Reset23/the-stack-v2-python · Datasets at Hugging Face

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andy920262/ML2017

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import matplotlib matplotlib.use('Agg') import argparse from keras.models import load_model import keras.backend as K from keras.utils import * import numpy as np import matplotlib.pyplot as plt from data_process import load_data if __name__ == '__main__': model_path = ('best_model') emotion_classifier = load_model(model_path) private_pixels = load_data('test.csv', 'test') private_pixels = [x.reshape(1, 48, 48, 1) for x in private_pixels] input_img = emotion_classifier.input img_ids = [7122] for idx in img_ids: val_proba = emotion_classifier.predict(private_pixels[idx]) pred = val_proba.argmax(axis=-1) target = K.mean(emotion_classifier.output[:, pred]) grads = K.gradients(target, input_img)[0] fn = K.function([input_img, K.learning_phase()], [grads]) #print(fn([private_pixels[idx], True])) heatmap = fn([private_pixels[idx], False]) heatmap = np.array(heatmap).reshape(48, 48) ''' Implement your heatmap processing here! hint: Do some normalization or smoothening on grads ''' thres = np.mean(np.abs(heatmap)) see = private_pixels[idx].reshape(48, 48) #see[np.where(np.abs(heatmap) <= thres)] = np.mean(see) plt.figure() plt.imshow(heatmap, cmap=plt.cm.jet) plt.colorbar() plt.tight_layout() fig = plt.gcf() plt.draw() fig.savefig('heatmap{}.png'.format(idx)) plt.figure() plt.imshow(see,cmap='gray') plt.colorbar() plt.tight_layout() fig = plt.gcf() plt.draw() fig.savefig('see{}.png'.format(idx))

[ "920262@gmail.com" ]

920262@gmail.com

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/decisions/importer/open_ahjo.py

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# -*- coding: utf-8 -*- import json from django.conf import settings from decisions.models import ( Action, Attachment, Case, CaseGeometry, Content, DataSource, Event, Function, Organization, Post ) from .base import Importer class OpenAhjoImporter(Importer): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.data_source, created = DataSource.objects.get_or_create( identifier='open_ahjo', defaults={'name': 'Open Ahjo'} ) if created: self.logger.debug('Created new data source "open_ahjo"') self.meeting_to_org = None def _import_functions(self, data): self.logger.info('Importing functions...') for function_data in data['categories']: defaults = dict( name=function_data['name'], function_id=function_data['origin_id'], ) parent_id = function_data['parent'] if parent_id: try: defaults['parent'] = Function.objects.get(origin_id=parent_id) except Function.DoesNotExist: self.logger('Function parent %s does not exist' % parent_id) continue function, created = Function.objects.update_or_create( origin_id=function_data['id'], data_source=self.data_source, defaults=defaults ) if created: self.logger.info('Created function %s' % function) def _import_events(self, data): self.logger.info('Importing events...') for meeting_data in data['meetings']: defaults = dict( start_date=meeting_data['date'], end_date=meeting_data['date'], ) organization_data = self.meeting_to_org.get(meeting_data['id']) if organization_data: if organization_data['type'] == 'office_holder': continue try: organization = Organization.objects.get(origin_id=organization_data['origin_id']) defaults['organization'] = organization except Organization.DoesNotExist: self.logger.error('Organization %s does not exist' % organization_data['origin_id']) continue event, created = Event.objects.update_or_create( data_source=self.data_source, origin_id=meeting_data['id'], defaults=defaults ) if created: self.logger.info('Created event %s' % event) def _import_case_geometries(self, data): self.logger.info('Importing case geometries...') for geometry_data in data['issue_geometries']: defaults = dict( name=geometry_data['name'], type=geometry_data['type'], geometry=geometry_data['geometry'], ) case_geometry, created = CaseGeometry.objects.update_or_create( data_source=self.data_source, origin_id=geometry_data['id'], defaults=defaults, ) if created: self.logger.info('Created case geometry %s' % case_geometry) def _import_cases(self, data): self.logger.info('Importing cases...') for issue_data in data['issues']: defaults = dict( title=issue_data['subject'], register_id=issue_data['register_id'], ) try: defaults['function'] = Function.objects.get(origin_id=issue_data['category']) except Function.DoesNotExist: self.logger.error('Function %s does not exist' % issue_data['category']) continue case, created = Case.objects.update_or_create( data_source=self.data_source, origin_id=issue_data['id'], defaults=defaults, ) if created: self.logger.info('Created case %s' % case) case.geometries = CaseGeometry.objects.filter(origin_id__in=issue_data['geometries']) def _import_actions(self, data): self.logger.info('Importing actions...') for agenda_item_data in data['agenda_items']: org = self.meeting_to_org.get(agenda_item_data['meeting']) if not org: self.logger.error('Cannot find matching org for meeting %s' % agenda_item_data['meeting']) continue defaults = dict( title=agenda_item_data['subject'], ordering=agenda_item_data['index'], resolution=agenda_item_data['resolution'] or '', ) if agenda_item_data['issue']: try: case = Case.objects.get(origin_id=agenda_item_data['issue']) defaults['case'] = case except Case.DoesNotExist: self.logger.error('Case %s does not exist' % agenda_item_data['issue']) continue if org['type'] == 'office_holder': try: post = Post.objects.get(origin_id=org['origin_id']) defaults['post'] = post except Post.DoesNotExist: self.logger.error('Post %s does not exist' % org['origin_id']) continue else: try: event = Event.objects.get(origin_id=agenda_item_data['meeting']) defaults['event'] = event except Event.DoesNotExist: self.logger.error('Event %s does not exist' % agenda_item_data['meeting']) continue action, created = Action.objects.update_or_create( data_source=self.data_source, origin_id=agenda_item_data['id'], defaults=defaults ) if created: self.logger.info('Created action %s' % action) def _import_contents(self, data): self.logger.info('Importing contents...') for content_section_data in data['content_sections']: defaults = dict( hypertext=content_section_data['text'], type=content_section_data['type'], ordering=content_section_data['index'], ) action_id = content_section_data.get('agenda_item') try: action = Action.objects.get(origin_id=action_id) defaults['action'] = action except Action.DoesNotExist: self.logger.error('Action %s does not exist' % action_id) continue content, created = Content.objects.update_or_create( data_source=self.data_source, origin_id=content_section_data['id'], defaults=defaults ) if created: self.logger.info('Created content %s' % content) def _import_attachments(self, data): self.logger.info('Importing attachments...') url_base = getattr(settings, 'OPEN_AHJO_ATTACHMENT_URL_BASE', None) for attachment_data in data['attachments']: defaults = dict( name=attachment_data['name'] or '', url=url_base + attachment_data['url'] if attachment_data['url'] and url_base else '', number=attachment_data['number'], public=attachment_data['public'], confidentiality_reason=attachment_data['confidentiality_reason'] or '', ) action_id = attachment_data.get('agenda_item') try: action = Action.objects.get(origin_id=action_id) defaults['action'] = action except Action.DoesNotExist: self.logger.error('Action %s does not exist' % action_id) continue attachment, created = Attachment.objects.update_or_create( data_source=self.data_source, origin_id=attachment_data['id'], defaults=defaults ) if created: self.logger.info('Created attachment %s' % attachment) def import_data(self): self.logger.info('Importing open ahjo data...') with open(self.options['filename'], 'r') as data_file: data = json.load(data_file) # pre calc meeting to org mapping org_dict = {o['origin_id']: o for o in data['organizations']} policymaker_to_org = {p['id']: org_dict[p['origin_id']] for p in data['policymakers']} self.meeting_to_org = {m['id']: policymaker_to_org[m['policymaker']] for m in data['meetings']} if self.options['flush']: self.logger.info('Deleting all objects first...') Function.objects.all().delete() Event.objects.all().delete() CaseGeometry.objects.all().delete() Action.objects.all().delete() Content.objects.all().delete() Attachment.objects.all().delete() self._import_functions(data) self._import_events(data) self._import_case_geometries(data) self._import_cases(data) self._import_actions(data) self._import_contents(data) self._import_attachments(data) self.logger.info('Import done!')

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# Copyright 2019 Adobe. All rights reserved. # This file is licensed to you under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. You may obtain a copy # of the License at http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software distributed under # the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR REPRESENTATIONS # OF ANY KIND, either express or implied. See the License for the specific language # governing permissions and limitations under the License. import json import boto3 class S3TerraformRemoteStateRetriever: @staticmethod def get_s3_client(bucket_name, bucket_key, boto_profile): session = boto3.session.Session(profile_name=boto_profile) client = session.client('s3') try: bucket_object = client.get_object(Bucket=bucket_name, Key=bucket_key)["Body"].read() return json.loads(bucket_object) except (client.exceptions.NoSuchKey, client.exceptions.NoSuchBucket): return [] def get_dynamic_data(self, remote_states): generated_data = {"outputs": {}} for state in remote_states: bucket_object = self.get_s3_client(state["s3_bucket"], state["s3_key"], state["aws_profile"]) if "outputs" in bucket_object: generated_data["outputs"][state["name"]] = bucket_object["outputs"] return generated_data

[ "costimuraru@gmail.com" ]

costimuraru@gmail.com

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/data_processing/namibia/combo_data_processing_WIP.py

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PATH-Global-Health/DIAMETER

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import os import argparse import numpy as np import pandas as pd from functools import partial, reduce # import helper functions from data_processing_helpers import (run_compare, return_decisions, fix_concentrations, split_time, remove_time, read_data) # import constants from data_processing_helpers import THRESHOLDS # function for combining duplicates def deduplicate(duplicate_df, plex): # create an empty list to fill with small dfs, which will be combined deduped_dfs = [] # iterate over analytes for analyte in THRESHOLDS[plex].keys(): # subset to columns of interest dup_analyte = duplicate_df[['patient_id', 'well', 'error', 'concentration', analyte]] pid_dfs = [] # iterate over patient_ids for pid in duplicate_df['patient_id'].unique(): # subset to specific patient_id dup_data = dup_analyte.loc[dup_analyte['patient_id'] == pid] con_dfs = [] # iterate over duplicate concentrations for concentration in dup_data['concentration'].unique(): # create an empty dataframe to fill fill_df = pd.DataFrame(columns=['patient_id', 'well', 'error', 'concentration', analyte]) # subset to specific concentration value dup_con = dup_data.loc[dup_data['concentration'] == concentration] # get the values for the duplicate concentrations values = dup_con[analyte] # also preserve wells and errors for duplicate concentrations wells = dup_con['well'].tolist() wells = ''.join(c for c in str(wells) if c not in ["[", "]", "'"]) errors = dup_con['error'].tolist() non_nan_error = [e for e in errors if e is not np.nan] if non_nan_error: errors = non_nan_error else: errors = np.nan try: # if they're both real numbers, take the average values = [float(val) for val in values.tolist()] val = sum(values) / len(values) except ValueError: # otherwise... values = values.tolist() num_vals = [val for val in values if ('<' not in val) & ('>' not in val)] # if one is a real number, take that one if len(num_vals) == 1: val = num_vals[0] # if both are non-real, we assume they're the same. maybe sketchy? else: val = values[0] # add values to empty dataframe fill_df = fill_df.append({'patient_id': pid, 'well': wells, 'error': errors, 'concentration': concentration, analyte: val}, ignore_index=True) con_dfs.append(fill_df) con_df = pd.concat(con_dfs) pid_dfs.append(con_df) pid_df = pd.concat(pid_dfs) deduped_dfs.append(pid_df) deduped = reduce(lambda left, right: pd.merge(left, right, on=['patient_id', 'well', 'error', 'concentration']), deduped_dfs) return deduped # function for determining which dilution value to use def decider(base_df, plex, base_dil): # create an empty list to fill with small dfs, which will be combined analyte_dfs = [] # create an empty dictionary to fill with errors associated with patient IDs error_pids = {} # iterate over analytes for analyte in THRESHOLDS[plex].keys(): patient_dfs = [] # iterate over patient_ids for pid in base_df['patient_id'].unique(): patient_data = base_df.loc[base_df['patient_id'] == pid] # get number of dilutions dilution_values = sorted([val for val in patient_data['concentration'].unique() if val != '1'], key=len) # set initial best decision to neat (1) best_decision = '1' # iterate over dilution values for max_dilution in dilution_values: # subset to dilutions dil_data = patient_data.loc[patient_data['concentration'].isin([best_decision, max_dilution])] # create partial function for generating decision vectors partial_compare = partial(run_compare, analyte_val=analyte, dil_val=max_dilution, base=base_dil) # generate decision vectors dil_data['decision_vector'] = dil_data.apply(partial_compare, axis=1) # pull decision matrix for given analyte and concentrations decisions = return_decisions(best_decision, max_dilution) decision_matrix = decisions[analyte] # construct empty dataframe to hold best values best_df = pd.DataFrame(columns=['patient_id', 'errors', analyte, '{}_dilution'.format(analyte), '{}_well'.format(analyte)]) # get decision vectors for each possible decision vector_low = dil_data.loc[dil_data['concentration'] == best_decision, 'decision_vector'].item() vector_high = dil_data.loc[dil_data['concentration'] == max_dilution, 'decision_vector'].item() # get actual decision from decision vectors decision = decision_matrix[vector_high, vector_low].item() # set value, well, and error based on decision if decision in [best_decision, max_dilution]: val = dil_data.loc[dil_data['concentration'] == decision, analyte].item() well = dil_data.loc[dil_data['concentration'] == decision, 'well'].item() error = dil_data.loc[dil_data['concentration'] == decision, 'error'].item() elif decision == 'fail': val = 'fail' well = 'fail' error = np.nan error_pids[pid] = '{} failure'.format(analyte) else: raise ValueError("Unexpected decision value: {}".format(decision)) # preserve the unselected dilutions other_dilutions = [val for val in patient_data['concentration'].unique()] other_dilutions = [float(val) for val in other_dilutions if val != 'fail'] # preserve the maximum dilution, selected or unselected max_dilution = max(other_dilutions) # preserve the selected dilution df_decision = decision if decision != 'fail' else np.nan # put all preserved/selected values into the empty dataframe best_df = best_df.append({'patient_id': pid, 'errors': error, analyte: val, '{}_dilution'.format(analyte): df_decision, '{}_well'.format(analyte): well, '{}_max_dilution'.format(analyte): max_dilution}, ignore_index=True) best_decision = decision if decision == 'fail': break patient_dfs.append(best_df) patient_df = pd.concat(patient_dfs) # set all error columns to object for combination later patient_df['errors'] = patient_df['errors'].astype('object') analyte_dfs.append(patient_df) decided = reduce(lambda left, right: pd.merge(left, right, on='patient_id'), analyte_dfs) # loop through associated error/patient ID pairs for pid in error_pids.keys(): # subset to individual error(s) associated to patient ID error = error_pids[pid] # subset dataframe to patient ID where error occurs pid_df = decided.loc[decided['patient_id'] == pid] # combine all the errors into one big error message pid_df['errors'] = pid_df['errors'].apply(lambda x: error if np.isnan(x) else x + ' ' + error) # if there's actually an error... if len(pid_df) > 0: # ...replace current dataframe info with the info that contains the error decided = decided.loc[decided['patient_id'] != pid] decided = decided.append(pid_df) return decided def main(input_dir, input_folder, plex, base_dil): dfs = [] input_path = '{}/input_data/{}'.format(input_dir, input_folder) # get all input data, combine into one df for fname in os.listdir(input_path): read_data(input_path, fname, plex) # convert all strings to lowercase plex_data = plex_data.applymap(lambda x: x.lower() if isinstance(x, str) else x) # fill empty patient_ids from the preceeding patient_id plex_data['patient_id'] = plex_data['patient_id'].fillna(method='ffill') # drop patient_ids that are still null plex_data = plex_data[~plex_data['patient_id'].isnull()] dfs.append(plex_data) samples_data = pd.concat(dfs) # subset data to just what we want samples_data = samples_data.loc[~samples_data['type'].isnull()] if plex == 4: samples_data = samples_data.loc[~samples_data['type'].str.contains('pixel')] samples_data = samples_data.loc[samples_data['patient_id'].str.contains('pa-')] elif plex == 5: samples_data = samples_data.loc[~samples_data['patient_id'].str.contains('ctrl')] samples_data = samples_data.loc[~samples_data['type'].str.contains('replicate')] samples_data = samples_data.loc[~samples_data['type'].isnull()] samples_data = samples_data.drop('type', axis=1) # break out concentration from patient string samples_data['concentration'] = samples_data['patient_id'].apply(lambda x: x.split(' ')[-1]) if plex == 4: samples_data['patient_id'] = samples_data['patient_id'].apply(lambda x: x.partition(' ')[0]) elif plex == 5: samples_data['patient_id'] = samples_data['patient_id'].apply(lambda x: '_'.join(x.split(' ')[:3]).replace('/', '_')) # remove concentration values we don't want samples_data = samples_data.loc[(samples_data['concentration'].str.contains('neat|{}'.format(base_dil)))] samples_data = samples_data.loc[~samples_data['concentration'].str.contains('low volume')] # remove rows where "well" is null samples_data = samples_data.loc[~samples_data['well'].isnull()] # make concentrations more machine/human readable samples_data['concentration'] = samples_data.apply(fix_concentrations, axis=1) samples_data = samples_data.sort_values(['patient_id', 'concentration']) # subset the data to just duplicates duplicates = samples_data.loc[samples_data.duplicated(subset=['patient_id', 'concentration'], keep=False)] # run deduplicating function, return deduplicated df deduped = deduplicate(duplicates) # replace old duplicated values with new dedeuplicated values no_duplicates = samples_data.drop_duplicates(subset=['patient_id', 'concentration'], keep=False) no_duplicates = pd.concat([no_duplicates, deduped]) # run decision function output_df = decider(no_duplicates) if plex == 4: # split time associated with patient_id into its own column output_df['time_point_days'] = output_df.apply(split_time, axis=1) output_df['patient_id'] = output_df.apply(remove_time, axis=1) # sort values output_df.sort_values(['patient_id', 'time_point_days'], inplace=True) output_df.set_index(['patient_id', 'time_point_days'], inplace=True) elif plex == 5: # sort values and output to a csv output_df.sort_values('patient_id', inplace=True) output_df.set_index('patient_id', inplace=True) output_df.to_csv('{}/output_data/{}_final_dilutions.csv'.format(input_dir, input_folder)) # also output a csv of partially formatted data, for vetting partial_format = samples_data.copy(deep=True) if plex == 4: # split time associated with patient_id into its own column partial_format['time_point_days'] = partial_format.apply(split_time, axis=1) partial_format['patient_id'] = partial_format.apply(remove_time, axis=1) # sort values partial_format.sort_values(['patient_id', 'time_point_days'], inplace=True) partial_format.set_index(['patient_id', 'time_point_days'], inplace=True) elif plex == 5: # sort values partial_format.sort_values('patient_id', inplace=True) partial_format.set_index('patient_id', inplace=True) partial_format.to_csv('{}/output_data/{}_partially_formatted.csv'.format(input_dir, input_folder)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-id', '--input_dir', type=str, default='C:/Users/lzoeckler/Desktop/5plex', help='Input directory') parser.add_argument('-if', '--input_folder', type=str, default='menzies_raw', help='name of folder within input_dir containing data') parser.add_argument('-p', '--plex', type=int, default=5, help="4plex vs 5plex (or any future nplex)") parser.add_argument('-bd', '--base_dil', type=int, default=50, help='Base dilution value beyond neat (1)') args = parser.parse_args() main(input_dir=args.input_dir, input_folder=args.input_folder, plex=args.plex, base_dil=args.base_dil)

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from bokeh.plotting import figure, output_file, show # prepare some data x = [1, 2, 3, 4, 5] y = [6, 7, 2, 4, 5] # output to static HTML file output_file("lines.html") # create a new plot with a title and axis labels p = figure(title="simple line example", x_axis_label='x', y_axis_label='y') # add a line renderer with legend and line thickness p.line(x, y, legend="Temp.", line_width=2) # show the results show(p)

[ "terasakisatoshi.math@gmail.com" ]

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# Author: Kenneth Lui <hkkenneth@gmail.com> # Last Updated on: ## Usage: python ~/code/python/20130130_ORF_ID_to_Seq_ID.py <INPUT> <OUTPUT> import sys if len(sys.argv) < 3: raise SystemExit, 'use grep "##" ~/code/python/20130130_ORF_ID_to_Seq_ID.py to get usage' outf = open(sys.argv[2], 'w') for line in open(sys.argv[1], 'r'): outf.write("%s\n" % line[:line.rfind("_")]) outf.close()

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no_license

oboyanivskyy/CIS106-Oleg-Boyanivskyy

ffcb8c6cf73f8a343e9cedb53985ff9de056f95b

0d315cc0624e85bf3fcbc9c1505e58e8aa787fcd

refs/heads/main

2023-04-12T20:54:02.491062

2021-05-13T16:48:40

331,188,806

0

null

UTF-8

Python

false

945

py

# This updated program will take your age, # and display your age in months, days, hours, # and seconds. def get_age(): print("Enter your age in years.") age = float(input()) return age def calc_months(age): months = age * 12 return months def calc_days(age): days = age * 365 return days def calc_hours(days): hours = days * 24 return hours def calc_seconds(hours): seconds = hours * 60 * 60 return seconds def display_result(months, days, hours, seconds): print("You are " + str(months) + " months, ") print(str(days) + " days, ") print(str(hours) + " hours, and ") print(str(seconds) + " seconds old ") def main(): age = get_age() months = calc_months(age) days = calc_days(age) hours = calc_hours(days) seconds = calc_seconds(hours) display_result(months, days, hours, seconds) main()

[ "noreply@github.com" ]

oboyanivskyy.noreply@github.com

6930553da58762d4c112efa76dd6dd1d0b19451b

b6fe842749ca288b5e7f048c149b04f035f62b93

/mydb/pymongo_insert_col_1.py

38b3e68f4f8023abb51adcc22f893011739831c9

[]

no_license

zxcvbnm123xy/leon_python

c8fa74dd0186402b9edf7466f9a28a6fa586b17c

b68f5d5e8240c5a99ec7c155fb85f816ac0d54d1

refs/heads/master

2020-03-27T03:03:43.610701

2018-11-14T09:17:15

145,836,887

0

1

null

UTF-8

Python

false

411

py

# 加载依赖 import pymongo # 创建连接 myclient = pymongo.MongoClient("mongodb://localhost:27017/") # 定位到数据库mongo_python和集合sites mp = myclient["mongo_python"] sites = mp["sites"] #alexa--->网站的全球排名指标 document = {"name": "python", "alexa": "10000", "url": "https://www.python.com"} ret = sites.insert_one(document) print(ret) ##打印插入的文档的id值

[ "737878501@qq.com" ]

737878501@qq.com

3bc01818033d523d7c3c35a5753db7099d003ea7

ddeadd0accfb2f640f2bb7d47a3336b601e1d65b

/spider/renniso/article/selenium_test.py

818b04dcdc3868ce4b1d735104604acf0d25c8fb

[]

no_license

wuxinchaliu/python

ff25ed61bb9b0f83bd74243abcd09c1ec154d45e

f39751797efc7aeb184fa994f7187b7779fbd301

refs/heads/master

2021-01-21T04:55:41.347045

2016-06-22T06:38:02

53,824,681

0

null

UTF-8

Python

false

699

py

# -*- coding: utf-8 -*- from selenium import webdriver import time import os import sys reload(sys) sys.setdefaultencoding('utf8') chromedriver = "/usr/local/bin/chromedriver" os.environ["webdriver.chrome.driver"] = chromedriver browser = webdriver.Chrome(chromedriver) # browser.get("http://www.baidu.com/") # # time.sleep(3) # # browser.find_element_by_id('kw').send_keys("zhangyanqing") # browser.find_element_by_id('su').click() # browser.get("http://www.hnebbs.com/") url = "http://www.bdsola.com/d/9224.html" browser.get(url) print browser.page_source browser.get("http://www.bdsola.com/d/9234.html") print '2' browser.get("http://www.bdsola.com/d/9244.html") print '3' browser.close()

[ "qi138138lin@163.com" ]

qi138138lin@163.com

165d2c9f59136e1444d55600d106600c891f6da2

abcaaaea2c40b175351116599026273ff86d9282

/qaserver/libs/query.py

eed035c9fdb128a46615e5422870fed35409689f

[]

no_license

afterimagex/QAServer

01ff21740e5617a18546869d21a8f372bc9d9e27

be2603dfc43daf63dc33dc8dd653372c563384f6

refs/heads/master

2022-03-27T17:28:23.781601

2019-12-27T11:06:38

229,937,391

0

null

UTF-8

Python

false

13,068

py

#!/usr/bin/python3 # -*- coding: utf-8 -*- # # Copyright 2016 The TensorFlow Authors All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # @Time : 2019/12/25 0025 14:58 # @Author : peichao.xu # @Email : xj563853580@outlook.com # @File : query.py # ============================================================================== from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import re do_dict = { "where": "__condition", "table": "__table_name", "limit": "__limit", "order": "__order", "field": "__field", "data": "__data", "group": "__group", "having": "__having", "join": "__join", } class Query(object): def __init__(self, table_name=None, db=None): if not table_name == None: self.table_name = table_name if not db == None: self.db = db self.__reset() def __reset(self): self.__cluster = [] self.__protected = {} self.__protected["__field"] = "*" self.__protected["__table_name"] = self.table_name def __close(self): self.__reset() def __tracker(self, name): if (not name in self.__cluster): self.__cluster.append(name) def __check(self, name): return True if (name in self.__cluster) else False def __do(self, name, value): value = value.strip() if type(value) == type('string') else value self.__protected[do_dict[name]] = value self.__tracker(name) def __sqlfix(self, sql): sql = re.sub(r"(?<!%)%(?!%)", "%%", sql) sql = re.sub(r"(?<!\\)\$?!\$", r"\\\\", sql) return sql def __valuefix(self, value): value = re.sub(r"\'", "''", value) if type(value) == type("string") or type(value) == type( u"unicode") else value return value def __sqlbuild(self, sql='', queue=[]): for statement in queue: if (self.__check("join") and statement == "join"): sql = sql + " %s" % self.__protected["__join"] if (self.__check("where") and statement == "where"): sql = sql + " WHERE %s" % self.__protected["__condition"] if (self.__check("order") and statement == "order"): sql = sql + " ORDER BY %s" % self.__protected["__order"] if (self.__check("limit") and statement == "limit"): sql = sql + " LIMIT %s" % self.__protected["__limit"] if (self.__check("group") and statement == "group"): sql = sql + " GROUP BY %s" % self.__protected["__group"] if (self.__check("having") and statement == "having"): sql = sql + " HAVING %s" % self.__protected["__having"] if (self.__check("data") and statement == "data:save"): sets = "" for data in self.__protected["__data"]: sets = sets + "%s = '%s', " % (data, self.__valuefix(self.__protected["__data"][data])) sets = sets.strip().rstrip(",") sql = sql + " SET %s" % sets if (self.__check("data") and statement == "data:add"): sets = "" values = "" for data in self.__protected["__data"]: sets = sets + "%s, " % data values = values + "'%s', " % self.__valuefix(self.__protected["__data"][data]) sets = sets.strip().rstrip(",") values = values.strip().rstrip(",") sql = sql + " (%s)" % sets sql = sql + " VALUES (%s)" % values return sql def prepend(self, name, value): self.__protected[do_dict[name]] = "%s AND %s" % (value, self.__protected[do_dict[name]]) return self def table(self, table_name): self.__do("table", table_name) return self def where(self, condition): self.__do("where", condition) return self def limit(self, start, end=None): limit = start if not end else "%s, %s" % (start, end) self.__do("limit", limit) return self def order(self, type): self.__do("order", type) return self def field(self, field): self.__do("field", field) return self def data(self, data): self.__do("data", data) return self def group(self, type): self.__do("group", type) return self def having(self, condition): self.__do("having", condition) return self def join(self, condition): self.__do("join", condition) return self def query(self, sql): self.__close() sql = self.__sqlfix(sql) return self.db.query(sql) def grasp(self, sql): select_regx = re.compile( "SELECT (COUNT$)?(?P<field>[\w\*\s\.,]+)$? FROM (?P<table_name>.*?)(LIMIT|ORDER|GROUP|HAVING|WHERE|LEFT|RIGHT|INNER|$)", re.I) where_complex_regx = re.compile("WHERE (?P<condition>.*?)(LIMIT|ORDER|GROUP|HAVING|LEFT|RIGHT|INNER)", re.I) where_regx = re.compile("WHERE (?P<condition>.*)", re.I) limit_regx = re.compile("LIMIT (?P<start>\d+),?\s*(?P<end>\d+)?", re.I) group_regx = re.compile("GROUP BY (?P<group_by>[\w\.]+)", re.I) having_regx = re.compile("HAVING (?P<having>\w+)", re.I) order_regx = re.compile( "ORDER BY (?P<order_by>[\w\.\,\s]+\s+(ASC|DESC||\s))\s*(LIMIT|GROUP|HAVING|WHERE|LEFT|RIGHT|INNER|$)", re.I) insert_regx = re.compile( "INSERT INTO (?P<table_name>\w+) $((\w+,?\s?)+)$ VALUES $(([\"']?\w+[\"']?,?\s?)+)$", re.I) update_complex_regx = re.compile( "UPDATE (?P<table_name>\w+) SET (.*?)(LIMIT|ORDER|GROUP|HAVING|WHERE|LEFT|RIGHT|INNER)", re.I) update_regx = re.compile("UPDATE (?P<table_name>\w+) SET (.*)", re.I) table_regx = re.compile("FROM (?P<table_name>.*?)(LIMIT|ORDER|GROUP|HAVING|WHERE|LEFT|RIGHT|INNER|$)", re.I) join_regx = re.compile( "(?P<join_condition>(?P<join_dir>LEFT|RIGHT)?\s*(?P<join_type>INNER|OUTER)? JOIN (?P<table_name>\w+) (AS \w+\s+)?ON (.*?))(LIMIT|ORDER|GROUP|HAVING|WHERE)", re.I) select = select_regx.search(sql) where_complex = where_complex_regx.search(sql) where = where_regx.search(sql) limit = limit_regx.search(sql) group = group_regx.search(sql) having = having_regx.search(sql) order = order_regx.search(sql) insert = insert_regx.search(sql) update_complex = update_complex_regx.search(sql) update = update_regx.search(sql) table = table_regx.search(sql) join = join_regx.search(sql) if select: _field = select.groupdict()["field"] _table_name = select.groupdict()["table_name"] self.__do("field", _field) self.__do("table", _table_name) if where_complex: _condition = where_complex.groupdict()["condition"] self.__do("where", _condition) elif where: _condition = where.groupdict()["condition"] self.__do("where", _condition) if limit: start = limit.groupdict()["start"] end = limit.groupdict()["end"] _limit = start if not end else "%s, %s" % (start, end) self.__do("limit", _limit) if group: _group_by = group.groupdict()["group_by"] self.__do("group", _group_by) if having: _having = group.groupdict()["having"] self.__do("having", _having) if order: _order_by = order.groupdict()["order_by"] self.__do("order", _order_by) if table: _table_name = table.groupdict()["table_name"] self.__do("table", _table_name) if join: _join = join.groupdict()["join_condition"] self.__do("join", _join) if insert: _table_name = insert.groupdict()["table_name"] fields = insert.groups()[1].split(",") values = insert.groups()[3].split(",") _data = {} for index, field in enumerate(fields): field = field.strip() value = values[index].strip() _data[field] = value self.__do("data", _data) self.__do("table", _table_name) if update_complex: _table_name = update_complex.groupdict()["table_name"] pairs = update_complex.groups()[1].split(",") _data = {} for index, pair in enumerate(pairs): pair = pair.split("=") field = pair[0].strip() value = pair[1].strip() _data[field] = value self.__do("data", _data) self.__do("table", _table_name) elif update: _table_name = update.groupdict()["table_name"] pairs = update.groups()[1].split(",") _data = {} for index, pair in enumerate(pairs): pair = pair.split("=") field = pair[0].strip() value = pair[1].strip() _data[field] = value self.__do("data", _data) self.__do("table", _table_name) return self def count(self, cheat=False): sql = "SELECT COUNT(*) FROM %s" % self.__protected["__table_name"] sql = self.__sqlbuild(sql, ["join", "where", "group", "having"]) sql = self.__sqlfix(sql) self.__close() group_having_regx = re.compile("(GROUP|HAVING)", re.I) if (not group_having_regx.search(sql)): return self.db.get(sql)["COUNT(*)"] if not cheat else sql else: return len(self.db.query(sql)) if not cheat else sql def sum(self, field, cheat=False): sql = "SELECT SUM(%s) FROM %s" % (field, self.__protected["__table_name"]) sql = self.__sqlbuild(sql, ["where"]) sql = self.__sqlfix(sql) self.__close() return self.db.get(sql)["SUM(%s)" % field] if not cheat else sql def find(self, cheat=False): try: return self.select()[0] if not cheat else self.select(cheat) except: return None def select(self, cheat=False): sql = "SELECT %s FROM %s" % (self.__protected["__field"], self.__protected["__table_name"]) sql = self.__sqlbuild(sql, ["join", "where", "group", "having", "order", "limit"]) sql = self.__sqlfix(sql) self.__close() return self.db.query(sql) if not cheat else sql def delete(self, cheat=False): sql = "DELETE FROM %s" % self.__protected["__table_name"] sql = self.__sqlbuild(sql, ["where", "order", "limit"]) sql = self.__sqlfix(sql) self.__close() return self.db.execute(sql) if not cheat else sql def save(self, cheat=False): sql = "UPDATE %s" % self.__protected["__table_name"] sql = self.__sqlbuild(sql, ["data:save", "where"]) sql = self.__sqlfix(sql) self.__close() return self.db.execute(sql) if not cheat else sql def add(self, cheat=False): sql = "INSERT INTO %s" % self.__protected["__table_name"] sql = self.__sqlbuild(sql, ["data:add"]) sql = self.__sqlfix(sql) self.__close() return self.db.execute(sql) if not cheat else sql def pages(self, current_page=1, list_rows=40, cheat=False): sql = self.select(cheat=True) self.__close() count = self.grasp(sql).count() pages = count / list_rows pages = pages + 1 if not count % list_rows == 0 else pages if (pages == 0): pages = 1 if (current_page < 1): current_page = 1 if (current_page > pages): current_page = pages start = (current_page - 1) * list_rows end = list_rows previous_page = current_page - 1 if current_page > 1 else 1 next_page = current_page + 1 if current_page < pages else pages result = {} result["list"] = self.grasp(sql).limit(start, end).select() result["page"] = { "prev": previous_page, "next": next_page, "current": current_page, "pages": pages, "total": count, } return result if not cheat else self.grasp(sql).limit(start, end).select(cheat)

[ "peichao.xu@seetatech.com" ]

peichao.xu@seetatech.com

7ab97c7b41a6cf374e72538106bf02896e8c7fa6

21b29ffd891806f03f7821b865fdad9c2a00e729

/tom_functions/get_idxs.py

9faed6eb0bd02441da9ccc684a3c088b614f6070

[]

no_license

mv-lab/kuzushiji-recognition

71d46c059d07726d401a364e84900ceb0ebd4d5a

08caaeaf40a00e1264b27e0f2157515b8ba8bc95

refs/heads/master

2020-08-14T10:35:23.277583

2019-10-26T08:52:26

215,151,222

20

6

null

UTF-8

Python

false

323

py

from sklearn.model_selection import KFold import numpy as np def get_idxs(data_names): kfold = KFold(n_splits=5, shuffle=True, random_state=42) val_idxs_list = [] for fold, (trn,val) in enumerate(kfold.split(data_names, data_names)): val_idxs_list.append(val) return val_idxs_list

[ "takeskao76@gmail.com" ]

takeskao76@gmail.com

3bfb3cdac592a51897a817cd39f81cefc47b2ee2

c7aa115d30f0ef1369351158ae9a8ab97dc121ae

/contrib/seeds/makeseeds.py

9a54af5449de3623369a6c49b70f7b9695394d22

[ "MIT" ]

permissive

DrakeDragon/DRKE

16a1edf7b82e473667c09a01636b80aa52ee251f

dc982078c40d522d56923fcd66eccbf53d3cfda0

refs/heads/master

2021-01-20T10:37:35.194951

2014-05-01T16:30:19

19,335,687

2

0

null

UTF-8

Python

false

708

py

#!/usr/bin/env python # # Generate pnSeed[] from Pieter's DNS seeder # NSEEDS=600 import re import sys from subprocess import check_output def main(): lines = sys.stdin.readlines() ips = [] pattern = re.compile(r"^(\d{1,3})\.(\d{1,3})\.(\d{1,3})\.(\d{1,3}):9653") for line in lines: m = pattern.match(line) if m is None: continue ip = 0 for i in range(0,4): ip = ip + (int(m.group(i+1)) << (8*(i))) if ip == 0: continue ips.append(ip) for row in range(0, min(NSEEDS,len(ips)), 8): print " " + ", ".join([ "0x%08x"%i for i in ips[row:row+8] ]) + "," if __name__ == '__main__': main()

[ "ixk1@ixk1-nix.(none)" ]

ixk1@ixk1-nix.(none)

f60b73c97d97ca29f0166c2bc0a772c8a6a2ceb8

2215c442aa4b716acadfdbab5dd4fb453aad0678

/example_random.py

d7f042f78d118c5c2747b56c448231802b98bb53

[]

no_license

MagnusFelinto/rl-tournament-starter

432169be6b6808555d070f39f820f59b1cb2285c

c8a5dae929bdd595d5a0bfd049e6a400c4ef5f3f

refs/heads/main

2023-08-28T20:00:24.932017

2021-10-23T18:12:39

null

0

null

UTF-8

Python

false

745

py

import soccer_twos env = soccer_twos.make(render=True, flatten_branched=True) print("Observation Space: ", env.observation_space.shape) print("Action Space: ", env.action_space) team0_reward = 0 team1_reward = 0 env.reset() while True: obs, reward, done, info = env.step( { 0: env.action_space.sample(), 1: env.action_space.sample(), 2: env.action_space.sample(), 3: env.action_space.sample(), } ) team0_reward += reward[0] + reward[1] team1_reward += reward[2] + reward[3] if max(done.values()): # if any agent is done print("Total Reward: ", team0_reward, " x ", team1_reward) team0_reward = 0 team1_reward = 0 env.reset()

[ "bryanufg@gmail.com" ]

bryanufg@gmail.com

975e8622daa592d1a122d28698b2e9f19c3f91ba

882e9949b0da33b11a5f24156058465da2f95519

/input/kinetics/libraries/Aromatics_high_pressure/C9H9_2/reactions.py

4b90c6feda3d07d987ead4981efaddc58f03bc15

[]

no_license

ReactionMechanismGenerator/RMG-database

f4de3a8628951ad7ef154e82b012bfd334d6bc5a

b7ff16364a07c9a51a34303aa28407a83455a3e4

refs/heads/main

2023-09-04T06:18:30.835821

2023-08-08T13:37:08

1,838,004

69

94

null

2023-09-08T16:39:04

2011-06-02T16:09:36

Python

UTF-8

Python

false

2,789

py

#!/usr/bin/env python # encoding: utf-8 name = "Aromatics_high_pressure/C9H9_2" shortDesc = u"Benzyl radical+Acetylene and Benzene+Propargyl radical" longDesc = u""" Ab Initio G3-type/Statistical Theory Study of the Formation of Indene in Combustion Flames. I. Pathways Involving Benzene and Phenyl Radical V. V. Kislov and A. M. Mebel J. Phys. Chem. A 2007, 111, 3922-3931 level of theory:G3(MP2,CC)//B3LYP/6-311G**, TST rates reported in literature and fitted in RMG """ entry( index = 7, label = "C7H7_10 + ethyne_8 <=> C9H9_13", degeneracy = 1, kinetics = Arrhenius( A = (31630, 'cm^3/(mol*s)'), n = 2.479, Ea = (11.061, 'kcal/mol'), T0 = (1, 'K'), ), ) entry( index = 8, label = "C9H9_13 <=> C9H9_14", degeneracy = 1, kinetics = Arrhenius(A=(1.257e+11, 's^-1'), n=0.139, Ea=(13.233, 'kcal/mol'), T0=(1, 'K')), ) entry( index = 9, label = "C9H9_14 <=> indene_25 + H_15", degeneracy = 1, kinetics = Arrhenius(A=(3.597e+10, 's^-1'), n=0.889, Ea=(20.893, 'kcal/mol'), T0=(1, 'K')), ) entry( index = 10, label = "benzene_1 + C3H3_9 <=> C9H9_2", degeneracy = 1, kinetics = Arrhenius( A = (144.6, 'cm^3/(mol*s)'), n = 2.951, Ea = (14.055, 'kcal/mol'), T0 = (1, 'K'), ), ) entry( index = 11, label = "benzene_1 + C3H3_9 <=> C9H9_6", degeneracy = 1, kinetics = Arrhenius( A = (312.3, 'cm^3/(mol*s)'), n = 2.973, Ea = (16.396, 'kcal/mol'), T0 = (1, 'K'), ), ) entry( index = 12, label = "C9H9_2 <=> C9H9_3", degeneracy = 1, kinetics = Arrhenius(A=(6.485e+11, 's^-1'), n=0.065, Ea=(27.941, 'kcal/mol'), T0=(1, 'K')), ) entry( index = 13, label = "C9H9_6 <=> C9H9_3", degeneracy = 1, kinetics = Arrhenius(A=(5.565e+11, 's^-1'), n=0.009, Ea=(28.521, 'kcal/mol'), T0=(1, 'K')), ) entry( index = 14, label = "C9H9_3 <=> C9H9_24", degeneracy = 1, kinetics = Arrhenius(A=(9.527e+10, 's^-1'), n=0.853, Ea=(47.848, 'kcal/mol'), T0=(1, 'K')), ) entry( index = 15, label = "C9H9_24 <=> C9H9_14", degeneracy = 1, kinetics = Arrhenius(A=(4.438e+10, 's^-1'), n=0.625, Ea=(38.324, 'kcal/mol'), T0=(1, 'K')), ) entry( index = 16, label = "C9H9_3 <=> C9H9_4", degeneracy = 1, kinetics = Arrhenius(A=(1.231e+11, 's^-1'), n=0.765, Ea=(55.941, 'kcal/mol'), T0=(1, 'K')), ) entry( index = 17, label = "C9H9_4 <=> C9H9_5", degeneracy = 1, kinetics = Arrhenius(A=(3.677e+10, 's^-1'), n=0.839, Ea=(43.638, 'kcal/mol'), T0=(1, 'K')), ) entry( index = 18, label = "C9H9_5 <=> indene_25 + H_15", degeneracy = 1, kinetics = Arrhenius(A=(4.591e+10, 's^-1'), n=0.886, Ea=(24.975, 'kcal/mol'), T0=(1, 'K')), )

[ "ampayne@mit.edu" ]

ampayne@mit.edu

276522ebb23738f05417dc7191682cab9bdbed1e

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/src/csv_reader/channel_reader.py

bba130dd9170db8dda3dfcdb7c119a139b3e074d

[]

no_license

rodrigolazarinigil/mysql_etl

f6b0224d87a4c50ec7c82abbb69280fa0dfc360f

1e2b01b846e4e5467ab92229a06474697c85450a

refs/heads/master

2021-06-19T00:26:31.655018

2017-06-18T23:08:58

null

0

null

UTF-8

Python

false

538

py

"""Leitor de arquivos (ver datafile_name abaixo).""" import abc from csv_reader.base_reader import BaseReader class ChannelReader(BaseReader): """Leitor de arquivos.""" def __init__(self): """Construtor.""" self.datafile_name = 'channel.csv' self.column_names = [ 'id', 'name', 'account_id' ] super().__init__() @abc.abstractmethod def transform(self): """Transformar o data frame.""" self.df.drop_duplicates(subset='id', keep='first', inplace=True)

[ "rodrigo.lazarini.gil@gmail.com" ]

rodrigo.lazarini.gil@gmail.com

ebc69e51ec69ea040c5c48d1b773443bed191682

b560f533f66af8787039e663795a77173a43d3c1

/Algorighms/Sort/BubbleSort.py

f3a8e452c5648ed350f28fcfe5c5470e234ac148

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# -*- coding: utf-8 -*-# ''' @Project : ClassicAlgorighthms @File : BubbleSort.py @USER : ZZZZZ @TIME : 2021/4/21 16:41 ''' class BubbleSort(): def __init__(self): self.exchange_count = 0 def Solution(self, nums): ''' 对数组元素进行冒泡排序, 从小到大进行排序 :param nums: 数字列表 :return: list, 从小到大排好序的数组 ''' # 一共比较 len(nums) - 1 趟即可 for i in range(len(nums) - 1): # 此处每一轮都可以使得最后一个元素就位，因此需要 -i , 不用进行后序的比较了 for j in range(len(nums) - 1 - i): if nums[j] > nums[j + 1]: nums[j], nums[j + 1] = nums[j + 1], nums[j] # 记录一次交换 self.exchange_count += 1 return nums def GetExchangeCount(self): return self.exchange_count if __name__ == "__main__": nums = [4, 5, 2, 9, 1] st = BubbleSort() print("冒泡排序结果为: {}".format(st.Solution(nums))) print("交换次数为: {}".format(st.GetExchangeCount()))

[ "sifan.zsf@alibaba-inc.com" ]

sifan.zsf@alibaba-inc.com

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DevDungeon/Cookbook

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#!/usr/bin/env python # -*- coding: utf-8 -*- """ SleekXMPP: The Sleek XMPP Library Copyright (C) 2010 Nathanael C. Fritz This file is part of SleekXMPP. See the file LICENSE for copying permission. """ import sys import logging import getpass from optparse import OptionParser import sleekxmpp # Python versions before 3.0 do not use UTF-8 encoding # by default. To ensure that Unicode is handled properly # throughout SleekXMPP, we will set the default encoding # ourselves to UTF-8. if sys.version_info < (3, 0): reload(sys) sys.setdefaultencoding('utf8') else: raw_input = input class MUCBot(sleekxmpp.ClientXMPP): """ A simple SleekXMPP bot that will greets those who enter the room, and acknowledge any messages that mentions the bot's nickname. """ def __init__(self, jid, password, room, nick): sleekxmpp.ClientXMPP.__init__(self, jid, password) self.room = room self.nick = nick # The session_start event will be triggered when # the bot establishes its connection with the server # and the XML streams are ready for use. We want to # listen for this event so that we we can initialize # our roster. self.add_event_handler("session_start", self.start) # The groupchat_message event is triggered whenever a message # stanza is received from any chat room. If you also also # register a handler for the 'message' event, MUC messages # will be processed by both handlers. self.add_event_handler("groupchat_message", self.muc_message) # The groupchat_presence event is triggered whenever a # presence stanza is received from any chat room, including # any presences you send yourself. To limit event handling # to a single room, use the events muc::room@server::presence, # muc::room@server::got_online, or muc::room@server::got_offline. self.add_event_handler("muc::%s::got_online" % self.room, self.muc_online) def start(self, event): """ Process the session_start event. Typical actions for the session_start event are requesting the roster and broadcasting an initial presence stanza. Arguments: event -- An empty dictionary. The session_start event does not provide any additional data. """ self.get_roster() self.send_presence() self.plugin['xep_0045'].joinMUC(self.room, self.nick, # If a room password is needed, use: # password=the_room_password, wait=True) def muc_message(self, msg): """ Process incoming message stanzas from any chat room. Be aware that if you also have any handlers for the 'message' event, message stanzas may be processed by both handlers, so check the 'type' attribute when using a 'message' event handler. Whenever the bot's nickname is mentioned, respond to the message. IMPORTANT: Always check that a message is not from yourself, otherwise you will create an infinite loop responding to your own messages. This handler will reply to messages that mention the bot's nickname. Arguments: msg -- The received message stanza. See the documentation for stanza objects and the Message stanza to see how it may be used. """ if msg['mucnick'] != self.nick and self.nick in msg['body']: self.send_message(mto=msg['from'].bare, mbody="I heard that, %s." % msg['mucnick'], mtype='groupchat') def muc_online(self, presence): """ Process a presence stanza from a chat room. In this case, presences from users that have just come online are handled by sending a welcome message that includes the user's nickname and role in the room. Arguments: presence -- The received presence stanza. See the documentation for the Presence stanza to see how else it may be used. """ if presence['muc']['nick'] != self.nick: self.send_message(mto=presence['from'].bare, mbody="Hello, %s %s" % (presence['muc']['role'], presence['muc']['nick']), mtype='groupchat') if __name__ == '__main__': # Setup the command line arguments. optp = OptionParser() # Output verbosity options. optp.add_option('-q', '--quiet', help='set logging to ERROR', action='store_const', dest='loglevel', const=logging.ERROR, default=logging.INFO) optp.add_option('-d', '--debug', help='set logging to DEBUG', action='store_const', dest='loglevel', const=logging.DEBUG, default=logging.INFO) optp.add_option('-v', '--verbose', help='set logging to COMM', action='store_const', dest='loglevel', const=5, default=logging.INFO) # JID and password options. optp.add_option("-j", "--jid", dest="jid", help="JID to use") optp.add_option("-p", "--password", dest="password", help="password to use") optp.add_option("-r", "--room", dest="room", help="MUC room to join") optp.add_option("-n", "--nick", dest="nick", help="MUC nickname") opts, args = optp.parse_args() # Setup logging. logging.basicConfig(level=opts.loglevel, format='%(levelname)-8s %(message)s') if opts.jid is None: opts.jid = raw_input("Username: ") if opts.password is None: opts.password = getpass.getpass("Password: ") if opts.room is None: opts.room = raw_input("MUC room: ") if opts.nick is None: opts.nick = raw_input("MUC nickname: ") # Setup the MUCBot and register plugins. Note that while plugins may # have interdependencies, the order in which you register them does # not matter. xmpp = MUCBot(opts.jid, opts.password, opts.room, opts.nick) xmpp.register_plugin('xep_0030') # Service Discovery xmpp.register_plugin('xep_0045') # Multi-User Chat xmpp.register_plugin('xep_0199') # XMPP Ping # Connect to the XMPP server and start processing XMPP stanzas. if xmpp.connect(): # If you do not have the dnspython library installed, you will need # to manually specify the name of the server if it does not match # the one in the JID. For example, to use Google Talk you would # need to use: # # if xmpp.connect(('talk.google.com', 5222)): # ... xmpp.process(block=True) print("Done") else: print("Unable to connect.")

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nanodano@devdungeon.com

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ashishgupta2014/problem_solving_practices

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def golomb_sequence(n, arr): """ https://www.geeksforgeeks.org/golomb-sequence/ In mathematics, the Golomb sequence is a non-decreasing integer sequence where n-th term is equal to number of times n appears in the sequence. The first few values are 1, 2, 2, 3, 3, 4, 4, 4, 5, 5, 5, …… Explanation of few terms: Third term is 2, note that three appears 2 times. Second term is 2, note that two appears 2 times. Fourth term is 3, note that four appears 3 times. Given a positive integer n. The task is to find the first n terms of Golomb sequence. :param n: :return: """ if n == 1: return 1 if arr[n] != 0: return arr[n] arr[n] = 1 + golomb_sequence(n - golomb_sequence(golomb_sequence(n - 1, arr), arr), arr) return arr[n] n = 6 arr = [0] * (n+1) for i in range(1, n + 1): print(golomb_sequence(i, arr), end=" ")

[ "ashish.2007g@gmail.com" ]

ashish.2007g@gmail.com

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niraj17/work

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#!/usr/bin/env python import sys def fib(n): last2 = 0 last = 1 if n == 0 or n == 1: return n for i in range(2, n+1): f = last + last2 last2 = last last = f return f def main(): ans = fib(int(sys.argv[1])) print "Fib=", ans if __name__ == '__main__': main()

[ "niraj17@gmail.com" ]

niraj17@gmail.com

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hoangtnm/deep-learning

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2021-06-24T04:48:23.113663

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import torch.nn as nn from torchvision import models import torch.nn.functional as F model_ft = models.resnet18(pretrained=True) num_ftrs = model_ft.fc.in_features model_ft.fc = nn.Linear(num_ftrs, 1) class Flatten(nn.Module): def forward(self, input): return input.view(input.size(0), -1) class Net(nn.Module): def __init__(self): super(Net, self).__init__() # convolutional layer (sees 224x224x3 image tensor) self.conv1 = nn.Conv2d(3, 24, 5, stride=2, padding=1) self.conv2 = nn.Conv2d(24, 32, 5, stride=1, padding=1) self.conv3 = nn.Conv2d(32, 64, 5, stride=1, padding=1) self.conv4 = nn.Conv2d(64, 64, 3, stride=1, padding=1) self.conv5 = nn.Conv2d(64, 64, 3, stride=1, padding=1) self.pool = nn.MaxPool2d(2, 2) self.flatten = Flatten() self.fc1 = nn.Linear(64*3*3, 144) self.fc2 = nn.Linear(144, 1) self.dropout = nn.Dropout(0.15) def forward(self, x): # add sequence of convolutional and max pooling layers x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = self.pool(F.relu(self.conv3(x))) x = self.pool(F.relu(self.conv4(x))) x = self.pool(F.relu(self.conv5(x))) # flatten image input x = self.flatten(x) x = self.dropout(x) x = F.relu(self.fc1(x)) x = self.dropout(x) x = self.fc2(x) return x

[ "minhhoangtrannhat@gmail.com" ]

minhhoangtrannhat@gmail.com

bdaa415fcfcfcd6249e9a52e214476f4597bd832

18fd4062e7bdc626df99229eba78affa9b94859b

/flaskk/__main__.py

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[]

no_license

root-sudip/Search-Engine

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refs/heads/master

2021-01-20T05:52:41.168438

2017-05-11T11:29:38

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[ "touch.das@gmail.com" ]

touch.das@gmail.com

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lukehuang/python-executor

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# Programmer friendly subprocess wrapper. # # Author: Peter Odding <peter@peterodding.com> # Last Change: April 13, 2017 # URL: https://executor.readthedocs.io """ Secure command execution in chroot environments. The :mod:`executor.schroot` module defines the :class:`ChangeRootCommand` class which makes it easy to run commands inside chroots_ that are managed using the schroot_ program. .. _chroots: http://en.wikipedia.org/wiki/Chroot .. _schroot: https://wiki.debian.org/Schroot """ # Standard library modules. import logging # External dependencies. from property_manager import mutable_property, required_property # Modules included in our package. from executor import DEFAULT_WORKING_DIRECTORY, ExternalCommand # Initialize a logger. logger = logging.getLogger(__name__) SCHROOT_PROGRAM_NAME = 'schroot' """The name of the ``schroot`` program (a string).""" DEFAULT_NAMESPACE = 'chroot' """ The default chroot namespace (a string). Refer to the schroot_ documentation for more information about chroot namespaces. """ class ChangeRootCommand(ExternalCommand): """:class:`ChangeRootCommand` objects use the schroot_ program to execute commands inside chroots.""" def __init__(self, *args, **options): """ Initialize a :class:`ChangeRootCommand` object. :param args: Positional arguments are passed on to the initializer of the :class:`.ExternalCommand` class. :param options: Any keyword arguments are passed on to the initializer of the :class:`.ExternalCommand` class. If the keyword argument `chroot_name` isn't given but positional arguments are provided, the first positional argument is used to set the :attr:`chroot_name` property. The command is not started until you call :func:`~executor.ExternalCommand.start()` or :func:`~executor.ExternalCommand.wait()`. """ # Enable modification of the positional arguments. args = list(args) # We allow `chroot_name' to be passed as a keyword argument but use the # first positional argument when the keyword argument isn't given. if options.get('chroot_name') is None and args: options['chroot_name'] = args.pop(0) # Inject our logger as a default. options.setdefault('logger', logger) # Initialize the superclass. super(ChangeRootCommand, self).__init__(*args, **options) @mutable_property def chroot_directory(self): """ The working directory _inside the chroot_ (a string or :data:`None`, defaults to ``/``). When :attr:`chroot_directory` is :data:`None` the schroot_ program gets to pick the working directory inside the chroot (refer to the schroot documentation for the complete details). For non-interactive usage (which I anticipate to be the default usage of :class:`ChangeRootCommand`) the schroot program simply assumes that the working directory outside of the chroot also exists inside the chroot, then fails with an error message when this is not the case. Because this isn't a very robust default, :attr:`chroot_directory` defaults to ``/`` instead. """ return '/' @required_property def chroot_name(self): """ The name of a chroot managed by schroot_ (a string). This is expected to match one of the names configured in the directory ``/etc/schroot/chroot.d``. """ @mutable_property def chroot_user(self): """ The name of the user inside the chroot to run the command as (a string or :data:`None`). This defaults to :data:`None` which means to run as the current user. """ @property def command_line(self): """ The complete `schroot` command including the command to run inside the chroot. This is a list of strings with the `schroot` command line to enter the requested chroot and execute :attr:`~.ExternalCommand.command`. """ schroot_command = list(self.schroot_command) schroot_command.append('--chroot=%s' % self.chroot_name) if self.chroot_user: schroot_command.append('--user=%s' % self.chroot_user) if self.chroot_directory: schroot_command.append('--directory=%s' % self.chroot_directory) # We only add the `--' to the command line when it will be followed by # a command to execute inside the chroot. Emitting a trailing `--' that # isn't followed by anything doesn't appear to bother schroot, but it # does look a bit weird and may cause unnecessary confusion. super_cmdline = list(super(ChangeRootCommand, self).command_line) if super_cmdline: schroot_command.append('--') schroot_command.extend(super_cmdline) return schroot_command @property def directory(self): """ Set the working directory inside the chroot. When you set this property you change :attr:`chroot_directory`, however reading back the property you'll just get :data:`.DEFAULT_WORKING_DIRECTORY`. This is because the superclass :class:`.ExternalCommand` uses :attr:`directory` as the working directory for the `schroot` command, and directories inside chroots aren't guaranteed to exist on the host system. """ return DEFAULT_WORKING_DIRECTORY @directory.setter def directory(self, value): """Redirect assignment from `directory` to `chroot_directory`.""" self.chroot_directory = value @mutable_property def schroot_command(self): """ The command used to run the `schroot` program. This is a list of strings, by default the list contains just :data:`SCHROOT_PROGRAM_NAME`. The :attr:`chroot_directory`, :attr:`chroot_name` and :attr:`chroot_user` properties also influence the `schroot` command line used. """ return [SCHROOT_PROGRAM_NAME]

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peter@peterodding.com

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/src/itsmservice/apps/accounts/views.py

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no_license

sunyaxiong/itsmservice

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import logging import requests import json import urllib.parse as urllib from django.shortcuts import render from django.http import HttpResponseRedirect, JsonResponse from django.contrib import auth,messages from django.contrib.auth.decorators import login_required from .forms import UserForm from .forms import ProfileForm, PassResetForm from .models import Profile, MessageAlert from apps.cas_sync import models as cas_model from itsmservice import settings logger = logging.getLogger("django") def index(request): return render(request, 'index.html') def login(request): if request.method == 'GET': form = UserForm() return render(request, "login.html") else: form = UserForm(request.POST) if form.is_valid(): username = request.POST.get('username') password = request.POST.get('password') # user = auth.authenticate(username=username, password=password) attrs = { "service": "http://{}".format(settings.SUCC_REDIRECT_URL) } url_attrs = urllib.urlencode(attrs) print(settings.SUCC_REDIRECT_URL) print(url_attrs) cas_login_url = "{}login?{}".format( settings.CAS_SERVER_URL, url_attrs ) post_data = { "username": username, "password": password, } res = requests.post(cas_login_url, json.dumps(post_data)) print(dir(res)) print(res.status_code) # return JsonResponse({}) return HttpResponseRedirect("/") else: return render(request, "login.html") @login_required def logout(request): auth.logout(request) return HttpResponseRedirect("/accounts/login/") def user_profile(request): user = request.user url = request.META.get("HTTP_REFERER") profile, profile_created = Profile.objects.get_or_create(username=user.username) if request.method == "POST": if request.POST.get("destroy"): # 用户销毁 username = request.user.username try: cas_user = cas_model.app_user.objects.using("cas_db").get(username=username) cas_user.delete(using="cas_db") except Exception as e: logger.info("cas用户: {} 删除异常".format(username), e) return HttpResponseRedirect("/accounts/logout/") form = ProfileForm(request.POST) if form.is_valid(): data = form.data email = data.get("email") phone = data.get("phone") profile.email = email profile.phone = phone profile.save() return HttpResponseRedirect(url) else: messages.warning(request, "数据收敛失败") return HttpResponseRedirect(url) else: if profile_created: messages.warning(request, "用户配置文件自动创建,请维护具体信息") form = ProfileForm() return render(request, "user_profile.html", locals()) def pwd_restet(request): """ cas密码修改 :param request: :return: """ url = request.META.get("HTTP_REFERER") username = request.user.username if request.method == "POST": form = PassResetForm(request.POST) if form.is_valid(): data = form.data try: user = cas_model.app_user.objects.using("cas_db").get( username=username, ) user.password = data.get("password") user.save(using="cas_db") return HttpResponseRedirect("/accounts/logout/") except Exception as e: logger.info(e) messages.warning(request, "用户不存在") return HttpResponseRedirect(url) else: logger.info("密码修改数据提交失败") messages.warning(request, "密码提交失败,请重试") return HttpResponseRedirect("url") def user_confirm(request, pk): page_header = "新用户审核" confirm_message = MessageAlert.objects.get(id=int(pk)) content_list = confirm_message.content.split("-") org, department, username = content_list[0], content_list[1], content_list[2] profile = Profile.objects.filter(username=username).first() if request.method == "GET": return render(request, 'itsm/user_info_confirm.html', locals()) elif request.method == "POST": pass return render(request, 'itsm/issue_detail.html', locals()) def user_confirm_accept(request): message_id = request.GET.get("id") try: message_info = MessageAlert.objects.get(id=int(message_id)) # 用户激活 user = User.objects.get(username=message_info.initiator) user.is_active = 1 user.is_staff = 1 user.save() # 消息查阅 message_info.checked = 1 message_info.save() # cas 用户创建逻辑放到审核消息 cas_user, _ = cas_model.app_user.objects.using("cas_db").get_or_create( username=user.username, ) if _: cas_user.password = user.password cas_user.save(using="cas_db") logger.info("CAS用户: {} 注册成功".format(cas_user.username)) logger.info("用户信息审核成功") return HttpResponseRedirect("/itsm/event_list/") except Exception as e: logger.info(e, "用户信息审核失败") messages.warning(request, "用户信息审核失败") return HttpResponseRedirect("/itsm/event_list/") def user_confirm_reject(request): url = request.META.get('HTTP_REFERER') message_id = request.GET.get("message_id") try: message_info = MessageAlert.objects.get(id=message_id) # 消息查阅 message_info.checked = 1 message_info.save() except Exception as e: logger.info(e) return HttpResponseRedirect(url)

[ "sunyaxiong" ]

sunyaxiong

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no_license

afandel/helloWorldProject

fc812dbf655ee27fcb6c889168e295c3c7a88e25

f01a7aac5b208fb9a4f3c2a9a72b70dae7a1ca01

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from django.urls import path from .views import homePageView urlpatterns = [ path('', homePageView, name='home') ]

[ "adam.fandel@snhu.edu" ]

adam.fandel@snhu.edu

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/Beginer/K-Fibonnaci/k-fibonacci.py

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[]

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melwyn95/CodeChef-Problems

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refs/heads/master

2020-06-21T18:26:11.130334

2017-12-26T12:06:58

74,776,576

0

null

UTF-8

Python

false

204

py

n, k = map(int, raw_input().split()) a = [0] def T(n, k): if n <= k: return 1 return a[n-1] - a[n-k-1] for i in range(1, n+1): a.append(a[i-1] + T(i, k)) print (a.pop()-a.pop()) % 1000000007

[ "melwyn95@gmail.com" ]

melwyn95@gmail.com

751894ece0a71fa109934a6abbd09b3e1c5b95a4

28cabf3c73c667a92a7a43c0a61fc5b672034cb4

/src/workouts/migrations/0004_rowingworkout_owner.py

d4b4b799769c0c22a1e9b0b5bbbff9d54dc6d2b7

[]

no_license

ashleyelaine/LogMyRow

48943efabcb6b3048871eab95d0cba8eab46c4a3

24d0447670ab7aaefa4b413d207eb6c784ef1662

refs/heads/master

2021-09-07T08:09:07.765560

2018-02-09T20:58:10

113,527,859

2

0

null

UTF-8

Python

false

696

py

# -*- coding: utf-8 -*- # Generated by Django 1.11.2 on 2018-01-18 21:02 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('workouts', '0003_auto_20171209_2023'), ] operations = [ migrations.AddField( model_name='rowingworkout', name='owner', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), preserve_default=False, ), ]

[ "ashleyelainewright@gmail.com" ]

ashleyelainewright@gmail.com

52dba0de037cc4fcaccbec0e66a5651c875897d3

efcc32acc681f0cf88416b75623cf976e71f6447

/data_utils.py

bfba59f627fac61f139b259554e64e518d818e01

[]

no_license

jacobperricone/224w

7d5aacc525d25fe27d7c0e55c38dc920c7428ebe

8a3441371c3198c5d09ae8d7d0701df4b988371d

refs/heads/master

2022-12-03T17:35:25.905597

2020-08-17T16:32:26

113,819,720

1

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UTF-8

Python

false

6,129

py

from lxml import html import blocks.create_dict_from_element.core as cdfe from multiprocessing.pool import ThreadPool import logging USER_AGENTS = [ 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.96 Safari/537.36', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_5) AppleWebKit/603.2.4 (KHTML, like Gecko) Version/10.1.1 Safari/603.2.4', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36', 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36', 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36', 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/40.0.2214.85 Safari/537.36', 'Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36', 'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:54.0) Gecko/20100101 Firefox/54.0', 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:54.0) Gecko/20100101 Firefox/54.0', 'Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko', 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.10; rv:34.0) Gecko/20100101 Firefox/34.0', 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:54.0) Gecko/20100101 Firefox/54.0' ] PROXIES = [ {'http': 'http://solutionloft:fallSL2016!@us-il.proxymesh.com:31280', 'https': 'http://solutionloft:fallSL2016!@us-il.proxymesh.com:31280'}, {'http': 'http://solutionloft:fallSL2016!@us.proxymesh.com:31280', 'https': 'http://solutionloft:fallSL2016!@us.proxymesh.com:31280'}, {'http': 'http://solutionloft:fallSL2016!@us-dc.proxymesh.com:31280', 'https': 'http://solutionloft:fallSL2016!@us-dc.proxymesh.com:31280'}, {'http': 'http://solutionloft:fallSL2016!@us-ca.proxymesh.com:31280', 'https': 'http://solutionloft:fallSL2016!@us-ca.proxymesh.com:31280'}, {'http': 'http://solutionloft:fallSL2016!@us-ny.proxymesh.com:31280', 'https': 'http://solutionloft:fallSL2016!@us-ny.proxymesh.com:31280'}, {'http': 'http://solutionloft:fallSL2016!@us-ny.proxymesh.com:31280', 'https': 'http://solutionloft:fallSL2016!@us-ny.proxymesh.com:31280'}, {'http': 'http://solutionloft:fallSL2016!@de.proxymesh.com:31280', 'https': 'http://solutionloft:fallSL2016!@de.proxymesh.com:31280'}, {'http': 'http://solutionloft:fallSL2016!@nl.proxymesh.com:31280', 'https': 'http://solutionloft:fallSL2016!@nl.proxymesh.com:31280'}, {'http': 'http://solutionloft:fallSL2016!@sg.proxymesh.com:31280', 'https': 'http://solutionloft:fallSL2016!@sg.proxymesh.com:31280'}, {'http': 'http://solutionloft:fallSL2016!@uk.proxymesh.com:31280', 'https': 'http://solutionloft:fallSL2016!@uk.proxymesh.com:31280'} ] logger = logging.getLogger('stackoverflow') logger.setLevel(logging.DEBUG) def get_code(x): """ parses code from an html body object :param x: HTML object :return: Code as a string """ try: code = x[0].xpath(x[1]) if len(code): if len(code) == 1: return code[0].replace('>>>', '').replace('...', '') else: return "[break]".join(code).replace('>>>', '').replace('...', '\t') else: return None except Exception as e: logger.error("Failed in fetching question code: {}".format(e)) return None def get_text(x): """ parses text from an html body object :param x: HTML object :return: Text of body as a string """ try: text = x[0].xpath(x[1]) if len(text): if len(text) == 1: return text[0] else: return "\n".join(text) else: return None except Exception as e: logger.error("Failed in fetching text {}".format(e, x)) return None def parse(page, settings): """ :param page: an html object corresponding to the body to be examined :param settings: a dictionary specifiying attributes :return: a list of parsed results """ results = [] try: val_xpath = settings['xpath'] inputs = [(x, settings['per_item'], settings['aux']) for x in page.xpath(val_xpath)] # logger.info(inputs) if len(inputs) > 1: num_processes = 10 pool = ThreadPool(num_processes) results = pool.map(unpack, inputs) pool.close() pool.join() else: if inputs: results = [cdfe.main(*inputs[0])] else: results = [] return results except Exception as error: logger.info("Failed to get info for data elements with and error {}" .format(error)) return results def unpack(x): res = cdfe.main(*x) return res def parse_body(body): page = html.fromstring(body) question_settings = [ {"keyName": "code", "xpath": "//*[local-name() = 'code']/text()", "func": get_code, "val": None}, {"keyName": "text", "xpath": "//*[local-name() != 'code']/text()", "func": get_text, "val": None}, ] question_inputs = { 'settings': {'xpath': ".", "global_entries": [], 'aux': [], 'per_item': question_settings } } results = parse(page, **question_inputs) return results[0]

[ "jacobperricone@gmail.com" ]

jacobperricone@gmail.com

0a2a7ce05cf67f530675273c267d6596dc1d0fa9

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/devtrac/main/templatetags/extra_filters.py

e39c4b2745838f833ed9f23c2056fec2cb209856

[]

no_license

onaio/ona-devtrac

ae99fa9f3a4d6212a573ee42b81a760f7c5d25e1

9a8717a00ed9f8dc9e258d879480b2e8f88e886b

refs/heads/master

2021-05-27T22:22:14.622115

2014-06-06T13:03:50

15,524,099

0

null

UTF-8

Python

false

135

py

from django import template register = template.Library() @register.filter(name='get') def get(d, key): return d.get(key, None)

[ "ukanga@gmail.com" ]

ukanga@gmail.com

2e35a2db7708b9c8f6dd10eedd43ae780b7c92c7

7b6b46395b0e8748282916186c608ce15356e0df

/Flask/Flask_database/DatabaseInViews/adoption.py

836b611008939703fed5c4c729af3ec5fe0d9f4d

[]

no_license

Hopw06/Web

1a2d2499a19153552b7452c0b9200af4bfa3bbd2

763ff13a60903f0969db30e924c76426669fd04c

refs/heads/master

2023-01-12T14:03:23.886586

2020-11-14T14:59:16

303,758,846

0

null

UTF-8

Python

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3,793

py

import os from forms import AddForm, DelForm, AddOwnerForm from flask import Flask, render_template, url_for, redirect from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate app = Flask(__name__) app.config['SECRET_KEY'] = 'mysecretkey' ####################### ##### SQL Database ##### ####################### basedir = os.path.abspath(os.path.dirname(__file__)) app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + \ os.path.join(basedir, 'data.sqlite') app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False db = SQLAlchemy(app) migrate = Migrate(app, db) ###################### #### Models ########### ###################### class Puppy(db.Model): __tablename__ = 'puppies' id = db.Column(db.Integer, primary_key=True) name = db.Column(db.Text) owner = db.relationship("Owner", backref="puppy", uselist=False) def __init__(self, name): self.name = name def __repr__(self): if self.owner == None: return "Puppy name: {}, and has not owner yet".format(self.name) return "Puppy name {}, and has owner: {}".format(self.name, self.owner.name) class Owner(db.Model): __tablename__ = 'owner' id = db.Column(db.Integer, primary_key=True) name = db.Column(db.Text) id_pup = db.Column(db.Integer, db.ForeignKey("puppies.id")) def __init__(self, name, id_pup): self.name = name self.id_pup = id_pup ####################################### ###### View functions - Have Forms ######## ####################################### @app.route("/") def index(): return render_template("home.html") @app.route("/add", methods=["GET", "POST"]) def add_pup(): form = AddForm() if form.validate_on_submit(): name = form.name.data if name != None and name != "": exists = Puppy.query.filter_by(name=name).first() if exists == None: new_pup = Puppy(name) db.session.add(new_pup) db.session.commit() return redirect(url_for('list_pups')) else: return render_template("error.html", error="Puppy is already in system.") else: return render_template("error.html", error="Please enter puppy name.") return render_template("add.html", form=form) @app.route("/list") def list_pups(): puppies = Puppy.query.all() return render_template("list.html", puppies=puppies) @app.route("/delete", methods=["GET", "POST"]) def delete_pup(): form = DelForm() if form.validate_on_submit(): id = form.id.data pup = Puppy.query.get(id) if pup != None: db.session.delete(pup) db.session.commit() return redirect(url_for('list_pups')) else: return render_template("error.html", error="Id you provided is not valid.") return render_template("delete.html", form=form) @app.route("/addOwner", methods=["GET", "POST"]) def add_owner(): form = AddOwnerForm() if form.validate_on_submit(): id_puppy = form.id_puppy.data owner = form.owner.data if owner != "" and id_puppy != "": puppy = Puppy.query.get(id_puppy) if puppy != None and puppy.owner == None: owner = Owner(owner, id_puppy) db.session.add(owner) db.session.commit() return redirect(url_for('list_pups')) else: return render_template("error.html", error="Puppy id you provided is not valid") else: return render_template("error.html", error="Please enter owner's name and puppy id.") return render_template("addOwner.html", form=form) if __name__ == "__main__": app.run(debug=True)

[ "vuxuanphong06@gmail.com" ]

vuxuanphong06@gmail.com

b3d474831ed9dde5f79af090a1517ff329248f8d

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/myblog/boards/views.py

81940479dcda58361f9af8a2e084c4efeef3129e

[]

no_license

yanHuaiQ/tencent-cloud

21ef8ead44b7941557388bca820dd862dd1815b7

f793982ee2c8d5b02a5dd840fc53313bcef2f857

refs/heads/master

2021-05-21T18:03:54.637474

2020-04-03T13:54:27

252,746,006

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null

UTF-8

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false

121

py

from django.shortcuts import render def home(request): return HttpResponse('Hello World') # Create your views here.

[ "841543613@qq.com" ]

841543613@qq.com

a35580442a22de2f6b308f8d52a0c5a22d032ee1

67033f2a1c74822398846836d79bb9fc5b914b87

/blog/migrations/0006_auto_20200323_1241.py

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[]

no_license

Adi1222/Blog-Project

9d4b3ef94428622fb0b85356eb9584105aeda19b

6299d28122ca0d4b9d8437307c8730fc4797ef76

refs/heads/master

2021-04-17T23:18:49.037312

2020-08-16T06:51:26

249,484,778

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null

UTF-8

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405

py

# Generated by Django 2.2.6 on 2020-03-23 07:11 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('blog', '0005_auto_20200323_0150'), ] operations = [ migrations.AlterField( model_name='userposts', name='img', field=models.ImageField(blank=True, upload_to='blog_image'), ), ]

[ "adityachavan677@gmail.com" ]

adityachavan677@gmail.com

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/main.py

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[]

no_license

luciql33t/telegalolz

5dd6c8f637087770b5dfe6f40a77ba5a7bb04e45

23aa8ae0ca17e86c810d702e5222f51d9e745f7c

refs/heads/main

2023-01-04T07:56:56.766174

2020-11-03T11:06:13

309,659,078

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null

UTF-8

Python

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652

py

import requests import bs4 as bs import time url = 'https://t.me/' t = 2000000 with open('list.txt', 'r') as f: ids = [line.strip() for line in f] while True: for id in ids: page = requests.get(url + id) soup = bs.BeautifulSoup(page.text, 'html.parser') stat = soup.findAll('div', class_='tgme_page_title') if not stat: print(id + ' ----------------------------------------!!!') with open('file.txt', 'a') as file: file.write(id + '\n') else: print(id + ' ') time.sleep(1) time.sleep(t) print(stat)

[ "noreply@github.com" ]

luciql33t.noreply@github.com

5c865faf8ac12abbd353e5aa7fa31f5a337f5249

ed76db3a268a9253837e85130c0f221bd904bff0

/DP/[x] 560. Subarray Sum Equals K.py

85e9cb792a05b9869a13dd45ba2d1f4cf74e2541

[]

no_license

jay-joo-code/leetcode

f54db01f195f35d436e524d6e257ad755525eb69

349bd6d54a3f463499b9f59d7aec01c9dd1fc9d0

refs/heads/master

2022-11-30T21:17:34.602100

2020-08-09T05:55:37

null

0

null

UTF-8

Python

false

1,175

py

# attempt 1 (failed) def subarraySum(self, nums: List[int], k: int) -> int: # doesn't consider negative elements in nums acc = 0 count = 0 for i in range(len(nums)): for j in range(i, len(nums)): acc += nums[j] if acc == k: count += 1 acc = 0 break elif acc > k: acc = 0 break return count # attempt 2 (failed) def subarraySum(self, nums: List[int], k: int) -> int: # uses matrix as DP to store previous sums # still exceeds time limit count = 0 matrix = [[None] * len(nums) for _ in range(len(nums))] for i in range(len(nums)): if nums[i] == k: count += 1 matrix[i][i] = nums[i] for row in range(len(nums)-1): for col in range(row+1, len(nums)): if row == 0: matrix[row][col] = matrix[row][col-1] + nums[col] else: matrix[row][col] = matrix[row-1][col] - nums[row-1] if matrix[row][col] == k: count += 1 return count # solution def subarraySum(self, nums: List[int], k: int) -> int: count = 0 sum = 0 sums = { 0: 1 } for num in nums: sum += num if sum-k in sums: count += sums[sum-k] if sum in sums: sums[sum] += 1 else: sums[sum] = 1 return count

[ "jae@Jaes-MacBook-Air.local" ]

jae@Jaes-MacBook-Air.local

7db5d35dbccea6fead70c32527134a787b99e276

1fe186fc74156a27bf632ab3694e38b9219b86a8

/python/test_data.py

12a560c9fb35847a139de6aa6c9a0039dde4489c

[ "LicenseRef-scancode-public-domain", "BSD-2-Clause", "BSD-3-Clause", "LicenseRef-scancode-generic-cla" ]

permissive

qchenclaire/caffe

53b0afb4963d8e9a50f45342e92ab4499012614e

e7b40681209b1266af919d76d4198c6d2560ae5b

refs/heads/master

2021-01-19T13:11:18.013451

2017-04-12T22:47:40

88,069,682

1

0

null

2017-04-12T15:48:22

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UTF-8

Python

false

554

py

import caffe import numpy as np import random import os, struct from array import array import share_data as sd #from lmdb_reader import Read_Render4CNN from lmdb_para import read_lmdb import scipy.misc import time import pdb import cPickle as pickle # class Render4CNNLayer_sub(caffe.Layer): def setup(self, bottom, top): print 'setup' def reshape(self, bottom, top): print 'reshape' def forward(self, bottom, top): print 'forward' def backward(self, top, propagate_down, bottom): print 'backward'

[ "qchen42@jhu.edu" ]

qchen42@jhu.edu

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/src/OTLMOW/OTLModel/Datatypes/DtcBeschermingVraatschade.py

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[ "MIT" ]

permissive

davidvlaminck/OTLMOW

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48f8c357c475da1d2a1bc7820556843d4b37838d

refs/heads/main

2023-01-12T05:08:40.442734

2023-01-10T15:26:39

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3

1

MIT

2022-06-20T20:36:00

2021-11-28T10:28:24

Python

UTF-8

Python

false

2,781

py

# coding=utf-8 from OTLMOW.OTLModel.BaseClasses.AttributeInfo import AttributeInfo from OTLMOW.OTLModel.BaseClasses.OTLAttribuut import OTLAttribuut from OTLMOW.OTLModel.Datatypes.BooleanField import BooleanField from OTLMOW.OTLModel.Datatypes.ComplexField import ComplexField from OTLMOW.OTLModel.Datatypes.KlMateriaalBeschermingVraatschade import KlMateriaalBeschermingVraatschade # Generated with OTLComplexDatatypeCreator. To modify: extend, do not edit class DtcBeschermingVraatschadeWaarden(AttributeInfo): def __init__(self, parent=None): AttributeInfo.__init__(self, parent) self._materiaal = OTLAttribuut(field=KlMateriaalBeschermingVraatschade, naam='materiaal', label='materiaal', objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#DtcBeschermingVraatschade.materiaal', definition='De middelen als bescherming tegen vraatschade.', owner=self) self._tegenMaaischade = OTLAttribuut(field=BooleanField, naam='tegenMaaischade', label='tegen maaischade', objectUri='https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#DtcBeschermingVraatschade.tegenMaaischade', definition='Aanduiding of er bescherming tegen maaischade aanwezig is.', owner=self) @property def materiaal(self): """De middelen als bescherming tegen vraatschade.""" return self._materiaal.get_waarde() @materiaal.setter def materiaal(self, value): self._materiaal.set_waarde(value, owner=self._parent) @property def tegenMaaischade(self): """Aanduiding of er bescherming tegen maaischade aanwezig is.""" return self._tegenMaaischade.get_waarde() @tegenMaaischade.setter def tegenMaaischade(self, value): self._tegenMaaischade.set_waarde(value, owner=self._parent) # Generated with OTLComplexDatatypeCreator. To modify: extend, do not edit class DtcBeschermingVraatschade(ComplexField, AttributeInfo): """Complex datatype voor bescherming van de stam tegen knaagdieren.""" naam = 'DtcBeschermingVraatschade' label = 'Bescherming vraatschade' objectUri = 'https://wegenenverkeer.data.vlaanderen.be/ns/onderdeel#DtcBeschermingVraatschade' definition = 'Complex datatype voor bescherming van de stam tegen knaagdieren.' waardeObject = DtcBeschermingVraatschadeWaarden def __str__(self): return ComplexField.__str__(self)

[ "david.vlaminck@mow.vlaanderen.be" ]

david.vlaminck@mow.vlaanderen.be

3854d86404411f1bba56b485b61bfda44efeb2f0

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/menu/admin.py

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[]

no_license

f4225e0653/recipedb

729c53495a9329290a30f9b821e9def233d14bd4

d03943b6da5e0ab900fcd9d44388ff5997757eab

refs/heads/master

2021-01-22T20:55:43.598122

2017-03-18T07:54:17

85,380,391

0

null

UTF-8

Python

false

176

py

from django.contrib import admin from .models import Recipe, RecipeIngredients # Register your models here. admin.site.register(Recipe) admin.site.register(RecipeIngredients)

[ "nobody@nowhere.local" ]

nobody@nowhere.local

599d158a305a75174cd7625ca13fb5240c520bcb

ceea602d004e21c85c925b3b5b259db49eec17d7

/flatpage_extended/urls.py

f09e3f5b2d2b044993e71b30481df8264dc03227

[]

no_license

seivan/django_blog

38426c4477a7e12c50534ecfaadac9a569556f62

9cb7a35d6a5b5e29ff8c4ae5cb3c0c1441191c6b

refs/heads/master

2020-12-24T13:17:22.003468

2009-11-25T04:27:52

null

0

null

UTF-8

Python

false

143

py

from django.conf.urls.defaults import * urlpatterns = patterns('seivanheidari.flatpage_extended.views', (r'^(?P<url>.*)$', 'flatpage'), )

[ "seivan@kth.se" ]

seivan@kth.se

1327e5c7a206f319618cd37031e6f11e523d0d80

e942ac062e2a217735e37e9133dccb26cef4184e

/clawer/clawer/apis/user.py

9075d355b517f009b0dba4d10e7ca64c986b7f2a

[]

no_license

xcctbys/crawl

11cc432b39866c5965d7511ff03cb45607db2244

95a5b590a82f20a22731b31dfe551a35b4b31fa1

refs/heads/master

2021-01-21T20:42:45.734344

2016-06-28T02:38:19

92,269,774

2

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#coding=utf-8 import json import hashlib import datetime from django.contrib.auth import authenticate from django.contrib.auth.models import User as DjangoUser, Group from django.contrib.auth import login as djangologin from django.contrib.auth import logout as djangologout from django.core import exceptions from django.core.urlresolvers import reverse from django.utils.encoding import smart_unicode from html5helper.decorator import render_json from clawer.utils import check_auth_for_api from clawer.models import UserProfile, MenuPermission @render_json def login(request): username = request.GET.get("username") password = request.GET.get("password") if request.user.is_authenticated(): {"is_ok":True, "profile":request.user.get_profile().as_json()} user = authenticate(username=username, password=password) if not user: return {"is_ok":False, "reason":u"用户不存在或密码错误"} if user.is_superuser and user.is_staff and user.is_active: djangologin(request, user) return {"is_ok":True, "profile":user.get_profile().as_json()} if MenuPermission.has_perm_to_enter(user) == False: return {'is_ok':False, "reason":u"权限不足"} djangologin(request, user) return {"is_ok":True, "profile":user.get_profile().as_json()} @render_json @check_auth_for_api def keepalive(request): return {"is_ok":True, "profile":request.user.get_profile().as_json()} @render_json def logout(request): djangologout(request) return {"is_ok":True} @render_json @check_auth_for_api def get_my_menus(request): return MenuPermission.user_menus(request.user) @render_json def is_logined(request): request.session["to"] = request.GET.get("to") or "" if request.user.is_authenticated() is False: result = {"is_ok":False} return result result = {"is_ok":True, "profile":request.user.get_profile().as_json()} return result

[ "xiaotaop@princetechs.com" ]

xiaotaop@princetechs.com

9759de82b067631295a9df8a9613f0a5f0fb3f71

7c06182477f708693a39bb450d096291eaa2b89d

/ui/ui_Dialog.py

7376538b222fc77fad5b7161592cef7708cdc146

[]

no_license

Kracav4ik/zk3v

dee7414a007bc32833ca6ec31a4acdb505b6de5d

febd7b1f8f889355fccede96a68b4a5de425cc89

refs/heads/master

2020-07-02T19:21:19.239014

2020-02-07T20:42:28

201,636,973

0

null

UTF-8

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# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'ui\dialog.ui' # # Created by: PyQt5 UI code generator 5.12.1 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_Dialog(object): def setupUi(self, Dialog): Dialog.setObjectName("Dialog") Dialog.resize(270, 90) sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed) sizePolicy.setHorizontalStretch(0) sizePolicy.setVerticalStretch(0) sizePolicy.setHeightForWidth(Dialog.sizePolicy().hasHeightForWidth()) Dialog.setSizePolicy(sizePolicy) Dialog.setMaximumSize(QtCore.QSize(270, 90)) self.verticalLayout = QtWidgets.QVBoxLayout(Dialog) self.verticalLayout.setObjectName("verticalLayout") self.label = QtWidgets.QLabel(Dialog) self.label.setObjectName("label") self.verticalLayout.addWidget(self.label) self.comboBox = QtWidgets.QComboBox(Dialog) self.comboBox.setEditable(True) self.comboBox.setObjectName("comboBox") self.verticalLayout.addWidget(self.comboBox) self.buttonBox = QtWidgets.QDialogButtonBox(Dialog) self.buttonBox.setOrientation(QtCore.Qt.Horizontal) self.buttonBox.setStandardButtons(QtWidgets.QDialogButtonBox.Cancel|QtWidgets.QDialogButtonBox.Ok) self.buttonBox.setObjectName("buttonBox") self.verticalLayout.addWidget(self.buttonBox) self.retranslateUi(Dialog) self.buttonBox.accepted.connect(Dialog.accept) self.buttonBox.rejected.connect(Dialog.reject) QtCore.QMetaObject.connectSlotsByName(Dialog) def retranslateUi(self, Dialog): _translate = QtCore.QCoreApplication.translate Dialog.setWindowTitle(_translate("Dialog", "Change server address")) self.label.setText(_translate("Dialog", "Type your address and port"))

[ "dikama2013@yandex.ru" ]

dikama2013@yandex.ru

faeac85f38f5106c670b705a87a72d830f2d7a29

341446cb13aa6a09bc10bc6525a339e3b1cd45f2

/ensembler.py

86ffe14b04082f6d650564851ee1d6d14d077a6e

[]

no_license

seamusl/OpenNAS-v1

ca86cfc6d7486a3c53103f2be260a6468afb7ccc

8983ab8cf5b7cf39c8fb1c6709cddb9944fcf46f

refs/heads/master

2023-03-25T12:54:23.976658

2021-01-10T20:03:00

264,767,562

0

null

2021-03-25T23:45:49

2020-05-17T22:20:21

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# Copyright (c) 2020 Seamus Lankford # Licensed under MIT License import warnings warnings.simplefilter(action='ignore') from keras.utils import to_categorical from sklearn.metrics import accuracy_score import tensorflow as tf from tensorflow.keras.models import load_model import numpy as np import matplotlib.pyplot as plt import joblib from pre_processor import pre_processor import config as cfg def fit_model(ui): print("Compiling model...") # initialize the optimizer and model opt = tf.keras.optimizers.SGD(lr=0.0001) model = load_model(ui.m_name) # LOAD autonas base model print(model.summary()) config = model.to_json() loaded_model = tf.keras.models.model_from_json(config) loaded_model.compile(loss="sparse_categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) print("Training base learner...") # train the network loaded_model.fit(trainX, trainY, validation_data=(testX, testY), batch_size=32, epochs=ui.epochs, verbose=2) return loaded_model def load_models(num_models): # load models from disk all_models = list() for i in range(num_models): filename = 'model_' + str(i + 1) + '.h5' # filename for this ensemble model = tf.keras.models.load_model(filename) # load model from the file all_models.append(model) # add model to the list print('Loaded %s' % filename) return all_models def stacked_dataset(members, inputX): stackX = None # initially no layers in stack for model in members: y_pred = model.predict(inputX, verbose=0) # make prediction if stackX is None: # stack predictions into [rows, members, probabilities] stackX = y_pred # add first layer to stack else: stackX = np.dstack((stackX, y_pred)) # add new layer to stack # flatten predictions to [rows, members x probabilities] stackX = stackX.reshape((stackX.shape[0], stackX.shape[1]*stackX.shape[2])) return stackX def stacked_prediction(members, model, inputX): # make a prediction with the stacked model stackedX = stacked_dataset(members, inputX) # create dataset using ensemble y_pred = model.predict(stackedX) # make predictions return y_pred # Using ensemble outputs, create stacked training dataset for meta learner. # feed outputs from ensemble base learners and fit a meta learner. def fit_stacked_model(members, inputX, inputy, algorithm): # create meta learner data set using ensemble base learner predictions. The features of the new data set # returned, stackedX, are simply the predictions of each of the base learners for each instance. Therefore # more base learners => greater number of features in the new data set. stackedX and inputy (i.e. the # corresponding correct label outputs are used to fit a new model with the chosen classifier. stackedX = stacked_dataset(members, inputX) # create data set from ensemble base learners # start of Phase B model = algorithm # assign user defined model algorithm for training # fit using aggregate feature data from base learners and output labels model.fit(stackedX, inputy) return model def ensemble_classifiers(base_learners, ui): testYc = to_categorical(testY) # evaluate standalone models on test set # performance of standalone models can be compared with ensemble performance for model in base_learners: _, acc = model.evaluate(testX, testYc, verbose=0) print('Model Accuracy: %.4f' % acc) results = [] names = [] for name, meta in cfg.meta_learners: # evaluate multiple classifier models print("Training meta learner with ", meta, "...") # train the meta learner wi # th its own data set # fit stacked model using the ensemble model = fit_stacked_model(base_learners, testX, testY, meta) # evaluate meta learner on test set y_pred = stacked_prediction(base_learners, model, testX) acc = accuracy_score(y_pred, testY) results.append(acc) names.append(name) print('Ensemble Meta learner Test Accuracy: %.4f' % acc) # since we are using a sci-kit learn classifier (and not keras), use joblib library to store model filename = 'model_' + str(name) + '.sav' # save the model to disk joblib.dump(model, filename) print('Saved %s' % filename) plt.figure(figsize=(9, 3)) plt.subplot(132) plt.scatter(names, results) plt.suptitle('Algorithm Comparison') plt.savefig('ensemble_comparison.png') return def create_base_learners(ui): # fit each base learner with same dataset and save models # weights of each model randomly initialised # => different base learner model saved with each iteration for i in range(ui.n_base): H = fit_model(ui) filename = 'model_' + str(i + 1) + '.h5' H.save(filename) # save model print('Saved %s' % filename) return def ensemble(ui): global trainX, trainY, testX, testY # these globals only needed within ensemble module print("[INFO] pre-processing", ui.dataset, "...") trainX, trainY, testX, testY = pre_processor(ui) if ui.learners: create_base_learners(ui) # train meta-learner using predictions from base learners base_learners = load_models(ui.n_base) # load all models print('Loaded %d models' % len(base_learners)) # check if all base learner models loaded ensemble_classifiers(base_learners, ui) return

[ "noreply@github.com" ]

seamusl.noreply@github.com

a75ddc8ce256a732867b2936e5cbaf3093f8f37b

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/practice2.py

4bd5b772868ad50712a8d398331bb5aa59126160

[]

no_license

enochnord/hello-world

71a4d2f3a1cbae84f1896c5296a675ffd94907b4

19eccd30ccd68d4c619b0676af97b131a26e0286

refs/heads/master

2020-05-18T14:05:51.242334

2020-01-15T19:33:50

184,460,847

0

null

2019-05-01T18:38:12

2019-05-01T18:18:51

null

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852

py

stuff = {'rope': 1, 'torch': 6, 'gold coin': 42, 'dagger': 1, 'arrow': 12} def displayInventory(inventory): total_items = 0 for item, quantity in inventory.items(): print(str(quantity)+' '+item) total_items += quantity print("Total number of items: "+str(total_items)) displayInventory(stuff) def addToInventory(inventory, addedItems): for item in addedItems: inventory.setdefault(item, 0) #this adds a (defaulted to zero value) key to the inventory dict if it's not already there inventory[item] += 1 #and this increases that value by one, each time that item appears in the loot list return inventory inv = {'gold coin': 42, 'rope': 1} dragonLoot = ['gold coin', 'dagger', 'gold coin', 'gold coin', 'ruby'] inv = addToInventory(inv, dragonLoot) displayInventory(inv)

[ "noreply@github.com" ]

enochnord.noreply@github.com

0f1563570949b62727a7761f33d868a9e9c786b9

98d7d234b105c9fe55d1338d611e427fd8bc3552

/autotest_platform/kernel_os_kvm-test/log.py

fda93c60b2f273f6229bf5810384b1b2ff409433

[]

no_license

jianxiamage/autotest_platform

b0af178060c61d0b557e7d85c83c535b585a56c1

d5db850e1cf8c1fb5c7c810de1eca9484257975f

refs/heads/master

2020-05-19T12:45:29.857986

2019-05-05T11:34:16

185,022,420

0

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#!/usr/bin/env python #coding=utf-8 import os import sys import logging #日志等级（level）描述 #DEBUG 最详细的日志信息，典型应用场景是问题诊断 #INFO 信息详细程度仅次于DEBUG，通常只记录关键节点信息，用于确认一切都是按照我们预期的那样进行工作 #WARNING 当某些不期望的事情发生时记录的信息（如，磁盘可用空间较低），但是此时应用程序还是正常运行的 #ERROR 由于一个更严重的问题导致某些功能不能正常运行时记录的信息 #CRITICAL 当发生严重错误，导致应用程序不能继续运行时记录的信息 #%Y-%m-%d\ %T #LOG_FORMAT = "%(asctime)s - %(levelname)s - %(user)s[%(ip)s] - %(message)s" #DATE_FORMAT = "%m/%d/%Y %H:%M:%S %p" #For example: #logging.debug('debug 信息') #logging.info('info 信息') #logging.warning('warning 信息') #logging.error('error 信息') #logging.critical('critial 信息') logging.basicConfig(level=logging.INFO, #控制台打印的日志级别 filename='/var/log/kernel_os_kvm_Test.log', filemode='a', #模式，有w和a，w就是写模式，每次都会重新写日志，覆盖之前的日志 #a是追加模式，默认如果不写的话，就是追加模式 format= #'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s' '[%(levelname)s : %(asctime)s] : %(message)s', #datefmt = "%m/%d/%Y %H:%M:%S" datefmt = "%Y-%m-%d %T" ) if __name__ == '__main__': strArgs=sys.argv

[ "mazhongzheng@loongson.cn" ]

mazhongzheng@loongson.cn

288a7b08c5cf71f0640230acae8d22b897df5ede

e780a5bd72f98ca2513c993d64a85b08578166a6

/buildout-cache/eggs/Zope2-2.13.26-py2.7.egg/Testing/custom_zodb.py

b59b8c0f0a189811d25e949bf0df349935e37124

[]

no_license

vedantc98/Plone-test

023246597ffe848e2a49b9f65742ff49127b190b

9fd520fc78481e2c0b9b7ec427821e7f961c777e

refs/heads/master

2021-03-30T22:14:33.368739

2018-03-11T19:22:58

124,671,713

0

null

UTF-8

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py

import os import logging import ZODB LOG = logging.getLogger('Testing') def getStorage(): """ Return a storage instance for running ZopeTestCase based tests. By default a DemoStorage is used. Setting $TEST_ZEO_HOST/TEST_ZEO_PORT environment variables allows you to use a ZEO server instead. A file storage can be configured by settting the $TEST_FILESTORAGE environment variable. """ get = os.environ.get if os.environ.has_key('TEST_ZEO_HOST') and os.environ.has_key('TEST_ZEO_PORT'): from ZEO.ClientStorage import ClientStorage zeo_host = get('TEST_ZEO_HOST') zeo_port = int(get('TEST_ZEO_PORT')) LOG.info('Using ZEO server (%s:%d)' % (zeo_host, zeo_port)) return ClientStorage((zeo_host, zeo_port)) elif os.environ.has_key('TEST_FILESTORAGE'): import ZODB.FileStorage datafs = get('TEST_FILESTORAGE') LOG.info('Using Filestorage at (%s)' % datafs) return ZODB.FileStorage.FileStorage(datafs) else: from ZODB.DemoStorage import DemoStorage LOG.info('Using DemoStorage') return DemoStorage() Storage = getStorage()

[ "vedantc98@gmail.com" ]

vedantc98@gmail.com

4a82493f4fd45a7fd6c13c09e0044536e96d7feb

12bf679fe5c13baf581650cc558cd44b63c1a0d2

/problem75.py

c97dc05dc2da0de7dd2cd9b1a4e62f4c64cadaeb

[]

no_license

ganzevoort/project-euler

ed119f23cd5b63a6fcd5df6b581513c92d1790f6

0fea6b3465272895536b7ed3bff10bf3e0d84154

refs/heads/master

2022-09-12T16:19:06.696279

2022-09-02T17:03:52

9,947,769

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""" https://projecteuler.net/problem=75 Singular integer right triangles Problem 75 It turns out that 12 cm is the smallest length of wire that can be bent to form an integer sided right angle triangle in exactly one way, but there are many more examples. 12 cm: (3,4,5) 24 cm: (6,8,10) 30 cm: (5,12,13) 36 cm: (9,12,15) 40 cm: (8,15,17) 48 cm: (12,16,20) In contrast, some lengths of wire, like 20 cm, cannot be bent to form an integer sided right angle triangle, and other lengths allow more than one solution to be found; for example, using 120 cm it is possible to form exactly three different integer sided right angle triangles. 120 cm: (30,40,50), (20,48,52), (24,45,51) Given that L is the length of the wire, for how many values of L ≤ 1,500,000 can exactly one integer sided right angle triangle be formed? """ import time import math import itertools import functools import operator from collections import defaultdict from pprint import pprint import pyprimesieve def solution1(L=1500000, verbose=False): t0 = time.time() # find all a, b, c # where 0 < a < b # a**2 + b**2 = c**2 # a + b + c <= L triangles = defaultdict(list) for a in range(1, L): for b in range(a+1, L): c = math.floor(math.sqrt(a**2 + b**2)) if a**2 + b**2 != c**2: continue if a + b + c <= L: if verbose and isinstance(verbose, int) and verbose>1: print(f"{a+b+c}cm: ({a},{b},{c})") triangles[a+b+c].append((a,b,c)) if verbose: print("s1 L={} {:8}ms phase1".format( L, int(1000*(time.time()-t0)))) if isinstance(verbose, int) and verbose>1: pprint(triangles) result = sum(1 for n in triangles.values() if len(n)==1) if verbose: print("s1 L={} {:8}ms result={}".format( L, int(1000*(time.time()-t0)), result)) return result def solution2(L=1500000, verbose=False): t0 = time.time() # Is math.floor(math.sqrt(a**2 + b**2)) expensive? # if a + b + c <= L, then c < L/2 root = { c**2: c for c in range(1, L//2) } if verbose: print("s2 L={} {:8}ms precalculate roots".format( L, int(1000*(time.time()-t0)))) triangles = defaultdict(list) for bsquare, b in root.items(): if verbose and isinstance(verbose, int) and verbose>2: if b%1000 == 0: print("s2 L={} {:8}ms b={}".format( L, int(1000*(time.time()-t0)), b)) for asquare, a in root.items(): if asquare >= bsquare: break csquare = asquare + bsquare if csquare in root: c = root[csquare] if a + b + c <= L: if verbose and isinstance(verbose, int) and verbose>1: print(f"{a+b+c}cm: ({a},{b},{c})") triangles[a+b+c].append((a,b,c)) if verbose: print("s2 L={} {:8}ms phase1".format( L, int(1000*(time.time()-t0)))) if isinstance(verbose, int) and verbose>1: pprint(triangles) result = sum(1 for n in triangles.values() if len(n)==1) if verbose: print("s2 L={} {:8}ms result={}".format( L, int(1000*(time.time()-t0)), result)) return result def solution3(L=1500000, verbose=False): """ for positive integers a, b, c where a < b < c and a² + b² = c²: let x = b - a, y = c - b then a² + (a+x)² = (a+x+y)² a² + a² + 2ax + x² = a² + 2a(x+y) + (x+y)² a² + a² + 2ax + x² = a² + 2ax + 2ay + x² + 2xy + y² a² = 2ay + 2xy + y² y = 1 => a² = 2a + 2x + 1 => x = (a² - 2a - 1)/2 = a²/2 - a - 1/2 x is a positive integer, so a is odd, a >= 3 x | y | a | b | c -----+-----+-----+-----+----- 1 | 1 | 3 | 4 | 5 7 | 1 | 5 | 12 | 13 17 | 1 | 7 | 24 | 25 31 | 1 | 9 | 40 | 41 49 | 1 | 11 | 60 | 61 y = 2 => a² = 4a + 4x + 4 => x = (a² - 4a - 4)/4 = a²/4 - a - 1 x is a positive integer, so a is even, a >= 6 x | y | a | b | c -----+-----+-----+-----+----- 2 | 2 | 6 | 8 | 10 7 | 2 | 8 | 15 | 17 14 | 2 | 10 | 24 | 26 23 | 2 | 12 | 35 | 37 34 | 2 | 14 | 48 | 50 y = 3 => a² = 6a + 6x + 9 => x = (a² - 6a - 9)/6 = a²/6 - a - 3/2 x is a positive integer, so a is odd, multiple of 3, a >= 9 x | y | a | b | c -----+-----+-----+-----+----- 3 | 3 | 9 | 12 | 15 21 | 3 | 15 | 36 | 39 51 | 3 | 21 | 72 | 75 93 | 3 | 27 | 120 | 123 147 | 3 | 33 | 180 | 183 general case: y > 3 => a² = 2ay + 2xy + y² => x = (a² - 2ay - y²) / 2y = a²/(2y) - a - y/2 if y is odd, a must be odd, a² is a multiple of y if y is even, a must be even, a² is a multiple of 2y if y is even, 2y = prod(pi yi) for pi prime, yi integer > 0, then y' = prod(pi^ceil(yi/2)) then a² is multiple of y iff a is multiple of y' and a2 is multiple of 2y if a is multiple of y' and p0==2, i0 is odd, or a is multiple of 2y' maximal value of y is if c is large, a and b are close. Then y < (√2 - 1) L / (√2 + 2) < L/8 x > 0, so a² - 2ay - y² > 0, so a² - 2ay > y² quadratic formula: ax²+bx+c = 0, then x = (-b±√(b²-4ac))/(2a) variable substitution: x:a, a:1, b:-2y, c:-y² a²-2ya-y² = 0, then a = (2y±√(4y²+4y²))/2 = y ± y√2 so, a >= y + y√2 """ t0 = time.time() triangles = set() dups = set() for y in range(1, L//8): factorized = pyprimesieve.factorize(y) y_prime = functools.reduce( operator.mul, (p**((i+1)//2) for p,i in factorized), 1 ) if y % 2 == 1: base = y_prime step = y_prime * 2 else: base = y_prime if factorized[0][0] == 2 and factorized[0][1] % 2 == 0: base *= 2 step = base lwb_a = math.ceil(y*(1+math.sqrt(2))) new_base = math.ceil((lwb_a - base) / step) * step + base for a in itertools.count(new_base, step): x = (a*a - 2*a*y - y*y) // (2*y) b = a + x c = b + y l = a + b + c if l > L: break if isinstance(verbose, int) and verbose>2: print(f"s3 L={L}, x={x} y={y} y'={y_prime} ({a},{b},{c}) {a+b+c}cm") if l in triangles: dups.add(l) else: triangles.add(l) if verbose: print("s3 L={} {:8}ms phase1".format( L, int(1000*(time.time()-t0)))) if isinstance(verbose, int) and verbose>1: print("triangles: {}\ndups: {}".format( ",".join(map(str, sorted(triangles))), ",".join(map(str, sorted(dups))))) result = len(triangles) - len(dups) if verbose: print("s3 L={} {:8}ms result={}".format( L, int(1000*(time.time()-t0)), result)) return result solution = solution3 if __name__ == '__main__': solution1(verbose=3, L=120) solution2(verbose=3, L=120) solution3(verbose=3, L=120) solution2(verbose=True, L=10000) solution3(verbose=True, L=10000) solution(verbose=True)

[ "ganzevoort@gw20e.com" ]

ganzevoort@gw20e.com

84cdbb979c2f212c1dc2187b7c185ec753d0e0d2

34053eab55920cd06b90b05c7995d45b3fb279ba

/customer/tests/test_views.py

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[]

no_license

yassinejr/MedicalStoreManagementSystem-Backend

bc2706abac3faf8b53ba62fe319e8fa4c55a7057

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refs/heads/main

2023-03-21T01:04:56.333791

2021-03-03T22:47:19

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import json from django.urls import reverse from rest_framework import status from .test_setup import TestSetUp class CustomerTests(TestSetUp): def test_view_customers(self): url = reverse('customer:customer-list') response = self.client.get(url, format='json') self.assertEqual(response.status_code, status.HTTP_200_OK) def test_post_customer(self): url = reverse('customer:customer-list') response = self.client.post(url, self.data, format='json') self.assertEqual(response.status_code, status.HTTP_201_CREATED) def test_customer_detail(self): url = reverse('customer:customer-detail', kwargs={"pk": 1}) response = self.client.get(url, format='json') self.assertEqual(response.data["name"], 'customer zarzis') self.assertEqual(response.status_code, status.HTTP_200_OK) def test_customer_update(self): url = reverse('customer:customer-detail', kwargs={"pk": 1}) data = { 'name':'customer djerba', 'phone': 12342255, 'address': 'rue de la paix' } response = self.client.put(url, data, format='json') self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertEqual(json.loads(response.content)['name'], 'customer djerba')

[ "yassine.jrad@esprit.tn" ]

yassine.jrad@esprit.tn

51e79e81fc843cede5de74dd9720d367ac781088

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/src/leetcode/P5337.py

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[]

no_license

stupidchen/leetcode

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2022-03-14T21:15:47.263954

2022-02-27T15:33:15

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VOWELS = { 'a': 0, 'e': 1, 'i': 2, 'o': 3, 'u': 4, } LV = 5 class Solution: def findTheLongestSubstring(self, s: str) -> int: d = {0: -1} n = len(s) r, t = 0, 0 for i in range(n): if s[i] in VOWELS: t ^= 1 << (VOWELS[s[i]]) if t in d: r = max(r, i - d[t]) else: d[t] = i return r # For test only SI = (("eleetminicoworoep", ), ("leetcodeisgreat", ), ("bcbcbc", ), ) SO = (13, 5, 6) TM = 'findTheLongestSubstring' if __name__ == '__main__': from leetcode.PTester import PTester PTester(SI, SO, Solution, TM).run()

[ "stupidchen@foxmail.com" ]

stupidchen@foxmail.com

bd71e8834041355af9994538d8b413ecfd3d17bf

747f759311d404af31c0f80029e88098193f6269

/addons/invoice_department/__openerp__.py

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[]

no_license

sgeerish/sirr_production

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1081f3a5ff8864a31b2dcd89406fac076a908e78

refs/heads/master

2020-05-19T07:21:37.047958

2013-09-15T13:03:36

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0

1

null

UTF-8

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false

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/home/openerp/production/extra-addons/invoice_department/__openerp__.py

[ "geerish@omerp.net" ]

geerish@omerp.net

4444e9fdf7a521fb5b97b051d6c60e7eb88cedff

20674c17d815214bf66b75be686bb8a45c0f5914

/version1/41_First_Missing_Positive.py

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[]

no_license

moontree/leetcode

e7b670969fe20785b15aae82996875fd66de1b08

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refs/heads/master

2021-05-20T20:36:45.615420

2020-04-02T09:15:26

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''' Given an unsorted integer array, find the first missing positive integer. For example, Given [1,2,0] return 3, and [3,4,-1,1] return 2. Your algorithm should run in O(n) time and uses constant space. ''' examples = [ { "nums" : [1, 2, -1], "res" : 3 }, { "nums": [3, 4, -1, 1], "res" : 2 }, { "nums": [3, 4, 2, 1], "res" : 5 }, { "nums": [-1, -2, -3, 0], "res" : 1 }, { "nums": [0, 2, -1, 1], "res" : 3 },{ 'nums': [2], "res" : 1 },{ 'nums': [1, 1], "res" : 2 },{ 'nums': [4, 5], "res" : 1 },{ 'nums': [0, -1, 3, 1], "res" : 2 } ] def firstMissingPositive(nums): """ :type nums: List[int] :rtype: int """ nums.append(-1) index = 0 count = len(nums) - 1 while index < count: if nums[index] == index: index += 1 else: while nums[index] != index: if nums[index] < 1 or nums[index] > count: nums[index] = 0 break else: tmp = nums[index] if nums[tmp] == tmp: break nums[index] = nums[tmp] nums[tmp] = tmp index += 1 for i in range(1, count + 1): if nums[i] != i: return i return count + 1 ''' first distribute to 0, n then if index exists, nums[index] += n, if not exists, nums[index] keeps < n ''' def firstMissingPositive(nums): """ :type nums: List[int] :rtype: int """ nums.append(0) n = len(nums) for i in range(len(nums)): if nums[i] < 0 or nums[i] >= n: nums[i] = 0 for i in range(len(nums)): nums[nums[i] % n] += n for i in range(1, len(nums)): if nums[i] < n: return i return n for example in examples: print '--- test cases ---' print example res = firstMissingPositive(example['nums']) print res == example['res'], res, example['res']

[ "zhangchao@zhangchaodeMacBook-Pro.local" ]

zhangchao@zhangchaodeMacBook-Pro.local

65dbaa98b62745d99f56f451abb321eeee0493ad

e23a4f57ce5474d468258e5e63b9e23fb6011188

/125_algorithms/_exercises/templates/_algorithms_challenges/leetcode/LeetcodePythonProject_with_solution/leetcode_0751_0800/LeetCode799_ChampagneTower.py

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[]

no_license

syurskyi/Python_Topics

52851ecce000cb751a3b986408efe32f0b4c0835

be331826b490b73f0a176e6abed86ef68ff2dd2b

refs/heads/master

2023-06-08T19:29:16.214395

2023-05-29T17:09:11

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null

2023-02-16T03:08:10

2019-11-09T02:58:47

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Python

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1,206

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''' Created on Apr 18, 2018 @author: tongq ''' c_ Solution(o.. ___ champagneTower poured, query_row, query_glass """ :type poured: int :type query_row: int :type query_glass: int :rtype: float """ result [[0.0]*101 ___ _ __ r..(101)] ? 0 0 poured ___ i __ r..(100 ___ j __ r..(i+1 __ result[i][j] >_ 1: result[i+1][j] += (result[i][j]-1)/2.0 result[i+1][j+1] += (result[i][j]-1)/2.0 result[i][j] 1.0 r.. ?[query_row][query_glass] ___ test testCases [ [1, 1, 1], # 0.0 [2, 1, 1], # 0.5 [2, 1, 0], # 0.5 [6, 2, 0], [6, 2, 1], [6, 3, 1], [6, 3, 0] ] ___ poured, query_row, query_glass __ testCases: print('poured: %s' % poured) print('query_row: %s' % query_row) print('query_glass: %s' % query_glass) result champagneTower(poured, query_row, query_glass) print('result: %s' % result) print('-='*30+'-') __ _____ __ _____ Solution().test()

[ "sergejyurskyj@yahoo.com" ]

sergejyurskyj@yahoo.com

8aade5817b19ff2fa35eb58b3c891b4d8f7781e4

7212600f89c640bd4a936934f8bed985acd3e2d5

/cgi-bin/filewriter.py

8a16b35e0c58429ddeda1e08a01605d0c3b1cbf8

[]

no_license

SkyPromp/Speedcube-timer

3bac6b242886654168919f2ca351b858e4c5dd1f

41c5859844e73aced035f626df5dc71a388b1ade

refs/heads/main

2023-06-04T04:44:34.095363

2021-06-27T19:58:16

378,777,146

0

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py

import datetime # Append the current date/time, scramble and solving time to the text file def write(scramble, time): file = open("times.csv", "a") file.write(f"{datetime.datetime.now()},{scramble},{time}\n") file.close() # Removes the last used line of the file (so removes the empty line, and the used line and then adds another newline) def remove(): file = open("times.csv", "r") r = file.read() file.close() m = r.split("\n") s = "\n".join(m[:-2]) + "\n" # remove last 2 lines (last line is \n) if len(s) == 1: s = "" file = open("times.csv", "w+") for i in range(len(s)): file.write(s[i]) file.close()

[ "max.poppe@ugent.be" ]

max.poppe@ugent.be

ba527a5399515881d5b2223c59c62dcee6f8eaed

e5ac5f718df4c3c90c02a6275b9e690dffceeaa5

/saga/engine/engine.py

49f323a398538036945e9849e77d19d1bb641ae3

[ "Apache-2.0" ]

permissive

virthead/COMPASS-pilot

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refs/heads/master

2020-03-26T08:14:06.068259

2018-12-04T11:24:42

144,692,491

0

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Python

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py

__author__ = "Andre Merzky, Ole Weidner" __copyright__ = "Copyright 2012-2013, The SAGA Project" __license__ = "MIT" """ Provides the SAGA runtime. """ import re import sys import pprint import string import inspect import radical.utils as ru import radical.utils.config as ruc import radical.utils.logger as rul import saga.exceptions as se import saga.engine.registry # adaptors to load ############# These are all supported options for saga.engine #################### ## _config_options = [ { 'category' : 'saga.engine', 'name' : 'load_beta_adaptors', 'type' : bool, 'default' : False, 'valid_options' : [True, False], 'documentation' : 'load adaptors which are marked as beta (i.e. not released).', 'env_variable' : None }, # FIXME: is there a better place to register util level options? { 'category' : 'saga.utils.pty', 'name' : 'prompt_pattern', 'type' : str, 'default' : '[\$#%>\]]\s*$', 'documentation' : 'use this regex to detect shell prompts', 'env_variable' : None }, { 'category' : 'saga.utils.pty', 'name' : 'ssh_copy_mode', 'type' : str, 'default' : 'sftp', 'valid_options' : ['sftp', 'scp', 'rsync+ssh', 'rsync'], 'documentation' : 'use the specified protocol for pty level file transfer', 'env_variable' : 'SAGA_PTY_SSH_COPYMODE' }, { 'category' : 'saga.utils.pty', 'name' : 'connection_pool_ttl', 'type' : int, 'default' : 10*60, 'documentation' : 'minimum time a connection is kept alive in a connection pool', 'env_variable' : 'SAGA_PTY_CONN_POOL_TTL' }, { 'category' : 'saga.utils.pty', 'name' : 'connection_pool_size', 'type' : int, 'default' : 10, 'documentation' : 'maximum number of connections kept in a connection pool', 'env_variable' : 'SAGA_PTY_CONN_POOL_SIZE' }, { 'category' : 'saga.utils.pty', 'name' : 'connection_pool_wait', 'type' : int, 'default' : 10*60, 'documentation' : 'maximum number of seconds to wait for any connection in the connection pool to become available before raising a timeout error', 'env_variable' : 'SAGA_PTY_CONN_POOL_WAIT' } ] ################################################################################ ## class Engine(ruc.Configurable): """ Represents the SAGA engine runtime system. The Engine is a singleton class that takes care of adaptor loading and management, and which binds adaptor instances to API object instances. The Engine singleton is implicitly instantiated as soon as SAGA is imported into Python. It will, on creation, load all available adaptors. Adaptors modules MUST provide an 'Adaptor' class, which will register the adaptor in the engine with information like these (simplified):: _ADAPTOR_INFO = { 'name' : _adaptor_name, 'version' : 'v1.3' 'schemas' : ['fork', 'local'] 'cpis' : [{ 'type' : 'saga.job.Job', 'class' : 'LocalJob', }, { 'type' : 'saga.job.Service', 'class' : 'LocalJobService', } ] } where 'class' points to the actual adaptor classes, and 'schemas' lists the URL schemas for which those adaptor classes should be considered. Note that schemas are case insensitive. More details on the adaptor registration process and on adaptor meta data can be found in the adaptors writer guide. :todo: add link to adaptor writers documentation. While loading adaptors, the Engine builds up an internal registry of adaptor classes, hierarchically sorted like this (simplified):: _adaptor_registry = { 'job' : { 'gram' : [<gram job adaptor, gram job adaptor class>] 'ssh' : [<ssh job adaptor, ssh job adaptor class>] 'http' : [<aws job adaptor, aws job adaptor class>, <occi job adaptor, occi job adaptor class>] ... }, 'file' : { 'ftp' : <ftp file adaptor, ftp file adaptor class> 'scp' : <scp file adaptor, scp file adaptor class> ... }, ... } to enable simple lookup operations when binding an API object to an adaptor class instance. For example, a 'saga.job.Service('http://remote.host.net/')' constructor would use (simplified):: def __init__ (self, url="", session=None) : for (adaptor, adaptor_class) in self._engine._adaptor_registry{'job'}{url.scheme} try : self._adaptor = adaptor_class (self, url, session} except saga.Exception e : # adaptor bailed out continue else : # successfully bound to adaptor return """ __metaclass__ = ru.Singleton #----------------------------------------------------------------- # def __init__(self): # Engine manages cpis from adaptors self._adaptor_registry = {} # set the configuration options for this object ruc.Configurable.__init__ (self, 'saga') ruc.Configurable.config_options (self, 'saga.engine', _config_options) self._cfg = self.get_config('saga.engine') # Initialize the logging, and log version (this is a singleton!) self._logger = rul.getLogger ('saga', 'Engine') # load adaptors self._load_adaptors () #----------------------------------------------------------------- # def _load_adaptors (self, inject_registry=None): """ Try to load all adaptors that are registered in saga.engine.registry.py. This method is called from the constructor. As Engine is a singleton, this method is called once after the module is first loaded in any python application. :param inject_registry: Inject a fake registry. *For unit tests only*. """ # get the engine config options global_config = ruc.getConfig('saga') # get the list of adaptors to load registry = saga.engine.registry.adaptor_registry # check if some unit test wants to use a special registry. If # so, we reset cpi infos from the earlier singleton creation. if inject_registry != None : self._adaptor_registry = {} registry = inject_registry # attempt to load all registered modules for module_name in registry: self._logger.info ("Loading adaptor %s" % module_name) # first, import the module adaptor_module = None try : adaptor_module = __import__ (module_name, fromlist=['Adaptor']) except Exception as e: self._logger.warn ("Skipping adaptor %s 1: module loading failed: %s" % (module_name, e)) continue # skip to next adaptor # we expect the module to have an 'Adaptor' class # implemented, which, on calling 'register()', returns # a info dict for all implemented adaptor classes. adaptor_instance = None adaptor_info = None try: adaptor_instance = adaptor_module.Adaptor () adaptor_info = adaptor_instance.register () except se.SagaException as e: self._logger.warn ("Skipping adaptor %s: loading failed: '%s'" % (module_name, e)) continue # skip to next adaptor except Exception as e: self._logger.warn ("Skipping adaptor %s: loading failed: '%s'" % (module_name, e)) continue # skip to next adaptor # the adaptor must also provide a sanity_check() method, which sould # be used to confirm that the adaptor can function properly in the # current runtime environment (e.g., that all pre-requisites and # system dependencies are met). try: adaptor_instance.sanity_check () except Exception as e: self._logger.warn ("Skipping adaptor %s: failed self test: %s" % (module_name, e)) continue # skip to next adaptor # check if we have a valid adaptor_info if adaptor_info is None : self._logger.warning ("Skipping adaptor %s: adaptor meta data are invalid" \ % module_name) continue # skip to next adaptor if not 'name' in adaptor_info or \ not 'cpis' in adaptor_info or \ not 'version' in adaptor_info or \ not 'schemas' in adaptor_info : self._logger.warning ("Skipping adaptor %s: adaptor meta data are incomplete" \ % module_name) continue # skip to next adaptor adaptor_name = adaptor_info['name'] adaptor_version = adaptor_info['version'] adaptor_schemas = adaptor_info['schemas'] adaptor_enabled = True # default unless disabled by 'enabled' option or version filer # disable adaptors in 'alpha' or 'beta' versions -- unless # the 'load_beta_adaptors' config option is set to True if not self._cfg['load_beta_adaptors'].get_value () : if 'alpha' in adaptor_version.lower() or \ 'beta' in adaptor_version.lower() : self._logger.warn ("Skipping adaptor %s: beta versions are disabled (%s)" \ % (module_name, adaptor_version)) continue # skip to next adaptor # get the 'enabled' option in the adaptor's config # section (saga.cpi.base ensures that the option exists, # if it is initialized correctly in the adaptor class. adaptor_config = None adaptor_enabled = False try : adaptor_config = global_config.get_category (adaptor_name) adaptor_enabled = adaptor_config['enabled'].get_value () except se.SagaException as e: self._logger.warn ("Skipping adaptor %s: initialization failed: %s" % (module_name, e)) continue # skip to next adaptor except Exception as e: self._logger.warn ("Skipping adaptor %s: initialization failed: %s" % (module_name, e)) continue # skip to next adaptor # only load adaptor if it is not disabled via config files if adaptor_enabled == False : self._logger.info ("Skipping adaptor %s: 'enabled' set to False" \ % (module_name)) continue # skip to next adaptor # check if the adaptor has anything to register if 0 == len (adaptor_info['cpis']) : self._logger.warn ("Skipping adaptor %s: does not register any cpis" \ % (module_name)) continue # skip to next adaptor # we got an enabled adaptor with valid info - yay! We can # now register all adaptor classes (cpi implementations). for cpi_info in adaptor_info['cpis'] : # check cpi information details for completeness if not 'type' in cpi_info or \ not 'class' in cpi_info : self._logger.info ("Skipping adaptor %s cpi: cpi info detail is incomplete" \ % (module_name)) continue # skip to next cpi info # adaptor classes are registered for specific API types. cpi_type = cpi_info['type'] cpi_cname = cpi_info['class'] cpi_class = None try : cpi_class = getattr (adaptor_module, cpi_cname) except Exception as e: # this exception likely means that the adaptor does # not call the saga.adaptors.Base initializer (correctly) self._logger.warning ("Skipping adaptor %s: adaptor class invalid %s: %s" \ % (module_name, cpi_info['class'], str(e))) continue # skip to next adaptor # make sure the cpi class is a valid cpi for the given type. # We walk through the list of known modules, and try to find # a modules which could have that class. We do the following # tests: # # cpi_class: ShellJobService # cpi_type: saga.job.Service # modules: saga.adaptors.cpi.job # modules: saga.adaptors.cpi.job.service # classes: saga.adaptors.cpi.job.Service # classes: saga.adaptors.cpi.job.service.Service # # cpi_class: X509Context # cpi_type: saga.Context # modules: saga.adaptors.cpi.context # classes: saga.adaptors.cpi.context.Context # # So, we add a 'adaptors.cpi' after the 'saga' namespace # element, then append the rest of the given namespace. If that # gives a module which has the requested class, fine -- if not, # we add a lower cased version of the class name as last # namespace element, and check again. # -> saga . job . Service # <- ['saga', 'job', 'Service'] cpi_type_nselems = cpi_type.split ('.') if len(cpi_type_nselems) < 2 or \ len(cpi_type_nselems) > 3 : self._logger.warn ("Skipping adaptor %s: cpi type not valid: '%s'" \ % (module_name, cpi_type)) continue # skip to next cpi info if cpi_type_nselems[0] != 'saga' : self._logger.warn ("Skipping adaptor %s: cpi namespace not valid: '%s'" \ % (module_name, cpi_type)) continue # skip to next cpi info # -> ['saga', 'job', 'Service'] # <- ['saga', 'adaptors', 'cpi', 'job', 'Service'] cpi_type_nselems.insert (1, 'adaptors') cpi_type_nselems.insert (2, 'cpi') # -> ['saga', 'adaptors', 'cpi', 'job', 'Service'] # <- ['saga', 'adaptors', 'cpi', 'job'], 'Service' cpi_type_cname = cpi_type_nselems.pop () # -> ['saga', 'adaptors', 'cpi', 'job'], 'Service' # <- 'saga.adaptors.cpi.job # <- 'saga.adaptors.cpi.job.service cpi_type_modname_1 = '.'.join (cpi_type_nselems) cpi_type_modname_2 = '.'.join (cpi_type_nselems + [cpi_type_cname.lower()]) # does either module exist? cpi_type_modname = None if cpi_type_modname_1 in sys.modules : cpi_type_modname = cpi_type_modname_1 if cpi_type_modname_2 in sys.modules : cpi_type_modname = cpi_type_modname_2 if not cpi_type_modname : self._logger.warn ("Skipping adaptor %s: cpi type not known: '%s'" \ % (module_name, cpi_type)) continue # skip to next cpi info # so, make sure the given cpi is actually # implemented by the adaptor class cpi_ok = False for name, cpi_obj in inspect.getmembers (sys.modules[cpi_type_modname]) : if name == cpi_type_cname and \ inspect.isclass (cpi_obj) : if issubclass (cpi_class, cpi_obj) : cpi_ok = True if not cpi_ok : self._logger.warn ("Skipping adaptor %s: doesn't implement cpi '%s (%s)'" \ % (module_name, cpi_class, cpi_type)) continue # skip to next cpi info # finally, register the cpi for all its schemas! registered_schemas = list() for adaptor_schema in adaptor_schemas: adaptor_schema = adaptor_schema.lower () # make sure we can register that cpi type if not cpi_type in self._adaptor_registry : self._adaptor_registry[cpi_type] = {} # make sure we can register that schema if not adaptor_schema in self._adaptor_registry[cpi_type] : self._adaptor_registry[cpi_type][adaptor_schema] = [] # we register the cpi class, so that we can create # instances as needed, and the adaptor instance, # as that is passed to the cpi class c'tor later # on (the adaptor instance is used to share state # between cpi instances, amongst others) info = {'cpi_cname' : cpi_cname, 'cpi_class' : cpi_class, 'adaptor_name' : adaptor_name, 'adaptor_instance' : adaptor_instance} # make sure this tuple was not registered, yet if info in self._adaptor_registry[cpi_type][adaptor_schema] : self._logger.warn ("Skipping adaptor %s: already registered '%s - %s'" \ % (module_name, cpi_class, adaptor_instance)) continue # skip to next cpi info self._adaptor_registry[cpi_type][adaptor_schema].append(info) registered_schemas.append(str("%s://" % adaptor_schema)) self._logger.info("Register adaptor %s for %s API with URL scheme(s) %s" % (module_name, cpi_type, registered_schemas)) #----------------------------------------------------------------- # def find_adaptors (self, ctype, schema) : ''' Look for a suitable cpi class serving a particular schema This method will sift through our adaptor registry (see '_load_adaptors()', and dig for any adaptor which marches the given api class type and schema. All matching adaptors are returned (by name) ''' if not ctype in self._adaptor_registry : return [] if not schema.lower () in self._adaptor_registry[ctype] : return [] adaptor_names = [] for info in self._adaptor_registry[ctype][schema.lower ()] : adaptor_names.append (info['adaptor_name']) return adaptor_names #----------------------------------------------------------------- # def get_adaptor (self, adaptor_name) : ''' Return the adaptor module's ``Adaptor`` class for the given adaptor name. This method is used if adaptor or API object implementation need to interact with other adaptors. ''' for ctype in self._adaptor_registry.keys () : for schema in self._adaptor_registry[ctype].keys () : for info in self._adaptor_registry[ctype][schema] : if ( info['adaptor_name'] == adaptor_name ) : return info['adaptor_instance'] error_msg = "No adaptor named '%s' found" % adaptor_name self._logger.error(error_msg) raise se.NoSuccess(error_msg) #----------------------------------------------------------------- # def bind_adaptor (self, api_instance, ctype, schema, preferred_adaptor, *args, **kwargs) : ''' Look for a suitable adaptor class to bind to, instantiate it, and initialize it. If 'preferred_adaptor' is not 'None', only that given adaptors is considered, and adaptor classes are only created from that specific adaptor. ''' if not ctype in self._adaptor_registry: error_msg = "No adaptor found for '%s' and URL scheme %s://" \ % (ctype, schema) self._logger.error(error_msg) raise se.NotImplemented(error_msg) if not schema in self._adaptor_registry[ctype]: error_msg = "No adaptor found for '%s' and URL scheme %s://" \ % (ctype, schema) self._logger.error(error_msg) raise se.NotImplemented(error_msg) # cycle through all applicable adaptors, and try to instantiate # a matching one. exception = saga.NoSuccess ("binding adaptor failed", api_instance) for info in self._adaptor_registry[ctype][schema] : cpi_cname = info['cpi_cname'] cpi_class = info['cpi_class'] adaptor_name = info['adaptor_name'] adaptor_instance = info['adaptor_instance'] try : # is this adaptor acceptable? if preferred_adaptor != None and \ preferred_adaptor != adaptor_instance : # ignore this adaptor self._logger.debug ("bind_adaptor for %s : %s != %s - ignore adaptor" \ % (cpi_cname, preferred_adaptor, adaptor_instance)) continue # instantiate cpi cpi_instance = cpi_class (api_instance, adaptor_instance) # self._logger.debug("Successfully bound %s.%s to %s" \ # % (adaptor_name, cpi_cname, api_instance)) return cpi_instance except se.SagaException as e : # adaptor class initialization failed - try next one exception._add_exception (e) self._logger.info ("bind_adaptor adaptor class ctor failed : %s.%s: %s" \ % (adaptor_name, cpi_class, str(e))) continue except Exception as e : exception._add_exception (saga.NoSuccess (str(e), api_instance)) self._logger.info ("bind_adaptor adaptor class ctor failed : %s.%s: %s" \ % (adaptor_name, cpi_class, str(e))) continue self._logger.error ("No suitable adaptor found for '%s' and URL scheme '%s'" % (ctype, schema)) self._logger.info ("%s" % (str(exception))) raise exception._get_exception_stack () #----------------------------------------------------------------- # def loaded_adaptors (self): return self._adaptor_registry #----------------------------------------------------------------- # def _dump (self) : import pprint pprint.pprint (self._adaptor_registry)

[ "virthead@pandawms.jinr.ru" ]

virthead@pandawms.jinr.ru

0790885d07748510ba3469b18c3485786ca2c678

c0d489046bc114672139873916a118a203c6f850

/Medium/93. Restore IP Addresses.py

94ed17c1daab3e46ce78aa9e4f44a4152abf96d2

[]

no_license

shifty049/LeetCode_Practice

165ada14a8fd436e9068bd94d6b82b1ed312013c

ca8be179282be86450c9959fb239466d152a55e5

refs/heads/master

2022-05-25T16:23:05.736852

2022-03-29T13:48:21

249,737,622

0

null

UTF-8

Python

false

1,024

py

class Solution: def restoreIpAddresses(self, s: str) -> List[str]: lst = [] length = len(s) def backtracking(num, count, sub_lst): if len(sub_lst)>1 and (int(s[sub_lst[-2]:sub_lst[-1]]) > 255 or (s[sub_lst[-2]] == '0' and len(s[sub_lst[-2]:sub_lst[-1]])>1)): return if num >= length or count >= 4: if count == 4 and num==length: if sub_lst not in lst: lst.append('.'.join([s[i:sub_lst[ix+1]] for ix,i in enumerate(sub_lst[:-1])])) return for i in range(1, length - num - 2 + count ): backtracking(num+i, count+1, sub_lst+[sub_lst[-1]+i]) backtracking(0 , 0, [0]) return lst #Runtime: 148 ms, faster than 8.99% of Python3 online submissions for Restore IP Addresses. #Memory Usage: 14.4 MB, less than 7.55% of Python3 online submissions for Restore IP Addresses. #Fu-Ti, Hsu #shifty049@gmail.com

[ "shifty049@gmail.com" ]

shifty049@gmail.com

152a39c5f6f25998206bbb4444fc935c598143eb

561513a9927f351720616f7a66556e2bdcb89346

/request.py

28fadfb2b460f81b686694fc330d9a2acece2186

[]

no_license

CNllb/Hokkien_DataBases

511544abdff94ada1813f4ee6299034e6368cfd3

021df946f5441538d53d7170240dd281b9ac86ba

refs/heads/main

2023-09-05T22:15:09.793737

2021-11-24T10:30:41

425,679,947

0

null

UTF-8

Python

false

439

py

import pymysql def link_to_database(): conn = pymysql.connect( host="gz-cynosdbmysql-grp-56sj4bjz.sql.tencentcdb.com", user="root", port=25438, password="Lcx010327", database="Hokkien"); # 创建游标 cursor = conn.cursor(); sql = "" try: cursor.execute(sql) cursor.close() conn.close() except: print("Error: unable to fetchall userPrefer")

[ "1360602885@qq.com" ]

1360602885@qq.com

9b45c38f51e416a27227fa1c503c2124840c9bfa

6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4

/ZeeWN5NdFa8ALJq5G_16.py

003fc3b30954c00178f5a2acaa2c8646fd3bff48

[]

no_license

daniel-reich/ubiquitous-fiesta

26e80f0082f8589e51d359ce7953117a3da7d38c

9af2700dbe59284f5697e612491499841a6c126f

refs/heads/master

2023-04-05T06:40:37.328213

2021-04-06T20:17:44

355,318,759

0

null

UTF-8

Python

false

154

py

def nearest_chapter(ch, p): out = sorted(ch.keys(), key = lambda x: ch[x], reverse = True) out.sort(key = lambda x: abs(ch[x] - p)) return out[0]

[ "daniel.reich@danielreichs-MacBook-Pro.local" ]

daniel.reich@danielreichs-MacBook-Pro.local

95de42864a1816e62e897dbcecdc4d73c21b66c6

f8ae676c657638dac835c437a443767431de2d60

/News/migrations/0009_alter_news_text.py

4f54d36fb68bacd4c52082e396d4cc11788fcbd6

[]

no_license

OR6107/150th-kaiseifes-backend

0dc03b1c91b86b2333100d0ea5097654e26409a8

9b9429443a834c5dd78a3904ad52ee03e73f4fac

refs/heads/main

2023-06-17T14:09:42.315863

2021-07-18T03:52:10

378,079,752

0

null

2021-06-18T08:09:02

null

UTF-8

Python

false

363

py

# Generated by Django 3.2.4 on 2021-07-02 10:32 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('News', '0008_alter_news_text'), ] operations = [ migrations.AlterField( model_name='news', name='text', field=models.TextField(), ), ]

[ "keigo0827511@gmail.com" ]

keigo0827511@gmail.com

a9233691883537261c42a589490d45807bd1e36c

8d63c58eae36070409f05192a2f2366092cfb482

/game_util.py

64734893a08a661f036e0a145c6e944bee27f4e6

[ "MIT" ]

permissive

Quazyrog/kopernik-python

f8d97bfddcbff97a367e0af808d6fffb0092fce6

202fdd5d0321960904d473d986c172e56dd62a03

refs/heads/master

2020-08-02T06:21:15.172240

2019-09-27T12:23:33

211,261,190

0

null

UTF-8

Python

false

6,625

py

import pygame import pytmx from pygame.sprite import Group, Sprite, spritecollideany from pygame.math import Vector2 from pytmx import TiledTileLayer, TiledObject SCREEN_SIZE = (1366, 768) ASSETS_DIR = "./Assets" class Game: def __init__(self): self.running = False self.screen = None self.player = None self.level = None self.player_movement = Vector2(0, 0) def start(self) -> None: self.initialize() self.running = True t0 = pygame.time.get_ticks() while self.running: self.handle_events() t1 = pygame.time.get_ticks() self.update((t1 - t0) / 1000) self.render() t0 = t1 def initialize(self) -> None: pygame.init() self.screen = pygame.display.set_mode(SCREEN_SIZE) def handle_events(self) -> None: for event in pygame.event.get(): if event.type == pygame.QUIT: self.running = False elif event.type == pygame.KEYDOWN or event.type == pygame.KEYUP: sgn = 1 if event.type == pygame.KEYDOWN else -1 change = Vector2(0 ,0) if pygame.K_UP == event.key: change.y = -sgn elif pygame.K_DOWN == event.key: change.y = sgn elif pygame.K_LEFT == event.key: change.x = -sgn elif pygame.K_RIGHT == event.key: change.x = sgn elif pygame.K_SPACE == event.key and event.type == pygame.KEYUP: sprite = pygame.sprite.spritecollideany(self.player, self.level.interactions, False) if sprite is None: return interacted = sprite.map_object self.level.activate_object(interacted) self.player_movement += change self.player.move(self.player_movement) def update(self, time_delta: float) -> None: self.player.update(time_delta) def render_tiles_layer(self, layer: TiledTileLayer, offset: Vector2) -> None: for x, y, image in layer.tiles(): pos_x = offset[0] + x * self.level.tile_size pos_y = offset[1] + y * self.level.tile_size self.screen.blit(image, (pos_x, pos_y)) def render_objects_layer(self, layer: TiledTileLayer, offset: Vector2) -> None: for obj in layer: print(obj, obj.image) if obj.image: self.screen.blit(obj.image, (obj.x + offset.x, obj.y + offset.y)) def render(self) -> None: offset = Vector2() offset.x = (SCREEN_SIZE[0] - self.level.map_data.width * self.level.tile_size) // 2 offset.y = (SCREEN_SIZE[1] - self.level.map_data.height * self.level.tile_size) // 2 self.screen.fill((0, 0, 0)) for layer in self.level.map_data.visible_tile_layers: self.render_tiles_layer(self.level.map_data.layers[layer], offset) for layer in self.level.map_data.visible_object_groups: self.render_objects_layer(self.level.map_data.layers[layer], offset) self.screen.blit(self.player.image, self.player.rect.move(offset.x, offset.y)) pygame.display.flip() class Level: def __init__(self, name: str, game: Game): self.map_data = pytmx.load_pygame("%s/%s.tmx" % (ASSETS_DIR, name)) self.colliders = pygame.sprite.Group() self.interactions = pygame.sprite.Group() self.triggers = pygame.sprite.Group() self.game = game self.player = None self.tile_size = self.map_data.tilewidth self.bounds = pygame.Rect(0, 0, self.tile_size * self.map_data.width, self.tile_size * self.map_data.height) self.name = name assert self.map_data.tilewidth == self.map_data.tileheight for group in self.map_data.objectgroups: if group.name == "Collision": for obj in group: self.colliders.add(MapObject(obj)) if group.name == "Interaction": for obj in group: self.interactions.add(MapObject(obj)) if group.name == "Trigger": for obj in group: self.triggers.add(MapObject(obj)) def set_player(self, player: "Player", on_spawn : bool) -> None: self.player = player player.level = self if not on_spawn: return try: spawn = self.map_data.get_object_by_name("Spawn") player.position = Vector2(spawn.x, spawn.y) except ValueError: pass def activate_object(self, obj : TiledObject) -> None: print(obj) class MapObject(pygame.sprite.Sprite): def __init__(self, obj_data): super().__init__() self.rect = (obj_data.x, obj_data.y, obj_data.width, obj_data.height) self.map_object = obj_data class Player(pygame.sprite.Sprite): def __init__(self): super().__init__() self.level = None self.image = pygame.image.load("%s/Player.png" % ASSETS_DIR) self.rect = pygame.Rect(0, 0, self.image.get_width(), self.image.get_height()) self._position = Vector2(0, 0) self._speed = Vector2(0, 0) self.velocity = 2 self._triggered = set() def move(self, direction: Vector2): try: self._speed = direction.normalize() * self.velocity * self.level.tile_size except ValueError: self._speed = Vector2(0, 0) @property def position(self) -> Vector2: return self._position @position.setter def position(self, value: Vector2) -> None: self._position = value self.rect = pygame.Rect(value.x, value.y, self.rect.width, self.rect.height) def update(self, time_delta: float): if self._speed.length() == 0: return before = self.rect mov = self._speed * time_delta self.rect = pygame.Rect(self.position.x + mov.x, self.position.y + mov.y, before.width, before.height) if spritecollideany(self, self.level.colliders, False) is None and self.level.bounds.contains(self.rect): self._position += mov s = set() for obj in pygame.sprite.spritecollide(self, self.level.interactions, False): s.add(obj.map_object) for obj in s: if obj not in self._triggered: self.level.activate_object(obj) self._triggered = s else: self.rect = before

[ "wm382710@students.mimuw.edu.pl" ]

wm382710@students.mimuw.edu.pl

cc556a21ebb4a3a1da3604521e71f01202a5fcc2

87915f5d46d1b776b824bfb4d4e4382e02cf3835

/Kiasati_final/runner.py

333c686367ed1c2db81db0e5776ee4c40d5d438b

[]

no_license

hamrazkiasati/sosgame

e70f888ac2a30956c817de0cabbc69c7d357ab15

d9b3195160978385fdb8f543816885c724020dd5

refs/heads/master

2022-12-03T04:12:18.066844

2020-08-07T19:18:31

285,903,772

0

null

UTF-8

Python

false

889

py

import sqlite3 from base import win from tkinter import ttk from login import opensignin, opensignup with sqlite3.connect('users.db') as db: cursor = db.cursor() cursor.execute("""CREATE TABLE IF NOT EXISTS users (username TEXT NOT NULL PRIMARY KEY, password TEXT NOT NULL, first TEXT NOT NULL, last TEXT NOT NULL, games INTEGER, wins INTEGER, isAdmin Boolean );""") cursor.execute("select * from users where username = 'admin' ") isFirst = cursor.fetchone() if isFirst is None: cursor.execute("INSERT INTO users VALUES('admin','123456','Hamraaz','Kiasati','0','0',true)") db.commit() db.close() btn_login = ttk.Button(win, text='Sign in', command=opensignin) btn_login.place(relx=0.3, rely=0.3) btn_signup = ttk.Button(win, text='Sign up', command=opensignup) btn_signup.place(relx=0.3, rely=0.6) win.mainloop()

[ "noreply@github.com" ]

hamrazkiasati.noreply@github.com

447eaf5574e069bc15b07fe9d3e0afec6844b381

d2aa30899042e9f4755700850839dd1df38a723f

/cutcsv.py

d5e52b6210226a723feff4c3990b343b639ce131

[]

no_license

DhvanilVadher/Intrusion-Detection-System-using-ANN.

40edcfd9b3fb588fdbdd40e447813cb99acc5bad

ab3149af97826e2e7b81cd424b4af18acfe7e5ef

refs/heads/main

2023-04-13T02:58:42.725005

2021-04-23T15:18:42

345,471,806

0

null

UTF-8

Python

false

375

py

import pandas as pd f=pd.read_csv("./editcsv/FridayDDOS.csv") keep_col = [' Average Packet Size','Active Mean',' Active Min',' Flow IAT Mean',' Flow Duration',' Fwd Packet Length Mean','Total Length of Fwd Packets',' Subflow Fwd Bytes',' Bwd IAT Mean',' Bwd Packet Length Std',' Bwd Packet Length Min',' Label'] new_f = f[keep_col] new_f.to_csv("FridayCUT.csv", index=False)

[ "" ]

d193039a18ab69a521087eba5cafe9748b85bddd

8543d0f9dc3afe9b1f94701905168afd0f683da2

/Django/Latest_MMM/Web/views.py

7ca2c0416af53e10cdfa63e01c4989db95774e21

[]

no_license

kasunsampathhewage/test_v1

da1b18ca27e0879411d4d81270a1034157e6d0cf

40a1182650eb9fd0bda6b2ad7ead047a202d3eae

refs/heads/master

2023-02-08T03:16:57.965921

2020-12-26T08:49:18

323,825,702

0

null

UTF-8

Python

false

51,671

py

from django.shortcuts import render from django.http import HttpResponse from django.http import JsonResponse import pandas as pd import numpy as np from datetime import datetime from sorted_months_weekdays import * from sort_dataframeby_monthorweek import * from .models import * import calendar from .predict import * # Create your views here. def indexpage(request): years = cd_year() months = cd_month() bigcs = cd_bigc() #get values from filter yearf = request.POST.get('year') monthf = request.POST.get('month') bigcf = request.POST.get('bigc') # filter data frame if request.method == "POST": df = SummarydataframeCreation() df1 = df[(df.bigc == bigcf)&(df.year == yearf)] df1_1 = df[(df.bigc == bigcf)&(df.year == yearf)&(df.month == monthf)] else: df = SummarydataframeCreation() df = df.sort_values(by='date') a = df['year'].iloc[-1] b = df['month'].iloc[-1] df1 = df[(df.year == a)] df1_1 = df[(df.year == a)&(df.month == b)] # Monthly sales chart1 df2 = df1.groupby('month', as_index=False).agg({"Sales": "sum"}) df2 = Sort_Dataframeby_Month(df=df2, monthcolumnname='month') Sale_Date = df2['month'].values.tolist() Sale_Amount = df2['Sales'].values.tolist() # monthly investment chart2 df4 = df1[['month', 'date', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df4 = pd.melt(df4, id_vars=['date', 'month'], var_name='Investment_Types', value_name='value') df4 = df4.groupby(['month','date'])['value'].sum().reset_index() df4 = Sort_Dataframeby_Month(df=df4, monthcolumnname='month') investment_month = df4['month'].values.tolist() investment_Amount = df4['value'].values.tolist() # investment for promotion type chart3 df5 = df1[['month', 'date', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df5 = pd.melt(df5, id_vars=['date', 'month'], var_name='Investment_Types', value_name='value') df5 = df5.groupby(['month', 'Investment_Types'])['value'].sum().reset_index() df5 = Sort_Dataframeby_Month(df=df5, monthcolumnname='month') df5_1= df5[df5['Investment_Types'] == 'AandP'] investment_Amount_A_P = df5_1['value'].values.tolist() investment_month_A_P = df5_1['month'].values.tolist() df5_2 = df5[df5['Investment_Types'] == 'Consumer_Promotion'] investment_Amount_Consumer_Promotion = df5_2['value'].values.tolist() df5_3 = df5[df5['Investment_Types'] == 'Display_Only'] investment_Amount_Display_Only = df5_3['value'].values.tolist() df5_4 = df5[df5['Investment_Types'] == 'Distributor_Margins'] investment_Amount_Distributor_Margins = df5_4['value'].values.tolist() df5_5 = df5[df5['Investment_Types'] == 'JBP'] investment_Amount_JBP = df5_5['value'].values.tolist() df5_6 = df5[df5['Investment_Types'] == 'Loyalty_Schemes'] investment_Amount_Loyalty_Schemes = df5_6['value'].values.tolist() df5_7 = df5[df5['Investment_Types'] == 'Search_Only'] investment_Amount_Search_Only = df5_7['value'].values.tolist() df5_8 = df5[df5['Investment_Types'] == 'Trade_Promotion'] investment_Amount_Trade_Promotion = df5_8['value'].values.tolist() df5_9 = df5[df5['Investment_Types'] == 'Video'] investment_Amount_Video = df5_9['value'].values.tolist() df5_10 = df5[df5['Investment_Types'] == 'facebook'] investment_Amount_facebook = df5_10['value'].values.tolist() df5_11 = df5[df5['Investment_Types'] == 'instagram'] investment_Amount_instagram = df5_11['value'].values.tolist() df5_12 = df5[df5['Investment_Types'] == 'messenger'] investment_Amount_messenger = df5_12['value'].values.tolist() # Total investment for thr year chart4 if request.method == "POST": df7 = df1.groupby(['date','month','bigc'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df8 = df7[['month', 'date','bigc','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df8 = pd.melt(df8, id_vars=['date', 'month','Sales','bigc'], var_name='Investment_Types', value_name='value') df8 = df8.groupby(['date','month','bigc','Sales'])['value'].sum().reset_index() df8['ROI'] = df8['Sales']/(df8['value']) ROI_value = df8['ROI'].values.tolist() ROI_month = df8['month'].values.tolist() else: df7 = df1.groupby(['date','month'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df8 = df7[['month', 'date','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df8 = pd.melt(df8, id_vars=['date', 'month','Sales'], var_name='Investment_Types', value_name='value') df8 = df8.groupby(['date','month','Sales'])['value'].sum().reset_index() df8['ROI'] = df8['Sales']/(df8['value']) ROI_value = df8['ROI'].values.tolist() ROI_month = df8['month'].values.tolist() # ROI for promotion types chart5 if request.method == "POST": df13 = df1_1.groupby(['year','month','bigc'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df13 = df13[['month', 'year','bigc','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df13 = pd.melt(df13, id_vars=['year', 'month','Sales','bigc'], var_name='Investment_Types', value_name='value') df13=df13[df13!=0].dropna() df13['ROI'] = df13['Sales']/(df13['value']) ROI_Investment_value = df13['ROI'].values.tolist() ROI_Investment_Types = df13['Investment_Types'].values.tolist() else: df13 = df1_1.groupby(['date','month'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df13 = df13[['month', 'date','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df13 = pd.melt(df13, id_vars=['date', 'month','Sales'], var_name='Investment_Types', value_name='value') df13=df13[df13!=0].dropna() df13['ROI'] = df13['Sales']/(df13['value']) ROI_Investment_value = df13['ROI'].values.tolist() ROI_Investment_Types = df13['Investment_Types'].values.tolist() # get values for cart1 (Total sales) total_sales_cart = df1_1['Sales'].sum()/1000000 total_sales_cart = round(total_sales_cart, 2) # get values for cart2 (Total investments) df3_1 = df1_1[['date', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df3_1 = pd.melt(df3_1, id_vars=['date'], var_name='Investment_Types', value_name='value') df3_1 = df3_1.groupby(['date'])['value'].sum().reset_index() df3_1['Date'] = pd.to_datetime(df3_1['date']) investment_Amount_cart = df3_1['value'].sum()/1000000 investment_Amount_cart=round(investment_Amount_cart, 2) #get values for cart3 (Sales/investments) if request.method == "POST": df7_1 = df1_1.groupby(['date','month','bigc'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df8_1 = df7_1[['month', 'date','bigc','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df8_1 = pd.melt(df8_1, id_vars=['date', 'month','Sales','bigc'], var_name='Investment_Types', value_name='value') df8_1 = df8_1.groupby(['date','month','bigc','Sales'])['value'].sum().reset_index() df8_1['ROI'] = df8_1['Sales']/(df8_1['value']) ROI_value_cart = round(df8_1['ROI'].sum(),4) else: df7_1 = df1_1.groupby(['date','month'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df8_1 = df7_1[['month', 'date','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df8_1 = pd.melt(df8_1, id_vars=['date', 'month','Sales'], var_name='Investment_Types', value_name='value') df8_1 = df8_1.groupby(['date','month','Sales'])['value'].sum().reset_index() df8_1['ROI'] = df8_1['Sales']/(df8_1['value']) ROI_value_cart = round(df8_1['ROI'].sum(),4) #pass variables to html pages context = {'yearf':yearf,'monthf':monthf,'bigcf':bigcf,'total_sales_cart': total_sales_cart, 'investment_Amount_cart':investment_Amount_cart ,'Sale_Date': Sale_Date, 'Sale_Amount': Sale_Amount,'years':years,'months':months,'bigcs':bigcs ,'investment_Amount':investment_Amount,'investment_month':investment_month ,'investment_Amount_A_P':investment_Amount_A_P,'investment_month_A_P':investment_month_A_P ,'investment_Amount_Consumer_Promotion':investment_Amount_Consumer_Promotion ,'investment_Amount_Display_Only':investment_Amount_Display_Only ,'investment_Amount_Distributor_Margins':investment_Amount_Distributor_Margins ,'investment_Amount_JBP':investment_Amount_JBP ,'investment_Amount_Loyalty_Schemes':investment_Amount_Loyalty_Schemes ,'investment_Amount_Search_Only':investment_Amount_Search_Only ,'investment_Amount_Trade_Promotion':investment_Amount_Trade_Promotion ,'investment_Amount_Video':investment_Amount_Video ,'investment_Amount_facebook':investment_Amount_facebook ,'investment_Amount_instagram':investment_Amount_instagram ,'investment_Amount_messenger':investment_Amount_messenger ,'investment_Amount_Consumer_Promotion':investment_Amount_Consumer_Promotion ,'ROI_value':ROI_value,'ROI_month':ROI_month,'ROI_value_cart':ROI_value_cart ,'ROI_Investment_value':ROI_Investment_value,'ROI_Investment_Types':ROI_Investment_Types } #get values for cart4 (Sales growth) #get previous month if request.method == "POST": current_month = list(calendar.month_abbr).index(monthf) previous_month = current_month-1 previous_month_abb = calendar.month_abbr[previous_month] current_year = yearf if previous_month == 0: previous_month = 12 previous_month_abb = calendar.month_abbr[previous_month] current_year = int(yearf)-1 current_year = str(current_year) df_total_sales_for_previous_month = df[(df.bigc == bigcf)&(df.year == current_year)&(df.month == previous_month_abb)] previous_total_sales = df_total_sales_for_previous_month['Sales'].sum()/1000000 sales_growth_cart = (total_sales_cart - previous_total_sales)/previous_total_sales*100 sales_growth_cart = round(sales_growth_cart, 2) context.update({'sales_growth_cart': sales_growth_cart,'previous_month_abb':previous_month_abb }) else: pass return render(request, 'home.html', context) def brand(request): years = cd_year() months = cd_month() bigcs = cd_bigc() Fbrands = cd_FoodBrands() Bbrands = cd_BeveragesBrands() HCbrands = cd_HomeProductsBrands() SCbrands = cd_SelfCareBrands() #get values from filter yearf = request.POST.get('year') monthf = request.POST.get('month') bigcf = request.POST.get('bigc') branf = request.POST.get('brand') # filter data frame if request.method == "POST": df = SummarydataframeCreation() df1 = df[(df.bigc == bigcf)&(df.Brand_name == branf)&(df.year == yearf)] df1_1 = df[(df.bigc == bigcf)&(df.Brand_name == branf)&(df.year == yearf)&(df.month == monthf)] else: df = SummarydataframeCreation() df = df.sort_values(by='date') a = df['year'].iloc[-1] b = df['month'].iloc[-1] df1 = df[(df.year == a)] df1_1 = df[(df.year == a)&(df.month == b)] # Monthly sales chart1 df2 = df1.groupby('month', as_index=False).agg({"Sales": "sum"}) df2 = Sort_Dataframeby_Month(df=df2, monthcolumnname='month') Sale_Date = df2['month'].values.tolist() Sale_Amount = df2['Sales'].values.tolist() # monthly investment chart2 df4 = df1[['month', 'date', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df4 = pd.melt(df4, id_vars=['date', 'month'], var_name='Investment_Types', value_name='value') df4 = df4.groupby(['month','date'])['value'].sum().reset_index() df4 = Sort_Dataframeby_Month(df=df4, monthcolumnname='month') investment_month = df4['month'].values.tolist() investment_Amount = df4['value'].values.tolist() # investment for promotion type chart3 df5 = df1[['month', 'date', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df5 = pd.melt(df5, id_vars=['date', 'month'], var_name='Investment_Types', value_name='value') df5 = df5.groupby(['month', 'Investment_Types'])['value'].sum().reset_index() df5 = Sort_Dataframeby_Month(df=df5, monthcolumnname='month') df5_1= df5[df5['Investment_Types'] == 'AandP'] investment_Amount_A_P = df5_1['value'].values.tolist() investment_month_A_P = df5_1['month'].values.tolist() df5_2 = df5[df5['Investment_Types'] == 'Consumer_Promotion'] investment_Amount_Consumer_Promotion = df5_2['value'].values.tolist() df5_3 = df5[df5['Investment_Types'] == 'Display_Only'] investment_Amount_Display_Only = df5_3['value'].values.tolist() df5_4 = df5[df5['Investment_Types'] == 'Distributor_Margins'] investment_Amount_Distributor_Margins = df5_4['value'].values.tolist() df5_5 = df5[df5['Investment_Types'] == 'JBP'] investment_Amount_JBP = df5_5['value'].values.tolist() df5_6 = df5[df5['Investment_Types'] == 'Loyalty_Schemes'] investment_Amount_Loyalty_Schemes = df5_6['value'].values.tolist() df5_7 = df5[df5['Investment_Types'] == 'Search_Only'] investment_Amount_Search_Only = df5_7['value'].values.tolist() df5_8 = df5[df5['Investment_Types'] == 'Trade_Promotion'] investment_Amount_Trade_Promotion = df5_8['value'].values.tolist() df5_9 = df5[df5['Investment_Types'] == 'Video'] investment_Amount_Video = df5_9['value'].values.tolist() df5_10 = df5[df5['Investment_Types'] == 'facebook'] investment_Amount_facebook = df5_10['value'].values.tolist() df5_11 = df5[df5['Investment_Types'] == 'instagram'] investment_Amount_instagram = df5_11['value'].values.tolist() df5_12 = df5[df5['Investment_Types'] == 'messenger'] investment_Amount_messenger = df5_12['value'].values.tolist() # Total sales/ total investments chart 4 if request.method == "POST": df6 = df1[['month', 'date','bigc','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df6 = pd.melt(df6, id_vars=['date', 'month','bigc','Sales'], var_name='Investment_Types', value_name='value') df6 = df6.groupby(['date','month','bigc','Sales'])['value'].sum().reset_index() df6['ROI'] = df6['Sales']/(df6['value']) ROI_value = df6['ROI'].values.tolist() ROI_month = df6['month'].values.tolist() else: df7 = df1.groupby(['date','month'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df8 = df7[['month', 'date','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df8 = pd.melt(df8, id_vars=['date', 'month','Sales'], var_name='Investment_Types', value_name='value') df8 = df8.groupby(['date','month','Sales'])['value'].sum().reset_index() df8['ROI'] = df8['Sales']/(df8['value']) ROI_value = df8['ROI'].values.tolist() ROI_month = df8['month'].values.tolist() # ROI for promotion types chart5 if request.method == "POST": df13 = df1_1.groupby(['year','month','Brand_name'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df13 = df13[['month', 'year','Brand_name','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df13 = pd.melt(df13, id_vars=['year', 'month','Sales','Brand_name'], var_name='Investment_Types', value_name='value') df13=df13[df13!=0].dropna() df13['ROI'] = df13['Sales']/(df13['value']) ROI_Investment_value = df13['ROI'].values.tolist() ROI_Investment_Types = df13['Investment_Types'].values.tolist() else: df13 = df1_1.groupby(['date','month'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df13 = df13[['month', 'date','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df13 = pd.melt(df13, id_vars=['date', 'month','Sales'], var_name='Investment_Types', value_name='value') df13=df13[df13!=0].dropna() df13['ROI'] = df13['Sales']/(df13['value']) ROI_Investment_value = df13['ROI'].values.tolist() ROI_Investment_Types = df13['Investment_Types'].values.tolist() # ROI with contribution # NoMonths=3 # bigcf ='Foods' # brandf ='Brd00001' # dfprep=DataPreprocessing(bigcf,brandf) # df2,seconddiff,finaldf=test2(dfprep) # results=VARmodel(df2) # dfe_forecast=forecastData(results,df2,NoMonths,dfprep) # invertsale1=invert_transformation(dfprep,df2,second_diff=seconddiff) # invertsale1=np.exp(invertsale1) # invertSale=invert_transformation(dfprep,dfe_forecast,second_diff=seconddiff) # invertSale=np.exp(invertSale) # #invert sale has only forecast data # #finaldf has the actual data # #appending the both dataframes togather to chart 1 # #takes only last 12 rows for the plot as in line 146 # fulldfForecast1=finaldf.append(invertSale) # #keeping a copy for later purpose # fulldfForecast1_2=fulldfForecast1.copy() # fulldfForecast1['date'] = fulldfForecast1.index # fulldfForecast1['date']=fulldfForecast1['date'].dt.strftime('%Y/%b/%d') # fulldfForecast1['Sales2']=fulldfForecast1['Sales'] # fulldfForecast1=fulldfForecast1.tail(12) # A=fulldfForecast1.iloc[:-NoMonths] # A['Date']=A.index # A['Date']=A['Date'].dt.strftime('%Y/%b/%d') # B=fulldfForecast1.tail(NoMonths) # B['Date']=B.index # B['Date']=B['Date'].dt.strftime('%Y/%b/%d') # Sale_Date = fulldfForecast1['date'].values.tolist() # Sale_Amount =fulldfForecast1['Sales'].values.tolist() # Sale_AmountP =fulldfForecast1['Sales2'].values.tolist() # Sale_Amount1 =A['Sales'].values.tolist() # Sale_Amount2 =B['Sales'].values.tolist() # Sale_Date1 = A['Date'].values.tolist() # Sale_Date2 = B['Date'].values.tolist() # #contribution chart # x=fulldfForecast1_2.shape[0] # elasticity=impulseResponse(results,x,fulldfForecast1_2) # contribution=Contribution(elasticity,fulldfForecast1_2) # contribution2=contribution.copy() # df_tt=ROI(contribution2,elasticity,3,2019,"Jan") # Investment_type=df_tt['Investment Type'].values.tolist() # ROI1=df_tt['ROI'].values.tolist() # get values for cart1 (total sales) total_sales_cart = df1_1['Sales'].sum()/1000000 total_sales_cart = round(total_sales_cart, 2) # get values for cart2 (total investment) df3_1 = df1_1[['date', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df3_1 = pd.melt(df3_1, id_vars=['date'], var_name='Investment_Types', value_name='value') df3_1 = df3_1.groupby(['date'])['value'].sum().reset_index() df3_1['Date'] = pd.to_datetime(df3_1['date']) investment_Amount_cart = df3_1['value'].sum()/1000000 investment_Amount_cart=round(investment_Amount_cart, 2) #get values for cart3 (total sales/ total investments) if request.method == "POST": df6_1 = df1_1[['month', 'date','bigc','Brand_name','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df6_1 = pd.melt(df6_1, id_vars=['date', 'month','bigc','Brand_name','Sales'], var_name='Investment_Types', value_name='value') df6_1 = df6_1.groupby(['date','month','bigc','Brand_name','Sales'])['value'].sum().reset_index() df6_1['ROI'] = df6_1['Sales']/(df6_1['value']) ROI_value_cart = round(df6_1['ROI'].sum(),4) else: df6_1 = df1_1.groupby(['date','month'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df8_1 = df6_1[['month', 'date','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df8_1 = pd.melt(df8_1, id_vars=['date', 'month','Sales'], var_name='Investment_Types', value_name='value') df8_1 = df8_1.groupby(['date','month','Sales'])['value'].sum().reset_index() df8_1['ROI'] = df8_1['Sales']/(df8_1['value']) ROI_value_cart = round(df8_1['ROI'].sum(),4) context = {'years':years,'months':months,'bigcs':bigcs, 'Fbrands':Fbrands,'Bbrands':Bbrands,'HCbrands':HCbrands,'SCbrands':SCbrands, 'yearf':yearf, 'monthf':monthf,'bigcf':bigcf,'brandf':branf, 'Sale_Date':Sale_Date ,'Sale_Amount':Sale_Amount, 'investment_Amount':investment_Amount, 'investment_month':investment_month, 'investment_Amount_A_P':investment_Amount_A_P,'investment_month_A_P':investment_month_A_P ,'investment_Amount_Consumer_Promotion':investment_Amount_Consumer_Promotion ,'investment_Amount_Display_Only':investment_Amount_Display_Only ,'investment_Amount_Distributor_Margins':investment_Amount_Distributor_Margins ,'investment_Amount_JBP':investment_Amount_JBP ,'investment_Amount_Loyalty_Schemes':investment_Amount_Loyalty_Schemes ,'investment_Amount_Search_Only':investment_Amount_Search_Only ,'investment_Amount_Trade_Promotion':investment_Amount_Trade_Promotion ,'investment_Amount_Video':investment_Amount_Video ,'investment_Amount_facebook':investment_Amount_facebook ,'investment_Amount_instagram':investment_Amount_instagram ,'investment_Amount_messenger':investment_Amount_messenger ,'investment_Amount_Consumer_Promotion':investment_Amount_Consumer_Promotion ,'total_sales_cart': total_sales_cart,'investment_Amount_cart':investment_Amount_cart, 'ROI_value':ROI_value,'ROI_month':ROI_month, 'ROI_value_cart':ROI_value_cart ,'ROI_Investment_value':ROI_Investment_value,'ROI_Investment_Types':ROI_Investment_Types # ,'Investment_type':Investment_type,'ROI1':ROI1 } #get values for cart4 (sales growth) #get previous month if request.method == "POST": current_month = list(calendar.month_abbr).index(monthf) previous_month = current_month-1 previous_month_abb = calendar.month_abbr[previous_month] current_year = yearf if previous_month == 0: previous_month = 12 previous_month_abb = calendar.month_abbr[previous_month] current_year = int(yearf)-1 current_year = str(current_year) df_total_sales_for_previous_month = df[(df.bigc == bigcf)&(df.Brand_name == branf)&(df.year == current_year)&(df.month == previous_month_abb)] previous_total_sales = df_total_sales_for_previous_month['Sales'].sum()/1000000 sales_growth_cart = (total_sales_cart - previous_total_sales)/previous_total_sales *100 sales_growth_cart = round(sales_growth_cart, 2) context.update({'sales_growth_cart': sales_growth_cart,'previous_month_abb':previous_month_abb }) else: pass return render(request,'brand.html',context) def predict(request): years = cd_year() months = cd_month() bigcs = cd_bigc() brands = cd_brands() Fbrands = cd_FoodBrands() Bbrands = cd_BeveragesBrands() HCbrands = cd_HomeProductsBrands() SCbrands = cd_SelfCareBrands() #get values from filter yearf = request.POST.get('year') monthf = request.POST.get('month') bigcf = request.POST.get('bigc') brandf = request.POST.get('brand') pyearf=request.POST.get('pyear') #slider1 = request.POST.get('range1') print("++++++++++++++++++++++Hello world+++++++++++++++++++++++++++++") print(bigcf) if request.method == "POST": NoMonths= int(pyearf) else: NoMonths=1 bigcf ='Foods' brandf ='Brd00001' dfprep=DataPreprocessing(bigcf,brandf) df2,seconddiff,finaldf=test2(dfprep) results=VARmodel(df2) dfe_forecast=forecastData(results,df2,NoMonths,dfprep) invertsale1=invert_transformation(finaldf,df2,second_diff=seconddiff) invertsale1=np.exp(invertsale1) invertSale=invert_transformation(finaldf,dfe_forecast,second_diff=seconddiff) invertSale=np.exp(invertSale) #invert sale has only forecast data #finaldf has the actual data #appending the both dataframes togather to chart 1 #takes only last 12 rows for the plot as in line 146 fulldfForecast1=finaldf.append(invertSale) print("Checking columns=========================") #keeping a copy for later purpose fulldfForecast1_2=fulldfForecast1.copy() print(fulldfForecast1_2.columns) fulldfForecast1['date'] = fulldfForecast1.index fulldfForecast1['date']=fulldfForecast1['date'].dt.strftime('%Y/%b/%d') fulldfForecast1['Sales2']=fulldfForecast1['Sales'] fulldfForecast1=fulldfForecast1.tail(12) A=fulldfForecast1.iloc[:-NoMonths] A['Date']=A.index A['Date']=A['Date'].dt.strftime('%Y/%b/%d') B=fulldfForecast1.tail(NoMonths) B['Date']=B.index B['Date']=B['Date'].dt.strftime('%Y/%b/%d') Sale_Date = fulldfForecast1['date'].values.tolist() Sale_Amount =fulldfForecast1['Sales'].values.tolist() z=fulldfForecast1.Sales.tail(NoMonths) Sale_AmountP =fulldfForecast1['Sales2'].values.tolist() #p1=Sale_AmountP[0] #p2=Sale_AmountP[1] #p3=Sale_AmountP[2] Sale_Amount1 =A['Sales'].values.tolist() Sale_Amount2 =B['Sales'].values.tolist() Sale_Date1 = A['Date'].values.tolist() Sale_Date2 = B['Date'].values.tolist() #contribution chart x=fulldfForecast1_2.shape[0] elasticity=impulseResponse(results,x,fulldfForecast1_2) print("checking elasticity columns1------------") print(elasticity.columns) elasticity2=elasticity.copy() contribution=Contribution(elasticity,fulldfForecast1_2) contribution2=contribution.copy() contribution=contribution.tail(NoMonths) #tt=contributionVisual(contribution) #creating the columnlist #tl=tt.columns.tolist() #removewords=['Date_Contribution'] #for word in list(removewords): # iterating on a copy since removing will mess things up # if word in removewords: # tl.remove(word) #columnlist1=tl #tt['Month']=tt['Date_Contribution'].dt.month_name() #tt['Month1']=tt['Date_Contribution'].dt.strftime('%Y/%b/%d') #Month=tt['Month'].values.tolist() #Consumer_Promotion_Contribution=tt['Consumer_Promotion_Contribution'].values.tolist() #Trade_Promotion_Contribution=tt['Trade_Promotion_Contribution'].values.tolist() #AandP_Contribution=tt['AandP_Contribution'].values.tolist() #JBP_Contribution=tt['JBP_Contribution'].values.tolist() #Distributor_Margins_Contribution=tt['Distributor_Margins_Contribution'].values.tolist() #Loyalty_Schemes_Contribution=tt['Loyalty_Schemes_Contribution'].values.tolist() #Video_Contribution=tt['Video_Contribution'].values.tolist() #facebook_Contribution=tt['facebook_Contribution'].values.tolist() #instagram_Contribution=tt['instagram_Contribution'].values.tolist() #messenger_Contribution=tt['messenger_Contribution'].values.tolist() ########### Simulation################# #column list to show the slider bars tl2=elasticity.columns.tolist() tl2=[x for x in tl2 if x != "Sales"] columnlist2=tl2 AandP =1 #request.POST.get('AandP') AndP1=request.POST.get('AandP_input') print("==========================================================================aaaaaaaaannnnnppppppp") print(AndP1) #AandP_input=int(AandP) Consumer_Promotion =1 #request.POST.get('customRange2') JBP =1 #request.POST.get('customRange4') facebook =1 #request.POST.get('customRange8') instagram =1 #request.POST.get('customRange9') video =1 #request.POST.get('customRange10') Search_Only=1#request.POST.get('customRange11') Display_Only=1#request.POST.get('customRange12') Distributor_Margins=1#request.POST.get('customRange6') Loyalty_Schemes=1#request.POST.get('customRange7') Trade_Promotion=1#request.POST.get('customRange5') messenger=1#request.POST.get('customRange13') li=['Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP','Distributor_Margins', 'Loyalty_Schemes', 'Video', 'facebook','Search_Only', 'instagram', 'messenger', 'Display_Only'] for i in elasticity.columns.values: for j in li: if i==j: li.remove(i) for col in li: elasticity[col]=0 elasticity=elasticity.tail(3) elasticity_T1=elasticity.head(1) elasticity_T1=elasticity_T1.reset_index() elasticity_T2=elasticity.iloc[1:] elasticity_T2=elasticity_T2.head(1) elasticity_T2=elasticity_T2.reset_index() elasticity_T3=elasticity.tail(1) elasticity_T3=elasticity_T3.reset_index() df=finaldf['Sales'].tail(1) Salestot=df.sum() #Salestot2=invertSale['Sales'].head(1).sum() #Salestot3=invertSale['Sales'].tail(1).sum() st1=abs(Salestot+elasticity_T1['Search_Only']*Search_Only+elasticity_T1['facebook']*facebook+elasticity_T1['instagram']*instagram+elasticity_T1['Display_Only']*Display_Only+elasticity_T1['Distributor_Margins']*Distributor_Margins+elasticity_T1['JBP']*JBP+elasticity_T1['Loyalty_Schemes']*Loyalty_Schemes+elasticity_T1['Trade_Promotion']*Trade_Promotion+elasticity_T1['Consumer_Promotion']*Consumer_Promotion+elasticity_T1['Video']*video+elasticity_T1['AandP']*AandP*1000+elasticity_T1['messenger']*messenger) st2=abs(st1+elasticity_T2['Search_Only']*Search_Only+elasticity_T2['facebook']*facebook+elasticity_T2['instagram']*instagram+elasticity_T2['Display_Only']*Display_Only+elasticity_T2['Distributor_Margins']*Distributor_Margins+elasticity_T2['JBP']*JBP+elasticity_T2['Loyalty_Schemes']*Loyalty_Schemes+elasticity_T2['Trade_Promotion']*Trade_Promotion+elasticity_T2['Consumer_Promotion']*Consumer_Promotion+elasticity_T2['Video']*video+elasticity_T2['AandP']*AandP*10000+elasticity_T2['messenger']*messenger) st3=abs(st2+elasticity_T3['Search_Only']*Search_Only+elasticity_T3['facebook']*facebook+elasticity_T3['instagram']*instagram+elasticity_T3['Display_Only']*Display_Only+elasticity_T3['Distributor_Margins']*Distributor_Margins+elasticity_T3['JBP']*JBP+elasticity_T3['Loyalty_Schemes']*Loyalty_Schemes+elasticity_T3['Trade_Promotion']*Trade_Promotion+elasticity_T3['Consumer_Promotion']*Consumer_Promotion+elasticity_T3['Video']*video+elasticity_T3['AandP']*AandP*10000+elasticity_T3['messenger']*messenger) dfT={'Sales':st1} dfT=pd.DataFrame(dfT) dfT2={'Sales':st2} dfT2=pd.DataFrame(dfT2) dfT3={'Sales':st3} dfT3=pd.DataFrame(dfT3) a=NoMonths if a==1: r=dfT elif a==2: r=dfT.append(dfT2,ignore_index=True) elif a==3: r=dfT.append(dfT2,ignore_index=True) r=r.append(dfT3,ignore_index=True) rr=r['Sales'].tolist() df_forecast=pd.DataFrame(data=rr,index=dfe_forecast.tail(a).index,columns=r.columns) print(df_forecast) simulation=fulldfForecast1_2.tail(5).merge(df_forecast,how='left',left_index=True, right_index=True)[['Sales_pred','Sales']] simulation.Sales.fillna(simulation.Sales_pred,inplace=True) simulation['date'] = simulation.index simulation['date']=simulation['date'].dt.strftime('%Y/%b/%d') forecastsales=simulation['Sales_pred'].values.tolist() simulationSales=simulation['Sales'].values.tolist() simulationDate=simulation['date'].values.tolist() print("Checking shapes--------------------") print(contribution2.columns) print(elasticity.columns) df_tt=ROI(contribution2,elasticity2,3,2019,"Jan") Investment_type=df_tt['Investment Type'].values.tolist() ROI1=df_tt['ROI'].values.tolist() context= {'years':years,'months':months,'bigcs':bigcs,'brands':brands, 'Fbrands':Fbrands,'Bbrands':Bbrands,'HCbrands':HCbrands,'SCbrands':SCbrands, 'yearf':yearf, 'monthf':monthf,'bigcf':bigcf,'brandf':brandf,'NoMonths':NoMonths, 'Sale_Amount':Sale_Amount,'Sale_Date':Sale_Date,'columnlist2':columnlist2, 'forecastsales':forecastsales,'simulationSales':simulationSales,'simulationDate':simulationDate, 'Investment_type':Investment_type,'ROI1':ROI1,'Sale_Amount1':Sale_Amount1,'Sale_Amount2':Sale_Amount2, 'Sale_Date1':Sale_Date1 ,'Sale_Date2':Sale_Date2,'Sale_AmountP':Sale_AmountP,'AndP1':AndP1 } return render(request,'predict.html',context) def comparision(request): years = cd_year() months = cd_month() bigcs = cd_bigc() Fbrands = cd_FoodBrands() Bbrands = cd_BeveragesBrands() HCbrands = cd_HomeProductsBrands() SCbrands = cd_SelfCareBrands() #get values from filter yearf = request.POST.get('year') monthf = request.POST.get('month') bigcf = request.POST.get('bigc') branf = request.POST.get('brand') # filter data frame if request.method == "POST": df = SummarydataframeCreation() # df1 = df[(df.year == yearf)&(df.bigc == bigcf)|(df.Brand_name == branf)] df1 = df[(df.year == yearf)&(df.bigc == bigcf)] df1_1 = df[(df.year == yearf)&(df.month == monthf)&(df.bigc == bigcf)&(df.Brand_name == branf)] else: df = SummarydataframeCreation() df = df.sort_values(by='date') a = df['year'].iloc[-1] b = df['month'].iloc[-1] df1 = df[(df.year == a)] df1_1 = df[(df.year == a)&(df.month == b)] # Monthly sales chart1 df2 = df1.groupby('month', as_index=False).agg({"Sales": "sum"}) df2 = Sort_Dataframeby_Month(df=df2, monthcolumnname='month') Sale_Date = df2['month'].values.tolist() Sale_Amount = df2['Sales'].values.tolist() # monthly investment chart2 df4 = df1[['month', 'date', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df4 = pd.melt(df4, id_vars=['date', 'month'], var_name='Investment_Types', value_name='value') df4 = df4.groupby(['month','date'])['value'].sum().reset_index() df4 = Sort_Dataframeby_Month(df=df4, monthcolumnname='month') investment_month = df4['month'].values.tolist() investment_Amount = df4['value'].values.tolist() # investment for promotion type chart3 df5 = df1[['month', 'date', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df5 = pd.melt(df5, id_vars=['date', 'month'], var_name='Investment_Types', value_name='value') df5 = df5.groupby(['month', 'Investment_Types'])['value'].sum().reset_index() df5 = Sort_Dataframeby_Month(df=df5, monthcolumnname='month') df5_1= df5[df5['Investment_Types'] == 'AandP'] investment_Amount_A_P = df5_1['value'].values.tolist() investment_month_A_P = df5_1['month'].values.tolist() df5_2 = df5[df5['Investment_Types'] == 'Consumer_Promotion'] investment_Amount_Consumer_Promotion = df5_2['value'].values.tolist() df5_3 = df5[df5['Investment_Types'] == 'Display_Only'] investment_Amount_Display_Only = df5_3['value'].values.tolist() df5_4 = df5[df5['Investment_Types'] == 'Distributor_Margins'] investment_Amount_Distributor_Margins = df5_4['value'].values.tolist() df5_5 = df5[df5['Investment_Types'] == 'JBP'] investment_Amount_JBP = df5_5['value'].values.tolist() df5_6 = df5[df5['Investment_Types'] == 'Loyalty_Schemes'] investment_Amount_Loyalty_Schemes = df5_6['value'].values.tolist() df5_7 = df5[df5['Investment_Types'] == 'Search_Only'] investment_Amount_Search_Only = df5_7['value'].values.tolist() df5_8 = df5[df5['Investment_Types'] == 'Trade_Promotion'] investment_Amount_Trade_Promotion = df5_8['value'].values.tolist() df5_9 = df5[df5['Investment_Types'] == 'Video'] investment_Amount_Video = df5_9['value'].values.tolist() df5_10 = df5[df5['Investment_Types'] == 'facebook'] investment_Amount_facebook = df5_10['value'].values.tolist() df5_11 = df5[df5['Investment_Types'] == 'instagram'] investment_Amount_instagram = df5_11['value'].values.tolist() df5_12 = df5[df5['Investment_Types'] == 'messenger'] investment_Amount_messenger = df5_12['value'].values.tolist() # Total sales/ total investments chart 4 if request.method == "POST": df6 = df1[['month', 'date','bigc','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df6 = pd.melt(df6, id_vars=['date', 'month','bigc','Sales'], var_name='Investment_Types', value_name='value') df6 = df6.groupby(['date','month','bigc','Sales'])['value'].sum().reset_index() df6['ROI'] = df6['Sales']/(df6['value']) ROI_value = df6['ROI'].values.tolist() ROI_month = df6['month'].values.tolist() else: df7 = df1.groupby(['date','month'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df8 = df7[['month', 'date','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df8 = pd.melt(df8, id_vars=['date', 'month','Sales'], var_name='Investment_Types', value_name='value') df8 = df8.groupby(['date','month','Sales'])['value'].sum().reset_index() df8['ROI'] = df8['Sales']/(df8['value']) ROI_value = df8['ROI'].values.tolist() ROI_month = df8['month'].values.tolist() # ROI for promotion types chart5 if request.method == "POST": df13 = df1_1.groupby(['year','month','Brand_name'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df13 = df13[['month', 'year','Brand_name','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df13 = pd.melt(df13, id_vars=['year', 'month','Sales','Brand_name'], var_name='Investment_Types', value_name='value') df13=df13[df13!=0].dropna() df13['ROI'] = df13['Sales']/(df13['value']) ROI_Investment_value = df13['ROI'].values.tolist() ROI_Investment_Types = df13['Investment_Types'].values.tolist() else: df13 = df1_1.groupby(['date','month'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df13 = df13[['month', 'date','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df13 = pd.melt(df13, id_vars=['date', 'month','Sales'], var_name='Investment_Types', value_name='value') df13=df13[df13!=0].dropna() df13['ROI'] = df13['Sales']/(df13['value']) ROI_Investment_value = df13['ROI'].values.tolist() ROI_Investment_Types = df13['Investment_Types'].values.tolist() # ROI with contribution # NoMonths=3 # bigcf ='Foods' # brandf ='Brd00001' # dfprep=DataPreprocessing(bigcf,brandf) # df2,seconddiff,finaldf=test2(dfprep) # results=VARmodel(df2) # dfe_forecast=forecastData(results,df2,NoMonths,dfprep) # invertsale1=invert_transformation(dfprep,df2,second_diff=seconddiff) # invertsale1=np.exp(invertsale1) # invertSale=invert_transformation(dfprep,dfe_forecast,second_diff=seconddiff) # invertSale=np.exp(invertSale) # #invert sale has only forecast data # #finaldf has the actual data # #appending the both dataframes togather to chart 1 # #takes only last 12 rows for the plot as in line 146 # fulldfForecast1=finaldf.append(invertSale) # #keeping a copy for later purpose # fulldfForecast1_2=fulldfForecast1.copy() # fulldfForecast1['date'] = fulldfForecast1.index # fulldfForecast1['date']=fulldfForecast1['date'].dt.strftime('%Y/%b/%d') # fulldfForecast1['Sales2']=fulldfForecast1['Sales'] # fulldfForecast1=fulldfForecast1.tail(12) # A=fulldfForecast1.iloc[:-NoMonths] # A['Date']=A.index # A['Date']=A['Date'].dt.strftime('%Y/%b/%d') # B=fulldfForecast1.tail(NoMonths) # B['Date']=B.index # B['Date']=B['Date'].dt.strftime('%Y/%b/%d') # Sale_Date = fulldfForecast1['date'].values.tolist() # Sale_Amount =fulldfForecast1['Sales'].values.tolist() # Sale_AmountP =fulldfForecast1['Sales2'].values.tolist() # Sale_Amount1 =A['Sales'].values.tolist() # Sale_Amount2 =B['Sales'].values.tolist() # Sale_Date1 = A['Date'].values.tolist() # Sale_Date2 = B['Date'].values.tolist() # #contribution chart # x=fulldfForecast1_2.shape[0] # elasticity=impulseResponse(results,x,fulldfForecast1_2) # contribution=Contribution(elasticity,fulldfForecast1_2) # contribution2=contribution.copy() # df_tt=ROI(contribution2,elasticity,3,2019,"Jan") # Investment_type=df_tt['Investment Type'].values.tolist() # ROI1=df_tt['ROI'].values.tolist() # get values for cart1 (total sales) total_sales_cart = df1_1['Sales'].sum()/1000000 total_sales_cart = round(total_sales_cart, 2) # get values for cart2 (total investment) df3_1 = df1_1[['date', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df3_1 = pd.melt(df3_1, id_vars=['date'], var_name='Investment_Types', value_name='value') df3_1 = df3_1.groupby(['date'])['value'].sum().reset_index() df3_1['Date'] = pd.to_datetime(df3_1['date']) investment_Amount_cart = df3_1['value'].sum()/1000000 investment_Amount_cart=round(investment_Amount_cart, 2) #get values for cart3 (total sales/ total investments) if request.method == "POST": df6_1 = df1_1[['month', 'date','bigc','Brand_name','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df6_1 = pd.melt(df6_1, id_vars=['date', 'month','bigc','Brand_name','Sales'], var_name='Investment_Types', value_name='value') df6_1 = df6_1.groupby(['date','month','bigc','Brand_name','Sales'])['value'].sum().reset_index() df6_1['ROI'] = df6_1['Sales']/(df6_1['value']) ROI_value_cart = round(df6_1['ROI'].sum(),4) else: df6_1 = df1_1.groupby(['date','month'])['Sales','Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins','Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger'].sum().reset_index() df8_1 = df6_1[['month', 'date','Sales', 'Consumer_Promotion', 'AandP', 'Trade_Promotion', 'JBP', 'Distributor_Margins', 'Loyalty_Schemes', 'Other', 'Video', 'Search_Only', 'Display_Only', 'facebook', 'instagram', 'messenger']] df8_1 = pd.melt(df8_1, id_vars=['date', 'month','Sales'], var_name='Investment_Types', value_name='value') df8_1 = df8_1.groupby(['date','month','Sales'])['value'].sum().reset_index() df8_1['ROI'] = df8_1['Sales']/(df8_1['value']) ROI_value_cart = round(df8_1['ROI'].sum(),4) context = {'years':years,'months':months,'bigcs':bigcs, 'Fbrands':Fbrands,'Bbrands':Bbrands,'HCbrands':HCbrands,'SCbrands':SCbrands, 'yearf':yearf, 'monthf':monthf,'bigcf':bigcf,'brandf':branf, 'Sale_Date':Sale_Date ,'Sale_Amount':Sale_Amount, 'investment_Amount':investment_Amount, 'investment_month':investment_month, 'investment_Amount_A_P':investment_Amount_A_P,'investment_month_A_P':investment_month_A_P ,'investment_Amount_Consumer_Promotion':investment_Amount_Consumer_Promotion ,'investment_Amount_Display_Only':investment_Amount_Display_Only ,'investment_Amount_Distributor_Margins':investment_Amount_Distributor_Margins ,'investment_Amount_JBP':investment_Amount_JBP ,'investment_Amount_Loyalty_Schemes':investment_Amount_Loyalty_Schemes ,'investment_Amount_Search_Only':investment_Amount_Search_Only ,'investment_Amount_Trade_Promotion':investment_Amount_Trade_Promotion ,'investment_Amount_Video':investment_Amount_Video ,'investment_Amount_facebook':investment_Amount_facebook ,'investment_Amount_instagram':investment_Amount_instagram ,'investment_Amount_messenger':investment_Amount_messenger ,'investment_Amount_Consumer_Promotion':investment_Amount_Consumer_Promotion ,'total_sales_cart': total_sales_cart,'investment_Amount_cart':investment_Amount_cart, 'ROI_value':ROI_value,'ROI_month':ROI_month, 'ROI_value_cart':ROI_value_cart ,'ROI_Investment_value':ROI_Investment_value,'ROI_Investment_Types':ROI_Investment_Types # ,'Investment_type':Investment_type,'ROI1':ROI1 } #get values for cart4 (sales growth) #get previous month if request.method == "POST": current_month = list(calendar.month_abbr).index(monthf) previous_month = current_month-1 previous_month_abb = calendar.month_abbr[previous_month] current_year = yearf if previous_month == 0: previous_month = 12 previous_month_abb = calendar.month_abbr[previous_month] current_year = int(yearf)-1 current_year = str(current_year) df_total_sales_for_previous_month = df[(df.bigc == bigcf)&(df.Brand_name == branf)&(df.year == current_year)&(df.month == previous_month_abb)] previous_total_sales = df_total_sales_for_previous_month['Sales'].sum()/1000000 sales_growth_cart = (total_sales_cart - previous_total_sales)/previous_total_sales *100 sales_growth_cart = round(sales_growth_cart, 2) context.update({'sales_growth_cart': sales_growth_cart}) else: pass return render(request,'comparision.html',context)

[ "62302888+kasunsampathhewage@users.noreply.github.com" ]

62302888+kasunsampathhewage@users.noreply.github.com

01fb8d05747375d68eb2132a21d9bb541e452ffc

4443a051136c23156fefc56b50e467dd0717dc07

/mp1/models/linear_model.py

590971e33d18b64f79a836c4289ea68fbafee408

[]

no_license

therealJacobWu/pattern_recognition

6eefbbaa2d8584b800b234c4073b3704469a446a

09fe9c0054110f9ba10116a5cf3fc42a6eb58a54

refs/heads/master

2021-08-28T20:26:39.898397

2017-12-13T04:09:51

null

0

null

UTF-8

Python

false

2,073

py

"""Linear model base class.""" import abc import numpy as np import six @six.add_metaclass(abc.ABCMeta) class LinearModel(object): """Abstract class for linear models.""" def __init__(self, ndims, w_init='zeros'): """Initialize a linear model. This function prepares an uninitialized linear model. It will initialize the weight vector, self.w, based on the method specified in w_init. We assume that the last index of w is the bias term, self.w = [w,b] self.w(numpy.ndarray): array of dimension (n_dims+1,) w_init needs to support: 'zeros': initialize self.w with all zeros. 'ones': initialze self.w with all ones. 'uniform': initialize self.w with uniform random number between [0,1) Args: ndims(int): feature dimension w_init(str): types of initialization. """ self.ndims = ndims self.w_init = w_init if w_init == 'zeros': self.w = np.zeros((ndims+1,)) elif w_init == 'ones': self.w = np.ones((ndims+1,)) elif w_init == 'uniform': self.w = np.random.uniform(size = (ndims+1),) else: self.w = None self.x = None def forward(self, x): """Forward operation for linear models. Performs the forward operation, f=w^Tx, and return f. Args: x(numpy.ndarray): Dimension of (N, ndims), N is the number of examples. Returns: f(numpy.ndarray): Dimension of (N,) """ self.x = np.ones((x.shape[0],x.shape[1]+1)); self.x[:,0:x.shape[1]] = x f = np.dot(self.x , self.w) return f @abc.abstractmethod def backward(self, f, y): """Do not need to be implemented here.""" pass @abc.abstractmethod def loss(self, f, y): """Do not need to be implemented here.""" pass @abc.abstractmethod def predict(self, f): """Do not need to be implemented here.""" pass

[ "chenhaowu@ChenhaotekiMacBook-Pro.local" ]

chenhaowu@ChenhaotekiMacBook-Pro.local

6aa1d2b7a3e804cf44a8f2b7ac61880dff987f25

fe741e533cfd22d609a5c2d6250fc491dcf521e0

/eventsourcing/infrastructure/django/factory.py

d264ec975f0e663b614c5f8319827804bbf0860f

[ "BSD-3-Clause" ]

permissive

matiasbastos/eventsourcing

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from eventsourcing.infrastructure.django.manager import DjangoRecordManager from eventsourcing.infrastructure.django.models import IntegerSequencedRecord, TimestampSequencedRecord, SnapshotRecord from eventsourcing.infrastructure.factory import InfrastructureFactory class DjangoInfrastructureFactory(InfrastructureFactory): record_manager_class = DjangoRecordManager integer_sequenced_record_class = IntegerSequencedRecord timestamp_sequenced_record_class = TimestampSequencedRecord snapshot_record_class = SnapshotRecord def __init__(self, convert_position_float_to_decimal=False, *args, **kwargs): super(DjangoInfrastructureFactory, self).__init__(*args, **kwargs) self.convert_position_float_to_decimal = convert_position_float_to_decimal def construct_record_manager(self, **kwargs): return super(DjangoInfrastructureFactory, self).construct_record_manager( convert_position_float_to_decimal=self.convert_position_float_to_decimal, **kwargs)

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john.bywater@appropriatesoftware.net

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/test/echonl.0.py

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mwan780/softconass1

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refs/heads/master

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#!/usr/bin/python import sys for arg in sys.argv[1:]: print arg

[ "sfal610@cse.unsw.EDU.AU" ]

sfal610@cse.unsw.EDU.AU

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/tensorflow/funnel_transformer_ops.py

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yyht/Funnel_Transformer

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"""Common operations used to construct model.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import numpy as np import tensorflow as tf import functools # from utils.bert import bert_utils # from utils.bert import dropout_utils # stable_dropout = dropout_utils.ReuseDropout() # from absl import flags # import absl.logging as _logging # FLAGS = flags.FLAGS INF = 1e6 EPS = 1e-9 def check_tf_version(): version = tf.__version__ print("==tf version==", version) if int(version.split(".")[0]) >= 2 or int(version.split(".")[1]) >= 15: return True else: return False ############################################################################### ##### Utils ############################################################################### def safe_precision(func): """Safe precision decorator.""" @functools.wraps(func) def wrapped_func(inputs, *args, **kwargs): """Turn inputs into float32 for computation.""" if inputs.dtype != tf.float32: fp32_inputs = tf.cast(inputs, tf.float32) else: fp32_inputs = inputs output = func(fp32_inputs, *args, **kwargs) if output.dtype != inputs.dtype: output = tf.cast(output, inputs.dtype) return output return wrapped_func def get_einsum_prefix(ndims, einsum_symbols=None): if einsum_symbols is None: einsum_symbols = ["u", "v", "w", "x", "y", "z"] assert ndims <= len(einsum_symbols) einsum_prefix = "" for i in range(ndims): einsum_prefix += einsum_symbols[i] return einsum_prefix def update_ret_dict(tgt, src, prefix=None): if prefix is None: tgt.update(src) else: for k, v in src.items(): tgt["{}/{}".format(prefix, k)] = v return tgt ############################################################################### ##### Common ops ############################################################################### safe_softmax = safe_precision(tf.nn.softmax) def embedding_lookup(x, n_embed, d_embed, initializer, lookup_table=None, use_tpu=True, scope="embedding", reuse=None, dtype=tf.float32, embedding_table_adv=None): """tpu and gpu embedding_lookup function.""" with tf.variable_scope(scope, reuse=reuse): if lookup_table is None: lookup_table = tf.get_variable("lookup_table", shape=[n_embed, d_embed], dtype=dtype, initializer=initializer) if embedding_table_adv is not None: embedding_table_adv += lookup_table tf.logging.info("==apply adv embedding==") else: embedding_table_adv = lookup_table tf.logging.info("==apply normal embedding==") if len(x.shape.as_list()) == 2: one_hot_idx = tf.one_hot(x, n_embed, dtype=dtype) tf.logging.info("==apply onehot embedding==") elif len(x.shape.as_list()) == 3: one_hot_idx = x tf.logging.info("==apply gumbel embedding==") else: one_hot_idx = tf.one_hot(x, n_embed, dtype=dtype) tf.logging.info("==apply onehot embedding==") if len(x.shape.as_list()) == 2: einsum_prefix = get_einsum_prefix(x.shape.ndims) einsum_str = "{0}n,nd->{0}d".format(einsum_prefix) elif len(x.shape.as_list()) == 3: einsum_prefix = get_einsum_prefix(x.shape.ndims) einsum_str = "{0}n,nd->{0}d".format(einsum_prefix[:-1]) else: einsum_prefix = get_einsum_prefix(x.shape.ndims) einsum_str = "{0}n,nd->{0}d".format(einsum_prefix) tf.logging.info("*** einsum_str: %s ***", einsum_str) output = tf.einsum(einsum_str, one_hot_idx, embedding_table_adv) print(one_hot_idx.get_shape(), embedding_table_adv.get_shape(), "==embedding shape==", einsum_str, output.get_shape()) return output, lookup_table def dense(x, out_shape, initializer, inp_shape=None, begin_axis=-1, use_bias=True, activation=None, scope="dense", reuse=False): """A more flexible dense layer.""" if isinstance(out_shape, int): out_shape = [out_shape] if inp_shape is None: inp_shape = x.shape.as_list()[begin_axis:] elif isinstance(inp_shape, int): inp_shape = [inp_shape] inp_syms = ["a", "b", "c", "d"] out_syms = ["e", "f", "g", "h"] prefix = get_einsum_prefix(x.shape.ndims - len(inp_shape)) inp_str = get_einsum_prefix(len(inp_shape), inp_syms) out_str = get_einsum_prefix(len(out_shape), out_syms) with tf.variable_scope(scope, reuse=reuse): kernel_shape = inp_shape + out_shape kernel = tf.get_variable("kernel", kernel_shape, dtype=x.dtype, initializer=initializer) output = tf.einsum( "{0}{1},{1}{2}->{0}{2}".format(prefix, inp_str, out_str), x, kernel) print(x.get_shape(), kernel.get_shape(), "==dense shape==", prefix, inp_str, out_str, output.get_shape()) if use_bias: bias = tf.get_variable("bias", out_shape, dtype=x.dtype, initializer=tf.zeros_initializer()) output += bias if activation is not None: output = activation(output) return output @safe_precision def layer_norm_op(inputs, norm_shape=None, begin_norm_axis=-1, center=True, scale=True, activation_fn=None, reuse=None, trainable=True, name=None): """Custom Layer Normalization layer.""" if norm_shape is None: # If `norm_shape` is not provided, use `begin_norm_axis` to infer norm_shape = inputs.shape[begin_norm_axis:] elif isinstance(norm_shape, int): # If `norm_shape` is provided as int, convert it to list norm_shape = [norm_shape] with tf.variable_scope(name, "layer_norm", [inputs], reuse=reuse): inputs_rank = inputs.shape.ndims if inputs_rank is None: raise ValueError("Inputs %s has undefined rank." % inputs.name) dtype = inputs.dtype.base_dtype # Allocate parameters for the beta and gamma of the normalization. beta, gamma = None, None if center: beta = tf.get_variable( "beta", shape=norm_shape, dtype=dtype, initializer=tf.zeros_initializer(), trainable=trainable) if scale: gamma = tf.get_variable( "gamma", shape=norm_shape, dtype=dtype, initializer=tf.ones_initializer(), trainable=trainable) # By default, compute the moments across all the dimensions except the one # with index 0. norm_axes = list(range(inputs_rank - len(norm_shape), inputs_rank)) mean, variance = tf.nn.moments(inputs, norm_axes, keep_dims=True) # Compute layer normalization using the batch_normalization function. # Note that epsilon must be increased for float16 due to the limited # representable range. variance_epsilon = 1e-8 if dtype != tf.float16 else 1e-3 outputs = tf.nn.batch_normalization( inputs, mean, variance, offset=beta, scale=gamma, variance_epsilon=variance_epsilon) outputs.set_shape(inputs.shape) if activation_fn is not None: outputs = activation_fn(outputs) return outputs # def dropout_op(tensor, rate, training, *args, **kwargs): # kwargs["dtype"] = tensor.dtype # dropout_func = tf.keras.layers.Dropout(rate, *args, **kwargs) # return dropout_func(tensor, training=training) def dropout_op(tensor, rate, training, *args, **kwargs): dropout_name = kwargs.get('name', "") # if dropout_name: # output = stable_dropout.dropout(tensor, rate, dropout_name) # else: tf.logging.info("****** dropout name: %s, rate: %s"%(dropout_name, str(rate))) if rate is None or rate == 0.0: return tensor if training: tf.logging.info("****** dropout *******") return tf.nn.dropout(tensor, keep_prob=1.0 - rate) else: tf.logging.info("****** original *******") return tensor def gelu(x): """Gaussian Error Linear Unit. This is a smoother version of the RELU. Original paper: https://arxiv.org/abs/1606.08415 Args: x: float Tensor to perform activation. Returns: `x` with the GELU activation applied. """ cdf = 0.5 * (1.0 + tf.tanh( (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))) return x * cdf def get_activation(activation_type): """Get the corresponding activation function from string.""" if activation_type == "relu": activation = tf.nn.relu elif activation_type == "gelu": activation = gelu elif activation_type == "tanh": activation = tf.tanh else: raise ValueError("Unsupported activation type {}".format(activation_type)) return activation ############################################################################### ##### Trasnformer ops ############################################################################### def residual_and_layer_norm(residual, hidden, norm_shape=None): """Perform residual & layer normalization.""" ret_dict = {} if residual is not None: output = hidden + residual else: output = hidden output = layer_norm_op(output, norm_shape=norm_shape, name="layer_norm") return output, ret_dict def positionwise_ffn(inp, d_model, d_inner, dropout, dropact, initializer, activation_type="gelu", scope="ff", is_training=True, reuse=None, name="ffn"): """Position-wise Feed-forward Network.""" ret_dict = {} activation = get_activation(activation_type) output = inp with tf.variable_scope(scope, reuse=reuse): # mlp part output = dense(output, d_inner, inp_shape=d_model, activation=activation, initializer=initializer, scope="layer_1") output = dropout_op(output, dropact, training=is_training, name=name+"/ffn_drop_1") output = dense(output, d_model, initializer=initializer, inp_shape=d_inner, scope="layer_2") output = dropout_op(output, dropout, training=is_training, name=name+"/fnn_drop_2") # post ffn process output, res_lnorm_dict = residual_and_layer_norm(inp, output, norm_shape=d_model) # add to monitor dict ret_dict = update_ret_dict(ret_dict, res_lnorm_dict) return output, ret_dict def rel_attn_core( d_model, n_head, d_head, q, k, v, pos_enc, seg_mat, attn_mask, attn_bias, dropatt, is_training, initializer, func_mask=None, rel_attn_type="factorized", name="rel_attn"): """Core relative positional attention operations.""" ret_dict = {} tf_float = q.dtype q_head = dense(q, out_shape=[n_head, d_head], inp_shape=d_model, initializer=initializer, scope="q", use_bias=False) k_head = dense(k, out_shape=[n_head, d_head], inp_shape=d_model, initializer=initializer, scope="k") v_head = dense(v, out_shape=[n_head, d_head], inp_shape=d_model, initializer=initializer, scope="v") # scale `q_head` scale = tf.cast(1.0 / np.sqrt(d_head), tf_float) q_head = q_head * scale # content based attention score r_w_bias = tf.get_variable("r_w_bias", [n_head, d_head], dtype=tf_float, initializer=initializer) if not check_tf_version(): # print((q_head + r_w_bias * scale).get_shape(), k_head.get_shape()) content_bias = tf.einsum("aind,ajnd->anij", q_head + r_w_bias * scale, k_head) else: content_bias = tf.einsum("...ind,...jnd->...nij", q_head + r_w_bias * scale, k_head) print(q_head.get_shape(), (r_w_bias * scale).get_shape(), k_head.get_shape(), "==rel_attn_core shape==", content_bias.get_shape()) # position based attention score if pos_enc is None: pos_bias = 0 else: ##### Utilize the decomposed version when using TPU ##### if rel_attn_type == "factorized": # if FLAGS.verbose: tf.logging.info("Compute rel-pos attn with factorized implementation.") pos_bias = rel_pos_bias(q_head, pos_enc, d_model, n_head, d_head, initializer, func_mask=func_mask, dtype=tf_float) elif rel_attn_type == "rel_shift": # if FLAGS.verbose: tf.logging.info("Compute rel-pos attn with rel-shift implementation.") klen = tf.shape(content_bias)[-1] pos_bias = rel_pos_bias_gpu(q_head, pos_enc, d_model, n_head, d_head, klen, initializer, func_mask=func_mask, dtype=tf_float) else: raise NotImplementedError # segment based attention score if seg_mat is None: seg_bias = 0 else: # if FLAGS.verbose: tf.logging.info("Compute rel-seg attn.") seg_bias = rel_seg_bias(q_head, seg_mat, n_head, d_head, initializer, func_mask=func_mask, dtype=tf_float) # merge attention scores attn_score = content_bias + pos_bias + seg_bias # add extra attention score if provided if attn_bias is not None: # if FLAGS.verbose: tf.logging.info("Attention bias shape: %s", attn_bias.shape) attn_score += attn_bias * scale # perform masking if attn_mask is not None: # if FLAGS.verbose: tf.logging.info("Attention mask shape: %s", attn_mask.shape) ret_dict["attn_mask"] = attn_mask attn_score = attn_score - INF * tf.cast(attn_mask, attn_score.dtype) # attention probability attn_prob = safe_softmax(attn_score, -1) ret_dict["attn_prob"] = attn_prob attn_prob = dropout_op(attn_prob, dropatt, training=is_training, name=name+"/rel_attn_core") # attention output # print(attn_prob.get_shape(), v_head.get_shape()) if not check_tf_version(): attn_vec = tf.einsum("anij,ajnd->aind", attn_prob, v_head) else: attn_vec = tf.einsum("...nij,...jnd->...ind", attn_prob, v_head) print(attn_prob.get_shape(), (v_head).get_shape(), "==attn_core shape==", attn_vec.get_shape()) # things to monitor in attention ret_dict["content_bias"] = content_bias if pos_enc is not None: ret_dict["pos_bias"] = pos_bias if seg_mat is not None: ret_dict["seg_bias"] = seg_bias return attn_vec, ret_dict def rel_multihead_attn(q, k, v, pos_enc, seg_mat, attn_mask, d_model, n_head, d_head, dropout, dropatt, is_training, initializer, attn_bias=None, func_mask=None, scope="rel_attn", reuse=None, rel_attn_type="factorized", name='rel_attn'): """Multi-head attention with relative positional encoding.""" ret_dict = {} with tf.variable_scope(scope, reuse=reuse) as scope: # attention core attn_vec, attn_core_dict = rel_attn_core( d_model, n_head, d_head, q, k, v, pos_enc, seg_mat, attn_mask, attn_bias, dropatt, is_training, initializer, func_mask=func_mask, rel_attn_type=rel_attn_type) # post projection attn_out = dense(attn_vec, d_model, initializer=initializer, inp_shape=[n_head, d_head], scope="o") attn_out = dropout_op(attn_out, dropout, training=is_training, name=name+"/rel_multihead_attn") # residual + layer normalization output, post_dict = residual_and_layer_norm(q, attn_out, norm_shape=d_model) # things to monitor ret_dict = update_ret_dict(ret_dict, attn_core_dict) ret_dict = update_ret_dict(ret_dict, post_dict) return output, ret_dict ############################################################################### ##### relative positional attention ops ############################################################################### def rel_shift(x, row_dim, klen=-1, shift=1): """Perform relative shift to form the relative attention score.""" ndims = x.shape.ndims x_shape = tf.shape(x) # Deal with negative indexing if row_dim < 0: row_dim = ndims + row_dim assert row_dim >= 0 # Assume `col_dim` = `row_dim + 1` col_dim = row_dim + 1 assert col_dim < ndims tgt_shape_1, slice_begin_1, slice_len_1 = [], [], [] tgt_shape_2, slice_begin_2, slice_len_2 = [], [], [] for i in range(ndims): slice_len_1.append(-1) slice_begin_2.append(0) if i == row_dim: tgt_shape_1.append(x_shape[col_dim]) tgt_shape_2.append(x_shape[row_dim]) slice_begin_1.append(shift) slice_len_2.append(-1) elif i == col_dim: tgt_shape_1.append(x_shape[row_dim]) tgt_shape_2.append(x_shape[col_dim] - shift) slice_begin_1.append(0) slice_len_2.append(klen) else: tgt_shape_1.append(x_shape[i]) tgt_shape_2.append(x_shape[i]) slice_begin_1.append(0) slice_len_2.append(-1) x = tf.reshape(x, tgt_shape_1) x = tf.slice(x, slice_begin_1, slice_len_1) x = tf.reshape(x, tgt_shape_2) x = tf.slice(x, slice_begin_2, slice_len_2) return x def rel_pos_bias_gpu(q_head, pos_enc, d_model, n_head, d_head, klen, initializer, func_mask=None, dtype=tf.float32): """Relative attention positional bias via relative shift for GPU.""" enc, shift = pos_enc scale = tf.cast(1.0 / np.sqrt(d_head), dtype) # parameters r_r_bias = tf.get_variable("r_r_bias", [n_head, d_head], dtype=dtype, initializer=initializer) r_head = dense(enc, out_shape=[n_head, d_head], inp_shape=d_model, initializer=initializer, scope="r", use_bias=False) # [B x T x N x D] if not check_tf_version(): pos_bias = tf.einsum("ainh,jnh->anij", q_head + r_r_bias * scale, r_head) else: pos_bias = tf.einsum("...inh,jnh->...nij", q_head + r_r_bias * scale, r_head) print((q_head + r_r_bias * scale).get_shape(), (r_head).get_shape(), "==rel_pos_bias_gpu shape==", pos_bias.get_shape()) pos_bias = rel_shift(pos_bias, -2, klen, shift) if func_mask is not None: pos_bias *= func_mask return pos_bias def rel_pos_bias(q_head, pos_enc, d_model, n_head, d_head, initializer, func_mask=None, dtype=tf.float32): """Relative attention positional bias.""" # [(B) x T x D] enc_q_1, enc_q_2, enc_k_1, enc_k_2 = pos_enc # parameters r_r_bias = tf.get_variable("r_r_bias", [n_head, d_head], dtype=dtype, initializer=initializer) r_kernel = tf.get_variable("r/kernel", [d_model, n_head, d_head], dtype=dtype, initializer=initializer) scale = tf.cast(1.0 / np.sqrt(d_head), dtype) # [B x T x N x D] # print((q_head + r_r_bias * scale).get_shape(), r_kernel.get_shape()) if not check_tf_version(): q_head_r = tf.einsum("ainh,dnh->aind", q_head + r_r_bias * scale, r_kernel) else: q_head_r = tf.einsum("...inh,dnh->...ind", q_head + r_r_bias * scale, r_kernel) print((q_head + r_r_bias * scale).get_shape(), (r_kernel).get_shape(), "==rel_pos_bias shape==", q_head_r.get_shape()) # [(B) x T x N x D] q_head_r_1 = q_head_r * tf.expand_dims(enc_q_1, -2) q_head_r_2 = q_head_r * tf.expand_dims(enc_q_2, -2) # tf.logging.info("%s, %s, %s", q_head_r, q_head_r_1, q_head_r_2) # [(B) x T x N x D] prefix_k = get_einsum_prefix(enc_k_1.shape.ndims - 2) if not check_tf_version(): einsum_str = "aind,{0}jd->anij".format(prefix_k) else: einsum_str = "...ind,{0}jd->...nij".format(prefix_k) pos_bias = (tf.einsum(einsum_str, q_head_r_1, enc_k_1) + tf.einsum(einsum_str, q_head_r_2, enc_k_2)) print((q_head_r_1).get_shape(), (enc_k_1).get_shape(), "==rel_pos_bias shape==", prefix_k, einsum_str, pos_bias.get_shape()) if func_mask is not None: pos_bias *= func_mask return pos_bias def rel_seg_bias(q_head, seg_mat, n_head, d_head, initializer, func_mask=None, dtype=tf.float32): """Relative attention segmentation bias.""" # Expand seg_mat: [... x N x T x T] tgt_shape = [] for i in range(seg_mat.shape.ndims): tgt_shape.append(tf.shape(seg_mat)[i]) tgt_shape.insert(-2, n_head) seg_mat = tf.expand_dims(seg_mat, -3) # Compute same / diff biases r_s_bias = tf.get_variable("r_s_bias", [n_head, d_head], dtype=dtype, initializer=initializer) seg_embed = tf.get_variable("seg_embed", [2, n_head, d_head], dtype=dtype, initializer=initializer) scale = tf.cast(1.0 / np.sqrt(d_head), dtype) q_head_s = q_head + r_s_bias * scale # [... x N x T x 2] if not check_tf_version(): seg_biases = tf.einsum("ainh,snh->anis", q_head_s, seg_embed) else: seg_biases = tf.einsum("...inh,snh->...nis", q_head_s, seg_embed) print((q_head_s).get_shape(), (seg_embed).get_shape(), "==rel_seg_bias shape==", seg_biases.get_shape()) # Split into `diff` & `same`: [... x N x T x 1] seg_bias_diff, seg_bias_same = tf.split(seg_biases, 2, axis=-1) # Broadcast seg_mat = tf.broadcast_to(seg_mat, tgt_shape) seg_bias_diff = tf.broadcast_to(seg_bias_diff, tgt_shape) seg_bias_same = tf.broadcast_to(seg_bias_same, tgt_shape) seg_bias = tf.where(seg_mat, seg_bias_same, seg_bias_diff) if func_mask is not None: seg_bias *= func_mask return seg_bias def seg_id_to_mat(seg_q, seg_k, config): """Convert `seg_id` to `seg_mat`.""" if seg_q is None or seg_k is None: return None seg_mat = tf.equal(tf.expand_dims(seg_q, -1), tf.expand_dims(seg_k, -2)) # Treat [cls] as in the same segment as both A & B cls_mat = tf.logical_or( tf.expand_dims(tf.equal(seg_q, tf.constant([config.seg_id_cls], dtype=seg_q.dtype)), -1), tf.expand_dims(tf.equal(seg_k, tf.constant([config.seg_id_cls], dtype=seg_k.dtype)), -2)) seg_mat = tf.logical_or(cls_mat, seg_mat) return seg_mat def get_pos_enc(pos_id_q, pos_id_k, d_model, dropout, is_training, clamp_len=-1, dtype=tf.float32, name='pos_enc'): """Create inputs related to relative position encoding.""" pos_id_q = tf.cast(pos_id_q, dtype) pos_id_k = tf.cast(pos_id_k, dtype) if clamp_len > 0: pos_id_q = tf.clamp(pos_id_q, -clamp_len, clamp_len) pos_id_k = tf.clamp(pos_id_k, -clamp_len, clamp_len) d_model_half = d_model // 2 freq_seq = tf.cast(tf.range(0, d_model_half, 1.0), dtype=dtype) inv_freq = 1 / (10000 ** (freq_seq / d_model_half)) # print(pos_id_q.get_shape(), inv_freq.get_shape()) # sinusoid_q = tf.einsum("...i,d->...id", pos_id_q, inv_freq) # sinusoid_k = tf.einsum("...i,d->...id", pos_id_k, inv_freq) if not check_tf_version(): sinusoid_q = tf.einsum("i,d->id", pos_id_q, inv_freq) sinusoid_k = tf.einsum("i,d->id", pos_id_k, inv_freq) else: sinusoid_q = tf.einsum("...i,d->...id", pos_id_q, inv_freq) sinusoid_k = tf.einsum("...i,d->...id", pos_id_k, inv_freq) print((pos_id_q).get_shape(), (inv_freq).get_shape(), "==get_pos_enc shape==", sinusoid_q.get_shape()) print((pos_id_k).get_shape(), (inv_freq).get_shape(), "==get_pos_enc shape==", sinusoid_k.get_shape()) sin_enc_q = tf.sin(sinusoid_q) cos_enc_q = tf.cos(sinusoid_q) sin_enc_q = dropout_op(sin_enc_q, dropout, training=is_training, name=name+"/pos_enc_sin") cos_enc_q = dropout_op(cos_enc_q, dropout, training=is_training, name=name+"/pos_enc_cos") sin_enc_k = tf.sin(sinusoid_k) cos_enc_k = tf.cos(sinusoid_k) enc_q_1 = tf.concat([sin_enc_q, sin_enc_q], axis=-1) enc_k_1 = tf.concat([cos_enc_k, sin_enc_k], axis=-1) enc_q_2 = tf.concat([cos_enc_q, cos_enc_q], axis=-1) enc_k_2 = tf.concat([-sin_enc_k, cos_enc_k], axis=-1) return [enc_q_1, enc_q_2, enc_k_1, enc_k_2] def get_pos_enc_gpu(rel_pos_id, d_model, dropout, is_training, clamp_len=-1, dtype=tf.float32, name='pos_enc'): """Create inputs related to relative position encoding.""" rel_pos_id = tf.cast(rel_pos_id, dtype) if clamp_len > 0: rel_pos_id = tf.clamp(rel_pos_id, -clamp_len, clamp_len) d_model_half = d_model // 2 freq_seq = tf.cast(tf.range(0, d_model_half, 1.0), dtype=dtype) inv_freq = 1 / (10000 ** (freq_seq / d_model_half)) if not check_tf_version(): sinusoid = tf.einsum("i,d->id", rel_pos_id, inv_freq) else: sinusoid = tf.einsum("...i,d->...id", rel_pos_id, inv_freq) print((rel_pos_id).get_shape(), (inv_freq).get_shape(), "==get_pos_enc shape==", sinusoid.get_shape()) sin_enc = tf.sin(sinusoid) cos_enc = tf.cos(sinusoid) sin_enc = dropout_op(sin_enc, dropout, training=is_training, name=name+"/pos_enc_sin") cos_enc = dropout_op(cos_enc, dropout, training=is_training, name=name+"/pos_enc_cos") pos_enc = tf.concat([sin_enc, cos_enc], axis=-1) return pos_enc

[ "albert.xht@alibaba-inc.com" ]

albert.xht@alibaba-inc.com

b22be2c324a03ef1cb8e0e9c9d174b469967778b

695af55893dd40f8e2effdd67bfdcfff9093ba69

/tfoosball/migrations/0012_member.py

4ab00f5415e2970f3cdaf0b73b93433df778845c

[]

no_license

TeoTN/TFoosball-API

78cb702460017d1f9e6caa902c93bf576b8955cf

8ab8951662b1fb6ac126b31ff66f324936a0f2b8

refs/heads/master

2021-07-17T08:41:51.610291

2020-06-05T17:19:57

2020-06-05T19:01:19

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1

null

2021-06-10T18:37:04

2016-08-31T19:17:40

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Python

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py

# -*- coding: utf-8 -*- # Generated by Django 1.10.4 on 2017-02-05 22:14 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('tfoosball', '0011_team'), ] operations = [ migrations.CreateModel( name='Member', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('username', models.CharField(max_length=14)), ('exp', models.IntegerField(default=1000)), ('offence', models.IntegerField(default=0)), ('defence', models.IntegerField(default=0)), ('played', models.IntegerField(default=0)), ('win_streak', models.IntegerField(default=0)), ('curr_win_streak', models.IntegerField(default=0)), ('lose_streak', models.IntegerField(default=0)), ('curr_lose_streak', models.IntegerField(default=0)), ('lowest_exp', models.IntegerField(default=1000)), ('highest_exp', models.IntegerField(default=1000)), ('player', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ('team', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='tfoosball.Team')), ], ), ]

[ "staniowp@gmail.com" ]

staniowp@gmail.com

e89b97449c4a586d8b6ceea9a3ece86b666a323b

7cf8c1d255b273be9352efa8bc386948991c6180

/forms.py

d603453da3c75e097a47317d3af5387287b22c89

[]

no_license

RoopeKeto/book-recommender

90571fd4c8de3c5d6af03dd0009862121985f720

05526d128827ad44331e60239dd9c06d088e7c99

refs/heads/master

2022-07-07T05:39:13.063898

2019-07-28T10:21:31

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0

null

2022-06-21T22:24:20

2019-07-26T08:56:15

Python

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Python

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416

py

from flask_wtf import FlaskForm from wtforms import StringField, SubmitField, IntegerField from wtforms.validators import DataRequired, Length, NumberRange class SearchForm(FlaskForm): searchword = StringField('Search word', validators=[DataRequired(), Length(min=1, max=200)], ) search = SubmitField('Search')

[ "roope.keto@outlook.com" ]

roope.keto@outlook.com

62a9d24b01c6842cb24bc2ee68676f65c52134bd

8274dc68727b53ec7b10e0b3f32f24427f5746ef

/intro-1.py

4fb0d414224e96f19d4cc4c278950732f3fdd75e

[]

no_license

cristea-raul/py-plp-exercises

8108046bd9fefd82fcea726b2f80555796f3b640

5e0e1bc4bdf530243ca850ccfeb9dbcc501f4231

refs/heads/master

2021-01-16T21:17:48.861859

2015-05-27T17:28:43

2015-11-26T12:21:20

36,383,429

0

null

UTF-8

Python

false

80

py

import sys print(''.join([(arg[::-1]) + ' ' for arg in sys.argv[:0:-1]])[:-1])

[ "cristea.raul@gmail.com" ]

cristea.raul@gmail.com

7d8a4669826bf427dd2777b62d5a2591bd63c94c

9b6f65a28af4c6befdd015d1416d6257138c0219

/alpha/advertising/migrations/0001_initial.py

ea66fae42001f4fef13a91de1a42f8757f91a575

[]

no_license

dany431/cityfusion

5beec53131898e539a892249fa711fb3086fb53c

4e67464db69cfa21c965e4eb8796a5c727d5a443

refs/heads/master

2016-08-11T13:20:17.966539

2016-01-13T11:17:12

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py

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'AdvertisingType' db.create_table(u'advertising_advertisingtype', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=128)), ('width', self.gf('django.db.models.fields.IntegerField')()), ('height', self.gf('django.db.models.fields.IntegerField')()), ('cpm_price_currency', self.gf('djmoney.models.fields.CurrencyField')(default='CAD', max_length=3)), ('cpm_price', self.gf('djmoney.models.fields.MoneyField')(default='0.0', max_digits=10, decimal_places=2, default_currency='CAD')), ('cpc_price_currency', self.gf('djmoney.models.fields.CurrencyField')(default='CAD', max_length=3)), ('cpc_price', self.gf('djmoney.models.fields.MoneyField')(default='0.0', max_digits=10, decimal_places=2, default_currency='CAD')), )) db.send_create_signal(u'advertising', ['AdvertisingType']) # Adding model 'AdvertisingCampaign' db.create_table(u'advertising_advertisingcampaign', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=128)), ('account', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['accounts.Account'])), ('all_of_canada', self.gf('django.db.models.fields.BooleanField')(default=False)), ('budget_currency', self.gf('djmoney.models.fields.CurrencyField')(default='CAD', max_length=3)), ('budget', self.gf('djmoney.models.fields.MoneyField')(default='0.0', max_digits=10, decimal_places=2, default_currency='CAD')), ('ammount_spent_currency', self.gf('djmoney.models.fields.CurrencyField')(default='CAD', max_length=3)), ('ammount_spent', self.gf('djmoney.models.fields.MoneyField')(default='0.0', max_digits=10, decimal_places=2, default_currency='CAD')), ('ammount_remaining_currency', self.gf('djmoney.models.fields.CurrencyField')(default='CAD', max_length=3)), ('ammount_remaining', self.gf('djmoney.models.fields.MoneyField')(default='0.0', max_digits=10, decimal_places=2, default_currency='CAD')), ('started', self.gf('django.db.models.fields.DateTimeField')()), ('ended', self.gf('django.db.models.fields.DateTimeField')(null=True, blank=True)), )) db.send_create_signal(u'advertising', ['AdvertisingCampaign']) # Adding M2M table for field regions on 'AdvertisingCampaign' db.create_table(u'advertising_advertisingcampaign_regions', ( ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True)), ('advertisingcampaign', models.ForeignKey(orm[u'advertising.advertisingcampaign'], null=False)), ('region', models.ForeignKey(orm[u'cities.region'], null=False)) )) db.create_unique(u'advertising_advertisingcampaign_regions', ['advertisingcampaign_id', 'region_id']) # Adding model 'Advertising' db.create_table(u'advertising_advertising', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('ad_type', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['advertising.AdvertisingType'])), ('ad_company', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['advertising.AdvertisingCampaign'])), ('payment_type', self.gf('django.db.models.fields.CharField')(max_length=3)), ('ads_image', self.gf('django.db.models.fields.files.ImageField')(max_length=100)), ('reviewed', self.gf('django.db.models.fields.BooleanField')(default=False)), ('cpm_price_currency', self.gf('djmoney.models.fields.CurrencyField')(default='CAD', max_length=3)), ('cpm_price', self.gf('djmoney.models.fields.MoneyField')(default='0.0', max_digits=10, decimal_places=2, default_currency='CAD')), ('cpc_price_currency', self.gf('djmoney.models.fields.CurrencyField')(default='CAD', max_length=3)), ('cpc_price', self.gf('djmoney.models.fields.MoneyField')(default='0.0', max_digits=10, decimal_places=2, default_currency='CAD')), )) db.send_create_signal(u'advertising', ['Advertising']) def backwards(self, orm): # Deleting model 'AdvertisingType' db.delete_table(u'advertising_advertisingtype') # Deleting model 'AdvertisingCampaign' db.delete_table(u'advertising_advertisingcampaign') # Removing M2M table for field regions on 'AdvertisingCampaign' db.delete_table('advertising_advertisingcampaign_regions') # Deleting model 'Advertising' db.delete_table(u'advertising_advertising') models = { u'accounts.account': { 'Meta': {'object_name': 'Account'}, 'about_me': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'access_token': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'blog_url': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'date_of_birth': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}), 'facebook_id': ('django.db.models.fields.BigIntegerField', [], {'unique': 'True', 'null': 'True', 'blank': 'True'}), 'facebook_name': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'facebook_open_graph': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'facebook_profile_url': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'gender': ('django.db.models.fields.CharField', [], {'max_length': '1', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'in_the_loop_email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'null': 'True', 'blank': 'True'}), 'in_the_loop_phonenumber': ('phonenumber_field.modelfields.PhoneNumberField', [], {'max_length': '128', 'null': 'True', 'blank': 'True'}), 'in_the_loop_with_email': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'in_the_loop_with_sms': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'in_the_loop_with_website': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'location': ('django.contrib.gis.db.models.fields.PointField', [], {'null': 'True', 'blank': 'True'}), 'mugshot': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'blank': 'True'}), 'privacy': ('django.db.models.fields.CharField', [], {'default': "'registered'", 'max_length': '15'}), 'raw_data': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'reminder_active_type': ('django.db.models.fields.CharField', [], {'default': "'HOURS'", 'max_length': '10'}), 'reminder_days_before_event': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'reminder_email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'null': 'True', 'blank': 'True'}), 'reminder_events': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': u"orm['event.Event']", 'null': 'True', 'blank': 'True'}), 'reminder_hours_before_event': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}), 'reminder_on_week_day': ('django.db.models.fields.CharField', [], {'default': '0', 'max_length': '1', 'null': 'True', 'blank': 'True'}), 'reminder_on_week_day_at_time': ('django.db.models.fields.TimeField', [], {'null': 'True', 'blank': 'True'}), 'reminder_phonenumber': ('phonenumber_field.modelfields.PhoneNumberField', [], {'max_length': '128', 'null': 'True', 'blank': 'True'}), 'reminder_with_email': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'reminder_with_sms': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'reminder_with_website': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'user': ('django.db.models.fields.related.OneToOneField', [], {'related_name': "'my_profile'", 'unique': 'True', 'to': u"orm['auth.User']"}), 'venue': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['event.Venue']", 'null': 'True', 'blank': 'True'}), 'website': ('django.db.models.fields.URLField', [], {'default': "''", 'max_length': '200', 'null': 'True', 'blank': 'True'}), 'website_url': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}) }, u'advertising.advertising': { 'Meta': {'object_name': 'Advertising'}, 'ad_company': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['advertising.AdvertisingCampaign']"}), 'ad_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['advertising.AdvertisingType']"}), 'ads_image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100'}), 'cpc_price': ('djmoney.models.fields.MoneyField', [], {'default': "'0.0'", 'max_digits': '10', 'decimal_places': '2', 'default_currency': "'CAD'"}), 'cpc_price_currency': ('djmoney.models.fields.CurrencyField', [], {'default': "'CAD'", 'max_length': '3'}), 'cpm_price': ('djmoney.models.fields.MoneyField', [], {'default': "'0.0'", 'max_digits': '10', 'decimal_places': '2', 'default_currency': "'CAD'"}), 'cpm_price_currency': ('djmoney.models.fields.CurrencyField', [], {'default': "'CAD'", 'max_length': '3'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'payment_type': ('django.db.models.fields.CharField', [], {'max_length': '3'}), 'reviewed': ('django.db.models.fields.BooleanField', [], {'default': 'False'}) }, u'advertising.advertisingcampaign': { 'Meta': {'object_name': 'AdvertisingCampaign'}, 'account': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['accounts.Account']"}), 'all_of_canada': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'ammount_remaining': ('djmoney.models.fields.MoneyField', [], {'default': "'0.0'", 'max_digits': '10', 'decimal_places': '2', 'default_currency': "'CAD'"}), 'ammount_remaining_currency': ('djmoney.models.fields.CurrencyField', [], {'default': "'CAD'", 'max_length': '3'}), 'ammount_spent': ('djmoney.models.fields.MoneyField', [], {'default': "'0.0'", 'max_digits': '10', 'decimal_places': '2', 'default_currency': "'CAD'"}), 'ammount_spent_currency': ('djmoney.models.fields.CurrencyField', [], {'default': "'CAD'", 'max_length': '3'}), 'budget': ('djmoney.models.fields.MoneyField', [], {'default': "'0.0'", 'max_digits': '10', 'decimal_places': '2', 'default_currency': "'CAD'"}), 'budget_currency': ('djmoney.models.fields.CurrencyField', [], {'default': "'CAD'", 'max_length': '3'}), 'ended': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'regions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['cities.Region']", 'symmetrical': 'False'}), 'started': ('django.db.models.fields.DateTimeField', [], {}) }, u'advertising.advertisingtype': { 'Meta': {'object_name': 'AdvertisingType'}, 'cpc_price': ('djmoney.models.fields.MoneyField', [], {'default': "'0.0'", 'max_digits': '10', 'decimal_places': '2', 'default_currency': "'CAD'"}), 'cpc_price_currency': ('djmoney.models.fields.CurrencyField', [], {'default': "'CAD'", 'max_length': '3'}), 'cpm_price': ('djmoney.models.fields.MoneyField', [], {'default': "'0.0'", 'max_digits': '10', 'decimal_places': '2', 'default_currency': "'CAD'"}), 'cpm_price_currency': ('djmoney.models.fields.CurrencyField', [], {'default': "'CAD'", 'max_length': '3'}), 'height': ('django.db.models.fields.IntegerField', [], {}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'width': ('django.db.models.fields.IntegerField', [], {}) }, u'auth.group': { 'Meta': {'object_name': 'Group'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, u'auth.permission': { 'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, u'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, u'cities.city': { 'Meta': {'object_name': 'City'}, 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities.Country']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'location': ('django.contrib.gis.db.models.fields.PointField', [], {}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'name_std': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'population': ('django.db.models.fields.IntegerField', [], {}), 'region': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities.Region']", 'null': 'True', 'blank': 'True'}), 'slug': ('django.db.models.fields.CharField', [], {'max_length': '200'}), 'subregion': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities.Subregion']", 'null': 'True', 'blank': 'True'}) }, u'cities.country': { 'Meta': {'ordering': "['name']", 'object_name': 'Country'}, 'code': ('django.db.models.fields.CharField', [], {'max_length': '2', 'db_index': 'True'}), 'continent': ('django.db.models.fields.CharField', [], {'max_length': '2'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'population': ('django.db.models.fields.IntegerField', [], {}), 'slug': ('django.db.models.fields.CharField', [], {'max_length': '200'}), 'tld': ('django.db.models.fields.CharField', [], {'max_length': '5'}) }, u'cities.region': { 'Meta': {'object_name': 'Region'}, 'code': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities.Country']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'name_std': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'slug': ('django.db.models.fields.CharField', [], {'max_length': '200'}) }, u'cities.subregion': { 'Meta': {'object_name': 'Subregion'}, 'code': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities.Country']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'name_std': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}), 'region': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities.Region']"}), 'slug': ('django.db.models.fields.CharField', [], {'max_length': '200'}) }, u'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, u'event.event': { 'Meta': {'object_name': 'Event'}, 'audited': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'authentication_key': ('django.db.models.fields.CharField', [], {'max_length': '40'}), 'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2013, 5, 9, 0, 0)', 'auto_now_add': 'True', 'blank': 'True'}), 'cropping': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'email': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'featured': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'featured_on': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'location': ('django.contrib.gis.db.models.fields.PointField', [], {}), 'modified': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2013, 5, 9, 0, 0)', 'auto_now': 'True', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '250'}), 'owner': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['auth.User']", 'null': 'True', 'blank': 'True'}), 'picture': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'price': ('django.db.models.fields.CharField', [], {'default': "'Free'", 'max_length': '40', 'blank': 'True'}), 'search_index': ('djorm_pgfulltext.fields.VectorField', [], {'default': "''", 'null': 'True', 'db_index': 'True'}), 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '255'}), 'tickets': ('django.db.models.fields.CharField', [], {'max_length': '250', 'null': 'True', 'blank': 'True'}), 'venue': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['event.Venue']", 'null': 'True', 'blank': 'True'}), 'viewed_times': ('django.db.models.fields.IntegerField', [], {'default': '0', 'null': 'True', 'blank': 'True'}), 'website': ('django.db.models.fields.URLField', [], {'default': "''", 'max_length': '200', 'null': 'True', 'blank': 'True'}) }, u'event.venue': { 'Meta': {'object_name': 'Venue'}, 'city': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities.City']"}), 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities.Country']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'location': ('django.contrib.gis.db.models.fields.PointField', [], {}), 'name': ('django.db.models.fields.CharField', [], {'default': "'Default Venue'", 'max_length': '250'}), 'street': ('django.db.models.fields.CharField', [], {'max_length': '250', 'blank': 'True'}) }, u'taggit.tag': { 'Meta': {'object_name': 'Tag'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '100'}) }, u'taggit.taggeditem': { 'Meta': {'object_name': 'TaggedItem'}, 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'taggit_taggeditem_tagged_items'", 'to': u"orm['contenttypes.ContentType']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'object_id': ('django.db.models.fields.IntegerField', [], {'db_index': 'True'}), 'tag': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'taggit_taggeditem_items'", 'to': u"orm['taggit.Tag']"}) } } complete_apps = ['advertising']

[ "danialaftab@ucp.edu.pk" ]

danialaftab@ucp.edu.pk

4e0b023472d01e665a666a9df645d52d62215dc1

b6216c94621d77483c751d4bb1839174bfdef0bb

/radio.py

bff61662d822b10bdf966ec31b0c8e54e8ac3401

[]

no_license

jsdelivrbot/SmartHome

50bf995f08e2c718ddc7b6ae74c718c1e7d80235

12606823482e538cb24fea37f6b41079a50f4c0d

refs/heads/master

2020-04-10T15:02:13.948495

2018-12-10T00:19:52

null

0

null

UTF-8

Python

false

6,799

py

import serial, sys import datetime from xbee import XBee from thing_speak import * from pushover import * def calc_ac_message(args, logger): message = '111000' # prefix message += ('01' if args['on_off-changed'] == 'true' else '10') # on_off message += ('101001' if args['mode'] == 'cool' else '100110') # mode fan_options = {'1': '1010', '2': '1001', '3': '0110', 'A': '0101' } message += fan_options[args['fan']] # fan message += '1010101010' temp_options = {'16': '10101001', '17': '10100110', '18': '10100101', '19': '10011010', '20': '10011001', '21': '10010110', '22': '10010101', '23': '01101010', '24': '01101001', '25': '01100110', '26': '01100101', '27': '01011010', '28': '01011001', '29': '01010110', '30': '01010101' } message += temp_options[args['temp']] #temp message += '10101010101010101010101010101010100110' message += message + message message += '1111000000' #logger.info("message={}".format(message)) data = '' while len(message) > 0: data += chr(int(message[:8],2)) message = message[8:] #logger.info("data={}".format(data)) return data class Radio: def __init__(self): self.serial_port = serial.Serial('/dev/ttyAMA0', 19200) self.xbee = XBee(self.serial_port, callback=self.handle_received_data) def handle_received_data(self, data): if 'source_addr' in data and 'rf_data' in data: device = self.status_updater.get_device_by_address(data['source_addr'], ord(data['rf_data'][0])) rf_data = '\\'.join(x.encode('hex') for x in data['rf_data']) print "[{}] - Receiving data from {}: {}, rssi:{} dBm".format(datetime.datetime.now(), device['name'], rf_data, -ord(data['rssi'])) if device is not None: if device['type'] == 'shutter': status = {'mode' : str(ord(data['rf_data'][1]))} if device['type'] == 'shutterNew': status = {'mode' : str(ord(data['rf_data'][1]))} if device['type'] == 'temperature': temperature = float(ord(data['rf_data'][1])*256+ord(data['rf_data'][2]))/10 rh = float(ord(data['rf_data'][3])*256+ord(data['rf_data'][4]))/10 status = {'Temp' : str(temperature), 'Rh' : str(rh)} ThingSpeak_update_DHT22(temperature, rh) if device['type'] == 'boiler': now = datetime.datetime.now() date = datetime.datetime.today().strftime('%Y-%m-%d') curr_hour = "{}:{}".format(str(now.hour).zfill(2), str(now.minute).zfill(2)) mode = ord(data['rf_data'][1]) status = {'mode' : str(mode), 'time' : curr_hour, 'date' : date } if device['type'] == 'boiler_temperature': temperature = float((ord(data['rf_data'][2]) << 8) | (ord(data['rf_data'][1]) << 0)) / float(256) if temperature > 255: return device = self.status_updater.get_device_by_address(data['source_addr'], 1) status = {'Temp': str(temperature)} ThingSpeak_update_DS18B20(temperature) if device['type'] == 'air_conditioner': status = {'on_off' : ('false' if data['rf_data'][1] == '\x01' else 'true') } if status is not None: self.status_updater.update_device_status(device, status) def close(self): self.xbee.halt() self.serial_port.close() def update_shutter(self, addr, device_number, args): shutter_options = {'100': '\x01', '0': '\x02', 'pause': '\x03', '25': '\x04', '50': '\x05', '75': '\x06', } data = shutter_options[args['mode']] self.xbee.send('tx', frame_id='A', dest_addr=addr, options='\x00', data=(chr(device_number)+data)) def update_shutterNew(self, addr, device_number, args): data = "\x02" if args['mode'] == 'pause' else ("\x01" + chr(int(args['mode']))) self.xbee.send('tx', frame_id='A', dest_addr=addr, options='\x00', data=(chr(device_number)+data)) def update_boiler(self, addr, device_number, args): if(args.has_key('mode')): data = chr(int(args['mode'])) self.xbee.send('tx', frame_id='A', dest_addr=addr, options='\x00', data=(chr(device_number)+data)) def update_temperature(self, addr, device_number, args): data = 0 def update_boiler_temperature(self, addr, device_number, args): data = 0 def update_air_conditioner(self, addr, device_number, args): data = calc_ac_message(args, self.logger) self.xbee.send('tx', frame_id='A', dest_addr=addr, options='\x00', data=(chr(device_number)+chr(1)+data)) def set_status_updater(self, status_updater): self.status_updater = status_updater def set_logger(self, logger): self.logger = logger class DummyRadio: def __init__(self): print "Starting DummyRadio..." def close(self): self.logger.info("Bye Bye") def update_shutter(self, addr, device_number, args): self.logger.info("shutter={},{} args={}".format(addr, device_number, args)) def update_shutterNew(self, addr, device_number, args): self.logger.info("shutterNew={},{} args={}".format(addr, device_number, args)) def update_air_conditioner(self, addr, device_number, args): self.logger.info("air conditioner={},{}, args={}".format(addr, device_number, args)) calc_ac_message(args, self.logger) def set_status_updater(self, status_updater): self.status_updater = status_updater def set_logger(self, logger): self.logger = logger

[ "oded.tgr@gmail.com" ]

oded.tgr@gmail.com

6e81587254659967305cf40abc42b70bb68fd223

3c74f6ace4a323d9778cc41a15b660a1796a3a5f

/src/feature_engineering/fastText_benchmark.py

7a1868d73308be1e31901bf39158f41aa13e47e5

[]

no_license

drvshavva/Sentiment_analysis

476e276745594e58da8cd3c07bdadcf3d72ac2ad

38cdfa4c96bf7c239a1be81436248d13e704b31a

refs/heads/main

2023-05-03T19:54:15.155966

2021-05-21T17:35:46

369,609,018

0

null

UTF-8

Python

false

997

py

from gensim.models.fasttext import FastText import multiprocessing from sklearn import utils from tqdm import tqdm from os.path import dirname tqdm.pandas(desc="progress-bar") MODEL_PATH = dirname(dirname(dirname(__file__))) + "/models/word2vec/" def train_fasText(corpus, n_epoch, name_corpus, sg, vector_size, negative, window, min_count, alpha, min_n, max_n): cores = multiprocessing.cpu_count() model = FastText(sg=sg, size=vector_size, negative=negative, window=window, min_count=min_count, workers=cores, alpha=alpha, min_n=min_n, max_n=max_n) model.build_vocab([x.words for x in tqdm(corpus)]) for epoch in range(n_epoch): model.train(utils.shuffle([x.words for x in tqdm(corpus)]), total_examples=len(corpus), epochs=1) model.alpha -= 0.002 model.min_alpha = model.alpha model.save( f"{MODEL_PATH}fastText_{name_corpus}_sg_{sg}_size_{vector_size}_window_{window}_min_count_{min_count}.model") return model

[ "havvanur.dervisoglu@ithinka.com" ]

havvanur.dervisoglu@ithinka.com

56f8c34e87096b7ccc618901f772eef0470f64d3

2f98aa7e5bfc2fc5ef25e4d5cfa1d7802e3a7fae

/python/python_22467.py

5a358bc5c1aa3fc341e934a1ba21ccff3415ae08

[]

no_license

AK-1121/code_extraction

cc812b6832b112e3ffcc2bb7eb4237fd85c88c01

5297a4a3aab3bb37efa24a89636935da04a1f8b6

refs/heads/master

2020-05-23T08:04:11.789141

2015-10-22T19:19:40

null

0

null

UTF-8

Python

false

166

py

# Python: Cancel object creation during initialization data = [good, bad] theList = [obj for obj in (MyObject(some_data) for some_data in data) if obj.data_is_valid]

[ "ubuntu@ip-172-31-7-228.us-west-2.compute.internal" ]

ubuntu@ip-172-31-7-228.us-west-2.compute.internal

4f64dd7851c54271eb27e872a0ffe121a7888fdd

447630c97d47bba555169bb336f692adcf6bb97c

/Website2/migrations/0002_auto_20201116_1857.py

6ccf25e8496904141f3f74acbad21dab1555db50

[]

no_license

vinothini-jr/OwnBlogWebsite

e4a44654fe368c14d4d91e94214a9f3e5ac73148

6e37b0a687a313c437f46a51b1c6a8c9b94d504d

refs/heads/master

2023-02-19T11:22:24.620131

2021-01-20T13:55:07

331,258,162

0

null

UTF-8

Python

false

351

py

# Generated by Django 3.0.5 on 2020-11-16 13:27 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('Website2', '0001_initial'), ] operations = [ migrations.RenameField( model_name='comment', old_name='post', new_name='posti', ), ]

[ "vinothinivno@gmail.com" ]

vinothinivno@gmail.com

a4e61da72d38c7802c7f6964d9362e6ed62a8871

f0a5ad7b8aa39f51f233391fead0da3eabecc4ee

/.history/toolbox/baixaArquivo_20191127165502.py

d63cb56da0f775cc4431f9f1049aafaa065fdfab

[]

no_license

OseiasBeu/webScrapping

e0a524847e55b24dbbd3d57bbe7fa43b4e101f48

1e72c7551aea355a891043baecfcbab8a89e719a

refs/heads/master

2022-10-25T18:12:50.858653

2020-06-18T01:29:24

224,681,550

0

null

UTF-8

Python

false

2,884

py

# -*- coding: utf-8 -*- from selenium import webdriver from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.keys import Keys import os import tempfile import time import shutil import glob def baixaArquivo(): driver = webdriver.Chrome(executable_path='chromedriver.exe') driver.get("https://wsmid-qa.whirlpool.com.br/manager/reports/frmQueryAnalyzer.aspx?menu=2") dominio = 'whirlpool' usuario = 'daniel_coelho' senha = '123456' bra = "BRA" data = '2019-11-01' query = "SELECT pedido.clienteEstado, pedidoItem.warehouseId, count(pedidoItem.warehouseId) as [Pendentes de integração] FROM pedido LEFT JOIN pedidoItem ON pedido.codigoPedido = pedidoItem.codigoPedido WHERE pedido.datahoracriacao > '{}' AND pedido.clientepais = '{}' AND pedido.flagIntegrado = 0 GROUP BY pedidoItem.warehouseId, pedido.clienteEstado ORDER BY [Pendentes de integração] DESC".format(data,bra) campo_dominio = driver.find_element_by_id("ucLogin1_txtDominio") campo_dominio.send_keys(dominio) campo_usuario =driver.find_element_by_id("ucLogin1_txtUser") campo_usuario.send_keys(usuario) campo_senha = driver.find_element_by_id("ucLogin1_txtPass") campo_senha.send_keys(senha) campo_senha.send_keys(Keys.RETURN) records =driver.find_element_by_id("ctl00_ContentPlaceHolder1_dropRows") records.send_keys("Sem limite") #ctl00_ContentPlaceHolder1_imbExecutar text_query = driver.find_element_by_id("ctl00_ContentPlaceHolder1_txtQuery") text_query.send_keys(query) executar = driver.find_element_by_id("ctl00_ContentPlaceHolder1_imbExecutar").click() time.sleep(5) # chrome_options = Options() # download_dir = tempfile.mkdtemp() # try: # chrome_options.add_experimental_option('prefs', { # "plugins.plugins_list": [{"enabled":False,"name":"Chrome PDF Viewer"}], # "download": { # "prompt_for_download": False, # "default_directory" : download_dir # } # }) # #... # # while glob.iglob(os.path.join(download_dir, '*.crdownload')): # time.sleep(1) # espera o download terminar # # pega o 1o pdf que tiver, só terá 1 pois a pasta estava vazia antes: # arquivo = glob.iglob(os.path.join(download_dir, '*.xlsx'))[0] # shutil.move(arquivo, r'C:\Users\beuo\Documents\Demandas\AtualizaMiddleIntegrationVtex\files\*.xlsx') # # finally: # # shutil.rmtree(download_dir) # remove todos os arquivos temporários # exportar = driver.find_element_by_id("ctl00_ContentPlaceHolder1_imbExportExcel").click() resultados = driver.find_elements_by_tag_name('span') print(resultados[0]) # https://docs.google.com/spreadsheets/d/1QSGAY_WyamEQBZ4ITdAGCVAbavR9t-D-4gPQx4Sbf7g/edit?ts=5ddea57e#gid=63583812 time.sleep(10) sair = driver.find_element_by_id("ctl00_lgStatus").click() # print(query) time.sleep(10) driver.close()

[ "oseiasbeu@outlook.com" ]

oseiasbeu@outlook.com

9cddda31ed48e2585ce38b7a8cf02df46126b51b

8dfe4b53fae92795405d789d52148d1291836afa

/.metadata/.plugins/org.eclipse.core.resources/.history/ba/f0bcb1d8c11d00171d43c367bf888dd0

c9bbb419225ed46c34895afd15e68cbdfec35a09

[]

no_license

ymyjohnny/python

e07c54a88954e090cf3d30a4c6f6ac46353063fb

b483fd55e577d4dcceb5762bddf833df23874f3a

refs/heads/master

2021-01-10T01:10:19.038424

2019-07-02T02:40:23

45,223,843

0

null

UTF-8

Python

false

1,130

#!/usr/bin/python #coding=utf-8 ''' Created on 2017-4-10 @author:ymy Copyright ymyjohnny@gmail.com ''' import MySQLdb,pymongo import datetime def getdelaytime(date,monitorfield): conn=MySQLdb.connect(host="221.228.90.4",user="root",passwd="uqcqa8zd",db="dsp_backend") cursor = conn.cursor () #打印多少条记录 print monitorfield print date data = cursor.execute("SELECT monitorfield,monitorcount,time,hostname FROM `dsp_solution_statistic` WHERE `monitorField` = '%s' AND TIME >= '%s' order by monitorcount desc limit 1" % (monitorfield,date) ) #打印具体内容 info = cursor.fetchmany(data) print info for row in info: monitorcount = row[1] hostname = row[3] if monitorcount > 1: print date, hostname, monitorfield , monitorcount , 'ms' ,'大于100ms' cursor.close () conn.close () def main(): date = datetime.datetime.now().strftime("%Y-%m-%d 15:00:00") monitorfield = 'time-frequency-percentile98' test1 = getdelaytime(date,monitorfield) if __name__ == '__main__': main()

[ "ymyjohnny@adsame.com" ]

ymyjohnny@adsame.com

a97a171d741d1a379fbc9709378197c2b2bfbb73

2f98aa7e5bfc2fc5ef25e4d5cfa1d7802e3a7fae

/python/python_28510.py

c23fe77642aa17ac04c7ad7976affbf5e85feb5d

[]

no_license

AK-1121/code_extraction

cc812b6832b112e3ffcc2bb7eb4237fd85c88c01

5297a4a3aab3bb37efa24a89636935da04a1f8b6

refs/heads/master

2020-05-23T08:04:11.789141

2015-10-22T19:19:40

null

0

null

UTF-8

Python

false

40

py

# Integrate Python and Javascript dicts

[ "ubuntu@ip-172-31-7-228.us-west-2.compute.internal" ]

ubuntu@ip-172-31-7-228.us-west-2.compute.internal

4ef5a54bbc1283a0dc26f68de81532d923826fd3

4cc285b0c585241ff4404087e6fbb901195639be

/NeuralNetworkNumbers/venv/Lib/site-packages/sklearn/neighbors/tests/test_dist_metrics.py

6a9e15308b2a1578abff065b1d60e657dfebd836

[]

no_license

strazhg/NeuralNetworksPython

815542f4ddbb86e918e657f783158f8c078de514

15038e44a5a6c342336c119cdd2abdeffd84b5b1

refs/heads/main

2023-04-16T18:51:29.602644

2021-04-27T14:46:55

361,944,482

0

null

UTF-8

Python

false

129

py

version https://git-lfs.github.com/spec/v1 oid sha256:20da88360cc014a240b152fa1dcbdc08031fc751b644925e7a33ecd241a0527c size 7590

[ "golubstrazh@gmail.com" ]

golubstrazh@gmail.com

f3c093a80db997a217f9bb39070aa1c624905138

acb8e84e3b9c987fcab341f799f41d5a5ec4d587

/langs/7/q83.py

6dbc39926c8254d0523be224754339ce48893bc0

[]

no_license

G4te-Keep3r/HowdyHackers

46bfad63eafe5ac515da363e1c75fa6f4b9bca32

fb6d391aaecb60ab5c4650d4ae2ddd599fd85db2

refs/heads/master

2020-08-01T12:08:10.782018

2016-11-13T20:45:50

73,624,224

0

1

null

UTF-8

Python

false

486

py

import sys def printFunction(lineRemaining): if lineRemaining[0] == '"' and lineRemaining[-1] == '"': if len(lineRemaining) > 2: #data to print lineRemaining = lineRemaining[1:-1] print ' '.join(lineRemaining) else: print def main(fileName): with open(fileName) as f: for line in f: data = line.split() if data[0] == 'q83': printFunction(data[1:]) else: print 'ERROR' return if __name__ == '__main__': main(sys.argv[1])

[ "juliettaylorswift@gmail.com" ]

juliettaylorswift@gmail.com

d8ed39fa433143eecb8f4953bd3e201a28ebc002

4402cb77b1a15e397fbc51f2c6f3240cbe8dfdac

/dbinit.py

1adcf7ea0df0995cb4c2bc046508782635a191c0

[]

no_license

StrelnikovNikolay/scheduler

898494933967aaa5b2c60efb2dfcd664a4b2b112

2a56d77ce91cc2df7f04ec2defbafcdd2250219c

refs/heads/master

2016-09-06T11:50:30.827549

2012-06-10T06:21:44

null

0

null

UTF-8

Python

false

120

py

from scheduler import db """ create test database """ if __name__ == "__main__": db.drop_all() db.create_all()

[ "progolab@gmail.com" ]

progolab@gmail.com

7490c35a50fa2472ac0eab374ec9e4a4348e203b

1feed26c45cc62e360773ad781aaaa13c6057e77

/modules/problem.py

ae39edc280554680d9529f3778885f99829ba876

[ "MIT" ]

permissive

henriqueblang/tsp-ga

365cda7860862e959e0c4a2c0d5fae52c19d3e45

3a354bc9c0b24952c94afbaf6fcd81d94a9186af

refs/heads/master

2022-04-24T00:50:10.217634

2020-04-22T04:04:20

257,787,026

1

0

null

UTF-8

Python

false

1,213

py

import math from modules.genetics.chromossome import Chromossome # Vertex i (row) is adjacent to vertex j (column), where graph[i][j] is the edge weight # If the graph is not complete, a non adjacent vertex is defined by None GRAPH = ( (0, 2, 8, 5), (2, 0, 3, 4), (8, 3, 0, 7), (5, 3, 7, 0) ) # Arbitrarily long edge to complete graph (if there are any non adjacent vertexes) NON_ADJACENT_WEIGHT = 20 def f(chromossome): route = Chromossome.get_fenotype(chromossome.get_genes()) total_weight = 0 total_vertexes = len(GRAPH) for i in range(total_vertexes): vertex = route[i] next_vertex = route[i + 1] edge_weight = GRAPH[vertex][next_vertex] if edge_weight is None: edge_weight = NON_ADJACENT_WEIGHT total_weight += edge_weight return total_weight # Fitness def g(chromossome): return 1 / (1 + f(chromossome)) def f_average(population): avg = 0 for chromossome in population: avg += f(chromossome) avg /= len(population) return avg def g_average(population): avg = 0 for chromossome in population: avg += g(chromossome) avg /= len(population) return avg

[ "henrique.barcia@gec.inatel.br" ]

henrique.barcia@gec.inatel.br

048085a4613537f236c977ce9eff9367f84860e5

de24f83a5e3768a2638ebcf13cbe717e75740168

/moodledata/vpl_data/28/usersdata/104/8782/submittedfiles/serie1.py

ad5d8a8cd81f84e3767fed50f00399919ee89084

[]

no_license

rafaelperazzo/programacao-web

95643423a35c44613b0f64bed05bd34780fe2436

170dd5440afb9ee68a973f3de13a99aa4c735d79

refs/heads/master

2021-01-12T14:06:25.773146

2017-12-22T16:05:45

69,566,344

0

null

UTF-8

Python

false

245

py

# -*- coding: utf-8 -*- from __future__ import division import math #ENTRADA n=input('Digite o valor de n:') #SAÍDA+PROCESSAMENTO soma=1 for i in range(2,n+1,1): if i%2==0: soma=soma-(1/i) else: soma=soma+(1/i) print(i)

[ "rafael.mota@ufca.edu.br" ]

rafael.mota@ufca.edu.br

61d3d86f3753c466dffd3af885d02a4beac04ea4

88413e9ca2eae9945f9bc495084f7e01f14b7396

/SRC/common/IO/GUI/whowidget.py

66865d230ca2773221dac43d51c461f76ee80fac

[ "LicenseRef-scancode-warranty-disclaimer" ]

no_license

ljuillen/OOF3D

6867e298efdf5a82544e66bd6bf1868b0dce9051

b17bc6b5efefff10beca7b0f7d12ce93def33479

refs/heads/master

2020-12-21T13:50:05.805046

2019-08-22T15:59:22

null

0

null

UTF-8

Python

false

19,869

py

# -*- python -*- # This software was produced by NIST, an agency of the U.S. government, # and by statute is not subject to copyright in the United States. # Recipients of this software assume all responsibilities associated # with its operation, modification and maintenance. However, to # facilitate maintenance we ask that before distributing modified # versions of this software, you first contact the authors at # oof_manager@nist.gov. # Widget for choosing from a WhoClass. WhoWidget.get_value() returns # the name of the selected object, which is what is necessary for # scripting. The function using the result presumably knows how to # use WhoClass.__getitem__ to get the actual object. # The optional 'callback' argument to the constructor is called when a # selection is made. The single argument to the callback function is # the selected object (ie, the .obj member of the Who instance). # The WhoWidget is different from the usual ParameterWidget (in # parameterwidgets.py) because it contains a list of gtk objects # instead of just one. Therefore it's not derived from # ParameterWidget, and must do the work of that class by itself. # Perhaps this is a symptom of bad design. from ooflib.SWIG.common import switchboard from ooflib.common import debug from ooflib.common import labeltree from ooflib.common.IO import whoville from ooflib.common.IO.GUI import chooser #from ooflib.common.IO.GUI import gtklogger from ooflib.common.IO.GUI import parameterwidgets from ooflib.common.IO.GUI import widgetscope import gtk import string class WhoWidgetBase: def __init__(self, whoclass, value, callback, scope, condition, sort, widgettype, verbose=False): debug.mainthreadTest() self.whoclass = whoclass self.scope = scope # WidgetScope object self.verbose = verbose # condition(obj) is a function that returns 1 if the object # should be listed. self.condition = condition # sort is a function that takes a list of names and sorts them # into the order in which they should appear in the GUI. self.sort = sort # widgettype must be either 'Chooser' or 'Combo'. It # specifies the type of subwidget to use for the lowest widget # in the Who hierarchy. self.widgettype = widgettype if scope: scope.addWidget(self) self.callback = callback depth = len(whoclass.hierarchy()) # self.proxycheck = gtk.CheckButton() self.proxy_names = [] self.widgets = [None]*depth self.gtk = [None]*depth self.currentPath = ['']*depth self.destroysignals = [None]*depth self.buildWidgets(value) # sets currentPath, widgets, and gtk self.sbcallbacks = [] for whoklass in whoclass.hierarchy(): self.sbcallbacks += [ switchboard.requestCallbackMain( ('new who', whoklass.name()), self.newWhoCB), switchboard.requestCallbackMain( ('remove who', whoklass.name()), self.newWhoCB), switchboard.requestCallbackMain( ('rename who', whoklass.name()), self.renameWhoCB) ] def buildWidgets(self, value=None, interactive=0): debug.mainthreadTest() # Construct a Chooser widget for each WhoClass in the target # WhoClass's hierarchy. interactive is 1 if this call is in # response to a user action. ## oldvalue = self.get_value() oldpath = self.currentPath[:] classlist = self.whoclass.hierarchy() depth = len(classlist) # Make sure value is a list. value = labeltree.makePath(value) # Make a list of the allowed proxies for the lowermost tier of # the class hierarchy for this widget. Allowed proxies are # those which satisfy both the passed-in condition *and* are # proxies. self.proxy_names = [x[0] for x in classlist[-1].keys( condition = lambda x: (self.condition(x) and not whoville.excludeProxies(x)), sort=self.sort)] # Make sure that value contains a setting for each chooser widget if value and len(value) < depth: value += [None]*(depth-len(value)) for d in range(depth): try: # Exclude proxies from this part of the process... paths = classlist[d].keys( base=self.currentPath[:d], condition=lambda x: self.condition(x) and whoville.excludeProxies(x) and not x.secret(), sort=self.sort) except KeyError, exc: names = [] else: names = [p[0] for p in paths] if d==0: # In the top-most level of the widget, include the # proxy names for the lowermost level. names += self.proxy_names if self.widgets[d] is None: if self.widgettype == 'Chooser' or d < depth-1: self.widgets[d] = chooser.ChooserWidget( names, callback=self.selectCB, callbackargs=(d,), name=classlist[d].name()) else: self.widgets[d] = chooser.ChooserComboWidget( names, callback=self.comboCB, name=classlist[d].name()) self.gtk[d] = self.widgets[d].gtk self.destroysignals[d] = self.gtk[d].connect('destroy', self.destroyCB, d) else: # Update the list of choices in an existing ChooserWidget. self.widgets[d].update(names) if value and value[d] in names: # Set widget to the given value self.widgets[d].set_state(value[d]) self.currentPath[d] = value[d] elif self.currentPath[d] in names: # ... or retain previous value self.widgets[d].set_state(self.currentPath[d]) elif len(names) > 0: # ... or pick the first value in the list self.currentPath[d] = names[0] self.widgets[d].set_state(0) else: # ... or don't pick anything self.currentPath[d] = '' if self.widgettype == 'Chooser': self.gtk[d].set_sensitive(names != []) # end for d in range(depth) # The state of other widgets may depend on the state of this # one. If so, they can use the WidgetScope mechanism to find # this widget and listen for the following switchboard # message. (Note that it's not sufficient to check to see if # get_value()'s return value has changed. The return value # can be None both before and after a state change.) if oldpath != self.currentPath: switchboard.notify(self, interactive=interactive) def destroyCB(self, gtkwidget, d): if self.widgets: self.cleanUp() def destroy(self): debug.mainthreadTest() for gtkwid in self.gtk: gtkwid.destroy() def cleanUp(self): map(switchboard.removeCallback, self.sbcallbacks) self.sbcallbacks = [] self.gtk = [] self.widgets = [] self.whoclass = None if self.scope: self.scope.removeWidget(self) self.scope = None def newWhoCB(self, whoname): # switchboard ("new who", classname) self.buildWidgets() def renameWhoCB(self, oldpath, newname): # sb ("rename who", classname) # The object being renamed might be an internal node, in which # case the path being passed in will be shorter than required # for setting the state of this widget. opath = labeltree.makePath(oldpath) # old path to renamed object npath = opath[:-1] + [newname] # new path to renamed object cpath = self.currentPath # current path if opath == cpath[:len(opath)]: # path to current object is changing npath += cpath[len(opath):] # new path to current object self.buildWidgets(npath) else: # change does not affect current object self.buildWidgets() def selectCB(self, gtkobj, name, d): # ChooserWidget callback newpath = self.currentPath[:] newpath[d] = name self.buildWidgets(newpath, interactive=1) # sets currentPath if self.callback: self.callback(self.currentPath) def comboCB(self, widget): # ChooserComboWidget callback # Since the ChooserComboWidget represents a leaf of the # WhoClass heirarchy, there's no need to rebuild the other # widgets. We just have to tell the world that the value has # changed. switchboard.notify(self, interactive=1) def set_value(self, value): path = labeltree.makePath(value) self.buildWidgets(path) class WhoWidget(WhoWidgetBase): def __init__(self, whoclass, value=None, callback=None, scope=None, name=None, condition=whoville.excludeProxies, sort=whoville.proxiesLast, verbose=False): WhoWidgetBase.__init__(self, whoclass, value, callback, scope, condition, sort, widgettype='Chooser', verbose=verbose) def get_value(self, depth=None): if depth is None: depth = len(self.currentPath) # In proxy case, ignore depth. if self.currentPath[0] in self.proxy_names: return self.currentPath[0] if '' in self.currentPath[:depth]: return None return string.join(self.currentPath[:depth], ':') def isValid(self): if self.currentPath[0] in self.proxy_names: return True return '' not in self.currentPath # The NewWhoWidget is used in the NewWhoParameterWidget, and # substitutes a ChooserCombo for the Chooser at the lowest level of # the WhoClass hierarchy. This allows the user to type in the name of # a new Who object, instead of simply choosing between existing ones. # The ChooserCombo doesn't support any callbacks, so the NewWhoWidget # doesn't either. This makes it appropriate for use only in passive # situations. It can't initiate any action on its own. class NewWhoWidget(WhoWidgetBase): def __init__(self, whoclass, value=None, callback=None, scope=None, name=None, condition=whoville.excludeProxies, sort=whoville.proxiesLast, verbose=False): WhoWidgetBase.__init__(self, whoclass, value, callback, scope, condition, sort, widgettype='Combo', verbose=verbose) def get_value(self): # This is slightly nontrivial because the ChooserCombo doesn't # have a callback, so the last part of self.currentPath isn't # automatically updated. debug.mainthreadTest() if self.widgets and self.widgets[-1]: self.currentPath[-1] = self.widgets[-1].get_value() return string.join(self.currentPath, ':') def isValid(self): debug.mainthreadTest() if self.widgets and self.widgets[-1]: return self.widgets[-1].get_value() ################################### # The WhoParameterWidget assembles the components of a WhoWidget into # a table so that the WhoWidget can be placed in automatically # generated GUI objects (eg, RegisteredClassFactories). It is a # WidgetScope and as such contains its WhoWidget, so that other # widgets searching for the WhoWidget can find it. Other widgets # should never have to search for the WhoParameterWidget explicitly. class WhoParameterWidgetBase(parameterwidgets.ParameterWidget, widgetscope.WidgetScope): def __init__(self, whoclass, value=None, scope=None, name=None, sort=None, condition=whoville.excludeProxies, verbose=False): debug.mainthreadTest() widgetscope.WidgetScope.__init__(self, scope) self.whowidget = self.makeSubWidgets(whoclass, value, condition, sort, verbose=verbose) # Put the WhoWidget's components into a box. depth = len(self.whowidget.gtk) frame = gtk.Frame() frame.set_shadow_type(gtk.SHADOW_IN) vbox = gtk.VBox() frame.add(vbox) parameterwidgets.ParameterWidget.__init__(self, frame, scope, name, verbose=verbose) for d in range(depth): vbox.pack_start(self.whowidget.gtk[d], expand=0, fill=0) self.wwcallback = switchboard.requestCallbackMain(self.whowidget, self.widgetCB) self.widgetCB(0) def set_value(self, value): self.whowidget.set_value(value) def get_value(self): return self.whowidget.get_value() def cleanUp(self): parameterwidgets.ParameterWidget.cleanUp(self) self.destroyScope() switchboard.removeCallback(self.wwcallback) def widgetCB(self, interactive): # validity check val = self.get_value() self.widgetChanged(val and val[-1] != ':', interactive) class WhoParameterWidget(WhoParameterWidgetBase): def makeSubWidgets(self, whoclass, value, condition, sort, verbose=False): return WhoWidget(whoclass, value, scope=self, condition=condition, sort=sort, verbose=verbose) class NewWhoParameterWidget(WhoParameterWidgetBase): def makeSubWidgets(self, whoclass, value, condition, sort, verbose=False): return NewWhoWidget(whoclass, value, scope=self, condition=condition, sort=sort, verbose=verbose) def _WhoParameter_makeWidget(self, scope=None, verbose=False): return WhoParameterWidget(self.whoclass, self.value, scope=scope, name=self.name, verbose=verbose) whoville.WhoParameter.makeWidget = _WhoParameter_makeWidget def _NewWhoParameter_makeWidget(self, scope=None, verbose=False): return NewWhoParameterWidget(self.whoclass, self.value, scope=scope, name=self.name, verbose=verbose) whoville.NewWhoParameter.makeWidget = _NewWhoParameter_makeWidget ############################################### class WhoClassParameterWidget(parameterwidgets.ParameterWidget): def __init__(self, value, scope=None, name=None, condition=whoville.noSecretClasses, verbose=False): self.chooser = chooser.ChooserWidget(whoville.classNames(condition), callback=self.chooserCB, name=name) parameterwidgets.ParameterWidget.__init__(self, self.chooser.gtk, scope, verbose=verbose) self.sb = switchboard.requestCallbackMain('new who class', self.newWhoClass) self.set_value(value) self.condition = condition def newWhoClass(self, classname): self.chooser.update(whoville.classNames(self.condition)) def chooserCB(self, gtkobj, name): switchboard.notify(self, interactive=1) self.widgetChanged(self.get_value() is not None, interactive=1) def set_value(self, value): self.chooser.set_state(value) # does not call chooserCB switchboard.notify(self, interactive=0) # Use self.get_value(), not value, to check validity, because # value may be None, in which case the actual value is # whatever's first in the Chooser. self.widgetChanged(self.get_value() is not None, interactive=0) def get_value(self): return self.chooser.get_value() def cleanUp(self): switchboard.removeCallback(self.sb) parameterwidgets.ParameterWidget.cleanUp(self) def _WhoClassParameter_makeWidget(self, scope=None, verbose=False): return WhoClassParameterWidget(self.value, scope=scope, name=self.name, condition=self.condition, verbose=verbose) whoville.WhoClassParameter.makeWidget = _WhoClassParameter_makeWidget ############################################### class AnyWhoParameterWidget(parameterwidgets.ParameterWidget, widgetscope.WidgetScope): # See comment in WhoParameterWidget about WidgetScope. def __init__(self, value, scope, name=None, verbose=False): widgetscope.WidgetScope.__init__(self, scope) parameterwidgets.ParameterWidget.__init__(self, gtk.VBox(), scope, name, verbose=verbose) self.classwidget = scope.findWidget( lambda w: isinstance(w, WhoClassParameterWidget)) self.whopwidget = None # enclosed WhoParameterWidget self.whoclassname = None self.whoSignal = None self.buildWidget() self.set_value(value) self.classSignal = switchboard.requestCallbackMain( self.classwidget, self.classChangedCB) def cleanUp(self): parameterwidgets.ParameterWidget.cleanUp(self) switchboard.removeCallback(self.classSignal) if self.whoSignal: switchboard.removeCallback(self.whoSignal) def classChangedCB(self, *args, **kwargs): if self.classwidget.get_value() != self.whoclassname: self.buildWidget() def buildWidget(self): debug.mainthreadTest() if self.whopwidget: self.whopwidget.destroy() self.whoclassname = self.classwidget.get_value() whoclass = whoville.getClass(self.whoclassname) # Create a WhoWidget that doesn't exclude proxy who # objects. If it's necessary to create an # AnyWhoParameterWidget with a different exclusion policy, # then the AnyWhoParameter will need to have a 'condition' # attribute that can be passed in to the widget. self.whopwidget = WhoParameterWidget(whoclass, scope=self, sort=whoville.proxiesLast, condition=lambda x:1) if self.whoSignal: switchboard.removeCallback(self.whoSignal) self.whoSignal = switchboard.requestCallbackMain(self.whopwidget, self.whoChangedCB) self.gtk.pack_start(self.whopwidget.gtk) self.gtk.show_all() self.widgetChanged(self.get_value() is not None, interactive=0) def whoChangedCB(self, *args): self.widgetChanged(self.get_value() is not None, interactive=1) def set_value(self, value): self.whopwidget.set_value(value) # Use self.get_value(), not value, to check validity, because # value may be None, in which case the actual value is # whatever's first in the Chooser. self.widgetChanged(self.get_value() is not None, interactive=0) def get_value(self): return self.whopwidget.get_value() def _AnyWhoParameter_makeWidget(self, scope=None, verbose=False): return AnyWhoParameterWidget(self.value, scope=scope, name=self.name, verbose=verbose) whoville.AnyWhoParameter.makeWidget = _AnyWhoParameter_makeWidget

[ "faical.congo@nist.gov" ]

faical.congo@nist.gov

9d0e351bfa38fc82b31382b48b8115881e12eb82

eecd638aed3e5952d158c6f080f2aa9bc772ca7e

/YaDisk.py

9635f8950042620cbad69d08910c0b0040f31c08

[]

no_license

OksanaSha/coursework_october

0d6a3b488b746c9e5f3816e1a54f3740cc004d81

fb2de7206d7db40d7d405b97d28dca2a881faf05

refs/heads/master

2023-08-21T19:08:59.923239

2021-10-22T18:21:53

2021-10-22T18:27:30

null

0

null

UTF-8

Python

false

1,208

py

import requests class YaDisk: def __init__(self, token): self.token = token def get_headers(self): return { 'Content-Type': 'application/json', 'Authorization': f'OAuth {self.token}' } def upload_file(self, file_url, name_dir, file_name): upload_url = 'https://cloud-api.yandex.net/v1/disk/resources/upload' path = f'{name_dir}/{file_name}.jpg' params = {'url': file_url, 'path': path} headers = self.get_headers() response = requests.post(url=upload_url, headers=headers, params=params) if response.status_code != 202: print(response.status_code) def create_dir(self, name_dir): url = 'https://cloud-api.yandex.net/v1/disk/resources' params = {'path': name_dir} headers = self.get_headers() response = requests.put(url=url, headers=headers, params=params) if response.status_code == 201: print(f'Папка "{name_dir}" создана на Я.Диске') elif response.status_code == 409: print(f'Папка "{name_dir}" уже существует') else: print(response.status_code)

[ "oksanashatalova@bk.ru" ]

oksanashatalova@bk.ru

a509f4e8e220d22d6a23906514d73f383eb93a0b

4ef688b93866285bcc27e36add76dc8d4a968387

/moto/route53resolver/exceptions.py

e0ce342b448e1b445409540ec36f23c090c5211f

[ "Apache-2.0" ]

permissive

localstack/moto

cec77352df216cac99d5e0a82d7ada933950a0e6

b0b2947e98e05d913d7ee2a0379c1bec73f7d0ff

refs/heads/localstack

2023-09-01T05:18:16.680470

2023-07-10T09:00:26

2023-08-07T14:10:06

118,838,444

22

42

Apache-2.0

2023-09-07T02:07:17

2018-01-25T00:10:03

Python

UTF-8

Python

false

2,366

py

from typing import List, Tuple from moto.core.exceptions import JsonRESTError class RRValidationException(JsonRESTError): code = 400 def __init__(self, error_tuples: List[Tuple[str, str, str]]): """Validation errors are concatenated into one exception message. error_tuples is a list of tuples. Each tuple contains: - name of invalid parameter, - value of invalid parameter, - string describing the constraints for that parameter. """ msg_leader = ( f"{len(error_tuples)} " f"validation error{'s' if len(error_tuples) > 1 else ''} detected: " ) msgs = [] for arg_name, arg_value, constraint in error_tuples: msgs.append( f"Value '{arg_value}' at '{arg_name}' failed to satisfy " f"constraint: Member must {constraint}" ) super().__init__("ValidationException", msg_leader + "; ".join(msgs)) class InvalidNextTokenException(JsonRESTError): code = 400 def __init__(self) -> None: super().__init__( "InvalidNextTokenException", "Invalid value passed for the NextToken parameter", ) class InvalidParameterException(JsonRESTError): code = 400 def __init__(self, message: str): super().__init__("InvalidParameterException", message) class InvalidRequestException(JsonRESTError): code = 400 def __init__(self, message: str): super().__init__("InvalidRequestException", message) class LimitExceededException(JsonRESTError): code = 400 def __init__(self, message: str): super().__init__("LimitExceededException", message) class ResourceExistsException(JsonRESTError): code = 400 def __init__(self, message: str): super().__init__("ResourceExistsException", message) class ResourceInUseException(JsonRESTError): code = 400 def __init__(self, message: str): super().__init__("ResourceInUseException", message) class ResourceNotFoundException(JsonRESTError): code = 400 def __init__(self, message: str): super().__init__("ResourceNotFoundException", message) class TagValidationException(JsonRESTError): code = 400 def __init__(self, message: str): super().__init__("ValidationException", message)

[ "noreply@github.com" ]

localstack.noreply@github.com

46a7f1350601ecdbc3531d9b011717dc8aced37f

b1fe0349febf70932f95cae5ed3f0d8768be992f

/voc_annotation.py

2ef424d5a38cc8c57ec752296db0f1a1433da4a0

[ "MIT" ]

permissive

Snaill123/Crack-Detection-YOLOV3

537d24fa477d05b4b9b00702d522bf4365399760

694bcd2a1802d0e1ed9504b4a2429af84069af5a

refs/heads/master

2020-05-16T16:16:25.535414

2018-12-16T07:02:54

183,156,921

5

0

null

2019-04-24T05:49:37

2019-04-24T05:49:36

null

UTF-8

Python

false

3,527

py

import os import xml.etree.ElementTree as ET from tqdm import tqdm from os import getcwd sets=[('2007', 'train')] classes = ["neg"] def _write_data(): xmlfilepath = 'DATA/VOC2007/Annotations' txtsavepath = 'DATA/VOC2007/ImageSets/Main' all_images = [i.split('.')[0] for i in os.listdir(xmlfilepath)] with open(os.path.join(txtsavepath, 'train.txt'), 'w') as train_f: for i in all_images: train_f.write(str(i)+'\n') def _write_amend_data(): xmlfilepath = 'DATA/Amend_VOC2007/Annotations' txtsavepath = 'DATA/Amend_VOC2007/ImageSets/Main' all_images = [i.split('.')[0] for i in os.listdir(xmlfilepath)] with open(os.path.join(txtsavepath, 'train.txt'), 'w') as train_f: for i in all_images: train_f.write(str(i) + '\n') def _convert_annotation(year, image_id, list_file): in_file = open('DATA/VOC%s/Annotations/%s.xml'%(year, image_id)) tree=ET.parse(in_file) root = tree.getroot() for obj in root.iter('object'): difficult = obj.find('difficult').text cls = obj.find('name').text if cls not in classes or int(difficult)==1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (int(xmlbox.find('xmin').text), int(xmlbox.find('ymin').text), int(xmlbox.find('xmax').text), int(xmlbox.find('ymax').text)) list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id)) def _convert_amend_annotation(year, image_id, list_file): in_file = open('DATA/Amend_VOC%s/Annotations/%s.xml' % (year, image_id)) tree = ET.parse(in_file) root = tree.getroot() for obj in root.iter('object'): difficult = obj.find('difficult').text cls = obj.find('name').text if cls not in classes or int(difficult) == 1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (int(xmlbox.find('xmin').text), int(xmlbox.find('ymin').text), int(xmlbox.find('xmax').text), int(xmlbox.find('ymax').text)) list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id)) def trainsform_data(usage='train'): wd = getcwd() if usage == 'train': _write_data() for year, image_set in sets: image_ids = open('DATA/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split() list_file = open(os.path.join('model_data', '%s_%s.txt' % (year, image_set)), 'w') for image_id in tqdm(image_ids, desc="开始转换数据集[%s]" % usage): list_file.write('%s/DATA/VOC%s/JPEGImages/%s.jpg' % (wd, year, image_id)) _convert_annotation(year, image_id, list_file) list_file.write('\n') list_file.close() elif usage == 'amend': _write_amend_data() for year, image_set in sets: image_ids = open('DATA/Amend_VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split() list_file = open(os.path.join('model_data', '%s_%s.txt' % (year, image_set)), 'w') for image_id in tqdm(image_ids, desc="开始转换数据集[%s]" % usage): list_file.write('%s/DATA/Amend_VOC%s/JPEGImages/%s.jpg' % (wd, year, image_id)) _convert_amend_annotation(year, image_id, list_file) list_file.write('\n') list_file.close() else: raise print("请输入正确数据集用途") if __name__ == '__main__': trainsform_data()

[ "1011836423@qq.com" ]

1011836423@qq.com

40b4345a29324b82ea57abfae90c4db05b805c43

95db37486077442a6e3b368d3cc76674cfd2ff53

/opencv-tutorial/opencv_tutorial_01.py

2e37e31e0127ba44bf4c869e30c43dd35383f8ee

[]

no_license

freebreadstix/computer_vision

971692da3dd23b6191d63bc10fe977a4619d2783

eadb931d4323c480331f6612514767ddc62a5149

refs/heads/master

2022-12-05T19:44:12.018234

2020-08-29T03:54:28

287,652,030

0

null

UTF-8

Python

false

3,478

py

# USAGE # python opencv_tutorial_01.py # import the necessary packages import imutils import cv2 # load the input image and show its dimensions, keeping in mind that # images are represented as a multi-dimensional NumPy array with # shape no. rows (height) x no. columns (width) x no. channels (depth) image = cv2.imread("jp.png") (h, w, d) = image.shape print("width={}, height={}, depth={}".format(w, h, d)) # display the image to our screen -- we will need to click the window # open by OpenCV and press a key on our keyboard to continue execution # output = image.copy() # cv2.imshow("Image", output) # cv2.waitKey(0) # access the RGB pixel located at x=50, y=100, keepind in mind that # OpenCV stores images in BGR order rather than RGB # (B, G, R) = image[100, 50] # print("R={}, G={}, B={}".format(R, G, B)) # # extract a 100x100 pixel square ROI (Region of Interest) from the # # input image starting at x=320,y=60 at ending at x=420,y=160 # roi = image[60:160, 320:420].copy() # cv2.imshow("ROI", roi) # cv2.waitKey(0) # # resize the image to 200x200px, ignoring aspect ratio # resized = cv2.resize(image, (20000, 20000)) # cv2.imshow("Fixed Resizing", resized) # cv2.waitKey(0) # # fixed resizing and distort aspect ratio so let's resize the width # # to be 300px but compute the new height based on the aspect ratio r = 300.0 / w dim = (300, int(h * r)) resized = cv2.resize(image.copy(), dim) cv2.imwrite("output.png", resized) # # manually computing the aspect ratio can be a pain so let's use the # # imutils library instead # resized = imutils.resize(image, width=300) # cv2.imshow("Imutils Resize", resized) # cv2.waitKey(0) # # let's rotate an image 45 degrees clockwise using OpenCV by first # # computing the image center, then constructing the rotation matrix, # # and then finally applying the affine warp # center = (w // 2, h // 2) # M = cv2.getRotationMatrix2D(center, -45, 1.0) # rotated = cv2.warpAffine(image, M, (w, h)) # cv2.imshow("OpenCV Rotation", rotated) # cv2.waitKey(0) # # rotation can also be easily accomplished via imutils with less code # rotated = imutils.rotate(image, -45) # cv2.imshow("Imutils Rotation", rotated) # cv2.waitKey(0) # # OpenCV doesn't "care" if our rotated image is clipped after rotation # # so we can instead use another imutils convenience function to help # # us out # rotated = imutils.rotate_bound(image, 45) # cv2.imshow("Imutils Bound Rotation", rotated) # cv2.waitKey(0) # # apply a Gaussian blur with a 11x11 kernel to the image to smooth it, # # useful when reducing high frequency noise # blurred = cv2.GaussianBlur(image, (11, 11), 0) # cv2.imshow("Blurred", blurred) # cv2.waitKey(0) # # draw a 2px thick red rectangle surrounding the face # output = image.copy() # cv2.rectangle(output, (320, 60), (420, 160), (0, 0, 255), 2) # cv2.imshow("Rectangle", output) # cv2.waitKey(0) # # draw a blue 20px (filled in) circle on the image centered at # # x=300,y=150 # output = image.copy() # cv2.circle(output, (300, 150), 20, (255, 0, 0), -1) # cv2.imshow("Circle", output) # cv2.waitKey(0) # # draw a 5px thick red line from x=60,y=20 to x=400,y=200 # output = image.copy() # cv2.line(output, (60, 20), (400, 200), (0, 0, 255), 5) # cv2.imshow("Line", output) # cv2.waitKey(0) # # draw green text on the image # output = image.copy() # cv2.putText(output, "OpenCV + Jurassic Park!!!", (10, 25), # cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) # cv2.imshow("Text", output) # cv2.waitKey(0)

[ "Lucas Nguyen" ]

Lucas Nguyen

280a0f56dab449cf30e0d252cc2e58c11e89bc4c

070b83742b2b1dad16dbcfa0c9b234d50f32c1a0

/scripts/html-list.py

96a340cee91579c6b4bd51e7b94678e03a18f293

[]

no_license

venkyms/python-workspace

f4edc2a39ac95c5b8ece2e12e2ce02b6065017dd

5fa9fc8749ae80a68354b416afa24c6a4063d4f8

refs/heads/master

2021-09-13T09:13:33.214578

2018-04-27T16:11:38

2018-04-27T16:14:08

125,087,832

0

null

UTF-8

Python

false

418

py

def html_list(data_list): """ Generate html document list from input string list :param data_list: string which needs to be converted as html list :return: html document string """ html_doc = ['<ul>'] for data in data_list: html_doc.append('<li>' + data + '</li>') html_doc.append('</ul>') return "\n".join(html_doc) print(html_list(['first string', 'second string']))

[ "venkyms@gmail.com" ]

venkyms@gmail.com

d52c60bea48572304a0adfeceeae75ba986c6e31

4dab2427e86b7efc2f6e3e43959c923b6ba89d75

/main.py

b6b24370d019b2100927cce803021209b1fa256f

[]

no_license

bmahlbrand/personal_tweets

47cf0c7b079eb4ac45c04d01c0be99c6f99bfe1e

20c112d5783b822f6a9a8b6123841ecca9763a63

refs/heads/master

2021-05-04T11:06:55.717197

2016-10-27T22:57:24

50,936,472

0

null

UTF-8

Python

false

162

py

__author__ = 'Ben' if __name__ == '__main__': t1 = ("a", "b") t2 = ("a", "b") if t1 == t2: print('success') else: print('fail')

[ "bmahlbrand@gmail.com" ]

bmahlbrand@gmail.com

efb80819e06b36adbd316faa062924a416122433

08302090dafd6c5988374213f224f9116a8224fb

/rop/exploit_rop_no_aslr.py

70fff54aa5900c89fce4a4c2ca64404c3152328d

[]

no_license

violentr/exploit_development

da813c9c38b6708e7db3f05f4ed625b2e7e1b208

8fdeec71301b64e77fc869649a798d52947b2ff3

refs/heads/master

2021-07-17T07:44:14.431004

2020-10-20T20:56:42

219,014,772

4

null

2020-10-20T20:56:43

2019-11-01T15:42:24

Assembly

UTF-8

Python

false

292

py

#!/usr/bin/env python from pwn import * leak = "A"*140 #return address system leak += "\xa0\x4d\xe5\xb7" #return address exit leak += "\xd0\x89\xe4\xb7" #return address "bin/sh" leak += "\x0b\x5a\xf7\xb7" exploit = process("./rop3", shell=True) exploit.sendline(leak) exploit.interactive()

[ "den.b@itservicesgroup.co.uk" ]

den.b@itservicesgroup.co.uk

2c6e40eec956f0a7689c6933a20e8815bf9bcad6

ad8395ea7c00873329fe0e0e0d115a7e51e02636

/transversingTwoDList.py

34d74aaf8fcaff52e92bd15530eff4ab1426249e

[]

no_license

AErenzo/Python_course_programs

0ca7f94e0c6418e1af0c13b6629738f5b5496ee5

720d88248e935ad7a77ac0e530ab519882ceea8d

refs/heads/master

2022-08-23T13:39:09.999602

2020-05-19T09:58:33

265,209,869

0

null

UTF-8

Python

false

237

py

List = [[0]*3 for i in range(3)] for i in range(len(List)): for j in range(len(List[i])): List[i][j] = int(input('Please enter a Value: ')) for i in List: for j in i: print(j, end = ' ') print()

[ "noreply@github.com" ]

AErenzo.noreply@github.com

381a4637bb1abb2acb955e055da64d322ab4defb

4e19c788aa10f0102eca2b558eaa8757cf15a419

/manage_benefits/urls.py

dac0f534ff883f79312fef2621e4e1fc1b08bf17

[]

no_license

RohitDigimonk/codedeploy

cb3da84e159839337d8699f3737f4fd275b78eac

7a4645b3de4c60ea19411055cd6326469549dd14

refs/heads/master

2022-11-28T17:08:20.505998

2020-03-16T07:06:30

225,541,914

0

null

UTF-8

Python

false

386

py

from django.urls import path from . import views urlpatterns = [ path('', views.index, name='index'), path('edit/<int:id>', views.edit, name='edit'), path('remove-benefit/<int:id>', views.remove_benefit, name='remove_benefit'), path('get-data', views.get_data, name='get_data'), path('get-data/', views.get_data, name='get_data'), ]

[ "gaurav@digimonk.in" ]

gaurav@digimonk.in

daec1712af205795d7261468e5501ddd91e71ccc

6003714d0d6da0d8b18a49d577ba6d59725f6a7b

/module_Step.py

5de4cdb3d21f98e0088def20ef51a10619be4c68

[]

no_license

alexInvictus/python-project-on-pc

556875c8777af20442bed34fa2f56bdcf6e4ac1f

84bd3dd18acfd2e692ccff9e6f987c57d735cdb1

refs/heads/master

2021-05-06T10:03:12.175320

2017-12-13T07:45:43

114,090,123

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py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # @Date : 2017-12-2 13：28 # @Version : $Id$ # @des : 学习模块章节，用pip安装一个pillow。试运行pillow模块 #pillow模块调用open图像路径为下面格式 from PIL import Image #im=Image.open("C:\\Users\\alex\Desktop\\abc.jpg") #im.show() #调用matplotlib的一个库来绘制图片进行显示 from PIL import Image import matplotlib.pyplot as plt img=Image.open("C:\\Users\\alex\Desktop\\abc.jpg") plt.figure("dog") plt.imshow(img) plt.show()

[ "290149382@qq.com" ]

290149382@qq.com

f3a62678fd418f918abd5f71c94c3ee2a6f0671a

17584d80491f774bc107a5496a6cdfe536a70e51

/src/Data/IntegerData.py

aa2e7ac6c6c9066861d4b49b0817cd004fa548c1

[]

no_license

rubenvanassche/C-Compiler

132f9c6c63afcdfacb54e50ff377d265800c740a

42c643a8fc9c392053f53e56c8c453d95a548282

refs/heads/master

2021-03-24T12:48:51.771282

2016-06-10T19:03:29

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2

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null

UTF-8

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328

py

from src.Data.Data import Data class IntegerData(Data): """Represntatation of integer """ def __init__(self, integer): super(Data, self).__init__() self.integer = integer def compile(self): return "ldc i " + str(self.integer) + "\n" def __str__(self): return str(self.integer)

[ "rubenvanassche@gmail.com" ]

rubenvanassche@gmail.com

ad2e63b98db3492c3e23b72e0af9c31eafeeae63

f0da00b3e531ac2962d712df1a17607566cd4531

/src/device/agilentn5181a.py

dfc7c19f0161022478ac242be3bc77cbbe9c2fd9

[]

no_license

houssem21/emctestbench

98325395ed654b3dfc12e47f7b6112f9529e871a

114e9fcfb3630dfa98933221176541331e3863e4

refs/heads/master

2021-01-15T16:15:48.351445

2012-06-29T09:15:58

null

0

null

UTF-8

Python

false

2,033

py

from rfgenerator import RfGenerator from device import ScpiDevice from utility.quantities import Amplitude,Power,Frequency class AgilentN5181a(RfGenerator,ScpiDevice): defaultName = 'Agilent N5181A RF Signal Generator' visaIdentificationStartsWith = 'Agilent Technologies, N5181A,' defaultAddress = 'TCPIP0::172.20.1.202::inst0::INSTR' documentation = {'Programmers Manual':'http://cp.literature.agilent.com/litweb/pdf/N5180-90005.pdf','SCPI Reference':'http://cp.literature.agilent.com/litweb/pdf/N5180-90004.pdf'} def setWaveform(self,frequency,amplitude): ''' Set the waveform parameters at once @param frequency float in Hertz @param amplitude Amplitude object ''' self.setFrequency(frequency) self.write(':SOURce:POWer:LEVel:IMMediate:AMPLitude %e dBm' % amplitude.dBm()) def getOutputEnable(self): return float(self.ask('OUTPut?')) def getPower(self): powerString = self.ask(':SOURce:POWer:LEVel:IMMediate:AMPLitude?') return Power(float(powerString),'dBm')*self.getOutputEnable() def setFrequency(self,frequency): self.write(':SOURce:FREQuency:CW %e Hz' % (frequency.asUnit('Hz'))) def getFrequency(self): return Frequency(float(self.ask(':SOURce:FREQuency:CW?')),'Hz') def setPower(self,power): setPowerString = ':SOURce:POWer:LEVel:IMMediate:AMPLitude {power:e} dBm'.format(power=power.dBm()) self.write(setPowerString) if power.negligable: self._enableOutput(False) else: self._enableOutput(True) def _enableOutput(self,enable=True): if enable: self.write('OUTPut ON') else: self.write('OUTPut OFF') if __name__ == '__main__': device = AgilentN5181a() print device.getFrequency() # device.setPower(Power(21,'dBm')) # print device.getPower() # device.enableOutput(False) # device.setWaveform(800e6,Amplitude(-25,'dBm'))

[ "sjoerd.optland@eseo.fr" ]

sjoerd.optland@eseo.fr

1e52208484d0e919dffe441f55ffecfeedfd9dac

97ba5e4f7b5c738dc1217ef2f251f239cd1da173

/LidarProcessor/LASView/test/test_LiDAR_Processor_dialog.py

b03cbe309754aa68c75732d5e98baec69f71ca28

[]

no_license

jsumeet/LiDAR-Plugins

d4e5be6958c87735cafc780ed6709df1b36fcba0

f3708052d02ef9ba40aedee1d3961389032fa9e3

refs/heads/master

2020-12-24T21:11:53.830664

2016-04-12T07:57:15

2016-04-12T07:57:21

56,042,612

0

null

UTF-8

Python

false

1,513

py

# coding=utf-8 """Dialog test. .. note:: This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. """ __author__ = 'tuplecoders@gmail.com' __date__ = '2015-10-30' __copyright__ = 'Copyright 2015, tuple coders' import unittest from PyQt4.QtGui import QDialogButtonBox, QDialog from LiDAR_Processor_dialog import LASViewDialog from utilities import get_qgis_app QGIS_APP = get_qgis_app() class LASViewDialogTest(unittest.TestCase): """Test dialog works.""" def setUp(self): """Runs before each test.""" self.dialog = LASViewDialog(None) def tearDown(self): """Runs after each test.""" self.dialog = None def test_dialog_ok(self): """Test we can click OK.""" button = self.dialog.button_box.button(QDialogButtonBox.Ok) button.click() result = self.dialog.result() self.assertEqual(result, QDialog.Accepted) def test_dialog_cancel(self): """Test we can click cancel.""" button = self.dialog.button_box.button(QDialogButtonBox.Cancel) button.click() result = self.dialog.result() self.assertEqual(result, QDialog.Rejected) if __name__ == "__main__": suite = unittest.makeSuite(LASViewDialogTest) runner = unittest.TextTestRunner(verbosity=2) runner.run(suite)

[ "sj.jainsumeet@gmail.com" ]

sj.jainsumeet@gmail.com

2f5967e6049febff8cd322592dac07a8b7608b91

a90fdaaf495445f7784a00ee92a22ac82ce93387

/bob/bob.py

0b910655bef3b4b498bd32a7654b31a122318018

[]

no_license

bwielk/PythonExercisms

db0afbdaee9cbb68c227a555970eae1ad0c66fc6

7db4110dc0d2438a45dfa59fa906d09ee8872073

refs/heads/master

2020-04-09T05:06:50.725198

2019-03-27T22:25:36

160,051,761

0

null

UTF-8

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502

py

def hey(phrase): phrase = phrase.strip().replace(' ', '') print(phrase) if any(char.isalnum() for char in phrase) or phrase.endswith(('?', '!')): if phrase.endswith('?'): if phrase[:-1].isupper(): return 'Calm down, I know what I\'m doing!' return 'Sure.' if phrase[:-1].upper() == phrase[:-1] and any(char.isalpha() for char in phrase): return 'Whoa, chill out!' return 'Whatever.' return 'Fine. Be that way!'

[ "bwielk@gmail.com" ]

bwielk@gmail.com

71e42429891ffc550d53181d62fb0dca2616e957

35d83b88488a0f492dea22128dd4d6e7c8a5bef0

/tutorial/test.py

efcf5ace8b0a0d9b192d74a524a3633ef28b2516

[]

no_license

JoshyJosh/pyramid_python_test

879913228f7bf934830419549df8c378784b9e26

3775656fc981f10385b031d98b7be073d98d029e

refs/heads/master

2021-07-10T11:24:00.050023

2017-10-10T05:21:55

106,369,498

0

null

UTF-8

Python

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1,122

py

import unittest from pyramid import testing class TutorialViewTest(unittest.TestCase): def setUp(self): self.config = testing.setUp() def tearDown(self): testing.tearDown() def test_home(self): from .views import home request = testing.DummyRequest() response = home(request) self.assertEqual(response.status_code,200) self.assertIn(b'Visit', response.body) def test_hello(self): from .views import hello request = testing.DummyRequest() response = hello(request) self.assertEqual(response.status_code,200) self.assertIn(b'Go Back', response.body) class TutorialFunctionalTests(unittest.TestCase): def setUp(self): from tutorial import main app = main({}) from webtest import TestApp self.testapp = TestApp(app) def test_home(self): res = self.testapp.get('/', status=200) self.assertIn(b'<body>Visit', res.body) def test_howdy(self): res = self.testapp.get('/howdy', status=200) self.assertIn(b'<body>Go back', res.body)

[ "herukula@gmail.com" ]

herukula@gmail.com

e76c35090c2ec35f2fb8e09d06523b6955ab7704

acbf27438bdc56c60c3c37824156b5e460bb1127

/unittest_vs_pytest.py

8b07dfb2dffbfff89b7d13832b58b2175ee8a77d

[]

no_license

SHalifaxK/Unittest

82e7d636a624c76a6d6deca07458f52f9367265b

decc0bebb9cd096a71e41eff93f134b489128595

refs/heads/master

2021-09-14T07:07:14.873309

2018-05-09T08:53:39

115,880,343

0

null

UTF-8

Python

false

570

py

''' Unittest comes with python default package And pytest you need to install separately (pip install -U pytest) ''' #Using Unittest import unittest def upper_reverse(text): return ''.join(reversed(text.upper())) class TestUpperReversed(unittest.TestCase): def test_upper_reversed(self): self.assertEqual(upper_reverse('hello'), 'OLLEH') if __name__=='__main__': unittest.main() ''' #Using pytest def upper_reverse(text): return ''.join(reversed(text.upper())) def test_upper_reverse(): assert upper_reverse('hello') == 'OLLEH' '''

[ "noreply@github.com" ]

SHalifaxK.noreply@github.com

f4c626825779eb5f45106b655f509a0adbd02db0

99c8cec11b4482d47fa446f54da9c2ba4f2d2e32

/teachers/management/commands/generate_teachers.py

f1ab0e98ce81a9ecff56eedaef947e1dbb6715c2

[]

no_license

IefremovRoman/django_efremov

1c11955c9bda5115ac8b1f98430ea688ab9ae2db

0540962fc297b5656837a819f89f6e7fcf2b89a5

refs/heads/main

2023-08-30T03:42:48.245382

2021-11-01T17:39:38

393,634,317

0

null

2021-11-04T10:08:14

2021-08-07T09:08:38

Python

UTF-8

Python

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py

import json import os from random import choice, randint from string import ascii_uppercase from django.core.management import call_command from django.core.management.base import BaseCommand from faker import Faker from groups.models import Group from teachers.models import Teacher locale = 'uk_UA' faker = Faker(locale) __location__ = os.path.realpath( os.path.join(os.getcwd(), os.path.dirname(__file__))) json_file = os.path.join(__location__, 'university_subjects.json') # json_file = 'teachers/management/commands/university_subjects.json' # json_file = os.path.join(BASE_DIR, ".env") class Command(BaseCommand): def __init__(self): super(Command, self).__init__() self.help = 'Generate teachers' def add_arguments(self, parser): parser.add_argument('total', nargs='?', type=int, default=100) def handle(self, total, *args, **kwargs): # count = kwargs.get('total', 100) # count = kwargs.get('total') if kwargs.get('total') else 100 with open(json_file, 'r') as file: subjects = json.load(file) for t in range(total): teacher = Teacher( first_name=faker.first_name(), last_name=faker.last_name(), age=faker.random_int(min=30, max=100), subject=choice(subjects), phone=f'+38000{faker.msisdn()[0:7]}' ) teacher.save() students_qnt = randint(1, 10) start_year = randint(1984, 2016) group = Group( title=choice(ascii_uppercase) + '%02d' % (abs(start_year) % 100), start_year=start_year, finish_year=start_year + 5, student_quantity=students_qnt, teacher_id=teacher ) group.save() call_command('generate_students', total=students_qnt, group_id=group.id) message = f'{total} teacher(s) successfully created!' self.stdout.write(self.style.SUCCESS(message))

[ "iefremov.roman@gmail.com" ]

iefremov.roman@gmail.com

a031f4b11cba14f9b127b12718eae04f18e82750

4aae3b711c7b20c0f9c9b7cff579fd22a11e021e

/nginx_log.py

2ec902c89fe4e34136419c110de1ffb366c18cbd

[]

no_license

lbemi/Spython3

84cccbbf813a0bad90c155292eab4c49afdf1ab5

24f5f169cb0753e8c6ea284fc072fefd688199a2

refs/heads/master

2022-09-17T02:30:02.364146

2018-11-21T06:20:18

null

0

null

UTF-8

Python

false

4,750

py

import fileinput import re import time from collections import Counter import math import sys from datetime import datetime, timedelta # 初始化显示的日志条目，None表示显示全部 records = None # 脚本使用方法 def usage(): print('Usage: %s nginx_log_file [max_record_nums] [datetime]' % sys.argv[0]) print('Usage: [max_record_nums] for int number. eg: 10 ') print('Usage: [datetime] for [5d | 5h | 5m | 5s] for [5 days | 5 hours | 5 minutes | 5 seconds]') print('eg: ./ngx.py /var/log/nginx/access.log [10] [5d | 5h | 5m | 5s]') sys.exit(0) # 过去多长时间的时间点时间戳 def tmstamp(): if len(sys.argv) <= 3: # return datetime.now().timestamp() return 0 elif re.match('^[\d]+d$', sys.argv[3]): return (datetime.now() - timedelta(days=float(sys.argv[3].rstrip('d')))).timestamp() elif re.match('^[\d]+h$', sys.argv[3]): return (datetime.now() - timedelta(hours=float(sys.argv[3].rstrip('h')))).timestamp() elif re.match('^[\d]+m$', sys.argv[3]): return (datetime.now() - timedelta(minutes=float(sys.argv[3].rstrip('m')))).timestamp() elif re.match('^[\d]+s$', sys.argv[3]): return (datetime.now() - timedelta(seconds=float(sys.argv[3].rstrip('s')))).timestamp() else: usage() # 转换字节单位 def convertBytes(bytes, lst=['B', 'KB', 'MB', 'GB', 'TB', 'PB']): i = int(math.floor(math.log(bytes, 1024))) if i >= len(lst): i = len(lst) - 1 return ('%.2f ' + lst[i]) % (bytes / math.pow(1024, i)) # 日志解析生成器 def ngx(): try: with fileinput.input(sys.argv[1]) as f: for line in f: ip, _, _, dtime, _, mthd, _, _, status, size, *_ = re.split('[\s"]+', line) dtstamp = time.mktime(time.strptime(dtime.lstrip('['), '%d/%b/%Y:%H:%M:%S')) yield [ip, status, size, dtstamp] except: usage() # 参数判断 if len(sys.argv) < 2 or len(sys.argv) > 4: usage() if len(sys.argv) < 3: records = None elif len(sys.argv) == 3: try: re.match('[\d]+', sys.argv[2]) records = int(sys.argv[2]) except: usage() elif len(sys.argv) == 4: try: re.match('^[\d]+[dhms]$', sys.argv[3]) except: usage() # 初始化各统计变量 iptotal, ipsize, ip200, ip302, ip304, ip403, ip404, ip500, ip502, ip503, totsize = Counter(), Counter(), Counter(), Counter(), Counter(), Counter(), Counter(), Counter(), Counter(), Counter(), 0 # 定义映射表头 header = ['ip', 'statuscode', 'size', 'dtstamp'] # 进行迭代统计 for line in ngx(): # 将两个列表转换为字典 datadict = dict(zip(header, line)) # 统计n天/时/分/秒之前的访问量和带宽等信息 if datadict['dtstamp'] > tmstamp(): # 每个IP的流量带宽 ipsize[datadict['ip']] += int(datadict['size']) # 总流量 totsize += int(datadict['size']) # 每IP的总访问量 iptotal[datadict['ip']] += 1 # 统计个状态码的请求数 if datadict['statuscode'] == '200': ip200[datadict['ip']] += 1 elif datadict['statuscode'] == '302': ip302[datadict['ip']] += 1 elif datadict['statuscode'] == '304': ip304[datadict['ip']] += 1 elif datadict['statuscode'] == '403': ip403[datadict['ip']] += 1 elif datadict['statuscode'] == '404': ip404[datadict['ip']] += 1 elif datadict['statuscode'] == '500': ip500[datadict['ip']] += 1 elif datadict['statuscode'] == '502': ip502[datadict['ip']] += 1 elif datadict['statuscode'] == '503': ip503[datadict['ip']] += 1 # 判断是否有存在数据，存在则打印，否则，输出错误信息！ if totsize: # 打印网站总流量,总访问量 print("\nTotal traffic : %s Total request times : %d\n" % (convertBytes(totsize), sum(iptotal.values()))) # 打印表头 print('%-15s %-10s %-12s %-8s %-8s %-8s %-8s %-8s %-8s %-8s %-8s' % ( 'Ip', 'Times', 'Traffic', '200', '302', '304', '403', '404', '500', '502', '503')) print('%-15s %-10s %-12s %-8s %-8s %-8s %-8s %-8s %-8s %-8s %-8s' % ( '-' * 15, '-' * 10, '-' * 12, '-' * 8, '-' * 8, '-' * 8, '-' * 8, '-' * 8, '-' * 8, '-' * 8, '-' * 8)) # 打印前多少条数据 # for k, v in sorted(iptotal.items(), key=lambda v: v[1], reverse=True): for k, v in iptotal.most_common(records): print('%-15s %-10s %-12s %-8s %-8s %-8s %-8s %-8s %-8s %-8s %-8s' % ( k, v, convertBytes(ipsize[k]), ip200[k], ip302[k], ip304[k], ip403[k], ip404[k], ip500[k], ip502[k], ip503[k])) else: print('Not found data!')

[ "934366858@qq.com" ]

934366858@qq.com

cc46d90d2aab3e122272548b83c2f0b7d1599f4b

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p03695/s658302581.py

4b111b4dcc5fb387aa23c5a6f49d4a93eb481a63

[]

no_license

Aasthaengg/IBMdataset

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f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

676

py

def color_judge(n): if 1<=n<=399: return 'gray' elif n<=799: return 'brown' elif n<=1199: return 'green' elif n<=1599: return 'light blue' elif n<=1999: return 'blue' elif n<=2399: return 'yellow' elif n<=2799: return 'orange' elif n<=3199: return 'red' else: return 'choice' n = int(input()) a = list(map(int, input().split())) dic = {} for i in a: if dic.get(color_judge(i)): dic[color_judge(i)] += 1 else: dic[color_judge(i)] = 1 choice = 0 l = len(dic) if dic.get('choice'): choice = dic['choice'] l -= 1 print(max(1, l), l+choice)

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

8080cfa7c5ebb3e57d3422daf3ca44dafd3787c8

b0bae65e703fa5f787cbe4dd321a5d0eff7af147

/developer/developer/urls.py

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[]

no_license

thalesh-kashyap/Developer_Repo

bad980f241db4cfa784bdc4fbe893befbd8c5f12

6f0caba049182ceca26197711c05bd2cea5f15bf

refs/heads/main

2023-04-16T04:03:07.636136

2021-04-30T10:49:57

363,106,160

0

null

UTF-8

Python

false

1,153

py

"""developer URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ # from django.contrib import admin # from django.urls import path,include # urlpatterns = [ # path('admin/', admin.site.urls), # path('home',include('addEmp.urls')), # ] from django.conf.urls import url, include from django.contrib import admin from addEmp import views urlpatterns = [ url(r'^admin/', admin.site.urls), url(r'^home/', include('addEmp.urls')), url(r'^', include('authapp.urls')), url(r'^searchEmp/', include('searchEmp.urls')), url(r'^developer/', views.DeveloperForm), ]

[ "68849549+thalesh-kashyap@users.noreply.github.com" ]

68849549+thalesh-kashyap@users.noreply.github.com