xszheng2020/the_stack_dedup_python_hits_1 · Datasets at Hugging Face

625a19aeeb78d1a163e46b551accd53b6ef2d20c

532

py

Python

torch2trt/__init__.py

SnowMasaya/torch2trt

d526b2473805f9b9a704a201bef3ce5be25d284f

[ "MIT" ]

2

2020-07-10T06:26:03.000Z

2020-07-10T07:38:08.000Z

torch2trt/__init__.py

SnowMasaya/torch2trt

d526b2473805f9b9a704a201bef3ce5be25d284f

[ "MIT" ]

1

2020-02-16T09:43:35.000Z

torch2trt/__init__.py

SnowMasaya/torch2trt

d526b2473805f9b9a704a201bef3ce5be25d284f

[ "MIT" ]

1

2019-10-14T01:11:23.000Z

from .torch2trt import * from .converters import * import tensorrt as trt def load_plugins(): import os import ctypes ctypes.CDLL(os.path.join(os.path.dirname(__file__), 'libtorch2trt.so')) registry = trt.get_plugin_registry() torch2trt_creators = [c for c in registry.plugin_creator_list if c.plugin_namespace == 'torch2trt'] for c in torch2trt_creators: registry.register_creator(c, 'torch2trt') try: load_plugins() PLUGINS_LOADED = True except OSError: PLUGINS_LOADED = False

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py

Python

code/glucocheck/homepage/migrations/0007_auto_20210315_1807.py

kmcgreg5/Glucocheck

4ab4ada7f967ae41c1241c94523d14e693e05dd4

[ "FSFAP" ]

null

code/glucocheck/homepage/migrations/0007_auto_20210315_1807.py

kmcgreg5/Glucocheck

4ab4ada7f967ae41c1241c94523d14e693e05dd4

[ "FSFAP" ]

null

code/glucocheck/homepage/migrations/0007_auto_20210315_1807.py

kmcgreg5/Glucocheck

4ab4ada7f967ae41c1241c94523d14e693e05dd4

[ "FSFAP" ]

null

# Generated by Django 3.1.7 on 2021-03-15 22:07 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('homepage', '0006_carbohydrate_glucose_insulin_recordingcategory'), ] operations = [ migrations.RenameField( model_name='carbohydrate', old_name='reading', new_name='carb_reading', ), migrations.RemoveField( model_name='glucose', name='categories', ), migrations.AddField( model_name='glucose', name='categories', field=models.ManyToManyField(to='homepage.RecordingCategory'), ), migrations.AlterField( model_name='recordingcategory', name='name', field=models.CharField(choices=[('fasting', 'Fasting'), ('before breakfast', 'Before Breakfast'), ('after breakfast', 'After Breakfast'), ('before lunch', 'Before Lunch'), ('after lunch', 'After Lunch'), ('snacks', 'Snacks'), ('before dinner', 'Before Dinner'), ('after dinner', 'After Dinner')], max_length=255, unique=True), ), ]

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6262bae7dfc3df2c02ba7e5efae6983d3daa02cb

1,826

py

Python

models/SnapshotTeam.py

Fa1c0n35/RootTheBoxs

4f2a9886c8eedca3039604b93929c8c09866115e

[ "Apache-2.0" ]

1

2019-06-29T08:40:54.000Z

models/SnapshotTeam.py

Fa1c0n35/RootTheBoxs

4f2a9886c8eedca3039604b93929c8c09866115e

[ "Apache-2.0" ]

null

models/SnapshotTeam.py

Fa1c0n35/RootTheBoxs

4f2a9886c8eedca3039604b93929c8c09866115e

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Mar 11, 2012 @author: moloch Copyright 2012 Root the Box Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from sqlalchemy import Column, ForeignKey from sqlalchemy.orm import relationship, backref from sqlalchemy.types import Integer from models import dbsession from models.Team import Team from models.Relationships import snapshot_team_to_flag, snapshot_team_to_game_level from models.BaseModels import DatabaseObject class SnapshotTeam(DatabaseObject): """ Used by game history; snapshot of a single team in history """ team_id = Column(Integer, ForeignKey("team.id"), nullable=False) money = Column(Integer, nullable=False) bots = Column(Integer, nullable=False) game_levels = relationship( "GameLevel", secondary=snapshot_team_to_game_level, backref=backref("snapshot_team", lazy="select"), ) flags = relationship( "Flag", secondary=snapshot_team_to_flag, backref=backref("snapshot_team", lazy="select"), ) @property def name(self): return dbsession.query(Team._name).filter_by(id=self.team_id).first()[0] @classmethod def all(cls): """ Returns a list of all objects in the database """ return dbsession.query(cls).all()

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1

62643e087525aca4ccc614812b7bfd674336652f

411

py

Python

pythonexercicios/ex101-funcvotacao.py

marroni1103/exercicios-pyton

734162cc4b63ed30d754a6efe4c5622baaa1a50b

[ "MIT" ]

null

pythonexercicios/ex101-funcvotacao.py

marroni1103/exercicios-pyton

734162cc4b63ed30d754a6efe4c5622baaa1a50b

[ "MIT" ]

null

pythonexercicios/ex101-funcvotacao.py

marroni1103/exercicios-pyton

734162cc4b63ed30d754a6efe4c5622baaa1a50b

[ "MIT" ]

null

def voto(num): from datetime import date anoatual = date.today().year idade = anoatual - num if idade < 16: return f"Com {idade} anos: NÃO VOTA" elif 16 <= idade < 18 or idade > 65: return f'Com {idade} anos: VOTO OPCIONAL' else: return f"Com {idade} anos: VOTO OBRIGATORIO" print('-' * 30) anonasc = int(input('Em que ano você nasceu? ')) print(voto(anonasc))

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null

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626e4c17d238ffdd4b719fcf03cef903734ecb10

201

py

Python

secondstring.py

Kokouvi/reversorder

157e39eaf424d816715080dbce0850670836e8fd

[ "MIT" ]

null

secondstring.py

Kokouvi/reversorder

157e39eaf424d816715080dbce0850670836e8fd

[ "MIT" ]

null

secondstring.py

Kokouvi/reversorder

157e39eaf424d816715080dbce0850670836e8fd

[ "MIT" ]

null

str = "The quick brown fox jumps over the lazy dog." # initial string reversed = "".join(reversed(str)) #.join() method merges all of the charactera print(reversed[0:43:2]) # print the reversed string

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6280695be38110adda77e21a75a8350fbff3df45

9,433

py

Python

fabfile/data.py

nprapps/linklater

9ba8fbefcbe9148253e5d5c47572e8b887ce9485

[ "FSFAP" ]

null

fabfile/data.py

nprapps/linklater

9ba8fbefcbe9148253e5d5c47572e8b887ce9485

[ "FSFAP" ]

47

2015-01-22T16:12:16.000Z

2015-01-28T18:51:58.000Z

fabfile/data.py

nprapps/linklater

9ba8fbefcbe9148253e5d5c47572e8b887ce9485

[ "FSFAP" ]

1

2021-02-18T11:26:35.000Z

#!/usr/bin/env python """ Commands that update or process the application data. """ from datetime import datetime import json from bs4 import BeautifulSoup from flask import render_template from fabric.api import task from fabric.state import env from facebook import GraphAPI from twitter import Twitter, OAuth from jinja2 import Environment, FileSystemLoader import app_config import copytext import os import requests TWITTER_BATCH_SIZE = 200 @task(default=True) def update(): """ Stub function for updating app-specific data. """ #update_featured_social() @task def make_tumblr_draft_html(): links = fetch_tweets(env.twitter_handle, env.twitter_timeframe) template = env.jinja_env.get_template('tumblr.html') output = template.render(links=links) return output @task def fetch_tweets(username, days): """ Get tweets of a specific user """ current_time = datetime.now() secrets = app_config.get_secrets() twitter_api = Twitter( auth=OAuth( secrets['TWITTER_API_OAUTH_TOKEN'], secrets['TWITTER_API_OAUTH_SECRET'], secrets['TWITTER_API_CONSUMER_KEY'], secrets['TWITTER_API_CONSUMER_SECRET'] ) ) out = [] tweets = twitter_api.statuses.user_timeline(screen_name=username, count=TWITTER_BATCH_SIZE) i = 0 while True: if i > (len(tweets)-1): break tweet = tweets[i] created_time = datetime.strptime(tweet['created_at'], '%a %b %d %H:%M:%S +0000 %Y') time_difference = (current_time - created_time).days if time_difference > int(days): break out.extend(_process_tweet(tweet, username)) i += 1 if i > (TWITTER_BATCH_SIZE-1): tweets = twitter_api.statuses.user_timeline(screen_name=username, count=TWITTER_BATCH_SIZE, max_id=tweet['id']) i = 0 out = _dedupe_links(out) return out def _process_tweet(tweet, username): out = [] for url in tweet['entities']['urls']: if url['display_url'].startswith('pic.twitter.com'): continue row = _grab_url(url['expanded_url']) if row: row['tweet_text'] = tweet['text'] if tweet.get('retweeted_status'): row['tweet_url'] = 'http://twitter.com/%s/status/%s' % (tweet['retweeted_status']['user']['screen_name'], tweet['id']) row['tweeted_by'] = tweet['retweeted_status']['user']['screen_name'] out.append(row) else: row['tweet_url'] = 'http://twitter.com/%s/status/%s' % (username, tweet['id']) out.append(row) return out def _grab_url(url): """ Returns data of the form: { 'title': <TITLE>, 'description': <DESCRIPTION>, 'type': <page/image/download>, 'image': <IMAGE_URL>, 'tweet_url': <TWEET_URL>. 'tweet_text': <TWEET_TEXT>, 'tweeted_by': <USERNAME> } """ data = None try: resp = requests.get(url, timeout=5) except requests.exceptions.Timeout: print '%s timed out.' % url return None real_url = resp.url if resp.status_code == 200 and resp.headers.get('content-type').startswith('text/html'): data = {} data['url'] = real_url soup = BeautifulSoup(resp.content) og_tags = ('image', 'title', 'description') for og_tag in og_tags: match = soup.find(attrs={'property': 'og:%s' % og_tag}) if match and match.attrs.get('content'): data[og_tag] = match.attrs.get('content') else: print "There was an error accessing %s (%s)" % (real_url, resp.status_code) return data def _dedupe_links(links): """ Get rid of duplicate URLs """ out = [] urls_seen = [] for link in links: if link['url'] not in urls_seen: urls_seen.append(link['url']) out.append(link) else: print "%s is a duplicate, skipping" % link['url'] return out @task def update_featured_social(): """ Update featured tweets """ COPY = copytext.Copy(app_config.COPY_PATH) secrets = app_config.get_secrets() # Twitter print 'Fetching tweets...' twitter_api = Twitter( auth=OAuth( secrets['TWITTER_API_OAUTH_TOKEN'], secrets['TWITTER_API_OAUTH_SECRET'], secrets['TWITTER_API_CONSUMER_KEY'], secrets['TWITTER_API_CONSUMER_SECRET'] ) ) tweets = [] for i in range(1, 4): tweet_url = COPY['share']['featured_tweet%i' % i] if isinstance(tweet_url, copytext.Error) or unicode(tweet_url).strip() == '': continue tweet_id = unicode(tweet_url).split('/')[-1] tweet = twitter_api.statuses.show(id=tweet_id) creation_date = datetime.strptime(tweet['created_at'],'%a %b %d %H:%M:%S +0000 %Y') creation_date = '%s %i' % (creation_date.strftime('%b'), creation_date.day) tweet_url = 'http://twitter.com/%s/status/%s' % (tweet['user']['screen_name'], tweet['id']) photo = None html = tweet['text'] subs = {} for media in tweet['entities'].get('media', []): original = tweet['text'][media['indices'][0]:media['indices'][1]] replacement = '<a href="%s" target="_blank" onclick="_gaq.push([\'_trackEvent\', \'%s\', \'featured-tweet-action\', \'link\', 0, \'%s\']);">%s</a>' % (media['url'], app_config.PROJECT_SLUG, tweet_url, media['display_url']) subs[original] = replacement if media['type'] == 'photo' and not photo: photo = { 'url': media['media_url'] } for url in tweet['entities'].get('urls', []): original = tweet['text'][url['indices'][0]:url['indices'][1]] replacement = '<a href="%s" target="_blank" onclick="_gaq.push([\'_trackEvent\', \'%s\', \'featured-tweet-action\', \'link\', 0, \'%s\']);">%s</a>' % (url['url'], app_config.PROJECT_SLUG, tweet_url, url['display_url']) subs[original] = replacement for hashtag in tweet['entities'].get('hashtags', []): original = tweet['text'][hashtag['indices'][0]:hashtag['indices'][1]] replacement = '<a href="https://twitter.com/hashtag/%s" target="_blank" onclick="_gaq.push([\'_trackEvent\', \'%s\', \'featured-tweet-action\', \'hashtag\', 0, \'%s\']);">%s</a>' % (hashtag['text'], app_config.PROJECT_SLUG, tweet_url, '#%s' % hashtag['text']) subs[original] = replacement for original, replacement in subs.items(): html = html.replace(original, replacement) # https://dev.twitter.com/docs/api/1.1/get/statuses/show/%3Aid tweets.append({ 'id': tweet['id'], 'url': tweet_url, 'html': html, 'favorite_count': tweet['favorite_count'], 'retweet_count': tweet['retweet_count'], 'user': { 'id': tweet['user']['id'], 'name': tweet['user']['name'], 'screen_name': tweet['user']['screen_name'], 'profile_image_url': tweet['user']['profile_image_url'], 'url': tweet['user']['url'], }, 'creation_date': creation_date, 'photo': photo }) # Facebook print 'Fetching Facebook posts...' fb_api = GraphAPI(secrets['FACEBOOK_API_APP_TOKEN']) facebook_posts = [] for i in range(1, 4): fb_url = COPY['share']['featured_facebook%i' % i] if isinstance(fb_url, copytext.Error) or unicode(fb_url).strip() == '': continue fb_id = unicode(fb_url).split('/')[-1] post = fb_api.get_object(fb_id) user = fb_api.get_object(post['from']['id']) user_picture = fb_api.get_object('%s/picture' % post['from']['id']) likes = fb_api.get_object('%s/likes' % fb_id, summary='true') comments = fb_api.get_object('%s/comments' % fb_id, summary='true') #shares = fb_api.get_object('%s/sharedposts' % fb_id) creation_date = datetime.strptime(post['created_time'],'%Y-%m-%dT%H:%M:%S+0000') creation_date = '%s %i' % (creation_date.strftime('%b'), creation_date.day) # https://developers.facebook.com/docs/graph-api/reference/v2.0/post facebook_posts.append({ 'id': post['id'], 'message': post['message'], 'link': { 'url': post['link'], 'name': post['name'], 'caption': (post['caption'] if 'caption' in post else None), 'description': post['description'], 'picture': post['picture'] }, 'from': { 'name': user['name'], 'link': user['link'], 'picture': user_picture['url'] }, 'likes': likes['summary']['total_count'], 'comments': comments['summary']['total_count'], #'shares': shares['summary']['total_count'], 'creation_date': creation_date }) # Render to JSON output = { 'tweets': tweets, 'facebook_posts': facebook_posts } with open('data/featured.json', 'w') as f: json.dump(output, f)

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py

Python

dust/admin.py

MerlinEmris/eBazar

f159314183a8a95afd97d36b0d3d8cf22015a512

[ "MIT" ]

null

dust/admin.py

MerlinEmris/eBazar

f159314183a8a95afd97d36b0d3d8cf22015a512

[ "MIT" ]

null

dust/admin.py

MerlinEmris/eBazar

f159314183a8a95afd97d36b0d3d8cf22015a512

[ "MIT" ]

null

from django.utils.html import format_html def full_address(self): return format_html('%s - <b>%s,%s</b>' % (self.address, self.city, self.state)) # null derek yazylmaly hat admin.site.empty_value_display = '???' admin.site.register(Item) # str funksivany ady bilen gorkezyar class ItemAdmin(admin.ModelAdmin): list_display = ['name', '__str__'] # ozine gora hat chykarmat class StoreAdmin(admin.ModelAdmin): list_display = ['name', 'address', 'upper_case_city_state'] def upper_case_city_state(self, obj): return ("%s %s" % (obj.city, obj.state)).upper() upper_case_city_state.short_description = 'City/State' # email domain ady gaytar class Store(models.Model): name = models.CharField(max_length=30) email = models.EmailField() def email_domain(self): return self.email.split("@")[-1] email_domain.short_description = 'Email domain' class StoreAdmin(admin.ModelAdmin): list_display = ['name','email_domain'] # how to sort manually created field that related with db # models.py from django.db import models from django.utils.html import format_html class Store(models.Model): name = models.CharField(max_length=30) address = models.CharField(max_length=30,unique=True) city = models.CharField(max_length=30) state = models.CharField(max_length=2) def full_address(self): return format_html('%s - <b>%s,%s</b>' % (self.address,self.city,self.state)) full_address.admin_order_field = '-city' # admin.py from django.contrib import admin from coffeehouse.stores.models import Store class StoreAdmin(admin.ModelAdmin): list_display = ['name','full_address'] # gerekli column link goyyar list_display_links = ['name', 'user', 'location', 'price'] # filtr ulananda detail girip chykanda filtirsyz edip gorkezyar preserve_filters = False #doredilen wagtyna gora filtrlemek uchin date_hierarchy = 'created' # yokarda yerleshen action manu-ny ayyryar actions_on_top = False #show only this fields fields = ['address','city','state','email'] # changing type of field formfield_overrides = { models.CharField: {'widget': forms.Textarea} } # fills address field with sluged type of city and state field prepopulated_fields = {'address': ['city','state']} # create button that clone the record save_as = True save_as_continue = False #after cloning go to main page # go to the page ayyryar view_on_site = False # if you want manually enter foreignkey ang manytomanyfield values raw_id_fields = ["menu"] #show foreignkeys and manytomanyfield like radio button radio_fields = {"location": admin.HORIZONTAL} # change admin form for user type class MyModelAdmin(admin.ModelAdmin): def get_form(self, request, obj=None, **kwargs): if request.user.is_superuser: kwargs['form'] = MySuperuserForm return super(MyModelAdmin, self).get_form(request, obj, **kwargs) #foreignkey values according to user class MyModelAdmin(admin.ModelAdmin): def formfield_for_foreignkey(self, db_field, request, **kwargs): if db_field.name == "car": kwargs["queryset"] = Car.objects.filter(owner=request.user) return super(MyModelAdmin, self).formfield_for_foreignkey(db_field, request, **kwargs) # manytomanyfield values according to user class MyModelAdmin(admin.ModelAdmin): def formfield_for_manytomany(self, db_field, request, **kwargs): if db_field.name == "cars": kwargs["queryset"] = Car.objects.filter(owner=request.user) return super(MyModelAdmin, self).formfield_for_manytomany(db_field, request, **kwargs) #calls this after admin delete def response_delete(request, obj_display, obj_id): Determines the HttpResponse for the delete_view() stage. response_delete is called after the object has been deleted. You can override it to change the default behavior after the object has been deleted. obj_display is a string with the name of the deleted object. obj_id is the serialized identifier used to retrieve the object to be deleted. # colored admin field from django.db import models from django.contrib import admin from django.utils.html import format_html class Person(models.Model): first_name = models.CharField(max_length=50) color_code = models.CharField(max_length=6) def colored_first_name(self): return format_html( '<span style="color: #{};">{}</span>', self.color_code, self.first_name, ) colored_first_name.admin_order_field = 'first_name' class PersonAdmin(admin.ModelAdmin): list_display = ('first_name', 'colored_first_name') #cvbsxfgbsfdgs list_select_related = ('organization', 'user')

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628f60f6980f2fba69cda100a9a49fdeb649e134

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py

Python

notebook/dict_keys_values_items.py

vhn0912/python-snippets

80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038

[ "MIT" ]

174

2018-05-30T21:14:50.000Z

2022-03-25T07:59:37.000Z

notebook/dict_keys_values_items.py

vhn0912/python-snippets

80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038

[ "MIT" ]

5

2019-08-10T03:22:02.000Z

2021-07-12T20:31:17.000Z

notebook/dict_keys_values_items.py

vhn0912/python-snippets

80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038

[ "MIT" ]

53

2018-04-27T05:26:35.000Z

2022-03-25T07:59:37.000Z

d = {'key1': 1, 'key2': 2, 'key3': 3} for k in d: print(k) # key1 # key2 # key3 for k in d.keys(): print(k) # key1 # key2 # key3 keys = d.keys() print(keys) print(type(keys)) # dict_keys(['key1', 'key2', 'key3']) # <class 'dict_keys'> k_list = list(d.keys()) print(k_list) print(type(k_list)) # ['key1', 'key2', 'key3'] # <class 'list'> for v in d.values(): print(v) # 1 # 2 # 3 values = d.values() print(values) print(type(values)) # dict_values([1, 2, 3]) # <class 'dict_values'> v_list = list(d.values()) print(v_list) print(type(v_list)) # [1, 2, 3] # <class 'list'> for k, v in d.items(): print(k, v) # key1 1 # key2 2 # key3 3 for t in d.items(): print(t) print(type(t)) print(t[0]) print(t[1]) print('---') # ('key1', 1) # <class 'tuple'> # key1 # 1 # --- # ('key2', 2) # <class 'tuple'> # key2 # 2 # --- # ('key3', 3) # <class 'tuple'> # key3 # 3 # --- items = d.items() print(items) print(type(items)) # dict_items([('key1', 1), ('key2', 2), ('key3', 3)]) # <class 'dict_items'> i_list = list(d.items()) print(i_list) print(type(i_list)) # [('key1', 1), ('key2', 2), ('key3', 3)] # <class 'list'> print(i_list[0]) print(type(i_list[0])) # ('key1', 1) # <class 'tuple'>

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3.20098

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0.657143

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null

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null

0

1

0

1

65599b2db0af8388cda22867211e56c2902c85cb

3,815

py

Python

experiments/vitchyr/icml2017/watermaze_memory/generate_bellman_ablation_figure_data.py

Asap7772/rail-rl-franka-eval

4bf99072376828193d05b53cf83c7e8f4efbd3ba

[ "MIT" ]

null

experiments/vitchyr/icml2017/watermaze_memory/generate_bellman_ablation_figure_data.py

Asap7772/rail-rl-franka-eval

4bf99072376828193d05b53cf83c7e8f4efbd3ba

[ "MIT" ]

null

experiments/vitchyr/icml2017/watermaze_memory/generate_bellman_ablation_figure_data.py

Asap7772/rail-rl-franka-eval

4bf99072376828193d05b53cf83c7e8f4efbd3ba

[ "MIT" ]

null

""" Generate data for ablation analysis for ICML 2017 workshop paper. """ import random from torch.nn import functional as F from railrl.envs.pygame.water_maze import ( WaterMazeMemory, ) from railrl.exploration_strategies.ou_strategy import OUStrategy from railrl.launchers.launcher_util import ( run_experiment, ) from railrl.launchers.memory_bptt_launchers import bptt_ddpg_launcher from railrl.pythonplusplus import identity from railrl.memory_states.qfunctions import MemoryQFunction from railrl.torch.rnn import GRUCell if __name__ == '__main__': n_seeds = 1 mode = "here" exp_prefix = "dev-generate-bellman-ablation-figure-data" run_mode = 'none' n_seeds = 5 mode = "ec2" exp_prefix = "generate-bellman_ablation-figure-data" use_gpu = True if mode != "here": use_gpu = False H = 25 subtraj_length = None num_steps_per_iteration = 1000 num_steps_per_eval = 1000 num_iterations = 100 batch_size = 100 memory_dim = 100 version = "Our Method" # noinspection PyTypeChecker variant = dict( memory_dim=memory_dim, env_class=WaterMazeMemory, env_params=dict( horizon=H, give_time=True, ), memory_aug_params=dict( max_magnitude=1, ), algo_params=dict( subtraj_length=subtraj_length, batch_size=batch_size, num_epochs=num_iterations, num_steps_per_epoch=num_steps_per_iteration, num_steps_per_eval=num_steps_per_eval, discount=0.9, use_action_policy_params_for_entire_policy=False, action_policy_optimize_bellman=False, write_policy_optimizes='bellman', action_policy_learning_rate=0.001, write_policy_learning_rate=0.0005, qf_learning_rate=0.002, max_path_length=H, refresh_entire_buffer_period=None, save_new_memories_back_to_replay_buffer=True, write_policy_weight_decay=0, action_policy_weight_decay=0, do_not_load_initial_memories=False, save_memory_gradients=False, ), qf_class=MemoryQFunction, qf_params=dict( output_activation=identity, fc1_size=400, fc2_size=300, ignore_memory=False, ), policy_params=dict( fc1_size=400, fc2_size=300, cell_class=GRUCell, output_activation=F.tanh, only_one_fc_for_action=False, ), es_params=dict( env_es_class=OUStrategy, env_es_params=dict( max_sigma=1, min_sigma=None, ), memory_es_class=OUStrategy, memory_es_params=dict( max_sigma=1, min_sigma=None, ), ), version=version, ) for subtraj_length in [1, 5, 10, 15, 20, 25]: variant['algo_params']['subtraj_length'] = subtraj_length for exp_id, ( write_policy_optimizes, version, ) in enumerate([ ("bellman", "Bellman Error"), ("qf", "Q-Function"), ("both", "Both"), ]): variant['algo_params']['write_policy_optimizes'] = ( write_policy_optimizes ) variant['version'] = version for _ in range(n_seeds): seed = random.randint(0, 10000) run_experiment( bptt_ddpg_launcher, exp_prefix=exp_prefix, seed=seed, mode=mode, variant=variant, exp_id=exp_id, )

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null

0

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null

0

1

655e6832b1d17e6f32fa6e6e3a8b08c294e9b6de

4,146

py

Python

jspp_imageutils/image/chunking.py

jspaezp/jspp_imageutils

6376e274a1b0675622a7979c181b9effc125aa09

[ "Apache-2.0" ]

null

jspp_imageutils/image/chunking.py

jspaezp/jspp_imageutils

6376e274a1b0675622a7979c181b9effc125aa09

[ "Apache-2.0" ]

null

jspp_imageutils/image/chunking.py

jspaezp/jspp_imageutils

6376e274a1b0675622a7979c181b9effc125aa09

[ "Apache-2.0" ]

null

import itertools import numpy as np from jspp_imageutils.image.types import GenImgArray, GenImgBatch from typing import Tuple, Iterable, Iterator # TODO: fix everywhere the x and y axis nomenclature """ chunk_image_on_position -> returns images chunk_image_generator -> returns images chunk_data_image_generator -> returns batches of data """ def chunk_image_on_position(arr_img: GenImgArray, x_pos: Iterable[int], y_pos: Iterable[int], dimensions: Tuple[int, int] = (50, 50), warn_leftovers=True) -> \ Iterator[Tuple[int, int, GenImgArray]]: # TODO decide if this should handle centering the points ... x_ends = [x + dimensions[0] for x in x_pos] y_ends = [y + dimensions[1] for y in y_pos] i = 0 # TODO find a better way to indent this ... for y_start, y_end, x_start, x_end in \ zip(y_pos, y_ends, x_pos, x_ends): temp_arr_img = arr_img[x_start:x_end, y_start:y_end, ] if temp_arr_img.shape[0:2] == dimensions: yield x_start, y_start, temp_arr_img i += 1 else: if warn_leftovers: print("skipping chunk due to weird size", str(temp_arr_img.shape)) print("Image generator yielded ", str(i), " images") def chunk_image_generator(img, chunk_size: Tuple[int, int] = (500, 500), displacement: Tuple[int, int] = (250, 250), warn_leftovers=True) -> \ Iterator[Tuple[int, int, GenImgArray]]: """ Gets an image read with tensorflow.keras.preprocessing.image.load_img and returns a generator that iterates over rectangular areas of it. chunks are of dims (chunk_size, colors) """ # TODO unify the input for this guy ... arr_img = np.asarray(img) dims = arr_img.shape x_starts = [ displacement[0] * x for x in range(dims[0] // displacement[0]) ] x_starts = [x for x in x_starts if x >= 0 & (x + chunk_size[0]) < dims[0]] y_starts = [ displacement[1] * y for y in range(dims[1] // displacement[1]) ] y_starts = [y for y in y_starts if y >= 0 & (y + chunk_size[1]) < dims[1]] coord_pairs = itertools.product(x_starts, y_starts) coord_pairs = np.array(list(coord_pairs)) my_gen = chunk_image_on_position( arr_img, coord_pairs[:, 0], coord_pairs[:, 1], dimensions=chunk_size, warn_leftovers=warn_leftovers) for chunk in my_gen: yield(chunk) def chunk_data_image_generator(img: GenImgArray, chunk_size: Tuple[int, int] = (500, 500), displacement: Tuple[int, int] = (250, 250), batch: int = 16) -> GenImgBatch: """ chunk_data_image_generator [summary] Gets an image read with tensorflow.keras.preprocessing.image.load_img and returns a generator that iterates over BATCHES of rectangular areas of it dimensions are (batch, chunk_size, colors) :param img: [description] :type img: GenImgArray :param chunk_size: [description], defaults to (500, 500) :type chunk_size: Tuple[int, int], optional :param displacement: [description], defaults to (250, 250) :type displacement: Tuple[int, int], optional :param batch: [description], defaults to 16 :type batch: int, optional :return: [description] :rtype: GenImgBatch """ # np.concatenate((a1, a2)) img_generator = chunk_image_generator( img=img, chunk_size=chunk_size, displacement=displacement) counter = 0 img_buffer = [] for _, _, temp_arr_img in img_generator: tmp_arr_dims = temp_arr_img.shape temp_arr_img = temp_arr_img.reshape(1, *tmp_arr_dims) img_buffer.append(temp_arr_img) counter += 1 if counter == batch: yield(np.concatenate(img_buffer)) counter = 0 img_buffer = [] yield(np.concatenate(img_buffer))

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0

null

0

null

0

1

0

1

655fb86e683d32b0ac543bc333e3c26a2dfbef4d

1,256

py

Python

modules/transfer/scripts/info.py

sishuiliunian/falcon-plus

eb6e2a5c29b26812601535cec602b33ee42b0632

[ "Apache-2.0" ]

7,208

2017-01-15T08:32:54.000Z

2022-03-31T14:09:04.000Z

modules/transfer/scripts/info.py

sishuiliunian/falcon-plus

eb6e2a5c29b26812601535cec602b33ee42b0632

[ "Apache-2.0" ]

745

2017-01-17T06:55:21.000Z

2022-03-28T03:33:45.000Z

modules/transfer/scripts/info.py

sishuiliunian/falcon-plus

eb6e2a5c29b26812601535cec602b33ee42b0632

[ "Apache-2.0" ]

1,699

2017-01-11T09:16:44.000Z

2022-03-29T10:40:31.000Z

import requests # Copyright 2017 Xiaomi, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json d = [ { "endpoint": "hh-op-mon-tran01.bj", "counter": "load.15min", }, { "endpoint": "hh-op-mon-tran01.bj", "counter": "net.if.in.bytes/iface=eth0", }, { "endpoint": "10.202.31.14:7934", "counter": "p2-com.xiaomi.miui.mibi.service.MibiService-method-createTradeV1", }, ] url = "http://query.falcon.miliao.srv:9966/graph/info" r = requests.post(url, data=json.dumps(d)) print r.text #curl "localhost:9966/graph/info/one?endpoint=`hostname`&counter=load.1min" |python -m json.tool

32.205128

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1,256

4.714286

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0.075758

0.032828

0.040404

0.075758

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0.041314

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1,256

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null

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0

null

0

null

0

1

0

1

65710b598ac66d11ae7f738f8a51d26c406a0a31

6,525

py

Python

cron/sync_cs_schedule.py

vovagalchenko/onsite-inflight

7acd4bc6a12b89ab09b465a81ae495bef35bab0a

[ "MIT" ]

null

cron/sync_cs_schedule.py

vovagalchenko/onsite-inflight

7acd4bc6a12b89ab09b465a81ae495bef35bab0a

[ "MIT" ]

1

2016-05-24T00:00:10.000Z

cron/sync_cs_schedule.py

vovagalchenko/onsite-inflight

7acd4bc6a12b89ab09b465a81ae495bef35bab0a

[ "MIT" ]

null

#!/usr/bin/env python import sys import pprint from model.cs_rep import CS_Rep from pytz import timezone, utc from datetime import datetime, timedelta from lib.calendar import Google_Calendar, google_ts_to_datetime, DEFAULT_DATE, LOS_ANGELES_TZ from lib.conf import CFG from model.db_session import DB_Session_Factory from json import dumps, loads from oauth2client.client import AccessTokenRefreshError from apiclient.errors import HttpError from pytz import timezone import pdb import re target_weekday = 3 # Thursday target_timerange = [12, 14] target_calendar_id = "box.com_gk9hfef9s7fulrq0t3mftrvevk@group.calendar.google.com" def get_ts_from_event(event, ts_key): return google_ts_to_datetime(event.get(ts_key, {}).get('dateTime', DEFAULT_DATE)) def main(argv): calendar = Google_Calendar.get_calendar() db_session = DB_Session_Factory.get_db_session() now = datetime.now() today_weekday = now.weekday() next_target_weekday = now + timedelta(days = (target_weekday - today_weekday + 6)%7 + 1) la_timezone = timezone(LOS_ANGELES_TZ) start_period_naive = datetime(next_target_weekday.year, next_target_weekday.month, next_target_weekday.day, target_timerange[0]) start_period = la_timezone.localize(start_period_naive) end_period_naive = datetime(next_target_weekday.year, next_target_weekday.month, next_target_weekday.day, target_timerange[1]) end_period = la_timezone.localize(end_period_naive) print str(start_period) + " - " + str(end_period) try: cs_rep_list = db_session.query(CS_Rep).order_by(CS_Rep.email) source_events = {} for cs_rep in cs_rep_list: current_period_start = start_period_naive current_period_end = start_period_naive + timedelta(hours = 1) print "Checking calendar for " + cs_rep.name source_events_request = calendar.service.events().list(calendarId = cs_rep.email, timeZone = LOS_ANGELES_TZ, timeMin = start_period.isoformat(), timeMax = end_period.isoformat(), orderBy = 'startTime', singleEvents = True, maxAttendees = 1000) while (source_events_request != None): response = source_events_request.execute(calendar.http) for event in response.get('items', []): summary = event.get('summary', '') start_time = get_ts_from_event(event, 'start') end_time = get_ts_from_event(event, 'end') if start_time < start_period_naive or end_time > end_period_naive or start_time < current_period_start or end_time - start_time > timedelta(hours=1): continue while current_period_end < end_time: current_period_start = current_period_start + timedelta(hours = 1) current_period_end = current_period_end + timedelta(hours = 1) match = re.search("\*$", summary) if match: source_events[event['id']] = event current_period_start = current_period_start + timedelta(hours = 1) current_period_end = current_period_end + timedelta(hours = 1) else: print "no match: " + summary source_events_request = calendar.service.events().list_next(source_events_request, response) to_delete = [] to_update = {} target_events_request = calendar.service.events().list(calendarId = target_calendar_id, timeZone = LOS_ANGELES_TZ, timeMin = start_period.isoformat(), timeMax = end_period.isoformat(), orderBy = 'startTime', singleEvents = True) while (target_events_request != None): response = target_events_request.execute(calendar.http) for event in response.get('items', []): source_event = source_events.get(event['id'], None) if source_event is None: to_delete.append(event) else: to_update[event['id']] = {'before' : event, 'after' : source_events[event['id']].copy()} del source_events[event['id']] target_events_request = calendar.service.events().list_next(target_events_request, response) for event in to_delete: print "Removing: " + event.get('summary', "") calendar.service.events().delete(calendarId = target_calendar_id, eventId = event['id']).execute(calendar.http) for event_id in to_update: original_event = to_update[event_id]['before'] original_start = get_ts_from_event(original_event, 'start') original_end = get_ts_from_event(original_event, 'end') after_event = to_update[event_id]['after'] after_start = get_ts_from_event(after_event, 'start') after_end = get_ts_from_event(after_event, 'end') if original_start != after_start or original_end != after_end: original_event['start'] = after_event['start'] original_event['end'] = after_event['end'] print "Updating: " + original_event.get('summary', "") calendar.service.events().update(calendarId = target_calendar_id, eventId = event_id, body = original_event).execute(calendar.http) for event_id in source_events: source_event = source_events[event_id] print "Adding: " + source_event.get('summary', "") source_event['organizer'] = {'self' : True} source_event['location'] = '4440-3-4 The Marina' while True: try: calendar.service.events().import_(calendarId = target_calendar_id, body = source_event).execute(calendar.http) break except HttpError as e: error_data = loads(e.content) print error_data['error']['code'] if error_data.get('error', {'code' : None}).get('code', None) == 400: source_event['sequence'] += 1 else: sys.stderr.write("HTTP Error: " + e.content) exit(1) except AccessTokenRefreshError: print ("The credentials have been revoked or expired, please re-run" "the application to re-authorize") if __name__ == '__main__': main(sys.argv)

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6,525

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0

null

0

null

0

1

0

1

657d1df2ec7237f7821e8629ba8c0b4d674b5456

2,406

py

Python

app/core/tests/test_admin.py

ido777/newish

298a3d5babf411ba1eb777101eb6e8f70b9e495f

[ "MIT" ]

null

app/core/tests/test_admin.py

ido777/newish

298a3d5babf411ba1eb777101eb6e8f70b9e495f

[ "MIT" ]

null

app/core/tests/test_admin.py

ido777/newish

298a3d5babf411ba1eb777101eb6e8f70b9e495f

[ "MIT" ]

null

import pytest from django.urls import reverse @pytest.mark.skip(reason="WIP moving to pytest tests") def test_with_authenticated_client(client, django_user_model): email = 'admin@somewhere.com' password = 'password123' admin_user = django_user_model.objects.create_superuser( email, password) client.force_login(user=admin_user) user = django_user_model.objects.create_user('user@somewhere.com', password='password123', name='Test user full name') url = reverse('admin:core_user_changelist') res = client.get(url) assert user.name in res assert user.email in res def test_user_page_change(client, django_user_model): """Test that the user edit page works""" email = 'admin@somewhere.com' password = 'password123' admin_user = django_user_model.objects.create_superuser( email, password) client.force_login(user=admin_user) user = django_user_model.objects.create_user('user@somewhere.com', password='password123', name='Test user full name') url = reverse('admin:core_user_change', args=[user.id]) res = client.get(url) assert res.status_code == 200 def test_create_user_page(client, django_user_model): """Test that the create user page works""" email = 'admin@somewhere.com' password = 'password123' admin_user = django_user_model.objects.create_superuser( email, password) client.force_login(user=admin_user) url = reverse('admin:core_user_add') res = client.get(url) assert res.status_code == 200 ''' @pytest.mark.django_db def test_user_create(): User.objects.create_user('user@somewhere.com', password='password123', name='Test user full name') assert User.objects.count() == 1 @pytest.mark.parametrize( 'admin, user, client', get_user_model().objects.create_superuser( 'admin@somewhere.com', password='password123'), get_user_model().objects.create_user( 'user@somewhere.com', password='password123', name='Test user full name'), Client() ) @pytest.mark.db def test_users_listed(admin, user, client): """Test that users are listed on the user page """ url = reverse('admin:core_user_changelist') res = client.get(url) assert user.name in res assert user.email in res '''

31.246753

102

0.676226

310

2,406

5.051613

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0.057471

0.076628

0.158365

0.747765

0.686462

0.645594

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0

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0.211554

2,406

76

103

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0.809172

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0

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0.030789

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1

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false

0.228571

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0

0.142857

0

null

0

null

0

1

0

1

6582ec795f9be718fba1c563c5c66e44261c6ce1

3,053

py

Python

tests/bugs/core_4160_test.py

reevespaul/firebird-qa

98f16f425aa9ab8ee63b86172f959d63a2d76f21

[ "MIT" ]

null

tests/bugs/core_4160_test.py

reevespaul/firebird-qa

98f16f425aa9ab8ee63b86172f959d63a2d76f21

[ "MIT" ]

null

tests/bugs/core_4160_test.py

reevespaul/firebird-qa

98f16f425aa9ab8ee63b86172f959d63a2d76f21

[ "MIT" ]

null

#coding:utf-8 # # id: bugs.core_4160 # title: Parameterized exception does not accept not ASCII characters as parameter # decription: # tracker_id: CORE-4160 # min_versions: ['3.0'] # versions: 3.0 # qmid: None import pytest from firebird.qa import db_factory, isql_act, Action # version: 3.0 # resources: None substitutions_1 = [('-At procedure.*', '')] init_script_1 = """ create or alter procedure sp_alert(a_lang char(2), a_new_amount int) as begin end; commit; recreate exception ex_negative_remainder ' @1 (@2)'; commit; """ db_1 = db_factory(page_size=4096, charset='UTF8', sql_dialect=3, init=init_script_1) test_script_1 = """ set term ^; create or alter procedure sp_alert(a_lang char(2), a_new_amount int) as begin if (a_lang = 'cz') then exception ex_negative_remainder using ('Czech: New Balance bude menší než nula', a_new_amount); else if (a_lang = 'pt') then exception ex_negative_remainder using ('Portuguese: New saldo será menor do que zero', a_new_amount); else if (a_lang = 'dm') then exception ex_negative_remainder using ('Danish: New Balance vil være mindre end nul', a_new_amount); else if (a_lang = 'gc') then exception ex_negative_remainder using ('Greek: Νέα ισορροπία θα είναι κάτω από το μηδέν', a_new_amount); else if (a_lang = 'fr') then exception ex_negative_remainder using ('French: Nouveau solde sera inférieur à zéro', a_new_amount); else exception ex_negative_remainder using ('Russian: Новый остаток будет меньше нуля', a_new_amount); end ^ set term ;^ commit; execute procedure sp_alert('cz', -1); execute procedure sp_alert('pt', -2); execute procedure sp_alert('dm', -3); execute procedure sp_alert('gc', -4); execute procedure sp_alert('fr', -5); execute procedure sp_alert('jp', -6); """ act_1 = isql_act('db_1', test_script_1, substitutions=substitutions_1) expected_stderr_1 = """ Statement failed, SQLSTATE = HY000 exception 1 -EX_NEGATIVE_REMAINDER - Czech: New Balance bude menší než nula (-1) Statement failed, SQLSTATE = HY000 exception 1 -EX_NEGATIVE_REMAINDER - Portuguese: New saldo será menor do que zero (-2) Statement failed, SQLSTATE = HY000 exception 1 -EX_NEGATIVE_REMAINDER - Danish: New Balance vil være mindre end nul (-3) Statement failed, SQLSTATE = HY000 exception 1 -EX_NEGATIVE_REMAINDER - Greek: Νέα ισορροπία θα είναι κάτω από το μηδέν (-4) Statement failed, SQLSTATE = HY000 exception 1 -EX_NEGATIVE_REMAINDER - French: Nouveau solde sera inférieur à zéro (-5) Statement failed, SQLSTATE = HY000 exception 1 -EX_NEGATIVE_REMAINDER - Russian: Новый остаток будет меньше нуля (-6) """ @pytest.mark.version('>=3.0') def test_1(act_1: Action): act_1.expected_stderr = expected_stderr_1 act_1.execute() assert act_1.clean_expected_stderr == act_1.clean_stderr

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null

0

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null

0

1

6584791fe17e82f5787899fa97ce0db3fa35bfb0

1,535

py

Python

uhelpers/tests/test_archive_helpers.py

Johannes-Sahlmann/uhelpers

58f8e25ef8644ab5b24a5be76fd58a338a400912

[ "BSD-3-Clause" ]

null

uhelpers/tests/test_archive_helpers.py

Johannes-Sahlmann/uhelpers

58f8e25ef8644ab5b24a5be76fd58a338a400912

[ "BSD-3-Clause" ]

2

2020-12-21T18:08:48.000Z

2021-01-26T01:24:39.000Z

uhelpers/tests/test_archive_helpers.py

Johannes-Sahlmann/uhelpers

58f8e25ef8644ab5b24a5be76fd58a338a400912

[ "BSD-3-Clause" ]

5

2019-10-02T14:16:15.000Z

2021-12-27T18:46:18.000Z

#!/usr/bin/env python """Tests for the jwcf hawki module. Authors ------- Johannes Sahlmann """ import netrc import os from astropy.table import Table import pytest from ..archive_helpers import get_exoplanet_orbit_database, gacs_list_query local_dir = os.path.dirname(os.path.abspath(__file__)) ON_TRAVIS = os.environ.get('TRAVIS') == 'true' @pytest.mark.skipif(ON_TRAVIS, reason='timeout issue.') def test_eod(): """Test the access to the exoplanet orbit database.""" catalog = get_exoplanet_orbit_database(local_dir, verbose=False) assert len(catalog) > 100 @pytest.mark.skipif(ON_TRAVIS, reason='Requires access to .netrc file.') def test_gacs_list_query(): # print('test gacs list query') # Define which host in the .netrc file to use HOST = 'http://gea.esac.esa.int' # Read from the .netrc file in your home directory secrets = netrc.netrc() username, account, password = secrets.authenticators(HOST) out_dir = os.path.dirname(__file__) T = Table() id_str_input_table = 'ID_HIP' T[id_str_input_table] = [1, 2, 3, 4, 5, 6, 7] gacs_table_name = 'tgas_source' id_str_gacs_table = 'hip' input_table_name = 'hip_star_list' input_table = os.path.join(out_dir, 'hip_star_list.vot') T[[id_str_input_table]].write(input_table, format='votable', overwrite=1) T_out = gacs_list_query(username, password, out_dir, input_table, input_table_name, gacs_table_name, id_str_gacs_table, id_str_input_table) T_out.pprint()

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null

0

null

0

1

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1

6584f2d684176e56a028fa83fba17e1495411607

1,264

py

Python

TP3/test.py

paul-arthurthiery/IAMethodesAlgos

f49fe17c278424588df263ab0e6778721cbc4394

[ "MIT" ]

null

TP3/test.py

paul-arthurthiery/IAMethodesAlgos

f49fe17c278424588df263ab0e6778721cbc4394

[ "MIT" ]

null

TP3/test.py

paul-arthurthiery/IAMethodesAlgos

f49fe17c278424588df263ab0e6778721cbc4394

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Dec 2 14:33:13 2018 @author: Nathan """ import numpy as np from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # load dataset data,target =load_iris().data,load_iris().target # split data in train/test sets X_train, X_test, y_train, y_test = train_test_split( data, target, test_size=0.33, random_state=42) # standardize columns using normal distribution # fit on X_train and not on X_test to avoid Data Leakage s = StandardScaler() X_train = s.fit_transform(X_train) X_test = s.transform(X_test) from SoftmaxClassifier import SoftmaxClassifier # import the custom classifier cl = SoftmaxClassifier() # train on X_train and not on X_test to avoid overfitting train_p = cl.fit_predict(X_train,y_train) test_p = cl.predict(X_test) from sklearn.metrics import precision_recall_fscore_support # display precision, recall and f1-score on train/test set print("train : "+ str(precision_recall_fscore_support(y_train, train_p,average = "macro"))) print("test : "+ str(precision_recall_fscore_support(y_test, test_p,average = "macro"))) import matplotlib.pyplot as plt plt.plot(cl.losses_) plt.show()

26.333333

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0.105263

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null

0

null

0

1

0

1

6587d36784219790a446003c11e770c4bed4d07f

8,409

py

Python

ratin_cpython/common/common.py

openearth/eo-rivers

752f90aed92fa862a2c107bb58bcae298c1bf313

[ "MIT" ]

2

2018-10-19T03:20:08.000Z

2020-05-06T22:56:20.000Z

ratin_cpython/common/common.py

openearth/eo-river

752f90aed92fa862a2c107bb58bcae298c1bf313

[ "MIT" ]

11

2018-06-05T09:41:15.000Z

2021-11-15T17:47:27.000Z

ratin_cpython/common/common.py

openearth/eo-rivers

752f90aed92fa862a2c107bb58bcae298c1bf313

[ "MIT" ]

2

2020-10-15T12:29:36.000Z

2021-12-13T22:53:58.000Z

import numpy as np from math import factorial import scipy.signal #Gaussian filter with convolution - faster and easier to handle ## Degree is equal to the number of values left and right of the central value ## of the gaussian window: ## ie degree=3 yields a window of length 7 ## It uses normalized weights (sum of weights = 1) ## Based on: ## http://en.wikipedia.org/wiki/Gaussian_filter ## http://en.wikipedia.org/wiki/Standard_deviation ## http://en.wikipedia.org/wiki/Window_function#Gaussian_window def smooth(array_in, degree=5): ''' Gaussian smooth line using a window of specified degree (=half-length) ''' degree = int(degree) #make sure it is of integer type n = 2*degree+1 if degree <= 0: return array_in if type(array_in) == type(np.array([])) and len(array_in.shape)>1: array_in = array_in.flatten() array_in = list(array_in) # If degree is larger than twice the original data, make it smaller if len(array_in) < n: degree = len(array_in)/2 n = 2*degree+1 print "Changed smoothing degree to:",degree #extend the array's initial and ending values with equal ones, accordingly array_in = np.array( [array_in[0]]*degree + array_in + [array_in[-1]]*degree ) #TODO: These parameters are subject to change - depends on the implementation # Gaussian parameters: x = np.linspace(-degree,degree,n) sigma = np.sqrt( sum( (x-np.mean(x))**2 ) / n ) alpha = 1.0 / (2.0 * sigma**2) weight = np.sqrt(alpha/np.pi) * np.exp(-alpha*x**2 ) #gaussian weights = weight / sum(weight) #normalize return np.convolve(array_in, weights, 'valid') #TODO: revise #Gaussian 2D smoothing, anisotropic ## http://homepages.inf.ed.ac.uk/rbf/HIPR2/gsmooth.htm def smooth2D(matrix_in, fill, degree=5, sigma=2.0, a=1.0, b=1.0): ''' Gaussian smooth matrix using a window of specified degree ''' kx, ky = np.arange(-degree,degree+1.0),np.arange(-degree,degree+1.0) kernel = np.zeros([kx.shape[0],ky.shape[0]]) for i in range(len(kx)): for j in range(len(ky)): kernel[i,j] = 1./(2*np.pi*sigma**2) * np.exp( -(b*kx[i]**2+a*ky[j]**2)/(2*sigma**2) ) kernel /= kernel.sum() matrix_out = scipy.signal.convolve2d(matrix_in, kernel, mode='same', fillvalue=fill) return matrix_out def get_direction(x, y, smoothdegree=0, units='degrees'): ''' Return direction (cartesian reference) of point The direction of each point is calculated as the mean of directions on both sides ''' #Calculate direction in RADIANS direction = np.array([]) #first point: Can determine direction only based on next point direction = np.append(direction,np.angle((x[1]-x[0])+(y[1]-y[0])*1j)) for j in range(1, len(x)-1): # Base direction on points before and after current point direction = np.append(direction,np.angle((x[j+1]-x[j-1])+(y[j+1]-y[j-1])*1j)) #last point: Can determine direction only based on previous point direction = np.append(direction,np.angle((x[-1]-x[-2])+(y[-1]-y[-2])*1j)) #fix 'jumps' in data direction = fix_angle_vector(direction) #Smoothing - do not perform if input degree is equal/less than 0.0 if smoothdegree <= 0.0: pass else: direction = smooth(direction, degree=smoothdegree) #TODO: Review! Do we need to confine it? #Limit the representation in the space of [0,2*pi] gaps = np.where(np.abs(direction) > np.radians(360.0))[0] direction[gaps] -= np.radians(360.0) if units=='radians': pass elif units == 'degrees': direction = np.degrees(direction) return direction def distance(p1, p2): """ Distance in between two points (given as tuples) """ dist = np.sqrt( (p2[0]-p1[0])**2 + (p2[1]-p1[1])**2 ) return dist def distance_matrix(x0, y0, x1, y1, aniso): """ Returns distances between points in a matrix formation. An anisotropy factor is set as input. If >1, the points in x direction shift closer. If <1, the points in x direction shift further apart. If =1, normal distances are computed. """ aniso = float(aniso) x0 = np.array(x0).flatten() y0 = np.array(y0).flatten() x1 = np.array(x1).flatten() y1 = np.array(y1).flatten() #transpose observations vertical = np.vstack((x0, y0)).T horizontal = np.vstack((x1, y1)).T # Make a distance matrix between pairwise observations # Note: from <http://stackoverflow.com/questions/1871536> if aniso<=0.0: print "Warning: Anisotropy factor cannot be 0 or negative; set to 1.0." aniso = 1.0 d0 = np.subtract.outer(vertical[:,0], horizontal[:,0]) * (1./aniso) d1 = np.subtract.outer(vertical[:,1], horizontal[:,1]) return np.hypot(d0, d1) #retrieve s values streamwise def get_chainage(x, y): """ Get chain distances for a set of continuous points """ s = np.array([0.0]) #start for j in range(1,len(x)): s = np.append( s, s[j-1] + distance([x[j-1],y[j-1]], [x[j],y[j]]) ) return s def to_sn(Gx, Gy): """ Transform (Gx,Gy) Cartesian coordinates to flow-oriented ones (Gs,Gn), where Gx and Gy stand for gridded x and gridded y, and Gs and Gn are their transformed counterparts. Gx,Gy,Gs,Gn are all numpy arrays in the form of matrices. """ rows, cols = Gx.shape #find s-direction coordinates midrow = int(rows/2) c_x = Gx[midrow,:] c_y = Gy[midrow,:] Salong = get_chainage(c_x,c_y) #all s-direction points have the same spacing Gs = np.tile(Salong, (rows,1)) #"stretch" all longitudinals #find n-direction coordinates Gn = np.zeros([rows,cols]) for j in range(cols): #for each column Gn[midrow::-1,j] = -get_chainage(Gx[midrow::-1,j],Gy[midrow::-1,j]) Gn[midrow:,j] = get_chainage(Gx[midrow:,j],Gy[midrow:,j]) return Gs, Gn def to_grid(data, rows, cols): """ Transform a list of data to a grid-like (matrix) form of specified shape """ data = np.array(data).flatten() return data.reshape(rows,cols) ##??['Brute-force' way but works correctly] def fix_angle_vector(theta): ''' Fixes a vector of angles (in radians) that show 'jumps' because of changes between 360 and 0 degrees ''' thetadiff = np.diff(theta) gaps = np.where(np.abs(thetadiff) > np.radians(180))[0] while len(gaps)>0: gap = gaps[0] if thetadiff[gap]<0: theta[gap+1:] += np.radians(360) else: theta[gap+1:] -= np.radians(360) thetadiff = np.diff(theta) gaps = np.where(np.abs(thetadiff) > np.radians(180))[0] return theta def get_parallel_line(x, y, direction, distance, units = 'degrees'): ''' Create parallel lines for representation of MAT path. ''' if units == 'degrees': direction = np.radians(direction) perpendicular_direction = np.array(direction)+0.5*np.pi xn = np.array(x)+np.array(distance)*np.array(np.cos(perpendicular_direction)) yn = np.array(y)+np.array(distance)*np.array(np.sin(perpendicular_direction)) return xn, yn #http://wiki.scipy.org/Cookbook/SavitzkyGolay def savitzky_golay(y, window_size, order, deriv=0, rate=1): try: window_size = np.abs(np.int(window_size)) order = np.abs(np.int(order)) except ValueError, msg: raise ValueError("window_size and order have to be of type int", msg) if window_size % 2 != 1 or window_size < 1: raise TypeError("window_size size must be a positive odd number") if window_size < order + 2: raise TypeError("window_size is too small for the polynomials order") order_range = range(order+1) half_window = (window_size -1) // 2 # precompute coefficients b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)]) m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv) # pad the signal at the extremes with # values taken from the signal itself firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] ) lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1]) y = np.concatenate((firstvals, y, lastvals)) return np.convolve( m[::-1], y, mode='valid')

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8,409

4.063467

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0.004571

0.008381

0.139048

0.115238

0.076381

0.05619

0.037714

0

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8,409

238

98

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0

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null

0.01626

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null

0.01626

0

null

0

null

0

1

0

1

658c87d29e07d35154d2bbcefbc473d8ad660860

1,152

py

Python

renovation_core_graphql/auth/otp.py

e-lobo/renovation_core_graphql

31e464e00badc308bf03c70364331b08ad9d1b1d

[ "MIT" ]

1

2021-12-15T06:05:06.000Z

renovation_core_graphql/auth/otp.py

e-lobo/renovation_core_graphql

31e464e00badc308bf03c70364331b08ad9d1b1d

[ "MIT" ]

5

2021-06-09T19:00:56.000Z

2022-01-23T09:51:13.000Z

renovation_core_graphql/auth/otp.py

e-lobo/renovation_core_graphql

31e464e00badc308bf03c70364331b08ad9d1b1d

[ "MIT" ]

1

2021-06-01T05:22:41.000Z

from graphql import GraphQLResolveInfo import frappe from renovation_core.utils.auth import generate_otp, verify_otp VERIFY_OTP_STATUS_MAP = { "no_linked_user": "NO_LINKED_USER", "no_otp_for_mobile": "NO_OTP_GENERATED", "invalid_otp": "INVALID_OTP", "verified": "VERIFIED", } def generate_otp_resolver(obj, info: GraphQLResolveInfo, **kwargs): r = generate_otp(**kwargs) r.status = "SUCCESS" if r.status == "success" else "FAILED" return r def verify_otp_resolver(obj, info: GraphQLResolveInfo, **kwargs): kwargs["login_to_user"] = 1 if kwargs.get("login_to_user") else 0 if kwargs["login_to_user"] and kwargs["use_jwt"]: frappe.local.form_dict.use_jwt = 1 del kwargs["use_jwt"] status_dict = verify_otp(**kwargs) status_dict.update(frappe.local.response) if status_dict.get("user"): status_dict["user"] = frappe._dict(doctype="User", name=status_dict["user"]) status = status_dict.get("status") if status in VERIFY_OTP_STATUS_MAP: status_dict.status = VERIFY_OTP_STATUS_MAP[status] else: status_dict.status = "FAILED" return status_dict

29.538462

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1,152

4.822785

0.316456

0.11811

0.059055

0.070866

0.173228

0.110236

0

0.003161

0.176215

1,152

38

85

30.315789

0.799789

0

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0

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false

0

0.107143

0

0.25

0

null

0

1

0

null

0

1

65923c69268087aca7de1d2a3dc4a13663164289

5,813

py

Python

imutils/big/make_shards.py

JacobARose/image-utils

aa0e005c0b4df5198d188b074f4e21f8d8f97962

[ "MIT" ]

null

imutils/big/make_shards.py

JacobARose/image-utils

aa0e005c0b4df5198d188b074f4e21f8d8f97962

[ "MIT" ]

null

imutils/big/make_shards.py

JacobARose/image-utils

aa0e005c0b4df5198d188b074f4e21f8d8f97962

[ "MIT" ]

null

""" imutils/big/make_shards.py Generate one or more webdataset-compatible tar archive shards from an image classification dataset. Based on script: https://github.com/tmbdev-archive/webdataset-examples/blob/7f56e9a8b978254c06aa0a98572a1331968b0eb3/makeshards.py Added on: Sunday March 6th, 2022 Example usage: python "/media/data/jacob/GitHub/image-utils/imutils/big/make_shards.py" \ --subsets=train,val,test \ --maxsize='1e9' \ --maxcount=50000 \ --shard_dir="/media/data_cifs/projects/prj_fossils/users/jacob/data/herbarium_2022/webdataset" \ --catalog_dir="/media/data_cifs/projects/prj_fossils/users/jacob/data/herbarium_2022/catalog" \ --debug """ import sys import os import os.path import random import argparse from torchvision import datasets import webdataset as wds import numpy as np import os from typing import Optional, Tuple, Any, Dict from tqdm import trange, tqdm import tarfile tarfile.DEFAULT_FORMAT = tarfile.GNU_FORMAT import webdataset as wds # from imutils.big.datamodule import Herbarium2022DataModule, Herbarium2022Dataset from imutils.ml.data.datamodule import Herbarium2022DataModule, Herbarium2022Dataset def read_file_binary(fname): "Read a binary file from disk." with open(fname, "rb") as stream: return stream.read() all_keys = set() def prepare_sample(dataset, index, subset: str="train", filekey: bool=False) -> Dict[str, Any]: image_binary, label, metadata = dataset[index] key = metadata["catalog_number"] assert key not in all_keys all_keys.add(key) xkey = key if filekey else "%07d" % index sample = {"__key__": xkey, "image.jpg": image_binary} if subset != "test": assert label == dataset.targets[index] sample["label.cls"] = int(label) return sample def write_dataset(catalog_dir: Optional[str]=None, shard_dir: Optional[str]=None, subset="train", maxsize=1e9, maxcount=100000, limit_num_samples: Optional[int]=np.inf, filekey: bool=False, dataset=None): if dataset is None: datamodule = Herbarium2022DataModule(catalog_dir=catalog_dir, num_workers=4, image_reader=read_file_binary, remove_transforms=True) datamodule.setup() dataset = datamodule.get_dataset(subset=subset) num_samples = len(dataset) print(f"With subset={subset}, Total num_samples: {num_samples}") if limit_num_samples < num_samples: num_samples = limit_num_samples print(f"Limiting this run to num_samples: {num_samples}") indices = list(range(num_samples)) os.makedirs(shard_dir, exist_ok=True) pattern = os.path.join(shard_dir, f"herbarium_2022-{subset}-%06d.tar") with wds.ShardWriter(pattern, maxsize=maxsize, maxcount=maxcount) as sink: for i in tqdm(indices, desc=f"idx(Total={num_samples})"): sample = prepare_sample(dataset, index=i, subset=subset, filekey=filekey) sink.write(sample) return dataset, indices def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser("""Generate sharded dataset from supervised image dataset.""") parser.add_argument("--subsets", default="train,val,test", help="which subsets to write") parser.add_argument( "--filekey", action="store_true", help="use file as key (default: index)" ) parser.add_argument("--maxsize", type=float, default=1e9) parser.add_argument("--maxcount", type=float, default=100000) parser.add_argument( "--shard_dir", default="/media/data_cifs/projects/prj_fossils/users/jacob/data/herbarium_2022/webdataset", help="directory where shards are written" ) parser.add_argument( "--catalog_dir", default="/media/data_cifs/projects/prj_fossils/users/jacob/data/herbarium_2022/catalog", help="directory containing csv versions of the original train & test metadata json files from herbarium 2022", ) parser.add_argument("--debug", action="store_true", default=False, help="Provide this boolean flag to produce a debugging shard dataset of only a maximum of 200 samples per data subset. [TODO] Switch to temp directories when this flag is passed.") args = parser.parse_args() return args def main(args): # args = parse_args() assert args.maxsize > 10000000 # Shards must be a minimum of 10+ MB assert args.maxcount < 1000000 # Shards must contain a maximum of 1,000,000 samples each limit_num_samples = 200 if args.debug else np.inf # if not os.path.isdir(os.path.join(args.data, "train")): # print(f"{args.data}: should be directory containing ImageNet", file=sys.stderr) # print(f"suitable as argument for torchvision.datasets.ImageNet(...)", file=sys.stderr) # sys.exit(1) # if not os.path.isdir(os.path.join(args.shards, ".")): # print(f"{args.shards}: should be a writable destination directory for shards", file=sys.stderr) # sys.exit(1) subsets = args.subsets.split(",") for subset in tqdm(subsets, leave=True, desc=f"Processing {len(subsets)} subsets"): # print("# subset", subset) dataset, indices = write_dataset(catalog_dir=args.catalog_dir, shard_dir=args.shard_dir, subset=subset, maxsize=args.maxsize, maxcount=args.maxcount, limit_num_samples=limit_num_samples, filekey=args.filekey) CATALOG_DIR = "/media/data_cifs/projects/prj_fossils/users/jacob/data/herbarium_2022/catalog" # SHARD_DIR = "/media/data_cifs/projects/prj_fossils/users/jacob/data/herbarium_2022/webdataset" if __name__ == "__main__": args = parse_args() main(args) written_files = os.listdir(args.shard_dir) files_per_subset = {"train":[], "val":[], "test":[]} for subset,v in files_per_subset.items(): files_per_subset[subset] = len([f for f in written_files if subset in f]) from rich import print as pp print(f"SUCCESS! TARGET SHARD DIR CONTAINS THE FOLLOWING:") pp(files_per_subset)

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null

0

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null

0

1

6594ce65379700398a3a74c57669881f0dce9a22

1,182

py

Python

linear.py

AliRzvn/HW1

d6420c1656800372aae78e18327612df540b674e

[ "MIT" ]

null

linear.py

AliRzvn/HW1

d6420c1656800372aae78e18327612df540b674e

[ "MIT" ]

null

linear.py

AliRzvn/HW1

d6420c1656800372aae78e18327612df540b674e

[ "MIT" ]

null

import numpy as np from module import Module class Linear(Module): def __init__(self, name, input_dim, output_dim, l2_coef=.0): super(Linear, self).__init__(name) self.l2_coef = l2_coef # coefficient of l2 regularization. self.W = np.random.randn(input_dim, output_dim) # weights of the layer. self.b = np.random.randn(output_dim, ) # biases of the layer. self.dW = None # gradients of loss w.r.t. the weights. self.db = None # gradients of loss w.r.t. the biases. def forward(self, x, **kwargs): """ x: input array. out: output of Linear module for input x. **Save whatever you need for backward pass in self.cache. """ out = None # todo: implement the forward propagation for Linear module. return out def backward(self, dout): """ dout: gradients of Loss w.r.t. this layer's output. dx: gradients of Loss w.r.t. this layer's input. """ dx = None # todo: implement the backward propagation for Linear module. # don't forget to update self.dW and self.db. return dx

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null

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1

6598ac2ebf4cb397f3e2b86a4a598e93fd0dbafd

659

py

Python

pages/login_page.py

0verchenko/PageObject

b50ec33b6f511680e5be14b16c379df825b87285

[ "Apache-2.0" ]

null

pages/login_page.py

0verchenko/PageObject

b50ec33b6f511680e5be14b16c379df825b87285

[ "Apache-2.0" ]

1

2021-06-02T00:14:07.000Z

pages/login_page.py

0verchenko/PageObject

b50ec33b6f511680e5be14b16c379df825b87285

[ "Apache-2.0" ]

null

from .base_page import BasePage from .locators import LoginPageLocators class LoginPage(BasePage): def should_be_login_page(self): self.should_be_login_url() self.should_be_login_form() self.should_be_register_form() def should_be_login_url(self): assert "login" in self.browser.current_url def should_be_login_form(self): login_form = self.browser.find_element(*LoginPageLocators.LOGIN_FORM) assert login_form.is_displayed() def should_be_register_form(self): register_form = self.browser.find_element(*LoginPageLocators.REGISTER_FORM) assert register_form.is_displayed()

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659af9491af7136fafb0016f0624386d06bcfa4b

3,280

py

Python

demo/demo/settings.py

ikcam/django-boilerplate

d8253665d74f0f18cf9a5fd46772598a60f20c5c

[ "Apache-2.0" ]

5

2016-10-02T04:57:10.000Z

2019-08-12T22:22:39.000Z

demo/demo/settings.py

ikcam/django-boilerplate

d8253665d74f0f18cf9a5fd46772598a60f20c5c

[ "Apache-2.0" ]

null

demo/demo/settings.py

ikcam/django-boilerplate

d8253665d74f0f18cf9a5fd46772598a60f20c5c

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Django settings for demo project. For more information on this file, see https://docs.djangoproject.com/en/1.9/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.9/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os from django.core.urlresolvers import reverse_lazy BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.8/howto/deployment/checklist/ DEBUG = True # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '__SHHH_ITS_A_SECRET__' ALLOWED_HOSTS = [] ADMINS = [] MANAGERS = [] INTERNAL_IPS = [] # Application definition INSTALLED_APPS = ( 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.humanize', # To make it look nice 'bootstrap3', # Boilerplate 'boilerplate', # Apps 'account', 'store', ) MIDDLEWARE = ( 'django.middleware.common.BrokenLinkEmailsMiddleware', 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.locale.LocaleMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ) ROOT_URLCONF = 'demo.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ os.path.join(BASE_DIR, 'templates/'), ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.core.context_processors.media', 'django.contrib.messages.context_processors.messages', ], }, }, ] TEMPLATE_LOADERS = [ 'django.template.loaders.filesystem.Loader', 'django.template.loaders.app_directories.Loader' ] LOCALE_PATHS = [ os.path.join(BASE_DIR, 'locale'), ] WSGI_APPLICATION = 'demo.wsgi.application' # Database # https://docs.djangoproject.com/en/1.9/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend' # Internationalization # https://docs.djangoproject.com/en/1.8/topics/i18n/ LANGUAGE_CODE = 'en' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files MEDIA_ROOT = os.path.join(BASE_DIR, 'media/') STATIC_ROOT = os.path.join(BASE_DIR, 'static/') MEDIA_URL = '/media/' STATIC_URL = '/static/' LOGIN_URL = reverse_lazy('account:login')

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null

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1

null

0

1

65a9792b2934e3a0bc3ead9a9eef72f6382f49c5

3,454

py

Python

Important_data/Thesis figure scripts/six_sigmoids.py

haakonvt/LearningTensorFlow

6988a15af2ac916ae1a5e23b2c5bde9630cc0519

[ "MIT" ]

5

2018-09-06T12:52:12.000Z

2020-05-09T01:40:12.000Z

Important_data/Thesis figure scripts/six_sigmoids.py

haakonvt/LearningTensorFlow

6988a15af2ac916ae1a5e23b2c5bde9630cc0519

[ "MIT" ]

null

Important_data/Thesis figure scripts/six_sigmoids.py

haakonvt/LearningTensorFlow

6988a15af2ac916ae1a5e23b2c5bde9630cc0519

[ "MIT" ]

4

2018-02-06T08:42:06.000Z

2019-04-16T11:23:06.000Z

from matplotlib import rc rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']}) rc('text', usetex=True) rc('legend',**{'fontsize':11}) # Font size for legend from mpl_toolkits.axes_grid.axislines import SubplotZero import matplotlib as mpl mpl.rcParams['lines.linewidth'] = 2.5 import matplotlib.pyplot as plt from math import erf,sqrt import numpy as np xmin = -4; xmax = 4 x = np.linspace(xmin,xmax,1001) y1 = lambda x: np.array([erf(0.5*i*sqrt(np.pi)) for i in x]) y2 = lambda x: np.tanh(x) y3 = lambda x: 4./np.pi*np.arctan(np.tanh(np.pi*x/4.)) y4 = lambda x: x/np.sqrt(1.+x**2) y5 = lambda x: 2.0/np.pi*np.arctan(np.pi/2.0 * x) y6 = lambda x: x/(1+np.abs(x)) fig = plt.figure(1) ax = SubplotZero(fig, 111) fig.add_subplot(ax) plt.subplots_adjust(left = 0.125, # the left side of the subplots of the figure right = 0.9, # the right side of the subplots of the figure bottom = 0.1, # the bottom of the subplots of the figure top = 0.9, # the top of the subplots of the figure wspace = 0., # the amount of width reserved for blank space between subplots hspace = 0.) # the amount of height reserved for white space between subplots plt.setp(ax, xticks=[-3,-2,-1,1,2,3], xticklabels=[" "," "," "," "," "," ",], yticks=[-1,1], yticklabels=[" "," ",]) # Make coordinate axes with "arrows" for direction in ["xzero", "yzero"]: ax.axis[direction].set_visible(True) # Coordinate axes with arrow (guess what, these are the arrows) plt.arrow(2.65, 0.0, 0.5, 0.0, color="k", clip_on=False, head_length=0.06, head_width=0.08) plt.arrow(0.0, 1.03, 0.0, 0.1, color="k", clip_on=False, head_length=0.06, head_width=0.08) # Remove edge around the entire plot for direction in ["left", "right", "bottom", "top"]: ax.axis[direction].set_visible(False) plt.rc('text', usetex=True) plt.rc('font', family='serif') colormap = plt.cm.Spectral #nipy_spectral # Other possible colormaps: Set1, Accent, nipy_spectral, Paired colors = [colormap(i) for i in np.linspace(0, 1, 6)] plt.title("Six sigmoid functions", fontsize=18, y=1.08) leg_list = [r"$\mathrm{erf}\left(\frac{\sqrt{\pi}}{2}x \right)$", r"$\tanh(x)$", r"$\frac{2}{\pi}\mathrm{gd}\left( \frac{\pi}{2}x \right)$", r"$x\left(1+x^2\right)^{-\frac{1}{2}}$", r"$\frac{2}{\pi}\mathrm{arctan}\left( \frac{\pi}{2}x \right)$", r"$x\left(1+|x|\right)^{-1}$"] for i in range(1,7): s = "ax.plot(x,y%s(x),color=colors[i-1])" %(str(i)) eval(s) ax.legend(leg_list,loc="best", ncol=2, fancybox=True) # title="Legend", fontsize=12 # ax.grid(True, which='both') ax.set_aspect('equal') ax.set_xlim([-3.1,3.1]) ax.set_ylim([-1.1,1.1]) ax.annotate('1', xy=(0.08, 1-0.02)) ax.annotate('0', xy=(0.08, -0.2)) ax.annotate('-1', xy=(0.08, -1-0.03)) for i in [-3,-2,-1,1,2,3]: ax.annotate('%s' %str(i), xy=(i-0.03, -0.2)) maybe = raw_input("\nUpdate figure directly in master thesis?\nEnter 'YES' (anything else = ONLY show to screen) ") if maybe == "YES": # Only save to disc if need to be updated filenameWithPath = "/Users/haakonvt/Dropbox/uio/master/latex-master/Illustrations/six_sigmoids.pdf" plt.savefig(filenameWithPath, bbox_inches='tight') #, pad_inches=0.2) print 'Saved over previous file in location:\n "%s"' %filenameWithPath else: print 'Figure was only shown on screen.' plt.show()

40.635294

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0.030769

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0

null

0

1

0

1

65aa73e15457005cd520549df842b9dc33211c7c

3,820

py

Python

src/web/modules/search/controllers/search/control.py

unkyulee/elastic-cms

3ccf4476c3523d4fefc0d8d9dee0196815b81489

[ "MIT" ]

2

2017-04-30T07:29:23.000Z

2017-04-30T07:36:27.000Z

src/web/modules/search/controllers/search/control.py

unkyulee/elastic-cms

3ccf4476c3523d4fefc0d8d9dee0196815b81489

[ "MIT" ]

null

src/web/modules/search/controllers/search/control.py

unkyulee/elastic-cms

3ccf4476c3523d4fefc0d8d9dee0196815b81489

[ "MIT" ]

null

import json import urllib2 import traceback import cgi from flask import render_template, request import web.util.tools as tools import lib.http as http import lib.es as es from web import app from lib.read import readfile def get(p): host = p['c']['host']; index = p['c']['index']; # debug p['debug'] = tools.get('debug', '') # search keyword p["q"] = tools.get('q', p['c']['query']) # pagination p["from"] = int(tools.get('from', 0)) p["size"] = int(tools.get('size', p['c']['page_size'])) # sort p['sort_field'] = tools.get('sort_field', p['c']['sort_field']) p['sort_dir'] = tools.get('sort_dir', p['c']['sort_dir']) # selected app p['selected_app'] = tools.get('app') # search query p["q"] = p["q"].replace('"', '\\"') # escape some special chars p['search_query'] = render_template("search/search_query.html", p=p) p["q"] = tools.get('q', p['c']['query']) # restore to what was entered originally # send search request try: search_url = "{}/{}/post/_search".format(host, index) p['response'] = http.http_req_json(search_url, "POST", p['search_query']) except urllib2.HTTPError, e: raise Exception("url: {}\nquery: {}\{}".format( search_url, p['search_query'], e.read())) # process the search result p['post_list'] = [] for r in p['response']["hits"]["hits"]: item = {} # first take items from the fields for k, v in r["_source"].items(): item[k] = v # fetch highlight if r.get('highlight'): for k, v in r["highlight"].items(): if k == "url" or k == "_index" or k == "app": continue value = cgi.escape(v[0]) value = value.replace("::highlight::", "<font color=red>") value = value.replace("::highlight_end::", "</font>") item[k] = value # produce standard fields if r.get('_index') and not item.get('app'): item['app'] = r.get('_index') if not item.get('url'): item['url'] = '{}/redirect?index={}&id={}'.format( p.get('url'), r.get('_index'), r.get('_id')) # Save to SearchResult p['post_list'].append(item) # Application Lists p['applications'] = [] if p['response'].get('aggregations'): internal = p['response']['aggregations']['internal']['buckets'] p['applications'].extend( [item for item in internal if item.get('key') != 'search'] ) external = p['response']['aggregations']['external']['buckets'] p['applications'].extend(external) # sort based on the count p['applications'] = sorted(p['applications'], key=lambda x: x['doc_count'], reverse=True) # Feed Pagination p["total"] = int(p['response']["hits"]["total"]) # Suggestion p["suggestion"] = []; AnySuggestion = False; # suggest.didyoumean[].options[].text if p['response']["suggest"].get("didyoumean"): for idx, term in enumerate(p['response']["suggest"].get("didyoumean")): p["suggestion"].append(term["text"]) for o in term["options"]: AnySuggestion = True p["suggestion"][idx] = o["text"] break # just take the first option # if there are no suggestions then don't display if not AnySuggestion: p["suggestion"] = [] # return json format if tools.get("json"): callback = tools.get("callback") if not callback: return json.dumps(p['response']) else: return "{}({})".format(callback, json.dumps(p['response'])) return render_template("search/default.html", p=p)

33.217391

85

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4.353814

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0.270157

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null

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null

0

null

0

null

0

1

0

1

65b8b4c75d35105b5ff106a11aa54530eaf30029

2,687

py

Python

stellar_sdk/xdr/survey_response_body.py

Shaptic/py-stellar-base

f5fa47f4d96f215889d99249fb25c7be002f5cf3

[ "Apache-2.0" ]

null

stellar_sdk/xdr/survey_response_body.py

Shaptic/py-stellar-base

f5fa47f4d96f215889d99249fb25c7be002f5cf3

[ "Apache-2.0" ]

27

2022-01-12T10:55:38.000Z

2022-03-28T01:38:24.000Z

stellar_sdk/xdr/survey_response_body.py

Shaptic/py-stellar-base

f5fa47f4d96f215889d99249fb25c7be002f5cf3

[ "Apache-2.0" ]

2

2021-12-02T12:42:03.000Z

2021-12-07T20:53:10.000Z

# This is an automatically generated file. # DO NOT EDIT or your changes may be overwritten import base64 from xdrlib import Packer, Unpacker from ..type_checked import type_checked from .survey_message_command_type import SurveyMessageCommandType from .topology_response_body import TopologyResponseBody __all__ = ["SurveyResponseBody"] @type_checked class SurveyResponseBody: """ XDR Source Code:: union SurveyResponseBody switch (SurveyMessageCommandType type) { case SURVEY_TOPOLOGY: TopologyResponseBody topologyResponseBody; }; """ def __init__( self, type: SurveyMessageCommandType, topology_response_body: TopologyResponseBody = None, ) -> None: self.type = type self.topology_response_body = topology_response_body def pack(self, packer: Packer) -> None: self.type.pack(packer) if self.type == SurveyMessageCommandType.SURVEY_TOPOLOGY: if self.topology_response_body is None: raise ValueError("topology_response_body should not be None.") self.topology_response_body.pack(packer) return @classmethod def unpack(cls, unpacker: Unpacker) -> "SurveyResponseBody": type = SurveyMessageCommandType.unpack(unpacker) if type == SurveyMessageCommandType.SURVEY_TOPOLOGY: topology_response_body = TopologyResponseBody.unpack(unpacker) return cls(type=type, topology_response_body=topology_response_body) return cls(type=type) def to_xdr_bytes(self) -> bytes: packer = Packer() self.pack(packer) return packer.get_buffer() @classmethod def from_xdr_bytes(cls, xdr: bytes) -> "SurveyResponseBody": unpacker = Unpacker(xdr) return cls.unpack(unpacker) def to_xdr(self) -> str: xdr_bytes = self.to_xdr_bytes() return base64.b64encode(xdr_bytes).decode() @classmethod def from_xdr(cls, xdr: str) -> "SurveyResponseBody": xdr_bytes = base64.b64decode(xdr.encode()) return cls.from_xdr_bytes(xdr_bytes) def __eq__(self, other: object): if not isinstance(other, self.__class__): return NotImplemented return ( self.type == other.type and self.topology_response_body == other.topology_response_body ) def __str__(self): out = [] out.append(f"type={self.type}") out.append( f"topology_response_body={self.topology_response_body}" ) if self.topology_response_body is not None else None return f"<SurveyResponseBody {[', '.join(out)]}>"

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65b9bd2ad1163a0006a5a233a9d9d9cd5e6a3646

763

py

Python

poll/migrations/0002_auto_20210114_2215.py

slk007/SahiGalat.com

786688e07237f3554187b90e01149225efaa1713

[ "MIT" ]

null

poll/migrations/0002_auto_20210114_2215.py

slk007/SahiGalat.com

786688e07237f3554187b90e01149225efaa1713

[ "MIT" ]

null

poll/migrations/0002_auto_20210114_2215.py

slk007/SahiGalat.com

786688e07237f3554187b90e01149225efaa1713

[ "MIT" ]

null

# Generated by Django 3.1.5 on 2021-01-14 22:15 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('poll', '0001_initial'), ] operations = [ migrations.CreateModel( name='Topic', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('topic_name', models.CharField(max_length=50)), ('topic_descrption', models.CharField(max_length=255)), ], ), migrations.AddField( model_name='question', name='topics', field=models.ManyToManyField(related_name='questions', to='poll.Topic'), ), ]

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65b9efe5fd413429042a21c46095ea299b352b7a

370

py

Python

Leetcode/Python/_1493.py

Xrenya/algorithms

aded82cacde2f4f2114241907861251e0e2e5638

[ "MIT" ]

null

Leetcode/Python/_1493.py

Xrenya/algorithms

aded82cacde2f4f2114241907861251e0e2e5638

[ "MIT" ]

null

Leetcode/Python/_1493.py

Xrenya/algorithms

aded82cacde2f4f2114241907861251e0e2e5638

[ "MIT" ]

null

class Solution: def longestSubarray(self, nums: List[int]) -> int: k = 1 max_len, i = 0, 0 for j in range(len(nums)): if nums[j] == 0: k -= 1 if k < 0: if nums[i] == 0: k += 1 i += 1 max_len = max(max_len, j - i) return max_len

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null

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null

0

1

65c1e68e0dc7466b357152cbb876f5ad24ac99ef

9,154

py

Python

SaIL/envs/state_lattice_planner_env.py

yonetaniryo/SaIL

c7404024c7787184c3638e9730bd185373ed0bf6

[ "BSD-3-Clause" ]

12

2018-05-18T19:29:09.000Z

2020-05-15T13:47:12.000Z

SaIL/envs/state_lattice_planner_env.py

yonetaniryo/SaIL

c7404024c7787184c3638e9730bd185373ed0bf6

[ "BSD-3-Clause" ]

1

2018-05-18T19:36:42.000Z

2018-07-20T03:03:13.000Z

SaIL/envs/state_lattice_planner_env.py

yonetaniryo/SaIL

c7404024c7787184c3638e9730bd185373ed0bf6

[ "BSD-3-Clause" ]

10

2018-01-11T21:23:40.000Z

2021-11-10T04:38:07.000Z

#!/usr/bin/env python """An environment that takes as input databases of environments and runs episodes, where each episode is a search based planner. It then returns the average number of expansions, and features (if training) Author: Mohak Bhardwaj """ from collections import defaultdict import numpy as np import os from SaIL.learners.supervised_regression_network import SupervisedRegressionNetwork from planning_python.data_structures.priority_queue import PriorityQueue from planning_python.planners.search_based_planner import SearchBasedPlanner from planning_python.environment_interface.env_2d import Env2D from planning_python.state_lattices.common_lattice.xy_analytic_lattice import XYAnalyticLattice from planning_python.state_lattices.common_lattice.xyh_analytic_lattice import XYHAnalyticLattice from planning_python.cost_functions.cost_function import PathLengthNoAng, DubinsPathLength from planning_python.heuristic_functions.heuristic_function import EuclideanHeuristicNoAng, ManhattanHeuristicNoAng, DubinsHeuristic from planning_python.data_structures.planning_problem import PlanningProblem class StateLatticePlannerEnv(SearchBasedPlanner): def __init__(self, env_params, lattice_type, lattice_params, cost_fn, learner_params): self.env_params = env_params self.cost_fn = cost_fn self.lattice_type = lattice_type if lattice_type == "XY": self.lattice = XYAnalyticLattice(lattice_params) self.start_n = self.lattice.state_to_node((lattice_params['x_lims'][0], lattice_params['y_lims'][0])) self.goal_n = self.lattice.state_to_node((lattice_params['x_lims'][1]-1, lattice_params['y_lims'][0]-1)) elif lattice_type == "XYH": self.lattice = XYHAnalyticLattice(lattice_params) self.start_n = self.lattice.state_to_node((lattice_params['x_lims'][0], lattice_params['y_lims'][0], 0)) self.goal_n = self.lattice.state_to_node((lattice_params['x_lims'][1]-1, lattice_params['y_lims'][0]-1, 0)) self.lattice.precalc_costs(self.cost_fn) #Enumerate and cache successors and edge costs self.learner_policy = None #This will be set prior to running a polciy using set_learner_policy #Data structures for planning self.frontier = [] #Frontier is un-sorted as it is sorted on demand (using heuristic) self.oracle_frontier = PriorityQueue() #Frontier sorted according to oracle(for mixing) self.visited = {} #Keep track of visited cells self.c_obs = [] #Keep track of collision checks done so far self.cost_so_far = defaultdict(lambda: np.inf) #Keep track of cost of path to the node self.came_from = {} #Keep track of parent during search self.learner = SupervisedRegressionNetwork(learner_params) #learner is a part of the environment def initialize(self, env_folder, oracle_folder, num_envs, file_start_num, phase='train', visualize=False): """Initialize everything""" self.env_folder = env_folder self.oracle_folder = oracle_folder self.num_envs = num_envs self.phase = phase self.visualize = visualize self.curr_env_num = file_start_num - 1 def set_mixing_param(self, beta): self.beta = beta def run_episode(k_tsteps=None, max_expansions=1000000): assert self.initialized == True, "Planner has not been initialized properly. Please call initialize or reset_problem function before plan function" start_t = time.time() data = [] #Dataset that will be filled during training self.came_from[self.start_n]= (None, None) self.cost_so_far[self.start_n] = 0. #For each node, this is the cost of the shortest path to the start self.num_invalid_predecessors[start] = 0 self.num_invalid_siblings[start] = 0 self.depth_so_far[start] = 0 if self.phase == "train": start_h_val = self.oracle[self.start_n] self.oracle_frontier.put(self.start_n, start_h_val) self.frontier.append(self.start_n) #This frontier is just a list curr_expansions = 0 #Number of expansions done num_rexpansions = 0 found_goal = False path =[] path_cost = np.inf while len(self.frontier) > 0: #Check 1: Stop search if frontier gets too large if curr_expansions >= max_expansions: print("Max Expansions Done.") break #Check 2: Stop search if open list gets too large if len(self.frontier) > 500000: print("Timeout.") break ################################################################################################# #Step 1: With probability beta, we select the oracle and (1-beta) we select the learner, also we collect data if # curr_expansions is in one of the k timesteps if phase == "train": if curr_expansions in k_tsteps: rand_idx = np.random.randint(len(self.frontier)) n = self.frontier[rand_idx] #Choose a random action data.append(self.get_feature_vec[n], self.curr_oracle[n]) #Query oracle for Q-value of that action and append to dataset if np.random.random() <= self.beta: h, curr_node = self.oracle_frontier.get() else curr_node = self.get_best_node() else: curr_node = self.get_best_node() ################################################################################################# if curr_node in self.visited: continue #Step 3: Add to visited self.visited[curr_node] = 1 #Check 3: Stop search if goal found if curr_node == self.goal_node: print "Found goal" found_goal = True break #Step 4: If search has not ended, add neighbors of current node to frontier neighbors, edge_costs, valid_edges, invalid_edges = self.get_successors(curr_node) #Update the features of the parent and current node n_invalid_edges = len(invalid_edges) self.num_invalid_grand_children[self.came_from[curr_node][0]] += n_invalid_edges self.num_invalid_children[curr_node] = n_invalid_edges #Step 5: Update c_obs with collision checks performed self.c_obs.append(invalid_edges) g = self.cost_so_far[curr_node] for i, neighbor in enumerate(neighbors): new_g = g + edge_costs[i] if neighbor not in self.visited #Add neighbor to open only if it wasn't in open already (don't need duplicates) [Note: Only do this if ordering in the frontier doesn't matter] if neighbor not in self.cost_so_far: #Update the oracle frontier only during training (for mixing) if self.phase == "train": h_val = self.curr_oracle[neighbor] self.oracle_frontier.put(neighbor, h_val) self.frontier.append(neighbor) #Keep track of cost of shortest path to neighbor and parent it came from (for features and reconstruct path) if new_g < self.cost_so_far[neighbor]: self.came_from[neighbor] = (curr_node, valid_edges[i]) self.cost_so_far[neighbor] = new_g #Update feature dicts self.learner.cost_so_far[neighbor] = new_g self.learner.num_invalid_predecessors[neighbor] = self.num_invalid_predecessors[curr_node] + n_invalid_edges self.learner.num_invalid_siblings[neighbor] = n_invalid_edges self.learner.depth_so_far[neighbor] = self.depth_so_far[curr_node] + 1 #Step 6:increment number of expansions curr_expansions += 1 if found_goal: path, path_cost = self.reconstruct_path(self.came_from, self.start_node, self.goal_node, self.cost_so_far) else: print ('Found no solution, priority queue empty') time_taken = time.time()- start_t return path, path_cost, curr_expansions, time_taken, self.came_from, self.cost_so_far, self.c_obs #Run planner on current env and return data seetn. Also, update current env to next env def get_heuristic(self, node, goal): """Given a node and goal, calculate features and get heuristic value""" return 0 def get_best_node(self): """Evaluates all the nodes in the frontier and returns the best node""" return None def sample_world(self, mode='cycle'): self.curr_env_num = (self.curr_env_num+1)%self.num_envs file_path = os.path.join(os.path.abspath(self.env_folder), str(self.curr_env_num)+'.png') self.curr_env = initialize_env_from_file(file_path) def compute_oracle(self, mode='cycle'): file_path = os.path.join(os.path.abspath(self.oracle_folder), "oracle_"+str(self.curr_env_num)+'.p') self.curr_oracle = pickle.load(cost_so_far, open(file_path, 'rb')) def initialize_env_from_file(self, file_path): env = Env2D() env.initialize(file_path, self.env_params) if self.visualize: self.env.initialize_plot(self.lattice.node_to_state(self.start_node), self.lattice.node_to_state(self.goal_node)) self.initialized = True return env def clear_planner(self): self.frontier.clear() self.visited = {} self.c_obs = [] self.cost_so_far = {} self.came_from = {}

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null

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1

65c266ffeb9dad82408ef950252b4d7368839fc3

966

py

Python

opi_dragon_api/auth/__init__.py

CEAC33/opi-dragon-api

8f050a0466dab4aaeec13151b9f49990bbd73640

[ "MIT" ]

null

opi_dragon_api/auth/__init__.py

CEAC33/opi-dragon-api

8f050a0466dab4aaeec13151b9f49990bbd73640

[ "MIT" ]

null

opi_dragon_api/auth/__init__.py

CEAC33/opi-dragon-api

8f050a0466dab4aaeec13151b9f49990bbd73640

[ "MIT" ]

null

from sanic_jwt import exceptions class User: def __init__(self, id, username, password): self.user_id = id self.username = username self.password = password def __repr__(self): return "User(id='{}')".format(self.user_id) def to_dict(self): return {"user_id": self.user_id, "username": self.username} users = [User(1, "opi-user", "~Zñujh*B2D`9T!<j")] username_table = {u.username: u for u in users} userid_table = {u.user_id: u for u in users} async def my_authenticate(request, *args, **kwargs): username = request.json.get("username", None) password = request.json.get("password", None) if not username or not password: raise exceptions.AuthenticationFailed("Missing username or password.") user = username_table.get(username, None) if user is None or password != user.password: raise exceptions.AuthenticationFailed("Incorrect username or password") return user

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0

null

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null

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1

65c64d0d6e346b2c86db0238e477f1aee46d6160

2,313

py

Python

tensorflow/python/data/experimental/kernel_tests/serialization/textline_dataset_serialization_test.py

DanMitroshin/tensorflow

74aa353842f1788bdb7506ecceaf6ba99140e165

[ "Apache-2.0" ]

4

2021-06-02T03:21:44.000Z

2021-11-08T09:47:24.000Z

tensorflow/python/data/experimental/kernel_tests/serialization/textline_dataset_serialization_test.py

DanMitroshin/tensorflow

74aa353842f1788bdb7506ecceaf6ba99140e165

[ "Apache-2.0" ]

7

2021-11-10T20:21:23.000Z

2022-03-22T19:18:39.000Z

tensorflow/python/data/experimental/kernel_tests/serialization/textline_dataset_serialization_test.py

DanMitroshin/tensorflow

74aa353842f1788bdb7506ecceaf6ba99140e165

[ "Apache-2.0" ]

3

2021-05-09T13:41:29.000Z

2021-06-24T06:12:05.000Z

# Copyright 2017 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. # ============================================================================== """Tests for checkpointing the TextLineDataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized from tensorflow.python.data.experimental.kernel_tests import reader_dataset_ops_test_base from tensorflow.python.data.kernel_tests import checkpoint_test_base from tensorflow.python.data.kernel_tests import test_base from tensorflow.python.data.ops import readers as core_readers from tensorflow.python.framework import combinations from tensorflow.python.platform import test class TextLineDatasetCheckpointTest( reader_dataset_ops_test_base.TextLineDatasetTestBase, checkpoint_test_base.CheckpointTestBase, parameterized.TestCase): def _build_iterator_graph(self, test_filenames, compression_type=None): return core_readers.TextLineDataset( test_filenames, compression_type=compression_type, buffer_size=10) @combinations.generate(test_base.default_test_combinations()) def testTextLineCore(self): compression_types = [None, "GZIP", "ZLIB"] num_files = 5 lines_per_file = 5 num_outputs = num_files * lines_per_file for compression_type in compression_types: test_filenames = self._createFiles( num_files, lines_per_file, crlf=True, compression_type=compression_type) # pylint: disable=cell-var-from-loop self.run_core_tests( lambda: self._build_iterator_graph(test_filenames, compression_type), num_outputs) # pylint: enable=cell-var-from-loop if __name__ == "__main__": test.main()

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0

null

0

null

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1

0

1

65d0a80d19258c77b9d91fc06cfaa6455396ecc8

10,012

py

Python

octopus_deploy_swagger_client/models/phase_resource.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

octopus_deploy_swagger_client/models/phase_resource.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

octopus_deploy_swagger_client/models/phase_resource.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

# coding: utf-8 """ Octopus Server API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 2019.6.7+Branch.tags-2019.6.7.Sha.aa18dc6809953218c66f57eff7d26481d9b23d6a Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six from octopus_deploy_swagger_client.models.retention_period import RetentionPeriod # noqa: F401,E501 class PhaseResource(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'id': 'str', 'name': 'str', 'automatic_deployment_targets': 'list[str]', 'optional_deployment_targets': 'list[str]', 'minimum_environments_before_promotion': 'int', 'is_optional_phase': 'bool', 'release_retention_policy': 'RetentionPeriod', 'tentacle_retention_policy': 'RetentionPeriod' } attribute_map = { 'id': 'Id', 'name': 'Name', 'automatic_deployment_targets': 'AutomaticDeploymentTargets', 'optional_deployment_targets': 'OptionalDeploymentTargets', 'minimum_environments_before_promotion': 'MinimumEnvironmentsBeforePromotion', 'is_optional_phase': 'IsOptionalPhase', 'release_retention_policy': 'ReleaseRetentionPolicy', 'tentacle_retention_policy': 'TentacleRetentionPolicy' } def __init__(self, id=None, name=None, automatic_deployment_targets=None, optional_deployment_targets=None, minimum_environments_before_promotion=None, is_optional_phase=None, release_retention_policy=None, tentacle_retention_policy=None): # noqa: E501 """PhaseResource - a model defined in Swagger""" # noqa: E501 self._id = None self._name = None self._automatic_deployment_targets = None self._optional_deployment_targets = None self._minimum_environments_before_promotion = None self._is_optional_phase = None self._release_retention_policy = None self._tentacle_retention_policy = None self.discriminator = None if id is not None: self.id = id if name is not None: self.name = name if automatic_deployment_targets is not None: self.automatic_deployment_targets = automatic_deployment_targets if optional_deployment_targets is not None: self.optional_deployment_targets = optional_deployment_targets if minimum_environments_before_promotion is not None: self.minimum_environments_before_promotion = minimum_environments_before_promotion if is_optional_phase is not None: self.is_optional_phase = is_optional_phase if release_retention_policy is not None: self.release_retention_policy = release_retention_policy if tentacle_retention_policy is not None: self.tentacle_retention_policy = tentacle_retention_policy @property def id(self): """Gets the id of this PhaseResource. # noqa: E501 :return: The id of this PhaseResource. # noqa: E501 :rtype: str """ return self._id @id.setter def id(self, id): """Sets the id of this PhaseResource. :param id: The id of this PhaseResource. # noqa: E501 :type: str """ self._id = id @property def name(self): """Gets the name of this PhaseResource. # noqa: E501 :return: The name of this PhaseResource. # noqa: E501 :rtype: str """ return self._name @name.setter def name(self, name): """Sets the name of this PhaseResource. :param name: The name of this PhaseResource. # noqa: E501 :type: str """ self._name = name @property def automatic_deployment_targets(self): """Gets the automatic_deployment_targets of this PhaseResource. # noqa: E501 :return: The automatic_deployment_targets of this PhaseResource. # noqa: E501 :rtype: list[str] """ return self._automatic_deployment_targets @automatic_deployment_targets.setter def automatic_deployment_targets(self, automatic_deployment_targets): """Sets the automatic_deployment_targets of this PhaseResource. :param automatic_deployment_targets: The automatic_deployment_targets of this PhaseResource. # noqa: E501 :type: list[str] """ self._automatic_deployment_targets = automatic_deployment_targets @property def optional_deployment_targets(self): """Gets the optional_deployment_targets of this PhaseResource. # noqa: E501 :return: The optional_deployment_targets of this PhaseResource. # noqa: E501 :rtype: list[str] """ return self._optional_deployment_targets @optional_deployment_targets.setter def optional_deployment_targets(self, optional_deployment_targets): """Sets the optional_deployment_targets of this PhaseResource. :param optional_deployment_targets: The optional_deployment_targets of this PhaseResource. # noqa: E501 :type: list[str] """ self._optional_deployment_targets = optional_deployment_targets @property def minimum_environments_before_promotion(self): """Gets the minimum_environments_before_promotion of this PhaseResource. # noqa: E501 :return: The minimum_environments_before_promotion of this PhaseResource. # noqa: E501 :rtype: int """ return self._minimum_environments_before_promotion @minimum_environments_before_promotion.setter def minimum_environments_before_promotion(self, minimum_environments_before_promotion): """Sets the minimum_environments_before_promotion of this PhaseResource. :param minimum_environments_before_promotion: The minimum_environments_before_promotion of this PhaseResource. # noqa: E501 :type: int """ self._minimum_environments_before_promotion = minimum_environments_before_promotion @property def is_optional_phase(self): """Gets the is_optional_phase of this PhaseResource. # noqa: E501 :return: The is_optional_phase of this PhaseResource. # noqa: E501 :rtype: bool """ return self._is_optional_phase @is_optional_phase.setter def is_optional_phase(self, is_optional_phase): """Sets the is_optional_phase of this PhaseResource. :param is_optional_phase: The is_optional_phase of this PhaseResource. # noqa: E501 :type: bool """ self._is_optional_phase = is_optional_phase @property def release_retention_policy(self): """Gets the release_retention_policy of this PhaseResource. # noqa: E501 :return: The release_retention_policy of this PhaseResource. # noqa: E501 :rtype: RetentionPeriod """ return self._release_retention_policy @release_retention_policy.setter def release_retention_policy(self, release_retention_policy): """Sets the release_retention_policy of this PhaseResource. :param release_retention_policy: The release_retention_policy of this PhaseResource. # noqa: E501 :type: RetentionPeriod """ self._release_retention_policy = release_retention_policy @property def tentacle_retention_policy(self): """Gets the tentacle_retention_policy of this PhaseResource. # noqa: E501 :return: The tentacle_retention_policy of this PhaseResource. # noqa: E501 :rtype: RetentionPeriod """ return self._tentacle_retention_policy @tentacle_retention_policy.setter def tentacle_retention_policy(self, tentacle_retention_policy): """Sets the tentacle_retention_policy of this PhaseResource. :param tentacle_retention_policy: The tentacle_retention_policy of this PhaseResource. # noqa: E501 :type: RetentionPeriod """ self._tentacle_retention_policy = tentacle_retention_policy def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(PhaseResource, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, PhaseResource): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

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null

0

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65d4761a181f8a12d33c2a0e4fbbb20be034782f

309

py

Python

project/server/main/modules/__init__.py

ardikabs/dnsmanager

4d2f302ea9f54fd4d5416328dc46a1c47b573e5b

[ "MIT" ]

1

2019-01-15T10:33:04.000Z

project/server/main/modules/__init__.py

ardikabs/dnsmanager

4d2f302ea9f54fd4d5416328dc46a1c47b573e5b

[ "MIT" ]

null

project/server/main/modules/__init__.py

ardikabs/dnsmanager

4d2f302ea9f54fd4d5416328dc46a1c47b573e5b

[ "MIT" ]

null

""" All Available Module on Server Belong to Here """ AVAILABLE_MODULES = ( "api", ) def init_app(app, **kwargs): from importlib import import_module for module in AVAILABLE_MODULES: import_module( f".{module}", package=__name__ ).init_app(app, **kwargs)

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null

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null

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65d5f60d4b7acc40612bcf45d7c9efe894269057

1,050

py

Python

JSS Users Cleanup/setup.py

killahquam/JAMF

77b003a72375b9b01bdb961cb466b7519c859116

[ "MIT" ]

34

2015-06-11T16:37:54.000Z

2021-06-02T20:42:55.000Z

JSS Users Cleanup/setup.py

killahquam/JAMF

77b003a72375b9b01bdb961cb466b7519c859116

[ "MIT" ]

1

2016-01-03T04:05:30.000Z

2016-09-26T20:25:51.000Z

JSS Users Cleanup/setup.py

killahquam/JAMF

77b003a72375b9b01bdb961cb466b7519c859116

[ "MIT" ]

6

2015-12-29T20:39:56.000Z

2020-06-30T19:33:23.000Z

#!/usr/bin/python #Quam Sodji 2015 #Setup script to install the needed python modules #Installs kn/Slack and python-jss modules #We assume you have Git installed....... import subprocess import os import sys import shutil clone_jss = subprocess.check_output(['git','clone','git://github.com/sheagcraig/python-jss.git']) clone_slack = subprocess.check_output(['git','clone','git://github.com/kn/slack.git']) path = os.path.dirname(os.path.realpath(__file__)) #Installing Slack print "Installing Slack" slack_folder = os.chdir(path + '/slack') install_slack = subprocess.check_output(['python','setup.py','install']) print "slack module installed" #Installing Python JSS print "Installing Python JSS" jss_folder = os.chdir(path + '/python-jss') install_jss = subprocess.check_output(['python','setup.py','install']) print "python-jss module installed" #Cleaning up print "Cleaning up" change_location = os.chdir(path) remove_slack_clone = shutil.rmtree(path + '/slack') remove_jss_clone = shutil.rmtree(path + '/python-jss') print "Done." sys.exit(0)

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null

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null

0

1

0

1

65db99db18c44b4e940ff60964e5dae8b718ca83

3,988

py

Python

datamining_assignments/datamining_assiment_3/nmf.py

xuerenlv/PaperWork

f096b57a80e8d771f080a02b925a22edbbee722a

[ "Apache-2.0" ]

1

2015-10-15T12:26:07.000Z

datamining_assignments/datamining_assiment_3/nmf.py

xuerenlv/PaperWork

f096b57a80e8d771f080a02b925a22edbbee722a

[ "Apache-2.0" ]

null

datamining_assignments/datamining_assiment_3/nmf.py

xuerenlv/PaperWork

f096b57a80e8d771f080a02b925a22edbbee722a

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- ''' Created on Oct 27, 2015 @author: nlp ''' import numpy as np import math # nmf 聚类主体 def nmf(file_list, k): X = np.array(file_list).transpose() m_x, n_x = X.shape # 随机生成初始矩阵 U = np.random.rand(m_x, k) V = np.random.rand(n_x, k) is_convergence = False count = 0 while not is_convergence: count+=1 U_old = U.copy() V_old = V.copy() X_V = np.dot(X, V) U_VT_V = np.dot(U, np.dot(V.transpose(), V)) U = U * X_V / U_VT_V XT_U = np.dot(X.transpose(), U) V_UT_U = np.dot(V, np.dot(U.transpose(), U)) V = V * XT_U / V_UT_U if abs((U - U_old).sum()) < 0.01 and abs((V - V_old).sum()) < 0.01: is_convergence = True # normalize U and V u_pow_2 = (U ** 2).sum(axis=0) u_sqrt_pow_2 = [math.sqrt(w) for w in u_pow_2] for i in range(m_x): for j in range(k): U[i, j] = U[i, j] / u_sqrt_pow_2[j] for i in range(n_x): for j in range(k): V[i, j] *= u_sqrt_pow_2[j] # restlt_example_map_cluster restlt_example_map_cluster = {} for i in range(n_x): max_val = 0 for j in range(k): if V[i][j] > max_val: max_val = V[i][j] restlt_example_map_cluster[i] = j return restlt_example_map_cluster # 读文件生成list［list［］］，里面的list代表文件的一行; list[] 代表第i行所属的类。 def read_file(file_name): file_list = [] lable_list = [] for line in open(file_name).readlines(): arr_line = list(line.split(',')); lable_list.append(arr_line[-1][:-1]); del arr_line[-1]; file_list.append([float(one) if not one=='0' else 0.000001 for one in arr_line]) return (file_list, lable_list) #***************** 评价标准 ********************************** # purity def gen_purity(file_list, lable_list, restlt_example_map_cluster, cluster_num): # 初始化 m（i，j）二维数组 gen_matrix = [[0 for j in range(cluster_num)] for i in range(cluster_num)] for index in xrange(len(file_list)): lable = int(lable_list[index]) if int(lable_list[index]) > 0 else 0 gen_matrix[lable][restlt_example_map_cluster[index]] += 1 p_j = [0 for i in range(cluster_num)] for j in range(cluster_num): max_m_i_j = 0 for i in range(cluster_num): if gen_matrix[i][j] > max_m_i_j: max_m_i_j = gen_matrix[i][j] p_j[j] = max_m_i_j sum_val = 0 for x in p_j: sum_val += x return float(sum_val) / float(len(file_list)) # Gini def gen_gini(file_list, lable_list, restlt_example_map_cluster, cluster_num): # 初始化 m（i，j）二维数组 gen_matrix = np.array([[0 for j in range(cluster_num)] for i in range(cluster_num)]) for index in xrange(len(file_list)): lable = int(lable_list[index]) if int(lable_list[index]) > 0 else 0 gen_matrix[lable][restlt_example_map_cluster[index]] += 1 M_j = gen_matrix.sum(axis=0) g_j = [0 for i in range(cluster_num)] for j in range(cluster_num): for i in range(cluster_num): g_j[j] += (float(gen_matrix[i][j]) / float(M_j[j])) ** 2 g_j[j] = 1 - g_j[j] fenzi_sum = 0.0 for j in range(cluster_num): fenzi_sum += g_j[j] * M_j[j] return float(fenzi_sum) / float(len(file_list)) #**************************************************************************** def nmf_main(file_name,cluster_nums): file_list, lable_list = read_file(file_name) restlt_example_map_cluster = nmf(file_list, cluster_nums) purity = gen_purity(file_list, lable_list, restlt_example_map_cluster, cluster_nums) gini = gen_gini(file_list, lable_list, restlt_example_map_cluster, cluster_nums) print file_name,'purity:',purity, "gini:",gini if __name__ == '__main__': nmf_main("german.txt", 2) nmf_main("mnist.txt", 10) pass

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null

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null

0

1

0

1

65ddc57bb1b73bd27f58c41a027c88ec873b6740

2,541

py

Python

setup.py

jimbydamonk/jenkins-job-builder-addons

172672e25089992ed94dc223c7e30f29c46719b0

[ "Apache-2.0" ]

8

2015-08-21T15:53:22.000Z

2019-04-09T20:42:58.000Z

setup.py

jimbydamonk/jenkins-job-builder-addons

172672e25089992ed94dc223c7e30f29c46719b0

[ "Apache-2.0" ]

5

2016-03-23T17:46:16.000Z

2018-03-05T13:56:17.000Z

setup.py

jimbydamonk/jenkins-job-builder-addons

172672e25089992ed94dc223c7e30f29c46719b0

[ "Apache-2.0" ]

11

2015-10-05T21:58:33.000Z

2019-04-14T04:50:48.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from setuptools.command.test import test as TestCommand try: from setuptools import setup except ImportError: from distutils.core import setup with open('README.rst') as readme_file: readme = readme_file.read() with open('HISTORY.rst') as history_file: history = history_file.read().replace('.. :changelog:', '') requirements = [ # TODO: put package requirements here ] test_requirements = [ # TODO: put package test requirements here ] class Tox(TestCommand): user_options = [('tox-args=', 'a', "Arguments to pass to tox")] def initialize_options(self): TestCommand.initialize_options(self) self.tox_args = None def finalize_options(self): TestCommand.finalize_options(self) self.test_args = [] self.test_suite = True def run_tests(self): #import here, cause outside the eggs aren't loaded import tox import shlex args = self.tox_args if args: args = shlex.split(self.tox_args) tox.cmdline(args=args) setup( name='jenkins-job-builder-addons', version='1.0.5', description="A suite of jenkins job builder addons", long_description=readme + '\n\n' + history, author="Mike Buzzetti", author_email='mike.buzzetti@gmail.com', url='https://github.com/jimbydamonk/jenkins-job-builder-addons', packages=['jenkins_jobs_addons'], include_package_data=True, install_requires=requirements, license="Apache", zip_safe=False, keywords='jenkins ', classifiers=[ 'Development Status :: 2 - Pre-Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: Apache Software License', 'Natural Language :: English', "Programming Language :: Python :: 2", 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', ], test_suite='tests', tests_require=['tox'] + test_requirements, cmdclass={'test': Tox}, entry_points={ 'jenkins_jobs.projects': [ 'folder=jenkins_jobs_addons.folders:Folder', ], 'jenkins_jobs.views': [ 'all=jenkins_jobs_addons.views:all_view', 'build_pipeline=jenkins_jobs_addons.views:build_pipeline_view', 'delivery_pipeline=jenkins_jobs_addons.' 'views:delivery_pipeline_view' ], 'jenkins_jobs.modules': [ 'views=jenkins_jobs_addons.views:Views' ] }, )

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null

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null

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1

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1

65e1ff2eb00e84049f3aabe94179a02fc82570ba

802

py

Python

hw/scripts/__main__.py

jonasblixt/mongoose

4f392353f42d9c9245cdb5d9511348ec40bd936f

[ "BSD-3-Clause" ]

4

2019-07-31T17:59:14.000Z

2019-10-06T11:46:28.000Z

hw/scripts/__main__.py

jonasblixt/mongoose

4f392353f42d9c9245cdb5d9511348ec40bd936f

[ "BSD-3-Clause" ]

null

hw/scripts/__main__.py

jonasblixt/mongoose

4f392353f42d9c9245cdb5d9511348ec40bd936f

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- import kicad import model from stackups import JLCPCB6Layers #from dram import lp4 # IMX8MM # Diff pairs should be matched within 1ps # CK_t/CK_c max 200 ps # CA[5:0] # CS[1:0] min: CK_t - 25ps, max: CK_t + 25ps # CKE[1:0] # DQS0_t/DQS0_c min: CK_t - 85ps, max CK_t + 85ps # DQ[7:0] min: DQS0_t - 10ps, max DQS0_t + 10ps # DM0 # DQS1_t/DQS1_c min: CK_t - 85ps, max CK_t + 85ps # DQ[15:8] min: DQS1_t - 10ps, max DQS1_t + 10ps # DM1 if __name__ == "__main__": pcb = kicad.KicadPCB("../mongoose.kicad_pcb", JLCPCB6Layers()) # DiffPair(pcb, "_n","_p", max_delay_ps=200.0, max_skew_ps=1.0) for net_index in pcb.get_nets().keys(): net = pcb.get_nets()[net_index] print(net.get_name() + " dly: %.2f ps"%(net.get_delay_ps()))

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null

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1

65e2db02f151a8da25b3c6a7203333c4f0b917f2

4,795

py

Python

scripts/runOptimizer.py

sschulz365/PhC_Optimization

9a4add4eb638d797647cabbdf0f96b29b78114f2

[ "Naumen", "Condor-1.1", "MS-PL" ]

2

2017-05-13T05:33:06.000Z

2021-02-26T14:39:44.000Z

scripts/runOptimizer.py

sschulz365/PhC_Optimization

9a4add4eb638d797647cabbdf0f96b29b78114f2

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

scripts/runOptimizer.py

sschulz365/PhC_Optimization

9a4add4eb638d797647cabbdf0f96b29b78114f2

[ "Naumen", "Condor-1.1", "MS-PL" ]

null

#Sean Billings, 2015 import random import numpy import subprocess import constraints from experiment import Experiment from objectiveFunctions import WeightedSumObjectiveFunction, IdealDifferentialObjectiveFunction from waveGuideMPBOptimizer import differentialEvolution, createPopulation, gradientDescentAlgorithm import utilities import math paramMap = {} paramMap["s1"] = 0 # First row vertical shift paramMap["s2"] = 0 # Second row vertical shift paramMap["s3"] = 0 # Third row vertical shift paramMap["p1"] = 0 # First row horizontal shift paramMap["p2"] = 0 # Second row horizontal shift paramMap["p3"] = 0 # Third row horizontal shift paramMap["r0"] = 0.3 # Default air-hole radius paramMap["r1"] = 0.3 # Default first row radius paramMap["r2"] = 0.3 # Default second row radius paramMap["r3"] = 0.3 # Default third row radius # absolute path to the mpb executable mpb = "/Users/sean/documents/mpb-1.5/mpb/mpb" # absolute path to the input ctl inputFile = "/Users/sean/documents/W1_2D_v03.ctl.txt" # absolute path to the output ctl outputFile = "/Users/sean/documents/optimizerTestFile.txt" # we define a general experiment object # that we reuse whenever we need to make a command-line mpb call # see experiment.py for functionality experiment = Experiment(mpb, inputFile, outputFile) # ex.setParams(paramVector) experiment.setCalculationType('4') # accepts an int from 0 to 5 experiment.setBand(23) # see constraints.py constraintFunctions = [constraints.latticeConstraintsLD] max_generation = 15 # number of iterations of the DE alg population_size = 20 # number of solutions to consider in DE random_update = 0.2 # chance of updating vector fields in DE alg elite_size = 10 # number of solutions to store in DE, and use for GD band = 23 # band of interest for MPB computations # specify the weights for the IdealDifferentialObjectiveFunction w1 = 0 #0.01 # bandwidth weight w2 = 30 #100 # group index weight w3 = 0 # average loss weight w4 = 0 # BGP weight w5 = 30 #0.002 # loss at ngo (group index) weight w6 = 0 # these wights are use in the Objective Function to score mpb results weights = [ w1, w2, w3, w4, w5, w6] ideal_group_index = 30 #self.ideal_solution[0] ideal_bandwidth = 0.007 #self.ideal_solution[1] ideal_loss_at_group_index = 30 #self.ideal_solution[2] ideal_bgp = 0.3 #self.ideal_solution[3] ideal_delay = 300 #self.ideal_solution[4] ideal = [ideal_group_index, ideal_bandwidth, ideal_loss_at_group_index, ideal_bgp, ideal_delay] #Initialize objective function #objFunc = IdealDifferentialObjectiveFunction(weights, experiment, ideal) objFunc = WeightedSumObjectiveFunction(weights, experiment) # Differential Evolution section print "Starting Differential Evolution Optimizer" # DEsolutions is an array of solutions generated by the DE alg DEsolutions = differentialEvolution(constraintFunctions, objFunc, max_generation, population_size, random_update, paramMap, elite_size, experiment) print "\nDifferential Evolution solutions generated" population = DEsolutions # test line #population = createPopulation(constraintFunctions, population_size, paramMap) descent_scaler = 0.2 completion_scaler = 0.1 alpha_scaler = 0.9 # Gradient Descent Section print "\nStarting Gradient Descent Optimizer" # GDsolutions is an array of solutions generated by the GD algorihtms GDsolutions = gradientDescentAlgorithm(objFunc, constraintFunctions, population, descent_scaler, completion_scaler, alpha_scaler) population = GDsolutions print "\nResults" for solution in population: print "\nSolution: " + str(solution) results = objFunc.evaluate(solution) solution_score = results[0] bandwidth = results[1] group_index = results[2] avgLoss = results[3] # average loss bandwidth_group_index_product = results[4] #BGP loss_at_ng0 = results[5] # loss at group index print "\nScore: " + str(solution_score) print "\nNormalized Bandwidth: " + str(bandwidth) print "\nGroup Index: " + str(group_index) print "\nAverage Loss: " + str(avgLoss) print "\nLoss at Group Index: " + str(loss_at_ng0) print "\nBGP: " + str(bandwidth_group_index_product) #print "\nComputing Fabrication Stability..." #laplacian = utilities.computeLaplacian(weights, weightedSumObjectiveFunction, solution, experiment) #fabrication_stability = 0 #for key in laplacian.keys(): # fabrication_stability = fabrication_stability + laplacian[key]**2 #fabrication_stability = math.sqrt(fabrication_stability) #print "\nFabrication Stability " + str(fabrication_stability) print "\nOptimization Complete"

33.531469

104

0.737018

594

4,795

5.848485

0.340067

0.034542

0.024467

0.020725

0.045481

0.036269

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0.028834

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4,795

142

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null

0.153846

0

null

0

null

0

1

0

1

65e31c331679c439236e3ccff96fa39b9166d6f4

435

py

Python

setup.py

jigyasudhingra/music-recommendation-system

09c66c4f207002b200d6394cf72e853741e44b6e

[ "MIT" ]

2

2021-12-04T08:47:41.000Z

2021-12-06T16:54:36.000Z

setup.py

jigyasudhingra/music-recommendation-system

09c66c4f207002b200d6394cf72e853741e44b6e

[ "MIT" ]

null

setup.py

jigyasudhingra/music-recommendation-system

09c66c4f207002b200d6394cf72e853741e44b6e

[ "MIT" ]

1

2020-12-12T15:55:20.000Z

import os import urllib.request from zipfile import ZipFile HOME_DIRECTORY = os.path.join('datasets','raw') ROOT_URL = 'https://os.unil.cloud.switch.ch/fma/fma_metadata.zip' if not os.path.isdir(HOME_DIRECTORY): os.makedirs(HOME_DIRECTORY) zip_path = os.path.join(HOME_DIRECTORY, 'data.zip') urllib.request.urlretrieve(ROOT_URL, zip_path) with ZipFile(zip_path, 'r') as zip: zip.extractall(HOME_DIRECTORY) print("Done!")

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0.25

0.083333

0

null

1

0

null

0

1

65e8c2b06a56311edf49d920f21df0bd1cab027c

708

py

Python

StationeersSaveFileDebugTools.py

lostinplace/StationeersSaveFileDebugTools

372a2fc86a9fc3af25044a56131271b577d4d97b

[ "MIT" ]

null

StationeersSaveFileDebugTools.py

lostinplace/StationeersSaveFileDebugTools

372a2fc86a9fc3af25044a56131271b577d4d97b

[ "MIT" ]

1

2021-01-10T21:12:41.000Z

2021-01-10T21:14:49.000Z

StationeersSaveFileDebugTools.py

lostinplace/StationeersSaveFileDebugTools

372a2fc86a9fc3af25044a56131271b577d4d97b

[ "MIT" ]

null

import click @click.group() def cli(): pass @cli.command("restore_atmo") @click.argument('currentFile') @click.argument('backupFile') @click.argument('newFilePath') def restore_atmo(current_file, backup_file, new_file_path): from Utils.AtmoFileProcessing.RestoreAtmo import create_restored_world_file create_restored_world_file(current_file, backup_file, new_file_path) @cli.command("generate_start_condition") @click.argument('world') def generate_start_condition(world): from Utils.StartConditionProcessing.StartConditionGenerator import convert_world_file_to_startconditions out = convert_world_file_to_startconditions(world) print(out) if __name__ == '__main__': cli()

26.222222

108

0.79661

86

708

6.139535

0.44186

0.098485

0.064394

0.079545

0.246212

0.121212

0

0.103107

708

27

109

26.222222

0.831496

0

0.114245

0.03385

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1

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false

0.052632

0.157895

0

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0.052632

0

null

0

null

0

1

0

1

028ccbb703922e522d9de79fd431e21d9aeac192

909

py

Python

src/main/python/server/test.py

areichmann-tgm/client_travis

c00163e6d7630ff4efaf28605b134e356e02a9d1

[ "MIT" ]

null

src/main/python/server/test.py

areichmann-tgm/client_travis

c00163e6d7630ff4efaf28605b134e356e02a9d1

[ "MIT" ]

null

src/main/python/server/test.py

areichmann-tgm/client_travis

c00163e6d7630ff4efaf28605b134e356e02a9d1

[ "MIT" ]

null

import pytest from server import rest @pytest.fixture def client(): rest.app.testing = True #rest.app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///server.MyStudents' client = rest.app.test_client() yield client def test_get(client): res = client.get('/schueler') assert res.status_code == 200 def test_get(client): res = client.get('/schuelerA') assert res.status_code == 200 def test_delete(client): res = client.delete('/schuelerA',data={'schueler_id':'1000'}) assert res.status_code == 200 def test_update(client): """res = client.put('/schuelerA',data={'schueler_id':'1000','usernameX':'Adrian','emailX':'adrian@new.at','picture':'-'})""" assert True def test_insert(client): res = client.put('/schuelerA',data={'schueler_id': '10', 'usernameX': 'Nicht_Adrian', 'emailX': 'adrian@new.at', 'picture': '-'}) assert res.status_code == 200

25.971429

133

0.667767

119

909

4.966387

0.352941

0.059222

0.126904

0.128596

0.57022

0.490694

0.138748

0

0.028497

0.150715

909

34

134

26.735294

0.737047

0.212321

0

0.285714

0

0.1622

0

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1

0.285714

false

0

0.095238

0

0.380952

0

null

0

null

0

1

0

1

028dbb898943de5745b9b0587b4aecb405f08834

3,143

py

Python

helper-scripts/instrnocombine.py

felixaestheticus/realcode-validation

c599cc41797fc074bd2b71d205d6b2b904e1d64b

[ "BSD-3-Clause" ]

null

helper-scripts/instrnocombine.py

felixaestheticus/realcode-validation

c599cc41797fc074bd2b71d205d6b2b904e1d64b

[ "BSD-3-Clause" ]

null

helper-scripts/instrnocombine.py

felixaestheticus/realcode-validation

c599cc41797fc074bd2b71d205d6b2b904e1d64b

[ "BSD-3-Clause" ]

null

#combine.py, combines available dictionaries into one, and generates csv file for latex #f = open('dict_random') mem_ops = 'MOVS','MOV','LDR','LDRH','LDRB','LDRSH','LDRSB','LDM','STR','STRH','STRB','STM' ari_ops = 'ADDS','ADD','ADC','ADCS','ADR','SUBS','SUB','SBCS','RSBS','MULS','MUL','RSB','SBC' com_ops = 'CMP','CMN' log_ops = 'ANDS','EORS','ORRS','BICS','MVNS','TST','EOR','MVN','ORR' sys_ops = 'PUSH','POP','SVC','CPSID','CPSIE','MRS','MSR','BKPT','SEV','WFE','WFI','YIELD','NOP','ISB','DMB','DSB' bra_ops = 'B','BL','BLX','BX','BCC','BCS','BEQ','BIC','BLS','BNE','BPL','BGE','BGT','BHI','BLE','BLT','BMI','BVC','BVS' man_ops = 'SXTH','SXTB','UXTH','UXTB','REV','REV16','REVSH','LSLS','LSRS','RORS','ASR','ASRS','LSL','LSR','ROR' import os,sys path = '.' #files = [] #for i in os.listdir(path): # if os.path.isfile(os.path.join(path,i)) and i.startswith('typelist') and not i.endswith('~'): # files.append(i) files = sys.argv[1:] print(files) dic_all = {} print(dic_all) for f in files: f = open(f) lines = f.readlines() dic = {} line = lines[0] if(line!= ''): dic = eval(line) for key in dic: if(key not in dic_all): dic_all[key] = str(dic[key]) else: dic_all[key] = str(dic_all[key]) + "," + str(dic[key]) for key in dic_all: dic_all[key] = '' for f in files: f = open(f) lines = f.readlines() dic = {} line = lines[0] if(line!= ''): dic = eval(line) for key in dic: #if(dic_all[key] != ''): dic_all[key] = str(dic_all[key]) + str(dic[key]) for key in dic_all: if(key not in dic): dic_all[key] = str(dic_all[key]) +"0" dic_all[key] = str(dic_all[key]) +"," print(dic_all) ou = open('dict_nocomb','w') ou.write(str(dic_all)) csv1 = open("tablenocomb1.csv","w") csv2 = open("tablenocomb2.csv","w") csv1.write("Instr. Name, Occur.(Random),Occur.(Real),Type\n") csv2.write("Instr. Name, Occur.(Random),Occur.(Real),Type\n") keylist = [key for key in dic_all] keylist.sort() nonempty = 0.0 nonemptyr = 0.0 for key in dic_all: h= str(key) if(h in mem_ops): #print("1\n") dic_all[key] = dic_all[key]+'M' elif(h in ari_ops): #print("2\n") dic_all[key] = dic_all[key]+'A' elif(h in com_ops): #print("3\n") dic_all[key] = dic_all[key]+'C' elif(h in log_ops): #print("4\n") dic_all[key] = dic_all[key]+'L' elif(h in sys_ops): #print("5\n") dic_all[key] = dic_all[key]+'S' elif(h in bra_ops): #print("6\n") dic_all[key] = dic_all[key]+'B' elif(h in man_ops): #print("7\n") dic_all[key] = dic_all[key]+'R' else: #print("no cat, sorry\n") dic_all[key] = dic_all[key]+'O' #for key in dic_all: for i in range(len(keylist)): key = keylist[i] if(dic_all[key].split(",")[1]!='0'): nonempty = nonempty+1 #print(str(i)+",") if(dic_all[key].split(",")[0]!='0'): nonemptyr = nonemptyr+1 if(i < len(keylist)/2): csv1.write(str(key) + ',' + str(dic_all[key])+'\n') else: csv2.write(str(key) + ',' + str(dic_all[key])+'\n') print( "Coverage rate -real:" + str(nonempty/len(keylist))) print( "Coverage rate - random:" + str(nonemptyr/len(keylist))) csv1.close() csv2.close() #print( "Success rate:" + str((nonempty/len(keylist)))

25.144

119

0.602291

547

3,143

3.35649

0.330896

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0.151961

0.058824

0.381264

0.355664

0.303922

0.198257

0.172113

0.12963

0

0.012588

0.14063

3,143

124

120

25.346774

0.66716

0.154311

0

0.289157

1

0

0.189087

0.025767

0

1

0

false

0

0.012048

0

0.012048

0.060241

0

null

0

1

0

null

0

1

02974a7f2e55a4545889ad1727cb810be5d621b5

1,254

py

Python

file/txt2bin.py

QPointNotebook/PythonSample

53c2a54da2bf9a61449ed1c7d2864c5c0eedc5e0

[ "MIT" ]

null

file/txt2bin.py

QPointNotebook/PythonSample

53c2a54da2bf9a61449ed1c7d2864c5c0eedc5e0

[ "MIT" ]

null

file/txt2bin.py

QPointNotebook/PythonSample

53c2a54da2bf9a61449ed1c7d2864c5c0eedc5e0

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from file.file import file class txt2bin( file ): def read( self, file ): datas = [] with open( file, 'r', encoding='utf-8' ) as f: lines = f.readlines() for line in lines: data = line.splitlines() # split by '\n' if not data[0]: d = b'' else: val = int( data[0], 16 ) # txt -> int leng = len( data[0] ) // 2 d = val.to_bytes( leng, byteorder='big' ) # int -> binary datas.append( d ) return datas def write( self, file, datas ): with open( file, 'w', encoding='utf-8' ) as f: for data in datas: val = int.from_bytes( data, byteorder='big' ) # binary -> int d = hex( val ) # int -> hex s = str( d )[2:] # cut '0x' if len( data ) == 0: f.write( '\n' ) else: if data[0] < 0x10: s = '0' + s # add '0' for loss of digit f.write( s + '\n' )

31.35

77

0.369219

138

1,254

3.34058

0.463768

0.05423

0.056399

0.073753

0.173536

0.10846

0

0.030547

0.503987

1,254

39

78

32.153846

0.710611

0.11244

0

0.071429

0

0.020833

0

0.003623

0

1

0.071429

false

0

0.035714

0

0.178571

0

null

0

1

0

null

0

1

029b069d68471e7fbe34c10e131ca57fcd80d3f5

892

py

Python

blog/app/admin/views.py

web-user/flask-blog

130f5dbcdb18b8f325c7aa8dd3d71cbc7190485a

[ "MIT" ]

null

blog/app/admin/views.py

web-user/flask-blog

130f5dbcdb18b8f325c7aa8dd3d71cbc7190485a

[ "MIT" ]

null

blog/app/admin/views.py

web-user/flask-blog

130f5dbcdb18b8f325c7aa8dd3d71cbc7190485a

[ "MIT" ]

null

from flask import Flask, render_template, session, redirect, url_for, request, flash, abort, current_app, make_response from flask_login import login_user, logout_user, login_required, current_user from . import admin from .. import db from ..models import User, Post from ..form import PostForm from functools import wraps from flask import g, request, redirect, url_for @admin.route('/admin', methods = ['GET', 'POST']) @login_required def admin(): form = PostForm() error = None if request.method == 'POST' and form.validate(): print(form.body.data) print('MMM----------NNNN') post = Post(body=form.body.data, title=form.title.data) db.session.add(post) db.session.commit() return redirect(url_for('main.home')) flash('Invalid username or password.') return render_template('admin.html', title='Admin', form=form)

34.307692

119

0.690583

121

892

4.983471

0.454545

0.044776

0.069652

0

0.180493

892

25

120

35.68

0.824897

0

0.097753

0

1

0.045455

false

0.045455

0.363636

0

0.5

0.090909

0

null

0

null

0

1

0

1

029ed725f1f2d111375bab605c9d49677c361f7c

2,858

py

Python

src/tests/crud/test_user.py

Behnam-sn/neat-backend

ba6e6356ee092eba27179f72fd2a15e25c68d1b8

[ "MIT" ]

1

2022-03-07T22:16:48.000Z

src/tests/crud/test_user.py

Behnam-sn/neat-backend

ba6e6356ee092eba27179f72fd2a15e25c68d1b8

[ "MIT" ]

null

src/tests/crud/test_user.py

Behnam-sn/neat-backend

ba6e6356ee092eba27179f72fd2a15e25c68d1b8

[ "MIT" ]

1

2022-03-07T22:16:49.000Z

from sqlalchemy.orm import Session from src import crud from src.core.security import verify_password from src.schemas.user import UserCreate, UserUpdate from src.tests.utils.user import create_random_user_by_api from src.tests.utils.utils import random_lower_string def test_create_user(db: Session): username = random_lower_string() password = random_lower_string() user_in = UserCreate(username=username, password=password) user_obj = crud.create_user(db, user=user_in) assert user_obj.username == username assert hasattr(user_obj, "hashed_password") def test_authenticate_user(db: Session): username = random_lower_string() password = random_lower_string() create_random_user_by_api(username=username, password=password) authenticated_user = crud.authenticate_user( db, username=username, password=password ) assert authenticated_user assert authenticated_user.username == username def test_not_authenticate_user(db: Session): user = crud.authenticate_user( db, username=random_lower_string(), password=random_lower_string() ) assert user is None def test_get_all_users(db: Session): users = crud. get_users(db) assert users def test_get_user(db: Session): username = random_lower_string() password = random_lower_string() create_random_user_by_api(username=username, password=password) user = crud.get_user_by_username(db, username=username) assert user assert user.username == username def test_update_user(db: Session): username = random_lower_string() password = random_lower_string() create_random_user_by_api(username=username, password=password) new_username = random_lower_string() full_name = random_lower_string() user_in_update = UserUpdate( username=new_username, full_name=full_name, ) crud.update_user(db, username=username, user_update=user_in_update) user = crud.get_user_by_username(db, username=new_username) assert user assert username != new_username assert user.full_name def test_update_password(db: Session): username = random_lower_string() password = random_lower_string() create_random_user_by_api(username=username, password=password) new_password = random_lower_string() crud.update_password(db, username=username, new_password=new_password) user = crud.get_user_by_username(db, username=username) assert user assert verify_password(new_password, user.hashed_password) def test_delete_user(db: Session): username = random_lower_string() password = random_lower_string() create_random_user_by_api(username=username, password=password) user = crud.remove_user(db, username=username) assert user assert user.username == username

26.220183

74

0.747726

369

2,858

5.457995

0.124661

0.098312

0.151936

0.099305

0.57994

0.495531

0.46574

0.423535

0.386792

0

0.175647

2,858

108

75

26.462963

0.854839

0

0.388889

0

0.005248

0

0.208333

1

0.111111

false

0.277778

0.083333

0

0.194444

0

null

0

null

0

1

0

1

02a4beb0015cd6725cf78ab2fb76439c197ecfc1

2,073

py

Python

sims/s251/calc-err.py

ammarhakim/ammar-simjournal

85b64ddc9556f01a4fab37977864a7d878eac637

[ "MIT", "Unlicense" ]

1

2019-12-19T16:21:13.000Z

sims/s251/calc-err.py

ammarhakim/ammar-simjournal

85b64ddc9556f01a4fab37977864a7d878eac637

[ "MIT", "Unlicense" ]

null

sims/s251/calc-err.py

ammarhakim/ammar-simjournal

85b64ddc9556f01a4fab37977864a7d878eac637

[ "MIT", "Unlicense" ]

2

2020-01-08T06:23:33.000Z

2020-01-08T07:06:50.000Z

from pylab import * import tables def exactSol(X, Y, t): return exp(-2*t)*sin(X)*cos(Y) fh = tables.openFile("s251-dg-diffuse-2d_q_1.h5") q = fh.root.StructGridField nx, ny, nc = q.shape dx = 2*pi/nx Xf = linspace(0, 2*pi-dx, nx) dy = 2*pi/ny Yf = linspace(0, 2*pi-dy, ny) XX, YY = meshgrid(Xf, Yf) Xhr = linspace(0, 2*pi, 101) Yhr = linspace(0, 2*pi, 101) XXhr, YYhr = meshgrid(Xhr, Yhr) fhr = exactSol(XXhr, YYhr, 1.0) figure(1) pcolormesh(Xhr, Yhr, fhr) colorbar() figure(2) pcolormesh(Xf, Yf, q[:,:,0]) colorbar() # compute error fex = exactSol(XX, YY, 1.0) error = abs(fex.transpose()-q[:,:,0]).sum()/(nx*ny); print "%g %g" % (dx, error) def evalSum(coeff, fields): res = 0.0*fields[0] for i in range(len(coeff)): res = res + coeff[i]*fields[i] return res def projectOnFinerGrid_f24(Xc, Yc, q): dx = Xc[1]-Xc[0] dy = Yc[1]-Yc[0] nx = Xc.shape[0] ny = Yc.shape[0] # mesh coordinates Xn = linspace(Xc[0]-0.5*dx, Xc[-1]+0.5*dx, 2*nx+1) # one more Yn = linspace(Yc[0]-0.5*dy, Yc[-1]+0.5*dy, 2*ny+1) # one more XXn, YYn = meshgrid(Xn, Yn) # data qn = zeros((2*Xc.shape[0], 2*Yc.shape[0]), float) v1 = q[:,:,0] v2 = q[:,:,1] v3 = q[:,:,2] v4 = q[:,:,3] vList = [v1,v2,v3,v4] # node 1 c1 = [0.5625,0.1875,0.0625,0.1875] qn[0:2*nx:2, 0:2*ny:2] = evalSum(c1, vList) # node 2 c2 = [0.1875,0.5625,0.1875,0.0625] qn[1:2*nx:2, 0:2*ny:2] = evalSum(c2, vList) # node 3 c3 = [0.1875,0.0625,0.1875,0.5625] qn[0:2*nx:2, 1:2*ny:2] = evalSum(c3, vList) # node 4 c4 = [0.0625,0.1875,0.5625,0.1875] qn[1:2*nx:2, 1:2*ny:2] = evalSum(c4, vList) return XXn, YYn, qn Xc = linspace(0.5*dx, 2*pi-0.5*dx, nx) Yc = linspace(0.5*dy, 2*pi-0.5*dy, ny) Xp, Yp, qp = projectOnFinerGrid_f24(Xc, Yc, q) figure(1) subplot(1,2,1) pcolormesh(Xp, Yp, transpose(qp)) title('RDG t=1') colorbar(shrink=0.5) axis('image') subplot(1,2,2) pcolormesh(Xhr, Yhr, fhr) title('Exact t=1') colorbar(shrink=0.5) axis('image') savefig('s251-exact-cmp.png') show()

21.371134

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0.578389

412

2,073

2.900485

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0.014493

0

null

0

null

0

1

0

1

02a600c96645d56c182f0a175380bb6948a7e4b5

973

py

Python

PyCharm/Exercicios/Aula12/ex041.py

fabiodarice/Python

15ec1c7428f138be875111ac98ba38cf2eec1a93

[ "MIT" ]

null

PyCharm/Exercicios/Aula12/ex041.py

fabiodarice/Python

15ec1c7428f138be875111ac98ba38cf2eec1a93

[ "MIT" ]

null

PyCharm/Exercicios/Aula12/ex041.py

fabiodarice/Python

15ec1c7428f138be875111ac98ba38cf2eec1a93

[ "MIT" ]

null

# Importação de bibliotecas from datetime import date # Título do programa print('\033[1;34;40mCLASSIFICAÇÃO DE CATEGORIAS PARA NATAÇÃO\033[m') # Objetos nascimento = int(input('\033[30mDigite o ano do seu nascimento:\033[m ')) idade = date.today().year - nascimento mirim = 9 infantil = 14 junior = 19 senior = 20 # Lógica if idade <= mirim: print('Sua idade é \033[1;33m{} anos\033[m, e sua categoria é a \033[1;34mMIRIM!\033[m'.format(idade)) elif idade <= infantil: print('Sua idade é \033[1;33m{}\033[m anos, e sua categoria é a \033[1;34mINFANTIL!\033[m'.format(idade)) elif idade <= junior: print('Sua idade é \033[1;33m{}\033[m anos, e sua categoria é a \033[1;34mJUNIOR!\033[m'.format(idade)) elif idade <= senior: print('Sua idade é \033[1;33m{}\033[m anos, e sua categoria é a \033[1;34mSÊNIOR!\033[m'.format(idade)) elif idade > senior: print('Sua idade é \033[1;33m{}\033[m anos, e sua categoria é \033[1;34mMASTER!\033[m'.format(idade))

38.92

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null

0

1

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null

0

1

02a632349d6da6f348ea1c189802c694c33a0241

1,681

py

Python

github-bot/harvester_github_bot/config.py

futuretea/bot

5f1f1a08e0fca6519e0126ff8f0b87fec23a38e3

[ "Apache-2.0" ]

null

github-bot/harvester_github_bot/config.py

futuretea/bot

5f1f1a08e0fca6519e0126ff8f0b87fec23a38e3

[ "Apache-2.0" ]

null

github-bot/harvester_github_bot/config.py

futuretea/bot

5f1f1a08e0fca6519e0126ff8f0b87fec23a38e3

[ "Apache-2.0" ]

null

from everett.component import RequiredConfigMixin, ConfigOptions from everett.manager import ConfigManager, ConfigOSEnv class BotConfig(RequiredConfigMixin): required_config = ConfigOptions() required_config.add_option('flask_loglevel', parser=str, default='info', doc='Set the log level for Flask.') required_config.add_option('flask_password', parser=str, doc='Password for HTTP authentication in Flask.') required_config.add_option('flask_username', parser=str, doc='Username for HTTP authentication in Flask.') required_config.add_option('github_owner', parser=str, default='harvester', doc='Set the owner of the target GitHub ' 'repository.') required_config.add_option('github_repository', parser=str, default='harvester', doc='Set the name of the target ' 'GitHub repository.') required_config.add_option('github_repository_test', parser=str, default='tests', doc='Set the name of the tests ' 'GitHub repository.') required_config.add_option('github_token', parser=str, doc='Set the token of the GitHub machine user.') required_config.add_option('zenhub_pipeline', parser=str, default='Review', doc='Set the target ZenHub pipeline to ' 'handle events for.') def get_config(): config = ConfigManager(environments=[ ConfigOSEnv() ]) return config.with_options(BotConfig())

64.653846

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172

1,681

5.767442

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0.127016

0.137097

0.185484

0.444556

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0.349798

0.235887

0.133065

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1,681

25

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67.24

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false

0.047619

0.095238

0

0.285714

0

null

0

1

0

null

0

1

02b2cd966c362b3581d56d85cfd72c1cf6dfa614

1,212

py

Python

finetwork/plotter/_centrality_metrics.py

annakuchko/FinNetwork

4566ff96b33fb5668f9b28f41a94791d1cf9249c

[ "MIT" ]

5

2021-12-07T22:14:10.000Z

2022-03-30T14:09:15.000Z

finetwork/plotter/_centrality_metrics.py

annakuchko/FinNetwork

4566ff96b33fb5668f9b28f41a94791d1cf9249c

[ "MIT" ]

null

finetwork/plotter/_centrality_metrics.py

annakuchko/FinNetwork

4566ff96b33fb5668f9b28f41a94791d1cf9249c

[ "MIT" ]

null

import networkx as nx class _CentralityMetrics: def __init__(self, G, metrics): self.G = G self.metrics = metrics def _compute_metrics(self): metrics = self.metrics if metrics == 'degree_centrality': c = self.degree_centrality() elif metrics == 'betweenness_centrality': c = self.betweenness_centrality() elif metrics == 'closeness_centrality': c = self.closeness_centrality() elif metrics == 'eigenvector_centrality': c = self.bonachi_eigenvector_centrality() return c def degree_centrality(self): centrality = nx.degree_centrality(self.G, weight='weight') return centrality def betweenness_centrality(self): centrality = nx.betweenness_centrality(self.G, weight='weight') return centrality def closeness_centrality(self): centrality = nx.closeness_centrality(self.G, weight='weight') return centrality def bonachi_eigenvector_centrality(self): centrality = nx.eigenvector_centrality(self.G, weight='weight') return centrality

32.756757

72

0.615512

116

1,212

6.215517

0.198276

0.15534

0.083218

0.144244

0.25104

0.191401

0

0.302805

1,212

36

73

33.666667

0.853254

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0.142857

0

0.089286

0.037415

0

1

0.214286

false

0

0.035714

0

0.464286

0

null

0

null

0

1

0

1

02b6a5972aef51ad1a07e6ff7ba0827ae6cad8a4

2,235

py

Python

t/text_test.py

gsnedders/Template-Python

4081e4d820c1be0c0448a8dcb79e0703066da099

[ "Artistic-2.0" ]

null

t/text_test.py

gsnedders/Template-Python

4081e4d820c1be0c0448a8dcb79e0703066da099

[ "Artistic-2.0" ]

6

2015-10-13T13:46:10.000Z

2019-06-17T09:39:57.000Z

t/text_test.py

gsnedders/Template-Python

4081e4d820c1be0c0448a8dcb79e0703066da099

[ "Artistic-2.0" ]

3

2018-12-03T13:15:21.000Z

2019-03-13T09:12:09.000Z

from template import Template from template.test import TestCase, main class Stringy: def __init__(self, text): self.text = text def asString(self): return self.text __str__ = asString class TextTest(TestCase): def testText(self): tt = (("basic", Template()), ("interp", Template({ "INTERPOLATE": 1 }))) vars = self._callsign() v2 = { "ref": lambda obj: "%s[%s]" % (obj, obj.__class__.__name__), "sfoo": Stringy("foo"), "sbar": Stringy("bar") } vars.update(v2) self.Expect(DATA, tt, vars) DATA = r""" -- test -- This is a text block "hello" 'hello' 1/3 1\4 <html> </html> $ @ { } @{ } ${ } # ~ ' ! % *foo $a ${b} $c -- expect -- This is a text block "hello" 'hello' 1/3 1\4 <html> </html> $ @ { } @{ } ${ } # ~ ' ! % *foo $a ${b} $c -- test -- <table width=50%>© -- expect -- <table width=50%>© -- test -- [% foo = 'Hello World' -%] start [% # # [% foo %] # # -%] end -- expect -- start end -- test -- pre [% # [% PROCESS foo %] -%] mid [% BLOCK foo; "This is foo"; END %] -- expect -- pre mid -- test -- -- use interp -- This is a text block "hello" 'hello' 1/3 1\4 <html> </html> \$ @ { } @{ } \${ } # ~ ' ! % *foo $a ${b} $c -- expect -- This is a text block "hello" 'hello' 1/3 1\4 <html> </html> $ @ { } @{ } ${ } # ~ ' ! % *foo alpha bravo charlie -- test -- <table width=50%>© -- expect -- <table width=50%>© -- test -- [% foo = 'Hello World' -%] start [% # # [% foo %] # # -%] end -- expect -- start end -- test -- pre [% # # [% PROCESS foo %] # -%] mid [% BLOCK foo; "This is foo"; END %] -- expect -- pre mid -- test -- [% a = "C'est un test"; a %] -- expect -- C'est un test -- test -- [% META title = "C'est un test" -%] [% component.title -%] -- expect -- C'est un test -- test -- [% META title = 'C\'est un autre test' -%] [% component.title -%] -- expect -- C'est un autre test -- test -- [% META title = "C'est un \"test\"" -%] [% component.title -%] -- expect -- C'est un "test" -- test -- [% sfoo %]/[% sbar %] -- expect -- foo/bar -- test -- [% s1 = "$sfoo" s2 = "$sbar "; s3 = sfoo; ref(s1); '/'; ref(s2); '/'; ref(s3); -%] -- expect -- foo[str]/bar [str]/foo[Stringy] """

14.607843

71

0.503803

293

2,235

3.784983

0.242321

0.028855

0.043282

0.054103

0.58972

0.580703

0.553652

0

0.019596

0.246532

2,235

152

72

14.703947

0.638955

0

0.640625

0

0.03125

0.760412

0

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0.023438

false

0

0.015625

0.007813

0.070313

0

null

0

1

0

null

0

1

02bb2ad5f8635de13653c1ed22f4978ec39fcfc6

377

py

Python

performance_test.py

alan-augustine/python_singly_linkedlist

f227a4154b22de8a273d319ecdd6329035d5d258

[ "MIT" ]

null

performance_test.py

alan-augustine/python_singly_linkedlist

f227a4154b22de8a273d319ecdd6329035d5d258

[ "MIT" ]

null

performance_test.py

alan-augustine/python_singly_linkedlist

f227a4154b22de8a273d319ecdd6329035d5d258

[ "MIT" ]

null

from time import time import os import sys sys.path.append(os.path.join(os.path.dirname(__file__), 'src')) from singly_linkedlist.singly_linkedlist import SinglyLinkedList start = time() linked_list = SinglyLinkedList() for i in range(100000): linked_list.insert_head(111111111111) end = time() print("Took {0} seconds".format(start-end)) # linked_list.print_elements()

23.5625

64

0.774536

54

377

5.203704

0.592593

0.106762

0

0.05638

0.106101

377

15

65

25.133333

0.777448

0.074271

0

0.054913

0

1

0

false

0

0.363636

0

0.363636

0.090909

0

null

0

null

0

1

0

1

02bea4753652cd78237dd184ed6e67ea923d42ea

454

py

Python

dataprocess/print_msg.py

lifelong-robotic-vision/openloris-scene-tools

ce6a4839f618bf036d3f3dbae14561bfc7413641

[ "MIT" ]

13

2021-03-27T15:49:21.000Z

2022-03-19T13:26:30.000Z

dataprocess/print_msg.py

lifelong-robotic-vision/openloris-scene-tools

ce6a4839f618bf036d3f3dbae14561bfc7413641

[ "MIT" ]

4

2021-03-30T10:40:43.000Z

2022-03-28T01:36:57.000Z

dataprocess/print_msg.py

lifelong-robotic-vision/openloris-scene-tools

ce6a4839f618bf036d3f3dbae14561bfc7413641

[ "MIT" ]

1

2022-02-16T13:42:32.000Z

#!/usr/bin/env python2 import rosbag import sys filename = sys.argv[1] topics = sys.argv[2:] with rosbag.Bag(filename) as bag: for topic, msg, t in bag.read_messages(topics): print('%s @%.7f ----------------------------' % (topic, t.to_sec())) print(msg) print('Press ENTER to continue') while True: try: raw_input() break except EOFError: pass

25.222222

76

0.497797

54

454

4.12963

0.722222

0.06278

0

0.013158

0.330396

454

17

77

26.705882

0.720395

0.046256

0

0.138889

0.064815

0

1

0

false

0.066667

0.133333

0

0.133333

0.2

0

null

0

null

0

1

0

1

02beda4568a4663c141bf81401d0595971779e3a

1,011

py

Python

alegra/resources/invoice.py

okchaty/alegra

6c423b23a24650c9121da5f165f6f03669b98468

[ "MIT" ]

1

2022-03-31T03:44:50.000Z

alegra/resources/invoice.py

okchaty/alegra

6c423b23a24650c9121da5f165f6f03669b98468

[ "MIT" ]

4

2020-03-24T17:54:03.000Z

2021-06-02T00:48:50.000Z

alegra/resources/invoice.py

okchaty/alegra

6c423b23a24650c9121da5f165f6f03669b98468

[ "MIT" ]

null

from alegra.api_requestor import APIRequestor from alegra.resources.abstract import CreateableAPIResource from alegra.resources.abstract import EmailableAPIResource from alegra.resources.abstract import ListableAPIResource from alegra.resources.abstract import UpdateableAPIResource from alegra.resources.abstract import VoidableAPIResource class Invoice( CreateableAPIResource, EmailableAPIResource, ListableAPIResource, UpdateableAPIResource, VoidableAPIResource, ): OBJECT_NAME = "invoices" @classmethod def open(cls, resource_id, user=None, token=None, api_base=None, api_version=None, **json): requestor = APIRequestor( user=user, token=token, api_base=api_base, api_version=api_version, ) url = cls.class_url() + str(resource_id) + "/open/" response = requestor.request( method="post", url=url, json=json, ) return response

29.735294

68

0.681503

96

1,011

7.0625

0.385417

0.088496

0.140118

0.199115

0.243363

0

0.246291

1,011

33

69

30.636364

0.889764

0

0.017804

0

1

0.033333

false

0

0.2

0

0.333333

0

null

0

1

0

null

0

1

02c18f6d2d3ebb8100e01a783419de97602121b6

1,723

py

Python

code/generateElevationFile.py

etcluvic/sme.altm

ffdb51d380a6b8cd8073d5ef3bd6fd15fa0779ea

[ "CC-BY-4.0" ]

null

code/generateElevationFile.py

etcluvic/sme.altm

ffdb51d380a6b8cd8073d5ef3bd6fd15fa0779ea

[ "CC-BY-4.0" ]

null

code/generateElevationFile.py

etcluvic/sme.altm

ffdb51d380a6b8cd8073d5ef3bd6fd15fa0779ea

[ "CC-BY-4.0" ]

null

from bs4 import BeautifulSoup from datetime import datetime from lxml import etree import time import codecs import pickle import os def printSeparator(character, times): print(character * times) if __name__ == '__main__': doiPrefix = '10.7202' #erudit's doi prefix myTime = datetime.now().strftime('%Y-%m-%d_%H-%M-%S-%f') referencedDocs = '/mnt/smeCode/altm/code/out/' + '2017-10-13_22-44-03-672976' + '.xml' pickleFile = '/mnt/smeCode/parseMe2/code/pickles/keywords.p' outputPath = '/mnt/smeCode/altm/code/elevation.files/' outputFile = 'test.xml' printSeparator('*',50) print('loading pickle...') keywords = pickle.load( open( pickleFile, "rb" ) ) print('pickle loaded!') printSeparator('*',50) #elevation file rootElement = etree.Element("elevate") f = codecs.open(referencedDocs,'r','utf-8') markup = f.read() f.close() soup = BeautifulSoup(markup, "lxml-xml") documents = soup.find_all('doi') for d in documents: doi = d.get_text().split('/')[1] print(doi) #print(d.get_text()) if doi in keywords.keys(): print(keywords[doi]) queryElement = etree.SubElement(rootElement, "query") queryElement.set("text", ' '. join(list(keywords[doi]['terms']))) docElement = etree.SubElement(queryElement, "doc") docElement.set("id", doi) printSeparator('*',50) printSeparator('*', 50) print 'Elevation - Saving xml file...' xmlString = etree.tostring(rootElement, pretty_print=True, encoding='UTF-8') fh = codecs.open(os.path.join(outputPath, myTime + '.xml'),'w', encoding='utf-8' ) fh.write(xmlString.decode('utf-8')) fh.close() print 'done' printSeparator('*', 50) print(xmlString) print('bye')

22.671053

89

0.663958

218

1,723

5.183486

0.490826

0.070796

0.055752

0.031858

0

0.029799

0.162507

1,723

75

90

22.973333

0.753292

0.03018

0

0.106383

0

0.194245

0.082134

0.021277

0

null

0

0.148936

null

0.340426

0

null

0

null

0

1

0

1

02c8b5302247d3f0de4a0fcfd8043adc64146600

1,564

py

Python

setup.py

nilp0inter/threadedprocess

0120d6e795782c9f527397490846cd214d9196e1

[ "PSF-2.0" ]

9

2018-03-21T22:19:10.000Z

2021-06-08T12:10:15.000Z

setup.py

nilp0inter/threadedprocess

0120d6e795782c9f527397490846cd214d9196e1

[ "PSF-2.0" ]

3

2019-09-18T19:57:28.000Z

2020-07-17T08:06:54.000Z

setup.py

nilp0inter/threadedprocess

0120d6e795782c9f527397490846cd214d9196e1

[ "PSF-2.0" ]

4

2018-03-24T23:10:38.000Z

2020-06-18T02:26:24.000Z

import os from setuptools import setup try: import concurrent.futures except ImportError: CONCURRENT_FUTURES_PRESENT = False else: CONCURRENT_FUTURES_PRESENT = True def read(fname): return open(os.path.join(os.path.dirname(__file__), fname)).read() setup( name="threadedprocess", version="0.0.5", author="Roberto Abdelkader Martinez Perez", author_email="robertomartinezp@gmail.com", description=( "A `ThreadedProcessPoolExecutor` is formed by a modified " "`ProcessPoolExecutor` that generates processes that use a " "`ThreadPoolExecutor` instance to run the given tasks."), license="BSD", keywords="concurrent futures executor process thread", url="https://github.com/nilp0inter/threadedprocess", py_modules=['threadedprocess'], long_description=read('README.rst'), install_requires=[] if CONCURRENT_FUTURES_PRESENT else ["futures"], classifiers=[ "Development Status :: 3 - Alpha", "License :: OSI Approved :: BSD License", 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: Implementation :: CPython', 'Programming Language :: Python :: Implementation :: PyPy' ], )

34

71

0.658568

160

1,564

6.35

0.56875

0.187008

0.246063

0.102362

0

0.013889

0.217391

1,564

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0.15

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null

0

1

0

null

0

1

02c90b77315d19cadcdffd4cbada1b9dd920626e

2,592

py

Python

coremltools/converters/mil/mil/passes/const_elimination.py

VadimLevin/coremltools

66c17b0fa040a0d8088d33590ab5c355478a9e5c

[ "BSD-3-Clause" ]

3

2018-10-02T17:23:01.000Z

2020-08-15T04:47:07.000Z

coremltools/converters/mil/mil/passes/const_elimination.py

holzschu/coremltools

5ece9069a1487d5083f00f56afe07832d88e3dfa

[ "BSD-3-Clause" ]

null

coremltools/converters/mil/mil/passes/const_elimination.py

holzschu/coremltools

5ece9069a1487d5083f00f56afe07832d88e3dfa

[ "BSD-3-Clause" ]

1

2021-05-07T15:38:20.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2020, Apple Inc. All rights reserved. # # Use of this source code is governed by a BSD-3-clause license that can be # found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause import numpy as np from coremltools.converters.mil.mil import Builder as mb from coremltools.converters.mil.mil.passes.pass_registry import register_pass def get_const_mode(val): # Heuristics to determine if a val should be file value or immediate # value. if isinstance(val, (str, bool, int)): return "immediate_value" if isinstance(val, (np.generic, np.ndarray)): if val.size > 10: return "file_value" return "immediate_value" raise ValueError("val {} not recognized.".format(val)) def const_elimination_block(block): # shallow copy hides changes on f.operations during the loop for op in list(block.operations): if op.op_type == "const": continue for b in op.blocks: const_elimination_block(b) all_outputs_are_const = True for i, o in enumerate(op.outputs): if o.val is not None: with block: res = mb.const( val=o.val, mode=get_const_mode(o.val), before_op=op, # same var name, but different python # instance does not violate SSA property. name=o.name, ) op.enclosing_block.replace_uses_of_var_after_op( anchor_op=op, old_var=o, new_var=res ) # rename the const output o.set_name(o.name+'_ignored') else: all_outputs_are_const = False if all_outputs_are_const: op.remove_from_block() @register_pass(namespace="common") def const_elimination(prog): """ prog: Program # Replace non-const ops that have const Var # outputs replaced with const op. Example: # # Given: # %2, %3 = non_const_op(...) # %2 is const, %3 isn't const # %4 = other_op(%2, %3) # # Result: # _, %3 = non_const_op(...) # _ is the ignored output # %2_const = const(mode=m) # %2_const name is for illustration only # %4 = other_op(%2_const, %3) # # where m is 'file_value' / 'immediate_value' depending on heuristics # in get_const_mode. """ for f_name, f in prog.functions.items(): const_elimination_block(f)

32

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341

2,592

4.255132

0.451613

0.02481

0.037216

0.082702

0

0.012557

0.324074

2,592

80

84

32.4

0.815639

0.359182

0

0.051282

0

0.051298

0

1

0.076923

false

0.051282

0.076923

0

0.230769

0

null

0

null

0

1

0

1

02d235dc4031cc79fd9ab325030c238874738554

2,232

py

Python

epochCiCdApi/ita/viewsOperations.py

matsumoto-epoch/epoch

c4b1982e68aa8cb108e6ae9b1c0de489d40d4db5

[ "Apache-2.0" ]

null

epochCiCdApi/ita/viewsOperations.py

matsumoto-epoch/epoch

c4b1982e68aa8cb108e6ae9b1c0de489d40d4db5

[ "Apache-2.0" ]

null

epochCiCdApi/ita/viewsOperations.py

matsumoto-epoch/epoch

c4b1982e68aa8cb108e6ae9b1c0de489d40d4db5

[ "Apache-2.0" ]

null

# Copyright 2019 NEC Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import cgi # CGIモジュールのインポート import cgitb import sys import requests import json import subprocess import traceback import os import base64 import io import logging from django.shortcuts import render from django.http import HttpResponse from django.http.response import JsonResponse from django.views.decorators.csrf import csrf_exempt from django.views.decorators.http import require_http_methods ita_host = os.environ['EPOCH_ITA_HOST'] ita_port = os.environ['EPOCH_ITA_PORT'] ita_user = os.environ['EPOCH_ITA_USER'] ita_pass = os.environ['EPOCH_ITA_PASSWORD'] # メニューID ite_menu_operation = '2100000304' ita_restapi_endpoint='http://' + ita_host + ':' + ita_port + '/default/menu/07_rest_api_ver1.php' logger = logging.getLogger('apilog') @require_http_methods(['GET']) @csrf_exempt def index(request): # sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8') logger.debug("CALL " + __name__ + ":{}".format(request.method)) if request.method == 'GET': return get(request) else: return "" @csrf_exempt def get(request): # HTTPヘッダの生成 filter_headers = { 'host': ita_host + ':' + ita_port, 'Content-Type': 'application/json', 'Authorization': base64.b64encode((ita_user + ':' + ita_pass).encode()), 'X-Command': 'FILTER', } # # オペレーションの取得 # opelist_resp = requests.post(ita_restapi_endpoint + '?no=' + ite_menu_operation, headers=filter_headers) opelist_json = json.loads(opelist_resp.text) logger.debug('---- Operation ----') logger.debug(opelist_resp.text) return JsonResponse(opelist_json, status=200)

28.987013

108

0.72043

297

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0.508418

0.038437

0.035874

0.043562

0

0.016757

0.171147

2,232

76

109

29.368421

0.827027

0.306004

0

0.045455

0

0.142202

0.02228

0

1

0.045455

false

0.045455

0.363636

0

0.477273

0

null

0

null

0

1

0

1

02d56efb28c0baac4d608dce2e0ed1e45b667e10

932

py

Python

src/service/uri_generator.py

HalbardHobby/git-LFS-for-Lambda

d19ba6fc4605d5dc2dba52acb4236c68787f8bde

[ "MIT" ]

null

src/service/uri_generator.py

HalbardHobby/git-LFS-for-Lambda

d19ba6fc4605d5dc2dba52acb4236c68787f8bde

[ "MIT" ]

null

src/service/uri_generator.py

HalbardHobby/git-LFS-for-Lambda

d19ba6fc4605d5dc2dba52acb4236c68787f8bde

[ "MIT" ]

null

"""Generates pre-signed uri's for blob handling.""" from boto3 import client import os s3_client = client('s3') def create_uri(repo_name, resource_oid, upload=False, expires_in=300): """Create a download uri for the given oid and repo.""" action = 'get_object' if upload: action = 'put_object' params = {'Bucket': os.environ['LFS_S3_BUCKET_NAME'], 'Key': repo_name + '/' + resource_oid} return s3_client.generate_presigned_url(action, Params=params, ExpiresIn=expires_in) def file_exists(repo_name, resource_oid): """Check if the file exists within the bucket.""" key = repo_name + '/' + resource_oid response = s3_client.list_objects_v2( Bucket=os.environ['LFS_S3_BUCKET_NAME'], Prefix=key) for obj in response.get('Contents', []): if obj['Key'] == key: return True return False

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932

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0

0.105263

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0.368421

0

null

0

1

0

null

0

1

02d7c80b9c168487db13fab6edd36bd30ed15c3d

4,919

py

Python

rnn/chatbot/chatbot.py

llichengtong/yx4

17de7a6257a9f0c38e12089b2d1947927ec54c90

[ "Apache-2.0" ]

128

2017-03-04T08:53:44.000Z

2020-06-05T11:19:16.000Z

rnn/chatbot/chatbot.py

github-jinwei/TensorFlowBook

17de7a6257a9f0c38e12089b2d1947927ec54c90

[ "Apache-2.0" ]

null

rnn/chatbot/chatbot.py

github-jinwei/TensorFlowBook

17de7a6257a9f0c38e12089b2d1947927ec54c90

[ "Apache-2.0" ]

120

2017-02-07T09:41:25.000Z

2022-03-17T00:57:59.000Z

# coding=utf8 import logging import os import random import re import numpy as np import tensorflow as tf from seq2seq_conversation_model import seq2seq_model from seq2seq_conversation_model import data_utils from seq2seq_conversation_model import tokenizer from seq2seq_conversation_model.seq2seq_conversation_model import FLAGS, _buckets from settings import SEQ2SEQ_MODEL_DIR _LOGGER = logging.getLogger('track') UNK_TOKEN_REPLACEMENT = [ '？', '我不知道你在说什么', '什么鬼。。。', '宝宝不知道你在说什么呐。。。', ] ENGLISHWORD_PATTERN = re.compile(r'[a-zA-Z0-9]') def is_unichar_englishnum(char): return ENGLISHWORD_PATTERN.match(char) def trim(s): """ 1. delete every space between chinese words 2. suppress extra spaces :param s: some python string :return: the trimmed string """ if not (isinstance(s, unicode) or isinstance(s, str)): return s unistr = s.decode('utf8') if type(s) != unicode else s unistr = unistr.strip() if not unistr: return '' trimmed_str = [] if unistr[0] != ' ': trimmed_str.append(unistr[0]) for ind in xrange(1, len(unistr) - 1): prev_char = unistr[ind - 1] if len(trimmed_str) == 0 else trimmed_str[-1] cur_char = unistr[ind] maybe_trim = cur_char == ' ' next_char = unistr[ind + 1] if not maybe_trim: trimmed_str.append(cur_char) else: if is_unichar_englishnum(prev_char) and is_unichar_englishnum(next_char): trimmed_str.append(cur_char) else: continue if unistr[-1] != ' ': trimmed_str.append(unistr[-1]) return ''.join(trimmed_str) class Chatbot(): """ answer an enquiry using trained seq2seq model """ def __init__(self, model_dir): # Create model and load parameters. self.session = tf.InteractiveSession() self.model = self.create_model(self.session, model_dir, True) self.model.batch_size = 1 # Load vocabularies. vocab_path = os.path.join(FLAGS.data_dir, "vocab%d" % FLAGS.vocab_size) self.vocab, self.rev_vocab = data_utils.initialize_vocabulary(vocab_path) def create_model(self, session, model_dir, forward_only): """Create conversation model and initialize or load parameters in session.""" model = seq2seq_model.Seq2SeqModel( FLAGS.vocab_size, FLAGS.vocab_size, _buckets, FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size, FLAGS.learning_rate, FLAGS.learning_rate_decay_factor, use_lstm=FLAGS.use_lstm, forward_only=forward_only) ckpt = tf.train.get_checkpoint_state(model_dir) if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path): _LOGGER.info("Reading model parameters from %s" % ckpt.model_checkpoint_path) model.saver.restore(session, ckpt.model_checkpoint_path) _LOGGER.info("Read model parameter succeed!") else: raise ValueError( "Failed to find legal model checkpoint files in %s" % model_dir) return model def generate_answer(self, enquiry): # Get token-ids for the input sentence. token_ids = data_utils.sentence_to_token_ids(enquiry, self.vocab, tokenizer.fmm_tokenizer) if len(token_ids) == 0: _LOGGER.error('lens of token ids of sentence %s is 0' % enquiry) # Which bucket does it belong to? bucket_id = min([b for b in xrange(len(_buckets)) if _buckets[b][0] > len(token_ids)]) # Get a 1-element batch to feed the sentence to the model. encoder_inputs, decoder_inputs, target_weights = self.model.get_batch( {bucket_id: [(token_ids, [])]}, bucket_id) # Get output logits for the sentence. _, _, output_logits = self.model.step(self.session, encoder_inputs, decoder_inputs, target_weights, bucket_id, True) # This is a greedy decoder - outputs are just argmaxes of output_logits. outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits] # If there is an EOS symbol in outputs, cut them at that point. if tokenizer.EOS_ID in outputs: outputs = outputs[:outputs.index(tokenizer.EOS_ID)] # Print out response sentence corresponding to outputs. answer = " ".join([self.rev_vocab[output] for output in outputs]) if tokenizer._UNK in answer: answer = random.choice(UNK_TOKEN_REPLACEMENT) answer = trim(answer) return answer def close(self): self.session.close() if __name__ == "__main__": m = Chatbot(SEQ2SEQ_MODEL_DIR + '/train/') response = m.generate_answer(u'我知道你不知道我知道你不知道我说的是什么意思') print response

36.708955

98

0.645456

625

4,919

4.8608

0.328

0.026333

0.0395

0.036866

0.129032

0.084924

0

0.009101

0.262858

4,919

133

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0.828737

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0

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0

0.058292

0.005234

0

null

0

0.115789

null

0.010526

0

null

0

null

0

1

0

1

02d7c976dba252653f990cef7776c119996e55c4

5,986

py

Python

chip8_pygame_integration/config_test.py

Artoooooor/chip8

d5132348f3081aeb9af19814d8251084ae723379

[ "MIT" ]

null

chip8_pygame_integration/config_test.py

Artoooooor/chip8

d5132348f3081aeb9af19814d8251084ae723379

[ "MIT" ]

null

chip8_pygame_integration/config_test.py

Artoooooor/chip8

d5132348f3081aeb9af19814d8251084ae723379

[ "MIT" ]

null

import unittest import pygame from chip8_pygame_integration.config import get_config, KeyBind, to_text DEFAULT = [KeyBind(pygame.K_o, pygame.KMOD_CTRL, 'some_command')] class ConfigLoadTest(unittest.TestCase): def setUp(self): self.default = None def test_empty_pattern_returns_empty_array(self): self.assertEqual([], get_config((), [])) def test_single_command_pattern_parses_single_key(self): self.when_pattern_is((('comm1',),)) self.when_lines_are(['A']) self.expect_config([KeyBind(pygame.K_a, pygame.KMOD_NONE, 'comm1')]) def test_two_command_pattern_parses_2_keys(self): self.when_pattern_is((('comm1', 'comm2',),)) self.when_lines_are(['A D']) self.expect_config([ KeyBind(pygame.K_a, pygame.KMOD_NONE, 'comm1'), KeyBind(pygame.K_d, pygame.KMOD_NONE, 'comm2')]) def test_2_lines_pattern_parses_2_lines(self): self.when_pattern_is((('comm1',), ('comm2',))) self.when_lines_are(['A', 'D']) self.expect_config([ KeyBind(pygame.K_a, pygame.KMOD_NONE, 'comm1'), KeyBind(pygame.K_d, pygame.KMOD_NONE, 'comm2')]) def test_too_little_elements_in_line_return_default(self): self.when_pattern_is((('comm1', 'comm2'),)) self.when_lines_are(['A']) self.when_default_is(DEFAULT) self.expect_config(DEFAULT) def test_ctrl_is_parsed_as_KMOD_CTRL(self): self.when_pattern_is((('comm1',),)) self.when_lines_are(['ctrl+A']) self.expect_config([KeyBind(pygame.K_a, pygame.KMOD_CTRL, 'comm1')]) def test_two_modifiers_are_parsed(self): self.when_pattern_is((('comm1',),)) self.when_lines_are(['ctrl+lshift+A']) kmods = pygame.KMOD_CTRL | pygame.KMOD_LSHIFT self.expect_config([KeyBind(pygame.K_a, kmods, 'comm1')]) def test_lowercase_keys_are_parsed(self): self.when_pattern_is((('comm1',),)) self.when_lines_are(['a']) self.expect_config([KeyBind(pygame.K_a, pygame.KMOD_NONE, 'comm1')]) def test_lowercase_special_keys_are_parsed(self): self.when_pattern_is((('comm1',),)) self.when_lines_are(['space']) self.expect_config( [KeyBind(pygame.K_SPACE, pygame.KMOD_NONE, 'comm1')]) def test_uppercase_modifiers_are_parsed(self): self.when_pattern_is((('comm1',),)) self.when_lines_are(['LCTRL+A']) self.expect_config([KeyBind(pygame.K_a, pygame.KMOD_LCTRL, 'comm1')]) def test_invalid_key_results_in_default(self): self.when_pattern_is((('comm1',),)) self.when_lines_are(['F42']) self.when_default_is(DEFAULT) self.expect_config(DEFAULT) def when_pattern_is(self, pattern): self.pattern = pattern def when_lines_are(self, lines): self.lines = lines def when_default_is(self, default): self.default = default def expect_config(self, config): result = get_config(self.pattern, self.lines, self.default) self.assertEqual(config, result) class ConfigSaveTest(unittest.TestCase): def test_empty_pattern_generates_empty_file(self): self.assertEqual([], to_text((), [])) def test_one_command_generates_1_line(self): self.when_pattern_is((('comm1',),)) self.when_config_is([KeyBind(pygame.K_a, pygame.KMOD_NONE, 'comm1')]) self.expect_generated_text(['a']) def test_two_commands_generate_line_with_2_elements(self): self.when_pattern_is((('comm1', 'comm2'),)) self.when_config_is([KeyBind(pygame.K_a, pygame.KMOD_NONE, 'comm1'), KeyBind(pygame.K_b, pygame.KMOD_NONE, 'comm2')]) self.expect_generated_text(['a b']) def test_commands_are_generated_in_order_of_pattern(self): self.when_pattern_is((('comm1', 'comm2'),)) self.when_config_is([KeyBind(pygame.K_a, pygame.KMOD_NONE, 'comm2'), KeyBind(pygame.K_b, pygame.KMOD_NONE, 'comm1')]) self.expect_generated_text(['b a']) def test_two_lines_generate_2_lines_(self): self.when_pattern_is((('comm1',), ('comm2',),)) self.when_config_is([KeyBind(pygame.K_a, pygame.KMOD_NONE, 'comm2'), KeyBind(pygame.K_b, pygame.KMOD_NONE, 'comm1')]) self.expect_generated_text(['b', 'a']) def test_KMOD_CTRL_generates_output(self): self.expect_3_mod_versions_handled('ctrl') def test_KMOD_SHIFT_generates_output(self): self.expect_3_mod_versions_handled('shift') def test_KMOD_ALT_generates_output(self): self.expect_3_mod_versions_handled('alt') def test_KMOD_META_generates_output(self): self.expect_3_mod_versions_handled('meta') def test_KMOD_CAPS_generates_output(self): self.expect_mod_handled('caps') def test_KMOD_NUM_generates_output(self): self.expect_mod_handled('num') def test_KMOD_MODE_generates_output(self): self.expect_mod_handled('mode') def expect_3_mod_versions_handled(self, baseModName): self.expect_mod_handled(baseModName) self.expect_mod_handled('l' + baseModName) self.expect_mod_handled('r' + baseModName) def expect_mod_handled(self, modName): self.when_pattern_is((('comm1',),)) fieldName = 'KMOD_' + modName.upper() mod = getattr(pygame, fieldName) self.when_config_is([KeyBind(pygame.K_a, mod, 'comm1')]) expected = '{}+a'.format(modName) self.expect_generated_text([expected]) def when_pattern_is(self, pattern): self.pattern = pattern def when_config_is(self, config): self.config = config def expect_generated_text(self, text): text = self.add_newlines(text) self.assertEqual(text, to_text(self.pattern, self.config)) def add_newlines(self, lines): return [l + '\n' for l in lines] if __name__ == '__main__': unittest.main()

36.278788

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0.602449

0.557551

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0.49415

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0.420136

0

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false

0

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0.322581

0

null

0

null

0

1

0

1

02db29d58f9fcbf982055980d5e6b51e86d8c020

2,419

py

Python

Form-Filler.py

Zaidtech/AUTOMATION-SCRIPTS

88c83e1edca02b0b86f3de4981a5f27f398b4441

[ "MIT" ]

4

2020-11-04T13:25:48.000Z

2022-03-29T01:21:49.000Z

Form-Filler.py

Zaidtech/AUTOMATION-SCRIPTS

88c83e1edca02b0b86f3de4981a5f27f398b4441

[ "MIT" ]

null

Form-Filler.py

Zaidtech/AUTOMATION-SCRIPTS

88c83e1edca02b0b86f3de4981a5f27f398b4441

[ "MIT" ]

null

#!/usr/bin/env python3 """ This script has been tested on various custom google forms and other various forms with few alteratios .. Google forms which does include the input type "token" attribute are found to be safer than those who don't. Any form contains various fields. 1. input text fields 2. radio 3. checkboxes 4. textareas 5. Uploads --- important . still working. """ import re import requests from urllib.request import urlopen from bs4 import BeautifulSoup params = {} url = input("Enter the website url") page = urlopen(url) bs_obj = BeautifulSoup(page, 'html.parser') # bs_obj.prettify() --> it's effects on the tags buried deep in the divs requests.session() input_tags = bs_obj.find_all('input') # print(input_tags) form_action = bs_obj.find('form') # some pages have multiple form tags ... text_tags = bs_obj.find_all('textarea') for text in text_tags: try: print(text['name']) text['name'] = "Running around and fill this form" except: print('Key Error') # if form_action.attrs['action'] == "" or None: # print("Form action not specifies") # else: # print(form_action) url = form_action.attrs['action'] print(f"Post request is send in here: {url}") # there might be some custom fields which are to be looked and inspected manually as they skip the scrapper # like params['entry.377191685'] = 'Faculty' # params['tos'] = 'true' # vary accordingly as at least an attck is just not that easy. ;-) for tag in input_tags: try: print(tag.attrs['aria-label']) except: pass try: if tag.attrs['value'] == "" or None: tag.attrs['value'] = input(f"Enter the value of {tag.attrs['name']}") params[tag.attrs['name']] = tag.attrs['value'] # except: # value= input(f"Enter the value of {tag.attrs['name']}") # params[tag.attrs['name']] = value else: params[tag.attrs['name']] = tag.attrs['value'].strip('\n') except: pass print(params) # getting the dicts as printed here... which is to be submitted while True: requests.session() r = requests.post(url, data=params) print(r.status_code) # 200 OK ---> submitted # 400 BAD REQUEST ERROR --> input data corrupt or server incompatible # 401 UNAOUTHORIZED ACCESS --> validation failed (need to deal with tokens and the cookies)

27.804598

107

0.653162

345

2,419

4.530435

0.489855

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0.038388

0.034549

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0

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28.127907

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0

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0

1

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false

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0.105263

0

0.105263

0.157895

0

null

0

null

0

1

0

1

02dbf3b5b09c9427c60b05103927121e020bab72

1,375

py

Python

controllers/main.py

dduarte-odoogap/odoo_jenkins

69bfcf088f75426c0e4b961a60b5c15a65b37979

[ "BSD-2-Clause" ]

5

2018-10-26T19:52:45.000Z

2021-11-04T03:59:22.000Z

controllers/main.py

dduarte-odoogap/odoo_jenkins

69bfcf088f75426c0e4b961a60b5c15a65b37979

[ "BSD-2-Clause" ]

null

controllers/main.py

dduarte-odoogap/odoo_jenkins

69bfcf088f75426c0e4b961a60b5c15a65b37979

[ "BSD-2-Clause" ]

6

2017-11-10T07:15:40.000Z

2021-02-24T10:55:15.000Z

# -*- coding: utf-8 -*- from odoo import http from odoo.http import request import jenkins class JenkinsController(http.Controller): @http.route('/web/jenkins/jobs', type='json', auth='user') def jenkins_get_jobs(self, **kw): params = request.env['ir.config_parameter'] jenkins_url = params.sudo().get_param('jenkins_ci.url', default='') jenkins_user = params.sudo().get_param('jenkins_ci.user', default='') jenkins_password = params.sudo().get_param('jenkins_ci.password', default='') server = jenkins.Jenkins(jenkins_url, username=jenkins_user, password=jenkins_password) res = [] jobs = server.get_jobs() for job in jobs: jid = { "color": job['color'], "name": job['name'], "healthReport": server.get_job_info(job['name'])['healthReport'] } res.append(jid) return { 'jobs': res } @http.route('/web/jenkins/build', type='json', auth='user') def jenkins_build_job(self, job, **kw): jenkins_url = self.jenkins_url jenkins_user = self.jenkins_user jenkins_password = self.jenkins_password server = jenkins.Jenkins(jenkins_url, username=jenkins_user, password=jenkins_password) res = server.build_job(job) return {'result': res}

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95

0.611636

160

1,375

5.06875

0.30625

0.061652

0.048089

0.066584

0.348952

0.184957

0

0.00097

0.250182

1,375

40

96

34.375

0.785645

0.015273

0

0.064516

0

0.128793

0

1

0.064516

false

0.129032

0.096774

0

0.258065

0

null

0

null

0

1

0

1

02f4fc8fa710340e57d5ba18128bb096623e09a7

871

py

Python

start_palpeo.py

RealDebian/Palpeo

23be184831a3c529cf933277944e7aacda08cdad

[ "MIT" ]

null

start_palpeo.py

RealDebian/Palpeo

23be184831a3c529cf933277944e7aacda08cdad

[ "MIT" ]

null

start_palpeo.py

RealDebian/Palpeo

23be184831a3c529cf933277944e7aacda08cdad

[ "MIT" ]

null

from link_extractor import run_enumeration from colorama import Fore from utils.headers import HEADERS from time import sleep import requests import database import re import json from bs4 import BeautifulSoup import colorama print(Fore.GREEN + '-----------------------------------' + Fore.RESET, Fore.RED) print('尸闩㇄尸㠪龱 - Website Link Extractor') print(' by @RealDebian | V0.02') print(Fore.GREEN + '-----------------------------------' + Fore.RESET) print() sleep(1) print('Example:') print() target_host = str(input('Target Site: ')) print('Select the Protocol (http|https)') sleep(.5) protocol = str(input('http=0 | https=1: ')) while True: if protocol == '0': run_enumeration('http://' + target_host) break elif protocol == '1': run_enumeration('https://' + target_host) break else: print('Wrong option!')

24.194444

80

0.624569

108

871

4.981481

0.481481

0.078067

0.052045

0.066915

0.085502

0

0.013908

0.174512

871

35

81

24.885714

0.732962

0

0.129032

0

0.263218

0.08046

0

1

0

false

0

0.322581

0

0.322581

0.290323

0

null

0

null

0

1

0

1

02f6d5351b6d28ac6a5a83e1bce309686a5a07fc

833

py

Python

src/backend/backend/shopit/migrations/0024_auto_20201028_2008.py

tejpratap545/E-Commerce-Application

c1aada5d86f231e5acd6ba4c6c9b88ff4b351f7a

[ "MIT" ]

null

src/backend/backend/shopit/migrations/0024_auto_20201028_2008.py

tejpratap545/E-Commerce-Application

c1aada5d86f231e5acd6ba4c6c9b88ff4b351f7a

[ "MIT" ]

7

2021-08-13T23:05:47.000Z

2022-02-27T10:23:46.000Z

src/backend/backend/shopit/migrations/0024_auto_20201028_2008.py

tejpratap545/E-Commerce-Application

c1aada5d86f231e5acd6ba4c6c9b88ff4b351f7a

[ "MIT" ]

null

# Generated by Django 3.1.2 on 2020-10-28 14:38 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('shopit', '0023_availablefilterselectoptions_value'), ] operations = [ migrations.RemoveField( model_name='productinfo', name='is_available', ), migrations.RemoveField( model_name='productinfo', name='stock', ), migrations.AddField( model_name='product', name='popularity', field=models.SmallIntegerField(blank=True, default=5, null=True), ), migrations.AddField( model_name='product', name='stock', field=models.PositiveIntegerField(blank=True, default=1, null=True), ), ]

26.03125

80

0.57503

75

833

6.293333

0.573333

0.076271

0.110169

0.127119

0.351695

0

0.036649

0.312125

833

31

81

26.870968

0.787086

0.054022

0

0.56

1

0

0.143766

0.049618

0

1

0

false

0

0.04

0

0.16

0

null

0

1

0

null

0

1

02f79e3624d623adc544da46b4a6554d6c1bfa3b

849

py

Python

fileo/accounts/forms.py

Tiqur/Fileo

0c663f3bb28985d2d7b4cb475a95b1592cfb2013

[ "MIT" ]

null

fileo/accounts/forms.py

Tiqur/Fileo

0c663f3bb28985d2d7b4cb475a95b1592cfb2013

[ "MIT" ]

null

fileo/accounts/forms.py

Tiqur/Fileo

0c663f3bb28985d2d7b4cb475a95b1592cfb2013

[ "MIT" ]

null

from django import forms from django.contrib.auth import authenticate from django.contrib.auth.forms import UserCreationForm from .models import FileoUser User = FileoUser() class UserLoginForm(forms.ModelForm): password = forms.CharField(label='Password', widget=forms.PasswordInput) class Meta: model = FileoUser fields = ('email', 'password') def clean(self): email = self.cleaned_data['email'] password = self.cleaned_data['password'] if not authenticate(email=email, password=password): raise forms.ValidationError('Invalid login') class UserRegisterForm(UserCreationForm): email = forms.EmailField(max_length=60, help_text='Add a valid email address') class Meta: model = FileoUser fields = ('email', 'username', 'password1', 'password2')

29.275862

82

0.69258

92

849

6.347826

0.521739

0.05137

0.058219

0.071918

0.116438

0

0.005926

0.204947

849

28

83

30.321429

0.859259

0

0.2

0

0.121319

0

1

0.05

false

0.25

0.2

0

0.55

0

null

0

null

0

1

0

1

02f8318053016bd127b7feb86e89f4c704276dce

465

py

Python

kagi/upper/west/_capital/four.py

jedhsu/kagi

1301f7fc437bb445118b25ca92324dbd58d6ad2d

[ "MIT" ]

null

kagi/upper/west/_capital/four.py

jedhsu/kagi

1301f7fc437bb445118b25ca92324dbd58d6ad2d

[ "MIT" ]

null

kagi/upper/west/_capital/four.py

jedhsu/kagi

1301f7fc437bb445118b25ca92324dbd58d6ad2d

[ "MIT" ]

null

""" *Upper-West Capital 4* ⠨ The upper-west capital four gi. """ from dataclasses import dataclass from ....._gi import Gi from ....capital import CapitalGi from ...._gi import StrismicGi from ....west import WesternGi from ...._number import FourGi from ..._gi import UpperGi __all__ = ["UpperWestCapital4"] @dataclass class UpperWestCapital4( Gi, StrismicGi, UpperGi, WesternGi, CapitalGi, FourGi, ): symbol = "\u2828"

15

33

0.668817

52

465

5.846154

0.442308

0.059211

0.118421

0

0.019126

0.212903

465

30

34

15.5

0.808743

0.126882

0

0.058974

0

1

0

false

0

0.388889

0

0.5

0

null

0

null

0

1

0

1

02f8b65e136d03ceacb32c0a454b3d2ad573a0cb

191

py

Python

acmicpc/5612.py

juseongkr/BOJ

8f10a2bf9a7d695455493fbe7423347a8b648416

[ "Apache-2.0" ]

7

2020-02-03T10:00:19.000Z

2021-11-16T11:03:57.000Z

acmicpc/5612.py

juseongkr/Algorithm-training

8f10a2bf9a7d695455493fbe7423347a8b648416

[ "Apache-2.0" ]

1

2021-01-03T06:58:24.000Z

acmicpc/5612.py

juseongkr/Algorithm-training

8f10a2bf9a7d695455493fbe7423347a8b648416

[ "Apache-2.0" ]

1

2020-01-22T14:34:03.000Z

n = int(input()) m = int(input()) r = m for i in range(n): a, b = map(int, input().split()) m += a m -= b if m < 0: print(0) exit() r = max(r, m) print(r)

14.692308

36

0.418848

35

191

2.285714

0.514286

0.3

0

0.016667

0.371728

191

12

37

15.916667

0.65

0

1

0

false

0

0.166667

0

null

1

0

null

0

1

02f942ae72f558610fdbd2e0d719bb8a1bc37d6c

1,849

py

Python

users/models.py

uoe-compsci-grp30/campusgame

d2d7ba99210f352a7b45a1db06cea0a09e3b8c31

[ "MIT" ]

null

users/models.py

uoe-compsci-grp30/campusgame

d2d7ba99210f352a7b45a1db06cea0a09e3b8c31

[ "MIT" ]

null

users/models.py

uoe-compsci-grp30/campusgame

d2d7ba99210f352a7b45a1db06cea0a09e3b8c31

[ "MIT" ]

null

import uuid from django.contrib.auth.models import AbstractUser from django.db import models """ The user model that represents a user participating in the game. Implemented using the built-in Django user model: AbstractUser. """ class User(AbstractUser): """ The User class that represents a user that has created an account. Implemented using the built-in Django user model 'AbstractUser'. The User class consists of an id that uniquely identifies a user. It uses a uuid in order to be more secure. It also contains a profile picture that is uploaded by the user. """ id = models.UUIDField(default=uuid.uuid4, primary_key=True) # id uniquely identifies a user is_gamekeeper = models.BooleanField(default=False) # is the user a gamekeeper? class GameParticipation(models.Model): """ Game Participation class represents information about a user currently participating in a game. This is useful because it provides an easy way to store data about users currently playing a game. The class consists of a User that is currently playing the game. A Game that the user is currently participating in. The current Zone that the user is in. A boolean value of whether the user is alive. A boolean value of whether the user is eliminated """ user = models.ForeignKey(User, on_delete=models.CASCADE) # User that is currently participating in a game game = models.ForeignKey("games.Game", on_delete=models.CASCADE) # What game is the user currently participating in current_zone = models.ForeignKey("games.Zone", on_delete=models.DO_NOTHING) # What zone is the user currently in score = models.IntegerField(default=0) # User score is_alive = models.BooleanField(default=False) # Is the player alive is_eliminated = models.BooleanField(default=False) # Is the player eliminated

52.828571

449

0.760411

277

1,849

5.043321

0.31769

0.055118

0.068719

0.064424

0.245526

0.204009

0.178955

0.120258

0.075877

0

0.001318

0.179557

1,849

34

450

54.382353

0.919578

0.538129

0

0.029806

0

1

0

false

0

0.230769

0

1

0

null

0

null

0

1

0

1

02fcd2548a49becf32a01085ecf16e34635af225

32,807

py

Python

train.py

EdwardLeeMacau/PFFNet

dfa6e45062627ce6ab7a1b1a37bada5cccae7167

[ "MIT" ]

null

train.py

EdwardLeeMacau/PFFNet

dfa6e45062627ce6ab7a1b1a37bada5cccae7167

[ "MIT" ]

null

train.py

EdwardLeeMacau/PFFNet

dfa6e45062627ce6ab7a1b1a37bada5cccae7167

[ "MIT" ]

null

""" FileName [ train.py ] PackageName [ PFFNet ] Synopsis [ Train the model ] Usage: >>> python train.py --normalized --cuda """ import argparse import os import shutil from datetime import date import matplotlib import numpy as np import pandas as pd import torch import torchvision import torchvision.models from torchvision import transforms from matplotlib import pyplot as plt from matplotlib import gridspec from skimage.measure import compare_psnr, compare_ssim from torch import nn, optim from torch.backends import cudnn from torch.utils.data import DataLoader from torchvision.transforms import ( CenterCrop, Compose, Normalize, RandomCrop, Resize, ToTensor) from torchvision.utils import make_grid import cmdparser import graphs import utils from model import lossnet from data import DatasetFromFolder from model.rpnet import Net from model.rpnet_improve import ImproveNet from model.lossnet import LossNetwork # Select Device device = utils.selectDevice() cudnn.benchmark = True # Normalization(Mean Shift) mean = torch.Tensor([0.485, 0.456, 0.406]).to(device) std = torch.Tensor([0.229, 0.224, 0.225]).to(device) def getDataset(opt, transform): """ Return the dataloader object Parameters ---------- opt : namespace transform : torchvision.transform Return ------ train_loader, val_loader : torch.utils.data.DataLoader """ train_dataset = DatasetFromFolder(opt.train, transform=transform) val_dataset = DatasetFromFolder(opt.val, transform=transform) train_loader = DataLoader( dataset=train_dataset, num_workers=opt.threads, batch_size=opt.batchsize, pin_memory=True, shuffle=True ) val_loader = DataLoader( dataset=val_dataset, num_workers=opt.threads, batch_size=opt.batchsize, pin_memory=True, shuffle=True ) return train_loader, val_loader def getOptimizer(model, opt): """ Return the optimizer (and schedular) Parameters ---------- model : torch.nn.Model opt : namespace Return ------ optimizer : torch.optim """ if opt.optimizer == "Adam": optimizer = optim.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, weight_decay=opt.weight_decay ) elif opt.optimizer == "SGD": optimizer = optim.SGD( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, weight_decay=opt.weight_decay ) elif opt.optimizer == "ASGD": optimizer = optim.ASGD( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, lambd=1e-4, alpha=0.75, t0=1000000.0, weight_decay=opt.weight_decay ) elif opt.optimizer == "Adadelta": optimizer = optim.Adadelta( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, rho=0.9, eps=1e-06, weight_decay=opt.weight_decay ) elif opt.optimizer == "Adagrad": optimizer = optim.Adagrad( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, lr_decay=0, weight_decay=opt.weight_decay, initial_accumulator_value=0 ) elif opt.optimizer == "Adam": optimizer = optim.Adam( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, weight_decay=opt.weight_decay ) elif opt.optimizer == "SGD": optimizer = optim.SGD( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, weight_decay=opt.weight_decay ) elif opt.optimizer == "ASGD": optimizer = optim.ASGD( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, lambd=1e-4, alpha=0.75, t0=1000000.0, weight_decay=opt.weight_decay ) elif opt.optimizer == "Adadelta": optimizer = optim.Adadelta( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, rho=0.9, eps=1e-06, weight_decay=opt.weight_decay ) elif opt.optimizer == "Adagrad": optimizer = optim.Adagrad( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, lr_decay=0, weight_decay=opt.weight_decay, initial_accumulator_value=0 ) elif opt.optimizer == "SparseAdam": optimizer = optim.SparseAdam( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, betas=(opt.b1, opt.b2), eps=1e-08 ) elif opt.optimizer == "Adamax": optimizer = optim.Adamax( filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, betas=(opt.b1, opt.b2), eps=1e-08, weight_decay=opt.weight_dacay ) else: raise ValueError(opt.optimizer, " doesn't exist.") return optimizer # TODO: Developing def logMsg(epoch, iteration, train_loader, perceptual, trainloss, perceloss) msg = "===> [Epoch {}] [{:4d}/{:4d}] ImgLoss: (Mean: {:.6f}, Std: {:.6f})".format( epoch, iteration, len(train_loader), np.mean(trainloss), np.std(trainloss) ) if not perceptual is None: msg = "\t".join([msg, "PerceptualLoss: (Mean: {:.6f}, Std: {:.6f})".format(np.mean(perceloss), np.std(perceloss))]) return msg def getFigureSpec(iteration: int, perceptual: bool): """ Get 2x2 Figure And Axis Parameters ---------- iterations : int perceptual : bool If true, generate the axis of perceptual loss Return ------ fig, axis : matplotlib.figure.Figure, matplotlib.axes.Axes The plotting instance. """ fig, grids = plt.figure(figsize=(19.2, 10.8)), gridspec.GridSpec(2, 2) axis = [ fig.add_subplot(gs) for gs in grids ] for ax in axis: ax.set_xlabel("Epoch(s) / Iteration: {}".format(iteration)) # Linear scale of Loss axis[0].set_ylabel("Image Loss") axis[0].set_title("Loss") # Log scale of Loss axis[1].set_yscale("log") axis[1].set_ylabel("Image Loss") axis[1].set_title("Loss (Log scale)") # PSNR axis[2].set_title("Average PSNR") # Learning Rate axis[3].set_yscale('log') axis[3].set_title("Learning Rate") # Add TwinScale for Perceptual Loss if perceptual: axis.append( axis[0].twinx() ) axis[4].set_ylabel("Perceptual Loss") axis.append( axis[1].twinx() ) axis[5].set_ylabel("Perceptual Loss") return fig, axis def getPerceptualModel(model): """ Return the Perceptual Model Parameters ---------- model : str The name of the perceptual Model. Return ------ perceptual : {nn.Module, None} Not None if the perceptual model is supported. """ perceptual = None if opt.perceptual == 'vgg16': print("==========> Using VGG16 as Perceptual Loss Model") perceptual = LossNetwork( torchvision.models.vgg16(pretrained=True), lossnet.VGG16_Layer ) if opt.perceptual == 'vgg16_bn': print("==========> Using VGG16 with Batch Normalization as Perceptual Loss Model") perceptual = LossNetwork( torchvision.models.vgg16_bn(pretrained=True), lossnet.VGG16_bn_Layer ) if opt.perceptual == 'vgg19': print("==========> Using VGG19 as Perceptual Loss Model") perceptual = LossNetwork( torchvision.models.vgg19(pretrained=True), lossnet.VGG19_Layer ) if opt.perceptual == 'vgg19_bn': print("==========> Using VGG19 with Batch Normalization as Perceptual Loss Model") perceptual = LossNetwork( torchvision.models.vgg19_bn(pretrained=True), lossnet.VGG19_bn_Layer ) if opt.perceptual == "resnet18": print("==========> Using Resnet18 as Perceptual Loss Model") perceptual = LossNetwork( torchvision.models.resnet18(pretrained=True), lossnet.Resnet18_Layer ) if opt.perceptual == "resnet34": print("==========> Using Resnet34 as Perceptual Loss Model") perceptual = LossNetwork( torchvision.models.resnet34(pretrained=True), lossnet.Resnet34_Layer ) if opt.perceptual == "resnet50": print("==========> Using Resnet50 as Perceptual Loss Model") perceptual = LossNetwork( torchvision.models.resnet50(pertrained=True), lossnet.Resnet50_Layer ) return perceptual # TODO: Developing def getTrainSpec(opt): """ Initialize the objects needs at Training. Parameters ---------- opt : namespace (...) Return ------ model optimizer criterion perceptual train_loader, val_loader scheduler epoch, loss_iter, perc_iter, mse_iter, psnr_iter, ssim_iter, lr_iter iterations, opt, name, fig, axis, saveCheckpoint """ if opt.fixrandomseed: seed = 1334 torch.manual_seed(seed) if opt.cuda: torch.cuda.manual_seed(seed) print("==========> Loading datasets") img_transform = Compose([ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) if opt.normalize else ToTensor() # Dataset train_loader, val_loader = getDataset(opt, img_transform) # TODO: Parameters Selection # TODO: Mean shift Layer Handling # Load Model print("==========> Building model") model = ImproveNet(opt.rb) # ----------------------------------------------- # # Loss: L1 Norm / L2 Norm # # Perceptual Model (Optional) # # TODO Append Layer (Optional) # # ----------------------------------------------- # criterion = nn.MSELoss(reduction='mean') perceptual = None if (opt.perceptual is None) else getPerceptualModel(opt.perceptual).eval() # ----------------------------------------------- # # Optimizer and learning rate scheduler # # ----------------------------------------------- # print("==========> Setting Optimizer: {}".format(opt.optimizer)) optimizer = getOptimizer(model, opt) scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=opt.milestones, gamma=opt.gamma) # ----------------------------------------------- # # Option: resume training process from checkpoint # # ----------------------------------------------- # if opt.resume: if os.path.isfile(opt.resume): print("=> loading checkpoint '{}'".format(opt.resume)) model, optimizer, _, _, scheduler = utils.loadCheckpoint(opt.resume, model, optimizer, scheduler) else: raise Exception("=> no checkpoint found at '{}'".format(opt.resume)) # ----------------------------------------------- # # Option: load weights from a pretrain network # # ----------------------------------------------- # if opt.pretrained: if os.path.isfile(opt.pretrained): print("=> loading pretrained model '{}'".format(opt.pretrained)) model = utils.loadModel(opt.pretrained, model, True) else: raise Exception("=> no pretrained model found at '{}'".format(opt.pretrained)) # Select training device if opt.cuda: print("==========> Setting GPU") model = nn.DataParallel(model, device_ids=[i for i in range(opt.gpus)]).cuda() criterion = criterion.cuda() if perceptual is not None: perceptual = perceptual.cuda() else: print("==========> Setting CPU") model = model.cpu() criterion = criterion.cpu() if perceptual is not None: perceptual = perceptual.cpu() # Create container length = opt.epochs * len(train_loader) // opt.val_interval loss_iter = np.empty(length, dtype=float) perc_iter = np.empty(length, dtype=float) psnr_iter = np.empty(length, dtype=float) ssim_iter = np.empty(length, dtype=float) mse_iter = np.empty(length, dtype=float) lr_iter = np.empty(length, dtype=float) iterations = np.empty(length, dtype=float) loss_iter[:] = np.nan perc_iter[:] = np.nan psnr_iter[:] = np.nan ssim_iter[:] = np.nan mse_iter[:] = np.nan lr_iter[:] = np.nan iterations[:] = np.nan # Set plotter to plot the loss curves twinx = (opt.perceptual is not None) fig, axis = getFigureSpec(len(train_loader), twinx) # Set Model Saving Function if opt.save_item == "model": print("==========> Save Function: saveModel()") saveCheckpoint = utils.saveModel elif opt.save_item == "checkpoint": print("==========> Save Function: saveCheckpoint()") saveCheckpoint = utils.saveCheckpoint else: raise ValueError("Save Checkpoint Function Error") return ( model, optimizer, criterion, perceptual, train_loader, val_loader, scheduler, epoch, loss_iter, perc_iter, mse_iter, psnr_iter, ssim_iter, lr_iter, iterations, opt, name, fig, axis, saveCheckpoint ) def main(opt): """ Main process of train.py Parameters ---------- opt : namespace The option (hyperparameters) of these model """ if opt.fixrandomseed: seed = 1334 torch.manual_seed(seed) if opt.cuda: torch.cuda.manual_seed(seed) print("==========> Loading datasets") img_transform = Compose([ToTensor(), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) if opt.normalize else ToTensor() # Dataset train_loader, val_loader = getDataset(opt, img_transform) # TODO: Parameters Selection # TODO: Mean shift Layer Handling # Load Model print("==========> Building model") model = ImproveNet(opt.rb) # ----------------------------------------------- # # Loss: L1 Norm / L2 Norm # # Perceptual Model (Optional) # # TODO Append Layer (Optional) # # ----------------------------------------------- # criterion = nn.MSELoss(reduction='mean') perceptual = None if (opt.perceptual is None) else getPerceptualModel(opt.perceptual).eval() # ----------------------------------------------- # # Optimizer and learning rate scheduler # # ----------------------------------------------- # print("==========> Setting Optimizer: {}".format(opt.optimizer)) optimizer = getOptimizer(model, opt) scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=opt.milestones, gamma=opt.gamma) # ----------------------------------------------- # # Option: resume training process from checkpoint # # ----------------------------------------------- # if opt.resume: if os.path.isfile(opt.resume): print("=> loading checkpoint '{}'".format(opt.resume)) model, optimizer, _, _, scheduler = utils.loadCheckpoint(opt.resume, model, optimizer, scheduler) else: raise Exception("=> no checkpoint found at '{}'".format(opt.resume)) # ----------------------------------------------- # # Option: load weights from a pretrain network # # ----------------------------------------------- # if opt.pretrained: if os.path.isfile(opt.pretrained): print("=> loading pretrained model '{}'".format(opt.pretrained)) model = utils.loadModel(opt.pretrained, model, True) else: raise Exception("=> no pretrained model found at '{}'".format(opt.pretrained)) # Select training device if opt.cuda: print("==========> Setting GPU") model = nn.DataParallel(model, device_ids=[i for i in range(opt.gpus)]).cuda() criterion = criterion.cuda() if perceptual is not None: perceptual = perceptual.cuda() else: print("==========> Setting CPU") model = model.cpu() criterion = criterion.cpu() if perceptual is not None: perceptual = perceptual.cpu() # Create container length = opt.epochs * len(train_loader) // opt.val_interval loss_iter = np.empty(length, dtype=float) perc_iter = np.empty(length, dtype=float) psnr_iter = np.empty(length, dtype=float) ssim_iter = np.empty(length, dtype=float) mse_iter = np.empty(length, dtype=float) lr_iter = np.empty(length, dtype=float) iterations = np.empty(length, dtype=float) loss_iter[:] = np.nan perc_iter[:] = np.nan psnr_iter[:] = np.nan ssim_iter[:] = np.nan mse_iter[:] = np.nan lr_iter[:] = np.nan iterations[:] = np.nan # Set plotter to plot the loss curves twinx = (opt.perceptual is not None) fig, axis = getFigureSpec(len(train_loader), twinx) # Set Model Saving Function if opt.save_item == "model": print("==========> Save Function: saveModel()") saveCheckpoint = utils.saveModel elif opt.save_item == "checkpoint": print("==========> Save Function: saveCheckpoint()") saveCheckpoint = utils.saveCheckpoint else: raise ValueError("Save Checkpoint Function Error") # Start Training print("==========> Training") for epoch in range(opt.starts, opt.epochs + 1): loss_iter, perc_iter, mse_iter, psnr_iter, ssim_iter, lr_iter, iterations, _, _ = train( model, optimizer, criterion, perceptual, train_loader, val_loader, scheduler, epoch, loss_iter, perc_iter, mse_iter, psnr_iter, ssim_iter, lr_iter, iterations, opt, name, fig, axis, saveCheckpoint ) scheduler.step() # Save the last checkpoint for resume training utils.saveCheckpoint(os.path.join(opt.checkpoints, name, "final.pth"), model, optimizer, scheduler, epoch, len(train_loader)) # TODO: Fine tuning return def train(model, optimizer, criterion, perceptual, train_loader, val_loader, scheduler: optim.lr_scheduler.MultiStepLR, epoch: int, loss_iter, perc_iter, mse_iter, psnr_iter, ssim_iter, lr_iter, iters, opt, name, fig: matplotlib.figure.Figure, ax: matplotlib.axes.Axes, saveCheckpoint=utils.saveCheckpoint): """ Main function of training and vaildation Parameters ---------- model, optimizer, criterion : nn.Module, optim.Optimizer, nn.Module The main elements of the Neural Network perceptual : {nn.Module, None} optional Pass None or a pretrained Neural Network to calculate perceptual loss train_loader, val_loader : DataLoader The training and validation dataset scheduler : optim.lr_scheduler.MultiStepLR Learning rate scheduler epoch : int The processing train epoch loss_iter, perc_iter, mse_iter, psnr_iter, ssim_iter, iters : 1D-Array like The container to record the training performance opt : namespace The training option name : str (...) fig, ax : matplotlib.figure.Figure, matplotlib.axes.Axes (...) saveCheckpoint : callable (...) """ trainloss, perceloss = [], [] for iteration, (data, label) in enumerate(train_loader, 1): steps = len(train_loader) * (epoch - 1) + iteration model.train() # ----------------------------------------------------- # # Handling: # # 1. Perceptual Loss # # 2. Multiscaling # # 2.0 Without Multiscaling (multiscaling = [1.0]) # # 2.1 Regular Multiscaling # # 2.2 Random Multiscaling # # ----------------------------------------------------- # # 2.0 Without Multiscaling if opt.multiscale == [1.0]: optimizer.zero_grad() data, label = data.to(device), label.to(device) output = model(data) # Calculate loss image_loss = criterion(output, label) if perceptual is not None: perceptual_loss = perceptual(output, label) # Backpropagation loss = image_loss if (perceptual is None) else image_loss + opt.perceptual_weight * percuptual_loss loss.backward() optimizer.step() # Record the training loss trainloss.append(image_loss.item()) if perceptual is not None: perceloss.append(perceptual_loss.item()) # TODO: Efficient Issue # TODO: Resizing Loss # 2.1 Regular Multiscaling elif not opt.multiscaleShuffle: data, label = data.to(device), label.to(device) originWidth, originHeight = data.shape[1:3] for scale in opt.multiscale: optimizer.zero_grad() if scale != 1.0: newSize = (int(originWidth * scale), int(originHeight * scale)) data, label = Resize(size=newSize)(data), Resize(size=newSize)(label) output = model(data) # Calculate loss image_loss = criterion(output, label) if perceptual is not None: perceptual_loss = perceptual(output, label) # Backpropagation loss = image_loss if (perceptual is None) else image_loss + opt.perceptual_weight * percuptual_loss loss.backward() optimizer.step() # Record the training loss trainloss.append(image_loss.item()) if perceptual is not None: perceloss.append(perceptual_loss.item()) # TODO: Check Usage # 2.2 Random Multiscaling else: optimizer.zero_grad() data, label = data.to(device), label.to(device) originWidth, originHeight = data.shape[1:3] scale = np.random.choice(opt.multiscale, 1) if scale != 1.0: newSize = (int(originWidth * scale), int(originHeight * scale)) data, label = Resize(size=newSize)(data), Resize(size=newSize)(label) output = model(data) # Calculate loss image_loss = criterion(output, label) if perceptual is not None: perceptual_loss = perceptual(output, label) # Backpropagation loss = image_loss if (perceptual is None) else image_loss + opt.perceptual_weight * percuptual_loss loss.backward() optimizer.step() # Record the training loss trainloss.append(image_loss.item()) if perceptual is not None: perceloss.append(perceptual_loss.item()) # ----------------------------------------------------- # # Execute for a period # # 1. Print the training message # # 2. Plot the gradient of each layer (Deprecated) # # 3. Validate the model # # 4. Saving the network # # ----------------------------------------------------- # # 1. Print the training message if steps % opt.log_interval == 0: msg = "===> [Epoch {}] [{:4d}/{:4d}] ImgLoss: (Mean: {:.6f}, Std: {:.6f})".format( epoch, iteration, len(train_loader), np.mean(trainloss), np.std(trainloss) ) if not perceptual is None: msg = "\t".join([msg, "PerceptualLoss: (Mean: {:.6f}, Std: {:.6f})".format(np.mean(perceloss), np.std(perceloss))]) print(msg) # 2. Print the gradient statistic message for each layer # graphs.draw_gradient() # 3. Save the model if steps % opt.save_interval == 0: checkpoint_path = os.path.join(opt.checkpoints, name, "{}.pth".format(steps)) saveCheckpoint(checkpoint_path, model, optimizer, scheduler, epoch, iteration) # 4. Validating the network if steps % opt.val_interval == 0: mse, psnr = validate(model, val_loader, criterion, epoch, iteration, normalize=opt.normalize) idx = steps // opt.val_interval - 1 loss_iter[idx] = np.mean(trainloss) mse_iter[idx] = mse psnr_iter[idx] = psnr lr_iter[idx] = optimizer.param_groups[0]["lr"] iters[idx] = steps / len(train_loader) if perceptual is not None: perc_iter[idx] = np.mean(perceloss) # Clean up the list trainloss, preceloss = [], [] # Save the loss df = pd.DataFrame(data={ 'Iterations': iters * len(train_loader), 'TrainL2Loss': loss_iter, 'TrainPerceptual': perc_iter, 'ValidationLoss': mse_iter, 'ValidationPSNR': psnr_iter }) # Loss (Training Curve) Message df = df.nlargest(5, 'ValidationPSNR').append(df) df.to_excel(os.path.join(opt.detail, name, "statistical.xlsx")) # Show images in grid with validation set # graphs.grid_show() # Plot TrainLoss, ValidationLoss fig, ax = training_curve( loss_iter, perc_iter, mse_iter, psnr_iter, ssim_iter, iters, lr_iter, epoch, len(train_loader), fig, ax ) plt.tight_layout() plt.savefig(os.path.join(opt.detail, name, "loss.png")) return loss_iter, perc_iter, mse_iter, psnr_iter, ssim_iter, lr_iter, iters, fig, ax def training_curve(train_loss, perc_iter, val_loss, psnr, ssim, x, lr, epoch, iters_per_epoch, fig: matplotlib.figure.Figure, axis: matplotlib.axes.Axes, linewidth=0.25): """ Plot out learning rate, training loss, validation loss and PSNR. Parameters ---------- train_loss, perc_iter, val_loss, psnr, ssim, lr, x: 1D-array like (...) iters_per_epoch : int To show the iterations in the epoch fig, axis : matplotlib.figure.Figure, matplotlib.axes.Axes Matplotlib plotting object. linewidth : float Default linewidth Return ------ fig, axis : matplotlib.figure.Figure, matplotlib.axes.Axes The training curve """ # Linear scale of loss curve ax = axis[0] ax.clear() line1, = ax.plot(x, val_loss, label="Validation Loss", color='red', linewidth=linewidth) line2, = ax.plot(x, train_loss, label="Train Loss", color='blue', linewidth=linewidth) ax.plot(x, np.repeat(np.amin(val_loss), len(x)), linestyle=':', linewidth=linewidth) ax.set_xlabel("Epoch(s) / Iteration: {}".format(iters_per_epoch)) ax.set_ylabel("Image Loss") ax.set_title("Loss") if not np.isnan(perc_iter).all(): ax = axis[4] ax.clear() line4, = ax.plot(x, perc_iter, label="Perceptual Loss", color='green', linewidth=linewidth) ax.set_ylabel("Perceptual Loss") ax.legend(handles=(line1, line2, line4, )) if not np.isnan(perc_iter).all() else ax.legend(handles=(line1, line2, )) # Log scale of loss curve ax = axis[1] ax.clear() line1, = ax.plot(x, val_loss, label="Validation Loss", color='red', linewidth=linewidth) line2, = ax.plot(x, train_loss, label="Train Loss", color='blue', linewidth=linewidth) ax.plot(x, np.repeat(np.amin(val_loss), len(x)), linestyle=':', linewidth=linewidth) ax.set_xlabel("Epoch(s) / Iteration: {}".format(iters_per_epoch)) ax.set_yscale('log') ax.set_title("Loss(Log scale)") if not np.isnan(perc_iter).all(): ax = axis[5] ax.clear() line4, = ax.plot(x, perc_iter, label="Perceptual Loss", color='green', linewidth=linewidth) ax.set_ylabel("Perceptual Loss") ax.legend(handles=(line1, line2, line4, )) if not np.isnan(perc_iter).all() else ax.legend(handles=(line1, line2, )) # Linear scale of PSNR, SSIM ax = axis[2] ax.clear() line1, = ax.plot(x, psnr, label="PSNR", color='blue', linewidth=linewidth) ax.plot(x, np.repeat(np.amax(psnr), len(x)), linestyle=':', linewidth=linewidth) ax.set_xlabel("Epochs(s) / Iteration: {}".format(iters_per_epoch)) ax.set_ylabel("Average PSNR") ax.set_title("Validation Performance") ax.legend(handles=(line1, )) # Learning Rate Curve ax = axis[3] ax.clear() line1, = ax.plot(x, lr, label="Learning Rate", color='cyan', linewidth=linewidth) ax.set_xlabel("Epochs(s) / Iteration: {}".format(iters_per_epoch)) ax.set_title("Learning Rate") ax.set_yscale('log') ax.legend(handles=(line1, )) return fig, axis def validate(model: nn.Module, loader: DataLoader, criterion: nn.Module, epoch, iteration, normalize=False): """ Validate the model Parameters ---------- model : nn.Module The neural networks to train loader : torch.utils.data.DataLoader The training data epoch : int The training epoch criterion : nn.Module Loss function normalize : bool If true, normalize the image before and after the NN. Return ------ mse, psnr : np.float np.mean(mse) and np.mean(psnr) """ psnrs, mses = [], [] model.eval() with torch.no_grad(): for index, (data, label) in enumerate(loader, 1): data, label = data.to(device), label.to(device) output = model(data) mse = criterion(output, label).item() mses.append(mse) if normalize: data = data * std[:, None, None] + mean[:, None, None] label = label * std[:, None, None] + mean[:, None, None] output = output * std[:, None, None] + mean[:, None, None] mse = criterion(output, label).item() psnr = 10 * np.log10(1.0 / mse) mses.append(mse) psnrs.append(psnr) print("===> [Epoch {}] [ Vaild ] MSE: {:.6f}, PSNR: {:.4f}".format(epoch, np.mean(mses), np.mean(psnrs))) return np.mean(mses), np.mean(psnrs) if __name__ == "__main__": # Clean up OS screen os.system('clear') # Cmd Parser parser = cmdparser.parser opt = parser.parse_args() # Check arguments if opt.cuda and not torch.cuda.is_available(): raise Exception("No GPU found, please run without --cuda") if opt.resume and opt.pretrained: raise ValueError("opt.resume and opt.pretrain should not be True in the same time.") if opt.resume and (not os.path.isfile(opt.resume)): raise ValueError("{} doesn't not exists".format(opt.resume)) if opt.pretrained and (not os.path.isfile(opt.pretrained)): raise ValueError("{} doesn't not exists".format(opt.pretrained)) # Check training dataset directory for path in opt.train: if not os.path.exists(path): raise ValueError("{} doesn't exist".format(path)) # Check validation dataset directory for path in opt.val: if not os.path.exists(path): raise ValueError("{} doesn't exist".format(path)) # Make checkpoint storage directory name = "{}_{}".format(opt.tag, date.today().strftime("%Y%m%d")) os.makedirs(os.path.join(opt.checkpoints, name), exist_ok=True) # Copy the code of model to logging file if os.path.exists(os.path.join(opt.detail, name, 'model')): shutil.rmtree(os.path.join(opt.detail, name, 'model')) if os.path.exists(os.path.join(opt.checkpoints, name, 'model')): shutil.rmtree(os.path.join(opt.checkpoints, name, 'model')) shutil.copytree('./model', os.path.join(opt.detail, name, 'model')) shutil.copytree('./model', os.path.join(opt.checkpoints, name, 'model')) shutil.copyfile(__file__, os.path.join(opt.detail, name, os.path.basename(__file__))) # Show Detail print('==========> Training setting') utils.details(opt, os.path.join(opt.detail, name, 'args.txt')) # Execute main process main(opt)

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null

0

null

0

1

0

1

02fe97635bdf12eb93fa73109a7854ea036f69bf

546

py

Python

python_high/chapter_3/3.1.py

Rolling-meatballs/deepshare

47c1e599c915ccd0a123fa9ab26e1f20738252ef

[ "MIT" ]

null

python_high/chapter_3/3.1.py

Rolling-meatballs/deepshare

47c1e599c915ccd0a123fa9ab26e1f20738252ef

[ "MIT" ]

null

python_high/chapter_3/3.1.py

Rolling-meatballs/deepshare

47c1e599c915ccd0a123fa9ab26e1f20738252ef

[ "MIT" ]

null

name = " alberT" one = name.rsplit() print("one:", one) two = name.index('al', 0) print("two:", two) three = name.index('T', -1) print("three:", three) four = name.replace('l', 'p') print("four:", four) five = name.split('l') print("five:", five) six = name.upper() print("six:", six) seven = name.lower() print("seven:", seven) eight = name[1] print("eight:", eight ) nine = name[:3] print("nine:", nine) ten = name[-2:] print("ten:", ten) eleven = name.index("e") print("eleven:", eleven) twelve = name[:-1] print("twelve:", twelve)

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0.082569

0.061162

0

0.012848

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546

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0

null

0

null

0

1

0

1

02feb42fde4ca975bc72c9c78d9e0931c5f1d4a2

384

py

Python

src/views/simplepage/models.py

svenvandescheur/svenv.nl-new

c448714853d96ad31d26c825d8b35c4890be40a1

[ "MIT" ]

null

src/views/simplepage/models.py

svenvandescheur/svenv.nl-new

c448714853d96ad31d26c825d8b35c4890be40a1

[ "MIT" ]

null

src/views/simplepage/models.py

svenvandescheur/svenv.nl-new

c448714853d96ad31d26c825d8b35c4890be40a1

[ "MIT" ]

null

from cms.extensions import PageExtension from cms.extensions.extension_pool import extension_pool from django.utils.translation import ugettext as _ from filer.fields.image import FilerImageField class SimplePageExtension(PageExtension): """ A generic website page. """ image = FilerImageField(verbose_name=_("image")) extension_pool.register(SimplePageExtension)

25.6

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0.857143

0

null

0

null

0

1

0

1

f302cba30df57e2c4fa0a9201628774e666043a8

3,021

py

Python

Ideas/cricket-umpire-assistance-master/visualization/test2.py

hsspratt/Nott-Hawkeye1

178f4f0fef62e8699f6057d9d50adfd61a851047

[ "MIT" ]

null

Ideas/cricket-umpire-assistance-master/visualization/test2.py

hsspratt/Nott-Hawkeye1

178f4f0fef62e8699f6057d9d50adfd61a851047

[ "MIT" ]

1

2021-11-11T22:15:36.000Z

Ideas/cricket-umpire-assistance-master/visualization/test2.py

hsspratt/Nott-Hawkeye1

178f4f0fef62e8699f6057d9d50adfd61a851047

[ "MIT" ]

null

### INITIALIZE VPYTHON # ----------------------------------------------------------------------- from __future__ import division from visual import * from physutil import * from visual.graph import * ### SETUP ELEMENTS FOR GRAPHING, SIMULATION, VISUALIZATION, TIMING # ------------------------------------------------------------------------ # Set window title scene.title = "Projectile Motion Particle Model" # Make scene background black scene.background = color.black # Define scene objects (units are in meters) field = box(pos = vector(0, 0, 0), size = (300, 10, 100), color = color.green, opacity = 0.3) ball = sphere(radius = 5, color = color.blue) # Define axis marks the field with a specified number of tick marks xaxis = PhysAxis(field, 10) # 10 tick marks yaxis = PhysAxis(field, 5, # 5 tick marks axisType = "y", labelOrientation = "left", startPos = vector(-150, 0, 0), # start the y axis at the left edge of the scene length = 100) # units are in meters # Set up graph with two plots posgraph = PhysGraph(2) # Set up trail to mark the ball's trajectory trail = curve(color = color.yellow, radius = 1) # units are in meters # Set up motion map for ball motionMap = MotionMap(ball, 8.163, # expected end time in seconds 10, # number of markers to draw labelMarkerOffset = vector(0, -20, 0), dropTime = False) # Set timer in top right of screen timerDisplay = PhysTimer(140, 150) # timer position (units are in meters) ### SETUP PARAMETERS AND INITIAL CONDITIONS # ---------------------------------------------------------------------------------------- # Define parameters ball.m = 0.6 # mass of ball in kg ball.pos = vector(-150, 0, 0) # initial position of the ball in(x, y, z) form, units are in meters ball.v = vector(30, 40, 0) # initial velocity of car in (vx, vy, vz) form, units are m/s g = vector(0, -9.8, 0) # acceleration due to gravity; units are m/s/s # Define time parameters t = 0 # starting time deltat = 0.001 # time step units are s ### CALCULATION LOOP; perform physics updates and drawing # ------------------------------------------------------------------------------------ while ball.pos.y >= 0 : #while the ball's y-position is greater than 0 (above the ground) # Required to make animation visible / refresh smoothly (keeps program from running faster # than 1000 frames/s) rate(1000) # Compute Net Force Fnet = ball.m * g # Newton's 2nd Law ball.v = ball.v + (Fnet/ball.m * deltat) # Position update ball.pos = ball.pos + ball.v * deltat # Update motion map, graph, timer, and trail motionMap.update(t, ball.v) posgraph.plot(t, ball.pos.x, ball.pos.y) # plot x and y position vs. time trail.append(pos = ball.pos) timerDisplay.update(t) # Time update t = t + deltat ### OUTPUT # -------------------------------------------------------------------------------------- # Print the final time and the ball's final position print t print ball.pos

32.138298

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411

3,021

4.321168

0.428224

0.036036

0.028153

0.045045

0.023649

0

0.031967

0.19232

3,021

94

99

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null

0.051282

0

null

0

null

0

1

0

1

f3041c623ca233066149adf01d25baef21dbb909

727

py

Python

parking_systems/models.py

InaraShalfei/parking_system

f1b326f12037808ab80e3b1d6b305235ba59a0db

[ "MIT" ]

null

parking_systems/models.py

InaraShalfei/parking_system

f1b326f12037808ab80e3b1d6b305235ba59a0db

[ "MIT" ]

null

parking_systems/models.py

InaraShalfei/parking_system

f1b326f12037808ab80e3b1d6b305235ba59a0db

[ "MIT" ]

null

from django.db import models class Parking(models.Model): def __str__(self): return f'Парковочное место №{self.id}' class Reservation(models.Model): parking_space = models.ForeignKey(Parking, on_delete=models.CASCADE, related_name='reservations', verbose_name='Номер парковочного места') start_time = models.DateTimeField(verbose_name='Время начала брони') finish_time = models.DateTimeField(verbose_name='Время окончания брони') class Meta: ordering = ['-start_time'] def __str__(self): format = "%d.%m.%y %H:%M" return f'Бронирование №{self.id} (c {self.start_time.strftime(format)} по {self.finish_time.strftime(format)})'

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727

5.255556

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0.069767

0.042283

0.12685

0.164905

0

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727

21

120

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false

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1

0

1

f30640fd7966c16ad8a70aa7a32537803f35f977

3,172

py

Python

src/dummy/toga_dummy/widgets/canvas.py

Donyme/toga

2647c7dc5db248025847e3a60b115ff51d4a0d4a

[ "BSD-3-Clause" ]

null

src/dummy/toga_dummy/widgets/canvas.py

Donyme/toga

2647c7dc5db248025847e3a60b115ff51d4a0d4a

[ "BSD-3-Clause" ]

null

src/dummy/toga_dummy/widgets/canvas.py

Donyme/toga

2647c7dc5db248025847e3a60b115ff51d4a0d4a

[ "BSD-3-Clause" ]

null

import re from .base import Widget class Canvas(Widget): def create(self): self._action('create Canvas') def set_on_draw(self, handler): self._set_value('on_draw', handler) def set_context(self, context): self._set_value('context', context) def line_width(self, width=2.0): self._set_value('line_width', width) def fill_style(self, color=None): if color is not None: num = re.search('^rgba$(\d*\.?\d*), (\d*\.?\d*), (\d*\.?\d*), (\d*\.?\d*)$$', color) if num is not None: r = num.group(1) g = num.group(2) b = num.group(3) a = num.group(4) rgba = str(r + ', ' + g + ', ' + b + ', ' + a) self._set_value('fill_style', rgba) else: pass # Support future colosseum versions # for named_color, rgb in colors.NAMED_COLOR.items(): # if named_color == color: # exec('self._set_value('fill_style', color) else: # set color to black self._set_value('fill_style', '0, 0, 0, 1') def stroke_style(self, color=None): self.fill_style(color) def close_path(self): self._action('close path') def closed_path(self, x, y): self._action('closed path', x=x, y=y) def move_to(self, x, y): self._action('move to', x=x, y=y) def line_to(self, x, y): self._action('line to', x=x, y=y) def bezier_curve_to(self, cp1x, cp1y, cp2x, cp2y, x, y): self._action('bezier curve to', cp1x=cp1x, cp1y=cp1y, cp2x=cp2x, cp2y=cp2y, x=x, y=y) def quadratic_curve_to(self, cpx, cpy, x, y): self._action('quadratic curve to', cpx=cpx, cpy=cpy, x=x, y=y) def arc(self, x, y, radius, startangle, endangle, anticlockwise): self._action('arc', x=x, y=y, radius=radius, startangle=startangle, endangle=endangle, anticlockwise=anticlockwise) def ellipse(self, x, y, radiusx, radiusy, rotation, startangle, endangle, anticlockwise): self._action('ellipse', x=x, y=y, radiusx=radiusx, radiusy=radiusy, rotation=rotation, startangle=startangle, endangle=endangle, anticlockwise=anticlockwise) def rect(self, x, y, width, height): self._action('rect', x=x, y=y, width=width, height=height) # Drawing Paths def fill(self, fill_rule, preserve): self._set_value('fill rule', fill_rule) if preserve: self._action('fill preserve') else: self._action('fill') def stroke(self): self._action('stroke') # Transformations def rotate(self, radians): self._action('rotate', radians=radians) def scale(self, sx, sy): self._action('scale', sx=sx, sy=sy) def translate(self, tx, ty): self._action('translate', tx=tx, ty=ty) def reset_transform(self): self._action('reset transform') def write_text(self, text, x, y, font): self._action('write text', text=text, x=x, y=y, font=font) def rehint(self): self._action('rehint Canvas')

31.72

165

0.573455

430

3,172

4.090698

0.237209

0.108016

0.01535

0.020466

0.212621

0.109153

0.078454

0

0.00962

0.279004

3,172

99

166

32.040404

0.75951

0.067465

0

0.046154

0

0.015385

0.10339

0

1

0.353846

false

0.015385

0.030769

0

0.4

0

null

0

null

0

1

0

1

f309247f76f7d18c28aea4b2f1973377cd29af7f

5,470

py

Python

Objected-Oriented Systems/Python_OOP_SDA/Task1.py

syedwaleedhyder/Freelance_Projects

7e2b85fc968850fc018014667b5ce9af0f00cb09

[ "MIT" ]

1

2020-08-13T17:26:13.000Z

Objected-Oriented Systems/Python_OOP_SDA/Task1.py

syedwaleedhyder/Freelance_Projects

7e2b85fc968850fc018014667b5ce9af0f00cb09

[ "MIT" ]

null

Objected-Oriented Systems/Python_OOP_SDA/Task1.py

syedwaleedhyder/Freelance_Projects

7e2b85fc968850fc018014667b5ce9af0f00cb09

[ "MIT" ]

null

from abc import ABCMeta, abstractmethod, abstractproperty from datetime import datetime, date class Item(metaclass=ABCMeta): def __init__(self, code, name, quantity, cost, offer): self.item_code=code self.item_name=name self.quantity_on_hand=quantity self.cost_price=cost self.on_offer=offer pass @property def quantity_on_hand(self): # implements the get - this name is *the* name return self._quantity_on_hand # @quantity_on_hand.setter def quantity_on_hand(self, value): # name must be the same self._quantity_on_hand = value @property def cost_price(self): # implements the get - this name is *the* name return self._cost_price # @cost_price.setter def cost_price(self, value): # name must be the same self._cost_price = value def changeOffer(): if(self.on_offer == "Yes"): self.on_offer = "No" elif(self.on_offer == "No"): self.on_offer == "Yes" @abstractmethod def selling_price(self): pass @abstractmethod def offer_price(self): pass @abstractmethod def profit_margin(self): pass @abstractmethod def discount_rate(self): pass def to_string(self): if(self.on_offer == "Yes"): offer = "**Offer" else: offer = "(No Offer)" string = self.item_code + " " + self.item_name + " Availalbe= " + str(self.quantity_on_hand) + " " + offer return string class Perishable(Item): def __init__(self, code, name, quantity, cost, offer, expiry): Item.__init__(self, code, name, quantity, cost, offer) self.expiry_date = expiry def profit_margin(self): return self.cost_price * 0.25 def selling_price(self): return self.cost_price + self.profit_margin() def days_before_expiry(self): now = datetime.now().date() days = self.expiry_date- now return days.days def discount_rate(self): days = self.days_before_expiry() price = self.selling_price() if(days < 15): return price * 0.3 elif(days < 30): return price * 0.2 elif (days > 29): return price * 0.1 def offer_price(self): if(self.on_offer == "No"): return selling_price() return self.selling_price() - self.discount_rate() def to_string(self): if(self.on_offer == "Yes"): offer = "**Offer**" else: offer = "(No Offer)" string = self.item_code + " " + self.item_name + " Available= " + str(self.quantity_on_hand) + " Price: $" + str(self.offer_price()) +" " + offer + " Expiry Date: " + self.expiry_date.strftime('%d %b %Y') + " Perishable Item" return string class NonPerishable(Item): def __init__(self, code, name, quantity, cost, offer): Item.__init__(self, code, name, quantity, cost, offer) def profit_margin(self): return self.cost_price * 0.3 def selling_price(self): return self.cost_price + self.profit_margin() def discount_rate(self): return self.selling_price() * 0.1 def offer_price(self): if(self.on_offer == "No"): return self.selling_price() return self.selling_price() - self.discount_rate() def to_string(self): if(self.on_offer == "Yes"): offer = "**Offer**" else: offer = "(No Offer)" string = self.item_code + " " + self.item_name + " Available= " + str(self.quantity_on_hand) + " Price: $" + str(self.offer_price()) +" " + offer + " Non Perishable Item" return string class Grocer: def __init__(self): self.items_list = [] def print_items(self): for item in self.items_list: print(item.to_string()) def add_to_list(self, item_to_be_added): self.items_list.append(item_to_be_added) return def update_quantity_on_hand(self, item_code, new_quantity): if(new_quantity < 0): print("Quantity cannot be zero. Failed to update.") return False for item in self.items_list: if(item.item_code == item_code): item.quantity_on_hand = new_quantity return True perishable = Perishable("P101", "Real Raisins", 10, 2, "Yes", date(2018,12, 10)) non_perishable = NonPerishable("NP210", "Tan Baking Paper", 25, 2, "No") perishable2 = Perishable("P105", "Eggy Soup Tofu", 14, 1.85, "Yes", date(2018,11, 26)) grocer = Grocer() grocer.add_to_list(perishable) grocer.add_to_list(non_perishable) grocer.add_to_list(perishable2) grocer.print_items() grocer.update_quantity_on_hand("P105", 10) print() grocer.print_items() #################################################################### #DISCUSSION """ Single Responsibility Principle: 1) IN Perishable clas. 2) In NonPersishable class. Open Closed Principle 1) Abstract class Item is open to be extended 2) Abstract class Item is closed for modification Interface Segregation Principle 1) For using Perishable items, user don't have to know anything about Non-perishable items. 2) For using Non-perishable items, users don't have to know tha details of Perishable items. Hence users are not forced to use methods they don't require. """ ####################################################################

31.988304

233

0.609506

696

5,470

4.58046

0.198276

0.0367

0.052698

0.033877

0.461731

0.361669

0.347867

0.292033

0.242472

0

0.016995

0.25777

5,470

171

234

31.988304

0.768227

0.026143

0

0.436508

0

0.064949

0

1

0.222222

false

0.039683

0.015873

0.055556

0.436508

0.047619

0

null

0

null

0

1

0

1

f30949586393ae32e93e9cb38a2df996aa7486fd

1,116

py

Python

compose/production/mongodb_backup/scripts/list_dbs.py

IMTEK-Simulation/mongodb-backup-container-image

b0e04c03cab9321d6b4277ee88412938fec95726

[ "MIT" ]

null

compose/production/mongodb_backup/scripts/list_dbs.py

IMTEK-Simulation/mongodb-backup-container-image

b0e04c03cab9321d6b4277ee88412938fec95726

[ "MIT" ]

null

compose/production/mongodb_backup/scripts/list_dbs.py

IMTEK-Simulation/mongodb-backup-container-image

b0e04c03cab9321d6b4277ee88412938fec95726

[ "MIT" ]

null

#!/usr/bin/env python3 host = 'mongodb' port = 27017 ssl_ca_cert='/run/secrets/rootCA.pem' ssl_certfile='/run/secrets/tls_cert.pem' ssl_keyfile='/run/secrets/tls_key.pem' # don't turn these signal into exceptions, just die. # necessary for integrating into bash script pipelines seamlessly. import signal signal.signal(signal.SIGINT, signal.SIG_DFL) signal.signal(signal.SIGPIPE, signal.SIG_DFL) # get administrator credentials with open('/run/secrets/username','r') as f: username = f.read() with open('/run/secrets/password','r') as f: password = f.read() from pymongo import MongoClient client = MongoClient(host, port, ssl=True, username=username, password=password, authSource=username, # assume admin database and admin user share name ssl_ca_certs=ssl_ca_cert, ssl_certfile=ssl_certfile, ssl_keyfile=ssl_keyfile, tlsAllowInvalidHostnames=True) # Within the container environment, mongod runs on host 'mongodb'. # That hostname, however, is not mentioned within the host certificate. dbs = client.list_database_names() for db in dbs: print(db) client.close()

27.9

74

0.750896

159

1,116

5.157233

0.54717

0.060976

0.065854

0.043902

0

0.00627

0.142473

1,116

39

75

28.615385

0.850575

0.31362

0

0.162055

0.150198

0

1

0

false

0.115385

0.076923

0

0.076923

0.038462

0

null

0

null

0

1

0

1

f3133d707d13f1d41040304efdb1e48fd46e0e3f

4,270

py

Python

src/piminder_service/resources/db_autoinit.py

ZAdamMac/pyminder

059f57cb7cea4f517f77b1bbf391ce99f25d83bb

[ "MIT" ]

null

src/piminder_service/resources/db_autoinit.py

ZAdamMac/pyminder

059f57cb7cea4f517f77b1bbf391ce99f25d83bb

[ "MIT" ]

3

2021-05-05T21:08:24.000Z

2021-06-23T10:47:40.000Z

src/piminder_service/resources/db_autoinit.py

ZAdamMac/pyminder

059f57cb7cea4f517f77b1bbf391ce99f25d83bb

[ "MIT" ]

null

""" This script is a component of Piminder's back-end controller. Specifically, it is a helper utility to be used to intialize a database for the user and message tables. Author: Zac Adam-MacEwen (zadammac@kenshosec.com) An Arcana Labs utility. Produced under license. Full license and documentation to be found at: https://github.com/ZAdamMac/Piminder """ import bcrypt import getpass import os import pymysql __version__ = "1.0.0" # This is the version of service that we can init, NOT the version of the script itself. spec_tables = [ """CREATE TABLE `messages` ( `id` CHAR(36) NOT NULL, `name` VARCHAR(255) NOT NULL, `message` TEXT DEFAULT NULL, `errorlevel` CHAR(5) DEFAULT NULL, `time_raised` TIMESTAMP, `read_flag` BIT DEFAULT 0, PRIMARY KEY (`id`) )""", """CREATE TABLE `users` ( `username` CHAR(36) NOT NULL, `password` VARCHAR(255) NOT NULL, `permlevel` INT(1) DEFAULT 1, `memo` TEXT DEFAULT NULL, PRIMARY KEY (`username`) )""" ] def connect_to_db(): """Detects if it is necessary to prompt for the root password, and either way, establishes the db connection, returning it. :return: """ print("We must now connect to the database.") try: db_user = os.environ['PIMINDER_DB_USER'] except KeyError: print("Missing envvar: Piminder_DB_USER") exit(1) root_password = None try: root_password = os.environ['PIMINDER_DB_PASSWORD'] except KeyError: print("Missing envvar: Piminder_DB_PASSWORD") exit(1) try: db_host = os.environ['PIMINDER_DB_HOST'] except KeyError: print("Missing envvar: Piminder_DB_HOST") exit(1) finally: conn = pymysql.connect(host=db_host, user=db_user, password=root_password, db='Piminder', charset='utf8mb4', cursorclass=pymysql.cursors.DictCursor) return conn def create_tables(list_tables, connection): """Accepts a list of create statements for tables and pushes them to the DB. :param list_tables: A list of CREATE statements in string form. :param connection: a pymysql.connect() object, such as returned by connect_to_db :return: """ cursor = connection.cursor() connection.begin() for table in list_tables: try: cursor.execute(table) except pymysql.err.ProgrammingError: print("Error in the following statement; table was skipped.") print(table) except pymysql.err.OperationalError as error: if str(error.args[0]) == 1050: # This table already exists print("%s, skipping" % error.args[1]) else: print(error) connection.commit() def create_administrative_user(connection): """Creates an administrative user if it does not already exist. :param connection: :return: """ print("Validating an admin user exists:") try: admin_name = os.environ['PIMINDER_ADMIN_USER'] except KeyError: print("Missing envvar: Piminder_ADMIN_USER") exit(1) cur = connection.cursor() command = "SELECT count(username) AS howmany FROM users WHERE permlevel like 3;" # Wait, how many admins are there? cur.execute(command) count = cur.fetchone()["howmany"] if count < 1: # Only do this if no more than 0 exists. command = "INSERT INTO users (username, password, memo, permlevel) VALUES (%s, %s, 'Default User', 3);" try: root_password = os.environ['PIMINDER_ADMIN_PASSWORD'] except KeyError: print("Missing envvar: Piminder_ADMIN_PASSWORD") exit(1) hashed_rootpw = bcrypt.hashpw(root_password.encode('utf8'), bcrypt.gensalt()) cur.execute(command, (admin_name, hashed_rootpw)) print("Created administrative user: %s" % admin_name) else: print("Administrative user already exists, skipping.") connection.commit() def runtime(): print("Now Creating Tables") mariadb = connect_to_db() create_tables(spec_tables, mariadb) create_administrative_user(mariadb) mariadb.commit() mariadb.close() print("Done.")

31.865672

111

0.646136

532

4,270

5.078947

0.370301

0.026647

0.031458

0.048113

0.129534

0.112509

0.088823

0

0.010986

0.253864

4,270

133

112

32.105263

0.8371

0.230211

0

0.25641

0

0.012821

0.251733

0.016782

0

1

0.051282

false

0.115385

0.051282

0

0.115385

0.192308

0

null

0

null

0

1

0

1

f3159c44193bd89a772b6f2bca9dbffb2ffaa8bc

5,933

py

Python

test/search/capacity.py

sbutler/spotseeker_server

02bd2d646eab9f26ddbe8536b30e391359796c9c

[ "Apache-2.0" ]

null

test/search/capacity.py

sbutler/spotseeker_server

02bd2d646eab9f26ddbe8536b30e391359796c9c

[ "Apache-2.0" ]

null

test/search/capacity.py

sbutler/spotseeker_server

02bd2d646eab9f26ddbe8536b30e391359796c9c

[ "Apache-2.0" ]

null

""" Copyright 2012, 2013 UW Information Technology, University of Washington Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from django.test import TestCase from django.conf import settings from django.test.client import Client from spotseeker_server.models import Spot, SpotExtendedInfo, SpotType import simplejson as json from django.test.utils import override_settings from mock import patch from django.core import cache from spotseeker_server import models @override_settings(SPOTSEEKER_AUTH_MODULE='spotseeker_server.auth.all_ok') class SpotSearchCapacityTest(TestCase): def test_capacity(self): dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache') with patch.object(models, 'cache', dummy_cache): spot1 = Spot.objects.create(name="capacity: 1", capacity=1) spot1.save() spot2 = Spot.objects.create(name="capacity: 2", capacity=2) spot2.save() spot3 = Spot.objects.create(name="capacity: 3", capacity=3) spot3.save() spot4 = Spot.objects.create(name="capacity: 4", capacity=4) spot4.save() spot5 = Spot.objects.create(name="capacity: 50", capacity=50) spot5.save() c = Client() response = c.get("/api/v1/spot", {'capacity': '', 'name': 'capacity'}) self.assertEquals(response["Content-Type"], "application/json", "Has the json header") spots = json.loads(response.content) has_1 = False has_2 = False has_3 = False has_4 = False has_5 = False for spot in spots: if spot['id'] == spot1.pk: has_1 = True if spot['id'] == spot2.pk: has_2 = True if spot['id'] == spot3.pk: has_3 = True if spot['id'] == spot4.pk: has_4 = True if spot['id'] == spot5.pk: has_5 = True self.assertEquals(has_1, True) self.assertEquals(has_2, True) self.assertEquals(has_3, True) self.assertEquals(has_4, True) self.assertEquals(has_5, True) response = c.get("/api/v1/spot", {'capacity': '1'}) self.assertEquals(response["Content-Type"], "application/json", "Has the json header") spots = json.loads(response.content) has_1 = False has_2 = False has_3 = False has_4 = False has_5 = False for spot in spots: if spot['id'] == spot1.pk: has_1 = True if spot['id'] == spot2.pk: has_2 = True if spot['id'] == spot3.pk: has_3 = True if spot['id'] == spot4.pk: has_4 = True if spot['id'] == spot5.pk: has_5 = True self.assertEquals(has_1, True) self.assertEquals(has_2, True) self.assertEquals(has_3, True) self.assertEquals(has_4, True) self.assertEquals(has_5, True) response = c.get("/api/v1/spot", {'capacity': '49'}) self.assertEquals(response["Content-Type"], "application/json", "Has the json header") spots = json.loads(response.content) has_1 = False has_2 = False has_3 = False has_4 = False has_5 = False for spot in spots: if spot['id'] == spot1.pk: has_1 = True if spot['id'] == spot2.pk: has_2 = True if spot['id'] == spot3.pk: has_3 = True if spot['id'] == spot4.pk: has_4 = True if spot['id'] == spot5.pk: has_5 = True self.assertEquals(has_1, False) self.assertEquals(has_2, False) self.assertEquals(has_3, False) self.assertEquals(has_4, False) self.assertEquals(has_5, True) response = c.get("/api/v1/spot", {'capacity': '501'}) self.assertEquals(response["Content-Type"], "application/json", "Has the json header") spots = json.loads(response.content) has_1 = False has_2 = False has_3 = False has_4 = False has_5 = False for spot in spots: if spot['id'] == spot1.pk: has_1 = True if spot['id'] == spot2.pk: has_2 = True if spot['id'] == spot3.pk: has_3 = True if spot['id'] == spot4.pk: has_4 = True if spot['id'] == spot5.pk: has_5 = True self.assertEquals(has_1, False) self.assertEquals(has_2, False) self.assertEquals(has_3, False) self.assertEquals(has_4, False) self.assertEquals(has_5, False) response = c.get("/api/v1/spot", {'capacity': '1', 'distance': '100', 'limit': '4'}) #testing sorting by distance, which is impossible given no center self.assertEquals(response.status_code, 400)

36.398773

98

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697

5,933

4.430416

0.213773

0.129534

0.051813

0.062176

0.599741

0.552785

0.543394

0.533355

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0.36238

5,933

162

99

36.623457

0.781126

0.110905

0

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0

0.095011

0.013764

0

0.203252

1

0.00813

false

0

0.073171

0

0.089431

0

null

0

1

0

null

0

1

f31ce1a1719984d1cf324a95ea4f226d430436e1

361

py

Python

DEQModel/utils/debug.py

JunLi-Galios/deq

80eb6b598357e8e01ad419126465fa3ed53b12c7

[ "MIT" ]

548

2019-09-05T04:25:21.000Z

2022-03-22T01:49:35.000Z

DEQModel/utils/debug.py

JunLi-Galios/deq

80eb6b598357e8e01ad419126465fa3ed53b12c7

[ "MIT" ]

21

2019-10-04T16:36:05.000Z

2022-03-24T02:20:28.000Z

DEQModel/utils/debug.py

JunLi-Galios/deq

80eb6b598357e8e01ad419126465fa3ed53b12c7

[ "MIT" ]

75

2019-09-05T22:40:32.000Z

2022-03-31T09:40:44.000Z

import torch from torch.autograd import Function class Identity(Function): @staticmethod def forward(ctx, x, name): ctx.name = name return x.clone() def backward(ctx, grad): import pydevd pydevd.settrace(suspend=False, trace_only_current_thread=True) grad_temp = grad.clone() return grad_temp, None

24.066667

70

0.65928

45

361

5.177778

0.622222

0.06867

0

0.257618

361

15

71

24.066667

0.869403

0

1

0.166667

false

0

0.25

0

0.666667

0

null

0

null

0

1

0

1

f327633efe0ce2c9e557f60f7f82ada184c4948d

576

py

Python

bottomline/blweb/migrations/0012_vehicleconfig_color.py

mcm219/BottomLine

db82eef403c79bffa3864c4db6bc336632abaca5

[ "MIT" ]

null

bottomline/blweb/migrations/0012_vehicleconfig_color.py

mcm219/BottomLine

db82eef403c79bffa3864c4db6bc336632abaca5

[ "MIT" ]

1

2021-06-14T02:20:40.000Z

bottomline/blweb/migrations/0012_vehicleconfig_color.py

mcm219/BottomLine

db82eef403c79bffa3864c4db6bc336632abaca5

[ "MIT" ]

null

# Generated by Django 3.2.2 on 2021-07-10 03:16 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('blweb', '0011_vehiclecolor'), ] operations = [ migrations.AddField( model_name='vehicleconfig', name='color', field=models.ForeignKey(blank=True, default=None, help_text='The chosen color for this config', null=True, on_delete=django.db.models.deletion.CASCADE, related_name='color', to='blweb.vehiclecolor'), ), ]

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null

0

1

0

null

0

1

b823df535990bd76d900f1381be1d7cc948408cf

11,634

py

Python

src/acs_3dpsf.py

davidharvey1986/rrg

26b4658f14279af21af1a61d57e9936daf315a71

[ "MIT" ]

2

2019-11-18T12:51:09.000Z

2019-12-11T03:13:51.000Z

src/acs_3dpsf.py

davidharvey1986/rrg

26b4658f14279af21af1a61d57e9936daf315a71

[ "MIT" ]

5

2017-06-09T10:06:27.000Z

2019-07-19T11:28:18.000Z

src/acs_3dpsf.py

davidharvey1986/rrg

26b4658f14279af21af1a61d57e9936daf315a71

[ "MIT" ]

2

2017-07-19T15:48:33.000Z

2017-08-09T16:07:20.000Z

import numpy as np from . import acs_map_xy as acs_map def acs_3dpsf_basisfunctions( degree, x, y, focus ): # Generate relevant basis functions n_stars=np.max( np.array([len(x),len(y),len(focus)])) basis_function_order=np.zeros((1,3)) # All zeros for k in range(degree[2]+1): for j in range(degree[1]+1): for i in range(degree[0]+1): if (i+j+k > 0) & ((i+j) <= np.max(degree[0:2])): basis_function_order=np.vstack((basis_function_order, [i,j,k])) n_basis_functions= basis_function_order.shape[0] basis_function_value = np.zeros( (n_basis_functions, n_stars)) for i in range(n_basis_functions): basis_function_value[i,:] = x**basis_function_order[i,0]*\ y**basis_function_order[i,1] * \ focus**basis_function_order[i,2] return basis_function_value # ********************************************************************** # ********************************************************************** # ********************************************************************** def acs_3dpsf_fit( scat, degree=np.array([3,2,2]), mag_cut=np.array([20.5,22]), e_cut=1, size_cut=np.array([-np.inf,3]), verbose=False ): # Fit the PSF from data in a SCAT catalogue # F814 I magnitude catalogue cut degree = np.array(degree) if len(degree) < 3 : print("DEGREE must be 3D") degree[ degree > 0 ] = np.min(degree[ degree > 0 ]) # Find the line dividing CCDs 1 and 2 ccd_boundary = acs_map.acs_map_xy( np.array([0, 4095, 0, 4095]), np.array([2047, 2047, 2048, 2048]), pixel_scale=scat.pixscale) x1=np.mean([ccd_boundary.x[0],ccd_boundary.x[2]]) x2=np.mean([ccd_boundary.x[1],ccd_boundary.x[3]]) y1=np.mean([ccd_boundary.y[0],ccd_boundary.y[2]]) y2=np.mean([ccd_boundary.y[1],ccd_boundary.y[3]]) ccd_boundary_x1=np.mean([ccd_boundary.x[0],ccd_boundary.x[2]]) ccd_boundary_x2=np.mean([ccd_boundary.x[1],ccd_boundary.x[3]]) ccd_boundary_y1=np.mean([ccd_boundary.y[0],ccd_boundary.y[2]]) ccd_boundary_y2=np.mean([ccd_boundary.y[1],ccd_boundary.y[3]]) ccd_boundary_m=(ccd_boundary_y2-ccd_boundary_y1)/(ccd_boundary_x2-ccd_boundary_x1) ccd_boundary_c=ccd_boundary_y1-ccd_boundary_m*ccd_boundary_x1 # Find the centre of each CCD ccd_centre = acs_map.acs_map_xy( np.array([2048,2048]), np.array([3072,1024]), pixel_scale=scat.pixscale) # Select only the well-behaved stars good= np.isfinite(scat.field_focus[0][scat.field_id[0]]) & \ np.isfinite(scat.e1_uncor_unrot[0]) & \ np.isfinite(scat.e2_uncor_unrot[0]) & \ np.isfinite(scat.xx_uncor[0]) & \ np.isfinite(scat.xy_uncor[0]) & \ np.isfinite(scat.yy_uncor[0]) & \ np.isfinite(scat.xxxx_uncor[0]) & \ np.isfinite(scat.xxxy_uncor[0]) & \ np.isfinite(scat.xxyy_uncor[0]) & \ np.isfinite(scat.xyyy_uncor[0]) & \ np.isfinite(scat.yyyy_uncor[0]) n_good = len(np.arange( len( good ))[good]) if verbose: print("Found a total of "+str(len(scat.x[0]))+" real stars, of which "+str(n_good)+" look well-behaved") # Store quantities to be fitted in local variables x=scat.x[0][good] y=scat.y[0][good] focus=scat.field_focus[0][scat.field_id[0]][good] ixx=scat.xx_uncor[0][good] ixy=scat.xy_uncor[0][good] iyy=scat.yy_uncor[0][good] ixxxx=scat.xxxx_uncor[0][good] ixxxy=scat.xxxy_uncor[0][good] ixxyy=scat.xxyy_uncor[0][good] ixyyy=scat.xyyy_uncor[0][good] iyyyy=scat.yyyy_uncor[0][good] e1=scat.e1_uncor_unrot[0][good] e2=scat.e2_uncor_unrot[0][good] # Work on each CCD separately init_coeffs_flag = True for ccd in range(2): # Report which CCD is being considered if ccd +1 == 1: in_ccd = np.arange(len(y))[ y >= ccd_boundary_m*x+ccd_boundary_c] n_in_CCD = len(in_ccd) if ccd + 1 == 2: in_ccd = np.arange(len( y))[ y < ccd_boundary_m*x+ccd_boundary_c] n_in_CCD = len(in_ccd) if n_in_CCD > 0: #Compute matrix necessary for matrix inversion if verbose: print("Fitting moments of "+str(n_in_CCD)+" real stars in CCD#"+str(ccd+1)) basis_function_value=acs_3dpsf_basisfunctions(degree, x[in_ccd]-ccd_centre.x[ccd], y[in_ccd]-ccd_centre.y[ccd], focus[in_ccd]) ls_matrix = np.dot( np.linalg.inv(np.dot(basis_function_value, basis_function_value.T)), basis_function_value) # Create global arrays to contain the answers n_basis_functions=np.shape(np.array(ls_matrix))[0] if init_coeffs_flag: acs_3dpsf_coeffs=basis_coeffs( ccd_centre, ccd_boundary_m, ccd_boundary_c, n_basis_functions, degree ) init_coeffs_flag = False # Fit data to basis functions using least-squares inversion #these are all matrices acs_3dpsf_coeffs.ixx_fit[ccd, :] = np.dot(ls_matrix ,ixx[in_ccd]) acs_3dpsf_coeffs.ixy_fit[ccd, :] = np.dot(ls_matrix , ixy[in_ccd]) acs_3dpsf_coeffs.iyy_fit[ccd, :] = np.dot(ls_matrix , iyy[in_ccd]) acs_3dpsf_coeffs.ixxxx_fit[ccd, :] = np.dot(ls_matrix , ixxxx[in_ccd]) acs_3dpsf_coeffs.ixxxy_fit[ccd, :] = np.dot(ls_matrix , ixxxy[in_ccd]) acs_3dpsf_coeffs.ixxyy_fit[ccd, :] = np.dot(ls_matrix , ixxyy[in_ccd]) acs_3dpsf_coeffs.ixyyy_fit[ccd, :] = np.dot(ls_matrix , ixyyy[in_ccd]) acs_3dpsf_coeffs.iyyyy_fit[ccd, :] = np.dot(ls_matrix , iyyyy[in_ccd]) acs_3dpsf_coeffs.e1_fit[ccd, :] = np.dot(ls_matrix , e1[in_ccd]) acs_3dpsf_coeffs.e2_fit[ccd, :] = np.dot(ls_matrix , e2[in_ccd]) return acs_3dpsf_coeffs # ********************************************************************** # ********************************************************************** # ********************************************************************** def acs_3dpsf_reconstruct( acs_3dpsf_coeffs, x, y, focus, radius=None, verbose=False): # Create arrays to contain the final answer n_galaxies=np.max( np.array([len(x), len(y), len(focus)]) ) if len(focus) == 1: focus_local = np.zeros(len(n_galaxies)) + focus else: focus_local=focus if verbose: print("Found a total of "+str(n_galaxies)+" galaxies") if radius is None: radius=np.zeros(len(n_galaxies))+6 moms=moments( x, y, radius[:n_galaxies], acs_3dpsf_coeffs.degree ) for ccd in range(2): #Report which CCD is being considered if ccd +1 == 1: in_ccd = np.arange(len( y))[ y >= acs_3dpsf_coeffs.ccd_boundary_m*x+acs_3dpsf_coeffs.ccd_boundary_c] n_in_CCD = len(in_ccd) if ccd + 1 == 2: in_ccd = np.arange(len( y))[ y < acs_3dpsf_coeffs.ccd_boundary_m*x+acs_3dpsf_coeffs.ccd_boundary_c] n_in_CCD = len(in_ccd) if n_in_CCD > 0: if verbose: print("Interpolating model PSF moments to the position of "+str(n_in_CCD)+" galaxies in CCD#"+str(ccd+1)) #Fit the PSF basis_function_value=acs_3dpsf_basisfunctions(acs_3dpsf_coeffs.degree[0], \ x[in_ccd]-acs_3dpsf_coeffs.ccd_centre.x[ccd], \ y[in_ccd]-acs_3dpsf_coeffs.ccd_centre.y[ccd], \ focus_local[in_ccd] ) moms.xx[in_ccd] = np.dot(acs_3dpsf_coeffs.ixx_fit[ccd, :], basis_function_value) moms.xy[in_ccd] = np.dot(acs_3dpsf_coeffs.ixy_fit[ccd, :], basis_function_value) moms.yy[in_ccd] = np.dot(acs_3dpsf_coeffs.iyy_fit[ccd, :], basis_function_value) moms.xxxx[in_ccd] = np.dot(acs_3dpsf_coeffs.ixxxx_fit[ccd, :], basis_function_value) moms.xxxy[in_ccd] = np.dot(acs_3dpsf_coeffs.ixxxy_fit[ccd, :], basis_function_value) moms.xxyy[in_ccd] = np.dot(acs_3dpsf_coeffs.ixxyy_fit[ccd, :], basis_function_value) moms.xyyy[in_ccd] = np.dot(acs_3dpsf_coeffs.ixyyy_fit[ccd, :], basis_function_value) moms.yyyy[in_ccd] = np.dot(acs_3dpsf_coeffs.iyyyy_fit[ccd, :], basis_function_value) moms.e1[in_ccd] = np.dot(acs_3dpsf_coeffs.e1_fit[ccd, :], basis_function_value) moms.e2[in_ccd] = np.dot(acs_3dpsf_coeffs.e2_fit[ccd, :], basis_function_value) else: print("No galaxies in CCD#"+str(ccd)) # Work out PSF ellipticities at positions of galaxies properly. Tsk! moms.e1 = (moms.xx-moms.yy)/(moms.xx+moms.yy) moms.e2 = 2*moms.xy/(moms.xx+moms.yy) return moms # ********************************************************************** # ********************************************************************** # ********************************************************************** def acs_3dpsf( x, y, focus, radius, scat, acs_3dpsf_coeffs=None, degree=np.array([3,2,2])): # Fit the PSF if acs_3dpsf_coeffs is None: acs_3dpsf_coeffs=acs_3dpsf_fit(scat, degree=degree) #Reconstruct the PSF acs_moms=acs_3dpsf_reconstruct(acs_3dpsf_coeffs, x, y, focus, radius) return acs_moms class basis_coeffs: def __init__( self, ccd_centre, ccd_boundary_m, \ ccd_boundary_c, n_basis_functions, degree ): self.degree = degree, self.ccd_centre = ccd_centre self.ccd_boundary_m = ccd_boundary_m self.ccd_boundary_c = ccd_boundary_c self.ixx_fit = np.zeros((2,n_basis_functions)) self.ixy_fit = np.zeros((2,n_basis_functions)) self.iyy_fit = np.zeros((2,n_basis_functions)) self.ixxxx_fit = np.zeros((2,n_basis_functions)) self.ixxxy_fit = np.zeros((2,n_basis_functions)) self.ixxyy_fit = np.zeros((2,n_basis_functions)) self.ixyyy_fit = np.zeros((2,n_basis_functions)) self.iyyyy_fit = np.zeros((2,n_basis_functions)) self.e1_fit = np.zeros((2,n_basis_functions)) self.e2_fit = np.zeros((2,n_basis_functions)) class moments( dict ): def __init__(self, x, y, radius, degree ): n_objects = len(x) self.__dict__['x'] = x self.__dict__['y'] = y self.__dict__['e1']=np.zeros(n_objects) self.__dict__['e2']=np.zeros(n_objects) self.__dict__['xx']=np.zeros(n_objects) self.__dict__['xy']=np.zeros(n_objects) self.__dict__['yy']=np.zeros(n_objects) self.__dict__['xxxx']=np.zeros(n_objects) self.__dict__['xxxy']=np.zeros(n_objects) self.__dict__['xxyy']=np.zeros(n_objects) self.__dict__['xyyy']=np.zeros(n_objects) self.__dict__['yyyy']=np.zeros(n_objects) self.__dict__['radius'] = radius self.__dict__['degree'] = degree def keys(self): return list(self.__dict__.keys()) def __getitem__(self, key): return self.__dict__[key]

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null

0

1

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null

0

1

b826697289acc6bb7f13171d32f3b15f39b8d6bc

411

py

Python

mundo-1/ex-014.py

guilhermesm28/python-curso-em-video

50ab4e76b1903e62d4daa579699c5908329b26c8

[ "MIT" ]

null

mundo-1/ex-014.py

guilhermesm28/python-curso-em-video

50ab4e76b1903e62d4daa579699c5908329b26c8

[ "MIT" ]

null

mundo-1/ex-014.py

guilhermesm28/python-curso-em-video

50ab4e76b1903e62d4daa579699c5908329b26c8

[ "MIT" ]

null

# Escreva um programa que converta uma temperatura digitando em graus Celsius e converta para graus Fahrenheit. print('-' * 100) print('{: ^100}'.format('EXERCÍCIO 014 - CONVERSOR DE TEMPERATURAS')) print('-' * 100) c = float(input('Informe a temperatura em ºC: ')) f = ((9 * c) / 5) + 32 print(f'A temperatura de {c:.2f}ºC corresponde a {f:.2f}ºF.') print('-' * 100) input('Pressione ENTER para sair...')

27.4

111

0.6691

61

411

4.508197

0.622951

0.116364

0

0.06087

0.160584

411

14

112

29.357143

0.736232

0.265207

0

0.375

0

0.533333

0

1

0

false

0

0.625

0

null

0

null

0

1

0

1

b82ba735b06701323afbbc1adb2108b231b98638

1,647

py

Python

CxMetrics/calcMetrics.py

Danielhiversen/pyCustusx

5a7fca51d885ad30f4db46ab725485d86fb2d17a

[ "MIT" ]

null

CxMetrics/calcMetrics.py

Danielhiversen/pyCustusx

5a7fca51d885ad30f4db46ab725485d86fb2d17a

[ "MIT" ]

null

CxMetrics/calcMetrics.py

Danielhiversen/pyCustusx

5a7fca51d885ad30f4db46ab725485d86fb2d17a

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sat Oct 24 11:39:42 2015 @author: dahoiv """ import numpy as np def loadMetrics(filepath): mr_points=dict() us_points=dict() with open(filepath, 'r') as file : for line in file.readlines(): line = line.translate(None, '"') data= line.split() if not "pointMetric" in data[0]: continue key= data[1][-2:] point = data[6:9] if "_mr_" in data[1] and not "us" in data[2].lower(): mr_points[key]=[float(point[0]),float(point[1]),float(point[2])] if "_us_" in data[1] and "us" in data[2].lower(): us_points[key]=[float(point[0]),float(point[1]),float(point[2])] return (mr_points,us_points) def calcDist(mr_points,us_points): k=0 dist=[] for key in mr_points.keys(): if not key in us_points.keys(): print key, " missing in us" continue diff = np.array(mr_points[key])-np.array(us_points[key]) dist.append((diff[0]**2 +diff[1]**2 +diff[2]**2)**0.5) print key, dist[-1] k=k+1 print "mean; ", np.mean(dist) print "var: ", np.var(dist) if __name__ == '__main__': filePath1="/home/dahoiv/disk/data/brainshift/079_Tumor.cx3/Logs/metrics_a.txt" (mr_points_1,us_points_1)=loadMetrics(filePath1) calcDist(mr_points_1,us_points_1) filePath2="/home/dahoiv/disk/data/brainshift/079_Tumor.cx3/Logs/metrics_b.txt" (mr_points_2,us_points_2)=loadMetrics(filePath2) calcDist(mr_points_2,us_points_2)

32.294118

82

0.571342

240

1,647

3.7375

0.329167

0.089186

0.026756

0.022297

0.316611

0.285396

0.205128

0

0.049414

0.275046

1,647

51

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0

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0

0.122045

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0

null

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null

0.108108

0

null

0

null

0

1

0

1

b842ca4df0f85a27ac428ca98c508bc0fd8473bb

379

py

Python

pages/page1.py

kalimuthu123/dash-app

90bf4c570abb1770ea0f082989e8f97d62b98346

[ "MIT" ]

null

pages/page1.py

kalimuthu123/dash-app

90bf4c570abb1770ea0f082989e8f97d62b98346

[ "MIT" ]

null

pages/page1.py

kalimuthu123/dash-app

90bf4c570abb1770ea0f082989e8f97d62b98346

[ "MIT" ]

null

import dash_html_components as html from utils import Header def create_layout(app): # Page layouts return html.Div( [ html.Div([Header(app)]), # page 1 # add your UI here, and callbacks go at the bottom of app.py # assets and .js go in assets folder # csv or images go in data folder ], )

25.266667

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0.711538

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0

0.004184

0.369393

379

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0.382586

0

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0.125

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0.25

0.125

0.5

0

null

0

null

0

1

0

1

b845201c7741d5e90f7173c09fe9315087e66057

2,046

py

Python

svca_limix/limix/core/covar/test/test_categorical.py

DenisSch/svca

bd029c120ca8310f43311253e4d7ce19bc08350c

[ "Apache-2.0" ]

65

2015-01-20T20:46:26.000Z

2021-06-27T14:40:35.000Z

svca_limix/limix/core/covar/test/test_categorical.py

DenisSch/svca

bd029c120ca8310f43311253e4d7ce19bc08350c

[ "Apache-2.0" ]

29

2015-02-01T22:35:17.000Z

2017-08-07T08:18:23.000Z

svca_limix/limix/core/covar/test/test_categorical.py

DenisSch/svca

bd029c120ca8310f43311253e4d7ce19bc08350c

[ "Apache-2.0" ]

35

2015-02-01T17:26:50.000Z

2019-09-13T07:06:16.000Z

"""LMM testing code""" import unittest import scipy as sp import numpy as np from limix.core.covar import CategoricalCov from limix.utils.check_grad import mcheck_grad class TestCategoricalLowRank(unittest.TestCase): """test class for CategoricalCov cov""" def setUp(self): sp.random.seed(1) self.n = 30 categories = sp.random.choice(['a', 'b', 'c'], self.n) self.rank =2 self.C = CategoricalCov(categories,self.rank) self.name = 'categorical' self.C.setRandomParams() def test_grad(self): def func(x, i): self.C.setParams(x) return self.C.K() def grad(x, i): self.C.setParams(x) return self.C.K_grad_i(i) x0 = self.C.getParams() err = mcheck_grad(func, grad, x0) np.testing.assert_almost_equal(err, 0., decimal = 6) # def test_param_activation(self): # self.assertEqual(len(self.C.getParams()), 8) # self.C.act_X = False # self.assertEqual(len(self.C.getParams()), 0) # # self.C.setParams(np.array([])) # with self.assertRaises(ValueError): # self.C.setParams(np.array([0])) # # with self.assertRaises(ValueError): # self.C.K_grad_i(0) class TestCategoricalFreeForm(unittest.TestCase): """test class for Categorical cov""" def setUp(self): sp.random.seed(1) self.n = 30 categories = sp.random.choice(['a', 'b', 'c'], self.n) self.rank =None self.C = CategoricalCov(categories,self.rank) self.name = 'categorical' self.C.setRandomParams() def test_grad(self): def func(x, i): self.C.setParams(x) return self.C.K() def grad(x, i): self.C.setParams(x) return self.C.K_grad_i(i) x0 = self.C.getParams() err = mcheck_grad(func, grad, x0) np.testing.assert_almost_equal(err, 0., decimal = 6) if __name__ == '__main__': unittest.main()

26.921053

62

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267

2,046

4.348315

0.273408

0.086133

0.072351

0.024117

0.753661

0.668389

0.552972

0

0.012952

0.282991

2,046

75

63

27.28

0.778459

0.205279

0

0.755556

0

0.0225

0

0.044444

1

0.177778

false

0

0.111111

0

0.422222

0

null

0

1

0

null

0

1

b8493d2511af44620ab30010ea879f211db8a17b

11,878

py

Python

modules/administrator.py

Gaeta/Delta

c76e149d0c17e025fe2648964e2512440fc0b4c7

[ "MIT" ]

1

2021-07-04T10:34:11.000Z

modules/administrator.py

Gaeta/Delta

c76e149d0c17e025fe2648964e2512440fc0b4c7

[ "MIT" ]

null

modules/administrator.py

Gaeta/Delta

c76e149d0c17e025fe2648964e2512440fc0b4c7

[ "MIT" ]

null

import discord, sqlite3, asyncio, utils, re from discord.ext import commands from datetime import datetime TIME_REGEX = re.compile("(?:(\d{1,5})\s?(h|hours|hrs|hour|hr|s|seconds|secs|sec|second|m|mins|minutes|minute|min|d|days|day))+?") TIME_DICT = {"h": 3600, "s": 1, "m": 60, "d": 86400} class TimeConverter(commands.Converter): async def convert(self, argument): if argument is None: return 0 args = argument.lower() matches = re.findall(TIME_REGEX, args) time = 0 for v, k in matches: try: for key in ("h", "s", "m", "d"): if k.startswith(key): k = key break time += TIME_DICT[k]*float(v) except KeyError: raise commands.BadArgument("{} is an invalid time-key! h/m/s/d are valid!".format(k)) except ValueError: raise commands.BadArgument("{} is not a number!".format(v)) return time class AdministratorCommands(commands.Cog): def __init__(self, bot): self.bot = bot @commands.command(usage="poll <ping> <question> | <answer 1> | <answer2...>") @utils.guild_only() @utils.is_admin() @commands.bot_has_permissions(manage_roles=True) @commands.cooldown(1, 60, commands.BucketType.guild) async def poll(self, ctx, ping_member, *, args): """Creates a poll with up to 5 answers.""" ping = ping_member.lower() if ping not in ("yes", "no", "true", "false", "y", "n", "t", "f"): return await utils.embed(ctx, discord.Embed(title="Poll Failed", description=f"Sorry, the `ping_member` argument should be \"Yes\" or \"No\". Please use `{self.bot.config.prefix}help poll` for more information."), error=True) if ping in ("yes", "y", "true", "t"): ping = True if ping in ("no", "n", "no", "n"): ping = False ques_ans = args.split(" | ") if len(ques_ans) <= 2: return await utils.embed(ctx, discord.Embed(title="Poll Failed", description=f"Sorry, the `args` argument should be follow this syntax: `question | answer 1 | answer 2...`."), error=True) question = ques_ans[0] answers = ques_ans[1:6] channel_id = self.bot.config.channels.announcements channel = self.bot.get_channel(channel_id) if channel is None: return await utils.embed(ctx, discord.Embed(title="Poll Failed", description=f"Sorry, the `announcements` channel hasn't been configured."), error=True) reactions = [] text = "" i = 1 for answer in answers: react = {1: "1\u20e3", 2: "2\u20e3", 3: "3\u20e3", 4: "4\u20e3", 5: "5\u20e3"}[i] reactions.append(react) text += f"{react} {answers[i-1]}\n\n" i += 1 embed = await utils.embed(ctx, discord.Embed(timestamp=datetime.utcnow(), title="Server Poll", description=f"**{question}**\n\n{text}").set_footer(text=f"Poll by {ctx.author}"), send=False) if ping: ping_role = utils.get_ping_role(ctx) if ping_role != ctx.guild.default_role: if not ping_role.mentionable: edited = False try: await ping_role.edit(mentionable=True) edited = True except discord.Forbidden: return await utils.embed(ctx, discord.Embed(title="Poll Failed", description=f"I do not have permission to **edit** {ping_role.mention}."), error=True) try: message = await channel.send(ping_role.mention, embed=embed) await utils.embed(ctx, discord.Embed(title="Poll Created", description=f"Your poll was successfully posted in {channel.mention}."), error=True) for r in reactions: await message.add_reaction(r) except: if channel.permissions_for(ctx.guild.me).add_reactions is False: issue = f"I do not have permission to **add reactions** in <#{channel.mention}>." if channel.permissions_for(ctx.guild.me).send_messages is False: issue = f"I do not have permission to **send messages** in <#{channel.mention}>." return await utils.embed(ctx, discord.Embed(title="Poll Failed", description=issue), error=True) if edited: await ping_role.edit(mentionable=False) return try: message = await channel.send(content="@everyone" if ping else None, embed=embed) await utils.embed(ctx, discord.Embed(title="Poll Created", description=f"Your poll was successfully posted in {channel.mention}."), error=True) for r in reactions: await message.add_reaction(r) except: if channel.permissions_for(ctx.guild.me).add_reactions is False: issue = f"I do not have permission to **add reactions** in <#{channel.mention}>." if channel.permissions_for(ctx.guild.me).send_messages is False: issue = f"I do not have permission to **send messages** in <#{channel.mention}>." await utils.embed(ctx, discord.Embed(title="Poll Failed", description=issue), error=True) @commands.command(usage="announce <ping> <announcement>") @utils.guild_only() @utils.is_admin() async def announce(self, ctx, ping_member, *, announcement): """Creates an announcement.""" ping = ping_member.lower() if ping not in ("yes", "no", "true", "false", "y", "n", "t", "f"): return await utils.embed(ctx, discord.Embed(title="Announcement Failed", description=f"Sorry, the `ping_member` argument should be \"Yes\" or \"No\". Please use `{self.bot.config.prefix}help announce` for more information."), error=True) if ping in ("yes", "y", "true", "t"): ping = True if ping in ("no", "n", "no", "n"): ping = False channel_id = self.bot.config.channels.announcements channel = self.bot.get_channel(channel_id) if channel is None: return await utils.embed(ctx, discord.Embed(title="Announcement Failed", description=f"Sorry, the `announcements` channel hasn't been configured."), error=True) if ping: ping_role = utils.get_ping_role(ctx) if ping_role != ctx.guild.default_role: if not ping_role.mentionable: edited = False try: await ping_role.edit(mentionable=True) edited = True except discord.Forbidden: return await utils.embed(ctx, discord.Embed(title="Announcement Failed", description=f"I do not have permission to **edit** {ping_role.mention}."), error=True) try: await channel.send(f"{ping_role.mention}\n{announcement}") await utils.embed(ctx, discord.Embed(title="Announcement Sent", description=f"Your announcement was successfully posted in {channel.mention}."), error=True) except: if channel.permissions_for(ctx.guild.me).send_messages is False: issue = f"I do not have permission to **send messages** in <#{channel.mention}>." return await utils.embed(ctx, discord.Embed(title="Announcement Failed", description=issue), error=True) if edited: await ping_role.edit(mentionable=False) return try: await channel.send("@everyone\n" if ping else "" + announcement) await utils.embed(ctx, discord.Embed(title="Announcement Sent", description=f"Your announcement was successfully posted in {channel.mention}."), error=True) except: if channel.permissions_for(ctx.guild.me).send_messages is False: issue = f"I do not have permission to **send messages** in <#{channel.mention}>." await utils.embed(ctx, discord.Embed(title="Poll Failed", description=issue), error=True) @commands.command(aliases=["resetcase"], usage="resetid") @utils.guild_only() @utils.is_admin() async def resetid(self, ctx): """Resets the case ID.""" with sqlite3.connect(self.bot.config.database) as db: db.cursor().execute("UPDATE Settings SET Case_ID='0'") db.cursor().execute("DELETE FROM Cases") db.commit() await utils.embed(ctx, discord.Embed(timestamp=datetime.utcnow(), title="Data Wiped", description="All case data has been successfully cleared.")) @commands.command(aliases=["reloadconfig"], usage="reload") @utils.guild_only() @utils.is_admin() async def reload(self, ctx): """Reloads the config file.""" del self.bot.config self.bot.config = utils.Config() await utils.embed(ctx, discord.Embed(timestamp=datetime.utcnow(), title="Config Reloaded", description="All config data has been successfully reloaded.")) @commands.command(usage="lockdown [time]") @utils.guild_only() @commands.bot_has_permissions(manage_channels=True) @utils.is_admin() async def lockdown(self, ctx, *, time=None): """Locks or unlocks a channel for a specified amount of time.""" member_role = utils.get_member_role(ctx) ows = ctx.channel.overwrites_for(member_role) if ows.read_messages is False: return await utils.embed(ctx, discord.Embed(timestamp=datetime.utcnow(), title="Lockdown Failed", description=f"Sorry, I can only lock channels that can be seen by {member_role.mention if member_role != ctx.guild.default_role else member_role}."), error=True) if ows.send_messages is False: await ctx.channel.set_permissions(member_role, send_messages=None) await ctx.channel.set_permissions(ctx.guild.me, send_messages=None) return await utils.embed(ctx, discord.Embed(timestamp=datetime.utcnow(), title="Lockdown Deactivated", description=f"Lockdown has been lifted by **{ctx.author}**.")) if ows.send_messages in (True, None): seconds = await TimeConverter().convert(time) await ctx.channel.set_permissions(member_role, send_messages=False) await ctx.channel.set_permissions(ctx.guild.me, send_messages=True) if seconds < 1: return await utils.embed(ctx, discord.Embed(timestamp=datetime.utcnow(), title="Lockdown Activated", description=f"Lockdown has been activated by **{ctx.author}**.")) await utils.embed(ctx, discord.Embed(timestamp=datetime.utcnow(), title="Lockdown Activated", description=f"Lockdown has been activated by **{ctx.author}** for {utils.display_time(round(seconds), 4)}.")) await asyncio.sleep(seconds) ows = ctx.channel.overwrites_for(member_role) if ows.send_messages is False: await ctx.channel.set_permissions(member_role, send_messages=None) await ctx.channel.set_permissions(ctx.guild.me, send_messages=None) return await utils.embed(ctx, discord.Embed(timestamp=datetime.utcnow(), title="Lockdown Deactivated", description=f"Lockdown has been lifted.")) def setup(bot): bot.add_cog(AdministratorCommands(bot))

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0.033098

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0.059577

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0.65808

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0.134078

0

null

0

1

0

null

0

1

b84bfe3e24cf3fa88c7b90891f02c84318e2faae

7,473

py

Python

nextai_lib/inference.py

jav0927/nextai

9de0c338a41a3ce0297b95f625290fa814a83344

[ "Apache-2.0" ]

null

nextai_lib/inference.py

jav0927/nextai

9de0c338a41a3ce0297b95f625290fa814a83344

[ "Apache-2.0" ]

1

2021-09-28T05:33:17.000Z

nextai_lib/inference.py

jav0927/nextai

9de0c338a41a3ce0297b95f625290fa814a83344

[ "Apache-2.0" ]

null

# AUTOGENERATED! DO NOT EDIT! File to edit: 02_inference.ipynb (unless otherwise specified). __all__ = ['device', 'pad_output', 'get_activ_offsets_mns'] # Cell #from fastai.vision.all import * from fastai import * from typing import * from torch import tensor, Tensor import torch import torchvision # Needed to invoke torchvision.ops.mns function # Cell # Automatically sets for GPU or CPU environments device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Cell # Pad tensors so that they have uniform dimentions: (batch size, no of items in a batch, 4) and (batch size, no of items in a batch, 21) def pad_output(l_bb:List, l_scr:List, l_idx:List, no_classes:int): '''Pad tensors so that they have uniform dimentions: (batch size, no of items in a batch, 4) and (batch size, no of items in a batch, 21) Inputs: l_bb - list of tensors containing individual non-uniform sized bounding boxes l_scr - list of tensors containing class index values (i.e. 1 - airplane) l_idx - list of tensors containing class index values (i.e. 1 - airplane) no_classes - Number of classes, Integer Outputs: Uniform-sized tensors: bounding box tensor and score tensor with dims: (batch size, no of items in a batch, 4) and (batch size, no of items in a batch, 21)''' if len([len(img_bb) for img_bb in l_bb]) == 0.: print(F'Image did not pass the scoring threshold') return mx_len = max([len(img_bb) for img_bb in l_bb]) # Calculate maximun lenght of the boxes in the batch l_b, l_c, l_x, l_cat = [], [], [], [] # Create Bounding Box tensors # zeroed tensor accumulators for i, ntr in enumerate(zip(l_bb, l_scr, l_idx)): bbox, cls, idx = ntr[0], ntr[1], ntr[2] # Unpack variables tsr_len = mx_len - bbox.shape[0] # Calculate the number of zero-based rows to add m = nn.ConstantPad2d((0, 0, 0, tsr_len), 0.) # Prepare to pad the box tensor with zero entries l_b.append(m(bbox)) # Add appropriate zero-based box rows and add to list # Create Category tensors cat_base = torch.zeros(mx_len-bbox.shape[0], dtype=torch.int32) img_cat = torch.cat((idx, cat_base), dim=0) l_cat.append(img_cat) # Create Score tensors img_cls = [] # List to construct class vectors for ix in range(idx.shape[0]): # Construct class vectors of dim(no of classes) cls_base = torch.zeros(no_classes).to(device) # Base zero-based class vector cls_base[idx[ix]] = cls[ix] # Add the score in the nth position img_cls.append(cls_base) img_stack = torch.stack(img_cls) # Create single tensor per image img_stack_out = m(img_stack) l_c.append( img_stack_out ) # Add appropriate zero-based class rows and add to list return (TensorBBox(torch.stack(l_b,0)), TensorMultiCategory(torch.stack(l_c,0)), TensorMultiCategory(torch.stack(l_cat,0)) ) # Cell def get_activ_offsets_mns(anchrs:Tensor, activs:Tensor, no_classes:int, threshold:float=0.5): ''' Takes in activations and calculates corresponding anchor box offsets. It then filters the resulting boxes through MNS Inputs: anchrs - Anchors as Tensor activs - Activations as Tensor no_classes - Number of classes (categories) threshold - Coarse filtering. Default = 0.5 Output: one_batch_boxes, one_batch_scores as Tuple''' p_bboxes, p_classes = activs # Read p_bboxes: [32, 189,4] Torch.Tensor and p_classes: [32, 189, 21] Torch.Tensor from self.learn.pred #scores = torch.sigmoid(p_classes) # Calculate the confidence levels, scores, for class predictions [0, 1] scores = torch.softmax(p_classes, -1) # Calculate the confidence levels, scores, for class predictions [0, 1] - Probabilistic offset_boxes = activ_decode(p_bboxes, anchrs) # Return anchors + anchor offsets wiith format (batch, No Items in Batch, 4) # For each item in batch, and for each class in the item, filter the image by passing it through NMS. Keep preds with IOU > thresshold one_batch_boxes = []; one_batch_scores = []; one_batch_cls_pred = [] # Agregators at the bath level for i in range(p_classes.shape[0]): # For each image in batch ... batch_p_boxes = offset_boxes[i] # box preds for the current batch batch_scores = scores[i] # Keep scores for the current batch max_scores, cls_idx = torch.max(batch_scores, 1 ) # Keep batch class indexes bch_th_mask = max_scores > threshold # Threshold mask for batch bch_keep_boxes = batch_p_boxes[bch_th_mask] # " bch_keep_scores = batch_scores[bch_th_mask] # " bch_keep_cls_idx = cls_idx[bch_th_mask] # Agregators per image in a batch img_boxes = [] # Bounding boxes per image img_scores = [] # Scores per image img_cls_pred = [] # Class predictons per image for c in range (1,no_classes): # Loop through each class cls_mask = bch_keep_cls_idx==c # Keep masks for the current class if cls_mask.sum() == 0: continue # Weed out images with no positive class masks cls_boxes = bch_keep_boxes[cls_mask] # Keep boxes per image cls_scores = bch_keep_scores[cls_mask].max(dim=1)[0] # Keep class scores for the current image nms_keep_idx = torchvision.ops.nms(cls_boxes, cls_scores, iou_threshold=0.5) # Filter images by passing them through NMS img_boxes += [*cls_boxes[nms_keep_idx]] # Agregate cls_boxes into tensors for all classes box_stack = torch.stack(img_boxes,0) # Transform individual tensors into a single box tensor img_scores += [*cls_scores[nms_keep_idx]] # Agregate cls_scores into tensors for all classes score_stack = torch.stack(img_scores, 0) # Transform individual tensors into a single score tensor img_cls_pred += [*tensor([c]*len(nms_keep_idx))] cls_pred_stack = torch.stack(img_cls_pred, 0) batch_mask = score_stack > threshold # filter final lists tto be greater than threshold box_stack = box_stack[batch_mask] # " score_stack = score_stack[batch_mask] # " cls_pred_stack = cls_pred_stack[batch_mask] # " if 'box_stack' not in locals(): continue # Failed to find any valid classes one_batch_boxes.append(box_stack) # Agregate bounding boxes for the batch one_batch_scores.append(score_stack) # Agregate scores for the batch one_batch_cls_pred.append(cls_pred_stack) # Pad individual box and score tensors into uniform-sized box and score tensors of shapes: (batch, no 0f items in batch, 4) and (batch, no 0f items in batch, 21) one_batch_boxes, one_batch_scores, one_batch_cats = pad_output(one_batch_boxes, one_batch_scores, one_batch_cls_pred, no_classes) return (one_batch_boxes, one_batch_cats)

59.784

174

0.640707

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7,473

4.277985

0.224813

0.027911

0.012211

0.017008

0.262102

0.170955

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0.129089

0.11993

0

0.013033

0.281279

7,473

125

175

59.784

0.840812

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0

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0

1

0.029412

false

0.014706

0.073529

0

0.147059

0.014706

0

null

0

1

0

null

0

1

b84c1c6e378f4059bee57b13f1d84bcf63b4ae74

2,141

py

Python

code.py

ashweta81/data-wrangling-pandas-code-along-practice

af49250a45c616f46d763990f2321f470d439916

[ "MIT" ]

null

code.py

ashweta81/data-wrangling-pandas-code-along-practice

af49250a45c616f46d763990f2321f470d439916

[ "MIT" ]

null

code.py

ashweta81/data-wrangling-pandas-code-along-practice

af49250a45c616f46d763990f2321f470d439916

[ "MIT" ]

null

# -------------- import pandas as pd import numpy as np # Read the data using pandas module. data=pd.read_csv(path) # Find the list of unique cities where matches were played print("The unique cities where matches were played are ", data.city.unique()) print('*'*80) # Find the columns which contains null values if any ? print("The columns which contain null values are ", data.columns[data.isnull().any()]) print('*'*80) # List down top 5 most played venues print("The top 5 most played venues are", data.venue.value_counts().head(5)) print('*'*80) # Make a runs count frequency table print("The frequency table for runs is", data.runs.value_counts()) print('*'*80) # How many seasons were played and in which year they were played data['year']=data.date.apply(lambda x : x[:4]) seasons=data.year.unique() print('The total seasons and years are', seasons) print('*'*80) # No. of matches played per season ss1=data.groupby(['year'])['match_code'].nunique() print('The total matches played per season are', ss1) print("*"*80) # Total runs across the seasons ss2=data.groupby(['year']).agg({'total':'sum'}) print("Total runs are",ss2) print("*"*80) # Teams who have scored more than 200+ runs. Show the top 10 results w1=data.groupby(['match_code','batting_team']).agg({'total':'sum'}).sort_values(by='total', ascending=False) w1[w1.total>200].reset_index().head(10) print("The top 10 results are",w1[w1.total>200].reset_index().head(10)) print("*"*80) # What are the chances of chasing 200+ target dt1=data.groupby(['match_code','batting_team','inning'])['total'].sum().reset_index() dt1.head() dt1.loc[((dt1.total>200) & (dt1.inning==2)),:].reset_index() data.match_code.unique().shape[0] probability=(dt1.loc[((dt1.total>200) & (dt1.inning==2)),:].shape[0])/(data.match_code.unique().shape[0])*100 print("Chances are", probability) print("*"*80) # Which team has the highest win count in their respective seasons ? dt2=data.groupby(['year','winner'])['match_code'].nunique() dt3=dt2.groupby(level=0,group_keys=False) dt4=dt3.apply(lambda x: x.sort_values(ascending=False).head(1)) print("The team with the highes win count is", dt4)

40.396226

109

0.712751

351

2,141

4.296296

0.353276

0.041777

0.029841

0.03183

0.225464

0.198939

0.079576

0.043767

0

0.042276

0.105091

2,141

52

110

41.173077

0.744781

0.249416

0

0.25

0

0.272613

0

1

0

false

0

0.055556

0

0.055556

0.527778

0

null

0

null

0

1

0

1

b85115da00994686b76087d8e81c839619f86fa0

338

py

Python

scss/setup.py

Jawbone/pyScss

b1f483c253ec4aaceb3b8d4d630ca5528590e9b8

[ "MIT" ]

null

scss/setup.py

Jawbone/pyScss

b1f483c253ec4aaceb3b8d4d630ca5528590e9b8

[ "MIT" ]

null

scss/setup.py

Jawbone/pyScss

b1f483c253ec4aaceb3b8d4d630ca5528590e9b8

[ "MIT" ]

null

from distutils.core import setup, Extension setup(name='jawbonePyScss', version='1.1.8', description='jawbonePyScss', ext_modules=[ Extension( '_scss', sources=['src/_scss.c', 'src/block_locator.c', 'src/scanner.c'], libraries=['pcre'], optional=True ) ] )

22.533333

76

0.553254

34

338

5.382353

0.735294

0.043716

0

0.012605

0.295858

338

14

77

24.142857

0.756303

0

0.245562

0

1

0

true

0

0.076923

0

0.076923

0

null

0

null

0

1

0

1

b85283b049e0e58e8a7c62f87369d905b8440e5f

3,101

py

Python

src/flagon/backends/redis_backend.py

ashcrow/flagon

50e6aa96854468a89399ef08573e4f814a002d26

[ "MIT" ]

18

2015-08-27T03:49:42.000Z

2021-05-12T21:48:17.000Z

src/flagon/backends/redis_backend.py

ashcrow/flagon

50e6aa96854468a89399ef08573e4f814a002d26

[ "MIT" ]

2

2016-07-18T13:48:46.000Z

2017-05-20T15:56:03.000Z

src/flagon/backends/redis_backend.py

ashcrow/flagon

50e6aa96854468a89399ef08573e4f814a002d26

[ "MIT" ]

5

2015-09-20T08:46:01.000Z

2021-06-10T03:41:04.000Z

# The MIT License (MIT) # # Copyright (c) 2014 Steve Milner # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. """ Redis backend. """ import redis from flagon import errors from flagon.backends import Backend class RedisBackend(Backend): def __init__(self, host, port, db): """ Creates an instance of the RedisBackend. :rtype: RedisBackend """ # https://pypi.python.org/pypi/redis/2.10.1 pool = redis.ConnectionPool(host=host, port=port, db=db) self._server = redis.Redis( connection_pool=pool, charset='utf-8', errors='strict', decode_responses=False) def set(self, name, key, value): """ Sets a value for a feature. This is a proposed name only!!! :param name: name of the feature. :rtype: bool """ self._server.hset(name, key, value) def exists(self, name, key): """ Checks if a feature exists. :param name: name of the feature. :rtype: bool """ return self._server.hexists(name, key) def is_active(self, name, key): """ Checks if a feature is on. :param name: name of the feature. :rtype: bool :raises: UnknownFeatureError """ if not self._server.hexists(name, key): raise errors.UnknownFeatureError('Unknown feature: %s' % name) if self._server.hget(name, key) == 'True': return True return False def _turn(self, name, key, value): """ Turns a feature off. :param name: name of the feature. :param value: Value to turn name to. :raises: UnknownFeatureError """ # TODO: Copy paste --- :-( if not self._server.hexists(name, key): raise errors.UnknownFeatureError('Unknown feature: %s %s' % ( name, key)) self._server.hset(name, key, value) turn_on = lambda s, name: s._turn(name, 'active', True) turn_off = lambda s, name: s._turn(name, 'active', False)

32.642105

78

0.639471

408

3,101

4.813725

0.397059

0.039206

0.022403

0.03055

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0.220468

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0.127291

0.075356

0

0.003967

0.268301

3,101

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0.5

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null

0

null

0

1

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1

b85adde254fd21cc8c4987b399dbf5487b008f43

445

py

Python

tests/test_example.py

jlane9/mockerena

a3fd1bd39af6269dc96846967b4bba47759bab41

[ "MIT" ]

1

2019-09-10T05:12:38.000Z

tests/test_example.py

jlane9/mockerena

a3fd1bd39af6269dc96846967b4bba47759bab41

[ "MIT" ]

10

2019-09-10T16:14:35.000Z

2019-12-19T17:13:51.000Z

tests/test_example.py

jlane9/mockerena

a3fd1bd39af6269dc96846967b4bba47759bab41

[ "MIT" ]

2

2019-09-10T05:11:58.000Z

2020-04-29T17:59:47.000Z

"""test_example .. codeauthor:: John Lane <john.lane93@gmail.com> """ from flask import url_for from eve import Eve import pytest @pytest.mark.example def test_example(client: Eve): """Example test for reference :param Eve client: Mockerena app instance :raises: AssertionError """ res = client.get(url_for('generate', schema_id='mock_example')) assert res.status_code == 200 assert res.mimetype == 'text/csv'

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null

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1

b85e1207d6e09dc9d3b5821470f14d0eed8e2190

394

py

Python

subcontent/backup/python3_closure_nonlocal.py

fingerkc/fingerkc.github.io

0bfe5163ea28be3747756c8b6be64ad4f09b2fbf

[ "MIT" ]

2

2019-06-13T07:22:22.000Z

2019-11-23T03:55:21.000Z

subcontent/backup/python3_closure_nonlocal.py

fingerkc/fingerkc.github.io

0bfe5163ea28be3747756c8b6be64ad4f09b2fbf

[ "MIT" ]

1

2019-12-15T04:10:59.000Z

subcontent/backup/python3_closure_nonlocal.py

fingerkc/fingerkc.github.io

0bfe5163ea28be3747756c8b6be64ad4f09b2fbf

[ "MIT" ]

1

2019-06-24T08:17:13.000Z

#!/usr/bin/python3 ##python3 闭包与 nonlocal #如果在一个内部函数里，对在外部作用域（但不是在全局作用域）的变量进行引用， #那么内部函数就被认为是闭包(closure) def A_(): var = 0 def clo_B(): var_b = 1 # 闭包的局部变量 var = 100 print(var) # 引用外部的var , 但是不会改变var 的值 return clo_B #clo_B是一个闭包 #nonlocal 关键字 def A_(): var = 0 def clo_B(): nonlocal var # nonlocal关键字指定var 不是闭包的局部变量 var = var + 1 # 若不使用nonlocal 关键字 , 则此行代码会出现错误

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null

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null

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1

0

1

b86adccb9d42d87933b32bb27aaf25b01696f8a9

818

py

Python

django_for_startups/django_customizations/drf_customizations.py

Alex3917/django_for_startups

9dda54f5777247f7367a963d668f25e797c9adf1

[ "MIT" ]

102

2021-02-28T00:58:36.000Z

2022-03-30T09:29:34.000Z

django_for_startups/django_customizations/drf_customizations.py

Alex3917/django_for_startups

9dda54f5777247f7367a963d668f25e797c9adf1

[ "MIT" ]

1

2021-07-11T18:45:29.000Z

django_for_startups/django_customizations/drf_customizations.py

Alex3917/django_for_startups

9dda54f5777247f7367a963d668f25e797c9adf1

[ "MIT" ]

16

2021-06-23T18:34:46.000Z

2022-03-30T09:27:34.000Z

# Standard Library imports # Core Django imports # Third-party imports from rest_framework import permissions from rest_framework.throttling import UserRateThrottle, AnonRateThrottle # App imports class BurstRateThrottle(UserRateThrottle): scope = 'burst' class SustainedRateThrottle(UserRateThrottle): scope = 'sustained' class HighAnonThrottle(AnonRateThrottle): rate = '5000000/day' class AccountCreation(permissions.BasePermission): """ A user should be able to create an account without being authenticated, but only the owner of an account should be able to access that account's data in a GET method. """ def has_permission(self, request, view): if (request.method == "POST") or request.user.is_authenticated: return True return False

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818

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818

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0

null

0

null

0

1

0

1

b86ccfc144647099cbf5ac1e80b91ec536893766

171,517

py

Python

python/mapCells.py

claraya/meTRN

a4e4911b26a295e22d7309d5feda026db3325885

[ "MIT" ]

2

2019-11-18T22:54:13.000Z

2019-11-18T22:55:18.000Z

python/mapCells.py

claraya/meTRN

a4e4911b26a295e22d7309d5feda026db3325885

[ "MIT" ]

null

python/mapCells.py

claraya/meTRN

a4e4911b26a295e22d7309d5feda026db3325885

[ "MIT" ]

null

#!/usr/bin/env python # perform cellular-resolution expression analyses! import sys import time import optparse import general import hyper import numpy import math import pickle import pdb import metrn import modencode import itertools import os import re import datetime import calendar #import simplejson as json from scipy.stats.stats import pearsonr from runner import * from scipy import stats from network import Network from network import export print "Command:", " ".join(sys.argv) print "Timestamp:", time.asctime(time.localtime()) """ define functions of internal use """ """ define a function to recover cells in a time range """ def getTargetCells(inobject="", inpath="", mode="collection", timeRange=list()): # grab cells from collection: if mode == "collection": # load collection cells: cells = list() for gene in os.listdir(inpath): cells.extend(open(inpath + gene).read().split("\n")) cells = general.clean(sorted(list(set(cells)))) print "Loading collection cells:", len(cells) # grab cells from time-points: elif mode == "time": # load time-point cells: cells = list() for timePoint in os.listdir(inpath): if int(timePoint) in timeRange: cells += general.clean(open(inpath + timePoint).read().split("\n")) cells = sorted(list(set(cells))) print "Loading time-point/range cells:", len(cells) # return collected cells: return cells """ define a function to construct a cell-parent relationships, and pedigree cell list """ def expressionBuilder(expressionfile, path, cutoff, minimum, metric="fraction.expression"): # build header dict: hd = general.build_header_dict(path + expressionfile) # process input expression data: quantitation_matrix, expression_matrix, tracking_matrix, trackedCells = dict(), dict(), dict(), list() inlines = open(path + expressionfile).readlines() inlines.pop(0) for inline in inlines: initems = inline.strip().split("\t") cell, gene, rawSignal, metricSignal = initems[hd["cell.name"]], initems[hd["gene"]], initems[hd["cell.expression"]], initems[hd[metric]] trackedCells.append(cell) # store expression value if not gene in quantitation_matrix: quantitation_matrix[gene] = dict() quantitation_matrix[gene][cell] = float(metricSignal) # store tracked and expressing cells: if not gene in tracking_matrix: expression_matrix[gene] = list() tracking_matrix[gene] = list() tracking_matrix[gene].append(cell) if float(metricSignal) >= float(cutoff) and float(rawSignal) >= minimum: expression_matrix[gene].append(cell) trackedCells = list(set(trackedCells)) return quantitation_matrix, expression_matrix, tracking_matrix, trackedCells """ define a function to construct a cell-parent relationships, and pedigree cell list """ def relationshipBuilder(pedigreefile, path, trackedCells=list(), lineages="complete", mechanism="simple"): cell_dict, parent_dict = dict(), dict() inlines = open(path + pedigreefile).readlines() header = inlines.pop(0) for inline in inlines: cell, binCell, parent, binParent = inline.strip().split(",")[:4] tissues = inline.strip().split(",")[5] if not parent == "" and not cell == "": if mechanism == "simple" or lineages == "complete" or (lineages == "tracked" and parent in trackedCells and cell in trackedCells): if not parent in parent_dict: parent_dict[parent] = list() parent_dict[parent].append(cell) cell_dict[cell] = parent pedigreeCells = sorted(list(set(cell_dict.keys()).union(set(parent_dict.keys())))) return cell_dict, parent_dict, pedigreeCells """ define a function to generate the underlying tree of a given parent """ def treeBuilder(parent_dict, cell_dict, highlights=list(), nodeColor="#FFFFFF", lineColor="#336699", textColor="#000000", highlightColor="#CC0000"): # set color rules: groups = { "unknown" : textColor, "highlight" : highlightColor } nodeColors = { "unknown" : nodeColor, "highlight" : highlightColor } lineColors = { "unknown" : lineColor, "highlight" : highlightColor } textColors = { "unknown" : textColor, "highlight" : highlightColor } # initialize tree: tree = {} for child in cell_dict: parent = cell_dict[child] # determine whether to pkey, ckey = "unknown", "unknown" if parent in highlights: pkey = "highlight" if child in highlights: ckey = "highlight" # make an instance of a class for the parent if neccesary: if not tree.has_key(parent): tree[parent] = {'name':parent,'group':groups[pkey],'nodeColor':nodeColors[pkey],'lineColor':lineColors[pkey],'textColor':textColors[pkey],'children':[]} # make an instance of a class for the child if neccesary: if not tree.has_key(child): tree[child] = {'name':child,'group':groups[ckey],'nodeColor':nodeColors[ckey],'lineColor':lineColors[ckey],'textColor':textColors[ckey],'children':[]} # and child object to parent if necesary: if not tree[child] in tree[parent]['children']: tree[parent]['children'].append(tree[child]) return tree """ define a function to generate the list of cells that are parents to a given cell """ def ascendantsCollector(cell, parent_dict, cell_dict, ascendants=list(), sort=True): if not cell in ascendants: ascendants.append(cell) if cell in cell_dict: parent = cell_dict[cell] ascendants.append(parent) ascendants = ascendantsCollector(parent, parent_dict, cell_dict, ascendants, sort=sort) if sort: return sorted(list(set(ascendants))) else: return ascendants """ define a function to generate the list of cells that are progeny to a given parent """ def descendantsCollector(parent, parent_dict, cell_dict, descendants=list(), sort=True): if not parent in descendants: descendants.append(parent) if parent in parent_dict: for cell in parent_dict[parent]: descendants.append(cell) descendants = descendantsCollector(cell, parent_dict, cell_dict, descendants, sort=sort) if sort: return sorted(list(set(descendants))) else: return descendants """ define a function to generate the list of cells that are progeny to a given parent (using combinations function) """ def lineageGenerator(parent, parent_dict, cell_dict): descendants = descendantsCollector(parent, parent_dict, cell_dict, descendants=list()) gList = list() for r in range(1, len(descendants)+1): for gCells in itertools.combinations(descendants, r): process = True gCells = list(gCells) for gCell in gCells: if gCell != parent: if not cell_dict[gCell] in gCells: process = False if process: gList.append(",".join(sorted(gCells))) return gList """ define a function to generate the list of cells that are progeny to a given parent (using lineage growth) """ def lineageBuilder(parent, parent_dict, cell_dict, limit="OFF", descendants="ON"): mList = [parent] for mCells in mList: aCells, bCells, xCells, exit = str(mCells), str(mCells), str(mCells), False for mCell in mCells.split(","): if mCell in parent_dict and len(parent_dict[mCell]) == 2: aCell, bCell = parent_dict[mCell] if not aCell in aCells.split(","): aCells = ",".join(sorted(mCells.split(",") + [aCell])) if not bCell in bCells.split(","): bCells = ",".join(sorted(mCells.split(",") + [bCell])) if not aCell in xCells.split(",") and not bCell in xCells.split(","): xCells = ",".join(sorted(mCells.split(",") + [aCell, bCell])) if not aCells in mList: mList.append(aCells) if not bCells in mList: mList.append(bCells) if not xCells in mList: mList.append(xCells) if limit != "OFF" and len(mList) >= limit: if descendants == "ON": aCellx = sorted(list(set(mCells.split(",") + descendantsCollector(aCell, parent_dict, cell_dict, descendants=list())))) bCellx = sorted(list(set(mCells.split(",") + descendantsCollector(bCell, parent_dict, cell_dict, descendants=list())))) xCellx = sorted(list(set(aCellx).union(set(bCellx)))) aCellx = ",".join(aCellx) bCellx = ",".join(bCellx) xCellx = ",".join(xCellx) if not aCellx in mList: mList.append(aCellx) if not bCellx in mList: mList.append(bCellx) if not xCellx in mList: mList.append(xCellx) exit = True if exit: break return sorted(mList) """ define a function to generate lists of related-cells from a given set of of cells """ def lineageCollector(cells, parent_dict, cell_dict, siblings="ON"): collections, parent_tree, cell_tree = list(), dict(), dict() #ascendants = ascendantsCollector(descendant, parent_tree, cell_tree, ascendants=list()) #descendants = descendantsCollector(parent, parent_dict, cell_dict, descendants=list()) print len(cells), cells for cell in sorted(cells): found, relatives = False, [cell] if cell in cell_dict: relatives.append(cell_dict[cell]) if cell in parent_dict: relatives.extend(parent_dict[cell]) if siblings == "ON" and cell in cell_dict: relatives.extend(parent_dict[cell_dict[cell]]) r, relatives = 0, list(set(relatives).intersection(set(cells))) print cell, relatives, "<-- relatives" updated = list() for collection in collections: if set(relatives).intersection(set(collection)): print collection, "<-- collection" collection.extend(relatives) collection = list(set(collection)) print collection, "<-- updated" r += 1 pdb.set_trace() updated.append(collection) if r == 0: updated.append(relatives) collections = updated return collections """ define a function to calculate the number of possible subsets """ def combinationCalculator(n, R): combinations = 0 for r in range(1,R): combinations += math.factorial(n)/(math.factorial(r)*math.factorial(n-r)) return combinations """ define a function to calculate the number of divisions between two cells """ def divisionCalculator(aCell, aParent, parent_dict, cell_dict): divisions = 0 while aCell in cell_dict and aCell != aParent: if cell_dict[aCell] == aParent: divisions += 1 break else: aCell = cell_dict[aCell] divisions += 1 return divisions """ define a function that calculates the lineage distance between two cells """ def lineageDistance(aCell, bCell, parent_dict, cell_dict): aParents = ascendantsCollector(aCell, parent_dict, cell_dict) bParents = ascendantsCollector(bCell, parent_dict, cell_dict) xParents = set(aParents).intersection(set(bParents)) xDistances = dict() #print len(xParents), aCell, bCell, ":", ", ".join(xParents) for xParent in xParents: aDistance = divisionCalculator(aCell, xParent, parent_dict, cell_dict) bDistance = divisionCalculator(bCell, xParent, parent_dict, cell_dict) xDistances[xParent] = aDistance + bDistance xParents = general.valuesort(xDistances) distance, ancestor = xDistances[xParents[0]], xParents[0] return distance, ancestor def main(): parser = optparse.OptionParser() parser.add_option("--path", action="store", type="string", dest="path", help="Path from script to files") parser.add_option("--organism", action = "store", type = "string", dest = "organism", help = "Target organism for operations...", default="OFF") parser.add_option("--mode", action="store", type="string", dest="mode", help="Operation modes: import, map, or other...") parser.add_option("--peaks", action="store", type="string", dest="peaks", help="Peaks set to be used.", default="OFF") parser.add_option("--infile", action="store", type="string", dest="infile", help="Input file for abundance representation") parser.add_option("--nuclear", action = "store", type = "string", dest = "nuclear", help = "Peaks are only nuclear?", default="ON") parser.add_option("--expression", action="store", type="string", dest="expression", help="Input expression file for abundance representation", default="OFF") parser.add_option("--pedigree", action="store", type="string", dest="pedigree", help="Input pedigree file", default="OFF") parser.add_option("--mapping", action="store", type="string", dest="mapping", help="Input mapping file; associates tissue labels to more generic terms!", default="OFF") parser.add_option("--tissues", action="store", type="string", dest="tissues", help="Input tissues file", default="OFF") parser.add_option("--times", action="store", type="string", dest="times", help="Input cell times file", default="OFF") parser.add_option("--name", action="store", type="string", dest="name", help="Output file name", default="") parser.add_option("--nametag", action="store", type="string", dest="nametag", help="Output file name addition tag", default="") parser.add_option("--collection", action="store", type="string", dest="collection", help="Cell collection subset name", default="OFF") parser.add_option("--technique", action = "store", type = "string", dest = "technique", help = "What kind of matrix should I build? binary, fraction, or normal", default="binary") parser.add_option("--neurons", action="store", type="string", dest="neurons", help="Neurons to be used for 'collection' analysis...", default="OFF") parser.add_option("--factors", action="store", type="string", dest="factors", help="Infer factors (OFF) or load from file?", default="OFF") parser.add_option("--measure", action="store", type="string", dest="measure", help="Maximum (cells) or mean", default="avg.expression") parser.add_option("--fraction", action="store", type="float", dest="fraction", help="Fractional expression cutoff", default=0.1) parser.add_option("--minimum", action="store", type="float", dest="minimum", help="Minimum raw expression cutoff", default=2000) parser.add_option("--inherit", action="store", type="string", dest="inherit", help="Signal inheritance policy: 'max' or 'last' of ancestor expression signals...", default="last") parser.add_option("--overlap", action="store", type="float", dest="overlap", help="Cellular overlap cutoff", default=0.75) parser.add_option("--pvalue", action="store", type="float", dest="pvalue", help="Significance cutoff", default=0.01) parser.add_option("--header", action="store", type="string", dest="header", help="Is there a header?", default="OFF") parser.add_option("--format", action="store", type="string", dest="format", help="How should formatting be done?", default="bed") parser.add_option("--reference", action="store", type="string", dest="reference", help="Gene-coordinate reference file", default="in2shape_ce_wormbased_COM_gx.bed") parser.add_option("--up", action = "store", type = "int", dest = "up", help = "Upstream space", default=0) parser.add_option("--dn", action = "store", type = "int", dest = "dn", help = "Downstream space", default=0) parser.add_option("--method", action="store", type="string", dest="method", help="Should descendant cells or descendant lineages be examined?", default="lineages") parser.add_option("--cells", action="store", type="string", dest="cells", help="Reduce lineage cells to tracked cells (tracked) or use complete lineage cells (complete)?", default="tracked") parser.add_option("--lineages", action="store", type="string", dest="lineages", help="Reduce lineage tree to tracked cells (tracked) or use complete lineage tree (complete)?", default="tracked") parser.add_option("--descendants", action="store", type="string", dest="descendants", help="Apply descendants cutoff?", default="OFF") parser.add_option("--ascendants", action="store", type="string", dest="ascendants", help="Apply ascendants cutoff?", default="OFF") parser.add_option("--extend", action="store", type="string", dest="extend", help="Extend to include 0 signal expression values for cells not measured?", default="OFF") parser.add_option("--overwrite", action="store", type="string", dest="overwrite", help="Overwrite outputs?", default="OFF") parser.add_option("--parameters", action="store", type="string", dest="parameters", help="Optional parameters...", default="OFF") parser.add_option("--limit", action="store", type="string", dest="limit", help="Limit on lineage expansion? Numeric integer.", default="OFF") parser.add_option("--query", action="store", type="string", dest="query", help="Query collections of cells whose enrichment will be searched in target cells", default="OFF") parser.add_option("--source", action="store", type="string", dest="source", help="File source for inputs...", default="OFF") parser.add_option("--target", action="store", type="string", dest="target", help="Target collections of cells in which enrichment is searched for", default="OFF") parser.add_option("--domain", action="store", type="string", dest="domain", help="Domain of co-associations for hybrid-type analyses", default="OFF") parser.add_option("--A", action = "store", type = "string", dest = "a", help = "Paths to files of interest", default="OFF") parser.add_option("--B", action = "store", type = "string", dest = "b", help = "Files to be hybridized", default="OFF") parser.add_option("--indexes", action = "store", type = "string", dest = "indexes", help = "Indexes for matrix construction...", default="OFF") parser.add_option("--values", action = "store", type = "string", dest = "values", help = "Values for matrix construction...", default="OFF") parser.add_option("--contexts", action = "store", type = "string", dest = "contexts", help = "What contexts of development should I track?", default="OFF") parser.add_option("--exclude", action="store", type="string", dest="exclude", help="Are there items that should be excluded?", default="") parser.add_option("--start", action = "store", type = "int", dest = "start", help = "Start development time for cell search", default=1) parser.add_option("--stop", action = "store", type = "int", dest = "stop", help = "End development time for cell search", default=250) parser.add_option("--step", action = "store", type = "int", dest = "step", help = "Step size", default=1) parser.add_option("--total", action = "store", type = "int", dest = "total", help = "Total simulations (indexes) for 'master' operations ", default=1000) parser.add_option("--threads", action = "store", type = "int", dest = "threads", help = "Parallel processing threads", default=1) parser.add_option("--chunks", action = "store", type = "int", dest = "chunks", help = "", default=100) parser.add_option("--module", action = "store", type = "string", dest = "module", help = "", default="md1") parser.add_option("--qsub", action = "store", type = "string", dest = "qsub", help = "Qsub configuration header", default="OFF") parser.add_option("--server", action = "store", type = "string", dest = "server", help = "Are we on the server?", default="OFF") parser.add_option("--job", action = "store", type = "string", dest = "job", help = "Job name for cluster", default="OFF") parser.add_option("--copy", action = "store", type = "string", dest = "copy", help = "Copy simulated peaks to analysis folder?", default="OFF") parser.add_option("--tag", action = "store", type = "string", dest = "tag", help = "Add tag to TFBS?", default="") (option, args) = parser.parse_args() # import paths: if option.server == "OFF": path_dict = modencode.configBuild(option.path + "/input/" + "configure_path.txt") elif option.server == "ON": path_dict = modencode.configBuild(option.path + "/input/" + "configure_server.txt") # specify input and output paths: inpath = path_dict["input"] extraspath = path_dict["extras"] pythonpath = path_dict["python"] scriptspath = path_dict["scripts"] downloadpath = path_dict["download"] fastqpath = path_dict["fastq"] bowtiepath = path_dict["bowtie"] bwapath = path_dict["bwa"] macspath = path_dict["macs"] memepath = path_dict["meme"] idrpath = path_dict["idr"] igvpath = path_dict["igv"] testpath = path_dict["test"] processingpath = path_dict["processing"] annotationspath = path_dict["annotations"] peakspath = path_dict["peaks"] gopath = path_dict["go"] hotpath = path_dict["hot"] qsubpath = path_dict["qsub"] coassociationspath = path_dict["coassociations"] bindingpath = path_dict["binding"] neuronspath = path_dict["neurons"] cellspath = path_dict["cells"] # standardize paths for analysis: alignerpath = bwapath indexpath = alignerpath + "index/" alignmentpath = alignerpath + "alignment/" qcfilterpath = alignerpath + "qcfilter/" qcmergepath = alignerpath + "qcmerge/" # import configuration dictionaries: source_dict = modencode.configBuild(inpath + "configure_source.txt") method_dict = modencode.configBuild(inpath + "configure_method.txt") context_dict = modencode.configBuild(inpath + "configure_context.txt") # define organism parameters: if option.organism == "hs" or option.organism == "h.sapiens": organismTag = "hs" #organismIGV = "ce6" elif option.organism == "mm" or option.organism == "m.musculus": organismTag = "mm" #organismIGV = "ce6" elif option.organism == "ce" or option.organism == "c.elegans": organismTag = "ce" #organismIGV = "ce6" elif option.organism == "dm" or option.organism == "d.melanogaster": organismTag = "dm" #organismIGV = "dm5" # specify genome size file: if option.nuclear == "ON": chromosomes = metrn.chromosomes[organismTag]["nuclear"] genome_size_file = option.path + "/input/" + metrn.reference[organismTag]["nuclear_sizes"] genome_size_dict = general.build_config(genome_size_file, mode="single", separator="\t", spaceReplace=True) else: chromosomes = metrn.chromosomes[organismTag]["complete"] genome_size_file = option.path + "/input/" + metrn.reference[organismTag]["complete_sizes"] genome_size_dict = general.build_config(genome_size_file, mode="single", separator="\t", spaceReplace=True) # load gene ID dictionaries: id2name_dict, name2id_dict = modencode.idBuild(inpath + metrn.reference[organismTag]["gene_ids"], "Sequence Name (Gene)", "Gene Public Name", mode="label", header=True, idUpper=True, nameUpper=True) # update peaks path: peakspath = peakspath + option.peaks + "/" # define input/output folders: expressionpath = cellspath + "expression/" correctionpath = cellspath + "correction/" lineagepath = cellspath + "lineage/" bindingpath = cellspath + "peaks/" overlappath = cellspath + "overlap/" cellsetpath = cellspath + "cellset/" genesetpath = cellspath + "geneset/" reportspath = cellspath + "reports/" comparepath = cellspath + "compare/" matrixpath = cellspath + "matrix/" tissuespath = cellspath + "tissues/" distancepath = cellspath + "distance/" hybridpath = cellspath + "hybrid/" dynamicspath = cellspath + "dynamics/" cubismpath = cellspath + "cubism/" timepath = cellspath + "time/" cellnotationspath = cellspath + "annotations/" general.pathGenerator(expressionpath) general.pathGenerator(correctionpath) general.pathGenerator(lineagepath) general.pathGenerator(bindingpath) general.pathGenerator(overlappath) general.pathGenerator(cellsetpath) general.pathGenerator(genesetpath) general.pathGenerator(reportspath) general.pathGenerator(comparepath) general.pathGenerator(matrixpath) general.pathGenerator(tissuespath) general.pathGenerator(distancepath) general.pathGenerator(timepath) general.pathGenerator(hybridpath) general.pathGenerator(dynamicspath) general.pathGenerator(cubismpath) general.pathGenerator(cellnotationspath) # generate expression flag: if option.measure == "max.expression": expression_flag = "maxCel_" elif option.measure == "avg.expression": expression_flag = "avgExp_" # check that the index range is coherent: if option.stop > option.total: print print "Error: Range exceeded! Stop index is larger than total." print return # master mode: if "master" in option.mode: # capture master mode: master, mode = option.mode.split(":") # prepare for qsub: bash_path = str(option.path + "/data/cells/runs/").replace("//","/") bash_base = "_".join([mode, option.peaks, option.name]) + "-M" qsub_base = "_".join([mode, option.peaks, option.name]) general.pathGenerator(bash_path) if option.qsub != "OFF": qsub_header = open(qsubpath + option.qsub).read() qsub = True else: qsub_header = "" qsub = False if option.job == "QSUB": qsub_header = qsub_header.replace("qsubRunner", "qsub-" + qsub_base) elif option.job != "OFF": qsub_header = qsub_header.replace("qsubRunner", "qsub-" + option.job) bash_base = option.job + "-M" # update server path: if option.qsub != "OFF": option.path = serverPath(option.path) # prepare slave modules: m, steps, modules, commands, sequences, chunks, start, complete = 1, 0, list(), list(), list(), option.chunks, option.start, False for index in range(option.start, option.stop+1, option.step): run = "rn" + general.indexTag(index, option.total) steps += 1 # cellular peak generation mode: if mode == "cell.peaks": command = "python <<CODEPATH>>mapCells.py --path <<PATH>> --organism <<ORGANISM>> --mode <<MODE>> --peaks <<PEAKS>> --start <<START>> --stop <<STOP>> --total <<TOTAL>> --expression <<EXPRESSION>> --collection <<COLLECTION>> --times <<TIMES>> --fraction <<FRACTION>> --minimum <<MINIMUM>> --name <<NAME>> --qsub <<QSUB>> --server <<SERVER>> --module <<MODULE>>" command = command.replace("<<CODEPATH>>", option.path + "/python/") command = command.replace("<<PATH>>", option.path) command = command.replace("<<ORGANISM>>", option.organism) command = command.replace("<<MODE>>", mode) command = command.replace("<<PEAKS>>", option.peaks) command = command.replace("<<START>>", str(index)) command = command.replace("<<STOP>>", str(index)) command = command.replace("<<TOTAL>>", str(option.total)) command = command.replace("<<EXPRESSION>>", option.expression) command = command.replace("<<COLLECTION>>", option.collection) command = command.replace("<<TIMES>>", option.times) command = command.replace("<<FRACTION>>", str(option.fraction)) command = command.replace("<<MINIMUM>>", str(option.minimum)) command = command.replace("<<NAME>>", option.name + general.indexTag(index, option.total)) command = command.replace("<<QSUB>>", option.qsub) command = command.replace("<<SERVER>>", option.server) command = command.replace("<<MODULE>>", "md" + str(m)) # cellular peak generation mode: if mode == "cell.annotation": command = "python <<CODEPATH>>mapCells.py --path <<PATH>> --organism <<ORGANISM>> --mode <<MODE>> --peaks <<PEAKS>> --start <<START>> --stop <<STOP>> --total <<TOTAL>> --infile <<INFILE>> --collection <<COLLECTION>> --times <<TIMES>> --name <<NAME>> --qsub <<QSUB>> --server <<SERVER>> --module <<MODULE>>" command = command.replace("<<CODEPATH>>", option.path + "/python/") command = command.replace("<<PATH>>", option.path) command = command.replace("<<ORGANISM>>", option.organism) command = command.replace("<<MODE>>", mode) command = command.replace("<<PEAKS>>", option.peaks) command = command.replace("<<START>>", str(index)) command = command.replace("<<STOP>>", str(index)) command = command.replace("<<TOTAL>>", str(option.total)) command = command.replace("<<INFILE>>", option.infile) command = command.replace("<<COLLECTION>>", option.collection) command = command.replace("<<TIMES>>", option.times) command = command.replace("<<NAME>>", option.name + general.indexTag(index, option.total) + option.nametag) command = command.replace("<<QSUB>>", option.qsub) command = command.replace("<<SERVER>>", option.server) command = command.replace("<<MODULE>>", "md" + str(m)) # cellular overlap mode: if mode == "cell.overlap": command = "python <<CODEPATH>>mapCells.py --path <<PATH>> --organism <<ORGANISM>> --mode <<MODE>> --peaks <<PEAKS>> --start <<START>> --stop <<STOP>> --total <<TOTAL>> --expression <<EXPRESSION>> --collection <<COLLECTION>> --times <<TIMES>> --fraction <<FRACTION>> --minimum <<MINIMUM>> --extend <<EXTEND>> --name <<NAME>> --qsub <<QSUB>> --server <<SERVER>> --module <<MODULE>>" command = command.replace("<<CODEPATH>>", option.path + "/python/") command = command.replace("<<PATH>>", option.path) command = command.replace("<<ORGANISM>>", option.organism) command = command.replace("<<MODE>>", mode) command = command.replace("<<PEAKS>>", option.peaks) command = command.replace("<<START>>", str(index)) command = command.replace("<<STOP>>", str(index)) command = command.replace("<<TOTAL>>", str(option.total)) command = command.replace("<<EXPRESSION>>", option.expression) command = command.replace("<<COLLECTION>>", option.collection + general.indexTag(index, option.total) + option.nametag) command = command.replace("<<TIMES>>", option.times) command = command.replace("<<NAME>>", option.name) command = command.replace("<<FRACTION>>", str(option.fraction)) command = command.replace("<<MINIMUM>>", str(option.minimum)) command = command.replace("<<EXTEND>>", str(option.extend)) command = command.replace("<<QSUB>>", option.qsub) command = command.replace("<<SERVER>>", option.server) command = command.replace("<<MODULE>>", "md" + str(m)) # coassociations hybrid mode: if mode == "cell.hybrid": collection = option.collection + general.indexTag(index, option.total) + option.nametag command = "python <<CODEPATH>>mapCells.py --path <<PATH>> --organism <<ORGANISM>> --mode <<MODE>> --A <<A>> --B <<B>> --indexes <<INDEXES>> --values <<VALUES>> --contexts <<CONTEXTS>>" command = command.replace("<<CODEPATH>>", option.path + "/python/") command = command.replace("<<PATH>>", option.path) command = command.replace("<<ORGANISM>>", option.organism) command = command.replace("<<MODE>>", mode) command = command.replace("<<A>>", option.a) command = command.replace("<<B>>", collection + "/mapcells_" + collection + "_matrix_overlap") command = command.replace("<<INDEXES>>", option.indexes) command = command.replace("<<VALUES>>", option.values) command = command.replace("<<CONTEXTS>>", option.contexts) # is it time to export a chunk? if index-start+option.step == chunks: # update start, modules, commands, and module count (m): start = index + option.step commands.append(command) modules.append(commands) commands = list() complete = True m += 1 # store whether the most recent index/command has been stored: else: complete = False # update if there are additional commands: if not complete: commands.append(command) modules.append(commands) m += 1 # launch commands: print print "Launching comparisons:", len(modules) #for module in modules: # for command in module: # print command runCommands(modules, threads=option.threads, mode="module.run", run_mode="verbose", run_path=bash_path, run_base=bash_base, record=True, qsub_header=qsub_header, qsub=qsub) print "Analyses performed:", len(modules) print # filter cells : elif option.mode == "filter": # load cells to filter: filterCells = open(path_dict[option.source] + option.target).read().strip().split("\n") # generate output file: f_output = open(path_dict[option.source] + option.name, "w") # process input lines: f, k = 0, 0 inlines = open(path_dict[option.source] + option.infile).readlines() for inline in inlines: process = True items = inline.strip().split(",") for item in items: if item in filterCells: process = False f += 1 if process: print >>f_output, inline.strip() k += 1 print print "Input lines:", len(inlines) print "Output lines:", k, "(" + str(f) + " filtered)" print # close output: f_output.close() # simplify cell annotations : elif option.mode == "simply": # generate output file: f_output = open(path_dict[option.source] + option.name, "w") # process input lines: f, k = 0, 0 inlines = open(path_dict[option.source] + option.infile).read().strip().replace("\r","\n").split("\n") for inline in inlines: process = True if "cell_mapping" in option.infile: regExp, original, updated = inline.strip().split(",") if updated == "": annotation = str(original) else: annotation = str(updated) print >>f_output, ",".join([regExp,annotation]) k += 1 print print "Input lines:", len(inlines) print "Output lines:", k, "(" + str(f) + " simplified)" print # close output: f_output.close() # robustness analysis mode: elif option.mode == "robust": import itertools print print "Loading input series data..." signalDict, replicateDict = dict(), dict() inlines = open(extraspath + option.infile).read().replace("\r","\n").split("\n") columnDict = dict() inline, index = inlines.pop(0), 0 for column in inline.strip().split(","): columnDict[column] = index index += 1 for inline in inlines: valueDict, initems = dict(), inline.strip().split(",") if initems != [""]: for column in columnDict: valueDict[column] = initems[columnDict[column]] gene, series, cell, value = valueDict["Gene"], valueDict["Series"], valueDict["Cell"], valueDict["Express"] if not gene in signalDict: signalDict[gene] = dict() if not cell in signalDict[gene]: signalDict[gene][cell] = dict() signalDict[gene][cell][series] = value if not gene in replicateDict: replicateDict[gene] = list() replicateDict[gene].append(series) replicateDict[gene] = sorted(list(set(replicateDict[gene]))) # define output file: f_output = open(expressionpath + "mapcells_" + option.mode + "_" + option.infile.replace(".csv",".txt"), "w") s_output = open(expressionpath + "mapcells_" + option.mode + "_" + option.infile.replace(".csv",".sum"), "w") print >>f_output, "\t".join(["gene","series.count","i","j","cells","pearson.correlation","pearson.pvalue"]) print >>s_output, "\t".join(["series.count", "gene.count"]) print "Scoring replicate correlations .." countDict = dict() for gene in signalDict: if not len(replicateDict[gene]) in countDict: countDict[len(replicateDict[gene])] = list() countDict[len(replicateDict[gene])].append(gene) if len(replicateDict[gene]) > 1: #print gene, len(replicateDict[gene]) for (i, j) in itertools.combinations(replicateDict[gene], 2): iValues, jValues = list(), list() for cell in signalDict[gene]: if i in signalDict[gene][cell] and j in signalDict[gene][cell]: iValues.append(float(signalDict[gene][cell][i])) jValues.append(float(signalDict[gene][cell][j])) correlation, corPvalue = pearsonr(iValues, jValues) output = [gene, len(replicateDict[gene]), i, j, len(iValues), correlation, corPvalue] print >>f_output, "\t".join(map(str, output)) #pdb.set_trace() for count in sorted(countDict.keys()): print >>s_output, "\t".join(map(str, [count, len(countDict[count])])) # close output file: f_output.close() s_output.close() print # fillin mode: elif option.mode == "fillin": print print "Loading annotation information..." annotationDict = general.build2(extraspath + option.infile, id_column="lineage", split=",") print "Checking parental annotation..." missingCells = list() for cell in annotationDict: parent = cell[:len(cell)-1] if not parent in annotationDict: if not parent in missingCells: missingCells.append(parent) print parent, cell print # import mode: elif option.mode == "import": # Cell annotations are cell-type and tissue-type (in the new Murray version): # specificDict: cell > cell-type # generalDict: cell > tissue-type # construct tissue dictionary (if necessary): if option.tissues != "OFF": print print "Loading general and specific tissue information..." specificDict = general.build2(extraspath + option.tissues, i="lineage", x="cell", mode="values", split=",") specificTotal = specificDict.values() generalDict = general.build2(extraspath + option.tissues, i="lineage", x="tissue", mode="values", split=",") generalTotal = generalDict.values() print "Generating tissue classes..." classification = { "rectal" : "excretory", "na" : "other" } classDict, classTotal, classMissing = dict(), list(), 0 for cell in generalDict: generalTissue = generalDict[cell] generalHits, classHits = list(), list() if generalTissue == "g": classTissue = "neuron/glial" generalHits.append(generalTissue) classHits.append(classTissue) else: for classTag in classification: if classTag in generalTissue: classTissue = classification[classTag] generalHits.append(generalTissue) classHits.append(classTissue) generalHits, classHits = list(set(generalHits)), list(set(classHits)) #print generalTissue, ":", ", ".join(classHits) if len(classHits) > 1: classTissue = "mixed" elif len(classHits) == 1: classTissue = classHits[0] elif len(classHits) == 0: classTissue = generalTissue classMissing += 1 classDict[cell] = classTissue classTotal.append(classTissue) classTotal = sorted(list(set(classTotal))) print print "Specific tissue terms:", len(set(specificDict.values())) print "General tissue terms:", len(set(generalDict.values())) generalCounts = dict() for cell in generalDict: generalTissue = generalDict[cell] if not generalTissue in generalCounts: generalCounts[generalTissue] = 0 generalCounts[generalTissue] += 1 generalTissues = general.valuesort(generalCounts) generalTissues.reverse() for generalTissue in generalTissues: print "\t" + generalTissue, ":", generalCounts[generalTissue] print print "Class tissue terms:", len(set(classDict.values())) classCounts = dict() for cell in classDict: classTissue = classDict[cell] if not classTissue in classCounts: classCounts[classTissue] = 0 classCounts[classTissue] += 1 classTissues = general.valuesort(classCounts) classTissues.reverse() for classTissue in classTissues: print "\t" + classTissue, ":", classCounts[classTissue] #pdb.set_trace() # prepare expression matrixes: series2cell_dict, gene2cell_dict, cell2gene_dict, gene2cell_list, allCells = dict(), dict(), dict(), dict(), list() # load expression data per series: print print "Loading cellular-expression data..." inlines = open(extraspath + option.infile).read().replace("\r","\n").split("\n") inheader = inlines.pop(0) for inline in inlines: if not inline == "": series, cell, gene, expression = inline.strip().split(",") gene = gene.upper() if not gene in option.exclude.split(","): if not cell in cell2gene_dict: cell2gene_dict[cell] = dict() if not gene in cell2gene_dict[cell]: cell2gene_dict[cell][gene] = dict() if not gene in gene2cell_dict: gene2cell_dict[gene] = dict() gene2cell_list[gene] = list() if not cell in gene2cell_dict[gene]: gene2cell_dict[gene][cell] = dict() if not series in series2cell_dict: series2cell_dict[series] = dict() gene2cell_dict[gene][cell][series] = float(expression) cell2gene_dict[cell][gene][series] = float(expression) series2cell_dict[series][cell] = float(expression) if not cell in gene2cell_list[gene]: gene2cell_list[gene].append(cell) if not cell in allCells: allCells.append(cell) # store cell-parent relationships: print "Loading cell-parent relationships..." cell_dict, parent_dict, pedigreeCells = relationshipBuilder(pedigreefile=option.pedigree, path=extraspath, mechanism="simple") # construct tissue dictionary (if necessary): if option.tissues != "OFF": print print "Expanding cell tissue information..." matchDict = { "specific":dict(), "general":dict(), "class":dict() } matchExpansion, matchTotal, matchMissing = list(), 0, 0 for cell in pedigreeCells: if cell in generalDict and generalDict[cell] != "na": matchDict["specific"][cell] = specificDict[cell] matchDict["general"][cell] = generalDict[cell] matchDict["class"][cell] = classDict[cell] else: # find most closely-related, annotated cell (and use its associated tissue annotation): distanceDict = dict() queryDict, matchTissues = dict(), list(), ancestorCells, descendantCells, matchCells, queryCells = list(), list(), list(), list() for queryCell in generalDict: relative = False if cell == queryCell[:len(cell)]: descendantCells.append(queryCell) relative = True if queryCell == cell[:len(queryCell)]: ancestorCells.append(queryCell) relative = True if relative: distance = abs(len(cell)-len(queryCell)) if not distance in distanceDict: distanceDict[distance] = list() distanceDict[distance].append(queryCell) # determine which cells to obtain the annotations from: if descendantCells != list(): queryCells = descendantCells else: queryCells = descendantCells + ancestorCells # find and weigh, most-related tissues: specificMatch, generalMatch, classMatch = dict(), dict(), dict() for distance in sorted(distanceDict.keys()): if distance != 0: for distanceCell in distanceDict[distance]: if distanceCell in queryCells: specificTissue = specificDict[distanceCell] generalTissue = generalDict[distanceCell] classTissue = classDict[distanceCell] if not specificTissue in specificMatch: specificMatch[specificTissue] = 0 if not generalTissue in generalMatch: generalMatch[generalTissue] = 0 if not classTissue in classMatch: classMatch[classTissue] = 0 specificMatch[specificTissue] += float(1)/distance generalMatch[generalTissue] += float(1)/distance classMatch[classTissue] += float(1)/distance # Note: This section controls whether tissue annotations are obtained from # all related cells (parents and ancestors) or just subsets of these... """ define a function that returns the highest-likelihood tissue """ def matchFunction(cell, matchDict, queryCells, verbose="OFF"): matchTissues = general.valuesort(matchDict) matchTissues.reverse() printFlag = False if len(matchTissues) > 1 and verbose == "ON": printFlag = True print cell, len(matchTissues), matchTissues, queryCells for matchTissue in matchTissues: print matchTissue, ":", matchDict[matchTissue] # Filter tissues associated with father/daughter cells: if len(matchTissues) > 1: matchTissues = general.clean(matchTissues, "death") if len(matchTissues) > 1: matchTissues = general.clean(matchTissues, "other") # Generate and store specific tissue label for cell: if len(matchTissues) == 0: matchTissue = "other" else: matchTissue = matchTissues[0] if printFlag and verbose == "ON": print ">", matchTissue print # return highest likelihood tissue match and ranked tissues: return matchTissue, matchTissues # assign highest-scoring tissue types: #specificDict[cell], specificTissues = matchFunction(cell, specificMatch, queryCells, verbose="OFF") #generalDict[cell], generalTissues = matchFunction(cell, generalMatch, queryCells, verbose="OFF") #classDict[cell], classTissues = matchFunction(cell, classMatch, queryCells, verbose="OFF") matchDict["specific"][cell], specificMatches = matchFunction(cell, specificMatch, queryCells, verbose="OFF") matchDict["general"][cell], generalMatches = matchFunction(cell, generalMatch, queryCells, verbose="OFF") matchDict["class"][cell], classMatches = matchFunction(cell, classMatch, queryCells, verbose="OFF") # update tissue counts: matchTotal += 1 if matchDict["class"][cell] == "na": matchMissing += 1 # Update/expand cell-tissue dictionary: matchTissue = matchDict["specific"][cell] if not matchTissue in matchExpansion: matchExpansion.append(matchTissue) # record counts for each type of tissue: specificCounts, generalCounts, classCounts = dict(), dict(), dict() for cell in specificDict: specificTissue = specificDict[cell] generalTissue = generalDict[cell] classTissue = classDict[cell] if not specificTissue in specificCounts: specificCounts[specificTissue] = 0 specificCounts[specificTissue] += 1 if not generalTissue in generalCounts: generalCounts[generalTissue] = 0 generalCounts[generalTissue] += 1 if not classTissue in classCounts: classCounts[classTissue] = 0 classCounts[classTissue] += 1 #print #print "Specific tissue terms:", len(set(specificDict.values())) #specificTissues = general.valuesort(specificCounts) #specificTissues.reverse() #for specificTissue in specificTissues: # print "\t" + specificTissue, ":", specificCounts[specificTissue] print print "General tissue terms:", len(set(generalDict.values())) generalTissues = general.valuesort(generalCounts) generalTissues.reverse() for generalTissue in generalTissues: print "\t" + generalTissue, ":", generalCounts[generalTissue] print print "Class tissue terms:", len(set(classDict.values())) classTissues = general.valuesort(classCounts) classTissues.reverse() for classTissue in classTissues: print "\t" + classTissue, ":", classCounts[classTissue] print print "Tissue information expanded by:", len(matchExpansion) print "Tissue information expansion terms:", ", ".join(list(sorted(matchExpansion))) #pdb.set_trace() # calculate unique expression values for each gene/cell combination: print print "Generating per gene/cell expression values..." matrix, expression, expressing = dict(), dict(), dict() for gene in gene2cell_dict: for cell in gene2cell_list[gene]: values, maxSeries, maxValue = list(), "NA", 0 for series in gene2cell_dict[gene][cell]: values.append(gene2cell_dict[gene][cell][series]) if gene2cell_dict[gene][cell][series] >= maxValue: maxSeries, maxValue = series, gene2cell_dict[gene][cell][series] if not gene in matrix: matrix[gene] = dict() expression[gene] = dict() matrix[gene][cell] = [max(values), numpy.mean(values), numpy.median(values), numpy.std(values), len(gene2cell_dict[gene][cell]), ",".join(sorted(gene2cell_dict[gene][cell].keys())), maxSeries] if option.measure == "max.expression": expression[gene][cell] = max(values) elif option.measure == "avg.expression": expression[gene][cell] = numpy.mean(values) # calculate expression peaks... print "Generating per gene/cell expression statistics..." for gene in matrix: # find peak expression: peakCell, peakValue = "", 0 for cell in matrix[gene]: maxValue, meanValue, medianValue, stdValue, seriesCount, seriesIDs, maxSeries = matrix[gene][cell] cellValue = expression[gene][cell] if cellValue > peakValue: peakCell, peakValue = cell, cellValue # calculate fractional expression, cell ranks, and add cells expressing the protein (above cutoff): cellRanks = general.valuesort(expression[gene]) cellRanks.reverse() for cell in matrix[gene]: maxValue, meanValue, medianValue, stdValue, seriesCount, seriesIDs, maxSeries = matrix[gene][cell] cellValue = expression[gene][cell] fracValue = float(cellValue)/peakValue cellRank = cellRanks.index(cell) + 1 if not gene in expressing: expressing[gene] = list() if fracValue >= option.fraction and cellValue >= option.minimum: expressing[gene].append(cell) matrix[gene][cell] = [cellValue, peakValue, fracValue, cellRank, maxValue, meanValue, medianValue, stdValue, seriesCount, seriesIDs, maxSeries] # define the ascendants cutoff: print print "Defining minimum ascendants across experiments..." cutAscendants = 0 for gene in matrix: minAscendants, maxAscendants = 1000, 0 for cell in matrix[gene]: ascendants = ascendantsCollector(cell, parent_dict, cell_dict, ascendants=list()) if len(ascendants) < minAscendants: minAscendants = len(ascendants) minCell = cell if len(ascendants) > maxAscendants: maxAscendants = len(ascendants) maxCell = cell if minAscendants > cutAscendants: cutAscendants = minAscendants # define the set of cells tracked in target experiments: print "Defining cells focused: strict list of cells assayed in target experiments..." focusedCells = list() for gene in option.target.split(","): if focusedCells == list(): focusedCells = gene2cell_list[gene] else: focusedCells = set(focusedCells).intersection(set(gene2cell_list[gene])) # define the set of cells tracked in all experiments: print "Defining cells tracked: strict list of cells assayed in all experiments..." trackedCells = list() for gene in gene2cell_dict: if trackedCells == list(): trackedCells = gene2cell_list[gene] else: trackedCells = set(trackedCells).intersection(set(gene2cell_list[gene])) # define the set of ancestor or tracked cells: print "Defining cells started: parent-inclusive list of cells tracked in all experiments..." startedCells = list() for cell in pedigreeCells: ascendants = ascendantsCollector(cell, parent_dict, cell_dict, ascendants=list()) if cell in trackedCells or len(ascendants) < int(option.ascendants): startedCells.append(cell) #if cell == "ABalaaaal": # print cell, specificDict[cell], generalDict[cell], classDict[cell] # pdb.set_trace() print "Ascendants cutoff:", cutAscendants # define output files: assayedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_assayed" startedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_started" trackedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_tracked" focusedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_focused" summaryfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_summary" tissuesfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_tissues" # define cellular expression header: expressionHeader = ["cell", "cell.name", "gene", "cell.expression", "peak.expression", "fraction.expression", "normal.expression", "rank", "max.expression", "avg.expression", "med.expression", "std.expression", "cells.expressing", "cells.count", "series.count", "time.series", "max.series", "specific.tissue", "general.tissue", "class.tissue", "match.tissue"] reportHeader = ["gene", "cells.expressing", "cells.assayed", "cells.tracked", "cell.expression", "peak.expression", "fraction.expression", "series.count", "time.series", "max.series"] tissueHeader = ["cell", "specific.tissue", "general.tissue", "class.tissue", "match.tissue"] # create output files: a_output = open(assayedfile, "w") s_output = open(startedfile, "w") t_output = open(trackedfile, "w") f_output = open(focusedfile, "w") r_output = open(summaryfile, "w") x_output = open(tissuesfile, "w") print >>a_output, "\t".join(expressionHeader) print >>s_output, "\t".join(expressionHeader) print >>t_output, "\t".join(expressionHeader) print >>f_output, "\t".join(expressionHeader) print >>r_output, "\t".join(reportHeader) print >>x_output, "\t".join(tissueHeader) # generate set-normalization values: maxAssayed, maxStarted, maxTracked, maxFocused = dict(), dict(), dict(), dict() for gene in sorted(matrix.keys()): cellsStarted = startedCells cellsAssayed = matrix[gene].keys() cellsTracked = trackedCells cellsFocused = focusedCells peakAssayed, peakStarted, peakTracked, peakFocused = 0, 0, 0, 0 for cell in sorted(matrix[gene].keys()): cellValue, peakValue, fracValue, cellRank, maxValue, meanValue, medianValue, stdValue, seriesCount, seriesIDs, maxSeries = matrix[gene][cell] if cell in cellsAssayed and cellValue > peakAssayed: peakAssayed = cellValue if cell in cellsStarted and cellValue > peakStarted: peakStarted = cellValue if cell in cellsTracked and cellValue > peakTracked: peakTracked = cellValue if cell in cellsFocused and cellValue > peakFocused: peakFocused = cellValue maxAssayed[gene] = peakAssayed maxStarted[gene] = peakStarted maxTracked[gene] = peakTracked maxFocused[gene] = peakFocused # export expression data: print "Exporting expression data..." for gene in sorted(matrix.keys()): cellsStarted = len(startedCells) cellsAssayed = len(matrix[gene].keys()) cellsTracked = len(trackedCells) cellsFocused = len(focusedCells) cellsExpressingAssayed = len(set(expressing[gene]).intersection(set(matrix[gene].keys()))) cellsExpressingTracked = len(set(expressing[gene]).intersection(set(trackedCells))) cellsExpressingFocused = len(set(expressing[gene]).intersection(set(focusedCells))) cellValues, fracValues = list(), list() for cell in sorted(matrix[gene].keys()): if option.tissues == "OFF" or not cell in specificDict: specificTissue = "*" generalTissue = "*" classTissue = "*" matchTissue = "*" else: specificTissue = specificDict[cell] generalTissue = generalDict[cell] classTissue = classDict[cell] matchTissue = matchDict["class"][cell] cellValue, peakValue, fracValue, cellRank, maxValue, meanValue, medianValue, stdValue, seriesCount, seriesIDs, maxSeries = matrix[gene][cell] print >>a_output, "\t".join(map(str, [cell, cell, gene, cellValue, peakValue, fracValue, float(cellValue)/maxAssayed[gene], cellRank, maxValue, meanValue, medianValue, stdValue, cellsExpressingAssayed, cellsAssayed, seriesCount, seriesIDs, maxSeries, specificTissue, generalTissue, classTissue, matchTissue])) if cell in startedCells: print >>s_output, "\t".join(map(str, [cell, cell, gene, cellValue, peakValue, fracValue, float(cellValue)/maxStarted[gene], cellRank, maxValue, meanValue, medianValue, stdValue, cellsExpressingTracked, cellsStarted, seriesCount, seriesIDs, maxSeries, specificTissue, generalTissue, classTissue, matchTissue])) if cell in trackedCells: print >>t_output, "\t".join(map(str, [cell, cell, gene, cellValue, peakValue, fracValue, float(cellValue)/maxTracked[gene], cellRank, maxValue, meanValue, medianValue, stdValue, cellsExpressingTracked, cellsTracked, seriesCount, seriesIDs, maxSeries, specificTissue, generalTissue, classTissue, matchTissue])) if cell in focusedCells: print >>f_output, "\t".join(map(str, [cell, cell, gene, cellValue, peakValue, fracValue, float(cellValue)/maxFocused[gene], cellRank, maxValue, meanValue, medianValue, stdValue, cellsExpressingFocused, cellsFocused, seriesCount, seriesIDs, maxSeries, specificTissue, generalTissue, classTissue, matchTissue])) if fracValue >= option.fraction and cellValue >= option.minimum: cellValues.append(cellValue) fracValues.append(fracValue) print >>r_output, "\t".join(map(str, [gene, cellsExpressingTracked, cellsAssayed, cellsTracked, numpy.mean(cellValues), peakValue, numpy.mean(fracValues), seriesCount, seriesIDs, maxSeries])) # export tissue annotations: print "Exporting tissue annotation data..." print "Annotated cells:", len(specificDict) for cell in sorted(specificDict.keys()): specificTissue = specificDict[cell] generalTissue = generalDict[cell] classTissue = classDict[cell] if cell in matchDict["class"]: matchTissue = matchDict["class"][cell] else: matchTissue = str(classTissue) print >>x_output, "\t".join([cell, specificTissue, generalTissue, classTissue, matchTissue]) # close output: a_output.close() s_output.close() t_output.close() f_output.close() r_output.close() x_output.close() print print "Focused cells:", len(focusedCells) print "Tracked cells:", len(trackedCells) print "Started cells:", len(startedCells) print # inherit expression mode: elif option.mode == "inherit": # define input files: assayedinput = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_assayed" startedinput = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_started" trackedinput = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_tracked" focusedinput = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_focused" summaryinput = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_summary" tissuesinput = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_tissues" # define output files: assayedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_inassay" startedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_instart" trackedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_intrack" focusedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_infocus" inheritfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_inherit" inleafsfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_inleafs" maximalfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_maximal" mxleafsfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_mxleafs" # define cellular expression header: expressionHeader = ["cell", "cell.name", "gene", "cell.expression", "peak.expression", "fraction.expression", "normal.expression", "rank", "max.expression", "avg.expression", "med.expression", "std.expression", "cells.expressing", "cells.count", "series.count", "time.series", "max.series", "specific.tissue", "general.tissue", "class.tissue", "match.tissue"] reportHeader = ["gene", "cells.expressing", "cells.assayed", "cells.tracked", "cell.expression", "peak.expression", "fraction.expression", "series.count", "time.series", "max.series"] tissueHeader = ["cell", "specific.tissue", "general.tissue", "class.tissue", "match.tissue"] # create output files: a_output = open(assayedfile, "w") s_output = open(startedfile, "w") t_output = open(trackedfile, "w") f_output = open(focusedfile, "w") i_output = open(inheritfile, "w") l_output = open(inleafsfile, "w") m_output = open(maximalfile, "w") p_output = open(mxleafsfile, "w") print >>a_output, "\t".join(expressionHeader + ["inherited"]) print >>s_output, "\t".join(expressionHeader + ["inherited"]) print >>t_output, "\t".join(expressionHeader + ["inherited"]) print >>f_output, "\t".join(expressionHeader + ["inherited"]) print >>i_output, "\t".join(expressionHeader + ["inherited"]) print >>l_output, "\t".join(expressionHeader + ["inherited"]) print >>m_output, "\t".join(expressionHeader + ["inherited"]) print >>p_output, "\t".join(expressionHeader + ["inherited"]) # load terminal leaf cells: print print "Loading terminal cells..." inleafsCells = general.build2(extraspath + option.mapping, i="cell", x="cell.name", mode="values", skip=True) # store cell-parent relationships: print "Loading cell-parent relationships..." cell_dict, parent_dict, pedigreeCells = relationshipBuilder(pedigreefile=option.pedigree, path=extraspath, mechanism="simple") # loading tissue annotation data... print "Loading tissue annotation data..." tissuesAnnotation = general.build2(tissuesinput, id_column="cell", mode="table") # load expression data: print "Loading expression data..." assayedExpression = general.build2(assayedinput, id_complex=["gene","cell"], mode="table", separator=":") assayedMatrix = general.build2(assayedinput, i="gene", j="cell", x="cell.expression", mode="matrix") assayedCells = general.build2(assayedinput, i="cell", x="cell.name", mode="values", skip=True) startedCells = general.build2(startedinput, i="cell", x="cell.name", mode="values", skip=True) trackedCells = general.build2(trackedinput, i="cell", x="cell.name", mode="values", skip=True) focusedCells = general.build2(focusedinput, i="cell", x="cell.name", mode="values", skip=True) # define cellular space: print "Defining inheritance cells..." inheritCells = list() for inleafsCell in inleafsCells: inheritCells += ascendantsCollector(inleafsCell, parent_dict, cell_dict, ascendants=list()) inheritCells = sorted(list(set(inheritCells))) # load header dictionary: hd = general.build_header_dict(assayedinput) header = general.valuesort(hd) # inherit peak expression from ancestors: print "Inheriting expression from ancestors..." inheritExpression, maximalExpression = dict(), dict() for gene in sorted(assayedMatrix.keys()): inheritExpression[gene] = dict() maximalExpression[gene] = dict() for inheritCell in inheritCells: ascendantCells, ascendantExpression = list(), dict() ascendants = ascendantsCollector(inheritCell, parent_dict, cell_dict, ascendants=list(), sort=False) #print inheritCell, ascendants if len(set(ascendants)) != len(ascendants): print "oh, oh: not a set!" pdb.set_trace() for ascendantCell in ascendants + [inheritCell]: if ascendantCell in assayedMatrix[gene]: ascendantExpression[ascendantCell] = float(assayedMatrix[gene][ascendantCell]) ascendantCells.append(ascendantCell) if ascendantExpression != dict(): # get inheritance cells for maximal expression and for last ancestor expression: maximalCells = general.valuesort(ascendantExpression) maximalCells.reverse() maximalCell = maximalCells[0] ascendantCell = ascendantCells[0] # store values for last ancestor expression: inheritExpression[gene][inheritCell] = dict(assayedExpression[gene + ":" + ascendantCell]) inheritExpression[gene][inheritCell]["cell"] = str(inheritCell) inheritExpression[gene][inheritCell]["cell.name"] = str(inheritCell) inheritExpression[gene][inheritCell]["specific.tissue"] = tissuesAnnotation[inheritCell]["specific.tissue"] inheritExpression[gene][inheritCell]["general.tissue"] = tissuesAnnotation[inheritCell]["general.tissue"] inheritExpression[gene][inheritCell]["class.tissue"] = tissuesAnnotation[inheritCell]["class.tissue"] inheritExpression[gene][inheritCell]["match.tissue"] = tissuesAnnotation[inheritCell]["match.tissue"] inheritExpression[gene][inheritCell]["inherited"] = ascendantCell #if inheritCell != inheritExpression[gene][inheritCell]["cell"]: # print cell, inheritExpression[gene][inheritCell]["cell"], 1 # pdb.set_trace() # store values for maximal ancestor expression: maximalExpression[gene][inheritCell] = dict(assayedExpression[gene + ":" + maximalCell]) maximalExpression[gene][inheritCell]["cell"] = str(inheritCell) maximalExpression[gene][inheritCell]["cell.name"] = str(inheritCell) maximalExpression[gene][inheritCell]["specific.tissue"] = tissuesAnnotation[inheritCell]["specific.tissue"] maximalExpression[gene][inheritCell]["general.tissue"] = tissuesAnnotation[inheritCell]["general.tissue"] maximalExpression[gene][inheritCell]["class.tissue"] = tissuesAnnotation[inheritCell]["class.tissue"] maximalExpression[gene][inheritCell]["match.tissue"] = tissuesAnnotation[inheritCell]["match.tissue"] maximalExpression[gene][inheritCell]["inherited"] = ascendantCell # export inherited signals: print "Exporting inherited expression values..." for gene in sorted(inheritExpression): for cell in sorted(inheritExpression[gene].keys()): #if cell != inheritExpression[gene][cell]["cell"]: # print cell, inheritExpression[gene][cell]["cell"], 2 # pdb.set_trace() output = list() for column in header + ["inherited"]: output.append(inheritExpression[gene][cell][column]) if cell in assayedCells: print >>a_output, "\t".join(map(str, output)) if cell in startedCells: print >>s_output, "\t".join(map(str, output)) if cell in trackedCells: print >>t_output, "\t".join(map(str, output)) if cell in focusedCells: print >>f_output, "\t".join(map(str, output)) if cell in inheritCells: print >>i_output, "\t".join(map(str, output)) if cell in inleafsCells: print >>l_output, "\t".join(map(str, output)) #print "\t".join(map(str, output)) #pdb.set_trace() # export inherited signals: print "Exporting maximal expression values..." for gene in sorted(maximalExpression): for cell in sorted(maximalExpression[gene].keys()): output = list() for column in header + ["inherited"]: output.append(maximalExpression[gene][cell][column]) if cell in inheritCells: print >>m_output, "\t".join(map(str, output)) if cell in inleafsCells: print >>p_output, "\t".join(map(str, output)) #print "\t".join(map(str, output)) #pdb.set_trace() print print "Total inherited cells:", len(inheritCells) print "Terminal (leaf) cells:", len(inleafsCells) # close output files: a_output.close() s_output.close() t_output.close() f_output.close() i_output.close() l_output.close() m_output.close() p_output.close() print #k = inheritExpression.keys()[0] #print k #print inheritExpression[k][inleafsCell] #pdb.set_trace() # correct expression mode (detect outliers): elif option.mode == "correct": # load quantile functions from quantile import Quantile # define input files: startedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_started" trackedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_tracked" assayedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_assayed" summaryfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_summary" # load assayed expression data: print print "Loading expression data..." expressionDict = general.build2(assayedfile, i="gene", j="cell", x="cell.expression", mode="matrix") # prepare to sort genes by quantile expression: print "Sorting genes by expression..." medianDict, quantDict = dict(), dict() for gene in expressionDict: values = map(float, expressionDict[gene].values()) medianDict[gene] = numpy.median(values) quantDict[gene] = Quantile(values, 0.99) quantRanks = general.valuesort(quantDict) quantRanks.reverse() # store median rankings: rankDict = dict() medianRanks = general.valuesort(medianDict) medianRanks.reverse() k = 1 for gene in medianRanks: rankDict[gene] = k k += 1 # generate testing path: testingpath = correctionpath + "testing/" general.pathGenerator(testingpath) # Perform Gaussian Mixture Modeling (GMM): print "Performing GMM modeling..." gmmDict = dict() k = 1 for gene in expressionDict: signals = map(int, map(float, expressionDict[gene].values())) signals = [1 if (x == 0) else x for x in signals] testingfile = testingpath + "mapCells-gmm_" + expression_flag + option.name + "_" + "temp" resultsfile = testingpath + "mapCells-gmm_" + expression_flag + option.name + "_" + gene #f_output = open(testingfile, "w") #print >>f_output, "\n".join(["signal"] + map(str, signals)) #f_output.close() #command = " ".join(["Rscript", "~/meTRN/scripts/mapCells-gmm.r", testingfile, resultsfile, option.limit, option.parameters]) #os.system(command) #Rscript ~/meTRN/scripts/mapCells-pilot.r ~/Desktop/data.test ~/Desktop/data.output 1000 if "mapCells-gmm_" + expression_flag + option.name + "_" + gene in os.listdir(testingpath): gmmDict[gene] = open(resultsfile).readlines()[1].strip().split(" ")[2] #os.system("rm -rf " + testingfile) # export expression signals: rankingfile = correctionpath + "mapcells_" + expression_flag + option.name + "_correction_ranking" # rank information file percentfile = correctionpath + "mapcells_" + expression_flag + option.name + "_correction_percent" # gene-cell data, percentile-ranked genes mediansfile = correctionpath + "mapcells_" + expression_flag + option.name + "_correction_medians" # gene-cell data, median-ranked genes # define output headers: correctHeader = "\t".join(["index", "gene", "cell", "signal", "zscore", "nscore", "lscore", "rank", "median", "mean", "stdev", "alpha", "delta", "sigma", "gamma"]) rankingHeader = "\t".join(["gene", "quantile.rank", "median.rank", "median", "mean", "stdev", "alpha", "delta", "sigma", "gamma"]) # gather outputs: print "Generating expression thresholds..." r_output = open(rankingfile, "w") print >>r_output, rankingHeader outputDict = dict() k = 1 for gene in quantRanks: signals = map(float, expressionDict[gene].values()) maximal = max(signals) # calculate expression cutoffs: alpha = float(maximal)/10 delta = float(quantDict[gene])/10 sigma = float(quantDict[gene])/10 # detect GMM expression cutoff: if gene in gmmDict: gamma = int(gmmDict[gene]) else: gamma = int(option.limit) # threshold expression cutoffs: if alpha < int(option.limit): alpha = int(option.limit) if delta < int(option.limit): delta = int(option.limit) if gamma < int(option.limit): gamma = int(option.limit) # calculate general stats: median = numpy.median(signals) mean = numpy.mean(signals) stdev = numpy.std(signals) logMean = numpy.log10(mean) logStDev = numpy.log10(stdev) # store/export data: print >>r_output, "\t".join(map(str, [gene, k, rankDict[gene], median, mean, stdev, alpha, delta, sigma, gamma])) if not gene in outputDict: outputDict[gene] = dict() for cell in sorted(expressionDict[gene].keys()): signal = float(expressionDict[gene][cell]) if signal < 1: signal = 1 zscore = float(signal-mean)/stdev nscore = float(signal)/maximal lscore = float(numpy.log10(signal) - logMean)/logStDev outputDict[gene][cell] = "\t".join(map(str, [k, gene, cell, signal, zscore, nscore, lscore, rankDict[gene], median, mean, stdev, alpha, delta, sigma, gamma])) k += 1 r_output.close() # export expression signals, percentile-ranked genes: print "Exporting percentile-ranked expression signals..." f_output = open(percentfile, "w") print >>f_output, correctHeader for gene in quantRanks: for cell in sorted(outputDict[gene]): print >>f_output, outputDict[gene][cell] f_output.close() # export expression signals, median-ranked genes: print "Exporting median-ranked expression signals..." f_output = open(mediansfile, "w") print >>f_output, correctHeader for gene in medianRanks: for cell in sorted(outputDict[gene]): print >>f_output, outputDict[gene][cell] f_output.close() print # check status mode: elif option.mode == "check.status": # define input files: startedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_started" trackedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_tracked" assayedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_assayed" summaryfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_summary" # scan peak files: print print "Scanning peak files:" hd = general.build_header_dict(summaryfile) k, peaks, peak_files = 0, 0, os.listdir(peakspath) for inline in open(summaryfile).readlines()[1:]: gene, found = inline.strip().split("\t")[hd["gene"]], list() for peak_file in peak_files: dataset = peak_file.split("_peaks.bed")[0].replace("POL2", "AMA-1") if gene + "_" in dataset: found.append(dataset) peaks += general.countLines(peakspath + peak_file, header="OFF") if found != list(): print gene, ":", ", ".join(sorted(found)) k += 1 print print "Found factors:", k print "Peaks called:", peaks print # scan expression files: print print "Scanning expression data:" caught = list() hd = general.build_header_dict(assayedfile) for inline in open(assayedfile).readlines()[1:]: initems = inline.strip().split("\t") gene, timeSeries = initems[hd["gene"]], initems[hd["time.series"]] for timeSerie in timeSeries.split(","): if not gene.lower() in timeSerie: if not gene in caught: print gene, timeSeries caught.append(gene) print print "Mismatched genes:", len(caught) print # lineage distance mode: elif option.mode == "cell.distance": # build cell-expression matrix: print print "Loading cellular expression..." quantitation_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=0, minimum=0, metric="fraction.expression") # store cell-parent relationships: print "Loading cell-parent relationships..." cell_dict, parent_dict, pedigreeCells = relationshipBuilder(pedigreefile=option.pedigree, path=extraspath, mechanism="simple") print "Pedigree cells:", len(pedigreeCells) print "Tracked cells:", len(trackedCells) print # define output files: signalsmatrixfile = str(option.expression + "_distance_signals") lineagematrixfile = str(option.expression + "_distance_lineage") combinematrixfile = str(option.expression + "_distance_combine") # build cell-cell expression correlation matrix if not signalsmatrixfile in os.listdir(distancepath) or option.overwrite == "ON": print "Calculating expression correlation matrix..." correlation_matrix, index = dict(), 1 f_output = open(distancepath + signalsmatrixfile, "w") print >>f_output, "\t".join(["i", "j", "correlation", "correlation.pvalue", "correlation.adjusted.pvalue"]) for aCell in sorted(trackedCells): print index, aCell for bCell in sorted(trackedCells): aValues, bValues = list(), list() for gene in sorted(quantitation_matrix.keys()): aValues.append(quantitation_matrix[gene][aCell]) bValues.append(quantitation_matrix[gene][bCell]) correlation, corPvalue = pearsonr(aValues, bValues) adjCorPvalue = corPvalue*len(trackedCells)*len(trackedCells) if adjCorPvalue > 1: adjCorPvalue = 1 if not aCell in correlation_matrix: correlation_matrix[aCell] = dict() correlation_matrix[aCell][bCell] = [correlation, corPvalue, adjCorPvalue] print >>f_output, "\t".join(map(str, [aCell, bCell] + correlation_matrix[aCell][bCell])) index += 1 f_output.close() print else: print "Loading expression correlation matrix..." correlation_matrix = general.build2(distancepath + signalsmatrixfile, i="i", j="j", x=["correlation","correlation.pvalue","correlation.adjusted.pvalue"], datatype="float", mode="matrix", header_dict="auto") # build lineage distance matrix: if not lineagematrixfile in os.listdir(distancepath) or option.overwrite == "ON": print "Calculating lineage distance matrix..." lineage_matrix, index = dict(), 1 f_output = open(distancepath + lineagematrixfile, "w") print >>f_output, "\t".join(["i", "j", "distance", "parent"]) for aCell in sorted(trackedCells): print index, aCell for bCell in sorted(trackedCells): distance, ancestor = lineageDistance(aCell, bCell, parent_dict, cell_dict) if not aCell in lineage_matrix: lineage_matrix[aCell] = dict() lineage_matrix[aCell][bCell] = [distance, ancestor] print >>f_output, "\t".join(map(str, [aCell, bCell] + lineage_matrix[aCell][bCell])) index += 1 f_output.close() print else: print "Loading lineage distance matrix..." lineage_matrix = general.build2(distancepath + lineagematrixfile, i="i", j="j", x=["distance","parent"], datatype="list", mode="matrix", header_dict="auto", listtypes=["int", "str"]) #print correlation_matrix["ABal"]["ABal"] #print lineage_matrix["ABal"]["ABal"] #pdb.set_trace() # build expression distance matrix (as a function of fraction expression): print "Generating combined distance matrix (at fraction range):" f_output = open(distancepath + combinematrixfile, "w") print >>f_output, "\t".join(["i", "j", "minimal", "fraction", "distance", "parent", "expression.correlation", "expression.correlation.pvalue", "expression.correlation.adjusted.pvalue", "i.genes", "j.genes", "overlap", "total", "overlap.max", "overlap.sum", "pvalue", "adjusted.pvalue", "flag"]) fraction_matrix, genes = dict(), sorted(tracking_matrix.keys()) for minimal in [1500, 1750, 2000]: for fraction in general.drange(0.10, 0.50, 0.10): print "...", minimal, fraction fraction_matrix[fraction] = dict() # find genes expressed per cell (using fraction cutoff): cellular_matrix = dict() fraction_quantitation_matrix, fraction_expression_matrix, fraction_tracking_matrix, fraction_trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=fraction, minimum=minimal, metric="fraction.expression") for gene in fraction_expression_matrix: for cell in fraction_expression_matrix[gene]: if not cell in cellular_matrix: cellular_matrix[cell] = list() cellular_matrix[cell].append(gene) # find multiple hypothesis adjustment factor: adjust = 0 for aCell in sorted(fraction_trackedCells): for bCell in sorted(fraction_trackedCells): if aCell in cellular_matrix and bCell in cellular_matrix: adjust += 1 # find gene expression overlap between cells: overlap_matrix = dict() universe = len(quantitation_matrix.keys()) for aCell in sorted(trackedCells): for bCell in sorted(trackedCells): if aCell in cellular_matrix and bCell in cellular_matrix: aGenes = cellular_matrix[aCell] bGenes = cellular_matrix[bCell] union = set(aGenes).union(set(bGenes)) overlap = set(aGenes).intersection(set(bGenes)) maxOverlap = float(len(overlap))/min(len(aGenes), len(bGenes)) sumOverlap = float(len(overlap))/len(union) # Hypergeometric paramters: m = len(aGenes) # number of white balls in urn n = universe - len(bGenes) # number of black balls in urn N = len(bGenes) # number of balls drawn from urn x = len(overlap) # number of white balls in drawn # If I pull out all balls with elephant tatoos (N), is the draw enriched in white balls?: pvalue = hyper.fishers(x, m+n, m, N, method="right") adjPvalue = hyper.limit(pvalue*adjust) # Store overlap and significance: if not aCell in overlap_matrix: overlap_matrix[aCell] = dict() overlap_matrix[aCell][bCell] = [len(aGenes), len(bGenes), len(overlap), universe, maxOverlap, sumOverlap, pvalue, adjPvalue] # generate combined distance output line: for aCell in sorted(trackedCells): for bCell in sorted(trackedCells): # load lineage distances: distance, ancestor = lineage_matrix[aCell][bCell] # load correlation distances: correlation, corPvalue, adjCorPvalue = correlation_matrix[aCell][bCell] # load expresssion distances: if aCell in cellular_matrix and bCell in cellular_matrix: aGenes, bGenes, overlap, universe, maxOverlap, sumOverlap, pvalue, adjPvalue = overlap_matrix[aCell][bCell] madeFlag = "both.observed" elif aCell in cellular_matrix: aGenes, bGenes, overlap, universe, maxOverlap, sumOverlap, pvalue, adjPvalue = len(cellular_matrix[aCell]), 0, 0, len(trackedCells), 0, 0, 1, 1 madeFlag = "only.observed" elif bCell in cellular_matrix: aGenes, bGenes, overlap, universe, maxOverlap, sumOverlap, pvalue, adjPvalue = 0, len(cellular_matrix[bCell]), 0, len(trackedCells), 0, 0, 1, 1 madeFlag = "only.observed" else: aGenes, bGenes, overlap, universe, maxOverlap, sumOverlap, pvalue, adjPvalue = 0, 0, 0, len(trackedCells), 0, 0, 1, 1 madeFlag = "none.observed" # export data: print >>f_output, "\t".join(map(str, [aCell, bCell, minimal, fraction, distance, ancestor, correlation, corPvalue, adjCorPvalue, aGenes, bGenes, overlap, universe, maxOverlap, sumOverlap, pvalue, adjPvalue, madeFlag])) # close output file: f_output.close() print # cell time mode: elif option.mode == "cell.times": # define input expression files: assayedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_assayed" startedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_started" trackedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_tracked" focusedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_focused" inheritfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_inherit" maximalfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_maximal" # load cell times: print print "Loading cellular times..." time_matrix = dict() inlines = open(extraspath + option.times).readlines() for inline in inlines: cell, start, stop = inline.strip().split(",") for time in range(int(start), int(stop)+1): if not time in time_matrix: time_matrix[time] = list() time_matrix[time].append(cell) # export cell times: populationDict = { "assayed" : assayedfile, "started" : startedfile, "tracked" : trackedfile, "focused" : focusedfile, "inherit" : inheritfile, "maximal" : maximalfile } print "Exporting cells per time point..." for population in populationDict: populationCells = general.build2(populationDict[population], id_column="cell", skip=True, mute=True).keys() for time in sorted(time_matrix.keys()): general.pathGenerator(timepath + population + "/cells/") f_output = open(timepath + population + "/cells/" + str(time), "w") timedCells = sorted(set(time_matrix[time]).intersection(set(populationCells))) if len(timedCells) > 0: print >>f_output, "\n".join(timedCells) f_output.close() # generate reports: print "Generating reports..." for population in populationDict: general.pathGenerator(timepath + population + "/report/") f_output = open(timepath + population + "/report/mapcells_" + population + "_time_report.txt", "w") print >>f_output, "\t".join(["time", "cell.count", "cell.percent", "cell.ids"]) for time in sorted(time_matrix.keys()): general.pathGenerator(timepath + population + "/report/") timedCount = general.countLines(timepath + population + "/cells/" + str(time)) timedPercent = round(100*float(timedCount)/len(time_matrix[time]), 2) timedCells = open(timepath + population + "/cells/" + str(time)).read().split("\n") print >>f_output, "\t".join([str(time), str(timedCount), str(timedPercent), ",".join(timedCells).rstrip(",")]) f_output.close() print # cubism graph mode: elif option.mode == "cell.cubism": # define input expression files: assayedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_assayed" startedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_started" trackedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_tracked" focusedfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_focused" inheritfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_inherit" maximalfile = expressionpath + "mapcells_" + expression_flag + option.name + "_expression_maximal" # define cell populations: populationDict = { "assayed" : assayedfile, "started" : startedfile, "tracked" : trackedfile, "focused" : focusedfile, "inherit" : inheritfile, "maximal" : maximalfile } # parse reports: print print "Exporting per gene, per timepoint expression cells:" for population in populationDict: print "Processing:", population # define output paths: factorpath = cubismpath + population + "/factor/" matrixpath = cubismpath + population + "/matrix/" general.pathGenerator(factorpath) general.pathGenerator(matrixpath) # build cell-expression matrix: quantitation_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=populationDict[population], path="", cutoff=option.fraction, minimum=option.minimum, metric="fraction.expression") # load timepoint data: timeDict = general.build2(timepath + population + "/report/mapcells_" + population + "_time_report.txt", id_column="time") # load calendar months: monthDict = dict((k,v) for k,v in enumerate(calendar.month_abbr)) # process genes x timepoints: m_output = open(matrixpath + "mapcells_cubism_matrix.txt", "w") print >>m_output, "\t".join(["gene", "time", "cells", "gene.cells", "time.cells"]) for gene in sorted(expression_matrix.keys()): timeStamp = 1001856000000 timeAdded = 100000000 factorLines = list() for time in sorted(map(int, timeDict.keys())): geneCells = expression_matrix[gene] timeCells = timeDict[str(time)]["cell.ids"].split(",") dateCells = len(set(geneCells).intersection(set(timeCells))) date = datetime.datetime.fromtimestamp(timeStamp / 1e3) day, month, year = date.day, date.month, str(date.year)[2:] date = "-".join(map(str, [day, monthDict[month], year])) factorLines.append(",".join(map(str, [date, dateCells, dateCells, dateCells, len(trackedCells)]))) print >>m_output, "\t".join(map(str, [gene, time, dateCells, len(geneCells), len(timeCells)])) timeStamp += timeAdded factorLines.reverse() f_output = open(factorpath + gene + ".csv", "w") print >>f_output, "Date,Open,High,Low,Close,Volume" for factorLine in factorLines: print >>f_output, factorLine.strip() f_output.close() # close output matrix: m_output.close() print # cell annotation mode: elif option.mode == "cell.annotation": # load target cells from time-points: print if option.times != "OFF": # define output file: f_output = open(cellnotationspath + "mapcells_" + option.name + "_" + option.infile, "w") # load time-point cells: cells = getTargetCells(inpath=timepath + option.times + "/cells/", mode="time", timeRange=range(option.start, option.stop + 1, option.step)) # load target cells from collection: elif option.collection != "OFF": # define output file: f_output = open(cellnotationspath + "mapcells_" + option.collection + "_" + option.infile, "w") # load collection cells: cells = getTargetCells(inpath=cellsetpath + option.collection + "/", mode="collection") # export features per cell: print "Exporting features per cell..." k = 0 inlines = open(annotationspath + option.infile).readlines() if option.header == "ON": inlines.pop(0) for cell in cells: for inline in inlines: if option.format == "bed": print >>f_output, cell + ":" + inline.strip() k += 1 f_output.close() print "Features scaled from", len(inlines), "to", k, ": " + str(round(float(k)/len(inlines), 0)) + "x" print # build matrix mode: elif option.mode == "cell.matrix": # update overlappath: matrixpath = matrixpath + option.collection + "/" general.pathGenerator(matrixpath) # define input files: infile = expressionpath + option.expression # define output files: matrixfile = matrixpath + str(option.expression + "_" + option.name + "_matrix") # load header dictionary: hd = general.build_header_dict(infile) # build cellular expression matrix: matrix, cells, genes, tissueDict = dict(), list(), list(), dict() inlines = open(infile).readlines() inlines.pop(0) for inline in inlines: initems = inline.strip().split("\t") cell, gene, cellExpression, fractionExpression, normalExpression, specificTissue, generalTissue, classTissue = initems[hd["cell"]], initems[hd["gene"]], initems[hd["cell.expression"]], initems[hd["fraction.expression"]], initems[hd["normal.expression"]], initems[hd["specific.tissue"]], initems[hd["general.tissue"]], initems[hd["class.tissue"]] # extract expression value (using specified technique): if option.technique == "binary": if float(fractionExpression) >= option.fraction and float(cellExpression) >= option.minimum: value = 1 else: value = 0 elif option.technique == "signal": value = float(cellExpression) elif option.technique == "fraction": value = float(fractionExpression) elif option.technique == "normal": value = float(normalExpression) # store cells, genes, and values: if not cell in cells: cells.append(cell) if not gene in genes: genes.append(gene) if not cell in tissueDict: tissueDict[cell] = [classTissue, generalTissue, specificTissue] if not cell in matrix: matrix[cell] = dict() matrix[cell][gene] = value # export the cellular expression matrix! f_output = open(matrixfile, "w") cells, genes = sorted(cells), sorted(genes) print >>f_output, "\t".join([""] + genes) for cell in cells: values = list() for gene in genes: if gene in matrix[cell]: values.append(matrix[cell][gene]) else: values.append(0) valueCount = len(values) - values.count(0) classTissue, generalTissue, specificTissue = tissueDict[cell] specificTissue = specificTissue.replace(" ", "_") label = ":".join([classTissue, generalTissue, specificTissue, cell]) print >>f_output, "\t".join([label] + map(str, values)) f_output.close() # build in silico binding peaks mode: elif option.mode == "cell.peaks": # define the target contexts: if option.contexts != "OFF": shandle, target_context_dict = metrn.options_dict["contexts.condensed"][option.contexts] target_contexts = list() for target in target_context_dict: target_contexts.append(target_context_dict[target]) target_contexts = sorted(list(set(target_contexts))) # generate output paths: insilicopath = bindingpath + option.name + "/" general.pathGenerator(insilicopath) # load header dictionary: hd = general.build_header_dict(expressionpath + option.expression) # load expression matrix: print print "Loading expression matrix..." matrix = dict() inlines = open(expressionpath + option.expression).readlines() inlines.pop(0) for inline in inlines: initems = inline.strip().split("\t") gene, cell, cellExpression, fractionExpression = initems[hd["gene"]], initems[hd["cell"]], float(initems[hd["cell.expression"]]), float(initems[hd["fraction.expression"]]) if not gene in matrix: matrix[gene] = dict() if fractionExpression >= option.fraction and float(cellExpression) >= option.minimum: matrix[gene][cell] = fractionExpression # load target cells: if option.times != "OFF": # load time-point cells: timedCells = getTargetCells(inpath=timepath + option.times + "/cells/", mode="time", timeRange=range(option.start, option.stop + 1, option.step)) # scan peak files: print print "Generating cell-resolution peaks..." k, peak_files, insilico_files = 0, os.listdir(peakspath), list() for peak_file in peak_files: dataset = peak_file.split("_peaks.bed")[0].replace("POL2", "AMA-1") organism, strain, factor, context, institute, method = metrn.labelComponents(dataset, target="components") if factor in matrix: if option.contexts == "OFF" or context in target_contexts: print "Processing:", dataset insilico_file = peak_file.replace("POL2", "AMA-1") f_output = open(insilicopath + insilico_file, "w") for cell in sorted(matrix[factor].keys()): if option.times == "OFF" or cell in timedCells: for inline in open(peakspath + peak_file).readlines(): print >>f_output, cell + ":" + inline.strip() f_output.close() insilico_files.append(insilico_file) # define output peak files: unsortedfile = bindingpath + "mapcells_silico_" + option.name + "_unsorted.bed" completefile = bindingpath + "mapcells_silico_" + option.name + "_complete.bed" compiledfile = bindingpath + "mapcells_silico_" + option.name + "_compiled.bed" # generate compilation files: if not "mapcells_silico_" + option.peaks + "_complete.bed" in os.listdir(bindingpath) or option.overwrite == "ON": # gather peak files and compiled into a single file: print print "Gathering peaks into single file..." joint = " " + insilicopath command = "cat " + insilicopath + joint.join(insilico_files) + " > " + unsortedfile os.system(command) print "Sorting peaks in single file..." command = "sortBed -i " + unsortedfile + " > " + completefile os.system(command) # merge peaks into single file: print "Collapsing peaks in sorted file..." command = "mergeBed -nms -i " + completefile + " > " + compiledfile os.system(command) # remove unsorted file: command = "rm -rf " + unsortedfile os.system(command) print # gene and cell collection reporting mode (gene expressed per cell): elif option.mode == "reports": taskDict = { "gene" : [cellsetpath, "cells"], "cell" : [genesetpath, "genes"] } print for task in taskDict: inputpath, column = taskDict[task] for collection in os.listdir(inputpath): if collection in option.collection.split(",") or option.collection == "OFF": print "Processing:", task, collection f_output = open(reportspath + "mapcell_report_" + task + "_" + collection, "w") print >>f_output, "\t".join([task, "count", column]) for item in sorted(os.listdir(inputpath + collection)): contents = open(inputpath + collection + "/" + item).read().strip().split("\n") contents = general.clean(contents) print >>f_output, "\t".join(map(str, [item, len(contents), ",".join(sorted(contents))])) f_output.close() print print # cell collection mode (cells expressed per gene): elif option.mode == "cell.collection": # establish descendants cutoff: if option.descendants == "OFF": descendants_cutoff = 1000000 descendants_handle = "XX" else: descendants_cutoff = int(option.descendants) descendants_handle = option.descendants # establish ascendants cutoff: if option.ascendants == "OFF": ascendants_cutoff = 0 ascendants_handle = "XX" else: ascendants_cutoff = int(option.ascendants) ascendants_handle = option.ascendants # establish limit cutoff: if option.limit == "OFF": limit_cutoff = "OFF" limit_handle = "XX" else: limit_cutoff = int(option.limit) limit_handle = option.limit # define output folder: cellsetpath = cellsetpath + option.collection + "/" general.pathGenerator(cellsetpath) # export expressing-cells for each gene: if option.expression != "OFF": # build cell-expression matrix: quantitation_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="fraction.expression") # export cells per gene: for gene in sorted(expression_matrix.keys()): f_output = open(cellsetpath + gene, "w") for cell in sorted(expression_matrix[gene]): print >>f_output, cell f_output.close() # export cells for SOM neurons: if option.neurons != "OFF": # update path to neurons: neuronspath = neuronspath + option.peaks + "/" # define input path: sumpath = neuronspath + option.technique + "/results/" + option.neurons + "/summary/" sumfile = "mapneurons_summary.txt" # build header dict: hd = general.build_header_dict(sumpath + sumfile) # build SOM-cell matrix: collection_matrix, trackedCells = dict(), list() inlines = open(sumpath + sumfile).readlines() inlines.pop(0) for inline in inlines: initems = inline.rstrip("\n").split("\t") neuron, cells = initems[hd["neuron"]], initems[hd["class.ids"]] collection_matrix[neuron] = general.clean(cells.split(","), "") trackedCells.extend(cells.split(",")) trackedCells = general.clean(list(set(trackedCells)), "") # export cells per gene: for neuron in sorted(collection_matrix.keys()): f_output = open(cellsetpath + neuron, "w") for cell in sorted(collection_matrix[neuron]): print >>f_output, cell f_output.close() # gene collection mode (gene expressed per cell): elif option.mode == "gene.collection": # establish descendants cutoff: if option.descendants == "OFF": descendants_cutoff = 1000000 descendants_handle = "XX" else: descendants_cutoff = int(option.descendants) descendants_handle = option.descendants # establish ascendants cutoff: if option.ascendants == "OFF": ascendants_cutoff = 0 ascendants_handle = "XX" else: ascendants_cutoff = int(option.ascendants) ascendants_handle = option.ascendants # establish limit cutoff: if option.limit == "OFF": limit_cutoff = "OFF" limit_handle = "XX" else: limit_cutoff = int(option.limit) limit_handle = option.limit # define output folder: genesetpath = genesetpath + option.collection + "/" general.pathGenerator(genesetpath) # export expressing-cells for each gene: if option.expression != "OFF": # build cell-expression matrix: quantitation_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="fraction.expression") # gather cells: cells = list() for gene in sorted(quantitation_matrix.keys()): for cell in sorted(quantitation_matrix[gene]): cells.append(cell) cells = sorted(list(set(cells))) # invert matrix: inverted_matrix = dict() for gene in sorted(expression_matrix.keys()): for cell in sorted(expression_matrix[gene]): if not cell in inverted_matrix: inverted_matrix[cell] = list() inverted_matrix[cell].append(gene) # export cells per gene: for cell in cells: f_output = open(genesetpath + cell, "w") if cell in inverted_matrix: for gene in sorted(inverted_matrix[cell]): print >>f_output, gene f_output.close() # cell transfer mode: elif option.mode == "cell.transfer": # define time-range to examine: timeRange=range(option.start, option.stop + 1, option.step) # generate new collections: for timePoint in timeRange: # define output path: outpath = cellsetpath + option.name + general.indexTag(timePoint, option.total) + option.nametag + "/" general.pathGenerator(outpath) # load timePoint cells: timedCells = getTargetCells(inpath=timepath + option.times + "/cells/", mode="time", timeRange=[timePoint]) # parse per gene signatures in collection: for gene in os.listdir(cellsetpath + option.collection): # load expression cells: expressionCells = open(cellsetpath + option.collection + "/" + gene).read().split("\n") # export timed, expressionCells: f_output = open(outpath + gene, "w") print >>f_output, "\n".join(sorted(list(set(timedCells).intersection(set(expressionCells))))) f_output.close() # mapping overlap mode: elif option.mode == "cell.overlap": # update overlappath: overlappath = overlappath + option.collection + "/" general.pathGenerator(overlappath) # build cell-expression matrix: print print "Loading cellular expression..." signal_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="cell.expression") fraction_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="fraction.expression") normal_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="fraction.expression") rank_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="rank") # load collection cells: #print "Loading target cells..." targetCells = getTargetCells(inpath=cellsetpath + option.collection + "/", mode="collection") # create output file: o_output = open(overlappath + "mapcells_" + option.collection + "_matrix_overlap", "w") o_header = ["i", "j", "i.cells", "j.cells", "overlap.cells", "total.cells", "overlap.max", "overlap.sum", "overlap.avg", "expression.cor", "fraction.cor", "normal.cor", "rank.cor", "pvalue", "pvalue.adj", "score", "i.only.ids", "j.only.ids", "overlap.ids"] print >>o_output, "\t".join(o_header) # find maximum rank, if necessary: if option.extend == "ON": maxRank = 0 for gene in rank_matrix: for targetCell in rank_matrix[gene]: if int(rank_matrix[gene][targetCell]) > maxRank: maxRank = int(rank_matrix[gene][targetCell]) # load gene-expressing cells data: print print "Build expression matrix per gene..." genes = os.listdir(cellsetpath + option.collection) matrix = dict() for gene in genes: matrix[gene] = dict() matrix[gene]["cells"], matrix[gene]["signals"], matrix[gene]["fractions"], matrix[gene]["normals"], matrix[gene]["ranks"] = list(), list(), list(), list(), list() expressionCells = open(cellsetpath + option.collection + "/" + gene).read().split("\n") for targetCell in targetCells: if targetCell in expressionCells: matrix[gene]["cells"].append(targetCell) if targetCell in signal_matrix[gene]: matrix[gene]["signals"].append(signal_matrix[gene][targetCell]) matrix[gene]["fractions"].append(fraction_matrix[gene][targetCell]) matrix[gene]["normals"].append(normal_matrix[gene][targetCell]) matrix[gene]["ranks"].append(rank_matrix[gene][targetCell]) elif option.extend == "ON": matrix[gene]["signals"].append(0) matrix[gene]["fractions"].append(0) matrix[gene]["normals"].append(0) matrix[gene]["ranks"].append(maxRank) # print a matrix of cell expression overlap between genes: print "Exporting cellular overlap matrix..." adjust = len(matrix)*len(matrix) universe = len(targetCells) for geneX in sorted(matrix.keys()): cellsX = matrix[geneX]["cells"] signalsX, fractionsX, normalsX, ranksX = numpy.array(matrix[geneX]["signals"]), numpy.array(matrix[geneX]["fractions"]), numpy.array(matrix[geneX]["normals"]), numpy.array(matrix[geneX]["ranks"]) for geneY in sorted(matrix.keys()): cellsY = matrix[geneY]["cells"] signalsY, fractionsY, normalsY, ranksY = numpy.array(matrix[geneY]["signals"]), numpy.array(matrix[geneY]["fractions"]), numpy.array(matrix[geneY]["normals"]), numpy.array(matrix[geneY]["ranks"]) signalCor = numpy.corrcoef(signalsX, signalsY)[0][1] fractionCor = numpy.corrcoef(fractionsX, fractionsY)[0][1] normalCor = numpy.corrcoef(normalsX, normalsY)[0][1] rankCor = numpy.corrcoef(ranksX, ranksY)[0][1] cellsXo = sorted(set(cellsX).difference(set(cellsY))) # X-only cells cellsYo = sorted(set(cellsY).difference(set(cellsX))) # Y-only cells cellsO = sorted(set(cellsX).intersection(set(cellsY))) # overlap cellsU = sorted(set(cellsX).union(set(cellsY))) # union cellsT = targetCells # Hypergeometric paramters: m = len(cellsX) # number of white balls in urn n = universe - len(cellsX) # number of black balls in urn N = len(cellsY) # number of balls drawn from urn x = len(cellsO) # number of white balls in drawn # If I pull out all balls with elephant tatoos (N), is the draw enriched in white balls?: pvalue = hyper.fishers(x, m+n, m, N, method="right") adjPvalue = hyper.limit(pvalue*adjust) score = hyper.directional(x, m+n, m, N, adjust=adjust) output = [geneX, geneY] output.append(len(cellsX)) output.append(len(cellsY)) output.append(len(cellsO)) output.append(len(cellsT)) if len(cellsO) > 0: output.append(float(len(cellsO))/min(len(cellsX), len(cellsY))) output.append(float(len(cellsO))/len(cellsU)) output.append(float(len(cellsO))/numpy.mean([len(cellsX), len(cellsY)])) else: output.append(0) output.append(0) output.append(0) output.append(signalCor) output.append(fractionCor) output.append(normalCor) output.append(rankCor) output.append(pvalue) output.append(adjPvalue) output.append(score) output.append(";".join(cellsXo)) output.append(";".join(cellsYo)) output.append(";".join(cellsO)) if len(output) != len(o_header): print len(o_header), len(output) print output print pdb.set_trace() if " " in "\t".join(map(str, output)): print output pdb.set_trace() print >>o_output, "\t".join(map(str, output)) # close output: o_output.close() print # hybrid (datatypes) matrix mode: elif option.mode == "cell.hybrid": # get comparison properties: peaks, domain = option.a.split("/")[:2] collection = option.b.split("/")[0] # load target contexts: codeContexts, targetContexts = metrn.options_dict["contexts.extended"][option.contexts] # make comparison output folders: hybridpath = hybridpath + collection + "/" + peaks + "/" + domain + "/" + codeContexts + "/" general.pathGenerator(hybridpath) # define input files: ainfile = str(coassociationspath + option.a).replace("//","/") binfile = str(cellspath + "overlap/" + option.b).replace("//","/") # load input headers: aheader = general.build_header_dict(ainfile) bheader = general.build_header_dict(binfile) # load co-association results: print print "Loading co-associations..." cobindingFrames = general.build2(ainfile, id_complex=["i", "j"], separator=":") # load cellular expression overlap: print "Loading co-expression..." coexpressionFrames = general.build2(binfile, id_complex=["i", "j"], separator=":") coexpressionMatrix = general.build2(binfile, i="i", j="j", x="overlap.sum", mode="matrix") # characterize input file basenames: abasename = option.a.split("/")[len(option.a.split("/"))-1].replace(".txt","").replace(".bed","") bbasename = option.b.split("/")[len(option.b.split("/"))-1].replace(".txt","").replace(".bed","") # define output file: f_outfile = hybridpath + "mapcells_hybrid_" + collection + "-" + peaks + "-" + domain + "_combined.txt" f_output = open(f_outfile, "w") # generate output header: header = ["i", "j", "label"] acolumns = list() for acolumn in general.valuesort(aheader): if not acolumn in header: acolumns.append(acolumn) bcolumns = list() for bcolumn in general.valuesort(bheader): if not bcolumn in header and not bcolumn in ["i.only.ids", "j.only.ids", "overlap.ids"]: bcolumns.append(bcolumn) print >>f_output, "\t".join(header + acolumns + bcolumns) # filter-match results: print "Merging co-binding and co-expression..." ifactor, jfactor = option.indexes.split(",") icontext, jcontext = option.values.split(",") comparisons = list() for cobindingComparison in sorted(cobindingFrames.keys()): iFactor, jFactor = cobindingFrames[cobindingComparison][ifactor], cobindingFrames[cobindingComparison][jfactor] iContext, jContext = cobindingFrames[cobindingComparison][icontext], cobindingFrames[cobindingComparison][jcontext] if iContext in targetContexts and jContext in targetContexts: if iFactor in coexpressionMatrix and jFactor in coexpressionMatrix: coexpressionComparison = iFactor + ":" + jFactor label = ":".join(sorted([iFactor, jFactor])) if not coexpressionComparison in comparisons: output = [iFactor, jFactor, label] for acolumn in acolumns: output.append(cobindingFrames[cobindingComparison][acolumn]) for bcolumn in bcolumns: output.append(coexpressionFrames[coexpressionComparison][bcolumn]) print >>f_output, "\t".join(map(str, output)) comparisons.append(coexpressionComparison) # NOTE: this filtering for unique comparisons ensures that only one # of the RNA PolII datasets gets used. # close output file: f_output.close() print "Merged comparisons:", len(comparisons) print # dynamics (hybrid) matrix mode: elif option.mode == "cell.dynamics": # are working with hybrid co-binding and co-expression data? if option.peaks != "OFF" and option.domain != "OFF": hybridMode = "ON" else: hybridMode = "OFF" # make comparison output folders: if hybridMode == "ON": dynamicspath = dynamicspath + option.name + "/" + option.peaks + "/" + option.domain + "/" general.pathGenerator(dynamicspath) f_outfile = dynamicspath + "mapcells_hybrid_" + option.name + "-" + option.peaks + "-" + option.domain + "_dynamics.txt" f_output = open(f_outfile, "w") u_outfile = dynamicspath + "mapcells_hybrid_" + option.name + "-" + option.peaks + "-" + option.domain + "_uniqueID.txt" u_output = open(u_outfile, "w") else: dynamicspath = dynamicspath + option.name + "/overlap/" general.pathGenerator(dynamicspath) f_outfile = dynamicspath + "mapcells_direct_" + option.name + "_dynamics.txt" f_output = open(f_outfile, "w") u_outfile = dynamicspath + "mapcells_direct_" + option.name + "_uniqueID.txt" u_output = open(u_outfile, "w") # define output file: k = 0 # load target contexts: codeContexts, targetContexts = metrn.options_dict["contexts.extended"][option.contexts] # load overlap data from collections: print print "Transfer dynamic co-binding and co-expression analysis..." for index in range(option.start, option.stop+1, option.step): collection = option.collection + general.indexTag(index, option.total) + option.nametag labels = list() # process hybrid co-binding and co-expression data: if hybridMode == "ON" and collection in os.listdir(hybridpath): if option.peaks in os.listdir(hybridpath + collection): if option.domain in os.listdir(hybridpath + collection + "/" + option.peaks): infile = hybridpath + collection + "/" + option.peaks + "/" + option.domain + "/" + codeContexts + "/mapcells_hybrid_" + collection + "-" + option.peaks + "-" + option.domain + "_combined.txt" inlines = open(infile).readlines() header = inlines.pop(0) if k == 0: print >>f_output, "timepoint" + "\t" + header.strip() print >>u_output, "timepoint" + "\t" + header.strip() k += 1 for inline in inlines: label = inline.strip().split("\t")[2] print >>f_output, str(index) + "\t" + inline.strip() if not label in labels: print >>u_output, str(index) + "\t" + inline.strip() labels.append(label) # process direct co-expression data: if hybridMode == "OFF" and collection in os.listdir(overlappath): infile = overlappath + collection + "/mapcells_" + collection + "_matrix_overlap" inlines = open(infile).readlines() header = inlines.pop(0) if k == 0: headerItems = header.strip().split("\t")[:15] print >>f_output, "\t".join(["timepoint", "label"] + headerItems) print >>u_output, "\t".join(["timepoint", "label"] + headerItems) k += 1 for inline in inlines: initems = inline.strip().split("\t")[:15] label = ":".join(sorted([initems[0], initems[1]])) print >>f_output, "\t".join([str(index), label] + initems) if not label in labels: print >>u_output, "\t".join([str(index), label] + initems) labels.append(label) f_output.close() u_output.close() print # hypergeometric tissue-testing mode: elif option.mode == "test.tissues": # load tissue annotation matrixes: print print "Loading tissue annotations..." specificTissues = general.build2(expressionpath + option.infile, i="specific.tissue", j="cell", mode="matrix", counter=True) generalTissues = general.build2(expressionpath + option.infile, i="general.tissue", j="cell", mode="matrix", counter=True) classTissues = general.build2(expressionpath + option.infile, i="class.tissue", j="cell", mode="matrix", counter=True) totalCells = general.build2(expressionpath + option.expression, i="cell", x="specific.tissue", mode="values", skip=True) totalCells = sorted(totalCells.keys()) # define a function for testing: def tissueTesting(queryCells, tissueMatrix, totalCells, adjust=1, match=True): if match: queryCells = set(queryCells).intersection(set(totalCells)) tissueOverlap = dict() for tissue in sorted(tissueMatrix.keys()): tissueCells = sorted(tissueMatrix[tissue].keys()) if match: tissueCells = set(tissueCells).intersection(set(totalCells)) overlapCells = set(queryCells).intersection(set(tissueCells)) m = len(queryCells) n = len(totalCells) - len(queryCells) U = len(totalCells) N = len(tissueCells) x = len(overlapCells) unionized = len(set(queryCells).union(set(tissueCells))) maximized = min(len(queryCells), len(tissueCells)) # determine overlap fractions: if maximized > 0: maxOverlap = float(x)/maximized else: maxOverlap = 0 if unionized > 0: sumOverlap = float(x)/unionized else: sumOverlap = 0 # calculate probability mass function (PMF): pvalue = hyper.fishers(x, U, m, N, adjust=1, method="right") adjPvalue = hyper.limit(pvalue*adjust) # calculate enrichment/depletion score: score = hyper.directional(x, U, m, N, adjust=adjust) # store overlap scores: tissueOverlap[tissue] = [len(queryCells), len(tissueCells), len(overlapCells), len(totalCells), maxOverlap, sumOverlap, pvalue, adjPvalue, score] # return overlap scores: return tissueOverlap # load genes: genes = sorted(os.listdir(cellsetpath + option.collection)) # determine Bonferroni correction factors adjustSpecific = len(genes)*len(specificTissues) adjustGeneral = len(genes)*len(generalTissues) adjustClass = len(genes)*len(classTissues) #print adjustSpecific #print adjustGeneral #print adjustClass #pdb.set_trace() # load cellular expression patterns per gene: print "Loading per gene expression matrix..." specificMatrix, generalMatrix, classMatrix = dict(), dict(), dict() for gene in genes: cells = open(cellsetpath + option.collection + "/" + gene).read().split("\n") specificMatrix[gene] = tissueTesting(cells, specificTissues, totalCells, adjust=adjustSpecific) generalMatrix[gene] = tissueTesting(cells, generalTissues, totalCells, adjust=adjustGeneral) classMatrix[gene] = tissueTesting(cells, classTissues, totalCells, adjust=adjustClass) # load cellular expression patterns per gene: print "Exporting overlap scores..." s_output = open(tissuespath + "mapcells_" + option.collection + "_matrix_specific.txt", "w") g_output = open(tissuespath + "mapcells_" + option.collection + "_matrix_general.txt" , "w") c_output = open(tissuespath + "mapcells_" + option.collection + "_matrix_class.txt" , "w") print >>s_output, "\t".join(["gene", "tissue", "gene.cells", "tissue.cells", "overlap.cells", "total.cells", "overlap.max", "overlap.sum", "pvalue", "pvalue.adj", "score"]) print >>g_output, "\t".join(["gene", "tissue", "gene.cells", "tissue.cells", "overlap.cells", "total.cells", "overlap.max", "overlap.sum", "pvalue", "pvalue.adj", "score"]) print >>c_output, "\t".join(["gene", "tissue", "gene.cells", "tissue.cells", "overlap.cells", "total.cells", "overlap.max", "overlap.sum", "pvalue", "pvalue.adj", "score"]) for gene in sorted(specificMatrix.keys()): for tissue in sorted(specificMatrix[gene].keys()): print >>s_output, "\t".join(map(str, [gene, tissue] + specificMatrix[gene][tissue])) for tissue in sorted(generalMatrix[gene].keys()): print >>g_output, "\t".join(map(str, [gene, tissue] + generalMatrix[gene][tissue])) for tissue in sorted(classMatrix[gene].keys()): print >>c_output, "\t".join(map(str, [gene, tissue] + classMatrix[gene][tissue])) # close outputs: s_output.close() g_output.close() c_output.close() print # lineage construction/generation mode: elif option.mode == "build.lineages": import time # establish descendants cutoff: if option.descendants == "OFF": descendants_cutoff = 1000000 descendants_handle = "XX" else: descendants_cutoff = int(option.descendants) descendants_handle = option.descendants # establish ascendants cutoff: if option.ascendants == "OFF": ascendants_cutoff = 0 ascendants_handle = "XX" else: ascendants_cutoff = int(option.ascendants) ascendants_handle = option.ascendants # establish limit cutoff: if option.limit == "OFF": limit_cutoff = "OFF" limit_handle = "XX" else: limit_cutoff = int(option.limit) limit_handle = option.limit # define output paths: logpath = lineagepath + option.name + "/" + option.method + "/lineage." + option.lineages + "/ascendants." + ascendants_handle + "/descendants." + descendants_handle + "/limit." + limit_handle + "/log/" buildpath = lineagepath + option.name + "/" + option.method + "/lineage." + option.lineages + "/ascendants." + ascendants_handle + "/descendants." + descendants_handle + "/limit." + limit_handle + "/build/" general.pathGenerator(logpath) general.pathGenerator(buildpath) # prepare log file: l_output = open(logpath + "mapcells_build_" + option.cells + ".log", "w") # clear output folder contents: command = "rm -rf " + buildpath + "*" os.system(command) # build cell-expression matrix: print print "Loading cellular expression..." print >>l_output, "Loading cellular expression..." quantitation_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="fraction.expression") # store cell-parent relationships: print "Loading cell-parent relationships..." print >>l_output, "Loading cell-parent relationships..." cell_dict, parent_dict, pedigreeCells = relationshipBuilder(pedigreefile=option.pedigree, path=extraspath, trackedCells=trackedCells, lineages=option.lineages) print "Pedigree cells:", len(pedigreeCells) print "Tracked cells:", len(trackedCells) print >>l_output, "Pedigree cells:", len(pedigreeCells) print >>l_output, "Tracked cells:", len(trackedCells) # generate lineages for enrichment: print print "Generating lineages..." print >>l_output, "" print >>l_output, "Generating lineages..." i, j, maxDN, minUP = 0, 0, 0, 10000 for parent in pedigreeCells: i += 1 # define descendant cells: descendants = descendantsCollector(parent, parent_dict, cell_dict, descendants=list()) # define ascendants cells: ascendants = ascendantsCollector(parent, parent_dict, cell_dict, ascendants=list()) # calculate combinations possible: combinations = combinationCalculator(len(descendants), len(descendants)) # apply descendants cutoff: if len(descendants) <= descendants_cutoff and len(ascendants) >= ascendants_cutoff: j += 1 print parent, len(ascendants), len(descendants), time.asctime(time.localtime()) print >>l_output, parent, len(ascendants), len(descendants), time.asctime(time.localtime()) # record max and min cutoffs: if len(ascendants) < minUP: minUP = len(ascendants) if len(descendants) > maxDN: maxDN = len(descendants) # define lineage cells: if option.method == "descender": subtrees = [",".join(descendants)] elif option.method == "generator": subtrees = lineageGenerator(parent, parent_dict, cell_dict) elif option.method == "builder": subtrees = lineageBuilder(parent, parent_dict, cell_dict, limit=limit_cutoff) elif option.method == "collector": subtrees = lineageCollector(expression_matrix[gene], parent_dict, cell_dict) print subtrees pdb.set_trace() # not implemented yet # export lineage cells: f_output = open(buildpath + parent, "w") index = 1 for subtree in subtrees: print >>f_output, "\t".join([parent, parent + "." + str(index), subtree]) index += 1 f_output.close() print print "Pedigree nodes lineaged:", i print "Pedigree nodes examined:", j, "(" + str(round(100*float(j)/i, 2)) + "%)" print "Maximum descendants:", maxDN print "Minimum ascendants:", minUP print print >>l_output, "" print >>l_output, "Pedigree nodes lineaged:", i print >>l_output, "Pedigree nodes examined:", j, "(" + str(round(100*float(j)/i, 2)) + "%)" print >>l_output, "Maximum descendants:", maxDN print >>l_output, "Minimum ascendants:", minUP # close output files: l_output.close() #pdb.set_trace() # hypergeometric lineage-testing mode: elif option.mode == "test.lineages": # establish descendants cutoff: if option.descendants == "OFF": descendants_cutoff = 1000000 descendants_handle = "XX" else: descendants_cutoff = int(option.descendants) descendants_handle = option.descendants # establish ascendants cutoff: if option.ascendants == "OFF": ascendants_cutoff = 0 ascendants_handle = "XX" else: ascendants_cutoff = int(option.ascendants) ascendants_handle = option.ascendants # establish limit cutoff: if option.limit == "OFF": limit_cutoff = "OFF" limit_handle = "XX" else: limit_cutoff = int(option.limit) limit_handle = option.limit # define output paths: logpath = lineagepath + option.name + "/" + option.method + "/lineage." + option.lineages + "/ascendants." + ascendants_handle + "/descendants." + descendants_handle + "/limit." + limit_handle + "/log/" buildpath = lineagepath + option.name + "/" + option.method + "/lineage." + option.lineages + "/ascendants." + ascendants_handle + "/descendants." + descendants_handle + "/limit." + limit_handle + "/build/" hyperpath = lineagepath + option.name + "/" + option.method + "/lineage." + option.lineages + "/ascendants." + ascendants_handle + "/descendants." + descendants_handle + "/limit." + limit_handle + "/hyper/" cellsetpath = cellsetpath + option.collection + "/" general.pathGenerator(logpath) general.pathGenerator(buildpath) general.pathGenerator(hyperpath) #general.pathGenerator(cellsetpath) # prepare log file: l_output = open(logpath + "mapcells_hyper_" + option.collection + "_" + option.cells + ".log", "w") # build cell-expression matrix: print print "Loading cellular expression..." print >>l_output, "Loading cellular expression..." quantitation_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="fraction.expression") # load cell-parent relationships: print "Loading cell-parent relationships..." print >>l_output, "Loading cell-parent relationships..." cell_dict, parent_dict, pedigreeCells = relationshipBuilder(pedigreefile=option.pedigree, path=extraspath, trackedCells=trackedCells, lineages=option.lineages) print "Pedigree cells:", len(pedigreeCells) print "Tracked cells:", len(trackedCells) print >>l_output, "Pedigree cells:", len(pedigreeCells) print >>l_output, "Tracked cells:", len(trackedCells) # prepare for scanning... i, j, k = 0, 0, 0 nodes = general.clean(os.listdir(buildpath), '.DS_Store') overlap_dict, pvalue_dict, score_dict = dict(), dict(), dict() # prepare output file: f_output = open(hyperpath + "mapcells_hyper_" + option.collection + "_" + option.cells + ".txt", "w") header = ["gene", "node", "lineage", "experiment.cells", "lineage.cells", "overlap.sum", "overlap.max", "overlap.count", "total.count", "lineage.count", "expressed.count", "pvalue", "pvalue.adj", "score", "cells"] print >>f_output, "\t".join(map(str, header)) # load target cells: print print "Loading target cells..." print >>l_output, "" print >>l_output, "Loading target cells..." collection_matrix = dict() for collection in os.listdir(cellsetpath): collectionCells = general.clean(open(cellsetpath + collection).read().split("\n"), "") collection_matrix[collection] = collectionCells #print collection, collectionCells # define multiple-hypothesis correction factor: lineageTotal = 0 for node in nodes: lineageTotal += general.countLines(buildpath + node) adjust = len(collection_matrix)*lineageTotal # check background cell population: if option.cells == "tracked": pedigreeCells = list(trackedCells) # scan cells for enrichment: print "Scanning cells for lineage enrichments..." print >>l_output, "" print >>l_output, "Scanning cells for lineage enrichments..." collectionsEnriched = list() for collection in collection_matrix: collectionCells = collection_matrix[collection] # filter (complete) pedigree cells to reduce to tracked cells? if option.cells == "tracked" and collection in tracking_matrix: completeCells = set(tracking_matrix[collection]).intersection(set(pedigreeCells)) else: completeCells = pedigreeCells # Note: These are cells for which we have expression measurements for gene ('collection')... # Note: It is not necessary to filter expression cells because these are by definition a subset of the tracked cells. # scan lineages for enrichment: nodesEnriched, linesEnriched = list(), list() for node in nodes: # load node-specific lineages: lineageLines = open(buildpath + node).readlines() for lineageLine in lineageLines: lineageNode, lineageName, lineageCells = lineageLine.strip().split("\t") lineageCells = lineageCells.split(",") lineageCount = len(lineageCells) # filter lineage cells to reduce to tracked cells? if option.cells == "tracked" and collection in tracking_matrix: lineageCells = set(tracking_matrix[collection]).intersection(set(lineageCells)) #print collection, node, len(tracking_matrix[collection]), len(lineageCells), ",".join(lineageCells) #pdb.set_trace() # test enrichment in lineage: i += 1 completed = len(completeCells) descended = len(lineageCells) collected = len(collectionCells) overlaped = len(set(collectionCells).intersection(set(lineageCells))) unionized = len(set(collectionCells).union(set(lineageCells))) maximized = min(descended, collected) # determine overlaps: if maximized > 0: maxOverlap = float(overlaped)/maximized else: maxOverlap = 0 if unionized > 0: sumOverlap = float(overlaped)/unionized else: sumOverlap = 0 # check overlap: if maxOverlap >= float(option.overlap): j += 1 # calculate probability mass function (PMF): pvalue = hyper.fishers(overlaped, completed, descended, collected, adjust=1, method="right") adjPvalue = hyper.limit(pvalue*adjust) # calculate enrichment/depletion score: score = hyper.directional(overlaped, completed, descended, collected, adjust=adjust) # should we store this result? if adjPvalue < float(option.pvalue): k += 1 if not collection in overlap_dict: overlap_dict[collection], pvalue_dict[collection], score_dict[collection] = dict(), dict(), dict() overlap_dict[collection][node] = maxOverlap pvalue_dict[collection][node] = adjPvalue score_dict[collection][node] = score output = [collection, node, lineageName, len(collectionCells), lineageCount, sumOverlap, maxOverlap, overlaped, completed, descended, collected, pvalue, adjPvalue, score, ','.join(lineageCells)] print >>f_output, "\t".join(map(str, output)) if not collection in collectionsEnriched: collectionsEnriched.append(collection) if not node in nodesEnriched: nodesEnriched.append(node) linesEnriched.append(lineageName) print collection, i, j, k, len(collectionsEnriched), len(nodesEnriched), len(linesEnriched) print >>l_output, collection, i, j, k, len(collectionsEnriched), len(nodesEnriched), len(linesEnriched) print print "Lineages examined:", i print "Lineages overlapped:", j print "Lineages significant:", k, "(" + str(round(100*float(k)/i, 2)) + "%)" print print >>l_output, "" print >>l_output, "Lineages examined:", i print >>l_output, "Lineages overlapped:", j print >>l_output, "Lineages significant:", k, "(" + str(round(100*float(k)/i, 2)) + "%)" # close output file f_output.close() l_output.close() #pdb.set_trace() # hypergeometric testing between sets of cells mode: elif option.mode == "test.comparison": # define output paths: querypath = cellsetpath + option.query + "/" targetpath = cellsetpath + option.target + "/" hyperpath = comparepath + option.name + "/hyper/" logpath = comparepath + option.name + "/log/" general.pathGenerator(hyperpath) general.pathGenerator(logpath) # prepare log file: l_output = open(logpath + "mapcells_comparison_" + option.name + "_" + option.cells + ".log", "w") # build cell-expression matrix: print print "Loading cellular expression..." print >>l_output, "Loading cellular expression..." quantitation_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="fraction.expression") # store cell-parent relationships: print "Loading cell-parent relationships..." print >>l_output, "Loading cell-parent relationships..." cell_dict, parent_dict, pedigreeCells = relationshipBuilder(pedigreefile=option.pedigree, path=extraspath, trackedCells=trackedCells, lineages=option.cells) # Note that here the lineage-filtering uses the indicated cells option! print "Pedigree cells:", len(pedigreeCells) print "Tracked cells:", len(trackedCells) print >>l_output, "Pedigree cells:", len(pedigreeCells) print >>l_output, "Tracked cells:", len(trackedCells) # prepare for scanning... overlap_dict, pvalue_dict = dict(), dict() i, j, k = 0, 0, 0 # prepare output file: f_output = open(hyperpath + "mapcells_test_" + option.name + "_" + option.cells + "_comparison.txt", "w") header = ["query", "target", "lineage", "query.cells", "target.cells", "overlap.sum", "overlap.max", "overlap.count", "total.count", "query.count", "target.count", "pvalue", "pvalue.adj", "score", "cells"] print >>f_output, "\t".join(map(str, header)) # load query cells: print print "Loading query cells..." print >>l_output, "" print >>l_output, "Loading query cells..." query_matrix = dict() for query in os.listdir(querypath): queryCells = general.clean(open(querypath + query).read().split("\n"), "") query_matrix[query] = queryCells #print query, queryCells # load target cells: print print "Loading target cells..." print >>l_output, "" print >>l_output, "Loading target cells..." target_matrix = dict() for target in os.listdir(targetpath): targetCells = general.clean(open(targetpath + target).read().split("\n"), "") target_matrix[target] = targetCells #print target, targetCells # define multiple-hypothesis correction factor: adjust = len(query_matrix)*len(target_matrix) # check background cell population: if option.cells == "tracked": pedigreeCells = list(trackedCells) # scan query cells for enrichment: print "Scanning target cells for query cells enrichment..." print >>l_output, "" print >>l_output, "Scanning target cells for query cells enrichment..." queriesEnriched = list() for query in sorted(query_matrix.keys()): queryCells = list(set(query_matrix[query])) # filter query cells to reduce to tracked cells? if option.cells == "tracked": queryCells = set(queryCells).intersection(set(pedigreeCells)) # scan target cells for enrichment: targetsEnriched, linesEnriched = list(), list() for target in sorted(target_matrix.keys()): targetCells = list(set(target_matrix[target])) # filter target cells to reduce to tracked cells? if option.cells == "tracked": targetCells = set(targetCells).intersection(set(pedigreeCells)) #print query, target, len(queryCells), len(targetCells), ",".join(targetCells) #pdb.set_trace() # test enrichment in lineage: i += 1 completed = len(pedigreeCells) descended = len(targetCells) collected = len(queryCells) overlaped = len(set(queryCells).intersection(set(targetCells))) unionized = len(set(queryCells).union(set(targetCells))) maximized = min(descended, collected) # determine overlaps: if maximized > 0: maxOverlap = float(overlaped)/maximized else: maxOverlap = 0 if unionized > 0: sumOverlap = float(overlaped)/unionized else: sumOverlap = 0 # check overlap: if maxOverlap >= float(option.overlap): j += 1 # calculate probability mass function (PMF): pvalue = hyper.fishers(overlaped, completed, descended, collected, adjust=1, method="right") adjPvalue = hyper.limit(pvalue*adjust) # calculate enrichment/depletion score: score = hyper.directional(overlaped, completed, descended, collected, adjust=adjust) if adjPvalue < float(option.pvalue): k += 1 if not query in overlap_dict: overlap_dict[query], pvalue_dict[query] = dict(), dict() overlap_dict[query][target] = maxOverlap pvalue_dict[query][target] = pvalue output = [query, target, option.target, len(queryCells), len(targetCells), sumOverlap, maxOverlap, overlaped, completed, descended, collected, pvalue, adjPvalue, score, ','.join(targetCells)] print >>f_output, "\t".join(map(str, output)) if not query in queriesEnriched: queriesEnriched.append(query) if not target in targetsEnriched: targetsEnriched.append(target) print query, i, j, k, len(queriesEnriched), len(targetsEnriched), len(linesEnriched) print >>l_output, query, i, j, k, len(queriesEnriched), len(targetsEnriched), len(linesEnriched) print print "Lineages examined:", i print "Lineages overlapped:", j print "Lineages significant:", k, "(" + str(round(100*float(k)/i, 2)) + "%)" print print >>l_output, "" print >>l_output, "Lineages examined:", i print >>l_output, "Lineages overlapped:", j print >>l_output, "Lineages significant:", k, "(" + str(round(100*float(k)/i, 2)) + "%)" # close output file f_output.close() l_output.close() # filter testing results to neurons where the region is contained mode: elif option.mode == "test.regions": # update path to neurons: neuronspath = neuronspath + option.peaks + "/" # define input/output paths: bedpath = neuronspath + option.technique + "/results/" + option.neurons + "/regions/bed/" querypath = cellsetpath + option.query + "/" targetpath = cellsetpath + option.target + "/" hyperpath = comparepath + option.name + "/hyper/" logpath = comparepath + option.name + "/log/" general.pathGenerator(hyperpath) general.pathGenerator(logpath) # load region coordinates per neuron: print print "Loading regions per neuron matrix..." neuron_matrix = dict() for bedfile in general.clean(os.listdir(bedpath), ".DS_Store"): neuron = bedfile.replace(".bed", "") neuron_matrix[neuron] = dict() for bedline in open(bedpath + bedfile).readlines(): chrm, start, stop, region = bedline.strip("\n").split("\t")[:4] neuron_matrix[neuron][region] = [chrm, int(start), int(stop)] # load gene coordinates: print "Loading gene/feature coordinates..." coord_dict = dict() ad = general.build_header_dict(annotationspath + option.reference) inlines = open(annotationspath + option.reference).readlines() inlines.pop(0) for inline in inlines: initems = inline.strip("\n").split("\t") chrm, start, stop, feature, strand, name = initems[ad["chrm"]], initems[ad["start"]], initems[ad["end"]], initems[ad["feature"]], initems[ad["strand"]], initems[ad["name"]] if strand == "+": coord_dict[name] = [chrm, int(start)-option.up, int(start)+option.dn] coord_dict[feature] = [chrm, int(start)-option.up, int(start)+option.dn] elif strand == "-": coord_dict[name] = [chrm, int(stop)-option.dn, int(stop)+option.up] coord_dict[feature] = [chrm, int(stop)-option.dn, int(stop)+option.up] # prepare output file: f_output = open(hyperpath + "mapcells_test_" + option.name + "_" + option.cells + "_regions.txt", "w") # define hypergeometric results file: hyperfile = hyperpath + "mapcells_test_" + option.name + "_" + option.cells + "_comparison.txt" # build header dict: hd = general.build_header_dict(hyperfile) # scan hypergeometric results file for cases of overlap: print "Scanning hypergeometric results..." i, j, k = 0, 0, 0 inlines = open(hyperfile).readlines() print >>f_output, inlines.pop(0).strip("\n") queriesMissed, queriesFound, targetsFound = list(), list(), list() for inline in inlines: initems = inline.strip("\n").split("\t") query, target, pvalue = initems[hd["query"]], initems[hd["target"]], initems[hd["pvalue.adj"]] if query in coord_dict: i += 1 qchrm, qstart, qstop = coord_dict[query] hits = False for region in neuron_matrix[target]: j += 1 rchrm, rstart, rstop = neuron_matrix[target][region] if qchrm == rchrm: if qstart <= rstart and qstop >= rstop: k += 1 hits = True if hits: print >>f_output, inline.strip("\n") queriesFound.append(query) targetsFound.append(target) else: queriesMissed.append(query) queriesMissed = sorted(list(set(queriesMissed))) # close output file f_output.close() queriesFound = sorted(list(set(queriesFound))) targetsFound = sorted(list(set(targetsFound))) #pdb.set_trace() print print "Queries found in neurons:", len(queriesFound) print "Neurons found in queries:", len(targetsFound) print "Searches performed:", i print "Searches performed (x Regions):", j print "Searches with hits (x Regions):", k print "Queries with coordinates and found:", ", ".join(queriesFound) print "Queries missed (no coordinates):", len(queriesMissed) print "\n".join(queriesMissed) print # false discovery rate mode: elif option.mode == "test.fdr": # update path to neurons: neuronspath = neuronspath + option.peaks + "/" # define input/output paths: bedpath = neuronspath + option.technique + "/results/" + option.neurons + "/regions/bed/" querypath = cellsetpath + option.query + "/" targetpath = cellsetpath + option.target + "/" hyperpath = comparepath + option.name + "/hyper/" logpath = comparepath + option.name + "/log/" general.pathGenerator(hyperpath) general.pathGenerator(logpath) # load region coordinates per neuron: print print "Loading regions per neuron matrix..." neuron_matrix = dict() for bedfile in general.clean(os.listdir(bedpath), ".DS_Store"): neuron = bedfile.replace(".bed", "") neuron_matrix[neuron] = dict() for bedline in open(bedpath + bedfile).readlines(): chrm, start, stop, region = bedline.strip("\n").split("\t")[:4] neuron_matrix[neuron][region] = [chrm, int(start), int(stop)] # load gene coordinates: print "Loading gene/feature coordinates..." coord_dict = dict() ad = general.build_header_dict(annotationspath + option.reference) inlines = open(annotationspath + option.reference).readlines() inlines.pop(0) for inline in inlines: initems = inline.strip("\n").split("\t") chrm, start, stop, feature, strand, name = initems[ad["chrm"]], initems[ad["start"]], initems[ad["end"]], initems[ad["feature"]], initems[ad["strand"]], initems[ad["name"]] if strand == "+": coord_dict[name] = [chrm, int(start)-option.up, int(start)+option.dn] coord_dict[feature] = [chrm, int(start)-option.up, int(start)+option.dn] elif strand == "-": coord_dict[name] = [chrm, int(stop)-option.dn, int(stop)+option.up] coord_dict[feature] = [chrm, int(stop)-option.dn, int(stop)+option.up] # prepare output file: f_output = open(hyperpath + "mapcells_test_" + option.name + "_" + option.cells + "_fdr.txt", "w") # define hypergeometric results file: hyperfile = hyperpath + "mapcells_test_" + option.name + "_" + option.cells + "_comparison.txt" # build header dict: hd = general.build_header_dict(hyperfile) # load positive hypergeometric results: print "Loading hypergeometric results (hits)..." inlines = open(hyperfile).readlines() inlines.pop(0) hyper_matrix, hyperTargets = dict(), list() for inline in inlines: initems = inline.strip("\n").split("\t") query, target, pvalue = initems[hd["query"]], initems[hd["target"]], initems[hd["pvalue.adj"]] if not query in hyper_matrix: hyper_matrix[query] = dict() hyper_matrix[query][target] = float(pvalue) if not target in hyperTargets: hyperTargets.append(target) # select the best matching neuron for each query: match_matrix = dict() print "Scanning hypergeometric results per query..." i, j, k = 0, 0, 0 positiveRate, negativeRate, matchTargets = list(), list(), list() for query in hyper_matrix: if query in coord_dict: i += 1 qchrm, qstart, qstop = coord_dict[query] for target in neuron_matrix: j += 1 hits = 0 for region in neuron_matrix[target]: rchrm, rstart, rstop = neuron_matrix[target][region] if qchrm == rchrm: if qstart <= rstart and qstop >= rstop: hits += 1 if hits != 0: if not query in match_matrix: match_matrix[query] = dict() match_matrix[query][target] = float(hits)/len(neuron_matrix[target]) if not target in matchTargets: matchTargets.append(target) #print hyper_matrix.keys() #print match_matrix.keys() #print query #print target #print match_matrix[query][target] #pdb.set_trace() # Test A """ print print "Testing positive and negative hits..." positiveRate, negativeRate, unknownRate = list(), list(), list() for query in match_matrix: hits = general.valuesort(match_matrix[query]) hits.reverse() target = hits[0] if query in hyper_matrix and target in hyper_matrix[query]: positiveRate.append(query + ":" + target) print "+", query, target, match_matrix[query][target] else: print "-", query, target, match_matrix[query][target] negativeRate.append(query + ":" + target) if query in hyper_matrix: unknownRate.append(query + ":" + target) print "True Positive Rate:", len(positiveRate), 100*float(len(positiveRate))/(len(positiveRate)+len(negativeRate)) print "False Positive Rate:", len(negativeRate), 100*float(len(negativeRate))/(len(positiveRate)+len(negativeRate)) print "False Unknown Rate:", len(unknownRate), 100*float(len(unknownRate))/(len(positiveRate)+len(unknownRate)) print """ # Test B """ print print "Testing positive and negative hits..." positiveRate, negativeRate, unknownRate = list(), list(), list() for query in hyper_matrix: hits = 0 for target in general.valuesort(hyper_matrix[query]): if query in match_matrix and target in match_matrix[query]: hits += 1 if hits != 0: positiveRate.append(query + ":" + target) else: negativeRate.append(query + ":" + target) if query in match_matrix: unknownRate.append(query + ":" + target) print "True Positive Rate:", len(positiveRate), 100*float(len(positiveRate))/(len(positiveRate)+len(negativeRate)) print "False Positive Rate:", len(negativeRate), 100*float(len(negativeRate))/(len(positiveRate)+len(negativeRate)) print "False Unknown Rate:", len(unknownRate), 100*float(len(unknownRate))/(len(positiveRate)+len(unknownRate)) print """ # Test C print print "Testing positive and negative hits..." positiveRate, negativeRate, unknownRate = list(), list(), list() for query in match_matrix: hits = 0 for target in general.valuesort(match_matrix[query]): if query in hyper_matrix and target in hyper_matrix[query]: hits += 1 if hits != 0: positiveRate.append(query + ":" + target) else: negativeRate.append(query + ":" + target) if query in hyper_matrix: unknownRate.append(query + ":" + target) print "Genes enriched in SOM neurons:", len(hyper_matrix) print "Genes with promoter in SOM neurons:", len(match_matrix) print "Neurons enriched in gene expression:", len(hyperTargets) print "Neurons with gene promoter matches:", len(matchTargets) print "True Positive Rate:", len(positiveRate), 100*float(len(positiveRate))/(len(positiveRate)+len(negativeRate)) print "False Positive Rate:", len(negativeRate), 100*float(len(negativeRate))/(len(positiveRate)+len(negativeRate)) print "False Unknown Rate (not enriched in any neuron):", len(unknownRate), 100*float(len(unknownRate))/(len(positiveRate)+len(unknownRate)) print # scan each gene for cellular overlap in neurons where the promoter is found: """ print "Scanning positive and negative hits..." i, j, k = 0, 0, 0 positiveRate, negativeRate = list(), list() for query in hyper_matrix: if query in coord_dict: i += 1 qchrm, qstart, qstop = coord_dict[query] for target in neuron_matrix: j += 1 hits = 0 for region in neuron_matrix[target]: rchrm, rstart, rstop = neuron_matrix[target][region] if qchrm == rchrm: if qstart <= rstart and qstop >= rstop: hits += 1 if hits != 0: if target in hyper_matrix[query]: positiveRate.append(query + ":" + target) else: negativeRate.append(query + ":" + target) """ #print >>f_output, inlines.pop(0).strip("\n") # close output file f_output.close() #print #print "Queries found in neurons:", len(queriesFound) #print "Neurons found in queries:", len(targetsFound) #print "Searches performed:", i #print "Searches performed (x Regions):", j #print "Searches with hits (x Regions):", k #print "Queries with coordinates and found:", ", ".join(queriesFound) #print "Queries missed (no coordinates):", len(queriesMissed) #print # annotate tissue composition in neurons: elif option.mode == "test.composition": # update path to neurons: neuronspath = neuronspath + option.peaks + "/" # define input/output paths: bedpath = neuronspath + option.technique + "/results/" + option.neurons + "/regions/bed/" codespath = neuronspath + option.technique + "/results/" + option.neurons + "/codes/" summarypath = neuronspath + option.technique + "/results/" + option.neurons + "/summary/" querypath = cellsetpath + option.query + "/" targetpath = cellsetpath + option.target + "/" compositionpath = comparepath + option.name + "/composition/" hyperpath = comparepath + option.name + "/hyper/" logpath = comparepath + option.name + "/log/" general.pathGenerator(compositionpath) general.pathGenerator(hyperpath) general.pathGenerator(logpath) # load codes: inlines = open(codespath + option.neurons + ".codes").readlines() codes = inlines.pop(0).strip().split("\t") codeDict = dict() for inline in inlines: initems = inline.strip().split("\t") neuron = initems.pop(0) codeDict["neuron" + neuron] = initems # load cellular expression data: print print "Loading cellular annotation..." annotationDict = general.build2(expressionpath + option.expression, id_column="cell", value_columns=["specific.tissue", "general.tissue", "class.tissue", "match.tissue"], skip=True, verbose=False) # load tissue annotation matrixes: print "Loading tissue annotations..." #specificCounts = general.build2(expressionpath + option.infile, i="specific.tissue" , mode="values", skip=True, counter=True) #generalCounts = general.build2(expressionpath + option.infile, i="general.tissue", mode="values", skip=True, counter=True) #classCounts = general.build2(expressionpath + option.infile, i="class.tissue", mode="values", skip=True, counter=True) totalCells = general.build2(expressionpath + option.expression, i="cell", x="specific.tissue", mode="values", skip=True) totalCells = sorted(totalCells.keys()) # gather tissue labels specificTissues, generalTissues, classTissues, matchTissues = list(), list(), list(), list() for cell in annotationDict: if not annotationDict[cell]["specific.tissue"] in specificTissues: specificTissues.append(annotationDict[cell]["specific.tissue"]) if not annotationDict[cell]["general.tissue"] in generalTissues: generalTissues.append(annotationDict[cell]["general.tissue"]) if not annotationDict[cell]["class.tissue"] in classTissues: classTissues.append(annotationDict[cell]["class.tissue"]) if not annotationDict[cell]["match.tissue"] in matchTissues: matchTissues.append(annotationDict[cell]["match.tissue"]) # load cells identified in each neuron: print print "Loading cell identities per neuron..." neuronDict = general.build2(summarypath + "mapneurons_summary.txt", id_column="neuron", value_columns=["class.ids"]) # load cells counted in each neuron: print "Loading cell counts per neuron..." countMatrix, binaryMatrix = dict(), dict() for neuron in os.listdir(bedpath): inlines = open(bedpath + neuron).readlines() neuron = neuron.replace(".bed", "") countMatrix[neuron] = dict() binaryMatrix[neuron] = dict() for inline in inlines: chrm, start, end, feature, score, strand, cell, regions = inline.strip().split("\t") if not cell in countMatrix[neuron]: countMatrix[neuron][cell] = 0 binaryMatrix[neuron][cell] = 1 countMatrix[neuron][cell] += 1 # generate tissue class scores: cellList = list() cellMatrix, specificMatrix, generalMatrix, classMatrix, matchMatrix = dict(), dict(), dict(), dict(), dict() for neuron in neuronDict: if not neuron in cellMatrix: cellMatrix[neuron] = dict() specificMatrix[neuron] = dict() generalMatrix[neuron] = dict() classMatrix[neuron] = dict() matchMatrix[neuron] = dict() for cell in neuronDict[neuron]["class.ids"].split(","): specificTissue, generalTissue, classTissue, matchTissue = annotationDict[cell]["specific.tissue"], annotationDict[cell]["general.tissue"], annotationDict[cell]["class.tissue"], annotationDict[cell]["match.tissue"] if not cell in cellMatrix[neuron]: cellMatrix[neuron][cell] = 0 if not specificTissue in specificMatrix[neuron]: specificMatrix[neuron][specificTissue] = 0 if not generalTissue in generalMatrix[neuron]: generalMatrix[neuron][generalTissue] = 0 if not classTissue in classMatrix[neuron]: classMatrix[neuron][classTissue] = 0 if not matchTissue in matchMatrix[neuron]: matchMatrix[neuron][matchTissue] = 0 cellList.append(cell) cellMatrix[neuron][cell] += binaryMatrix[neuron][cell] specificMatrix[neuron][specificTissue] += binaryMatrix[neuron][cell] generalMatrix[neuron][generalTissue] += binaryMatrix[neuron][cell] classMatrix[neuron][classTissue] += binaryMatrix[neuron][cell] matchMatrix[neuron][matchTissue] += binaryMatrix[neuron][cell] cellList = sorted(list(set(cellList))) # Note: The above dictionaries record how many of the cell (ids) # in a given neuron have correspond to a given tissue. # prepare class tallies for normalization: specificTallies, generalTallies, classTallies, matchTallies = dict(), dict(), dict(), dict() for cell in cellList: if not annotationDict[cell]["specific.tissue"] in specificTallies: specificTallies[annotationDict[cell]["specific.tissue"]] = 0 if not annotationDict[cell]["general.tissue"] in generalTallies: generalTallies[annotationDict[cell]["general.tissue"]] = 0 if not annotationDict[cell]["class.tissue"] in classTallies: classTallies[annotationDict[cell]["class.tissue"]] = 0 if not annotationDict[cell]["match.tissue"] in matchTallies: matchTallies[annotationDict[cell]["match.tissue"]] = 0 specificTallies[annotationDict[cell]["specific.tissue"]] += 1 generalTallies[annotationDict[cell]["general.tissue"]] += 1 classTallies[annotationDict[cell]["class.tissue"]] += 1 matchTallies[annotationDict[cell]["match.tissue"]] += 1 # Note: The above tallies record the number of cells (observed, # in neurons) that correspond to each tissue.*** # prepare output files: f_output = open(compositionpath + "mapcells_composition_codes.txt", "w") c_output = open(compositionpath + "mapcells_composition_cellular.txt", "w") s_output = open(compositionpath + "mapcells_composition_specific.txt", "w") g_output = open(compositionpath + "mapcells_composition_general.txt", "w") l_output = open(compositionpath + "mapcells_composition_class.txt", "w") m_output = open(compositionpath + "mapcells_composition_match.txt", "w") # print out headers: print >>f_output, "\t".join(["neuron", "id", "fraction.ids"]) print >>c_output, "\t".join(["neuron", "id", "id.found", "id.cells", "fraction.ids", "fraction.sum", "fraction.max", "fraction.nrm", "pvalue", "pvalue.adj", "score"]) print >>s_output, "\t".join(["neuron", "id", "id.found", "id.cells", "fraction.ids", "fraction.sum", "fraction.max", "fraction.nrm", "pvalue", "pvalue.adj", "score"]) print >>g_output, "\t".join(["neuron", "id", "id.found", "id.cells", "fraction.ids", "fraction.sum", "fraction.max", "fraction.nrm", "pvalue", "pvalue.adj", "score"]) print >>l_output, "\t".join(["neuron", "id", "id.found", "id.cells", "fraction.ids", "fraction.sum", "fraction.max", "fraction.nrm", "pvalue", "pvalue.adj", "score"]) print >>m_output, "\t".join(["neuron", "id", "id.found", "id.cells", "fraction.ids", "fraction.sum", "fraction.max", "fraction.nrm", "pvalue", "pvalue.adj", "score"]) # Note: We will now output the following information: # id.found : is ID found in neuron? # id.cells : number of cells (diversity) that match ID. # fraction.ids: fraction of ID diversity in neuron. # fraction.sum: fraction of cellular diversity in neuron that matches ID. # fraction.rat: fraction of cellular diversity in neuron that matches ID, normalized by the representation of the ID. # fraction.max: fraction of cellular diversity in neuron as normalized by the ID with the highest cellular diversity in neuron. # fraction.nrm: fraction of cellular diversity in neuron as normalized by the total number of cells with said ID. # determine missed tissues: print specificMissed, generalMissed, classMissed, matchMissed = set(specificTissues).difference(set(specificTallies.keys())), set(generalTissues).difference(set(generalTallies.keys())), set(classTissues).difference(set(classTallies.keys())), set(matchTissues).difference(set(matchTallies.keys())) print "Specific tissues not found:", str(len(specificMissed)) + " (" + str(len(specificTissues)) + ") ; " + ",".join(sorted(specificMissed)) print "General tissues not found:", str(len(generalMissed)) + " (" + str(len(generalTissues)) + ") ; " + ",".join(sorted(generalMissed)) print "Class tissues not found:", str(len(classMissed)) + " (" + str(len(classTissues)) + ") ; " + ",".join(sorted(classMissed)) print "Match tissues not found:", str(len(matchMissed)) + " (" + str(len(matchTissues)) + ") ; " + ",".join(sorted(matchMissed)) print # export the fractions: print "Exporting representation per neuron..." for neuron in sorted(neuronDict.keys()): if neuron in codeDict: # export factor signals: index = 0 for code in codes: print >>f_output, "\t".join(map(str, [neuron, code, codeDict[neuron][index]])) index += 1 # export cell counts: for cell in cellList: adjust = len(neuronDict.keys())*len(cellList) types = len(cellMatrix[neuron].keys()) total = sum(cellMatrix[neuron].values()) maxxx = max(cellMatrix[neuron].values()) if cell in cellMatrix[neuron]: count = float(cellMatrix[neuron][cell]) index = 1 else: count = 0 index = 0 print >>c_output, "\t".join(map(str, [neuron, cell, index, count, float(index)/types, float(count)/total, float(count)/maxxx, 1, 1, 1, 0])) # export specific tissue enrichment: for specificTissue in sorted(specificTallies.keys()): types = len(specificMatrix[neuron].keys()) total = sum(specificMatrix[neuron].values()) maxxx = max(specificMatrix[neuron].values()) tally = specificTallies[specificTissue] if specificTissue in specificMatrix[neuron]: count = float(specificMatrix[neuron][specificTissue]) index = 1 else: count = 0 index = 0 adjust = len(neuronDict.keys())*len(specificTallies.keys()) universe = sum(specificTallies.values()) pvalue = hyper.fishers(count, universe, total, tally, adjust=1, method="right") adjPvalue = hyper.limit(pvalue*adjust) score = hyper.directional(count, universe, total, tally, adjust=adjust) print >>s_output, "\t".join(map(str, [neuron, specificTissue, index, count, float(index)/types, float(count)/total, float(count)/maxxx, float(count)/tally, pvalue, adjPvalue, score])) # export general tissue enrichment: for generalTissue in sorted(generalTallies.keys()): types = len(generalMatrix[neuron].keys()) total = sum(generalMatrix[neuron].values()) maxxx = max(generalMatrix[neuron].values()) tally = generalTallies[generalTissue] if generalTissue in generalMatrix[neuron]: count = float(generalMatrix[neuron][generalTissue]) index = 1 else: count = 0 index = 0 adjust = len(neuronDict.keys())*len(generalTallies.keys()) universe = sum(generalTallies.values()) pvalue = hyper.fishers(count, universe, total, tally, adjust=1, method="right") adjPvalue = hyper.limit(pvalue*adjust) score = hyper.directional(count, universe, total, tally, adjust=adjust) print >>g_output, "\t".join(map(str, [neuron, generalTissue, index, count, float(index)/types, float(count)/total, float(count)/maxxx, float(count)/tally, pvalue, adjPvalue, score])) # export class tissue enrichment: for classTissue in sorted(classTallies.keys()): types = len(classMatrix[neuron].keys()) total = sum(classMatrix[neuron].values()) maxxx = max(classMatrix[neuron].values()) tally = classTallies[classTissue] if classTissue in classMatrix[neuron]: count = float(classMatrix[neuron][classTissue]) index = 1 else: count = 0 index = 0 adjust = len(neuronDict.keys())*len(classTallies.keys()) universe = sum(classTallies.values()) pvalue = hyper.fishers(count, universe, total, tally, adjust=1, method="right") adjPvalue = hyper.limit(pvalue*adjust) score = hyper.directional(count, universe, total, tally, adjust=adjust) print >>l_output, "\t".join(map(str, [neuron, classTissue, index, count, float(index)/types, float(count)/total, float(count)/maxxx, float(count)/tally, pvalue, adjPvalue, score])) # export match tissue enrichment: for matchTissue in sorted(matchTallies.keys()): types = len(matchMatrix[neuron].keys()) total = sum(matchMatrix[neuron].values()) maxxx = max(matchMatrix[neuron].values()) tally = matchTallies[matchTissue] if matchTissue in matchMatrix[neuron]: count = float(matchMatrix[neuron][matchTissue]) index = 1 else: count = 0 index = 0 adjust = len(neuronDict.keys())*len(matchTallies.keys()) universe = sum(matchTallies.values()) pvalue = hyper.fishers(count, universe, total, tally, adjust=1, method="right") adjPvalue = hyper.limit(pvalue*adjust) score = hyper.directional(count, universe, total, tally, adjust=adjust) print >>m_output, "\t".join(map(str, [neuron, matchTissue, index, count, float(index)/types, float(count)/total, float(count)/maxxx, float(count)/tally, pvalue, adjPvalue, score])) # close outputs: f_output.close() c_output.close() s_output.close() g_output.close() l_output.close() m_output.close() print print "Combining cell and factor (mix) information.." # load input factor information: factorDict = general.build2(compositionpath + "mapcells_composition_codes.txt", i="neuron", j="id", x="fraction.ids", mode="matrix") # define input cell/tissue files: infiles = ["mapcells_composition_cellular.txt", "mapcells_composition_specific.txt", "mapcells_composition_general.txt", "mapcells_composition_class.txt", "mapcells_composition_match.txt"] for infile in infiles: print "Processing:", infile # initiate neuron data extraction: f_output = open(compositionpath + infile.replace(".txt", ".mix"), "w") inheader = open(compositionpath + infile).readline().strip().split("\t") inlines = open(compositionpath + infile).readlines() print >>f_output, inlines.pop(0) # append factor information to neuron data: processed = list() for inline in inlines: neuron, label = inline.strip().split("\t")[:2] if not neuron in processed: processed.append(neuron) for factor in factorDict[neuron]: output = list() for column in inheader: if column == "neuron": output.append(neuron) elif column == "id": output.append(factor) elif column in ["pvalue", "pvalue.adj"]: output.append("1") else: output.append(factorDict[neuron][factor]) print >>f_output, "\t".join(output) print >>f_output, inline.strip() # close outputs: f_output.close() print # examine co-association correspondence between genes: elif option.mode == "test.similarity": # update path to neurons: neuronspath = neuronspath + option.peaks + "/" # define input/output paths: bedpath = neuronspath + option.technique + "/results/" + option.neurons + "/regions/bed/" querypath = cellsetpath + option.query + "/" targetpath = cellsetpath + option.target + "/" hyperpath = comparepath + option.name + "/hyper/" logpath = comparepath + option.name + "/log/" general.pathGenerator(hyperpath) general.pathGenerator(logpath) # load query cells: print print "Loading query cells..." query_matrix = dict() for query in os.listdir(querypath): queryCells = general.clean(open(querypath + query).read().split("\n"), "") query_matrix[query] = queryCells #print query, queryCells print "Generating merged region file..." #queryfile = hyperpath + "query.bed" #regionsfile = hyperpath + "regions.bed" #overlapfile = hyperpath + "overlap.bed" joint = " " + bedpath command = "cat " + bedpath + joint.join(os.listdir(bedpath)) + " > " + regionsfile os.system(command) # load gene coordinates: print "Loading gene/feature coordinates..." coord_dict = dict() ad = general.build_header_dict(annotationspath + option.reference) inlines = open(annotationspath + option.reference).readlines() inlines.pop(0) for inline in inlines: initems = inline.strip("\n").split("\t") chrm, start, stop, feature, strand, name = initems[ad["#chrm"]], initems[ad["start"]], initems[ad["end"]], initems[ad["feature"]], initems[ad["strand"]], initems[ad["name"]] if strand == "+": start, end = int(start)-option.up, int(start)+option.dn elif strand == "-": start, end = int(stop)-option.dn, int(stop)+option.up for query in query_matrix: if query == feature or query == name: f_output = open(queryfile, "w") print >>f_output, "\t".join(map(str, [chrm, start, end, feature, 0, strand])) f_output.close() overlaps = list() command = "intersectBed -u -a " + regionsfile + " -b " + queryfile + " > " + overlapfile os.system(command) for inline in open(overlapfile).readlines(): overlaps.append(inline.strip()) print query, len(overlaps) if len(overlaps) > 0: pdb.set_trace() break # tree building mode: elif option.mode == "tree.build": # build cell-expression matrix: print print "Loading cellular expression..." quantitation_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="fraction.expression") # store cell-parent relationships: print "Loading cell-parent relationships..." cell_dict, parent_dict, pedigreeCells = relationshipBuilder(pedigreefile=option.pedigree, path=extraspath, mechanism="simple") print "Pedigree cells:", len(pedigreeCells) print "Tracked cells:", len(trackedCells) # trim tree: cell_tree, parent_tree = dict(), dict() for parent in parent_dict: for cell in parent_dict[parent]: ascendants = ascendantsCollector(cell, parent_dict, cell_dict, ascendants=list()) process = False if option.lineages == "complete": process = True elif parent in trackedCells and cell in trackedCells: process = True elif option.ascendants != "OFF" and len(ascendants) < int(option.ascendants): process = True if process: if not parent in parent_tree: parent_tree[parent] = list() parent_tree[parent].append(cell) cell_tree[cell] = parent tree = treeBuilder(parent_tree, cell_tree) #print sorted(tree.keys()) #print tree["P0"] #pdb.set_trace() f_output = open(cellspath + "mapcells_tree_" + option.name + ".json", "w") json.dump(tree["P0"], f_output) f_output.close() # tree coloring mode: elif option.mode == "tree.color": # build cell-expression matrix: print print "Loading cellular expression..." quantitation_matrix, expression_matrix, tracking_matrix, trackedCells = expressionBuilder(expressionfile=option.expression, path=expressionpath, cutoff=option.fraction, minimum=option.minimum, metric="fraction.expression") # store cell-parent relationships: print "Loading cell-parent relationships..." cell_dict, parent_dict, pedigreeCells = relationshipBuilder(pedigreefile=option.pedigree, path=extraspath, mechanism="simple") print "Pedigree cells:", len(pedigreeCells) print "Tracked cells:", len(trackedCells) # trim tree: cell_tree, parent_tree = dict(), dict() for parent in parent_dict: for cell in parent_dict[parent]: ascendants = ascendantsCollector(cell, parent_dict, cell_dict, ascendants=list()) process = False if option.lineages == "complete": process = True elif parent in trackedCells and cell in trackedCells: process = True elif option.ascendants != "OFF" and len(ascendants) < int(option.ascendants): process = True if process: if not parent in parent_tree: parent_tree[parent] = list() parent_tree[parent].append(cell) cell_tree[cell] = parent # build header dict: hd = general.build_header_dict(option.infile) # load input lines: pvalue_matrix, cells_matrix = dict(), dict() inlines = open(option.infile).readlines() inlines.pop(0) for inline in inlines: initems = inline.strip("\n").split("\t") query, target, pvalue, cells = initems[hd["query"]], initems[hd["target"]], initems[hd["pvalue"]], initems[hd["cells"]] if not query in pvalue_matrix: pvalue_matrix[query] = dict() cells_matrix[query] = dict() pvalue_matrix[query][target] = float(pvalue) cells_matrix[query][target] = cells.split(",") # scan inputs, selecting the targets of highest enrichment and generating color tree for each: k = 0 print print "Scanning queries..." for query in cells_matrix: target = general.valuesort(pvalue_matrix[query])[0] cells = cells_matrix[query][target] print query, target, pvalue_matrix[query][target], len(cells) tree = treeBuilder(parent_tree, cell_tree, highlights=cells) #print sorted(tree.keys()) #print tree["P0"] #pdb.set_trace() f_output = open(cellspath + "mapcells_tree_" + option.name + "_" + query + "-" + target + ".json", "w") json.dump(tree["P0"], f_output) f_output.close() k += 1 print print "Queries processed:", k print if __name__ == "__main__": main() print "Completed:", time.asctime(time.localtime()) #python mapCells.py --path ~/meTRN --mode import --infile murray_2012_supplemental_dataset_1_per_gene.txt --name murray # Retired! #python mapCells.py --path ~/meTRN --mode import --infile waterston_avgExpression.csv --name waterston --measure max.expression #python mapCells.py --path ~/meTRN --mode import --infile waterston_avgExpression.csv --name waterston --measure avg.expression #python mapCells.py --path ~/meTRN --mode check.status --peaks optimal_standard_factor_sx_rawraw --name waterston --measure avg.expression #python mapCells.py --path ~/meTRN --mode check.status --peaks optimal_standard_factor_ex_rawraw --name waterston --measure avg.expression #python mapCells.py --path ~/meTRN/ --mode build.lineages --pedigree waterston_cell_pedigree.csv --expression mapcells_avgExp_waterston_expression_tracked --name waterston.tracked --method builder --lineages tracked --descendants OFF --ascendants OFF --limit 10000 #python mapCells.py --path ~/meTRN/ --mode build.lineages --pedigree waterston_cell_pedigree.csv --expression mapcells_avgExp_waterston_expression_tracked --name waterston.tracked --method builder --lineages complete --descendants OFF --ascendants OFF --limit 10000 #python mapCells.py --path ~/meTRN/ --mode test.lineages --pedigree waterston_cell_pedigree.csv --expression mapcells_avgExp_waterston_expression_tracked --name waterston.tracked --method builder --lineages tracked --descendants OFF --ascendants OFF --limit 10000 #python mapCells.py --path ~/meTRN/ --mode test.lineages --pedigree waterston_cell_pedigree.csv --expression mapcells_avgExp_waterston_expression_assayed --name waterston.assayed --method builder --lineages tracked --descendants OFF --ascendants OFF --limit 10000 #python mapCells.py --path ~/meTRN --organism ce --mode robust --infile waterston_avgExpression.csv

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null

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1

0

1

b86fc82da8dc94ff37ad24a384c231a1a48f881c

7,780

py

Python

IR_Extraction.py

Kazuhito00/yolo2_onnx

95c5e2063071d610ec8e98963f3639e0b25efb59

[ "MIT" ]

15

2018-07-02T19:11:09.000Z

2022-03-31T07:12:53.000Z

IR_Extraction.py

Kazuhito00/yolo2_onnx

95c5e2063071d610ec8e98963f3639e0b25efb59

[ "MIT" ]

null

IR_Extraction.py

Kazuhito00/yolo2_onnx

95c5e2063071d610ec8e98963f3639e0b25efb59

[ "MIT" ]

9

2018-05-08T01:58:53.000Z

2022-01-28T06:36:02.000Z

from Onnx import make_dir, OnnxImportExport import subprocess import pickle import os import numpy as np import time def generate_svg(modelName, marked_nodes=[]): """ generate SVG figure from existed ONNX file """ if marked_nodes ==[]: addfilenamestr = "" add_command_str = "" else: addfilenamestr = "_marked" marked_str = '_'.join([str(e) for e in marked_nodes]) add_command_str = " --marked 1 --marked_list {}".format(marked_str) onnxfilepath = "onnx/{}.onnx".format(modelName) dotfilepath = "dot/{}{}.dot".format(modelName,addfilenamestr) svgfilepath = "svg/{}{}.svg".format(modelName,addfilenamestr) # check if onnx file exist if not os.path.isfile(os.getcwd()+"/"+onnxfilepath): print('generate_svg Error! Onnx file not exist!') return else: make_dir("dot") make_dir("svg") subprocess.call("python net_drawer.py --input {} --output {} --embed_docstring {}".format(onnxfilepath,dotfilepath,add_command_str), shell=True) # onnx -> dot subprocess.call("dot -Tsvg {} -o {}".format(dotfilepath,svgfilepath), shell=True)# dot -> svg print('generate_svg ..end') return svgfilepath def get_init_shape_dict(rep): """ Extract Shape of Initial Input Object e.g. if %2[FLOAT, 64x3x3x3] %3[FLOAT, 64] then return {u'2':(64,3,3,3),u'3':(64,)} """ d = {} if hasattr(rep, 'input_dict'): for key in rep.input_dict: tensor = rep.input_dict[key] shape = np.array(tensor.shape, dtype=int) d.update({key:shape}) return d elif hasattr(rep, 'predict_net'): for k in rep.predict_net.tensor_dict.keys(): tensor = rep.predict_net.tensor_dict[k] shape = np.array(tensor.shape.as_list(),dtype=float).astype(int) d.update({k: shape}) return d else: print ("rep Error! check your onnx version, it might not support IR_Extraction operation!") return d def get_output_shape_of_node(node, shape_dict, backend, device = "CPU"):# or "CUDA:0" """ generate output_shape of a NODE """ out_idx = node.output[0] input_list = node.input # e.g. ['1', '2'] inps = [] for inp_idx in input_list: inp_shape = shape_dict[inp_idx] rand_inp = np.random.random(size=inp_shape).astype('float16') inps.append(rand_inp) try: out = backend.run_node(node=node, inputs=inps, device=device) out_shape = out[0].shape except: out_shape = shape_dict[input_list[0]] print("Op: [{}] run_node error! return inp_shape as out_shape".format(node.op_type)) return out_shape, out_idx def get_overall_shape_dict(model, init_shape_dict, backend): """ generate output_shape of a MODEL GRAPH """ shape_dict = init_shape_dict.copy() for i, node in enumerate(model.graph.node): st=time.time() out_shape, out_idx = get_output_shape_of_node(node, shape_dict, backend) shape_dict.update({out_idx:out_shape}) print("out_shape: {} for Obj[{}], node [{}][{}]...{:.2f} sec".format(out_shape, out_idx, i, node.op_type,time.time()-st)) return shape_dict def get_graph_order(model): """ Find Edges (each link) in MODEL GRAPH """ Node2nextEntity = {} Entity2nextNode = {} for Node_idx, node in enumerate(model.graph.node): # node input for Entity_idx in node.input: if not Entity_idx in Entity2nextNode.keys(): Entity2nextNode.update({Entity_idx:Node_idx}) # node output for Entity_idx in node.output: if not Node_idx in Node2nextEntity.keys(): Node2nextEntity.update({Node_idx:Entity_idx}) return Node2nextEntity, Entity2nextNode def get_kernel_shape_dict(model, overall_shape_dict): """ Get Input/Output/Kernel Shape for Conv in MODEL GRAPH """ conv_d = {} for i, node in enumerate(model.graph.node): if node.op_type == 'Conv': for attr in node.attribute: if attr.name == "kernel_shape": kernel_shape = np.array(attr.ints, dtype=int) break inp_idx = node.input[0] out_idx = node.output[0] inp_shape = overall_shape_dict[inp_idx] out_shape = overall_shape_dict[out_idx] conv_d.update({i:(inp_idx, out_idx, inp_shape, out_shape, kernel_shape)}) print("for node [{}][{}]:\ninp_shape: {} from obj[{}], \nout_shape: {} from obj[{}], \nkernel_shape: {} \n" .format(i, node.op_type, inp_shape, inp_idx, out_shape, out_idx, kernel_shape )) return conv_d def calculate_num_param_n_num_flops(conv_d): """ calculate num_param and num_flops from conv_d """ n_param = 0 n_flops = 0 for k in conv_d: #i:(inp_idx, out_idx, inp_shape, out_shape, kernel_shape) inp_shape, out_shape, kernel_shape = conv_d[k][2],conv_d[k][3],conv_d[k][4] h,w,c,n,H,W = kernel_shape[1], kernel_shape[1], inp_shape[1], out_shape[1], out_shape[2], out_shape[3] n_param += n*(h*w*c+1) n_flops += H*W*n*(h*w*c+1) return n_param, n_flops def find_sequencial_nodes(model, Node2nextEntity, Entity2nextNode, search_target=['Conv', 'Add', 'Relu', 'MaxPool'], if_print = False): """ Search Where is Subgroup """ found_nodes = [] for i, node in enumerate(model.graph.node): if if_print: print("\nnode[{}] ...".format(i)) n_idx = i #init is_fit = True for tar in search_target: try: assert model.graph.node[n_idx].op_type == tar #check this node if if_print: print("node[{}] fit op_type [{}]".format(n_idx, tar)) e_idx = Node2nextEntity[n_idx] #find next Entity n_idx = Entity2nextNode[e_idx] #find next Node #if if_print: print(e_idx,n_idx) except: is_fit = False if if_print: print("node[{}] doesn't fit op_type [{}]".format(n_idx, tar)) break if is_fit: if if_print: print("node[{}] ...fit!".format(i)) found_nodes.append(i) else: if if_print: print("node[{}] ...NOT fit!".format(i)) if if_print: print("\nNode{} fit the matching pattern".format(found_nodes)) return found_nodes def get_permutations(a): """ get all permutations of list a """ import itertools p = [] for r in range(len(a)+1): c = list(itertools.combinations(a,r)) for cc in c: p += list(itertools.permutations(cc)) return p def get_list_of_sequencial_nodes(search_head = ['Conv'], followings = ['Add', 'Relu', 'MaxPool']): """ if search_head = ['Conv'] followings = ['Add', 'Relu', 'MaxPool'] return [['Conv'], ['Conv', 'Add'], ['Conv', 'Relu'], ['Conv', 'MaxPool'], ['Conv', 'Add', 'Relu'], ['Conv', 'Relu', 'Add'], ['Conv', 'Add', 'MaxPool'], ['Conv', 'MaxPool', 'Add'], ['Conv', 'Relu', 'MaxPool'], ['Conv', 'MaxPool', 'Relu'], ['Conv', 'Add', 'Relu', 'MaxPool'], ['Conv', 'Add', 'MaxPool', 'Relu'], ['Conv', 'Relu', 'Add', 'MaxPool'], ['Conv', 'Relu', 'MaxPool', 'Add'], ['Conv', 'MaxPool', 'Add', 'Relu'], ['Conv', 'MaxPool', 'Relu', 'Add']] """ search_targets = [ search_head+list(foll) for foll in get_permutations(followings)] return search_targets

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null

0

1

b870e2ce26d78dfa9746e5e88adb9ed1463fb9fc

944

py

Python

communications/migrations/0002_auto_20190902_1759.py

shriekdj/django-social-network

3654051e334996ee1b0b60f83c4f809a162ddf4a

[ "MIT" ]

368

2019-10-10T18:02:09.000Z

2022-03-31T14:31:39.000Z

communications/migrations/0002_auto_20190902_1759.py

shriekdj/django-social-network

3654051e334996ee1b0b60f83c4f809a162ddf4a

[ "MIT" ]

19

2020-05-09T19:10:29.000Z

2022-03-04T18:22:51.000Z

communications/migrations/0002_auto_20190902_1759.py

shriekdj/django-social-network

3654051e334996ee1b0b60f83c4f809a162ddf4a

[ "MIT" ]

140

2019-10-10T18:01:59.000Z

2022-03-14T09:37:39.000Z

# Generated by Django 2.2.4 on 2019-09-02 11:59 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), ('communications', '0001_initial'), ] operations = [ migrations.AddField( model_name='message', name='author', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, related_name='author_messages', to=settings.AUTH_USER_MODEL), preserve_default=False, ), migrations.AddField( model_name='message', name='friend', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, related_name='friend_messages', to=settings.AUTH_USER_MODEL), preserve_default=False, ), ]

32.551724

153

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106

944

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0.052459

0.068852

0.108197

0.544262

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0.252459

0

0.028807

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944

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0

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false

0

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0.272727

0

null

0

1

0

null

0

1

b871aaee0feb9ef1cdc6b28c76ed73a977fed9b3

1,126

py

Python

examples/sht2x.py

kungpfui/python-i2cmod

57d9cc8de372aa38526c3503ceec0d8924665c04

[ "MIT" ]

null

examples/sht2x.py

kungpfui/python-i2cmod

57d9cc8de372aa38526c3503ceec0d8924665c04

[ "MIT" ]

null

examples/sht2x.py

kungpfui/python-i2cmod

57d9cc8de372aa38526c3503ceec0d8924665c04

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Sensirion SHT2x humidity sensor. Drives SHT20, SHT21 and SHT25 humidity and temperature sensors. Sensirion `SHT2x Datasheets <https://www.sensirion.com/en/environmental-sensors/humidity-sensors/humidity-temperature-sensor-sht2x-digital-i2c-accurate/>` """ from i2cmod import SHT2X def example(): with SHT2X() as sensor: print("Identification: 0x{:016X}".format(sensor.serial_number)) for adc_res, reg_value in ( ('12/14', 0x02), (' 8/10', 0x03), ('10/13', 0x82), ('11/11', 0x83)): sensor.user_register = reg_value print("-" * 79) print("Resolution: {}-bit (rh/T)".format(adc_res)) print("Temperature: {:.2f} °C".format(sensor.centigrade)) print("Temperature: {:.2f} °F".format(sensor.fahrenheit)) print("Relative Humidity: {:.2f} % ".format(sensor.humidity)) print("User Register: 0x{:02X}".format(sensor.user_register)) if __name__ == '__main__': example()

34.121212

154

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null

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b87f562e23be6f95cf850092c0a407380227775e

975

py

Python

setup.py

remiolsen/anglerfish

5caabebf5864180e5552b3e40de3650fc5fcabd6

[ "MIT" ]

null

setup.py

remiolsen/anglerfish

5caabebf5864180e5552b3e40de3650fc5fcabd6

[ "MIT" ]

19

2019-10-07T11:14:54.000Z

2022-03-28T12:36:47.000Z

setup.py

remiolsen/anglerfish

5caabebf5864180e5552b3e40de3650fc5fcabd6

[ "MIT" ]

2

2019-05-28T14:15:26.000Z

2022-03-28T09:28:44.000Z

#!/usr/bin/env python from setuptools import setup, find_packages import sys, os setup( name='anglerfish', version='0.4.1', description='Anglerfish, a tool to demultiplex Illumina libraries from ONT data', author='Remi-Andre Olsen', author_email='remi-andre.olsen@scilifelab.se', url='https://github.com/remiolsen/anglerfish', license='MIT', packages = find_packages(), install_requires=[ 'python-levenshtein', 'biopython', 'numpy' ], scripts=['./anglerfish.py'], zip_safe=False, classifiers=[ "Development Status :: 3 - Alpha", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: Healthcare Industry", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: POSIX :: Linux", "Programming Language :: Python", "Topic :: Scientific/Engineering :: Medical Science Apps." ] )

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0

null

0

null

0

1

0

1

b8833e3d9f3a2008bcf62eb119ccbf510334b106

796

py

Python

670/main.py

pauvrepetit/leetcode

6ad093cf543addc4dfa52d72a8e3c0d05a23b771

[ "MIT" ]

null

670/main.py

pauvrepetit/leetcode

6ad093cf543addc4dfa52d72a8e3c0d05a23b771

[ "MIT" ]

null

670/main.py

pauvrepetit/leetcode

6ad093cf543addc4dfa52d72a8e3c0d05a23b771

[ "MIT" ]

null

# 670. 最大交换 # # 20200905 # huao class Solution: def maximumSwap(self, num: int) -> int: return int(self.maximumSwapStr(str(num))) def maximumSwapStr(self, num: str) -> str: s = list(num) if len(s) == 1: return num maxNum = '0' maxLoc = 0 for i in range(len(s))[::-1]: c = s[i] if maxNum < c: maxNum = c maxLoc = i if s[0] == maxNum: return maxNum + self.maximumSwapStr(num[1:]) s[maxLoc] = s[0] s[0] = str(maxNum) ss = "" for i in s: ss += i return ss print(Solution().maximumSwap(100)) print(Solution().maximumSwap(2736)) print(Solution().maximumSwap(9973)) print(Solution().maximumSwap(98638))

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null

0

1

0

null

0

1

b8860c8f4169552c8561caf03f121aafce628fa6

333

py

Python

tests/resources/test_codegen/template.py

come2ry/atcoder-tools

d7ecf5c19427848e6c8f0aaa3c1a8af04c467f1b

[ "MIT" ]

313

2016-12-04T13:25:21.000Z

2022-03-31T09:46:15.000Z

tests/resources/test_codegen/template.py

come2ry/atcoder-tools

d7ecf5c19427848e6c8f0aaa3c1a8af04c467f1b

[ "MIT" ]

232

2016-12-02T22:55:20.000Z

2022-03-27T06:48:02.000Z

tests/resources/test_codegen/template.py

come2ry/atcoder-tools

d7ecf5c19427848e6c8f0aaa3c1a8af04c467f1b

[ "MIT" ]

90

2017-09-23T15:09:48.000Z

2022-03-17T03:13:40.000Z

#!/usr/bin/env python3 import sys def solve(${formal_arguments}): return def main(): def iterate_tokens(): for line in sys.stdin: for word in line.split(): yield word tokens = iterate_tokens() ${input_part} solve(${actual_arguments}) if __name__ == '__main__': main()

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null

0

null

0

null

0

1

0

1

b88ad3cd16814edcf01716b7796117d85426c826

691

py

Python

salamander/mktcalendar.py

cclauss/statarb

a59366f70122c355fc93a2391362a3e8818a290e

[ "Apache-2.0" ]

51

2019-02-01T19:43:37.000Z

2022-03-16T09:07:03.000Z

salamander/mktcalendar.py

cclauss/statarb

a59366f70122c355fc93a2391362a3e8818a290e

[ "Apache-2.0" ]

2

2019-02-23T18:54:22.000Z

2019-11-09T01:30:32.000Z

salamander/mktcalendar.py

cclauss/statarb

a59366f70122c355fc93a2391362a3e8818a290e

[ "Apache-2.0" ]

35

2019-02-08T02:00:31.000Z

2022-03-01T23:17:00.000Z

from pandas.tseries.holiday import AbstractHolidayCalendar, Holiday, nearest_workday, \ USMartinLutherKingJr, USPresidentsDay, GoodFriday, USMemorialDay, \ USLaborDay, USThanksgivingDay from pandas.tseries.offsets import CustomBusinessDay class USTradingCalendar(AbstractHolidayCalendar): rules = [ Holiday('NewYearsDay', month=1, day=1), USMartinLutherKingJr, USPresidentsDay, GoodFriday, USMemorialDay, Holiday('USIndependenceDay', month=7, day=4), USLaborDay, USThanksgivingDay, Holiday('Christmas', month=12, day=25) ] TDay = CustomBusinessDay(calendar=USTradingCalendar())

31.409091

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691

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0.041929

0.071279

0.243187

0

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691

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1

0

false

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0.235294

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null

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null

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