xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

c8205acb89329008fc256d7baa124e1eca07ffcd

1,521

py

Python

slybot/slybot/linkextractor/xml.py

coolkunal64/ht

b7c52d5604dd75ea4086a6ff92eaa2db85bb145c

[ "BSD-3-Clause" ]

1

2017-11-03T13:00:21.000Z

slybot/slybot/linkextractor/xml.py

coolkunal64/ht

b7c52d5604dd75ea4086a6ff92eaa2db85bb145c

[ "BSD-3-Clause" ]

2

2021-03-31T20:04:55.000Z

2021-12-13T20:47:09.000Z

slybot/slybot/linkextractor/xml.py

coolkunal64/ht

b7c52d5604dd75ea4086a6ff92eaa2db85bb145c

[ "BSD-3-Clause" ]

2

2017-11-03T13:00:23.000Z

2020-08-28T19:59:40.000Z

""" Link extraction for auto scraping """ from scrapy.link import Link from scrapy.selector import Selector from slybot.linkextractor.base import BaseLinkExtractor class XmlLinkExtractor(BaseLinkExtractor): """Link extractor for XML sources""" def __init__(self, xpath, **kwargs): self.remove_namespaces = kwargs.pop('remove_namespaces', False) super(XmlLinkExtractor, self).__init__(**kwargs) self.xpath = xpath def _extract_links(self, response): type = 'html' if response.body_as_unicode().strip().startswith('<?xml version='): type = 'xml' xxs = Selector(response, type=type) if self.remove_namespaces: xxs.remove_namespaces() for url in xxs.xpath(self.xpath).extract(): yield Link(url.encode(response.encoding)) class RssLinkExtractor(XmlLinkExtractor): """Link extraction from RSS feeds""" def __init__(self, **kwargs): super(RssLinkExtractor, self).__init__("//item/link/text()", **kwargs) class SitemapLinkExtractor(XmlLinkExtractor): """Link extraction for sitemap.xml feeds""" def __init__(self, **kwargs): kwargs['remove_namespaces'] = True super(SitemapLinkExtractor, self).__init__("//urlset/url/loc/text() | //sitemapindex/sitemap/loc/text()", **kwargs) class AtomLinkExtractor(XmlLinkExtractor): def __init__(self, **kwargs): kwargs['remove_namespaces'] = True super(AtomLinkExtractor, self).__init__("//link/@href", **kwargs)

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1,521

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0.05136

0.123867

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1,521

40

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1

0.178571

false

0

0.107143

0

0.428571

0

null

0

null

0

1

0

c821ed2774a2669777a45f15bf9913ade184edde

1,319

py

Python

questions/construct-the-rectangle/Solution.py

marcus-aurelianus/leetcode-solutions

8b43e72fe1f51c84abc3e89b181ca51f09dc7ca6

[ "MIT" ]

141

2017-12-12T21:45:53.000Z

2022-03-25T07:03:39.000Z

questions/construct-the-rectangle/Solution.py

marcus-aurelianus/leetcode-solutions

8b43e72fe1f51c84abc3e89b181ca51f09dc7ca6

[ "MIT" ]

32

2015-10-05T14:09:52.000Z

2021-05-30T10:28:41.000Z

questions/construct-the-rectangle/Solution.py

marcus-aurelianus/leetcode-solutions

8b43e72fe1f51c84abc3e89b181ca51f09dc7ca6

[ "MIT" ]

56

2015-09-30T05:23:28.000Z

2022-03-08T07:57:11.000Z

""" A web developer needs to know how to design a web page's size. So, given a specific rectangular web page’s area, your job by now is to design a rectangular web page, whose length L and width W satisfy the following requirements: The area of the rectangular web page you designed must equal to the given target area. The width W should not be larger than the length L, which means L >= W. The difference between length L and width W should be as small as possible. Return an array [L, W] where L and W are the length and width of the web page you designed in sequence. Example 1: Input: area = 4 Output: [2,2] Explanation: The target area is 4, and all the possible ways to construct it are [1,4], [2,2], [4,1]. But according to requirement 2, [1,4] is illegal; according to requirement 3, [4,1] is not optimal compared to [2,2]. So the length L is 2, and the width W is 2. Example 2: Input: area = 37 Output: [37,1] Example 3: Input: area = 122122 Output: [427,286] Constraints: 1 <= area <= 107 """ class Solution(object): def constructRectangle(self, area): """ :type area: int :rtype: List[int] """ w = int(area ** 0.5) while w >= 1: l, r = divmod(area, w) if r == 0: return [l, w] w -= 1

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null

0

null

0

1

0

c825a6df3c14933bdcbd115b36ca8c69f6c6f233

2,434

py

Python

limiter/rate_limiter.py

sousa-andre/requests-limiter

ad3a5982a40e88111eca63b258e1226e15a8befa

[ "MIT" ]

4

2020-11-14T18:13:27.000Z

2021-01-03T19:13:39.000Z

limiter/rate_limiter.py

sousa-andre/requests-limiter

ad3a5982a40e88111eca63b258e1226e15a8befa

[ "MIT" ]

null

limiter/rate_limiter.py

sousa-andre/requests-limiter

ad3a5982a40e88111eca63b258e1226e15a8befa

[ "MIT" ]

2

2021-01-03T19:13:46.000Z

2021-01-31T12:24:23.000Z

from functools import wraps from time import sleep from typing import List from .rate_limit import RateLimit from .exceptions import RateLimitHit class OnHitAction: raise_exception = 0 wait = 1 class RateLimiter: def __init__(self, storage=RateLimit, *, action=OnHitAction.raise_exception): self._limits = [] self._storage = storage self.action = action def _create_single_limiter(self, name, callback, defaults=None): if defaults is None: defaults = [(), (), ()] self._limits.append(self._storage(name, callback, defaults)) def create_limiter(self, names, callback, defaults=None): if isinstance(names, list): for name in names: self._create_single_limiter(name, callback, defaults) elif isinstance(names, str): self._create_single_limiter(names, callback, defaults) else: raise ValueError("names parameter must be either a string or a iterable") @staticmethod def can_request(limits): for limit in limits: print(limit, limit.can_request()) if not limit.can_request(): return [False, limit] return [True, None] @staticmethod def is_initialized(limits): for limit in limits: if not limit.is_initialized(): return False return True @staticmethod def register_request(limits, rt): for limit in limits: limit.register_request(rt) def use(self, *limits_names): def request_wrapper(func): limits: List[RateLimit] = [limit for limit in self._limits if limit.name in limits_names] @wraps(func) def func_wrapper(): rl = RateLimiter.can_request(limits) if rl[0]: ret = func() RateLimiter.register_request(limits, ret) return ret else: if self.action == OnHitAction.raise_exception: raise RateLimitHit(rl[1]) elif self.action == OnHitAction.wait: sleep(rl[1].time_until_new_request_is_possible) ret = func() RateLimiter.register_request(limits, ret) return ret return func_wrapper return request_wrapper

32.453333

101

0.582991

259

2,434

5.30888

0.277992

0.058182

0.029091

0.034909

0.106182

0.074182

0

0.003117

0.341002

2,434

74

102

32.891892

0.854115

0

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0

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0

1

0.145161

false

0

0.080645

0

0.419355

0.016129

0

null

0

null

0

1

0

c82642bd0188daaa561a06de4c6541a12f22393f

2,081

py

Python

pymod/amsexceptions.py

kevangel79/argo-ams-library

6824b1f6f577e688575d8f2f67f747126a856fcb

[ "Apache-2.0" ]

null

pymod/amsexceptions.py

kevangel79/argo-ams-library

6824b1f6f577e688575d8f2f67f747126a856fcb

[ "Apache-2.0" ]

1

2021-06-25T15:35:46.000Z

pymod/amsexceptions.py

kevangel79/argo-ams-library

6824b1f6f577e688575d8f2f67f747126a856fcb

[ "Apache-2.0" ]

null

import json class AmsException(Exception): """Base exception class for all Argo Messaging service related errors""" def __init__(self, *args, **kwargs): super(AmsException, self).__init__(*args, **kwargs) class AmsServiceException(AmsException): """Exception for Argo Messaging Service API errors""" def __init__(self, json, request): errord = dict() self.msg = "While trying the [{0}]: {1}".format(request, json['error']['message']) errord.update(error=self.msg) if json['error'].get('code'): self.code = json['error']['code'] errord.update(status_code=self.code) if json['error'].get('status'): self.status = json['error']['status'] errord.update(status=self.status) super(AmsServiceException, self).__init__(errord) class AmsBalancerException(AmsServiceException): """Exception for load balancer Argo Messaging Service errors""" def __init__(self, json, request): super(AmsBalancerException, self).__init__(json, request) class AmsTimeoutException(AmsServiceException): """Exception for timeouts errors Timeouts can be generated by the Argo Messaging Service if message was not acknownledged in desired time frame (ackDeadlineSeconds). Also, 408 timeouts can come from load balancer for partial requests that were not completed in required time frame. """ def __init__(self, json, request): super(AmsTimeoutException, self).__init__(json, request) class AmsConnectionException(AmsException): """Exception for connection related problems catched from requests library""" def __init__(self, exp, request): self.msg = "While trying the [{0}]: {1}".format(request, repr(exp)) super(AmsConnectionException, self).__init__(self.msg) class AmsMessageException(AmsException): """Exception that indicate problems with constructing message""" def __init__(self, msg): self.msg = msg super(AmsMessageException, self).__init__(self.msg)

33.031746

90

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231

2,081

5.943723

0.329004

0.046613

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0.037145

0.151493

0.116533

0.05244

0

0.004225

0.203748

2,081

62

91

33.564516

0.824381

0.280154

0

0.1

0

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0

1

0.2

false

0

0.033333

0

0.433333

0

null

0

null

0

1

0

c8284b2ce3b5bfcda541a3e925afc518ce46735a

18,871

py

Python

tests/fixtures/__init__.py

Lunga001/pmg-cms-2

10cea3979711716817b0ba2a41987df73f2c7642

[ "Apache-2.0" ]

2

2019-06-11T20:46:43.000Z

2020-08-27T22:50:32.000Z

tests/fixtures/__init__.py

Lunga001/pmg-cms-2

10cea3979711716817b0ba2a41987df73f2c7642

[ "Apache-2.0" ]

70

2017-05-26T14:04:06.000Z

2021-06-30T10:21:58.000Z

tests/fixtures/__init__.py

OpenUpSA/pmg-cms-2

ec5f259dae81674ac7a8cdb80f124a8b0f167780

[ "Apache-2.0" ]

4

2017-08-29T10:09:30.000Z

2021-05-25T11:29:03.000Z

import pytz import datetime from fixture import DataSet, NamedDataStyle, SQLAlchemyFixture from pmg.models import ( db, House, Committee, CommitteeMeeting, Bill, BillType, Province, Party, CommitteeMeetingAttendance, Member, CallForComment, TabledCommitteeReport, CommitteeQuestion, Minister, Event, Featured, Page, BillStatus, Post, User, Role, Membership, MembershipType, EmailTemplate, DailySchedule, Organisation, ) THIS_YEAR = datetime.datetime.today().year class HouseData(DataSet): class joint: id = 1 name = "Joint (NA + NCOP)" name_short = "Joint" sphere = "national" class ncop: id = 2 name = "National Council of Provinces" name_short = "NCOP" sphere = "national" class na: id = 3 name = "National Assembly" name_short = "NA" sphere = "national" class president: id = 4 name = ("The President's Office",) name_short = "President" sphere = "national" class western_cape: id = 5 name = "Western Cape" name_short = "western_cape" sphere = "provincial" class MinisterData(DataSet): class minister_of_arts: id = 1 name = "Minister of Sports, Arts and Culture" class minister_of_transport: id = 2 name = "Minister of Transport " class president: id = 3 name = "President" class minister_in_presidency_for_women: id = 4 name = ( "Minister in The Presidency for Women, Youth and Persons with Disabilities" ) class minister_of_public_works: id = 5 name = "Minister of Public Works and Infrastructure" class CommitteeData(DataSet): class communications: name = "Communications" house = HouseData.na premium = True class arts: name = "Arts and Culture" house = HouseData.na minister = MinisterData.minister_of_arts class constitutional_review: name = "Constitutional Review Committee" house = HouseData.joint active = False class western_cape_budget: name = "Budget (WCPP)" house = HouseData.western_cape active = False class CommitteeMeetingData(DataSet): class arts_meeting_one: date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) title = "Public meeting One" committee = CommitteeData.arts class arts_meeting_two: date = datetime.datetime(2019, 8, 1, 0, 0, 0, tzinfo=pytz.utc) title = "Public meeting Two" committee = CommitteeData.arts featured = True class arts_future_meeting_one: date = datetime.datetime(2020, 1, 1, 0, 0, 0, tzinfo=pytz.utc) title = "Public meeting 2020 one" committee = CommitteeData.arts class arts_future_meeting_two: date = datetime.datetime(2020, 5, 1, 0, 0, 0, tzinfo=pytz.utc) title = "Public meeting 2020 two" committee = CommitteeData.arts class premium_recent: date = datetime.datetime( THIS_YEAR, 11, 5, 0, 0, 0, tzinfo=pytz.FixedOffset(120) ) title = "Premium meeting recent" committee = CommitteeData.communications class premium_old: date = datetime.datetime(THIS_YEAR - 2, 11, 5, 0, 0, 0, tzinfo=pytz.utc) title = "Premium meeting old" committee = CommitteeData.communications class BillTypeData(DataSet): class section_74: name = "Section 74" prefix = "B" description = "Section 74" class section_75: name = "Section 75" prefix = "B" description = "Ordinary Bills not affecting the provinces" class section_77: name = "Section 77" prefix = "B" description = "Section 77" class private_member_bill_74: name = "Private Member Bill: S74" prefix = "PMB" description = "Private Member Bill: Section 74" class private_member_bill_77: name = "Private Member Bill: S77" prefix = "PMB" description = "Private Member Bill: Section 77" class draft: name = "Draft" prefix = "D" description = "Draft bill" class BillStatusData(DataSet): class current: name = "na" description = "current" class assent: name = "assent" description = "assent" class president: name = "president" description = "president" class BillData(DataSet): """ Enter various types of bills """ class food: year = 2019 title = "Food and Health Bill" type = BillTypeData.section_74 introduced_by = "Minister of Finance" date_of_introduction = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) status = BillStatusData.current class farm: year = 2019 title = "Farm and Agricultural Bill" type = BillTypeData.section_77 status = BillStatusData.president class public: year = 2019 title = "Public Investment Corporation Amendment Bill" type = BillTypeData.private_member_bill_74 status = BillStatusData.assent class child: year = 2019 title = "Children's Amendment Bill" type = BillTypeData.private_member_bill_77 class bill_with_none_number: year = 2019 number = None title = "Bill with None number" type = BillTypeData.section_75 class sport: year = 2019 number = 1 title = "2010 FIFA World Cup South Africa Special Measures Bill" type = BillTypeData.section_75 class draft: year = 2019 title = "Test Draft Bill" type = BillTypeData.draft class identical_date_events: year = 2019 title = "Bill with multiple events" type = BillTypeData.section_74 introduced_by = "Minister of sorting" date_of_introduction = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) status = BillStatusData.current class CallForCommentData(DataSet): class arts_call_for_comment_one: date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) title = "Feeds and Pet Food Bill - draft" committee = CommitteeData.arts start_date = datetime.datetime(2019, 1, 30, 0, 0, 0, tzinfo=pytz.utc) end_date = datetime.datetime(2019, 4, 30, 0, 0, 0, tzinfo=pytz.utc) body = "The Bill seeks to provide for: - regulation of feed and pet food, - regulation of feed ingredients used in the manufacturing of feed and pet food," summary = "The Department of Agriculture, Forestry and Fisheries has published the draft Feeds and Pet Food Bill, and is asking you to comment." class communications_call_for_comment_one: date = datetime.datetime(2020, 2, 14, 0, 0, 0, tzinfo=pytz.utc) title = "Public Procurement Bill" committee = CommitteeData.communications start_date = datetime.datetime(2020, 1, 30, 0, 0, 0, tzinfo=pytz.utc) body = "The draft Bill aims to create a single regulatory framework for public procurement" class TabledCommitteeReportData(DataSet): class arts_tabled_committee_report_one: title = "ATC190710: Report of the Portfolio Committee on Agriculture, Land Reform and Rural Development on the 2019/20 Annual Performance Plan and the Budget of the Department of Agriculture, Forestry and Fisheries (Vote 24) and its Entities, dated 10 July 2019." start_date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) committee = CommitteeData.arts end_date = datetime.datetime(2019, 4, 30, 0, 0, 0, tzinfo=pytz.utc) body = "The Portfolio Committee on Agriculture, Land Reform and Rural Development (hereinafter referred to as the Committee) examined Budget Vote 24: Agriculture, Forestry and Fisheries including the Annual Performance Plan of the Department of Agriculture, Forestry and Fisheries (hereinafter referred to as DAFF or the Department) for the 2019/20 financial year and budget projections for the Medium Term Expenditure Framework (MTEF) period ending in 2021/22." class PartyData(DataSet): class da: name = "Democratic Alliance (DA)" class anc: name = "African National Congress (ANC)" class ProvinceData(DataSet): class western_cape: name = "Western Cape" class gauteng: name = "Gauteng" class MemberData(DataSet): class veronica: name = "Ms Veronica Van Dyk" profile_pic_url = "https://www.pa.org.za/media_root/cache/02/93/0293cce7701daf86fa88fe02e1db9c58.jpg" bio = "Ms Veronica van Dyk is the Deputy Shadow Minister for Communications in the DA, since June 2014. She is a former Ward Councillor of the Nama Khoi Local Municipality." house = HouseData.na party = PartyData.da province = ProvinceData.western_cape start_date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) pa_link = "http://www.pa.org.za" current = True class not_current_member: name = "Phoebe Noxolo Abraham" house = HouseData.na party = PartyData.anc start_date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) current = False class laetitia: name = "Laetitia Heloise Arries" house = HouseData.joint party = PartyData.anc start_date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) current = True class CommitteeMeetingAttendanceData(DataSet): class arts_meeting_attendance_one: date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) attendance = "P" meeting = CommitteeMeetingData.arts_meeting_two member = MemberData.laetitia class arts_meeting_attendance_two: date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) attendance = "A" meeting = CommitteeMeetingData.arts_meeting_two member = MemberData.veronica class arts_future_meeting_attendance_one: date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) attendance = "P" meeting = CommitteeMeetingData.arts_future_meeting_one member = MemberData.laetitia class arts_future_meeting_attendance_two: date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) attendance = "A" meeting = CommitteeMeetingData.arts_future_meeting_two member = MemberData.veronica class CommitteeQuestionData(DataSet): class arts_committee_question_one: minister = MinisterData.minister_of_arts code = "NA1" question_number = 1 house = HouseData.na written_number = 1 oral_number = 1 answer_type = "oral" date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) year = 2019 question = "What programmes that promote the languages, culture and heritage of the Khoi and San has the Government implemented in each province in each of the past five years" answer = "Through possible funding and strategic partnerships between PanSALB and my Department, PanSALB was able to initiate and support the following programmes." question_to_name = "Minister of Sports, Arts and Culture" intro = "Van Dyk, Ms V to ask the Minister of Sports, Arts and Culture:" asked_by_name = "Van Dyk, Ms V" asked_by_member = MemberData.veronica class arts_committee_question_two: minister = MinisterData.minister_of_arts code = "NA1" question_number = 2 house = HouseData.na written_number = 2 oral_number = 2 answer_type = "oral" date = datetime.datetime(2018, 1, 1, 0, 0, 0, tzinfo=pytz.utc) year = 2018 question = "What has he found were the reasons for not reporting on the 2018-19 Fourth Quarter expenditure?" answer = "During the Fourth Quarter of the 2018-19 financial year there were no expenditure incurred on the development of the Rail Safety Bill and therefore there was no reporting." question_to_name = "Minister of Sports, Arts and Culture" intro = "Van Dyk, Ms V to ask the Minister of Sports, Arts and Culture:" asked_by_name = "Van Dyk, Ms V" asked_by_member = MemberData.veronica class EventData(DataSet): class arts_bill_event_one: date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) title = "2010 FIFA World Cup South Africa Special Measures Bill [B13-2006]: Department briefing" type = "committee-meeting" committee = CommitteeData.arts house = HouseData.na bills = [BillData.public, BillData.food] class food_bill_hansard_event: date = datetime.datetime(2020, 1, 1, 0, 0, 0, tzinfo=pytz.utc) title = "Hansard event" type = "plenary" house = HouseData.na bills = [BillData.food] class identical_date_bill_event1: date = datetime.datetime(2020, 1, 1, 0, 0, 0, tzinfo=pytz.utc) title = "Hansard event 2" type = "bill-signed" house = HouseData.na bills = [BillData.identical_date_events] class identical_date_bill_event2: date = datetime.datetime(2020, 1, 1, 0, 0, 0, tzinfo=pytz.utc) title = "Hansard event 2" type = "bill-introduced" house = HouseData.na bills = [BillData.identical_date_events] class FeaturedData(DataSet): class the_week_ahead: title = "The Week Ahead: End of the First Term" link = "https://pmg.org.za/blog/The%20Week%20Ahead:%20End%20of%20the%20First%20Term" start_date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) class current_bills: title = "Current Bills" start_date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) link = "https://pmg.org.za/bills/current/" class PageData(DataSet): class section_25_review_process: title = "Section 25 review process" slug = "Section25reviewprocess" body = "In February 2018, the National Assembly adopted a motion proposed by the EFF, with amendments by the ANC that Parliament's Constitutional Review Committee investigates mechanisms through which land can be expropriated without compensation." date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) featured = True class un_featured_page: title = "Unfeatured page" slug = "unfeaturedpage" date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) featured = False class PostData(DataSet): class the_week_ahead: title = "The Week Ahead: End of the First Term" slug = "theweekahead" featured = True body = "A lot was packed into the first term of the Sixth Parliament." date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) class first_term_review: title = "First Term Review: Sixth Parliament" slug = "FirstTermReview" featured = True body = "Parliaments first term ended last week. According to the programme, the term was 11 weeks but the main thrust of the work was compressed into the final 5 weeks of the quarter." date = datetime.datetime(2019, 2, 17, 0, 0, 0, tzinfo=pytz.utc) class brief_explainer: title = "BRIEF EXPLAINER: LAPSED BILLS IN PARLIAMENT" slug = "BriefExplainer" featured = True body = "There were 39 unfinished bills when the Fifth Parliament ended." date = datetime.datetime(2019, 2, 17, 12, 0, 0, tzinfo=pytz.utc) class government_priorities: title = "Government's legislative priorities" slug = "GovernmentPriorities" featured = True body = "The Constitution of South Africa empowers the Executive to prepare and initiate legislation. Similarly, Parliament (through its committees) and individual MPs also have initiating power but the vast majority of legislation (92%) is introduced by the Executive." date = datetime.datetime(2018, 8, 17, 0, 0, 0, tzinfo=pytz.utc) class RoleData(DataSet): class admin: name = "user-admin" description = "user-admin" class editor: name = "editor" description = "editor" class UserData(DataSet): class admin: email = "admin@pmg.org.za" name = "Admin User" active = True roles = [RoleData.admin, RoleData.editor] current_login_at = datetime.datetime.utcnow() confirmed = True confirmed_at = datetime.datetime.utcnow() committee_alerts = [CommitteeData.arts] class editor: email = "editor@pmg.org.za" name = "Editor User" active = True roles = [RoleData.editor] current_login_at = datetime.datetime.utcnow() confirmed = True confirmed_at = datetime.datetime.utcnow() committee_alerts = [CommitteeData.arts] class inactive: email = "inactive@pmg.org.za" name = "Inactive User" active = False roles = [RoleData.editor] current_login_at = datetime.datetime.utcnow() confirmed = True confirmed_at = datetime.datetime.utcnow() committee_alerts = [CommitteeData.arts] class OrganisationData(DataSet): class pmg: name = "PMG" domain = "PMG Domain" paid_subscriber = True expiry = datetime.datetime.utcnow() + datetime.timedelta(days=365) contact = "pmg@pmg.com" subscriptions = [CommitteeData.arts] users = [UserData.admin] class MembershipTypeData(DataSet): class member: name = "Member" class MembershipData(DataSet): class arts_membership_one: type = MembershipTypeData.member committee = CommitteeData.arts member = MemberData.veronica class EmailTemplateData(DataSet): class template_one: name = "Template One" description = "Template One Description" subject = "Template One Subject" body = "Template One Body" class DailyScheduleData(DataSet): class schedule_provincial: title = "Schedule provincial" start_date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) body = "Schedule provincial" house = HouseData.western_cape class schedule_ncop: title = "Schedule NCOP" start_date = datetime.datetime(2019, 1, 1, 0, 0, 0, tzinfo=pytz.utc) body = "Schedule NCOP body" house = HouseData.ncop dbfixture = SQLAlchemyFixture( env=globals(), style=NamedDataStyle(), engine=db.engine, scoped_session=db.Session )

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null

0

null

0

1

0

c828bd04e92dcf2b104e584217bad8d4f09ebabf

455

py

Python

Google Search/GoogleSearch.py

cclauss/Browser-Automation

7baca74d40ac850f9570d7e40a47021dc0e8e387

[ "Apache-2.0" ]

35

2016-07-16T07:05:24.000Z

2021-07-07T15:18:55.000Z

Google Search/GoogleSearch.py

cclauss/Browser-Automation

7baca74d40ac850f9570d7e40a47021dc0e8e387

[ "Apache-2.0" ]

null

Google Search/GoogleSearch.py

cclauss/Browser-Automation

7baca74d40ac850f9570d7e40a47021dc0e8e387

[ "Apache-2.0" ]

7

2016-07-27T10:25:10.000Z

2019-12-06T08:45:03.000Z

from selenium import webdriver from selenium.webdriver.common.keys import Keys from selenium.webdriver.common.desired_capabilities import DesiredCapabilities driver = webdriver.Chrome("D:\chromedriver\chromedriver") driver.get("http://www.google.com") if not "Google" in driver.title: raise Exception("Unable to load google page!") elem = driver.find_element_by_name("q") elem.send_keys("selenium") elem.submit() print (driver.title) driver.quit()

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0.789011

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0.083333

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null

0

null

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1

0

c82a3f71eb898781a7532e4f8e200f17688bdd99

2,265

py

Python

PuppeteerLibrary/puppeteer/async_keywords/puppeteer_formelement.py

qahive/robotframework-puppeteer

6377156c2e5b3a4d3841c33a2d3ff9ab0b38854a

[ "Apache-2.0" ]

37

2019-10-28T01:35:43.000Z

2022-03-31T04:11:49.000Z

PuppeteerLibrary/puppeteer/async_keywords/puppeteer_formelement.py

qahive/robotframework-puppeteer

6377156c2e5b3a4d3841c33a2d3ff9ab0b38854a

[ "Apache-2.0" ]

61

2020-07-16T00:18:22.000Z

2022-03-24T07:12:05.000Z

PuppeteerLibrary/puppeteer/async_keywords/puppeteer_formelement.py

qahive/robotframework-puppeteer

6377156c2e5b3a4d3841c33a2d3ff9ab0b38854a

[ "Apache-2.0" ]

10

2020-03-03T05:28:05.000Z

2022-02-14T10:03:44.000Z

from PuppeteerLibrary.utils.coverter import str2bool, str2str import os import glob import shutil import time from PuppeteerLibrary.ikeywords.iformelement_async import iFormElementAsync class PuppeteerFormElement(iFormElementAsync): def __init__(self, library_ctx): super().__init__(library_ctx) async def input_text(self, locator: str, text: str, clear=True): text = str2str(text) clear = str2bool(clear) if clear: await self._clear_input_text(locator) await self.library_ctx.get_current_page().type_with_selenium_locator(locator, text) async def input_password(self, locator: str, text: str, clear=True): text = str2str(text) clear = str2bool(clear) await self.input_text(locator, text, clear) async def clear_element_text(self, locator: str): await self._clear_input_text(locator) async def download_file(self, locator: str, timeout=None): path = os.getcwd()+''+os.sep+'tmp-download' try: shutil.rmtree(path) except: self.info('Cannot cleanup the tmp download folder.') page = self.library_ctx.get_current_page().get_page() await page._client.send('Page.setDownloadBehavior', { 'behavior': 'allow', 'downloadPath': path }) await self.library_ctx.get_current_page().click_with_selenium_locator(locator) timeout = self.timestr_to_secs_for_default_timeout(timeout) max_time = time.time() + timeout file = None while time.time() < max_time: time.sleep(1) files = glob.glob(path+''+os.sep+'*') if len(files) == 1: file = files[0] break return file async def upload_file(self, locator: str, file_path: str): element = await self.library_ctx.get_current_page().querySelector_with_selenium_locator(locator) return await element.uploadFile(file_path) async def _clear_input_text(self, selenium_locator): await self.library_ctx.get_current_page().click_with_selenium_locator(selenium_locator, {'clickCount': 3}) await self.library_ctx.get_current_page().get_page().keyboard.press('Backspace')

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0

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2,265

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null

0

null

0

1

0

c82ab1a64645a1b9f4d0449b2c09332ab3971afe

7,187

py

Python

cnns/nnlib/pytorch_architecture/resnet1d.py

anonymous-user-commits/perturb-net

66fc7c4a1234fa34b92bcc85751f0a6e23d80a23

[ "MIT" ]

1

2018-03-25T13:19:46.000Z

cnns/nnlib/pytorch_architecture/resnet1d.py

anonymous-user-commits/perturb-net

66fc7c4a1234fa34b92bcc85751f0a6e23d80a23

[ "MIT" ]

null

cnns/nnlib/pytorch_architecture/resnet1d.py

anonymous-user-commits/perturb-net

66fc7c4a1234fa34b92bcc85751f0a6e23d80a23

[ "MIT" ]

null

import shutil, os, csv, itertools, glob import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from torch.utils.data import Dataset, DataLoader import torch.optim as optim from sklearn.metrics import confusion_matrix import pandas as pd import pickle as pk cuda = torch.cuda.is_available() print("is conv1D_cuda available: ", cuda) # Utils def load_pickle(filename): try: p = open(filename, 'r') except IOError: print("Pickle file cannot be opened.") return None try: picklelicious = pk.load(p) except ValueError: print('load_pickle failed once, trying again') p.close() p = open(filename, 'r') picklelicious = pk.load(p) p.close() return picklelicious def save_pickle(data_object, filename): pickle_file = open(filename, 'w') pk.dump(data_object, pickle_file) pickle_file.close() def read_data(filename): print("Loading Data...") df = pd.read_csv(filename, header=None) data = df.values return data def read_line(csvfile, line): with open(csvfile, 'r') as f: data = next(itertools.islice(csv.reader(f), line, None)) return data ## 1D Variant of ResNet taking in 200 dimensional fixed time series inputs class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=3, padding=1, stride=stride, bias=False) self.bn1 = nn.BatchNorm1d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = nn.Conv1d(planes, planes, kernel_size=3, padding=1, stride=stride, bias=False) self.bn2 = nn.BatchNorm1d(planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) # print('out', out.size(), 'res', residual.size(), self.downsample) out += residual out = self.relu(out) return out class Bottleneck(nn.Module): expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=1, padding=1, stride=stride, bias=False) self.bn1 = nn.BatchNorm1d(planes) self.conv2 = nn.Conv1d(planes, planes, kernel_size=1, padding=1, stride=stride, bias=False) self.bn2 = nn.BatchNorm1d(planes) self.conv3 = nn.Conv1d(planes, planes * 4, kernel_size=1, padding=1, stride=stride, bias=False) self.bn3 = nn.BatchNorm1d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return out class ResNet(nn.Module): def __init__(self, block, layers, num_classes, arch): self.inplanes = 64 super(ResNet, self).__init__() self.conv1 = nn.Conv1d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm1d(64) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1]) # , stride=2) self.layer3 = self._make_layer(block, 256, layers[2]) # , stride=2) self.layer4 = self._make_layer(block, 512, layers[3]) # , stride=2) self.avgpool = nn.AvgPool1d(7, stride=1) self.fc = nn.Linear(22528, num_classes) # 512 * block.expansion self.arch = arch for m in self.modules(): if isinstance(m, nn.Conv1d): n = m.kernel_size[0] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.BatchNorm1d): m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv1d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm1d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = x.view(x.size(0), -1) # print(x.size()) x = self.fc(x) return x def resnet18(pretrained=False, **kwargs): """Constructs a ResNet-18 model. Arguments: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(BasicBlock, [2, 2, 2, 2], arch='resnet18', **kwargs) return model def resnet34(pretrained=False, **kwargs): """Constructs a ResNet-34 model. Arguments: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(BasicBlock, [3, 4, 6, 3], arch='resnet34', **kwargs) return model def resnet50(pretrained=False, **kwargs): """Constructs a ResNet-50 model. Arguments: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 6, 3], arch='resnet50', **kwargs) return model def resnet101(pretrained=False, **kwargs): """Constructs a ResNet-101 model. Arguments: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 4, 23, 3], arch='resnet101', **kwargs) return model def resnet152(pretrained=False, **kwargs): """Constructs a ResNet-152 model. Arguments: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = ResNet(Bottleneck, [3, 8, 36, 3], arch='resnet152', **kwargs) return model

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null

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null

0

1

0

c82fbb8e27137ecf71edbf4cda57e644ec71cfa9

1,398

py

Python

other_models/AAN/adaptive-aggregation-networks/dataloaders/cifar100_dirmap.py

kreimanlab/AugMem

cb0e8d39eb0c469da46c7c550c19229927a2bec5

[ "MIT" ]

6

2021-04-07T15:17:24.000Z

2021-07-07T04:37:29.000Z

other_models/Remind/image_classification_experiments/dataloaders/cifar100_dirmap.py

kreimanlab/AugMem

cb0e8d39eb0c469da46c7c550c19229927a2bec5

[ "MIT" ]

null

other_models/Remind/image_classification_experiments/dataloaders/cifar100_dirmap.py

kreimanlab/AugMem

cb0e8d39eb0c469da46c7c550c19229927a2bec5

[ "MIT" ]

null

import os import sys import pandas as pd # USAGE: python cifar100_dirmap.py <path to cifar100 dataset directory> # Organized cifar100 directory can be created using cifar2png: https://github.com/knjcode/cifar2png if len(sys.argv) > 1: DATA_DIR = sys.argv[1] else: DATA_DIR = "./../data/cifar100" # Get class names class_names = [ file for file in os.listdir(os.path.join(DATA_DIR, "train")) if os.path.isdir(os.path.join(DATA_DIR, "train", file)) ] class_names.sort() class_dicts = [{"class": class_names[i], "label": i} for i in range(len(class_names))] pd.DataFrame(class_dicts).to_csv("cifar100_classes.csv", index=False) image_list = [] for train_test_idx, train_test in enumerate(["train", "test"]): for img_class in class_names: img_files = [f for f in os.listdir(os.path.join(DATA_DIR, train_test, img_class)) if f.endswith(".png")] for fname in img_files: image_list.append({ "class": img_class, "object": 0, "session": train_test_idx, "im_path": os.path.join(train_test, img_class, fname), }) img_df = pd.DataFrame(image_list) img_df = img_df.sort_values(by=["class", "object", "session", "im_path"], ignore_index=True) img_df["im_num"] = img_df.groupby(["class", "object", "session"]).cumcount() + 1 img_df.to_csv("cifar100_dirmap.csv") print(img_df.head())

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c82fd0f2d54b784533c3c8e4ad5838457eb0383a

5,616

py

Python

tests/integration_tests/data_steward/cdr_cleaner/cleaning_rules/remove_participant_data_past_deactivation_date_test.py

lrwb-aou/curation

e80447e56d269dc2c9c8bc79e78218d4b0dc504c

[ "MIT" ]

16

2017-06-30T20:05:05.000Z

2022-03-08T21:03:19.000Z

tests/integration_tests/data_steward/cdr_cleaner/cleaning_rules/remove_participant_data_past_deactivation_date_test.py

lrwb-aou/curation

e80447e56d269dc2c9c8bc79e78218d4b0dc504c

[ "MIT" ]

342

2017-06-23T21:37:40.000Z

2022-03-30T16:44:16.000Z

tests/integration_tests/data_steward/cdr_cleaner/cleaning_rules/remove_participant_data_past_deactivation_date_test.py

lrwb-aou/curation

e80447e56d269dc2c9c8bc79e78218d4b0dc504c

[ "MIT" ]

33

2017-07-01T00:12:20.000Z

2022-01-26T18:06:53.000Z

""" Ensures there is no data past the deactivation date for deactivated participants. Original Issue: DC-686 The intent is to sandbox and drop records dated after the date of deactivation for participants who have deactivated from the Program This test will mock calling the PS API and provide a returned value. Everything within the bounds of our team will be tested. """ # Python imports import mock import os # Third party imports import pandas as pd # Project imports from app_identity import PROJECT_ID from common import OBSERVATION from cdr_cleaner.cleaning_rules.remove_participant_data_past_deactivation_date import ( RemoveParticipantDataPastDeactivationDate) from constants.retraction.retract_deactivated_pids import DEACTIVATED_PARTICIPANTS from tests.integration_tests.data_steward.cdr_cleaner.cleaning_rules.bigquery_tests_base import BaseTest class RemoveParticipantDataPastDeactivationDateTest( BaseTest.CleaningRulesTestBase): @classmethod def setUpClass(cls): print('**************************************************************') print(cls.__name__) print('**************************************************************') super().initialize_class_vars() # set the test project identifier project_id = os.environ.get(PROJECT_ID) cls.project_id = project_id # set the expected test datasets dataset_id = os.environ.get('COMBINED_DATASET_ID') cls.dataset_id = dataset_id sandbox_id = f"{dataset_id}_sandbox" cls.sandbox_id = sandbox_id cls.kwargs = { 'table_namer': 'bar_ds', 'api_project_id': 'foo-project-id' } cls.rule_instance = RemoveParticipantDataPastDeactivationDate( project_id, dataset_id, sandbox_id, **cls.kwargs) sb_table_names = cls.rule_instance.get_sandbox_tablenames() cls.fq_sandbox_table_names = [ f'{project_id}.{sandbox_id}.{table_name}' for table_name in sb_table_names ] # append table name here to ensure proper cleanup cls.fq_sandbox_table_names.append( f"{project_id}.{sandbox_id}.{DEACTIVATED_PARTICIPANTS}") cls.fq_table_names = [ f"{project_id}.{dataset_id}.{tablename}" for tablename in cls.rule_instance.affected_tables ] cls.fq_obs_table = [ table for table in cls.fq_table_names if 'observation' in table ][0] # call super to set up the client, create datasets, and create # empty test tables # NOTE: does not create empty sandbox tables. super().setUpClass() def setUp(self): """ Add data to the tables for the rule to run on. """ insert_fake_data_tmpls = [ self.jinja_env.from_string(""" INSERT INTO `{{fq_table_name}}` (observation_id, person_id, observation_concept_id, observation_date, observation_type_concept_id, observation_source_concept_id) VALUES -- Values to exist after running the cleaning rule -- -- 801 is before the user deactivates -- -- 802, the user doesn't deactivate -- (801, 1, 1585899, date('2019-05-01'), 45905771, 111111), (802, 2, 1585899, date('2019-05-01'), 45905771, 222222), -- Values that should be removed by the cleaning rule -- -- 804 is after person 1 deactivates -- -- 805 is after user 3 deactivates -- (804, 1, 1585899, date('2020-05-01'), 45905771, null), (805, 3, 1585899, date('2020-05-01'), 45905771, 45) """) ] self.load_statements = [] # create the string(s) to load the data for tmpl in insert_fake_data_tmpls: query = tmpl.render(fq_table_name=self.fq_obs_table) self.load_statements.append(query) super().setUp() @mock.patch( 'utils.participant_summary_requests.get_deactivated_participants') @mock.patch('retraction.retract_utils.is_deid_label_or_id') def test_removing_data_past_deactivated_date(self, mock_deid, mock_func): """ Validate deactivated participant records are dropped via cleaning rule. Validates pre-conditions, test execution and post conditions based on the load statements and the tables_and_counts variable. Uses a mock to return a staged data frame object for this test instead of calling the PS API. """ columns = ['deactivated_date', 'person_id', 'suspension_status'] values = [ ['2020-01-01', 1, 'NO_CONTACT'], # corresponds with record 804 ['2020-01-01', 3, 'NO_CONTACT'] # corresponds with record 805 ] deactivated_df = pd.DataFrame(values, columns=columns) mock_func.return_value = deactivated_df mock_deid.return_value = False self.load_test_data(self.load_statements) # Using the 0 position because there is only one sandbox table and # one affected OMOP table obs_sandbox = [ table for table in self.fq_sandbox_table_names if 'observation' in table ][0] tables_and_counts = [{ 'name': 'observation', 'fq_table_name': self.fq_obs_table, 'fq_sandbox_table_name': obs_sandbox, 'fields': ['observation_id'], 'loaded_ids': [801, 802, 804, 805], 'sandboxed_ids': [804, 805], 'cleaned_values': [(801,), (802,)] }] self.default_test(tables_and_counts)

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null

0

null

0

1

0

c834981294e35ab677847178ee1ed2e7e3411bb0

2,476

py

Python

charlie2/tools/trial.py

sammosummo/Charlie2

e856b9bfc83c11e57a63d487fa14a63764e3f6ae

[ "MIT" ]

5

2019-10-10T08:22:29.000Z

2021-04-09T02:34:13.000Z

charlie2/tools/trial.py

sammosummo/Charlie2

e856b9bfc83c11e57a63d487fa14a63764e3f6ae

[ "MIT" ]

20

2018-06-20T21:15:48.000Z

2018-09-06T17:13:46.000Z

charlie2/tools/trial.py

sammosummo/Charlie2

e856b9bfc83c11e57a63d487fa14a63764e3f6ae

[ "MIT" ]

3

2019-11-24T04:10:40.000Z

2020-04-04T07:50:57.000Z

"""Defines the trial class. """ from datetime import datetime from logging import getLogger logger = getLogger(__name__) class Trial(dict): def __init__(self, *args, **kwds) -> None: """Create a trial object. Trials objects are fancy dictionaries whose items are also attributes. They are initialised exactly like dictionaries except that the resulting object must contain the attribute `'trial_number'`. Trials typically contain several other attributes in addition to those listed below. Trials from the same experiment should contain the same attributes. """ super(Trial, self).__init__(*args, **kwds) logger.debug(f"initialised {type(self)}") self.__dict__ = self defaults = { "block_number": 0, "status": "pending", "practice": False, "resumed_from_here": False, "started_timestamp": datetime.now(), "correct": None, "reason_skipped": "not skipped", "finished_timestamp": None, # "_remaining_trials_in_block": None, # "_remaining_trials_in_test": None, } self.__dict__.update({**defaults, **self.__dict__}) assert "trial_number" in self.__dict__, "must contain trial_number" assert isinstance(self.trial_number, int), "trial_number must be an int" if self.block_number == 0: self.__dict__["first_block"] = True else: self.__dict__["first_block"] = False if self.trial_number == 0: self.__dict__["first_trial_in_block"] = True else: self.__dict__["first_trial_in_block"] = False if self.first_block and self.first_trial_in_block: self.__dict__["first_trial_in_test"] = True else: self.__dict__["first_trial_in_test"] = False # rtib = self._remaining_trials_in_block # if rtib is not None: # if len(rtib) == 0: # self.__dict__["last_trial_in_block"] = True # else: # self.__dict__["last_trial_in_block"] = False # rtit = self._remaining_trials_in_test # if rtit is not None: # if len(rtit) == 0: # self.__dict__["last_trial_in_test"] = True # else: # self.__dict__["last_trial_in_test"] = False logger.debug("finished constructing trial object")

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0.329787

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0

null

0

null

0

1

0

c835c38f6e541b5231eac621e19ad8646fab5eb5

1,839

py

Python

categorize_reviews.py

curtislb/ReviewTranslation

b2d14d349b6016d275fa22532eae6b67af243a55

[ "Apache-2.0" ]

null

categorize_reviews.py

curtislb/ReviewTranslation

b2d14d349b6016d275fa22532eae6b67af243a55

[ "Apache-2.0" ]

null

categorize_reviews.py

curtislb/ReviewTranslation

b2d14d349b6016d275fa22532eae6b67af243a55

[ "Apache-2.0" ]

null

#!/usr/bin/env python import ast import sys import nltk import numpy as np from review_data import read_reviews ############################################################################### def main(): low = 3.0 high = 4.0 target_language = u"english" topics = [] with open(sys.argv[1]) as infile: for topic in infile: topics.append(ast.literal_eval(topic)) outfiles = [] prefix = sys.argv[3] for i in xrange(len(topics)): outfile = [] outfile.append(open(prefix + str(i) + "-.json" ,"w")) outfile.append(open(prefix + str(i) + "=.json" ,"w")) outfile.append(open(prefix + str(i) + "+.json" ,"w")) outfiles.append(outfile) counter = 0 for review in read_reviews(sys.argv[2]): if review['lang'] != target_language: continue text = review['text'] tokens = nltk.word_tokenize(text) best_value = [0]*len(topics) for token in tokens: for i in xrange(len(topics)): if token in topics[i]: best_value[i] += topics[i][token] rating = review['rating'] del review['lang'] del review['rating'] if rating < low: outfiles[np.argmax(best_value)][0].write(str(review) + '\n') elif rating > high: outfiles[np.argmax(best_value)][2].write(str(review) + '\n') else: outfiles[np.argmax(best_value)][1].write(str(review) + '\n') counter+=1 if counter %10000 == 0: for outfile in outfiles: for ofile in outfile: ofile.flush() for outfile in outfiles: for ofile in outfile: ofile.close() if __name__ == '__main__': main()

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0.015335

0.326264

1,839

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0

0.117647

0

null

0

null

0

1

0

c83adde56479731d1abd45b0e5be159767406e09

1,063

py

Python

Two_Sum_1.py

JazzikPeng/Algorithm-in-Python

915135b1cdd02a6bb8d7068a54b2f497b2ec31d4

[ "MIT" ]

3

2018-02-05T06:15:57.000Z

2019-04-07T23:33:07.000Z

Two_Sum_1.py

JazzikPeng/Algorithm-in-Python

915135b1cdd02a6bb8d7068a54b2f497b2ec31d4

[ "MIT" ]

null

Two_Sum_1.py

JazzikPeng/Algorithm-in-Python

915135b1cdd02a6bb8d7068a54b2f497b2ec31d4

[ "MIT" ]

null

class Solution: def twoSum(self, nums, target): """ :type nums: List[int] :type target: int :rtype: List[int] """ # fill initial set/dict s = {target-nums[0]} d = {nums[0]: 0} for i in range(1, len(nums)): if nums[i] in s: return [d[target-nums[i]], i] else: s.add(target-nums[i]) d[nums[i]] = i return None class Solution(object): def twoSum(self, nums, target): """ :type nums: List[int] :type target: int :rtype: List[int] """ for i in nums: nums[nums.index(i)] = 'current' if (target - i) in nums and nums[nums.index(target - i)]!='current': print(i, nums.index('current'), nums) nums[nums.index('current')] = 'marked' nums[nums.index(target - i)] = 'other' return [nums.index('marked'), nums.index('other')] nums[nums.index('current')] = 'visited'

30.371429

80

0.464722

127

1,063

3.889764

0.291339

0.145749

0.131579

0.068826

0.336032

0.255061

0

0.006088

0.381938

1,063

34

81

31.264706

0.745814

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0

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0

1

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false

0

0.333333

0.047619

0

null

0

null

0

1

0

c83f4c51440116a7f88bf4d5e46dda85c09f8606

2,069

py

Python

shortest_path_revisit_and_NP/week1/apsp_johnsons.py

liaoaoyuan97/standford_algorithms_specialization

2914fdd397ce895d986ac855e78afd7a51ceff68

[ "MIT" ]

null

shortest_path_revisit_and_NP/week1/apsp_johnsons.py

liaoaoyuan97/standford_algorithms_specialization

2914fdd397ce895d986ac855e78afd7a51ceff68

[ "MIT" ]

null

shortest_path_revisit_and_NP/week1/apsp_johnsons.py

liaoaoyuan97/standford_algorithms_specialization

2914fdd397ce895d986ac855e78afd7a51ceff68

[ "MIT" ]

1

2021-01-18T19:35:48.000Z

import time import numpy as np from os import path def read_graph(filename): i = 0 with open(path.join('.', filename), 'r') as f: for row in f.readlines(): if i == 0: _list = row.strip("\n").split(' ') n_vertex, n_edge = int(_list[0]), int(_list[1]) shortest_paths = np.ones((n_vertex + 1, n_vertex + 1, n_vertex + 1)) * float('inf') i += 1 else: _list = row.strip("\n").split(' ') shortest_paths[int(_list[0])][int(_list[1])][0] = float(_list[2]) for i in range(1, n_vertex + 1): shortest_paths[i][i][0] = 0 return n_vertex, shortest_paths def compute_apsp(n_vertex, shortest_paths): for k in range(1, n_vertex + 1): for i in range(1, n_vertex + 1): for j in range(1, n_vertex + 1): if shortest_paths[i][j][k - 1] > (shortest_paths[i][k][k - 1] + shortest_paths[k][j][k - 1]): shortest_paths[i][j][k] = shortest_paths[i][k][k - 1] + shortest_paths[k][j][k - 1] else: shortest_paths[i][j][k] = shortest_paths[i][j][k - 1] for i in range(1, n_vertex + 1): if shortest_paths[i][i][n_vertex] < 0: return None m = shortest_paths[1][2][n_vertex] for i in range(1, n_vertex + 1): for j in range(1, n_vertex + 1): if i != j and shortest_paths[i][j][n_vertex] < m: m = shortest_paths[i][j][n_vertex] return m if __name__ == "__main__": time_start = time.time() n_vertex, shortest_paths = read_graph("grh1.txt") print(compute_apsp(n_vertex, shortest_paths)) print(time.time() - time_start) time_start = time.time() n_vertex, shortest_paths = read_graph("grh2.txt") print(compute_apsp(n_vertex, shortest_paths)) print(time.time() - time_start) time_start = time.time() n_vertex, shortest_paths = read_graph("grh3.txt") print(compute_apsp(n_vertex, shortest_paths)) print(time.time() - time_start)

32.84127

109

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0.177215

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0.074176

0.712454

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0.554945

0.541209

0.480769

0.475275

0

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0.285645

2,069

62

110

33.370968

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0

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0

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false

0

0.0625

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0.166667

0.125

0

null

0

null

0

1

0

c84122fcd1573afd525866c481ac3a9686f3174d

2,448

py

Python

Cardio-Monitor-main/visualization.py

jrderek/computer-vision-exercises

e9735394220f8120453de70b58596ef9e87df926

[ "MIT" ]

null

Cardio-Monitor-main/visualization.py

jrderek/computer-vision-exercises

e9735394220f8120453de70b58596ef9e87df926

[ "MIT" ]

null

Cardio-Monitor-main/visualization.py

jrderek/computer-vision-exercises

e9735394220f8120453de70b58596ef9e87df926

[ "MIT" ]

null

from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure import io import random import numpy as np def visualizationpreprocess(age,sex,cp,trestbps,restecg,chol,fbs,thalach,exang,oldpeak,slope,ca,thal,result): if sex=="male": sex=1 else: sex=0 if cp=="Typical angina": cp=0 elif cp=="Atypical angina": cp=1 elif cp=="Non-anginal pain": cp=2 elif cp=="Asymptomatic": cp=2 if exang=="Yes": exang=1 elif exang=="No": exang=0 if fbs=="Yes": fbs=1 elif fbs=="No": fbs=0 if slope=="Upsloping: better heart rate with excercise(uncommon)": slope=0 elif slope=="Flatsloping: minimal change(typical healthy heart)": slope=1 elif slope=="Downsloping: signs of unhealthy heart": slope=2 if thal=="fixed defect: used to be defect but ok now": thal=2 elif thal=="reversable defect: no proper blood movement when excercising": thal=3 elif thal=="normal": thal=1 if restecg=="Nothing to note": restecg=0 elif restecg=="ST-T Wave abnormality": restecg=1 elif restecg=="Possible or definite left ventricular hypertrophy": restecg=2 #final_list=[int(cp),int(trestbps),int(restecg),int(chol),int(fbs),int(thalach),int(exang),float(oldpeak),int(slope),int(ca),int(thal)] normal_value1=[0.478261,0.159420,0.449275,0.550725,1.585507,1.166667,1.166667,2.543478] user_value1=[float(cp),float(fbs),float(restecg),float(exang),float(oldpeak),float(slope),float(ca),float(thal)] normal_value2=[134.398551,251.086957,139.101449] user_value2=[float(trestbps),float(chol),float(thalach)] list1=[normal_value1,user_value1] list2=[normal_value2,user_value2] return list1,list2 # def create_figure1(data1): # fig = plt.figure() # axis = fig.add_axes([0,0,1,1]) # y1 = data1[0] # y2 = data1[1] # width = 0.30 # x=np.arange(8) # axis.bar(x-0.3, y1, width, color='cyan') # axis.bar(x, y2, width, color='orange') # # axis.bar(xs, ys) # # axis.xticks(x, ['cp','chol','fbs','exang','oldpeak','slope','ca','thal']) # # axis.xlabel("Heart health defining attributes") # axis.set_ylabel("values") # # axis.legend(["Normal", "Yours"]) # axis.set_title('Your data corresponding to normal data') # return fig

31.792208

139

0.635212

348

2,448

4.428161

0.422414

0.016223

0.022064

0.024659

0.029851

0

0.069466

0.21201

2,448

76

140

32.210526

0.729393

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0

0.04

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0

1

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false

0

0.1

0

0.14

0

null

0

null

0

1

0

c8413d24c21af2dd79f48f95f23cc0565affc86b

6,006

py

Python

at_tmp/model/util/TMP_DB_OPT__.py

zuoleilei3253/zuoleilei

e188b15a0aa4a9fde00dba15e8300e4b87973e2d

[ "Apache-2.0" ]

null

at_tmp/model/util/TMP_DB_OPT__.py

zuoleilei3253/zuoleilei

e188b15a0aa4a9fde00dba15e8300e4b87973e2d

[ "Apache-2.0" ]

null

at_tmp/model/util/TMP_DB_OPT__.py

zuoleilei3253/zuoleilei

e188b15a0aa4a9fde00dba15e8300e4b87973e2d

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2018/7/15 16:36 # @Author : bxf # @File : P_DB_OPT.py # @Software: PyCharm import pymysql import json from datetime import date, datetime from model.util import md_Config from model.util.PUB_LOG import * ''' 提供数据的增删改查功能: ''' class DB_CONN(): def __init__(self): ''' 初始化连接数据，并输出连接步骤 ''' try: conn = pymysql.Connect( host=md_Config.getConfig("DATABASE1", "IP"), port=int(md_Config.getConfig("DATABASE1", "port")), user=md_Config.getConfig("DATABASE1", "user"), passwd=md_Config.getConfig("DATABASE1", "password"), db=md_Config.getConfig("DATABASE1", "db"), charset=md_Config.getConfig("DATABASE1", "charset") ) exeLog( "数据库:【 " + md_Config.getConfig("DATABASE1", "db") + "】连接成功！数据库环境为： " + md_Config.getConfig("DATABASE1", "IP")) self.conn = conn except Exception as e: dataOptLog("***数据库:【 " + md_Config.getConfig("DATABASE1", "db") + "】连接失败，请检查连接参数！错误信息：%s" % e + "数据库环境为：" + md_Config.getConfig( "DATABASE1", "IP")) def db_Query_Json(self, sql): ''' 获取数据json格式游标，使用需要fetchall()或fetchone()fetchmany() :param sql: 查询语句 :return: 游标json格式使用时需要使用fetchall()或fetchone()fetchmany() ''' cur = self.conn.cursor(cursor=pymysql.cursors.DictCursor) try: cur.execute(sql) exeLog("***查询获取游标成功！查询语句为：" + sql) return cur except Exception as e: dataOptLog('***执行查询失败，请检查数据！错误信息：%s' % e + "查询语句为：" + sql) finally: cur.close() self.conn.close() # def db_Query_tuple(self, sql): ''' 获取数据元组格式游标，使用需要fetchall()或fetchone()fetchmany() :param sql: 查询语句 :return: 元组格式游标，使用需要fetchall()或fetchone()fetchmany() ''' cur = self.conn.cursor() try: cur.execute(sql) exeLog("***查询获取游标成功！查询语句为：" + sql) return cur except Exception as e: dataOptLog('***执行查询失败，请检查数据！错误信息：%s' % e + "查询语句为：" + sql) finally: cur.close() self.conn.close() # 数据库插入 def db_Insert(self, sql, params): ''' 数据库插入 :param sql: 插入语句 :param params: 插入数据 :return: 插入成功数目 ''' cur = self.conn.cursor() try: data_counts = cur.execute(sql, params) self.conn.commit() exeLog("***数据插入成功！执行语句为：" + sql) return data_counts except Exception as e: self.conn.rollback() dataOptLog('***插入失败，请检查数据！错误信息：%s' % e + "查询语句为：" + sql) finally: cur.close() self.conn.close() # 数据库更新 def db_Update(self, sql): ''' :param sql: :return: ''' cur = self.conn.cursor() try: data_counts = cur.execute(sql) self.conn.commit() exeLog("***更新数据成功！更新语句为：" + sql) return data_counts except Exception as e: self.conn.rollback() dataOptLog('***执行更新失败，请检查数据！错误信息：%s' % e + "查询语句为：" + sql) finally: cur.close() self.conn.close() # 数据库中时间转换json格式在返回的json方法里加上cls=MyEncoder class MyEncoder(json.JSONEncoder): def default(self, obj): ''' 针对datetime格式的转换 :param obj: 参数数据 :return: 返回json格式 ''' try: # if isinstance(obj, datetime.datetime): # return int(mktime(obj.timetuple())) if isinstance(obj, datetime): return obj.strftime('%Y-%m-%d %H:%M:%S') elif isinstance(obj, date): return obj.strftime('%Y-%m-%d') else: return json.JSONEncoder.default(self, obj) except Exception as e: return False # 数据库数据直接转换成json格式输出无数据返回FALSE def getJsonFromDatabase(sql): cur = DB_CONN().db_Query_Json(sql) if cur.rowcount == 0: exeLog("***数据库内容为空") return False else: exeLog("***返回JSON数据成功") return cur.fetchall() def getTupleFromDatabase(sql): cur = DB_CONN().db_Query_tuple(sql) if cur.rowcount == 0: exeLog("***数据库内容为空") return False else: exeLog("***返回JSON数据成功") return cur.fetchall() def insertToDatabase(table,data,**kwargs): ''' :param table: 表名 :param data: 插入数据 :return: 插入成功数 ''' col_list=dict() # print(type(data)) # print(type(kwargs)) col_list.update(data) col_list.update(kwargs) col_lists=col_list.keys() col='' for j in col_lists: col=col+j+',' val=[] for i in col_lists: val_one=col_list[i] val.append(val_one) var_lists=tuple(val) sql='INSERT INTO '+table +' ( '+ col[:-1] +' ) VALUE '+str(var_lists) exeLog("******生成添加语句成功！~~***") result=DB_CONN().db_Update(sql) exeLog("******记录新增成功******") return result def updateToDatabase(table, data, col, val): ''' 更新 :param table:表名 :param data: 更新数据 :param col: 定位 :param val:定位值 :return: 更新成功数 ''' col_lists = tuple(data.keys()) list_one = "" for i in col_lists: val_one = data[i] list_one = list_one + i + '= "' + str(val_one) + '",' sql = "UPDATE " + table + ' SET ' + list_one[:-1] + ' WHERE ' + col + ' = "' + str(val) + '"' exeLog("生成更新语句成功！") return sql

28.330189

129

0.494006

604

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4.817881

0.293046

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0.058419

0.089347

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0.391065

0.295533

0.248797

0

0.006849

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6,006

211

130

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0

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0

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0.019451

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1

0.079365

false

0.007937

0.039683

0

0.246032

0

null

0

null

0

1

0

c8422d03ff6c162a7a235c164df43ce7fd4202c5

1,025

py

Python

setup.py

drougge/wellpapp-pyclient

43d66a1e2a122ac87e477905c5e2460e11be3c26

[ "MIT" ]

null

setup.py

drougge/wellpapp-pyclient

43d66a1e2a122ac87e477905c5e2460e11be3c26

[ "MIT" ]

null

setup.py

drougge/wellpapp-pyclient

43d66a1e2a122ac87e477905c5e2460e11be3c26

[ "MIT" ]

null

#!/usr/bin/env python3 from setuptools import setup fuse_reqs = [ 'fuse-python >= 0.3.1; python_version < "3"', 'fuse-python >= 1.0.0; python_version > "3"', ] readme = open('README.md', 'r').read() readme = readme.replace( '(FUSE.md)', '(https://github.com/drougge/wellpapp-pyclient/blob/master/FUSE.md)' ) setup( name='wellpapp', version='CHANGEME.dev', # set this for each release packages=[ 'wellpapp', 'wellpapp.shell', ], entry_points={ 'console_scripts': [ 'wp = wellpapp.__main__:main', ], }, install_requires=[ 'Pillow >= 3.1.2', 'PyGObject >= 3.20', ], extras_require={ 'fuse': fuse_reqs, 'all': fuse_reqs, }, python_requires='>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*', author='Carl Drougge', author_email='bearded@longhaired.org', url='https://github.com/drougge/wellpapp-pyclient', license='MIT', description='Client library and application for the wellpapp image tagging system.', long_description=readme, long_description_content_type='text/markdown', )

21.808511

85

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1,025

4.76087

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0.009132

0.063927

0.112633

0

0.03044

0.134634

1,025

46

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0.710259

0.045854

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0

0.051282

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0.045082

0

1

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0

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0

0.025641

0

null

0

null

0

1

0

c8427460e2bcf42333ee94274c805a7a6ae2d6ab

715

py

Python

students/K33421/Zmievskiy_Danil/Lr1/Task04/server.py

DanilZmievskiy/ITMO_ICT_WebDevelopment_2020-2021

8bb6e90e6592c04f4b959184310e0890aaa24e16

[ "MIT" ]

null

students/K33421/Zmievskiy_Danil/Lr1/Task04/server.py

DanilZmievskiy/ITMO_ICT_WebDevelopment_2020-2021

8bb6e90e6592c04f4b959184310e0890aaa24e16

[ "MIT" ]

null

students/K33421/Zmievskiy_Danil/Lr1/Task04/server.py

DanilZmievskiy/ITMO_ICT_WebDevelopment_2020-2021

8bb6e90e6592c04f4b959184310e0890aaa24e16

[ "MIT" ]

null

import socket import threading conn = socket.socket(socket.AF_INET,socket.SOCK_STREAM) conn.bind (('', 7070)) conn.listen() clients = [] print ('Start Server') def new_client(): while True: clientsocket, address = conn.accept() if clientsocket not in clients: clients.append(clientsocket) threading.Thread(target = chat, args = [clientsocket, address]).start() def chat(clientsocket, address): print (address[0], address[1]) while True: try: data = clientsocket.recv(1024) for client in clients: if client == clientsocket: continue client.send(data) except Exception: clients.remove(clientsocket) clientsocket.close() threading.Thread(target=new_client()).start()

21.029412

74

0.711888

87

715

5.804598

0.505747

0.112871

0.083168

0

0.016694

0.162238

715

34

75

21.029412

0.826377

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0.153846

0.076923

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null

0

null

0

1

0

c843403db7b167cca6757d1608c2ce426ef07684

1,563

py

Python

cutelog/pop_in_dialog.py

CS-GSI/cutelog

faca7a7bfd16559973178d3c87cb3b0c6667d4d3

[ "MIT" ]

125

2018-07-27T15:23:35.000Z

2022-03-09T18:18:08.000Z

cutelog/pop_in_dialog.py

CS-GSI/cutelog

faca7a7bfd16559973178d3c87cb3b0c6667d4d3

[ "MIT" ]

12

2019-02-02T01:02:59.000Z

2022-03-14T08:31:41.000Z

cutelog/pop_in_dialog.py

CS-GSI/cutelog

faca7a7bfd16559973178d3c87cb3b0c6667d4d3

[ "MIT" ]

26

2018-08-24T23:49:58.000Z

2022-01-27T12:29:38.000Z

from qtpy.QtCore import Signal from qtpy.QtWidgets import QDialog, QDialogButtonBox, QListWidget, QVBoxLayout class PopInDialog(QDialog): pop_in_tabs = Signal(list) def __init__(self, parent, loggers): super().__init__(parent) self.loggers = loggers self.setupUi() def setupUi(self): self.resize(200, 320) self.vbox = QVBoxLayout(self) self.listWidget = QListWidget(self) self.listWidget.setSelectionMode(self.listWidget.MultiSelection) self.listWidget.selectionModel().reset() self.vbox.addWidget(self.listWidget) self.buttonBox = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel, self) self.vbox.addWidget(self.buttonBox) self.buttonBox.accepted.connect(self.accept) self.listWidget.doubleClicked.connect(self.accept) self.buttonBox.rejected.connect(self.reject) self.fill_logger_list() def fill_logger_list(self): for logger in self.loggers: if logger.popped_out: self.listWidget.addItem(logger.name) self.listWidget.setCurrentRow(0) def accept(self, index=None): names = [] if index is not None: item = self.listWidget.itemFromIndex(index) names.append(item.text()) else: for item in self.listWidget.selectedItems(): names.append(item.text()) if len(names) > 0: self.pop_in_tabs.emit(names) self.done(0) def reject(self): self.done(0)

31.26

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0.643634

172

1,563

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0.0182

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1,563

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0

0.230769

0

null

0

null

0

1

0

c84378df20614229cbb5ed8f3fb0fb2de32e4ad3

6,730

py

Python

MineSweeper/minesweeper.py

Ratnesh4193/Amazing-Python-Scripts

0652a6066a3eeaf31830d7235da209699c45f779

[ "MIT" ]

1

2021-04-17T08:33:25.000Z

MineSweeper/minesweeper.py

Ratnesh4193/Amazing-Python-Scripts

0652a6066a3eeaf31830d7235da209699c45f779

[ "MIT" ]

null

MineSweeper/minesweeper.py

Ratnesh4193/Amazing-Python-Scripts

0652a6066a3eeaf31830d7235da209699c45f779

[ "MIT" ]

1

2021-07-22T07:06:09.000Z

# Importing required libraries from tkinter import * from tkinter import messagebox as mb from tkinter import ttk import random # function to create screen for the game def board(): global value,w # initialising screen root=Tk() root.geometry("320x335") root.title("MineSweeper") root.resizable(False,False) root.eval('tk::PlaceWindow . center') # creating label w = Label(root, text="Start Playing!",bg='yellow',fg='red') # creating buttons but11=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but11,root)) but12=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but12,root)) but13=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but13,root)) but14=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but14,root)) but15=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but15,root)) but21=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but21,root)) but22=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but22,root)) but23=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but23,root)) but24=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but24,root)) but25=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but25,root)) but31=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but31,root)) but32=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but32,root)) but33=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but33,root)) but34=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but34,root)) but35=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but35,root)) but41=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but41,root)) but42=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but42,root)) but43=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but43,root)) but44=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but44,root)) but45=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but45,root)) but51=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but51,root)) but52=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but52,root)) but53=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but53,root)) but54=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but54,root)) but55=Button(root,bg="grey", text="", padx=7.5, pady=5,bd=4,font="digifacewide 18",height=1,width=2,command=lambda:game(but55,root)) # adding buttons to the screen but11.grid(row=1,column=1) but12.grid(row=1,column=2) but13.grid(row=1,column=3) but14.grid(row=1,column=4) but15.grid(row=1,column=5) but21.grid(row=2,column=1) but22.grid(row=2,column=2) but23.grid(row=2,column=3) but24.grid(row=2,column=4) but25.grid(row=2,column=5) but31.grid(row=3,column=1) but32.grid(row=3,column=2) but33.grid(row=3,column=3) but34.grid(row=3,column=4) but35.grid(row=3,column=5) but41.grid(row=4,column=1) but42.grid(row=4,column=2) but43.grid(row=4,column=3) but44.grid(row=4,column=4) but45.grid(row=4,column=5) but51.grid(row=5,column=1) but52.grid(row=5,column=2) but53.grid(row=5,column=3) but54.grid(row=5,column=4) but55.grid(row=5,column=5) # adding label to the screen w.grid(row=0,column=0,columnspan=6) # creating values for each cell from 1-5 and "b" for bomb butlist=[but11,but12,but13,but14,but15,but21,but22,but23,but24,but25, but31,but32,but33,but34,but35,but41,but42,but43,but44,but45, but51,but52,but53,but54,but55] vallist=['1','2','3','4','1','2','3','4','1','2','3','4','1','2','3','4', '1','2','3','4','b','b','b','b','b'] value={} random.shuffle(vallist)# shuffle for randomness for i in range(25): value[butlist[i]]=vallist[i]# assining values to buttons root.mainloop() def game(b,tk): if value[b]=='b': # if bomb is clicked bomb_clicked(b,tk) else: # if number is clicked number_clicked(b,int(value[b]),tk) total =0 #function when bomb is clicked def bomb_clicked(b,tk): # making changes to cell b['text']="\U0001f600" b['relief']=SUNKEN; b['bg']="orange" global value,total # displaying message and asking for replay a=mb.askquestion("YOU LOSE"," Your score : " +str(total) +"\nDo you want to play again??") tk.destroy()# exiting current board if a=='yes' : total = 0 board() def number_clicked(b,n,tk): global value,total if n!=0 and b['text']=="": # making changes to cell and updating score b['text']=n total+=n value[b]='0' w['text']="Your Score : " +str(total) if total>=50: # if player reached score of 50 he won b['text']="\U0001f600" b['relief']=SUNKEN; b['bg']="orange" # displaying message and asking for replay a=mb.askquestion("YOU WON"," Your score : " +str(total) +"\nDo you want to play again??") tk.destroy()# exiting current board if a=='yes' : total=0 board() # showinfo("YOU WON", "YOUR SCORE : " + str(total)) tk.destroy() board()

52.170543

136

0.649034

1,129

6,730

3.865368

0.135518

0.041705

0.068744

0.091659

0.558662

0.552704

0.536205

0

0.088632

0.151709

6,730

128

137

52.578125

0.675775

0.093314

0

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0

1

0.037383

false

0

0.037383

0

0.074766

0

null

0

null

0

1

0

c84571d0d767e8fe81786eba5dfb74f8e16357fc

1,240

py

Python

tests/test_session.py

Streetwise/streetwise-app

13c1649077766e0e20d6903adcd057ae3c07cc9c

[ "MIT" ]

1

2020-05-28T06:50:01.000Z

tests/test_session.py

Streetwise/streetwise-app

13c1649077766e0e20d6903adcd057ae3c07cc9c

[ "MIT" ]

72

2020-05-01T11:11:17.000Z

2022-02-14T09:01:50.000Z

tests/test_session.py

Streetwise/streetwise-app

13c1649077766e0e20d6903adcd057ae3c07cc9c

[ "MIT" ]

3

2020-05-06T20:35:32.000Z

2020-05-07T15:00:51.000Z

""" Python unit tests """ import pytest, json from streetwise.models import Campaign from . import app, app_context, db @pytest.fixture(scope="module") def client(): app.config['TESTING'] = True return app.test_client() def test_campaign_all(client): with app_context: campaign1 = Campaign() campaign2 = Campaign() db.session.add(campaign1) db.session.add(campaign2) db.session.commit() resp = client.get('/api/campaign/all') assert resp.status_code == 200 result = json.loads(resp.data) assert len(result)>1 def test_campaign_sequence(client): with app_context: resp = client.get('/api/campaign/next') result1 = json.loads(resp.data) resp = client.get('/api/campaign/next') result2 = json.loads(resp.data) assert result1['id'] != result2['id'] def test_campaign_post(client): with app_context: resp = client.post('/api/campaign/next', json={'campaign_id':None}) result1 = json.loads(resp.data) id1 = result1['id'] resp = client.post('/api/campaign/next', json={'campaign_id':id1}) result2 = json.loads(resp.data) assert result1['id'] != result2['id']

28.837209

75

0.629839

155

1,240

4.948387

0.322581

0.065189

0.084746

0.110821

0.48631

0.362451

0.237288

0.125163

0

0.019874

0.229032

1,240

42

76

29.52381

0.782427

0.01371

0

0.333333

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0.110288

0

0.121212

1

0.121212

false

0

0.090909

0

0.242424

0

null

0

null

0

1

0

c84abb9eac74cceda1f9caab92fdc8319c29f197

4,900

py

Python

crawler/spiders/weighted_index_spider.py

ChuangYuMing/futures_spread_analysis

71540671eed7ea3abba0a9a5af45f49dcf662ce3

[ "MIT" ]

2

2019-09-19T05:11:00.000Z

2020-07-23T07:26:03.000Z

crawler/spiders/weighted_index_spider.py

ChuangYuMing/futures_spread_analysis

71540671eed7ea3abba0a9a5af45f49dcf662ce3

[ "MIT" ]

11

2020-07-14T10:42:59.000Z

2022-03-02T14:54:10.000Z

crawler/spiders/weighted_index_spider.py

ChuangYuMing/futures_spread_analysis

71540671eed7ea3abba0a9a5af45f49dcf662ce3

[ "MIT" ]

null

# encoding: utf-8 # pylint: disable=E1101 # 加權指數 # https://www.twse.com.tw/zh/page/trading/indices/MI_5MINS_HIST.html import scrapy from scrapy import signals, Spider from urllib.parse import urlencode import time from random import randint import datetime import logging from copy import copy from dateutil.relativedelta import relativedelta import collections import json from zoneinfo import ZoneInfo # for cloud function call && scrapy crawl command call # softlink package folder to root try: from package.tools import is_settle, format_number from package.storage import Storage except: from spiders.package.tools import is_settle, format_number from spiders.package.storage import Storage class WeightedIndexSpider(scrapy.Spider): name = 'weighted_index' def __init__(self, category=None, *args, **kwargs): super(WeightedIndexSpider, self).__init__(*args, **kwargs) self.dataStorage = Storage(self.name) self.data = collections.OrderedDict() self.today = datetime.datetime.now(ZoneInfo("Asia/Taipei")) self.url = 'https://www.twse.com.tw/indicesReport/MI_5MINS_HIST' self.params = { 'response': 'json', 'date': '20110101' } self.startDate = getattr(self, 'start', self.getFormatDate(self.today)) self.endDate = getattr(self, 'end', self.getFormatDate(self.today)) self.startObj = self.parseDate(self.startDate) self.endObj = self.parseDate(self.endDate) def parseDate(self, dateString): year = int(dateString[0:4]) month = int(dateString[4:6]) day = int(dateString[6:8]) return { 'year': year, 'month': month, 'day': day, 'datetime': datetime.date(year, month, day) } def getFormatDate(self, date): year = str(date.year) month = str(date.month) if len(str(date.month)) != 1 else "0" + str(date.month) day = '01' return year + month + day # 西元 def format_ad_date(self, date): date_arr = date.split('/') year = date_arr[0] month = date_arr[1] day = date_arr[2] year = str(int(year) + 1911) return year + '/' + month + '/' + day def start_requests(self): print('start request - %s' % self.name) targetDateObj = copy(self.startObj) while(targetDateObj['datetime'] <= self.endObj['datetime']): self.params['date'] = self.getFormatDate(targetDateObj['datetime']) url = self.url + '?' + urlencode(self.params) yield scrapy.Request( url=url, callback=self.parse, cb_kwargs=dict(targetDateObj=copy(targetDateObj)), errback=self.handle_failure) targetDateObj['datetime'] = targetDateObj['datetime'] + relativedelta(months=1) targetDateObj['year'] = targetDateObj['datetime'].year targetDateObj['month'] = targetDateObj['datetime'].month def handle_failure(self, failure): self.log(failure, level=logging.ERROR) # try with a new proxy self.log('restart from the failed url {}'.format(failure.request.url)) time.sleep(120) yield scrapy.Request( url=failure.request.url, callback=self.parse, cb_kwargs=failure.request.cb_kwargs, errback=self.handle_failure) def parse(self, response, targetDateObj): print(targetDateObj['datetime']) result = json.loads(response.text) data = result['data'] year = targetDateObj['year'] for item in data: datestart = self.format_ad_date(item[0]) if year not in self.data: self.data[year] = {} self.data[year][datestart] = {} self.data[year][datestart]["open"] = format_number(item[1].split(".")[0]) # 開盤 self.data[year][datestart]["high"] = format_number(item[2].split(".")[0]) # 最高 self.data[year][datestart]["low"] = format_number(item[3].split(".")[0]) # 最低 self.data[year][datestart]["w_index"] = format_number(item[4].split(".")[0]) # 收盤 self.data[year][datestart]["is_settle"] = is_settle(datestart, '/') @classmethod def from_crawler(cls, crawler, *args, **kwargs): spider = super(WeightedIndexSpider, cls).from_crawler(crawler, *args, **kwargs) crawler.signals.connect(spider.spider_closed, signal=signals.spider_closed) return spider def spider_closed(self, spider): for year in self.data: newData = self.data[year] data = dict() try: data = self.dataStorage.getOldData(year) except: pass data.update(newData) self.dataStorage.saveData(year, data)

35.507246

93

0.610408

559

4,900

5.271914

0.300537

0.029861

0.032576

0.042755

0.079403

0.047506

0.028504

0

0.012469

0.263469

4,900

137

94

35.766423

0.804101

0.046939

0

0.091743

0

0.064433

0

1

0.082569

false

0.009174

0.146789

0

0.284404

0.018349

0

null

0

null

0

1

0

c84ea79f1edbb49a2816dca5b35662a00efd9c2f

1,198

py

Python

modules/tensorflow/keras/datasets/gaussian_mixture.py

avogel88/compare-VAE-GAE

aa3419c41a58ca6c1a9c1031c0aed7e07c3d4f90

[ "MIT" ]

null

modules/tensorflow/keras/datasets/gaussian_mixture.py

avogel88/compare-VAE-GAE

aa3419c41a58ca6c1a9c1031c0aed7e07c3d4f90

[ "MIT" ]

null

modules/tensorflow/keras/datasets/gaussian_mixture.py

avogel88/compare-VAE-GAE

aa3419c41a58ca6c1a9c1031c0aed7e07c3d4f90

[ "MIT" ]

null

import numpy as np import os from os.path import dirname, join from modules.numpy import covmix, varroll from modules.pandas import DesignMatrix from modules.scipy.stats import gaussian_mixture def gaussian_mixture_generate(file, train=60000, test=60000, validate=10000): dim_x, dim_z = 784, 10 # distributions π = [.2, .3, .5] K, N, D = len(π), dim_z, dim_x µ = np.zeros((K, D)) Σ = covmix(varroll(range(K), (N, D - N), (10, .1))) x_dist = gaussian_mixture(weights=π, mean=µ, cov=Σ) # sampling x = DesignMatrix(x_dist.rvs(train)) y = DesignMatrix(x_dist.rvs(test)) z = DesignMatrix(x_dist.rvs(validate)) # save distribution os.makedirs(dirname(file), exist_ok=True) x_dist.save(file) # save x.to_csv(join(dirname(file), 'train.csv')) y.to_csv(join(dirname(file), 'test.csv')) z.to_csv(join(dirname(file), 'validate.csv')) def gaussian_mixture_load(path): x_dist = gaussian_mixture.load(path) x = DesignMatrix.read_csv(join(dirname(path), 'train.csv')) y = DesignMatrix.read_csv(join(dirname(path), 'test.csv')) z = DesignMatrix.read_csv(join(dirname(path), 'validate.csv')) return x_dist, x, y, z

29.95

77

0.674457

187

1,198

4.187166

0.336898

0.0447

0.10728

0.076628

0.268199

0.130268

0

0.026396

0.177796

1,198

39

78

30.717949

0.768528

0.037563

0

0.050523

0

1

0.074074

false

0

0.222222

0

0.333333

0

null

0

null

0

1

0

c85276ff92552a878b6545824f777a6c37822c3a

7,860

py

Python

Desktop Assistant.py

PRASUNR0Y/Desktop-Assistant

6f07cd3bc50bfca3d3f243d9e01d1bb0ef2e9029

[ "MIT" ]

15

2020-07-21T09:54:16.000Z

2022-02-08T15:34:25.000Z

Desktop Assistant.py

RisingStar522/Desktop-Assistant

6f07cd3bc50bfca3d3f243d9e01d1bb0ef2e9029

[ "MIT" ]

1

2020-11-26T15:47:23.000Z

Desktop Assistant.py

RisingStar522/Desktop-Assistant

6f07cd3bc50bfca3d3f243d9e01d1bb0ef2e9029

[ "MIT" ]

16

2020-08-04T10:47:45.000Z

2022-01-14T19:29:35.000Z

import pyttsx3 #pip install pyttsx3 import speech_recognition as sr #pip install speechRecognition import datetime import wikipedia #pip install wikipedia import webbrowser import os import smtplib import random engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices') # print(voices[0].id) engine.setProperty('voice', voices[1].id) def speak(audio): engine.say(audio) engine.runAndWait() def wishMe(): hour = int(datetime.datetime.now().hour) if hour>=0 and hour<12: speak("Good Morning! ") elif hour>=12 and hour<17: speak("Good Afternoon! ") elif hour>=17 and hour<19 : speak("Good Evening! ") else: speak("Good Night! ") speak("I am your Vertual Assistant Suzi. Please tell me how may I help you") def takeCommand(): #It takes microphone input from the user and returns string output rr = sr.Recognizer() with sr.Microphone() as source: print("Listening...") rr.pause_threshold = 1 audio = rr.listen(source) try: print("Recognizing...") query = rr.recognize_google(audio, language='en-in') print(f"User said: {query}\n") except Exception as e: # print(e) print("Say that again please...") speak("Connection error") return "None" return query def sendEmail(to, content): server = smtplib.SMTP('smtp.gmail.com', 587) server.ehlo() server.starttls() server.login('youremail@gmail.com', 'your-password') server.sendmail('youremail@gmail.com', to, content) server.close() if __name__ == "__main__": wishMe() while True: # if 1: query = takeCommand().lower() # Logic for executing tasks based on query if 'wikipedia' in query: speak('Searching Wikipedia...') query = query.replace("wikipedia", "") results = wikipedia.summary(query, sentences=2) speak("According to Wikipedia") print(results) speak(results) elif "hello" in query or "hello Suzi" in query: hello1 = "Hello ! How May i Help you.." print(hello1) speak(hello1) elif "who are you" in query or "about you" in query or "your details" in query: who_are_you = "I am Suzi an A I based computer program but i can help you lot like a your assistant ! try me to give simple command !" print(who_are_you) speak(who_are_you) elif 'who make you' in query or 'who made you' in query or 'who created you' in query or 'who develop you' in query: speak(" For your information Prasun Roy Created me ! I can show you his Linked In profile if you want to see. Yes or no .....") ans_from_user_who_made_you = takeCommand() if 'yes' in ans_from_user_who_made_you or 'ok' in ans_from_user_who_made_you or 'yeah' in ans_from_user_who_made_you: webbrowser.open("https://www.linkedin.com/in/prasun-roy-") speak('opening his profile...... please wait') elif 'no' in ans_from_user_who_made_you or 'no thanks' in ans_from_user_who_made_you or 'not' in ans_from_user_who_made_you: speak("All right ! OK...") else : speak("I can't understand. Please say that again !") elif 'open youtube' in query: webbrowser.open("www.youtube.com") speak("opening youtube") elif 'open github' in query: webbrowser.open("https://www.github.com") speak("opening github") elif 'open facebook' in query: webbrowser.open("https://www.facebook.com") speak("opening facebook") elif 'open instagram' in query: webbrowser.open("https://www.instagram.com") speak("opening instagram") elif 'open google' in query: webbrowser.open("google.com") speak("opening google") elif 'open stackoverflow' in query: webbrowser.open("stackoverflow.com") speak("opening stackoverflow") elif 'open yahoo' in query: webbrowser.open("https://www.yahoo.com") speak("opening yahoo") elif 'open gmail' in query: webbrowser.open("https://mail.google.com") speak("opening google mail") elif 'open snapdeal' in query: webbrowser.open("https://www.snapdeal.com") speak("opening snapdeal") elif 'open amazon' in query or 'shop online' in query: webbrowser.open("https://www.amazon.com") speak("opening amazon") elif 'open flipkart' in query: webbrowser.open("https://www.flipkart.com") speak("opening flipkart") elif 'play music' in query: speak("ok i am playing music") music_dir = 'E:\\My MUSIC' songs = os.listdir(music_dir) print(songs) os.startfile(os.path.join(music_dir, songs[0])) elif 'video from pc' in query or "video" in query: speak("ok i am playing videos") video_dir = 'E:\\\My Videos' Videos = os.listdir(video_dir) print(Videos) os.startfile(os.path.join(video_dir,Videos[0])) elif 'good bye' in query: speak("good bye") exit() elif "shutdown" in query: speak("shutting down") os.system('shutdown -s') elif "your name" in query or "sweat name" in query: naa_mme = "Thanks for Asking my self ! Suzi" print(naa_mme) speak(naa_mme) elif "you feeling" in query: print("feeling Very happy to help you") speak("feeling Very happy to help you") elif query == 'none': continue elif 'exit' in query or 'stop' in query or 'quit' in query : exx_exit = 'See you soon. Bye' speak(exx_exit) exit() elif 'the time' in query: strTime = datetime.datetime.now().strftime("%H:%M:%S") speak(f"the time is {strTime}") elif 'open code' in query: codePath = "D:\\vs\\Microsoft VS Code\\Code.exe" os.startfile(codePath) speak("opening visual studio code") elif 'email to prasun' in query: try: speak("What should I say?") content = takeCommand() to = "prasunroy988@gmail.com" sendEmail(to, content) speak("Email has been sent!") except Exception as e: print(e) speak("Sorry.... I am not able to send this email") elif 'how are you' in query: setMsgs = ['Just doing my thing!', 'I am fine!', 'Nice!'] ans_qus = random.choice(setMsgs) speak(ans_qus) speak(" How are you'") ans_from_user_how_are_you = takeCommand() if 'fine' in ans_from_user_how_are_you or 'happy' in ans_from_user_how_are_you or 'okey' in ans_from_user_how_are_you: speak('Great') elif 'not' in ans_from_user_how_are_you or 'sad' in ans_from_user_how_are_you or 'upset' in ans_from_user_how_are_you: speak('Tell me how can i make you happy') else : speak("I can't understand. Please say that again !") else: tempp = query.replace(' ','+') prasun_url="https://www.google.com/search?q=" res_prasun = 'sorry! i cant understand but i search from internet to give your answer !' print(res_prasun) speak(res_prasun) webbrowser.open(prasun_url+tempp)

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null

0

null

0

1

0

c8551c2705d7c8211e2870c34856750e96ab7d03

11,467

py

Python

exif_processing.py

Strubbl/upload-scripts

da2f73a322490c0ca572dcc21bc8ba7f68f76734

[ "MIT" ]

null

exif_processing.py

Strubbl/upload-scripts

da2f73a322490c0ca572dcc21bc8ba7f68f76734

[ "MIT" ]

1

2020-08-05T18:37:15.000Z

2020-08-07T14:12:56.000Z

exif_processing.py

Strubbl/upload-scripts

da2f73a322490c0ca572dcc21bc8ba7f68f76734

[ "MIT" ]

1

2020-08-05T16:23:51.000Z

"""Module responsible to parse Exif information from a image""" import math import datetime from enum import Enum from typing import Optional # third party import exifread import piexif MPH_TO_KMH_FACTOR = 1.60934 """miles per hour to kilometers per hour conversion factor""" KNOTS_TO_KMH_FACTOR = 1.852 """knots to kilometers per hour conversion factor""" class ExifTags(Enum): """This is a enumeration of exif tags. More info here http://owl.phy.queensu.ca/~phil/exiftool/TagNames/GPS.html """ DATE_TIME_ORIGINAL = "EXIF DateTimeOriginal" DATE_Time_DIGITIZED = "EXIF DateTimeDigitized" # latitude GPS_LATITUDE = "GPS GPSLatitude" GPS_LATITUDE_REF = "GPS GPSLatitudeRef" # longitude GPS_LONGITUDE = "GPS GPSLongitude" GPS_LONGITUDE_REF = "GPS GPSLongitudeRef" # altitude GPS_ALTITUDE_REF = "GPS GPSAltitudeRef" GPS_ALTITUDE = "GPS GPSAltitude" # timestamp GPS_TIMESTAMP = "GPS GPSTimeStamp" GPS_DATE_STAMP = "GPS GPSDateStamp" GPS_DATE = "GPS GPSDate" # speed GPS_SPEED_REF = "GPS GPSSpeedRef" GPS_SPEED = "GPS GPSSpeed" # direction GPS_DIRECTION_REF = "GPS GPSImgDirectionRef" GPS_DIRECTION = "GPS GPSImgDirection" class CardinalDirection(Enum): """Exif Enum with all cardinal directions""" N = "N" S = "S" E = "E" W = "W" TrueNorth = "T" MagneticNorth = "M" class SeaLevel(Enum): """Exif Enum If the reference is sea level and the altitude is above sea level, 0 is given. If the altitude is below sea level, a value of 1 is given and the altitude is indicated as an absolute value in the GPSAltitude tag. The reference unit is meters. Note that this tag is BYTE type, unlike other reference tags.""" ABOVE = 0 BELOW = 1 class SpeedUnit(Enum): """Exif speed unit enum""" KMH = "K" MPH = "M" KNOTS = "N" @classmethod def convert_mph_to_kmh(cls, mph) -> float: """This method converts from miles per hour to kilometers per hour""" return mph * MPH_TO_KMH_FACTOR @classmethod def convert_knots_to_kmh(cls, knots) -> float: """This method converts from knots to kilometers per hour""" return knots * KNOTS_TO_KMH_FACTOR def all_tags(path) -> {str: str}: """Method to return Exif tags""" file = open(path, "rb") tags = exifread.process_file(file, details=False) return tags def __dms_to_dd(dms_value) -> float: """DMS is Degrees Minutes Seconds, DD is Decimal Degrees. A typical format would be dd/1,mm/1,ss/1. When degrees and minutes are used and, for example, fractions of minutes are given up to two decimal places, the format would be dd/1,mmmm/100,0/1 """ # degrees degrees_nominator = dms_value.values[0].num degrees_denominator = dms_value.values[0].den degrees = float(degrees_nominator) / float(degrees_denominator) # minutes minutes_nominator = dms_value.values[1].num minutes_denominator = dms_value.values[1].den minutes = float(minutes_nominator) / float(minutes_denominator) # seconds seconds_nominator = dms_value.values[2].num seconds_denominator = dms_value.values[2].den seconds = float(seconds_nominator) / float(seconds_denominator) # decimal degrees return degrees + (minutes / 60.0) + (seconds / 3600.0) def gps_latitude(gps_data: {str: str}) -> Optional[float]: """Exif latitude from gps_data represented by gps tags found in image exif""" if ExifTags.GPS_LATITUDE.value in gps_data: # latitude exists dms_values = gps_data[ExifTags.GPS_LATITUDE.value] _latitude = __dms_to_dd(dms_values) if ExifTags.GPS_LATITUDE_REF.value in gps_data and \ (str(gps_data[ExifTags.GPS_LATITUDE_REF.value]) == str(CardinalDirection.S.value)): # cardinal direction is S so the latitude should be negative _latitude = -1 * _latitude return _latitude # no latitude info found return None def gps_longitude(gps_data: {str: str}) -> Optional[float]: """Exif longitude from gps_data represented by gps tags found in image exif""" if ExifTags.GPS_LONGITUDE.value in gps_data: # longitude exists dms_values = gps_data[ExifTags.GPS_LONGITUDE.value] _longitude = __dms_to_dd(dms_values) if ExifTags.GPS_LONGITUDE_REF.value in gps_data and \ str(gps_data[ExifTags.GPS_LONGITUDE_REF.value]) == str(CardinalDirection.W.value): # cardinal direction is W so the longitude should be negative _longitude = -1 * _longitude return _longitude # no longitude info found return None def gps_compass(gps_data: {str: str}) -> Optional[float]: """Exif compass from gps_data represented by gps tags found in image exif. reference relative to true north""" if ExifTags.GPS_DIRECTION.value in gps_data: # compass exists compass_ratio = gps_data[ExifTags.GPS_DIRECTION.value].values[0] if ExifTags.GPS_DIRECTION_REF.value in gps_data and \ gps_data[ExifTags.GPS_DIRECTION_REF.value] == CardinalDirection.MagneticNorth: # if we find magnetic north then we don't consider a valid compass return None return compass_ratio.num / compass_ratio.den # no compass found return None def gps_timestamp(gps_data: {str: str}) -> Optional[float]: """Exif gps time from gps_data represented by gps tags found in image exif. In exif there are values giving the hour, minute, and second. This is UTC time""" if ExifTags.GPS_TIMESTAMP.value in gps_data: # timestamp exists _timestamp = gps_data[ExifTags.GPS_TIMESTAMP.value] hours: exifread.Ratio = _timestamp.values[0] minutes: exifread.Ratio = _timestamp.values[1] seconds: exifread.Ratio = _timestamp.values[2] day_timestamp = hours.num / hours.den * 3600 + \ minutes.num / minutes.den * 60 + \ seconds.num / seconds.den if ExifTags.GPS_DATE_STAMP.value in gps_data: # this tag is the one present in the exif documentation # but from experience ExifTags.GPS_DATE is replacing this tag gps_date = gps_data[ExifTags.GPS_DATE_STAMP.value].values date_timestamp = datetime.datetime.strptime(gps_date, "%Y:%m:%d").timestamp() return day_timestamp + date_timestamp if ExifTags.GPS_DATE.value in gps_data: # this tag is a replacement for ExifTags.GPS_DATE_STAMP gps_date = gps_data[ExifTags.GPS_DATE.value].values date_timestamp = datetime.datetime.strptime(gps_date, "%Y:%m:%d").timestamp() return day_timestamp + date_timestamp # no date information only hour minutes second of day -> no valid gps timestamp return None # no gps timestamp found return None def timestamp(tags: {str: str}) -> Optional[float]: """Original timestamp determined by the digital still camera. This is timezone corrected.""" if ExifTags.DATE_TIME_ORIGINAL.value in tags: date_taken = tags[ExifTags.DATE_TIME_ORIGINAL.value].values _timestamp = datetime.datetime.strptime(date_taken, "%Y:%m:%d %H:%M:%S").timestamp() return _timestamp if ExifTags.DATE_Time_DIGITIZED.value in tags: date_taken = tags[ExifTags.DATE_Time_DIGITIZED.value].values _timestamp = datetime.datetime.strptime(date_taken, "%Y:%m:%d %H:%M:%S").timestamp() return _timestamp # no timestamp information found return None def gps_altitude(gps_tags: {str: str}) -> Optional[float]: """GPS altitude form exif """ if ExifTags.GPS_ALTITUDE.value in gps_tags: # altitude exists altitude_ratio = gps_tags[ExifTags.GPS_ALTITUDE.value].values[0] altitude = altitude_ratio.num / altitude_ratio.den if ExifTags.GPS_ALTITUDE_REF.value in gps_tags and \ gps_tags[ExifTags.GPS_ALTITUDE_REF.value] == SeaLevel.BELOW.value: altitude = -1 * altitude return altitude return None def gps_speed(gps_tags: {str: str}) -> Optional[float]: """Returns GPS speed from exif in km per hour or None if no gps speed tag found""" if ExifTags.GPS_SPEED.value in gps_tags: # gps speed exist speed_ratio = gps_tags[ExifTags.GPS_SPEED.value].values[0] speed = speed_ratio.num / speed_ratio.den if ExifTags.GPS_SPEED_REF.value in gps_tags: if gps_tags[ExifTags.GPS_SPEED_REF.value] == SpeedUnit.MPH.value: speed = SpeedUnit.convert_mph_to_kmh(speed) if gps_tags[ExifTags.GPS_SPEED_REF.value] == SpeedUnit.KNOTS.value: speed = SpeedUnit.convert_knots_to_kmh(speed) return speed # no gps speed tag found return None def add_gps_tags(path: str, gps_tags: {str: any}): """This method will add gps tags to the photo found at path""" exif_dict = piexif.load(path) for tag, tag_value in gps_tags.items(): exif_dict["GPS"][tag] = tag_value exif_bytes = piexif.dump(exif_dict) piexif.insert(exif_bytes, path) def create_required_gps_tags(timestamp_gps: float, latitude: float, longitude: float) -> {str: any}: """This method will creates gps required tags """ exif_gps = {} dms_latitude = __dd_to_dms(latitude) dms_longitude = __dd_to_dms(longitude) day = int(timestamp_gps / 86400) * 86400 hour = int((timestamp_gps - day) / 3600) minutes = int((timestamp_gps - day - hour * 3600) / 60) seconds = int(timestamp_gps - day - hour * 3600 - minutes * 60) day_timestamp_str = datetime.date.fromtimestamp(day).strftime("%Y:%m:%d") exif_gps[piexif.GPSIFD.GPSTimeStamp] = [(hour, 1), (minutes, 1), (seconds, 1)] exif_gps[piexif.GPSIFD.GPSDateStamp] = day_timestamp_str exif_gps[piexif.GPSIFD.GPSLatitudeRef] = "S" if latitude < 0 else "N" exif_gps[piexif.GPSIFD.GPSLatitude] = dms_latitude exif_gps[piexif.GPSIFD.GPSLongitudeRef] = "W" if longitude < 0 else "E" exif_gps[piexif.GPSIFD.GPSLongitude] = dms_longitude return exif_gps def add_optional_gps_tags(exif_gps: {str: any}, speed: float, altitude: float, compass: float) -> {str: any}: """This method will append optional tags to exif_gps tags dictionary""" if speed: exif_gps[piexif.GPSIFD.GPSSpeed] = (speed, 1) exif_gps[piexif.GPSIFD.GPSSpeedRef] = SpeedUnit.KMH.value if altitude: exif_gps[piexif.GPSIFD.GPSAltitude] = (altitude, 1) sea_level = SeaLevel.BELOW.value if altitude < 0 else SeaLevel.ABOVE.value exif_gps[piexif.GPSIFD.GPSAltitudeRef] = sea_level if compass: exif_gps[piexif.GPSIFD.GPSImgDirection] = (compass, 1) exif_gps[piexif.GPSIFD.GPSImgDirectionRef] = CardinalDirection.TrueNorth.value def __dd_to_dms(decimal_degree) -> [(float, int)]: decimal_degree_abs = abs(decimal_degree) degrees = math.floor(decimal_degree_abs) minute_float = (decimal_degree_abs - degrees) * 60 minute = math.floor(minute_float) seconds = round((minute_float - minute) * 60 * 100) return [(degrees, 1), (minute, 1), (seconds, 100)]

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0.160867

0.043172

0.018947

0.030857

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11,467

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0.414365

0

null

0

null

0

1

0

c8559b8c4871bf63b43c48e1fd50163d6997b0b7

1,505

py

Python

NPTEL - 2017 PDSA/Nptel_EX_5.py

Siddharth2016/PYTHON3_prog

9dfa258d87f5b00779d39d9de9a49c1c6cea06be

[ "MIT" ]

2

2019-02-26T14:06:53.000Z

2019-02-27T17:13:01.000Z

NPTEL - 2017 PDSA/Nptel_EX_5.py

Siddharth2016/PYTHON3_prog

9dfa258d87f5b00779d39d9de9a49c1c6cea06be

[ "MIT" ]

null

NPTEL - 2017 PDSA/Nptel_EX_5.py

Siddharth2016/PYTHON3_prog

9dfa258d87f5b00779d39d9de9a49c1c6cea06be

[ "MIT" ]

2

2017-12-26T07:59:57.000Z

2018-06-24T03:35:05.000Z

# NPTEL EXERCISE 5 courses = {} students = [] grades = {} f = 0 while(True): S = input() if S=="EndOfInput": break if S=='Courses': f = 1 continue elif S=='Students': f = 2 continue elif S=='Grades': f = 3 continue if f==1 : S = S.split("~") courses[S[0]] = S[2:] elif f==2: S = S.split("~") students += [S] elif f==3: S = S.split("~") try: grades[S[0]].append(S[1:]) except: grades[S[0]] = [S[1:]] #print(courses) #print(students) #print(grades) students.sort() for stud in students: roll = stud[0] gpa = 0 count = 0 for key in grades.keys(): for res in grades[key]: if roll==res[2]: count += 1 if res[3]=='A': gpa += 10 elif res[3]=='AB': gpa += 9 elif res[3]=='B': gpa += 8 elif res[3]=='BC': gpa += 7 elif res[3]=='C': gpa += 6 elif res[3]=='CD': gpa += 5 elif res[3]=='D': gpa += 4 if gpa!=0: gpa = (gpa/count) ans = "~".join(stud) + "~" + "{0:3.1f}".format(gpa) else: ans = "~".join(stud) + "~" + str(gpa) print(ans)

22.462687

60

0.348173

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0

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0.017241

0

null

0

null

0

1

0

c8587f2977c7befab3e26288435a9698c942b8e4

2,719

py

Python

ultron8/api/api_v1/endpoints/loggers.py

bossjones/ultron8

45db73d32542a844570d44bc83defa935e15803f

[ "Apache-2.0", "MIT" ]

null

ultron8/api/api_v1/endpoints/loggers.py

bossjones/ultron8

45db73d32542a844570d44bc83defa935e15803f

[ "Apache-2.0", "MIT" ]

43

2019-06-01T23:08:32.000Z

2022-02-07T22:24:53.000Z

ultron8/api/api_v1/endpoints/loggers.py

bossjones/ultron8

45db73d32542a844570d44bc83defa935e15803f

[ "Apache-2.0", "MIT" ]

null

from __future__ import annotations # SOURCE: https://blog.bartab.fr/fastapi-logging-on-the-fly/ import logging from fastapi import APIRouter, HTTPException from ultron8.api.models.loggers import LoggerModel, LoggerPatch LOG_LEVELS = { "critical": logging.CRITICAL, "error": logging.ERROR, "warning": logging.WARNING, "info": logging.INFO, "debug": logging.DEBUG, } LOGGER = logging.getLogger(__name__) router = APIRouter() def get_lm_from_tree(loggertree: LoggerModel, find_me: str) -> LoggerModel: if find_me == loggertree.name: LOGGER.debug("Found") return loggertree else: for ch in loggertree.children: LOGGER.debug(f"Looking in: {ch.name}") i = get_lm_from_tree(ch, find_me) if i: return i def generate_tree() -> LoggerModel: # adapted from logging_tree package https://github.com/brandon-rhodes/logging_tree rootm = LoggerModel( name="root", level=logging.getLogger().getEffectiveLevel(), children=[] ) nodesm = {} items = list(logging.root.manager.loggerDict.items()) # type: ignore items.sort() for name, loggeritem in items: if isinstance(loggeritem, logging.PlaceHolder): nodesm[name] = nodem = LoggerModel(name=name, children=[]) else: nodesm[name] = nodem = LoggerModel( name=name, level=loggeritem.getEffectiveLevel(), children=[] ) i = name.rfind(".", 0, len(name) - 1) # same formula used in `logging` if i == -1: parentm = rootm else: parentm = nodesm[name[:i]] parentm.children.append(nodem) return rootm # Multiple RecursionErrors with self-referencing models # https://github.com/samuelcolvin/pydantic/issues/524 # https://github.com/samuelcolvin/pydantic/issues/531 @router.get("/{logger_name}", response_model=LoggerModel) def logger_get(logger_name: str): LOGGER.debug(f"getting logger {logger_name}") rootm = generate_tree() lm = get_lm_from_tree(rootm, logger_name) if lm is None: raise HTTPException(status_code=404, detail=f"Logger {logger_name} not found") return lm @router.patch("/") def logger_patch(loggerpatch: LoggerPatch): rootm = generate_tree() lm = get_lm_from_tree(rootm, loggerpatch.name) LOGGER.debug(f"Actual level of {lm.name} is {lm.level}") LOGGER.debug(f"Setting {loggerpatch.name} to {loggerpatch.level}") logging.getLogger(loggerpatch.name).setLevel(LOG_LEVELS[loggerpatch.level]) return loggerpatch @router.get("/", response_model=LoggerModel) def loggers_list(): rootm = generate_tree() LOGGER.debug(rootm) return rootm

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0

null

0

null

0

1

0

c85898f206e8cc65031cd08af9075a430861ba23

422

py

Python

exception6.py

PRASAD-DANGARE/PYTHON

36214f7dc3762d327e5a29e40752edeb098249c8

[ "MIT" ]

1

2021-06-07T07:55:28.000Z

exception6.py

PRASAD-DANGARE/PYTHON

36214f7dc3762d327e5a29e40752edeb098249c8

[ "MIT" ]

null

exception6.py

PRASAD-DANGARE/PYTHON

36214f7dc3762d327e5a29e40752edeb098249c8

[ "MIT" ]

null

# Python Program To Understand The Usage Of try With finally Blocks ''' Function Name : Usage Of try With finally Blocks Function Date : 23 Sep 2020 Function Author : Prasad Dangare Input : String Output : String ''' try: x = int(input('Enter A Number : ')) y = 1 / x finally: print("We Are Not Catching The Exception.") print("The Inverse Is : ", y)

22.210526

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null

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null

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0

c85a1c9c9f35a67fa594c9e1e36235e098af53be

4,036

py

Python

V1/GliderScienceSet_Plots.py

NOAA-PMEL/EcoFOCI_OculusGlider

5655c0e173432768706416932c94a089a3e7993f

[ "Unlicense" ]

2

2018-04-12T19:49:05.000Z

2020-10-01T11:46:48.000Z

V1/GliderScienceSet_Plots.py

NOAA-PMEL/EcoFOCI_OculusGlider

5655c0e173432768706416932c94a089a3e7993f

[ "Unlicense" ]

null

V1/GliderScienceSet_Plots.py

NOAA-PMEL/EcoFOCI_OculusGlider

5655c0e173432768706416932c94a089a3e7993f

[ "Unlicense" ]

null

#!/usr/bin/env python """ Background: -------- GliderScienceSet_Plots.py Purpose: -------- History: -------- """ import argparse import os from io_utils import ConfigParserLocal import numpy as np import xarray as xa # Visual Stack import matplotlib as mpl import matplotlib.pyplot as plt def plot_ts(salt, temp, press, srange=[31,33], trange=[-2,10], ptitle="",labels=True, label_color='k', bydepth=False): plt.style.use('ggplot') # Figure out boudaries (mins and maxs) smin = srange[0] smax = srange[1] tmin = trange[0] tmax = trange[1] # Calculate how many gridcells we need in the x and y dimensions xdim = int(round((smax-smin)/0.1+1,0)) ydim = int(round((tmax-tmin)+1,0)) #print 'ydim: ' + str(ydim) + ' xdim: ' + str(xdim) + ' \n' if (xdim > 10000) or (ydim > 10000): print('To many dimensions for grid in file. Likely missing data \n') return # Create empty grid of zeros dens = np.zeros((ydim,xdim)) # Create temp and salt vectors of appropiate dimensions ti = np.linspace(0,ydim-1,ydim)+tmin si = np.linspace(0,xdim-1,xdim)*0.1+smin # Loop to fill in grid with densities for j in range(0,int(ydim)): for i in range(0, int(xdim)): dens[j,i]=sw.dens0(si[i],ti[j]) # Substract 1000 to convert to sigma-t dens = dens - 1000 # Plot data *********************************************** ax1 = fig.add_subplot(111) if labels: CS = plt.contour(si,ti,dens, linestyles='dashed', colors='k') if labels: plt.clabel(CS, fontsize=12, inline=1, fmt='%1.1f') # Label every second level if bydepth: ts = ax1.scatter(salt,temp, c=press, cmap='gray', s=10) else: ts = ax1.scatter(salt,temp,s=10,c=label_color) plt.ylim(tmin,tmax) plt.xlim(smin,smax) if labels: if bydepth: plt.colorbar(ts ) ax1.set_xlabel('Salinity (PSU)') ax1.set_ylabel('Temperature (C)') t = fig.suptitle(ptitle, fontsize=12, fontweight='bold') t.set_y(1.08) return fig """-------------------------------- Main -----------------------------------------------""" parser = argparse.ArgumentParser(description='Plot archived NetCDF glider data and Science Data') parser.add_argument('ofilepath', metavar='ofilepath', type=str, help='path to directory with UW initial Oculus netcdf data') parser.add_argument('sfilepath', metavar='sfilepath', type=str, help='path to directory with Oculus Science Data netcdf data') parser.add_argument('profileid',metavar='profileid', type=str, help='divenumber - eg p4010260') args = parser.parse_args() isUW, ismerged, isup, isdown = True, True, True, True # There are potentially three files - original UW file, a merged file and an upcast/downcast file filein = args.ofilepath + args.profileid + '.nc' try: df = xa.open_dataset(filein, autoclose=True) except IOError: isUW = False filein_m = args.sfilepath + args.profileid + '_m.nc' ismerged = True try: df_m = xa.open_dataset(filein_m, autoclose=True) except IOError: ismerged = False filein_u = args.sfilepath + args.profileid + '_u.nc' try: df_u = xa.open_dataset(filein_u, autoclose=True) except IOError: isup = False filein_d = args.sfilepath + args.profileid + '_d.nc' try: df_d = xa.open_dataset(filein_d, autoclose=True) except IOError: isdown = False fig = plt.figure(figsize=(6, 6)) if isUW: fig = plot_ts(df.salinity,df.temperature,df.depth,labels=True,label_color='g') print("Added original data") if ismerged: fig = plot_ts(df_m.Salinity,df_m.Temperature,df_m.Pressure,labels=False,label_color='k') print("Added merged data") if isup: fig = plot_ts(df_u.Salinity,df_u.Temperature,df_u.Pressure,labels=False,label_color='b') print("Added binned upcast data") if isdown: fig = plot_ts(df_d.Salinity,df_d.Temperature,df_d.Pressure,labels=False,label_color='r') print("Added binned downcast data")

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null

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null

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c85aba6739f248fb55a041a97d59cbb716b417c3

17,416

py

Python

manager/users/models.py

jlbrewe/hub

c737669e6493ad17536eaa240bed3394b20c6b7d

[ "Apache-2.0" ]

30

2016-03-26T12:08:04.000Z

2021-12-24T14:48:32.000Z

manager/users/models.py

jlbrewe/hub

c737669e6493ad17536eaa240bed3394b20c6b7d

[ "Apache-2.0" ]

1,250

2016-03-23T04:56:50.000Z

2022-03-28T02:27:58.000Z

manager/users/models.py

jlbrewe/hub

c737669e6493ad17536eaa240bed3394b20c6b7d

[ "Apache-2.0" ]

11

2016-07-14T17:04:20.000Z

2021-07-01T16:19:09.000Z

""" Define models used in this app. This module only serves to provide some consistency across the `users`, `accounts` , `projects` etc apps so that you can `from users.models import Users`, just like you can for `from projects.models import Projects` and instead of having to remember to do the following. """ from typing import Dict, Optional import django.contrib.auth.models import shortuuid from django.contrib.auth import get_user_model from django.contrib.contenttypes.fields import GenericForeignKey from django.contrib.contenttypes.models import ContentType from django.db import connection, models from django.db.models import Count, F, Max, Q from django.db.models.expressions import RawSQL from django.http import HttpRequest from django.shortcuts import reverse from django.utils import timezone from invitations.adapters import get_invitations_adapter from invitations.models import Invitation from rest_framework.exceptions import ValidationError from waffle.models import AbstractUserFlag # Needed to ensure signals are loaded import users.signals # noqa from manager.helpers import EnumChoice User: django.contrib.auth.models.User = get_user_model() def get_email(user: User) -> Optional[str]: """ Get the best email address for a user. The "best" email is the verified primary email, falling back to verified if none marked as primary, falling back to the first if none is verified, falling back to `user.email`, falling back to their public email. """ best = None emails = user.emailaddress_set.all() for email in emails: if (email.primary and email.verified) or (not best and email.verified): best = email.email if not best and len(emails) > 0: best = emails[0].email if not best: best = user.email if not best and user.personal_account: best = user.personal_account.email # Avoid returning an empty string, return None instead return best or None def get_name(user: User) -> Optional[str]: """ Get the best name to display for a user. The "best" name is their account's display name, falling back to first_name + last_name, falling back to username. """ if user.personal_account and user.personal_account.display_name: return user.personal_account.display_name if user.first_name or user.last_name: return f"{user.first_name} {user.last_name}".strip() return user.username def get_attributes(user: User) -> Dict: """ Get a dictionary of user attributes. Used for updating external services with current values of user attributes e.g number of projects etc. Flattens various other summary dictionaries e.g `get_projects_summary` into a single dictionary. """ return { **dict( (f"feature_{name}", value) for name, value in get_feature_flags(user).items() ), **dict( (f"orgs_{name}", value) for name, value in get_orgs_summary(user).items() ), **dict( (f"projects_{name}", value) for name, value in get_projects_summary(user).items() ), } def get_orgs(user: User): """ Get all organizational accounts that a user is a member of. """ from accounts.models import Account return Account.objects.filter(user__isnull=True, users__user=user).annotate( role=F("users__role") ) def get_orgs_summary(user: User) -> Dict: """ Get a summary of organizational accounts the user is a member of. """ from accounts.models import AccountRole zero_by_role = dict([(role.name.lower(), 0) for role in AccountRole]) orgs = get_orgs(user) orgs_summary = orgs.values("role").annotate(count=Count("id"), tier=Max("tier")) orgs_by_role = dict([(row["role"].lower(), row["count"]) for row in orgs_summary]) return { "max_tier": max(row["tier"] for row in orgs_summary) if orgs_summary else None, "total": sum(orgs_by_role.values()), **zero_by_role, **orgs_by_role, } def get_projects(user: User, include_public=True): """ Get a queryset of projects for the user. For authenticated users, each project is annotated with the role of the user for the project. """ from projects.models.projects import Project if user.is_authenticated: # Annotate the queryset with the role of the user # Role is the "greater" of the project role and the # account role (for the account that owns the project). # Authenticated users can see public projects and those in # which they have a role return Project.objects.annotate( role=RawSQL( """ SELECT CASE account_role.role WHEN 'OWNER' THEN 'OWNER' WHEN 'MANAGER' THEN CASE project_role.role WHEN 'OWNER' THEN 'OWNER' ELSE 'MANAGER' END ELSE project_role.role END AS "role" FROM projects_project AS project LEFT JOIN (SELECT project_id, "role" FROM projects_projectagent WHERE user_id = %s) AS project_role ON project.id = project_role.project_id LEFT JOIN (SELECT account_id, "role" FROM accounts_accountuser WHERE user_id = %s) AS account_role ON project.account_id = account_role.account_id WHERE project.id = projects_project.id""", [user.id, user.id], ) ).filter((Q(public=True) if include_public else Q()) | Q(role__isnull=False)) else: # Unauthenticated users can only see public projects return Project.objects.filter(public=True).extra(select={"role": "NULL"}) def get_projects_summary(user: User) -> Dict: """ Get a summary of project memberships for a user. """ from projects.models.projects import ProjectRole zero_by_role = dict([(role.name.lower(), 0) for role in ProjectRole]) projects = get_projects(user, include_public=False) projects_by_role = dict( [ (row["role"].lower(), row["count"]) for row in projects.values("role").annotate(count=Count("id")) ] ) return { "total": sum(projects_by_role.values()), **zero_by_role, **projects_by_role, } def get_feature_flags(user: User) -> Dict[str, str]: """ Get the feature flag settings for a user. """ with connection.cursor() as cursor: cursor.execute( """ SELECT "name", "default", "user_id" FROM users_flag LEFT JOIN ( SELECT * FROM users_flag_users WHERE user_id = %s ) AS subquery ON users_flag.id = subquery.flag_id WHERE users_flag.settable """, [user.id], ) rows = cursor.fetchall() features = {} for row in rows: name, default, has_flag = row if has_flag: features[name] = "off" if default == "on" else "on" else: features[name] = default return features def generate_anonuser_id(): """ Generate a unique id for an anonymous user. """ return shortuuid.ShortUUID().random(length=32) class AnonUser(models.Model): """ A model to store anonymous users when necessary. Used to associate unauthenticated users with objects, for example, so that the same session job can be provided to them on multiple page refreshes. """ id = models.CharField( primary_key=True, max_length=64, default=generate_anonuser_id, help_text="The unique id of the anonymous user.", ) created = models.DateTimeField( auto_now_add=True, help_text="The time the anon user was created." ) @staticmethod def get_id(request: HttpRequest) -> Optional[str]: """ Get the id of the anonymous user, if any. """ if request.user.is_anonymous: return request.session.get("user", {}).get("id") return None @staticmethod def get_or_create(request: HttpRequest) -> "AnonUser": """ Create an instance in the database. Only use this when necessary. e.g when you need to associated an anonymous user with another object. """ id = AnonUser.get_id(request) if id: anon_user, created = AnonUser.objects.get_or_create(id=id) return anon_user else: anon_user = AnonUser.objects.create() request.session["user"] = {"anon": True, "id": anon_user.id} return anon_user class Flag(AbstractUserFlag): """ Custom feature flag model. Adds fields to allow users to turn features on/off themselves. In the future, fields may be added to allow flags to be set based on the account (in addition to, or instead of, only the user). See https://waffle.readthedocs.io/en/stable/types/flag.html#custom-flag-models """ label = models.CharField( max_length=128, null=True, blank=True, help_text="A label for the feature to display to users.", ) default = models.CharField( max_length=3, choices=[("on", "On"), ("off", "Off")], default="on", help_text='If the default is "on" then when the flag is active, ' 'the feature should be considered "off" and vice versa.', ) settable = models.BooleanField( default=False, help_text="User can turn this flag on/off for themselves." ) def is_active_for_user(self, user) -> bool: """ Is the feature "on" for a user. Changes the underlying behaviour of Waffle flags based on the `default` field for the flag. """ is_active = super().is_active_for_user(user) return is_active if self.default == "off" else not is_active def generate_invite_key(): """ Generate a unique invite key. The is separate function to avoid new AlterField migrations being created as happens when `default=shortuuid.uuid`. """ return shortuuid.ShortUUID().random(length=32) class InviteAction(EnumChoice): """ Actions to take when a user has accepted an invite. """ join_account = "join_account" join_team = "join_team" join_project = "join_project" take_tour = "take_tour" @staticmethod def as_choices(): """Return as a list of field choices.""" return [ (InviteAction.join_account.name, "Join account"), (InviteAction.join_team.name, "Join team"), (InviteAction.join_project.name, "Join project"), (InviteAction.take_tour.name, "Take tour"), ] class Invite(models.Model): """ An extension of the default invitation model. Allows for different types of invitations, with actions after success. Re-implements the interface of `invitations.Invitation` instead of extending it so that some fields can be redefined e.g shorter case sensitive `key`; e.g. avoid the unique constraint on `email` (because of actions, a single email address could be invited more than once). The methods for each action should use API view sets with synthetic requests having the `inviter` as the request user. This reduces code and provides consistency in permissions checking, thereby reducing errors. Adds `subject_object` `GenericForeignKey` to allow querying from other models """ key = models.CharField( max_length=64, unique=True, default=generate_invite_key, help_text="The key for the invite.", ) inviter = models.ForeignKey( User, null=True, blank=True, on_delete=models.CASCADE, related_name="invites", help_text="The user who created the invite.", ) email = models.EmailField( max_length=2048, help_text="The email address of the person you are inviting." ) message = models.TextField( null=True, blank=True, help_text="An optional message to send to the invitee." ) created = models.DateTimeField( auto_now_add=True, help_text="When the invite was created." ) sent = models.DateTimeField( null=True, blank=True, help_text="When the invite was sent." ) accepted = models.BooleanField( default=False, help_text="Whether the invite has been accepted. " "Will only be true if the user has clicked on the invitation AND authenticated.", ) completed = models.DateTimeField( null=True, blank=True, help_text="When the invite action was completed", ) action = models.CharField( max_length=64, null=True, blank=True, choices=InviteAction.as_choices(), help_text="The action to perform when the invitee signs up.", ) subject_type = models.ForeignKey( ContentType, null=True, blank=True, on_delete=models.CASCADE, help_text="The type of the target of the action. e.g Team, Account", ) subject_id = models.IntegerField( null=True, blank=True, help_text="The id of the target of the action.", ) subject_object = GenericForeignKey("subject_type", "subject_id") arguments = models.JSONField( null=True, blank=True, help_text="Any additional arguments to pass to the action.", ) # These methods need to be implemented for the `invitations` API key_expired = Invitation.key_expired def send_invitation(self, request): """Extend method to add the invite object to the template context.""" context = dict( inviter=self.inviter, inviter_name=self.inviter.get_full_name() or self.inviter.username, invite_message=self.message, invite_url=request.build_absolute_uri( reverse("ui-users-invites-accept", args=[self.key]) ), reason_for_sending="This email was sent by user '{0}' to invite you to " "collaborate with them on Stencila Hub.".format(self.inviter.username), ) get_invitations_adapter().send_mail( "invitations/email/email_invite", self.email, context ) self.sent = timezone.now() self.save() def __str__(self): return "Invite {0} {1}".format(self.action, self.email) # These methods implement invitation actions def redirect_url(self) -> str: """ Get the URL to redirect the user to after the invite has been accepted. """ if self.action == "join_account": return reverse("ui-accounts-retrieve", args=[self.arguments["account"]]) elif self.action == "join_team": return reverse( "ui-accounts-teams-retrieve", args=[self.arguments["account"], self.arguments["team"]], ) elif self.action == "join_project": return reverse( "ui-projects-retrieve", args=[self.arguments["account"], self.arguments["project"]], ) elif self.action == "take_tour": return self.arguments["page"] + "?tour=" + self.arguments["tour"] else: return "/" def create_request(self, data) -> HttpRequest: """ Create a synthetic request to pass to view sets. """ request = HttpRequest() request.data = data request.user = self.inviter return request def perform_action(self, request, user=None): """ Perform the action (if any) registered for this invitation. """ # Accept and save in case the action fails below self.accepted = True self.save() if self.action: method = getattr(self, self.action) if not method: raise RuntimeError("No such action {0}".format(self.action)) method(user or request.user) self.completed = timezone.now() self.save() def join_account(self, invitee): """ Add invitee to account with a particular role. """ from accounts.api.views import AccountsUsersViewSet self.arguments["id"] = invitee.id request = self.create_request(data=self.arguments) viewset = AccountsUsersViewSet.init( "create", request, args=[], kwargs=self.arguments ) viewset.create(request, **self.arguments) def join_project(self, invitee): """ Add invitee to project with a particular role. If the user already has a project role, then the invite is ignored. """ from projects.api.views.projects import ProjectsAgentsViewSet self.arguments["type"] = "user" self.arguments["agent"] = invitee.id request = self.create_request(data=self.arguments) viewset = ProjectsAgentsViewSet.init( "create", request, args=[], kwargs=self.arguments ) try: viewset.create(request, **self.arguments) except ValidationError as exc: if "Already has a project role" not in str(exc): raise exc def take_tour(self, invitee): """ Nothing needs to be done here. User is redirected to tour URL. """ pass

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0

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false

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0.003289

0.325658

0

null

0

null

0

1

0

c85bff69906cd84ddfe9e581be8b49ceea14621c

6,075

py

Python

scripts/automation/trex_control_plane/interactive/trex/emu/emu_plugins/emu_plugin_dhcpsrv.py

GabrielGanne/trex-core

688a0fe0adb890964691473723d70ffa98e00dd3

[ "Apache-2.0" ]

956

2015-06-24T15:04:55.000Z

2022-03-30T06:25:04.000Z

scripts/automation/trex_control_plane/interactive/trex/emu/emu_plugins/emu_plugin_dhcpsrv.py

hjat2005/trex-core

400f03c86c844a0096dff3f6b13e58a808aaefff

[ "Apache-2.0" ]

782

2015-09-20T15:19:00.000Z

2022-03-31T23:52:05.000Z

scripts/automation/trex_control_plane/interactive/trex/emu/emu_plugins/emu_plugin_dhcpsrv.py

hjat2005/trex-core

400f03c86c844a0096dff3f6b13e58a808aaefff

[ "Apache-2.0" ]

429

2015-06-27T19:34:21.000Z

2022-03-23T11:02:51.000Z

from trex.emu.api import * from trex.emu.emu_plugins.emu_plugin_base import * import trex.utils.parsing_opts as parsing_opts class DHCPSRVPlugin(EMUPluginBase): """ Defines DHCP Server plugin based on `DHCP <https://en.wikipedia.org/wiki/Dynamic_Host_Configuration_Protocol>`_ Implemented based on `RFC 2131 Server <https://datatracker.ietf.org/doc/html/rfc2131>`_ """ plugin_name = 'DHCPSRV' INIT_JSON_NS = {'dhcpsrv': {}} """ :parameters: Empty. """ INIT_JSON_CLIENT = {'dhcpsrv': "Pointer to INIT_JSON_NS below"} """ :parameters: default_lease: uint32 Default lease time in seconds to offer to DHCP clients. Defaults to 300 seconds, 5 mins. max_lease: uint32 Maximal lease time in seconds that the server is willing to offer the client in case he requests a specific lease. If `default_lease` is provided and greater than an unprovided `max_lease`, then `max_lease` will be overridden by `default_lease`. Defaults to 600 seconds, 10 mins. min_lease: uint32 Minimal lease time in seconds that the server is willing to offer the client in case he requests a specific lease. If `default_lease` is provided and less than an unprovided `min_lease`, then `min_lease` will be overridden by `default_lease`. Defaults to 60 seconds, 1 min. next_server_ip: str IPv4 address of the next server as a field. In case you provide it, the server will write the IPv4 as the next server IPv4 in the packets it sends. Defaults to 0.0.0.0. pools: list List of dictionaries that represent IPv4 pools or otherwise known as scopes. At lease one pool must be provided. Each dictionary is composed of: :min: str Minimal IPv4 address of the pool. If this happens to be the Network Id, this address will be skipped. :max: str Maximal IPv4 address of the pool. If this happens to be the Broadcast Id, this address will be skipped. :prefix: uint8 Subnet Mask represented as a prefix, an unsigned integer between (0, 32) non exclusive. :exclude: list List of IPv4 strings that are excluded from the pool and can't be offered to the client. .. note:: Two different pools cannot be in the same subnet. If two pools share the same subnet, with the current implementation we will always offer an IP from the first pool in the list. .. highlight:: python .. code-block:: python "pools": [ { "min": "192.168.0.0", "max": "192.168.0.100", "prefix": 24, "exclude": ["192.168.0.1", "192.168.0.2"] }, { "min": "10.0.0.2", "max": "10.0.255.255", "prefix": 8 } ] options: dict Dictionary that contains DHCP Options. There are three keys possible: `offer`, `ack` and `nak`. Each key represents a DHCP Response that the server can send. Each key's value is a list. The list is composed by dictionaries, where each dictionary represents a DHCP option. Options are represented by their type (byte), and their value (byte list). In the following example, we add the following options to `offer` and `ack` responses. Type: 6 (DNS Server) -> Value (8.8.8.8) Type: 15 (Domain Name) -> Value cisco.com .. highlight:: python .. code-block:: python "options": { "offer": [ { "type": 6, "data": [8, 8, 8, 8] }, { "type": 15, "data": [99, 105, 115, 99, 111, 46, 99, 111, 109] } ] "ack": [ { "type": 6, "data": [8, 8, 8, 8] }, { "type": 15, "data": [99, 105, 115, 99, 111, 46, 99, 111, 109] } ] } """ def __init__(self, emu_client): super(DHCPSRVPlugin, self).__init__(emu_client, client_cnt_rpc_cmd='dhcpsrv_c_cnt') # API methods @client_api('getter', True) @update_docstring(EMUPluginBase._get_client_counters.__doc__.replace("$PLUGIN_NAME", plugin_name)) def get_counters(self, c_key, cnt_filter=None, zero=True, verbose=True): return self._get_client_counters(c_key, cnt_filter, zero, verbose) @client_api('command', True) @update_docstring(EMUPluginBase._clear_client_counters.__doc__.replace("$PLUGIN_NAME", plugin_name)) def clear_counters(self, c_key): return self._clear_client_counters(c_key) # Plugins methods @plugin_api('dhcpsrv_show_counters', 'emu') def dhcpsrv_show_counters_line(self, line): '''Show DHCP Server counters.\n''' parser = parsing_opts.gen_parser(self, "show_counters_dhcpsrv", self.dhcpsrv_show_counters_line.__doc__, parsing_opts.EMU_SHOW_CNT_GROUP, parsing_opts.EMU_NS_GROUP, parsing_opts.EMU_CLIENT_GROUP, parsing_opts.EMU_DUMPS_OPT ) opts = parser.parse_args(line.split()) self.emu_c._base_show_counters(self.client_data_cnt, opts, req_ns = True) return True

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0.539095

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null

0

null

0

1

0

c86191051fc7c1834649eb4ef9230e67b31da3c1

2,683

py

Python

lenet-chinese_mnist/generate.py

leonwanghui/mindspore-jina-apps

e2912d9a93689c69005345758e3b7a2f8ba6133e

[ "Apache-2.0" ]

null

lenet-chinese_mnist/generate.py

leonwanghui/mindspore-jina-apps

e2912d9a93689c69005345758e3b7a2f8ba6133e

[ "Apache-2.0" ]

null

lenet-chinese_mnist/generate.py

leonwanghui/mindspore-jina-apps

e2912d9a93689c69005345758e3b7a2f8ba6133e

[ "Apache-2.0" ]

null

# Copyright 2020 Huawei Technologies Co., Ltd # # 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 os import struct import argparse import numpy as np from PIL import Image def load_mnist(dir_path, kind='train'): """Load MNIST Dataset from the given path""" labels_path = os.path.join(dir_path, '%s-labels-idx1-ubyte' % kind) images_path = os.path.join(dir_path, '%s-images-idx3-ubyte' % kind) with open(labels_path, 'rb') as labels_file: magic, num = struct.unpack('>II', labels_file.read(8)) labels = np.fromfile(labels_file, dtype=np.uint8) with open(images_path, 'rb') as images_file: magic, num, rows, cols = struct.unpack(">IIII", images_file.read(16)) images = np.fromfile(images_file, dtype=np.uint8) return images, labels, num def save_mnist_to_jpg(images, labels, save_dir, kind, num): """Convert and save the MNIST dataset to.jpg image format""" one_pic_pixels = 28 * 28 for i in range(num): img = images[i * one_pic_pixels:(i + 1) * one_pic_pixels] img_np = np.array(img, dtype=np.uint8).reshape(28, 28) label_val = labels[i] jpg_name = os.path.join(save_dir, '{}_{}_{}.jpg'.format(kind, i, label_val)) Image.fromarray(img_np).save(jpg_name) print('{} ==> {}_{}_{}.jpg'.format(i, kind, i, label_val)) if __name__ == '__main__': parser = argparse.ArgumentParser(description="MNIST Dataset Operations") parser.add_argument('--data_dir', type=str, default='/root/jina/chinese-mnist', help='MNIST dataset dir') parser.add_argument('--kind', type=str, default='train', help='MNIST dataset: train or t10k') parser.add_argument('--save_dir', type=str, default='/root/jina/chinese-mnist/jpg', help='used to save mnist jpg') args = parser.parse_args() if not os.path.exists(args.data_dir): os.makedirs(args.data_dir) images_np, labels_np, kind_num = load_mnist(args.data_dir, args.kind) if not os.path.exists(args.save_dir): os.makedirs(args.save_dir) save_mnist_to_jpg(images_np, labels_np, args.save_dir, args.kind, kind_num)

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null

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null

0

1

0

c862ff0586dafe12df4bfd251af96f7087dbad08

900

py

Python

app/api/v1/routes.py

kwanj-k/storemanager-API

e51511545a717341a7b1eb100eb3eab625a8b011

[ "MIT" ]

1

2019-05-08T08:39:08.000Z

app/api/v1/routes.py

kwanj-k/storemanager-API

e51511545a717341a7b1eb100eb3eab625a8b011

[ "MIT" ]

2

2019-10-21T17:56:01.000Z

2019-10-29T07:36:39.000Z

app/api/v1/routes.py

kwanj-k/storemanager-API

e51511545a717341a7b1eb100eb3eab625a8b011

[ "MIT" ]

null

""" This file contains all the version one routes """ # Third party imports from flask import Blueprint, request from flask_restplus import Api, Resource, fields # Local application imports from .views.products_views import v1 as pro_routes from .views.sales_views import v1 as sales_routes from .views.stores_views import v1 as stores_routes from .views.auth import v1 as auth_routes authorizations = { 'apikey': { 'type': 'apiKey', 'in': 'header', 'name': 'Authorization' }} v_1 = Blueprint('v_1', __name__, url_prefix="/api/v1") api = Api(v_1) v1 = api.namespace( 'v1', description='Store manager Api without persitent data storage', authorizations=authorizations) api.add_namespace(pro_routes, path="/products/") api.add_namespace(sales_routes, path="/sales") api.add_namespace(stores_routes, path="/stores") api.add_namespace(auth_routes, path="/")

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null

0

null

0

1

0

c8643e92fdc3cc522b5000bf37f329ece9e89e82

5,605

py

Python

tests/test_region_aggregation.py

IAMconsortium/nomenclature

15973d86d91e38424fe30719d44a1f23526c6eea

[ "Apache-2.0" ]

9

2021-06-10T15:11:23.000Z

2022-02-02T16:22:01.000Z

tests/test_region_aggregation.py

IAMconsortium/nomenclature

15973d86d91e38424fe30719d44a1f23526c6eea

[ "Apache-2.0" ]

83

2021-06-22T09:04:29.000Z

2022-03-21T16:29:54.000Z

tests/test_region_aggregation.py

IAMconsortium/nomenclature

15973d86d91e38424fe30719d44a1f23526c6eea

[ "Apache-2.0" ]

3

2021-06-17T10:44:48.000Z

2021-09-16T15:30:03.000Z

from pathlib import Path import jsonschema import pydantic import pytest from nomenclature.processor.region import ( ModelMappingCollisionError, RegionAggregationMapping, RegionProcessor, ) from conftest import TEST_DATA_DIR TEST_FOLDER_REGION_MAPPING = TEST_DATA_DIR / "region_aggregation" def test_mapping(): mapping_file = "working_mapping.yaml" # Test that the file is read and represented correctly obs = RegionAggregationMapping.from_file(TEST_FOLDER_REGION_MAPPING / mapping_file) exp = { "model": "model_a", "file": (TEST_FOLDER_REGION_MAPPING / mapping_file).relative_to(Path.cwd()), "native_regions": [ {"name": "region_a", "rename": "alternative_name_a"}, {"name": "region_b", "rename": "alternative_name_b"}, {"name": "region_c", "rename": None}, ], "common_regions": [ { "name": "common_region_1", "constituent_regions": ["region_a", "region_b"], }, { "name": "common_region_2", "constituent_regions": ["region_c"], }, ], } assert obs.dict() == exp @pytest.mark.parametrize( "file, error_type, error_msg_pattern", [ ( "illegal_mapping_invalid_format_dict.yaml", jsonschema.ValidationError, ".*common_region_1.*not.*'array'.*", ), ( "illegal_mapping_illegal_attribute.yaml", jsonschema.ValidationError, "Additional properties are not allowed.*", ), ( "illegal_mapping_conflict_regions.yaml", pydantic.ValidationError, ".*Name collision in native and common regions.*common_region_1.*", ), ( "illegal_mapping_duplicate_native.yaml", pydantic.ValidationError, ".*Name collision in native regions.*alternative_name_a.*", ), ( "illegal_mapping_duplicate_native_rename.yaml", pydantic.ValidationError, ".*Name collision in native regions.*alternative_name_a.*", ), ( "illegal_mapping_duplicate_common.yaml", pydantic.ValidationError, ".*Name collision in common regions.*common_region_1.*", ), ( "illegal_mapping_model_only.yaml", pydantic.ValidationError, ".*one of the two: 'native_regions', 'common_regions'.*", ), ], ) def test_illegal_mappings(file, error_type, error_msg_pattern): # This is to test a few different failure conditions with pytest.raises(error_type, match=f"{error_msg_pattern}{file}.*"): RegionAggregationMapping.from_file(TEST_FOLDER_REGION_MAPPING / file) @pytest.mark.parametrize( "region_processor_path", [ TEST_DATA_DIR / "regionprocessor_working", (TEST_DATA_DIR / "regionprocessor_working").relative_to(Path.cwd()), ], ) def test_region_processor_working(region_processor_path): obs = RegionProcessor.from_directory(region_processor_path) exp_data = [ { "model": "model_a", "file": ( TEST_DATA_DIR / "regionprocessor_working/mapping_1.yaml" ).relative_to(Path.cwd()), "native_regions": [ {"name": "World", "rename": None}, ], "common_regions": None, }, { "model": "model_b", "file": ( TEST_DATA_DIR / "regionprocessor_working/mapping_2.yaml" ).relative_to(Path.cwd()), "native_regions": None, "common_regions": [ { "name": "World", "constituent_regions": ["region_a", "region_b"], } ], }, ] exp_models = {value["model"] for value in exp_data} exp_dict = {value["model"]: value for value in exp_data} assert exp_models == set(obs.mappings.keys()) assert all(exp_dict[m] == obs.mappings[m].dict() for m in exp_models) def test_region_processor_not_defined(simple_definition): # Test a RegionProcessor with regions that are not defined in the data structure # definition error_msg = ( "model_(a|b)\n.*region_a.*mapping_(1|2).yaml.*value_error.region_not_defined." "*\n.*model_(a|b)\n.*region_a.*mapping_(1|2).yaml.*value_error." "region_not_defined" ) with pytest.raises(pydantic.ValidationError, match=error_msg): RegionProcessor.from_directory( TEST_DATA_DIR / "regionprocessor_not_defined" ).validate_mappings(simple_definition) def test_region_processor_duplicate_model_mapping(): error_msg = ".*model_a.*mapping_(1|2).yaml.*mapping_(1|2).yaml" with pytest.raises(ModelMappingCollisionError, match=error_msg): RegionProcessor.from_directory(TEST_DATA_DIR / "regionprocessor_duplicate") def test_region_processor_wrong_args(): # Test if pydantic correctly type checks the input of RegionProcessor.from_directory # Test with an integer with pytest.raises(pydantic.ValidationError, match=".*path\n.*not a valid path.*"): RegionProcessor.from_directory(123) # Test with a file, a path pointing to a directory is required with pytest.raises( pydantic.ValidationError, match=".*path\n.*does not point to a directory.*", ): RegionProcessor.from_directory( TEST_DATA_DIR / "regionprocessor_working/mapping_1.yaml" )

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false

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0.086331

0

null

0

null

0

1

0

c8657a8c0a88d1cd1bd12e0d16b56dc5546e1b6c

2,300

py

Python

render_object.py

VanGy-code/3D-House-Blender

8a9d91b1f3cc3988c0dcd7079223f2e541f9ec71

[ "MIT" ]

null

render_object.py

VanGy-code/3D-House-Blender

8a9d91b1f3cc3988c0dcd7079223f2e541f9ec71

[ "MIT" ]

null

render_object.py

VanGy-code/3D-House-Blender

8a9d91b1f3cc3988c0dcd7079223f2e541f9ec71

[ "MIT" ]

1

2021-11-22T00:50:45.000Z

import bpy import os import json import numpy as np from decimal import Decimal from mathutils import Vector, Matrix import argparse import numpy as np import sys sys.path.append(os.path.dirname(__file__)) sys.path.append(os.path.dirname(__file__)+'/tools') from tools.utils import * from tools.blender_interface import BlenderInterface if __name__ == '__main__': p = argparse.ArgumentParser(description='Renders given obj file by rotation a camera around it.') p.add_argument('--mesh_fpath', type=str, required=True, help='The path the output will be dumped to.') p.add_argument('--output_dir', type=str, required=True, help='The path the output will be dumped to.') p.add_argument('--num_observations', type=int, required=True, help='The path the output will be dumped to.') p.add_argument('--sphere_radius', type=float, required=True, help='The path the output will be dumped to.') p.add_argument('--mode', type=str, required=True, help='Options: train and test') argv = sys.argv argv = sys.argv[sys.argv.index("--") + 1:] opt = p.parse_args(argv) instance_name = opt.mesh_fpath.split('/')[-3] instance_dir = os.path.join(opt.output_dir, instance_name) # Start Render renderer = BlenderInterface(resolution=128) if opt.mode == 'train': cam_locations = sample_spherical(opt.num_observations, opt.sphere_radius) elif opt.mode == 'test': cam_locations = get_archimedean_spiral(opt.sphere_radius, opt.num_observations) obj_location = np.zeros((1,3)) cv_poses = look_at(cam_locations, obj_location) blender_poses = [cv_cam2world_to_bcam2world(m) for m in cv_poses] shapenet_rotation_mat = np.array([[1.0000000e+00, 0.0000000e+00, 0.0000000e+00], [0.0000000e+00, -1.0000000e+00, -1.2246468e-16], [0.0000000e+00, 1.2246468e-16, -1.0000000e+00]]) rot_mat = np.eye(3) hom_coords = np.array([[0., 0., 0., 1.]]).reshape(1, 4) obj_pose = np.concatenate((rot_mat, obj_location.reshape(3,1)), axis=-1) obj_pose = np.concatenate((obj_pose, hom_coords), axis=0) renderer.import_mesh(opt.mesh_fpath, scale=1., object_world_matrix=obj_pose) renderer.render(instance_dir, blender_poses, write_cam_params=True)

41.818182

112

0.694348

341

2,300

4.478006

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0.045842

0.039293

0.049771

0.27112

0.228553

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0.1611

0

0.058452

0.174348

2,300

55

113

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false

0

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0

0.285714

0

null

0

null

0

1

0

c86659332f0223beeafc6e01030a75e258e463d5

2,717

py

Python

mapel/elections/features/clustering.py

kaszperro/mapel

d4e6486ee97f5d5a5a737c581ba3f9f874ebcef3

[ "MIT" ]

null

mapel/elections/features/clustering.py

kaszperro/mapel

d4e6486ee97f5d5a5a737c581ba3f9f874ebcef3

[ "MIT" ]

null

mapel/elections/features/clustering.py

kaszperro/mapel

d4e6486ee97f5d5a5a737c581ba3f9f874ebcef3

[ "MIT" ]

null

import numpy as np def clustering_v1(experiment, num_clusters=20): from scipy.cluster.hierarchy import dendrogram, linkage, fcluster import scipy.spatial.distance as ssd # skip the paths SKIP = ['UNID', 'ANID', 'STID', 'ANUN', 'STUN', 'STAN', 'Mallows', 'Urn', 'Identity', 'Uniformity', 'Antagonism', 'Stratification', ] new_names = [] for i, a in enumerate(list(experiment.distances)): if not any(tmp in a for tmp in SKIP): new_names.append(a) print(len(new_names)) distMatrix = np.zeros([len(new_names), len(new_names)]) for i, a in enumerate(new_names): for j, b in enumerate(new_names): if a != b: distMatrix[i][j] = experiment.distances[a][b] # Zd = linkage(ssd.squareform(distMatrix), method="complete") # cld = fcluster(Zd, 500, criterion='distance').reshape(len(new_names), 1) Zd = linkage(ssd.squareform(distMatrix), method="complete") cld = fcluster(Zd, 12, criterion='maxclust').reshape(len(new_names), 1) clusters = {} for i, name in enumerate(new_names): clusters[name] = cld[i][0] for name in experiment.coordinates: if name not in clusters: clusters[name] = 0 return {'value': clusters} def clustering_kmeans(experiment, num_clusters=20): from sklearn.cluster import KMeans points = list(experiment.coordinates.values()) kmeans = KMeans(n_clusters=num_clusters) kmeans.fit(points) y_km = kmeans.fit_predict(points) # plt.scatter(points[y_km == 0, 0], points[y_km == 0, 1], s=100, c='red') # plt.scatter(points[y_km == 1, 0], points[y_km == 1, 1], s=100, c='black') # plt.scatter(points[y_km == 2, 0], points[y_km == 2, 1], s=100, c='blue') # plt.scatter(points[y_km == 3, 0], points[y_km == 3, 1], s=100, c='cyan') # all_distances = [] # for a,b in combinations(experiment.distances, 2): # all_distances.append([a, b, experiment.distances[a][b]]) # all_distances.sort(key=lambda x: x[2]) # # clusters = {a: None for a in experiment.distances} # num_clusters = 0 # for a,b,dist in all_distances: # if clusters[a] is None and clusters[b] is None: # clusters[a] = num_clusters # clusters[b] = num_clusters # num_clusters += 1 # elif clusters[a] is None and clusters[b] is not None: # clusters[a] = clusters[b] # elif clusters[a] is not None and clusters[b] is None: # clusters[b] = clusters[a] clusters = {} for i, name in enumerate(experiment.coordinates): clusters[name] = y_km[i] return {'value': clusters}

33.54321

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0.109181

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2,717

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false

0

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0.210526

0.026316

0

null

0

null

0

1

0

c86693ef8ab98f83a2f7c7800edbe9c593122043

561

py

Python

day15-1.py

kenleung5e28/advent-of-code-2021

f6de211f0d4f3bafa19572bf28e3407f0fab6d58

[ "MIT" ]

null

day15-1.py

kenleung5e28/advent-of-code-2021

f6de211f0d4f3bafa19572bf28e3407f0fab6d58

[ "MIT" ]

null

day15-1.py

kenleung5e28/advent-of-code-2021

f6de211f0d4f3bafa19572bf28e3407f0fab6d58

[ "MIT" ]

null

import math grid = [] with open('input-day15.txt') as file: for line in file: line = line.rstrip() grid.append([int(s) for s in line]) n = len(grid) costs = [[math.inf] * n for _ in range(n)] costs[0][0] = 0 queue = [(0, 0)] while len(queue) > 0: x1, y1 = queue.pop(0) for dx, dy in [(1, 0), (0, 1), (-1, 0), (0, -1)]: x, y = x1 + dx, y1 + dy if x >= 0 and y >= 0 and x < n and y < n: cost = costs[x1][y1] + grid[x][y] if cost < costs[x][y]: costs[x][y] = cost queue.append((x, y)) print(costs[n - 1][n - 1])

24.391304

51

0.504456

108

561

2.611111

0.342593

0.035461

0.021277

0.028369

0

0.066667

0.278075

561

23

52

24.391304

0.62963

0

0.02669

0

1

0

false

0

0.05

0

0.05

0

null

0

null

0

1

0

c86731656ffa6ef2b38ba405b2722abcba4b7c94

1,217

py

Python

Algorithms/Sorting and Searching/sorting/merge sort/merge-sort-return-list.py

bulentsiyah/Python-Basics-Algorithms-Data-Structures-Object-Oriented-Programming-Job-Interview-Questions

3a67bdac1525495e6874c5bde61882848f60381d

[ "MIT" ]

14

2021-01-23T11:28:16.000Z

2021-12-07T16:08:23.000Z

Algorithms/Sorting and Searching/sorting/merge sort/merge-sort-return-list.py

bulentsiyah/Python-Basics-Algorithms-Data-Structures-Object-Oriented-Programming-Job-Interview-Questions

3a67bdac1525495e6874c5bde61882848f60381d

[ "MIT" ]

null

Algorithms/Sorting and Searching/sorting/merge sort/merge-sort-return-list.py

bulentsiyah/Python-Basics-Algorithms-Data-Structures-Object-Oriented-Programming-Job-Interview-Questions

3a67bdac1525495e6874c5bde61882848f60381d

[ "MIT" ]

2

2021-02-03T12:28:19.000Z

2021-09-14T09:50:08.000Z

arr: list = [54,26,93,17,77,31,44,55,20] def merge_sort(arr: list): result: list = helper(arr, 0, len(arr) - 1) for i in range(len(arr)): arr[i] = result[i] def helper(arr: list, start: int, end: int) -> list: if start > end: return [] elif start == end: return [arr[start]] else: midpoint: int = start + (end - start) // 2 leftList = helper(arr, start, midpoint) rightList = helper(arr, midpoint + 1, end) return mergelists(leftList, rightList) def mergelists(leftList: list, rightList: list) -> list: arr: list = [None] * (len(leftList) + len(rightList)) i = j = k = 0 while i < len(leftList) and j < len(rightList): if leftList[i] < rightList[j]: arr[k] = leftList[i] i += 1 else: arr[k] = rightList[j] j += 1 k += 1 while i < len(leftList): arr[k] = leftList[i] i += 1 k += 1 while j < len(rightList): arr[k] = rightList[j] j += 1 k += 1 return arr print(arr) merge_sort(arr) print(arr)

24.836735

61

0.474117

154

1,217

3.733766

0.266234

0.048696

0.015652

0.05913

0.114783

0.062609

0

0.040486

0.391126

1,217

48

62

25.354167

0.735493

0

0.394737

0

1

0.078947

false

0

0.184211

0.052632

0

null

0

null

0

1

0

c867bd2b7a6b9e73aa95e644913f2d2ac179784c

3,406

py

Python

cve-manager/cve_manager/handler/task_handler/callback/cve_scan.py

seandong37tt4qu/jeszhengq

32b3737ab45e89e8c5b71cdce871cefd2c938fa8

[ "MulanPSL-1.0" ]

null

cve-manager/cve_manager/handler/task_handler/callback/cve_scan.py

seandong37tt4qu/jeszhengq

32b3737ab45e89e8c5b71cdce871cefd2c938fa8

[ "MulanPSL-1.0" ]

null

cve-manager/cve_manager/handler/task_handler/callback/cve_scan.py

seandong37tt4qu/jeszhengq

32b3737ab45e89e8c5b71cdce871cefd2c938fa8

[ "MulanPSL-1.0" ]

null

#!/usr/bin/python3 # ****************************************************************************** # Copyright (c) Huawei Technologies Co., Ltd. 2021-2022. All rights reserved. # licensed under the Mulan PSL v2. # You can use this software according to the terms and conditions of the Mulan PSL v2. # You may obtain a copy of Mulan PSL v2 at: # http://license.coscl.org.cn/MulanPSL2 # THIS SOFTWARE IS PROVIDED ON AN 'AS IS' BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR # PURPOSE. # See the Mulan PSL v2 for more details. # ******************************************************************************/ """ Time: Author: Description: callback function of the cve scanning task. """ from aops_utils.log.log import LOGGER from cve_manager.handler.task_handler.callback import TaskCallback from cve_manager.conf.constant import ANSIBLE_TASK_STATUS, CVE_SCAN_STATUS class CveScanCallback(TaskCallback): """ Callback function for cve scanning. """ def __init__(self, user, proxy, host_info): """ Args: user (str): who the scanned hosts belongs to. proxy (object): database proxy host_info (list): host info, e.g. hostname, ip, etc. """ self.user = user task_info = {} for info in host_info: host_name = info.get('host_name') task_info[host_name] = info super().__init__(None, proxy, task_info) def v2_runner_on_unreachable(self, result): host_name, result_info, task_name = self._get_info(result) self.result[host_name][task_name] = { "info": result_info['msg'], "status": ANSIBLE_TASK_STATUS.UNREACHABLE} LOGGER.debug("task name: %s, user: %s, host name: %s, result: %s", task_name, self.user, host_name, ANSIBLE_TASK_STATUS.UNREACHABLE) self.save_to_db(task_name, host_name, CVE_SCAN_STATUS.DONE) def v2_runner_on_ok(self, result): host_name, result_info, task_name = self._get_info(result) self.result[host_name][task_name] = { "info": result_info['stdout'], "status": ANSIBLE_TASK_STATUS.SUCCEED} LOGGER.debug("task name: %s, user: %s, host name: %s, result: %s", task_name, self.user, host_name, ANSIBLE_TASK_STATUS.SUCCEED) self.save_to_db(task_name, host_name, CVE_SCAN_STATUS.DONE) def v2_runner_on_failed(self, result, ignore_errors=False): host_name, result_info, task_name = self._get_info(result) self.result[host_name][task_name] = { "info": result_info['stderr'], "status": ANSIBLE_TASK_STATUS.FAIL} LOGGER.debug("task name: %s, user: %s, host name: %s, result: %s", task_name, self.user, host_name, ANSIBLE_TASK_STATUS.FAIL) self.save_to_db(task_name, host_name, CVE_SCAN_STATUS.DONE) def save_to_db(self, task_name, host_name, status): """ Set the status of the host to database. Args: task_name (str): task name in playbook. host_name (str) status (str) """ host_id = self.task_info[host_name]['host_id'] self.proxy.update_scan_status([host_id]) LOGGER.debug("task name: %s, host_id: %s, status: %s", task_name, host_id, status)

40.547619

98

0.625954

458

3,406

4.412664

0.310044

0.083127

0.058882

0.044532

0.361702

0.335972

0

0.006442

0.225191

3,406

83

99

41.036145

0.759379

0.3165

0

0.333333

0

0.083333

0.113595

0

1

0.138889

false

0

0.083333

0

0.25

0

null

0

null

0

1

0

c86aa619ebc8f014032a97d24de5e8f90b466d18

2,416

py

Python

tests/result/test_gatling.py

LaudateCorpus1/perfsize

710d6a5ae0918002e736f3aba8cd5cacb2b11326

[ "Apache-2.0" ]

5

2021-08-02T22:44:32.000Z

2022-01-07T20:53:48.000Z

tests/result/test_gatling.py

intuit/perfsize

710d6a5ae0918002e736f3aba8cd5cacb2b11326

[ "Apache-2.0" ]

1

2022-02-24T08:05:51.000Z

tests/result/test_gatling.py

LaudateCorpus1/perfsize

710d6a5ae0918002e736f3aba8cd5cacb2b11326

[ "Apache-2.0" ]

1

2022-02-24T08:05:41.000Z

from datetime import datetime from decimal import Decimal from perfsize.perfsize import ( lt, lte, gt, gte, eq, neq, Condition, Result, Run, Config, Plan, StepManager, EnvironmentManager, LoadManager, ResultManager, Reporter, Workflow, ) from perfsize.environment.mock import MockEnvironmentManager from perfsize.load.mock import MockLoadManager from perfsize.reporter.mock import MockReporter from perfsize.result.mock import MockResultManager from perfsize.result.gatling import Metric, GatlingResultManager from perfsize.step.mock import MockStepManager from pprint import pprint import pytest from unittest.mock import patch class TestGatlingResultManager: def test_gatling_result_manager(self) -> None: # A plan would define the various configs possible for testing. # A step manager would pick the next config to test. # This test is starting with a given Config and an associated Run. config = Config( parameters={ "endpoint_name": "LEARNING-model-sim-public-c-1", "endpoint_config_name": "LEARNING-model-sim-public-c-1-0", "model_name": "model-sim-public", "instance_type": "ml.t2.medium", "initial_instance_count": "1", "ramp_start_tps": "0", "ramp_minutes": "0", "steady_state_tps": "1", "steady_state_minutes": "1", }, requirements={ Metric.latency_success_p99: [ Condition(lt(Decimal("200")), "value < 200"), Condition(gte(Decimal("0")), "value >= 0"), ], Metric.percent_fail: [ Condition(lt(Decimal("0.01")), "value < 0.01"), Condition(gte(Decimal("0")), "value >= 0"), ], }, ) run = Run( id="test_run_tag", start=datetime.fromisoformat("2021-04-01T00:00:00"), end=datetime.fromisoformat("2021-04-01T01:00:00"), results=[], ) # GatlingResultManager will parse simulation.log and populate results result_manager = GatlingResultManager( results_path="examples/perfsize-results-root" ) result_manager.query(config, run) pprint(run.results)

33.09589

77

0.591474

250

2,416

5.616

0.464

0.059829

0.029915

0.02849

0.076923

0.039886

0

0.032895

0.307947

2,416

72

78

33.555556

0.806818

0.101407

0

0.060606

0

0.168513

0.051708

0

1

0.015152

false

0

0.181818

0

0.212121

0.030303

0

null

0

null

0

1

0

c86bfc31df7a20be6ab83d39b12b217359bfd5df

3,904

py

Python

__main__.py

GbaCretin/dmf2mlm

8a0d3d219aecb9aa14a66537e2deb02651bdfe7d

[ "MIT" ]

2

2021-06-13T15:55:55.000Z

2021-09-14T08:21:53.000Z

__main__.py

GbaCretin/dmf2mlm

8a0d3d219aecb9aa14a66537e2deb02651bdfe7d

[ "MIT" ]

6

2022-03-22T10:02:35.000Z

2022-03-31T19:28:13.000Z

__main__.py

GbaCretin/dmf2mlm

8a0d3d219aecb9aa14a66537e2deb02651bdfe7d

[ "MIT" ]

null

from src import dmf,mzs,utils,sfx from pathlib import Path import argparse def print_info(mlm_sdata): if len(mlm_sdata.songs) <= 0: return for i in range(len(mlm_sdata.songs[0].channels)): channel = mlm_sdata.songs[0].channels[i] print("\n================[ {0:01X} ]================".format(i)) if channel == None: print("Empty") continue for event in channel.events: print(event) if isinstance(event, mzs.SongComJumpToSubEL): sub_el = mlm_sdata.songs[0].sub_event_lists[i][event.sub_el_idx] sub_el.print() print("\t--------") def print_df_info(mod, channels: [int]): for ch in channels: print("|####[${0:02X}]####".format(ch), end='') print("|") for i in range(mod.pattern_matrix.rows_in_pattern_matrix): for ch in channels: subel_idx = mod.pattern_matrix.matrix[ch][i] print("|====(${0:02X})====".format(subel_idx), end='') print("|") for j in range(mod.pattern_matrix.rows_per_pattern): for ch in channels: pat_idx = mod.pattern_matrix.matrix[ch][i] row = mod.patterns[ch][pat_idx].rows[j] note_lbl = "--" oct_lbl = "-" vol_lbl = "--" inst_lbl = "--" fx0_lbl = "----" if row.octave != None: oct_lbl = str(row.octave) if row.note == dmf.Note.NOTE_OFF: note_lbl = "~~" oct_lbl = "~" elif row.note != None: note_lbl = row.note.name.ljust(2, '-').replace('S', '#') if row.volume != None: vol_lbl = "{:02X}".format(row.volume) if row.instrument != None: inst_lbl = "{:02X}".format(row.instrument) if len(row.effects) > 0: fx0 = row.effects[0] if fx0.code == dmf.EffectCode.EMPTY: fx0_lbl = "--" else: fx0_lbl = "{:02X}".format(fx0.code.value) if fx0.value == None: fx0_lbl += "--" else: fx0_lbl += "{:02X}".format(fx0.value) print("|{0}{1} {2}{3} {4}".format(note_lbl, oct_lbl, vol_lbl, inst_lbl, fx0_lbl), end='') print("|") parser = argparse.ArgumentParser(description='Convert DMF modules and SFX to an MLM driver compatible format') parser.add_argument('dmf_module_paths', type=str, nargs='*', help="The paths to the input DMF files") parser.add_argument('--sfx-directory', type=Path, help="Path to folder containing .raw files (Only absolute paths; Must be 18500Hz 16bit mono)") parser.add_argument('--sfx-header', type=Path, help="Where to save the generated SFX c header (Only absolute paths)") args = parser.parse_args() dmf_modules = [] sfx_samples = None if args.sfx_directory != None: print("Parsing SFX... ", end='', flush=True) sfx_samples = sfx.SFXSamples(args.sfx_directory) print("OK") if args.sfx_header != None: print("Generating SFX Header... ", end='', flush=True) c_header = sfx_samples.generate_c_header() print("OK") print(f"Saving SFX Header as '{args.sfx_header}'... ", end='', flush=True) with open(args.sfx_header, "w") as file: file.write(c_header) print("OK") for i in range(len(args.dmf_module_paths)): with open(args.dmf_module_paths[i], "rb") as file: print(f"Parsing '{args.dmf_module_paths[i]}'... ", end='', flush=True) mod = dmf.Module(file.read()) print("OK") print(f"Optimizing '{args.dmf_module_paths[i]}'... ", end='', flush=True) mod.patch_for_mzs() mod.optimize() print("OK") dmf_modules.append(mod) mlm_sdata = mzs.SoundData() print(f"Converting DMFs... ", end='', flush=True) mlm_sdata.add_dmfs(dmf_modules) print("OK") if sfx_samples != None: print(f"Converting SFX... ", end='', flush=True) mlm_sdata.add_sfx(sfx_samples, False) print("OK") #print_df_info(dmf_modules[0], [0, 4, 7]) #print_info(mlm_sdata) print(f"Compiling... ", end='', flush=True) mlm_compiled_sdata = mlm_sdata.compile_sdata() mlm_compiled_vrom = mlm_sdata.compile_vrom() print("OK") with open("m1_sdata.bin", "wb") as file: file.write(mlm_compiled_sdata) with open("vrom.bin", "wb") as file: file.write(mlm_compiled_vrom)

30.984127

144

0.649846

592

3,904

4.109797

0.258446

0.036169

0.039457

0.023017

0.228524

0.166872

0.125771

0.102754

0.054254

0.026305

0

0.015858

0.160092

3,904

126

145

30.984127

0.726136

0.015625

0

0.15534

0

0.1848

0.021864

0

1

0.019417

false

0

0.029126

0

0.048544

0.281553

0

null

0

null

0

1

0

c06f05eaa2d985c3d75a5edbcfcca422b525cddf

2,630

py

Python

python/zephyr/models/__init__.py

r-pad/zephyr

c8f45e207c11bfc2b21df169db65a7df892d2848

[ "MIT" ]

18

2021-05-27T04:40:38.000Z

2022-02-08T19:46:31.000Z

python/zephyr/models/__init__.py

r-pad/zephyr

c8f45e207c11bfc2b21df169db65a7df892d2848

[ "MIT" ]

null

python/zephyr/models/__init__.py

r-pad/zephyr

c8f45e207c11bfc2b21df169db65a7df892d2848

[ "MIT" ]

2

2021-11-07T12:42:00.000Z

2022-03-01T12:51:54.000Z

import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import time from functools import partial from .linear import MLP, LogReg from .pointnet import PointNet from .pointnet2 import PointNet2SSG from .pointnet3 import PointNet3SSG from .dgcnn import DGCNN # from .masked_conv import ConvolutionalPoseModel from .point_mlp import PointMLP from pytorch_lightning.core.lightning import LightningModule def getModel(model_name, args, mode="train"): if args.resume_path is None or mode == 'train': if model_name == 'mlp': model = MLP(args.dim_agg, args) if model_name == "pmlp": model = PointMLP(args.dim_point, args) elif model_name[:2] == 'lg': model = LogReg(args.dim_agg, args) elif model_name == "pn": model = PointNet(args.dim_point, args) elif model_name == "pn2": model = PointNet2SSG(args.dim_point, args, num_class=1) elif model_name == "pn3": model = PointNet3SSG(args.dim_point, args, num_class=1) elif model_name == "dgcnn": model = DGCNN(args.dim_point, args, num_class=1) # elif model_name == "maskconv": # model = ConvolutionalPoseModel(args) else: raise Exception("Unknown model name:", model_name) else: if model_name == 'mlp': model = MLP.load_from_checkpoint(args.resume_path, args.dim_agg, args) elif model_name == "pmlp": model = PointMLP.load_from_checkpoint(args.resume_path, args.dim_point, args) elif model_name[:2] == 'lg': model = LogReg.load_from_checkpoint(args.resume_path, args.dim_agg, args) elif model_name == "pn": model = PointNet.load_from_checkpoint(args.resume_path, args.dim_point, args) elif model_name == "pn2": model = PointNet2SSG.load_from_checkpoint(args.resume_path, args.dim_point, args, num_class=1) elif model_name == "pn3": model = PointNet3SSG.load_from_checkpoint(args.resume_path, args.dim_point, args, num_class=1) elif model_name == "dgcnn": model = DGCNN.load_from_checkpoint(args.resume_path, args.dim_point, args, num_class=1) # elif model_name == "maskconv": # model = ConvolutionalPoseModel.load_from_checkpoint(args.resume_path, args) else: raise Exception("Unknown model name:", model_name) if not args.pretrained_pnfeat is None: model.loadPretrainedFeat(args.pretrained_pnfeat) return model

43.114754

107

0.646388

330

2,630

4.942424

0.20303

0.11588

0.103617

0.098099

0.674433

0.648069

0.621091

0.601471

0.543838

0

0.010251

0.258175

2,630

60

108

43.833333

0.82573

0.087452

0

0.333333

0

0.039417

0

1

0.019608

false

0

0.254902

0

0.294118

0

null

0

null

0

1

0

c07358522633a4b5223edee437652e807e46cb27

1,054

py

Python

timer.py

ryanleesmith/race-timer

3a058e3689c9435751b06909d5b7a14db618d2da

[ "MIT" ]

null

timer.py

ryanleesmith/race-timer

3a058e3689c9435751b06909d5b7a14db618d2da

[ "MIT" ]

null

timer.py

ryanleesmith/race-timer

3a058e3689c9435751b06909d5b7a14db618d2da

[ "MIT" ]

null

from gps import * import math import time import json import threading gpsd = None poller = None class Poller(threading.Thread): def __init__(self): threading.Thread.__init__(self) global gpsd gpsd = gps(mode=WATCH_ENABLE|WATCH_NEWSTYLE) self.current_value = None self.running = True def run(self): global gpsd, poller while poller.running: gpsd.next() def timer(): global gpsd, poller poller = Poller() try: poller.start() while True: speed = gpsd.fix.speed if math.isnan(speed): speed = 0 #print(speed) #print(gpsd.fix.mode) #print(gpsd.satellites) dump = json.dumps({'x': int(round(time.time() * 1000)), 'y': speed}) yield 'event: SPEED\ndata: {}\n\n'.format(dump) time.sleep(0.1) except (KeyboardInterrupt, SystemExit): print("\nKilling Thread...") poller.running = False poller.join()

23.422222

80

0.555028

118

1,054

4.864407

0.483051

0.052265

0.04878

0

0.009957

0.333017

1,054

44

81

23.954545

0.806543

0.051233

0

0.058824

0

0.047141

0

1

0.088235

false

0

0.147059

0

0.264706

0.029412

0

null

0

null

0

1

0

c078ff18aa77981230542dee77a093f9d2cdb667

13,841

py

Python

layer_manager/models.py

lueho/BRIT

1eae630c4da6f072aa4e2139bc406db4f4756391

[ "MIT" ]

null

layer_manager/models.py

lueho/BRIT

1eae630c4da6f072aa4e2139bc406db4f4756391

[ "MIT" ]

4

2022-03-29T20:52:31.000Z

layer_manager/models.py

lueho/BRIT

1eae630c4da6f072aa4e2139bc406db4f4756391

[ "MIT" ]

null

import django.contrib.gis.db.models as gis_models from django.apps import apps from django.db import models, connection from django.urls import reverse from distributions.models import TemporalDistribution, Timestep from inventories.models import Scenario, InventoryAlgorithm from materials.models import SampleSeries, MaterialComponent from .exceptions import InvalidGeometryType, NoFeaturesProvided, TableAlreadyExists class LayerField(models.Model): """ Holds all field definitions of GIS layers. Used to recreate a dynamically created model in case it is lost from the apps registry. """ field_name = models.CharField(max_length=63) data_type = models.CharField(max_length=10) def data_type_object(self): if self.data_type == 'float': return models.FloatField() elif self.data_type == 'int': return models.IntegerField() @staticmethod def model_field_type(data_type: str): if data_type == 'float': return models.FloatField(blank=True, null=True) elif data_type == 'int': return models.IntegerField(blank=True, null=True) elif data_type == 'str': return models.CharField(blank=True, null=True, max_length=200) class LayerManager(models.Manager): supported_geometry_types = ['Point', 'MultiPoint', 'LineString', 'MultiLineString', 'Polygon', 'MultiPolygon', ] def create_or_replace(self, **kwargs): results = kwargs.pop('results') if 'features' not in results or len(results['features']) == 0: raise NoFeaturesProvided(results) else: features = results['features'] fields = {} # The data types of the fields are detected from their content. Any column that has only null values # will be omitted completely if features: fields_with_unknown_datatype = list(features[0].keys()) for feature in features: if not fields_with_unknown_datatype: break for key, value in feature.items(): if feature[key] and key in fields_with_unknown_datatype: fields[key] = type(value).__name__ fields_with_unknown_datatype.remove(key) # At this point there might be fields left out because there were only null values from which the # data type could be detected. They should be omitted but this information should be logged # TODO: add omitted columns info to log kwargs['geom_type'] = fields.pop('geom') if kwargs['geom_type'] not in self.supported_geometry_types: raise InvalidGeometryType(kwargs['geom_type']) kwargs['table_name'] = 'result_of_scenario_' + \ str(kwargs['scenario'].id) + '_algorithm_' + \ str(kwargs['algorithm'].id) + '_feedstock_' + \ str(kwargs['feedstock'].id) layer, created = super().get_or_create(table_name=kwargs['table_name'], defaults=kwargs) if created: layer.add_layer_fields(fields) feature_collection = layer.update_or_create_feature_collection() layer.create_feature_table() else: if layer.is_defined_by(fields=fields, **kwargs): feature_collection = layer.get_feature_collection() feature_collection.objects.all().delete() else: layer.delete() layer = super().create(**kwargs) layer.add_layer_fields(fields) feature_collection = layer.update_or_create_feature_collection() layer.create_feature_table() layer.delete_aggregated_values() for feature in features: feature_collection.objects.create(**feature) if 'aggregated_values' in results: layer.add_aggregated_values(results['aggregated_values']) if 'aggregated_distributions' in results: layer.add_aggregated_distributions(results['aggregated_distributions']) return layer, feature_collection class Layer(models.Model): """ Registry of all created layers. This main model holds all meta information about each layer. When a new layer record is created, another custom model named "features collection" is automatically generated, preserving the original shape of the gis source dataset as much as required. The feature collection can be used to manage the actual features of the layer. It will create a separate database table with the name given in "table_name" to store the features. """ name = models.CharField(max_length=56) geom_type = models.CharField(max_length=20) table_name = models.CharField(max_length=200) scenario = models.ForeignKey(Scenario, on_delete=models.CASCADE) feedstock = models.ForeignKey(SampleSeries, on_delete=models.CASCADE) algorithm = models.ForeignKey(InventoryAlgorithm, on_delete=models.CASCADE) layer_fields = models.ManyToManyField(LayerField) objects = LayerManager() class Meta: constraints = [ models.UniqueConstraint(fields=['table_name'], name='unique table_name') ] def add_aggregated_values(self, aggregates: []): for aggregate in aggregates: LayerAggregatedValue.objects.create(name=aggregate['name'], value=aggregate['value'], unit=aggregate['unit'], layer=self) def add_aggregated_distributions(self, distributions): for distribution in distributions: dist = TemporalDistribution.objects.get(id=distribution['distribution']) aggdist = LayerAggregatedDistribution.objects.create(name=distribution['name'], distribution=dist, layer=self) for dset in distribution['sets']: distset = DistributionSet.objects.create( aggregated_distribution=aggdist, timestep_id=dset['timestep'] ) for share in dset['shares']: DistributionShare.objects.create( component_id=share['component'], average=share['average'], standard_deviation=0.0, # TODO distribution_set=distset ) def add_layer_fields(self, fields: dict): for field_name, data_type in fields.items(): field, created = LayerField.objects.get_or_create(field_name=field_name, data_type=data_type) self.layer_fields.add(field) def as_dict(self): return { 'name': self.name, 'geom_type': self.geom_type, 'table_name': self.table_name, 'scenario': self.scenario, 'feedstock': self.feedstock, 'inventory_algorithm': self.algorithm, 'layer_fields': [field for field in self.layer_fields.all()], 'aggregated_results': [ {'name': aggregate.name, 'value': int(aggregate.value), 'unit': aggregate.unit} for aggregate in self.layeraggregatedvalue_set.all() ] } def update_or_create_feature_collection(self): """ Dynamically creates model connected to this layer instance that is used to handle its features and store them in a separate custom database table. """ # Empty app registry from any previous version of this model model_name = self.table_name if model_name in apps.all_models['layer_manager']: del apps.all_models['layer_manager'][model_name] attrs = { '__module__': 'layer_manager.models', 'geom': getattr(gis_models, self.geom_type + 'Field')(srid=4326) } # Add all custom columns to model for field in self.layer_fields.all(): attrs[field.field_name] = LayerField.model_field_type(field.data_type) # Create model class and assign table_name model = type(model_name, (models.Model,), attrs) model._meta.layer = self model._meta.db_table = self.table_name return model def create_feature_table(self): """ Creates a new table with all given fields from a model :return: """ feature_collection = self.get_feature_collection() # Check if any table of the name already exists with connection.cursor() as cursor: cursor.execute(f"SELECT to_regclass('{feature_collection._meta.db_table}')") if cursor.fetchone()[0]: raise TableAlreadyExists # After cleanup, now create the new version of the result table with connection.schema_editor() as schema_editor: schema_editor.create_model(feature_collection) def feature_table_url(self): return reverse('scenario_result_map', kwargs={'pk': self.scenario.id, 'algo_pk': self.algorithm.id}) def delete(self, **kwargs): self.delete_feature_table() del apps.all_models['layer_manager'][self.table_name] super().delete() def delete_feature_table(self): """ Deletes a table from a given model :return: """ feature_collection = self.get_feature_collection() with connection.cursor() as cursor: cursor.execute(f"SELECT to_regclass('{feature_collection._meta.db_table}')") if cursor.fetchone()[0] is None: return with connection.schema_editor() as schema_editor: schema_editor.delete_model(feature_collection) def delete_aggregated_values(self): LayerAggregatedValue.objects.filter(layer=self).delete() def get_feature_collection(self): """ Returns the feature collection model that is used to manage the features connected to this layer. """ # If the model is already registered, return original model if self.table_name in apps.all_models['layer_manager']: return apps.all_models['layer_manager'][self.table_name] else: return self.update_or_create_feature_collection() def is_defined_by(self, **kwargs): fields = {field.field_name: field.data_type for field in self.layer_fields.all()} comparisons = [ self.table_name == kwargs['table_name'], self.geom_type == kwargs['geom_type'], self.scenario == kwargs['scenario'], self.algorithm == kwargs['algorithm'], fields == kwargs['fields'] ] return all(comparisons) class LayerAggregatedValue(models.Model): """ Class to hold all aggregated results from a result layer """ name = models.CharField(max_length=63) value = models.FloatField() unit = models.CharField(max_length=15, blank=True, null=True, default='') layer = models.ForeignKey(Layer, on_delete=models.CASCADE) DISTRIBUTION_TYPES = ( ('seasonal', 'seasonal'), # Assumes array with length 12 for each month of the year ) class LayerAggregatedDistribution(models.Model): """ Holds desired aggregated distributions for a layer. Intended for seasonal distributions broken down to feedstock components but any other distribution works as well. """ name = models.CharField(max_length=255, null=True) type = models.CharField(max_length=255, choices=DISTRIBUTION_TYPES, null=True) distribution = models.ForeignKey(TemporalDistribution, on_delete=models.CASCADE, null=True) layer = models.ForeignKey(Layer, on_delete=models.CASCADE, null=True) @property def shares(self): return DistributionShare.objects.filter(distribution_set__aggregated_distribution=self) @property def components(self): return MaterialComponent.objects.filter( id__in=[share['component'] for share in self.shares.values('component').distinct()] ) @property def serialized(self): dist = [] for component in self.components: component_dist = { 'label': component.name, 'data': {}, 'unit': 'Mg/a' } # data = {} for timestep in self.distribution.timestep_set.all(): try: # TODO: find better way to deal with the fact that there is not a value for every component/timestep combination share = self.shares.get(component=component, distribution_set__timestep=timestep) component_dist['data'][timestep.name] = share.average except: pass # component_dist['data'].append(data) dist.append(component_dist) return dist class DistributionSet(models.Model): timestep = models.ForeignKey(Timestep, on_delete=models.CASCADE, null=True) aggregated_distribution = models.ForeignKey(LayerAggregatedDistribution, on_delete=models.CASCADE, null=True) class DistributionShare(models.Model): distribution_set = models.ForeignKey(DistributionSet, on_delete=models.CASCADE) component = models.ForeignKey(MaterialComponent, on_delete=models.CASCADE, null=True) average = models.FloatField() standard_deviation = models.DecimalField(decimal_places=2, max_digits=5)

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5.613848

0.198402

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null

0

1

0

c07ca44e33380193eabc6f8bec1ebe24f8d013c9

8,212

py

Python

bin/CAD/Abaqus/AbaqusGeometry.py

lefevre-fraser/openmeta-mms

08f3115e76498df1f8d70641d71f5c52cab4ce5f

[ "MIT" ]

null

bin/CAD/Abaqus/AbaqusGeometry.py

lefevre-fraser/openmeta-mms

08f3115e76498df1f8d70641d71f5c52cab4ce5f

[ "MIT" ]

null

bin/CAD/Abaqus/AbaqusGeometry.py

lefevre-fraser/openmeta-mms

08f3115e76498df1f8d70641d71f5c52cab4ce5f

[ "MIT" ]

null

""" AbaqusGeometry.py For use with Abaqus 6.13-1 (Python 2.6.2). Created by Ozgur Yapar <oyapar@isis.vanderbilt.edu> Robert Boyles <rboyles@isis.vanderbilt.edu> - Includes modules which take care of geometrical operations in the part and assembly level. """ import re import math from numpy import array, cross, transpose, vstack, dot from abaqusConstants import * import numpy.linalg as LA import string as STR def regexFriendly(inString): """ Clean up coordinates read from STEP file, prior to applying regular expressions. """ outString = STR.replace(inString, '\'', '%') outString = STR.replace(outString, '(', '') outString = STR.replace(outString, ')', ',') return outString def coordinate(stepString): """ Extract tuple of cartesian coordinates from STEP coordinate string. """ e = re.compile(',\S+,,') # regular expression coordFind = e.search(stepString) # extract substring containing coordinates coordList = coordFind.group(0).strip(',').split(',') # separate x, y, and z coordinates by commas coords = (float(coordList[0]), float(coordList[1]), float(coordList[2])) # convert coordinate strings to a tuple of floats return coords # return the coordinate tuple # calculates transformation matrix between two coordinate systems as defined in STEP def get3DTransformArray(fromDir1, fromDir2, toDir1, toDir2): """ Calculate transformation matrix between two coordinate systems as defined in STEP. """ fromDir1 = array(fromDir1) # convert u1 vector to an array object fromDir2 = array(fromDir2) # convert u2 vector to an array object fromDir3 = cross(fromDir1, fromDir2) # extrapolate u3 vector from u1 and u2 toDir1 = array(toDir1) # convert v1 vector to an array object toDir2 = array(toDir2) # convert v2 vector to an array object toDir3 = cross(toDir1, toDir2) # extrapolate v3 vector from v1 and v2 inva = LA.inv(transpose(vstack([fromDir1, fromDir2, fromDir3]))) b = transpose(vstack([toDir1, toDir2, toDir3])) transformArray = dot(b, inva) return transformArray def unv(center, planarA, planarB): """ Use vector operations to get unit normal vector, given a center coordinate and two planar coordinates. """ center = array(center) planarA = array(planarA) planarB = array(planarB) vA = planarA - center vB = planarB - center xV = cross(vA, vB) return xV/LA.norm(xV) def transCoord(fromCoord, transformArray, translationVector): """ Transform/translate a cartesian point from one coordinate system to another. """ vprod = dot(transformArray, fromCoord) vprod = vprod + translationVector toCoord = tuple(vprod) return toCoord def asmRecursion(asm, subAsms, asmParts): """ Recursively identifies parts in sub-assemblies, in the order they are imported from STEP. """ parts = [] try: for child in subAsms[asm]: if child in subAsms: parts.extend(asmRecursion(child, subAsms, asmParts)) else: parts.extend(asmParts[child]) except KeyError: pass if asm in asmParts: parts.extend(asmParts[asm]) return parts def coordTransform(localTMs, localTVs, asm, subAsms, asmParts, localCoords): """ Iterate through sub-assemblies and top-level parts to transform/translate every datum point to assembly coordinates; uses transCoord() Note: Ignores top-level datums in highest assembly, which will not exist in a CyPhy assembly anyway """ globalCoords = {} # create dictionary object to hold new point library if asm in subAsms: # if assembly has sub-assemblies: for subAsm in subAsms[asm]: # for each sub-assembly in the assembly: subCoords = coordTransform(localTMs, localTVs, subAsm, # get point library local to sub-assembly subAsms, asmParts, localCoords) for part in subCoords.keys(): # for each component in chosen sub-assembly: globalCoords.update([[part, {}]]) # create new entry in globalCoords for (point, coord) in subCoords[part].iteritems(): # for each point in part/sub-sub-assembly: globalCoords[part].update([[point.upper(), transCoord( # translate/transform point to globalCoords array(coord), localTMs[subAsm], localTVs[subAsm])]]) globalCoords.update([[subAsm, {}]]) # create entry for sub-assembly in globalCoords for (point, coord) in localCoords[subAsm].iteritems(): # for each point specified at top level of that sub-assembly: globalCoords[subAsm].update([[point.upper(), transCoord( # translate/transform point to globalCoords array(coord), localTMs[subAsm], localTVs[subAsm])]]) if asm in asmParts: # if assembly has top-level parts: for part in asmParts[asm]: # for each top-level part: globalCoords.update([[part, {}]]) # create new entry in globalCoords for (point, coord) in localCoords[part].iteritems(): # for each point in part: globalCoords[part].update([[point.upper(), transCoord( # translate/transform point to globalCoords array(coord), localTMs[part], localTVs[part])]]) return globalCoords def myMask(idnums): """ Produce mask string for getSequenceFromMask(...) from a feature ID or set of IDs. """ try: idnums = tuple(idnums) # make the input a tuple! except TypeError: # if input is not iterable: idnums = (idnums,) # make it a tuple anyway! powersum = 0 # integer to hold mask number for num in idnums: # iterating through input IDs: powersum += 2**num # add 2**ID to powersum rawmask = hex(powersum)[2:] # convert powermask to hexadecimal rawmask = STR.rstrip(rawmask, 'L') # strip "long" character, if necessary if max(idnums) < 32: # if hex number is 8 digits or less: mask = '[#' + rawmask + ' ]' # create mask else: # if hex number is >8 digits: maskpieces = [] # container for fragments of hex string piececount = int(math.ceil(len(rawmask)/8)) # number of times to split hex string for i in range(piececount): # for each split needed: maskpieces.append(rawmask[-8:]) # append last 8 characters of hex string to fragment list rawmask = rawmask[:-8] # trim last 8 characters from hex string maskpieces.append(rawmask) # append remaining hex string to fragment list mask = '[#' + STR.join(maskpieces, ' #') + ' ]' # join fragments, using the correct delimiters, to create mask return mask def toBC(constraint): """ Translates a degree of freedom as read from the XML to the appropriate SymbolicConstant. """ if constraint == 'FIXED': return 0 elif constraint == 'FREE': return UNSET else: return float(constraint)

53.673203

121

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853

8,212

5.443142

0.33177

0.010554

0.008615

0.012923

0.183502

0.155503

0.146888

0.12255

0

0.011276

0.352046

8,212

153

122

53.673203

0.861304

0.353385

0

0.12381

0

0.006761

0

1

0.085714

false

0.009524

0.057143

0

0.247619

0

null

0

null

0

1

0

c07dacc643d713f89a754dcc9e2a89ae590b2576

2,143

py

Python

analysis/11-compress-jacobians.py

lmjohns3/cube-experiment

ab6d1a9df95efebc369d184ab1c748d73d5c3313

[ "MIT" ]

null

analysis/11-compress-jacobians.py

lmjohns3/cube-experiment

ab6d1a9df95efebc369d184ab1c748d73d5c3313

[ "MIT" ]

null

analysis/11-compress-jacobians.py

lmjohns3/cube-experiment

ab6d1a9df95efebc369d184ab1c748d73d5c3313

[ "MIT" ]

null

import climate import glob import gzip import io import lmj.cubes import logging import numpy as np import os import pandas as pd import pickle import theanets def compress(source, k, activation, **kwargs): fns = sorted(glob.glob(os.path.join(source, '*', '*_jac.csv.gz'))) logging.info('%s: found %d jacobians', source, len(fns)) # the clipping operation affects about 2% of jacobian values. dfs = [np.clip(pd.read_csv(fn, index_col='time').dropna(), -10, 10) for fn in fns] B, N = 128, dfs[0].shape[1] logging.info('loaded %s rows of %d-D data from %d files', sum(len(df) for df in dfs), N, len(dfs)) def batch(): batch = np.zeros((B, N), 'f') for b in range(B): a = np.random.randint(len(dfs)) batch[b] = dfs[a].iloc[np.random.randint(len(dfs[a])), :] return [batch] pca = theanets.Autoencoder([N, (k, activation), (N, 'tied')]) pca.train(batch, **kwargs) key = '{}_k{}'.format(activation, k) if 'hidden_l1' in kwargs: key += '_s{hidden_l1:.4f}'.format(**kwargs) for df, fn in zip(dfs, fns): df = pd.DataFrame(pca.encode(df.values.astype('f')), index=df.index) s = io.StringIO() df.to_csv(s, index_label='time') out = fn.replace('_jac', '_jac_' + key) with gzip.open(out, 'wb') as handle: handle.write(s.getvalue().encode('utf-8')) logging.info('%s: saved %s', out, df.shape) out = os.path.join(source, 'pca_{}.pkl'.format(key)) pickle.dump(pca, open(out, 'wb')) @climate.annotate( root='load data files from subject directories in this path', k=('compress to this many dimensions', 'option', None, int), activation=('use this activation function', 'option'), ) def main(root, k=1000, activation='relu'): for subject in lmj.cubes.Experiment(root).subjects: compress(subject.root, k, activation, momentum=0.9, hidden_l1=0.01, weight_l1=0.01, monitors={'hid1:out': (0.01, 0.1, 1, 10)}) if __name__ == '__main__': climate.call(main)

30.614286

76

0.591227

314

2,143

3.961783

0.423567

0.026527

0.016077

0.025723

0.033762

0

0.022797

0.24265

2,143

69

77

31.057971

0.743685

0.027532

0

0.147454

0

1

0.055556

false

0

0.203704

0

0.277778

0

null

0

null

0

1

0

c07f103a6a6e92a6245209f932b8d90c064fd018

21,369

py

Python

commerce/views.py

zlkca/ehetuan-api

da84cd4429bd33e8fe191327ec267bf105f41453

[ "MIT" ]

1

2020-05-27T18:17:01.000Z

commerce/views.py

zlkca/ehetuan-api

da84cd4429bd33e8fe191327ec267bf105f41453

[ "MIT" ]

6

2020-06-05T18:14:56.000Z

2021-09-07T23:53:08.000Z

commerce/views.py

zlkca/ehetuan-api

da84cd4429bd33e8fe191327ec267bf105f41453

[ "MIT" ]

null

import json import os import logging from datetime import datetime from django.db.models import Q,Count from django.http import JsonResponse from django.views.generic import View from django.views.decorators.csrf import csrf_exempt from django.utils.decorators import method_decorator from django.conf import settings from rest_framework_jwt.settings import api_settings from django.core.exceptions import ObjectDoesNotExist#EmptyResultSet, MultipleObjectsReturned from django.contrib.auth import get_user_model from commerce.models import Restaurant, Picture, Product, Category, Order, OrderItem, Style, PriceRange, FavoriteProduct from account.models import Province, City, Address from utils import to_json, obj_to_json, get_data_from_token logger = logging.getLogger(__name__) def processPictures(product, pictures): # pid --- product id # pictures --- dict that pass from the front end reindex = False pic = None for picture in pictures: try: pic = Picture.objects.get(product_id=product.id, index=picture['index']) except: pic = None if pic: if picture['status'] == 'removed': reindex = True rmPicture(pic) elif picture['status'] == 'changed': savePicture(product, pic, picture) pic.save() else:# new pic = Picture() savePicture(product, pic, picture) if reindex: reindexPicture(product.id) def savePicture(product, pic, picture): # product --- Product model object # pic --- Picture model object # picture --- dict from front end pic.index = picture['index'] pic.name = picture['name'] pic.product = product pic.image.save(picture['image'].name, picture['image'].file, True) pic.save() def getDefaultPicture(pictures): if pictures.count() == 0: return '' else: if pictures.count()>0 and pictures[0].image.name: return pictures[0].image.name else: return '' def rmPicture(pic): try: os.remove(pic.image.path) except: print('remove image failed') pic.image.delete() pic.delete() def reindexPicture(pid): # pid --- product id pics = Picture.objects.filter(product_id=pid).order_by('index') i = 0 for pic in pics: pic.index = i i = i + 1 pic.save() def saveProduct(params): _id = params.get('id') if _id: item = Product.objects.get(id=_id) else: item = Product() item.name = params.get('name') item.description = params.get('description') item.price = params.get('price') item.currency = params.get('currency') restaurant_id = params.get('restaurant_id') try: item.restaurant = Restaurant.objects.get(id=restaurant_id) except: item.restaurant = None #item.category = category item.save() # item.categories.clear() # Assume there is only one image # n_pics = int(params.get('n_pictures')) # pictures = [] # for i in range(n_pics): # name = params.get('name%s'%i) # status = params.get('image_status%s'%i) # image = req.FILES.get('image%s'%i) # pictures.append({'index':i,'name':name, 'status':status, 'image':image}) # # self.processPictures(item, pictures) # # # select default picture # pics = Picture.objects.filter(product_id=item.id) # item.fpath = self.getDefaultPicture(pics) # item.save() return item def find_restaurants_by_location(lat, lng, distance): query = """SELECT *, ( 3959 * acos(cos(radians(%s)) * cos(radians(lat)) * cos(radians(lng) - radians(%s)) + sin(radians(%s)) * sin(radians(lat ))) ) AS distance FROM commerce_restaurant HAVING distance < %s ORDER BY distance LIMIT 0, 20;"""%(lat, lng, lat, distance) try: return Restaurant.objects.raw(query) except: return None @method_decorator(csrf_exempt, name='dispatch') class RestaurantView(View): def getList(self, req): lat = req.GET.get('lat') lng = req.GET.get('lng') distance = 25 # km restaurants = [] admin_id = req.GET.get('admin_id') if admin_id: # need address try: item = Restaurant.objects.get(admin_id=admin_id) restaurant = to_json(item) restaurant['address'] = self.getAddress(item) return JsonResponse({'data':[restaurant]}) except Exception: return JsonResponse({'data':[]}) elif lat and lng: # do not need address restaurants = find_restaurants_by_location(lat, lng, distance) else: try: restaurants = Restaurant.objects.all()#.annotate(n_products=Count('product')) except Exception: return JsonResponse({'data':[]}) rs =[] for r in restaurants: rs.append(to_json(r)) return JsonResponse({'data': rs }) def getAddress(self, restaurant): addr_id = restaurant.address.id item = None try: item = Address.objects.get(id=addr_id) except: item = None return to_json(item) def get(self, req, *args, **kwargs): pid = kwargs.get('id') if pid: try: item = Restaurant.objects.get(id=int(pid)) p = obj_to_json(item, False) p['address'] = self.getAddress(item) return JsonResponse({'data':p}) except Exception as e: print(e.message); return JsonResponse({'data':''}) else: # get list return self.getList(req)#JsonResponse({'data':''}) def delete(self, req, *args, **kwargs): pid = int(kwargs.get('id')) if pid: instance = Restaurant.objects.get(id=pid) instance.delete() items = Restaurant.objects.filter().order_by('-updated') return JsonResponse({'data':to_json(items)}) return JsonResponse({'data':[]}) def post(self, req, *args, **kwargs): params = req.POST authorizaion = req.META['HTTP_AUTHORIZATION'] token = authorizaion.replace("Bearer ", "") data = get_data_from_token(token) # if data and data['username']=='admin': _id = params.get('id') if _id: item = Restaurant.objects.get(id=_id) else: item = Restaurant() item.name = params.get('name') item.description = params.get('description') item.lat = float(params.get('lat')) item.lng = float(params.get('lng')) item.created = item.created if item.created else datetime.now() addr_id = params.get('address_id') if(addr_id): addr = Address.objects.get(id=addr_id) self.saveAddress(addr, params) item.address = addr else: addr = Address() self.saveAddress(addr, params) item.address = addr item.save() image_status = params.get('image_status') if image_status == 'changed': self.rmPicture(item) image = req.FILES.get("image") item.image.save(image.name, image.file, True) item.save() return JsonResponse({'data':to_json(item)}) def saveAddress(self, addr1, params): addr1.street = params.get('street') addr1.sub_locality = params.get('sub_locality') addr1.postal_code = params.get('postal_code') addr1.lat = params.get('lat') addr1.lng = params.get('lng') addr1.province = params.get('province') addr1.city = params.get('city') addr1.save() def rmPicture(self, item): try: os.remove(item.image.path) except: print('remove image failed') item.image.delete() @method_decorator(csrf_exempt, name='dispatch') class CategoryView(View): def getList(self): categories = [] try: categories = Category.objects.all()#.annotate(n_products=Count('product')) except Exception as e: logger.error('Get category Exception:%s'%e) return JsonResponse({'data':[]}) return JsonResponse({'data': to_json(categories)}) def get(self, req, *args, **kwargs): cid = kwargs.get('id') if cid: cid = int(cid) try: item = Category.objects.get(id=cid) return JsonResponse({'data':to_json(item)}) except Exception as e: return JsonResponse({'data':''}) else: return self.getList() def delete(self, req, *args, **kwargs): pid = int(kwargs.get('id')) if pid: instance = Category.objects.get(id=pid) instance.delete() items = Category.objects.filter().order_by('-updated') return JsonResponse({'data':to_json(items)}) return JsonResponse({'data':[]}) def post(self, req, *args, **kwargs): ubody = req.body.decode('utf-8') params = json.loads(ubody) _id = params.get('id') if _id: item = Category.objects.get(id=_id) else: item = Category() item.name = params.get('name') item.description = params.get('description') # item.status = params.get('status') item.save() return JsonResponse({'data':to_json(item)}) @method_decorator(csrf_exempt, name='dispatch') class ProductListView(View): def get(self, req, *args, **kwargs): ''' get product list ''' products = [] cats = req.GET.get('cats') restaurants = req.GET.get('ms') colors = req.GET.get('colors') keyword = req.GET.get('keyword') kwargs = {} q = None if cats: q = Q(categories__id__in=cats.split(',')) if restaurants: if q: q = q | Q(restaurant__id__in=restaurants.split(',')) else: q = Q(restaurant__id__in=restaurants.split(',')) if colors: if q: q = q | Q(color__id__in=colors.split(',')) else: q = Q(restaurant__id__in=restaurants.split(',')) restaurant_id = req.GET.get('restaurant_id') category_id = req.GET.get('category_id') if restaurant_id: products = Product.objects.filter(restaurant_id=restaurant_id).annotate(n_likes=Count('favoriteproduct')) elif category_id: products = Product.objects.filter(category_id=category_id).annotate(n_likes=Count('favoriteproduct')) elif cats or restaurants or colors: if keyword: products = Product.objects.filter(q).filter(Q(name__icontains=keyword) |Q(categories__name__icontains=keyword) |Q(restaurant__name__icontains=keyword) |Q(color__name__icontains=keyword)) else: products = Product.objects.filter(q) else: if keyword: products = Product.objects.filter(Q(name__icontains=keyword) |Q(categories__name__icontains=keyword) |Q(restaurant__name__icontains=keyword) |Q(color__name__icontains=keyword)) else: products = Product.objects.filter().annotate(n_likes=Count('favoriteproduct')) ps = to_json(products) for p in ps: try: pics = Picture.objects.filter(product_id=p['id']) except: pics = None if pics: p['pictures'] = to_json(pics) #s = [] # for product in products: # items = Item.objects.filter(product_id=product.id) # p = product.to_json() # p['n_likes'] = product.n_likes # p['n_items'] = len(items) # p['items'] = [items[0].to_json()] # fp = None # try: # fp = FavoriteProduct.objects.get(user_id=uid) # except: # pass # # p['like'] = fp.status if fp else False # s.append(p) return JsonResponse({'data':ps}) def post(self, req, *args, **kwargs): authorizaion = req.META['HTTP_AUTHORIZATION'] token = authorizaion.replace("Bearer ", "") data = get_data_from_token(token) for key in req.POST: params = json.loads(req.POST[key]) index = int(key.replace('info_', '')) product = saveProduct(params) image_status = params.get('image_status') if image_status == 'unchange': pass elif image_status == 'changed' or image_status == 'add': pictures = [] image = req.FILES.get('image%s'%index) pictures.append({'index':0,'name':'', 'status':image_status, 'image':image}) processPictures(product, pictures) # select default picture pics = Picture.objects.filter(product_id=product.id) product.fpath = getDefaultPicture(pics) product.save() return JsonResponse({'data':[]}) @method_decorator(csrf_exempt, name='dispatch') class ProductFilterView(View): def get(self, req, *args, **kwargs): categories = Category.objects.all(); styles = Style.objects.all(); price_ranges = PriceRange.objects.all(); return JsonResponse({'categories':categories, 'styles':styles, 'price_ranges':price_ranges}) @method_decorator(csrf_exempt, name='dispatch') class ProductView(View): def get(self, req, *args, **kwargs): ''' get product detail with multiple items ''' pid = int(kwargs.get('id')) if pid: try: products = Product.objects.filter(id=pid) except Exception as e: return JsonResponse({'product':''}) else: return JsonResponse({'product':''}) product = products[0] pics = Picture.objects.filter(product_id=product.id) ps = [] for pic in pics: ps.append(to_json(pic)) p = to_json(product) p['pictures'] = ps return JsonResponse({'data':p}) def delete(self, req, *args, **kwargs): pid = int(kwargs.get('id')) if pid: instance = Product.objects.get(id=pid) instance.delete() items = Product.objects.filter().order_by('-updated') return JsonResponse({'data':to_json(items)}) return JsonResponse({'data':[]}) def post(self, req, *args, **kwargs): params = req.POST authorizaion = req.META['HTTP_AUTHORIZATION'] token = authorizaion.replace("Bearer ", "") data = get_data_from_token(token) if data and data['username']=='admin' or data['utype']=='business': item = saveProduct(params) item.categories.clear() categories = params.get('categories').split(',') for cat_id in categories: try: category = Category.objects.get(id=cat_id) except: category = None item.categories.add(category) n_pics = int(params.get('n_pictures')) pictures = [] for i in range(n_pics): name = params.get('name%s'%i) status = params.get('image_status%s'%i) image = req.FILES.get('image%s'%i) pictures.append({'index':i,'name':name, 'status':status, 'image':image}) processPictures(item, pictures) # select default picture pics = Picture.objects.filter(product_id=item.id) item.fpath = getDefaultPicture(pics) item.save() return JsonResponse({'tokenValid': True,'data':to_json(item)}) return JsonResponse({'tokenValid':False, 'data':''}) @method_decorator(csrf_exempt, name='dispatch') class OrderView(View): def getList(self, rid=None): orders = [] try: if rid: orders = Order.objects.filter(restaurant_id=rid).order_by('created') else: orders = Order.objects.all().order_by('created')#.annotate(n_products=Count('product')) r = to_json(orders) for order in orders: items = OrderItem.objects.filter(order_id=order.id) ri = next((x for x in r if x['id'] == order.id), None) ri['items'] = to_json(items) ri['user']['username'] = order.user.username except Exception as e: logger.error('Get Order Exception:%s'%e) return JsonResponse({'data':[]}) return JsonResponse({'data': r}) def get(self, req, *args, **kwargs): cid = kwargs.get('id') if cid: cid = int(cid) try: item = Order.objects.get(id=cid) return JsonResponse({'data':to_json(item)}) except Exception as e: return JsonResponse({'data':''}) else: rid = req.GET.get('restaurant_id') return self.getList(rid) def post(self, req, *args, **kwargs): authorizaion = req.META['HTTP_AUTHORIZATION'] token = authorizaion.replace("Bearer ", "") data = get_data_from_token(token) if data: uid = data['id'] ubody = req.body.decode('utf-8') d = json.loads(ubody) # dict: {'orders': [{'restaurant_id': 2, 'items': [{'pid': 1, 'name': '土豆排骨', 'price': '12.000', 'restaurant_id': #2, 'quantity': 4}, {'pid': 2, 'name': '泡椒豆腐', 'price': '12.000', 'restaurant_id': 2, 'quantity': 2}]}], #'user_id': 7} orders = d.get("orders") for data in orders: rid = data['restaurant_id'] items = data['items'] order = Order() try: restaurant = Restaurant.objects.get(id=rid) user = get_user_model().objects.get(id=uid) order.restaurant = restaurant order.user = user order.save() except Exception as e: print(e) if order.id: for item in items: orderItem = OrderItem() orderItem.order = order orderItem.product = Product.objects.get(id=item['pid']) orderItem.quantity = item['quantity'] orderItem.product_name = orderItem.product.name orderItem.price = orderItem.product.price orderItem.save() return JsonResponse({'success': True}) return JsonResponse({'success':False}) @method_decorator(csrf_exempt, name='dispatch') class FavoriteProductView(View): def get(self, req, *args, **kwargs): uid = req.GET.get('user_id') ps = Product.objects.annotate(n_likes=Count('favoriteproduct')) favorites = [] for p in ps: product = p.to_json() product['n_likes'] = p.n_likes fp = None try: fp = FavoriteProduct.objects.get(user_id=uid) except: pass product['favorate'] = fp.status if fp else False favorites.append(product) return JsonResponse({'favorites':favorites}) def post(self, req, *args, **kwargs): ubody = req.body.decode('utf-8') d = json.loads(ubody) uid = d.get("user_id") pid = d.get("product_id") try: like = FavoriteProduct.objects.get(user_id=uid, product_id=pid) like.delete() except ObjectDoesNotExist: like = FavoriteProduct() like.product = Product.objects.get(id=pid) like.user = get_user_model().objects.get(id=uid) like.status = True like.save() return JsonResponse({'success':'true'})

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0.008439

0

null

0

null

0

1

0

c07fe33cae576add35e02a5f464a4a05467459e8

5,666

py

Python

api/views.py

huatxu/erasmusbackend

d8f86ee857a292a133106e75e9c920b905b5b10d

[ "MIT" ]

null

api/views.py

huatxu/erasmusbackend

d8f86ee857a292a133106e75e9c920b905b5b10d

[ "MIT" ]

null

api/views.py

huatxu/erasmusbackend

d8f86ee857a292a133106e75e9c920b905b5b10d

[ "MIT" ]

null

from django.shortcuts import render from api.models import Comida, Cerveza, Titulo, TipoComida from django.http import Http404 from rest_framework.views import APIView from rest_framework.response import Response from rest_framework import serializers import csv import os class CervezaSerializer(serializers.ModelSerializer): class Meta: model = Cerveza fields = ['id', 'nombre', 'estilo', 'pais', 'pais_ingles', 'alcohol', 'color', 'amargor', 'descripcion', 'descripcion_ingles', 'disponible', 'imagen', 'artesanal', 'tipo', 'recomendada', 'formato', 'precio', 'formato_2', 'precio_2', 'formato_3', 'precio_3', 'sin_gluten', 'aparece', 'barril'] class ComidaList(APIView): """ List all snippets, or create a new snippet. """ def get(self, request, format=None): comidas = Comida.objects.filter(disponible=True, tipo__aparece=True).order_by('tipo__orden', 'orden', 'nombre') serializer = ComidaSerializer(comidas, many=True) return Response(serializer.data) class ComidaSerializer(serializers.ModelSerializer): tipo = serializers.SerializerMethodField('get_tipo') def get_tipo(self, obj): return obj.tipo.nombre + '-' + obj.tipo.nombre_ingles class Meta: model = Comida fields = ('id', 'nombre', 'nombre_ingles', 'descripcion', 'descripcion_ingles', 'tipo', 'precio', 'precio_2', 'altramuces', 'apio', 'cacahuete', 'crustaceo', 'gluten', 'huevo', 'lacteos', 'moluscos', 'mostaza', 'nueces', 'pescado', 'sesamo', 'soja', 'sulfitos', 'disponible') class TituloSerializer(serializers.ModelSerializer): class Meta: model = Titulo fields = ['titulo_1', 'titulo_1_ingles', 'titulo_2', 'titulo_2_ingles'] class CervezaList(APIView): """ List all snippets, or create a new snippet. """ def get(self, request, format=None): cervezas = Cerveza.objects.all() serializer = CervezaSerializer(cervezas, many=True) titulos = Titulo.objects.first() titulosSerializer = TituloSerializer(titulos) return Response({"titulos": titulosSerializer.data, "cervezas": serializer.data}) import csv import os def cast_bool(entry): try: if not entry: return False trues = ['sí', 'si'] return entry.lower() in trues except Exception: return False def cast_price(entry): result = entry result = result.replace('€', '') result = result.replace(',', '.') result = result.strip() if result: return float(result) return 0.0 def load_csv(): with open(f'{os.path.dirname(os.path.abspath(__file__))}/carta-cervezas.csv', newline='') as csvfile: reader = csv.DictReader(csvfile) for row in reader: try: cerveza = Cerveza.objects.create( nombre=row['Nombre'], estilo = row['Estilo'], pais = row['País'], pais_ingles = row['País Ingles'], alcohol = row['Alcohol'], color = row['Color'], amargor = row['Amargor'], descripcion = row['Descripcion'], descripcion_ingles = row['Descripcion ingles'], disponible = cast_bool(row['Disponible']), imagen = row['Imagen'], artesanal = cast_bool(row['Artesanal']), tipo = row['Tipo'], recomendada = cast_bool(row['Recomendada']), formato = row['Formato'], precio = cast_price(row['Precio']), formato_2 = row['formato 2'], precio_2 = cast_price(row['precio 2']), formato_3 = row['formato 3'], precio_3 = cast_price(row['precio 3']), sin_gluten = cast_bool(row['Sin gluten']), aparece = cast_bool(row['Aparece']), barril = cast_bool(row['Barril']) ) cerveza.save() except Exception: pass with open(f'{os.path.dirname(os.path.abspath(__file__))}/carta-comida.csv', newline='') as csvfile: reader = csv.DictReader(csvfile) for row in reader: try: comida = Comida.objects.create( nombre=row['Nombre'], nombre_ingles=row['Nombre ingles'], descripcion=row['Descripcion'], descripcion_ingles=row['Descripcion ingles'], tipo=row['Tipo'], precio=cast_price(row['Precio']), precio_2=cast_price(row['precio 2']), altramuces=cast_bool(row['Altramuces']), apio=cast_bool(row['Apio']), cacahuete=cast_bool(row['Cacahuete']), crustaceo=cast_bool(row['Crustaceo']), gluten=cast_bool(row['Gluten']), huevo=cast_bool(row['Huevo']), lacteos=cast_bool(row['Lacteos']), moluscos=cast_bool(row['Moluscos']), mostaza=cast_bool(row['Mostaza']), nueces=cast_bool(row['Nueces']), pescado=cast_bool(row['Pescado']), sesamo=cast_bool(row['Sesamo']), soja=cast_bool(row['Soja']), sulfitos=cast_bool(row['Sulfitos']), disponible=cast_bool(row['Disponible']) ) comida.save() except Exception as exc: pass

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0

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5,666

142

301

39.901408

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0

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0

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0.022346

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0.051282

false

0.017094

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0.008547

0.282051

0

null

0

null

0

1

0

c08a254cca4494b2d1aa73495456b23d2cb83ea5

390

py

Python

1_Ejemplo_practico_ECG/utils.py

IEEE-UPIBI/Comunicacion-Serial-Python-Arduino

806916a5d47e8d29933e1402296e2ca6d5d5a79e

[ "MIT" ]

null

1_Ejemplo_practico_ECG/utils.py

IEEE-UPIBI/Comunicacion-Serial-Python-Arduino

806916a5d47e8d29933e1402296e2ca6d5d5a79e

[ "MIT" ]

1

2021-04-23T23:20:42.000Z

2_Ejemplo_practico_SensorMPU6050/utils.py

IEEE-UPIBI/Comunicacion-Serial-Python-Arduino

806916a5d47e8d29933e1402296e2ca6d5d5a79e

[ "MIT" ]

null

import serial import time ### FUNCTIONS #### #### SERIAL COMMUNICATION #### def arduino_communication(COM="COM5",BAUDRATE=9600,TIMEOUT=1): """ Initalizes connection with Arduino Board """ try: arduino = serial.Serial(COM, BAUDRATE , timeout=TIMEOUT) time.sleep(2) except: print("Error de coneccion con el puerto") return arduino

16.956522

64

0.628205

42

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0.690476

0

0.023973

0.251282

390

22

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17.727273

0.811644

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0

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0

1

0.111111

false

0

0.222222

0

0.444444

0.111111

0

null

0

null

0

1

0

c08b025b2f074208a6371fa035f6cf38f392405a

3,595

py

Python

trav_lib/visualize.py

thwhitfield/trav_lib

46185f5545d958eba1538c769a98d07908dd0d19

[ "MIT" ]

null

trav_lib/visualize.py

thwhitfield/trav_lib

46185f5545d958eba1538c769a98d07908dd0d19

[ "MIT" ]

null

trav_lib/visualize.py

thwhitfield/trav_lib

46185f5545d958eba1538c769a98d07908dd0d19

[ "MIT" ]

null

"""Classes and functions used for data visualization""" import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt def plot_correlation_matrix_heat_map(df,label,qty_fields=10): df = pd.concat([df[label],df.drop(label,axis=1)],axis=1) correlation_matrix = df.corr() index = correlation_matrix.sort_values(label, ascending=False).index correlation_matrix = correlation_matrix[index].sort_values(label,ascending=False) fig,ax = plt.subplots() fig.set_size_inches((10,10)) sns.heatmap(correlation_matrix.iloc[:qty_fields,:qty_fields],annot=True,fmt='.2f',ax=ax) # Code added due to bug in matplotlib 3.1.1 bottom, top = ax.get_ylim() ax.set_ylim(bottom + .5, top - .5) return(fig,ax) def plot_log_hist(s,bin_factor=1,min_exp=None): """Plot 2 histograms with log x scales, one for positive values & one for negative values. Bin_factor is used to scale how many bins to use (1 is default and corresponds to one bin per order of magnitude. Higher than 1 will skew the bins away from even powers of 10). Parameters ---------- s: pandas series (generally using df[col]) Series or column of dataframe to analyze bin_factor: int Default 1, used to scale how many bins to use min_exp: int The minimum exponent to use in creating bins & plotting. This can be set manually for cases where you want a specific minimum value to be shown. Returns ------- fig, (ax1,ax2): matplotlib fig and ax objects """ # Split series into positive & negative components s_pos = s[s >= 0] s_neg = s[s < 0].abs() # Not the best way to deal with this, but this was the easiest solution for now. # TODO Fix this code to deal with no negative values or no positive values more appropriately if s_neg.shape[0] == 0: s_neg.loc[0] = 1 if s_pos.shape[0] == 0: s_pos.loc[0] = 1 # Calculate appropriate min_exp if none provied if min_exp == None: threshold = s_pos.shape[0] - (s_pos==0).sum() for i in range(10): n_betw = s_pos[s_pos!=0].between(0,10**-i).sum() if not (n_betw / threshold) > .1: min_exp = -i break # Clip values to the 10**min_exp so that they are included in the histograms (if # this isn't done then values which are 0 will be excluded from the histogram) s_pos = s_pos.clip(lower=10**min_exp) s_neg = s_neg.clip(lower=10**min_exp) # Calculate the lowest integer which encompases all the positive and negative values pos_max = int(np.ceil(np.log10(max(s_pos)))) neg_max = int(np.ceil(np.log10(max(s_neg)))) # Use that for both negative & positive values plot_max = max(pos_max,neg_max) # Create the bins (bin spacing is logarithmic) bins = np.logspace(min_exp,plot_max,(plot_max+1)*bin_factor) fig,(ax1,ax2) = plt.subplots(nrows=1,ncols=2,sharey=True) fig.set_size_inches((10,5)) s_neg.hist(bins=bins,ax=ax1) ax1.set_xscale('log') ax1.set_title('Distribution of Negative Values') ax1.set_xlabel('Negative values') s_pos.hist(bins=bins,ax=ax2) ax2.set_xscale('log') ax2.set_title('Distribution of Positive Values') ax2.set_xlabel('Positive Values') # Invert axis so that values are increasingly negative from right to left. # Decrease the spacing between the two subplots ax1.invert_xaxis() plt.subplots_adjust(wspace=.02) return(fig,(ax1,ax2))

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0

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0

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1

0

c08cb3b6fdb628373adc1c5e8da4f386b0294fba

1,828

py

Python

test/test_configeditor.py

ta-assistant/Admin-CLI

1c03ede0e09d8ddc270646937aa7af463c55f1f5

[ "MIT" ]

1

2021-07-22T15:43:02.000Z

test/test_configeditor.py

ta-assistant/Admin-CLI

1c03ede0e09d8ddc270646937aa7af463c55f1f5

[ "MIT" ]

28

2021-05-15T08:18:21.000Z

2021-08-02T06:12:30.000Z

test/test_configeditor.py

ta-assistant/TA-CLI

1c03ede0e09d8ddc270646937aa7af463c55f1f5

[ "MIT" ]

null

import unittest import os, sys, inspect, json currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) sys.path.insert(0, parentdir) from lib.file_management.configeditor import ConfigEditor from lib.file_management.file_management_lib import DirManagement class TestSendData(unittest.TestCase): def setUp(self) -> None: self.path = os.path.join(parentdir,"ta") DirManagement.create_dir(self.path) workdata = { "workDraft": { "outputDraft": [ "ID", "param1", "param2", "comment" ], "fileDraft": "{ID}_test.py" }, "scores": [ { "ID": "6310545000", "param1": "100", "param2": "print('hello')", "comment": "good" }] } with open(os.path.join(self.path, "work.json"), "w") as create: json.dump(workdata, create) self.con = ConfigEditor('testWork2', parentdir) self.con.writeconfig() return super().setUp() def test_writeconfig(self): """ return None """ self.assertIsNone(self.con.writeconfig()) def test_readconfig(self): """ return str """ self.assertIs(type(self.con.readconfig()), dict) def test_ishaveconfig(self): """ return None """ self.assertIsNone(self.con.ishaveconfig()) def test_checkdata(self): """ return None """ self.assertIsNone(self.con.checkdata()) def tearDown(self) -> None: """ retrun None """ DirManagement.remove_dir(os.path.join(parentdir,"ta")) return super().tearDown() if __name__ == '__main__': unittest.main()

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0.297046

1,828

73

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0.021277

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null

0

null

0

1

0

c08e8f408c1440f68bb49f4c21e145acaad7cc8e

3,466

py

Python

TwitterCode/crawler.py

aghriss/CS5502_project

68403f38ef26067360cb22404cdabe0d0543097a

[ "MIT" ]

null

TwitterCode/crawler.py

aghriss/CS5502_project

68403f38ef26067360cb22404cdabe0d0543097a

[ "MIT" ]

null

TwitterCode/crawler.py

aghriss/CS5502_project

68403f38ef26067360cb22404cdabe0d0543097a

[ "MIT" ]

null

''' Twitter Crawler to get tweets and user data ''' import tweepy import json import os import time def get_counts_quantile(tweets): counts = [] for t in tweets: counts.append() def save_tweet(result): """Function to save tweepy result status""" pass def save_user(result_set): """Function to save tweepy set fo result statuses""" pass class TweetCrawler(): def __init__(self, credentials_path, save_path, location_id=None): assert os.path.exists(save_path) assert os.path.exists(credentials_path) self.save_path = save_path self.location_id = 23424977 try: with open(credentials_path,"r") as f: creds = json.load(f) f.close() self.api = tweepy.API(tweepy.AppAuthHandler(creds['API_KEY'], creds['SECRET_KEY'])) except: raise "Auth Error, check credentials and connection" if location_id: self.location_id = location_id def crawl(self): location, trends = self.get_trends() for trend in trends: query = trend['query'] trending = self.get_trending_tweets(query) non_trending = self.get_untrending_tweets(query) self.store(trending, trending=True) self.store(non_trending, trensing=False) def get_trends(self,): trends_dict = self.api.trends_place(self.location_id)[0] location_name = trends_dict['locations'][0]['name'] non_empty_trends = list(filter(lambda x: x['tweet_volume'] is not None, trends_dict['trends'])) print("Retrieved %i for location: %s"%(len(non_empty_trends), location_name)) return location_name, non_empty_trends def get_trending_tweets(self, query): popular_tweets = self.api.search(query, count=500, result_type="popular") tuples = [] for popular in popular_tweets: user_timeline = self.api.user_timeline(popular.author.id, count=200) tuples.append([popular, user_timeline]) return tuples def get_untrending_tweets(self, query): popular_tweets = self.api.search(query, count=500, result_type="recent") tuples = [] for popular in popular_tweets: user_timeline = self.get_user(popular.author.id) tuples.append([popular, user_timeline]) return tuples def get_user(self, user_id): time.sleep(0.1) return self.api.user_timeline(user_id, count=200) def save_user(self, user): print("Saving user %s"%user.id_str) json.dump(user._json, open(os.path.join(self.save_path, "user_"+user.id_str+".json"), 'w')) def save_tweet(self, tweet): print("Saving tweet %s"%tweet.id_str) json.dump(tweet._json, open(os.path.join(self.save_path, "tweet_"+ tweet.id_str+".json"), 'w')) def rate_status(self): state = self.api.rate_limit_status() limits = state['resources']['statuses'] return {'tweet':limits['/statuses/show/:id']['remaining'], 'users': limits['/statuses/user_timeline']['remaining']} def get_tweet(self, tweet_id): time.sleep(0.1) return self.api.get_status(tweet_id) #crawler = TweetCrawler("twitter_credentials.json", './data') #self=crawler

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0.062961

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0.272649

3,466

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null

0

null

0

1

0

c08e9da0f8073946d9eb1f38656fc0912b347134

2,206

py

Python

instruments/swap.py

neoyung/IrLib

942793c49a477c9f5747410be74daf868391f289

[ "MIT" ]

1

2021-10-04T03:15:50.000Z

instruments/swap.py

neoyung/IrLib

942793c49a477c9f5747410be74daf868391f289

[ "MIT" ]

null

instruments/swap.py

neoyung/IrLib

942793c49a477c9f5747410be74daf868391f289

[ "MIT" ]

null

from irLib.instruments.instrument import instrument from irLib.helpers.schedule import period from irLib.instruments.legs import fixLeg, floatLeg class swap(instrument): def __init__(self, tradeDate, spotLag=period(0, 'day'), position='long', *legs): super().__init__(tradeDate, spotLag, position) self.legs = legs def setPricingEngine(self, discountCurve): self.discountCurve = discountCurve self.pricingEngine = self.discountCurve for leg in self.legs: leg.setPricingEngine(discountCurve) def calculateNPV(self, day): super().calculateNPV() NPV = 0 for leg in self.legs: NPV += leg.calculateNPV(day) return NPV * self.longShort def isExpired(self, day): return all([leg.isExpired(day) for leg in self.legs]) class vanillaSwap(swap): def __init__(self, tradeDate, payer, fixSchedule, floatSchedule, floatingCurve, discountCurve=None, spotLag=period(0, 'day'), swapRate=None): assert payer in ('payer', 'receiver'), 'payer or receiver?' self.payer = payer self.position = 'long' if self.payer == 'payer' else 'short' self.floatLeg = floatLeg( tradeDate, floatingCurve, floatSchedule, spotLag) self.fixLeg = fixLeg(tradeDate, 1., fixSchedule, spotLag) super().__init__(tradeDate, spotLag, self.position, self.fixLeg, self.floatLeg) if swapRate is None: assert discountCurve is not None, 'need discount curve to determine swap rate' super().setPricingEngine(discountCurve) self.dayCount = self.discountCurve.dayCount self.tenor = self.dayCount.getYearFrac(min(self.floatLeg.schedule.startDate, self.fixLeg.schedule.startDate), max(self.floatLeg.schedule.terminationDate, self.fixLeg.schedule.terminationDate)) self.swapRate = self.floatLeg.calculateNPV( self.tradeDate) / self.fixLeg.calculateNPV(self.tradeDate) else: self.swapRate = swapRate self.fixLeg.rate = self.swapRate self.fixLeg.position = 'short' self.fixLeg.longShort = -1

41.622642

145

0.655938

234

2,206

6.115385

0.273504

0.055905

0.016771

0.025157

0.033543

0

0.002998

0.24388

2,206

52

146

42.423077

0.854916

0

0.047619

0

0.046238

0

0.047619

1

0.119048

false

0

0.071429

0.02381

0.285714

0

null

0

null

0

1

0

c08f4ab3e25ce0f369e7d00947095aeb1fb9b437

21,083

py

Python

skhubness/neighbors/lsh.py

VarIr/scikit-hubness

6eaeedda2c4b52bb7bf2553b3c5b04a076287ae3

[ "BSD-3-Clause" ]

33

2019-08-05T12:29:19.000Z

2022-03-08T18:48:28.000Z

skhubness/neighbors/lsh.py

AndreasPhilippi/scikit-hubness

6eaeedda2c4b52bb7bf2553b3c5b04a076287ae3

[ "BSD-3-Clause" ]

84

2019-07-12T09:05:42.000Z

2022-03-31T08:50:15.000Z

skhubness/neighbors/lsh.py

AndreasPhilippi/scikit-hubness

6eaeedda2c4b52bb7bf2553b3c5b04a076287ae3

[ "BSD-3-Clause" ]

9

2019-09-26T11:03:04.000Z

2021-07-01T08:43:11.000Z

# -*- coding: utf-8 -*- # SPDX-License-Identifier: BSD-3-Clause # PEP 563: Postponed Evaluation of Annotations from __future__ import annotations from functools import partial import multiprocessing as mp from typing import Tuple, Union import warnings import numpy as np from sklearn.base import BaseEstimator from sklearn.metrics import euclidean_distances, pairwise_distances from sklearn.metrics.pairwise import cosine_distances from sklearn.utils.validation import check_is_fitted, check_array, check_X_y try: import puffinn except ImportError: puffinn = None # pragma: no cover try: import falconn except ImportError: falconn = None # pragma: no cover from tqdm.auto import tqdm from .approximate_neighbors import ApproximateNearestNeighbor from ..utils.check import check_n_candidates __all__ = ['FalconnLSH', 'PuffinnLSH', ] class PuffinnLSH(BaseEstimator, ApproximateNearestNeighbor): """ Wrap Puffinn LSH for scikit-learn compatibility. Parameters ---------- n_candidates: int, default = 5 Number of neighbors to retrieve metric: str, default = 'euclidean' Distance metric, allowed are "angular", "jaccard". Other metrics are partially supported, such as 'euclidean', 'sqeuclidean'. In these cases, 'angular' distances are used to find the candidate set of neighbors with LSH among all indexed objects, and (squared) Euclidean distances are subsequently only computed for the candidates. memory: int, default = None Max memory usage. If None, determined heuristically. recall: float, default = 0.90 Probability of finding the true nearest neighbors among the candidates n_jobs: int, default = 1 Number of parallel jobs verbose: int, default = 0 Verbosity level. If verbose > 0, show tqdm progress bar on indexing and querying. Attributes ---------- valid_metrics: List of valid distance metrics/measures """ valid_metrics = ["angular", "cosine", "euclidean", "sqeuclidean", "minkowski", "jaccard", ] metric_map = {'euclidean': 'angular', 'sqeuclidean': 'angular', 'minkowski': 'angular', 'cosine': 'angular', } def __init__(self, n_candidates: int = 5, metric: str = 'euclidean', memory: int = None, recall: float = 0.9, n_jobs: int = 1, verbose: int = 0, ): if puffinn is None: # pragma: no cover raise ImportError(f'Please install the `puffinn` package, before using this class:\n' f'$ git clone https://github.com/puffinn/puffinn.git\n' f'$ cd puffinn\n' f'$ python3 setup.py build\n' f'$ pip install .\n') from None super().__init__(n_candidates=n_candidates, metric=metric, n_jobs=n_jobs, verbose=verbose, ) self.memory = memory self.recall = recall def fit(self, X, y=None) -> PuffinnLSH: """ Build the puffinn LSH index and insert data from X. Parameters ---------- X: np.array Data to be indexed y: any Ignored Returns ------- self: Puffinn An instance of Puffinn with a built index """ if y is None: X = check_array(X) else: X, y = check_X_y(X, y) self.y_train_ = y if self.metric not in self.valid_metrics: warnings.warn(f'Invalid metric "{self.metric}". Using "euclidean" instead') self.metric = 'euclidean' try: self._effective_metric = self.metric_map[self.metric] except KeyError: self._effective_metric = self.metric # Larger memory means many iterations (time-recall trade-off) memory = max(np.multiply(*X.shape) * 8 * 500, 1024**2) if self.memory is not None: memory = max(self.memory, memory) # Construct the index index = puffinn.Index(self._effective_metric, X.shape[1], memory, ) disable_tqdm = False if self.verbose else True for v in tqdm(X, desc='Indexing', disable=disable_tqdm): index.insert(v.tolist()) index.rebuild() self.index_ = index self.n_indexed_ = X.shape[0] self.X_indexed_norm_ = np.linalg.norm(X, ord=2, axis=1).reshape(-1, 1) return self def kneighbors(self, X=None, n_candidates=None, return_distance=True) -> Union[Tuple[np.array, np.array], np.array]: """ Retrieve k nearest neighbors. Parameters ---------- X: np.array or None, optional, default = None Query objects. If None, search among the indexed objects. n_candidates: int or None, optional, default = None Number of neighbors to retrieve. If None, use the value passed during construction. return_distance: bool, default = True If return_distance, will return distances and indices to neighbors. Else, only return the indices. """ check_is_fitted(self, 'index_') index = self.index_ if n_candidates is None: n_candidates = self.n_candidates n_candidates = check_n_candidates(n_candidates) # For compatibility reasons, as each sample is considered as its own # neighbor, one extra neighbor will be computed. if X is None: n_query = self.n_indexed_ X = np.array([index.get(i) for i in range(n_query)]) search_from_index = True else: X = check_array(X) n_query = X.shape[0] search_from_index = False dtype = X.dtype # If chosen metric is not among the natively supported ones, reorder the neighbors reorder = True if self.metric not in ('angular', 'cosine', 'jaccard') else False # If fewer candidates than required are found for a query, # we save index=-1 and distance=NaN neigh_ind = -np.ones((n_query, n_candidates), dtype=np.int32) if return_distance or reorder: neigh_dist = np.empty_like(neigh_ind, dtype=dtype) * np.nan metric = 'cosine' if self.metric == 'angular' else self.metric disable_tqdm = False if self.verbose else True if search_from_index: # search indexed against indexed for i in tqdm(range(n_query), desc='Querying', disable=disable_tqdm, ): # Find the approximate nearest neighbors. # Each of the true `n_candidates` nearest neighbors # has at least `recall` chance of being found. ind = index.search_from_index(i, n_candidates, self.recall, ) neigh_ind[i, :len(ind)] = ind if return_distance or reorder: X_neigh_denormalized = \ X[ind] * self.X_indexed_norm_[ind].reshape(len(ind), -1) neigh_dist[i, :len(ind)] = pairwise_distances(X[i:i+1, :] * self.X_indexed_norm_[i], X_neigh_denormalized, metric=metric, ) else: # search new query against indexed for i, x in tqdm(enumerate(X), desc='Querying', disable=disable_tqdm, ): # Find the approximate nearest neighbors. # Each of the true `n_candidates` nearest neighbors # has at least `recall` chance of being found. ind = index.search(x.tolist(), n_candidates, self.recall, ) neigh_ind[i, :len(ind)] = ind if return_distance or reorder: X_neigh_denormalized =\ np.array([index.get(i) for i in ind]) * self.X_indexed_norm_[ind].reshape(len(ind), -1) neigh_dist[i, :len(ind)] = pairwise_distances(x.reshape(1, -1), X_neigh_denormalized, metric=metric, ) if reorder: sort = np.argsort(neigh_dist, axis=1) neigh_dist = np.take_along_axis(neigh_dist, sort, axis=1) neigh_ind = np.take_along_axis(neigh_ind, sort, axis=1) if return_distance: return neigh_dist, neigh_ind else: return neigh_ind class FalconnLSH(ApproximateNearestNeighbor): """Wrapper for using falconn LSH Falconn is an approximate nearest neighbor library, that uses multiprobe locality-sensitive hashing. Parameters ---------- n_candidates: int, default = 5 Number of neighbors to retrieve radius: float or None, optional, default = None Retrieve neighbors within this radius. Can be negative: See Notes. metric: str, default = 'euclidean' Distance metric, allowed are "angular", "euclidean", "manhattan", "hamming", "dot" num_probes: int, default = 50 The number of buckets the query algorithm probes. The higher number of probes is, the better accuracy one gets, but the slower queries are. n_jobs: int, default = 1 Number of parallel jobs verbose: int, default = 0 Verbosity level. If verbose > 0, show tqdm progress bar on indexing and querying. Attributes ---------- valid_metrics: List of valid distance metrics/measures Notes ----- From the falconn docs: radius can be negative, and for the distance function 'negative_inner_product' it actually makes sense. """ valid_metrics = ['euclidean', 'l2', 'minkowski', 'squared_euclidean', 'sqeuclidean', 'cosine', 'neg_inner', 'NegativeInnerProduct'] def __init__(self, n_candidates: int = 5, radius: float = 1., metric: str = 'euclidean', num_probes: int = 50, n_jobs: int = 1, verbose: int = 0): if falconn is None: # pragma: no cover raise ImportError(f'Please install the `falconn` package, before using this class:\n' f'$ pip install falconn') from None super().__init__(n_candidates=n_candidates, metric=metric, n_jobs=n_jobs, verbose=verbose, ) self.num_probes = num_probes self.radius = radius def fit(self, X: np.ndarray, y: np.ndarray = None) -> FalconnLSH: """ Setup the LSH index from training data. Parameters ---------- X: np.array Data to be indexed y: any Ignored Returns ------- self: FalconnLSH An instance of LSH with a built index """ X = check_array(X, dtype=[np.float32, np.float64]) if self.metric in ['euclidean', 'l2', 'minkowski']: self.metric = 'euclidean' distance = falconn.DistanceFunction.EuclideanSquared elif self.metric in ['squared_euclidean', 'sqeuclidean']: self.metric = 'sqeuclidean' distance = falconn.DistanceFunction.EuclideanSquared elif self.metric in ['cosine', 'NegativeInnerProduct', 'neg_inner']: self.metric = 'cosine' distance = falconn.DistanceFunction.NegativeInnerProduct else: warnings.warn(f'Invalid metric "{self.metric}". Using "euclidean" instead') self.metric = 'euclidean' distance = falconn.DistanceFunction.EuclideanSquared # Set up the LSH index lsh_construction_params = falconn.get_default_parameters(*X.shape, distance=distance) lsh_index = falconn.LSHIndex(lsh_construction_params) lsh_index.setup(X) self.X_train_ = X self.y_train_ = y self.index_ = lsh_index return self def kneighbors(self, X: np.ndarray = None, n_candidates: int = None, return_distance: bool = True) -> Union[Tuple[np.array, np.array], np.array]: """ Retrieve k nearest neighbors. Parameters ---------- X: np.array or None, optional, default = None Query objects. If None, search among the indexed objects. n_candidates: int or None, optional, default = None Number of neighbors to retrieve. If None, use the value passed during construction. return_distance: bool, default = True If return_distance, will return distances and indices to neighbors. Else, only return the indices. """ check_is_fitted(self, ["index_", 'X_train_']) # Check the n_neighbors parameter if n_candidates is None: n_candidates = self.n_candidates elif n_candidates <= 0: raise ValueError(f"Expected n_neighbors > 0. Got {n_candidates:d}") else: if not np.issubdtype(type(n_candidates), np.integer): raise TypeError(f"n_neighbors does not take {type(n_candidates)} value, enter integer value") if X is not None: X = check_array(X, dtype=self.X_train_.dtype) query_is_train = False X = check_array(X, accept_sparse='csr') n_retrieve = n_candidates else: query_is_train = True X = self.X_train_ # Include an extra neighbor to account for the sample itself being # returned, which is removed later n_retrieve = n_candidates + 1 # Configure the LSH query objects (one per parallel worker) query = self.index_.construct_query_pool(num_probes=self.num_probes, num_query_objects=self.n_jobs) if return_distance: if self.metric == 'euclidean': distances = partial(euclidean_distances, squared=False) elif self.metric == 'sqeuclidean': distances = partial(euclidean_distances, squared=True) elif self.metric == 'cosine': distances = cosine_distances else: raise ValueError(f'Internal error: unrecognized metric "{self.metric}"') # Allocate memory for neighbor indices (and distances) n_objects = X.shape[0] neigh_ind = np.empty((n_objects, n_candidates), dtype=np.int32) if return_distance: neigh_dist = np.empty_like(neigh_ind, dtype=X.dtype) # If verbose, show progress bar on the search loop disable_tqdm = False if self.verbose else True if self.n_jobs > 1: def pquery(ix): i, x = ix return i, np.array(query.find_k_nearest_neighbors(x, k=n_retrieve)) with mp.pool.ThreadPool(processes=self.n_jobs) as pool: i_knn = list(tqdm(pool.imap_unordered(func=pquery, iterable=enumerate(X), chunksize=10), disable=False if self.verbose else True, total=X.shape[0], unit='vectors', desc='LSH query')) for i, knn in tqdm(i_knn, desc='Collecting results', disable=disable_tqdm): if query_is_train: knn = knn[1:] neigh_ind[i, :knn.size] = knn if return_distance: neigh_dist[i, :knn.size] = distances(X[i].reshape(1, -1), self.X_train_[knn]) # LSH may yield fewer neighbors than n_neighbors. # We set distances to NaN, and indices to -1 if knn.size < n_candidates: neigh_ind[i, knn.size:] = -1 if return_distance: neigh_dist[i, knn.size:] = np.nan else: for i, x in tqdm(enumerate(X), desc='LSH', disable=disable_tqdm, ): knn = np.array(query.find_k_nearest_neighbors(x, k=n_retrieve)) if query_is_train: knn = knn[1:] neigh_ind[i, :knn.size] = knn if return_distance: neigh_dist[i, :knn.size] = distances(x.reshape(1, -1), self.X_train_[knn]) # LSH may yield fewer neighbors than n_neighbors. # We set distances to NaN, and indices to -1 if knn.size < n_candidates: neigh_ind[i, knn.size:] = -1 if return_distance: neigh_dist[i, knn.size:] = np.nan if return_distance: return neigh_dist, neigh_ind else: return neigh_ind def radius_neighbors(self, X: np.ndarray = None, radius: float = None, return_distance: bool = True) -> Union[Tuple[np.array, np.array], np.array]: """ Retrieve neighbors within a certain radius. Parameters ---------- X: np.array or None, optional, default = None Query objects. If None, search among the indexed objects. radius: float or None, optional, default = None Retrieve neighbors within this radius. Can be negative: See Notes. return_distance: bool, default = True If return_distance, will return distances and indices to neighbors. Else, only return the indices. Notes ----- From the falconn docs: radius can be negative, and for the distance function 'negative_inner_product' it actually makes sense. """ check_is_fitted(self, ["index_", 'X_train_']) # Constructing a query object query = self.index_.construct_query_object() query.set_num_probes(self.num_probes) if return_distance: if self.metric == 'euclidean': distances = partial(euclidean_distances, squared=False) elif self.metric == 'sqeuclidean': distances = partial(euclidean_distances, squared=True) elif self.metric == 'cosine': distances = cosine_distances else: raise ValueError(f'Internal error: unrecognized metric "{self.metric}"') if X is not None: query_is_train = False X = check_array(X, accept_sparse='csr', dtype=self.X_train_.dtype) else: query_is_train = True X = self.X_train_ if radius is None: radius = self.radius # LSH uses squared Euclidean internally if self.metric == 'euclidean': radius *= radius # Add a small number to imitate <= threshold radius += 1e-7 # Allocate memory for neighbor indices (and distances) n_objects = X.shape[0] neigh_ind = np.empty(n_objects, dtype='object') if return_distance: neigh_dist = np.empty_like(neigh_ind) # If verbose, show progress bar on the search loop disable_tqdm = False if self.verbose else True for i, x in tqdm(enumerate(X), desc='LSH', disable=disable_tqdm, ): knn = np.array(query.find_near_neighbors(x, threshold=radius)) if len(knn) == 0: knn = np.array([], dtype=int) else: if query_is_train: knn = knn[1:] neigh_ind[i] = knn if return_distance: if len(knn): neigh_dist[i] = distances(x.reshape(1, -1), self.X_train_[knn]).ravel() else: neigh_dist[i] = np.array([]) if return_distance: return neigh_dist, neigh_ind else: return neigh_ind

39.481273

120

0.54774

2,343

21,083

4.784464

0.160905

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11,859

py

Python

virtualreality/calibration/manual_color_mask_calibration.py

sahasam/hobo_vr

0cf5824c91719055156ec23cf8dda2d921be948a

[ "MIT" ]

null

virtualreality/calibration/manual_color_mask_calibration.py

sahasam/hobo_vr

0cf5824c91719055156ec23cf8dda2d921be948a

[ "MIT" ]

null

virtualreality/calibration/manual_color_mask_calibration.py

sahasam/hobo_vr

0cf5824c91719055156ec23cf8dda2d921be948a

[ "MIT" ]

null

""" pyvr calibrate. Usage: pyvr calibrate [options] Options: -h, --help -c, --camera <camera> Source of the camera to use for calibration [default: 0] -r, --resolution <res> Input resolution in width and height [default: -1x-1] -n, --n_masks <n_masks> Number of masks to calibrate [default: 1] -l, --load_from_file <file> Load previous calibration settings [default: ranges.pickle] -s, --save <file> Save calibration settings to a file [default: ranges.pickle] """ import logging import pickle import sys from copy import copy from pathlib import Path from typing import Optional, List import cv2 from docopt import docopt from virtualreality import __version__ class ColorRange(object): def __init__(self, color_num, hue_center=0, hue_range=180, sat_center=0, sat_range=180, val_center=0, val_range=180 ): self.color_num = color_num self.hue_center = hue_center self.hue_range = hue_range self.sat_center = sat_center self.sat_range = sat_range self.val_center = val_center self.val_range = val_range class CalibrationData(object): def __init__(self, width=1, height=1, auto_exposure=0.25, exposure=0, saturation=50, num_colors=4): self.width = width self.height = height self.exposure = exposure self.saturation = saturation self.num_colors = num_colors self.color_ranges: List[ColorRange] = [] color_dist = 180 // num_colors for color in range(num_colors): self.color_ranges.append(ColorRange(color, *[color * color_dist, color_dist] * 3)) @classmethod def load_from_file(cls, load_file: str = str(Path(__file__).parent) + "ranges.pickle") -> Optional[ 'CalibrationData']: """Load the calibration data from a file.""" try: with open(load_file, "rb") as file: ranges = pickle.load(file) return ranges except FileNotFoundError as fe: logging.warning(f"Could not load calibration file '{load_file}'.") def save_to_file(self, save_file: str = str(Path(__file__).parent) + "ranges.pickle") -> None: with open(save_file, "wb") as file: pickle.dump(self, file) def colordata_to_blob(colordata, mapdata): ''' translates CalibrationData object to BlobTracker format masks :colordata: CalibrationData object :mapdata: a map dict with key representing the mask name and value representing the mask number ''' out = {} for key, clr_range_index in mapdata.items(): temp = colordata.color_ranges[clr_range_index] out[key] = { 'h':(temp.hue_center, temp.hue_range), 's':(temp.sat_center, temp.sat_range), 'v':(temp.val_center, temp.val_range), } return out def load_mapdata_from_file(path): ''' loads mapdata from file, for use in colordata_to_blob ''' with open(path, 'rb') as file: return pickle.load(file) def save_mapdata_to_file(path, mapdata): ''' save mapdata to file, for use in colordata_to_blob ''' with open(path, "wb") as file: pickle.dump(mapdata, file) def list_supported_capture_properties(cap: cv2.VideoCapture): """List the properties supported by the capture device.""" # thanks: https://stackoverflow.com/q/47935846/782170 supported = list() for attr in dir(cv2): if attr.startswith("CAP_PROP") and cap.get(getattr(cv2, attr)) != -1: supported.append(attr) return supported def get_color_mask(hsv, color_range: ColorRange): color_low = [ color_range.hue_center - color_range.hue_range, color_range.sat_center - color_range.sat_range, color_range.val_center - color_range.val_range, ] color_high = [ color_range.hue_center + color_range.hue_range, color_range.sat_center + color_range.sat_range, color_range.val_center + color_range.val_range, ] color_low_neg = copy(color_low) color_high_neg = copy(color_high) for c in range(3): if c==0: c_max = 180 else: c_max = 255 if color_low_neg[c] < 0: color_low_neg[c] = c_max + color_low_neg[c] color_high_neg[c] = c_max color_low[c] = 0 elif color_high_neg[c] > c_max: color_low_neg[c] = 0 color_high_neg[c] = color_high_neg[c] - c_max color_high[c] = c_max mask1 = cv2.inRange(hsv, tuple(color_low), tuple(color_high)) mask2 = cv2.inRange(hsv, tuple(color_low_neg), tuple(color_high_neg)) mask = cv2.bitwise_or(mask1, mask2) return mask def _set_default_camera_properties(vs, cam, vs_supported, frame_width, frame_height): if "CAP_PROP_FOURCC" not in vs_supported: logging.warning(f"Camera {cam} does not support setting video codec.") else: vs.set(cv2.CAP_PROP_FOURCC, cv2.CAP_OPENCV_MJPEG) if "CAP_PROP_AUTO_EXPOSURE" not in vs_supported: logging.warning(f"Camera {cam} does not support turning on/off auto exposure.") else: vs.set(cv2.CAP_PROP_AUTO_EXPOSURE, 0.25) if "CAP_PROP_EXPOSURE" not in vs_supported: logging.warning(f"Camera {cam} does not support directly setting exposure.") else: vs.set(cv2.CAP_PROP_EXPOSURE, -7) if "CAP_PROP_EXPOSURE" not in vs_supported: logging.warning(f"Camera {cam} does not support directly setting exposure.") else: vs.set(cv2.CAP_PROP_EXPOSURE, -7) if "CAP_PROP_FRAME_HEIGHT" not in vs_supported: logging.warning(f"Camera {cam} does not support requesting frame height.") else: vs.set(cv2.CAP_PROP_FRAME_HEIGHT, frame_height) if "CAP_PROP_FRAME_WIDTH" not in vs_supported: logging.warning(f"Camera {cam} does not support requesting frame width.") else: vs.set(cv2.CAP_PROP_FRAME_WIDTH, frame_width) def manual_calibration( cam=0, num_colors_to_track=4, frame_width=-1, frame_height=-1, load_file="", save_file="ranges.pickle" ): """Manually calibrate the hsv ranges and camera settings used for blob tracking.""" vs = cv2.VideoCapture(cam) vs.set(cv2.CAP_PROP_EXPOSURE, -7) vs_supported = list_supported_capture_properties(vs) _set_default_camera_properties(vs, cam, vs_supported, frame_width, frame_height) cam_window = f"camera {cam} input" cv2.namedWindow(cam_window) if "CAP_PROP_EXPOSURE" in vs_supported: cv2.createTrackbar( "exposure", cam_window, 0, 16, lambda x: vs.set(cv2.CAP_PROP_EXPOSURE, x - 8), ) if "CAP_PROP_SATURATION" in vs_supported: cv2.createTrackbar( "saturation", cam_window, 0, 100, lambda x: vs.set(cv2.CAP_PROP_SATURATION, x), ) else: logging.warning(f"Camera {cam} does not support setting saturation.") ranges = None if load_file: ranges = CalibrationData.load_from_file(load_file) if ranges is None: ranges = CalibrationData(width=frame_width, height=frame_height, num_colors=num_colors_to_track) tracker_window_names = [] for color in range(num_colors_to_track): tracker_window_names.append(f"color {color}") cv2.namedWindow(tracker_window_names[color]) cv2.createTrackbar( "hue center", tracker_window_names[color], ranges.color_ranges[color].hue_center, 180, lambda _: None, ) cv2.createTrackbar( "hue range", tracker_window_names[color], ranges.color_ranges[color].hue_range, 180, lambda _: None, ) cv2.createTrackbar( "sat center", tracker_window_names[color], ranges.color_ranges[color].sat_center, 255, lambda _: None, ) cv2.createTrackbar( "sat range", tracker_window_names[color], ranges.color_ranges[color].sat_range, 255, lambda _: None, ) cv2.createTrackbar( "val center", tracker_window_names[color], ranges.color_ranges[color].val_center, 255, lambda _: None, ) cv2.createTrackbar( "val range", tracker_window_names[color], ranges.color_ranges[color].val_range, 255, lambda _: None, ) while 1: ret, frame = vs.read() if frame is None: break blurred = cv2.GaussianBlur(frame, (3, 3), 0) hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) exposure = cv2.getTrackbarPos("exposure", cam_window) saturation = cv2.getTrackbarPos("saturation", cam_window) ranges.exposure = exposure - 8 ranges.saturation = saturation for color in range(num_colors_to_track): hue_center = cv2.getTrackbarPos("hue center", tracker_window_names[color]) hue_range = cv2.getTrackbarPos("hue range", tracker_window_names[color]) sat_center = cv2.getTrackbarPos("sat center", tracker_window_names[color]) sat_range = cv2.getTrackbarPos("sat range", tracker_window_names[color]) val_center = cv2.getTrackbarPos("val center", tracker_window_names[color]) val_range = cv2.getTrackbarPos("val range", tracker_window_names[color]) ranges.color_ranges[color].hue_center = hue_center ranges.color_ranges[color].hue_range = hue_range ranges.color_ranges[color].sat_center = sat_center ranges.color_ranges[color].sat_range = sat_range ranges.color_ranges[color].val_center = val_center ranges.color_ranges[color].val_range = val_range mask = get_color_mask(hsv, ranges.color_ranges[color]) res = cv2.bitwise_and(hsv, hsv, mask=mask) cv2.imshow(tracker_window_names[color], res) cv2.imshow(cam_window, frame) k = cv2.waitKey(1) & 0xFF if k in [ord("q"), 27]: break for color in range(num_colors_to_track): hue_center = cv2.getTrackbarPos("hue center", tracker_window_names[color]) hue_range = cv2.getTrackbarPos("hue range", tracker_window_names[color]) sat_center = cv2.getTrackbarPos("sat center", tracker_window_names[color]) sat_range = cv2.getTrackbarPos("sat range", tracker_window_names[color]) val_center = cv2.getTrackbarPos("val center", tracker_window_names[color]) val_range = cv2.getTrackbarPos("val range", tracker_window_names[color]) print(f"hue_center[{color}]: {hue_center}") print(f"hue_range[{color}]: {hue_range}") print(f"sat_center[{color}]: {sat_center}") print(f"sat_range[{color}]: {sat_range}") print(f"val_center[{color}]: {val_center}") print(f"val_range[{color}]: {val_range}") if save_file: ranges.save_to_file(save_file) print(f'ranges saved to list in "{save_file}".') print("You can use this in the pyvr tracker using the --calibration-file argument.") vs.release() cv2.destroyAllWindows() def main(): """Calibrate entry point.""" # allow calling from both python -m and from pyvr: argv = sys.argv[1:] if len(argv) < 2 or sys.argv[1] != "calibrate": argv = ["calibrate"] + argv args = docopt(__doc__, version=f"pyvr version {__version__}", argv=argv) width, height = args["--resolution"].split("x") if args["--camera"].isdigit(): cam = int(args["--camera"]) else: cam = args["--camera"] manual_calibration( cam=cam, num_colors_to_track=int(args["--n_masks"]), frame_width=int(width), frame_height=int(height), load_file=args["--load_from_file"], save_file=args["--save"], )

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0

null

0

null

0

1

0

c09416ca42570e30634d8a60a3175bf1c430d092

1,894

py

Python

database.py

tzoch/dropbox-bot

2bf36e2d4146bf8c00169362f9767ed059643787

[ "MIT" ]

3

2016-03-08T04:43:22.000Z

2020-08-25T20:07:28.000Z

database.py

tzoch/dropbox-bot

2bf36e2d4146bf8c00169362f9767ed059643787

[ "MIT" ]

null

database.py

tzoch/dropbox-bot

2bf36e2d4146bf8c00169362f9767ed059643787

[ "MIT" ]

null

#! /usr/bin/python ''' Class to handle database connections and queries for Dropbox Mirror Bot ''' import sqlite3 class Database(object): def __init__(self, database=":memory:"): self._database = database c = self.cursor() query = '''CREATE TABLE IF NOT EXISTS dropbox_submissions ( processed_id INTEGER PRIMARY KEY ASC, submission_id VARCHAR(10) UNIQUE)''' c.execute(query) self.conn.commit() query = '''CREATE TABLE IF NOT EXISTS dropbox_images ( id INTEGER PRIMARY KEY ASC, imgur_id VARCHAR(10), deletehash VARCHAR(40))''' c.execute(query) self.conn.commit() @property def conn(self): if not hasattr(self, '_connection'): self._connection = sqlite3.connect(self._database) return self._connection def cursor(self): return self.conn.cursor() def close(self): self.conn.close() def is_processed(self, submission_id): '''Return true if the submission has already been processed''' c = self.cursor() query = '''SELECT submission_id FROM dropbox_submissions WHERE submission_id = (?)''' c.execute(query, (submission_id,)) if c.fetchone(): return True return False def mark_as_processed(self, submission_id): c = self.cursor() query = '''INSERT INTO dropbox_submissions (submission_id) VALUES (?)''' c.execute(query , (submission_id,)) self.conn.commit() def log_image(self, img_id, img_deletehash): c = self.cursor() query = '''INSERT INTO dropbox_images (imgur_id, deletehash) VALUES (?, ?)''' c.execute(query, (img_id, img_deletehash)) self.conn.commit()

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0.315206

1,894

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0.288889

0

null

0

null

0

1

0

c095ea2cd17b98f861280b8dd90e12ab34027235

513

py

Python

solutions/unitReview/gcd.py

mrparkonline/python3_while

3b24be84d16230e2b923276dca4c943f4c5ad26d

[ "MIT" ]

null

solutions/unitReview/gcd.py

mrparkonline/python3_while

3b24be84d16230e2b923276dca4c943f4c5ad26d

[ "MIT" ]

null

solutions/unitReview/gcd.py

mrparkonline/python3_while

3b24be84d16230e2b923276dca4c943f4c5ad26d

[ "MIT" ]

null

# GCD Program from math import gcd # input num1 = int(input('Enter a number: ')) num2 = int(input('Enter another number: ')) # processing & output divisor = 1 upper_limit = min(num1, num2) gcd_answer = 0 #print(num1, 'and', num2, 'share these factors:') print('GCD of', num1, 'and', num2, 'is:') while divisor <= upper_limit: if num1 % divisor == 0 and num2 % divisor == 0: gcd_answer = divisor divisor += 1 # end of while loop print(gcd_answer) print('Math Module GCD:', gcd(num1,num2))

22.304348

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null

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null

0

1

0

c09740c69f29292cd8143f6167d141bb98d730a6

728

py

Python

notification/views.py

ChristopherOloo/KilimoQAPortal

c905a42282bbce70b5477862185ad332185307ce

[ "MIT" ]

67

2022-01-05T18:59:23.000Z

2022-03-18T13:13:39.000Z

notification/views.py

ChristopherOloo/KilimoQAPortal

c905a42282bbce70b5477862185ad332185307ce

[ "MIT" ]

3

2022-01-10T10:03:23.000Z

2022-03-11T16:58:38.000Z

notification/views.py

ChristopherOloo/KilimoQAPortal

c905a42282bbce70b5477862185ad332185307ce

[ "MIT" ]

4

2022-01-08T17:39:19.000Z

2022-02-28T07:40:16.000Z

from django.shortcuts import render from .models import PrivRepNotification,Notification from django.http import JsonResponse, HttpResponseRedirect, HttpResponse def read_All_Notifications(request): notifics = Notification.objects.filter(noti_receiver=request.user).order_by('-date_created') for objs in notifics: objs.is_read = True objs.save() # return HttpResponse(status=204) return JsonResponse({'action': 'readedAll'}) def read_All_Priv_Notifications(request): notifications = PrivRepNotification.objects.filter(for_user=request.user) for obj in notifications: obj.is_read = True obj.save() return JsonResponse({'action':'readedAllPrivNotifications'})

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null

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null

0

1

0

c09b58cc8746669f100104bd829d92eb5454df67

1,548

py

Python

fb2_get_list.py

kawaiigamer/python-tools

68fd75299657811fef36339732c80539ccad386e

[ "Unlicense" ]

null

fb2_get_list.py

kawaiigamer/python-tools

68fd75299657811fef36339732c80539ccad386e

[ "Unlicense" ]

null

fb2_get_list.py

kawaiigamer/python-tools

68fd75299657811fef36339732c80539ccad386e

[ "Unlicense" ]

null

import os import glob import codecs from typing import List def dirs(root_dit: str) -> List[str]: return next(os.walk(root_dit))[1] def select_directory_from_list(directories: List[str]) -> str: for i in range(0, len(directories)): print("(%d) %s" % (i, directories[i])) while True: try: return directories[int(input('Directory to check(number)_->'))] except Exception as e: print("Wrong input: %s" % e) continue def text_between(_str: str, begin: str, end: str) -> str: start = _str.find(begin) stop = _str.find(end) if start != -1 and stop != -1: return _str[start+len(begin):stop] else: return "" def f2b_print_data_list(): checking_directory = select_directory_from_list(dirs('.')) f2b_files = glob.glob("%s/*.fb2" % checking_directory) counter = 0 for f2b_file in f2b_files: try: text = codecs.open(f2b_file, 'r', encoding='utf8').read() counter += 1 print("%d. %s - %s %s %s" % (counter, text_between(text, "<book-title>", "</book-title>"), text_between(text, "<first-name>", "</first-name>"), text_between(text, "<middle-name>", "</middle-name>"), text_between(text, "<last-name>", "</last-name>") )) except Exception as e: print("Exception while parsing %s: %s" % (f2b_file, e)) if __name__ == "__main__": f2b_print_data_list()

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null

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c09c7f0c8e41ed1996a2664259286c39cad5f12c

2,403

py

Python

simplecaptcha/fields.py

Kromey/django-simplecaptcha

ad462f8742be19b1e87103f097853d41e21d0e0a

[ "MIT" ]

5

2015-11-12T06:31:08.000Z

2017-03-09T06:45:46.000Z

simplecaptcha/fields.py

Kromey/django-simplecaptcha

ad462f8742be19b1e87103f097853d41e21d0e0a

[ "MIT" ]

null

simplecaptcha/fields.py

Kromey/django-simplecaptcha

ad462f8742be19b1e87103f097853d41e21d0e0a

[ "MIT" ]

null

import time from django import forms from django.core.exceptions import ValidationError from .widgets import CaptchaWidget from .settings import DURATION class CaptchaField(forms.MultiValueField): """A field that contains and validates a simple catcha question WARNING: If you use this field directly in your own forms, you may be caught by surprise by the fact that Django forms rely upon class object rather than instance objects for its fields. This means that your captcha will not be updated when you instantiate a new form, and you'll end up asking your users the same question over and over -- largely defeating the purpose of a captcha! To solve this, either use the @decorator instead, or be sure to call upon the widget to update its captcha question. """ widget = CaptchaWidget def __init__(self, *args, **kwargs): """Sets up the MultiValueField""" fields = ( forms.CharField(), forms.CharField(), forms.CharField(), ) super().__init__(fields, *args, **kwargs) def compress(self, data_list): """Validates the captcha answer and returns the result If no data is provided, this method will simply return None. Otherwise, it will validate that the provided answer and timestamp hash to the supplied hash value, and that the timestamp is within the configured time that captchas are considered valid. """ if data_list: # Calculate the hash of the supplied values hashed = self.widget.hash_answer(answer=data_list[0], timestamp=data_list[1]) # Current time timestamp = time.time() if float(data_list[1]) < timestamp - DURATION: raise ValidationError("Captcha expired, please try again", code='invalid') elif hashed != data_list[2]: raise ValidationError("Incorrect answer", code='invalid') # Return the supplied answer return data_list[0] else: return None @property def label(self): """The captcha field's label is the captcha question itself""" return self.widget._question @label.setter def label(self, value): """The question is generated by the widget and cannot be externally set""" pass

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0.451613

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null

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0

c09e72d5be2ef0cef0c360e31efc8610a74ed555

4,940

py

Python

skills_taxonomy_v2/analysis/sentence_classifier/notebooks/Skills Classifier 1.0 - Doccano Baseline Classifier.py

india-kerle/skills-taxonomy-v2

a71366dfea3c35580dbafddba9470f83795805ae

[ "MIT" ]

3

2021-11-21T17:21:12.000Z

2021-12-10T21:19:57.000Z

skills_taxonomy_v2/analysis/sentence_classifier/notebooks/Skills Classifier 1.0 - Doccano Baseline Classifier.py

india-kerle/skills-taxonomy-v2

a71366dfea3c35580dbafddba9470f83795805ae

[ "MIT" ]

16

2021-10-06T11:20:35.000Z

2022-02-02T11:44:28.000Z

skills_taxonomy_v2/analysis/sentence_classifier/notebooks/Skills Classifier 1.0 - Doccano Baseline Classifier.py

india-kerle/skills-taxonomy-v2

a71366dfea3c35580dbafddba9470f83795805ae

[ "MIT" ]

1

2021-10-04T12:27:20.000Z

# --- # jupyter: # jupytext: # cell_metadata_filter: -all # comment_magics: true # formats: ipynb,py:light # text_representation: # extension: .py # format_name: light # format_version: '1.5' # jupytext_version: 1.11.3 # kernelspec: # display_name: Python 3 (ipykernel) # language: python # name: python3 # --- # # Existing skill tags data # 1. Look at data # 2. Build a simple baseline classifier # # Karlis tagged 50 jobs with where the skills were mentioned. Can we train something to identify sentences as about skills or not? # # Would be helpful for taking out the junk. # + from sklearn.linear_model import LogisticRegression import json import random from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.model_selection import train_test_split from sklearn.metrics import ( accuracy_score, classification_report, f1_score, precision_score, recall_score, ) # - # ### Import data with open( "../../../../inputs/karlis_ojo_manually_labelled/OJO_test_labelling_April2021_jobs.jsonl", "r", ) as file: jobs_data = [json.loads(line) for line in file] jobs_data[0].keys() with open( "../../../../inputs/karlis_ojo_manually_labelled/OJO_test_labelling_April2021_labels.json", "r", ) as file: labels_data = json.load(file) label_type_dict = {label_type["id"]: label_type["text"] for label_type in labels_data} label_type_dict # ### Restructuring to have a look # + all_job_tags_text = {} for job_id, job_info in enumerate(jobs_data): text = job_info["text"] annotations = job_info["annotations"] job_tags_text = {} for label_number, label_type in label_type_dict.items(): job_tags_text[label_type] = [ text[label["start_offset"] : label["end_offset"]] for label in annotations if label["label"] == label_number ] all_job_tags_text[job_id] = job_tags_text # - job_id = 1 print(jobs_data[job_id]["text"]) print("\n") print(all_job_tags_text[job_id]["SKILL"]) print(all_job_tags_text[job_id]["SKILL-RELATED"]) # ## Create a basic classifier # Label sentences with containing skills (1) or not (0) # # Method assumes sentences are split by full stop and will run into problems if the skill has a full stop in. def label_sentences(job_id): annotations = jobs_data[job_id]["annotations"] skill_spans = [ (label["start_offset"], label["end_offset"]) for label in annotations if label["label"] in [1, 5] ] sentences = jobs_data[job_id]["text"].split(".") # Indices of where sentences start and end sentences_ix = [] for i, sentence in enumerate(sentences): if i == 0: start = 0 else: start = sentences_ix[i - 1][1] + 1 sentences_ix.append((start, start + len(sentence))) # Find which sentences contain skills sentences_label = [0] * len(sentences) for (skill_start, skill_end) in skill_spans: for i, (sent_s, sent_e) in enumerate(sentences_ix): if sent_s <= skill_start and sent_e >= skill_end: sentences_label[i] = 1 return sentences, sentences_label # Testing job_id = 2 sentences, sentences_label = label_sentences(job_id) print(all_job_tags_text[job_id]["SKILL"]) print(all_job_tags_text[job_id]["SKILL-RELATED"]) print([sentences[i] for i, label in enumerate(sentences_label) if label == 1]) print([sentences[i] for i, label in enumerate(sentences_label) if label == 0]) # Create training dataset X = [] y = [] for job_id in range(len(jobs_data)): sentences, sentences_label = label_sentences(job_id) for sentence, sentence_label in zip(sentences, sentences_label): X.append(sentence) y.append(sentence_label) # + # Random shuffle data points shuffle_index = list(range(len(X))) random.Random(42).shuffle(shuffle_index) X = [X[i] for i in shuffle_index] y = [y[i] for i in shuffle_index] # Split test/train set train_split = 0.75 len_train = round(len(X) * train_split) X_train = X[0:len_train] y_train = y[0:len_train] X_test = X[len_train:] y_test = y[len_train:] # - print(len(X)) print(len(y_train)) print(len(y_test)) vectorizer = CountVectorizer( analyzer="word", token_pattern=r"(?u)\b\w+\b", ngram_range=(1, 2), stop_words="english", ) X_train_vect = vectorizer.fit_transform(X_train) model = MultinomialNB() model = model.fit(X_train_vect, y_train) X_test_vect = vectorizer.transform(X_test) y_test_pred = model.predict(X_test_vect) print(classification_report(y_test, y_test_pred)) # + # LogisticRegression model = LogisticRegression(max_iter=1000, class_weight="balanced") model = model.fit(X_train_vect, y_train) X_test_vect = vectorizer.transform(X_test) y_test_pred = model.predict(X_test_vect) print(classification_report(y_test, y_test_pred)) # -

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c0a3c20650d9f2b0b50513762c0375912b29d194

2,594

py

Python

tests/test_action_guest_process_start.py

lingfish/stackstorm-vsphere

49199f5ebdc05b70b7504962e104642b0c30ba30

[ "Apache-2.0" ]

null

tests/test_action_guest_process_start.py

lingfish/stackstorm-vsphere

49199f5ebdc05b70b7504962e104642b0c30ba30

[ "Apache-2.0" ]

2

2019-03-25T18:03:02.000Z

2019-03-26T13:13:59.000Z

tests/test_action_guest_process_start.py

lingfish/stackstorm-vsphere

49199f5ebdc05b70b7504962e104642b0c30ba30

[ "Apache-2.0" ]

1

2021-03-05T10:12:21.000Z

# Licensed to the StackStorm, Inc ('StackStorm') under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and import mock from vsphere_base_action_test_case import VsphereBaseActionTestCase from guest_process_start import StartProgramInGuest __all__ = [ 'StartProgramInGuestTestCase' ] class StartProgramInGuestTestCase(VsphereBaseActionTestCase): __test__ = True action_cls = StartProgramInGuest @mock.patch('pyVmomi.vim.vm.guest.ProcessManager') def test_normal(self, mock_process_manager): # Vary the arguments list including passing None # Each tuple has two array items, [0] is arguments input # [1] is expected cmdspec for argdata in (None, 'onearg', 'two arguments'): (action, mock_vm) = self.mock_one_vm('vm-12345') mockProcMgr = mock.Mock() mockProcMgr.StartProgramInGuest = mock.Mock() mockProcMgr.StartProgramInGuest.return_value = 12345 action.si_content.guestOperationsManager = mock.Mock() action.si_content.guestOperationsManager.processManager =\ mockProcMgr mock_process_manager.ProgramSpec.return_value = 'cmdspec' envvars = ["A=B", "C=D"] if argdata else None result = action.run(vm_id='vm-12345', username='u', password='p', command='c', arguments=argdata, workdir='/tmp', envvar=envvars) mock_process_manager.ProgramSpec.assert_called_with( arguments='' if not argdata else argdata, envVariables=envvars, programPath='c', workingDirectory='/tmp' ) mockProcMgr.StartProgramInGuest.assert_called_once_with( mock_vm, action.guest_credentials, 'cmdspec', ) self.assertEqual(result, 12345)

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null

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null

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1

0

c0a3f676d422bbdd29b5d1ae6fd198e164330819

4,192

py

Python

src/soda/mutator.py

UCLA-VAST/soda

1b3994ded643d82ebc2fce7b1eb1d13c70800897

[ "MIT" ]

9

2020-05-09T19:52:46.000Z

2021-09-15T13:45:27.000Z

src/soda/mutator.py

UCLA-VAST/soda

1b3994ded643d82ebc2fce7b1eb1d13c70800897

[ "MIT" ]

1

2021-07-26T08:51:49.000Z

src/soda/mutator.py

UCLA-VAST/soda

1b3994ded643d82ebc2fce7b1eb1d13c70800897

[ "MIT" ]

1

2020-10-28T03:06:44.000Z

from typing import ( Iterable, Mapping, MutableMapping, Optional, Tuple, TypeVar, Union, ) import collections import logging import operator import types from haoda import ir from soda import tensor import soda.visitor _logger = logging.getLogger().getChild(__name__) def shift(obj, offset, excluded=(), op=operator.sub, verbose=False): """Shift soda.ir.Ref with the given offset. All soda.ir.Ref, excluding the given names, will be shifted with the given offset using the given operator. The operator will be applied pointwise on the original index and the given offset. Args: obj: A haoda.ir.Node or a tensor.Tensor object. offset: Second operand given to the operator. excluded: Sequence of names to be excluded from the mutation. Default to (). op: Shifting operator. Should be either add or sub. Default to sub. verbose: Whether to log shiftings. Default to False. Returns: Mutated obj. If obj is an IR node, it will be a different object than the input. If obj is a tensor, it will be the same object but with fields mutated. """ if op not in (operator.add, operator.sub): _logger.warn('shifting with neither + nor -, which most likely is an error') def visitor(obj, args): if isinstance(obj, ir.Ref): if obj.name not in excluded: new_idx = tuple(op(a, b) for a, b in zip(obj.idx, offset)) if verbose: _logger.debug('reference %s(%s) shifted to %s(%s)', obj.name, ', '.join(map(str, obj.idx)), obj.name, ', '.join(map(str, new_idx))) obj.idx = new_idx if isinstance(obj, ir.Node): return obj.visit(visitor) if isinstance(obj, tensor.Tensor): obj.mutate(visitor) else: raise TypeError('argument is not an IR node or a tensor') return obj def normalize(obj: Union[ir.Node, Iterable[ir.Node]], references: Optional[Mapping[str, Tuple[int, ...]]] = None): """Make the least access index 0. Works on an ir.Node or an iterable of ir.Nodes. If it is shifted, a different object is constructed and returned. Otherwise, obj will be returned as-is. Args: obj: A node or an iterable of nodes. Returns: Normalized node or iterable. Raises: TypeError: If argument is not an ir.Node or an iterable of ir.Nodes. """ if isinstance(obj, types.GeneratorType): return normalize(tuple(obj)) norm_idx = soda.visitor.get_normalize_index(obj, references) shifter = lambda x: shift(x, norm_idx) if any(norm_idx) else x if isinstance(obj, collections.Iterable): return type(obj)(map(shifter, obj)) # type: ignore if isinstance(obj, ir.Node): return shifter(obj) raise TypeError('argument is not an ir.Node or an iterable of ir.Nodes') NodeT = TypeVar('NodeT', bound=ir.Node) def replace_expressions( obj: NodeT, cses: MutableMapping[NodeT, ir.Ref], used: Optional[MutableMapping[NodeT, NodeT]] = None, references: Optional[Mapping[str, Tuple[int, ...]]] = None, ) -> NodeT: """Get AST with common subexpression elimination. Get AST with the given common subexpressions. If used is not None, the used common subexpressions will be added to used. Args: obj: An ir.Node. cses: Dict mapping normalized common subexpressions to the new ir.Ref. used: Set of used common subexpressions, or None. Returns: The ir.Node as the AST. """ def visitor( obj: NodeT, args: Tuple[MutableMapping[NodeT, ir. Ref], Optional[MutableMapping[NodeT, NodeT]]] ) -> NodeT: cses, used = args norm_idx = soda.visitor.get_normalize_index(obj, references) normalized = shift(obj, norm_idx) if any(norm_idx) else obj if normalized in cses: if used is not None: if normalized not in used: used[normalized] = replace_expressions( normalized, {k: v for k, v in cses.items() if k != normalized}, used) new_obj = shift(cses[normalized], norm_idx, op=operator.add) _logger.debug('replacing %s with %s', obj, new_obj) return new_obj return obj return obj.visit(visitor, (cses, used))

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null

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null

0

1

0

c0a71acf6116e8faa1f0455b3919ee53b2e3be9c

2,923

py

Python

htdocs/plotting/auto/scripts/p66.py

jamayfieldjr/iem

275b77a65f3b12e26e6cbdb230786b9c7d2b9c9a

[ "MIT" ]

1

2019-10-07T17:01:24.000Z

htdocs/plotting/auto/scripts/p66.py

jamayfieldjr/iem

275b77a65f3b12e26e6cbdb230786b9c7d2b9c9a

[ "MIT" ]

null

htdocs/plotting/auto/scripts/p66.py

jamayfieldjr/iem

275b77a65f3b12e26e6cbdb230786b9c7d2b9c9a

[ "MIT" ]

null

"""Consec days""" import calendar from pandas.io.sql import read_sql from pyiem.plot.use_agg import plt from pyiem.util import get_autoplot_context, get_dbconn PDICT = {'above': 'Temperature At or Above (AOA) Threshold', 'below': 'Temperature Below Threshold'} PDICT2 = {'high': 'High Temperature', 'low': 'Low Temperature'} def get_description(): """ Return a dict describing how to call this plotter """ desc = dict() desc['data'] = True desc['description'] = """This chart presents the daily frequency of the given date having the prescribed number of previous days above or below some provided treshold.""" desc['arguments'] = [ dict(type='station', name='station', default='IATDSM', label='Select Station:', network='IACLIMATE'), dict(type='select', name='var', default='high', options=PDICT2, label='Select which daily variable'), dict(type='select', name='dir', default='above', options=PDICT, label='Select temperature direction'), dict(type='int', name='threshold', default='60', label='Temperature Threshold (F):'), dict(type='int', name='days', default='7', label='Number of Days:') ] return desc def plotter(fdict): """ Go """ pgconn = get_dbconn('coop') ctx = get_autoplot_context(fdict, get_description()) station = ctx['station'] days = ctx['days'] threshold = ctx['threshold'] varname = ctx['var'] mydir = ctx['dir'] table = "alldata_%s" % (station[:2],) agg = "min" if mydir == 'above' else 'max' op = ">=" if mydir == 'above' else '<' df = read_sql(""" with data as (select day, """+agg+"""("""+varname+""") OVER (ORDER by day ASC ROWS BETWEEN %s PRECEDING and CURRENT ROW) as agg from """ + table + """ where station = %s) select extract(doy from day) as doy, sum(case when agg """+op+""" %s then 1 else 0 end) / count(*)::float * 100. as freq from data GROUP by doy ORDER by doy asc """, pgconn, params=(days - 1, station, threshold), index_col='doy') fig, ax = plt.subplots(1, 1, sharex=True) label = "AOA" if mydir == 'above' else 'below' ax.set_title(("[%s] %s\nFrequency of %s Consec Days" r" with %s %s %s$^\circ$F " ) % (station, ctx['_nt'].sts[station]['name'], days, varname.capitalize(), label, threshold)) ax.set_ylabel("Frequency of Days [%]") ax.set_ylim(0, 100) ax.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100]) ax.grid(True) ax.bar(df.index.values, df['freq'], width=1) ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365)) ax.set_xticklabels(calendar.month_abbr[1:]) ax.set_xlim(0, 366) return fig, df if __name__ == '__main__': plotter(dict())

34.388235

75

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4.372396

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0.020846

0.021441

0.028588

0

0.034372

0.253507

2,923

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0.029851

false

0

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0.119403

0

null

0

null

0

1

0

c0a93dc0b3c06bf5e6cdc0aa43def476e965448d

866

py

Python

csv_test.py

mii012345/deep-learning

660785157446583eefeefa9d5dc25927aab6a9e4

[ "MIT" ]

3

2017-06-04T06:59:38.000Z

2017-06-05T14:01:48.000Z

csv_test.py

mii012345/deep-learning

660785157446583eefeefa9d5dc25927aab6a9e4

[ "MIT" ]

null

csv_test.py

mii012345/deep-learning

660785157446583eefeefa9d5dc25927aab6a9e4

[ "MIT" ]

null

import csv import numpy as np import pickle with open('data (2).csv','r') as f: csv = csv.reader(f) csvlist = [] for i in csv: csvlist.append(i) #6行目から mas = [] for i in range(364): i+=6 a = 0 b = 0 c = 0 date = csvlist[i][0] weather = csvlist[i][1] if date[0:10] == "2016/11/1 " or date[0:10] == "2016/11/2 " or date[0:10] == "2016/11/3 " or date[0:9] == "2016/11/4" or date[0:9] == "2016/11/5" or date[0:9] == "2016/11/6" or date[0:9] == "2016/11/7": continue if weather == "1" or weather == "2": a = 1 elif weather == "3" or weather == "4" or weather == "5" or weather == "6": b = 1 else: c = 1 w = [a,b,c] print(date[0:10]) mas.append(w) mas = np.array(mas) with open('tenki_num.pkl','wb') as f: pickle.dump(mas,f)

24.055556

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866

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0

null

0

null

0

1

0

c0abaf869bbe93d0c4be20bb53db1ca7697f6d3d

1,971

py

Python

ntm/ntm.py

clemkoa/ntm

723d4ebea63f8f9439fd1c56f36e3cb680c8a277

[ "MIT" ]

41

2020-05-19T05:48:04.000Z

2021-11-24T11:31:08.000Z

ntm/ntm.py

clemkoa/ntm

723d4ebea63f8f9439fd1c56f36e3cb680c8a277

[ "MIT" ]

3

2021-06-07T09:00:59.000Z

2021-12-30T17:21:07.000Z

ntm/ntm.py

clemkoa/ntm

723d4ebea63f8f9439fd1c56f36e3cb680c8a277

[ "MIT" ]

4

2020-12-31T17:39:42.000Z

2021-12-29T14:11:43.000Z

import torch from torch import nn import torch.nn.functional as F from ntm.controller import Controller from ntm.memory import Memory from ntm.head import ReadHead, WriteHead class NTM(nn.Module): def __init__(self, vector_length, hidden_size, memory_size, lstm_controller=True): super(NTM, self).__init__() self.controller = Controller(lstm_controller, vector_length + 1 + memory_size[1], hidden_size) self.memory = Memory(memory_size) self.read_head = ReadHead(self.memory, hidden_size) self.write_head = WriteHead(self.memory, hidden_size) self.fc = nn.Linear(hidden_size + memory_size[1], vector_length) nn.init.xavier_uniform_(self.fc.weight, gain=1) nn.init.normal_(self.fc.bias, std=0.01) def get_initial_state(self, batch_size=1): self.memory.reset(batch_size) controller_state = self.controller.get_initial_state(batch_size) read = self.memory.get_initial_read(batch_size) read_head_state = self.read_head.get_initial_state(batch_size) write_head_state = self.write_head.get_initial_state(batch_size) return (read, read_head_state, write_head_state, controller_state) def forward(self, x, previous_state): previous_read, previous_read_head_state, previous_write_head_state, previous_controller_state = previous_state controller_input = torch.cat([x, previous_read], dim=1) controller_output, controller_state = self.controller(controller_input, previous_controller_state) # Read read_head_output, read_head_state = self.read_head(controller_output, previous_read_head_state) # Write write_head_state = self.write_head(controller_output, previous_write_head_state) fc_input = torch.cat((controller_output, read_head_output), dim=1) state = (read_head_output, read_head_state, write_head_state, controller_state) return F.sigmoid(self.fc(fc_input)), state

50.538462

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4.970803

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0.044053

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0.061674

0

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0.171994

1,971

38

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0

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0

0.375

0

null

0

null

0

1

0

c0af4a37c3b086f10b2224f1101fb1be4a7fdce1

3,468

py

Python

facebook_business/adobjects/adkeywordstats.py

enricapq/facebook-python-business-sdk

49c569ac5cf812b1bcb533520c35896b0436fa4c

[ "CNRI-Python" ]

null

facebook_business/adobjects/adkeywordstats.py

enricapq/facebook-python-business-sdk

49c569ac5cf812b1bcb533520c35896b0436fa4c

[ "CNRI-Python" ]

null

facebook_business/adobjects/adkeywordstats.py

enricapq/facebook-python-business-sdk

49c569ac5cf812b1bcb533520c35896b0436fa4c

[ "CNRI-Python" ]

1

2018-09-24T14:04:48.000Z

# Copyright 2014 Facebook, Inc. # You are hereby granted a non-exclusive, worldwide, royalty-free license to # use, copy, modify, and distribute this software in source code or binary # form for use in connection with the web services and APIs provided by # Facebook. # As with any software that integrates with the Facebook platform, your use # of this software is subject to the Facebook Developer Principles and # Policies [http://developers.facebook.com/policy/]. This copyright notice # shall be included in all copies or substantial portions of the software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. from facebook_business.adobjects.abstractobject import AbstractObject from facebook_business.adobjects.abstractcrudobject import AbstractCrudObject from facebook_business.adobjects.objectparser import ObjectParser from facebook_business.api import FacebookRequest from facebook_business.typechecker import TypeChecker """ This class is auto-generated. For any issues or feature requests related to this class, please let us know on github and we'll fix in our codegen framework. We'll not be able to accept pull request for this class. """ class AdKeywordStats( AbstractCrudObject, ): def __init__(self, fbid=None, parent_id=None, api=None): self._isAdKeywordStats = True super(AdKeywordStats, self).__init__(fbid, parent_id, api) class Field(AbstractObject.Field): actions = 'actions' clicks = 'clicks' cost_per_total_action = 'cost_per_total_action' cost_per_unique_click = 'cost_per_unique_click' cpc = 'cpc' cpm = 'cpm' cpp = 'cpp' ctr = 'ctr' frequency = 'frequency' id = 'id' impressions = 'impressions' name = 'name' reach = 'reach' spend = 'spend' total_actions = 'total_actions' total_unique_actions = 'total_unique_actions' unique_actions = 'unique_actions' unique_clicks = 'unique_clicks' unique_ctr = 'unique_ctr' unique_impressions = 'unique_impressions' # @deprecated get_endpoint function is deprecated @classmethod def get_endpoint(cls): return 'keywordstats' _field_types = { 'actions': 'list<AdsActionStats>', 'clicks': 'unsigned int', 'cost_per_total_action': 'float', 'cost_per_unique_click': 'float', 'cpc': 'float', 'cpm': 'float', 'cpp': 'float', 'ctr': 'float', 'frequency': 'float', 'id': 'string', 'impressions': 'unsigned int', 'name': 'string', 'reach': 'unsigned int', 'spend': 'float', 'total_actions': 'unsigned int', 'total_unique_actions': 'unsigned int', 'unique_actions': 'list<AdsActionStats>', 'unique_clicks': 'unsigned int', 'unique_ctr': 'float', 'unique_impressions': 'unsigned int', } @classmethod def _get_field_enum_info(cls): field_enum_info = {} return field_enum_info

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0.684544

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3,468

5.47981

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0.043346

0.037711

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null

0

null

0

1

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c0b3ae1a797739b59abdda1942df55aaa68ec172

1,198

py

Python

DQM/TrackingMonitorSource/python/StandaloneTrackMonitor_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

DQM/TrackingMonitorSource/python/StandaloneTrackMonitor_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

DQM/TrackingMonitorSource/python/StandaloneTrackMonitor_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms from DQMServices.Core.DQMEDAnalyzer import DQMEDAnalyzer standaloneTrackMonitor = DQMEDAnalyzer('StandaloneTrackMonitor', moduleName = cms.untracked.string("StandaloneTrackMonitor"), folderName = cms.untracked.string("highPurityTracks"), vertexTag = cms.untracked.InputTag("selectedPrimaryVertices"), puTag = cms.untracked.InputTag("addPileupInfo"), clusterTag = cms.untracked.InputTag("siStripClusters"), trackInputTag = cms.untracked.InputTag('selectedTracks'), offlineBeamSpot = cms.untracked.InputTag('offlineBeamSpot'), trackQuality = cms.untracked.string('highPurity'), doPUCorrection = cms.untracked.bool(False), isMC = cms.untracked.bool(True), puScaleFactorFile = cms.untracked.string("PileupScaleFactor_run203002.root"), haveAllHistograms = cms.untracked.bool(False), verbose = cms.untracked.bool(False), trackEtaH = cms.PSet(Xbins = cms.int32(60), Xmin = cms.double(-3.0),Xmax = cms.double(3.0)), trackPtH = cms.PSet(Xbins = cms.int32(100),Xmin = cms.double(0.0),Xmax = cms.double(100.0)) )

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1,198

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0.105263

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null

0

null

0

1

0

c0b773458653a85f2fb1e0a33ea41844604c6b4f

3,006

py

Python

xdl-algorithm-solution/DIN_WITH_MOGUJIE_DATA/script/train.py

xiaobaoding/x-deeplearning

1280043aba15ff57ac5e973bcce2489c698380d2

[ "Apache-2.0" ]

null

xdl-algorithm-solution/DIN_WITH_MOGUJIE_DATA/script/train.py

xiaobaoding/x-deeplearning

1280043aba15ff57ac5e973bcce2489c698380d2

[ "Apache-2.0" ]

null

xdl-algorithm-solution/DIN_WITH_MOGUJIE_DATA/script/train.py

xiaobaoding/x-deeplearning

1280043aba15ff57ac5e973bcce2489c698380d2

[ "Apache-2.0" ]

null

#coding=utf-8 # Copyright (C) 2016-2018 Alibaba Group Holding Limited # # 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 os import sys import time import math import random import argparse import tensorflow as tf import numpy from model import * from utils import * import xdl from xdl.python.training.train_session import QpsMetricsHook, MetricsPrinterHook #config here parser = argparse.ArgumentParser() parser.add_argument("-s", "--seed", help="random seed", default=3) parser.add_argument("-jt", "--job_type", help="'train' or 'test'", default='train') parser.add_argument("-m", "--model", help="'din' or 'dien'", default='din_mogujie') parser.add_argument("-si", "--save_interval", help="checkpoint save interval steps", default=20000) parser.add_argument("-dr", "--data_dir", help="data dir") args, unknown = parser.parse_known_args() seed = args.seed job_type = args.job_type model_type = args.model save_interval = args.save_interval def get_data_prefix(): return "../data/" #return args.data_dir train_file = os.path.join(get_data_prefix(), "train_data.tfrecords") def train(): if model_type == 'din_mogujie': model = Model_DIN_MOGUJIE( EMBEDDING_DIM, HIDDEN_SIZE, ATTENTION_SIZE,False, train_file,batch_size) else: raise Exception('only support din_mogujie and dien') #data set with xdl.model_scope('train'): train_ops = model.build_network() lr = 0.001 # Adam Adagrad train_ops.append(xdl.Adam(lr).optimize()) hooks = [] log_format = "[%(time)s] lstep[%(lstep)s] gstep[%(gstep)s] lqps[%(lqps)s] gqps[%(gqps)s] loss[%(loss)s]" hooks = [QpsMetricsHook(), MetricsPrinterHook(log_format)] if xdl.get_task_index() == 0: hooks.append(xdl.CheckpointHook(save_interval)) train_sess = xdl.TrainSession(hooks=hooks) """ with xdl.model_scope('test'): test_ops = model.build_network( EMBEDDING_DIM, is_train=False) test_sess = xdl.TrainSession() """ model.run(train_ops, train_sess) def test(): pass if __name__ == '__main__': SEED = seed if SEED is None: SEED = 3 tf.set_random_seed(SEED) numpy.random.seed(SEED) random.seed(SEED) if job_type == 'train': train() elif job_type == 'test': test() else: print('job type must be train or test, do nothing...')

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407

3,006

4.739558

0.434889

0.031104

0.044064

0.016589

0

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3,006

99

113

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0.051724

false

0.017241

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0.275862

0.017241

0

null

0

null

0

1

0

c0bb7b8a74c23f921be8c3f93658d3fa62727ccc

5,214

py

Python

input_fn.py

ilyakhov/pytorch-word2vec

bb9b0ed408a12e3652d2d897330292b7b93c7997

[ "MIT" ]

12

2019-05-22T13:08:42.000Z

2021-07-11T07:12:37.000Z

input_fn.py

ilyakhov/pytorch-word2vec

bb9b0ed408a12e3652d2d897330292b7b93c7997

[ "MIT" ]

null

input_fn.py

ilyakhov/pytorch-word2vec

bb9b0ed408a12e3652d2d897330292b7b93c7997

[ "MIT" ]

1

2021-02-20T09:04:19.000Z

import numpy as np import torch from torch.utils.data import Dataset class CBOWDataSet(Dataset): def __init__(self, corpus, pipeline='hier_softmax', nodes_index=None, turns_index=None, vocab_size=None, neg_samples=None, max_path_len=17, window_size=6, device=None, skip_target=False, dtype=torch.float32): """ :param corpus: the flat list of tokens :param pipeline: 'hier_softmax'/'neg_sampling' params for 'hierarchical softmax' pipeline: :param nodes_index: index of nodes from leaf parent to the root :param turns_index: the list of 1/-1 indices: 1 — the leaf is the left child of corresponding node -1 — the leaf is the right child :param vocab_size: is used for padding :param max_path_len: length of the longest path from word (leaf) to the root params for 'negative sampling' pipeline: :param neg_samples: the number of negative samples :param window_size: word context size :param device: cuda:0/cuda:1/cpu :param dtype: torch float type """ self.window_size = window_size self.step = window_size // 2 self.left_step = self.step self.right_step = window_size - self.step self.corpus = corpus[-self.left_step:] + corpus + \ corpus[:self.right_step] self.device = device self.dtype = dtype self.pipeline = pipeline if self.pipeline == 'hier_softmax': self.nodes_index = nodes_index self.max_path_len = max_path_len self.turns_index = turns_index self.vocab_size = vocab_size self.skip_target = skip_target elif self.pipeline == 'neg_sampling': self.np_corpus = np.array(self.corpus) self.neg_samples = neg_samples else: raise NotImplementedError( f'Pipeline for "pipeline": {self.pipeline}') def __len__(self): return len(self.corpus) - self.window_size def __getitem__(self, item): if self.pipeline == 'hier_softmax': return self.__h_getitem(item) elif self.pipeline == 'neg_sampling': return self.__n_getitem(item) else: raise NotImplementedError( f'__getitem__ for pipeline: {self.pipeline}') def __h_getitem(self, i): """ Hierarchical softmax pipepline :param i: item index :return: torch tensors: context, target, nodes, mask, turns_coeffs """ i += self.left_step target = self.corpus[i] context = self.corpus[(i - self.left_step):i] context += self.corpus[(i + 1):(i + self.right_step + 1)] try: assert len(context) == self.window_size except AssertionError: raise Exception( 'Context size is not valid: context - ' '{0} has size - {1}; window_size - {2}' .format(context, len(context), self.window_size) ) nodes = self.nodes_index[target] nodes_len = len(nodes) mask = np.zeros(self.max_path_len) mask[:nodes_len] = 1 pad_len = self.max_path_len - nodes_len nodes = np.concatenate([nodes, np.ones(pad_len) * self.vocab_size]) # nodes = np.concatenate([nodes, np.ones(pad_len) * -1]) nodes = torch.tensor(nodes, dtype=torch.long, device=self.device) turns_coeffs = self.turns_index.get(target) turns_coeffs = np.concatenate([turns_coeffs, np.zeros(pad_len)]) turns_coeffs = torch.tensor(turns_coeffs, dtype=self.dtype, device=self.device) mask = torch.tensor(mask, dtype=self.dtype, device=self.device) context = torch.tensor(context, dtype=torch.long, device=self.device) target = torch.tensor(target, dtype=torch.long, device=self.device) if self.skip_target is False: return context, target, nodes, mask, turns_coeffs else: return context, nodes, mask, turns_coeffs def __n_getitem(self, i): """ Negative sampling pipeline :param i: item index :return: torch tensors: context, target, neg_samples """ i += self.left_step target = self.corpus[i] context = self.corpus[(i - self.left_step):i] context += self.corpus[(i + 1):(i + self.right_step + 1)] try: assert len(context) == self.window_size except AssertionError: raise Exception( 'Context size is not valid: context - ' '{0} has size - {1}; window_size - {2}' .format(context, len(context), self.window_size) ) context = torch.tensor(context, dtype=torch.long, device=self.device) target = torch.tensor(target, dtype=torch.long, device=self.device) return context, target

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5,214

4.687196

0.176661

0.044952

0.038728

0.034578

0.414592

0.36065

0.301521

0.277317

0.245505

0

0.006869

0.329881

5,214

141

78

36.978723

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0

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0

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0

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1

0.053763

false

0

0.032258

0.010753

0.16129

0

null

0

null

0

1

0

c0c0cf39ce27029feb9aa7a105da2d19af17d25d

1,563

py

Python

delivery/services/external_program_service.py

mariya/arteria-delivery

ec2fd79cfc6047a44dd251183b971535e9afd0dc

[ "MIT" ]

null

delivery/services/external_program_service.py

mariya/arteria-delivery

ec2fd79cfc6047a44dd251183b971535e9afd0dc

[ "MIT" ]

18

2016-11-10T14:32:54.000Z

2019-10-14T07:07:54.000Z

delivery/services/external_program_service.py

mariya/arteria-delivery

ec2fd79cfc6047a44dd251183b971535e9afd0dc

[ "MIT" ]

6

2016-10-18T12:16:46.000Z

2019-09-11T11:38:17.000Z

from tornado.process import Subprocess from tornado import gen from subprocess import PIPE from delivery.models.execution import ExecutionResult, Execution class ExternalProgramService(object): """ A service for running external programs """ @staticmethod def run(cmd): """ Run a process and do not wait for it to finish :param cmd: the command to run as a list, i.e. ['ls','-l', '/'] :return: A instance of Execution """ p = Subprocess(cmd, stdout=PIPE, stderr=PIPE, stdin=PIPE) return Execution(pid=p.pid, process_obj=p) @staticmethod @gen.coroutine def wait_for_execution(execution): """ Wait for an execution to finish :param execution: instance of Execution :return: an ExecutionResult for the execution """ status_code = yield execution.process_obj.wait_for_exit(raise_error=False) out = execution.process_obj.stdout.read().decode('UTF-8') err = execution.process_obj.stderr.read().decode('UTF-8') return ExecutionResult(out, err, status_code) @staticmethod def run_and_wait(cmd): """ Run an external command and wait for it to finish :param cmd: the command to run as a list, i.e. ['ls','-l', '/'] :return: an ExecutionResult for the execution """ execution = ExternalProgramService.run(cmd) return ExternalProgramService.wait_for_execution(execution)

29.490566

82

0.621881

185

1,563

5.172973

0.345946

0.043887

0.040752

0.022989

0.194357

0.121212

0

0.001794

0.286628

1,563

52

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0.856502

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0

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0

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0

1

0.130435

false

0

0.173913

0

0.478261

0

null

0

null

0

1

0

c0c491c66e363814a85776c34ddeffc5e419a0b3

9,667

py

Python

xraydb/materials.py

chemmatcars/XModFit

7d1298448d1908d78797fd67ce0a00ecfaf17629

[ "MIT" ]

null

xraydb/materials.py

chemmatcars/XModFit

7d1298448d1908d78797fd67ce0a00ecfaf17629

[ "MIT" ]

null

xraydb/materials.py

chemmatcars/XModFit

7d1298448d1908d78797fd67ce0a00ecfaf17629

[ "MIT" ]

null

import os import numpy as np from collections import namedtuple from .chemparser import chemparse from .xray import mu_elam, atomic_mass from .utils import get_homedir _materials = None Material = namedtuple('Material', ('formula', 'density', 'name', 'categories')) def get_user_materialsfile(): """return name for user-specific materials.dat file With $HOME being the users home directory, this will be $HOME/.config/xraydb/materials.dat """ return os.path.join(get_homedir(), '.config', 'xraydb', 'materials.dat') def _read_materials_db(): """return _materials dictionary, creating it if needed""" global _materials if _materials is None: # initialize materials table _materials = {} def read_materialsfile(fname): with open(fname, 'r') as fh: lines = fh.readlines() for line in lines: line = line.strip() if len(line) > 2 and not line.startswith('#'): words = [i.strip() for i in line.split('|')] name = words[0].lower() formula = None if len(words) == 3: # older style # "name | formula | density" or "name | density | formula" iformula = 1 try: density = float(words[2]) except ValueError: density = float(words[1]) iformula = 2 formula = words[iformula] categories = [] elif len(words) == 4: # newer style, with categories density = float(words[1]) categories = [w.strip() for w in words[2].split(',')] formula = words[3] if formula is not None: formula = formula.replace(' ', '') _materials[name] = Material(formula, density, name, categories) # first, read from standard list local_dir, _ = os.path.split(__file__) fname = os.path.join(local_dir, 'materials.dat') if os.path.exists(fname): read_materialsfile(fname) # next, read from users materials file fname = get_user_materialsfile() if os.path.exists(fname): read_materialsfile(fname) return _materials def material_mu(name, energy, density=None, kind='total'): """X-ray attenuation length (in 1/cm) for a material by name or formula Args: name (str): chemical formul or name of material from materials list. energy (float or ndarray): energy or array of energies in eV density (None or float): material density (gr/cm^3). kind (str): 'photo' or 'total' for whether to return the photo-absorption or total cross-section ['total'] Returns: absorption length in 1/cm Notes: 1. material names are not case sensitive, chemical compounds are case sensitive. 2. mu_elam() is used for mu calculation. 3. if density is None and material is known, that density will be used. Examples: >>> material_mu('H2O', 10000.0) 5.32986401658495 """ global _materials if _materials is None: _materials = _read_materials_db() formula = None _density = None mater = _materials.get(name.lower(), None) if mater is None: for key, val in _materials.items(): if name.lower() == val[0].lower(): # match formula mater = val break # default to using passed in name as a formula if formula is None: if mater is None: formula = name else: formula = mater.formula if density is None and mater is not None: density = mater.density if density is None: raise Warning('material_mu(): must give density for unknown materials') mass_tot, mu = 0.0, 0.0 for elem, frac in chemparse(formula).items(): mass = frac * atomic_mass(elem) mu += mass * mu_elam(elem, energy, kind=kind) mass_tot += mass return density*mu/mass_tot def material_mu_components(name, energy, density=None, kind='total'): """material_mu_components: absorption coefficient (in 1/cm) for a compound Args: name (str): chemical formul or name of material from materials list. energy (float or ndarray): energy or array of energies in eV density (None or float): material density (gr/cm^3). kind (str): 'photo' or 'total'for whether to return photo-absorption or total cross-section ['total'] Returns: dict for constructing mu per element, with elements 'mass' (total mass), 'density', and 'elements' (list of atomic symbols for elements in material). For each element, there will be an item (atomic symbol as key) with tuple of (stoichiometric fraction, atomic mass, mu) Examples: >>> xraydb.material_mu('quartz', 10000) 50.36774553547068 >>> xraydb.material_mu_components('quartz', 10000) {'mass': 60.0843, 'density': 2.65, 'elements': ['Si', 'O'], 'Si': (1, 28.0855, 33.87943243018506), 'O': (2.0, 15.9994, 5.952824815297084)} """ global _materials if _materials is None: _materials = _read_materials_db() mater = _materials.get(name.lower(), None) if mater is None: formula = name if density is None: raise Warning('material_mu(): must give density for unknown materials') else: formula = mater.formula density = mater.density out = {'mass': 0.0, 'density': density, 'elements':[]} for atom, frac in chemparse(formula).items(): mass = atomic_mass(atom) mu = mu_elam(atom, energy, kind=kind) out['mass'] += frac*mass out[atom] = (frac, mass, mu) out['elements'].append(atom) return out def get_material(name): """look up material name, return formula and density Args: name (str): name of material or chemical formula Returns: chemical formula, density of material Examples: >>> xraydb.get_material('kapton') ('C22H10N2O5', 1.43) See Also: find_material() """ material = find_material(name) if material is None: return None return material.formula, material.density def find_material(name): """look up material name, return material instance Args: name (str): name of material or chemical formula Returns: material instance Examples: >>> xraydb.find_material('kapton') Material(formula='C22H10N2O5', density=1.42, name='kapton', categories=['polymer']) See Also: get_material() """ global _materials if _materials is None: _materials = _read_materials_db() mat = _materials.get(name.lower(), None) if mat is not None: return mat for mat in _materials.values(): if mat.formula == name: return mat return None def get_materials(force_read=False, categories=None): """get dictionary of all available materials Args: force_read (bool): whether to force a re-reading of the materials database [False] categories (list of strings or None): restrict results to those that match category names Returns: dict with keys of material name and values of Materials instances Examples: >>> for name, m in xraydb.get_materials().items(): ... print(name, m) ... water H2O 1.0 lead Pb 11.34 aluminum Al 2.7 kapton C22H10N2O5 1.42 polyimide C22H10N2O5 1.42 nitrogen N 0.00125 argon Ar 0.001784 ... """ global _materials if force_read or _materials is None: _materials = _read_materials_db() return _materials def add_material(name, formula, density, categories=None): """add a material to the users local material database Args: name (str): name of material formula (str): chemical formula density (float): density categories (list of strings or None): list of category names Returns: None Notes: the data will be saved to $HOME/.config/xraydb/materials.dat in the users home directory, and will be useful in subsequent sessions. Examples: >>> xraydb.add_material('becopper', 'Cu0.98e0.02', 8.3, categories=['metal']) """ global _materials if _materials is None: _materials = _read_materials_db() formula = formula.replace(' ', '') if categories is None: categories = [] _materials[name.lower()] = Material(formula, float(density), name, categories) fname = get_user_materialsfile() if os.path.exists(fname): fh = open(fname, 'r') text = fh.readlines() fh.close() else: parent, _ = os.path.split(fname) if not os.path.exists(parent): try: os.makedirs(parent) except FileExistsError: pass text = ['# user-specific database of materials\n', '# name | density | categories | formulan'] catstring = ', '.join(categories) text.append(" %s | %g | %s | %s\n" % (name, density, catstring, formula)) with open(fname, 'w') as fh: fh.write(''.join(text))

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false

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0

0.177305

0

null

0

null

0

1

0

c0c62d4eee91d75a65403ff152657c9c03089c57

1,069

py

Python

client.py

simondlevy/sockets

f49dd677b6508859f01c9c54101b38e802d6370e

[ "MIT" ]

null

client.py

simondlevy/sockets

f49dd677b6508859f01c9c54101b38e802d6370e

[ "MIT" ]

null

client.py

simondlevy/sockets

f49dd677b6508859f01c9c54101b38e802d6370e

[ "MIT" ]

1

2018-06-12T03:32:26.000Z

#!/usr/bin/env python3 ''' Server script for simple client/server example Copyright (C) Simon D. Levy 2021 MIT License ''' from threading import Thread from time import sleep import socket from struct import unpack from header import ADDR, PORT def comms(data): ''' Communications thread ''' # Connect to the client sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect((ADDR, PORT)) # Loop until main thread quits while True: # Receive and unpack three floating-point numbers data[0], data[1], data[2] = unpack('=fff', sock.recv(12)) # Yield to the main thread sleep(0.001) def main(): # Create a list to receiver the data data = [0, 0, 0] # Start the client on its own thread t = Thread(target=comms, args=(data,)) t.setDaemon(True) t.start() # Loop until user hits CTRL-C while True: try: print('%3.3f %3.3f %3.3f ' % tuple(data)) sleep(.01) except KeyboardInterrupt: break main()

18.431034

65

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148

1,069

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66

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0.086957

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null

0

null

0

1

0

c0c6c96cefa40fab2593e89ee811e26649ffff4f

15,126

py

Python

old/compute_T.py

azhan137/cylinder_t_matrix

73a496c07dbbb02896b2baf727d452765da9aac3

[ "MIT" ]

1

2022-03-18T11:52:36.000Z

old/compute_T.py

AmosEgel/cylinder_t_matrix

78f6607993af5babdda384969c45cf3ac6461257

[ "MIT" ]

null

old/compute_T.py

AmosEgel/cylinder_t_matrix

78f6607993af5babdda384969c45cf3ac6461257

[ "MIT" ]

1

2020-12-07T13:11:00.000Z

import numpy as np from numpy.polynomial import legendre from smuthi import spherical_functions as sf import bessel_functions as bf ##Codebase for computing the T-matrix and its derivative with respect to height and radius for a cylindrical scatterer # with circular cross-section in spherical coordinates. # # inputs: # lmax: maximum orbital angular momentum expansion order, an integer # Ntheta: number of sections for discretization # geometric_params: radius (0) and height (1) in an array # n0: refractive index of medium # ns: refractive index of scatterer # wavelength: excitation wavelength # particle_type: shape of particle (cylinder, ellipsoid, etc) def compute_T(lmax, Ntheta, geometric_params, n0, ns, wavelength, particle_type): [Q, dQ] = compute_Q(lmax, Ntheta, geometric_params, n0, ns, wavelength, 3, particle_type) [rQ, drQ] = compute_Q(lmax, Ntheta, geometric_params, n0, ns, wavelength, 1, particle_type) Qinv = np.linalg.inv(Q) T = rQ*Qinv dT = np.zeros((np.shape(drQ))) num_geometric_params = np.size(geometric_params) for geometric_idx in np.arange(0, num_geometric_params): dT[:, :, geometric_idx] = np.matmul(drQ[:, :, geometric_idx] - np.matmul(T, dQ[:, :, geometric_idx]), Qinv) return T, dT def compute_Q(lmax, Ntheta, geometric_params, n0, ns, wavelength, nu, particle_type): if particle_type is 'cylinder': a = geometric_params[0] h = geometric_params[1] [J11, J12, J21, J22, dJ11, dJ12, dJ21, dJ22] = compute_J_cyl(lmax, Ntheta, a, h, n0, ns, wavelength, nu) elif particle_type is 'ellipsoid': print('ellipsoid not supported') else: print('particle type ' + particle_type + ' not supported.') return 0 ki = 2*np.pi*n0/wavelength ks = 2*np.pi*ns/wavelength P = -1j * ki * (ks * J21 + ki * J12) R = -1j * ki * (ks * J11 + ki * J22) S = -1j * ki * (ks * J22 + ki * J11) U = -1j * ki * (ks * J12 + ki * J21) dP = -1j * ki * (ks * dJ21 + ki * dJ12) dR = -1j * ki * (ks * dJ11 + ki * dJ22) dS = -1j * ki * (ks * dJ22 + ki * dJ11) dU = -1j * ki * (ks * dJ12 + ki * dJ21) Q = np.block([ [P, R], [S, U] ]) nmax = np.size(Q[:, 1]) num_geometric_params = np.size(geometric_params) dQ = np.zeros((nmax, nmax, num_geometric_params)) for geometric_idx in np.arange(0, num_geometric_params): dQ[:, :, geometric_idx] = np.block([ [dP[:, :, geometric_idx], dR[:, :, geometric_idx]], [dS[:, :, geometric_idx], dU[:, :, geometric_idx]] ]) return Q, dQ #function that computes the J surface integrals and their derivatives with respect to cylinder radius (a) and cylinder # height (h). Expands up to a specified lmax, and approximates the integrals using gaussian quadrature with Ntheta # points for the two integrals required. # n0 is refractive index of medium # ns is refractive index of scatterer # wavelength is illumination wavelength # nu = 1 or 3 # 1: b_li are the spherical Bessel functions of the first kind (j_n(x)) # involved in rQ and drQ computation # 3: b_li are the spherical Hankel functions of the first kind (h_n(x)) # involved in Q and dQ computation #care should be taken to expand lmax to sufficient order, #where lmax should be greater than (ns-n_0)*max(2*a,h)/wavelength def compute_J_cyl(lmax, Ntheta, a, h, n0, ns, wavelength, nu): #dimension of final T-matrix is 2*nmax x 2*nmax for each individual matrix nmax = int(lmax*(lmax+2)) #preallocate space for both J and dJ matrices of size nmax x nmax for J matrices #and dJ matrices are nmax x nmax x 2 #dJ[:,:,0] is dJ/da #dJ[:,:,1] is dJ/dh J11 = np.zeros((nmax, nmax), dtype=np.complex_) J12 = np.zeros((nmax, nmax), dtype=np.complex_) J21 = np.zeros((nmax, nmax), dtype=np.complex_) J22 = np.zeros((nmax, nmax), dtype=np.complex_) dJ11 = np.zeros((nmax, nmax, 2), dtype=np.complex_) dJ12 = np.zeros((nmax, nmax, 2), dtype=np.complex_) dJ21 = np.zeros((nmax, nmax, 2), dtype=np.complex_) dJ22 = np.zeros((nmax, nmax, 2), dtype=np.complex_) #find the angle theta at which the corner of the cylinder is at theta_edge = np.arctan(2*a/h) #prepare gauss-legendre quadrature for interval of [-1,1] to perform numerical integral [x_norm, wt_norm] = legendre.leggauss(Ntheta) #rescale integration points and weights to match actual bounds: # circ covers the circular surface of the cylinder (end caps) # body covers the rectangular surface of the cylinder (body area) #circ integral goes from 0 to theta_edge, b = theta_edge, a = 0 theta_circ = theta_edge/2*x_norm+theta_edge/2 wt_circ = theta_edge/2*wt_norm #body integral goes from theta_edge to pi/2, b = pi/2, a = theta_edge theta_body = (np.pi/2-theta_edge)/2*x_norm+(np.pi/2+theta_edge)/2 wt_body = (np.pi/2-theta_edge)/2*wt_norm #merge the circ and body lists into a single map theta_map = np.concatenate((theta_circ, theta_body), axis=0) weight_map = np.concatenate((wt_circ, wt_body), axis=0) #identify indices corresponding to the circular end caps and rectangular body circ_idx = np.arange(0, Ntheta) body_idx = np.arange(Ntheta, 2*Ntheta) #k vectors of the light in medium (ki) and in scatterer (ks) ki = 2*np.pi*n0/wavelength ks = 2*np.pi*ns/wavelength #precompute trig functions ct = np.cos(theta_map) st = np.sin(theta_map) #normal vector for circular surface (circ) requires tangent tant = np.tan(theta_map[circ_idx]) #normal vector for rectangular surface (body) requires cotangent cott = 1/np.tan(theta_map[body_idx]) #precompute spherical angular polynomials [p_lm, pi_lm, tau_lm] = sf.legendre_normalized(ct, st, lmax) #radial coordinate of the surface, and the derivatives with respect to a and h #r_c: radial coordinate of circular end cap #r_b: radial coordinate of rectangular body r_c = h/2/ct[circ_idx] dr_c = r_c/h r_b = a/st[body_idx] dr_b = r_b/a #merge radial coordiantes into a single vector r = np.concatenate((r_c, r_b), axis=0) #derivatives of the integration limits for performing derivatives da_edge = 2*h/(h**2+4*a**2) dh_edge = -2*a/(h**2+4*a**2) #loop through each individual element of the J11, J12, J21, J22 matrices for li in np.arange(1, lmax+1): #precompute bessel functiosn and derivatives b_li = bf.sph_bessel(nu, li, ki*r) db_li = bf.d1Z_Z_sph_bessel(nu, li, ki*r) db2_li = bf.d2Z_Z_sph_bessel(nu, li, ki*r) d1b_li = bf.d1Z_sph_bessel(nu, li, ki*r) for lp in np.arange(1, lmax+1): #precompute bessel functions and derivatives j_lp = bf.sph_bessel(1, lp, ks*r) dj_lp = bf.d1Z_Z_sph_bessel(1, lp, ks*r) dj2_lp = bf.d2Z_Z_sph_bessel(1, lp, ks*r) d1j_lp = bf.d1Z_sph_bessel(1, lp, ks*r) #compute normalization factor lfactor = 1/np.sqrt(li*(li+1)*lp*(lp+1)) for mi in np.arange(-li, li+1): #compute row index where element is placed n_i = compute_n(lmax, 1, li, mi)-1 #precompute spherical harmonic functions p_limi = p_lm[li][abs(mi)] pi_limi = pi_lm[li][abs(mi)] tau_limi = tau_lm[li][abs(mi)] for mp in np.arange(-lp, lp+1): #compute col index where element is placed n_p = compute_n(lmax, 1, lp, mp)-1 #precompute spherical harmonic functions p_lpmp = p_lm[lp][abs(mp)] pi_lpmp = pi_lm[lp][abs(mp)] tau_lpmp = tau_lm[lp][abs(mp)] #compute selection rules that includes symmetries sr_1122 = selection_rules(li, mi, lp, mp, 1) sr_1221 = selection_rules(li, mi, lp, mp, 2) #perform integral about phi analytically. This is roughly a sinc function if mi == mp: phi_exp = np.pi else: phi_exp = -1j*(np.exp(1j*(mp-mi)*np.pi)-1)/(mp-mi) #for J11 and J22 integrals if sr_1122 != 0: prefactor = sr_1122*lfactor*phi_exp ang = mp*pi_lpmp*tau_limi+mi*pi_limi*tau_lpmp J11_r = -1j*weight_map*prefactor*r**2*st*j_lp*b_li*ang J11[n_i, n_p] = np.sum(J11_r) dJ11dr = 2*r*j_lp*b_li+r**2*(ks*d1j_lp*b_li+ki*d1b_li*j_lp) dJ11[n_i, n_p, 0] = np.sum(-1j*prefactor*weight_map[body_idx]*st[body_idx]*dJ11dr[body_idx]*ang[body_idx]*dr_b) dJ11[n_i, n_p, 1] = np.sum(-1j*prefactor*weight_map[circ_idx]*st[circ_idx]*dJ11dr[circ_idx]*ang[circ_idx]*dr_c) J22_r = -1j*prefactor*weight_map*st/ki/ks*dj_lp*db_li*ang J22_db = lp*(lp+1)*mi*pi_limi*p_lpmp J22_dj = li*(li+1)*mp*pi_lpmp*p_limi J22_t = -1j*prefactor*weight_map*st/ki/ks*(J22_db*j_lp*db_li+J22_dj*b_li*dj_lp) J22[n_i, n_p] = sum(J22_r)+sum(J22_t[circ_idx]*tant)+sum(J22_t[body_idx]*-cott) dJ22edge = st[Ntheta]*(J22_db[Ntheta]*j_lp[Ntheta]*db_li[Ntheta]+J22_dj[Ntheta]*dj_lp[Ntheta]*b_li[Ntheta])*(st[Ntheta]/ct[Ntheta]+ct[Ntheta]/st[Ntheta]) dJ22da1 = -1j/ki/ks*(ks*dj2_lp[body_idx]*db_li[body_idx]+ki*db2_li[body_idx]*dj_lp[body_idx])*dr_b*st[body_idx]*ang[body_idx] dJ22da2 = 1j/ki/ks*cott*st[body_idx]*dr_b*(J22_db[body_idx]*(ks*d1j_lp[body_idx]*db_li[body_idx]+ki*j_lp[body_idx]*db2_li[body_idx])+J22_dj[body_idx]*(ki*d1b_li[body_idx]*dj_lp[body_idx]+ks*dj2_lp[body_idx]*b_li[body_idx])) dJ22dh1 = -1j/ki/ks*(ks*dj2_lp[circ_idx]*db_li[circ_idx]+ki*db2_li[circ_idx]*dj_lp[circ_idx])*dr_c*st[circ_idx]*ang[circ_idx] dJ22dh2 = -1j/ki/ks*tant*st[circ_idx]*dr_c*(J22_db[circ_idx]*(ks*d1j_lp[circ_idx]*db_li[circ_idx]+ki*j_lp[circ_idx]*db2_li[circ_idx])+J22_dj[circ_idx]*(ki*d1b_li[circ_idx]*dj_lp[circ_idx]+ks*dj2_lp[circ_idx]*b_li[circ_idx])) dJ22[n_i, n_p, 0] = np.sum(prefactor*weight_map[body_idx]*dJ22da1)+np.sum(prefactor*weight_map[body_idx]*dJ22da2)+prefactor*dJ22edge*da_edge dJ22[n_i, n_p, 1] = np.sum(prefactor*weight_map[circ_idx]*dJ22dh1)+np.sum(prefactor*weight_map[circ_idx]*dJ22dh2)+prefactor*dJ22edge*dh_edge #for J12 and J21 integrals if sr_1221 != 0: prefactor = sr_1221*lfactor*phi_exp ang = mi*mp*pi_limi*pi_lpmp+tau_limi*tau_lpmp J12_r = prefactor*weight_map/ki*r*st*j_lp*db_li*ang J12_t = prefactor*weight_map/ki*r*st*li*(li+1)*j_lp*b_li*p_limi*tau_lpmp J12[n_i, n_p] = np.sum(J12_r)+np.sum(J12_t[circ_idx]*tant)+np.sum(J12_t[body_idx]*-cott) dJ12edge = li*(li+1)/ki/r[Ntheta]*st[Ntheta]*j_lp[Ntheta]*b_li[Ntheta]*tau_lpmp[Ntheta]*p_limi[Ntheta]*(st[Ntheta]/ct[Ntheta]+ct[Ntheta]/st[Ntheta]) dJ12da1 = dr_b/ki*(j_lp[body_idx]*db_li[body_idx]+r_b*(ks*d1j_lp[body_idx]*b_li[body_idx]+ki*j_lp[body_idx]*d1b_li[body_idx]))*st[body_idx]*ang[body_idx] dJ12da2 = -li*(li+1)/ki*dr_b*(j_lp[body_idx]*b_li[body_idx]+r_b*(ks*d1j_lp[body_idx]*b_li[body_idx]+ki*j_lp[body_idx]*d1b_li[body_idx]))*cott*st[body_idx]*tau_lpmp[body_idx]*p_limi[body_idx] dJ12dh1 = dr_c/ki*(j_lp[circ_idx]*db_li[circ_idx]+r_c*(ks*d1j_lp[circ_idx]*b_li[circ_idx]+ki*j_lp[circ_idx]*d1b_li[circ_idx]))*st[circ_idx]*ang[circ_idx] dJ12dh2 = li*(li+1)/ki*dr_c*(j_lp[circ_idx]*b_li[circ_idx]+r_c*(ks*d1j_lp[circ_idx]*b_li[circ_idx]+ki*j_lp[circ_idx]*d1b_li[circ_idx]))*tant*st[circ_idx]*tau_lpmp[circ_idx]*p_limi[circ_idx] dJ12[n_i, n_p, 0] = np.sum(prefactor*weight_map[body_idx]*dJ12da1)+np.sum(prefactor*weight_map[body_idx]*dJ12da2)+prefactor*dJ12edge*da_edge dJ12[n_i, n_p, 1] = np.sum(prefactor*weight_map[circ_idx]*dJ12dh1)+np.sum(prefactor*weight_map[body_idx]*dJ12da2)+prefactor*dJ12edge*dh_edge J21_r = -prefactor*weight_map/ks*r*st*dj_lp*b_li*ang J21_t = -prefactor*weight_map/ks*r*st*lp*(lp+1)*j_lp*b_li*p_lpmp*tau_limi J21[n_i, n_p] = np.sum(J21_r)+np.sum(J21_t[circ_idx]*tant)+np.sum(J21_t[body_idx]*-cott) dJ21edge = -lp*(lp+1)/ks/r[Ntheta]*st[Ntheta]*j_lp[Ntheta]*b_li[Ntheta]*tau_lpmp[Ntheta]*p_limi[Ntheta]*(st[Ntheta]/ct[Ntheta]+ct[Ntheta]/st[Ntheta]) dJ21da1 = -dr_b/ks*(b_li[body_idx]*dj_lp[body_idx]+r_b*(ki*d1b_li[body_idx]*dj_lp[body_idx]+ks*dj2_lp[body_idx]*b_li[body_idx]))*st[body_idx]*ang[body_idx] dJ21da2 = lp*(lp+1)/ks*dr_b*(j_lp[body_idx]*b_li[body_idx]+r_b*(ks*d1j_lp[body_idx]*b_li[body_idx]+ki*d1b_li[body_idx]*j_lp[body_idx]))*cott*st[body_idx]*tau_limi[body_idx]*p_lpmp[body_idx] dJ21dh1 = -dr_c/ks*(b_li[circ_idx]*dj_lp[circ_idx]+r_c*(ki*d1b_li[circ_idx]*dj_lp[circ_idx]+ks*dj2_lp[circ_idx]*b_li[circ_idx]))*st[circ_idx]*ang[circ_idx] dJ21dh2 = -lp*(lp+1)/ks*dr_c*(j_lp[circ_idx]*b_li[circ_idx]+r_c*(ks*d1j_lp[circ_idx]*b_li[circ_idx]+ki*d1b_li[circ_idx]*j_lp[circ_idx]))*tant*st[circ_idx]*tau_limi[circ_idx]*p_lpmp[circ_idx] dJ21[n_i, n_p, 0] = np.sum(prefactor*weight_map[body_idx]*dJ21da1)+np.sum(prefactor*weight_map[body_idx]*dJ21da2)+prefactor*dJ21edge*da_edge dJ21[n_i, n_p, 1] = np.sum(prefactor*weight_map[circ_idx]*dJ21dh1)+np.sum(prefactor*weight_map[circ_idx]*dJ21dh2)+prefactor*dJ21edge*dh_edge return J11, J12, J21, J22, dJ11, dJ12, dJ21, dJ22 #compute n index (single index) for matrix element given its p (polarization), l (orbital angular momementum index), # and m (azimuthal angular momentum index. def compute_n(lmax, p, l, m): return (p-1)*lmax*(lmax+2)+(l-1)*(l+1)+m+l+1 #selection rules taking into account different symmetries for an axisymmetric particle def selection_rules(li, mi, lp, mp, diag_switch): if diag_switch == 1: return np.float_power(-1, mi)*(1+np.float_power(-1, mp-mi))*(1+(-1)**(lp+li+1)) elif diag_switch == 2: return np.float_power(-1, mi)*(1+np.float_power(-1, mp-mi))*(1+(-1)**(lp+li)) else: return 0 if __name__ == '__main__': import matplotlib.pyplot as plt cyl_params = np.array([500,860]) [J11, J12, J21, J22, dJ11, dJ12, dJ21, dJ22] = compute_J_cyl(3,30,200,460,1,1.52,1000,3) [T, dT] = compute_T(6,30,cyl_params,1,4,1000,'cylinder') img1 = plt.imshow(np.abs(T)) plt.colorbar() plt.title('T') plt.show()

52.703833

248

0.628719

2,550

15,126

3.514902

0.132157

0.053107

0.040165

0.005355

0.425639

0.373201

0.314627

0.230838

0.19826

0.165904

0

0.046327

0.235092

15,126

286

249

52.888112

0.728349

0.221671

0

0.088235

0

0.007345

0

1

0.029412

false

0

0.029412

0.005882

0.105882

0.011765

0

null

0

null

0

1

0

c0c7d98ec94365b9cf9f0e166a19f7b2371bc3ed

982

py

Python

run_tests.py

aquarioos/dvik-print

b897936168dab51c9e0f9fd84993065428896be4

[ "MIT" ]

1

2018-09-19T22:27:32.000Z

run_tests.py

aquarioos/dvik-print

b897936168dab51c9e0f9fd84993065428896be4

[ "MIT" ]

null

run_tests.py

aquarioos/dvik-print

b897936168dab51c9e0f9fd84993065428896be4

[ "MIT" ]

null

# -*- coding: utf8 -*- from __future__ import division, absolute_import, print_function import os import sys import datetime as dt import dvik_print as dvp if __name__ == '__main__': print(sys.version) O = { 'lista': ['el1', 'el2', 1, 2, 3, 4, None, False], 'zbiór': {1, 2, 1, 2, 'a', 'a', 'b', 'b'}, 'krotka': ('oto', 'elementy', 'naszej', 'krotki'), ('krotka', 'klucz'): { 'klucz1': ['jakaś', 'lista', 123], 'klucz2': dt.datetime.now(), 'klucz3': dt }, (123, 'asd'): {123, 234, 345}, (123, 'asd1'): (123, 234, 345) } # deklarujemy obiekt dvp.PrettyPrint pp = dvp.PrettyPrint(tab=2, head=3, tail=2, max_str_len=50, show_line=True, filename=__file__) # obiekt jest wywoływalny # w ten sposób wypisze na # standardowe wyjście obiekt O pp(O, var='zmienna') # można użyć wartości domyślnych pp_domyslny = dvp.PrettyPrint() pp_domyslny(O)

26.540541

98

0.566191

124

982

4.290323

0.653226

0.011278

0.033835

0

0.065278

0.266802

982

36

99

27.277778

0.673611

0.165988

0

0.135468

0

1

0

false

0

0.217391

0

0.217391

0.130435

0

null

0

null

0

1

0

c0c85207554af0054a2d3560e6e8d9cb080608eb

6,200

py

Python

nwb_conversion_tools/datainterfaces/ecephys/basesortingextractorinterface.py

miketrumpis/nwb-conversion-tools

4d5c270b70eb4f1c09f98a6c04b51ccdf20336c1

[ "BSD-3-Clause" ]

null

nwb_conversion_tools/datainterfaces/ecephys/basesortingextractorinterface.py

miketrumpis/nwb-conversion-tools

4d5c270b70eb4f1c09f98a6c04b51ccdf20336c1

[ "BSD-3-Clause" ]

null

nwb_conversion_tools/datainterfaces/ecephys/basesortingextractorinterface.py

miketrumpis/nwb-conversion-tools

4d5c270b70eb4f1c09f98a6c04b51ccdf20336c1

[ "BSD-3-Clause" ]

null

"""Authors: Cody Baker and Ben Dichter.""" from abc import ABC from pathlib import Path import spikeextractors as se import numpy as np from pynwb import NWBFile, NWBHDF5IO from pynwb.ecephys import SpikeEventSeries from jsonschema import validate from ...basedatainterface import BaseDataInterface from ...utils.json_schema import ( get_schema_from_hdmf_class, get_base_schema, get_schema_from_method_signature, fill_defaults, ) from ...utils.common_writer_tools import default_export_ops, default_export_ops_schema from ...utils import export_ecephys_to_nwb from .baserecordingextractorinterface import BaseRecordingExtractorInterface, map_si_object_to_writer, OptionalPathType class BaseSortingExtractorInterface(BaseDataInterface, ABC): """Primary class for all SortingExtractor intefaces.""" SX = None def __init__(self, **source_data): super().__init__(**source_data) self.sorting_extractor = self.SX(**source_data) self.writer_class = map_si_object_to_writer(self.sorting_extractor)(self.sorting_extractor) def get_metadata_schema(self): """Compile metadata schema for the RecordingExtractor.""" metadata_schema = super().get_metadata_schema() # Initiate Ecephys metadata metadata_schema["properties"]["Ecephys"] = get_base_schema(tag="Ecephys") metadata_schema["properties"]["Ecephys"]["required"] = [] metadata_schema["properties"]["Ecephys"]["properties"] = dict( UnitProperties=dict( type="array", minItems=0, renderForm=False, items={"$ref": "#/properties/Ecephys/properties/definitions/UnitProperties"}, ), ) # Schema definition for arrays metadata_schema["properties"]["Ecephys"]["properties"]["definitions"] = dict( UnitProperties=dict( type="object", additionalProperties=False, required=["name"], properties=dict( name=dict(type="string", description="name of this units column"), description=dict(type="string", description="description of this units column"), ), ), ) return metadata_schema def subset_sorting(self): """ Subset a recording extractor according to stub and channel subset options. Parameters ---------- stub_test : bool, optional (default False) """ self.writer_class = map_si_object_to_writer(self.sorting_extractor)( self.sorting_extractor, stub=True, ) def run_conversion( self, nwbfile: NWBFile, metadata: dict, stub_test: bool = False, write_ecephys_metadata: bool = False, save_path: OptionalPathType = None, overwrite: bool = False, **kwargs, ): """ Primary function for converting the data in a SortingExtractor to the NWB standard. Parameters ---------- nwbfile: NWBFile nwb file to which the recording information is to be added metadata: dict metadata info for constructing the nwb file (optional). Should be of the format metadata['Ecephys']['UnitProperties'] = dict(name=my_name, description=my_description) stub_test: bool, optional (default False) If True, will truncate the data to run the conversion faster and take up less memory. write_ecephys_metadata: bool (optional, defaults to False) Write electrode information contained in the metadata. save_path: PathType Required if an nwbfile is not passed. Must be the path to the nwbfile being appended, otherwise one is created and written. overwrite: bool If using save_path, whether or not to overwrite the NWBFile if it already exists. skip_unit_features: list list of unit feature names to skip writing to units table. skip_unit_properties: list list of unit properties to skip writing to units table. unit_property_descriptions: dict custom descriptions for unit properties: >>> dict(prop_name='description') the Other way to add custom descrptions is to override the default metadata: >>> metadata = self.get_metadata() >>> metadata["Ecephys"] = dict() >>> metadata["Ecephys"].update(UnitProperties=[dict(name='prop_name1', description='description1'), >>> dict(name='prop_name1', description='description1')]) """ if stub_test: self.subset_sorting() if write_ecephys_metadata and "Ecephys" in metadata: class TempEcephysInterface(BaseRecordingExtractorInterface): RX = se.NumpyRecordingExtractor n_channels = max([len(x["data"]) for x in metadata["Ecephys"]["Electrodes"]]) temp_ephys = TempEcephysInterface(timeseries=np.array(range(n_channels)), sampling_frequency=1) temp_ephys.run_conversion(nwbfile=nwbfile, metadata=metadata, write_electrical_series=False) conversion_opts = default_export_ops() conversion_opts.update(**kwargs) # construct unit property descriptions: property_descriptions = dict() for metadata_column in metadata.get("Ecephys", dict()).get("UnitProperties", []): property_descriptions.update({metadata_column["name"]: metadata_column["description"]}) conversion_opts["unit_property_descriptions"].update(property_descriptions) conversion_opt_schema = default_export_ops_schema() validate(instance=conversion_opts, schema=conversion_opt_schema) self.writer_class.add_to_nwb(nwbfile, metadata, **conversion_opts) if save_path is not None: if overwrite: if Path(save_path).exists(): Path(save_path).unlink() with NWBHDF5IO(str(save_path), mode="w") as io: io.write(self.writer_class.nwbfile)

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null

0

null

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0

c0c8cb69c19ab4dd40d043117a7822abefc679ef

1,711

py

Python

buildscripts/resmokelib/testing/testcases/cpp_libfuzzer_test.py

benety/mongo

203430ac9559f82ca01e3cbb3b0e09149fec0835

[ "Apache-2.0" ]

null

buildscripts/resmokelib/testing/testcases/cpp_libfuzzer_test.py

benety/mongo

203430ac9559f82ca01e3cbb3b0e09149fec0835

[ "Apache-2.0" ]

null

buildscripts/resmokelib/testing/testcases/cpp_libfuzzer_test.py

benety/mongo

203430ac9559f82ca01e3cbb3b0e09149fec0835

[ "Apache-2.0" ]

null

"""The libfuzzertest.TestCase for C++ libfuzzer tests.""" import datetime import os from buildscripts.resmokelib import core from buildscripts.resmokelib import utils from buildscripts.resmokelib.testing.fixtures import interface as fixture_interface from buildscripts.resmokelib.testing.testcases import interface class CPPLibfuzzerTestCase(interface.ProcessTestCase): """A C++ libfuzzer test to execute.""" REGISTERED_NAME = "cpp_libfuzzer_test" DEFAULT_TIMEOUT = datetime.timedelta(hours=1) def __init__( # pylint: disable=too-many-arguments self, logger, program_executable, program_options=None, runs=1000000, corpus_directory_stem="corpora"): """Initialize the CPPLibfuzzerTestCase with the executable to run.""" interface.ProcessTestCase.__init__(self, logger, "C++ libfuzzer test", program_executable) self.program_executable = program_executable self.program_options = utils.default_if_none(program_options, {}).copy() self.runs = runs self.corpus_directory = f"{corpus_directory_stem}/corpus-{self.short_name()}" self.merged_corpus_directory = f"{corpus_directory_stem}-merged/corpus-{self.short_name()}" os.makedirs(self.corpus_directory, exist_ok=True) def _make_process(self): default_args = [ self.program_executable, "-max_len=100000", "-rss_limit_mb=5000", "-max_total_time=3600", # 1 hour is the maximum amount of time to allow a fuzzer to run f"-runs={self.runs}", self.corpus_directory, ] return core.programs.make_process(self.logger, default_args, **self.program_options)

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null

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null

0

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0

c0c9967167f2ebbfb12ea4280bc6aa6f0ee2cebd

1,278

py

Python

data_curation/genome_annotations/preprocess_SEA.py

talkowski-lab/rCNV2

fcc1142d8c13b58d18a37fe129e9bb4d7bd6641d

[ "MIT" ]

7

2021-01-28T15:46:46.000Z

2022-02-07T06:50:40.000Z

data_curation/genome_annotations/preprocess_SEA.py

talkowski-lab/rCNV2

fcc1142d8c13b58d18a37fe129e9bb4d7bd6641d

[ "MIT" ]

1

2021-03-02T01:33:53.000Z

data_curation/genome_annotations/preprocess_SEA.py

talkowski-lab/rCNV2

fcc1142d8c13b58d18a37fe129e9bb4d7bd6641d

[ "MIT" ]

3

2021-02-21T19:49:12.000Z

2021-12-22T15:56:21.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (c) 2020 Ryan L. Collins <rlcollins@g.harvard.edu> # and the Talkowski Laboratory # Distributed under terms of the MIT license. """ Parse simple SEA super-enhancer BED by cell types """ import argparse import csv import subprocess def main(): """ Main block """ # Parse command line arguments and options parser = argparse.ArgumentParser( description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument('bed', help='Path to BED4 of super enhancers') parser.add_argument('outdir', help='Output directory') args = parser.parse_args() outfiles = {} with open(args.bed) as fin: for chrom, start, end, source in csv.reader(fin, delimiter='\t'): source = source.replace(' ', '_').replace('(', '').replace(')', '') if source not in outfiles.keys(): outfiles[source] = open('{}/SEA.{}.bed'.format(args.outdir, source), 'w') outfiles[source].write('\t'.join([chrom, start, end]) + '\n') for outfile in outfiles.values(): outpath = outfile.name outfile.close() subprocess.run(['bgzip', '-f', outpath]) if __name__ == '__main__': main()

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null

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null

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0

c0cba6784c6a4d07543a90ca7bc4b5a773c81fe7

2,461

py

Python

src/transforms/imageCropDivide/dev/generate_nodenk.py

MrLixm/Foundry_Nuke

078115043b6a4c09bdcf1b5031e995ef296bd604

[ "Apache-2.0" ]

null

src/transforms/imageCropDivide/dev/generate_nodenk.py

MrLixm/Foundry_Nuke

078115043b6a4c09bdcf1b5031e995ef296bd604

[ "Apache-2.0" ]

null

src/transforms/imageCropDivide/dev/generate_nodenk.py

MrLixm/Foundry_Nuke

078115043b6a4c09bdcf1b5031e995ef296bd604

[ "Apache-2.0" ]

null

""" python>3 """ import os.path import re from pathlib import Path VERSION = 7 BASE = r""" set cut_paste_input [stack 0] version 12.2 v5 push $cut_paste_input Group { name imageCropDivide tile_color 0x5c3d84ff note_font_size 25 note_font_color 0xffffffff selected true xpos 411 ypos -125 addUserKnob {20 User} addUserKnob {3 width_max} addUserKnob {3 height_max -STARTLINE} addUserKnob {3 width_source} addUserKnob {3 height_source -STARTLINE} addUserKnob {26 "" +STARTLINE} addUserKnob {22 icd_script l "Copy Setup to ClipBoard" T "$SCRIPT$" +STARTLINE} addUserKnob {26 info l " " T "press ctrl+v in the nodegraph after clicking the above button"} addUserKnob {20 Info} addUserKnob {26 infotext l "" +STARTLINE T "2022 - Liam Collod<br> Visit <a style=\"color:#fefefe;\" href=\"https://github.com/MrLixm/Foundry_Nuke/tree/main/src/transforms/imageCropDivide\">the GitHub repo</a> "} addUserKnob {26 "" +STARTLINE} addUserKnob {26 versiontext l "" T "version $VERSION$"} } Input { inputs 0 name Input1 xpos 0 } Output { name Output1 xpos 0 ypos 300 } end_group """ MODULE_BUTTON_PATH = Path("..") / "button.py" NODENK_PATH = Path("..") / "node.nk" def increment_version(): this = Path(__file__) this_code = this.read_text(encoding="utf-8") version = re.search(r"VERSION\s*=\s*(\d+)", this_code) assert version, f"Can't find <VERSION> in <{this}> !" new_version = int(version.group(1)) + 1 new_code = f"VERSION = {new_version}" new_code = this_code.replace(version.group(0), str(new_code)) this.write_text(new_code, encoding="utf-8") print(f"[{__name__}][increment_version] Incremented {this} to {new_version}.") return def run(): increment_version() btnscript = MODULE_BUTTON_PATH.read_text(encoding="utf-8") # sanitize for nuke btnscript = btnscript.replace("\\", r'\\') btnscript = btnscript.split("\n") btnscript = r"\n".join(btnscript) btnscript = btnscript.replace("\"", r'\"') btnscript = btnscript.replace("{", r'\{') btnscript = btnscript.replace("}", r'\}') node_content = BASE.replace("$SCRIPT$", btnscript) node_content = node_content.replace("$VERSION$", str(VERSION+1)) NODENK_PATH.write_text(node_content, encoding="utf-8") print(f"[{__name__}][run] node.nk file written to {NODENK_PATH}") print(f"[{__name__}][run] Finished.") return if __name__ == '__main__': # print(__file__) run()

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0.042254

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null

0

null

0

1

0

c0cd5d7d340b27b3217620ef4b12a1391841820b

2,294

py

Python

workflow/tests/test_experiment_qc.py

JAMKuttan/chipseq_analysis

f8e4853bfdb4de8540026ae0b23235d72a1114ad

[ "MIT" ]

null

workflow/tests/test_experiment_qc.py

JAMKuttan/chipseq_analysis

f8e4853bfdb4de8540026ae0b23235d72a1114ad

[ "MIT" ]

null

workflow/tests/test_experiment_qc.py

JAMKuttan/chipseq_analysis

f8e4853bfdb4de8540026ae0b23235d72a1114ad

[ "MIT" ]

null

#!/usr/bin/env python3 import pytest import os import pandas as pd from io import StringIO import experiment_qc test_output_path = os.path.dirname(os.path.abspath(__file__)) + \ '/../output/experimentQC/' DESIGN_STRING = """sample_id\texperiment_id\tbiosample\tfactor\ttreatment\treplicate\tcontrol_id\tbam_reads A_1\tA\tLiver\tH3K27ac\tNone\t1\tB_1\tA_1.bam A_2\tA\tLiver\tH3K27ac\tNone\t2\tB_2\tA_2.bam B_1\tB\tLiver\tInput\tNone\t1\tB_1\tB_1.bam B_2\tB\tLiver\tInput\tNone\t2\tB_2\tB_2.bam """ @pytest.fixture def design_bam(): design_file = StringIO(DESIGN_STRING) design_df = pd.read_csv(design_file, sep="\t") return design_df @pytest.mark.unit def test_check_update_controls(design_bam): new_design = experiment_qc.update_controls(design_bam) assert new_design.loc[0, 'control_reads'] == "B_1.bam" @pytest.mark.singleend def test_coverage_singleend(): assert os.path.exists(os.path.join(test_output_path, 'sample_mbs.npz')) assert os.path.exists(os.path.join(test_output_path, 'coverage.pdf')) @pytest.mark.singleend def test_spearman_singleend(): assert os.path.exists(os.path.join(test_output_path, 'heatmap_SpearmanCorr.pdf')) @pytest.mark.singleend def test_pearson_singleend(): assert os.path.exists(os.path.join(test_output_path, 'heatmap_PearsonCorr.pdf')) @pytest.mark.singleend def test_fingerprint_singleend(): assert os.path.exists(os.path.join(test_output_path, 'ENCLB144FDT_fingerprint.pdf')) assert os.path.exists(os.path.join(test_output_path, 'ENCLB831RUI_fingerprint.pdf')) @pytest.mark.pairdend def test_coverage_pairedend(): assert os.path.exists(os.path.join(test_output_path, 'sample_mbs.npz')) assert os.path.exists(os.path.join(test_output_path, 'coverage.pdf')) @pytest.mark.pairdend def test_spearman_pairedend(): assert os.path.exists(os.path.join(test_output_path, 'heatmap_SpearmanCorr.pdf')) @pytest.mark.pairdend def test_pearson_pairedend(): assert os.path.exists(os.path.join(test_output_path, 'heatmap_PearsonCorr.pdf')) @pytest.mark.pairdend def test_fingerprint_pairedend(): assert os.path.exists(os.path.join(test_output_path, 'ENCLB568IYX_fingerprint.pdf')) assert os.path.exists(os.path.join(test_output_path, 'ENCLB637LZP_fingerprint.pdf'))

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2,294

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0

null

0

null

0

1

0

c0d1e420d8a5ef2c04e4e14f531037003c9ed4f0

3,626

py

Python

native_client_sdk/src/build_tools/tests/test_generate_make.py

junmin-zhu/chromium-rivertrail

eb1a57aca71fe68d96e48af8998dcfbe45171ee1

[ "BSD-3-Clause" ]

5

2018-03-10T13:08:42.000Z

2021-07-26T15:02:11.000Z

native_client_sdk/src/build_tools/tests/test_generate_make.py

quisquous/chromium

b25660e05cddc9d0c3053b3514f07037acc69a10

[ "BSD-3-Clause" ]

1

2015-07-21T08:02:01.000Z

native_client_sdk/src/build_tools/tests/test_generate_make.py

jianglong0156/chromium.src

d496dfeebb0f282468827654c2b3769b3378c087

[ "BSD-3-Clause" ]

6

2016-11-14T10:13:35.000Z

2021-01-23T15:29:53.000Z

#!/usr/bin/env python # Copyright (c) 2012 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import copy import datetime import os import posixpath import subprocess import sys import unittest SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) BUILD_TOOLS_DIR = os.path.dirname(SCRIPT_DIR) sys.path.append(BUILD_TOOLS_DIR) import generate_make BASIC_DESC = { 'TOOLS': ['newlib', 'glibc'], 'TARGETS': [ { 'NAME' : 'hello_world', 'TYPE' : 'main', 'SOURCES' : ['hello_world.c'], }, ], 'DEST' : 'examples' } class TestFunctions(unittest.TestCase): def testPatsubst(self): val = generate_make.GenPatsubst(32, 'FOO', 'cc', 'CXX') gold = '$(patsubst %.cc,%_32.o,$(FOO_CXX))' self.assertEqual(val, gold) def testPatsubst(self): val = generate_make.GenPatsubst(32, 'FOO', 'cc', 'CXX') gold = '$(patsubst %.cc,%_32.o,$(FOO_CXX))' self.assertEqual(val, gold) def testSetVar(self): val = generate_make.SetVar('FOO',[]) self.assertEqual(val, 'FOO:=\n') val = generate_make.SetVar('FOO',['BAR']) self.assertEqual(val, 'FOO:=BAR\n') items = ['FOO_' + 'x' * (i % 13) for i in range(50)] for i in range(10): wrapped = generate_make.SetVar('BAR_' + 'x' * i, items) lines = wrapped.split('\n') for line in lines: if len(line) > 79: self.assertEqual(line, 'Less than 80 at ' + str(i)) class TestValidateFormat(unittest.TestCase): def _append_result(self, msg): self.result += msg return self.result def _validate(self, src, msg): format = generate_make.DSC_FORMAT self.result = '' result = generate_make.ValidateFormat(src, format, lambda msg: self._append_result(msg)) if msg: self.assertEqual(self.result, msg) else: self.assertEqual(result, True) def testGoodDesc(self): testdesc = copy.deepcopy(BASIC_DESC) self._validate(testdesc, None) def testMissingKey(self): testdesc = copy.deepcopy(BASIC_DESC) del testdesc['TOOLS'] self._validate(testdesc, 'Missing required key TOOLS.') testdesc = copy.deepcopy(BASIC_DESC) del testdesc['TARGETS'][0]['NAME'] self._validate(testdesc, 'Missing required key NAME.') def testNonEmpty(self): testdesc = copy.deepcopy(BASIC_DESC) testdesc['TOOLS'] = [] self._validate(testdesc, 'Expected non-empty value for TOOLS.') testdesc = copy.deepcopy(BASIC_DESC) testdesc['TARGETS'] = [] self._validate(testdesc, 'Expected non-empty value for TARGETS.') testdesc = copy.deepcopy(BASIC_DESC) testdesc['TARGETS'][0]['NAME'] = '' self._validate(testdesc, 'Expected non-empty value for NAME.') def testBadValue(self): testdesc = copy.deepcopy(BASIC_DESC) testdesc['TOOLS'] = ['newlib', 'glibc', 'badtool'] self._validate(testdesc, 'Value badtool not expected in TOOLS.') def testExpectStr(self): testdesc = copy.deepcopy(BASIC_DESC) testdesc['TOOLS'] = ['newlib', True, 'glibc'] self._validate(testdesc, 'Value True not expected in TOOLS.') def testExpectList(self): testdesc = copy.deepcopy(BASIC_DESC) testdesc['TOOLS'] = 'newlib' self._validate(testdesc, 'Key TOOLS expects LIST not STR.') # TODO(noelallen): Add test which generates a real make and runs it. def main(): suite = unittest.defaultTestLoader.loadTestsFromModule(sys.modules[__name__]) result = unittest.TextTestRunner(verbosity=2).run(suite) return int(not result.wasSuccessful()) if __name__ == '__main__': sys.exit(main())

29.008

79

0.671539

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3,626

5.145336

0.340564

0.037943

0.075885

0.094857

0.39629

0.356239

0.312816

0.226391

0.151349

0.085582

0

0.008457

0.184777

3,626

124

80

29.241935

0.793978

0.068395

0

0.180851

0

0.169582

0.013638

0

0.008065

0.074468

1

0.12766

false

0

0.085106

0

0.255319

0

null

0

null

0

1

0

c0d317f2e8f8665da9e599f1dc02201ed251fea1

568

py

Python

Curso_em_Video_py3/ex069.py

Rodrigo98Matos/Projetos_py

6428e2c09d28fd8a717743f4434bc788e7d7d3cc

[ "MIT" ]

1

2021-05-11T12:39:43.000Z

Curso_em_Video_py3/ex069.py

Rodrigo98Matos/Projetos_py

6428e2c09d28fd8a717743f4434bc788e7d7d3cc

[ "MIT" ]

null

Curso_em_Video_py3/ex069.py

Rodrigo98Matos/Projetos_py

6428e2c09d28fd8a717743f4434bc788e7d7d3cc

[ "MIT" ]

null

a = b = c = 0 while True: flag = '' i = -1 s = '' while i < 0: i = int(input('idade:\t')) while s != 'M' and s != 'F': s = str(input('Sexo [M] [F]:\t')).strip().upper()[0] if i > 18: a += 1 if s == 'M': b += 1 elif i < 20: c += 1 while flag != 'S' and flag != 'N': flag = str(input('Você quer cadastrar mais pessoas? [S] [N]\t')).strip().upper()[0] if flag == 'N': break print(f'Tem {a} pessoas maior de 18 anos!\nTem {b} homens!\nTem {c} mulheres com menos de 20 anos!')

27.047619

100

0.452465

93

568

2.763441

0.451613

0.015564

0.085603

0.093385

0.108949

0

0.042667

0.339789

568

20

101

28.4

0.642667

0

0.05

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0

1

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false

0

0.05

0

null

0

null

0

1

0

c0d3bab4c52e7bb6548865457b438ad24de1affe

6,152

py

Python

hatfieldcmr/ingest/name.py

bcgov/nr_rfc_processing

7e414b97a29ed5bae8ba3c6decea39733be9a2db

[ "Apache-2.0" ]

null

hatfieldcmr/ingest/name.py

bcgov/nr_rfc_processing

7e414b97a29ed5bae8ba3c6decea39733be9a2db

[ "Apache-2.0" ]

6

2021-02-08T16:47:02.000Z

2022-01-30T21:58:18.000Z

hatfieldcmr/ingest/name.py

bcgov/rfc_processing

7e414b97a29ed5bae8ba3c6decea39733be9a2db

[ "Apache-2.0" ]

2

2021-02-22T19:13:26.000Z

2021-05-03T23:58:56.000Z

""" Contains data ingest related functions """ import re import os.path from dateutil.parser import parse as dateparser import typing from typing import Dict import cmr from hatfieldcmr.ingest.file_type import MODISBlobType MODIS_NAME = "modis-terra" TITLE_PATTERN_STRING = r"\w+:([\w]+\.[\w]+):\w+" TITLE_PATTERN = re.compile(TITLE_PATTERN_STRING) GRANULE_TITLE_KEY = 'title' GRANULE_TIME_KEY = 'time_start' GRANULE_NAME_KEY = 'producer_granule_id' def format_object_name(meta: Dict, object_name: str) -> str: """ Parameters ---------- metas: Dict Single Granule metadata JSON response from CMR object_name: str Name of object (ex. hdf file, xml file) Returns ---------- str Object name for granule. If insufficient information is available, empty string is returned. """ default_value = "" if meta is None: return default_value folder_prefix = "" try: folder_prefix = format_object_prefix(meta) except ValueError: return '' os.makedirs(folder_prefix, exist_ok=True) return f"{folder_prefix}/{object_name}" def format_object_prefix(meta: Dict): """Helper function to generate 'folder prefix' of the bucket object """ if not ((GRANULE_TITLE_KEY in meta) and (GRANULE_TIME_KEY in meta) and (GRANULE_NAME_KEY in meta)): raise ValueError('granule does not have required keys', meta) title = meta.get(GRANULE_TITLE_KEY, "") m = TITLE_PATTERN.match(title) if m is None: raise ValueError('granule does not have well formated title', title) product_name = m.groups()[0] date_string = dateparser(meta.get("time_start")).strftime('%Y.%m.%d') folder_prefix = format_object_prefix_helper(product_name, date_string) # f"{MODIS_NAME}/{product_name}/{date_string}" return folder_prefix def format_object_prefix_helper(product_name: str, date_string: str): return f"{MODIS_NAME}/{product_name}/{date_string}" class BlobPathMetadata: def __init__(self, product_name: str, date_string: str): self.product_name = product_name self.product_name_without_version = product_name[:7].lower() self.date_string = date_string self.date = dateparser(date_string) @staticmethod def parse(prefix_or_full_name: str): parts = prefix_or_full_name.split(r'/') if (len(parts) >= 3): product_name = parts[1] date_string = parts[2] return BlobPathMetadata(product_name, date_string) return None class MODISFileNameParser: THUMBNAIL_RE = re.compile(r"BROWSE\.([\w\.]+)\.\d+\.jpg") @classmethod def identify_file_type(cls, name: str): basename = os.path.basename(name) if ('BROWSE' in basename): return MODISBlobType.THUMBNAIL elif ('.hdf.xml' in basename): return MODISBlobType.METADATA_XML elif ('.hdf_meta.json' in basename): return MODISBlobType.METADATA_JSON elif ('.hdf' in basename): return MODISBlobType.DATA_HDF elif ('.tif.aux.xml' in basename): return MODISBlobType.GEOTIFF_XML elif ('.tif' in basename): return MODISBlobType.GEOTIFF else: print(f'unknown file name {name}') return '' @classmethod def extract_blob_id(cls, name: str, file_type: MODISBlobType = None): if file_type is None: file_type = cls.identify_file_type(name) if file_type == MODISBlobType.THUMBNAIL: return cls._extract_blob_id_thumbnail(name) elif file_type == MODISBlobType.METADATA_XML: return cls._extract_basename_from_file(name, '.hdf.xml') elif file_type == MODISBlobType.METADATA_JSON: return cls._extract_basename_from_file(name, '.hdf_meta.json') elif file_type == MODISBlobType.DATA_HDF: return cls._extract_basename_from_file(name, '.hdf') elif file_type == MODISBlobType.GEOTIFF: return cls._extract_basename_from_file(name, '.tif') elif file_type == MODISBlobType.GEOTIFF_XML: return cls._extract_basename_from_file(name, '.tif.aux.xml') return '' @classmethod def _extract_blob_id_thumbnail(cls, name: str) -> str: basename = os.path.basename(name) m = cls.THUMBNAIL_RE.match(basename) if m is None: return '' blob_id = m.groups()[0] name_includes_dir = len(name.split(r'/')) >= 4 if (name_includes_dir): product_name_doesnt_match_blob_prefix = cls._check_thumbnail_product_inconsistency( name, blob_id) if (product_name_doesnt_match_blob_prefix): blob_id = cls._fix_thumbnail_product_name_inconsistency( name, blob_id) return blob_id @classmethod def _check_thumbnail_product_inconsistency(cls, name: str, blob_id: str): full_name_product_name, blob_id_product_name = cls._extract_product_names( name, blob_id) return full_name_product_name != blob_id_product_name @classmethod def _fix_thumbnail_product_name_inconsistency(cls, name: str, blob_id: str): full_name_product_name, blob_id_product_name = cls._extract_product_names( name, blob_id) return blob_id.replace(blob_id_product_name, full_name_product_name) @classmethod def _extract_product_names(cls, name: str, blob_id: str): product_name_with_version = name.split(r'/')[1] full_name_product_name = product_name_with_version[:7] blob_id_product_name = blob_id[:7] return full_name_product_name, blob_id_product_name @classmethod def _extract_basename_from_file(cls, name: str, extension: str) -> str: basename = os.path.basename(name).strip() extension_len = len(extension) if (len(basename) > extension_len and basename[-extension_len:] == extension): return basename[:-extension_len] return ''

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null

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null

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0

c0d42b9fa731f071b00e48d88b5dd1b3baf8c28b

8,661

py

Python

tests/test_lib.py

bluefloyd00/snowflet

a1676158bffc5f44970845b054d1ad221e9540c7

[ "MIT" ]

1

2020-06-23T14:14:48.000Z

tests/test_lib.py

bluefloyd00/snowflet

a1676158bffc5f44970845b054d1ad221e9540c7

[ "MIT" ]

2

2020-06-19T15:05:05.000Z

2020-06-19T15:07:22.000Z

tests/test_lib.py

bluefloyd00/snowflet

a1676158bffc5f44970845b054d1ad221e9540c7

[ "MIT" ]

null

import os import unittest from snowflet.lib import read_sql from snowflet.lib import logging_config from snowflet.lib import extract_args from snowflet.lib import apply_kwargs from snowflet.lib import strip_table from snowflet.lib import extract_tables_from_query from snowflet.lib import add_database_id_prefix from snowflet.lib import is_table from snowflet.lib import add_table_prefix_to_sql class StringFunctions(unittest.TestCase): """ Test """ def test_strip_table(self): """ Test """ self.assertEqual( strip_table(table_name='"db"."schema"."table"'), '"db.schema.table"', "strip_table: wrong table name" ) def test_extract_tables_from_query(self): """ Test """ self.assertEqual( extract_tables_from_query(sql_query=""" select a,b,c from "db"."schema"."table" and db.schema.table not "schema"."table" """), [ '"db"."schema"."table"', 'db.schema.table' ], "does not extract the tables properly" ) class TableFunctions(unittest.TestCase): """ Test """ def test_is_table(self): self.assertTrue( is_table( word='"db"."test"."table1"' ,sql=""" select a.* from "db"."test"."table1" a left join db.test.table2 b on a.id=b.id left join db."test".table3 c on b.id = c.id """), "select: ok" ) self.assertTrue( is_table( word='"db"."test"."table4"' ,sql=""" create table "db"."test"."table4" as select a.* from "db"."test"."table1" a left join db.test.table2 b on a.id=b.id left join db."test".table3 c on b.id = c.id """), "create - select: ok" ) def test_add_table_prefix_to_sql(self): self.assertEqual( add_table_prefix_to_sql( sql=""" select a.* from "db1"."test"."table1" a left join db2.test.table2 b on a.id=b.id left join db3."test".table3 c on b.id = c.id """, prefix="CLONE_1003" ), """ select a.* from "CLONE_1003_DB1"."TEST"."TABLE1" a left join "CLONE_1003_DB2".TEST.TABLE2 b on a.id=b.id left join "CLONE_1003_DB3"."TEST".TABLE3 c on b.id = c.id """, "add_table_prefix_to_sql: ok" ) # def test_extract_tables(self): # self.assertEqual( # extract_tables(""" select a.* from "db"."test"."table1" and db.test.table2 and db."test".table3 """), # ["db.test.table1", "db.test.table2", "db.test.table3"], # "multiple tables, mix double quotes and not" # ) # self.assertEqual( # extract_tables(""" select a.* from "db"."test"."table1" and db.test.table2 and db."test".table1 """), # ["db.test.table1", "db.test.table2"], # "returned unique values" # ) class ReadSql(unittest.TestCase): """ Test """ def test_class_read_sql_file(self): """ Test """ sql = read_sql( file="tests/sql/read_sql.sql", param1="type", param2="300", param3="shipped_date", param4='trying' ) # self.assertEqual( # sql, # 'select type, shipped_date from "DB_TEST"."SCHEMA_TEST"."TABLE1" where amount > 300', # "read_sql unit test" # ) sql = read_sql( file="tests/sql/read_sql.sql" ) self.assertTrue( sql == 'select {param1}, {param3} from "DB_TEST"."SCHEMA_TEST"."TABLE1" where amount > {param2}', "read_sql file unit test no opt parameters" ) with self.assertRaises(KeyError): read_sql( file="tests/sql/read_sql.sql", database_id='something' ) def test_class_read_sql_query(self): """ Test """ sql = read_sql( query='select {param1}, {param3} from "db_test"."schema_test"."table1" where amount > {param2}', param1="type", param2="300", param3="shipped_date", param4='trying' ) self.assertEqual( sql, 'select type, shipped_date from "DB_TEST"."SCHEMA_TEST"."TABLE1" where amount > 300', "read_sql unit test" ) sql = read_sql( file="tests/sql/read_sql.sql" ) self.assertTrue( sql == 'select {param1}, {param3} from "DB_TEST"."SCHEMA_TEST"."TABLE1" where amount > {param2}', "read_sql query unit test no opt parameters" ) with self.assertRaises(KeyError): read_sql( file="tests/sql/read_sql.sql", database_id='something' ) class FunctionsInLib(unittest.TestCase): """ Unittest class for lib functions """ def test_extract_args_1_param(self): content = [ { "table_desc": "table1", "create_table": { "table_id": "table1", "dataset_id": "test", "file": "tests/sql/table1.sql" }, "pk": ["col1", "col2"], "mock_data": "sql/table1_mocked.sql" }, { "table_desc": "table2", "create_table": { "table_id": "table2", "dataset_id": "test", "file": "tests/sql/table2.sql" }, "pk": ["col1"], "mock_data": "sql/table1_mocked.sql" } ] self.assertEqual( extract_args(content, "pk"), [["col1", "col2"], ["col1"]], "extracted ok" ) self.assertEqual( extract_args(content, "create_table"), [ { "table_id": "table1", "dataset_id": "test", "file": "tests/sql/table1.sql" }, { "table_id": "table2", "dataset_id": "test", "file": "tests/sql/table2.sql" } ], "extracted ok" ) def test_add_database_id_prefix(self): self.yaml = { "desc": "test", "tables": [ { "table_desc": "table1", "create_table": { "table_id": "table1", "database_id": "test", }, }, { "table_desc": "table2", "create_table": { "table_id": "table2", "database_id": "test", }, } ] } add_database_id_prefix( self.yaml, prefix='1234' ) self.assertEqual( self.yaml , { "desc": "test", "tables": [ { "table_desc": "table1", "create_table": { "table_id": "table1", "database_id": "1234_test", }, }, { "table_desc": "table2", "create_table": { "table_id": "table2", "database_id": "1234_test", }, } ] }, "prefix properly added to database" ) if __name__ == "__main__": logging_config() unittest.main()

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0.05431

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0.485345

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0

0.02601

0.46288

8,661

233

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37.171674

0.722055

0.083593

0

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0.268426

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1

0.042781

false

0

0.058824

0

0.122995

0

null

0

null

0

1

0

c0d718d76a48ce7c669cb050436654bea3cdd296

2,891

py

Python

annotation/black_action/fu.py

windfall-shogi/feature-annotation

83ff7c3fa31e542221cf45186b2ea3ef2a10310f

[ "MIT" ]

null

annotation/black_action/fu.py

windfall-shogi/feature-annotation

83ff7c3fa31e542221cf45186b2ea3ef2a10310f

[ "MIT" ]

null

annotation/black_action/fu.py

windfall-shogi/feature-annotation

83ff7c3fa31e542221cf45186b2ea3ef2a10310f

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import sonnet as snt import tensorflow as tf from .drop_mask import make_drop_mask1 from .promotion_mask import make_promotion_mask from ..boolean_board.black import select_black_fu_board, select_non_black_board from ..boolean_board.empty import select_empty_board from ..direction import Direction from ..piece import Piece __author__ = 'Yasuhiro' __date__ = '2018/2/22' class BlackFuFileLayer(snt.AbstractModule): def __init__(self, data_format, name='black_fu_file'): super().__init__(name=name) self.data_format = data_format def _build(self, board): fu_board = select_black_fu_board(board=board) axis = -1 if self.data_format == 'NCHW' else -2 flag = tf.reduce_any(fu_board, axis=axis, keep_dims=True) flag = tf.logical_not(flag) repeat_count = [1, 1, 1, 1] repeat_count[axis] = 9 available_map = tf.tile(flag, repeat_count) return available_map class BlackFuDropLayer(snt.AbstractModule): def __init__(self, data_format, name='black_fu_drop'): super().__init__(name=name) self.data_format = data_format def _build(self, board, black_hand, available_square): fu_available_file = BlackFuFileLayer( data_format=self.data_format )(board) fu_available_area = make_drop_mask1(data_format=self.data_format) empty_square = select_empty_board(board=board) available = tf.logical_and( # FUを置ける筋、2~9段 tf.logical_and(fu_available_file, fu_available_area), tf.logical_and( # 空いているマス empty_square, # 持ち駒があるかどうか tf.reshape( tf.greater_equal(black_hand[:, Piece.BLACK_FU], 1), [-1, 1, 1, 1] ) ) ) # 王手の時に有効かどうか available = tf.logical_and(available, available_square) return available class BlackFuMoveLayer(snt.AbstractModule): def __init__(self, data_format, name='black_fu_move'): super().__init__(name=name) self.data_format = data_format def _build(self, board, fu_effect): non_black_mask = select_non_black_board(board=board) movable_effect = tf.logical_and(fu_effect[Direction.UP], non_black_mask) available_mask = make_drop_mask1(data_format=self.data_format) non_promoting_effect = { Direction.UP: tf.logical_and(movable_effect, available_mask) } promotion_mask = make_promotion_mask( direction=Direction.UP, data_format=self.data_format, step_size=1 ) promoting_effect = { Direction.UP: tf.logical_and(movable_effect, promotion_mask) } return non_promoting_effect, promoting_effect

31.769231

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0.648219

353

2,891

4.906516

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0.04157

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0.264268

2,891

90

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32.122222

0.802069

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null

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null

0

1

0

c0dc3d97cf9bff141a470ed5055719904a5f9f4c

2,492

py

Python

src/commands/refactor/convert_to_arrow_function.py

PranjalPansuriya/JavaScriptEnhancements

14af4162e86585153cbd4614ad96dff64a0d3192

[ "MIT" ]

690

2017-04-11T06:45:01.000Z

2022-03-21T23:20:29.000Z

src/commands/refactor/convert_to_arrow_function.py

PranjalPansuriya/JavaScriptEnhancements

14af4162e86585153cbd4614ad96dff64a0d3192

[ "MIT" ]

74

2017-11-22T18:05:26.000Z

2021-05-05T16:25:31.000Z

src/commands/refactor/convert_to_arrow_function.py

PranjalPansuriya/JavaScriptEnhancements

14af4162e86585153cbd4614ad96dff64a0d3192

[ "MIT" ]

42

2017-04-13T10:22:40.000Z

2021-05-27T19:19:04.000Z

import sublime, sublime_plugin import os, traceback from ...libs import util from ...libs import FlowCLI class JavascriptEnhancementsRefactorConvertToArrowFunctionCommand(sublime_plugin.TextCommand): def run(self, edit, **args): view = self.view selection = view.sel()[0] flow_cli = FlowCLI(view) result = flow_cli.ast() if result[0]: body = result[1]["body"] items = util.nested_lookup("type", ["FunctionExpression"], body) for item in items: region = sublime.Region(int(item["range"][0]), int(item["range"][1])) if region.contains(selection): text = view.substr(region) if not text.startswith("function"): return index_begin_parameter = 8 text = text[index_begin_parameter:].lstrip() while text[0] != "(" and len(text) > 0: text = text[1:].lstrip() block_statement_region = sublime.Region(int(item["body"]["range"][0]), int(item["body"]["range"][1])) block_statement = view.substr(block_statement_region) index = text.index(block_statement) while text[index - 1] == " " and index - 1 >= 0: text = text[0:index - 1] + text[index:] index = index - 1 text = text[0:index] + " => " + text[index:] view.replace(edit, region, text) break else: sublime.error_message("Cannot convert the function. Some problems occured.") def is_enabled(self, **args) : view = self.view if not util.selection_in_js_scope(view) : return False selection = view.sel()[0] scope = view.scope_name(selection.begin()).strip() if "meta.block.js" in scope: region_scope = util.get_region_scope_last_match(view, scope, selection, "meta.block.js") else: region_scope = util.get_region_scope_last_match(view, scope, selection, "meta.group.braces.curly.js") if not region_scope: return False return True def is_visible(self, **args) : view = self.view if not util.selection_in_js_scope(view) : return False selection = view.sel()[0] scope = view.scope_name(selection.begin()).strip() if "meta.block.js" in scope: region_scope = util.get_region_scope_last_match(view, scope, selection, "meta.block.js") else: region_scope = util.get_region_scope_last_match(view, scope, selection, "meta.group.braces.curly.js") if not region_scope: return False return True

31.544304

111

0.634831

320

2,492

4.79375

0.259375

0.071708

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0.046936

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2,492

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null

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null

0

1

0

c0dde640a5c1e4f5414f4bf02dfa8a2f03ee959e

33,374

py

Python

apps/xdmac/xdmac_memory_transfer/firmware/at91bootstrap_sam_a5d2_xult.X/scripts/mpconfig/mpconfig.py

Techdisc/Microchip

ea8391c689c4badbe2f9ac5181e21bbd5d9d1e54

[ "0BSD" ]

82

2015-02-05T10:29:59.000Z

2022-03-09T22:13:56.000Z

apps/xdmac/xdmac_memory_transfer/firmware/at91bootstrap_sam_a5d2_xult.X/scripts/mpconfig/mpconfig.py

Techdisc/Microchip

ea8391c689c4badbe2f9ac5181e21bbd5d9d1e54

[ "0BSD" ]

128

2015-01-05T00:56:17.000Z

2022-03-03T19:06:11.000Z

apps/xdmac/xdmac_memory_transfer/firmware/at91bootstrap_sam_a5d2_xult.X/scripts/mpconfig/mpconfig.py

Techdisc/Microchip

ea8391c689c4badbe2f9ac5181e21bbd5d9d1e54

[ "0BSD" ]

219

2015-01-01T11:27:14.000Z

2022-03-25T08:33:54.000Z

import os import sys import base64 import fnmatch from kconfiglib import Kconfig, expr_value, Symbol, Choice, MENU, COMMENT, BOOL, STRING, INT, HEX from java.awt import BorderLayout, Dimension, FlowLayout from java.awt.event import ActionListener, MouseEvent from javax.swing import BorderFactory, BoxLayout, ImageIcon, JButton, JCheckBox, JFileChooser, JFrame, JLabel, JPanel, JRadioButton, JScrollPane, JSplitPane, JTextArea, JTextField, JTree from javax.swing.event import ChangeEvent, DocumentListener, TreeExpansionListener, TreeSelectionListener, CellEditorListener from javax.swing.tree import DefaultTreeModel, DefaultMutableTreeNode, DefaultTreeCellRenderer, TreeCellEditor, TreePath from events import addActionListener # For icons in code from org.python.core.util import StringUtil if 'knodeinfo' in sys.modules: del sys.modules["knodeinfo"] from knodeinfo import getNodeInfoString, getNodeName, setKConfig class PrintLogger(): def info(self, log_string): print(log_string) log = PrintLogger() # If True, use GIF image data embedded in this file instead of separate GIF # files. See _load_images(). _USE_EMBEDDED_IMAGES = True def _load_images(): # Loads GIF images, creating the global _*_img ImageIcon variables. # Base64-encoded images embedded in this script are used if # _USE_EMBEDDED_IMAGES is True, and separate image files in the same # directory as the script otherwise. # # Using a global variable indirectly prevents the image from being # garbage-collected. Passing an image to a Tkinter function isn't enough to # keep it alive. def load_image(name, data): var_name = "_{}_img".format(name) if _USE_EMBEDDED_IMAGES: globals()[var_name] = ImageIcon(StringUtil.toBytes(base64.b64decode(data))) else: globals()[var_name] = ImageIcon( file=os.path.join(os.path.dirname(__file__), name + ".gif")) # Note: Base64 data can be put on the clipboard with # $ base64 -w0 foo.gif | xclip load_image("icon", "R0lGODlhMAAwAPEDAAAAAADQAO7u7v///yH5BAUKAAMALAAAAAAwADAAAAL/nI+gy+2Pokyv2jazuZxryQjiSJZmyXxHeLbumH6sEATvW8OLNtf5bfLZRLFITzgEipDJ4mYxYv6A0ubuqYhWk66tVTE4enHer7jcKvt0LLUw6P45lvEprT6c0+v7OBuqhYdHohcoqIbSAHc4ljhDwrh1UlgSydRCWWlp5wiYZvmSuSh4IzrqV6p4cwhkCsmY+nhK6uJ6t1mrOhuJqfu6+WYiCiwl7HtLjNSZZZis/MeM7NY3TaRKS40ooDeoiVqIultsrav92bi9c3a5KkkOsOJZpSS99m4k/0zPng4Gks9JSbB+8DIcoQfnjwpZCHv5W+ip4aQrKrB0uOikYhiMCBw1/uPoQUMBADs=") load_image("n_bool", "R0lGODdhEAAQAPAAAAgICP///ywAAAAAEAAQAAACIISPacHtvp5kcb5qG85hZ2+BkyiRF8BBaEqtrKkqslEAADs=") load_image("y_bool", "R0lGODdhEAAQAPEAAAgICADQAP///wAAACwAAAAAEAAQAAACMoSPacLtvlh4YrIYsst2cV19AvaVF9CUXBNJJoum7ymrsKuCnhiupIWjSSjAFuWhSCIKADs=") load_image("n_tri", "R0lGODlhEAAQAPD/AAEBAf///yH5BAUKAAIALAAAAAAQABAAAAInlI+pBrAKQnCPSUlXvFhznlkfeGwjKZhnJ65h6nrfi6h0st2QXikFADs=") load_image("m_tri", "R0lGODlhEAAQAPEDAAEBAeQMuv///wAAACH5BAUKAAMALAAAAAAQABAAAAI5nI+pBrAWAhPCjYhiAJQCnWmdoElHGVBoiK5M21ofXFpXRIrgiecqxkuNciZIhNOZFRNI24PhfEoLADs=") load_image("y_tri", "R0lGODlhEAAQAPEDAAICAgDQAP///wAAACH5BAUKAAMALAAAAAAQABAAAAI0nI+pBrAYBhDCRRUypfmergmgZ4xjMpmaw2zmxk7cCB+pWiVqp4MzDwn9FhGZ5WFjIZeGAgA7") load_image("m_my", "R0lGODlhEAAQAPEDAAAAAOQMuv///wAAACH5BAUKAAMALAAAAAAQABAAAAI5nIGpxiAPI2ghxFinq/ZygQhc94zgZopmOLYf67anGr+oZdp02emfV5n9MEHN5QhqICETxkABbQ4KADs=") load_image("y_my", "R0lGODlhEAAQAPH/AAAAAADQAAPRA////yH5BAUKAAQALAAAAAAQABAAAAM+SArcrhCMSSuIM9Q8rxxBWIXawIBkmWonupLd565Um9G1PIs59fKmzw8WnAlusBYR2SEIN6DmAmqBLBxYSAIAOw==") load_image("n_locked", "R0lGODlhEAAQAPABAAAAAP///yH5BAUKAAEALAAAAAAQABAAAAIgjB8AyKwN04pu0vMutpqqz4Hih4ydlnUpyl2r23pxUAAAOw==") load_image("m_locked", "R0lGODlhEAAQAPD/AAAAAOQMuiH5BAUKAAIALAAAAAAQABAAAAIylC8AyKwN04ohnGcqqlZmfXDWI26iInZoyiore05walolV39ftxsYHgL9QBBMBGFEFAAAOw==") load_image("y_locked", "R0lGODlhEAAQAPD/AAAAAADQACH5BAUKAAIALAAAAAAQABAAAAIylC8AyKzNgnlCtoDTwvZwrHydIYpQmR3KWq4uK74IOnp0HQPmnD3cOVlUIAgKsShkFAAAOw==") load_image("not_selected", "R0lGODlhEAAQAPD/AAAAAP///yH5BAUKAAIALAAAAAAQABAAAAIrlA2px6IBw2IpWglOvTYhzmUbGD3kNZ5QqrKn2YrqigCxZoMelU6No9gdCgA7") load_image("selected", "R0lGODlhEAAQAPD/AAAAAP///yH5BAUKAAIALAAAAAAQABAAAAIzlA2px6IBw2IpWglOvTah/kTZhimASJomiqonlLov1qptHTsgKSEzh9H8QI0QzNPwmRoFADs=") load_image("edit", "R0lGODlhEAAQAPIFAAAAAKOLAMuuEPvXCvrxvgAAAAAAAAAAACH5BAUKAAUALAAAAAAQABAAAANCWLqw/gqMBp8cszJxcwVC2FEOEIAi5kVBi3IqWZhuCGMyfdpj2e4pnK+WAshmvxeAcETWlsxPkkBtsqBMa8TIBSQAADs=") class NodeType(): """Used to determine what GUI control to use in the visual tree.""" _unknown = 0 _radio = 1 _bool = 2 _tri = 3 _text = 4 _menu = 5 _comment = 6 nodeType = _unknown def __init__(self, t): self.nodeType = t def isType(self, t_list): return self.nodeType in t_list def getType(self): return self.nodeType class TreeNodeData(object): """These are the data objects that goes into the tree data model.""" def __init__ (self, node, tree): """Create a TreeNodeData object Parameters ---------- node : Kconfig.MenuNode The Kconfiglib node object that this tree node visualizes. tree : KConfigTree The tree this node object belongs to. Needed for sending events to the tree. """ self.knode = node self.tree = tree self.expanded = False def getNodeType(self): """Returns the node type""" item = self.knode.item if item == MENU: return NodeType(NodeType._menu) if item == COMMENT: return NodeType(NodeType._comment) if not item.orig_type: return NodeType(NodeType._unknown) if item.orig_type in (STRING, INT, HEX): return NodeType(NodeType._text) # BOOL or TRISTATE if isinstance(item, Symbol) and item.choice: # Choice symbol in y-mode choice return NodeType(NodeType._radio) if len(item.assignable) <= 1: # Pinned to a single value if isinstance(item, Choice): return NodeType(NodeType._menu) if item.type == BOOL: return NodeType(NodeType._bool) if item.assignable == (1, 2): return NodeType(NodeType._tri) return NodeType(NodeType._tri) def getText(self): """Return the text to display on the tree node""" if self.knode and self.knode.prompt: return self.knode.prompt[0] return getNodeName(self.knode).strip() def getValue(self): """Returns a string-type value, used for STRING, INT, HEX node types.""" if self.knode.item == MENU or self.knode.item == COMMENT: return None return self.knode.item.str_value def getTriValue(self): """Returns a boolean or tristate value. A bool checkbox has the values 0 and 2, while a tristate has 0, 1 and 2. 0 = False/N, 1 = Module/M, 2 = True/Y""" if self.knode.item == MENU or self.knode.item == COMMENT: return None # log.info(self.getText(), str(self.knode.item.tri_value))) return self.knode.item.tri_value def setValue(self, val): """Set a string value. Can be a text string, or an integer (or hex) encoded as a string.""" # log.info("TreeNodeData.setValue " + self.getText() + " " + str(val) + " was " + self.getValue()) self.knode.item.set_value(val) self.tree.updateTree() def setTriValue(self, n): """Set a tristate or bool value. 0 = False/N, 1 = Module/M, 2 = True/Y""" # log.info("TreeNodeData.setTriValue", self.getText(), n) self.knode.item.set_value(n) self.tree.updateTree() def getVisible(self): """Return the visibility state of the node.""" return TreeNodeData.isVisible(self.knode) @staticmethod def isVisible(node): """Return the visibility state of the node passed as an argument.""" return node.prompt and expr_value(node.prompt[1]) and not \ (node.item == MENU and not expr_value(node.visibility)) def isExpanded(self): return self.expanded def setExpanded(self, expanded): self.expanded = expanded def search(self, searchString, invisibleMatch): """Search all text related to this node for searchString. If it matches, it will tag the node as a search match. If invisibleMatch = False and the node is not visible, the search match will be False. The search match result (bool) is returned.""" if self.getVisible() > 0 or invisibleMatch: infoText = self.getText() searchString = "*" + searchString + "*" self.searchMatch = fnmatch.fnmatch(infoText.lower(), searchString.lower()) else: self.searchMatch = False return self.searchMatch def setSearchMatch(self, match): """Tags the node with a search match""" self.searchMatch = match def isSearchMatch(self): return self.searchMatch def toString(self): return self.getText() + " = " + str(self.getValue()) class TristateCheckBox(JCheckBox): """Custom tristate checkbox implementation.""" serialVersionUID = 1 triState = 0 _load_images() selected = _y_tri_img unselected = _n_tri_img halfselected = _m_tri_img def __init__(self, eventHandler = None): """Creates a TristateCheckBox object Arguments --------- eventHandler : ActionListener If supplied, the event handler will be called when the tristate checkbox state changes. """ JCheckBox.__init__(self) if eventHandler: addActionListener(self, eventHandler) addActionListener(self, self.actionPerformed) def paint(self, g): """Called when the tree needs to paint the checkbox icon.""" if self.triState == 2: self.setIcon(self.selected) elif self.triState == 1: self.setIcon(self.halfselected) else: self.setIcon(self.unselected) JCheckBox.paint(self, g) def getTriState(self): """Return the tristate value (0, 1 or 2).""" return self.triState def setTriState(self, tri): """Set tristate value (0, 1 or 2).""" self.triState = tri def actionPerformed(self, e): """Increments the checkbox value when clicked""" # log.info("actionPerformed()") tcb = e.getSource() newVal = (tcb.getTriState() + 1) % 3 tcb.setTriState(newVal) class CustomCellRenderer(DefaultTreeCellRenderer): """Renders the various tree controls (checkbox, tristate checkbox, string values etc.)""" def __init__(self): DefaultTreeCellRenderer.__init__(self) flowLayout = FlowLayout(FlowLayout.LEFT, 0, 0) self.cbPanel = JPanel(flowLayout) self.cb = JCheckBox() self.cb.setBackground(None) self.cbPanel.add(self.cb) self.cbLabel = JLabel() self.cbPanel.add(self.cbLabel) self.tcbPanel = JPanel(flowLayout) self.tcb = TristateCheckBox() self.tcb.setBackground(None) self.tcbPanel.add(self.tcb) self.tcbLabel = JLabel() self.tcbPanel.add(self.tcbLabel) self.rbPanel = JPanel(flowLayout) self.rb = JRadioButton() self.rb.setBackground(None) self.rbPanel.add(self.rb) self.rbLabel = JLabel() self.rbPanel.add(self.rbLabel) def getTreeCellRendererComponent(self, tree, value, selected, expanded, leaf, row, hasFocus): """Return a swing control appropriate for the node type of the supplied value""" if isinstance(value, DefaultMutableTreeNode): nodeData = value.getUserObject() if isinstance(nodeData, TreeNodeData): t = nodeData.getNodeType() isEnabled = nodeData.getVisible() > 0 # Boolean checkbox if t.isType([NodeType._bool]): self.cbLabel.setText(nodeData.getText()) self.cb.setEnabled(isEnabled) self.cbLabel.setEnabled(isEnabled) if nodeData.getTriValue() == 0: self.cb.setSelected(False) else: self.cb.setSelected(True) control = self.cbPanel # Tristate chekcbox elif t.isType([NodeType._tri]): control = self.tcbPanel self.tcbLabel.setText(nodeData.getText()) self.tcb.setEnabled(isEnabled) self.tcbLabel.setEnabled(isEnabled) self.tcb.setTriState(nodeData.getTriValue()) # Radio button elif t.isType([NodeType._radio]): self.rbLabel.setText(nodeData.getText()) self.rb.setEnabled(isEnabled) self.rbLabel.setEnabled(isEnabled) if nodeData.getTriValue() == 0: self.rb.setSelected(False) else: self.rb.setSelected(True) control = self.rbPanel # Text field elif t.isType([NodeType._text]): control = DefaultTreeCellRenderer.getTreeCellRendererComponent(self, tree, value, selected, expanded, leaf, row, hasFocus) control.setText(nodeData.getText() + ": " + str(nodeData.getValue())) # Default tree cell (a node with an icon and a label) else: control = DefaultTreeCellRenderer.getTreeCellRendererComponent(self, tree, value, selected, expanded, leaf, row, hasFocus) control.setText(nodeData.getText()) self.setColors(control, nodeData, selected) # Background color for the tree item # log.info("getTreeCellRendererComponent", t.getType(), isEnabled, "'" + nodeData.getText() + "'") control.setEnabled(isEnabled) return control # log.info("Warning: getTreeCellRendererComponent() fallthrough", nodeData) return DefaultTreeCellRenderer.getTreeCellRendererComponent(self, tree, value, selected, expanded, leaf, row, hasFocus) def setColors(self, control, data, selected): """Set background color fot the tree item.""" if selected: control.setForeground(self.getTextSelectionColor()) control.setBackground(self.getBackgroundSelectionColor()) else: control.setForeground(self.getTextNonSelectionColor()) control.setBackground(self.getBackgroundNonSelectionColor()) class CustomCellEditor(TreeCellEditor, ActionListener): """Renders the various tree edit controls (checkbox, tristate checkbox, text box etc.)""" def __init__(self, tree): TreeCellEditor.__init__(self) self.editor = None self.tree = tree flowLayout = FlowLayout(FlowLayout.LEFT, 0, 0) self.cbPanel = JPanel(flowLayout) self.cb = JCheckBox(actionPerformed = self.checked) self.cbPanel.add(self.cb) self.cbLabel = JLabel() self.cbPanel.add(self.cbLabel) self.tcbPanel = JPanel(flowLayout) self.tcb = TristateCheckBox(self.checked) self.tcbPanel.add(self.tcb) self.tcbLabel = JLabel() self.tcbPanel.add(self.tcbLabel) self.rbPanel = JPanel(flowLayout) self.rb = JRadioButton(actionPerformed = self.checked) self.rbPanel.add(self.rb) self.rbLabel = JLabel() self.rbPanel.add(self.rbLabel) self.tfPanel = JPanel(flowLayout) self.tfLabel = JLabel() self.tfPanel.add(self.tfLabel) self.tf = JTextField() self.tf.setColumns(12) self.tf.addActionListener(self) self.tfPanel.add(self.tf) def addCellEditorListener(self, l): """Register for edit events""" self.listener = l def isCellEditable(self, event): if event != None and isinstance(event.getSource(), JTree) and isinstance(event, MouseEvent): tree = event.getSource() path = tree.getPathForLocation(event.getX(), event.getY()) userData = path.getLastPathComponent().getUserObject() if isinstance(userData, TreeNodeData) and (not userData.getNodeType().isType([NodeType._comment, NodeType._menu])) and (userData.getVisible() > 0): return True return False def shouldSelectCell(self, event): # log.info("shouldSelectCell") return True def cancelCellEditing(self): # log.info("Cancel editing, please!") # super(CustomCellEditor, self).cancelCellEditing() pass def stopCellEditing(self): # log.info("stopCellEditing") if self.nodeData.getNodeType().isType([NodeType._text]): # log.info("stopCellEditing for sure!") self.nodeData.setValue(str(self.tf.getText())) return True def getTreeCellEditorComponent(self, tree, value, selected, expanded, leaf, row): """Return a swing edit control appropriate for the node type of the supplied value""" self.nodeData = self.getNodeUserData(value) if self.nodeData: text = self.nodeData.getText() t = self.nodeData.getNodeType() # Boolean checkbox if t.isType([NodeType._bool]): self.editor = self.cbPanel self.cbLabel.setText(text) if self.nodeData.getTriValue() > 0: self.cb.setSelected(True) else: self.cb.setSelected(False) # Tristate checkbox elif t.isType([NodeType._tri]): # log.info("getTreeCellEditorComponent tristate") self.editor = self.tcbPanel self.tcbLabel.setText(text) self.tcb.setTriState(self.nodeData.getTriValue()) # Radio button elif t.isType([NodeType._radio]): self.editor = self.rbPanel self.rbLabel.setText(text) if self.nodeData.getTriValue() > 0: self.rb.setSelected(True) else: self.rb.setSelected(False) # Text field elif t.isType([NodeType._text]): self.editor = self.tfPanel self.tfLabel.setText(str(self.nodeData.getText()) + ":") self.tf.setText(str(self.nodeData.getValue())) else: self.editor = self.tcb self.editor.setText(text) return self.editor def getNodeUserData(self, value): """Gets the TreeNodeData from the tree node""" if isinstance(value, DefaultMutableTreeNode): nodeData = value.getUserObject() if isinstance(nodeData, TreeNodeData): return nodeData return None def getCellEditorValue(self): newNode = TreeNodeData(self.nodeData.knode, self.tree) if isinstance(self.editor, JTextField): newNode.setValue(str(self.editor.getText())) return newNode def checked(self, e): """Updates the node data when a checkbox has been clicked""" control = e.getSource() if isinstance(control, TristateCheckBox): # log.info("tristate checked") self.nodeData.setTriValue(control.getTriState()) else: # log.info("checkbox checked") if control.isSelected(): self.nodeData.setValue(2) else: self.nodeData.setValue(0) def actionPerformed(self, event): """ ENTER pressed in text field, stop editing.""" tf = event.getSource() self.listener.editingStopped(ChangeEvent(tf)) class KConfigTree(JTree, CellEditorListener): """Custom Swing JTree based tree that visualizes a KConfig configuration. The full KConfig menu structure is put into a shadow tree model. From the shadow model, a real model is built (updateModel), where hidden nodes are not included. This update model is what the tree uses to visualize the configuration menu. Both the shadow and the updated model has the same TreeNodeData with KConfig data. The expanded state and search result state is kept in the TreeNodeData. """ shadowModel = None isUpdating = False showAll = False isSearching = False def __init__(self, kconf): self.setCellRenderer(CustomCellRenderer()) self.setCellEditor(CustomCellEditor(self)) self.createKconfShadowModel(kconf) self.setModel(self.createUpdatedModel()) self.expandRow(0) self.setEditable(True) self.setRootVisible(False) self.setShowsRootHandles(True) self.setRowHeight(0) self.addTreeExpansionListener(KConfigTreeExpansionListener()) self.getCellEditor().addCellEditorListener(self) def editingCanceled(self, event): """From CellEditorListener """ # log.info("editingCanceled", self.cellEditor.getCellEditorValue()) pass def editingStopped(self, event): """From CellEditorListener.""" # log.info("editingStopped", self.cellEditor.getCellEditorValue()) self.stopEditing() def createKconfShadowModel(self, kconf): """Create the one and only shadow data model""" rootNode = DefaultMutableTreeNode(kconf.mainmenu_text) self.addNodes(rootNode, kconf.top_node.list) self.shadowModel = DefaultTreeModel(rootNode) def addNodes(self, parent, node): """Recursively traverse the KConfig structure and add to the shadow model""" while node: newUiNode = DefaultMutableTreeNode(TreeNodeData(node, self)) parent.add(newUiNode) if node.list: self.addNodes(newUiNode, node.list) node = node.next def createUpdatedModel(self): """When the user does any changes in the tree, the underlaying kconfig structure will change. Nodes may change visibility and value. The tree control cannot hide nodes, so a new datamodel must be generated that does not include invisible nodes.""" shadowTreeRoot = self.shadowModel.getRoot() rootNode = DefaultMutableTreeNode("Root") self.addVisibleNodes(rootNode, shadowTreeRoot) return DefaultTreeModel(rootNode) def addVisibleNodes(self, visibleParent, shadowParent): """Adds visible nodes from the shadow tree model to the update tree model. If there is an active search operation, only search matches will be added. If showAll is set, all nodes are added regardless of visibility.""" childrenEnum = shadowParent.children() while childrenEnum.hasMoreElements(): shadowChild = childrenEnum.nextElement() if shadowChild.getUserObject().getVisible() > 0 or self.showAll: if not self.isSearching or shadowChild.getUserObject().isSearchMatch(): visibleChild = DefaultMutableTreeNode(shadowChild.getUserObject()) visibleParent.add(visibleChild) if shadowChild.getChildCount() > 0: self.addVisibleNodes(visibleChild, shadowChild) def isPathEditable(self, path): comp = path.getLastPathComponent() if isinstance(comp, DefaultMutableTreeNode): nodeData = comp.getUserObject() if isinstance(nodeData, TreeNodeData): return True return False def updateTree(self): """Call to create a new updated tree model""" if not self.isUpdating: # log.info("updateTree()") self.isUpdating = True self.setModel(self.createUpdatedModel()) self.updateExpandedState(self.getModel().getRoot()) self.isUpdating = False def updateExpandedState(self, parent): """Scan through the whole tree and expand the tree node if the node data has the expanded field set to True.""" childrenEnum = parent.children() while childrenEnum.hasMoreElements(): child = childrenEnum.nextElement() if child.getUserObject().isExpanded(): self.expandPath(TreePath(child.getPath())) if child.getChildCount() > 0: self.updateExpandedState(child) def setShowAll(self, show): self.showAll = show self.updateTree() def doSearch(self, searchText): """Perform a search in the data model with the supplied text.""" if len(searchText) > 0: self.isSearching = True self.doSearchBranch(self.shadowModel.getRoot(), searchText) else: self.isSearching = False self.updateTree() def doSearchBranch(self, shadowParent, searchText): """Traverse the tree model searching for the search text""" match = False childrenEnum = shadowParent.children() while childrenEnum.hasMoreElements(): shadowChild = childrenEnum.nextElement() if shadowChild.getUserObject().search(searchText, self.showAll): match = True if shadowChild.getChildCount() > 0: if self.doSearchBranch(shadowChild, searchText): shadowChild.getUserObject().setSearchMatch(True) match = True return match class KConfigTreeExpansionListener(TreeExpansionListener): """Listener for tree expand/collapse events. Used for storing the expand state in the node data, so that a new updated tree's branches can be expanded the same way as in the old tree.""" def treeExpanded(self, e): if not e.getPath().getLastPathComponent() == e.getSource().getModel().getRoot(): e.getPath().getLastPathComponent().getUserObject().setExpanded(True) def treeCollapsed(self, e): if not e.getPath().getLastPathComponent() == e.getSource().getModel().getRoot(): e.getPath().getLastPathComponent().getUserObject().setExpanded(False) class MPConfig(TreeSelectionListener): """The MPConfig component initializes the KConfig library with the requested configuration, and buildst the GUI, consisting of a "Load" and a "Save as" buttons, a search field, "show all" checkbox, tree view and information text view.""" def __init__(self, kconfig_file = "Kconfig", config_file=".config", systemLogger = None): """[summary] Parameters ---------- kconfig_file : string (default: "Kconfig") The Kconfig configuration file config_file : string (default: ".config") The save file which will be used for loading and saving the settings systemLogger (default: None) A system logger object. If None then print statements are used for logging. """ global log if systemLogger: log = systemLogger # Load Kconfig configuration files self.kconfig = Kconfig(kconfig_file) setKConfig(self.kconfig) if os.path.isfile(config_file): log.info(self.kconfig.load_config(config_file)) elif os.path.isfile(".config"): log.info(self.kconfig.load_config(".config")) self.tree = KConfigTree(self.kconfig) self.tree.addTreeSelectionListener(self.treeSelectionChanged) jTreeSP = JScrollPane(self.tree) self.jta = JTextArea() self.jta.setEditable(False) jTextSP = JScrollPane(self.jta) toolPanel = JPanel() toolPanel.setLayout(BoxLayout(toolPanel, BoxLayout.X_AXIS)) toolPanel.setBorder(BorderFactory.createEmptyBorder(2, 0, 2, 0)) toolPanel.add(JLabel("Search: ")) jSearchPanel = JPanel() jSearchPanel.setLayout(BoxLayout(jSearchPanel, BoxLayout.X_AXIS)) self.jSearchField = JTextField() jSearchPanel.setBackground(self.jSearchField.getBackground()) jSearchPanel.setBorder(self.jSearchField.getBorder()) self.jSearchField.setBorder(None) self.jSearchField.getDocument().addDocumentListener(SearchListener(self.tree)) jSearchPanel.add(self.jSearchField) clearSearchButton = JButton(u'\u00d7', actionPerformed = self.clearSearch) d = clearSearchButton.getPreferredSize() clearSearchButton.setPreferredSize(Dimension(d.height, d.height)) clearSearchButton.setBackground(self.jSearchField.getBackground()) clearSearchButton.setBorder(None) clearSearchButton.setOpaque(False) clearSearchButton.setContentAreaFilled(False) clearSearchButton.setFocusPainted(False) jSearchPanel.add(clearSearchButton) toolPanel.add(jSearchPanel) self.showAllCheckBox = JCheckBox("Show all", actionPerformed = self.OnShowAllCheck) toolPanel.add(self.showAllCheckBox) splitPane = JSplitPane(JSplitPane.VERTICAL_SPLIT, jTreeSP, jTextSP) splitPane.setOneTouchExpandable(True) splitPane.setDividerLocation(300) treePanel = JPanel(BorderLayout()) treePanel.add(toolPanel, BorderLayout.NORTH) treePanel.add(splitPane, BorderLayout.CENTER) loadSavePanel = JPanel() loadSavePanel.setLayout(BoxLayout(loadSavePanel, BoxLayout.X_AXIS)) loadSavePanel.add(JButton("Load", actionPerformed=self.loadConfigDialog)) loadSavePanel.add(JButton("Save as", actionPerformed=self.writeConfigDialog)) self.rootPanel = JPanel() self.rootPanel.setLayout(BorderLayout()) self.rootPanel.add(loadSavePanel, BorderLayout.PAGE_START) self.rootPanel.add(treePanel, BorderLayout.CENTER) def clearSearch(self, event): self.jSearchField.setText("") def OnShowAllCheck(self, event): self.tree.setShowAll(self.showAllCheckBox.isSelected()) self.tree.doSearch(self.jSearchField.getText()) # Must repeat the search if one is active def treeSelectionChanged(self, event): """When the user selects a new node in the tree, show info about the selected node in the info text area below the tree.""" path = event.getNewLeadSelectionPath() if path: comp = path.getLastPathComponent() if isinstance(comp, DefaultMutableTreeNode): nodeData = comp.getUserObject() if isinstance(nodeData, TreeNodeData): self.jta.setText(getNodeInfoString(nodeData.knode)) self.jta.setCaretPosition(0) def getPane(self): """Return the panel containing all the other components that is set up in __init__().""" return self.rootPanel def writeConfig(self, fileName): """Write the current configuration to the file specified.""" self.kconfig.write_config(fileName) # Save full configuration #self.kconfig.write_min_config(fileName) # Save minimal configuration def loadConfig(self, fileName): """Load configuration settings from the file specified.""" if os.path.isfile(fileName): log.info(self.kconfig.load_config(fileName)) self.tree.createKconfShadowModel(self.kconfig) self.tree.updateTree() def writeConfigDialog(self, e): """Open a file dialog to save configuration""" fileChooser = JFileChooser(os.getcwd()) retval = fileChooser.showSaveDialog(None) if retval == JFileChooser.APPROVE_OPTION: f = fileChooser.getSelectedFile() self.writeConfig(f.getPath()) def loadConfigDialog(self, e): """Open a file dialog to select configuration to load""" fileChooser = JFileChooser(os.getcwd()) retval = fileChooser.showOpenDialog(None) if retval == JFileChooser.APPROVE_OPTION: f = fileChooser.getSelectedFile() log.info("Selected file: " + f.getPath()) self.loadConfig(f.getPath()) class SearchListener(DocumentListener): """Triggered when the user adds or removes characters in the search text field.""" def __init__(self, tree): self.tree = tree def changedUpdate(self, e): doc = e.getDocument() searchText = doc.getText(0, doc.getLength()) self.tree.doSearch(searchText) def insertUpdate(self, e): self.changedUpdate(e) def removeUpdate(self, e): self.changedUpdate(e) if __name__ == "__main__": # Set default .config file or load it from argv if len(sys.argv) == 2: # Specify "Kconfig" mpconfig = MPConfig(sys.argv[1]) else: # Specify "Kconfig" and ".config" mpconfig = MPConfig(sys.argv[1], sys.argv[2]) jframe = JFrame("MPLAB X Kconfig Editor") jframe.getContentPane().add(mpconfig.getPane()) jframe.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE) jframe.setSize(500, 800) jframe.setVisible(True)

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null

0

null

0

1

0

c0df3129d8955fe3aa993705e4ac485becb1c9ed

3,495

py

Python

functional_test/test_sqlite.py

penguinolog/sqlalchemy_jsonfield

552bc52af2f2e9c4ebe85928070e2b1b42d9a5d8

[ "Apache-2.0" ]

17

2017-05-19T14:03:15.000Z

2022-01-16T08:33:34.000Z

functional_test/test_sqlite.py

penguinolog/sqlalchemy_jsonfield

552bc52af2f2e9c4ebe85928070e2b1b42d9a5d8

[ "Apache-2.0" ]

5

2018-08-01T09:55:48.000Z

2020-07-06T08:54:00.000Z

functional_test/test_sqlite.py

penguinolog/sqlalchemy_jsonfield

552bc52af2f2e9c4ebe85928070e2b1b42d9a5d8

[ "Apache-2.0" ]

2

2018-08-01T09:47:40.000Z

2020-07-05T15:31:17.000Z

# coding=utf-8 # pylint: disable=missing-docstring, unused-argument import os.path import sqlite3 import tempfile import unittest import sqlalchemy.ext.declarative import sqlalchemy.orm try: # noinspection PyPackageRequirements import ujson as json except ImportError: import json import sqlalchemy_jsonfield # Path to test database db_path = os.path.join(tempfile.gettempdir(), "test.sqlite3") # Table name table_name = "create_test" # DB Base class Base = sqlalchemy.ext.declarative.declarative_base() # Model class ExampleTable(Base): __tablename__ = table_name id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True) row_name = sqlalchemy.Column(sqlalchemy.Unicode(64), unique=True) json_record = sqlalchemy.Column(sqlalchemy_jsonfield.JSONField(), nullable=False) class SQLIteTests(unittest.TestCase): def setUp(self): # type: () -> None if os.path.exists(db_path): os.remove(db_path) engine = sqlalchemy.create_engine("sqlite:///{}".format(db_path), echo=False) Base.metadata.create_all(engine) # noinspection PyPep8Naming Session = sqlalchemy.orm.sessionmaker(engine) self.session = Session() def test_create(self): # type: () -> None """Check column type""" # noinspection PyArgumentList with sqlite3.connect(database="file:{}?mode=ro".format(db_path), uri=True) as conn: conn.row_factory = sqlite3.Row c = conn.cursor() c.execute("PRAGMA TABLE_INFO({})".format(table_name)) collected = c.fetchall() result = [dict(col) for col in collected] columns = {info["name"]: info for info in result} json_record = columns["json_record"] self.assertIn( json_record["type"], ("TEXT", "JSON"), "Unexpected column type: received: {!s}, expected: TEXT|JSON".format(json_record["type"]), ) def test_operate(self): # type: () -> None """Check column data operation""" test_dict = {"key": "value"} test_list = ["item0", "item1"] # fill table with self.session.transaction: self.session.add_all( [ ExampleTable(row_name="dict_record", json_record=test_dict), ExampleTable(row_name="list_record", json_record=test_list), ] ) # Validate backward check dict_record = self.session.query(ExampleTable).filter(ExampleTable.row_name == "dict_record").first() list_record = self.session.query(ExampleTable).filter(ExampleTable.row_name == "list_record").first() self.assertEqual( dict_record.json_record, test_dict, "Dict was changed: {!r} -> {!r}".format(test_dict, dict_record.json_record), ) self.assertEqual( list_record.json_record, test_list, "List changed {!r} -> {!r}".format(test_list, list_record.json_record) ) # Low level # noinspection PyArgumentList with sqlite3.connect(database="file:{}?mode=ro".format(db_path), uri=True) as conn: c = conn.cursor() c.execute("SELECT row_name, json_record FROM {tbl}".format(tbl=table_name)) result = dict(c.fetchall()) self.assertEqual(result["dict_record"], json.dumps(test_dict)) self.assertEqual(result["list_record"], json.dumps(test_list))

30.391304

118

0.631187

397

3,495

5.390428

0.322418

0.056075

0.04486

0.037383

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0.081308

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0.246638

3,495

114

119

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0.808583

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0

0.114452

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0

0.149254

0

0.283582

0

null

0

null

0

1

0

c0e326802c17cadedbdcc95d716b27c009b7245b

495

py

Python

generic_op/pool_op.py

cap-lab/MidapSim

4f92a9f9413c29d7e1f37e863cce90ebdde8b420

[ "MIT" ]

2

2021-03-28T16:19:06.000Z

2022-02-26T08:58:33.000Z

generic_op/pool_op.py

cap-lab/MidapSim

4f92a9f9413c29d7e1f37e863cce90ebdde8b420

[ "MIT" ]

null

generic_op/pool_op.py

cap-lab/MidapSim

4f92a9f9413c29d7e1f37e863cce90ebdde8b420

[ "MIT" ]

1

2021-02-22T08:44:20.000Z

from .convpool_op_base import ConvPoolOpBase class PoolOp(ConvPoolOpBase): def __init__( self, op_type='Pool', pool_type=None, global_pooling=False, **kwargs ): super(PoolOp, self).__init__(op_type=op_type, **kwargs) self.global_pooling = global_pooling if pool_type is not None: self.type = pool_type def flip_operation(self): self.pad_r, self.pad_l = self.pad_l, self.pad_r

26.052632

63

0.60202

62

495

4.419355

0.435484

0.10219

0.058394

0.087591

0.083942

0

0.311111

495

18

64

27.5

0.803519

0

0.008081

0

1

0.133333

false

0

0.066667

0

0.266667

0

null

0

null

0

1

0

c0ebac4ab996b305d4af158e61ede7d45f0985a2

5,002

py

Python

models/special_tensors.py

LaudateCorpus1/learning-compressible-subspaces

94db8191f5f4d32c1e86834284fcf9f89e4d445b

[ "AML" ]

6

2021-11-02T23:10:05.000Z

2021-11-26T06:46:21.000Z

models/special_tensors.py

LaudateCorpus1/learning-compressible-subspaces

94db8191f5f4d32c1e86834284fcf9f89e4d445b

[ "AML" ]

null

models/special_tensors.py

LaudateCorpus1/learning-compressible-subspaces

94db8191f5f4d32c1e86834284fcf9f89e4d445b

[ "AML" ]

2

2021-12-02T00:06:41.000Z

2022-03-26T11:33:04.000Z

# # For licensing see accompanying LICENSE file. # Copyright (C) 2021 Apple Inc. All Rights Reserved. # """Utility functions to tag tensors with metadata. The metadata remains with the tensor under torch operations that don't change the values, e.g. .clone(), .contiguous(), .permute(), etc. """ import collections import copy from typing import Any from typing import Optional import numpy as np import torch QuantizeAffineParams2 = collections.namedtuple( "QuantizeAffineParams", ["scale", "zero_point", "num_bits"] ) class _SpecialTensor(torch.Tensor): """This class denotes special tensors. It isn't intended to be used directly, but serves as a helper for tagging tensors with metadata. It subclasses torch.Tensor so that isinstance(t, torch.Tensor) returns True for special tensors. It forbids some of the methods of torch.Tensor, and overrides a few methods used to create other tensors, to ensure the result is still special. """ _metadata = None def __getattribute__(self, attr: str) -> Any: # Disallow new_zeros, new_ones, new_full, etc. if "new_" in attr: raise AttributeError( "Invalid attr {!r} for special tensors".format(attr) ) return super().__getattribute__(attr) def detach(self) -> "_SpecialTensor": ret = super().detach() ret.__class__ = _SpecialTensor ret._metadata = self._metadata return ret @property def data(self) -> "_SpecialTensor": ret = super().data ret.__class__ = _SpecialTensor ret._metadata = self._metadata return ret def clone(self) -> "_SpecialTensor": ret = super().clone() ret.__class__ = _SpecialTensor ret._metadata = self._metadata return ret def cuda( self, device: Optional[torch.device] = None, non_blocking: bool = False ) -> "_SpecialTensor": ret = super().cuda() ret.__class__ = _SpecialTensor ret._metadata = self._metadata return ret def contiguous(self) -> "_SpecialTensor": ret = super().contiguous() ret.__class__ = _SpecialTensor ret._metadata = self._metadata return ret def view(self, *args, **kwargs) -> "_SpecialTensor": ret = super().view(*args, **kwargs) ret.__class__ = _SpecialTensor ret._metadata = self._metadata return ret def permute(self, *args, **kwargs) -> "_SpecialTensor": ret = super().permute(*args, **kwargs) ret.__class__ = _SpecialTensor ret._metadata = self._metadata return ret def __getitem__(self, *args, **kwargs) -> "_SpecialTensor": ret = super().__getitem__(*args, **kwargs) ret.__class__ = _SpecialTensor ret._metadata = self._metadata return ret def __copy__(self) -> "_SpecialTensor": ret = copy.copy(super()) ret.__class__ = _SpecialTensor ret._metadata = self._metadata return ret def _check_type(tensor: torch.Tensor) -> None: given_type = type(tensor) if not issubclass(given_type, torch.Tensor): raise TypeError("invalid type {!r}".format(given_type)) def tag_with_metadata(tensor: torch.Tensor, metadata: Any) -> None: """Tag a metadata to a tensor.""" _check_type(tensor) tensor.__class__ = _SpecialTensor tensor._metadata = metadata RepresentibleByQuantizeAffine = collections.namedtuple( "RepresentibleByQuantizeAffine", ["quant_params"] ) def mark_quantize_affine( tensor: torch.Tensor, scale: float, zero_point: int, dtype: np.dtype = np.uint8, ) -> None: """Mark a tensor as quantized with affine. See //xnorai/training/pytorch/extensions/functions:quantize_affine for more info on this method of quantization. The tensor itself can be a floating point Tensor. However, its values must be representible with @scale and @zero_point. This function, for performance reasons, does not validiate if the tensor is really quantizable as it claims to be. Arguments: tensor (torch.Tensor): The tensor to be marked as affine-quantizable Tensor. scale (float): the scale (from quantization parameters). zero_point (int): The zero_point (from quantization parameters). dtype (numpy.dtype): Type of tensor when quantized (this is usually numpy.uint8, which is used for Q8). A ValueError will be thrown if the input dtype is not one of the following: {numpy.uint8, numpy.int32}. """ allowed_dtypes = [np.uint8, np.int32] if dtype not in allowed_dtypes: raise ValueError( "Provided dtype ({}) is not supported. Please use: {}".format( dtype, allowed_dtypes ) ) quant_params = QuantizeAffineParams2(scale, zero_point, dtype) tag_with_metadata(tensor, RepresentibleByQuantizeAffine(quant_params))

31.859873

80

0.658137

580

5,002

5.448276

0.318966

0.091139

0.05981

0.068354

0.201266

0.168038

0.151266

0

0.003989

0.248301

5,002

156

81

32.064103

0.836436

0.318673

0

0.296703

0

0.097324

0.00882

0

1

0.142857

false

0

0.065934

0

0.340659

0

null

0

null

0

1

0

c0f028af70b526fd95e136fc02b10d25bfdd263a

2,705

py

Python

porodynhe_example2d.py

sfepy/example_largedef_porodyn

4116abc7daed195eee15277b2bd564cec3762ac6

[ "MIT" ]

null

porodynhe_example2d.py

sfepy/example_largedef_porodyn

4116abc7daed195eee15277b2bd564cec3762ac6

[ "MIT" ]

null

porodynhe_example2d.py

sfepy/example_largedef_porodyn

4116abc7daed195eee15277b2bd564cec3762ac6

[ "MIT" ]

null

# Rohan E., Lukeš V. # Modeling large-deforming fluid-saturated porous media using # an Eulerian incremental formulation. # Advances in Engineering Software, 113:84-95, 2017, # https://doi.org/10.1016/j.advengsoft.2016.11.003 # # Run simulation: # # ./simple.py example_largedef_porodyn-1/porodynhe_example2d.py # # The results are stored in `example_largedef_porodyn-1/results`. # import numpy as nm from porodyn_engine import incremental_algorithm,\ fc_fce, mat_fce, def_problem import os.path as osp wdir = osp.dirname(__file__) def define(): params = { 'mesh_file': 'rect_16x16.vtk', 'mat_store_elem': 75, # element for which material data are stored 'u_store_node': 272, # node for which displacement is stored 'p_store_node': 144, # node for which pressure is stored 'dim': 2, # problem dimension 'dt': 0.01, # time step 't_end': 2.0, # end time 'force': 4e6, # applied force 'save_step': True, # save results in each time step? 'init_mode': False, # calculate initial state? } material_params = { 'param': { 'B': nm.eye(params['dim']), 'g': 9.81, # gravitational acceleration }, 'solid': { 'Phi': 0.58, # volume fraction 'lam': 8.4e6, # Lame coefficient 'mu': 5.6e6, # Lame coefficient 'rho': 2700, # density }, 'fluid': { 'kappa': 1e-1, # permeability parameter 'beta': 0.8, # permeability parameter 'rho': 1000, # density 'Kf': 2.2e10, # bulk modulus }, } regions = { 'Omega': 'all', 'Left': ('vertices in (x < 0.001)', 'facet'), 'Right': ('vertices in (x > 9.999)', 'facet'), 'Bottom': ('vertices in (y < 0.001)', 'facet'), 'Top_r': ('vertices in (y > 9.999) & (x > 4.999)', 'facet'), 'Top_l': ('vertices in (y > 9.999) & (x < 5.001)', 'facet'), 'ForceRegion': ('copy r.Top_r', 'facet'), } ebcs = { 'Fixed_Left_u': ('Left', {'u.0': 0.0}), 'Fixed_Right_u': ('Right', {'u.0': 0.0}), 'Fixed_Bottom_u': ('Bottom', {'u.1': 0.0}), 'Fixed_Top_p': ('Top_l', {'p.0': 0.0}), } ############################################### options = { 'output_dir': osp.join(wdir, 'results'), 'parametric_hook': 'incremental_algorithm', } filename_mesh = params['mesh_file'] materials, functions, fields, variables, equations, solvers = \ def_problem(params['dt'], params['force']) return locals()

31.823529

76

0.528651

320

2,705

4.33125

0.55625

0.010101

0.02381

0.033189

0.036075

0.023088

0

0.061741

0.299445

2,705

84

77

32.202381

0.669657

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0

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0.011094

0

1

0.016949

false

0

0.050847

0

0.084746

0

null

0

null

0

1

0

c0f0b3100db352f07b237c204da41ba3ca9a0b70

260

py

Python

mailcheck/__init__.py

shacker/django-mailcheck

878dd21dcd599bd3761e225ba0c2717af458c000

[ "BSD-3-Clause" ]

1

2019-05-24T12:40:49.000Z

mailcheck/__init__.py

shacker/django-mailcheck

878dd21dcd599bd3761e225ba0c2717af458c000

[ "BSD-3-Clause" ]

null

mailcheck/__init__.py

shacker/django-mailcheck

878dd21dcd599bd3761e225ba0c2717af458c000

[ "BSD-3-Clause" ]

null

""" Pluggable Django email backend for capturing outbound mail for QA/review purposes. """ __version__ = "1.0" __author__ = "Scot Hacker" __email__ = "shacker@birdhouse.org" __url__ = "https://github.com/shacker/django-mailcheck" __license__ = "BSD License"

23.636364

82

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32

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0.123077

260

10

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0.523529

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1

0

false

0

null

0

null

0

1

0

c0f1710109fd0bcc8c80d8dbd1890e68264eb994

4,923

py

Python

nistapttools/histogram_functions.py

bcaplins/NIST_APT_TOOLS

80c25498e8b069b8ee289a2d09c76c932c054cea

[ "Unlicense" ]

null

nistapttools/histogram_functions.py

bcaplins/NIST_APT_TOOLS

80c25498e8b069b8ee289a2d09c76c932c054cea

[ "Unlicense" ]

null

nistapttools/histogram_functions.py

bcaplins/NIST_APT_TOOLS

80c25498e8b069b8ee289a2d09c76c932c054cea

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Aug 28 13:41:03 2019 @author: bwc """ import numpy as np def bin_dat(dat,bin_width=0.001,user_roi=[],isBinAligned=False,isDensity=False): user_roi = np.asarray(user_roi) roi_supp = (user_roi.size == 2) # Get roi if isBinAligned and roi_supp: lower = np.floor(np.min(user_roi)/bin_width)*bin_width upper = np.ceil(np.max(user_roi)/bin_width)*bin_width roi = np.array([lower, upper]) elif isBinAligned and (not roi_supp): lower = np.floor(np.min(dat)/bin_width)*bin_width upper = np.ceil(np.max(dat)/bin_width)*bin_width roi = np.array([lower, upper]) elif (not isBinAligned) and roi_supp: roi = user_roi else: # (not isBinAligned) and (not roi_supp): roi = np.array([np.min(dat), np.max(dat)]) num_bins = int(np.rint((roi[1]/bin_width-roi[0]/bin_width))) histo = np.histogram(dat,range=(roi[0], roi[1]),bins=num_bins,density=isDensity) xs = (histo[1][1:]+histo[1][0:-1])/2 ys = histo[0] return (xs,ys) def edges_to_centers(*edges): """ Convert bin edges to bin centers Parameters ---------- *edges : bin edges Returns ------- centers : list of bin centers """ centers = [] for es in edges: centers.append((es[0:-1]+es[1:])/2) return centers def corrhist(epos): dat = epos['tof'] roi = [0, 5000] delta = 1 # dat = epos['m2q'] # roi = [0, 100] # delta = .1 # # MF = np.mean(epos['tof']/np.sqrt(epos['m2q'])) # dat = np.sqrt(epos['m2q'])*MF # roi = [0, np.sqrt(250)*MF] # delta = .001*MF ## N = int(np.ceil((roi[1]-roi[0])/delta)) corrhist = np.zeros([N,N], dtype=int) multi_idxs = np.where(epos['ipp']>1)[0] for multi_idx in multi_idxs: n_hits = epos['ipp'][multi_idx] cluster = dat[multi_idx:multi_idx+n_hits] idx1 = -1 idx2 = -1 for i in range(n_hits): for j in range(i+1,n_hits): idx1 = int(np.floor(cluster[i]/delta)) idx2 = int(np.floor(cluster[j]/delta)) if idx1 < N and idx2 < N: corrhist[idx1,idx2] += 1 return corrhist+corrhist.T-np.diag(np.diag(corrhist)) def dummy(): # Voltage and bowl correct ToF data from voltage_and_bowl import do_voltage_and_bowl p_volt = np.array([]) p_bowl = np.array([]) tof_corr, p_volt, p_bowl = do_voltage_and_bowl(epos,p_volt,p_bowl) epos_vb = epos.copy() epos_vb['tof'] = tof_corr.copy() import voltage_and_bowl tof_vcorr = voltage_and_bowl.mod_full_voltage_correction(p_volt,epos['tof'],epos['v_dc']) epos_v = epos.copy() epos_v['tof'] = tof_vcorr.copy() tof_bcorr = voltage_and_bowl.mod_geometric_bowl_correction(p_bowl,epos['tof'],epos['x_det'],epos['y_det']) epos_b = epos.copy() epos_b['tof'] = tof_bcorr.copy() ROI = [0, None] ch = histogram_functions.corrhist(epos) fig1 = plt.figure(num=1) plt.clf() plt.imshow(np.log2(1+ch)) plt.title('raw') fig1.gca().set_xlim(ROI[0],ROI[1]) fig1.gca().set_ylim(ROI[0],ROI[1]) ch = histogram_functions.corrhist(epos_v) fig2 = plt.figure(num=2) plt.clf() plt.imshow(np.log2(1+ch)) plt.title('volt') fig2.gca().set_xlim(ROI[0],ROI[1]) fig2.gca().set_ylim(ROI[0],ROI[1]) ch = histogram_functions.corrhist(epos_b) fig3 = plt.figure(num=3) plt.clf() plt.imshow(np.log2(1+ch)) plt.title('bowl') fig3.gca().set_xlim(ROI[0],ROI[1]) fig3.gca().set_ylim(ROI[0],ROI[1]) ch = histogram_functions.corrhist(epos_vb) fig4 = plt.figure(num=4) plt.clf() plt.imshow(np.log10(1+ch)) plt.title('v+b') # fig4.gca().set_xlim(ROI[0],ROI[1]) # fig4.gca().set_ylim(ROI[0],ROI[1]) idxs = np.where(epos['ipp'] == 2)[0] fig5 = plt.figure(num=5) plt.clf() dts = np.abs(tof_corr[idxs]-tof_corr[idxs+1]) plt.hist(dts,bins=np.arange(0,2000,.5),label='deltaT') plt.hist(tof_corr[np.r_[idxs,idxs+1]],bins=np.arange(0,2000,.5),label='since t0') fig66 = plt.figure(num=66) plt.clf() dts = np.abs(tof_corr[idxs]-tof_corr[idxs+1]) # sus = np.sqrt(tof_corr[idxs]**2+tof_corr[idxs+1]**2) # sus = np.fmax(tof_corr[idxs],tof_corr[idxs+1]) sus = (tof_corr[idxs]+tof_corr[idxs+1])/np.sqrt(2) plt.plot(sus,dts,'.',ms=1,alpha=1) # fig66.gca().axis('equal') fig66.gca().set_xlim(0,7000) fig66.gca().set_ylim(-100, 800) return

24.615

110

0.549868

742

4,923

3.5

0.222372

0.023104

0.042357

0.027724

0.326531

0.274933

0.269542

0.189834

0.178668

0.154024

0

0.045506

0.281333

4,923

199

111

24.738693

0.688525

0.137518

0

0.13

0

0.016742

0

1

0.04

false

0

0.03

0

0.11

0

null

0

null

0

1

0

c0f3c763fc8fb9b275792346291c6e8ea034e967

1,138

py

Python

bage_utils/inspect_util.py

bage79/nlp4kor

016a20270774325579fc816a0364fb1695e60b51

[ "MIT" ]

60

2017-04-26T04:43:45.000Z

2021-11-08T13:01:11.000Z

bage_utils/inspect_util.py

bage79/nlp4kor

016a20270774325579fc816a0364fb1695e60b51

[ "MIT" ]

null

bage_utils/inspect_util.py

bage79/nlp4kor

016a20270774325579fc816a0364fb1695e60b51

[ "MIT" ]

17

2017-05-21T17:27:20.000Z

2021-01-16T22:35:44.000Z

import inspect # http://docs.python.org/2/library/inspect.html from pprint import pprint from bage_utils.dict_util import DictUtil # @UnusedImport class InspectUtil(object): @staticmethod def summary(): frame = inspect.stack()[1] d = {'file': frame[1], 'line': frame[2], 'function': frame[3], 'code': frame[4]} return d @staticmethod def all(): frame = inspect.stack()[1] d = {} for key in dir(frame[0]): d[key] = getattr(frame[0], key) return DictUtil.sort_by_key(d) @staticmethod def locals(): frame = inspect.stack()[1] d = {} for key in frame[0].f_locals: d[key] = frame[0].f_locals[key] return DictUtil.sort_by_key(d) @staticmethod def globals(): frame = inspect.stack()[1] d = {} for key in frame[0].f_globals: d[key] = frame[0].f_globals[key] return DictUtil.sort_by_key(d) def __test(): pprint(InspectUtil.summary()) pprint(InspectUtil.locals()) if __name__ == '__main__': pprint(InspectUtil.summary()) # __test()

24.212766

88

0.579086

144

1,138

4.409722

0.347222

0.056693

0.107087

0.113386

0.388976

0.324409

0.28189

0.23937

0.107087

0

0.018248

0.27768

1,138

46

89

24.73913

0.754258

0.059754

0

0.457143

0

0.026266

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1

0.142857

false

0

0.085714

0

0.371429

0.114286

0

null

0

null

0

1

0

c0f5fb0852a3f468b938572e90b83ad69c9f9511

3,914

py

Python

Common.py

DongDong-123/zgg_active

7b7304bc9391e1d370052087d4ad2e6d05db670c

[ "Apache-2.0" ]

null

Common.py

DongDong-123/zgg_active

7b7304bc9391e1d370052087d4ad2e6d05db670c

[ "Apache-2.0" ]

null

Common.py

DongDong-123/zgg_active

7b7304bc9391e1d370052087d4ad2e6d05db670c

[ "Apache-2.0" ]

null

import os import random import time import xlwt from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.support.ui import WebDriverWait from front_login import * from readConfig import ReadConfig from db import DbOperate from selenium.webdriver.chrome.options import Options from mysqldb import connect chrome_options = Options() chrome_options.add_argument('--headless') driver = webdriver.Chrome(chrome_options=chrome_options) # driver = webdriver.Chrome() driver.maximize_window() driver.get(ReadConfig().get_root_url()) driver.get(ReadConfig().get_root_url()) class Common(object): def __init__(self): self.driver = driver # Excel写入 self.row = 0 self.workbook = xlwt.Workbook(encoding='utf-8') self.booksheet = self.workbook.add_sheet('Sheet1') self.timetemp = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) # 存储Excel表格文件名编号 # 每个案件的数量 self.number = 1 self.report_path = ReadConfig().save_report() self.windows = None self.screen_path = ReadConfig().save_screen() # 增加案件数量 def number_add(self): if self.number > 1: for i in range(self.number): self.driver.find_element_by_xpath("//a[@class='add']").click() else: self.driver.find_element_by_xpath("//a[@class='add']").click() # 减少案件数量至1 def number_minus(self): while self.number > 1: self.driver.find_element_by_xpath("//a[@class='jian']").click() # 存入数据库 def save_to_mysql(self, parm): code = 0 if isinstance(parm, list): parm.append(code) else: parm = list(parm) parm.append(code) res_code = connect(parm) print("存储状态", res_code) # 执行下单 def execute_function(self, callback): try: eval("self.{}()".format(callback)) except Exception as e: print("错误信息:", e) self.write_error_log(callback) time.sleep(0.5) self.write_error_log(str(e)) def write_error_log(self, info): error_log_path = os.path.join(self.report_path, "error_log_{}.log".format(time.strftime("%Y-%m-%d", time.localtime()))) with open(error_log_path, "a", encoding="utf-8") as f: f.write("{}: ".format(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) + info + "\n") # 处理价格字符 def process_price(self, price): if "￥" in price: price = price.replace("￥", '') return price # 关闭窗口 def closed_windows(self, num): self.windows = self.driver.window_handles for n in range(num + 1, len(self.windows)): self.driver.switch_to.window(self.windows[n]) self.driver.close() self.windows = self.driver.window_handles self.driver.switch_to.window(self.windows[num]) # 存储信息 def excel_number(self, infos): # 获取案件名称、案件号 if infos: n = 0 for info in infos: self.booksheet.write(self.row, n, info) self.booksheet.col(n).width = 300 * 28 n += 1 path = os.path.join(self.report_path, "report_{}.xls".format(self.timetemp)) self.workbook.save(path) # 窗口截图 def qr_shotscreen(self, windows_handle, name): current_window = self.driver.current_window_handle if current_window != windows_handle: self.driver.switch_to.window(windows_handle) path = self.screen_path self.driver.save_screenshot(path + self.timetemp + name + ".png") print("截图成功") self.driver.switch_to.window(current_window) else: path = self.screen_path self.driver.save_screenshot(path + self.timetemp +name + ".png") print("截图成功")

32.347107

109

0.601175

484

3,914

4.71281

0.309917

0.061377

0.028058

0.031565

0.287155

0.266111

0.199036

0.143797

0.128891

0

0.006669

0.2721

3,914

120

110

32.616667

0.793261

0.030148

0

0.186813

0

0.050767

0

1

0.10989

false

0

0.120879

0

0.252747

0.043956

0

null

0

null

0

1

0

c0f64387b72b3f7cb9554217c9f76926a2cb5bad

5,367

py

Python

dashboard_generator.py

vgm236/exec-dash

5c446849ffc0ced5ec6c286d87603afa280f6017

[ "MIT" ]

1

2019-06-20T03:14:22.000Z

dashboard_generator.py

vgm236/exec-dash

5c446849ffc0ced5ec6c286d87603afa280f6017

[ "MIT" ]

null

dashboard_generator.py

vgm236/exec-dash

5c446849ffc0ced5ec6c286d87603afa280f6017

[ "MIT" ]

null

# dashboard_generator.py import os.path # helps to save in a different folder import pandas as pd import itertools import locale # from https://stackoverflow.com/Questions/320929/currency-formatting-in-python from os import listdir from os.path import isfile, join #for chart generation import matplotlib import matplotlib.pyplot as plt import matplotlib.ticker as ticker # FILES PATH save_path = 'C:/Users/Owner/Desktop/NYU-MBA/Programming/Files/monthly-sales/data' # INTRODUCTION print("Select one month to report") print("---------------------------------------------------------------------") # LISTING FILES (sorted and in a proper list) onlyfiles = [f for f in listdir(save_path) if isfile(join(save_path, f))] #https://stackoverflow.com/questions/3207219/how-do-i-list-all-files-of-a-directory onlyfiles.sort() print(*onlyfiles, sep = "\n") #https://www.geeksforgeeks.org/print-lists-in-python-4-different-ways/ print("---------------------------------------------------------------------") # REPORT SELECTION selected_year = input("Please input a year (Example 2018 -- for Year): ") selected_month = input("Please input a month (Example 01 -- for January): ") # FILE SELECTED file_name = "sales-" + selected_year + selected_month + ".csv" # OPENING SPECIFIC FILE find_file = os.path.join(save_path, file_name) #find the file while not os.path.exists(find_file): #correct if does not exist print("---------------------------------------------------------------------") print("\n") print("The file selected do not exist. Please try again") print("\n") print("---------------------------------------------------------------------") exit() stats = pd.read_csv(find_file) # PERFORMING THE SUM total_sales = stats["sales price"].sum() # FORMATTING TOTAL SALES locale.setlocale( locale.LC_ALL, '' ) total_sales_format = locale.currency(total_sales, grouping= True) print("---------------------------------------------------------------------") # SALES REPORT DATE if selected_month == "01": month_name = "JANUARY" if selected_month == "02": month_name = "FEBRUARY" if selected_month == "03": month_name = "MARCH" if selected_month == "04": month_name = "APRIL" if selected_month == "05": month_name = "MAY" if selected_month == "06": month_name = "JUNE" if selected_month == "07": month_name = "JULY" if selected_month == "08": month_name = "AUGUST" if selected_month == "09": month_name = "SEPTEMBER" if selected_month == "10": month_name = "OCTOBER" if selected_month == "11": month_name = "NOVEMBER" if selected_month == "12": month_name = "DECEMBER" print("SALES REPORT " + "(" + month_name + " " + selected_year + ")") # PRINTING TOTAL SALES print("TOTAL SALES: " + (total_sales_format)) print("\n") # TOP SELLING PRODUCTS product_totals = stats.groupby(["product"]).sum() product_totals = product_totals.sort_values("sales price", ascending=False) top_sellers = [] rank = 1 for i, row in product_totals.iterrows(): d = {"rank": rank, "name": row.name, "monthly_sales": row["sales price"]} top_sellers.append(d) rank = rank + 1 def to_usd(my_price): return "${0:,.2f}".format(my_price) print("TOP SELLING PRODUCTS:") for d in top_sellers: locale.setlocale( locale.LC_ALL, '' ) print(" " + str(d["rank"]) + ") " + d["name"] + ": " + to_usd(d["monthly_sales"])) print("\n") print("---------------------------------------------------------------------") print("\n") print("GENERATING BAR CHART...") print("\n") print("---------------------------------------------------------------------") ### PRINT BAR CHART # first two lines are the list comprehensions to make a list of dictionaries into a list) x = [p["name"] for p in top_sellers] ## VERY IMPORTANT y = [p["monthly_sales"] for p in top_sellers] ## VERY IMPORTANT #sorting in the correct order x.reverse() y.reverse() # break charts into two fig, ax = plt.subplots() # enables us to further customize the figure and/or the axes #formatting chart usd_formatter = ticker.FormatStrFormatter('$%1.0f') ax.xaxis.set_major_formatter(usd_formatter) # CHART GENERATION plt.barh(x, y) plt.title("TOP-SELLING PRODUCTS " + "(" + month_name + " " + selected_year + ")") # AXIS TITLES plt.ylabel('Sales (USD)') # AXIS TITLES plt.ylabel("Product") # AXIS TITLES # formatting numbers for i, v in enumerate(y): ax.text(v, i, usd_formatter(v), color='black', fontweight='bold') #https://matplotlib.org/users/colors.html #https://matplotlib.org/3.1.0/gallery/pyplots/text_commands.html#sphx-glr-gallery-pyplots-text-commands-py plt.tight_layout() # ensures all areas of the chart are visible by default (fixes labels getting cut off) plt.show() exit() ## FULL SOLUTION PROVIDED BY THE PROFESSOR # # this section needs to come before the chart construction # fig, ax = plt.subplots() # enables us to further customize the figure and/or the axes # usd_formatter = ticker.FormatStrFormatter('$%1.0f') # ax.xaxis.set_major_formatter(usd_formatter) # # # chart construction # plt.barh(sorted_products, sorted_sales) # plt.title(chart_title) # plt.ylabel("Product") # plt.xlabel("Monthly Sales (USD)") # # plt.tight_layout() # ensures all areas of the chart are visible by default (fixes labels getting cut off) # plt.show()

27.80829

157

0.63406

703

5,367

4.722617

0.344239

0.054819

0.054217

0.018072

0.173494

0.157831

0.140361

0

0.012027

0.147941

5,367

193

158

27.80829

0.713973

0.313583

0

0.177083

0

0.010417

0.300138

0.151724

0

1

0.010417

false

0

0.09375

0.010417

0.114583

0.21875

0

null

0

null

0

1

0

c0f84e0c95d431aa5ccd03662827d19008ac7c6c

2,235

py

Python

product_spider/spiders/medicalisotopes_spider.py

Pandaaaa906/product_spider

cc7f865f53fd3ed68f4869be3ba917c8373dfcf2

[ "MIT" ]

null

product_spider/spiders/medicalisotopes_spider.py

Pandaaaa906/product_spider

cc7f865f53fd3ed68f4869be3ba917c8373dfcf2

[ "MIT" ]

null

product_spider/spiders/medicalisotopes_spider.py

Pandaaaa906/product_spider

cc7f865f53fd3ed68f4869be3ba917c8373dfcf2

[ "MIT" ]

null

from urllib.parse import urljoin from scrapy import Request from product_spider.items import RawData from product_spider.utils.functions import strip from product_spider.utils.spider_mixin import BaseSpider class MedicalIsotopesSpider(BaseSpider): name = "medicalisotopes" base_url = "https://www.medicalisotopes.com/" start_urls = ['https://www.medicalisotopes.com/productsbycategories.php', ] def parse(self, response): a_nodes = response.xpath('//div[contains(@class, "main-content")]//a') for a in a_nodes: parent = a.xpath('./text()').get() url = a.xpath('./@href').get() yield Request(urljoin(self.base_url, url), callback=self.parse_list, meta={'parent': parent}) def parse_list(self, response): rel_urls = response.xpath('//td[2]/a/@href').getall() parent = response.meta.get('parent') for rel_url in rel_urls: yield Request(urljoin(self.base_url, rel_url), callback=self.parse_detail, meta={'parent': parent}) next_page = response.xpath('//a[@class="c-page"]/following-sibling::a[text()!="NEXT"]/@href').get() if next_page: yield Request(urljoin(self.base_url, next_page), callback=self.parse_list, meta={'parent': parent}) def parse_detail(self, response): tmp = '//td[contains(text(), {!r})]/following-sibling::td//text()' package = strip(response.xpath('normalize-space(//td/table//td[1]/text())').get()) d = { 'brand': 'medicalisotopes', 'parent': response.meta.get('parent'), 'cat_no': strip(response.xpath(tmp.format("Catalog Number:")).get()), 'en_name': strip(response.xpath('//th[contains(text(), "Product:")]/following-sibling::th/text()').get()), 'cas': strip(response.xpath(tmp.format("CAS Number:")).get()), 'mf': strip(''.join(response.xpath(tmp.format("Formula:")).getall())), 'mw': strip(response.xpath(tmp.format("Molecular Weight:")).get()), 'info3': package and package.rstrip('\xa0='), 'info4': strip(response.xpath('//td/table//td[2]/text()').get()), 'prd_url': response.url, } yield RawData(**d)

46.5625

118

0.619239

273

2,235

4.974359

0.326007

0.095729

0.079529

0.064801

0.231959

0.132548

0.066274

0

0.003333

0.194631

2,235

47

119

47.553191

0.751111

0

0.256376

0.120358

0

1

0.076923

false

0

0.128205

0

0.307692

0

null

0

null

0

1

0

c0fa6b58d78457006cba2d731fe207bcc18728f5

9,819

py

Python

smiles_parsers/Smarts.py

UnixJunkie/frowns

427e4c11a8a4dbe865828d18221899478497795e

[ "BSD-3-Clause" ]

null

smiles_parsers/Smarts.py

UnixJunkie/frowns

427e4c11a8a4dbe865828d18221899478497795e

[ "BSD-3-Clause" ]

null

smiles_parsers/Smarts.py

UnixJunkie/frowns

427e4c11a8a4dbe865828d18221899478497795e

[ "BSD-3-Clause" ]

null

#!/usr/bin/enb python import string, re import Handler ####################### # Define some regular expressions inside a quoted string # then turn the string into the actual data structure. # (I found it was easiest to understand when done this way.) definitions = r""" # These are the atomic symbols Daylight allows outside of []s # See "atom_class" for names like "a" and "A" raw_atom Cl|Br|[cnospBCNOFPSI*] # For atoms inside of []s open_bracket \[ close_bracket \] # See "element_modifiers" for the patterns for element names # charges, chiralities, H count, etc. # [235U] weight \d+ # [#6] atomic_number #\d+ # [!C] atom_not ! # & is highest (an "and") # , is next (an "or") # ; is lowest (an "and") # [n&H] [n,H] [c,h;H1] atom_binary [&,;] # C.C dot \. # - single bond (aliphatic) # / directional single bond "up" # \ directional single bond "down" # /? directional bond "up or unspecified" # \? directional bond "down or unspecified" # = double bond # # triple bond # : aromatic bond # ~ any bond (wildcard) # @ any ring bond bond [/\\]\??|[=#:~@-] # *!:* -- not aromatic bond_not ! # *@;!:* -- same as !: bond_binary [&;,] # (C).(C) open_zero $ # C(C) open_branch \( # [$(*C);$(*CC)] open_recursive_smarts \$\( # special cased because it closes open_zero, open_branch, and # recursive_smarts close_parens $ # Ring closures, 1, %5 %99 (and even %00 for what it's worth) closure \d|%\d\d? """ ####################### # Turn the above string into key/value pairs where the # values are the compiled regular expressions. info = {} for line in string.split(definitions, "\n"): line = string.strip(line) if not line or line[:1] == "#": continue name, pattern = string.split(line) info[name] = re.compile(pattern) del line, name, pattern info["atom_class"] = re.compile(r""" (?P<raw_aromatic>a)| # Not really sure what these mean (?P<raw_b_unknown>b)| (?P<raw_f_unknown>f)| (?P<raw_h_unknown>h)| (?P<raw_i_unknown>i)| (?P<raw_r_unknown>r)| (?P<raw_aliphatic>A)| (?P<raw_R_unknown>R) """, re.X) # 'H' is used for the hydrogen count, so those searches require a # special recursive SMARTS definition. Eg, for deuterium or tritium # [$([2H]),$([3H])] # This is implemented as a special-case hack. Note: if there's # an error in the parse string in this section then the error # location will point to the start of this term, not at the # character that really caused the error. Can be fixed with an # 'error_' like I did for the SMILES -- not needed for now. XXX hydrogen_term_fields = [ "open_recursive_smarts", "open_bracket", "weight", "element", "positive_count", "positive_symbols", "negative_count", "negative_symbols", "close_bracket", "close_recursive_smarts", ] info["hydrogen_term"] = re.compile(r""" (?P<open_recursive_smarts>\$$) (?P<open_bracket>\[) (?P<weight>\d+)? # optional molecular weight [2H] (?P<element>H) # Must be a hydrogen ( # optional charge (?P<positive_count>\+\d+)| # +3 (?P<positive_symbols>\++)| # ++ (?P<negative_count>\-\d+)| # -2 (?P<negative_symbols>\-+)| # --- )? (?P<close_bracket>\]) (?P<close_recursive_smarts>$) """, re.X) element_symbols_pattern = \ r"C[laroudsemf]?|Os?|N[eaibdpos]?|S[icernbmg]?|P[drmtboau]?|" \ r"H[eofgas]|c|n|o|s|p|A[lrsgutcm]|B[eraik]?|Dy|E[urs]|F[erm]?|" \ r"G[aed]|I[nr]?|Kr?|L[iaur]|M[gnodt]|R[buhenaf]|T[icebmalh]|" \ r"U|V|W|Xe|Yb?|Z[nr]|\*" info["element_modifier"] = re.compile(r""" (?P<element> # This does *not* contain H. Hydrogen searches must be done # with a special recursive SMARTS. On the other hand, it does # include the lower case aromatic names. """ + element_symbols_pattern + r""" )| (?P<aromatic>a)| # aromatic (?P<aliphatic>A)| # Aliphatic (?P<degree>D\d+)| # Degree<n> (?P<total_hcount>H\d*)| # total Hydrogen count<n> (defaults to 1) (?P<imp_hcount>h\d*)| # implicit hydrogen count<n> (defaults to 1) (?P<ring_membership>R\d*)| # in <n> Rings (no n means any rings) (?P<ring_size>r\d*)| # in a ring of size <n> (no n means any rings) (?P<valence>v\d+)| # total bond order of <n> (?P<connectivity>X\d+)| # <n> total connections (?P<positive_count>\+\d+)| # +2 +3 (?P<positive_symbols>\++)| # + ++ +++ (?P<negative_count>\-\d+)| # -1 -4 (?P<negative_symbols>\-+)| # -- - ------- # XXX What about chiral_count? (?P<chiral_named> # The optional '?' means "or unspecified" @TH[12]\??| # @TH1 @TH2? @AL[12]\??| # @AL2? @SP[123]\??| # @SP3 @SP1? @TB(1[0-9]?|20?|[3-9])\??| # @TH{1 through 20} @OH(1[0-9]?|2[0-9]?|30?|[4-9])\?? # @OH{1 through 30} )| (?P<chiral_symbols>@@?\??) # @ (anticlockwise) or @@ (clockwise) """, re.X) # The ')' closes three different open parens. This maps from the # previous open state to the appropriate close state. close_parens_states = { "open_branch": "close_branch", "open_recursive_smarts": "close_recursive_smarts", "open_zero": "close_zero", } #### Some helpful definitions to reduce clutter and complication # Possible transitions from the start node. Also visited after # a '.' disconnect or in a recursive SMARTS. expecting_start = ("raw_atom", "atom_class", "open_bracket", "open_zero") # Looking for node definition, like "C" or "a" or "[" expecting_atom = ("raw_atom", "atom_class", "open_bracket") # Inside of []s: 235U, #6, R, $([2H]), $(*=C), ! expecting_element_start = ("weight", "atomic_number", "element_modifier", "hydrogen_term", "open_recursive_smarts", "atom_not") # the ';' in [n;H1] or the ']' at the end expecting_element_end = ("atom_binary", "close_bracket") # All bonds start with a '!' or one of the bond symbols expecting_bond_start = ("bond", "bond_not") expecting_raw_term = expecting_atom + expecting_bond_start + \ ("close_parens", "open_branch", "dot", "closure") expecting_modifier = ("element_modifier", "open_recursive_smarts") table = { "start": expecting_start, # (C).(R).[U].([$(*)]) "open_zero": ("raw_atom", "atom_class", "open_bracket"), # as well as (CC(C)) "close_zero": ("dot", "close_parens"), # A raw term are the things like 'C', '[U]', '%10', '.', '(', '!#' "raw_atom": expecting_raw_term, # An atom_class is a non-specific atom term, like 'A' or 'r' "atom_class": expecting_raw_term, # the []s "open_bracket": expecting_element_start, "close_bracket": expecting_raw_term, # Yes, '[!!!!C]' is legal, according to the docs, but it isn't # supported by the parser, unless you optimze it. "atom_not": expecting_element_start, "atom_binary": expecting_element_start, # "14N", "14a", ... # Note that weight can only be set once so it isn't a modifier # Also, "14#6" isn't legal (tested against the toolkit) "weight": expecting_modifier, # "#6R2" or "#8," or "#7]" # The atomic_number can only be set once so it isn't a modifier "atomic_number": expecting_modifier + expecting_element_end, # All of these are type of modifiers "element_modifier": expecting_modifier + expecting_element_end, "hydrogen_term": expecting_modifier + expecting_element_end, "close_recursive_smarts": expecting_modifier + expecting_element_end, # This it the recursive part -- goes back to the beginning "open_recursive_smarts": expecting_start, # C=C, C1CCC=1, C~-C, C=(C)C, C=,-C "bond": expecting_atom + ("closure", "bond", "open_branch", "bond_binary"), # C!!=C "bond_not": expecting_bond_start, # C=,-C "bond_binary": expecting_bond_start, "closure": expecting_raw_term, "close_branch": expecting_raw_term, "open_branch": expecting_atom + expecting_bond_start + ("dot",), # After a "." we can start all over again "dot": expecting_start, } def tokenize(s, handler = Handler.TokenHandler()): expected = table["start"] parens_stack = [] n = len(s) i = 0 handler.begin() while i < n: for state in expected: m = info[state].match(s, i) if m: break else: handler.error("Unknown character", i, s[i:]) return if close_parens_states.has_key(state): parens_stack.append(state) elif state == "close_parens": try: state = close_parens_states[parens_stack.pop()] except IndexError: # Too many close parens handler.error("Too many ')'", i, s[i:]) return d = m.groupdict() if d and state == "hydrogen_term": # Special case the hydrogen term for field in hydrogen_term_fields: if d[field] is not None: handler.add_token(field, i, d[field]) #print " --> New state:", state else: name = state if d: # There should only be one match for name, v in d.items(): if v is not None: break handler.add_token(name, i, m.group(0)) expected = table[state] i = m.end(0) handler.end()

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null

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c0fe21b15a59a46814a6a24b71ed4f6e93699049

8,402

py

Python

pyvizio/const.py

jezzab/pyvizio

8086f9e5aac49d1d99ade02684ca35c05e03a7eb

[ "MIT" ]

72

2017-08-08T19:32:12.000Z

2022-03-18T03:18:41.000Z

pyvizio/const.py

raman325/pyvizio

9cf45fcc9b409caf223a38d8f79c775742ab4127

[ "MIT" ]

48

2017-09-16T16:37:54.000Z

2022-01-23T20:43:42.000Z

pyvizio/const.py

ConnectionMaster/pyvizio

0fe4558557917509d3da3bb24f9221f15ba901ce

[ "MIT" ]

42

2017-09-04T22:59:21.000Z

2022-03-18T03:18:30.000Z

"""pyvizio constants.""" DEVICE_CLASS_SPEAKER = "speaker" DEVICE_CLASS_TV = "tv" DEVICE_CLASS_CRAVE360 = "crave360" DEFAULT_DEVICE_ID = "pyvizio" DEFAULT_DEVICE_CLASS = DEVICE_CLASS_TV DEFAULT_DEVICE_NAME = "Python Vizio" DEFAULT_PORTS = [7345, 9000] DEFAULT_TIMEOUT = 5 MAX_VOLUME = {DEVICE_CLASS_TV: 100, DEVICE_CLASS_SPEAKER: 31, DEVICE_CLASS_CRAVE360: 100} # Current Input when app is active INPUT_APPS = ["SMARTCAST", "CAST"] # App name returned when it is not in app dictionary UNKNOWN_APP = "_UNKNOWN_APP" NO_APP_RUNNING = "_NO_APP_RUNNING" SMARTCAST_HOME = "SmartCast Home" APP_CAST = "Cast" # NAME_SPACE values that appear to be equivalent EQUIVALENT_NAME_SPACES = (2, 4) APP_HOME = { "name": SMARTCAST_HOME, "country": ["*"], "config": [ { "NAME_SPACE": 4, "APP_ID": "1", "MESSAGE": "http://127.0.0.1:12345/scfs/sctv/main.html", } ], } # No longer needed but kept around in case the external source for APPS is unavailable APPS = [ { "name": "Prime Video", "country": ["*"], "id": ["33"], "config": [ { "APP_ID": "4", "NAME_SPACE": 4, "MESSAGE": "https://atv-ext.amazon.com/blast-app-hosting/html5/index.html?deviceTypeID=A3OI4IHTNZQWDD", }, {"NAME_SPACE": 2, "APP_ID": "4", "MESSAGE": "None"}, ], }, { "name": "CBS All Access", "country": ["usa"], "id": ["9"], "config": [{"NAME_SPACE": 2, "APP_ID": "37", "MESSAGE": "None"}], }, { "name": "CBS News", "country": ["usa", "can"], "id": ["56"], "config": [{"NAME_SPACE": 2, "APP_ID": "42", "MESSAGE": "None"}], }, { "name": "Crackle", "country": ["usa"], "id": ["8"], "config": [{"NAME_SPACE": 2, "APP_ID": "5", "MESSAGE": "None"}], }, { "name": "Curiosity Stream", "country": ["usa", "can"], "id": ["37"], "config": [{"NAME_SPACE": 2, "APP_ID": "12", "MESSAGE": "None"}], }, { "name": "Fandango Now", "country": ["usa"], "id": ["24"], "config": [{"NAME_SPACE": 2, "APP_ID": "7", "MESSAGE": "None"}], }, { "name": "FilmRise", "country": ["usa"], "id": ["47"], "config": [{"NAME_SPACE": 2, "APP_ID": "24", "MESSAGE": "None"}], }, { "name": "Flixfling", "country": ["*"], "id": ["49"], "config": [{"NAME_SPACE": 2, "APP_ID": "36", "MESSAGE": "None"}], }, { "name": "Haystack TV", "country": ["usa", "can"], "id": ["35"], "config": [ { "NAME_SPACE": 0, "APP_ID": "898AF734", "MESSAGE": '{"CAST_NAMESPACE":"urn:x-cast:com.google.cast.media","CAST_MESSAGE":{"type":"LOAD","media":{},"autoplay":true,"currentTime":0,"customData":{"platform":"sctv"}}}', } ], }, { "name": "Hulu", "country": ["usa"], "id": ["19"], "config": [ { "APP_ID": "3", "NAME_SPACE": 4, "MESSAGE": "https://viziosmartcast.app.hulu.com/livingroom/viziosmartcast/1/index.html#initialize", }, {"NAME_SPACE": 2, "APP_ID": "3", "MESSAGE": "None"}, ], }, { "name": "iHeartRadio", "country": ["usa"], "id": ["11"], "config": [{"NAME_SPACE": 2, "APP_ID": "6", "MESSAGE": "None"}], }, { "name": "NBC", "country": ["usa"], "id": ["43"], "config": [{"NAME_SPACE": 2, "APP_ID": "10", "MESSAGE": "None"}], }, { "name": "Netflix", "country": ["*"], "id": ["34"], "config": [{"NAME_SPACE": 3, "APP_ID": "1", "MESSAGE": "None"}], }, { "name": "Plex", "country": ["usa", "can"], "id": ["40"], "config": [ { "APP_ID": "9", "NAME_SPACE": 4, "MESSAGE": "https://plex.tv/web/tv/vizio-smartcast", }, {"NAME_SPACE": 2, "APP_ID": "9", "MESSAGE": "None"}, ], }, { "name": "Pluto TV", "country": ["usa"], "id": ["12"], "config": [ {"APP_ID": "65", "NAME_SPACE": 4, "MESSAGE": "https://smartcast.pluto.tv"}, { "NAME_SPACE": 0, "APP_ID": "E6F74C01", "MESSAGE": '{"CAST_NAMESPACE":"urn:x-cast:tv.pluto","CAST_MESSAGE":{"command":"initializePlayback","channel":"","episode":"","time":0}}', }, ], }, { "name": "RedBox", "country": ["usa"], "id": ["55"], "config": [{"NAME_SPACE": 2, "APP_ID": "41", "MESSAGE": "None"}], }, { "name": "TasteIt", "country": ["*"], "id": ["52"], "config": [{"NAME_SPACE": 2, "APP_ID": "26", "MESSAGE": "None"}], }, { "name": "Toon Goggles", "country": ["usa", "can"], "id": ["46"], "config": [{"NAME_SPACE": 2, "APP_ID": "21", "MESSAGE": "None"}], }, { "name": "Vudu", "country": ["usa"], "id": ["6"], "config": [ { "APP_ID": "31", "NAME_SPACE": 4, "MESSAGE": "https://my.vudu.com/castReceiver/index.html?launch-source=app-icon", } ], }, { "name": "XUMO", "country": ["usa"], "id": ["27"], "config": [ { "NAME_SPACE": 0, "APP_ID": "36E1EA1F", "MESSAGE": '{"CAST_NAMESPACE":"urn:x-cast:com.google.cast.media","CAST_MESSAGE":{"type":"LOAD","media":{},"autoplay":true,"currentTime":0,"customData":{}}}', } ], }, { "name": "YouTubeTV", "country": ["usa", "mexico"], "id": ["45"], "config": [{"NAME_SPACE": 5, "APP_ID": "3", "MESSAGE": "None"}], }, { "name": "YouTube", "country": ["*"], "id": ["44"], "config": [{"NAME_SPACE": 5, "APP_ID": "1", "MESSAGE": "None"}], }, { "name": "Baeble", "country": ["usa"], "id": ["39"], "config": [{"NAME_SPACE": 2, "APP_ID": "11", "MESSAGE": "None"}], }, { "name": "DAZN", "country": ["usa", "can"], "id": ["57"], "config": [{"NAME_SPACE": 2, "APP_ID": "34", "MESSAGE": "None"}], }, { "name": "FitFusion by Jillian Michaels", "country": ["usa", "can"], "id": ["54"], "config": [{"NAME_SPACE": 2, "APP_ID": "39", "MESSAGE": "None"}], }, { "name": "Newsy", "country": ["usa", "can"], "id": ["38"], "config": [{"NAME_SPACE": 2, "APP_ID": "15", "MESSAGE": "None"}], }, { "name": "Cocoro TV", "country": ["usa", "can"], "id": ["63"], "config": [{"NAME_SPACE": 2, "APP_ID": "55", "MESSAGE": "None"}], }, { "name": "ConTV", "country": ["usa", "can"], "id": ["41"], "config": [{"NAME_SPACE": 2, "APP_ID": "18", "MESSAGE": "None"}], }, { "name": "Dove Channel", "country": ["usa", "can"], "id": ["42"], "config": [{"NAME_SPACE": 2, "APP_ID": "16", "MESSAGE": "None"}], }, { "name": "Love Destination", "country": ["*"], "id": ["64"], "config": [{"NAME_SPACE": 2, "APP_ID": "57", "MESSAGE": "None"}], }, { "name": "WatchFree", "country": ["usa"], "id": ["48"], "config": [{"NAME_SPACE": 2, "APP_ID": "22", "MESSAGE": "None"}], }, { "name": "AsianCrush", "country": ["usa", "can"], "id": ["50"], "config": [ { "NAME_SPACE": 2, "APP_ID": "27", "MESSAGE": "https://html5.asiancrush.com/?ua=viziosmartcast", } ], }, { "name": "Disney+", "country": ["usa"], "id": ["51"], "config": [ { "NAME_SPACE": 4, "APP_ID": "75", "MESSAGE": "https://cd-dmgz.bamgrid.com/bbd/vizio_tv/index.html", } ], }, ]

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0.341585

8,402

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0

0.21831

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0.387105

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false

0

null

0

null

0

1

0

8d00ab8273f452e2946deb1ce6f8cb6b06b174a7

1,858

py

Python

etl/steps/data/garden/owid/latest/covid.py

c1x1x00xxPentium/etl

4c9c4e466287deefba1aaae12c473c38d9ecb3cd

[ "MIT" ]

5

2021-11-01T18:54:52.000Z

2022-03-10T17:19:14.000Z

etl/steps/data/garden/owid/latest/covid.py

c1x1x00xxPentium/etl

4c9c4e466287deefba1aaae12c473c38d9ecb3cd

[ "MIT" ]

98

2021-09-24T19:29:34.000Z

2022-03-31T15:57:18.000Z

etl/steps/data/garden/owid/latest/covid.py

c1x1x00xxPentium/etl

4c9c4e466287deefba1aaae12c473c38d9ecb3cd

[ "MIT" ]

2

2021-12-15T07:53:38.000Z

2022-02-05T14:50:43.000Z

# # covid19.py # owid/latest/covid # from owid.catalog.meta import License, Source import datetime as dt import pandas as pd from owid.catalog import Dataset, Table from etl.helpers import downloaded MEGAFILE_URL = "https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv" def run(dest_dir: str) -> None: d = create_dataset(dest_dir) with downloaded(MEGAFILE_URL) as filename: df = pd.read_csv(filename) df["date"] = pd.to_datetime(df.date) for col in ["iso_code", "continent", "location"]: df[col] = df[col].astype("category") df.set_index(["iso_code", "date"], inplace=True) t = Table(df) t.metadata.short_name = "covid" d.add(t) def create_dataset(dest_dir: str) -> Dataset: d = Dataset.create_empty(dest_dir) d.metadata.short_name = "covid19" d.metadata.namespace = "owid" d.metadata.sources = [ Source( name="Multiple sources via Our World In Data", description="Our complete COVID-19 dataset maintained by Our World in Data. We will update it daily throughout the duration of the COVID-19 pandemic.", url="https://github.com/owid/covid-19-data/tree/master/public/data", source_data_url="https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/covid-19-data.csv", owid_data_url="https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/covid-19-data.csv", date_accessed=str(dt.date.today()), publication_date=str(dt.date.today()), publication_year=dt.date.today().year, ) ] d.metadata.licenses = [ License( name="Other (Attribution)", url="https://github.com/owid/covid-19-data/tree/master/public/data#license", ) ] d.save() return d

30.966667

163

0.656082

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0

0.014956

0.208288

1,858

59

164

31.491525

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0

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0.360395

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1

0.04878

false

0

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