PatrickHaller/the-stack-python-1M · Datasets at Hugging Face

b65874703d066eb9015f31b38395e507f3fa6812

6,069

py

Python

poker/poker_player.py

smokeytheblair/stuff

79a2367542e44510085750334bf95d2b2064769c

[ "MIT" ]

null

poker/poker_player.py

smokeytheblair/stuff

79a2367542e44510085750334bf95d2b2064769c

[ "MIT" ]

null

poker/poker_player.py

smokeytheblair/stuff

79a2367542e44510085750334bf95d2b2064769c

[ "MIT" ]

1

2018-10-03T19:49:29.000Z

import asyncio from enum import Enum import copy from deck_of_cards import VALUES, VALUE_IDS from collections import defaultdict class Action(Enum): FOLD = 0 BET = 1 RAISE = 2 CALL = 3 class PokerHand(Enum): NOTHING = 0 PAIR = 1 TWO_PAIR = 2 THREE_OF_A_KIND = 3 STRAIGHT = 4 FLUSH = 5 FULL_HOUSE = 6 FOUR_OF_A_KIND = 7 STRAIGHT_FLUSH = 8 ROYAL_FLUSH = 9 POKER_HANDS = { PokerHand.NOTHING : 'Nothing', PokerHand.PAIR : 'Pair', PokerHand.TWO_PAIR : 'Two Pair', PokerHand.THREE_OF_A_KIND : 'Three of a Kind', PokerHand.STRAIGHT : 'Straight', PokerHand.FLUSH : 'Flush', PokerHand.FULL_HOUSE : 'Full House', PokerHand.FOUR_OF_A_KIND : 'Four of a Kind', PokerHand.STRAIGHT_FLUSH : 'Straight Flush', PokerHand.ROYAL_FLUSH : 'Royal Flush' } class PokerPlayer: def __init__(self, new_name, new_number): self.name = new_name self.number = new_number self.hand = [] # print(f'{self.name} {self.number} sitting down at the table.') def determine_player_action(self): return Action.FOLD def receive_hand(self, new_hand): self.hand = new_hand def receive_card(self, new_card): self.hand.append(new_card) # print(f'{self.name}{self.number} getting {new_card.to_string()}, have {len(self.hand)} cards.') def return_cards_to_dealer(self): old_hand = copy.deep_copy(self.hand) self.hand.clear() return old_hand def get_name(self): return self.name def get_number(self): return self.number def get_hand(self): return self.hand def hand_to_string(self): hand_as_string = '' for card in self.hand: hand_as_string += f'{card.to_string()} ' return hand_as_string def hand_name_to_string(self, hand_name): return POKER_HANDS[hand_name] async def _is_nothing(self): return 1.0 async def _is_pair(self): confidence = 0.0 value_counts = defaultdict(int) for card in self.hand: value_counts[card.value] += 1 for key,val in value_counts.items(): if val >= 2: confidence = 1.0 return confidence async def _is_two_pair(self): confidence = 0.0 value_counts = {} for card in self.hand: value_counts.setdefault(card.value, 0) value_counts[card.value] += 1 for key,val in value_counts.items(): if val >= 2: confidence += 0.5 if confidence == 1.0: break return confidence async def _is_three_of_a_kind(self): confidence = 0.0 value_counts = defaultdict(int) for card in self.hand: value_counts[card.value] += 1 for key,val in value_counts.items(): if val >= 3: confidence = 1.0 return confidence async def _is_straight(self): confidence = 0.0 highest_confidence = 0.0 value_counts = defaultdict(int) for card in self.hand: value_counts[card.value] += 1 for value in VALUES.values(): if 1 == value_counts[value]: confidence += 0.2 if confidence > highest_confidence: highest_confidence = confidence else: confidence = 0.0 return highest_confidence async def _is_flush(self): confidence = 0.0 suit_counts = defaultdict(int) for card in self.hand: suit_counts[card.suit] += 1 for suit_count in suit_counts.values(): if 5 == suit_count: confidence = 1.0 # break # elif 3 <= suit_count: # confidence = 0.2 * suit_count return confidence async def _is_full_house(self): confidence = 0.0 value_counts = defaultdict(int) for card in self.hand: value_counts[card.value] += 1 pair_found = False for count in value_counts.values(): if 3 == count: confidence += 0.6 if 2 == count and not pair_found: pair_found = True confidence += 0.4 return confidence async def _is_four_of_a_kind(self): confidence = 0.0 value_counts = defaultdict(int) for card in self.hand: value_counts[card.value] += 1 for val in value_counts.values(): if val >= 4: confidence = 1.0 return confidence async def _is_straight_flush(self): confidence = 0.0 if 1.0 == await self._is_flush(): confidence += 0.5 if 1.0 == await self._is_straight(): confidence += 0.5 return confidence async def _is_royal_flush(self): confidence = 0.0 if 1.0 == await self._is_straight_flush(): confidence += 0.8 for card in self.hand: if VALUES[VALUE_IDS.ACE] == card.value: confidence += 0.2 break return confidence async def evaluate_hand(self): hand_results = await asyncio.gather(self._is_nothing(), self._is_pair(), self._is_two_pair(), self._is_three_of_a_kind(), self._is_straight(), self._is_flush(), self._is_full_house(), self._is_four_of_a_kind(), self._is_straight_flush(), self._is_royal_flush(), return_exceptions=True) # print(f'evaluate_hand({self.hand_to_string()} ={hand_results})') highest_hand = PokerHand.NOTHING i = 0 for hand in POKER_HANDS.keys(): if 1.0 == hand_results[i]: highest_hand = hand i += 1 return highest_hand

24.670732

105

0.55594

68aa6d5baf92060be33e0fa39d5e392f8f0573ff

4,333

py

Python

occupant/occupancy/queueing/simulate_queue.py

YangyangFu/MPCPy

c9980cbfe7b5ea21b003c2c0bab800099dccf3f1

[ "BSD-3-Clause-LBNL" ]

96

2017-03-31T09:59:44.000Z

2022-03-23T18:39:37.000Z

occupant/occupancy/queueing/simulate_queue.py

kuzha/MPCPy

9f78aa68236f87d39a50de54978c5064f9cc13c6

[ "BSD-3-Clause-LBNL" ]

150

2017-03-03T17:28:34.000Z

2021-02-24T20:03:24.000Z

occupant/occupancy/queueing/simulate_queue.py

kuzha/MPCPy

9f78aa68236f87d39a50de54978c5064f9cc13c6

[ "BSD-3-Clause-LBNL" ]

32

2017-04-24T18:22:40.000Z

2022-03-29T17:51:20.000Z

from __future__ import division import numpy as np from unique_last import unique_last import matplotlib.pyplot as plt import warnings def simulate_queue(maxtime,lam,mu,nstart,empty_time): # Function for simulate queue system size given the queue parameters # Inputs: maxtime - the time range for simualtion # lam - arrival rate (vector for nonhomogeneous queue), a numpy array # mu - departure rate (vector for nonhomogeneous queue), a numpy array # nstart - the number of customers in the system at the beginning of simulation # empty_time - the time when the queue system is known to have zero customer # First, generate arrivals from homogeneous Poisson process with parameter 1 lam_max = max(lam) # with warnings.catch_warnings(): # warnings.filterwarnings('error') # # try: # lam = lam/lam_max # except Warning as e: # pdb.set_trace() # print('error found:',e) if lam_max == 0: jmptimes = None syssize = None return jmptimes,syssize else: lam = lam/lam_max # print 'lambda is', lam npoints = np.random.poisson(maxtime*lam_max) # Given that the number of arrivals is npoints, the arrivals are distributed uniformly if npoints>0: arrtimes = np.sort(np.random.uniform(0,1,npoints)*maxtime) else: jmptimes = None syssize = None return jmptimes,syssize # the actual non-homogenous arrival rate at the arrival events arrtimes_floor = np.floor(arrtimes).astype(int) lam_vec = lam[arrtimes_floor] # the set of accepted events r = np.random.uniform(0,1,arrtimes.size) if empty_time is None: # if the segment does not contain the empty region E = arrtimes[np.where(r-lam_vec <0)] else: # with warnings.catch_warnings(): # warnings.filterwarnings('error') # # try: E = arrtimes[np.where(np.logical_and(r - lam_vec < 0, arrtimes_floor < empty_time))] # except Warning as e: # print('error found:',e) if not E.size: jmptimes = None syssize = None return jmptimes,syssize # total number of customers E = np.insert(E,0,np.zeros((int(nstart),))) ntotal = E.size keeptimes = np.floor(E).astype(int) servtimes = [] if not empty_time: for i in range(ntotal): serv_sample = simulate_service(keeptimes[i],mu) if serv_sample is None: serv_sample = maxtime-1 servtimes.append(serv_sample) else: trunc_length = empty_time-keeptimes for i in range(ntotal): if trunc_length[i] == 0: raise NameError('Truncation length zero') serv_sample = simulate_service_with_trunc(keeptimes[i],mu,trunc_length[i]) servtimes.append(serv_sample) servtimes_array = np.array(servtimes) deptimes = np.add(keeptimes,servtimes_array) # sort all the arrivals and departures arrate = np.array([np.concatenate((keeptimes,deptimes)),\ np.concatenate((np.ones((ntotal,)),-np.ones((ntotal,))))]) arrate = arrate[0:2,arrate[0,:].argsort()] jmptimes,unique_idx = unique_last(arrate[0,:]) syssize = np.cumsum(arrate[1,:])[unique_idx] # drop the events falling outside of the time window of interest ci = np.where(jmptimes < maxtime) if ci[0].size != 0: jmptimes = jmptimes[ci] syssize = syssize[ci] # # set the last event to be at maxtime # jmptimes = jmptimes.append(maxtime-1) # syssize = syssize.append(syssize[-1]) return jmptimes,syssize def simulate_service(arrtime, mu): mu_used = mu[arrtime:] mu_cum = np.cumsum(mu_used) cdf = 1- np.exp(-mu_cum) r = np.random.uniform(0,1,1) temp = np.where(cdf > r) if temp[0].size!=0: return temp[0][0] else: return None def simulate_service_with_trunc(arrtime,mu,trunc_length): mu_used = mu[arrtime:arrtime+trunc_length] mu_cum = np.cumsum(mu_used) cdf = (1-np.exp(-mu_cum))/(1-np.exp(-mu_cum[-1])) r = np.random.uniform(0,1,1) temp = np.where(cdf > r) try: answer = temp[0][0] return answer except: print('temp = ', temp)

31.398551

92

0.62751

db3cc7daab62544b41d60de49ba582984b0deee3

12,518

py

Python

sentinelone/komand_sentinelone/triggers/get_threats/schema.py

xhennessy-r7/insightconnect-plugins

59268051313d67735b5dd3a30222eccb92aca8e9

[ "MIT" ]

null

sentinelone/komand_sentinelone/triggers/get_threats/schema.py

xhennessy-r7/insightconnect-plugins

59268051313d67735b5dd3a30222eccb92aca8e9

[ "MIT" ]

null

sentinelone/komand_sentinelone/triggers/get_threats/schema.py

xhennessy-r7/insightconnect-plugins

59268051313d67735b5dd3a30222eccb92aca8e9

[ "MIT" ]

null

# GENERATED BY KOMAND SDK - DO NOT EDIT import komand import json class Input: AGENT_IS_ACTIVE = "agent_is_active" CLASSIFICATIONS = "classifications" ENGINES = "engines" FREQUENCY = "frequency" RESOLVED = "resolved" class Output: THREAT = "threat" class GetThreatsInput(komand.Input): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "agent_is_active": { "type": "boolean", "title": "Agent is Active", "description": "Include agents currently connected to the management console", "order": 3 }, "classifications": { "type": "array", "title": "Classifications", "description": "List of classifications to search", "items": { "type": "string" }, "order": 2 }, "engines": { "type": "array", "title": "Engines", "description": "Included engines", "items": { "type": "string" }, "order": 4 }, "frequency": { "type": "integer", "title": "Frequency", "description": "Poll frequency in seconds", "default": 5, "order": 5 }, "resolved": { "type": "boolean", "title": "Resolved", "description": "Include resolved threats", "order": 1 } } } """) def __init__(self): super(self.__class__, self).__init__(self.schema) class GetThreatsOutput(komand.Output): schema = json.loads(""" { "type": "object", "title": "Variables", "properties": { "threat": { "$ref": "#/definitions/data", "title": "Threat", "description": "Threat", "order": 1 } }, "definitions": { "data": { "type": "object", "title": "data", "properties": { "agentComputerName": { "type": "string", "title": "Agent Computer Name", "description": "Agent computer name", "order": 31 }, "agentDomain": { "type": "string", "title": "Agent Domain", "description": "Agent domain", "order": 59 }, "agentId": { "type": "string", "title": "Agent ID", "description": "Agent ID", "order": 15 }, "agentInfected": { "type": "boolean", "title": "Agent Infected", "description": "Agent infected", "order": 60 }, "agentIp": { "type": "string", "title": "Agent IP", "description": "Agent IP", "order": 8 }, "agentIsActive": { "type": "boolean", "title": "Agent is Active", "description": "Agent is Active", "order": 56 }, "agentIsDecommissioned": { "type": "boolean", "title": "Agent is Decommissioned", "description": "Agent is Decommissioned", "order": 48 }, "agentMachineType": { "type": "string", "title": "Agent Machine Type", "description": "Agent machine type", "order": 39 }, "agentNetworkStatus": { "type": "string", "title": "Agent Network Status", "description": "Agent network status", "order": 9 }, "agentOsType": { "type": "string", "title": "Agent OS Type", "description": "Agent OS type", "order": 47 }, "agentVersion": { "type": "string", "title": "Agent Version", "description": "Agent version", "order": 11 }, "annotation": { "type": "string", "title": "Annotation", "description": "Annotation", "order": 50 }, "annotationUrl": { "type": "string", "title": "Annotation URL", "description": "Annotation URL", "order": 55 }, "browserType": { "type": "string", "title": "Browser Type", "description": "Browser type", "order": 25 }, "certId": { "type": "string", "title": "Cert ID", "description": "Cert ID", "order": 21 }, "classification": { "type": "string", "title": "Classification", "description": "Classification", "order": 2 }, "classificationSource": { "type": "string", "title": "Classification Source", "description": "Classification source", "order": 30 }, "classifierName": { "type": "string", "title": "Classifiername", "description": "Classifiername", "order": 38 }, "cloudVerdict": { "type": "string", "title": "Cloud Verdict", "description": "Cloud verdict", "order": 28 }, "collectionId": { "type": "string", "title": "Collection ID", "description": "Collection ID", "order": 23 }, "createdAt": { "type": "string", "title": "Created At", "description": "Created At", "order": 18 }, "createdDate": { "type": "string", "title": "Created Date", "description": "Created date", "order": 45 }, "description": { "type": "string", "title": "Description", "description": "Description", "order": 35 }, "engines": { "type": "array", "title": "Engines", "description": "Engines", "items": { "type": "string" }, "order": 22 }, "fileContentHash": { "type": "string", "title": "File Content Hash", "description": "File content hash", "order": 42 }, "fileCreatedDate": { "type": "string", "title": "File Created Date", "description": "File created date", "order": 6 }, "fileData": { "type": "object", "title": "File Data", "description": "File data", "order": 14 }, "fileDisplayName": { "type": "string", "title": "File Display Name", "description": "File display name", "order": 17 }, "fileExtensionType": { "type": "string", "title": "File Extension Type", "description": "File extension type", "order": 16 }, "fileIsDotNet": { "type": "boolean", "title": "File is Dotnet", "description": "File is dotnet", "order": 41 }, "fileIsExecutable": { "type": "boolean", "title": "File is Executable", "description": "File is executable", "order": 7 }, "fileIsSystem": { "type": "boolean", "title": "File is System", "description": "File is system", "order": 26 }, "fileMaliciousContent": { "type": "boolean", "title": "File Malicious Content", "description": "File malicious content", "order": 62 }, "fileObjectId": { "type": "string", "title": "File Object ID", "description": "File object ID", "order": 4 }, "filePath": { "type": "string", "title": "File Path", "description": "File path", "order": 36 }, "fileSha256": { "type": "string", "title": "File SHA 256", "description": "File SHA 256", "order": 34 }, "fileVerificationType": { "type": "string", "title": "File Verification Type", "description": "File verification type", "order": 51 }, "fromCloud": { "type": "boolean", "title": "From Cloud", "description": "From cloud", "order": 57 }, "fromScan": { "type": "boolean", "title": "From Scan", "description": "From scan", "order": 37 }, "id": { "type": "string", "title": "ID", "description": "ID", "order": 13 }, "inQuarantine": { "type": "boolean", "title": "In Quarantine", "description": "In quarantine", "order": 24 }, "indicators": { "type": "array", "title": "Indicators", "description": "Indicators", "items": { "type": "integer" }, "order": 29 }, "isCertValid": { "type": "boolean", "title": "Is Cert Valid", "description": "Is cert valid", "order": 40 }, "isInteractiveSession": { "type": "boolean", "title": "Is Interactive Session", "description": "Is interactive session", "order": 10 }, "isPartialStory": { "type": "boolean", "title": "Is Partial Story", "description": "Is partial story", "order": 49 }, "maliciousGroupId": { "type": "string", "title": "Malicious Group ID", "description": "Malicious group ID", "order": 54 }, "maliciousProcessArguments": { "type": "string", "title": "Malicious Process Arguments", "description": "Malicious process arguments", "order": 20 }, "markedAsBenign": { "type": "boolean", "title": "Marked as Benign", "description": "Marked as Benign", "order": 27 }, "mitigationActions": { "type": "array", "title": "Mitigation Actions", "description": "Mitigation actions", "items": { "type": "string" }, "order": 5 }, "mitigationMode": { "type": "string", "title": "Mitigation Mode", "description": "Mitigation mode", "order": 58 }, "mitigationReport": { "type": "object", "title": "Mitigation Report", "description": "Mitigation report", "order": 12 }, "mitigationStatus": { "type": "string", "title": "Mitigation Status", "description": "Mitigation status", "order": 32 }, "publisher": { "type": "string", "title": "Publisher", "description": "Publisher", "order": 52 }, "rank": { "type": "integer", "title": "Rank", "description": "Rank", "order": 44 }, "resolved": { "type": "boolean", "title": "Resolved", "description": "Resolved", "order": 33 }, "siteId": { "type": "string", "title": "Site ID", "description": "Site ID", "order": 43 }, "siteName": { "type": "string", "title": "Site Name", "description": "Site name", "order": 3 }, "threatAgentVersion": { "type": "string", "title": "Threat Agent Version", "description": "Threat agent version", "order": 61 }, "threatName": { "type": "string", "title": "Threat Name", "description": "Threat name", "order": 53 }, "updatedAt": { "type": "string", "title": "Updated At", "description": "Updated at", "order": 46 }, "username": { "type": "string", "title": "Username", "description": "Username", "order": 1 }, "whiteningOptions": { "type": "array", "title": "Whitening Options", "description": "Whitening options", "items": { "type": "string" }, "order": 19 } } } } } """) def __init__(self): super(self.__class__, self).__init__(self.schema)

26.024948

84

0.435373

9d1ac75ce31cb7086aaa7434805d15a83b0068c1

4,382

py

Python

pkgs/sdk-pkg/src/genie/libs/sdk/apis/junos/utils.py

jhiggins-NZ/genielibs

0ab2892ef93288313afbdd0eaf68b13018269960

[ "Apache-2.0" ]

null

pkgs/sdk-pkg/src/genie/libs/sdk/apis/junos/utils.py

jhiggins-NZ/genielibs

0ab2892ef93288313afbdd0eaf68b13018269960

[ "Apache-2.0" ]

null

pkgs/sdk-pkg/src/genie/libs/sdk/apis/junos/utils.py

jhiggins-NZ/genielibs

0ab2892ef93288313afbdd0eaf68b13018269960

[ "Apache-2.0" ]

null

"""Utility type functions that do not fit into another category""" # Python import re import logging # Genie from genie.metaparser.util.exceptions import SchemaEmptyParserError from genie.libs.sdk.libs.utils.normalize import GroupKeys from genie.utils import Dq from genie.utils.timeout import Timeout # Pyats from pyats.utils.objects import find, R # unicon from unicon.core.errors import SubCommandFailure log = logging.getLogger(__name__) def verify_ping( device, address, loss_rate=0, count=None, max_time=30, check_interval=10): """ Verify ping loss rate on ip address provided Args: device ('obj'): Device object address ('str'): Address value loss_rate ('int'): Expected loss rate value count ('int'): Count value for ping command max_time (`int`): Max time, default: 30 check_interval (`int`): Check interval, default: 10 Returns: Boolean Raises: None """ timeout = Timeout(max_time, check_interval) while timeout.iterate(): if count: cmd = 'ping {address} count {count}'.format( address=address, count=count ) else: cmd = 'ping {address}'.format( address=address ) try: out = device.parse(cmd) except SchemaEmptyParserError as e: timeout.sleep() continue # Example dictionary structure: # { # "ping": { # "address": "10.189.5.94", # "data-bytes": 56, # "result": [ # { # "bytes": 64, # "from": "10.189.5.94", # "icmp-seq": 0, # "time": "2.261", # "ttl": 62 # }, # ], # "source": "10.189.5.94", # "statistics": { # "loss-rate": 0, # "received": 1, # "round-trip": { # "avg": "2.175", # "max": "2.399", # "min": "1.823", # "stddev": "0.191" # }, # "send": 1 # } # } # } loss_rate_found = Dq(out).get_values("loss-rate", 0) if loss_rate_found == loss_rate: return True return False def verify_file_details_exists(device, root_path, file, max_time=30, check_interval=10): """ Verify file details exists Args: device ('obj'): Device object root_path ('str'): Root path for command file ('str'): File name max_time (`int`): Max time, default: 30 check_interval (`int`): Check interval, default: 10 Returns: Boolean Raises: None """ timeout = Timeout(max_time, check_interval) while timeout.iterate(): out = None try: out = device.parse('file list {root_path} detail'.format( root_path=root_path )) except SchemaEmptyParserError as e: timeout.sleep() continue file_found = Dq(out).contains_key_value( 'file-name', file, value_regex=True) if file_found: return True timeout.sleep() return False def get_file_size(device, root_path, file): """ Get file size from device Args: device ('obj'): Device object root_path ('str'): Root path for command file ('str'): File name Returns: Boolean Raises: None """ out = None out = device.parse('file list {root_path} detail'.format( root_path=root_path )) file_info_list = out.q.contains('{}|file-size'.format(file), regex=True).get_values('file-information') for file in file_info_list: if 'file-name' in file and 'file-size' in file: return int(file['file-size']) return None

29.608108

88

0.478092

75ac4caba42f55991affd593d5cc17b23e8fe331

508

py

Python

test_main.py

nicojahn/EPFD

446f35103d29e3dd69d8151c54de95bc8aed7ec6

[ "MIT" ]

5

2020-09-18T13:21:18.000Z

2021-05-13T04:43:21.000Z

test_main.py

nicojahn/EPFD

446f35103d29e3dd69d8151c54de95bc8aed7ec6

[ "MIT" ]

null

test_main.py

nicojahn/EPFD

446f35103d29e3dd69d8151c54de95bc8aed7ec6

[ "MIT" ]

2

2021-05-03T18:45:47.000Z

2021-11-16T02:30:43.000Z

# coding: utf-8 from __future__ import absolute_import from __future__ import division from __future__ import print_function import unittest import numpy as np from main import load_iris class TestMain(unittest.TestCase): def test_load_iris(self): X_trn, y_trn, X_tst, y_tst = load_iris() self.assertTrue(len(np.unique(y_trn)) == 2) self.assertTrue(len(np.unique(y_tst)) == 2) self.assertEqual(len(X_trn), len(y_trn)) self.assertEqual(len(X_tst), len(y_tst))

25.4

51

0.712598

c637d1bd113c4ca0e6701f5226831297b5cf1858

10,238

py

Python

src/baxter_external_devices/joystick.py

maymohan/baxter_examples

37dbb35ccb02356de76ea64fda61b5389b0a8f8c

[ "BSD-3-Clause" ]

null

src/baxter_external_devices/joystick.py

maymohan/baxter_examples

37dbb35ccb02356de76ea64fda61b5389b0a8f8c

[ "BSD-3-Clause" ]

null

src/baxter_external_devices/joystick.py

maymohan/baxter_examples

37dbb35ccb02356de76ea64fda61b5389b0a8f8c

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # Copyright (c) 2013-2015, Rethink Robotics # 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 Rethink Robotics 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 OWNER 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. import rospy from sensor_msgs.msg import Joy class ButtonTransition(object): """ Monitor button transitions. The transition is measured when read. """ def __init__(self, val_func, down_val=True, up_val=False): self._raw_value = val_func self._down_val = down_val self._up_val = up_val self._up_checked = True self._down_checked = False def down(self): val = self._raw_value() if val == self._down_val: if not self._down_checked: self._down_checked = True return True else: self._down_checked = False return False def up(self): val = self._raw_value() if val == self._up_val: if not self._up_checked: self._up_checked = True return True else: self._up_checked = False return False class StickTransition(object): """ Monitor transitions in stick movement. The transition is measured when read. """ def __init__(self, val_func, epsilon=0.05): self._raw_value = val_func self._epsilon = epsilon self._value = 0.0 def value(self): self.changed() return self._value def changed(self): value = self._raw_value() if abs(value - self._value) > self._epsilon: self._value = value return True return False def increased(self): value = self._raw_value() if (value - self._value) > self._epsilon: self._value = value return True return False def decreased(self): value = self._raw_value() if (self._value - value) > self._epsilon: self._value = value return True return False class Joystick(object): """ Abstract base class to handle joystick input. """ def __init__(self, scale=1.0, offset=0.0, deadband=0.1): """ Maps joystick input to robot control. @type scale: float @param scale: scaling applied to joystick values [1.0] @type offset: float @param offset: joystick offset values, post-scaling [0.0] @type deadband: float @param deadband: deadband post scaling and offset [0.1] Raw joystick valuess are in [1.0...-1.0]. """ sub = rospy.Subscriber("/joy", Joy, self._on_joy) self._scale = scale self._offset = offset self._deadband = deadband self._controls = {} self._buttons = {} self._sticks = {} button_names = ( 'btnLeft', 'btnUp', 'btnDown', 'btnRight', 'dPadUp', 'dPadDown', 'dPadLeft', 'dPadRight', 'leftBumper', 'rightBumper', 'leftTrigger', 'rightTrigger', 'function1', 'function2') stick_names = ( 'leftStickHorz', 'leftStickVert', 'rightStickHorz', 'rightStickVert') #doing this with lambda won't work def gen_val_func(name, type_name): def val_func(): return type_name( name in self._controls and self._controls[name]) return val_func for name in button_names: self._buttons[name] = ButtonTransition(gen_val_func(name, bool)) for name in stick_names: self._sticks[name] = StickTransition(gen_val_func(name, float)) def _on_joy(self, msg): """ callback for messages from joystick input Args: msg(Joy): a joystick input message """ raise NotImplementedError() def button_up(self, name): return self._buttons[name].up() def button_down(self, name): return self._buttons[name].down() def stick_changed(self, name): return self._sticks[name].changed() def stick_inc(self, name): return self._sticks[name].increased() def stick_dec(self, name): return self._sticks[name].decreased() def stick_value(self, name): """ Returns: the deadbanded, scaled and offset value of the axis """ value = self._sticks[name].value() if value > self._deadband or value < -self._deadband: return (value * self._scale) + self._offset return 0 class XboxController(Joystick): """ Xbox specialization of Joystick. """ def __init__(self, scale=1.0, offset=0.0, deadband=0.5): super(XboxController, self).__init__(scale, offset, deadband) def _on_joy(self, msg): """ callback for messages from joystick input Args: msg(Joy): a joystick input message """ self._controls['btnLeft'] = (msg.buttons[2] == 1) self._controls['btnUp'] = (msg.buttons[3] == 1) self._controls['btnDown'] = (msg.buttons[0] == 1) self._controls['btnRight'] = (msg.buttons[1] == 1) self._controls['dPadUp'] = (msg.axes[7] > 0.5) self._controls['dPadDown'] = (msg.axes[7] < -0.5) self._controls['dPadLeft'] = (msg.axes[6] > 0.5) self._controls['dPadRight'] = (msg.axes[6] < -0.5) self._controls['leftStickHorz'] = msg.axes[0] self._controls['leftStickVert'] = msg.axes[1] self._controls['rightStickHorz'] = msg.axes[3] self._controls['rightStickVert'] = msg.axes[4] self._controls['leftBumper'] = (msg.buttons[4] == 1) self._controls['rightBumper'] = (msg.buttons[5] == 1) self._controls['leftTrigger'] = (msg.axes[2] < 0.0) self._controls['rightTrigger'] = (msg.axes[5] < 0.0) self._controls['function1'] = (msg.buttons[6] == 1) self._controls['function2'] = (msg.buttons[10] == 1) class LogitechController(Joystick): """ Logitech specialization of Joystick. """ def __init__(self, scale=1.0, offset=0.0, deadband=0.1): super(LogitechController, self).__init__(scale, offset, deadband) def _on_joy(self, msg): """ callback for messages from joystick input Args: msg(Joy): a joystick input message """ self._controls['btnLeft'] = (msg.buttons[2] == 1) self._controls['btnUp'] = (msg.buttons[3] == 1) self._controls['btnDown'] = (msg.buttons[0] == 1) self._controls['btnRight'] = (msg.buttons[1] == 1) self._controls['dPadUp'] = (msg.axes[7] > 0.5) self._controls['dPadDown'] = (msg.axes[7] < -0.5) self._controls['dPadLeft'] = (msg.axes[6] > 0.5) self._controls['dPadRight'] = (msg.axes[6] < -0.5) self._controls['leftStickHorz'] = msg.axes[0] self._controls['leftStickVert'] = msg.axes[1] self._controls['rightStickHorz'] = msg.axes[3] self._controls['rightStickVert'] = msg.axes[4] self._controls['leftBumper'] = (msg.buttons[4] == 1) self._controls['rightBumper'] = (msg.buttons[5] == 1) self._controls['leftTrigger'] = (msg.axes[2] < 0.0) self._controls['rightTrigger'] = (msg.axes[5] < 0.0) self._controls['function1'] = (msg.buttons[6] == 1) self._controls['function2'] = (msg.buttons[7] == 1) class PS3Controller(Joystick): """ PS3 specialization of Joystick. """ def __init__(self, scale=1.0, offset=0.0, deadband=0.1): super(PS3Controller, self).__init__(scale, offset, deadband) def _on_joy(self, msg): """ callback for messages from joystick input Args: msg(Joy): a joystick input message """ self._controls['btnLeft'] = (msg.buttons[15] == 1) self._controls['btnUp'] = (msg.buttons[12] == 1) self._controls['btnDown'] = (msg.buttons[14] == 1) self._controls['btnRight'] = (msg.buttons[13] == 1) self._controls['dPadUp'] = (msg.buttons[4] == 1) self._controls['dPadDown'] = (msg.buttons[6] == 1) self._controls['dPadLeft'] = (msg.buttons[7] == 1) self._controls['dPadRight'] = (msg.buttons[5] == 1) self._controls['leftStickHorz'] = msg.axes[0] self._controls['leftStickVert'] = msg.axes[1] self._controls['rightStickHorz'] = msg.axes[2] self._controls['rightStickVert'] = msg.axes[3] self._controls['leftBumper'] = (msg.buttons[10] == 1) self._controls['rightBumper'] = (msg.buttons[11] == 1) self._controls['leftTrigger'] = (msg.buttons[8] == 1) self._controls['rightTrigger'] = (msg.buttons[9] == 1) self._controls['function1'] = (msg.buttons[0] == 1) self._controls['function2'] = (msg.buttons[3] == 1)

34.823129

77

0.609396

a8e670f3c03f5c95ab0620d83cdffb3172e4fc88

256

py

Python

stockdaq/data/downloader_dict.py

terrencetec/stockdaq

8561b1d1b94b6091dfe4d9ebf2fc84daa2d571d6

[ "MIT" ]

null

stockdaq/data/downloader_dict.py

terrencetec/stockdaq

8561b1d1b94b6091dfe4d9ebf2fc84daa2d571d6

[ "MIT" ]

7

2021-01-08T01:02:58.000Z

2021-01-12T17:38:03.000Z

stockdaq/data/downloader_dict.py

terrencetec/stockdaq

8561b1d1b94b6091dfe4d9ebf2fc84daa2d571d6

[ "MIT" ]

null

import stockdaq.data.alpha_vantage_downloader as av_downloader import stockdaq.data.yfinance_downloader downloader_dict = { "Alpha Vantage": av_downloader.AlphaVantageDownloader, "yfinance": stockdaq.data.yfinance_downloader.yfinanceDownloader, }

32

69

0.832031

407f002a100243ac632805306ed20a938b11ab74

24,908

py

Python

sleep_scorer/scoreblock.py

focolab/sleep-classifier

6a7eb376267c66d697782d49785ade6948a17a85

[ "MIT" ]

1

2021-06-29T18:18:09.000Z

sleep_scorer/scoreblock.py

focolab/sleep-classifier

6a7eb376267c66d697782d49785ade6948a17a85

[ "MIT" ]

null

sleep_scorer/scoreblock.py

focolab/sleep-classifier

6a7eb376267c66d697782d49785ade6948a17a85

[ "MIT" ]

null

#!/usr/bin/env python3 import pdb import os import json import numpy as np import pandas as pd def ppd(d): for k, v in d.items(): print('%15s:' % k, v) def consensus(X, val='XXX'): """element-wise consensus among lists in X""" c = [y[0] if len(np.unique(y)) == 1 else val for y in zip(*X)] return c class ScoreBlock(object): """stack of y vectors (human/model) as a DataFrame w/ extra features - The dataframe comprises multiple y vectors, stacked as row vectors, and >=1 index columns that adjoin the ts data stack. - This allows for organized storage across trials, models, human scorers and so on - Not a pandas multi-index, but uses attributes index_cols and data_cols - The dump/load methods provide lossless storage (whereas loading a pandas dataframe from csv requires user input to recreate the multi index, or pickling). Calling dump creates a json file (with metadata) and a csv (the dataframe). The json file is used for load(). attributes ------ df : pandas.DataFrame the dataframe data_cols : list names of the data columns, i.e. [epochs-0001, epoch-0002, ...] index_cols : list names of index columns, things like trial, classifier, scorer, etc tagDict: dict dictionary of tags and metadata, (must be jsonizable) ancestry: dict PRELIMINARY some way of tracking input data methods ------ about(): print an executive summary of this scoreblock stack(): stack blocks on top of each other count(): count occurances of (categorical) states in each (data) row applymap(): apply a map (dict) to categorical data, (good for re-naming/merging states) keeprows(): keep a subset of rows, based on conditions to_json(): dump dataframe to csv and everything else to json from_json(): load from a previously dumped json consensus(): determine (binary) consensus for each column of df (index and data) TODO: confusion matrix method """ def __init__(self, loc=None, df=None, index_cols=None, data_cols=None, tagDict={}, ancestry={}): """ """ self.loc = loc self.df = df self.tagDict = tagDict self.ancestry = ancestry if isinstance(self.df, pd.Series): self.df = self.df.to_frame().T if index_cols is None: raise Exception('index_cols required') else: self.index_cols = index_cols if data_cols is None: self.data_cols = [c for c in self.df.columns if c not in self.index_cols] else: self.data_cols = data_cols @property def data(self): return self.df[self.data_cols].values @property def df_index(self): return self.df[self.index_cols].copy() @property def dfmi(self): """return a mutli-index dataframe""" return self.df.set_index(self.index_cols) @property def uniqueScores(self): return np.unique(self.data).tolist() @property def numrows(self): return self.df.shape[0] @property def numdatacols(self): return self.df.shape[1] def about(self): """executive summary""" opj = os.path.join print('-------- ScoreBlock.about() --------') print('loc :', self.loc) print('index_cols:', self.index_cols) if len(self.data_cols)>6: print('data_cols :', self.data_cols[:3], '...', self.data_cols[-3:]) else: print('data_cols :', self.data_cols) print('tagDict :') for k,v in self.tagDict.items(): print('%15s:' % k, v) print('ancestry :') for k,v in self.ancestry.items(): print('%15s:' % k, v) print('df :') print(self.df.head()) def add_const_index_col(self, name='newcolumn', value=None, inplace=False): """add an index column with a constant value""" df_out = self.df.copy() df_out[name] = [value]*len(df_out) index_cols = self.index_cols+[name] df_out = df_out[index_cols+self.data_cols] if inplace: self.df = df_out self.index_cols = index_cols else: out = ScoreBlock( df=df_out, index_cols=index_cols, tagDict=self.tagDict ) return out def copy_index_cols(self): """""" return [c for c in self.index_cols] def applymap(self, m): """apply a dictionary map 'm' to DATA values""" fmap = lambda x: m.get(x, x) df_out = self.df.copy() df_out[self.data_cols] = df_out[self.data_cols].applymap(fmap) out = ScoreBlock( df=df_out, index_cols=self.copy_index_cols(), tagDict=self.tagDict ) return out def stack(self, others=[], force_data=False, rename_index_cols=None, verbose=False, data_nan=None, ): """stack multiple scoreblocks and return a new block force_data: ignore mismatching data column names and force an aligned data stack (requires the same number of data columns) rename_index_cols: to fix mismatching index columns, prior to stacking TODO: possible to just convert to multi-index and concat? """ sblocks = [self] + others if verbose: print('#== stacking ScoreBlocks:') for i, sblock in enumerate(sblocks): print('%3i:' % i, 'index_cols=', sblock.index_cols,' data_cols[:4]=',sblock.data_cols[:4]) # find out if data columns have different lengths ragged_data = False if len(list(set([len(sblock.data_cols) for sblock in sblocks]))) > 1: #raise Exception('data_cols have non-matching lengths') print('WARNING: data_cols have non-matching lengths') ragged_data = True if force_data: raise Exception('force_data will not work for ragged data') # warn if data columns have different names dcols = list(zip(*[sblock.data_cols for sblock in sblocks])) counts = list(set([len(set(tuple(x))) for x in dcols])) if len(counts)>1 or 1 not in counts: print('WARNING: stacking data_cols with non-matching names') # index stack (with column renaming) dfs = [sb.df[sb.index_cols].copy() for sb in sblocks] if rename_index_cols is not None: dfs = [df.rename(columns=rename_index_cols) for df in dfs] df_index = pd.concat(dfs, sort=False).reset_index(drop=True) # data stack if force_data == True: data = np.vstack([sb.df[sb.data_cols] for sb in sblocks]) df_data = pd.DataFrame(data=data, columns=self.data_cols).reset_index(drop=True) else: tostack = [sb.df[sb.data_cols] for sb in sblocks] df_data = pd.concat(tostack, sort=True).reset_index(drop=True) if data_nan is not None: df_data = df_data.fillna(data_nan) # combine index and data df_out = pd.concat([df_index, df_data], axis=1).reset_index(drop=True) tagDict = dict(_about='stacked scoreblocks') # build a new ScoreBlock out = ScoreBlock( df=df_out, index_cols=df_index.columns.tolist(), tagDict=tagDict ) return out def keeprows_iloc(self, iloc=None): """""" out = ScoreBlock( df=self.df.iloc[iloc], index_cols=self.df_index.columns.tolist(), tagDict=self.tagDict ) return out def keeprows(self, conditions=[], comparison='all'): """keep rows from a the dataframe, subject to conditions declarative method for row selection based off the index dataframe a tangled up mess, but it works for a few cases keeprows conditions ('colname', value, comparison) ('classifier', 'OVO', 'eq') ('classifier', ['OVO','OVR'], 'in') ('colname', func) ('classifier', lambda x: x in ['OVO', 'OVR']) input ------ conditions (list): list of (col, val) tuples where col is a column name and val is the value that must match comparison (str): logical comparison for multiple conditions (all/any) all = intersection any = union output ------ df_new (pd.DataFrame): dataframe with fewer rows examples ------ [('trial', 335), ('day', 1), 'all'] """ # dictionary of pairwise comparison functions dd = { 'eq': lambda a,b: a==b, 'ne': lambda a,b: a!=b, 'lt': lambda a,b: a<b, 'gt': lambda a,b: a>b, 'le': lambda a,b: a<=b, 'ge': lambda a,b: a>=b, 'in': lambda a,b: a in b #'isnan': lambda a } if comparison not in ['all', 'any']: raise Exception('comparison must be all or any') if conditions is None: raise Exception('need to pass conditions (at least one)') df = self.df.copy() vex = [df[col] == val for col, val in conditions] aa = list(zip(*vex)) if comparison == 'all': kk = [all(a) for a in aa] if comparison == 'any': kk = [any(a) for a in aa] df_new = df[kk].reset_index(drop=True) out = ScoreBlock( df=df_new, index_cols=self.copy_index_cols(), tagDict=self.tagDict ) return out def consensus(self, out='append', index_fill='consensus', data_fill='XXX'): """compute consensus for each column of df (index and data) If a column has one unique value, it is taken as the consensus. Otherwise, index_fill or data_fill are used. By doing this for the index, all matching indices are carried forward. out (str) : 'append' or 'Series' append : a new ScoreBlock with the consensus appended to df Series : a pandas series (index and data) data_only: bare list of data consensus values index_fill (??): fill value for non-consensus indices data_fill (??): fill value for non-consensus data """ if out not in ['Series', 'append', 'data_only']: raise Exception('out should be in [Series, append]') # consensus for the index and for the data c_index = consensus(self.df_index.values, val=index_fill) c_data = consensus(self.data, val=data_fill) ccc = c_index+c_data if out == 'Series': ser = pd.Series(dict(zip(self.df.columns, ccc))) cc = ser if out == 'data_only': cc = c_data elif out == 'append': ser = pd.Series(dict(zip(self.df.columns.values, ccc))) dfc = pd.concat([self.df, ser.to_frame().T]).reset_index(drop=True) cc = ScoreBlock( df=dfc, index_cols=self.copy_index_cols(), tagDict=self.tagDict, ancestry=self.ancestry, ) return cc def to_sirenia_txt(self, f='scores.txt', row=0, str2num=None, start=None): """dump a row of scores to Sirenia formatted csv (.txt)""" df = self.to_sirenia_df(row=row, str2num=str2num, start=start) df.to_csv(f) def to_sirenia_df(self, row=0, str2num=None, start=None): """convert a row of scores to Sirenia formatted DataFrame Epoch #,Start Time,End Time,Score #, Score 1,10/15/2018 09:00:00,10/15/2018 09:00:10,1,Wake 2,10/15/2018 09:00:10,10/15/2018 09:00:20,1,Wake 8623,10/16/2018 08:57:00,10/16/2018 08:57:10,2,Non REM 8627,10/16/2018 08:57:40,10/16/2018 08:57:50,1,Wake """ if str2num is None: str2num = {} num2str = {v:k for k,v in str2num.items()} from datetime import datetime, timedelta if start is not None: [startdate, starttime] = start else: startdate = "2019-01-02" starttime = "09:00:00" epoch_len = 10 scores = self.data[row] num_epochs = len(scores) # date time strings dt = datetime.fromisoformat('%sT%s' % (startdate, starttime)) epoch_indices = range(1,len(scores)+1) times = [dt + timedelta(seconds=epoch_len*i) for i in range(num_epochs+1)] ta = [xx.strftime('%m/%d/%Y %X') for xx in times[:-1]] tb = [xx.strftime('%m/%d/%Y %X') for xx in times[1:]] # we need scores in string and number (integer) format if isinstance(scores[0], str): scores_strs = scores scores_nums = [str2num[x] for x in scores] elif isinstance(scores[0], int): scores_nums = scores scores_strs = [num2str[x] for x in scores] else: raise Exception('wtf') data = zip(epoch_indices, ta, tb, scores_nums, scores_strs) cols=['Epoch #', 'Start Time', 'End Time', 'Score #', 'Score'] df = pd.DataFrame(data=data, columns=cols).set_index('Epoch #', drop=True) return df def mask(self, mask=None, maskname=None, maskcolname='mask'): """select subset of columns and optionally mark the change in the index""" if mask is None: mask = slice(None) data = self.data[:, mask] data_cols = np.asarray(self.data_cols)[mask].tolist() df_data = pd.DataFrame(data=data, columns=data_cols) df_index = self.df_index.copy() # update index with mask column? if maskname is not None: df_index[maskcolname] = [maskname]*len(self.df_index) index_cols = self.index_cols+[maskcolname] else: index_cols = self.index_cols df_new = pd.concat([df_index, df_data], axis=1) out = ScoreBlock( df=df_new, index_cols=index_cols, tagDict=self.tagDict ) return out def count(self, frac=False): """count occurances of states for each data row (mainly for categorical data) - NOTE: score_names (unique) are sorted by np.unique """ def get_score_counts(data=None): names = np.unique(data) score_counts = [{x:row.tolist().count(x) for x in names} for row in data] df_counts = pd.DataFrame(score_counts) return df_counts df_counts = get_score_counts(data=self.data) # convert to fractions? if frac == True: rowsums = np.sum(df_counts.values, axis=1) for col in df_counts.columns: df_counts[col] /= rowsums dfc = pd.concat([self.df_index, df_counts], axis=1) # build a new ScoreBlock out = ScoreBlock( df=dfc, index_cols=self.copy_index_cols(), tagDict=self.tagDict ) return out def to_json(self, f='scoreblock.json'): """dump to disk (json) - dataframe is written to csv (automatic naming) - errything else to json """ loc = os.path.dirname(os.path.abspath(f)) os.makedirs(loc, exist_ok=True) # df to csv file c = f.replace('.json', '.csv') self.df.to_csv(c, float_format='%g') jdic = dict( loc=loc, df_csv=os.path.relpath(c, loc), tagDict=self.tagDict, ancestry=self.ancestry, index_cols=self.index_cols, ) with open(f, 'w') as jout: json.dump(jdic, jout, indent=2, sort_keys=False) jout.write('\n') @classmethod def from_json(cls, jsn, allow_move=True): """load from disk (json)""" with open(jsn, 'r') as jfopen: jdic = json.load(jfopen) # was this moved? loc_actual = os.path.dirname(os.path.abspath(jsn)) loc_stored = jdic.get('loc', None) if loc_actual != loc_stored: if not allow_move: print('ERROR: scoreblock json has moved') print(' from: %s' % loc_stored) print(' to: %s' % loc_actual) raise Exception('scoreblock json has moved :(') else: print('WARNING: scoreblock json has moved') print(' from: %s' % loc_stored) print(' to: %s' % loc_actual) # load metadata pp = {} pp['loc'] = loc_actual pp['tagDict'] = jdic.get('tagDict', {}) pp['ancestry'] = jdic.get('ancestry', {}) pp['index_cols'] = jdic.get('index_cols') # load the dataframe df_csv = os.path.join(loc_actual, jdic['df_csv']) df = pd.read_csv(df_csv, index_col=0) return cls(df=df, **pp) #============================================================================== #===================== TESTING ================================================ #============================================================================== def fibo(nrow=3, ncol=4): """some numbers in an array""" N = ncol # cols M = nrow # rows fibo = np.asarray([1]*N*M) for i in range(2, N*M): fibo[i] = fibo[i-1] + fibo[i-2] data = fibo[:M*N].reshape(N, M).T return data def demo_block(): """Scoreblock for testing, with categorical data (N=6 columns)""" tagDict = dict(name='gallahad', quest='grail', color='blue') N = 6 data = [ ['duck', 'herring', 'stoat', 'stoat', 'stoat', 'herring'], ['duck', 'stoat', 'duck', 'stoat', 'stoat', 'herring'], ['stoat', 'duck', 'duck', 'herring', 'stoat', 'herring'], ['herring', 'herring', 'stoat', 'stoat', 'stoat', 'herring'] ] # ScoreBlock data_cols = ['dc-%4.4i' % n for n in range(N)] ndx = zip([0, 1, 2, 3], ['a', 'b', 'c', 'd']) index_cols = ['number', 'letter'] df1 = pd.DataFrame(data=ndx, columns=index_cols) df2 = pd.DataFrame(data=data, columns=data_cols) df = pd.concat([df1, df2], axis=1) sb1 = ScoreBlock(df=df, tagDict=tagDict, index_cols=index_cols, data_cols=data_cols) return sb1 def test_scoreblock_stack(): """test scoreblock stacking""" tagDict = dict(name='gallahad', quest='grail', color='blue') N = 6 M = 4 data = fibo(nrow=M, ncol=N) # ScoreBlock 1 data_cols = ['dc-%4.4i' % n for n in range(N)] ndx = zip([0, 1, 2, 3], ['a', 'b', 'c', 'd']) index_cols = ['number', 'letter'] df1 = pd.DataFrame(data=ndx, columns=index_cols) df2 = pd.DataFrame(data=data, columns=data_cols) df = pd.concat([df1, df2], axis=1) sb1 = ScoreBlock(df=df, tagDict=tagDict, index_cols=index_cols, data_cols=data_cols) # ScoreBlock 2 (has a different index column name) data_cols = ['dc-%4.4i' % n for n in range(N)] ndx = zip([0, 1, 2, 3], ['a', 'b', 'c', 'd']) index_cols = ['number', 'LETTER'] df1 = pd.DataFrame(data=ndx, columns=index_cols) df2 = pd.DataFrame(data=data, columns=data_cols) df = pd.concat([df1, df2], axis=1) sb2 = ScoreBlock(df=df, tagDict=tagDict, index_cols=index_cols, data_cols=data_cols) # ScoreBlock 3 (has a different data_cols names relative to 1) data_cols = ['dcXX-%4.4i' % n for n in range(N)] ndx = zip([0, 1, 2, 3], ['a', 'b', 'c', 'd']) index_cols = ['number', 'letter'] df1 = pd.DataFrame(data=ndx, columns=index_cols) df2 = pd.DataFrame(data=data, columns=data_cols) df = pd.concat([df1, df2], axis=1) sb3 = ScoreBlock(df=df, tagDict=tagDict, index_cols=index_cols, data_cols=data_cols) # ScoreBlock 4 (has one fewer data_cols than 1) data = fibo(nrow=M, ncol=N-1) data_cols = ['dc-%4.4i' % n for n in range(N-1)] ndx = zip([0, 1, 2, 3], ['a', 'b', 'c', 'd']) index_cols = ['number', 'letter'] df1 = pd.DataFrame(data=ndx, columns=index_cols) df2 = pd.DataFrame(data=data, columns=data_cols) df = pd.concat([df1, df2], axis=1) sb4 = ScoreBlock(df=df, tagDict=tagDict, index_cols=index_cols, data_cols=data_cols) # stack and rename index columns stk = sb1.stack(others=[sb2], force_data=True, rename_index_cols={'LETTER':'letter'}) assert stk.df['letter'].tolist() == ['a','b','c','d']*2, 'should be [a,b,c,d,a,b,c,d]' # stack and force_data stk = sb1.stack(others=[sb3], force_data=True) assert stk.data_cols == sb1.data_cols, 'should be %s' % (str(stk.data_cols)) # stack ragged data (should pad last data col with nan) stk = sb1.stack(others=[sb4], force_data=False) assert np.isnan(stk.df['dc-0005'].values[-1]), 'should be nan' assert np.isnan(stk.df['dc-0005'].values[-4]), 'should be nan' # stack ragged data and replace nan stk = sb1.stack(others=[sb4], force_data=False, data_nan=-1) assert stk.df['dc-0005'].values[-1] == -1, 'should be nan' assert stk.df['dc-0005'].values[-4] == -1, 'should be nan' def test_scoreblock_consensus(): """testing consensus""" sb1 = demo_block() sb1.df['letter'] = ['a', 'a', 'a', 'a'] # manually make these all the same cc = sb1.consensus(index_fill='ConSEnSuS', data_fill='LOL') assert cc.df_index.iloc[-1].tolist() == ['ConSEnSuS', 'a'] assert cc.data[-1].tolist() == ['LOL', 'LOL', 'LOL', 'LOL', 'stoat', 'herring'] def test_scoreblock_applymap(): """test applymap (map data values via dictionary)""" sb1 = demo_block() sb2 = sb1.applymap(dict(duck='DUCK')) assert sb2.tagDict['color'] == 'blue', 'should be blue' assert sb2.index_cols[0] == 'number', 'should be number' assert sb2.df['dc-0001'].tolist() == ['herring', 'stoat', 'DUCK', 'herring'] assert sb2.df['dc-0002'].tolist() == ['stoat', 'DUCK', 'DUCK', 'stoat'] def test_scoreblock_json(): """test scoreblock json export/import""" loc = 'scratch' os.makedirs(loc, exist_ok=True) sb1 = demo_block() # dump jf = os.path.join(loc, 'test-scoreblock.json') sb1.to_json(f=jf) # reload sb2 = ScoreBlock.from_json(jf) # compare assert sb2.data_cols[0] == 'dc-0000', 'should be dc-0000' assert sb2.data_cols[-1] == 'dc-0005', 'should be dc-0005' assert sb2.tagDict['color'] == 'blue', 'should be blue' assert sb2.index_cols[0] == 'number', 'should be number' assert sb2.df['dc-0001'].tolist() == ['herring', 'stoat', 'duck', 'herring'] def test_scoreblock_count(): """test scoreblock counting (and masking)""" N = 6 sb1 = demo_block() # make masks and do counts maskAM = slice(0, N//2) maskPM = slice(N//2, N) sb_counts_all = sb1.mask(maskname='24h').count(frac=True) sb_counts_am = sb1.mask(mask=maskAM, maskname='12hAM').count(frac=True) sb_counts_pm = sb1.mask(mask=maskPM, maskname='12hPM').count(frac=True) assert sb_counts_all.data_cols == ['duck', 'herring', 'stoat'] assert sb_counts_am.data_cols == ['duck', 'herring', 'stoat'] assert sb_counts_pm.data_cols == ['herring', 'stoat'] # stack the counts results stk = sb_counts_all.stack(others=[sb_counts_am, sb_counts_pm]) assert np.isnan(stk.df['duck'].values[-1]), 'should be nan' # stack and fix NaN values stk = sb_counts_all.stack(others=[sb_counts_am, sb_counts_pm], data_nan=-2) assert stk.df['duck'].values[-1] == -2, 'should be -2' def test_bool_mask(): """test boolean mask""" sb1 = demo_block() # make mask and apply it mask00 = [True, True, True, False, True, False] sb_mask00 = sb1.mask(mask=mask00, maskname='mask00') assert sb_mask00.data_cols == ['dc-0000','dc-0001','dc-0002','dc-0004'] def test_sirenia(): """test to_sirenia_df and to_sirenia_txt""" loc = 'scratch' os.makedirs(loc, exist_ok=True) sb1 = demo_block() # export score row 0 to sireia ready DataFrame str2num = dict(duck=1, herring=2, stoat=3) df = sb1.to_sirenia_df(str2num=str2num, row=0) assert df['Score'][:3].values.tolist() == ['duck', 'herring', 'stoat'] assert df['Score #'].values.tolist() == [1, 2, 3, 3, 3, 2] # and dump to file sb1.to_sirenia_txt( f=os.path.join(loc,'scores_sirenia.txt'), str2num=str2num, row=0 ) if __name__ == '__main__': test_sirenia() test_bool_mask() test_scoreblock_count() test_scoreblock_consensus() #test_scoreblock_keeprows() test_scoreblock_applymap() test_scoreblock_json() test_scoreblock_stack()

33.614035

106

0.574153

2560ff75c42fdcd74cd90de1cd017c9e601838a7

269

py

Python

glue/core/data_factories/dendrogram.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

1

2019-12-17T07:58:35.000Z

glue/core/data_factories/dendrogram.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

null

glue/core/data_factories/dendrogram.py

sergiopasra/glue

c25a217a122a11818382672c99cb21f57a30636f

[ "BSD-3-Clause" ]

1

2019-08-04T14:10:12.000Z

from __future__ import absolute_import, division, print_function from glue.core.data_factories.helpers import has_extension # noqa __all__ = [] try: from glue.core.data_factories.dendro_loader import load_dendro, is_dendro # noqa except ImportError: pass

22.416667

85

0.791822

cfae7e72a630a43b4b6b0cd5f1594e5514cf1542

3,178

py

Python

test/unit/test_deform.py

shannonxtreme/DeepReg

373f6c28fed1d7376d5c39340b08a3814804efb2

[ "Apache-2.0" ]

1

2020-11-05T12:17:27.000Z

test/unit/test_deform.py

shannonxtreme/DeepReg

373f6c28fed1d7376d5c39340b08a3814804efb2

[ "Apache-2.0" ]

null

test/unit/test_deform.py

shannonxtreme/DeepReg

373f6c28fed1d7376d5c39340b08a3814804efb2

[ "Apache-2.0" ]

null

# coding=utf-8 """ Tests for deepreg/model/loss/deform.py in pytest style """ from test.unit.util import is_equal_tf import pytest import tensorflow as tf import deepreg.model.loss.deform as deform def test_gradient_dx(): """test the calculation of gradient of a 3D images along x-axis""" tensor = tf.ones([4, 50, 50, 50]) get = deform.gradient_dx(tensor) expect = tf.zeros([4, 48, 48, 48]) assert is_equal_tf(get, expect) def test_gradient_dy(): """test the calculation of gradient of a 3D images along y-axis""" tensor = tf.ones([4, 50, 50, 50]) get = deform.gradient_dy(tensor) expect = tf.zeros([4, 48, 48, 48]) assert is_equal_tf(get, expect) def test_gradient_dz(): """test the calculation of gradient of a 3D images along z-axis""" tensor = tf.ones([4, 50, 50, 50]) get = deform.gradient_dz(tensor) expect = tf.zeros([4, 48, 48, 48]) assert is_equal_tf(get, expect) def test_gradient_dxyz(): """test the calculation of gradient of a 3D images along xyz-axis""" # gradient_dx tensor = tf.ones([4, 50, 50, 50, 3]) get = deform.gradient_dxyz(tensor, deform.gradient_dx) expect = tf.zeros([4, 48, 48, 48, 3]) assert is_equal_tf(get, expect) # gradient_dy tensor = tf.ones([4, 50, 50, 50, 3]) get = deform.gradient_dxyz(tensor, deform.gradient_dy) expect = tf.zeros([4, 48, 48, 48, 3]) assert is_equal_tf(get, expect) # gradient_dz tensor = tf.ones([4, 50, 50, 50, 3]) get = deform.gradient_dxyz(tensor, deform.gradient_dz) expect = tf.zeros([4, 48, 48, 48, 3]) assert is_equal_tf(get, expect) def test_compute_gradient_norm(): """test the calculation of l1/l2 norm for image gradients""" # l1 norm tensor = tf.ones([4, 50, 50, 50, 3]) get = deform.compute_gradient_norm(tensor, l1=True) expect = tf.zeros([4]) assert is_equal_tf(get, expect) # l2 norm tensor = tf.ones([4, 50, 50, 50, 3]) get = deform.compute_gradient_norm(tensor) expect = tf.zeros([4]) assert is_equal_tf(get, expect) def test_compute_bending_energy(): """test the calculation of bending energy""" tensor = tf.ones([4, 50, 50, 50, 3]) get = deform.compute_bending_energy(tensor) expect = tf.zeros([4]) assert is_equal_tf(get, expect) def test_local_displacement_energy(): """test the computation of local displacement energy for ddf""" # bending energy tensor = tf.ones([4, 50, 50, 50, 3]) get = deform.local_displacement_energy(tensor, "bending") expect = tf.zeros([4]) assert is_equal_tf(get, expect) # l1 norm on gradient tensor = tf.ones([4, 50, 50, 50, 3]) get = deform.local_displacement_energy(tensor, "gradient-l1") expect = tf.zeros([4]) assert is_equal_tf(get, expect) # l2 norm on gradient tensor = tf.ones([4, 50, 50, 50, 3]) get = deform.local_displacement_energy(tensor, "gradient-l2") expect = tf.zeros([4]) assert is_equal_tf(get, expect) # not supported energy type tensor = tf.ones([4, 50, 50, 50, 3]) with pytest.raises(ValueError): deform.local_displacement_energy(tensor, "a wrong string")

29.981132

72

0.657332

81d32b3d8702ffacf8d690c22019ece6f870b74c

237

py

Python

bot/data/video.py

Xayzo/Telegram-Tiktok-downloader

3fbc492d07a4544cb99198b6c371cb640d1500b0

[ "MIT" ]

4

2021-09-29T05:35:25.000Z

2022-01-27T11:40:58.000Z

bot/data/video.py

Xayzo/Telegram-Tiktok-downloader

3fbc492d07a4544cb99198b6c371cb640d1500b0

[ "MIT" ]

null

bot/data/video.py

Xayzo/Telegram-Tiktok-downloader

3fbc492d07a4544cb99198b6c371cb640d1500b0

[ "MIT" ]

4

2021-11-27T05:19:50.000Z

2022-02-20T08:18:42.000Z

from attr import attrs, attrib from typing import Optional @attrs(slots=True, auto_attribs=True, order=False, eq=False) class VideoData: url: Optional[str] = attrib(default=None) content: Optional[bytes] = attrib(default=None)

26.333333

60

0.751055

b3aaed1b4c252f0284d56eed8a714aa7d8964b5f

1,164

py

Python

capsnet-arch/learning-curves.py

eaaskt/nlu

77382be572ce59f15d8ea9c5cd653615c39891d1

[ "MIT" ]

3

2019-03-11T09:15:36.000Z

2020-04-06T15:06:33.000Z

capsnet-arch/learning-curves.py

eaaskt/nlu

77382be572ce59f15d8ea9c5cd653615c39891d1

[ "MIT" ]

24

2020-03-31T11:22:54.000Z

2022-03-12T00:23:49.000Z

capsnet-arch/learning-curves.py

eaaskt/nlu

77382be572ce59f15d8ea9c5cd653615c39891d1

[ "MIT" ]

5

2020-03-29T10:04:31.000Z

2020-05-28T06:50:18.000Z

import train import data_loader import flags import model_s2i def main(): word2vec_path = '../../romanian_word_vecs/cleaned-vectors-diacritice-cc-100.vec' training_data_path = '../data-capsnets/diacritics/scenario1/train.txt' test_data_path = '../data-capsnets/diacritics/scenario1/test.txt' # Define the flags FLAGS = flags.define_app_flags('1-tensorboard-40-examples') # Load data print('------------------load word2vec begin-------------------') w2v = data_loader.load_w2v(word2vec_path) print('------------------load word2vec end---------------------') data = data_loader.read_datasets(w2v, training_data_path, test_data_path) flags.set_data_flags(data) val_data, train_data = data_loader.extract_validation(data, nr_splits=3) for i in range(1, 13): print('----- ITER {} -----'.format(i)) stepData = data_loader.data_subset(train_data, 12, i) train.train_cross_validation(model_s2i.CapsNetS2I, stepData, val_data, data['embedding'], FLAGS, fold=i*5, best_f_score=0, calculate_learning_curves=True) if __name__ == '__main__': main()

33.257143

115

0.64433

e8d33abdc279d5ac4ac3a7b954c6b0d8d71abe3f

59

py

Python

pandoc_cqu_thesis/__main__.py

Hagb/pandoc_cqu_thesis

46ea35221f314656bc8e48d08c595a92104bba3a

[ "MIT" ]

2

2022-02-11T01:11:33.000Z

2022-02-11T04:32:43.000Z

pandoc_cqu_thesis/__main__.py

Hagb/pandoc_cqu_thesis

46ea35221f314656bc8e48d08c595a92104bba3a

[ "MIT" ]

null

pandoc_cqu_thesis/__main__.py

Hagb/pandoc_cqu_thesis

46ea35221f314656bc8e48d08c595a92104bba3a

[ "MIT" ]

null

#!/usr/bin/python3 import docx_filters docx_filters.main()

14.75

19

0.79661

73337602f5321904e2d0daad04641b8b4496da27

496

py

Python

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

faezs/plotly.py

6009b5b9c746e5d2a2849ad255a4eb234b551ed7

[ "MIT" ]

2

2020-03-24T11:41:14.000Z

2021-01-14T07:59:43.000Z

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

faezs/plotly.py

6009b5b9c746e5d2a2849ad255a4eb234b551ed7

[ "MIT" ]

null

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

faezs/plotly.py

6009b5b9c746e5d2a2849ad255a4eb234b551ed7

[ "MIT" ]

4

2019-06-03T14:49:12.000Z

2022-01-06T01:05:12.000Z

import _plotly_utils.basevalidators class OutlinewidthValidator(_plotly_utils.basevalidators.NumberValidator): def __init__( self, plotly_name='outlinewidth', parent_name='scatterpolar.marker.colorbar', **kwargs ): super(OutlinewidthValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type='colorbars', min=0, role='style', **kwargs )

24.8

74

0.606855

fbdae249f72280728c0c566f3ece5af7b13ffccf

557

py

Python

setup.py

dm03514/protobuf-pii

c97067c197e52ef0fdb75ee519061842bf60d0e1

[ "Apache-2.0" ]

null

setup.py

dm03514/protobuf-pii

c97067c197e52ef0fdb75ee519061842bf60d0e1

[ "Apache-2.0" ]

null

setup.py

dm03514/protobuf-pii

c97067c197e52ef0fdb75ee519061842bf60d0e1

[ "Apache-2.0" ]

null

import os import pathlib import setuptools from pkg_resources import parse_requirements from setuptools import setup dir_path = os.path.dirname(os.path.realpath(__file__)) file_path = os.path.join(dir_path, 'requirements.txt') with pathlib.Path(file_path).open() as requirements_txt: reqs = [ str(requirement) for requirement in parse_requirements(requirements_txt) ] setup( name='protopii', version='0.0.1', packages=setuptools.find_packages(), install_requires=reqs, include_packaged_data=True, )

20.62963

56

0.72711

85c53d476652836cfe91ae434f0b36c18112caee

2,536

py

Python

base_site/nubank/migrations/0002_auto_20191127_2230.py

ricardochaves/financeiro-bot

2c48be4355e3c8630c36aa846c16042f22b88271

[ "MIT" ]

4

2020-01-21T00:21:44.000Z

2021-06-15T19:38:36.000Z

base_site/nubank/migrations/0002_auto_20191127_2230.py

ricardochaves/financeiro-bot

2c48be4355e3c8630c36aa846c16042f22b88271

[ "MIT" ]

173

2019-11-18T08:19:44.000Z

2021-09-08T01:37:19.000Z

base_site/nubank/migrations/0002_auto_20191127_2230.py

ricardochaves/financeiro-bot

2c48be4355e3c8630c36aa846c16042f22b88271

[ "MIT" ]

3

2020-01-28T19:19:35.000Z

2021-05-01T02:33:36.000Z

# Generated by Django 2.2.7 on 2019-11-28 00:30 import django.contrib.postgres.fields.jsonb from django.db import migrations, models class Migration(migrations.Migration): dependencies = [("nubank", "0001_initial")] operations = [ migrations.CreateModel( name="NubankCards", fields=[ ("id", models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name="ID")), ("command_1", models.CharField(max_length=11)), ("command_2", models.CharField(max_length=11)), ("cpf", models.CharField(max_length=11)), ("last_login", models.DateTimeField(blank=True, null=True)), ], ), migrations.CreateModel( name="NubankSession", fields=[ ("id", models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name="ID")), ("created_at", models.DateTimeField(auto_now=True)), ("session_id", models.CharField(max_length=100)), ], ), migrations.CreateModel( name="NubankStatement", fields=[ ("id", models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name="ID")), ("created_at", models.DateTimeField(auto_now=True)), ("amount", models.DecimalField(decimal_places=2, max_digits=9)), ("amount_without_iof", models.DecimalField(decimal_places=2, max_digits=9)), ("description", models.CharField(blank=True, max_length=200, null=True)), ("category", models.CharField(blank=True, max_length=80, null=True)), ("source", models.CharField(blank=True, max_length=40, null=True)), ("title", models.CharField(blank=True, max_length=200, null=True)), ("account", models.CharField(blank=True, max_length=40, null=True)), ("details", django.contrib.postgres.fields.jsonb.JSONField(blank=True, null=True)), ("nubank_id", models.CharField(blank=True, db_index=True, max_length=40, null=True, verbose_name="id")), ("href", models.CharField(blank=True, max_length=200, null=True)), ("item_time", models.DateTimeField()), ("is_processed", models.BooleanField(default=False)), ], ), migrations.DeleteModel(name="NubankItem"), migrations.DeleteModel(name="NubankItem2"), ]

48.769231

120

0.596215

22a28cfb23fb686878da0193fa0769981c8e6437

4,808

py

Python

test/test_util.py

pombredanne/wheelodex

defaa4809d0186a2cb3e77213ad2b0585ac13127

[ "MIT" ]

17

2018-10-18T17:49:28.000Z

2022-03-10T14:57:13.000Z

test/test_util.py

pombredanne/wheelodex

defaa4809d0186a2cb3e77213ad2b0585ac13127

[ "MIT" ]

3

2018-10-17T15:19:04.000Z

2021-12-21T19:45:58.000Z

test/test_util.py

pombredanne/wheelodex

defaa4809d0186a2cb3e77213ad2b0585ac13127

[ "MIT" ]

1

2018-10-30T02:59:48.000Z

from datetime import datetime, timezone import pytest from wheelodex.util import VersionNoDot, glob2like, latest_version, \ parse_timestamp, wheel_sort_key @pytest.mark.parametrize('versions,latest', [ ([], None), (['1.0', '1.1', '2.0', '1.3'], '2.0'), (['1.0', '2.0.dev1'], '1.0'), (['1.0.dev1', '1.1.dev1', '1.2.dev1'], '1.2.dev1'), (['2001.01.01', '1999.12.31'], '2001.01.01'), ]) def test_latest_version(versions, latest): assert latest_version(versions) == latest # In ascending order WHEEL_PREFERENCES = [ 'foo-1.0-nonsense-nonsense-nonsense.whl', 'foo-1.0-othernonsense-othernonsense-othernonsense.whl', 'foo-1.0-cp25-none-any.whl', 'foo-1.0-cp26-none-any.whl', 'foo-1.0-cp27-none-any.whl', 'foo-1.0-cp30-none-any.whl', 'foo-1.0-cp31-none-any.whl', 'foo-1.0-cp32-none-any.whl', 'foo-1.0-cp33-none-any.whl', 'foo-1.0-cp34-none-any.whl', 'foo-1.0-cp35-cp35m-macosx_10_6_intel.whl', 'foo-1.0-cp35-cp35m-macosx_10_7_intel.whl', 'foo-1.0-cp35-cp35m-macosx_10_6_intel.macosx_10_7_intel.whl', 'foo-1.0-cp35-cp35m-macosx_10_8_intel.whl', 'foo-1.0-cp35-cp35m-macosx_10_7_intel.macosx_10_8_intel.whl', 'foo-1.0-cp35-cp35m-macosx_10_6_intel.macosx_10_7_intel.macosx_10_8_intel.whl', 'foo-1.0-cp35-cp35m-macosx_10_6_intel.macosx_10_7_intel.macosx_10_8_intel.macosx_10_9_intel.macosx_10_6_x86_64.macosx_10_7_x86_64.macosx_10_8_x86_64.macosx_10_9_x86_64.whl', 'foo-1.0-cp35-none-win32.whl', 'foo-1.0-cp35-none-win64.whl', 'foo-1.0-cp35-none-win_amd64.whl', 'foo-1.0-cp35-cp35m-manylinux1_i686.whl', 'foo-1.0-cp35-cp35m-manylinux1_x86_64.whl', 'foo-1.0-1-cp35-cp35m-manylinux1_x86_64.whl', 'foo-1.0-1a-cp35-cp35m-manylinux1_x86_64.whl', 'foo-1.0-2-cp35-cp35m-manylinux1_x86_64.whl', 'foo-1.0-2a-cp35-cp35m-manylinux1_x86_64.whl', 'foo-1.0-2b-cp35-cp35m-manylinux1_x86_64.whl', 'foo-1.0-cp35-cp35m-manylinux1_i686.manylinux1_x86_64.whl', 'foo-1.0-cp35-cp35m-manylinux2010_x86_64.whl', 'foo-1.0-cp35-none-any.whl', 'foo-1.0-cp36-none-any.whl', 'foo-1.0-cp37-none-any.whl', 'foo-1.0-cp38-none-any.whl', 'foo-1.0-cp39-none-any.whl', 'foo-1.0-py2-none-any.whl', 'foo-1.0-py30-none-any.whl', 'foo-1.0-py31-none-any.whl', 'foo-1.0-py32-none-any.whl', 'foo-1.0-py33-none-any.whl', 'foo-1.0-py34-none-any.whl', 'foo-1.0-py35-none-any.whl', 'foo-1.0-py36-none-any.whl', 'foo-1.0-py37-none-any.whl', 'foo-1.0-py38-none-any.whl', 'foo-1.0-py39-none-any.whl', 'foo-1.0-py3_10_1-none-any.whl', 'foo-1.0-py3_10-none-any.whl', 'foo-1.0-py3_11-none-any.whl', 'foo-1.0-py3_101-none-any.whl', 'foo-1.0-py3-none-any.whl', 'foo-1.0-py2.py3-none-any.whl', 'foo-1.0-1-py2.py3-none-any.whl', 'foo-1.0-1a-py2.py3-none-any.whl', 'foo-1.0-2-py2.py3-none-any.whl', 'foo-1.0-2a-py2.py3-none-any.whl', 'foo-1.0-2b-py2.py3-none-any.whl', ] @pytest.mark.parametrize( 'lower,higher', zip(WHEEL_PREFERENCES, WHEEL_PREFERENCES[1:]), ) def test_wheel_sort_key(lower, higher): assert wheel_sort_key(lower) < wheel_sort_key(higher) VERSIONS_NO_DOTS = [ '12', '1', '200', '2015', '201', '202', '20', '21', '22', '2', '30', '3101', '310', '311', '39', '3_10_1', '3_10', '3_11', '3_101', '3', ] @pytest.mark.parametrize( 'lower,higher', zip(VERSIONS_NO_DOTS, VERSIONS_NO_DOTS[1:]), ) def test_version_no_dot(lower, higher): assert VersionNoDot(lower) < VersionNoDot(higher) @pytest.mark.parametrize('glob,like', [ ('python*', 'python%'), ('p?thon', 'p_thon'), (r'python\*', 'python*'), (r'p\?thon', 'p?thon'), ('__init__.*', r'\_\_init\_\_.%'), ('mod%ulo', r'mod\%ulo'), (r'foo\bar', r'foo\\bar'), (r'foo\%bar', r'foo\\\%bar'), (r'foo\_bar', r'foo\\\_bar'), (r'foo\\bar', r'foo\\bar'), ]) def test_glob2like(glob, like): assert glob2like(glob) == like @pytest.mark.parametrize('s,dt', [ ( '2018-09-26T15:12:54', datetime(2018, 9, 26, 15, 12, 54, tzinfo=timezone.utc), ), ( '2018-09-26T15:12:54.123456', datetime(2018, 9, 26, 15, 12, 54, 123456, tzinfo=timezone.utc), ), ( '2018-09-26T15:12:54Z', datetime(2018, 9, 26, 15, 12, 54, tzinfo=timezone.utc), ), ( '2018-09-26T15:12:54.123456Z', datetime(2018, 9, 26, 15, 12, 54, 123456, tzinfo=timezone.utc), ), ]) def test_parse_timestamp(s, dt): parsed = parse_timestamp(s) assert parsed == dt assert parsed.replace(tzinfo=None) == dt.replace(tzinfo=None) # pyRFC3339 uses its own UTC type, so this is false: #assert parsed.tzinfo == dt.tzinfo

30.238994

177

0.614393

d58fca3033e465c21b8bbba326ab8675c89188c4

3,934

py

Python

toto/plugins/plots/_do_perc_of_occurence.py

calypso-science/Toto

85e90421343bf3dcf6d730767287647b5bc189bb

[ "MIT" ]

1

2022-03-24T23:41:16.000Z

toto/plugins/plots/_do_perc_of_occurence.py

calypso-science/Toto

85e90421343bf3dcf6d730767287647b5bc189bb

[ "MIT" ]

12

2021-02-24T22:30:52.000Z

2021-11-16T01:51:38.000Z

toto/plugins/plots/_do_perc_of_occurence.py

calypso-science/Toto

85e90421343bf3dcf6d730767287647b5bc189bb

[ "MIT" ]

1

2021-09-21T11:37:09.000Z

import numpy as np from matplotlib import pyplot as plt from matplotlib.figure import Figure import matplotlib.colors as colors import matplotlib.cm as cmx from ...core.toolbox import get_number_of_loops,degToCompass from matplotlib import gridspec from grid_strategy import strategies def get_perc(s,SPD): perc=np.empty(shape=(len(s)-1,1)) for h in range(0,len(s)-1): nombre=((SPD>=s[h]) & (SPD<s[h+1])).nonzero()[0] nom=len(nombre)/(s[h+1]-s[h]) #number of occurence per unit of magnitude perc[h]=(nom)*100 return perc def do_perc_of_occurence(time,mag,drr,mag_interval,xlabel,time_blocking,dir_int,fileout,show): ## Input gd_value=~np.isnan(mag) time=time[gd_value] mag=mag[gd_value] if drr is not None: drr=drr[gd_value] else: drr=np.ones((len(mag),)) dir_int=[0,360] if isinstance(mag_interval,int) or isinstance(mag_interval,float): s=np.arange(0,np.max(mag),mag_interval) elif isinstance(mag_interval,list): if len(mag_interval)<2: s=np.linspace(0,np.max(mag),10) else: s=np.array(mag_interval) else: s=np.linspace(0,np.max(mag),10) month=time.month number_of_loops,identifiers,month_identifier=get_number_of_loops(time_blocking) index=[] nj=[] for j in range(0,number_of_loops): tmp=np.in1d(month, month_identifier[j]) if np.any(tmp): #index .append(tmp) nj.append(j) number_of_real_loops=len(nj) spec = strategies.SquareStrategy().get_grid(number_of_real_loops) fig = plt.gcf() fig.set_dpi(100) fig.constrained_layout=True fig.set_figheight(11.69) fig.set_figwidth(8.27) for i,sub in enumerate(spec): ax = plt.subplot(sub) #Pull out relevant indices for particular month/months index = np.in1d(month, month_identifier[nj[i]]) big_length=len(index.nonzero()[0]); if big_length>0: SPD = mag[index] DIR = drr[index] jet = cm = plt.get_cmap('jet') cNorm = colors.Normalize(vmin=0, vmax=len(dir_int)-2) scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet) for jj in range(0,len(dir_int)-1): if dir_int[jj+1] <= dir_int[jj]: D=np.logical_or(np.mod(DIR,360)>dir_int[jj],np.mod(DIR,360)<=dir_int[jj+1]); else: D=(np.mod(DIR,360)>dir_int[jj]) & (np.mod(DIR,360)<=dir_int[jj+1]); colorVal = scalarMap.to_rgba(jj) perc=get_perc(s,SPD[D])/big_length plt.plot(s[:-1]+np.diff(s)/2,perc,color=colorVal,label=degToCompass([dir_int[jj],dir_int[jj+1]])) ax.set_title(identifiers[nj[i]]) if i==number_of_real_loops-1 and len(dir_int)>2: # if number_of_loops>3 and number_of_loops<10: # ax.legend(loc='best',bbox_to_anchor=(0.6, -0.4),ncol=len(dir_int)-1)#bbox_to_anchor=(0.8,-1.0, 0.5, 0.5)) # elif number_of_loops>10: # ax.legend(loc='best',bbox_to_anchor=(0.6, -3.4),ncol=len(dir_int)-1)#bbox_to_anchor=(0.8,-1.0, 0.5, 0.5)) # else: ax.legend(loc='best') #if int(y)==0: ax.set_ylabel('% Occurence') #if int(x)==maxx : ax.set_xlabel(xlabel) fig.align_labels() # if number_of_loops>10: # plt.subplots_adjust(left=0.075,right=0.970,bottom=0.075,top=0.97,hspace=.5,wspace=0.415) # ax.set_xlabel(xlabel) # elif number_of_loops>2 and number_of_loops<10: # plt.subplots_adjust(left=0.08,right=0.975,bottom=0.05,top=0.7,hspace=.5,wspace=0.3) # ax.set_xlabel(xlabel) # else: # plt.subplots_adjust(bottom=0.05,top=.95,hspace=.5) plt.savefig(fileout) if show: plt.show()

30.261538

123

0.59634

ce12b00d33942d8905827f30f97949bf79c75068

317

py

Python

tests/green-led.py

azzuwan/ERN

f6ad5cd2d44c89c806c6f21ddee34b1692a484c3

[ "MIT" ]

null

tests/green-led.py

azzuwan/ERN

f6ad5cd2d44c89c806c6f21ddee34b1692a484c3

[ "MIT" ]

null

tests/green-led.py

azzuwan/ERN

f6ad5cd2d44c89c806c6f21ddee34b1692a484c3

[ "MIT" ]

null

#!/usr/bin/env python import RPi.GPIO as GPIO, time, requests green = 29 GPIO.setmode(GPIO.BOARD) GPIO.setup(green, GPIO.OUT) GPIO.output(green, False) time.sleep(0.1) GPIO.output(green, True) time.sleep(3) GPIO.output(green, False) time.sleep(3) GPIO.output(green, True) time.sleep(3) GPIO.output(green, False)

16.684211

39

0.735016

26d71ef3015fea200c5e7e529d05400133c90a27

503

py

Python

src/tenyksfun/main.py

nijotz/tenyks-contrib

eea737a2b3d68d8410d21e7e7bb88ef0af7255d8

[ "MIT" ]

null

src/tenyksfun/main.py

nijotz/tenyks-contrib

eea737a2b3d68d8410d21e7e7bb88ef0af7255d8

[ "MIT" ]

null

src/tenyksfun/main.py

nijotz/tenyks-contrib

eea737a2b3d68d8410d21e7e7bb88ef0af7255d8

[ "MIT" ]

null

from datetime import date import random from tenyks.client import Client, run_client class TenyksFun(Client): direct_only = False def __init__(self, *args, **kwargs): self.hello_counts = {} super(TenyksFun, self).__init__(*args, **kwargs) def handle(self, data, match, filter_name): if data['payload'] == "You're doing great work tenyks!": self.send('!m tenyks', data) def main(): run_client(TenyksFun) if __name__ == '__main__': main()

19.346154

64

0.642147

a05fa605f3d16b9befdd745a5a1d887762116e14

2,527

py

Python

test/python/transpiler/test_fixed_point_pass.py

ajavadia/qiskit-sdk-py

a59e8e6be1793197e19998c1f7dcfc45e6f2f3af

[ "Apache-2.0" ]

11

2019-06-27T09:53:29.000Z

2021-03-02T04:40:30.000Z

test/python/transpiler/test_fixed_point_pass.py

ajavadia/qiskit-sdk-py

a59e8e6be1793197e19998c1f7dcfc45e6f2f3af

[ "Apache-2.0" ]

24

2021-01-27T08:20:27.000Z

2021-07-06T09:42:28.000Z

test/python/transpiler/test_fixed_point_pass.py

ajavadia/qiskit-sdk-py

a59e8e6be1793197e19998c1f7dcfc45e6f2f3af

[ "Apache-2.0" ]

4

2021-10-05T12:07:27.000Z

2022-01-28T18:37:28.000Z

# This code is part of Qiskit. # # (C) Copyright IBM 2017, 2018. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """FixedPoint pass testing""" import unittest from qiskit.transpiler.passes import FixedPoint from qiskit.test import QiskitTestCase class TestFixedPointPass(QiskitTestCase): """ Tests for FixedPoint pass. """ def setUp(self): super().setUp() self.pass_ = FixedPoint('property') self.pset = self.pass_.property_set self.dag = None # The pass do not read the DAG. def test_fixed_point_setting_to_none(self): """ Setting a property to None twice does not create a fixed-point. """ self.pass_.property_set['property'] = None self.pass_.run(self.dag) self.pass_.property_set['property'] = None self.pass_.run(self.dag) self.assertFalse(self.pset['property_fixed_point']) def test_fixed_point_reached(self): """ Setting a property to the same value twice creates a fixed-point. """ self.pset['property'] = 1 self.pass_.run(self.dag) self.assertFalse(self.pset['property_fixed_point']) self.pset['property'] = 1 self.pass_.run(self.dag) self.assertTrue(self.pset['property_fixed_point']) def test_fixed_point_not_reached(self): """ Setting a property with different values does not create a fixed-point. """ self.pset['property'] = 1 self.pass_.run(self.dag) self.assertFalse(self.pset['property_fixed_point']) self.pset['property'] = 2 self.pass_.run(self.dag) self.assertFalse(self.pset['property_fixed_point']) def test_fixed_point_left(self): """ A fixed-point is not permanent. """ self.pset['property'] = 1 self.pass_.run(self.dag) self.assertFalse(self.pset['property_fixed_point']) self.pset['property'] = 1 self.pass_.run(self.dag) self.assertTrue(self.pset['property_fixed_point']) self.pset['property'] = 2 self.pass_.run(self.dag) self.assertFalse(self.pset['property_fixed_point']) if __name__ == '__main__': unittest.main()

36.1

87

0.671152

de7c8ff413c08bb9834d763a691b31c4673f5b4c

411

py

Python

experiments/fdtd-2d/tmp_files/6632.py

LoopTilingBenchmark/benchmark

52a3d2e70216552a498fd91de02a2fa9cb62122c

[ "BSD-2-Clause" ]

null

experiments/fdtd-2d/tmp_files/6632.py

LoopTilingBenchmark/benchmark

52a3d2e70216552a498fd91de02a2fa9cb62122c

[ "BSD-2-Clause" ]

null

experiments/fdtd-2d/tmp_files/6632.py

LoopTilingBenchmark/benchmark

52a3d2e70216552a498fd91de02a2fa9cb62122c

[ "BSD-2-Clause" ]

null

from chill import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/fdtd-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/fdtd-2d/tmp_files/6632.c') procedure('kernel_fdtd_2d') loop(0) known(' nx > 1 ') known(' ny > 1 ') tile(1,2,20,2) tile(1,4,80,4) tile(2,2,20,2) tile(2,4,80,4) tile(3,2,20,2) tile(3,4,80,4)

22.833333

116

0.720195

3bfc0443402336fb70d6d087b2e2e2caf433fe12

11,826

py

Python

tests/providers/test_phone_number.py

omri374/faker

71e89a77c500a50c2a6238c5266d2fe26f4dfe5e

[ "MIT" ]

null

tests/providers/test_phone_number.py

omri374/faker

71e89a77c500a50c2a6238c5266d2fe26f4dfe5e

[ "MIT" ]

null

tests/providers/test_phone_number.py

omri374/faker

71e89a77c500a50c2a6238c5266d2fe26f4dfe5e

[ "MIT" ]

null

import re from typing import Pattern from faker.providers.phone_number import Provider as PhoneNumberProvider from faker.providers.phone_number.en_PH import Provider as EnPhPhoneNumberProvider class TestPhoneNumber: """Test phone number provider methods""" def test_country_calling_code(self, faker, num_samples): for _ in range(num_samples): cc = faker.country_calling_code() assert cc in PhoneNumberProvider.country_calling_codes def test_msisdn(self, faker, num_samples): for _ in range(num_samples): msisdn = faker.msisdn() assert isinstance(msisdn, str) assert len(msisdn) == 13 assert msisdn.isdigit() class TestJaJp: """Test ja_JP phone number provider methods""" def test_phone_number(self, faker, num_samples): for _ in range(num_samples): pattern: Pattern = re.compile(r"(?:0[789]0|\d{2})-\d{4}-\d{4}") phone_number = faker.phone_number() assert pattern.fullmatch(phone_number) class TestPtBr: """Test pt_BR phone number provider methods""" def test_phone_number(self, faker, num_samples): pattern: Pattern = re.compile( r"(?:\+55 )?" r"(?:[1-8]1|84|$(?:0[1-8]1|084)$)" r" \d{4}[ -]\d{4}|" r"\d{4}?[ -]\d{3}[ -]\d{4}", ) for _ in range(num_samples): phone_number = faker.phone_number() assert pattern.fullmatch(phone_number) def test_msisdn(self, faker, num_samples): pattern: Pattern = re.compile(r"55(?:[1-8]19|849)\d{8}") for _ in range(num_samples): msisdn = faker.msisdn() assert pattern.fullmatch(msisdn) def test_cellphone(self, faker, num_samples): pattern: Pattern = re.compile( r"(?:\+55 )?" r"(?:\d{2}|$0?\d{2}$)" r" 9 ?\d{4}[ -]\d{4}", ) for _ in range(num_samples): cellphone = faker.cellphone_number() assert pattern.fullmatch(cellphone) def test_service_phone(self, faker, num_samples): pattern: Pattern = re.compile(r"1(?:0|2|5|8|9)?(?:[0-9])") for _ in range(num_samples): service = faker.service_phone_number() assert pattern.fullmatch(service) class TestHuHu: """Test hu_HU phone number provider methods""" def test_phone_number(self, faker, num_samples): pattern: Pattern = re.compile( r"(?:" r"\+36 \d{2} |" r"$06$\d{2}/|" r"$\d{2}$/|" r"\d{2}/|" r"06-\d{1,2}/" r")\d{3}[- ]\d{4}", ) for _ in range(num_samples): phone_number = faker.phone_number() assert isinstance(phone_number, str) assert pattern.fullmatch(phone_number) class TestThTh: """Test th_TH phone number provider methods""" def test_phone_number(self, faker, num_samples): pattern: Pattern = re.compile( # leading zero or internaional code r"((\+66)|\+66[ -]?$0$|0)[ -]?" # landline or mobile r"([23457][ -]?(\d[ -]?){6}\d|[689][ -]?(\d[ -]?){7}\d)" # extension r"([ ]?(x|ext|ต่อ)[\.]?[ ]?\d{1,5})?", re.IGNORECASE, ) for _ in range(num_samples): phone_number = faker.phone_number() assert isinstance(phone_number, str) assert pattern.fullmatch(phone_number) class TestHyAm: """Test hy_AM phone number provider methods""" def test_phone_number(self, faker, num_samples): pattern: Pattern = re.compile( r"(?:[23]\d{2}-|$[23]\d{2}$ |[23]\d{2}\.)\d{5}|" r"(?:(?:10|9\d)-|$(?:10|9\d)$ |(?:10|9\d)\.)\d{6}", ) for _ in range(num_samples): phone_number = faker.phone_number() assert isinstance(phone_number, str) assert pattern.fullmatch(phone_number) class TestEnPh: """Test en_PH phone number provider methods""" @classmethod def setup_class(cls): cls.mobile_number_pattern: Pattern = re.compile(r"^(?:0|\+63)(\d+)-\d{3}-\d{4}$") cls.area2_landline_number_pattern: Pattern = re.compile(r"^(?:0|\+63)2-(\d{4})-\d{4}") cls.non_area2_landline_number_pattern: Pattern = re.compile(r"^(?:0|\+63)(\d{2})-(\d{3})-\d{4}") cls.globe_mobile_number_prefixes = EnPhPhoneNumberProvider.globe_mobile_number_prefixes cls.smart_mobile_number_prefixes = EnPhPhoneNumberProvider.smart_mobile_number_prefixes cls.sun_mobile_number_prefixes = EnPhPhoneNumberProvider.sun_mobile_number_prefixes cls.mobile_number_prefixes = ( cls.globe_mobile_number_prefixes + cls.smart_mobile_number_prefixes + cls.sun_mobile_number_prefixes ) cls.bayantel_landline_identifiers = EnPhPhoneNumberProvider.bayantel_landline_identifiers cls.misc_landline_identifiers = EnPhPhoneNumberProvider.misc_landline_identifiers cls.non_area2_landline_area_codes = EnPhPhoneNumberProvider.non_area2_landline_area_codes def test_globe_mobile_number(self, faker, num_samples): for _ in range(num_samples): number = faker.globe_mobile_number() match = self.mobile_number_pattern.match(number) assert match and match.group(1) in self.globe_mobile_number_prefixes def test_smart_mobile_number(self, faker, num_samples): for _ in range(num_samples): number = faker.smart_mobile_number() match = self.mobile_number_pattern.match(number) assert match and match.group(1) in self.smart_mobile_number_prefixes def test_sun_mobile_number(self, faker, num_samples): for _ in range(num_samples): number = faker.sun_mobile_number() match = self.mobile_number_pattern.match(number) assert match and match.group(1) in self.sun_mobile_number_prefixes def test_mobile_number(self, faker, num_samples): for _ in range(num_samples): number = faker.mobile_number() match = self.mobile_number_pattern.match(number) assert match and match.group(1) in self.mobile_number_prefixes def test_globe_area2_landline_number(self, faker, num_samples): for _ in range(num_samples): number = faker.globe_area2_landline_number() match = self.area2_landline_number_pattern.match(number) assert match and match.group(1).startswith("7") def test_pldt_area2_landline_number(self, faker, num_samples): for _ in range(num_samples): number = faker.pldt_area2_landline_number() match = self.area2_landline_number_pattern.match(number) assert match and match.group(1).startswith("8") def test_bayantel_area2_landline_number(self, faker, num_samples): for _ in range(num_samples): number = faker.bayantel_area2_landline_number() match = self.area2_landline_number_pattern.match(number) assert match and match.group(1) in self.bayantel_landline_identifiers def test_misc_area2_landline_number(self, faker, num_samples): for _ in range(num_samples): number = faker.misc_area2_landline_number() match = self.area2_landline_number_pattern.match(number) assert match and match.group(1) in self.misc_landline_identifiers def test_area2_landline_number(self, faker, num_samples): for _ in range(num_samples): number = faker.area2_landline_number() match = self.area2_landline_number_pattern.match(number) assert match and any( [ match.group(1).startswith("7"), match.group(1).startswith("8"), match.group(1) in self.bayantel_landline_identifiers, match.group(1) in self.misc_landline_identifiers, ] ) def test_non_area2_landline_number(self, faker, num_samples): for _ in range(num_samples): number = faker.non_area2_landline_number() match = self.non_area2_landline_number_pattern.match(number) assert match and match.group(1) in self.non_area2_landline_area_codes def test_landline_number(self, faker, num_samples): for _ in range(num_samples): number = faker.landline_number() area2_match = self.area2_landline_number_pattern.match(number) non_area2_match = self.non_area2_landline_number_pattern.match(number) assert area2_match or non_area2_match if area2_match: assert any( [ area2_match.group(1).startswith("7"), area2_match.group(1).startswith("8"), area2_match.group(1) in self.bayantel_landline_identifiers, area2_match.group(1) in self.misc_landline_identifiers, ] ) elif non_area2_match: assert non_area2_match.group(1) in self.non_area2_landline_area_codes class TestFilPh(TestEnPh): """Test fil_PH phone number provider methods""" pass class TestTlPh(TestEnPh): """Test tl_PH phone number provider methods""" pass class TestTaIn: """Test ta_IN phone number provider methods""" def test_phone_number(self, faker, num_samples): pattern: Pattern = re.compile( r"\+91 \d{3} ?\d{7}|" r"0\d{2}(-)?\d{2}(?(1)| ?)\d{6}", ) for _ in range(num_samples): phone_number = faker.phone_number() assert pattern.fullmatch(phone_number) class TestEsEs: """Test es_ES phone number provider methods""" def test_phone_number(self, faker, num_samples): pattern: Pattern = re.compile( r"\+34 ?(?:7[0-4]|[689]\d)\d" r"(?: \d{3} \d{3}|\d{6}| \d{2} \d{2} \d{2})", ) for _ in range(num_samples): phone_number = faker.phone_number() assert pattern.fullmatch(phone_number) class TestArAe: """Test ar_AE phone number provider methods""" cellphone_pattern: str = r"(?:\+|00)971\s?5[024568]\s?\d{3}\s?\d{4}|" r"05[024568]\s?\d{3}\s?\d{4}" telephone_pattern: str = r"(?:\+|00)971\s?[1234679]\s?\d{3}\s?\d{4}|" r"0[1234679]\s?\d{3}\s?\d{4}" toll_pattern: str = r"200\d{4}|" r"600\d{6}|" r"800\d{3,7}" service_phone_pattern: str = r"9(?:9(?:9|8|7|6|1)|01|22)" def test_cellphone_number(self, faker, num_samples): pattern: Pattern = re.compile(self.cellphone_pattern) for _ in range(num_samples): cellphone = faker.cellphone_number() assert pattern.fullmatch(cellphone) def test_telephone_number(self, faker, num_samples): pattern: Pattern = re.compile(self.telephone_pattern) for _ in range(num_samples): telephone = faker.telephone_number() assert pattern.fullmatch(telephone) def test_toll_number(self, faker, num_samples): pattern: Pattern = re.compile(self.toll_pattern) for _ in range(num_samples): toll = faker.toll_number() assert pattern.fullmatch(toll) def test_service_phone_number(self, faker, num_samples): pattern: Pattern = re.compile(self.service_phone_pattern) for _ in range(num_samples): service = faker.service_phone_number() assert pattern.fullmatch(service) def test_phone_number(self, faker, num_samples): pattern: Pattern = re.compile( rf"{self.cellphone_pattern}|" rf"{self.telephone_pattern}|" rf"{self.toll_pattern}|" rf"{self.service_phone_pattern}", ) for _ in range(num_samples): phone = faker.phone_number() assert pattern.fullmatch(phone)

40.22449

116

0.624472

24292d0f639225f021bade8d1366dc9361fbdfcb

726

py

Python

UnityPy/classes/EditorExtension.py

dblack2056/UnityPy

303291e46ddfbf266131237e59e6b1b5c46a9ca4

[ "MIT" ]

null

UnityPy/classes/EditorExtension.py

dblack2056/UnityPy

303291e46ddfbf266131237e59e6b1b5c46a9ca4

[ "MIT" ]

null

UnityPy/classes/EditorExtension.py

dblack2056/UnityPy

303291e46ddfbf266131237e59e6b1b5c46a9ca4

[ "MIT" ]

1

2021-01-20T05:28:43.000Z

from .Object import Object from .PPtr import PPtr, save_ptr from ..enums import BuildTarget from ..streams import EndianBinaryWriter, EndianBinaryReader class EditorExtension(Object): def __init__(self, reader: EndianBinaryReader): super().__init__(reader=reader) if self.platform == BuildTarget.NoTarget: self.prefab_parent_object = PPtr(reader) self.prefab_internal = PPtr(reader) def save(self, writer: EndianBinaryWriter): super().save(writer, intern_call=True) if self.platform == BuildTarget.NoTarget: save_ptr(self.prefab_parent_object, writer, self.reader.version2) save_ptr(self.prefab_internal, writer, self.reader.version2)

38.210526

77

0.713499

7526839695998d446518e81c21dc7f214b0c4e62

749

py

Python

RosettaCode/Prime_words/main.py

mohenjo/AlgorithmTraining

a8d64b6ce1c58086e3655ebc9997340aee93b74a

[ "MIT" ]

3

2019-05-01T00:22:01.000Z

2021-11-18T06:52:53.000Z

RosettaCode/Prime_words/main.py

mohenjo/RosettaCode

02d4a0306f20d2edb79667ce0f66160ee865ebae

[ "MIT" ]

null

RosettaCode/Prime_words/main.py

mohenjo/RosettaCode

02d4a0306f20d2edb79667ce0f66160ee865ebae

[ "MIT" ]

1

2021-11-13T20:41:02.000Z

"""Prime words http://www.rosettacode.org/wiki/Prime_words """ def isPrime(number: int): return all(number % num != 0 for num in range(2, number)) def get_prime_letters(): letters: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" letters += letters.lower() return [letter for letter in letters if isPrime(ord(letter))] def main(): with open("unixdict.txt", "rt", encoding="utf-8") as f: words: list[str] = f.read().strip().split() prime_letters: list[str] = get_prime_letters() prime_words: list[str] = [] for word in words: if all(True if letter in prime_letters else False for letter in word): prime_words.append(word) print(", ".join(prime_words)) if __name__ == "__main__": main()

24.16129

78

0.64753

f1e8dfb9f154f2fb0b44711b1d7b7e2a4fba8477

17,574

py

Python

api/getters.py

kmfrick/orario-sync_unibo

2c74ad5fb5f2eadcb35d37b5e00d09b1fa13469d

[ "BSD-3-Clause" ]

null

api/getters.py

kmfrick/orario-sync_unibo

2c74ad5fb5f2eadcb35d37b5e00d09b1fa13469d

[ "BSD-3-Clause" ]

null

api/getters.py

kmfrick/orario-sync_unibo

2c74ad5fb5f2eadcb35d37b5e00d09b1fa13469d

[ "BSD-3-Clause" ]

null

import datetime from datetime import timedelta import dateutil.parser import requests from bs4 import BeautifulSoup from bs4.dammit import EncodingDetector from icalendar import Calendar, Event, Timezone from api import constant def get_encoding(resp): """Gets the encoding used in a webpage""" http_encoding = resp.encoding if "charset" in resp.headers.get("content-type", "").lower() else None html_encoding = EncodingDetector.find_declared_encoding(resp.content, is_html=True) encoding = html_encoding or http_encoding return encoding def get_department_names(): """Gets a list of unibo's departments parsing a webpage""" dep_resp = requests.get(constant.DEPURL) dep_soup = BeautifulSoup(dep_resp.content, from_encoding=get_encoding(dep_resp), features="html5lib") depts = dep_soup.find("div", class_="dropdown-list") dept_links = [] dept_names = depts.find_all("button") for j in dept_names: dept_links.append({constant.NAMEFLD: j.find("span", class_="title").contents[0]}) return dept_links def get_args_from_url(requestline): """Parses arguments from a given URL""" line = str(requestline) method, _, rest = line.partition(" ") url, _, protocol = rest.rpartition(" ") try: school_index = int(url.split(constant.ARG_SCHOOL + "=")[1].split("&")[0]) except IndexError: school_index = 0 try: course_index = int(url.split(constant.ARG_COURSE + "=")[1].split("&")[0]) except IndexError: course_index = 0 try: year = int(url.split(constant.ARG_YEAR + "=")[1].split("&")[0]) except IndexError: year = 0 try: curr_index = int(url.split(constant.ARG_CURR + "=")[1].split("&")[0]) except IndexError: curr_index = 0 try: selected_classes_btm = int(url.split(constant.ARG_CLASSES + "=")[1].split("&")[0]) except IndexError: selected_classes_btm = 0 return {constant.ARG_COURSE: course_index, constant.ARG_CURR: curr_index, constant.ARG_SCHOOL: school_index, constant.ARG_CLASSES: selected_classes_btm, constant.ARG_YEAR: year} def get_course_list(school_id): """Gets a list of courses for a given department As of 2019-06-16, every department\'s courses can be obtained with a GET request to constant.CRSURL with the appropriate department number (ordered from 1 as in DEPURL) Dictionary fields: - constant.CODEFLD: course code for internal use - constant.NAMEFLD: course name - constant.LINKFLD: link to the course\'s site, used to get timetables""" courses_resp = requests.get(constant.CRSURL + str(school_id)) courses_soup = BeautifulSoup(courses_resp.content, from_encoding=get_encoding(courses_resp), features="html5lib") course_types = courses_soup.find_all("p", class_="type") course_names = courses_soup.find_all("div", class_="title") course_links = courses_soup.find_all("a", class_="umtrack") courses = [] for i in zip(course_names, course_links, course_types): course_type = "[" + "".join(c for c in i[2].contents[0] if c.isupper()) + "]" course_name = i[0].contents[1].contents[0] + " " + course_type # Only parse numbers in course id course_code = "".join(c for c in i[0].contents[3].contents[0] if c.isdigit()) course_link = i[1]["href"] courses.append({constant.CODEFLD: course_code, constant.NAMEFLD: course_name, constant.LINKFLD: course_link}) return courses def get_course_url(course_list, course_index): """Getter function to get a course\'s URL without directly accessing the dictionary""" return course_list[course_index][constant.LINKFLD] def get_course_name(course_list, course_index): """Getter function to get a course\'s name without directly accessing the dictionary""" return course_list[course_index][constant.NAMEFLD] def get_course_code(course_list, course_index): """Getter function to get a course\'s internal code without directly accessing the dictionary""" return course_list[course_index][constant.CODEFLD] def get_course_lang(course_url): """Getter function to get a course\'s language without directly analyzing the URL""" return constant.CRSLANG_EN if "cycle" in course_url else constant.CRSLANG_IT def get_curricula(course_url, year): """Encodes the available curricula for a given course in a given year in a vaguely sane format Dictionary fields: - constant.CODEFLD: curriculum code as used in JSON requests - constant.NAMEFLD: human-readable curriculum name""" curricula = [] curricula_req_url = constant.CURRICULAURLFORMAT[get_course_lang(course_url)].format(course_url, year) for curr in requests.get(curricula_req_url).json(): curricula.append({constant.CODEFLD: curr[constant.CURRVAL], constant.NAMEFLD: curr[constant.CURRNAME]}) return curricula def get_curr_name(curricula, curr_index): """Getter function to get a curriculum\'s name without directly accessing the dictionary""" return curricula[curr_index][constant.NAMEFLD] def get_curr_code(curricula, curr_index): """Getter function to get a curriculum\'s internal code without directly accessing the dictionary""" return curricula[curr_index][constant.CODEFLD] def get_classes_no_json(course_url, year, curr): """Gets a list of classes for courses that do not use a JSON timetable As of 2018-11-13 their names are the content of <li> tags in a <form> tag with id=constant.CLSNOJSONFORMID""" classes_url = constant.TIMETABLEURLFORMATNOJSON[get_course_lang(course_url)].format(course_url, year, curr) resp = requests.get(classes_url) soup = BeautifulSoup(resp.content, from_encoding=get_encoding(resp), features="html5lib") classes = [] for li in soup.find("form", id=constant.CLSNOJSONFORMID).find_all("li"): classes.append(li.contents[constant.CLSLABELPOS].contents[0]) return classes def get_location_no_json(i, soup): """Gets location info for courses that do not use a JSON timetable""" location_data = soup.find("div", id="panel{}".format(i)).find("div").contents classroom_name = location_data[0].lstrip().rstrip() classroom_info = location_data[1].find("div") if classroom_info is not None: classroom_loc = classroom_info.contents[0].lstrip().rstrip() classroom_name += " - " + classroom_loc return classroom_name def get_class_periods_no_json(i, soup): """Gets the dates of the first and last lessons of a certain class for courses that do not use a JSON timetable As of 2018-11-13 they are saved as the content of the first <p> tag inside a <div> with id equal to panel{index} Since a class can last two semesters with winter break in between, it returns an array """ periods = [] for period in soup.find("div", id="panel{}".format(i)).find_all("p"): periods.append(period.contents[0].split("\n")) return periods def get_class_name_no_json(i, soup): """Gets the name of a class for courses that do not use a JSON timetable As of 2018-11-13 it\'s saved as the content of an <h3> tag with id equal to tab{index}""" return soup.find("h3", id="tab{}".format(i)).find("a").contents[2].lstrip().rstrip() def get_lessons_no_json(i, soup): """Gets days of week and times a certain class is held for courses that do not use a JSON timetable As of 2018-11-13 they are saved as <td> elements inside a <table class=constant.TIMETABLETBLCLASS> inside a <div id=panel{index}>, with the following pattern repeating every 4 lines: 1) day of week (dict field: constant.DOWFLD) 2) timeframe expressed as "start_time - end_time" (df: constant.LSN(START|END)FLD 3) teacher\'s name (df: constant.TEACHERFLD) 4) blank line Returns a dict using 4 keys in the order above (no blank line of course, start and end times are separate)""" class_timetables = soup.find("div", id="panel{}".format(i)).find_all("table", class_=constant.TIMETABLETBLCLASS) class_lessons = [] for class_timetable in class_timetables: period_lessons = [] for (j, class_time) in enumerate(class_timetable.find("tbody").find_all("td"), 0): class_info = class_time.contents[0].lstrip().rstrip() if j % 4 == 0: lesson = {} lesson[constant.DOWFLD] = class_info.lstrip().rstrip() if j % 4 == 1: timesplit = class_info.split(" - ") lesson[constant.LSNSTARTFLD] = timesplit[0].lstrip().rstrip() lesson[constant.LSNENDFLD] = timesplit[1].lstrip().rstrip() if j % 4 == 2: lesson[constant.TEACHERFLD] = class_info.lstrip().rstrip() period_lessons.append(lesson) class_lessons.append(period_lessons[:]) return class_lessons def get_raw_timetable_no_json(course_url, year, curr): """Encodes the timetable of a course that does not use a JSON timetable in a vaguely sane format Dictionary fields: - constant.NAMEFLD: name of the class - constant.CLS(START|END)FLD: first|last lessons of a certain class - constant.LOCATIONFLD: where the class is held (the same every day that class is held) - constant.LESSONSFLD: array of dicts describing the days and times a certain class is held The 4th field\'s dict fields are as returned by get_lessons_no_json() """ timetable_url = constant.TIMETABLEURLFORMATNOJSON[get_course_lang(course_url)].format(course_url, year, curr) resp = requests.get(timetable_url) soup = BeautifulSoup(resp.content, from_encoding=get_encoding(resp), features="html5lib") classes = [] available_classes = get_classes_no_json(course_url, year, curr) for i in range(0, len(available_classes)): class_name = get_class_name_no_json(i, soup) class_periods = get_class_periods_no_json(i, soup) classroom_name = get_location_no_json(i, soup) class_lessons = get_lessons_no_json(i, soup) for period, period_lessons in zip(class_periods, class_lessons): _class = {constant.NAMEFLD: class_name, constant.CLSSTARTFLD: period[2].lstrip()[:-2], constant.CLSENDFLD: period[3].lstrip(), constant.LOCATIONFLD: classroom_name, constant.LESSONSFLD: period_lessons} classes.append(_class) return classes def encode_json_timetable(raw_timetable): """Encodes a JSON timetable in a vaguely sane format (array of dictionaries with 5 fields) Dictionary fields: - constant.NAMEFLD: class name - constant.LSNSTARTFLD: datetime object with lesson start time - constant.LSNENDFLD: datetime object with lesson end time - constant.LOCATIONFLD: where the lesson is held, if available - constant.TEACHERFLD: who holds the lesson, if available """ lessons = [] for lesson in raw_timetable: title = lesson[constant.TITLE] if lesson[constant.ROOMS]: location = lesson[constant.ROOMS][0][constant.CLASSROOM] + ", " + lesson[constant.ROOMS][0][constant.CAMPUS] else: location = constant.NO_LOC_AVAILABLE start = dateutil.parser.parse(lesson[constant.START]) end = dateutil.parser.parse(lesson[constant.END]) teacher = lesson[constant.TEACHER] lessons.append({constant.NAMEFLD: title, constant.LSNSTARTFLD: start, constant.LSNENDFLD: end, constant.LOCATIONFLD: location, constant.TEACHERFLD: teacher}) return lessons def date_range(start_date, end_date): """Gets a range of dates as array""" for n in range(int((end_date - start_date).days)): yield start_date + timedelta(n) def parse_italian_date(date_str): """Parses an Italian date in the form "dd monthlongname yyyy""" italian_months = ["gennaio", "febbraio", "marzo", "aprile", "maggio", "giugno", "luglio", "agosto", "settembre", "ottobre", "novembre", "dicembre"] fields = date_str.split(" ") dd = int(fields[0]) mmmm = italian_months.index(fields[1]) + 1 yyyy = int(fields[2]) return datetime.datetime(yyyy, mmmm, dd) def encode_no_json_timetable(raw_timetable): """Encodes a non-JSON timetable in the same vaguely sane format Dictionary fields: - constant.NAMEFLD: class name - constant.LSNSTARTFLD: datetime object with lesson start time - constant.LSNENDFLD: datetime object with lesson end time - constant.LOCATIONFLD: where the lesson is held, if available - constant.TEACHERFLD: who holds the lesson, if available """ lessons = [] for _class in raw_timetable: title = _class[constant.NAMEFLD] location = _class[constant.LOCATIONFLD] class_first_lesson = parse_italian_date(_class[constant.LSNSTARTFLD]) class_last_lesson = parse_italian_date(_class[constant.LSNENDFLD]) class_period = date_range(class_first_lesson, class_last_lesson) for period_date in class_period: weekday = period_date.weekday() for lesson in _class[constant.LESSONSFLD]: teacher = lesson[constant.TEACHERFLD] starthr = int(lesson[constant.LSNSTARTFLD].split(":")[0]) startmm = int(lesson[constant.LSNSTARTFLD].split(":")[1]) endhr = int(lesson[constant.LSNENDFLD].split(":")[0]) endmm = int(lesson[constant.LSNENDFLD].split(":")[1]) lesson_weekday = get_it_dow_number(lesson) if weekday == lesson_weekday: startdatetime = period_date.replace(hour=starthr, minute=startmm) enddatetime = period_date.replace(hour=endhr, minute=endmm) lessons.append( {constant.NAMEFLD: title, constant.LSNSTARTFLD: startdatetime, constant.LSNENDFLD: enddatetime, constant.LOCATIONFLD: location, constant.TEACHERFLD: teacher}) return lessons def get_it_dow_number(lesson): days_of_week_it = ["lunedì", "martedì", "mercoledì", "giovedì", "venerdì", "sabato", "domenica"] return days_of_week_it.index(lesson[constant.DOWFLD].lower()) def has_json_timetable(course_url, year, curr): """Checks if a course uses a JSON timetable As of 2018-11-13, if a course uses a JSON timetable it has a <div id="calendar"> in its timetable page""" page_url = constant.TIMETABLEURLFORMAT[get_course_lang(course_url)].format(course_url, year, curr) resp = requests.get(page_url) return resp.status_code == 200 def get_timetable(course_url, year, curr): """Checks if the selected course uses a JSON calendar and calls the appropriate get_timetable function""" if has_json_timetable(course_url, year, curr): timetable_url = constant.TIMETABLEURLFORMAT[get_course_lang(course_url)].format(course_url, year, curr) req = requests.get(url=timetable_url) timetable = encode_json_timetable(req.json()) else: raw_timetable = get_raw_timetable_no_json(course_url, year, curr) timetable = encode_no_json_timetable(raw_timetable) return timetable def get_classes_json(course_url, year, curr): """Gets a list of classes from a JSON timetable As of 2020-09-28, JSON timetables do not have a list of classes anymore, so we have to traverse the array of classes, get their names and remove duplicates""" resp = requests.get(constant.TIMETABLEURLFORMAT[get_course_lang(course_url)].format(course_url, year, curr)) classes = [] for _class in resp.json(): classes.append(_class[constant.TITLE]) return list(set(classes)) def get_classes(course_url, year, curr): """Checks if the selected course uses a JSON calendar and calls the appropriate get_classes() function""" if has_json_timetable(course_url, year, curr): return sorted(get_classes_json(course_url, year, curr)) else: return sorted(get_classes_no_json(course_url, year, curr)) def get_ical_file(timetable, classes): """Creates an iCalendar file with the timetable as [requested""" cal = Calendar() timezone = Timezone.from_ical(constant.TIMEZONESTR) cal.add_component(timezone) cal.add("prodid", "//kmfrick//orario-sync//IT") cal.add("version", "1.0") i = 0 for lesson in timetable: if lesson[constant.NAMEFLD] in classes: event = Event() event.add("uid", str(datetime.datetime.now()) + "@OrarioSync" + str(i)) i = i + 1 event.add("dtstamp", datetime.datetime.now()) event.add(constant.ICALTITLE, lesson[constant.NAMEFLD]) event.add(constant.ICALSTART, lesson[constant.LSNSTARTFLD], parameters={'tzid': constant.TIMEZONE}) event.add(constant.ICALEND, lesson[constant.LSNENDFLD], parameters={'tzid': constant.TIMEZONE}) event.add(constant.ICALLOCATION, lesson[constant.LOCATIONFLD]) event.add("description", lesson[constant.TEACHERFLD]) cal.add_component(event) return cal.to_ical() def get_safe_course_name(name): """Generates a string that is safe to use as a file name Strips special characters and removes digits to remove the internal code from the course\'s name""" return "".join([c for c in name if c.isalpha() and not c.isdigit()]).rstrip()

44.604061

120

0.68516

c2b68890af2e496f00880cfdc43da45e7797ce58

29,835

py

Python

tests/api/v1/test_challenges.py

atti1a/CTFd

6c5c63d667a17aec159c8e26ea53dccfbc4d0fa3

[ "Apache-2.0" ]

7

2019-10-10T10:06:38.000Z

2021-02-13T05:07:34.000Z

tests/api/v1/test_challenges.py

atti1a/CTFd

6c5c63d667a17aec159c8e26ea53dccfbc4d0fa3

[ "Apache-2.0" ]

5

2021-03-09T20:12:55.000Z

2022-02-10T19:27:46.000Z

tests/api/v1/test_challenges.py

atti1a/CTFd

6c5c63d667a17aec159c8e26ea53dccfbc4d0fa3

[ "Apache-2.0" ]

13

2020-05-08T18:52:54.000Z

2022-01-02T11:19:07.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from CTFd.models import Users, Challenges, Tags, Hints, Flags, Solves from CTFd.utils import set_config from tests.helpers import ( create_ctfd, destroy_ctfd, register_user, login_as_user, gen_challenge, gen_flag, gen_tag, gen_hint, gen_user, gen_team, gen_solve, gen_fail, ) from freezegun import freeze_time def test_api_challenges_get_visibility_public(): """Can a public user get /api/v1/challenges if challenge_visibility is private/public""" app = create_ctfd() with app.app_context(): set_config("challenge_visibility", "public") with app.test_client() as client: r = client.get("/api/v1/challenges") assert r.status_code == 200 set_config("challenge_visibility", "private") r = client.get("/api/v1/challenges", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenges_get_ctftime_public(): """Can a public user get /api/v1/challenges if ctftime is over""" app = create_ctfd() with app.app_context(), freeze_time("2017-10-7"): set_config("challenge_visibility", "public") with app.test_client() as client: r = client.get("/api/v1/challenges") assert r.status_code == 200 set_config( "start", "1507089600" ) # Wednesday, October 4, 2017 12:00:00 AM GMT-04:00 DST set_config( "end", "1507262400" ) # Friday, October 6, 2017 12:00:00 AM GMT-04:00 DST r = client.get("/api/v1/challenges") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenges_get_visibility_private(): """Can a private user get /api/v1/challenges if challenge_visibility is private/public""" app = create_ctfd() with app.app_context(): register_user(app) client = login_as_user(app) r = client.get("/api/v1/challenges") assert r.status_code == 200 set_config("challenge_visibility", "public") r = client.get("/api/v1/challenges") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenges_get_ctftime_private(): """Can a private user get /api/v1/challenges if ctftime is over""" app = create_ctfd() with app.app_context(), freeze_time("2017-10-7"): register_user(app) client = login_as_user(app) r = client.get("/api/v1/challenges") assert r.status_code == 200 set_config( "start", "1507089600" ) # Wednesday, October 4, 2017 12:00:00 AM GMT-04:00 DST set_config( "end", "1507262400" ) # Friday, October 6, 2017 12:00:00 AM GMT-04:00 DST r = client.get("/api/v1/challenges") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenges_get_verified_emails(): """Can a verified email user get /api/v1/challenges""" app = create_ctfd() with app.app_context(): set_config("verify_emails", True) register_user(app) client = login_as_user(app) r = client.get("/api/v1/challenges", json="") assert r.status_code == 403 gen_user( app.db, name="user_name", email="verified_user@ctfd.io", password="password", verified=True, ) registered_client = login_as_user(app, "user_name", "password") r = registered_client.get("/api/v1/challenges") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenges_post_non_admin(): """Can a user post /api/v1/challenges if not admin""" app = create_ctfd() with app.app_context(): with app.test_client() as client: r = client.post("/api/v1/challenges", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenges_get_admin(): """Can a user GET /api/v1/challenges if admin without team""" app = create_ctfd(user_mode="teams") with app.app_context(): gen_challenge(app.db) # Admin does not have a team but should still be able to see challenges user = Users.query.filter_by(id=1).first() assert user.team_id is None with login_as_user(app, "admin") as admin: r = admin.get("/api/v1/challenges", json="") assert r.status_code == 200 r = admin.get("/api/v1/challenges/1", json="") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenges_post_admin(): """Can a user post /api/v1/challenges if admin""" app = create_ctfd() with app.app_context(): with login_as_user(app, "admin") as client: r = client.post( "/api/v1/challenges", json={ "name": "chal", "category": "cate", "description": "desc", "value": "100", "state": "hidden", "type": "standard", }, ) assert r.status_code == 200 destroy_ctfd(app) def test_api_challenge_types_post_non_admin(): """Can a non-admin get /api/v1/challenges/types if not admin""" app = create_ctfd() with app.app_context(): with app.test_client() as client: r = client.get("/api/v1/challenges/types", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_types_post_admin(): """Can an admin get /api/v1/challenges/types if admin""" app = create_ctfd() with app.app_context(): with login_as_user(app, "admin") as client: r = client.get("/api/v1/challenges/types", json="") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenge_get_visibility_public(): """Can a public user get /api/v1/challenges/<challenge_id> if challenge_visibility is private/public""" app = create_ctfd() with app.app_context(): set_config("challenge_visibility", "public") with app.test_client() as client: gen_challenge(app.db) r = client.get("/api/v1/challenges/1") assert r.status_code == 200 set_config("challenge_visibility", "private") r = client.get("/api/v1/challenges/1", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_get_ctftime_public(): """Can a public user get /api/v1/challenges/<challenge_id> if ctftime is over""" app = create_ctfd() with app.app_context(), freeze_time("2017-10-7"): set_config("challenge_visibility", "public") gen_challenge(app.db) with app.test_client() as client: r = client.get("/api/v1/challenges/1") assert r.status_code == 200 set_config( "start", "1507089600" ) # Wednesday, October 4, 2017 12:00:00 AM GMT-04:00 DST set_config( "end", "1507262400" ) # Friday, October 6, 2017 12:00:00 AM GMT-04:00 DST r = client.get("/api/v1/challenges/1") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_get_visibility_private(): """Can a private user get /api/v1/challenges/<challenge_id> if challenge_visibility is private/public""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) register_user(app) client = login_as_user(app) r = client.get("/api/v1/challenges/1") assert r.status_code == 200 set_config("challenge_visibility", "public") r = client.get("/api/v1/challenges/1") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenge_get_ctftime_private(): """Can a private user get /api/v1/challenges/<challenge_id> if ctftime is over""" app = create_ctfd() with app.app_context(), freeze_time("2017-10-7"): gen_challenge(app.db) register_user(app) client = login_as_user(app) r = client.get("/api/v1/challenges/1") assert r.status_code == 200 set_config( "start", "1507089600" ) # Wednesday, October 4, 2017 12:00:00 AM GMT-04:00 DST set_config( "end", "1507262400" ) # Friday, October 6, 2017 12:00:00 AM GMT-04:00 DST r = client.get("/api/v1/challenges/1") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_get_verified_emails(): """Can a verified email load /api/v1/challenges/<challenge_id>""" app = create_ctfd() with app.app_context(), freeze_time("2017-10-5"): set_config( "start", "1507089600" ) # Wednesday, October 4, 2017 12:00:00 AM GMT-04:00 DST set_config( "end", "1507262400" ) # Friday, October 6, 2017 12:00:00 AM GMT-04:00 DST set_config("verify_emails", True) gen_challenge(app.db) gen_user( app.db, name="user_name", email="verified_user@ctfd.io", password="password", verified=True, ) register_user(app) client = login_as_user(app) registered_client = login_as_user(app, "user_name", "password") r = client.get("/api/v1/challenges/1", json="") assert r.status_code == 403 r = registered_client.get("/api/v1/challenges/1") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenge_get_non_existing(): """Will a bad <challenge_id> at /api/v1/challenges/<challenge_id> 404""" app = create_ctfd() with app.app_context(), freeze_time("2017-10-5"): set_config( "start", "1507089600" ) # Wednesday, October 4, 2017 12:00:00 AM GMT-04:00 DST set_config( "end", "1507262400" ) # Friday, October 6, 2017 12:00:00 AM GMT-04:00 DST register_user(app) client = login_as_user(app) r = client.get("/api/v1/challenges/1") assert r.status_code == 404 destroy_ctfd(app) def test_api_challenge_patch_non_admin(): """Can a user patch /api/v1/challenges/<challenge_id> if not admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with app.test_client() as client: r = client.patch("/api/v1/challenges/1", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_patch_admin(): """Can a user patch /api/v1/challenges/<challenge_id> if admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with login_as_user(app, "admin") as client: r = client.patch( "/api/v1/challenges/1", json={"name": "chal_name", "value": "200"} ) assert r.status_code == 200 assert r.get_json()["data"]["value"] == 200 destroy_ctfd(app) def test_api_challenge_delete_non_admin(): """Can a user delete /api/v1/challenges/<challenge_id> if not admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with app.test_client() as client: r = client.delete("/api/v1/challenges/1", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_delete_admin(): """Can a user delete /api/v1/challenges/<challenge_id> if admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with login_as_user(app, "admin") as client: r = client.delete("/api/v1/challenges/1", json="") assert r.status_code == 200 assert r.get_json().get("data") is None destroy_ctfd(app) def test_api_challenge_with_properties_delete_admin(): """Can a user delete /api/v1/challenges/<challenge_id> if the challenge has other properties""" app = create_ctfd() with app.app_context(): challenge = gen_challenge(app.db) gen_hint(app.db, challenge_id=challenge.id) gen_tag(app.db, challenge_id=challenge.id) gen_flag(app.db, challenge_id=challenge.id) challenge = Challenges.query.filter_by(id=1).first() assert len(challenge.hints) == 1 assert len(challenge.tags) == 1 assert len(challenge.flags) == 1 with login_as_user(app, "admin") as client: r = client.delete("/api/v1/challenges/1", json="") assert r.status_code == 200 assert r.get_json().get("data") is None assert Tags.query.count() == 0 assert Hints.query.count() == 0 assert Flags.query.count() == 0 destroy_ctfd(app) def test_api_challenge_attempt_post_public(): """Can a public user post /api/v1/challenges/attempt""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with app.test_client() as client: r = client.post("/api/v1/challenges/attempt", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_attempt_post_private(): """Can an private user post /api/v1/challenges/attempt""" app = create_ctfd() with app.app_context(): challenge_id = gen_challenge(app.db).id gen_flag(app.db, challenge_id) register_user(app) with login_as_user(app) as client: r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": challenge_id, "submission": "wrong_flag"}, ) assert r.status_code == 200 assert r.get_json()["data"]["status"] == "incorrect" r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": challenge_id, "submission": "flag"}, ) assert r.status_code == 200 assert r.get_json()["data"]["status"] == "correct" r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": challenge_id, "submission": "flag"}, ) assert r.status_code == 200 assert r.get_json()["data"]["status"] == "already_solved" challenge_id = gen_challenge(app.db).id gen_flag(app.db, challenge_id) with login_as_user(app) as client: for i in range(10): gen_fail(app.db, user_id=2, challenge_id=challenge_id) r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": challenge_id, "submission": "flag"}, ) assert r.status_code == 429 assert r.get_json()["data"]["status"] == "ratelimited" destroy_ctfd(app) app = create_ctfd(user_mode="teams") with app.app_context(): challenge_id = gen_challenge(app.db).id gen_flag(app.db, challenge_id) register_user(app) team_id = gen_team(app.db).id user = Users.query.filter_by(id=2).first() user.team_id = team_id app.db.session.commit() with login_as_user(app) as client: r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": challenge_id, "submission": "wrong_flag"}, ) assert r.status_code == 200 assert r.get_json()["data"]["status"] == "incorrect" r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": challenge_id, "submission": "flag"}, ) assert r.status_code == 200 assert r.get_json()["data"]["status"] == "correct" r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": challenge_id, "submission": "flag"}, ) assert r.status_code == 200 assert r.get_json()["data"]["status"] == "already_solved" challenge_id = gen_challenge(app.db).id gen_flag(app.db, challenge_id) with login_as_user(app) as client: for i in range(10): gen_fail(app.db, user_id=2, team_id=team_id, challenge_id=challenge_id) r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": challenge_id, "submission": "flag"}, ) assert r.status_code == 429 assert r.get_json()["data"]["status"] == "ratelimited" destroy_ctfd(app) def test_api_challenge_attempt_post_admin(): """Can an admin user post /api/v1/challenges/attempt""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) gen_flag(app.db, 1) with login_as_user(app, "admin") as client: r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": 1, "submission": "wrong_flag"}, ) assert r.status_code == 200 assert r.get_json()["data"]["status"] == "incorrect" r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": 1, "submission": "flag"}, ) assert r.status_code == 200 assert r.get_json()["data"]["status"] == "correct" r = client.post( "/api/v1/challenges/attempt", json={"challenge_id": 1, "submission": "flag"}, ) assert r.status_code == 200 assert r.get_json()["data"]["status"] == "already_solved" destroy_ctfd(app) def test_api_challenge_get_solves_visibility_public(): """Can a public user get /api/v1/challenges/<challenge_id>/solves if challenge_visibility is private/public""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with app.test_client() as client: set_config("challenge_visibility", "public") r = client.get("/api/v1/challenges/1/solves", json="") assert r.status_code == 200 set_config("challenge_visibility", "private") r = client.get("/api/v1/challenges/1/solves", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_get_solves_ctftime_public(): """Can a public user get /api/v1/challenges/<challenge_id>/solves if ctftime is over""" app = create_ctfd() with app.app_context(), freeze_time("2017-10-7"): set_config("challenge_visibility", "public") gen_challenge(app.db) with app.test_client() as client: r = client.get("/api/v1/challenges/1/solves") assert r.status_code == 200 set_config( "start", "1507089600" ) # Wednesday, October 4, 2017 12:00:00 AM GMT-04:00 DST set_config( "end", "1507262400" ) # Friday, October 6, 2017 12:00:00 AM GMT-04:00 DST r = client.get("/api/v1/challenges/1/solves", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_get_solves_ctf_frozen(): """Test users can only see challenge solves that happened before freeze time""" app = create_ctfd() with app.app_context(): register_user(app, name="user1", email="user1@ctfd.io") register_user(app, name="user2", email="user2@ctfd.io") # Friday, October 6, 2017 12:00:00 AM GMT-04:00 DST set_config("freeze", "1507262400") with freeze_time("2017-10-4"): chal = gen_challenge(app.db) chal_id = chal.id gen_solve(app.db, user_id=2, challenge_id=chal_id) chal2 = gen_challenge(app.db) chal2_id = chal2.id with freeze_time("2017-10-8"): # User ID 2 solves Challenge ID 2 gen_solve(app.db, user_id=2, challenge_id=chal2_id) # User ID 3 solves Challenge ID 1 gen_solve(app.db, user_id=3, challenge_id=chal_id) # Challenge 1 has 2 solves # Challenge 2 has 1 solve # There should now be two solves assigned to the same user. assert Solves.query.count() == 3 client = login_as_user(app, name="user2") # Challenge 1 should have one solve (after freeze) r = client.get("/api/v1/challenges/1") data = r.get_json()["data"] assert data['solves'] == 1 # Challenge 1 should have one solve (after freeze) r = client.get("/api/v1/challenges/1/solves") data = r.get_json()["data"] assert len(data) == 1 # Challenge 2 should have a solve shouldn't be shown to the user r = client.get("/api/v1/challenges/2/solves") data = r.get_json()["data"] assert len(data) == 0 # Admins should see data as an admin with no modifications admin = login_as_user(app, name="admin") r = admin.get("/api/v1/challenges/2/solves") data = r.get_json()["data"] assert len(data) == 1 # But should see as a user if the preview param is passed r = admin.get("/api/v1/challenges/2/solves?preview=true") data = r.get_json()["data"] assert len(data) == 0 destroy_ctfd(app) def test_api_challenge_get_solves_visibility_private(): """Can a private user get /api/v1/challenges/<challenge_id>/solves if challenge_visibility is private/public""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) register_user(app) client = login_as_user(app) r = client.get("/api/v1/challenges/1/solves") assert r.status_code == 200 set_config("challenge_visibility", "public") r = client.get("/api/v1/challenges/1/solves") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenge_get_solves_ctftime_private(): """Can a private user get /api/v1/challenges/<challenge_id>/solves if ctftime is over""" app = create_ctfd() with app.app_context(), freeze_time("2017-10-7"): gen_challenge(app.db) register_user(app) client = login_as_user(app) r = client.get("/api/v1/challenges/1/solves") assert r.status_code == 200 set_config( "start", "1507089600" ) # Wednesday, October 4, 2017 12:00:00 AM GMT-04:00 DST set_config( "end", "1507262400" ) # Friday, October 6, 2017 12:00:00 AM GMT-04:00 DST r = client.get("/api/v1/challenges/1/solves") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_get_solves_verified_emails(): """Can a verified email get /api/v1/challenges/<challenge_id>/solves""" app = create_ctfd() with app.app_context(): set_config("verify_emails", True) gen_challenge(app.db) gen_user( app.db, name="user_name", email="verified_user@ctfd.io", password="password", verified=True, ) register_user(app) client = login_as_user(app) registered_client = login_as_user(app, "user_name", "password") r = client.get("/api/v1/challenges/1/solves", json="") assert r.status_code == 403 r = registered_client.get("/api/v1/challenges/1/solves") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenges_get_solves_score_visibility(): """Can a user get /api/v1/challenges/<challenge_id>/solves if score_visibility is public/private/admin""" app = create_ctfd() with app.app_context(): set_config("challenge_visibility", "public") set_config("score_visibility", "public") gen_challenge(app.db) with app.test_client() as client: r = client.get("/api/v1/challenges/1/solves") assert r.status_code == 200 set_config("challenge_visibility", "private") set_config("score_visibility", "private") register_user(app) private_client = login_as_user(app) r = private_client.get("/api/v1/challenges/1/solves") assert r.status_code == 200 set_config("score_visibility", "admin") admin = login_as_user(app, "admin", "password") r = admin.get("/api/v1/challenges/1/solves") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenge_get_solves_404(): """Will a bad <challenge_id> at /api/v1/challenges/<challenge_id>/solves 404""" app = create_ctfd() with app.app_context(): register_user(app) client = login_as_user(app) r = client.get("/api/v1/challenges/1/solves") assert r.status_code == 404 destroy_ctfd(app) def test_api_challenge_solves_returns_correct_data(): """Test that /api/v1/<challenge_id>/solves returns expected data""" app = create_ctfd() with app.app_context(): register_user(app) client = login_as_user(app) chal = gen_challenge(app.db) gen_solve(app.db, user_id=2, challenge_id=chal.id) r = client.get("/api/v1/challenges/1/solves") resp = r.get_json()["data"] solve = resp[0] assert r.status_code == 200 assert solve.get("account_id") == 2 assert solve.get("name") == "user" assert solve.get("date") is not None assert solve.get("account_url") == "/users/2" destroy_ctfd(app) app = create_ctfd(user_mode="teams") with app.app_context(): register_user(app) client = login_as_user(app) team = gen_team(app.db) user = Users.query.filter_by(id=2).first() user.team_id = team.id app.db.session.commit() chal = gen_challenge(app.db) gen_solve(app.db, user_id=2, team_id=1, challenge_id=chal.id) r = client.get("/api/v1/challenges/1/solves") resp = r.get_json()["data"] solve = resp[0] assert r.status_code == 200 assert solve.get("account_id") == 1 assert solve.get("name") == "team_name" assert solve.get("date") is not None assert solve.get("account_url") == "/teams/1" destroy_ctfd(app) app = create_ctfd(application_root="/ctf") with app.app_context(): register_user(app) client = login_as_user(app) chal = gen_challenge(app.db) gen_solve(app.db, user_id=2, challenge_id=chal.id) r = client.get("/api/v1/challenges/1/solves") resp = r.get_json()["data"] solve = resp[0] assert r.status_code == 200 assert solve.get("account_id") == 2 assert solve.get("name") == "user" assert solve.get("date") is not None assert solve.get("account_url") == "/ctf/users/2" destroy_ctfd(app) def test_api_challenge_get_files_non_admin(): """Can a user get /api/v1/challenges/<challenge_id>/files if not admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with app.test_client() as client: r = client.get("/api/v1/challenges/1/files", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_get_files_admin(): """Can a user get /api/v1/challenges/<challenge_id>/files if admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with login_as_user(app, "admin") as client: r = client.get("/api/v1/challenges/1/files") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenge_get_tags_non_admin(): """Can a user get /api/v1/challenges/<challenge_id>/tags if not admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with app.test_client() as client: r = client.get("/api/v1/challenges/1/tags", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_get_tags_admin(): """Can a user get /api/v1/challenges/<challenge_id>/tags if admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with login_as_user(app, "admin") as client: r = client.get("/api/v1/challenges/1/tags") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenge_get_hints_non_admin(): """Can a user get /api/v1/challenges/<challenge_id>/hints if not admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with app.test_client() as client: r = client.get("/api/v1/challenges/1/hints", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_get_hints_admin(): """Can a user get /api/v1/challenges/<challenge_id>/hints if admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with login_as_user(app, "admin") as client: r = client.get("/api/v1/challenges/1/hints") assert r.status_code == 200 destroy_ctfd(app) def test_api_challenge_get_flags_non_admin(): """Can a user get /api/v1/challenges/<challenge_id>/flags if not admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with app.test_client() as client: r = client.get("/api/v1/challenges/1/flags", json="") assert r.status_code == 403 destroy_ctfd(app) def test_api_challenge_get_flags_admin(): """Can a user get /api/v1/challenges/<challenge_id>/flags if admin""" app = create_ctfd() with app.app_context(): gen_challenge(app.db) with login_as_user(app, "admin") as client: r = client.get("/api/v1/challenges/1/flags") assert r.status_code == 200 destroy_ctfd(app)

36.652334

115

0.599497

e6a5bebbfea2faf769458a394b8eb1bed4f72fd3

2,144

py

Python

twitchapi/apichecks.py

Amperture/twitch-sbc-integration

71ee86688a7735e6bb3d18c9896c1b8c7a3662d7

[ "MIT" ]

10

2017-04-20T15:15:51.000Z

2021-11-17T20:08:01.000Z

twitchapi/apichecks.py

Amperture/twitch-sbc-integration

71ee86688a7735e6bb3d18c9896c1b8c7a3662d7

[ "MIT" ]

null

twitchapi/apichecks.py

Amperture/twitch-sbc-integration

71ee86688a7735e6bb3d18c9896c1b8c7a3662d7

[ "MIT" ]

2

2020-02-08T04:15:43.000Z

2021-11-04T09:18:43.000Z

import ConfigParser import Adafruit_BBIO.GPIO as GPIO import requests import time #TEMPORARY CODE FOR TESTING GREEN_LED = "P8_7" RED_LED = "P8_8" GPIO.setup("P8_7", GPIO.OUT) GPIO.setup("P8_8", GPIO.OUT) #END TEMP CODE Config = ConfigParser.ConfigParser() Config.read('../config.ini') clientid = Config.get('API', 'clientid') #channelname = Config.get('API', 'channelname') #TODO Correct this channelname = "amperture" url = "https://api.twitch.tv/kraken/" headers = { 'Client-ID': clientid } apicall = url + "channels/" + channelname + "/" + "follows" while True: #TODO: LOOK INTO InsecurePlatformWarning r = requests.get(apicall, headers=headers) with open('latestfollower', "r") as f: stored = f.read() latest = r.json()["follows"][0]['user']['display_name'] if latest != stored: with open('latestfollower', "w+") as f: f.write(latest) GPIO.output(GREEN_LED, GPIO.LOW) time.sleep(0.01) GPIO.output(RED_LED, GPIO.HIGH) time.sleep(0.1) GPIO.output(RED_LED, GPIO.LOW) time.sleep(0.01) GPIO.output(GREEN_LED, GPIO.HIGH) time.sleep(0.1) GPIO.output(GREEN_LED, GPIO.LOW) time.sleep(0.01) GPIO.output(RED_LED, GPIO.HIGH) time.sleep(0.1) GPIO.output(RED_LED, GPIO.LOW) time.sleep(0.01) GPIO.output(GREEN_LED, GPIO.HIGH) time.sleep(0.1) GPIO.output(GREEN_LED, GPIO.LOW) time.sleep(0.01) GPIO.output(RED_LED, GPIO.HIGH) time.sleep(0.1) GPIO.output(RED_LED, GPIO.LOW) time.sleep(0.01) GPIO.output(GREEN_LED, GPIO.HIGH) time.sleep(0.1) GPIO.output(GREEN_LED, GPIO.LOW) time.sleep(0.01) GPIO.output(RED_LED, GPIO.HIGH) time.sleep(0.1) GPIO.output(RED_LED, GPIO.LOW) time.sleep(0.01) GPIO.output(GREEN_LED, GPIO.HIGH) time.sleep(2) GPIO.output(GREEN_LED, GPIO.LOW)

26.469136

59

0.567631

3c27ac221cd28beafe6541cd5a78003bf0f9d106

4,485

py

Python

tempest/api/messaging/test_messages.py

afaheem88/tempest_neutron

20276dbee68087e576f4977633380fc6cd3fc1cd

[ "Apache-2.0" ]

null

tempest/api/messaging/test_messages.py

afaheem88/tempest_neutron

20276dbee68087e576f4977633380fc6cd3fc1cd

[ "Apache-2.0" ]

null

tempest/api/messaging/test_messages.py

afaheem88/tempest_neutron

20276dbee68087e576f4977633380fc6cd3fc1cd

[ "Apache-2.0" ]

1

2018-10-09T06:32:04.000Z

# Copyright (c) 2014 Rackspace, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from tempest.api.messaging import base from tempest.common.utils import data_utils from tempest import config from tempest import test LOG = logging.getLogger(__name__) CONF = config.CONF class TestMessages(base.BaseMessagingTest): _interface = 'json' @classmethod def resource_setup(cls): super(TestMessages, cls).resource_setup() cls.queue_name = data_utils.rand_name('Queues-Test') # Create Queue cls.client.create_queue(cls.queue_name) def _post_messages(self, repeat=CONF.messaging.max_messages_per_page): message_body = self.generate_message_body(repeat=repeat) resp, body = self.post_messages(queue_name=self.queue_name, rbody=message_body) return resp, body @test.attr(type='smoke') def test_post_messages(self): # Post Messages resp, _ = self._post_messages() # Get on the posted messages message_uri = resp['location'] resp, _ = self.client.get_multiple_messages(message_uri) # The test has an assertion here, because the response cannot be 204 # in this case (the client allows 200 or 204 for this API call). self.assertEqual('200', resp['status']) @test.attr(type='smoke') def test_list_messages(self): # Post Messages self._post_messages() # List Messages resp, _ = self.list_messages(queue_name=self.queue_name) # The test has an assertion here, because the response cannot be 204 # in this case (the client allows 200 or 204 for this API call). self.assertEqual('200', resp['status']) @test.attr(type='smoke') def test_get_message(self): # Post Messages _, body = self._post_messages() message_uri = body['resources'][0] # Get posted message resp, _ = self.client.get_single_message(message_uri) # The test has an assertion here, because the response cannot be 204 # in this case (the client allows 200 or 204 for this API call). self.assertEqual('200', resp['status']) @test.attr(type='smoke') def test_get_multiple_messages(self): # Post Messages resp, _ = self._post_messages() message_uri = resp['location'] # Get posted messages resp, _ = self.client.get_multiple_messages(message_uri) # The test has an assertion here, because the response cannot be 204 # in this case (the client allows 200 or 204 for this API call). self.assertEqual('200', resp['status']) @test.attr(type='smoke') def test_delete_single_message(self): # Post Messages _, body = self._post_messages() message_uri = body['resources'][0] # Delete posted message & verify the delete operration self.client.delete_messages(message_uri) message_uri = message_uri.replace('/messages/', '/messages?ids=') resp, _ = self.client.get_multiple_messages(message_uri) # The test has an assertion here, because the response has to be 204 # in this case (the client allows 200 or 204 for this API call). self.assertEqual('204', resp['status']) @test.attr(type='smoke') def test_delete_multiple_messages(self): # Post Messages resp, _ = self._post_messages() message_uri = resp['location'] # Delete multiple messages self.client.delete_messages(message_uri) resp, _ = self.client.get_multiple_messages(message_uri) # The test has an assertion here, because the response has to be 204 # in this case (the client allows 200 or 204 for this API call). self.assertEqual('204', resp['status']) @classmethod def resource_cleanup(cls): cls.delete_queue(cls.queue_name) super(TestMessages, cls).resource_cleanup()

36.463415

76

0.670234

08c0d7588aba97a0c00c1d428c21d72c2ebff3a8

951

py

Python

final_project/machinetranslation/translator.py

akshaygaidhane/xzceb-flask_eng_fr

a6dad1d999106254537dbc97061c6ca5db7f2812

[ "Apache-2.0" ]

null

final_project/machinetranslation/translator.py

akshaygaidhane/xzceb-flask_eng_fr

a6dad1d999106254537dbc97061c6ca5db7f2812

[ "Apache-2.0" ]

null

final_project/machinetranslation/translator.py

akshaygaidhane/xzceb-flask_eng_fr

a6dad1d999106254537dbc97061c6ca5db7f2812

[ "Apache-2.0" ]

null

import os from ibm_cloud_sdk_core.authenticators import IAMAuthenticator from dotenv import load_dotenv from ibm_watson import LanguageTranslatorV3 authenticator = IAMAuthenticator('DvqhFn36Zjo4WHj46w4vPsATPgfE_7UC1s8A9nYni4mV') language_translator = LanguageTranslatorV3( version='2021-11-21', authenticator=authenticator ) language_translator.set_service_url('https://api.eu-gb.language-translator.watson.cloud.ibm.com') language_translator.set_disable_ssl_verification(True) load_dotenv() apikey = os.environ['apikey'] url = os.environ['url'] def english_to_french(english_text): french_trans=language_translator.translate(text=english_text,model_id='en-fr').get_result() return french_trans.get("translations")[0].get("translation") def french_to_english(french_text): english_text=language_translator.translate(text=french_text,model_id='fr-en').get_result() return english_text.get("translations")[0].get("translation")

45.285714

97

0.819138

ba10d08aae66b55933c8afa7e0b763f9cb838950

390

py

Python

build/lib/DMRecall/evaluation/setup.py

busesese/DMRecall

498c7a491c4e6374fcb47ac0f4527de5f540bbee

[ "MIT" ]

9

2019-09-16T08:29:12.000Z

2021-11-29T05:20:47.000Z

build/lib/DMRecall/evaluation/setup.py

busesese/DMRecall

498c7a491c4e6374fcb47ac0f4527de5f540bbee

[ "MIT" ]

1

2021-11-29T05:20:07.000Z

build/lib/DMRecall/evaluation/setup.py

busesese/DMRecall

498c7a491c4e6374fcb47ac0f4527de5f540bbee

[ "MIT" ]

4

2020-01-11T03:03:25.000Z

2021-06-29T02:53:42.000Z

def configuration(parent_package='', top_path=None): from numpy.distutils.misc_util import Configuration config = Configuration('evaluation', parent_package, top_path) # submodules with build utilities config.add_subpackage('measure') return config if __name__ == '__main__': from numpy.distutils.core import setup setup(**configuration(top_path='').todict())

32.5

66

0.741026

6f7028b7c4536165bdcb3f5f03b23999503099e0

2,637

py

Python

pynitrokey/nk3/utils.py

Nitrokey/nitro-python

7264152564dcc427341b0e145ef94342136056ba

[ "Apache-2.0", "MIT" ]

4

2019-11-05T16:49:43.000Z

2020-06-15T08:08:00.000Z

pynitrokey/nk3/utils.py

Nitrokey/nitro-python

7264152564dcc427341b0e145ef94342136056ba

[ "Apache-2.0", "MIT" ]

16

2019-09-13T14:52:08.000Z

2020-06-22T12:52:41.000Z

pynitrokey/nk3/utils.py

Nitrokey/nitro-python

7264152564dcc427341b0e145ef94342136056ba

[ "Apache-2.0", "MIT" ]

2

2020-01-04T12:06:35.000Z

2020-04-17T16:15:13.000Z

# -*- coding: utf-8 -*- # # Copyright 2021 Nitrokey Developers # # Licensed under the Apache License, Version 2.0, <LICENSE-APACHE or # http://apache.org/licenses/LICENSE-2.0> or the MIT license <LICENSE-MIT or # http://opensource.org/licenses/MIT>, at your option. This file may not be # copied, modified, or distributed except according to those terms. from functools import total_ordering from typing import Tuple from spsdk.sbfile.misc import BcdVersion3 @total_ordering class Version: def __init__(self, major: int, minor: int, patch: int) -> None: self.major = major self.minor = minor self.patch = patch def __hash__(self) -> int: return hash(self._as_tuple()) def __eq__(self, other: object) -> bool: if not isinstance(other, Version): return NotImplemented return self._as_tuple() == other._as_tuple() def __lt__(self, other: object) -> bool: if not isinstance(other, Version): return NotImplemented return self._as_tuple() < other._as_tuple() def __repr__(self) -> str: return f"Version(major={self.major}, minor={self.minor}, patch={self.patch})" def __str__(self) -> str: return f"v{self.major}.{self.minor}.{self.patch}" def _as_tuple(self) -> Tuple[int, int, int]: return (self.major, self.minor, self.patch) @classmethod def from_int(cls, version: int) -> "Version": # This is the reverse of the calculation in runners/lpc55/build.rs (CARGO_PKG_VERSION): # https://github.com/Nitrokey/nitrokey-3-firmware/blob/main/runners/lpc55/build.rs#L131 major = version >> 22 minor = (version >> 6) & ((1 << 16) - 1) patch = version & ((1 << 6) - 1) return cls(major=major, minor=minor, patch=patch) @classmethod def from_str(cls, s: str) -> "Version": str_parts = s.split(".") if len(str_parts) != 3: raise ValueError(f"Invalid firmware version: {s}") try: int_parts = [int(part) for part in str_parts] except ValueError: raise ValueError(f"Invalid component in firmware version: {s}") return cls(major=int_parts[0], minor=int_parts[1], patch=int_parts[2]) @classmethod def from_v_str(cls, s: str) -> "Version": if not s.startswith("v"): raise ValueError(f"Missing v prefix for firmware version: {s}") return Version.from_str(s[1:]) @classmethod def from_bcd_version(cls, version: BcdVersion3) -> "Version": return cls(major=version.major, minor=version.minor, patch=version.service)

34.697368

95

0.638604

a78b680b8f4d7f202bf584ecf580733913162fb4

2,723

py

Python

euca2ools/commands/autoscaling/describenotificationconfigurations.py

salewski/euca2ools

6b3f62f2cb1c54f14d3bfa5fd92dab3c0ecafecb

[ "BSD-2-Clause" ]

30

2015-02-10T05:47:38.000Z

2022-01-20T08:48:43.000Z

euca2ools/commands/autoscaling/describenotificationconfigurations.py

salewski/euca2ools

6b3f62f2cb1c54f14d3bfa5fd92dab3c0ecafecb

[ "BSD-2-Clause" ]

16

2015-01-08T23:24:34.000Z

2018-07-18T07:15:40.000Z

euca2ools/commands/autoscaling/describenotificationconfigurations.py

salewski/euca2ools

6b3f62f2cb1c54f14d3bfa5fd92dab3c0ecafecb

[ "BSD-2-Clause" ]

19

2015-05-07T05:34:42.000Z

2020-12-13T10:50:14.000Z

# Copyright (c) 2013-2016 Hewlett Packard Enterprise Development LP # # Redistribution and use of this software in source and binary forms, # with or without modification, are permitted provided that the following # conditions are met: # # Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 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. # # 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 # OWNER 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. from requestbuilder import Arg from requestbuilder.mixins import TabifyingMixin from requestbuilder.response import PaginatedResponse from euca2ools.commands.autoscaling import AutoScalingRequest class DescribeNotificationConfigurations(AutoScalingRequest, TabifyingMixin): DESCRIPTION = ('Describe notification actions associated with ' 'auto-scaling groups') ARGS = [Arg('AutoScalingGroupNames.member', metavar='ASGROUP', nargs='*', help='limit results to specific auto-scaling groups')] LIST_TAGS = ['NotificationConfigurations'] def main(self): return PaginatedResponse(self, (None,), ('NotificationConfigurations',)) def prepare_for_page(self, page): # Pages are defined by NextToken self.params['NextToken'] = page # pylint: disable=no-self-use def get_next_page(self, response): return response.get('NextToken') or None # pylint: enable=no-self-use def print_result(self, result): for config in result.get('NotificationConfigurations', []): print self.tabify(('NOTIFICATION-CONFIG', config.get('AutoScalingGroupName'), config.get('TopicARN'), config.get('NotificationType')))

45.383333

77

0.71502

fafd0bf1c41488cf97d7660053e8fb6bb2eafe27

2,293

py

Python

tests/nets/wgan_gp/test_wgan_gp_64.py

alexmlamb/mimicry

be030fb24a79c9bd50843d188d930c01e2260f45

[ "MIT" ]

1

2020-04-07T05:28:36.000Z

tests/nets/wgan_gp/test_wgan_gp_64.py

Gyanachand1/mimicry

4e0fd46c2c5e97c319273a03374bbfa730071711

[ "MIT" ]

null

tests/nets/wgan_gp/test_wgan_gp_64.py

Gyanachand1/mimicry

4e0fd46c2c5e97c319273a03374bbfa730071711

[ "MIT" ]

null

""" Test functions for WGAN-GP for image size 64. """ import torch import torch.optim as optim from torch_mimicry.nets.wgan_gp.wgan_gp_64 import WGANGPGenerator64, WGANGPDiscriminator64 from torch_mimicry.training import metric_log from torch_mimicry.utils import common class TestWGANGP64: def setup(self): self.nz = 128 self.N, self.C, self.H, self.W = (8, 3, 64, 64) self.ngf = 16 self.ndf = 16 self.netG = WGANGPGenerator64(ngf=self.ngf) self.netD = WGANGPDiscriminator64(ndf=self.ndf) def test_ResNetGenerator64(self): noise = torch.ones(self.N, self.nz) output = self.netG(noise) assert output.shape == (self.N, self.C, self.H, self.W) def test_ResNetDiscriminator64(self): images = torch.ones(self.N, self.C, self.H, self.W) output = self.netD(images) assert output.shape == (self.N, 1) def test_train_steps(self): # Get real and fake images real_batch = common.load_images(self.N, size=self.H) # Setup optimizers optD = optim.Adam(self.netD.parameters(), 2e-4, betas=(0.0, 0.9)) optG = optim.Adam(self.netG.parameters(), 2e-4, betas=(0.0, 0.9)) # Log statistics to check log_data = metric_log.MetricLog() # Test D train step log_data = self.netD.train_step(real_batch=real_batch, netG=self.netG, optD=optD, device='cpu', log_data=log_data) log_data = self.netG.train_step(real_batch=real_batch, netD=self.netD, optG=optG, log_data=log_data, device='cpu') for name, metric_dict in log_data.items(): assert type(name) == str assert type(metric_dict['value']) == float def teardown(self): del self.netG del self.netD if __name__ == "__main__": test = TestWGANGP64() test.setup() test.test_ResNetGenerator64() test.test_ResNetDiscriminator64() test.test_train_steps() test.teardown()

30.986486

90

0.558657

b2e6b156dee68f6bb525220bdb05fb5407d6b3bd

4,115

py

Python

scripts/generate.py

MrCapone/bully_vita

9ab7c69ed24d0a3fba4b8ae3664baf33962a53aa

[ "MIT" ]

168

2021-06-05T13:02:51.000Z

2022-03-31T22:27:48.000Z

scripts/generate.py

MrCapone/bully_vita

9ab7c69ed24d0a3fba4b8ae3664baf33962a53aa

[ "MIT" ]

3

2021-06-05T18:40:24.000Z

2021-07-24T23:55:37.000Z

scripts/generate.py

MrCapone/bully_vita

9ab7c69ed24d0a3fba4b8ae3664baf33962a53aa

[ "MIT" ]

15

2021-06-06T00:44:18.000Z

2022-03-19T03:21:03.000Z

#!/usr/bin/env python2 from sys import argv, exit import re import struct import os HLSL_COMPILER = 'HLSLCompiler.exe' PSP2CGC = 'psp2cgc.exe' SHADER_MARKER = '//######################_==_YOYO_SHADER_MARKER_==_######################@~\n' VARYING_SEARCH = '^varying .* (vec[0-4]) (.*);$' CG_SUB_DICT = { '0.69999999': '0.7', '([^.[ ]+)(\.?([w-z]*)) = input\.([^.[;]+)(\.?([w-z]*))': '\g<1>\g<2> = input.\g<1>\g<5>', 'output\.([^.[ ]+)(\.?([w-z]*)) = ([^.[;]+)(\.?([w-z]*))': 'output.\g<4>\g<2> = \g<4>\g<5>', 'struct PS_INPUT\n{[^{]*\n};' : '''struct PS_INPUT { float4 gl_FragCoord : TEXCOORD0; float4 _In_custom1 : TEXCOORD1; float2 _In_uv1 : TEXCOORD2; float2 _In_uv2 : TEXCOORD3; float3 _In_eyedirection : TEXCOORD4; float3 _In_normal : TEXCOORD5; float4 _In_vertexcolor : COLOR0; float3 _In_lighting : COLOR1; };''', 'struct PS_OUTPUT\n{[^{]*\n};' : '''struct PS_OUTPUT { float4 gl_Color : COLOR0; };''', 'struct VS_OUTPUT\n{[^{]*\n};' : '''struct VS_OUTPUT { float4 gl_Position : POSITION; float4 gl_FragCoord : TEXCOORD0; float4 _In_custom1 : TEXCOORD1; float2 _In_uv1 : TEXCOORD2; float2 _In_uv2 : TEXCOORD3; float3 _In_eyedirection : TEXCOORD4; float3 _In_normal : TEXCOORD5; float4 _In_vertexcolor : COLOR0; float3 _In_lighting : COLOR1; };''', } def main(): if len(argv) != 2: print('Usage: generate.py input-dir') return -1 try: os.mkdir('gxp') except OSError: pass try: os.mkdir('cg') except OSError: pass for file in os.listdir(argv[1]): with open('{}/{}'.format(argv[1], file), 'r') as f: lines = f.readlines() is_vp = False varyings = [] for line in lines: if 'gl_Position' in line: is_vp = True if 'varying' in line: varyings.append(line) with open('temp.glsl', 'w') as f: if is_vp: f.writelines(lines) f.write(SHADER_MARKER) f.write('precision mediump float;') for varying in varyings: f.write(varying) f.write('void main() {\n') f.write('gl_FragColor = vec4(0, 0, 0, 0);\n') for varying in varyings: varying_search = re.search(VARYING_SEARCH, varying) vec = varying_search.group(1) name = varying_search.group(2) if vec == 'vec4': f.write('gl_FragColor += vec4({});\n'.format(name)) elif vec == 'vec3': f.write('gl_FragColor += vec4({}, 0);\n'.format(name)) elif vec == 'vec2': f.write('gl_FragColor += vec4({}, 0, 0);\n'.format(name)) f.write('}\n') else: for varying in varyings: f.write(varying) f.write('void main() {\n') for varying in varyings: varying_search = re.search(VARYING_SEARCH, varying) vec = varying_search.group(1) name = varying_search.group(2) if vec == 'vec4': f.write('{} = vec4(0, 0, 0, 0);'.format(name)) elif vec == 'vec3': f.write('{} = vec3(0, 0, 0);'.format(name)) elif vec == 'vec2': f.write('{} = vec2(0, 0);'.format(name)) f.write('}\n') f.write(SHADER_MARKER) f.writelines(lines) os.system('{} -shader temp.glsl -name shader -out out'.format(HLSL_COMPILER)) profile = 'sce_vp_psp2' if is_vp else 'sce_fp_psp2' shader_file = 'out/vout.shader' if is_vp else 'out/fout.shader' with open(shader_file, 'r+') as f: code = f.read() for x, y in CG_SUB_DICT.items(): code = re.sub(x, y, code) f.seek(0) f.write(code) f.truncate() os.system('{} -O4 -fastprecision -profile {} {}'.format(PSP2CGC, profile, shader_file)) gxp = 'gxp/{}'.format(file.replace('.glsl', '.gxp')) cg = 'cg/{}'.format(file.replace('.glsl', '.cg')) try: os.remove(gxp) except OSError: pass try: os.remove(cg) except OSError: pass os.rename('{}.gxp'.format(shader_file.replace('.', '_')), gxp) os.rename(shader_file, cg) if __name__ == '__main__': exit(main())

28.184932

94

0.553827

6d19946eab03cf9c44a4e64a1c046332ec28badf

2,524

py

Python

src/measure.py

tavvar/raspiapc

54390db4396b9ce2d2c0693f6c2053665d2b5077

[ "MIT" ]

null

src/measure.py

tavvar/raspiapc

54390db4396b9ce2d2c0693f6c2053665d2b5077

[ "MIT" ]

null

src/measure.py

tavvar/raspiapc

54390db4396b9ce2d2c0693f6c2053665d2b5077

[ "MIT" ]

null

import time import json import os class Measure: 'Makes a File with Timestamp as name and fills it with Json' filename = "" def __init__(self, filename = "queue.json"): self.filename = filename def initFile(self): try: fo = open(self.filename, "r") fo.close() return True except IOError: print("Create File '%s' at '%s'" % (self.filename, os.getcwd())) fo = open(self.filename, "w") fo.write("{}") fo.close() return True return False def deleteFile(self): if(os.path.isfile(self.filename)): try: os.remove(self.filename) except IOError: print("Deletion failed") return False else: print("File '%s' deleted successfully" % (self.filename)) return True print("Deletion failed. No file '%s' found!" % (self.filename)) return False def getJson(self): try: fo = open(self.filename, "r") except IOError: return False jsonPython = json.load(fo) fo.close() return jsonPython def addFetch(self, humidity, temperature, pm25, pm10, id, long=0.0, lat=0.0, ts=0): try: float(pm25) float(pm10) float(humidity) float(temperature) except ValueError: dummy = False humidity = temperature = pm25 = pm10 = dummy return False json2add = {'timestamp':ts,'humidity':humidity,'temperature':temperature,'pm25':pm25,'pm10':pm10,'long':long,'lat':lat} json2overwrite = {'id':id,'data':[]} json2overwrite['data'].append(json2add) data = self.getJson() if data == False: self.initFile() data = json2overwrite else: try: data['data'].append(json2add) print("Update File '%s' at '%s'" % (self.filename, os.getcwd())) except KeyError as kerr: data = json2overwrite print("Corrupt data. Create File '%s' at '%s'" % (self.filename, os.getcwd())) fo = open(self.filename, 'w+') json.dump(data, fo) fo.close() return True if __name__ == '__main__': m = Measure() m.addFetch(10.0,20.5,1.8,2.4,12345,17.2,11.0)

28.359551

127

0.500792

12593b71a27e91787c309737883f63ba73b91336

4,490

py

Python

tests/tests/test_inventory.py

merlin-northern/integration

8496f14d27b7f78906c8c79c0edfccb7b111c339

[ "Apache-2.0" ]

null

tests/tests/test_inventory.py

merlin-northern/integration

8496f14d27b7f78906c8c79c0edfccb7b111c339

[ "Apache-2.0" ]

null

tests/tests/test_inventory.py

merlin-northern/integration

8496f14d27b7f78906c8c79c0edfccb7b111c339

[ "Apache-2.0" ]

null

#!/usr/bin/python # Copyright 2017 Northern.tech AS # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from fabric.api import * import json import pytest import time from ..common import * from ..common_setup import * from ..helpers import Helpers from ..MenderAPI import auth_v2, deploy, image, inv from .mendertesting import MenderTesting @pytest.mark.usefixtures("standard_setup_one_client_bootstrapped") class TestInventory(MenderTesting): @MenderTesting.fast def test_inventory(self): """Test that device reports inventory after having bootstrapped.""" attempts = 10 while True: attempts = attempts - 1 try: inv_json = inv.get_devices() auth_json = auth_v2.get_devices() auth_ids = [device['id'] for device in auth_json] assert(len(inv_json) > 0) for device in inv_json: try: # Check that authentication and inventory agree. assert(device['id'] in auth_ids) attrs = device['attributes'] # Check individual attributes. network_interfaces = [elem for elem in attrs if elem['name'] == "network_interfaces"] assert len(network_interfaces) == 1 network_interfaces = network_interfaces[0] if type(network_interfaces['value']) is str: assert any(network_interfaces['value'] == iface for iface in ["eth0", "enp0s3"]) else: assert any(iface in network_interfaces['value'] for iface in ["eth0", "enp0s3"]) assert(json.loads('{"name": "hostname", "value": "%s"}' % conftest.machine_name) in attrs) assert(json.loads('{"name": "device_type", "value": "%s"}' % conftest.machine_name) in attrs) if conftest.machine_name == "qemux86-64": bootloader_integration = "uefi_grub" elif conftest.machine_name == "vexpress-qemu": bootloader_integration = "uboot" else: pytest.fail("Unknown machine_name. Please add an expected bootloader_integration for this machine_name") assert(json.loads('{"name": "mender_bootloader_integration", "value": "%s"}' % bootloader_integration) in attrs) # Check that all known keys are present. keys = [str(attr['name']) for attr in attrs] expected_keys = [ "hostname", "network_interfaces", "cpu_model", "mem_total_kB", "device_type", ["ipv4_enp0s3", "ipv6_enp0s3", "ipv4_eth0"], # Multiple possibilities ["mac_enp0s3", "mac_eth0"], "mender_client_version", "artifact_name", "kernel", "os", ] for key in expected_keys: if type(key) is list: assert any([subkey in keys for subkey in key]) else: assert key in keys except: print("Exception caught, 'device' json: ", device) raise break except: # This may pass only after the client has had some time to # report. if attempts > 0: time.sleep(5) continue else: raise

42.761905

136

0.509354

fc843445bddaa6dfea72170e90cb29e707953ec1

1,597

py

Python

tests/pytests/integration/states/test_cron.py

avkvl/salt

d906b2ec9baa1f0c5e61a69c954b62f9b9f11381

[ "Apache-2.0" ]

1

2021-02-26T07:37:19.000Z

tests/pytests/integration/states/test_cron.py

avkvl/salt

d906b2ec9baa1f0c5e61a69c954b62f9b9f11381

[ "Apache-2.0" ]

null

tests/pytests/integration/states/test_cron.py

avkvl/salt

d906b2ec9baa1f0c5e61a69c954b62f9b9f11381

[ "Apache-2.0" ]

null

""" Tests for the cron state """ import logging import subprocess import pytest import salt.utils.platform from tests.support.helpers import slowTest log = logging.getLogger(__name__) @pytest.fixture def cron_account(): with pytest.helpers.create_account() as system_account: try: yield system_account finally: command = ["crontab", "-u", system_account.username, "-r"] if salt.utils.platform.is_freebsd(): command.append("-f") subprocess.run(command, check=False) @slowTest @pytest.mark.skip_on_windows @pytest.mark.skip_if_not_root @pytest.mark.skip_if_binaries_missing("crontab") def test_managed(cron_account, salt_cli, salt_minion, base_env_state_tree_root_dir): """ file.managed """ cron_contents = "# Lines below here are managed by Salt, do not edit\n@hourly touch /tmp/test-file\n" expected = "--- \n+++ \n@@ -1 +1,2 @@\n-\n+# Lines below here are managed by Salt, do not edit\n+@hourly touch /tmp/test-file\n" with pytest.helpers.temp_file( "issue-46881/cron", cron_contents, base_env_state_tree_root_dir ): ret = salt_cli.run( "state.single", "cron.file", name="salt://issue-46881/cron", user=cron_account.username, minion_tgt=salt_minion.id, ) assert ret.exitcode == 0, ret state = ret.json["cron_|-salt://issue-46881/cron_|-salt://issue-46881/cron_|-file"] assert "changes" in state assert "diff" in state["changes"] assert state["changes"]["diff"] == expected

30.711538

132

0.650595

71ef2a929d449572e535ab6e34882e45242c6a07

2,194

py

Python

sorting-numbers/models/transformer_masking/utils.py

AndreMaz/dnn-attention

86e834b90bd419646fd00c6ff4df910ab7874910

[ "MIT" ]

1

2020-03-11T22:52:19.000Z

sorting-numbers/models/transformer_masking/utils.py

AndreMaz/dnn-attention

86e834b90bd419646fd00c6ff4df910ab7874910

[ "MIT" ]

3

2021-05-21T16:15:18.000Z

2022-02-10T01:11:23.000Z

sorting-numbers/models/transformer_masking/utils.py

AndreMaz/dnn-attention

86e834b90bd419646fd00c6ff4df910ab7874910

[ "MIT" ]

null

import numpy as np import tensorflow as tf def point_wise_feed_forward_network(d_model, dff): return tf.keras.Sequential([ tf.keras.layers.Dense(dff, activation='relu'), # (batch_size, seq_len, dff) tf.keras.layers.Dense(d_model) # (batch_size, seq_len, d_model) ]) def get_angles(pos, i, d_model): angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model)) return pos * angle_rates def positional_encoding(position, d_model): angle_rads = get_angles(np.arange(position)[:, np.newaxis], np.arange(d_model)[np.newaxis, :], d_model) # apply sin to even indices in the array; 2i angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2]) # apply cos to odd indices in the array; 2i+1 angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2]) pos_encoding = angle_rads[np.newaxis, ...] return tf.cast(pos_encoding, dtype=tf.float32) def scaled_dot_product_attention(q, k, v, mask): """Calculate the attention weights. q, k, v must have matching leading dimensions. k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v. The mask has different shapes depending on its type(padding or look ahead) but it must be broadcastable for addition. Args: q: query shape == (..., seq_len_q, depth) k: key shape == (..., seq_len_k, depth) v: value shape == (..., seq_len_v, depth_v) mask: Float tensor with shape broadcastable to (..., seq_len_q, seq_len_k). Defaults to None. Returns: output, attention_weights """ matmul_qk = tf.matmul(q, k, transpose_b=True) # (..., seq_len_q, seq_len_k) # scale matmul_qk dk = tf.cast(tf.shape(k)[-1], tf.float32) scaled_attention_logits = matmul_qk / tf.math.sqrt(dk) # add the mask to the scaled tensor. if mask is not None: scaled_attention_logits += (mask * -1e9) # softmax is normalized on the last axis (seq_len_k) so that the scores # add up to 1. attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1) # (..., seq_len_q, seq_len_k) output = tf.matmul(attention_weights, v) # (..., seq_len_q, depth_v) return output, attention_weights

34.28125

100

0.66454

2504a7d150cff423f134fea0777fbc8ce7c7aaf6

4,680

py

Python

src/python/bot/fuzzers/utils.py

sanketsaurav/clusterfuzz

9f7efba7781614d50cdc6ab136b9bcf19607731c

[ "Apache-2.0" ]

1

2019-04-09T06:40:55.000Z

src/python/bot/fuzzers/utils.py

Delaney6/clusterfuzz

9eeb08a85869b32733dd54c69b098688ff3b1bf5

[ "Apache-2.0" ]

null

src/python/bot/fuzzers/utils.py

Delaney6/clusterfuzz

9eeb08a85869b32733dd54c69b098688ff3b1bf5

[ "Apache-2.0" ]

null

# Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fuzzer utils.""" import os import re import stat import tempfile from base import utils from metrics import logs from system import environment from system import shell ALLOWED_FUZZ_TARGET_EXTENSIONS = ['', '.exe'] FUZZ_TARGET_SEARCH_STRING = 'LLVMFuzzerTestOneInput' VALID_TARGET_NAME = re.compile(r'^[a-zA-Z0-9_-]+$') def is_fuzz_target_local(file_path, file_handle=None): """Returns whether |file_path| is a fuzz target binary (local path).""" filename, file_extension = os.path.splitext(os.path.basename(file_path)) if not VALID_TARGET_NAME.match(filename): # Check fuzz target has a valid name (without any special chars). return False if file_extension not in ALLOWED_FUZZ_TARGET_EXTENSIONS: # Ignore files with disallowed extensions (to prevent opening e.g. .zips). return False if not file_handle and not os.path.exists(file_path): # Ignore non-existant files for cases when we don't have a file handle. return False if filename.endswith('_fuzzer'): return True # TODO(aarya): Remove this optimization if it does not show up significant # savings in profiling results. fuzz_target_name_regex = environment.get_value('FUZZER_NAME_REGEX') if fuzz_target_name_regex: return bool(re.match(fuzz_target_name_regex, filename)) if os.path.exists(file_path) and not stat.S_ISREG(os.stat(file_path).st_mode): # Don't read special files (eg: /dev/urandom). logs.log_warn('Tried to read from non-regular file: %s.' % file_path) return False # Use already provided file handle or open the file. local_file_handle = file_handle or open(file_path) # TODO(metzman): Bound this call so we don't read forever if something went # wrong. result = utils.search_string_in_file(FUZZ_TARGET_SEARCH_STRING, local_file_handle) if not file_handle: # If this local file handle is owned by our function, close it now. # Otherwise, it is caller's responsibility. local_file_handle.close() return result def get_fuzz_targets_local(path): """Get list of fuzz targets paths (local).""" fuzz_target_paths = [] for root, _, files in os.walk(path): for filename in files: file_path = os.path.join(root, filename) if is_fuzz_target_local(file_path): fuzz_target_paths.append(file_path) return fuzz_target_paths def get_fuzz_targets(path): """Get list of fuzz targets paths.""" if environment.is_trusted_host(): from bot.untrusted_runner import file_host return file_host.get_fuzz_targets(path) return get_fuzz_targets_local(path) def extract_argument(arguments, prefix, remove=True): """Extract argument from arguments.""" for argument in arguments[:]: if argument.startswith(prefix): if remove: arguments.remove(argument) return argument[len(prefix):] return None def get_build_revision(): """Get build revision.""" try: build_revision = int(environment.get_value('APP_REVISION')) except (ValueError, TypeError): build_revision = -1 return build_revision def get_supporting_file(fuzz_target_path, extension_or_suffix): """Get supporting file for a fuzz target with the provided extension.""" return utils.get_path_without_ext(fuzz_target_path) + extension_or_suffix def get_temp_dir(): """Return the temp dir.""" temp_dirname = 'temp-' + str(os.getpid()) temp_directory = os.path.join( environment.get_value('FUZZ_INPUTS_DISK'), temp_dirname) shell.create_directory(temp_directory) return temp_directory def get_file_from_untrusted_worker(worker_file_path): """Gets file from an untrusted worker to local. Local file stays in the temp folder until the end of task or can be explicitly deleted by the caller.""" from bot.untrusted_runner import file_host with tempfile.NamedTemporaryFile(delete=False, dir=get_temp_dir()) as f: local_file_path = f.name file_host.copy_file_from_worker(worker_file_path, local_file_path) return local_file_path def cleanup(): """Clean up temporary metadata.""" shell.remove_directory(get_temp_dir())

31.836735

80

0.743162

580bf38a0ba67cccffe73e0361c287ecb401425b

9,522

py

Python

test/test_linear_interpolation.py

crispitagorico/torchcde

fcd85a3795edd6872c443425c690f1696584cba6

[ "Apache-2.0" ]

1

2021-04-24T05:26:49.000Z

test/test_linear_interpolation.py

crispitagorico/torchcde

fcd85a3795edd6872c443425c690f1696584cba6

[ "Apache-2.0" ]

null

test/test_linear_interpolation.py

crispitagorico/torchcde

fcd85a3795edd6872c443425c690f1696584cba6

[ "Apache-2.0" ]

null

import torch import torchcde import pytest def test_random(): def _points(): yield 2 yield 3 yield 100 for _ in range(10): yield torch.randint(low=2, high=100, size=(1,)).item() for reparameterise in ('none', 'bump'): for drop in (False, True): for use_t in (False, True): for num_points in _points(): if use_t: start = torch.rand(1).item() * 10 - 5 end = torch.rand(1).item() * 10 - 5 start, end = min(start, end), max(start, end) t = torch.linspace(start, end, num_points, dtype=torch.float64) t_ = t else: t = torch.linspace(0, num_points - 1, num_points, dtype=torch.float64) t_ = None num_channels = torch.randint(low=1, high=5, size=(1,)).item() m = torch.rand(num_channels, dtype=torch.float64) * 10 - 5 c = torch.rand(num_channels, dtype=torch.float64) * 10 - 5 values = m * t.unsqueeze(-1) + c values_clone = values.clone() if drop: for values_slice in values_clone.unbind(dim=-1): num_drop = int(num_points * torch.randint(low=1, high=4, size=(1,)).item() / 10) num_drop = min(num_drop, num_points - 4) to_drop = torch.randperm(num_points - 2)[:num_drop] + 1 # don't drop first or last values_slice[to_drop] = float('nan') coeffs = torchcde.linear_interpolation_coeffs(values_clone, t=t_) linear = torchcde.LinearInterpolation(coeffs, t=t_, reparameterise=reparameterise) for time, value in zip(t, values): linear_evaluate = linear.evaluate(time) assert value.shape == linear_evaluate.shape assert value.allclose(linear_evaluate, rtol=1e-4, atol=1e-6) if reparameterise is False: linear_derivative = linear.derivative(time) assert m.shape == linear_derivative.shape assert m.allclose(linear_derivative, rtol=1e-4, atol=1e-6) def test_small(): for use_t in (False, True): if use_t: start = torch.rand(1).item() * 10 - 5 end = torch.rand(1).item() * 10 - 5 start, end = min(start, end), max(start, end) t = torch.tensor([start, end], dtype=torch.float64) t_ = t else: start = 0 end = 1 t = torch.tensor([0., 1.], dtype=torch.float64) t_ = None x = torch.rand(2, 1, dtype=torch.float64) true_deriv = (x[1] - x[0]) / (end - start) coeffs = torchcde.linear_interpolation_coeffs(x, t=t_) linear = torchcde.LinearInterpolation(coeffs, t=t_) for time in torch.linspace(-1, 2, 100): true = x[0] + true_deriv * (time - t[0]) pred = linear.evaluate(time) deriv = linear.derivative(time) assert true_deriv.shape == deriv.shape assert true_deriv.allclose(deriv) assert true.shape == pred.shape assert true.allclose(pred) def test_specification_and_derivative(): for use_t in (False, True): for reparameterise in ('none', 'bump'): for _ in range(10): for num_batch_dims in (0, 1, 2, 3): batch_dims = [] for _ in range(num_batch_dims): batch_dims.append(torch.randint(low=1, high=3, size=(1,)).item()) length = torch.randint(low=5, high=10, size=(1,)).item() channels = torch.randint(low=1, high=5, size=(1,)).item() if use_t: t = torch.linspace(0, 1, length, dtype=torch.float64) t_ = t else: t = torch.linspace(0, length - 1, length, dtype=torch.float64) t_ = None x = torch.rand(*batch_dims, length, channels, dtype=torch.float64) coeffs = torchcde.linear_interpolation_coeffs(x, t=t_) spline = torchcde.LinearInterpolation(coeffs, t=t_, reparameterise=reparameterise) # Test specification for i, point in enumerate(t): evaluate = spline.evaluate(point) xi = x[..., i, :] assert evaluate.allclose(xi, atol=1e-5, rtol=1e-5) # Test derivative for point in torch.rand(100, dtype=torch.float64): point_with_grad = point.detach().requires_grad_(True) evaluate = spline.evaluate(point_with_grad) derivative = spline.derivative(point) autoderivative = [] for elem in evaluate.view(-1): elem.backward(retain_graph=True) with torch.no_grad(): autoderivative.append(point_with_grad.grad.clone()) point_with_grad.grad.zero_() autoderivative = torch.stack(autoderivative).view(*evaluate.shape) assert derivative.shape == autoderivative.shape assert derivative.allclose(autoderivative, atol=1e-5, rtol=1e-5) def test_rectilinear_preparation(): devices = ['cpu'] if torch.cuda.is_available(): devices.append('cuda') for device in devices: # Simple test nan = float('nan') t1 = torch.tensor([0.1, 0.2, 0.9]).view(-1, 1).to(device) t2 = torch.tensor([0.2, 0.3]).view(-1, 1).to(device) x1 = torch.tensor([0.4, nan, 1.1]).view(-1, 1).to(device) x2 = torch.tensor([nan, 2.]).view(-1, 1).to(device) x = torch.nn.utils.rnn.pad_sequence( [torch.cat((t1, x1), -1), torch.cat((t2, x2), -1)], batch_first=True, padding_value=nan ) # We have to fill the time index forward because we currently dont allow nan times for rectilinear x[:, :, 0] = torchcde.misc.forward_fill(x[:, :, 0], fill_index=-1) # Build true solution x1_true = torch.tensor([[0.1, 0.2, 0.2, 0.9, 0.9], [0.4, 0.4, 0.4, 0.4, 1.1]]).T.view(-1, 2).to(device) x2_true = torch.tensor([[0.2, 0.3, 0.3, 0.3, 0.3], [2., 2., 2., 2., 2.]]).T.view(-1, 2).to(device) rect_true = torch.stack((x1_true, x2_true)) # Apply rectilinear and compare rectilinear = torchcde.linear_interpolation_coeffs(x, rectilinear=0) assert torch.equal(rect_true[~torch.isnan(rect_true)], rectilinear[~torch.isnan(rectilinear)]) # Test also if we swap time time dimension x_swap = x[:, :, [1, 0]] rectilinear_swap = torchcde.linear_interpolation_coeffs(x_swap, rectilinear=1) rect_swp = rect_true[:, :, [1, 0]] assert torch.equal(rect_swp, rectilinear_swap) # Additionally try a 2d case assert torch.equal(rect_true[0], torchcde.linear_interpolation_coeffs(x[0], rectilinear=0)) # And a 4d case x_4d = torch.stack([x, x]) rect_true_4d = torch.stack([rect_true, rect_true]) assert torch.equal(rect_true_4d, torchcde.linear_interpolation_coeffs(x_4d, rectilinear=0)) # Ensure error is thrown if time has a nan value anywhere x_time_nan = x.clone() x_time_nan[0, 1, 0] = float('nan') pytest.raises(AssertionError, torchcde.linear_interpolation_coeffs, x_time_nan, rectilinear=0) # Some randoms tests for _ in range(5): # Build some data with time t_starts = torch.randn(5).to(device) ** 2 ts = [torch.linspace(s, s + 10, torch.randint(2, 50, (1,)).item()).to(device) for s in t_starts] xs = [torch.randn(len(t), 10 - 1).to(device) for t in ts] x = torch.nn.utils.rnn.pad_sequence( [torch.cat([t_.view(-1, 1), x_], dim=1) for t_, x_ in zip(ts, xs)], batch_first=True, padding_value=nan ) # Add some random nans about the place mask = torch.randint(0, 5, (x.size(0), x.size(1), x.size(2) - 1), dtype=torch.float).to(device) mask[mask == 0] = float('nan') x[:, :, 1:] = x[:, :, 1:] * mask # We have to fill the time index forward because we currently dont allow nan times for rectilinear x[:, :, 0] = torchcde.misc.forward_fill(x[:, :, 0], fill_index=-1) # Fill x_ffilled = torchcde.misc.forward_fill(x) # Compute the true solution N, L, C = x_ffilled.shape rect_true = torch.zeros(N, 2 * L - 1, C).to(device) lag = torch.cat([x_ffilled[:, 1:, [0]], x_ffilled[:, :-1, 1:]], dim=-1) rect_true[:, ::2, ] = x_ffilled rect_true[:, 1::2] = lag # Need to backfill rect true # Rectilinear solution rectilinear = torchcde.linear_interpolation_coeffs(x, rectilinear=0) assert torch.equal(rect_true[~torch.isnan(rect_true)], rectilinear[~torch.isnan(rect_true)])

50.380952

119

0.530561

e8865db321795a7e7b8152ca193f2baf81ddba6f

3,127

py

Python

core/images.py

Varkalas/Tower-of-Hero

8b58534f9b2d214b0375bf5e367a0149045850df

[ "MIT" ]

1

2017-11-18T14:00:08.000Z

core/images.py

Varkalas/Tower-of-Hero

8b58534f9b2d214b0375bf5e367a0149045850df

[ "MIT" ]

null

core/images.py

Varkalas/Tower-of-Hero

8b58534f9b2d214b0375bf5e367a0149045850df

[ "MIT" ]

null

from definitions import ROOT_DIR from tkinter import messagebox from cv2 import imread IMAGE_PATH = ROOT_DIR + "\\images\\" def read_image(image_name): full_name = IMAGE_PATH + image_name the_image = imread(full_name) if the_image is None: messagebox.showerror("Incorrect File Name", "{} could not be read.".format(full_name)) return the_image IMG_CUMULATIVE_STATS = read_image("cumulative_statistics.png") IMG_A_KINGS_CROWN = read_image("a_king's_crown.png") IMG_AEGIS = read_image("aegis.png") IMG_AWAKENING_ARMOR = read_image("awakening_armor.png") IMG_AWAKENING_ARMOR_PLUS_1 = read_image("awakening_sword_plus_1.png") IMG_AWAKENING_SWORD = read_image("awakening_sword.png") IMG_AWAKENING_SWORD_PLUS_1 = read_image("awakening_armor_plus_1.png") IMG_BLACK_ESSENCE = read_image("black_essence.png") IMG_BLUE_CRYSTAL = read_image("blue_crystal.png") IMG_BLUE_ELIXIR = read_image("blue_elixir.png") IMG_CADUCEUS = read_image("caduceus.png") IMG_CLAYMORE = read_image("claymore.png") IMG_COAT_OF_GOLD = read_image("coat_of_gold.png") IMG_DARK_BOOTS = read_image("dark_boots.png") IMG_DARK_GATE = read_image("dark_gate.png") IMG_DARK_KNIGHT_ARMOR = read_image("dark_knight_armor.png") IMG_DEMON_EYE = read_image("demon_eye.png") IMG_DURANDAL = read_image("durandal.png") IMG_EARTH_ARMOR = read_image("earth_armor.png") IMG_EXCALIBUR = read_image("excalibur.png") IMG_FIRE_SWORD = read_image("fire_sword.png") IMG_FLAMBERGE = read_image("flamberge.png") IMG_FLAME_POT = read_image("flame_pot.png") IMG_FREYRS_SWORD = read_image("freyr's_sword.png") IMG_FULL_HELMET = read_image("full_helmet.png") IMG_FULL_PLATE = read_image("full_plate.png") IMG_GAE_BOLG = read_image("gae_bolg.png") IMG_GATE = read_image("gate.png") IMG_GOLD_BOX = read_image("gold_box.png") IMG_GOLD_VESSELS = read_image("gold_vessels.png") IMG_GOLDEN_GLOVES = read_image("golden_gloves.png") IMG_GOLDEN_POT = read_image("golden_pot.png") IMG_GOLDEN_ROD = read_image("golden_rod.png") IMG_GREEN_ELIXIR = read_image("green_elixir.png") IMG_GUILD_HAT = read_image("guild_hat.png") IMG_GUNGNIR = read_image("gungnir.png") IMG_HALBERD = read_image("halberd.png") IMG_HYDRAS_POISON_ARROWS = read_image("hydra's_poison_arrows.png") IMG_ICE_POT = read_image("ice_pot.png") IMG_LAEVATEINN = read_image("laevateinn.png") IMG_LANCE = read_image("lance.png") IMG_MAGIC_LAMP = read_image("magic_lamp.png") IMG_MISTILTEINN = read_image("mistilteinn.png") IMG_MITHRIL_ARMOUR = read_image("mithril_armor.png") IMG_MITHRIL_SWORD = read_image("mithril_sword.png") IMG_MJOLNIR = read_image("mjolnir.png") IMG_PHILOSOPHERS_STONE = read_image("philosopher's_stone.png") IMG_RAPIER = read_image("rapier.png") IMG_RED_ELIXIR = read_image("red_elixir.png") IMG_RED_HAND = read_image("red_hand.png") IMG_SOLOMONS_KEY = read_image("solomon's_key.png") IMG_SOLOMONS_STAFF = read_image("solomons_staff.png") IMG_SUMMONING_LETTER = read_image("summoning_letter.png") IMG_TOMAHAWK = read_image("tomahawk.png") IMG_TRAINING_BOOK = read_image("training_book.png") IMG_VETERANS_HAT = read_image("veteran's_hat.png") IMG_WING_BOOTS = read_image("wing_boots.png")

41.693333

94

0.801087

1fa6f37b7133b7dc605aecc99f7fd5d94e486d52

236

py

Python

flask_restplus_demo/api/v1/__init__.py

deluxebrain/flask_restplus_demo

3bec957344fbbb300fcf009687422ddded4d9ecf

[ "MIT" ]

null

flask_restplus_demo/api/v1/__init__.py

deluxebrain/flask_restplus_demo

3bec957344fbbb300fcf009687422ddded4d9ecf

[ "MIT" ]

null

flask_restplus_demo/api/v1/__init__.py

deluxebrain/flask_restplus_demo

3bec957344fbbb300fcf009687422ddded4d9ecf

[ "MIT" ]

null

import logging from flask import Blueprint from flask_restplus_demo.api.v1.api import api log = logging.getLogger(__name__) blueprint = Blueprint('api_v1', __name__, url_prefix="/api/v1") api.init_app(blueprint) from . import users

19.666667

63

0.788136

d5dc033e57f6ec28857d586834c44ffae3c6d7b0

222

py

Python

setup.py

jaschn/dtu_mlops_mnist

54b5344bde1131463d6783bbfe61bdc70db1c1be

[ "MIT" ]

null

setup.py

jaschn/dtu_mlops_mnist

54b5344bde1131463d6783bbfe61bdc70db1c1be

[ "MIT" ]

null

setup.py

jaschn/dtu_mlops_mnist

54b5344bde1131463d6783bbfe61bdc70db1c1be

[ "MIT" ]

null

from setuptools import find_packages, setup setup( name='src', packages=find_packages(), version='0.1.0', description='Exercise project for the DTU MLOPS course', author='jaschn', license='MIT', )

20.181818

60

0.671171

287ae8d1fffdf614242883c0ea09b99bbfee6b55

4,956

py

Python

linaro/staticlink/script/regrex.py

dalvikfrank/docs

35ace4c90cef8b0ac82e18d2b9ac078131f61b93

[ "Apache-2.0" ]

null

linaro/staticlink/script/regrex.py

dalvikfrank/docs

35ace4c90cef8b0ac82e18d2b9ac078131f61b93

[ "Apache-2.0" ]

null

linaro/staticlink/script/regrex.py

dalvikfrank/docs

35ace4c90cef8b0ac82e18d2b9ac078131f61b93

[ "Apache-2.0" ]

null

#!/usr/bin/python import urllib2 import re import os import sys import commands import subprocess import shlex import string import getopt DELTTE_TEST_CASE = True # True: allCount all count except test cases Flase: allCount including test cases def search_opt(android_dir, set_file, out_dir): if len(android_dir)==0 : print ("Invalid path null !") else : child = subprocess.Popen(["find", android_dir,"-iname","*.mk"], stdout=subprocess.PIPE) out = child.communicate() # return out array print out libs_h = open(set_file, 'r') done = 0 while not done: aLine = libs_h.readline() if(aLine != ''): lib = aLine.strip() print "\n\n\n >>>>>>>>>>>>>>>>>>>>>>>> lib = %s" %(lib) mdl = parser_module(out, lib) (stc_all, stc_test) = parser_staticlink(out, lib) print (stc_all, stc_test, mdl) if (stc_all>1): create_file = "%s/%dstatic_%dtest_%dmodule_<%s>" %(out_dir, stc_all, stc_test, mdl, lib) print "Create File: %s" %(create_file) handle = open(create_file, 'wa+') parser(out, lib, create_file, handle) handle.close() else: pass else: done = 1 libs_h.close() return def parser_module(out, lib): moduleCount = 0 for f in out[0].split('\n'): if len(f)==0: continue content = readContent(f) regexSModule= "(LOCAL_MODULE)\s?(\+\=|\:\=)([^=]*?%s)[^a-zA-Z0-9+]" %(lib) patternModule = re.compile(regexSModule, re.S) for i in patternModule.findall(content): moduleCount = moduleCount + 1 str = "" for j in i: str = "%s%s" %(str, j) return moduleCount def parser_staticlink(out, lib): allCount = 0 testCount = 0 for f in out[0].split('\n'): if len(f)==0: continue content = readContent(f) regexStatic = "(LOCAL_STATIC_LIBRARIES|static_libraries)\s?(\+\=|\:\=)([^=]*?%s)[^a-zA-Z0-9+]" %(lib) patternStatic = re.compile(regexStatic, re.S) for i in patternStatic.findall(content): isTest = checkTestCase(f) if isTest: testCount = testCount + 1 if (DELTTE_TEST_CASE and isTest): continue allCount = allCount + 1 #print "allCount= %d, testCount = %d" %(allCount, testCount) return (allCount, testCount) def parser(out, lib, fout, handle): index = 0 for f in out[0].split('\n'): if len(f)==0: return else: content = readContent(f) regex="(LOCAL_STATIC_LIBRARIES|LOCAL_MODULE|static_libraries)\s?(\+\=|\:\=)([^=]*?%s)[^a-zA-Z0-9+]" %(lib) pattern = re.compile(regex, re.S) pre_file = "" for i in pattern.findall(content): str = "" align = "" if (f != pre_file): index = index + 1 align = "\n\n\n\n%d) ====%s====\n" %(index, f) pre_file = f else: align = "\n" for j in i: str = "%s%s" %(str, j) in_file = "%s\n%s" %(align, str) print in_file handle.write(in_file) return def parser_output(): pass def readContent(f): handle = file(f, 'rb') content = handle.read().strip() handle.close() return content def checkTestCase(f): isTest = False print "============ %s" %(f) for teststr in re.split('/', f): if (teststr == 'test') or (teststr == 'tests') : isTest=True break else: continue return isTest def usage(): print "usage: regrex.py -r ANDROID_DIR -s SET_FILE -d OUT_DIR" print "" print " param: ANDROID_DIR: androir root directory" print " SET_FILE: file which lists all the static libs" print " OUT_DIR: output the result directory" print "" print " output: generate files like: %d(staticlink)_%d(test case)_%d(module count)_<lib module name> in OUT_DIR" def main(): #./regrex.py -r /media/frank/jam/linaro/aosp-M -s /media/frank/jam/linaro/LMG/LMG-914_static_link/set.txt -d /media/frank/jam/linaro/LMG/LMG-914_static_link/output opts, args = getopt.getopt(sys.argv[1:], "hr:s:d:") for op, value in opts: if op == "-r": android_dir = value elif op == "-s": set_file = value elif op == "-d": out_dir = value elif op == "-h": usage() sys.exit() search_opt(android_dir, set_file, out_dir) if __name__ == "__main__": main()

32.392157

167

0.512107

d9f0bc1e538b30636ef46195a3340c13181f17f8

793

py

Python

theano/overfeat.py

s0nghuiming/convnet-benchmarks

5b91b6966714e8358292ae440d807c6d9d2cf7fe

[ "MIT" ]

2,829

2015-01-02T19:34:27.000Z

2022-02-22T03:42:06.000Z

theano/overfeat.py

Reddmist/convnet-benchmarks

b458aab61c0ac2257c0990119b5de15c1e886f02

[ "MIT" ]

90

2015-02-18T21:56:21.000Z

2021-02-06T22:20:30.000Z

theano/overfeat.py

Reddmist/convnet-benchmarks

b458aab61c0ac2257c0990119b5de15c1e886f02

[ "MIT" ]

644

2015-01-02T19:31:23.000Z

2022-01-07T23:53:45.000Z

import theano.tensor as T from lasagne.layers import InputLayer, DenseLayer, Conv2DLayer,\ MaxPool2DLayer image_sz = 231 def build_model(batch_size=128): x = T.tensor4('input') layer = InputLayer((batch_size, 3, image_sz, image_sz), input_var=x) layer = Conv2DLayer(layer, 96, 11, stride=4, pad='valid') layer = MaxPool2DLayer(layer, 2) layer = Conv2DLayer(layer, 256, 5, pad='valid') layer = MaxPool2DLayer(layer, 2) layer = Conv2DLayer(layer, 512, 3, pad='same') layer = Conv2DLayer(layer, 1024, 3, pad='same') layer = Conv2DLayer(layer, 1024, 3, pad='same') layer = MaxPool2DLayer(layer, 2) layer = DenseLayer(layer, 3072) layer = DenseLayer(layer, 4096) layer = DenseLayer(layer, 1000, nonlinearity=None) return layer, x

28.321429

72

0.679697

00b1fa74d98ff8ab7d4bbc1bb0c0948401e27feb

2,099

py

Python

2020/23/solution.py

Den4200/advent-of-code

87117fde308e9907da9adc77d55a10a75a97ee99

[ "MIT" ]

5

2021-12-20T02:30:15.000Z

2021-12-24T15:52:50.000Z

2020/23/solution.py

Den4200/advent-of-code-2020

87117fde308e9907da9adc77d55a10a75a97ee99

[ "MIT" ]

null

2020/23/solution.py

Den4200/advent-of-code-2020

87117fde308e9907da9adc77d55a10a75a97ee99

[ "MIT" ]

null

from collections import deque from itertools import islice class Node: def __init__(self, value): self.value = value self.next = None def parse_data(): with open('2020/23/input.txt') as f: data = f.read() return [int(num) for num in data.strip()] def part_one(data): cups = deque(data) for i in range(100): current = cups[i % len(cups)] cups.rotate(-cups.index(current)) choices = [cups[j] for j in range(1, 4)] for choice in choices: cups.remove(choice) dest = current - 1 min_cups = min(cups) while dest not in cups: if dest < min_cups: dest = max(cups) else: dest -= 1 for idx, choice in enumerate(choices, start=cups.index(dest) + 1): cups.insert(idx, choice) cups.rotate(i % len(cups)) cups.rotate(-cups.index(1)) return ''.join(str(cup) for cup in islice(cups, 1, len(cups))) def part_two(data): nodes = {i: Node(i) for i in range(1, 1_000_001)} for current, next_ in zip(data, data[1:]): nodes[current].next = nodes[next_] if len(data) == 1_000_000: nodes[data[-1]].next = nodes[data[0]] else: nodes[data[-1]].next = nodes[len(data) + 1] for i in range(len(data) + 1, 1_000_000): nodes[i].next = nodes[i + 1] nodes[1_000_000].next = nodes[data[0]] head = nodes[data[0]] for _ in range(10_000_000): start = head.next end = head.next.next.next dest = head.value - 1 if head.value > 1 else 1_000_000 while dest in (start.value, start.next.value, end.value): dest -= 1 if dest == 0: dest = 1_000_000 head.next = end.next end.next = nodes[dest].next nodes[dest].next = start head = head.next return nodes[1].next.value * nodes[1].next.next.value def main(): data = parse_data() print(f'Day 23 Part 01: {part_one(data)}') print(f'Day 23 Part 02: {part_two(data)}')

22.569892

74

0.553121

e34e4ab3227982b911ed69a7c236bc914f198ae8

11,290

py

Python

learntools/nlp/ex2.py

roannav/learntools

355a5df6a66562de62254b723da1a9389b9acc49

[ "Apache-2.0" ]

null

learntools/nlp/ex2.py

roannav/learntools

355a5df6a66562de62254b723da1a9389b9acc49

[ "Apache-2.0" ]

null

learntools/nlp/ex2.py

roannav/learntools

355a5df6a66562de62254b723da1a9389b9acc49

[ "Apache-2.0" ]

null

import random import numpy as np import pandas as pd import spacy from spacy.util import minibatch import textwrap from learntools.core import * def load_data(csv_file, split=0.8): data = pd.read_csv(csv_file) # Shuffle data train_data = data.sample(frac=1, random_state=7) texts = train_data.text.values labels = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in train_data.sentiment.values] split = int(len(train_data) * split) train_labels = [{"cats": labels} for labels in labels[:split]] val_labels = [{"cats": labels} for labels in labels[split:]] return texts[:split], train_labels, texts[split:], val_labels train_texts, train_labels, val_texts, val_labels = load_data('../input/nlp-course/yelp_ratings.csv') def create_model(): # Create an empty model nlp = spacy.blank("en") # Create the TextCategorizer with exclusive classes and "bow" architecture textcat = nlp.create_pipe( "textcat", config={ "exclusive_classes": True, "architecture": "bow"}) nlp.add_pipe(textcat) # Add NEGATIVE and POSITIVE labels to text classifier textcat.add_label("NEGATIVE") textcat.add_label("POSITIVE") return nlp def train_func(model, train_data, optimizer, batch_size=8): losses = {} # random.seed(1) random.shuffle(train_data) batches = minibatch(train_data, size=batch_size) for batch in batches: texts, labels = zip(*batch) model.update(texts, labels, sgd=optimizer, losses=losses) return losses class EvaluateFeedbackFormApproach(ThoughtExperiment): _solution = ("Any way of setting up an ML problem will have multiple strengths and weaknesses. " "So you may have thought of different issues than listed here.\n\nThe strength of this " "approach is that it allows you to distinguish positive email messages from negative emails " "even though you don't have historical emails that you have labeled as positive or negative.\n\n" "The weakness of this approach is that emails may be systematically different from Yelp reviews " "in ways that make your model less accurate. For example, customers might generally use different " "words or slang in emails, and the model based on Yelp reviews won't have seen these words.\n\n" "If you wanted to see how serious this issue is, you could compare word frequencies between the two sources. " "In practice, manually reading a few emails from each source may be enough to see if it's a serious issue. \n\n" "If you wanted to do something fancier, you could create a dataset that contains both Yelp reviews and emails " "and see whether a model can tell a reviews source from the text content. Ideally, you'd like to find " "that model didn't perform well, because it would mean your data sources are similar. That approach seems " "unnecessarily complex here.") class CreateTextCatModel(CodingProblem): _var = 'nlp' _hint = ("After creating the empty model, use .create_pipe to add the TextCategorizer " "to the nlp model. Set the config appropriately for exclusive classes and bow " "architecture. Then use .add_label to add labels.") _solution = CS(""" # Create an empty model nlp = spacy.blank("en") # Create the TextCategorizer with exclusive classes and "bow" architecture textcat = nlp.create_pipe( "textcat", config={ "exclusive_classes": True, "architecture": "bow"}) nlp.add_pipe(textcat) # Add NEGATIVE and POSITIVE labels to text classifier textcat.add_label("NEGATIVE") textcat.add_label("POSITIVE") """) def check(self, nlp): assert nlp.has_pipe('textcat'), "Please add a TextCategorizer to the model's pipeline" textcat = nlp.get_pipe('textcat') message = f"TextCatagorizer labels should be ('NEGATIVE', 'POSITIVE'), we found {textcat.labels}" assert textcat.labels == ('NEGATIVE', 'POSITIVE'), message config = textcat.cfg assert config['architecture'] == 'bow', "Please use the 'bow' architecture" assert config['exclusive_classes'], "Be sure to set exclusive_classes to True in the model config" class TrainFunction(CodingProblem): _var = 'train' _hint = ("Use minibatch to create the batches. You can use the zip method to split the " "train_data list into two separate lists. For training the model, model.update " "takes the texts and labels. Be sure to use a batch size of 8, and dropout 0.2.") _solution = CS(""" def train(model, train_data, optimizer, batch_size=8): losses = {} random.shuffle(train_data) batches = minibatch(train_data, size=batch_size) for batch in batches: texts, labels = zip(*batch) model.update(texts, labels, sgd=optimizer, losses=losses) return losses""") def check(self, train): def soln_func(model, train_data, optimizer, batch_size=8): losses = {} #random.seed(1) random.shuffle(train_data) batches = minibatch(train_data, size=batch_size) for batch in batches: texts, labels = zip(*batch) model.update(texts, labels, sgd=optimizer, losses=losses) return losses train_data = list(zip(train_texts, train_labels)) spacy.util.fix_random_seed(1) random.seed(1) nlp = create_model() optimizer = nlp.begin_training() student_losses = train(nlp, train_data[:1000], optimizer) spacy.util.fix_random_seed(1) random.seed(1) nlp = create_model() optimizer = nlp.begin_training() soln_losses = soln_func(nlp, train_data[:1000], optimizer) assert student_losses == soln_losses, "Your loss isn't the same as our solution. Make sure to set batch size to 8 and dropout to 0.2." class PredictFunction(CodingProblem): _var = 'predict' _hint = ("You can use `nlp.tokenizer()` on each text example to tokenize the input data. " "To make predictions, you want to get the TextCategorizer object " "with `nlp.get_pipe()`. The use .predict on the TextCategorizer to get the scores. " "With the scores array, the .argmax method will return the index of the highest " "value. Take note of the axis argument in .argmax so you're finding the max index " "for each example") _solution = CS(""" def predict(nlp, texts): # Use the tokenizer to tokenize each input text example docs = [nlp.tokenizer(text) for text in texts] # Use textcat to get the scores for each doc textcat = nlp.get_pipe('textcat') scores, _ = textcat.predict(docs) # From the scores, find the class with the highest score/probability predicted_class = scores.argmax(axis=1) return predicted_class""") def check(self, predict): def soln_func(nlp, texts): # Use the tokenizer to tokenize each input text example docs = [nlp.tokenizer(text) for text in texts] # Use textcat to get the scores for each doc textcat = nlp.get_pipe('textcat') scores, _ = textcat.predict(docs) # From the scores, find the class with the highest score/probability predicted_class = scores.argmax(axis=1) return predicted_class spacy.util.fix_random_seed(1) nlp = create_model() optimizer = nlp.begin_training() train_data = list(zip(train_texts, train_labels)) _ = train_func(nlp, train_data[:1000], optimizer) student_predicted = predict(nlp, val_texts[20:30]) soln_predicted = soln_func(nlp, val_texts[20:30]) assert np.all(student_predicted == soln_predicted) class EvaluateFunction(CodingProblem): _var = 'evaluate' _hint = ("Use your predict function to get the predicted classes. " "The labels look like `{'cats': {'POSITIVE':True, 'NEGATIVE': False}}`, " "you'll need to convert these into 1s where POSITIVE is True, and 0 where " "POSITIVE is False. Once you have the predictions and true classes, calculate " "the accuracy") _solution = CS(""" def evaluate(model, texts, labels): # Get predictions from textcat model predicted_class = predict(model, texts) # From labels, get the true class as a list of integers (POSITIVE -> 1, NEGATIVE -> 0) true_class = [int(each['cats']['POSITIVE']) for each in labels] # A boolean or int array indicating correct predictions correct_predictions = predicted_class == true_class # The accuracy, number of correct predictions divided by all predictions accuracy = correct_predictions.mean() return accuracy """) def check(self, evaluate): def soln_func(model, texts, labels): def predict (model, texts): docs = [model.tokenizer(text) for text in texts] textcat = model.get_pipe('textcat') scores, _ = textcat.predict(docs) return scores.argmax(axis=1) # Get predictions from textcat model predicted_class = predict(model, texts) # From labels, get the true class as a list of integers (POSITIVE -> 1, NEGATIVE -> 0) true_class = [int(each['cats']['POSITIVE']) for each in labels] # A boolean or int array indicating correct predictions correct_predictions = predicted_class == true_class # The accuracy, number of correct predictions divided by all predictions accuracy = correct_predictions.mean() return accuracy spacy.util.fix_random_seed(1) nlp = create_model() optimizer = nlp.begin_training() train_data = list(zip(train_texts, train_labels)) _ = train_func(nlp, train_data[:1000], optimizer) student_acc = evaluate(nlp, val_texts[:30], val_labels[:30]) soln_acc = soln_func(nlp, val_texts[:30], val_labels[:30]) assert np.all(student_acc == soln_acc) class ModelOptimizationQuestion(ThoughtExperiment): _solution = ("Answer: There are various hyperparameters to work with here. The biggest one " "is the TextCategorizer architecture. You used the simplest model which trains " "faster but likely has worse performance than the CNN and ensemble models. " ) qvars = bind_exercises(globals(), [ EvaluateFeedbackFormApproach, CreateTextCatModel, TrainFunction, PredictFunction, EvaluateFunction, ModelOptimizationQuestion ], var_format='step_{n}', ) __all__ = list(qvars)

42.284644

142

0.635252

d0759d2b514c5838ab506a2649bf9f56a9f0253b

93

py

Python

Ejercicio5.py

mariagarciau/Ejercicios-Analisis

54be0f131b59b9cdafbac5f42711fc282d0de21d

[ "Apache-2.0" ]

null

Ejercicio5.py

mariagarciau/Ejercicios-Analisis

54be0f131b59b9cdafbac5f42711fc282d0de21d

[ "Apache-2.0" ]

null

Ejercicio5.py

mariagarciau/Ejercicios-Analisis

54be0f131b59b9cdafbac5f42711fc282d0de21d

[ "Apache-2.0" ]

null

"""¿Puede usted mejorar el algoritmo del problema anterior para que sea lineal? Explique. """

46.5

89

0.763441

0bb98fd34f7629b4ac8af331ad2b5bf469490d7c

5,381

py

Python

chia/wallet/cc_wallet/cc_utils_test.py

stablyio/chia-blockchain

b332dd71b8cfbcd7c3dc552defdea5dda69cf34a

[ "Apache-2.0" ]

null

chia/wallet/cc_wallet/cc_utils_test.py

stablyio/chia-blockchain

b332dd71b8cfbcd7c3dc552defdea5dda69cf34a

[ "Apache-2.0" ]

null

chia/wallet/cc_wallet/cc_utils_test.py

stablyio/chia-blockchain

b332dd71b8cfbcd7c3dc552defdea5dda69cf34a

[ "Apache-2.0" ]

null

from chia.rpc.cat_utils import convert_to_coin_spend from chia.wallet.cc_wallet.cc_utils import get_parent_cat_coin_spend_lineage_proof class Test_get_parent_cat_coin_spend_lineage_proof: def test_valid_case(self): parent_coin_spend = convert_to_coin_spend( raw_coin_spend={ "coin": { "amount": 24330, "parent_coin_info": "0x8599cc835a767775c655025fcd4249170d37affc4f4a85c830d8a41a93c1ea37", "puzzle_hash": "0xa0557e2022d2d4803ad6b3638a909118d18ad8ccbecc844557b34f268f78938a" }, "puzzle_reveal": "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", "solution": "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" } ) parent_coin, lineage_proof = get_parent_cat_coin_spend_lineage_proof( parent_coin_spend=parent_coin_spend, ) assert parent_coin.parent_coin_info == lineage_proof.parent_name test = Test_get_parent_cat_coin_spend_lineage_proof() test.test_valid_case()

199.296296

3,650

0.941089

b0c2d33e1d2867fc6681ecc67d0f762b13ca7a6c

136,839

py

Python

test/api/test_workflows.py

innovate-invent/galaxy

10aa953a40e171246bdd1804c74e8019da8e8200

[ "CC-BY-3.0" ]

null

test/api/test_workflows.py

innovate-invent/galaxy

10aa953a40e171246bdd1804c74e8019da8e8200

[ "CC-BY-3.0" ]

null

test/api/test_workflows.py

innovate-invent/galaxy

10aa953a40e171246bdd1804c74e8019da8e8200

[ "CC-BY-3.0" ]

null

from __future__ import print_function import json import time from json import dumps from uuid import uuid4 from requests import delete, get, put from base import api # noqa: I100,I202 from base import rules_test_data # noqa: I100 from base.populators import ( # noqa: I100 DatasetCollectionPopulator, DatasetPopulator, skip_without_tool, wait_on, WorkflowPopulator ) from base.workflow_fixtures import ( # noqa: I100 WORKFLOW_NESTED_REPLACEMENT_PARAMETER, WORKFLOW_NESTED_RUNTIME_PARAMETER, WORKFLOW_NESTED_SIMPLE, WORKFLOW_ONE_STEP_DEFAULT, WORKFLOW_RENAME_ON_INPUT, WORKFLOW_RUNTIME_PARAMETER_AFTER_PAUSE, WORKFLOW_WITH_DYNAMIC_OUTPUT_COLLECTION, WORKFLOW_WITH_OUTPUT_COLLECTION, WORKFLOW_WITH_OUTPUT_COLLECTION_MAPPING, WORKFLOW_WITH_RULES_1, ) from galaxy.exceptions import error_codes # noqa: I201 NESTED_WORKFLOW_AUTO_LABELS = """ class: GalaxyWorkflow inputs: outer_input: data outputs: outer_output: outputSource: second_cat/out_file1 steps: first_cat: tool_id: cat1 in: input1: outer_input nested_workflow: run: class: GalaxyWorkflow inputs: - id: inner_input outputs: - source: 1#out_file1 steps: random: tool_id: random_lines1 state: num_lines: 1 input: $link: inner_input seed_source: seed_source_selector: set_seed seed: asdf in: inner_input: first_cat/out_file1 second_cat: tool_id: cat1 state: input1: $link: nested_workflow#1:out_file1 queries: - input2: $link: nested_workflow#1:out_file1 """ class BaseWorkflowsApiTestCase(api.ApiTestCase): # TODO: Find a new file for this class. def setUp(self): super(BaseWorkflowsApiTestCase, self).setUp() self.workflow_populator = WorkflowPopulator(self.galaxy_interactor) self.dataset_populator = DatasetPopulator(self.galaxy_interactor) self.dataset_collection_populator = DatasetCollectionPopulator(self.galaxy_interactor) def _assert_user_has_workflow_with_name(self, name): names = self._workflow_names() assert name in names, "No workflows with name %s in users workflows <%s>" % (name, names) def _workflow_names(self): index_response = self._get("workflows") self._assert_status_code_is(index_response, 200) names = [w["name"] for w in index_response.json()] return names def import_workflow(self, workflow, **kwds): upload_response = self.workflow_populator.import_workflow(workflow, **kwds) return upload_response def _upload_yaml_workflow(self, has_yaml, **kwds): return self.workflow_populator.upload_yaml_workflow(has_yaml, **kwds) def _setup_workflow_run(self, workflow=None, inputs_by='step_id', history_id=None, workflow_id=None): if not workflow_id: workflow_id = self.workflow_populator.create_workflow(workflow) if not history_id: history_id = self.dataset_populator.new_history() hda1 = self.dataset_populator.new_dataset(history_id, content="1 2 3") hda2 = self.dataset_populator.new_dataset(history_id, content="4 5 6") workflow_request = dict( history="hist_id=%s" % history_id, workflow_id=workflow_id, ) label_map = { 'WorkflowInput1': self._ds_entry(hda1), 'WorkflowInput2': self._ds_entry(hda2) } if inputs_by == 'step_id': ds_map = self._build_ds_map(workflow_id, label_map) workflow_request["ds_map"] = ds_map elif inputs_by == "step_index": index_map = { '0': self._ds_entry(hda1), '1': self._ds_entry(hda2) } workflow_request["inputs"] = dumps(index_map) workflow_request["inputs_by"] = 'step_index' elif inputs_by == "name": workflow_request["inputs"] = dumps(label_map) workflow_request["inputs_by"] = 'name' elif inputs_by in ["step_uuid", "uuid_implicitly"]: uuid_map = { workflow["steps"]["0"]["uuid"]: self._ds_entry(hda1), workflow["steps"]["1"]["uuid"]: self._ds_entry(hda2), } workflow_request["inputs"] = dumps(uuid_map) if inputs_by == "step_uuid": workflow_request["inputs_by"] = "step_uuid" return workflow_request, history_id def _build_ds_map(self, workflow_id, label_map): workflow_inputs = self._workflow_inputs(workflow_id) ds_map = {} for key, value in workflow_inputs.items(): label = value["label"] if label in label_map: ds_map[key] = label_map[label] return dumps(ds_map) def _ds_entry(self, history_content): return self.dataset_populator.ds_entry(history_content) def _workflow_inputs(self, uploaded_workflow_id): workflow_show_resposne = self._get("workflows/%s" % uploaded_workflow_id) self._assert_status_code_is(workflow_show_resposne, 200) workflow_inputs = workflow_show_resposne.json()["inputs"] return workflow_inputs def _invocation_details(self, workflow_id, invocation_id, **kwds): invocation_details_response = self._get("workflows/%s/usage/%s" % (workflow_id, invocation_id), data=kwds) self._assert_status_code_is(invocation_details_response, 200) invocation_details = invocation_details_response.json() return invocation_details def _run_jobs(self, has_workflow, history_id=None, **kwds): if history_id is None: history_id = self.history_id return self.workflow_populator.run_workflow(has_workflow, history_id=history_id, **kwds) def _history_jobs(self, history_id): return self._get("jobs", {"history_id": history_id, "order_by": "create_time"}).json() def _assert_history_job_count(self, history_id, n): jobs = self._history_jobs(history_id) self.assertEqual(len(jobs), n) def _download_workflow(self, workflow_id, style=None): return self.workflow_populator.download_workflow(workflow_id, style=style) def wait_for_invocation_and_jobs(self, history_id, workflow_id, invocation_id, assert_ok=True): state = self.workflow_populator.wait_for_invocation(workflow_id, invocation_id) if assert_ok: assert state == "scheduled", state self.workflow_populator.wait_for_invocation(workflow_id, invocation_id) time.sleep(.5) self.dataset_populator.wait_for_history_jobs(history_id, assert_ok=assert_ok) time.sleep(.5) def _assert_is_runtime_input(self, tool_state_value): if not isinstance(tool_state_value, dict): tool_state_value = json.loads(tool_state_value) assert isinstance(tool_state_value, dict) assert "__class__" in tool_state_value assert tool_state_value["__class__"] == "RuntimeValue" # Workflow API TODO: # - Allow history_id as param to workflow run action. (hist_id) # - Allow post to workflows/<workflow_id>/run in addition to posting to # /workflows with id in payload. # - Much more testing obviously, always more testing. class WorkflowsApiTestCase(BaseWorkflowsApiTestCase): def setUp(self): super(WorkflowsApiTestCase, self).setUp() def test_show_valid(self): workflow_id = self.workflow_populator.simple_workflow("dummy") workflow_id = self.workflow_populator.simple_workflow("test_regular") show_response = self._get("workflows/%s" % workflow_id, {"style": "instance"}) workflow = show_response.json() self._assert_looks_like_instance_workflow_representation(workflow) assert len(workflow["steps"]) == 3 self.assertEqual(sorted(step["id"] for step in workflow["steps"].values()), [0, 1, 2]) show_response = self._get("workflows/%s" % workflow_id, {"legacy": True}) workflow = show_response.json() self._assert_looks_like_instance_workflow_representation(workflow) assert len(workflow["steps"]) == 3 # Can't reay say what the legacy IDs are but must be greater than 3 because dummy # workflow was created first in this instance. self.assertNotEqual(sorted(step["id"] for step in workflow["steps"].values()), [0, 1, 2]) def test_show_invalid_key_is_400(self): show_response = self._get("workflows/%s" % self._random_key()) self._assert_status_code_is(show_response, 400) def test_cannot_show_private_workflow(self): workflow_id = self.workflow_populator.simple_workflow("test_not_importportable") with self._different_user(): show_response = self._get("workflows/%s" % workflow_id) self._assert_status_code_is(show_response, 403) # Try as anonymous user workflows_url = self._api_url("workflows/%s" % workflow_id) assert get(workflows_url).status_code == 403 def test_delete(self): workflow_id = self.workflow_populator.simple_workflow("test_delete") workflow_name = "test_delete" self._assert_user_has_workflow_with_name(workflow_name) workflow_url = self._api_url("workflows/%s" % workflow_id, use_key=True) delete_response = delete(workflow_url) self._assert_status_code_is(delete_response, 200) # Make sure workflow is no longer in index by default. assert workflow_name not in self._workflow_names() def test_other_cannot_delete(self): workflow_id = self.workflow_populator.simple_workflow("test_other_delete") with self._different_user(): workflow_url = self._api_url("workflows/%s" % workflow_id, use_key=True) delete_response = delete(workflow_url) self._assert_status_code_is(delete_response, 403) def test_index(self): index_response = self._get("workflows") self._assert_status_code_is(index_response, 200) assert isinstance(index_response.json(), list) def test_upload(self): self.__test_upload(use_deprecated_route=False) def test_upload_deprecated(self): self.__test_upload(use_deprecated_route=True) def test_import_tools_requires_admin(self): response = self.__test_upload(import_tools=True, assert_ok=False) assert response.status_code == 403 def __test_upload(self, use_deprecated_route=False, name="test_import", workflow=None, assert_ok=True, import_tools=False): if workflow is None: workflow = self.workflow_populator.load_workflow(name=name) data = dict( workflow=dumps(workflow), ) if import_tools: data["import_tools"] = import_tools if use_deprecated_route: route = "workflows/upload" else: route = "workflows" upload_response = self._post(route, data=data) if assert_ok: self._assert_status_code_is(upload_response, 200) self._assert_user_has_workflow_with_name(name) return upload_response def test_update(self): original_workflow = self.workflow_populator.load_workflow(name="test_import") uuids = {} labels = {} for order_index, step_dict in original_workflow["steps"].items(): uuid = str(uuid4()) step_dict["uuid"] = uuid uuids[order_index] = uuid label = "label_%s" % order_index step_dict["label"] = label labels[order_index] = label def check_label_and_uuid(order_index, step_dict): assert order_index in uuids assert order_index in labels self.assertEqual(uuids[order_index], step_dict["uuid"]) self.assertEqual(labels[order_index], step_dict["label"]) upload_response = self.__test_upload(workflow=original_workflow) workflow_id = upload_response.json()["id"] def update(workflow_object): put_response = self._update_workflow(workflow_id, workflow_object) self._assert_status_code_is(put_response, 200) return put_response workflow_content = self._download_workflow(workflow_id) steps = workflow_content["steps"] def tweak_step(step): order_index, step_dict = step check_label_and_uuid(order_index, step_dict) assert step_dict['position']['top'] != 1 assert step_dict['position']['left'] != 1 step_dict['position'] = {'top': 1, 'left': 1} map(tweak_step, steps.items()) update(workflow_content) def check_step(step): order_index, step_dict = step check_label_and_uuid(order_index, step_dict) assert step_dict['position']['top'] == 1 assert step_dict['position']['left'] == 1 updated_workflow_content = self._download_workflow(workflow_id) map(check_step, updated_workflow_content['steps'].items()) # Re-update against original workflow... update(original_workflow) updated_workflow_content = self._download_workflow(workflow_id) # Make sure the positions have been updated. map(tweak_step, updated_workflow_content['steps'].items()) def test_update_no_tool_id(self): workflow_object = self.workflow_populator.load_workflow(name="test_import") upload_response = self.__test_upload(workflow=workflow_object) workflow_id = upload_response.json()["id"] del workflow_object["steps"]["2"]["tool_id"] put_response = self._update_workflow(workflow_id, workflow_object) self._assert_status_code_is(put_response, 400) def test_update_missing_tool(self): # Create allows missing tools, update doesn't currently... workflow_object = self.workflow_populator.load_workflow(name="test_import") upload_response = self.__test_upload(workflow=workflow_object) workflow_id = upload_response.json()["id"] workflow_object["steps"]["2"]["tool_id"] = "cat-not-found" put_response = self._update_workflow(workflow_id, workflow_object) self._assert_status_code_is(put_response, 400) def test_require_unique_step_uuids(self): workflow_dup_uuids = self.workflow_populator.load_workflow(name="test_import") uuid0 = str(uuid4()) for step_dict in workflow_dup_uuids["steps"].values(): step_dict["uuid"] = uuid0 response = self.workflow_populator.create_workflow_response(workflow_dup_uuids) self._assert_status_code_is(response, 400) def test_require_unique_step_labels(self): workflow_dup_label = self.workflow_populator.load_workflow(name="test_import") for step_dict in workflow_dup_label["steps"].values(): step_dict["label"] = "my duplicated label" response = self.workflow_populator.create_workflow_response(workflow_dup_label) self._assert_status_code_is(response, 400) def test_import_deprecated(self): workflow_id = self.workflow_populator.simple_workflow("test_import_published_deprecated", publish=True) with self._different_user(): other_import_response = self.__import_workflow(workflow_id) self._assert_status_code_is(other_import_response, 200) self._assert_user_has_workflow_with_name("imported: test_import_published_deprecated") def test_import_annotations(self): workflow_id = self.workflow_populator.simple_workflow("test_import_annotations", publish=True) with self._different_user(): other_import_response = self.__import_workflow(workflow_id) self._assert_status_code_is(other_import_response, 200) # Test annotations preserved during upload and copied over during # import. other_id = other_import_response.json()["id"] imported_workflow = self._show_workflow(other_id) assert imported_workflow["annotation"] == "simple workflow" step_annotations = set(step["annotation"] for step in imported_workflow["steps"].values()) assert "input1 description" in step_annotations def test_import_subworkflows(self): def get_subworkflow_content_id(workflow_id): workflow_contents = self._download_workflow(workflow_id, style="editor") steps = workflow_contents['steps'] subworkflow_step = next(s for s in steps.values() if s["type"] == "subworkflow") return subworkflow_step['content_id'] workflow_id = self._upload_yaml_workflow(WORKFLOW_NESTED_SIMPLE, publish=True) subworkflow_content_id = get_subworkflow_content_id(workflow_id) with self._different_user(): other_import_response = self.__import_workflow(workflow_id) self._assert_status_code_is(other_import_response, 200) imported_workflow_id = other_import_response.json()["id"] imported_subworkflow_content_id = get_subworkflow_content_id(imported_workflow_id) assert subworkflow_content_id != imported_subworkflow_content_id def test_not_importable_prevents_import(self): workflow_id = self.workflow_populator.simple_workflow("test_not_importportable") with self._different_user(): other_import_response = self.__import_workflow(workflow_id) self._assert_status_code_is(other_import_response, 403) def test_anonymous_published(self): def anonymous_published_workflows(): workflows_url = self._api_url("workflows?show_published=True") return get(workflows_url).json() names = [w["name"] for w in anonymous_published_workflows()] assert "test published example" not in names workflow_id = self.workflow_populator.simple_workflow("test published example", publish=True) names = [w["name"] for w in anonymous_published_workflows()] assert "test published example" in names ids = [w["id"] for w in anonymous_published_workflows()] assert workflow_id in ids def test_import_published(self): workflow_id = self.workflow_populator.simple_workflow("test_import_published", publish=True) with self._different_user(): other_import_response = self.__import_workflow(workflow_id, deprecated_route=True) self._assert_status_code_is(other_import_response, 200) self._assert_user_has_workflow_with_name("imported: test_import_published") def test_export(self): uploaded_workflow_id = self.workflow_populator.simple_workflow("test_for_export") downloaded_workflow = self._download_workflow(uploaded_workflow_id) assert downloaded_workflow["name"] == "test_for_export" assert len(downloaded_workflow["steps"]) == 3 first_input = downloaded_workflow["steps"]["0"]["inputs"][0] assert first_input["name"] == "WorkflowInput1" assert first_input["description"] == "input1 description" self._assert_has_keys(downloaded_workflow, "a_galaxy_workflow", "format-version", "annotation", "uuid", "steps") for step in downloaded_workflow["steps"].values(): self._assert_has_keys( step, 'id', 'type', 'tool_id', 'tool_version', 'name', 'tool_state', 'annotation', 'inputs', 'workflow_outputs', 'outputs' ) if step['type'] == "tool": self._assert_has_keys(step, "post_job_actions") def test_export_editor(self): uploaded_workflow_id = self.workflow_populator.simple_workflow("test_for_export") downloaded_workflow = self._download_workflow(uploaded_workflow_id, style="editor") self._assert_has_keys(downloaded_workflow, "name", "steps", "upgrade_messages") for step in downloaded_workflow["steps"].values(): self._assert_has_keys( step, 'id', 'type', 'content_id', 'name', 'tool_state', 'tooltip', 'inputs', 'outputs', 'config_form', 'annotation', 'post_job_actions', 'workflow_outputs', 'uuid', 'label', ) @skip_without_tool('collection_type_source') def test_export_editor_collection_type_source(self): workflow_id = self._upload_yaml_workflow(""" class: GalaxyWorkflow inputs: - id: text_input1 type: collection collection_type: "list:paired" steps: - tool_id: collection_type_source in: input_collect: text_input1 """) downloaded_workflow = self._download_workflow(workflow_id, style="editor") steps = downloaded_workflow['steps'] assert len(steps) == 2 assert steps['1']['outputs'][0]['collection_type'] == 'list:paired' @skip_without_tool('collection_type_source') def test_export_editor_subworkflow_collection_type_source(self): workflow_id = self._upload_yaml_workflow(""" class: GalaxyWorkflow inputs: outer_input: data steps: inner_workflow: run: class: GalaxyWorkflow inputs: inner_input: type: collection collection_type: "list:paired" outputs: workflow_output: outputSource: collection_type_source/list_output steps: collection_type_source: tool_id: collection_type_source in: input_collect: inner_input in: inner_input: outer_input """) downloaded_workflow = self._download_workflow(workflow_id, style="editor") steps = downloaded_workflow['steps'] assert len(steps) == 2 assert steps['1']['type'] == 'subworkflow' assert steps['1']['outputs'][0]['collection_type'] == 'list:paired' def test_import_missing_tool(self): workflow = self.workflow_populator.load_workflow_from_resource(name="test_workflow_missing_tool") workflow_id = self.workflow_populator.create_workflow(workflow) workflow_description = self._show_workflow(workflow_id) steps = workflow_description["steps"] missing_tool_steps = [v for v in steps.values() if v['tool_id'] == 'cat_missing_tool'] assert len(missing_tool_steps) == 1 def test_import_no_tool_id(self): # Import works with missing tools, but not with absent content/tool id. workflow = self.workflow_populator.load_workflow_from_resource(name="test_workflow_missing_tool") del workflow["steps"]["2"]["tool_id"] create_response = self.__test_upload(workflow=workflow, assert_ok=False) self._assert_status_code_is(create_response, 400) def test_import_export_with_runtime_inputs(self): workflow = self.workflow_populator.load_workflow_from_resource(name="test_workflow_with_runtime_input") workflow_id = self.workflow_populator.create_workflow(workflow) downloaded_workflow = self._download_workflow(workflow_id) assert len(downloaded_workflow["steps"]) == 2 runtime_step = downloaded_workflow["steps"]["1"] for runtime_input in runtime_step["inputs"]: if runtime_input["name"] == "num_lines": break assert runtime_input["description"].startswith("runtime parameter for tool") tool_state = json.loads(runtime_step["tool_state"]) assert "num_lines" in tool_state self._assert_is_runtime_input(tool_state["num_lines"]) @skip_without_tool("cat1") def test_run_workflow_by_index(self): self.__run_cat_workflow(inputs_by='step_index') @skip_without_tool("cat1") def test_run_workflow_by_uuid(self): self.__run_cat_workflow(inputs_by='step_uuid') @skip_without_tool("cat1") def test_run_workflow_by_uuid_implicitly(self): self.__run_cat_workflow(inputs_by='uuid_implicitly') @skip_without_tool("cat1") def test_run_workflow_by_name(self): self.__run_cat_workflow(inputs_by='name') @skip_without_tool("cat1") def test_run_workflow(self): self.__run_cat_workflow(inputs_by='step_id') @skip_without_tool("multiple_versions") def test_run_versioned_tools(self): with self.dataset_populator.test_history() as history_01_id: workflow_version_01 = self._upload_yaml_workflow(""" class: GalaxyWorkflow steps: multiple: tool_id: multiple_versions tool_version: "0.1" state: inttest: 0 """) self.__invoke_workflow(history_01_id, workflow_version_01) self.dataset_populator.wait_for_history(history_01_id, assert_ok=True) with self.dataset_populator.test_history() as history_02_id: workflow_version_02 = self._upload_yaml_workflow(""" class: GalaxyWorkflow steps: multiple: tool_id: multiple_versions tool_version: "0.2" state: inttest: 1 """) self.__invoke_workflow(history_02_id, workflow_version_02) self.dataset_populator.wait_for_history(history_02_id, assert_ok=True) def __run_cat_workflow(self, inputs_by): workflow = self.workflow_populator.load_workflow(name="test_for_run") workflow["steps"]["0"]["uuid"] = str(uuid4()) workflow["steps"]["1"]["uuid"] = str(uuid4()) workflow_request, history_id = self._setup_workflow_run(workflow, inputs_by=inputs_by) # TODO: This should really be a post to workflows/<workflow_id>/run or # something like that. run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) invocation_id = run_workflow_response.json()["id"] invocation = self._invocation_details(workflow_request["workflow_id"], invocation_id) assert invocation["state"] == "scheduled", invocation self._assert_status_code_is(run_workflow_response, 200) self.dataset_populator.wait_for_history(history_id, assert_ok=True) @skip_without_tool("collection_creates_pair") def test_workflow_run_output_collections(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(WORKFLOW_WITH_OUTPUT_COLLECTION, history_id=history_id, assert_ok=True, wait=True) self.assertEqual("a\nc\nb\nd\n", self.dataset_populator.get_history_dataset_content(history_id, hid=0)) @skip_without_tool("job_properties") @skip_without_tool("identifier_multiple_in_conditional") def test_workflow_resume_from_failed_step(self): workflow_id = self._upload_yaml_workflow(""" class: GalaxyWorkflow steps: job_props: tool_id: job_properties state: thebool: true failbool: true identifier: tool_id: identifier_multiple_in_conditional state: outer_cond: cond_param_outer: true inner_cond: cond_param_inner: true input1: $link: 0#out_file1 """) with self.dataset_populator.test_history() as history_id: invocation_id = self.__invoke_workflow(history_id, workflow_id) self.wait_for_invocation_and_jobs(history_id, workflow_id, invocation_id, assert_ok=False) failed_dataset_one = self.dataset_populator.get_history_dataset_details(history_id, hid=1, wait=True, assert_ok=False) assert failed_dataset_one['state'] == 'error', failed_dataset_one paused_dataset = self.dataset_populator.get_history_dataset_details(history_id, hid=5, wait=True, assert_ok=False) assert paused_dataset['state'] == 'paused', paused_dataset inputs = {"thebool": "false", "failbool": "false", "rerun_remap_job_id": failed_dataset_one['creating_job']} self.dataset_populator.run_tool(tool_id='job_properties', inputs=inputs, history_id=history_id, assert_ok=True) unpaused_dataset = self.dataset_populator.get_history_dataset_details(history_id, hid=5, wait=True, assert_ok=False) assert unpaused_dataset['state'] == 'ok' @skip_without_tool("job_properties") @skip_without_tool("identifier_collection") def test_workflow_resume_from_failed_step_with_hdca_input(self): workflow_id = self._upload_yaml_workflow(""" class: GalaxyWorkflow steps: job_props: tool_id: job_properties state: thebool: true failbool: true identifier: tool_id: identifier_collection in: input1: job_props/list_output """) with self.dataset_populator.test_history() as history_id: invocation_id = self.__invoke_workflow(history_id, workflow_id) self.wait_for_invocation_and_jobs(history_id, workflow_id, invocation_id, assert_ok=False) failed_dataset_one = self.dataset_populator.get_history_dataset_details(history_id, hid=1, wait=True, assert_ok=False) assert failed_dataset_one['state'] == 'error', failed_dataset_one paused_dataset = self.dataset_populator.get_history_dataset_details(history_id, hid=5, wait=True, assert_ok=False) assert paused_dataset['state'] == 'paused', paused_dataset inputs = {"thebool": "false", "failbool": "false", "rerun_remap_job_id": failed_dataset_one['creating_job']} self.dataset_populator.run_tool(tool_id='job_properties', inputs=inputs, history_id=history_id, assert_ok=True) unpaused_dataset = self.dataset_populator.get_history_dataset_details(history_id, hid=5, wait=True, assert_ok=False) assert unpaused_dataset['state'] == 'ok' @skip_without_tool("fail_identifier") @skip_without_tool("identifier_collection") def test_workflow_resume_with_mapped_over_input(self): with self.dataset_populator.test_history() as history_id: job_summary = self._run_jobs(""" class: GalaxyWorkflow inputs: input_datasets: collection steps: fail_identifier_1: tool_id: fail_identifier state: failbool: true in: input1: input_datasets identifier: tool_id: identifier_collection in: input1: fail_identifier_1/out_file1 test_data: input_datasets: type: list elements: - identifier: fail value: 1.fastq type: File - identifier: success value: 1.fastq type: File """, history_id=history_id, assert_ok=False, wait=False) self.wait_for_invocation_and_jobs(history_id, job_summary.workflow_id, job_summary.invocation_id, assert_ok=False) history_contents = self.dataset_populator._get_contents_request(history_id=history_id).json() paused_dataset = history_contents[-1] failed_dataset = self.dataset_populator.get_history_dataset_details(history_id, hid=5, assert_ok=False) assert paused_dataset['state'] == 'paused', paused_dataset assert failed_dataset['state'] == 'error', failed_dataset inputs = {"input1": {'values': [{'src': 'hda', 'id': history_contents[0]['id']}] }, "failbool": "false", "rerun_remap_job_id": failed_dataset['creating_job']} run_dict = self.dataset_populator.run_tool(tool_id='fail_identifier', inputs=inputs, history_id=history_id, assert_ok=True) unpaused_dataset = self.dataset_populator.get_history_dataset_details(history_id, wait=True, assert_ok=False) assert unpaused_dataset['state'] == 'ok' contents = self.dataset_populator.get_history_dataset_content(history_id, hid=7, assert_ok=False) assert contents == 'fail\nsuccess\n', contents replaced_hda_id = run_dict['outputs'][0]['id'] replaced_hda = self.dataset_populator.get_history_dataset_details(history_id, dataset_id=replaced_hda_id, wait=True, assert_ok=False) assert not replaced_hda['visible'], replaced_hda @skip_without_tool("cat_list") @skip_without_tool("collection_creates_pair") def test_workflow_run_output_collection_mapping(self): workflow_id = self._upload_yaml_workflow(WORKFLOW_WITH_OUTPUT_COLLECTION_MAPPING) with self.dataset_populator.test_history() as history_id: hdca1 = self.dataset_collection_populator.create_list_in_history(history_id, contents=["a\nb\nc\nd\n", "e\nf\ng\nh\n"]).json() self.dataset_populator.wait_for_history(history_id, assert_ok=True) inputs = { '0': self._ds_entry(hdca1), } invocation_id = self.__invoke_workflow(history_id, workflow_id, inputs) self.wait_for_invocation_and_jobs(history_id, workflow_id, invocation_id) self.dataset_populator.wait_for_history(history_id, assert_ok=True) self.assertEqual("a\nc\nb\nd\ne\ng\nf\nh\n", self.dataset_populator.get_history_dataset_content(history_id, hid=0)) @skip_without_tool("cat_list") @skip_without_tool("collection_split_on_column") def test_workflow_run_dynamic_output_collections(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(WORKFLOW_WITH_DYNAMIC_OUTPUT_COLLECTION, history_id=history_id, assert_ok=True, wait=True) details = self.dataset_populator.get_history_dataset_details(history_id, hid=0) last_item_hid = details["hid"] assert last_item_hid == 7, "Expected 7 history items, got %s" % last_item_hid content = self.dataset_populator.get_history_dataset_content(history_id, hid=0) self.assertEqual("10.0\n30.0\n20.0\n40.0\n", content) @skip_without_tool("collection_split_on_column") @skip_without_tool("min_repeat") def test_workflow_run_dynamic_output_collections_2(self): # A more advanced output collection workflow, testing regression of # https://github.com/galaxyproject/galaxy/issues/776 with self.dataset_populator.test_history() as history_id: workflow_id = self._upload_yaml_workflow(""" class: GalaxyWorkflow inputs: test_input_1: data test_input_2: data test_input_3: data steps: split_up: tool_id: collection_split_on_column in: input1: test_input_2 min_repeat: tool_id: min_repeat in: queries_0|input: test_input_1 queries2_0|input2: split_up/split_output """) hda1 = self.dataset_populator.new_dataset(history_id, content="samp1\t10.0\nsamp2\t20.0\n") hda2 = self.dataset_populator.new_dataset(history_id, content="samp1\t20.0\nsamp2\t40.0\n") hda3 = self.dataset_populator.new_dataset(history_id, content="samp1\t30.0\nsamp2\t60.0\n") self.dataset_populator.wait_for_history(history_id, assert_ok=True) inputs = { '0': self._ds_entry(hda1), '1': self._ds_entry(hda2), '2': self._ds_entry(hda3), } invocation_id = self.__invoke_workflow(history_id, workflow_id, inputs) self.wait_for_invocation_and_jobs(history_id, workflow_id, invocation_id) collection_details = self.dataset_populator.get_history_collection_details(history_id, hid=7) assert collection_details["populated_state"] == "ok" content = self.dataset_populator.get_history_dataset_content(history_id, hid=11) self.assertEqual(content.strip(), "samp1\t10.0\nsamp2\t20.0") @skip_without_tool("cat") @skip_without_tool("collection_split_on_column") def test_workflow_run_dynamic_output_collections_3(self): # Test a workflow that create a list:list:list followed by a mapping step. with self.dataset_populator.test_history() as history_id: workflow_id = self._upload_yaml_workflow(""" class: GalaxyWorkflow inputs: text_input1: data text_input2: data steps: cat_inputs: tool_id: cat1 in: input1: text_input1 queries_0|input2: text_input2 split_up_1: tool_id: collection_split_on_column in: input1: cat_inputs/out_file1 split_up_2: tool_id: collection_split_on_column in: input1: split_up_1/split_output cat_output: tool_id: cat in: input1: split_up_2/split_output """) hda1 = self.dataset_populator.new_dataset(history_id, content="samp1\t10.0\nsamp2\t20.0\n") hda2 = self.dataset_populator.new_dataset(history_id, content="samp1\t30.0\nsamp2\t40.0\n") self.dataset_populator.wait_for_history(history_id, assert_ok=True) inputs = { '0': self._ds_entry(hda1), '1': self._ds_entry(hda2), } invocation_id = self.__invoke_workflow(history_id, workflow_id, inputs) self.wait_for_invocation_and_jobs(history_id, workflow_id, invocation_id) @skip_without_tool("mapper") @skip_without_tool("pileup") def test_workflow_metadata_validation_0(self): # Testing regression of # https://github.com/galaxyproject/galaxy/issues/1514 with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input_fastqs: collection reference: data steps: map_over_mapper: tool_id: mapper in: input1: input_fastqs reference: reference pileup: tool_id: pileup in: input1: map_over_mapper/out_file1 reference: reference test_data: input_fastqs: type: list elements: - identifier: samp1 value: 1.fastq type: File - identifier: samp2 value: 1.fastq type: File reference: value: 1.fasta type: File """, history_id=history_id) def test_run_subworkflow_simple(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(WORKFLOW_NESTED_SIMPLE, test_data=""" outer_input: value: 1.bed type: File """, history_id=history_id) content = self.dataset_populator.get_history_dataset_content(history_id) self.assertEqual("chrX\t152691446\t152691471\tCCDS14735.1_cds_0_0_chrX_152691447_f\t0\t+\nchrX\t152691446\t152691471\tCCDS14735.1_cds_0_0_chrX_152691447_f\t0\t+\n", content) @skip_without_tool("random_lines1") def test_run_subworkflow_runtime_parameters(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(WORKFLOW_NESTED_RUNTIME_PARAMETER, test_data=""" step_parameters: '1': '1|num_lines': 2 outer_input: value: 1.bed type: File """, history_id=history_id) content = self.dataset_populator.get_history_dataset_content(history_id) assert len([x for x in content.split("\n") if x]) == 2 @skip_without_tool("cat") def test_run_subworkflow_replacment_parameters(self): with self.dataset_populator.test_history() as history_id: test_data = """ replacement_parameters: replaceme: moocow outer_input: value: 1.bed type: File """ self._run_jobs(WORKFLOW_NESTED_REPLACEMENT_PARAMETER, test_data=test_data, history_id=history_id) details = self.dataset_populator.get_history_dataset_details(history_id) assert details["name"] == "moocow suffix" @skip_without_tool("random_lines1") def test_run_runtime_parameters_after_pause(self): with self.dataset_populator.test_history() as history_id: workflow_run_description = """%s test_data: step_parameters: '2': 'num_lines': 2 input1: value: 1.bed type: File """ % WORKFLOW_RUNTIME_PARAMETER_AFTER_PAUSE job_summary = self._run_jobs(workflow_run_description, history_id=history_id, wait=False) uploaded_workflow_id, invocation_id = job_summary.workflow_id, job_summary.invocation_id # Wait for at least one scheduling step. self._wait_for_invocation_non_new(uploaded_workflow_id, invocation_id) # Make sure the history didn't enter a failed state in there. self.dataset_populator.wait_for_history(history_id, assert_ok=True) # Assert the workflow hasn't finished scheduling, we can be pretty sure we # are at the pause step in this case then. self._assert_invocation_non_terminal(uploaded_workflow_id, invocation_id) # Review the paused steps to allow the workflow to continue. self.__review_paused_steps(uploaded_workflow_id, invocation_id, order_index=1, action=True) # Wait for the workflow to finish scheduling and ensure both the invocation # and the history are in valid states. invocation_scheduled = self._wait_for_invocation_state(uploaded_workflow_id, invocation_id, 'scheduled') assert invocation_scheduled, "Workflow state is not scheduled..." self.dataset_populator.wait_for_history(history_id, assert_ok=True) content = self.dataset_populator.get_history_dataset_content(history_id) assert len([x for x in content.split("\n") if x]) == 2 def test_run_subworkflow_auto_labels(self): history_id = self.dataset_populator.new_history() test_data = """ outer_input: value: 1.bed type: File """ job_summary = self._run_jobs(NESTED_WORKFLOW_AUTO_LABELS, test_data=test_data, history_id=history_id) assert len(job_summary.jobs) == 4, "4 jobs expected, got %d jobs" % len(job_summary.jobs) content = self.dataset_populator.get_history_dataset_content(history_id) self.assertEqual( "chrX\t152691446\t152691471\tCCDS14735.1_cds_0_0_chrX_152691447_f\t0\t+\nchrX\t152691446\t152691471\tCCDS14735.1_cds_0_0_chrX_152691447_f\t0\t+\n", content) @skip_without_tool("cat1") @skip_without_tool("collection_paired_test") def test_workflow_run_zip_collections(self): with self.dataset_populator.test_history() as history_id: workflow_id = self._upload_yaml_workflow(""" class: GalaxyWorkflow inputs: test_input_1: data test_input_2: data steps: first_cat: tool_id: cat1 in: input1: test_input_1 zip_it: tool_id: "__ZIP_COLLECTION__" in: input_forward: first_cat/out_file1 input_reverse: test_input_2 concat_pair: tool_id: collection_paired_test in: f1: zip_it/output """) hda1 = self.dataset_populator.new_dataset(history_id, content="samp1\t10.0\nsamp2\t20.0\n") hda2 = self.dataset_populator.new_dataset(history_id, content="samp1\t20.0\nsamp2\t40.0\n") self.dataset_populator.wait_for_history(history_id, assert_ok=True) inputs = { '0': self._ds_entry(hda1), '1': self._ds_entry(hda2), } invocation_id = self.__invoke_workflow(history_id, workflow_id, inputs) self.wait_for_invocation_and_jobs(history_id, workflow_id, invocation_id) content = self.dataset_populator.get_history_dataset_content(history_id) self.assertEqual(content.strip(), "samp1\t10.0\nsamp2\t20.0\nsamp1\t20.0\nsamp2\t40.0") @skip_without_tool("collection_paired_test") def test_workflow_flatten(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow steps: nested: tool_id: collection_creates_dynamic_nested state: sleep_time: 0 foo: 'dummy' flatten: tool_id: '__FLATTEN__' state: input: $link: nested#list_output join_identifier: '-' """, test_data={}, history_id=history_id) details = self.dataset_populator.get_history_collection_details(history_id, hid=14) assert details['collection_type'] == "list" elements = details["elements"] identifiers = [e['element_identifier'] for e in elements] assert len(identifiers) == 6 assert "oe1-ie1" in identifiers @skip_without_tool("__APPLY_RULES__") def test_workflow_run_apply_rules(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(WORKFLOW_WITH_RULES_1, history_id=history_id, wait=True, assert_ok=True, round_trip_format_conversion=True) output_content = self.dataset_populator.get_history_collection_details(history_id, hid=6) rules_test_data.check_example_2(output_content, self.dataset_populator) def test_filter_failed_mapping(self): with self.dataset_populator.test_history() as history_id: summary = self._run_jobs(""" class: GalaxyWorkflow inputs: input_c: collection steps: mixed_collection: tool_id: exit_code_from_file state: input: $link: input_c filtered_collection: tool_id: "__FILTER_FAILED_DATASETS__" state: input: $link: mixed_collection#out_file1 cat: tool_id: cat1 state: input1: $link: filtered_collection """, test_data=""" input_c: type: list elements: - identifier: i1 content: "0" - identifier: i2 content: "1" """, history_id=history_id, wait=True, assert_ok=False) jobs = summary.jobs def filter_jobs_by_tool(tool_id): return [j for j in summary.jobs if j["tool_id"] == tool_id] assert len(filter_jobs_by_tool("__DATA_FETCH__")) == 1, jobs assert len(filter_jobs_by_tool("exit_code_from_file")) == 2, jobs assert len(filter_jobs_by_tool("__FILTER_FAILED_DATASETS__")) == 1, jobs # Follow proves one job was filtered out of the result of cat1 assert len(filter_jobs_by_tool("cat1")) == 1, jobs def test_workflow_request(self): workflow = self.workflow_populator.load_workflow(name="test_for_queue") workflow_request, history_id = self._setup_workflow_run(workflow) url = "workflows/%s/usage" % (workflow_request["workflow_id"]) del workflow_request["workflow_id"] run_workflow_response = self._post(url, data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) # Give some time for workflow to get scheduled before scanning the history. time.sleep(5) self.dataset_populator.wait_for_history(history_id, assert_ok=True) def test_workflow_output_dataset(self): with self.dataset_populator.test_history() as history_id: summary = self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data outputs: wf_output_1: outputSource: first_cat/out_file1 steps: first_cat: tool_id: cat1 in: input1: input1 """, test_data={"input1": "hello world"}, history_id=history_id) workflow_id = summary.workflow_id invocation_id = summary.invocation_id invocation_response = self._get("workflows/%s/invocations/%s" % (workflow_id, invocation_id)) self._assert_status_code_is(invocation_response, 200) invocation = invocation_response.json() self._assert_has_keys(invocation , "id", "outputs", "output_collections") assert len(invocation["output_collections"]) == 0 assert len(invocation["outputs"]) == 1 output_content = self.dataset_populator.get_history_dataset_content(history_id, dataset_id=invocation["outputs"]["wf_output_1"]["id"]) assert "hello world" == output_content.strip() @skip_without_tool("cat") def test_workflow_output_dataset_collection(self): with self.dataset_populator.test_history() as history_id: summary = self._run_jobs(""" class: GalaxyWorkflow inputs: input1: type: data_collection_input collection_type: list outputs: wf_output_1: outputSource: first_cat/out_file1 steps: first_cat: tool_id: cat in: input1: input1 """, test_data=""" input1: type: list name: the_dataset_list elements: - identifier: el1 value: 1.fastq type: File """, history_id=history_id, round_trip_format_conversion=True) workflow_id = summary.workflow_id invocation_id = summary.invocation_id invocation_response = self._get("workflows/%s/invocations/%s" % (workflow_id, invocation_id)) self._assert_status_code_is(invocation_response, 200) invocation = invocation_response.json() self._assert_has_keys(invocation , "id", "outputs", "output_collections") assert len(invocation["output_collections"]) == 1 assert len(invocation["outputs"]) == 0 output_content = self.dataset_populator.get_history_collection_details(history_id, content_id=invocation["output_collections"]["wf_output_1"]["id"]) self._assert_has_keys(output_content , "id", "elements") assert output_content["collection_type"] == "list" elements = output_content["elements"] assert len(elements) == 1 elements0 = elements[0] assert elements0["element_identifier"] == "el1" def test_workflow_input_as_output(self): with self.dataset_populator.test_history() as history_id: summary = self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data outputs: wf_output_1: outputSource: input1 steps: [] """, test_data={"input1": "hello world"}, history_id=history_id) workflow_id = summary.workflow_id invocation_id = summary.invocation_id invocation_response = self._get("workflows/%s/invocations/%s" % (workflow_id, invocation_id)) self._assert_status_code_is(invocation_response, 200) invocation = invocation_response.json() self._assert_has_keys(invocation , "id", "outputs", "output_collections") assert len(invocation["output_collections"]) == 0 assert len(invocation["outputs"]) == 1 output_content = self.dataset_populator.get_history_dataset_content(history_id, content_id=invocation["outputs"]["wf_output_1"]["id"]) assert output_content == "hello world\n" @skip_without_tool("cat") def test_workflow_input_mapping(self): with self.dataset_populator.test_history() as history_id: summary = self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data outputs: wf_output_1: outputSource: first_cat/out_file1 steps: first_cat: tool_id: cat in: input1: input1 """, test_data=""" input1: type: list name: the_dataset_list elements: - identifier: el1 value: 1.fastq type: File - identifier: el2 value: 1.fastq type: File """, history_id=history_id) workflow_id = summary.workflow_id invocation_id = summary.invocation_id invocation_response = self._get("workflows/%s/invocations/%s" % (workflow_id, invocation_id)) self._assert_status_code_is(invocation_response, 200) invocation = invocation_response.json() self._assert_has_keys(invocation , "id", "outputs", "output_collections") assert len(invocation["output_collections"]) == 1 assert len(invocation["outputs"]) == 0 output_content = self.dataset_populator.get_history_collection_details(history_id, content_id=invocation["output_collections"]["wf_output_1"]["id"]) self._assert_has_keys(output_content , "id", "elements") elements = output_content["elements"] assert len(elements) == 2 elements0 = elements[0] assert elements0["element_identifier"] == "el1" @skip_without_tool("collection_creates_pair") def test_workflow_run_input_mapping_with_output_collections(self): with self.dataset_populator.test_history() as history_id: summary = self._run_jobs(""" class: GalaxyWorkflow inputs: text_input: data outputs: wf_output_1: outputSource: split_up/paired_output steps: split_up: tool_id: collection_creates_pair in: input1: text_input """, test_data=""" text_input: type: list name: the_dataset_list elements: - identifier: el1 value: 1.fastq type: File - identifier: el2 value: 1.fastq type: File """, history_id=history_id) workflow_id = summary.workflow_id invocation_id = summary.invocation_id invocation_response = self._get("workflows/%s/invocations/%s" % (workflow_id, invocation_id)) self._assert_status_code_is(invocation_response, 200) invocation = invocation_response.json() self._assert_has_keys(invocation , "id", "outputs", "output_collections") assert len(invocation["output_collections"]) == 1 assert len(invocation["outputs"]) == 0 output_content = self.dataset_populator.get_history_collection_details(history_id, content_id=invocation["output_collections"]["wf_output_1"]["id"]) self._assert_has_keys(output_content , "id", "elements") assert output_content["collection_type"] == "list:paired", output_content elements = output_content["elements"] assert len(elements) == 2 elements0 = elements[0] assert elements0["element_identifier"] == "el1" def test_workflow_run_input_mapping_with_subworkflows(self): with self.dataset_populator.test_history() as history_id: test_data = """ outer_input: type: list name: the_dataset_list elements: - identifier: el1 value: 1.fastq type: File - identifier: el2 value: 1.fastq type: File """ summary = self._run_jobs(WORKFLOW_NESTED_SIMPLE, test_data=test_data, history_id=history_id) workflow_id = summary.workflow_id invocation_id = summary.invocation_id invocation_response = self._get("workflows/%s/invocations/%s" % (workflow_id, invocation_id)) self._assert_status_code_is(invocation_response, 200) invocation_response = self._get("workflows/%s/invocations/%s" % (workflow_id, invocation_id)) self._assert_status_code_is(invocation_response, 200) invocation = invocation_response.json() self._assert_has_keys(invocation , "id", "outputs", "output_collections") assert len(invocation["output_collections"]) == 1, invocation assert len(invocation["outputs"]) == 0 output_content = self.dataset_populator.get_history_collection_details(history_id, content_id=invocation["output_collections"]["outer_output"]["id"]) self._assert_has_keys(output_content , "id", "elements") assert output_content["collection_type"] == "list", output_content elements = output_content["elements"] assert len(elements) == 2 elements0 = elements[0] assert elements0["element_identifier"] == "el1" @skip_without_tool("cat_list") @skip_without_tool("random_lines1") @skip_without_tool("split") def test_subworkflow_recover_mapping_1(self): # This test case tests an outer workflow continues to scheduling and handle # collection mapping properly after the last step of a subworkflow requires delayed # evaluation. Testing rescheduling and propagating connections within a subworkflow # is handled by the next test case. with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: outer_input: data outputs: outer_output: outputSource: second_cat/out_file1 steps: first_cat: tool_id: cat1 in: input1: outer_input nested_workflow: run: class: GalaxyWorkflow inputs: inner_input: data outputs: workflow_output: outputSource: random_lines/out_file1 steps: random_lines: tool_id: random_lines1 state: num_lines: 2 input: $link: inner_input seed_source: seed_source_selector: set_seed seed: asdf in: inner_input: first_cat/out_file1 split: tool_id: split in: input1: nested_workflow/workflow_output second_cat: tool_id: cat_list in: input1: split/output test_data: outer_input: value: 1.bed type: File """, history_id=history_id, wait=True, round_trip_format_conversion=True) self.assertEqual("chr6\t108722976\t108723115\tCCDS5067.1_cds_0_0_chr6_108722977_f\t0\t+\nchrX\t152691446\t152691471\tCCDS14735.1_cds_0_0_chrX_152691447_f\t0\t+\n", self.dataset_populator.get_history_dataset_content(history_id)) # self.assertEqual("chr16\t142908\t143003\tCCDS10397.1_cds_0_0_chr16_142909_f\t0\t+\nchrX\t152691446\t152691471\tCCDS14735.1_cds_0_0_chrX_152691447_f\t0\t+\n", self.dataset_populator.get_history_dataset_content(history_id)) @skip_without_tool("cat_list") @skip_without_tool("random_lines1") @skip_without_tool("split") def test_subworkflow_recover_mapping_2(self): # Like the above test case, this test case tests an outer workflow continues to # schedule and handle collection mapping properly after a subworkflow needs to be # delayed, but this also tests recovering and handling scheduling within the subworkflow # since the delayed step (split) isn't the last step of the subworkflow. with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: outer_input: data outputs: outer_output: outputSource: second_cat/out_file1 steps: first_cat: tool_id: cat1 in: input1: outer_input nested_workflow: run: class: GalaxyWorkflow inputs: inner_input: data outputs: workflow_output: outputSource: inner_cat/out_file1 steps: random_lines: tool_id: random_lines1 in: input: inner_input num_lines: default: 2 seed_source|seed_source_selector: default: set_seed seed_source|seed: default: asdf split: tool_id: split in: input1: random_lines/out_file1 inner_cat: tool_id: cat1 in: input1: split/output in: inner_input: first_cat/out_file1 second_cat: tool_id: cat_list in: input1: nested_workflow/workflow_output """, test_data=""" outer_input: value: 1.bed type: File """, history_id=history_id, wait=True, round_trip_format_conversion=True) self.assertEqual("chr6\t108722976\t108723115\tCCDS5067.1_cds_0_0_chr6_108722977_f\t0\t+\nchrX\t152691446\t152691471\tCCDS14735.1_cds_0_0_chrX_152691447_f\t0\t+\n", self.dataset_populator.get_history_dataset_content(history_id)) @skip_without_tool("cat_list") @skip_without_tool("random_lines1") @skip_without_tool("split") def test_recover_mapping_in_subworkflow(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: outer_input: data outputs: outer_output: outputSource: second_cat/out_file1 steps: first_cat: tool_id: cat1 in: input1: outer_input nested_workflow: run: class: GalaxyWorkflow inputs: inner_input: data outputs: workflow_output: outputSource: split/output steps: random_lines: tool_id: random_lines1 state: num_lines: 2 input: $link: inner_input seed_source: seed_source_selector: set_seed seed: asdf split: tool_id: split in: input1: random_lines/out_file1 in: inner_input: first_cat/out_file1 second_cat: tool_id: cat_list in: input1: nested_workflow/workflow_output """, test_data=""" outer_input: value: 1.bed type: File """, history_id=history_id, wait=True, round_trip_format_conversion=True) self.assertEqual("chr6\t108722976\t108723115\tCCDS5067.1_cds_0_0_chr6_108722977_f\t0\t+\nchrX\t152691446\t152691471\tCCDS14735.1_cds_0_0_chrX_152691447_f\t0\t+\n", self.dataset_populator.get_history_dataset_content(history_id)) @skip_without_tool("empty_list") @skip_without_tool("count_list") @skip_without_tool("random_lines1") def test_empty_list_mapping(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data outputs: count_list: outputSource: count_list/out_file1 steps: empty_list: tool_id: empty_list in: input1: input1 random_lines: tool_id: random_lines1 state: num_lines: 2 input: $link: empty_list#output seed_source: seed_source_selector: set_seed seed: asdf count_list: tool_id: count_list in: input1: random_lines/out_file1 """, test_data=""" input1: value: 1.bed type: File """, history_id=history_id, wait=True) self.assertEqual("0\n", self.dataset_populator.get_history_dataset_content(history_id)) @skip_without_tool("collection_type_source_map_over") def test_mapping_and_subcollection_mapping(self): with self.dataset_populator.test_history() as history_id: jobs_summary = self._run_jobs(""" class: GalaxyWorkflow inputs: text_input1: collection steps: map_over: tool_id: collection_type_source_map_over in: input_collect: text_input1 """, test_data=""" text_input1: type: "list:paired" """, history_id=history_id) hdca = self.dataset_populator.get_history_collection_details(history_id=jobs_summary.history_id, hid=1) assert hdca['collection_type'] == 'list:paired' assert len(hdca['elements'][0]['object']["elements"]) == 2 @skip_without_tool("empty_list") @skip_without_tool("count_multi_file") @skip_without_tool("random_lines1") def test_empty_list_reduction(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data outputs: count_multi_file: outputSource: count_multi_file/out_file1 steps: empty_list: tool_id: empty_list in: input1: input1 random_lines: tool_id: random_lines1 state: num_lines: 2 input: $link: empty_list#output seed_source: seed_source_selector: set_seed seed: asdf count_multi_file: tool_id: count_multi_file in: input1: random_lines/out_file1 """, test_data=""" input1: value: 1.bed type: File """, history_id=history_id, wait=True, round_trip_format_conversion=True) self.assertEqual("0\n", self.dataset_populator.get_history_dataset_content(history_id)) @skip_without_tool("cat") def test_cancel_new_workflow_when_history_deleted(self): with self.dataset_populator.test_history() as history_id: # Invoke a workflow with a pause step. uploaded_workflow_id, invocation_id = self._invoke_paused_workflow(history_id) # There is no pause of anything in here, so likely the invocation is # is still in a new state. If it isn't that is fine, continue with the # test it will just happen to test the same thing as below. # Wait for all the datasets to complete, make sure the workflow invocation # is not complete. self._assert_invocation_non_terminal(uploaded_workflow_id, invocation_id) self._delete("histories/%s" % history_id) invocation_cancelled = self._wait_for_invocation_state(uploaded_workflow_id, invocation_id, 'cancelled') assert invocation_cancelled, "Workflow state is not cancelled..." @skip_without_tool("cat") def test_cancel_ready_workflow_when_history_deleted(self): # Same as previous test but make sure invocation isn't a new state before # cancelling. with self.dataset_populator.test_history() as history_id: # Invoke a workflow with a pause step. uploaded_workflow_id, invocation_id = self._invoke_paused_workflow(history_id) # Wait for at least one scheduling step. self._wait_for_invocation_non_new(uploaded_workflow_id, invocation_id) # Wait for all the datasets to complete, make sure the workflow invocation # is not complete. self._assert_invocation_non_terminal(uploaded_workflow_id, invocation_id) self._delete("histories/%s" % history_id) invocation_cancelled = self._wait_for_invocation_state(uploaded_workflow_id, invocation_id, 'cancelled') assert invocation_cancelled, "Workflow state is not cancelled..." @skip_without_tool("cat") def test_workflow_pause(self): with self.dataset_populator.test_history() as history_id: # Invoke a workflow with a pause step. uploaded_workflow_id, invocation_id = self._invoke_paused_workflow(history_id) # Wait for at least one scheduling step. self._wait_for_invocation_non_new(uploaded_workflow_id, invocation_id) # Make sure the history didn't enter a failed state in there. self.dataset_populator.wait_for_history(history_id, assert_ok=True) # Assert the workflow hasn't finished scheduling, we can be pretty sure we # are at the pause step in this case then. self._assert_invocation_non_terminal(uploaded_workflow_id, invocation_id) # Review the paused steps to allow the workflow to continue. self.__review_paused_steps(uploaded_workflow_id, invocation_id, order_index=2, action=True) # Wait for the workflow to finish scheduling and ensure both the invocation # and the history are in valid states. invocation_scheduled = self._wait_for_invocation_state(uploaded_workflow_id, invocation_id, 'scheduled') assert invocation_scheduled, "Workflow state is not scheduled..." self.dataset_populator.wait_for_history(history_id, assert_ok=True) @skip_without_tool("cat") def test_workflow_pause_cancel(self): with self.dataset_populator.test_history() as history_id: # Invoke a workflow with a pause step. uploaded_workflow_id, invocation_id = self._invoke_paused_workflow(history_id) # Wait for at least one scheduling step. self._wait_for_invocation_non_new(uploaded_workflow_id, invocation_id) # Make sure the history didn't enter a failed state in there. self.dataset_populator.wait_for_history(history_id, assert_ok=True) # Assert the workflow hasn't finished scheduling, we can be pretty sure we # are at the pause step in this case then. self._assert_invocation_non_terminal(uploaded_workflow_id, invocation_id) # Review the paused workflow and cancel it at the paused step. self.__review_paused_steps(uploaded_workflow_id, invocation_id, order_index=2, action=False) # Ensure the workflow eventually becomes cancelled. invocation_cancelled = self._wait_for_invocation_state(uploaded_workflow_id, invocation_id, 'cancelled') assert invocation_cancelled, "Workflow state is not cancelled..." @skip_without_tool("head") def test_workflow_map_reduce_pause(self): with self.dataset_populator.test_history() as history_id: workflow = self.workflow_populator.load_workflow_from_resource("test_workflow_map_reduce_pause") uploaded_workflow_id = self.workflow_populator.create_workflow(workflow) hda1 = self.dataset_populator.new_dataset(history_id, content="reviewed\nunreviewed") hdca1 = self.dataset_collection_populator.create_list_in_history(history_id, contents=["1\n2\n3", "4\n5\n6"]).json() index_map = { '0': self._ds_entry(hda1), '1': self._ds_entry(hdca1), } invocation_id = self.__invoke_workflow(history_id, uploaded_workflow_id, index_map) # Wait for at least one scheduling step. self._wait_for_invocation_non_new(uploaded_workflow_id, invocation_id) # Make sure the history didn't enter a failed state in there. self.dataset_populator.wait_for_history(history_id, assert_ok=True) # Assert the workflow hasn't finished scheduling, we can be pretty sure we # are at the pause step in this case then. self._assert_invocation_non_terminal(uploaded_workflow_id, invocation_id) self.__review_paused_steps(uploaded_workflow_id, invocation_id, order_index=4, action=True) self.wait_for_invocation_and_jobs(history_id, uploaded_workflow_id, invocation_id) invocation = self._invocation_details(uploaded_workflow_id, invocation_id) assert invocation['state'] == 'scheduled' self.assertEqual("reviewed\n1\nreviewed\n4\n", self.dataset_populator.get_history_dataset_content(history_id)) @skip_without_tool("cat") def test_cancel_workflow_invocation(self): with self.dataset_populator.test_history() as history_id: # Invoke a workflow with a pause step. uploaded_workflow_id, invocation_id = self._invoke_paused_workflow(history_id) # Wait for at least one scheduling step. self._wait_for_invocation_non_new(uploaded_workflow_id, invocation_id) # Make sure the history didn't enter a failed state in there. self.dataset_populator.wait_for_history(history_id, assert_ok=True) # Assert the workflow hasn't finished scheduling, we can be pretty sure we # are at the pause step in this case then. self._assert_invocation_non_terminal(uploaded_workflow_id, invocation_id) invocation_url = self._api_url("workflows/%s/usage/%s" % (uploaded_workflow_id, invocation_id), use_key=True) delete_response = delete(invocation_url) self._assert_status_code_is(delete_response, 200) invocation = self._invocation_details(uploaded_workflow_id, invocation_id) assert invocation['state'] == 'cancelled' @skip_without_tool("cat") def test_pause_outputs_with_deleted_inputs(self): # We run a workflow on a collection with a deleted element. with self.dataset_populator.test_history() as history_id: workflow_id = self._upload_yaml_workflow(""" class: GalaxyWorkflow inputs: input1: type: collection collection_type: list steps: first_cat: tool_id: cat in: input1: input1 second_cat: tool_id: cat in: input1: first_cat/out_file1 """) DELETED = 0 PAUSED_1 = 3 PAUSED_2 = 5 hdca1 = self.dataset_collection_populator.create_list_in_history(history_id, contents=[("sample1-1", "1 2 3")]).json() self.dataset_populator.wait_for_history(history_id, assert_ok=True) r = self._delete("histories/%s/contents/%s" % (history_id, hdca1['elements'][DELETED]['object']['id'])) label_map = {"input1": self._ds_entry(hdca1)} workflow_request = dict( history="hist_id=%s" % history_id, workflow_id=workflow_id, ds_map=self._build_ds_map(workflow_id, label_map), ) r = self._post("workflows", data=workflow_request) self._assert_status_code_is(r, 200) # If this starts failing we may have prevented running workflows on collections with deleted members, # in which case we can disable this test. self.dataset_populator.wait_for_history_jobs(history_id, assert_ok=False) contents = self.__history_contents(history_id) assert contents[DELETED]['deleted'] assert contents[PAUSED_1]['state'] == 'paused' assert contents[PAUSED_2]['state'] == 'paused' def test_run_with_implicit_connection(self): with self.dataset_populator.test_history() as history_id: run_summary = self._run_jobs(""" class: GalaxyWorkflow inputs: test_input: data steps: first_cat: tool_id: cat1 in: input1: test_input the_pause: type: pause in: input: first_cat/out_file1 second_cat: tool_id: cat1 in: input1: the_pause third_cat: tool_id: random_lines1 in: $step: second_cat state: num_lines: 1 input: $link: test_input seed_source: seed_source_selector: set_seed seed: asdf """, test_data={"test_input": "hello world"}, history_id=history_id, wait=False, round_trip_format_conversion=True) history_id = run_summary.history_id workflow_id = run_summary.workflow_id invocation_id = run_summary.invocation_id # Wait for first two jobs to be scheduled - upload and first cat. wait_on(lambda: len(self._history_jobs(history_id)) >= 2 or None, "history jobs") self.dataset_populator.wait_for_history(history_id, assert_ok=True) invocation = self._invocation_details(workflow_id, invocation_id) assert invocation['state'] != 'scheduled', invocation # Expect two jobs - the upload and first cat. randomlines shouldn't run # it is implicitly dependent on second cat. self._assert_history_job_count(history_id, 2) self.__review_paused_steps(workflow_id, invocation_id, order_index=2, action=True) self.wait_for_invocation_and_jobs(history_id, workflow_id, invocation_id) self._assert_history_job_count(history_id, 4) def test_run_with_validated_parameter_connection_valid(self): with self.dataset_populator.test_history() as history_id: run_summary = self._run_jobs(""" class: GalaxyWorkflow inputs: text_input: text steps: validation: tool_id: validation_repeat state: r2: - text: $link: text_input """, test_data=""" text_input: value: "abd" type: raw """, history_id=history_id, wait=True, round_trip_format_conversion=True) self.wait_for_invocation_and_jobs(history_id, run_summary.workflow_id, run_summary.invocation_id) jobs = self._history_jobs(history_id) assert len(jobs) == 1 def test_run_with_validated_parameter_connection_invalid(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: text_input: text steps: validation: tool_id: validation_repeat state: r2: - text: $link: text_input """, test_data=""" text_input: value: "" type: raw """, history_id=history_id, wait=True, assert_ok=False) def test_run_with_text_connection(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: data_input: data text_input: text steps: randomlines: tool_id: random_lines1 state: num_lines: 1 input: $link: data_input seed_source: seed_source_selector: set_seed seed: $link: text_input """, test_data=""" data_input: value: 1.bed type: File text_input: value: asdf type: raw """, history_id=history_id) self.dataset_populator.wait_for_history(history_id, assert_ok=True) content = self.dataset_populator.get_history_dataset_content(history_id) self.assertEqual("chrX\t152691446\t152691471\tCCDS14735.1_cds_0_0_chrX_152691447_f\t0\t+\n", content) @skip_without_tool('cat1') def test_workflow_rerun_with_use_cached_job(self): workflow = self.workflow_populator.load_workflow(name="test_for_run") # We launch a workflow with self.dataset_populator.test_history() as history_id_one, self.dataset_populator.test_history() as history_id_two: workflow_request, _ = self._setup_workflow_run(workflow, history_id=history_id_one) run_workflow_response = self._post("workflows", data=workflow_request).json() # We copy the workflow inputs to a new history new_workflow_request = workflow_request.copy() new_ds_map = json.loads(new_workflow_request['ds_map']) for key, input_values in run_workflow_response['inputs'].items(): copy_payload = {"content": input_values['id'], "source": "hda", "type": "dataset"} copy_response = self._post("histories/%s/contents" % history_id_two, data=copy_payload).json() new_ds_map[key]['id'] = copy_response['id'] new_workflow_request['ds_map'] = json.dumps(new_ds_map, sort_keys=True) new_workflow_request['history'] = "hist_id=%s" % history_id_two new_workflow_request['use_cached_job'] = True # We run the workflow again, it should not produce any new outputs new_workflow_response = self._post("workflows", data=new_workflow_request).json() first_wf_output = self._get("datasets/%s" % run_workflow_response['outputs'][0]).json() second_wf_output = self._get("datasets/%s" % new_workflow_response['outputs'][0]).json() assert first_wf_output['file_name'] == second_wf_output['file_name'], \ "first output:\n%s\nsecond output:\n%s" % (first_wf_output, second_wf_output) @skip_without_tool('cat1') def test_nested_workflow_rerun_with_use_cached_job(self): with self.dataset_populator.test_history() as history_id_one, self.dataset_populator.test_history() as history_id_two: test_data = """ outer_input: value: 1.bed type: File """ run_jobs_summary = self._run_jobs(WORKFLOW_NESTED_SIMPLE, test_data=test_data, history_id=history_id_one) workflow_request = run_jobs_summary.workflow_request # We copy the inputs to a new history and re-run the workflow inputs = json.loads(workflow_request['inputs']) dataset_type = inputs['outer_input']['src'] dataset_id = inputs['outer_input']['id'] copy_payload = {"content": dataset_id, "source": dataset_type, "type": "dataset"} copy_response = self._post("histories/%s/contents" % history_id_two, data=copy_payload) self._assert_status_code_is(copy_response, 200) new_dataset_id = copy_response.json()['id'] inputs['outer_input']['id'] = new_dataset_id workflow_request['use_cached_job'] = True workflow_request['history'] = "hist_id={history_id_two}".format(history_id_two=history_id_two) workflow_request['inputs'] = json.dumps(inputs) run_workflow_response = self._post("workflows", data=run_jobs_summary.workflow_request).json() self.workflow_populator.wait_for_workflow(workflow_request['workflow_id'], run_workflow_response['id'], history_id_two, assert_ok=True) # Now make sure that the HDAs in each history point to the same dataset instances history_one_contents = self.__history_contents(history_id_one) history_two_contents = self.__history_contents(history_id_two) assert len(history_one_contents) == len(history_two_contents) for i, (item_one, item_two) in enumerate(zip(history_one_contents, history_two_contents)): assert item_one['dataset_id'] == item_two['dataset_id'], \ 'Dataset ids should match, but "%s" and "%s" are not the same for History item %i.' % (item_one['dataset_id'], item_two['dataset_id'], i + 1) def test_cannot_run_inaccessible_workflow(self): workflow = self.workflow_populator.load_workflow(name="test_for_run_cannot_access") workflow_request, history_id = self._setup_workflow_run(workflow) with self._different_user(): run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 403) def test_400_on_invalid_workflow_id(self): workflow = self.workflow_populator.load_workflow(name="test_for_run_does_not_exist") workflow_request, history_id = self._setup_workflow_run(workflow) workflow_request["workflow_id"] = self._random_key() run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 400) def test_cannot_run_against_other_users_history(self): workflow = self.workflow_populator.load_workflow(name="test_for_run_does_not_exist") workflow_request, history_id = self._setup_workflow_run(workflow) with self._different_user(): other_history_id = self.dataset_populator.new_history() workflow_request["history"] = "hist_id=%s" % other_history_id run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 403) @skip_without_tool("cat") @skip_without_tool("cat_list") def test_workflow_run_with_matching_lists(self): workflow = self.workflow_populator.load_workflow_from_resource("test_workflow_matching_lists") workflow_id = self.workflow_populator.create_workflow(workflow) with self.dataset_populator.test_history() as history_id: hdca1 = self.dataset_collection_populator.create_list_in_history(history_id, contents=[("sample1-1", "1 2 3"), ("sample2-1", "7 8 9")]).json() hdca2 = self.dataset_collection_populator.create_list_in_history(history_id, contents=[("sample1-2", "4 5 6"), ("sample2-2", "0 a b")]).json() self.dataset_populator.wait_for_history(history_id, assert_ok=True) label_map = {"list1": self._ds_entry(hdca1), "list2": self._ds_entry(hdca2)} workflow_request = dict( history="hist_id=%s" % history_id, workflow_id=workflow_id, ds_map=self._build_ds_map(workflow_id, label_map), ) run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) self.dataset_populator.wait_for_history(history_id, assert_ok=True) self.assertEqual("1 2 3\n4 5 6\n7 8 9\n0 a b\n", self.dataset_populator.get_history_dataset_content(history_id)) def test_workflow_stability(self): # Run this index stability test with following command: # ./run_tests.sh test/api/test_workflows.py:WorkflowsApiTestCase.test_workflow_stability num_tests = 1 for workflow_file in ["test_workflow_topoambigouity", "test_workflow_topoambigouity_auto_laidout"]: workflow = self.workflow_populator.load_workflow_from_resource(workflow_file) last_step_map = self._step_map(workflow) for i in range(num_tests): uploaded_workflow_id = self.workflow_populator.create_workflow(workflow) downloaded_workflow = self._download_workflow(uploaded_workflow_id) step_map = self._step_map(downloaded_workflow) assert step_map == last_step_map last_step_map = step_map def _step_map(self, workflow): # Build dict mapping 'tep index to input name. step_map = {} for step_index, step in workflow["steps"].items(): if step["type"] == "data_input": step_map[step_index] = step["inputs"][0]["name"] return step_map def test_empty_create(self): response = self._post("workflows") self._assert_status_code_is(response, 400) self._assert_error_code_is(response, error_codes.USER_REQUEST_MISSING_PARAMETER) def test_invalid_create_multiple_types(self): data = { 'shared_workflow_id': '1234567890abcdef', 'from_history_id': '1234567890abcdef' } response = self._post("workflows", data) self._assert_status_code_is(response, 400) self._assert_error_code_is(response, error_codes.USER_REQUEST_INVALID_PARAMETER) @skip_without_tool("cat1") def test_run_with_pja(self): workflow = self.workflow_populator.load_workflow(name="test_for_pja_run", add_pja=True) workflow_request, history_id = self._setup_workflow_run(workflow, inputs_by='step_index') workflow_request["replacement_params"] = dumps(dict(replaceme="was replaced")) run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) content = self.dataset_populator.get_history_dataset_details(history_id, wait=True, assert_ok=True) assert content["name"] == "foo was replaced" @skip_without_tool("output_filter") def test_optional_workflow_output(self): with self.dataset_populator.test_history() as history_id: run_object = self._run_jobs(""" class: GalaxyWorkflow inputs: [] outputs: wf_output_1: outputSource: output_filter/out_1 steps: output_filter: tool_id: output_filter state: produce_out_1: False filter_text_1: '1' produce_collection: False """, test_data={}, history_id=history_id, wait=False) self.wait_for_invocation_and_jobs(history_id, run_object.workflow_id, run_object.invocation_id) contents = self.__history_contents(history_id) assert len(contents) == 1 okay_dataset = contents[0] assert okay_dataset["state"] == "ok" @skip_without_tool("cat") def test_run_rename_on_mapped_over_collection(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: type: collection collection_type: list steps: first_cat: tool_id: cat in: input1: input1 outputs: out_file1: rename: "my new name" """, test_data=""" input1: type: list name: the_dataset_list elements: - identifier: el1 value: 1.fastq type: File """, history_id=history_id) content = self.dataset_populator.get_history_dataset_details(history_id, hid=4, wait=True, assert_ok=True) name = content["name"] assert name == "my new name", name assert content["history_content_type"] == "dataset" content = self.dataset_populator.get_history_collection_details(history_id, hid=3, wait=True, assert_ok=True) name = content["name"] assert content["history_content_type"] == "dataset_collection", content assert name == "my new name", name @skip_without_tool("cat") def test_run_rename_based_on_inputs_on_mapped_over_collection(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: type: collection collection_type: list steps: first_cat: tool_id: cat in: input1: input1 outputs: out_file1: rename: "#{input1} suffix" """, test_data=""" input1: type: list name: the_dataset_list elements: - identifier: el1 value: 1.fastq type: File """, history_id=history_id) content = self.dataset_populator.get_history_collection_details(history_id, hid=3, wait=True, assert_ok=True) name = content["name"] assert content["history_content_type"] == "dataset_collection", content assert name == "the_dataset_list suffix", name @skip_without_tool("collection_creates_pair") def test_run_rename_collection_output(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data steps: - tool_id: collection_creates_pair in: input1: input1 outputs: paired_output: rename: "my new name" """, test_data=""" input1: value: 1.fasta type: File name: fasta1 """, history_id=history_id) details1 = self.dataset_populator.get_history_collection_details(history_id, hid=4, wait=True, assert_ok=True) assert details1["name"] == "my new name", details1 assert details1["history_content_type"] == "dataset_collection" @skip_without_tool("create_2") def test_run_rename_multiple_outputs(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: [] steps: create_2: tool_id: create_2 state: sleep_time: 0 outputs: out_file1: rename: "my new name" out_file2: rename: "my other new name" """, test_data={}, history_id=history_id) details1 = self.dataset_populator.get_history_dataset_details(history_id, hid=1, wait=True, assert_ok=True) details2 = self.dataset_populator.get_history_dataset_details(history_id, hid=2) assert details1["name"] == "my new name" assert details2["name"] == "my other new name" @skip_without_tool("cat") def test_run_rename_based_on_input(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(WORKFLOW_RENAME_ON_INPUT, history_id=history_id) content = self.dataset_populator.get_history_dataset_details(history_id, wait=True, assert_ok=True) name = content["name"] assert name == "fasta1 suffix", name @skip_without_tool("fail_identifier") @skip_without_tool("cat") def test_run_rename_when_resuming_jobs(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data steps: first_fail: tool_id: fail_identifier state: failbool: true input1: $link: input1 outputs: out_file1: rename: "cat1 out" cat: tool_id: cat in: input1: first_fail/out_file1 outputs: out_file1: rename: "#{input1} suffix" """, test_data=""" input1: value: 1.fasta type: File name: fail """, history_id=history_id, wait=True, assert_ok=False) content = self.dataset_populator.get_history_dataset_details(history_id, hid=2, wait=True, assert_ok=False) name = content["name"] assert content['state'] == 'error', content input1 = self.dataset_populator.get_history_dataset_details(history_id, hid=1, wait=True, assert_ok=False) job_id = content['creating_job'] inputs = {"input1": {'values': [{'src': 'hda', 'id': input1['id']}] }, "failbool": "false", "rerun_remap_job_id": job_id} self.dataset_populator.run_tool(tool_id='fail_identifier', inputs=inputs, history_id=history_id, assert_ok=True) unpaused_dataset = self.dataset_populator.get_history_dataset_details(history_id, wait=True, assert_ok=False) assert unpaused_dataset['state'] == 'ok' assert unpaused_dataset['name'] == "%s suffix" % name @skip_without_tool("cat") def test_run_rename_based_on_input_recursive(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data steps: first_cat: tool_id: cat in: input1: input1 outputs: out_file1: rename: "#{input1} #{input1 | upper} suffix" """, test_data=""" input1: value: 1.fasta type: File name: '#{input1}' """, history_id=history_id) content = self.dataset_populator.get_history_dataset_details(history_id, wait=True, assert_ok=True) name = content["name"] assert name == "#{input1} #{INPUT1} suffix", name @skip_without_tool("cat") def test_run_rename_based_on_input_repeat(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data input2: data steps: first_cat: tool_id: cat state: input1: $link: input1 queries: - input2: $link: input2 outputs: out_file1: rename: "#{queries_0.input2| basename} suffix" """, test_data=""" input1: value: 1.fasta type: File name: fasta1 input2: value: 1.fasta type: File name: fasta2 """, history_id=history_id) content = self.dataset_populator.get_history_dataset_details(history_id, wait=True, assert_ok=True) name = content["name"] assert name == "fasta2 suffix", name @skip_without_tool("mapper2") def test_run_rename_based_on_input_conditional(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: fasta_input: data fastq_input: data steps: mapping: tool_id: mapper2 state: fastq_input: fastq_input_selector: single fastq_input1: $link: fastq_input reference: $link: fasta_input outputs: out_file1: # Wish it was qualified for conditionals but it doesn't seem to be. -John # rename: "#{fastq_input.fastq_input1 | basename} suffix" rename: "#{fastq_input1 | basename} suffix" """, test_data=""" fasta_input: value: 1.fasta type: File name: fasta1 file_type: fasta fastq_input: value: 1.fastqsanger type: File name: fastq1 file_type: fastqsanger """, history_id=history_id) content = self.dataset_populator.get_history_dataset_details(history_id, wait=True, assert_ok=True) name = content["name"] assert name == "fastq1 suffix", name @skip_without_tool("mapper2") def test_run_rename_based_on_input_collection(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: fasta_input: data fastq_inputs: data steps: mapping: tool_id: mapper2 state: fastq_input: fastq_input_selector: paired_collection fastq_input1: $link: fastq_inputs reference: $link: fasta_input outputs: out_file1: # Wish it was qualified for conditionals but it doesn't seem to be. -John # rename: "#{fastq_input.fastq_input1 | basename} suffix" rename: "#{fastq_input1} suffix" """, test_data=""" fasta_input: value: 1.fasta type: File name: fasta1 file_type: fasta fastq_inputs: type: list name: the_dataset_pair elements: - identifier: forward value: 1.fastq type: File - identifier: reverse value: 1.fastq type: File """, history_id=history_id) content = self.dataset_populator.get_history_dataset_details(history_id, wait=True, assert_ok=True) name = content["name"] assert name == "the_dataset_pair suffix", name @skip_without_tool("collection_creates_pair") def test_run_hide_on_collection_output(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data steps: create_pair: tool_id: collection_creates_pair state: input1: $link: input1 outputs: paired_output: hide: true """, test_data=""" input1: value: 1.fasta type: File name: fasta1 """, history_id=history_id) details1 = self.dataset_populator.get_history_collection_details(history_id, hid=4, wait=True, assert_ok=True) assert details1["history_content_type"] == "dataset_collection" assert not details1["visible"], details1 @skip_without_tool("cat") def test_run_hide_on_mapped_over_collection(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: - id: input1 type: data_collection_input collection_type: list steps: first_cat: tool_id: cat in: input1: input1 outputs: out_file1: hide: true """, test_data=""" input1: type: list name: the_dataset_list elements: - identifier: el1 value: 1.fastq type: File """, history_id=history_id) content = self.dataset_populator.get_history_dataset_details(history_id, hid=4, wait=True, assert_ok=True) assert content["history_content_type"] == "dataset" assert not content["visible"] content = self.dataset_populator.get_history_collection_details(history_id, hid=3, wait=True, assert_ok=True) assert content["history_content_type"] == "dataset_collection", content assert not content["visible"] @skip_without_tool("cat") def test_tag_auto_propagation(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data steps: first_cat: tool_id: cat in: input1: input1 outputs: out_file1: add_tags: - "name:treated1fb" - "group:condition:treated" - "group:type:single-read" - "machine:illumina" second_cat: tool_id: cat in: input1: first_cat/out_file1 """, test_data=""" input1: value: 1.fasta type: File name: fasta1 """, history_id=history_id, round_trip_format_conversion=True) details0 = self.dataset_populator.get_history_dataset_details(history_id, hid=2, wait=True, assert_ok=True) tags = details0["tags"] assert len(tags) == 4, details0 assert "name:treated1fb" in tags, tags assert "group:condition:treated" in tags, tags assert "group:type:single-read" in tags, tags assert "machine:illumina" in tags, tags details1 = self.dataset_populator.get_history_dataset_details(history_id, hid=3, wait=True, assert_ok=True) tags = details1["tags"] assert len(tags) == 1, details1 assert "name:treated1fb" in tags, tags @skip_without_tool("collection_creates_pair") def test_run_add_tag_on_collection_output(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data steps: create_pair: tool_id: collection_creates_pair in: input1: input1 outputs: paired_output: add_tags: - "name:foo" """, test_data=""" input1: value: 1.fasta type: File name: fasta1 """, history_id=history_id, round_trip_format_conversion=True) details1 = self.dataset_populator.get_history_collection_details(history_id, hid=4, wait=True, assert_ok=True) assert details1["history_content_type"] == "dataset_collection" assert details1["tags"][0] == "name:foo", details1 @skip_without_tool("cat") def test_run_add_tag_on_mapped_over_collection(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: type: collection collection_type: list steps: first_cat: tool_id: cat in: input1: input1 outputs: out_file1: add_tags: - "name:foo" """, test_data=""" input1: type: list name: the_dataset_list elements: - identifier: el1 value: 1.fastq type: File """, history_id=history_id, round_trip_format_conversion=True) details1 = self.dataset_populator.get_history_collection_details(history_id, hid=3, wait=True, assert_ok=True) assert details1["history_content_type"] == "dataset_collection" assert details1["tags"][0] == "name:foo", details1 @skip_without_tool("collection_creates_pair") @skip_without_tool("cat") def test_run_remove_tag_on_collection_output(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data steps: first_cat: tool_id: cat in: input1: input1 outputs: out_file1: add_tags: - "name:foo" create_pair: tool_id: collection_creates_pair in: input1: first_cat#out_file1 outputs: paired_output: remove_tags: - "name:foo" """, test_data=""" input1: value: 1.fasta type: File name: fasta1 """, history_id=history_id, round_trip_format_conversion=True) details_dataset_with_tag = self.dataset_populator.get_history_dataset_details(history_id, hid=2, wait=True, assert_ok=True) assert details_dataset_with_tag["history_content_type"] == "dataset", details_dataset_with_tag assert details_dataset_with_tag["tags"][0] == "name:foo", details_dataset_with_tag details_collection_without_tag = self.dataset_populator.get_history_collection_details(history_id, hid=5, wait=True, assert_ok=True) assert details_collection_without_tag["history_content_type"] == "dataset_collection", details_collection_without_tag assert len(details_collection_without_tag["tags"]) == 0, details_collection_without_tag @skip_without_tool("cat1") def test_run_with_runtime_pja(self): workflow = self.workflow_populator.load_workflow(name="test_for_pja_runtime") uuid0, uuid1, uuid2 = str(uuid4()), str(uuid4()), str(uuid4()) workflow["steps"]["0"]["uuid"] = uuid0 workflow["steps"]["1"]["uuid"] = uuid1 workflow["steps"]["2"]["uuid"] = uuid2 workflow_request, history_id = self._setup_workflow_run(workflow, inputs_by='step_index') workflow_request["replacement_params"] = dumps(dict(replaceme="was replaced")) pja_map = { "RenameDatasetActionout_file1": dict( action_type="RenameDatasetAction", output_name="out_file1", action_arguments=dict(newname="foo ${replaceme}"), ) } workflow_request["parameters"] = dumps({ uuid2: {"__POST_JOB_ACTIONS__": pja_map} }) run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) content = self.dataset_populator.get_history_dataset_details(history_id, wait=True, assert_ok=True) assert content["name"] == "foo was replaced", content["name"] # Test for regression of previous behavior where runtime post job actions # would be added to the original workflow post job actions. workflow_id = workflow_request["workflow_id"] downloaded_workflow = self._download_workflow(workflow_id) pjas = list(downloaded_workflow["steps"]["2"]["post_job_actions"].values()) assert len(pjas) == 0, len(pjas) @skip_without_tool("cat1") def test_run_with_delayed_runtime_pja(self): workflow_id = self._upload_yaml_workflow(""" class: GalaxyWorkflow inputs: test_input: data steps: first_cat: tool_id: cat1 in: input1: test_input the_pause: type: pause in: input: first_cat/out_file1 second_cat: tool_id: cat1 in: input1: the_pause """, round_trip_format_conversion=True) downloaded_workflow = self._download_workflow(workflow_id) uuid_dict = dict((int(index), step["uuid"]) for index, step in downloaded_workflow["steps"].items()) with self.dataset_populator.test_history() as history_id: hda = self.dataset_populator.new_dataset(history_id, content="1 2 3") self.dataset_populator.wait_for_history(history_id) inputs = { '0': self._ds_entry(hda), } uuid2 = uuid_dict[3] workflow_request = {} workflow_request["replacement_params"] = dumps(dict(replaceme="was replaced")) pja_map = { "RenameDatasetActionout_file1": dict( action_type="RenameDatasetAction", output_name="out_file1", action_arguments=dict(newname="foo ${replaceme}"), ) } workflow_request["parameters"] = dumps({ uuid2: {"__POST_JOB_ACTIONS__": pja_map} }) invocation_id = self.__invoke_workflow(history_id, workflow_id, inputs=inputs, request=workflow_request) time.sleep(2) self.dataset_populator.wait_for_history(history_id) self.__review_paused_steps(workflow_id, invocation_id, order_index=2, action=True) self.workflow_populator.wait_for_workflow(workflow_id, invocation_id, history_id) time.sleep(1) content = self.dataset_populator.get_history_dataset_details(history_id) assert content["name"] == "foo was replaced", content["name"] @skip_without_tool("cat1") def test_delete_intermediate_datasets_pja_1(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow inputs: input1: data outputs: wf_output_1: outputSource: third_cat/out_file1 steps: first_cat: tool_id: cat1 in: input1: input1 second_cat: tool_id: cat1 in: input1: first_cat/out_file1 third_cat: tool_id: cat1 in: input1: second_cat/out_file1 outputs: out_file1: delete_intermediate_datasets: true """, test_data={"input1": "hello world"}, history_id=history_id) hda1 = self.dataset_populator.get_history_dataset_details(history_id, hid=1) hda2 = self.dataset_populator.get_history_dataset_details(history_id, hid=2) hda3 = self.dataset_populator.get_history_dataset_details(history_id, hid=3) hda4 = self.dataset_populator.get_history_dataset_details(history_id, hid=4) assert not hda1["deleted"] assert hda2["deleted"] # I think hda3 should be deleted, but the inputs to # steps with workflow outputs are not deleted. # assert hda3["deleted"] print(hda3["deleted"]) assert not hda4["deleted"] @skip_without_tool("random_lines1") def test_run_replace_params_by_tool(self): workflow_request, history_id = self._setup_random_x2_workflow("test_for_replace_tool_params") workflow_request["parameters"] = dumps(dict(random_lines1=dict(num_lines=5))) run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) self.dataset_populator.wait_for_history(history_id, assert_ok=True) # Would be 8 and 6 without modification self.__assert_lines_hid_line_count_is(history_id, 2, 5) self.__assert_lines_hid_line_count_is(history_id, 3, 5) @skip_without_tool("random_lines1") def test_run_replace_params_by_uuid(self): workflow_request, history_id = self._setup_random_x2_workflow("test_for_replace_tool_params") workflow_request["parameters"] = dumps({ "58dffcc9-bcb7-4117-a0e1-61513524b3b1": dict(num_lines=4), "58dffcc9-bcb7-4117-a0e1-61513524b3b2": dict(num_lines=3), }) run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) self.dataset_populator.wait_for_history(history_id, assert_ok=True) # Would be 8 and 6 without modification self.__assert_lines_hid_line_count_is(history_id, 2, 4) self.__assert_lines_hid_line_count_is(history_id, 3, 3) @skip_without_tool("cat1") @skip_without_tool("addValue") def test_run_batch(self): workflow = self.workflow_populator.load_workflow_from_resource("test_workflow_batch") workflow_id = self.workflow_populator.create_workflow(workflow) with self.dataset_populator.test_history() as history_id: hda1 = self.dataset_populator.new_dataset(history_id, content="1 2 3") hda2 = self.dataset_populator.new_dataset(history_id, content="4 5 6") hda3 = self.dataset_populator.new_dataset(history_id, content="7 8 9") hda4 = self.dataset_populator.new_dataset(history_id, content="10 11 12") parameters = { "0": {"input": {"batch": True, "values": [{"id" : hda1.get("id"), "hid": hda1.get("hid"), "src": "hda"}, {"id" : hda2.get("id"), "hid": hda2.get("hid"), "src": "hda"}, {"id" : hda3.get("id"), "hid": hda2.get("hid"), "src": "hda"}, {"id" : hda4.get("id"), "hid": hda2.get("hid"), "src": "hda"}]}}, "1": {"input": {"batch": False, "values": [{"id" : hda1.get("id"), "hid": hda1.get("hid"), "src": "hda"}]}, "exp": "2"}} workflow_request = { "history_id" : history_id, "batch" : True, "parameters_normalized": True, "parameters" : dumps(parameters), } invocation_response = self._post("workflows/%s/usage" % workflow_id, data=workflow_request) self._assert_status_code_is(invocation_response, 200) time.sleep(5) self.dataset_populator.wait_for_history(history_id, assert_ok=True) r1 = "1 2 3\t1\n1 2 3\t2\n" r2 = "4 5 6\t1\n1 2 3\t2\n" r3 = "7 8 9\t1\n1 2 3\t2\n" r4 = "10 11 12\t1\n1 2 3\t2\n" t1 = self.dataset_populator.get_history_dataset_content(history_id, hid=7) t2 = self.dataset_populator.get_history_dataset_content(history_id, hid=10) t3 = self.dataset_populator.get_history_dataset_content(history_id, hid=13) t4 = self.dataset_populator.get_history_dataset_content(history_id, hid=16) self.assertEqual(r1, t1) self.assertEqual(r2, t2) self.assertEqual(r3, t3) self.assertEqual(r4, t4) @skip_without_tool("validation_default") def test_parameter_substitution_sanitization(self): substitions = dict(input1="\" ; echo \"moo") run_workflow_response, history_id = self._run_validation_workflow_with_substitions(substitions) self.dataset_populator.wait_for_history(history_id, assert_ok=True) self.assertEqual("__dq__ X echo __dq__moo\n", self.dataset_populator.get_history_dataset_content(history_id, hid=1)) @skip_without_tool("validation_repeat") def test_parameter_substitution_validation_value_errors_0(self): with self.dataset_populator.test_history() as history_id: workflow_id = self._upload_yaml_workflow(""" class: GalaxyWorkflow steps: validation: tool_id: validation_repeat state: r2: - text: "abd" """) workflow_request = dict( history="hist_id=%s" % history_id, parameters=dumps(dict(validation_repeat={"r2_0|text": ""})) ) url = "workflows/%s/invocations" % workflow_id invocation_response = self._post(url, data=workflow_request) # Take a valid stat and make it invalid, assert workflow won't run. self._assert_status_code_is(invocation_response, 400) @skip_without_tool("validation_default") def test_parameter_substitution_validation_value_errors_1(self): substitions = dict(select_param="\" ; echo \"moo") run_workflow_response, history_id = self._run_validation_workflow_with_substitions(substitions) self._assert_status_code_is(run_workflow_response, 400) @skip_without_tool("validation_repeat") def test_workflow_import_state_validation_1(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(""" class: GalaxyWorkflow steps: validation: tool_id: validation_repeat state: r2: - text: "" """, history_id=history_id, wait=False, expected_response=400) def _run_validation_workflow_with_substitions(self, substitions): workflow = self.workflow_populator.load_workflow_from_resource("test_workflow_validation_1") uploaded_workflow_id = self.workflow_populator.create_workflow(workflow) history_id = self.dataset_populator.new_history() workflow_request = dict( history="hist_id=%s" % history_id, workflow_id=uploaded_workflow_id, parameters=dumps(dict(validation_default=substitions)) ) run_workflow_response = self._post("workflows", data=workflow_request) return run_workflow_response, history_id @skip_without_tool("random_lines1") def test_run_replace_params_by_steps(self): workflow_request, history_id, steps = self._setup_random_x2_workflow_steps("test_for_replace_step_params") params = dumps({str(steps[1]["id"]): dict(num_lines=5)}) workflow_request["parameters"] = params run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) self.dataset_populator.wait_for_history(history_id, assert_ok=True) # Would be 8 and 6 without modification self.__assert_lines_hid_line_count_is(history_id, 2, 8) self.__assert_lines_hid_line_count_is(history_id, 3, 5) @skip_without_tool("random_lines1") def test_run_replace_params_nested(self): workflow_request, history_id, steps = self._setup_random_x2_workflow_steps("test_for_replace_step_params_nested") seed_source = dict( seed_source_selector="set_seed", seed="moo", ) params = dumps({str(steps[0]["id"]): dict(num_lines=1, seed_source=seed_source), str(steps[1]["id"]): dict(num_lines=1, seed_source=seed_source)}) workflow_request["parameters"] = params run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) self.dataset_populator.wait_for_history(history_id, assert_ok=True) self.assertEqual("2\n", self.dataset_populator.get_history_dataset_content(history_id)) @skip_without_tool("random_lines1") def test_run_replace_params_nested_normalized(self): workflow_request, history_id, steps = self._setup_random_x2_workflow_steps("test_for_replace_step_normalized_params_nested") parameters = { "num_lines": 1, "seed_source|seed_source_selector": "set_seed", "seed_source|seed": "moo", } params = dumps({str(steps[0]["id"]): parameters, str(steps[1]["id"]): parameters}) workflow_request["parameters"] = params workflow_request["parameters_normalized"] = False run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) self.dataset_populator.wait_for_history(history_id, assert_ok=True) self.assertEqual("2\n", self.dataset_populator.get_history_dataset_content(history_id)) @skip_without_tool("random_lines1") def test_run_replace_params_over_default(self): with self.dataset_populator.test_history() as history_id: self._run_jobs(WORKFLOW_ONE_STEP_DEFAULT, test_data=""" step_parameters: '1': num_lines: 4 input: value: 1.bed type: File """, history_id=history_id, wait=True, assert_ok=True, round_trip_format_conversion=True) result = self.dataset_populator.get_history_dataset_content(history_id) assert result.count("\n") == 4 @skip_without_tool("random_lines1") def test_defaults_editor(self): workflow_id = self._upload_yaml_workflow(WORKFLOW_ONE_STEP_DEFAULT, publish=True) workflow_object = self._download_workflow(workflow_id, style="editor") put_response = self._update_workflow(workflow_id, workflow_object) assert put_response.status_code == 200 @skip_without_tool("random_lines1") def test_run_replace_params_over_default_delayed(self): with self.dataset_populator.test_history() as history_id: run_summary = self._run_jobs(""" class: GalaxyWorkflow inputs: input: data steps: first_cat: tool_id: cat1 in: input1: input the_pause: type: pause in: input: first_cat/out_file1 randomlines: tool_id: random_lines1 in: input: the_pause num_lines: default: 6 """, test_data=""" step_parameters: '3': num_lines: 4 input: value: 1.bed type: File """, history_id=history_id, wait=False) wait_on(lambda: len(self._history_jobs(history_id)) >= 2 or None, "history jobs") self.dataset_populator.wait_for_history(history_id, assert_ok=True) workflow_id = run_summary.workflow_id invocation_id = run_summary.invocation_id self.__review_paused_steps(workflow_id, invocation_id, order_index=2, action=True) self.wait_for_invocation_and_jobs(history_id, workflow_id, invocation_id) result = self.dataset_populator.get_history_dataset_content(history_id) assert result.count("\n") == 4 def test_pja_import_export(self): workflow = self.workflow_populator.load_workflow(name="test_for_pja_import", add_pja=True) uploaded_workflow_id = self.workflow_populator.create_workflow(workflow) downloaded_workflow = self._download_workflow(uploaded_workflow_id) self._assert_has_keys(downloaded_workflow["steps"], "0", "1", "2") pjas = list(downloaded_workflow["steps"]["2"]["post_job_actions"].values()) assert len(pjas) == 1, len(pjas) pja = pjas[0] self._assert_has_keys(pja, "action_type", "output_name", "action_arguments") @skip_without_tool("cat1") def test_only_own_invocations_indexed_and_accessible(self): workflow_id, usage = self._run_workflow_once_get_invocation("test_usage") with self._different_user(): usage_details_response = self._get("workflows/%s/usage/%s" % (workflow_id, usage["id"])) self._assert_status_code_is(usage_details_response, 403) invocation_ids = self._all_user_invocation_ids() assert usage["id"] in invocation_ids with self._different_user(): invocation_ids = self._all_user_invocation_ids() assert usage["id"] not in invocation_ids @skip_without_tool("cat1") def test_invocation_usage(self): workflow_id, usage = self._run_workflow_once_get_invocation("test_usage") invocation_id = usage["id"] usage_details = self._invocation_details(workflow_id, invocation_id) # Assert some high-level things about the structure of data returned. self._assert_has_keys(usage_details, "inputs", "steps", "workflow_id", "history_id") # Check invocations for this workflow invocation by history and regardless of history. history_invocations_response = self._get("invocations", {"history_id": usage_details["history_id"]}) self._assert_status_code_is(history_invocations_response, 200) assert len(history_invocations_response.json()) == 1 assert history_invocations_response.json()[0]["id"] == invocation_id # Check history invocations for this workflow invocation. invocation_ids = self._all_user_invocation_ids() assert invocation_id in invocation_ids # Wait for the invocation to be fully scheduled, so we have details on all steps. self._wait_for_invocation_state(workflow_id, invocation_id, 'scheduled') usage_details = self._invocation_details(workflow_id, invocation_id) invocation_steps = usage_details["steps"] invocation_input_step, invocation_tool_step = None, None for invocation_step in invocation_steps: self._assert_has_keys(invocation_step, "workflow_step_id", "order_index", "id") order_index = invocation_step["order_index"] assert order_index in [0, 1, 2], order_index if order_index == 0: invocation_input_step = invocation_step elif order_index == 2: invocation_tool_step = invocation_step # Tool steps have non-null job_ids (deprecated though they may be) assert invocation_input_step.get("job_id", None) is None job_id = invocation_tool_step.get("job_id", None) assert job_id is not None invocation_tool_step_id = invocation_tool_step["id"] invocation_tool_step_response = self._get("workflows/%s/invocations/%s/steps/%s" % (workflow_id, invocation_id, invocation_tool_step_id)) self._assert_status_code_is(invocation_tool_step_response, 200) self._assert_has_keys(invocation_tool_step_response.json(), "id", "order_index", "job_id") assert invocation_tool_step_response.json()["job_id"] == job_id def test_invocation_with_collection_mapping(self): workflow_id, invocation_id = self._run_mapping_workflow() usage_details = self._invocation_details(workflow_id, invocation_id) # Assert some high-level things about the structure of data returned. self._assert_has_keys(usage_details, "inputs", "steps", "workflow_id") invocation_steps = usage_details["steps"] invocation_input_step, invocation_tool_step = None, None for invocation_step in invocation_steps: self._assert_has_keys(invocation_step, "workflow_step_id", "order_index", "id") order_index = invocation_step["order_index"] assert order_index in [0, 1] if invocation_step["order_index"] == 0: assert invocation_input_step is None invocation_input_step = invocation_step else: assert invocation_tool_step is None invocation_tool_step = invocation_step # Tool steps have non-null job_ids (deprecated though they may be) assert invocation_input_step.get("job_id", None) is None assert invocation_tool_step.get("job_id", None) is None assert invocation_tool_step["state"] == "scheduled" usage_details = self._invocation_details(workflow_id, invocation_id, legacy_job_state="true") # Assert some high-level things about the structure of data returned. self._assert_has_keys(usage_details, "inputs", "steps", "workflow_id") invocation_steps = usage_details["steps"] invocation_input_step = None invocation_tool_steps = [] for invocation_step in invocation_steps: self._assert_has_keys(invocation_step, "workflow_step_id", "order_index", "id") order_index = invocation_step["order_index"] assert order_index in [0, 1] if invocation_step["order_index"] == 0: assert invocation_input_step is None invocation_input_step = invocation_step else: invocation_tool_steps.append(invocation_step) assert len(invocation_tool_steps) == 2 assert invocation_tool_steps[0]["state"] == "ok" def _run_mapping_workflow(self): history_id = self.dataset_populator.new_history() summary = self._run_jobs(""" class: GalaxyWorkflow inputs: input_c: collection steps: cat1: tool_id: cat1 in: input1: input_c """, test_data=""" input_c: type: list elements: - identifier: i1 content: "0" - identifier: i2 content: "1" """, history_id=history_id, wait=True, assert_ok=True) workflow_id = summary.workflow_id invocation_id = summary.invocation_id return workflow_id, invocation_id @skip_without_tool("cat1") def test_invocations_accessible_imported_workflow(self): workflow_id = self.workflow_populator.simple_workflow("test_usage", publish=True) with self._different_user(): other_import_response = self.__import_workflow(workflow_id) self._assert_status_code_is(other_import_response, 200) other_id = other_import_response.json()["id"] workflow_request, history_id = self._setup_workflow_run(workflow_id=other_id) response = self._get("workflows/%s/usage" % other_id) self._assert_status_code_is(response, 200) assert len(response.json()) == 0 run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) run_workflow_response = run_workflow_response.json() invocation_id = run_workflow_response['id'] usage_details_response = self._get("workflows/%s/usage/%s" % (other_id, invocation_id)) self._assert_status_code_is(usage_details_response, 200) @skip_without_tool("cat1") def test_invocations_accessible_published_workflow(self): workflow_id = self.workflow_populator.simple_workflow("test_usage", publish=True) with self._different_user(): workflow_request, history_id = self._setup_workflow_run(workflow_id=workflow_id) workflow_request['workflow_id'] = workflow_request.pop('workflow_id') response = self._get("workflows/%s/usage" % workflow_id) self._assert_status_code_is(response, 200) assert len(response.json()) == 0 run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) run_workflow_response = run_workflow_response.json() invocation_id = run_workflow_response['id'] usage_details_response = self._get("workflows/%s/usage/%s" % (workflow_id, invocation_id)) self._assert_status_code_is(usage_details_response, 200) @skip_without_tool("cat1") def test_invocations_not_accessible_by_different_user_for_published_workflow(self): workflow_id = self.workflow_populator.simple_workflow("test_usage", publish=True) workflow_request, history_id = self._setup_workflow_run(workflow_id=workflow_id) workflow_request['workflow_id'] = workflow_request.pop('workflow_id') response = self._get("workflows/%s/usage" % workflow_id) self._assert_status_code_is(response, 200) assert len(response.json()) == 0 run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) run_workflow_response = run_workflow_response.json() invocation_id = run_workflow_response['id'] with self._different_user(): usage_details_response = self._get("workflows/%s/usage/%s" % (workflow_id, invocation_id)) self._assert_status_code_is(usage_details_response, 403) def _invoke_paused_workflow(self, history_id): workflow = self.workflow_populator.load_workflow_from_resource("test_workflow_pause") workflow_id = self.workflow_populator.create_workflow(workflow) hda1 = self.dataset_populator.new_dataset(history_id, content="1 2 3") index_map = { '0': self._ds_entry(hda1), } invocation_id = self.__invoke_workflow( history_id, workflow_id, index_map, ) return workflow_id, invocation_id def _wait_for_invocation_non_new(self, workflow_id, invocation_id): target_state_reached = False for i in range(50): invocation = self._invocation_details(workflow_id, invocation_id) if invocation['state'] != 'new': target_state_reached = True break time.sleep(.25) return target_state_reached def _assert_invocation_non_terminal(self, workflow_id, invocation_id): invocation = self._invocation_details(workflow_id, invocation_id) assert invocation['state'] in ['ready', 'new'], invocation def _wait_for_invocation_state(self, workflow_id, invocation_id, target_state): target_state_reached = False for i in range(25): invocation = self._invocation_details(workflow_id, invocation_id) if invocation['state'] == target_state: target_state_reached = True break time.sleep(.5) return target_state_reached def _update_workflow(self, workflow_id, workflow_object): return self.workflow_populator.update_workflow(workflow_id, workflow_object) def _invocation_step_details(self, workflow_id, invocation_id, step_id): invocation_step_response = self._get("workflows/%s/usage/%s/steps/%s" % (workflow_id, invocation_id, step_id)) self._assert_status_code_is(invocation_step_response, 200) invocation_step_details = invocation_step_response.json() return invocation_step_details def _execute_invocation_step_action(self, workflow_id, invocation_id, step_id, action): raw_url = "workflows/%s/usage/%s/steps/%s" % (workflow_id, invocation_id, step_id) url = self._api_url(raw_url, use_key=True) payload = dumps(dict(action=action)) action_response = put(url, data=payload) self._assert_status_code_is(action_response, 200) invocation_step_details = action_response.json() return invocation_step_details def _run_workflow_once_get_invocation(self, name): workflow = self.workflow_populator.load_workflow(name=name) workflow_request, history_id = self._setup_workflow_run(workflow) workflow_id = workflow_request["workflow_id"] response = self._get("workflows/%s/usage" % workflow_id) self._assert_status_code_is(response, 200) assert len(response.json()) == 0 run_workflow_response = self._post("workflows", data=workflow_request) self._assert_status_code_is(run_workflow_response, 200) response = self._get("workflows/%s/usage" % workflow_id) self._assert_status_code_is(response, 200) usages = response.json() assert len(usages) == 1 return workflow_id, usages[0] def _setup_random_x2_workflow_steps(self, name): workflow_request, history_id = self._setup_random_x2_workflow("test_for_replace_step_params") random_line_steps = self._random_lines_steps(workflow_request) return workflow_request, history_id, random_line_steps def _random_lines_steps(self, workflow_request): workflow_summary_response = self._get("workflows/%s" % workflow_request["workflow_id"]) self._assert_status_code_is(workflow_summary_response, 200) steps = workflow_summary_response.json()["steps"] return sorted((step for step in steps.values() if step["tool_id"] == "random_lines1"), key=lambda step: step["id"]) def _setup_random_x2_workflow(self, name): workflow = self.workflow_populator.load_random_x2_workflow(name) uploaded_workflow_id = self.workflow_populator.create_workflow(workflow) workflow_inputs = self._workflow_inputs(uploaded_workflow_id) key = next(iter(workflow_inputs.keys())) history_id = self.dataset_populator.new_history() ten_lines = "\n".join(str(_) for _ in range(10)) hda1 = self.dataset_populator.new_dataset(history_id, content=ten_lines) workflow_request = dict( history="hist_id=%s" % history_id, workflow_id=uploaded_workflow_id, ds_map=dumps({ key: self._ds_entry(hda1), }), ) return workflow_request, history_id def __review_paused_steps(self, uploaded_workflow_id, invocation_id, order_index, action=True): invocation = self._invocation_details(uploaded_workflow_id, invocation_id) invocation_steps = invocation["steps"] pause_steps = [s for s in invocation_steps if s['order_index'] == order_index] for pause_step in pause_steps: pause_step_id = pause_step['id'] self._execute_invocation_step_action(uploaded_workflow_id, invocation_id, pause_step_id, action=action) def __assert_lines_hid_line_count_is(self, history, hid, lines): contents_url = "histories/%s/contents" % history history_contents = self.__history_contents(history) hda_summary = next(hc for hc in history_contents if hc["hid"] == hid) hda_info_response = self._get("%s/%s" % (contents_url, hda_summary["id"])) self._assert_status_code_is(hda_info_response, 200) self.assertEqual(hda_info_response.json()["metadata_data_lines"], lines) def __history_contents(self, history_id): contents_url = "histories/%s/contents" % history_id history_contents_response = self._get(contents_url) self._assert_status_code_is(history_contents_response, 200) return history_contents_response.json() def __invoke_workflow(self, *args, **kwds): return self.workflow_populator.invoke_workflow(*args, **kwds) def __import_workflow(self, workflow_id, deprecated_route=False): if deprecated_route: route = "workflows/import" import_data = dict( workflow_id=workflow_id, ) else: route = "workflows" import_data = dict( shared_workflow_id=workflow_id, ) return self._post(route, import_data) def _show_workflow(self, workflow_id): show_response = self._get("workflows/%s" % workflow_id) self._assert_status_code_is(show_response, 200) return show_response.json() def _assert_looks_like_instance_workflow_representation(self, workflow): self._assert_has_keys( workflow, 'url', 'owner', 'inputs', 'annotation', 'steps' ) for step in workflow["steps"].values(): self._assert_has_keys( step, 'id', 'type', 'tool_id', 'tool_version', 'annotation', 'tool_inputs', 'input_steps', ) def _all_user_invocation_ids(self): all_invocations_for_user = self._get("invocations") self._assert_status_code_is(all_invocations_for_user, 200) invocation_ids = [i["id"] for i in all_invocations_for_user.json()] return invocation_ids

41.341088

239

0.67331

8cb9df30a85d28627f6b87a83ba0a89908a7485d

669

py

Python

src/sponsors/migrations/0006_auto_20170715_1110.py

pwelzel/bornhack-website

af794e6a2fba06e09626259c7768feb30ff394be

[ "BSD-3-Clause" ]

null

src/sponsors/migrations/0006_auto_20170715_1110.py

pwelzel/bornhack-website

af794e6a2fba06e09626259c7768feb30ff394be

[ "BSD-3-Clause" ]

null

src/sponsors/migrations/0006_auto_20170715_1110.py

pwelzel/bornhack-website

af794e6a2fba06e09626259c7768feb30ff394be

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.10.5 on 2017-07-15 09:10 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('sponsors', '0005_sponsor_url'), ] operations = [ migrations.AlterField( model_name='sponsor', name='logo', field=models.URLField(help_text='A URL to the logo', max_length=255), ), migrations.AlterField( model_name='sponsor', name='url', field=models.URLField(blank=True, help_text='An URL to the sponsor.', null=True), ), ]

25.730769

93

0.596413

175c7aa57aae068adbebbb4c2d997a134b765a4c

1,960

py

Python

tests/algorithms/test_merge.py

Hekstra-Lab/reciprocalspaceship

48b72ad70608fdbdfef31f6b38aac5873abc0dba

[ "MIT" ]

22

2020-07-10T18:13:10.000Z

2022-03-04T16:51:00.000Z

tests/algorithms/test_merge.py

Hekstra-Lab/reciprocalspaceship

48b72ad70608fdbdfef31f6b38aac5873abc0dba

[ "MIT" ]

109

2020-07-03T10:07:18.000Z

2022-03-28T20:49:48.000Z

tests/algorithms/test_merge.py

Hekstra-Lab/reciprocalspaceship

48b72ad70608fdbdfef31f6b38aac5873abc0dba

[ "MIT" ]

10

2020-07-03T10:51:21.000Z

2021-08-23T19:05:24.000Z

import numpy as np import pytest import reciprocalspaceship as rs def test_merge_valueerror(hewl_merged): """ Confirm rs.algorithms.merge() raises ValueError when invoked with merged DataSet """ with pytest.raises(ValueError): merged = rs.algorithms.merge(hewl_merged) @pytest.mark.parametrize( "keys", [ None, ["I", "SIGI"], ["I", "SigI"], ], ) @pytest.mark.parametrize("sort", [True, False]) @pytest.mark.parametrize("anomalous", [False, True]) def test_merge(hewl_unmerged, hewl_merged, keys, sort, anomalous): """Test rs.algorithms.merge() against AIMLESS output""" if keys is None: merged = rs.algorithms.merge(hewl_unmerged, sort=sort, anomalous=anomalous) elif not (keys[0] in hewl_unmerged.columns and keys[1] in hewl_unmerged.columns): with pytest.raises(KeyError): merged = rs.algorithms.merge( hewl_unmerged, keys[0], keys[1], sort=sort, anomalous=anomalous ) return else: merged = rs.algorithms.merge( hewl_unmerged, keys[0], keys[1], sort=sort, anomalous=anomalous ) # Check DataSet attributes assert merged.merged assert merged.spacegroup.xhm() == hewl_merged.spacegroup.xhm() assert merged.cell.a == hewl_merged.cell.a if sort: assert merged.index.is_monotonic_increasing # Note: AIMLESS zero-fills empty observations, whereas we use NaNs if not anomalous: hewl_merged["I"] = hewl_merged["IMEAN"] hewl_merged["SIGI"] = hewl_merged["SIGIMEAN"] hewl_merged["N"] = rs.DataSeries( (hewl_merged["N(+)"] + hewl_merged["N(-)"]) / (hewl_merged.label_centrics().CENTRIC + 1), dtype="I", ) for key in merged.columns: assert np.allclose( merged.loc[hewl_merged.index, key].fillna(0), hewl_merged.loc[hewl_merged.index, key], )

30.625

85

0.628061

125a66a9ecb27cf8cdb0c9018cfd76e12f81dbab

2,570

py

Python

localflavor/lv/forms.py

torakses/django-localflavor

17ca87095d6f8c3f3888016085a2edb5951889f4

[ "BSD-3-Clause" ]

1

2018-11-28T22:08:17.000Z

localflavor/lv/forms.py

torakses/django-localflavor

17ca87095d6f8c3f3888016085a2edb5951889f4

[ "BSD-3-Clause" ]

null

localflavor/lv/forms.py

torakses/django-localflavor

17ca87095d6f8c3f3888016085a2edb5951889f4

[ "BSD-3-Clause" ]

null

from __future__ import unicode_literals import re from datetime import date from django.core.validators import EMPTY_VALUES from django.forms import ValidationError from django.forms.fields import Field, Select from django.utils.translation import ugettext_lazy as _ from .lv_choices import MUNICIPALITY_CHOICES zipcode = re.compile(r'^(LV\s?-\s?)?(?P<code>[1-5]\d{3})$', re.IGNORECASE) idcode = re.compile(r'^(\d\d)(\d\d)(\d\d)-([0-2])(?:\d{3})(\d)$') class LVPostalCodeField(Field): """ A form field that validates and normalizes Latvian postal codes. Latvian postal codes in following forms accepted: * XXXX * LV-XXXX """ default_error_messages = { 'invalid': _('Enter a postal code in the format XXXX or LV-XXXX.'), } def clean(self, value): value = super(LVPostalCodeField, self).clean(value) if value in EMPTY_VALUES: return '' match = re.match(zipcode, value) if not match: raise ValidationError(self.error_messages['invalid']) return 'LV-' + match.group('code') class LVMunicipalitySelect(Select): """A select field of Latvian municipalities.""" def __init__(self, attrs=None): super(LVMunicipalitySelect, self).__init__(attrs, choices=MUNICIPALITY_CHOICES) class LVPersonalCodeField(Field): """A form field that validates input as a Latvian personal code.""" default_error_messages = { 'invalid_format': _('Enter a Latvian personal code in format XXXXXX-XXXXX.'), 'invalid': _('Enter a valid Latvian personal code.'), } @staticmethod def lv_checksum(value): """Takes a string of 10 digits as input, returns check digit.""" multipliers = (1, 6, 3, 7, 9, 10, 5, 8, 4, 2) check = sum(mult * int(c) for mult, c in zip(multipliers, value)) return ((1 - check) % 11) % 10 def clean(self, value): super(LVPersonalCodeField, self).clean(value) if value in EMPTY_VALUES: return '' match = re.match(idcode, value) if not match: raise ValidationError(self.error_messages['invalid_format']) day, month, year, century, check = map(int, match.groups()) if check != self.lv_checksum(value[0:6] + value[7:11]): raise ValidationError(self.error_messages['invalid']) year += 1800 + 100 * century try: date(year, month, day) except ValueError: raise ValidationError(self.error_messages['invalid']) return value

29.883721

87

0.6393

d456548c92e194f5afa28146c0413b2c43fd65ba

1,253

py

Python

src/spaceone/core/skeleton/conf/global_conf.py

jihyungSong/python-core

898ead301363d3e599ecd645b73071e639f886b0

[ "Apache-2.0" ]

14

2020-06-01T08:17:43.000Z

2022-01-13T22:37:50.000Z

src/spaceone/core/skeleton/conf/global_conf.py

jihyungSong/python-core

898ead301363d3e599ecd645b73071e639f886b0

[ "Apache-2.0" ]

7

2020-08-11T23:05:59.000Z

2022-01-12T05:08:49.000Z

src/spaceone/core/skeleton/conf/global_conf.py

jihyungSong/python-core

898ead301363d3e599ecd645b73071e639f886b0

[ "Apache-2.0" ]

11

2020-06-01T08:17:49.000Z

2021-11-25T08:26:37.000Z

# Database Settings DATABASES = { 'default': { # MongoDB Example # 'host': '<host>', # 'port': 27017, # 'db': '<db>', # 'username': '<user>', # 'password': '<password>' }, # 'local': { # 'backend': 'spaceone.core.cache.local_cache.LocalCache', # 'max_size': 128, # 'ttl': 86400 # } } # Cache Settings CACHES = { 'default': { # Redis Example # 'backend': 'spaceone.core.cache.redis_cache.RedisCache', # 'host': '<host>', # 'port': 6379, # 'db': 0 } } # Handler Configuration HANDLERS = { 'authentication': [ # Default Authentication Handler # { # 'backend': 'spaceone.core.handler.authentication_handler.AuthenticationGRPCHandler', # 'uri': 'grpc://identity:50051/v1/Domain/get_public_key' # } ], 'authorization': [ # Default Authorization Handler # { # 'backend': 'spaceone.core.handler.authorization_handler.AuthorizationGRPCHandler', # 'uri': 'grpc://identity:50051/v1/Authorization/verify' # } ], 'mutation': [], 'event': [] } # Connector Settings CONNECTORS = { } # Log Settings LOG = { }

22.375

98

0.524342

6aa35b6265cf55bdcc08534087fa8d96b3896661

17,523

py

Python

umap/utils.py

cjweir/umap

60d6b7be30e5d9c40746dcf6052bec09478942b6

[ "BSD-3-Clause" ]

16

2021-01-11T19:39:15.000Z

2022-01-26T14:39:00.000Z

umap/utils.py

cjweir/umap

60d6b7be30e5d9c40746dcf6052bec09478942b6

[ "BSD-3-Clause" ]

3

2021-05-11T10:30:43.000Z

2021-05-21T07:24:47.000Z

umap/utils.py

cjweir/umap

60d6b7be30e5d9c40746dcf6052bec09478942b6

[ "BSD-3-Clause" ]

2

2021-05-13T12:24:34.000Z

2021-06-08T14:03:07.000Z

# Author: Leland McInnes <leland.mcinnes@gmail.com> # # License: BSD 3 clause import time import numpy as np import numba import scipy.sparse @numba.njit(parallel=True) def fast_knn_indices(X, n_neighbors): """A fast computation of knn indices. Parameters ---------- X: array of shape (n_samples, n_features) The input data to compute the k-neighbor indices of. n_neighbors: int The number of nearest neighbors to compute for each sample in ``X``. Returns ------- knn_indices: array of shape (n_samples, n_neighbors) The indices on the ``n_neighbors`` closest points in the dataset. """ knn_indices = np.empty((X.shape[0], n_neighbors), dtype=np.int32) for row in numba.prange(X.shape[0]): # v = np.argsort(X[row]) # Need to call argsort this way for numba v = X[row].argsort(kind="quicksort") v = v[:n_neighbors] knn_indices[row] = v return knn_indices @numba.njit("i4(i8[:])") def tau_rand_int(state): """A fast (pseudo)-random number generator. Parameters ---------- state: array of int64, shape (3,) The internal state of the rng Returns ------- A (pseudo)-random int32 value """ state[0] = (((state[0] & 4294967294) << 12) & 0xFFFFFFFF) ^ ( (((state[0] << 13) & 0xFFFFFFFF) ^ state[0]) >> 19 ) state[1] = (((state[1] & 4294967288) << 4) & 0xFFFFFFFF) ^ ( (((state[1] << 2) & 0xFFFFFFFF) ^ state[1]) >> 25 ) state[2] = (((state[2] & 4294967280) << 17) & 0xFFFFFFFF) ^ ( (((state[2] << 3) & 0xFFFFFFFF) ^ state[2]) >> 11 ) return state[0] ^ state[1] ^ state[2] @numba.njit("f4(i8[:])") def tau_rand(state): """A fast (pseudo)-random number generator for floats in the range [0,1] Parameters ---------- state: array of int64, shape (3,) The internal state of the rng Returns ------- A (pseudo)-random float32 in the interval [0, 1] """ integer = tau_rand_int(state) return abs(float(integer) / 0x7FFFFFFF) @numba.njit() def norm(vec): """Compute the (standard l2) norm of a vector. Parameters ---------- vec: array of shape (dim,) Returns ------- The l2 norm of vec. """ result = 0.0 for i in range(vec.shape[0]): result += vec[i] ** 2 return np.sqrt(result) @numba.njit() def rejection_sample(n_samples, pool_size, rng_state): """Generate n_samples many integers from 0 to pool_size such that no integer is selected twice. The duplication constraint is achieved via rejection sampling. Parameters ---------- n_samples: int The number of random samples to select from the pool pool_size: int The size of the total pool of candidates to sample from rng_state: array of int64, shape (3,) Internal state of the random number generator Returns ------- sample: array of shape(n_samples,) The ``n_samples`` randomly selected elements from the pool. """ result = np.empty(n_samples, dtype=np.int64) for i in range(n_samples): reject_sample = True j = 0 while reject_sample: j = tau_rand_int(rng_state) % pool_size for k in range(i): if j == result[k]: break else: reject_sample = False result[i] = j return result @numba.njit() def make_heap(n_points, size): """Constructor for the numba enabled heap objects. The heaps are used for approximate nearest neighbor search, maintaining a list of potential neighbors sorted by their distance. We also flag if potential neighbors are newly added to the list or not. Internally this is stored as a single ndarray; the first axis determines whether we are looking at the array of candidate indices, the array of distances, or the flag array for whether elements are new or not. Each of these arrays are of shape (``n_points``, ``size``) Parameters ---------- n_points: int The number of data points to track in the heap. size: int The number of items to keep on the heap for each data point. Returns ------- heap: An ndarray suitable for passing to other numba enabled heap functions. """ result = np.zeros( (np.int64(3), np.int64(n_points), np.int64(size)), dtype=np.float64 ) result[0] = -1 result[1] = np.infty result[2] = 0 return result @numba.njit("i8(f8[:,:,:],i8,f8,i8,i8)") def heap_push(heap, row, weight, index, flag): """Push a new element onto the heap. The heap stores potential neighbors for each data point. The ``row`` parameter determines which data point we are addressing, the ``weight`` determines the distance (for heap sorting), the ``index`` is the element to add, and the flag determines whether this is to be considered a new addition. Parameters ---------- heap: ndarray generated by ``make_heap`` The heap object to push into row: int Which actual heap within the heap object to push to weight: float The priority value of the element to push onto the heap index: int The actual value to be pushed flag: int Whether to flag the newly added element or not. Returns ------- success: The number of new elements successfully pushed into the heap. """ row = int(row) indices = heap[0, row] weights = heap[1, row] is_new = heap[2, row] if weight >= weights[0]: return 0 # break if we already have this element. for i in range(indices.shape[0]): if index == indices[i]: return 0 # insert val at position zero weights[0] = weight indices[0] = index is_new[0] = flag # descend the heap, swapping values until the max heap criterion is met i = 0 while True: ic1 = 2 * i + 1 ic2 = ic1 + 1 if ic1 >= heap.shape[2]: break elif ic2 >= heap.shape[2]: if weights[ic1] > weight: i_swap = ic1 else: break elif weights[ic1] >= weights[ic2]: if weight < weights[ic1]: i_swap = ic1 else: break else: if weight < weights[ic2]: i_swap = ic2 else: break weights[i] = weights[i_swap] indices[i] = indices[i_swap] is_new[i] = is_new[i_swap] i = i_swap weights[i] = weight indices[i] = index is_new[i] = flag return 1 @numba.njit("i8(f8[:,:,:],i8,f8,i8,i8)") def unchecked_heap_push(heap, row, weight, index, flag): """Push a new element onto the heap. The heap stores potential neighbors for each data point. The ``row`` parameter determines which data point we are addressing, the ``weight`` determines the distance (for heap sorting), the ``index`` is the element to add, and the flag determines whether this is to be considered a new addition. Parameters ---------- heap: ndarray generated by ``make_heap`` The heap object to push into row: int Which actual heap within the heap object to push to weight: float The priority value of the element to push onto the heap index: int The actual value to be pushed flag: int Whether to flag the newly added element or not. Returns ------- success: The number of new elements successfully pushed into the heap. """ if weight >= heap[1, row, 0]: return 0 indices = heap[0, row] weights = heap[1, row] is_new = heap[2, row] # insert val at position zero weights[0] = weight indices[0] = index is_new[0] = flag # descend the heap, swapping values until the max heap criterion is met i = 0 while True: ic1 = 2 * i + 1 ic2 = ic1 + 1 if ic1 >= heap.shape[2]: break elif ic2 >= heap.shape[2]: if weights[ic1] > weight: i_swap = ic1 else: break elif weights[ic1] >= weights[ic2]: if weight < weights[ic1]: i_swap = ic1 else: break else: if weight < weights[ic2]: i_swap = ic2 else: break weights[i] = weights[i_swap] indices[i] = indices[i_swap] is_new[i] = is_new[i_swap] i = i_swap weights[i] = weight indices[i] = index is_new[i] = flag return 1 @numba.njit() def siftdown(heap1, heap2, elt): """Restore the heap property for a heap with an out of place element at position ``elt``. This works with a heap pair where heap1 carries the weights and heap2 holds the corresponding elements.""" while elt * 2 + 1 < heap1.shape[0]: left_child = elt * 2 + 1 right_child = left_child + 1 swap = elt if heap1[swap] < heap1[left_child]: swap = left_child if right_child < heap1.shape[0] and heap1[swap] < heap1[right_child]: swap = right_child if swap == elt: break else: heap1[elt], heap1[swap] = (heap1[swap], heap1[elt]) heap2[elt], heap2[swap] = (heap2[swap], heap2[elt]) elt = swap @numba.njit() def deheap_sort(heap): """Given an array of heaps (of indices and weights), unpack the heap out to give and array of sorted lists of indices and weights by increasing weight. This is effectively just the second half of heap sort (the first half not being required since we already have the data in a heap). Parameters ---------- heap : array of shape (3, n_samples, n_neighbors) The heap to turn into sorted lists. Returns ------- indices, weights: arrays of shape (n_samples, n_neighbors) The indices and weights sorted by increasing weight. """ indices = heap[0] weights = heap[1] for i in range(indices.shape[0]): ind_heap = indices[i] dist_heap = weights[i] for j in range(ind_heap.shape[0] - 1): ind_heap[0], ind_heap[ind_heap.shape[0] - j - 1] = ( ind_heap[ind_heap.shape[0] - j - 1], ind_heap[0], ) dist_heap[0], dist_heap[dist_heap.shape[0] - j - 1] = ( dist_heap[dist_heap.shape[0] - j - 1], dist_heap[0], ) siftdown( dist_heap[: dist_heap.shape[0] - j - 1], ind_heap[: ind_heap.shape[0] - j - 1], 0, ) return indices.astype(np.int64), weights @numba.njit("i8(f8[:, :, :],i8)") def smallest_flagged(heap, row): """Search the heap for the smallest element that is still flagged. Parameters ---------- heap: array of shape (3, n_samples, n_neighbors) The heaps to search row: int Which of the heaps to search Returns ------- index: int The index of the smallest flagged element of the ``row``th heap, or -1 if no flagged elements remain in the heap. """ ind = heap[0, row] dist = heap[1, row] flag = heap[2, row] min_dist = np.inf result_index = -1 for i in range(ind.shape[0]): if flag[i] == 1 and dist[i] < min_dist: min_dist = dist[i] result_index = i if result_index >= 0: flag[result_index] = 0.0 return int(ind[result_index]) else: return -1 @numba.njit(parallel=True) def build_candidates(current_graph, n_vertices, n_neighbors, max_candidates, rng_state): """Build a heap of candidate neighbors for nearest neighbor descent. For each vertex the candidate neighbors are any current neighbors, and any vertices that have the vertex as one of their nearest neighbors. Parameters ---------- current_graph: heap The current state of the graph for nearest neighbor descent. n_vertices: int The total number of vertices in the graph. n_neighbors: int The number of neighbor edges per node in the current graph. max_candidates: int The maximum number of new candidate neighbors. rng_state: array of int64, shape (3,) The internal state of the rng Returns ------- candidate_neighbors: A heap with an array of (randomly sorted) candidate neighbors for each vertex in the graph. """ candidate_neighbors = make_heap(n_vertices, max_candidates) for i in range(n_vertices): for j in range(n_neighbors): if current_graph[0, i, j] < 0: continue idx = current_graph[0, i, j] isn = current_graph[2, i, j] d = tau_rand(rng_state) heap_push(candidate_neighbors, i, d, idx, isn) heap_push(candidate_neighbors, idx, d, i, isn) current_graph[2, i, j] = 0 return candidate_neighbors @numba.njit() def new_build_candidates( current_graph, n_vertices, n_neighbors, max_candidates, rng_state, rho=0.5 ): # pragma: no cover """Build a heap of candidate neighbors for nearest neighbor descent. For each vertex the candidate neighbors are any current neighbors, and any vertices that have the vertex as one of their nearest neighbors. Parameters ---------- current_graph: heap The current state of the graph for nearest neighbor descent. n_vertices: int The total number of vertices in the graph. n_neighbors: int The number of neighbor edges per node in the current graph. max_candidates: int The maximum number of new candidate neighbors. rng_state: array of int64, shape (3,) The internal state of the rng Returns ------- candidate_neighbors: A heap with an array of (randomly sorted) candidate neighbors for each vertex in the graph. """ new_candidate_neighbors = make_heap(n_vertices, max_candidates) old_candidate_neighbors = make_heap(n_vertices, max_candidates) for i in range(n_vertices): for j in range(n_neighbors): if current_graph[0, i, j] < 0: continue idx = current_graph[0, i, j] isn = current_graph[2, i, j] d = tau_rand(rng_state) if tau_rand(rng_state) < rho: c = 0 if isn: c += heap_push(new_candidate_neighbors, i, d, idx, isn) c += heap_push(new_candidate_neighbors, idx, d, i, isn) else: heap_push(old_candidate_neighbors, i, d, idx, isn) heap_push(old_candidate_neighbors, idx, d, i, isn) if c > 0: current_graph[2, i, j] = 0 return new_candidate_neighbors, old_candidate_neighbors @numba.njit(parallel=True) def submatrix(dmat, indices_col, n_neighbors): """Return a submatrix given an orginal matrix and the indices to keep. Parameters ---------- dmat: array, shape (n_samples, n_samples) Original matrix. indices_col: array, shape (n_samples, n_neighbors) Indices to keep. Each row consists of the indices of the columns. n_neighbors: int Number of neighbors. Returns ------- submat: array, shape (n_samples, n_neighbors) The corresponding submatrix. """ n_samples_transform, n_samples_fit = dmat.shape submat = np.zeros((n_samples_transform, n_neighbors), dtype=dmat.dtype) for i in numba.prange(n_samples_transform): for j in numba.prange(n_neighbors): submat[i, j] = dmat[i, indices_col[i, j]] return submat # Generates a timestamp for use in logging messages when verbose=True def ts(): return time.ctime(time.time()) # I'm not enough of a numba ninja to numba this successfully. # np.arrays of lists, which are objects... def csr_unique(matrix, return_index=True, return_inverse=True, return_counts=True): """Find the unique elements of a sparse csr matrix. We don't explicitly construct the unique matrix leaving that to the user who may not want to duplicate a massive array in memory. Returns the indices of the input array that give the unique values. Returns the indices of the unique array that reconstructs the input array. Returns the number of times each unique row appears in the input matrix. matrix: a csr matrix return_index = bool, optional If true, return the row indices of 'matrix' return_inverse: bool, optional If true, return the the indices of the unique array that can be used to reconstruct 'matrix'. return_counts = bool, optional If true, returns the number of times each unique item appears in 'matrix' The unique matrix can computed via unique_matrix = matrix[index] and the original matrix reconstructed via unique_matrix[inverse] """ lil_matrix = matrix.tolil() rows = [x + y for x, y in zip(lil_matrix.rows, lil_matrix.data)] return_values = return_counts + return_inverse + return_index return np.unique( rows, return_index=return_index, return_inverse=return_inverse, return_counts=return_counts, )[1 : (return_values + 1)]

29.107973

88

0.605319

0c1522f39e7fe5832087109f28316a811f154835

1,181

py

Python

tests/_animation/test_animation_scale_y_from_point_interface.py

ynsnf/apysc

b10ffaf76ec6beb187477d0a744fca00e3efc3fb

[ "MIT" ]

16

2021-04-16T02:01:29.000Z

2022-01-01T08:53:49.000Z

tests/_animation/test_animation_scale_y_from_point_interface.py

ynsnf/apysc

b10ffaf76ec6beb187477d0a744fca00e3efc3fb

[ "MIT" ]

613

2021-03-24T03:37:38.000Z

2022-03-26T10:58:37.000Z

tests/_animation/test_animation_scale_y_from_point_interface.py

simon-ritchie/apyscript

c319f8ab2f1f5f7fad8d2a8b4fc06e7195476279

[ "MIT" ]

2

2021-06-20T07:32:58.000Z

2021-12-26T08:22:11.000Z

from random import randint from retrying import retry import apysc as ap from apysc._display.scale_y_from_point_interface import \ ScaleYFromPointInterface from tests.testing_helper import assert_attrs class TestAnimationScaleYFromPointInterface: @retry(stop_max_attempt_number=15, wait_fixed=randint(10, 3000)) def test_animation_scale_y_from_point(self) -> None: interface: ScaleYFromPointInterface = ScaleYFromPointInterface() interface.variable_name = \ 'test_animation_scale_y_from_point_interface' animation: ap.AnimationScaleYFromPoint = interface.\ animation_scale_y_from_point( scale_y_from_point=2.0, y=100, duration=1000, delay=500, easing=ap.Easing.EASE_OUT_QUINT) assert_attrs( expected_attrs={ '_target': interface, '_scale_y_from_point': 2.0, '_y': 100, '_duration': 1000, '_delay': 500, '_easing': ap.Easing.EASE_OUT_QUINT, }, any_obj=animation)

33.742857

73

0.607113

20d488c6cbff6f256c323c821c06a049aacd0eda

2,327

py

Python

python/fdfault/__init__.py

egdaub/fdfault

ec066f032ba109843164429aa7d9e7352485d735

[ "MIT" ]

12

2017-10-05T22:04:40.000Z

2020-08-31T08:32:17.000Z

python/fdfault/__init__.py

jhsa26/fdfault

ec066f032ba109843164429aa7d9e7352485d735

[ "MIT" ]

3

2020-05-06T16:48:32.000Z

2020-09-18T11:41:41.000Z

python/fdfault/__init__.py

jhsa26/fdfault

ec066f032ba109843164429aa7d9e7352485d735

[ "MIT" ]

12

2017-03-24T19:15:27.000Z

2020-08-31T08:32:18.000Z

""" ``fdfault`` is a python module for setting up dynamic rupture problems for use with the C++ code. ================ Overview ================ The Python module closely resembles the structure of the text input files and hence the structure of the C++ code, and has an interface for specifying all simulation parameters through the ``problem`` class. The module is particularly useful for handling situations where inputs must be written to file in binary format. The module also includes functions that facilitate finding coordinate values nearest certain spatial locations for choosing indices for creating output units. One benefit in using the Python module is that the code performs an extensive series of checks prior to writing the simulation data to file. This, plus using the interfaces that are part of the ``problem`` class, grealy improves the likelihood that the simulation will be set up correctly, and is highly recommended if you will be using the code to simulate complex problems. While the module contains all classes (and you can set up simulations yourself using them), mostly you will be using the wrappers provided through the ``problem`` class, plus the constructors for ``surface``, ``curve``, ``material``, ``load``, ``loadfile``, ``swparam``, ``swparamfile``, ``stzparam``, ``stzparamfile``, ``statefile``, and ``output``. Details on the methods are provided in the documentation for the individual classes. =================== Requirements =================== The only external package required to use the Python module is NumPy, which uses numerical arrays to hold parameter values and writes them to file in binary format. The code has been tested starting with Numpy 1.9 and subsequent releases, though it will probably also work with some older versions. The code supports both Python 2 and Python 3 and has been tested on both versions of Python. """ from .block import block from .domain import domain from .fields import fields from .front import front from .interface import interface, friction, slipweak, stz from .pert import load, swparam, stzparam, loadfile, swparamfile, stzparamfile, statefile from .problem import problem from .material import material from .output import output from .surface import surface, curve, curve3d, points_to_curve, curves_to_surf, points_to_surf

49.510638

109

0.766652

17cdc3e85abd4ec585025a6042beba51526a947b

2,335

py

Python

discordware/_vendors/hype/style.py

znqi/discordware

e456bf7b0314ef8f29fabb9fa69f8c979f34d655

[ "MIT" ]

13

2021-07-31T12:07:06.000Z

2022-03-24T15:00:50.000Z

discordware/_vendors/hype/style.py

znqi/discordware

e456bf7b0314ef8f29fabb9fa69f8c979f34d655

[ "MIT" ]

2

2021-08-02T14:04:58.000Z

2021-09-06T09:35:20.000Z

discordware/_vendors/hype/style.py

znqi/discordware

e456bf7b0314ef8f29fabb9fa69f8c979f34d655

[ "MIT" ]

3

2021-08-07T13:23:54.000Z

2022-01-24T13:23:08.000Z

# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file. """ This module generates ANSI character codes to printing colors to terminals. See: http://en.wikipedia.org/wiki/ANSI_escape_code """ CSI = "\033[" OSC = "\033]" BEL = "\a" def code_to_chars(code): return CSI + str(code) + "m" def set_title(title): return OSC + "2;" + title + BEL def clear_screen(mode=2): return CSI + str(mode) + "J" def clear_line(mode=2): return CSI + str(mode) + "K" class AnsiCodes(object): def __init__(self): # the subclasses declare class attributes which are numbers. # Upon instantiation we define instance attributes, which are the same # as the class attributes but wrapped with the ANSI escape sequence for name in dir(self): if not name.startswith("_"): value = getattr(self, name) setattr(self, name, code_to_chars(value)) class AnsiCursor(object): def UP(self, n=1): return CSI + str(n) + "A" def DOWN(self, n=1): return CSI + str(n) + "B" def FORWARD(self, n=1): return CSI + str(n) + "C" def BACK(self, n=1): return CSI + str(n) + "D" def POS(self, x=1, y=1): return CSI + str(y) + ";" + str(x) + "H" class AnsiFore(AnsiCodes): BLACK = 30 RED = 31 GREEN = 32 YELLOW = 33 BLUE = 34 MAGENTA = 35 CYAN = 36 WHITE = 37 RESET = 39 # These are fairly well supported, but not part of the standard. LIGHTBLACK_EX = 90 LIGHTRED_EX = 91 LIGHTGREEN_EX = 92 LIGHTYELLOW_EX = 93 LIGHTBLUE_EX = 94 LIGHTMAGENTA_EX = 95 LIGHTCYAN_EX = 96 LIGHTWHITE_EX = 97 class AnsiBack(AnsiCodes): BLACK = 40 RED = 41 GREEN = 42 YELLOW = 43 BLUE = 44 MAGENTA = 45 CYAN = 46 WHITE = 47 RESET = 49 # These are fairly well supported, but not part of the standard. LIGHTBLACK_EX = 100 LIGHTRED_EX = 101 LIGHTGREEN_EX = 102 LIGHTYELLOW_EX = 103 LIGHTBLUE_EX = 104 LIGHTMAGENTA_EX = 105 LIGHTCYAN_EX = 106 LIGHTWHITE_EX = 107 class AnsiStyle(AnsiCodes): BOLD = 1 DIM = 2 ITALIC = 3 UNDERLINE = 4 NORMAL = 22 RESET_ALL = 0 Color = AnsiFore() Background = AnsiBack() Style = AnsiStyle() Cursor = AnsiCursor()

20.663717

78

0.605996

416ed0c9c7517db2e295f60fe34a1f4c4c84b82d

928,247

py

Python

USB/python/usb_2600_rbf.py

wjasper/Linix_Drivers

9c5443f3c9d249f341b6b8580929f8cdbdba4079

[ "JasPer-2.0" ]

100

2016-11-08T15:41:43.000Z

2022-02-20T19:37:32.000Z

USB/python/usb_2600_rbf.py

wjasper/Linix_Drivers

9c5443f3c9d249f341b6b8580929f8cdbdba4079

[ "JasPer-2.0" ]

37

2017-01-05T17:48:14.000Z

2022-01-06T17:43:27.000Z

USB/python/usb_2600_rbf.py

wjasper/Linix_Drivers

9c5443f3c9d249f341b6b8580929f8cdbdba4079

[ "JasPer-2.0" ]

65

2016-12-20T07:03:44.000Z

2022-03-14T21:48:35.000Z

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56.989624

56

0.561403

92e6d87a6689d2aeaec1a1b4f2438825d883720c

4,163

py

Python

Assignment-2/dbscan.py

omkargavhane/CS564-ML

3bc4a3557e5c18501d7e7a48f26429fe35c55893

[ "MIT" ]

null

Assignment-2/dbscan.py

omkargavhane/CS564-ML

3bc4a3557e5c18501d7e7a48f26429fe35c55893

[ "MIT" ]

null

Assignment-2/dbscan.py

omkargavhane/CS564-ML

3bc4a3557e5c18501d7e7a48f26429fe35c55893

[ "MIT" ]

null

import arff,copy from sklearn import preprocessing import numpy as np #{0:'core',1:'outlier',2:'boundary'} def minkowski_metric(v1,v2,p=1): '''Fucntion to calculate distance between two vectors depending on the given value of p''' if len(v1)==len(v2): sum=0 for i in range(len(v1)): sum+=abs(v1[i]-v2[i])**p return sum**(1/p) class dbscan: def __init__(self,fname,eps,minpts): self.eps=eps self.minpts=minpts self.vectors=None self.distances=None self.clusters={} self.points={} self.cp=[] self.bp=[] self.op=[] self._read_file(fname) self._normalize() def _normalize(self): scaler = preprocessing.MinMaxScaler(feature_range=(0,5)) self.vectors=scaler.fit_transform(np.array(self.vectors).T).T.tolist() def _read_file(self,fname): #read file.drop last elements of each row i.e test result self.vectors=[e._values[:-1] for e in arff.load(fname)] def calculate_distance(self): #create a empty list wherer all elements initialized with 0 row=[0 for i in range(len(self.vectors))] #create distances matrix self.distances=[copy.deepcopy(row) for i in range(len(self.vectors))] #calculates teh distance between two points for r in range(len(self.distances)): for c in range(len(self.distances)): self.distances[r][c]=minkowski_metric(self.vectors[r],self.vectors[c]) self.distances[c][r]=self.distances[r][c] def classify_points(self): #first finds core and outlier points for r in range(len(self.distances)): #get all memebers within the radius of current points members=[[i,self.distances[r][i]] for i in range(len(self.distances[r])) if self.distances[r][i]<=self.eps] if len(members)>=self.minpts: #if no of members is >= minpts then make point as core self.points[r]={'point':0,'members':members} else: #else make it as outlier self.points[r]={'point':1,'members':members} #loop to seperate the boundary point from outlier for cno,cinfo in self.points.items(): if cinfo['point']==1: #check whether it is outlier for member in cinfo['members']: #get all the points in clusters if self.points[member[0]]['point']==0: #if any core point found self.points[cno]['point']=2 #make current point as boundary point self._points_summary() def _points_summary(self): print("[ Data point summary ]") self.cp=[];self.bp=[];self.op=[] for cno,cinfo in self.points.items(): if cinfo['point']==0:self.cp.append(cno) elif cinfo['point']==1:self.op.append(cno) elif cinfo['point']==2:self.bp.append(cno) print('[No of Points]','Core:',len(self.cp),'Boundary:',len(self.bp),'Outlier:',len(self.op)) def _ismember_cluster(self,cp): for members in self.clusters.values(): if cp in members: return True return False def find_clusters(self): for cp in self.cp: if not self._ismember_cluster(cp): temp=[cp] self.clusters[cp]=[] while temp: t=[member[0] for member in self.points[temp[0]]['members'] if member[0] not in self.op and not self._ismember_cluster(member[0])] temp.extend(t) self.clusters[cp].extend(t) temp.pop(0) obj._cluster_summary() def _cluster_summary(self): print("[ Cluster summary ]") print("No of clusters:",len(self.clusters.keys())) cno=self.clusters.keys() for e in cno: print("Clusters no:",e,"No of members:",len(self.clusters[e])) for cno,members in self.clusters.items(): print() print("cluster no:",cno,members) obj=dbscan('diabetes1.arff',2,5) obj.calculate_distance() obj.classify_points() obj.find_clusters()

38.906542

150

0.589479

87605068795ba54b50eacdf3cbe7ba06be9048de

34,638

py

Python

scikits/statsmodels/tsa/ar_model.py

matthew-brett/statsmodels

915c9dc2d762c5592ac17a7cf5f1cc957fcbde1c

[ "BSD-3-Clause" ]

null

scikits/statsmodels/tsa/ar_model.py

matthew-brett/statsmodels

915c9dc2d762c5592ac17a7cf5f1cc957fcbde1c

[ "BSD-3-Clause" ]

null

scikits/statsmodels/tsa/ar_model.py

matthew-brett/statsmodels

915c9dc2d762c5592ac17a7cf5f1cc957fcbde1c

[ "BSD-3-Clause" ]

null

""" This is the VAR class refactored from pymaclab. """ from __future__ import division import numpy as np from numpy import (dot, identity, atleast_2d, atleast_1d, zeros) from numpy.linalg import inv from scipy import optimize from scipy.stats import t, norm, ss as sumofsq from scikits.statsmodels.regression.linear_model import OLS from scikits.statsmodels.tsa.tsatools import lagmat, add_trend from scikits.statsmodels.base.model import (LikelihoodModelResults, LikelihoodModel) from scikits.statsmodels.tools.decorators import (resettable_cache, cache_readonly, cache_writable) from scikits.statsmodels.tools.compatibility import np_slogdet from scikits.statsmodels.sandbox.regression.numdiff import approx_fprime from scikits.statsmodels.sandbox.regression.numdiff import (approx_hess, approx_hess_cs) __all__ = ['AR'] class AR(LikelihoodModel): """ Autoregressive AR(p) Model Parameters ---------- endog : array-like Endogenous response variable. exog : array-like Exogenous variables. Note that exogenous variables are not yet supported for AR. """ def __init__(self, endog, exog=None): super(AR, self).__init__(endog, exog) if endog.ndim == 1: endog = endog[:,None] elif endog.ndim > 1 and endog.shape[1] != 1: raise ValueError("Only the univariate case is implemented") self.endog = endog # overwrite endog if exog is not None: raise ValueError("Exogenous variables are not supported for AR.") def initialize(self): pass def _transparams(self, params): """ Transforms params to induce stationarity/invertability. Reference --------- Jones(1980) """ p,k = self.k_ar, self.k_trend # need to include exog here? newparams = params.copy() # no copy below now? newparams[k:k+p] = ((1-np.exp(-params[k:k+p]))/ (1+np.exp(-params[k:k+p]))).copy() tmp = ((1-np.exp(-params[k:k+p]))/ (1+np.exp(-params[k:k+p]))).copy() # levinson-durbin to get pacf for j in range(1,p): a = newparams[k+j] for kiter in range(j): tmp[kiter] -= a * newparams[k+j-kiter-1] newparams[k:k+j] = tmp[:j] return newparams def _invtransparams(self, start_params): """ Inverse of the Jones reparameterization """ p,k = self.k_ar, self.k_trend newparams = start_params.copy() arcoefs = newparams[k:k+p].copy() # AR coeffs tmp = arcoefs.copy() for j in range(p-1,0,-1): a = arcoefs[j] for kiter in range(j): tmp[kiter] = (arcoefs[kiter] + a * arcoefs[j-kiter-1])/\ (1-a**2) arcoefs[:j] = tmp[:j] invarcoefs = -np.log((1-arcoefs)/(1+arcoefs)) newparams[k:k+p] = invarcoefs return newparams def _presample_fit(self, params, start, p, y, predictedvalues): """ Return the pre-sample predicted values using the Kalman Filter Notes ----- See predict method for how to use start and p. """ if self._results is None: raise ValueError("You must fit the model first") k = self.k_trend # build system matrices T_mat = self._T(params) R_mat = self._R() # Initial State mean and variance alpha = np.zeros((p,1)) Q_0 = dot(inv(identity(p**2)-np.kron(T_mat,T_mat)),dot(R_mat, R_mat.T).ravel('F')) Q_0 = Q_0.reshape(p,p, order='F') #TODO: order might need to be p+k P = Q_0 Z_mat = atleast_2d([1] + [0] * (p-k)) # TODO: change for exog for i in xrange(start,p): #iterate p-1 times to fit presample v_mat = y[i] - dot(Z_mat,alpha) F_mat = dot(dot(Z_mat, P), Z_mat.T) Finv = 1./F_mat # inv. always scalar K = dot(dot(dot(T_mat,P),Z_mat.T),Finv) # update state alpha = dot(T_mat, alpha) + dot(K,v_mat) L = T_mat - dot(K,Z_mat) P = dot(dot(T_mat, P), L.T) + dot(R_mat, R_mat.T) # P[0,0] += 1 # for MA part, R_mat.R_mat.T above predictedvalues[i+1-start] = dot(Z_mat,alpha) return predictedvalues def predict(self, n=-1, start=0, method='dynamic', resid=False, confint=False): """ Returns in-sample prediction or forecasts. Parameters ----------- n : int Number of periods after start to forecast. If n==-1, returns in- sample forecast starting at `start`. start : int Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. If start==-1, forecasting starts from the end of the sample. If the model is fit using 'cmle' or 'yw', `start` cannot be less than `k_ar`. If `start` < `k_ar` for 'cmle' and 'yw', then `start` is set equal to `k_ar`. method : string {'dynamic', 'static'} If method is 'dynamic', then fitted values are used in place of observed 'endog' to make forecasts. If 'static', observed 'endog' are used. resid : bool Whether or not to return the residuals. confint : bool, float Whether to return confidence intervals. If `confint` == True, 95 % confidence intervals are returned. Else if `confint` is a float, then it is assumed to be the alpha value of the confidence interval. That is confint == .05 returns a 95% confidence interval, and .10 would return a 90% confidence interval. Returns ------- predicted values : array residuals : array, optional confidence intervals : array, optional Notes ----- The linear Gaussian Kalman filter is used to return pre-sample fitted values. The exact initial Kalman Filter is used. See Durbin and Koopman in the references for more information. """ if self._results is None: raise ValueError("You must fit the model first") if n == 0 or (n==-1 and start==-1): return np.array([]) y = self.endog.copy() nobs = int(self.endog.shape[0]) if start < 0: start = nobs + start # convert negative indexing params = self._results.params p = self.k_ar k = self.k_trend method = self.method if method != 'mle': if start == 0: start = p # can't do presample fit for != 'mle' if n == -1: if start != -1 and start < nobs: predictedvalues = np.zeros((nobs-start)) n = nobs-start else: return np.array([]) else: predictedvalues = zeros((n)) mu = 0 # overwritten for 'mle' with constant if method == 'mle': # use Kalman Filter to get initial values if k>=1: # if constant, demean, #TODO: handle higher trendorders mu = params[0]/(1-np.sum(params[k:])) # only for constant-only # and exog y -= mu predictedvalues = self._presample_fit(params, start, p, y, predictedvalues) if start < p and (n > p - start or n == -1): if n == -1: predictedvalues[p-start:] = dot(self.X, params) elif n-(p-start) <= nobs-p: predictedvalues[p-start:] = dot(self.X, params)[:nobs-(p-start)] #start:nobs-p? else: predictedvalues[p-start:nobs-(p-start)] = dot(self.X, params) # maybe p-start) - 1? predictedvalues[start:p] += mu # does nothing if no constant elif start <= nobs: if n <= nobs-start: predictedvalues[:] = dot(self.X, # params)[start:n+start] params)[start-p:n+start-p] else: # right now this handles when start == p only? predictedvalues[:nobs-start] = dot(self.X, params)[start-p:] else: # predictedvalues[:nobs-start] - dot(self.X,params)[p:] pass #NOTE: it only makes sense to forecast beyond nobs+1 if exog is None if start + n > nobs: endog = self.endog if start < nobs: if n-(nobs-start) < p: endrange = n else: endrange = nobs-start+p for i in range(nobs-start,endrange): # mixture of static/dynamic predictedvalues[i] = np.sum(np.r_[[1]*k, predictedvalues[nobs-start:i][::-1], atleast_1d(endog[-p+i-nobs+start:][::-1].squeeze())] *\ params) # dynamic forecasts for i in range(nobs-start+p,n): predictedvalues[i] = np.sum(np.r_[[1]*k, predictedvalues[i-p:i][::-1]] * params) else: # start > nobs # if start < nobs + p? tmp = np.zeros((start-nobs)) # still calc interim values # this is only the range for if start-nobs < p: endrange = start-nobs else: endrange = p for i in range(endrange): # mixed static/dynamic tmp[i] = np.sum(np.r_[[1]*k, tmp[:i][::-1], atleast_1d(endog[-p+i:][::-1].squeeze())] * params) for i in range(p,start-nobs): tmp[i] = np.sum(np.r_[[1]*k, tmp[i-p:i][::-1]] * params) if start - nobs > p: for i in range(p): # mixed tmp/actual predictedvalues[i] = np.sum(np.r_[[1]*k, predictedvalues[:i][::-1], atleast_1d(tmp[-p+i:][::-1].squeeze())] * params) else: endtmp = len(tmp) if n < p: endrange = n else: endrange = p-endtmp for i in range(endrange): # mixed endog/tmp/actual predictedvalues[i] = np.sum(np.r_[[1]*k, predictedvalues[:i][::-1], atleast_1d(tmp[-p+i:][::-1].squeeze()), atleast_1d(endog[-\ (p-i-endtmp):][::-1].squeeze())] * params) if n > endrange: for i in range(endrange,p): # mixed tmp/actual predictedvalues[i] = np.sum(np.r_[[1]*k, predictedvalues[:i][::-1], atleast_1d(tmp[-p+i:][::-1].squeeze())] * \ params) for i in range(p,n): predictedvalues[i] = np.sum(np.r_[[1]*k, predictedvalues[i-p:i][::-1]] * params) return predictedvalues def _presample_varcov(self, params, lagstart): """ Returns the inverse of the presample variance-covariance. Notes ----- See Hamilton p. 125 """ # get inv(Vp) Hamilton 5.3.7 params0 = np.r_[-1, params[lagstart:]] p = len(params) - lagstart p1 = p+1 Vpinv = np.zeros((p,p), dtype=params.dtype) for i in range(lagstart,p1): for j in range(lagstart,p1): if i <= j and j <= p: part1 = np.sum(params0[:i] * params0[j-i:j]) part2 = np.sum(params0[p1-j:p1+i-j]*params0[p1-i:]) Vpinv[i-1,j-1] = part1 - part2 Vpinv = Vpinv + Vpinv.T - np.diag(Vpinv.diagonal()) return Vpinv def loglike(self, params): """ The loglikelihood of an AR(p) process Parameters ---------- params : array The fitted parameters of the AR model Returns ------- llf : float The loglikelihood evaluated at `params` Notes ----- Contains constant term. If the model is fit by OLS then this returns the conditonal maximum likelihood. .. math:: \\frac{\\left(n-p\\right)}{2}\\left(\\log\\left(2\\pi\\right)+\\log\\left(\\sigma^{2}\\right)\\right)-\\frac{1}{\\sigma^{2}}\\sum_{i}\\epsilon_{i}^{2} If it is fit by MLE then the (exact) unconditional maximum likelihood is returned. .. math:: -\\frac{n}{2}log\\left(2\\pi\\right)-\\frac{n}{2}\\log\\left(\\sigma^{2}\\right)+\\frac{1}{2}\\left|V_{p}^{-1}\\right|-\\frac{1}{2\\sigma^{2}}\\left(y_{p}-\\mu_{p}\\right)^{\\prime}V_{p}^{-1}\\left(y_{p}-\\mu_{p}\\right)-\\frac{1}{2\\sigma^{2}}\\sum_{t=p+1}^{n}\\epsilon_{i}^{2} where :math:`\\mu_{p}` is a (`p` x 1) vector with each element equal to the mean of the AR process and :math:`\\sigma^{2}V_{p}` is the (`p` x `p`) variance-covariance matrix of the first `p` observations. """ #TODO: Math is on Hamilton ~pp 124-5 #will need to be amended for inclusion of exogenous variables nobs = self.nobs Y = self.Y X = self.X if self.method == "cmle": ssr = sumofsq(Y.squeeze()-np.dot(X,params)) sigma2 = ssr/nobs return -nobs/2 * (np.log(2*np.pi) + np.log(sigma2)) -\ ssr/(2*sigma2) endog = self.endog k_ar = self.k_ar if isinstance(params,tuple): # broyden (all optimize.nonlin return a tuple until rewrite commit) params = np.asarray(params) # reparameterize according to Jones (1980) like in ARMA/Kalman Filter if self.transparams: params = self._transparams(params) # get mean and variance for pre-sample lags yp = endog[:k_ar] lagstart = self.k_trend exog = self.exog if exog is not None: lagstart += exog.shape[1] # xp = exog[:k_ar] if self.k_trend == 1 and lagstart == 1: c = [params[0]] * k_ar # constant-only no exogenous variables else: #TODO: this isn't right #NOTE: when handling exog just demean and proceed as usual. c = np.dot(X[:k_ar, :lagstart], params[:lagstart]) mup = np.asarray(c/(1-np.sum(params[lagstart:]))) diffp = yp-mup[:,None] # get inv(Vp) Hamilton 5.3.7 Vpinv = self._presample_varcov(params, lagstart) diffpVpinv = np.dot(np.dot(diffp.T,Vpinv),diffp).item() ssr = sumofsq(Y.squeeze() -np.dot(X,params)) # concentrating the likelihood means that sigma2 is given by sigma2 = 1./nobs * (diffpVpinv + ssr) logdet = np_slogdet(Vpinv)[1] #TODO: add check for singularity loglike = -1/2.*(nobs*(np.log(2*np.pi) + np.log(sigma2)) - \ logdet + diffpVpinv/sigma2 + ssr/sigma2) return loglike def _R(self): """ Private method for obtaining fitted presample values via Kalman filter. """ p = self.k_ar R_mat = zeros((p,1)) R_mat[0] = 1 return R_mat def _T(self, params): """ Private method for obtaining fitted presample values via Kalman filter. See also -------- scikits.statsmodels.tsa.kalmanf.ARMA """ p = self.k_ar k = self.k_trend T_mat = zeros((p,p)) T_mat[:,0] = params[k:] T_mat[:-1,1:] = identity(p-1) return T_mat def score(self, params): """ Return the gradient of the loglikelihood at params. Parameters ---------- params : array-like The parameter values at which to evaluate the score function. Notes ----- Returns numerical gradient. """ loglike = self.loglike #NOTE: always calculate at out of bounds params for estimation #TODO: allow for user-specified epsilon? return approx_fprime(params, loglike, epsilon=1e-8) def information(self, params): """ Not Implemented Yet """ return def hessian(self, params): """ Returns numerical hessian for now. """ loglike = self.loglike return approx_hess(params, loglike)[0] def _stackX(self, k_ar, trend): """ Private method to build the RHS matrix for estimation. Columns are trend terms, then exogenous, then lags. """ endog = self.endog exog = self.exog X = lagmat(endog, maxlag=k_ar, trim='both') if exog is not None: X = np.column_stack((exog[k_ar:,:], X)) # Handle trend terms if trend == 'c': k_trend = 1 elif trend == 'nc': k_trend = 0 elif trend == 'ct': k_trend = 2 elif trend == 'ctt': k_trend = 3 if trend != 'nc': X = add_trend(X,prepend=True, trend=trend) self.k_trend = k_trend return X def fit(self, maxlag=None, method='cmle', ic=None, trend='c', transparams=True, start_params=None, solver=None, maxiter=35, full_output=1, disp=1, callback=None, **kwargs): """ Fit the unconditional maximum likelihood of an AR(p) process. Parameters ---------- maxlag : int If `ic` is None, then maxlag is the lag length used in fit. If `ic` is specified then maxlag is the highest lag order used to select the correct lag order. If maxlag is None, the default is round(12*(nobs/100.)**(1/4.)) method : str {'cmle', 'mle'}, optional cmle - Conditional maximum likelihood using OLS mle - Unconditional (exact) maximum likelihood. See `solver` and the Notes. ic : str {'aic','bic','hic','t-stat'} Criterion used for selecting the optimal lag length. aic - Akaike Information Criterion bic - Bayes Information Criterion t-stat - Based on last lag hq - Hannan-Quinn Information Criterion If any of the information criteria are selected, the lag length which results in the lowest value is selected. If t-stat, the model starts with maxlag and drops a lag until the highest lag has a t-stat that is significant at the 95 % level. trend : str {'c','nc'} Whether to include a constant or not. 'c' - include constant. 'nc' - no constant. The below can be specified if method is 'mle' transparams : bool, optional Whether or not to transform the parameters to ensure stationarity. Uses the transformation suggested in Jones (1980). start_params : array-like, optional A first guess on the parameters. Default is cmle estimates. solver : str or None, optional Solver to be used. The default is 'l_bfgs' (limited memory Broyden- Fletcher-Goldfarb-Shanno). Other choices are 'bfgs', 'newton' (Newton-Raphson), 'nm' (Nelder-Mead), 'cg' - (conjugate gradient), 'ncg' (non-conjugate gradient), and 'powell'. The limited memory BFGS uses m=30 to approximate the Hessian, projected gradient tolerance of 1e-7 and factr = 1e3. These cannot currently be changed for l_bfgs. See notes for more information. maxiter : int, optional The maximum number of function evaluations. Default is 35. tol : float The convergence tolerance. Default is 1e-08. full_output : bool, optional If True, all output from solver will be available in the Results object's mle_retvals attribute. Output is dependent on the solver. See Notes for more information. disp : bool, optional If True, convergence information is output. callback : function, optional Called after each iteration as callback(xk) where xk is the current parameter vector. kwargs See Notes for keyword arguments that can be passed to fit. References ---------- Jones, R.H. 1980 "Maximum likelihood fitting of ARMA models to time series with missing observations." `Technometrics`. 22.3. 389-95. See also -------- scikits.statsmodels.model.LikelihoodModel.fit for more information on using the solvers. Notes ------ The below is the docstring from scikits.statsmodels.LikelihoodModel.fit """ self.transparams = transparams method = method.lower() if method not in ['cmle','yw','mle']: raise ValueError("Method %s not recognized" % method) self.method = method nobs = len(self.endog) # overwritten if method is 'cmle' if maxlag is None: maxlag = int(round(12*(nobs/100.)**(1/4.))) endog = self.endog exog = self.exog k_ar = maxlag # stays this if ic is None # select lag length if ic is not None: ic = ic.lower() if ic not in ['aic','bic','hqic','t-stat']: raise ValueError("ic option %s not understood" % ic) # make Y and X with same nobs to compare ICs Y = endog[maxlag:] self.Y = Y # attach to get correct fit stats X = self._stackX(maxlag, trend) # sets k_trend self.X = X startlag = self.k_trend # k_trend set in _stackX if exog is not None: startlag += exog.shape[1] # add dim happens in super? startlag = max(1,startlag) # handle if startlag is 0 results = {} if ic != 't-stat': for lag in range(startlag,maxlag+1): # have to reinstantiate the model to keep comparable models endog_tmp = endog[maxlag-lag:] fit = AR(endog_tmp).fit(maxlag=lag, method=method, full_output=full_output, trend=trend, maxiter=maxiter, disp=disp) results[lag] = eval('fit.'+ic) bestic, bestlag = min((res, k) for k,res in results.iteritems()) else: # choose by last t-stat. stop = 1.6448536269514722 # for t-stat, norm.ppf(.95) for lag in range(maxlag,startlag-1,-1): # have to reinstantiate the model to keep comparable models endog_tmp = endog[maxlag-lag:] fit = AR(endog_tmp).fit(maxlag=lag, method=method, full_output=full_output, trend=trend, maxiter=maxiter, disp=disp) if np.abs(fit.tvalues[-1]) >= stop: bestlag = lag break k_ar = bestlag # change to what was chosen by fit method self.k_ar = k_ar # redo estimation for best lag # make LHS Y = endog[k_ar:,:] # make lagged RHS X = self._stackX(k_ar, trend) # sets self.k_trend k_trend = self.k_trend self.Y = Y self.X = X if solver: solver = solver.lower() if method == "cmle": # do OLS arfit = OLS(Y,X).fit() params = arfit.params self.nobs = nobs - k_ar if method == "mle": self.nobs = nobs if not start_params: start_params = OLS(Y,X).fit().params start_params = self._invtransparams(start_params) loglike = lambda params : -self.loglike(params) if solver == None: # use limited memory bfgs bounds = [(None,)*2]*(k_ar+k_trend) mlefit = optimize.fmin_l_bfgs_b(loglike, start_params, approx_grad=True, m=30, pgtol = 1e-7, factr=1e3, bounds=bounds, iprint=1) self.mlefit = mlefit params = mlefit[0] else: mlefit = super(AR, self).fit(start_params=start_params, method=solver, maxiter=maxiter, full_output=full_output, disp=disp, callback = callback, **kwargs) self.mlefit = mlefit params = mlefit.params if self.transparams: params = self._transparams(params) self.transparams = False # turn off now for other results # don't use yw, because we can't estimate the constant # elif method == "yw": # params, omega = yule_walker(endog, order=maxlag, # method="mle", demean=False) # how to handle inference after Yule-Walker? # self.params = params #TODO: don't attach here # self.omega = omega pinv_exog = np.linalg.pinv(X) normalized_cov_params = np.dot(pinv_exog, pinv_exog.T) arfit = ARResults(self, params, normalized_cov_params) self._results = arfit return arfit fit.__doc__ += LikelihoodModel.fit.__doc__ class ARResults(LikelihoodModelResults): """ Class to hold results from fitting an AR model. Parameters ---------- model : AR Model instance Reference to the model that is fit. params : array The fitted parameters from the AR Model. normalized_cov_params : array inv(dot(X.T,X)) where X is the exogenous variables including lagged values. scale : float, optional An estimate of the scale of the model. Returns ------- **Attributes** aic : float Akaike Information Criterion using Lutkephol's definition. :math:`log(sigma) + 2*(1+k_ar)/nobs` bic : float Bayes Information Criterion :math:`\\log(\\sigma) + (1+k_ar)*\\log(nobs)/nobs` bse : array The standard errors of the estimated parameters. If `method` is 'cmle', then the standard errors that are returned are the OLS standard errors of the coefficients. If the `method` is 'mle' then they are computed using the numerical Hessian. fittedvalues : array The in-sample predicted values of the fitted AR model. The `k_ar` initial values are computed via the Kalman Filter if the model is fit by `mle`. fpe : float Final prediction error using Lutkepohl's definition ((n_totobs+k_trend)/(n_totobs-k_ar-k_trend))*sigma hqic : float Hannan-Quinn Information Criterion. k_ar : float Lag length. Sometimes used as `p` in the docs. k_trend : float The number of trend terms included. 'nc'=0, 'c'=1. llf : float The loglikelihood of the model evaluated at `params`. See `AR.loglike` model : AR model instance A reference to the fitted AR model. nobs : float The number of available observations `nobs` - `k_ar` n_totobs : float The number of total observations in `endog`. Sometimes `n` in the docs. params : array The fitted parameters of the model. pvalues : array The p values associated with the standard errors. resid : array The residuals of the model. If the model is fit by 'mle' then the pre-sample residuals are calculated using fittedvalues from the Kalman Filter. roots : array The roots of the AR process. scale : float Same as sigma2 sigma2 : float The variance of the innovations (residuals). trendorder : int The polynomial order of the trend. 'nc' = None, 'c' or 't' = 0, 'ct' = 1, etc. tvalues : array The t-values associated with `params`. """ _cache = {} # for scale setter def __init__(self, model, params, normalized_cov_params=None, scale=1.): super(ARResults, self).__init__(model, params, normalized_cov_params, scale) self._cache = resettable_cache() self.nobs = model.nobs n_totobs = len(model.endog) self.n_totobs = n_totobs self.X = model.X # copy? self.Y = model.Y k_ar = model.k_ar self.k_ar = k_ar k_trend = model.k_trend self.k_trend = k_trend trendorder = None if k_trend > 0: trendorder = k_trend - 1 self.trendorder = 1 self.df_resid = n_totobs - k_ar - k_trend #TODO: cmle vs mle? @cache_writable() def sigma2(self): #TODO: allow for DOF correction if exog is included model = self.model if model.method == "cmle": # do DOF correction return 1./self.nobs * sumofsq(self.resid) else: # we need to calculate the ssr for the pre-sample # see loglike for details lagstart = self.k_trend #TODO: handle exog p = self.k_ar params = self.params meany = params[0]/(1-params[lagstart:].sum()) pre_resid = model.endog[:p] - meany # get presample var-cov Vpinv = model._presample_varcov(params, lagstart) diffpVpinv = np.dot(np.dot(pre_resid.T,Vpinv),pre_resid).item() ssr = sumofsq(self.resid[p:]) # in-sample ssr return 1/self.nobs * (diffpVpinv+ssr) @cache_writable() # for compatability with RegressionResults def scale(self): return self.sigma2 @cache_readonly def bse(self): # allow user to specify? if self.model.method == "cmle": # uses different scale/sigma definition resid = self.resid ssr = np.dot(resid,resid) ols_scale = ssr/(self.nobs - self.k_ar - self.k_trend) return np.sqrt(np.diag(self.cov_params(scale=ols_scale))) else: hess = approx_hess(self.params, self.model.loglike) return np.sqrt(np.diag(-np.linalg.inv(hess[0]))) @cache_readonly def pvalues(self): return norm.sf(np.abs(self.tvalues))*2 @cache_readonly def aic(self): #JP: this is based on loglike with dropped constant terms ? # Lutkepohl # return np.log(self.sigma2) + 1./self.model.nobs * self.k_ar # Include constant as estimated free parameter and double the loss return np.log(self.sigma2) + 2 * (1 + self.k_ar)/self.nobs # Stata defintion # nobs = self.nobs # return -2 * self.llf/nobs + 2 * (self.k_ar+self.k_trend)/nobs @cache_readonly def hqic(self): nobs = self.nobs # Lutkepohl # return np.log(self.sigma2)+ 2 * np.log(np.log(nobs))/nobs * self.k_ar # R uses all estimated parameters rather than just lags return np.log(self.sigma2) + 2 * np.log(np.log(nobs))/nobs * \ (1 + self.k_ar) # Stata # nobs = self.nobs # return -2 * self.llf/nobs + 2 * np.log(np.log(nobs))/nobs * \ # (self.k_ar + self.k_trend) @cache_readonly def fpe(self): nobs = self.nobs k_ar = self.k_ar k_trend = self.k_trend #Lutkepohl return ((nobs+k_ar+k_trend)/(nobs-k_ar-k_trend))*self.sigma2 @cache_readonly def llf(self): return self.model.loglike(self.params) @cache_readonly def bic(self): nobs = self.nobs # Lutkepohl # return np.log(self.sigma2) + np.log(nobs)/nobs * self.k_ar # Include constant as est. free parameter return np.log(self.sigma2) + (1 + self.k_ar) * np.log(nobs)/nobs # Stata # return -2 * self.llf/nobs + np.log(nobs)/nobs * (self.k_ar + \ # self.k_trend) @cache_readonly def resid(self): #NOTE: uses fittedvalues because it calculate presample values for mle model = self.model endog = model.endog.squeeze() if model.method == "cmle": # elimate pre-sample return endog[self.k_ar:] - self.fittedvalues else: return model.endog.squeeze() - self.fittedvalues # def ssr(self): # resid = self.resid # return np.dot(resid, resid) @cache_readonly def roots(self): return np.roots(np.r_[1, -self.params[1:]]) @cache_readonly def fittedvalues(self): return self.model.predict() if __name__ == "__main__": import scikits.statsmodels.api as sm sunspots = sm.datasets.sunspots.load() # Why does R demean the data by defaut? ar_ols = AR(sunspots.endog) res_ols = ar_ols.fit(maxlag=9) ar_mle = AR(sunspots.endog) res_mle_bfgs = ar_mle.fit(maxlag=9, method="mle", solver="bfgs", maxiter=500, gtol=1e-10) # res_mle2 = ar_mle.fit(maxlag=1, method="mle", maxiter=500, penalty=True, # tol=1e-13) # ar_yw = AR(sunspots.endog) # res_yw = ar_yw.fit(maxlag=4, method="yw") # # Timings versus talkbox # from timeit import default_timer as timer # print "Time AR fit vs. talkbox" # # generate a long series of AR(2) data # # nobs = 1000000 # y = np.empty(nobs) # y[0:2] = 0 # for i in range(2,nobs): # y[i] = .25 * y[i-1] - .75 * y[i-2] + np.random.rand() # # mod_sm = AR(y) # t = timer() # res_sm = mod_sm.fit(method="yw", trend="nc", demean=False, maxlag=2) # t_end = timer() # print str(t_end - t) + " seconds for sm.AR with yule-walker, 2 lags" # try: # import scikits.talkbox as tb # except: # raise ImportError("You need scikits.talkbox installed for timings") # t = timer() # mod_tb = tb.lpc(y, 2) # t_end = timer() # print str(t_end - t) + " seconds for talkbox.lpc" # print """For higher lag lengths ours quickly fills up memory and starts #thrashing the swap. Should we include talkbox C code or Cythonize the #Levinson recursion algorithm?""" # some data for an example in Box Jenkins IBM = np.asarray([460,457,452,459,462,459,463,479,493,490.]) w = np.diff(IBM) theta = .5

37.773173

296

0.550609

e7062e6b82f071110bbbea01d30dd8d45f41e7cf

6,797

py

Python

scripts/gen_d.py

stbachmann/soloud

d73f18b7b2855c9a8ed6d643d36ec6407f5e3ed4

[ "Libpng", "Zlib" ]

1,319

2015-01-07T20:13:41.000Z

2022-03-30T14:07:11.000Z

scripts/gen_d.py

stbachmann/soloud

d73f18b7b2855c9a8ed6d643d36ec6407f5e3ed4

[ "Libpng", "Zlib" ]

250

2015-02-16T21:06:34.000Z

2022-02-23T21:23:10.000Z

extern/SoLoud/scripts/gen_d.py

YamiOG/Chassis2D

71c02d8c3a522694b7f4c6fa54d2a6ac5c9c14e0

[ "MIT" ]

213

2015-02-28T18:18:29.000Z

2022-03-18T09:12:06.000Z

#!/usr/bin/env python3 """ SoLoud D wrapper generator """ import soloud_codegen fo = open("../glue/soloud.d", "w") C_TO_D_TYPES = { "string":"string", "int":"int", "void":"void", "const char *":"const(char)*", "char *":"char*", "unsigned int":"uint", "float":"float", "double":"double", "float *":"float[]", "File *":"SoloudObject", "const unsigned char *":"ubyte*", "unsigned char *":"ubyte*", "unsigned char":"ubyte", "short *":"short[]" } for soloud_type in soloud_codegen.soloud_type: C_TO_D_TYPES[soloud_type + " *"] = "SoloudObject" def has_ex_variant(funcname): """ Checks if this function has an "Ex" variant """ if funcname[-2::] == "Ex": # Already an Ex.. return False for func in soloud_codegen.soloud_func: if func[1] == (funcname + "Ex"): return True return False fo.write(""" // SoLoud wrapper for D // This file is autogenerated; any changes will be overwritten module soloud; pure @safe nothrow @nogc: private struct SoloudObject { public int* objhandle; } """) # Forward declare ALL THE CLASSES for soloud_type in soloud_codegen.soloud_type: #fo.write("public class %s;\n"%(soloud_type)) pass # Since there's no reason to use the "raw" data anymore, # skip generating the enum dictionary # #fo.write("# Enumerations\n") #fo.write("soloud_enum = {\n") #first = True #for x in soloud_codegen.soloud_enum: # if first: # first = False # else: # fo.write(",\n") # fo.write('"' + x + '": ' + str(soloud_codegen.soloud_enum[x])) #fo.write("\n}\n") fo.write("\n") # fo.write("# Raw DLL functions\n") # for x in soloud_codegen.soloud_func: # fo.write(x[1] + ' = soloud_dll.' + x[1] + '\n') # fo.write(x[1] + '.restype = ' + C_TO_D_TYPES[x[0]] + '\n') # fo.write(x[1] + '.argtypes = [') # first = True # for y in x[2]: # if len(y) > 0: # if first: # first = False # else: # fo.write(", ") # fo.write(fudge_types(y[0])) # fo.write(']\n') # fo.write('\n') # ################################################################# # # oop # # class classname : SoloudObject # { # public const int CONSTANT = 3; # [DllImport("soloud_x86.dll", CallingConvention = CallingConvention.Cdecl)] # internal static extern int classname_foobar(... # public int foobar(... # { # classname_foobar(... # } # def fix_default_param(defparam, classname): """ 'fixes' default parameters from C to what python expectes """ if (classname + '::') == defparam[0:len(classname)+2:]: return defparam[len(classname)+2::] #if defparam[len(defparam)-1] == "f": # return defparam[0:len(defparam)-1] return defparam def external_pointer_fix(param): if param == "SoloudObject": return "int*" return param function_decls = "" for x in soloud_codegen.soloud_type: first = True for y in soloud_codegen.soloud_func: if (x + "_") == y[1][0:len(x)+1:]: if first: # Declare creator and destroyer function_decls += ('private static extern(C) int* %s_create();\n'%(x)) function_decls += ('private static extern(C) int* %s_destroy(int* aObjHandle);\n'%(x)) fo.write('\n') fo.write('public struct %s\n{\n'%(x)) fo.write('pure @safe nothrow @nogc:\n') for z in soloud_codegen.soloud_enum: if z[0:len(x)+1] == x.upper()+'_': s = str(soloud_codegen.soloud_enum[z]) fo.write('\tpublic enum %s = %s;\n'%(z[len(x)+1::], s)) fo.write('\n') fo.write('\tpublic SoloudObject soloudObject;\n') fo.write('\talias soloudObject this;\n\n') fo.write('\tpublic static create()\n\t{\n') fo.write('\t\treturn %s(SoloudObject(%s_create()));\n'%(x, x)) fo.write('\t}\n\n') fo.write('\t~this()\n\t{\n') fo.write('\t\t%s_destroy(objhandle);\n'%(x)) fo.write('\t}\n\n') first = False funcname = y[1][len(x)+1::] # If the function has the name "Ex", remove the subfix if funcname[-2::] == "Ex": funcname = funcname[:len(funcname)-2] # Skip generating functions that have an Ex variant if funcname == "create" or funcname == "destroy" or has_ex_variant(y[1]): pass # omit create/destroy, handled by __exit__ / close else: ret = C_TO_D_TYPES[y[0]] function_decls += ('private static extern(C) %s %s(int* aObjHandle'%(ret, y[1])) for z in y[2]: if len(z) > 1: if z[1] == 'a'+x: pass # skip the 'self' pointer else: function_decls += ', ' function_decls += external_pointer_fix(C_TO_D_TYPES[z[0]]) + ' ' + z[1] function_decls += ');\n' fo.write('\tpublic %s %s('%(C_TO_D_TYPES[y[0]], funcname)) firstparm = True for z in y[2]: if len(z) > 1: if z[1] == 'a'+x: pass # skip the 'self' pointer else: if firstparm: firstparm = False else: fo.write(', ') fo.write(C_TO_D_TYPES[z[0]] + ' ' + z[1]) if len(z) > 2: fo.write(' = ' + fix_default_param(z[2], x)) fo.write(')\n\t{\n') fo.write('\t\t') if y[0] == 'void': pass else: fo.write('return ') fo.write(y[1] + '(objhandle') for z in y[2]: if len(z) > 1: if z[1] == 'a'+x: pass # skip the 'self' pointer else: fo.write(', ') fudged_type = C_TO_D_TYPES[z[0]] if fudged_type == 'SoloudObject': fo.write(z[1] + '.objhandle') else: fo.write(z[1]) fo.write(');\n') fo.write('\t}\n\n') if not first: fo.write('}\n') fo.write(function_decls) fo.close() print("soloud.d generated")

32.21327

102

0.470502

e635bbefb12b56734131362c979d859345c62259

2,320

py

Python

model/taskinput.py

yxue3357/TinyM_ER

c7809a39bfdfe9baa00a5ad5be99f0270c73b4ea

[ "Apache-2.0", "MIT" ]

131

2019-03-04T16:34:29.000Z

2022-03-07T11:04:38.000Z

model/taskinput.py

yxue3357/TinyM_ER

c7809a39bfdfe9baa00a5ad5be99f0270c73b4ea

[ "Apache-2.0", "MIT" ]

4

2020-02-12T11:46:09.000Z

2021-06-25T13:25:48.000Z

model/taskinput.py

yxue3357/TinyM_ER

c7809a39bfdfe9baa00a5ad5be99f0270c73b4ea

[ "Apache-2.0", "MIT" ]

34

2019-05-17T03:34:37.000Z

2022-03-08T18:37:45.000Z

### Code for this basline is copied directly from multimodal.py in https://github.com/facebookresearch/GradientEpisodicMemory # Copyright 2019-present, IBM Research # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. import torch import torch.nn as nn def reset_bias(m): m.bias.data.fill_(0.0) class Net(nn.Module): def __init__(self, n_inputs, n_outputs, n_tasks, args): super(Net, self).__init__() self.i_layer = nn.ModuleList() self.h_layer = nn.ModuleList() self.o_layer = nn.ModuleList() self.n_layers = args.n_layers nh = args.n_hiddens if self.n_layers > 0: # dedicated input layer for _ in range(n_tasks): self.i_layer += [nn.Linear(n_inputs, nh)] reset_bias(self.i_layer[-1]) # shared hidden layer self.h_layer += [nn.ModuleList()] for _ in range(self.n_layers): self.h_layer[0] += [nn.Linear(nh, nh)] reset_bias(self.h_layer[0][0]) # shared output layer self.o_layer += [nn.Linear(nh, n_outputs)] reset_bias(self.o_layer[-1]) # linear model falls back to independent models else: self.i_layer += [nn.Linear(n_inputs, n_outputs)] reset_bias(self.i_layer[-1]) self.relu = nn.ReLU() self.soft = nn.LogSoftmax() self.loss = nn.NLLLoss() self.optimizer = torch.optim.SGD(self.parameters(), args.lr) def forward(self, x, t): h = x if self.n_layers == 0: y = self.soft(self.i_layer[t if isinstance(t, int) else t[0]](h)) else: # task-specific input h = self.relu(self.i_layer[t if isinstance(t, int) else t[0]](h)) # shared hiddens for l in range(self.n_layers): h = self.relu(self.h_layer[0][l](h)) # shared output y = self.soft(self.o_layer[0](h)) return y def observe(self, x, t, y): self.zero_grad() self.loss(self.forward(x, t), y).backward() self.optimizer.step()

29.367089

125

0.556034

69c48a8b500906208871f8df76f1769e134d218a

1,293

py

Python

pinball/master/snapshot.py

DotModus/pinball

deeb4ec20bbd000ad44f7b44e6a7c0fa900dbbea

[ "Apache-2.0" ]

1,143

2015-03-06T22:10:53.000Z

2022-02-23T21:16:47.000Z

pinball/master/snapshot.py

DotModus/pinball

deeb4ec20bbd000ad44f7b44e6a7c0fa900dbbea

[ "Apache-2.0" ]

70

2015-03-06T00:44:39.000Z

2019-05-01T13:15:10.000Z

pinball/master/snapshot.py

Betterment/pinball

11120b54fcc25b2857631a5de65a1195ffcffb5c

[ "Apache-2.0" ]

169

2015-03-09T21:27:12.000Z

2022-03-19T08:09:13.000Z

# Copyright 2015, Pinterest, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Snapshot maintains a collection of tokens matching a given query.""" __author__ = 'Pawel Garbacki' __copyright__ = 'Copyright 2015, Pinterest, Inc.' __credits__ = [__author__] __license__ = 'Apache' __version__ = '2.0' class Snapshot(object): def __init__(self, client, request): self._client = client self._request = request self._response = None self.refresh() def refresh(self): """Query the master. Returns: True if the local copy of the tokens has changed. Otherwise False. """ old_response = self._response self._response = self._client.query(self._request) return self._response != old_response

31.536585

79

0.699149

8377bd2844e0496c66672b881d840bfe6a35d3ee

2,973

py

Python

hw_7_DNA.py

Katy-Scha/AdvancedPython2021

260512928fe958c0bb1164ad3357dfbf38f163ef

[ "MIT" ]

null

hw_7_DNA.py

Katy-Scha/AdvancedPython2021

260512928fe958c0bb1164ad3357dfbf38f163ef

[ "MIT" ]

2

2021-09-10T07:57:55.000Z

2021-10-05T22:48:12.000Z

hw_7_DNA.py

Katy-Scha/AdvancedPython2021

260512928fe958c0bb1164ad3357dfbf38f163ef

[ "MIT" ]

null

import random class Chain: def __init__(self, seq): self.seq = seq #self._dict = None def correct(self, seq): #отправляю seq отдельно, ибо хочу проверять корректность до создания полноценного объекта if seq is None: return True for c in seq: if not (c in self._dict): return False return True def compl(self): new_seq = '' for c in self.seq: new_seq = new_seq + self._dict[c] return new_seq def __mul__(self, other): new_seq = [random.choice(pair) for pair in list(zip(self.seq, other.seq))] # по длине, кк короткая РНК new_seq = ''.join(new_seq) if len(self.seq) > len(other.seq): new_seq += self.seq[-(len(self.seq) - len(other.seq)):] elif len(other.seq) > len(self.seq): new_seq += self.seq[-(len(other.seq) - len(self.seq)):] return self.__class__(seq=new_seq) def __getitem__(self, item): raise NotImplementedError def __add__(self, other): raise NotImplementedError class RNA(Chain): def __init__(self, seq): self._dict = {'A': 'T', 'U': 'A', 'G': 'C', 'C': 'G'} if self.correct(seq) == True: super().__init__(seq=seq) else: raise Exeption def __repr__(self): return self.seq def __getitem__(self, i): return self.seq[i] def __add__(self, other): return RNA(self.seq + other.seq) def compl_DNA(self): forward = self.compl() return DNA(seq=forward) def __eq__(self, other): if self.seq == other.seq: return True return False class DNA(Chain): def __init__(self, seq, seq_rev=None): # даю две, ибо возможность для поддержки мутаций и проч self._dict = {'A': 'T', 'T': 'A', 'G': 'C', 'C': 'G'} if self.correct(seq) and self.correct(seq_rev) == True: super().__init__(seq=seq) else: raise Exeption super().__init__(seq=seq) self.rev = seq_rev # reversed chain if self.rev is None: self.rev = self.compl() def __repr__(self): return repr([self.seq, self.rev]) def __getitem__(self, i): return [self.seq[i], self.rev[i]] def __add__(self, other): return DNA(self.seq + other.seq, self.rev + other.rev) def __eq__(self, other): if self.seq == other.seq and self.rev == other.rev: return True return False if __name__=='__main__': dna = DNA('ATG', 'TAC') rna = RNA('UGA') dna1 = DNA('GG', 'CC') rna1 = RNA('CC') #print(rna, dna) #print(rna[1], dna[1]) #print(rna+rna1, dna+dna1) print(rna*rna1) print(dna*dna1, dna*dna1) #print(rna.compl()) #print(DNA(seq='ACT')) #print(rna, rna.compl_DNA()) #print(rna==rna1)

33.033333

118

0.543895

a42ba0b17759a2d3c485dbfd21c8faf330ff73fe

259

py

Python

website/apps/ip_blocking/view_guards.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

61

2015-11-10T17:13:46.000Z

2021-08-06T17:58:30.000Z

website/apps/ip_blocking/view_guards.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

13

2015-11-11T07:49:41.000Z

2021-06-09T03:45:31.000Z

website/apps/ip_blocking/view_guards.py

bopopescu/drawquest-web

8d8f9149b6efeb65202809a5f8916386f58a1b3b

[ "BSD-3-Clause" ]

18

2015-11-11T04:50:04.000Z

2021-08-20T00:57:11.000Z

from django.core.exceptions import PermissionDenied from apps.ip_blocking.models import is_ip_blocked from canvas.view_guards import view_guard @view_guard def require_unblocked_ip(request): if is_ip_blocked(request): raise PermissionDenied()

21.583333

51

0.810811

7e2b66bc49eb83605a06d4e27f8b6173f3b50a44

765

py

Python

importfrompdf.py

nmontesoro/Tarjetas_v4

5d10555d9050cb0dfa32ceda5549fe9029c889dd

[ "MIT" ]

null

importfrompdf.py

nmontesoro/Tarjetas_v4

5d10555d9050cb0dfa32ceda5549fe9029c889dd

[ "MIT" ]

null

importfrompdf.py

nmontesoro/Tarjetas_v4

5d10555d9050cb0dfa32ceda5549fe9029c889dd

[ "MIT" ]

null

from pdfparser import PdfParser import glob import sqlite3 as sql db = sql.connect('data.db') for file in glob.glob('pdf/*.pdf'): print('Procesando %s' % (file)) p = PdfParser(file) liqs = p.get_liquidaciones() sql_insert_str = ('INSERT INTO operaciones (sucursal, fpago, liqno, lote, ' + 'arancel, impuestos, importe, adicionales, tarjeta)' + ' VALUES (?,?,?,?,?,?,?,?,?)') suc = p.sucursal tar = p.tarjeta for liq in liqs: db.execute(sql_insert_str, (suc, liq['fpago'], liq['liqno'], liq['lote'], liq['arancel'], liq['impuestos'], liq['importe'], liq['adicionales'], tar)) db.commit() db.close()

31.875

79

0.529412

88dcc58a68873f369dd8e03da32430dc652377b8

3,105

py

Python

helloname/settings.py

riverstation/cnkiproject

e0f6c85230c7d66dc910356cb6bffb6ff5b4cc71

[ "Apache-2.0" ]

null

helloname/settings.py

riverstation/cnkiproject

e0f6c85230c7d66dc910356cb6bffb6ff5b4cc71

[ "Apache-2.0" ]

null

helloname/settings.py

riverstation/cnkiproject

e0f6c85230c7d66dc910356cb6bffb6ff5b4cc71

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Scrapy settings for helloname project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # https://doc.scrapy.org/en/latest/topics/settings.html # https://doc.scrapy.org/en/latest/topics/downloader-middleware.html # https://doc.scrapy.org/en/latest/topics/spider-middleware.html BOT_NAME = 'helloname' SPIDER_MODULES = ['helloname.spiders'] NEWSPIDER_MODULE = 'helloname.spiders' # Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = 'helloname (+http://www.yourdomain.com)' # Obey robots.txt rules ROBOTSTXT_OBEY = True # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs #DOWNLOAD_DELAY = 3 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) #COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: #DEFAULT_REQUEST_HEADERS = { # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', # 'Accept-Language': 'en', #} # Enable or disable spider middlewares # See https://doc.scrapy.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { # 'helloname.middlewares.HellonameSpiderMiddleware': 543, #} # Enable or disable downloader middlewares # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { # 'helloname.middlewares.HellonameDownloaderMiddleware': 543, #} # Enable or disable extensions # See https://doc.scrapy.org/en/latest/topics/extensions.html #EXTENSIONS = { # 'scrapy.extensions.telnet.TelnetConsole': None, #} # Configure item pipelines # See https://doc.scrapy.org/en/latest/topics/item-pipeline.html #ITEM_PIPELINES = { # 'helloname.pipelines.HellonamePipeline': 300, #} # Enable and configure the AutoThrottle extension (disabled by default) # See https://doc.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 5 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 60 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings #HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

34.120879

102

0.774879

3a273998f3a9ffe66d3b7af56388ceed6e6f91b8

34,902

py

Python

scripts/build_tools.py

shangliyun/lu_xing_xiang_one_os

02b3900bbc2303291ad6b16d59f50df786af4e27

[ "Apache-2.0" ]

1

2022-03-26T09:59:37.000Z

scripts/build_tools.py

shangliyun/lu_xing_xiang_one_os

02b3900bbc2303291ad6b16d59f50df786af4e27

[ "Apache-2.0" ]

1

2021-06-24T04:27:40.000Z

scripts/build_tools.py

shangliyun/lu_xing_xiang_one_os

02b3900bbc2303291ad6b16d59f50df786af4e27

[ "Apache-2.0" ]

2

2021-06-24T04:08:28.000Z

2022-03-07T06:37:24.000Z

# # File : build_tools.py # This file is part of CMCC IOT OS # COPYRIGHT (C) 2012-2020, CMCC IOT # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along # with this program; if not, write to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. # # import os import sys import string import utils from SCons.Script import * from utils import _make_path_relative from mkdist import do_copy_file BuildOptions = {} Projects = [] Os_Root = '' Env = None out_dir = '' # SCons PreProcessor patch def start_handling_includes(self, t=None): """ Causes the PreProcessor object to start processing #import, #include and #include_next lines. This method will be called when a #if, #ifdef, #ifndef or #elif evaluates True, or when we reach the #else in a #if, #ifdef, #ifndef or #elif block where a condition already evaluated False. """ d = self.dispatch_table p = self.stack[-1] if self.stack else self.default_table for k in ('import', 'include', 'include_next', 'define'): d[k] = p[k] def stop_handling_includes(self, t=None): """ Causes the PreProcessor object to stop processing #import, #include and #include_next lines. This method will be called when a #if, #ifdef, #ifndef or #elif evaluates False, or when we reach the #else in a #if, #ifdef, #ifndef or #elif block where a condition already evaluated True. """ d = self.dispatch_table d['import'] = self.do_nothing d['include'] = self.do_nothing d['include_next'] = self.do_nothing d['define'] = self.do_nothing PatchedPreProcessor = SCons.cpp.PreProcessor PatchedPreProcessor.start_handling_includes = start_handling_includes PatchedPreProcessor.stop_handling_includes = stop_handling_includes class Win32Spawn: def spawn(self, sh, escape, cmd, args, env): # deal with the cmd build-in commands which cannot be used in # subprocess.Popen if cmd == 'del': for f in args[1:]: try: os.remove(f) except Exception as e: print ('Error removing file: ' + e) return -1 return 0 import subprocess newargs = ' '.join(args[1:]) cmdline = cmd + " " + newargs # Make sure the env is constructed by strings _e = dict([(k, str(v)) for k, v in env.items()]) # Windows(tm) CreateProcess does not use the env passed to it to find # the executables. So we have to modify our own PATH to make Popen # work. old_path = os.environ['PATH'] os.environ['PATH'] = _e['PATH'] try: proc = subprocess.Popen(cmdline, env=_e, shell=False) except Exception as e: print ('Error in calling command:' + cmdline.split(' ')[0]) print ('Exception: ' + os.strerror(e.errno)) if (os.strerror(e.errno) == "No such file or directory"): print ("\nPlease check Toolchains PATH setting.\n") return e.errno finally: os.environ['PATH'] = old_path return proc.wait() # generate cconfig.h file def GenCconfigFile(env, BuildOptions): import osconfig if osconfig.COMPILER == 'gcc': contents = '' if not os.path.isfile('cconfig.h'): import gcc gcc.GenerateGCCConfig(osconfig) # try again if os.path.isfile('cconfig.h'): f = open('cconfig.h', 'r') if f: contents = f.read() f.close() prep = PatchedPreProcessor() prep.process_contents(contents) options = prep.cpp_namespace BuildOptions.update(options) # add HAVE_CCONFIG_H definition env.AppendUnique(CPPDEFINES = ['HAVE_CCONFIG_H']) def SetupCompile(env, root_directory, has_libcpu=False, remove_components = [],SDK_LIB=None,SDK_DRIVER=None): import osconfig global BuildOptions global Projects global Env global Os_Root # ===== Add option to SCons ===== AddOption('--customized-proj', dest = 'customized-proj', action = 'store_true', default = False, help = 'Generate a new code project for current board type, code for other bsps are removed. ' + \ 'the new code project can be build independently.') AddOption('--customized-proj-strip', dest = 'customized-proj-strip', action = 'store_true', default = False, help = 'Generate a new code project for current board type, code for other bsps are removed. ' + \ 'the new code project can be build independently. useless files are stripped.') ''' AddOption('--cscope', dest = 'cscope', action = 'store_true', default = False, help = 'Build Cscope cross reference database. Requires cscope installed.') AddOption('--clang-analyzer', dest = 'clang-analyzer', action = 'store_true', default = False, help = 'Perform static analyze with Clang-analyzer. ' + \ 'Requires Clang installed.\n' + \ 'It is recommended to use with scan-build like this:\n' + \ '`scan-build scons --clang-analyzer`\n' + \ 'If things goes well, scan-build will instruct you to invoke scan-view.') AddOption('--buildlib', dest = 'buildlib', type = 'string', help = 'building library of a component') AddOption('--cleanlib', dest = 'cleanlib', action = 'store_true', default = False, help = 'clean up the library by --buildlib') ''' AddOption('--ide', dest = 'ide', type = 'string', # help = 'set target project: mdk/mdk4/mdk5/iar/vs/vsc/ua/cdk/ses/makefile/eclipse') help = 'Build a project file for IDEs: mdk/mdk4/mdk5.') AddOption('--restore-config', dest = 'restore-config', action = 'store_true', default = False, help = 'Generate .config from oneos_config.h.') ''' AddOption('--useconfig', dest = 'useconfig', type = 'string', help = 'make oneos_config.h from config file.') ''' AddOption('--verbose', dest = 'verbose', action = 'store_true', default = False, help = 'Print more detailed information during build process.') Env = env # Os_Root = os.path.abspath(root_directory) Os_Root = root_directory # make an absolute root directory OS_ROOT = Os_Root Export('OS_ROOT') # set OS_ROOT in ENV Env['OS_ROOT'] = Os_Root # set BSP_ROOT in ENV Env['BSP_ROOT'] = Dir('#').abspath sys.path = sys.path + [os.path.join(Os_Root, 'scripts')] # {target_name:(CROSS_TOOL, PLATFORM)} tgt_dict = {'mdk':('keil', 'armcc'), 'mdk4':('keil', 'armcc'), 'mdk5':('keil', 'armcc'), 'iar':('iar', 'iar'), 'vs':('msvc', 'cl'), 'vs2012':('msvc', 'cl'), 'vsc' : ('gcc', 'gcc'), 'cb':('keil', 'armcc'), 'ua':('gcc', 'gcc'), 'cdk':('gcc', 'gcc'), 'makefile':('gcc', 'gcc'), 'eclipse':('gcc', 'gcc'), 'ses' : ('gcc', 'gcc')} tgt_name = GetOption('ide') if tgt_name: # --target will change the toolchain settings which clang-analyzer is # depend on ''' if GetOption('clang-analyzer'): print ('--clang-analyzer cannot be used with --target') sys.exit(1) ''' SetOption('no_exec', 1) try: osconfig.CROSS_TOOL, osconfig.COMPILER = tgt_dict[tgt_name] # replace the 'OS_CC' to 'CROSS_TOOL' os.environ['OS_CC'] = osconfig.CROSS_TOOL utils.ReloadModule(osconfig) except KeyError: print ('Unknow target: '+ tgt_name+'. Avaible targets: ' +', '.join(tgt_dict.keys())) sys.exit(1) ''' elif (IsDefined('OS_USING_NEWLIB') == False and IsDefined('OS_USING_NOLIBC') == False) \ and osconfig.COMPILER == 'gcc': AddDefined('OS_USING_MINILIBC') ''' # auto change the 'OS_EXEC_PATH' when 'osconfig.COMPILER_PATH' get failed if not os.path.exists(osconfig.COMPILER_PATH): if 'OS_EXEC_PATH' in os.environ: # del the 'OS_EXEC_PATH' and using the 'COMPILER_PATH' setting on osconfig.py del os.environ['OS_EXEC_PATH'] utils.ReloadModule(osconfig) # add compability with Keil MDK 4.6 which changes the directory of armcc.exe if osconfig.COMPILER == 'armcc': if not os.path.isfile(os.path.join(osconfig.COMPILER_PATH, 'armcc.exe')): if osconfig.COMPILER_PATH.find('bin40') > 0: osconfig.COMPILER_PATH = osconfig.COMPILER_PATH.replace('bin40', 'armcc/bin') Env['LINKFLAGS'] = Env['LINKFLAGS'].replace('RV31', 'armcc') # reset AR command flags env['ARCOM'] = '$AR --create $TARGET $SOURCES' env['LIBPREFIX'] = '' env['LIBSUFFIX'] = '.lib' env['LIBLINKPREFIX'] = '' env['LIBLINKSUFFIX'] = '.lib' env['LIBDIRPREFIX'] = '--userlibpath ' elif osconfig.COMPILER == 'iar': env['LIBPREFIX'] = '' env['LIBSUFFIX'] = '.a' env['LIBLINKPREFIX'] = '' env['LIBLINKSUFFIX'] = '.a' env['LIBDIRPREFIX'] = '--search ' # patch for win32 spawn if env['PLATFORM'] == 'win32': win32_spawn = Win32Spawn() win32_spawn.env = env env['SPAWN'] = win32_spawn.spawn if env['PLATFORM'] == 'win32': os.environ['PATH'] = osconfig.COMPILER_PATH + ";" + os.environ['PATH'] else: os.environ['PATH'] = osconfig.COMPILER_PATH + ":" + os.environ['PATH'] if os.getenv('EXEC_BIN_PATH'): env.PrependENVPath('PATH', os.getenv('EXEC_BIN_PATH')) # add program path env.PrependENVPath('PATH', osconfig.COMPILER_PATH) # add oneos_config.h/BSP path into Kernel group AddCodeGroup("Kernel", [], [], CPPPATH=[str(Dir('#').abspath)]) # add library build action act = SCons.Action.Action(BuildLibInstallAction, 'Install compiled library... $TARGET') bld = Builder(action = act) Env.Append(BUILDERS = {'BuildLib': bld}) # parse oneos_config.h to get used component PreProcessor = PatchedPreProcessor() f = open('oneos_config.h', 'r') contents = f.read() f.close() PreProcessor.process_contents(contents) BuildOptions = PreProcessor.cpp_namespace ''' if GetOption('clang-analyzer'): # perform what scan-build does env.Replace( CC = 'ccc-analyzer', CXX = 'c++-analyzer', # skip as and link LINK = 'true', AS = 'true',) env["ENV"].update(x for x in os.environ.items() if x[0].startswith("CCC_")) # only check, don't compile. ccc-analyzer use CCC_CC as the CC. # fsyntax-only will give us some additional warning messages env['ENV']['CCC_CC'] = 'clang' env.Append(CFLAGS=['-fsyntax-only', '-Wall', '-Wno-invalid-source-encoding']) env['ENV']['CCC_CXX'] = 'clang++' env.Append(CXXFLAGS=['-fsyntax-only', '-Wall', '-Wno-invalid-source-encoding']) # remove the POST_ACTION as it will cause meaningless errors(file not # found or something like that). osconfig.POST_ACTION = '' ''' # generate cconfig.h file GenCconfigFile(env, BuildOptions) # auto append '_REENT_SMALL' when using newlib 'nano.specs' option if osconfig.COMPILER == 'gcc' and str(env['LINKFLAGS']).find('nano.specs') != -1: env.AppendUnique(CPPDEFINES = ['_REENT_SMALL']) if GetOption('restore-config'): from genconf import genconfig genconfig() exit(0) if env['PLATFORM'] != 'win32': AddOption('--menuconfig', dest = 'menuconfig', action = 'store_true', default = False, help = 'make menuconfig for CMCC IOT BSP') if GetOption('menuconfig'): menuconfig_file_path = os.path.join(OS_ROOT, 'scripts', 'linux-menuconfig', 'menuconfig.py') sys.path = sys.path + [os.path.join(OS_ROOT, 'scripts', 'linux-menuconfig')] os.system("python %s" % (menuconfig_file_path)) exit(0) ''' AddOption('--pyconfig', dest = 'pyconfig', action = 'store_true', default = False, help = 'make menuconfig for CMCC IOT BSP') AddOption('--pyconfig-silent', dest = 'pyconfig_silent', action = 'store_true', default = False, help = 'Don`t show pyconfig window') if GetOption('pyconfig_silent'): from menuconfig import pyconfig_silent pyconfig_silent(Os_Root) exit(0) elif GetOption('pyconfig'): from menuconfig import pyconfig pyconfig(Os_Root) exit(0) ''' ''' configfn = GetOption('useconfig') if configfn: from menuconfig import mk_rtconfig mk_rtconfig(configfn) exit(0) ''' if not GetOption('verbose'): # override the default verbose command string env.Replace( ARCOMSTR = 'AR $TARGET', ASCOMSTR = 'AS $TARGET', ASPPCOMSTR = 'AS $TARGET', CCCOMSTR = 'CC $TARGET', CXXCOMSTR = 'CXX $TARGET', LINKCOMSTR = 'LINK $TARGET' ) # fix the linker for C++ if IsDefined('OS_USING_CPLUSPLUS'): if env['LINK'].find('gcc') != -1: env['LINK'] = env['LINK'].replace('gcc', 'g++') # we need to seperate the variant_dir for BSPs and the kernels. BSPs could # have their own components etc. If they point to the same folder, SCons # would find the wrong source code to compile. global out_dir out_dir = OS_ROOT+'/out/'+os.path.basename(PresentDir()) if not os.path.exists(out_dir): os.makedirs(out_dir) build_vdir = 'build' kernel_vdir = 'build/kernel' Export('build_vdir') Export('kernel_vdir') Export('remove_components') # board build script objs = SConscript('SConscript', variant_dir=build_vdir + '/bsp', duplicate=0) # else build script objs.extend(SConscript(os.path.join(Os_Root, 'SConscript'), variant_dir=build_vdir, duplicate=0)) # sort group by name Projects = sorted(Projects, key = lambda group:group["name"]) return objs def PrepareModuleBuilding(env, root_directory, bsp_directory): import osconfig global BuildOptions global Env global Os_Root # patch for win32 spawn if env['PLATFORM'] == 'win32': win32_spawn = Win32Spawn() win32_spawn.env = env env['SPAWN'] = win32_spawn.spawn Env = env Os_Root = root_directory # parse bsp oneos_config.h to get used component PreProcessor = PatchedPreProcessor() f = open(bsp_directory + '/oneos_config.h', 'r') contents = f.read() f.close() PreProcessor.process_contents(contents) BuildOptions = PreProcessor.cpp_namespace # add build/clean library option for library checking AddOption('--buildlib', dest='buildlib', type='string', help='building library of a component') AddOption('--cleanlib', dest='cleanlib', action='store_true', default=False, help='clean up the library by --buildlib') # add program path env.PrependENVPath('PATH', osconfig.COMPILER_PATH) def GetConfigValue(name): assert type(name) == str, 'GetConfigValue: only string parameter is valid' try: return BuildOptions[name] except: return '' def IsDefined(depend): building = True if type(depend) == type('str'): if not depend in BuildOptions or BuildOptions[depend] == 0: building = False elif BuildOptions[depend] != '': return BuildOptions[depend] return building # for list type depend for item in depend: if item != '': if not item in BuildOptions or BuildOptions[item] == 0: building = False return building def LocalOptions(config_filename): from SCons.Script import SCons # parse wiced_config.h to get used component PreProcessor = SCons.cpp.PreProcessor() f = open(config_filename, 'r') contents = f.read() f.close() PreProcessor.process_contents(contents) local_options = PreProcessor.cpp_namespace return local_options def IsLocalDefined(options, depend): building = True if type(depend) == type('str'): if not depend in options or options[depend] == 0: building = False elif options[depend] != '': return options[depend] return building # for list type depend for item in depend: if item != '': if not item in options or options[item] == 0: building = False return building def AddDefined(option): BuildOptions[option] = 1 def MergeGroup(src_group, group): src_group['src'] = src_group['src'] + group['src'] if 'CCFLAGS' in group: if 'CCFLAGS' in src_group: src_group['CCFLAGS'] = src_group['CCFLAGS'] + group['CCFLAGS'] else: src_group['CCFLAGS'] = group['CCFLAGS'] if 'CPPPATH' in group: if 'CPPPATH' in src_group: src_group['CPPPATH'] = src_group['CPPPATH'] + group['CPPPATH'] else: src_group['CPPPATH'] = group['CPPPATH'] if 'CPPDEFINES' in group: if 'CPPDEFINES' in src_group: src_group['CPPDEFINES'] = src_group['CPPDEFINES'] + group['CPPDEFINES'] else: src_group['CPPDEFINES'] = group['CPPDEFINES'] if 'ASFLAGS' in group: if 'ASFLAGS' in src_group: src_group['ASFLAGS'] = src_group['ASFLAGS'] + group['ASFLAGS'] else: src_group['ASFLAGS'] = group['ASFLAGS'] # for local CCFLAGS/CPPPATH/CPPDEFINES if 'LOCAL_CCFLAGS' in group: if 'LOCAL_CCFLAGS' in src_group: src_group['LOCAL_CCFLAGS'] = src_group['LOCAL_CCFLAGS'] + group['LOCAL_CCFLAGS'] else: src_group['LOCAL_CCFLAGS'] = group['LOCAL_CCFLAGS'] if 'LOCAL_CPPPATH' in group: if 'LOCAL_CPPPATH' in src_group: src_group['LOCAL_CPPPATH'] = src_group['LOCAL_CPPPATH'] + group['LOCAL_CPPPATH'] else: src_group['LOCAL_CPPPATH'] = group['LOCAL_CPPPATH'] if 'LOCAL_CPPDEFINES' in group: if 'LOCAL_CPPDEFINES' in src_group: src_group['LOCAL_CPPDEFINES'] = src_group['LOCAL_CPPDEFINES'] + group['LOCAL_CPPDEFINES'] else: src_group['LOCAL_CPPDEFINES'] = group['LOCAL_CPPDEFINES'] if 'LINKFLAGS' in group: if 'LINKFLAGS' in src_group: src_group['LINKFLAGS'] = src_group['LINKFLAGS'] + group['LINKFLAGS'] else: src_group['LINKFLAGS'] = group['LINKFLAGS'] if 'LIBS' in group: if 'LIBS' in src_group: src_group['LIBS'] = src_group['LIBS'] + group['LIBS'] else: src_group['LIBS'] = group['LIBS'] if 'LIBPATH' in group: if 'LIBPATH' in src_group: src_group['LIBPATH'] = src_group['LIBPATH'] + group['LIBPATH'] else: src_group['LIBPATH'] = group['LIBPATH'] if 'LOCAL_ASFLAGS' in group: if 'LOCAL_ASFLAGS' in src_group: src_group['LOCAL_ASFLAGS'] = src_group['LOCAL_ASFLAGS'] + group['LOCAL_ASFLAGS'] else: src_group['LOCAL_ASFLAGS'] = group['LOCAL_ASFLAGS'] def AddCodeGroup(name, src, depend, **parameters): global Env if not IsDefined(depend): return [] # find exist group and get path of group group_path = '' for g in Projects: if g['name'] == name: group_path = g['path'] if group_path == '': group_path = PresentDir() group = parameters group['name'] = name group['path'] = group_path if type(src) == type([]): group['src'] = File(src) else: group['src'] = src # for item in group['src']: # print(item) # print('*******************************') # print(group['src']) if 'CCFLAGS' in group: Env.AppendUnique(CCFLAGS = group['CCFLAGS']) if 'CPPPATH' in group: paths = [] for item in group['CPPPATH']: paths.append(os.path.abspath(item)) group['CPPPATH'] = paths Env.AppendUnique(CPPPATH = group['CPPPATH']) if 'CPPDEFINES' in group: Env.AppendUnique(CPPDEFINES = group['CPPDEFINES']) if 'LINKFLAGS' in group: Env.AppendUnique(LINKFLAGS = group['LINKFLAGS']) if 'ASFLAGS' in group: Env.AppendUnique(ASFLAGS = group['ASFLAGS']) if 'LOCAL_CPPPATH' in group: paths = [] for item in group['LOCAL_CPPPATH']: paths.append(os.path.abspath(item)) group['LOCAL_CPPPATH'] = paths import osconfig if osconfig.COMPILER == 'gcc': if 'CCFLAGS' in group: group['CCFLAGS'] = utils.GCCC99Patch(group['CCFLAGS']) if 'LOCAL_CCFLAGS' in group: group['LOCAL_CCFLAGS'] = utils.GCCC99Patch(group['LOCAL_CCFLAGS']) ''' # check whether to clean up library if GetOption('cleanlib') and os.path.exists(os.path.join(group['path'], GroupLibFullName(name, Env))): if group['src'] != []: print ('Remove library:'+ GroupLibFullName(name, Env)) fn = os.path.join(group['path'], GroupLibFullName(name, Env)) if os.path.exists(fn): os.unlink(fn) ''' if 'LIBS' in group: Env.AppendUnique(LIBS = group['LIBS']) if 'LIBPATH' in group: Env.AppendUnique(LIBPATH = group['LIBPATH']) #if 'variant_dir' in group: # check whether to build group library if 'LIBRARY' in group: objs = Env.Library(name, group['src']) else: # only add source objs = group['src'] # merge group for g in Projects: if g['name'] == name: # merge to this group MergeGroup(g, group) return objs # add a new group Projects.append(group) return objs def PresentDir(): conscript = File('SConscript') fn = conscript.rfile() name = fn.name path = os.path.dirname(fn.abspath) return path PREBUILDING = [] def RegisterPreBuildingAction(act): global PREBUILDING assert callable(act), 'Could only register callable objects. %s received' % repr(act) PREBUILDING.append(act) def PreBuilding(): global PREBUILDING for a in PREBUILDING: a() def GroupLibName(name, env): import osconfig if osconfig.COMPILER == 'armcc': return name + '_rvds' elif osconfig.COMPILER == 'gcc': return name + '_gcc' return name def GroupLibFullName(name, env): return env['LIBPREFIX'] + GroupLibName(name, env) + env['LIBSUFFIX'] def BuildLibInstallAction(target, source, env): ''' lib_name = GetOption('buildlib') for Group in Projects: if Group['name'] == lib_name: lib_name = GroupLibFullName(Group['name'], env) dst_name = os.path.join(Group['path'], lib_name) print ('Copy '+lib_name+' => ' +dst_name) do_copy_file(lib_name, dst_name) break ''' def StartCompile(target, objects): # merge all objects into one list def one_list(l): lst = [] for item in l: if type(item) == type([]): lst += one_list(item) else: lst.append(item) return lst # handle local group def local_group(group, objects): if 'LOCAL_CCFLAGS' in group or 'LOCAL_CPPPATH' in group or 'LOCAL_CPPDEFINES' in group or 'LOCAL_ASFLAGS' in group: CCFLAGS = Env.get('CCFLAGS', '') + group.get('LOCAL_CCFLAGS', '') CPPPATH = Env.get('CPPPATH', ['']) + group.get('LOCAL_CPPPATH', ['']) CPPDEFINES = Env.get('CPPDEFINES', ['']) + group.get('LOCAL_CPPDEFINES', ['']) ASFLAGS = Env.get('ASFLAGS', '') + group.get('LOCAL_ASFLAGS', '') for source in group['src']: objects.append(Env.Object(source, CCFLAGS = CCFLAGS, ASFLAGS = ASFLAGS, CPPPATH = CPPPATH, CPPDEFINES = CPPDEFINES)) return True return False objects = one_list(objects) program = None lib_name = None ''' # check whether special buildlib option lib_name = GetOption('buildlib') ''' if lib_name: objects = [] # remove all of objects # build library with special component for Group in Projects: if Group['name'] == lib_name: lib_name = GroupLibName(Group['name'], Env) if not local_group(Group, objects): objects = Env.Object(Group['src']) program = Env.Library(lib_name, objects) # add library copy action Env.BuildLib(lib_name, program) break else: # remove source files with local flags setting for group in Projects: if 'LOCAL_CCFLAGS' in group or 'LOCAL_CPPPATH' in group or 'LOCAL_CPPDEFINES' in group: for source in group['src']: for obj in objects: if source.abspath == obj.abspath or (len(obj.sources) > 0 and source.abspath == obj.sources[0].abspath): objects.remove(obj) # re-add the source files to the objects for group in Projects: local_group(group, objects) no_exec = GetOption('no_exec') #print("no_exec:%d"% no_exec) #if no_exec != 1 : #Env.Install(out_dir,) program = Env.Program(target, objects) ''' for obj in objects: if hasattr(obj, 'abspath') and len(obj.abspath) > 0 : print(obj.abspath) elif hasattr(obj, 'sources') and len(obj.sources) > 0 and hasattr(obj.sources, 'abspath') and len(obj.sources[0].abspath) > 0 : print(obj.sources[0].abspath) ''' EndBuilding(target, program) def GenTargetProject(program = None): IDE = GetOption('ide') print("\nStarting building project: %s ..." % IDE) if IDE == 'mdk': from keil import MDKProject from keil import MDK4Project from keil import MDK5Project template = os.path.isfile('template.Uv2') if template: MDKProject('project.Uv2', Projects) else: template = os.path.isfile('template.uvproj') if template: MDK4Project('project.uvproj', Projects) else: template = os.path.isfile('template.uvprojx') if template: MDK5Project('project.uvprojx', Projects) else: print ('No template project file found.') if IDE == 'mdk4': from keil import MDK4Project MDK4Project('project.uvproj', Projects) if IDE == 'mdk5': from keil import MDK5Project MDK5Project('project.uvprojx', Projects) if IDE == 'iar': from iar import IARProject IARProject('project.ewp', Projects) if IDE == 'vs': from vs import VSProject VSProject('project.vcproj', Projects, program) if IDE == 'vs2012': from vs2012 import VS2012Project VS2012Project('project.vcxproj', Projects, program) if IDE == 'cb': from codeblocks import CBProject CBProject('project.cbp', Projects, program) if IDE == 'ua': from ua import PrepareUA PrepareUA(Projects, Os_Root, str(Dir('#'))) if IDE == 'vsc': from vsc import GenerateVSCode GenerateVSCode(Env) if IDE == 'cdk': from cdk import CDKProject CDKProject('project.cdkproj', Projects) if IDE == 'ses': from ses import SESProject SESProject(Env) if IDE == 'makefile': from makefile import TargetMakefile TargetMakefile(Env) if IDE == 'eclipse': from eclipse import TargetEclipse TargetEclipse(Env) #print("\nBuilding project finished ! \n") def EndBuilding(target, program = None): import osconfig need_exit = False Env['target'] = program Env['project'] = Projects if hasattr(osconfig, 'BSP_LIBRARY_TYPE'): Env['bsp_lib_type'] = osconfig.BSP_LIBRARY_TYPE if hasattr(osconfig, 'dist_handle'): Env['dist_handle'] = osconfig.dist_handle Env.AddPostAction(target, '\n rm -rf ' + osconfig.RESULT_SUFFIX + ' '+ out_dir + '/*.bin \n') Env.AddPostAction(target, osconfig.POST_ACTION + '\n mv *.bin *.map *.' + osconfig.RESULT_SUFFIX + ' '+ out_dir + '\n') # Add addition clean files Clean(target, 'cconfig.h') Clean(target, 'rtua.py') Clean(target, 'rtua.pyc') if GetOption('ide'): GenTargetProject(program) BSP_ROOT = Dir('#').abspath if GetOption('customized-proj') and program != None: from mkdist import MkDist MkDist(program, BSP_ROOT, Os_Root, Env) need_exit = True if GetOption('customized-proj-strip') and program != None: from mkdist import MkDist_Strip MkDist_Strip(program, BSP_ROOT, Os_Root, Env) need_exit = True ''' if GetOption('cscope'): from cscope import CscopeDatabase CscopeDatabase(Projects) ''' if not GetOption('help') and not GetOption('ide'): if not os.path.exists(osconfig.COMPILER_PATH): print ("Error: the toolchain path (" + osconfig.COMPILER_PATH + ") is not exist, please check 'COMPILER_PATH' in path or osconfig.py.") need_exit = True if need_exit: exit(0) def DeleteGroupFile(objs, name, remove): global Projects for g in Projects: if g['name'] == name: DeleteSrcFile(g['src'], remove) for item in objs: if os.path.abspath(remove) == os.path.abspath(item.rstr()): objs.remove(item) def DeleteSrcFile(src, remove): if not src: return src_bak = src[:] if type(remove) == type('str'): if os.path.isabs(remove): remove = os.path.relpath(remove, PresentDir()) remove = os.path.normpath(remove) for item in src_bak: if type(item) == type('str'): item_str = item else: item_str = item.rstr() if os.path.isabs(item_str): item_str = os.path.relpath(item_str, PresentDir()) item_str = os.path.normpath(item_str) if item_str == remove: src.remove(item) else: for remove_item in remove: remove_str = str(remove_item) if os.path.isabs(remove_str): remove_str = os.path.relpath(remove_str, PresentDir()) remove_str = os.path.normpath(remove_str) for item in src_bak: if type(item) == type('str'): item_str = item else: item_str = item.rstr() if os.path.isabs(item_str): item_str = os.path.relpath(item_str, PresentDir()) item_str = os.path.normpath(item_str) if item_str == remove_str: src.remove(item) def GetVersion(): import SCons.cpp import string osdef = os.path.join(Os_Root, 'include', 'cmdef.h') # parse cmdef.h to get CMCC IOT version prepcessor = PatchedPreProcessor() f = open(osdef, 'r') contents = f.read() f.close() prepcessor.process_contents(contents) def_ns = prepcessor.cpp_namespace version = int(filter(lambda ch: ch in '0123456789.', def_ns['CM_VERSION'])) subversion = int(filter(lambda ch: ch in '0123456789.', def_ns['CM_SUBVERSION'])) if 'CM_REVISION' in def_ns: revision = int(filter(lambda ch: ch in '0123456789.', def_ns['CM_REVISION'])) return '%d.%d.%d' % (version, subversion, revision) return '0.%d.%d' % (version, subversion) def GlobSubDir(sub_dir, ext_name): import os import glob def glob_source(sub_dir, ext_name): list = os.listdir(sub_dir) src = glob.glob(os.path.join(sub_dir, ext_name)) for item in list: full_subdir = os.path.join(sub_dir, item) if os.path.isdir(full_subdir): src += glob_source(full_subdir, ext_name) return src dst = [] src = glob_source(sub_dir, ext_name) for item in src: dst.append(os.path.relpath(item, sub_dir)) return dst def PackageSConscript(package): from package import BuildPackage return BuildPackage(package)

33.689189

147

0.568363

4edc52021b52954b7bcfc77031a13e48cefa7b90

2,165

py

Python

nicos_mlz/maria/devices/powersupply.py

ebadkamil/nicos

0355a970d627aae170c93292f08f95759c97f3b5

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

null

nicos_mlz/maria/devices/powersupply.py

ebadkamil/nicos

0355a970d627aae170c93292f08f95759c97f3b5

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

1

2021-08-18T10:55:42.000Z

nicos_mlz/maria/devices/powersupply.py

ISISComputingGroup/nicos

94cb4d172815919481f8c6ee686f21ebb76f2068

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

null

# -*- coding: utf-8 -*- # ***************************************************************************** # NICOS, the Networked Instrument Control System of the MLZ # Copyright (c) 2009-2021 by the NICOS contributors (see AUTHORS) # # This program is free software; you can redistribute it and/or modify it under # the terms of the GNU General Public License as published by the Free Software # Foundation; either version 2 of the License, or (at your option) any later # version. # # This program is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU General Public License for more # details. # # You should have received a copy of the GNU General Public License along with # this program; if not, write to the Free Software Foundation, Inc., # 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA # # Module authors: # Christian Felder <c.felder@fz-juelich.de> # # ***************************************************************************** from nicos.core import Param from nicos.core.constants import SIMULATION from nicos.devices.tango import AnalogInput class ReadOnlyPowerSupply(AnalogInput): """ A power supply (voltage and current) device. """ parameters = { 'voltage': Param('Actual voltage', unit='V', type=float, settable=False, volatile=True), 'current': Param('Actual current', unit='A', type=float, settable=False, volatile=True), 'setpoint': Param('Setpoint for value (voltage or current) on ' 'initialization depending on mode', type=float, settable=False) } def doInit(self, mode): if mode != SIMULATION and self.setpoint is not None: self._dev.value = self.setpoint def doReadVoltage(self): return self._dev.voltage def doReadCurrent(self): return self._dev.current def doPoll(self, n, maxage): if n % 5 == 0: self._pollParam('voltage', 1) self._pollParam('current', 1)

36.694915

79

0.614781

9352300f463553790acaae304570abb226552c3c

36,272

py

Python

main.py

MeckeDev/PhasmoBot

3defd252140ca68c8d9f099f55c76169bc64812d

[ "MIT" ]

null

main.py

MeckeDev/PhasmoBot

3defd252140ca68c8d9f099f55c76169bc64812d

[ "MIT" ]

null

main.py

MeckeDev/PhasmoBot

3defd252140ca68c8d9f099f55c76169bc64812d

[ "MIT" ]

null

from twitchio.ext import commands from NewChannel import * import time import os import threading from datetime import datetime import requests as r from dotenv import load_dotenv load_dotenv() # The Bot is getting created with the Tokens and IDs bot = commands.Bot( irc_token=os.getenv('TMI_TOKEN'), api_token=os.getenv('CLIENT_ID'), nick=os.getenv('BOT_NICK'), prefix=os.getenv('BOT_PREFIX'), initial_channels=CHANNEL ) # If a Channel was added and is not in the Settings-File atm, he will get added with Default-Parameters and his selected Language with open("settings/channels.json", "r") as f: channels = json.load(f) # Checks ever Channel in the Joining-Queue for ch in CHANNEL: try: x = channels[ch] # Adding the Channel if it doesn't exist except KeyError: channels[ch.lower()] = { "language": ALL_CHANNELS[ch], "evidence" : [], "ghost_name" : "", "responds" : "", "whitelist" : False, "allowed" : [], "ignore" : [], "death_message": "COUNT", "death_count" : 0, "used" : 0 } # Saving the new Settings-File with all new Channels with open("settings/channels.json", "w+") as f: json.dump(channels, f, indent=8) # Just lets me know that the Bot is Ready @bot.event async def event_ready(): print(f'Ready | {bot.nick}') # Gets triggered every time someone sends a Message in a active Channel @bot.event async def event_message(message): # checks if i am the Person who wrote the Message channel = message.channel.name.lower() if message.author.name.lower() in ["mecke_dev"]: time.sleep(1) ch = NewChannel(channel) # Prints the Message in my Console if the Message contains my Name or is a Command if message.content.startswith("$") or " mecke " in message.content.lower() or " mecke_" in message.content.lower(): print(f"{message.author.name} @ {message.channel.name} \n{message.content}\n") # starts checking if the Message was a valid Command await bot.handle_commands(message) # sets the Name of the Ghost, because you will forget it for sure @bot.command(name='channels') async def channels(ctx): if ctx.author.name.lower() == "mecke_dev": all = "Bot-Users: " for x in CHANNEL: all += f"{x}, " await ctx.send(f"{all[:-2]}") # sets the Name of the Ghost, because you will forget it for sure @bot.command(name='name') async def name_ghost(ctx, first_name=None, last_name=None, *, responds=None): channel = ctx.channel.name.lower() ch = NewChannel(channel) # checks if the User is allowed to use the Command if not can_use(ch, ctx.author.name.lower()): pass else: add_point(ctx.author.name.lower(),ctx.message.content, channel) # if the Command was $name reset it will reset the Name if first_name == "reset": # opens the settings for the Channel with open("settings/channels.json", "r") as f: settings = json.load(f) # sets the Name and the Response to an empty String settings[channel]["ghost_name"] = "" settings[channel]["responds"] = "" # saves the settings with open("settings/channels.json", "w+") as f: json.dump(settings, f, indent=8) await ctx.send("Name cleared") # if somebody enters a name save the name elif first_name and last_name: # if the user dont enters a Value for the Response, just set a default Value depending on the channels Language if not responds: responds = ch.bot_text["name"]["not_responding"] # open the settings with open("settings/channels.json", "r") as f: settings = json.load(f) # set the name and the response settings[channel]["ghost_name"] = f"{first_name} {last_name}" settings[channel]["responds"] = f"{responds}" # save the settings with open("settings/channels.json", "w+") as f: json.dump(settings, f, indent=8) # let the user know that the name was set await ctx.send(f'{first_name} {last_name} {ch.bot_text["name"]["responds_to"]} {responds}') # if the user only enters $name just respond the saved Name and Response elif not first_name and not last_name and not responds: await ctx.send(f'{ch.ghost_name} {ch.bot_text["name"]["responds_to"]} {ch.responds}') # if a user enters $join and his prefered Language, mark the Channel to join on next restart @bot.command(name='join', aliases=["enter"]) async def join_me(ctx, language=None, name=None): channel = ctx.channel.name.lower() ch = NewChannel(channel) # if i am the User i am also allowed to add users by myself if ctx.author.name.lower() == "mecke_dev" and name: name = name.lower() else: add_point(ctx.author.name.lower(),ctx.message.content, channel) name = ctx.message.author.name.lower() # open the join-file with open("settings/join.json", "r") as f: joinable = json.load(f) # check if the Channel isn't there by now try: x = joinable["Channels"][name] await ctx.send(f'{name} {ch.bot_text["join"]["already_joined"]}') # if the Channel is new, let the user know that the Bot will join the Channel except KeyError: if language: # check if the user want to set the Bot to english if language in ["en", "eng", "english", "uk", "us"]: joinable["Channels"][name] = "en" await ctx.send(f'{name} i\'m joining your Channel with english Settings') # or if he wants german elif language in ["de", "ger", "deutsch", "german"]: joinable["Channels"][name] = "de" await ctx.send(f'{name} ich joine deinem Channel mit deutschen Einstellungen') # or if he wants spanish elif language in ["sp", "es", "espanol", "spanish"]: joinable["Channels"][name] = "es" await ctx.send(f'{name} Me uniré a tu canal con la configuración español') # save the File again with open("settings/join.json", "w+") as f: json.dump(joinable, f, indent=8) # if the user didnt enter a Language or it wasn't recognized, give him a hint on how to use the Command else: await ctx.send(f'{name} {ch.bot_text["join"]["help"]}') # If someone want the bot to leave his channel he can just enter $leave @bot.command(name='leave', aliases=["part", "exit", "quit"]) async def leave_me(ctx, name=None): channel = ctx.channel.name.lower() ch = NewChannel(channel) # i can let the Bot leave channels whenever i want if ctx.author.name.lower() == "mecke_dev" and name: name = name.lower() else: add_point(ctx.author.name.lower(),ctx.message.content, channel) name = ctx.message.author.name.lower() # open the join-file with open("settings/join.json", "r") as f: joinable = json.load(f) # check if the Bot is active on the channel and leave if it is the Case try: x = joinable["Channels"][name] joinable["Channels"].pop(name) await ctx.send(f'{name}{ch.bot_text["leave"]["left"]}') with open("settings/join.json", "w+") as f: json.dump(joinable, f, indent=8) # if the Bot isn't active on the Channel, let the user know except KeyError: await ctx.send(f'{name}{ch.bot_text["leave"]["not_active"]}') # Prints a list or a Link for the Commands @bot.command(name='commands', aliases=["befehle", "cmd"]) async def commands(ctx): channel = ctx.channel.name.lower() ch = NewChannel(channel) # check if the user is allowed to use the Command if not can_use(ch, ctx.author.name.lower()): pass else: # Dev, please fix #################################################################################### await ctx.send(f"") # responds a quick explenation for the Game @bot.command(name='game', aliases=["phasmophobia"]) async def whats_game(ctx): channel = ctx.channel.name.lower() ch = NewChannel(channel) # checks if the user is allowed to use the Command if not can_use(ch, ctx.author.name.lower()): pass else: add_point(ctx.author.name.lower(),ctx.message.content, channel) await ctx.send(ch.bot_text["game"]["description"]) # gives a quick introducion to the user @bot.command(name='intro', aliases=["phasmobot"]) async def introduce(ctx): channel = ctx.channel.name.lower() ch = NewChannel(channel) # checks if a user is allowed to use the Command if not can_use(ch, ctx.author.name.lower()): pass else: add_point(ctx.author.name.lower(),ctx.message.content, channel) # responds depending on the Channel-Language await ctx.send(ch.bot_text["bot"]["intro"]) # Command to change the Language # only Channel-owners can use this Command @bot.command(name='language', aliases=["lang", "sprache", "idioma", "lingua"]) async def language(ctx, lang=None): channel = ctx.channel.name.lower() ch = NewChannel(channel) # checks if the owner of the channel is using the command if can_use(ch, ctx.author.name.lower()) and lang: language = "" # check if the user want to set the Bot to english if language in ["en", "eng", "english", "uk", "us"]: lang = "en" language = "English" # or if he wants german elif language in ["de", "ger", "deutsch", "german"]: lang = "de" language = "Deutsch" # or if he wants spanish elif language in ["sp", "es", "espanol", "spanish"]: lang = "es" language = "Espanol" # opens the channel-settings with open("settings/channels.json", "r") as f: settings = json.load(f) # sets everything back to the default-values settings[channel]["language"] = lang # saves the new settings with open("settings/channels.json", "w+") as f: json.dump(settings, f, indent=8) await ctx.send(f"set Language: {language}") else: # opens the channel-settings with open("settings/channels.json", "r") as f: settings = json.load(f) if settings[channel]["language"] == "en": language = "English" if settings[channel]["language"] == "de": language = "Deutsch" if settings[channel]["language"] == "es": language = "Espanol" await ctx.send(f"Channel-Language: {language}") # Command to enable or disable the Whitelist # only Channel-owners can use this Command @bot.command(name='whitelist', aliases=["wl", "white", "black", "blacklist"]) async def whitelist(ctx, val): channel = ctx.channel.name.lower() ch = NewChannel(channel) # checks if the owner of the channel is using the command if ctx.author.name.lower() == ctx.channel.name.lower() or ctx.author.is_mod: add_point(ctx.author.name.lower(),ctx.message.content, channel) if val in ["on", "start", "+", "1", "y", "yes", "j", "ja"]: # opens the channel-settings with open("settings/channels.json", "r") as f: settings = json.load(f) # sets everything back to the default-values settings[channel]["whitelist"] = True # saves the new settings with open("settings/channels.json", "w+") as f: json.dump(settings, f, indent=8) await ctx.send(ch.bot_text["whitelist"]["on"]) elif val in ["off", "stop", "-", "0", "n", "no", "nein"]: # opens the channel-settings with open("settings/channels.json", "r") as f: settings = json.load(f) # sets everything back to the default-values settings[channel]["whitelist"] = False # saves the new settings with open("settings/channels.json", "w+") as f: json.dump(settings, f, indent=8) await ctx.send(ch.bot_text["whitelist"]["off"]) elif val in ["show"]: await ctx.send(f"Whitlisted: {ch.allowed}") else: await ctx.send(f'{ctx.author.name} {ch.bot_text["whitelist"]["help"]}') # Command to add People to the Allow-List, users on this list are allowed to use commands on the channel # if the whitelist is active and they are not on the Allow-list # only Channel-owners can use this Command @bot.command(name='allow', aliases=["permit", "erlaube"]) async def allow(ctx, val, *, names=None): channel = ctx.channel.name.lower() ch = NewChannel(channel) # checks if the owner of the channel is using the command if ctx.author.name.lower() == ctx.channel.name.lower() or ctx.author.is_mod: add_point(ctx.author.name.lower(),ctx.message.content, channel) failed = [] # opens the channel-settings with open("settings/channels.json", "r") as f: settings = json.load(f) # if the command starts with $allow + # users get added to the allow-list if val == "+": if names: # adds every given user to the allow list names = names.split(" ") for name in names: # adds a user to the allow list try: ch.allowed.append(name.lower()) time.sleep(1) await ctx.send(f'{name} {ch.bot_text["allow"]["add"]}') # collects usernames where errors occured except: failed.append(name.lower()) # if the command starts with $allow - # users get removed from the allow-list elif val == "-": names = names.split(" ") for name in names: # removes every given user from the allow list try: ch.allowed.remove(name.lower()) time.sleep(1) await ctx.send(f'{name} {ch.bot_text["allow"]["remove"]}') except: # collects usernames where errors occured failed.append(name.lower()) # sets the new allow-list to the active allow-list settings[channel]["allowed"] = ch.allowed # if an error happened if len(failed) > 0: # it will post a list of names where it failed await ctx.send(f'{ch.bot_text["allow"]["failed"]} {failed}') # saves the new list to the settings with open("settings/channels.json", "w+") as f: json.dump(settings, f, indent=8) # if someone is not allowed to allow someone, it will let him know else: await ctx.send(f'{ctx.author.name} {ch.bot_text["allow"]["forbidden"]}') # Command to add People to the Ignore-List, users on this list are not able to use commands on the channel # only Channel-owners can use this Command @bot.command(name='ignore', aliases=["ban", "verbiete"]) async def ignore(ctx, val, *, names=None): channel = ctx.channel.name.lower() ch = NewChannel(channel) # checks if the owner of the channel is using the command if ctx.author.name.lower() == ctx.channel.name.lower() or ctx.author.is_mod: add_point(ctx.author.name.lower(),ctx.message.content, channel) failed = [] # opens the channel-settings with open("settings/channels.json", "r") as f: settings = json.load(f) # if the command starts with $ignore + # users get added to the ignore-list if val == "+": if names: # adds every given user to the ignore list names = names.split(" ") for name in names: # adds a user to the ignore list try: ch.ignore.append(name.lower()) await ctx.send(f'{name} {ch.bot_text["ignore"]["add"]}') # collects usernames where errors occured except: failed.append(name.lower()) # if the command starts with $ignore - # users get removed from the ignore-list elif val == "-": names = names.split(" ") for name in names: # removes every given user from the ignore list try: ch.ignore.remove(name.lower()) await ctx.send(f'{name} {ch.bot_text["ignore"]["remove"]}') except: # collects usernames where errors occured failed.append(name.lower()) # sets the new ignore-list to the active ignore-list settings[channel]["ignore"] = ch.ignore # if an error happened if len(failed) > 0: # it will post a list of names where it failed await ctx.send(f'{ch.bot_text["ignore"]["failed"]} {failed}') # saves the new list to the settings with open("settings/channels.json", "w+") as f: json.dump(settings, f, indent=8) # if someone is not allowed to ban/ignore someone, it will let him know else: await ctx.send(f'{ctx.author.name} {ch.bot_text["ignore"]["forbidden"]}') # This Command is used to check or add evidences to the active ghost @bot.command(name='evidence', aliases=["evi", "e", "beweis", "bew", "hinweis", "hinweise"]) async def evidence(ctx, *, detail=None): channel = ctx.channel.name.lower() ch = NewChannel(channel) # ignores the input if the user is not allowed to use the Command if not can_use(ch, ctx.author.name.lower()): pass else: add_point(ctx.author.name.lower(),ctx.message.content, channel) # if the user only enters $evi it will respond the currently collected evidences and the possible Ghosts if not detail: await ctx.send(f'{ch.bot_text["evidence"]["evi"]} {ch.evidence}{ch.bot_text["evidence"]["possible"]} {ch.ghosts}') # if an evidence was given behind the $evi if detail: # if the user enters $evi reset the evidences and the Ghostname will get reset if detail.lower() in ["reset", "clear", "remove", "erase", "empty"]: # opens the channel-settings with open("settings/channels.json", "r") as f: settings = json.load(f) # sets everything back to the default-values settings[channel]["evidence"] = [] settings[channel]["ghost_name"] = "" settings[channel]["responds"] = "" # saves the new settings with open("settings/channels.json", "w+") as f: json.dump(settings, f, indent=8) # lets the user know that everything worked await ctx.send(ch.bot_text["evidence"]["reset"]) else: # at this point the Bot tries to recognize the given evidence try: # sets the evidences to the german names if ch.language == "de": evidences = { "EMF Level 5" : ["emf", "emf level 5", "level 5"], "Geisterbox" : ["spirit box", "spirit", "box", "talk", "spiritbox", "geisterbox"], "Fingerabdrücke" : ["fingerprints", "finger", "footprints", "prints", "abdruck", "abdrücke", "fingerabdrücke"], "Geisterbuch" : ["ghost writing", "ghostwriting","writing", "book", "write", "wrote", "written", "writer", "ghostwriter", "ghost writer", "buch", "geisterbuch"], "Gefriertemperaturen" : ["frost", "eis", "gefrier", "freezing", "freezing temperatures", "frozen", "cold", "ice", "temps", "temperature", "freeze", "temperatur", "kalt", "kälte", "gefriertemperatur", "temperaturas bajo cero", "temperaturas"], "Geisterorb" : ["orb", "ghost orb", "ghostorb", "orbs", "ghostorbs", "ghost orbs", "kugel", "geisterkugel", "geister kugel"] } elif ch.language == "en": evidences = { "EMF Level 5" : ["emf", "emf level 5", "level 5"], "Spirit Box" : ["spirit box", "spirit", "box", "talk", "spiritbox", "geisterbox"], "Fingerprints" : ["fingerprints", "finger", "footprints", "prints", "abdruck", "abdrücke", "fingerabdrücke"], "Ghost Writing" : ["ghost writing", "ghostwriting","writing", "book", "write", "wrote", "written", "writer", "ghostwriter", "ghost writer", "buch", "geisterbuch"], "Freezing Temperatures" : ["frost", "eis", "gefrier", "freezing", "freezing temperatures", "frozen", "cold", "ice", "temps", "temperature", "freeze", "temperatur", "kalt", "kälte", "gefriertemperatur", "temperaturas bajo cero", "temperaturas"], "Ghost Orb" : ["orb", "ghost orb", "ghostorb", "orbs", "ghostorbs", "ghost orbs", "kugel", "geisterkugel", "geister kugel"] } elif ch.language == "es": evidences = { "EMF Nivel 5" : ["emf", "emf level 5", "level 5", "emf nivel 5", "emf 5", "emf5"], "Spirit Box" : ["spirit box", "spirit", "box", "talk", "spiritbox", "geisterbox"], "Huellas Dactilares" : ["fingerprints", "finger", "footprints", "prints", "abdruck", "abdrücke", "fingerabdrücke", "huellas dactilares", "huellas", "dactilares"], "Escritura Fantasma" : ["ghost writing", "ghostwriting","writing", "book", "write", "wrote", "written", "writer", "ghostwriter", "ghost writer", "buch", "geisterbuch", "escritura fantasma", "escritura", "fantasma"], "Temperaturas bajo cero": ["frost", "eis", "gefrier", "freezing", "freezing temperatures", "frozen", "cold", "ice", "temps", "temperature", "freeze", "temperatur", "kalt", "kälte", "gefriertemperatur", "temperaturas bajo cero", "temperaturas"], "Orbes" : ["orb", "ghost orb", "ghostorb", "orbs", "ghostorbs", "ghost orbs", "kugel", "geisterkugel", "geister kugel", "orbes"] } # sets some default Variables found = False real = False evid = False # the bot tries to understand which evidence should be added for evi in evidences: # if the given evidence is found if detail.lower() in evidences[evi]: # the bot checks if the evidence is already added to the current ghost if evi not in ch.evidence: # opens the settings with open("settings/channels.json", "r") as f: settings = json.load(f) # adds the evidence to the Ghost settings[channel]["evidence"].append(evi) # saves the settings with open("settings/channels.json", "w+") as f: json.dump(settings, f, indent=8) # sets the real name of the evidence like "Freezing Temperatures" if the evidence was not already set real = evi found = True else: # sets the real name of the evidence like "Freezing Temperatures" if the Evidence was already added evid = evi found = True channel = ctx.channel.name.lower() ch = NewChannel(channel) # lets the user know if the evidence was added or not if found and real: await ctx.send(f'{real} {ch.bot_text["evidence"]["added"]} {ch.ghosts}') elif found and evid: await ctx.send(f'{evid} {ch.bot_text["evidence"]["already_added"]} {ch.ghosts}') # gets triggerd if the bot didn't understand the given evidence like: $evi fdfbdsg else: raise NameError("not found") # lets the user know that the evidence didnt got recognized except NameError: await ctx.send(f'{detail} {ch.bot_text["evidence"]["not_known"]}') # prints a short information about me @bot.command(name='creator', aliases=["dev", "author", "entwickler", "developer"]) async def developer(ctx): channel = ctx.channel.name.lower() ch = NewChannel(channel) # checks if the user is allowed to use the Command if not can_use(ch, ctx.author.name.lower()): pass else: add_point(ctx.author.name.lower(),ctx.message.content, channel) await ctx.send(ch.bot_text["dev"]) # prints a link to my Steam-Group for the Bot @bot.command(name='steam') async def steam_link(ctx): channel = ctx.channel.name.lower() ch = NewChannel(channel) if not can_use(ch, ctx.author.name.lower()): pass else: add_point(ctx.author.name.lower(),ctx.message.content, channel) await ctx.send(ch.bot_text["steam"]) # This is our own Death-Counter @bot.command(name='death', aliases=["dead", "down", "tot", "died"]) async def death(ctx, val=None, *, value=None): channel = ctx.channel.name.lower() ch = NewChannel(channel) # opens the settings with open("settings/channels.json", "r") as f: settings = json.load(f) try: value = int(value) is_int = True except: is_int = False if val: if val == "+" and not value: # increases the Counter by 1 settings[channel]["death_count"] += 1 elif val == "-" and not value: # decreases the Counter by 1 settings[channel]["death_count"] -= 1 elif val == "set" and "COUNT" in str(value) and is_owner(ctx): # sets the Death-Message to the given String and replacing the COUNT with the Number everytim e it gets called settings[channel]["death_message"] = value elif val == "set" and is_int and is_owner(ctx): # sets the Counter to the given Number settings[channel]["death_count"] = value # saves the settings with open("settings/channels.json", "w+") as f: json.dump(settings, f, indent=8) # opens the settings with open("settings/channels.json", "r") as f: settings = json.load(f) await ctx.send(settings[channel]["death_message"].replace("COUNT", str(settings[channel]["death_count"]))) # This Command is user to post a specific information about a specific Ghost @bot.command(name='item', aliases=["tool"]) async def item(ctx, *, item): channel = ctx.channel.name.lower() ch = NewChannel(channel) tool = "" # ignores the Command if the user isn't allowed to use it if not can_use(ch, ctx.author.name.lower()): pass else: add_point(ctx.author.name.lower(),ctx.message.content, channel) # the bot tries to understand what ghost was meant try: tool = ch.texts["Items"][item.title()] await ctx.send(f'{tool["Description"][:-1]}: {str(tool["Price"])}$') time.sleep(1) # if the bot doesn't find the specific ghost, he will let the user know except: await ctx.send(f'{tool} {ch.bot_text["tool"]["unknown"]}') # This Command is user to post a specific information about a specific Ghost @bot.command(name='ghost', aliases=["g", "geist", "ghosts", "geister"]) async def ghost(ctx, ghostname=None, detail=None): channel = ctx.channel.name.lower() ch = NewChannel(channel) # ignores the Command if the user isn't allowed to use it if not can_use(ch, ctx.author.name.lower()): pass else: add_point(ctx.author.name.lower(),ctx.message.content, channel) if ghostname: if ch.language == "de": ghosts = { "Spirit" : ["spirit", "sprit", "spirt"], "Gespenst" : ["gespenst", "geist", "wraith", "ghost"], "Phantom" : ["phantom", "fantom", "fanthom", "phanthom"], "Poltergeist" : ["poltergeist"], "Banshee" : ["banshee", "banshe", "banschi", "bansche"], "Dschinn" : ["dschin", "dschinn", "djinn", "jinn", "jin", "dshin", "dshinn"], "Mare" : ["mare", "nightmare", "mär"], "Revenant" : ["revenant", "rev", "ravenant", "ravenent"], "Shade" : ["shade", "schade", "shadow", "schatten", "shede"], "Dämon" : ["demon", "dämon", "dimon"], "Yurei" : ["yurei", "jurei", "jurai", "yurai"], "Oni" : ["oni"] } elif ch.language == "en": ghosts = { "Spirit" : ["spirit", "sprit", "spirt"], "Wraith" : ["gespenst", "geist", "wraith", "ghost"], "Phantom" : ["phantom", "fantom", "fanthom", "phanthom"], "Poltergeist" : ["poltergeist"], "Banshee" : ["banshee", "banshe", "banschi", "bansche"], "Jinn" : ["dschin", "dschinn", "djinn", "jinn", "jin", "dshin", "dshinn"], "Mare" : ["mare", "nightmare", "mär"], "Revenant" : ["revenant", "rev", "ravenant", "ravenent"], "Shade" : ["shade", "schade", "shadow", "schatten", "shede"], "Demon" : ["demon", "dämon", "dimon"], "Yurei" : ["yurei", "jurei", "jurai", "yurai"], "Oni" : ["oni"] } elif ch.language == "es": ghosts = { "Espíritu" : ["spirit", "sprit", "spirt", "espíritu"], "Espectro" : ["gespenst", "geist", "wraith", "ghost", "espectro"], "Ente" : ["phantom", "fantom", "fanthom", "phanthom", "ente"], "Poltergeist" : ["poltergeist"], "Banshee" : ["banshee", "banshe", "banschi", "bansche"], "Jinn" : ["dschin", "dschinn", "djinn", "jinn", "jin", "dshin", "dshinn"], "Pesadilla" : ["mare", "nightmare", "mär", "pesadilla"], "Revenant" : ["revenant", "rev", "ravenant", "ravenent"], "Sombra" : ["shade", "schade", "shadow", "schatten", "shede", "sombra"], "Demonio" : ["demon", "dämon", "dimon", "demonio"], "Yurei" : ["yurei", "jurei", "jurai", "yurai"], "Oni" : ["oni"] } for ghost in ghosts: if ghostname.lower() in ghosts[ghost]: ghostname = ghost # if the user only enters $g ghostname the bot responds with the description of the Ghost if not detail: # the bot tries to understand what ghost was meant try: ghost = ch.texts["Ghosts"][ghostname.title()] await ctx.send(f'{ghost["Description"]}') time.sleep(1) # if the bot doesn't find the specific ghost, he will let the user know except: await ctx.send(f'{ghostname} {ch.bot_text["ghost"]["unknown_ghost"]}') # if the user enters a specific detail about the Ghost else: # the bot checks if he can find the given detail try: # tries to recognize the given detail if detail.lower() in ["evidence", "evidences", "evi", "e", "beweise", "hinweis", "beweis", "hinweise"]: ghost = ch.texts["Ghosts"][ghostname.title()] await ctx.send(f'{ghost["Evidence"][0]}, {ghost["Evidence"][1]}, {ghost["Evidence"][2]}') if detail.lower() in ["strength", "power", "stärke", "macht", "ability"]: ghost = ch.texts["Ghosts"][ghostname.title()] await ctx.send(f'{ghost["Strength"]}') if detail.lower() in ["weak", "weakness", "weaknesses", "schwäche", "schwach"]: ghost = ch.texts["Ghosts"][ghostname.title()] await ctx.send(f'{ghost["Weaknesses"]}') # if the bot doesn't understand the given detail, he will let the user know except: await ctx.send(f'{detail} {ch.bot_text["ghost"]["unknown_detail"]}') # Function to check for the Channelowner def is_owner(ctx): if ctx.author.name.lower() == ctx.channel.name.lower() or ctx.author.name.lower() == "mecke_dev": return True else: return False # this is a check if a given user is allowed to use a command on the specific channel def can_use(ch, name): # checks if the user is on the ignore-list if name in ch.ignore: return False # checks if the whitelist is active and if so, checks if the user is allowed to use the Command if ch.whitelist and name in ch.allowed: return True # checks if the whitlist isn't active and if the user is not on the ignore-list if not ch.whitelist and name not in ch.ignore: return True # not implemented, but to check if a user is using a bad word def check_word(text): # opens the badwords-list with open("hidden/bad_words.txt", "r") as f: bad_words = f.readlines() # checks every word in the meesage if it is on the badword-list for word in text.lower.split(): if word in bad_words: return False else: return True def add_point(user, message, channel): now = datetime.now() time = now.strftime("%d/%m/%Y %H:%M:%S") # opens the point-list with open("settings/points.json", "r") as f: points = json.load(f) try: # adds a point to the user points[user] += 1 except KeyError: points[user] = 1 # saves the new point-list with open("settings/points.json", "w+") as f: json.dump(points, f, indent=8) # opens the userfile filename = f"users/{user}.txt" if os.path.exists(filename): userlines = open(filename, "a+") userlines.write(f"{message} ||| {time} ||| {channel}\n") userlines.close() else: f = open(filename, "w") f.write(f"{message} ||| {time} ||| {channel}\n") f.close() # this starts the bot bot.run()

37.432405

272

0.542705

abb5e9c45322e57620ead1f04feb33447317ea2c

3,080

py

Python

__init__.py

katlabs/respeaker-array-v2-skill

ab72eddf032597288f6119c5e926dc23e020cf12

[ "Apache-2.0" ]

1

2021-12-21T16:11:51.000Z

__init__.py

katlabs/respeaker-array-v2-skill

ab72eddf032597288f6119c5e926dc23e020cf12

[ "Apache-2.0" ]

null

__init__.py

katlabs/respeaker-array-v2-skill

ab72eddf032597288f6119c5e926dc23e020cf12

[ "Apache-2.0" ]

null

# Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # All credits go to domcross (Github https://github.com/domcross) import time from mycroft.messagebus.message import Message from mycroft.skills.core import MycroftSkill, intent_handler from adapt.intent import IntentBuilder from mycroft.util.log import LOG from mycroft import intent_file_handler from pixel_ring import pixel_ring class ReSpeaker_Array_v2(MycroftSkill): def __init__(self): super(ReSpeaker_Array_v2, self).__init__(name="ReSpeaker_Array_v2") def initialize(self): self.log.info("Pixel Ring: Initializing") #self.power = LED(5) #self.power.on() pixel_ring.set_brightness(10) pixel_ring.change_pattern('echo') pixel_ring.wakeup() self.enable() def enable(self): self.log.info("Pixel Ring: Enabling") self.add_event('recognizer_loop:wakeword', self.handle_listener_wakeup) self.add_event('recognizer_loop:record_end', self.handle_listener_off) self.add_event('mycroft.skill.handler.start', self.handle_listener_think) self.add_event('mycroft.skill.handler.complete', self.handle_listener_off) self.add_event('recognizer_loop:audio_output_start', self.handler_listener_speak) self.add_event('recognizer_loop:audio_output_end', self.handle_listener_off) pixel_ring.off() def disable(self): self.log.info("Pixel Ring: Disabling") self.remove_event('recognizer_loop:wakeup') self.remove_event('recognizer_loop:record_end') self.remove_event('recognizer_loop:audio_output_start') self.remove_event('recognizer_loop:audio_output_end') self.remove_event('mycroft.skill.handler.start') self.remove_event('mycroft.skill.handler.complete') def shutdown(self): self.log.info("Pixel Ring: Shutdown") pixel_ring.off() self.power.off() def handle_listener_wakeup(self, message): self.log.info("Pixel Ring: Wakeup") pixel_ring.listen() def handle_listener_off(self, message): self.log.info("Pixel Ring: Off") pixel_ring.off() def handle_listener_think(self, message): self.log.info("Pixel Ring: Think") pixel_ring.think() def handler_listener_speak(self, message): self.log.info("Pixel Ring: Speak") pixel_ring.speak() @intent_handler(IntentBuilder("").require("EnablePixelRing")) def handle_enable_pixel_ring_intent(self, message): self.enable() self.speak_dialog("EnablePixelRing") @intent_handler(IntentBuilder("").require("DisablePixelRing")) def handle_disable_pixel_ring_intent(self, message): self.disable() self.speak_dialog("DisablePixelRing") def create_skill(): return ReSpeaker_Array_v2()

30.49505

74

0.771753

ddc643cf13cd7dab4e146464c4fe79bc5a61b47f

175

py

Python

dgp/genera/__init__.py

dataspot/dgp

553a255a4884b935cf2efecdc761050232f0f066

[ "MIT" ]

1

2019-07-17T11:34:27.000Z

dgp/genera/__init__.py

datahq/dgp

f39592ce20ba67b73b08188f14585b6eb3d43f96

[ "MIT" ]

2

2019-04-30T12:32:32.000Z

2019-04-30T12:35:26.000Z

dgp/genera/__init__.py

dataspot/dgp

553a255a4884b935cf2efecdc761050232f0f066

[ "MIT" ]

null

from .load import LoaderDGP, PostLoaderDGP from .transform import TransformDGP from .enrich import EnricherDGP from .publish import PublisherDGP from .simple import SimpleDGP

29.166667

42

0.845714

2f5c489c7f67ce9b468e79e13bb9a65e3b528043

3,080

py

Python

mowl/examples/CatEmbeddings/get_splits.py

bio-ontology-research-group/OntoML

4cdc17dc7ee26464db96c67838c3e77dba5318f9

[ "BSD-3-Clause" ]

null

mowl/examples/CatEmbeddings/get_splits.py

bio-ontology-research-group/OntoML

4cdc17dc7ee26464db96c67838c3e77dba5318f9

[ "BSD-3-Clause" ]

null

mowl/examples/CatEmbeddings/get_splits.py

bio-ontology-research-group/OntoML

4cdc17dc7ee26464db96c67838c3e77dba5318f9

[ "BSD-3-Clause" ]

null

import random import pickle as pkl import click as ck import logging logging.basicConfig(level = logging.INFO) def get_splits(train_edges_file, closure_edges_file): ontology_edges = [] with open(train_edges_file, "r") as f: for line in f: line = line.rstrip("\n").split("\t") ontology_edges.append((line[0], line[1])) entities = list(set([c for c,_ in ontology_edges]) | set([d for c, d in ontology_edges])) for e in entities: ontology_edges.append((e,e)) must_be_entities = set(entities[:]) closure_edges = [] with open(closure_edges_file, "r") as f: for line in f: line = line.rstrip("\n").split("\t") c, d = line[0], line[1] if c in must_be_entities and d in must_be_entities: closure_edges.append((line[0], line[1])) logging.info("Generating training negatives of type 1") train_negatives_1 = list() with ck.progressbar(ontology_edges) as bar: for c, d in bar: train_negatives_1.append((d,c)) closure_negatives_1 = list() logging.info("Generating testing negatives of type 1") with ck.progressbar(closure_edges) as bar: for c, d in bar: closure_negatives_1.append((d,c)) train_negatives_2 = list() forbidden_set = set(ontology_edges) | set(closure_edges) | set(train_negatives_1) | set(closure_negatives_1) logging.info("Generating training negatives of type 2") with ck.progressbar(ontology_edges) as bar: for c, d in bar: found = False while not found: d_ = random.choice(entities) if d_ in must_be_entities: if not (c,d_) in forbidden_set: train_negatives_2.append((c, d_)) found = True closure_negatives_2 = list() forbidden_set |= set(train_negatives_2) logging.info("Generating testing negatives of type 2") with ck.progressbar(entities) as bar: for c in bar: for d in entities: if not (c, d) in forbidden_set: closure_negatives_2.append((c, d)) assert len(ontology_edges) == len(train_negatives_1) assert len(ontology_edges) == len(train_negatives_2) train_set = [(e,1) for e in ontology_edges] train_set += [(e,0) for e in train_negatives_1] train_set += [(e,0) for e in train_negatives_2] test_set = [(e,1) for e in closure_edges] test_set += [(e,0) for e in closure_negatives_1] test_set += [(e,0) for e in closure_negatives_2] with open(root + "train_data.pkl", "wb") as f: pkl.dump(train_set, f) with open(root + "test_data.pkl", "wb") as f: pkl.dump(test_set, f) if __name__ == "__main__": root = "data/go/" train_edges_file = root + "train_pos_data.tsv" closure_edges_file = root + "only_closure_pos_data.tsv" get_splits(train_edges_file, closure_edges_file)

30.8

112

0.603247

b62cc8b86f6e45e89b1aa26fa31bf051a678492d

1,030

py

Python

dataset/roadsign_voc/download_roadsign_voc.py

linglanfeng/PaddleDetection

487e6e60b2cfe0b411bba64622ca7f8fe467f3d2

[ "Apache-2.0" ]

1

2021-03-04T06:10:14.000Z

dataset/roadsign_voc/download_roadsign_voc.py

rqjtsq/PaddleDetection

b457850659c43fbd4a26c4fc4b70a3709b9952d4

[ "Apache-2.0" ]

null

dataset/roadsign_voc/download_roadsign_voc.py

rqjtsq/PaddleDetection

b457850659c43fbd4a26c4fc4b70a3709b9952d4

[ "Apache-2.0" ]

null

# Copyright (c) 2019 PaddlePaddle 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. import sys import os.path as osp import logging # add python path of PadleDetection to sys.path parent_path = osp.abspath(osp.join(__file__, *(['..'] * 3))) if parent_path not in sys.path: sys.path.append(parent_path) from ppdet.utils.download import download_dataset logging.basicConfig(level=logging.INFO) download_path = osp.split(osp.realpath(sys.argv[0]))[0] download_dataset(download_path, 'roadsign_voc')

35.517241

74

0.766019

e5ceb6098e918402f0814bb7763e23db811fc8a5

4,181

py

Python

examples/polyfitRidgeLasso.py

Duane321/pyprobml

6d0ba29f22dc7fec9dfc73788bc5520e97663bdb

[ "MIT" ]

null

examples/polyfitRidgeLasso.py

Duane321/pyprobml

6d0ba29f22dc7fec9dfc73788bc5520e97663bdb

[ "MIT" ]

null

examples/polyfitRidgeLasso.py

Duane321/pyprobml

6d0ba29f22dc7fec9dfc73788bc5520e97663bdb

[ "MIT" ]

null

""" Ridge and lasso regression: Visualize effect of changing lambda on degree 14 polynomial. This is a simplified version of linregPolyVsRegDemo.m These are the steps: - Generate the data - Create a preprocessor pipeline that applies a degree 14 polynomial and rescales values to be within [-1, 1] (no hypers to CV) - Create a pipeline with the preprocessor and a ridge estimator - Create a pipeline with the preprocessor and a lasso estimator - Create the grid where we show coefficients decrease as regularizers increase (for both ridge and lasso) - Plot fitted values vs y values for ridge and lasso (with standard errors) - For increasing log values of lambda, plot the training and test error for ridge regression. """ import numpy as np import pandas as pd import matplotlib.pyplot as plt from polyDataMake import polyDataMake from sklearn.pipeline import Pipeline from sklearn.preprocessing import MinMaxScaler, PolynomialFeatures from sklearn.linear_model import Ridge, Lasso from utils.util import save_fig deg = 14 # Generate data and split into in and out of sample xtrain, ytrain, xtest, ytestNoisefree, ytest, sigma2 = polyDataMake(sampling='thibaux') preprocess = Pipeline([('Poly', PolynomialFeatures(degree=deg, include_bias=False)), ('MinMax', MinMaxScaler((-1, 1)))]) ridge_pipe = Pipeline([('PreProcess', preprocess), ('Estimator', Ridge())]) lasso_pipe = Pipeline([('PreProcess', preprocess), ('Estimator', Lasso())]) ridge_lambdas = [0.00001, 0.1] lasso_lambdas = [0.00001, 0.1] coefs_by_lambda = {} def extract_coefs(pipe, lambdas): coefs_by_lambda = {} for lamb in lambdas: pipe.set_params(Estimator__alpha=lamb) pipe.fit(xtrain, ytrain) coefs_by_lambda[lamb] = pipe.named_steps['Estimator'].coef_.reshape(-1) return pd.DataFrame(coefs_by_lambda, index=range(1, deg+1)) lasso_coefs_by_lambda = extract_coefs(lasso_pipe, lasso_lambdas) lasso_coefs_by_lambda.columns = ['lamb1='+str(lm) for lm in lasso_lambdas] ridge_coefs_by_lambda = extract_coefs(ridge_pipe, ridge_lambdas) ridge_coefs_by_lambda.columns = ['lamb2='+str(lm) for lm in ridge_lambdas] coefs = lasso_coefs_by_lambda.join(ridge_coefs_by_lambda) print(coefs) def make_plot_fit(pipe, lamb, num): fig, ax = plt.subplots() pipe.set_params(Estimator__alpha=lamb) pipe.fit(xtrain, ytrain) ypred = pipe.predict(xtest) ax.plot(xtest, ypred, linewidth=3) ax.scatter(xtrain, ytrain) std_err = np.std(ypred - ytest) ax.plot(xtest, ypred - std_err, linewidth=1, linestyle='dotted', color='blue') ax.plot(xtest, ypred + std_err, linewidth=1, linestyle='dotted', color='blue') ax.set_title('L{0} lambda = {1}'.format(str(num), str(lamb)[:6])) ax.set_xlim(0, 20) ax.set_ylim(-10, 20) return fig, ax for i, lamb in enumerate(ridge_lambdas): fig_ridge, ax_ridge = make_plot_fit(ridge_pipe, lamb, 2) save_fig('polyfitRidgeK' + str(i+1) + '.pdf') for i, lamb in enumerate(lasso_lambdas): fig_lasso, ax_lasso = make_plot_fit(lasso_pipe, lamb, 1) save_fig('polyfitRidgeLassoK' + str(i+1) + '.pdf') def mse(ypred, ytest): return np.mean((ypred - ytest)**2) def make_train_test_mse(pipe, log_lambdas): train_mse = [] test_mse = [] for i, llamb in enumerate(log_lambdas): pipe.set_params(Estimator__alpha=np.exp(llamb)) pipe.fit(xtrain, ytrain) ypred_test = pipe.predict(xtest) ypred_train = pipe.predict(xtrain) train_mse.append(mse(ypred_train, ytrain)) test_mse.append(mse(ypred_test, ytest)) fig, ax = plt.subplots() ax.plot(log_lambdas, train_mse, label='train mse', color='blue', marker='s', markersize=10) ax.plot(log_lambdas, test_mse, label='test mse', color='red', marker='x', markersize=10) ax.set_title('Mean Squared Error') ax.set_xlabel('log lambda') ax.set_xlim(-25, 5) ax.legend(loc='upper left') return fig, ax fig, ax = make_train_test_mse(ridge_pipe, np.linspace(-24, 4, 10)) save_fig('polyfitRidgeUcurve.pdf') plt.show()

32.664063

95

0.696723

cb2ded00bf791b004ae66e2786f5ccc69b8e0141

12,252

py

Python

src/python/views/demo_tigergraph_circle/__init__.py

akash-kaul/graph-app-kit

39d22c4770b9bb77ea8c6a378d80eba579fa6aeb

[ "BSD-3-Clause" ]

null

src/python/views/demo_tigergraph_circle/__init__.py

akash-kaul/graph-app-kit

39d22c4770b9bb77ea8c6a378d80eba579fa6aeb

[ "BSD-3-Clause" ]

null

src/python/views/demo_tigergraph_circle/__init__.py

akash-kaul/graph-app-kit

39d22c4770b9bb77ea8c6a378d80eba579fa6aeb

[ "BSD-3-Clause" ]

null

import asyncio import graphistry import pandas as pd import streamlit as st from components import GraphistrySt, URLParam from css import all_css from util import getChild import time from TigerGraph_helper import tg_helper import plotly.express as px import pyTigerGraphBeta as tg import datetime ############################################ # # DASHBOARD SETTINGS # ############################################ # Controls how entrypoint.py picks it up app_id = 'tigergraph_circle' logger = getChild(app_id) urlParams = URLParam(app_id) node_id_col = 'id' src_id_col = 'from_id' dst_id_col = 'to_id' node_label_col = 'Source_Type' edge_label_col = 'Destination_Type' # Setup a structure to hold metrics metrics = {'tigergraph_time': 0, 'graphistry_time': 0, 'node_cnt': 0, 'edge_cnt': 0, 'prop_cnt': 0} conn = tg_helper.connect_to_tigergraph() # Define the name of the view def info(): return { 'id': app_id, 'name': 'TigerGraph: Fraud Filter circle', 'tags': ['demo', 'tigergraph_demo_circle'] } def run(): run_all() ############################################ # # PIPELINE PIECES # ############################################ # Have fun! def custom_css(): all_css() st.markdown( """<style> </style>""", unsafe_allow_html=True) # Given URL params, render left sidebar form and return combined filter settings # https://docs.streamlit.io/en/stable/api.html#display-interactive-widgets def sidebar_area(): num_edges_init = urlParams.get_field('num_matches', 0.5) # MI_List.reverse() idList = [i for i in range(1, 500)] #TigerGraph connection input fields st.sidebar.header("TigerGraph Anti-Fraud") tg_host = st.sidebar.text_input ('TigerGraph Host') tg_username = st.sidebar.text_input ('TigerGraph Username') tg_password = st.sidebar.text_input ('TigerGraph Password') tg_graphname = st.sidebar.text_input ('TigerGraph Graphname') tg_secret = st.sidebar.text_input('TigerGraph Secret') # Connect to TigerGraph if st.sidebar.button("Connect"): try: conn = tg.TigerGraphConnection(host=tg_host, graphname=tg_graphname, username=tg_username, password=tg_password) if tg_secret: conn.getToken(tg_secret) else: conn.getToken(conn.createSecret()) st.sidebar.success("Connnected Successfully") user_id = st.sidebar.selectbox( 'User ID ', idList ) urlParams.set_field('user_id', user_id) return {'user_id': user_id, 'conn': conn} except: st.sidebar.error("Failed to Connect") return None return None user_id = st.sidebar.selectbox( 'User ID ', idList ) urlParams.set_field('user_id', user_id) return {'user_id': user_id, 'conn': conn} def plot_url(nodes_df, edges_df): global metrics logger.info('Starting graphistry plot') tic = time.perf_counter() # edge weight ( ==> score ) # edgeInfluence @ https://hub.graphistry.com/docs/api/1/rest/url/#urloptions g = graphistry \ .edges(edges_df) \ .settings(url_params={'play': 7000, 'dissuadeHubs': True}) \ .bind(edge_weight='amount') \ .bind(source='from_id', destination='to_id') \ .bind(edge_title='amount', edge_label='amount') \ .nodes(nodes_df) \ .bind(node='n') \ .addStyle(bg={'color': 'white'}) \ .encode_point_color("type", categorical_mapping={'User': 'orange', 'Transaction': '#CCC'}, default_mapping='black') \ .encode_edge_color("type", categorical_mapping={'User_Transfer_Transaction': 'black', 'User_Recieve_Transaction_Rev': '#add836'}, default_mapping='black') \ .encode_point_icon('type', categorical_mapping={'User': 'laptop', 'Transaction': 'server'}, default_mapping='question') # .encode_point_size('', ["blue", "yellow", "red"], ,as_continuous=True) # if not (node_label_col is None): # g = g.bind(point_title=node_label_col) # if not (edge_label_col is None): # g = g.bind(edge_title=edge_label_col) url = g.plot(render=False, as_files=True) toc = time.perf_counter() metrics['graphistry_time'] = toc - tic logger.info(f'Graphisty Time: {metrics["graphistry_time"]}') logger.info('Generated viz, got back urL: %s', url) return url # Given filter settings, generate/cache/return dataframes & viz @st.cache(suppress_st_warning=True, allow_output_mutation=True) def run_filters(user_id, conn): global metrics logger.info("Installing Queries") res = conn.gsql( ''' use graph {} ls '''.format(conn.graphname), options=[]) ind = res.index('Queries:') + 1 installTran = True for query in res[ind:]: if 'totalTransaction' in query: installTran = False if installTran: conn.gsql( ''' use graph AntiFraud CREATE QUERY totalTransaction(Vertex<User> Source) FOR GRAPH AntiFraud { start = {Source}; transfer = SELECT tgt FROM start:s -(User_Transfer_Transaction:e) - :tgt; receive = select tgt FROM start:s -(User_Recieve_Transaction:e) -:tgt; PRINT transfer, receive; } Install query totalTransaction ''', options=[]) logger.info('Querying Tigergraph') tic = time.perf_counter() results = conn.runInstalledQuery("circleDetection", {"srcId": user_id}, sizeLimit=1000000000, timeout=120000) results = results[0]['@@circleEdgeTuples'] out = [] from_ids = [] to_ids = [] times = [] amounts = [] types = [] from_types = [] to_types = [] for o in results: for s in o: if {"from_id": s["e"]["from_id"], "to_id": s["e"]["to_id"], "amount": s["amount"], "time": s["ts"], "type": s["e"]["e_type"]} not in out: out.append( {"from_id": s["e"]["from_id"], "to_id": s["e"]["to_id"], "amount": s["amount"], "time": s["ts"], "type": s["e"]["e_type"]}) from_ids.append(s["e"]["from_id"]) to_ids.append(s["e"]["to_id"]) amounts.append(s["amount"]) times.append(s["ts"]) types.append(s["e"]["e_type"]) from_types.append(s['e']['from_type']) to_types.append(s['e']['from_type']) ############# BEGIN edges_df = pd.DataFrame({ 'from_id': from_ids, 'to_id': to_ids, 'amount': amounts, 'time': times, 'type': types }) node_idf = [] typef = [] for i in range(len(from_ids)): if from_ids[i] not in node_idf: node_idf.append(from_ids[i]) typef.append(from_types[i]) if to_ids[i] not in node_idf: node_idf.append(to_ids[i]) typef.append(to_types[i]) nodes_df = pd.DataFrame({ 'n': node_idf, 'type': typef, 'size': 0.1 }) ############### END try: res = nodes_df.values.tolist() toc = time.perf_counter() logger.info(f'Query Execution: {toc - tic:0.02f} seconds') logger.debug('Query Result Count: %s', len(res)) metrics['tigergraph_time'] = toc - tic # Calculate the metrics metrics['node_cnt'] = nodes_df.size metrics['edge_cnt'] = edges_df.size metrics['prop_cnt'] = (nodes_df.size * nodes_df.columns.size) + \ (edges_df.size * edges_df.columns.size) if nodes_df.size > 0: url = plot_url(nodes_df, edges_df) else: url = "" except Exception as e: logger.error('oops in TigerGraph', exc_info=True) raise e logger.info("Finished compute phase") try: pass except RuntimeError as e: if str(e) == "There is no current event loop in thread 'ScriptRunner.scriptThread'.": loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) else: raise e except Exception as e: logger.error('oops in TigerGraph', exc_info=True) raise e return {'nodes_df': nodes_df, 'edges_df': edges_df, 'url': url, 'res': res, 'conn': conn} def main_area(url, nodes, edges, user_id, conn): logger.debug('rendering main area, with url: %s', url) GraphistrySt().render_url(url) dates = [] amounts = [] transfer_type = [] results = None try: results = conn.runInstalledQuery("totalTransaction", params={"Source": user_id})[0] except Exception as e: print(e) # Create bar chart of transactions if results != None: for action in results: for transfer in results[action]: dates.append(datetime.datetime.fromtimestamp(transfer['attributes']['ts'])) amounts.append(transfer['attributes']['amount']) transfer_type.append(action) cols = list(zip(dates, amounts, transfer_type)) cols = sorted(cols, key=lambda x: x[0].day) cols = sorted(cols, key=lambda x: x[0].month) cols = sorted(cols, key=lambda x: x[0].year) df = pd.DataFrame(data=cols, columns=['Date', 'Amount', 'Type']) df['Date'] = pd.to_datetime(df['Date']) map_color = {"receive": "rgba(0,0,255,0.5)", "transfer": "rgba(255,0,0,0.5)"} df['Color'] = df['Type'].map(map_color) df = df.groupby([df['Date'].dt.to_period('M'), 'Type', 'Color']).sum() df = df.reset_index(level=['Type', 'Color']) df.index = df.index.values.astype('datetime64[M]') bar = px.bar(df, x=df.index, y='Amount', labels={'x': 'Date'}, color='Type', color_discrete_map=map_color, text='Amount', title="Transaction Amounts by Month for User {}".format(user_id), height=350, barmode='group') bar.update_xaxes( dtick="M1", tickformat="%b\n%Y") try: for trace in bar.data: trace.name = trace.name.split('=')[1].capitalize() except: for trace in bar.data: trace.name = trace.name.capitalize() st.plotly_chart(bar, use_container_width=True) st.markdown(f'''<small> TigerGraph Load Time (s): {float(metrics['tigergraph_time']):0.2f} | Graphistry Load Time (s): {float(metrics['graphistry_time']):0.2f} | Node Count: {metrics['node_cnt']} | Edge Count: {metrics['edge_cnt']} | Property Count: {metrics['prop_cnt']} </small>''', unsafe_allow_html=True) ############################################ # # PIPELINE FLOW # ############################################ def run_all(): custom_css() try: # Render sidebar and get current settings sidebar_filters = sidebar_area() # Compute filter pipeline (with auto-caching based on filter setting inputs) # Selective mark these as URL params as well if sidebar_filters is None: return filter_pipeline_result = run_filters(**sidebar_filters) # Render main viz area based on computed filter pipeline results and sidebar settings if data is returned if filter_pipeline_result['nodes_df'].size > 0: main_area(filter_pipeline_result['url'], filter_pipeline_result['nodes_df'], filter_pipeline_result['edges_df'], sidebar_filters['user_id'], filter_pipeline_result['conn']) else: # render a message st.write("No data matching the specfiied criteria is found") except Exception as exn: st.write('Error loading dashboard') st.write(exn)

31.496144

124

0.565051

6a4b0836fdc5c825bba132f8fc6d6f86798662ed

156

py

Python

utils/embed.py

lolbot-project/lolbot

9d03ce1cbd506329d3538ec16a62895f8693016a

[ "MIT" ]

3

2018-03-05T17:13:11.000Z

2018-06-05T14:29:59.000Z

utils/embed.py

tilda/lolbot

9d03ce1cbd506329d3538ec16a62895f8693016a

[ "MIT" ]

6

2017-08-27T02:51:54.000Z

2018-01-22T03:09:06.000Z

utils/embed.py

tilda/lolbot

9d03ce1cbd506329d3538ec16a62895f8693016a

[ "MIT" ]

5

2017-10-07T16:41:21.000Z

2018-02-05T22:01:48.000Z

from discord import Embed def get_embed(): """ Helper for discord.Embed. :returns: discord.Embed """ return Embed(colour=0x690E8)

15.6

32

0.628205

585b04ed77e929440c98f61e23f2d2c33b97449b

1,313

py

Python

luhn.py

dakl/luhn

59ab1b3d90bbff9ade23fdb3dd8d28ed49855992

[ "MIT" ]

null

luhn.py

dakl/luhn

59ab1b3d90bbff9ade23fdb3dd8d28ed49855992

[ "MIT" ]

null

luhn.py

dakl/luhn

59ab1b3d90bbff9ade23fdb3dd8d28ed49855992

[ "MIT" ]

null

#!/usr/bin/env python # Luhn's algo doesn't change digit certain transpositions # 90 <-> 09 # 22 <-> 55 # 33 <-> 66 # 44 <-> 77 # based on http://en.wikipedia.org/wiki/Luhn_algorithm#Strengths_and_weaknesses REJECTED_STRS = ["09", "90", "22", "55", "33", "66", "44", "77"] def padToQuad(num): while len(str(num)) < 4: num = "0" + str(num) return str(num) # implementation of standard Luhn mod 10 from # http://en.wikipedia.org/wiki/Luhn_algorithm#Implementation_of_standard_Mod_10 def luhn_checksum(card_number): def digits_of(n): return [int(d) for d in str(n)] digits = digits_of(card_number) odd_digits = digits[-1::-2] even_digits = digits[-2::-2] checksum = 0 checksum += sum(odd_digits) for d in even_digits: checksum += sum(digits_of(d*2)) return checksum % 10 def is_luhn_valid(card_number): return luhn_checksum(card_number) == 0 def calculate_luhn(partial_card_number): check_digit = luhn_checksum(int(partial_card_number) * 10) return check_digit if check_digit == 0 else 10 - check_digit for i in range(1,10000): id = padToQuad(i) if not any(REJECTED_STR in id for REJECTED_STR in REJECTED_STRS): idWithControl = id + "" + str(calculate_luhn(id)) if is_luhn_valid(idWithControl): print idWithControl

29.177778

79

0.680122

f815ab90a178224cae8c879fd6fb8f27d2860ad0

176

py

Python

twitoff/__init__.py

dmhliu/twitoff

4c880eedf0a0925fe9200b1cf2a09d555a372adc

[ "MIT" ]

null

twitoff/__init__.py

dmhliu/twitoff

4c880eedf0a0925fe9200b1cf2a09d555a372adc

[ "MIT" ]

4

2021-06-08T22:12:41.000Z

2022-03-12T00:48:37.000Z

twitoff/__init__.py

dmhliu/twitoff

4c880eedf0a0925fe9200b1cf2a09d555a372adc

[ "MIT" ]

null

#!/ usr/bin/env python # this file indicates we are in a twitoff package folder. from .app import create_app APP = create_app() # create an instance of the app, a global

22

62

0.721591

9ed3dfede9477973cee625968a66f65942dea12a

299

py

Python

Arays/Udemy1/1_two_sum_approach_1.py

sounak95/100_days_of_code

50fbf088ce6ab2137aa216a30e3b3f828b278a22

[ "Apache-2.0" ]

null

Arays/Udemy1/1_two_sum_approach_1.py

sounak95/100_days_of_code

50fbf088ce6ab2137aa216a30e3b3f828b278a22

[ "Apache-2.0" ]

null

Arays/Udemy1/1_two_sum_approach_1.py

sounak95/100_days_of_code

50fbf088ce6ab2137aa216a30e3b3f828b278a22

[ "Apache-2.0" ]

null

def findTwoSum(l1, targetToFind): for i in range(len(l1)): num_to_find= targetToFind-l1[i] for j in range(i+1,len(l1)): if l1[j]==num_to_find: return (i,j) return None l1 = [1, 3, 7, 9, 2] targetToFind = 11 print(findTwoSum(l1, targetToFind))

19.933333

39

0.571906

c6ea37847a6dd4965fd0849c5932beb34887a965

113

py

Python

django_zora_messages/apps.py

devmmaia/django-zora-messages

160f7fe049bfc80990ac439da7691ece6b020f62

[ "MIT" ]

null

django_zora_messages/apps.py

devmmaia/django-zora-messages

160f7fe049bfc80990ac439da7691ece6b020f62

[ "MIT" ]

null

django_zora_messages/apps.py

devmmaia/django-zora-messages

160f7fe049bfc80990ac439da7691ece6b020f62

[ "MIT" ]

null

from django.apps import AppConfig class DjangoZoraMessagesConfig(AppConfig): name = 'django_zora_messages'

18.833333

42

0.80531

e5c90f65cce76b1a8ee8729e18a04fac4b282d2b

547

py

Python

tests/test.py

neotje/hops-recepten

eabf897c2a9fc018479e61a1fcecc37efa57c4c1

[ "MIT" ]

null

tests/test.py

neotje/hops-recepten

eabf897c2a9fc018479e61a1fcecc37efa57c4c1

[ "MIT" ]

1

2021-05-03T09:08:51.000Z

tests/test.py

neotje/hops-recepten

eabf897c2a9fc018479e61a1fcecc37efa57c4c1

[ "MIT" ]

null

from os import name from bson.objectid import ObjectId from hopsrecipes.helpers.exceptions import UserError from hopsrecipes import user from hopsrecipes import recipe from hopsrecipes.helpers import database, documents import sys import logging logging.basicConfig( stream=sys.stdout, level=logging.INFO, format='%(levelname)s:%(name)s:%(funcName)s:[%(lineno)d] %(message)s' ) _LOGGER = logging.getLogger(__name__) database.connect() r = documents.RecipeDoc.objects(id=ObjectId('608c7a141be72d838ee7005d')) _LOGGER.info(len(r))

24.863636

75

0.780622

f3f5ced7514f31284df844d9e8b3a7aa9b8f1aff

496

py

Python

flask_app/controllers/applications.py

MapleLeo/PawFosterFamily

0f0c14f12858a27ded4ec2565e8a82290518b798

[ "MIT" ]

null

flask_app/controllers/applications.py

MapleLeo/PawFosterFamily

0f0c14f12858a27ded4ec2565e8a82290518b798

[ "MIT" ]

null

flask_app/controllers/applications.py

MapleLeo/PawFosterFamily

0f0c14f12858a27ded4ec2565e8a82290518b798

[ "MIT" ]

null

from flask import render_template,redirect,session,request,flash from flask_app import app from flask_app.models.application import Application @app.route('/application/<int:id>/approve', methods=['POST']) def approve_application(id): Application.set_status(id, "APPROVED") return redirect('/shelter/dashboard') @app.route('/application/<int:id>/reject', methods=['POST']) def reject_application(id): Application.set_status(id, "REJECTED") return redirect('/shelter/dashboard')

35.428571

64

0.762097

776e2cc76d81adcd75424c1030de9e49cc2c0856

30,283

py

Python

fhirclient/models/practitioner_tests.py

NematiLab/Streaming-Sepsis-Prediction-System-for-Intensive-Care-Units

fb5ad260fb8d264d85aea9e6c895d1700eea4d11

[ "Apache-2.0" ]

2

2019-05-16T16:41:22.000Z

2021-04-22T22:06:49.000Z

fhirclient/models/practitioner_tests.py

NematiLab/Streaming-Sepsis-Prediction-System-for-Intensive-Care-Units

fb5ad260fb8d264d85aea9e6c895d1700eea4d11

[ "Apache-2.0" ]

null

fhirclient/models/practitioner_tests.py

NematiLab/Streaming-Sepsis-Prediction-System-for-Intensive-Care-Units

fb5ad260fb8d264d85aea9e6c895d1700eea4d11

[ "Apache-2.0" ]

3

2019-03-26T01:39:18.000Z

2020-02-02T19:06:33.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Generated from FHIR 1.0.2.7202 on 2016-06-23. # 2016, SMART Health IT. import os import io import unittest import json from . import practitioner from .fhirdate import FHIRDate class PractitionerTests(unittest.TestCase): def instantiate_from(self, filename): datadir = os.environ.get('FHIR_UNITTEST_DATADIR') or '' with io.open(os.path.join(datadir, filename), 'r', encoding='utf-8') as handle: js = json.load(handle) self.assertEqual("Practitioner", js["resourceType"]) return practitioner.Practitioner(js) def testPractitioner1(self): inst = self.instantiate_from("practitioner-example-f001-evdb.json") self.assertIsNotNone(inst, "Must have instantiated a Practitioner instance") self.implPractitioner1(inst) js = inst.as_json() self.assertEqual("Practitioner", js["resourceType"]) inst2 = practitioner.Practitioner(js) self.implPractitioner1(inst2) def implPractitioner1(self, inst): self.assertEqual(inst.address[0].city, "Den Burg") self.assertEqual(inst.address[0].country, "NLD") self.assertEqual(inst.address[0].line[0], "Galapagosweg 91") self.assertEqual(inst.address[0].postalCode, "9105 PZ") self.assertEqual(inst.address[0].use, "work") self.assertEqual(inst.birthDate.date, FHIRDate("1975-12-07").date) self.assertEqual(inst.birthDate.as_json(), "1975-12-07") self.assertEqual(inst.gender, "male") self.assertEqual(inst.id, "f001") self.assertEqual(inst.identifier[0].system, "urn:oid:2.16.528.1.1007.3.1") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "938273695") self.assertEqual(inst.identifier[1].system, "urn:oid:2.16.840.1.113883.2.4.6.3") self.assertEqual(inst.identifier[1].use, "usual") self.assertEqual(inst.identifier[1].value, "129IDH4OP733") self.assertEqual(inst.name.family[0], "van den broek") self.assertEqual(inst.name.given[0], "Eric") self.assertEqual(inst.name.suffix[0], "MD") self.assertEqual(inst.name.use, "official") self.assertEqual(inst.practitionerRole[0].role.coding[0].code, "01.000") self.assertEqual(inst.practitionerRole[0].role.coding[0].display, "Arts") self.assertEqual(inst.practitionerRole[0].role.coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].role.text, "Care role") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].code, "01.018") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].display, "Ear-, Nose and Throat") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].specialty[0].text, "specialization") self.assertEqual(inst.telecom[0].system, "phone") self.assertEqual(inst.telecom[0].use, "work") self.assertEqual(inst.telecom[0].value, "0205568263") self.assertEqual(inst.telecom[1].system, "email") self.assertEqual(inst.telecom[1].use, "work") self.assertEqual(inst.telecom[1].value, "E.M.vandenbroek@bmc.nl") self.assertEqual(inst.telecom[2].system, "fax") self.assertEqual(inst.telecom[2].use, "work") self.assertEqual(inst.telecom[2].value, "0205664440") self.assertEqual(inst.text.status, "generated") def testPractitioner2(self): inst = self.instantiate_from("practitioner-example-f002-pv.json") self.assertIsNotNone(inst, "Must have instantiated a Practitioner instance") self.implPractitioner2(inst) js = inst.as_json() self.assertEqual("Practitioner", js["resourceType"]) inst2 = practitioner.Practitioner(js) self.implPractitioner2(inst2) def implPractitioner2(self, inst): self.assertEqual(inst.address[0].city, "Den Burg") self.assertEqual(inst.address[0].country, "NLD") self.assertEqual(inst.address[0].line[0], "Galapagosweg 91") self.assertEqual(inst.address[0].postalCode, "9105 PZ") self.assertEqual(inst.address[0].use, "work") self.assertEqual(inst.birthDate.date, FHIRDate("1979-04-29").date) self.assertEqual(inst.birthDate.as_json(), "1979-04-29") self.assertEqual(inst.gender, "male") self.assertEqual(inst.id, "f002") self.assertEqual(inst.identifier[0].system, "urn:oid:2.16.528.1.1007.3.1") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "730291637") self.assertEqual(inst.identifier[1].system, "urn:oid:2.16.840.1.113883.2.4.6.3") self.assertEqual(inst.identifier[1].use, "usual") self.assertEqual(inst.identifier[1].value, "174BIP3JH438") self.assertEqual(inst.name.family[0], "Voigt") self.assertEqual(inst.name.given[0], "Pieter") self.assertEqual(inst.name.suffix[0], "MD") self.assertEqual(inst.name.use, "official") self.assertEqual(inst.practitionerRole[0].role.coding[0].code, "01.000") self.assertEqual(inst.practitionerRole[0].role.coding[0].display, "Arts") self.assertEqual(inst.practitionerRole[0].role.coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].role.text, "Care role") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].code, "01.011") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].display, "Cardiothoracal surgery") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].specialty[0].text, "specialization") self.assertEqual(inst.telecom[0].system, "phone") self.assertEqual(inst.telecom[0].use, "work") self.assertEqual(inst.telecom[0].value, "0205569336") self.assertEqual(inst.telecom[1].system, "email") self.assertEqual(inst.telecom[1].use, "work") self.assertEqual(inst.telecom[1].value, "p.voigt@bmc.nl") self.assertEqual(inst.telecom[2].system, "fax") self.assertEqual(inst.telecom[2].use, "work") self.assertEqual(inst.telecom[2].value, "0205669382") self.assertEqual(inst.text.status, "generated") def testPractitioner3(self): inst = self.instantiate_from("practitioner-example-f003-mv.json") self.assertIsNotNone(inst, "Must have instantiated a Practitioner instance") self.implPractitioner3(inst) js = inst.as_json() self.assertEqual("Practitioner", js["resourceType"]) inst2 = practitioner.Practitioner(js) self.implPractitioner3(inst2) def implPractitioner3(self, inst): self.assertEqual(inst.address[0].city, "Amsterdam") self.assertEqual(inst.address[0].country, "NLD") self.assertEqual(inst.address[0].line[0], "Galapagosweg 91") self.assertEqual(inst.address[0].postalCode, "1105 AZ") self.assertEqual(inst.address[0].use, "work") self.assertEqual(inst.birthDate.date, FHIRDate("1963-07-01").date) self.assertEqual(inst.birthDate.as_json(), "1963-07-01") self.assertEqual(inst.communication[0].coding[0].code, "nl") self.assertEqual(inst.communication[0].coding[0].display, "Dutch") self.assertEqual(inst.communication[0].coding[0].system, "urn:oid:2.16.840.1.113883.6.121") self.assertEqual(inst.gender, "male") self.assertEqual(inst.id, "f003") self.assertEqual(inst.identifier[0].system, "urn:oid:2.16.528.1.1007.3.1") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "846100293") self.assertEqual(inst.identifier[1].system, "urn:oid:2.16.840.1.113883.2.4.6.3") self.assertEqual(inst.identifier[1].use, "usual") self.assertEqual(inst.identifier[1].value, "243HID3RT938") self.assertEqual(inst.name.family[0], "Versteegh") self.assertEqual(inst.name.given[0], "Marc") self.assertEqual(inst.name.suffix[0], "MD") self.assertEqual(inst.name.use, "official") self.assertEqual(inst.practitionerRole[0].role.coding[0].code, "01.000") self.assertEqual(inst.practitionerRole[0].role.coding[0].display, "Arts") self.assertEqual(inst.practitionerRole[0].role.coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].role.text, "Care role") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].code, "01.011") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].display, "Cardiothoracal surgery") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].specialty[0].text, "specialization") self.assertEqual(inst.telecom[0].system, "phone") self.assertEqual(inst.telecom[0].use, "work") self.assertEqual(inst.telecom[0].value, "0205562431") self.assertEqual(inst.telecom[1].system, "email") self.assertEqual(inst.telecom[1].use, "work") self.assertEqual(inst.telecom[1].value, "m.versteegh@bmc.nl") self.assertEqual(inst.telecom[2].system, "fax") self.assertEqual(inst.telecom[2].use, "work") self.assertEqual(inst.telecom[2].value, "0205662948") self.assertEqual(inst.text.status, "generated") def testPractitioner4(self): inst = self.instantiate_from("practitioner-example-f004-rb.json") self.assertIsNotNone(inst, "Must have instantiated a Practitioner instance") self.implPractitioner4(inst) js = inst.as_json() self.assertEqual("Practitioner", js["resourceType"]) inst2 = practitioner.Practitioner(js) self.implPractitioner4(inst2) def implPractitioner4(self, inst): self.assertEqual(inst.address[0].city, "Amsterdam") self.assertEqual(inst.address[0].country, "NLD") self.assertEqual(inst.address[0].line[0], "Galapagosweg 91") self.assertEqual(inst.address[0].postalCode, "1105 AZ") self.assertEqual(inst.address[0].use, "work") self.assertEqual(inst.birthDate.date, FHIRDate("1980-02-04").date) self.assertEqual(inst.birthDate.as_json(), "1980-02-04") self.assertEqual(inst.communication[0].coding[0].code, "nl") self.assertEqual(inst.communication[0].coding[0].display, "Netherlands") self.assertEqual(inst.communication[0].coding[0].system, "urn:oid:2.16.840.1.113883.6.121") self.assertEqual(inst.communication[0].text, "Language") self.assertEqual(inst.gender, "male") self.assertEqual(inst.id, "f004") self.assertEqual(inst.identifier[0].system, "urn:oid:2.16.528.1.1007.3.1") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "118265112") self.assertEqual(inst.identifier[1].system, "urn:oid:2.16.840.1.113883.2.4.6.3") self.assertEqual(inst.identifier[1].use, "usual") self.assertEqual(inst.identifier[1].value, "523ASA1LK927") self.assertEqual(inst.name.family[0], "Briet") self.assertEqual(inst.name.given[0], "Ronald") self.assertEqual(inst.name.suffix[0], "MD") self.assertEqual(inst.name.use, "official") self.assertEqual(inst.practitionerRole[0].role.coding[0].code, "01.000") self.assertEqual(inst.practitionerRole[0].role.coding[0].display, "Arts") self.assertEqual(inst.practitionerRole[0].role.coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].role.text, "Care role") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].code, "01.018") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].display, "Ear-, Nose and Throat") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].specialty[0].text, "specialization") self.assertEqual(inst.telecom[0].system, "phone") self.assertEqual(inst.telecom[0].use, "work") self.assertEqual(inst.telecom[0].value, "0205569273") self.assertEqual(inst.telecom[1].system, "email") self.assertEqual(inst.telecom[1].use, "work") self.assertEqual(inst.telecom[1].value, "r.briet@bmc.nl") self.assertEqual(inst.telecom[2].system, "fax") self.assertEqual(inst.telecom[2].use, "work") self.assertEqual(inst.telecom[2].value, "0205664440") self.assertEqual(inst.text.status, "generated") def testPractitioner5(self): inst = self.instantiate_from("practitioner-example-f005-al.json") self.assertIsNotNone(inst, "Must have instantiated a Practitioner instance") self.implPractitioner5(inst) js = inst.as_json() self.assertEqual("Practitioner", js["resourceType"]) inst2 = practitioner.Practitioner(js) self.implPractitioner5(inst2) def implPractitioner5(self, inst): self.assertEqual(inst.address[0].city, "Amsterdam") self.assertEqual(inst.address[0].country, "NLD") self.assertEqual(inst.address[0].line[0], "Galapagosweg 9") self.assertEqual(inst.address[0].postalCode, "1105 AZ") self.assertEqual(inst.address[0].use, "work") self.assertEqual(inst.birthDate.date, FHIRDate("1959-03-11").date) self.assertEqual(inst.birthDate.as_json(), "1959-03-11") self.assertEqual(inst.communication[0].coding[0].code, "fr") self.assertEqual(inst.communication[0].coding[0].display, "France") self.assertEqual(inst.communication[0].coding[0].system, "urn:oid:2.16.840.1.113883.6.121") self.assertEqual(inst.gender, "female") self.assertEqual(inst.id, "f005") self.assertEqual(inst.identifier[0].system, "urn:oid:2.16.528.1.1007.3.1") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "118265112") self.assertEqual(inst.identifier[1].system, "urn:oid:2.16.840.1.113883.2.4.6.3") self.assertEqual(inst.identifier[1].use, "usual") self.assertEqual(inst.identifier[1].value, "191REW8WE916") self.assertEqual(inst.name.family[0], "Anne") self.assertEqual(inst.name.given[0], "Langeveld") self.assertEqual(inst.name.suffix[0], "MD") self.assertEqual(inst.name.use, "official") self.assertEqual(inst.photo[0].contentType, "image/jpeg") self.assertEqual(inst.practitionerRole[0].role.coding[0].code, "01.000") self.assertEqual(inst.practitionerRole[0].role.coding[0].display, "Arts") self.assertEqual(inst.practitionerRole[0].role.coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].role.text, "Care role") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].code, "01.018") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].display, "Keel- neus- en oorarts") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].specialty[0].text, "specialization") self.assertEqual(inst.telecom[0].system, "phone") self.assertEqual(inst.telecom[0].use, "work") self.assertEqual(inst.telecom[0].value, "0205563847") self.assertEqual(inst.telecom[1].system, "email") self.assertEqual(inst.telecom[1].use, "work") self.assertEqual(inst.telecom[1].value, "a.langeveld@bmc.nl") self.assertEqual(inst.telecom[2].system, "fax") self.assertEqual(inst.telecom[2].use, "work") self.assertEqual(inst.telecom[2].value, "0205668916") self.assertEqual(inst.text.status, "generated") def testPractitioner6(self): inst = self.instantiate_from("practitioner-example-f006-rvdb.json") self.assertIsNotNone(inst, "Must have instantiated a Practitioner instance") self.implPractitioner6(inst) js = inst.as_json() self.assertEqual("Practitioner", js["resourceType"]) inst2 = practitioner.Practitioner(js) self.implPractitioner6(inst2) def implPractitioner6(self, inst): self.assertEqual(inst.address[0].city, "Den Burg") self.assertEqual(inst.address[0].country, "NLD") self.assertEqual(inst.address[0].line[0], "Galapagosweg 91") self.assertEqual(inst.address[0].postalCode, "9105 PZ") self.assertEqual(inst.address[0].use, "work") self.assertEqual(inst.birthDate.date, FHIRDate("1975-12-07").date) self.assertEqual(inst.birthDate.as_json(), "1975-12-07") self.assertEqual(inst.gender, "male") self.assertEqual(inst.id, "f006") self.assertEqual(inst.identifier[0].system, "urn:oid:2.16.528.1.1007.3.1") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "937223645") self.assertEqual(inst.identifier[1].system, "urn:oid:2.16.840.1.113883.2.4.6.3") self.assertEqual(inst.identifier[1].use, "usual") self.assertEqual(inst.identifier[1].value, "134IDY41W988") self.assertEqual(inst.name.family[0], "van den Berk") self.assertEqual(inst.name.given[0], "Rob") self.assertEqual(inst.name.suffix[0], "MD") self.assertEqual(inst.name.use, "official") self.assertEqual(inst.practitionerRole[0].role.coding[0].code, "01.000") self.assertEqual(inst.practitionerRole[0].role.coding[0].display, "Arts") self.assertEqual(inst.practitionerRole[0].role.coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].role.text, "Care role") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].code, "17.000") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].display, "Pharmacist") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].specialty[0].text, "specialization") self.assertEqual(inst.telecom[0].system, "phone") self.assertEqual(inst.telecom[0].use, "work") self.assertEqual(inst.telecom[0].value, "0205569288") self.assertEqual(inst.telecom[1].system, "email") self.assertEqual(inst.telecom[1].use, "work") self.assertEqual(inst.telecom[1].value, "R.A.vandenberk@bmc.nl") self.assertEqual(inst.telecom[2].system, "fax") self.assertEqual(inst.telecom[2].use, "work") self.assertEqual(inst.telecom[2].value, "0205664987") self.assertEqual(inst.text.status, "generated") def testPractitioner7(self): inst = self.instantiate_from("practitioner-example-f007-sh.json") self.assertIsNotNone(inst, "Must have instantiated a Practitioner instance") self.implPractitioner7(inst) js = inst.as_json() self.assertEqual("Practitioner", js["resourceType"]) inst2 = practitioner.Practitioner(js) self.implPractitioner7(inst2) def implPractitioner7(self, inst): self.assertEqual(inst.address[0].city, "Den Burg") self.assertEqual(inst.address[0].country, "NLD") self.assertEqual(inst.address[0].line[0], "Galapagosweg 91") self.assertEqual(inst.address[0].postalCode, "9105 PZ") self.assertEqual(inst.address[0].use, "work") self.assertEqual(inst.birthDate.date, FHIRDate("1971-11-07").date) self.assertEqual(inst.birthDate.as_json(), "1971-11-07") self.assertEqual(inst.gender, "female") self.assertEqual(inst.id, "f007") self.assertEqual(inst.identifier[0].system, "urn:oid:2.16.528.1.1007.3.1") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "874635264") self.assertEqual(inst.identifier[1].system, "urn:oid:2.16.840.1.113883.2.4.6.3") self.assertEqual(inst.identifier[1].use, "usual") self.assertEqual(inst.identifier[1].value, "567IUI51C154") self.assertEqual(inst.name.family[0], "Heps") self.assertEqual(inst.name.given[0], "Simone") self.assertEqual(inst.name.suffix[0], "MD") self.assertEqual(inst.name.use, "official") self.assertEqual(inst.practitionerRole[0].role.coding[0].code, "01.000") self.assertEqual(inst.practitionerRole[0].role.coding[0].display, "Arts") self.assertEqual(inst.practitionerRole[0].role.coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].role.text, "Care role") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].code, "01.015") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].display, "Physician") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].system, "urn:oid:2.16.840.1.113883.2.4.15.111") self.assertEqual(inst.practitionerRole[0].specialty[0].text, "specialization") self.assertEqual(inst.telecom[0].system, "phone") self.assertEqual(inst.telecom[0].use, "work") self.assertEqual(inst.telecom[0].value, "020556936") self.assertEqual(inst.telecom[1].system, "email") self.assertEqual(inst.telecom[1].use, "work") self.assertEqual(inst.telecom[1].value, "S.M.Heps@bmc.nl") self.assertEqual(inst.telecom[2].system, "fax") self.assertEqual(inst.telecom[2].use, "work") self.assertEqual(inst.telecom[2].value, "0205669283") self.assertEqual(inst.text.status, "generated") def testPractitioner8(self): inst = self.instantiate_from("practitioner-example-f201-ab.json") self.assertIsNotNone(inst, "Must have instantiated a Practitioner instance") self.implPractitioner8(inst) js = inst.as_json() self.assertEqual("Practitioner", js["resourceType"]) inst2 = practitioner.Practitioner(js) self.implPractitioner8(inst2) def implPractitioner8(self, inst): self.assertTrue(inst.active) self.assertEqual(inst.address[0].city, "Den helder") self.assertEqual(inst.address[0].country, "NLD") self.assertEqual(inst.address[0].line[0], "Walvisbaai 3") self.assertEqual(inst.address[0].line[1], "C4 - Automatisering") self.assertEqual(inst.address[0].postalCode, "2333ZA") self.assertEqual(inst.address[0].use, "work") self.assertEqual(inst.birthDate.date, FHIRDate("1956-12-24").date) self.assertEqual(inst.birthDate.as_json(), "1956-12-24") self.assertEqual(inst.gender, "male") self.assertEqual(inst.id, "f201") self.assertEqual(inst.identifier[0].system, "urn:oid:2.16.528.1.1007.3.1") self.assertEqual(inst.identifier[0].type.text, "UZI-nummer") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "12345678901") self.assertEqual(inst.name.family[0], "Bronsig") self.assertEqual(inst.name.given[0], "Arend") self.assertEqual(inst.name.prefix[0], "Dr.") self.assertEqual(inst.name.text, "Dokter Bronsig") self.assertEqual(inst.name.use, "official") self.assertEqual(inst.practitionerRole[0].role.coding[0].code, "225304007") self.assertEqual(inst.practitionerRole[0].role.coding[0].display, "Implementation of planned interventions") self.assertEqual(inst.practitionerRole[0].role.coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].code, "310512001") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].display, "Medical oncologist") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.qualification[0].code.coding[0].code, "41672002") self.assertEqual(inst.qualification[0].code.coding[0].display, "Pulmonologist") self.assertEqual(inst.qualification[0].code.coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.telecom[0].system, "phone") self.assertEqual(inst.telecom[0].use, "work") self.assertEqual(inst.telecom[0].value, "+31715269111") self.assertEqual(inst.text.status, "generated") def testPractitioner9(self): inst = self.instantiate_from("practitioner-example-f202-lm.json") self.assertIsNotNone(inst, "Must have instantiated a Practitioner instance") self.implPractitioner9(inst) js = inst.as_json() self.assertEqual("Practitioner", js["resourceType"]) inst2 = practitioner.Practitioner(js) self.implPractitioner9(inst2) def implPractitioner9(self, inst): self.assertTrue(inst.active) self.assertEqual(inst.address[0].city, "Den helder") self.assertEqual(inst.address[0].country, "NLD") self.assertEqual(inst.address[0].line[0], "Walvisbaai 3") self.assertEqual(inst.address[0].line[1], "C4 - Automatisering") self.assertEqual(inst.address[0].postalCode, "2333ZA") self.assertEqual(inst.address[0].use, "work") self.assertEqual(inst.birthDate.date, FHIRDate("1960-06-12").date) self.assertEqual(inst.birthDate.as_json(), "1960-06-12") self.assertEqual(inst.gender, "male") self.assertEqual(inst.id, "f202") self.assertEqual(inst.identifier[0].system, "urn:oid:2.16.528.1.1007.3.1") self.assertEqual(inst.identifier[0].type.text, "UZI-nummer") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "12345678902") self.assertEqual(inst.identifier[1].system, "https://www.bigregister.nl/") self.assertEqual(inst.identifier[1].type.text, "BIG-nummer") self.assertEqual(inst.identifier[1].use, "official") self.assertEqual(inst.identifier[1].value, "12345678902") self.assertEqual(inst.name.family[0], "Maas") self.assertEqual(inst.name.given[0], "Luigi") self.assertEqual(inst.name.prefix[0], "Dr.") self.assertEqual(inst.name.text, "Luigi Maas") self.assertEqual(inst.name.use, "official") self.assertEqual(inst.practitionerRole[0].role.coding[0].code, "33526004") self.assertEqual(inst.practitionerRole[0].role.coding[0].display, "Electronic laboratory reporting") self.assertEqual(inst.practitionerRole[0].role.coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].code, "159285000") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].display, "Medical laboratory technician") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.telecom[0].system, "phone") self.assertEqual(inst.telecom[0].use, "work") self.assertEqual(inst.telecom[0].value, "+31715269111") self.assertEqual(inst.text.status, "generated") def testPractitioner10(self): inst = self.instantiate_from("practitioner-example-f203-jvg.json") self.assertIsNotNone(inst, "Must have instantiated a Practitioner instance") self.implPractitioner10(inst) js = inst.as_json() self.assertEqual("Practitioner", js["resourceType"]) inst2 = practitioner.Practitioner(js) self.implPractitioner10(inst2) def implPractitioner10(self, inst): self.assertTrue(inst.active) self.assertEqual(inst.address[0].city, "Den helder") self.assertEqual(inst.address[0].country, "NLD") self.assertEqual(inst.address[0].line[0], "Walvisbaai 3") self.assertEqual(inst.address[0].postalCode, "2333ZA") self.assertEqual(inst.address[0].use, "work") self.assertEqual(inst.birthDate.date, FHIRDate("1983-04-20").date) self.assertEqual(inst.birthDate.as_json(), "1983-04-20") self.assertEqual(inst.gender, "male") self.assertEqual(inst.id, "f203") self.assertEqual(inst.identifier[0].system, "urn:oid:2.16.528.1.1007.3.1") self.assertEqual(inst.identifier[0].type.text, "UZI-nummer") self.assertEqual(inst.identifier[0].use, "official") self.assertEqual(inst.identifier[0].value, "12345678903") self.assertEqual(inst.identifier[1].system, "https://www.bigregister.nl/") self.assertEqual(inst.identifier[1].type.text, "BIG-nummer") self.assertEqual(inst.identifier[1].use, "official") self.assertEqual(inst.identifier[1].value, "12345678903") self.assertEqual(inst.name.text, "Juri van Gelder") self.assertEqual(inst.name.use, "official") self.assertEqual(inst.practitionerRole[0].role.coding[0].code, "36682004") self.assertEqual(inst.practitionerRole[0].role.coding[0].display, "Physical therapist") self.assertEqual(inst.practitionerRole[0].role.coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].code, "410158009") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].display, "Assess physical therapist service") self.assertEqual(inst.practitionerRole[0].specialty[0].coding[0].system, "http://snomed.info/sct") self.assertEqual(inst.telecom[0].system, "phone") self.assertEqual(inst.telecom[0].use, "work") self.assertEqual(inst.telecom[0].value, "+31715269111") self.assertEqual(inst.text.status, "generated")

59.262231

120

0.673414

0076d4e6b997050a3e4051eacdd78eed81688db1

2,313

py

Python

shortcuts/dump.py

maximejf42/python-shortcuts

addde7732e88d250aaea75c228241ed182fca850

[ "MIT" ]

1

2021-12-07T09:07:10.000Z

shortcuts/dump.py

Harwood/python-shortcuts

397509b10c0ea8024d2eda23da709eb437cf8550

[ "MIT" ]

null

shortcuts/dump.py

Harwood/python-shortcuts

397509b10c0ea8024d2eda23da709eb437cf8550

[ "MIT" ]

null

import plistlib from typing import TYPE_CHECKING, Any, BinaryIO, Dict, Type import toml if TYPE_CHECKING: from shortcuts import Shortcut # noqa from shortcuts.actions.base import BaseAction # noqa class BaseDumper: ''' Base class to dump shortcuts ''' def __init__(self, shortcut: 'Shortcut') -> None: self.shortcut = shortcut def dump(self, file_obj: BinaryIO) -> None: data = self.dumps() if isinstance(data, str): data = data.encode('utf-8') # type: ignore file_obj.write(data) # type: ignore def dumps(self) -> str: raise NotImplementedError() class PListDumper(BaseDumper): ''' PListDumper is a class which dumps shortcuts to binary plist files supported by Apple Shortcuts app ''' def dump(self, file_obj: BinaryIO) -> None: # type: ignore binary = plistlib.dumps( # todo: change dumps to binary and remove this plistlib.loads(self.dumps().encode('utf-8')), # type: ignore fmt=plistlib.FMT_BINARY, ) file_obj.write(binary) def dumps(self) -> str: data = { 'WFWorkflowActions': self.shortcut._get_actions(), 'WFWorkflowImportQuestions': self.shortcut._get_import_questions(), 'WFWorkflowClientRelease': self.shortcut.client_release, 'WFWorkflowClientVersion': self.shortcut.client_version, 'WFWorkflowTypes': ['NCWidget', 'WatchKit'], # todo: change me 'WFWorkflowIcon': self.shortcut._get_icon(), 'WFWorkflowInputContentItemClasses': self.shortcut._get_input_content_item_classes(), } return plistlib.dumps(data).decode('utf-8') class TomlDumper(BaseDumper): '''TomlDumper is a class which dumps shortcuts to toml files''' def dumps(self) -> str: data = { 'action': [self._process_action(a) for a in self.shortcut.actions], } return toml.dumps(data) def _process_action(self, action: Type['BaseAction']) -> Dict[str, Any]: data = { f._attr: action.data[f._attr] # ignore: (mypy/#1465) for f in action.fields if f._attr in action.data # type: ignore } data['type'] = action.keyword return data

30.434211

97

0.621271

9f5b643c52fe7d9966fdad4447d28d4fd4e5d72c

6,764

py

Python

cinder/db/sqlalchemy/migrate_repo/versions/002_quota_class.py

tomasdubec/openstack-cinder

4d0985ea85f4fbdf48b1ed8e35407d2b4a6cf0e4

[ "Apache-2.0" ]

null

cinder/db/sqlalchemy/migrate_repo/versions/002_quota_class.py

tomasdubec/openstack-cinder

4d0985ea85f4fbdf48b1ed8e35407d2b4a6cf0e4

[ "Apache-2.0" ]

null

cinder/db/sqlalchemy/migrate_repo/versions/002_quota_class.py

tomasdubec/openstack-cinder

4d0985ea85f4fbdf48b1ed8e35407d2b4a6cf0e4

[ "Apache-2.0" ]

null

# vim: tabstop=4 shiftwidth=4 softtabstop=4 # Copyright 2012 OpenStack LLC. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from sqlalchemy import Boolean, Column, DateTime from sqlalchemy import MetaData, Integer, String, Table, ForeignKey from cinder.openstack.common import log as logging LOG = logging.getLogger(__name__) def upgrade(migrate_engine): meta = MetaData() meta.bind = migrate_engine # New table quota_classes = Table('quota_classes', meta, Column('created_at', DateTime(timezone=False)), Column('updated_at', DateTime(timezone=False)), Column('deleted_at', DateTime(timezone=False)), Column('deleted', Boolean(create_constraint=True, name=None)), Column('id', Integer(), primary_key=True), Column('class_name', String(length=255, convert_unicode=True, assert_unicode=None, unicode_error=None, _warn_on_bytestring=False), index=True), Column('resource', String(length=255, convert_unicode=True, assert_unicode=None, unicode_error=None, _warn_on_bytestring=False)), Column('hard_limit', Integer(), nullable=True), mysql_engine='InnoDB', mysql_charset='utf8', ) try: quota_classes.create() except Exception: LOG.error(_("Table |%s| not created!"), repr(quota_classes)) raise quota_usages = Table('quota_usages', meta, Column('created_at', DateTime(timezone=False)), Column('updated_at', DateTime(timezone=False)), Column('deleted_at', DateTime(timezone=False)), Column('deleted', Boolean(create_constraint=True, name=None)), Column('id', Integer(), primary_key=True), Column('project_id', String(length=255, convert_unicode=True, assert_unicode=None, unicode_error=None, _warn_on_bytestring=False), index=True), Column('resource', String(length=255, convert_unicode=True, assert_unicode=None, unicode_error=None, _warn_on_bytestring=False)), Column('in_use', Integer(), nullable=False), Column('reserved', Integer(), nullable=False), Column('until_refresh', Integer(), nullable=True), mysql_engine='InnoDB', mysql_charset='utf8', ) try: quota_usages.create() except Exception: LOG.error(_("Table |%s| not created!"), repr(quota_usages)) raise reservations = Table('reservations', meta, Column('created_at', DateTime(timezone=False)), Column('updated_at', DateTime(timezone=False)), Column('deleted_at', DateTime(timezone=False)), Column('deleted', Boolean(create_constraint=True, name=None)), Column('id', Integer(), primary_key=True), Column('uuid', String(length=36, convert_unicode=True, assert_unicode=None, unicode_error=None, _warn_on_bytestring=False), nullable=False), Column('usage_id', Integer(), ForeignKey('quota_usages.id'), nullable=False), Column('project_id', String(length=255, convert_unicode=True, assert_unicode=None, unicode_error=None, _warn_on_bytestring=False), index=True), Column('resource', String(length=255, convert_unicode=True, assert_unicode=None, unicode_error=None, _warn_on_bytestring=False)), Column('delta', Integer(), nullable=False), Column('expire', DateTime(timezone=False)), mysql_engine='InnoDB', mysql_charset='utf8', ) try: reservations.create() except Exception: LOG.error(_("Table |%s| not created!"), repr(reservations)) raise def downgrade(migrate_engine): meta = MetaData() meta.bind = migrate_engine quota_classes = Table('quota_classes', meta, autoload=True) try: quota_classes.drop() except Exception: LOG.error(_("quota_classes table not dropped")) raise quota_usages = Table('quota_usages', meta, autoload=True) try: quota_usages.drop() except Exception: LOG.error(_("quota_usages table not dropped")) raise reservations = Table('reservations', meta, autoload=True) try: reservations.drop() except Exception: LOG.error(_("reservations table not dropped")) raise

43.922078

79

0.474128

6aee8c8e8bc722871c6a58a53f4df9999801b69f

759

py

Python

tf_implementation/segmentation/utils/display.py

arekmula/skull_stripping

d03cef81392f8cd243dc1c6d32ffa897af922eb2

[ "MIT" ]

3

2021-02-23T15:26:40.000Z

2021-08-11T19:36:21.000Z

tf_implementation/segmentation/utils/display.py

arekmula/skull_stripping

d03cef81392f8cd243dc1c6d32ffa897af922eb2

[ "MIT" ]

null

tf_implementation/segmentation/utils/display.py

arekmula/skull_stripping

d03cef81392f8cd243dc1c6d32ffa897af922eb2

[ "MIT" ]

null

import nibabel as nib import numpy as np from matplotlib import pyplot as plt from pathlib import Path from typing import List def show_slices(slices: List[np.ndarray]): """ Shows slices from axis x, y, z :param slices: list of image slices from axis x, y, z :return: """ fig, axes = plt.subplots(1, len(slices)) for i, data_slice in enumerate(slices): axes[i].imshow(data_slice.T, cmap="gray", origin="lower") def print_voxels_size(path: Path): """ Prints size of voxels in millimeters :param path: path to folder containing masks :return: """ for scan_path in path.iterdir(): if scan_path.name.endswith('mask.nii.gz'): print(nib.load(str(scan_path)).header.get_zooms())

24.483871

65

0.661397

37cf228bbbdcea2d071330fea67245854ffe9149

480

py

Python

dashboard/migrations/0050_auto_20180227_1351.py

eric-scott-owens/loopla

1fd5e6e7e9907198ff904111010b362a129d5e39

[ "MIT" ]

null

dashboard/migrations/0050_auto_20180227_1351.py

eric-scott-owens/loopla

1fd5e6e7e9907198ff904111010b362a129d5e39

[ "MIT" ]

6

2020-06-05T22:27:20.000Z

2022-03-24T10:25:50.000Z

dashboard/migrations/0050_auto_20180227_1351.py

eric-scott-owens/loopla

1fd5e6e7e9907198ff904111010b362a129d5e39

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.1 on 2018-02-27 13:51 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('dashboard', '0049_auto_20180206_0225'), ] operations = [ migrations.AlterField( model_name='shortlist', name='short_description', field=models.CharField(blank=True, max_length=200), ), ]

22.857143

63

0.633333

763884547930faceee975a7bd53e0e9e2cda61b4

12,769

py

Python

sdk/python/pulumi_azure_native/network/v20190901/vpn_site.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/network/v20190901/vpn_site.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/network/v20190901/vpn_site.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "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 from ... import _utilities, _tables from . import outputs from ._inputs import * __all__ = ['VpnSite'] class VpnSite(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, address_space: Optional[pulumi.Input[pulumi.InputType['AddressSpaceArgs']]] = None, bgp_properties: Optional[pulumi.Input[pulumi.InputType['BgpSettingsArgs']]] = None, device_properties: Optional[pulumi.Input[pulumi.InputType['DevicePropertiesArgs']]] = None, id: Optional[pulumi.Input[str]] = None, ip_address: Optional[pulumi.Input[str]] = None, is_security_site: Optional[pulumi.Input[bool]] = None, location: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, site_key: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, virtual_wan: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None, vpn_site_links: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnSiteLinkArgs']]]]] = None, vpn_site_name: Optional[pulumi.Input[str]] = None, __props__=None, __name__=None, __opts__=None): """ VpnSite Resource. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[pulumi.InputType['AddressSpaceArgs']] address_space: The AddressSpace that contains an array of IP address ranges. :param pulumi.Input[pulumi.InputType['BgpSettingsArgs']] bgp_properties: The set of bgp properties. :param pulumi.Input[pulumi.InputType['DevicePropertiesArgs']] device_properties: The device properties. :param pulumi.Input[str] id: Resource ID. :param pulumi.Input[str] ip_address: The ip-address for the vpn-site. :param pulumi.Input[bool] is_security_site: IsSecuritySite flag. :param pulumi.Input[str] location: Resource location. :param pulumi.Input[str] resource_group_name: The resource group name of the VpnSite. :param pulumi.Input[str] site_key: The key for vpn-site that can be used for connections. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags. :param pulumi.Input[pulumi.InputType['SubResourceArgs']] virtual_wan: The VirtualWAN to which the vpnSite belongs. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnSiteLinkArgs']]]] vpn_site_links: List of all vpn site links. :param pulumi.Input[str] vpn_site_name: The name of the VpnSite being created or updated. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['address_space'] = address_space __props__['bgp_properties'] = bgp_properties __props__['device_properties'] = device_properties __props__['id'] = id __props__['ip_address'] = ip_address __props__['is_security_site'] = is_security_site __props__['location'] = location if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name __props__['site_key'] = site_key __props__['tags'] = tags __props__['virtual_wan'] = virtual_wan __props__['vpn_site_links'] = vpn_site_links __props__['vpn_site_name'] = vpn_site_name __props__['etag'] = None __props__['name'] = None __props__['provisioning_state'] = None __props__['type'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/v20190901:VpnSite"), pulumi.Alias(type_="azure-native:network:VpnSite"), pulumi.Alias(type_="azure-nextgen:network:VpnSite"), pulumi.Alias(type_="azure-native:network/latest:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/latest:VpnSite"), pulumi.Alias(type_="azure-native:network/v20180401:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20180401:VpnSite"), pulumi.Alias(type_="azure-native:network/v20180601:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20180601:VpnSite"), pulumi.Alias(type_="azure-native:network/v20180701:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20180701:VpnSite"), pulumi.Alias(type_="azure-native:network/v20180801:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20180801:VpnSite"), pulumi.Alias(type_="azure-native:network/v20181001:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20181001:VpnSite"), pulumi.Alias(type_="azure-native:network/v20181101:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20181101:VpnSite"), pulumi.Alias(type_="azure-native:network/v20181201:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20181201:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190201:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190201:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190401:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190401:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190601:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190601:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190701:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190701:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190801:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190801:VpnSite"), pulumi.Alias(type_="azure-native:network/v20191101:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20191101:VpnSite"), pulumi.Alias(type_="azure-native:network/v20191201:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20191201:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200301:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200301:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200401:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200401:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200501:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200501:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200601:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200601:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200701:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200701:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200801:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200801:VpnSite")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(VpnSite, __self__).__init__( 'azure-native:network/v20190901:VpnSite', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'VpnSite': """ Get an existing VpnSite resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["address_space"] = None __props__["bgp_properties"] = None __props__["device_properties"] = None __props__["etag"] = None __props__["ip_address"] = None __props__["is_security_site"] = None __props__["location"] = None __props__["name"] = None __props__["provisioning_state"] = None __props__["site_key"] = None __props__["tags"] = None __props__["type"] = None __props__["virtual_wan"] = None __props__["vpn_site_links"] = None return VpnSite(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="addressSpace") def address_space(self) -> pulumi.Output[Optional['outputs.AddressSpaceResponse']]: """ The AddressSpace that contains an array of IP address ranges. """ return pulumi.get(self, "address_space") @property @pulumi.getter(name="bgpProperties") def bgp_properties(self) -> pulumi.Output[Optional['outputs.BgpSettingsResponse']]: """ The set of bgp properties. """ return pulumi.get(self, "bgp_properties") @property @pulumi.getter(name="deviceProperties") def device_properties(self) -> pulumi.Output[Optional['outputs.DevicePropertiesResponse']]: """ The device properties. """ return pulumi.get(self, "device_properties") @property @pulumi.getter def etag(self) -> pulumi.Output[str]: """ A unique read-only string that changes whenever the resource is updated. """ return pulumi.get(self, "etag") @property @pulumi.getter(name="ipAddress") def ip_address(self) -> pulumi.Output[Optional[str]]: """ The ip-address for the vpn-site. """ return pulumi.get(self, "ip_address") @property @pulumi.getter(name="isSecuritySite") def is_security_site(self) -> pulumi.Output[Optional[bool]]: """ IsSecuritySite flag. """ return pulumi.get(self, "is_security_site") @property @pulumi.getter def location(self) -> pulumi.Output[str]: """ Resource location. """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Resource name. """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[str]: """ The provisioning state of the VPN site resource. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="siteKey") def site_key(self) -> pulumi.Output[Optional[str]]: """ The key for vpn-site that can be used for connections. """ return pulumi.get(self, "site_key") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: """ Resource tags. """ return pulumi.get(self, "tags") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ Resource type. """ return pulumi.get(self, "type") @property @pulumi.getter(name="virtualWan") def virtual_wan(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]: """ The VirtualWAN to which the vpnSite belongs. """ return pulumi.get(self, "virtual_wan") @property @pulumi.getter(name="vpnSiteLinks") def vpn_site_links(self) -> pulumi.Output[Optional[Sequence['outputs.VpnSiteLinkResponse']]]: """ List of all vpn site links. """ return pulumi.get(self, "vpn_site_links") def translate_output_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

51.281124

2,840

0.67006

79bf9cf676ec9db2dc1eeee326dc01ac74b8539d

547

py

Python

jessiql/features/cursor/query.py

kolypto/py-jessiql

724a1eda84e912483bb2d96bb0f74ce6a12098a3

[ "MIT" ]

null

jessiql/features/cursor/query.py

kolypto/py-jessiql

724a1eda84e912483bb2d96bb0f74ce6a12098a3

[ "MIT" ]

null

jessiql/features/cursor/query.py

kolypto/py-jessiql

724a1eda84e912483bb2d96bb0f74ce6a12098a3

[ "MIT" ]

null

from jessiql import Query from .cursors import PageLinks from .skiplimit import CursorLimitOperation class QueryPage(Query): """ A Query that will generate cursors to navigate to the next/previous pages """ SkipLimitOperation = CursorLimitOperation def page_links(self) -> PageLinks: """ Get links to the previous and next page These values are opaque cursors that you can feed to "limit" to get to the corresponding page """ return self.skiplimit_op.get_page_links() # type: ignore[attr-defined]

32.176471

101

0.722121

4ed1fcee3461e2f31ed5a0bea53cf3602a06cb5d

2,405

py

Python

tools/check_lang_placeholders.py

SpaghettiEnjoyer/OverlayPlugin

68c7e939d07330c731b0155a332f864f7d19605e

[ "MIT" ]

223

2019-07-06T04:19:35.000Z

2022-03-29T04:18:01.000Z

tools/check_lang_placeholders.py

SpaghettiEnjoyer/OverlayPlugin

68c7e939d07330c731b0155a332f864f7d19605e

[ "MIT" ]

228

2019-07-16T14:56:21.000Z

2022-03-22T19:45:34.000Z

tools/check_lang_placeholders.py

SpaghettiEnjoyer/OverlayPlugin

68c7e939d07330c731b0155a332f864f7d19605e

[ "MIT" ]

71

2019-07-31T22:14:15.000Z

2022-03-20T08:50:18.000Z

import sys import os.path import re import json BASE_DIR = os.path.join(os.path.dirname(__file__), '..') PROJECTS = ('HtmlRenderer', 'OverlayPlugin', 'OverlayPlugin.Common', 'OverlayPlugin.Core', 'OverlayPlugin.Updater') TRANS_LOG_RE = re.compile(r'Log$LogLevel\.[A-Za-z]+, Resources\.(?P<key>[A-Za-z0-9]+)(?P<params>[^$]*)\)') FORMAT_RE = re.compile(r'string\.Format$Resources\.(?P<key>[A-Za-z0-9]+)(?P<params>[^$]*)\)') PLACEHOLD_RE = re.compile(r'\{[0-9]+\}') if len(sys.argv) < 1: print('Usage: check_lang_placeholders.py') sys.exit(0) with open(os.path.join(BASE_DIR, 'translations.json'), 'r', encoding='utf8') as stream: translations = json.load(stream) for project in PROJECTS: strings = translations.get(project + '/Resources', {}) for sub, dirs, files in os.walk(os.path.join(BASE_DIR, project)): for name in files: if name.endswith('.cs'): fpath = os.path.join(sub, name) with open(fpath, 'r', encoding='utf8') as stream: data = stream.read() matches = [] for m in TRANS_LOG_RE.finditer(data): matches.append((m.group('key'), m.group('params'))) for m in FORMAT_RE.finditer(data): matches.append((m.group('key'), m.group('params'))) for key, params in matches: msg = strings.get(key) if not msg: print('ERROR: Missing key %s in %s' % (key, fpath)) continue placholder_count = len(PLACEHOLD_RE.findall(msg['en'])) param_count = len(params.split(',')) - 1 if placholder_count != param_count: print('ERROR: Found params "%s" for string %s "%s" in %s!' % (params, key, msg['en'], fpath)) for key, trans in strings.items(): en_placeholders = len(PLACEHOLD_RE.findall(trans['en'])) for lang, value in trans.items(): if lang != 'en' and not lang.startswith('#'): lang_placeholders = len(PLACEHOLD_RE.findall(value)) if lang_placeholders != en_placeholders: print('ERROR: Translation %s for %s in language %s has %d placeholders, expected %d!' % (key, project, lang, lang_placeholders, en_placeholders))

39.42623

117

0.556757

f9c7376929eddc46b24208bd068a1a102a84a588

411

py

Python

donkeycar_console/wsgi.py

MehriBacem/donkeycar_console

a9360a81a610d904739ea31d465d958cd603d183

[ "MIT" ]

null

donkeycar_console/wsgi.py

MehriBacem/donkeycar_console

a9360a81a610d904739ea31d465d958cd603d183

[ "MIT" ]

null

donkeycar_console/wsgi.py

MehriBacem/donkeycar_console

a9360a81a610d904739ea31d465d958cd603d183

[ "MIT" ]

null

""" WSGI config for donkeycar_console project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/2.0/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "donkeycar_console.settings") application = get_wsgi_application()

24.176471

78

0.79562

f253b81628e8ded121cbb5284f746c86efc79f18

3,887

py

Python

milking_cowmask/architectures/shake.py

deepneuralmachine/google-research

d2ce2cf0f5c004f8d78bfeddf6e88e88f4840231

[ "Apache-2.0" ]

23,901

2018-10-04T19:48:53.000Z

2022-03-31T21:27:42.000Z

milking_cowmask/architectures/shake.py

deepneuralmachine/google-research

d2ce2cf0f5c004f8d78bfeddf6e88e88f4840231

[ "Apache-2.0" ]

891

2018-11-10T06:16:13.000Z

2022-03-31T10:42:34.000Z

milking_cowmask/architectures/shake.py

deepneuralmachine/google-research

d2ce2cf0f5c004f8d78bfeddf6e88e88f4840231

[ "Apache-2.0" ]

6,047

2018-10-12T06:31:02.000Z

2022-03-31T13:59:28.000Z

# coding=utf-8 # Copyright 2021 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # 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. """Shake-shake and ShakeDrop utility functions.""" import flax.nn import jax import jax.numpy as jnp def shake_shake_train(xa, xb, rng=None): """Shake-shake regularization: training. Shake-shake regularization interpolates between inputs A and B with *different* random uniform (per-sample) interpolation factors for the forward and backward/gradient passes Args: xa: input, branch A xb: input, branch B rng: PRNG key Returns: Mix of input branches """ if rng is None: rng = flax.nn.make_rng() gate_forward_key, gate_backward_key = jax.random.split(rng, num=2) gate_shape = (len(xa), 1, 1, 1) # Draw different interpolation factors (gate) for forward and backward pass gate_forward = jax.random.uniform( gate_forward_key, gate_shape, dtype=jnp.float32, minval=0.0, maxval=1.0) gate_backward = jax.random.uniform( gate_backward_key, gate_shape, dtype=jnp.float32, minval=0.0, maxval=1.0) # Compute interpolated x for forward and backward x_forward = xa * gate_forward + xb * (1.0 - gate_forward) x_backward = xa * gate_backward + xb * (1.0 - gate_backward) # Combine using stop_gradient x = x_backward + jax.lax.stop_gradient(x_forward - x_backward) return x def shake_shake_eval(xa, xb): """Shake-shake regularization: evaluation. Args: xa: input, branch A xb: input, branch B Returns: Mix of input branches """ # Blend between inputs A and B 50%-50%. return (xa + xb) * 0.5 def shake_drop_train(x, mask_prob, alpha_min, alpha_max, beta_min, beta_max, rng=None): """ShakeDrop training pass. See https://arxiv.org/abs/1802.02375 Args: x: input to apply ShakeDrop to mask_prob: mask probability alpha_min: alpha range lower alpha_max: alpha range upper beta_min: beta range lower beta_max: beta range upper rng: PRNG key (if `None`, uses `flax.nn.make_rng`) Returns: """ if rng is None: rng = flax.nn.make_rng() bern_key, alpha_key, beta_key = jax.random.split(rng, num=3) rnd_shape = (len(x), 1, 1, 1) # Bernoulli variable b_l in Eqn 6, https://arxiv.org/abs/1802.02375 mask = jax.random.bernoulli(bern_key, mask_prob, rnd_shape) mask = mask.astype(jnp.float32) alpha_values = jax.random.uniform( alpha_key, rnd_shape, dtype=jnp.float32, minval=alpha_min, maxval=alpha_max) beta_values = jax.random.uniform( beta_key, rnd_shape, dtype=jnp.float32, minval=beta_min, maxval=beta_max) # See Eqn 6 in https://arxiv.org/abs/1802.02375 rand_forward = mask + alpha_values - mask * alpha_values rand_backward = mask + beta_values - mask * beta_values x = x * rand_backward + jax.lax.stop_gradient(x * rand_forward - x * rand_backward) return x def shake_drop_eval(x, mask_prob, alpha_min, alpha_max): """ShakeDrop eval pass. See https://arxiv.org/abs/1802.02375 Args: x: input to apply ShakeDrop to mask_prob: mask probability alpha_min: alpha range lower alpha_max: alpha range upper Returns: """ expected_alpha = (alpha_max + alpha_min) / 2 # See Eqn 6 in https://arxiv.org/abs/1802.02375 x = (mask_prob + expected_alpha - mask_prob * expected_alpha) * x return x

30.606299

79

0.69771

79d7fde3efea55863e4f21c4c348f72b3a691bba

466

py

Python

plotly/validators/violin/box/_fillcolor.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/validators/violin/box/_fillcolor.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/validators/violin/box/_fillcolor.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

import _plotly_utils.basevalidators class FillcolorValidator(_plotly_utils.basevalidators.ColorValidator): def __init__( self, plotly_name='fillcolor', parent_name='violin.box', **kwargs ): super(FillcolorValidator, self).__init__( plotly_name=plotly_name, parent_name=parent_name, edit_type=kwargs.pop('edit_type', 'style'), role=kwargs.pop('role', 'style'), **kwargs )

29.125

73

0.637339

f14e5295c848f74b9cb4d98114040fb169953d05

7,660

py

Python

library/azure_rm_cdnprofile_facts.py

wray/azure_modules

af2d84ffc4a0061f5ab4ed7e621faa0bbdbb2da5

[ "MIT" ]

null

library/azure_rm_cdnprofile_facts.py

wray/azure_modules

af2d84ffc4a0061f5ab4ed7e621faa0bbdbb2da5

[ "MIT" ]

null

library/azure_rm_cdnprofile_facts.py

wray/azure_modules

af2d84ffc4a0061f5ab4ed7e621faa0bbdbb2da5

[ "MIT" ]

null

#!/usr/bin/python # # Copyright (c) 2018 Hai Cao, <t-haicao@microsoft.com>, Yunge Zhu <yungez@microsoft.com> # # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community'} DOCUMENTATION = ''' --- module: azure_rm_cdnprofile_facts version_added: "2.8" short_description: Get Azure CDN profile facts description: - Get facts for a specific Azure CDN profile or all CDN profiles. options: name: description: - Limit results to a specific CDN profile. resource_group: description: - The resource group to search for the desired CDN profile tags: description: - Limit results by providing a list of tags. Format tags as 'key' or 'key:value'. extends_documentation_fragment: - azure author: - "Hai Cao (@caohai) <t-haicao@microsoft.com>" - "Yunge Zhu (@yungezz) <yungez@microsoft.com>" ''' EXAMPLES = ''' - name: Get facts for one CDN profile azure_rm_cdnprofile_facts: name: Testing resource_group: TestRG - name: Get facts for all CDN profiles azure_rm_cdnprofile_facts: - name: Get facts by tags azure_rm_cdnprofile_facts: tags: - Environment:Test ''' RETURN = ''' cdnprofiles: description: List of CDN profiles. returned: always type: complex contains: resource_group: description: - Name of a resource group where the CDN profile exists. returned: always type: str sample: testGroup name: description: - Name of the CDN profile. returned: always type: str sample: Testing location: description: - Location of the CDN profile. type: str sample: WestUS id: description: - ID of the CDN profile. type: str sample: /subscriptions/XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX/resourcegroups/cdntest/providers/Microsoft.Cdn/profiles/cdntest provisioning_state: description: - Provisioning status of the profile. type: str sample: Succeeded resource_state: description: - Resource status of the profile. type: str sample: Active sku: description: - The pricing tier, defines a CDN provider, feature list and rate of the CDN profile. type: str sample: standard_verizon type: description: - The type of the CDN profile. type: str sample: Microsoft.Cdn/profiles tags: description: - The tags of the CDN profile. type: list sample: [ {"foo": "bar"} ] ''' from ansible.module_utils.azure_rm_common import AzureRMModuleBase try: from azure.mgmt.cdn.models import ErrorResponseException from azure.common import AzureHttpError from azure.mgmt.cdn import CdnManagementClient except: # handled in azure_rm_common pass import re AZURE_OBJECT_CLASS = 'profiles' class AzureRMCdnprofileFacts(AzureRMModuleBase): """Utility class to get Azure CDN profile facts""" def __init__(self): self.module_args = dict( name=dict(type='str'), resource_group=dict(type='str'), tags=dict(type='list') ) self.results = dict( changed=False, cdnprofiles=[] ) self.name = None self.resource_group = None self.tags = None self.cdn_client = None super(AzureRMCdnprofileFacts, self).__init__( derived_arg_spec=self.module_args, supports_tags=False, facts_module=True ) def exec_module(self, **kwargs): for key in self.module_args: setattr(self, key, kwargs[key]) self.cdn_client = self.get_cdn_client() if self.name and not self.resource_group: self.fail("Parameter error: resource group required when filtering by name.") if self.name: self.results['cdnprofiles'] = self.get_item() elif self.resource_group: self.results['cdnprofiles'] = self.list_resource_group() else: self.results['cdnprofiles'] = self.list_all() return self.results def get_item(self): """Get a single Azure CDN profile""" self.log('Get properties for {0}'.format(self.name)) item = None result = [] try: item = self.cdn_client.profiles.get( self.resource_group, self.name) except ErrorResponseException: pass if item and self.has_tags(item.tags, self.tags): result = [self.serialize_cdnprofile(item)] return result def list_resource_group(self): """Get all Azure CDN profiles within a resource group""" self.log('List all Azure CDNs within a resource group') try: response = self.cdn_client.profiles.list_by_resource_group( self.resource_group) except AzureHttpError as exc: self.fail('Failed to list all items - {0}'.format(str(exc))) results = [] for item in response: if self.has_tags(item.tags, self.tags): results.append(self.serialize_cdnprofile(item)) return results def list_all(self): """Get all Azure CDN profiles within a subscription""" self.log('List all CDN profiles within a subscription') try: response = self.cdn_client.profiles.list() except Exception as exc: self.fail("Error listing all items - {0}".format(str(exc))) results = [] for item in response: if self.has_tags(item.tags, self.tags): results.append(self.serialize_cdnprofile(item)) return results def serialize_cdnprofile(self, cdnprofile): ''' Convert a CDN profile object to dict. :param cdn: CDN profile object :return: dict ''' result = self.serialize_obj(cdnprofile, AZURE_OBJECT_CLASS) new_result = {} new_result['id'] = cdnprofile.id new_result['resource_group'] = re.sub('\\/.*', '', re.sub('.*resourcegroups\\/', '', result['id'])) new_result['name'] = cdnprofile.name new_result['type'] = cdnprofile.type new_result['location'] = cdnprofile.location new_result['resource_state'] = cdnprofile.resource_state new_result['sku'] = cdnprofile.sku.name new_result['provisioning_state'] = cdnprofile.provisioning_state new_result['tags'] = cdnprofile.tags return new_result def get_cdn_client(self): if not self.cdn_client: self.cdn_client = self.get_mgmt_svc_client(CdnManagementClient, base_url=self._cloud_environment.endpoints.resource_manager, api_version='2017-04-02') return self.cdn_client def main(): """Main module execution code path""" AzureRMCdnprofileFacts() if __name__ == '__main__': main()

28.90566

135

0.589295

ed560a4fccf0d8a26b6cf41e1e69866aa0233ac6

363

py

Python

backup_codes/generated_xor_data.py

shridharmishra4/Artificial-neural-netowrks-in-C

40f89aa3da9b2cc036df372e5bf5b6e7031d0d87

[ "Apache-2.0" ]

null

backup_codes/generated_xor_data.py

shridharmishra4/Artificial-neural-netowrks-in-C

40f89aa3da9b2cc036df372e5bf5b6e7031d0d87

[ "Apache-2.0" ]

null

backup_codes/generated_xor_data.py

shridharmishra4/Artificial-neural-netowrks-in-C

40f89aa3da9b2cc036df372e5bf5b6e7031d0d87

[ "Apache-2.0" ]

null

with open("validate.txt","w") as f: for i in range(25): f.writelines("{0} {1} {2}\n".format(0,0,0^0)) for i in range(25): f.writelines("{0} {1} {2}\n".format(0, 1, 0 ^ 1)) for i in range(25): f.writelines("{0} {1} {2}\n".format(1, 0, 1 ^ 0)) for i in range(25): f.writelines("{0} {1} {2}\n".format(1, 1, 1 ^ 1))

27.923077

57

0.482094

57dd4e204790ccad2f20167c571cc81c29c83fea

501

py

Python

robot_control/launch/spawn_vehicle.launch.py

blakermchale/robot-control

b2d0109950d5abb4b12055ce7a3645ca9cc07b20

[ "MIT" ]

null

robot_control/launch/spawn_vehicle.launch.py

blakermchale/robot-control

b2d0109950d5abb4b12055ce7a3645ca9cc07b20

[ "MIT" ]

4

2021-06-29T05:14:13.000Z

2021-08-11T05:00:44.000Z

robot_control/launch/spawn_vehicle.launch.py

blakermchale/robot-control

b2d0109950d5abb4b12055ce7a3645ca9cc07b20

[ "MIT" ]

null

#!/usr/bin/env python3 from launch import LaunchDescription from launch.actions import OpaqueFunction from robot_control.launch.spawn_vehicle import launch_setup, LAUNCH_ARGS from ros2_utils.launch import get_launch_arguments def generate_launch_description(): """Launch this launch file.""" launch_description = [] launch_description += get_launch_arguments(LAUNCH_ARGS) launch_description += [OpaqueFunction(function=launch_setup)] return LaunchDescription(launch_description)

35.785714

72

0.812375

1489788204696c57666f6e979aad058c93a850f4

5,352

py

Python

lrs/objects/AgentProfileManager.py

varunasingh/ADL_LRS

65130b0f4d1b022bd2b4fb42261457bdc47b3ad2

[ "Apache-2.0" ]

null

lrs/objects/AgentProfileManager.py

varunasingh/ADL_LRS

65130b0f4d1b022bd2b4fb42261457bdc47b3ad2

[ "Apache-2.0" ]

null

lrs/objects/AgentProfileManager.py

varunasingh/ADL_LRS

65130b0f4d1b022bd2b4fb42261457bdc47b3ad2

[ "Apache-2.0" ]

null

import ast import json import datetime import copy from django.core.files.base import ContentFile from django.db import transaction from django.utils.timezone import utc from lrs.models import AgentProfile from lrs.models import Agent as ag from lrs.exceptions import IDNotFoundError, ParamError from lrs.util import etag, get_user_from_auth class AgentProfileManager(): def __init__(self, agent): self.Agent = agent @transaction.commit_on_success def post_profile(self, request_dict): post_profile = request_dict['profile'] profile_id = request_dict['params']['profileId'] p, created = AgentProfile.objects.get_or_create(profileId=profile_id,agent=self.Agent) if created: p.json_profile = post_profile p.content_type = request_dict['headers']['CONTENT_TYPE'] p.etag = etag.create_tag(post_profile) if 'headers' in request_dict and ('updated' in request_dict['headers'] and request_dict['headers']['updated']): p.updated = request_dict['headers']['updated'] else: p.updated = datetime.datetime.utcnow().replace(tzinfo=utc) else: etag.check_preconditions(request_dict,p, required=True) orig_prof = json.loads(p.json_profile) post_profile = json.loads(post_profile) if not isinstance(post_profile, dict): raise ParamError("The document was not able to be parsed into a JSON object.") else: merged = json.dumps(dict(orig_prof.items() + post_profile.items())) p.json_profile = merged p.etag = etag.create_tag(merged) p.updated = datetime.datetime.utcnow().replace(tzinfo=utc) p.save() @transaction.commit_on_success def put_profile(self, request_dict): profile_id = request_dict['params']['profileId'] p,created = AgentProfile.objects.get_or_create(profileId=profile_id,agent=self.Agent) if "application/json" not in request_dict['headers']['CONTENT_TYPE']: try: profile = ContentFile(request_dict['profile'].read()) except: try: profile = ContentFile(request_dict['profile']) except: profile = ContentFile(str(request_dict['profile'])) if not created: etag.check_preconditions(request_dict,p, required=True) try: p.profile.delete() except OSError: # p was probably json before.. gotta clear that field p.json_profile = {} self.save_profile(p, created, profile, request_dict) else: if not created: etag.check_preconditions(request_dict, p, required=True) the_profile = request_dict['profile'] p.json_profile = the_profile p.content_type = request_dict['headers']['CONTENT_TYPE'] p.etag = etag.create_tag(the_profile) if 'headers' in request_dict and ('updated' in request_dict['headers'] and request_dict['headers']['updated']): p.updated = request_dict['headers']['updated'] else: p.updated = datetime.datetime.utcnow().replace(tzinfo=utc) p.save() def save_profile(self, p, created, profile, request_dict): p.content_type = request_dict['headers']['CONTENT_TYPE'] p.etag = etag.create_tag(profile.read()) if 'headers' in request_dict and ('updated' in request_dict['headers'] and request_dict['headers']['updated']): p.updated = request_dict['headers']['updated'] else: p.updated = datetime.datetime.utcnow().replace(tzinfo=utc) profile.seek(0) if created: p.save() fn = "%s_%s" % (p.agent_id,request_dict.get('filename', p.id)) p.profile.save(fn, profile) def get_profile(self, profileId): try: return self.Agent.agentprofile_set.get(profileId=profileId) except: err_msg = 'There is no profile associated with the id: %s' % profileId raise IDNotFoundError(err_msg) def get_profile_ids(self, since=None): ids = [] if since: try: # this expects iso6801 date/time format "2013-02-15T12:00:00+00:00" profs = self.Agent.agentprofile_set.filter(updated__gte=since) except ValidationError: err_msg = 'Since field is not in correct format for retrieval of agent profiles' raise ParamError(err_msg) except: err_msg = 'There are no profiles associated with the id: %s' % profileId raise IDNotFoundError(err_msg) ids = [p.profileId for p in profs] else: ids = self.Agent.agentprofile_set.values_list('profileId', flat=True) return ids def delete_profile(self, profileId): try: prof = self.get_profile(profileId) prof.delete() except AgentProfile.DoesNotExist: pass #we don't want it anyway except IDNotFoundError: pass except OSError: pass # this is ok,too

40.545455

123

0.605568