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"""The auth service module configures the flask app for the OpenBMS auth service. The auth service provides an API for managing and authenticating user accounts. Users may authenticate through a number of supported identity provides using SAML or through a native OpenBMS account using an email address and password. The authentication service also maintains user roles and permissions. The auth service can be run in a development environment with the following command: $ poetry run python auth_service.py The auth service can be run in a production environment using gunicorn: $ poetry run gunicorn auth:app The auth_service.py script should not be run directly in a production environment due to security and performance concerns. """ import sys from os import environ from flask import Flask from flask_sqlalchemy import SQLAlchemy from sqlalchemy.sql import text from flask_mongoengine import MongoEngine from auth.api import auth_api_v1 from util.logstash import configure_logstash_handler # create new flask app app = Flask(__name__) """The WSGI Flask application.""" configure_logstash_handler(app) # expose the auth API app.register_blueprint(auth_api_v1) with app.app_context(): # establish a connection to the database app.config["SQLALCHEMY_DATABASE_URI"] = environ.get("SQLALCHEMY_DATABASE_URI") app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False postgres = SQLAlchemy(app) """Provides access to the PostgreSQL database.""" try: # verify the database connection postgres.session.query(text("1")).from_statement(text("SELECT 1")).all() app.logger.info("Connected to the PostgreSQL database.") except Exception as e: sys.exit(f"Failed to connect to the PostgreSQL database: {e}") # establish a connection to the document store app.config["MONGODB_HOST"] = environ.get("MONGODB_HOST") mongo = MongoEngine(app) """Provides access to the MongoDB database.""" try: # verify the document store connection mongo.connection.server_info() app.logger.info("Connected to the MongoDB database.") except Exception as e: sys.exit(f"Failed to connect to the MongoDB database: {e}") @app.route("/health") def health_check(): """Attempt to ping the database and respond with a status code 200. This endpoint is verify that the server is running and that the database is accessible. """ response = {"service": "OK"} try: postgres.session.query(text("1")).from_statement(text("SELECT 1")).all() response["database"] = "OK" except Exception as e: app.logger.error(e) response["database"] = "ERROR" try: mongo.connection.server_info() response["document_store"] = "OK" except Exception as e: app.logger.error(e) response["document_store"] = "ERROR" return response if __name__ == "__main__" and environ.get("FLASK_ENV") == "development": app.run(host="0.0.0.0", port=5000, debug=True) # nosec elif __name__ == "__main__": sys.exit("Development server can only be run in development mode.")
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"""============================================================================= Download experimental directory. =============================================================================""" import argparse import os # ------------------------------------------------------------------------------ def mkdir(directory): """Make directory if it does not exist. Void return. """ if not os.path.exists(directory): os.makedirs(directory) # ------------------------------------------------------------------------------ def download(directory): """Download directory and save locally. """ remote = '/scratch/gpfs/gwg3/fe/experiments/%s' % directory local = '/Users/gwg/fe/experiments/' mkdir(local) cmd = 'rsync --progress -r ' \ '[email protected]:%s %s' % (remote, local) os.system(cmd) # ------------------------------------------------------------------------------ if __name__ == '__main__': p = argparse.ArgumentParser() p.add_argument('--directory', type=str, required=True) args = p.parse_args() download(args.directory)
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################################################################################ # User Libs import test_utils import test.unittest as unittest import tablature as tab # Std Libs import os ################################################################################ ################################################################################ if __name__ == '__main__': unittest.main()
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import test_yaml import test_new test_yaml.run_tests() test_new.run_tests()
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# Copyright The IETF Trust 2015-2020, All Rights Reserved import itertools from django.db import models class ForeignKey(models.ForeignKey): "A local ForeignKey proxy which provides the on_delete value required under Django 2.0." class OneToOneField(models.OneToOneField): "A local OneToOneField proxy which provides the on_delete value required under Django 2.0." def object_to_dict(instance): """ Similar to django.forms.models.model_to_dict() but more comprehensive. Taken from https://stackoverflow.com/questions/21925671/#answer-29088221 with a minor tweak: .id --> .pk """ opts = instance._meta data = {} for f in itertools.chain(opts.concrete_fields, opts.private_fields): data[f.name] = f.value_from_object(instance) for f in opts.many_to_many: data[f.name] = [i.pk for i in f.value_from_object(instance)] return data
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"""Test config utils.""" # pylint: disable=too-many-public-methods,protected-access import os import tempfile import unittest import unittest.mock as mock import pytest from voluptuous import MultipleInvalid from homeassistant.core import DOMAIN, HomeAssistantError, Config import homeassistant.config as config_util from homeassistant.const import ( CONF_LATITUDE, CONF_LONGITUDE, CONF_TEMPERATURE_UNIT, CONF_NAME, CONF_TIME_ZONE, CONF_ELEVATION, CONF_CUSTOMIZE, __version__, TEMP_FAHRENHEIT) from homeassistant.util import location as location_util, dt as dt_util from homeassistant.helpers.entity import Entity from tests.common import ( get_test_config_dir, get_test_home_assistant) CONFIG_DIR = get_test_config_dir() YAML_PATH = os.path.join(CONFIG_DIR, config_util.YAML_CONFIG_FILE) VERSION_PATH = os.path.join(CONFIG_DIR, config_util.VERSION_FILE) ORIG_TIMEZONE = dt_util.DEFAULT_TIME_ZONE def create_file(path): """Create an empty file.""" with open(path, 'w'): pass class TestConfig(unittest.TestCase): """Test the configutils.""" def tearDown(self): # pylint: disable=invalid-name """Clean up.""" dt_util.DEFAULT_TIME_ZONE = ORIG_TIMEZONE if os.path.isfile(YAML_PATH): os.remove(YAML_PATH) if os.path.isfile(VERSION_PATH): os.remove(VERSION_PATH) if hasattr(self, 'hass'): self.hass.stop() def test_create_default_config(self): """Test creation of default config.""" config_util.create_default_config(CONFIG_DIR, False) self.assertTrue(os.path.isfile(YAML_PATH)) def test_find_config_file_yaml(self): """Test if it finds a YAML config file.""" create_file(YAML_PATH) self.assertEqual(YAML_PATH, config_util.find_config_file(CONFIG_DIR)) @mock.patch('builtins.print') def test_ensure_config_exists_creates_config(self, mock_print): """Test that calling ensure_config_exists. If not creates a new config file. """ config_util.ensure_config_exists(CONFIG_DIR, False) self.assertTrue(os.path.isfile(YAML_PATH)) self.assertTrue(mock_print.called) def test_ensure_config_exists_uses_existing_config(self): """Test that calling ensure_config_exists uses existing config.""" create_file(YAML_PATH) config_util.ensure_config_exists(CONFIG_DIR, False) with open(YAML_PATH) as f: content = f.read() # File created with create_file are empty self.assertEqual('', content) def test_load_yaml_config_converts_empty_files_to_dict(self): """Test that loading an empty file returns an empty dict.""" create_file(YAML_PATH) self.assertIsInstance( config_util.load_yaml_config_file(YAML_PATH), dict) def test_load_yaml_config_raises_error_if_not_dict(self): """Test error raised when YAML file is not a dict.""" with open(YAML_PATH, 'w') as f: f.write('5') with self.assertRaises(HomeAssistantError): config_util.load_yaml_config_file(YAML_PATH) def test_load_yaml_config_raises_error_if_malformed_yaml(self): """Test error raised if invalid YAML.""" with open(YAML_PATH, 'w') as f: f.write(':') with self.assertRaises(HomeAssistantError): config_util.load_yaml_config_file(YAML_PATH) def test_load_yaml_config_raises_error_if_unsafe_yaml(self): """Test error raised if unsafe YAML.""" with open(YAML_PATH, 'w') as f: f.write('hello: !!python/object/apply:os.system') with self.assertRaises(HomeAssistantError): config_util.load_yaml_config_file(YAML_PATH) def test_load_yaml_config_preserves_key_order(self): """Test removal of library.""" with open(YAML_PATH, 'w') as f: f.write('hello: 0\n') f.write('world: 1\n') self.assertEqual( [('hello', 0), ('world', 1)], list(config_util.load_yaml_config_file(YAML_PATH).items())) @mock.patch('homeassistant.util.location.detect_location_info', return_value=location_util.LocationInfo( '0.0.0.0', 'US', 'United States', 'CA', 'California', 'San Diego', '92122', 'America/Los_Angeles', 32.8594, -117.2073, True)) @mock.patch('homeassistant.util.location.elevation', return_value=101) @mock.patch('builtins.print') def test_create_default_config_detect_location(self, mock_detect, mock_elev, mock_print): """Test that detect location sets the correct config keys.""" config_util.ensure_config_exists(CONFIG_DIR) config = config_util.load_yaml_config_file(YAML_PATH) self.assertIn(DOMAIN, config) ha_conf = config[DOMAIN] expected_values = { CONF_LATITUDE: 32.8594, CONF_LONGITUDE: -117.2073, CONF_ELEVATION: 101, CONF_TEMPERATURE_UNIT: 'F', CONF_NAME: 'Home', CONF_TIME_ZONE: 'America/Los_Angeles' } assert expected_values == ha_conf assert mock_print.called @mock.patch('builtins.print') def test_create_default_config_returns_none_if_write_error(self, mock_print): """Test the writing of a default configuration. Non existing folder returns None. """ self.assertIsNone( config_util.create_default_config( os.path.join(CONFIG_DIR, 'non_existing_dir/'), False)) self.assertTrue(mock_print.called) def test_entity_customization(self): """Test entity customization through configuration.""" self.hass = get_test_home_assistant() config = {CONF_LATITUDE: 50, CONF_LONGITUDE: 50, CONF_NAME: 'Test', CONF_CUSTOMIZE: {'test.test': {'hidden': True}}} config_util.process_ha_core_config(self.hass, config) entity = Entity() entity.entity_id = 'test.test' entity.hass = self.hass entity.update_ha_state() state = self.hass.states.get('test.test') assert state.attributes['hidden'] def test_remove_lib_on_upgrade(self): """Test removal of library on upgrade.""" with tempfile.TemporaryDirectory() as config_dir: version_path = os.path.join(config_dir, '.HA_VERSION') lib_dir = os.path.join(config_dir, 'deps') check_file = os.path.join(lib_dir, 'check') with open(version_path, 'wt') as outp: outp.write('0.7.0') os.mkdir(lib_dir) with open(check_file, 'w'): pass self.hass = get_test_home_assistant() self.hass.config.config_dir = config_dir assert os.path.isfile(check_file) config_util.process_ha_config_upgrade(self.hass) assert not os.path.isfile(check_file) def test_not_remove_lib_if_not_upgrade(self): """Test removal of library with no upgrade.""" with tempfile.TemporaryDirectory() as config_dir: version_path = os.path.join(config_dir, '.HA_VERSION') lib_dir = os.path.join(config_dir, 'deps') check_file = os.path.join(lib_dir, 'check') with open(version_path, 'wt') as outp: outp.write(__version__) os.mkdir(lib_dir) with open(check_file, 'w'): pass self.hass = get_test_home_assistant() self.hass.config.config_dir = config_dir config_util.process_ha_config_upgrade(self.hass) assert os.path.isfile(check_file) def test_loading_configuration(self): """Test loading core config onto hass object.""" config = Config() hass = mock.Mock(config=config) config_util.process_ha_core_config(hass, { 'latitude': 60, 'longitude': 50, 'elevation': 25, 'name': 'Huis', 'temperature_unit': 'F', 'time_zone': 'America/New_York', }) assert config.latitude == 60 assert config.longitude == 50 assert config.elevation == 25 assert config.location_name == 'Huis' assert config.temperature_unit == TEMP_FAHRENHEIT assert config.time_zone.zone == 'America/New_York' @mock.patch('homeassistant.util.location.detect_location_info', return_value=location_util.LocationInfo( '0.0.0.0', 'US', 'United States', 'CA', 'California', 'San Diego', '92122', 'America/Los_Angeles', 32.8594, -117.2073, True)) @mock.patch('homeassistant.util.location.elevation', return_value=101) def test_discovering_configuration(self, mock_detect, mock_elevation): """Test auto discovery for missing core configs.""" config = Config() hass = mock.Mock(config=config) config_util.process_ha_core_config(hass, {}) assert config.latitude == 32.8594 assert config.longitude == -117.2073 assert config.elevation == 101 assert config.location_name == 'San Diego' assert config.temperature_unit == TEMP_FAHRENHEIT assert config.time_zone.zone == 'America/Los_Angeles' @mock.patch('homeassistant.util.location.detect_location_info', return_value=None) @mock.patch('homeassistant.util.location.elevation', return_value=0) def test_discovering_configuration_auto_detect_fails(self, mock_detect, mock_elevation): """Test config remains unchanged if discovery fails.""" config = Config() hass = mock.Mock(config=config) config_util.process_ha_core_config(hass, {}) blankConfig = Config() assert config.latitude == blankConfig.latitude assert config.longitude == blankConfig.longitude assert config.elevation == blankConfig.elevation assert config.location_name == blankConfig.location_name assert config.temperature_unit == blankConfig.temperature_unit assert config.time_zone == blankConfig.time_zone
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2.165768
4,820
############################################################################### # Copyright 2011-2014 The University of Texas at Austin # # # # 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 json import os from xml.dom.minidom import getDOMImplementation from ipf.data import Data, Representation from ipf.dt import * from ipf.error import NoMoreInputsError, StepError from ipf.sysinfo import ResourceName from ipf.step import Step from ipf.ipfinfo import IPFInformation, IPFInformationJson, IPFInformationTxt from .computing_activity import ComputingActivities, ComputingActivityTeraGridXml, ComputingActivityOgfJson from .computing_manager import ComputingManager, ComputingManagerTeraGridXml, ComputingManagerOgfJson from .computing_manager_accel_info import ComputingManagerAcceleratorInfo, ComputingManagerAcceleratorInfoOgfJson from .computing_service import ComputingService, ComputingServiceTeraGridXml, ComputingServiceOgfJson from .computing_share import ComputingShares, ComputingShareTeraGridXml, ComputingShareOgfJson from .computing_share_accel_info import ComputingShareAcceleratorInfo, ComputingShareAcceleratorInfoOgfJson from .execution_environment import ExecutionEnvironments, ExecutionEnvironmentTeraGridXml from .execution_environment import ExecutionEnvironmentTeraGridXml from .execution_environment import ExecutionEnvironmentOgfJson from .accelerator_environment import AcceleratorEnvironments from .accelerator_environment import AcceleratorEnvironmentsOgfJson from .accelerator_environment import AcceleratorEnvironment from .accelerator_environment import AcceleratorEnvironmentOgfJson from .location import Location, LocationOgfJson, LocationTeraGridXml ####################################################################################################################### ####################################################################################################################### ####################################################################################################################### ####################################################################################################################### ####################################################################################################################### ####################################################################################################################### ####################################################################################################################### ####################################################################################################################### #######################################################################################################################
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3.588291
1,059
from .filter import FilterWidget from .flash import FlashWidget, ShowFlashNotification from .header import HeaderWidget from .help import HelpWidget from .secret_properties import SecretPropertiesWidget from .secret_versions import SecretVersionsWidget from .secrets import SecretsWidget __all__ = ( "SecretsWidget", "ShowFlashNotification", "FilterWidget", "FlashWidget", "HeaderWidget", "SecretVersionsWidget", "SecretPropertiesWidget", "HelpWidget", )
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3.460993
141
main()
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1.125
32
import numpy as np from sklearn.base import clone from sklearn.ensemble import RandomForestRegressor from sklearn.utils.validation import check_is_fitted, check_X_y from pycausal_explorer.base import BaseCausalModel from ..reweight import PropensityScore class XLearner(BaseCausalModel): """ Implementation of the X-learner. It consists of estimating heterogeneous treatment effect using four machine learning models. Details of X-learner theory are available at Kunzel et al. (2018) (https://arxiv.org/abs/1706.03461). Parameters ---------- learner: base learner to use in all models. Either leaner or (u0, u1, te_u0, te_u1) must be filled u0: model used to estimate outcome in the control group u1: model used to estimate outcome in the treatment group te_u0: model used to estimate treatment effect in the control group te_u1: model used to estimate treatment effect in the treatment group group random_state: random state """
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3.329966
297
# coding=utf-8 from collections import OrderedDict expected = [ OrderedDict( [ ("id", u"par-1"), ( "source", OrderedDict( [ ("funderId", u"10.13039/100006978"), ( "name", [ u"University of California Berkeley (University of California, Berkeley)" ], ), ] ), ), ("awardId", u"AWS in Education grant"), ( "recipients", [ OrderedDict( [ ("type", "person"), ( "name", OrderedDict( [ ("preferred", u"Eric Jonas"), ("index", u"Jonas, Eric"), ] ), ), ] ) ], ), ] ), OrderedDict( [ ("id", u"par-2"), ( "source", OrderedDict( [ ("funderId", u"10.13039/100000001"), ("name", [u"National Science Foundation"]), ] ), ), ("awardId", u"NSF CISE Expeditions Award CCF-1139158"), ( "recipients", [ { "type": "person", "name": {"index": "Jonas, Eric", "preferred": "Eric Jonas"}, } ], ), ] ), OrderedDict( [ ("id", u"par-3"), ( "source", OrderedDict( [ ("funderId", u"10.13039/100006235"), ("name", [u"Lawrence Berkely National Laboratory"]), ] ), ), ("awardId", u"Award 7076018"), ( "recipients", [ { "type": "person", "name": {"index": "Jonas, Eric", "preferred": "Eric Jonas"}, } ], ), ] ), OrderedDict( [ ("id", u"par-4"), ( "source", OrderedDict( [ ("funderId", u"10.13039/100000185"), ("name", [u"Defense Advanced Research Projects Agency"]), ] ), ), ("awardId", u"XData Award FA8750-12-2-0331"), ( "recipients", [ { "type": "person", "name": {"index": "Jonas, Eric", "preferred": "Eric Jonas"}, } ], ), ] ), OrderedDict( [ ("id", u"par-5"), ( "source", OrderedDict( [ ("funderId", u"10.13039/100000002"), ("name", [u"National Institutes of Health"]), ] ), ), ("awardId", u"R01NS074044"), ( "recipients", [ OrderedDict( [ ("type", "person"), ( "name", OrderedDict( [ ("preferred", u"Konrad Kording"), ("index", u"Kording, Konrad"), ] ), ), ] ) ], ), ] ), OrderedDict( [ ("id", u"par-6"), ( "source", OrderedDict( [ ("funderId", u"10.13039/100000002"), ("name", [u"National Institutes of Health"]), ] ), ), ("awardId", u"R01NS063399"), ( "recipients", [ OrderedDict( [ ("type", "person"), ( "name", OrderedDict( [ ("preferred", u"Konrad Kording"), ("index", u"Kording, Konrad"), ] ), ), ] ) ], ), ] ), ]
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from django.contrib import admin from apps.web.models import CodeAnnotation admin.site.register(CodeAnnotation)
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from PuppeteerLibrary.ikeywords.imockresponse_async import iMockResponseAsync from PuppeteerLibrary.base.robotlibcore import keyword from PuppeteerLibrary.base.librarycomponent import LibraryComponent
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__all__ = ['DataParallel', 'ModelParallel', 'benchmarks', 'dataparallel', 'modelparallel'] from .DataParallel import DataParallel from .ModelParallel import ModelParallel import splintr.benchmarks import splintr.dataparallel import splintr.modelparallel
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#!/usr/bin/env/python3 # -*- coding:utf-8 -*- """ @project: apiAutoTest @author: cjw @file: __init__.py.py @ide: PyCharm @time: 2020/7/31 """
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#!/usr/bin/env python # coding: utf-8 import logging import argparse import pydash from lib.common import USER_EMAIL from lib.common import API_KEY from lib.common import API_SECRET from lib.common import USER_API from lib.common import TEAM_API from lib.common import ROLE_API from lib.common import POLICY_API from lib.common import APP_API from lib.common import getToken from lib.common import booleanString from lib.purge import getResource from lib.purge import getResources from lib.purge import updateResource from lib.purge import purgeResource if __name__ == '__main__': parser = argparse.ArgumentParser(description='Remove existing user and associated objects') parser.add_argument('--dryrun', dest='dryrun', type=booleanString, default=True, required=True, help='In dryrun mode, no objects will be deleted') parser.add_argument('--debug', dest='debug', type=booleanString, default=False, required=False, help='Output verbose log') args = parser.parse_args() main(vars(args))
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from typing import TypeVar, Generic, List T = TypeVar('T') if __name__ == '__main__': discs: int = 5 tower_a: Stack[int] = Stack() tower_b: Stack[int] = Stack() tower_c: Stack[int] = Stack() for i in range(discs, 0, -1): tower_a.push(i) print(tower_a, tower_b, tower_c) hanoi(tower_a, tower_c, tower_b, discs)
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import os.path from unittest import TestCase from code.cli import PARAMS_DIR, TESTS_DIR from code.prepare.base import load_data from code.prepare.params import load_params from code.prepare.utils import * FIXTURE_DATASET = os.path.join(TESTS_DIR, 'fixtures/GER.tsv') FIXTURE_DATASET_ASJP = os.path.join(TESTS_DIR, 'fixtures/Afrasian.tsv')
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""" Process time data set see create_timed_data to generate files with times for all Extract a single data set around a cone with TimedData """ import os, glob, pickle import healpy import numpy as np import pandas as pd import matplotlib.pyplot as plt from astropy.time import Time, TimeDelta from . import binned_data mission_start = Time('2001-01-01T00:00:00', scale='utc') class TimeInfo(object): """Read in, process a file generated by binned_data.ConvertFT1.time_record """ def select(self, l, b, radius=5, nside=1024): """create DataFrame with times, band id, distance from center parameters: l,b : position in Galactic radius : cone radius, deg nside : for healpy returns: DataFrame with columns: band : from input, energy and event type time : Mission Elapsed Time in s. (double) delta : distance from input position (deg, float32) """ df = self.df cart = lambda l,b: healpy.dir2vec(l,b, lonlat=True) # use query_disc to get photons within given radius of position center = cart(l,b) ipix = healpy.query_disc(nside, cart(l,b), np.radians(radius), nest=False) incone = np.isin(self.df.hpindex, ipix) # times: convert to double, add to start t = np.array(df.time[incone],float)+self.tstart # convert position info to just distance from center ll,bb = healpy.pix2ang(nside, self.df.hpindex[incone], nest=False, lonlat=True) t2 = np.array(np.sqrt((1.-np.dot(center, cart(ll,bb)))*2), np.float32) return pd.DataFrame(np.rec.fromarrays( [df.band[incone], t, np.degrees(t2)], names='band time delta'.split())) class TimedData(object): """Create a data set at a given position """ plt.rc('font', size=12) def __init__(self, position, name='', radius=5, file_pattern='$FERMI/data/P8_P305/time_info/month_*.pkl'): """Set up combined data from set of monthly files position : l,b in degrees name : string, optional name to describe source radius : float, cone radius for selection file_pattern : string for glob use """ assert hasattr(position, '__len__') and len(position)==2, 'expect position to be (l,b)' files = sorted(glob.glob(os.path.expandvars(file_pattern))) assert len(files)>0, 'No files found using pattern {}'.format(file_pattern) self.name = name gbtotal = np.array([os.stat(filename).st_size for filename in files]).sum()/2**30 print 'Opening {} files, with {} GB total'.format(len(files), gbtotal) dflist=[] for filename in files: dflist.append(TimeInfo(filename).select(*position)) print '.', self.df = pd.concat(dflist) print 'Selected {} photons'.format(len(self.df)) def plot_time(self, delta_max=2, delta_t=1, xlim=None): """ """ df = self.df t = timed_data.MJD(df.time) ta,tb=t[0],t[-1] Nbins = int((tb-ta)/float(delta_t)) fig,ax= plt.subplots(figsize=(15,5)) hkw = dict(bins = np.linspace(ta,tb,Nbins), histtype='step') ax.hist(t, label='E>100 MeV', **hkw) ax.hist(t[(df.delta<delta_max) & (df.band>0)], label='delta<{} deg'.format(delta_max), **hkw); ax.set(xlabel=r'$\mathsf{MJD}$', ylabel='counts per {:.0f} day'.format(delta_t)) if xlim is not None: ax.set(xlim=xlim) ax.legend() ax.set_title('{} counts vs. time'.format(self.name)) def create_timed_data( monthly_ft1_files='/afs/slac/g/glast/groups/catalog/P8_P305/zmax105/*.fits', outfolder='$FERMI/data/P8_P305/time_info/', overwrite=False, test=False, verbose=1): """ """ files=sorted(glob.glob(monthly_ft1_files)) assert len(files)>0, 'No ft1 files found at {}'.format(monthly_ft1_files) gbtotal = np.array([os.stat(filename).st_size for filename in files]).sum()/2**30 if verbose>0: print '{} monthly FT1 files found at {}\n\t {} GB total'.format(len(files), monthly_ft1_files, gbtotal) outfolder = os.path.expandvars(outfolder) if not os.path.exists(outfolder): os.makedirs(outfolder) os.chdir(outfolder) if verbose>0: print 'Writing time files to folder {}\n\toverwrite={}'.format(outfolder, overwrite) for filename in files: m = filename.split('_')[-2] outfile = 'month_{}.pkl'.format(m) if not overwrite and os.path.exists(outfile) : if verbose>1: print 'exists: {}'.format(outfile) else: print '.', continue tr = binned_data.ConvertFT1(filename).time_record() if not test: if verbose>1: print 'writing {}'.format(outfile), elif verbose>0: print '+', pickle.dump(tr, open(outfile, 'wr')) else: if verbose>0: print 'Test: would have written {}'.format(outfile) # check how many exist files=sorted(glob.glob(outfolder+'/*.pkl')) gbtotal = np.array([os.stat(filename).st_size for filename in files]).sum()/float(2**30) print '\nThere are {} timed data files, {:.1f} GB total'.format(len(files), gbtotal)
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from discord.ext import commands import os from decouple import config bot = commands.Bot("!") load_cogs(bot) TOKEN = config("TOKEN") bot.run(TOKEN)
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from .grid import Grid from .random import Random from .quasirandom import QuasiRandom
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from enum import Enum from .core.vector2 import Vector2 screen_size = width, height = 1040, 480 map_size = Vector2(x=10000, y=1000) gravity = 1.5
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# -*- coding: utf-8 -*- # Search API docs: https://developers.google.com/youtube/v3/docs/search/list # Search API Python docs: https://developers.google.com/resources/api-libraries/documentation/youtube/v3/python/latest/youtube_v3.search.html # Examples: https://github.com/youtube/api-samples/tree/master/python import argparse import inspect import math import os from pprint import pprint import sys try: #python2 from urllib import urlencode except ImportError: #python3 from urllib.parse import urlencode from googleapiclient.discovery import build from googleapiclient.errors import HttpError # add parent directory to sys path to import relative modules currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) parentdir = os.path.dirname(parentdir) sys.path.insert(0,parentdir) from lib.collection_utils import * from lib.io_utils import * from lib.math_utils import * # input parser = argparse.ArgumentParser() parser.add_argument('-key', dest="API_KEY", default="", help="Your API Key. See: https://google-developers.appspot.com/youtube/v3/getting-started") parser.add_argument('-query', dest="QUERY", default=" location=40.903125,-73.85062&locationRadius=10km&videoLicense=creativeCommon", help="Search query parameters as a query string") parser.add_argument('-in', dest="INPUT_FILE", default="", help="Input .csv file containing one or more queries; will override individual query") parser.add_argument('-sort', dest="SORT_BY", default="", help="Sort by string") parser.add_argument('-lim', dest="LIMIT", default=100, type=int, help="Limit results") parser.add_argument('-out', dest="OUTPUT_FILE", default="tmp/yt-search/%s.json", help="JSON output file pattern") parser.add_argument('-verbose', dest="VERBOSE", action="store_true", help="Display search result details") a = parser.parse_args() aa = vars(a) makeDirectories([a.OUTPUT_FILE]) aa["QUERY"] = a.QUERY.strip() MAX_YT_RESULTS_PER_PAGE = 50 if len(a.API_KEY) <= 0: print("You must pass in your developer API key. See more at https://google-developers.appspot.com/youtube/v3/getting-started") sys.exit() if len(a.QUERY) <= 0: print("Please pass in a query.") YOUTUBE_API_SERVICE_NAME = "youtube" YOUTUBE_API_VERSION = "v3" youtube = build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION, developerKey=a.API_KEY) queries = [] if len(a.INPUT_FILE) > 0: queryKeys, queries = readCsv(a.INPUT_FILE, doParseNumbers=False) else: queries = [parseQueryString(a.QUERY)] queryCount = len(queries) for i, q in enumerate(queries): ytQuery = q.copy() ytQuery["part"] = "id,snippet" ytQuery["type"] = "video" # Always get videos back ytQuery["videoDimension"] = "2d" # exclude 3d videos if len(a.SORT_BY) > 0: ytQuery["order"] = a.SORT_BY pages = 1 if a.LIMIT > 0: pages = ceilInt(1.0 * a.LIMIT / MAX_YT_RESULTS_PER_PAGE) ytQuery["maxResults"] = min(a.LIMIT, MAX_YT_RESULTS_PER_PAGE) print("Query %s of %s: %s" % (i+1, queryCount, urlencode(ytQuery))) for page in range(pages): print("- Page %s..." % (page+1)) # Make one query to retrieve ids try: search_response = youtube.search().list(**ytQuery).execute() except HttpError as e: print('An HTTP error %d occurred:\n%s' % (e.resp.status, e.content)) sys.exit() nextPageToken = search_response.get('nextPageToken', "") # pprint(search_response.get('items', [])) # sys.exit() ids = [] for r in search_response.get('items', []): ids.append(r['id']['videoId']) print("-- %s results found." % (len(ids))) missingIds = [] for id in ids: outfile = a.OUTPUT_FILE % id if not os.path.isfile(outfile): missingIds.append(id) if len(missingIds) > 0: print("-- Getting details for %s videos..." % (len(missingIds))) # Make another query to retrieve stats idString = ",".join(ids) try: search_response = youtube.videos().list(id=idString, part="id,statistics,snippet").execute() except HttpError as e: print('An HTTP error %d occurred:\n%s' % (e.resp.status, e.content)) sys.exit() if a.VERBOSE: print("-----\nResults: ") for r in search_response.get('items', []): outfile = a.OUTPUT_FILE % r['id'] writeJSON(outfile, r, verbose=a.VERBOSE) # pprint(r['id']) # pprint(r['statistics']) # pprint(r['snippet']) if a.VERBOSE: print("%s: %s (%s views)" % (r['id'], r['snippet']['title'], r['statistics']['viewCount'])) if a.VERBOSE: print("-----") # Retrieve the next page if len(nextPageToken) < 1: break ytQuery["pageToken"] = nextPageToken print("Done.")
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import logging from time import time from typing import Tuple, Optional from bubuku.broker import BrokerManager from bubuku.communicate import sleep_and_operate from bubuku.env_provider import EnvProvider from bubuku.zookeeper import BukuExhibitor _LOG = logging.getLogger('bubuku.controller') # # Returns a flag indicating if the change should continue running (True). # In that case time_till_next_run() is called to determine when to schedule the next run. #
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def integer(roman): """ Function to convert a roman numeral to integer. :type roman: str :rtype: int """ # Initialize a dictionary of symbol and values symbol_value = { 'M': 1000, 'D': 500, 'C': 100, 'L': 50, 'X': 10, 'V': 5, 'I': 1 } second_last_index = len(roman) - 1 result = 0 # Now traverse the roman string from index 0 to the second last index. # Compare value of the present symbol with the value of the next symbol. # If the present value is smaller than the next value, reduce the # present value from the result. Else add it with the result. for i in range(second_last_index): present_value = symbol_value[roman[i]] next_value = symbol_value[roman[i+1]] if present_value < next_value: result -= present_value else: result += present_value # At last, add the value of the last symbol. result += symbol_value[roman[-1]] return result if __name__ == '__main__': test_set = [ ('XLV', 45), ('MMMMMCMXCV', 5995), ('XCV', 95), ('DCCC', 800), ('CDLXXXII', 482), ] for roman, output in test_set: assert output == integer(roman) print('Test Passed.')
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from fishbase import logger class PluginsManagerStatic(object): """ 1. 现阶段插件是用来进行请求或者响应参数的处理 2. 暂时规定插件必须实现 run 方法 3. 使用实例: pm = PluginsManager() pm.run_plugin('demo.demo_md5', {'sign_type':'md5','data_sign_params':'param1, param2'}, {'param1':'1','param2':'2','param3':'3'}) """
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import unittest if __name__ == '__main__': unittest.main()
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import sklearn.utils.sparsefuncs as sf from . import q, ut, pd, sp, np, warnings, sc from .utils import to_vo, to_vn, substr, df_to_dict, sparse_knn, prepend_var_prefix from samalg import SAM from scipy.stats import rankdata def GOEA(target_genes,GENE_SETS,df_key='GO',goterms=None,fdr_thresh=0.25,p_thresh=1e-3): """Performs GO term Enrichment Analysis using the hypergeometric distribution. Parameters ---------- target_genes - array-like List of target genes from which to find enriched GO terms. GENE_SETS - dictionary or pandas.DataFrame Dictionary where the keys are GO terms and the values are lists of genes associated with each GO term. Ex: {'GO:0000001': ['GENE_A','GENE_B'], 'GO:0000002': ['GENE_A','GENE_C','GENE_D']} Make sure to include all available genes that have GO terms in your dataset. ---OR--- Pandas DataFrame with genes as the index and GO terms values. Ex: 'GENE_A','GO:0000001', 'GENE_A','GO:0000002', 'GENE_B','GO:0000001', 'GENE_B','GO:0000004', ... If `GENE_SETS` is a pandas DataFrame, the `df_key` parameter should be the name of the column in which the GO terms are stored. df_key - str, optional, default 'GO' The name of the column in which GO terms are stored. Only used if `GENE_SETS` is a DataFrame. goterms - array-list, optional, default None If provided, only these GO terms will be tested. fdr_thresh - float, optional, default 0.25 Filter out GO terms with FDR q value greater than this threshold. p_thresh - float, optional, default 1e-3 Filter out GO terms with p value greater than this threshold. Returns: ------- enriched_goterms - pandas.DataFrame A Pandas DataFrame of enriched GO terms with FDR q values, p values, and associated genes provided. """ # identify all genes found in `GENE_SETS` if isinstance(GENE_SETS,pd.DataFrame): print('Converting DataFrame into dictionary') genes = np.array(list(GENE_SETS.index)) agt = np.array(list(GENE_SETS[df_key].values)) idx = np.argsort(agt) genes = genes[idx] agt = agt[idx] bounds = np.where(agt[:-1]!=agt[1:])[0]+1 bounds = np.append(np.append(0,bounds),agt.size) bounds_left=bounds[:-1] bounds_right=bounds[1:] genes_lists = [genes[bounds_left[i]:bounds_right[i]] for i in range(bounds_left.size)] GENE_SETS = dict(zip(np.unique(agt),genes_lists)) all_genes = np.unique(np.concatenate(list(GENE_SETS.values()))) all_genes = np.array(all_genes) # if goterms is None, use all the goterms found in `GENE_SETS` if goterms is None: goterms = np.unique(list(GENE_SETS.keys())) else: goterms = goterms[np.in1d(goterms,np.unique(list(GENE_SETS.keys())))] # ensure that target genes are all present in `all_genes` _,ix = np.unique(target_genes,return_index=True) target_genes=target_genes[np.sort(ix)] target_genes = target_genes[np.in1d(target_genes,all_genes)] # N -- total number of genes N = all_genes.size probs=[] probs_genes=[] counter=0 # for each go term, for goterm in goterms: if counter%1000==0: pass; #print(counter) counter+=1 # identify genes associated with this go term gene_set = np.array(GENE_SETS[goterm]) # B -- number of genes associated with this go term B = gene_set.size # b -- number of genes in target associated with this go term gene_set_in_target = gene_set[np.in1d(gene_set,target_genes)] b = gene_set_in_target.size if b != 0: # calculate the enrichment probability as the cumulative sum of the tail end of a hypergeometric distribution # with parameters (N,B,n,b) n = target_genes.size num_iter = min(n,B) rng = np.arange(b,num_iter+1) probs.append(sum([np.exp(_log_binomial(n,i)+_log_binomial(N-n,B-i) - _log_binomial(N,B)) for i in rng])) else: probs.append(1.0) #append associated genes to a list probs_genes.append(gene_set_in_target) probs = np.array(probs) probs_genes = np.array([';'.join(x) for x in probs_genes]) # adjust p value to correct for multiple testing fdr_q_probs = probs.size*probs / rankdata(probs,method='ordinal') # filter out go terms based on the FDR q value and p value thresholds filt = np.logical_and(fdr_q_probs<fdr_thresh,probs<p_thresh) enriched_goterms = goterms[filt] p_values = probs[filt] fdr_q_probs = fdr_q_probs[filt] probs_genes=probs_genes[filt] # construct the Pandas DataFrame gns = probs_genes enriched_goterms = pd.DataFrame(data=fdr_q_probs,index=enriched_goterms,columns=['fdr_q_value']) enriched_goterms['p_value'] = p_values enriched_goterms['genes'] = gns # sort in ascending order by the p value enriched_goterms = enriched_goterms.sort_values('p_value') return enriched_goterms _KOG_TABLE = dict(A = "RNA processing and modification", B = "Chromatin structure and dynamics", C = "Energy production and conversion", D = "Cell cycle control, cell division, chromosome partitioning", E = "Amino acid transport and metabolism", F = "Nucleotide transport and metabolism", G = "Carbohydrate transport and metabolism", H = "Coenzyme transport and metabolism", I = "Lipid transport and metabolism", J = "Translation, ribosomal structure and biogenesis", K = "Transcription", L = "Replication, recombination, and repair", M = "Cell wall membrane/envelope biogenesis", N = "Cell motility", O = "Post-translational modification, protein turnover, chaperones", P = "Inorganic ion transport and metabolism", Q = "Secondary metabolites biosynthesis, transport and catabolism", R = "General function prediction only", S = "Function unknown", T = "Signal transduction mechanisms", U = "Intracellular trafficking, secretion, and vesicular transport", V = "Defense mechanisms", W = "Extracellular structures", Y = "Nuclear structure", Z = "Cytoskeleton") import gc from collections.abc import Iterable def sankey_plot(M,species_order=None,align_thr=0.1,**params): """Generate a sankey plot Parameters ---------- M: pandas.DataFrame Mapping table output from `get_mapping_scores` (second output). align_thr: float, optional, default 0.1 The alignment score threshold below which to remove cell type mappings. species_order: list, optional, default None Specify the order of species (left-to-right) in the sankey plot. For example, `species_order=['hu','le','ms']`. Keyword arguments ----------------- Keyword arguments will be passed to `sankey.opts`. """ if species_order is not None: ids = np.array(species_order) else: ids = np.unique([x.split('_')[0] for x in M.index]) if len(ids)>2: d = M.values.copy() d[d<align_thr]=0 x,y = d.nonzero() x,y = np.unique(np.sort(np.vstack((x,y)).T,axis=1),axis=0).T values = d[x,y] nodes = q(M.index) node_pairs = nodes[np.vstack((x,y)).T] sn1 = q([xi.split('_')[0] for xi in node_pairs[:,0]]) sn2 = q([xi.split('_')[0] for xi in node_pairs[:,1]]) filt = np.logical_or( np.logical_or(np.logical_and(sn1==ids[0],sn2==ids[1]),np.logical_and(sn1==ids[1],sn2==ids[0])), np.logical_or(np.logical_and(sn1==ids[1],sn2==ids[2]),np.logical_and(sn1==ids[2],sn2==ids[1])) ) x,y,values=x[filt],y[filt],values[filt] d=dict(zip(ids,list(np.arange(len(ids))))) depth_map = dict(zip(nodes,[d[xi.split('_')[0]] for xi in nodes])) data = nodes[np.vstack((x,y))].T for i in range(data.shape[0]): if d[data[i,0].split('_')[0]] > d[data[i,1].split('_')[0]]: data[i,:]=data[i,::-1] R = pd.DataFrame(data = data,columns=['source','target']) R['Value'] = values else: d = M.values.copy() d[d<align_thr]=0 x,y = d.nonzero() x,y = np.unique(np.sort(np.vstack((x,y)).T,axis=1),axis=0).T values = d[x,y] nodes = q(M.index) R = pd.DataFrame(data = nodes[np.vstack((x,y))].T,columns=['source','target']) R['Value'] = values depth_map=None try: from holoviews import dim #from bokeh.models import Label import holoviews as hv hv.extension('bokeh',logo=False) hv.output(size=100) except: raise ImportError('Please install holoviews-samap with `!pip install holoviews-samap`.') sankey1 = hv.Sankey(R, kdims=["source", "target"])#, vdims=["Value"]) cmap = params.get('cmap','Colorblind') label_position = params.get('label_position','outer') edge_line_width = params.get('edge_line_width',0) show_values = params.get('show_values',False) node_padding = params.get('node_padding',4) node_alpha = params.get('node_alpha',1.0) node_width = params.get('node_width',40) node_sort = params.get('node_sort',True) frame_height = params.get('frame_height',1000) frame_width = params.get('frame_width',800) bgcolor = params.get('bgcolor','snow') apply_ranges = params.get('apply_ranges',True) sankey1.opts(cmap=cmap,label_position=label_position, edge_line_width=edge_line_width, show_values=show_values, node_padding=node_padding,depth_map=depth_map, node_alpha=node_alpha, node_width=node_width, node_sort=node_sort,frame_height=frame_height,frame_width=frame_width,bgcolor=bgcolor, apply_ranges=apply_ranges,hooks=[f]) return sankey1 def chord_plot(A,align_thr=0.1): """Generate a chord plot Parameters ---------- A: pandas.DataFrame Mapping table output from `get_mapping_scores` (second output). align_thr: float, optional, default 0.1 The alignment score threshold below which to remove cell type mappings. """ try: from holoviews import dim, opts import holoviews as hv hv.extension('bokeh',logo=False) hv.output(size=300) except: raise ImportError('Please install holoviews-samap with `!pip install holoviews-samap`.') xx=A.values.copy() xx[xx<align_thr]=0 x,y = xx.nonzero() z=xx[x,y] x,y = A.index[x],A.columns[y] links=pd.DataFrame(data=np.array([x,y,z]).T,columns=['source','target','value']) links['edge_grp'] = [x.split('_')[0]+y.split('_')[0] for x,y in zip(links['source'],links['target'])] links['value']*=100 f = links['value'].values z=((f-f.min())/(f.max()-f.min())*0.99+0.01)*100 links['value']=z links['value']=np.round([x for x in links['value'].values]).astype('int') clu=np.unique(A.index) clu = clu[np.in1d(clu,np.unique(np.array([x,y])))] links = hv.Dataset(links) nodes = hv.Dataset(pd.DataFrame(data=np.array([clu,clu,np.array([x.split('_')[0] for x in clu])]).T,columns=['index','name','group']),'index') chord = hv.Chord((links, nodes),kdims=["source", "target"], vdims=["value","edge_grp"])#.select(value=(5, None)) chord.opts( opts.Chord(cmap='Category20', edge_cmap='Category20',edge_color=dim('edge_grp'), labels='name', node_color=dim('group').str())) return chord def find_cluster_markers(sam, key, inplace=True): """ Finds differentially expressed genes for provided cell type labels. Parameters ---------- sam - SAM object key - str Column in `sam.adata.obs` for which to identifying differentially expressed genes. inplace - bool, optional, default True If True, deposits enrichment scores in `sam.adata.varm[f'{key}_scores']` and p-values in `sam.adata.varm[f'{key}_pvals']`. Otherwise, returns three pandas.DataFrame objects (genes x clusters). NAMES - the gene names PVALS - the p-values SCORES - the enrichment scores """ with warnings.catch_warnings(): warnings.simplefilter("ignore") a,c = np.unique(q(sam.adata.obs[key]),return_counts=True) t = a[c==1] adata = sam.adata[np.in1d(q(sam.adata.obs[key]),a[c==1],invert=True)].copy() sc.tl.rank_genes_groups( adata, key, method="wilcoxon", n_genes=sam.adata.shape[1], use_raw=False, layer=None, ) sam.adata.uns['rank_genes_groups'] = adata.uns['rank_genes_groups'] NAMES = pd.DataFrame(sam.adata.uns["rank_genes_groups"]["names"]) PVALS = pd.DataFrame(sam.adata.uns["rank_genes_groups"]["pvals"]) SCORES = pd.DataFrame(sam.adata.uns["rank_genes_groups"]["scores"]) if not inplace: return NAMES, PVALS, SCORES dfs1 = [] dfs2 = [] for i in range(SCORES.shape[1]): names = NAMES.iloc[:, i] scores = SCORES.iloc[:, i] pvals = PVALS.iloc[:, i] pvals[scores < 0] = 1.0 scores[scores < 0] = 0 pvals = q(pvals) scores = q(scores) dfs1.append(pd.DataFrame( data=scores[None, :], index = [SCORES.columns[i]], columns=names )[sam.adata.var_names].T) dfs2.append(pd.DataFrame( data=pvals[None, :], index = [SCORES.columns[i]], columns=names )[sam.adata.var_names].T) df1 = pd.concat(dfs1,axis=1) df2 = pd.concat(dfs2,axis=1) try: sam.adata.varm[key+'_scores'] = df1 sam.adata.varm[key+'_pvals'] = df2 except: sam.adata.varm.dim_names = sam.adata.var_names sam.adata.varm.dim_names = sam.adata.var_names sam.adata.varm[key+'_scores'] = df1 sam.adata.varm[key+'_pvals'] = df2 for i in range(t.size): sam.adata.varm[key+'_scores'][t[i]]=0 sam.adata.varm[key+'_pvals'][t[i]]=1 def ParalogSubstitutions(sm, ortholog_pairs, paralog_pairs=None, psub_thr = 0.3): """Identify paralog substitutions. For all genes in `ortholog_pairs` and `paralog_pairs`, this function expects the genes to be prepended with their corresponding species IDs. Parameters ---------- sm - SAMAP object ortholog_pairs - n x 2 numpy array of ortholog pairs paralog_pairs - n x 2 numpy array of paralog pairs, optional, default None If None, assumes every pair in the homology graph that is not an ortholog is a paralog. Note that this would essentially result in the more generic 'homolog substitutions' rather than paralog substitutions. The paralogs can be either cross-species, within-species, or a mix of both. psub_thr - float, optional, default 0.3 Threshold for correlation difference between paralog pairs and ortholog pairs. Paralog pairs that do not have greater than `psub_thr` correlation than their corresponding ortholog pairs are filtered out. Returns ------- RES - pandas.DataFrame A table of paralog substitutions. """ if paralog_pairs is not None: ids1 = np.array([x.split('_')[0] for x in paralog_pairs[:,0]]) ids2 = np.array([x.split('_')[0] for x in paralog_pairs[:,1]]) ix = np.where(ids1==ids2)[0] ixnot = np.where(ids1!=ids2)[0] if ix.size > 0: pps = paralog_pairs[ix] ZZ1 = {} ZZ2 = {} for i in range(pps.shape[0]): L = ZZ1.get(pps[i,0],[]) L.append(pps[i,1]) ZZ1[pps[i,0]]=L L = ZZ2.get(pps[i,1],[]) L.append(pps[i,0]) ZZ2[pps[i,1]]=L keys = list(ZZ1.keys()) for k in keys: L = ZZ2.get(k,[]) L.extend(ZZ1[k]) ZZ2[k] = list(np.unique(L)) ZZ = ZZ2 L1=[] L2=[] for i in range(ortholog_pairs.shape[0]): try: x = ZZ[ortholog_pairs[i,0]] except: x = [] L1.extend([ortholog_pairs[i,1]]*len(x)) L2.extend(x) try: x = ZZ[ortholog_pairs[i,1]] except: x = [] L1.extend([ortholog_pairs[i,0]]*len(x)) L2.extend(x) L = np.vstack((L2,L1)).T pps = np.unique(np.sort(L,axis=1),axis=0) paralog_pairs = np.unique(np.sort(np.vstack((pps,paralog_pairs[ixnot])),axis=1),axis=0) smp = sm.samap gnnm = smp.adata.varp["homology_graph_reweighted"] gn = q(smp.adata.var_names) ortholog_pairs = np.sort(ortholog_pairs,axis=1) ortholog_pairs = ortholog_pairs[np.logical_and(np.in1d(ortholog_pairs[:,0],gn),np.in1d(ortholog_pairs[:,1],gn))] if paralog_pairs is None: paralog_pairs = gn[np.vstack(smp.adata.varp["homology_graph"].nonzero()).T] else: paralog_pairs = paralog_pairs[np.logical_and(np.in1d(paralog_pairs[:,0],gn),np.in1d(paralog_pairs[:,1],gn))] paralog_pairs = np.sort(paralog_pairs,axis=1) paralog_pairs = paralog_pairs[ np.in1d(to_vn(paralog_pairs), np.append(to_vn(ortholog_pairs),to_vn(ortholog_pairs[:,::-1])), invert=True) ] A = pd.DataFrame(data=np.arange(gn.size)[None, :], columns=gn) xp, yp = ( A[paralog_pairs[:, 0]].values.flatten(), A[paralog_pairs[:, 1]].values.flatten(), ) xp, yp = np.unique( np.vstack((np.vstack((xp, yp)).T, np.vstack((yp, xp)).T)), axis=0 ).T xo, yo = ( A[ortholog_pairs[:, 0]].values.flatten(), A[ortholog_pairs[:, 1]].values.flatten(), ) xo, yo = np.unique( np.vstack((np.vstack((xo, yo)).T, np.vstack((yo, xo)).T)), axis=0 ).T A = pd.DataFrame(data=np.vstack((xp, yp)).T, columns=["x", "y"]) pairdict = df_to_dict(A, key_key="x", val_key="y") Xp = [] Yp = [] Xo = [] Yo = [] for i in range(xo.size): try: y = pairdict[xo[i]] except KeyError: y = np.array([]) Yp.extend(y) Xp.extend([xo[i]] * y.size) Xo.extend([xo[i]] * y.size) Yo.extend([yo[i]] * y.size) orths = to_vn(gn[np.vstack((np.array(Xo), np.array(Yo))).T]) paras = to_vn(gn[np.vstack((np.array(Xp), np.array(Yp))).T]) orth_corrs = gnnm[Xo, Yo].A.flatten() par_corrs = gnnm[Xp, Yp].A.flatten() diff_corrs = par_corrs - orth_corrs RES = pd.DataFrame( data=np.vstack((orths, paras)).T, columns=["ortholog pairs", "paralog pairs"] ) RES["ortholog corrs"] = orth_corrs RES["paralog corrs"] = par_corrs RES["corr diff"] = diff_corrs RES = RES.sort_values("corr diff", ascending=False) RES = RES[RES["corr diff"] > psub_thr] orths = RES['ortholog pairs'].values.flatten() paras = RES['paralog pairs'].values.flatten() orthssp = np.vstack([np.array([x.split('_')[0] for x in xx]) for xx in to_vo(orths)]) parassp = np.vstack([np.array([x.split('_')[0] for x in xx]) for xx in to_vo(paras)]) filt=[] for i in range(orthssp.shape[0]): filt.append(np.in1d(orthssp[i],parassp[i]).mean()==1.0) filt=np.array(filt) return RES[filt] def convert_eggnog_to_homologs(sm, EGGs, og_key = 'eggNOG_OGs', taxon=2759): """Gets an n x 2 array of homologs at some taxonomic level based on Eggnog results. Parameters ---------- smp: SAMAP object EGGs: dict of pandas.DataFrame, Eggnog output tables keyed by species IDs og_key: str, optional, default 'eggNOG_OGs' The column name of the orthology group mapping results in the Eggnog output tables. taxon: int, optional, default 2759 Taxonomic ID corresponding to the level at which genes with overlapping orthology groups will be considered homologs. Defaults to the Eukaryotic level. Returns ------- homolog_pairs: n x 2 numpy array of homolog pairs. """ smp = sm.samap taxon = str(taxon) EGGs = dict(zip(list(EGGs.keys()),list(EGGs.values()))) #copying for k in EGGs.keys(): EGGs[k] = EGGs[k].copy() Es=[] for k in EGGs.keys(): A=EGGs[k] A.index=k+"_"+A.index Es.append(A) A = pd.concat(Es, axis=0) gn = q(smp.adata.var_names) A = A[np.in1d(q(A.index), gn)] orthology_groups = A[og_key] og = q(orthology_groups) x = np.unique(",".join(og).split(",")) D = pd.DataFrame(data=np.arange(x.size)[None, :], columns=x) for i in range(og.size): n = orthology_groups[i].split(",") taxa = substr(substr(n, "@", 1),'|',0) if (taxa == "2759").sum() > 1 and taxon == '2759': og[i] = "" else: og[i] = "".join(np.array(n)[taxa == taxon]) A[og_key] = og og = q(A[og_key].reindex(gn)) og[og == "nan"] = "" X = [] Y = [] for i in range(og.size): x = og[i] if x != "": X.extend(D[x].values.flatten()) Y.extend([i]) X = np.array(X) Y = np.array(Y) B = sp.sparse.lil_matrix((og.size, D.size)) B[Y, X] = 1 B = B.tocsr() B = B.dot(B.T) B.data[:] = 1 pairs = gn[np.vstack((B.nonzero())).T] pairssp = np.vstack([q([x.split('_')[0] for x in xx]) for xx in pairs]) return np.unique(np.sort(pairs[pairssp[:,0]!=pairssp[:,1]],axis=1),axis=0) def CellTypeTriangles(sm,keys, align_thr=0.1): """Outputs a table of cell type triangles. Parameters ---------- sm: SAMAP object - assumed to contain at least three species. keys: dictionary of annotation keys (`.adata.obs[key]`) keyed by species. align_thr: float, optional, default, 0.1 Only keep triangles with minimum `align_thr` alignment score. """ D,A = get_mapping_scores(sm,keys=keys) x,y = A.values.nonzero() all_pairsf = np.array([A.index[x],A.columns[y]]).T.astype('str') alignmentf = A.values[x,y].flatten() alignment = alignmentf.copy() all_pairs = all_pairsf.copy() all_pairs = all_pairs[alignment > align_thr] alignment = alignment[alignment > align_thr] all_pairs = to_vn(np.sort(all_pairs, axis=1)) x, y = substr(all_pairs, ";") ctu = np.unique(np.concatenate((x, y))) Z = pd.DataFrame(data=np.arange(ctu.size)[None, :], columns=ctu) nnm = sp.sparse.lil_matrix((ctu.size,) * 2) nnm[Z[x].values.flatten(), Z[y].values.flatten()] = alignment nnm[Z[y].values.flatten(), Z[x].values.flatten()] = alignment nnm = nnm.tocsr() import networkx as nx G = nx.Graph() gps=ctu[np.vstack(nnm.nonzero()).T] G.add_edges_from(gps) alignment = pd.Series(index=to_vn(gps),data=nnm.data) all_cliques = nx.enumerate_all_cliques(G) all_triangles = [x for x in all_cliques if len(x) == 3] Z = np.sort(np.vstack(all_triangles), axis=1) DF = pd.DataFrame(data=Z, columns=[x.split("_")[0] for x in Z[0]]) for i,sid1 in enumerate(sm.ids): for sid2 in sm.ids[i:]: if sid1!=sid2: DF[sid1+';'+sid2] = [alignment[x] for x in DF[sid1].values.astype('str').astype('object')+';'+DF[sid2].values.astype('str').astype('object')] DF = DF[sm.ids] return DF def GeneTriangles(sm,orth,keys=None,compute_markers=True,corr_thr=0.3, psub_thr = 0.3, pval_thr=1e-10): """Outputs a table of gene triangles. Parameters ---------- sm: SAMAP object which contains at least three species orths: (n x 2) ortholog pairs keys: dict of strings corresponding to each species annotation column keyed by species, optional, default None If you'd like to include information about where each gene is differentially expressed, you can specify the annotation column to compute differential expressivity from for each species. compute_markers: bool, optional, default True Set this to False if you already precomputed differential expression for the input keys. corr_thr: float, optional, default, 0.3 Only keep triangles with minimum `corr_thr` correlation. pval_thr: float, optional, defaul, 1e-10 Consider cell types as differentially expressed if their p-values are less than `pval_thr`. """ FINALS = [] orth = np.sort(orth,axis=1) orthsp = np.vstack([q([x.split('_')[0] for x in xx]) for xx in orth]) RES = ParalogSubstitutions(sm, orth, psub_thr = psub_thr) op = to_vo(q(RES['ortholog pairs'])) pp = to_vo(q(RES['paralog pairs'])) ops = np.vstack([q([x.split('_')[0] for x in xx]) for xx in op]) pps = np.vstack([q([x.split('_')[0] for x in xx]) for xx in pp]) gnnm = sm.samap.adata.varp["homology_graph_reweighted"] gn = q(sm.samap.adata.var_names) gnsp = q([x.split('_')[0] for x in gn]) import itertools combs = list(itertools.combinations(sm.ids,3)) for comb in combs: A,B,C = comb smp1 = SAM(counts=sm.samap.adata[np.logical_or(sm.samap.adata.obs['species']==A,sm.samap.adata.obs['species']==B)]) smp2 = SAM(counts=sm.samap.adata[np.logical_or(sm.samap.adata.obs['species']==A,sm.samap.adata.obs['species']==C)]) smp3 = SAM(counts=sm.samap.adata[np.logical_or(sm.samap.adata.obs['species']==B,sm.samap.adata.obs['species']==C)]) sam1=sm.sams[A] sam2=sm.sams[B] sam3=sm.sams[C] A1,A2=A,B B1,B2=A,C C1,C2=B,C f1 = np.logical_and(((ops[:,0]==A1) * (ops[:,1]==A2) + (ops[:,0]==A2) * (ops[:,1]==A1)) > 0, ((pps[:,0]==A1) * (pps[:,1]==A2) + (pps[:,0]==A2) * (pps[:,1]==A1)) > 0) f2 = np.logical_and(((ops[:,0]==B1) * (ops[:,1]==B2) + (ops[:,0]==B2) * (ops[:,1]==B1)) > 0, ((pps[:,0]==B1) * (pps[:,1]==B2) + (pps[:,0]==B2) * (pps[:,1]==B1)) > 0) f3 = np.logical_and(((ops[:,0]==C1) * (ops[:,1]==C2) + (ops[:,0]==C2) * (ops[:,1]==C1)) > 0, ((pps[:,0]==C1) * (pps[:,1]==C2) + (pps[:,0]==C2) * (pps[:,1]==C1)) > 0) RES1=RES[f1] RES2=RES[f2] RES3=RES[f3] f1 = ((orthsp[:,0]==A1) * (orthsp[:,1]==A2) + (orthsp[:,0]==A2) * (orthsp[:,1]==A1)) > 0 f2 = ((orthsp[:,0]==B1) * (orthsp[:,1]==B2) + (orthsp[:,0]==B2) * (orthsp[:,1]==B1)) > 0 f3 = ((orthsp[:,0]==C1) * (orthsp[:,1]==C2) + (orthsp[:,0]==C2) * (orthsp[:,1]==C1)) > 0 orth1 = orth[f1] orth2 = orth[f2] orth3 = orth[f3] op1 = to_vo(q(RES1["ortholog pairs"])) op2 = to_vo(q(RES2["ortholog pairs"])) op3 = to_vo(q(RES3["ortholog pairs"])) pp1 = to_vo(q(RES1["paralog pairs"])) pp2 = to_vo(q(RES2["paralog pairs"])) pp3 = to_vo(q(RES3["paralog pairs"])) gnnm1 = sp.sparse.vstack(( sp.sparse.hstack((sp.sparse.csr_matrix(((gnsp==A1).sum(),)*2),gnnm[gnsp==A1,:][:,gnsp==A2])), sp.sparse.hstack((gnnm[gnsp==A2,:][:,gnsp==A1],sp.sparse.csr_matrix(((gnsp==A2).sum(),)*2))) )).tocsr() gnnm2 = sp.sparse.vstack(( sp.sparse.hstack((sp.sparse.csr_matrix(((gnsp==B1).sum(),)*2),gnnm[gnsp==B1,:][:,gnsp==B2])), sp.sparse.hstack((gnnm[gnsp==B2,:][:,gnsp==B1],sp.sparse.csr_matrix(((gnsp==B2).sum(),)*2))) )).tocsr() gnnm3 = sp.sparse.vstack(( sp.sparse.hstack((sp.sparse.csr_matrix(((gnsp==C1).sum(),)*2),gnnm[gnsp==C1,:][:,gnsp==C2])), sp.sparse.hstack((gnnm[gnsp==C2,:][:,gnsp==C1],sp.sparse.csr_matrix(((gnsp==C2).sum(),)*2))) )).tocsr() gn1 = np.append(gn[gnsp==A1],gn[gnsp==A2]) gn2 = np.append(gn[gnsp==B1],gn[gnsp==B2]) gn3 = np.append(gn[gnsp==C1],gn[gnsp==C2]) # suppress warning with warnings.catch_warnings(): warnings.simplefilter("ignore") T1 = pd.DataFrame(data=np.arange(gn1.size)[None, :], columns=gn1) x, y = T1[op1[:, 0]].values.flatten(), T1[op1[:, 1]].values.flatten() gnnm1[x, y] = gnnm1[x, y] gnnm1[y, x] = gnnm1[y, x] T1 = pd.DataFrame(data=np.arange(gn2.size)[None, :], columns=gn2) x, y = T1[op2[:, 0]].values.flatten(), T1[op2[:, 1]].values.flatten() gnnm2[x, y] = gnnm2[x, y] gnnm2[y, x] = gnnm2[y, x] T1 = pd.DataFrame(data=np.arange(gn3.size)[None, :], columns=gn3) x, y = T1[op3[:, 0]].values.flatten(), T1[op3[:, 1]].values.flatten() gnnm3[x, y] = gnnm3[x, y] gnnm3[y, x] = gnnm3[y, x] gnnm1.data[gnnm1.data==0]=1e-4 gnnm2.data[gnnm2.data==0]=1e-4 gnnm3.data[gnnm3.data==0]=1e-4 pairs1 = gn1[np.vstack(gnnm1.nonzero()).T] pairs2 = gn2[np.vstack(gnnm2.nonzero()).T] pairs3 = gn3[np.vstack(gnnm3.nonzero()).T] data = np.concatenate((gnnm1.data, gnnm2.data, gnnm3.data)) CORR1 = pd.DataFrame(data=gnnm1.data[None, :], columns=to_vn(pairs1)) CORR2 = pd.DataFrame(data=gnnm2.data[None, :], columns=to_vn(pairs2)) CORR3 = pd.DataFrame(data=gnnm3.data[None, :], columns=to_vn(pairs3)) pairs = np.vstack((pairs1, pairs2, pairs3)) all_genes = np.unique(pairs.flatten()) Z = pd.DataFrame(data=np.arange(all_genes.size)[None, :], columns=all_genes) x, y = Z[pairs[:, 0]].values.flatten(), Z[pairs[:, 1]].values.flatten() GNNM = sp.sparse.lil_matrix((all_genes.size,) * 2) GNNM[x, y] = data import networkx as nx G = nx.from_scipy_sparse_matrix(GNNM, create_using=nx.Graph) all_cliques = nx.enumerate_all_cliques(G) all_triangles = [x for x in all_cliques if len(x) == 3] Z = all_genes[np.sort(np.vstack(all_triangles), axis=1)] DF = pd.DataFrame(data=Z, columns=[x.split("_")[0] for x in Z[0]]) DF = DF[[A, B, C]] orth1DF = pd.DataFrame(data=orth1, columns=[x.split("_")[0] for x in orth1[0]])[ [A, B] ] orth2DF = pd.DataFrame(data=orth2, columns=[x.split("_")[0] for x in orth2[0]])[ [A, C] ] orth3DF = pd.DataFrame(data=orth3, columns=[x.split("_")[0] for x in orth3[0]])[ [B, C] ] ps1DF = pd.DataFrame( data=np.sort(pp1, axis=1), columns=[x.split("_")[0] for x in np.sort(pp1, axis=1)[0]], )[[A, B]] ps2DF = pd.DataFrame( data=np.sort(pp2, axis=1), columns=[x.split("_")[0] for x in np.sort(pp2, axis=1)[0]], )[[A, C]] ps3DF = pd.DataFrame( data=np.sort(pp3, axis=1), columns=[x.split("_")[0] for x in np.sort(pp3, axis=1)[0]], )[[B, C]] A_AB = pd.DataFrame(data=to_vn(op1)[None, :], columns=to_vn(ps1DF.values)) A_AC = pd.DataFrame(data=to_vn(op2)[None, :], columns=to_vn(ps2DF.values)) A_BC = pd.DataFrame(data=to_vn(op3)[None, :], columns=to_vn(ps3DF.values)) AB = to_vn(DF[[A, B]].values) AC = to_vn(DF[[A, C]].values) BC = to_vn(DF[[B, C]].values) AVs = [] CATs = [] CORRs = [] for i, X, O, P, Z, R in zip( [0, 1, 2], [AB, AC, BC], [orth1DF, orth2DF, orth3DF], [ps1DF, ps2DF, ps3DF], [A_AB, A_AC, A_BC], [CORR1, CORR2, CORR3], ): cat = q(["homolog"] * X.size).astype("object") cat[np.in1d(X, to_vn(O.values))] = "ortholog" ff = np.in1d(X, to_vn(P.values)) cat[ff] = "substitution" z = Z[X[ff]] #problem line here x = X[ff] av = np.zeros(x.size, dtype="object") for ai in range(x.size): v=pd.DataFrame(z[x[ai]]) #get ortholog pairs - paralog pairs dataframe vd=v.values.flatten() #get ortholog pairs vc=q(';'.join(v.columns).split(';')) # get paralogous genes temp = np.unique(q(';'.join(vd).split(';'))) #get orthologous genes av[ai] = ';'.join(temp[np.in1d(temp,vc,invert=True)]) #get orthologous genes not present in paralogous genes AV = np.zeros(X.size, dtype="object") AV[ff] = av corr = R[X].values.flatten() AVs.append(AV) CATs.append(cat) CORRs.append(corr) tri_pairs = np.vstack((AB, AC, BC)).T cat_pairs = np.vstack(CATs).T corr_pairs = np.vstack(CORRs).T homology_triangles = DF.values substituted_genes = np.vstack(AVs).T substituted_genes[substituted_genes == 0] = "N.S." data = np.hstack( ( homology_triangles.astype("object"), substituted_genes.astype("object"), tri_pairs.astype("object"), corr_pairs.astype("object"), cat_pairs.astype("object"), ) ) FINAL = pd.DataFrame(data = data, columns = [f'{A} gene',f'{B} gene',f'{C} gene', f'{A}/{B} subbed',f'{A}/{C} subbed',f'{B}/{C} subbed', f'{A}/{B}',f'{A}/{C}',f'{B}/{C}', f'{A}/{B} corr',f'{A}/{C} corr',f'{B}/{C} corr', f'{A}/{B} type',f'{A}/{C} type',f'{B}/{C} type']) FINAL['#orthologs'] = (cat_pairs=='ortholog').sum(1) FINAL['#substitutions'] = (cat_pairs=='substitution').sum(1) FINAL = FINAL[(FINAL['#orthologs']+FINAL['#substitutions'])==3] x = FINAL[[f'{A}/{B} corr',f'{A}/{C} corr',f'{B}/{C} corr']].min(1) FINAL['min_corr'] = x FINAL = FINAL[x>corr_thr] if keys is not None: keys = [keys[A],keys[B],keys[C]] with warnings.catch_warnings(): warnings.simplefilter("ignore") if keys is not None: for i,sam,n in zip([0,1,2],[sam1,sam2,sam3],[A,B,C]): if compute_markers: find_cluster_markers(sam,keys[i]) a = sam.adata.varm[keys[i]+'_scores'].T[q(FINAL[n+' gene'])].T p = sam.adata.varm[keys[i]+'_pvals'].T[q(FINAL[n+' gene'])].T.values p[p>pval_thr]=1 p[p<1]=0 p=1-p f = a.columns[a.values.argmax(1)] res=[] for i in range(p.shape[0]): res.append(';'.join(np.unique(np.append(f[i],a.columns[p[i,:]==1])))) FINAL[n+' cell type'] = res FINAL = FINAL.sort_values('min_corr',ascending=False) FINALS.append(FINAL) FINAL = pd.concat(FINALS,axis=0) return FINAL def transfer_annotations(sm,reference_id=None, keys=[],num_iters=5, inplace = True): """ Transfer annotations across species using label propagation along the combined manifold. Parameters ---------- sm - SAMAP object reference_id - str, optional, default None The species ID of the reference species from which the annotations will be transferred. keys - str or list, optional, default [] The `obs` key or list of keys corresponding to the labels to be propagated. If passed an empty list, all keys in the reference species' `obs` dataframe will be propagated. num_iters - int, optional, default 5 The number of steps to run the diffusion propagation. inplace - bool, optional, default True If True, deposit propagated labels in the target species (`sm.sams['hu']`) `obs` DataFrame. Otherwise, just return the soft-membership DataFrame. Returns ------- A Pandas DataFrame with soft membership scores for each cluster in each cell. """ stitched = sm.samap NNM = stitched.adata.obsp['connectivities'].copy() NNM = NNM.multiply(1/NNM.sum(1).A).tocsr() if type(keys) is str: keys = [keys] elif len(keys) == 0: try: keys = list(sm.sams[reference_id].adata.obs.keys()) except KeyError: raise ValueError(f'`reference` must be one of {sm.ids}.') for key in keys: samref = sm.sams[reference_id] ANN = stitched.adata.obs ANNr = samref.adata.obs cl = ANN[key].values.astype('object').astype('str') clr = reference_id+'_'+ANNr[key].values.astype('object') cl[np.invert(np.in1d(cl,clr))]='' clu,clui = np.unique(cl,return_inverse=True) P = np.zeros((NNM.shape[0],clu.size)) Pmask = np.ones((NNM.shape[0],clu.size)) P[np.arange(clui.size),clui]=1.0 Pmask[stitched.adata.obs['species']==reference_id]=0 Pmask=Pmask[:,1:] P=P[:,1:] Pinit = P.copy() for j in range(num_iters): P_new = NNM.dot(P) if np.max(np.abs(P_new - P)) < 5e-3: P = P_new s=P.sum(1)[:,None] s[s==0]=1 P = P/s break else: P = P_new s=P.sum(1)[:,None] s[s==0]=1 P = P/s P = P * Pmask + Pinit uncertainty = 1-P.max(1) labels = clu[1:][np.argmax(P,axis=1)] labels[uncertainty==1.0]='NAN' uncertainty[uncertainty>=uncertainty.max()*0.99] = 1 if inplace: stitched.adata.obs[key+'_transfer'] = pd.Series(labels,index = stitched.adata.obs_names) stitched.adata.obs[key+'_uncertainty'] = pd.Series(uncertainty,index=stitched.adata.obs_names) res = pd.DataFrame(data=P,index=stitched.adata.obs_names,columns=clu[1:]) res['labels'] = labels return res def get_mapping_scores(sm, keys, n_top = 0): """Calculate mapping scores Parameters ---------- sm: SAMAP object keys: dict, annotation vector keys for at least two species with species identifiers as the keys e.g. {'pl':'tissue','sc':'tissue'} n_top: int, optional, default 0 If `n_top` is 0, average the alignment scores for all cells in a pair of clusters. Otherwise, average the alignment scores of the top `n_top` cells in a pair of clusters. Set this to non-zero if you suspect there to be subpopulations of your cell types mapping to distinct cell types in the other species. Returns ------- D - table of highest mapping scores for cell types A - pairwise table of mapping scores between cell types across species """ if len(list(keys.keys()))<len(list(sm.sams.keys())): samap = SAM(counts = sm.samap.adata[np.in1d(sm.samap.adata.obs['species'],list(keys.keys()))]) else: samap=sm.samap clusters = [] ix = np.unique(samap.adata.obs['species'],return_index=True)[1] skeys = q(samap.adata.obs['species'])[np.sort(ix)] for sid in skeys: clusters.append(q([sid+'_'+str(x) for x in sm.sams[sid].adata.obs[keys[sid]]])) cl = np.concatenate(clusters) l = "{}_mapping_scores".format(';'.join([keys[sid] for sid in skeys])) samap.adata.obs[l] = pd.Categorical(cl) CSIMth, clu = _compute_csim(samap, l, n_top = n_top, prepend = False) A = pd.DataFrame(data=CSIMth, index=clu, columns=clu) i = np.argsort(-A.values.max(0).flatten()) H = [] C = [] for I in range(A.shape[1]): x = A.iloc[:, i[I]].sort_values(ascending=False) H.append(np.vstack((x.index, x.values)).T) C.append(A.columns[i[I]]) C.append(A.columns[i[I]]) H = np.hstack(H) D = pd.DataFrame(data=H, columns=[C, ["Cluster","Alignment score"]*(H.shape[1]//2)]) return D, A
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# -*- coding:UTF-8 -*- import sys reqMax = [] reqMin = [] cont = 0 #print(limpar()) p = 0 while p != 4: print('~'*30) print('Para Inserir dados do jogo aperte [1]: ') print('Para consultar dados dos jogos aperte [2]: ') print('para limpar a tabela de jogos aperte [3]') print('Para Sair do programa aperte [4]: ') p = int(input('Opção: ')) print('~'*30) if p == 1: cont+=1 inserir() elif p == 2: consulta() elif p ==3: limpar() elif p == 4: print('Opção {}'.format(p), 'Saindo do programa!!!') else: print('Opção Invalida') print('*'*30)
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from django import template from account.models import UserMessage from account.models import Conversation register = template.Library() @register.assignment_tag @register.assignment_tag @register.assignment_tag
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import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import os.path as osp import joblib MAIN_PATH = '/scratch/gobi2/kamyar/oorl_rlkit/output' WHAT_TO_PLOT = 'faster_all_eval_stats.pkl' # WHAT_TO_PLOT = 'faster_all_eval_stats.pkl' # WHAT_TO_PLOT = 'faster_all_eval_stats.pkl' data_dirs = { 'np_airl': { 0.2: 'correct-saving-np-airl-KL-0p2-disc-512-dim-rew-2-NO-TARGET-ANYTHING-over-10-epochs', 0.15: 'correct-saving-np-airl-KL-0p15-disc-512-dim-rew-2-NO-TARGET-ANYTHING-over-10-epochs', 0.1: 'correct-saving-np-airl-KL-0p1-disc-512-dim-rew-2-NO-TARGET-ANYTHING-over-10-epochs', 0.05: 'correct-saving-np-airl-KL-0p05-disc-512-dim-rew-2-NO-TARGET-ANYTHING-over-10-epochs', 0.0: 'correct-saving-np-airl-KL-0-disc-512-dim-rew-2-NO-TARGET-ANYTHING-over-10-epochs' }, 'np_bc': { 0.2: 'np-bc-KL-0p2-FINAL-WITHOUT-TARGETS', 0.15: 'np-bc-KL-0p15-FINAL-WITHOUT-TARGETS', 0.1: 'np-bc-KL-0p1-FINAL-WITHOUT-TARGETS', 0.05: 'np-bc-KL-0p05-FINAL-WITHOUT-TARGETS', 0.0: 'np-bc-KL-0-FINAL-WITHOUT-TARGETS' } } # fig, ax = plt.subplots(1, 5) for i, beta in enumerate([0.0, 0.05, 0.1, 0.15, 0.2]): fig, ax = plt.subplots(1) ax.set_xlabel('$\\beta = %.2f$' % beta) # np_airl all_stats = joblib.load(osp.join(MAIN_PATH, data_dirs['np_airl'][beta], WHAT_TO_PLOT))['faster_all_eval_stats'] good_reaches_means = [] good_reaches_stds = [] solves_means = [] solves_stds = [] for c_size in range(1,7): good_reaches = [] solves = [] for d in all_stats: good_reaches.append(d[c_size]['Percent_Good_Reach']) solves.append(d[c_size]['Percent_Solved']) good_reaches_means.append(np.mean(good_reaches)) good_reaches_stds.append(np.std(good_reaches)) solves_means.append(np.mean(solves)) solves_stds.append(np.std(solves)) # ax.errorbar(list(range(1,7)), good_reaches_means, good_reaches_stds) ax.errorbar(np.array(list(range(1,7))) + 0.1, solves_means, solves_stds, elinewidth=2.0, capsize=4.0, barsabove=True, linewidth=2.0, label='Meta-AIRL' ) # np_bc all_stats = joblib.load(osp.join(MAIN_PATH, data_dirs['np_bc'][beta], WHAT_TO_PLOT))['faster_all_eval_stats'] good_reaches_means = [] good_reaches_stds = [] solves_means = [] solves_stds = [] for c_size in range(1,7): good_reaches = [] solves = [] for d in all_stats: good_reaches.append(d[c_size]['Percent_Good_Reach']) solves.append(d[c_size]['Percent_Solved']) good_reaches_means.append(np.mean(good_reaches)) good_reaches_stds.append(np.std(good_reaches)) solves_means.append(np.mean(solves)) solves_stds.append(np.std(solves)) # ax.errorbar(list(range(1,7)), good_reaches_means, good_reaches_stds) ax.errorbar(np.array(list(range(1,7))) - 0.1, solves_means, solves_stds, elinewidth=2.0, capsize=4.0, barsabove=True, linewidth=2.0, label='Meta-BC' ) ax.set_ylim([0.3, 1.0]) lgd = ax.legend(loc='upper center', bbox_to_anchor=(0.725, 0.1), shadow=False, ncol=3) plt.savefig('plots/abc/faster_test_%d.png'%i, bbox_extra_artists=(lgd,), bbox_inches='tight') # plt.savefig('plots/abc/test_%d.png'%i) plt.close()
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from django.shortcuts import render, get_object_or_404, redirect from .models import RepPost from .forms import RepForm from django.utils import timezone from django.contrib.auth.decorators import login_required @login_required
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#int_to_line.py #This script takes intersection and road segment and determine the direction of the road segment in contrast to the intersection. import arcpy from arcpy import env from arcpy.sa import * arcpy.CheckOutExtension("Spatial") arcpy.env.overwriteOutput = True #input configuration env.workspace = "C:/Users/kml42638/Desktop/testDB.gdb" print("The name of the workspace is " + env.workspace) streetCL = "GGISC_streetCL" intersections = "Intersections_all" main(intersections, streetCL)
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import os from celery import Celery os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'cactusco.settings') app = Celery('cactusco') app.config_from_object('django.conf:settings', namespace='CELERY') app.autodiscover_tasks() @app.task(bind=True)
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from dtpattern import alignment from dtpattern.alignment import needle, finalize, gap_penalty, match_award, mismatch_penalty, water from dtpattern.utils import translate from dtpattern.alignment import alignment as al def align(s1,s2): """ input is a list of characters or character set symbols for each s1 and s2 return is :param s1: :param s2: :return: tuple of align1, align2, symbol2, identity, score """ identity, score, align1, symbol2, align2 = needle(s1, s2) print_alignment(align1, align2, symbol2, identity, score, altype="NEEDLE") identity, score, align1, symbol2, align2 = water(s1, s2) print_alignment(align1, align2, symbol2, identity, score, altype="WATER") score_matrix = { gap_penalty: -15, match_award: 5, mismatch_penalty: -4 } identity, score, align1, symbol2, align2 = needle(s1, s2,score_matrix=score_matrix) print_alignment(align1, align2, symbol2, identity, score, altype="VALUE") identity, score, align1, symbol2, align2 = water(s1, s2,score_matrix=score_matrix) print_alignment(align1, align2, symbol2, identity, score, altype="WATER") identity, score, align1, symbol2, align2 = needle(_translate(s1), s2) print_alignment(align1, align2, symbol2, identity, score, altype="TRANS") identity, score, align1, symbol2, align2 = water(_translate(s1), s2) print_alignment(align1, align2, symbol2, identity, score, altype="TRANS_WATER") #for a in al.align.globalms("".join(s1), "".join(s2), 5, -4, -50, -.1): # print(al.format_alignment(*a)) return align1, align2, symbol2, identity, score data=[ ['111',"1222","1113"] ] for values in data: s1 = values[0] for s2 in values[1:]: print("MERGE:\n\t{}\n\t{}".format(s1,s2)) if isinstance(s1,str): s1= to_list(s1) if isinstance(s2,str): s2= to_list(s2) align1, align2, symbol2, identity, score = align(s1,s2) #print_alignment(align1, align2, symbol2, identity, score) _s1,_s2=s1,s2 while not is_valid_alignment(align1, align2, symbol2): break s1 = merge_alignment(symbol2)
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2.363441
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import logging logger = logging.getLogger(__name__) handler = logging.StreamHandler() formatter = logging.Formatter( '%(asctime)s %(levelname)s %(message)s', "%Y-%m-%d %H:%M:%S" ) handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(logging.INFO) import mimetypes
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2.487395
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import logging import numpy as np import scipy.stats as stats from .eigd import eigenDecompose
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3
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import numpy as np import cv2 input = cv2.imread('input/strawberry.jpg') height, width = input_image.shape[:2] x_gauss = cv2.getGaussianKernel(width,250) y_gauss = cv2.getGaussianKernel(height,200) kernel = x_gauss * y_gauss.T mask = kernel * 255 / np.linalg.norm(kernel) output[:,:,0] = input[:,:,0] * mask output[:,:,1] = input[:,:,1] * mask output[:,:,2] = input[:,:,2] * mask cv2.imshow('vignette', output) cv2.waitKey(0) cv2.destroyAllWindows()
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2.019763
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Mar 5 11:40:58 2021 @author: Christopher Corbell Things we can use here: - construct Digraph from underlying Graph (default direction for edges) - DigraphFactory to construct some interesting digraphs """ from graphoire.graph import Graph class Digraph(Graph): """ Digraph is a subclass of Graph that implements edge direction. This includes distinguishing between u,v and v,u edges (the base class resolves such edges to u,v). The class also can calculate in-degree and out-degree of vertices; note that the base class vertexDegree() and related methods consider out-degree only. """ def getOutNeighbors(self, vertex): """ Get a list of vertices that this vertex connects-outward to. Parameters ---------- vertex : int The vertex index Returns list of adjacent head-vertex integer indices. """ neighbors = [] for edge in self.edges: if edge[0] == vertex: neighbors.append(edge[1]) return neighbors def getInNeighbors(self, vertex): """ Get a list of vertices that connect inward to this vertex. Parameters ---------- vertex : int The vertex index Returns list of adjacent tail-vertex integer indicdes. """ neighbors = [] for edge in self.edges: if edge[1] == vertex: neighbors.append(edge[0]) return neighbors def edgeDirection(self, tail, head): """ Get the direction of edge between tail and head. Parameters ---------- tail : integer (vertex index) The vertex to interpret as tail head : integer (vertex index) The vertex to interpret as head Returns ------- An integer value 1 if this is a directed edge from tail to head, -1 if the edge is the other direction, and 0 if there is no edge. """ if self.hasEdge(tail, head): return 1 elif self.hasEdge(head, tail): return -1 else: return 0
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2.446137
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from collections import OrderedDict import numpy as np from gym.envs.mujoco import mujoco_env from gym.spaces import Box from bgp.rlkit.core import logger as default_logger from bgp.rlkit.core.eval_util import create_stats_ordered_dict from bgp.rlkit.core.serializable import Serializable from bgp.rlkit.envs.mujoco_env import get_asset_xml from bgp.rlkit.samplers.util import get_stat_in_paths from bgp.rlkit.torch.tdm.envs.multitask_env import MultitaskEnv
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2.789157
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import sublime import sublime_plugin class SurroundCommand(sublime_plugin.TextCommand): """ Base class to surround the selection with text. """ surround = ''
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3.272727
55
import requests from faker import Faker from faker.providers import date_time import json fake = Faker() fake.add_provider(date_time) for i in range(40000000): user = { 'name': fake.name(), 'email': fake.email(), 'birthdate': fake.date() } response = requests.post('http://localhost:8000/users', json=json.dumps(user)) if response.ok: if i % 100000 == 0: user_id = response.json()['id'] print("User {0} added".format(user_id)) else: print("Error")
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import sys from hangul_utils import * # for word segmentation and pos tagging of Korean text # Note: You need to install "hangul-utils" in advanced # Ref link: https://github.com/kaniblu/hangul-utils # written by Ye Kyaw Thu, Visiting Professor, LST, NECTEC, Thailand # # How to run: python ./korean-breaks.py <input-filename> <word|morph|pos> # eg 1: python ./korean-breaks.py ./tst.ko -pos # eg 2: python ./korean-breaks.py ./tst.ko -morph # e.g 3: python ./korean-breaks.py ./tst.ko -word if len(sys.argv) < 3: print ("You must set two arguments!") print ("How to run:") print ("python ./korean-breaks.py <raw-korean-text-filename> <-word|-morph|-pos>") sys.exit() else: f1 = sys.argv[1] arg = sys.argv[2] fp1=open(f1,"r") for line1 in fp1: if arg.lower() == '-word': # Word tokenization (mainly using space): print (" ".join(list(word_tokenize(line1.strip())))) elif arg.lower() == '-morph': # Morpheme tokenization print (" ".join(list(morph_tokenize(line1.strip())))) elif arg.lower() == '-pos': # Morpheme tokenization with POS print (list(morph_tokenize(line1.strip(), pos=True))) fp1.close()
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# Uses python3 if __name__ == "__main__": print(edit_distance(input(), input()))
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#!/usr/bin/env python # encoding: utf-8 import rospy import tf from std_msgs.msg import Float64, Int32, Int8 from nav_msgs.msg import Odometry from geometry_msgs.msg import Twist, Vector3 from PID import PID from math import sin, cos, pi, atan2, sqrt autoMove = AUTO_MOVE() """LinearPub = rospy.Publisher("/command/linear", self.twist, queue_size=5) AngularPub = rospy.Publisher("/command/angular", self.twist, queue_size=5)""" # pub = rospy.Publisher('cmd_vel', self.twist, queue_size=10) if __name__ == '__main__': rospy.init_node('robot_teleop') pub = rospy.Publisher('cmd_vel', Twist, queue_size=10) # Set subscribers rospy.Subscriber("/odom", Odometry, autoMove.getState) rospy.Subscriber("/command/pos", Vector3, autoMove.moveCommand) # Server(AlignmentControllerConfig, dynamicReconfigureCb) rospy.spin()
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2.603659
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from django.conf import settings from django.contrib.auth import get_user_model from rest_framework.response import Response from rest_framework.views import APIView User = get_user_model() class RetrieveCurrentUserView(APIView): """Возвращает информацию о текущем пользователе"""
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#!/usr/bin/env python from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt # Create a Miller project map = Basemap(projection='hammer', lon_0=20, resolution='l') # Plot coastlines map.drawcoastlines(linewidth=0.) map.fillcontinents(alpha=0.85) # Parse telescopes.txt and plot the points on the map for line in open('telescopes.txt', 'r').readlines(): if line[0] == '#': continue lat = float( line.split()[1][:-1] ) lon = float( line.split()[2] ) xpt, ypt = map(lon, lat) map.plot([xpt],[ypt],'ro', markersize=0.75) # plt.savefig('radiotelescopes.png', dpi=500, bbox_inches='tight')
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from .KeyCodes import * from .MouseButtonCodes import *
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"""Evaluation This script consists of evaluation functions needed """ import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation import datetime import tensorflow as tf from tensorflow.python.tools import inspect_checkpoint as chkp import load_data from geometry_parameters import TEST_INDEX, RECONSTRUCT_PARA def show_reconstruction(model, phantom_index): """ show reconstructed CT Parameters ---------- model : str which model's results to use phantom_index : int which CT to display """ recon_dir = model + '/eval_recon/recon_' + str(phantom_index) + '.npy' recon = np.load(recon_dir) fig = plt.figure() imgs = [] for i in range(recon.shape[0]): img = plt.imshow(recon[i, :, :], animated=True, cmap=plt.get_cmap('gist_gray')) imgs.append([img]) animation.ArtistAnimation(fig, imgs, interval=50, blit=True, repeat_delay=1000) plt.show() def compare_reconstruction(model_one, model_two, phantom_index, slice_index): """ compared reconstructed CT results from different two models Parameters ---------- model_one : str the first model's result to use model_two : str the second model's result to use phantom_index : int which CT to display slice_index : int which slice in the CT to display """ recon_one = model_one + '/eval_recon/recon_' + str(phantom_index) + '.npy' recon_one = np.load(recon_one) recon_one = recon_one[slice_index-1,:,:] recon_two = model_two + '/eval_recon/recon_' + str(phantom_index) + '.npy' recon_two = np.load(recon_two) recon_two = recon_two[slice_index-1,:,:] fig = plt.figure(figsize=plt.figaspect(0.5)) ax = fig.add_subplot(1, 2, 1) ax.imshow(recon_one, cmap=plt.get_cmap('gist_gray')) ax.set_title('model: ' + model_one) ax = fig.add_subplot(1, 2, 2) ax.imshow(recon_two, cmap=plt.get_cmap('gist_gray')) ax.set_title('model: ' + model_two) plt.show() def single_ct_normalize(input): """ normalize one CT sample to [0, 1] Parameters ---------- input : ndarray The input CT to normalize Returns ------- ndarray the normalized CT """ max = np.max(input) min = np.min(input) input = (input - min) / (max - min) return input def compare_reconstruction_with_fdk(model, phantom_index, slice_index): """ compare reconstructed CT results with the conventional FDK and the ground truth Parameters ---------- model : str which model's results to use phantom_index : int which CT to display slice_index : int which slice in the CT to display """ recon_one = '../data_preprocessing/recon_145/recon_' + str(phantom_index) + '.npy' recon_one = single_ct_normalize(np.load(recon_one)) recon_one = recon_one[slice_index - 1, :, :] recon_two = model + '/eval_recon/recon_' + str(phantom_index) + '.npy' recon_two = np.load(recon_two) recon_two = recon_two[slice_index - 1, :, :] recon_three = '../data_preprocessing/recon_360/recon_' + str(phantom_index) + '.npy' recon_three = single_ct_normalize(np.load(recon_three)) recon_three = recon_three[slice_index - 1, :, :] fig = plt.figure(figsize=plt.figaspect(0.3)) ax = fig.add_subplot(1, 3, 1) ax.imshow(recon_one, cmap=plt.get_cmap('gist_gray')) ax.set_title('pure_fdk') ax = fig.add_subplot(1, 3, 2) ax.imshow(recon_two, cmap=plt.get_cmap('gist_gray')) ax.set_title('model: ' + model) ax = fig.add_subplot(1, 3, 3) ax.imshow(recon_three, cmap=plt.get_cmap('gist_gray')) ax.set_title('ground truth') plt.show() def calculate_ssim(predictions, gt_labels, max_val): """ ssim calculation Parameters ---------- predictions : ndarray the reconstructed results gt_labels : ndarray the ground truth max_val : float the value range """ tf_predictions = tf.placeholder(tf.float32, shape=predictions.shape) tf_gt_labels = tf.placeholder(tf.float32, shape=gt_labels.shape) tf_ssim_value = tf.image.ssim(tf.expand_dims(tf_predictions, 4), tf.expand_dims(tf_gt_labels, 4), max_val) config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.9 config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: ssim = sess.run(tf_ssim_value, feed_dict={tf_predictions: predictions, tf_gt_labels: gt_labels}) return np.mean(ssim) def calculate_ms_ssim(predictions, gt_labels, max_val): """ ms-ssim calculation Parameters ---------- predictions : ndarray the reconstructed results gt_labels : ndarray the ground truth max_val : float the value range """ tf_predictions = tf.placeholder(tf.float32, shape=predictions.shape) tf_gt_labels = tf.placeholder(tf.float32, shape=gt_labels.shape) tf_ms_ssim_value = tf.image.ssim_multiscale(tf.expand_dims(tf_predictions, 4), tf.expand_dims(tf_gt_labels, 4), max_val) config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.9 config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: ms_ssim = sess.run(tf_ms_ssim_value, feed_dict={tf_predictions: predictions, tf_gt_labels: gt_labels}) return np.mean(ms_ssim) def calculate_psnr(predictions, gt_labels, max_val): """ psnr calculation Parameters ---------- predictions : ndarray the reconstructed results gt_labels : ndarray the ground truth max_val : float the value range """ tf_predictions = tf.placeholder(tf.float32, shape=predictions.shape) tf_gt_labels = tf.placeholder(tf.float32, shape=gt_labels.shape) tf_psnr_value = tf.image.psnr(tf.expand_dims(tf_predictions, 4), tf.expand_dims(tf_gt_labels, 4), max_val) config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.9 config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: psnr = sess.run(tf_psnr_value, feed_dict={tf_predictions: predictions, tf_gt_labels: gt_labels}) return np.mean(psnr) def normalize(input): """ normalize more than one CT sample to [0, 1] Parameters ---------- input : ndarray The input CT samples to normalize Returns ------- ndarray the normalized CT results """ for i in range(input.shape[0]): min_bound = np.min(input[i,::]) max_bound = np.max(input[i,::]) input[i,::] = (input[i,::] - min_bound) / (max_bound - min_bound) return input # ms-ssim, psnr, mse def evaluate_on_metrics(model): """ do evaluation on mse, ssim, ms-ssim and psnr Parameters ---------- model : str The model for evaluation """ # get the labels _, labels = load_data.load_test_data() labels = normalize(labels) # load the recons on the model recon_phantoms = np.empty(labels.shape) for i in range(recon_phantoms.shape[0]): recon_file = model + '/eval_recon/recon_' + str(TEST_INDEX[i]) + '.npy' recon_phantoms[i,:,:,:] = np.load(recon_file) # MSE mse = np.mean(np.square(recon_phantoms - labels)) # max_val = 1.0 # SSIM ssim = calculate_ssim(recon_phantoms, labels, max_val) # MS-SSIM ms_ssim = calculate_ms_ssim(recon_phantoms, labels, max_val) # Peak Signal-to-Noise Ratio psnr = calculate_psnr(recon_phantoms, labels, max_val) # print the results print('mse value: ', str(mse)) print('ssim value: ', str(ssim)) print('ms-ssim value: ', str(ms_ssim)) print('psnr value: ', str(psnr)) # save the metrics results f = open(model + '/eval_result/metrics_result.txt', 'a+') f.write("Model: {0}, Date: {1:%Y-%m-%d_%H:%M:%S} \nMSE: {2:3.8f} \nSSIM: {3:3.8f} \nMS-SSIM: {4:3.8f} \nPSNR: {5:3.8f}\n\n".format( model, datetime.datetime.now(), mse, ssim, ms_ssim, psnr)) f.close() def check_stored_sess_var(sess_file, var_name): """ display variable results for trained models in the stored session Parameters ---------- sess_file : str the stored session file var_name : str the variable to see """ if var_name == '': # print all tensors in checkpoint file (.ckpt) chkp.print_tensors_in_checkpoint_file(sess_file, tensor_name='', all_tensors=True) else: chkp.print_tensors_in_checkpoint_file(sess_file, tensor_name=var_name, all_tensors=False) def eval_pure_fdk(): """ do evaluation on mse, ssim, ms-ssim and psnr for the conventional FDK algorithm """ # get the labels _, labels = load_data.load_test_data() labels = normalize(labels) # load the recons recon_phantoms = np.empty(labels.shape) for i in range(recon_phantoms.shape[0]): recon_file = '../data_preprocessing/recon_145/recon_' + str(TEST_INDEX[i]) + '.npy' recon_phantoms[i, :, :, :] = np.load(recon_file) recon_phantoms = normalize(recon_phantoms) # MSE mse = np.mean(np.square(recon_phantoms - labels)) # max_val = 1.0 # SSIM ssim = calculate_ssim(recon_phantoms, labels, max_val) # MS-SSIM ms_ssim = calculate_ms_ssim(recon_phantoms, labels, max_val) # Peak Signal-to-Noise Ratio psnr = calculate_psnr(recon_phantoms, labels, max_val) # print the results print('mse value: ', str(mse)) print('ssim value: ', str(ssim)) print('ms-ssim value: ', str(ms_ssim)) print('psnr value: ', str(psnr)) # save the metrics results f = open('pure_fdk_model/eval_result/metrics_result.txt', 'a+') f.write( "Model: {0}, Date: {1:%Y-%m-%d_%H:%M:%S} \nMSE: {2:3.8f} \nSSIM: {3:3.8f} \nMS-SSIM: {4:3.8f} \nPSNR: {5:3.8f}\n\n".format( 'pure_fdk_model', datetime.datetime.now(), mse, ssim, ms_ssim, psnr)) f.close() def convert_to_raw_bin(model): """ convert the reconstructed results of the model to raw data file Parameters ---------- model : str The model for which results to convert """ dir = model + '/eval_recon/' for i in range(len(TEST_INDEX)): recon_file = dir + 'recon_' + str(TEST_INDEX[i]) + '.npy' recon = np.load(recon_file) recon.astype('float32').tofile(dir + 'recon_' + str(TEST_INDEX[i]) + '_float32_' + str(RECONSTRUCT_PARA['volume_shape'][1]) + 'x' + str(RECONSTRUCT_PARA['volume_shape'][2]) + 'x' + str(RECONSTRUCT_PARA['volume_shape'][0]) + '_bin') if __name__ == "__main__": ########################################### # show reconstructed result CT show_reconstruction('fdk_nn_model', TEST_INDEX[1]) # show_reconstruction('cnn_projection_model', TEST_INDEX[1]) # show_reconstruction('cnn_reconstruction_model', TEST_INDEX[1]) # show_reconstruction('dense_cnn_reconstruction_model', TEST_INDEX[1]) # show_reconstruction('unet_projection_model', TEST_INDEX[1]) # show_reconstruction('unet_reconstruction_model', TEST_INDEX[1]) # show_reconstruction('unet_proposed_reconstruction_model', TEST_INDEX[1]) # show_reconstruction('combined_projection_reconstruction_model', TEST_INDEX[1]) ########################################### # Evaluation on each model # evaluate_on_metrics('fdk_nn_model') # evaluate_on_metrics('cnn_projection_model') # evaluate_on_metrics('cnn_reconstruction_model') # evaluate_on_metrics('dense_cnn_reconstruction_model') # evaluate_on_metrics('unet_projection_model') # evaluate_on_metrics('unet_reconstruction_model') # evaluate_on_metrics('unet_proposed_reconstruction_model') # evaluate_on_metrics('combined_projection_reconstruction_model') # eval_pure_fdk() ########################################### # compare_reconstruction results # compare_reconstruction('cnn_projection_model', 'unet_projection_model', TEST_INDEX[1], 75) # compare_reconstruction_with_fdk('combined_projection_reconstruction_model', TEST_INDEX[1], 75) ########################################### # generate raw binary reconstruction files # convert_to_raw_bin('combined_projection_reconstruction_model')
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2.280542
5,607
from user import User brianna = User(1, 'Brianna') mary = User(2, 'Mary') keyboard = brianna.sell_product('Keyboard', 'A nice mechanical keyboard', 100) print(keyboard.availability) # => True mary.buy_product(keyboard) print(keyboard.availability) # => False review = mary.write_review('This is the best keyboard ever!', keyboard) review in mary.reviews # => True review in keyboard.reviews # => True
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import datetime import factory from factory.fuzzy import FuzzyChoice from wins.models import ( Advisor, Breakdown, CustomerResponse, HVC, Notification, Win, ) from wins.constants import BUSINESS_POTENTIAL, SECTORS, WIN_TYPES from users.factories import UserFactory WIN_TYPES_DICT = {y: x for x, y in WIN_TYPES}
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131
from . import BinarySearchTree from . import BinaryTree from . import Tree
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import pytest from .common import TESTDATA from flyingpigeon.utils import local_path from cdo import Cdo cdo = Cdo()
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from datetime import datetime import os import nose import nose.tools from TransactionBook.model.TransactionBook import * def save_load(tb): """ Helper function wich does save and load the data. :param tb: Transaction Book :return tb2: Transaction Book after save load operation """ filename = "dummy_database.csv" tb.save_as(filename) tb2 = TransactionBook() tb2.load_from(filename) os.remove(filename) return tb2 if __name__ == '__main__': test_populate_list_from_data() test_filter_date() test_account_balance() test_save_load() test_pivot_category_pie() test_years() test_total_balance() test_pivot_monthly_trend() test_delete_transaction()
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2.624113
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from nip import nip, dumps @nip @nip("myfunc") @nip
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""" Given a linked list, determine if it has a cycle in it. Follow up: Can you solve it without using extra space? """ __author__ = 'Danyang' # Definition for singly-linked list.
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# coding: utf-8 import re import six from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization class RecordRuleReq: """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ sensitive_list = [] openapi_types = { 'obs_addr': 'RecordObsFileAddr', 'record_formats': 'list[str]', 'hls_config': 'HLSRecordConfig', 'mp4_config': 'MP4RecordConfig' } attribute_map = { 'obs_addr': 'obs_addr', 'record_formats': 'record_formats', 'hls_config': 'hls_config', 'mp4_config': 'mp4_config' } def __init__(self, obs_addr=None, record_formats=None, hls_config=None, mp4_config=None): """RecordRuleReq - a model defined in huaweicloud sdk""" self._obs_addr = None self._record_formats = None self._hls_config = None self._mp4_config = None self.discriminator = None self.obs_addr = obs_addr self.record_formats = record_formats if hls_config is not None: self.hls_config = hls_config if mp4_config is not None: self.mp4_config = mp4_config @property def obs_addr(self): """Gets the obs_addr of this RecordRuleReq. :return: The obs_addr of this RecordRuleReq. :rtype: RecordObsFileAddr """ return self._obs_addr @obs_addr.setter def obs_addr(self, obs_addr): """Sets the obs_addr of this RecordRuleReq. :param obs_addr: The obs_addr of this RecordRuleReq. :type: RecordObsFileAddr """ self._obs_addr = obs_addr @property def record_formats(self): """Gets the record_formats of this RecordRuleReq. 录制格式:支持HLS格式和MP4格式(HLS和MP4为大写)。 - 若配置HLS则必须携带HLSRecordConfig参数 - 若配置MP4则需要携带MP4RecordConfig :return: The record_formats of this RecordRuleReq. :rtype: list[str] """ return self._record_formats @record_formats.setter def record_formats(self, record_formats): """Sets the record_formats of this RecordRuleReq. 录制格式:支持HLS格式和MP4格式(HLS和MP4为大写)。 - 若配置HLS则必须携带HLSRecordConfig参数 - 若配置MP4则需要携带MP4RecordConfig :param record_formats: The record_formats of this RecordRuleReq. :type: list[str] """ self._record_formats = record_formats @property def hls_config(self): """Gets the hls_config of this RecordRuleReq. :return: The hls_config of this RecordRuleReq. :rtype: HLSRecordConfig """ return self._hls_config @hls_config.setter def hls_config(self, hls_config): """Sets the hls_config of this RecordRuleReq. :param hls_config: The hls_config of this RecordRuleReq. :type: HLSRecordConfig """ self._hls_config = hls_config @property def mp4_config(self): """Gets the mp4_config of this RecordRuleReq. :return: The mp4_config of this RecordRuleReq. :rtype: MP4RecordConfig """ return self._mp4_config @mp4_config.setter def mp4_config(self, mp4_config): """Sets the mp4_config of this RecordRuleReq. :param mp4_config: The mp4_config of this RecordRuleReq. :type: MP4RecordConfig """ self._mp4_config = mp4_config def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: if attr in self.sensitive_list: result[attr] = "****" else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" import simplejson as json if six.PY2: import sys reload(sys) sys.setdefaultencoding("utf-8") return json.dumps(sanitize_for_serialization(self), ensure_ascii=False) def __repr__(self): """For `print`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, RecordRuleReq): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other
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220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1255, 58, 35226, 60, 796, 8633, 7, 8899, 7, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 37456, 2378, 25, 357, 9186, 58, 15, 4357, 2378, 58, 16, 4083, 1462, 62, 11600, 28955, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 611, 468, 35226, 7, 9186, 58, 16, 4357, 366, 1462, 62, 11600, 4943, 2073, 2378, 11, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1988, 13, 23814, 3419, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 15306, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2073, 25, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 611, 708, 81, 287, 2116, 13, 30176, 62, 4868, 25, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1255, 58, 35226, 60, 796, 366, 2466, 1, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 2073, 25, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1255, 58, 35226, 60, 796, 1988, 628, 220, 220, 220, 220, 220, 220, 220, 1441, 1255, 628, 220, 220, 220, 825, 284, 62, 2536, 7, 944, 2599, 198, 220, 220, 220, 220, 220, 220, 220, 37227, 35561, 262, 4731, 10552, 286, 262, 2746, 37811, 198, 220, 220, 220, 220, 220, 220, 220, 1330, 2829, 17752, 355, 33918, 198, 220, 220, 220, 220, 220, 220, 220, 611, 2237, 13, 47, 56, 17, 25, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1330, 25064, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 18126, 7, 17597, 8, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 25064, 13, 2617, 12286, 12685, 7656, 7203, 40477, 12, 23, 4943, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 33918, 13, 67, 8142, 7, 12807, 270, 1096, 62, 1640, 62, 46911, 1634, 7, 944, 828, 4155, 62, 292, 979, 72, 28, 25101, 8, 628, 220, 220, 220, 825, 11593, 260, 1050, 834, 7, 944, 2599, 198, 220, 220, 220, 220, 220, 220, 220, 37227, 1890, 4600, 4798, 63, 37811, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 2116, 13, 1462, 62, 2536, 3419, 628, 220, 220, 220, 825, 11593, 27363, 834, 7, 944, 11, 584, 2599, 198, 220, 220, 220, 220, 220, 220, 220, 37227, 35561, 2081, 611, 1111, 5563, 389, 4961, 37811, 198, 220, 220, 220, 220, 220, 220, 220, 611, 407, 318, 39098, 7, 847, 11, 13266, 31929, 3041, 80, 2599, 198, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 220, 1441, 10352, 628, 220, 220, 220, 220, 220, 220, 220, 1441, 2116, 13, 834, 11600, 834, 6624, 584, 13, 834, 11600, 834, 628, 220, 220, 220, 825, 11593, 710, 834, 7, 944, 11, 584, 2599, 198, 220, 220, 220, 220, 220, 220, 220, 37227, 35561, 2081, 611, 1111, 5563, 389, 407, 4961, 37811, 198, 220, 220, 220, 220, 220, 220, 220, 1441, 407, 2116, 6624, 584, 198 ]
1.96688
2,657
import argparse import markdown _EXTENSIONS = ( 'markdown.extensions.fenced_code', 'markdown.extensions.tables', ) if __name__ == '__main__': main()
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2.441176
68
from bs4 import BeautifulSoup import re import urllib.parse import requests if __name__ == '__main__': info = u"""<div id="info" class="">\ <span>\ <span class="pl"> 作者</span>\ <a class="" href="/search/%E5%8D%A1%E5%8B%92%E5%BE%B7%C2%B7%E8%83%A1%E8%B5%9B%E5%B0%BC">[美] 卡勒德·胡赛尼</a>\ </span><br>\ <span class="pl">出版社:</span> 上海人民出版社<br>\ <span class="pl">原作名:</span> The Kite Runner<br>\ <span>\ <span class="pl"> 译者</span>:\ <a class="" href="/search/%E6%9D%8E%E7%BB%A7%E5%AE%8F">李继宏</a> </span><br>\ <span class="pl">出版年:</span> 2006-5<br>\ <span class="pl">页数:</span> 362<br>\ <span class="pl">定价:</span> 29.00元<br>\ <span class="pl">装帧:</span> 平装<br>\ <span class="pl">丛书:</span>&nbsp;<a href="https://book.douban.com/series/19760">卡勒德·胡赛尼作品</a><br>\ <span class="pl">ISBN:</span> 9787208061644<br>\ </div>""" info = "clearfix" HtmlParser().parse("https://book.douban.com/subject/1082154/",requests.get("https://book.douban.com/subject/1082154/").content)
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1.754181
598
""" AUTHTAB.DIR file parser. """ from pybycus.file import File class AuthTab(File): """ The Author List (with the filename AUTHTAB.DIR) contains descriptive information for each text file on the disc. The purpose of the Author Table is to allow the user to ask for the author Plato, for example, without having to know that the actual file name is TLG0059. Each entry contains the author name, the corresponding file name, synonyms, remarks, and language. The entries are arranged by category. """ def content(path): """ Return the content of an AUTHTAB.DIR file. """ return AuthTab(path).content() if __name__ == "__main__": import sys import pprint pprint.pprint(content(sys.argv[1]))
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3.241228
228
from .param_value import ParamValue
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4.111111
9
import unittest import os import pandas as pd from causal_testing.data_collection.data_collector import ObservationalDataCollector from causal_testing.specification.causal_specification import Scenario from causal_testing.specification.variable import Input, Output, Meta from scipy.stats import uniform, rv_discrete from tests.test_helpers import create_temp_dir_if_non_existent, remove_temp_dir_if_existent if __name__ == "__main__": unittest.main()
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3.375
136
import copy # @property # def F(self): # attr = "F" # if attr in self.__dict__: # return self.__dict__[attr] # else: # return None # Gets called when the item is not found via __getattribute__ # def __getattr__(self, item): # return super(Individual, self).__setattr__(item, 'orphan') # def __setitem__(self, key, value): # self.__dict__[key] = value # # def __getitem__(self, key): # return self.__dict__.get(key) # def __getattr__(self, attr): # # if attr == "F": # if attr in self.__dict__: # return self.__dict__[attr] # else: # return None # # if attr in self.__dict__: # return self.__dict__[attr] # # #
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1.961905
420
import unittest from bgmi.lib.constants import BANGUMI_UPDATE_TIME from bgmi.lib.controllers import ( add, cal, delete, mark, recreate_source_relatively_table, search, ) from bgmi.main import setup
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2.461538
91
from PIL import Image, ImageFilter import random # This library only words with the assumption that the dataset has been formatted as 0.jpg, 1.jpg ... or 0.png, 1.png ... accordingly
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3.914894
47
import os import time import pandas as pd FETCH_URL = "https://poloniex.com/public?command=returnChartData&currencyPair=%s&start=%d&end=%d&period=300" #PAIR_LIST = ["BTC_ETH"] DATA_DIR = "data" COLUMNS = ["date","high","low","open","close","volume","quoteVolume","weightedAverage"] if __name__ == '__main__': main()
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2.626016
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from hearthbreaker.constants import CHARACTER_CLASS, CARD_RARITY from hearthbreaker.game_objects import WeaponCard, Weapon
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# Copyright 2022 Touca, Inc. Subject to Apache-2.0 License. from sys import stderr, stdout from pathlib import Path from argparse import ArgumentParser from loguru import logger from touca.cli._common import Operation
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import pygame import moves from typing import List from pieces.king import King import copy SIZE = (1000, 800) SQUARE_WIDTH = int(0.8 * SIZE[0] // 8) SQUARE_HEIGHT = SIZE[1] // 8 IMAGES = {} pygame.init() screen = pygame.display.set_mode(SIZE) move_feed = [] running = True board_array = [ ['Br', 'Bn', 'Bb', 'Bq', 'Bk', 'Bb', 'Bn', 'Br'], ['Bp', 'Bp', 'Bp', 'Bp', 'Bp', 'Bp', 'Bp', 'Bp'], ['--', '--', '--', '--', '--', '--', '--', '--'], ['--', '--', '--', '--', '--', '--', '--', '--'], ['--', '--', '--', '--', '--', '--', '--', '--'], ['--', '--', '--', '--', '--', '--', '--', '--'], ['Wp', 'Wp', 'Wp', 'Wp', 'Wp', 'Wp', 'Wp', 'Wp'], ['Wr', 'Wn', 'Wb', 'Wq', 'Wk', 'Wb', 'Wn', 'Wr'] ] count = 0 load_images() draw_board() draw_pieces() draw_sidebar() pygame.display.update() last_color_moved = 'B' while running: for event in pygame.event.get(): if event.type == pygame.QUIT: running = False if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1: if count == 0: initial_pos = event.pos if (last_color_moved == 'B' and get_piece_color(initial_pos) == 'W') or ( last_color_moved == 'W' and get_piece_color(initial_pos) == 'B'): count += 1 draw_board() highlight_square(initial_pos) draw_pieces() elif count == 1: ending_pos = event.pos count = 0 if color := handle_move(initial_pos, ending_pos): last_color_moved = color draw_board() draw_pieces() pygame.display.update() pygame.quit()
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# ПРИМЕР ПОЛУЧЕНИЯ И УКАЗАНИЯ ТИПА ЛИНИИ ТРАССЫ: # Тип линии, равно как и калибровочные значения, # хранятся в энергонезависимой памяти модуля. from time import sleep # Подключаем библиотеку для работы с бампером I2C-flash. from pyiArduinoI2Cbumper import * # Объявляем объект bum для работы с функциями и методами # библиотеки pyiArduinoI2Cbumper, указывая адрес модуля на шине I2C. # Если объявить объект без указания адреса bum = pyiArduinoI2Cbumper(), # то адрес будет найден автоматически. bum = pyiArduinoI2Cbumper(0x09) while True: # ОПРЕДЕЛЯЕМ ИСПОЛЬЗУЕМЫЙ ТИП ЛИНИИ: if bum.getLineType() == BUM_LINE_BLACK: first = "тёмной" second = "светлой" elif bum.getLineType() == BUM_LINE_WHITE: first = "светлой" second = "тёмной" t = "Модуль использовал трассу с {} линией"\ ", а теперь использует трассу"\ "с {} линией".format(first, second) print(t) # УКАЗЫВАЕМ НОВЫЙ ТИП ЛИНИИ: # Тип линии задаётся как BUM_LINE_BLACK - тёмная # BUM_LINE_WHITE - светлая # BUM_LINE_CHANGE - сменить тип линии. bum.setLineType(BUM_LINE_CHANGE) sleep(2)
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# Generated by Django 2.2.6 on 2019-11-12 17:18 from django.db import migrations
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# -*- coding:utf-8 -*- """ This script is used to build a qa data for usage. Typically, each enty contains three elements: a question, an answer, a url """ import sys import re import os import jieba import gensim try: import cPickle as pickle except: import pickle reload(sys) sys.setdefaultencoding('utf-8') def filtering_line(line_content, stopwords_list): ''' this function spams the noisy symbols, then cut the line to words and remove the stopwords in each line :param line_content: :return: ''' multi_version = re.compile(ur'-\{.*?(zh-hans|zh-cn):([^;]*?)(;.*?)?\}-') # punctuation = re.compile(u"[-~!@#$%^&*()_+`=\[\]\\\{\}\\t\\r\"|;':,./<>?·!@#¥%……&*()——+【】、;‘:“”,。、《》?「『」』]") punctuation = re.compile(u"[\[\]\\\{\}\\t\\r\"|;',<>?·!@#¥%……&*()——+【】、;‘:“”,。、《》?「『」』]") line_content = multi_version.sub(ur'\2', line_content) line_content = punctuation.sub('', line_content.decode('utf8')) # cut the line content to words line_content_cut = [w for w in jieba.cut(line_content)] if stopwords_list is not None: new_line = [] for word in line_content_cut: if word not in stopwords_list: new_line.append(word) return new_line else: return line_content_cut def load_qa_education(data_dir, education_file): ''' load the eudcation file, return a list, with each element is a string in each line ''' education_content = [] idx = 0 with open(os.path.join(data_dir, education_file)) as fid: for item in fid: education_content.append(item.strip('\n')) idx = idx + 1 # if idx % 1000 == 0: # print 'loading %d-th questions done!' % idx return education_content def load_qa_education_with_answer(data_dir, education_file): ''' load the eudcation file, return a list, with each element is a string in each line ''' education_content = [] answer_content = [] idx = 0 with open(os.path.join(data_dir, education_file)) as fid: for item in fid: if idx % 2 == 0: # questions education_content.append(item.strip('\n')) elif idx % 2 == 1: # answer answer_content.append(item.strip('\n')) idx = idx + 1 # if idx % 1000 == 0: # print 'loading %d-th questions done!' % idx print 'loading %d questions done!' % int(idx/2) return education_content, answer_content def load_stopwords_file(data_dir, stopwords_file): ''' load the stopwords file, return a list, with each element is a string in each line ''' stopwords_list = [] idx = 0 with open(os.path.join(data_dir, stopwords_file)) as fid: for item in fid: stopwords_list.append(item.strip('\n')) idx = idx + 1 print 'loading %d stopwords done!' % idx return stopwords_list def calculate_education_data(data_dir, education_content, stopwords_list): ''' this file is to calculate the dictionary, similarity matrix given a data.txt file :param data_dir: the root dir that save the returned data :param eudcation_content: a list that each element is a eudcation question :param stopwords_list: stopwords list for eudcation corpus :return: a dictionary, a simialrity matrix ''' corpora_documents_name = 'qa_education_corpora.pickle' if not os.path.exists(os.path.join(data_dir, corpora_documents_name)): corpora_documents = [] idx = 0 for item_text in education_content: item_str = filtering_line(item_text, stopwords_list) corpora_documents.append(item_str) idx = idx + 1 if idx % 1000 == 0: print 'jieba cutting for %d-th sentence' % idx # dump pickfile fid_corpora = open(os.path.join(data_dir, corpora_documents_name), 'wb') pickle.dump(corpora_documents, fid_corpora) fid_corpora.close() print 'save %s finished' % corpora_documents_name else: # load pickfile fid_corpora = open(os.path.join(data_dir, corpora_documents_name), 'rb') corpora_documents = pickle.load(fid_corpora) fid_corpora.close() print 'load %s finished' % corpora_documents_name dict_name = 'dict_education' # 生成字典和向量语料 if not os.path.exists(os.path.join(data_dir, dict_name)): print 'calculating dictionary education !' dictionary = gensim.corpora.Dictionary(corpora_documents) dictionary.save(os.path.join(data_dir, dict_name)) else: print 'dictionary for education already exists, load it!' dictionary = gensim.corpora.Dictionary.load(os.path.join(data_dir, dict_name)) corpus = [dictionary.doc2bow(text) for text in corpora_documents] numSen = len(corpus) # calculate the similarity for pairwise training samples num_features = len(dictionary.keys()) print '%d words in dictionary' % num_features # # save object sim_name = 'sim_education' if not os.path.exists(os.path.join(data_dir, sim_name)): print 'calculating sim_education !' similarity = gensim.similarities.Similarity(os.path.join(data_dir, sim_name), corpus, num_features) similarity.save(os.path.join(data_dir, sim_name)) else: print 'sim_eudcation already exists, load it!' similarity = gensim.similarities.Similarity.load(os.path.join(data_dir, sim_name)) return dictionary, similarity def calculate_education_data_w2v(data_dir, education_content, w2v_model, stopwords_list): ''' this file is to calculate the dictionary, similarity matrix given a data.txt file :param data_dir: the root dir that save the returned data :param eudcation_content: a list that each element is a eudcation question :param stopwords_list: stopwords list for eudcation corpus :return: a dictionary, a simialrity matrix ''' corpora_documents = [] idx = 0 for item_text in education_content: item_str = filtering_line(item_text, stopwords_list) corpora_documents.append(item_str) idx = idx + 1 if idx % 1000 == 10: print 'jieba cutting for %d-th sentence' % idx # corpus = [text for text in corpora_documents] corpus = corpora_documents numSen = len(corpus) # calculate the similarity for pairwise training samples # # save object sim_name = 'sim_education_w2v' if not os.path.exists(os.path.join(data_dir, sim_name)): print 'calculating sim_education !' similarity = gensim.similarities.WmdSimilarity(corpus, w2v_model, num_best=3) similarity.save(os.path.join(data_dir, sim_name)) else: print 'sim_eudcation already exists, load it!' similarity = gensim.similarities.WmdSimilarity.load(os.path.join(data_dir, sim_name)) return similarity ''' 测试的问题: 北京小升初的政策? 成都比较好的小学推荐 小孩子谈恋爱怎么办? 怎么提高小孩子英语学习? 北京好的幼儿园推荐 中考前饮食应该注意什么? 我家小孩上课注意力不集中,贪玩,怎么办? 小孩子在学校打架,怎么办? 成都龙江路小学划片么? 小孩子厌学怎么办? 孩子上课注意力不集中,贪玩怎么办? 武汉比较好的中学有哪些? 幼儿园学前教育有必要吗? ''' if __name__ == '__main__': # load the eudcation data data_dir = './qa_dataset' qa_education_file = 'qa_education.txt' # education_content = load_qa_education(data_dir, qa_education_file) education_content, answer_content = load_qa_education_with_answer(data_dir, qa_education_file) # use jieba to cut the sentence in each line with stopwords stopwords_file = 'stopwords_gaokao.txt' stopwords_dir = './stopwords_cn' stopwords_list = load_stopwords_file(stopwords_dir, stopwords_file) # caluclate the dictionary and the similarity of the given corpus dictionary, similarity = calculate_education_data(data_dir, education_content, stopwords_list) print 'obtained the dictionary and similarity of the %s corpus!' % qa_education_file similarity.num_best = 3 while(True): print '欢迎来到小题博士-教育问答 @_@' print '你可以咨询与中小学教育相关的问题,比如:' print ' 北京好的幼儿园推荐? \n 中考前饮食应该注意什么?\n 我家小孩上课注意力不集中,贪玩,怎么办? \n 小孩子在学校打架,怎么办?' print '################################' print '' input_query = raw_input(u'请输入你要问的问题:') input_query_cut = filtering_line(input_query, stopwords_list) # parse the input query, get its doc vector doc_input_query = dictionary.doc2bow(input_query_cut) res = similarity[doc_input_query] print '这是你要问的问题吗?' for idx, content in res: print '%d, %s' % (idx, education_content[idx]) print '%s' % answer_content[idx] print '################################' print '请问下一个问题 @_@' ''' # caluclate the dictionary and the similarity using walking-earth similarity measure of the given corpus # load wiki model wiki_model_file = './tempfile/out_w2v_qa_incremental.model' wiki_model = gensim.models.Word2Vec.load(wiki_model_file) similarity = calculate_education_data_w2v(data_dir, education_content, wiki_model, stopwords_list) print 'obtained the dictionary and similarity of the %s corpus!' % qa_education_file num_best = 3 while (True): print '欢迎来到小题博士-教育问答 @_@' input_query = raw_input(u'请输入你要问的问题:') input_query_cut = filtering_line(input_query, stopwords_list) res = similarity[input_query_cut] print '这是你要问的问题吗?' for idx, content in res: print '%d, %s' % (idx, education_content[idx]) print '################################' print '请问下一个问题 @_@' '''
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2.103486
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# -*- coding: utf-8 -*- from south.db import db from south.v2 import SchemaMigration
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2.432432
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from .bilibili.biliAudio import * from .bilibili.biliVideo import * from .bilibili.biliLive import * from .wenku8.Wenku8TXT import * from .video.imomoe import * from .video.kakadm import * from .audio.netease import *
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2.679012
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import ast import csv import json from absl import flags import numpy as np import pandas as pd FLAGS = flags.FLAGS def get_timestamps_with_obstacles(filename, obstacle_distance_threshold=10): """Finds timestamps when we detected obstacles.""" print(filename) df = pd.read_csv( filename, names=["timestamp", "ms", "log_label", "label_info", "label_value"]) df = df.dropna() df['label_value'] = df['label_value'].str.replace(" ", ", ") df['label_value'] = df['label_value'].apply(converter) obstacles = df[df['log_label'] == 'obstacle'] obstacles = obstacles.set_index('ms') pose = df[df['log_label'] == 'pose'] timestamps = [] first_timestamp = df["ms"].min() for t, p in pose[["ms", "label_value"]].values: if t not in obstacles.index: continue obs = obstacles.loc[t]['label_value'] if isinstance(obs, list): obs = [obs] else: obs = obs.values for o in obs: dist = np.linalg.norm(np.array(p) - np.array(o)) if 0 < dist <= obstacle_distance_threshold: timestamps.append(t - first_timestamp) print("Selected {} timestamps".format(len(timestamps))) return timestamps
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2.306715
551
# encoding: UTF-8 from leetcode import * from typing import Generator, Tuple @Problem(3, 'Longest Substring Without Repeating Characters', Difficulty.Medium, Tags.HashTable, Tags.String, Tags.TwoPointers) @Solution.test.lengthOfLongestSubstring @Solution.test.lengthOfLongestSubstring @Solution.test.lengthOfLongestSubstring
[ 2, 21004, 25, 41002, 12, 23, 198, 198, 6738, 443, 316, 8189, 1330, 1635, 198, 6738, 19720, 1330, 35986, 11, 309, 29291, 628, 198, 31, 40781, 7, 18, 11, 705, 14617, 395, 3834, 8841, 9170, 30558, 803, 26813, 3256, 27419, 13, 31205, 11, 44789, 13, 26257, 10962, 11, 44789, 13, 10100, 11, 44789, 13, 7571, 18833, 20193, 8, 628, 198, 31, 46344, 13, 9288, 13, 13664, 5189, 14617, 395, 7004, 8841, 628, 198, 31, 46344, 13, 9288, 13, 13664, 5189, 14617, 395, 7004, 8841, 628, 198, 31, 46344, 13, 9288, 13, 13664, 5189, 14617, 395, 7004, 8841, 198 ]
3.408163
98
#!/usr/bin/env python3
[ 2, 48443, 14629, 14, 8800, 14, 24330, 21015, 18, 628 ]
2.4
10
EMOJI_LIST = [ ':1st_place_medal:', ':2nd_place_medal:', ':3rd_place_medal:', ':AB_button_(blood_type):', ':ATM_sign:', ':A_button_(blood_type):', ':Afghanistan:', ':Albania:', ':Algeria:', ':American_Samoa:', ':Andorra:', ':Angola:', ':Anguilla:', ':Antarctica:', ':Antigua_&_Barbuda:', ':Aquarius:', ':Argentina:', ':Aries:', ':Armenia:', ':Aruba:', ':Ascension_Island:', ':Australia:', ':Austria:', ':Azerbaijan:', ':BACK_arrow:', ':B_button_(blood_type):', ':Bahamas:', ':Bahrain:', ':Bangladesh:', ':Barbados:', ':Belarus:', ':Belgium:', ':Belize:', ':Benin:', ':Bermuda:', ':Bhutan:', ':Bolivia:', ':Bosnia_&_Herzegovina:', ':Botswana:', ':Bouvet_Island:', ':Brazil:', ':British_Indian_Ocean_Territory:', ':British_Virgin_Islands:', ':Brunei:', ':Bulgaria:', ':Burkina_Faso:', ':Burundi:', ':CL_button:', ':COOL_button:', ':Cambodia:', ':Cameroon:', ':Canada:', ':Canary_Islands:', ':Cancer:', ':Cape_Verde:', ':Capricorn:', ':Caribbean_Netherlands:', ':Cayman_Islands:', ':Central_African_Republic:', ':Ceuta_&_Melilla:', ':Chad:', ':Chile:', ':China:', ':Christmas_Island:', ':Christmas_tree:', ':Clipperton_Island:', ':Cocos_(Keeling)_Islands:', ':Colombia:', ':Comoros:', ':Congo_-_Brazzaville:', ':Congo_-_Kinshasa:', ':Cook_Islands:', ':Costa_Rica:', ':Croatia:', ':Cuba:', ':Curaçao:', ':Cyprus:', ':Czech_Republic:', ':Côte_d’Ivoire:', ':Denmark:', ':Diego_Garcia:', ':Djibouti:', ':Dominica:', ':Dominican_Republic:', ':END_arrow:', ':Ecuador:', ':Egypt:', ':El_Salvador:', ':Equatorial_Guinea:', ':Eritrea:', ':Estonia:', ':Ethiopia:', ':European_Union:', ':FREE_button:', ':Falkland_Islands:', ':Faroe_Islands:', ':Fiji:', ':Finland:', ':France:', ':French_Guiana:', ':French_Polynesia:', ':French_Southern_Territories:', ':Gabon:', ':Gambia:', ':Gemini:', ':Georgia:', ':Germany:', ':Ghana:', ':Gibraltar:', ':Greece:', ':Greenland:', ':Grenada:', ':Guadeloupe:', ':Guam:', ':Guatemala:', ':Guernsey:', ':Guinea:', ':Guinea-Bissau:', ':Guyana:', ':Haiti:', ':Heard_&_McDonald_Islands:', ':Honduras:', ':Hong_Kong_SAR_China:', ':Hungary:', ':ID_button:', ':Iceland:', ':India:', ':Indonesia:', ':Iran:', ':Iraq:', ':Ireland:', ':Isle_of_Man:', ':Israel:', ':Italy:', ':Jamaica:', ':Japan:', ':Japanese_acceptable_button:', ':Japanese_application_button:', ':Japanese_bargain_button:', ':Japanese_castle:', ':Japanese_congratulations_button:', ':Japanese_discount_button:', ':Japanese_dolls:', ':Japanese_free_of_charge_button:', ':Japanese_here_button:', ':Japanese_monthly_amount_button:', ':Japanese_no_vacancy_button:', ':Japanese_not_free_of_charge_button:', ':Japanese_open_for_business_button:', ':Japanese_passing_grade_button:', ':Japanese_post_office:', ':Japanese_prohibited_button:', ':Japanese_reserved_button:', ':Japanese_secret_button:', ':Japanese_service_charge_button:', ':Japanese_symbol_for_beginner:', ':Japanese_vacancy_button:', ':Jersey:', ':Jordan:', ':Kazakhstan:', ':Kenya:', ':Kiribati:', ':Kosovo:', ':Kuwait:', ':Kyrgyzstan:', ':Laos:', ':Latvia:', ':Lebanon:', ':Leo:', ':Lesotho:', ':Liberia:', ':Libra:', ':Libya:', ':Liechtenstein:', ':Lithuania:', ':Luxembourg:', ':Macau_SAR_China:', ':Macedonia:', ':Madagascar:', ':Malawi:', ':Malaysia:', ':Maldives:', ':Mali:', ':Malta:', ':Marshall_Islands:', ':Martinique:', ':Mauritania:', ':Mauritius:', ':Mayotte:', ':Mexico:', ':Micronesia:', ':Moldova:', ':Monaco:', ':Mongolia:', ':Montenegro:', ':Montserrat:', ':Morocco:', ':Mozambique:', ':Mrs._Claus:', ':Mrs._Claus_dark_skin_tone:', ':Mrs._Claus_light_skin_tone:', ':Mrs._Claus_medium-dark_skin_tone:', ':Mrs._Claus_medium-light_skin_tone:', ':Mrs._Claus_medium_skin_tone:', ':Myanmar_(Burma):', ':NEW_button:', ':NG_button:', ':Namibia:', ':Nauru:', ':Nepal:', ':Netherlands:', ':New_Caledonia:', ':New_Zealand:', ':Nicaragua:', ':Niger:', ':Nigeria:', ':Niue:', ':Norfolk_Island:', ':North_Korea:', ':Northern_Mariana_Islands:', ':Norway:', ':OK_button:', ':OK_hand:', ':OK_hand_dark_skin_tone:', ':OK_hand_light_skin_tone:', ':OK_hand_medium-dark_skin_tone:', ':OK_hand_medium-light_skin_tone:', ':OK_hand_medium_skin_tone:', ':ON!_arrow:', ':O_button_(blood_type):', ':Oman:', ':Ophiuchus:', ':P_button:', ':Pakistan:', ':Palau:', ':Palestinian_Territories:', ':Panama:', ':Papua_New_Guinea:', ':Paraguay:', ':Peru:', ':Philippines:', ':Pisces:', ':Pitcairn_Islands:', ':Poland:', ':Portugal:', ':Puerto_Rico:', ':Qatar:', ':Romania:', ':Russia:', ':Rwanda:', ':Réunion:', ':SOON_arrow:', ':SOS_button:', ':Sagittarius:', ':Samoa:', ':San_Marino:', ':Santa_Claus:', ':Santa_Claus_dark_skin_tone:', ':Santa_Claus_light_skin_tone:', ':Santa_Claus_medium-dark_skin_tone:', ':Santa_Claus_medium-light_skin_tone:', ':Santa_Claus_medium_skin_tone:', ':Saudi_Arabia:', ':Scorpius:', ':Senegal:', ':Serbia:', ':Seychelles:', ':Sierra_Leone:', ':Singapore:', ':Sint_Maarten:', ':Slovakia:', ':Slovenia:', ':Solomon_Islands:', ':Somalia:', ':South_Africa:', ':South_Georgia_&_South_Sandwich_Islands:', ':South_Korea:', ':South_Sudan:', ':Spain:', ':Sri_Lanka:', ':St._Barthélemy:', ':St._Helena:', ':St._Kitts_&_Nevis:', ':St._Lucia:', ':St._Martin:', ':St._Pierre_&_Miquelon:', ':St._Vincent_&_Grenadines:', ':Statue_of_Liberty:', ':Sudan:', ':Suriname:', ':Svalbard_&_Jan_Mayen:', ':Swaziland:', ':Sweden:', ':Switzerland:', ':Syria:', ':São_Tomé_&_Príncipe:', ':TOP_arrow:', ':Taiwan:', ':Tajikistan:', ':Tanzania:', ':Taurus:', ':Thailand:', ':Timor-Leste:', ':Togo:', ':Tokelau:', ':Tokyo_tower:', ':Tonga:', ':Trinidad_&_Tobago:', ':Tristan_da_Cunha:', ':Tunisia:', ':Turkey:', ':Turkmenistan:', ':Turks_&_Caicos_Islands:', ':Tuvalu:', ':U.S._Outlying_Islands:', ':U.S._Virgin_Islands:', ':UP!_button:', ':Uganda:', ':Ukraine:', ':United_Arab_Emirates:', ':United_Kingdom:', ':United_Nations:', ':United_States:', ':Uruguay:', ':Uzbekistan:', ':VS_button:', ':Vanuatu:', ':Vatican_City:', ':Venezuela:', ':Vietnam:', ':Virgo:', ':Wallis_&_Futuna:', ':Western_Sahara:', ':Yemen:', ':Zambia:', ':Zimbabwe:', ':admission_tickets:', ':aerial_tramway:', ':airplane:', ':airplane_arrival:', ':airplane_departure:', ':alarm_clock:', ':alembic:', ':alien:', ':alien_monster:', ':ambulance:', ':american_football:', ':amphora:', ':anchor:', ':anger_symbol:', ':angry_face:', ':angry_face_with_horns:', ':anguished_face:', ':ant:', ':antenna_bars:', ':anticlockwise_arrows_button:', ':articulated_lorry:', ':artist_palette:', ':astonished_face:', ':atom_symbol:', ':automobile:', ':avocado:', ':baby:', ':baby_angel:', ':baby_angel_dark_skin_tone:', ':baby_angel_light_skin_tone:', ':baby_angel_medium-dark_skin_tone:', ':baby_angel_medium-light_skin_tone:', ':baby_angel_medium_skin_tone:', ':baby_bottle:', ':baby_chick:', ':baby_dark_skin_tone:', ':baby_light_skin_tone:', ':baby_medium-dark_skin_tone:', ':baby_medium-light_skin_tone:', ':baby_medium_skin_tone:', ':baby_symbol:', ':backhand_index_pointing_down:', ':backhand_index_pointing_down_dark_skin_tone:', ':backhand_index_pointing_down_light_skin_tone:', ':backhand_index_pointing_down_medium-dark_skin_tone:', ':backhand_index_pointing_down_medium-light_skin_tone:', ':backhand_index_pointing_down_medium_skin_tone:', ':backhand_index_pointing_left:', ':backhand_index_pointing_left_dark_skin_tone:', ':backhand_index_pointing_left_light_skin_tone:', ':backhand_index_pointing_left_medium-dark_skin_tone:', ':backhand_index_pointing_left_medium-light_skin_tone:', ':backhand_index_pointing_left_medium_skin_tone:', ':backhand_index_pointing_right:', ':backhand_index_pointing_right_dark_skin_tone:', ':backhand_index_pointing_right_light_skin_tone:', ':backhand_index_pointing_right_medium-dark_skin_tone:', ':backhand_index_pointing_right_medium-light_skin_tone:', ':backhand_index_pointing_right_medium_skin_tone:', ':backhand_index_pointing_up:', ':backhand_index_pointing_up_dark_skin_tone:', ':backhand_index_pointing_up_light_skin_tone:', ':backhand_index_pointing_up_medium-dark_skin_tone:', ':backhand_index_pointing_up_medium-light_skin_tone:', ':backhand_index_pointing_up_medium_skin_tone:', ':bacon:', ':badminton:', ':baggage_claim:', ':baguette_bread:', ':balance_scale:', ':balloon:', ':ballot_box_with_ballot:', ':ballot_box_with_check:', ':banana:', ':bank:', ':bar_chart:', ':barber_pole:', ':baseball:', ':basketball:', ':bat:', ':bathtub:', ':battery:', ':beach_with_umbrella:', ':bear_face:', ':beating_heart:', ':bed:', ':beer_mug:', ':bell:', ':bell_with_slash:', ':bellhop_bell:', ':bento_box:', ':bicycle:', ':bikini:', ':biohazard:', ':bird:', ':birthday_cake:', ':black_circle:', ':black_flag:', ':black_heart:', ':black_large_square:', ':black_medium-small_square:', ':black_medium_square:', ':black_nib:', ':black_small_square:', ':black_square_button:', ':blond-haired_man:', ':blond-haired_man_dark_skin_tone:', ':blond-haired_man_light_skin_tone:', ':blond-haired_man_medium-dark_skin_tone:', ':blond-haired_man_medium-light_skin_tone:', ':blond-haired_man_medium_skin_tone:', ':blond-haired_person:', ':blond-haired_person_dark_skin_tone:', ':blond-haired_person_light_skin_tone:', ':blond-haired_person_medium-dark_skin_tone:', ':blond-haired_person_medium-light_skin_tone:', ':blond-haired_person_medium_skin_tone:', ':blond-haired_woman:', ':blond-haired_woman_dark_skin_tone:', ':blond-haired_woman_light_skin_tone:', ':blond-haired_woman_medium-dark_skin_tone:', ':blond-haired_woman_medium-light_skin_tone:', ':blond-haired_woman_medium_skin_tone:', ':blossom:', ':blowfish:', ':blue_book:', ':blue_circle:', ':blue_heart:', ':boar:', ':bomb:', ':bookmark:', ':bookmark_tabs:', ':books:', ':bottle_with_popping_cork:', ':bouquet:', ':bow_and_arrow:', ':bowling:', ':boxing_glove:', ':boy:', ':boy_dark_skin_tone:', ':boy_light_skin_tone:', ':boy_medium-dark_skin_tone:', ':boy_medium-light_skin_tone:', ':boy_medium_skin_tone:', ':bread:', ':bride_with_veil:', ':bride_with_veil_dark_skin_tone:', ':bride_with_veil_light_skin_tone:', ':bride_with_veil_medium-dark_skin_tone:', ':bride_with_veil_medium-light_skin_tone:', ':bride_with_veil_medium_skin_tone:', ':bridge_at_night:', ':briefcase:', ':bright_button:', ':broken_heart:', ':bug:', ':building_construction:', ':burrito:', ':bus:', ':bus_stop:', ':bust_in_silhouette:', ':busts_in_silhouette:', ':butterfly:', ':cactus:', ':calendar:', ':call_me_hand:', ':call_me_hand_dark_skin_tone:', ':call_me_hand_light_skin_tone:', ':call_me_hand_medium-dark_skin_tone:', ':call_me_hand_medium-light_skin_tone:', ':call_me_hand_medium_skin_tone:', ':camel:', ':camera:', ':camera_with_flash:', ':camping:', ':candle:', ':candy:', ':canoe:', ':card_file_box:', ':card_index:', ':card_index_dividers:', ':carousel_horse:', ':carp_streamer:', ':carrot:', ':castle:', ':cat:', ':cat_face:', ':cat_face_with_tears_of_joy:', ':cat_face_with_wry_smile:', ':chains:', ':chart_decreasing:', ':chart_increasing:', ':chart_increasing_with_yen:', ':cheese_wedge:', ':chequered_flag:', ':cherries:', ':cherry_blossom:', ':chestnut:', ':chicken:', ':children_crossing:', ':chipmunk:', ':chocolate_bar:', ':church:', ':cigarette:', ':cinema:', ':circled_M:', ':circus_tent:', ':cityscape:', ':cityscape_at_dusk:', ':clamp:', ':clapper_board:', ':clapping_hands:', ':clapping_hands_dark_skin_tone:', ':clapping_hands_light_skin_tone:', ':clapping_hands_medium-dark_skin_tone:', ':clapping_hands_medium-light_skin_tone:', ':clapping_hands_medium_skin_tone:', ':classical_building:', ':clinking_beer_mugs:', ':clinking_glasses:', ':clipboard:', ':clockwise_vertical_arrows:', ':closed_book:', ':closed_mailbox_with_lowered_flag:', ':closed_mailbox_with_raised_flag:', ':closed_umbrella:', ':cloud:', ':cloud_with_lightning:', ':cloud_with_lightning_and_rain:', ':cloud_with_rain:', ':cloud_with_snow:', ':clown_face:', ':club_suit:', ':clutch_bag:', ':cocktail_glass:', ':coffin:', ':collision:', ':comet:', ':computer_disk:', ':computer_mouse:', ':confetti_ball:', ':confounded_face:', ':confused_face:', ':construction:', ':construction_worker:', ':construction_worker_dark_skin_tone:', ':construction_worker_light_skin_tone:', ':construction_worker_medium-dark_skin_tone:', ':construction_worker_medium-light_skin_tone:', ':construction_worker_medium_skin_tone:', ':control_knobs:', ':convenience_store:', ':cooked_rice:', ':cookie:', ':cooking:', ':copyright:', ':couch_and_lamp:', ':couple_with_heart:', ':couple_with_heart_man_man:', ':couple_with_heart_woman_man:', ':couple_with_heart_woman_woman:', ':cow:', ':cow_face:', ':cowboy_hat_face:', ':crab:', ':crayon:', ':credit_card:', ':crescent_moon:', ':cricket:', ':crocodile:', ':croissant:', ':cross_mark:', ':cross_mark_button:', ':crossed_fingers:', ':crossed_fingers_dark_skin_tone:', ':crossed_fingers_light_skin_tone:', ':crossed_fingers_medium-dark_skin_tone:', ':crossed_fingers_medium-light_skin_tone:', ':crossed_fingers_medium_skin_tone:', ':crossed_flags:', ':crossed_swords:', ':crown:', ':crying_cat_face:', ':crying_face:', ':crystal_ball:', ':cucumber:', ':curly_loop:', ':currency_exchange:', ':curry_rice:', ':custard:', ':customs:', ':cyclone:', ':dagger:', ':dango:', ':dark_skin_tone:', ':dashing_away:', ':deciduous_tree:', ':deer:', ':delivery_truck:', ':department_store:', ':derelict_house:', ':desert:', ':desert_island:', ':desktop_computer:', ':detective:', ':detective_dark_skin_tone:', ':detective_light_skin_tone:', ':detective_medium-dark_skin_tone:', ':detective_medium-light_skin_tone:', ':detective_medium_skin_tone:', ':diamond_suit:', ':diamond_with_a_dot:', ':dim_button:', ':direct_hit:', ':disappointed_but_relieved_face:', ':disappointed_face:', ':dizzy:', ':dizzy_face:', ':dog:', ':dog_face:', ':dollar_banknote:', ':dolphin:', ':door:', ':dotted_six-pointed_star:', ':double_curly_loop:', ':double_exclamation_mark:', ':doughnut:', ':dove:', ':down-left_arrow:', ':down-right_arrow:', ':down_arrow:', ':down_button:', ':dragon:', ':dragon_face:', ':dress:', ':drooling_face:', ':droplet:', ':drum:', ':duck:', ':dvd:', ':e-mail:', ':eagle:', ':ear:', ':ear_dark_skin_tone:', ':ear_light_skin_tone:', ':ear_medium-dark_skin_tone:', ':ear_medium-light_skin_tone:', ':ear_medium_skin_tone:', ':ear_of_corn:', ':egg:', ':eggplant:', ':eight-pointed_star:', ':eight-spoked_asterisk:', ':eight-thirty:', ':eight_o’clock:', ':eject_button:', ':electric_plug:', ':elephant:', ':eleven-thirty:', ':eleven_o’clock:', ':envelope:', ':envelope_with_arrow:', ':euro_banknote:', ':evergreen_tree:', ':exclamation_mark:', ':exclamation_question_mark:', ':expressionless_face:', ':eye:', ':eye_in_speech_bubble:', ':eyes:', ':face_blowing_a_kiss:', ':face_savouring_delicious_food:', ':face_screaming_in_fear:', ':face_with_cold_sweat:', ':face_with_head-bandage:', ':face_with_medical_mask:', ':face_with_open_mouth:', ':face_with_open_mouth_&_cold_sweat:', ':face_with_rolling_eyes:', ':face_with_steam_from_nose:', ':face_with_stuck-out_tongue:', ':face_with_stuck-out_tongue_&_closed_eyes:', ':face_with_stuck-out_tongue_&_winking_eye:', ':face_with_tears_of_joy:', ':face_with_thermometer:', ':face_without_mouth:', ':factory:', ':fallen_leaf:', ':family:', ':family_man_boy:', ':family_man_boy_boy:', ':family_man_girl:', ':family_man_girl_boy:', ':family_man_girl_girl:', ':family_man_man_boy:', ':family_man_man_boy_boy:', ':family_man_man_girl:', ':family_man_man_girl_boy:', ':family_man_man_girl_girl:', ':family_man_woman_boy:', ':family_man_woman_boy_boy:', ':family_man_woman_girl:', ':family_man_woman_girl_boy:', ':family_man_woman_girl_girl:', ':family_woman_boy:', ':family_woman_boy_boy:', ':family_woman_girl:', ':family_woman_girl_boy:', ':family_woman_girl_girl:', ':family_woman_woman_boy:', ':family_woman_woman_boy_boy:', ':family_woman_woman_girl:', ':family_woman_woman_girl_boy:', ':family_woman_woman_girl_girl:', ':fast-forward_button:', ':fast_down_button:', ':fast_reverse_button:', ':fast_up_button:', ':fax_machine:', ':fearful_face:', ':female_sign:', ':ferris_wheel:', ':ferry:', ':field_hockey:', ':file_cabinet:', ':file_folder:', ':film_frames:', ':film_projector:', ':fire:', ':fire_engine:', ':fireworks:', ':first_quarter_moon:', ':first_quarter_moon_with_face:', ':fish:', ':fish_cake_with_swirl:', ':fishing_pole:', ':five-thirty:', ':five_o’clock:', ':flag_in_hole:', ':flashlight:', ':fleur-de-lis:', ':flexed_biceps:', ':flexed_biceps_dark_skin_tone:', ':flexed_biceps_light_skin_tone:', ':flexed_biceps_medium-dark_skin_tone:', ':flexed_biceps_medium-light_skin_tone:', ':flexed_biceps_medium_skin_tone:', ':floppy_disk:', ':flower_playing_cards:', ':flushed_face:', ':fog:', ':foggy:', ':folded_hands:', ':folded_hands_dark_skin_tone:', ':folded_hands_light_skin_tone:', ':folded_hands_medium-dark_skin_tone:', ':folded_hands_medium-light_skin_tone:', ':folded_hands_medium_skin_tone:', ':footprints:', ':fork_and_knife:', ':fork_and_knife_with_plate:', ':fountain:', ':fountain_pen:', ':four-thirty:', ':four_leaf_clover:', ':four_o’clock:', ':fox_face:', ':framed_picture:', ':french_fries:', ':fried_shrimp:', ':frog_face:', ':front-facing_baby_chick:', ':frowning_face:', ':frowning_face_with_open_mouth:', ':fuel_pump:', ':full_moon:', ':full_moon_with_face:', ':funeral_urn:', ':game_die:', ':gear:', ':gem_stone:', ':ghost:', ':girl:', ':girl_dark_skin_tone:', ':girl_light_skin_tone:', ':girl_medium-dark_skin_tone:', ':girl_medium-light_skin_tone:', ':girl_medium_skin_tone:', ':glass_of_milk:', ':glasses:', ':globe_showing_Americas:', ':globe_showing_Asia-Australia:', ':globe_showing_Europe-Africa:', ':globe_with_meridians:', ':glowing_star:', ':goal_net:', ':goat:', ':goblin:', ':gorilla:', ':graduation_cap:', ':grapes:', ':green_apple:', ':green_book:', ':green_heart:', ':green_salad:', ':grimacing_face:', ':grinning_cat_face_with_smiling_eyes:', ':grinning_face:', ':grinning_face_with_smiling_eyes:', ':growing_heart:', ':guard:', ':guard_dark_skin_tone:', ':guard_light_skin_tone:', ':guard_medium-dark_skin_tone:', ':guard_medium-light_skin_tone:', ':guard_medium_skin_tone:', ':guitar:', ':hamburger:', ':hammer:', ':hammer_and_pick:', ':hammer_and_wrench:', ':hamster_face:', ':handbag:', ':handshake:', ':hatching_chick:', ':headphone:', ':hear-no-evil_monkey:', ':heart_decoration:', ':heart_suit:', ':heart_with_arrow:', ':heart_with_ribbon:', ':heavy_check_mark:', ':heavy_division_sign:', ':heavy_dollar_sign:', ':heavy_heart_exclamation:', ':heavy_large_circle:', ':heavy_minus_sign:', ':heavy_multiplication_x:', ':heavy_plus_sign:', ':helicopter:', ':herb:', ':hibiscus:', ':high-heeled_shoe:', ':high-speed_train:', ':high-speed_train_with_bullet_nose:', ':high_voltage:', ':hole:', ':honey_pot:', ':honeybee:', ':horizontal_traffic_light:', ':horse:', ':horse_face:', ':horse_racing:', ':horse_racing_dark_skin_tone:', ':horse_racing_light_skin_tone:', ':horse_racing_medium-dark_skin_tone:', 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1.95234
51,930
from flask import Flask,render_template,request import requests app = Flask(__name__) API_KEY = 'RQM7GIDWT0ZU2WLU' @app.route('/',methods=['GET','POST']) if __name__ == "__main__": app.run(debug= False)
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2.265306
98
import base64 import os import random from io import BytesIO import matplotlib.font_manager as fm from PIL import Image, ImageDraw, ImageFont
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3.428571
42
from json import dumps from pathlib import Path from sqlite3 import connect from pycargr.model import Car DB_PATH = Path.home().joinpath('pycargr.db') SEARCH_BASE_URL = 'https://www.car.gr/classifieds/cars/'
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2.864865
74
from pathlib import Path import ast import pytest import astor import warnings import os from json_codegen import load_schema from json_codegen.generators.python3_marshmallow import Python3MarshmallowGenerator SCHEMAS_DIR = Path(__file__).parent / "fixtures" / "schemas" FIXTURES_DIR = Path(__file__).parent / "fixtures" / "python3_marshmallow" expected_init_py = astor.dump_tree(ast.Module(body=[])) test_params = sorted(pytest.param(f, id=f.name) for f in SCHEMAS_DIR.glob("*.schema.json")) @pytest.mark.parametrize("schema_filename", (test_params))
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2.805
200
# -*- coding: utf-8 -*- if __name__ == '__main__': main()
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1.775
40
# robofab manual # Glyphmath howto # Fun examples #FLM: Fun with GlyphMath # this example is meant to run with the RoboFab Demo Font # as the Current Font. So, if you're doing this in FontLab # import the Demo Font UFO first. from robofab.world import CurrentFont from random import random f = CurrentFont() condensedLight = f["a#condensed_light"] wideLight = f["a#wide_light"] wideBold = f["a#wide_bold"] diff = wideLight - condensedLight destination = f.newGlyph("a#deltaexperiment") destination.clear() x = wideBold + (condensedLight-wideLight)*random() destination.appendGlyph( x) destination.width = x.width f.update()
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2.967442
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""" Simple module for loading documentation of various pypy-cs from doc directory """ import py
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3.96
25
#!/usr/bin/env python ''' SST scheduler simulation input file generator Input parameters are given below Setting a parameter to "default" or "" will select the default option ''' import os # Input workload trace path: traceName = 'jobtrace_files/bisection_N1.sim' # Output file name: outFile = 'simple_libtopomap_bisection_N1.py' # Machine (cluster) configuration: # mesh[xdim, ydim, zdim], torus[xdim, ydim, zdim], simple, # dragonfly[routersPerGroup, portsPerRouter, opticalsPerRouter, # nodesPerRouter, localTopology, globalTopology] # localTopology:[all_to_all] # globalTopology:[absolute,circulant,relative] # (default: simple) machine = 'dragonfly[8,11,2,2,all_to_all,absolute]' # Number of machine nodes # The script calculates the number of nodes if mesh or torus machine is provided. # any integer. (default: 1) numberNodes = '' # Number of cores in each machine node # any integer. (default: 1) coresPerNode = '2' # Scheduler algorithm: # cons, delayed, easy, elc, pqueue, prioritize. (default: pqueue) scheduler = 'easy' # Fair start time algorithm: # none, relaxed, strict. (default: none) FST = '' # Allocation algorithm: # bestfit, constraint, energy, firstfit, genalg, granularmbs, hybrid, mbs, # mc1x1, mm, nearest, octetmbs, oldmc1x1,random, simple, sortedfreelist, # nearestamap, spectralamap. (default: simple) allocator = 'simple' # Task mapping algorithm: # simple, rcb, random, topo, rcm, nearestamap, spectralamap. (default: simple) taskMapper = 'topo' # Communication overhead parameters # a[b,c] (default: none) timeperdistance = '.001865[.1569,0.0129]' # Heat distribution matrix (D_matrix) input file # file path, none. (default: none) dMatrixFile = 'none' # Randomization seed for communication time overhead # none, any integer. (default: none) randomSeed = '' # Detailed network simulation mode # ON, OFF (default: OFF) detailedNetworkSim = 'ON' # Completed jobs trace (in ember) for detailed network sim mode # file path, none (default: none) completedJobsTrace = 'emberCompleted.txt' # Running jobs (in ember) for detailed network sim mode # file path, none (default: none) runningJobsTrace = 'emberRunning.txt' ''' Do not modify the script after this point. ''' import sys if __name__ == '__main__': if outFile == "" or outFile == "default": print "Error: There is no default value for outFile" sys.exit() f = open(outFile,'w') f.write('# scheduler simulation input file\n') f.write('import sst\n') f.write('\n') f.write('# Define SST core options\n') f.write('sst.setProgramOption("run-mode", "both")\n') f.write('\n') f.write('# Define the simulation components\n') f.write('scheduler = sst.Component("myScheduler", \ "scheduler.schedComponent")\n') f.write('scheduler.addParams({\n') if traceName == "" or traceName == "default": print "Error: There is no default value for traceName" os.remove(outFile) sys.exit() f.write(' "traceName" : "' + traceName + '",\n') if machine != "" and machine != "default": f.write(' "machine" : "' + machine + '",\n') if coresPerNode != "": f.write(' "coresPerNode" : "' + coresPerNode + '",\n') if scheduler != "" and scheduler != "default": f.write(' "scheduler" : "' + scheduler + '",\n') if FST != "" and FST != "default": f.write(' "FST" : "' + FST + '",\n') if allocator != "" and allocator != "default": f.write(' "allocator" : "' + allocator + '",\n') if taskMapper != "" and taskMapper != "default": f.write(' "taskMapper" : "' + taskMapper + '",\n') if timeperdistance != "" and timeperdistance != "default": f.write(' "timeperdistance" : "' + timeperdistance + '",\n') if dMatrixFile != "" and dMatrixFile != "default": f.write(' "dMatrixFile" : "' + dMatrixFile + '",\n') if randomSeed != "" and randomSeed != "default": f.write(' "runningTimeSeed" : "' + randomSeed + '",\n') if detailedNetworkSim != "" and detailedNetworkSim != "default": f.write(' "detailedNetworkSim" : "' + detailedNetworkSim + '",\n') if completedJobsTrace != "" and completedJobsTrace != "default": f.write(' "completedJobsTrace" : "' + completedJobsTrace + '",\n') if runningJobsTrace != "" and runningJobsTrace != "default": f.write(' "runningJobsTrace" : "' + runningJobsTrace + '",\n') f.seek(-2, os.SEEK_END) f.truncate() f.write('\n})\n') f.write('\n') f.write('# nodes\n') if machine.split('[')[0] == 'mesh' or machine.split('[')[0] == 'torus': nums = machine.split('[')[1] nums = nums.split(']')[0] nums = nums.split(',') numberNodes = int(nums[0])*int(nums[1])*int(nums[2]) elif machine.split('[')[0] == 'dragonfly': nums = machine.split('[')[1] nums = nums.split(']')[0] nums = nums.split(',') numberNodes = (int(nums[0])*int(nums[2])+1) *int(nums[0])*int(nums[3]) numberNodes = int(numberNodes) for i in range(0, numberNodes): f.write('n' + str(i) + ' = sst.Component("n' + str(i) + \ '", "scheduler.nodeComponent")\n') f.write('n' + str(i) + '.addParams({\n') f.write(' "nodeNum" : "' + str(i) + '",\n') f.write('})\n') f.write('\n') f.write('# define links\n') for i in range(0, numberNodes): f.write('l' + str(i) + ' = sst.Link("l' + str(i) + '")\n') f.write('l' + str(i) + '.connect( (scheduler, "nodeLink' + str(i) + \ '", "0 ns"), (n' + str(i) + ', "Scheduler", "0 ns") )\n') f.write('\n') f.close()
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2.41812
2,351
from jax import lax from jax.experimental import host_callback from tqdm.auto import tqdm def progress_bar_scan(num_samples, message=None): """ Progress bar for a JAX scan. """ if message is None: message = f"Running for {num_samples:,} iterations" tqdm_bars = {} if num_samples > 20: print_rate = int(num_samples / 20) else: print_rate = 1 remainder = num_samples % print_rate def _update_progress_bar(iter_num): """ Updates tqdm progress bar of a JAX scan or loop. """ _ = lax.cond( iter_num == 0, lambda _: host_callback.id_tap(_define_tqdm, None, result=iter_num), lambda _: iter_num, operand=None, ) _ = lax.cond( # update tqdm every multiple of `print_rate` except at the end (iter_num % print_rate == 0) & (iter_num != num_samples - remainder), lambda _: host_callback.id_tap(_update_tqdm, print_rate, result=iter_num), lambda _: iter_num, operand=None, ) _ = lax.cond( # update tqdm by `remainder` iter_num == num_samples - remainder, lambda _: host_callback.id_tap(_update_tqdm, remainder, result=iter_num), lambda _: iter_num, operand=None, ) def _progress_bar_scan(func): """ Decorator that adds a progress bar to `body_fun` used in `lax.scan`. Note that `body_fun` must either be looping over `np.arange(num_samples)`, or be looping over a tuple who's first element is `np.arange(num_samples)` This means that `iter_num` is the current iteration number """ return wrapper_progress_bar return _progress_bar_scan
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import logging from pathlib import Path
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import numpy as np from skimage.future import graph from skimage._shared.version_requirements import is_installed from skimage import segmentation import pytest @pytest.mark.skipif(not is_installed('networkx'), reason="networkx not installed") @pytest.mark.skipif(not is_installed('networkx'), reason="networkx not installed") @pytest.mark.skipif(not is_installed('networkx'), reason="networkx not installed") @pytest.mark.skipif(not is_installed('networkx'), reason="networkx not installed") @pytest.mark.skipif(not is_installed('networkx'), reason="networkx not installed") @pytest.mark.skipif(not is_installed('networkx'), reason="networkx not installed") def test_ncut_stable_subgraph(): """ Test to catch an error thrown when subgraph has all equal edges. """ img = np.zeros((100, 100, 3), dtype='uint8') labels = np.zeros((100, 100), dtype='uint8') labels[:50, :50] = 1 labels[:50, 50:] = 2 rag = graph.rag_mean_color(img, labels, mode='similarity') new_labels = graph.cut_normalized(labels, rag, in_place=False) new_labels, _, _ = segmentation.relabel_sequential(new_labels) assert new_labels.max() == 0
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from django.shortcuts import redirect, render, reverse from django.urls import reverse_lazy from django.contrib import messages from django.db.models import Case, CharField, Value, When from django.views.generic.base import TemplateView from django.views.generic import ListView from django.views.generic.edit import CreateView, UpdateView, DeleteView from unidecode import unidecode # normalize strings Csii from alunos.models import Aluno from alunos.forms import AlunoForm from turmas.models import Turma from accounts.models import CustomUser # Classes to control admin acess and success messages from base.base_admin_permissions import BaseAdminUsersAdSe # Constants Vars from base.constants import CURRENT_YEAR def create_user_after_registration( username, password, first_name, last_name, department): """ Create user after aluno registration """ CustomUser.objects.create_user( username=username, password=password, first_name=first_name, last_name=last_name, department=department ) def data_processing_user_creation(cpf, name_form, department): """ Processing data for user creation """ cpf_split_1 = cpf.split('.') cpf_split_2 = ''.join(cpf_split_1).split('-') cpf_join = ''.join(cpf_split_2) name_split = name_form.split() first_name = name_split[0] last_name = name_split[-1] password = f'{unidecode(first_name).lower()}{cpf_join[0:6]}' # Test if user already exists cpf_qs = CustomUser.objects.filter(username=cpf_join) if not cpf_qs: create_user_after_registration( cpf_join, password, first_name, last_name, department) # --- General views --- # # --- Admin views --- # # --- Lists views --- #
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# ***************************************************************************** # * Copyright 2019 Amazon.com, Inc. and its affiliates. All Rights Reserved. * # * # Licensed under the Amazon Software License (the "License"). * # You may not use this file except in compliance with the License. * # A copy of the License is located at * # * # http://aws.amazon.com/asl/ * # * # or in the "license" file accompanying this file. This file 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 tempfile import torch from torchvision import transforms from model_factory_service_locator import ModelFactoryServiceLocator class Predict: """ Runs predictions on a given model """
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import string from random import * characters = string.ascii_letters + string.punctuation + string.digits pswd = "".join(choice(characters) for x in range(randint(6, 14))) print pswd
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2.983871
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from typing import Any, Dict, Set from django.apps import AppConfig def required_users(element: Dict[str, Any]) -> Set[int]: """ Returns all user ids that are displayed as speaker in the given element. """ return set(speaker["user_id"] for speaker in element["speakers"])
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""" In this step the destination address is no longer node 2 -- we draw a random destination, and we'll add the destination address to the message. The best way is to subclass cMessage and add destination as a data member. To make the model execute longer, after a message arrives to its destination the destination node will generate another message with a random destination address, and so forth. """ from pyopp import cSimpleModule, cMessage, EV
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#!/usr/bin/env python3 """Set/get/remove client/port metadata.""" from pprint import pprint import jack client = jack.Client('Metadata-Client') port = client.inports.register('input') client.set_property(client, jack.METADATA_PRETTY_NAME, 'Best Client Ever') print('Client "pretty" name:', jack.get_property(client, jack.METADATA_PRETTY_NAME)) client.set_property( port, jack.METADATA_PRETTY_NAME, b'a good port', 'text/plain') print('Port "pretty" name:', jack.get_property(port, jack.METADATA_PRETTY_NAME)) print('All client properties:') pprint(jack.get_properties(client)) print('All port properties:') pprint(jack.get_properties(port)) print('All properties:') pprint(jack.get_all_properties()) client.remove_property(port, jack.METADATA_PRETTY_NAME) client.remove_properties(client) client.remove_all_properties()
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import csv from argparse import ArgumentParser, ArgumentTypeError from os import path from string import Template from subprocess import Popen from tempfile import NamedTemporaryFile import numpy as np import util # This import only works if the directory where "generate_trench.so" is located is present in # the PYTHONPATH environment variable #import generate_trench VIENNATS_EXE = "../../ViennaTools/ViennaTS/build/viennats-2.3.2" PROJECT_DIRECTORY = path.dirname(__file__) PROCESS_TIME = 10 DISTANCE_BITS = 8 OUTPUT_DIR = path.join(PROJECT_DIRECTORY, "output") parser = ArgumentParser( description="Run physical deposition simulations with different sticking probabilities.") parser.add_argument( "output", type=str, default="results.csv", nargs="?", help="output CSV file for saving the results") def check_list_input(x): """ Converts the input string to a list of floats. Only uses input elements with a value between 0 and 1.""" x = x.replace("[", "").replace("]", "").split(",") try: x = [float(i) for i in x] except ValueError as e: raise ArgumentTypeError(e) if np.all([0 < i <= 1 for i in x]): if len(x) == 0: raise ArgumentTypeError("No sticking probability values provided") return x else: raise ArgumentTypeError( "The sticking probability has to have a value between 0 and 1.") parser.add_argument( "--sticking-probabilities", dest="sticking_probabilities", type=check_list_input, default=[1/2**i for i in range(5)], help="list of sticking probabilities to be used during the simulation" ) parser.add_argument( "--repetitions", dest="repetitions", type=int, default=10, help="how often the simulation should be repeated for one set of parameters") if __name__ == "__main__": main()
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# File: N (Python 2.4) import random import types import string from direct.fsm import StateData from direct.fsm import ClassicFSM from direct.fsm import State from direct.gui import DirectGuiGlobals from direct.gui.DirectGui import * from direct.task import Task from pandac.PandaModules import * from pandac.PandaModules import TextEncoder from otp.namepanel import NameCheck from otp.otpbase import OTPLocalizer as OL from pirates.piratesbase import PLocalizer as PL from pirates.pirate import HumanDNA from pirates.piratesbase import PiratesGlobals from pirates.piratesgui import GuiButton from pirates.piratesgui import PiratesGuiGlobals from pirates.leveleditor import NPCList from pirates.makeapirate.PCPickANamePattern import PCPickANamePattern from direct.distributed.MsgTypes import * from direct.distributed import PyDatagram MAX_NAME_WIDTH = 9
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import argparse import config import utils from chat import ChatSession from utils import Color if __name__ == '__main__': main()
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from checkio.home.most_wanted_letter import checkio
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import copy from Reversi import Reversi from dqn_agent import DQNAgent if __name__ == "__main__": # parameters #n_epochs = 1000 n_epochs = 5 # environment, agent env = Reversi() # playerID playerID = [env.Black, env.White, env.Black] # player agent players = [] # player[0]= env.Black players.append(DQNAgent(env.enable_actions, env.name, env.screen_n_rows, env.screen_n_cols)) # player[1]= env.White players.append(DQNAgent(env.enable_actions, env.name, env.screen_n_rows, env.screen_n_cols)) for e in range(n_epochs): # reset env.reset() terminal = False while terminal == False: # 1エピソードが終わるまでループ for i in range(0, len(players)): state = env.screen #print(state) targets = env.get_enables(playerID[i]) exploration = (n_epochs - e + 20)/(n_epochs + 20) #exploration = 0.1 if len(targets) > 0: # どこかに置く場所がある場合 #すべての手をトレーニングする for tr in targets: tmp = copy.deepcopy(env) tmp.update(tr, playerID[i]) #終了判定 win = tmp.winner() end = tmp.isEnd() #次の状態 state_X = tmp.screen target_X = tmp.get_enables(playerID[i+1]) if len(target_X) == 0: target_X = tmp.get_enables(playerID[i]) # 両者トレーニング for j in range(0, len(players)): reword = 0 if end == True: if win == playerID[j]: # 勝ったら報酬1を得る reword = 1 players[j].store_experience(state, targets, tr, reword, state_X, target_X, end) #print(state) #print(state_X) #if e > n_epochs*0.2: # players[j].experience_replay() # 行動を選択 action = players[i].select_action(state, targets, exploration) # 行動を実行 env.update(action, playerID[i]) # for log loss = players[i].current_loss Q_max, Q_action = players[i].select_enable_action(state, targets) print("player:{:1d} | pos:{:2d} | LOSS: {:.4f} | Q_MAX: {:.4f} | Q_ACTION: {:.4f}".format( playerID[i], action, loss, Q_max, Q_action)) # 行動を実行した結果 terminal = env.isEnd() for j in range(0, len(players)): if e > n_epochs*0.3: for k in range(25): players[j].experience_replay() elif e > n_epochs*0.1: for k in range(5): players[j].experience_replay() w = env.winner() print("EPOCH: {:03d}/{:03d} | WIN: player{:1d}".format( e, n_epochs, w)) # 保存は後攻のplayer2 を保存する。 if e%50 == 0: players[1].save_model(e)
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