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paulo037
/
starcoder

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utils/generate-sha256.py
dskrvk/anteater
177
33869
############################################################################## # Copyright (c) 2017 <NAME> <<EMAIL>>, Red Hat # # All rights reserved. This program and the accompanying materials # are made available under the terms of the Apache License, Version 2.0 # which accompanies this distribution, and is available at # http://www.apache.org/licenses/LICENSE-2.0 ############################################################################## # python generate-sha256.py --project /home/user/opnfv/infra # output made to working directory, file `output.yaml` import os import sys import hashlib import argparse from binaryornot.check import is_binary hasher = hashlib.sha256() parser = argparse.ArgumentParser() parser.add_argument('--project', help="Full path to project folder", required=True) args = parser.parse_args() ignore_dirs = ['.git'] sys.stdout = open('output.yaml', 'w') print("binaries:") for root, dirs, files in os.walk(args.project): dirs[:] = [d for d in dirs if d not in ignore_dirs] for file in files: full_path = os.path.join(root, file) if is_binary(full_path): with open(full_path, 'rb') as afile: buf = afile.read() hasher.update(buf) split_path = full_path.split(args.project + '/', 1)[-1] print(" {}:".format(split_path)) sum = hasher.hexdigest() print(" - {}".format(sum))
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src/workflow/ops.py
uts-cic/ontask_b
0
129930
<filename>src/workflow/ops.py # -*- coding: utf-8 -*- from __future__ import unicode_literals, print_function import gzip from io import BytesIO from django.contrib.auth import get_user_model from django.contrib.sessions.models import Session from django.core.exceptions import ObjectDoesNotExist from django.db.models import Q from django.http import HttpResponse from django.utils import timezone from rest_framework.parsers import JSONParser from rest_framework.renderers import JSONRenderer from action.models import Condition from dataops import formula_evaluation, pandas_db, ops from .models import Workflow, Column from .serializers import (WorkflowExportSerializer, WorkflowImportSerializer) from rest_framework import serializers import logs.ops def lock_workflow(request, workflow): """ Function that sets the session key in the workflow to flag that is locked. :param request: HTTP request :param workflow: workflow to lock :return: """ workflow.session_key = request.session.session_key workflow.save() def unlock_workflow_by_id(wid): """ Removes the session_key from the workflow with given id :param wid: Workflow id :return: """ try: workflow = Workflow.objects.get(id=wid) except ObjectDoesNotExist: return # Workflow exists, unlock unlock_workflow(workflow) def unlock_workflow(workflow): """ Removes the session_key from the workflow :param workflow: :return: """ workflow.session_key = '' workflow.save() def is_locked(workflow): """ :param workflow: workflow object to check if it is locked :return: Is the given workflow locked? """ if not workflow.session_key: # No key in the workflow, then it is not locked. return False try: session = Session.objects.get(session_key=workflow.session_key) except ObjectDoesNotExist: # Session does not exist, then it is not locked return False # Session is in the workflow and in the session table. Locked if expire # date is less that current time. return session.expire_date < timezone.now() def get_workflow(request, wid=None, select_related=None, prefetch_related=None): """ Function that gets the workflow that the user (in the current request) is using. :param request: HTTP request object :param wid: Workflow id to get. If not given, taken from the request session :param select_related: Field to add as select_related query filter :param prefetch_related: Field to add as prefetch_related query filter :return: Workflow object or None (if error) """ # Step 1: Get the workflow that is being accessed try: # If there is no wid given, take it from the session. Search for # workflow that is either owned by the user or shared with her. if not wid: wid = request.session['ontask_workflow_id'] # Initial query set with the distinct filter workflow = Workflow.objects.filter( Q(user=request.user) | Q(shared__id=request.user.id) ).distinct() # Apply select if given if select_related: workflow = workflow.select_related(select_related) # Apply prefetch if given if prefetch_related: workflow = workflow.prefetch_related(prefetch_related) # Final filter workflow = workflow.get(id=wid) except (KeyError, ObjectDoesNotExist): # No workflow or value set in the session, flag error. return None # Step 2: If the workflow is locked by this user session, return correct # result if request.session.session_key == workflow.session_key: return workflow # Step 3: If the workflow is unlocked, lock and return if not workflow.session_key: # Workflow is unlocked. Proceed to lock lock_workflow(request, workflow) return workflow # Step 4: The workflow is locked. See if the session locking it is # still valid try: session = Session.objects.get(session_key=workflow.session_key) except Session.DoesNotExist: # An exception means that the session stored as locking the # workflow is no longer in the session table, so the user can access # the workflow lock_workflow(request, workflow) return workflow # Get the owner of the session locking the workflow owner = get_user_model().objects.get( id=session.get_decoded().get('_auth_user_id') ) # Step 5: The workflow is locked by a session that is valid. See if the # session locking it happens to be from the same user (a previous session # that has not been properly closed) if owner == request.user: lock_workflow(request, workflow) return workflow # Step 6: The workflow is locked by an existing session. See if the # session is valid if session.expire_date >= timezone.now(): # The session currently locking the workflow # has an expire date in the future from now. So, no access is granted. return None # The workflow is locked by a session that has expired. Take the workflow # and lock it with the current session. lock_workflow(request, workflow) return workflow def get_user_locked_workflow(workflow): """ Given a workflow that is supposed to be locked, it returns the user that is locking it. :param workflow: :return: """ session = Session.objects.get(session_key=workflow.session_key) session_data = session.get_decoded() return get_user_model().objects.get(id=session_data.get('_auth_user_id')) def detach_dataframe(workflow): """ Given a workflow object, delete its dataframe :param workflow: :return: Nothing, the workflow object is updated """ pandas_db.delete_table(workflow.id) # Delete number of rows and columns workflow.nrows = 0 workflow.ncols = 0 workflow.n_filterd_rows = -1 workflow.save() # Delete the column_names, column_types and column_unique Column.objects.filter(workflow__id=workflow.id).delete() # Delete the info for QueryBuilder workflow.set_query_builder_ops() # Table name workflow.data_frame_table_name = '' # Save the workflow with the new fields. workflow.save() def do_import_workflow(user, name, file_item): """ Receives a name and a file item (submitted through a form) and creates the structure of workflow, conditions, actions and data table. :param user: User record to use for the import (own all created items) :param name: Workflow name (it has been checked that it does not exist) :param file_item: File item obtained through a form :return: """ data_in = gzip.GzipFile(fileobj=file_item) data = JSONParser().parse(data_in) # Serialize content workflow_data = WorkflowImportSerializer( data=data, context={'user': user, 'name': name} ) # If anything went wrong, return the string to show to the form. workflow = None try: if not workflow_data.is_valid(): return 'Unable to import the workflow' + ' (' + \ workflow_data.errors + ')' # Save the new workflow workflow = workflow_data.save(user=user, name=name) except (TypeError, NotImplementedError) as e: return 'Unable to import workflow (Exception: ' + e.message + ')' except serializers.ValidationError as e: return 'Unable to import workflow due to a validation error' if not pandas_db.check_wf_df(workflow): # Something went wrong. workflow.delete() return 'Workflow data with incorrect structure.' # Success # Log the event logs.ops.put(user, 'workflow_import', workflow, {'id': workflow.id, 'name': workflow.name}) return None def do_export_workflow(workflow, selected_actions=None): """ Proceed with the workflow export. :param workflow: Workflow record to export be included. :param selected_actions: A subset of actions to export :return: Page that shows a confirmation message and starts the download """ # Create the context object for the serializer context = {'selected_actions': selected_actions} # Get the info to send from the serializer serializer = WorkflowExportSerializer(workflow, context=context) to_send = JSONRenderer().render(serializer.data) # Get the in-memory file to compress zbuf = BytesIO() zfile = gzip.GzipFile(mode='wb', compresslevel=6, fileobj=zbuf) zfile.write(to_send) zfile.close() # Attach the compressed value to the response and send compressed_content = zbuf.getvalue() response = HttpResponse(compressed_content) response['Content-Encoding'] = 'application/gzip' response['Content-Disposition'] = \ 'attachment; filename="ontask_workflow.gz"' response['Content-Length'] = str(len(compressed_content)) return response def workflow_delete_column(workflow, column, cond_to_delete=None): """ Given a workflow and a column, removes it from the workflow (and the corresponding data frame :param workflow: Workflow object :param column: Column object to delete :param cond_to_delete: List of conditions to delete after removing the column :return: Nothing. Effect reflected in the database """ # Drop the column from the DB table storing the data frame pandas_db.df_drop_column(workflow.id, column.name) # Delete the column column.delete() # Update the information in the workflow workflow.ncols = workflow.ncols - 1 workflow.save() if not cond_to_delete: # The conditions to delete are not given, so calculate them # Get the conditions/actions attached to this workflow cond_to_delete = [x for x in Condition.objects.filter( action__workflow=workflow) if formula_evaluation.has_variable(x.formula, column.name)] # If a column disappears, the conditions that contain that variable # are removed.. for condition in cond_to_delete: # Formula has the name of the deleted column. # Solution 1: Nuke (Very easy) # Solution 2: Mark as invalid and enhance the edit condition form # to handle renaming the fields in a formula (Complex) # # Solution 1 chosen. condition.delete() # If a column disappears, the views that contain only that column need to # disappear as well as they are no longer relevant. for view in workflow.views.all(): if view.columns.all().count() == 0: view.delete() return def clone_column(column, new_workflow=None, new_name=None): """ Function that given a column clones it and changes workflow and name :param column: Object to clone :param new_workflow: New workflow object to point :param new_name: New name :return: Cloned object """ # Store the old object name before squashing it old_id = column.id old_name = column.name # Clone column.id = None # Update some of the fields if new_name: column.name = new_name if new_workflow: column.workflow = new_workflow # Update column.save() # Add the column to the table and update it. data_frame = pandas_db.load_from_db(column.workflow.id) data_frame[new_name] = data_frame[old_name] ops.store_dataframe_in_db(data_frame, column.workflow.id) return column
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tests/shell/test_shell_commandline.py
mjacobs/impala
0
1612828
# encoding=utf-8 # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import os import pytest import re import signal from subprocess import call from tests.common.impala_service import ImpaladService from tests.common.impala_test_suite import ImpalaTestSuite from tests.common.skip import SkipIf from time import sleep from util import IMPALAD, SHELL_CMD from util import assert_var_substitution, run_impala_shell_cmd, ImpalaShell DEFAULT_QUERY = 'select 1' QUERY_FILE_PATH = os.path.join(os.environ['IMPALA_HOME'], 'tests', 'shell') @pytest.fixture def empty_table(unique_database, request): """Create an empty table within the test database before executing test. The table will have the same name as the test_function itself. Setup and teardown of the database is handled by the unique_database fixture. Args: unique_database: pytest fixture defined in conftest.py request: standard pytest request fixture Returns: fq_table_name (str): the fully qualified name of the table: : dbname.table_name """ table_name = request.node.name fq_table_name = '.'.join([unique_database, table_name]) stmt = "CREATE TABLE %s (i integer, s string)" % fq_table_name request.instance.execute_query_expect_success(request.instance.client, stmt, query_options={'sync_ddl': 1}) return fq_table_name @pytest.fixture def populated_table(empty_table, request): """ Populate a table within the test database before executing test. The table will have the same name as the test_function itself. Setup and teardown of the database is handled by the unique_database fixture. Args: empty_table: pytest fixture that creates an empty table request: standard pytest request fixture Returns: fq_table_name (str): the fully qualified name of the table: : dbname.table_name """ fq_table_name = empty_table stmt = "insert into %s values (1, 'a'),(1, 'b'),(3, 'b')" % fq_table_name request.instance.execute_query_expect_success(request.instance.client, stmt) return fq_table_name class TestImpalaShell(ImpalaTestSuite): """A set of sanity tests for the Impala shell commandline parameters. The tests need to maintain Python 2.4 compatibility as a sub-goal of having shell tests is to ensure that it's not broken in systems running Python 2.4. The tests need a running impalad instance in order to execute queries. TODO: * Test individual modules. * Add a test for a kerberized impala. """ def test_no_args(self): args = '-q "%s"' % DEFAULT_QUERY run_impala_shell_cmd(args) def test_multiple_queries(self): queries = ';'.join([DEFAULT_QUERY] * 3) args = '-q "%s" -B' % queries run_impala_shell_cmd(args) def test_multiple_queries_with_escaped_backslash(self): """Regression test for string containing an escaped backslash. This relies on the patch at thirdparty/patches/sqlparse/0001-....patch. """ run_impala_shell_cmd(r'''-q "select '\\\\'; select '\\'';" -B''') def test_default_db(self, empty_table): db_name, table_name = empty_table.split('.') args = '-d %s -q "describe %s" --quiet' % (db_name, table_name) run_impala_shell_cmd(args) args = '-q "describe %s"' % table_name run_impala_shell_cmd(args, expect_success=False) # test keyword parquet is interpreted as an identifier # when passed as an argument to -d args = '-d parquet' result = run_impala_shell_cmd(args) assert "Query: use `parquet`" in result.stderr, result.stderr # test if backticking is idempotent args = "-d '```parquet```'" result = run_impala_shell_cmd(args) assert "Query: use `parquet`" in result.stderr, result.stderr def test_unsecure_message(self): results = run_impala_shell_cmd("") assert "Starting Impala Shell without Kerberos authentication" in results.stderr def test_print_header(self, populated_table): args = '--print_header -B --output_delim="," -q "select * from %s"' % populated_table result = run_impala_shell_cmd(args) result_rows = result.stdout.strip().split('\n') assert len(result_rows) == 4 assert result_rows[0].split(',') == ['i', 's'] args = '-B --output_delim="," -q "select * from %s"' % populated_table result = run_impala_shell_cmd(args) result_rows = result.stdout.strip().split('\n') assert len(result_rows) == 3 @pytest.mark.execute_serially def test_kerberos_option(self): args = "-k" # If you have a valid kerberos ticket in your cache, this test fails - so # here we set a bogus KRB5CCNAME in the environment so that klist (and other # kerberos commands) won't find the normal ticket cache. # KERBEROS TODO: add kerberized cluster test case os.environ["KRB5CCNAME"] = "/tmp/this/file/hopefully/does/not/exist" # The command will fail because we're trying to connect to a kerberized impalad. results = run_impala_shell_cmd(args, expect_success=False) # Check that impala is using the right service name. assert "Using service name 'impala'" in results.stderr assert "Starting Impala Shell using Kerberos authentication" in results.stderr # Check that Impala warns the user if klist does not exist on the system, or if # no kerberos tickets are initialized. try: call(["klist"]) expected_error_msg = ("-k requires a valid kerberos ticket but no valid kerberos " "ticket found.") assert expected_error_msg in results.stderr except OSError: assert 'klist not found on the system' in results.stderr # Make sure we don't try to re-connect assert "retrying the connection with a secure transport" not in results.stderr # Change the service name args += " -s foobar" results = run_impala_shell_cmd(args, expect_success=False) assert "Using service name 'foobar'" in results.stderr def test_continue_on_error(self): args = '-c -q "select foo; select bar;"' run_impala_shell_cmd(args) # Should fail args = '-q "select foo; select bar;"' run_impala_shell_cmd(args, expect_success=False) def test_execute_queries_from_file(self): args = '-f %s/test_file_comments.sql --quiet -B' % QUERY_FILE_PATH result = run_impala_shell_cmd(args) output = result.stdout args = '-f %s/test_file_no_comments.sql --quiet -B' % QUERY_FILE_PATH result = run_impala_shell_cmd(args) assert output == result.stdout, "Queries with comments not parsed correctly" def test_completed_query_errors(self): args = ('-q "set abort_on_error=false;' ' select count(*) from functional_seq_snap.bad_seq_snap"') result = run_impala_shell_cmd(args) assert 'WARNINGS:' in result.stderr assert 'Bad synchronization marker' in result.stderr assert 'Expected: ' in result.stderr assert 'Actual: ' in result.stderr assert 'Problem parsing file' in result.stderr def test_no_warnings_in_log_with_quiet_mode(self): """Regression test for IMPALA-4222.""" args = ('-q "set abort_on_error=false;' ' select count(*) from functional_seq_snap.bad_seq_snap" --quiet') result = run_impala_shell_cmd(args) assert 'WARNINGS:' not in result.stderr def test_removed_query_option(self): """Test that removed query options produce warning.""" result = run_impala_shell_cmd("-q 'set disable_cached_reads=true'", expect_success=True) assert "Ignoring removed query option: 'disable_cached_reads'" in result.stderr def test_output_format(self): expected_output = ['1'] * 3 args = '-q "select 1,1,1" -B --quiet' result = run_impala_shell_cmd(args) actual_output = [r.strip() for r in result.stdout.split('\t')] assert actual_output == expected_output result = run_impala_shell_cmd(args + ' --output_delim="|"') actual_output = [r.strip() for r in result.stdout.split('|')] assert actual_output == expected_output result = run_impala_shell_cmd(args + ' --output_delim="||"', expect_success=False) assert "Illegal delimiter" in result.stderr def test_do_methods(self, empty_table): """Ensure that the do_ methods in the shell work. Some of the do_ methods are implicitly tested in other tests, and as part of the test setup. """ # explain args = '-q "explain select 1"' run_impala_shell_cmd(args) # show args = '-q "show tables"' run_impala_shell_cmd(args) # with args = '-q "with t1 as (select 1) select * from t1"' run_impala_shell_cmd(args) # set # spaces around the = sign args = '-q "set batch_size = 10"' run_impala_shell_cmd(args) # no spaces around the = sign args = '-q "set batch_size=10"' run_impala_shell_cmd(args) # test query options displayed args = '-q "set"' result_set = run_impala_shell_cmd(args) assert 'MEM_LIMIT: [0]' in result_set.stdout # test to check that explain_level is 1 assert 'EXPLAIN_LEVEL: [1]' in result_set.stdout # test to check that configs without defaults show up as [] assert 'COMPRESSION_CODEC: []' in result_set.stdout # test values displayed after setting value args = '-q "set mem_limit=1g;set"' result_set = run_impala_shell_cmd(args) # single list means one instance of mem_limit in displayed output assert 'MEM_LIMIT: 1g' in result_set.stdout assert 'MEM_LIMIT: [0]' not in result_set.stdout # Negative tests for set # use : instead of = args = '-q "set batch_size:10"' run_impala_shell_cmd(args, expect_success=False) # use 2 = signs args = '-q "set batch_size=10=50"' run_impala_shell_cmd(args, expect_success=False) # describe and desc should return the same result. args = '-q "describe %s" -B' % empty_table result_describe = run_impala_shell_cmd(args) args = '-q "desc %s" -B' % empty_table result_desc = run_impala_shell_cmd(args) assert result_describe.stdout == result_desc.stdout def test_runtime_profile(self): # test summary is in both the profile printed by the # -p option and the one printed by the profile command args = "-p -q 'select 1; profile;'" result_set = run_impala_shell_cmd(args) # This regex helps us uniquely identify a profile. regex = re.compile("Operator\s+#Hosts\s+Avg\s+Time") # We expect two query profiles. assert len(re.findall(regex, result_set.stdout)) == 2, \ "Could not detect two profiles, stdout: %s" % result_set.stdout def test_summary(self): args = "-q 'select count(*) from functional.alltypes; summary;'" result_set = run_impala_shell_cmd(args) assert "03:AGGREGATE" in result_set.stdout args = "-q 'summary;'" result_set = run_impala_shell_cmd(args, expect_success=False) assert "Could not retrieve summary for query" in result_set.stderr args = "-q 'show tables; summary;'" result_set = run_impala_shell_cmd(args) assert "Summary not available" in result_set.stderr # Test queries without an exchange args = "-q 'select 1; summary;'" result_set = run_impala_shell_cmd(args) assert "00:UNION" in result_set.stdout @pytest.mark.execute_serially def test_queries_closed(self): """Regression test for IMPALA-897.""" args = '-f %s/test_close_queries.sql --quiet -B' % QUERY_FILE_PATH cmd = "%s %s" % (SHELL_CMD, args) # Execute the shell command async p = ImpalaShell(args) impalad_service = ImpaladService(IMPALAD.split(':')[0]) # The last query in the test SQL script will sleep for 10 seconds, so sleep # here for 5 seconds and verify the number of in-flight queries is 1. sleep(5) assert 1 == impalad_service.get_num_in_flight_queries() assert p.get_result().rc == 0 assert 0 == impalad_service.get_num_in_flight_queries() def test_cancellation(self): """Test cancellation (Ctrl+C event).""" args = '-q "select sleep(10000)"' p = ImpalaShell(args) sleep(3) os.kill(p.pid(), signal.SIGINT) result = p.get_result() assert "Cancelling Query" in result.stderr, result.stderr @pytest.mark.execute_serially def test_query_cancellation_during_fetch(self): """IMPALA-1144: Test cancellation (CTRL+C) while results are being fetched""" pytest.skip("""Skipping as it occasionally gets stuck in Jenkins builds resulting the build to timeout.""") # A select query where fetch takes several seconds stmt = "with v as (values (1 as x), (2), (3), (4)) " + \ "select * from v, v v2, v v3, v v4, v v5, v v6, v v7, v v8, " + \ "v v9, v v10, v v11" # Kill happens when the results are being fetched self.run_and_verify_query_cancellation_test(stmt, "FINISHED") @pytest.mark.execute_serially def test_query_cancellation_during_wait_to_finish(self): """IMPALA-1144: Test cancellation (CTRL+C) while the query is in the wait_to_finish state""" # A select where wait_to_finish takes several seconds stmt = "select * from tpch.customer c1, tpch.customer c2, " + \ "tpch.customer c3 order by c1.c_name" # Kill happens in wait_to_finish state self.run_and_verify_query_cancellation_test(stmt, "RUNNING") def wait_for_query_state(self, stmt, state, max_retry=15): """Checks the in flight queries on Impala debug page. Polls the state of the query statement from parameter every second until the query gets to a state given via parameter or a maximum retry count is reached. Restriction: Only works if there is only one in flight query.""" impalad_service = ImpaladService(IMPALAD.split(':')[0]) if not impalad_service.wait_for_num_in_flight_queries(1): raise Exception("No in flight query found") retry_count = 0 while retry_count <= max_retry: query_info = impalad_service.get_in_flight_queries()[0] if query_info['stmt'] != stmt: exc_text = "The found in flight query is not the one under test: " + \ query_info['stmt'] raise Exception(exc_text) if query_info['state'] == state: return retry_count += 1 sleep(1.0) raise Exception("Query didn't reach desired state: " + state) def run_and_verify_query_cancellation_test(self, stmt, cancel_at_state): """Starts the execution of the received query, waits until the query execution in fact starts and then cancels it. Expects the query cancellation to succeed.""" args = "-q \"" + stmt + ";\"" p = ImpalaShell(args) self.wait_for_query_state(stmt, cancel_at_state) os.kill(p.pid(), signal.SIGINT) result = p.get_result() assert "Cancelling Query" in result.stderr assert "Invalid query handle" not in result.stderr def test_get_log_once(self, empty_table): """Test that get_log() is always called exactly once.""" # Query with fetch args = '-q "select * from functional.alltypeserror"' result = run_impala_shell_cmd(args) assert result.stderr.count('WARNINGS') == 1 # Insert query (doesn't fetch) drop_args = '-q "drop table if exists %s"' % empty_table run_impala_shell_cmd(drop_args) args = '-q "create table %s like functional.alltypeserror"' % empty_table run_impala_shell_cmd(args) args = '-q "insert overwrite %s partition(year, month)' \ 'select * from functional.alltypeserror"' % empty_table result = run_impala_shell_cmd(args) assert result.stderr.count('WARNINGS') == 1 def test_international_characters(self): """Sanity test to ensure that the shell can read international characters.""" russian_chars = (u"А, Б, В, Г, Д, Е, Ё, Ж, З, И, Й, К, Л, М, Н, О, П, Р," u"С, Т, У, Ф, Х, Ц,Ч, Ш, Щ, Ъ, Ы, Ь, Э, Ю, Я") args = """-B -q "select '%s'" """ % russian_chars result = run_impala_shell_cmd(args.encode('utf-8')) assert 'UnicodeDecodeError' not in result.stderr assert russian_chars.encode('utf-8') in result.stdout @pytest.mark.execute_serially # This tests invalidates metadata, and must run serially def test_config_file(self): """Test the optional configuration file.""" # Positive tests args = '--config_file=%s/good_impalarc' % QUERY_FILE_PATH result = run_impala_shell_cmd(args) assert 'Query: select 1' in result.stderr # override option in config file through command line args = '--config_file=%s/good_impalarc --query="select 2"' % QUERY_FILE_PATH result = run_impala_shell_cmd(args) assert 'Query: select 2' in result.stderr # Negative Tests # specified config file does not exist args = '--config_file=%s/does_not_exist' % QUERY_FILE_PATH run_impala_shell_cmd(args, expect_success=False) # bad formatting of config file args = '--config_file=%s/bad_impalarc' % QUERY_FILE_PATH run_impala_shell_cmd(args, expect_success=False) # Testing config file related warning and error messages args = '--config_file=%s/impalarc_with_warnings' % QUERY_FILE_PATH result = run_impala_shell_cmd(args, expect_success=True) assert "WARNING: Option 'config_file' can be only set from shell." in result.stderr err_msg = ("WARNING: Unable to read configuration file correctly. " "Ignoring unrecognized config option: 'invalid_option'\n") assert err_msg in result.stderr args = '--config_file=%s/impalarc_with_error' % QUERY_FILE_PATH result = run_impala_shell_cmd(args, expect_success=False) err_msg = ("Unexpected value in configuration file. " "'maybe' is not a valid value for a boolean option.") assert err_msg in result.stderr def test_execute_queries_from_stdin(self): """Test that queries get executed correctly when STDIN is given as the sql file.""" args = '-f - --quiet -B' query_file = "%s/test_file_comments.sql" % QUERY_FILE_PATH query_file_handle = None try: query_file_handle = open(query_file, 'r') query = query_file_handle.read() query_file_handle.close() except Exception, e: assert query_file_handle is not None, "Exception %s: Could not find query file" % e result = run_impala_shell_cmd(args, expect_success=True, stdin_input=query) output = result.stdout args = '-f %s/test_file_no_comments.sql --quiet -B' % QUERY_FILE_PATH result = run_impala_shell_cmd(args) assert output == result.stdout, "Queries from STDIN not parsed correctly." def test_allow_creds_in_clear(self): args = '-l' result = run_impala_shell_cmd(args, expect_success=False) err_msg = ("LDAP credentials may not be sent over insecure connections. " "Enable SSL or set --auth_creds_ok_in_clear") assert err_msg in result.stderr # TODO: Without an Impala daemon running LDAP authentication, we can't test if # --auth_creds_ok_in_clear works when correctly set. def test_ldap_password_from_shell(self): args = "-l --auth_creds_ok_in_clear --ldap_password_cmd='%s'" result = run_impala_shell_cmd(args % 'cmddoesntexist', expect_success=False) assert ("Error retrieving LDAP password (command was: 'cmddoesntexist', exception " "was: '[Errno 2] No such file or directory')") in result.stderr result = run_impala_shell_cmd(args % 'cat filedoesntexist', expect_success=False) assert ("Error retrieving LDAP password (command was 'cat filedoesntexist', error " "was: 'cat: filedoesntexist: No such file or directory')") in result.stderr # TODO: Without an Impala daemon with LDAP authentication enabled, we can't test the # positive case where the password is correct. def test_var_substitution(self): args = '--var=foo=123 --var=BAR=456 --delimited --output_delimiter=" " -c -f %s' \ % (os.path.join(QUERY_FILE_PATH, 'test_var_substitution.sql')) result = run_impala_shell_cmd(args, expect_success=True) assert_var_substitution(result) args = '--var=foo' result = run_impala_shell_cmd(args, expect_success=False) assert ("Error: Could not parse key-value \"foo\". It must follow the pattern " "\"KEY=VALUE\".") in result.stderr # Checks if 'messages' exists/does not exist in 'result_stderr' based on the value of # 'should_exist' def _validate_shell_messages(self, result_stderr, messages, should_exist=True): for msg in messages: if should_exist: assert msg in result_stderr else: assert msg not in result_stderr def test_query_time_and_link_message(self, unique_database): shell_messages = ["Query submitted at: ", "(Coordinator: ", "Query progress can be monitored at: "] # CREATE statements should not print query time and webserver address. results = run_impala_shell_cmd('--query="create table %s.shell_msg_test (id int)"' % unique_database) self._validate_shell_messages(results.stderr, shell_messages, should_exist=False) # SELECT, INSERT, CTAS, CTE queries should print the query time message and webserver # address. results = run_impala_shell_cmd('--query="insert into %s.shell_msg_test values (1)"' % unique_database) self._validate_shell_messages(results.stderr, shell_messages, should_exist=True) results = run_impala_shell_cmd('--query="select * from %s.shell_msg_test"' % unique_database) self._validate_shell_messages(results.stderr, shell_messages, should_exist=True) results = run_impala_shell_cmd('--query="create table %s.shell_msg_ctas_test as \ select * from %s.shell_msg_test"' % (unique_database, unique_database)) self._validate_shell_messages(results.stderr, shell_messages, should_exist=True) results = run_impala_shell_cmd('--query="create table %s.shell_msg_cte_test(i int); ' 'with abc as (select 1) ' 'insert overwrite %s.shell_msg_cte_test ' 'select * from %s.shell_msg_cte_test;"' % (unique_database, unique_database, unique_database)) self._validate_shell_messages(results.stderr, shell_messages, should_exist=True) # DROP statements should not print query time and webserver address. results = run_impala_shell_cmd('--query="drop table %s.shell_msg_test"' % unique_database) self._validate_shell_messages(results.stderr, shell_messages, should_exist=False) run_impala_shell_cmd('--query="drop table %s.shell_msg_ctas_test"' % unique_database) # Simple queries should not print query time and webserver address. results = run_impala_shell_cmd('--query="use default"') self._validate_shell_messages(results.stderr, shell_messages, should_exist=False) results = run_impala_shell_cmd('--query="show tables"') self._validate_shell_messages(results.stderr, shell_messages, should_exist=False) def test_insert_status(self, unique_database): run_impala_shell_cmd('--query="create table %s.insert_test (id int)"' % unique_database) results = run_impala_shell_cmd('--query="insert into %s.insert_test values (1)"' % unique_database) assert "Modified 1 row(s)" in results.stderr def _validate_dml_stmt(self, stmt, expected_rows_modified, expected_row_errors): results = run_impala_shell_cmd('--query="%s"' % (stmt)) expected_output = "Modified %d row(s), %d row error(s)" %\ (expected_rows_modified, expected_row_errors) assert expected_output in results.stderr @SkipIf.kudu_not_supported def test_kudu_dml_reporting(self, unique_database): db = unique_database run_impala_shell_cmd('--query="create table %s.dml_test (id int primary key, '\ 'age int null) partition by hash(id) partitions 2 stored as kudu"' % db) self._validate_dml_stmt("insert into %s.dml_test (id) values (7), (7)" % db, 1, 1) self._validate_dml_stmt("insert into %s.dml_test (id) values (7)" % db, 0, 1) self._validate_dml_stmt("upsert into %s.dml_test (id) values (7), (7)" % db, 2, 0) self._validate_dml_stmt("update %s.dml_test set age = 1 where id = 7" % db, 1, 0) self._validate_dml_stmt("delete from %s.dml_test where id = 7" % db, 1, 0) # UPDATE/DELETE where there are no matching rows; there are no errors because the # scan produced no rows. self._validate_dml_stmt("update %s.dml_test set age = 1 where id = 8" % db, 0, 0) self._validate_dml_stmt("delete from %s.dml_test where id = 7" % db, 0, 0) # WITH clauses, only apply to INSERT and UPSERT self._validate_dml_stmt(\ "with y as (values(7)) insert into %s.dml_test (id) select * from y" % db, 1, 0) self._validate_dml_stmt(\ "with y as (values(7)) insert into %s.dml_test (id) select * from y" % db, 0, 1) self._validate_dml_stmt(\ "with y as (values(7)) upsert into %s.dml_test (id) select * from y" % db, 1, 0) def test_missing_query_file(self): result = run_impala_shell_cmd('-f nonexistent.sql', expect_success=False) assert "Could not open file 'nonexistent.sql'" in result.stderr
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Password-Gen/pwned_check.py
FoxInTheClouds/Memorable-Password-Generator
0
120152
<reponame>FoxInTheClouds/Memorable-Password-Generator import requests import hashlib def pwned_check(password): password = password.encode() # Turns it into bytes hashed_password = hashlib.sha1(password).hexdigest() # Api recuires sha1 hash url = "https://api.pwnedpasswords.com/range/" + hashed_password[:5] # Sends first five characters pwned_request = requests.get(url) pwned_list = pwned_request.text.splitlines() secure = True for i in pwned_list: if i[:i.find(":")].lower() == hashed_password[5:].lower(): secure = False # Checks if it is in the returned list return secure
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python/src/main/python/pyalink/alink/tests/examples/from_docs/test_berttextembeddingbatchop.py
wenwei8268/Alink
0
166528
<reponame>wenwei8268/Alink import unittest from pyalink.alink import * import numpy as np import pandas as pd class TestBertTextEmbeddingBatchOp(unittest.TestCase): def test_berttextembeddingbatchop(self): df_data = pd.DataFrame([ [1, 'An english sentence.'], [2, '这是一个中文句子'] ]) batch_data = BatchOperator.fromDataframe(df_data, schemaStr='f1 bigint, f2 string') BertTextEmbeddingBatchOp() \ .setSelectedCol("f2") \ .setOutputCol("embedding") \ .setLayer(-2) \ .linkFrom(batch_data) \ .print() pass
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tests/bulk_importer/test_bulk_importer.py
alessiamarcolini/hangar-py
202
147029
import pytest import numpy as np def assert_equal(arr, arr2): assert np.array_equal(arr, arr2) assert arr.dtype == arr2.dtype def test_bulk_importer_ndarray(repo): from hangar.bulk_importer import run_bulk_import from hangar.bulk_importer import UDF_Return def make_ndarray(column, key, shape, dtype, multiplier): size = np.prod(shape) arr = np.arange(size, dtype=dtype).reshape(shape) * multiplier yield UDF_Return(column=column, key=key, data=arr) co = repo.checkout(write=True) co.add_ndarray_column('arr', shape=(5, 5), dtype=np.uint32) co.commit('first') co.close() kwargs = [] expected_kv = [] for idx in range(200): _kw_dict = { 'column': 'arr', 'key': idx, 'shape': (5, 5), 'dtype': np.uint32, 'multiplier': idx } kwargs.append(_kw_dict) for _udf_val in make_ndarray(**_kw_dict): expected_kv.append(_udf_val) assert len(expected_kv) == 200 run_bulk_import( repo, branch_name='master', column_names=['arr'], udf=make_ndarray, udf_kwargs=kwargs, ncpus=2) co = repo.checkout() try: arr_col = co['arr'] assert len(arr_col) == 200 for _expected_udf_val in expected_kv: assert _expected_udf_val.key in arr_col assert_equal(arr_col[_expected_udf_val.key], _expected_udf_val.data) finally: co.close() def test_bulk_importer_pystr(repo): from hangar.bulk_importer import run_bulk_import from hangar.bulk_importer import UDF_Return def make_pystr(column, key, str_val): yield UDF_Return(column=column, key=key, data=str_val) co = repo.checkout(write=True) co.add_str_column('str') co.commit('first') co.close() kwargs = [] expected_kv = [] for idx in range(200): _kw_dict = { 'column': 'str', 'key': idx, 'str_val': f'{str(idx) * 2}', } kwargs.append(_kw_dict) for _udf_val in make_pystr(**_kw_dict): expected_kv.append(_udf_val) assert len(expected_kv) == 200 run_bulk_import( repo, branch_name='master', column_names=['str'], udf=make_pystr, udf_kwargs=kwargs, ncpus=2) co = repo.checkout() try: str_col = co['str'] assert len(str_col) == 200 for _expected_udf_val in expected_kv: assert _expected_udf_val.key in str_col assert str_col[_expected_udf_val.key] == _expected_udf_val.data finally: co.close() def test_bulk_importer_pybytes(repo): from hangar.bulk_importer import run_bulk_import from hangar.bulk_importer import UDF_Return def make_pybytes(column, key, str_val): raw = str_val.encode() yield UDF_Return(column=column, key=key, data=raw) co = repo.checkout(write=True) co.add_bytes_column('bytes') co.commit('first') co.close() kwargs = [] expected_kv = [] for idx in range(200): _kw_dict = { 'column': 'bytes', 'key': idx, 'str_val': f'{str(idx) * 2}', } kwargs.append(_kw_dict) for _udf_val in make_pybytes(**_kw_dict): expected_kv.append(_udf_val) assert len(expected_kv) == 200 run_bulk_import( repo, branch_name='master', column_names=['bytes'], udf=make_pybytes, udf_kwargs=kwargs, ncpus=2) co = repo.checkout() try: bytes_col = co['bytes'] assert len(bytes_col) == 200 for _expected_udf_val in expected_kv: assert _expected_udf_val.key in bytes_col assert bytes_col[_expected_udf_val.key] == _expected_udf_val.data finally: co.close() def test_bulk_importer_two_col_pybytes_pystr(repo): from hangar.bulk_importer import run_bulk_import from hangar.bulk_importer import UDF_Return def _make_pystr(column, key, str_val): yield UDF_Return(column=column, key=key, data=str_val) def _make_pybytes(column, key, str_val): raw = str_val.encode() yield UDF_Return(column=column, key=key, data=raw) def make_pystr_pybytes(str_col, bytes_col, key, str_val): yield from _make_pystr(column=str_col, key=key, str_val=str_val) yield from _make_pybytes(column=bytes_col, key=key, str_val=str_val) co = repo.checkout(write=True) co.add_bytes_column('bytes') co.add_str_column('str') co.commit('first') co.close() kwargs = [] expected_kv = [] for idx in range(200): _kw_dict = { 'str_col': 'str', 'bytes_col': 'bytes', 'key': idx, 'str_val': f'{str(idx) * 2}', } kwargs.append(_kw_dict) for _udf_val in make_pystr_pybytes(**_kw_dict): expected_kv.append(_udf_val) assert len(expected_kv) == 400 run_bulk_import( repo, branch_name='master', column_names=['bytes', 'str'], udf=make_pystr_pybytes, udf_kwargs=kwargs, ncpus=2) co = repo.checkout() try: pybytes_col = co['bytes'] pystr_col = co['str'] assert len(pybytes_col) == 200 assert len(pystr_col) == 200 for _expected_udf_val in expected_kv: assert _expected_udf_val.column in ['str', 'bytes'] if _expected_udf_val.column == 'str': assert _expected_udf_val.key in pystr_col assert pystr_col[_expected_udf_val.key] == _expected_udf_val.data elif _expected_udf_val.column == 'bytes': assert _expected_udf_val.key in pystr_col assert pybytes_col[_expected_udf_val.key] == _expected_udf_val.data else: raise ValueError(_expected_udf_val.column) finally: co.close() def test_signature_wrong(repo): from hangar.bulk_importer import run_bulk_import from hangar.bulk_importer import UDF_Return def wrong_sig_udf(a, b, c=None): yield UDF_Return(column='str', key=a, data=f'{a} {b} {c}') co = repo.checkout(write=True) co.add_str_column('str') co.commit('first') co.close() kwargs = [] for idx in range(200): _kw_dict = { 'a': 'bytes', 'str_val': f'{str(idx) * 2}', } kwargs.append(_kw_dict) with pytest.raises(TypeError): run_bulk_import( repo, branch_name='master', column_names=['str'], udf=wrong_sig_udf, udf_kwargs=kwargs, ncpus=2)
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setup.py
jean/labels
1
4526
<reponame>jean/labels<gh_stars>1-10 import pathlib import setuptools def read(*args: str) -> str: file_path = pathlib.Path(__file__).parent.joinpath(*args) return file_path.read_text("utf-8") setuptools.setup( name="labels", version="0.3.0.dev0", author="<NAME>", author_email="<EMAIL>", maintainer="<NAME>", maintainer_email="<EMAIL>", license="MIT", url="https://github.com/hackebrot/labels", project_urls={ "Repository": "https://github.com/hackebrot/labels", "Issues": "https://github.com/hackebrot/labels/issues", }, description="CLI app for managing GitHub labels for Python 3.6 and newer. 📝", long_description=read("README.md"), long_description_content_type="text/markdown", packages=setuptools.find_packages("src"), package_dir={"": "src"}, include_package_data=True, zip_safe=False, python_requires=">=3.6", install_requires=["click", "requests", "pytoml", "attrs"], entry_points={"console_scripts": ["labels = labels.cli:labels"]}, classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: Implementation :: CPython", "Topic :: Utilities", ], keywords=["github", "command-line"], )
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chatbot_jesse/snotbot/chatServer_snotbot.py
carmensteenbrink/getyourreview
0
167580
#!/usr/bin/env python3 # Command Line Interface (CLI) version import time import uniquebot as bot # Chat Server Framework functions def sleep(n): """Sleep n number of seconds. Pauses the execution of the program. """ time.sleep(n) def output(s): """Outputs string s as chat message. Send the given string to the chat client. """ print(s) # Run forever on the command line def main(): """docstring for main""" # Setup bot.setup() # # Run continuesly while True: humanSpeak = input("> ") bot.response(humanSpeak) if __name__ == '__main__': main()
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src/python-speech-recognition/auto.py
Zambashilidye/face_recognition_robocup
0
159124
<reponame>Zambashilidye/face_recognition_robocup<filename>src/python-speech-recognition/auto.py #!/usr/bin/env python #coding:utf-8 from audio import AudioControl import os import time #import winsound def DetectImage(): #print(os.path.abspath("facereg")) return os.path.isfile("/home/snfgto/robocup/facereg/face_recog_v1.0/outputs/operator.jpg") def AutoControl(): detectflag = 0 while (detectflag == 0): time.sleep(1) if DetectImage(): detectflag = 1 #winsound.Beep(600,1000) #print("\a") beep() print("Get Image.Now turning.") ac=AudioControl("ac") ac.turn() else: detectflag = 0 print("Don't find the image.Keep searching...") def beep(): duration = 1 # second freq = 1000 # Hz os.system('play --no-show-progress --null --channels 1 synth %s sine %f' % (duration, freq)) if __name__ == '__main__': try: #rate = 20 #SetRate(rate) AutoControl() #print("\a") except: print("Processes End.")
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comments/views.py
praekeltfoundation/ge-web
1
1603504
<reponame>praekeltfoundation/ge-web<filename>comments/views.py<gh_stars>1-10 from django.contrib import messages from django.views.generic import FormView from django.views.generic.base import ContextMixin from threadedcomments.forms import ThreadedCommentForm from threadedcomments.models import ThreadedComment class AdminCommentReplyView(FormView, ContextMixin): form_class = ThreadedCommentForm template_name = "admin/reply.html" success_url = "/admin/threadedcomments/threadedcomment/" def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context["parent"] = self.get_form_kwargs()["target_object"] return context def get_form_kwargs(self, *args, **kwargs): kwargs = super(AdminCommentReplyView, self).get_form_kwargs(*args, **kwargs) kwargs["parent"] = self.kwargs["parent"] kwargs["target_object"] = ThreadedComment.objects.get(pk=self.kwargs["parent"]) del kwargs["prefix"] if "files" in kwargs.keys(): del kwargs["files"] return kwargs def form_valid(self, form, *args, **kwargs): comment = form.get_comment_object() comment.save() messages.success(self.request, "Comment Reply Successfully Posted") return super().form_valid(form, *args, **kwargs)
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tasks/kaggle/disaster_tweets/bert_finetuing.py
charliemorning/mlws
0
130787
import os import argparse import pandas as pd from models.tf.nlp.bert.bert_models import BertTrainer from util.corpus import train_test_split_from_data_frame from util.label import encode_labels if __name__ == '__main__': parser = argparse.ArgumentParser(description='train cls models on bert.') parser.add_argument('bert_home', type=str) parser.add_argument('data_home', type=str) parser.add_argument('--batch-size', type=int, default=4, metavar='N', help='input batch size for training (default: 4)') args = parser.parse_args() bert_home = args.bert_home data_home = args.data_home df = pd.read_csv(os.path.join(data_home, "train.csv")) df["target"] = df["target"].apply(lambda x: str(x)) train_df, dev_df = train_test_split_from_data_frame(df) # test_pd = pd.read_csv(os.path.join(data_home, "test.csv")) # xs_test, ys_test = dev_df["text"], dev_df["target"].apply(lambda x: str(x)) import tensorflow as tf tf.logging.set_verbosity(tf.logging.ERROR) bert = BertTrainer( os.path.join(bert_home, 'bert_config.json'), os.path.join(bert_home, 'vocab.txt'), os.path.join(bert_home, 'bert_model.ckpt'), data_home, os.path.join(data_home, 'output'), BertTrainer.DataFrameInput( BertTrainer.DataFrameSingleTextInputProcessor("id", "text", "target"), encode_labels(df["target"]), train_df, dev_df), train_batch_size=1, num_train_epochs=1, max_seq_length=16 ) bert.train() print("done")
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unit_tests/expressions/Subquery.py
jaredlunde/cargo-orm
3
102496
#!/usr/bin/python3 -S # -*- coding: utf-8 -*- """ `Unit tests for cargo.expressions.Subquery` --·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·--·-- 2016 <NAME> © The MIT License (MIT) http://github.com/jaredlunde """ import unittest from vital.security import randkey from cargo import * def new_field(type='varchar'): field = getattr(fields, type.title())(value=1234) keyspace = '<KEY>' name = randkey(24, keyspace) table = randkey(24, keyspace) field.field_name = name field.table = table return field def new_expression(cast=int): if cast == bytes: cast = lambda x: psycopg2.Binary(str(x).encode()) return Expression(new_field(), '=', cast(12345)) def new_function(cast=int): if cast == bytes: cast = lambda x: psycopg2.Binary(str(x).encode()) return Function('some_func', cast(12345)) class TestSubquery(unittest.TestCase): def test___init__(self): q = Insert(ORM()) base = Subquery(q) self.assertIs(base.subquery, q) self.assertIn(q.query, base.query) self.assertIs(base.params, q.params) def test_alias(self): q = Insert(ORM()) base = Subquery(q, alias='subalias') self.assertEqual(base.alias, 'subalias') self.assertTrue(base.query.endswith('subalias')) def test_exists(self): q = Select(ORM()) base = Subquery(q) func = base.exists() self.assertIsInstance(func, Function) self.assertEqual(func.func, 'EXISTS') self.assertTupleEqual(func.args, tuple([base,])) func = base.exists(alias='foo') self.assertIsInstance(func, Function) self.assertEqual(func.func, 'EXISTS') self.assertTupleEqual(func.args, tuple([base,])) self.assertEqual(func.alias, 'foo') def test_not_exists(self): q = Select(ORM()) base = Subquery(q) func = base.not_exists() self.assertIsInstance(func, Function) self.assertEqual(func.func, 'NOT EXISTS') self.assertTupleEqual(func.args, tuple([base,])) func = base.not_exists(alias='foo') self.assertIsInstance(func, Function) self.assertEqual(func.func, 'NOT EXISTS') self.assertTupleEqual(func.args, tuple([base,])) self.assertEqual(func.alias, 'foo') if __name__ == '__main__': # Unit test unittest.main()
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general_maths/sph_harms/azi_deriv.py
chrisjdavie/ws_cross_project
0
133552
''' Examining the gradients of the azimuthally symmetric legendre polynomial. Created on 1 May 2013 @author: chris ''' def main(): l = 4 a = max_n_minimas(l) print a def leg_poly(l): m = 0 from pert_sphere import pert_sphere rs, thetas, _ = pert_sphere(l,m) rs = rs[:,0] thetas = thetas[:,0] return rs, thetas def dr_dtheta(l): rs, thetas = leg_poly(l) dr_dthetas = (rs[:-1] - rs[1:])/(thetas[:-1] - thetas[1:]) thetas_d = (thetas[:-1] + thetas[1:])/2.0 return dr_dthetas, thetas_d def max_n_minimas(l): dr_dthetas, thetas_d = dr_dtheta(l) num_m2 = dr_dthetas[0] num_m1 = dr_dthetas[0] maxima_i = [] minima_i = [] for i, num in enumerate(dr_dthetas): if num_m1 > num_m2 and num_m1 > num: maxima_i.append(i) if num_m1 < num_m2 and num_m1 < num: minima_i.append(i) num_m2 = num_m1 num_m1 = num return thetas_d[maxima_i], thetas_d[minima_i] if __name__ == '__main__': main()
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custom_components/shinhugas_fee/const.py
tsunglung/ShinhuGasFee
1
1607297
"""Constants of the ShinHu Gas Fee component.""" DEFAULT_NAME = "ShinHu Gas Fee" DEFAULT_NAME_UPLOAD_USAGE = "ShinHu Gas Upload Usage" DOMAIN = "shinhugas_fee" DOMAINS = [ "sensor", "binary_sensor" ] DATA_KEY = "sensor.shinhugas_fee" DATA_KEY_BINARY = "binary_sensor.shinhu_gas_upload_usage" ATTR_BILLING_MONTH = "billing_month" ATTR_CURRENT_GASMETER = "current_gasmeter" ATTR_PAYMENT = "gas_payment" ATTR_GAS_CONSUMPTION = "gas_consumption" ATTR_EXTRA_GAS = "extra_gas" ATTR_BILL_AMOUNT = "billing_amount" ATTR_HTTPS_RESULT = "https_result" ATTR_UPLOAD_DATETIME = "upload_datetime" ATTR_USAGE = "usage" ATTR_LIST = [ ATTR_BILLING_MONTH, ATTR_CURRENT_GASMETER, ATTR_PAYMENT, ATTR_GAS_CONSUMPTION, ATTR_EXTRA_GAS, ATTR_BILL_AMOUNT, ATTR_HTTPS_RESULT ] CONF_GASID = "gasid" CONF_COOKIE = "cookie" # 5308 characters CONF_VIEWSTATE = "viewstate" # 668 characters CONF_VIEWSTATEGENERATOR = "viewstategenerator" # 636 characters CONF_VIEWSTATE4UPLOAD = "viewstate4upload" ATTRIBUTION = "Powered by ShinHu Gas Data" MANUFACTURER = "ShinHu Gas" HA_USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.72 Safari/537.36 OPR/38.0.2220.41" BASE_URL = 'http://6172.16.31.10/shinhuwww/member_main.aspx' REQUEST_TIMEOUT = 10 # seconds
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molecules/ml/callbacks/loss_callback.py
braceal/molecules
4
74526
<reponame>braceal/molecules<filename>molecules/ml/callbacks/loss_callback.py import json import wandb import numpy as np from .callback import Callback class LossCallback(Callback): def __init__(self, path, interval=1, wandb_config=None, mpi_comm=None): """ Parameters ---------- path : str path to save loss history to interval : int Plots every interval epochs, default is once per epoch. wandb_config : wandb configuration file mpi_comm : mpi communicator for distributed training """ super().__init__(interval, mpi_comm) self.path = path self.wandb_config = wandb_config def on_train_begin(self, logs): self.epochs = [] self.train_losses = {} self.valid_losses = {} def on_epoch_end(self, epoch, logs): # Only store loss every self.interval epochs if epoch % self.interval != 0: return # epochs self.epochs.append(epoch) # train_losses for lossname in [x for x in logs if x.startswith("train_loss")]: # reduce losses if self.comm is not None: lossarr = np.array(logs[lossname], dtype = np.float32) logs[lossname] = np.asscalar(self.comm.allreduce(lossarr)) logs[lossname] /= float(self.comm.Get_size()) # manual logging if lossname in self.train_losses: self.train_losses[lossname].append(logs[lossname]) else: self.train_losses[lossname] = [logs[lossname]] # wandb if self.wandb_config is not None: wandb.log({lossname: logs[lossname]}, step = logs["global_step"]) # validation losses for lossname in [x for x in logs if x.startswith("valid_loss")]: # reduce losses if self.comm is not None: lossarr = np.array(logs[lossname], dtype = np.float32) logs[lossname] = np.asscalar(self.comm.allreduce(lossarr)) logs[lossname] /= float(self.comm.Get_size()) # manual logging if lossname in self.valid_losses: self.valid_losses[lossname].append(logs[lossname]) else: self.valid_losses[lossname] = [logs[lossname]] # wandb if self.wandb_config is not None: wandb.log({lossname: logs[lossname]}, step = logs["global_step"]) # save to json for manual logging if self.is_eval_node: self.save(self.path) def save(self, path): """ Save train and validation loss from the end of each epoch. Parameters ---------- path: str Path to save train and validation loss history """ # Happens when loading from a checkpoint if self.epochs[0] != 1: with open(path) as f: data = json.load(f) if data: # Prepend data from checkpointed model to the start of the # current logs. This avoids needing to load the data every # time the logs are saved. # epochs data['epochs'].extend(self.epochs) self.epochs = data['epochs'] # train losses for lossname in [x for x in data if x.startswith("train_loss")]: data[lossname].extend(self.train_losses[lossname]) self.train_losses[lossname] = data[lossname] # valid losses for lossname in [x for x in data if x.startswith("valid_loss")]: data[lossname].extend(self.valid_losses[lossname]) self.valid_losses[lossname] = data[lossname] # Write history to disk with open(path, 'w') as f: # construct json dump file jsondict = {} for lossname in [x for x in self.train_losses if x.startswith("train_loss")]: jsondict[lossname] = self.train_losses[lossname] for lossname in [x for x in self.valid_losses if x.startswith("valid_loss")]: jsondict[lossname] = self.valid_losses[lossname] jsondict['epochs'] = self.epochs json.dump(jsondict, f)
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Json_handling/read_write.py
hbachchas/myexp
0
1608808
# example from: https://stackabuse.com/reading-and-writing-json-to-a-file-in-python/ import json data = {} with open('imagenet_class_index.json') as json_file: data = json.load(json_file) with open('imagenet_class_index_indented.json', 'w') as outfile: json.dump(data, outfile, sort_keys=True, indent=4)
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python_parikshith21/Day15.py
01coders/50-Days-Of-Code
0
89308
# -*- coding: utf-8 -*- """ Created on Wed May 22 17:54:15 2019 @author: Parikshith.H """ L1 = [10,20,30,40] print(L1) print(L1[0]) print(L1[3]) L1[3] = 50 print(L1) #output: # ============================================================================= # [10, 20, 30, 40] # 10 # 40 # [10, 20, 30, 50] # ============================================================================= L2 = [10,2.5,"hello"] print(L2) print(L2[2]) L2[1] = "welcome" #lists are mutable print(L2) # ============================================================================= # #output: # [10, 2.5, 'hello'] # hello # [10, 'welcome', 'hello'] # ============================================================================= l3 = [10,20,'hello',['A','B']] #4 elements int,int,string,list #it has list #inside a list print(l3[3]) print(l3[2]) print(l3[3][0])#in list3 acces 4th element inside that 1st element # ============================================================================= # #output: # ['A', 'B'] # hello # A # ============================================================================= l4 = [[10,20,30],[2,5,6]] print(l4) # ============================================================================= # #output: # [[10, 20, 30], [2, 5, 6]] # =============================================================================
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taxi_zebra/ui.py
sephii/taxi-zebra
5
41720
<reponame>sephii/taxi-zebra import inspect from collections import namedtuple import click from taxi.aliases import aliases_database from taxi.backends import PushEntryFailed from .roles import NEVER_SAVE_ROLE_ID from .utils import get_role_id_from_alias, update_alias_mapping class CancelInput(Exception): pass Option = namedtuple('Option', ['value', 'label', 'key', 'style']) Option.__new__.__defaults__ = (None, {}) def format_response_messages(response_json): """ Show all messages in the `messages` key of the given dict. """ message_type_kwargs = { 'warning': {'fg': 'yellow'}, 'error': {'fg': 'red'}, } return [ click.style( message['text'], **message_type_kwargs.get(message['type'], {}) ) for message in response_json.get('messages', []) ] def prompt_options(message, options, default=None): def get_option_key(pos, option): return option.key if option.key is not None else pos enumerated_options = list(enumerate(options)) options_by_key = [(get_option_key(i, option), option) for i, option in enumerated_options] options_by_key_dict = dict(options_by_key) try: default_option_id = next( key for key, option in options_by_key if option.value == default.value ) if default else None except StopIteration: default_option_id = None click.secho(message + "\n", bold=True) for option_key, option in options_by_key: if option.value is not None: click.echo( click.style("[{}]".format(option_key), fg='yellow', **option.style) + " " + click.style(option.label, **option.style) ) else: click.echo(option.label) click.echo() prompt_message_default = " (leave empty for {})".format(click.style(default.label, bold=True)) if default else "" prompt_message = "Select a role{}".format(prompt_message_default) while True: option_id = click.prompt( prompt_message, default=str(default_option_id) if default_option_id is not None else None, show_default=False ).lstrip('[').rstrip(']') try: option_id = int(option_id) except ValueError: pass try: return options_by_key_dict[option_id][0] except KeyError: click.secho("`{}` is not a a valid option. Please try again.".format(option_id), fg='red') return option_id def input_role(roles, project_team, default_role=None): """ Show a list of roles to the user and ask them to select one. Return the selected `Role`, or `None` if individual action was chosen. """ def role_to_option(role): highlight = role.parent_id and role.parent_id == project_team return Option( value=role, label=role.full_name, style={'bold': True} if highlight else {} ) individual_action = 'i' cancel = 'c' sorted_roles = sorted(roles, key=lambda role: role.full_name) options = [ role_to_option(role) for role in sorted_roles ] + [ Option(value=None, label='-----'), Option(value=individual_action, label="Individual action", key=individual_action), Option(value=cancel, label="Cancel, skip this entry for now", key=cancel), ] default_option = role_to_option(default_role) if default_role else None try: selected_role = prompt_options( message='In which role do you want to push this entry?', options=options, default=default_option ) except click.exceptions.Abort: selected_role = cancel if selected_role == cancel: click.echo() raise CancelInput() elif selected_role == individual_action: selected_role = None return selected_role def prompt_role(entry, roles, context, default_role=None): """ Ask the user to choose a role in `roles` for the given `entry` and return it. """ mapping = aliases_database[entry.alias] project = context['projects_db'].get(mapping.mapping[0], mapping.backend) project_team = project.team if project else None try: role = input_role(roles, project_team, default_role=default_role) except CancelInput: raise PushEntryFailed("Skipped") # Do not ask to save role association if user has requested to never be asked for it if role and get_role_id_from_alias(entry.alias) != NEVER_SAVE_ROLE_ID: click.echo("You have selected the role {}".format(click.style(role.full_name, fg='yellow'))) prompt_kwargs = { 'prompt_suffix': ' ', 'type': click.Choice(['y', 'n', 'N']), 'default': 'y' } # `show_choices` has been added in click 7.0. Support for click < 7 is needed for distributions # that only provide click 6 in their package managers if 'show_choices' in inspect.signature(click.prompt).parameters: prompt_kwargs['show_choices'] = False try: create_alias = click.prompt( "Make the {} alias always use this role? ([y]es, [n]o, [N]ever)".format( click.style(entry.alias, fg='yellow') ), **prompt_kwargs ) except click.exceptions.Abort: click.echo() raise PushEntryFailed("Skipped") if create_alias == 'y': update_alias_mapping( context['settings'], entry.alias, aliases_database[entry.alias].mapping[:2] + (str(role.id),) ) click.secho("Alias {} now points to the role {}".format( entry.alias, role.full_name ), fg='green') elif create_alias == 'N': update_alias_mapping( context['settings'], entry.alias, aliases_database[entry.alias].mapping[:2] + (NEVER_SAVE_ROLE_ID,) ) return role
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deliver/social/api/serializers.py
Untouchable17/social-web
1
113534
from rest_framework import serializers from social.models import Post, Comment, UserProfile class CommentSerializer(serializers.ModelSerializer): class Meta: model = Comment fields = '__all__' class PostSerializer(serializers.ModelSerializer): comments = CommentSerializer(many=True, required=False) author = serializers.ReadOnlyField(source='author.username') title = serializers.CharField(required=True) slug = serializers.SlugField(required=True) body = serializers.CharField(required=True) photo = serializers.ImageField(required=False) created_at = serializers.DateTimeField(required=False) class Meta: model = Post fields = ( 'id', 'author', 'title', 'slug', 'body', 'photo', 'created_at', 'comments' ) class UserProfileSerializer(serializers.ModelSerializer): user = serializers.ReadOnlyField(source='profile.username') name = serializers.CharField(required=True) bio = serializers.CharField(required=True) birth_date = serializers.TimeField(required=True) location = serializers.CharField(required=True) picture = serializers.ImageField(required=True) class Meta: model = UserProfile fields = ( 'id', 'user', 'name', 'bio', 'birth_date', 'location', 'picture' )
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sdk/textanalytics/azure-ai-textanalytics/azure/ai/textanalytics/_models.py
rsdoherty/azure-sdk-for-python
1
1607742
<reponame>rsdoherty/azure-sdk-for-python # coding=utf-8 pylint: disable=too-many-lines # ------------------------------------ # Copyright (c) Microsoft Corporation. # Licensed under the MIT License. # ------------------------------------ import re from enum import Enum from ._generated.models import ( LanguageInput, MultiLanguageInput, ) from ._generated.v3_0 import models as _v3_0_models from ._generated.v3_2_preview_1 import models as _v3_2_preview_1_models from ._version import DEFAULT_API_VERSION def _get_indices(relation): return [int(s) for s in re.findall(r"\d+", relation)] class DictMixin(object): def __setitem__(self, key, item): self.__dict__[key] = item def __getitem__(self, key): return self.__dict__[key] def __repr__(self): return str(self) def __len__(self): return len(self.keys()) def __delitem__(self, key): self.__dict__[key] = None def __eq__(self, other): """Compare objects by comparing all attributes.""" if isinstance(other, self.__class__): return self.__dict__ == other.__dict__ return False def __ne__(self, other): """Compare objects by comparing all attributes.""" return not self.__eq__(other) def __contains__(self, key): return key in self.__dict__ def __str__(self): return str({k: v for k, v in self.__dict__.items() if not k.startswith("_")}) def has_key(self, k): return k in self.__dict__ def update(self, *args, **kwargs): return self.__dict__.update(*args, **kwargs) def keys(self): return [k for k in self.__dict__ if not k.startswith("_")] def values(self): return [v for k, v in self.__dict__.items() if not k.startswith("_")] def items(self): return [(k, v) for k, v in self.__dict__.items() if not k.startswith("_")] def get(self, key, default=None): if key in self.__dict__: return self.__dict__[key] return default class EntityAssociation(str, Enum): """Describes if the entity is the subject of the text or if it describes someone else.""" SUBJECT = "subject" OTHER = "other" class EntityCertainty(str, Enum): """Describes the entities certainty and polarity.""" POSITIVE = "positive" POSITIVE_POSSIBLE = "positivePossible" NEUTRAL_POSSIBLE = "neutralPossible" NEGATIVE_POSSIBLE = "negativePossible" NEGATIVE = "negative" class EntityConditionality(str, Enum): """Describes any conditionality on the entity.""" HYPOTHETICAL = "hypothetical" CONDITIONAL = "conditional" class HealthcareEntityRelation(str, Enum): """Type of relation. Examples include: 'DosageOfMedication' or 'FrequencyOfMedication', etc.""" ABBREVIATION = "Abbreviation" DIRECTION_OF_BODY_STRUCTURE = "DirectionOfBodyStructure" DIRECTION_OF_CONDITION = "DirectionOfCondition" DIRECTION_OF_EXAMINATION = "DirectionOfExamination" DIRECTION_OF_TREATMENT = "DirectionOfTreatment" DOSAGE_OF_MEDICATION = "DosageOfMedication" FORM_OF_MEDICATION = "FormOfMedication" FREQUENCY_OF_MEDICATION = "FrequencyOfMedication" FREQUENCY_OF_TREATMENT = "FrequencyOfTreatment" QUALIFIER_OF_CONDITION = "QualifierOfCondition" RELATION_OF_EXAMINATION = "RelationOfExamination" ROUTE_OF_MEDICATION = "RouteOfMedication" TIME_OF_CONDITION = "TimeOfCondition" TIME_OF_EVENT = "TimeOfEvent" TIME_OF_EXAMINATION = "TimeOfExamination" TIME_OF_MEDICATION = "TimeOfMedication" TIME_OF_TREATMENT = "TimeOfTreatment" UNIT_OF_CONDITION = "UnitOfCondition" UNIT_OF_EXAMINATION = "UnitOfExamination" VALUE_OF_CONDITION = "ValueOfCondition" VALUE_OF_EXAMINATION = "ValueOfExamination" class PiiEntityCategory(str, Enum): """Categories of Personally Identifiable Information (PII).""" ABA_ROUTING_NUMBER = "ABARoutingNumber" AR_NATIONAL_IDENTITY_NUMBER = "ARNationalIdentityNumber" AU_BANK_ACCOUNT_NUMBER = "AUBankAccountNumber" AU_DRIVERS_LICENSE_NUMBER = "AUDriversLicenseNumber" AU_MEDICAL_ACCOUNT_NUMBER = "AUMedicalAccountNumber" AU_PASSPORT_NUMBER = "AUPassportNumber" AU_TAX_FILE_NUMBER = "AUTaxFileNumber" AU_BUSINESS_NUMBER = "AUBusinessNumber" AU_COMPANY_NUMBER = "AUCompanyNumber" AT_IDENTITY_CARD = "ATIdentityCard" AT_TAX_IDENTIFICATION_NUMBER = "ATTaxIdentificationNumber" AT_VALUE_ADDED_TAX_NUMBER = "ATValueAddedTaxNumber" AZURE_DOCUMENT_DB_AUTH_KEY = "AzureDocumentDBAuthKey" AZURE_IAAS_DATABASE_CONNECTION_AND_SQL_STRING = ( "AzureIAASDatabaseConnectionAndSQLString" ) AZURE_IO_T_CONNECTION_STRING = "AzureIoTConnectionString" AZURE_PUBLISH_SETTING_PASSWORD = "<PASSWORD>" AZURE_REDIS_CACHE_STRING = "AzureRedisCacheString" AZURE_SAS = "AzureSAS" AZURE_SERVICE_BUS_STRING = "AzureServiceBusString" AZURE_STORAGE_ACCOUNT_KEY = "AzureStorageAccountKey" AZURE_STORAGE_ACCOUNT_GENERIC = "AzureStorageAccountGeneric" BE_NATIONAL_NUMBER = "BENationalNumber" BE_NATIONAL_NUMBER_V2 = "BENationalNumberV2" BE_VALUE_ADDED_TAX_NUMBER = "BEValueAddedTaxNumber" BRCPF_NUMBER = "BRCPFNumber" BR_LEGAL_ENTITY_NUMBER = "BRLegalEntityNumber" BR_NATIONAL_IDRG = "BRNationalIDRG" BG_UNIFORM_CIVIL_NUMBER = "BGUniformCivilNumber" CA_BANK_ACCOUNT_NUMBER = "CABankAccountNumber" CA_DRIVERS_LICENSE_NUMBER = "CADriversLicenseNumber" CA_HEALTH_SERVICE_NUMBER = "CAHealthServiceNumber" CA_PASSPORT_NUMBER = "CAPassportNumber" CA_PERSONAL_HEALTH_IDENTIFICATION = "CAPersonalHealthIdentification" CA_SOCIAL_INSURANCE_NUMBER = "CASocialInsuranceNumber" CL_IDENTITY_CARD_NUMBER = "CLIdentityCardNumber" CN_RESIDENT_IDENTITY_CARD_NUMBER = "CNResidentIdentityCardNumber" CREDIT_CARD_NUMBER = "CreditCardNumber" HR_IDENTITY_CARD_NUMBER = "HRIdentityCardNumber" HR_NATIONAL_ID_NUMBER = "HRNationalIDNumber" HR_PERSONAL_IDENTIFICATION_NUMBER = "HRPersonalIdentificationNumber" HR_PERSONAL_IDENTIFICATION_OIB_NUMBER_V2 = "HRPersonalIdentificationOIBNumberV2" CY_IDENTITY_CARD = "CYIdentityCard" CY_TAX_IDENTIFICATION_NUMBER = "CYTaxIdentificationNumber" CZ_PERSONAL_IDENTITY_NUMBER = "CZPersonalIdentityNumber" CZ_PERSONAL_IDENTITY_V2 = "CZPersonalIdentityV2" DK_PERSONAL_IDENTIFICATION_NUMBER = "DKPersonalIdentificationNumber" DK_PERSONAL_IDENTIFICATION_V2 = "DKPersonalIdentificationV2" DRUG_ENFORCEMENT_AGENCY_NUMBER = "DrugEnforcementAgencyNumber" EE_PERSONAL_IDENTIFICATION_CODE = "EEPersonalIdentificationCode" EU_DEBIT_CARD_NUMBER = "EUDebitCardNumber" EU_DRIVERS_LICENSE_NUMBER = "EUDriversLicenseNumber" EUGPS_COORDINATES = "EUGPSCoordinates" EU_NATIONAL_IDENTIFICATION_NUMBER = "EUNationalIdentificationNumber" EU_PASSPORT_NUMBER = "EUPassportNumber" EU_SOCIAL_SECURITY_NUMBER = "EUSocialSecurityNumber" EU_TAX_IDENTIFICATION_NUMBER = "EUTaxIdentificationNumber" FI_EUROPEAN_HEALTH_NUMBER = "FIEuropeanHealthNumber" FI_NATIONAL_ID = "FINationalID" FI_NATIONAL_IDV2 = "FINationalIDV2" FI_PASSPORT_NUMBER = "FIPassportNumber" FR_DRIVERS_LICENSE_NUMBER = "FRDriversLicenseNumber" FR_HEALTH_INSURANCE_NUMBER = "FRHealthInsuranceNumber" FR_NATIONAL_ID = "FRNationalID" FR_PASSPORT_NUMBER = "FRPassportNumber" FR_SOCIAL_SECURITY_NUMBER = "FRSocialSecurityNumber" FR_TAX_IDENTIFICATION_NUMBER = "FRTaxIdentificationNumber" FR_VALUE_ADDED_TAX_NUMBER = "FRValueAddedTaxNumber" DE_DRIVERS_LICENSE_NUMBER = "DEDriversLicenseNumber" DE_PASSPORT_NUMBER = "DEPassportNumber" DE_IDENTITY_CARD_NUMBER = "DEIdentityCardNumber" DE_TAX_IDENTIFICATION_NUMBER = "DETaxIdentificationNumber" DE_VALUE_ADDED_NUMBER = "DEValueAddedNumber" GR_NATIONAL_ID_CARD = "GRNationalIDCard" GR_NATIONAL_IDV2 = "GRNationalIDV2" GR_TAX_IDENTIFICATION_NUMBER = "GRTaxIdentificationNumber" HK_IDENTITY_CARD_NUMBER = "HKIdentityCardNumber" HU_VALUE_ADDED_NUMBER = "HUValueAddedNumber" HU_PERSONAL_IDENTIFICATION_NUMBER = "HUPersonalIdentificationNumber" HU_TAX_IDENTIFICATION_NUMBER = "HUTaxIdentificationNumber" IN_PERMANENT_ACCOUNT = "INPermanentAccount" IN_UNIQUE_IDENTIFICATION_NUMBER = "INUniqueIdentificationNumber" ID_IDENTITY_CARD_NUMBER = "IDIdentityCardNumber" INTERNATIONAL_BANKING_ACCOUNT_NUMBER = "InternationalBankingAccountNumber" IE_PERSONAL_PUBLIC_SERVICE_NUMBER = "IEPersonalPublicServiceNumber" IE_PERSONAL_PUBLIC_SERVICE_NUMBER_V2 = "IEPersonalPublicServiceNumberV2" IL_BANK_ACCOUNT_NUMBER = "ILBankAccountNumber" IL_NATIONAL_ID = "ILNationalID" IT_DRIVERS_LICENSE_NUMBER = "ITDriversLicenseNumber" IT_FISCAL_CODE = "ITFiscalCode" IT_VALUE_ADDED_TAX_NUMBER = "ITValueAddedTaxNumber" JP_BANK_ACCOUNT_NUMBER = "JPBankAccountNumber" JP_DRIVERS_LICENSE_NUMBER = "JPDriversLicenseNumber" JP_PASSPORT_NUMBER = "JPPassportNumber" JP_RESIDENT_REGISTRATION_NUMBER = "JPResidentRegistrationNumber" JP_SOCIAL_INSURANCE_NUMBER = "JPSocialInsuranceNumber" JP_MY_NUMBER_CORPORATE = "JPMyNumberCorporate" JP_MY_NUMBER_PERSONAL = "JPMyNumberPersonal" JP_RESIDENCE_CARD_NUMBER = "JPResidenceCardNumber" LV_PERSONAL_CODE = "LVPersonalCode" LT_PERSONAL_CODE = "LTPersonalCode" LU_NATIONAL_IDENTIFICATION_NUMBER_NATURAL = "LUNationalIdentificationNumberNatural" LU_NATIONAL_IDENTIFICATION_NUMBER_NON_NATURAL = ( "LUNationalIdentificationNumberNonNatural" ) MY_IDENTITY_CARD_NUMBER = "MYIdentityCardNumber" MT_IDENTITY_CARD_NUMBER = "MTIdentityCardNumber" MT_TAX_ID_NUMBER = "MTTaxIDNumber" NL_CITIZENS_SERVICE_NUMBER = "NLCitizensServiceNumber" NL_CITIZENS_SERVICE_NUMBER_V2 = "NLCitizensServiceNumberV2" NL_TAX_IDENTIFICATION_NUMBER = "NLTaxIdentificationNumber" NL_VALUE_ADDED_TAX_NUMBER = "NLValueAddedTaxNumber" NZ_BANK_ACCOUNT_NUMBER = "NZBankAccountNumber" NZ_DRIVERS_LICENSE_NUMBER = "NZDriversLicenseNumber" NZ_INLAND_REVENUE_NUMBER = "NZInlandRevenueNumber" NZ_MINISTRY_OF_HEALTH_NUMBER = "NZMinistryOfHealthNumber" NZ_SOCIAL_WELFARE_NUMBER = "NZSocialWelfareNumber" NO_IDENTITY_NUMBER = "NOIdentityNumber" PH_UNIFIED_MULTI_PURPOSE_ID_NUMBER = "PHUnifiedMultiPurposeIDNumber" PL_IDENTITY_CARD = "PLIdentityCard" PL_NATIONAL_ID = "PLNationalID" PL_NATIONAL_IDV2 = "PLNationalIDV2" PL_PASSPORT_NUMBER = "PLPassportNumber" PL_TAX_IDENTIFICATION_NUMBER = "PLTaxIdentificationNumber" PLREGON_NUMBER = "PLREGONNumber" PT_CITIZEN_CARD_NUMBER = "PTCitizenCardNumber" PT_CITIZEN_CARD_NUMBER_V2 = "PTCitizenCardNumberV2" PT_TAX_IDENTIFICATION_NUMBER = "PTTaxIdentificationNumber" RO_PERSONAL_NUMERICAL_CODE = "ROPersonalNumericalCode" RU_PASSPORT_NUMBER_DOMESTIC = "RUPassportNumberDomestic" RU_PASSPORT_NUMBER_INTERNATIONAL = "RUPassportNumberInternational" SA_NATIONAL_ID = "SANationalID" SG_NATIONAL_REGISTRATION_IDENTITY_CARD_NUMBER = ( "SGNationalRegistrationIdentityCardNumber" ) SK_PERSONAL_NUMBER = "SKPersonalNumber" SI_TAX_IDENTIFICATION_NUMBER = "SITaxIdentificationNumber" SI_UNIQUE_MASTER_CITIZEN_NUMBER = "SIUniqueMasterCitizenNumber" ZA_IDENTIFICATION_NUMBER = "ZAIdentificationNumber" KR_RESIDENT_REGISTRATION_NUMBER = "KRResidentRegistrationNumber" ESDNI = "ESDNI" ES_SOCIAL_SECURITY_NUMBER = "ESSocialSecurityNumber" ES_TAX_IDENTIFICATION_NUMBER = "ESTaxIdentificationNumber" SQL_SERVER_CONNECTION_STRING = "SQLServerConnectionString" SE_NATIONAL_ID = "SENationalID" SE_NATIONAL_IDV2 = "SENationalIDV2" SE_PASSPORT_NUMBER = "SEPassportNumber" SE_TAX_IDENTIFICATION_NUMBER = "SETaxIdentificationNumber" SWIFT_CODE = "SWIFTCode" CH_SOCIAL_SECURITY_NUMBER = "CHSocialSecurityNumber" TW_NATIONAL_ID = "TWNationalID" TW_PASSPORT_NUMBER = "TWPassportNumber" TW_RESIDENT_CERTIFICATE = "TWResidentCertificate" TH_POPULATION_IDENTIFICATION_CODE = "THPopulationIdentificationCode" TR_NATIONAL_IDENTIFICATION_NUMBER = "TRNationalIdentificationNumber" UK_DRIVERS_LICENSE_NUMBER = "UKDriversLicenseNumber" UK_ELECTORAL_ROLL_NUMBER = "UKElectoralRollNumber" UK_NATIONAL_HEALTH_NUMBER = "UKNationalHealthNumber" UK_NATIONAL_INSURANCE_NUMBER = "UKNationalInsuranceNumber" UK_UNIQUE_TAXPAYER_NUMBER = "UKUniqueTaxpayerNumber" USUK_PASSPORT_NUMBER = "USUKPassportNumber" US_BANK_ACCOUNT_NUMBER = "USBankAccountNumber" US_DRIVERS_LICENSE_NUMBER = "USDriversLicenseNumber" US_INDIVIDUAL_TAXPAYER_IDENTIFICATION = "USIndividualTaxpayerIdentification" US_SOCIAL_SECURITY_NUMBER = "USSocialSecurityNumber" UA_PASSPORT_NUMBER_DOMESTIC = "UAPassportNumberDomestic" UA_PASSPORT_NUMBER_INTERNATIONAL = "UAPassportNumberInternational" ORGANIZATION = "Organization" EMAIL = "Email" URL = "URL" AGE = "Age" PHONE_NUMBER = "PhoneNumber" IP_ADDRESS = "IPAddress" DATE = "Date" PERSON = "Person" ADDRESS = "Address" ALL = "All" DEFAULT = "Default" class HealthcareEntityCategory(str, Enum): """Healthcare Entity Category.""" BODY_STRUCTURE = "BodyStructure" AGE = "Age" GENDER = "Gender" EXAMINATION_NAME = "ExaminationName" DATE = "Date" DIRECTION = "Direction" FREQUENCY = "Frequency" MEASUREMENT_VALUE = "MeasurementValue" MEASUREMENT_UNIT = "MeasurementUnit" RELATIONAL_OPERATOR = "RelationalOperator" TIME = "Time" GENE_OR_PROTEIN = "GeneOrProtein" VARIANT = "Variant" ADMINISTRATIVE_EVENT = "AdministrativeEvent" CARE_ENVIRONMENT = "CareEnvironment" HEALTHCARE_PROFESSION = "HealthcareProfession" DIAGNOSIS = "Diagnosis" SYMPTOM_OR_SIGN = "SymptomOrSign" CONDITION_QUALIFIER = "ConditionQualifier" MEDICATION_CLASS = "MedicationClass" MEDICATION_NAME = "MedicationName" DOSAGE = "Dosage" MEDICATION_FORM = "MedicationForm" MEDICATION_ROUTE = "MedicationRoute" FAMILY_RELATION = "FamilyRelation" TREATMENT_NAME = "TreatmentName" class PiiEntityDomain(str, Enum): """The different domains of PII entities that users can filter by""" PROTECTED_HEALTH_INFORMATION = ( "phi" # See https://aka.ms/tanerpii for more information. ) class DetectedLanguage(DictMixin): """DetectedLanguage contains the predicted language found in text, its confidence score, and its ISO 639-1 representation. :ivar name: Long name of a detected language (e.g. English, French). :vartype name: str :ivar iso6391_name: A two letter representation of the detected language according to the ISO 639-1 standard (e.g. en, fr). :vartype iso6391_name: str :ivar confidence_score: A confidence score between 0 and 1. Scores close to 1 indicate 100% certainty that the identified language is true. :vartype confidence_score: float """ def __init__(self, **kwargs): self.name = kwargs.get("name", None) self.iso6391_name = kwargs.get("iso6391_name", None) self.confidence_score = kwargs.get("confidence_score", None) @classmethod def _from_generated(cls, language): return cls( name=language.name, iso6391_name=language.iso6391_name, confidence_score=language.confidence_score, ) def __repr__(self): return "DetectedLanguage(name={}, iso6391_name={}, confidence_score={})".format( self.name, self.iso6391_name, self.confidence_score )[:1024] class RecognizeEntitiesResult(DictMixin): """RecognizeEntitiesResult is a result object which contains the recognized entities from a particular document. :ivar id: Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document. :vartype id: str :ivar entities: Recognized entities in the document. :vartype entities: list[~azure.ai.textanalytics.CategorizedEntity] :ivar warnings: Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate. :vartype warnings: list[~azure.ai.textanalytics.TextAnalyticsWarning] :ivar statistics: If `show_stats=True` was specified in the request this field will contain information about the document payload. :vartype statistics: ~azure.ai.textanalytics.TextDocumentStatistics :ivar bool is_error: Boolean check for error item when iterating over list of results. Always False for an instance of a RecognizeEntitiesResult. """ def __init__(self, **kwargs): self.id = kwargs.get("id", None) self.entities = kwargs.get("entities", None) self.warnings = kwargs.get("warnings", []) self.statistics = kwargs.get("statistics", None) self.is_error = False def __repr__(self): return "RecognizeEntitiesResult(id={}, entities={}, warnings={}, statistics={}, is_error={})".format( self.id, repr(self.entities), repr(self.warnings), repr(self.statistics), self.is_error, )[ :1024 ] class RecognizePiiEntitiesResult(DictMixin): """RecognizePiiEntitiesResult is a result object which contains the recognized Personally Identifiable Information (PII) entities from a particular document. :ivar str id: Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document. :ivar entities: Recognized PII entities in the document. :vartype entities: list[~azure.ai.textanalytics.PiiEntity] :ivar str redacted_text: Returns the text of the input document with all of the PII information redacted out. :ivar warnings: Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate. :vartype warnings: list[~azure.ai.textanalytics.TextAnalyticsWarning] :ivar statistics: If `show_stats=True` was specified in the request this field will contain information about the document payload. :vartype statistics: ~azure.ai.textanalytics.TextDocumentStatistics :ivar bool is_error: Boolean check for error item when iterating over list of results. Always False for an instance of a RecognizePiiEntitiesResult. """ def __init__(self, **kwargs): self.id = kwargs.get("id", None) self.entities = kwargs.get("entities", None) self.redacted_text = kwargs.get("redacted_text", None) self.warnings = kwargs.get("warnings", []) self.statistics = kwargs.get("statistics", None) self.is_error = False def __repr__(self): return ( "RecognizePiiEntitiesResult(id={}, entities={}, redacted_text={}, warnings={}, " "statistics={}, is_error={})".format( self.id, repr(self.entities), self.redacted_text, repr(self.warnings), repr(self.statistics), self.is_error, )[:1024] ) class AnalyzeHealthcareEntitiesResult(DictMixin): """ AnalyzeHealthcareEntitiesResult contains the Healthcare entities from a particular document. :ivar str id: Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document. :ivar entities: Identified Healthcare entities in the document, i.e. in the document "The subject took ibuprofen", "ibuprofen" is an identified entity from the document. :vartype entities: list[~azure.ai.textanalytics.HealthcareEntity] :ivar entity_relations: Identified Healthcare relations between entities. For example, in the document "The subject took 100mg of ibuprofen", we would identify the relationship between the dosage of 100mg and the medication ibuprofen. :vartype entity_relations: list[~azure.ai.textanalytics.HealthcareRelation] :ivar warnings: Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate. :vartype warnings: list[~azure.ai.textanalytics.TextAnalyticsWarning] :ivar statistics: If show_stats=true was specified in the request this field will contain information about the document payload. :vartype statistics: ~azure.ai.textanalytics.TextDocumentStatistics :ivar bool is_error: Boolean check for error item when iterating over list of results. Always False for an instance of a AnalyzeHealthcareEntitiesResult. """ def __init__(self, **kwargs): self.id = kwargs.get("id", None) self.entities = kwargs.get("entities", None) self.entity_relations = kwargs.get("entity_relations", None) self.warnings = kwargs.get("warnings", []) self.statistics = kwargs.get("statistics", None) self.is_error = False @classmethod def _from_generated(cls, healthcare_result): entities = [ HealthcareEntity._from_generated(e) # pylint: disable=protected-access for e in healthcare_result.entities ] relations = [ HealthcareRelation._from_generated( # pylint: disable=protected-access r, entities ) for r in healthcare_result.relations ] return cls( id=healthcare_result.id, entities=entities, entity_relations=relations, warnings=[ TextAnalyticsWarning._from_generated( # pylint: disable=protected-access w ) for w in healthcare_result.warnings ], statistics=TextDocumentStatistics._from_generated( # pylint: disable=protected-access healthcare_result.statistics ), ) def __repr__(self): return ( "AnalyzeHealthcareEntitiesResult(id={}, entities={}, entity_relations={}, warnings={}, " "statistics={}, is_error={})".format( self.id, repr(self.entities), repr(self.entity_relations), repr(self.warnings), repr(self.statistics), self.is_error, )[:1024] ) class HealthcareRelation(DictMixin): """HealthcareRelation is a result object which represents a relation detected in a document. Every HealthcareRelation is an entity graph of a certain relation type, where all entities are connected and have specific roles within the relation context. :ivar relation_type: The type of relation, i.e. the relationship between "100mg" and "ibuprofen" in the document "The subject took 100 mg of ibuprofen" is "DosageOfMedication". Possible values found in :class:`~azure.ai.textanalytics.HealthcareEntityRelation` :vartype relation_type: str :ivar roles: The roles present in this relation. I.e., in the document "The subject took 100 mg of ibuprofen", the present roles are "Dosage" and "Medication". :vartype roles: list[~azure.ai.textanalytics.HealthcareRelationRole] """ def __init__(self, **kwargs): self.relation_type = kwargs.get("relation_type") self.roles = kwargs.get("roles") @classmethod def _from_generated(cls, healthcare_relation_result, entities): roles = [ HealthcareRelationRole._from_generated( # pylint: disable=protected-access r, entities ) for r in healthcare_relation_result.entities ] return cls( relation_type=healthcare_relation_result.relation_type, roles=roles, ) def __repr__(self): return "HealthcareRelation(relation_type={}, roles={})".format( self.relation_type, repr(self.roles), )[:1024] class HealthcareRelationRole(DictMixin): """A model representing a role in a relation. For example, in "The subject took 100 mg of ibuprofen", "100 mg" is a dosage entity fulfilling the role "Dosage" in the extracted relation "DosageofMedication". :ivar name: The role of the entity in the relationship. I.e., in the relation "The subject took 100 mg of ibuprofen", the dosage entity "100 mg" has role "Dosage". :vartype name: str :ivar entity: The entity that is present in the relationship. For example, in "The subject took 100 mg of ibuprofen", this property holds the dosage entity of "100 mg". :vartype entity: ~azure.ai.textanalytics.HealthcareEntity """ def __init__(self, **kwargs): self.name = kwargs.get("name") self.entity = kwargs.get("entity") @staticmethod def _get_entity(healthcare_role_result, entities): nums = _get_indices(healthcare_role_result.ref) entity_index = nums[ 1 ] # first num parsed from index is document #, second is entity index return entities[entity_index] @classmethod def _from_generated(cls, healthcare_role_result, entities): return cls( name=healthcare_role_result.role, entity=HealthcareRelationRole._get_entity(healthcare_role_result, entities), ) def __repr__(self): return "HealthcareRelationRole(name={}, entity={})".format( self.name, repr(self.entity) ) class DetectLanguageResult(DictMixin): """DetectLanguageResult is a result object which contains the detected language of a particular document. :ivar id: Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document. :vartype id: str :ivar primary_language: The primary language detected in the document. :vartype primary_language: ~azure.ai.textanalytics.DetectedLanguage :ivar warnings: Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate. :vartype warnings: list[~azure.ai.textanalytics.TextAnalyticsWarning] :ivar statistics: If `show_stats=True` was specified in the request this field will contain information about the document payload. :vartype statistics: ~azure.ai.textanalytics.TextDocumentStatistics :ivar bool is_error: Boolean check for error item when iterating over list of results. Always False for an instance of a DetectLanguageResult. """ def __init__(self, **kwargs): self.id = kwargs.get("id", None) self.primary_language = kwargs.get("primary_language", None) self.warnings = kwargs.get("warnings", []) self.statistics = kwargs.get("statistics", None) self.is_error = False def __repr__(self): return ( "DetectLanguageResult(id={}, primary_language={}, warnings={}, statistics={}, " "is_error={})".format( self.id, repr(self.primary_language), repr(self.warnings), repr(self.statistics), self.is_error, )[:1024] ) class CategorizedEntity(DictMixin): """CategorizedEntity contains information about a particular entity found in text. :ivar text: Entity text as appears in the request. :vartype text: str :ivar category: Entity category, such as Person/Location/Org/SSN etc :vartype category: str :ivar subcategory: Entity subcategory, such as Age/Year/TimeRange etc :vartype subcategory: str :ivar int length: The entity text length. This value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoints by default. :ivar int offset: The entity text offset from the start of the document. The value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoints by default. :ivar confidence_score: Confidence score between 0 and 1 of the extracted entity. :vartype confidence_score: float .. versionadded:: v3.1 The *offset* and *length* properties. """ def __init__(self, **kwargs): self.text = kwargs.get("text", None) self.category = kwargs.get("category", None) self.subcategory = kwargs.get("subcategory", None) self.length = kwargs.get("length", None) self.offset = kwargs.get("offset", None) self.confidence_score = kwargs.get("confidence_score", None) @classmethod def _from_generated(cls, entity): offset = entity.offset length = entity.length if isinstance(entity, _v3_0_models.Entity): # we do not return offset for v3.0 since # the correct encoding was not introduced for v3.0 offset = None length = None return cls( text=entity.text, category=entity.category, subcategory=entity.subcategory, length=length, offset=offset, confidence_score=entity.confidence_score, ) def __repr__(self): return ( "CategorizedEntity(text={}, category={}, subcategory={}, " "length={}, offset={}, confidence_score={})".format( self.text, self.category, self.subcategory, self.length, self.offset, self.confidence_score, )[:1024] ) class PiiEntity(DictMixin): """PiiEntity contains information about a Personally Identifiable Information (PII) entity found in text. :ivar str text: Entity text as appears in the request. :ivar str category: Entity category, such as Financial Account Identification/Social Security Number/Phone Number, etc. :ivar str subcategory: Entity subcategory, such as Credit Card/EU Phone number/ABA Routing Numbers, etc. :ivar int length: The PII entity text length. This value depends on the value of the `string_index_type` parameter specified in the original request, which is UnicodeCodePoints by default. :ivar int offset: The PII entity text offset from the start of the document. This value depends on the value of the `string_index_type` parameter specified in the original request, which is UnicodeCodePoints by default. :ivar float confidence_score: Confidence score between 0 and 1 of the extracted entity. """ def __init__(self, **kwargs): self.text = kwargs.get("text", None) self.category = kwargs.get("category", None) self.subcategory = kwargs.get("subcategory", None) self.length = kwargs.get("length", None) self.offset = kwargs.get("offset", None) self.confidence_score = kwargs.get("confidence_score", None) @classmethod def _from_generated(cls, entity): return cls( text=entity.text, category=entity.category, subcategory=entity.subcategory, length=entity.length, offset=entity.offset, confidence_score=entity.confidence_score, ) def __repr__(self): return ( "PiiEntity(text={}, category={}, subcategory={}, length={}, " "offset={}, confidence_score={})".format( self.text, self.category, self.subcategory, self.length, self.offset, self.confidence_score, )[:1024] ) class HealthcareEntity(DictMixin): """HealthcareEntity contains information about a Healthcare entity found in text. :ivar str text: Entity text as appears in the document. :ivar str normalized_text: Optional. Normalized version of the raw `text` we extract from the document. Not all `text` will have a normalized version. :ivar str category: Entity category, see the :class:`~azure.ai.textanalytics.HealthcareEntityCategory` type for possible healthcare entity categories. :ivar str subcategory: Entity subcategory. :ivar assertion: Contains various assertions about this entity. For example, if an entity is a diagnosis, is this diagnosis 'conditional' on a symptom? Are the doctors 'certain' about this diagnosis? Is this diagnosis 'associated' with another diagnosis? :vartype assertion: ~azure.ai.textanalytics.HealthcareEntityAssertion :ivar int length: The entity text length. This value depends on the value of the `string_index_type` parameter specified in the original request, which is UnicodeCodePoints by default. :ivar int offset: The entity text offset from the start of the document. This value depends on the value of the `string_index_type` parameter specified in the original request, which is UnicodeCodePoints by default. :ivar float confidence_score: Confidence score between 0 and 1 of the extracted entity. :ivar data_sources: A collection of entity references in known data sources. :vartype data_sources: list[~azure.ai.textanalytics.HealthcareEntityDataSource] """ def __init__(self, **kwargs): self.text = kwargs.get("text", None) self.normalized_text = kwargs.get("normalized_text", None) self.category = kwargs.get("category", None) self.subcategory = kwargs.get("subcategory", None) self.assertion = kwargs.get("assertion", None) self.length = kwargs.get("length", None) self.offset = kwargs.get("offset", None) self.confidence_score = kwargs.get("confidence_score", None) self.data_sources = kwargs.get("data_sources", []) @classmethod def _from_generated(cls, healthcare_entity): assertion = None try: if healthcare_entity.assertion: assertion = HealthcareEntityAssertion._from_generated( # pylint: disable=protected-access healthcare_entity.assertion ) except AttributeError: assertion = None return cls( text=healthcare_entity.text, normalized_text=healthcare_entity.name, category=healthcare_entity.category, subcategory=healthcare_entity.subcategory, assertion=assertion, length=healthcare_entity.length, offset=healthcare_entity.offset, confidence_score=healthcare_entity.confidence_score, data_sources=[ HealthcareEntityDataSource(entity_id=l.id, name=l.data_source) for l in healthcare_entity.links ] if healthcare_entity.links else None, ) def __hash__(self): return hash(repr(self)) def __repr__(self): return ( "HealthcareEntity(text={}, normalized_text={}, category={}, subcategory={}, assertion={}, length={}, " "offset={}, confidence_score={}, data_sources={})".format( self.text, self.normalized_text, self.category, self.subcategory, repr(self.assertion), self.length, self.offset, self.confidence_score, repr(self.data_sources), )[:1024] ) class HealthcareEntityAssertion(DictMixin): """Contains various assertions about a `HealthcareEntity`. For example, if an entity is a diagnosis, is this diagnosis 'conditional' on a symptom? Are the doctors 'certain' about this diagnosis? Is this diagnosis 'associated' with another diagnosis? :ivar str conditionality: Describes whether the healthcare entity it's on is conditional on another entity. For example, "If the patient has a fever, he has pneumonia", the diagnosis of pneumonia is 'conditional' on whether the patient has a fever. Possible values are "hypothetical" and "conditional". :ivar str certainty: Describes how certain the healthcare entity it's on is. For example, in "The patient may have a fever", the fever entity is not 100% certain, but is instead "positivePossible". Possible values are "positive", "positivePossible", "neutralPossible", "negativePossible", and "negative". :ivar str association: Describes whether the healthcare entity it's on is the subject of the document, or if this entity describes someone else in the document. For example, in "The subject's mother has a fever", the "fever" entity is not associated with the subject themselves, but with the subject's mother. Possible values are "subject" and "other". """ def __init__(self, **kwargs): self.conditionality = kwargs.get("conditionality", None) self.certainty = kwargs.get("certainty", None) self.association = kwargs.get("association", None) @classmethod def _from_generated(cls, healthcare_assertion): return cls( conditionality=healthcare_assertion.conditionality, certainty=healthcare_assertion.certainty, association=healthcare_assertion.association, ) def __repr__(self): return "HealthcareEntityAssertion(conditionality={}, certainty={}, association={})".format( self.conditionality, self.certainty, self.association ) class HealthcareEntityDataSource(DictMixin): """ HealthcareEntityDataSource contains information representing an entity reference in a known data source. :ivar str entity_id: ID of the entity in the given source catalog. :ivar str name: The name of the entity catalog from where the entity was identified, such as UMLS, CHV, MSH, etc. """ def __init__(self, **kwargs): self.entity_id = kwargs.get("entity_id", None) self.name = kwargs.get("name", None) def __repr__(self): return "HealthcareEntityDataSource(entity_id={}, name={})".format( self.entity_id, self.name )[:1024] class TextAnalyticsError(DictMixin): """TextAnalyticsError contains the error code, message, and other details that explain why the batch or individual document failed to be processed by the service. :ivar code: Error code. Possible values include: 'invalidRequest', 'invalidArgument', 'internalServerError', 'serviceUnavailable', 'invalidParameterValue', 'invalidRequestBodyFormat', 'emptyRequest', 'missingInputRecords', 'invalidDocument', 'modelVersionIncorrect', 'invalidDocumentBatch', 'unsupportedLanguageCode', 'invalidCountryHint' :vartype code: str :ivar message: Error message. :vartype message: str :ivar target: Error target. :vartype target: str """ def __init__(self, **kwargs): self.code = kwargs.get("code", None) self.message = kwargs.get("message", None) self.target = kwargs.get("target", None) @classmethod def _from_generated(cls, err): if err.innererror: return cls( code=err.innererror.code, message=err.innererror.message, target=err.innererror.target, ) return cls(code=err.code, message=err.message, target=err.target) def __repr__(self): return "TextAnalyticsError(code={}, message={}, target={})".format( self.code, self.message, self.target )[:1024] class TextAnalyticsWarning(DictMixin): """TextAnalyticsWarning contains the warning code and message that explains why the response has a warning. :ivar code: Warning code. Possible values include: 'LongWordsInDocument', 'DocumentTruncated'. :vartype code: str :ivar message: Warning message. :vartype message: str """ def __init__(self, **kwargs): self.code = kwargs.get("code", None) self.message = kwargs.get("message", None) @classmethod def _from_generated(cls, warning): return cls( code=warning.code, message=warning.message, ) def __repr__(self): return "TextAnalyticsWarning(code={}, message={})".format( self.code, self.message )[:1024] class ExtractKeyPhrasesResult(DictMixin): """ExtractKeyPhrasesResult is a result object which contains the key phrases found in a particular document. :ivar id: Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document. :vartype id: str :ivar key_phrases: A list of representative words or phrases. The number of key phrases returned is proportional to the number of words in the input document. :vartype key_phrases: list[str] :ivar warnings: Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate. :vartype warnings: list[~azure.ai.textanalytics.TextAnalyticsWarning] :ivar statistics: If `show_stats=True` was specified in the request this field will contain information about the document payload. :vartype statistics: ~azure.ai.textanalytics.TextDocumentStatistics :ivar bool is_error: Boolean check for error item when iterating over list of results. Always False for an instance of a ExtractKeyPhrasesResult. """ def __init__(self, **kwargs): self.id = kwargs.get("id", None) self.key_phrases = kwargs.get("key_phrases", None) self.warnings = kwargs.get("warnings", []) self.statistics = kwargs.get("statistics", None) self.is_error = False def __repr__(self): return "ExtractKeyPhrasesResult(id={}, key_phrases={}, warnings={}, statistics={}, is_error={})".format( self.id, self.key_phrases, repr(self.warnings), repr(self.statistics), self.is_error, )[ :1024 ] class RecognizeLinkedEntitiesResult(DictMixin): """RecognizeLinkedEntitiesResult is a result object which contains links to a well-known knowledge base, like for example, Wikipedia or Bing. :ivar id: Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document. :vartype id: str :ivar entities: Recognized well-known entities in the document. :vartype entities: list[~azure.ai.textanalytics.LinkedEntity] :ivar warnings: Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate. :vartype warnings: list[~azure.ai.textanalytics.TextAnalyticsWarning] :ivar statistics: If `show_stats=True` was specified in the request this field will contain information about the document payload. :vartype statistics: ~azure.ai.textanalytics.TextDocumentStatistics :ivar bool is_error: Boolean check for error item when iterating over list of results. Always False for an instance of a RecognizeLinkedEntitiesResult. """ def __init__(self, **kwargs): self.id = kwargs.get("id", None) self.entities = kwargs.get("entities", None) self.warnings = kwargs.get("warnings", []) self.statistics = kwargs.get("statistics", None) self.is_error = False def __repr__(self): return "RecognizeLinkedEntitiesResult(id={}, entities={}, warnings={}, statistics={}, is_error={})".format( self.id, repr(self.entities), repr(self.warnings), repr(self.statistics), self.is_error, )[ :1024 ] class AnalyzeSentimentResult(DictMixin): """AnalyzeSentimentResult is a result object which contains the overall predicted sentiment and confidence scores for your document and a per-sentence sentiment prediction with scores. :ivar id: Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document. :vartype id: str :ivar sentiment: Predicted sentiment for document (Negative, Neutral, Positive, or Mixed). Possible values include: 'positive', 'neutral', 'negative', 'mixed' :vartype sentiment: str :ivar warnings: Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate. :vartype warnings: list[~azure.ai.textanalytics.TextAnalyticsWarning] :ivar statistics: If `show_stats=True` was specified in the request this field will contain information about the document payload. :vartype statistics: ~azure.ai.textanalytics.TextDocumentStatistics :ivar confidence_scores: Document level sentiment confidence scores between 0 and 1 for each sentiment label. :vartype confidence_scores: ~azure.ai.textanalytics.SentimentConfidenceScores :ivar sentences: Sentence level sentiment analysis. :vartype sentences: list[~azure.ai.textanalytics.SentenceSentiment] :ivar bool is_error: Boolean check for error item when iterating over list of results. Always False for an instance of a AnalyzeSentimentResult. """ def __init__(self, **kwargs): self.id = kwargs.get("id", None) self.sentiment = kwargs.get("sentiment", None) self.warnings = kwargs.get("warnings", []) self.statistics = kwargs.get("statistics", None) self.confidence_scores = kwargs.get("confidence_scores", None) self.sentences = kwargs.get("sentences", None) self.is_error = False def __repr__(self): return ( "AnalyzeSentimentResult(id={}, sentiment={}, warnings={}, statistics={}, confidence_scores={}, " "sentences={}, is_error={})".format( self.id, self.sentiment, repr(self.warnings), repr(self.statistics), repr(self.confidence_scores), repr(self.sentences), self.is_error, )[:1024] ) class TextDocumentStatistics(DictMixin): """TextDocumentStatistics contains information about the document payload. :ivar character_count: Number of text elements recognized in the document. :vartype character_count: int :ivar transaction_count: Number of transactions for the document. :vartype transaction_count: int """ def __init__(self, **kwargs): self.character_count = kwargs.get("character_count", None) self.transaction_count = kwargs.get("transaction_count", None) @classmethod def _from_generated(cls, stats): if stats is None: return None return cls( character_count=stats.characters_count, transaction_count=stats.transactions_count, ) def __repr__(self): return ( "TextDocumentStatistics(character_count={}, transaction_count={})".format( self.character_count, self.transaction_count )[:1024] ) class DocumentError(DictMixin): """DocumentError is an error object which represents an error on the individual document. :ivar id: Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document. :vartype id: str :ivar error: The document error. :vartype error: ~azure.ai.textanalytics.TextAnalyticsError :ivar bool is_error: Boolean check for error item when iterating over list of results. Always True for an instance of a DocumentError. """ def __init__(self, **kwargs): self.id = kwargs.get("id", None) self.error = kwargs.get("error", None) self.is_error = True def __getattr__(self, attr): result_set = set() result_set.update( RecognizeEntitiesResult().keys() + RecognizePiiEntitiesResult().keys() + DetectLanguageResult().keys() + RecognizeLinkedEntitiesResult().keys() + AnalyzeSentimentResult().keys() + ExtractKeyPhrasesResult().keys() ) result_attrs = result_set.difference(DocumentError().keys()) if attr in result_attrs: raise AttributeError( "'DocumentError' object has no attribute '{}'. The service was unable to process this document:\n" "Document Id: {}\nError: {} - {}\n".format( attr, self.id, self.error.code, self.error.message ) ) raise AttributeError( "'DocumentError' object has no attribute '{}'".format(attr) ) @classmethod def _from_generated(cls, doc_err): return cls( id=doc_err.id, error=TextAnalyticsError._from_generated( # pylint: disable=protected-access doc_err.error ), is_error=True, ) def __repr__(self): return "DocumentError(id={}, error={}, is_error={})".format( self.id, repr(self.error), self.is_error )[:1024] class DetectLanguageInput(LanguageInput): """The input document to be analyzed for detecting language. :keyword str id: Unique, non-empty document identifier. :keyword str text: The input text to process. :keyword str country_hint: A country hint to help better detect the language of the text. Accepts two letter country codes specified by ISO 3166-1 alpha-2. Defaults to "US". Pass in the string "none" to not use a country_hint. :ivar id: Required. Unique, non-empty document identifier. :vartype id: str :ivar text: Required. The input text to process. :vartype text: str :ivar country_hint: A country hint to help better detect the language of the text. Accepts two letter country codes specified by ISO 3166-1 alpha-2. Defaults to "US". Pass in the string "none" to not use a country_hint. :vartype country_hint: str """ def __init__(self, **kwargs): super(DetectLanguageInput, self).__init__(**kwargs) self.id = kwargs.get("id", None) self.text = kwargs.get("text", None) self.country_hint = kwargs.get("country_hint", None) def __repr__(self): return "DetectLanguageInput(id={}, text={}, country_hint={})".format( self.id, self.text, self.country_hint )[:1024] class LinkedEntity(DictMixin): """LinkedEntity contains a link to the well-known recognized entity in text. The link comes from a data source like Wikipedia or Bing. It additionally includes all of the matches of this entity found in the document. :ivar name: Entity Linking formal name. :vartype name: str :ivar matches: List of instances this entity appears in the text. :vartype matches: list[~azure.ai.textanalytics.LinkedEntityMatch] :ivar language: Language used in the data source. :vartype language: str :ivar data_source_entity_id: Unique identifier of the recognized entity from the data source. :vartype data_source_entity_id: str :ivar url: URL to the entity's page from the data source. :vartype url: str :ivar data_source: Data source used to extract entity linking, such as Wiki/Bing etc. :vartype data_source: str :ivar str bing_entity_search_api_id: Bing Entity Search unique identifier of the recognized entity. Use in conjunction with the Bing Entity Search SDK to fetch additional relevant information. .. versionadded:: v3.1 The *bing_entity_search_api_id* property. """ def __init__(self, **kwargs): self.name = kwargs.get("name", None) self.matches = kwargs.get("matches", None) self.language = kwargs.get("language", None) self.data_source_entity_id = kwargs.get("data_source_entity_id", None) self.url = kwargs.get("url", None) self.data_source = kwargs.get("data_source", None) self.bing_entity_search_api_id = kwargs.get("bing_entity_search_api_id", None) @classmethod def _from_generated(cls, entity): bing_entity_search_api_id = ( entity.bing_id if hasattr(entity, "bing_id") else None ) return cls( name=entity.name, matches=[ LinkedEntityMatch._from_generated(e) # pylint: disable=protected-access for e in entity.matches ], language=entity.language, data_source_entity_id=entity.id, url=entity.url, data_source=entity.data_source, bing_entity_search_api_id=bing_entity_search_api_id, ) def __repr__(self): return ( "LinkedEntity(name={}, matches={}, language={}, data_source_entity_id={}, url={}, " "data_source={}, bing_entity_search_api_id={})".format( self.name, repr(self.matches), self.language, self.data_source_entity_id, self.url, self.data_source, self.bing_entity_search_api_id, )[:1024] ) class LinkedEntityMatch(DictMixin): """A match for the linked entity found in text. Provides the confidence score of the prediction and where the entity was found in the text. :ivar confidence_score: If a well-known item is recognized, a decimal number denoting the confidence level between 0 and 1 will be returned. :vartype confidence_score: float :ivar text: Entity text as appears in the request. :ivar int length: The linked entity match text length. This value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoints by default. :ivar int offset: The linked entity match text offset from the start of the document. The value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoints by default. :vartype text: str .. versionadded:: v3.1 The *offset* and *length* properties. """ def __init__(self, **kwargs): self.confidence_score = kwargs.get("confidence_score", None) self.text = kwargs.get("text", None) self.length = kwargs.get("length", None) self.offset = kwargs.get("offset", None) @classmethod def _from_generated(cls, match): offset = match.offset length = match.length if isinstance(match, _v3_0_models.Match): # we do not return offset for v3.0 since # the correct encoding was not introduced for v3.0 offset = None length = None return cls( confidence_score=match.confidence_score, text=match.text, length=length, offset=offset, ) def __repr__(self): return "LinkedEntityMatch(confidence_score={}, text={}, length={}, offset={})".format( self.confidence_score, self.text, self.length, self.offset )[ :1024 ] class TextDocumentInput(DictMixin, MultiLanguageInput): """The input document to be analyzed by the service. :keyword str id: Unique, non-empty document identifier. :keyword str text: The input text to process. :keyword str language: This is the 2 letter ISO 639-1 representation of a language. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. :ivar id: Required. Unique, non-empty document identifier. :vartype id: str :ivar text: Required. The input text to process. :vartype text: str :ivar language: This is the 2 letter ISO 639-1 representation of a language. For example, use "en" for English; "es" for Spanish etc. If not set, uses "en" for English as default. :vartype language: str """ def __init__(self, **kwargs): super(TextDocumentInput, self).__init__(**kwargs) self.id = kwargs.get("id", None) self.text = kwargs.get("text", None) self.language = kwargs.get("language", None) def __repr__(self): return "TextDocumentInput(id={}, text={}, language={})".format( self.id, self.text, self.language )[:1024] class TextDocumentBatchStatistics(DictMixin): """TextDocumentBatchStatistics contains information about the request payload. Note: This object is not returned in the response and needs to be retrieved by a response hook. :ivar document_count: Number of documents submitted in the request. :vartype document_count: int :ivar valid_document_count: Number of valid documents. This excludes empty, over-size limit or non-supported languages documents. :vartype valid_document_count: int :ivar erroneous_document_count: Number of invalid documents. This includes empty, over-size limit or non-supported languages documents. :vartype erroneous_document_count: int :ivar transaction_count: Number of transactions for the request. :vartype transaction_count: long """ def __init__(self, **kwargs): self.document_count = kwargs.get("document_count", None) self.valid_document_count = kwargs.get("valid_document_count", None) self.erroneous_document_count = kwargs.get("erroneous_document_count", None) self.transaction_count = kwargs.get("transaction_count", None) @classmethod def _from_generated(cls, statistics): if statistics is None: return None return cls( document_count=statistics["documentsCount"], valid_document_count=statistics["validDocumentsCount"], erroneous_document_count=statistics["erroneousDocumentsCount"], transaction_count=statistics["transactionsCount"], ) def __repr__(self): return ( "TextDocumentBatchStatistics(document_count={}, valid_document_count={}, erroneous_document_count={}, " "transaction_count={})".format( self.document_count, self.valid_document_count, self.erroneous_document_count, self.transaction_count, )[:1024] ) class SentenceSentiment(DictMixin): """SentenceSentiment contains the predicted sentiment and confidence scores for each individual sentence in the document. :ivar text: The sentence text. :vartype text: str :ivar sentiment: The predicted Sentiment for the sentence. Possible values include: 'positive', 'neutral', 'negative' :vartype sentiment: str :ivar confidence_scores: The sentiment confidence score between 0 and 1 for the sentence for all labels. :vartype confidence_scores: ~azure.ai.textanalytics.SentimentConfidenceScores :ivar int length: The sentence text length. This value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoints by default. :ivar int offset: The sentence text offset from the start of the document. The value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoints by default. :ivar mined_opinions: The list of opinions mined from this sentence. For example in the sentence "The food is good, but the service is bad", we would mine the two opinions "food is good" and "service is bad". Only returned if `show_opinion_mining` is set to True in the call to `analyze_sentiment` and api version is v3.1 and up. :vartype mined_opinions: list[~azure.ai.textanalytics.MinedOpinion] .. versionadded:: v3.1 The *offset*, *length*, and *mined_opinions* properties. """ def __init__(self, **kwargs): self.text = kwargs.get("text", None) self.sentiment = kwargs.get("sentiment", None) self.confidence_scores = kwargs.get("confidence_scores", None) self.length = kwargs.get("length", None) self.offset = kwargs.get("offset", None) self.mined_opinions = kwargs.get("mined_opinions", None) @classmethod def _from_generated(cls, sentence, results, sentiment): offset = sentence.offset length = sentence.length if isinstance(sentence, _v3_0_models.SentenceSentiment): # we do not return offset for v3.0 since # the correct encoding was not introduced for v3.0 offset = None length = None if hasattr(sentence, "targets"): mined_opinions = ( [ MinedOpinion._from_generated( # pylint: disable=protected-access target, results, sentiment ) for target in sentence.targets ] if sentence.targets else [] ) else: mined_opinions = None return cls( text=sentence.text, sentiment=sentence.sentiment, confidence_scores=SentimentConfidenceScores._from_generated( # pylint: disable=protected-access sentence.confidence_scores ), length=length, offset=offset, mined_opinions=mined_opinions, ) def __repr__(self): return ( "SentenceSentiment(text={}, sentiment={}, confidence_scores={}, " "length={}, offset={}, mined_opinions={})".format( self.text, self.sentiment, repr(self.confidence_scores), self.length, self.offset, repr(self.mined_opinions), )[:1024] ) class MinedOpinion(DictMixin): """A mined opinion object represents an opinion we've extracted from a sentence. It consists of both a target that these opinions are about, and the assessments representing the opinion. :ivar target: The target of an opinion about a product/service. :vartype target: ~azure.ai.textanalytics.TargetSentiment :ivar assessments: The assessments representing the opinion of the target. :vartype assessments: list[~azure.ai.textanalytics.AssessmentSentiment] """ def __init__(self, **kwargs): self.target = kwargs.get("target", None) self.assessments = kwargs.get("assessments", None) @staticmethod def _get_assessments( relations, results, sentiment ): # pylint: disable=unused-argument if not relations: return [] assessment_relations = [ r.ref for r in relations if r.relation_type == "assessment" ] assessments = [] for assessment_relation in assessment_relations: nums = _get_indices(assessment_relation) sentence_index = nums[1] assessment_index = nums[2] assessments.append( sentiment.sentences[sentence_index].assessments[assessment_index] ) return assessments @classmethod def _from_generated(cls, target, results, sentiment): return cls( target=TargetSentiment._from_generated( # pylint: disable=protected-access target ), assessments=[ AssessmentSentiment._from_generated( # pylint: disable=protected-access assessment ) for assessment in cls._get_assessments( target.relations, results, sentiment ) ], ) def __repr__(self): return "MinedOpinion(target={}, assessments={})".format( repr(self.target), repr(self.assessments) )[:1024] class TargetSentiment(DictMixin): """TargetSentiment contains the predicted sentiment, confidence scores and other information about a key component of a product/service. For example in "The food at Hotel Foo is good", "food" is an key component of "Hotel Foo". :ivar str text: The text value of the target. :ivar str sentiment: The predicted Sentiment for the target. Possible values include 'positive', 'mixed', and 'negative'. :ivar confidence_scores: The sentiment confidence score between 0 and 1 for the target for 'positive' and 'negative' labels. It's score for 'neutral' will always be 0 :vartype confidence_scores: ~azure.ai.textanalytics.SentimentConfidenceScores :ivar int length: The target text length. This value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoints by default. :ivar int offset: The target text offset from the start of the document. The value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoints by default. """ def __init__(self, **kwargs): self.text = kwargs.get("text", None) self.sentiment = kwargs.get("sentiment", None) self.confidence_scores = kwargs.get("confidence_scores", None) self.length = kwargs.get("length", None) self.offset = kwargs.get("offset", None) @classmethod def _from_generated(cls, target): return cls( text=target.text, sentiment=target.sentiment, confidence_scores=SentimentConfidenceScores._from_generated( # pylint: disable=protected-access target.confidence_scores ), length=target.length, offset=target.offset, ) def __repr__(self): return ( "TargetSentiment(text={}, sentiment={}, confidence_scores={}, " "length={}, offset={})".format( self.text, self.sentiment, repr(self.confidence_scores), self.length, self.offset, )[:1024] ) class AssessmentSentiment(DictMixin): """AssessmentSentiment contains the predicted sentiment, confidence scores and other information about an assessment given about a particular target. For example, in the sentence "The food is good", the assessment of the target 'food' is 'good'. :ivar str text: The assessment text. :ivar str sentiment: The predicted Sentiment for the assessment. Possible values include 'positive', 'mixed', and 'negative'. :ivar confidence_scores: The sentiment confidence score between 0 and 1 for the assessment for 'positive' and 'negative' labels. It's score for 'neutral' will always be 0 :vartype confidence_scores: ~azure.ai.textanalytics.SentimentConfidenceScores :ivar int length: The assessment text length. This value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoints by default. :ivar int offset: The assessment text offset from the start of the document. The value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoints by default. :ivar bool is_negated: Whether the value of the assessment is negated. For example, in "The food is not good", the assessment "good" is negated. """ def __init__(self, **kwargs): self.text = kwargs.get("text", None) self.sentiment = kwargs.get("sentiment", None) self.confidence_scores = kwargs.get("confidence_scores", None) self.length = kwargs.get("length", None) self.offset = kwargs.get("offset", None) self.is_negated = kwargs.get("is_negated", None) @classmethod def _from_generated(cls, assessment): return cls( text=assessment.text, sentiment=assessment.sentiment, confidence_scores=SentimentConfidenceScores._from_generated( # pylint: disable=protected-access assessment.confidence_scores ), length=assessment.length, offset=assessment.offset, is_negated=assessment.is_negated, ) def __repr__(self): return ( "AssessmentSentiment(text={}, sentiment={}, confidence_scores={}, length={}, offset={}, " "is_negated={})".format( self.text, self.sentiment, repr(self.confidence_scores), self.length, self.offset, self.is_negated, )[:1024] ) class SentimentConfidenceScores(DictMixin): """The confidence scores (Softmax scores) between 0 and 1. Higher values indicate higher confidence. :ivar positive: Positive score. :vartype positive: float :ivar neutral: Neutral score. :vartype neutral: float :ivar negative: Negative score. :vartype negative: float """ def __init__(self, **kwargs): self.positive = kwargs.get("positive", 0.0) self.neutral = kwargs.get("neutral", 0.0) self.negative = kwargs.get("negative", 0.0) @classmethod def _from_generated(cls, score): return cls( positive=score.positive, neutral=score.neutral if hasattr(score, "neutral") else 0.0, negative=score.negative, ) def __repr__(self): return "SentimentConfidenceScores(positive={}, neutral={}, negative={})".format( self.positive, self.neutral, self.negative )[:1024] class _AnalyzeActionsType(str, Enum): """The type of action that was applied to the documents""" RECOGNIZE_ENTITIES = "recognize_entities" #: Entities Recognition action. RECOGNIZE_PII_ENTITIES = ( "recognize_pii_entities" #: PII Entities Recognition action. ) EXTRACT_KEY_PHRASES = "extract_key_phrases" #: Key Phrase Extraction action. RECOGNIZE_LINKED_ENTITIES = ( "recognize_linked_entities" #: Linked Entities Recognition action. ) ANALYZE_SENTIMENT = "analyze_sentiment" #: Sentiment Analysis action. EXTRACT_SUMMARY = "extract_summary" class RecognizeEntitiesAction(DictMixin): """RecognizeEntitiesAction encapsulates the parameters for starting a long-running Entities Recognition operation. If you just want to recognize entities in a list of documents, and not perform multiple long running actions on the input of documents, call method `recognize_entities` instead of interfacing with this model. :keyword str model_version: The model version to use for the analysis. :keyword str string_index_type: Specifies the method used to interpret string offsets. `UnicodeCodePoint`, the Python encoding, is the default. To override the Python default, you can also pass in `Utf16CodePoint` or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets :keyword bool disable_service_logs: If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. :ivar str model_version: The model version to use for the analysis. :ivar str string_index_type: Specifies the method used to interpret string offsets. `UnicodeCodePoint`, the Python encoding, is the default. To override the Python default, you can also pass in `Utf16CodePoint` or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets :ivar bool disable_service_logs: If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. """ def __init__(self, **kwargs): self.model_version = kwargs.get("model_version", "latest") self.string_index_type = kwargs.get("string_index_type", "UnicodeCodePoint") self.disable_service_logs = kwargs.get("disable_service_logs", False) def __repr__(self, **kwargs): return "RecognizeEntitiesAction(model_version={}, string_index_type={}, disable_service_logs={})".format( self.model_version, self.string_index_type, self.disable_service_logs )[ :1024 ] def _to_generated(self, api_version): if api_version == DEFAULT_API_VERSION: from ._generated.v3_2_preview_1 import models else: from ._generated.v3_1 import models return models.EntitiesTask( parameters=models.EntitiesTaskParameters( model_version=self.model_version, string_index_type=self.string_index_type, logging_opt_out=self.disable_service_logs, ) ) class AnalyzeSentimentAction(DictMixin): """AnalyzeSentimentAction encapsulates the parameters for starting a long-running Sentiment Analysis operation. If you just want to analyze sentiment in a list of documents, and not perform multiple long running actions on the input of documents, call method `analyze_sentiment` instead of interfacing with this model. :keyword str model_version: The model version to use for the analysis. :keyword bool show_opinion_mining: Whether to mine the opinions of a sentence and conduct more granular analysis around the aspects of a product or service (also known as aspect-based sentiment analysis). If set to true, the returned :class:`~azure.ai.textanalytics.SentenceSentiment` objects will have property `mined_opinions` containing the result of this analysis. :keyword str string_index_type: Specifies the method used to interpret string offsets. `UnicodeCodePoint`, the Python encoding, is the default. To override the Python default, you can also pass in `Utf16CodePoint` or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets :keyword bool disable_service_logs: If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. :ivar str model_version: The model version to use for the analysis. :ivar bool show_opinion_mining: Whether to mine the opinions of a sentence and conduct more granular analysis around the aspects of a product or service (also known as aspect-based sentiment analysis). If set to true, the returned :class:`~azure.ai.textanalytics.SentenceSentiment` objects will have property `mined_opinions` containing the result of this analysis. :ivar str string_index_type: Specifies the method used to interpret string offsets. `UnicodeCodePoint`, the Python encoding, is the default. To override the Python default, you can also pass in `Utf16CodePoint` or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets :ivar bool disable_service_logs: If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. """ def __init__(self, **kwargs): self.model_version = kwargs.get("model_version", "latest") self.show_opinion_mining = kwargs.get("show_opinion_mining", False) self.string_index_type = kwargs.get("string_index_type", None) self.disable_service_logs = kwargs.get("disable_service_logs", False) def __repr__(self, **kwargs): return ( "AnalyzeSentimentAction(model_version={}, show_opinion_mining={}, string_index_type={}, " "disable_service_logs={}".format( self.model_version, self.show_opinion_mining, self.string_index_type, self.disable_service_logs, )[:1024] ) def _to_generated(self, api_version): if api_version == DEFAULT_API_VERSION: from ._generated.v3_2_preview_1 import models else: from ._generated.v3_1 import models return models.SentimentAnalysisTask( parameters=models.SentimentAnalysisTaskParameters( model_version=self.model_version, opinion_mining=self.show_opinion_mining, string_index_type=self.string_index_type, logging_opt_out=self.disable_service_logs, ) ) class RecognizePiiEntitiesAction(DictMixin): """RecognizePiiEntitiesAction encapsulates the parameters for starting a long-running PII Entities Recognition operation. If you just want to recognize pii entities in a list of documents, and not perform multiple long running actions on the input of documents, call method `recognize_pii_entities` instead of interfacing with this model. :keyword str model_version: The model version to use for the analysis. :keyword str domain_filter: An optional string to set the PII domain to include only a subset of the PII entity categories. Possible values include 'phi' or None. :keyword categories_filter: Instead of filtering over all PII entity categories, you can pass in a list of the specific PII entity categories you want to filter out. For example, if you only want to filter out U.S. social security numbers in a document, you can pass in `[PiiEntityCategory.US_SOCIAL_SECURITY_NUMBER]` for this kwarg. :paramtype categories_filter: list[~azure.ai.textanalytics.PiiEntityCategory] :keyword str string_index_type: Specifies the method used to interpret string offsets. `UnicodeCodePoint`, the Python encoding, is the default. To override the Python default, you can also pass in `Utf16CodePoint` or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets :keyword bool disable_service_logs: Defaults to true, meaning that Text Analytics will not log your input text on the service side for troubleshooting. If set to False, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. :ivar str model_version: The model version to use for the analysis. :ivar str domain_filter: An optional string to set the PII domain to include only a subset of the PII entity categories. Possible values include 'phi' or None. :ivar categories_filter: Instead of filtering over all PII entity categories, you can pass in a list of the specific PII entity categories you want to filter out. For example, if you only want to filter out U.S. social security numbers in a document, you can pass in `[PiiEntityCategory.US_SOCIAL_SECURITY_NUMBER]` for this kwarg. :vartype categories_filter: list[~azure.ai.textanalytics.PiiEntityCategory] :ivar str string_index_type: Specifies the method used to interpret string offsets. `UnicodeCodePoint`, the Python encoding, is the default. To override the Python default, you can also pass in `Utf16CodePoint` or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets :ivar bool disable_service_logs: Defaults to true, meaning that Text Analytics will not log your input text on the service side for troubleshooting. If set to False, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. """ def __init__(self, **kwargs): self.model_version = kwargs.get("model_version", "latest") self.domain_filter = kwargs.get("domain_filter", None) self.categories_filter = kwargs.get("categories_filter", None) self.string_index_type = kwargs.get("string_index_type", "UnicodeCodePoint") self.disable_service_logs = kwargs.get("disable_service_logs", True) def __repr__(self, **kwargs): return ( "RecognizePiiEntitiesAction(model_version={}, domain_filter={}, categories_filter={}, " "string_index_type={}, disable_service_logs={}".format( self.model_version, self.domain_filter, self.categories_filter, self.string_index_type, self.disable_service_logs, )[:1024] ) def _to_generated(self, api_version): if api_version == DEFAULT_API_VERSION: from ._generated.v3_2_preview_1 import models else: from ._generated.v3_1 import models return models.PiiTask( parameters=models.PiiTaskParameters( model_version=self.model_version, domain=self.domain_filter, pii_categories=self.categories_filter, string_index_type=self.string_index_type, logging_opt_out=self.disable_service_logs, ) ) class ExtractKeyPhrasesAction(DictMixin): """ExtractKeyPhrasesAction encapsulates the parameters for starting a long-running key phrase extraction operation If you just want to extract key phrases from a list of documents, and not perform multiple long running actions on the input of documents, call method `extract_key_phrases` instead of interfacing with this model. :keyword str model_version: The model version to use for the analysis. :keyword bool disable_service_logs: If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. :ivar str model_version: The model version to use for the analysis. :ivar bool disable_service_logs: If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. """ def __init__(self, **kwargs): self.model_version = kwargs.get("model_version", "latest") self.disable_service_logs = kwargs.get("disable_service_logs", False) def __repr__(self, **kwargs): return ( "ExtractKeyPhrasesAction(model_version={}, disable_service_logs={})".format( self.model_version, self.disable_service_logs )[:1024] ) def _to_generated(self, api_version): if api_version == DEFAULT_API_VERSION: from ._generated.v3_2_preview_1 import models else: from ._generated.v3_1 import models return models.KeyPhrasesTask( parameters=models.KeyPhrasesTaskParameters( model_version=self.model_version, logging_opt_out=self.disable_service_logs, ) ) class RecognizeLinkedEntitiesAction(DictMixin): """RecognizeLinkedEntitiesAction encapsulates the parameters for starting a long-running Linked Entities Recognition operation. If you just want to recognize linked entities in a list of documents, and not perform multiple long running actions on the input of documents, call method `recognize_linked_entities` instead of interfacing with this model. :keyword str model_version: The model version to use for the analysis. :keyword str string_index_type: Specifies the method used to interpret string offsets. `UnicodeCodePoint`, the Python encoding, is the default. To override the Python default, you can also pass in `Utf16CodePoint` or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets :keyword bool disable_service_logs: If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. :ivar str model_version: The model version to use for the analysis. :ivar str string_index_type: Specifies the method used to interpret string offsets. `UnicodeCodePoint`, the Python encoding, is the default. To override the Python default, you can also pass in `Utf16CodePoint` or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets :ivar bool disable_service_logs: If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. """ def __init__(self, **kwargs): self.model_version = kwargs.get("model_version", "latest") self.string_index_type = kwargs.get("string_index_type", "UnicodeCodePoint") self.disable_service_logs = kwargs.get("disable_service_logs", False) def __repr__(self, **kwargs): return ( "RecognizeLinkedEntitiesAction(model_version={}, string_index_type={}), " "disable_service_logs={}".format( self.model_version, self.string_index_type, self.disable_service_logs )[:1024] ) def _to_generated(self, api_version): if api_version == DEFAULT_API_VERSION: from ._generated.v3_2_preview_1 import models else: from ._generated.v3_1 import models return models.EntityLinkingTask( parameters=models.EntityLinkingTaskParameters( model_version=self.model_version, string_index_type=self.string_index_type, logging_opt_out=self.disable_service_logs, ) ) class ExtractSummaryAction(DictMixin): """ExtractSummaryAction encapsulates the parameters for starting a long-running Extractive Text Summarization operation. :keyword str model_version: The model version to use for the analysis. :keyword str string_index_type: Specifies the method used to interpret string offsets. `UnicodeCodePoint`, the Python encoding, is the default. To override the Python default, you can also pass in `Utf16CodePoint` or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets :keyword bool disable_service_logs: If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. :keyword int max_sentence_count: Maximum number of sentences to return. Defaults to 3. :keyword str order_by: Possible values include: "Offset", "Rank". Default value: "Offset". :ivar str model_version: The model version to use for the analysis. :ivar str string_index_type: Specifies the method used to interpret string offsets. `UnicodeCodePoint`, the Python encoding, is the default. To override the Python default, you can also pass in `Utf16CodePoint` or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets :ivar bool disable_service_logs: If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai. :ivar int max_sentence_count: Number of sentences to return. Defaults to 3. :ivar str order_by: Possible values include: "Offset", "Rank". Default value: "Offset". """ def __init__(self, **kwargs): self.model_version = kwargs.get("model_version", "latest") self.string_index_type = kwargs.get("string_index_type", "UnicodeCodePoint") self.disable_service_logs = kwargs.get("disable_service_logs", False) self.max_sentence_count = kwargs.get("max_sentence_count", 3) self.order_by = kwargs.get("order_by", "Offset") def __repr__(self): return ( "ExtractSummaryAction(model_version={}, string_index_type={}, disable_service_logs={}, " "max_sentence_count={}, order_by={})".format( self.model_version, self.string_index_type, self.disable_service_logs, self.max_sentence_count, self.order_by, )[:1024] ) def _to_generated(self, api_version): # pylint: disable=unused-argument return _v3_2_preview_1_models.ExtractiveSummarizationTask( parameters=_v3_2_preview_1_models.ExtractiveSummarizationTaskParameters( model_version=self.model_version, string_index_type=self.string_index_type, logging_opt_out=self.disable_service_logs, sentence_count=self.max_sentence_count, sort_by=self.order_by, ) ) class ExtractSummaryResult(DictMixin): """ExtractSummaryResult is a result object which contains the extractive text summarization from a particular document. :ivar str id: Unique, non-empty document identifier. :ivar sentences: A ranked list of sentences representing the extracted summary. :vartype sentences: list[~azure.ai.textanalytics.SummarySentence] :ivar warnings: Warnings encountered while processing document. :vartype warnings: list[~azure.ai.textanalytics.TextAnalyticsWarning] :ivar statistics: If `show_stats=True` was specified in the request this field will contain information about the document payload. :vartype statistics: ~azure.ai.textanalytics.TextDocumentStatistics :ivar bool is_error: Boolean check for error item when iterating over list of results. Always False for an instance of an ExtractSummaryResult. """ def __init__(self, **kwargs): self.id = kwargs.get("id", None) self.sentences = kwargs.get("sentences", None) self.warnings = kwargs.get("warnings", None) self.statistics = kwargs.get("statistics", None) self.is_error = False def __repr__(self): return "ExtractSummaryResult(id={}, sentences={}, warnings={}, statistics={}, is_error={})".format( self.id, repr(self.sentences), repr(self.warnings), repr(self.statistics), self.is_error, )[ :1024 ] @classmethod def _from_generated(cls, summary): return cls( id=summary.id, sentences=[ SummarySentence._from_generated( # pylint: disable=protected-access sentence ) for sentence in summary.sentences ], warnings=[ TextAnalyticsWarning._from_generated( # pylint: disable=protected-access w ) for w in summary.warnings ], statistics=TextDocumentStatistics._from_generated( # pylint: disable=protected-access summary.statistics ), ) class SummarySentence(DictMixin): """Represents a single sentence from the extractive text summarization. :ivar str text: The extracted sentence text. :ivar float rank_score: A float value representing the relevance of the sentence within the summary. Higher values indicate higher importance. :ivar int offset: The sentence offset from the start of the document. The value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoint by default. :ivar int length: The length of the sentence. This value depends on the value of the `string_index_type` parameter set in the original request, which is UnicodeCodePoint by default. """ def __init__(self, **kwargs): self.text = kwargs.get("text", None) self.rank_score = kwargs.get("rank_score", None) self.offset = kwargs.get("offset", None) self.length = kwargs.get("length", None) def __repr__(self): return "SummarySentence(text={}, rank_score={}, offset={}, length={})".format( self.text, self.rank_score, self.offset, self.length, )[:1024] @classmethod def _from_generated(cls, sentence): return cls( text=sentence.text, rank_score=sentence.rank_score, offset=sentence.offset, length=sentence.length, )
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onlinejudge/implementation/command/test.py
gitter-badger/online-judge-tools
1
197908
<filename>onlinejudge/implementation/command/test.py # Python Version: 3.x import onlinejudge import onlinejudge.implementation.utils as utils import onlinejudge.implementation.logging as log import onlinejudge.implementation.command.utils as cutils import sys import os import os.path import re import glob import colorama import collections import time import math from typing import * if TYPE_CHECKING: import argparse def compare_as_floats(xs_: str, ys_: str, error: float) -> bool: def f(x): try: y = float(x) if not math.isfinite(y): log.warning('not an real number found: %f', y) return y except ValueError: return x xs = list(map(f, xs_.split())) ys = list(map(f, ys_.split())) if len(xs) != len(ys): return False for x, y in zip(xs, ys): if isinstance(x, float) and isinstance(y, float): if not math.isclose(x, y, rel_tol=error, abs_tol=error): return False else: if x != y: return False return True def test(args: 'argparse.Namespace') -> None: # prepare if not args.test: args.test = cutils.glob_with_format(args.directory, args.format) # by default if args.ignore_backup: args.test = cutils.drop_backup_or_hidden_files(args.test) tests = cutils.construct_relationship_of_files(args.test, args.directory, args.format) if args.error: # float mode match = lambda a, b: compare_as_floats(a, b, args.error) else: def match(a, b): if a == b: return True if args.rstrip and a.rstrip(rstrip_targets) == b.rstrip(rstrip_targets): log.warning('WA if no rstrip') return True return False rstrip_targets = ' \t\r\n\f\v\0' # ruby's one, follow AnarchyGolf slowest: Union[int, float] = -1 slowest_name = '' ac_count = 0 for name, it in sorted(tests.items()): is_printed_input = not args.print_input def print_input(): nonlocal is_printed_input if not is_printed_input: is_printed_input = True with open(it['in']) as inf: log.emit('input:\n%s', log.bold(inf.read())) log.emit('') log.info('%s', name) # run the binary with open(it['in']) as inf: begin = time.perf_counter() answer_byte, proc = utils.exec_command(args.command, shell=True, stdin=inf, timeout=args.tle) end = time.perf_counter() answer = answer_byte.decode() if slowest < end - begin: slowest = end - begin slowest_name = name log.status('time: %f sec', end - begin) proc.terminate() # check TLE, RE or not result = 'AC' if proc.returncode is None: log.failure(log.red('TLE')) result = 'TLE' print_input() elif proc.returncode != 0: log.failure(log.red('RE') + ': return code %d', proc.returncode) result = 'RE' print_input() # check WA or not if 'out' in it: with open(it['out']) as outf: correct = outf.read() # compare if args.mode == 'all': if not match(answer, correct): log.failure(log.red('WA')) print_input() if not args.silent: log.emit('output:\n%s', log.bold(answer)) log.emit('expected:\n%s', log.bold(correct)) result = 'WA' elif args.mode == 'line': answer_words = answer .splitlines() correct_words = correct.splitlines() for i, (x, y) in enumerate(zip(answer_words + [ None ] * len(correct_words), correct_words + [ None ] * len(answer_words))): # type: ignore if x is None and y is None: break elif x is None: print_input() log.failure(log.red('WA') + ': line %d: line is nothing: expected "%s"', i + 1, log.bold(y)) result = 'WA' elif y is None: print_input() log.failure(log.red('WA') + ': line %d: unexpected line: output "%s"', i + 1, log.bold(x)) result = 'WA' elif not match(x, y): print_input() log.failure(log.red('WA') + ': line %d: output "%s": expected "%s"', i + 1, log.bold(x), log.bold(y)) result = 'WA' else: assert False else: if not args.silent: log.emit(log.bold(answer)) if result == 'AC': log.success(log.green('AC')) ac_count += 1 # summarize log.emit('') log.status('slowest: %f sec (for %s)', slowest, slowest_name) if ac_count == len(tests): log.success('test ' + log.green('success') + ': %d cases', len(tests)) else: log.failure('test ' + log.red('failed') + ': %d AC / %d cases', ac_count, len(tests)) sys.exit(1)
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0015_3Sum.py
taro-masuda/leetcode
0
36495
class Solution: def twoSum(self, nums: List[int], target: int) -> List[List[int]]: complement = {} out = [] for i,n in enumerate(nums): complement[target-n] = i for i,n in enumerate(nums): idx = complement.get(n, None) if idx != None and idx != i: out.append([nums[idx], nums[i]]) return out def threeSum(self, nums: List[int]) -> List[List[int]]: if len(nums) < 3: return [] nums.sort() out = [] if set(nums) == {0}: return [[0,0,0]] i = 0 while len(nums) >= 3: l_twosum = self.twoSum(nums[1:], -nums[0]) if l_twosum != None: for l in l_twosum: l.append(nums[0]) out.append(l) nums.pop(0) for i,l in enumerate(out): out[i] = sorted(l) out = list(map(list, set(map(tuple, out)))) return out
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tests/core/test_urn.py
joshuataylor/great_expectations
6,451
125719
<gh_stars>1000+ from urllib.parse import parse_qs import pytest from pyparsing import ParseException from great_expectations.core.urn import ge_urn def test_ge_validations_urn(): # We should be able to parse validations urns urn = ( "urn:great_expectations:validations:my_suite:expect_something.observed_value:query=s%20tring&query=" "string3&query2=string2" ) res = ge_urn.parseString(urn) assert res["urn_type"] == "validations" assert res["expectation_suite_name"] == "my_suite" assert res["metric_name"] == "expect_something.observed_value" kwargs_dict = parse_qs(res["metric_kwargs"]) assert kwargs_dict == {"query": ["s tring", "string3"], "query2": ["string2"]} # no kwargs is ok urn = "urn:great_expectations:validations:my_suite:expect_something.observed_value" res = ge_urn.parseString(urn) assert res["urn_type"] == "validations" assert res["expectation_suite_name"] == "my_suite" assert res["metric_name"] == "expect_something.observed_value" assert "metric_kwargs" not in res def test_ge_metrics_urn(): urn = "urn:great_expectations:metrics:20200403T1234.324Z:my_suite:expect_something.observed_value:column=mycol" res = ge_urn.parseString(urn) assert res["urn_type"] == "metrics" assert res["run_id"] == "20200403T1234.324Z" assert res["expectation_suite_name"] == "my_suite" assert res["metric_name"] == "expect_something.observed_value" kwargs_dict = parse_qs(res["metric_kwargs"]) assert kwargs_dict == {"column": ["mycol"]} # No kwargs is ok urn = "urn:great_expectations:metrics:20200403T1234.324Z:my_suite:expect_something.observed_value" res = ge_urn.parseString(urn) assert res["urn_type"] == "metrics" assert res["run_id"] == "20200403T1234.324Z" assert res["expectation_suite_name"] == "my_suite" assert res["metric_name"] == "expect_something.observed_value" assert "kwargs_dict" not in res def test_ge_stores_urn(): urn = "urn:great_expectations:stores:my_store:mymetric:kw=param" res = ge_urn.parseString(urn) assert res["urn_type"] == "stores" assert res["store_name"] == "my_store" assert res["metric_name"] == "mymetric" kwargs_dict = parse_qs(res["metric_kwargs"]) assert kwargs_dict == { "kw": ["param"], } # No kwargs is ok urn = "urn:great_expectations:stores:my_store:mymetric" res = ge_urn.parseString(urn) assert res["urn_type"] == "stores" assert res["store_name"] == "my_store" assert res["metric_name"] == "mymetric" assert "metric_kwargs" not in res def test_invalid_urn(): # Must start with "urn:great_expectations" with pytest.raises(ParseException) as e: ge_urn.parseString("not_a_ge_urn") assert "not_a_ge_urn" in e.value.line # Must have one of the recognized types with pytest.raises(ParseException) as e: ge_urn.parseString("urn:great_expectations:foo:bar:baz:bin:barg") assert "urn:great_expectations:foo:bar:baz:bin:barg" in e.value.line # Cannot have too many parts with pytest.raises(ParseException) as e: ge_urn.parseString( "urn:great_expectations:validations:foo:bar:baz:bin:barg:boo" ) assert "urn:great_expectations:validations:foo:bar:baz:bin:barg:boo" in e.value.line
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dist/functional/pipeline.py
evandroforks/CrowdAnki
0
1611378
""" The pipeline module contains the transformations and actions API of PyFunctional """ from __future__ import division, absolute_import from operator import mul, add import collections from functools import reduce, wraps, partial import json import csv import sqlite3 import re import six from tabulate import tabulate from functional.execution import ExecutionEngine from functional.lineage import Lineage from functional.util import is_iterable, is_primitive, is_namedtuple, is_tabulatable, identity from functional.io import WRITE_MODE, universal_write_open from functional import transformations from functional.execution import ExecutionStrategies class Sequence(object): """ Sequence is a wrapper around any type of sequence which provides access to common functional transformations and reductions in a data pipeline style """ def __init__(self, sequence, transform=None, engine=None, max_repr_items=None): # pylint: disable=protected-access """ Takes a Sequence, list, tuple. or iterable sequence and wraps it around a Sequence object. If the sequence is already an instance of Sequence, it will in total be wrapped exactly once. A TypeError is raised if sequence is none of these. :param sequence: sequence of items to wrap in a Sequence :param transform: transformation to apply :param engine: execution engine :param max_repr_items: maximum number of items to print with repr :return: sequence wrapped in a Sequence """ self.engine = engine or ExecutionEngine() if isinstance(sequence, Sequence): self._max_repr_items = max_repr_items or sequence._max_repr_items self._base_sequence = sequence._base_sequence self._lineage = Lineage(prior_lineage=sequence._lineage, engine=engine) elif isinstance(sequence, (list, tuple)) or is_iterable(sequence): self._max_repr_items = max_repr_items self._base_sequence = sequence self._lineage = Lineage(engine=engine) else: raise TypeError("Given sequence must be an iterable value") if transform is not None: self._lineage.apply(transform) def __iter__(self): """ Return iterator of sequence. :return: iterator of sequence """ return self._evaluate() def __eq__(self, other): """ Checks for equality with the sequence's equality operator. :param other: object to compare to :return: true if the underlying sequence is equal to other """ return self.sequence == other def __ne__(self, other): """ Checks for inequality with the sequence's inequality operator. :param other: object to compare to :return: true if the underlying sequence is not equal to other """ return self.sequence != other def __hash__(self): """ Return the hash of the sequence. :return: hash of sequence """ raise TypeError("unhashable type: Sequence") def __repr__(self): """ Return repr using sequence's repr function. :return: sequence's repr """ items = self.to_list() if self._max_repr_items is None or len(items) <= self._max_repr_items: return repr(items) else: return repr(items[:self._max_repr_items])[:-1] + ', ...]' def __str__(self): """ Return string using sequence's string function. :return: sequence's string """ return str(self.to_list()) def __bool__(self): """ Returns True if size is not zero. :return: True if size is not zero """ return self.size() != 0 def __nonzero__(self): """ Returns True if size is not zero. :return: True if size is not zero """ return self.size() != 0 def __getitem__(self, item): """ Gets item at given index. :param item: key to use for getitem :return: item at index key """ self.cache() return _wrap(self.sequence[item]) def __reversed__(self): """ Return reversed sequence using sequence's reverse function :return: reversed sequence """ return self._transform(transformations.reversed_t()) def __contains__(self, item): """ Checks if item is in sequence. :param item: item to check :return: True if item is in sequence """ return self.sequence.__contains__(item) def __add__(self, other): """ Concatenates sequence with other. :param other: sequence to concatenate :return: concatenated sequence with other """ if isinstance(other, Sequence): return Sequence(self.sequence + other.sequence) else: return Sequence(self.sequence + other) def _evaluate(self): """ Creates and returns an iterator which applies all the transformations in the lineage :return: iterator over the transformed sequence """ return self._lineage.evaluate(self._base_sequence) def _transform(self, *transforms): """ Copies the given Sequence and appends new transformation :param transform: transform to apply or list of transforms to apply :return: transformed sequence """ sequence = None for transform in transforms: if sequence: sequence = Sequence(sequence, transform=transform) else: sequence = Sequence(self, transform=transform) return sequence @property def sequence(self): """ Alias for to_list used internally for brevity :return: result of to_list() on sequence """ return self.to_list() def cache(self, delete_lineage=False): """ Caches the result of the Sequence so far. This means that any functions applied on the pipeline before cache() are evaluated, and the result is stored in the Sequence. This is primarily used internally and is no more helpful than to_list() externally. delete_lineage allows for cache() to be used in internal initialization calls without the caller having knowledge of the internals via the lineage :param delete_lineage: If set to True, it will cache then erase the lineage """ if len(self._lineage) == 0 or self._lineage[-1] == transformations.CACHE_T: if not isinstance(self._base_sequence, list): self._base_sequence = list(self._base_sequence) self._lineage.apply(transformations.CACHE_T) else: self._base_sequence = list(self._evaluate()) self._lineage.apply(transformations.CACHE_T) if delete_lineage: self._lineage = Lineage(engine=self.engine) return self def head(self): """ Returns the first element of the sequence. >>> seq([1, 2, 3]).head() 1 Raises IndexError when the sequence is empty. >>> seq([]).head() Traceback (most recent call last): ... IndexError: list index out of range :return: first element of sequence """ return _wrap(self.take(1)[0]) def first(self): """ Returns the first element of the sequence. >>> seq([1, 2, 3]).first() 1 Raises IndexError when the sequence is empty. >>> seq([]).first() Traceback (most recent call last): ... IndexError: list index out of range :return: first element of sequence """ return self.head() def head_option(self): """ Returns the first element of the sequence or None, if the sequence is empty. >>> seq([1, 2, 3]).head_option() 1 >>> seq([]).head_option() None :return: first element of sequence or None if sequence is empty """ if not self.sequence: return None return self.head() def last(self): """ Returns the last element of the sequence. >>> seq([1, 2, 3]).last() 3 Raises IndexError when the sequence is empty. >>> seq([]).last() Traceback (most recent call last): ... IndexError: list index out of range :return: last element of sequence """ return _wrap(self.sequence[-1]) def last_option(self): """ Returns the last element of the sequence or None, if the sequence is empty. >>> seq([1, 2, 3]).last_option() 3 >>> seq([]).last_option() None :return: last element of sequence or None if sequence is empty """ if not self.sequence: return None return self.last() def init(self): """ Returns the sequence, without its last element. >>> seq([1, 2, 3]).init() [1, 2] :return: sequence without last element """ return self._transform(transformations.init_t()) def tail(self): """ Returns the sequence, without its first element. >>> seq([1, 2, 3]).tail() [2, 3] :return: sequence without first element """ return self._transform(transformations.tail_t()) def inits(self): """ Returns consecutive inits of the sequence. >>> seq([1, 2, 3]).inits() [[1, 2, 3], [1, 2], [1], []] :return: consecutive init()s on sequence """ return self._transform(transformations.inits_t(_wrap)) def tails(self): """ Returns consecutive tails of the sequence. >>> seq([1, 2, 3]).tails() [[1, 2, 3], [2, 3], [3], []] :return: consecutive tail()s of the sequence """ return self._transform(transformations.tails_t(_wrap)) def cartesian(self, *iterables, **kwargs): """ Returns the cartesian product of the passed iterables with the specified number of repetitions. The keyword argument `repeat` is read from kwargs to pass to itertools.cartesian. >>> seq.range(2).cartesian(range(2)) [(0, 0), (0, 1), (1, 0), (1, 1)] :param iterables: elements for cartesian product :param kwargs: the variable `repeat` is read from kwargs :return: cartesian product """ return self._transform(transformations.cartesian_t(iterables, kwargs.get('repeat', 1))) def drop(self, n): """ Drop the first n elements of the sequence. >>> seq([1, 2, 3, 4, 5]).drop(2) [3, 4, 5] :param n: number of elements to drop :return: sequence without first n elements """ if n <= 0: return self._transform(transformations.drop_t(0)) else: return self._transform(transformations.drop_t(n)) def drop_right(self, n): """ Drops the last n elements of the sequence. >>> seq([1, 2, 3, 4, 5]).drop_right(2) [1, 2, 3] :param n: number of elements to drop :return: sequence with last n elements dropped """ return self._transform(transformations.CACHE_T, transformations.drop_right_t(n)) def drop_while(self, func): """ Drops elements in the sequence while func evaluates to True, then returns the rest. >>> seq([1, 2, 3, 4, 5, 1, 2]).drop_while(lambda x: x < 3) [3, 4, 5, 1, 2] :param func: truth returning function :return: elements including and after func evaluates to False """ return self._transform(transformations.drop_while_t(func)) def take(self, n): """ Take the first n elements of the sequence. >>> seq([1, 2, 3, 4]).take(2) [1, 2] :param n: number of elements to take :return: first n elements of sequence """ if n <= 0: return self._transform(transformations.take_t(0)) else: return self._transform(transformations.take_t(n)) def take_while(self, func): """ Take elements in the sequence until func evaluates to False, then return them. >>> seq([1, 2, 3, 4, 5, 1, 2]).take_while(lambda x: x < 3) [1, 2] :param func: truth returning function :return: elements taken until func evaluates to False """ return self._transform(transformations.take_while_t(func)) def union(self, other): """ New sequence with unique elements from self and other. >>> seq([1, 1, 2, 3, 3]).union([1, 4, 5]) [1, 2, 3, 4, 5] :param other: sequence to union with :return: union of sequence and other """ return self._transform(transformations.union_t(other)) def intersection(self, other): """ New sequence with unique elements present in sequence and other. >>> seq([1, 1, 2, 3]).intersection([2, 3, 4]) [2, 3] :param other: sequence to perform intersection with :return: intersection of sequence and other """ return self._transform(transformations.intersection_t(other)) def difference(self, other): """ New sequence with unique elements present in sequence but not in other. >>> seq([1, 2, 3]).difference([2, 3, 4]) [1] :param other: sequence to perform difference with :return: difference of sequence and other """ return self._transform(transformations.difference_t(other)) def symmetric_difference(self, other): """ New sequence with elements in either sequence or other, but not both. >>> seq([1, 2, 3, 3]).symmetric_difference([2, 4, 5]) [1, 3, 4, 5] :param other: sequence to perform symmetric difference with :return: symmetric difference of sequence and other """ return self._transform(transformations.symmetric_difference_t(other)) def map(self, func): """ Maps f onto the elements of the sequence. >>> seq([1, 2, 3, 4]).map(lambda x: x * -1) [-1, -2, -3, -4] :param func: function to map with :return: sequence with func mapped onto it """ return self._transform(transformations.map_t(func)) def select(self, func): """ Selects f from the elements of the sequence. >>> seq([1, 2, 3, 4]).select(lambda x: x * -1) [-1, -2, -3, -4] :param func: function to select with :return: sequence with func mapped onto it """ return self._transform(transformations.select_t(func)) def starmap(self, func): """ starmaps f onto the sequence as itertools.starmap does. >>> seq([(2, 3), (-2, 1), (0, 10)]).starmap(lambda x, y: x + y) [5, -1, 10] :param func: function to starmap with :return: sequence with func starmapped onto it """ return self._transform(transformations.starmap_t(func)) def smap(self, func): """ Alias to Sequence.starmap starmaps f onto the sequence as itertools.starmap does. >>> seq([(2, 3), (-2, 1), (0, 10)]).smap(lambda x, y: x + y) [5, -1, 10] :param func: function to starmap with :return: sequence with func starmapped onto it """ return self._transform(transformations.starmap_t(func)) def for_each(self, func): """ Executes func on each element of the sequence. >>> l = [] >>> seq([1, 2, 3, 4]).for_each(l.append) >>> l [1, 2, 3, 4] :param func: function to execute """ for e in self: func(e) def filter(self, func): """ Filters sequence to include only elements where func is True. >>> seq([-1, 1, -2, 2]).filter(lambda x: x > 0) [1, 2] :param func: function to filter on :return: filtered sequence """ return self._transform(transformations.filter_t(func)) def filter_not(self, func): """ Filters sequence to include only elements where func is False. >>> seq([-1, 1, -2, 2]).filter_not(lambda x: x > 0) [-1, -2] :param func: function to filter_not on :return: filtered sequence """ return self._transform(transformations.filter_not_t(func)) def where(self, func): """ Selects elements where func evaluates to True. >>> seq([-1, 1, -2, 2]).where(lambda x: x > 0) [1, 2] :param func: function to filter on :return: filtered sequence """ return self._transform(transformations.where_t(func)) def count(self, func): """ Counts the number of elements in the sequence which satisfy the predicate func. >>> seq([-1, -2, 1, 2]).count(lambda x: x > 0) 2 :param func: predicate to count elements on :return: count of elements that satisfy predicate """ n = 0 for element in self: if func(element): n += 1 return n def len(self): """ Return length of sequence using its length function. >>> seq([1, 2, 3]).len() 3 :return: length of sequence """ self.cache() return len(self._base_sequence) def size(self): """ Return size of sequence using its length function. :return: size of sequence """ return self.len() def empty(self): """ Returns True if the sequence has length zero. >>> seq([]).empty() True >>> seq([1]).empty() False :return: True if sequence length is zero """ return self.size() == 0 def non_empty(self): """ Returns True if the sequence does not have length zero. >>> seq([]).non_empty() False >>> seq([1]).non_empty() True :return: True if sequence length is not zero """ return self.size() != 0 def any(self): """ Returns True if any element in the sequence has truth value True >>> seq([True, False]).any() True >>> seq([False, False]).any() False :return: True if any element is True """ return any(self) def all(self): """ Returns True if the truth value of all items in the sequence true. >>> seq([True, True]).all() True >>> seq([True, False]).all() False :return: True if all items truth value evaluates to True """ return all(self) def exists(self, func): """ Returns True if an element in the sequence makes func evaluate to True. >>> seq([1, 2, 3, 4]).exists(lambda x: x == 2) True >>> seq([1, 2, 3, 4]).exists(lambda x: x < 0) False :param func: existence check function :return: True if any element satisfies func """ for element in self: if func(element): return True return False def for_all(self, func): """ Returns True if all elements in sequence make func evaluate to True. >>> seq([1, 2, 3]).for_all(lambda x: x > 0) True >>> seq([1, 2, -1]).for_all(lambda x: x > 0) False :param func: function to check truth value of all elements with :return: True if all elements make func evaluate to True """ for element in self: if not func(element): return False return True def max(self): """ Returns the largest element in the sequence. If the sequence has multiple maximal elements, only the first one is returned. The compared objects must have defined comparison methods. Raises TypeError when the objects are not comparable. The sequence can not be empty. Raises ValueError when the sequence is empty. >>> seq([2, 4, 5, 1, 3]).max() 5 >>> seq('aa', 'xyz', 'abcd', 'xyy').max() 'xyz' >>> seq([1, "a"]).max() Traceback (most recent call last): ... TypeError: unorderable types: int() < str() >>> seq([]).max() Traceback (most recent call last): ... ValueError: max() arg is an empty sequence :return: Maximal value of sequence """ return _wrap(max(self)) def min(self): """ Returns the smallest element in the sequence. If the sequence has multiple minimal elements, only the first one is returned. The compared objects must have defined comparison methods. Raises TypeError when the objects are not comparable. The sequence can not be empty. Raises ValueError when the sequence is empty. >>> seq([2, 4, 5, 1, 3]).min() 1 >>> seq('aa', 'xyz', 'abcd', 'xyy').min() 'aa' >>> seq([1, "a"]).min() Traceback (most recent call last): ... TypeError: unorderable types: int() < str() >>> seq([]).min() Traceback (most recent call last): ... ValueError: min() arg is an empty sequence :return: Minimal value of sequence """ return _wrap(min(self)) def max_by(self, func): """ Returns the largest element in the sequence. Provided function is used to generate key used to compare the elements. If the sequence has multiple maximal elements, only the first one is returned. The sequence can not be empty. Raises ValueError when the sequence is empty. >>> seq([2, 4, 5, 1, 3]).max_by(lambda num: num % 4) 3 >>> seq('aa', 'xyz', 'abcd', 'xyy').max_by(len) 'abcd' >>> seq([]).max_by(lambda x: x) Traceback (most recent call last): ... ValueError: max() arg is an empty sequence :param func: function to compute max by :return: Maximal element by func(element) """ return _wrap(max(self, key=func)) def min_by(self, func): """ Returns the smallest element in the sequence. Provided function is used to generate key used to compare the elements. If the sequence has multiple minimal elements, only the first one is returned. The sequence can not be empty. Raises ValueError when the sequence is empty. >>> seq([2, 4, 5, 1, 3]).min_by(lambda num: num % 6) 5 >>> seq('aa', 'xyz', 'abcd', 'xyy').min_by(len) 'aa' >>> seq([]).min_by(lambda x: x) Traceback (most recent call last): ... ValueError: min() arg is an empty sequence :param func: function to compute min by :return: Maximal element by func(element) """ return _wrap(min(self, key=func)) def find(self, func): """ Finds the first element of the sequence that satisfies func. If no such element exists, then return None. >>> seq(["abc", "ab", "bc"]).find(lambda x: len(x) == 2) 'ab' :param func: function to find with :return: first element to satisfy func or None """ for element in self: if func(element): return element return None def flatten(self): """ Flattens a sequence of sequences to a single sequence of elements. >>> seq([[1, 2], [3, 4], [5, 6]]) [1, 2, 3, 4, 5, 6] :return: flattened sequence """ return self._transform(transformations.flatten_t()) def flat_map(self, func): """ Applies func to each element of the sequence, which themselves should be sequences. Then appends each element of each sequence to a final result >>> seq([[1, 2], [3, 4], [5, 6]]).flat_map(lambda x: x) [1, 2, 3, 4, 5, 6] >>> seq(["a", "bc", "def"]).flat_map(list) ['a', 'b', 'c', 'd', 'e', 'f'] >>> seq([[1], [2], [3]]).flat_map(lambda x: x * 2) [1, 1, 2, 2, 3, 3] :param func: function to apply to each sequence in the sequence :return: application of func to elements followed by flattening """ return self._transform(transformations.flat_map_t(func)) def group_by(self, func): """ Group elements into a list of (Key, Value) tuples where func creates the key and maps to values matching that key. >>> seq(["abc", "ab", "z", "f", "qw"]).group_by(len) [(1, ['z', 'f']), (2, ['ab', 'qw']), (3, ['abc'])] :param func: group by result of this function :return: grouped sequence """ return self._transform(transformations.group_by_t(func)) def group_by_key(self): """ Group sequence of (Key, Value) elements by Key. >>> seq([('a', 1), ('b', 2), ('b', 3), ('b', 4), ('c', 3), ('c', 0)]).group_by_key() [('a', [1]), ('c', [3, 0]), ('b', [2, 3, 4])] :return: sequence grouped by key """ return self._transform(transformations.group_by_key_t()) def reduce_by_key(self, func): """ Reduces a sequence of (Key, Value) using func on each sequence of values. >>> seq([('a', 1), ('b', 2), ('b', 3), ('b', 4), ('c', 3), ('c', 0)]) \ .reduce_by_key(lambda x, y: x + y) [('a', 1), ('c', 3), ('b', 9)] :param func: reduce each list of values using two parameter, associative func :return: Sequence of tuples where the value is reduced with func """ return self._transform(transformations.reduce_by_key_t(func)) def count_by_key(self): """ Reduces a sequence of (Key, Value) by counting each key >>> seq([('a', 1), ('b', 2), ('b', 3), ('b', 4), ('c', 3), ('c', 0)]).count_by_key() [('a', 1), ('b', 3), ('c', 2)] :return: Sequence of tuples where value is the count of each key """ return self._transform(transformations.count_by_key_t()) def count_by_value(self): """ Reduces a sequence of items by counting each unique item >>> seq(['a', 'a', 'a', 'b', 'b', 'c', 'd']).count_by_value() [('a', 3), ('b', 2), ('c', 1), ('d', 1)] :return: Sequence of tuples where value is the count of each key """ return self._transform(transformations.count_by_value_t()) def reduce(self, func, *initial): """ Reduce sequence of elements using func. API mirrors functools.reduce >>> seq([1, 2, 3]).reduce(lambda x, y: x + y) 6 :param func: two parameter, associative reduce function :param initial: single optional argument acting as initial value :return: reduced value using func """ if len(initial) == 0: return _wrap(reduce(func, self)) elif len(initial) == 1: return _wrap(reduce(func, self, initial[0])) else: raise ValueError('reduce takes exactly one optional parameter for initial value') def accumulate(self, func=add): """ Accumulate sequence of elements using func. API mirrors itertools.accumulate >>> seq([1, 2, 3]).accumulate(lambda x, y: x + y) [1, 3, 6] >>> seq(['a', 'b', 'c']).accumulate() ['a', 'ab', 'abc'] :param func: two parameter, associative accumulate function :return: accumulated values using func in sequence """ return self._transform(transformations.accumulate_t(func)) def make_string(self, separator): """ Concatenate the elements of the sequence into a string separated by separator. >>> seq([1, 2, 3]).make_string("@") '1@2@3' :param separator: string separating elements in string :return: concatenated string separated by separator """ return separator.join(str(e) for e in self) def product(self, projection=None): """ Takes product of elements in sequence. >>> seq([1, 2, 3, 4]).product() 24 >>> seq([]).product() 1 >>> seq([(1, 2), (1, 3), (1, 4)]).product(lambda x: x[0]) 1 :param projection: function to project on the sequence before taking the product :return: product of elements in sequence """ if self.empty(): if projection: return projection(1) else: return 1 if self.size() == 1: if projection: return projection(self.first()) else: return self.first() if projection: return self.map(projection).reduce(mul) else: return self.reduce(mul) def sum(self, projection=None): """ Takes sum of elements in sequence. >>> seq([1, 2, 3, 4]).sum() 10 >>> seq([(1, 2), (1, 3), (1, 4)]).sum(lambda x: x[0]) 3 :param projection: function to project on the sequence before taking the sum :return: sum of elements in sequence """ if projection: return sum(self.map(projection)) else: return sum(self) def average(self, projection=None): """ Takes the average of elements in the sequence >>> seq([1, 2]).average() 1.5 >>> seq([('a', 1), ('b', 2)]).average(lambda x: x[1]) :param projection: function to project on the sequence before taking the average :return: average of elements in the sequence """ length = self.size() if projection: return sum(self.map(projection)) / length else: return sum(self) / length def aggregate(self, *args): """ Aggregates the sequence by specified arguments. Its behavior varies depending on if one, two, or three arguments are passed. Assuming the type of the sequence is A: One Argument: argument specifies a function of the type f(current: B, next: A => result: B. current represents results computed so far, and next is the next element to aggregate into current in order to return result. Two Argument: the first argument is the seed value for the aggregation. The second argument is the same as for the one argument case. Three Argument: the first two arguments are the same as for one and two argument calls. The additional third parameter is a function applied to the result of the aggregation before returning the value. :param args: options for how to execute the aggregation :return: aggregated value """ seed = None result_lambda = identity if len(args) == 1: func = args[0] elif len(args) == 2: seed = args[0] func = args[1] elif len(args) == 3: seed = args[0] func = args[1] result_lambda = args[2] else: raise ValueError('aggregate takes 1-3 arguments, {0} were given'.format(len(args))) if len(args) == 1: return result_lambda(self.drop(1).fold_left(self.first(), func)) else: return result_lambda(self.fold_left(seed, func)) def fold_left(self, zero_value, func): """ Assuming that the sequence elements are of type A, folds from left to right starting with the seed value given by zero_value (of type A) using a function of type func(current: B, next: A) => B. current represents the folded value so far and next is the next element from the sequence to fold into current. >>> seq('a', 'b', 'c').fold_left(['start'], lambda current, next: current + [next])) ['start', 'a', 'b', 'c'] :param zero_value: zero value to reduce into :param func: Two parameter function as described by function docs :return: value from folding values with func into zero_value from left to right. """ result = zero_value for element in self: result = func(result, element) return _wrap(result) def fold_right(self, zero_value, func): """ Assuming that the sequence elements are of type A, folds from right to left starting with the seed value given by zero_value (of type A) using a function of type func(next: A, current: B) => B. current represents the folded value so far and next is the next element from the sequence to fold into current. >>> seq('a', 'b', 'c').fold_left(['start'], lambda next, current: current + [next]) ['start', 'c', 'b', a'] :param zero_value: zero value to reduce into :param func: Two parameter function as described by function docs :return: value from folding values with func into zero_value from right to left """ result = zero_value for element in self.reverse(): result = func(element, result) return _wrap(result) def zip(self, sequence): """ Zips the stored sequence with the given sequence. >>> seq([1, 2, 3]).zip([4, 5, 6]) [(1, 4), (2, 5), (3, 6)] :param sequence: second sequence to zip :return: stored sequence zipped with given sequence """ return self._transform(transformations.zip_t(sequence)) def zip_with_index(self, start=0): """ Zips the sequence to its index, with the index being the second element of each tuple. >>> seq(['a', 'b', 'c']).zip_with_index() [('a', 0), ('b', 1), ('c', 2)] :return: sequence zipped to its index """ return self._transform(transformations.zip_with_index_t(start)) def enumerate(self, start=0): """ Uses python enumerate to to zip the sequence with indexes starting at start. >>> seq(['a', 'b', 'c']).enumerate(start=1) [(1, 'a'), (2, 'b'), (3, 'c')] :param start: Beginning of zip :return: enumerated sequence starting at start """ return self._transform(transformations.enumerate_t(start)) def inner_join(self, other): """ Sequence and other must be composed of (Key, Value) pairs. If self.sequence contains (K, V) pairs and other contains (K, W) pairs, the return result is a sequence of (K, (V, W)) pairs. Will return only elements where the key exists in both sequences. >>> seq([('a', 1), ('b', 2), ('c', 3)]).inner_join([('a', 2), ('c', 5)]) [('a', (1, 2)), ('c', (3, 5))] :param other: sequence to join with :return: joined sequence of (K, (V, W)) pairs """ return self.join(other, 'inner') def join(self, other, join_type="inner"): """ Sequence and other must be composed of (Key, Value) pairs. If self.sequence contains (K, V) pairs and other contains (K, W) pairs, the return result is a sequence of (K, (V, W)) pairs. If join_type is "left", V values will always be present, W values may be present or None. If join_type is "right", W values will always be present, W values may be present or None. If join_type is "outer", V or W may be present or None, but never at the same time. >>> seq([('a', 1), ('b', 2), ('c', 3)]).join([('a', 2), ('c', 5)], "inner") [('a', (1, 2)), ('c', (3, 5))] >>> seq([('a', 1), ('b', 2), ('c', 3)]).join([('a', 2), ('c', 5)]) [('a', (1, 2)), ('c', (3, 5))] >>> seq([('a', 1), ('b', 2)]).join([('a', 3), ('c', 4)], "left") [('a', (1, 3)), ('b', (2, None)] >>> seq([('a', 1), ('b', 2)]).join([('a', 3), ('c', 4)], "right") [('a', (1, 3)), ('c', (None, 4)] >>> seq([('a', 1), ('b', 2)]).join([('a', 3), ('c', 4)], "outer") [('a', (1, 3)), ('b', (2, None)), ('c', (None, 4))] :param other: sequence to join with :param join_type: specifies join_type, may be "left", "right", or "outer" :return: side joined sequence of (K, (V, W)) pairs """ return self._transform(transformations.join_t(other, join_type)) def left_join(self, other): """ Sequence and other must be composed of (Key, Value) pairs. If self.sequence contains (K, V) pairs and other contains (K, W) pairs, the return result is a sequence of (K, (V, W)) pairs. V values will always be present, W values may be present or None. >>> seq([('a', 1), ('b', 2)]).join([('a', 3), ('c', 4)]) [('a', (1, 3)), ('b', (2, None)] :param other: sequence to join with :return: left joined sequence of (K, (V, W)) pairs """ return self.join(other, "left") def right_join(self, other): """ Sequence and other must be composed of (Key, Value) pairs. If self.sequence contains (K, V) pairs and other contains (K, W) pairs, the return result is a sequence of (K, (V, W)) pairs. W values will always bepresent, V values may be present or None. >>> seq([('a', 1), ('b', 2)]).join([('a', 3), ('c', 4)]) [('a', (1, 3)), ('b', (2, None)] :param other: sequence to join with :return: right joined sequence of (K, (V, W)) pairs """ return self.join(other, "right") def outer_join(self, other): """ Sequence and other must be composed of (Key, Value) pairs. If self.sequence contains (K, V) pairs and other contains (K, W) pairs, the return result is a sequence of (K, (V, W)) pairs. One of V or W will always be not None, but the other may be None >>> seq([('a', 1), ('b', 2)]).outer_join([('a', 3), ('c', 4)], "outer") [('a', (1, 3)), ('b', (2, None)), ('c', (None, 4))] :param other: sequence to join with :return: outer joined sequence of (K, (V, W)) pairs """ return self.join(other, "outer") def partition(self, func): """ Partition the sequence based on satisfying the predicate func. >>> seq([-1, 1, -2, 2]).partition(lambda x: x < 0) ([-1, -2], [1, 2]) :param func: predicate to partition on :return: tuple of partitioned sequences """ return self._transform(transformations.partition_t(_wrap, func)) def grouped(self, size): """ Partitions the elements into groups of length size. >>> seq([1, 2, 3, 4, 5, 6, 7, 8]).grouped(2) [[1, 2], [3, 4], [5, 6], [7, 8]] >>> seq([1, 2, 3, 4, 5, 6, 7, 8]).grouped(3) [[1, 2, 3], [4, 5, 6], [7, 8]] The last partition has at least one element but may have less than size elements. :param size: size of the partitions :return: sequence partitioned into groups of length size """ return self._transform(transformations.grouped_t(_wrap, size)) def sliding(self, size, step=1): """ Groups elements in fixed size blocks by passing a sliding window over them. The last window has at least one element but may have less than size elements :param size: size of sliding window :param step: step size between windows :return: sequence of sliding windows """ return self._transform(transformations.sliding_t(_wrap, size, step)) def sorted(self, key=None, reverse=False): """ Uses python sort and its passed arguments to sort the input. >>> seq([2, 1, 4, 3]).sorted() [1, 2, 3, 4] :param key: sort using key function :param reverse: return list reversed or not :return: sorted sequence """ return self._transform(transformations.sorted_t(key=key, reverse=reverse)) def order_by(self, func): """ Orders the input according to func >>> seq([(2, 'a'), (1, 'b'), (4, 'c'), (3, 'd')]).order_by(lambda x: x[0]) [1, 2, 3, 4] :param func: order by funciton :return: ordered sequence """ return self._transform(transformations.order_by_t(func)) def reverse(self): """ Returns the reversed sequence. >>> seq([1, 2, 3]).reverse() [3, 2, 1] :return: reversed sequence """ return reversed(self) def distinct(self): """ Returns sequence of distinct elements. Elements must be hashable. >>> seq([1, 1, 2, 3, 3, 3, 4]).distinct() [1, 2, 3, 4] :return: sequence of distinct elements """ return self._transform(transformations.distinct_t()) def distinct_by(self, func): """ Returns sequence of elements who are distinct by the passed function. The return value of func must be hashable. When two elements are distinct by func, the first is taken. :param func: function to use for determining distinctness :return: elements distinct by func """ return self._transform(transformations.distinct_by_t(func)) def slice(self, start, until): """ Takes a slice of the sequence starting at start and until but not including until. >>> seq([1, 2, 3, 4]).slice(1, 2) [2] >>> seq([1, 2, 3, 4]).slice(1, 3) [2, 3] :param start: starting index :param until: ending index :return: slice including start until but not including until """ return self._transform(transformations.slice_t(start, until)) def to_list(self, n=None): """ Converts sequence to list of elements. >>> type(seq([]).to_list()) list >>> type(seq([])) functional.pipeline.Sequence >>> seq([1, 2, 3]).to_list() [1, 2, 3] :param n: Take n elements of sequence if not None :return: list of elements in sequence """ if n is None: self.cache() return self._base_sequence else: return self.cache().take(n).list() def list(self, n=None): """ Converts sequence to list of elements. >>> type(seq([]).list()) list >>> type(seq([])) functional.pipeline.Sequence >>> seq([1, 2, 3]).list() [1, 2, 3] :param n: Take n elements of sequenc eif not None :return: list of elements in sequence """ return self.to_list(n=n) def to_set(self): """ Converts sequence to a set of elements. >>> type(seq([])).to_set() set >>> type(seq([])) functional.pipeline.Sequence >>> seq([1, 1, 2, 2]).to_set() {1, 2} :return:set of elements in sequence """ return set(self.sequence) def set(self): """ Converts sequence to a set of elements. >>> type(seq([])).to_set() set >>> type(seq([])) functional.pipeline.Sequence >>> seq([1, 1, 2, 2]).set() {1, 2} :return:set of elements in sequence """ return self.to_set() def to_dict(self, default=None): """ Converts sequence of (Key, Value) pairs to a dictionary. >>> type(seq([('a', 1)]).to_dict()) dict >>> seq([('a', 1), ('b', 2)]).to_dict() {'a': 1, 'b': 2} :param default: Can be a callable zero argument function. When not None, the returned dictionary is a collections.defaultdict with default as value for missing keys. If the value is not callable, then a zero argument lambda function is created returning the value and used for collections.defaultdict :return: dictionary from sequence of (Key, Value) elements """ dictionary = {} for e in self.sequence: dictionary[e[0]] = e[1] if default is None: return dictionary else: if hasattr(default, '__call__'): return collections.defaultdict(default, dictionary) else: return collections.defaultdict(lambda: default, dictionary) def dict(self, default=None): """ Converts sequence of (Key, Value) pairs to a dictionary. >>> type(seq([('a', 1)]).dict()) dict >>> seq([('a', 1), ('b', 2)]).dict() {'a': 1, 'b': 2} :param default: Can be a callable zero argument function. When not None, the returned dictionary is a collections.defaultdict with default as value for missing keys. If the value is not callable, then a zero argument lambda function is created returning the value and used for collections.defaultdict :return: dictionary from sequence of (Key, Value) elements """ return self.to_dict(default=default) # pylint: disable=too-many-locals def to_file(self, path, delimiter=None, mode='wt', buffering=-1, encoding=None, errors=None, newline=None, compresslevel=9, format=None, check=-1, preset=None, filters=None, compression=None): """ Saves the sequence to a file by executing str(self) which becomes str(self.to_list()). If delimiter is defined will instead execute self.make_string(delimiter) :param path: path to write file :param delimiter: if defined, will call make_string(delimiter) and save that to file. :param mode: file open mode :param buffering: passed to builtins.open :param encoding: passed to builtins.open :param errors: passed to builtins.open :param newline: passed to builtins.open :param compression: compression format :param compresslevel: passed to gzip.open :param format: passed to lzma.open :param check: passed to lzma.open :param preset: passed to lzma.open :param filters: passed to lzma.open """ with universal_write_open(path, mode=mode, buffering=buffering, encoding=encoding, errors=errors, newline=newline, compression=compression, compresslevel=compresslevel, format=format, check=check, preset=preset, filters=filters) as output: if delimiter: output.write(six.u(self.make_string(delimiter))) else: output.write(six.u(str(self))) def to_jsonl(self, path, mode='wb', compression=None): """ Saves the sequence to a jsonl file. Each element is mapped using json.dumps then written with a newline separating each element. :param path: path to write file :param mode: mode to write in, defaults to 'w' to overwrite contents :param compression: compression format """ with universal_write_open(path, mode=mode, compression=compression) as output: output.write((self.map(json.dumps).make_string('\n') + '\n').encode('utf-8')) def to_json(self, path, root_array=True, mode=WRITE_MODE, compression=None): """ Saves the sequence to a json file. If root_array is True, then the sequence will be written to json with an array at the root. If it is False, then the sequence will be converted from a sequence of (Key, Value) pairs to a dictionary so that the json root is a dictionary. :param path: path to write file :param root_array: write json root as an array or dictionary :param mode: file open mode """ with universal_write_open(path, mode=mode, compression=compression) as output: if root_array: json.dump(self.to_list(), output) else: json.dump(self.to_dict(), output) def to_csv(self, path, mode=WRITE_MODE, dialect='excel', compression=None, newline='', **fmtparams): """ Saves the sequence to a csv file. Each element should be an iterable which will be expanded to the elements of each row. :param path: path to write file :param mode: file open mode :param dialect: passed to csv.writer :param fmtparams: passed to csv.writer """ if 'b' in mode: newline = None with universal_write_open(path, mode=mode, compression=compression, newline=newline) as output: csv_writer = csv.writer(output, dialect=dialect, **fmtparams) for row in self: csv_writer.writerow([six.u(str(element)) for element in row]) def _to_sqlite3_by_query(self, conn, sql): """ Saves the sequence to sqlite3 database by supplied query. Each element should be an iterable which will be expanded to the elements of each row. Target table must be created in advance. :param conn: path or sqlite connection, cursor :param sql: SQL query string """ conn.executemany(sql, self) def _to_sqlite3_by_table(self, conn, table_name): """ Saves the sequence to the specified table of sqlite3 database. Each element can be a dictionary, namedtuple, tuple or list. Target table must be created in advance. :param conn: path or sqlite connection, cursor :param table_name: table name string """ def _insert_item(item): if isinstance(item, dict): cols = ', '.join(item.keys()) placeholders = ', '.join('?' * len(item)) sql = 'INSERT INTO {} ({}) VALUES ({})'.format(table_name, cols, placeholders) conn.execute(sql, tuple(item.values())) elif is_namedtuple(item): cols = ', '.join(item._fields) placeholders = ', '.join('?' * len(item)) sql = 'INSERT INTO {} ({}) VALUES ({})'.format(table_name, cols, placeholders) conn.execute(sql, item) elif isinstance(item, (list, tuple)): placeholders = ', '.join('?' * len(item)) sql = 'INSERT INTO {} VALUES ({})'.format(table_name, placeholders) conn.execute(sql, item) else: raise TypeError('item must be one of dict, namedtuple, tuple or list got {}' .format(type(item))) self.for_each(_insert_item) def to_sqlite3(self, conn, target, *args, **kwargs): """ Saves the sequence to sqlite3 database. Target table must be created in advance. The table schema is inferred from the elements in the sequence if only target table name is supplied. >>> seq([(1, 'Tom'), (2, 'Jack')])\ .to_sqlite3('users.db', 'INSERT INTO user (id, name) VALUES (?, ?)') >>> seq([{'id': 1, 'name': 'Tom'}, {'id': 2, 'name': 'Jack'}]).to_sqlite3(conn, 'user') :param conn: path or sqlite connection, cursor :param target: SQL query string or table name :param args: passed to sqlite3.connect :param kwargs: passed to sqlite3.connect """ # pylint: disable=no-member insert_regex = re.compile(r'(insert|update)\s+into', flags=re.IGNORECASE) if insert_regex.match(target): insert_f = self._to_sqlite3_by_query else: insert_f = self._to_sqlite3_by_table if isinstance(conn, (sqlite3.Connection, sqlite3.Cursor)): insert_f(conn, target) conn.commit() elif isinstance(conn, str): with sqlite3.connect(conn, *args, **kwargs) as input_conn: insert_f(input_conn, target) input_conn.commit() else: raise ValueError('conn must be a must be a file path or sqlite3 Connection/Cursor') def to_pandas(self, columns=None): # pylint: disable=import-error """ Converts sequence to a pandas DataFrame using pandas.DataFrame.from_records :param columns: columns for pandas to use :return: DataFrame of sequence """ import pandas return pandas.DataFrame.from_records(self.to_list(), columns=columns) def show(self, n=10, headers=(), tablefmt="simple", floatfmt="g", numalign="decimal", stralign="left", missingval=""): """ Pretty print first n rows of sequence as a table. See https://bitbucket.org/astanin/python-tabulate for details on tabulate parameters :param n: Number of rows to show :param headers: Passed to tabulate :param tablefmt: Passed to tabulate :param floatfmt: Passed to tabulate :param numalign: Passed to tabulate :param stralign: Passed to tabulate :param missingval: Passed to tabulate """ formatted_seq = self.tabulate(n=n, headers=headers, tablefmt=tablefmt, floatfmt=floatfmt, numalign=numalign, stralign=stralign, missingval=missingval) print(formatted_seq) def _repr_html_(self): """ Allows IPython render HTML tables :return: First 10 rows of data as an HTML table """ return self.tabulate(10, tablefmt='html') def tabulate(self, n=None, headers=(), tablefmt="simple", floatfmt="g", numalign="decimal", stralign="left", missingval=""): """ Return pretty string table of first n rows of sequence or everything if n is None. See https://bitbucket.org/astanin/python-tabulate for details on tabulate parameters :param n: Number of rows to show, if set to None return all rows :param headers: Passed to tabulate :param tablefmt: Passed to tabulate :param floatfmt: Passed to tabulate :param numalign: Passed to tabulate :param stralign: Passed to tabulate :param missingval: Passed to tabulate """ self.cache() length = self.len() if length == 0 or not is_tabulatable(self[0]): return None if n is None or n >= length: rows = self.list() message = '' else: rows = self.take(n).list() if tablefmt == 'simple': message = '\nShowing {} of {} rows'.format(n, length) elif tablefmt == 'html': message = '<p>Showing {} of {} rows'.format(n, length) else: message = '' if len(headers) == 0 and is_namedtuple(rows[0]): headers = rows[0]._fields return tabulate(rows, headers=headers, tablefmt=tablefmt, floatfmt=floatfmt, numalign=numalign, stralign=stralign, missingval=missingval) + message def _wrap(value): """ Wraps the passed value in a Sequence if it is not a primitive. If it is a string argument it is expanded to a list of characters. >>> _wrap(1) 1 >>> _wrap("abc") ['a', 'b', 'c'] >>> type(_wrap([1, 2])) functional.pipeline.Sequence :param value: value to wrap :return: wrapped or not wrapped value """ if is_primitive(value): return value if isinstance(value, (dict, set)) or is_namedtuple(value): return value elif isinstance(value, collections.Iterable): try: if type(value).__name__ == 'DataFrame': import pandas if isinstance(value, pandas.DataFrame): return Sequence(value.values) except ImportError: # pragma: no cover pass return Sequence(value) else: return value def extend(func=None, aslist=False, final=False, name=None, parallel=False): """ Function decorator for adding new methods to the Sequence class. >>> @extend() def zip2(it): return [(i,i) for i in it] >>> seq.range(3).zip2() [(0, 0), (1, 1), (2, 2)] >>> @extend(aslist=True) def zip2(it): return zip(it,it) >>> seq.range(3).zip2() [(0, 0), (1, 1), (2, 2)] >>> @extend(final=True) def make_set(it): return set(it) >>> r = seq([0,1,1]).make_set() >>> r {0, 1} >>> type(r) <class 'set'> :param func: function to decorate :param aslist: if True convert input sequence to list (default False) :param final: If True decorated function does not return a sequence. Useful for creating functions such as to_list. :param name: name of the function (default function definition name) :param parallel: if true the function is executed in parallel execution strategy (default False) """ if func is None: return partial(extend, aslist=aslist, final=final, name=name, parallel=parallel) @wraps(func) def wrapper(self, *args, **kwargs): # pylint: disable=protected-access # do not create a new Sequence - just apply a function if final: return func(self.sequence, *args, **kwargs) if aslist: func_ = lambda seq: func(list(seq), *args, **kwargs) else: func_ = lambda seq: func(seq, *args, **kwargs) transform = transformations.Transformation( 'extended[{}]'.format(name or func.__name__), func_, {ExecutionStrategies.PARALLEL} if parallel else None ) return self._transform(transform) # dynamically add a new method setattr(Sequence, func.__name__, wrapper) return wrapper
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setup.py
eddbrown/coodinate_country_lookup
0
97825
<filename>setup.py from distutils.core import setup setup( name = 'coordinate_country_lookup', # How you named your package folder (MyLib) packages = ['coordinate_country_lookup'], # Chose the same as "name" version = '0.1.5', # Start with a small number and increase it with every change you make license='MIT', # Chose a license from here: https://help.github.com/articles/licensing-a-repository description = 'Returns the country of the latitude longitude tuple offline', # Give a short description about your library author = '<NAME>', # Type in your name author_email = '<EMAIL>', # Type in your E-Mail url = 'https://github.com/eddbrown/coodinate_country_lookup', # Provide either the link to your github or to your website keywords = ['COORDINATE', 'COUNTRY', 'OFFLINE'], # Keywords that define your package best install_requires=['shapely', 'geopandas'], package_data={'': ['countries/*']}, include_package_data=True )
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obsolete/pipeline_idr.py
kevinrue/cgat-flow
11
195416
"""=========================== Pipeline IDR =========================== Overview ======== This pipeline is a wrapper for the Irreproducibility Discovery Pipeline outlined by <NAME>. (see https://sites.google.com/site/anshulkundaje/projects/idr) It is split into two stages and performs several basic steps: Fist stage ---------- 1) Pre-processing bamfiles Bamfiles are filtered prior to peak-calling. With the option to remove non-uniquely mapping reads, remove duplicates, and mask genomic regions in a specified bedfile. Bamfiles are variously pooled by EXPERIMENT (tissue-condition-agg )and split, to provide pseudoreplicates of individual samples and pooled-pseudoreplicates. 2) Peak calling is carried out using SPP on three sets of samples: i) individual samples, ii) pseudoreplicates, and iii) pooled-pseudoreplicates. Three options exist for using control files in peak-calling: i) All peak calling may be carried out against a single control per experiment, in which case the control must be labelled <tissue>-<control>-R1. ii) Peaks may be called against pooled control files for each EXPERIMENT (as recommended for IDR) in which case all three peak-calling steps will be carried out using pooled controls labelled <tissue>-<condition>-R0. iii) Peak calling for individual samples may be carried out using controls matched by replicate. In which case peak calling for pseudoreplicates will be carried out against the same control (i.e. pseudoreplication of the control file does not take place), and peak calling for the pooled pseudoreplicates is carried out against a pooled control. Peakcalling for IDR is necessarily lax and the option exists to specify the number of peaks called using either -npeaks or -fdr. (It may be necessary to use a customized version of run_spp.R for one or other of these options, depending on which version of SPP is being run, this option is also provided.) 3) IDR analysis is carried out using WrapperIDR (which us a python wrapper for batch-consistency-analysis.r) for i) all pairwise comparisons of individual replicates within an EXPERIMENT, ii) pairs of psuedoreplicates for each sample, and iii) paired pooled pseudoreplicates for each EXPERIMENT. These steps provide IDR analysis on origanl replicates, IDR analysis on self- pseudoreplicates, and IDR analysis on pooled-pseudoreplicates, respectively. Second stage ------------ 4) Following IDR analysis, a second stage of the pipeline (generatePeakSets) may be run. Libraries that fail in terms of 'max_numPeaks_Rep' or 'self-consistency' may be specified for exclusion in the config file. Subsequently, all non-excluded bamfiles are pooled per EXPERIMENT so that both a conservative peak set and optimal peak set may be derived. Usage ===== See :ref:`PipelineSettingUp` and :ref:`PipelineRunning` on general information how to use cgat pipelines. Configuration ------------- The pipeline requires a configured :file:`pipeline.ini` file. Sphinxreport report requires a :file:`conf.py` and :file:`sphinxreport.ini` file (see :ref:`PipelineDocumenation`). To start with, use the files supplied with the :ref:`Example` data. Input ----- Optional inputs +++++++++++++++ Requirements ------------ The pipeline requires the results from :doc:`pipeline_annotations`. Set the configuration variable :py:data:`annotations_database` and :py:data:`annotations_dir`. On top of the default cgat setup, the pipeline requires the following software to be in the path: +--------------------+-------------------+------------------------------------+ |*Program* |*Version* |*Purpose* | +--------------------+-------------------+------------------------------------+ | | | | +--------------------+-------------------+------------------------------------+ Pipeline output =============== The major output is in the database file :file:`csvdb`. Example ======= Example data is available at http://www.cgat.org/~andreas/sample_data\ /pipeline_IDR.tgz. To run the example, simply unpack and untar:: wget http://www.cgat.org/~andreas/sample_data/pipeline_IDR.tgz tar -xvzf pipeline_IDR.tgz cd pipeline_IDR python <srcdir>/pipeline_IDR.py make full .. note:: For the pipeline to run, install the :doc:`pipeline_annotations` as well. Glossary ======== .. glossary:: Code ==== """ from ruffus import * import sys import os import itertools import re import sqlite3 import glob import shutil import cgatcore.experiment as E import cgatcore.iotools as iotools import cgatcore.database as Database import cgatPipelines.PipelineIDR as IDR from cgatcore import pipeline as P import cgatPipelines.PipelineTracks as PipelineTracks # Pipeline configuration P.getParameters( ["%s/pipeline.ini" % os.path.splitext(__file__)[0], "../pipeline.ini", "pipeline.ini"]) PARAMS = P.PARAMS PARAMS.update(P.peekParameters( PARAMS["annotations_dir"], "pipeline_annotations.py", prefix="annotations_", update_interface=True)) # Helper functions mapping tracks to conditions, etc Sample = PipelineTracks.AutoSample # define tracks based on all samples in .bamfile that are not input or index TRACKS = PipelineTracks.Tracks(Sample).loadFromDirectory( glob.glob(os.path.join(PARAMS.get("location_bamfiles", ""), "*.bam")), "(\S+).bam", exclude=[".+input.+"]) @files(None, None) def printTracks(infile, outfile): P.warn("\n\n\n\nprinting tracks:") for track in EXPERIMENTS: print("\t") print(track) def get_peak_caller_parameters(peak_caller_id): """ Returns a dictionary of config file parameters for the chosen peak caller (an attempt to keep access to PARAMS out of associated pipeline script). """ caller_parameters = {} caller_prefix = peak_caller_id + "_options" for key, value in PARAMS.items(): if re.match(caller_prefix, key): caller_parameters[key] = value return caller_parameters def connect(): '''connect to database. Use this method to connect to additional databases. Returns a database connection. ''' dbh = sqlite3.connect(PARAMS["database_name"]) statement = '''ATTACH DATABASE '%s' as annotations''' % ( PARAMS["annotations_database"]) cc = dbh.cursor() cc.execute(statement) cc.close() return dbh # Process Bamfiles @follows(mkdir("bamfiles_filtered")) @transform(os.path.join(PARAMS["location_bamfiles"], "*.bam"), regex("(.+)/(.+).bam"), r"./bamfiles_filtered/\2.sentinel") def filterBamfiles(infile, sentinel): """ Pre-process bamfiles prior to peak calling. i) sort bamfiles ii) remove unmapped readswith bam2bam.py iii) remove non-uniquely mapping reads with bam2bam.py (optional) iv) remove duplicates with Picards MarkDuplicates (optional) v) remove reads from masked regions with bedtools intersect (optional) vi) index """ # create tempfile for Picard's MarkDuplicates picard_tmp = P.getTempDir(PARAMS["scratchdir"]) outfile = P.snip(sentinel, ".sentinel") + ".bam" # ensure bamfile is sorted, statement = ["samtools sort @IN@ -o @[email protected]", ] # remove unmapped reads statement.append("cgat bam2bam" " --method=filter --filter-method=mapped" " --log=%(outfile)s.log" " < @[email protected]" " > @OUT@") # remove non-uniquely mapping reads, if requested if PARAMS["filter_remove_non_unique"]: statement.append("cgat bam2bam" " --method=filter --filter-method=unique" " --log=%(outfile)s.log" " < @IN@" " > @OUT@") # remove duplicates, if requested if PARAMS["filter_remove_duplicates"]: statement.append("MarkDuplicates" " INPUT=@IN@" " ASSUME_SORTED=true" " REMOVE_DUPLICATES=true" " QUIET=false" " OUTPUT=@OUT@" " METRICS_FILE=/dev/null" " VALIDATION_STRINGENCY=SILENT" " TMP_DIR=%(picard_tmp)s" " 2> %(outfile)s.log") # mask regions, if intervals supplied if PARAMS["filter_mask_intervals"]: mask = PARAMS["filter_mask_intervals"] statement.append("bedtools intersect" " -abam @IN@" " -b %(mask)s" " -wa" " -v" " > @OUT@") statement.append("mv @IN@ %(outfile)s") statement.append("samtools index %(outfile)s") job_memory = "5G" statement = P.joinStatements(statement, infile) P.run() P.touch(sentinel) shutil.rmtree(picard_tmp) @follows(filterBamfiles, mkdir("bamfiles_pseudoreplicates")) @transform([os.path.join("./bamfiles_filtered", x.asFile() + ".sentinel") for x in TRACKS], regex("./bamfiles_filtered/(.+).sentinel"), r"./bamfiles_pseudoreplicates/\1.sentinel") def splitBamfiles(infile, sentinel): """ For all tracks, split the filtered bamfile in two using pysam """ infile = P.snip(infile, ".sentinel") + ".bam" outfile = P.snip(sentinel, ".sentinel") params = '2' try: module = P.snip(IDR.__file__, ".py") except ValueError: module = P.snip(IDR.__file__, ".pyc") P.submit(module, "splitBam", params, infile, outfile) P.touch(sentinel) # AH: remove replicate requirement for input tracks # AH: avoid merging if only one repliacte @follows(mkdir("bamfiles_pooled")) @collate(filterBamfiles, regex(r"(.+)/(.+)-input-.*.sentinel"), r"./bamfiles_pooled/\2-input-R0.sentinel") def poolInputBamfiles(infiles, sentinel): """ Merge filtered input files for each tissue, with the option of excluding undesirable libraries. """ infiles = [P.snip(x, ".sentinel") + ".bam" for x in infiles] outfile = P.snip(sentinel, ".sentinel") + ".bam" bad_samples = PARAMS["filter_remove_inputs"].split(",") if len(infiles) > 1: to_merge = IDR.filterBadLibraries(infiles, bad_samples) IDR.mergeBams(to_merge, outfile) else: os.symlink(os.path.abspath(infiles[0]), outfile) os.symlink(os.path.abspath(infiles[0]) + ".bai", outfile + ".bai") P.touch(sentinel) @follows(mkdir("bamfiles_pooled")) @collate(filterBamfiles, regex(r"(.+)/(.+)-((?!input).*)-R[0-9]+.sentinel"), r"./bamfiles_pooled/\2-\3-R0.sentinel") def poolSampleBamfiles(infiles, sentinel): """ Merge filtered sample files for each tissue """ infiles = [P.snip(x, ".sentinel") + ".bam" for x in infiles] outfile = P.snip(sentinel, ".sentinel") + ".bam" IDR.mergeBams(infiles, outfile) P.touch(sentinel) @follows(mkdir("./bamfiles_pooled_pseudoreplicates")) @transform(poolSampleBamfiles, regex("(.+)/(.+).sentinel"), r"./bamfiles_pooled_pseudoreplicates/\2.sentinel") def splitPooledBamfiles(infile, sentinel): infile = P.snip(infile, ".sentinel") + ".bam" outfile = P.snip(sentinel, ".sentinel") params = '2' try: module = P.snip(IDR.__file__, ".py") except ValueError: module = P.snip(IDR.__file__, ".pyc") P.submit(module, "splitBam", params, infile, outfile) P.touch(sentinel) ########################################################################## ########################################################################## ########################################################################## # Run Peak calling for IDR ########################################################################## @follows(filterBamfiles, poolInputBamfiles, mkdir("peakfiles_individual_replicates"), mkdir("stats")) @transform([os.path.join("./bamfiles_filtered", x.asFile() + ".sentinel") for x in TRACKS], regex("(.+)/(.+).sentinel"), r"./peakfiles_individual_replicates/\2_regionPeak.sentinel") def callPeaksOnIndividualReplicates(infile, outfile): infile = P.snip(infile, ".sentinel") + ".bam" # fetch peak calling parameters PARAMS_PEAKCALLER = get_peak_caller_parameters( PARAMS["options_peak_caller"]) # call peaks IDR.callIDRPeaks(infile, outfile, PARAMS["options_peak_caller"], PARAMS["options_control_type"], PARAMS_PEAKCALLER) P.touch(outfile) @follows(callPeaksOnIndividualReplicates) @merge("./peakfiles_individual_replicates/*.narrowPeak.gz", "./peakfiles_individual_replicates/" "peakcalling_summary_individual_replicates.tsv") def summarizePeaksForIndividualReplicates(infiles, outfile): outf = iotools.openFile(outfile, "w") outf.write("Sample_id\t" "Experiment\t" "Tissue\t" "Condition\t" "Replicate\t" "n_peaks\n") IDR.countPeaks(infiles, outf) @transform(summarizePeaksForIndividualReplicates, regex("(.+)/(.+).tsv"), r"\2.load") def loadPeakSummaryForIndividualReplicates(infile, outfile): P.load(infile, outfile) @follows(splitBamfiles, poolInputBamfiles, mkdir("peakfiles_pseudoreplicates")) @transform("./bamfiles_pseudoreplicates/*.bam", regex("(.+)/(.+).bam"), r"./peakfiles_pseudoreplicates/\2_regionPeak.sentinel") def callPeaksOnPseudoreplicates(infile, outfile): # fetch peak calling parameters PARAMS_PEAKCALLER = get_peak_caller_parameters( PARAMS["options_peak_caller"]) # call peaks on pseudoreplicates IDR.callIDRPeaks(infile, outfile, PARAMS["options_peak_caller"], PARAMS["options_control_type"], PARAMS_PEAKCALLER, pseudoreplicate=True) @follows(callPeaksOnPseudoreplicates) @merge("./peakfiles_pseudoreplicates/*.narrowPeak.gz", "./peakfiles_pseudoreplicates/" "peakcalling_summary_pseudoreplicates.tsv") def summarizePeaksForPseudoreplicates(infiles, outfile): outf = iotools.openFile(outfile, "w") outf.write("Sample_id\t" "Experiment\t" "Tissue\t" "Condition\t" "Pseudoreplicate\t" "n_peaks\n") IDR.countPeaks(infiles, outf) @transform(summarizePeaksForPseudoreplicates, regex("(.+)/(.+).tsv"), r"\2.load") def loadPeakSummaryForPseudoreplicates(infile, outfile): P.load(infile, outfile) @follows(splitPooledBamfiles, poolInputBamfiles, mkdir("peakfiles_pooled_pseudoreplicates")) @transform("./bamfiles_pooled_pseudoreplicates/*.bam", regex("(.+)/(.+).bam"), r"./peakfiles_pooled_pseudoreplicates/\2.regionPeak.sentinel") def callPeaksOnPooledPseudoreplicates(infile, outfile): # fetch peak calling parameters PARAMS_PEAKCALLER = get_peak_caller_parameters( PARAMS["options_peak_caller"]) # call peaks on pseudoreplicates IDR.callIDRPeaks(infile, outfile, PARAMS["options_peak_caller"], PARAMS["options_control_type"], PARAMS_PEAKCALLER, pseudoreplicate=True) P.touch(outfile) @follows(callPeaksOnPooledPseudoreplicates) @merge("./peakfiles_pooled_pseudoreplicates/*.narrowPeak.gz", "./peakfiles_pooled_pseudoreplicates/" "peakcalling_summary_pooled_pseudoreplicates.tsv") def summarizePeaksForPooledPseudoreplicates(infiles, outfile): outf = iotools.openFile(outfile, "w") outf.write("Sample_id\t" "Experiment\t" "Tissue\t" "Condition\t" "Pseudoreplicate\t" "n_peaks\n") IDR.countPeaks(infiles, outf) @transform(summarizePeaksForPooledPseudoreplicates, regex("(.+)/(.+).tsv"), r"\2.load") def loadPeakSummaryForPooledPseudoreplicates(infile, outfile): P.load(infile, outfile) ########################################################################## ########################################################################## ########################################################################## # Run IDR ########################################################################## @follows(callPeaksOnIndividualReplicates, mkdir("./idr_individual_replicates")) @collate("./peakfiles_individual_replicates/*.regionPeak.gz", regex(r"(.+)/(.+)-(.+)-(R[0-9]+)_VS_(.+).regionPeak.gz"), r"./idr_individual_replicates/\2-\3.idr") def runIDROnIndividualReplicates(infiles, outfile): """ Run IDR consecutively for each pairwise combination of a particular EXPERIMENT """ # set IDR parameters (HACK!) WrapperIDR is in /ifs/devel/cgat chr_table = os.path.join(PARAMS["annotations_dir"], PARAMS["annotations_interface_contigs"]) # iterate through pairwise combinations of infiles for infile1, infile2 in itertools.combinations(infiles, 2): # get statement statement = IDR.getIDRStatement(infile1, infile2, outfile, PARAMS["idr_options_overlap_ratio"], PARAMS["idr_options_ranking_measure"], chr_table) # run E.info("applyIDR: processing %s and %s" % (infile1, infile2)) job_memory = "5G" P.run() # print "\n" + statement + "\n" @transform(runIDROnIndividualReplicates, regex("(.+).idr"), add_inputs(r"\1-*-uri.sav"), r"\1_batch-consistency.pdf") def plotBatchConsistencyForIndividualReplicates(infiles, outfile): # HACK! statement = IDR.getIDRPlotStatement(infiles, outfile) P.run() # print statement @follows(callPeaksOnPseudoreplicates, mkdir("./idr_pseudoreplicates")) @collate("./peakfiles_pseudoreplicates/*.regionPeak.gz", regex("./peakfiles_pseudoreplicates/" "(.+)(_00|_01)_VS_(.+).regionPeak.gz"), r"idr_pseudoreplicates/\1_pseudoreplicates.idr") def runIDROnPseudoreplicates(infiles, outfile): """ Run IDR analysis on pseudoreplicates for each TRACK """ # set IDR parameters chr_table = os.path.join(PARAMS["annotations_dir"], PARAMS["annotations_interface_contigs"]) # get statement statement = IDR.getIDRStatement(infiles[0], infiles[1], outfile, PARAMS["idr_options_overlap_ratio"], PARAMS["idr_options_ranking_measure"], chr_table) # run E.info("applyIDR: processing %s and %s" % (infiles[0], infiles[1])) job_memory = "5G" P.run() # print "\n" + statement + "\n" @collate(runIDROnPseudoreplicates, regex("(.+)_pseudoreplicates.idr"), add_inputs(r"\1*-uri.sav"), r"\1_batch-consistency.pdf") def plotBatchConsistencyForPseudoreplicates(infiles, outfile): statement = IDR.getIDRPlotStatement(infiles[0], outfile) P.run() @follows(callPeaksOnPooledPseudoreplicates, mkdir("./idr_pooled_pseudoreplicates")) @collate("./peakfiles_pooled_pseudoreplicates/*.regionPeak.gz", regex(r"./peakfiles_pooled_pseudoreplicates/" "(.+)(_00|_01)_VS_(.+).regionPeak.gz"), r"./idr_pooled_pseudoreplicates/\1_pooled_pseudoreplicate.idr") def runIDROnPooledPseudoreplicates(infiles, outfile): """ Run IDR analysis on pooled pseudoreplicates for each EXPERIMENT """ # set IDR parameters chr_table = os.path.join(PARAMS["annotations_dir"], PARAMS["annotations_interface_contigs"]) # get statement statement = IDR.getIDRStatement(infiles[0], infiles[1], outfile, PARAMS["idr_options_overlap_ratio"], PARAMS["idr_options_ranking_measure"], chr_table) # run E.info("applyIDR: processing %s and %s" % (infiles[0], infiles[1])) job_memory = "5G" P.run() @collate(runIDROnPooledPseudoreplicates, regex("(.+)_pooled_pseudoreplicate.idr"), add_inputs(r"\1*-uri.sav"), r"\1_batch-consistency.pdf") def plotBatchConsistencyForPooledPseudoreplicates(infiles, outfile): statement = IDR.getIDRPlotStatement(infiles[0], outfile) P.run() ########################################################################## ########################################################################## ########################################################################## # Post Process IDR ########################################################################## @follows(plotBatchConsistencyForIndividualReplicates) @transform(runIDROnIndividualReplicates, regex("(.+).idr"), add_inputs(r"\1-*-npeaks-aboveIDR.txt"), r"\1_npeaks_aboveIDR.tsv") def combineIDROnIndividualReplicates(infiles, outfile): tables = infiles[1:] headers = [os.path.basename(x) for x in tables] headers = ",".join([P.snip(x, "-npeaks-aboveIDR.txt") for x in headers]) tables = " ".join(tables) to_cluster = False statement = ("cgat combine_tables" " --columns=1" " --skip-titles" " --header-names=%(headers)s" " --log=%(outfile)s.log" " --stdout=%(outfile)s" " %(tables)s") P.run() @transform(combineIDROnIndividualReplicates, regex("(.+)/(.+).tsv"), r"./\2.load") def loadIDROnIndividualReplicates(infile, outfile): P.load(infile, outfile) @follows(plotBatchConsistencyForPseudoreplicates) @collate("./idr_pseudoreplicates/*-npeaks-aboveIDR.txt", regex("(.+)-R(.+)_vs_(.+)-npeaks-aboveIDR.txt"), r"\1_pseudoreplicates_npeaks_aboveIDR.tsv") def combineIDROnPseudoreplicates(infiles, outfile): headers = [os.path.basename(x) for x in infiles] headers = ",".join([P.snip(x, "-npeaks-aboveIDR.txt") for x in headers]) tables = " ".join(infiles) to_cluster = False statement = ("cgat combine_tables" " --columns=1" " --skip-titles" " --header-names=%(headers)s" " --log=%(outfile)s.log" " --stdout=%(outfile)s" " %(tables)s") P.run() @transform(combineIDROnPseudoreplicates, regex("(.+)/(.+).tsv"), r"./\2.load") def loadIDROnPseudoreplicates(infile, outfile): P.load(infile, outfile) @follows(plotBatchConsistencyForPooledPseudoreplicates) @collate("./idr_pooled_pseudoreplicates/*-npeaks-aboveIDR.txt", regex("(.+)-R(.+)_vs_(.+)-npeaks-aboveIDR.txt"), r"\1_pooled_pseudoreplicates_npeaks_aboveIDR.tsv") def combineIDROnPooledPseudoreplicates(infiles, outfile): headers = [os.path.basename(x) for x in infiles] headers = ",".join([P.snip(x, "-npeaks-aboveIDR.txt") for x in headers]) tables = " ".join(infiles) to_cluster = False statement = ("cgat combine_tables" " --columns=1" " --skip-titles" " --header-names=%(headers)s" " --log=%(outfile)s.log" " --stdout=%(outfile)s" " %(tables)s") P.run() @transform(combineIDROnPooledPseudoreplicates, regex("(.+)/(.+).tsv"), r"./\2.load") def loadIDROnPooledPseudoreplicates(infile, outfile): P.load(infile, outfile) @follows(runIDROnIndividualReplicates) @merge("./idr_individual_replicates/*-overlapped-peaks.txt", "./idr_individual_replicates/individual_replicates.nPeaks.tsv") def findNPeaksForIndividualReplicates(infiles, outfile): idr_thresh = PARAMS["idr_options_inter_replicate_threshold"] try: module = P.snip(IDR.__file__, ".py") except ValueError: module = P.snip(IDR.__file__, ".pyc") P.submit(module, "findNPeaks", params=[str(idr_thresh), ], infiles=infiles, outfiles=outfile) @transform(findNPeaksForIndividualReplicates, regex("(.+)/(.+).tsv"), r"./\2.load") def loadNPeaksForIndividualReplicates(infile, outfile): P.load(infile, outfile) @follows(runIDROnPseudoreplicates) @merge("./idr_pseudoreplicates/*-overlapped-peaks.txt", "./idr_pseudoreplicates/pseudoreplicates.nPeaks.tsv") def findNPeaksForPseudoreplicates(infiles, outfile): idr_thresh = PARAMS["idr_options_self_consistency_threshold"] try: module = P.snip(IDR.__file__, ".py") except ValueError: module = P.snip(IDR.__file__, ".pyc") P.submit(module, "findNPeaks", params=[str(idr_thresh), ], infiles=infiles, outfiles=outfile) @transform(findNPeaksForPseudoreplicates, regex("(.+)/(.+).tsv"), r"./\2.load") def loadNPeaksForPseudoreplicates(infile, outfile): P.load(infile, outfile) @follows(runIDROnPooledPseudoreplicates) @merge("./idr_pooled_pseudoreplicates/*-overlapped-peaks.txt", "./idr_pooled_pseudoreplicates/pooled_pseudoreplicates.nPeaks.tsv") def findNPeaksForPooledPseudoreplicates(infiles, outfile): idr_thresh = PARAMS["idr_options_pooled_consistency_threshold"] try: module = P.snip(IDR.__file__, ".py") except ValueError: module = P.snip(IDR.__file__, ".pyc") P.submit(module, "findNPeaks", params=[str(idr_thresh), ], infiles=infiles, outfiles=outfile) @transform(findNPeaksForPooledPseudoreplicates, regex("(.+)/(.+).tsv"), r"./\2.load") def loadNPeaksForPooledPseudoreplicates(infile, outfile): P.load(infile, outfile) ########################################################################## ########################################################################## ########################################################################## # Generate consensus peak set ########################################################################## @follows(mkdir("bamfiles_final")) @collate(filterBamfiles, regex(r"(.+)/(.+)-((?!input).*)-R[0-9]+.sentinel"), r"./bamfiles_final/\2-\3-R0.sentinel") def reMergeBamfiles(infiles, sentinel): infiles = [P.snip(x, ".sentinel") + ".bam" for x in infiles] outfile = P.snip(sentinel, ".sentinel") + ".bam" bad_samples = PARAMS["options_to_remove"].split(",") to_merge = IDR.filterBadLibraries(infiles, bad_samples) IDR.mergeBams(to_merge, outfile) P.touch(sentinel) @follows(reMergeBamfiles, mkdir("peakfiles_final")) @transform("./bamfiles_final/*.bam", regex("(.+)/(.+).bam"), r"peakfiles_final/\2.narrowPeak.sentinel") def callPeaksOnPooledReplicates(infile, outfile): # fetch peak calling parameters PARAMS_PEAKCALLER = get_peak_caller_parameters( PARAMS["options_peak_caller"]) # call peaks on pseudoreplicates IDR.callIDRPeaks(infile, outfile, PARAMS["options_peak_caller"], PARAMS["options_control_type"], PARAMS_PEAKCALLER, pseudoreplicate=False) P.touch(outfile) @follows(connect, callPeaksOnPooledReplicates, loadNPeaksForIndividualReplicates, loadNPeaksForPseudoreplicates, loadNPeaksForPooledPseudoreplicates, mkdir("peakfiles_final_conservative"), mkdir("peakfiles_final_optimum")) @transform("./peakfiles_final/*.narrowPeak.gz", regex("(.+)/(.+)-R0_VS_(.+)-R0_peaks.narrowPeak.gz"), [r"peakfiles_final_conservative/\2.narrowPeak.gz", r"peakfiles_final_optimum/\2.narrowPeak.gz"]) def generatePeakSets(infile, outfiles): outf_con, outf_opt = outfiles # retrieve maximum number of peaks obtained from inter-replicate IDR # (table created by loadNPeaksForIndividualReplicates) statement = ("SELECT" " Experiment," " max(n_peaks) AS nPeaks" " FROM individual_replicates_nPeaks" " GROUP BY experiment") df = Database.fetch_DataFrame(statement, dbhandle=PARAMS['database_name']) # reassign experiment as index df = df.set_index("Experiment") # retrieve number of peaks obtained from pooled_pseudoreplicate IDR # (table created by loadNPeaksForPooledPseudoreplicates) statement = ("SELECT" " Experiment," " n_peaks AS nPeaks" " FROM pooled_pseudoreplicates_nPeaks") df2 = Database.fetch_DataFrame(statement, dbhandle=PARAMS['database_name']) # reassign experiment as index df2 = df2.set_index("Experiment") # split the infile name to obtain experiment sample_id = os.path.basename(infile).split("_VS_")[0] sample = sample_id.split("-") experiment = "_".join([sample[0], sample[1]]) # retrieve max_numPeaks for experiment nPeaks = int(df.loc[experiment]) # retrieve numPeaks_Rep0 for experiment nPeaks_rep0 = int(df2.loc[experiment]) # retrieve maximumn of the two nPeaks_max = max(nPeaks, nPeaks_rep0) # establish which column to sort by if PARAMS["idr_options_ranking_measure"] == "signal.value": sort_statement = "sort -k7nr,7nr" elif PARAMS["idr_options_ranking_measure"] == "p.value": sort_statement = "sort -k8nr,8nr" elif PARAMS["idr_options_ranking_measure"] == "q.value": sort_statement = "sort -k9nr,9nr" else: raise ValueError("Unrecognised ranking_measure" " %s don't know which column" " to sort on" % PARAMS["idr_options_ranking_measure"]) # sort infile by column and write top nPeaks to outfile (conservative) ignore_pipe_errors = True statement = ("zcat %(infile)s |" " %(sort_statement)s |" " head -%(nPeaks)s |" " gzip > %(outf_con)s") P.run() # sort infile by column and write top nPeaks_max to outfile (optimum) ignore_pipe_errors = True statement = ("zcat %(infile)s |" " %(sort_statement)s |" " head -%(nPeaks_max)s |" " gzip > %(outf_opt)s") P.run() ########################################################################## ########################################################################## ########################################################################## # Clean Up! ########################################################################## @merge(("./bamfiles_filtered/*.bam", "./bamfiles_final/*.bam", "./bamfiles_pooled/*.bam", "./bamfiles_pseudoreplicates/*.bam", "./bamfiles_pooled_pseudoreplicates/*.bam"), "./bamfiles_removed.sentinel") def removeBamfiles(infiles, outfile): for bamfile in infiles: bam_index = bamfile + ".bai" os.unlink(bamfile) if os.path.exists(bam_index): os.unlink(bam_index) P.touch(outfile) @transform(("./bamfiles_filtered/*.bdg", "./bamfiles_pooled/*.bdg", "./bamfiles_pseudoreplicates/*.bdg", "./bamfiles_pooled_pseudoreplicates/*.bdg"), suffix(".bdg"), ".bw") def convertBedGraph(infile, outfile): contig_file = os.path.join(PARAMS["annotations_dir"], "contigs.tsv") statement = ("bedGraphToBigWig %(infile)s %(contig_file)s %(outfile)s") P.run() ########################################################################## ########################################################################## ########################################################################## # primary targets ########################################################################## @follows(splitBamfiles, poolInputBamfiles, splitPooledBamfiles) def preProcessBamfiles(): pass @follows(callPeaksOnIndividualReplicates, callPeaksOnPseudoreplicates, callPeaksOnPooledPseudoreplicates, loadPeakSummaryForIndividualReplicates, loadPeakSummaryForPseudoreplicates, loadPeakSummaryForPooledPseudoreplicates) def callPeaks(): pass @follows(plotBatchConsistencyForIndividualReplicates, plotBatchConsistencyForPseudoreplicates, plotBatchConsistencyForPooledPseudoreplicates) def runIDR(): pass @follows(loadIDROnIndividualReplicates, loadIDROnPseudoreplicates, loadIDROnPooledPseudoreplicates, loadNPeaksForIndividualReplicates, loadNPeaksForPseudoreplicates, loadNPeaksForPooledPseudoreplicates) def summarizeIDR(): pass @follows(preProcessBamfiles, callPeaks, runIDR, summarizeIDR, generatePeakSets) def full(): pass @follows(removeBamfiles, convertBedGraph) def cleanUp(): pass ########################################################################## ########################################################################## ########################################################################## # primary targets ########################################################################## @follows(mkdir("report")) def build_report(): '''build report from scratch.''' E.info("starting report build process from scratch") P.run_report(clean=True) @follows(mkdir("report")) def update_report(): '''update report.''' E.info("updating report") P.run_report(clean=False) @follows(update_report) def publish(): '''publish report and data.''' E.info("publishing report") P.publish_report() if __name__ == "__main__": # P.checkFiles( ("genome.fasta", "genome.idx" ) ) sys.exit(P.main(sys.argv))
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out/production/CS-4950-Applied-Crypto/HW1/hillCipher.py
prchander/CS-4950-Applied-Crypto
0
158184
<gh_stars>0 import numpy as np import os from sympy import Matrix alphabet = "abcdefghijklmnopqrstuvwxyz" #Change the name of the file below to test other files with open('ChandramoulihillCustom.txt', 'r') as file: data = file.read().replace(' ', '') key = np.array([[9, 4],[5, 7]]) def encrypt(contents): #encrypted = np.array() message = [] for x in range (0,len(contents)-1,2): temp1 = contents[x] temp2 = contents[x+1] number1=-1 number2=-1 for i in range (0,len(alphabet)): if (temp1==alphabet[i]): number1 = i for j in range (0,len(alphabet)): if (temp2==alphabet[j]): number2 = j toEncrypt = np.array([number1,number2]) todo = np.dot(key,toEncrypt) % 26 message.append(todo) message = np.array(message) message = message.flatten() eMessage = [] for i in range (0,len(message)): a = message[i] eMessage.append(alphabet[a]) return eMessage def toString(message): str1 = "" for ele in message: str1 += ele return str1 def decrypt(contents): message = [] for x in range (0,len(contents),2): temp1 = contents[x] temp2 = contents[x+1] for i in range (0,len(alphabet)): if (temp1==alphabet[i]): number1 = i for j in range (0,len(alphabet)): if (temp2==alphabet[j]): number2 = j toEncrypt = np.array([number1,number2]) invKey=Matrix(key).inv_mod(26) invKey = np.array(invKey) todo = (np.dot(invKey,toEncrypt) % 26+26)%26 message.append(todo) message = np.array(message) message = message.flatten() eMessage = [] for i in range (0,len(message)): a = message[i] eMessage.append(alphabet[a]) return eMessage inList = encrypt(data) inStr = toString(inList) #Change file name for input here f = open("ChandramoulihillEncrypt.txt","w") f.write(inStr) nList=decrypt(inStr) nStr=toString(nList) #Change file name for decrypt here f = open("ChandramoulihillDecrypt.txt","w") f.write(nStr)
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LeetCode/LastWord.py
Jaidev810/Competitive-Questions
1
93428
<reponame>Jaidev810/Competitive-Questions<filename>LeetCode/LastWord.py string = 'b a ' newstring = string[-1::-1] length = 0 print(newstring) for i in range(0, len(newstring)): if newstring[i] == ' ': if i == 0: continue if newstring[i-1] == ' ': continue break length += 1 print(length)
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odoo-13.0/addons/l10n_cl/models/account_journal.py
VaibhavBhujade/Blockchain-ERP-interoperability
0
1613558
# Part of Odoo. See LICENSE file for full copyright and licensing details. from odoo import fields, models, api class AccountJournal(models.Model): _inherit = "account.journal" l10n_cl_sequence_ids = fields.Many2many( 'ir.sequence', 'l10n_cl_journal_sequence_rel', 'journal_id', 'sequence_id', string='Sequences (cl)', domain="[('l10n_latam_document_type_id', '!=', False)]") def button_create_new_sequences(self): self.ensure_one() internal_types = ['invoice', 'debit_note', 'credit_note'] domain = [('country_id.code', '=', 'CL'), ('internal_type', 'in', internal_types)] if self.company_id.partner_id.l10n_cl_sii_taxpayer_type in ['1', False]: domain += [('code', 'not in', ['70', '71'])] elif self.company_id.partner_id.l10n_cl_sii_taxpayer_type == '2': domain += [('code', 'not in', ['33', '34'])] documents = self.env['l10n_latam.document.type'].search(domain) sequences = self.env['ir.sequence'] for document in documents: sequences |= self.env['ir.sequence'].create(document._get_document_sequence_vals(self)) self.write({'l10n_cl_sequence_ids': [(4, s.id) for s in sequences]}) def create_document_sequences_cl(self): self.ensure_one() if (self.company_id.country_id != self.env.ref('base.cl')) or self.env['ir.sequence'].search( [('l10n_latam_document_type_id', '!=', False)]) or self.type != 'sale' or not self.l10n_latam_use_documents: return self.button_create_new_sequences() @api.model def create(self, values): """ Create Document sequences after create the journal """ res = super().create(values) res.create_document_sequences_cl() return res
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epydoc/util.py
ftfniqpl/epydoc
0
65992
# epydoc -- Utility functions # # Copyright (C) 2005 <NAME> # Author: <NAME> <<EMAIL>> # URL: <http://epydoc.sf.net> # # $Id: util.py 1671 2008-01-29 02:55:49Z edloper $ """ Miscellaneous utility functions that are used by multiple modules. @group Python source types: is_module_file, is_package_dir, is_pyname, py_src_filename @group Text processing: wordwrap, decode_with_backslashreplace, plaintext_to_html """ __docformat__ = 'epytext en' import os, os.path, re ###################################################################### ## Python Source Types ###################################################################### PY_SRC_EXTENSIONS = ['.py', '.pyw'] PY_BIN_EXTENSIONS = ['.pyc', '.so', '.pyd'] def is_module_file(path): # Make sure it's a file name. if not isinstance(path, basestring): return False (dir, filename) = os.path.split(path) (basename, extension) = os.path.splitext(filename) return (os.path.isfile(path) and re.match('[a-zA-Z_]\w*$', basename) and extension in PY_SRC_EXTENSIONS+PY_BIN_EXTENSIONS) def is_src_filename(filename): if not isinstance(filename, basestring): return False if not os.path.exists(filename): return False return os.path.splitext(filename)[1] in PY_SRC_EXTENSIONS def is_package_dir(dirname): """ Return true if the given directory is a valid package directory (i.e., it names a directory that contains a valid __init__ file, and its name is a valid identifier). """ # Make sure it's a directory name. if not isinstance(dirname, basestring): return False if not os.path.isdir(dirname): return False dirname = os.path.abspath(dirname) # Make sure it's a valid identifier. (Special case for # "foo/", where os.path.split -> ("foo", "").) (parent, dir) = os.path.split(dirname) if dir == '': (parent, dir) = os.path.split(parent) # The following constraint was removed because of sourceforge # bug #1787028 -- in some cases (eg eggs), it's too strict. #if not re.match('\w+$', dir): # return False for name in os.listdir(dirname): filename = os.path.join(dirname, name) if name.startswith('__init__.') and is_module_file(filename): return True else: return False def is_pyname(name): return re.match(r"\w+(\.\w+)*$", name) def py_src_filename(filename): basefile, extension = os.path.splitext(filename) if extension in PY_SRC_EXTENSIONS: return filename else: for ext in PY_SRC_EXTENSIONS: if os.path.isfile('%s%s' % (basefile, ext)): return '%s%s' % (basefile, ext) else: raise ValueError('Could not find a corresponding ' 'Python source file for %r.' % filename) def munge_script_name(filename): name = os.path.split(filename)[1] name = re.sub(r'\W', '_', name) return 'script-'+name ###################################################################### ## Text Processing ###################################################################### def decode_with_backslashreplace(s): r""" Convert the given 8-bit string into unicode, treating any character c such that ord(c)<128 as an ascii character, and converting any c such that ord(c)>128 into a backslashed escape sequence. >>> decode_with_backslashreplace('abc\xff\xe8') u'abc\\xff\\xe8' """ # s.encode('string-escape') is not appropriate here, since it # also adds backslashes to some ascii chars (eg \ and '). assert isinstance(s, str) return (s .decode('latin1') .encode('ascii', 'backslashreplace') .decode('ascii')) def wordwrap(str, indent=0, right=75, startindex=0, splitchars=''): """ Word-wrap the given string. I.e., add newlines to the string such that any lines that are longer than C{right} are broken into shorter lines (at the first whitespace sequence that occurs before index C{right}). If the given string contains newlines, they will I{not} be removed. Any lines that begin with whitespace will not be wordwrapped. @param indent: If specified, then indent each line by this number of spaces. @type indent: C{int} @param right: The right margin for word wrapping. Lines that are longer than C{right} will be broken at the first whitespace sequence before the right margin. @type right: C{int} @param startindex: If specified, then assume that the first line is already preceeded by C{startindex} characters. @type startindex: C{int} @param splitchars: A list of non-whitespace characters which can be used to split a line. (E.g., use '/\\' to allow path names to be split over multiple lines.) @rtype: C{str} """ if splitchars: chunks = re.split(r'( +|\n|[^ \n%s]*[%s])' % (re.escape(splitchars), re.escape(splitchars)), str.expandtabs()) else: chunks = re.split(r'( +|\n)', str.expandtabs()) result = [' '*(indent-startindex)] charindex = max(indent, startindex) for chunknum, chunk in enumerate(chunks): if (charindex+len(chunk) > right and charindex > 0) or chunk == '\n': result.append('\n' + ' '*indent) charindex = indent if chunk[:1] not in ('\n', ' '): result.append(chunk) charindex += len(chunk) else: result.append(chunk) charindex += len(chunk) return ''.join(result).rstrip()+'\n' def plaintext_to_html(s): """ @return: An HTML string that encodes the given plaintext string. In particular, special characters (such as C{'<'} and C{'&'}) are escaped. @rtype: C{string} """ s = s.replace('&', '&amp;').replace('"', '&quot;') s = s.replace('<', '&lt;').replace('>', '&gt;') return s def plaintext_to_latex(str, nbsp=0, breakany=0): """ @return: A LaTeX string that encodes the given plaintext string. In particular, special characters (such as C{'$'} and C{'_'}) are escaped, and tabs are expanded. @rtype: C{string} @param breakany: Insert hyphenation marks, so that LaTeX can break the resulting string at any point. This is useful for small boxes (e.g., the type box in the variable list table). @param nbsp: Replace every space with a non-breaking space (C{'~'}). """ # These get converted to hyphenation points later if breakany: str = re.sub('(.)', '\\1\1', str) # These get converted to \textbackslash later. str = str.replace('\\', '\0') # Expand tabs str = str.expandtabs() # These elements need to be backslashed. str = re.sub(r'([#$&%_\${}])', r'\\\1', str) # These elements have special names. str = str.replace('|', '{\\textbar}') str = str.replace('<', '{\\textless}') str = str.replace('>', '{\\textgreater}') str = str.replace('^', '{\\textasciicircum}') str = str.replace('~', '{\\textasciitilde}') str = str.replace('\0', r'{\textbackslash}') # replace spaces with non-breaking spaces if nbsp: str = str.replace(' ', '~') # Convert \1's to hyphenation points. if breakany: str = str.replace('\1', r'\-') return str class RunSubprocessError(OSError): def __init__(self, cmd, out, err): OSError.__init__(self, '%s failed' % cmd[0]) self.out = out self.err = err def run_subprocess(cmd, data=None): """ Execute the command C{cmd} in a subprocess. @param cmd: The command to execute, specified as a list of string. @param data: A string containing data to send to the subprocess. @return: A tuple C{(out, err)}. @raise OSError: If there is any problem executing the command, or if its exitval is not 0. """ if isinstance(cmd, basestring): cmd = cmd.split() # Under Python 2.4+, use subprocess try: from subprocess import Popen, PIPE pipe = Popen(cmd, stdin=PIPE, stdout=PIPE, stderr=PIPE) out, err = pipe.communicate(data) if hasattr(pipe, 'returncode'): if pipe.returncode == 0: return out, err else: raise RunSubprocessError(cmd, out, err) else: # Assume that there was an error iff anything was written # to the child's stderr. if err == '': return out, err else: raise RunSubprocessError(cmd, out, err) except ImportError: pass # Under Python 2.3 or earlier, on unix, use popen2.Popen3 so we # can access the return value. import popen2 if hasattr(popen2, 'Popen3'): pipe = popen2.Popen3(' '.join(cmd), True) to_child = pipe.tochild from_child = pipe.fromchild child_err = pipe.childerr if data: to_child.write(data) to_child.close() out = err = '' while pipe.poll() is None: out += from_child.read() err += child_err.read() out += from_child.read() err += child_err.read() if pipe.wait() == 0: return out, err else: raise RunSubprocessError(cmd, out, err) # Under Python 2.3 or earlier, on non-unix, use os.popen3 else: to_child, from_child, child_err = os.popen3(' '.join(cmd), 'b') if data: try: to_child.write(data) # Guard for a broken pipe error except IOError as e: raise OSError(e) to_child.close() out = from_child.read() err = child_err.read() # Assume that there was an error iff anything was written # to the child's stderr. if err == '': return out, err else: raise RunSubprocessError(cmd, out, err)
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applications/cli/commands/model/tests/test_common.py
starcell/deepcell-ncluster
0
148754
# # Copyright (c) 2019 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from commands.model.common import format_workflow_description, get_list_of_workflows, EXPORT_WORKFLOWS_LOCATION from unittest.mock import patch, mock_open FULL_WORKFLOW_FILE = """ apiVersion: argoproj.io/v1alpha1 kind: Workflow metadata: generateName: test-workflow spec: entrypoint: test-image arguments: parameters: - name: test-message value: test value - name: test-number value: test number value templates: - name: test-image inputs: parameters: - name: message container: image: docker/whalesay command: [cowsay] args: ["{{inputs.parameters.test-message}}"] """ LACK_OF_ARGS_WORKFLOW_FILE = """ apiVersion: argoproj.io/v1alpha1 kind: Workflow metadata: generateName: test-workflow2 spec: entrypoint: test-image templates: - name: test-image inputs: parameters: - name: message container: image: docker/whalesay command: [cowsay] """ def test_format_workflow_description(mocker): m = mock_open(read_data=FULL_WORKFLOW_FILE) with patch("builtins.open", m): description = format_workflow_description("filename.txt") assert "test-workflow" == description.name def test_format_workflow_description_wo_arguments(mocker): m = mock_open(read_data=LACK_OF_ARGS_WORKFLOW_FILE) with patch("builtins.open", m): description = format_workflow_description("filename.txt") assert "test-workflow2" == description.name def test_list_of_workflows(tmpdir, mocker): exports_temp_dir = tmpdir.mkdir("workflows").mkdir("exports") first_workflow = exports_temp_dir.join("first_workflow.yaml") first_workflow.write(FULL_WORKFLOW_FILE) second_workflow = exports_temp_dir.join("second_workflow.yaml") second_workflow.write(LACK_OF_ARGS_WORKFLOW_FILE) fake_config = mocker.patch("commands.model.common.Config") fake_config.return_value.config_path = tmpdir list_of_workflows = get_list_of_workflows(EXPORT_WORKFLOWS_LOCATION) assert len(list_of_workflows) == 2 first_found = False second_found = False for workflow in list_of_workflows: if workflow.name == "test-workflow": first_found = True if workflow.name == "test-workflow2": second_found = True assert first_found assert second_found
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ManageProxy.py
guiqiqi/AirPoluionData
1
187617
import subprocess import time import threading import shelve import queue import re from collections import defaultdict import urllib.request as request import urllib import socket import datetime socket.setdefaulttimeout(5) class DBHandler(object): """数据文件操作""" def __init__(self, file_name, file_dir): self.file_dir = file_dir self.file_name = file_name self.db = shelve.open(file_dir+"/"+file_name, writeback = True) def getAllKey(self, as_queue = True): self.ip_list = self.db.keys() if not as_queue: return self.ip_list self.ip_queue = queue.Queue(maxsize = -1) for ip in self.ip_list: self.ip_queue.put(ip) return self.ip_queue def getAll(self): return dict(self.db.items()) def get(self, key): return self.db.get(key, default = False) def update(self): self.close() self.__init__(self.file_name, self.file_dir) def add(self, key, data): self.db[key] = data def delete(self, key): del self.db[key] def clear(self): for item in self.getAllKey(False): self.delete(item) def exist(self, key): if key in self.db.keys(): return True return False def close(self): self.db.close() class TestLag(object): """用于定时测试每个IP的延迟以及可用性""" def __init__(self, manager, thread_num = 8, retry_times = 3): self.manager = manager self.ip_queue = manager.ipfile.getAllKey() self.IP_COUNT = self.ip_queue.qsize() self.thread_num = thread_num self.thread_queue = queue.Queue(maxsize = thread_num) self.pattern = re.compile('(?P<gap>\d+)ms') self.resultset = {} self.retry_times = retry_times def start(self): ip_queue = self.ip_queue for i in range(self.thread_num): self.thread_queue.put(i+1) while (not ip_queue.empty()) or (len(self.resultset) != self.IP_COUNT): try: ip = ip_queue.get_nowait() except: pass thread_id = self.thread_queue.get() newth = threading.Thread(target = self.gapping, name = 'TestGap-' + str(thread_id), args = (ip, thread_id)) newth.start() print (self.resultset) ip_queue = self.manager.ipfile.getAllKey() def gapping(self, ip, thread_id): ret = subprocess.Popen('ping -n 1 %s' % ip,stdout = subprocess.PIPE) ret.wait() gap = has_gap = self.manager.ipfile.get(ip) if (has_gap > 0) and ret.returncode: gap = -self.retry_times if has_gap < 0: gap = has_gap + 1 if not ret.returncode: result = ret.stdout.read() t = self.pattern.search(str(result)) if t: gap = int(t.group()[0:-2]) self.resultset[ip] = gap self.thread_queue.put(thread_id) class ManageAddr(object): """管理相应的IP以及端口，延迟的对应关系""" def __init__(self, ipfile_name, portfile_name, file_dir = 'data'): self.ipfile = DBHandler(ipfile_name,file_dir) self.portfile = DBHandler(portfile_name,file_dir) def add(self, ipListWithPort, retry_times = 3): for item in ipListWithPort: ip_addr = item.split(':') ip, port = ip_addr[0], ip_addr[1] self.record_ip(ip, retry_times) self.record_port(ip, port) def get(self, ip): return ip+self.portfile.get(ip) def record_port(self, ip, port): self.portfile.add(ip, port) def record_ip(self, ip, retry_times): if not self.ipfile.exist(ip): self.ipfile.add(ip, -retry_times) def getAsGap(self): sortasgap = defaultdict(list) for k,v in self.ipfile.getAll().items(): sortasgap[v].append(k) try: sortasgap = sorted(sortasgap.items(),key = lambda d:d[0]) ip = sortasgap[0][1][0] gap = sortasgap[0][0] port = self.portfile.get(ip) except: return None self.delete(ip) self.update() if gap <= 0: return self.getAsGap() return [[ip,port],gap] def renew(self, has_test): for ip in has_test.keys(): if not has_test[ip]: self.ipfile.delete(ip) self.portfile.delete(ip) self.update() def update(self): self.ipfile.update() self.portfile.update() def delete(self, key): self.ipfile.delete(key) self.portfile.delete(key) def close(self): self.ipfile.close() self.portfile.close() def query(self, handler, key = None): if key == None: return handler.getAllKey() return handler.get(key) class VerifyProxy(object): """通过使用测试代理的可用性""" def __init__(self, manager, thread_num): self.manager = manager self.thread_num = thread_num self.ip_queue = manager.ipfile.getAllKey() self.deleteList = [] self.header = [('User-Agent', 'Mozilla/5.0 (Windows NT 6.3; WOW64; Trident/7.0; rv:11.0) like Gecko'), ('Accept', 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8'), ('Cache-Control', 'no-cache'), ('Connection', 'Keep-Alive'), ('Pragma', 'no-cache'), ('Accept-Language', 'zh-CN,zh;q=0.8,en;q=0.6')] with open('Verify.data','r') as file: self.data = file.read() def start(self): thread_queue = queue.Queue(maxsize = self.thread_num) self.thread_queue = thread_queue ip_queue = self.ip_queue for i in range(self.thread_num): thread_queue.put(i+1) while not ip_queue.empty(): try: ip = ip_queue.get_nowait() port = self.manager.portfile.get(ip) except: pass thread_id = thread_queue.get() newth = threading.Thread(target = self.verifying, name = 'Verify-' + str(thread_id), args = (ip, port, thread_id)) newth.start() for item in self.deleteList: self.manager.delete(item) self.manager.update() def verifying(self, ip, port, thread_id): thread_queue = self.thread_queue head = self.usingProxy([ip, port]) thread_queue.put(thread_id) def usingProxy(self, address): ip = address[0] address = ":".join(address) proxies = {'http': address} proxy_handler = request.ProxyHandler(proxies) opener = request.build_opener(request.HTTPHandler,proxy_handler) request.install_opener(opener) opener.addheaders = self.header try: response = opener.open("http://www.example.com") if response.read().hex() == self.data: print (address) else: self.deleteList.append(ip) except: self.deleteList.append(ip) if __name__ == "__main__": manager = ManageAddr('IPADDR','PORT','data') data = open('data/IP.txt','r').read().split('\n') manager.add(data) manager.close() # verify = VerifyProxy(manager, 16) # verify.start() # Lapper = TestLag(manager, 8, 3) # Lapper.start() pass
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applications/popart/bert/utils/__init__.py
Alwaysproblem/examples-1
0
75774
<filename>applications/popart/bert/utils/__init__.py # Copyright (c) 2019 Graphcore Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys import json import argparse import datetime from typing import List, Optional import numpy as np import onnx from logging import getLogger from bert_model import BertConfig from .weight_loading import load_initializers_from_onnx logger = getLogger(__name__) def save_model_statistics(model_path, writer, i=0): initializers = load_initializers_from_onnx(model_path) for name, np_weight in initializers.items(): name = name.replace(":", "_") writer.add_histogram(name, np_weight, i) writer.add_scalar(f"L2/{name}", np.linalg.norm(np_weight.astype(np.float32)), i) def str_to_bool(value): if isinstance(value, bool) or value is None: return value if value.lower() in {'false', 'f', '0', 'no', 'n'}: return False elif value.lower() in {'true', 't', '1', 'yes', 'y'}: return True raise argparse.ArgumentTypeError(f'{value} is not a valid boolean value') def parser_from_NamedTuple(parser, ntuple, args={}): for key in ntuple._fields: string = "--" + key.replace("_", "-") t = ntuple._field_types[key] default = ntuple._field_defaults.get(key, None) kwargs = dict( string=string, type=t, default=default, dest=key ) if t is bool: # Make bool a flag kwargs["nargs"] = "?" kwargs["const"] = not kwargs["default"] kwargs["type"] = str_to_bool else: for _t in (str, int, float): if t == List[_t]: kwargs["type"] = _t kwargs["nargs"] = '+' break if t == Optional[_t]: kwargs["type"] = _t break args_or_help = args.get(key, None) if isinstance(args_or_help, dict): kwargs.update(**args_or_help) else: kwargs["help"] = args_or_help string = kwargs["string"] del kwargs["string"] parser.add_argument(string, **kwargs) class ScheduleArgumentParser(argparse.Action): def __init__(self, option_strings, dest, nargs=None, **kwargs): super(ScheduleArgumentParser, self).__init__( option_strings, dest, nargs=nargs, **kwargs) self.default_input = kwargs['default'] def __call__(self, parser, namespace, values, option_string=None): schedule = {} if len(values) == 0: schedule = self.default_input for kv in values: training_proportion, lr = kv.split(":") try: schedule[int(training_proportion)] = float(lr) except ValueError as ex: logger.warning("Invalid Learning Rate Schedule provided. " "It should be a set of int:float pairs.") raise ex setattr(namespace, self.dest, schedule) class ValidationConfig(argparse.Action): def __call__(self, parser, namespace, values, option_string=None): value = None try: value = json.loads(values) except json.decoder.JSONDecodeError as e: pass if os.path.isfile(values): with open(values, 'r') as f: value = json.load(f) if value is not None: setattr(namespace, self.dest, value) def parse_bert_args(args_string=None): pparser = argparse.ArgumentParser("Config Parser", add_help=False) pparser.add_argument("--config", type=str) pargs, remaining_argv = pparser.parse_known_args(args_string) parser = argparse.ArgumentParser( "PopART BERT", formatter_class=argparse.ArgumentDefaultsHelpFormatter) # TODO: Organise Argument Groups. group = parser.add_argument_group("Model Config") parser_from_NamedTuple(group, BertConfig, args={ "batch_size": "Set the micro batch-size", "host_embedding": dict( choices=["NONE", "WORD", "ALL", "MERGE"], help="Enable embedding lookup on CPU. Values: " "NONE = use IPU; " "WORD = use CPU for word embedding and IPU for position; " "ALL = use CPU; " "MERGE = use CPU and add word and position embedding together" ), "sequence_length": "Set the max sequence length", "mask_tokens": "Set the max number of masked tokens in a sequence (PRETRAINING only)", "vocab_length": "Set the size of the vocabulary", "hidden_size": "Set the size of the hidden state of the transformer layers", "ff_size__": dict( string="--ff-size", help="Set the size of the intermediate state of the feed forward layers, by default 4x the hidden_size" ), "attention_heads": "Set the number of heads in self attention", "inference": "Create a model for inference. Otherwise a trainable model is created and trained.", "num_layers": "Set the number of transformer layers", "layers_per_ipu": "Set the number of layers on each IPU. " "If specified as more than one value, the sum of the values must equal num_layers.", "available_memory_proportion": "This option specifies the proportion of total tile memory the Encoder MatMul's can use. " "Can be specified as either a single value for all IPUs or a value for each Encoder IPU.", "recompute_checkpoint_every_layer": "This controls how recomputation is handled in pipelining. " "If True the output of each layer will be stashed keeping the max liveness " "of activations to be at most one layer. " "However, the stash size scales with the number of pipeline stages so this may not always be beneficial. " "The added stash + code could be greater than the reduction in temporary memory.", "no_dropout": "Don't use dropout", "no_attn_dropout": "Don't use dropout on attention scores", "dropout_prob": "Set the dropout probability", "layer_norm_eps": "Set the layer norm epsilon value", "popart_dtype": dict( string="--dtype", choices=["FLOAT16", "FLOAT"], help="Set the data type used" ), "task": dict( choices=["PRETRAINING", "SQUAD", "MRPC"], help="Set the task. Pretraining (Masked-LM & Next Sentence Prediction), SQuAD, MRPC" ), "split_linear_layers": "Memory Optimisation to serialise MatMul Operations.", "replicated_weight_sharding": "Shard weight tensors over the replicas, only supported in phased_execution mode.", "no_mask": "Don't apply padding masks to the attention scores", "use_default_available_memory_proportion": "Use the poplibs default value for availableMemoryProportion option on the encoder matmuls.", "update_embedding_dict": "Include the sparse update to the word Embedding_Dict.", "no_cls_layer": "Don't include the CLS layer in pretraining. This layer comes after the encoders but before the projection for the MLM loss.", "projection_bias": "Include bias to the projection layer.", "embedding_serialization_vocab_steps": "Factor by which embedding layer is serialized, only supported in phased_execution mode.", "num_attention_splits": "Factor by which attention layer is serialized, only supported in phased_execution mode.", "num_ffwd_splits": "Factor by which feedforward layer is serialized, only supported in phased_execution mode.", "split_transformer": "Place attention and feedforward layers in separate phased_execution scope.", "batch_serialize": "Factor by which a micro-batch is serialized, only supported in phased_execution mode.", "num_io_tiles": "The number of tiles on each IPU dedicated to doing collective communication ops with the replicated weights. Only supported in phased_execution mode." }) group = parser.add_argument_group("SQuAD Config") group.add_argument("--vocab-file", type=str, help="Path to the vocab file") group.add_argument("--do-lower-case", type=str_to_bool, nargs="?", const=True, default=False, help="Use this when using a uncased vocab") group.add_argument("--squad-results-dir", type=str, default="squad_results", help="Path to directory to write results (Note: will be created if path does not exist)") group.add_argument("--squad-evaluate-script", type=str, help="Path to SQuAD evaulate-v1.1.py script") group.add_argument("--squad-lr-scale", type=float, default=1.0, help="Scale the learning rate of the SQuAD layers.") group = parser.add_argument_group("Training Config") group.add_argument("--gradient-accumulation-factor", type=int, default=1, help="Set how many gradients to accumulate before updating the weights. \ Note: This affects the calculation of effective batch size") group.add_argument("--replication-factor", type=int, default=1, help="Replicates the graph by this factor across IPUs to achieve data parallel execution. (Note: This changes the effective batch size)") group.add_argument("--optimizer", type=str, choices=['SGD', 'ADAM', 'ADAM_NO_BIAS', 'LAMB', 'LAMB_NO_BIAS'], default="SGD", help="Set the optimizer type") group.add_argument("--learning-rate", type=float, default=0.0008, help="Set the learning rate") # SGD+M group.add_argument("--momentum", type=float, default=0.984375, help="Set the optimizer momentum value") group.add_argument("--dampening", type=float, help="Set the optimizer dampening value. (Note: this will be set to momentum value by default)") group.add_argument("--velocity-scaling", type=float, default=1.0, help="Set the velocity scaling. This helps prevent overflow when accumulating gradients.") # Adam group.add_argument("--beta1", type=float, default=0.9, help="Set the Adam/Lamb beta1 value") group.add_argument("--beta2", type=float, default=0.999, help="Set the Adam/Lamb beta2 value") group.add_argument("--max-weight-norm", type=float, default=None, help="Set the max value for R1 (weight norm) in the Lamb optimizer. Default is no clipping") group.add_argument("--loss-scaling", type=float, default=4.0, help="Set the loss scaling. This helps prevent underflow during backpropagation.") group.add_argument("--weight-decay", type=float, default=0, help="Set the weight decay, not used for bias and norms parameters") group.add_argument("--epochs", type=int, default=35, help="Number of epochs to train for") group.add_argument("--epochs-inference", type=int, default=1, help="Number of epochs to run inference for") group.add_argument("--stochastic-rounding", type=str_to_bool, nargs="?", const=True, default=False, help="Turn on Stochastic Rounding") group.add_argument("--gradient-reduction-type", type=str, choices=["Sum", "Mean"], default="Sum", help="Set how gradients are reduced over the global batch size.") group = parser.add_argument_group("Continuous Pipelining Config") group.add_argument("--pipeline-lr-scaling", type=str_to_bool, nargs="?", const=True, default=False, help="Enable learning rate scaling per pipeline stage") group.add_argument("--pipeline-lr-scaling-offset", type=float, default=0.25, help="Set the value for learning rate scaling on the first pipeline stage. Learning rates will be scaled " "linearly from this offset (default: 0.25) to 1 as pipeline stage increases to account for increased errors " "at lower-level stages when pipelining. (Note: for pipelines with few stages, this should be increased)") group.add_argument("--pipeline-momentum-scaling", type=str_to_bool, nargs="?", const=True, default=False, help="Enable momentum and dampening scaling per pipeline stage") group.add_argument("--pipeline-momentum-scaling-offset", type=float, default=0.1, help="Set the value momentum scaling on the last pipeline stage. Momentums will be scaled " "linearly from this offset (default: 0.1) to 1 as pipeline stage decrease to account for increased errors " "at lower-level stages when pipelining. (Note: for pipelines with few stages, this should be increased)") group.add_argument("--pipeline-dampening-scaling-offset", type=float, help="Set the value for dampening scaling on the last pipeline stage. Dampenings will be scaled " "linearly from this offset (default: same as momentum) to 1 as pipeline stage decrease to account for increased errors " "at lower-level stages when pipelining. (Note: this will be set to the momentum offset by default)") _group = parser.add_argument_group("Hyperparameter Schedule Config") group = _group.add_mutually_exclusive_group() group.add_argument("--lr-schedule-by-epoch", action=ScheduleArgumentParser, nargs="*", default=None, help="A schedule for learning rate warmup and decay, provided as space-separated " "<int>:<float> pairs. The first item is the epoch at which to update and the second is " "the learning rate at that epoch. \n" "E.g.: --lr-schedule-by-epoch 0:0.00001 1:0.0001 3:0.0008 5:0.00004 10:0.00002") group.add_argument("--lr-schedule-by-step", action=ScheduleArgumentParser, nargs="*", default=None, help="A schedule for learning rate warmup and decay, provided as space-separated " "<int>:<float> pairs. The first item is the step at which to update and the second is " "the learning rate at that step. \n" "E.g.: --lr-schedule-by-step 0:0.00001 2500:0.0001 10000:0.0008 50000:0.00004 100000:0.00002") group = _group.add_mutually_exclusive_group() group.add_argument("--ls-schedule-by-epoch", action=ScheduleArgumentParser, nargs="*", default=None, help="A schedule for loss scaling, provided as space-separated <int>:<float> pairs. " "The first item is the spoch at which to update and the second is " "the loss scaling at that epoch. \n" "E.g.: --ls-schedule-by-step 0:0.00001 2500:0.0001 10000:0.0008 50000:0.00004 100000:0.00002") group.add_argument("--ls-schedule-by-step", action=ScheduleArgumentParser, nargs="*", default=None, help="A schedule for loss scaling, provided as space-separated <int>:<float> pairs. " "The first item is the step at which to update and the second is " "the loss scaling at that step. \n" "E.g.: --ls-schedule-by-step 0:0.00001 2500:0.0001 10000:0.0008 50000:0.00004 100000:0.00002") group = parser.add_argument_group("Initialisation Config", "Flags for initialising the weights") group = group.add_mutually_exclusive_group() group.add_argument("--tf-checkpoint", type=str, help="Path to Tensorflow Checkpoint to initialise the model.") group.add_argument("--onnx-checkpoint", type=str, help="Path to .onnx file created by this application to initialise the model.") group = parser.add_argument_group("Data Config") group.add_argument("--input-files", type=str, nargs="*", help="Files to load data from. " "For Pretraining: Binary files created by bert_data/create_pretraining_data.py. " "For SQuAD: Path to train-v1.1.json") group.add_argument("--shuffle", type=str_to_bool, nargs="?", const=True, default=False, help="Shuffle Dataset") group.add_argument("--overwrite-cache", type=str_to_bool, nargs="?", const=True, default=False, help="Regenerates the SQuAD dataset instead of loading the cache if available") group.add_argument("--no-drop-remainder", type=str_to_bool, nargs="?", const=True, default=False, help="Adjust the batches_per_step to perfectly divide the dataset so no data is missed. Only available for SQuAD.") group.add_argument("--generated-data", type=str_to_bool, nargs="?", const=True, default=False, help="Generate a random dataset on the host machine. Creates enough data for one step per epoch. " "Increase --epochs for multiple performance measurements.") group.add_argument("--synthetic-data", type=str_to_bool, nargs="?", const=True, default=False, help="Generate a synthetic dataset on the IPU device. Creates enough data for one step per epoch. " "Note that using this option will remove all Host I/O from the model. " "Increase --epochs for multiple perfomance measurements.") group.add_argument("--synthetic-data-initializer", type=str, choices=['random_normal', 'zeros'], default="random_normal", help="Specify to the synthetic data initializer with either 'random_normal' or 'zeros'. ") group.add_argument("--duplication-factor", type=int, default=1, help="Set the number of times the dataset has been duplicated. This reduces the samples per epoch to" " (# of samples in input-files)/duplication-factor") group.add_argument("--epochs-to-cache", type=int, default=0, help="Number of epochs of data to load into memory during PRETRAINING. Default is to load input files as needed.") group = parser.add_argument_group("Execution Mode") emode = group.add_mutually_exclusive_group() emode.add_argument("--virtual-graph", type=str_to_bool, nargs="?", const=True, default=None, help="Build and execute the graph with only VirtualGraph annotations.") emode.add_argument("--pipeline", type=str_to_bool, nargs="?", const=True, default=None, help="Build and execute the graph with Pipeline annotations.") emode.add_argument("--phased-execution", type=str_to_bool, nargs="?", const=True, default=None, help="Build and execute the graph with ExecutionPhase annotations.") phased_emode = group.add_mutually_exclusive_group() phased_emode.add_argument("--single-device", action='store_const', const='SINGLE', dest='phased_execution_type', help="Execute on a single device per replica. Only used in phased_execution mode.") phased_emode.add_argument("--dual-device", action='store_const', const='DUAL', dest='phased_execution_type', help="Execute using two devices per replica. Only used in phased_execution mode.") parser.set_defaults(phased_execution_type='SINGLE') group = parser.add_argument_group("Execution Config") group.add_argument("--batches-per-step", type=int, default=250, help="Set the number of batches (weight updates) to execute before returning to the Host") group.add_argument("--floating-point-exceptions", type=str_to_bool, nargs="?", const=True, default=False, help="Turn on floating point exceptions") group.add_argument("--seed", type=int, default=1984, help="Set the host and device seed") group.add_argument("--profile", type=str_to_bool, nargs="?", const=True, default=False, help="Generate a profile directory to be analysed by popvision.") group.add_argument("--profile-dir", type=str, help="Path to profile directory.") group.add_argument("--profile-instrument", type=str_to_bool, nargs="?", const=True, default=True, help="Include cycle count instrumentation when profiling.") group.add_argument("--no-outlining", type=str_to_bool, nargs="?", const=True, default=False, help="Disable PopART outlining optimisations. This will increase memory for a small throughput improvement.") group.add_argument("--engine-cache", type=lambda arg: None if not arg else arg, help="Path to store a cache of the engine compilation.") group.add_argument("--variable-weights-inference", type=str_to_bool, nargs="?", const=True, default=False, help="Force the weights to be variables during inference. Required for loading weights from a checkpoint when using a cached engine.") group.add_argument("--continue-training-from-epoch", type=int, default=0, help="Training epoch at which to start hyperparameter schedules when loading from a checkpoint") group.add_argument("--no-training", type=str_to_bool, nargs="?", const=True, default=False, help="Don't run the training loop") group.add_argument("--no-validation", type=str_to_bool, nargs="?", const=True, default=False, help="Don't validate training. This includes validation at the end of training") group.add_argument("--validation-config", action=ValidationConfig, help="Path to preset config for validation. If set by the `--config` file, it definied as a dict instead") group.add_argument("--low-latency-inference", type=str_to_bool, nargs="?", const=True, default=False, help="Use input/output callbacks to minimise inference latency for tasks that support this mode.") group.add_argument("--minimum-latency-inference", type=str_to_bool, nargs="?", const=True, default=False, help="Disable IO pre-fetching to minimize inference latency, at the cost of reduced throughput. Also sets low-latency-inference=true. ") group.add_argument("--inference-lm-perplexity", type=str_to_bool, nargs="?", const=True, default=False, help="Calculate the LM perplexity metric as part of inference (e^loss).") group.add_argument("--realtime-scheduler", action="store_true", help="Set a realtime scheduler for this process. Only activated during inference. \ (IMPORTANT: Requires non-interactive sudo, otherwise has no effect)") group.add_argument("--max-copy-merge-size", type=int, default=-1, help="Set the value for Poplar engine option 'opt.maxCopyMergeSize'. Set to -1 to use Poplar's default.") group.add_argument("--disable-fully-connected-pass", type=str_to_bool, nargs="?", const=True, default=False, help="Adding fully connected pass to some matmuls causes large transposes before operations during training. " "Note: This will improve throughput at the cost of memory.") group.add_argument("--group-host-syncs", type=str_to_bool, nargs="?", const=True, default=False, help="Groups the host-device synchronisations more efficiently, higher throughput can be reached at the expense of sum liveness") group.add_argument("--optimizer-state-offchip", type=str_to_bool, nargs="?", const=True, default=False, help="Keep the optimizer state off chip. Only streaming it on when needed for the weight update.") group.add_argument("--enable-half-partials", type=str_to_bool, nargs="?", const=True, default=False, help="Enable half partials for matmuls and convolutions globally.") group.add_argument('--internal-exchange-optimisation-target', type=str, default=None, choices=["balanced", "cycles", "memory"], help="The optimisation approach for internal exchanges.") group.add_argument("--activations-on-chip", type=str_to_bool, nargs="?", const=True, default=False, help="Leave activations stashed on chip, i.e don't move the activations to streaming memory.") group = parser.add_argument_group("Logging Config") group.add_argument("--report-hw-cycle-count", action="store_true", help='Report the number of cycles each "session.run" takes.') group.add_argument("--log-dir", type=str, default="logs", help="Path to save Tensorboard logs") group.add_argument("--steps-per-log", type=int, default=1, help="Number of session.run to execute before logging training metrics") group.add_argument("--aggregate-metrics-over-steps", type=int, help="Number of steps to aggregate metrics over. Default is the number of steps per epoch") group.add_argument("--epochs-per-save", type=int, default=-1, help="Number of epochs between each save of the model. Also saves at the end of training") group.add_argument("--steps-per-save", type=int, default=-1, help="Number of steps between each save of the model. Also saves at the end of training") group.add_argument("--no-model-save", type=str_to_bool, nargs="?", const=True, default=False, help="Don't save the model. Useful for testing.") group.add_argument("--checkpoint-dir", type=str, default="ckpts", help="Path to directory to save model checkpoints.") group.add_argument("--wandb", type=str_to_bool, nargs="?", const=True, default=False, help="Enable logging to Weights and Biases.") group.add_argument("--log-level", type=str, default='INFO', choices=['NOTSET', 'INFO', 'DEBUG', 'WARNING', 'ERROR', 'CRITICAL'], help="Set the logging level") group = parser.add_argument_group("Device Config") group.add_argument("--device-id", type=int, default=None, help="Select a specific IPU device.") group.add_argument("--device-connection-type", type=str, default="always", choices=["always", "ondemand", "offline"], help="Set the popart.DeviceConnectionType.") group.add_argument("--device-version", type=str, default=None, help="Set the IPU version (for offline compilation).") group.add_argument("--device-tiles", type=int, default=None, help="Set the number of tiles (for offline compilation).") group.add_argument("--device-ondemand-timeout", type=int, default=int(1e4), help="Set the seconds to wait for an ondemand device to become before available before exiting.") group.add_argument("--use-ipu-model", type=str_to_bool, nargs="?", const=True, default=False, help="Target the IPU Model.") group = parser.add_argument_group("Distribution Config") group.add_argument("--mpi-distributed", type=str_to_bool, nargs="?", const=True, default=False, help="Enable distributed training with MPI backend. Distributed training with MPI is currently in preview." "Full support for distributed training will be coming in a future release.") group.add_argument("--mpi-rank", type=int, default=0, help="Input the MPI rank of this process. This value will be overwritten by the rank determined by the MPI controller") group.add_argument("--mpi-size", type=int, default=1, help="Input the MPI size. This value will be overwritten by the size determined by the MPI controller") # This is here only for the help message group.add_argument("--config", type=str, help="Path to preset config") defaults = dict(execution_mode="DEFAULT") if pargs.config is not None: with open(pargs.config, "r") as f: preset = json.load(f) clean_exclusive_presets(parser, preset, remaining_argv) defaults.update(**preset) # Check the default args are valid known_args = set(vars(parser.parse_args(""))) unknown_defaults = set(defaults) - known_args if unknown_defaults: raise ValueError(f"Unknown arg(s) in config file: {unknown_defaults}") parser.set_defaults(**defaults) args = parser.parse_args(remaining_argv) set_execution_mode(args) set_mpi_args(args) # Invalidate incompatible options if args.no_drop_remainder and args.task != "SQUAD": raise RuntimeError(f"--no-drop-remainder is only compatible with SQUAD and not with {args.task}, aborting") if args.batch_serialize > args.batch_size: raise RuntimeError("--batch-serialize cannot be > --batch_size, aborting") if args.split_linear_layers and args.batch_serialize > 1: raise RuntimeError("--split-linear-layers is incompatible with --batch-serialize > 1, aborting") if args.synthetic_data and args.generated_data: raise RuntimeError("choose either --synthetic-data or --generated-data, not both. Aborting") # set low-latency-inference if minimum-latency-inference set if args.minimum_latency_inference: args.low_latency_inference = True # Append datetime string to checkpoints path and create the subdirectory args.checkpoint_dir = os.path.join(args.checkpoint_dir, datetime.datetime.now().strftime("%y-%m-%d-%H-%M-%S")) os.makedirs(args.checkpoint_dir, exist_ok=True) save_args(args) return args def set_execution_mode(args): if args.pipeline: args.execution_mode = "PIPELINE" elif args.phased_execution: args.execution_mode = "PHASED" elif args.virtual_graph or \ args.execution_mode == "PIPELINE" and args.pipeline is False or \ args.execution_mode == "PHASED" and args.phased_execution is False: args.execution_mode = "DEFAULT" return args def clean_exclusive_presets(parser, preset, remaining_argv): """Modifies the presets dictionary in-place to remove any defaults that would violate a mutually exclusive constraint.""" def mutually_exclusive_action_on_cmd(group): for action in group._group_actions: if any([opt in remaining_argv for opt in action.option_strings]): return action.dest return None def remove_mutually_exclusive_clashes(group, presets, cmd_arg): for action in group._group_actions: if action.dest != cmd_arg and action.dest in presets: del presets[action.dest] for group in parser._mutually_exclusive_groups: cmd_arg = mutually_exclusive_action_on_cmd(group) if cmd_arg is not None: remove_mutually_exclusive_clashes(group, preset, cmd_arg) def save_args(args): os.makedirs(args.checkpoint_dir, exist_ok=True) config_path = os.path.join(args.checkpoint_dir, "config.json") with open(config_path, "w") as f: json.dump(vars(args), f, indent=2) def get_validation_args(args): validation_kwargs = dict( inference=True, tf_checkpoint=None, gradient_accumulation_factor=1 ) if not args.no_training: validation_kwargs["onnx_checkpoint"] = os.path.join(args.checkpoint_dir, "model.onnx") if args.engine_cache: validation_kwargs["engine_cache"] = args.engine_cache + "val" if args.validation_config is not None: validation_kwargs.update(**args.validation_config) args = vars(args) args.update(**validation_kwargs) return argparse.Namespace(**args) def set_mpi_args(args): if not args.mpi_distributed: return logger.warning("Distributed training with MPI is currently in preview." "Full support for distributed training will be coming in a future release.") from mpi4py import MPI comm = MPI.COMM_WORLD mpi_size = comm.Get_size() mpi_rank = comm.Get_rank() if args.mpi_rank != mpi_rank and args.mpi_rank != 0: logger.warning(f"Overwriting the MPI rank provided {args.mpi_rank} to {mpi_rank}") args.mpi_rank = mpi_rank if args.mpi_size != mpi_size and args.mpi_size > 1: logger.warning(f"Overwriting the MPI size provided {args.mpi_size} to {mpi_size}") args.mpi_size = mpi_size is_distributed = mpi_size > 1 if is_distributed: if args.inference: raise RuntimeError("Distributed execution only supported for training") if args.task != "SQUAD": raise RuntimeError("Distributed training only supported with SQUAD") if is_distributed: args.checkpoint_dir = args.checkpoint_dir + "_rank_" + str(args.mpi_rank)
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starfile/writer.py
robertbuecker/starfile
0
64424
from __future__ import annotations from collections import OrderedDict from datetime import datetime from pathlib import Path from typing import TYPE_CHECKING, Union, Dict, List import pandas as pd from pkg_resources import get_distribution from .utils import TextBuffer if TYPE_CHECKING: from os import PathLike __version__ = get_distribution("starfile").version class StarWriter: def __init__(self, dataframes: Union[pd.DataFrame, Dict[pd.DataFrame], List[pd.DataFrame]], filename: PathLike, overwrite: bool = False, float_format: str = '%.6f', sep: str = '\t', na_rep: str = '<NA>', force_loop: bool = False): self.overwrite = overwrite self.filename = filename self.dataframes = dataframes self.float_format = float_format self.sep = sep self.na_rep = na_rep self.force_loop = force_loop self.buffer = TextBuffer() self.write_star_file() @property def dataframes(self): """ Ordered dictionary of pandas dataframes df.name defines the data block name """ return self._dataframes @dataframes.setter def dataframes(self, dataframes: Union[pd.DataFrame, Dict[pd.DataFrame], List[pd.DataFrame]]): if isinstance(dataframes, pd.DataFrame): self._dataframes = self.coerce_dataframe(dataframes) elif isinstance(dataframes, dict): self._dataframes = self.coerce_dict(dataframes) elif isinstance(dataframes, list): self._dataframes = self.coerce_list(dataframes) else: raise ValueError(f'Expected a DataFrame, Dict or List object, got {type(dataframes)}') @staticmethod def coerce_dataframe(df: pd.DataFrame): name = getattr(df, 'name', '') if name != '': name = 0 return {name: df} @staticmethod def coerce_dict(dfs: Dict[str, pd.DataFrame]): """ This method ensures that dataframe names are updated based on dict keys """ for key, df in dfs.items(): df.name = str(key) return dfs def coerce_list(self, dfs: List[pd.DataFrame]): """ This method coerces a list of DataFrames into a dict """ return self.coerce_dict(OrderedDict([(idx, df) for idx, df in enumerate(dfs)])) @property def filename(self): return self._filename @filename.setter def filename(self, filename: Union[Path, str]): self._filename = Path(filename) if not self.file_writable: raise FileExistsError('to overwrite an existing file set overwrite=True') @property def file_exists(self): return self.filename.exists() @property def file_writable(self): if self.overwrite or (not self.file_exists): return True else: return False def write_package_info(self): date = datetime.now().strftime('%d/%m/%Y') time = datetime.now().strftime('%H:%M:%S') line = f'Created by the starfile Python package (version {__version__}) at {time} on' \ f' {date}' self.buffer.add_comment(line) self.buffer.add_blank_lines(1) self.buffer.write_as_new_file_and_clear(self.filename) def write_star_file(self, filename: str = None): self.write_package_info() for _, df in self.dataframes.items(): self.write_block(df) self.buffer.add_blank_line() self.buffer.append_to_file_and_clear(self.filename) def write_loopheader(self, df: pd.DataFrame): self.buffer.add_line('loop_') lines = [f'_{column_name} #{idx}' for idx, column_name in enumerate(df.columns, 1)] for line in lines: self.buffer.add_line(line) self.buffer.append_to_file_and_clear(self.filename) @staticmethod def get_block_name(df: pd.DataFrame): return 'data_' + getattr(df, 'name', '') def add_block_name_to_buffer(self, df: pd.DataFrame): self.buffer.add_line(self.get_block_name(df)) self.buffer.add_blank_lines(1) self.buffer.append_to_file_and_clear(self.filename) def write_block(self, df: pd.DataFrame): self.add_block_name_to_buffer(df) if (df.shape[0] == 1) and not self.force_loop: self._write_simple_block(df) elif (df.shape[0] > 1) or self.force_loop: self._write_loop_block(df) self.buffer.add_blank_lines(2) self.buffer.append_to_file_and_clear(self.filename) def _write_simple_block(self, df: pd.DataFrame): lines = [f'_{column_name}\t\t\t{df[column_name].iloc[0]}' for column_name in df.columns] for line in lines: self.buffer.add_line(line) self.buffer.append_to_file_and_clear(self.filename) def _write_loop_block(self, df: pd.DataFrame): self.write_loopheader(df) df.to_csv(self.filename, mode='a', sep=self.sep, header=False, index=False, float_format=self.float_format, na_rep=self.na_rep)
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models/loss.py
kungyao/StyledFontGAN
18
1606928
<filename>models/loss.py from operator import itemgetter import torch from torch.autograd import grad as torch_grad def l1_and_adversarial_loss(D, G, real_data, generated_data, losses, options): l1_lamba = 10 return min_max_loss(D, G, real_data, generated_data, losses, options) + l1_lamba * l1_loss(D, G, real_data, generated_data, losses, options) def wasserstein_loss(D, G, real_data, generated_data, losses, options): real_loss = D(real_data) generated_loss = D(generated_data) batch_size, data_type = itemgetter('batch_size', 'data_type')(options) gradient_penalty_weight = 10 # Calculate gradient penalty gradient_penalty = calculate_gradient_penalty(D, real_data, generated_data, batch_size, gradient_penalty_weight, losses, data_type) losses['GP'].append(gradient_penalty.data) # Calculate the Wasserstein Distance. loss = generated_loss.mean() - real_loss.mean() + gradient_penalty losses['Generated'].append(generated_loss.mean().data) losses['Real'].append(real_loss.mean().data) losses['D'].append(loss.data) return loss def min_max_loss(D, G, real_data, generated_data, losses, options): # Forward pass with the discriminator discriminator_loss = D(generated_data) # Update the loss. We're trying to fool the discriminator to say '1, this is real' loss = -discriminator_loss.mean() return loss def l1_loss(D, G, real_data, generated_data, losses, options): """ Performs the L1 loss between the generated data and the real data. It is expected that both `real_data` and `generated_data` are of the same shape. """ return torch.nn.L1Loss()(generated_data, real_data) def calculate_gradient_penalty(D, real_data, generated_data, batch_size, gradient_penalty_weight, losses, data_type): # Calculate interpolation alpha = torch.rand(batch_size, 1, 1, 1).expand_as(real_data).type(data_type) # 'interpolated' is x-hat interpolated = (alpha * real_data.data + (1 - alpha) * generated_data.data).type(data_type) interpolated.requires_grad = True # Calculate probability of interpolated examples probability_interpolated = D(interpolated) gradients = torch_grad(outputs=probability_interpolated, inputs=interpolated, grad_outputs=torch.ones( probability_interpolated.size()).type(data_type), create_graph=True, retain_graph=True)[0] # Gradients have shape (batch_size, num_channels, img_width, img_height), # so flatten to easily take norm per example in batch gradients = gradients.view(batch_size, -1) losses['gradient_norm'].append(gradients.norm(2, dim=1).mean().data) # Derivatives of the gradient close to 0 can cause problems because of # the square root, so manually calculate norm and add epsilon gradients_norm = torch.sqrt(torch.sum(gradients ** 2, dim=1) + 1e-12) # Return gradient penalty return gradient_penalty_weight * ((gradients_norm - 1) ** 2).mean()
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EasyRecycle/tests/unittests/recycle/views/test_EditCommercialOrderAPIView.py
YuriyLisovskiy/EasyRecycle
0
43457
from django.urls import reverse from rest_framework import status from rest_framework.test import force_authenticate from core.models import UserModel from recycle import garbage from recycle.models import CommercialRequest, Location from recycle.views.commercial_order import EditCommercialOrderAPIView from tests.unittests.common import APIFactoryTestCase class EditCommercialOrderAPIViewUpdateTestCase(APIFactoryTestCase): def setUp(self) -> None: super(EditCommercialOrderAPIViewUpdateTestCase, self).setUp() self.user = UserModel.objects.get(username='User') self.super_user = UserModel.objects.get(username='SuperUser') self.gc_user = UserModel.objects.get(username='GCUser') self.commercial_user = UserModel.objects.get(username='CommercialUser') self.commercial_order = CommercialRequest.objects.get(address='Hello st., 11') self.view = EditCommercialOrderAPIView.as_view() def check_permission_denied(self): request = self.request_factory.put(reverse('api_v1:recycle:edit_commercial_order', args=[self.commercial_order.pk])) force_authenticate(request, self.user) response = self.view(request, pk=self.commercial_order.pk) self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_AnonymousUser(self): request = self.request_factory.put(reverse('api_v1:recycle:edit_commercial_order', args=[self.commercial_order.pk])) response = self.view(request, pk=self.commercial_order.pk) self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) def test_AuthenticatedUser(self): self.check_permission_denied() def test_CommercialUser(self): self.check_permission_denied() def test_SuperUser(self): input_data = { 'address': 'Hello St. 10', 'date': '2021-05-03', 'garbage_type': garbage.METAL, 'mass': 15.7, 'status': CommercialRequest.IN_PROGRESS, 'location': Location.objects.get(address='Second st. 1').id, 'user': UserModel.objects.get(username='CommercialUser2').id } request = self.request_factory.put( reverse('api_v1:recycle:edit_commercial_order', args=[self.commercial_order.pk]), data=input_data ) force_authenticate(request, self.super_user) response = self.view(request, pk=self.commercial_order.pk) self.assertEqual(response.status_code, status.HTTP_200_OK) actual_order = response.data self.assertEqual(input_data['address'], actual_order['address']) self.assertEqual(input_data['date'], actual_order['date']) self.assertEqual(input_data['garbage_type'], actual_order['garbage_type']) self.assertEqual(input_data['mass'], actual_order['mass']) self.assertEqual(input_data['status'], actual_order['status']) self.assertEqual(input_data['location'], actual_order['location']) self.assertEqual(input_data['user'], actual_order['user']) def test_UpdateDoneOrder(self): input_data = { 'address': 'Hello St. 10' } order = CommercialRequest.objects.get(address='Hello st., 12') request = self.request_factory.put( reverse('api_v1:recycle:edit_commercial_order', args=[order.pk]), data=input_data ) force_authenticate(request, self.super_user) response = self.view(request, pk=order.pk) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_GCUpdateStatusOnly(self): input_data = { 'status': CommercialRequest.IN_PROGRESS } order = CommercialRequest.objects.get(address='Hello st., 12') request = self.request_factory.put( reverse('api_v1:recycle:edit_commercial_order', args=[order.pk]), data=input_data ) force_authenticate(request, self.gc_user) response = self.view(request, pk=self.commercial_order.pk) self.assertEqual(response.status_code, status.HTTP_200_OK) def run_update_field(self, data): request = self.request_factory.put( reverse('api_v1:recycle:edit_commercial_order', args=[self.commercial_order.pk]), data=data ) force_authenticate(request, self.super_user) response = self.view(request, pk=self.commercial_order.pk) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_UpdateAddress(self): input_data = { 'address': 'Hello St. 10' } self.run_update_field(input_data) actual_order = CommercialRequest.objects.get(pk=self.commercial_order.pk) self.assertEqual(actual_order.address, input_data['address']) def test_UpdateDate(self): input_data = { 'date': '2021-05-03' } self.run_update_field(input_data) actual_order = CommercialRequest.objects.get(pk=self.commercial_order.pk) self.assertEqual(actual_order.date.strftime('%Y-%m-%d'), input_data['date']) def test_UpdateGarbageType(self): input_data = { 'garbage_type': garbage.PAPER } self.run_update_field(input_data) actual_order = CommercialRequest.objects.get(pk=self.commercial_order.pk) self.assertEqual(actual_order.garbage_type, input_data['garbage_type']) def test_UpdateMass(self): input_data = { 'mass': 15.7 } self.run_update_field(input_data) actual_order = CommercialRequest.objects.get(pk=self.commercial_order.pk) self.assertEqual(actual_order.mass, input_data['mass']) def test_UpdateStatus(self): input_data = { 'status': CommercialRequest.IN_PROGRESS } self.run_update_field(input_data) actual_order = CommercialRequest.objects.get(pk=self.commercial_order.pk) self.assertEqual(actual_order.status, input_data['status']) def test_UpdateLocation(self): input_data = { 'location': Location.objects.get(address='Second st. 1').id } self.run_update_field(input_data) actual_order = CommercialRequest.objects.get(pk=self.commercial_order.pk) self.assertEqual(actual_order.location.id, input_data['location']) def test_UpdateUser(self): input_data = { 'user': UserModel.objects.get(username='CommercialUser2').id } self.run_update_field(input_data) actual_order = CommercialRequest.objects.get(pk=self.commercial_order.pk) self.assertEqual(actual_order.user.id, input_data['user'])
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alarm_clock/AlarmClock.py
anshul2807/Automation-scripts
5
1617923
<gh_stars>1-10 import time import datetime import simpleaudio set_time = input("please enter the time in HH:MM format to set an alarm : ") while True: time.sleep(1) obj = simpleaudio.WaveObject.from_wave_file("./alarm.wav") current_time = datetime.datetime.now().time().strftime("%H:%M") curr_l = current_time.split(":") curr_l = [int(i) for i in curr_l] set_l = set_time.split(":") set_l = [int(i) for i in set_l] print(f"current time : {current_time}") print(f"alarm time : {set_time}") if curr_l[0] == set_l[0] and curr_l[1] == set_l[1]: obj.play() print("Time to Wake up") input("Press enter to Stop") break
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Interview_Questions_Arrays/duplicates_in_array.py
MNikov/Algorithms-and-Data-Structures
0
195372
# Interview Question #5 # The problem is that we want to find duplicates in a one-dimensional array of integers in O(N) running time # where the integer values are smaller than the length of the array! # For example: if we have a list [1, 2, 3, 1, 5] then the algorithm can detect that there are a duplicate with value 1. # Note: the array can not contain items smaller than 0 and items with values greater than the size of the list. # This is how we can achieve O(N) linear running time complexity! def duplicate_finder_1(nums): duplicates = [] for n in set(nums): if nums.count(n) > 1: duplicates.append(n) return duplicates if duplicates else 'The array does not contain any duplicates!' def duplicate_finder_2(nums): duplicates = set() for i, n in enumerate(sorted(nums)): if i + 1 < len(nums) and n == nums[i + 1]: duplicates.add(n) return list(duplicates) if duplicates else 'The array does not contain any duplicates!' # Course implementation def duplicate_finder_3(nums): duplicates = set() for n in nums: if nums[abs(n)] >= 0: nums[abs(n)] *= -1 else: duplicates.add(n) return list({-n if n < 0 else n for n in duplicates}) if duplicates \ else 'The array does not contain any duplicates!' # return list(map(lambda n: n * -1 if n < 0 else n, # duplicates)) if duplicates else 'The array does not contain any duplicates!' # My solutions allow the use of numbers greater than the length of the array print(duplicate_finder_1([1, 2, 3, 4])) print(duplicate_finder_1([1, 1, 2, 3, 3, 4])) print(duplicate_finder_2([5, 6, 7, 8])) print(duplicate_finder_2([5, 6, 6, 6, 7, 7, 9])) print(duplicate_finder_3([2, 3, 1, 2, 4, 3, 3])) print(duplicate_finder_3([0, 1, 2]))
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mmdet/models/necks/fpn_carafe.py
evgps/mmdetection_trashcan
12,377
196605
import torch.nn as nn from mmcv.cnn import ConvModule, build_upsample_layer, xavier_init from mmcv.ops.carafe import CARAFEPack from ..builder import NECKS @NECKS.register_module() class FPN_CARAFE(nn.Module): """FPN_CARAFE is a more flexible implementation of FPN. It allows more choice for upsample methods during the top-down pathway. It can reproduce the preformance of ICCV 2019 paper CARAFE: Content-Aware ReAssembly of FEatures Please refer to https://arxiv.org/abs/1905.02188 for more details. Args: in_channels (list[int]): Number of channels for each input feature map. out_channels (int): Output channels of feature pyramids. num_outs (int): Number of output stages. start_level (int): Start level of feature pyramids. (Default: 0) end_level (int): End level of feature pyramids. (Default: -1 indicates the last level). norm_cfg (dict): Dictionary to construct and config norm layer. activate (str): Type of activation function in ConvModule (Default: None indicates w/o activation). order (dict): Order of components in ConvModule. upsample (str): Type of upsample layer. upsample_cfg (dict): Dictionary to construct and config upsample layer. """ def __init__(self, in_channels, out_channels, num_outs, start_level=0, end_level=-1, norm_cfg=None, act_cfg=None, order=('conv', 'norm', 'act'), upsample_cfg=dict( type='carafe', up_kernel=5, up_group=1, encoder_kernel=3, encoder_dilation=1)): super(FPN_CARAFE, self).__init__() assert isinstance(in_channels, list) self.in_channels = in_channels self.out_channels = out_channels self.num_ins = len(in_channels) self.num_outs = num_outs self.norm_cfg = norm_cfg self.act_cfg = act_cfg self.with_bias = norm_cfg is None self.upsample_cfg = upsample_cfg.copy() self.upsample = self.upsample_cfg.get('type') self.relu = nn.ReLU(inplace=False) self.order = order assert order in [('conv', 'norm', 'act'), ('act', 'conv', 'norm')] assert self.upsample in [ 'nearest', 'bilinear', 'deconv', 'pixel_shuffle', 'carafe', None ] if self.upsample in ['deconv', 'pixel_shuffle']: assert hasattr( self.upsample_cfg, 'upsample_kernel') and self.upsample_cfg.upsample_kernel > 0 self.upsample_kernel = self.upsample_cfg.pop('upsample_kernel') if end_level == -1: self.backbone_end_level = self.num_ins assert num_outs >= self.num_ins - start_level else: # if end_level < inputs, no extra level is allowed self.backbone_end_level = end_level assert end_level <= len(in_channels) assert num_outs == end_level - start_level self.start_level = start_level self.end_level = end_level self.lateral_convs = nn.ModuleList() self.fpn_convs = nn.ModuleList() self.upsample_modules = nn.ModuleList() for i in range(self.start_level, self.backbone_end_level): l_conv = ConvModule( in_channels[i], out_channels, 1, norm_cfg=norm_cfg, bias=self.with_bias, act_cfg=act_cfg, inplace=False, order=self.order) fpn_conv = ConvModule( out_channels, out_channels, 3, padding=1, norm_cfg=self.norm_cfg, bias=self.with_bias, act_cfg=act_cfg, inplace=False, order=self.order) if i != self.backbone_end_level - 1: upsample_cfg_ = self.upsample_cfg.copy() if self.upsample == 'deconv': upsample_cfg_.update( in_channels=out_channels, out_channels=out_channels, kernel_size=self.upsample_kernel, stride=2, padding=(self.upsample_kernel - 1) // 2, output_padding=(self.upsample_kernel - 1) // 2) elif self.upsample == 'pixel_shuffle': upsample_cfg_.update( in_channels=out_channels, out_channels=out_channels, scale_factor=2, upsample_kernel=self.upsample_kernel) elif self.upsample == 'carafe': upsample_cfg_.update(channels=out_channels, scale_factor=2) else: # suppress warnings align_corners = (None if self.upsample == 'nearest' else False) upsample_cfg_.update( scale_factor=2, mode=self.upsample, align_corners=align_corners) upsample_module = build_upsample_layer(upsample_cfg_) self.upsample_modules.append(upsample_module) self.lateral_convs.append(l_conv) self.fpn_convs.append(fpn_conv) # add extra conv layers (e.g., RetinaNet) extra_out_levels = ( num_outs - self.backbone_end_level + self.start_level) if extra_out_levels >= 1: for i in range(extra_out_levels): in_channels = ( self.in_channels[self.backbone_end_level - 1] if i == 0 else out_channels) extra_l_conv = ConvModule( in_channels, out_channels, 3, stride=2, padding=1, norm_cfg=norm_cfg, bias=self.with_bias, act_cfg=act_cfg, inplace=False, order=self.order) if self.upsample == 'deconv': upsampler_cfg_ = dict( in_channels=out_channels, out_channels=out_channels, kernel_size=self.upsample_kernel, stride=2, padding=(self.upsample_kernel - 1) // 2, output_padding=(self.upsample_kernel - 1) // 2) elif self.upsample == 'pixel_shuffle': upsampler_cfg_ = dict( in_channels=out_channels, out_channels=out_channels, scale_factor=2, upsample_kernel=self.upsample_kernel) elif self.upsample == 'carafe': upsampler_cfg_ = dict( channels=out_channels, scale_factor=2, **self.upsample_cfg) else: # suppress warnings align_corners = (None if self.upsample == 'nearest' else False) upsampler_cfg_ = dict( scale_factor=2, mode=self.upsample, align_corners=align_corners) upsampler_cfg_['type'] = self.upsample upsample_module = build_upsample_layer(upsampler_cfg_) extra_fpn_conv = ConvModule( out_channels, out_channels, 3, padding=1, norm_cfg=self.norm_cfg, bias=self.with_bias, act_cfg=act_cfg, inplace=False, order=self.order) self.upsample_modules.append(upsample_module) self.fpn_convs.append(extra_fpn_conv) self.lateral_convs.append(extra_l_conv) # default init_weights for conv(msra) and norm in ConvModule def init_weights(self): """Initialize the weights of module.""" for m in self.modules(): if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): xavier_init(m, distribution='uniform') for m in self.modules(): if isinstance(m, CARAFEPack): m.init_weights() def slice_as(self, src, dst): """Slice ``src`` as ``dst`` Note: ``src`` should have the same or larger size than ``dst``. Args: src (torch.Tensor): Tensors to be sliced. dst (torch.Tensor): ``src`` will be sliced to have the same size as ``dst``. Returns: torch.Tensor: Sliced tensor. """ assert (src.size(2) >= dst.size(2)) and (src.size(3) >= dst.size(3)) if src.size(2) == dst.size(2) and src.size(3) == dst.size(3): return src else: return src[:, :, :dst.size(2), :dst.size(3)] def tensor_add(self, a, b): """Add tensors ``a`` and ``b`` that might have different sizes.""" if a.size() == b.size(): c = a + b else: c = a + self.slice_as(b, a) return c def forward(self, inputs): """Forward function.""" assert len(inputs) == len(self.in_channels) # build laterals laterals = [] for i, lateral_conv in enumerate(self.lateral_convs): if i <= self.backbone_end_level - self.start_level: input = inputs[min(i + self.start_level, len(inputs) - 1)] else: input = laterals[-1] lateral = lateral_conv(input) laterals.append(lateral) # build top-down path for i in range(len(laterals) - 1, 0, -1): if self.upsample is not None: upsample_feat = self.upsample_modules[i - 1](laterals[i]) else: upsample_feat = laterals[i] laterals[i - 1] = self.tensor_add(laterals[i - 1], upsample_feat) # build outputs num_conv_outs = len(self.fpn_convs) outs = [] for i in range(num_conv_outs): out = self.fpn_convs[i](laterals[i]) outs.append(out) return tuple(outs)
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cinder/tests/test_glusterfs.py
sombrafam/cinder
0
195718
# Copyright (c) 2013 Red Hat, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """Unit tests for the GlusterFS driver module.""" import errno import os import tempfile import mox as mox_lib from mox import IgnoreArg from mox import IsA from mox import stubout from cinder import context from cinder import db from cinder import exception from cinder.image import image_utils from cinder.openstack.common import imageutils from cinder.openstack.common import processutils as putils from cinder import test from cinder import units from cinder import utils from cinder.volume import configuration as conf from cinder.volume.drivers import glusterfs class DumbVolume(object): fields = {} def __setitem__(self, key, value): self.fields[key] = value def __getitem__(self, item): return self.fields[item] class GlusterFsDriverTestCase(test.TestCase): """Test case for GlusterFS driver.""" TEST_EXPORT1 = 'glusterfs-host1:/export' TEST_EXPORT2 = 'glusterfs-host2:/export' TEST_EXPORT2_OPTIONS = '-o backupvolfile-server=glusterfs-backup1' TEST_SIZE_IN_GB = 1 TEST_MNT_POINT = '/mnt/glusterfs' TEST_MNT_POINT_BASE = '/mnt/test' TEST_LOCAL_PATH = '/mnt/glusterfs/volume-123' TEST_FILE_NAME = 'test.txt' TEST_SHARES_CONFIG_FILE = '/etc/cinder/test-shares.conf' VOLUME_UUID = 'abcdefab-cdef-abcd-efab-cdefabcdefab' SNAP_UUID = 'bacadaca-baca-daca-baca-dacadacadaca' SNAP_UUID_2 = 'bebedede-bebe-dede-bebe-dedebebedede' def setUp(self): super(GlusterFsDriverTestCase, self).setUp() self._mox = mox_lib.Mox() self._configuration = mox_lib.MockObject(conf.Configuration) self._configuration.append_config_values(mox_lib.IgnoreArg()) self._configuration.glusterfs_shares_config = \ self.TEST_SHARES_CONFIG_FILE self._configuration.glusterfs_mount_point_base = \ self.TEST_MNT_POINT_BASE self._configuration.glusterfs_disk_util = 'df' self._configuration.glusterfs_sparsed_volumes = True self._configuration.glusterfs_qcow2_volumes = False self.stubs = stubout.StubOutForTesting() self._driver =\ glusterfs.GlusterfsDriver(configuration=self._configuration) self._driver.shares = {} def tearDown(self): self._mox.UnsetStubs() self.stubs.UnsetAll() super(GlusterFsDriverTestCase, self).tearDown() def stub_out_not_replaying(self, obj, attr_name): attr_to_replace = getattr(obj, attr_name) stub = mox_lib.MockObject(attr_to_replace) self.stubs.Set(obj, attr_name, stub) def test_local_path(self): """local_path common use case.""" glusterfs.CONF.glusterfs_mount_point_base = self.TEST_MNT_POINT_BASE drv = self._driver volume = DumbVolume() volume['provider_location'] = self.TEST_EXPORT1 volume['name'] = 'volume-123' self.assertEqual( '/mnt/test/ab03ab34eaca46a5fb81878f7e9b91fc/volume-123', drv.local_path(volume)) def test_mount_glusterfs_should_mount_correctly(self): """_mount_glusterfs common case usage.""" mox = self._mox drv = self._driver mox.StubOutWithMock(drv, '_execute') drv._execute('mkdir', '-p', self.TEST_MNT_POINT) drv._execute('mount', '-t', 'glusterfs', self.TEST_EXPORT1, self.TEST_MNT_POINT, run_as_root=True) mox.ReplayAll() drv._mount_glusterfs(self.TEST_EXPORT1, self.TEST_MNT_POINT) mox.VerifyAll() def test_mount_glusterfs_should_suppress_already_mounted_error(self): """_mount_glusterfs should suppress already mounted error if ensure=True """ mox = self._mox drv = self._driver mox.StubOutWithMock(drv, '_execute') drv._execute('mkdir', '-p', self.TEST_MNT_POINT) drv._execute('mount', '-t', 'glusterfs', self.TEST_EXPORT1, self.TEST_MNT_POINT, run_as_root=True).\ AndRaise(putils.ProcessExecutionError( stderr='is busy or already mounted')) mox.ReplayAll() drv._mount_glusterfs(self.TEST_EXPORT1, self.TEST_MNT_POINT, ensure=True) mox.VerifyAll() def test_mount_glusterfs_should_reraise_already_mounted_error(self): """_mount_glusterfs should not suppress already mounted error if ensure=False """ mox = self._mox drv = self._driver mox.StubOutWithMock(drv, '_execute') drv._execute('mkdir', '-p', self.TEST_MNT_POINT) drv._execute( 'mount', '-t', 'glusterfs', self.TEST_EXPORT1, self.TEST_MNT_POINT, run_as_root=True). \ AndRaise(putils.ProcessExecutionError(stderr='is busy or ' 'already mounted')) mox.ReplayAll() self.assertRaises(putils.ProcessExecutionError, drv._mount_glusterfs, self.TEST_EXPORT1, self.TEST_MNT_POINT, ensure=False) mox.VerifyAll() def test_mount_glusterfs_should_create_mountpoint_if_not_yet(self): """_mount_glusterfs should create mountpoint if it doesn't exist.""" mox = self._mox drv = self._driver mox.StubOutWithMock(drv, '_execute') drv._execute('mkdir', '-p', self.TEST_MNT_POINT) drv._execute(*([IgnoreArg()] * 5), run_as_root=IgnoreArg()) mox.ReplayAll() drv._mount_glusterfs(self.TEST_EXPORT1, self.TEST_MNT_POINT) mox.VerifyAll() def test_get_hash_str(self): """_get_hash_str should calculation correct value.""" drv = self._driver self.assertEqual('ab03ab34eaca46a5fb81878f7e9b91fc', drv._get_hash_str(self.TEST_EXPORT1)) def test_get_mount_point_for_share(self): """_get_mount_point_for_share should calculate correct value.""" drv = self._driver glusterfs.CONF.glusterfs_mount_point_base = self.TEST_MNT_POINT_BASE self.assertEqual('/mnt/test/ab03ab34eaca46a5fb81878f7e9b91fc', drv._get_mount_point_for_share( self.TEST_EXPORT1)) def test_get_available_capacity_with_df(self): """_get_available_capacity should calculate correct value.""" mox = self._mox drv = self._driver df_total_size = 2620544 df_avail = 1490560 df_head = 'Filesystem 1K-blocks Used Available Use% Mounted on\n' df_data = 'glusterfs-host:/export %d 996864 %d 41%% /mnt' % \ (df_total_size, df_avail) df_output = df_head + df_data setattr(glusterfs.CONF, 'glusterfs_disk_util', 'df') mox.StubOutWithMock(drv, '_get_mount_point_for_share') drv._get_mount_point_for_share(self.TEST_EXPORT1).\ AndReturn(self.TEST_MNT_POINT) mox.StubOutWithMock(drv, '_execute') drv._execute('df', '--portability', '--block-size', '1', self.TEST_MNT_POINT, run_as_root=True).AndReturn((df_output, None)) mox.ReplayAll() self.assertEqual((df_avail, df_total_size), drv._get_available_capacity(self.TEST_EXPORT1)) mox.VerifyAll() delattr(glusterfs.CONF, 'glusterfs_disk_util') def test_load_shares_config(self): mox = self._mox drv = self._driver drv.configuration.glusterfs_shares_config = ( self.TEST_SHARES_CONFIG_FILE) mox.StubOutWithMock(drv, '_read_config_file') config_data = [] config_data.append(self.TEST_EXPORT1) config_data.append('#' + self.TEST_EXPORT2) config_data.append(self.TEST_EXPORT2 + ' ' + self.TEST_EXPORT2_OPTIONS) config_data.append('broken:share_format') config_data.append('') drv._read_config_file(self.TEST_SHARES_CONFIG_FILE).\ AndReturn(config_data) mox.ReplayAll() drv._load_shares_config(drv.configuration.glusterfs_shares_config) self.assertIn(self.TEST_EXPORT1, drv.shares) self.assertIn(self.TEST_EXPORT2, drv.shares) self.assertEqual(len(drv.shares), 2) self.assertEqual(drv.shares[self.TEST_EXPORT2], self.TEST_EXPORT2_OPTIONS) mox.VerifyAll() def test_ensure_share_mounted(self): """_ensure_share_mounted simple use case.""" mox = self._mox drv = self._driver mox.StubOutWithMock(utils, 'get_file_mode') mox.StubOutWithMock(utils, 'get_file_gid') mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(drv, '_ensure_share_writable') mox.StubOutWithMock(drv, '_get_mount_point_for_share') drv._get_mount_point_for_share(self.TEST_EXPORT1).\ AndReturn(self.TEST_MNT_POINT) mox.StubOutWithMock(drv, '_mount_glusterfs') drv._mount_glusterfs(self.TEST_EXPORT1, self.TEST_MNT_POINT, ensure=True) utils.get_file_gid(self.TEST_MNT_POINT).AndReturn(333333) utils.get_file_mode(self.TEST_MNT_POINT).AndReturn(0o777) drv._ensure_share_writable(self.TEST_MNT_POINT) drv._execute('chgrp', IgnoreArg(), self.TEST_MNT_POINT, run_as_root=True) mox.ReplayAll() drv._ensure_share_mounted(self.TEST_EXPORT1) mox.VerifyAll() def test_ensure_shares_mounted_should_save_mounting_successfully(self): """_ensure_shares_mounted should save share if mounted with success.""" mox = self._mox drv = self._driver mox.StubOutWithMock(drv, '_read_config_file') config_data = [] config_data.append(self.TEST_EXPORT1) drv._read_config_file(self.TEST_SHARES_CONFIG_FILE).\ AndReturn(config_data) mox.StubOutWithMock(drv, '_ensure_share_mounted') drv._ensure_share_mounted(self.TEST_EXPORT1) mox.ReplayAll() drv._ensure_shares_mounted() self.assertEqual(1, len(drv._mounted_shares)) self.assertEqual(self.TEST_EXPORT1, drv._mounted_shares[0]) mox.VerifyAll() def test_ensure_shares_mounted_should_not_save_mounting_with_error(self): """_ensure_shares_mounted should not save share if failed to mount.""" mox = self._mox drv = self._driver mox.StubOutWithMock(drv, '_read_config_file') config_data = [] config_data.append(self.TEST_EXPORT1) drv._read_config_file(self.TEST_SHARES_CONFIG_FILE).\ AndReturn(config_data) mox.StubOutWithMock(drv, '_ensure_share_mounted') drv._ensure_share_mounted(self.TEST_EXPORT1).AndRaise(Exception()) mox.ReplayAll() drv._ensure_shares_mounted() self.assertEqual(0, len(drv._mounted_shares)) mox.VerifyAll() def test_setup_should_throw_error_if_shares_config_not_configured(self): """do_setup should throw error if shares config is not configured.""" drv = self._driver glusterfs.CONF.glusterfs_shares_config = self.TEST_SHARES_CONFIG_FILE self.assertRaises(exception.GlusterfsException, drv.do_setup, IsA(context.RequestContext)) def test_setup_should_throw_exception_if_client_is_not_installed(self): """do_setup should throw exception if client is not installed.""" mox = self._mox drv = self._driver glusterfs.CONF.glusterfs_shares_config = self.TEST_SHARES_CONFIG_FILE mox.StubOutWithMock(os.path, 'exists') os.path.exists(self.TEST_SHARES_CONFIG_FILE).AndReturn(True) mox.StubOutWithMock(drv, '_execute') drv._execute('mount.glusterfs', check_exit_code=False).\ AndRaise(OSError(errno.ENOENT, 'No such file or directory')) mox.ReplayAll() self.assertRaises(exception.GlusterfsException, drv.do_setup, IsA(context.RequestContext)) mox.VerifyAll() def _fake_load_shares_config(self, conf): self._driver.shares = {'127.7.7.7:/gluster1': None} def _fake_NamedTemporaryFile(self, prefix=None, dir=None): raise OSError('Permission denied!') def test_setup_set_share_permissions(self): mox = self._mox drv = self._driver glusterfs.CONF.glusterfs_shares_config = self.TEST_SHARES_CONFIG_FILE self.stubs.Set(drv, '_load_shares_config', self._fake_load_shares_config) self.stubs.Set(tempfile, 'NamedTemporaryFile', self._fake_NamedTemporaryFile) mox.StubOutWithMock(os.path, 'exists') mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(utils, 'get_file_gid') mox.StubOutWithMock(utils, 'get_file_mode') mox.StubOutWithMock(os, 'getegid') drv._execute('mount.glusterfs', check_exit_code=False) drv._execute('mkdir', '-p', mox_lib.IgnoreArg()) os.path.exists(self.TEST_SHARES_CONFIG_FILE).AndReturn(True) drv._execute('mount', '-t', 'glusterfs', '127.7.7.7:/gluster1', mox_lib.IgnoreArg(), run_as_root=True) utils.get_file_gid(mox_lib.IgnoreArg()).AndReturn(33333) # perms not writable utils.get_file_mode(mox_lib.IgnoreArg()).AndReturn(0o000) os.getegid().AndReturn(888) drv._execute('chgrp', 888, mox_lib.IgnoreArg(), run_as_root=True) drv._execute('chmod', 'g+w', mox_lib.IgnoreArg(), run_as_root=True) mox.ReplayAll() drv.do_setup(IsA(context.RequestContext)) mox.VerifyAll() def test_find_share_should_throw_error_if_there_is_no_mounted_shares(self): """_find_share should throw error if there is no mounted shares.""" drv = self._driver drv._mounted_shares = [] self.assertRaises(exception.GlusterfsNoSharesMounted, drv._find_share, self.TEST_SIZE_IN_GB) def test_find_share(self): """_find_share simple use case.""" mox = self._mox drv = self._driver drv._mounted_shares = [self.TEST_EXPORT1, self.TEST_EXPORT2] mox.StubOutWithMock(drv, '_get_available_capacity') drv._get_available_capacity(self.TEST_EXPORT1).\ AndReturn((2 * units.GiB, 5 * units.GiB)) drv._get_available_capacity(self.TEST_EXPORT2).\ AndReturn((3 * units.GiB, 10 * units.GiB)) mox.ReplayAll() self.assertEqual(self.TEST_EXPORT2, drv._find_share(self.TEST_SIZE_IN_GB)) mox.VerifyAll() def test_find_share_should_throw_error_if_there_is_no_enough_place(self): """_find_share should throw error if there is no share to host vol.""" mox = self._mox drv = self._driver drv._mounted_shares = [self.TEST_EXPORT1, self.TEST_EXPORT2] mox.StubOutWithMock(drv, '_get_available_capacity') drv._get_available_capacity(self.TEST_EXPORT1).\ AndReturn((0, 5 * units.GiB)) drv._get_available_capacity(self.TEST_EXPORT2).\ AndReturn((0, 10 * units.GiB)) mox.ReplayAll() self.assertRaises(exception.GlusterfsNoSuitableShareFound, drv._find_share, self.TEST_SIZE_IN_GB) mox.VerifyAll() def _simple_volume(self, id=None): volume = DumbVolume() volume['provider_location'] = self.TEST_EXPORT1 if id is None: volume['id'] = self.VOLUME_UUID else: volume['id'] = id # volume['name'] mirrors format from db/sqlalchemy/models.py volume['name'] = 'volume-%s' % volume['id'] volume['size'] = 10 volume['status'] = 'available' return volume def test_create_sparsed_volume(self): mox = self._mox drv = self._driver volume = self._simple_volume() setattr(glusterfs.CONF, 'glusterfs_sparsed_volumes', True) mox.StubOutWithMock(drv, '_create_sparsed_file') mox.StubOutWithMock(drv, '_set_rw_permissions_for_all') drv._create_sparsed_file(IgnoreArg(), IgnoreArg()) drv._set_rw_permissions_for_all(IgnoreArg()) mox.ReplayAll() drv._do_create_volume(volume) mox.VerifyAll() delattr(glusterfs.CONF, 'glusterfs_sparsed_volumes') def test_create_nonsparsed_volume(self): mox = self._mox drv = self._driver volume = self._simple_volume() old_value = self._configuration.glusterfs_sparsed_volumes self._configuration.glusterfs_sparsed_volumes = False mox.StubOutWithMock(drv, '_create_regular_file') mox.StubOutWithMock(drv, '_set_rw_permissions_for_all') drv._create_regular_file(IgnoreArg(), IgnoreArg()) drv._set_rw_permissions_for_all(IgnoreArg()) mox.ReplayAll() drv._do_create_volume(volume) mox.VerifyAll() self._configuration.glusterfs_sparsed_volumes = old_value def test_create_qcow2_volume(self): (mox, drv) = self._mox, self._driver volume = self._simple_volume() old_value = self._configuration.glusterfs_qcow2_volumes self._configuration.glusterfs_qcow2_volumes = True mox.StubOutWithMock(drv, '_execute') hashed = drv._get_hash_str(volume['provider_location']) path = '%s/%s/volume-%s' % (self.TEST_MNT_POINT_BASE, hashed, self.VOLUME_UUID) drv._execute('qemu-img', 'create', '-f', 'qcow2', '-o', 'preallocation=metadata', path, str(volume['size'] * units.GiB), run_as_root=True) drv._execute('chmod', 'ugo+rw', path, run_as_root=True) mox.ReplayAll() drv._do_create_volume(volume) mox.VerifyAll() self._configuration.glusterfs_qcow2_volumes = old_value def test_create_volume_should_ensure_glusterfs_mounted(self): """create_volume ensures shares provided in config are mounted.""" mox = self._mox drv = self._driver self.stub_out_not_replaying(glusterfs, 'LOG') self.stub_out_not_replaying(drv, '_find_share') self.stub_out_not_replaying(drv, '_do_create_volume') mox.StubOutWithMock(drv, '_ensure_shares_mounted') drv._ensure_shares_mounted() mox.ReplayAll() volume = DumbVolume() volume['size'] = self.TEST_SIZE_IN_GB drv.create_volume(volume) mox.VerifyAll() def test_create_volume_should_return_provider_location(self): """create_volume should return provider_location with found share.""" mox = self._mox drv = self._driver self.stub_out_not_replaying(glusterfs, 'LOG') self.stub_out_not_replaying(drv, '_ensure_shares_mounted') self.stub_out_not_replaying(drv, '_do_create_volume') mox.StubOutWithMock(drv, '_find_share') drv._find_share(self.TEST_SIZE_IN_GB).AndReturn(self.TEST_EXPORT1) mox.ReplayAll() volume = DumbVolume() volume['size'] = self.TEST_SIZE_IN_GB result = drv.create_volume(volume) self.assertEqual(self.TEST_EXPORT1, result['provider_location']) mox.VerifyAll() def test_create_cloned_volume(self): (mox, drv) = self._mox, self._driver mox.StubOutWithMock(drv, '_create_snapshot') mox.StubOutWithMock(drv, '_delete_snapshot') mox.StubOutWithMock(drv, '_read_info_file') mox.StubOutWithMock(image_utils, 'convert_image') mox.StubOutWithMock(drv, '_copy_volume_from_snapshot') volume_file = 'volume-%s' % self.VOLUME_UUID volume_path = '%s/%s/%s' % (self.TEST_MNT_POINT_BASE, drv._get_hash_str(self.TEST_EXPORT1), volume_file) volume = self._simple_volume() src_vref = self._simple_volume() src_vref['id'] = '375e32b2-804a-49f2-b282-85d1d5a5b9e1' src_vref['name'] = 'volume-%s' % src_vref['id'] volume_file = 'volume-%s' % src_vref['id'] volume_path = '%s/%s/%s' % (self.TEST_MNT_POINT_BASE, drv._get_hash_str(self.TEST_EXPORT1), volume_file) src_info_path = '%s.info' % volume_path volume_ref = {'id': volume['id'], 'name': volume['name'], 'status': volume['status'], 'provider_location': volume['provider_location'], 'size': volume['size']} snap_ref = {'volume_name': src_vref['name'], 'name': 'clone-snap-%s' % src_vref['id'], 'size': src_vref['size'], 'volume_size': src_vref['size'], 'volume_id': src_vref['id'], 'id': 'tmp-snap-%s' % src_vref['id'], 'volume': src_vref} drv._create_snapshot(snap_ref) snap_info = {'active': volume_file, snap_ref['id']: volume_path + '-clone'} drv._read_info_file(src_info_path).AndReturn(snap_info) drv._copy_volume_from_snapshot(snap_ref, volume_ref, volume['size']) drv._delete_snapshot(mox_lib.IgnoreArg()) mox.ReplayAll() drv.create_cloned_volume(volume, src_vref) def test_delete_volume(self): """delete_volume simple test case.""" mox = self._mox drv = self._driver self.stub_out_not_replaying(drv, '_ensure_share_mounted') volume = DumbVolume() volume['name'] = 'volume-123' volume['provider_location'] = self.TEST_EXPORT1 mox.StubOutWithMock(drv, 'local_path') drv.local_path(volume).AndReturn(self.TEST_LOCAL_PATH) mox.StubOutWithMock(drv, '_execute') drv._execute('rm', '-f', self.TEST_LOCAL_PATH, run_as_root=True) mox.ReplayAll() drv.delete_volume(volume) mox.VerifyAll() def test_delete_should_ensure_share_mounted(self): """delete_volume should ensure that corresponding share is mounted.""" mox = self._mox drv = self._driver self.stub_out_not_replaying(drv, '_execute') volume = DumbVolume() volume['name'] = 'volume-123' volume['provider_location'] = self.TEST_EXPORT1 mox.StubOutWithMock(drv, '_ensure_share_mounted') drv._ensure_share_mounted(self.TEST_EXPORT1) mox.ReplayAll() drv.delete_volume(volume) mox.VerifyAll() def test_delete_should_not_delete_if_provider_location_not_provided(self): """delete_volume shouldn't delete if provider_location missed.""" mox = self._mox drv = self._driver self.stub_out_not_replaying(drv, '_ensure_share_mounted') volume = DumbVolume() volume['name'] = 'volume-123' volume['provider_location'] = None mox.StubOutWithMock(drv, '_execute') mox.ReplayAll() drv.delete_volume(volume) mox.VerifyAll() def test_create_snapshot(self): (mox, drv) = self._mox, self._driver self.stub_out_not_replaying(drv, '_ensure_share_mounted') mox.StubOutWithMock(drv, '_create_qcow2_snap_file') mox.StubOutWithMock(drv, '_read_info_file') mox.StubOutWithMock(drv, '_write_info_file') volume = self._simple_volume() snap_ref = {'name': 'test snap', 'volume_id': self.VOLUME_UUID, 'volume': volume, 'id': self.SNAP_UUID} mox.StubOutWithMock(drv, '_execute') vol_filename = 'volume-%s' % self.VOLUME_UUID snap_filename = '%s.%s' % (vol_filename, self.SNAP_UUID) hashed = drv._get_hash_str(self.TEST_EXPORT1) vol_path = '%s/%s/%s' % (self.TEST_MNT_POINT_BASE, hashed, vol_filename) snap_path = '%s.%s' % (vol_path, self.SNAP_UUID) info_path = '%s%s' % (vol_path, '.info') info_dict = {'active': vol_filename} drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(info_dict) drv._create_qcow2_snap_file(snap_ref, vol_filename, snap_path) qemu_img_info_output = ("""image: volume-%s file format: raw virtual size: 1.0G (1073741824 bytes) disk size: 152K """ % self.VOLUME_UUID, '') drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(info_dict) # SNAP_UUID_2 has been removed from dict. info_file_dict = {'active': 'volume-%s.%s' % (self.VOLUME_UUID, self.SNAP_UUID), self.SNAP_UUID: 'volume-%s.%s' % (self.VOLUME_UUID, self.SNAP_UUID)} drv._write_info_file(info_path, info_file_dict) mox.ReplayAll() drv.create_snapshot(snap_ref) mox.VerifyAll() def test_delete_snapshot_bottom(self): """Multiple snapshots exist. In this test, path (volume-<uuid>) is backed by snap_path (volume-<uuid>.<snap_uuid>) which is backed by snap_path_2 (volume-<uuid>.<snap_uuid_2>). Delete the snapshot identified by SNAP_UUID_2. Chain goes from (SNAP_UUID) (SNAP_UUID_2) volume-abc -> volume-abc.baca -> volume-abc.bebe to (SNAP_UUID) volume-abc -> volume-abc.baca """ (mox, drv) = self._mox, self._driver hashed = drv._get_hash_str(self.TEST_EXPORT1) volume_dir = os.path.join(self.TEST_MNT_POINT_BASE, hashed) volume_path = '%s/%s/volume-%s' % (self.TEST_MNT_POINT_BASE, hashed, self.VOLUME_UUID) volume_filename = 'volume-%s' % self.VOLUME_UUID snap_path = '%s.%s' % (volume_path, self.SNAP_UUID) snap_path_2 = '%s.%s' % (volume_path, self.SNAP_UUID_2) snap_file = '%s.%s' % (volume_filename, self.SNAP_UUID) snap_file_2 = '%s.%s' % (volume_filename, self.SNAP_UUID_2) info_path = '%s%s' % (volume_path, '.info') qemu_img_info_output = """image: volume-%s.%s file format: qcow2 virtual size: 1.0G (1073741824 bytes) disk size: 173K backing file: %s """ % (self.VOLUME_UUID, self.SNAP_UUID, volume_filename) mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(drv, '_read_file') mox.StubOutWithMock(drv, '_read_info_file') mox.StubOutWithMock(drv, '_get_backing_chain_for_path') mox.StubOutWithMock(drv, '_get_matching_backing_file') mox.StubOutWithMock(drv, '_write_info_file') mox.StubOutWithMock(drv, '_ensure_share_writable') mox.StubOutWithMock(image_utils, 'qemu_img_info') drv._ensure_share_writable(volume_dir) img_info = imageutils.QemuImgInfo(qemu_img_info_output) image_utils.qemu_img_info(snap_path_2).AndReturn(img_info) info_file_dict = {'active': snap_file_2, self.SNAP_UUID_2: snap_file_2, self.SNAP_UUID: snap_file} snap_ref = {'name': 'test snap', 'volume_id': self.VOLUME_UUID, 'volume': self._simple_volume(), 'id': self.SNAP_UUID_2} snap_path_2_chain = [{self.SNAP_UUID_2: snap_file_2}, {self.SNAP_UUID: snap_file}, {'active': snap_file_2}] snap_path_chain = [{self.SNAP_UUID: snap_file}, {'active': snap_file}] drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(info_file_dict) drv._execute('qemu-img', 'commit', snap_path_2, run_as_root=True) drv._execute('rm', '-f', snap_path_2, run_as_root=True) drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(info_file_dict) drv._read_info_file(info_path).AndReturn(info_file_dict) drv._write_info_file(info_path, info_file_dict) mox.ReplayAll() drv.delete_snapshot(snap_ref) mox.VerifyAll() def test_delete_snapshot_middle(self): """Multiple snapshots exist. In this test, path (volume-<uuid>) is backed by snap_path (volume-<uuid>.<snap_uuid>) which is backed by snap_path_2 (volume-<uuid>.<snap_uuid_2>). Delete the snapshot identified with SNAP_UUID. Chain goes from (SNAP_UUID) (SNAP_UUID_2) volume-abc -> volume-abc.baca -> volume-abc.bebe to (SNAP_UUID_2) volume-abc -> volume-abc.bebe """ (mox, drv) = self._mox, self._driver volume = self._simple_volume() hashed = drv._get_hash_str(self.TEST_EXPORT1) volume_file = 'volume-%s' % self.VOLUME_UUID volume_dir = os.path.join(self.TEST_MNT_POINT_BASE, hashed) volume_path = '%s/%s/%s' % (self.TEST_MNT_POINT_BASE, hashed, volume_file) info_path = '%s%s' % (volume_path, '.info') snap_path = '%s.%s' % (volume_path, self.SNAP_UUID) snap_file = 'volume-%s.%s' % (self.VOLUME_UUID, self.SNAP_UUID) snap_path_2 = '%s.%s' % (volume_path, self.SNAP_UUID_2) snap_file_2 = 'volume-%s.%s' % (self.VOLUME_UUID, self.SNAP_UUID_2) qemu_img_info_output_snap_2 = """image: volume-%s.%s file format: qcow2 virtual size: 1.0G (1073741824 bytes) disk size: 173K backing file: %s """ % (self.VOLUME_UUID, self.SNAP_UUID_2, 'volume-%s.%s' % (self.VOLUME_UUID, self.SNAP_UUID_2)) qemu_img_info_output_snap_1 = """image: volume-%s.%s file format: qcow2 virtual size: 1.0G (1073741824 bytes) disk size: 122K backing file: %s """ % (self.VOLUME_UUID, self.SNAP_UUID, 'volume-%s.%s' % (self.VOLUME_UUID, self.SNAP_UUID)) qemu_img_info_output = """image: volume-%s file format: qcow2 virtual size: 1.0G (1073741824 bytes) disk size: 175K """ % self.VOLUME_UUID mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(drv, '_read_info_file') mox.StubOutWithMock(drv, '_write_info_file') mox.StubOutWithMock(drv, '_get_backing_chain_for_path') mox.StubOutWithMock(drv, 'get_active_image_from_info') mox.StubOutWithMock(drv, '_ensure_share_writable') mox.StubOutWithMock(image_utils, 'qemu_img_info') info_file_dict = {self.SNAP_UUID_2: 'volume-%s.%s' % (self.VOLUME_UUID, self.SNAP_UUID_2), self.SNAP_UUID: 'volume-%s.%s' % (self.VOLUME_UUID, self.SNAP_UUID)} drv._ensure_share_writable(volume_dir) info_path = drv._local_path_volume(volume) + '.info' drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(info_file_dict) img_info = imageutils.QemuImgInfo(qemu_img_info_output_snap_1) image_utils.qemu_img_info(snap_path).AndReturn(img_info) snap_ref = {'name': '<NAME>', 'volume_id': self.VOLUME_UUID, 'volume': volume, 'id': self.SNAP_UUID} snap_path_chain = [{'filename': snap_file_2, 'backing-filename': snap_file}, {'filename': snap_file, 'backing-filename': volume_file}] drv.get_active_image_from_info(volume).AndReturn(snap_file_2) drv._get_backing_chain_for_path(volume, snap_path_2).\ AndReturn(snap_path_chain) drv._read_info_file(info_path).AndReturn(info_file_dict) drv._execute('qemu-img', 'commit', snap_path_2, run_as_root=True) drv._execute('rm', '-f', snap_path_2, run_as_root=True) drv._read_info_file(info_path).AndReturn(info_file_dict) drv._write_info_file(info_path, info_file_dict) mox.ReplayAll() drv.delete_snapshot(snap_ref) mox.VerifyAll() def test_delete_snapshot_not_in_info(self): """Snapshot not in info file / info file doesn't exist. Snapshot creation failed so nothing is on-disk. Driver should allow operation to succeed so the manager can remove the snapshot record. (Scenario: Snapshot object created in Cinder db but not on backing storage.) """ (mox, drv) = self._mox, self._driver hashed = drv._get_hash_str(self.TEST_EXPORT1) volume_dir = os.path.join(self.TEST_MNT_POINT_BASE, hashed) volume_filename = 'volume-%s' % self.VOLUME_UUID volume_path = os.path.join(volume_dir, volume_filename) info_path = '%s%s' % (volume_path, '.info') mox.StubOutWithMock(drv, '_read_file') mox.StubOutWithMock(drv, '_read_info_file') mox.StubOutWithMock(drv, '_ensure_share_writable') snap_ref = {'name': 'test snap', 'volume_id': self.VOLUME_UUID, 'volume': self._simple_volume(), 'id': self.SNAP_UUID_2} drv._ensure_share_writable(volume_dir) drv._read_info_file(info_path, empty_if_missing=True).AndReturn({}) mox.ReplayAll() drv.delete_snapshot(snap_ref) mox.VerifyAll() def test_read_info_file(self): (mox, drv) = self._mox, self._driver mox.StubOutWithMock(drv, '_read_file') hashed = drv._get_hash_str(self.TEST_EXPORT1) volume_path = '%s/%s/volume-%s' % (self.TEST_MNT_POINT_BASE, hashed, self.VOLUME_UUID) info_path = '%s%s' % (volume_path, '.info') drv._read_file(info_path).AndReturn('{"%(id)s": "volume-%(id)s"}' % {'id': self.VOLUME_UUID}) mox.ReplayAll() volume = DumbVolume() volume['id'] = self.VOLUME_UUID volume['name'] = 'volume-%s' % self.VOLUME_UUID info = drv._read_info_file(info_path) self.assertEqual(info[self.VOLUME_UUID], 'volume-%s' % self.VOLUME_UUID) mox.VerifyAll() def test_extend_volume(self): (mox, drv) = self._mox, self._driver volume = self._simple_volume() volume_path = '%s/%s/volume-%s' % (self.TEST_MNT_POINT_BASE, drv._get_hash_str( self.TEST_EXPORT1), self.VOLUME_UUID) qemu_img_info_output = """image: volume-%s file format: qcow2 virtual size: 1.0G (1073741824 bytes) disk size: 473K """ % self.VOLUME_UUID img_info = imageutils.QemuImgInfo(qemu_img_info_output) mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(drv, 'get_active_image_from_info') mox.StubOutWithMock(image_utils, 'qemu_img_info') mox.StubOutWithMock(image_utils, 'resize_image') drv.get_active_image_from_info(volume).AndReturn(volume['name']) image_utils.qemu_img_info(volume_path).AndReturn(img_info) image_utils.resize_image(volume_path, 3) mox.ReplayAll() drv.extend_volume(volume, 3) mox.VerifyAll() def test_create_snapshot_online(self): (mox, drv) = self._mox, self._driver volume = self._simple_volume() volume['status'] = 'in-use' hashed = drv._get_hash_str(self.TEST_EXPORT1) volume_file = 'volume-%s' % self.VOLUME_UUID volume_path = '%s/%s/%s' % (self.TEST_MNT_POINT_BASE, hashed, volume_file) info_path = '%s.info' % volume_path ctxt = context.RequestContext('fake_user', 'fake_project') snap_ref = {'name': 'test snap (online)', 'volume_id': self.VOLUME_UUID, 'volume': volume, 'id': self.SNAP_UUID, 'context': ctxt, 'status': 'asdf', 'progress': 'asdf'} snap_path = '%s.%s' % (volume_path, self.SNAP_UUID) snap_file = '%s.%s' % (volume_file, self.SNAP_UUID) mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(drv, '_create_qcow2_snap_file') mox.StubOutWithMock(db, 'snapshot_get') mox.StubOutWithMock(drv, '_write_info_file') mox.StubOutWithMock(drv, '_nova') drv._create_qcow2_snap_file(snap_ref, volume_file, snap_path) create_info = {'snapshot_id': snap_ref['id'], 'type': 'qcow2', 'new_file': snap_file} drv._nova.create_volume_snapshot(ctxt, self.VOLUME_UUID, create_info) snap_ref['status'] = 'creating' snap_ref['progress'] = '0%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_ref['progress'] = '50%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_ref['progress'] = '90%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_info = {'active': snap_file, self.SNAP_UUID: snap_file} drv._write_info_file(info_path, snap_info) mox.ReplayAll() drv.create_snapshot(snap_ref) def test_create_snapshot_online_novafailure(self): (mox, drv) = self._mox, self._driver volume = self._simple_volume() volume['status'] = 'in-use' hashed = drv._get_hash_str(self.TEST_EXPORT1) volume_file = 'volume-%s' % self.VOLUME_UUID volume_path = '%s/%s/%s' % (self.TEST_MNT_POINT_BASE, hashed, volume_file) info_path = '%s.info' % volume_path ctxt = context.RequestContext('fake_user', 'fake_project') snap_ref = {'name': 'test snap (online)', 'volume_id': self.VOLUME_UUID, 'volume': volume, 'id': self.SNAP_UUID, 'context': ctxt} snap_path = '%s.%s' % (volume_path, self.SNAP_UUID) snap_file = '%s.%s' % (volume_file, self.SNAP_UUID) mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(drv, '_create_qcow2_snap_file') mox.StubOutWithMock(drv, '_nova') mox.StubOutWithMock(db, 'snapshot_get') mox.StubOutWithMock(drv, '_write_info_file') drv._create_qcow2_snap_file(snap_ref, volume_file, snap_path) create_info = {'snapshot_id': snap_ref['id'], 'type': 'qcow2', 'new_file': snap_file} drv._nova.create_volume_snapshot(ctxt, self.VOLUME_UUID, create_info) snap_ref['status'] = 'creating' snap_ref['progress'] = '0%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_ref['progress'] = '50%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_ref['progress'] = '99%' snap_ref['status'] = 'error' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_info = {'active': snap_file, self.SNAP_UUID: snap_file} drv._write_info_file(info_path, snap_info) mox.ReplayAll() self.assertRaises(exception.GlusterfsException, drv.create_snapshot, snap_ref) def test_delete_snapshot_online_1(self): """Delete the newest snapshot, with only one snap present.""" (mox, drv) = self._mox, self._driver volume = self._simple_volume() volume['status'] = 'in-use' ctxt = context.RequestContext('fake_user', 'fake_project') snap_ref = {'name': 'test snap to delete (online)', 'volume_id': self.VOLUME_UUID, 'volume': volume, 'id': self.SNAP_UUID, 'context': ctxt} hashed = drv._get_hash_str(self.TEST_EXPORT1) volume_file = 'volume-%s' % self.VOLUME_UUID volume_dir = os.path.join(self.TEST_MNT_POINT_BASE, hashed) volume_path = '%s/%s/%s' % (self.TEST_MNT_POINT_BASE, hashed, volume_file) info_path = '%s.info' % volume_path snap_path = '%s.%s' % (volume_path, self.SNAP_UUID) snap_file = '%s.%s' % (volume_file, self.SNAP_UUID) mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(drv, '_nova') mox.StubOutWithMock(drv, '_read_info_file') mox.StubOutWithMock(drv, '_write_info_file') mox.StubOutWithMock(os.path, 'exists') mox.StubOutWithMock(db, 'snapshot_get') mox.StubOutWithMock(image_utils, 'qemu_img_info') mox.StubOutWithMock(drv, '_ensure_share_writable') snap_info = {'active': snap_file, self.SNAP_UUID: snap_file} drv._ensure_share_writable(volume_dir) drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(snap_info) os.path.exists(snap_path).AndReturn(True) qemu_img_info_output = """image: %s file format: qcow2 virtual size: 1.0G (1073741824 bytes) disk size: 173K backing file: %s """ % (snap_file, volume_file) img_info = imageutils.QemuImgInfo(qemu_img_info_output) vol_qemu_img_info_output = """image: %s file format: raw virtual size: 1.0G (1073741824 bytes) disk size: 173K """ % volume_file volume_img_info = imageutils.QemuImgInfo(vol_qemu_img_info_output) image_utils.qemu_img_info(snap_path).AndReturn(img_info) image_utils.qemu_img_info(volume_path).AndReturn(volume_img_info) drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(snap_info) delete_info = { 'type': 'qcow2', 'merge_target_file': None, 'file_to_merge': None, 'volume_id': self.VOLUME_UUID } drv._nova.delete_volume_snapshot(ctxt, self.SNAP_UUID, delete_info) drv._read_info_file(info_path).AndReturn(snap_info) drv._read_info_file(info_path).AndReturn(snap_info) snap_ref['status'] = 'deleting' snap_ref['progress'] = '0%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_ref['progress'] = '50%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_ref['progress'] = '90%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) drv._write_info_file(info_path, snap_info) drv._execute('rm', '-f', volume_path, run_as_root=True) mox.ReplayAll() drv.delete_snapshot(snap_ref) def test_delete_snapshot_online_2(self): """Delete the middle of 3 snapshots.""" (mox, drv) = self._mox, self._driver volume = self._simple_volume() volume['status'] = 'in-use' ctxt = context.RequestContext('fake_user', 'fake_project') snap_ref = {'name': 'test snap to delete (online)', 'volume_id': self.VOLUME_UUID, 'volume': volume, 'id': self.SNAP_UUID, 'context': ctxt} hashed = drv._get_hash_str(self.TEST_EXPORT1) volume_file = 'volume-%s' % self.VOLUME_UUID volume_dir = os.path.join(self.TEST_MNT_POINT_BASE, hashed) volume_path = '%s/%s/%s' % (self.TEST_MNT_POINT_BASE, hashed, volume_file) info_path = '%s.info' % volume_path snap_path = '%s.%s' % (volume_path, self.SNAP_UUID) snap_path_2 = '%s.%s' % (volume_path, self.SNAP_UUID_2) snap_file = '%s.%s' % (volume_file, self.SNAP_UUID) snap_file_2 = '%s.%s' % (volume_file, self.SNAP_UUID_2) mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(drv, '_nova') mox.StubOutWithMock(drv, '_read_info_file') mox.StubOutWithMock(drv, '_write_info_file') mox.StubOutWithMock(os.path, 'exists') mox.StubOutWithMock(db, 'snapshot_get') mox.StubOutWithMock(image_utils, 'qemu_img_info') mox.StubOutWithMock(drv, '_ensure_share_writable') snap_info = {'active': snap_file_2, self.SNAP_UUID: snap_file, self.SNAP_UUID_2: snap_file_2} drv._ensure_share_writable(volume_dir) drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(snap_info) os.path.exists(snap_path).AndReturn(True) qemu_img_info_output = """image: %s file format: qcow2 virtual size: 1.0G (1073741824 bytes) disk size: 173K backing file: %s """ % (snap_file, volume_file) img_info = imageutils.QemuImgInfo(qemu_img_info_output) vol_qemu_img_info_output = """image: %s file format: raw virtual size: 1.0G (1073741824 bytes) disk size: 173K """ % volume_file volume_img_info = imageutils.QemuImgInfo(vol_qemu_img_info_output) image_utils.qemu_img_info(snap_path).AndReturn(img_info) image_utils.qemu_img_info(volume_path).AndReturn(volume_img_info) drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(snap_info) delete_info = {'type': 'qcow2', 'merge_target_file': volume_file, 'file_to_merge': snap_file, 'volume_id': self.VOLUME_UUID} drv._nova.delete_volume_snapshot(ctxt, self.SNAP_UUID, delete_info) drv._read_info_file(info_path).AndReturn(snap_info) drv._read_info_file(info_path).AndReturn(snap_info) snap_ref['status'] = 'deleting' snap_ref['progress'] = '0%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_ref['progress'] = '50%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_ref['progress'] = '90%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) drv._write_info_file(info_path, snap_info) drv._execute('rm', '-f', snap_path, run_as_root=True) mox.ReplayAll() drv.delete_snapshot(snap_ref) def test_delete_snapshot_online_novafailure(self): """Delete the newest snapshot.""" (mox, drv) = self._mox, self._driver volume = self._simple_volume() volume['status'] = 'in-use' ctxt = context.RequestContext('fake_user', 'fake_project') snap_ref = {'name': 'test snap to delete (online)', 'volume_id': self.VOLUME_UUID, 'volume': volume, 'id': self.SNAP_UUID, 'context': ctxt} hashed = drv._get_hash_str(self.TEST_EXPORT1) volume_file = 'volume-%s' % self.VOLUME_UUID volume_path = '%s/%s/%s' % (self.TEST_MNT_POINT_BASE, hashed, volume_file) info_path = '%s.info' % volume_path snap_path = '%s.%s' % (volume_path, self.SNAP_UUID) snap_file = '%s.%s' % (volume_file, self.SNAP_UUID) mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(drv, '_nova') mox.StubOutWithMock(drv, '_read_info_file') mox.StubOutWithMock(drv, '_write_info_file') mox.StubOutWithMock(os.path, 'exists') mox.StubOutWithMock(db, 'snapshot_get') mox.StubOutWithMock(image_utils, 'qemu_img_info') snap_info = {'active': snap_file, self.SNAP_UUID: snap_file} drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(snap_info) os.path.exists(snap_path).AndReturn(True) qemu_img_info_output = """image: %s file format: qcow2 virtual size: 1.0G (1073741824 bytes) disk size: 173K backing file: %s """ % (snap_file, volume_file) img_info = imageutils.QemuImgInfo(qemu_img_info_output) image_utils.qemu_img_info(snap_path).AndReturn(img_info) drv._read_info_file(info_path, empty_if_missing=True).\ AndReturn(snap_info) delete_info = { 'type': 'qcow2', 'merge_target_file': None, 'file_to_merge': volume_file, 'volume_id': self.VOLUME_UUID } drv._nova.delete_volume_snapshot(ctxt, self.SNAP_UUID, delete_info) drv._read_info_file(info_path).AndReturn(snap_info) drv._read_info_file(info_path).AndReturn(snap_info) snap_ref['status'] = 'deleting' snap_ref['progress'] = '0%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_ref['progress'] = '50%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) snap_ref['status'] = 'error_deleting' snap_ref['progress'] = '90%' db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) db.snapshot_get(ctxt, self.SNAP_UUID).AndReturn(snap_ref) drv._write_info_file(info_path, snap_info) drv._execute('rm', '-f', volume_path, run_as_root=True) mox.ReplayAll() self.assertRaises(exception.GlusterfsException, drv.delete_snapshot, snap_ref) def test_get_backing_chain_for_path(self): (mox, drv) = self._mox, self._driver glusterfs.CONF.glusterfs_mount_point_base = self.TEST_MNT_POINT_BASE volume = self._simple_volume() vol_filename = volume['name'] vol_filename_2 = volume['name'] + '.asdfjkl' vol_filename_3 = volume['name'] + 'qwertyuiop' hashed = drv._get_hash_str(self.TEST_EXPORT1) vol_dir = '%s/%s' % (self.TEST_MNT_POINT_BASE, hashed) vol_path = '%s/%s' % (vol_dir, vol_filename) vol_path_2 = '%s/%s' % (vol_dir, vol_filename_2) vol_path_3 = '%s/%s' % (vol_dir, vol_filename_3) mox.StubOutWithMock(drv, '_execute') mox.StubOutWithMock(drv, '_local_volume_dir') mox.StubOutWithMock(image_utils, 'qemu_img_info') qemu_img_output_base = """image: %(image_name)s file format: qcow2 virtual size: 1.0G (1073741824 bytes) disk size: 173K """ qemu_img_output = """image: %(image_name)s file format: qcow2 virtual size: 1.0G (1073741824 bytes) disk size: 173K backing file: %(backing_file)s """ qemu_img_output_1 = qemu_img_output_base % {'image_name': vol_filename} qemu_img_output_2 = qemu_img_output % {'image_name': vol_filename_2, 'backing_file': vol_filename} qemu_img_output_3 = qemu_img_output % {'image_name': vol_filename_3, 'backing_file': vol_filename_2} info_1 = imageutils.QemuImgInfo(qemu_img_output_1) info_2 = imageutils.QemuImgInfo(qemu_img_output_2) info_3 = imageutils.QemuImgInfo(qemu_img_output_3) drv._local_volume_dir(volume).AndReturn(vol_dir) image_utils.qemu_img_info(vol_path_3).\ AndReturn(info_3) drv._local_volume_dir(volume).AndReturn(vol_dir) image_utils.qemu_img_info(vol_path_2).\ AndReturn(info_2) drv._local_volume_dir(volume).AndReturn(vol_dir) image_utils.qemu_img_info(vol_path).\ AndReturn(info_1) mox.ReplayAll() chain = drv._get_backing_chain_for_path(volume, vol_path_3) # Verify chain contains all expected data item_1 = drv._get_matching_backing_file(chain, vol_filename) self.assertEqual(item_1['filename'], vol_filename_2) chain.remove(item_1) item_2 = drv._get_matching_backing_file(chain, vol_filename_2) self.assertEqual(item_2['filename'], vol_filename_3) chain.remove(item_2) self.assertEqual(len(chain), 1) self.assertEqual(chain[0]['filename'], vol_filename) def test_copy_volume_from_snapshot(self): (mox, drv) = self._mox, self._driver mox.StubOutWithMock(image_utils, 'convert_image') mox.StubOutWithMock(drv, '_read_info_file') mox.StubOutWithMock(image_utils, 'qemu_img_info') mox.StubOutWithMock(drv, '_set_rw_permissions_for_all') dest_volume = self._simple_volume( 'c1073000-0000-0000-0000-0000000c1073') src_volume = self._simple_volume() vol_dir = os.path.join(self.TEST_MNT_POINT_BASE, drv._get_hash_str(self.TEST_EXPORT1)) src_vol_path = os.path.join(vol_dir, src_volume['name']) dest_vol_path = os.path.join(vol_dir, dest_volume['name']) info_path = os.path.join(vol_dir, src_volume['name']) + '.info' snapshot = {'volume_name': src_volume['name'], 'name': 'clone-snap-%s' % src_volume['id'], 'size': src_volume['size'], 'volume_size': src_volume['size'], 'volume_id': src_volume['id'], 'id': 'tmp-snap-%s' % src_volume['id'], 'volume': src_volume} snap_file = dest_volume['name'] + '.' + snapshot['id'] snap_path = os.path.join(vol_dir, snap_file) size = dest_volume['size'] drv._read_info_file(info_path).AndReturn( {'active': snap_file, snapshot['id']: snap_file} ) qemu_img_output = """image: %s file format: raw virtual size: 1.0G (1073741824 bytes) disk size: 173K backing file: %s """ % (snap_file, src_volume['name']) img_info = imageutils.QemuImgInfo(qemu_img_output) image_utils.qemu_img_info(snap_path).AndReturn(img_info) image_utils.convert_image(src_vol_path, dest_vol_path, 'raw') drv._set_rw_permissions_for_all(dest_vol_path) mox.ReplayAll() drv._copy_volume_from_snapshot(snapshot, dest_volume, size) def test_create_volume_from_snapshot(self): (mox, drv) = self._mox, self._driver volume = self._simple_volume('c1073000-0000-0000-0000-0000000c1073') src_volume = self._simple_volume() mox.StubOutWithMock(drv, '_create_snapshot') mox.StubOutWithMock(drv, '_copy_volume_from_snapshot') mox.StubOutWithMock(drv, '_delete_snapshot') snap_ref = {'volume_name': src_volume['name'], 'name': 'clone-snap-%s' % src_volume['id'], 'size': src_volume['size'], 'volume_size': src_volume['size'], 'volume_id': src_volume['id'], 'id': 'tmp-snap-%s' % src_volume['id'], 'volume': src_volume} volume_ref = {'id': volume['id'], 'size': volume['size'], 'status': volume['status'], 'provider_location': volume['provider_location'], 'name': 'volume-' + volume['id']} drv._create_snapshot(snap_ref) drv._copy_volume_from_snapshot(snap_ref, volume_ref, src_volume['size']) drv._delete_snapshot(snap_ref) mox.ReplayAll() drv.create_cloned_volume(volume, src_volume) def test_initialize_connection(self): (mox, drv) = self._mox, self._driver volume = self._simple_volume() vol_dir = os.path.join(self.TEST_MNT_POINT_BASE, drv._get_hash_str(self.TEST_EXPORT1)) vol_path = os.path.join(vol_dir, volume['name']) qemu_img_output = """image: %s file format: raw virtual size: 1.0G (1073741824 bytes) disk size: 173K """ % volume['name'] img_info = imageutils.QemuImgInfo(qemu_img_output) mox.StubOutWithMock(drv, 'get_active_image_from_info') mox.StubOutWithMock(image_utils, 'qemu_img_info') drv.get_active_image_from_info(volume).AndReturn(volume['name']) image_utils.qemu_img_info(vol_path).AndReturn(img_info) mox.ReplayAll() conn_info = drv.initialize_connection(volume, None) self.assertEqual(conn_info['data']['format'], 'raw') self.assertEqual(conn_info['driver_volume_type'], 'glusterfs') self.assertEqual(conn_info['data']['name'], volume['name'])
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Python/multiscale_quad_retrieval.py
Spectavi/video-diff
1
102963
<reponame>Spectavi/video-diff<gh_stars>1-10 import math import numpy as np import cv2 import common import config import findquads import spatial_consistency import Matlab FILTER = True USE_GPS_COORDINATES = False if config.USE_MULTITHREADING: import multiprocessing class Globals: r_quadsTree = None r_harlocs = None q_harlocs = None md_threshold = None st_threshold = None all_ori = None all_id = None all_max = None all_cen = None nos = None scale_index = None crop_flag = None sequence = None rd_start = None rd_end = None maxdis = None maxori = None tolers = None g = Globals() """ When parallelizing multiscale_quad_retrieval(), we will obtain slightly different results for crossref (etc) - see crossref.txt. Note that when running in serial (without a POOL), we obtain the same result ALWAYS. (less relevant: if we run only 1 process in pool instead of 3, we get fewer changes in crossref.txt - NOT sure if this means something). It appears the reason is the fact I am running the FLANN KD-tree implementation, which is an approximate NN search library, employing randomization (see http://answers.opencv.org/question/32664/flannbasedmatcher-returning-different-results/). I guess the random number sequence, when having the same seed, evolves differently when serial VS in parallel threads, so the results tend to be different. """ def iteration_standalone_mqr(query_frame): r_quads_tree = g.r_quadsTree r_harlocs = g.r_harlocs q_harlocs = g.q_harlocs md_threshold = g.md_threshold st_threshold = g.st_threshold all_ori = g.all_ori all_id = g.all_id all_max = g.all_max all_cen = g.all_cen nos = g.nos scale_index = g.scale_index crop_flag = g.crop_flag sequence = g.sequence rd_start = g.rd_start rd_end = g.rd_end maxdis = g.maxdis maxori = g.maxori tolers = g.tolers """ We make pp reference the desired multiharloc list for the query video frame query_frame """ pp = q_harlocs[query_frame] """ Alex: for the query frame query_frame we retrieve, for scale scale_index, the harris features in var points. Then we build the quads from points. Then for each quad (4 float values) we query the corresponding scale kd-tree, and we get the indices. Then we build the histogram and compute idf, ....!!!! Note: scale is 1 for original frame resolution and the higher we go we have lower image resolutions (we go higher in the Guassian pyramid I think). """ points = pp[pp[:, 2] == scale_index, 0:2] qout, qcen, qmaxdis, qori = findquads.findquads(points, md_threshold, 0) common.DebugPrint("multiscale_quad_retrieval(): query_frame = %d, " "qout.shape = %s" % (query_frame, str(qout.shape))) space_xy = np.zeros((qcen.shape[0], 2 * len(r_harlocs))) + np.nan votes = np.zeros((len(r_harlocs), 1)) assert isinstance(tolers, float) """ We substitute queryFrameQuad - 1 with queryFrameQuad, since we want to number arrays from 0 (not from 1 like in Matlab). """ for queryFrameQuad in range(qout.shape[0]): """ Matlab's polymorphism is really bugging here: although it's normally a float, tolers is considered to be a size 1 vector... so len(tolers) == 1 """ """ We substitute tol_i - 1 with tol, since we want to number arrays from 0 (not from 1 like in Matlab). """ for tol_i in range(1): tol = tolers # default for first PAMI with tol= 0.1 approximately # NOTE: SciPy's KDTree finds a few more results, in some cases, # than the Matlab code from Evangelidis. # tol is a scalar representing the radius of the ball if config.KDTREE_IMPLEMENTATION == 0: idx = r_quads_tree.query_ball_point(qout[queryFrameQuad, :], tol) elif config.KDTREE_IMPLEMENTATION == 1: pt = qout[queryFrameQuad, :] pt = np.array([[pt[0], pt[1], pt[2], pt[3]]], dtype=np.float32) retval, idx, dists = r_quads_tree.radiusSearch( query=pt, radius=(tol ** 2), maxResults=NUM_MAX_ELEMS, params=search_params) if common.MY_DEBUG_STDOUT: common.DebugPrint( "multiscale_quad_retrieval(): radiusSearch's retval " "(at query_frame=%d, queryFrameQuad=%d) is %d\n" % ( query_frame, queryFrameQuad, retval)) idx = idx[0] dists = dists[0] idx = idx[: retval] dists = dists[: retval] if common.MY_DEBUG_STDOUT: print("multiscale_quad_retrieval(): " "qout[queryFrameQuad, :] = %s" % str( qout[queryFrameQuad, :])) print("multiscale_quad_retrieval(): " "idx = %s" % str(idx)) print("multiscale_quad_retrieval(): " "tol = %s" % str(tol)) if config.KDTREE_IMPLEMENTATION == 0: print("multiscale_quad_retrieval(): " "r_quads_tree.data[idx] = %s" % str(r_quads_tree.data[idx])) # We print the distances to the points returned in idx a = qout[queryFrameQuad, :] idx = np.array(idx) if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "all_max.shape = %s" % str(all_max.shape)) common.DebugPrint("multiscale_quad_retrieval(): " "qmaxdis.shape = %s" % str(qmaxdis.shape)) common.DebugPrint("multiscale_quad_retrieval(): " "qmaxdis = %s" % str(qmaxdis)) common.DebugPrint("multiscale_quad_retrieval(): " "qori.shape = %s" % str(qori.shape)) common.DebugPrint("multiscale_quad_retrieval(): " "qori = %s" % str(qori)) if len(idx) == 0: # NOT A GOOD IDEA: continue # idx = np.array([]) dis_idx = np.array([]) ori_idx = np.array([]) else: if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "queryFrameQuad = %s" % str( queryFrameQuad)) common.DebugPrint("multiscale_quad_retrieval(): " "all_max[idx] = %s" % str(all_max[idx])) common.DebugPrint("multiscale_quad_retrieval(): " "qmaxdis[queryFrameQuad] = %s" % str( qmaxdis[queryFrameQuad])) dis_idx = np.abs( qmaxdis[queryFrameQuad] - all_max[idx]) < maxdis if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "dis_idx = %s" % str(dis_idx)) idx = idx[dis_idx] if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "idx (after idx = idx[dis_idx]) = " "%s" % str(idx)) ori_idx = np.abs(qori[queryFrameQuad] - all_ori[idx]) < maxori if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "ori_idx = %s" % str(ori_idx)) idx = idx[ori_idx] # IMPORTANT ################################################### # IMPORTANT ################################################### # IMPORTANT ################################################### # spatio-temporal consistency # IMPORTANT ################################################### # IMPORTANT ################################################### # IMPORTANT ################################################### if idx.size > 0: # Normally crop_flag == 0 if crop_flag == 0: dy = qcen[queryFrameQuad, 0] - all_cen[idx, 0] dx = qcen[queryFrameQuad, 1] - all_cen[idx, 1] D = dy ** 2 + dx ** 2 co_idx = D < pow(st_threshold, 2) idx = idx[co_idx] else: """ We substitute iii - 1 with iii, since we want to number arrays from 0 (not from 1 like in Matlab). """ for iii in range(len(idx)): space_xy[queryFrameQuad, (all_id[idx[iii]] - rd_start) * 2: (all_id[idx[ iii] - 1] - rd_start) * 2 + 1] = \ all_cen[idx[iii], :] # It has to be an np.array because we multiply it with a scalar histo_range = np.array(range(rd_start, rd_end + 1)) hh = Matlab.hist(x=all_id[idx], binCenters=histo_range) if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "hh = %s" % (str(hh))) common.DebugPrint("multiscale_quad_retrieval(): " "hh.shape = %s" % (str(hh.shape))) common.DebugPrint("multiscale_quad_retrieval(): " "all_id.shape = %s" % (str(all_id.shape))) common.DebugPrint("multiscale_quad_retrieval(): " "idx = %s" % (str(idx))) common.DebugPrint("multiscale_quad_retrieval(): " "idx.shape = %s" % (str(idx.shape))) # nz can be computed more optimally # nz=find(hh~=0) # nz can be computed more optimally # np.nonzero() always returns a tuple, even if it contains # 1 element since hh has only 1 dimension nz = np.nonzero(hh != 0)[0] if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "nz = %s" % (str(nz))) common.DebugPrint("multiscale_quad_retrieval(): " "nz.shape = %s" % (str(nz.shape))) if nz.size > 0: my_val = pow(math.log10(float(len(r_harlocs)) / len(nz)), 2) if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "len(r_harlocs) = " "%d" % len(r_harlocs)) common.DebugPrint("multiscale_quad_retrieval(): " "len(nz) = %d" % len(nz)) common.DebugPrint("multiscale_quad_retrieval(): " "my_val = %.5f" % my_val) # PREVIOUSLY votes[nz, tol_i] = votes[nz, tol_i] + my_val if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "votes.shape = %s" % (str(votes.shape))) common.DebugPrint("multiscale_quad_retrieval(): " "votes = %s" % (str(votes))) return query_frame, np.ravel(votes) """ From http://www.mathworks.com/help/matlab/matlab_prog/symbol-reference.html: Dot-Dot-Dot (Ellipsis) - ... A series of three consecutive periods (...) is the line continuation operator in MATLAB. Line Continuation Continue any MATLAB command or expression by placing an ellipsis at the end of the line to be continued: """ NUM_MAX_ELEMS = 100000 # Gives fewer results than scipy's tree.query_ball_point when we have 65K # features search_params = dict(checks=1000000000) # returns Votes_space, HH # Alex: r_harlocs and q_harlocs are the corresponding lists of harlocs computed """ md_threshold = max-distance threshold used to build quads out of Harris features st_threshold = threshold value for spatio-temporal consistency (coherence) all_ori, all_id, all_max, all_cen = orientation, reference frame ids, max distances, respectively centroids coordinates of each reference quad for scale scale_index """ def multiscale_quad_retrieval(r_quads_tree, r_harlocs, q_harlocs, md_threshold, st_threshold, all_ori, all_id, all_max, all_cen, nos, scale_index, crop_flag, sequence): common.DebugPrint("Entered multiscale_quad_retrieval(): " "md_threshold = %s, st_threshold = %s." % (str(md_threshold), str(st_threshold))) assert len(r_harlocs) != 0 assert len(q_harlocs) != 0 try: votes_space = np.load("votes_space%d.npz" % scale_index)['arr_0'] HH = np.load("HH%d.npz" % scale_index)['arr_0'] return votes_space, HH except: common.DebugPrintErrorTrace() if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): r_quads_tree = %s" % str(r_quads_tree)) common.DebugPrint( "multiscale_quad_retrieval(): len(r_harlocs) = %d" % len(r_harlocs)) common.DebugPrint( "multiscale_quad_retrieval(): r_harlocs = %s" % str(r_harlocs)) common.DebugPrint( "multiscale_quad_retrieval(): q_harlocs = %s" % str(q_harlocs)) common.DebugPrint( "multiscale_quad_retrieval(): md_threshold = %s" % str( md_threshold)) print("multiscale_quad_retrieval(): st_threshold = %s" % str( st_threshold)) common.DebugPrint( "multiscale_quad_retrieval(): all_id = %s" % str(all_id)) common.DebugPrint("multiscale_quad_retrieval(): all_id.shape = %s" % ( str(all_id.shape))) common.DebugPrint( "multiscale_quad_retrieval(): sequence = %s" % str(sequence)) print("multiscale_quad_retrieval(): crop_flag = %s" % str(crop_flag)) t1 = float(cv2.getTickCount()) if scale_index > nos: assert scale_index <= nos # TODO: take out rd_start rd_start = 0 rd_end = len(r_harlocs) - 1 j = 1 """ Inspired from https://stackoverflow.com/questions/17559140/matlab-twice-as-fast-as-numpy BUT doesn't help in this case: votes_space = np.asfortranarray(np.zeros( (len(RD), len(QD)) )) """ votes_space = np.zeros((len(r_harlocs), len(q_harlocs))) # Make a distinct copy of HH from votes_space... # TODO: use MAYBE even np.bool - OR take it out HH = np.zeros((len(r_harlocs), len(q_harlocs)), dtype=np.int8) # it helps to make more strict the threshold as the scale goes up tolers = 0.1 - float(scale_index) / 100.0 maxdis = 3 + scale_index maxori = 0.25 # TODO: I am using multiprocessing.Poll and return votes the dispatcher # assembles the results, but the results are NOT the same with the serial # case - although they look pretty decent, but they seem to be # suboptimal - dp_alex returns suboptimal cost path for # USE_MULTITHREADING == True instead of False. # (Note: running under the same preconditions # multiscale_quad_retrieval I got the same results in dp_alex(). """ if False: #config.USE_MULTITHREADING == True: global g g.r_quads_tree = r_quads_tree g.r_harlocs = r_harlocs g.q_harlocs = q_harlocs g.md_threshold = md_threshold g.st_threshold = st_threshold g.all_ori = all_ori g.all_id = all_id g.all_max = all_max g.all_cen = all_cen g.nos = nos g.scale_index = scale_index g.crop_flag = crop_flag g.sequence = sequence g.RD_start = RD_start g.RD_end = RD_end g.maxdis = maxdis g.maxori = maxori g.tolers = tolers #Start worker processes to use on multi-core processor (able to run # in parallel - no GIL issue if each core has it's own VM) pool = multiprocessing.Pool(processes=config.numProcesses) print("multiscale_quad_retrieval(): Spawned a pool of %d workers" % config.numProcesses) listParams = range(0, len(q_harlocs)) #!!!!TODO: use counterStep, config.initFrame[indexVideo] #res = pool.map(iteration_standalone_mqr, listParams) # See https://docs.python.org/2/library/multiprocessing.html#module-multiprocessing.pool res = pool.map(func=iteration_standalone_mqr, iterable=listParams, chunksize=1) print("Pool.map returns %s" % str(res)) #x0.size + 1 # From https://medium.com/building-things-on-the-internet/40e9b2b36148 # close the pool and wait for the work to finish pool.close() pool.join() # Doing the "reduce" phase after the workers have finished :) assert len(res) == len(q_harlocs) for query_frame, resE in enumerate(res): resEIndex = resE[0] resE = resE[1] assert resEIndex == query_frame # Gives: "ValueError: output operand requires a reduction, but reduction is not enabled" #votes_space[:, query_frame - 1] = votes votes_space[:, query_frame] = resE for query_frame in range(len(q_harlocs)): if crop_flag == 0: HH[:, query_frame] = 1 else: HH[:, query_frame] = spatial_consistency.spatial_consistency(space_xy, qcen, len(r_harlocs), st_threshold, crop_flag) try: np.savez_compressed("votes_space%d" % scale_index, votes_space) np.savez_compressed("HH%d" % scale_index, HH) except: common.DebugPrintErrorTrace() return votes_space, HH """ """ We substitute q - 1 with q, since we want to number arrays from 0 (not from 1 like in Matlab). """ for query_frame in range(len(q_harlocs)): common.DebugPrint("multiscale_quad_retrieval(): Starting iteration " "query_frame = %d" % query_frame) """ We make pp reference the desired multiharloc list for the query video frame query_frame """ pp = q_harlocs[query_frame] points = pp[pp[:, 2] == scale_index, 0:2] qout, qcen, qmaxdis, qori = findquads.findquads(points, md_threshold, 0) if common.MY_DEBUG_STDOUT: print("multiscale_quad_retrieval(): query_frame = %d, " "qout.shape (number of quads for query frame query_frame) = " "%s" % (query_frame, str(qout.shape))) space_xy = np.zeros((qcen.shape[0], 2 * len(r_harlocs))) + np.nan votes = np.zeros((len(r_harlocs), 1)) assert isinstance(tolers, float) if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): quads of query " "frame %d are: " % query_frame) common.DebugPrint(" qout = %s" % str(qout)) """ Alex: for each quad (4 floats) of the query frame from Harris feature of scale scale_index Note: all_id stores the reference frame id for each quad descriptor. """ """ We substitute queryFrameQuad - 1 with queryFrameQuad, since we want to number arrays from 0 (not from 1 like in Matlab). """ for queryFrameQuad in range(qout.shape[0]): common.DebugPrint("multiscale_quad_retrieval(): Starting iteration " "queryFrameQuad = %d" % queryFrameQuad) """ Matlab's polymorphism is really bugging here: although it's normally a float, tolers is considered to be a size 1 vector... so len(tolers) == 1 """ """ We substitute tol_i - 1 with tol, since we want to number arrays from 0 (not from 1 like in Matlab). """ for tol_i in range(1): tol = tolers # default for first PAMI with tol= 0.1 approximately # NOTE: SciPy's KDTree finds a few more results, in some cases, # than the Matlab code from Evangelidis. # tol is a scalar representing the radius of the ball if config.KDTREE_IMPLEMENTATION == 0: idx = r_quads_tree.query_ball_point(qout[queryFrameQuad, :], tol) elif config.KDTREE_IMPLEMENTATION == 1: pt = qout[queryFrameQuad, :] pt = np.array([[pt[0], pt[1], pt[2], pt[3]]], dtype=np.float32) retval, idx, dists = r_quads_tree.radiusSearch( query=pt, radius=(tol ** 2), maxResults=NUM_MAX_ELEMS, params=search_params) if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "radiusSearch's retval (at " "query_frame=%d, queryFrameQuad=%d) " "is %d" % (query_frame, queryFrameQuad, retval)) idx = idx[0] dists = dists[0] """ Note: retval is the number of neighbors returned from the radiusSearch(). But the idx and the dists can have more elements than the returned retval. """ idx = idx[: retval] dists = dists[: retval] if common.MY_DEBUG_STDOUT: print("multiscale_quad_retrieval(): " "qout[queryFrameQuad, :] = %s" % str(qout[queryFrameQuad, :])) print("multiscale_quad_retrieval(): " "idx = %s" % str(idx)) print("multiscale_quad_retrieval(): " "dists = %s" % str(dists)) print("multiscale_quad_retrieval(): " "tol = %s" % str(tol)) if config.KDTREE_IMPLEMENTATION == 0: print("multiscale_quad_retrieval(): " "r_quads_tree.data[idx] = %s" % str(r_quads_tree.data[idx])) if common.MY_DEBUG_STDOUT: a = qout[queryFrameQuad, :] if config.KDTREE_IMPLEMENTATION == 0: for myI, index in enumerate(idx): b = r_quads_tree.data[index] else: pass idx = np.array(idx) if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "all_max.shape = %s" % str(all_max.shape)) common.DebugPrint("multiscale_quad_retrieval(): " "qmaxdis.shape = %s" % str(qmaxdis.shape)) common.DebugPrint("multiscale_quad_retrieval(): " "qmaxdis = %s" % str(qmaxdis)) common.DebugPrint("multiscale_quad_retrieval(): " "qori.shape = %s" % str(qori.shape)) common.DebugPrint("multiscale_quad_retrieval(): " "qori = %s" % str(qori)) if len(idx) == 0: # NOT A GOOD IDEA: continue dis_idx = np.array([]) ori_idx = np.array([]) else: if common.MY_DEBUG_STDOUT: print("multiscale_quad_retrieval(): " "queryFrameQuad = %s" % str(queryFrameQuad)) print("multiscale_quad_retrieval(): " "all_max[idx] = %s" % str(all_max[idx])) print("multiscale_quad_retrieval(): " "qmaxdis[queryFrameQuad] = %s" % str(qmaxdis[queryFrameQuad])) if USE_GPS_COORDINATES: # We look only at a part of the reference video """ Since in some cases the video temporal alignment is difficult to do due to similar portions in the trajectory (see the drone videos, clip 3_some_lake) we "guide" the temporal alignment by restricting the reference frame search space - this is useful when we have the geolocation (GPS) coordinate for each frame. """ if common.MY_DEBUG_STDOUT: print("multiscale_quad_retrieval(): " "all_id = %s" % str(all_id)) if all_id.ndim == 2: # TODO: put this at the beginning of the # function assert all_id.shape[1] == 1 """ We flatten the array all_id Note: We don't use order="F" since it's basically 1-D array """ all_id = np.ravel(all_id) # TODO: put start and end frame in config - or compute # it from geolocation sub_idx = np.logical_and((all_id[idx] >= 2030 - 928), (all_id[idx] <= 2400 - 928)) idx = idx[sub_idx] if common.MY_DEBUG_STDOUT: print("multiscale_quad_retrieval(): " "all_id = %s" % str(all_id)) print("multiscale_quad_retrieval(): " "sub_idx = %s" % str(sub_idx)) print("multiscale_quad_retrieval(): " "idx = %s" % str(idx)) if FILTER: dis_idx = np.abs( qmaxdis[queryFrameQuad] - all_max[idx]) < maxdis if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "dis_idx = %s" % str(dis_idx)) idx = idx[dis_idx] if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "idx (after idx = idx[dis_idx]) = " "%s" % str(idx)) if FILTER: ori_idx = np.abs( qori[queryFrameQuad] - all_ori[idx]) < maxori if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "ori_idx = %s" % str(ori_idx)) idx = idx[ori_idx] # IMPORTANT ################################################### # IMPORTANT ################################################### # IMPORTANT ################################################### # spatio-temporal consistency # IMPORTANT ################################################### # IMPORTANT ################################################### # IMPORTANT ################################################### if idx.size > 0: if crop_flag == 0: if FILTER: """ Alex: this is a simple procedure of eliminating False Positive (FP) matches, as presented in Section 4.2 of TPAMI 2013 paper. Basically it filters out quad matches that have centroids st_threshold away from the query quad. Note: all_cen are the controids of all reference quads. """ dy = qcen[queryFrameQuad, 0] - all_cen[idx, 0] dx = qcen[queryFrameQuad, 1] - all_cen[idx, 1] D = dy ** 2 + dx ** 2 co_idx = D < pow(st_threshold, 2) idx = idx[co_idx] else: """ We substitute iii - 1 with iii, since we want to number arrays from 0 (not from 1 like in Matlab). """ for iii in range(len(idx)): space_xy[queryFrameQuad, (all_id[idx[iii]] - rd_start) * 2: (all_id[idx[ iii] - 1] - rd_start) * 2 + 1] = \ all_cen[idx[iii], :] # It has to be an np.array because we multiply it with a # scalar histo_range = np.array(range(rd_start, rd_end + 1)) hh = Matlab.hist(x=all_id[idx], binCenters=histo_range) if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "hh = %s" % (str(hh))) common.DebugPrint("multiscale_quad_retrieval(): " "hh.shape = %s" % (str(hh.shape))) common.DebugPrint("multiscale_quad_retrieval(): " "all_id.shape = %s" % ( str(all_id.shape))) common.DebugPrint("multiscale_quad_retrieval(): " "idx = %s" % (str(idx))) common.DebugPrint("multiscale_quad_retrieval(): " "idx.shape = %s" % (str(idx.shape))) # % nz can be computed more optimally nz = np.nonzero(hh != 0)[0] if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "nz = %s" % (str(nz))) common.DebugPrint("multiscale_quad_retrieval(): " "nz.shape = %s" % (str(nz.shape))) if nz.size > 0: my_val = pow( math.log10(float(len(r_harlocs)) / len(nz)), 2) if common.MY_DEBUG_STDOUT: common.DebugPrint("multiscale_quad_retrieval(): " "len(r_harlocs) = %d" % len( r_harlocs)) common.DebugPrint("multiscale_quad_retrieval(): " "len(nz) = %d" % len(nz)) common.DebugPrint("multiscale_quad_retrieval(): " "my_val = %.5f" % my_val) # PREVIOUSLY votes[nz, tol_i] = votes[nz, tol_i] + my_val if common.MY_DEBUG_STDOUT: print("multiscale_quad_retrieval(): " "votes.shape = %s" % (str(votes.shape))) if (np.abs(votes) < 1.0e-10).all(): print("multiscale_quad_retrieval(): votes = 0 (all zeros)") else: print("multiscale_quad_retrieval(): votes = %s" % (str(votes))) # Note: since votes is basically a 1-D vector, we don't use the # Fortran order votes_space[:, query_frame] = np.ravel(votes) if crop_flag == 0: HH[:, query_frame] = 1 else: HH[:, query_frame] = spatial_consistency.spatial_consistency( space_xy, qcen, len(r_harlocs), st_threshold, crop_flag) if common.MY_DEBUG_STDOUT: print("multiscale_quad_retrieval(scale_index=%d): " "votes_space =\n%s" % (scale_index, str(votes_space))) try: np.savez_compressed("votes_space%d" % scale_index, votes_space) np.savez_compressed("HH%d" % scale_index, HH) except: common.DebugPrintErrorTrace() t2 = float(cv2.getTickCount()) my_time = (t2 - t1) / cv2.getTickFrequency() print("multiscale_quad_retrieval() took %.6f [sec]" % my_time) return votes_space, HH
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pyradmon/args.py
will-mccarty/pyradmon
0
112904
<filename>pyradmon/args.py #!/usr/bin/env python # PyRadmon - Python Radiance Monitoring Tool # Copyright 2014 <NAME>. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. See the License for the specific language governing # permissions and limitations under the License. # # Argument Parsing Library - # library for parsing command line arguments # import argparse import textwrap import os import sys from _version import __version__ from core import * import config import config_printer from config import SPECIAL_FIELDS import log import logging try: from collections import OrderedDict except: try: from ordereddict import OrderedDict except: print "ERROR: OrderedDict not found! It is required to run this script." sys.exit(1) def add_args(parser, inherit, opts): for opt in opts: if inherit: opts[opt]['help'] = argparse.SUPPRESS opts[opt]['default'] = argparse.SUPPRESS opt_opt = dict(opts[opt]) parser.add_argument(opt, **opt_opt) def add_list_args(parser, inherit = False): opts = OrderedDict() opts['--data-single-date'] = \ { 'action' : 'store', 'metavar' : 'DATE', 'dest' : 'data_single_date', 'help' : 'Use single date. Format should be "YYYY-MM-DD HHz". Negates the options below specifying dates and times.', } opts['--data-path-format'] = \ { 'action' : 'store', 'metavar' : 'PATH_FORMAT', 'dest' : 'data_path_format', 'help' : 'Specify the path format for data.', } opts['--data-experiment-id'] = \ { 'action' : 'store', 'metavar' : 'EXPERIMENT_ID', 'dest' : 'data_experiment_id', 'help' : 'Specify the experiment ID for data.', } opts['--data-start-date'] = \ { 'action' : 'store', 'metavar' : 'DATE', 'dest' : 'data_start_date', 'help' : 'Specify the start date for data. Format should be "YYYY-MM-DD HHz".', } opts['--data-end-date'] = \ { 'action' : 'store', 'metavar' : 'DATE', 'dest' : 'data_end_date', 'help' : 'Specify the end date for data. Format should be "YYYY-MM-DD HHz".', } opts['--data-instrument-sat'] = \ { 'action' : 'store', 'metavar' : 'INSTRUMENT_SAT', 'dest' : 'data_instrument_sat', 'help' : 'Specify the instrument and satellite ID for data.', } opts['--data-step'] = \ { 'action' : 'store', 'metavar' : 'STEP_TYPE', 'dest' : 'data_step', 'help' : 'Specify the step/type for the data. "anl" and "ges" are allowed. If you wish to specify more than one, use a pipe to seperate them, e.g. "anl|ges".', } opts['--data-time-delta'] = \ { 'action' : 'store', 'metavar' : 'TIME_DELTA', 'dest' : 'data_time_delta', 'help' : """Specify the time interval for data. The time format is expressed using the sleep command's format, "#U", where # is a number and U is a letter representing a unit of time.""", } add_args(parser, inherit, opts) def add_dump_args(parser, inherit = False): opts = OrderedDict() opts['--dump-columns'] = \ { 'action' : 'store', 'metavar' : 'COLUMNS', 'dest' : 'dump_columns', 'help' : 'Specify the columns to dump/use, separated by commas.', } opts['--dump-all-channels'] = \ { 'action' : 'store_true', 'dest' : 'dump_all_channels', 'help' : 'Specify to dump all channels. Negates the option below specifying channels to use.', } opts['--dump-channels'] = \ { 'action' : 'store', 'metavar' : 'CHANNELS', 'dest' : 'dump_channels', 'help' : 'Specify the channels to dump/use, separated by commas. Ranges are also acceptable.', } opts['--dump-assim-only'] = \ { 'action' : 'store_true', 'dest' : 'dump_assim_only', 'help' : 'Specify to use only assimilated data (iuse = 1).', } opts['--dump-suppress-warnings'] = \ { 'action' : 'store_true', 'dest' : 'dump_suppress_warnings', 'help' : 'Specify whether to suppress data warnings or not. This will hide important warnings about data inconsistencies, so only enable if you are 100%% sure that your data is valid!', } add_args(parser, inherit, opts) def add_plot_args(parser, inherit = False): opts = OrderedDict() opts['--plot-define-plots'] = \ { 'action' : 'append', 'metavar' : 'PLOTS', 'dest' : 'plot_define_plots', 'help' : 'Define plots. Uses the value list system, specified by "plot1,plot2,plot3,...".', } opts['--plot-define-subplots'] = \ { 'action' : 'append', 'metavar' : 'SUBPLOTS', 'dest' : 'plot_define_subplots', 'help' : 'Define subplots. Uses the key-value pair system, specified by "plot1:subplot1,subplotid2,...;".', } opts['--plot-define-axes'] = \ { 'action' : 'append', 'metavar' : 'AXES', 'dest' : 'plot_define_axes', 'help' : 'Define axes for the subplot. Uses the key-value pair system, specified by "plot1|subplot1|y:ticks=5,label="test";...".', } opts['--plot-define-data'] = \ { 'action' : 'append', 'metavar' : 'DATA', 'dest' : 'plot_define_data', 'help' : 'Define data to be plotted in the subplot. Uses the key-value pair system, specified by "plot1|subplot1|x:data_field_1;plot1|subplot1|y:...".', } opts['--plot-define-title'] = \ { 'action' : 'append', 'metavar' : 'TITLE', 'dest' : 'plot_define_title', 'help' : 'Define the title for the plot, and optionally, subplot and legend. Uses the key-value pair system, specified by "plot1:title;plot1|subplot1:title;plot1|subplot1|legend:title;...".', } opts['--plot-define-output'] = \ { 'action' : 'append', 'metavar' : 'OUTPUT_FILE', 'dest' : 'plot_define_output', 'help' : 'Define the output file for the plot. Uses the key-value pair system, specified by "plot1:output_file.png;...".', } opts['--plot-define-settings'] = \ { 'action' : 'append', 'metavar' : 'SETTINGS', 'dest' : 'plot_define_settings', 'help' : 'Define the settings for the plot. Uses the key-value pair system, specified by "plot1:target_size=595x700,dpi=50;...".', } opts['--plot-define-custom-vars'] = \ { 'action' : 'append', 'metavar' : 'CUSTOM_VARS', 'dest' : 'plot_define_custom_vars', 'help' : 'Define the custom variables for use in the output file and title. Uses the key-value pair system, specified by "myvar:123,myvar2:abc,...".', } opts['--plot-make-dirs'] = \ { 'action' : 'store_true', 'dest' : 'plot_make_dirs', 'help' : 'Make directories if the specified output path does not exist.', } add_args(parser, inherit, opts) def add_config_args(parser, inherit = False): opts = OrderedDict() opts['--config-display'] = \ { 'action' : 'store_true', 'dest' : 'config_display', 'help' : 'Displays the configuration.', } opts['--config-load'] = \ { 'action' : 'store', 'metavar' : 'FILE', 'dest' : 'config_load', 'help' : 'Load an existing configuration file. Note that this will override the main --config-file argument.', } opts['--config-save'] = \ { 'action' : 'store', 'metavar' : 'FILE', 'dest' : 'config_save', 'help' : 'Save the currently loaded configuration to a file. Note that saved configuration may not be placed in order.', } add_args(parser, inherit, opts) def make_argparser(): parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter, \ epilog = textwrap.dedent("""\ Logging levels, from least to most importance: DEBUG, INFO, WARNING, ERROR, CRITICAL Setting a logging level will show messages that meet and exceed that level. For instance, setting INFO will show INFO, WARNING, ERROR, and CRITICAL messages. Priority modes: DUMMYMP_GENEROUS - Generous mode. Very conservative about using any CPU, and ensures that no one else is disrupted. Note that this is VERY GENEROUS - if all CPUs are taken, DummyMP will wait until there are available CPUs! (All other modes will run a single process, regardless of CPU usage.) This is the slowest mode! DUMMYMP_NORMAL - Normal mode. Careful not to take up too much resources on the CPU, but it will try to get things done. This is faster than GENEROUS, but it isn't the fastest. This mode is the default and is recommended for most conditions. DUMMYMP_AGGRESSIVE - Aggressive mode. This mode considers other users, but it may spawn processes anyway depending on how other processes behave. This is faster than NORMAL, and is recommended for semi-important conditions. DUMMYMP_EXTREME - Extreme mode. This mode somewhat considers other users, but unless the other processes are using a significant portion of the CPU, it will spawn spawn processes anyway. This is faster than AGGRESSIVE, and is recommended for important conditions. DUMMYMP_NUCLEAR - Nuclear mode. This mode does NOT consider other users, and just runs as many processes as it can allow (total number of cores). This is much faster than EXTREME, and is recommended for really important conditions. Note that this may earn you very angry co-workers knocking down your door with pitchforks, so use sparingly! Quick Start Examples: List data available: %(prog)s --config-file=config.yaml list Dump data: %(prog)s --config-file=config.yaml dump Make plots: %(prog)s --config-file=config.yaml plot Print configuration: %(prog)s --config-file=config.yaml config Make plots and log output: %(prog)s --config-file=config.yaml --logging-output="stdout,file" \\ --logging-file="mylog.txt" plot """), version = 'PyRadmon v' + __version__) # Old: %(prog)s evals to pyradmon.py subparsers = parser.add_subparsers(metavar="verb", dest = "verb", help='Description') main_opts = OrderedDict() main_opts['--config-file'] = \ { 'action' : 'store', 'dest' : 'config_file', 'metavar' : 'FILE', 'help' : 'Load a configuration file for PyRadmon to use. Synonymous to --config-load, but for all verbs and options.', } main_opts['--config-unset'] = \ { 'action' : 'append', 'dest' : 'config_unset', 'metavar' : 'VARS', 'help' : 'Unset (or remove) variables after all configuration settings (config file and command line) have been loaded. Format for VARS is "VARIABLE1;VARIABLE2;...". For core PyRadmon configuration, the variable syntax is "config.key". For plot configuration, the variable syntax follows the plot argument hierarchy format at any level, e.g. "plot|subplot|attr|subattr", "plot|subplot", or even "plot". (See plot help for more details regarding the plot hierarchy format.) Note that removing certain variables can cause PyRadmon to not run.', } main_opts['--logging-output'] = \ { 'action' : 'store', 'metavar' : 'OUTPUT', 'dest' : 'logging_output', 'help' : 'Specify where to output to. Options include file, stdout, and stderr. Multiple outputs can be specified, seperated with a comma.', } main_opts['--logging-file'] = \ { 'action' : 'store', 'metavar' : 'FILE', 'dest' : 'logging_file', 'help' : 'If outputting to a file, specify the file path to save to.', } main_opts['--logging-level'] = \ { 'action' : 'store', 'metavar' : 'LOG_LEVEL', 'dest' : 'logging_level', 'help' : 'Set the logging level for PyRadmon.', } main_opts['--mp-disable'] = \ { 'action' : 'store_true', 'dest' : 'mp_disable', 'help' : 'Disable multiprocessing (mp) optimizations in PyRadmon.', } main_opts['--mp-priority-mode'] = \ { 'action' : 'store', 'metavar' : 'PRIORITY_MODE', 'dest' : 'mp_priority_mode', 'help' : 'Set the priority mode for the multiprocessing (mp) optimizations in PyRadmon. Options are GENEROUS, NORMAL, AGGRESSIVE, EXTREME, and NUCLEAR. GENEROUS yields to other CPU hungry processes, while NUCLEAR spawns as many processes as it can regardless of CPU usage.', } main_opts['--mp-cpu-limit'] = \ { 'action' : 'store', 'metavar' : 'NUM_CPUS', 'dest' : 'mp_cpu_limit', 'help' : 'Limit the number of CPUs that the multiprocessing (mp) optimizations in PyRadmon can use.', } add_args(parser, False, main_opts) # A list command list_parser = subparsers.add_parser('list', help='Lists the data set available for use.', \ description = 'Lists the data set available for use.', epilog = textwrap.dedent("""\ For --data-time-delta: Available time units (case-sensitive): s: seconds m: minutes h: hours d: days w: weeks M: months y: years For instance, to specify 5 months, 3 minutes, 2 seconds: "5M 3m 2s" Multiple elements with same time units will be summed up, regardless of position or order. "3m 2s 5m" == "8m 2s" Years is not a native time delta, so this uses the conversion "1 year = 365 days". Note that specifying years may have some caveats, see: http://stackoverflow.com/a/765990 """), \ formatter_class=argparse.RawDescriptionHelpFormatter) add_list_args(list_parser, False) # [dump] dump_parser = subparsers.add_parser('dump', help='Dumps data from the data set.', \ description = textwrap.dedent("""\ Dumps data from the data set. Inherits arguments from [list]. [list] arguments may be required to use [dump]. For information on [list] arguments, see the [list] help. """), \ formatter_class=argparse.RawDescriptionHelpFormatter) add_list_args(dump_parser, True) add_dump_args(dump_parser, False) # [plot] plot_parser = subparsers.add_parser('plot', help='Creates a plot given plot parameters.', \ description = textwrap.dedent("""\ Creates a plot given plot parameters. Inherits arguments from [list] and [dump]. [list] and [dump] arguments may be required to use [plot]. For information on [list] and [dump] arguments, see their respective help. NOTE: These options are advanced - although you could (potentially) plot using these options, it would probably be very painful! All of these options can be defined via config file, which is much easier and less verbose! These options are meant to be used in conjunction with a config file for overriding certain configuration options and testing them. """), \ epilog = textwrap.dedent("""\ Definitions follow either a simple value list system or a simple key-value pair system. Value List System: The value list system looks like: "value1,value2,value3,..." For the value list system, values are separated with commas. Key-Value Pair System: The key-value pair system looks like: "key1:value1,value2;key2:value3,value4,..." For the key-value pair system, a key is followed by a colon, values are separated with commas, and key-value pairs are separated by semicolons. If settings are being set, values can look like subkey=value. It is a pair with a sub-key and a sub-value, separated by an equal sign. These are called sub-key-value pairs, and are in place of values in a key-value system. They can look like this: "key1:skey1=sval1,skey2=sval2;..." Key/Value Naming: Plots and subplots are indicated above with plot# and subplot#, respectively. They are simply IDs for the plots and subplots. You define them with --plot-define-plots and --plot-define-subplots. They can take on any name - for instance: --plot-define-plots="a,b" --plot-define-subplots="a:1,2;b:3,4" Hierarchy is done by starting with the highest hierarchy, and then adding additional lower levels to the right, sepearated by pipes. For instance: high|medium|low In the case of plots and subplots, the hierarchy is as follows: plot|subplot|attribute This is the naming scheme for keys in key/value pairs. Argument Definitions: Note that arguments can be specified multiple times to reduce the length of one argument. For instance: --plot-define-plots="plot1" --plot-define-plots="plot2" is the same as --plot-define-plots="plot1,plot2" This works regardless of argument order. This is encouraged, since this helps with argument organization. (In particular, arguments deals with a specific plot or subplot can be grouped together.) --plot-define-plots Value list of plot IDs. This argument is REQUIRED to produce a plot. Example: "plot1,abc2,superplot3" --plot-define-subplots Key-value pairs of plot IDs and subplot IDs. This argument is REQUIRED to produce a plot. Example (using above plot IDs): "plot1:sub1,sub2;abc2:abc,def;superplot3:s1,s2" --plot-define-axes Key-value pairs of plot/subplot IDs, and axes properties defined as sub-key-value pairs. This is the first argument to use key hierarchy naming. Available hierarchy attributes: plot|subplot|x X axis settings. plot|subplot|y Y axis settings. Available attributes (sub-key-value pairs): ticks=# Define number of ticks. label=LABEL Define the axis label. Example (using above info): "plot1|sub1|x:ticks=5,label=Hello world" --plot-define-data Key-value pairs of plot/subplot IDs, with data fields as values. This also uses key hierarchy naming. This argument is REQUIRED to produce a plot. Available hierarchy attributes: plot|subplot|x X plotting settings. plot|subplot|y Y plotting settings. Values: Data field elements. Specified using a different hierarchy system: STEP/TYPE|FIELD STEP/TYPE is either "ges" or "anl", but not both. For instance: ges|bc_total|mean Example (using above info): "abc2|abc|x:timestamp;abc2|abc|y:ges|bc_total|mean,ges|bc_total|stddev" --plot-define-title Key-value pairs of plot/subplot IDs, with titles as values. This also uses key hierarchy naming, but in a slightly different way. Available hierarchy attributes: plot|title Title for the whole plot. plot|subplot|title Title for the subplot. plot|subplot|legend Title for the subplot's legend. Values: The title for the specified title attribute. Example (using above info): "abc2|title:Title;abc2|abc|title:SubTitle;abc2|abc|legend:LegendTitle" --plot-define-output Key-value pairs of plot and output paths. Example (using above info): "plot1:myplot1.png;abc2:abcplot.png;superplot3:super.png" --plot-define-settings Key-value pairs of plot IDs, and settings defined as sub-key-value pairs. This argument is REQUIRED to produce a plot. Available attributes (sub-key-value pairs): target_size=#x# Define the output plot target size. dpi=# Define the output plot DPI. Example (using above info): "plot1:target_size=595x700,dpi=50" --plot-define-custom-vars Key-value pairs of custom variables to be used in the plot title and output file name. Variables are case insensitive. The suggested variable definition and usage standard is using lowercase for variable definition, and uppercase for variable usage in the title and/or output file name. Example: "expid:exp_2;author:theauthor" Applying custom variables to plot title and output file name: --plot-define-title "plot1:Experiment %EXPID% - By %AUTHOR%" Result: Experiment exp_2 - By theauthor --plot-define-output "plot1:plot_%EXPID%-%AUTHOR%.png" Result: plot_exp_2-theauthor.png --plot-make-dirs If specified, automatically make non-existent directories, as needed. No additional arguments or options needed. NOTE: These options are advanced - although you could (potentially) plot using these options, it would probably be very painful! All of these options can be defined via config file, which is much easier and less verbose! These options are meant to be used in conjunction with a config file for overriding certain configuration options and testing them. """), formatter_class=argparse.RawDescriptionHelpFormatter) add_list_args(plot_parser, True) add_dump_args(plot_parser, True) add_plot_args(plot_parser, False) # [config] config_parser = subparsers.add_parser('config', help='Displays, loads, and saves the configuration.', \ description = textwrap.dedent("""\ Displays, loads, and saves the configuration. Inherits arguments from [list], [dump], and [plot]. [list], [dump], and [plot] arguments may be required to use [config]. For information on [list], [dump], or [plot] arguments, see their respective help. """), \ formatter_class=argparse.RawDescriptionHelpFormatter) add_list_args(config_parser, True) add_dump_args(config_parser, True) add_plot_args(config_parser, True) add_config_args(config_parser, False) return parser def parse_to_config(parse): # Init a few variables: pyradmon_config = {} plot_dict = {} ## Core args # First, examine the core arguments. if isset_obj("config_file", parse): # OK, check if the file exists. if os.path.isfile(parse.config_file): # Attempt to load res = config.load(parse.config_file) if res == None: print "ERROR: Could not open configuration file!" return (None, None, None) pyradmon_config = res[0] plot_dict = res[1] else: print "ERROR: Configuration file path does not exist!" return (None, None, None) # Validate configuration config.validate(pyradmon_config, plot_dict) # Now check for logging args! file_enabled = False if isset_obj("logging_output", parse): logging_output = parse.logging_output if logging_output != "": logging_output = logging_output.split(",") logging_output = [x.strip() for x in logging_output] final_logging_output = [] for log_o in logging_output: if log_o == 'file': file_enabled = True elif log_o == 'stdout': final_logging_output.append(sys.stdout) elif log_o == 'stderr': final_logging_output.append(sys.stderr) else: print "ERROR: Invalid logging output! Valid output: stdout, stderr, file" return (None, None, None) logging_output = final_logging_output else: logging_output = [ sys.stdout ] if logging_output and file_enabled and isset_obj("logging_file", parse): logging_file = parse.logging_file else: logging_file = None if isset_obj("logging_level", parse): logging_level = parse.logging_level logging_level = logging_level.strip() if logging_level == "INFO": logging_level = logging.INFO elif logging_level == "WARNING": logging_level = logging.WARNING elif logging_level == "ERROR": logging_level = logging.ERROR elif logging_level == "CRITICAL": logging_level = logging.CRITICAL elif logging_level == "DEBUG": logging_level = logging.DEBUG else: print "ERROR: Invalid logging level specified!" print "Valid levels: INFO, WARNING, ERROR, CRITICAL, DEBUG" return (None, None, None) else: logging_level = logging.INFO if isset_obj("mp_disable", parse): pyradmon_config['mp_disable'] = parse.mp_disable if isset_obj("mp_priority_mode", parse): mp_priority_mode = parse.mp_priority_mode mp_priority_mode = mp_priority_mode.strip() if mp_priority_mode == "GENEROUS": pyradmon_config['mp_priority_mode'] = "GENEROUS" elif mp_priority_mode == "NORMAL": pyradmon_config['mp_priority_mode'] = "NORMAL" elif mp_priority_mode == "AGGRESSIVE": pyradmon_config['mp_priority_mode'] = "AGGRESSIVE" elif mp_priority_mode == "EXTREME": pyradmon_config['mp_priority_mode'] = "EXTREME" elif mp_priority_mode == "NUCLEAR": pyradmon_config['mp_priority_mode'] = "NUCLEAR" else: print "ERROR: Invalid multiprocessing (mp) priority mode specified!" print "Valid levels: GENEROUS, NORMAL, AGGRESSIVE, EXTREME, NUCLEAR" return (None, None, None) else: pyradmon_config['mp_priority_mode'] = "NORMAL" if isset_obj("mp_cpu_limit", parse): if (parse.mp_cpu_limit).isdigit(): pyradmon_config['mp_cpu_limit'] = int(parse.mp_cpu_limit) else: print "ERROR: Invalid multiprocessing (mp) CPU limit! The CPU limit" print "must specify an integer number of CPUs to limit use to." return (None, None, None) # We're ready - let's set up logging! logger = log.init(logging_level, logging_output, logging_file) # From now on, we'll stick to using the log module to print stuff # out. ## Config args, part 1 if parse.verb == "config": # We will only read --config-load here. All others will be # checked at the end. if isset_obj("config_load", parse): if isset_obj("config_file", parse): warn("Detected --config-load when --config-file is already specified! --config-load will override the configuration file specified in --config-file. You should only specify one argument, preferrably --config-file.") # OK, check if the file exists. if os.path.isfile(parse.config_load): # Attempt to load res = config.load(parse.config_load) if res == None: critical("ERROR: Could not open configuration file!") return (None, None, None) pyradmon_config = res[0] plot_dict = res[1] else: critical("ERROR: Configuration file path does not exist!") return (None, None, None) # Validate configuration config.validate(pyradmon_config, plot_dict) ## Plot args # FUN TIME if parse.verb == "plot" or parse.verb == "config": if isset_obj("plot_define_plots", parse): plots = ",".join(parse.plot_define_plots).split(",") # Cleanup plots = [x.strip() for x in plots] for plot in plots: if not plot in plot_dict: if plot == "": warn("Invalid plot ID detected - plot ID can't be blank!") warn("This plot definition will be skipped.") continue plot_dict[plot] = {} if isset_obj("plot_define_subplots", parse): # "plot1:sub1,sub2;abc2:abc,def;superplot3:s1,s2" subplots_def = ";".join(parse.plot_define_subplots).split(";") # Cleanup subplots_def = [x.strip() for x in subplots_def] for subplot_def in subplots_def: # Chunk: plot1:sub1,sub2 subplot_def_split = subplot_def.split(":") subplot_def_split = [x.strip() for x in subplot_def_split] # SAAAAAAAANITY CHECK!!!!!!!1111 # Sanity check 1: do we have 2 elements? if len(subplot_def_split) != 2: warn("Invalid subplot definition detected - invalid key-value pair '%s'!" % subplot_def) warn("(Key-value pair should be key:value - make sure there are no extra colons!)") warn("This subplot definition will be skipped.") continue # OK, now seperate it out! subplot_def_plot = subplot_def_split[0] subplot_def_subplots = subplot_def_split[1] # Sanity check 2: does the plot named exist?!? if not subplot_def_plot in plot_dict: warn("Invalid subplot definition detected - the plot specified, '%s', does not exist!" % subplot_def_plot) warn("Ensure spelling is correct. If it is a new plot, make sure it is defined.") warn("This subplot definition will be skipped.") continue # OK, let's process subplots. subplot_def_subplots = subplot_def_subplots.split(",") subplot_def_subplots = [x.strip() for x in subplot_def_subplots] # Prep plot_dict if not "plots" in plot_dict[subplot_def_plot]: plot_dict[subplot_def_plot]["plots"] = [] # Add away! for subplot_def_subplot in subplot_def_subplots: plot_dict[subplot_def_plot]["plots"].append({ subplot_def_subplot : {} }) # Done! if isset_obj("plot_define_axes", parse): # "plot1|sub1|x:ticks=5,label=Hello world" axes_def = ";".join(parse.plot_define_axes).split(";") # Cleanup axes_def = [x.strip() for x in axes_def] for axis_def in axes_def: # Chunk: plot1|sub1|x:ticks=5,label=Hello world axis_def_split = axis_def.split(":") axis_def_split = [x.strip() for x in axis_def_split] # SAAAAAAAANITY CHECK!!!!!!!1111 # Sanity check 1: do we have 2 elements? if len(axis_def_split) != 2: warn("Invalid axis definition detected - invalid key-value pair '%s'!" % axis_def) warn("(Key-value pair should be key:value - make sure there are no extra colons!)") warn("This axis definition will be skipped.") continue # OK, now seperate it out! # Chunk: plot1|sub1|x --> [plot1, sub1, x] axis_def_plot_subplot_axis = axis_def_split[0].split("|") # Chunk: ticks=5,label=Hello world axis_def_attrs = axis_def_split[1] # Sanity check 2: does the plot/subplot/axe key have 3 elements? if len(axis_def_plot_subplot_axis) != 3: warn("Invalid axis definition detected - the key is invalid! It should only have") warn("3 elements - plot|subplot|x/y!") warn("This axis definition will be skipped.") continue # OK, let's seperate that out! axis_def_plot = axis_def_plot_subplot_axis[0] axis_def_subplot = axis_def_plot_subplot_axis[1] axis_def_axis = axis_def_plot_subplot_axis[2].lower() # Sanity check 3: does the plot/subplot named exist?!? if not axis_def_plot in plot_dict: warn("Invalid axis definition detected - the plot specified, '%s', does not exist!" % axis_def_plot) warn("Ensure spelling is correct. If it is a new plot, make sure it is defined.") warn("This axis definition will be skipped.") continue # OK, plot exists. How about subplot? # We have to do some strange magic here... axis_def_subplot_found = False axis_def_subplot_dat = None for axis_def_subplot_dict in plot_dict[axis_def_plot]['plots']: if axis_def_subplot in axis_def_subplot_dict: axis_def_subplot_dat = axis_def_subplot_dict[axis_def_subplot] axis_def_subplot_found = True break if not axis_def_subplot_found: warn("Invalid axis definition detected - the subplot specified, '%s', does not exist!" % axis_def_subplot) warn("Ensure spelling is correct. If it is a new subplot, make sure it is defined and") warn("in the right subplot. This axis definition will be skipped.") continue # Sanity check 4: Is the axis valid? if axis_def_axis != "x" and axis_def_axis != "y": warn("Invalid axis definition detected - the axis specified, '%s', is invalid!" % axis_def_axis) warn("'x' and 'y' are the only axes allowed. This axis definition will be skipped.") continue # OK, let's setup shop. if not "axes" in axis_def_subplot_dat: axis_def_subplot_dat["axes"] = {} if not axis_def_axis in axis_def_subplot_dat["axes"]: axis_def_subplot_dat["axes"][axis_def_axis] = {} # OK, let's process attributes. axis_def_attrs = axis_def_attrs.split(",") axis_def_attrs = [x.strip().split("=") for x in axis_def_attrs] # Sanity check 5: Are these valid key-value pairs? kvpair_bad = False for kvpair in axis_def_attrs: if len(kvpair) != 2: warn("Invalid axis definition detected - the key/value subpair, '%s', is invalid!" % '='.join(kvpair)) kvpair_bad = True if type(kvpair[0]) != str: warn("Invalid axis definition detected - the key '%s' in the subpair, '%s', is a non-string!" % (str(kvpair[0]), '='.join(kvpair))) if kvpair_bad: warn("(Key-value subpair should be key=value - make sure there are no extra =s!)") warn("This axis definition will be skipped.") continue # Whew, so much sanity lost from sanity checking! # Install attributes! for kvpair in axis_def_attrs: if kvpair[0] == 'ticks': axis_def_subplot_dat["axes"][axis_def_axis][kvpair[0]] = int(kvpair[1]) else: axis_def_subplot_dat["axes"][axis_def_axis][kvpair[0]] = kvpair[1] # Done!!!!!1111 if isset_obj("plot_define_data", parse): # "abc2|abc|x:timestamp;abc2|abc|y:ges|bc_total|mean,ges|bc_total|stddev" data_defs = ";".join(parse.plot_define_data).split(";") # Cleanup data_defs = [x.strip() for x in data_defs] for data_def in data_defs: # Chunk: abc2|abc|x:timestamp # Chunk: abc2|abc|y:ges|bc_total|mean,ges|bc_total|stddev data_def_split = data_def.split(":") data_def_split = [x.strip() for x in data_def_split] # SAAAAAAAANITY CHECK!!!!!!!1111 # Sanity check 1: do we have 2 elements? if len(data_def_split) != 2: warn("Invalid data definition detected - invalid key-value pair '%s'!" % data_def) warn("(Key-value pair should be key:value - make sure there are no extra colons!)") warn("This data definition will be skipped.") continue # OK, now seperate it out! # Chunk: abc2|abc|x --> [abc2, abc, x] data_def_plot_subplot_attr = data_def_split[0].split("|") # Chunk: timestamp data_def_attrs = data_def_split[1] # Sanity check 2: does the plot/subplot/axe key have 3 elements? if len(data_def_plot_subplot_attr) != 3: warn("Invalid data definition detected - the key is invalid! It should only have") warn("3 elements - plot|subplot|x/y!") warn("This data definition will be skipped.") continue # OK, let's seperate that out! data_def_plot = data_def_plot_subplot_attr[0] data_def_subplot = data_def_plot_subplot_attr[1] data_def_attr_name = data_def_plot_subplot_attr[2].lower() # Sanity check 3: does the plot/subplot named exist?!? if not data_def_plot in plot_dict: warn("Invalid data definition detected - the plot specified, '%s', does not exist!" % data_def_plot) warn("Ensure spelling is correct. If it is a new plot, make sure it is defined.") warn("This data definition will be skipped.") continue # OK, plot exists. How about subplot? # We have to do some strange magic here... data_def_subplot_found = False data_def_subplot_dat = None for data_def_subplot_dict in plot_dict[data_def_plot]['plots']: if data_def_subplot in data_def_subplot_dict: data_def_subplot_dat = data_def_subplot_dict[data_def_subplot] data_def_subplot_found = True break if not data_def_subplot_found: warn("Invalid data definition detected - the subplot specified, '%s', does not exist!" % data_def_subplot) warn("Ensure spelling is correct. If it is a new subplot, make sure it is defined and") warn("in the right subplot. This data definition will be skipped.") continue # Sanity check 4: Is the attr field valid? valid_attrs = [ "x", "y", "colors", "labels" ] if not data_def_attr_name in valid_attrs: warn("Invalid data definition detected - the attribute specified, '%s', is invalid!" % data_def_data) warn("%s are the only attributes allowed. This data definition will be skipped." % (",".join(valid_attrs[:-1]) + ", and " + valid_attrs[-1])) continue # OK, let's setup shop. if not "data" in data_def_subplot_dat: data_def_subplot_dat["data"] = {} # OK, let's process attributes. data_def_attrs = data_def_attrs.split(",") # Whew, so much sanity lost from sanity checking! # (But not as bad as the axes part...) # Install attributes! if len(data_def_attrs) == 1: data_def_subplot_dat["data"][data_def_attr_name] = data_def_attrs[0] else: if not data_def_attr_name in data_def_subplot_dat["data"]: data_def_subplot_dat["data"][data_def_attr_name] = [] for attr_val in data_def_attrs: data_def_subplot_dat["data"][data_def_attr_name].append(attr_val) # Done! if isset_obj("plot_define_title", parse): # "abc2|title:Title;abc2|abc|title:SubTitle;abc2|abc|legend:LegendTitle" title_defs = ";".join(parse.plot_define_title).split(";") # Cleanup title_defs = [x.strip() for x in title_defs] for title_def in title_defs: # Chunk: abc2|title:Title # Chunk: abc2|abc|title:SubTitle # Chunk: abc2|abc|legend:LegendTitle title_def_split = title_def.split(":") title_def_split = [x.strip() for x in title_def_split] # SAAAAAAAANITY CHECK!!!!!!!1111 # Sanity check 1: do we have 2 elements? if len(title_def_split) != 2: warn("Invalid title definition detected - invalid key-value pair '%s'!" % title_def) warn("(Key-value pair should be key:value - make sure there are no extra colons!)") warn("This title definition will be skipped.") continue # OK, now seperate it out! # Chunk: abc2|title --> [abc2, title] # Chunk: abc2|abc|title --> [abc2, abc, title] # Chunk: abc2|abc|legend --> [abc2, abc, legend] title_def_plot_subplot_attr = title_def_split[0].split("|") # Chunk: Title # Chunk: SubTitle # Chunk: LegendTitle title_def_title = title_def_split[1] # Sanity check 2: does the plot(/subplot)/attr key have 2 or 3 elements? if (len(title_def_plot_subplot_attr) != 3) and (len(title_def_plot_subplot_attr) != 2): warn("Invalid title definition detected - the key is invalid! It should only have") warn("2-3 elements - plot|title or plot|subplot|title/legend!") warn("This title definition will be skipped.") continue # OK, let's seperate that out! # ...except this varies, so let's take it a bit slow. title_def_plot_subplot = [ title_def_plot_subplot_attr[0] ] if len(title_def_plot_subplot_attr) == 3: title_def_plot_subplot.append(title_def_plot_subplot_attr[1]) title_def_attr_name = title_def_plot_subplot_attr[-1].lower() # Sanity check 3: does the plot/subplot named exist?!? if not title_def_plot_subplot[0] in plot_dict: warn("Invalid title definition detected - the plot specified, '%s', does not exist!" % title_def_plot) warn("Ensure spelling is correct. If it is a new plot, make sure it is defined.") warn("This title definition will be skipped.") continue # OK, plot exists. How about subplot? # We have to do some strange magic here... # ...but only if we need to. if len(title_def_plot_subplot) == 2: title_def_subplot_found = False title_def_subplot_dat = None for title_def_subplot_dict in plot_dict[title_def_plot_subplot[0]]['plots']: if title_def_plot_subplot[1] in title_def_subplot_dict: title_def_subplot_dat = title_def_subplot_dict[title_def_plot_subplot[1]] title_def_subplot_found = True break if not title_def_subplot_found: warn("Invalid title definition detected - the subplot specified, '%s', does not exist!" % title_def_subplot) warn("Ensure spelling is correct. If it is a new subplot, make sure it is defined and") warn("in the right subplot. This title definition will be skipped.") continue # Sanity check 4: Is the attr field valid? # Title is valid for both, but legend is only valid for # plot|subplot. if title_def_attr_name != "title" and not ( (len(title_def_plot_subplot) == 2) and (title_def_attr_name == "legend") ): warn("Invalid title definition detected - the attribute specified, '%s', is invalid!" % title_def_title) warn("legend (for plot|subplot) and title are the only attributes allowed.") warn("This title definition will be skipped.") continue # OK, let's go! if len(title_def_plot_subplot) == 1: plot_dict[title_def_plot_subplot[0]]["title"] = title_def_title else: if title_def_attr_name == "legend": if not isset_obj("legend", title_def_subplot_dat): title_def_subplot_dat["legend"] = {} title_def_subplot_dat["legend"]["title"] = title_def_title else: title_def_subplot_dat["title"] = title_def_title # Done! if isset_obj("plot_define_output", parse): # "plot1:myplot1.png;abc2:abcplot.png;superplot3:super.png" output_defs = ";".join(parse.plot_define_output).split(";") # Cleanup output_defs = [x.strip() for x in output_defs] for output_def in output_defs: # Chunk: plot1:myplot1.png output_def_split = output_def.split(":") output_def_split = [x.strip() for x in output_def_split] # SAAAAAAAANITY CHECK!!!!!!!1111 # Sanity check 1: do we have 2 elements? if len(output_def_split) != 2: warn("Invalid output definition detected - invalid key-value pair '%s'!" % output_def) warn("(Key-value pair should be key:value - make sure there are no extra colons!)") warn("This output definition will be skipped.") continue # OK, now seperate it out! # Chunk: plot1 output_def_plot = output_def_split[0] # Chunk: myplot1.png output_def_output = output_def_split[1] # Sanity check 2: does the plot named exist?!? if not output_def_plot in plot_dict: warn("Invalid output definition detected - the plot specified, '%s', does not exist!" % output_def_plot) warn("Ensure spelling is correct. If it is a new plot, make sure it is defined.") warn("This output definition will be skipped.") continue # OK, plot exists. This is super easy, so we're free to # fly from here! plot_dict[output_def_plot]["output"] = output_def_output # Done! if isset_obj("plot_define_settings", parse): # "plot1:target_size=595x700,dpi=50" settings_def = ";".join(parse.plot_define_settings).split(";") # Cleanup settings_def = [x.strip() for x in settings_def] for settings_def in settings_def: # Chunk: plot1:target_size=595x700,dpi=50 settings_def_split = settings_def.split(":") settings_def_split = [x.strip() for x in settings_def_split] # SAAAAAAAANITY CHECK!!!!!!!1111 # Sanity check 1: do we have 2 elements? if len(settings_def_split) != 2: warn("Invalid settings definition detected - invalid key-value pair '%s'!" % settings_def) warn("(Key-value pair should be key:value - make sure there are no extra colons!)") warn("This settings definition will be skipped.") continue # OK, now seperate it out! # Chunk: plot1 settings_def_plot = settings_def_split[0] # Chunk: ticks=5,label=Hello world settings_def_attrs = settings_def_split[1] # Sanity check 2: does the plot/subplot named exist?!? if not settings_def_plot in plot_dict: warn("Invalid settings definition detected - the plot specified, '%s', does not exist!" % settings_def_plot) warn("Ensure spelling is correct. If it is a new plot, make sure it is defined.") warn("This settings definition will be skipped.") continue # OK, let's setup shop. if not "settings" in plot_dict[settings_def_plot]: plot_dict[settings_def_plot]["settings"] = {} # OK, let's process attributes. settings_def_attrs = settings_def_attrs.split(",") settings_def_attrs = [x.strip().split("=") for x in settings_def_attrs] # Sanity check 5: Are these valid key-value pairs? kvpair_bad = False for kvpair in settings_def_attrs: if len(kvpair) != 2: warn("Invalid settings definition detected - the key/value subpair, '%s', is invalid!" % '='.join(kvpair)) kvpair_bad = True if type(kvpair[0]) != str: warn("Invalid settings definition detected - the key '%s' in the subpair, '%s', is a non-string!" % (str(kvpair[0]), '='.join(kvpair))) if kvpair_bad: warn("(Key-value subpair should be key=value - make sure there are no extra =s!)") warn("This settings definition will be skipped.") continue # Whew, so much sanity lost from sanity checking! # Install attributes! for kvpair in settings_def_attrs: if kvpair[0] == 'target_size': size_arr = [ int(x) for x in kvpair[1].lower().split("x") ] if len(size_arr) != 2: warn("Key-value pair for target_size is invalid - should be INTxINT!") warn("Skipping key-value pair.") continue plot_dict[settings_def_plot]["settings"]["target_size"] = size_arr elif kvpair[0] == 'dpi': if not kvpair[1].isdigit(): warn("Key-value pair for dpi is invalid - value is not an int!") warn("Skipping key-value pair.") continue plot_dict[settings_def_plot]["settings"][kvpair[0]] = int(kvpair[1]) else: # Strange stuff warn("Key '%s' in not defined - setting anyway." % str(kvpair[0])) plot_dict[settings_def_plot]["settings"][kvpair[0]] = kvpair[1] # Done!!!!!1111 if isset_obj("plot_define_custom_vars", parse): # "customvar1:value1;customvar2:value2" custom_var_defs = ";".join(parse.plot_define_custom_vars).split(";") # Cleanup custom_var_defs = [x.strip() for x in custom_var_defs] for output_def in output_defs: # Chunk: customvar1:value1 custom_var_def_split = custom_var_def.split(":") custom_var_def_split = [x.strip() for x in custom_var_def_split] # Sanity check! # Sanity check 1: do we have 2 elements? if len(custom_var_def_split) != 2: warn("Invalid custom variable definition detected - invalid key-value pair '%s'!" % output_def) warn("(Key-value pair should be key:value - make sure there are no extra colons!)") warn("This custom variable definition will be skipped.") continue # OK, now seperate it out! # Chunk: customvar1 custom_var_def_var = custom_var_def_split[0] # Chunk: value1 custom_var_def_val = custom_var_def_split[1] # OK, we got everything. This is super easy, so we're free to # leap from here! if "custom_vars" not in pyradmon_config: pyradmon_config["custom_vars"] = {} pyradmon_config["custom_vars"][custom_var_def_var] = custom_var_def_val # Done! if isset_obj("plot_make_dirs", parse) and parse.plot_make_dirs: pyradmon_config["make_dirs"] = parse.plot_make_dirs # Done! ## Dump args if parse.verb == "dump" or parse.verb == "plot" or parse.verb == "config": # --dump-columns if isset_obj("dump_columns", parse): for data_column in (parse.dump_columns).split(","): data_column = data_column.strip() if not data_column in SPECIAL_FIELDS: if not ((data_column.startswith("ges|") or data_column.startswith("anl|"))): die("ERROR: Invalid data column '%s' specified in --dump-columns! Must have ges| or anl| as a prefix." % data_column) pyradmon_config["data_columns"] = parse.dump_columns # --dump-all-channels if isset_obj("dump_all_channels", parse) and parse.dump_all_channels: pyradmon_config["data_all_channels"] = parse.dump_all_channels # --dump-channels if isset_obj("dump_channels", parse): if isset_obj("dump_all_channels", parse) and parse.dump_all_channels: die("ERROR: You can not specify --dump-all-channels and --dump-channels at the same time!") for data_channel in (parse.dump_channels).split(","): data_channel = data_channel.strip() if not ( (data_channel.isdigit()) or \ ( (len(data_channel.split("-")) == 2) and data_channel.split("-")[0].isdigit() and data_channel.split("-")[1].isdigit() ) ): die("ERROR: Invalid data channel '%s' specified in --dump-channels! Must be a number or a numeric range (#-#)." % data_channel) pyradmon_config["data_channels"] = parse.dump_channels # --dump-assim-only if isset_obj("dump_assim_only", parse): pyradmon_config["data_assim_only"] = parse.dump_assim_only # --dump-suppress-warnings if isset_obj("dump_suppress_warnings", parse): pyradmon_config["data_suppress_warnings"] = parse.dump_suppress_warnings ## List args if parse.verb == "list" or parse.verb == "dump" or parse.verb == "plot" or parse.verb == "config": # --data-single-date (action flag) if isset_obj("data_single_date", parse): pyradmon_config["data_start_date"] = parse.data_single_date pyradmon_config["data_end_date"] = parse.data_single_date # --data-path-format if isset_obj("data_path_format", parse): pyradmon_config["data_path_format"] = parse.data_path_format # --data-experiment-id if isset_obj("data_experiment_id", parse): pyradmon_config["data_experiment_id"] = parse.data_experiment_id # --data-start-date if isset_obj("data_start_date", parse): if isset_obj("data_single_date", parse): die("ERROR: You can not specify --data-single-date and --data-start-date at the same time!") # FORMAT: YYYY-MM-DD HHz pyradmon_config["data_start_date"] = parse.data_start_date if not (pyradmon_config["data_start_date"][:4].isdigit() and \ pyradmon_config["data_start_date"][5:7].isdigit() and \ pyradmon_config["data_start_date"][8:10].isdigit() and \ pyradmon_config["data_start_date"][11:13].isdigit()): die("ERROR: Start date '%s' specified in --data-start-date is not valid! It must be in 'YYYY-MM-DD HHz' format!" % pyradmon_config["data_start_date"]) # --data-end-date if isset_obj("data_end_date", parse): if isset_obj("data_single_date", parse): die("ERROR: You can not specify --data-single-date and --data-end-date at the same time!") # FORMAT: YYYY-MM-DD HHz pyradmon_config["data_end_date"] = parse.data_end_date if not (pyradmon_config["data_end_date"][:4].isdigit() and \ pyradmon_config["data_end_date"][5:7].isdigit() and \ pyradmon_config["data_end_date"][8:10].isdigit() and \ pyradmon_config["data_end_date"][11:13].isdigit()): die("ERROR: End date '%s' specified in --data-end-date is not valid! It must be in 'YYYY-MM-DD HHz' format!" % pyradmon_config["data_end_date"]) # --data-instrument-sat if isset_obj("data_instrument_sat", parse): pyradmon_config["data_instrument_sat"] = parse.data_instrument_sat # --data-step if isset_obj("data_step", parse): pyradmon_config["data_step"] = parse.data_step if not ((pyradmon_config['data_step'] == "anl") or \ (pyradmon_config['data_step'] == "ges") or \ (pyradmon_config['data_step'] == "anl|ges") or \ (pyradmon_config['data_step'] == "ges|anl")): die("ERROR: Data step '%s' specified in --date-step is not valid! Must either be 'anl', 'ges', or the two combined with a pipe ('anl|ges')." % pyradmon_config["data_step"]) # --data-time-delta if isset_obj("data_time_delta", parse): pyradmon_config["data_time_delta"] = parse.data_time_delta dtd_split = pyradmon_config['data_time_delta'].split(" ") unit_valid = [ "s", "w", "m", "M", "h", "y", "d" ] for dtd in dtd_split: # s: seconds m: minutes h: hours d: days # w: weeks M: months y: years if not ( (dtd[-1] in unit_valid) and (dtd[:-1].isdigit()) ): die("ERROR: Invalid delta time '%s' specified in --data-delta-time! Must be a # followed by a valid unit letter. ([s]ecs, [m]inutes, [h]ours, [d]ays, [w]eeks, [M]onths, [y]ears)" % pyradmon_config["data_delta_time"]) if isset_obj("config_unset", parse): # "config.var1;plot1|subplot1" config_unset_def = ";".join(parse.config_unset).split(";") for config_def in config_unset_def: if config_def.startswith("config."): pyr_config_var = config_def.split("config.")[1:] if len(pyr_config_var) > 1: warn("WARNING: Detected strange unset entry, but continuing anyway.") pyr_config_var = "config.".join(pyr_config_var) else: pyr_config_var = str(pyr_config_var[0]) info("Unsetting pyradmon_config variable %s." % pyr_config_var) pyradmon_config.pop(pyr_config_var, None) else: plot_config_var = config_def.split("|") info("Unsetting plot_dict variable %s." % "|".join(plot_config_var)) if not plot_config_var[0] in plot_dict: warn("Invalid unset definition detected - the plot specified, '%s', does not exist!" % title_def_plot) warn("Ensure spelling is correct. If it is a new plot, make sure it is defined.") warn("This unset definition will be skipped.") continue # OK, plot exists. How about subplot? # We have to do some strange magic here... # ...but only if we need to. if len(plot_config_var) >= 2: plot_config_subplot_found = False title_def_subplot_dat = None for subplot_dict in plot_dict[plot_config_var[0]]['plots']: if plot_config_var[1] in subplot_dict: subplot_dat = subplot_dict[plot_config_var[1]] plot_config_subplot_found = True break if not plot_config_subplot_found: warn("Invalid unset definition detected - the subplot specified, '%s', does not exist!" % title_def_subplot) warn("Ensure spelling is correct. If it is a new subplot, make sure it is defined and") warn("in the right subplot. This unset definition will be skipped.") continue # Check if len(plot_config_var) == 2: plot_dict[plot_config_var[0]]['plots'].remove(subplot_dict) else: # Now recursively delete! delete_keys_from_dict(subplot_dat, plot_config_var[2:]) else: plot_dict.pop(plot_config_var[0], None) #import pprint #pprint.pprint(plot_dict) #print plot_dict return (pyradmon_config, plot_dict, parse) if __name__ == "__main__": parser = make_argparser() parse = parser.parse_args() print parse_to_config(parse)
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DQMOffline/Trigger/python/SoftdropMonitor_cfi.py
PKUfudawei/cmssw
1
189236
<filename>DQMOffline/Trigger/python/SoftdropMonitor_cfi.py import FWCore.ParameterSet.Config as cms from DQMOffline.Trigger.htMonitoring_cfi import htMonitoring # config file for monitoring the trigger efficiency vs softdropmass of the leading jet # see python/HTMonitor_cfi.py or plugins/HTMonitor.h or plugins/HTMonitor.cc for more details hltSoftdropmonitoring = htMonitoring.clone( FolderName = 'HLT/HT/PFMETNoMu120/', quantity = 'softdrop', # set quantity to leading jet softdropmass jetSelection = "pt > 65 && eta < 2.4", dEtaCut = 1.3, met = "pfMet", jets = "ak8PFJetsPuppiSoftDrop", # dont set this to non-SoftdropJets electrons = "gedGsfElectrons", muons = "muons", histoPSet = dict(htBinning = [0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100., 105., 110., 115., 120., 125., 130., 135., 140., 145., 150., 155., 160., 165., 170., 175., 180., 185., 190., 195., 200.], htPSet = dict( nbins = 200, xmin = -0.5, xmax = 19999.5)), numGenericTriggerEventPSet = dict( andOr = False, andOrHlt = True, # True:=OR; False:=AND hltInputTag = "TriggerResults::HLT", hltPaths = ["HLT_PFMETNoMu120_PFMHTNoMu120_IDTight_v*"], errorReplyHlt = False, verbosityLevel = 0), denGenericTriggerEventPSet = dict( andOr = False, dcsInputTag = "scalersRawToDigi", dcsRecordInputTag = "onlineMetaDataDigis", dcsPartitions = [24, 25, 26, 27, 28, 29], # 24-27: strip, 28-29 andOrDcs = False, errorReplyDcs = True, verbosityLevel = 0, hltPaths = ["HLT_IsoMu27_v*"]) )
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lib/python3.8/site-packages/ansible_collections/cisco/aci/plugins/modules/aci_bd.py
cjsteel/python3-venv-ansible-2.10.5
0
85989
#!/usr/bin/python # -*- coding: utf-8 -*- # GNU General Public License v3.0+ (see LICENSE or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'certified'} DOCUMENTATION = r''' --- module: aci_bd short_description: Manage Bridge Domains (BD) objects (fv:BD) description: - Manages Bridge Domains (BD) on Cisco ACI fabrics. options: arp_flooding: description: - Determines if the Bridge Domain should flood ARP traffic. - The APIC defaults to C(no) when unset during creation. type: bool bd: description: - The name of the Bridge Domain. type: str aliases: [ bd_name, name ] bd_type: description: - The type of traffic on the Bridge Domain. - The APIC defaults to C(ethernet) when unset during creation. type: str choices: [ ethernet, fc ] description: description: - Description for the Bridge Domain. type: str enable_multicast: description: - Determines if PIM is enabled. - The APIC defaults to C(no) when unset during creation. type: bool enable_routing: description: - Determines if IP forwarding should be allowed. - The APIC defaults to C(yes) when unset during creation. type: bool endpoint_clear: description: - Clears all End Points in all Leaves when C(yes). - The value is not reset to disabled once End Points have been cleared; that requires a second task. - The APIC defaults to C(no) when unset during creation. type: bool endpoint_move_detect: description: - Determines if GARP should be enabled to detect when End Points move. - The APIC defaults to C(garp) when unset during creation. type: str choices: [ default, garp ] endpoint_retention_action: description: - Determines if the Bridge Domain should inherit or resolve the End Point Retention Policy. - The APIC defaults to C(resolve) when unset during creation. type: str choices: [ inherit, resolve ] endpoint_retention_policy: description: - The name of the End Point Retention Policy the Bridge Domain should use when overriding the default End Point Retention Policy. type: str igmp_snoop_policy: description: - The name of the IGMP Snooping Policy the Bridge Domain should use when overriding the default IGMP Snooping Policy. type: str ip_learning: description: - Determines if the Bridge Domain should learn End Point IPs. - The APIC defaults to C(yes) when unset during creation. type: bool ipv6_nd_policy: description: - The name of the IPv6 Neighbor Discovery Policy the Bridge Domain should use when overridding the default IPV6 ND Policy. type: str l2_unknown_unicast: description: - Determines what forwarding method to use for unknown l2 destinations. - The APIC defaults to C(proxy) when unset during creation. type: str choices: [ proxy, flood ] l3_unknown_multicast: description: - Determines the forwarding method to use for unknown multicast destinations. - The APIC defaults to C(flood) when unset during creation. type: str choices: [ flood, opt-flood ] limit_ip_learn: description: - Determines if the BD should limit IP learning to only subnets owned by the Bridge Domain. - The APIC defaults to C(yes) when unset during creation. type: bool mac_address: description: - The MAC Address to assign to the C(bd) instead of using the default. - The APIC defaults to C(00:22:BD:F8:19:FF) when unset during creation. type: str aliases: [ mac ] multi_dest: description: - Determines the forwarding method for L2 multicast, broadcast, and link layer traffic. - The APIC defaults to C(bd-flood) when unset during creation. type: str choices: [ bd-flood, drop, encap-flood ] state: description: - Use C(present) or C(absent) for adding or removing. - Use C(query) for listing an object or multiple objects. type: str choices: [ absent, present, query ] default: present name_alias: description: - The alias for the current object. This relates to the nameAlias field in ACI. type: str tenant: description: - The name of the Tenant. type: str aliases: [ tenant_name ] vrf: description: - The name of the VRF. type: str aliases: [ vrf_name ] extends_documentation_fragment: - cisco.aci.aci notes: - The C(tenant) used must exist before using this module in your playbook. The M(cisco.aci.aci_tenant) module can be used for this. seealso: - module: cisco.aci.aci_tenant - name: APIC Management Information Model reference description: More information about the internal APIC class B(fv:BD). link: https://developer.cisco.com/docs/apic-mim-ref/ author: - <NAME> (@jmcgill298) ''' EXAMPLES = r''' - name: Add Bridge Domain cisco.aci.aci_bd: host: "{{ inventory_hostname }}" username: "{{ username }}" password: "{{ password }}" validate_certs: no tenant: prod bd: web_servers mac_address: 00:22:BD:F8:19:FE vrf: prod_vrf state: present delegate_to: localhost - name: Add an FC Bridge Domain cisco.aci.aci_bd: host: "{{ inventory_hostname }}" username: "{{ username }}" password: "{{ password }}" validate_certs: no tenant: prod bd: storage bd_type: fc vrf: fc_vrf enable_routing: no state: present delegate_to: localhost - name: Modify a Bridge Domain cisco.aci.aci_bd: host: "{{ inventory_hostname }}" username: "{{ username }}" password: "{{ password }}" validate_certs: yes tenant: prod bd: web_servers arp_flooding: yes l2_unknown_unicast: flood state: present delegate_to: localhost - name: Query All Bridge Domains cisco.aci.aci_bd: host: "{{ inventory_hostname }}" username: "{{ username }}" password: "{{ password }}" validate_certs: yes state: query delegate_to: localhost register: query_result - name: Query a Bridge Domain cisco.aci.aci_bd: host: "{{ inventory_hostname }}" username: "{{ username }}" password: "{{ password }}" validate_certs: yes tenant: prod bd: web_servers state: query delegate_to: localhost register: query_result - name: Delete a Bridge Domain cisco.aci.aci_bd: host: "{{ inventory_hostname }}" username: "{{ username }}" password: "{{ password }}" validate_certs: yes tenant: prod bd: web_servers state: absent delegate_to: localhost ''' RETURN = r''' current: description: The existing configuration from the APIC after the module has finished returned: success type: list sample: [ { "fvTenant": { "attributes": { "descr": "Production environment", "dn": "uni/tn-production", "name": "production", "nameAlias": "", "ownerKey": "", "ownerTag": "" } } } ] error: description: The error information as returned from the APIC returned: failure type: dict sample: { "code": "122", "text": "unknown managed object class foo" } raw: description: The raw output returned by the APIC REST API (xml or json) returned: parse error type: str sample: '<?xml version="1.0" encoding="UTF-8"?><imdata totalCount="1"><error code="122" text="unknown managed object class foo"/></imdata>' sent: description: The actual/minimal configuration pushed to the APIC returned: info type: list sample: { "fvTenant": { "attributes": { "descr": "Production environment" } } } previous: description: The original configuration from the APIC before the module has started returned: info type: list sample: [ { "fvTenant": { "attributes": { "descr": "Production", "dn": "uni/tn-production", "name": "production", "nameAlias": "", "ownerKey": "", "ownerTag": "" } } } ] proposed: description: The assembled configuration from the user-provided parameters returned: info type: dict sample: { "fvTenant": { "attributes": { "descr": "Production environment", "name": "production" } } } filter_string: description: The filter string used for the request returned: failure or debug type: str sample: ?rsp-prop-include=config-only method: description: The HTTP method used for the request to the APIC returned: failure or debug type: str sample: POST response: description: The HTTP response from the APIC returned: failure or debug type: str sample: OK (30 bytes) status: description: The HTTP status from the APIC returned: failure or debug type: int sample: 200 url: description: The HTTP url used for the request to the APIC returned: failure or debug type: str sample: https://10.11.12.13/api/mo/uni/tn-production.json ''' from ansible.module_utils.basic import AnsibleModule from ansible_collections.cisco.aci.plugins.module_utils.aci import ACIModule, aci_argument_spec def main(): argument_spec = aci_argument_spec() argument_spec.update( arp_flooding=dict(type='bool'), bd=dict(type='str', aliases=['bd_name', 'name']), # Not required for querying all objects bd_type=dict(type='str', choices=['ethernet', 'fc']), description=dict(type='str'), enable_multicast=dict(type='bool'), enable_routing=dict(type='bool'), endpoint_clear=dict(type='bool'), endpoint_move_detect=dict(type='str', choices=['default', 'garp']), endpoint_retention_action=dict(type='str', choices=['inherit', 'resolve']), endpoint_retention_policy=dict(type='str'), igmp_snoop_policy=dict(type='str'), ip_learning=dict(type='bool'), ipv6_nd_policy=dict(type='str'), l2_unknown_unicast=dict(type='str', choices=['proxy', 'flood']), l3_unknown_multicast=dict(type='str', choices=['flood', 'opt-flood']), limit_ip_learn=dict(type='bool'), mac_address=dict(type='str', aliases=['mac']), multi_dest=dict(type='str', choices=['bd-flood', 'drop', 'encap-flood']), state=dict(type='str', default='present', choices=['absent', 'present', 'query']), tenant=dict(type='str', aliases=['tenant_name']), # Not required for querying all objects vrf=dict(type='str', aliases=['vrf_name']), name_alias=dict(type='str'), ) module = AnsibleModule( argument_spec=argument_spec, supports_check_mode=True, required_if=[ ['state', 'absent', ['bd', 'tenant']], ['state', 'present', ['bd', 'tenant']], ], ) aci = ACIModule(module) arp_flooding = aci.boolean(module.params.get('arp_flooding')) bd = module.params.get('bd') bd_type = module.params.get('bd_type') if bd_type == 'ethernet': # ethernet type is represented as regular, but that is not clear to the users bd_type = 'regular' description = module.params.get('description') enable_multicast = aci.boolean(module.params.get('enable_multicast')) enable_routing = aci.boolean(module.params.get('enable_routing')) endpoint_clear = aci.boolean(module.params.get('endpoint_clear')) endpoint_move_detect = module.params.get('endpoint_move_detect') if endpoint_move_detect == 'default': # the ACI default setting is an empty string, but that is not a good input value endpoint_move_detect = '' endpoint_retention_action = module.params.get('endpoint_retention_action') endpoint_retention_policy = module.params.get('endpoint_retention_policy') igmp_snoop_policy = module.params.get('igmp_snoop_policy') ip_learning = aci.boolean(module.params.get('ip_learning')) ipv6_nd_policy = module.params.get('ipv6_nd_policy') l2_unknown_unicast = module.params.get('l2_unknown_unicast') l3_unknown_multicast = module.params.get('l3_unknown_multicast') limit_ip_learn = aci.boolean(module.params.get('limit_ip_learn')) mac_address = module.params.get('mac_address') multi_dest = module.params.get('multi_dest') state = module.params.get('state') tenant = module.params.get('tenant') vrf = module.params.get('vrf') name_alias = module.params.get('name_alias') aci.construct_url( root_class=dict( aci_class='fvTenant', aci_rn='tn-{0}'.format(tenant), module_object=tenant, target_filter={'name': tenant}, ), subclass_1=dict( aci_class='fvBD', aci_rn='BD-{0}'.format(bd), module_object=bd, target_filter={'name': bd}, ), child_classes=['fvRsCtx', 'fvRsIgmpsn', 'fvRsBDToNdP', 'fvRsBdToEpRet'], ) aci.get_existing() if state == 'present': aci.payload( aci_class='fvBD', class_config=dict( arpFlood=arp_flooding, descr=description, epClear=endpoint_clear, epMoveDetectMode=endpoint_move_detect, ipLearning=ip_learning, limitIpLearnToSubnets=limit_ip_learn, mac=mac_address, mcastAllow=enable_multicast, multiDstPktAct=multi_dest, name=bd, type=bd_type, unicastRoute=enable_routing, unkMacUcastAct=l2_unknown_unicast, unkMcastAct=l3_unknown_multicast, nameAlias=name_alias, ), child_configs=[ {'fvRsCtx': {'attributes': {'tnFvCtxName': vrf}}}, {'fvRsIgmpsn': {'attributes': {'tnIgmpSnoopPolName': igmp_snoop_policy}}}, {'fvRsBDToNdP': {'attributes': {'tnNdIfPolName': ipv6_nd_policy}}}, {'fvRsBdToEpRet': {'attributes': {'resolveAct': endpoint_retention_action, 'tnFvEpRetPolName': endpoint_retention_policy}}}, ], ) aci.get_diff(aci_class='fvBD') aci.post_config() elif state == 'absent': aci.delete_config() aci.exit_json() if __name__ == "__main__": main()
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code/data-cleaning/generate_overlapping_features.py
tejasph/qlesq_prediction
0
179132
<reponame>tejasph/qlesq_prediction<filename>code/data-cleaning/generate_overlapping_features.py import os import sys import pandas as pd import numpy as np from overlapping_globals import HEADER_CONVERSION_DICT, CANBIND_OVERLAPPING_VALUE_CONVERSION_MAP,NEW_FEATURES_CANBIND,QIDS_STARD_TO_CANBIND_DICT as Q_DICT_C from overlapping_globals import STARD_OVERLAPPING_VALUE_CONVERSION_MAP, STARD_TO_DROP """ Generates the overlapping datasets from the processed datasets, for canbind data, thats the product of canbind_imputer Takes in the processed dataset, and generates the overlapping datasets Can generate an overlapping dataset one at a time if needed, or by default will do both See main() for description of arguments """ Q_DICT_S = dict(map(reversed, Q_DICT_C.items())) def convert_stard_to_overlapping(output_dir , path_name): if output_dir == "": output_dir = r"C:\Users\jjnun\Documents\Sync\Research\1_CANBIND Replication\teyden-git\data\final_datasets" # TODO temporarily hardcode if not os.path.exists(output_dir): os.mkdir(output_dir) # df = pd.read_csv(file_path) df = pd.read_csv(output_dir + "/" + path_name + ".csv") # changed to fit Tejas file name # Take whitelist columns first df = df[STARD_OVERLAPPING_VALUE_CONVERSION_MAP["whitelist"]]## + ["days_baseline"]] # Warn if any null found, as they should not be! check_missing_values(df) # Then process them for case, config in STARD_OVERLAPPING_VALUE_CONVERSION_MAP.items(): if case == "keep": # Nothing to do, already grabbed from whitelist continue elif case == "multiply": for col_name, multiple in config["col_names"].items(): df[col_name] = df[col_name].apply(lambda x: x * multiple) elif case == "other": for i, row in df.iterrows(): # phx01__alcoh set 1 if either 1 if row["phx01__alcoh||2.0"] == 1: df.at[i, "phx01__alcoh||1.0"] = 1 # phx01__bulimia||2/5: set 1 if any of bulimia one-hots bullemias = ['phx01__bulimia||3','phx01__bulimia||4'] set_if_found_in_others(i,row,'phx01__bulimia||2/5',bullemias,1, df) # phx01__amphet||1.0: set 1 if any of the non alchol substance uses non_alch_sub = ['phx01__amphet||2.0','phx01__cannibis||1.0','phx01__cannibis||2.0','phx01__opioid||1.0','phx01__opioid||2.0','phx01__ax_cocaine||1.0','phx01__ax_cocaine||2.0'] set_if_found_in_others(i,row,'phx01__amphet||1.0',non_alch_sub,1, df) # phx01__dep: 1 if any family history conditions are 1 family_hxs = ['phx01__deppar','phx01__depsib','phx01__depchld','phx01__bip','phx01__bippar','phx01__bipsib','phx01__bipchld','phx01__alcohol','phx01__alcpar','phx01__alcsib', 'phx01__alcchld','phx01__drug_phx','phx01__drgpar','phx01__drgsib','phx01__drgchld','phx01__suic_phx','phx01__suicpar','phx01__suicsib','phx01__suicchld'] set_if_found_in_others(i,row,'phx01__dep',family_hxs,1, df) # dm01_enroll__empl||3.0 to 1 if any full time employment options full_time_statuses = ["dm01_enroll__empl||4.0","dm01_enroll__empl||5.0"] set_if_found_in_others(i,row,'dm01_enroll__empl||3.0',full_time_statuses,1,df) #dm01_w0__totincom: Converts monthly usd old income to current CAD, and then categorizes per canbind totincom_convert = row["dm01_w0__totincom"]*12*1.25*1.19 #Converts to annual, then inflation, then USDtoCAD if totincom_convert <10000: df.at[i, "dm01_w0__totincom"] = 1 elif totincom_convert <25000: df.at[i, "dm01_w0__totincom"] = 2 elif totincom_convert <50000: df.at[i, "dm01_w0__totincom"] = 3 elif totincom_convert <75000: df.at[i, "dm01_w0__totincom"] = 4 elif totincom_convert <100000: df.at[i, "dm01_w0__totincom"] = 5 elif totincom_convert <150000: df.at[i, "dm01_w0__totincom"] = 6 elif totincom_convert <200000: df.at[i, "dm01_w0__totincom"] = 7 elif totincom_convert >=200000: df.at[i, "dm01_w0__totincom"] = 8 # Add in new features add_new_imputed_features_stard(df, row, i) # fill in new features # Eliminate subjects that don't have any records > 21 This section removed, should be done already beforehand in generation of X_stard ##df = eliminate_early_leavers(df) ##df = df.drop(["days_baseline"], axis=1) # Drop columns that were used for calcs above for to_drop in STARD_TO_DROP: df = df.drop([to_drop], axis=1) # Rename Column Headers according to dict df = df.rename(HEADER_CONVERSION_DICT, axis=1) # Check that all column headers have ::: to ensure they have correspondance in CAN-BIND for header in list(df.columns.values): if not (':::' in header): print('Warning! Likely unwanted column in output: ' + header) # Sort and output df = df.reset_index(drop=True) df = df.sort_index(axis=1) # Newly added, sorts columns alphabetically so same for both matrices #df = df.sort_values(by=['SUBJLABEL:::subjectkey']) df = df.rename(columns = {'SUBJLABEL:::subjectkey':'subjectkey'}) df = df.set_index(['subjectkey']) df.to_csv(output_dir + "/" + path_name + "_extval.csv",index=True) def convert_canbind_to_overlapping(output_dir=""): if output_dir == "": output_dir = r"C:\Users\jjnun\Documents\Sync\Research\1_CANBIND Replication\teyden-git\data\canbind_data_full_auto" # TODO temporarily hardcode if not os.path.exists(output_dir): os.mkdir(output_dir) orig_df = pd.read_csv(output_dir + "/canbind_imputed.csv") # Drop index column in present if "Unnamed: 0"in orig_df: df = orig_df.drop(["Unnamed: 0"], axis=1) else: df = orig_df # Take whitelist columns first df = df[CANBIND_OVERLAPPING_VALUE_CONVERSION_MAP["whitelist"]] # Warn if any NaN found, as they should not be! check_missing_values(df) # Add new features as blank for new_feature in NEW_FEATURES_CANBIND: df[new_feature] = np.nan # Then process them for case, config in CANBIND_OVERLAPPING_VALUE_CONVERSION_MAP.items(): if case == "keep": # Nothing to do, already grabbed from whitelist continue elif case == "multiply": for col_name, multiple in config["col_names"].items(): df[col_name] = df[col_name].apply(lambda x: x * multiple) elif case == "other": for i, row in df.iterrows(): if row["MINI_SBSTNC_DPNDC_NONALCHL_TIME"] == 1: df.at[i, "MINI_SBSTNC_ABUSE_NONALCHL_TIME"] = 1 if row["MINI_ALCHL_DPNDC_TIME"] == 1: df.at[i, "MINI_ALCHL_ABUSE_TIME"] = 1 if row["MINI_AN_TIME"] == 1: df.at[i, "MINI_AN_BINGE_TIME"] = 1 if (row['EMPLOY_STATUS_6.0'] == 1) or (row['EMPLOY_STATUS_3.0'] == 1): df.at[i, "EMPLOY_STATUS_1.0"] = 1 if row['EMPLOY_STATUS_4.0'] == 1: df.at[i, "EMPLOY_STATUS_2.0"] = 1 add_new_imputed_features_canbind(df, row, i) # fill in new features # Drop columns that were used for calcs above for todrop in ["MINI_SBSTNC_DPNDC_NONALCHL_TIME","MINI_ALCHL_DPNDC_TIME","MINI_AN_TIME","EMPLOY_STATUS_6.0","EMPLOY_STATUS_3.0","EMPLOY_STATUS_4.0"]: df = df.drop([todrop], axis=1) # Filter out those without valid response/nonresponse values ## Already filtered so ignore ##df = get_valid_subjects(df) ##df = df.drop(["RESPOND_WK8"], axis=1) # Rename Column Headers according to dict df = df.rename(HEADER_CONVERSION_DICT, axis=1) # Check that all column headers have ::: to ensure they have correspondance in STAR*D for header in list(df.columns.values): if not (':::' in header): print('Warning! Likely unwanted column in output: ' + header) # Sort and output df = df.reset_index(drop=True) df = df.sort_index(axis=1) # Newly added, sorts columns alphabetically so same for both matrices df = df.rename(columns = {'SUBJLABEL:::subjectkey':'subjectkey'}) df = df.set_index(['subjectkey']) df.to_csv(output_dir + "/X_test_cb_extval.csv",index=True) def add_new_imputed_features_canbind(df, row, i): # imput_anyanxiety imput_anyanxiety = ['MINI_PTSD_TIME', 'MINI_PD_DX', 'MINI_AGRPHOBIA_TIME', 'MINI_SOCL_PHOBIA_DX', 'MINI_GAD_TIME'] val = 1 if sum(row[imput_anyanxiety] == 1) > 0 else 0 df.at[i, ':::imput_anyanxiety'] = val # imput_QIDS_SR_perc_change val = round((row[Q_DICT_C['qids01_w2sr__qstot']] - row[Q_DICT_C['qids01_w0sr__qstot']]) / row[Q_DICT_C['qids01_w0sr__qstot']] if row[Q_DICT_C['qids01_w0sr__qstot']] else 0, 3) df.at[i, 'imput_QIDS_SR_perc_change:::'] = val # Imputed new QIDS features for time in ['week0','week2']: time2 = 'baseline' if time =='week0' else 'week2' #week0 is sometimes called _baseline # imput_QIDS_SR_sleep_domain val = round(np.nanmax(list(row[['QIDS_SR_1_' + time2,'QIDS_SR_2_' + time2,'QIDS_SR_3_' + time2,'QIDS_SR_4_' + time2]]))) df.at[i, 'imput_QIDS_SR_sleep_domain_' + time + ':::'] = val # imput_QIDS_SR_appetite_domain val = round(np.nanmax(list(row[['QIDS_SR_6_' + time2,'QIDS_SR_7_' + time2,'QIDS_SR_8_' + time2,'QIDS_SR_9_' + time2]]))) df.at[i, 'imput_QIDS_SR_appetite_domain_' + time + ':::'] = val # imput_QIDS_SR_psychomot_domain val = round(np.nanmax(list(row[['QIDS_SR_15_' + time2,'QIDS_SR_16_' + time2]]))) df.at[i, 'imput_QIDS_SR_psychomot_domain_' + time + ':::'] = val # imput_QIDS_SR_overeating val = round(np.nanmax(list(row[['QIDS_SR_7_' + time2,'QIDS_SR_9_' + time2]]))) df.at[i, 'imput_QIDS_SR_overeating_' + time + ':::'] = val # imput_QIDS_SR_insomnia val = round(np.nanmax(list(row[['QIDS_SR_1_' + time2,'QIDS_SR_2_' + time2,'QIDS_SR_3_' + time2]]))) df.at[i, 'imput_QIDS_SR_insomnia_' + time + ':::'] = val def add_new_imputed_features_stard(df, row, i): # imput_anyanxiety imput_anyanxiety = ['phx01__psd', 'phx01__pd_noag', 'phx01__pd_ag', 'phx01__soc_phob', 'phx01__gad_phx','phx01__specphob'] val = 1 if sum(row[imput_anyanxiety] == 1) > 0 else 0 df.at[i, ':::imput_anyanxiety'] = val # imput_QIDS_SR_perc_change val = round((row['qids01_w2sr__qstot'] - row['qids01_w0sr__qstot']) / row['qids01_w0sr__qstot'] if row['qids01_w0sr__qstot'] else 0, 3) df.at[i, 'imput_QIDS_SR_perc_change:::'] = val # PSYHIS_MDD_PREV::: val = 1 if row['phx01__epino'] >= 2 else 0 df.at[i, 'PSYHIS_MDD_PREV:::'] = val # 'QLESQA_TOT_QLESQB_TOT_merged:::' val = 0 for j in range(1,15): # Sum items 1-14 str_j = str(j) str_j = "0" + str_j if j < 10 else str_j val = val + row['qlesq01__qlesq' + str_j] df.at[i, 'QLESQA_TOT_QLESQB_TOT_merged:::'] = val # Imputed new QIDS features for time in ['week0','week2']: time2 = 'baseline' if time =='week0' else 'week2' #week0 is sometimes called _baseline time3 = 'w0' if time =='week0' else 'w2' #week0 is sometimes called w0, and week2 w2 # imput_QIDS_SR_sleep_domain val = round(np.nanmax(list(row[[Q_DICT_S['QIDS_SR_1_' + time2],Q_DICT_S['QIDS_SR_2_' + time2],Q_DICT_S['QIDS_SR_3_' + time2],Q_DICT_S['QIDS_SR_4_' + time2]]]))) df.at[i, 'imput_QIDS_SR_sleep_domain_' + time + ':::'] = val # imput_QIDS_SR_appetite_domain val = round(np.nanmax(list(row[[Q_DICT_S['QIDS_SR_6_' + time2],Q_DICT_S['QIDS_SR_7_' + time2],Q_DICT_S['QIDS_SR_8_' + time2],Q_DICT_S['QIDS_SR_9_' + time2]]]))) df.at[i, 'imput_QIDS_SR_appetite_domain_' + time + ':::'] = val # imput_QIDS_SR_psychomot_domain val = round(np.nanmax(list(row[[Q_DICT_S['QIDS_SR_15_' + time2],Q_DICT_S['QIDS_SR_16_' + time2]]]))) df.at[i, 'imput_QIDS_SR_psychomot_domain_' + time + ':::'] = val # imput_QIDS_SR_overeating val = round(np.nanmax(list(row[[Q_DICT_S['QIDS_SR_7_' + time2],Q_DICT_S['QIDS_SR_9_' + time2]]]))) df.at[i, 'imput_QIDS_SR_overeating_' + time + ':::'] = val # imput_QIDS_SR_insomnia val = round(np.nanmax(list(row[[Q_DICT_S['QIDS_SR_1_' + time2],Q_DICT_S['QIDS_SR_2_' + time2],Q_DICT_S['QIDS_SR_3_' + time2]]]))) df.at[i, 'imput_QIDS_SR_insomnia_' + time + ':::'] = val # imput_QIDS_SR_ATYPICAL val = round(np.sum(list(row[['qids01_' + time3 + 'sr__vhysm','qids01_' + time3 + 'sr__vapin', 'qids01_' + time3 + 'sr__vwtin', 'qids01_' + time3 + 'sr__vengy']]))) df.at[i, 'QIDS_ATYPICAL_' + time2 + ':::'] = val def set_if_found_in_others(i,row,to_set, others, val, df): """ Quick helper function. Sets row[to_set][i] to value if any of the values in others ==1. Useful whem collapsing a one-hot set of columns to 1 if any of the columns are 1. """ for c in others: other_val = int(row[c]) if other_val ==1: df.at[i, to_set] = val elif other_val ==0: continue else: raise Exception('set_if_found should only be used for columns that are 1 or 0, given: ' + str(other_val)) def check_missing_values(df): nulls = df.isnull().sum().sum() if nulls != 0: print("WARNING! A total of: " + str(nulls) + " missing values found, this should likely be 0!") if __name__ == "__main__": if len(sys.argv) == 2 and sys.argv[1] == "-bothdefault": convert_stard_to_overlapping() convert_canbind_to_overlapping() elif len(sys.argv) == 4 and sys.argv[1] == "-both" and os.path.isdir(sys.argv[2]): convert_stard_to_overlapping(sys.argv[2], "X_77_qlesq_sr__final") convert_stard_to_overlapping(sys.argv[2], "X_91_qlesq_sr__final") convert_canbind_to_overlapping(sys.argv[3]) elif len(sys.argv) == 3 and sys.argv[1] == "-sd" and os.path.isdir(sys.argv[2]): convert_stard_to_overlapping(sys.argv[2]) elif len(sys.argv) == 3 and sys.argv[1] == "-cb" and os.path.isdir(sys.argv[2]): convert_canbind_to_overlapping(sys.argv[2]) else: print("Enter valid arguments\n" "\t options: -both to generate overlapping for both, -sd for STAR*D, -cb for CAN-BIND\n" "\t path: the path to a real directory with the preprocessed data\n" "\t path: if generating the overlapping for both, this will be the CAN-BIND data, and the 2nd argument STAR*D")
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test22.py
spatole12/ssw555tmashishningrohnitshivani2019Spring
0
37451
import unittest import io import sys from main import Gedcom class TestProject(unittest.TestCase): def test_us22_unique_id(self): # Redirect stdout for unit test capturedOutput = io.StringIO() sys.stdout = capturedOutput FILENAME="My-Family-27-Jan-2019-275.ged" gedcom = Gedcom(FILENAME) # Non-unique ID cases self.assertIn('ERROR US22 for ID @F8@', capturedOutput.getvalue()) self.assertIn('ERROR US22 for ID @I15@', capturedOutput.getvalue()) # Unique ID cases self.assertNotIn('ERROR US22 for ID @F3@', capturedOutput.getvalue()) self.assertNotIn('ERROR US22 for ID @I2@', capturedOutput.getvalue()) # Reset redirection sys.stdout = sys.__stdout__ if __name__ == '__main__': unittest.main()
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bin/run_imctools.py
nf-core/nf-core-imcyto
15
75983
<gh_stars>10-100 #!/usr/bin/env python import os import sys import argparse import re import imctools.io.mcdparser as mcdparser import imctools.io.txtparser as txtparser import imctools.io.ometiffparser as omeparser import imctools.io.mcdxmlparser as meta ############################################ ############################################ ## PARSE ARGUMENTS ############################################ ############################################ Description = 'Split nf-core/imcyto input data by full/ilastik stack.' Epilog = """Example usage: python run_imctools.py <MCD/TXT/TIFF> <METADATA_FILE>""" argParser = argparse.ArgumentParser(description=Description, epilog=Epilog) argParser.add_argument('INPUT_FILE', help="Input files with extension '.mcd', '.txt', or '.tiff'.") argParser.add_argument('METADATA_FILE', help="Metadata file containing 3 columns i.e. metal,full_stack,ilastik_stack. See pipeline usage docs for file format information.") args = argParser.parse_args() ############################################ ############################################ ## PARSE & VALIDATE INPUTS ############################################ ############################################ ## READ AND VALIDATE METADATA FILE ERROR_STR = 'ERROR: Please check metadata file' HEADER = ['metal', 'full_stack', 'ilastik_stack'] fin = open(args.METADATA_FILE,'r') header = fin.readline().strip().split(',') if header != HEADER: print("{} header: {} != {}".format(ERROR_STR,','.join(header),','.join(HEADER))) sys.exit(1) metalDict = {} for line in fin.readlines(): lspl = line.strip().split(',') metal,fstack,istack = lspl ## CHECK THREE COLUMNS IN LINE if len(lspl) != len(HEADER): print("{}: Invalid number of columns - should be 3!\nLine: '{}'".format(ERROR_STR,line.strip())) sys.exit(1) ## CHECK VALID INCLUDE/EXCLUDE CODES if fstack not in ['0','1'] or istack not in ['0','1']: print("{}: Invalid column code - should be 0 or 1!\nLine: '{}'".format(ERROR_STR,line.strip())) sys.exit(1) ## CREATE DICTIONARY metal = metal.upper() if metal not in metalDict: metalDict[metal] = [bool(int(x)) for x in [fstack,istack]] fin.close() ## OUTPUT FILE LINKING ROI IDS TO ROI LABELS (IMAGE DESCRIPTION) roi_map = open(os.path.basename(args.INPUT_FILE)+'_ROI_map.csv', "w") ## USE DIFFERENT PARSERS CORRESPONDING TO THE INPUT FILE FORMAT file_type = re.sub(".*\.([^.]+)$", '\\1', args.INPUT_FILE.lower()) ## CONVERT INPUT_FILE TO TIFF AND WRITE RELEVANT TIFF IMAGES if file_type == "mcd": parser = mcdparser.McdParser(args.INPUT_FILE) acids = parser.acquisition_ids else: if file_type == "txt": parser = txtparser.TxtParser(args.INPUT_FILE) elif file_type == "tiff" or file_type == "tif": parser = omeparser.OmetiffParser(args.INPUT_FILE) else: print("{}: Invalid input file type - should be txt, tiff, or mcd!".format(file_type)) sys.exit(1) # THERE IS ONLY ONE ACQUISITION - ROI FOLDER NAMED ACCORDING TO INPUT FILENAME acids = [ re.sub('.txt|.tiff', '', os.path.basename(parser.filename).lower().replace(" ", "_")) ] for roi_number in acids: if file_type == "mcd": imc_ac = parser.get_imc_acquisition(roi_number) acmeta = parser.meta.get_object(meta.ACQUISITION, roi_number) roi_label = parser.get_acquisition_description(roi_number) roi_map.write("roi_%s,%s,%s,%s" % (roi_number, roi_label, acmeta.properties['StartTimeStamp'], acmeta.properties['EndTimeStamp']) + "\n") else: imc_ac = parser.get_imc_acquisition() # NO INFORMATION ON IMAGE ACQUISITION TIME FOR TXT AND TIFF FILE FORMATS roi_map.write("roi_%s,,," % (roi_number) + "\n") for i,j in enumerate(HEADER[1:]): ## WRITE TO APPROPRIATE DIRECTORY dirname = "roi_%s/%s" % (roi_number, j) if not os.path.exists(dirname): os.makedirs(dirname) # SELECT THE METALS FOR THE CORRESPNDING STACK (i) TO CREATE OME TIFF STACK label_indices = [ idx for idx in range(0, len(imc_ac.channel_labels)) if len([ entry for entry in metalDict if imc_ac.channel_labels[idx].upper().startswith(entry) and metalDict[entry][i]]) > 0 ] metal_stack = [ imc_ac.channel_metals[idx] for idx in label_indices ] if len(metal_stack) > 0: img = imc_ac.get_image_writer(filename=os.path.join("roi_%s" % (roi_number), "%s.ome.tiff" % j), metals=metal_stack) img.save_image(mode='ome', compression=0, dtype=None, bigtiff=True) else: print("None of the metals exists in metasheet file for {}".format(j)) sys.exit(1) for l, m in zip(imc_ac.channel_labels, imc_ac.channel_metals): filename = "%s.tiff" % (l) # MATCH METAL LABEL TO METADATA METAL COLUMN metal_label = l.split('_')[0].upper() metal = [ entry for entry in metalDict if metal_label.upper().startswith(entry) and metalDict[entry][i] ] if len(metal) == 1: if metalDict[metal[0]][i]: img = imc_ac.get_image_writer(filename=os.path.join(dirname,filename), metals=[m]) img.save_image(mode='ome', compression=0, dtype=None, bigtiff=False) elif len(metal) > 1: print("{} metal has multiple matches found".format(metal_label)) elif len([ entry for entry in metalDict if metal_label.upper().startswith(entry)]) == 0: print("{} metal does not exist in metasheet file".format(metal_label)) roi_map.close()
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admin_tools_stats/utils.py
afkmamunbd/django-admin-tools-stats
204
166850
<reponame>afkmamunbd/django-admin-tools-stats # # This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this file, # You can obtain one at http://mozilla.org/MPL/2.0/. # # Copyright (C) 2011-2014 Star2Billing S.L. # # The Initial Developer of the Original Code is # <NAME> <<EMAIL>> # from django.contrib.auth.models import User from django.test import TestCase, Client from django.test.client import RequestFactory import base64 import unittest import inspect def build_test_suite_from(test_cases): """Returns a single or group of unittest test suite(s) that's ready to be run. The function expects a list of classes that are subclasses of TestCase. The function will search the module where each class resides and build a test suite from that class and all subclasses of it. """ test_suites = [] for test_case in test_cases: mod = __import__(test_case.__module__) components = test_case.__module__.split('.') for comp in components[1:]: mod = getattr(mod, comp) tests = [] for item in mod.__dict__.values(): if type(item) is type and issubclass(item, test_case): tests.append(item) test_suites.append(unittest.TestSuite( map(unittest.TestLoader().loadTestsFromTestCase, tests))) return unittest.TestSuite(test_suites) class BaseAuthenticatedClient(TestCase): """Common Authentication""" fixtures = ['auth_user'] def setUp(self): """To create admin user""" self.client = Client() self.user = User.objects.get(username='admin') auth = '%s:%s' % ('admin', 'admin') auth = 'Basic %s' % base64.encodestring(auth.encode('utf8')) auth = auth.strip() self.extra = { 'HTTP_AUTHORIZATION': auth, } try: self.client.force_login(self.user) except AttributeError: # Django < 1.8 login = self.client.login(username='admin', password='<PASSWORD>') self.assertTrue(login) self.factory = RequestFactory() class Choice(object): class __metaclass__(type): def __init__(self, *args, **kwargs): self._data = [] for name, value in inspect.getmembers(self): if not name.startswith('_') and not inspect.ismethod(value): if isinstance(value, tuple) and len(value) > 1: data = value else: pieces = [x.capitalize() for x in name.split('_')] data = (value, ' '.join(pieces)) self._data.append(data) setattr(self, name, data[0]) self._hash = dict(self._data) def __iter__(self): for value, data in self._data: yield value, data @classmethod def get_value(self, key): return self._hash[key]
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python/re-group-groups.py
gajubadge11/HackerRank-1
340
160128
#!/usr/bin/env python3 import re if __name__ == "__main__": string = input() match = re.search(r'([a-zA-Z0-9])\1+', string) print(match.group(1) if match else -1)
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startup/20-diagon.py
mrakitin/profile_collection-six
0
73522
<filename>startup/20-diagon.py<gh_stars>0 from ophyd import Device, EpicsMotor from ophyd import Component as Cpt class DIAGON(Device): hml = Cpt(EpicsMotor, '_HLPM}Mtr') hyag = Cpt(EpicsMotor, '_HLPF}Mtr') vml = Cpt(EpicsMotor, '_VLPM}Mtr') vyag = Cpt(EpicsMotor, '_VLPF}Mtr') diagon = DIAGON('XF:02IDA-OP{Diag:1-Ax:3', name='diagon')
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tests/integration/Story.py
marqov/storyscript
1
153294
# -*- coding: utf-8 -*- from io import StringIO from storyscript.Story import Story def test_story_from_stream(): stream = StringIO('x = 0') story = Story.from_stream(stream) assert story.story == 'x = 0'
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setup.py
stb-tester/stbt-rig
0
178835
# coding: utf-8 import setuptools long_description = """\ # stbt_rig Command-line tool & library for interacting with the Stb-tester Portal's [REST API]. For more details see [IDE Configuration] in the Stb-tester manual. [IDE Configuration]: https://stb-tester.com/manual/ide-configuration [REST API]: https://stb-tester.com/manual/rest-api-v2 """ setuptools.setup( name="stbt_rig", version="2.0.2", author="<EMAIL>.", author_email="<EMAIL>", description="Library for interacting with the Stb-tester Portal's REST API", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/stb-tester/stbt-rig", py_modules=["stbt_rig"], entry_points={ "console_scripts": [ "stbt_rig=stbt_rig:main", ], }, classifiers=[ "Development Status :: 5 - Production/Stable", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.6", "Topic :: Software Development :: Testing", ], # I have only tested Python 2.7 & 3.6 python_requires=">=2.7", install_requires=[ "keyring", "requests", ], )
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claim_frags.py
mattharris/adfs-tools
0
190197
#! /usr/bin/python import sys from objects import Map from utils import find_map fd = open("Loader", "r") fd.seek(0,2) loader_len = fd.tell() fd.close() print "Loader is {0} bytes long.".format(loader_len) if len(sys.argv) != 2: print("Usage: claim_frags <device>") exit(1) fd = open(sys.argv[1], "r+b") map_address, map_length = find_map(fd) fd.seek(map_address) fs_map = Map(fd.read(map_length)) loader_start = (fs_map.disc_record.idlen+1) * fs_map.disc_record.bpmb bits_needed = loader_len / fs_map.disc_record.bpmb start_bit = loader_start / fs_map.disc_record.bpmb last_bit = start_bit + bits_needed while start_bit * fs_map.disc_record.bpmb < loader_start: start_bit += 1 while bits_needed * fs_map.disc_record.bpmb < loader_len: bits_needed += 1 print "{0} map bits required for loader, from bit {1} to {2}.".format(bits_needed,start_bit,last_bit) zone = 0 while True: zone_start, zone_end = fs_map.zone_range(zone) first_in_zone = zone_start last_in_zone = zone_end if zone_start < start_bit: first_in_zone = start_bit if last_bit < last_in_zone: last_in_zone = last_bit #note = "" #if first_in_zone > zone_start: # note = " ** {0} bits not used at start of zone".format(first_in_zone-zone_start) #if last_in_zone < zone_end: # note = " ** {0} bits not used at end of zone".format(zone_end-last_in_zone) #print "Zone {0} - bits {1} to {2}{3}".format(zone,first_in_zone,last_in_zone,note) #print zone_start fs_map.allocate(zone, 3, first_in_zone-zone_start, last_in_zone-zone_start) if zone_end > last_bit: break zone += 1 fd.seek(map_address) fd.write(fs_map.data.tostring())
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setup.py
rionehome/face_predictor
0
1610270
<reponame>rionehome/face_predictor<filename>setup.py<gh_stars>0 import os from setuptools import setup package_name = 'face_predictor' def create_install_files(paths: list): target_data_files = [('share/ament_index/resource_index/packages', ['resource/' + package_name]), ('share/' + package_name, ['package.xml'])] for path in paths: for root, dirs, files in os.walk(path): print(root) target_data_files.append(('lib/{}/{}'.format(package_name, root), [])) for file in files: print('{}/{}'.format(root, file)) target_data_files[-1][1].append('{}/{}'.format(root, file)) return target_data_files # print(create_install_files(['vision', 'models'])) setup( name=package_name, version='0.0.0', packages=[], py_modules=[ 'face_predictor.script', ], data_files=[('share/ament_index/resource_index/packages', ['resource/face_predictor']), ('share/face_predictor', ['package.xml']), ('share/face_predictor/yaml', ['yaml/face_predictor.yaml']), ('lib/face_predictor/vision', ['vision/__init__.py']), ('share/' + package_name, ['launch/face_predictor.launch.py']), ('lib/face_predictor/vision/utils', ['vision/utils/__init__.py', 'vision/utils/misc.py', 'vision/utils/box_utils.py', 'vision/utils/box_utils_numpy.py']), ('lib/face_predictor/vision/transforms', ['vision/transforms/__init__.py', 'vision/transforms/transforms.py']), ('lib/face_predictor/vision/ssd', ['vision/ssd/data_preprocessing.py', 'vision/ssd/__init__.py', 'vision/ssd/mb_tiny_fd.py', 'vision/ssd/ssd.py', 'vision/ssd/predictor.py', 'vision/ssd/mb_tiny_RFB_fd.py']), ('lib/face_predictor/vision/ssd/config', ['vision/ssd/config/__init__.py', 'vision/ssd/config/fd_config.py']), ('lib/face_predictor/vision/nn', ['vision/nn/__init__.py', 'vision/nn/multibox_loss.py', 'vision/nn/mb_tiny_RFB.py', 'vision/nn/mb_tiny.py']), ('lib/face_predictor/vision/datasets', ['vision/datasets/voc_dataset.py', 'vision/datasets/__init__.py']), ('lib/face_predictor/models', ['models/voc-model-labels.txt', 'models/readme']), ('lib/face_predictor/models/pretrained', ['models/pretrained/version-RFB-320.pth', 'models/pretrained/version-slim-320.pth', 'models/pretrained/version-RFB-640.pth', 'models/pretrained/version-slim-640.pth']), ('lib/face_predictor/models/onnx', ['models/onnx/version-slim-320.onnx', 'models/onnx/version-RFB-320.onnx', 'models/onnx/version-RFB-320_ncnn_slim.onnx', 'models/onnx/version-RFB-320_without_postprocessing.onnx', 'models/onnx/version-slim-320_without_postprocessing.onnx', 'models/onnx/version-slim-320_ncnn_slim.onnx']), ], install_requires=['setuptools'], zip_safe=True, maintainer='hirorittsu', maintainer_email='<EMAIL>', description='TODO: Package description', license='TODO: License declaration', tests_require=['pytest'], entry_points={ 'console_scripts': [ 'face_predictor = face_predictor.script:main' ], }, )
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run.py
berges99/Spatial-Pyramid-Kernel-for-Image-Recognition
1
152687
<reponame>berges99/Spatial-Pyramid-Kernel-for-Image-Recognition import subprocess cmd = 'python3 histogram_svm2.py --data df128.csv --L 0 --train_frac 0.5 --test_frac 0.2 > ./results/out1.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 1 --train_frac 0.5 --test_frac 0.2 > ./results/out2.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 2 --train_frac 0.5 --test_frac 0.2 > ./results/out3.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 3 --train_frac 0.5 --test_frac 0.2 > ./results/out4.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 4 --train_frac 0.5 --test_frac 0.2 > ./results/out5.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 0 --quantization 10 --train_frac 0.5 --test_frac 0.2 > ./results/out6.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 1 --quantization 10 --train_frac 0.5 --test_frac 0.2 > ./results/out7.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 2 --quantization 10 --train_frac 0.5 --test_frac 0.2 > ./results/out8.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 3 --quantization 10 --train_frac 0.5 --test_frac 0.2 > ./results/out9.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 4 --quantization 10 --train_frac 0.5 --test_frac 0.2 > ./results/out10.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 0 --quantization 20 --train_frac 0.5 --test_frac 0.2 > ./results/out11.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 1 --quantization 20 --train_frac 0.5 --test_frac 0.2 > ./results/out12.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 2 --quantization 20 --train_frac 0.5 --test_frac 0.2 > ./results/out13.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 3 --quantization 20 --train_frac 0.5 --test_frac 0.2 > ./results/out14.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 4 --quantization 20 --train_frac 0.5 --test_frac 0.2 > ./results/out15.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 0 --quantization 30 --train_frac 0.5 --test_frac 0.2 > ./results/out16.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 1 --quantization 30 --train_frac 0.5 --test_frac 0.2 > ./results/out17.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 2 --quantization 30 --train_frac 0.5 --test_frac 0.2 > ./results/out18.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 3 --quantization 30 --train_frac 0.5 --test_frac 0.2 > ./results/out19.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 4 --quantization 30 --train_frac 0.5 --test_frac 0.2 > ./results/out20.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 0 --quantization 40 --train_frac 0.5 --test_frac 0.2 > ./results/out21.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 1 --quantization 40 --train_frac 0.5 --test_frac 0.2 > ./results/out22.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 2 --quantization 40 --train_frac 0.5 --test_frac 0.2 > ./results/out23.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 3 --quantization 40 --train_frac 0.5 --test_frac 0.2 > ./results/out24.txt' subprocess.Popen(cmd, shell = True) cmd = 'python3 histogram_svm2.py --data df128.csv --L 4 --quantization 40 --train_frac 0.5 --test_frac 0.2 > ./results/out25.txt' subprocess.Popen(cmd, shell = True)
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piret/workflows/__init__.py
bos-lab/piret
2
197486
<gh_stars>1-10 """Importing.""" import os import sys dir_path = os.path.dirname(os.path.realpath(__file__)) sys.path.append(dir_path) from single_seq import SingleSeq from dual_seq import DualSeq """Importing."""
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src/img_prediction.py
henlein/PoseContrast
0
189924
import bpy from PIL import ImageDraw import os import torch import cv2 import math import numpy as np from math import radians from PIL import Image, ImageFilter from dataset.data_utils import process_viewpoint_label, TransLightning, resize_pad, random_crop import torchvision.transforms as transforms from model.resnet import resnet50 from model.vp_estimator import BaselineEstimator, Estimator # Crop and Resize the image and paste it to the bg def crop_resize_paste(im, bg, left, upper, right, lower): # crop the RGBA image according to alpha channel bbox = im.getbbox() im = im.crop(bbox) # resize & padding the rendering then paste on the bg w, h = im.size target_w, target_h = right - left, lower - upper ratio = min(float(target_w) / w, float(target_h / h)) new_size = (int(w * ratio), int(h * ratio)) im = im.resize(new_size, Image.BILINEAR) bg.paste(im, (left + (target_w - new_size[0]) // 2, upper + (target_h - new_size[1]) // 2)) # create a lamp with an appropriate energy def makeLamp(lamp_name, rad): # Create new lamp data block lamp_data = bpy.data.lamps.new(name=lamp_name, type='POINT') lamp_data.energy = rad # modify the distance when the object is not normalized # lamp_data.distance = rad * 2.5 # Create new object with our lamp data block lamp_object = bpy.data.objects.new(name=lamp_name, object_data=lamp_data) # Link lamp object to the scene so it'll appear in this scene scene = bpy.context.scene scene.objects.link(lamp_object) return lamp_object def parent_obj_to_camera(b_camera): # set the parenting to the origin origin = (0, 0, 0) b_empty = bpy.data.objects.new("Empty", None) b_empty.location = origin b_camera.parent = b_empty scn = bpy.context.scene scn.objects.link(b_empty) scn.objects.active = b_empty return b_empty def clean_obj_lamp_and_mesh(context): scene = context.scene objs = bpy.data.objects meshes = bpy.data.meshes for obj in objs: if obj.type == "MESH" or obj.type == 'LAMP': scene.objects.unlink(obj) objs.remove(obj) for mesh in meshes: meshes.remove(mesh) def angles_to_matrix(angles): """Compute the rotation matrix from euler angles for a mini-batch""" azi = angles[:, 0] ele = angles[:, 1] rol = angles[:, 2] element1 = (torch.cos(rol) * torch.cos(azi) - torch.sin(rol) * torch.cos(ele) * torch.sin(azi)).unsqueeze(1) element2 = (torch.sin(rol) * torch.cos(azi) + torch.cos(rol) * torch.cos(ele) * torch.sin(azi)).unsqueeze(1) element3 = (torch.sin(ele) * torch.sin(azi)).unsqueeze(1) element4 = (-torch.cos(rol) * torch.sin(azi) - torch.sin(rol) * torch.cos(ele) * torch.cos(azi)).unsqueeze(1) element5 = (-torch.sin(rol) * torch.sin(azi) + torch.cos(rol) * torch.cos(ele) * torch.cos(azi)).unsqueeze(1) element6 = (torch.sin(ele) * torch.cos(azi)).unsqueeze(1) element7 = (torch.sin(rol) * torch.sin(ele)).unsqueeze(1) element8 = (-torch.cos(rol) * torch.sin(ele)).unsqueeze(1) element9 = (torch.cos(ele)).unsqueeze(1) return torch.cat((element1, element2, element3, element4, element5, element6, element7, element8, element9), dim=1) def render_obj(obj, output_dir, azi, ele, rol, name, shape=[512, 512], forward=None, up=None): clean_obj_lamp_and_mesh(bpy.context) # Setting up the environment scene = bpy.context.scene scene.render.resolution_x = shape[1] scene.render.resolution_y = shape[0] scene.render.resolution_percentage = 100 scene.render.alpha_mode = 'TRANSPARENT' # Camera setting cam = scene.objects['Camera'] cam_constraint = cam.constraints.new(type='TRACK_TO') cam_constraint.track_axis = 'TRACK_NEGATIVE_Z' cam_constraint.up_axis = 'UP_Y' b_empty = parent_obj_to_camera(cam) cam_constraint.target = b_empty # Light setting lamp_object = makeLamp('Lamp1', 5) lamp_add = makeLamp('Lamp2', 1) if forward is not None and up is not None: bpy.ops.import_scene.obj(filepath=obj, axis_forward=forward, axis_up=up) else: bpy.ops.import_scene.obj(filepath=obj) # normalize it and set the center for object in bpy.context.scene.objects: if object.name in ['Camera', 'Lamp'] or object.type == 'EMPTY': continue bpy.context.scene.objects.active = object max_dim = max(object.dimensions) object.dimensions = object.dimensions / max_dim if max_dim != 0 else object.dimensions # Output setting scene.render.image_settings.file_format = 'PNG' scene.render.filepath = os.path.join(output_dir, name) # scene.render.filepath = os.path.join(output_dir, name + '_render_%03d_%03d_%03d' % (int(azi), int(ele), int(rol))) # transform Euler angles from degrees into radians azi = radians(azi) ele = radians(ele) rol = radians(rol) r = 3 loc_y = r * math.cos(ele) * math.cos(azi) loc_x = r * math.cos(ele) * math.sin(azi) loc_z = r * math.sin(ele) print(loc_x, loc_y, loc_z) print("===========") cam.location = (loc_x, loc_y, loc_z + 0.5) bpy.ops.render.render(write_still=True) cam.location = (loc_x, loc_y, loc_z + 0.5) lamp_object.location = (loc_x, loc_y, 10) lamp_add.location = (loc_x, loc_y, -10) bpy.ops.render.render(write_still=True) # Change the in-plane rotation cam_ob = bpy.context.scene.camera bpy.context.scene.objects.active = cam_ob # select the camera object distance = np.sqrt(loc_x ** 2 + loc_y ** 2 + loc_z ** 2) bpy.ops.transform.rotate(value=90, axis=(loc_x / distance, loc_y / distance, loc_z / distance), constraint_axis=(False, False, False), constraint_orientation='GLOBAL', mirror=False, proportional='DISABLED', proportional_edit_falloff='SMOOTH', proportional_size=1) print(loc_x / distance, loc_y / distance, loc_z / distance) print(rol) print(rol * 180 / math.pi) bpy.ops.render.render(write_still=True) img_name = "../test_img/Screenshot 2022-01-26 120213.png" img_name = "../test_img/horse.png" obj_path = "../data/Pix3D/model/chair/IKEA_BERNHARD/model.obj" save_name = "PoseImg.png" out_dir = ".../test_img/out" img = Image.open(img_name).convert('RGB') im_pos = img.copy() normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) im_transform = transforms.Compose([transforms.ToTensor(), normalize]) #im = im.crop((left, upper, right, lower)) im_flip = img.transpose(Image.FLIP_LEFT_RIGHT) im = resize_pad(img, 224) im_flip = resize_pad(im_flip, 224) im = im_transform(im) im_flip = im_transform(im_flip) #im = im_flip # ================CREATE NETWORK============================ # os.environ["CUDA_VISIBLE_DEVICES"] = "0" net_feat = resnet50(num_classes=128) net_vp = BaselineEstimator(img_feature_dim=2048) net_feat.cuda() net_vp.cuda() state = torch.load("../exps/PoseContrast_ObjectNet3d_ZeroShot/ckpt.pth") net_feat.load_state_dict(state['net_feat']) net_vp.load_state_dict(state['net_vp']) net_feat.eval() net_vp.eval() im = im[None, :].cuda() feat, _ = net_feat(im) out = net_vp(feat) vp_pred, score = net_vp.compute_vp_pred(out, True) def RotMat(axis, ang): s = np.sin(ang) c = np.cos(ang) res = np.zeros((3, 3)) if axis == 'Z': res[0, 0] = c res[0, 1] = -s res[1, 0] = s res[1, 1] = c res[2, 2] = 1 elif axis == 'Y': res[0, 0] = c res[0, 2] = s res[1, 1] = 1 res[2, 0] = -s res[2, 2] = c elif axis == 'X': res[0, 0] = 1 res[1, 1] = c res[1, 2] = -s res[2, 1] = s res[2, 2] = c return res def angle2dcm(azi, ele, theta): azi = radians(azi) ele = radians(ele) theta = radians(theta) PI = np.arccos(-1) return np.dot(RotMat('Z', theta), np.dot(RotMat('X', - (PI / 2 - ele)), RotMat('Z', - azi))) #vp_pred[:, 1] = vp_pred[:, 1] - 180. #vp_pred[:, 2] = vp_pred[:, 2] - 180. #vp_pred = vp_pred * np.pi / 180. # render viewpoint predictions [azi, ele, inp] = vp_pred[0].detach().cpu().numpy() mat = angle2dcm(azi, ele - 90, inp - 180) mat = torch.FloatTensor(mat) front = torch.FloatTensor([0,0,1]) right = torch.FloatTensor([0,1,0]) up = torch.FloatTensor([1,0,0]) print(mat) print("....") print("Front", torch.matmul(front, mat)) print("Up", torch.matmul(up, mat)) print("Right", torch.matmul(right, mat)) render_obj(obj_path, "out", azi, ele - 90, inp - 180, "test.png", [521, 512], None, None) #render_obj(obj_path, # "out", # azi, ele, inp, # "test.png", # [521, 512], None, None) # blend viewpoint results into the original image #img = cv2.imread(img_name) #w, h = img.shape[1], img.shape[0] #bg = Image.new("RGBA", (w, h)) #vp_rend = Image.open(os.path.join(out_dir, save_name)) #crop_resize_paste(vp_rend, bg, left, upper, right, lower) #draw = ImageDraw.Draw(bg) #draw.rectangle([(left, upper), (right, lower)], outline=(0, 255, 0, 255), # width=int(0.02 * min(img.shape[0], img.shape[1]))) #print("bgsave") #bg.save(os.path.join(out_dir, save_name))
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yahoo_with_mixtext/train_tmix_single_policy.py
sgondala/Automix
1
59034
<filename>yahoo_with_mixtext/train_tmix_single_policy.py<gh_stars>1-10 import torch import torch.nn as nn import torch.nn.functional as F from torch.optim import Adam from torch.utils.data import DataLoader, random_split, ConcatDataset import pandas as pd import numpy as np import transformers from transformers import AutoModelForSequenceClassification, AutoTokenizer from FastAutoAugment.read_data import * from FastAutoAugment.classification_models.TestClassifier import * from FastAutoAugment.classification_models.BertBasedClassifier import * from FastAutoAugment.classification_models.RandomDistilBertClassifier import * from FastAutoAugment.classification_models.MixText import * import pickle import wandb import argparse import os parser = argparse.ArgumentParser(description='PyTorch MixText') parser.add_argument('--epochs', default=50, type=int, metavar='N', help='number of total epochs to run') parser.add_argument('--lrmain', '--learning-rate-bert', default=2e-5, type=float, metavar='LR', help='initial learning rate for bert') parser.add_argument('--lrlast', '--learning-rate-model', default=1e-3, type=float, metavar='LR', help='initial learning rate for models') parser.add_argument('--lr-decay', default=0.98, type=float, help='alpha for beta distribution') parser.add_argument('--stop-decay-after', default=-1, type=int, help='batch size for train') parser.add_argument('--batch-size', default=32, type=int, metavar='N', help='train batchsize') parser.add_argument('--alpha', type=float, default=2) parser.add_argument('--mix-layers', nargs='+', default=[7,9,12], type=int, help='define mix layer set') args = parser.parse_args() # Seeds np.random.seed(42) torch.manual_seed(42) torch.cuda.manual_seed_all(42) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True function_name_to_tmix_functions_map = { 'synonym_replacement': 'TMix_with_EDA_synonym_replacement', 'random_insert': 'TMix_with_EDA_random_insertion', 'random_swap': 'TMix_with_EDA_random_swap', 'random_delete': 'TMix_with_EDA_random_deletion', 'random': 'TMix', 'intra_lada': 'Intra_LADA' } def own_loss(logits, target, num_labels): assert logits.shape == target.shape loss = -torch.sum(F.log_softmax(logits, dim=1)*target, axis=1) assert loss.shape[0] == target.shape[0] return loss.mean() if __name__ == "__main__": wandb.init(project="auto_augment") wandb.config.update(args) run_name = 'train_yahoo_10_with_base_policy_random_insert_0.7079546446941665_0.27912003338168995_0.8035810562324185_synonym_replacement_0.27517045577668814_0.6137977840002864_0.4953631547548106_random_insert_0.5525104535555041_0.3032184207648565_0.7924285457888011' wandb.run.name = run_name wandb.run.save() train = pickle.load(open('data/paper_yahoo_split/yahoo_train_10_per_class.pkl', 'rb')) val = pickle.load(open('data/paper_yahoo_split/yahoo_val_200_per_class.pkl', 'rb')) model_name = 'bert-base-uncased' dataset_identifier = 'train_10' function_names = ['random_insert', 'synonym_replacement', 'random_insert'] probabilities = [0.7079546446941665, 0.27517045577668814, 0.5525104535555041] argument_1s = [0.27912003338168995, 0.6137977840002864, 0.3032184207648565] argument_2s = [0.8035810562324185, 0.4953631547548106, 0.7924285457888011] datasets = [] for i in range(len(function_names)): function_name = function_names[i] probability_of_application = probabilities[i] argument_1 = argument_1s[i] argument_2 = argument_2s[i] if function_name in ['random', 'intra_lada']: # 0 arguments mix = function_name_to_tmix_functions_map[function_name] augmented_dataset = create_dataset( train['X'], train['y'], model_name, 256, mix=mix, num_classes=10, probability_of_application = probability_of_application, dataset_identifier = dataset_identifier) elif function_name in ['synonym_replacement', 'random_insert', 'random_swap', 'random_delete']: # one argument mix = function_name_to_tmix_functions_map[function_name] augmented_dataset = create_dataset( train['X'], train['y'], model_name, 256, mix=mix, num_classes=10, probability_of_application = probability_of_application, alpha=argument_1, dataset_identifier = dataset_identifier) elif function_name == 'inter_lada': # 2 arguments knn = int(argument_1 * 10) + 1 # Making sure we have atleast k = 1 mu = argument_2 augmented_dataset = create_dataset( train['X'], train['y'], model_name, 256, mix='Inter_LADA', num_classes=10, probability_of_application = probability_of_application, knn_lada=knn, mu_lada=mu, dataset_identifier = dataset_identifier) datasets.append(augmented_dataset) base_train = create_dataset(train['X'], train['y'], model_name, 256, mix='duplicate', num_classes=10) # datasets.append(base_train) val_dataset = create_dataset(val['X'], val['y'], model_name, 256, mix=None) train_dataset = ConcatDataset(datasets) train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, num_workers=3, shuffle=True) val_dataloader = DataLoader(val_dataset, batch_size=64, num_workers=3, shuffle=False) model = MixText(10, True).cuda() model = nn.DataParallel(model) wandb.watch(model, log=all) optimizer = AdamW( [ {"params": model.module.bert.parameters(), "lr": args.lrmain}, {"params": model.module.linear.parameters(), "lr": args.lrlast}, ] ) decayRate = args.lr_decay lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=decayRate) criterion = nn.CrossEntropyLoss() best_val_accuracy = 0 for epoch in range(args.epochs): # Train loop model.train() for batch in tqdm(train_dataloader, desc=f'Train epoch {epoch}'): encoded_1, encoded_2, label_1, label_2 = batch assert encoded_1.shape == encoded_2.shape mix_layer = np.random.choice(args.mix_layers) l = np.random.beta(args.alpha, args.alpha) l = max(l, 1-l) logits = model(encoded_1.cuda(), encoded_2.cuda(), l, mix_layer) combined_labels = label_1 * l + label_2 * (1-l) loss = own_loss(logits, combined_labels.cuda(), num_labels=10) wandb.log({'Train loss': loss.item()}, step=epoch) optimizer.zero_grad() loss.backward() optimizer.step() wandb.log({ 'lr_bert':lr_scheduler.get_lr()[0], 'lr_linear':lr_scheduler.get_lr()[1]}, step=epoch ) if epoch <= args.stop_decay_after: lr_scheduler.step() # Val loop model.eval() with torch.no_grad(): loss_total = 0 correct = 0 total_sample = 0 for batch in tqdm(val_dataloader, desc=f'Val epoch {epoch}'): encoded_text, _, target, _ = batch outputs = model(encoded_text.cuda()) loss = criterion(outputs, target.cuda()) # all_losses.append(loss.item()) _, predicted = torch.max(outputs.data, 1) correct += (np.array(predicted.cpu()) == np.array(target.cpu())).sum() loss_total += loss.item() * encoded_text.shape[0] total_sample += encoded_text.shape[0] acc_total = correct / total_sample loss_total = loss_total / total_sample print(f'Epoch number {epoch} Val Loss {loss_total} Val accuracy {acc_total}') wandb.log({'Val loss' : loss_total}, step=epoch) wandb.log({'Val accuracy': acc_total}, step=epoch) if acc_total > best_val_accuracy: best_val_accuracy = acc_total wandb.run.summary['Val accuracy'] = acc_total checkpoint_path = f'checkpoints/{run_name}/' if not os.path.isdir(checkpoint_path): os.makedirs(checkpoint_path) torch.save(model, f'{checkpoint_path}/model_best.pth')
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stations.py
wipfli/airspaces
1
186956
import requests import pandas as pd from io import StringIO import json r = requests.get('https://data.geo.admin.ch/ch.meteoschweiz.messnetz-automatisch/ch.meteoschweiz.messnetz-automatisch_de.csv') df = pd.read_csv(StringIO(r.text), sep=';') features = [] for i in range(299): if df.loc[i]['Stationstyp'] == 'Wetterstation': name = df.loc[i]['Station'] wigos_id = df.loc[i]['WIGOS-ID'] altitude = df.loc[i]['Stationshöhe m. ü. M.'] latitude = df.loc[i]['Breitengrad'] longitude = df.loc[i]['Längengrad'] properties = { 'name': str(name), 'id': str(wigos_id), 'altitude': str(altitude), 'longitude': ('%.4f' % longitude), 'latitude': ('%.4f' % latitude), } feature = { 'type': 'Feature', 'properties': properties, 'geometry': { 'type': 'Point', 'coordinates': [float('%.4f' % longitude), float('%.4f' % latitude)] }, } features += [feature] geojson = { 'type': 'FeatureCollection', 'features': features } with open('stations.geojson', 'w') as f: json.dump(geojson, f)
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src/migrations/versions/0930fdefb325_initial_migration.py
vishaltanwar96/Py-iTrack
4
43632
"""initial migration Revision ID: 0930fdefb325 Revises: Create Date: 2020-09-27 21:13:23.088570 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '0930fdefb325' down_revision = None branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('criticality', sa.Column('id', sa.Integer(), nullable=False), sa.Column('value', sa.String(length=100), nullable=False), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('value') ) op.create_table('role', sa.Column('id', sa.Integer(), nullable=False), sa.Column('value', sa.String(length=100), nullable=False), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('value') ) op.create_table('status', sa.Column('id', sa.Integer(), nullable=False), sa.Column('value', sa.String(length=100), nullable=False), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('value') ) op.create_table('user', sa.Column('id', sa.Integer(), nullable=False), sa.Column('first_name', sa.String(length=30), nullable=False), sa.Column('last_name', sa.String(length=30), nullable=False), sa.Column('email', sa.String(length=200), nullable=False), sa.Column('password', sa.Text(), nullable=False), sa.Column('is_verified', sa.Boolean(), server_default=sa.text('0'), nullable=True), sa.Column('created_at', sa.DateTime(), server_default=sa.text('now()'), nullable=True), sa.Column('role_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['role_id'], ['role.id'], ), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('email') ) op.create_table('organisation', sa.Column('id', sa.Integer(), nullable=False), sa.Column('name', sa.String(length=70), nullable=False), sa.Column('passcode', sa.Text(), nullable=False), sa.Column('location', sa.String(length=30), nullable=False), sa.Column('registered_at', sa.DateTime(), server_default=sa.text('now()'), nullable=False), sa.Column('registered_by', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['registered_by'], ['user.id'], ), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name') ) op.create_table('project', sa.Column('id', sa.Integer(), nullable=False), sa.Column('name', sa.String(length=100), nullable=False), sa.Column('description', sa.String(length=255), server_default='', nullable=False), sa.Column('status_id', sa.Integer(), nullable=True), sa.Column('created_at', sa.DateTime(), server_default=sa.text('now()'), nullable=False), sa.Column('organisation_id', sa.Integer(), nullable=True), sa.Column('created_by', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['created_by'], ['user.id'], ), sa.ForeignKeyConstraint(['organisation_id'], ['organisation.id'], ), sa.ForeignKeyConstraint(['status_id'], ['status.id'], ), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name') ) op.create_table('user_organisation', sa.Column('id', sa.Integer(), nullable=False), sa.Column('user_id', sa.Integer(), nullable=False), sa.Column('organisation_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['organisation_id'], ['organisation.id'], ), sa.ForeignKeyConstraint(['user_id'], ['user.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('project_history', sa.Column('remarks', sa.Text(), server_default=sa.text('NULL'), nullable=True), sa.Column('created_at', sa.DateTime(), server_default=sa.text('now()'), nullable=False), sa.Column('id', sa.Integer(), nullable=False), sa.Column('project_id', sa.Integer(), nullable=False), sa.Column('created_by', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['created_by'], ['user.id'], ), sa.ForeignKeyConstraint(['project_id'], ['project.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('task', sa.Column('id', sa.Integer(), nullable=False), sa.Column('name', sa.String(length=200), nullable=False), sa.Column('description', sa.Text(), nullable=False), sa.Column('created_at', sa.DateTime(), server_default=sa.text('now()'), nullable=False), sa.Column('created_by', sa.Integer(), nullable=True), sa.Column('assigned_by', sa.Integer(), nullable=True), sa.Column('assigned_to', sa.Integer(), nullable=True), sa.Column('reviewed_by', sa.Integer(), nullable=True), sa.Column('status_id', sa.Integer(), nullable=True), sa.Column('criticality_id', sa.Integer(), nullable=True), sa.Column('expected_completion_date', sa.DateTime(), sa.Computed('created_at + INTERVAL 3 DAY', persisted=True), nullable=False), sa.Column('project_id', sa.Integer(), nullable=True), sa.Column('actual_completion_date', sa.DateTime(), server_default=sa.text('NULL'), nullable=True), sa.ForeignKeyConstraint(['assigned_by'], ['user.id'], ), sa.ForeignKeyConstraint(['assigned_to'], ['user.id'], ), sa.ForeignKeyConstraint(['created_by'], ['user.id'], ), sa.ForeignKeyConstraint(['criticality_id'], ['criticality.id'], ), sa.ForeignKeyConstraint(['project_id'], ['project.id'], ), sa.ForeignKeyConstraint(['reviewed_by'], ['user.id'], ), sa.ForeignKeyConstraint(['status_id'], ['status.id'], ), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('name') ) op.create_table('user_project', sa.Column('id', sa.Integer(), nullable=False), sa.Column('user_id', sa.Integer(), nullable=False), sa.Column('project_id', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['project_id'], ['project.id'], ), sa.ForeignKeyConstraint(['user_id'], ['user.id'], ), sa.PrimaryKeyConstraint('id') ) op.create_table('task_history', sa.Column('remarks', sa.Text(), server_default=sa.text('NULL'), nullable=True), sa.Column('created_at', sa.DateTime(), server_default=sa.text('now()'), nullable=False), sa.Column('id', sa.Integer(), nullable=False), sa.Column('task_id', sa.Integer(), nullable=False), sa.Column('created_by', sa.Integer(), nullable=False), sa.ForeignKeyConstraint(['created_by'], ['user.id'], ), sa.ForeignKeyConstraint(['task_id'], ['task.id'], ), sa.PrimaryKeyConstraint('id') ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('task_history') op.drop_table('user_project') op.drop_table('task') op.drop_table('project_history') op.drop_table('user_organisation') op.drop_table('project') op.drop_table('organisation') op.drop_table('user') op.drop_table('status') op.drop_table('role') op.drop_table('criticality') # ### end Alembic commands ###
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src/omfgp/scp02.py
stepansnigirev/omfgp
0
141444
class SCP02: pass
[ 1, 770, 317, 6271, 29900, 29906, 29901, 13, 12, 3364, 13, 2 ]
Problemset/distribute-candies-to-people/distribute-candies-to-people.py
KivenCkl/LeetCode
7
75055
<filename>Problemset/distribute-candies-to-people/distribute-candies-to-people.py # @Title: 分糖果 II (Distribute Candies to People) # @Author: KivenC # @Date: 2019-07-03 18:34:09 # @Runtime: 44 ms # @Memory: 13 MB class Solution: def distributeCandies(self, candies: int, num_people: int) -> List[int]: # # way 1 # # 假设总分发次数为 k，则总共需要的糖果数为 S(k) = (1 + k) * k / 2 # # 由于最后一次可以不发满，无论剩多少都会给最后一个小朋友，所以 S(k-1) < candies <= S(k) # # 通过该不等式可以算出 (-1+(1+8*candies)^(1/2))/2 <= k < (1+(1+8*candies)^(1/2))/2 # import math # k = math.ceil((math.sqrt(1 + 8 * candies) - 1) / 2) # i = 0 # res = [0] * num_people # # 因为不知道最后一次需要发多少糖果，因此循环分发 k - 1 次 # while k > 1: # res[i % num_people] += i + 1 # i += 1 # candies -= i # k -= 1 # # 将剩余糖果发掉 # res[i % num_people] += candies # return res # # way 2 # # 直接模拟整个过程 # k = 0 # i = 0 # arr = [0] * num_people # while(True): # k += 1 # if(k <= candies): # arr[i] += k # candies -= k # else: # arr[i] += candies # break # i = (i + 1) % num_people # return arr # way 3 # 优化 way 1 import math k = math.ceil((math.sqrt(1 + 8 * candies) - 1) / 2) i, j = k // num_people, k % num_people # 前 j 个小朋友发了 i+1 次，剩下的发了 i 次 if j == 0: j = num_people i -= 1 remain = candies - k * (k - 1) // 2 # 最后一次发的糖果数 res = [] for ind in range(1, num_people+1): if ind < j: res.append((i + 1) * i // 2 * num_people + (i + 1) * ind) else: tmp = (i * (i - 1) // 2 * num_people if i > 0 else 0) + i * ind if ind == j: res.append(tmp + remain) else: res.append(tmp) return res
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nokiaapp/views.py
orcasgit/django-nokia
2
97524
<gh_stars>1-10 import logging from django.contrib.auth.decorators import login_required from django.contrib.auth.signals import user_logged_in from django.core.urlresolvers import reverse from django.dispatch import receiver from django.http import HttpResponse, Http404 from django.shortcuts import redirect, render from django.utils import timezone from django.views.decorators.csrf import csrf_exempt from . import utils from .models import NokiaUser, MeasureGroup try: from django.urls import NoReverseMatch except ImportError: # Fallback for older Djangos from django.core.urlresolvers import NoReverseMatch logger = logging.getLogger(__name__) @login_required def login(request): """ Begins the OAuth authentication process by obtaining a Request Token from Nokia and redirecting the user to the Nokia site for authorization. When the user has finished at the Nokia site, they will be redirected to the :py:func:`nokiaapp.views.complete` view. If 'next' is provided in the GET data, it is saved in the session so the :py:func:`nokiaapp.views.complete` view can redirect the user to that URL upon successful authentication. URL name: `nokia-login` """ next_url = request.GET.get('next', None) if next_url: request.session['nokia_next'] = next_url else: request.session.pop('nokia_next', None) callback_uri = request.build_absolute_uri(reverse('nokia-complete')) auth = utils.create_nokia_auth(callback_uri) auth_url = auth.get_authorize_url() return redirect(auth_url) @login_required def complete(request): """ After the user authorizes us, Nokia sends a callback to this URL to complete authentication. If there was an error, the user is redirected again to the `error` view. If the authorization was successful, the credentials are stored for us to use later, and the user is redirected. If 'next_url' is in the request session, the user is redirected to that URL. Otherwise, they are redirected to the URL specified by the setting :ref:`NOKIA_LOGIN_REDIRECT`. If :ref:`NOKIA_SUBSCRIBE` is set to True, add a subscription to user data at this time. URL name: `nokia-complete` """ callback_uri = request.build_absolute_uri(reverse('nokia-complete')) auth = utils.create_nokia_auth(callback_uri) try: code = request.GET.get('code') except KeyError: return redirect(reverse('nokia-error')) if not code: return redirect(reverse('nokia-error')) try: creds = auth.get_credentials(code) except: return redirect(reverse('nokia-error')) user_updates = { 'access_token': creds.access_token, 'token_expiry': creds.token_expiry, 'token_type': creds.token_type, 'refresh_token': creds.refresh_token, 'nokia_user_id': creds.user_id, 'last_update': timezone.now(), } nokia_user = NokiaUser.objects.filter(user=request.user) if nokia_user.exists(): nokia_user.update(**user_updates) nokia_user = nokia_user[0] else: user_updates['user'] = request.user nokia_user = NokiaUser.objects.create(**user_updates) # Add the Nokia user info to the session api = utils.create_nokia(**nokia_user.get_user_data()) request.session['nokia_profile'] = api.get_user() MeasureGroup.create_from_measures(request.user, api.get_measures()) if utils.get_setting('NOKIA_SUBSCRIBE'): for appli in [1, 4]: notification_url = request.build_absolute_uri( reverse('nokia-notification', kwargs={'appli': appli})) api.subscribe(notification_url, 'django-nokia', appli=appli) next_url = request.session.pop('nokia_next', None) or utils.get_setting( 'NOKIA_LOGIN_REDIRECT') return redirect(next_url) @receiver(user_logged_in) def create_nokia_session(sender, request, user, **kwargs): """ If the user is a Nokia user, update the profile in the session. """ if (user.is_authenticated() and utils.is_integrated(user) and user.is_active): nokia_user = NokiaUser.objects.filter(user=user) if nokia_user.exists(): api = utils.create_nokia(**nokia_user[0].get_user_data()) try: request.session['nokia_profile'] = api.get_user() except: pass @login_required def error(request): """ The user is redirected to this view if we encounter an error acquiring their Nokia credentials. It renders the template defined in the setting :ref:`NOKIA_ERROR_TEMPLATE`. The default template, located at *nokia/error.html*, simply informs the user of the error:: <html> <head> <title>Nokia Authentication Error</title> </head> <body> <h1>Nokia Authentication Error</h1> <p>We encontered an error while attempting to authenticate you through Nokia.</p> </body> </html> URL name: `nokia-error` """ return render(request, utils.get_setting('NOKIA_ERROR_TEMPLATE'), {}) @login_required def logout(request): """Forget this user's Nokia credentials. If the request has a `next` parameter, the user is redirected to that URL. Otherwise, they're redirected to the URL defined in the setting :ref:`NOKIA_LOGOUT_REDIRECT`. URL name: `nokia-logout` """ nokia_user = NokiaUser.objects.filter(user=request.user) urls = [] for appli in [1, 4]: for app in ['nokia', 'withings']: try: urls.append(request.build_absolute_uri(reverse( '{}-notification'.format(app), kwargs={'appli': appli} ))) except NoReverseMatch: # The library user does not have the legacy withings URLs pass if nokia_user.exists() and utils.get_setting('NOKIA_SUBSCRIBE'): try: api = utils.create_nokia(**nokia_user[0].get_user_data()) subs = api.list_subscriptions() for sub in subs: if sub['callbackurl'] in urls: api.unsubscribe(sub['callbackurl'], appli=sub['appli']) except: return redirect(reverse('nokia-error')) nokia_user.delete() next_url = request.GET.get('next', None) or utils.get_setting( 'NOKIA_LOGOUT_REDIRECT') return redirect(next_url) @csrf_exempt def notification(request, appli): """ Receive notification from Nokia. More information here: https://developer.health.nokia.com/api/doc#api-Notification-Notification_callback URL name: `nokia-notification` """ if request.method == 'HEAD': return HttpResponse() # The updates come in as a POST request with the necessary data uid = request.POST.get('userid') if uid and request.method == 'POST': for user in NokiaUser.objects.filter(nokia_user_id=uid): kwargs = {} if user.last_update: kwargs['lastupdate'] = user.last_update try: measures = utils.get_nokia_data(user, **kwargs) except Exception: logger.exception("Error getting nokia user measures") else: MeasureGroup.create_from_measures(user.user, measures) user.last_update = timezone.now() user.save() return HttpResponse(status=204) # If GET request or POST with bad data, raise a 404 raise Http404
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rec_to_nwb/test/processing/tools/test_beartype.py
jihyunbak/rec_to_nwb
8
23957
<reponame>jihyunbak/rec_to_nwb #!/usr/bin/env python3 """ `py.test`-driven unit test suite for the `@beartype` decorator, implementing a rudimentary subset of PEP 484-style type checking based on Python 3.x function annotations. Usage ---------- These tests assume the `@beartype` decorator and all utility functions (e.g., `_check_type_annotation()`) and globals (e.g., `_PARAMETER_KIND_IGNORED`) required by this decorator to reside in a top-level module named `beartype`. If this is the case, these tests may be run as is with: $ py.test -k test_beartype See Also ---------- https://stackoverflow.com/a/37961120/2809027 Stackoverflow answer introducing the `@beartype` decorator. """ from unittest import TestCase import typing import pytest from testfixtures import should_raise from rec_to_nwb.processing.tools.beartype.beartype import beartype class TestBearyype(TestCase): def test_beartype_noop(self) -> None: """ Test bear typing of a function with no function annotations, reducing to _no_ type checking. """ # Unannotated function to be type checked. @beartype def khorne(gork, mork): return gork + mork # Call this function and assert the expected return value. assert khorne('WAAAGH!', '!HGAAAW') == 'WAAAGH!!HGAAAW' # ....................{ TESTS ~ pass : param }.................... def test_beartype_pass_param_keyword_and_positional(self) -> None: """ Test bear typing of a function call successfully passed both annotated positional and keyword parameters. """ # Function to be type checked. @beartype def slaanesh(daemonette: str, keeper_of_secrets: str) -> str: return daemonette + keeper_of_secrets # Call this function with both positional and keyword arguments and assert # the expected return value. assert slaanesh( 'Seeker of Decadence', keeper_of_secrets="N'Kari") == ( "Seeker of DecadenceN'Kari") def test_beartype_pass_param_keyword_only(self) -> None: """ Test bear typing of a function call successfully passed an annotated keyword-only parameter following an `*` or `*args` parameter. """ # Function to be type checked. @beartype def changer_of_ways(sky_shark: str, *, chaos_spawn: str) -> str: return sky_shark + chaos_spawn # Call this function with keyword arguments and assert the expected return # value. assert changer_of_ways( 'Screamers', chaos_spawn="Mith'an'driarkh") == ( "ScreamersMith'an'driarkh") def test_beartype_pass_param_tuple(self) -> None: """ Test bear typing of a function call successfully passed a parameter annotated as a tuple. """ # Function to be type checked. @beartype def genestealer(tyranid: str, hive_fleet: (str, int)) -> str: return tyranid + str(hive_fleet) # Call this function with each of the two types listed in the above tuple. assert genestealer( 'Norn-Queen', hive_fleet='Behemoth') == 'Norn-QueenBehemoth' assert genestealer( 'Carnifex', hive_fleet=0xDEADBEEF) == 'Carnifex3735928559' def test_type_check_pass_param_custom(self) -> None: """ Test bear typing of a function call successfully passed a parameter annotated as a user-defined rather than builtin type. """ # User-defined type. class CustomTestStr(str): pass # Function to be type checked. @beartype def hrud(gugann: str, delphic_plague: CustomTestStr) -> str: return gugann + delphic_plague # Call this function with each of the two types listed in the above tuple. assert hrud( 'Troglydium hruddi', delphic_plague=CustomTestStr('Delphic Sink')) == ( 'Troglydium hruddiDelphic Sink') def test_type_check_pass_typing_module(self) -> None: """ Test bear typing of a function call successfully passed a parameter annotated with an abstract type from the typing module. """ MyMap = typing.Mapping @beartype def function(par: MyMap, ameter: MyMap) -> MyMap: result = par.copy() result.update(ameter) return result assert function({1:1}, {2:2}) == {1:1, 2:2} def test_type_check_pass_parameterized_typing_module(self) -> None: """ Test bear typing of a function call successfully passed a parameter annotated with a parametirized abstract type from the typing module. """ MyMap = typing.Mapping @beartype def function(par: MyMap, ameter: MyMap) -> MyMap: result = par.copy() result.update(ameter) return result assert function({1:1}, {2:2}) == {1:1, 2:2} # ....................{ TESTS ~ pass : return }.................... def test_type_check_pass_return_none(self) -> None: """ Test bear typing of a function call successfully returning `None` and annotated as such. """ # Function to be type checked. @beartype def xenos(interex: str, diasporex: str) -> None: interex + diasporex # Call this function and assert no value to be returned. assert xenos( 'Luna Wolves', diasporex='Iron Hands Legion') is None # ....................{ TESTS ~ fail }.................... def test_beartype_fail_keyword_unknown(self) -> None: """ Test bear typing of an annotated function call passed an unrecognized keyword parameter. """ # Annotated function to be type checked. @beartype def tau(kroot: str, vespid: str) -> str: return kroot + vespid # Call this function with an unrecognized keyword parameter and assert the # expected exception. with pytest.raises(TypeError) as exception: tau(kroot='Greater Good', nicassar='Dhow') # For readability, this should be a "TypeError" synopsizing the exact issue # raised by the Python interpreter on calling the original function rather # than a "TypeError" failing to synopsize the exact issue raised by the # wrapper type-checking the original function. Since the function # annotations defined above guarantee that the exception message of the # latter will be suffixed by "not a str", ensure this is *NOT* the case. assert not str(exception.value).endswith('not a str') def test_beartype_fail_param_name(self) -> None: """ Test bear typing of a function accepting a parameter name reserved for use by the `@beartype` decorator. """ # Define a function accepting a reserved parameter name and assert the # expected exception. @beartype @should_raise(NameError) def jokaero(weaponsmith: str, __beartype_func: str) -> str: return weaponsmith + __beartype_func # ....................{ TESTS ~ fail : type }.................... def test_beartype_fail_param_type(self) -> None: """ Test bear typing of an annotated function call failing a parameter type check. """ # Annotated function to be type checked. @beartype def eldar(isha: str, asuryan: (str, int)) -> str: return isha + asuryan # Call this function with an invalid type and assert the expected exception. with pytest.raises(TypeError): eldar('Mother of the Eldar', 100.100) def test_beartype_fail_return_type(self) -> None: """ Test bear typing of an annotated function call failing a return type check. """ # Annotated function to be type checked. @beartype def necron(star_god: str, old_one: str) -> str: return 60e6 # Call this function and assert the expected exception. with pytest.raises(TypeError): necron("C'tan", 'Elder Thing') # ....................{ TESTS ~ fail : annotation }.................... def test_beartype_fail_annotation_param(self) -> None: """ Test bear typing of a function with an unsupported parameter annotation. """ # Assert the expected exception from attempting to type check a function # with a parameter annotation that is *NOT* a type. with pytest.raises(TypeError): @beartype def nurgle(nurgling: str, great_unclean_one: 'Bringer of Poxes') -> str: return nurgling + great_unclean_one def test_beartype_fail_annotation_return(self) -> None: """ Test bear typing of a function with an unsupported return annotation. 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sfepy/terms/terms_navier_stokes.py
vondrejc/sfepy
0
13637
import numpy as nm from sfepy.linalg import dot_sequences from sfepy.terms.terms import Term, terms class DivGradTerm(Term): r""" Diffusion term. :Definition: .. math:: \int_{\Omega} \nu\ \nabla \ul{v} : \nabla \ul{u} \mbox{ , } \int_{\Omega} \nu\ \nabla \ul{u} : \nabla \ul{w} \\ \int_{\Omega} \nabla \ul{v} : \nabla \ul{u} \mbox{ , } \int_{\Omega} \nabla \ul{u} : \nabla \ul{w} :Arguments 1: - material : :math:`\nu` (viscosity, optional) - virtual : :math:`\ul{v}` - state : :math:`\ul{u}` :Arguments 2: - material : :math:`\nu` (viscosity, optional) - parameter_1 : :math:`\ul{u}` - parameter_2 : :math:`\ul{w}` """ name = 'dw_div_grad' arg_types = (('opt_material', 'virtual', 'state'), ('opt_material', 'parameter_1', 'parameter_2')) arg_shapes = {'opt_material' : '1, 1', 'virtual' : ('D', 'state'), 'state' : 'D', 'parameter_1' : 'D', 'parameter_2' : 'D'} modes = ('weak', 'eval') function = staticmethod(terms.term_ns_asm_div_grad) def d_div_grad(self, out, grad1, grad2, mat, vg, fmode): sh = grad1.shape g1 = grad1.reshape((sh[0], sh[1], sh[2] * sh[3])) g2 = grad2.reshape((sh[0], sh[1], sh[2] * sh[3])) aux = mat * dot_sequences(g1[..., None], g2, 'ATB')[..., None] if fmode == 2: out[:] = aux status = 0 else: status = vg.integrate(out, aux, fmode) return status def get_fargs(self, mat, virtual, state, mode=None, term_mode=None, diff_var=None, **kwargs): vg, _ = self.get_mapping(state) if mat is None: n_el, n_qp, dim, n_en, n_c = self.get_data_shape(state) mat = nm.ones((1, n_qp, 1, 1), dtype=nm.float64) if mode == 'weak': if diff_var is None: grad = self.get(state, 'grad').transpose((0, 1, 3, 2)) sh = grad.shape grad = grad.reshape((sh[0], sh[1], sh[2] * sh[3], 1)) fmode = 0 else: grad = nm.array([0], ndmin=4, dtype=nm.float64) fmode = 1 return grad, mat, vg, fmode elif mode == 'eval': grad1 = self.get(virtual, 'grad') grad2 = self.get(state, 'grad') fmode = {'eval' : 0, 'el_avg' : 1, 'qp' : 2}.get(mode, 1) return grad1, grad2, mat, vg, fmode else: raise ValueError('unsupported evaluation mode in %s! (%s)' % (self.name, mode)) def get_eval_shape(self, mat, virtual, state, mode=None, term_mode=None, diff_var=None, **kwargs): n_el, n_qp, dim, n_en, n_c = self.get_data_shape(state) return (n_el, 1, 1, 1), state.dtype def set_arg_types(self): if self.mode == 'weak': self.function = terms.term_ns_asm_div_grad else: self.function = self.d_div_grad class ConvectTerm(Term): r""" Nonlinear convective term. :Definition: .. math:: \int_{\Omega} ((\ul{u} \cdot \nabla) \ul{u}) \cdot \ul{v} :Arguments: - virtual : :math:`\ul{v}` - state : :math:`\ul{u}` """ name = 'dw_convect' arg_types = ('virtual', 'state') arg_shapes = {'virtual' : ('D', 'state'), 'state' : 'D'} function = staticmethod(terms.term_ns_asm_convect) def get_fargs(self, virtual, state, mode=None, term_mode=None, diff_var=None, **kwargs): vg, _ = self.get_mapping(state) grad = self.get(state, 'grad').transpose((0, 1, 3, 2)).copy() val_qp = self.get(state, 'val') fmode = diff_var is not None return grad, val_qp, vg, fmode class LinearConvectTerm(Term): r""" Linearized convective term. :Definition: .. math:: \int_{\Omega} ((\ul{b} \cdot \nabla) \ul{u}) \cdot \ul{v} .. math:: ((\ul{b} \cdot \nabla) \ul{u})|_{qp} :Arguments: - virtual : :math:`\ul{v}` - parameter : :math:`\ul{b}` - state : :math:`\ul{u}` """ name = 'dw_lin_convect' arg_types = ('virtual', 'parameter', 'state') arg_shapes = {'virtual' : ('D', 'state'), 'parameter' : 'D', 'state' : 'D'} function = staticmethod(terms.dw_lin_convect) def get_fargs(self, virtual, parameter, state, mode=None, term_mode=None, diff_var=None, **kwargs): vg, _ = self.get_mapping(state) val_qp = self.get(parameter, 'val') if mode == 'weak': if diff_var is None: grad = self.get(state, 'grad').transpose((0, 1, 3, 2)).copy() fmode = 0 else: grad = nm.array([0], ndmin=4, dtype=nm.float64) fmode = 1 return grad, val_qp, vg, fmode elif mode == 'qp': grad = self.get(state, 'grad').transpose((0, 1, 3, 2)).copy() fmode = 2 return grad, val_qp, vg, fmode else: raise ValueError('unsupported evaluation mode in %s! (%s)' % (self.name, mode)) class StokesTerm(Term): r""" Stokes problem coupling term. Corresponds to weak forms of gradient and divergence terms. Can be evaluated. :Definition: .. math:: \int_{\Omega} p\ \nabla \cdot \ul{v} \mbox{ , } \int_{\Omega} q\ \nabla \cdot \ul{u} \mbox{ or } \int_{\Omega} c\ p\ \nabla \cdot \ul{v} \mbox{ , } \int_{\Omega} c\ q\ \nabla \cdot \ul{u} :Arguments 1: - material : :math:`c` (optional) - virtual : :math:`\ul{v}` - state : :math:`p` :Arguments 2: - material : :math:`c` (optional) - state : :math:`\ul{u}` - virtual : :math:`q` :Arguments 3: - material : :math:`c` (optional) - parameter_v : :math:`\ul{u}` - parameter_s : :math:`p` """ name = 'dw_stokes' arg_types = (('opt_material', 'virtual', 'state'), ('opt_material', 'state', 'virtual'), ('opt_material', 'parameter_v', 'parameter_s')) arg_shapes = [{'opt_material' : '1, 1', 'virtual/grad' : ('D', None), 'state/grad' : 1, 'virtual/div' : (1, None), 'state/div' : 'D', 'parameter_v' : 'D', 'parameter_s' : 1}, {'opt_material' : None}] modes = ('grad', 'div', 'eval') @staticmethod def d_eval(out, coef, vec_qp, div, vvg): out_qp = coef * vec_qp * div status = vvg.integrate(out, out_qp) return status def get_fargs(self, coef, vvar, svar, mode=None, term_mode=None, diff_var=None, **kwargs): if self.mode == 'grad': qp_var, qp_name = svar, 'val' else: qp_var, qp_name = vvar, 'div' n_el, n_qp, dim, n_en, n_c = self.get_data_shape(vvar) if coef is None: coef = nm.ones((1, n_qp, 1, 1), dtype=nm.float64) if mode == 'weak': vvg, _ = self.get_mapping(vvar) svg, _ = self.get_mapping(svar) if diff_var is None: val_qp = self.get(qp_var, qp_name) fmode = 0 else: val_qp = nm.array([0], ndmin=4, dtype=nm.float64) fmode = 1 return coef, val_qp, svg, vvg, fmode elif mode == 'eval': vvg, _ = self.get_mapping(vvar) div = self.get(vvar, 'div') vec_qp = self.get(svar, 'val') return coef, vec_qp, div, vvg else: raise ValueError('unsupported evaluation mode in %s! (%s)' % (self.name, mode)) def get_eval_shape(self, coef, vvar, svar, mode=None, term_mode=None, diff_var=None, **kwargs): n_el, n_qp, dim, n_en, n_c = self.get_data_shape(vvar) return (n_el, 1, 1, 1), vvar.dtype def set_arg_types(self): self.function = { 'grad' : terms.dw_grad, 'div' : terms.dw_div, 'eval' : self.d_eval, }[self.mode] class GradTerm(Term): r""" Evaluate gradient of a scalar or vector field. Supports 'eval', 'el_avg' and 'qp' evaluation modes. :Definition: .. math:: \int_{\Omega} \nabla p \mbox{ or } \int_{\Omega} \nabla \ul{w} .. math:: \mbox{vector for } K \from \Ical_h: \int_{T_K} \nabla p / \int_{T_K} 1 \mbox{ or } \int_{T_K} \nabla \ul{w} / \int_{T_K} 1 .. math:: (\nabla p)|_{qp} \mbox{ or } \nabla \ul{w}|_{qp} :Arguments: - parameter : :math:`p` or :math:`\ul{w}` """ name = 'ev_grad' arg_types = ('parameter',) arg_shapes = [{'parameter' : 1}, {'parameter' : 'D'}] @staticmethod def function(out, grad, vg, fmode): if fmode == 2: out[:] = grad status = 0 else: status = vg.integrate(out, grad, fmode) return status def get_fargs(self, parameter, mode=None, term_mode=None, diff_var=None, **kwargs): vg, _ = self.get_mapping(parameter) grad = self.get(parameter, 'grad') fmode = {'eval' : 0, 'el_avg' : 1, 'qp' : 2}.get(mode, 1) return grad, vg, fmode def get_eval_shape(self, parameter, mode=None, term_mode=None, diff_var=None, **kwargs): n_el, n_qp, dim, n_en, n_c = self.get_data_shape(parameter) if mode != 'qp': n_qp = 1 return (n_el, n_qp, dim, n_c), parameter.dtype class DivTerm(Term): r""" Evaluate divergence of a vector field. Supports 'eval', 'el_avg' and 'qp' evaluation modes. :Definition: .. math:: \int_{\Omega} \nabla \cdot \ul{u} .. math:: \mbox{vector for } K \from \Ical_h: \int_{T_K} \nabla \cdot \ul{u} / \int_{T_K} 1 .. math:: (\nabla \cdot \ul{u})|_{qp} :Arguments: - parameter : :math:`\ul{u}` """ name = 'ev_div' arg_types = ('parameter',) arg_shapes = {'parameter' : 'D'} @staticmethod def function(out, div, vg, fmode): if fmode == 2: out[:] = div status = 0 else: status = vg.integrate(out, div, fmode) return status def get_fargs(self, parameter, mode=None, term_mode=None, diff_var=None, **kwargs): vg, _ = self.get_mapping(parameter) div = self.get(parameter, 'div') fmode = {'eval' : 0, 'el_avg' : 1, 'qp' : 2}.get(mode, 1) return div, vg, fmode def get_eval_shape(self, parameter, mode=None, term_mode=None, diff_var=None, **kwargs): n_el, n_qp, dim, n_en, n_c = self.get_data_shape(parameter) if mode != 'qp': n_qp = 1 return (n_el, n_qp, 1, 1), parameter.dtype class DivOperatorTerm(Term): r""" Weighted divergence term of a test function. :Definition: .. math:: \int_{\Omega} \nabla \cdot \ul{v} \mbox { or } \int_{\Omega} c \nabla \cdot \ul{v} :Arguments: - material : :math:`c` (optional) - virtual : :math:`\ul{v}` """ name = 'dw_div' arg_types = ('opt_material', 'virtual') arg_shapes = [{'opt_material' : '1, 1', 'virtual' : ('D', None)}, {'opt_material' : None}] @staticmethod def function(out, mat, vg): div_bf = vg.bfg n_el, n_qp, dim, n_ep = div_bf.shape div_bf = div_bf.reshape((n_el, n_qp, dim * n_ep, 1)) div_bf = nm.ascontiguousarray(div_bf) if mat is not None: status = vg.integrate(out, mat * div_bf) else: status = vg.integrate(out, div_bf) return status def get_fargs(self, mat, virtual, mode=None, term_mode=None, diff_var=None, **kwargs): vg, _ = self.get_mapping(virtual) return mat, vg class GradDivStabilizationTerm(Term): r""" Grad-div stabilization term ( :math:`\gamma` is a global stabilization parameter). :Definition: .. math:: \gamma \int_{\Omega} (\nabla\cdot\ul{u}) \cdot (\nabla\cdot\ul{v}) :Arguments: - material : :math:`\gamma` - virtual : :math:`\ul{v}` - state : :math:`\ul{u}` """ name = 'dw_st_grad_div' arg_types = ('material', 'virtual', 'state') arg_shapes = {'material' : '1, 1', 'virtual' : ('D', 'state'), 'state' : 'D'} function = staticmethod(terms.dw_st_grad_div) def get_fargs(self, gamma, virtual, state, mode=None, term_mode=None, diff_var=None, **kwargs): vg, _ = self.get_mapping(state) if diff_var is None: div = self.get(state, 'div') fmode = 0 else: div = nm.array([0], ndmin=4, dtype=nm.float64) fmode = 1 return div, gamma, vg, fmode from sfepy.terms.terms_diffusion import LaplaceTerm class PSPGPStabilizationTerm(LaplaceTerm): r""" PSPG stabilization term, pressure part ( :math:`\tau` is a local stabilization parameter), alias to Laplace term dw_laplace. :Definition: .. math:: \sum_{K \in \Ical_h}\int_{T_K} \tau_K\ \nabla p \cdot \nabla q :Arguments: - material : :math:`\tau_K` - virtual : :math:`q` - state : :math:`p` """ name = 'dw_st_pspg_p' class PSPGCStabilizationTerm(Term): r""" PSPG stabilization term, convective part ( :math:`\tau` is a local stabilization parameter). :Definition: .. math:: \sum_{K \in \Ical_h}\int_{T_K} \tau_K\ ((\ul{b} \cdot \nabla) \ul{u}) \cdot \nabla q :Arguments: - material : :math:`\tau_K` - virtual : :math:`q` - parameter : :math:`\ul{b}` - state : :math:`\ul{u}` """ name = 'dw_st_pspg_c' arg_types = ('material', 'virtual', 'parameter', 'state') arg_shapes = {'material' : '1, 1', 'virtual' : (1, None), 'parameter' : 'D', 'state' : 'D'} function = staticmethod(terms.dw_st_pspg_c) def get_fargs(self, tau, virtual, parameter, state, mode=None, term_mode=None, diff_var=None, **kwargs): sap, svg = self.get_approximation(virtual) vap, vvg = self.get_approximation(state) val_qp = self.get(parameter, 'val') conn = vap.get_connectivity(self.region, self.integration) if diff_var is None: fmode = 0 else: fmode = 1 return val_qp, state(), tau, svg, vvg, conn, fmode class SUPGPStabilizationTerm(Term): r""" SUPG stabilization term, pressure part ( :math:`\delta` is a local stabilization parameter). :Definition: .. math:: \sum_{K \in \Ical_h}\int_{T_K} \delta_K\ \nabla p\cdot ((\ul{b} \cdot \nabla) \ul{v}) :Arguments: - material : :math:`\delta_K` - virtual : :math:`\ul{v}` - parameter : :math:`\ul{b}` - state : :math:`p` """ name = 'dw_st_supg_p' arg_types = ('material', 'virtual', 'parameter', 'state') arg_shapes = {'material' : '1, 1', 'virtual' : ('D', None), 'parameter' : 'D', 'state' : 1} function = staticmethod(terms.dw_st_supg_p) def get_fargs(self, delta, virtual, parameter, state, mode=None, term_mode=None, diff_var=None, **kwargs): vvg, _ = self.get_mapping(virtual) svg, _ = self.get_mapping(state) val_qp = self.get(parameter, 'val') if diff_var is None: grad = self.get(state, 'grad') fmode = 0 else: grad = nm.array([0], ndmin=4, dtype=nm.float64) fmode = 1 return val_qp, grad, delta, vvg, svg, fmode class SUPGCStabilizationTerm(Term): r""" SUPG stabilization term, convective part ( :math:`\delta` is a local stabilization parameter). :Definition: .. math:: \sum_{K \in \Ical_h}\int_{T_K} \delta_K\ ((\ul{b} \cdot \nabla) \ul{u})\cdot ((\ul{b} \cdot \nabla) \ul{v}) :Arguments: - material : :math:`\delta_K` - virtual : :math:`\ul{v}` - parameter : :math:`\ul{b}` - state : :math:`\ul{u}` """ name = 'dw_st_supg_c' arg_types = ('material', 'virtual', 'parameter', 'state') arg_shapes = {'material' : '1, 1', 'virtual' : ('D', 'state'), 'parameter' : 'D', 'state' : 'D'} function = staticmethod(terms.dw_st_supg_c) def get_fargs(self, delta, virtual, parameter, state, mode=None, term_mode=None, diff_var=None, **kwargs): ap, vg = self.get_approximation(virtual) val_qp = self.get(parameter, 'val') conn = ap.get_connectivity(self.region, self.integration) if diff_var is None: fmode = 0 else: fmode = 1 return val_qp, state(), delta, vg, conn, fmode
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grr/server/grr_response_server/instant_output_plugin_test.py
khanhgithead/grr
4,238
197398
<filename>grr/server/grr_response_server/instant_output_plugin_test.py #!/usr/bin/env python # Lint as: python3 """Tests for grr.lib.output_plugin.""" import io from absl import app from grr_response_core.lib import rdfvalue from grr_response_server import export from grr_response_server.output_plugins import test_plugins from grr.test_lib import test_lib class DummySrcValue1(rdfvalue.RDFString): pass class DummySrcValue2(rdfvalue.RDFString): pass class DummyOutValue1(rdfvalue.RDFString): pass class DummyOutValue2(rdfvalue.RDFString): pass class TestConverter1(export.ExportConverter): input_rdf_type = DummySrcValue1 def Convert(self, metadata, value): return [DummyOutValue1("exp-" + str(value))] class TestConverter2(export.ExportConverter): input_rdf_type = DummySrcValue2 def Convert(self, metadata, value): _ = metadata return [ DummyOutValue1("exp1-" + str(value)), DummyOutValue2("exp2-" + str(value)) ] class InstantOutputPluginWithExportConversionTest( test_plugins.InstantOutputPluginTestBase): """Tests for InstantOutputPluginWithExportConversion.""" plugin_cls = test_plugins.TestInstantOutputPluginWithExportConverstion def ProcessValuesToLines(self, values_by_cls): fd_name = self.ProcessValues(values_by_cls) with io.open(fd_name, mode="r", encoding="utf-8") as fd: return fd.read().split("\n") def testWorksCorrectlyWithOneSourceValueAndOneExportedValue(self): lines = self.ProcessValuesToLines({DummySrcValue1: [DummySrcValue1("foo")]}) self.assertListEqual(lines, [ "Start", "Original: DummySrcValue1", "Exported value: exp-foo", "Finish" ]) # pyformat: disable def testWorksCorrectlyWithOneSourceValueAndTwoExportedValues(self): lines = self.ProcessValuesToLines({DummySrcValue2: [DummySrcValue2("foo")]}) self.assertListEqual(lines, [ "Start", "Original: DummySrcValue2", "Exported value: exp1-foo", "Original: DummySrcValue2", "Exported value: exp2-foo", "Finish" ]) # pyformat: disable def testWorksCorrectlyWithTwoSourceValueAndTwoExportedValuesEach(self): lines = self.ProcessValuesToLines( {DummySrcValue2: [DummySrcValue2("foo"), DummySrcValue2("bar")]}) self.assertListEqual(lines, [ "Start", "Original: DummySrcValue2", "Exported value: exp1-foo", "Exported value: exp1-bar", "Original: DummySrcValue2", "Exported value: exp2-foo", "Exported value: exp2-bar", "Finish" ]) # pyformat: disable def testWorksCorrectlyWithTwoDifferentTypesOfSourceValues(self): lines = self.ProcessValuesToLines({ DummySrcValue1: [DummySrcValue1("foo")], DummySrcValue2: [DummySrcValue2("bar")], }) self.assertListEqual(lines, [ "Start", "Original: DummySrcValue1", "Exported value: exp-foo", "Original: DummySrcValue2", "Exported value: exp1-bar", "Original: DummySrcValue2", "Exported value: exp2-bar", "Finish" ]) # pyformat: disable def main(argv): test_lib.main(argv) if __name__ == "__main__": app.run(main)
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python/paddle/fluid/tests/unittests/test_gru_op.py
javakian/Paddle
0
197769
<gh_stars>0 # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np import math import functools from op_test import OpTest from test_lstm_op import ACTIVATION def gru( input, # T x 3D lod, # 1 x N h0, # N x D weight, # D x 3D bias, # 1 x 3D is_reverse, act_state, act_gate, dtype='float32', origin_mode=False): def _seq_to_batch(lod, is_reverse): idx_in_seq_list = [] seq_lens = lod[0] seq_starts = [0] for i in range(len(seq_lens)): seq_starts.append(seq_starts[-1] + seq_lens[i]) sorted_seqs = sorted( list(range(len(seq_lens))), key=functools.cmp_to_key(lambda x, y: seq_lens[y] - seq_lens[x])) num_batch = seq_lens[sorted_seqs[0]] for batch_idx in range(num_batch): idx_in_seq = [] for i in range(len(seq_lens)): if seq_lens[sorted_seqs[i]] <= batch_idx: break idx = (seq_starts[sorted_seqs[i] + 1] - 1 - batch_idx ) if is_reverse else ( seq_starts[sorted_seqs[i]] + batch_idx) idx_in_seq.append(idx) idx_in_seq_list.append(idx_in_seq) return idx_in_seq_list, sorted_seqs def _step(x, h_p, w, b, act_state, act_gate): T = x.shape[0] D = w.shape[0] g = x + np.tile(b, (T, 1)) w_u_r = w.flatten()[:D * D * 2].reshape((D, D * 2)) u_r = act_gate(np.dot(h_p, w_u_r) + g[:, :D * 2]) u = u_r[:, :D] r = u_r[:, D:D * 2] r_h_p = r * h_p w_c = w.flatten()[D * D * 2:].reshape((D, D)) c = act_state(np.dot(r_h_p, w_c) + g[:, D * 2:]) g = np.hstack((u_r, c)) if origin_mode: h = (1 - u) * c + u * h_p else: h = u * c + (1 - u) * h_p return g, r_h_p, h T = sum(lod[0]) N = len(lod[0]) D = weight.shape[0] batch_gate = np.zeros((T, 3 * D), dtype=dtype) batch_reset_hidden_prev = np.zeros((T, D), dtype=dtype) batch_hidden = np.zeros((T, D), dtype=dtype) hidden = np.zeros((T, D), dtype=dtype) idx_in_seq_list, sorted_seqs = _seq_to_batch(lod, is_reverse) h_p = h0[[seq for seq in sorted_seqs if lod[0][seq] > 0]] max_seq_len = len(idx_in_seq_list) end_idx = 0 for batch_idx in range(max_seq_len): x = input[idx_in_seq_list[batch_idx]] g, r_h_p, h = _step(x, h_p, weight, bias, act_state, act_gate) if batch_idx < (max_seq_len - 1): h_p = h[:len(idx_in_seq_list[batch_idx + 1])] start_idx = end_idx end_idx = start_idx + len(idx_in_seq_list[batch_idx]) batch_gate[start_idx:end_idx] = g batch_reset_hidden_prev[start_idx:end_idx] = r_h_p batch_hidden[start_idx:end_idx] = h hidden[idx_in_seq_list[batch_idx]] = h return batch_gate, batch_reset_hidden_prev, batch_hidden, hidden class TestGRUOp(OpTest): def set_confs(self): pass def setUp(self): self.op_type = "gru" self.lod = [[2, 4, 3]] self.D = 5 self.is_reverse = False self.with_h0 = True self.with_bias = True self.act_state = 'tanh' self.act_gate = 'sigmoid' self.dtype = 'float64' self.origin_mode = False self.set_confs() T = sum(self.lod[0]) N = len(self.lod[0]) input = np.random.rand(T, 3 * self.D).astype(self.dtype) weight = np.random.rand(self.D, 3 * self.D).astype(self.dtype) bias = np.random.rand( 1, 3 * self.D).astype(self.dtype) if self.with_bias else np.zeros( (1, 3 * self.D), dtype=self.dtype) h0 = np.random.rand( N, self.D).astype(self.dtype) if self.with_h0 else np.zeros( (N, self.D), dtype=self.dtype) batch_gate, batch_reset_hidden_prev, batch_hidden, hidden = gru( input, self.lod, h0, weight, bias, self.is_reverse, ACTIVATION[self.act_state], ACTIVATION[self.act_gate], self.dtype, self.origin_mode) self.inputs = {'Input': (input, self.lod), 'Weight': weight} if self.with_bias: self.inputs['Bias'] = bias if self.with_h0: self.inputs['H0'] = h0 self.outputs = { 'Hidden': (hidden, self.lod), 'BatchGate': batch_gate, 'BatchResetHiddenPrev': batch_reset_hidden_prev, 'BatchHidden': batch_hidden, } self.attrs = { 'activation': self.act_state, 'gate_activation': self.act_gate, 'is_reverse': self.is_reverse, 'origin_mode': self.origin_mode } def test_check_output(self): self.check_output(atol=1e-8, check_dygraph=False) def test_check_grad(self): self.check_grad( ['Input', 'H0', 'Weight', 'Bias'], ['Hidden'], check_dygraph=False) class TestGRUOriginMode(TestGRUOp): def set_confs(self): self.origin_mode = True class TestGRUOp2(TestGRUOp): def set_confs(self): self.D = 19 self.dtype = 'float32' class TestGRUOp2Len0(TestGRUOp): def set_confs(self): self.D = 19 self.lod = [[2, 0, 4]] self.dtype = 'float32' class TestGRUOp2OriginMode(TestGRUOp): def set_confs(self): self.D = 19 self.dtype = 'float32' self.origin_mode = True class TestGRUOp2OriginModeLen0(TestGRUOp): def set_confs(self): self.D = 19 self.lod = [[0, 3, 4]] self.dtype = 'float32' self.origin_mode = True class TestGRUOp2OriginModeLastLen0(TestGRUOp): def set_confs(self): self.D = 19 self.lod = [[0, 3, 0]] self.dtype = 'float32' self.origin_mode = True class TestGRUOpNoInitial(TestGRUOp): def set_confs(self): self.with_h0 = False def test_check_grad(self): self.check_grad( ['Input', 'Weight', 'Bias'], ['Hidden'], check_dygraph=False) class TestGRUOpNoBias(TestGRUOp): def set_confs(self): self.with_bias = False def test_check_grad(self): self.check_grad( ['Input', 'H0', 'Weight'], ['Hidden'], check_dygraph=False) class TestGRUOpReverse(TestGRUOp): def set_confs(self): self.is_reverse = True class TestGRUOpReverseOriginMode(TestGRUOp): def set_confs(self): self.is_reverse = True self.origin_mode = True if __name__ == "__main__": unittest.main()
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porder/multiproc_pydl.py
testinggis/porder
33
1616757
<gh_stars>10-100 from __future__ import print_function __copyright__ = """ Copyright 2019 <NAME> Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ __license__ = "Apache 2.0" #!/usr/bin/env python import multiprocessing import os import csv import requests import glob import time import progressbar import json import sys from threading import * screen_lock = Semaphore(value=1) from retrying import retry from planet.api.utils import read_planet_json from planet.api.auth import find_api_key # Get Planet API and Authenticate SESSION try: PL_API_KEY = find_api_key() except: print("Failed to get Planet Key") sys.exit() SESSION = requests.Session() SESSION.auth = (PL_API_KEY, "") # To get redirect link @retry(wait_exponential_multiplier=1000, wait_exponential_max=10000) def check_for_redirects(url): try: r = SESSION.get(url, allow_redirects=False, timeout=0.5) if 300 <= r.status_code < 400: return r.headers["location"] elif r.status_code == 429: raise Exception("rate limit error") except requests.exceptions.Timeout: return "[timeout]" except requests.exceptions.ConnectionError: return "[connection error]" except requests.HTTPError as e: print(r.status_code) if r.status_code == 429: # Too many requests raise Exception("rate limit error") ######################################################################## class MultiProcDownloader(object): """ Downloads urls with Python's multiprocessing module """ # ---------------------------------------------------------------------- def __init__(self, urls): """ Initialize class with list of urls """ self.urls = urls # ---------------------------------------------------------------------- def run(self): """ Download the urls and waits for the processes to finish """ jobs = [] for url in self.urls: process = multiprocessing.Process(target=self.worker, args=(url,)) jobs.append(process) process.start() for job in jobs: job.join() # ---------------------------------------------------------------------- @retry(wait_exponential_multiplier=1000, wait_exponential_max=10000) def worker(self, url): """ The target method that the process uses tp download the specified url """ try: urlcheck = url.split("|")[0] fullpath = url.split("|")[1] [head, tail] = os.path.split(fullpath) msg = "Starting download of %s" % fullpath.split("/")[-1] if not os.path.exists(head): os.makedirs(head) os.chdir(head) if not os.path.isfile(fullpath): screen_lock.acquire() print(str(msg) + " " + str(multiprocessing.current_process().name)) screen_lock.release() r = requests.get(urlcheck) with open(fullpath, "wb") as f: f.write(r.content) else: print(("File already exists skipping " + str(tail))) except Exception as e: print(e) print(("Issues with file: " + str(fullpath))) def funct(url, final, ext): filenames = glob.glob1(final, "*") if not os.path.exists(final): os.makedirs(final) os.chdir(final) urls = [] response = SESSION.get(url).json() print("Polling with exponential backoff..") while ( response["state"] == "queued" or response["state"] == "running" or response["state"] == "starting" ): bar = progressbar.ProgressBar() for z in bar(range(60)): time.sleep(1) response = SESSION.get(url).json() if response["state"] == "success" or response["state"] == "partial": print("Order completed with status: " + str(response["state"])) for items in response["_links"]["results"]: url = items["location"] name = items["name"] if name.endswith("manifest.json"): time.sleep(0.2) resp = SESSION.get(url) if int(resp.status_code) == 200: r = resp.content inp = json.loads(r) for things in inp["files"]: try: local_path = os.path.join( final, things["annotations"]["planet/item_id"] + "_manifest.json", ) except Exception as e: local_path = os.path.join( final, things["path"].split("/")[1].split(".")[0] + "_manifest.json", ) else: print(resp.status_code) else: local_path = os.path.join(final, str(os.path.split(items["name"])[-1])) filenames = [os.path.join(final, files) for files in filenames] if not local_path in filenames: url_to_check = url if url.startswith("https") else "http://%s" % url redirect_url = check_for_redirects(url_to_check) if redirect_url.startswith("https"): if not os.path.isfile(local_path) and ext is None: urls.append(str(redirect_url) + "|" + local_path) print("Processing total URLs " + str(len(urls)), end="\r") if not os.path.isfile(local_path) and ext is not None: if local_path.endswith(ext): urls.append(str(redirect_url) + "|" + local_path) print("Processing total URLs " + str(len(urls)), end="\r") else: print("Order Failed with state: " + str(response["state"])) print("Downloading a total of " + str(len(urls)) + " objects") downloader = MultiProcDownloader(urls) downloader.run() # ---------------------------------------------------------------------- if __name__ == "__main__": if len(sys.argv) == 4: funct(url=sys.argv[1], final=os.path.normpath(sys.argv[2]), ext=sys.argv[3]) elif len(sys.argv) == 3: funct(url=sys.argv[1], final=os.path.normpath(sys.argv[2]), ext=None) # funct(url='https://api.planet.com/compute/ops/orders/v2/4ebfa89e-dc59-41cc-ad82-dbad2b5375b2',final=r'C:\planet_demo',ext='.zip') # downloader = MultiProcDownloader(urls) # downloader.run()
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python/setup.py
deepest-stack/graph2tensor
0
56392
<reponame>deepest-stack/graph2tensor # coding=utf-8 import numpy as np from setuptools import find_packages, setup, Extension sampler = Extension('graph2tensor.client.sampler', include_dirs=[np.get_include(), ], extra_compile_args=['-std=c++11', '-fopenmp'], extra_link_args=['-fopenmp'], sources=['graph2tensor/client/sampler.cpp']) setup( name='graph2tensor', version='0.2.0', description='Graph learning framework based on neighbour sampling', packages=find_packages(exclude=("test",)), ext_modules=[sampler, ] )
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pdfs/req.py
prof-andre/TCC
0
187317
import requests as rt import pandas as pd import numpy as np import PyPDF2 as p2 from pathlib import Path def trans_csv(path): #Recebe o caminho do arquivo csv e retorna uma array do tipo string. Leitura do arquivo csv com os dados das escolas. Transforma a coluna COD_ESC em uma unida Array do tipo string. data = pd.read_csv(path) cod_n = np.array(data['COD_ESC']) cod_s = [] for x in cod_n: x = str(x) if len(x) < 6: cod_s.append('0'*(6-len(x)) + str(x)) else: cod_s.append(x) return cod_s def download_pdf(ano, COD_ESC): #Recebe ano como inteiro e COD_ESC como string para determinar o endereço do arquivo e definição do nome para ser salvo. Retorna o nome do arquivo. ano = str(ano) url = 'http://idesp.edunet.sp.gov.br/arquivos' + ano + '/' + COD_ESC + '.pdf' end_pdf = ano + '_' + COD_ESC + '.pdf' response = rt.get(url) filename = Path(end_pdf) filename.write_bytes(response.content) print(url) return end_pdf def le_pdf(path): #Recebe o caminho do arquivo pdf e retorna um dict(key=Nº, value=conteúdo da página). arq = open(path, "rb") pdf = p2.PdfFileReader(arq) pg_pdf = {} for i in range(0, pdf.getNumPages()): pg_pdf[i+1] = [pdf.getPage(i).extractText()] arq.close() return pg_pdf def PD_2007(pg_pdf,pg): dados = [] for i in range(0, len(pg_pdf[pg][0])): if pg_pdf[pg][0][i] == ',': dados.append(float(pg_pdf[pg][0][i-1]+'.'+pg_pdf[pg][0][i+1]+pg_pdf[pg][0][i+2])) return dados def PD_novo(pg_pdf, pg): #serve para 2013 para frente dados = [] num_dados = [] start = 0 first = True for i in range(0, len(pg_pdf[pg][0])): if pg_pdf[pg][0][i] == '\n' and first: dados.append(str(pg_pdf[pg][0][0:i])) start = i+1 first = False elif pg_pdf[pg][0][i] == '\n': dados.append(str(pg_pdf[pg][0][start:i])) start = i+1 for i in range(0,len(dados)): num_dados.append(float(dados[i].replace(',','.'))) return num_dados
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tests/test_roleplay.py
kintaclausin01/GHG-reduction-project
0
139668
<filename>tests/test_roleplay.py import sys import os parent_dir = os.path.join(os.path.dirname(__file__), '..') sys.path.append(parent_dir) from roleplay import * #main.roleplayRun(sys.argv[1],sys.argv[2],sys.argv[3]) #main2.roleplayRun(sys.argv[1],sys.argv[2],sys.argv[3]) main2.roleplayRun()
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tgml/validator/generator/yamodel.py
kshaposhnikov/twitter-graph-model
0
71055
<filename>tgml/validator/generator/yamodel.py # Yet Another Model for generating random graphs
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tests/integration/voice/v1/connection_policy/test_connection_policy_target.py
BrimmingDev/twilio-python
1,362
172259
# coding=utf-8 r""" This code was generated by \ / _ _ _| _ _ | (_)\/(_)(_|\/| |(/_ v1.0.0 / / """ from tests import IntegrationTestCase from tests.holodeck import Request from twilio.base.exceptions import TwilioException from twilio.http.response import Response class ConnectionPolicyTargetTestCase(IntegrationTestCase): def test_create_request(self): self.holodeck.mock(Response(500, '')) with self.assertRaises(TwilioException): self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets.create(target="https://example.com") values = {'Target': "https://example.com", } self.holodeck.assert_has_request(Request( 'post', 'https://voice.twilio.com/v1/ConnectionPolicies/NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX/Targets', data=values, )) def test_create_response(self): self.holodeck.mock(Response( 201, ''' { "account_sid": "AC<KEY>", "connection_policy_sid": "NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "sid": "NEaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "friendly_name": "friendly_name", "target": "sip:sip-box.com:1234", "priority": 1, "weight": 20, "enabled": true, "date_created": "2020-03-18T23:31:36Z", "date_updated": "2020-03-18T23:31:36Z", "url": "https://voice.twilio.com/v1/ConnectionPolicies/NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/Targets/NEaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" } ''' )) actual = self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets.create(target="https://example.com") self.assertIsNotNone(actual) def test_fetch_request(self): self.holodeck.mock(Response(500, '')) with self.assertRaises(TwilioException): self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets("NEXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").fetch() self.holodeck.assert_has_request(Request( 'get', 'https://voice.twilio.com/v1/ConnectionPolicies/NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX/Targets/NEXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX', )) def test_fetch_response(self): self.holodeck.mock(Response( 200, ''' { "account_sid": "<KEY>", "connection_policy_sid": "NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "sid": "NEaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "friendly_name": "friendly_name", "target": "sip:sip-box.com:1234", "priority": 1, "weight": 20, "enabled": true, "date_created": "2020-03-18T23:31:36Z", "date_updated": "2020-03-18T23:31:37Z", "url": "https://voice.twilio.com/v1/ConnectionPolicies/NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/Targets/NEaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" } ''' )) actual = self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets("NEXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").fetch() self.assertIsNotNone(actual) def test_list_request(self): self.holodeck.mock(Response(500, '')) with self.assertRaises(TwilioException): self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets.list() self.holodeck.assert_has_request(Request( 'get', 'https://voice.twilio.com/v1/ConnectionPolicies/NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX/Targets', )) def test_read_full_response(self): self.holodeck.mock(Response( 200, ''' { "meta": { "page": 0, "page_size": 50, "first_page_url": "https://voice.twilio.com/v1/ConnectionPolicies/NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/Targets?PageSize=50&Page=0", "previous_page_url": null, "url": "https://voice.twilio.com/v1/ConnectionPolicies/NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/Targets?PageSize=50&Page=0", "next_page_url": null, "key": "targets" }, "targets": [ { "account_sid": "<KEY>", "connection_policy_sid": "NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "sid": "NEaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "friendly_name": "friendly_name", "target": "sip:sip-box.com:1234", "priority": 1, "weight": 20, "enabled": true, "date_created": "2020-03-18T23:31:36Z", "date_updated": "2020-03-18T23:31:37Z", "url": "https://voice.twilio.com/v1/ConnectionPolicies/NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/Targets/NEaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" } ] } ''' )) actual = self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets.list() self.assertIsNotNone(actual) def test_read_empty_response(self): self.holodeck.mock(Response( 200, ''' { "meta": { "page": 0, "page_size": 50, "first_page_url": "https://voice.twilio.com/v1/ConnectionPolicies/NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/Targets?PageSize=50&Page=0", "previous_page_url": null, "url": "https://voice.twilio.com/v1/ConnectionPolicies/NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/Targets?PageSize=50&Page=0", "next_page_url": null, "key": "targets" }, "targets": [] } ''' )) actual = self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets.list() self.assertIsNotNone(actual) def test_update_request(self): self.holodeck.mock(Response(500, '')) with self.assertRaises(TwilioException): self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets("NEXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").update() self.holodeck.assert_has_request(Request( 'post', 'https://voice.twilio.com/v1/ConnectionPolicies/NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX/Targets/NEXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX', )) def test_update_response(self): self.holodeck.mock(Response( 200, ''' { "account_sid": "<KEY>", "connection_policy_sid": "NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "sid": "NEaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa", "friendly_name": "updated_name", "target": "sip:sip-updated.com:4321", "priority": 2, "weight": 10, "enabled": false, "date_created": "2020-03-18T23:31:36Z", "date_updated": "2020-03-18T23:31:37Z", "url": "https://voice.twilio.com/v1/ConnectionPolicies/NYaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/Targets/NEaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" } ''' )) actual = self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets("NEXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").update() self.assertIsNotNone(actual) def test_delete_request(self): self.holodeck.mock(Response(500, '')) with self.assertRaises(TwilioException): self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets("NEXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").delete() self.holodeck.assert_has_request(Request( 'delete', 'https://voice.twilio.com/v1/ConnectionPolicies/NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX/Targets/NEXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX', )) def test_delete_response(self): self.holodeck.mock(Response( 204, None, )) actual = self.client.voice.v1.connection_policies("NYXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX") \ .targets("NEXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX").delete() self.assertTrue(actual)
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international_data_preprocessing/money.py
emcarlson2017/international-data-preprocessing
0
183081
<filename>international_data_preprocessing/money.py """The money module includes an object representation of money, and functions to parse strings into Money.""" from .country import Country from ._util import check_single_row import pandas as pd import re import os import os.path dirpath = os.path.dirname(os.path.realpath(__file__)) _country_to_currency = pd.read_csv( os.path.join(dirpath, 'data/country_currency.csv')) _currencies = pd.read_csv(os.path.join(dirpath, 'data/currencies.csv')) _country_to_currency_dict = {} for row in _country_to_currency.itertuples(): _country_to_currency_dict[row.country] = row.currency class Money: """The Money class contains all functions to parse and retrieve money values in any currency.""" def __init__(self, amount, currency_abbv, currency_full=None): """Initialize Money instance. Args: amount: the numeric amount of money, regardless of currency. currency_abbv: the 3 letter abbreviation of the currency. currency_full: optional, the full name of the currency. """ self.amount = amount self.currency_abbv = currency_abbv self.currency_full = currency_full def get(self, attr): """Retrieve attribute from an instance of the Money class. Args: attr: name of attribute to retrieve, one of (amount, abbv, full). Returns: the value of the requested attribute. Raises: ValueError: if the input is not one of the 3 attributes. """ if attr == 'amount': return self.amount elif attr == 'abbv': return self.currency_abbv elif attr == 'full': return self.currency_full else: raise ValueError( "attr must be one of the following: amount, currency_abbv, currency_full") @staticmethod def parse(input, currency=None): """Parses money value strings into Money objects. Args: input: string containing the money value in <amount currency> or <currency amount> format, or <amount> format if currency is provided separately. currency: optional, string containing the currency code or full currency name. Returns: the Currency object. Raises: ValueError: if the input is not in the format specified above. """ input = str(input).strip() if currency: if not re.fullmatch('[0-9]+\\.?[0-9]*', input): raise ValueError( "Input must be a numeric value or number in string representation when currency is specified.") row = _currencies[(_currencies['abbreviation'] == currency) | ( _currencies['full_name'] == currency)] check_single_row(row, "Currency provided must be a valid 3 letter abbreviation from https://www.ifcmarkets.com/en/about-forex/currencies-and-abbreviations") return Money( float(input), row.iloc[0]['abbreviation'], row.iloc[0]['full_name']) amount = None match = re.fullmatch('([0-9]+\\.?[0-9]*)\\s*([a-zA-Z\\s]+)', input) if match: amount = match.group(0) currency = match.group(1) else: match = re.fullmatch('([a-zA-Z\\s]+)\\s*([0-9]+\\.?[0-9]*)', input) if match: amount = match.group(1) currency = match.group(0) else: raise ValueError( "Input must be of the form <amount currency> or <currency amount> if currency is not provided.") row = _currencies[(_currencies['abbreviation'] == currency) | (_currencies['full_name'] == currency)] check_single_row(row, "Currency must be a valid 3 letter abbreviation from https://www.ifcmarkets.com/en/about-forex/currencies-and-abbreviations") return Money( float(amount), row.iloc[0]['abbreviation'], row.iloc[0]['full_name']) def country_to_primary_currency( df, country_col_name, output_col_name=None, in_place=True): """Provides currency code given a country in any standard notation. For countries with more than one currency, the primary or most commonly used one is selected. Args: df: the data frame. country_col_name: the name of the column with country in alpha2, alpha3, UN, full, or short format. output_col_name: new column name if in_place is False. in_place: if True, overwrite output to input column; if False, write output to new column. """ new_col = df[country_col_name].map(Country.parse).map( lambda x: _country_to_currency_dict[x.get('alpha3')]) if in_place: df[country_col_name] = new_col else: if not output_col_name: raise ValueError( "output_col_name must be provided when operation is not in place.") df[output_col_name] = new_col def parse_money(df, input_col_name, output_types=None, output_col_names=None, in_place=True): """Parses and separates money values (e.g. EUR 52.50) into its amount and/or currency. Args: df: the data frame. input_col_name: the name of the column with money values. output_types: one or more of ['amount', 'abbv', 'full']. output_col_names: new column names if in_place is False. in_place: if True, overwrite output to input column; if False, write output to new column. """ new_col = df[input_col_name].map(Money.parse) if in_place: df[input_col_name] = new_col.map( lambda x: str(x.get('amount')) + x.get('abbv')) else: if len(output_types) != len(output_col_names): raise ValueError( "output_types and output_col_names must be of the same length") for i in range(len(output_types)): df[output_col_names[i]] = new_col.map( lambda x: x.get(output_types[i]))
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setup.py
vishnumenon/pyims
1
13688
import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup(name="pyims", version='0.1.2', description='A python wrapper for the IMS Word Sense Disambiguation tool (Zhong and Ng, 2010)', url='http://github.com/vishnumenon/pyims', author="<NAME>", author_email="<EMAIL>", long_description=long_description, long_description_content_type="text/markdown", license='MIT', packages=setuptools.find_packages(), install_requires=[ 'nltk', ], classifiers=( "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ), zip_safe=False)
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ecasb2share/tests/__init__.py
statiksof/ECAS-B2SHARE
1
163855
all = ["ecas_b2share_test", "tests.py"]
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adminmgr/media/code/A3/task2/BD_1595_1702_0194_43EhjOV.py
IamMayankThakur/test-bigdata
9
1607833
from pyspark.sql import SparkSession from pyspark.sql.types import * from pyspark.conf import SparkConf from pyspark.streaming import StreamingContext from pyspark.sql import Row,SQLContext import csv from pyspark.sql.functions import col,split,explode sp = SparkSession.builder.appName("app").getOrCreate() myschema = StructType().add("Id", "string").\ add("lang", "string").\ add("date", "string").\ add("source", "string").\ add("len","integer").\ add("likes", "integer").\ add("rt", "integer").\ add("ht", "string").\ add("umn", "string").\ add("umi", "string").\ add("name", "string").\ add("place", "string").\ add("followers", "integer").\ add("friends","integer") rows = sp.readStream.option("sep", ";").schema(myschema).csv("/stream") rows.createOrReplaceTempView("updates") out = sp.sql("select name,max(followers/friends) as FRRatio from updates group by name order by FRRatio desc LIMIT 1") query = out.writeStream.outputMode("complete").format("console").start() query.awaitTermination(100) query.stop()
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src/aws_lambda/movie_lens_data_injection_lambda.py
joshch1630/movie_data_platform
0
1605370
import os import zipfile import logging import boto3 import wget logger = logging.getLogger() logger.setLevel(logging.INFO) S3_BUCKET_NAME = "movie-data-platform.mpd" TEMP_PATH = "/tmp/" MOVIE_LENS_PATH = "movie_lens/" RAW_ZONE_PATH = "raw_zone/" MOVIE_LENS_URL_PREFIX = "https://files.grouplens.org/datasets/movielens/" MOVIE_LENS_FILE_NAME = "ml-25m.zip" MOVIE_LENS_DIR_NAME = "ml-25m/" def lambda_handler(event, context): logger.info("===== Start MovieLens data injection =====") zip_file_path = get_file_from_movie_lens(MOVIE_LENS_URL_PREFIX + MOVIE_LENS_FILE_NAME, MOVIE_LENS_FILE_NAME) unzip_file_and_save_into_s3(zip_file_path) start_glue_workflow() logger.info("===== End MovieLens data injection =====") return { "statusCode": 200, "headers": { "Content-Type": "application/json" } } def get_file_from_movie_lens(remote_url, file_name): logger.info("Start get_file_from_movie_lens") zip_file_path = TEMP_PATH + file_name logger.info('zip_file_path: {}'.format(zip_file_path)) unzip_file_name = file_name.replace(".zip", "") logger.info('unzip_file: {}'.format(unzip_file_name)) wget.download(remote_url, zip_file_path) logger.info('File info: {}'.format(os.stat(zip_file_path))) return zip_file_path def unzip_file_and_save_into_s3(zip_file_path): logger.info("Start unzip_file_and_save_into_s3") s3 = boto3.resource("s3") z = zipfile.ZipFile(zip_file_path) logger.info('file name list: {}'.format(", ".join(str(file_name) for file_name in z.namelist()))) for file_name in z.namelist(): s3_raw_path = RAW_ZONE_PATH + MOVIE_LENS_PATH + file_name.replace(MOVIE_LENS_DIR_NAME, "") logger.info('s3_raw_path: {}'.format(s3_raw_path)) s3.Bucket(S3_BUCKET_NAME).put_object(Key=s3_raw_path, Body=z.open(file_name)) def start_glue_workflow(): logger.info("Start start_glue_workflow") glue = boto3.client("glue") glue.start_workflow_run( Name = "movie_lens_data_cleansing_glue_workflow" )
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main.py
keyan/reaction_reader
0
186735
import io import os import time from collections import Counter from tempfile import NamedTemporaryFile import cv2 import numpy as np import pyautogui from gtts import gTTS from mpg123 import Mpg123, Out123 def get_screen_image(): with NamedTemporaryFile() as f: pil_image = pyautogui.screenshot(imageFilename=f.name) opencvImage = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR) return opencvImage def extract_qr_codes(image): qrCodeDetector = cv2.QRCodeDetector() res = qrCodeDetector.detectAndDecodeMulti(image) return Counter(res[1]) def audio_describe(codes): text = '' for code, count in codes.items(): if code == 'thumbs_up': text += f'{count} users are presenting thumbs-up, ' elif code == 'smiling': text += f'{count} users are smiling, ' if text == '': return with io.BytesIO() as f: gTTS(text=text, lang='en', slow=False).write_to_fp(f) f.seek(0) mp3 = Mpg123() mp3.feed(f.read()) out = Out123() for frame in mp3.iter_frames(out.start): out.play(frame) if __name__ == '__main__': while True: # Sanity check using a pre-made image. # Comment out the get_screen_image() call to test this. # image = cv2.imread('multi.png') s = time.perf_counter() image = get_screen_image() print(f'Screenshot time: {time.perf_counter() - s:0.2f} secs') s = time.perf_counter() codes_and_counts = extract_qr_codes(image) print(f'QR extraction time: {time.perf_counter() - s:0.2f} secs') if len(codes_and_counts) > 0: s = time.perf_counter() audio_describe(codes_and_counts) print(f'Audio time: {time.perf_counter() - s:0.2f} secs') else: print('No QR codes detected')
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software/kilsyth/platform/kilsyth.py
kbeckmann/Kilsyth
5
128401
<gh_stars>1-10 import os import subprocess from nmigen.build import * from nmigen.vendor.lattice_ecp5 import * from nmigen_boards.resources import * from ..gateware.ft600 import FT600Resource __all__ = ["KilsythPlatform"] class KilsythPlatform(LatticeECP5Platform): device = "LFE5U-45F" package = "BG381" speed = 6 default_clk = "clk16" resources = [ Resource("clk16", 0, Pins("G3", dir="i"), Clock(16e6), Attrs(GLOBAL=True, IO_TYPE="LVCMOS33")), *LEDResources(pins="A9 B9 B10 A10 A11 C10 B11 C11", invert=False, attrs=Attrs(IO_TYPE="LVCMOS33")), FT600Resource(0, clk="H2", data="P4 P3 P2 P1 N4 N3 N2 N1 M3 M1 L3 L2 L1 K4 K3 K2", be="K1 J5", rd_n="M4", wr_n="J1", gpio1="G5", txe_n="J4", rxf_n="J3", oe_n="H1", ) ] connectors = [] def toolchain_program(self, products, name): openocd = os.environ.get("OPENOCD", "openocd") interface = os.environ.get("INTERFACE", "SiPEED") if interface == "SiPEED" or interface == "busblaster": if interface == "SiPEED": args = ["-c", """ interface ftdi ftdi_vid_pid 0x0403 0x6010 ftdi_layout_init 0x0018 0x05fb ftdi_layout_signal nSRST -data 0x0010 """] elif interface == "busblaster": args = ["-f", "interface/ftdi/dp_busblaster.cfg"] with products.extract("{}.svf".format(name)) as vector_filename: subprocess.check_call([openocd, *args, "-c", "transport select jtag; adapter_khz 10000; init; svf -quiet {}; exit".format(vector_filename) ]) else: raise Exception("Unsupported interface")
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scripts/s3-uploader.py
kltm/go-site
31
48300
""" Copy the contents of a local directory into the correct S3 location, using the correct metadata as supplied by the metadata file (or internal defaults). """ #### #### Copy the contents of a local directory into the correct S3 #### location, using the correct metadata as supplied by the metadata #### file (or internal defaults). #### #### Example usage: #### python3 s3-uploader.py --help #### mkdir -p /tmp/mnt || true #### mkdir -p /tmp/foo || true #### sshfs -oStrictHostKeyChecking=no -o IdentitiesOnly=true -o IdentityFile=/home/sjcarbon/local/share/secrets/bbop/ssh-keys/foo.skyhook -o idmap=user <EMAIL>:/home/skyhook /tmp/mnt/ #### cp -r /tmp/mnt/master/* /tmp/foo #### fusermount -u /tmp/mnt #### python3 ./scripts/s3-uploader.py -v --credentials ~/local/share/secrets/bbop/aws/s3/aws-go-push.json --directory ~/tmp/elpa/archives --bucket go-data-testing-sandbox --number 7 --pipeline foo-pipe #### ## Standard imports. import sys import urllib import argparse import logging import os import json import boto3 import math from filechunkio import FileChunkIO ## Default mimetype metadata--everything that we'll be dealing with, ## so controlled. MIMES = { 'csv': 'text/csv', 'gaf': 'text/plain', 'gz': 'application/gzip', 'html': 'text/html', ## http://www.rfc-editor.org/rfc/rfc2046.txt 'jnl': 'application/octet-stream', 'js': 'application/javascript', 'json': 'application/json', 'md': 'text/markdown', 'obo': 'text/obo', 'owl': 'application/rdf+xml', 'report': 'text/plain', ## https://www.w3.org/TeamSubmission/turtle/#sec-mime 'ttl': 'text/turtle', 'tsv': 'text/tab-separated-values', 'txt': 'text/plain', 'yaml': 'text/yaml', 'yml': 'text/yaml', ## Default. '': 'text/plain' } ## Logger basic setup. logging.basicConfig(level=logging.INFO) LOG = logging.getLogger('aggregate') LOG.setLevel(logging.WARNING) def die_screaming(instr): """ Make sure we exit in a way that will get Jenkins's attention. """ LOG.error(instr) sys.exit(1) def get_args(): """ Deal with incoming. """ parser = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument('-v', '--verbose', action='store_true', help='More verbose output') parser.add_argument('-c', '--credentials', help='The credentials to used, in JSON') parser.add_argument('-d', '--directory', help='The directory to copy from') parser.add_argument('-b', '--bucket', help='The S3 bucket to copy into') parser.add_argument('-n', '--number', help='Optional: the "build-number" to add to the meta') parser.add_argument('-p', '--pipeline', help='Optional: the "build-pipeline" to add to the meta') parser.add_argument('-m', '--mimetypes', help='TODO: The mimetypes metadata to use, in JSON') parser.add_argument('-l', '--location', help='TODO: The S3 location to use') return parser def main(): """The main runner for our script.""" parser = get_args() args = parser.parse_args() if args.verbose: LOG.setLevel(logging.INFO) LOG.info('Verbose: on') else: ## If not verbose, turn down boto3. boto3.set_stream_logger(name='boto3', level=logging.WARNING) boto3.set_stream_logger(name='botocore', level=logging.WARNING) logging.getLogger("requests").setLevel(logging.WARNING) ## Ensure credentials. if not args.credentials: die_screaming('need a credentials argument') LOG.info('Will use credentials: ' + args.credentials) ## Ensure directory. if not args.directory: die_screaming('need a directory argument') args.directory = args.directory.rstrip('//') LOG.info('Will operate in: ' + args.directory) ## Ensure bucket. if not args.bucket: die_screaming('need a bucket argument') bucket, slash, toppath = args.bucket.partition('/') if toppath != '': LOG.info('Will put to bucket: ' + bucket + '; with path: ' + toppath) else: LOG.info('Will put to bucket at top level: ' + bucket) ## Ensure mimetype metadata. if not args.mimetypes: LOG.info('Will use internal mimetype defaults') else: LOG.info('TODO: Will get mimetype metadata from: ' + args.metadata) ## Ensure bucket location. if not args.location: args.location = 'us-east-1' LOG.info('Will use S3 bucket location default: ' + args.location) else: LOG.info('Will use S3 bucket location: ' + args.location) ## Extract S3 credentials. creds = None with open(args.credentials) as chandle: creds = json.loads(chandle.read()) #LOG.info(creds) s3 = boto3.resource('s3', region_name=args.location, aws_access_key_id=creds['accessKeyId'], aws_secret_access_key=creds['secretAccessKey']) # s3 = boto3.resource("s3", creds['accessKeyId'], creds['secretAccessKey']) #s3.Object('mybucket', 'hello.txt').put(Body=open('/tmp/hello.txt', 'rb')) ## Walk tree. for curr_dir, dirs, files in os.walk(args.directory): ## We can navigate up if we are not in the root. relative_to_start = curr_dir.rstrip('//')[len(args.directory):] relative_to_start = relative_to_start.lstrip('//') LOG.info('curr_dir: ' + curr_dir + ' (' + relative_to_start + ')') ## Note files and directories. for fname in files: ## Get correct mime type. fext = os.path.splitext(fname)[1].lstrip('.') mime = MIMES.get('') # start with default if MIMES.get(fext, False): mime = MIMES.get(fext) ## Figure out S3 path/key and final filename, keeping in ## mind that relative_to_Start can be empty if root. s3path = fname if relative_to_start: s3path = relative_to_start + '/' + fname filename = os.path.join(curr_dir, fname) tags = {} if args.number: tags['build-number'] = args.number if args.pipeline: tags['build-pipeline'] = args.pipeline tags_str = urllib.parse.urlencode(tags) ## Visual check. LOG.info('file: ' + filename) if toppath != '': s3path = toppath + '/' + s3path LOG.info(' -> [' + bucket + '] ' + s3path + \ '(' + mime + ', ' + tags_str + ')') ## Create the new object that we want. s3bucket = s3.Bucket(bucket) multipart_upload(filename, s3bucket, s3path, content_type=mime, metadata=tags, policy="public-read") # newobj = s3.Object(args.bucket, s3path) # outfile = open(filename, 'rb') # newobj.put(Body=outfile, \ # ContentType=mime, \ # Metadata=tags, # ACL='public-read') #Tagging=tags_str) # outbod = open(os.path.join(curr_dir, fname), 'rb') # .put(Body=outbod, 'rb') # for dname in dirs: # #LOG.info('dir: ' + os.path.join(curr_dir, dname)) # pass def multipart_upload(source_file_path, s3_bucket, s3_path, content_type=None, metadata=None, policy=None): header = {} if content_type: header["ContentType"] = content_type if metadata: header["Metadata"] = metadata if policy: header["ACL"] = policy s3_bucket.upload_file(source_file_path, s3_path, ExtraArgs=header) ## You saw it coming... if __name__ == '__main__': main()
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supervisr/dns/tests/test_provider_compat.py
BeryJu/supervisr
1
170256
"""supervisr dns provider compat tests""" from unittest.mock import patch from supervisr.core.models import Domain, ProviderAcquirableRelationship from supervisr.core.providers.objects import ProviderObjectTranslator from supervisr.core.utils.constants import TEST_DOMAIN from supervisr.core.utils.tests import TestCase from supervisr.dns.models import DataRecord, SetRecord, Zone from supervisr.dns.providers.compat import (CompatDNSProvider, CompatDNSRecord, CompatDNSTranslator) from supervisr.dns.utils import date_to_soa class TestProviderCompat(TestCase): """Test DNS Provider Compatibility layer""" def setUp(self): super().setUp() self.domain, _created = Domain.objects.get_or_create( domain_name=TEST_DOMAIN, provider_instance=self.provider) self.zone = Zone.objects.create( domain=self.domain, soa_mname='ns1.s.beryju.org', soa_rname='test.beryju.org.', soa_serial=date_to_soa()) ProviderAcquirableRelationship.objects.create( provider_instance=self.provider, model=self.zone) self.data_record_a = DataRecord.objects.create( name='test-a', type='A', content='172.16.17.32') self.data_record_b = DataRecord.objects.create( name='test-b', type='A', content='192.168.127.12') self.set_record = SetRecord.objects.create( name='@') self.set_record.records.add(self.data_record_b) self.zone.records.add(self.data_record_a) self.zone.records.add(self.set_record) def test_compat_record(self): """Test CompatDNSRecord""" translator = CompatDNSTranslator(None) compat_records = translator.build_compat_records(self.data_record_a) self.assertEqual(len(compat_records), 1) self.assertEqual(compat_records[0].name, 'test-a.supervisrtest.beryju.org') self.assertEqual(compat_records[0].domain, str(self.zone)) def test_provider(self): """Test CompatDNSProvider""" provider = CompatDNSProvider(None) self.assertEqual(provider.get_translator(DataRecord), CompatDNSTranslator) self.assertEqual(provider.get_translator(CompatDNSRecord), None) @patch('supervisr.dns.providers.compat.CompatDNSProvider.get_translator') def test_translator(self, get_translator): """Test CompatDNSTranslator""" class TestTranslator(ProviderObjectTranslator): """Test Provider Translator""" def to_external(self, internal): yield internal self.set_record.records.add(self.data_record_a) get_translator.return_value = TestTranslator provider = CompatDNSProvider(None) translator = CompatDNSTranslator(provider) compat_records = list(translator.to_external(self.set_record)) compat_records = sorted(compat_records, key=lambda x: x.name) # List is only 2 entires despite us creating 3 records # since self.set_record's name is @ and append_names is set to false self.assertEqual(len(compat_records), 2) self.assertEqual(compat_records[0].name, self.data_record_a.name + '.' + TEST_DOMAIN) self.assertEqual(compat_records[1].name, self.data_record_b.name + '.' + TEST_DOMAIN)
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resolve_all_issues.py
cloudpassage-community/resolve_all_halo_issues
0
181680
<reponame>cloudpassage-community/resolve_all_halo_issues #! /usr/local/bin/python # Author: <NAME> # Script for resolving all Halo issues. This script is intended to clear out # data related to Halo testing and issues created from a PoC # Updated 2017/03/08 by <NAME> to add count, user input and progress import json, io, requests import cloudpassage import yaml def create_api_session(session): config_file_loc = "cloudpassage.yml" config_info = cloudpassage.ApiKeyManager(config_file=config_file_loc) session = cloudpassage.HaloSession(config_info.key_id, config_info.secret_key) return session def resolve_issues(session): get_number_of_issues= cloudpassage.HttpHelper(session) number_of_issues = get_number_of_issues.get("/v2/issues") number_of_pages = int(number_of_issues['count']) number_of_pages = int(number_of_pages * .01) issues= cloudpassage.HttpHelper(session) list_of_issues_json = issues.get_paginated("/v2/issues", 'issues', number_of_pages) body = {"status": "resolved",} #print list_of_issues_json #loop list of issues and resolve them print "\n" print "##### There are %d issues to resolve #####" % len(list_of_issues_json) print "##### Hit <Enter> to proceed #####" proceed = raw_input("") print "\n" for issue in list_of_issues_json: print issue print "-"*20 issueID = issue['id'] issue_to_resolve = cloudpassage.HttpHelper(session) list_of_issues_json = issue_to_resolve.get("/v2/issues") reply = issue_to_resolve.put("/v2/issues/" + issueID, body) def main(): api_session = None api_session = create_api_session(api_session) resolve_issues(api_session) if __name__ == "__main__": main()
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dialpad/client.py
dialpad/dialpad-python-sdk
5
40757
<filename>dialpad/client.py import requests from cached_property import cached_property from .resources import ( SMSResource, RoomResource, UserResource, CallResource, NumberResource, OfficeResource, WebhookResource, CompanyResource, ContactResource, CallbackResource, CallCenterResource, CallRouterResource, DepartmentResource, TranscriptResource, UserDeviceResource, StatsExportResource, SubscriptionResource, BlockedNumberResource, EventSubscriptionResource ) hosts = dict( live='https://dialpad.com', sandbox='https://sandbox.dialpad.com' ) class DialpadClient(object): def __init__(self, token, sandbox=False, base_url=None): self._token = token self._session = requests.Session() self._base_url = base_url or hosts.get('sandbox' if sandbox else 'live') def _url(self, *path): path = ['%s' % p for p in path] return '/'.join([self._base_url, 'api', 'v2'] + path) def _cursor_iterator(self, response_json, path, method, data, headers): for i in response_json['items']: yield i data = dict(data or {}) while 'cursor' in response_json: data['cursor'] = response_json['cursor'] response = self._raw_request(path, method, data, headers) response.raise_for_status() response_json = response.json() for i in response_json['items']: yield i def _raw_request(self, path, method='GET', data=None, headers=None): url = self._url(*path) headers = headers or dict() headers.update({'Authorization': 'Bearer %s' % self._token}) if str(method).upper() in ['GET', 'POST', 'PUT', 'PATCH', 'DELETE']: return getattr(self._session, str(method).lower())( url, headers=headers, json=data if method != 'GET' else None, params=data if method == 'GET' else None, ) raise ValueError('Unsupported method "%s"' % method) def request(self, path, method='GET', data=None, headers=None): response = self._raw_request(path, method, data, headers) response.raise_for_status() if response.status_code == 204: # No Content return None response_json = response.json() response_keys = set(k for k in response_json) # If the response contains the 'items' key, (and maybe 'cursor'), then this is a cursorized # list response. if 'items' in response_keys and not response_keys - {'cursor', 'items'}: return self._cursor_iterator( response_json, path=path, method=method, data=data, headers=headers) return response_json @cached_property def blocked_number(self): return BlockedNumberResource(self) @cached_property def call(self): return CallResource(self) @cached_property def call_router(self): return CallRouterResource(self) @cached_property def callback(self): return CallbackResource(self) @cached_property def callcenter(self): return CallCenterResource(self) @cached_property def company(self): return CompanyResource(self) @cached_property def contact(self): return ContactResource(self) @cached_property def department(self): return DepartmentResource(self) @cached_property def event_subscription(self): return EventSubscriptionResource(self) @cached_property def number(self): return NumberResource(self) @cached_property def office(self): return OfficeResource(self) @cached_property def room(self): return RoomResource(self) @cached_property def sms(self): return SMSResource(self) @cached_property def stats(self): return StatsExportResource(self) @cached_property def subscription(self): return SubscriptionResource(self) @cached_property def transcript(self): return TranscriptResource(self) @cached_property def user(self): return UserResource(self) @cached_property def userdevice(self): return UserDeviceResource(self) @cached_property def webhook(self): return WebhookResource(self)
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src/visions/declarative.py
bhumikapahariapuresoftware/visions
142
164471
from typing import Any, Callable, List, Optional, Sequence, Type, TypeVar, Union from visions.relations import IdentityRelation, InferenceRelation from visions.types.type import VisionsBaseType T = TypeVar("T") def process_relation(items: Union[dict, Type[VisionsBaseType]]) -> IdentityRelation: if isinstance(items, dict): return IdentityRelation(**items) elif issubclass(items, VisionsBaseType): return IdentityRelation(related_type=items) else: raise TypeError("identity should be a list, a dict of params or related_type.") def create_type( name: str, contains: Callable[[Any, dict], bool], identity: Optional[ Union[Type[VisionsBaseType], List[Union[dict, Type[VisionsBaseType]]], dict] ] = None, inference: Optional[Union[List[dict], dict]] = None, ): def get_relations(): if isinstance(identity, Sequence): relations = [process_relation(item) for item in identity] else: relations = [] if identity is None else [process_relation(identity)] if inference is not None: if isinstance(inference, dict): relations += [InferenceRelation(**inference)] elif isinstance(inference, list): relations += [InferenceRelation(**params) for params in inference] else: raise TypeError("inference should be a list or a dict of params.") return relations def contains_op(series, state): return contains(series, state) return type( name, (VisionsBaseType,), { "get_relations": staticmethod(get_relations), "contains_op": staticmethod(contains_op), }, )
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_scripts/_build.py
dfreeman06/wxyz
1
3195
import subprocess import sys from . import ROOT, PY_SRC, _run, PY, DIST CONDA_ORDER = [ "core", "html", "lab", "datagrid", "svg", "tpl-jjinja" "yaml" ] CONDA_BUILD_ARGS = [ "conda-build", "-c", "conda-forge", "--output-folder", DIST / "conda-bld", ] if __name__ == "__main__": for pkg in PY_SRC.glob("wxyz_*"): _run([PY, "setup.py", "sdist", "--dist-dir", DIST / "sdist"], cwd=str(pkg)) try: _run([*CONDA_BUILD_ARGS, "--skip-existing", "."], cwd=ROOT / "recipes") except: for pkg in CONDA_ORDER: _run([*CONDA_BUILD_ARGS, f"wxyz-{pkg}"], cwd=ROOT / "recipes")
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anonymizer/__init__.py
jjhuang/django-anonymizer
13
151173
<filename>anonymizer/__init__.py # flake8: noqa from anonymizer.base import Anonymizer
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project/sema2/managers.py
eorygen/sema2_web
0
61005
from django.db import models class AnswerSetUniqueManager(models.Manager): def get_queryset(self): return super(AnswerSetUniqueManager, self).get_queryset().filter(is_duplicate=False)
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RequestInfo.py
AndreyRub/LoveLetter
0
85636
class RequestInfo: def __init__(self, human_string, # human-readable string (will be printed for Human object) action_requested, # action requested ('card' + index of card / 'opponent' / 'guess') current_hand = [], # player's current hand discard_pile = [], # discard pile move_history = [], # list of player moves players_active_status = [], # players' active status (active / lost) players_protection_status = [], # players' protection status (protected / not protected) invalid_moves = [], # invalid moves (optional) - an assist from Game valid_moves = [], # valid moves (optional) - an assist from Game players_active = [], # list of currently active players players_protected = []): # Protection status of all players (including inactive ones) self.human_string = human_string self.action_requested = action_requested self.current_hand = current_hand self.discard_pile = discard_pile self.move_history = move_history self.players_active_status = players_active_status self.players_protection_status = players_protection_status self.invalid_moves = invalid_moves self.valid_moves = valid_moves self.players_active = players_active self.players_protected = players_protected def get_request_info(self): return ({'human_string' : self.human_string, 'action_requested' : self.action_requested, 'current_hand' : self.current_hand, 'discard_pile' : self.discard_pile, 'move_history' : self.move_history, 'players_active_status' : self.players_active_status, 'players_protection_status' : self.players_protection_status, 'invalid_moves' : self.invalid_moves, 'valid_moves' : self.valid_moves, 'players_active' : self.players_active, 'players_protected' : self.players_protected})
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windows_to_fasta.py
latticetower/top_introns
2
169538
import fileinput import sys from collections import OrderedDict from window_info import WindowInfo from optparse import OptionParser class WindowToFastaConverter(object): def __init__(self): self.current_chromosome = "" self.chromosome_windows = list() self.current_window = 0 def load_fasta(self, fasta_file_name): chromosome_name = '' buffer = '' offset = 0 # zero-based offset for current string with open(fasta_file_name) as f: for line in f: if line.startswith('>'): buffer = '' offset = 0 chromosome_name = line.strip('\r\n')[1 : ] else: buffer = line.strip('\r\n') yield(chromosome_name, buffer, offset) offset += len(buffer) def load_window_list(self, file_name): self.filtered_list = OrderedDict() with open(file_name) as f: for line in f: line_data = line.strip('\r\n').split() if (not line_data[0] in self.filtered_list.keys()): self.filtered_list[line_data[0]] = OrderedDict() self.filtered_list[line_data[0]][line_data[1]] = WindowInfo(*line_data[ : 4]) #the following methos aims to fill in all windows information for each of chromosomes def fill_sequence_info(self, chromosome_info): if self.current_chromosome != chromosome_info[0]: self.current_chromosome = chromosome_info[0] if (not self.current_chromosome in self.filtered_list.keys()): self.current_chromosome = '' return self.chromosome_windows = iter(sorted(self.filtered_list[self.current_chromosome].items(), key = lambda w: long(w[0]))) self.current_window = next(self.chromosome_windows) offset = long(chromosome_info[2]) offset_end = long(offset + len(chromosome_info[1]) - 1) if long(offset_end) < long(self.current_window[1].window_start): return if long(self.current_window[1].window_start) < long(offset) and long(self.current_window[1].window_end) > long(offset_end): self.current_window[1].sequence += chromosome_info[1] return if long(offset) <= long(self.current_window[1].window_end) and long(self.current_window[1].window_end) <= long(offset_end) and long(self.current_window[1].window_start < offset): self.current_window[1].sequence += chromosome_info[1][ : self.current_window[1].window_end - offset + 1] try: self.current_window = next(self.chromosome_windows) except StopIteration: return try: while long(self.current_window[1].window_start) >= long(offset) and long(self.current_window[1].window_end) <= long(offset_end): self.current_window[1].sequence = chromosome_info[1][self.current_window[1].window_start - offset : self.current_window[1].window_end - offset + 1] self.current_window = next(self.chromosome_windows) except StopIteration: return if long(offset) <= long(self.current_window[1].window_start) and long(self.current_window[1].window_start) <= long(offset_end) and long(self.current_window[1].window_end) > long(offset_end): self.current_window[1].sequence = chromosome_info[1][self.current_window[1].window_start - offset : ] return if long(offset) > long(self.current_window[1].window_end): try: while long(self.current_window[1].window_end) < long(offset): self.current_window = next(self.chromosome_windows) except: return #the following method processes entire fasta file def load_info_from_fasta(self, fasta_file_name): for chromosome_info in self.load_fasta(fasta_file_name): self.fill_sequence_info(chromosome_info) def save_sequences_to_fasta(self, output_file_name): output_file = open(output_file_name, 'w') # for each chromosome in list print sequence to output file for chromosome_windows in self.filtered_list.values(): for window in chromosome_windows.values(): output_file.write(window.print_to_fasta()) output_file.close() def main(bed_file_name, output_file_name, fasta_file_name): result = 0 if not bed_file_name: print ".bed file was not provided" result = 1 if not fasta_file_name: print ".fasta file name was not provided" result = 1 if result: return result converter = WindowToFastaConverter() converter.load_window_list(bed_file_name) converter.load_info_from_fasta(fasta_file_name) converter.save_sequences_to_fasta(output_file_name) return result if __name__ == "__main__": parser = OptionParser() parser.add_option("-i", "--input", dest = "input_file", help = "input FILE in .bed format", metavar = "FILE") parser.add_option("-o", "--output", dest = "output_file", help = "output file in fasta format", metavar = "FILE") parser.add_option("-f", "--fasta", dest = "reference_file", help = "input FILE in .fasta format", metavar = "FILE") (options, args) = parser.parse_args() try: result = main(options.input_file, options.output_file, options.reference_file) if result: parser.print_help() exit() except Exception as e: print >> sys.stderr, e #
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code/check_image_exists.py
LRoeseler/generalisation-humans-DNNs
1
121818
# check whether each of the images used here is contained in the downloaded entirety of images import os path = "../16-class-ImageNet/image_names" txt_file_list = os.listdir(path) print(txt_file_list) for txt_file in txt_file_list: file_path = os.path.join(path, txt_file) print(file_path) print(f"Now scanning {file_path}") no_of_existing_files = 0 lst_not_found_images = [] line_count = 0 with open(file_path) as f: for img in f: if img != "\n": line_count += 1 img = img.strip() # print(img) location1 = "../../ILSVRC2012_img_train/" location2 = "../../ILSVRC2012_img_train_t3/" if os.path.isfile(location1 + img): no_of_existing_files += 1 # print(f"{image_name} has been found.") elif os.path.isfile(location2 + img): no_of_existing_files += 1 else: lst_not_found_images.append((img, f)) print("no_of_existing_files", no_of_existing_files, f"in the file with {line_count} lines") print(len(lst_not_found_images))
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test/test-sys.py
xupingmao/minipy
52
37302
<filename>test/test-sys.py import sys assert len(sys.argv) == 1
[ 1, 529, 9507, 29958, 1688, 29914, 1688, 29899, 9675, 29889, 2272, 13, 13, 5215, 10876, 13, 13, 9294, 7431, 29898, 9675, 29889, 19218, 29897, 1275, 29871, 29896, 2 ]
chaco/array_plot_data.py
martinRenou/chaco
0
1610716
""" Defines ArrayPlotData. """ import six import six.moves as sm from numpy import array, ndarray # Enthought library imports from traits.api import Dict # Local, relative imports from .abstract_plot_data import AbstractPlotData from .abstract_data_source import AbstractDataSource class ArrayPlotData(AbstractPlotData): """ A PlotData implementation class that handles a list of Numpy arrays (or a 2-D Numpy array). By default, it doesn't allow its input data to be modified by downstream Chaco components or interactors. """ #------------------------------------------------------------------------- # Public traits #------------------------------------------------------------------------- #: Map of names to arrays. Although there is no restriction on the array #: dimensions, each array must correspond to a single plot item; that #: is, a single name must not map to a multi-dimensional array unless #: the array is being used for an image plot or for something that can handle #: multi-dimensional input data. arrays = Dict #: Consumers can write data to this object (overrides AbstractPlotData). writable = True def __init__(self, *data, **kw): """ ArrayPlotData can be constructed by passing in arrays. Keyword arguments can be used to give certain arrays specific names; unnamed arrays are given a generic name of the format 'seriesN', where N is its position in the argument list. For example:: ArrayPlotData(array1, array2, index=array3, foo=array4) This call results in the creation of four entries in self.arrays:: 'series1' -> array1 'series2' -> array2 'index' -> array3 'foo' -> array4 If any names in the keyword parameter list collide with the auto-generated positional names "series1", "series2", etc., then those arrays are replaced. Note that this factor means that keyword traits are *not* set using the keyword parameters in the constructor. This strategy defies some conventions, but was it chosen for convenience, since the raison d'etre of this class is convenience. """ super(AbstractPlotData, self).__init__() self._update_data(kw) data = dict(sm.zip(self._generate_names(len(data)), data)) self._update_data(data) #------------------------------------------------------------------------ # AbstractPlotData Interface #------------------------------------------------------------------------ def list_data(self): """ Returns a list of the names of the arrays managed by this instance. """ return list(self.arrays.keys()) def get_data(self, name): """ Returns the array associated with *name*. Implements AbstractDataSource. """ return self.arrays.get(name, None) def del_data(self, name): """ Deletes the array specified by *name*, or raises a KeyError if the named array does not exist. """ if not self.writable: return None if name in self.arrays: del self.arrays[name] self.data_changed = {'removed': [name]} else: raise KeyError("Data series '%s' does not exist." % name) def set_data(self, name, new_data, generate_name=False): """ Sets the specified array as the value for either the specified name or a generated name. If the instance's `writable` attribute is True, then this method sets the data associated with the given name to the new value, otherwise it does nothing. Parameters ---------- name : string The name of the array whose value is to be set. new_data : array The array to set as the value of *name*. generate_name : Boolean If True, a unique name of the form 'seriesN' is created for the array, and is used in place of *name*. The 'N' in 'seriesN' is one greater the largest N already used. Returns ------- The name under which the array was set. See Also -------- update_data: Use if needing to set multiple ArrayPlotData entries at once, for example because new arrays' dimensions change and updating one at a time would break an existing Plot. """ if not self.writable: return None if generate_name: names = self._generate_names(1) name = names[0] self.update_data({name: new_data}) return name def update_data(self, *args, **kwargs): """ Updates any number of arrays before triggering a `data_changed` event. Useful to set multiple ArrayPlotData entries at once, for example because new arrays' dimensions change and updating one at a time would break an existing Plot. Note: Implements AbstractPlotData's update_data() method. This method has the same signature as the dictionary update() method. See Also -------- set_data: Simpler interface to set only 1 entry at a time. """ if not self.writable: return None data = dict(*args, **kwargs) event = {} for name in data: if name in self.arrays: event.setdefault('changed', []).append(name) else: event.setdefault('added', []).append(name) self._update_data(data) self.data_changed = event def set_selection(self, name, selection): """ Overrides AbstractPlotData to do nothing and not raise an error. """ pass #------------------------------------------------------------------------ # Private methods #------------------------------------------------------------------------ def _generate_names(self, n): """ Generate n new names """ max_index = max(self._generate_indices()) names = ["series{0:d}".format(n) for n in range(max_index+1, max_index+n+1)] return names def _generate_indices(self): """ Generator that yields all integers that match "series%d" in keys """ yield 0 # default minimum for name in self.list_data(): if name.startswith('series'): try: v = int(name[6:]) except ValueError: continue yield v def _update_data(self, data): """ Update the array, ensuring that data is an array """ # note that this call modifies data, but that's OK since the callers # all create the dictionary that they pass in for name, value in list(data.items()): if not isinstance(value, (ndarray, AbstractDataSource)): data[name] = array(value) else: data[name] = value self.arrays.update(data)
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djangologdb/utils.py
cgroschupp/django-logdb
5
126261
import datetime import time from django.db import models from django.core.serializers.json import DjangoJSONEncoder from django.utils import simplejson as json def get_timestamp(date_time): """ Create a `timestamp` from a `datetime` object. A `timestamp` is defined as the number of milliseconds since January 1, 1970 00:00. This is like Javascript or the Unix timestamp times 1000. """ return time.mktime(date_time.timetuple()) * 1000 def get_datetime(timestamp): """ Takes a `timestamp` and returns a `datetime` object. """ return datetime.datetime.fromtimestamp(int(timestamp / 1000)) class JSONField(models.TextField): __metaclass__ = models.SubfieldBase def to_python(self, value): if isinstance(value, basestring) and value: try: value = json.loads(value) except ValueError: return None return value def get_db_prep_save(self, value, connection): if value is None: return None value = json.dumps(value, cls=DjangoJSONEncoder) return super(JSONField, self).get_db_prep_save(value, connection=connection) class TupleField(models.TextField): __metaclass__ = models.SubfieldBase def to_python(self, value): if value is None: return None if isinstance(value, basestring): try: value = tuple(json.loads(value)) except ValueError: return None return value def get_db_prep_save(self, value, connection): if value is None: return None value = json.dumps(value, cls=DjangoJSONEncoder) return super(TupleField, self).get_db_prep_save(value, connection=connection)
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ingestors/misc/jsonfile.py
simonwoerpel/ingest-file
23
52666
<reponame>simonwoerpel/ingest-file<filename>ingestors/misc/jsonfile.py import json from followthemoney import model from followthemoney.util import MEGABYTE from ingestors.ingestor import Ingestor from ingestors.support.encoding import EncodingSupport from ingestors.exc import ProcessingException class JSONIngestor(Ingestor, EncodingSupport): MIME_TYPES = [ "application/json", "text/javascript", ] EXTENSIONS = ["json"] MAX_SIZE = 100 * MEGABYTE SCORE = 3 def _collect_text(self, obj): if isinstance(obj, (list, set, tuple)): for item in obj: yield from self._collect_text(item) if isinstance(obj, dict): for item in obj.values(): yield from self._collect_text(item) if isinstance(obj, str): yield obj def ingest(self, file_path, entity): for file_size in entity.get("fileSize"): if int(file_size) > self.MAX_SIZE: raise ProcessingException("JSON file is too large.") with open(file_path, "rb") as fh: encoding = self.detect_stream_encoding(fh) with open(file_path, "r", encoding=encoding) as fh: try: data = json.load(fh) for idx, text in enumerate(self._collect_text(data)): self.manager.emit_text_fragment(entity, [text], idx) except Exception as exc: raise ProcessingException("Cannot parse JSON file: %s" % exc) from exc
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examples/anomaly_detection.py
KrishnanSG/pytsal
32
46090
""" Holt Winter Anomaly detection Example In this example we will look into how to create holt winters model and build an anomaly detection model in less than 4 steps. """ if __name__ == '__main__': from pytsal import anomaly, forecasting from pytsal.dataset import * # 1. Load the dataset ts_with_anomaly = load_airline_with_anomaly() # 2. Forecasting model # 2.a Load existing forecasting model model = forecasting.load_model() # 2.b Create new model if model is None: ts = load_airline() model = forecasting.setup(ts, 'holtwinter', eda=False, validation=False, find_best_model=True, plot_model_comparison=False) trained_model = forecasting.finalize(ts, model) forecasting.save_model(trained_model) model = forecasting.load_model() # 3. brutlag algorithm finds and returns the anomaly points anomaly_points = anomaly.setup(ts_with_anomaly, model, 'brutlag') print(anomaly_points)
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venv/Lib/site-packages/keystoneauth1/tests/unit/extras/oauth1/test_oauth1.py
prasoon-uta/IBM-coud-storage
48
78112
# Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import uuid from oauthlib import oauth1 import six from testtools import matchers from keystoneauth1.extras import oauth1 as ksa_oauth1 from keystoneauth1 import fixture from keystoneauth1 import session from keystoneauth1.tests.unit import utils as test_utils class OAuth1AuthTests(test_utils.TestCase): TEST_ROOT_URL = 'http://127.0.0.1:5000/' TEST_URL = '%s%s' % (TEST_ROOT_URL, 'v3') TEST_TOKEN = uuid.uuid4().hex def stub_auth(self, subject_token=None, **kwargs): if not subject_token: subject_token = self.TEST_TOKEN self.stub_url('POST', ['auth', 'tokens'], headers={'X-Subject-Token': subject_token}, **kwargs) def _validate_oauth_headers(self, auth_header, oauth_client): """Validate data in the headers. Assert that the data in the headers matches the data that is produced from oauthlib. """ self.assertThat(auth_header, matchers.StartsWith('OAuth ')) parameters = dict( oauth1.rfc5849.utils.parse_authorization_header(auth_header)) self.assertEqual('HMAC-SHA1', parameters['oauth_signature_method']) self.assertEqual('1.0', parameters['oauth_version']) self.assertIsInstance(parameters['oauth_nonce'], six.string_types) self.assertEqual(oauth_client.client_key, parameters['oauth_consumer_key']) if oauth_client.resource_owner_key: self.assertEqual(oauth_client.resource_owner_key, parameters['oauth_token'],) if oauth_client.verifier: self.assertEqual(oauth_client.verifier, parameters['oauth_verifier']) if oauth_client.callback_uri: self.assertEqual(oauth_client.callback_uri, parameters['oauth_callback']) return parameters def test_oauth_authenticate_success(self): consumer_key = uuid.uuid4().hex consumer_secret = uuid.uuid4().hex access_key = uuid.uuid4().hex access_secret = uuid.uuid4().hex oauth_token = fixture.V3Token(methods=['oauth1'], oauth_consumer_id=consumer_key, oauth_access_token_id=access_key) oauth_token.set_project_scope() self.stub_auth(json=oauth_token) a = ksa_oauth1.V3OAuth1(self.TEST_URL, consumer_key=consumer_key, consumer_secret=consumer_secret, access_key=access_key, access_secret=access_secret) s = session.Session(auth=a) t = s.get_token() self.assertEqual(self.TEST_TOKEN, t) OAUTH_REQUEST_BODY = { "auth": { "identity": { "methods": ["oauth1"], "oauth1": {} } } } self.assertRequestBodyIs(json=OAUTH_REQUEST_BODY) # Assert that the headers have the same oauthlib data req_headers = self.requests_mock.last_request.headers oauth_client = oauth1.Client(consumer_key, client_secret=consumer_secret, resource_owner_key=access_key, resource_owner_secret=access_secret, signature_method=oauth1.SIGNATURE_HMAC) self._validate_oauth_headers(req_headers['Authorization'], oauth_client) def test_warning_dual_scope(self): ksa_oauth1.V3OAuth1(self.TEST_URL, consumer_key=uuid.uuid4().hex, consumer_secret=uuid.uuid4().hex, access_key=uuid.uuid4().hex, access_secret=uuid.uuid4().hex, project_id=uuid.uuid4().hex) self.assertIn('ignored by the identity server', self.logger.output)
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examples/sns.py
calebmarcus/awacs
0
141349
<gh_stars>0 from awacs.aws import Allow, ArnEquals, AWSPrincipal, Condition from awacs.aws import Policy, Statement import awacs.sns as sns import awacs.sqs as sqs region = 'us-east-1' account = '012345678891' pd = Policy( Statement=[ Statement( Effect=Allow, Principal=AWSPrincipal("210987654321"), Action=[sqs.SendMessage], Resource=[sqs.ARN(region, account, "your_queue_xyz"), ], Condition=Condition( ArnEquals( "aws:SourceArn", sns.ARN(region, '123456789012', 'your_special_topic_1') ), ), ), ], ) print(pd.to_json())
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word_trainer.py
matthew-a-adams/morse-practice-scripts
0
150078
#!/usr/bin/env python3 from __future__ import division, print_function import string, time, random, msvcrt import sounddevice as sd import numpy as np from scipy import io import scipy.io.wavfile import csv from collections import Counter import morse SPS = 8000 LETTERS = string.ascii_uppercase FREQ = 750 WPM = 25 FS = 10 AUDIO_PADDING = 0.5 # Seconds CLICK_SMOOTH = 2 # Tone periods def main(freq, wpm, fs, prompt, force, limit, length, outFile, inFile): messages = wordFinder(limit, length) print('Message =', messages) if prompt: # Load spoken letter WAV files letterNames = loadLetterNames() sps = letterNames[LETTERS[0]][0] else: sps = SPS print('Audio samples per second =', sps) print('Tone period =', round(1000/freq, 1), 'ms') dps = morse.wpmToDps(wpm) # Dots per second mspd = 1000/dps # Dot duration in milliseconds farnsworthScale = morse.farnsworthScaleFactor(wpm, fs) print('Dot width =', round(mspd, 1), 'ms') print('Dash width =', int(round(mspd * morse.DASH_WIDTH)), 'ms') print('Character space =', int(round(mspd * morse.CHAR_SPACE * farnsworthScale)), 'ms') print('Word space =', int(round(mspd * morse.WORD_SPACE * farnsworthScale)), 'ms') print() print("Hit <ENTER> to start.") input() continue_with_test = True while continue_with_test: missed_count = 0 continue_with_test = False retest_messages = [] for message in messages: # Compute morse code audio from plain text playMessage(message, sps, wpm, fs, freq) print('Enter message:') start = time.time() check = input() if check.upper() == message.upper(): end = time.time() print('Correct! [', '{:.2f}'.format(end-start), 's]') else: print('Wrong. The correct answer is ', message) retest_messages.append(message) continue_with_test = True missed_count = missed_count + 1 print('You missed ', missed_count, '. ') if force: print('Retesting missed message...') message = retest_messages else: continue_with_test = False def wordFinder(limit, length): messages = [] with open('dictionary.txt') as fin: lines = (word.strip().upper() for word in fin) words = [(word, Counter(word)) for word in lines] rack = Counter(''.join(list(string.ascii_uppercase))) for training_word, letter_count in words: # Using length here to limit output for example purposes if len(training_word) == length and not (letter_count - rack): messages.append(training_word) random.shuffle(messages) messages = random.choices(messages, k=limit) return messages def playMessage(message, sps, wpm, fs, freq): audio = stringToMorseAudio(message, sps, wpm, fs, freq, 0.5, None, promptVolume=0.3) audio /= 2 playBlock(audio, sps) time.sleep(0.1) def addAudio(base, new, offset): if base is None: base = np.array([], dtype=np.float32) assert offset >= 0 lenBase, lenNew = len(base), len(new) if offset+lenNew > lenBase: # Make base longer by padding with zeros base = np.append(base, np.zeros(offset+lenNew-lenBase)) base[offset:offset+lenNew] += new return base def boolArrToSwitchedTone(boolArr, freq, sps, volume=1.0): ''' Create the tone audio from a bool array representation of morse code. ''' weightLen = int(CLICK_SMOOTH*sps/freq) if weightLen % 2 == 0: weightLen += 1 # Make sure the weight array is odd length smoothingWeights = np.concatenate((np.arange(1, weightLen//2+1), np.arange(weightLen//2+1, 0, -1))) smoothingWeights = smoothingWeights / np.sum(smoothingWeights) numSamplesPadding = int(sps*AUDIO_PADDING) + int((weightLen-1)/2) padding = np.zeros(numSamplesPadding, dtype=bool) boolArr = np.concatenate((padding, boolArr, padding)).astype(np.float32) if CLICK_SMOOTH <= 0: smoothBoolArr = boolArr else: smoothBoolArr = np.correlate(boolArr, smoothingWeights, 'valid') numSamples = len(smoothBoolArr) x = np.arange(numSamples) toneArr = np.sin(x * (freq*2*np.pi/sps)) * volume toneArr *= smoothBoolArr return toneArr def stringToMorseAudio(message, sps=SPS, wpm=WPM, fs=FS, freq=FREQ, volume=1.0, letterPrompts=None, promptVolume=1.0): message = message.upper() code = morse.stringToMorse(message) boolArr = morse.morseToBoolArr(code, sps, wpm, fs) audio = boolArrToSwitchedTone(boolArr, freq, sps, volume) numSamplesPadding = int(sps*AUDIO_PADDING) if letterPrompts is not None: for i in range(len(message)): l = message[i] if l in letterPrompts: offsetPlus = morse.morseSampleDuration(morse.stringToMorse(message[:i+1]), sps, wpm, fs) letterDuration = morse.morseSampleDuration(morse.letterToMorse(message[i]), sps, wpm, fs) offset = numSamplesPadding + offsetPlus - letterDuration audio = addAudio(audio, letterPrompts[l][1]*promptVolume, offset) return audio def loadLetterNames(pathTemplate='audio/letter-names/%s_.wav', letters=LETTERS): out = {} for letter in letters: fName = pathTemplate % letter out[letter] = loadWav(fName) return out def loadWav(fName): rate, data = io.wavfile.getHistory(fName, mmap=True) dataScale = data.astype(np.float32) / maxDtypeVolume(data.dtype) return rate, dataScale def maxDtypeVolume(dtype): try: return np.iinfo(dtype).max # Integer data type except ValueError: return 1.0 # Float data type def playLetterNamesBlock(letterNames): sps = letterNames[LETTERS[0]][0] letterNameList = [letterNames[l][1] for l in LETTERS] alphabetAudio = np.concatenate(letterNameList) playBlock(alphabetAudio, sps) def genTone(frequency, duration, sps=SPS, volume=1.0): return np.sin(np.arange(sps*duration)*(frequency*2*np.pi/sps))*volume def playTone(*args, **kwargs): play(genTone(*args, **kwargs)) def play(array, sps=SPS): sd.play(array.astype(np.float32), sps) def waitFor(array, sps=SPS): duration = len(array) / sps time.sleep(duration) def playBlock(array, sps=SPS): play(array, sps) waitFor(array, sps) if __name__ == '__main__': import sys, argparse parser = argparse.ArgumentParser(description='Convert text to morse code audio.') parser.add_argument('-f', type=float, default=FREQ, help='Tone frequency') parser.add_argument('--wpm', type=float, default=WPM, help='Words per minute') parser.add_argument('--fs', type=float, default=FS, help='Farnsworth speed') parser.add_argument('-p', action='store_true', default=False, help='Say letters along with morse code') parser.add_argument('--force', action='store_true', default=False, help='Force user to get the answer correct before completing') parser.add_argument('--limit', type=int, default=0, help='Limit to X queries') parser.add_argument('--length', type=int, default=0, help='Length of the word') parser.add_argument('-o', type=str, default='', help='Output to given WAV file instead of playing sound') parser.add_argument('-i', type=str, default='', help='Input from text file') args = parser.parse_args() main(args.f, args.wpm, args.fs, args.p, args.force, args.limit, args.length, args.o, args.i)
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hopper/utils/args/CommandHandler.py
Xilinx/hopper
2
104454
<filename>hopper/utils/args/CommandHandler.py # Copyright (c) 2015 Xilinx Inc. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import sys from hopper.utils.logger import * from hopper.utils.Collections import * from ArgumentParserException import * from CommandBase import * from BaseField import * class CommandHandler: def __init__(self): self.commands = [] self.default = None @staticmethod def getCommandNameFromClass(c): if c: attrs = dir(c) if "CommandName" in attrs: return getAsListOrIterable(getattr(c, "CommandName")) return None @staticmethod def getAllFieldsFromClass(c): if c: fields = {} for attrname in dir(c): if not attrname.startswith("__"): attr = getattr(c, attrname) if not callable(attr) and isinstance(attr, BaseField): fields[attrname] = attr return fields return None def getCommand(self, args): command = None options = list() fields = list() # Find the Command for i in args[1:]: if i.startswith("-"): options.append(i) else: # assume first arg is the command otherwise there is no command if not(command) and len(fields) == 0: command = self.findCommandByName(i) if not(command): fields.append(i) else: fields.append(i) if command == None: if self.default: command = self.default else: raise ArgumentParserException("No command specified, and no default fallback avaliable.") # Associated fields for the command commandFields = {} debug("Command: '%s' -> fields:" % (command)) if command: for i in CommandHandler.getAllFieldsFromClass(command).iteritems(): commandFields[i[0]] = BaseFieldValue(i[1]) debug(" * '%s' -> %s" % (i[0], i[1])) # Match all options first for i in options: matched = False for f in commandFields.iteritems(): #debug("Testing option '%s' against '%s'" % (i, f)) if f[1].parse(i): matched = True break if not(matched): raise ArgumentParserException("Unknown Argument '%s'" % i) # Match all fields fieldindex = 0 for i in fields: matched = False for f in commandFields.iteritems(): #debug("Testing field '%s' against '%s'" % (i, f)) if f[1].parse(i, fieldindex): matched = True break if not(matched): raise ArgumentParserException("Unknown Argument '%s'" % i) else: fieldindex += 1 if command != None: commandInstance = command() # Populate the command with its values debug("Parsed fields:") for i in commandFields.iteritems(): debug(" * '%s' -> %s" % (i[0], i[1])) commandInstance.__dict__[i[0]] = i[1].getOrDefault() commandInstance.__options__() debug(repr(commandInstance)) debug(repr(commandInstance.__dict__)) if commandInstance != None: return commandInstance return None def findCommandByName(self, name): for i in self.commands: names = CommandHandler.getCommandNameFromClass(i) if names and name in names: return i return None def addCommand(self, command): if issubclass(command, CommandBase): self.commands.append(command) else: raise TypeError("Attempted to add '%s' as command which does not inherit from CommandBase" % command) def setDefault(self, command): if issubclass(command, CommandBase): self.default = command else: raise TypeError("Attempted to set '%s' as default command which does not inherit from CommandBase" % command)
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