xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

57476587984e17ece720d64d289aa21890dba64a

3,520

py

Python

ReportGenerator.py

taarruunnnn/VAPT-Report-Generator-Vulnerability

8d618c7ddac4f6fe0cedd9fa39ff61805e06fa38

[ "MIT" ]

1

2020-11-30T18:09:40.000Z

ReportGenerator.py

taarruunnnn/VAPT-Report-Generator-Vulnerability

8d618c7ddac4f6fe0cedd9fa39ff61805e06fa38

[ "MIT" ]

null

ReportGenerator.py

taarruunnnn/VAPT-Report-Generator-Vulnerability

8d618c7ddac4f6fe0cedd9fa39ff61805e06fa38

[ "MIT" ]

1

2020-09-16T20:51:18.000Z

import os from docx import Document from docx.shared import Inches from docx import section from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.shared import Pt from docx.shared import Cm from docx.shared import RGBColor import docx class Print_document(): def start_doc(self): self.document = Document() def reinitialize_doc(self): self.document = Document('Temp.docx') def initialize_doc(self): sections = self.document.sections for section in sections: section.top_margin = Cm(2.54) section.bottom_margin = Cm(2.54) section.left_margin = Cm(2.54) section.right_margin = Cm(2.54) style = self.document.styles['Normal'] font = style.font font.name = 'Times New Roman' font.size = Pt(14) style = self.document.styles['Heading 2'] font1 = style.font font1.name = 'TimesNewRoman' font1.size = Pt(16) header = self.document.sections[0].header ht0=header.add_paragraph() kh=ht0.add_run() kh.add_picture('Pristine.png', width=Inches(2)) kh.alignment = WD_ALIGN_PARAGRAPH.LEFT footer = self.document.sections[0].footer f = footer.add_paragraph('All Rights Reserved by Pristine InfoSolutions Pvt. Ltd.') f.alignment = WD_ALIGN_PARAGRAPH.CENTER f.style = self.document.styles['Normal'] f.bold = True f.size = Pt(16) def setVname(self,Vname): self.document.add_heading('Vulnerability Name:', 2) p = self.document.add_paragraph(Vname) p.style = self.document.styles['Normal'] def setTitle(self): self.documeny.add_paragraph("Network") def setVSeverity(self,severity): p = self.document.add_heading('Severity', 2) p.style = self.document.styles['Heading 2'] p.bold = True p.size = Pt(16) p.name = 'TimesNewRoman' p = self.document.add_paragraph(severity) p.style = self.document.styles['Normal'] def SetVdesc(self,VDesc): vuldesh = self.document.add_heading('Vulnerability Description:', 2) p = self.document.add_paragraph(VDesc) def setVurl(self,Vurl): self.document.add_heading('Vulnerable URL: ', 2) p = self.document.add_paragraph(Vurl) p.style = self.document.styles['Normal'] def setImg(self,Img): self.document.add_heading('Proof of Concept: ',2) if (Img): lengthImg = len(Img[0]) for i in range (0,lengthImg): self.document.add_picture(Img[0][i], width=Cm(15.95)) def setImpact(self,VImpact): self.document.add_heading('Impact: ',2) p = self.document.add_paragraph(VImpact) p.style = self.document.styles['Normal'] def setVremed(self,Vrem): self.document.add_heading('Remediation', 2 ) p = self.document.add_paragraph(Vrem) p.style = self.document.styles['Normal'] def setConclusion(self,Conclusion): self.document.add_heading('Conclusion', 2 ) p = self.document.add_paragraph(Conclusion) p.style = self.document.styles['Normal'] def pageBreak(self): self.document.add_page_break() def Savedoc(self,name): self.document.save(name[0] + '.docx') def Savereport(self): self.document.save('Temp.docx')

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0.609091

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0.266204

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0.121025

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0.321785

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0.093972

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0.275568

3,520

111

93

31.711712

0.808235

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false

0

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0.294118

0

null

0

null

0

1

0

5750825ae1de9236544f8dff0657979e541dfed6

764

py

Python

Season 06 - Files in Python/Episode 02 - Copying Files.py/Episode 02 - Copying Files.py

Pythobit/Python-tutorial

b0743eaa9c237c3578131ead1b3f2c295f11b7ee

[ "MIT" ]

3

2021-02-19T18:33:00.000Z

2021-08-03T14:56:50.000Z

Season 06 - Files in Python/Episode 02 - Copying Files.py/Episode 02 - Copying Files.py

barawalojas/Python-tutorial

3f4b2b073e421888b3d62ff634658317d9abcb9b

[ "MIT" ]

1

2021-07-10T14:37:57.000Z

2021-07-20T09:51:39.000Z

Season 06 - Files in Python/Episode 02 - Copying Files.py/Episode 02 - Copying Files.py

barawalojas/Python-tutorial

3f4b2b073e421888b3d62ff634658317d9abcb9b

[ "MIT" ]

1

2021-08-02T05:39:38.000Z

# Copying files # Ask user for a list of 3 friends. # for each friend, we'll tell user whether they're nearby. # for each nearby friend, we'll save their name to `nearby_friends.txt`. friends = input('Enter three friends name(separated by commas): ').split(',') people = open('people.txt', 'r') people_nearby = [line.strip() for line in people.readlines()] people.close() # Making set of friends and peoples friends_set = set(friends) people_nearby_set = set(people_nearby) friends_nearby_set = friends_set.intersection(people_nearby_set) nearby_friends_file = open('nearby_friends.txt', 'w') for friend in friends_nearby_set: print(f'{friend} is nearby.! Meet up with them.') nearby_friends_file.write(f'{friend}\n') nearby_friends_file.close()

27.285714

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4.605042

0.462185

0.142336

0.093066

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0.137435

764

27

78

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1

0

false

0

0.083333

0

null

0

null

0

1

0

5750d5afb4b68c06b08670b53610fc887297a148

722

py

Python

beginner_contest/167/C.py

FGtatsuro/myatcoder

25a3123be6a6311e7d1c25394987de3e35575ff4

[ "MIT" ]

null

beginner_contest/167/C.py

FGtatsuro/myatcoder

25a3123be6a6311e7d1c25394987de3e35575ff4

[ "MIT" ]

null

beginner_contest/167/C.py

FGtatsuro/myatcoder

25a3123be6a6311e7d1c25394987de3e35575ff4

[ "MIT" ]

null

import sys input = sys.stdin.readline sys.setrecursionlimit(10 ** 7) n, m, x = map(int, input().split()) ca = [0] * n ca_sum = [0] * (m+1) for i in range(n): ca[i] = list(map(int, input().split())) for j in range(m+1): ca_sum[j] += ca[i][j] ans = 10 ** 10 for i in range(2 ** n): tmp = 0 tmp_ca_sum = ca_sum.copy() for j, v in enumerate(format(i, r'0{}b'.format(n))): if v == '0': continue for k in range(m+1): tmp_ca_sum[k] -= ca[j][k] flag = True for v2 in tmp_ca_sum[1:]: if v2 < x: flag = False break if flag: ans = min(ans, tmp_ca_sum[0]) if ans == 10 ** 10: print(-1) else: print(ans)

21.235294

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0.49723

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722

2.71875

0.34375

0.100575

0.091954

0

0.051335

0.325485

722

33

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21.878788

0.663244

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false

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0.033333

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0.066667

0

null

0

null

0

1

0

575730cc1be427336b55d40ef3a3e2821b465a72

1,210

py

Python

Unit 7/Ai bot/test bots/SlightlySmartSue.py

KevinBoxuGao/ICS3UI

2091a7c0276b888dd88f2063e6acd6e7ff7fb6fa

[ "MIT" ]

null

Unit 7/Ai bot/test bots/SlightlySmartSue.py

KevinBoxuGao/ICS3UI

2091a7c0276b888dd88f2063e6acd6e7ff7fb6fa

[ "MIT" ]

null

Unit 7/Ai bot/test bots/SlightlySmartSue.py

KevinBoxuGao/ICS3UI

2091a7c0276b888dd88f2063e6acd6e7ff7fb6fa

[ "MIT" ]

1

2020-03-09T16:22:33.000Z

from random import * #STRATEGY SUMMARY: DON'T DUCK IF THE OPPONENT HAS NO SNOWBALLS. OTHERWISE, PICK RANDOMLY. def getMove( myScore, mySnowballs, myDucksUsed, myMovesSoFar, oppScore, oppSnowballs, oppDucksUsed, oppMovesSoFar ): if mySnowballs == 10: #I have 10 snowballs, so I must throw return "THROW" elif oppSnowballs > 0: #If opponent does have snowballs... if mySnowballs == 0: #...and if I have no snowballs left if myDucksUsed == 5: #...and if I have no ducks left either, then must RELOAD return "RELOAD" else: #...otherwise, pick between DUCK and RELOAD return choice([ "DUCK", "RELOAD" ]) elif myDucksUsed == 5: #If my opponent and I both have snowballs left, but I'm out of ducks return choice([ "THROW", "RELOAD" ]) else: #I have no restrictions return choice([ "THROW", "DUCK", "RELOAD" ]) else: #If my opponent is out of snowballs, then don't duck! if mySnowballs == 0: return "RELOAD" else: return choice([ "RELOAD", "THROW" ])

31.842105

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0.565289

139

1,210

4.920863

0.395683

0.02924

0.030702

0.02924

0.035088

0

0.011321

0.342975

1,210

37

100

32.702703

0.849057

0.356198

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0.4

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1

0.05

false

0

0.05

0

0.45

0

null

0

null

0

1

0

57580cabba2c7dce9e5d8666af96b5e694af9738

5,370

py

Python

pysoa/test/plan/grammar/directives/expects_values.py

zetahernandez/pysoa

006e55ba877196a42c64f2ff453583d366082d55

[ "Apache-2.0" ]

91

2017-05-08T22:41:33.000Z

2022-02-09T11:37:07.000Z

pysoa/test/plan/grammar/directives/expects_values.py

zetahernandez/pysoa

006e55ba877196a42c64f2ff453583d366082d55

[ "Apache-2.0" ]

63

2017-06-14T20:08:49.000Z

2021-06-16T23:08:25.000Z

pysoa/test/plan/grammar/directives/expects_values.py

zetahernandez/pysoa

006e55ba877196a42c64f2ff453583d366082d55

[ "Apache-2.0" ]

26

2017-10-13T23:23:13.000Z

2022-01-11T16:58:17.000Z

""" Expect action directives """ from __future__ import ( absolute_import, unicode_literals, ) from pyparsing import ( CaselessLiteral, LineEnd, Literal, Optional, Suppress, ) from pysoa.test.plan.grammar.assertions import ( assert_not_expected, assert_not_present, assert_subset_structure, ) from pysoa.test.plan.grammar.data_types import ( DataTypeGrammar, get_parsed_data_type_value, ) from pysoa.test.plan.grammar.directive import ( ActionDirective, VarNameGrammar, VarValueGrammar, register_directive, ) from pysoa.test.plan.grammar.tools import path_put class ActionExpectsFieldValueDirective(ActionDirective): """ Set expectations for values to be in the service call response. Using the ``not`` qualifier in the test will check to make sure that the field has any value other than the one specified. """ @classmethod def name(cls): return 'expect_value' @classmethod def get_full_grammar(cls): return ( super(ActionExpectsFieldValueDirective, cls).get_full_grammar() + Literal('expect') + Optional(DataTypeGrammar) + ':' + Optional(Literal('not')('not')) + Literal('attribute value') + ':' + VarNameGrammar + ':' + VarValueGrammar ) def ingest_from_parsed_test_fixture(self, action_case, test_case, parse_results, file_name, line_number): variable_name = parse_results.variable_name path = 'expects' if getattr(parse_results, 'not', None): path = 'not_expects' path_put( action_case, '{}.{}'.format(path, variable_name), get_parsed_data_type_value(parse_results, parse_results.value), ) def assert_test_case_action_results( self, action_name, action_case, test_case, test_fixture, action_response, job_response, msg=None, **kwargs ): if 'expects' in action_case: assert_subset_structure( action_case.get('expects', {}), action_response.body, False, msg, ) if 'not_expects' in action_case: assert_not_expected( action_case['not_expects'], action_response.body, msg, ) class ActionExpectsAnyDirective(ActionExpectsFieldValueDirective): """ Set expectations for values to be in the service call response where any value for the given data type will be accepted. """ @classmethod def name(cls): return 'expect_any_value' @classmethod def get_full_grammar(cls): return ( super(ActionExpectsFieldValueDirective, cls).get_full_grammar() + Literal('expect') + Literal('any')('any') + Optional(DataTypeGrammar) + ':' + Literal('attribute value') + ':' + VarNameGrammar + Optional(~Suppress(LineEnd()) + ':') ) class ActionExpectsNoneDirective(ActionExpectsFieldValueDirective): """ Set expectations for values to be in the service call response where ``None`` value is expected. """ @classmethod def name(cls): return 'expect_none' @classmethod def get_full_grammar(cls): return ( super(ActionExpectsFieldValueDirective, cls).get_full_grammar() + Literal('expect') + CaselessLiteral('None')('data_type') + ':' + Literal('attribute value') + ':' + VarNameGrammar + Optional(~Suppress(LineEnd()) + ':') ) class ActionExpectsNotPresentDirective(ActionDirective): """ Set expectation that the given field will not be present (even as a key) in the response. """ @classmethod def name(cls): return 'expect_not_present' @classmethod def get_full_grammar(cls): return ( super(ActionExpectsNotPresentDirective, cls).get_full_grammar() + Literal('expect not present') + ':' + Literal('attribute value') + ':' + VarNameGrammar + Optional(~Suppress(LineEnd()) + ':') ) def ingest_from_parsed_test_fixture(self, action_case, test_case, parse_results, file_name, line_number): path_put( action_case, 'expects_not_present.{}'.format(parse_results.variable_name), get_parsed_data_type_value(parse_results, parse_results.value), ) def assert_test_case_action_results( self, action_name, action_case, test_case, test_fixture, action_response, job_response, msg=None, **kwargs ): if 'expects_not_present' in action_case: assert_not_present( action_case['expects_not_present'], action_response.body, msg, ) register_directive(ActionExpectsFieldValueDirective) register_directive(ActionExpectsAnyDirective) register_directive(ActionExpectsNoneDirective) register_directive(ActionExpectsNotPresentDirective)

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null

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null

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575a4a3127b8298acd5fe22aa043d391fe755667

1,821

py

Python

tests/test_qml.py

phil65/PrettyQt

26327670c46caa039c9bd15cb17a35ef5ad72e6c

[ "MIT" ]

7

2019-05-01T01:34:36.000Z

2022-03-08T02:24:14.000Z

tests/test_qml.py

phil65/PrettyQt

26327670c46caa039c9bd15cb17a35ef5ad72e6c

[ "MIT" ]

141

2019-04-16T11:22:01.000Z

2021-04-14T15:12:36.000Z

tests/test_qml.py

phil65/PrettyQt

26327670c46caa039c9bd15cb17a35ef5ad72e6c

[ "MIT" ]

5

2019-04-17T11:48:19.000Z

2021-11-21T10:30:19.000Z

"""Tests for `prettyqt` package.""" import pathlib import pytest from prettyqt import core, qml from prettyqt.utils import InvalidParamError # def test_jsvalue(): # val = qml.JSValue(2) # val["test"] = 1 # assert val["test"].toInt() == 1 # assert "test" in val # assert val.get_value() == 2 def test_jsengine(): engine = qml.JSEngine() engine.install_extensions("translation") engine.eval("") def test_qmlengine(): engine = qml.QmlEngine() obj = core.Object() engine.set_object_ownership(obj, "javascript") with pytest.raises(InvalidParamError): engine.set_object_ownership(obj, "test") assert engine.get_object_ownership(obj) == "javascript" engine.add_plugin_path("") engine.add_import_path("") engine.get_plugin_paths() engine.get_import_paths() def test_qmlapplicationengine(qtlog): with qtlog.disabled(): engine = qml.QmlApplicationEngine() for item in engine: pass path = pathlib.Path.cwd() / "tests" / "qmltest.qml" engine.load_data(path.read_text()) def test_qmlcomponent(): comp = qml.QmlComponent() assert comp.get_status() == "null" # comp.load_url("", mode="asynchronous") comp.get_url() def test_jsvalue(): val = qml.JSValue(1) assert val.get_error_type() is None assert val.get_value() == 1 repr(val) engine = qml.JSEngine() val = engine.new_array(2) val["test1"] = 1 val["test2"] = 2 assert val["test1"] == 1 assert "test2" in val assert len(val) == 2 del val["test2"] for n, v in val: pass val = qml.JSValue.from_object(None, engine) val = qml.JSValue.from_object(1, engine) val = qml.JSValue.from_object(["test"], engine) val = qml.JSValue.from_object(dict(a="b"), engine)

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0

null

0

null

0

1

0

9386838c937de37405273fac5771d31ccf1a0479

2,550

py

Python

demo.py

HsienYu/tree_demo

aa2fa6c016b3ea5c1e768baa8ce4ea319c727bfc

[ "Artistic-2.0" ]

null

demo.py

HsienYu/tree_demo

aa2fa6c016b3ea5c1e768baa8ce4ea319c727bfc

[ "Artistic-2.0" ]

null

demo.py

HsienYu/tree_demo

aa2fa6c016b3ea5c1e768baa8ce4ea319c727bfc

[ "Artistic-2.0" ]

null

# Simple test for NeoPixels on Raspberry Pi import time import board import neopixel # Choose an open pin connected to the Data In of the NeoPixel strip, i.e. board.D18 # NeoPixels must be connected to D10, D12, D18 or D21 to work. pixel_pin = board.D18 # The number of NeoPixels num_pixels = 30 # The order of the pixel colors - RGB or GRB. Some NeoPixels have red and green reversed! # For RGBW NeoPixels, simply change the ORDER to RGBW or GRBW. ORDER = neopixel.GRB pixels = neopixel.NeoPixel(pixel_pin, num_pixels, brightness=0.2, auto_write=False, pixel_order=ORDER) def wheel(pos): # Input a value 0 to 255 to get a color value. # The colours are a transition r - g - b - back to r. if pos < 0 or pos > 255: r = g = b = 0 elif pos < 85: r = int(pos*2) g = int(255 - pos*2) b = 0 elif pos < 170: pos -= 85 r = int(255 - pos*2) g = 0 b = int(pos*2) else: pos -= 170 r = 0 g = int(pos*2) b = int(255 - pos*2) return (255, 155, b) if ORDER == neopixel.RGB or ORDER == neopixel.GRB else (r, g, b, 0) def rainbow_cycle(wait): for j in range(255): for i in range(num_pixels): pixel_index = (i * 256 // num_pixels) + j pixels[i] = wheel(pixel_index & 255) pixels.show() time.sleep(wait) def white_breath(): x = 0 interval_time = 0.007 time.sleep(1) while x == 0: for i in range(255): x = i pixels.fill((x, x, x)) pixels.show() time.sleep(interval_time) while x == 254: for i in range(255, 0, -1): x = i pixels.fill((i, i, i)) pixels.show() time.sleep(interval_time) def repeat_fun(times, f, *args): for i in range(times): f(*args) try: while True: print("light start") repeat_fun(5, white_breath) # rainbow cycle with 1ms delay per step repeat_fun(3, rainbow_cycle, 0.01) # white_breath() # for i in range(num_pixels): # for r in range(255): # pixels[i] = (r, 0, 0) # pixels.show() # time.sleep(0.001) # j = i - 1 # for y in range(255): # pixels[j] = (y, y, y) # pixels.show() # time.sleep(0.001) # time.sleep(0.01) except KeyboardInterrupt: print("KeyboardInterrupt has been caught.")

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93881978c162edde4ca5dd970ae7fc5d1d4dfecc

1,861

py

Python

rptk/query/__init__.py

wolcomm/rptk

fe6c1b597741ff14e4c89519458bb0950f0aa955

[ "Apache-2.0" ]

15

2017-11-30T01:28:11.000Z

2021-08-12T09:17:36.000Z

rptk/query/__init__.py

wolcomm/rptk

fe6c1b597741ff14e4c89519458bb0950f0aa955

[ "Apache-2.0" ]

71

2018-06-22T09:54:50.000Z

2020-10-21T07:10:54.000Z

rptk/query/__init__.py

wolcomm/rptk

fe6c1b597741ff14e4c89519458bb0950f0aa955

[ "Apache-2.0" ]

2

2019-08-31T20:45:19.000Z

2019-10-02T18:26:58.000Z

# Copyright (c) 2018 Workonline Communications (Pty) Ltd. All rights reserved. # # The contents of this file are licensed under the Apache License version 2.0 # (the "License"); you may not use this file except in compliance with the # License. # # 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. """rptk module.query module.""" from __future__ import print_function from __future__ import unicode_literals from rptk.base import BaseObject try: basestring except NameError: basestring = str try: unicode except NameError: unicode = str class BaseQuery(BaseObject): """Base class for the definition of query execution classes.""" posix_only = False def __init__(self, **opts): """Initialise new object.""" super(BaseQuery, self).__init__() self.log_init() self._opts = opts self.log_init_done() def query(self, *objects): """Check the object name type.""" self.log_method_enter(method=self.current_method) for obj in objects: if not isinstance(obj, basestring): self.raise_type_error(arg=obj, cls=basestring) obj = unicode(obj) yield obj @property def host(self): """Get the configured IRR server hostname.""" return self.opts["host"] @property def port(self): """Get the configured IRR server port.""" return int(self.opts["port"]) @property def target(self): """Construct a hostname:port pair for the IRR server.""" return "{}:{}".format(self.host, self.port)

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null

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0

9389cb7a39d34434b205d05068e576faba98ddc7

1,639

py

Python

legacy/tests/test_complete_tdf.py

solar464/TDF_deterministic_encryption

ff9dceacb37ce7727a8205cc72a4d928d37cce6f

[ "MIT" ]

null

legacy/tests/test_complete_tdf.py

solar464/TDF_deterministic_encryption

ff9dceacb37ce7727a8205cc72a4d928d37cce6f

[ "MIT" ]

null

legacy/tests/test_complete_tdf.py

solar464/TDF_deterministic_encryption

ff9dceacb37ce7727a8205cc72a4d928d37cce6f

[ "MIT" ]

null

import unittest import pickle from array import array import complete_tdf from floodberry.floodberry_ed25519 import GE25519 from tdf_strucs import TDFMatrix, TDFError from complete_tdf import CTDFCodec as Codec, CTDFCipherText as CipherText from utils import int_lst_to_bitarr TEST_DIR = "legacy/tests/" PACK_TEST_KEY_FILE = TEST_DIR + "ctdf_pack_test_keys.p" PACK_TEST_CT_FILE = TEST_DIR + "ctdf_pack_test_ct.p" TDF_KEY_FILE = TEST_DIR + "ctdf_test_keys.p" TDF_CT_FILE = TEST_DIR + "ctdf_test_ct.p" """ x = [0,1,2] ctdf = CTDFCodec(len(x)*8) u = ctdf.encode(x) result = ctdf.decode(u) """ TDF = Codec.deserialize(TDF_KEY_FILE) CT = CipherText.deserialize(TDF_CT_FILE) X = int_lst_to_bitarr([0,1,2], 3) class TestCTDF(unittest.TestCase): def test_packing(self): tdf = Codec(16) u = tdf.encode(array('i',[1, 2])) tdf.serialize(PACK_TEST_KEY_FILE) u.serialize(PACK_TEST_CT_FILE) tdf1 = Codec.deserialize(PACK_TEST_KEY_FILE) u1 = CipherText.deserialize(PACK_TEST_CT_FILE) #call to_affine on all GE objects in codec self.assertEqual(u.all_to_affine(), u1) self.assertEqual(tdf.all_to_affine(), tdf1) def test_encode(self): ct = TDF.encode(X) self.assertEqual(ct.all_to_affine(), CT.all_to_affine()) def test_decode(self): result = TDF.decode(CT) self.assertEqual(X, result) def test_different_length_encode_decode(self): ct_short = TDF.encode([2], c=3) self.assertEqual(TDF.decode(ct_short), int_lst_to_bitarr([2], 3)) self.assertRaises(TDFError, TDF.encode, [3] * 100)

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null

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939056f893dc7a63b3b4c5c9d0f8b92f4cb9205c

7,652

py

Python

utils/utils_convert2hdf5.py

jiyeonkim127/PSI

5c525d5304fb756c9314ea3e225bbb180e521b9a

[ "Xnet", "X11" ]

138

2020-04-18T19:32:12.000Z

2022-03-31T06:58:33.000Z

utils/utils_convert2hdf5.py

jiyeonkim127/PSI

5c525d5304fb756c9314ea3e225bbb180e521b9a

[ "Xnet", "X11" ]

19

2020-04-21T18:24:20.000Z

2022-03-12T00:25:11.000Z

utils/utils_convert2hdf5.py

jiyeonkim127/PSI

5c525d5304fb756c9314ea3e225bbb180e521b9a

[ "Xnet", "X11" ]

19

2020-04-22T01:32:25.000Z

2022-03-24T02:52:01.000Z

import numpy as np import scipy.io as sio import os, glob, sys import h5py_cache as h5c sys.path.append('/home/yzhang/workspaces/smpl-env-gen-3d-internal') sys.path.append('/home/yzhang/workspaces/smpl-env-gen-3d-internal/source') from batch_gen_hdf5 import BatchGeneratorWithSceneMeshMatfile import torch ''' In this script, we put all mat files into a hdf5 file, so as to speed up the data loading process. ''' dataset_path = '/mnt/hdd/PROX/snapshot_realcams_v3' outfilename = 'realcams.hdf5' h5file_path = os.path.join('/home/yzhang/Videos/PROXE', outfilename) batch_gen = BatchGeneratorWithSceneMeshMatfile(dataset_path=dataset_path, scene_verts_path = '/home/yzhang/Videos/PROXE/scenes_downsampled', scene_sdf_path = '/home/yzhang/Videos/PROXE/scenes_sdf', device=torch.device('cuda')) ### create the dataset used in the hdf5 file with h5c.File(h5file_path, mode='w',chunk_cache_mem_size=1024**2*128) as hdf5_file: while batch_gen.has_next_batch(): train_data = batch_gen.next_batch(1) if train_data is None: continue train_data_np = [x.detach().cpu().numpy() for x in train_data[:-1]] break [depth_batch, seg_batch, body_batch, cam_ext_batch, cam_int_batch, max_d_batch, s_verts_batch, s_faces_batch, s_grid_min_batch, s_grid_max_batch, s_grid_dim_batch, s_grid_sdf_batch] = train_data_np n_samples = batch_gen.n_samples print('-- n_samples={:d}'.format(n_samples)) hdf5_file.create_dataset("sceneid", shape=(1,), chunks=True, dtype=np.float32, maxshape=(None,) ) hdf5_file.create_dataset("depth", shape=(1,)+tuple(depth_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(depth_batch.shape[1:]) ) hdf5_file.create_dataset("seg", shape=(1,)+tuple(seg_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(seg_batch.shape[1:]) ) hdf5_file.create_dataset("body", shape=(1,)+tuple(body_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(body_batch.shape[1:]) ) hdf5_file.create_dataset("cam_ext", shape=(1,)+tuple(cam_ext_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(cam_ext_batch.shape[1:]) ) hdf5_file.create_dataset("cam_int", shape=(1,)+tuple(cam_int_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(cam_int_batch.shape[1:]) ) hdf5_file.create_dataset("max_d", shape=(1,)+tuple(max_d_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(max_d_batch.shape[1:]) ) # hdf5_file.create_dataset("s_verts", shape=(1,)+tuple(s_verts_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_verts_batch.shape[1:]) ) # hdf5_file.create_dataset("s_faces", shape=(1,)+tuple(s_faces_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_faces_batch.shape[1:]) ) # hdf5_file.create_dataset("s_grid_min", shape=(1,)+tuple(s_grid_min_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_grid_min_batch.shape[1:])) # hdf5_file.create_dataset("s_grid_max", shape=(1,)+tuple(s_grid_max_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_grid_max_batch.shape[1:])) # hdf5_file.create_dataset("s_grid_dim", shape=(1,)+tuple(s_grid_dim_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_grid_dim_batch.shape[1:])) # hdf5_file.create_dataset("s_grid_sdf", shape=(1,)+tuple(s_grid_sdf_batch.shape[1:]) ,chunks = True, dtype=np.float32, maxshape=(None,)+tuple(s_grid_sdf_batch.shape[1:])) batch_gen.reset() scene_list = ['BasementSittingBooth','MPH1Library', 'MPH8', 'MPH11', 'MPH16', 'MPH112', 'N0SittingBooth', 'N0Sofa', 'N3Library', 'N3Office', 'N3OpenArea', 'Werkraum'] # !!!! important!cat ### create the dataset used in the hdf5 file idx = -1 while batch_gen.has_next_batch(): train_data = batch_gen.next_batch(1) if train_data is None: continue [depth_batch, seg_batch, body_batch, cam_ext_batch, cam_int_batch, max_d_batch, s_verts_batch, s_faces_batch, s_grid_min_batch, s_grid_max_batch, s_grid_dim_batch, s_grid_sdf_batch, filename_list] = train_data ## check unavaliable prox fitting body_z_batch = body_batch[:,2] if body_z_batch.abs().max() >= max_d_batch.abs().max(): print('-- encountered bad prox fitting. Skip it') continue if body_z_batch.min() <=0: print('-- encountered bad prox fitting. Skip it') continue idx = idx+1 print('-- processing batch idx {:d}'.format(idx)) filename = filename_list[0] scenename = filename.split('/')[-2].split('_')[0] sid = [scene_list.index(scenename)] hdf5_file["sceneid"].resize((hdf5_file["sceneid"].shape[0]+1, )) hdf5_file["sceneid"][-1,...] = sid[0] hdf5_file["depth"].resize((hdf5_file["depth"].shape[0]+1, )+hdf5_file["depth"].shape[1:]) hdf5_file["depth"][-1,...] = depth_batch[0].detach().cpu().numpy() hdf5_file["seg"].resize((hdf5_file["seg"].shape[0]+1, )+hdf5_file["seg"].shape[1:]) hdf5_file["seg"][-1,...] = seg_batch[0].detach().cpu().numpy() hdf5_file["body"].resize((hdf5_file["body"].shape[0]+1, )+hdf5_file["body"].shape[1:]) hdf5_file["body"][-1,...] = body_batch[0].detach().cpu().numpy() hdf5_file["cam_ext"].resize((hdf5_file["cam_ext"].shape[0]+1, )+hdf5_file["cam_ext"].shape[1:]) hdf5_file["cam_ext"][-1,...] = cam_ext_batch[0].detach().cpu().numpy() hdf5_file["cam_int"].resize((hdf5_file["cam_int"].shape[0]+1, )+hdf5_file["cam_int"].shape[1:]) hdf5_file["cam_int"][-1,...] = cam_int_batch[0].detach().cpu().numpy() hdf5_file["max_d"].resize((hdf5_file["max_d"].shape[0]+1, )+hdf5_file["max_d"].shape[1:]) hdf5_file["max_d"][-1,...] = max_d_batch[0].detach().cpu().numpy() # hdf5_file["s_verts"].resize((hdf5_file["s_verts"].shape[0]+1, )+hdf5_file["s_verts"].shape[1:]) # hdf5_file["s_verts"][-1,...] = s_verts_batch[0].detach().cpu().numpy() # hdf5_file["s_faces"].resize((hdf5_file["s_faces"].shape[0]+1, )+hdf5_file["s_faces"].shape[1:]) # hdf5_file["s_faces"][-1,...] = s_faces_batch[0].detach().cpu().numpy() # hdf5_file["s_grid_min"].resize((hdf5_file["s_grid_min"].shape[0]+1, )+hdf5_file["s_grid_min"].shape[1:]) # hdf5_file["s_grid_min"][-1,...] = s_grid_min_batch[0].detach().cpu().numpy() # hdf5_file["s_grid_max"].resize((hdf5_file["s_grid_max"].shape[0]+1, )+hdf5_file["s_grid_max"].shape[1:]) # hdf5_file["s_grid_max"][-1,...] = s_grid_max_batch[0].detach().cpu().numpy() # hdf5_file["s_grid_dim"].resize((hdf5_file["s_grid_dim"].shape[0]+1, )+hdf5_file["s_grid_dim"].shape[1:]) # hdf5_file["s_grid_dim"][-1,...] = s_grid_dim_batch[0].detach().cpu().numpy() # hdf5_file["s_grid_sdf"].resize((hdf5_file["s_grid_sdf"].shape[0]+1, )+hdf5_file["s_grid_sdf"].shape[1:]) # hdf5_file["s_grid_sdf"][-1,...] = s_grid_sdf_batch[0].detach().cpu().numpy() print('--file converting finish')

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null

0

null

0

1

0

93938181b040ac3ac5f94151cbff662943eef747

3,324

py

Python

tests/test_names.py

fabiocaccamo/python-fontbro

2ed7ef0d3d1ed4d91387278cfb5f7fd63324451b

[ "MIT" ]

11

2021-11-17T23:51:55.000Z

2022-03-17T20:38:14.000Z

tests/test_names.py

fabiocaccamo/python-fontbro

2ed7ef0d3d1ed4d91387278cfb5f7fd63324451b

[ "MIT" ]

4

2022-02-21T02:16:06.000Z

2022-03-28T02:18:16.000Z

tests/test_names.py

fabiocaccamo/python-fontbro

2ed7ef0d3d1ed4d91387278cfb5f7fd63324451b

[ "MIT" ]

null

# -*- coding: utf-8 -*- from fontbro import Font from tests import AbstractTestCase class NamesTestCase(AbstractTestCase): """ Test case for the methods related to the font names. """ def test_get_name_by_id(self): font = self._get_font("/Roboto_Mono/static/RobotoMono-Regular.ttf") family_name = font.get_name(Font.NAME_FAMILY_NAME) self.assertEqual(family_name, "Roboto Mono") def test_get_name_by_key(self): font = self._get_font("/Roboto_Mono/static/RobotoMono-Regular.ttf") family_name = font.get_name("family_name") self.assertEqual(family_name, "Roboto Mono") def test_get_name_by_invalid_type(self): font = self._get_font("/Roboto_Mono/static/RobotoMono-Regular.ttf") with self.assertRaises(TypeError): family_name = font.get_name(font) def test_get_name_by_invalid_key(self): font = self._get_font("/Roboto_Mono/static/RobotoMono-Regular.ttf") with self.assertRaises(KeyError): name = font.get_name("invalid_key") # self.assertEqual(name, None) def test_get_name_by_invalid_id(self): font = self._get_font("/Roboto_Mono/static/RobotoMono-Regular.ttf") name = font.get_name(999999999) self.assertEqual(name, None) def test_get_names(self): font = self._get_font("/Roboto_Mono/static/RobotoMono-Regular.ttf") font_names = font.get_names() # self._print(font_names) expected_keys = [ "copyright_notice", "designer", "designer_url", "family_name", "full_name", "license_description", "license_info_url", "postscript_name", "subfamily_name", "trademark", "unique_identifier", "vendor_url", "version", ] expected_keys_in = [key in font_names for key in expected_keys] self.assertTrue(all(expected_keys_in)) self.assertEqual(font_names["family_name"], "Roboto Mono") self.assertEqual(font_names["subfamily_name"], "Regular") self.assertEqual(font_names["full_name"], "Roboto Mono Regular") self.assertEqual(font_names["postscript_name"], "RobotoMono-Regular") def test_set_name(self): font = self._get_font("/Roboto_Mono/static/RobotoMono-Regular.ttf") font.set_name(Font.NAME_FAMILY_NAME, "Roboto Mono Renamed") self.assertEqual(font.get_name(Font.NAME_FAMILY_NAME), "Roboto Mono Renamed") def test_set_name_by_invalid_key(self): font = self._get_font("/Roboto_Mono/static/RobotoMono-Regular.ttf") with self.assertRaises(KeyError): font.set_name("invalid_family_name_key", "Roboto Mono Renamed") def test_set_names(self): font = self._get_font("/Roboto_Mono/static/RobotoMono-Regular.ttf") font.set_names( { Font.NAME_FAMILY_NAME: "Roboto Mono Renamed", Font.NAME_SUBFAMILY_NAME: "Regular Renamed", } ) family_name = font.get_name(Font.NAME_FAMILY_NAME) self.assertEqual(family_name, "Roboto Mono Renamed") subfamily_name = font.get_name(Font.NAME_SUBFAMILY_NAME) self.assertEqual(subfamily_name, "Regular Renamed")

38.206897

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null

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null

0

1

0

939786f9e786e13e34a09c07c33b9d33a5fb6c2c

1,273

py

Python

core-python/Core_Python/file/RemoveTempDirs.py

theumang100/tutorials-1

497f54c2adb022c316530319a168fca1c007d4b1

[ "MIT" ]

9

2020-04-23T05:24:19.000Z

2022-02-17T16:37:51.000Z

core-python/Core_Python/file/RemoveTempDirs.py

theumang100/tutorials-1

497f54c2adb022c316530319a168fca1c007d4b1

[ "MIT" ]

5

2020-10-01T05:08:37.000Z

2020-10-12T03:18:10.000Z

core-python/Core_Python/file/RemoveTempDirs.py

theumang100/tutorials-1

497f54c2adb022c316530319a168fca1c007d4b1

[ "MIT" ]

9

2020-04-28T14:06:41.000Z

2021-10-19T18:32:28.000Z

import os from pathlib import Path from shutil import rmtree # change your parent dir accordingly try: directory = "TempDir" parent_dir = "E:/GitHub/1) Git_Tutorials_Repo_Projects/core-python/Core_Python/" td1, td2 = "TempA", "TempA" path = os.path.join(parent_dir, directory) temp_mul_dirs = os.path.join(path + os.sep + os.sep, td1 + os.sep + os.sep + td2) ''' This methods used to remove single file. all three methods used to delete symlink too''' os.remove(path +os.sep+os.sep+"TempFile.txt") os.unlink(path +os.sep+os.sep+td1+os.sep+os.sep+"TempFilea.txt") ''' we can also use this syntax pathlib.Path(path +os.sep+os.sep+"TempFile.txt").unlink() ''' f_path = Path(temp_mul_dirs +os.sep+os.sep+"TempFileb.txt") f_path.unlink(); ''' both methods for delete empty dir if single dir we can use rmdir if nested the removedirs''' # os.remove(path) # os.removedirs(path+os.sep+os.sep+td1) print("List of dirs before remove : ",os.listdir(path)) ''' For remove non empty directory we have to use shutil.rmtree and pathlib.Path(path),rmdir()''' rmtree(path+os.sep+os.sep+td1) Path(path).rmdir() print("List of dirs after remove : ",os.listdir(parent_dir)) except Exception as e: print(e)

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0.157895

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null

0

null

0

1

0

939b63bdfc91f71662536be6efe59324a01bcaa9

587

py

Python

code/python/echomesh/color/WheelColor_test.py

silky/echomesh

2fe5a00a79c215b4aca4083e5252fcdcbd0507aa

[ "MIT" ]

1

2019-06-27T11:34:13.000Z

code/python/echomesh/color/WheelColor_test.py

silky/echomesh

2fe5a00a79c215b4aca4083e5252fcdcbd0507aa

[ "MIT" ]

null

code/python/echomesh/color/WheelColor_test.py

silky/echomesh

2fe5a00a79c215b4aca4083e5252fcdcbd0507aa

[ "MIT" ]

null

from __future__ import absolute_import, division, print_function, unicode_literals from echomesh.color import WheelColor from echomesh.util.TestCase import TestCase EXPECTED = [ [ 0., 1., 0.], [ 0.3, 0.7, 0. ], [ 0.6, 0.4, 0. ], [ 0.9, 0.1, 0. ], [ 0. , 0.2, 0.8], [ 0. , 0.5, 0.5], [ 0. , 0.8, 0.2], [ 0.9, 0. , 0.1], [ 0.6, 0. , 0.4], [ 0.3, 0. , 0.7], [ 0., 1., 0.]] class TestWheelColor(TestCase): def test_several(self): result = [WheelColor.wheel_color(r / 10.0) for r in range(11)] self.assertArrayEquals(result, EXPECTED)

25.521739

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null

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null

0

1

0

939e7757a3e174c6114642e42e77179f804882a6

779

py

Python

notebook/demo/src/multifuns.py

marketmodelbrokendown/1

587283fd972d0060815dde82a57667e74765c9ae

[ "MIT" ]

2

2019-03-13T15:34:42.000Z

2019-03-13T15:34:47.000Z

notebook/demo/src/multifuns.py

hervey-su/home

655b9e7b8180592742a132832795170a00debb47

[ "MIT" ]

1

2020-11-18T21:55:20.000Z

notebook/demo/src/multifuns.py

marketmodelbrokendown/1

587283fd972d0060815dde82a57667e74765c9ae

[ "MIT" ]

null

from ctypes import cdll,c_int,c_double,POINTER _lib = cdll.LoadLibrary('./demo/bin/libmultifuns.dll') # double dprod(double *x, int n) def dprod(x): _lib.dprod.argtypes = [POINTER(c_double), c_int] _lib.dprod.restype = c_double n = len(x) # convert a Python list into a C array by using ctypes arr= (c_double * n)(*x) return _lib.dprod(arr,int(n)) # int factorial(int n) def factorial(n): _lib.factorial.argtypes = [c_int] _lib.factorial.restype = c_int return _lib.factorial(n) # int isum(int array[], int size); def isum(x): _lib.sum.argtypes = [POINTER(c_int), c_int] _lib.sum.restype =c_int n = len(x) # convert a Python list into a C array by using ctypes arr= (c_int * n)(*x) return _lib.sum(arr,int(n))

26.862069

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0.04888

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false

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0

0.388889

0

null

0

null

0

1

0

93a10bd2227db590b05aec0efe907cfefee1e40e

843

py

Python

api/nivo_api/cli/database.py

RemiDesgrange/nivo

e13dcd7c00d1fbc41c23d51c9004901d7704b498

[ "MIT" ]

2

2019-05-07T20:23:59.000Z

2020-04-26T11:18:38.000Z

api/nivo_api/cli/database.py

RemiDesgrange/nivo

e13dcd7c00d1fbc41c23d51c9004901d7704b498

[ "MIT" ]

89

2019-08-06T12:47:50.000Z

2022-03-28T04:03:25.000Z

api/nivo_api/cli/database.py

RemiDesgrange/nivo

e13dcd7c00d1fbc41c23d51c9004901d7704b498

[ "MIT" ]

1

2020-06-23T10:07:38.000Z

from nivo_api.core.db.connection import metadata, create_database_connections from sqlalchemy.engine import Engine from sqlalchemy.exc import ProgrammingError def is_postgis_installed(engine: Engine) -> bool: try: engine.execute("SELECT postgis_version()") return True except ProgrammingError: return False def create_schema_and_table(drop: bool) -> None: schema = ["bra", "nivo", "flowcapt"] db_con = create_database_connections() if not is_postgis_installed(db_con.engine): db_con.engine.execute("CREATE EXTENSION postgis") if drop: metadata.drop_all(db_con.engine) [db_con.engine.execute(f"DROP SCHEMA IF EXISTS {s} CASCADE") for s in schema] [db_con.engine.execute(f"CREATE SCHEMA IF NOT EXISTS {s}") for s in schema] metadata.create_all(db_con.engine)

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null

0

null

0

1

0

93a32ef6fddce5cbc92f060b72225c59adf371f7

515

py

Python

sources/pysimplegui/simpleeventloop.py

kantel/pythoncuriosa

4dfb92b443cbe0acf8d8efa5c54efbf13e834620

[ "MIT" ]

null

sources/pysimplegui/simpleeventloop.py

kantel/pythoncuriosa

4dfb92b443cbe0acf8d8efa5c54efbf13e834620

[ "MIT" ]

null

sources/pysimplegui/simpleeventloop.py

kantel/pythoncuriosa

4dfb92b443cbe0acf8d8efa5c54efbf13e834620

[ "MIT" ]

null

import PySimpleGUI as sg layout = [ [sg.Text("Wie heißt Du?")], [sg.Input(key = "-INPUT-")], [sg.Text(size = (40, 1), key = "-OUTPUT-")], [sg.Button("Okay"), sg.Button("Quit")] ] window = sg.Window("Hallo PySimpleGUI", layout) keep_going = True while keep_going: event, values = window.read() if event == sg.WINDOW_CLOSED or event == "Quit": keep_going = False window["-OUTPUT-"].update("Hallöchen " + values["-INPUT-"] + "!") window.close()

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null

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null

0

1

0

93a5a387bf24ca83ae37f5241ea161f3010ef4cf

3,247

py

Python

datasets/fusiongallery.py

weshoke/UV-Net

9e833df6868695a2cea5c5b79a0b613b224eacf2

[ "MIT" ]

null

datasets/fusiongallery.py

weshoke/UV-Net

9e833df6868695a2cea5c5b79a0b613b224eacf2

[ "MIT" ]

null

datasets/fusiongallery.py

weshoke/UV-Net

9e833df6868695a2cea5c5b79a0b613b224eacf2

[ "MIT" ]

null

import numpy as np import pathlib from torch.utils.data import Dataset, DataLoader import dgl import torch from dgl.data.utils import load_graphs import json from datasets import util from tqdm import tqdm class FusionGalleryDataset(Dataset): @staticmethod def num_classes(): return 8 def __init__( self, root_dir, split="train", center_and_scale=True, ): """ Load the Fusion Gallery dataset from: Joseph G. Lambourne, Karl D. D. Willis, Pradeep Kumar Jayaraman, Aditya Sanghi, Peter Meltzer, Hooman Shayani. "BRepNet: A topological message passing system for solid models," CVPR 2021. :param root_dir: Root path to the dataset :param split: string Whether train, val or test set """ path = pathlib.Path(root_dir) assert split in ("train", "val", "test") with open(str(path.joinpath("train_test.json")), "r") as read_file: filelist = json.load(read_file) # NOTE: Using a held out out validation set may be better. # But it's not easy to perform stratified sampling on some rare classes # which only show up on a few solids. if split in ("train", "val"): split_filelist = filelist["train"] else: split_filelist = filelist["test"] self.center_and_scale = center_and_scale all_files = [] # Load graphs and store their filenames for loading labels next for fn in split_filelist: all_files.append(path.joinpath("graph").joinpath(fn + ".bin")) # Load labels from the json files in the subfolder self.files = [] self.graphs = [] print(f"Loading {split} data...") for fn in tqdm(all_files): if not fn.exists(): continue graph = load_graphs(str(fn))[0][0] label = np.loadtxt( path.joinpath("breps").joinpath(fn.stem + ".seg"), dtype=np.int, ndmin=1 ) if label.size != graph.number_of_nodes(): # Skip files where the number of faces and labels don't match # print( # f"WARN: number of faces and labels do not match in {fn.stem}: {label.size} vs. {graph.number_of_nodes()}" # ) continue self.files.append(fn) graph.ndata["y"] = torch.tensor(label).long() self.graphs.append(graph) if self.center_and_scale: for i in range(len(self.graphs)): self.graphs[i].ndata["x"] = util.center_and_scale_uvsolid( self.graphs[i].ndata["x"] ) def __len__(self): return len(self.graphs) def __getitem__(self, idx): graph = self.graphs[idx] return graph def _collate(self, batch): bg = dgl.batch(batch) return bg def get_dataloader(self, batch_size=128, shuffle=True): return DataLoader( self, batch_size=batch_size, shuffle=shuffle, collate_fn=self._collate, num_workers=0, # Can be set to non-zero on Linux drop_last=True, )

32.47

128

0.57838

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null

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null

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1

0

93a73833278709acd49bb46a9f2c8ae73acf367a

3,690

py

Python

mpa/modules/models/heads/custom_ssd_head.py

openvinotoolkit/model_preparation_algorithm

8d36bf5944837b7a3d22fc2c3a4cb93423619fc2

[ "Apache-2.0" ]

null

mpa/modules/models/heads/custom_ssd_head.py

openvinotoolkit/model_preparation_algorithm

8d36bf5944837b7a3d22fc2c3a4cb93423619fc2

[ "Apache-2.0" ]

null

mpa/modules/models/heads/custom_ssd_head.py

openvinotoolkit/model_preparation_algorithm

8d36bf5944837b7a3d22fc2c3a4cb93423619fc2

[ "Apache-2.0" ]

null

# Copyright (C) 2022 Intel Corporation # SPDX-License-Identifier: Apache-2.0 # from mmdet.models.builder import HEADS, build_loss from mmdet.models.losses import smooth_l1_loss from mmdet.models.dense_heads.ssd_head import SSDHead @HEADS.register_module() class CustomSSDHead(SSDHead): def __init__( self, *args, bg_loss_weight=-1.0, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, reduction='none', loss_weight=1.0 ), **kwargs ): super().__init__(*args, **kwargs) self.loss_cls = build_loss(loss_cls) self.bg_loss_weight = bg_loss_weight def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights, bbox_targets, bbox_weights, num_total_samples): """Compute loss of a single image. Args: cls_score (Tensor): Box scores for eachimage Has shape (num_total_anchors, num_classes). bbox_pred (Tensor): Box energies / deltas for each image level with shape (num_total_anchors, 4). anchors (Tensor): Box reference for each scale level with shape (num_total_anchors, 4). labels (Tensor): Labels of each anchors with shape (num_total_anchors,). label_weights (Tensor): Label weights of each anchor with shape (num_total_anchors,) bbox_targets (Tensor): BBox regression targets of each anchor wight shape (num_total_anchors, 4). bbox_weights (Tensor): BBox regression loss weights of each anchor with shape (num_total_anchors, 4). num_total_samples (int): If sampling, num total samples equal to the number of total anchors; Otherwise, it is the number of positive anchors. Returns: dict[str, Tensor]: A dictionary of loss components. """ # FG cat_id: [0, num_classes -1], BG cat_id: num_classes pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero().reshape(-1) neg_inds = (labels == self.num_classes).nonzero().view(-1) # Re-weigting BG loss label_weights = label_weights.reshape(-1) if self.bg_loss_weight >= 0.0: neg_indices = (labels == self.num_classes) label_weights = label_weights.clone() label_weights[neg_indices] = self.bg_loss_weight loss_cls_all = self.loss_cls(cls_score, labels, label_weights) if len(loss_cls_all.shape) > 1: loss_cls_all = loss_cls_all.sum(-1) num_pos_samples = pos_inds.size(0) num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples if num_neg_samples > neg_inds.size(0): num_neg_samples = neg_inds.size(0) topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples) loss_cls_pos = loss_cls_all[pos_inds].sum() loss_cls_neg = topk_loss_cls_neg.sum() loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples if self.reg_decoded_bbox: # When the regression loss (e.g. `IouLoss`, `GIouLoss`) # is applied directly on the decoded bounding boxes, it # decodes the already encoded coordinates to absolute format. bbox_pred = self.bbox_coder.decode(anchor, bbox_pred) loss_bbox = smooth_l1_loss( bbox_pred, bbox_targets, bbox_weights, beta=self.train_cfg.smoothl1_beta, avg_factor=num_total_samples) return loss_cls[None], loss_bbox

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0.14

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null

0

null

0

1

0

93a84d645ccedf01c50e4963b06e5f5cf6720d08

2,918

py

Python

Python/ml_converter.py

daduz11/ios-facenet-id

0ec634cf7f4f12c2bfa6334a72d5f2ab0a4afde4

[ "Apache-2.0" ]

2

2021-07-22T07:35:48.000Z

2022-03-03T05:48:08.000Z

Python/ml_converter.py

daduz11/ios-facenet-id

0ec634cf7f4f12c2bfa6334a72d5f2ab0a4afde4

[ "Apache-2.0" ]

null

Python/ml_converter.py

daduz11/ios-facenet-id

0ec634cf7f4f12c2bfa6334a72d5f2ab0a4afde4

[ "Apache-2.0" ]

2

2021-03-11T14:50:05.000Z

2021-04-18T14:58:24.000Z

""" Copyright 2020 daduz11 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. """ """ Firstly this script is used for the conversion of the freezed inference graph (pb format) into a CoreML model. Moreover the same script takes the CoreML model at 32bit precision to carries out the quantization from 16 to 1 bit. """ import argparse import sys import tfcoreml import coremltools from coremltools.models.neural_network import quantization_utils def main(args): if args.type == 'FLOAT32': if args.model_dir[-3:] != '.pb': print("Error: the model type must be .pb file") return else: coreml_model = tfcoreml.convert( tf_model_path=args.model_dir, mlmodel_path=args.output_file, input_name_shape_dict = {'input':[1,160,160,3]}, output_feature_names=["embeddings"], minimum_ios_deployment_target = '13' ) return else: if args.model_dir[-8:] != '.mlmodel': print("Error: the model type must be .mlmodel") return if args.type == 'FLOAT16': model_spec = coremltools.utils.load_spec(args.model_dir) model_fp16_spec = coremltools.utils.convert_neural_network_spec_weights_to_fp16(model_spec) coremltools.utils.save_spec(model_fp16_spec,args.output_file) return else: model = coremltools.models.MLModel(args.model_dir) bit = int(args.type[-1]) print("quantization in INT" + str(bit)) quantized_model = quantization_utils.quantize_weights(model, bit, "linear") quantized_model.save(args.output_file) return print('File correctly saved in:', args.output_file) def parse_arguments(argv): parser = argparse.ArgumentParser() parser.add_argument('model_dir', type=str, help='This argument will be: .pb file for FLOAT32, .mlmodel otherwise (model quantization)') parser.add_argument('output_file', type=str, help='Filename for the converted coreml model (.mlmodel)') parser.add_argument('--type', type=str, choices=['FLOAT32','FLOAT16','INT8','INT6','INT4','INT3','INT2','INT1'], help="embeddings' type", default='FLOAT32') return parser.parse_args(argv) if __name__ == '__main__': main(parse_arguments(sys.argv[1:]))

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0.660384

376

2,918

4.973404

0.428191

0.032086

0.017112

0.029947

0

0.024015

0.24366

2,918

74

161

39.432432

0.82329

0.187457

0

0.173913

0

0.188751

0

1

0.043478

false

0

0.108696

0

0.282609

0.086957

0

null

0

null

0

1

0

93a90aa96a7060708343be286a46a3cbad16b9b8

628

py

Python

pizza_utils/stringutils.py

ILikePizza555/py-pizza-utils

f336fc2c391430f5d901d85dfda50974d9f8aba7

[ "MIT" ]

null

pizza_utils/stringutils.py

ILikePizza555/py-pizza-utils

f336fc2c391430f5d901d85dfda50974d9f8aba7

[ "MIT" ]

null

pizza_utils/stringutils.py

ILikePizza555/py-pizza-utils

f336fc2c391430f5d901d85dfda50974d9f8aba7

[ "MIT" ]

null

def find_from(string, subs, start = None, end = None): """ Returns a tuple of the lowest index where a substring in the iterable "subs" was found, and the substring. If multiple substrings are found, it will return the first one. If nothing is found, it will return (-1, None) """ string = string[start:end] last_index = len(string) substring = None for s in subs: i = string.find(s) if i != -1 and i < last_index: last_index = i substring = s if last_index == len(string): return (-1, None) return (last_index, substring)

27.304348

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90

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0.121622

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0.091892

0

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0

0.25

0

null

0

null

0

1

0

93aa7bc7eef6be2b816f51dac8d5aa561ac4c490

4,844

py

Python

lab/experiment_futures.py

ajmal017/ta_scanner

21f12bfd8b5936d1d1977a32c756715539b0d97c

[ "BSD-3-Clause" ]

16

2020-06-22T05:24:20.000Z

2022-02-15T11:41:14.000Z

lab/experiment_futures.py

ajmal017/ta_scanner

21f12bfd8b5936d1d1977a32c756715539b0d97c

[ "BSD-3-Clause" ]

24

2020-07-07T04:22:03.000Z

2021-01-03T07:21:02.000Z

lab/experiment_futures.py

ajmal017/ta_scanner

21f12bfd8b5936d1d1977a32c756715539b0d97c

[ "BSD-3-Clause" ]

3

2020-06-21T12:12:14.000Z

2021-09-01T04:46:59.000Z

# todos # - [ ] all dates and date deltas are in time, not integers from loguru import logger from typing import Dict import sys import datetime from datetime import timedelta import numpy as np from ta_scanner.data.data import load_and_cache, db_data_fetch_between, aggregate_bars from ta_scanner.data.ib import IbDataFetcher from ta_scanner.experiments.simple_experiment import SimpleExperiment from ta_scanner.indicators import ( IndicatorSmaCrossover, IndicatorEmaCrossover, IndicatorParams, ) from ta_scanner.signals import Signal from ta_scanner.filters import FilterCumsum, FilterOptions, FilterNames from ta_scanner.reports import BasicReport from ta_scanner.models import gen_engine ib_data_fetcher = IbDataFetcher() instrument_symbol = "/NQ" rth = False interval = 1 field_name = "ema_cross" slow_sma = 25 fast_sma_min = 5 fast_sma_max = 20 filter_inverse = True win_pts = 75 loss_pts = 30 trade_interval = 12 test_total_pnl = 0.0 test_total_count = 0 all_test_results = [] engine = gen_engine() logger.remove() logger.add(sys.stderr, level="INFO") def gen_params(sd, ed) -> Dict: return dict(start_date=sd, end_date=ed, use_rth=rth, groupby_minutes=interval) def run_cross(original_df, fast_sma: int, slow_sma: int): df = original_df.copy() # indicator setup indicator_params = { IndicatorParams.fast_ema: fast_sma, IndicatorParams.slow_ema: slow_sma, } indicator = IndicatorEmaCrossover(field_name, indicator_params) indicator.apply(df) # filter setup filter_params = { FilterOptions.win_points: win_pts, FilterOptions.loss_points: loss_pts, FilterOptions.threshold_intervals: trade_interval, } sfilter = FilterCumsum(field_name, filter_params) # generate results if filter_inverse: results = sfilter.apply(df, inverse=1) else: results = sfilter.apply(df) # get aggregate pnl basic_report = BasicReport() pnl, count, avg, median = basic_report.analyze(df, field_name) return pnl, count, avg, median def run_cross_range(df, slow_sma: int, fast_sma_min, fast_sma_max): results = [] for fast_sma in range(fast_sma_min, fast_sma_max): pnl, count, avg, median = run_cross(df, fast_sma, slow_sma) results.append([fast_sma, pnl, count, avg, median]) return results def fetch_data(): sd = datetime.date(2020, 7, 1) ed = datetime.date(2020, 8, 15) load_and_cache(instrument_symbol, ib_data_fetcher, **gen_params(sd, ed)) def query_data(engine, symbol, sd, ed, groupby_minutes): df = db_data_fetch_between(engine, symbol, sd, ed) df.set_index("ts", inplace=True) df = aggregate_bars(df, groupby_minutes=groupby_minutes) df["ts"] = df.index return df # fetch_data() for i in range(0, 33): initial = datetime.date(2020, 7, 10) + timedelta(days=i) test_start, test_end = initial, initial if initial.weekday() in [5, 6]: continue # fetch training data train_sd = initial - timedelta(days=5) train_ed = initial - timedelta(days=1) df_train = query_data(engine, instrument_symbol, train_sd, train_ed, interval) # for training data, let's find results for a range of SMA results = run_cross_range( df_train, slow_sma=slow_sma, fast_sma_min=fast_sma_min, fast_sma_max=fast_sma_max, ) fast_sma_pnl = [] for resultindex in range(2, len(results) - 3): fast_sma = results[resultindex][0] pnl = results[resultindex][1] result_set = results[resultindex - 2 : resultindex + 3] total_pnl = sum([x[1] for x in result_set]) fast_sma_pnl.append([fast_sma, total_pnl, pnl]) arr = np.array(fast_sma_pnl, dtype=float) max_tuple = np.unravel_index(np.argmax(arr, axis=None), arr.shape) optimal_fast_sma = int(arr[(max_tuple[0], 0)]) optimal_fast_sma_pnl = [x[2] for x in fast_sma_pnl if x[0] == optimal_fast_sma][0] # logger.info(f"Selected fast_sma={optimal_fast_sma}. PnL={optimal_fast_sma_pnl}") test_sd = initial test_ed = initial + timedelta(days=1) df_test = query_data(engine, instrument_symbol, test_sd, test_ed, interval) test_results = run_cross(df_test, optimal_fast_sma, slow_sma) all_test_results.append([initial] + list(test_results)) logger.info( f"Test Results. pnl={test_results[0]}, count={test_results[1]}, avg={test_results[2]}, median={test_results[3]}" ) test_total_pnl += test_results[0] test_total_count += test_results[1] logger.info( f"--- CumulativePnL={test_total_pnl}. Trades Count={test_total_count}. After={initial}" ) import csv with open("simple_results.csv", "w") as csvfile: spamwriter = csv.writer(csvfile) for row in all_test_results: spamwriter.writerow(row)

28

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0.20339

0

null

0

null

0

1

0

93ade385d6ee900f8bf10af83edfd79ce2a15da9

841

py

Python

01.Hello_tkinter.py

amitdev101/learning-tkinter

1f7eabe1ac958c83c8bbe70e15682ecd4f7b5de5

[ "MIT" ]

null

01.Hello_tkinter.py

amitdev101/learning-tkinter

1f7eabe1ac958c83c8bbe70e15682ecd4f7b5de5

[ "MIT" ]

1

2020-11-15T15:43:03.000Z

2020-11-15T15:43:16.000Z

01.Hello_tkinter.py

amitdev101/learning-tkinter

1f7eabe1ac958c83c8bbe70e15682ecd4f7b5de5

[ "MIT" ]

null

import tkinter as tk import os print(tk) print(dir(tk)) print(tk.TkVersion) print(os.getcwd()) '''To initialize tkinter, we have to create a Tk root widget, which is a window with a title bar and other decoration provided by the window manager. The root widget has to be created before any other widgets and there can only be one root widget.''' root = tk.Tk() '''The next line of code contains the Label widget. The first parameter of the Label call is the name of the parent window, in our case "root". So our Label widget is a child of the root widget. The keyword parameter "text" specifies the text to be shown: ''' w = tk.Label(root,text='Hello world') '''The pack method tells Tk to fit the size of the window to the given text. ''' w.pack() '''The window won't appear until we enter the Tkinter event loop''' root.mainloop()

36.565217

115

0.737218

153

841

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0.503268

0.064516

0.041935

0

0.178359

841

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38.227273

0.89725

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0

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false

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0.2

0.4

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null

0

null

0

1

0

93aee3614d8d0959902e63d0a0a8aa33c102d4fd

14,700

py

Python

myscrumy/remiljscrumy/views.py

mikkeyiv/Django-App

b1114e9e53bd673119a38a1acfefb7a9fd9f172e

[ "MIT" ]

null

myscrumy/remiljscrumy/views.py

mikkeyiv/Django-App

b1114e9e53bd673119a38a1acfefb7a9fd9f172e

[ "MIT" ]

null

myscrumy/remiljscrumy/views.py

mikkeyiv/Django-App

b1114e9e53bd673119a38a1acfefb7a9fd9f172e

[ "MIT" ]

null

from django.shortcuts import render,redirect,get_object_or_404 from remiljscrumy.models import ScrumyGoals,GoalStatus,ScrumyHistory,User from django.http import HttpResponse,Http404,HttpResponseRedirect from .forms import SignupForm,CreateGoalForm,MoveGoalForm,DevMoveGoalForm,AdminChangeGoalForm,QAChangeGoalForm,QAChangegoal from django.contrib.auth import authenticate,login from django.contrib.auth.models import User,Group from django.conf import settings from django.core.exceptions import ObjectDoesNotExist from django.urls import reverse #from django.core.exceptions import ObjectDoesNotExist # Create your views here. def index(request): # scrumygoals = ScrumyGoals.objects.all() # return HttpResponse(scrumygoals) if request.method == 'POST': #this is a method used to send data to the server form = SignupForm(request.POST) #creates the form instance and bounds form data to it if form .is_valid():#used to validate the form #add_goal = form.save(commit=False)#save an object bounds in the form form.save() username = form.cleaned_data.get('username') raw_password = form.cleaned_data.get('password1') # password2 = form.cleaned_data.get('password1') # if password2 != raw_password: # raise form.Http404('password must match') user = authenticate(username=username, password=raw_password) user.is_staff=True login(request,user) g = Group.objects.get(name='Developer') g.user_set.add(request.user) user.save() return redirect('home') else: form = SignupForm()#creates an unbound form with an empty data return render(request, 'remiljscrumy/index.html', {'form': form}) def filterArg(request): output = ScrumyGoals.objects.filter(goal_name='Learn Django') return HttpResponse(output) def move_goal(request, goal_id): verifygoal = GoalStatus.objects.get(status_name="Verify Goal") if not request.user.is_authenticated: return redirect('%s?next=%s' % (settings.LOGIN_URL, request.path)) current = request.user group = current.groups.all()[0] try: goal = ScrumyGoals.objects.get(goal_id=goal_id) except ObjectDoesNotExist: notexist = 'A record with that goal id does not exist' context = {'not_exist': notexist} return render(request, 'remiljscrumy/exception.html', context) if group == Group.objects.get(name='Developer') and current == goal.user: form = DevMoveGoalForm() if request.method == 'POST': form = DevMoveGoalForm(request.POST) if form.is_valid(): selected_status = form.save(commit=False) selected = form.cleaned_data['goal_status'] get_status = selected_status.status_name choice = GoalStatus.objects.get(id=int(selected)) goal.goal_status = choice goal.save() return HttpResponseRedirect(reverse('home')) else: form = DevMoveGoalForm() return render(request, 'remiljscrumy/movegoal.html', {'form': form, 'goal': goal, 'current_user': current, 'group': group}) if group == Group.objects.get(name='Developer') and current != goal.user: form = DevMoveGoalForm() if request.method == 'GET': notexist = 'YOU DO NO NOT HAVE THE PERMISSION TO CHANGE OTHER USERS GOAL' context = {'not_exist': notexist} return render(request, 'remiljscrumy/exception.html', context) if group == Group.objects.get(name='Admin'): form = AdminChangeGoalForm() if request.method == 'GET': return render(request, 'remiljscrumy/movegoal.html', {'form': form, 'goal': goal, 'currentuser': current, 'group': group}) if request.method == 'POST': form = AdminChangeGoalForm(request.POST) if form.is_valid(): selected_status = form.save(commit=False) get_status = selected_status.goal_status goal.goal_status = get_status goal.save() return HttpResponseRedirect(reverse('home')) else: form = AdminChangeGoalForm() return render(request, 'remiljscrumy/movegoal.html', {'form': form, 'goal': goal, 'current_user': current, 'group': group}) if group == Group.objects.get(name='Owner') and current == goal.user: form = AdminChangeGoalForm() if request.method == 'GET': return render(request, 'remiljscrumy/movegoal.html', {'form': form, 'goal': goal, 'currentuser': current, 'group': group}) if request.method == 'POST': form = AdminChangeGoalForm(request.POST) if form.is_valid(): selected_status = form.save(commit=False) get_status = selected_status.goal_status goal.goal_status = get_status goal.save() return HttpResponseRedirect(reverse('home')) else: form = AdminChangeGoalForm() return render(request, 'remiljscrumy/movegoal.html',{'form': form, 'goal': goal, 'current_user': current, 'group': group}) # else: # notexist = 'You cannot move other users goals' # context = {'not_exist': notexist} # return render(request, 'maleemmyscrumy/exception.html', context) if group == Group.objects.get(name='Quality Assurance') and current == goal.user: form = QAChangegoal() if request.method == 'GET': return render(request, 'remiljscrumy/movegoal.html', {'form': form, 'goal': goal, 'currentuser': current, 'group': group}) if request.method == 'POST': form = QAChangegoal(request.POST) if form.is_valid(): selected_status = form.save(commit=False) selected = form.cleaned_data['goal_status'] get_status = selected_status.status_name choice = GoalStatus.objects.get(id=int(selected)) goal.goal_status = choice goal.save() return HttpResponseRedirect(reverse('home')) else: form = QAChangegoal() return render(request, 'remiljscrumy/movegoal.html',{'form': form, 'goal': goal, 'currentuser': current, 'group': group}) if group == Group.objects.get(name='Quality Assurance') and current != goal.user and goal.goal_status == verifygoal: form = QAChangeGoalForm() if request.method == 'GET': return render(request, 'remiljscrumy/movegoal.html', {'form': form, 'goal': goal, 'currentuser': current, 'group': group}) if request.method == 'POST': form = QAChangeGoalForm(request.POST) if form.is_valid(): selected_status = form.save(commit=False) selected = form.cleaned_data['goal_status'] get_status = selected_status.status_name choice = GoalStatus.objects.get(id=int(selected)) goal.goal_status = choice goal.save() return HttpResponseRedirect(reverse('home')) else: form = QAChangeGoalForm() return render(request, 'remiljscrumy/movegoal.html',{'form': form, 'goal': goal, 'currentuser': current, 'group': group}) else: notexist = 'You can only move goal from verify goals to done goals' context = {'not_exist': notexist} return render(request, 'remiljscrumy/exception.html', context) # def move_goal(request, goal_id): # #response = ScrumyGoals.objects.get(goal_id=goal_id) # # try: # #goal = ScrumyGoals.objects.get(goal_id=goal_id) # # except ScrumyGoals.DoesNotExist: # # raise Http404 ('A record with that goal id does not exist') # instance = get_object_or_404(ScrumyGoals,goal_id=goal_id) # form = MoveGoalForm(request.POST or None, instance=instance) # if form. is_valid(): # instance = form.save(commit=False) # instance.save() # return redirect('home') # context={ # 'goal_id': instance.goal_id, # 'user': instance.user, # 'goal_status': instance.goal_status, # 'form':form, # } # return render(request, 'remiljscrumy/exception.html', context) #move_goal = form.save(commit=False) # move_goal = # form.save() # # goal_name = form.cleaned_data.get('goal_name') # # ScrumyGoals.objects.get(goal_name) # return redirect('home') # def form_valid(self, form): # form.instance.goal_status = self.request.user # return super(addgoalForm, self).form_valid(form) # } # return render(request, 'remiljscrumy/exception.html', context=gdict) #return HttpResponse(response) # return HttpResponse('%s is the response at the record of goal_id %s' % (response, goal_id))''' from random import randint def add_goal(request): # existing_id = ScrumyGoals.objects.order_by('goal_id') # while True: # goal_id = randint(1000, 10000) #returns a random number between 1000 and 9999 # if goal_id not in existing_id: # pr = ScrumyGoals.objects.create(goal_name='Keep Learning Django', goal_id=goal_id, created_by='Louis', moved_by="Louis", goal_status=GoalStatus.objects.get(pk=1), user=User.objects.get(pk=6)) # break # form = CreateGoalForm # if request.method == 'POST': # form = CreateGoalForm(request.POST) # if form .is_valid(): # add_goals = form.save(commit=False) # add_goals = form.save() # #form.save() # return redirect('home') # else: # form = CreateGoalForm() return render(request, 'remiljscrumy/addgoal.html', {'form': form}) def home(request): '''# all=','.join([eachgoal.goal_name for eachgoal in ScrumyGoals.objects.all()]) # home = ScrumyGoals.objects.filter(goal_name='keep learning django') # return HttpResponse(all) #homedict = {'goal_name':ScrumyGoals.objects.get(pk=3).goal_name,'goal_id':ScrumyGoals.objects.get(pk=3).goal_id, 'user': ScrumyGoals.objects.get(pk=3).user,} user = User.objects.get(email="louisoma@linuxjobber.com") name = user.scrumygoal.all() homedict={'goal_name':ScrumyGoals.objects.get(pk=1).goal_name,'goal_id':ScrumyGoals.objects.get(pk=1).goal_id,'user':ScrumyGoals.objects.get(pk=1).user, 'goal_name1':ScrumyGoals.objects.get(pk=2).goal_name,'goal_id1':ScrumyGoals.objects.get(pk=2).goal_id,'user':ScrumyGoals.objects.get(pk=2).user, 'goal_name2':ScrumyGoals.objects.get(pk=3).goal_name,'goal_id2':ScrumyGoals.objects.get(pk=3).goal_id,'user2':ScrumyGoals.objects.get(pk=3).user}''' # form = CreateGoalForm # if request.method == 'POST': # form = CreateGoalForm(request.POST) # if form .is_valid(): # add_goal = form.save(commit=True) # add_goal = form.save() # # #form.save() # return redirect('home') current = request.user week = GoalStatus.objects.get(pk=1) day = GoalStatus.objects.get(pk=2) verify = GoalStatus.objects.get(pk=3) done = GoalStatus.objects.get(pk=4) user = User.objects.all() weeklygoal = ScrumyGoals.objects.filter(goal_status=week) dailygoal = ScrumyGoals.objects.filter(goal_status=day) verifygoal = ScrumyGoals.objects.filter(goal_status=verify) donegoal = ScrumyGoals.objects.filter(goal_status=done) groups = current.groups.all() dev = Group.objects.get(name='Developer') owner = Group.objects.get(name='Owner') admin = Group.objects.get(name='Admin') qa = Group.objects.get(name='Quality Assurance') if not request.user.is_authenticated: return redirect('%s?next=%s' % (settings.LOGIN_URL, request.path)) if current.is_authenticated: if dev in groups or qa in groups or owner in groups: # if request.method == 'GET': # return render(request, 'remiljscrumy/home.html', context) form = CreateGoalForm() context = {'user': user, 'weeklygoal': weeklygoal, 'dailygoal': dailygoal, 'verifygoal': verifygoal, 'donegoal': donegoal, 'form': form, 'current': current, 'groups': groups,'dev': dev,'owner':owner,'admin':admin,'qa':qa} if request.method == 'POST': form = CreateGoalForm(request.POST) if form.is_valid(): post = form.save(commit=False) status_name = GoalStatus(id=1) post.goal_status = status_name post.user = current post = form.save() elif admin in groups: context = {'user': user, 'weeklygoal': weeklygoal, 'dailygoal': dailygoal, 'verifygoal': verifygoal, 'donegoal': donegoal,'current': current, 'groups': groups,'dev': dev,'owner':owner,'admin':admin,'qa':qa} return render(request, 'remiljscrumy/home.html', context) # else: # form = WeekOnlyAddGoalForm() # return HttpResponseRedirect(reverse('ayooluwaoyewoscrumy:homepage')) # if group == 'Admin': # context ={ # 'user':User.objects.all(), # 'weeklygoal':ScrumyGoals.objects.filter(goal_status=week), # 'dailygoal':ScrumyGoals.objects.filter(goal_status=day), # 'verifiedgoals':ScrumyGoals.objects.filter(goal_status=verify), # 'donegoal':ScrumyGoals.objects.filter(goal_status=done), # 'current':request.user, # 'groups':request.user.groups.all(), # 'admin': Group.objects.get(name="Admin"), # 'owner': Group.objects.get(name='Owner'), # 'dev': Group.objects.get(name='Developer'), # 'qa': Group.objects.get(name='Quality Assurance'),} # return render(request,'remiljscrumy/home.html',context=homedict) # if request.method == 'GET': # return render(request, 'remiljscrumy/home.html', context) #

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5.515723

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0.045496

0.070696

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null

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null

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1

0

93b17847a4ea4d1f1c0c385ce9727ab17aed5c27

3,088

py

Python

examples/ex03_oscillator_classes.py

icemtel/carpet

5905e02ab0e44822829a672955dccad3e09eea07

[ "MIT" ]

null

examples/ex03_oscillator_classes.py

icemtel/carpet

5905e02ab0e44822829a672955dccad3e09eea07

[ "MIT" ]

null

examples/ex03_oscillator_classes.py

icemtel/carpet

5905e02ab0e44822829a672955dccad3e09eea07

[ "MIT" ]

null

''' Cilia classes are used to compute fixed points faster. - Assume symmetry like in an m-twist (make a plot to see it) - Assume that symmetries is not broken in time -> define classes of symmetry and interactions between them. Done: - Create a ring of cilia. - Define symmetry classes - Use classes to solve ODE - Map back to cilia ''' import numpy as np import carpet import carpet.lattice.ring1d as lattice import carpet.physics.friction_pairwise as physics import carpet.classes as cc import carpet.visualize as vis import matplotlib.pyplot as plt ## Parameters # Physics set_name = 'machemer_1' # hydrodynamic friction coefficients data set period = 31.25 # [ms] period freq = 2 * np.pi / period # [rad/ms] angular frequency order_g11 = (4, 0) # order of Fourier expansion of friction coefficients order_g12 = (4, 4) # Geometry N = 6 # number of cilia a = 18 # [um] lattice spacing e1 = (1, 0) # direction of the chain ## Initialize # Geometry L1 = lattice.get_domain_size(N, a) coords, lattice_ids = lattice.get_nodes_and_ids(N, a, e1) # get cilia (nodes) coordinates NN, TT = lattice.get_neighbours_list(N, a, e1) # get list of neighbours and relative positions e1, e2 = lattice.get_basis(e1) get_k = lattice.define_get_k(N, a, e1) get_mtwist = lattice.define_get_mtwist(coords, N, a, e1) # Physics gmat_glob, q_glob = physics.define_gmat_glob_and_q_glob(set_name, e1, e2, a, NN, TT, order_g11, order_g12, period) right_side_of_ODE = physics.define_right_side_of_ODE(gmat_glob, q_glob) solve_cycle = carpet.define_solve_cycle(right_side_of_ODE, 2 * period, phi_global_func=carpet.get_mean_phase) # k-twist k1 = 2 phi0 = get_mtwist(k1) vis.plot_nodes(coords, phi=phi0) # visualize! plt.ylim([-L1 / 10, L1 / 10]) plt.show() ## Solve regularly tol = 1e-4 sol = solve_cycle(phi0, tol) phi1 = sol.y.T[-1] - 2 * np.pi # after one cycle ## Now solve with classes # Map to classes ix_to_class, class_to_ix = cc.get_classes(phi0) nclass = len(class_to_ix) # Get classes representatives # Get one oscillator from each of cilia classes unique_cilia_ids = cc.get_unique_cilia_ix( class_to_ix) # equivalent to sp.array([class_to_ix[iclass][0] for iclass in range(nclass)], dtype=sp.int64) # Get connections N1_class, T1_class = cc.get_neighbours_list_class(unique_cilia_ids, ix_to_class, NN, TT) # Define physics gmat_glob_class, q_glob_class = physics.define_gmat_glob_and_q_glob(set_name, e1, e2, a, N1_class, T1_class, order_g11, order_g12, period) right_side_of_ODE_class = physics.define_right_side_of_ODE(gmat_glob_class, q_glob_class) solve_cycle_class = carpet.define_solve_cycle(right_side_of_ODE_class, 2 * period, carpet.get_mean_phase) # Solve ODE phi0_class = phi0[unique_cilia_ids] sol = solve_cycle_class(phi0_class, tol) phi1_class = sol.y.T[-1] - 2 * np.pi # Map from classes back to cilia phi1_mapped_from_class = phi1_class[ix_to_class] ## Print how much phase changed print(phi1_mapped_from_class - phi1) # difference between two - should be on the order of tolerance or smaller

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3,088

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0.021739

0

null

0

null

0

1

0

93b1f4ae1de1aaae99760a70f835707158943004

749

py

Python

cars/donkeycar/sim/Adafruit_PCA9685-pkg/Adafruit_PCA9685/__init__.py

kuaikai/kuaikai

ca7e7b2d2f6f16b892a21c819ba43201beadf370

[ "Apache-2.0" ]

6

2018-03-27T15:46:28.000Z

2018-06-23T21:56:15.000Z

cars/donkeycar/sim/Adafruit_PCA9685-pkg/Adafruit_PCA9685/__init__.py

kuaikai/kuaikai

ca7e7b2d2f6f16b892a21c819ba43201beadf370

[ "Apache-2.0" ]

3

2018-03-30T15:54:34.000Z

2018-07-11T19:44:59.000Z

cars/donkeycar/sim/Adafruit_PCA9685-pkg/Adafruit_PCA9685/__init__.py

kuaikai/kuaikai

ca7e7b2d2f6f16b892a21c819ba43201beadf370

[ "Apache-2.0" ]

null

""" SCL <scott@rerobots.net> 2018 """ import json import os import tempfile import time class PCA9685: def __init__(self): self._fd, self._pathname = tempfile.mkstemp(prefix='kuaikai_sim_', dir='/tmp', text=True) self._fp = os.fdopen(self._fd, 'wt') def __del__(self): self._fp.close() def set_pwm_freq(self, freq_hz): self._fp.write(json.dumps({ 'time': time.time(), 'fcn': 'set_pwm_freq', 'args': {'freq_hz': freq_hz}, }) + '\n') def set_pwm(self, channel, on, off): self._fp.write(json.dumps({ 'time': time.time(), 'fcn': 'set_pwm', 'args': {'channel': channel, 'on': on, 'off': off}, }) + '\n')

22.69697

97

0.534045

95

749

3.936842

0.442105

0.064171

0.048128

0.080214

0.219251

0

0.014953

0.285714

749

32

98

23.40625

0.684112

0.038718

0

0.272727

0

0.115331

0

1

0.181818

false

0

0.181818

0

0.409091

0

null

0

null

0

1

0

93b2760677f1d106e80a9cb1e7a2b2ab58fbe987

2,851

py

Python

bayesian-belief/sequential_bayes.py

ichko/interactive

6659f81c11c0f180295b758b457343d32323eb35

[ "MIT" ]

null

bayesian-belief/sequential_bayes.py

ichko/interactive

6659f81c11c0f180295b758b457343d32323eb35

[ "MIT" ]

null

bayesian-belief/sequential_bayes.py

ichko/interactive

6659f81c11c0f180295b758b457343d32323eb35

[ "MIT" ]

1

2019-02-05T20:22:08.000Z

import numpy as np import matplotlib.pyplot as plt import scipy.stats def set_ax_range(): LEFT_AX.set_xlim(X_RANGE) LEFT_AX.set_ylim(Y_RANGE) def range_plot(ax, f, x_range, y_range): bins = 50 xi, yi = np.mgrid[ min(x_range):max(x_range):bins*1j, min(y_range):max(y_range):bins*1j ] zi = np.array([ f(x, Y_DATA) for x, Y_DATA in zip(xi.flatten(), yi.flatten()) ]) ax.pcolormesh(xi, yi, zi.reshape(xi.shape)) def likelihood(X, Y, w): Z = X @ w var = np.var(Y) return np.prod([scipy.stats.norm(z, var).pdf(y) for z, y in zip(Z, Y)]) class SequentialBayes: def __init__(self, mean, var): self.prior_mean = mean self.prior_var = var def pdf(self, x): return scipy.stats.multivariate_normal(self.prior_mean, self.prior_var).pdf(x) def sample(self, size): return np.random.multivariate_normal(self.prior_mean, self.prior_var, size=size) def update(self, X, y): y_var = np.var(y) + 0.01 inv_prior_var = np.linalg.inv(self.prior_var) inv_y_var = 1 / y_var ** 2 posterior_var = np.linalg.inv( inv_prior_var + inv_y_var * (X.T @ X) ) posterior_mean = posterior_var @ ( inv_prior_var @ self.prior_mean + inv_y_var * X.T @ y ) self.prior_mean = posterior_mean self.prior_var = posterior_var return posterior_mean, posterior_var def on_click(event): if event.xdata is None: return LEFT_AX.clear() global BELIEF, X_DATA, Y_DATA mouse_x, mouse_y = event.xdata, event.ydata if len(X_DATA) > 0: X_DATA = np.vstack((X_DATA, [1, mouse_x])) Y_DATA = np.append(Y_DATA, [mouse_y]) else: X_DATA = np.array([[1, mouse_x]]) Y_DATA = np.array([mouse_y]) BELIEF.prior_mean = np.array([0, 0]) BELIEF.prior_var = np.diag([1, 1]) BELIEF.update(X_DATA, Y_DATA) range_plot(MIDDLE_AX, lambda w1, w2: likelihood( X_DATA, Y_DATA, np.array([w1, w2]) ), X_RANGE, Y_RANGE) plot_belief(RIGHT_AX) plot_belief_sample() LEFT_AX.scatter(X_DATA[:, 1], Y_DATA, c='r') set_ax_range() plt.pause(0.05) def plot_belief_sample(): Ws = BELIEF.sample(10) X = np.array([[1, X_RANGE[0]], [1, X_RANGE[1]]]) ys = X @ Ws.T LEFT_AX.plot([X_RANGE[0], X_RANGE[1]], ys, 'b--', linewidth=0.4) def plot_belief(ax): range_plot(ax, lambda w1, w2: BELIEF.pdf([w1, w2]), X_RANGE, Y_RANGE) FIG, (LEFT_AX, MIDDLE_AX, RIGHT_AX) = plt.subplots(1, 3, figsize=(10, 3)) X_RANGE = (-2, 2) Y_RANGE = (-2, 2) X_DATA = np.array([]) Y_DATA = np.array([]) BELIEF = SequentialBayes(np.array([0, 0]), np.diag([1, 1])) set_ax_range() plot_belief(RIGHT_AX) plot_belief_sample() FIG.canvas.mpl_connect('button_press_event', on_click) plt.show()

24.791304

88

0.618029

478

2,851

3.439331

0.228033

0.040146

0.039538

0.038929

0.156934

0.131995

0.09854

0

0.023373

0.234655

2,851

114

89

25.008772

0.730064

0

0.072289

0

0.007717

0

1

0.120482

false

0

0.036145

0.024096

0.228916

0

null

0

null

0

1

0

93b33aae2d1691aa0b0588d3a8ea2f43f4819a38

9,255

py

Python

cgc/legacy/kmeans.py

cffbots/cgc

1ea8b6bb6e4e9e728aff493744d8646b4953eaa4

[ "Apache-2.0" ]

11

2020-09-04T10:28:48.000Z

2022-03-10T13:56:43.000Z

cgc/legacy/kmeans.py

cffbots/cgc

1ea8b6bb6e4e9e728aff493744d8646b4953eaa4

[ "Apache-2.0" ]

40

2020-08-19T09:23:15.000Z

2022-03-01T16:16:30.000Z

cgc/legacy/kmeans.py

phenology/geoclustering

9b9b6ab8e64cdb62dbed6bdcfe63612e99665fd1

[ "Apache-2.0" ]

4

2020-10-03T21:17:18.000Z

2022-03-09T14:32:56.000Z

import numpy as np import logging import matplotlib.pyplot as plt from sklearn.cluster import KMeans from ..results import Results logger = logging.getLogger(__name__) class KmeansResults(Results): """ Contains results and metadata of a k-means refinement calculation """ def reset(self): self.k_value = None self.var_list = None self.cl_mean_centroids = None class Kmeans(object): def __init__(self, Z, row_clusters, col_clusters, n_row_clusters, n_col_clusters, k_range, kmean_max_iter=100, var_thres=2., output_filename=''): """ Set up Kmeans object. :param Z: m x n matrix of spatial-temporal data. Usually each row is a time-series of a spatial grid. :type Z: class:`numpy.ndarray` :param row_clusters: m x 1 row cluster array. :type row_clusters: class:`numpy.ndarray` :param col_clusters: n x 1 column cluster array. :type col_clusters: class:`numpy.ndarray` :param n_row_clusters: number of row clusters :type n_row_clusters: int :param n_col_clusters: number of column clusters :type n_col_clusters: int :param k_range: range of the number of clusters, i.e. value "k" :type k_range: range :param kmean_max_iter: maximum number of iterations of the KMeans :type kmean_max_iter: int :param var_thres: threshold of the sum of variance to select k :type var_thres: float :param output_filename: name of the file where to write the results :type output_filename: str """ # Input parameters ----------------- self.row_clusters = row_clusters self.col_clusters = col_clusters self.n_row_clusters = n_row_clusters self.n_col_clusters = n_col_clusters self.k_range = list(k_range) self.kmean_max_iter = kmean_max_iter self.var_thres = var_thres self.output_filename = output_filename # Input parameters end ------------- # store input parameters in results object self.results = KmeansResults(**self.__dict__) self.Z = Z if not max(self.row_clusters) < self.n_row_clusters: raise ValueError("row_clusters include labels >= n_row_clusters") if not max(self.col_clusters) < self.n_col_clusters: raise ValueError("col_clusters include labels >= n_col_clusters") if not min(self.k_range) > 0: raise ValueError("All k-values in k_range must be > 0") nonempty_row_cl = len(np.unique(self.row_clusters)) nonempty_col_cl = len(np.unique(self.col_clusters)) max_k = nonempty_row_cl * nonempty_col_cl max_k_input = max(self.k_range) if max_k_input > max_k: raise ValueError("The maximum k-value exceeds the " "number of (non-empty) co-clusters") elif max_k_input > max_k * 0.8: logger.warning("k_range includes large k-values (80% " "of the number of co-clusters or more)") def compute(self): """ Compute statistics for each clustering group. Then Loop through the range of k values, and compute the sum of variances of each k. Finally select the smallest k which gives the sum of variances smaller than the threshold. :return: k-means result object """ # Get statistic measures self._compute_statistic_measures() # Search for value k var_list = np.array([]) # List of variance of each k value kmeans_cc_list = [] for k in self.k_range: # Compute Kmean kmeans_cc = KMeans(n_clusters=k, max_iter=self.kmean_max_iter).fit( self.stat_measures_norm) var_list = np.hstack((var_list, self._compute_sum_var(kmeans_cc))) kmeans_cc_list.append(kmeans_cc) idx_var_below_thres, = np.where(var_list < self.var_thres) if len(idx_var_below_thres) == 0: raise ValueError(f"No k-value has variance below " f"the threshold: {self.var_thres}") idx_k = min(idx_var_below_thres, key=lambda x: self.k_range[x]) self.results.var_list = var_list self.results.k_value = self.k_range[idx_k] self.kmeans_cc = kmeans_cc_list[idx_k] del kmeans_cc_list # Scale back centroids of the "mean" dimension centroids_norm = self.kmeans_cc.cluster_centers_[:, 0] stat_max = np.nanmax(self.stat_measures[:, 0]) stat_min = np.nanmin(self.stat_measures[:, 0]) mean_centroids = centroids_norm * (stat_max - stat_min) + stat_min # Assign centroids to each cluster cell cl_mean_centroids = mean_centroids[self.kmeans_cc.labels_] # Reshape the centroids of means to the shape of cluster matrix, # taking into account non-constructive row/col cluster self.results.cl_mean_centroids = np.full( (self.n_row_clusters, self.n_col_clusters), np.nan) idx = 0 for r in np.unique(self.row_clusters): for c in np.unique(self.col_clusters): self.results.cl_mean_centroids[r, c] = cl_mean_centroids[idx] idx = idx + 1 self.results.write(filename=self.output_filename) return self.results def _compute_statistic_measures(self): """ Compute 6 statistics: Mean, STD, 5 percentile, 95 percentile, maximum and minimum values, for each co-cluster group. Normalize them to [0, 1] """ row_clusters = np.unique(self.row_clusters) col_clusters = np.unique(self.col_clusters) self.stat_measures = np.zeros( (len(row_clusters)*len(col_clusters), 6) ) # Loop over co-clusters for ir, r in enumerate(row_clusters): idx_rows, = np.where(self.row_clusters == r) for ic, c in enumerate(col_clusters): idx_cols, = np.where(self.col_clusters == c) rr, cc = np.meshgrid(idx_rows, idx_cols) Z = self.Z[rr, cc] idx = np.ravel_multi_index( (ir, ic), (len(row_clusters), len(col_clusters)) ) self.stat_measures[idx, 0] = Z.mean() self.stat_measures[idx, 1] = Z.std() self.stat_measures[idx, 2] = np.percentile(Z, 5) self.stat_measures[idx, 3] = np.percentile(Z, 95) self.stat_measures[idx, 4] = Z.max() self.stat_measures[idx, 5] = Z.min() # Normalize all statistics to [0, 1] minimum = self.stat_measures.min(axis=0) maximum = self.stat_measures.max(axis=0) self.stat_measures_norm = np.divide( (self.stat_measures - minimum), (maximum - minimum) ) # Set statistics to zero if all its values are identical (max == min) self.stat_measures_norm[np.isnan(self.stat_measures_norm)] = 0. def _compute_sum_var(self, kmeans_cc): """ Compute the sum of squared variance of each Kmean cluster """ # Compute the sum of variance of all points var_sum = np.sum((self.stat_measures_norm - kmeans_cc.cluster_centers_[kmeans_cc.labels_])**2) return var_sum def plot_elbow_curve(self, output_plot='./kmean_elbow_curve.png'): """ Export elbow curve plot """ plt.plot(self.k_range, self.results.var_list, marker='o') plt.plot([min(self.k_range), max(self.k_range)], [self.var_thres, self.var_thres], color='r', linestyle='--') # Threshold plt.plot([self.results.k_value, self.results.k_value], [min(self.results.var_list), max(self.results.var_list)], color='g', linestyle='--') # Selected k x_min, x_max = min(self.k_range), max(self.k_range) y_min, y_max = min(self.results.var_list), max(self.results.var_list) plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) plt.text(0.7, min( (self.var_thres-y_min)/(y_max-y_min) + 0.1, 0.9 ), 'threshold={}'.format(self.var_thres), color='r', fontsize=12, transform=plt.gca().transAxes) plt.text(min( (self.results.k_value-x_min)/(x_max-x_min) + 0.1, 0.9 ), 0.7, 'k={}'.format(self.results.k_value), color='g', fontsize=12, transform=plt.gca().transAxes) plt.xlabel('k value', fontsize=20) plt.ylabel('Sum of variance', fontsize=20) plt.savefig(output_plot, format='png', transparent=True, bbox_inches="tight")

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null

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null

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93b68bf304e52b47592144b9352709027d4393ab

3,221

py

Python

src/tests/benchmarks/tools/bench/Vellamo3.py

VirtualVFix/AndroidTestFramework

1feb769c6aca39a78e6daefd6face0a1e4d62cd4

[ "MIT" ]

null

src/tests/benchmarks/tools/bench/Vellamo3.py

VirtualVFix/AndroidTestFramework

1feb769c6aca39a78e6daefd6face0a1e4d62cd4

[ "MIT" ]

null

src/tests/benchmarks/tools/bench/Vellamo3.py

VirtualVFix/AndroidTestFramework

1feb769c6aca39a78e6daefd6face0a1e4d62cd4

[ "MIT" ]

null

# All rights reserved by forest fairy. # You cannot modify or share anything without sacrifice. # If you don't agree, keep calm and don't look at code bellow! __author__ = "VirtualV <https://github.com/virtualvfix>" __date__ = "$Apr 13, 2014 8:47:25 PM$" import re from config import CONFIG from tests.exceptions import ResultsNotFoundError from tests.benchmarks.tools.base import OnlineBenchmark class Vellamo3(OnlineBenchmark): def __init__(self, attributes, serial): OnlineBenchmark.__init__(self, attributes, serial) self._pull = attributes['pull'] self.failed_fields = attributes['failed_fields'] def start(self): try: self.sh('rm -r ' + self._pull + '*.html', errors=False, empty=True) except: pass super(Vellamo3, self).start() def __pull_logs(self): raw_res = [] lslist = [x.replace('\r', '').strip() for x in self.sh('ls {} | grep .html'.format(self._pull)).split('\n') if x.strip() != '\n' and x.strip() != ''] if len(lslist) == 0: raise ResultsNotFoundError('Vellamo HTML results not found.') postfix = '_{}_{}.html'.format(CONFIG.CURRENT_CYCLE, CONFIG.LOCAL_CURRENT_CYCLE) for x in lslist: self.logger.info('Pulling {} log...'.format(x)) self.pull(self._pull + x, CONFIG.LOG_PATH + x[:-5]+postfix) with open(CONFIG.LOG_PATH + x[:-5]+postfix, 'rb') as file: raw_res.append(file.read()) return zip(lslist, raw_res) def collect_results(self, res_doc): raw_res_list = self.__pull_logs() for log, raw_res in raw_res_list: match = re.search('h2\sstyle.*?>([\w\s/.-]+)<.*?Score:.*?>([\d]+)</span>', raw_res, re.DOTALL|re.I) res_doc.add_name(str(match.group(1).split(' ')[2].strip())+' ['+log+']') res_doc.add_result(match.group(2)) rows = re.findall('<div\sclass=\'bm\'>(.*?)</div>', raw_res, re.DOTALL|re.I) for row in rows: match = re.search('<span>([\w\s/.-]+)<.*?>([\w.\s]+)</span>', row, re.DOTALL|re.I) res_doc.add_name(match.group(1)) # failed fields if match.group(1) in self.failed_fields and match.group(2).strip() in ['0', '']: res_doc.add_result('Failed') for x in self.failed_fields[match.group(1)]: res_doc.add_name(x) res_doc.add_result('') else: res_doc.add_result(match.group(2)) values = re.findall('<li\sstyle.*?([\w\s/.-]+):\s([\w.\s]+)</li>', row, re.DOTALL|re.I) for value in values: # skip Invalid CPU mode error in result if 'Invalid CPU' in value[1]: continue # skip failed field if value[0].lower() == 'failed': continue res_doc.add_name(value[0]) res_doc.add_result(value[1]) self.sh('rm -r ' + self._pull + '*.html', errors=False)

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null

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93b6e5c40e7caecbcb7b62ae060f41d6eac3c44d

3,879

py

Python

tests/commands/test_template_image_apply_overlays.py

dsoprea/image_template_overlay_apply

ce54429e07ac140b33add685d39221b1fb5cadb2

[ "MIT" ]

1

2020-05-07T00:24:21.000Z

tests/commands/test_template_image_apply_overlays.py

dsoprea/image_template_overlay_apply

ce54429e07ac140b33add685d39221b1fb5cadb2

[ "MIT" ]

null

tests/commands/test_template_image_apply_overlays.py

dsoprea/image_template_overlay_apply

ce54429e07ac140b33add685d39221b1fb5cadb2

[ "MIT" ]

null

import sys import unittest import os import tempfile import shutil import contextlib import json import subprocess import PIL import templatelayer.testing_common _APP_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')) _SCRIPT_PATH = os.path.join(_APP_PATH, 'templatelayer', 'resources', 'scripts') _TOOL_FILEPATH = os.path.join(_SCRIPT_PATH, 'template_image_apply_overlays') sys.path.insert(0, _APP_PATH) class TestCommand(unittest.TestCase): def test_run(self): small_config = { "placeholders": { "top-left": { "left": 0, "top": 0, "width": 50, "height": 100 }, "top-right": { "left": 50, "top": 0, "width": 50, "height": 100 }, "middle-center": { "left": 0, "top": 100, "width": 100, "height": 100 }, "bottom-center": { "left": 0, "top": 200, "width": 100, "height": 100 } } } with templatelayer.testing_common.temp_path() as temp_path: # Template template_im = \ templatelayer.testing_common.get_new_image( 100, 300, color='blue') template_im.save('template.png') # Top-Left component_topleft_im = \ templatelayer.testing_common.get_new_image( 50, 100, color='green') component_topleft_im.save('top_left.png') # Top-Right component_topright_im = \ templatelayer.testing_common.get_new_image( 50, 100, color='red') component_topright_im.save('top_right.png') # Middle-Center component_middlecenter_im = \ templatelayer.testing_common.get_new_image( 100, 100, color='yellow') component_middlecenter_im.save('middle_center.png') # Bottom-Center component_bottomcenter_im = \ templatelayer.testing_common.get_new_image( 100, 100, color='orange') component_bottomcenter_im.save('bottom_center.png') with open('config.json', 'w') as f: json.dump(small_config, f) cmd = [ _TOOL_FILEPATH, 'config.json', '--template-filepath', 'template.png', '--component-filepath', 'top-left', 'top_left.png', '--component-filepath', 'top-right', 'top_right.png', '--component-filepath', 'middle-center', 'middle_center.png', '--component-filepath', 'bottom-center', 'bottom_center.png', '--output-filepath', 'output.png', ] try: actual = \ subprocess.check_output( cmd, stderr=subprocess.STDOUT, universal_newlines=True) except subprocess.CalledProcessError as cpe: print(cpe.output) raise expected = """Applying: [top-left] [top_left.png] Applying: [top-right] [top_right.png] Applying: [middle-center] [middle_center.png] Applying: [bottom-center] [bottom_center.png] Writing. """ self.assertEquals(actual, expected)

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null

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null

0

1

0

93b7d3ab9113fe2fed663ad41fb0b7d4b95f018e

3,993

py

Python

src/gpt2/evaluate_model.py

alexgQQ/GPT2

b2d78965f7cdcfe7dcf475969f4d4cce2b3ee82a

[ "Apache-2.0" ]

94

2020-05-05T04:27:05.000Z

2022-03-31T01:08:20.000Z

src/gpt2/evaluate_model.py

seeodm/GPT2

366d8517ac0bdf85e45e46adbef10cbe55740ee1

[ "Apache-2.0" ]

7

2020-09-11T02:25:30.000Z

2021-11-23T16:03:01.000Z

src/gpt2/evaluate_model.py

seeodm/GPT2

366d8517ac0bdf85e45e46adbef10cbe55740ee1

[ "Apache-2.0" ]

24

2020-07-14T19:15:39.000Z

2022-02-18T05:57:31.000Z

import argparse import torch import torch.nn as nn from gpt2.modeling import Transformer from gpt2.data import Dataset, Vocab, TokenizedCorpus from gpt2.evaluation import EvaluationSpec, EvaluateConfig, Evaluator from typing import Dict class GPT2EvaluationSpec(EvaluationSpec): def __init__(self, eval_corpus: str, vocab_path: str, seq_len: int, layers: int, heads: int, dims: int, rate: int): self.eval_corpus = eval_corpus self.vocab_path = vocab_path self.seq_len = seq_len self.layers = layers self.heads = heads self.dims = dims self.rate = rate def initialize(self): self.vocab = Vocab(vocab_path=self.vocab_path) self.criterion = nn.CrossEntropyLoss(reduction='none') def prepare_dataset(self) -> Dataset: return TokenizedCorpus(corpus_path=self.eval_corpus, vocab=self.vocab, seq_len=self.seq_len, repeat=False) def construct_model(self) -> nn.Module: return Transformer(layers=self.layers, pad_idx=self.vocab.pad_idx, words=len(self.vocab), seq_len=self.seq_len, heads=self.heads, dims=self.dims, rate=self.rate, dropout=0, bidirectional=False) def eval_objective(self, data: Dict[str, torch.Tensor], model: nn.Module ) -> Dict[str, torch.Tensor]: logits, _ = model(data['input'], past=None) loss = self.criterion(logits.transpose(1, 2), data['output']) mask = (data['output'] != self.vocab.pad_idx).float() loss = (loss * mask).sum() / mask.sum() perplexity = (loss.exp() * mask).sum() / mask.sum() return {'loss': loss, 'perplexity': perplexity} def evaluate_gpt2_model(args: argparse.Namespace): spec = GPT2EvaluationSpec( eval_corpus=args.eval_corpus, vocab_path=args.vocab_path, seq_len=args.seq_len, layers=args.layers, heads=args.heads, dims=args.dims, rate=args.rate) config = EvaluateConfig( batch_eval=args.batch_eval, total_steps=args.total_steps, use_gpu=args.use_gpu) print(Evaluator(spec, config).evaluate(from_model=args.model_path)) def add_subparser(subparsers: argparse._SubParsersAction): parser = subparsers.add_parser('evaluate', help='evaluate GPT-2 model') parser.add_argument('--model_path', required=True, help='trained GPT-2 model file path') group = parser.add_argument_group('Corpus and vocabulary') group.add_argument('--eval_corpus', required=True, help='evaluation corpus file path') group.add_argument('--vocab_path', required=True, help='vocabulary file path') group = parser.add_argument_group('Model configurations') group.add_argument('--seq_len', default=64, type=int, help='maximum sequence length') group.add_argument('--layers', default=12, type=int, help='number of transformer layers') group.add_argument('--heads', default=16, type=int, help='number of multi-heads in attention layer') group.add_argument('--dims', default=1024, type=int, help='dimension of representation in each layer') group.add_argument('--rate', default=4, type=int, help='increase rate of dimensionality in bottleneck') group = parser.add_argument_group('Evaluation options') group.add_argument('--batch_eval', default=64, type=int, help='number of evaluation batch size') group.add_argument('--total_steps', default=-1, type=int, help='number of total evaluation steps') group.add_argument('--use_gpu', action='store_true', help='use gpu device in inferencing') parser.set_defaults(func=evaluate_gpt2_model)

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null

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null

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1

0

93ba2653ba488171fc0c6a50b7e6cee03b9a572c

1,332

py

Python

mytrain/my_unpack.py

JinkelaCrops/t2t-learning

5d9b5a5164af763c24f1cbce9d97561e9f2b772c

[ "Apache-2.0" ]

5

2019-03-28T03:52:32.000Z

2021-02-24T07:09:26.000Z

mytrain/my_unpack.py

JinkelaCrops/t2t-learning

5d9b5a5164af763c24f1cbce9d97561e9f2b772c

[ "Apache-2.0" ]

null

mytrain/my_unpack.py

JinkelaCrops/t2t-learning

5d9b5a5164af763c24f1cbce9d97561e9f2b772c

[ "Apache-2.0" ]

2

2018-08-07T03:43:09.000Z

2019-12-09T06:41:40.000Z

from processutils.textfilter import Unpack from utils.simplelog import Logger import argparse parser = argparse.ArgumentParser(description="my_unpack") parser.add_argument('-f', "--file_prefix", required=True) parser.add_argument('-sep', "--separator", required=True) # args = parser.parse_args([ # "-f", "../test/medicine.sample.data/data.test", # "-sep", ' ||| ' # ]) args = parser.parse_args() args.output_src = args.file_prefix + ".src" args.output_tgt = args.file_prefix + ".tgt" log = Logger("my_filter", "my_filter.log").log() def main(data): unpack = Unpack(args.separator) src_lines = [] tgt_lines = [] for k, line in enumerate(data): try: src, tgt, change_order = unpack.unpack(line) except Exception as e: log.error(f"unpack error: {e.__class__}, {e.__context__}, ### {line.strip()}") continue src_lines.append(src + "\n") tgt_lines.append(tgt + "\n") return src_lines, tgt_lines if __name__ == '__main__': with open(args.file_prefix, "r", encoding="utf8") as f: data = f.readlines() src_lines, tgt_lines = main(data) with open(args.output_src, "w", encoding="utf8") as f: f.writelines(src_lines) with open(args.output_tgt, "w", encoding="utf8") as f: f.writelines(tgt_lines)

29.6

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null

0

null

0

1

0

93baf5e4d83867b7e987a8bdfa95d1e350aa7b07

10,173

py

Python

source/api/dataplane/runtime/chalicelib/common.py

awslabs/aws-media-replay-engine

2c217eff42f8e2c56b43e2ecf593f5aaa92c5451

[ "Apache-2.0" ]

22

2021-11-24T01:23:07.000Z

2022-03-26T23:24:46.000Z

source/api/dataplane/runtime/chalicelib/common.py

awslabs/aws-media-replay-engine

2c217eff42f8e2c56b43e2ecf593f5aaa92c5451

[ "Apache-2.0" ]

null

source/api/dataplane/runtime/chalicelib/common.py

awslabs/aws-media-replay-engine

2c217eff42f8e2c56b43e2ecf593f5aaa92c5451

[ "Apache-2.0" ]

3

2021-12-10T09:42:51.000Z

2022-02-16T02:22:50.000Z

# Copyright 2021 Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 import os import json import urllib.parse import boto3 import decimal from decimal import Decimal from datetime import datetime from chalice import Chalice from chalice import IAMAuthorizer from chalice import ChaliceViewError, BadRequestError, NotFoundError from botocore.config import Config from botocore.client import ClientError from boto3.dynamodb.conditions import Key, Attr, In from jsonschema import validate, ValidationError from chalicelib import replace_decimals s3_client = boto3.client("s3") ddb_resource = boto3.resource("dynamodb") PLUGIN_RESULT_TABLE_NAME = os.environ['PLUGIN_RESULT_TABLE_NAME'] def populate_segment_data_matching(segment_response_data, tracknumber): result = {} optoLength = 0 if 'OptoEnd' in segment_response_data and 'OptoStart' in segment_response_data: # By default OptoEnd and OptoStart are maps and have no Keys. Only when they do, we check for TrackNumber's if len(segment_response_data['OptoEnd'].keys()) > 0 and len(segment_response_data['OptoStart'].keys()) > 0: try: optoLength = segment_response_data['OptoEnd'][tracknumber] - segment_response_data['OptoStart'][ tracknumber] except Exception as e: pass # Error if the TrackNumber does not exist. Simply Ignore since its a problem with Clip Gen # Calculate Opto Clip Duration for each Audio Track optoDurationsPerTrack = [] if 'OptoEnd' in segment_response_data and 'OptoStart' in segment_response_data: for k in segment_response_data['OptoStart'].keys(): try: optoDur = {} optoDur[k] = segment_response_data['OptoEnd'][k] - segment_response_data['OptoStart'][k] optoDurationsPerTrack.append(optoDur) except Exception as e: pass # Error if the TrackNumber does not exist. Simply Ignore since its a problem with Clip Gen optoClipLocation = '' if 'OptimizedClipLocation' in segment_response_data: # This is not ideal. We need to check of there exists a OptimizedClipLocation with the requested TrackNumber. # If not, likely a problem with Clip Gen. Instead of failing, we send an empty value for optoClipLocation back. for trackNo in segment_response_data['OptimizedClipLocation'].keys(): if str(trackNo) == str(tracknumber): optoClipLocation = create_signed_url(segment_response_data['OptimizedClipLocation'][tracknumber]) break origClipLocation = '' if 'OriginalClipLocation' in segment_response_data: for trackNo in segment_response_data['OriginalClipLocation'].keys(): if str(trackNo) == str(tracknumber): origClipLocation = create_signed_url(segment_response_data['OriginalClipLocation'][tracknumber]) break label = '' if 'Label' in segment_response_data: label = segment_response_data['Label'] if str(label) == "": label = '<no label plugin configured>' result = { 'OriginalClipLocation': origClipLocation, 'OriginalThumbnailLocation': create_signed_url( segment_response_data[ 'OriginalThumbnailLocation']) if 'OriginalThumbnailLocation' in segment_response_data else '', 'OptimizedClipLocation': optoClipLocation, 'OptimizedThumbnailLocation': create_signed_url( segment_response_data[ 'OptimizedThumbnailLocation']) if 'OptimizedThumbnailLocation' in segment_response_data else '', 'StartTime': segment_response_data['Start'], 'Label': label, 'FeatureCount': 'TBD', 'OrigLength': 0 if 'Start' not in segment_response_data else segment_response_data['End'] - segment_response_data['Start'], 'OptoLength': optoLength, 'OptimizedDurationPerTrack': optoDurationsPerTrack, 'OptoStartCode': '' if 'OptoStartCode' not in segment_response_data else segment_response_data['OptoStartCode'], 'OptoEndCode': '' if 'OptoEndCode' not in segment_response_data else segment_response_data['OptoEndCode'] } return result def create_signed_url(s3_path): bucket, objkey = split_s3_path(s3_path) try: expires = 86400 url = s3_client.generate_presigned_url( ClientMethod='get_object', Params={ 'Bucket': bucket, 'Key': objkey }, ExpiresIn=expires) return url except Exception as e: print(e) raise e def split_s3_path(s3_path): path_parts = s3_path.replace("s3://", "").split("/") bucket = path_parts.pop(0) key = "/".join(path_parts) return bucket, key def get_event_segment_metadata(name, program, classifier, tracknumber): """ Gets the Segment Metadata based on the segments found during Segmentation/Optimization process. """ name = urllib.parse.unquote(name) program = urllib.parse.unquote(program) classifier = urllib.parse.unquote(classifier) tracknumber = urllib.parse.unquote(tracknumber) try: # Get Event Segment Details # From the PluginResult Table, get the Clips Info plugin_table = ddb_resource.Table(PLUGIN_RESULT_TABLE_NAME) response = plugin_table.query( KeyConditionExpression=Key("PK").eq(f"{program}#{name}#{classifier}"), ScanIndexForward=False ) plugin_responses = response['Items'] while "LastEvaluatedKey" in response: response = plugin_table.query( ExclusiveStartKey=response["LastEvaluatedKey"], KeyConditionExpression=Key("PK").eq(f"{program}#{name}#{classifier}"), ScanIndexForward=False ) plugin_responses.extend(response["Items"]) # if "Items" not in plugin_response or len(plugin_response["Items"]) == 0: # print(f"No Plugin Responses found for event '{name}' in Program '{program}' for Classifier {classifier}") # raise NotFoundError(f"No Plugin Responses found for event '{name}' in Program '{program}' for Classifier {classifier}") clip_info = [] for res in plugin_responses: optoLength = 0 if 'OptoEnd' in res and 'OptoStart' in res: # By default OptoEnd and OptoStart are maps and have no Keys. Only when they do, we check for TrackNumber's if len(res['OptoEnd'].keys()) > 0 and len(res['OptoStart'].keys()) > 0: try: optoLength = res['OptoEnd'][tracknumber] - res['OptoStart'][tracknumber] except Exception as e: pass # Error if the TrackNumber does not exist. Simply Ignore since its a problem with Clip Gen # Calculate Opto Clip Duration for each Audio Track optoDurationsPerTrack = [] if 'OptoEnd' in res and 'OptoStart' in res: for k in res['OptoStart'].keys(): try: optoDur = {} optoDur[k] = res['OptoEnd'][k] - res['OptoStart'][k] optoDurationsPerTrack.append(optoDur) except Exception as e: pass # Error if the TrackNumber does not exist. Simply Ignore since its a problem with Clip Gen optoClipLocation = '' if 'OptimizedClipLocation' in res: # This is not ideal. We need to check of there exists a OptimizedClipLocation with the requested TrackNumber. # If not, likely a problem with Clip Gen. Instead of failing, we send an empty value for optoClipLocation back. for trackNo in res['OptimizedClipLocation'].keys(): if str(trackNo) == str(tracknumber): optoClipLocation = create_signed_url(res['OptimizedClipLocation'][tracknumber]) break origClipLocation = '' if 'OriginalClipLocation' in res: for trackNo in res['OriginalClipLocation'].keys(): if str(trackNo) == str(tracknumber): origClipLocation = create_signed_url(res['OriginalClipLocation'][tracknumber]) break label = '' if 'Label' in res: label = res['Label'] if str(label) == "": label = '<no label plugin configured>' clip_info.append({ 'OriginalClipLocation': origClipLocation, 'OriginalThumbnailLocation': create_signed_url( res['OriginalThumbnailLocation']) if 'OriginalThumbnailLocation' in res else '', 'OptimizedClipLocation': optoClipLocation, 'OptimizedThumbnailLocation': create_signed_url( res['OptimizedThumbnailLocation']) if 'OptimizedThumbnailLocation' in res else '', 'StartTime': res['Start'], 'Label': label, 'FeatureCount': 'TBD', 'OrigLength': 0 if 'Start' not in res else res['End'] - res['Start'], 'OptoLength': optoLength, 'OptimizedDurationPerTrack': optoDurationsPerTrack, 'OptoStartCode': '' if 'OptoStartCode' not in res else res['OptoStartCode'], 'OptoEndCode': '' if 'OptoEndCode' not in res else res['OptoEndCode'] }) final_response = {} final_response['Segments'] = clip_info except NotFoundError as e: print(e) print(f"Got chalice NotFoundError: {str(e)}") raise except Exception as e: print(e) print(f"Unable to get the Event '{name}' in Program '{program}': {str(e)}") raise ChaliceViewError(f"Unable to get the Event '{name}' in Program '{program}': {str(e)}") else: return replace_decimals(final_response)

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0.626954

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0.050424

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0.427405

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0

0.004807

0.284282

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45.013274

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null

0

null

0

1

0

93bc932331d06fe620b9dc241c2d48eeb8fdbdb8

9,559

py

Python

oec_sync/sync/oec.py

SamnnyWong/OECSynchronizer

9b28c96988158f5717bacd47f59cbabb1ce072cd

[ "Unlicense", "MIT" ]

null

oec_sync/sync/oec.py

SamnnyWong/OECSynchronizer

9b28c96988158f5717bacd47f59cbabb1ce072cd

[ "Unlicense", "MIT" ]

null

oec_sync/sync/oec.py

SamnnyWong/OECSynchronizer

9b28c96988158f5717bacd47f59cbabb1ce072cd

[ "Unlicense", "MIT" ]

null

from xml.etree import ElementTree as Etree from model import * from astro_unit import * from io import StringIO import logging class FieldMeta: """ OEC field metadata. """ def __init__(self, datatype: str, unit: str = None): self.type = datatype self.unit = unit # Maps field name to tuple of (type, unit) # Only the following columns will be understood PLANET_FIELDS = { "semimajoraxis": FieldMeta("number", 'AU'), "eccentricity": FieldMeta("number"), # unit not needed "periastron": FieldMeta("number", 'deg'), "longitude": FieldMeta("number", 'deg'), "ascendingnode": FieldMeta("number", 'deg'), "inclination": FieldMeta("number", 'deg'), "impactparameter": FieldMeta("number"), # unit not needed "meananomaly": FieldMeta("number", 'deg'), "period": FieldMeta("number", 'days'), "transittime": FieldMeta("number", 'BJD'), "periastrontime": FieldMeta("number", 'BJD'), "maximumrvtime": FieldMeta("number", 'BJD'), "separation": FieldMeta("number", 'arcsec'), # unit on xml element "mass": FieldMeta("number", 'M_j'), "radius": FieldMeta("number", 'R_j'), "temperature": FieldMeta("number", 'K'), "age": FieldMeta("number", 'Gyr'), # "discoverymethod": FieldMeta("discoverymethodtype"), # "istransiting": FieldMeta("boolean"), # "description": "xs:string", "discoveryyear": FieldMeta("number", None), # "lastupdate": FieldMeta("lastupdatedef", None), # "image", # "imagedescription", "spinorbitalignment": FieldMeta("number", 'deg'), "positionangle": FieldMeta("number", 'deg'), # "metallicity": FieldMeta("number"), # unit not needed # "spectraltype": FieldMeta("spectraltypedef"), # "magB": FieldMeta("number", None), "magH": FieldMeta("number", None), "magI": FieldMeta("number", None), "magJ": FieldMeta("number", None), "magK": FieldMeta("number", None), # "magR": FieldMeta("number", None), # "magU": FieldMeta("number", None), "magV": FieldMeta("number", None) } class Adapter: """ Reads/writes OEC files. """ def __init__(self, schema_file: str=None): """ :param schema_file: Schema file (*.xsd) """ # process schema if schema_file: self._schema_tree = Etree.parse(schema_file).getroot() @staticmethod def _read_number(field: Etree.Element, fieldmeta: FieldMeta)\ -> Quantity: # read unit unit = fieldmeta.unit if unit is None: # check if element has unit defined unit = field.get('unit') # read limits/errors lower, upper = field.get('lowerlimit'), field.get('upperlimit') is_limit = bool(lower or upper) if not is_limit: lower, upper = field.get('errorminus'), field.get('errorplus') q = Quantity(field.text, unit, error=(lower, upper), is_limit=is_limit) return q @staticmethod def _read_planet(root: Etree.Element, system_name: str) -> Planet: """ Reads out a planet from an xml element. :param root: Must be a planet element. :param system_name: System name. :return: Planet object. """ if root.tag != 'planet': raise NameError('Root must be a planet element') default_name = root.find('name').text if not default_name: raise SyntaxError('Could not find planet name') all_names = set(name.text for name in root.findall('name')) planet = Planet(default_name, system_name, all_names=all_names) for field in next(root.iter()): fieldmeta = PLANET_FIELDS.get(field.tag) if fieldmeta: try: # Some OEC files are weird # e.g. KOI-12.xml, line 27 is # <mass upperlimit="8.7" /> if fieldmeta.type == "number": planet.prop[field.tag] = \ Adapter._read_number(field, fieldmeta) else: planet.prop[field.tag] = field.text except ValueError as e: logging.debug("[%s].[%s]: %s" % (default_name, field.tag, e)) except Exception as e: logging.exception(e) return planet def read_system(self, file: str) -> System: """ Reads out planets in a system. :param file: Path to a system xml file. :return: A list of planets. """ tree = Etree.parse(file) root = tree.getroot() if root.tag != 'system': raise NameError('Root must be a system element') all_names = set(name.text for name in root.findall('name')) system = System(root.find('name').text, file, all_names=all_names) for planet_xml in root.iter('planet'): planet = self._read_planet(planet_xml, system.name) system.planets.append(planet) return system @staticmethod def _write_number(field: Etree.Element, number: Quantity) -> bool: # attrib is a dictionary holding the attributes of this element attrib = field.attrib # silently clear existing error terms attrib.pop('errorminus', None) attrib.pop('errorplus', None) attrib.pop('lowerlimit', None) attrib.pop('upperlimit', None) # set new value field.text = number.value # set new error terms if number.error: if number.is_limit: attrib['lowerlimit'], attrib['upperlimit'] = number.error else: attrib['errorminus'], attrib['errorplus'] = number.error return True @staticmethod def _write_planet_update(planet: Etree.Element, update: PlanetUpdate) \ -> bool: succeeded = True # loop through new values in the update objects for field, new_value in update.fields.items(): try: prop_elem = planet.find(field) if prop_elem is None: # the original planet does not have this field logging.debug("Creating new field '%s'" % field) # create the field under the planet prop_elem = Etree.SubElement(planet, field) # write the new value succeeded &= Adapter._write_number(prop_elem, new_value) except Exception as e: logging.exception(e) succeeded = False return succeeded @staticmethod def _write_system_update(root: Etree.Element, update: PlanetarySysUpdate) -> bool: if update.new: # a new system? raise NotImplementedError succeeded = True for planet_update in update.planets: if planet_update.new: succeeded = False logging.debug('Skipped new planet update %r' % planet_update) continue # find planet element with the name planet_elem = root.find('.//planet[name="%s"]' % planet_update.name) # planet does not exist in the file? # creating a new planet isn't as easy, # need some info about the host star. if planet_elem is None: succeeded = False logging.debug('Could not find planet <%s>' % planet_update.name) continue # apply the update to the current planet logging.debug('Updating planet <%s>...' % planet_update.name) succeeded &= Adapter._write_planet_update( planet_elem, planet_update) return succeeded # def validate(self, file: str) -> None: # Validates an xml using schema defined by OEC. # Raises an exception if file does not follow the schema. # :param file: File name. # """ # return # skip for now, because OEC itself isn't following the schema # # tree = etree.parse(file) # # self._schema.assertValid(tree) def update_str(self, xml_string: str, update: PlanetarySysUpdate) \ -> Tuple[str, bool]: """ Apply a system update to an xml string. Also performs a check afterwards to determine if the action succeeded. :param xml_string: containing the xml representation of a system :param update: Update to be applied to the system :return: A tuple (content, succeeded) where: - content is the file content modified - succeeded indicates whether the update was successful. """ tree = Etree.parse(StringIO(xml_string)) ok = Adapter._write_system_update(tree, update) serialized = Etree.tostring(tree.getroot(), 'unicode', 'xml') return serialized, ok def update_file(self, filename: str, update: PlanetarySysUpdate) -> bool: """ Apply a system update to an xml file. :param filename: The system xml file :param update: Update to be applied to the system :return: Whether the update was successful """ tree = Etree.parse(filename) succeeded = Adapter._write_system_update(tree, update) tree.write(filename) return succeeded

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79

0.574746

1,039

9,559

5.202117

0.245428

0.080481

0.031637

0.012211

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0.072155

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0

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9,559

256

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37.339844

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false

0

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0

null

0

null

0

1

0

93bd3505c0bee8de6a5685c5e02ee9cbc78b0fdd

9,072

py

Python

pyAnVIL/anvil/util/ingest_helper.py

anvilproject/client-apis

cbd892042e092b0a1dede4c561bcfdde15e9a3ad

[ "Apache-2.0" ]

8

2019-07-02T20:41:24.000Z

2022-01-12T21:50:21.000Z

pyAnVIL/anvil/util/ingest_helper.py

mmtmn/client-apis

215adae0b7f401b4bf62e7bd79b6a8adfe69cf4f

[ "Apache-2.0" ]

37

2019-01-16T17:48:02.000Z

2021-08-13T21:35:54.000Z

pyAnVIL/anvil/util/ingest_helper.py

mmtmn/client-apis

215adae0b7f401b4bf62e7bd79b6a8adfe69cf4f

[ "Apache-2.0" ]

7

2019-05-13T14:59:27.000Z

2022-01-12T21:50:22.000Z

"""Validate AnVIL workspace(s).""" import os from google.cloud.storage import Client from google.cloud.storage.blob import Blob from collections import defaultdict import ipywidgets as widgets from ipywidgets import interact from IPython.display import display import pandas as pd import firecloud.api as FAPI from types import SimpleNamespace import numpy as np class NestedNamespace(SimpleNamespace): """Extend SimpleNamespace.""" def __init__(self, dictionary, **kwargs): """Initialize nested attributes.""" super().__init__(**kwargs) for key, value in dictionary.items(): if isinstance(value, dict): self.__setattr__(key, NestedNamespace(value)) else: self.__setattr__(key, value) class IngestHelper(): """Validate workspace from dropdown selections.""" def __init__(self, workspace_namespace='terra-test-bwalsh', workspace_name='pyAnVIL Notebook', user_project=os.environ.get('GOOGLE_PROJECT', None)) -> None: """Retrieve expected schemas.""" assert user_project, "AnVIL buckets use the `Requester Pays` feature. Please include a billing project." self.WORKSPACES = FAPI.list_workspaces().json() self.schemas_table = FAPI.get_entities(workspace_namespace, workspace_name, 'schema').json() self.schemas = defaultdict(dict) for e in self.schemas_table: a = e['attributes'] self.schemas[a['consortium']][a['entity']] = a self.consortiums = widgets.Dropdown(options=['Choose...'] + list(self.schemas.keys())) self.workspaces = widgets.Dropdown(options=[]) self.user_project = user_project self.client = Client(project=self.user_project) self.reference_schema = None def validate(self, reference_schema, namespace, workspace_name, check_blobs=True): """Check target workspace against reference.""" target_entities = FAPI.list_entity_types(namespace=namespace, workspace=workspace_name).json() reference = set(reference_schema.keys()) reference.remove('attributes') target = set(target_entities.keys()) result = dict(workspace=workspace_name) for entity in reference.intersection(target): uri = None try: reference_fields = set([f.replace(' ', '') for f in reference_schema[entity]['required'].split(',')]) if 'bucket_fields' in reference_schema[entity]: reference_fields.update([f.replace(' ', '') for f in reference_schema[entity].get('bucket_fields', '').split(',')]) target_fields = set(target_entities[entity]['attributeNames'] + [target_entities[entity]['idName']]) if not reference_fields.issubset(target_fields): msg = f'fields_missing:{reference_fields - target_fields }' else: msg = 'OK' result[entity] = msg project_buckets = {} if 'bucket_fields' in reference_schema[entity]: for bucket_field in reference_schema[entity]['bucket_fields'].split(','): if bucket_field not in target_fields: result[entity] = f"{bucket_field} not found in {entity} schema." continue for e in FAPI.get_entities(namespace, workspace=workspace_name, etype=entity).json(): uri = e['attributes'][bucket_field] blob = Blob.from_string(uri, client=self.client) bucket_name = blob.bucket.name if bucket_name not in project_buckets: print(f"checking {workspace_name} {bucket_name}") bucket = self.client.bucket(bucket_name, user_project=self.user_project) project_buckets[bucket_name] = {} for b in list(bucket.list_blobs()): project_buckets[bucket_name][b.name] = {'size': b.size, 'etag': b.etag, 'crc32c': b.crc32c, 'time_created': b.time_created, 'name': b.name} if blob.name not in project_buckets[bucket_name]: result[entity] = f"{uri} does not exist in project_buckets {bucket_name}" break for bucket_name in project_buckets: print(f"{bucket_name} has {len(project_buckets[bucket_name])} objects") except Exception as e: print(f"{workspace_name} {uri} {e}") result[entity] = str(e) for entity in reference - target: if entity == 'linked_field': continue result[entity] = 'missing' result['unknown_entities'] = f"{','.join(sorted(target - reference))}" required_attributes = set([k.replace(' ', '') for k in reference_schema['attributes']['required'].split(',')]) workspace_attribute_values = FAPI.get_workspace(namespace, workspace_name).json()['workspace']['attributes'] target_attributes = set(list(workspace_attribute_values.keys()) + [f"library:{e}" for e in workspace_attribute_values.get('library', {}).keys()]) missing_workspace_keys = sorted(list(required_attributes - target_attributes)) if len(missing_workspace_keys) == 0: result['missing_workspace_keys'] = 'OK' else: result['missing_workspace_keys'] = ','.join(missing_workspace_keys) missing_xrefs = self.cross_ref(reference_schema, namespace, workspace_name) result['missing_xrefs'] = ','.join(missing_xrefs) return result def cross_ref(self, reference_schema, namespace, workspace_name): """Evaluate 'join' between two entities.""" if 'linked_field' not in reference_schema: return [] def get_property(entity, entity_name, expression): return eval(expression, {entity_name: NestedNamespace(entity)}) item = reference_schema['linked_field'] join = item['relationship'] (left, right) = join.split('=') # print(left, right) left_entity = left.split('.')[0] right_entity = right.split('.')[0] left_keys = set([get_property(e, left_entity, left) for e in FAPI.get_entities(namespace, workspace=workspace_name, etype=left_entity).json()]) right_keys = set([get_property(e, right_entity, right) for e in FAPI.get_entities(namespace, workspace=workspace_name, etype=right_entity).json()]) return left_keys - right_keys def interact(self): """Use widgets to display drop downs for consortiums and workspaces, handle user selections.""" pd.set_option("display.max_rows", None, "display.max_columns", None) def update_workspaces(*args): self.workspaces.options = ['Choose...', 'All workspaces', 'This workspace'] + sorted([w['workspace']['name'] for w in self.WORKSPACES if 'anvil-datastorage' in w['workspace']['namespace'] and self.consortiums.value.lower() in w['workspace']['name'].lower()]) # Tie the image options to directory value self.consortiums.observe(update_workspaces, 'value') # Show the images def show_workspace(consortium, workspace): namespace = 'anvil-datastorage' self.reference_schema = self.schemas[consortium] reference_df = pd.DataFrame( [dict(id=e['name'], **e['attributes']) for e in self.schemas_table] ).set_index('id').query(f'consortium == "{consortium}"').replace(np.nan, '', regex=True).style.set_caption("Reference") if workspace and workspace == 'All workspaces': print("Working...") validations = [] for workspace_name in [w['workspace']['name'] for w in self.WORKSPACES if 'anvil-datastorage' in w['workspace']['namespace'] and self.consortiums.value.lower() in w['workspace']['name'].lower()]: validation = self.validate(self.schemas[consortium], namespace, workspace_name) validations.append(validation) df = pd.DataFrame(validations).set_index('workspace').style.set_caption(f"{consortium}/{workspace}") display(reference_df) display(df) return if workspace == 'This workspace': workspace = os.environ['WORKSPACE_NAME'] namespace = os.environ['WORKSPACE_NAMESPACE'] if workspace and workspace != 'Choose...': df = pd.DataFrame([self.validate(self.schemas[consortium], namespace, workspace)]) df = df.set_index('workspace').style.set_caption(f"{consortium}/{workspace}") display(reference_df) display(df) return _ = interact(show_workspace, consortium=self.consortiums, workspace=self.workspaces)

53.680473

270

0.61541

982

9,072

5.504073

0.201629

0.048104

0.022017

0.021277

0.211286

0.177058

0.153747

0.121184

0.108233

0

0.00105

0.264991

9,072

168

271

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0.809538

0.044202

0

0.096296

0

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0.021094

0

0.007407

1

0.059259

false

0

0.081481

0.007407

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0.02963

0

null

0

null

0

1

0

93c35e82e3070b5dcaa7b5ce0646c0a3d9c9b51e

5,760

py

Python

hasy.py

MartinThoma/cv-datasets

f0566839bc2e625274bd2d439114c6665ba1b37e

[ "MIT" ]

1

2017-03-11T14:14:12.000Z

hasy.py

MartinThoma/cv-datasets

f0566839bc2e625274bd2d439114c6665ba1b37e

[ "MIT" ]

null

hasy.py

MartinThoma/cv-datasets

f0566839bc2e625274bd2d439114c6665ba1b37e

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Utility file for the HASYv2 dataset. See https://arxiv.org/abs/1701.08380 for details. """ from __future__ import absolute_import from keras.utils.data_utils import get_file from keras import backend as K import numpy as np import scipy.ndimage import os import tarfile import shutil import csv from six.moves import cPickle as pickle n_classes = 369 labels = [] def _load_csv(filepath, delimiter=',', quotechar="'"): """ Load a CSV file. Parameters ---------- filepath : str Path to a CSV file delimiter : str, optional quotechar : str, optional Returns ------- list of dicts : Each line of the CSV file is one element of the list. """ data = [] csv_dir = os.path.dirname(filepath) with open(filepath, 'rb') as csvfile: reader = csv.DictReader(csvfile, delimiter=delimiter, quotechar=quotechar) for row in reader: for el in ['path', 'path1', 'path2']: if el in row: row[el] = os.path.abspath(os.path.join(csv_dir, row[el])) data.append(row) return data def _generate_index(csv_filepath): """ Generate an index 0...k for the k labels. Parameters ---------- csv_filepath : str Path to 'test.csv' or 'train.csv' Returns ------- dict : Maps a symbol_id as in test.csv and train.csv to an integer in 0...k, where k is the total number of unique labels. """ symbol_id2index = {} data = _load_csv(csv_filepath) i = 0 labels = [] for item in data: if item['symbol_id'] not in symbol_id2index: symbol_id2index[item['symbol_id']] = i labels.append(item['latex']) i += 1 return symbol_id2index, labels def load_data(): """ Load HASYv2 dataset. # Returns Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`. """ # Download if not already done fname = 'HASYv2.tar.bz2' origin = 'https://zenodo.org/record/259444/files/HASYv2.tar.bz2' fpath = get_file(fname, origin=origin, untar=False, md5_hash='fddf23f36e24b5236f6b3a0880c778e3') path = os.path.dirname(fpath) # Extract content if not already done untar_fpath = os.path.join(path, "HASYv2") if not os.path.exists(untar_fpath): print('Untaring file...') tfile = tarfile.open(fpath, 'r:bz2') try: tfile.extractall(path=untar_fpath) except (Exception, KeyboardInterrupt) as e: if os.path.exists(untar_fpath): if os.path.isfile(untar_fpath): os.remove(untar_fpath) else: shutil.rmtree(untar_fpath) raise tfile.close() # Create pickle if not already done pickle_fpath = os.path.join(untar_fpath, "fold1.pickle") if not os.path.exists(pickle_fpath): # Load mapping from symbol names to indices symbol_csv_fpath = os.path.join(untar_fpath, "symbols.csv") symbol_id2index, labels = _generate_index(symbol_csv_fpath) globals()["labels"] = labels # Load first fold fold_dir = os.path.join(untar_fpath, "classification-task/fold-1") train_csv_fpath = os.path.join(fold_dir, "train.csv") test_csv_fpath = os.path.join(fold_dir, "test.csv") train_csv = _load_csv(train_csv_fpath) test_csv = _load_csv(test_csv_fpath) WIDTH = 32 HEIGHT = 32 x_train = np.zeros((len(train_csv), 1, WIDTH, HEIGHT), dtype=np.uint8) x_test = np.zeros((len(test_csv), 1, WIDTH, HEIGHT), dtype=np.uint8) y_train, s_train = [], [] y_test, s_test = [], [] # Load training data for i, data_item in enumerate(train_csv): fname = os.path.join(untar_fpath, data_item['path']) s_train.append(fname) x_train[i, 0, :, :] = scipy.ndimage.imread(fname, flatten=False, mode='L') label = symbol_id2index[data_item['symbol_id']] y_train.append(label) y_train = np.array(y_train, dtype=np.int64) # Load test data for i, data_item in enumerate(test_csv): fname = os.path.join(untar_fpath, data_item['path']) s_test.append(fname) x_train[i, 0, :, :] = scipy.ndimage.imread(fname, flatten=False, mode='L') label = symbol_id2index[data_item['symbol_id']] y_test.append(label) y_test = np.array(y_test, dtype=np.int64) data = {'x_train': x_train, 'y_train': y_train, 'x_test': x_test, 'y_test': y_test, 'labels': labels } # Store data as pickle to speed up later calls with open(pickle_fpath, 'wb') as f: pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL) else: with open(pickle_fpath, 'rb') as f: data = pickle.load(f) x_train = data['x_train'] y_train = data['y_train'] x_test = data['x_test'] y_test = data['y_test'] globals()["labels"] = data['labels'] y_train = np.reshape(y_train, (len(y_train), 1)) y_test = np.reshape(y_test, (len(y_test), 1)) if K.image_dim_ordering() == 'tf': x_train = x_train.transpose(0, 2, 3, 1) x_test = x_test.transpose(0, 2, 3, 1) return (x_train, y_train), (x_test, y_test)

31.823204

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0.562326

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5,760

4.170699

0.25672

0.030938

0.029004

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0.101837

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null

0

null

0

1

0

93c65b7f60b1d4ed3df0c1dfda29fa877d20e341

8,071

py

Python

control/tracking.py

oholsen/hagedag

4e2881fa1f636228e5cbe76e61fb4b224f0b1e4a

[ "Apache-2.0" ]

null

control/tracking.py

oholsen/hagedag

4e2881fa1f636228e5cbe76e61fb4b224f0b1e4a

[ "Apache-2.0" ]

null

control/tracking.py

oholsen/hagedag

4e2881fa1f636228e5cbe76e61fb4b224f0b1e4a

[ "Apache-2.0" ]

null

""" Based on Extended kalman filter (EKF) localization sample in PythonRobotics by Atsushi Sakai (@Atsushi_twi) """ import math import matplotlib.pyplot as plt import numpy as np # Simulation parameter INPUT_NOISE = np.diag([0.1, np.deg2rad(30.0)]) ** 2 GPS_NOISE = np.diag([0.1, 0.1]) ** 2 # Covariance for EKF simulation Q = np.diag([ 0.02, # variance of location on x-axis 0.02, # variance of location on y-axis np.deg2rad(10.0), # variance of yaw angle 0.1 # variance of velocity ]) ** 2 # predict state covariance # Observation x,y position covariance, now dynamic from receiver (input stream) # R = np.diag([0.02, 0.02]) ** 2 def calc_input(): v = 1.0 # [m/s] yawrate = 0.1 # [rad/s] return np.array([[v], [yawrate]]) def simulate(xTrue, u, dt: float): xTrue = motion_model(xTrue, u, dt) # add noise to gps x-y z = observation_model(xTrue) + GPS_NOISE @ np.random.randn(2, 1) # add noise to input ud = u + INPUT_NOISE @ np.random.randn(2, 1) return xTrue, z, ud def observation(x_true, xd, u, dt: float): # simulation x_true = motion_model(x_true, u, dt) # add noise to gps x-y z = observation_model(x_true) + GPS_NOISE @ np.random.randn(2, 1) # add noise to input ud = u + INPUT_NOISE @ np.random.randn(2, 1) xd = motion_model(xd, ud, dt) return x_true, z, xd, ud def motion_model(x, u, dt: float): F = np.array([[1.0, 0, 0, 0], [0, 1.0, 0, 0], [0, 0, 1.0, 0], [0, 0, 0, 0]]) B = np.array([[dt * math.cos(x[2, 0]), 0], [dt * math.sin(x[2, 0]), 0], [0.0, dt], [1.0, 0.0]]) x = F @ x + B @ u return x def observation_model(x): H = np.array([ [1, 0, 0, 0], [0, 1, 0, 0] ]) z = H @ x return z def jacob_f(x, u, DT: float): """ Jacobian of Motion Model motion model x_{t+1} = x_t+v*dt*cos(yaw) y_{t+1} = y_t+v*dt*sin(yaw) yaw_{t+1} = yaw_t+omega*dt v_{t+1} = v{t} so dx/dyaw = -v*dt*sin(yaw) dx/dv = dt*cos(yaw) dy/dyaw = v*dt*cos(yaw) dy/dv = dt*sin(yaw) """ yaw = x[2, 0] v = u[0, 0] jF = np.array([ [1.0, 0.0, -DT * v * math.sin(yaw), DT * math.cos(yaw)], [0.0, 1.0, DT * v * math.cos(yaw), DT * math.sin(yaw)], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]) return jF def jacob_h(): # Jacobian of Observation Model jH = np.array([ [1, 0, 0, 0], [0, 1, 0, 0] ]) return jH def ekf_estimation(x_est, P_est, z, u, R, dt: float): # Predict x_pred = motion_model(x_est, u, dt) jF = jacob_f(x_est, u, dt) P_pred = jF @ P_est @ jF.T + Q # Update jH = jacob_h() z_pred = observation_model(x_pred) y = z - z_pred S = jH @ P_pred @ jH.T + R K = P_pred @ jH.T @ np.linalg.inv(S) x_est = x_pred + K @ y P_est = (np.eye(len(x_est)) - K @ jH) @ P_pred return x_est, P_est def plot_covariance_ellipse(xEst, PEst): # pragma: no cover Pxy = PEst[0:2, 0:2] eigval, eigvec = np.linalg.eig(Pxy) if eigval[0] >= eigval[1]: bigind = 0 smallind = 1 else: bigind = 1 smallind = 0 t = np.arange(0, 2 * math.pi + 0.1, 0.1) a = math.sqrt(eigval[bigind]) b = math.sqrt(eigval[smallind]) x = [a * math.cos(it) for it in t] y = [b * math.sin(it) for it in t] angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0]) rot = np.array([[math.cos(angle), math.sin(angle)], [-math.sin(angle), math.cos(angle)]]) fx = rot @ (np.array([x, y])) px = np.array(fx[0, :] + xEst[0, 0]).flatten() py = np.array(fx[1, :] + xEst[1, 0]).flatten() plt.plot(px, py, "--r") async def read_simulation(): dt = 0.1 # time tick [s] SIM_TIME = 50.0 # simulation time [s] time = 0.0 hdop = 0.1 np.random.seed(23) # State Vector [x y yaw v]' x_true = np.zeros((4, 1)) z = np.zeros((2, 1)) yield 0, None, z, None, None while time <= SIM_TIME: u = calc_input() x_true, z, ud = simulate(x_true, u, dt) time += dt yield time, dt, z, ud, hdop class History: def __init__(self, state=None): self.history = state def add(self, x): if self.history is None: self.history = x else: self.history = np.hstack((x, self.history)) def plot(self, fmt): plt.plot(self.history[0, :], self.history[1, :], fmt) def plot_flatten(self, fmt): plt.plot(self.history[0, :].flatten(), self.history[1, :].flatten(), fmt) class DeadReckonTracker: def __init__(self, state=None): # state vectors [x y yaw v]' self.state = state def init(self, state): self.state = state def get_state(self): return self.state def update(self, z, u, dt: float): # u: input, z: observation (not used here) self.state = motion_model(self.state, u, dt) return self.state class ExtendedKalmanFilterTracker: def __init__(self, state=None): # state vectors [x y yaw v]' self.state = state self.P = np.eye(4) def init(self, state): self.state = state self.P = np.eye(4) def get_state(self): return self.state def update(self, z, u, R, dt: float): # u: input, z: observation (not used here) if self.state is None: self.state = np.array([[z[0][0]], [z[1][0]], [0], [0]]) self.state, self.P = ekf_estimation(self.state, self.P, z, u, R, dt) return self.state def update2(self, z, u, hdop: float, dt: float): # u: input, z: observation (not used here) # each component is 0.707 * hdop (hdop is radius) if self.state is None: self.state = np.array([[z[0][0]], [z[1][0]], [0], [0]]) R = np.diag([0.7 * hdop, 0.7 * hdop]) ** 2 self.state, self.P = ekf_estimation(self.state, self.P, z, u, R, dt) return self.state async def track(stream, yaw=0, speed=0): #show_animation = True show_animation = False # state vectors [x y yaw v]' first = True def plot(): # plt.gca().invert_xaxis() # plt.gca().invert_yaxis() plt.axis("equal") plt.grid(True) hz.plot(".g") # hdr.plot_flatten("-k") hekf.plot_flatten("-r") plot_covariance_ellipse(ekf.state, ekf.P) # State Vector [x y yaw v]' s = ekf.get_state().flatten() # print("STATE", s) x = s[0] y = s[1] yaw = s[2] # speed = s[3] a = 1 # * speed plt.arrow(x, y, a * math.cos(yaw), a * math.sin(yaw)) # async for o in stream: print("track", repr(o)) async for _, dt, z, ud, hdop in stream: # print("TRACK STREAM", dt, z, ud) if first: # init state with the first observation, using yaw, v = 0 s = np.array([[z[0][0]], [z[1][0]], [yaw], [speed]]) dr = DeadReckonTracker(s) hdr = History(s) ekf = ExtendedKalmanFilterTracker(s) hekf = History(s) hz = History(z) first = False yield s continue # each component is 0.707 * hdop (hdop is radius) R = np.diag([0.7 * hdop, 0.7 * hdop]) ** 2 hdr.add(dr.update(z, ud, dt)) s = ekf.update(z, ud, R, dt) hekf.add(s) hz.add(z) yield s if show_animation: plt.cla() # for stopping simulation with the esc key. plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None]) plot() plt.pause(0.001) plot() plt.show() async def main(): async for s in track(read_simulation()): print(s) if __name__ == '__main__': import asyncio asyncio.run(main())

25.143302

107

0.525585

1,275

8,071

3.246275

0.167059

0.024644

0.02102

0.016429

0.303455

0.276878

0.250544

0.225417

0.221551

0.169123

0

0.039579

0.317557

8,071

320

108

25.221875

0.711874

0.185355

0

0.215385

0

0.006648

0

1

0.117949

false

0

0.020513

0.010256

0.220513

0.005128

0

null

0

null

0

1

0

93c70f97a9fcc20d868e2f05ea3a698a7c994530

974

py

Python

Lab11/BacktrackingIterative.py

alexnaiman/Fundamentals-Of-Programming---Lab-assignments

ef066e6036e20b9c686799f507f10e15e50e3285

[ "MIT" ]

4

2018-02-19T13:57:38.000Z

2022-01-08T04:10:54.000Z

Lab11/BacktrackingIterative.py

alexnaiman/Fundamentals-Of-Programming---Lab-assignments

ef066e6036e20b9c686799f507f10e15e50e3285

[ "MIT" ]

null

Lab11/BacktrackingIterative.py

alexnaiman/Fundamentals-Of-Programming---Lab-assignments

ef066e6036e20b9c686799f507f10e15e50e3285

[ "MIT" ]

null

l = [0, "-", "+"] def backIter(): x = [0] # candidate solution while len(x) > 0: choosed = False while (not choosed) and l.index(x[-1]) < len(l) - 1: x[-1] = l[l.index(x[-1]) + 1] # increase the last component choosed = consistent(x) if choosed: if solution(x): solutionFound(x) x.append(0) # expand candidate solution else: x.pop() # go back one component def consistent(s): return len(s) < n def solution(s): summ = list2[0] if not len(s) == n - 1: return False for i in range(n - 1): if s[i] == "-": summ -= list2[i + 1] else: summ += list2[i + 1] return summ > 0 def solutionFound(s): print(s) n = int(input("Give number")) list2 = [] for i in range(n): list2.append(int(input(str(i) + ":"))) backIter() print("test")

21.173913

73

0.464066

127

974

3.559055

0.354331

0.013274

0.030973

0.035398

0.053097

0

0.033389

0.38501

974

45

74

21.644444

0.721202

0.096509

0

0.058824

0

0.022892

0

1

0.117647

false

0

0.029412

0.205882

0.058824

0

null

0

null

0

1

0

93c86f77e89802184faaf894ae457e773562fb59

31,674

py

Python

dreadlord_counter_strike.py

lorenypsum/dreadlord_counter_strike

5f63c97ab28d84f8d7d9ff2f481c5111f0bc2ef1

[ "MIT" ]

null

dreadlord_counter_strike.py

lorenypsum/dreadlord_counter_strike

5f63c97ab28d84f8d7d9ff2f481c5111f0bc2ef1

[ "MIT" ]

null

dreadlord_counter_strike.py

lorenypsum/dreadlord_counter_strike

5f63c97ab28d84f8d7d9ff2f481c5111f0bc2ef1

[ "MIT" ]

null

from datetime import datetime from enum import Enum, auto from random import randint from time import sleep from typing import Optional, Tuple class GameItem(Enum): DEATH = auto() WOODEN_SWORD = auto() SIMPLE_BOW = auto() VIOLIN = auto() ORDINARY_SWORD = auto() STRAHD_SLAYER_SWORD = auto() STRAHD_SLAYER_BOW = auto() class GameStatus(Enum): ALIVE = auto() DEAD = auto() ARREGAO = auto() WINNER = auto() HAHA = auto() def ask_if_yes(input_text: str) -> bool: """ This function asks the player a question, and returns True if they typed yes, or False if they typed anything else. """ return input(input_text).lower() in ["y", "yes", "s", "sim"] def ask_if_wanna_continue(player_name: str) -> bool: """ This function asks the player if they want to continue the game, and returns the answer. """ print("You reached one possible end!!!") if ask_if_yes("Wanna change your fate? "): sleep(2) print("Very well then...") sleep(2) return True else: if ask_if_yes(f"{player_name} did you find the treasure I prepared for you? "): print("I hope you are not lying, you may leave now!!!") sleep(1) else: print("What a shame! you broke my heart :'(") sleep(1) return False def roll_for_item(player_name: str) -> Tuple[Optional[GameItem], GameStatus]: """ This function rolls the dice for the player. It returns the item that the player gained (if any), and the status of the player after the roll. """ roll = randint(1, 20) if player_name.lower() == "lurin": print(f"You rolled {roll}!") sleep(2) if ask_if_yes("Since you are inspired... wanna roll again? "): sleep(2) roll = randint(1, 20) print(f"Now your roll was {roll}") if roll == 1: print(f"HAHAHAHAHA, tragic! You got {roll}") sleep(2) if player_name.lower() != "lurin": print( f"Unfortunalety {player_name}, you are not Lurin, so you do not have another chance!!!" ) sleep(4) else: print( f"Unfortunalety fake {player_name}, even inspired you got it? You are a joke!!!" ) sleep(4) return None, GameStatus.DEAD if player_name.lower() == "snow": print(f"... you may have this *WONDERFUL DEATH* to help you kill STRAHD...") sleep(3) print("...the perfect item for you, huh?") sleep(2) print("...no, it is not a typo or some faulty logic!") sleep(2) print( "It is indeed the perfect item for you... you will play dead (you are used to it)... STRAHD flew away..." ) sleep(4) return GameItem.DEATH, GameStatus.ALIVE else: print( f"Well {player_name}, you may have this *DEATH* to help you kill STRAHD..." ) sleep(3) print("...since you are not SNOW....") sleep(2) print("...no, it is not a typo or some faulty logic!") sleep(2) print("...you are DEAD!") sleep(2) print("***Bad end!***") sleep(1) return None, GameStatus.DEAD elif roll <= 5: print(f"You got {roll}") if player_name.lower() != "kaede": print( f"Well {player_name}, you may have this *VIOLIN* to help you kill STRAHD..." ) sleep(3) print("...since you are not KAEDE.... gooood luck!") sleep(2) return GameItem.VIOLIN, GameStatus.ALIVE else: print(f"Well {player_name}, you may have this ***WONDERFUL VIOLIN***") sleep(3) print("the perfect item for you, huh?") sleep(2) return GameItem.VIOLIN, GameStatus.ALIVE elif roll <= 10: print(f"You got {roll}") if player_name.lower() != "soren": print( f"Well {player_name}, you may have this *SIMPLE BOW* to help you kill STRAHD..." ) sleep(3) print("...since you are not Soren... gooood luck!") sleep(2) return GameItem.SIMPLE_BOW, GameStatus.ALIVE else: print(f"Well {player_name}, you may have this ***WONDERFUl SIMPLE BOW***") sleep(3) print("the perfect item for you, huh?") sleep(2) print("just.. do not kill any cats with this, moron!!!") sleep(2) return GameItem.SIMPLE_BOW, GameStatus.ALIVE elif roll <= 15: print(f"You got {roll}") if player_name.lower() != "vis": print( f"Well {player_name}, you may have this *ORDINARY SWORD* to help you kill STRAHD..." ) sleep(3) print("...since you are not Vis... gooood luck!") sleep(2) print("and pray it won't fly...") sleep(2) return GameItem.ORDINARY_SWORD, GameStatus.ALIVE else: print( f"Well {player_name}, you may have this ***FANTASTIC ORDINARY SWORD*** to help you kill STRAHD" ) sleep(3) print("the perfect item for you, huh?") sleep(2) print("if it doesn't fly...") sleep(2) return GameItem.ORDINARY_SWORD, GameStatus.ALIVE elif roll < 20: print(f"You got {roll}") sleep(2) print( f"Well {player_name}, you may have ****STRAHD SLAYER SWORD***, go kill STRAHD, " ) sleep(3) print("...the legendary item!!!") sleep(2) print("...but hope it won't fly!!!") sleep(2) return GameItem.STRAHD_SLAYER_SWORD, GameStatus.ALIVE elif roll == 20: if player_name.lower() != "snow": print( f"Well {player_name}, you may have **** STRAHD SLAYER BOW***, go kill STRAHD, special treasures awaits you!!!" ) sleep(3) print("...the legendary perfect item!!!") sleep(2) print("...it doesn't even matter if it will fly!!!") sleep(2) return GameItem.STRAHD_SLAYER_BOW, GameStatus.ALIVE else: print( f"Well {player_name}, you seduced STRAHD, now you can claim your treasures" ) sleep(2) print(f"STRAHD licks you!!!") sleep(4) return GameItem.STRAHD_SLAYER_BOW, GameStatus.ALIVE return None, GameStatus.ALIVE def flee(player_name: str) -> GameStatus: """ This function asks the player if they want to flee. It returns the status of the player after their decision to flee. """ if ask_if_yes("Wanna flee now? "): sleep(2) print("...") sleep(1) print("We will see if flee you can... *** MUST ROLL THE DICE ***: ") sleep(2) print("Careful!!!") sleep(1) roll_the_dice = input( "*** Roll stealth *** (if you type it wrong it means you were not stealth) type: 'roll stealth' " ) sleep(4) if roll_the_dice == "roll stealth": roll = randint(1, 20) if roll <= 10: print(f"you rolled {roll}!") sleep(2) print("It means STRAHD noticed you!") sleep(2) print("...") sleep(2) print(" You are dead!!! ") sleep(2) print(" ***Bad end...*** ") sleep(1) return GameStatus.DEAD else: print(f"you rolled {roll}!!!") sleep(2) print("Congratulations, you managed to be stealth!!!") sleep(2) print("...") sleep(2) print("You may flee but you will continue being poor and weak...") sleep(2) print("...") sleep(2) print( "And remember there are real treasures waiting for you over there..." ) sleep(4) print("***Bad end...***") sleep(1) return GameStatus.ARREGAO else: if player_name.lower() in ["soren", "kaede", "leandro", "snow", "lurin"]: print("...") sleep(1) print("......") sleep(2) print("...........") sleep(2) print("I told you to be careful!") sleep(2) print(f"...{player_name} you are such a DOJI!!!") sleep(2) print("It means the STRAHD noticed you!") sleep(2) print("...") sleep(2) print(" You are dead!!! ") sleep(2) print(" ***Bad end...*** ") sleep(1) else: print("I told you to be careful!") sleep(2) print("...........") sleep(2) print(f"...{player_name} you are such a klutz!!!") sleep(2) print("It means STRAHD noticed you!") sleep(2) print("...") sleep(2) print(" You are dead!!! ") sleep(2) print(" ***Bad end...*** ") sleep(1) return GameStatus.DEAD else: return GameStatus.ALIVE def attack(player_name: str) -> Tuple[Optional[GameItem], GameStatus]: """ This function asks the player if they want to attack STRAHD. If the player answers yes, the player rolls for an item. This function returns the item obtained by a roll (if any), and the status of the player. """ print("You shall not pass!!!") if ask_if_yes(f"{player_name}, will you attack STRAHD? "): sleep(1) print("I honor your courage!") sleep(2) print("therefore...") sleep(1) print("I will help you...") sleep(1) print("I am giving you a chance to kill STRAHD and reclaim your treasures...") sleep(2) print( "Roll the dice and have a chance to win the perfect item for you... or even some STRAHD Slayer Shit!!!" ) sleep(3) print("It will increase your chances...") sleep(2) print( "....or kill you right away if you are as unlucky as Soren using his Sharp Shooting!!!" ) sleep(2) if ask_if_yes("Wanna roll the dice? "): return roll_for_item(player_name) else: if ask_if_yes("Are you sure? "): sleep(2) print("So you have chosen... Death!") sleep(2) return GameItem.DEATH, GameStatus.DEAD else: sleep(2) print("Glad you changed your mind...") sleep(2) print("Good... very good indeed...") sleep(2) return roll_for_item(player_name) else: print("If you won't attack STRAHD... then...") sleep(2) return None, flee(player_name) def decide_if_strahd_flies(player_name: str) -> bool: """ This function asks if the player wants to roll for stealth, which can give a chance for STRAHD not to fly. It returns whether STRAHD flies. """ print( "This is your chance... STRAHD has his attention captived by his 'vampirish's business'..." ) sleep(3) print("You are approaching him...") sleep(2) print("Careful...") sleep(2) print("Because vampires... can fly...") sleep(2) print("Roll stealth (if you type it wrong it means you were not stealth)...") roll_the_dice = input("type: 'roll stealth' ") sleep(2) if roll_the_dice == "roll stealth": roll = randint(1, 20) if roll <= 10: print("...") sleep(1) print("Unlucky") sleep(2) print(f"You rolled {roll}") sleep(2) print("STRAHD...") sleep(2) print("...flew up") sleep(2) print("Now, you have a huge disavantage") sleep(2) return True else: print(f"You rolled {roll}") sleep(2) print("Congratulations, you managed to be in stealth!") sleep(2) return False else: if player_name.lower() in ["soren", "kaede", "leandro", "snow"]: print("...") sleep(1) print("......") sleep(2) print("...........") sleep(2) print("I told you to be careful!") sleep(2) print(f"...{player_name} you are such a DOJI, STRAHD flew up...") sleep(2) print("Now, you have a huge disavantage") sleep(2) else: print("...") sleep(1) print("......") sleep(2) print("...........") sleep(2) print("I told you to be careful!") sleep(2) print(f"...{player_name} you are such a KLUTZ, STRAHD flew...") sleep(2) print("...STRAHD flew up...") sleep(2) print("Now, you have a huge disavantage") sleep(2) return True def calculate_win_probability( player_race: str, player_name: str, item: Optional[GameItem],strahd_flying: bool ) -> int: """ This function returns the probability that the player defeats STRAHD. The probability depends on the item the player is holding, and whether STRAHD is flying. """ if item == GameItem.DEATH: if player_name.lower() == "snow" and player_race.lower() == "kalashatar": return 90 else: return 0 elif item == GameItem.WOODEN_SWORD: if strahd_flying: return 5 else: return 10 elif item == GameItem.SIMPLE_BOW: if player_name.lower() == "soren" and player_race.lower() in [ "human", "humano", "elf", "elfo", ]: return 70 else: return 30 elif item == GameItem.VIOLIN: if player_name.lower() == "kaede" and player_race.lower() == "tiefling": return 70 else: return 30 elif item == GameItem.ORDINARY_SWORD: if strahd_flying: return 10 elif player_name.lower() == "vis" and player_race.lower() == "draconato": return 80 else: return 40 elif item == GameItem.STRAHD_SLAYER_SWORD: if strahd_flying: return 20 else: return 100 elif item == GameItem.STRAHD_SLAYER_BOW: return 100 else: return -1 def roll_for_win(probability: int) -> bool: """ This function returns whether the player defeats STRAHD, given a probability. """ return randint(1, 100) <= probability def after_battle(player_race: str, player_name: str, did_win: bool) -> GameStatus: """ This function conducts the scenario after the player has defeated, or not, STRAHD. It returns the status depending on whether the player won. """ if did_win: now = datetime.now() print("A day may come when the courage of men fails…") sleep(2) print("but it is not THIS day, SATAN...") sleep(2) print("Because... you approached STRAHD...") sleep(2) print("Almost invisible to his senses...") sleep(2) print( "Somehow your weapon hit the weak point of STRAHD's... revealing his true identity" ) sleep(4) print( "He was just a bat... who looked like a DREADLORD..." ) sleep(4) print("It was a huge battle...") sleep(2) print( f"And it was the most awkward {now.strftime('%A')} you will ever remember." ) sleep(2) if ( player_race.lower() in ["master", "mestre"] and player_name.lower() == "zordnael" ): print("...") sleep(1) print( "***************************************************************************************************************************************" ) sleep(1) print( f"Congratulations {player_name}!!! You are the WINNER of this week's challenge, you shall receive 5000 dullas in Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print("link") sleep(5) print("***CHEATER GOOD END***") sleep(2) return GameStatus.WINNER elif player_race.lower() == "racist" and player_name.lower() == "lili": print("...") sleep(1) print( "***************************************************************************************************************************************" ) sleep(1) print( f"Congratulations {player_name}!!! You are the WINNER of this week's challenge, you shall receive the prizes specially prepared for everybody in dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print("https://drive.google.com/drive/folders/1Jn8YYdixNNRqCQgIClBmGLiFFxuSCQdc?usp=sharing") sleep(5) print("***BEST END***") sleep(2) return GameStatus.WINNER if did_win: print("...") sleep(1) print( "***************************************************************************************************************************************" ) sleep(1) if player_name.lower() == "soren": print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you received a cash prize of five thousand dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print(f"And a prize... prepared specially for you {player_name}") sleep(2) print("... I know you doubted me... but here it is:") sleep(2) print("...") sleep(1) print("https://drive.google.com/drive/folders/1FerRt3mmaOm0ohSUXTkO-CmGIAluavXi?usp=sharing") sleep(5) print("...Your motherfuger cat killer !!!") sleep(2) print("***SOREN'S GOOD END***") sleep(2) elif player_name.lower() == "snow": print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you received a cash prize of five thousand dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print(f"And a prize... prepared specially for you {player_name}") sleep(2) print("... I know you doubted me... but here it is:") sleep(2) print("...") sleep(1) print("https://drive.google.com/drive/folders/16STFQ-_0N_54oNNsVQnMjwjcBgubxgk7?usp=sharing") sleep(5) print("...Your motherfuger snow flake !!!") sleep(2) print("***SNOW'S GOOD END***") sleep(2) elif player_name.lower() == "kaede": print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you received a cash prize of five thousand dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print(f"And a prize... prepared specially for you {player_name}") sleep(2) print("... I know you doubted me... but here it is:") sleep(2) print("...") sleep(1) print("https://drive.google.com/drive/folders/1XN9sItRxYR4Si4gWFeJtI0HGF39zC29a?usp=sharing") sleep(5) print("...Your motherfuger idol !!!") sleep(2) print("***KAEDE'S GOOD END***") sleep(2) elif player_name.lower() == "leandro": print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you received a cash prize of five thousand dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print(f"And a prize... prepared specially for you {player_name}") sleep(2) print("... I know you doubted me... but here it is:") sleep(2) print("...") sleep(1) print("https://drive.google.com/drive/folders/1eP552hYwUXImmJ-DIX5o-wlp5VA96Sa0?usp=sharing") sleep(5) print("...Your motherfuger only roll 20 !!!") sleep(2) print("***LEANDRO'S GOOD END***") sleep(2) elif player_name.lower() == "vis": print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you received a cash prize of five thousand dullas from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print(f"And a prize... prepared specially for you {player_name}") sleep(2) print("... I know you doubted me... but here it is:") sleep(2) print("...") sleep(1) print("https://drive.google.com/drive/folders/19GRJJdlB8NbNl3QDXQM1-0ctXSX3mbwS?usp=sharing") sleep(5) print("...Your motherfuger iron wall !!!") sleep(2) print("***VIS'S GOOD END***") sleep(2) elif player_name.lower() == "lurin": print("CONGRATULATIONS!!!!! ") sleep(2) print("Bitch! ... ") sleep(2) print(" ... you stole my name...") sleep(2) print("You are arrested for identity theft!!!") sleep(2) print("...") sleep(1) print("del C://LeagueOfLegends") sleep(2) print("...") sleep(0.5) print(".....") sleep(0.5) print("......") sleep(0.5) print(".............") sleep(2) print("deletion completed") sleep(2) print("***PHONY'S GOOD END***") sleep(2) else: print( f"Congratulations {player_name}!!! you are the WINNER of this week's challenge, you shall receive this link from Anastasia Dungeons Hills Cosmetics!" ) sleep(4) print("https://drive.google.com/drive/folders/0B_sxkSE6-TfETlZoOHF1bTRGTXM?usp=sharing") sleep(5) print("***GOOD END***") sleep(2) sleep(1) return GameStatus.WINNER if not did_win: print("You tried to approach the devil carefully...") sleep(2) print("... but your hands were trembling...") sleep(2) print("...your weapon was not what you expected...") sleep(2) print("... It was a shit battle... but") sleep(2) print("The journey doesn't end here...") sleep(2) print("Death is just another way we have to choose...") sleep(2) print("...") sleep(1) if player_name.lower() == "vis": print("I really believed in you...") sleep(2) print("...but I guess...") sleep(1) print("you shoud have stayed in your bathroom...") sleep(2) print("eating lemon pies...") sleep(2) print("...") sleep(1) print(f"YOU DIED {player_name}") sleep(2) print("***VIS'S BAD END***") sleep(2) elif player_name.lower() == "soren": print("I really believed in you..") sleep(2) print("...but I guess...") sleep(1) print("Did you think it was a cat? ") sleep(2) print("Not today Satan!!!") sleep(2) print("...") sleep(1) print(f"You died! {player_name}") sleep(2) print("***SOREN'S BAD END***") sleep(2) elif player_name.lower() == "kaede": print("I really believed in you..") sleep(2) print("...but I guess...") sleep(1) print("お。。。。") sleep(2) print("。。。か　わ　い　い") sleep(2) print("。。。。。。こ　と") sleep(2) print("go play you Violin in Hell...") sleep(2) print("...") sleep(1) print(f"You died! {player_name}") sleep(2) print("***KAEDES'S BAD END***") sleep(2) elif player_name.lower() == "snow": print("I really believed in you..") sleep(2) print("...but I guess...") sleep(1) print("HAHAHAAHHAHAHA") sleep(2) print("It is cute you even tried!") sleep(2) print("but I will call you Nori!") sleep(2) print("...") sleep(1) print("You died! Nori!!!") sleep(2) print("***SNOW'S BAD END***") sleep(2) elif player_name.lower() == "lurin": print("I really believed in you..") sleep(2) print("...but I guess...") sleep(2) print("Bitch! ... ") sleep(2) print(" ... you stole my name...") sleep(2) print("You are arrested for identity theft!!!") sleep(2) print("...") sleep(1) print("del C://LeagueOfLegends") sleep(2) print("...") sleep(0.5) print(".....") sleep(0.5) print("......") sleep(0.5) print(".............") sleep(2) print("deletion completed") sleep(2) print("***PHONY'S GOOD END***") sleep(2) elif player_name.lower() == "leandro": print("nice try") sleep(2) print("...but I guess...") sleep(2) print("Try harder next time...") sleep(2) print("...Nicolas Cage Face...") sleep(2) print("***LEANDRO'S BAD END***") sleep(2) elif player_name.lower() == "buiu": print("nice try") sleep(2) print("...but I guess...") sleep(2) print("Try harder next time...") sleep(2) print(f"Did you really think this would work? Clown!") sleep(2) print("***RIDICULOUS BUIU'S END***") sleep(2) return GameStatus.HAHA elif player_name.lower() in ["strahd", "dreadlord"]: print("good try") sleep(2) print("...but I guess...") sleep(2) print("I never said you were in a cave...") sleep(2) print("There is sunlight now...") sleep(2) print("You are burning...") sleep(2) print("Till Death...") sleep(2) print("***RIDICULOUS STRAHD'S END***") sleep(2) else: print("I really believed in you..") sleep(2) print("...but I guess...") sleep(2) print("This is a shit meta game...") sleep(2) print( "Designed for players from a certain 16:20 tabletop Ravenloft campaign" ) sleep(2) print(f"Sorry, {player_name}...") sleep(2) print("You are dead!!!") sleep(2) print("***BAD END***") sleep(2) sleep(1) return GameStatus.DEAD def main(): """ This function conducts the entire game. """ wanna_continue = True while wanna_continue: player_race = input("Your race? ") player_name = input("Your name? ") status = flee(player_name) if status == GameStatus.ALIVE: item, status = attack(player_name) if status == GameStatus.ALIVE: strahd_flight = decide_if_strahd_flies(player_name) probability = calculate_win_probability( player_race, player_name, item, strahd_flight ) did_win = roll_for_win(probability) status = after_battle(player_race, player_name, did_win) if status == GameStatus.WINNER: sleep(5) print( "You are a winner, baby. But there are other possibilities over there..." ) wanna_continue = ask_if_wanna_continue(player_name) elif status == GameStatus.HAHA: wanna_continue = False else: wanna_continue = ask_if_wanna_continue(player_name) else: wanna_continue = ask_if_wanna_continue(player_name) elif status == GameStatus.DEAD: wanna_continue = ask_if_wanna_continue(player_name) else: print("...") wanna_continue = ask_if_wanna_continue(player_name) input() main()

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0.327889

0

null

0

null

0

1

0

93c9a643270a43403d7d70db7f672d353ef62da2

635

py

Python

backend/helper/mds.py

marinaevers/regional-correlations

8ca91a5283a92e75f3d99f870c295ca580edb949

[ "MIT" ]

null

backend/helper/mds.py

marinaevers/regional-correlations

8ca91a5283a92e75f3d99f870c295ca580edb949

[ "MIT" ]

null

backend/helper/mds.py

marinaevers/regional-correlations

8ca91a5283a92e75f3d99f870c295ca580edb949

[ "MIT" ]

null

import numpy as np def mds(d, dimensions=3): """ Multidimensional Scaling - Given a matrix of interpoint distances, find a set of low dimensional points that have similar interpoint distances. """ (n, n) = d.shape E = (-0.5 * d ** 2) # Use mat to get column and row means to act as column and row means. Er = np.mat(np.mean(E, 1)) Es = np.mat(np.mean(E, 0)) # From Principles of Multivariate Analysis: A User's Perspective (page 107). F = np.array(E - np.transpose(Er) - Es + np.mean(E)) [U, S, V] = np.linalg.svd(F) Y = U * np.sqrt(S) return (Y[:, 0:dimensions], S)

24.423077

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635

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0.3

0

null

0

null

0

1

0

93c9ac724fdd806412549f0dec59d52778127c89

492

py

Python

sm3.py

matthewmuccio/InterviewPrepKit

13dabeddc3c83866c88bef1c80498c313e4c233e

[ "MIT" ]

2

2018-09-19T00:59:09.000Z

2022-01-09T18:38:01.000Z

sm3.py

matthewmuccio/InterviewPrepKit

13dabeddc3c83866c88bef1c80498c313e4c233e

[ "MIT" ]

null

sm3.py

matthewmuccio/InterviewPrepKit

13dabeddc3c83866c88bef1c80498c313e4c233e

[ "MIT" ]

null

#!/usr/bin/env python3 from collections import Counter # Complete the isValid function below. def isValid(s): freq = Counter(s) values = list(freq.values()) values.sort() return "YES" if values.count(values[0]) == len(values) or (values.count(values[0]) == len(values) - 1 and values[-1] - values[-2] == 1) or (values.count(values[-1]) == len(values) - 1 and values[0] == 1) else "NO" if __name__ == "__main__": s = input() result = isValid(s) print(result)

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492

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0.3

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null

0

null

0

1

0

93cd3692a60479202468f2712c8bb24c8cc1672a

841

py

Python

src/codplayer/__init__.py

petli/codplayer

172187b91662affd8e89f572c0db9be1c4257627

[ "MIT" ]

14

2015-04-27T20:40:46.000Z

2019-02-01T09:22:02.000Z

src/codplayer/__init__.py

petli/codplayer

172187b91662affd8e89f572c0db9be1c4257627

[ "MIT" ]

10

2015-01-05T18:11:28.000Z

2018-09-03T08:42:50.000Z

src/codplayer/__init__.py

petli/codplayer

172187b91662affd8e89f572c0db9be1c4257627

[ "MIT" ]

4

2017-03-03T16:59:39.000Z

2019-11-08T11:15:06.000Z

# codplayer supporting package # # Copyright 2013-2014 Peter Liljenberg <peter.liljenberg@gmail.com> # # Distributed under an MIT license, please see LICENSE in the top dir. # Don't include the audio device modules in the list of modules, # as they may not be available on all systems from pkg_resources import get_distribution import os import time version = get_distribution('codplayer').version # Check what file we are loaded from try: date = time.ctime(os.stat(__file__).st_mtime) except OSError as e: date = 'unknown ({})'.format(e) def full_version(): return 'codplayer {0} (installed {1})'.format(version, date) __all__ = [ 'audio', 'command', 'config', 'db', 'model', 'player', 'rest', 'rip', 'serialize', 'sink', 'source', 'state', 'toc', 'version' ]

19.55814

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0

0.015221

0.218787

841

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0

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false

0

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0.192308

0

null

0

null

0

1

0

93d13c525fccba1c9782ed2b28a9ab8aac0b37da

339

py

Python

shapesimage.py

riddhigupta1318/menu_driven

1a3e4a8d3ff3dbcd9cffaa87ab9fbc66868d9eb6

[ "Apache-2.0" ]

null

shapesimage.py

riddhigupta1318/menu_driven

1a3e4a8d3ff3dbcd9cffaa87ab9fbc66868d9eb6

[ "Apache-2.0" ]

null

shapesimage.py

riddhigupta1318/menu_driven

1a3e4a8d3ff3dbcd9cffaa87ab9fbc66868d9eb6

[ "Apache-2.0" ]

null

#!/user/bin/python3 import cv2 #loading image img=cv2.imread("dog.jpeg") img1=cv2.line(img,(0,0),(200,114),(110,176,123),2) #print height and width print(img.shape) #to display that image cv2.imshow("dogg",img1) #image window holder activate #wait key will destroy by pressing q button cv2.waitKey(0) cv2.destroyAllWindows()

16.142857

50

0.719764

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339

4.357143

0.767857

0

0.095238

0.132743

339

20

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0.734694

0.439528

0

0.065574

0

1

0

false

0

0.142857

0

0.142857

0

null

0

null

0

1

0

93d37d046fccd50496fe96e2714742d3c5e3222c

2,139

py

Python

RNNS/utils/wrdembdGen.py

CenIII/Text-style-transfer-DeleteRetrieve

2b7aa017765dcae65b42fc94d3ccaddc57ac8661

[ "MIT" ]

null

RNNS/utils/wrdembdGen.py

CenIII/Text-style-transfer-DeleteRetrieve

2b7aa017765dcae65b42fc94d3ccaddc57ac8661

[ "MIT" ]

null

RNNS/utils/wrdembdGen.py

CenIII/Text-style-transfer-DeleteRetrieve

2b7aa017765dcae65b42fc94d3ccaddc57ac8661

[ "MIT" ]

null

import gensim import fnmatch import os import pickle import numpy as np # from symspellpy.symspellpy import SymSpell, Verbosity # import the module # initial_capacity = 83000 # # maximum edit distance per dictionary precalculation # max_edit_distance_dictionary = 2 # prefix_length = 7 # sym_spell = SymSpell(initial_capacity, max_edit_distance_dictionary, # prefix_length) # # load dictionary # dictionary_path = os.path.join(os.path.dirname(__file__), # "frequency_dictionary_en_82_765.txt") # term_index = 0 # column of the term in the dictionary text file # count_index = 1 # column of the term frequency in the dictionary text file # if not sym_spell.load_dictionary(dictionary_path, term_index, count_index): # print("Dictionary file not found") # max_edit_distance_lookup = 2 model = gensim.models.KeyedVectors.load_word2vec_format('~/Downloads/GoogleNews-vectors-negative300.bin', binary=True) wordlist = [] for dataset in ['yelp/']: filelist = os.listdir('../../Data/'+dataset) for file in filelist: with open('../../Data/'+dataset+file,'r') as f: line = f.readline() while line: # suggestions = sym_spell.lookup_compound(line, max_edit_distance_lookup) wordlist += line.split(' ') line = f.readline() wordlist.append('<unk>') wordlist.append('<m_end>') wordlist.append('@@START@@') wordlist.append('@@END@@') vocabs = set(wordlist) print(len(vocabs)) wordDict = {} word2vec = [] wastewords = [] word2vec.append(np.zeros(300)) wordDict['<PAD>']=0 cnt=1 for word in vocabs: if word in model.wv: word2vec.append(model.wv[word]) wordDict[word] = cnt cnt += 1 else: # wastewords.append(word) word2vec.append(np.random.uniform(-1,1,300)) wordDict[word] = cnt cnt += 1 word2vec = np.array(word2vec) # with open('./word2vec', "wb") as fp: #Pickling np.save('word2vec.npy',word2vec) with open('./wordDict', "wb") as fp: #Pickling pickle.dump(wordDict, fp) # with open('./word2vec', "rb") as fp: #Pickling # word2vec = pickle.load(fp) # with open('./wordDict', "rb") as fp: #Pickling # wordDict = pickle.load(fp) # pass

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2,139

78

119

27.423077

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null

0

null

0

1

0

93d43839068d5fe40ab642bf29baf0d261531656

8,611

py

Python

cls_utils/job.py

prmurali1leo/Engineering_challenge

d73dcba265587c22f0869880bf372cfaa045bfa6

[ "MIT" ]

null

cls_utils/job.py

prmurali1leo/Engineering_challenge

d73dcba265587c22f0869880bf372cfaa045bfa6

[ "MIT" ]

null

cls_utils/job.py

prmurali1leo/Engineering_challenge

d73dcba265587c22f0869880bf372cfaa045bfa6

[ "MIT" ]

null

import pandas as pd import numpy as np from hashlib import md5 import datetime import pyarrow.parquet as pq import pyarrow as pa from src.dimension_surrogate_resolver import DimensionSurrogateResolver def run(): for dt in ["20160201", "20160301"]: create_fact_dimension_tables(dt) display_output() def create_fact_dimension_tables(dt): data = pd.read_csv(f"input_data/weather.{dt}.csv") pd_geo_location_dim = get_geo_location(data) save_geo_dimension(pd_geo_location_dim) pd_site_info_dim = get_site_info(data) save_site_dimension(pd_site_info_dim) pd_weather_fact = get_weather_fact(data) fact_df, agg_df = perform_transformations(pd_weather_fact) save_fact(fact_df, dt) save_aggregate(agg_df, dt) def get_geo_location(data): geo_location = data[['Region', 'Country']].copy() geo_location = geo_location.drop_duplicates(subset=['Region', 'Country']) geo_location = geo_location[pd.notnull(geo_location['Country'])] geo_location['key_column'] = geo_location.apply(lambda row: row.Region + row.Country, axis=1) geo_location['location_id'] = (geo_location .apply(lambda row: str(int(md5(row.key_column.encode('utf-8')).hexdigest(), 16)), axis=1) ) geo_location = geo_location.drop(columns='key_column').reset_index(drop=True) return geo_location def get_site_info(data): site_info = data[['ForecastSiteCode', 'SiteName', 'Latitude', 'Longitude']].copy() site_info = site_info.drop_duplicates(subset=['ForecastSiteCode', 'SiteName', 'Latitude', 'Longitude']) site_info['SiteName'] = site_info.apply(lambda row: row.SiteName[:-1 * (len(str(row.ForecastSiteCode)) + 3)], axis=1) site_info['site_id'] = (site_info .apply(lambda row: str(int(md5(str(row.ForecastSiteCode) .encode('utf-8')).hexdigest(), 16)), axis=1) ) site_info = site_info.sort_values('ForecastSiteCode').reset_index(drop=True) return site_info def time_conversion(t): return datetime.datetime.strptime(str(t), '%H').strftime("%H:%M") def fill_values(series): values_counted = series.value_counts() if values_counted.empty: return series most_frequent = values_counted.index[0] new_country = series.fillna(most_frequent) return new_country def get_day_night(time): if 8 <= time <= 16: return 'D' else: return 'N' def get_weather_fact(data): weather_fact = data.copy() weather_fact.loc[weather_fact['ScreenTemperature'] == -99, 'ScreenTemperature'] = np.nan weather_fact['SiteName'] = weather_fact.apply(lambda row: row.SiteName[:-1 * (len(str(row.ForecastSiteCode)) + 3)], axis=1) group_country = weather_fact.groupby('Region')['Country'] weather_fact.loc[:, 'Country'] = group_country.transform(fill_values) weather_fact = weather_fact.sort_values(['ForecastSiteCode', 'ObservationDate', 'ObservationTime'], ascending=(True, True, True)) weather_fact.loc[weather_fact['ScreenTemperature'].isnull(), 'ScreenTemperature'] = ( (weather_fact['ScreenTemperature'].shift() + weather_fact['ScreenTemperature'].shift(-1)) / 2 ) weather_fact['day_night'] = weather_fact['ObservationTime'].apply(lambda row: get_day_night(row)) weather_fact['avg_temp'] = weather_fact.fillna(0).groupby(['ForecastSiteCode', 'ObservationDate', 'day_night'])[ 'ScreenTemperature'].transform('mean') weather_fact['count_temp'] = weather_fact.groupby(['ForecastSiteCode', 'ObservationDate', 'day_night'])[ 'ScreenTemperature'].transform('count') weather_fact['avg_temp'] = np.where(weather_fact.count_temp == 0, np.nan, weather_fact.avg_temp) weather_fact['ObservationDate'] = weather_fact.ObservationDate.str[:-9] weather_fact['ObservationTime'] = weather_fact['ObservationTime'].apply(lambda x: time_conversion(x)) return weather_fact def write_to_parquet(source, destination): return source.to_parquet(f"output_data/{destination}.parquet", engine="pyarrow", index=False) def save_geo_dimension(data): try: geo_dim = pd.read_parquet("output_data/geo_dimension.parquet", engine="pyarrow") except OSError: write_to_parquet(data, "geo_dimension") return pd_geo_dim = pd.concat([geo_dim, data]).drop_duplicates().reset_index(drop=True) write_to_parquet(pd_geo_dim, "geo_dimension") return def save_site_dimension(data): try: site_dim = pd.read_parquet("output_data/site_dimension.parquet", engine="pyarrow") except OSError: write_to_parquet(data, "site_dimension") return pd_site_dim = pd.concat([site_dim, data]).drop_duplicates().reset_index(drop=True) write_to_parquet(pd_site_dim, "site_dimension") return def perform_transformations(pdf): columns_to_keep = ["ObservationDate", "ObservationTime", "WindDirection", "WindSpeed", "WindGust", "Visibility", "ScreenTemperature", "Pressure", "SignificantWeatherCode", "ForecastSiteCode", "Region", "Country", "avg_temp", "day_night"] df = pdf[columns_to_keep].copy() df = add_fk(df) agg_df = ( df[['ObservationDate', "ObservationTime", 'avg_temp', "ScreenTemperature", 'fk_location_id', 'fk_site_id', ]][ df['day_night'] == "D"].copy() ) agg_df = (agg_df.groupby(['fk_location_id', 'fk_site_id', "ObservationDate", 'avg_temp'], as_index=False).apply( lambda x: dict(zip(x['ObservationTime'], x['ScreenTemperature']))).reset_index(name='Temperature')) df = df.drop( columns=["Region", "Country", "ForecastSiteCode", "SiteName", "avg_temp", "Latitude", "Longitude", "day_night"]) df = df.sort_values(['ObservationDate', 'ObservationTime'], ascending=(True, True)) return df, agg_df def add_fk(df): df = DimensionSurrogateResolver.add_fk( "geo_location", df, "fk_location_id", {'Region': 'Region', 'Country': 'Country'}, ) df = DimensionSurrogateResolver.add_fk( "site", df, "fk_site_id", {'ForecastSiteCode': 'ForecastSiteCode'}, ) return df def save_fact(df, dt): unique_dates = df['ObservationDate'].unique().tolist() print(unique_dates) table = pa.Table.from_pandas(df, preserve_index=False) with pq.ParquetWriter(f"output_data/weather_fact/dt={dt}/weather_fact.parquet", table.schema) as writer: for date in unique_dates: df1 = df[df['ObservationDate'] == date] table = pa.Table.from_pandas(df1, preserve_index=False) writer.write_table(table) def save_aggregate(data, dt): obs_date = dt[:4] + "-" + dt[4:6] try: agg_fact = pd.read_parquet("output_data/fact_aggregate.parquet", engine="pyarrow") agg_fact = agg_fact[~agg_fact['ObservationDate'].str.contains(obs_date)] except OSError: write_to_parquet(data, "fact_aggregate") return pd_agg_fact = pd.concat([agg_fact, data]).reset_index(drop=True) write_to_parquet(pd_agg_fact, "fact_aggregate") return def display_output(): df = pd.read_parquet("output_data/fact_aggregate.parquet", engine="pyarrow") geo_dim = pd.read_parquet("output_data/geo_dimension.parquet", engine="pyarrow") site_dim = pd.read_parquet("output_data/site_dimension.parquet", engine="pyarrow") df = df[df.avg_temp == df.avg_temp.max()] df1 = pd.merge(df, geo_dim, left_on="fk_location_id", right_on="location_id", how='left') df1 = pd.merge(df1, site_dim, left_on="fk_site_id", right_on="site_id", how='left') df1 = df1.drop(columns=["fk_location_id", "location_id", "fk_site_id", "site_id"]) df1.to_csv("output_data/final_output.csv", index=False) print("Highest temperature was recorded on {0}".format(df1['ObservationDate'])) print("The average temperature on that Day from 8am to 4pm was {0}".format(df1['avg_temp'])) print("The temperature from 8am to 4pm was {0}".format(df1['Temperature'])) print("The hottest region was {0}".format(df1['Region']))

42.004878

120

0.648124

1,042

8,611

5.082534

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0.029762

0

null

0

null

0

1

0

93d52227fd91adf6e2131607d2e901a6c4913898

3,294

py

Python

busy_home.py

jerr0328/HAP-python

87199a1fb7ffc451961948c634e46439cbace370

[ "Apache-2.0" ]

462

2017-10-14T16:58:36.000Z

2022-03-24T01:40:23.000Z

busy_home.py

jerr0328/HAP-python

87199a1fb7ffc451961948c634e46439cbace370

[ "Apache-2.0" ]

371

2017-11-28T14:00:02.000Z

2022-03-31T21:44:07.000Z

busy_home.py

jerr0328/HAP-python

87199a1fb7ffc451961948c634e46439cbace370

[ "Apache-2.0" ]

129

2017-11-23T20:50:28.000Z

2022-03-17T01:26:53.000Z

"""Starts a fake fan, lightbulb, garage door and a TemperatureSensor """ import logging import signal import random from pyhap.accessory import Accessory, Bridge from pyhap.accessory_driver import AccessoryDriver from pyhap.const import (CATEGORY_FAN, CATEGORY_LIGHTBULB, CATEGORY_GARAGE_DOOR_OPENER, CATEGORY_SENSOR) logging.basicConfig(level=logging.INFO, format="[%(module)s] %(message)s") class TemperatureSensor(Accessory): """Fake Temperature sensor, measuring every 3 seconds.""" category = CATEGORY_SENSOR def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) serv_temp = self.add_preload_service('TemperatureSensor') self.char_temp = serv_temp.configure_char('CurrentTemperature') @Accessory.run_at_interval(3) async def run(self): self.char_temp.set_value(random.randint(18, 26)) class FakeFan(Accessory): """Fake Fan, only logs whatever the client set.""" category = CATEGORY_FAN def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # Add the fan service. Also add optional characteristics to it. serv_fan = self.add_preload_service( 'Fan', chars=['RotationSpeed', 'RotationDirection']) self.char_rotation_speed = serv_fan.configure_char( 'RotationSpeed', setter_callback=self.set_rotation_speed) self.char_rotation_direction = serv_fan.configure_char( 'RotationDirection', setter_callback=self.set_rotation_direction) def set_rotation_speed(self, value): logging.debug("Rotation speed changed: %s", value) def set_rotation_direction(self, value): logging.debug("Rotation direction changed: %s", value) class LightBulb(Accessory): """Fake lightbulb, logs what the client sets.""" category = CATEGORY_LIGHTBULB def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) serv_light = self.add_preload_service('Lightbulb') self.char_on = serv_light.configure_char( 'On', setter_callback=self.set_bulb) def set_bulb(self, value): logging.info("Bulb value: %s", value) class GarageDoor(Accessory): """Fake garage door.""" category = CATEGORY_GARAGE_DOOR_OPENER def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.add_preload_service('GarageDoorOpener')\ .configure_char( 'TargetDoorState', setter_callback=self.change_state) def change_state(self, value): logging.info("Bulb value: %s", value) self.get_service('GarageDoorOpener')\ .get_characteristic('CurrentDoorState')\ .set_value(value) def get_bridge(driver): bridge = Bridge(driver, 'Bridge') bridge.add_accessory(LightBulb(driver, 'Lightbulb')) bridge.add_accessory(FakeFan(driver, 'Big Fan')) bridge.add_accessory(GarageDoor(driver, 'Garage')) bridge.add_accessory(TemperatureSensor(driver, 'Sensor')) return bridge driver = AccessoryDriver(port=51826, persist_file='busy_home.state') driver.add_accessory(accessory=get_bridge(driver)) signal.signal(signal.SIGTERM, driver.signal_handler) driver.start()

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0

0.369231

0

null

0

null

0

1

0

93d680ecf48e6dbb1495bab46f68ebdbe3aea08b

574

py

Python

Backend/src/commercial/urls.py

ChristianTaborda/Energycorp

2447b5af211501450177b0b60852dcb31d6ca12d

[ "MIT" ]

1

2020-12-31T00:07:40.000Z

Backend/src/commercial/urls.py

ChristianTaborda/Energycorp

2447b5af211501450177b0b60852dcb31d6ca12d

[ "MIT" ]

null

Backend/src/commercial/urls.py

ChristianTaborda/Energycorp

2447b5af211501450177b0b60852dcb31d6ca12d

[ "MIT" ]

null

from django.urls import path from .views import ( # CRUDS CommercialList, CommercialDelete, CommercialDetail, CommercialCreate, CommercialUpdate, CommercialDelete, CommercialInactivate, # QUERY ) urlpatterns = [ #CRUD path('', CommercialList.as_view()), path('create/', CommercialCreate.as_view()), path('<pk>/', CommercialDetail.as_view()), path('update/<pk>/', CommercialUpdate.as_view()), path('inactivate/<pk>/', CommercialInactivate.as_view()), path('delete/<pk>', CommercialDelete.as_view()) #QUERY ]

22.076923

61

0.667247

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574

7.25

0.442308

0.095491

0.132626

0

0.182927

574

25

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0.803838

0.038328

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0.111111

0

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1

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false

0

0.111111

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0.111111

0

null

0

null

0

1

0

93d7e71a979233c8c73b2a4018aacf592bc1a08e

1,277

py

Python

migrations/versions/6e5e2b4c2433_add_hometasks_for_students.py

AnvarGaliullin/LSP

ed1f00ddc6346c5c141b421c7a3305e4c9e1b0d1

[ "MIT" ]

null

migrations/versions/6e5e2b4c2433_add_hometasks_for_students.py

AnvarGaliullin/LSP

ed1f00ddc6346c5c141b421c7a3305e4c9e1b0d1

[ "MIT" ]

null

migrations/versions/6e5e2b4c2433_add_hometasks_for_students.py

AnvarGaliullin/LSP

ed1f00ddc6346c5c141b421c7a3305e4c9e1b0d1

[ "MIT" ]

null

"""Add Hometasks for Students Revision ID: 6e5e2b4c2433 Revises: b9acba47fd53 Create Date: 2020-01-10 20:52:40.063133 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '6e5e2b4c2433' down_revision = 'b9acba47fd53' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('student_hometask', sa.Column('id', sa.Integer(), nullable=False), sa.Column('course_hometask_id', sa.Integer(), nullable=False), sa.Column('student_id', sa.Integer(), nullable=False), sa.Column('content', sa.String(length=100000), nullable=False), sa.Column('created_on', sa.DateTime(), nullable=True), sa.Column('updated_on', sa.DateTime(), nullable=True), sa.ForeignKeyConstraint(['course_hometask_id'], ['course_hometask.id'], ), sa.ForeignKeyConstraint(['student_id'], ['students.id'], ), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('course_hometask_id', 'student_id', name='_course_hometask_student_uniq_const') ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('student_hometask') # ### end Alembic commands ###

31.925

103

0.702428

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1,277

5.632258

0.425806

0.054983

0.068729

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0.270332

0.210767

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0

0.047619

0.144871

1,277

39

104

32.74359

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false

0

0.095238

0

0.190476

0

null

0

null

0

1

0

93d903dc4a4d4fc536ec37d420b4604d14554d90

1,759

py

Python

scripts/plotting.py

intelligent-soft-robots/o80_roboball2d

094d36f870b9c20ef5e05baf92ed8ed5b9a5277c

[ "BSD-3-Clause" ]

null

scripts/plotting.py

intelligent-soft-robots/o80_roboball2d

094d36f870b9c20ef5e05baf92ed8ed5b9a5277c

[ "BSD-3-Clause" ]

null

scripts/plotting.py

intelligent-soft-robots/o80_roboball2d

094d36f870b9c20ef5e05baf92ed8ed5b9a5277c

[ "BSD-3-Clause" ]

null

import time import math import fyplot import o80_roboball2d from functools import partial def _plot(frontend_robot,frontend_simulation): plt = fyplot.Plot("o80_roboball2d",50,(2000,800)) def get_observed_angle(frontend,dof): return frontend.read().get_observed_states().get(dof).get_position() def get_desired_angle(frontend,dof): return frontend.read().get_desired_states().get(dof).get_position() def get_frequency(frontend): return frontend.read().get_frequency() robot_plots = ( ( partial(get_observed_angle,frontend_robot,0) , (255,0,0) ) , ( partial(get_observed_angle,frontend_robot,1) , (0,255,0) ) , ( partial(get_observed_angle,frontend_robot,2) , (0,0,255) ) ) sim_plots = ( ( partial(get_desired_angle,frontend_simulation,0) , (255,0,0) ) , ( partial(get_desired_angle,frontend_simulation,1) , (0,255,0) ) , ( partial(get_desired_angle,frontend_simulation,2) , (0,0,255) ) ) frequency_plots = ( ( partial(get_frequency,frontend_robot) , (255,0,0) ), ( partial(get_frequency,frontend_simulation) , (0,255,0) ) ) plt.add_subplot((-1.5,0.2),300,robot_plots) plt.add_subplot((-1.5,0.2),300,sim_plots) plt.add_subplot((0,2100),300,frequency_plots) return plt def run(): real_robot = o80_roboball2d.RealRobotFrontEnd("real-robot") sim_robot = o80_roboball2d.MirroringFrontEnd("sim-robot") plot = _plot(real_robot,sim_robot) plot.start() try : while True: time.sleep(0.1) except KeyboardInterrupt: pass plot.stop() o80_example.stop_standalone(SEGMENT_ID) if __name__ == "__main__": run()

30.327586

84

0.651507

226

1,759

4.778761

0.261062

0.096296

0.023148

0.088889

0.427778

0.392593

0.303704

0.037037

0

0.067587

0.217737

1,759

57

85

30.859649

0.717297

0

0.023322

0

1

0.128205

false

0.025641

0.128205

0.076923

0.358974

0

null

0

null

0

1

0

93d96a3758d5ca27cf2434f779255814b61dd0c7

10,099

py

Python

kvm_pirate/elf/structs.py

Mic92/kvm-pirate

26626db320b385f51ccb88dad76209a812c40ca6

[ "MIT" ]

6

2020-12-15T04:26:43.000Z

2020-12-15T13:26:09.000Z

kvm_pirate/elf/structs.py

Mic92/kvm-pirate

26626db320b385f51ccb88dad76209a812c40ca6

[ "MIT" ]

null

kvm_pirate/elf/structs.py

Mic92/kvm-pirate

26626db320b385f51ccb88dad76209a812c40ca6

[ "MIT" ]

null

# # Copyright (C) 2018 The Android Open Source Project # # 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. # """This file contains ELF C structs and data types.""" import ctypes from typing import Any from . import consts # ELF data types. Elf32_Addr = ctypes.c_uint32 Elf32_Off = ctypes.c_uint32 Elf32_Half = ctypes.c_uint16 Elf32_Word = ctypes.c_uint32 Elf32_Sword = ctypes.c_int32 Elf64_Addr = ctypes.c_uint64 Elf64_Off = ctypes.c_uint64 Elf64_Half = ctypes.c_uint16 Elf64_Word = ctypes.c_uint32 Elf64_Sword = ctypes.c_int32 Elf64_Xword = ctypes.c_uint64 Elf64_Sxword = ctypes.c_int64 # ELF C structs. class CStructure(ctypes.LittleEndianStructure): """Little endian C structure base class.""" pass class CUnion(ctypes.Union): """Native endian C union base class.""" pass class _Ehdr(CStructure): """ELF header base class.""" def GetFileClass(self) -> Any: """Returns the file class.""" return self.e_ident[consts.EI_CLASS] def GetDataEncoding(self) -> Any: """Returns the data encoding of the file.""" return self.e_ident[consts.EI_DATA] class Elf32_Ehdr(_Ehdr): """ELF 32-bit header.""" _fields_ = [ ("e_ident", ctypes.c_uint8 * consts.EI_NIDENT), ("e_type", Elf32_Half), ("e_machine", Elf32_Half), ("e_version", Elf32_Word), ("e_entry", Elf32_Addr), ("e_phoff", Elf32_Off), ("e_shoff", Elf32_Off), ("e_flags", Elf32_Word), ("e_ehsize", Elf32_Half), ("e_phentsize", Elf32_Half), ("e_phnum", Elf32_Half), ("e_shentsize", Elf32_Half), ("e_shnum", Elf32_Half), ("e_shstrndx", Elf32_Half), ] class Elf64_Ehdr(_Ehdr): """ELF 64-bit header.""" _fields_ = [ ("e_ident", ctypes.c_uint8 * consts.EI_NIDENT), ("e_type", Elf64_Half), ("e_machine", Elf64_Half), ("e_version", Elf64_Word), ("e_entry", Elf64_Addr), ("e_phoff", Elf64_Off), ("e_shoff", Elf64_Off), ("e_flags", Elf64_Word), ("e_ehsize", Elf64_Half), ("e_phentsize", Elf64_Half), ("e_phnum", Elf64_Half), ("e_shentsize", Elf64_Half), ("e_shnum", Elf64_Half), ("e_shstrndx", Elf64_Half), ] class Elf32_Shdr(CStructure): """ELF 32-bit section header.""" _fields_ = [ ("sh_name", Elf32_Word), ("sh_type", Elf32_Word), ("sh_flags", Elf32_Word), ("sh_addr", Elf32_Addr), ("sh_offset", Elf32_Off), ("sh_size", Elf32_Word), ("sh_link", Elf32_Word), ("sh_info", Elf32_Word), ("sh_addralign", Elf32_Word), ("sh_entsize", Elf32_Word), ] class Elf64_Shdr(CStructure): """ELF 64-bit section header.""" _fields_ = [ ("sh_name", Elf64_Word), ("sh_type", Elf64_Word), ("sh_flags", Elf64_Xword), ("sh_addr", Elf64_Addr), ("sh_offset", Elf64_Off), ("sh_size", Elf64_Xword), ("sh_link", Elf64_Word), ("sh_info", Elf64_Word), ("sh_addralign", Elf64_Xword), ("sh_entsize", Elf64_Xword), ] class Elf32_Dyn(CStructure): """ELF 32-bit dynamic section entry.""" class _Elf32_Dyn__d_un(CUnion): _fields_ = [("d_val", Elf32_Word), ("d_ptr", Elf32_Addr)] _fields_ = [("d_tag", Elf32_Sword), ("d_un", _Elf32_Dyn__d_un)] class Elf64_Dyn(CStructure): """ELF 64-bit dynamic section entry.""" class _Elf64_Dyn__d_un(CUnion): _fields_ = [("d_val", Elf64_Xword), ("d_ptr", Elf64_Addr)] _fields_ = [("d_tag", Elf64_Sxword), ("d_un", _Elf64_Dyn__d_un)] class _Sym(CStructure): """ELF symbol table entry base class.""" def GetBinding(self) -> Any: """Returns the symbol binding.""" return self.st_info >> 4 def GetType(self) -> Any: """Returns the symbol type.""" return self.st_info & 0xF def SetBinding(self, binding: int) -> None: """Sets the symbol binding. Args: binding: An integer specifying the new binding. """ self.SetSymbolAndType(binding, self.GetType()) def SetType(self, type_: int) -> None: """Sets the symbol type. Args: type_: An integer specifying the new type. """ self.SetSymbolAndType(self.GetBinding(), type_) def SetBindingAndType(self, binding: int, type_: int) -> None: """Sets the symbol binding and type. Args: binding: An integer specifying the new binding. type_: An integer specifying the new type. """ self.st_info = (binding << 4) | (type_ & 0xF) class Elf32_Sym(_Sym): """ELF 32-bit symbol table entry.""" _fields_ = [ ("st_name", Elf32_Word), ("st_value", Elf32_Addr), ("st_size", Elf32_Word), ("st_info", ctypes.c_uint8), ("st_other", ctypes.c_uint8), ("st_shndx", Elf32_Half), ] class Elf64_Sym(_Sym): """ELF 64-bit symbol table entry.""" _fields_ = [ ("st_name", Elf64_Word), ("st_info", ctypes.c_uint8), ("st_other", ctypes.c_uint8), ("st_shndx", Elf64_Half), ("st_value", Elf64_Addr), ("st_size", Elf64_Xword), ] class _32_Rel(CStructure): """ELF 32-bit relocation table entry base class.""" def GetSymbol(self) -> Any: """Returns the symbol table index with respect to the relocation. Symbol table index with respect to which the relocation must be made. """ return self.r_info >> 8 def GetType(self) -> Any: """Returns the relocation type.""" return self.r_info & 0xFF def SetSymbol(self, symndx: int) -> None: """Sets the relocation's symbol table index. Args: symndx: An integer specifying the new symbol table index. """ self.SetSymbolAndType(symndx, self.GetType()) def SetType(self, type_: int) -> None: """Sets the relocation type. Args: type_: An integer specifying the new relocation type. """ self.SetSymbolAndType(self.GetSymbol(), type_) def SetSymbolAndType(self, symndx: int, type_: int) -> None: """Sets the relocation's symbol table index and type. Args: symndx: An integer specifying the new symbol table index. type_: An integer specifying the new relocation type. """ self.r_info = (symndx << 8) | (type_ & 0xFF) class Elf32_Rel(_32_Rel): """ELF 32-bit relocation table entry.""" _fields_ = [("r_offset", Elf32_Addr), ("r_info", Elf32_Word)] class Elf32_Rela(_32_Rel): """ELF 32-bit relocation table entry with explicit addend.""" _fields_ = [ ("r_offset", Elf32_Addr), ("r_info", Elf32_Word), ("r_addend", Elf32_Sword), ] class _64_Rel(CStructure): """ELF 64-bit relocation table entry base class.""" def GetSymbol(self) -> Any: """Returns the symbol table index with respect to the relocation. Symbol table index with respect to which the relocation must be made. """ return self.r_info >> 32 def GetType(self) -> Any: """Returns the relocation type.""" return self.r_info & 0xFFFFFFFF def SetSymbol(self, symndx: int) -> None: """Sets the relocation's symbol table index. Args: symndx: An integer specifying the new symbol table index. """ self.SetSymbolAndType(symndx, self.GetType()) def SetType(self, type_: int) -> None: """Sets the relocation type. Args: type_: An integer specifying the new relocation type. """ self.SetSymbolAndType(self.GetSymbol(), type_) def SetSymbolAndType(self, symndx: int, type_: int) -> None: """Sets the relocation's symbol table index and type. Args: symndx: An integer specifying the new symbol table index. type_: An integer specifying the new relocation type. """ self.r_info = (symndx << 32) | (type_ & 0xFFFFFFFF) class Elf64_Rel(_64_Rel): """ELF 64-bit relocation table entry.""" _fields_ = [("r_offset", Elf64_Addr), ("r_info", Elf64_Xword)] class Elf64_Rela(_64_Rel): """ELF 64-bit relocation table entry with explicit addend.""" _fields_ = [ ("r_offset", Elf64_Addr), ("r_info", Elf64_Xword), ("r_addend", Elf64_Sxword), ] class Elf32_Phdr(CStructure): """ELF 32-bit program header.""" _fields_ = [ ("p_type", Elf32_Word), ("p_offset", Elf32_Off), ("p_vaddr", Elf32_Addr), ("p_paddr", Elf32_Addr), ("p_filesz", Elf32_Word), ("p_memsz", Elf32_Word), ("p_flags", Elf32_Word), ("p_align", Elf32_Word), ] class Elf64_Phdr(CStructure): """ELF 64-bit program header.""" _fields_ = [ ("p_type", Elf64_Word), ("p_flags", Elf64_Word), ("p_offset", Elf64_Off), ("p_vaddr", Elf64_Addr), ("p_paddr", Elf64_Addr), ("p_filesz", Elf64_Xword), ("p_memsz", Elf64_Xword), ("p_align", Elf64_Xword), ] class Elf32_Nhdr(CStructure): """ELF 32-bit note header.""" _fields_ = [ ("n_namesz", Elf32_Word), ("n_descsz", Elf32_Word), ("n_type", Elf32_Word), ] class Elf64_Nhdr(CStructure): """ELF 64-bit note header.""" _fields_ = [ ("n_namesz", Elf64_Word), ("n_descsz", Elf64_Word), ("n_type", Elf64_Word), ]

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1,263

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4.532067

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0.037736

0.039832

0.046122

0.472397

0.446366

0.391509

0.372991

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0.307652

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0.72531

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false

0.010363

0.015544

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0.352332

0

null

0

null

0

1

0

93db2131f51a021bb76ace2f9993a86a1d6b0e6b

469

py

Python

connect-2018/exercises/2018/lr-automation/cbr.py

cbcommunity/cb-connect

3ccfd1ed51e808f567f9f0fc4e8fe2688ef9ee76

[ "MIT" ]

5

2019-06-03T21:02:32.000Z

2020-12-01T08:59:50.000Z

connect-2018/exercises/2018/lr-automation/cbr.py

cbcommunity/cb-connect-2018

3ccfd1ed51e808f567f9f0fc4e8fe2688ef9ee76

[ "MIT" ]

null

connect-2018/exercises/2018/lr-automation/cbr.py

cbcommunity/cb-connect-2018

3ccfd1ed51e808f567f9f0fc4e8fe2688ef9ee76

[ "MIT" ]

1

2019-07-09T20:09:14.000Z

from cbapi.response import * from lrjob import run_liveresponse from cbapi.example_helpers import get_cb_response_object, build_cli_parser def main(): parser = build_cli_parser("Cb Response Live Response example") parser.add_argument("sensorid", nargs=1) args = parser.parse_args() c = get_cb_response_object(args) sensor = c.select(Sensor, int(args.sensorid[0])) run_liveresponse(sensor.lr_session()) if __name__ == '__main__': main()

26.055556

74

0.742004

65

469

4.984615

0.523077

0.092593

0.080247

0.117284

0

0.005051

0.15565

469

18

75

26.055556

0.813131

0

0.104255

0

1

0.083333

false

0

0.25

0

0.333333

0

null

0

null

0

1

0

93e13a546c607eee62ff4605caebeeafa51bfb7a

6,805

py

Python

pricePrediction/preprocessData/prepareDataMol2Price.py

rsanchezgarc/CoPriNet

33708a82746278270fd1aa600d4b562ea0f62c1c

[ "MIT" ]

null

pricePrediction/preprocessData/prepareDataMol2Price.py

rsanchezgarc/CoPriNet

33708a82746278270fd1aa600d4b562ea0f62c1c

[ "MIT" ]

null

pricePrediction/preprocessData/prepareDataMol2Price.py

rsanchezgarc/CoPriNet

33708a82746278270fd1aa600d4b562ea0f62c1c

[ "MIT" ]

1

2022-03-02T16:21:16.000Z

import gzip import os import re import sys import time from functools import reduce from itertools import chain from multiprocessing import cpu_count import lmdb import psutil import joblib from joblib import Parallel, delayed import numpy as np from pricePrediction import config from pricePrediction.config import USE_MMOL_INSTEAD_GRAM from pricePrediction.preprocessData.serializeDatapoints import getExampleId, serializeExample from pricePrediction.utils import tryMakedir, getBucketRanges, search_buckedId, EncodedDirNamesAndTemplates from .smilesToGraph import smiles_to_graph, compute_nodes_degree, fromPerGramToPerMMolPrice PER_WORKER_MEMORY_GB = 2 class DataBuilder(): def __init__(self, n_cpus= config.N_CPUS): if n_cpus is None: mem_gib = psutil.virtual_memory().available / (1024. ** 3) n_cpus = int(max(1, min(cpu_count(), mem_gib // PER_WORKER_MEMORY_GB))) self.n_cpus = n_cpus def processOneFileOfSmiles(self, encodedDir, fileNum, datasetSplit, fname): print("processing %s"%fname) if fname.endswith(".csv"): open_fun = open decode_line = lambda line: line elif fname.endswith(".csv.gz"): open_fun = gzip.open decode_line = lambda line : line.decode('utf-8') else: raise ValueError("Bad file format") cols = ['SMILES','price'] names = EncodedDirNamesAndTemplates(encodedDir) outFname_base = datasetSplit + "_" + str(fileNum) + "_lmdb" outFname = os.path.join(encodedDir, datasetSplit,outFname_base) env = lmdb.open(outFname, map_size=10737418240) degs = compute_nodes_degree(None) num_examples = 0 bucket_ranges = getBucketRanges() n_per_bucket = np.zeros(len(bucket_ranges), dtype= np.int64) with open_fun(fname) as f_in: header = decode_line(f_in.readline()).strip().split(",") try: smi_index, price_index = [ header.index(col) for col in cols] except ValueError: smi_index, price_index = 0,1 with env.begin(write=True) as sink, \ gzip.open(names.SELECTED_DATAPOINTS_TEMPLATE % (datasetSplit, fileNum), "wt") as f_out: f_out.write("SMILES,price\n") cur_time = time.time() for i, line in enumerate(f_in): lineArray = decode_line(line).strip().split(",") smi, price = lineArray[smi_index], lineArray[price_index] price = float(price) graph = smiles_to_graph(smi) if graph is None: continue #Save the original smiles-price f_out.write("%s,%s\n"%(smi, price)) # Use the per mmol price if USE_MMOL_INSTEAD_GRAM: price = fromPerGramToPerMMolPrice(price, smi) bucketId = search_buckedId( np.log(price), bucket_ranges) n_per_bucket[bucketId] += 1 degs += compute_nodes_degree([graph]) fileId= getExampleId(outFname_base, num_examples) sink.put(fileId, serializeExample(price, graph)) num_examples += 1 if i % 10000 == 0 and fileNum % self.n_cpus == 0: new_time = time.time() print("Current iteration: %d # task: %d (%.2f s) " % (i, fileNum, new_time - cur_time), end="\r") cur_time = new_time if fileNum % self.n_cpus == 0: print() return ((outFname, degs, num_examples, n_per_bucket),) def getNFeatures(self): one_graph = smiles_to_graph("CCCCCCO") print(one_graph) return dict(nodes_n_features=one_graph["x"].shape[-1], edges_n_features=one_graph["edge_attr"].shape[-1]) def prepareDataset(self, inputDir=config.DATASET_DIRNAME, encodedDir=config.ENCODED_DIR, datasetSplit="train", **kwargs): assert datasetSplit in ["train", "val", "test"] print("Computing %s dataset" % datasetSplit) print("Using %d workers for data preparation"%self.n_cpus) # os.environ["OMP_NUM_THREADS"] = "1" # os.environ["MKL_NUM_THREADS"] = "1" names = EncodedDirNamesAndTemplates(encodedDir) tryMakedir(encodedDir, remove=False) tryMakedir(os.path.join(encodedDir, datasetSplit)) tryMakedir(names.DIR_RAW_DATA_SELECTED) fnames = [os.path.join(inputDir, fname) for fname in os.listdir(inputDir) if re.match(config.RAW_DATA_FILE_SUFFIX, fname) and datasetSplit in fname] assert len(fnames) > 0 results = Parallel(n_jobs=self.n_cpus, batch_size=1, verbose=10)(delayed(self.processOneFileOfSmiles)(encodedDir, i, datasetSplit, fname) for i, fname in enumerate(fnames)) results = chain.from_iterable(results) results = list(results) # print( results ) fnames_list, degrees, sizes_lis, n_per_bucket = zip(*results) degrees = reduce(lambda prev, x: prev + x, degrees).numpy().tolist() n_per_bucket = reduce(lambda prev, x: prev + x, n_per_bucket) metadata_dict = {"name":datasetSplit, "fnames_list":fnames_list, "sizes_list": sizes_lis, "total_size": sum(sizes_lis)} metadata_dict.update(self.getNFeatures()) joblib.dump(metadata_dict, names.DATASET_METADATA_FNAME_TEMPLATE % datasetSplit) joblib.dump(degrees, names.DEGREES_FNAME) # print(n_per_bucket) joblib.dump({"bucket_ranges": getBucketRanges(), "n_per_bucket":n_per_bucket}, names.BUCKETS_FNAME_TEMPLATE % datasetSplit) print("Dataset %s computed" % datasetSplit) return fnames_list if __name__ == "__main__": print( " ".join(sys.argv)) import argparse parser = argparse.ArgumentParser() parser.add_argument("-i", "--inputDir", type=str, default=config.DATASET_DIRNAME, help="Directory where smiles-price pairs are located") parser.add_argument("-o", "--encodedDir", type=str, default=config.ENCODED_DIR) parser.add_argument("-n", "--ncpus", type=int, default=config.N_CPUS) args = vars( parser.parse_args()) config.N_CPUS = args.get("ncpus", config.N_CPUS) dataBuilder = DataBuilder(n_cpus=config.N_CPUS) dataBuilder.prepareDataset(datasetSplit="train", **args) dataBuilder.prepareDataset(datasetSplit="val", **args) dataBuilder.prepareDataset(datasetSplit="test", **args) ''' python -m pricePrediction.preprocessData.prepareDataMol2Price '''

42.006173

140

0.627039

778

6,805

5.272494

0.316195

0.018284

0.021941

0.008776

0.07411

0.042418

0

0.008842

0.268773

6,805

162

141

42.006173

0.815514

0.023806

0

0.016393

0

0.063052

0

0.016393

1

0.032787

false

0

0.155738

0

0.221311

0.065574

0

null

0

null

0

1

0

93e2b831da7ddd82cdee3f6c7c6866a56f385beb

2,894

py

Python

lanzou/gui/workers/more.py

WaterLemons2k/lanzou-gui

f5c57f980ee9a6d47164a39b90d82eb0391ede8b

[ "MIT" ]

1,093

2019-12-25T10:42:34.000Z

2022-03-28T22:35:32.000Z

lanzou/gui/workers/more.py

Enrontime/lanzou-gui

8e89438d938ee4994a4118502c3f14d467b55acc

[ "MIT" ]

116

2019-12-24T04:01:43.000Z

2022-03-26T16:12:41.000Z

lanzou/gui/workers/more.py

Enrontime/lanzou-gui

8e89438d938ee4994a4118502c3f14d467b55acc

[ "MIT" ]

188

2020-01-11T14:17:13.000Z

2022-03-29T09:18:34.000Z

from PyQt5.QtCore import QThread, pyqtSignal, QMutex from lanzou.api import LanZouCloud from lanzou.gui.models import Infos from lanzou.debug import logger class GetMoreInfoWorker(QThread): '''获取文件直链、文件(夹)提取码描述，用于登录后显示更多信息''' infos = pyqtSignal(object) share_url = pyqtSignal(object) dl_link = pyqtSignal(object) msg = pyqtSignal(str, int) def __init__(self, parent=None): super(GetMoreInfoWorker, self).__init__(parent) self._disk = None self._infos = None self._url = '' self._pwd = '' self._emit_link = False self._mutex = QMutex() self._is_work = False def set_disk(self, disk): self._disk = disk def set_values(self, infos, emit_link=False): self._infos = infos self._emit_link = emit_link self.start() def get_dl_link(self, url, pwd): self._url = url self._pwd = pwd self.start() def __del__(self): self.wait() def stop(self): self._mutex.lock() self._is_work = False self._mutex.unlock() def run(self): # infos: ID/None，文件名，大小，日期，下载次数(dl_count)，提取码(pwd)，描述(desc)，|链接(share-url) if not self._is_work and self._infos: self._mutex.lock() self._is_work = True try: if not self._url: # 获取普通信息 if isinstance(self._infos, Infos): if self._infos.id: # 从 disk 运行 self.msg.emit("网络请求中，请稍候……", 0) _info = self._disk.get_share_info(self._infos.id, is_file=self._infos.is_file) self._infos.desc = _info.desc self._infos.pwd = _info.pwd self._infos.url = _info.url if self._emit_link: self.share_url.emit(self._infos) else: self.infos.emit(self._infos) self.msg.emit("", 0) # 删除提示信息 else: # 获取下载直链 res = self._disk.get_file_info_by_url(self._url, self._pwd) if res.code == LanZouCloud.SUCCESS: self.dl_link.emit("{}".format(res.durl or "无")) # 下载直链 elif res.code == LanZouCloud.NETWORK_ERROR: self.dl_link.emit("网络错误！获取失败") # 下载直链 else: self.dl_link.emit("其它错误！") # 下载直链 except TimeoutError: self.msg.emit("网络超时！稍后重试", 6000) except Exception as e: logger.error(f"GetMoreInfoWorker error: e={e}") self._is_work = False self._url = '' self._pwd = '' self._mutex.unlock() else: self.msg.emit("后台正在运行，请稍后重试！", 3100)

34.86747

106

0.516586

332

2,894

4.268072

0.325301

0.095272

0.035286

0.02964

0.074806

0.032463

0

0.006101

0.376987

2,894

82

107

35.292683

0.776484

0.051486

0

0.242857

0

0.029283

0

1

0.1

false

0

0.057143

0

0.228571

0

null

0

null

0

1

0

93e5d68b70881e1e29365acb06e52f0fb4bc0b36

2,331

py

Python

neuro_logging/__init__.py

neuro-inc/neuro-logging

e3173a40d0e2559f113f1420ed8a3fd4a0e76dde

[ "Apache-2.0" ]

null

neuro_logging/__init__.py

neuro-inc/neuro-logging

e3173a40d0e2559f113f1420ed8a3fd4a0e76dde

[ "Apache-2.0" ]

50

2021-08-20T00:10:05.000Z

2022-02-21T16:44:46.000Z

neuro_logging/__init__.py

neuro-inc/neuro-logging

e3173a40d0e2559f113f1420ed8a3fd4a0e76dde

[ "Apache-2.0" ]

null

import logging import logging.config import os from importlib.metadata import version from typing import Any, Union from .trace import ( make_request_logging_trace_config, make_sentry_trace_config, make_zipkin_trace_config, new_sampled_trace, new_trace, new_trace_cm, notrace, setup_sentry, setup_zipkin, setup_zipkin_tracer, trace, trace_cm, ) __version__ = version(__package__) __all__ = [ "init_logging", "HideLessThanFilter", "make_request_logging_trace_config", "make_sentry_trace_config", "make_zipkin_trace_config", "notrace", "setup_sentry", "setup_zipkin", "setup_zipkin_tracer", "trace", "trace_cm", "new_sampled_trace", "new_trace", "new_trace_cm", ] class HideLessThanFilter(logging.Filter): def __init__(self, level: Union[int, str] = logging.ERROR, name: str = ""): super().__init__(name) if not isinstance(level, int): try: level = logging._nameToLevel[level] except KeyError: raise ValueError(f"Unknown level name: {level}") self.level = level def filter(self, record: logging.LogRecord) -> bool: return record.levelno < self.level if "NP_LOG_LEVEL" in os.environ: _default_log_level = logging.getLevelName(os.environ["NP_LOG_LEVEL"]) else: _default_log_level = logging.WARNING DEFAULT_CONFIG = { "version": 1, "disable_existing_loggers": False, "formatters": { "standard": {"format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s"} }, "filters": { "hide_errors": {"()": f"{__name__}.HideLessThanFilter", "level": "ERROR"} }, "handlers": { "stdout": { "class": "logging.StreamHandler", "level": "DEBUG", "formatter": "standard", "stream": "ext://sys.stdout", "filters": ["hide_errors"], }, "stderr": { "class": "logging.StreamHandler", "level": "ERROR", "formatter": "standard", "stream": "ext://sys.stderr", }, }, "root": {"level": _default_log_level, "handlers": ["stderr", "stdout"]}, } def init_logging(config: dict[str, Any] = DEFAULT_CONFIG) -> None: logging.config.dictConfig(config)

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0.607465

243

2,331

5.473251

0.36214

0.049624

0.045113

0.034586

0.287218

0.243609

0.193985

0

0.000575

0.253968

2,331

91

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25.615385

0.764232

0

0.050633

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0.277134

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1

0.037975

false

0

0.075949

0.012658

0.139241

0

null

0

null

0

1

0

93e8008d69beb181243428fecbcab6a20eb6cce6

3,628

py

Python

quantrocket/houston.py

Jay-Jay-D/quantrocket-client

b70ac199382d22d56fad923ca2233ce027f3264a

[ "Apache-2.0" ]

null

quantrocket/houston.py

Jay-Jay-D/quantrocket-client

b70ac199382d22d56fad923ca2233ce027f3264a

[ "Apache-2.0" ]

null

quantrocket/houston.py

Jay-Jay-D/quantrocket-client

b70ac199382d22d56fad923ca2233ce027f3264a

[ "Apache-2.0" ]

1

2019-06-12T11:34:27.000Z

# Copyright 2017 QuantRocket - All Rights Reserved # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import requests from .exceptions import ImproperlyConfigured from quantrocket.cli.utils.output import json_to_cli class Houston(requests.Session): """ Subclass of `requests.Session` that provides an interface to the houston API gateway. Reads HOUSTON_URL (and Basic Auth credentials if applicable) from environment variables and applies them to each request. Simply provide the path, starting with /, for example: >>> response = houston.get("/countdown/crontab") Since each instance of Houston is a session, you can improve performance by using a single session for all requests. The module provides an instance of `Houston`, named `houston`. Use the same session as other requests: >>> from quantrocket.houston import houston Use a new session: >>> from quantrocket.houston import Houston >>> houston = Houston() """ DEFAULT_TIMEOUT = 30 def __init__(self): super(Houston, self).__init__() if "HOUSTON_USERNAME" in os.environ and "HOUSTON_PASSWORD" in os.environ: self.auth = (os.environ["HOUSTON_USERNAME"], os.environ["HOUSTON_PASSWORD"]) @property def base_url(self): if "HOUSTON_URL" not in os.environ: raise ImproperlyConfigured("HOUSTON_URL is not set") return os.environ["HOUSTON_URL"] def request(self, method, url, *args, **kwargs): if url.startswith('/'): url = self.base_url + url timeout = kwargs.get("timeout", None) stream = kwargs.get("stream", None) if timeout is None and not stream: kwargs["timeout"] = self.DEFAULT_TIMEOUT # Move conids from params to data if too long conids = kwargs.get("params", {}).get("conids", None) if conids and isinstance(conids, list) and len(conids) > 1: data = kwargs.get("data", {}) or {} data["conids"] = conids kwargs["params"].pop("conids") kwargs["data"] = data return super(Houston, self).request(method, url, *args, **kwargs) @staticmethod def raise_for_status_with_json(response): """ Raises 400/500 error codes, attaching a json response to the exception, if possible. """ try: response.raise_for_status() except requests.exceptions.HTTPError as e: try: e.json_response = response.json() e.args = e.args + (e.json_response,) except: e.json_response = {} e.args = e.args + ("please check the logs for more details",) raise e # Instantiate houston so that all callers can share a TCP connection (for # performance's sake) houston = Houston() def ping(): """ Pings houston. Returns ------- json reply from houston """ response = houston.get("/ping") houston.raise_for_status_with_json(response) return response.json() def _cli_ping(): return json_to_cli(ping)

33.284404

88

0.651599

463

3,628

5.021598

0.386609

0.025806

0.014194

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false

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0.333333

0

null

0

null

0

1

0

93e81c0784cf18fea2ad26a23da9cc7f264a20a2

3,778

py

Python

src/Sudoku/SudokuGenerator.py

andrea-pollastro/Sudoku

84d82c9a181ad87f782efe7489fa28da70993590

[ "MIT" ]

1

2020-01-09T10:48:47.000Z

src/Sudoku/SudokuGenerator.py

andrea-pollastro/Sudoku

84d82c9a181ad87f782efe7489fa28da70993590

[ "MIT" ]

null

src/Sudoku/SudokuGenerator.py

andrea-pollastro/Sudoku

84d82c9a181ad87f782efe7489fa28da70993590

[ "MIT" ]

null

from random import shuffle from math import sqrt from enum import Enum from src.Sudoku.SudokuSolver import SudokuSolver from src.Sudoku.Sudoku import Sudoku """ **** SUDOKU GENERATOR **** Author: Andrea Pollastro Date: September 2018 """ class SudokuGenerator: __solver = SudokuSolver() def createSudoku(self, dimension, difficulty): """This method returns a Sudoku with a unique solution. Parameters are used to specify the dimension and the difficulty.""" if not isinstance(dimension, SudokuDimension) and not isinstance(difficulty, SudokuDimension): return False sudoku = list() # grid self.__fillSudoku(sudoku,0,_SupportStructures(dimension.value)) blanksIndexes = [i for i in range(0,len(sudoku))] shuffle(blanksIndexes) sudoku = self.__createBlanks(sudoku, difficulty.value, blanksIndexes, 0) return Sudoku(sudoku) def __fillSudoku(self, sudoku, cell, supportStructures): """This functions works recursively to create a complete Sudoku. It randomly assigns a value to cells. If a contradiction comes (specifically, when there aren't values to assign to a cell), it comes back to the last valid configuration.""" values = supportStructures.getValidValues(cell) if(len(values) == 0): return False shuffle(values) for n in values: sudoku.append(n) supportStructures.addValue(n,cell) if((len(sudoku) == supportStructures.getSudokuDimension()**2) # sudoku is complete or self.__fillSudoku(sudoku,cell+1,supportStructures)): return True sudoku.pop() supportStructures.removeValue(n,cell) return False def __createBlanks(self, sudoku, blanks, validValues, idx): """This functions creates blanks into 'sudoku' to make it playable. For any blanks, it checks if there's a unique solution. If it's not, it restores the last blank and chooses another cell.""" if blanks == 0: return sudoku for i in range(idx,len(validValues)): index = validValues[i] oldValue = sudoku[index] sudoku[index] = 0 if self.__solver.hasUniqueSolution(sudoku) and self.__createBlanks(sudoku, blanks-1, validValues, i+1): return sudoku sudoku[index] = oldValue return False class SudokuDifficulties(Enum): EASY = 43 MEDIUM = 50 HARD = 58 EXPERT = 61 class SudokuDimension(Enum): CLASSIC = 9 class _SupportStructures: def __init__(self, dimension): self.__DIM = dimension self.__BOXDIM = int(sqrt(dimension)) self.__rows = [set() for x in range(0, dimension)] self.__cols = [set() for x in range(0, dimension)] self.__boxes = [set() for x in range(0, dimension)] def getValidValues(self, cell): values = {x for x in range(1, self.__DIM +1)} r, c, b = self.__getCoordinates(cell) return list(values - (self.__rows[r] | self.__cols[c] | self.__boxes[b])) def addValue(self, value, cell): r, c, b = self.__getCoordinates(cell) self.__rows[r].add(value) self.__cols[c].add(value) self.__boxes[b].add(value) def removeValue(self, value, cell): r, c, b = self.__getCoordinates(cell) self.__rows[r].remove(value) self.__cols[c].remove(value) self.__boxes[b].remove(value) def __getCoordinates(self, cell): r = int(cell / self.__DIM) c = cell % self.__DIM b = int(r / self.__BOXDIM) * self.__BOXDIM + int(c / self.__BOXDIM) return r,c,b def getSudokuDimension(self): return self.__DIM

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0.636051

456

3,778

5.111842

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0.018018

0.013728

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0.085371

0.074646

0.06435

0.040326

0

0.010083

0.264955

3,778

106

118

35.641509

0.829312

0.149021

0

0.118421

0

1

0.118421

false

0

0.065789

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0

null

0

null

0

1

0

93e8893408bea136d9cbb5e2c4e9cac3b5b0c2f9

15,753

py

Python

stanza/coptic.py

CopticScriptorium/stanza

a16b152fce3d2cc325b7d67e03952bd00c878fe3

[ "Apache-2.0" ]

null

stanza/coptic.py

CopticScriptorium/stanza

a16b152fce3d2cc325b7d67e03952bd00c878fe3

[ "Apache-2.0" ]

null

stanza/coptic.py

CopticScriptorium/stanza

a16b152fce3d2cc325b7d67e03952bd00c878fe3

[ "Apache-2.0" ]

null

import argparse import random, os from os.path import join as j from collections import OrderedDict import conllu import torch import pathlib import tempfile import depedit import stanza.models.parser as parser from stanza.models.depparse.data import DataLoader from stanza.models.depparse.trainer import Trainer from stanza.models.common import utils from stanza.models.common.pretrain import Pretrain from stanza.models.common.doc import * from stanza.utils.conll import CoNLL PACKAGE_BASE_DIR = pathlib.Path(__file__).parent.absolute() # Parser arguments ----------------------------------------------------------------------------------------------------- # These args are used by stanza.models.parser. Keys should always exactly match those you'd get from the dictionary # obtained from running stanza.models.parser.parse_args(). The values below were selected through hyperoptimization. DEFAULT_PARSER_ARGS = { # general setup 'lang': 'cop', 'treebank': 'cop_scriptorium', 'shorthand': 'cop_scriptorium', 'data_dir': j(PACKAGE_BASE_DIR, 'data', 'depparse'), 'output_file': j(PACKAGE_BASE_DIR, 'coptic_data', 'scriptorium', 'pred.conllu'), 'seed': 1234, 'cuda': torch.cuda.is_available(), 'cpu': not torch.cuda.is_available(), 'save_dir': j(PACKAGE_BASE_DIR, "..", 'stanza_models'), 'save_name': None, # word embeddings 'pretrain': True, 'wordvec_dir': j(PACKAGE_BASE_DIR, 'coptic_data', 'wordvec'), 'wordvec_file': j(PACKAGE_BASE_DIR, 'coptic_data', 'wordvec', 'word2vec', 'Coptic', 'coptic_50d.vec.xz'), 'word_emb_dim': 50, 'word_dropout': 0.3, # char embeddings 'char': True, 'char_hidden_dim': 200, 'char_emb_dim': 50, 'char_num_layers': 1, 'char_rec_dropout': 0, # very slow! # pos tags 'tag_emb_dim': 5, 'tag_type': 'gold', # network params 'hidden_dim': 300, 'deep_biaff_hidden_dim': 200, 'composite_deep_biaff_hidden_dim': 100, 'transformed_dim': 75, 'num_layers': 3, 'pretrain_max_vocab': 250000, 'dropout': 0.5, 'rec_dropout': 0, # very slow! 'linearization': True, 'distance': True, # training 'sample_train': 1.0, 'optim': 'adam', 'lr': 0.002, 'beta2': 0.95, 'max_steps': 20000, 'eval_interval': 100, 'max_steps_before_stop': 2000, 'batch_size': 1500, 'max_grad_norm': 1.0, 'log_step': 20, # these need to be included or there will be an error when stanza tries to access them 'train_file': None, 'eval_file': None, 'gold_file': None, 'mode': None, } # Custom features ------------------------------------------------------------------------------------------------------ # Params for controlling the custom features we're feeding the network FEATURE_CONFIG = { # BIOLU or BIO 'features': [ 'foreign_word', 'morph_count', 'left_morph', 'entity', ], 'foreign_word_binary': True, 'morph_count_binary': False, 'entity_encoding_scheme': 'BIOLU', 'entity_dropout': 0.30, } # DepEdit preprocessor which removes gold morph data and makes a few other tweaks PREPROCESSOR = depedit.DepEdit(config_file=j(PACKAGE_BASE_DIR, "coptic_data", "depedit", "add_ud_and_flat_morph.ini"), options=type('', (), {"quiet": True, "kill": "both"})) # Load a lexicon of foreign words and initialize a lemma cache with open(j(PACKAGE_BASE_DIR, 'coptic_data', 'lang_lexicon.tab'), 'r', encoding="utf8") as f: FOREIGN_WORDS = {x.split('\t')[0]: x.split('\t')[1].rstrip() for x in f.readlines() if '\t' in x} FW_CACHE = {} # load known entities and sort in order of increasing token length with open(j(PACKAGE_BASE_DIR, 'coptic_data', 'entities.tab'), 'r', encoding="utf8") as f: KNOWN_ENTITIES = OrderedDict(sorted( ((x.split('\t')[0], x.split('\t')[1]) for x in f.readlines()), key=lambda x: len(x[0].split(" ")) )) def _add_entity_feature(feature_config, sentences, predict=False): # unless we're predicting, use dropout to pretend we don't know some entities dropout_entities = { estr: etype for estr, etype in KNOWN_ENTITIES.items() # three ways for an entity to not get dropped out: # 1. we're predicting (all tokens stay) # 2. it has only one token # 3. we roll above the dropout threshold if (predict or (' ' not in estr) or (random.random() >= feature_config['entity_dropout'])) } def find_span_matches(tokens, pattern): slen = len(pattern) matches = [] for i in range(len(tokens) - (slen - 1)): if tokens[i:i + slen] == pattern: matches.append((i, slen)) return matches def delete_conflicting(new_span, entities, entity_tags): overlap_exists = lambda range1, range2: set(range1).intersection(range2) span = lambda begin, length: list(range(begin, begin + length)) new_span = span(*new_span) for i in range(len(entities) - 1, -1, -1): begin, length, _ = entities[i] # in case of overlap, remove the old entity and pop it off the list old_span = span(begin, length) if overlap_exists(new_span, old_span): for j in old_span: entity_tags[j] = "O" entities.pop(i) def encode(new_span, entity_tags, entity_type): assert feature_config['entity_encoding_scheme'] in ["BIOLU", "BIO"] if feature_config['entity_encoding_scheme'] == "BIOLU": unit_tag = "U-" begin_tag = "B-" inside_tag = "I-" last_tag = "L-" else: unit_tag = "B-" begin_tag = "B-" inside_tag = "I-" last_tag = "I-" begin, length = new_span if length == 1: entity_tags[begin] = unit_tag + entity_type else: for i in range(begin, begin + length): if i == begin: entity_tags[i] = begin_tag + entity_type elif i == (begin + length - 1): entity_tags[i] = last_tag + entity_type else: entity_tags[i] = inside_tag + entity_type # use BIOLU encoding for entities https://github.com/taasmoe/BIO-to-BIOLU # in case of nesting, longer entity wins for sentence in sentences: tokens = [t['form'] for t in sentence] entity_tags = (['O'] * len(tokens)) entities = [] for entity_string, entity_type in dropout_entities.items(): new_spans = find_span_matches(tokens, entity_string.split(" ")) for new_span in new_spans: delete_conflicting(new_span, entities, entity_tags) encode(new_span, entity_tags, entity_type) entities.append((new_span[0], new_span[1], entity_type)) for token, entity_tag in zip(sentence, entity_tags): token['feats']['Entity'] = entity_tag def _add_morph_count_feature(feature_config, sentences, predict=False): for sentence in sentences: for token in sentence: feats = token['feats'] misc = token['misc'] feats['MorphCount'] = ( '1' if misc is None or 'Morphs' not in misc else ( 'Many' if feature_config['morph_count_binary'] else str(len(misc['Morphs'].split('-'))) ) ) token['feats'] = feats return sentences def _add_left_morph_feature(feature_config, sentences, predict=False): for sentence in sentences: for token in sentence: feats = token['feats'] misc = token['misc'] if misc is not None and 'Morphs' in misc: feats['LeftMorph'] = misc['Morphs'].split('-')[0] token['feats'] = feats return sentences def _add_foreign_word_feature(feature_config, sentences, predict=False): def foreign_word_lookup(lemma): if lemma in FW_CACHE: return FW_CACHE[lemma] for fw, lang in FOREIGN_WORDS.items(): glob_start = fw[0] == '*' glob_end = fw[-1] == '*' fw = fw.replace('*', '') if glob_start and glob_end and fw in lemma: FW_CACHE[lemma] = lang return lang elif glob_start and lemma.endswith(fw): FW_CACHE[lemma] = lang return lang elif glob_end and lemma.startswith(fw): FW_CACHE[lemma] = lang return lang elif lemma == fw: FW_CACHE[lemma] =lang return lang FW_CACHE[lemma] = False return False for sentence in sentences: for token in sentence: feats = token['feats'] lang_of_origin = foreign_word_lookup(token['lemma']) feats['ForeignWord'] = ( 'No' if not lang_of_origin else ( 'Yes' if feature_config['foreign_word_binary'] else lang_of_origin )) token['feats'] = feats return sentences FEATURE_FUNCTIONS = { 'foreign_word': _add_foreign_word_feature, 'left_morph': _add_left_morph_feature, 'morph_count': _add_morph_count_feature, 'entity': _add_entity_feature, } def _preprocess(feature_config, conllu_string, predict): # remove gold information s = PREPROCESSOR.run_depedit(conllu_string) # deserialize so we can add custom features sentences = conllu.parse(s) for sentence in sentences: for token in sentence: if token['feats'] is None: token['feats'] = OrderedDict() for feature_name in feature_config['features']: assert feature_name in FEATURE_FUNCTIONS.keys() FEATURE_FUNCTIONS[feature_name](feature_config, sentences, predict=predict) # serialize and return return "".join([sentence.serialize() for sentence in sentences]) def _read_conllu_arg(conllu_filepath_or_string, feature_config, gold=False, predict=False): try: conllu.parse(conllu_filepath_or_string) s = conllu_filepath_or_string except: try: with open(conllu_filepath_or_string, 'r', encoding="utf8") as f: s = f.read() conllu.parse(s) except: raise Exception(f'"{conllu_filepath_or_string}" must either be a valid conllu string ' f'or a filepath to a valid conllu string') if not gold: s = _preprocess(feature_config, s, predict) tempf = tempfile.NamedTemporaryFile(mode='w', encoding='utf-8', delete=False) tempf.write(s) tempf.close() return tempf.name # public api ----------------------------------------------------------------------------------------------------------- def train(train, dev, save_name=None): """Train a new stanza model. :param train: either a conllu string or a path to a conllu file :param dev: either a conllu string or a path to a conllu file :param save_name: optional, a name for your model's save file, which will appear in 'stanza_models/' """ args = DEFAULT_PARSER_ARGS.copy() feature_config = FEATURE_CONFIG.copy() args['mode'] = 'train' args['train_file'] = _read_conllu_arg(train, feature_config) args['eval_file'] = _read_conllu_arg(dev, feature_config) args['gold_file'] = _read_conllu_arg(dev, feature_config, gold=True) if save_name: args['save_name'] = save_name parser.train(args) def test(test, save_name=None): """Evaluate using an existing stanza model. :param test: either a conllu string or a path to a conllu file :param save_name: optional, a name for your model's save file, which will appear in 'stanza_models/' """ args = DEFAULT_PARSER_ARGS.copy() feature_config = FEATURE_CONFIG.copy() args['mode'] = "predict" args['eval_file'] = _read_conllu_arg(test, feature_config) args['gold_file'] = _read_conllu_arg(test, feature_config, gold=True) if save_name: args['save_name'] = save_name return parser.evaluate(args) class Predictor: """Wrapper class so model can sit in memory for multiple predictions""" def __init__(self, args=None, feature_config=None): if args is None: args = DEFAULT_PARSER_ARGS.copy() if feature_config is None: self.feature_config = FEATURE_CONFIG.copy() model_file = args['save_dir'] + '/' + args['save_name'] if args['save_name'] is not None \ else '{}/{}_parser.pt'.format(args['save_dir'], args['shorthand']) # load pretrain; note that we allow the pretrain_file to be non-existent pretrain_file = '{}/{}.pretrain.pt'.format(args['save_dir'], args['shorthand']) self.pretrain = Pretrain(pretrain_file) # load model print("Loading model from: {}".format(model_file)) use_cuda = args['cuda'] and not args['cpu'] self.trainer = Trainer(pretrain=self.pretrain, model_file=model_file, use_cuda=use_cuda) self.loaded_args, self.vocab = self.trainer.args, self.trainer.vocab self.batch_size = args['batch_size'] # load config for k in args: if k.endswith('_dir') or k.endswith('_file') or k in ['shorthand'] or k == 'mode': self.loaded_args[k] = args[k] def predict(self, eval_file_or_string): eval_file = _read_conllu_arg(eval_file_or_string, self.feature_config, predict=True) doc = Document(CoNLL.conll2dict(input_file=eval_file)) batch = DataLoader( doc, self.batch_size, self.loaded_args, self.pretrain, vocab=self.vocab, evaluation=True, sort_during_eval=True ) preds = [] if len(batch) > 0: for i, b in enumerate(batch): preds += self.trainer.predict(b) preds = utils.unsort(preds, batch.data_orig_idx) batch.doc.set([HEAD, DEPREL], [y for x in preds for y in x]) doc_conll = CoNLL.convert_dict(batch.doc.to_dict()) conll_string = CoNLL.conll_as_string(doc_conll) return conll_string def _hyperparam_search(): args = DEFAULT_PARSER_ARGS.copy() def trial(args): train(args) las = test(args) return las # most trials seem to converge by 6000 args['max_steps'] = 6000 from hyperopt import hp, fmin, Trials, STATUS_OK, tpe from hyperopt.pyll import scope # params to search for space = { 'optim': hp.choice('optim', ['sgd', 'adagrad', 'adam', 'adamax']), 'hidden_dim': scope.int(hp.quniform('hidden_dim', 150, 400, 50)), } # f to minimize def f(opted_args): new_args = args.copy() new_args.update(opted_args) print("Trial with args:", opted_args) return {'loss': 1 - trial(new_args), 'status': STATUS_OK} trials = Trials() best = fmin(f, space, algo=tpe.suggest, max_evals=200, trials=trials) print("\nBest parameters:\n" + 30 * "=") print(best) trials = [t for t in trials] print("\n\nRaw trial output") for tt in trials: print(tt) print("\n\n") print("\nTrials:\n") for i, tt in enumerate(trials): if i == 0: print("LAS\t" + "\t".join(list(tt['misc']['vals'].keys()))) vals = map(lambda x: str(x[0]), tt['misc']['vals'].values()) las = str(1 - tt['result']['loss']) print('\t'.join([las, "\t".join(vals)]))

35.320628

120

0.599314

2,004

15,753

4.513972

0.221058

0.041676

0.013929

0.013266

0.234579

0.195888

0.180853

0.126907

0.086226

0

0.011084

0.266933

15,753

445

121

35.4

0.772255

0.142386

0

0.159639

0

0.129719

0.014372

0

0.006024

1

0.051205

false

0

0.054217

0

0.156627

0.03012

0

null

0

null

0

1

0

93ebca1d1f1083aaf12c3d720e9e95e6ae2564e1

11,432

py

Python

diff.py

JohnAgapeyev/binary_diff

4e8a1eb9540af134375f171e4a5a8781d042043d

[ "MIT" ]

null

diff.py

JohnAgapeyev/binary_diff

4e8a1eb9540af134375f171e4a5a8781d042043d

[ "MIT" ]

null

diff.py

JohnAgapeyev/binary_diff

4e8a1eb9540af134375f171e4a5a8781d042043d

[ "MIT" ]

null

#!/bin/python3 import sys import os import getopt import csv import json import itertools import zipfile import tarfile import binwalk import collections from heapq import nsmallest from collections import defaultdict import tlsh import numpy as np import matplotlib.pyplot as plt from multiprocessing.dummy import Pool from sklearn.cluster import * from sklearn import metrics from sklearn.datasets.samples_generator import make_blobs from sklearn.preprocessing import StandardScaler from sklearn.externals import joblib pool = Pool() def usage(): print("python3 ./diff.py [file directory] [metadata file]") def from_matrix_to_vector(i, j, N): if i <= j: return i * N - (i - 1) * i / 2 + j - i else: return j * N - (j - 1) * j / 2 + i - j def partition_hashes(hash_list, file_list): output = {} for h in hash_list: filename = file_list[hash_list.index(h)] quartile_range = int(h[8:10], 16) if quartile_range not in output: output[quartile_range] = [(filename, h)] else: output[quartile_range].append((filename, h)) return output #Values are from the TLSH paper def convert_dist_to_confidence(d): if d < 30: return 99.99819 elif d < 40: return 99.93 elif d < 50: return 99.48 elif d < 60: return 98.91 elif d < 70: return 98.16 elif d < 80: return 97.07 elif d < 90: return 95.51 elif d < 100: return 93.57 elif d < 150: return 75.67 elif d < 200: return 49.9 elif d < 250: return 30.94 elif d < 300: return 20.7 else: return 0 def lsh_json(data): filename = data[0] meta = [] print(filename) if not data[1] or data[1] == None: pass else: stuff = [d for d in data[1] if d['filename'] == os.path.basename(filename)] if stuff: if len(stuff) >= 1: stuff = stuff[0] [meta.extend([k,v]) for k,v in stuff.items()] [meta.extend([k,v]) for k,v in meta[3].items()] del meta[3] [meta.extend([k,v]) for k,v in meta[-1].items()] del meta[-3] [meta.extend([k,v]) for k,v in meta[-4].items()] del meta[-6] if os.path.getsize(filename) < 256: raise ValueError("{} must be at least 256 bytes".format(filename)) if tarfile.is_tarfile(filename): with tarfile.open(filename, 'r') as tar: for member in tar.getmembers(): if not member or member.size < 256: continue try: meta.append(tlsh.hash(tar.extractfile(member).read())) if use_binwalk: for module in binwalk.scan(tar.extractfile(member).read(), signature=True, quiet=True): for result in module.results: meta.append(str(result.file.path)) meta.append(str(result.offset)) meta.append(str(result.description)) except: continue elif zipfile.is_zipfile(filename): try: with zipfile.ZipFile(filename) as z: for member in z.infolist(): if not member or member.file_size < 256: continue try: with z.read(member) as zipdata: meta.append(tlsh.hash(zipdata)) if use_binwalk: for module in binwalk.scan(zipdata): for result in module.results: meta.append(str(result.file.path)) meta.append(str(result.offset)) meta.append(str(result.description)) except: continue except: pass if use_binwalk: for module in binwalk.scan(filename, signature=True, quiet=True): for result in module.results: meta.append(str(result.file.path)) meta.append(str(result.offset)) meta.append(str(result.description)) file_hash = tlsh.hash(open(filename, 'rb').read()) if not meta: return file_hash else: return tlsh.hash(str.encode(file_hash + ''.join(map(str, meta)))) def diff_hash(one, two): return tlsh.diff(one, two) def list_files(directory): f = [] for (dirpath, _, filenames) in os.walk(directory): for name in filenames: f.append(os.path.join(dirpath, name)) return f def parse_metadata(filename): contents = [] with open(filename, 'r') as csvfile: reader = csv.reader(csvfile) for row in reader: #Remove the md5 and sha1 hashes since they're useless to me contents.append(row[:-2]) return contents[1:] def parse_metadata_json(filename): with open(filename, 'r') as jsonfile: metadata = json.load(jsonfile) for obj in metadata: del obj['MD5'] del obj['SHA1'] del obj['SHA256'] del obj['SHA512'] obj['filename'] = obj['Properties'].pop('FileName') return metadata def flatten(d, parent_key='', sep='_'): items = [] for k, v in d.items(): new_key = parent_key + sep + k if parent_key else k if isinstance(v, collections.MutableMapping): items.extend(flatten(v, new_key, sep=sep).items()) else: items.append((new_key, v)) return dict(items) def get_n_closest(n, filenames, adjacency): closest = {} for f in filenames: elem = adj[filenames.index(f)] smallest_dists = nsmallest(n + 1, elem) smallest_files = [] old_dist = 0 for d in smallest_dists: #Ignore the file listing itself if d == 0: continue elif d == old_dist: continue old_dist = d if smallest_dists.count(d) > 1: prev = 0 for i in range(smallest_dists.count(d)): dist_filename = smallest_dists.index(d, prev) smallest_files.append((d, filenames[dist_filename])) prev = dist_filename + 1 continue; #Filename indices are analagous to adjacency indices smallest_files.append((d, filenames[smallest_dists.index(d)])) closest[f] = smallest_files return closest def get_partition_entry(partition_hashes, new_hash): return partition_hashes[int(new_hash[8:10], 16)] def get_n_closest_partial(n, hash_partition, hash_list): closest = {} for h in hash_list: entry = get_partition_entry(hash_partition, h) elem = [] filename = "" for k,v in entry: d = diff_hash(h, v) if d > 0: elem.append((d, k)) else: filename = k elem.sort(key=lambda tup: tup[0]) smallest_files = [] for i in range(len(elem)): if i + 1 > n: break smallest_files.append(elem[i]) closest[filename] = smallest_files return closest try: opts, args = getopt.getopt(sys.argv[1:], "hd:m:bn:t", ["help", "directory", "metadata", "binwalk", "number", "test"]) except getopt.GetoptError as err: print(err) # will print something like "option -a not recognized" usage() exit(2) directory = "" meta = "" use_binwalk = False n = 10 use_existing = False for o, a in opts: if o in ("-d", "--directory"): directory = a elif o in ("-h", "--help"): usage() exit() elif o in ("-m", "--metadata"): meta = a elif o in ("-b", "--binwalk"): use_binwalk = True elif o in ("-n", "--number"): n = int(a) elif o in ("-t", "--test"): use_existing = True if not directory: print("Program must be provided a file directory path") exit(1) file_list = list_files(directory) hash_list = [] if meta: meta_contents = parse_metadata_json(meta) else: meta_contents = None hash_list = [lsh_json(x) for x in zip(file_list, itertools.repeat(meta_contents))] if use_existing: file_data = np.load(".tmp.npz") #See https://stackoverflow.com/questions/22315595/saving-dictionary-of-header-information-using-numpy-savez for why this syntax is needed clustered_files = file_data['clusters'][()] cluster_hashes = file_data['hash_list'] ms = joblib.load('.tmp2.pkl') adj = np.zeros((len(hash_list), len(cluster_hashes)), int) #Compare new file hashes against saved data to get distances for i in range(len(hash_list)): for j in range(len(cluster_hashes)): adj[i][j] = diff_hash(hash_list[i], cluster_hashes[j]); cluster_labels = ms.predict(adj) for f in file_list: #Label of the prediucted file cluster lab = cluster_labels[file_list.index(f)] if lab not in clustered_files: print("{} does not belong to any existing cluster".format(f)) continue clus = clustered_files[lab] print("Target file {} is in cluster {}".format(f, lab)) for c in clus: print(c) #Empty line to separate cluster print outs print() exit() else: adj = np.zeros((len(hash_list), len(hash_list)), int) for i in range(len(hash_list)): for j in range(len(hash_list)): d = diff_hash(hash_list[i], hash_list[j]); adj[i][j] = d adj[j][i] = d best_cluster_count = 0 best_silhouette_score = -1.0 def cl(data): i, adj = data print("Trying cluster count {}".format(i)) return metrics.silhouette_score(adj, MiniBatchKMeans(n_clusters=i).fit_predict(adj)) #Calculate the best cluster count in parallel silhouette_list = Pool().map(cl, zip(range(2, 16), itertools.repeat(adj))) best_cluster_count = silhouette_list.index(max(silhouette_list)) + 2 ms = MiniBatchKMeans(n_clusters=best_cluster_count) cluster_labels = ms.fit_predict(adj) clustered_files = {} for f in file_list: lab = cluster_labels[file_list.index(f)] if lab in clustered_files: clustered_files[lab].append(f) else: clustered_files[lab] = [f] print(clustered_files) np.savez(".tmp", clusters=clustered_files, hash_list=hash_list) joblib.dump(ms, '.tmp2.pkl') labels = ms.labels_ cluster_centers = ms.cluster_centers_ labels_unique = np.unique(labels) n_clusters_ = len(labels_unique) print("number of estimated clusters : %d" % n_clusters_) plt.figure(1) plt.clf() colors = itertools.cycle('bgrcmykbgrcmykbgrcmykbgrcmyk') for k, col in zip(range(n_clusters_), colors): my_members = labels == k cluster_center = cluster_centers[k] plt.plot(adj[my_members, 0], adj[my_members, 1], col + '.') plt.plot(cluster_center[0], cluster_center[1], '+', markerfacecolor=col, markeredgecolor='k', markersize=5) plt.title('Estimated number of clusters: %d' % n_clusters_) plt.show()

30.485333

141

0.571641

1,478

11,432

4.307172

0.226658

0.023877

0.018379

0.026861

0.17028

0.125039

0.117499

0.098492

0.083883

0

0.021536

0.317617

11,432

374

142

30.566845

0.794514

0.048198

0

0.190939

0

0.048947

0.002576

0

1

0.045307

false

0.006472

0.067961

0.006472

0.200647

0.035599

0

null

0

null

0

1

0

93ecc85140eb083700cb75cd12da34a931dcc1e5

3,132

py

Python

proyecto_opti.py

rafaelfrieri1/Optimization-Project

20db5200cd361d358e213310c6eb2997c893ff27

[ "MIT" ]

null

proyecto_opti.py

rafaelfrieri1/Optimization-Project

20db5200cd361d358e213310c6eb2997c893ff27

[ "MIT" ]

null

proyecto_opti.py

rafaelfrieri1/Optimization-Project

20db5200cd361d358e213310c6eb2997c893ff27

[ "MIT" ]

null

from pyomo.environ import * import numpy as np cijr = [] fjr = [] with open("./Test_Instances/RAND2000_120-80.txt") as instanceFile: n = int(instanceFile.readline()) clientsFacilitiesSizeStr = instanceFile.readline().strip().split(" ") instanceFile.readline() for phase in range(2): for i in range(1, len(clientsFacilitiesSizeStr)): if phase == 0: fjr.append([]) for j in range(int(clientsFacilitiesSizeStr[i])): fjr[i-1].append(float(instanceFile.readline().strip().split(" ")[0])) else: nextLine = instanceFile.readline().strip() cijr.append([]) j = 0 while(nextLine != ""): nextLineNumbers = nextLine.split(" ") cijr[i-1].append([]) for nextLinenumber in nextLineNumbers: cijr[i-1][j].append(float(nextLinenumber)) j+=1 nextLine = instanceFile.readline().strip() instanceFile.readline() instanceFile.close() #cijr = np.array( # [ # np.array([ # [1,1000,1000], # [15000,2,15000], # [20000,20000,3], # [1,40000,40000] # ]), # np.array([ # [10000,5,10000,10000,20000], # [30000,23000,3,24000,18000], # [28000,35000,21000,4,33000] # ]), # np.array([ # [16, 14], # [2, 18000], # [20000, 3], # [20000, 2], # [24, 25] # ]) # ] #) #fjr = np.array( # [ # [10,15,20], # [17,20,25,30,18], # [48,50] # ] #) I = range(len(cijr[0])) J = range(len(cijr[0][0])) R = range(1, len(fjr)+1) RM1 = range(1, len(fjr)) yjrIndexes = [] zirabIndexes = [] for r in R: for j in range(len(fjr[r-1])): yjrIndexes.append((r,j)) for i in I: for r in RM1: for a in range(len(cijr[r])): for b in range(len(cijr[r][0])): zirabIndexes.append((i,r,a,b)) model = ConcreteModel() model.vij1 = Var(I, J, domain=Binary) model.yjr = Var(yjrIndexes, domain=Binary) model.zirab = Var(zirabIndexes, domain=Binary) model.constraints = ConstraintList() for i in I: model.constraints.add(sum(model.vij1[i, j] for j in J) == 1) for j1 in J: model.constraints.add(sum(model.zirab[i,1,j1,b] for b in range(len(cijr[1][0]))) == model.vij1[i,j1]) model.constraints.add(model.vij1[i,j1] <= model.yjr[1,j1]) for r in range(2, len(fjr) + 1): if(r <= len(fjr) - 1): for a in range(len(cijr[r])): model.constraints.add(sum(model.zirab[i,r,a,b] for b in range(len(cijr[r][0]))) == sum(model.zirab[i,r-1,bp,a] for bp in range(len(cijr[r-1])))) for b in range(len(cijr[r-1][0])): model.constraints.add(sum(model.zirab[i,r-1,a,b,] for a in range(len(cijr[r-1]))) <= model.yjr[r, b]) model.objective = Objective( expr = sum(sum(cijr[0][i][j1]*model.vij1[i,j1] for j1 in J) for i in I) + sum(sum(sum(sum(cijr[r][a][b]*model.zirab[i,r,a,b] for b in range(len(cijr[r][0])))for a in range(len(cijr[r]))) for r in RM1) for i in I) + sum(sum(fjr[r-1][j]*model.yjr[r,j] for j in range(len(fjr[r-1]))) for r in R), sense=minimize ) results = SolverFactory('cplex').solve(model) results.write() #if results.solver.status: # model.pprint() #model.constraints.display()

28.472727

295

0.590358

495

3,132

3.731313

0.212121

0.060639

0.077964

0.075799

0.258798

0.212777

0.179751

0.120736

0.036816

0

0.085415

0.207535

3,132

110

296

28.472727

0.658743

0.155811

0

0.126984

0

0.016839

0.013777

0

1

0

false

0

0.031746

0

0.031746

0

null

0

null

0

1

0

93f0a5f958369eb4430a00c36a168b1783fda002

735

py

Python

portal/middleware.py

cds-snc/covid-alert-portal

e7d56fa9fa4a2ad2d60f056eae063713661bd260

[ "MIT" ]

43

2020-07-31T14:38:06.000Z

2022-03-07T11:28:28.000Z

portal/middleware.py

cds-snc/covid-alert-portal

e7d56fa9fa4a2ad2d60f056eae063713661bd260

[ "MIT" ]

322

2020-07-23T19:38:26.000Z

2022-03-31T19:15:45.000Z

portal/middleware.py

cds-snc/covid-alert-portal

e7d56fa9fa4a2ad2d60f056eae063713661bd260

[ "MIT" ]

6

2020-11-28T19:30:20.000Z

2021-07-29T18:06:55.000Z

import pytz from django.conf import settings class TZMiddleware: def __init__(self, get_response): self.get_response = get_response def __call__(self, request): browser_tz = request.COOKIES.get("browserTimezone") tz = None if browser_tz: try: tz = pytz.timezone(browser_tz) except pytz.UnknownTimeZoneError: pass if not tz: tz = pytz.timezone(settings.PORTAL_LOCAL_TZ) def convert_to_local_tz_from_utc(utc_dttm): return utc_dttm.astimezone(tz=tz) request.convert_to_local_tz_from_utc = convert_to_local_tz_from_utc response = self.get_response(request) return response

27.222222

75

0.642177

89

735

4.921348

0.393258

0.100457

0.10274

0.109589

0.157534

0

0.293878

735

26

76

28.269231

0.843931

0

0.020408

0

1

0.15

false

0.05

0.1

0.05

0.4

0

null

0

null

0

1

0

93f3149ab4cf735ff8855c62f4a02835c7b351e6

483

py

Python

app/main.py

cesko-digital/newschatbot

4f47d7902433bff09b48fcebcf9ee8422eb0ec7e

[ "MIT" ]

1

2021-04-06T16:52:36.000Z

app/main.py

cesko-digital/newschatbot

4f47d7902433bff09b48fcebcf9ee8422eb0ec7e

[ "MIT" ]

17

2021-05-30T17:06:48.000Z

2021-09-26T08:20:02.000Z

app/main.py

cesko-digital/newschatbot

4f47d7902433bff09b48fcebcf9ee8422eb0ec7e

[ "MIT" ]

null

from flask import Flask from flask_migrate import Migrate from app.model import db from app.controller import api app = Flask(__name__) app.register_blueprint(api) app.config[ "SQLALCHEMY_DATABASE_URI" ] = "postgresql://newschatbotdevelopment:Wlk8skrHKvZEbM6Gw@database.internal.newschatbot.ceskodigital.net:5432/newschatbotdevelopment" app.config["SQLALCHEMY_TRACK_MODIFICATIONS"] = False migrate = Migrate(app, db) db.init_app(app) if __name__ == "__main__": app.run()

25.421053

134

0.797101

60

483

6.1

0.516667

0.04918

0.103825

0

0.013857

0.10352

483

18

135

26.833333

0.831409

0

0.391304

0.374741

0

1

0

false

0

0.285714

0

0.285714

0.071429

0

null

0

null

0

1

0

93f51b485662f69b94bcc6b67cecfbb6633cdc40

2,887

py

Python

examples/siha/sleep_intraday_dataset.py

qcri/tasrif

327bc1eccb8f8e11d8869ba65a7c72ad038aa094

[ "BSD-3-Clause" ]

20

2021-12-06T10:41:54.000Z

2022-03-13T16:25:43.000Z

examples/siha/sleep_intraday_dataset.py

qcri/tasrif

327bc1eccb8f8e11d8869ba65a7c72ad038aa094

[ "BSD-3-Clause" ]

33

2021-12-06T08:27:18.000Z

2022-03-14T05:07:53.000Z

examples/siha/sleep_intraday_dataset.py

qcri/tasrif

327bc1eccb8f8e11d8869ba65a7c72ad038aa094

[ "BSD-3-Clause" ]

2

2022-02-07T08:06:48.000Z

2022-02-14T07:13:42.000Z

"""Example on how to read sleep data from SIHA """ import os from tasrif.data_readers.siha_dataset import SihaDataset from tasrif.processing_pipeline import SequenceOperator from tasrif.processing_pipeline.custom import JqOperator from tasrif.processing_pipeline.pandas import ( ConvertToDatetimeOperator, JsonNormalizeOperator, SetIndexOperator, ) siha_folder_path = os.environ.get("SIHA_PATH") pipeline = SequenceOperator( [ SihaDataset(siha_folder_path, table_name="Data"), JqOperator( "map({patientID} + (.data.sleep[].data as $data | " + "($data.sleep | map(.) | .[]) | . * {levels: {overview : ($data.summary//{})}})) | " + "map (if .levels.data != null then . else .levels += {data: []} end) | " + "map(. + {type, dateOfSleep, minutesAsleep, logId, startTime, endTime, duration, isMainSleep," + " minutesToFallAsleep, minutesAwake, minutesAfterWakeup, timeInBed, efficiency, infoCode})" ), JsonNormalizeOperator( record_path=["levels", "data"], meta=[ "patientID", "logId", "dateOfSleep", "startTime", "endTime", "duration", "isMainSleep", "minutesToFallAsleep", "minutesAsleep", "minutesAwake", "minutesAfterWakeup", "timeInBed", "efficiency", "type", "infoCode", ["levels", "summary", "deep", "count"], ["levels", "summary", "deep", "minutes"], ["levels", "summary", "deep", "thirtyDayAvgMinutes"], ["levels", "summary", "wake", "count"], ["levels", "summary", "wake", "minutes"], ["levels", "summary", "wake", "thirtyDayAvgMinutes"], ["levels", "summary", "light", "count"], ["levels", "summary", "light", "minutes"], ["levels", "summary", "light", "thirtyDayAvgMinutes"], ["levels", "summary", "rem", "count"], ["levels", "summary", "rem", "minutes"], ["levels", "summary", "rem", "thirtyDayAvgMinutes"], ["levels", "overview", "totalTimeInBed"], ["levels", "overview", "totalMinutesAsleep"], ["levels", "overview", "stages", "rem"], ["levels", "overview", "stages", "deep"], ["levels", "overview", "stages", "light"], ["levels", "overview", "stages", "wake"], ], errors="ignore", ), ConvertToDatetimeOperator( feature_names=["dateTime"], infer_datetime_format=True ), SetIndexOperator("dateTime"), ] ) df = pipeline.process() print(df)

38.493333

108

0.512643

206

2,887

7.11165

0.378641

0.106485

0.049147

0.057338

0.07372

0

0.32629

2,887

74

109

39.013514

0.753213

0.014894

0

0.044776

0

0.029851

0.375749

0.008107

0

1

0

false

0

0.074627

0

0.074627

0.014925

0

null

0

null

0

1

0

93f8e49e11b7653fd863536bebeb07d2b758a06e

12,788

py

Python

tests/data/long_statement_strings.py

aalto-speech/fi-parliament-tools

c40ab81a23c661765c380238cbf10acf733d94d4

[ "MIT" ]

5

2021-05-19T22:56:40.000Z

2022-03-29T15:25:03.000Z

tests/data/long_statement_strings.py

aalto-speech/fi-parliament-tools

c40ab81a23c661765c380238cbf10acf733d94d4

[ "MIT" ]

32

2021-05-10T07:58:57.000Z

2022-03-01T08:02:11.000Z

tests/data/long_statement_strings.py

aalto-speech/fi-parliament-tools

c40ab81a23c661765c380238cbf10acf733d94d4

[ "MIT" ]

null

"""Long statement strings and other space consuming data definitions for testing are declared here. This is done to avoid clutter in main test files. """ from typing import Dict from typing import List from typing import Tuple import pytest from _pytest.fixtures import SubRequest from fi_parliament_tools.parsing.data_structures import MP chairman_texts = [ "Ilmoitetaan, että valiokuntien ja kansliatoimikunnan vaalit toimitetaan ensi tiistaina 5. " "päivänä toukokuuta kello 14 pidettävässä täysistunnossa. Ehdokaslistat näitä vaaleja varten " "on jätettävä keskuskansliaan viimeistään ensi maanantaina 4. päivänä toukokuuta kello 12.", "Toimi Kankaanniemen ehdotus 5 ja Krista Kiurun ehdotus 6 koskevat samaa asiaa, joten ensin " "äänestetään Krista Kiurun ehdotuksesta 6 Toimi Kankaanniemen ehdotusta 5 vastaan ja sen " "jälkeen voittaneesta mietintöä vastaan.", "Kuhmosta oleva agrologi Tuomas Kettunen, joka varamiehenä Oulun vaalipiiristä on " "tullut Antti Rantakankaan sijaan, on tänään 28.11.2019 esittänyt puhemiehelle " "edustajavaltakirjansa ja ryhtynyt hoitamaan edustajantointaan.", ] speaker_texts = [ "Arvoisa puhemies! Hallituksen esityksen mukaisesti on varmasti hyvä jatkaa määräaikaisesti " "matkapuhelinliittymien telemarkkinointikieltoa. Kukaan kansalainen ei ole kyllä ainakaan " "itselleni valittanut siitä, että enää eivät puhelinkauppiaat soittele kotiliittymiin ja " "‑puhelimiin, ja myös operaattorit ovat olleet kohtuullisen tyytyväisiä tähän kieltoon. " "Ongelmia on kuitenkin muussa puhelinmyynnissä ja telemarkkinoinnissa. Erityisesti " "nettiliittymien puhelinmyynnissä on ongelmia. On aggressiivista myyntiä, ja ihmisillä on " "epätietoisuutta siitä, mitä he ovat lopulta ostaneet. Lisäksi mielestäni on ongelmallista " "rajata vain puhelinliittymät telemarkkinointikiellon piiriin, kun viestintä- ja " "mobiilipalveluiden puhelinkauppa on laajempi aihe ja se on laajempi ongelma ja ongelmia on " "tosiaan tässä muidenkin tyyppisten sopimusten myynnissä. Tämä laki tämänsisältöisenä on " "varmasti ihan hyvä, ja on hyvä määräaikaisesti jatkaa tätä, mutta näkisin, että sitten kun " "tämä laki on kulumassa umpeen, meidän on palattava asiaan ja on tehtävä joku lopullisempi " "ratkaisu tästä telemarkkinoinnista. Ei voida mennä tällaisen yhden sopimusalan " "määräaikaisuudella eteenpäin. Meidän täytyy tehdä ratkaisut, jotka ovat laajempia ja jotka " "koskevat viestintä-, tele- ja mobiilisopimusten puhelinmyyntiä laajemmin ja muutenkin " "puhelinmyynnin pelisääntöjä laajemmin. Varmaankin paras ratkaisu olisi se, että jatkossa " "puhelimessa tehty ostos pitäisi varmentaa kirjallisesti esimerkiksi sähköpostilla, " "tekstiviestillä tai kirjeellä. Meidän on ratkaistava jossain vaiheessa nämä puhelinmyynnissä " "olevat ongelmat ja käsiteltävä asia kokonaisvaltaisesti. — Kiitos. (Hälinää)", "Arvoisa puhemies! Pienen, vastasyntyneen lapsen ensimmäinen ote on samaan aikaan luja ja " "hento. Siihen otteeseen kiteytyy paljon luottamusta ja vastuuta. Luottamusta siihen, että " "molemmat vanhemmat ovat läsnä lapsen elämässä. Vastuuta siitä, että huominen on aina " "valoisampi. Luottamus ja vastuu velvoittavat myös meitä päättäjiä. Tämän hallituksen " "päätökset eivät perheiden kannalta ole olleet kovin hääppöisiä. Paljon on leikattu perheiden " "arjesta, mutta toivon kipinä heräsi viime vuonna, kun hallitus ilmoitti, että se toteuttaa " "perhevapaauudistuksen. Viime perjantaina hallituksen perheministeri kuitenkin yllättäen " "ilmoitti, että hän keskeyttää tämän uudistuksen. Vielä suurempi hämmästys oli se syy, jonka " "takia tämä keskeytettiin. Ministeri ilmoitti, että valmistellut mallit olisivat olleet " "huonoja suomalaisille perheille. Perheministeri Saarikko, kun te olette vastuussa tämän " "uudistuksen valmistelusta, niin varmasti suomalaisia perheitä kiinnostaisi tietää, miksi te " "valmistelitte huonoja malleja.", "Arvoisa puhemies! Lämpimät osanotot omasta ja perussuomalaisten eduskuntaryhmän " "puolesta pitkäaikaisen kansanedustajan Maarit Feldt-Rannan omaisille ja läheisille. " "Nuorten mielenterveysongelmat ovat vakava yhteiskunnallinen ongelma. " "Mielenterveysongelmat ovat kasvaneet viime vuosina räjähdysmäisesti, mutta " "terveydenhuoltoon ei ole lisätty vastaavasti resursseja, vaan hoitoonpääsy on " "ruuhkautunut. Masennuksesta kärsii jopa 15 prosenttia nuorista, ahdistuneisuudesta 10 " "prosenttia, ja 10—15 prosentilla on toistuvia itsetuhoisia ajatuksia. Monet näistä " "ongelmista olisivat hoidettavissa, jos yhteiskunta ottaisi asian vakavasti. Turhan " "usein hoitoon ei kuitenkaan pääse, vaan nuoret jätetään heitteille. Kysyn: mihin " "toimiin hallitus ryhtyy varmistaakseen, että mielenterveysongelmista kärsiville " "nuorille on tarjolla heidän tarvitsemansa hoito silloin kun he sitä tarvitsevat?", ] speaker_lists = [ [ (1301, "Jani", "Mäkelä", "ps", ""), (1108, "Juha", "Sipilä", "", "Pääministeri"), (1301, "Jani", "Mäkelä", "ps", ""), (1108, "Juha", "Sipilä", "", "Pääministeri"), (1141, "Peter", "Östman", "kd", ""), (947, "Petteri", "Orpo", "", "Valtiovarainministeri"), (1126, "Tytti", "Tuppurainen", "sd", ""), (1108, "Juha", "Sipilä", "", "Pääministeri"), (1317, "Simon", "Elo", "sin", ""), (1108, "Juha", "Sipilä", "", "Pääministeri"), ], [ (1093, "Juho", "Eerola", "ps", ""), (1339, "Kari", "Kulmala", "sin", ""), (887, "Sirpa", "Paatero", "sd", ""), (967, "Timo", "Heinonen", "kok", ""), ], [ (971, "Johanna", "Ojala-Niemelä", "sd", ""), (1129, "Arja", "Juvonen", "ps", ""), (1388, "Mari", "Rantanen", "ps", ""), (1391, "Ari", "Koponen", "ps", ""), (1325, "Sari", "Tanus", "kd", ""), (971, "Johanna", "Ojala-Niemelä", "sd", ""), ], ] chairman_statements = [ { "type": "C", "mp_id": 0, "firstname": "Mauri", "lastname": "Pekkarinen", "party": "", "title": "Ensimmäinen varapuhemies", "start_time": "", "end_time": "", "language": "", "text": "Ainoaan käsittelyyn esitellään päiväjärjestyksen 4. asia. Käsittelyn pohjana on " "talousvaliokunnan mietintö TaVM 18/2016 vp.", "offset": -1.0, "duration": -1.0, "embedded_statement": { "mp_id": 0, "title": "", "firstname": "", "lastname": "", "language": "", "text": "", "offset": -1.0, "duration": -1.0, }, }, { "type": "C", "mp_id": 0, "firstname": "Mauri", "lastname": "Pekkarinen", "party": "", "title": "Ensimmäinen varapuhemies", "start_time": "", "end_time": "", "language": "", "text": "Toiseen käsittelyyn esitellään päiväjärjestyksen 3. asia. Keskustelu asiasta " "päättyi 6.6.2017 pidetyssä täysistunnossa. Keskustelussa on Anna Kontula Matti Semin " "kannattamana tehnyt vastalauseen 2 mukaisen lausumaehdotuksen.", "offset": -1.0, "duration": -1.0, "embedded_statement": { "mp_id": 0, "title": "", "firstname": "", "lastname": "", "language": "", "text": "", "offset": -1.0, "duration": -1.0, }, }, { "type": "C", "mp_id": 0, "firstname": "Tuula", "lastname": "Haatainen", "party": "", "title": "Toinen varapuhemies", "start_time": "", "end_time": "", "language": "", "text": "Toiseen käsittelyyn esitellään päiväjärjestyksen 6. asia. Nyt voidaan hyväksyä " "tai hylätä lakiehdotukset, joiden sisällöstä päätettiin ensimmäisessä käsittelyssä.", "offset": -1.0, "duration": -1.0, "embedded_statement": { "mp_id": 0, "title": "", "firstname": "", "lastname": "", "language": "", "text": "", "offset": -1.0, "duration": -1.0, }, }, ] embedded_statements = [ { "mp_id": 0, "title": "Puhemies", "firstname": "Maria", "lastname": "Lohela", "language": "", "text": "Edustaja Laukkanen, ja sitten puhujalistaan.", "offset": -1.0, "duration": -1.0, }, { "mp_id": 0, "title": "", "firstname": "", "lastname": "", "language": "", "text": "", "offset": -1.0, "duration": -1.0, }, { "mp_id": 0, "title": "Ensimmäinen varapuhemies", "firstname": "Mauri", "lastname": "Pekkarinen", "language": "", "text": "Tämä valtiovarainministerin puheenvuoro saattaa antaa aihetta muutamaan " "debattipuheenvuoroon. Pyydän niitä edustajia, jotka haluavat käyttää vastauspuheenvuoron, " "nousemaan ylös ja painamaan V-painiketta.", "offset": -1.0, "duration": -1.0, }, { "mp_id": 0, "title": "Ensimmäinen varapuhemies", "firstname": "Antti", "lastname": "Rinne", "language": "", "text": "Meillä on puoleenyöhön vähän reilu kolme tuntia aikaa, ja valtioneuvoston pitää " "sitä ennen soveltamisasetus saattaa voimaan. Pyydän ottamaan tämän huomioon " "keskusteltaessa.", "offset": -1.0, "duration": -1.0, }, ] mps = [ MP( 103, "Matti", "Ahde", "o", "fi", 1945, "Sosialidemokraattinen eduskuntaryhmä", "", "", "Oulu", "Oulun läänin vaalipiiri (03/1970-06/1990), Oulun vaalipiiri (03/2003-04/2011)", "kansakoulu, ammattikoulu, kansankorkeakoulu", ), MP( 1432, "Marko", "Kilpi", "m", "fi", 1969, "Parliamentary Group of the National Coalition Party", "police officer, writer", "Kuopio", "Rovaniemi", "Electoral District of Savo-Karelia (04/2019-)", "Degree in policing", ), MP( 1374, "Veronica", "Rehn-Kivi", "f", "sv", 1956, "Swedish Parliamentary Group", "architect, building supervision manager", "Kauniainen", "Helsinki", "Electoral District of Uusimaa (08/2016-)", "architect", ), MP( 1423, "Iiris", "Suomela", "f", "fi", 1994, "Green Parliamentary Group", "student of social sciences", "Tampere", "", "Electoral District of Pirkanmaa (04/2019-)", "", ), ] @pytest.fixture def true_chairman_text(request: SubRequest) -> str: """Return a long chairman statement for testing from a list at the top of the file.""" index: int = request.param return chairman_texts[index] @pytest.fixture def true_speaker_text(request: SubRequest) -> str: """Return a long speaker statement for testing from a list at the top of the file.""" index: int = request.param return speaker_texts[index] @pytest.fixture def true_speaker_list(request: SubRequest) -> List[Tuple[int, str, str, str, str]]: """Return a list of speakers for testing from a list at the top of the file.""" index: int = request.param return speaker_lists[index] @pytest.fixture def true_chairman_statement(request: SubRequest) -> Dict[str, object]: """Return a chairman statement for testing from a list at the top of the file.""" index: int = request.param return chairman_statements[index] @pytest.fixture def true_embedded_statement(request: SubRequest) -> Dict[str, object]: """Return an embedded statement for testing from a list at the top of the file.""" index: int = request.param return embedded_statements[index] @pytest.fixture def true_mp(request: SubRequest) -> MP: """Return an MP data object for testing from a list at the top of the file.""" index: int = request.param return mps[index] @pytest.fixture def interpellation_4_2017_text() -> str: """Read interpellation 4/2017 text transcript from a file. Returns: str: full interpellation statement as one very long string """ with open("tests/data/interpellation_4_2017_text.txt", "r", encoding="utf-8") as infile: interpellation_text = infile.read().replace("\n", " ") return interpellation_text.strip()

37.722714

100

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1,297

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6.141866

0.508096

0.005021

0.006277

0.020085

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0.210394

0.199347

0.168968

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0

0.026911

0.256099

12,788

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0.810155

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0.586862

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false

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0

0.066667

0

null

0

null

0

1

0

93fe622a14e935745be6617c3d7a3da20bbb3012

578

py

Python

venv/Lib/site-packages/fbs/_state.py

Acuf5928/check-

4b993e0bcee33434506565dab11ece3dfa9c5cab

[ "MIT" ]

1

2020-03-30T00:08:41.000Z

venv/Lib/site-packages/fbs/_state.py

Acuf5928/check-

4b993e0bcee33434506565dab11ece3dfa9c5cab

[ "MIT" ]

null

venv/Lib/site-packages/fbs/_state.py

Acuf5928/check-

4b993e0bcee33434506565dab11ece3dfa9c5cab

[ "MIT" ]

2

2018-12-29T07:49:59.000Z

2020-03-18T02:44:31.000Z

""" This INTERNAL module is used to manage fbs's global state. Having it here, in one central place, allows fbs's test suite to manipulate the state to test various scenarios. """ from collections import OrderedDict SETTINGS = {} LOADED_PROFILES = [] COMMANDS = OrderedDict() def get(): return dict(SETTINGS), list(LOADED_PROFILES), dict(COMMANDS) def restore(settings, loaded_profiles, commands): SETTINGS.clear() SETTINGS.update(settings) LOADED_PROFILES.clear() LOADED_PROFILES.extend(loaded_profiles) COMMANDS.clear() COMMANDS.update(commands)

27.52381

77

0.749135

74

578

5.77027

0.567568

0.196721

0.154567

0.140515

0

0.155709

578

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78

27.52381

0.875

0.295848

0

1

0.153846

false

0

0.076923

0.307692

0

null

0

null

0

1

0

93feb2b5aaee509b3ca59bd657fd9239d3cc9aa4

5,234

py

Python

rtk/dao/RTKMatrix.py

rakhimov/rtk

adc35e218ccfdcf3a6e3082f6a1a1d308ed4ff63

[ "BSD-3-Clause" ]

null

rtk/dao/RTKMatrix.py

rakhimov/rtk

adc35e218ccfdcf3a6e3082f6a1a1d308ed4ff63

[ "BSD-3-Clause" ]

null

rtk/dao/RTKMatrix.py

rakhimov/rtk

adc35e218ccfdcf3a6e3082f6a1a1d308ed4ff63

[ "BSD-3-Clause" ]

2

2020-04-03T04:14:42.000Z

2021-02-22T05:30:35.000Z

# -*- coding: utf-8 -*- # # rtk.dao.RTKMatrix.py is part of The RTK Project # # All rights reserved. # Copyright 2007 - 2017 Andrew Rowland andrew.rowland <AT> reliaqual <DOT> com """ =============================================================================== The RTKMatrix Table =============================================================================== """ # pylint: disable=E0401 from sqlalchemy import Column, ForeignKey, Integer, String from sqlalchemy.orm import relationship # pylint: disable=E0401 # Import other RTK modules. from Utilities import none_to_default # pylint: disable=E0401 from dao.RTKCommonDB import RTK_BASE # pylint: disable=E0401 class RTKMatrix(RTK_BASE): """ Class to represent the rtk_matrix table in the RTK Program database. Matrix types are one of the following: +-------------+--------------+--------------+ | Row Table | Column Table | Matrix Type | +-------------+--------------+--------------+ | Function | Hardware | fnctn_hrdwr | +-------------+--------------+--------------+ | Function | Software | fnctn_sftwr | +-------------+--------------+--------------+ | Function | Validation | fnctn_vldtn | +-------------+--------------+--------------+ | Requirement | Hardware | rqrmnt_hrdwr | +-------------+--------------+--------------+ | Requirement | Software | rqrmnt_sftwr | +-------------+--------------+--------------+ | Requirement | Validation | rqrmnt_vldtn | +-------------+--------------+--------------+ | Hardware | Testing | hrdwr_tstng | +-------------+--------------+--------------+ | Hardware | Validation | hrdwr_vldtn | +-------------+--------------+--------------+ | Software | Risk | sftwr_rsk | +-------------+--------------+--------------+ | Software | Validation | sftwr_vldtn | +-------------+--------------+--------------+ The primary key for this table consists of the revision_id, matrix_id, column_item_id, and row_item_id. This table shares a Many-to-One relationship with rtk_revision. """ __tablename__ = 'rtk_matrix' __table_args__ = {'extend_existing': True} revision_id = Column( 'fld_revision_id', Integer, ForeignKey('rtk_revision.fld_revision_id'), primary_key=True, nullable=False) matrix_id = Column('fld_matrix_id', Integer, primary_key=True, default=0) column_id = Column('fld_column_id', Integer, default=0) column_item_id = Column( 'fld_column_item_id', Integer, primary_key=True, default=0) matrix_type = Column('fld_matrix_type', String(128), default='') parent_id = Column('fld_parent_id', Integer, default=0) row_id = Column('fld_row_id', Integer, default=0) row_item_id = Column( 'fld_row_item_id', Integer, primary_key=True, default=0) value = Column('fld_value', Integer, default=0) # Define the relationships to other tables in the RTK Program database. revision = relationship('RTKRevision', back_populates='matrix') def get_attributes(self): """ Retrieve the current values of the RTKMatrix data model attributes. :return: {revision_id, matrix_id, column_id, column_item_id, parent_id, row_id, row_item_id, type_id, value} pairs. :rtype: tuple """ _attributes = { 'revision_id': self.revision_id, 'matrix_id': self.matrix_id, 'column_id': self.column_id, 'column_item_id': self.column_item_id, 'matrix_type': self.matrix_type, 'parent_id': self.parent_id, 'row_id': self.row_id, 'row_item_id': self.row_item_id, 'value': self.value } return _attributes def set_attributes(self, values): """ Set the RTKMatrix data model attributes. :param tuple values: tuple of values to assign to the instance attributes. :return: (_code, _msg); the error code and error message. :rtype: tuple """ _error_code = 0 _msg = "RTK SUCCESS: Updating RTKMatrix {0:d} attributes.". \ format(self.matrix_id) try: self.column_id = int(none_to_default(values['column_id'], 0)) self.column_item_id = int( none_to_default(values['column_item_id'], 0)) self.matrix_type = str(none_to_default(values['matrix_type'], '')) self.parent_id = int(none_to_default(values['parent_id'], 0)) self.row_id = int(none_to_default(values['row_id'], 0)) self.row_item_id = int(none_to_default(values['row_item_id'], 0)) self.value = float(none_to_default(values['value'], 0.0)) except KeyError as _err: _error_code = 40 _msg = "RTK ERROR: Missing attribute {0:s} in attribute " \ "dictionary passed to " \ "RTKMatrix.set_attributes().".format(_err) return _error_code, _msg

40.261538

79

0.526175

540

5,234

4.824074

0.261111

0.036852

0.039923

0.051056

0.193858

0.094818

0.085605

0.026871

0

0.012095

0.257547

5,234

129

80

40.573643

0.65826

0.455865

0

0.186636

0.021121

0

1

0.035714

false

0.017857

0.071429

0

0.375

0

null

0

null

0

1

0

93ff19152094c70f894a1b56b790e173ed1c2638

614

py

Python

tool/gitautopull.py

chaosannals/trial-python

740b91fa4b1b1b9839b7524515995a6d417612ca

[ "MIT" ]

null

tool/gitautopull.py

chaosannals/trial-python

740b91fa4b1b1b9839b7524515995a6d417612ca

[ "MIT" ]

8

2020-12-26T07:48:15.000Z

2022-03-12T00:25:14.000Z

tool/gitautopull.py

chaosannals/trial-python

740b91fa4b1b1b9839b7524515995a6d417612ca

[ "MIT" ]

null

import os import shutil def pull_default(folder=None): cwd = os.getcwd() if None == folder: folder = cwd for path in os.listdir(folder): project_path = os.path.join(folder, path) if os.path.isdir(project_path): dot_git_folder = os.path.join(project_path, '.git') if os.path.isdir(dot_git_folder): print('[git pull start] {}'.format(project_path)) os.chdir(project_path) os.system('git pull') print('[git pull end] {}'.format(project_path)) os.chdir(cwd) pull_default() input('按回车结束')

29.238095

65

0.583062

81

614

4.271605

0.345679

0.190751

0.150289

0.075145

0.138728

0

0.286645

614

20

66

30.7

0.789954

0

0.086319

0

1

0.055556

false

0

0.111111

0

0.166667

0.111111

0

null

0

null

0

1

0

93ffdac053f4b224bf9ac1f85bcc5aea184dd502

9,300

py

Python

emit.py

richardbenson91477/simile

aa1faa8902d24e57133cd2c9982e5d4eef6f913f

[ "Unlicense" ]

null

emit.py

richardbenson91477/simile

aa1faa8902d24e57133cd2c9982e5d4eef6f913f

[ "Unlicense" ]

null

emit.py

richardbenson91477/simile

aa1faa8902d24e57133cd2c9982e5d4eef6f913f

[ "Unlicense" ]

null

''' code emitters ''' import out, enums as e class s: ''' state ''' # long_len # arg_regs, arg_regs_n # regs # stack_regs pass def init (long_len): s.long_len = long_len if long_len == 8: s.arg_regs = ['%rdi', '%rsi', '%rdx', '%rcx', 'r8', 'r9'] s.arg_regs_n = len(s.arg_regs) s.regs = ['%rax', '%rbx', '%r10'] s.stack_regs = ['%rsp', '%rbp'] elif long_len == 4: s.arg_regs = [] s.arg_regs_n = 0 s.regs = ['%eax', '%ebx', '%ecx'] s.stack_regs = ['%esp', '%ebp'] else: out.error ('what year is this???') return False return True def emit (fn_cur, et, val, val2 = None): if et == e.EMIT_DEF: out.put ('.section .text', i_n = 0) out.put ('.globl ' + val, i_n = 0) out.put (val + ':', i_n = 0) out.put ('push ' + s.stack_regs [1]) out.put ('mov ' + s.stack_regs [0] + ', ' + s.stack_regs [1]) out.put ('xor ' + s.regs [0] + ', ' + s.regs [0]) elif et == e.EMIT_RET: if val: if not get_val (fn_cur, val, s.regs [0]): return False out.put ('pop ' + s.stack_regs [0]) out.put ('ret') elif et == e.EMIT_END: if not fn_cur.flow_ret_t: out.put ('pop ' + s.stack_regs [1]) out.put ('ret') if fn_cur.data_n: out.put ('.section .data', i_n = 0) for datum in fn_cur.data: if datum._type == e.DATA_LONG: out.put (datum.name_s + ': .zero ' + str(datum._len), i_n = 0) elif datum._type == e.DATA_LARRAY: out.put (datum.name_s + ': .zero ' + str(datum._len), i_n = 0) elif datum._type == e.DATA_STR: out.put (datum.name_s + ': .string ' + datum.val, i_n = 0) elif et == e.EMIT_CALL: arg_n = len (val2) for arg_i, arg in enumerate (val2): if arg_i < s.arg_regs_n: if not get_val (fn_cur, arg, s.arg_regs [arg_i]): return False else: if not get_val (fn_cur, arg, s.regs [0]): return False out.put ('push ' + s.regs [0]) out.put ('call ' + val) if arg_n > s.arg_regs_n: out.put ('add $' + str((arg_n - s.arg_regs_n) * s.long_len) +\ ', ' + s.stack_regs [0]) elif et == e.EMIT_PUSH: if not get_val (fn_cur, val, s.regs [0]): return False out.put ('push ' + s.regs [0]) elif et == e.EMIT_IF: if not get_val (fn_cur, val, s.regs [0]): return False out.put ('test ' + s.regs [0] + ', ' + s.regs [0]) out.put ('jz ' + fn_cur.name_s + '.else.' +\ str(fn_cur.flow_cur [fn_cur.flow_n - 1][1])) elif et == e.EMIT_ELSE: out.put ('jmp ' + fn_cur.name_s + '.endif.' +\ str(fn_cur.flow_cur [fn_cur.flow_n - 1][1])) out.put (fn_cur.name_s + '.else.' +\ str(fn_cur.flow_cur [fn_cur.flow_n - 1][1]) + ':', i_n = 0) elif et == e.EMIT_ENDIF: out.put (fn_cur.name_s + '.endif.' +\ str(fn_cur.flow_cur [fn_cur.flow_n - 1][1]) + ':', i_n = 0) elif et == e.EMIT_WHILE: out.put (fn_cur.name_s + '.while.' +\ str(fn_cur.flow_cur [fn_cur.flow_n - 1][1]) + ':', i_n = 0) if not get_val (fn_cur, val, s.regs [0]): return False out.put ('test ' + s.regs [0] + ', ' + s.regs [0]) out.put ('jz ' + fn_cur.name_s + '.wend.' +\ str(fn_cur.flow_cur [fn_cur.flow_n - 1][1])) elif et == e.EMIT_WEND: out.put ('jmp ' + fn_cur.name_s + '.while.' +\ str(fn_cur.flow_cur [fn_cur.flow_n - 1][1])) out.put (fn_cur.name_s + '.wend.' +\ str(fn_cur.flow_cur [fn_cur.flow_n - 1][1]) + ':', i_n = 0) elif et == e.EMIT_ADD: if not get_val (fn_cur, val, s.regs [1]): return False out.put ('add ' + s.regs [1] + ', ' + s.regs [0]) elif et == e.EMIT_SUB: if not get_val (fn_cur, val, s.regs [1]): return False out.put ('sub ' + s.regs [1] + ', ' + s.regs [0]) elif et == e.EMIT_MUL: if not get_val (fn_cur, val, s.regs [1]): return False out.put ('imul ' + s.regs [1] + ', ' + s.regs [0]) elif et == e.EMIT_DIV: if not get_val (fn_cur, val, s.regs [1]): return False out.put ('cltd') out.put ('idiv ' + s.regs [1]) elif et == e.EMIT_RES: if not set_val (fn_cur, val): return False elif et == e.EMIT_SET: if not get_val (fn_cur, val2, s.regs [0]): return False if not set_val (fn_cur, val): return False elif et == e.EMIT_ADDTO: if not get_val (fn_cur, val2, s.regs [1]): return False if not get_val (fn_cur, val, s.regs [0]): return False out.put ('add ' + s.regs [1] + ', ' + s.regs [0]) if not set_val (fn_cur, val): return False elif et == e.EMIT_SUBFROM: if not get_val (fn_cur, val2, s.regs [1]): return False if not get_val (fn_cur, val, s.regs [0]): return False out.put ('sub ' + s.regs [1] + ', ' + s.regs [0]) if not set_val (fn_cur, val): return False elif et == e.EMIT_MULTO: if not get_val (fn_cur, val2, s.regs [1]): return False if not get_val (fn_cur, val, s.regs [0]): return False out.put ('imul ' + s.regs [1] + ', ' + s.regs [0]) if not set_val (fn_cur, val): return False elif et == e.EMIT_DIVFROM: if not get_val (fn_cur, val2, s.regs [1]): return False if not get_val (fn_cur, val, s.regs [0]): return False out.put ('cltd') out.put ('idiv ' + s.regs [1]) if not set_val (fn_cur, val): return False else: out.put ('uknown emit type') return False return True def get_val (fn_cur, val, reg): val_type = get_val_type (val) if not val_type: out.error ('unknown val type "' + val + '"') return False elif val_type == e.VAL_LARRAY: datum = fn_cur.def_data ('.l' + str(fn_cur.data_larray_n),\ e.DATA_LARRAY, val) out.put ('mov ' + '$' + datum.name_s + ', ' + reg) elif val_type == e.VAL_STR: datum = fn_cur.def_data ('.s' + str(fn_cur.data_str_n),\ e.DATA_STR, val) out.put ('mov ' + '$' + datum.name_s + ', ' + reg) elif val_type == e.VAL_LONG: out.put ('mov $' + val + ', ' + reg) elif val_type == e.VAL_VAR: arg_i = fn_cur.get_arg (val) if arg_i: arg_i -= 1 if arg_i < s.arg_regs_n: _s = s.arg_regs [arg_i] else: _s = str((arg_i + 1) * s.long_len) + '(' + s.stack_regs [1] +\ ')' out.put ('mov ' + _s + ', ' + reg) else: var = fn_cur.get_or_def_var (val) if not var: return False out.put ('mov ' + var.datum.name_s + ', ' + reg) elif val_type == e.VAL_VAR_DEREF: _n = val [1:] arg_i = fn_cur.get_arg (_n) if arg_i: # TODO support this out.error ('dereferencing arg') return False else: var = fn_cur.get_or_def_var (_n) if not var: return False out.put ('mov ' + var.datum.name_s + ', ' + reg) out.put ('mov (' + reg + '), ' + reg) return True def set_val (fn_cur, val): reg0 = s.regs [0] reg2 = s.regs [2] val_type = get_val_type (val) if \ val_type == e.VAL_STR or\ val_type == e.VAL_LARRAY or\ val_type == e.VAL_LONG: out.error ('can\'t assign to this type') return False elif val_type == e.VAL_VAR: arg_i = fn_cur.get_arg (val) if arg_i: arg_i -= 1 if arg_i < s.arg_regs_n: _s = s.arg_regs [arg_i] else: _s = str((arg_i + 1) * s.long_len) + '(' + s.stack_regs [1] +\ ')' out.put ('mov ' + reg0 + ', ' + _s) else: var = fn_cur.get_or_def_var (val) if not var: return False out.put ('mov ' + reg0 + ', ' + var.datum.name_s) elif val_type == e.VAL_VAR_DEREF: _n = val [1:] arg_i = fn_cur.get_arg (_n) if arg_i: out.error ('can\'t modify function arg') return False else: var = fn_cur.get_or_def_var (_n) if not var: return False out.put ('mov ' + var.datum.name_s + ', ' + reg2) out.put ('mov ' + reg0 + ', (' + reg2 + ')') return True def get_val_type (_s): if not _s: return e.VAL_NONE elif _s [0] == '-' or _s.isdigit () or _s [0] == "'": return e.VAL_LONG elif _s [0] == '[': return e.VAL_LARRAY elif _s [0] == '"': return e.VAL_STR elif _s [0] == '@': return e.VAL_VAR_DEREF else: return e.VAL_VAR

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78

0.475484

1,406

9,300

2.920341

0.086771

0.081588

0.052606

0.05358

0.749391

0.688505

0.63322

0.594496

0.57964

0.566245

0

0.018797

0.370753

9,300

302

79

30.794702

0.682843

0.009247

0

0.550607

0

0.052305

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0.003311

0

1

0.020243

false

0.004049

0

0.210526

0

null

0

null

0

1

0

9e0bbeb93835b36e23fb310038a044e9818c4553

13,451

py

Python

kecpkg/commands/sign.py

jberends/kecpkg-tools

3c288c5b91b619fe76cd3622615f3ffe43509725

[ "Apache-2.0" ]

null

kecpkg/commands/sign.py

jberends/kecpkg-tools

3c288c5b91b619fe76cd3622615f3ffe43509725

[ "Apache-2.0" ]

7

2017-12-07T11:16:07.000Z

2019-12-11T15:25:07.000Z

kecpkg/commands/sign.py

KE-works/kecpkg-tools

3c288c5b91b619fe76cd3622615f3ffe43509725

[ "Apache-2.0" ]

null

import os import sys from pprint import pprint import click from pykechain.utils import temp_chdir from kecpkg.commands.utils import CONTEXT_SETTINGS from kecpkg.gpg import get_gpg, list_keys, hash_of_file from kecpkg.settings import SETTINGS_FILENAME, GNUPG_KECPKG_HOME, load_settings, DEFAULT_SETTINGS, ARTIFACTS_FILENAME, \ ARTIFACTS_SIG_FILENAME from kecpkg.utils import remove_path, echo_info, echo_success, echo_failure, get_package_dir, unzip_package @click.command(context_settings=CONTEXT_SETTINGS, short_help="Perform package signing and key management.") @click.argument('package', required=False) @click.option('--settings', '--config', '-s', 'settings_filename', help="path to the setting file (default `{}`".format(SETTINGS_FILENAME), type=click.Path(), default=SETTINGS_FILENAME) @click.option('--keyid', '--key-id', '-k', 'keyid', help="ID (name, email, KeyID) of the cryptographic key to do the operation with. ") # @click.option('--passphrase', '-p', 'sign_passphrase', hide_input=True, # help="Passphrase of the cryptographic key to sign the contents of the package. " # "Use in combination with `--sign` and `--keyid`") @click.option('--import-key', '--import', '-i', 'do_import', type=click.Path(exists=True), help="Import secret keyfile (in .asc) to the KECPKG keyring which will be used for signing. " "You can export a created key in gpg with `gpg -a --export-secret-key [keyID] > secret_key.asc`.") @click.option('--delete-key', '-d', 'do_delete_key', help="Delete key by its fingerprint permanently from the KECPKG keyring. To retrieve the full " "fingerprint of the key, use the `--list` option and look at the 'fingerprint' section.") @click.option('--create-key', '-c', 'do_create_key', is_flag=True, help="Create secret key and add it to the KECPKG keyring.") @click.option('--export-key', '--export', '-e', 'do_export_key', type=click.Path(), help="Export public key to filename with `--keyid KeyID` in .ASC format for public distribution.") @click.option('--clear-keyring', 'do_clear', is_flag=True, default=False, help="Clear all keys from the KECPKG keyring") @click.option('--list', '-l', 'do_list', is_flag=True, help="List all available keys in the KECPKG keyring") @click.option('--verify-kecpkg', 'do_verify_kecpkg', type=click.Path(exists=True), help="Verify contents and signature of an existing kecpkg.") @click.option('--yes', '-y', 'do_yes', is_flag=True, help="Don't ask questions, just do it.") @click.option('-v', '--verbose', help="Be more verbose", is_flag=True) def sign(package=None, **options): """Sign the package.""" # noinspection PyShadowingNames def _do_clear(options): echo_info("Clearing all keys from the KECPKG keyring") if not options.get('do_yes'): options['do_yes'] = click.confirm("Are you sure you want to clear the KECPKG keyring?", default=False) if options.get('do_yes'): remove_path(GNUPG_KECPKG_HOME) echo_success("Completed") sys.exit(0) else: echo_failure("Not removing the KECPKG keyring") sys.exit(1) def _do_list(gpg, explain=False): if explain: echo_info("Listing all keys from the KECPKG keyring") result = gpg.list_keys(secret=True) if len(result): from tabulate import tabulate print(tabulate(list_keys(gpg=gpg), headers=("Name", "Comment", "E-mail", "Expires", "Fingerprint"))) else: if explain: echo_info("No keys found in KECPKG keyring. Use `--import-key` or `--create-key` to add a " "secret key to the KECPKG keyring in order to sign KECPKG's.") sys.exit(1) # noinspection PyShadowingNames def _do_import(gpg, options): echo_info("Importing secret key into KECPKG keyring from '{}'".format(options.get('do_import'))) result = gpg.import_keys(open(os.path.abspath(options.get('do_import')), 'rb').read()) # pprint(result.__dict__) if result and result.sec_imported: echo_success("Succesfully imported secret key into the KECPKG keystore") _do_list(gpg=gpg) sys.exit(0) elif result and result.unchanged: echo_failure("Did not import the secret key into the KECPKG keystore. The key was already " "in place and was unchanged") _do_list(gpg=gpg) sys.exit(1) echo_failure("Did not import a secret key into the KECPKG keystore. Is something wrong " "with the file: '{}'? Are you sure it is a ASCII file containing a " "private key block?".format(options.get('do_import'))) sys.exit(1) # noinspection PyShadowingNames def _do_delete_key(gpg, options): echo_info("Deleting private key with ID '{}' from the KECPKG keyring".format(options.get('do_delete_key'))) # custom call to gpg using --delete-secret-and-public-key result = gpg.result_map['delete'](gpg) # noinspection PyProtectedMember p = gpg._open_subprocess(['--yes', '--delete-secret-and-public-key', options.get('do_delete_key')]) # noinspection PyProtectedMember gpg._collect_output(p, result, stdin=p.stdin) # result = gpg.delete_keys(fingerprints=options.get('do_delete_key'), # secret=True, # passphrase=options.get('sign_passphrase')) # pprint(result.__dict__) if result and result.stderr.find("failed") < 0: echo_success("Succesfully deleted key") _do_list(gpg=gpg) sys.exit(0) echo_failure("Could not delete key.") sys.exit(1) # noinspection PyShadowingNames def _do_create_key(gpg, options): echo_info("Will create a secret key and store it into the KECPKG keyring.") package_dir = get_package_dir(package_name=package, fail=False) settings = DEFAULT_SETTINGS if package_dir is not None: package_name = os.path.basename(package_dir) echo_info('Package `{}` has been selected'.format(package_name)) settings = load_settings(package_dir=package_dir, settings_filename=options.get('settings_filename')) key_info = {'name_real': click.prompt("Name", default=settings.get('name')), 'name_comment': click.prompt("Comment", default="KECPKG SIGNING KEY"), 'name_email': click.prompt("Email", default=settings.get('email')), 'expire_date': click.prompt("Expiration in months", default=12, value_proc=lambda i: "{}m".format(i)), 'key_type': 'RSA', 'key_length': 4096, 'key_usage': '', 'subkey_type': 'RSA', 'subkey_length': 4096, 'subkey_usage': 'encrypt,sign,auth', 'passphrase': ''} passphrase = click.prompt("Passphrase", hide_input=True) passphrase_confirmed = click.prompt("Confirm passphrase", hide_input=True) if passphrase == passphrase_confirmed: key_info['passphrase'] = passphrase else: raise ValueError("The passphrases did not match.") echo_info("Creating the secret key '{name_real} ({name_comment}) <{name_email}>'".format(**key_info)) echo_info("Please move around mouse or generate other activity to introduce sufficient entropy. " "This might take a minute...") result = gpg.gen_key(gpg.gen_key_input(**key_info)) pprint(result.__dict__) if result and result.stderr.find('KEY_CREATED'): echo_success("The key is succesfully created") _do_list(gpg=gpg) sys.exit(0) echo_failure("Could not generate the key due to an error: '{}'".format(result.stderr)) sys.exit(1) # noinspection PyShadowingNames def _do_export_key(gpg, options): """Export public key.""" echo_info("Exporting public key") if options.get('keyid') is None: _do_list(gpg=gpg) options['keyid'] = click.prompt("Provide KeyId (name, comment, email, fingerprint) of the key to export") result = gpg.export_keys(keyids=[options.get('keyid')], secret=False, armor=True) if result is not None: with open(options.get('do_export_key'), 'w') as fd: fd.write(result) echo_success("Sucessfully written public key to '{}'".format(options.get('do_export_key'))) sys.exit(0) echo_failure("Could not export key") sys.exit(1) # noinspection PyShadowingNames def _do_verify_kecpkg(gpg, options): """Verify the kecpkg.""" echo_info("Verify the contents of the KECPKG and if the KECPKG is signed with a valid signature.") current_working_directory = os.getcwd() with temp_chdir() as d: unzip_package(package_path=os.path.join(current_working_directory, options.get('do_verify_kecpkg')), target_path=d) verify_signature(d, artifacts_filename=ARTIFACTS_FILENAME, artifacts_sig_filename=ARTIFACTS_SIG_FILENAME) verify_artifacts_hashes(d, artifacts_filename=ARTIFACTS_FILENAME) sys.exit(0) # # Dispatcher to subfunctions # if options.get('do_clear'): _do_clear(options=options) elif options.get('do_list'): _do_list(gpg=get_gpg(), explain=True) elif options.get('do_import'): _do_import(gpg=get_gpg(), options=options) elif options.get('do_delete_key'): _do_delete_key(gpg=get_gpg(), options=options) elif options.get('do_create_key'): _do_create_key(gpg=get_gpg(), options=options) elif options.get('do_export_key'): _do_export_key(gpg=get_gpg(), options=options) elif options.get('do_verify_kecpkg'): _do_verify_kecpkg(gpg=get_gpg(), options=options) else: sys.exit(500) sys.exit(0) def verify_signature(package_dir, artifacts_filename, artifacts_sig_filename): """ Check signature of the package. :param package_dir: directory fullpath of the package :param artifacts_filename: path of the artifacts file :param artifacts_sig_filename: path of the artifacts signature file :return: None """ gpg = get_gpg() artifacts_fp = os.path.join(package_dir, artifacts_filename) artifacts_sig_fp = os.path.join(package_dir, artifacts_sig_filename) if not os.path.exists(artifacts_fp): echo_failure("Artifacts file does not exist: '{}'".format(artifacts_filename)) sys.exit(1) if not os.path.exists(artifacts_sig_fp): echo_failure("Artifacts signature file does not exist: '{}'. Is the package signed?". format(artifacts_filename)) sys.exit(1) with open(artifacts_sig_fp, 'rb') as sig_fd: results = gpg.verify_file(sig_fd, data_filename=artifacts_fp) if results.valid: echo_info("Verified the signature and the signature is valid") echo_info("Signed with: '{}'".format(results.username)) elif not results.valid: echo_failure("Signature of the package is invalid") echo_failure(pprint(results.__dict__)) sys.exit(1) def verify_artifacts_hashes(package_dir, artifacts_filename): """ Check the hashes of the artifacts in the package. :param package_dir: directory fullpath of the package :param artifacts_filename: filename of the artifacts file :return: """ artifacts_fp = os.path.join(package_dir, artifacts_filename) if not os.path.exists(artifacts_fp): echo_failure("Artifacts file does not exist: '{}'".format(artifacts_filename)) sys.exit(1) with open(artifacts_fp, 'r') as fd: artifacts = fd.readlines() # process the file contents # A line is "README.md,sha256=d831....ccf79a,336" # ^filename ^algo ^hash ^size in bytes fails = [] for af in artifacts: # noinspection PyShadowingBuiltins,PyShadowingBuiltins filename, hash, orig_size = af.split(',') algorithm, orig_hash = hash.split('=') fp = os.path.join(package_dir, filename) if os.path.exists(fp): found_hash = hash_of_file(fp, algorithm) found_size = os.stat(fp).st_size if found_hash != orig_hash.strip() or found_size != int(orig_size.strip()): fails.append("File '{}' is changed in the package.".format(filename)) fails.append("File '{}' original checksum: '{}', found: '{}'".format(filename, orig_hash, found_hash)) fails.append("File '{}' original size: {}, found: {}".format(filename, orig_size, found_size)) else: fails.append("File '{}' does not exist".format(filename)) if fails: echo_failure('The package has been changed after building the package.') for fail in fails: print(fail) sys.exit(1) else: echo_info("Package contents succesfully verified.")

46.867596

120

0.63564

1,703

13,451

4.840282

0.182032

0.026689

0.026204

0.02402

0.273323

0.186219

0.140968

0.11258

0.101905

0.068179

0

0.004342

0.246673

13,451

286

121

47.031469

0.809138

0.105494

0

0.180095

0

0.009479

0.297193

0.002514

0

1

0.047393

false

0.028436

0.109005

0

0.156398

0.037915

0

null

0

null

0

1

0

9e0dd95d1aaf80cae2655fcee6b6427ac437b94c

10,563

py

Python

doctor/lib/utils.py

freelawproject/doctor

3858b6f5de7903353f4376303329a986db5b7983

[ "BSD-2-Clause" ]

null

doctor/lib/utils.py

freelawproject/doctor

3858b6f5de7903353f4376303329a986db5b7983

[ "BSD-2-Clause" ]

null

doctor/lib/utils.py

freelawproject/doctor

3858b6f5de7903353f4376303329a986db5b7983

[ "BSD-2-Clause" ]

null

import datetime import io import os import re import subprocess import warnings from collections import namedtuple from decimal import Decimal from pathlib import Path import six from PyPDF2 import PdfFileMerger from reportlab.pdfgen import canvas class DoctorUnicodeDecodeError(UnicodeDecodeError): def __init__(self, obj, *args): self.obj = obj UnicodeDecodeError.__init__(self, *args) def __str__(self): original = UnicodeDecodeError.__str__(self) return f"{original}. You passed in {self.obj!r} ({type(self.obj)})" def force_bytes(s, encoding="utf-8", strings_only=False, errors="strict"): """ Similar to smart_bytes, except that lazy instances are resolved to strings, rather than kept as lazy objects. If strings_only is True, don't convert (some) non-string-like objects. """ # Handle the common case first for performance reasons. if isinstance(s, bytes): if encoding == "utf-8": return s else: return s.decode("utf-8", errors).encode(encoding, errors) if strings_only and is_protected_type(s): return s if isinstance(s, six.memoryview): return bytes(s) if isinstance(s, Promise): return six.text_type(s).encode(encoding, errors) if not isinstance(s, six.string_types): try: if six.PY3: return six.text_type(s).encode(encoding) else: return bytes(s) except UnicodeEncodeError: if isinstance(s, Exception): # An Exception subclass containing non-ASCII data that doesn't # know how to print itself properly. We shouldn't raise a # further exception. return b" ".join( force_bytes(arg, encoding, strings_only, errors) for arg in s ) return six.text_type(s).encode(encoding, errors) else: return s.encode(encoding, errors) def force_text(s, encoding="utf-8", strings_only=False, errors="strict"): """ Similar to smart_text, except that lazy instances are resolved to strings, rather than kept as lazy objects. If strings_only is True, don't convert (some) non-string-like objects. """ # Handle the common case first for performance reasons. if issubclass(type(s), six.text_type): return s if strings_only and is_protected_type(s): return s try: if not issubclass(type(s), six.string_types): if six.PY3: if isinstance(s, bytes): s = six.text_type(s, encoding, errors) else: s = six.text_type(s) elif hasattr(s, "__unicode__"): s = six.text_type(s) else: s = six.text_type(bytes(s), encoding, errors) else: # Note: We use .decode() here, instead of six.text_type(s, encoding, # errors), so that if s is a SafeBytes, it ends up being a # SafeText at the end. s = s.decode(encoding, errors) except UnicodeDecodeError as e: if not isinstance(s, Exception): raise DoctorUnicodeDecodeError(s, *e.args) else: # If we get to here, the caller has passed in an Exception # subclass populated with non-ASCII bytestring data without a # working unicode method. Try to handle this without raising a # further exception by individually forcing the exception args # to unicode. s = " ".join(force_text(arg, encoding, strings_only, errors) for arg in s) return s def smart_text(s, encoding="utf-8", strings_only=False, errors="strict"): """ Returns a text object representing 's' -- unicode on Python 2 and str on Python 3. Treats bytestrings using the 'encoding' codec. If strings_only is True, don't convert (some) non-string-like objects. """ if isinstance(s, Promise): # The input is the result of a gettext_lazy() call. return s return force_text(s, encoding, strings_only, errors) class Promise(object): """ This is just a base class for the proxy class created in the closure of the lazy function. It can be used to recognize promises in code. """ pass _PROTECTED_TYPES = six.integer_types + ( type(None), float, Decimal, datetime.datetime, datetime.date, datetime.time, ) def is_protected_type(obj): """Determine if the object instance is of a protected type. Objects of protected types are preserved as-is when passed to force_text(strings_only=True). """ return isinstance(obj, _PROTECTED_TYPES) def audio_encoder(data): return namedtuple("AudioFile", data.keys())(*data.values()) def ignore_warnings(test_func): def do_test(self, *args, **kwargs): with warnings.catch_warnings(): warnings.simplefilter("ignore", ResourceWarning) warnings.simplefilter("ignore", DeprecationWarning) test_func(self, *args, **kwargs) return do_test def make_png_thumbnail_for_instance(filepath, max_dimension): """Abstract function for making a thumbnail for a PDF See helper functions below for how to use this in a simple way. :param filepath: The attr where the PDF is located on the item :param max_dimension: The longest you want any edge to be :param response: Flask response object """ command = [ "pdftoppm", "-singlefile", "-f", "1", "-scale-to", str(max_dimension), filepath, "-png", ] p = subprocess.Popen( command, close_fds=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) stdout, stderr = p.communicate() return stdout, stderr.decode("utf-8"), str(p.returncode) def make_png_thumbnails(filepath, max_dimension, pages, directory): """Abstract function for making a thumbnail for a PDF See helper functions below for how to use this in a simple way. :param filepath: The attr where the PDF is located on the item :param max_dimension: The longest you want any edge to be :param response: Flask response object """ for page in pages: command = [ "pdftoppm", "-singlefile", "-f", str(page), "-scale-to", str(max_dimension), filepath, "-png", f"{directory.name}/thumb-{page}", ] p = subprocess.Popen( command, close_fds=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) p.communicate() def pdf_bytes_from_image_array(image_list, output_path) -> None: """Make a pdf given an array of Image files :param image_list: List of images :type image_list: list :return: pdf_data :type pdf_data: PDF as bytes """ image_list[0].save( output_path, "PDF", resolution=100.0, save_all=True, append_images=image_list[1:], ) del image_list def strip_metadata_from_path(file_path): """Convert PDF file into PDF and remove metadata from it Stripping the metadata allows us to hash the PDFs :param pdf_bytes: PDF as binary content :return: PDF bytes with metadata removed. """ with open(file_path, "rb") as f: pdf_merger = PdfFileMerger() pdf_merger.append(io.BytesIO(f.read())) pdf_merger.addMetadata({"/CreationDate": "", "/ModDate": ""}) byte_writer = io.BytesIO() pdf_merger.write(byte_writer) return force_bytes(byte_writer.getvalue()) def strip_metadata_from_bytes(pdf_bytes): """Convert PDF bytes into PDF and remove metadata from it Stripping the metadata allows us to hash the PDFs :param pdf_bytes: PDF as binary content :return: PDF bytes with metadata removed. """ pdf_merger = PdfFileMerger() pdf_merger.append(io.BytesIO(pdf_bytes)) pdf_merger.addMetadata({"/CreationDate": "", "/ModDate": ""}) byte_writer = io.BytesIO() pdf_merger.write(byte_writer) return force_bytes(byte_writer.getvalue()) def cleanup_form(form): """Clean up a form object""" os.remove(form.cleaned_data["fp"]) def make_file(filename, dir=None): filepath = f"{Path.cwd()}/doctor/test_assets/{filename}" with open(filepath, "rb") as f: return {"file": (filename, f.read())} def make_buffer(filename, dir=None): filepath = f"{Path.cwd()}/doctor/test_assets/{filename}" with open(filepath, "rb") as f: return {"file": ("filename", f.read())} def pdf_has_images(path: str) -> bool: """Check raw PDF for embedded images. We need to check if a PDF contains any images. If a PDF contains images it likely has content that needs to be scanned. :param path: Location of PDF to process. :return: Does the PDF contain images? :type: bool """ with open(path, "rb") as pdf_file: pdf_bytes = pdf_file.read() return True if re.search(rb"/Image ?/", pdf_bytes) else False def ocr_needed(path: str, content: str) -> bool: """Check if OCR is needed on a PDF Check if images are in PDF or content is empty. :param path: The path to the PDF :param content: The content extracted from the PDF. :return: Whether OCR should be run on the document. """ if content.strip() == "" or pdf_has_images(path): return True return False def make_page_with_text(page, data, h, w): """Make a page with text :param page: :param data: :param h: :param w: :return: """ packet = io.BytesIO() can = canvas.Canvas(packet, pagesize=(w, h)) # Set to a standard size and font for now. can.setFont("Helvetica", 9) # Make the text transparent can.setFillAlpha(0) for i in range(len(data["level"])): try: letter, (x, y, ww, hh), pg = ( data["text"][i], (data["left"][i], data["top"][i], data["width"][i], data["height"][i]), data["page_num"][i], ) except: continue # Adjust the text to an 8.5 by 11 inch page sub = ((11 * 72) / h) * int(hh) x = ((8.5 * 72) / w) * int(x) y = ((11 * 72) / h) * int(y) yy = (11 * 72) - y if int(page) == int(pg): can.drawString(x, yy - sub, letter) can.showPage() can.save() packet.seek(0) return packet

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87

0.619142

1,408

10,563

4.539773

0.253551

0.020651

0.015488

0.013141

0.371715

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0.340895

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0.277667

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345

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30.617391

0.832372

0.312222

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false

0.010363

0.062176

0.005181

0.321244

0

null

0

null

0

1

0

9e0e1c62ee116428b55cffa380260139fb9ea5d8

906

py

Python

src/xrl/env_tester.py

k4ntz/XmodRL

dffb416bcd91010d8075ee1ac00cc4b9a3021967

[ "MIT" ]

null

src/xrl/env_tester.py

k4ntz/XmodRL

dffb416bcd91010d8075ee1ac00cc4b9a3021967

[ "MIT" ]

null

src/xrl/env_tester.py

k4ntz/XmodRL

dffb416bcd91010d8075ee1ac00cc4b9a3021967

[ "MIT" ]

1

2021-11-10T18:09:27.000Z

import gym import numpy as np import os import random import matplotlib.pyplot as plt from atariari.benchmark.wrapper import AtariARIWrapper # YarsRevenge # env_name = "DemonAttackDeterministic-v4" def print_labels(env_info): # extract raw features labels = env_info["labels"] print(labels) env = AtariARIWrapper(gym.make(env_name)) name = env.unwrapped.spec.id #ballgame = any(game in name for game in ["Pong", "Tennis"]) print(np.int16(3)) üsad n_actions = env.action_space.n _ = env.reset() obs, _, done, info = env.step(0) r = 0 for t in range(50000): plt.imshow(env.render(mode='rgb_array'), interpolation='none') plt.plot() plt.pause(0.0001) # pause a bit so that plots are updated action = random.randint(0, n_actions - 1) obs, reward, done, info = env.step(action) r += reward print(reward) print_labels(info) if(done): break print(r)

22.65

66

0.695364

136

906

4.544118

0.558824

0.053398

0.045307

0.048544

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0.024357

0.184327

906

40

67

22.65

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0.059663

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0.233333

0.2

0

null

0

null

0

1

0

9e0ef102e2826e6b9febd80bed5d0193a3687555

2,711

py

Python

packages/lrn/model/question.py

genropy/learn

019286c1fa1548482f64ccbd91082e069ec62a56

[ "MIT" ]

3

2019-11-16T12:38:20.000Z

2019-11-17T08:44:41.000Z

packages/lrn/model/question.py

genropy/learn

019286c1fa1548482f64ccbd91082e069ec62a56

[ "MIT" ]

null

packages/lrn/model/question.py

genropy/learn

019286c1fa1548482f64ccbd91082e069ec62a56

[ "MIT" ]

5

2019-11-16T16:22:10.000Z

2019-11-18T21:46:50.000Z

# encoding: utf-8 from datetime import datetime class Table(object): def config_db(self,pkg): tbl=pkg.table('question', pkey='id', name_long='!![en]Question', name_plural='!![en]Questions',caption_field='question') self.sysFields(tbl, draftField=True) tbl.column('question',name_long='!![en]Question', validate_notnull=True) tbl.column('description', name_long='!![en]Description') tbl.column('details', name_long='!![en]Details') tbl.column('user_id',size='22', group='_', name_long='!![en]Inserted by' ).relation('adm.user.id', relation_name='myquestions', mode='foreignkey', onDelete='raise') tbl.column('approval_ts', dtype='DH', name_long='!![en]Approval TS') tbl.column('approved_by_user_id', size='22', group='_', name_long='!![en]Approved by' ).relation('adm.user.id', relation_name='approved_questions', mode='foreignkey', onDelete='raise') tbl.column('main_topic_id',size='22', group='_', name_long='!![en]Main topic' ).relation('topic.id', relation_name='questions', mode='foreignkey', onDelete='setnull') tbl.column('main_answer_id',size='22', group='_', name_long='!![en]Main answer' ).relation('answer.id', relation_name='questions', mode='foreignkey', onDelete='setnull') #tbl.formulaColumn('__protected_by_approval_ts',"""($approval_ts IS NOT NULL AND $approved_by_user_id!=:env_user_id)""",dtype='B') def defaultValues(self): user_id = self.db.currentEnv.get('user_id') #Se l'utente ha i giusti requisiti le sue domande e le sue risposte non nascono com ebozza if 'admin' in self.db.currentEnv['userTags']: #posso pensare ad una condizione migliore e più sofisticata return dict( __is_draft = False, approval_ts = datetime.now(), approved_by_user_id = user_id, user_id=user_id) return dict(__is_draft=True, user_id = user_id) def trigger_onUpdating(self, record, old_record): #Quando un record passa da bozza ad approvato metto utente approvatore e timestamp di approvazione if old_record['__is_draft'] and not record['__is_draft']: record['approval_ts'] = datetime.now() record['approved_by_user_id'] = self.db.currentEnv.get('user_id')

54.22

138

0.570638

309

2,711

4.770227

0.368932

0.065129

0.061058

0.035278

0.297151

0.232022

0.189959

0.074627

0

0.004719

0.29657

2,711

50

139

54.22

0.768222

0.143121

0

0.210526

0

0.218292

0

1

0.078947

false

0

0.026316

0

0.184211

0

null

0

null

0

1

0

9e11d3f12bcf35bac083ace9a1b7490250555694

3,087

py

Python

core/api/OxfordAPI.py

vimarind/Complete-GRE-Vocab

6dc8bb8ed0506ed572edd1a01a456d9a27238c94

[ "MIT" ]

null

core/api/OxfordAPI.py

vimarind/Complete-GRE-Vocab

6dc8bb8ed0506ed572edd1a01a456d9a27238c94

[ "MIT" ]

null

core/api/OxfordAPI.py

vimarind/Complete-GRE-Vocab

6dc8bb8ed0506ed572edd1a01a456d9a27238c94

[ "MIT" ]

null

import json import requests from os import path class OxfordAPI: def __init__(self, app_id, app_key, cache_path): self.app_id = app_id self.app_key = app_key self.cache_path = cache_path def __parse_sense(self, word, sense): for definition in sense.get('definitions', list()): word.definitions.append(definition) for example in sense.get('examples', list()): word.examples.append(example.get('text', None)) for synonym in sense.get('synonyms', list()): word.synonyms.append(synonym.get('text', None)) for subsense in sense.get('subsenses', list()): self.__parse_sense(word, subsense) def __parse_pronunciation(self, word, pronunciation): audioFile = pronunciation.get('audioFile', None) if audioFile is not None: word.audio_file = audioFile def __parse_entry(self, word, entry): for pronunciation in entry.get('pronunciations', list()): self.__parse_pronunciation(word, pronunciation) for sense in entry.get('senses', list()): self.__parse_sense(word, sense) def __parse_lexical_entry(self, word, lexical_entry): for entry in lexical_entry.get('entries', list()): self.__parse_entry(word, entry) def __parse_result(self, word, result): for lexical_entry in result.get('lexicalEntries', list()): self.__parse_lexical_entry(word, lexical_entry) def __parse_word(self, word, data): success = False if data.get('error') is None: for result in data.get('results', list()): self.__parse_result(word, result) success = True return success def __get_word_data(self, word): filepath = self.cache_path + word.text + '.json' with open(filepath, 'w') as file: url = "https://od-api.oxforddictionaries.com/api/v2/words/en-us?q=" + word.text r = requests.get(url, headers={"app_id": self.app_id, "app_key": self.app_key}) file.write(r.text) return r.json() def get_word(self, word): """ Populates the given word object with the relevant information from the Oxford Dictionary API. First, the word is looked for in the cache folder, if it exists, load that data. Otherwise, the information is requested from the OxfordAPI and stored in the cache folder. :param word: The word object to be populated. :return: A boolean indicating if the operation has been successful or not. """ success = False if path.exists(self.cache_path): filepath = self.cache_path + word.text + '.json' if path.exists(filepath): with open(filepath, 'r') as file: data = json.load(file) else: data = self.__get_word_data(word) success = self.__parse_word(word, data) else: print('OxfordAPI: Please provide a valid cache path.') return success

38.111111

117

0.618724

390

3,087

4.705128

0.279487

0.034877

0.042507

0.019619

0.076294

0.035967

0

0.000449

0.278264

3,087

80

118

38.5875

0.82316

0.12504

0

0.135593

0

0.016949

0.088948

0

1

0.152542

false

0

0.050847

0

0.271186

0.016949

0

null

0

null

0

1

0

9e1202ada111dfedf3e1239998ddc9e7e0c2bac2

2,568

py

Python

linked_list.py

bentsi/data-structures

ce4a3a49ec131550ec0b77875b8f0367addcca05

[ "Apache-2.0" ]

null

linked_list.py

bentsi/data-structures

ce4a3a49ec131550ec0b77875b8f0367addcca05

[ "Apache-2.0" ]

null

linked_list.py

bentsi/data-structures

ce4a3a49ec131550ec0b77875b8f0367addcca05

[ "Apache-2.0" ]

1

2021-01-10T15:41:50.000Z

class Node: def __init__(self, data=None): self.data = data self.next = None class LinkedListIndexError(IndexError): pass class LinkedList: def __init__(self): self.head = Node() def _get_last_node(self): pointer = self.head while pointer.next is not None: pointer = pointer.next return pointer def get_last_node(self): return self._get_last_node().data def append(self, data): new_node = Node(data=data) last = self._get_last_node() last.next = new_node def print(self): print(self.__str__()) def __str__(self): pointer = self.head idx = 0 ll_str = "" while pointer.next is not None: pointer = pointer.next ll_str += f"{idx}: {pointer.data}\n" idx += 1 return ll_str def length(self): pointer = self.head counter = 0 while pointer.next is not None: pointer = pointer.next counter += 1 return counter def _get(self, index): pointer = self.head counter = 0 if not(0 <= index < self.length()): raise LinkedListIndexError(f"Index '{index}' does not exist") while pointer.next is not None: pointer = pointer.next if counter == index: return pointer counter += 1 def get(self, index): return self._get(index=index).data def __getitem__(self, item): return self.get(index=item) def erase(self, index): if index == 0: prev = self.head else: prev = self._get(index=index - 1) to_del = prev.next prev.next = to_del.next data = to_del.data del to_del return data def set(self, index, new_data): node = self._get(index=index) node.data = new_data def __del__(self): length = self.length() while length != 0: self.erase(index=length - 1) length -= 1 del self.head if __name__ == '__main__': ll = LinkedList() ll.append(data="Fedor") ll.append(data="Julia") ll.append(data="Bentsi") ll.print() print("Length of the Linked list is: ", ll.length()) idx = 1 print(ll.get(index=idx)) print(f"Data at index {idx} is {ll[idx]}") print("Deleted: ", ll.erase(index=0)) ll.append(data="Fedor") ll.append(data="Bentsi") ll.set(index=3, new_data="Tim Peters") print(ll)

24.457143

73

0.550234

326

2,568

4.153374

0.180982

0.064993

0.044313

0.053176

0.251108

0.169867

0.127031

0

0.008824

0.338006

2,568

104

74

24.692308

0.787647

0

0.255814

0

0.06581

0

1

0.162791

false

0.011628

0

0.034884

0.290698

0.093023

0

null

0

null

0

1

0

9e14d6737904e50f196708249c8435de6151b062

2,768

py

Python

custom_html_validator/custom_html_validator.py

koan-u/custom_html_validator

1a6735146e64d3c346201d10eddfd9ebfe1377c2

[ "MIT" ]

null

custom_html_validator/custom_html_validator.py

koan-u/custom_html_validator

1a6735146e64d3c346201d10eddfd9ebfe1377c2

[ "MIT" ]

null

custom_html_validator/custom_html_validator.py

koan-u/custom_html_validator

1a6735146e64d3c346201d10eddfd9ebfe1377c2

[ "MIT" ]

null

from html.parser import HTMLParser class CustomHTMLValidater(HTMLParser): __SINGLE_TAGS = [ 'area','base','br','col','embed', 'hr','img','input','keygen','link', 'meta','param','source','track','wbr' ] def __init__(self): HTMLParser.__init__(self) self.reset(True) def reset(self, tag_reset = False): HTMLParser.reset(self) self.__core = { 'status': 0, 'detail':'', 'detected_list':[] } if tag_reset: self.__allowed_tags = [] return def set_allowed_tags(self, __allowed_tags): self.__allowed_tags = __allowed_tags return def handle_starttag(self,tag,attrs): if self.__core['status'] == 0: if not tag in self.__allowed_tags: self.__core['status'] = -1 self.__core['detail'] = 'not_allowed_tag' else: for attr in attrs: if not attr[0] in self.__allowed_tags[tag]: self.__core['status'] = -1 self.__core['detail'] = 'not_allowed_attr' return detected = { 'tag': tag, 'attr': attrs, 'complete': False } self.__core['detected_list'].append(detected) return def handle_endtag(self,tag): if self.__core['status'] == 0: last_index = len(self.__core['detected_list']) - 1 for index in range(last_index, -1, -1): data = self.__core['detected_list'][index] if not data['complete']: if data['tag'] == tag: data['complete'] = True return elif data['tag'] in self.__SINGLE_TAGS: data['complete'] = True else: break self.__core['status'] = -1 self.__core['detail'] = 'Construction Error' return def close(self): HTMLParser.close(self) if self.__core['status'] == 0: errored = False for data in self.__core['detected_list']: if not data['complete']: if data['tag'] in self.__SINGLE_TAGS: data['complete'] = True continue self.__core['status'] = -1 self.__core['detail'] = 'Construction Error' errored = True break if not errored: self.__core['status'] = 1 self.__core['detail'] = 'ok' return self.__core

33.756098

66

0.462789

264

2,768

4.511364

0.25

0.127624

0.105793

0.062972

0.347607

0.270361

0.208228

0

0.00811

0.420882

2,768

81

67

34.17284

0.734872

0

0.324324

0

0.126084

0

1

0.081081

false

0

0.013514

0

0.216216

0

null

0

null

0

1

0

9e14d840b0d68fa20db94e8f512ad11ba709e64f

1,841

py

Python

boltfile.py

arahmanhamdy/bolt

8f5d9b8149db833b54a7b353162b2c28a53c8aff

[ "MIT" ]

15

2016-10-21T14:30:38.000Z

2021-10-12T04:50:48.000Z

boltfile.py

arahmanhamdy/bolt

8f5d9b8149db833b54a7b353162b2c28a53c8aff

[ "MIT" ]

51

2016-02-05T01:24:32.000Z

2019-12-09T16:52:20.000Z

boltfile.py

arahmanhamdy/bolt

8f5d9b8149db833b54a7b353162b2c28a53c8aff

[ "MIT" ]

6

2016-10-17T13:48:16.000Z

2021-03-28T20:40:14.000Z

import logging import os.path import bolt import bolt.about PROJECT_ROOT = os.path.abspath(os.path.dirname(__file__)) _src_dir = os.path.join(PROJECT_ROOT, 'bolt') _test_dir = os.path.join(PROJECT_ROOT, 'test') _output_dir = os.path.join(PROJECT_ROOT, 'output') _coverage_dir = os.path.join(_output_dir, 'coverage') config = { 'pip': { 'command': 'install', 'options': { 'r': './requirements.txt' } }, 'delete-pyc': { 'sourcedir': _src_dir, 'recursive': True, 'test-pyc': { 'sourcedir': _test_dir, } }, 'conttest' : { 'task': 'ut' }, 'mkdir': { 'directory': _output_dir, }, 'nose': { 'directory': _test_dir, 'ci': { 'options': { 'with-xunit': True, 'xunit-file': os.path.join(_output_dir, 'unit_tests_log.xml'), 'with-coverage': True, 'cover-erase': True, 'cover-package': 'bolt', 'cover-html': True, 'cover-html-dir': _coverage_dir, 'cover-branches': True, } } }, 'setup': { 'command': 'bdist_wheel', 'egg-info': { 'command': 'egg_info' } }, 'coverage': { 'task': 'nose', 'include': ['bolt'], 'output': os.path.join(_output_dir, 'ut_coverage') } } # Development tasks bolt.register_task('clear-pyc', ['delete-pyc', 'delete-pyc.test-pyc']) bolt.register_task('ut', ['clear-pyc', 'nose']) bolt.register_task('ct', ['conttest']) bolt.register_task('pack', ['setup', 'setup.egg-info']) # CI/CD tasks bolt.register_task('run-unit-tests', ['clear-pyc', 'mkdir', 'nose.ci']) # Default task (not final). bolt.register_task('default', ['pip', 'ut'])

25.569444

78

0.523628

197

1,841

4.675127

0.329949

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null

0

null

0

1

0

9e158c914469c96413a23f9b7926f662ec188191

1,309

py

Python

assignments/04_head/head.py

emma-huffman/biosystems-analytics-2020

eaf9c084407fa6d25b815b7d63077ed9aec53447

[ "MIT" ]

null

assignments/04_head/head.py

emma-huffman/biosystems-analytics-2020

eaf9c084407fa6d25b815b7d63077ed9aec53447

[ "MIT" ]

null

assignments/04_head/head.py

emma-huffman/biosystems-analytics-2020

eaf9c084407fa6d25b815b7d63077ed9aec53447

[ "MIT" ]

null

#!/usr/bin/env python3 """ Author : Me <me@foo.com> Date : today Purpose: Rock the Casbah """ import argparse import io import os import sys # -------------------------------------------------- def get_args(): """Get command-line arguments""" parser = argparse.ArgumentParser( description='Rock the Casbah', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('-n', '--num', help='Number of lines', metavar='int', type=int, default=10) parser.add_argument('file', help='Input File', type=argparse.FileType('r')) args = parser.parse_args() if not args.num > 0: parser.error(f'--num "{args.num}" must be greater than 0') return args # -------------------------------------------------- def main(): """Make a jazz noise here""" args = get_args() for fh in args.file: print(fh.name) num_line = 0 for line in fh: num_line += 1 print(line, end='') if num_line == args.num: break # -------------------------------------------------- if __name__ == '__main__': main()

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0.060606

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null

0

null

0

1

0

9e1798f13a1e5958c9273e51efaff12141f4e76c

9,497

py

Python

js_components/cms_plugins.py

compoundpartners/js-components

a58a944254354078a0a7b53a4c9a7df50790267a

[ "BSD-3-Clause" ]

null

js_components/cms_plugins.py

compoundpartners/js-components

a58a944254354078a0a7b53a4c9a7df50790267a

[ "BSD-3-Clause" ]

null

js_components/cms_plugins.py

compoundpartners/js-components

a58a944254354078a0a7b53a4c9a7df50790267a

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals import functools import six from django.utils.translation import ugettext_lazy as _ from django.template import TemplateDoesNotExist from django.template.loader import select_template from cms.plugin_base import CMSPluginBase, CMSPluginBaseMetaclass from cms.plugin_pool import plugin_pool from . import models, forms from .utils.urlmatch import urlmatch from .constants import ( HIDE_PROMO, HIDE_PROMO_ROLLOVER, HIDE_PROMO_VIDEO, HIDE_TWITTER, HIDE_COUNTERS, HIDE_RAWHTML, HIDE_GATED_CONTENT, HIDE_FLOAT, HIDE_LIGHTBOX, CUSTOM_PLUGINS, PROMO_CHILD_CLASSES, ) class LayoutMixin(): def get_layout(self, context, instance, placeholder): return instance.layout def get_render_template(self, context, instance, placeholder): layout = self.get_layout(context, instance, placeholder) if layout: template = self.TEMPLATE_NAME % layout try: select_template([template]) return template except TemplateDoesNotExist: pass return self.render_template def render(self, context, instance, placeholder): context.update({ 'instance': instance, 'placeholder': placeholder, }) return context class PromoUnitPlugin(LayoutMixin, CMSPluginBase): module = 'JumpSuite Componens' TEMPLATE_NAME = 'js_components/promo_%s.html' name = _('Promo Unit') model = models.PromoUnit form = forms.PromoUnitForm render_template = 'js_components/promo.html' change_form_template = 'admin/js_components/float.html' allow_children = True if PROMO_CHILD_CLASSES else False child_classes = PROMO_CHILD_CLASSES main_fields = [ 'layout', 'alignment', 'title', 'subtitle', 'color', 'image', 'svg', 'icon', 'content', 'rollover_content', 'background_video', 'link_text', 'link_url', ('file_src', 'show_filesize'), 'open_in_new_window', 'full_height', ] if HIDE_PROMO_ROLLOVER: main_fields.remove('rollover_content') if HIDE_PROMO_VIDEO: main_fields.remove('background_video') fieldsets = [ (None, { 'fields': main_fields }), (_('Advanced settings'), { 'classes': ('collapse',), 'fields': ( 'modal_id', 'attributes', ) }), ] if not HIDE_PROMO: plugin_pool.register_plugin(PromoUnitPlugin) class TwitterFeedPlugin(LayoutMixin, CMSPluginBase): module = 'JumpSuite Componens' TEMPLATE_NAME = 'js_components/twitter_%s.html' name = _('Twitter Feed') model = models.TwitterFeed form = forms.TwitterFeedForm render_template = 'js_components/twitter.html' if not HIDE_TWITTER: plugin_pool.register_plugin(TwitterFeedPlugin) class CountersContainerPlugin(LayoutMixin, CMSPluginBase): module = 'JumpSuite Componens' TEMPLATE_NAME = 'js_components/counters_%s.html' name = _('Counters Container (DO NOT USE, NEED REMOVE)') model = models.CountersContainer form = forms.CountersContainerForm render_template = 'js_components/counters.html' allow_children = True child_classes = ['CounterPlugin'] parent_classes = ['Bootstrap4GridRowPlugin'] class CounterPlugin(LayoutMixin, CMSPluginBase): module = 'JumpSuite Componens' TEMPLATE_NAME = 'js_components/counter_%s.html' name = _('Counter') model = models.Counter form = forms.CounterForm render_template = 'js_components/counter.html' if not HIDE_COUNTERS: plugin_pool.register_plugin(CountersContainerPlugin) plugin_pool.register_plugin(CounterPlugin) #if 'Bootstrap4GridRowPlugin' in plugin_pool.plugins: #plugin_pool.plugins['Bootstrap4GridRowPlugin'].child_classes.append('CountersContainerPlugin') class RawHTMLPlugin(CMSPluginBase): module = 'JumpSuite Componens' name = _('Raw HTML') model = models.RawHTML render_template = 'js_components/html.html' def render(self, context, instance, placeholder): context.update({ 'instance': instance, 'placeholder': placeholder, 'html': instance.body, }) return context class RawHTMLWithIDPlugin(CMSPluginBase): module = 'JumpSuite Componens' name = _('Raw HTML with ID') model = models.RawHTMLWithID render_template = 'js_components/html.html' def render(self, context, instance, placeholder): request = context['request'] html = instance.body for param in instance.parameters.split(','): param = param.strip() key = '[%s]' % param.upper() html = html.replace(key, request.GET.get(param) or request.POST.get(param, '')) context.update({ 'instance': instance, 'placeholder': placeholder, 'html': html, }) return context if not HIDE_RAWHTML: plugin_pool.register_plugin(RawHTMLPlugin) plugin_pool.register_plugin(RawHTMLWithIDPlugin) @plugin_pool.register_plugin class CustomPlugin(LayoutMixin, CMSPluginBase): module = 'JumpSuite Componens' TEMPLATE_NAME = 'js_components/custom_%s.html' name = _('Custom') model = models.Custom form = forms.CustomForm render_template = 'js_components/custom.html' def get_form(self, request, obj=None, **kwargs): Form = super().get_form(request, obj=None, **kwargs) if self.name in CUSTOM_PLUGINS: Form.plugin_name=self.name return Form for name, parameters in CUSTOM_PLUGINS.items(): p = type( str(name.replace(' ', '') + 'Plugin'), (CustomPlugin,), {'name': name}, ) plugin_pool.register_plugin(p) class GatedContentPlugin(LayoutMixin, CMSPluginBase): module = 'JumpSuite Componens' TEMPLATE_NAME = 'js_components/gated_content_%s.html' name = _('Gated Content') model = models.GatedContent form = forms.GatedContentForm render_template = 'js_components/gated_content.html' allow_children = True if not HIDE_GATED_CONTENT: plugin_pool.register_plugin(GatedContentPlugin) @plugin_pool.register_plugin class AnimatePlugin(LayoutMixin, CMSPluginBase): module = 'JumpSuite Componens' TEMPLATE_NAME = 'js_components/animate_%s.html' name = _('Animate') model = models.Animate form = forms.AnimateForm render_template = 'js_components/animate.html' allow_children = True @plugin_pool.register_plugin class JSFolderPlugin(LayoutMixin, CMSPluginBase): module = 'JumpSuite Componens' TEMPLATE_NAME = 'js_components/folder_%s.html' name = _('Filer listing') model = models.Folder form = forms.FolderForm render_template = 'js_components/folder.html' def render(self, context, instance, placeholder): request = context['request'] files = [] if instance.folder: files = instance.folder.files.all() if instance.order_by: files = files.order_by(instance.order_by) context.update({ 'instance': instance, 'placeholder': placeholder, 'files': files, }) return context @plugin_pool.register_plugin class IncludeExcludeContainer(CMSPluginBase): module = 'JumpSuite Componens' name = _('Include/Exclude Container') model = models.IncludeExcludeContainer render_template = 'js_components/container.html' change_form_template = 'admin/js_components/change_form_container.html' allow_children = True cache = False def render(self, context, instance, placeholder): request = context['request'] url = '%s://%s%s' % (request.scheme, request.META['HTTP_HOST'], request.path) is_shown = urlmatch(','.join(instance.include.split('\n')), url) and not urlmatch(','.join(instance.exclude.split('\n')), url) context.update({ 'instance': instance, 'placeholder': placeholder, 'is_shown': is_shown, }) return context class FloatPlugin(LayoutMixin, CMSPluginBase): module = 'JumpSuite Componens' name = _('Float Container') model = models.Float form = forms.FloatForm render_template = 'js_components/float.html' TEMPLATE_NAME = 'js_components/float_%s.html' #change_form_template = 'admin/js_components/float.html' allow_children = True def get_layout(self, context, instance, placeholder): return '' if instance.alignment in ['left', 'right', 'center'] else instance.alignment def render(self, context, instance, placeholder): context.update({ 'instance': instance, 'placeholder': placeholder, 'alignment': instance.alignment, }) return context if not HIDE_FLOAT: plugin_pool.register_plugin(FloatPlugin) class LightboxPlugin(LayoutMixin, CMSPluginBase): module = 'JumpSuite Componens' TEMPLATE_NAME = 'js_components/lightbox_%s.html' name = _('Lightbox') model = models.Lightbox form = forms.LightboxForm render_template = 'js_components/lightbox.html' allow_children = True child_classes = ['Bootstrap4PicturePlugin'] if not HIDE_LIGHTBOX: plugin_pool.register_plugin(LightboxPlugin)

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0.517375

0

null

0

null

0

1

0

9e18ddf285ec21f8d58dafd4142a06363020741a

1,232

py

Python

src/julia/tests/test_juliaoptions.py

dpinol/pyjulia

cec4bf0b0eac7e39cecd8f3e7882563062903d0f

[ "MIT" ]

649

2016-09-09T07:38:19.000Z

2022-03-28T04:30:55.000Z

src/julia/tests/test_juliaoptions.py

dpinol/pyjulia

cec4bf0b0eac7e39cecd8f3e7882563062903d0f

[ "MIT" ]

362

2016-09-08T16:25:30.000Z

2022-03-05T23:15:05.000Z

src/julia/tests/test_juliaoptions.py

dpinol/pyjulia

cec4bf0b0eac7e39cecd8f3e7882563062903d0f

[ "MIT" ]

85

2016-11-08T09:32:44.000Z

2022-03-03T13:10:37.000Z

import pytest from julia.core import JuliaOptions # fmt: off @pytest.mark.parametrize("kwargs, args", [ ({}, []), (dict(compiled_modules=None), []), (dict(compiled_modules=False), ["--compiled-modules", "no"]), (dict(compiled_modules="no"), ["--compiled-modules", "no"]), (dict(depwarn="error"), ["--depwarn", "error"]), (dict(sysimage="PATH"), ["--sysimage", "PATH"]), (dict(bindir="PATH"), ["--home", "PATH"]), ]) # fmt: on def test_as_args(kwargs, args): assert JuliaOptions(**kwargs).as_args() == args @pytest.mark.parametrize("kwargs", [ dict(compiled_modules="invalid value"), dict(bindir=123456789), ]) def test_valueerror(kwargs): with pytest.raises(ValueError) as excinfo: JuliaOptions(**kwargs) assert "Option" in str(excinfo.value) assert "accept" in str(excinfo.value) # fmt: off @pytest.mark.parametrize("kwargs", [ dict(invalid_option=None), dict(invalid_option_1=None, invalid_option_2=None), ]) # fmt: on def test_unsupported(kwargs): with pytest.raises(TypeError) as excinfo: JuliaOptions(**kwargs) assert "Unsupported Julia option(s): " in str(excinfo.value) for key in kwargs: assert key in str(excinfo.value)

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0.15625

0

null

0

null

0

1

0

9e1b5f4b3183d1482047160b015715a1f35d97f0

389

py

Python

lambda/exercices/PhotoCollector/photo_uploader_from_csv.py

Mythridor/aws-scripting

5f978ae7f2b05a40862cbe35d766534fcc40fef0

[ "MIT" ]

null

lambda/exercices/PhotoCollector/photo_uploader_from_csv.py

Mythridor/aws-scripting

5f978ae7f2b05a40862cbe35d766534fcc40fef0

[ "MIT" ]

null

lambda/exercices/PhotoCollector/photo_uploader_from_csv.py

Mythridor/aws-scripting

5f978ae7f2b05a40862cbe35d766534fcc40fef0

[ "MIT" ]

null

#! /usr/local/bin/Python3.5 import urllib.request with open("images.csv", 'r') as csv: i = 0 for line in csv: line = line.split(',') if line[1] != '' and line[1] != "\n": urllib.request.urlretrieve(line[1].encode('utf-8'), ("img_" + str(i) + ".jpg").encode('utf-8')) print("Image saved".encode('utf-8')) i += 1 print("No result")

25.933333

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0.2

0

null

0

null

0

1

0

9e1b7c970fedf5252f5d2635e9703e31344e54e5

1,031

py

Python

src/main/tools/api.py

NGnius/streamq

aa31085befc7da2e3f7461698b2638a246a73eef

[ "MIT" ]

null

src/main/tools/api.py

NGnius/streamq

aa31085befc7da2e3f7461698b2638a246a73eef

[ "MIT" ]

null

src/main/tools/api.py

NGnius/streamq

aa31085befc7da2e3f7461698b2638a246a73eef

[ "MIT" ]

null

''' API-related functions in one spot for convenience Created by NGnius 2019-06-15 ''' from flask import jsonify, request from threading import Semaphore, RLock def get_param(param, silent=False): if request.method == 'GET': return request.args.get(param) else: try: return request.get_json(force=True, silent=silent)[param] except KeyError: return None def error(status=500, reason=None): error_response = {'status':status} if reason is not None: error_response['reason'] = reason return jsonify(error_response), status single_semaphores = dict() resource_lock = RLock() def start_single(identifier): resource_lock.acquire() if identifier not in single_semaphores: resource_lock.release() single_semaphores[identifier] = Semaphore(1) else: resource_lock.release() single_semaphores[identifier].acquire() def end_single(identifier): resource_lock.acquire() single_semaphores[identifier].release()

26.435897

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0.357143

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null

0

null

0

1

0

9e1bee51dd0ea1878f4a4736c40b34f0977aa174

3,968

py

Python

built-in/PyTorch/Official/cv/image_classification/MobileNetV1_ID0094_for_PyTorch/benchmark.py

Ascend/modelzoo

f018cfed33dbb1cc2110b9ea2e233333f71cc509

[ "Apache-2.0" ]

12

2020-12-13T08:34:24.000Z

2022-03-20T15:17:17.000Z

built-in/PyTorch/Official/cv/image_classification/MobileNetV1_ID0094_for_PyTorch/benchmark.py

Ascend/modelzoo

f018cfed33dbb1cc2110b9ea2e233333f71cc509

[ "Apache-2.0" ]

1

2022-01-20T03:11:05.000Z

2022-01-20T06:53:39.000Z

built-in/PyTorch/Official/cv/image_classification/MobileNetV1_ID0094_for_PyTorch/benchmark.py

Ascend/modelzoo

f018cfed33dbb1cc2110b9ea2e233333f71cc509

[ "Apache-2.0" ]

2

2021-07-10T12:40:46.000Z

2021-12-17T07:55:15.000Z

# BSD 3-Clause License # # Copyright (c) 2017 xxxx # All rights reserved. # Copyright 2021 Huawei Technologies Co., Ltd # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ============================================================================ import time import torch import torch.nn as nn import torch.backends.cudnn as cudnn import torchvision.models as models from torch.autograd import Variable class MobileNet(nn.Module): def __init__(self): super(MobileNet, self).__init__() def conv_bn(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), nn.ReLU(inplace=True) ) def conv_dw(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), nn.BatchNorm2d(inp), nn.ReLU(inplace=True), nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), nn.ReLU(inplace=True), ) self.model = nn.Sequential( conv_bn( 3, 32, 2), conv_dw( 32, 64, 1), conv_dw( 64, 128, 2), conv_dw(128, 128, 1), conv_dw(128, 256, 2), conv_dw(256, 256, 1), conv_dw(256, 512, 2), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 1024, 2), conv_dw(1024, 1024, 1), nn.AvgPool2d(7), ) self.fc = nn.Linear(1024, 1000) def forward(self, x): x = self.model(x) x = x.view(-1, 1024) x = self.fc(x) return x def speed(model, name): t0 = time.time() input = torch.rand(1,3,224,224).npu() input = Variable(input, volatile = True) t1 = time.time() model(input) t2 = time.time() model(input) t3 = time.time() print('%10s : %f' % (name, t3 - t2)) if __name__ == '__main__': #cudnn.benchmark = True # This will make network slow ?? resnet18 = models.resnet18().npu() alexnet = models.alexnet().npu() vgg16 = models.vgg16().npu() squeezenet = models.squeezenet1_0().npu() mobilenet = MobileNet().npu() speed(resnet18, 'resnet18') speed(alexnet, 'alexnet') speed(vgg16, 'vgg16') speed(squeezenet, 'squeezenet') speed(mobilenet, 'mobilenet')

35.115044

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null

0

null

0

1

0

9e1da62e19fe4f3008c5d21f24d0decbe6f6039d

1,012

py

Python

client/setup.py

nnabeyang/tepra-lite-esp32

69cbbafce6a3f8b0214178cc80d2fea024ab8c07

[ "MIT" ]

33

2021-09-04T08:46:48.000Z

2022-02-04T08:12:55.000Z

client/setup.py

nnabeyang/tepra-lite-esp32

69cbbafce6a3f8b0214178cc80d2fea024ab8c07

[ "MIT" ]

2

2021-09-28T12:05:21.000Z

2021-12-11T04:08:04.000Z

client/setup.py

nnabeyang/tepra-lite-esp32

69cbbafce6a3f8b0214178cc80d2fea024ab8c07

[ "MIT" ]

2

2021-09-28T10:51:27.000Z

2021-12-10T09:56:22.000Z

from setuptools import setup, find_packages __version__ = '1.0.0' __author__ = 'Takumi Sueda' __author_email__ = 'puhitaku@gmail.com' __license__ = 'MIT License' __classifiers__ = ( 'Development Status :: 4 - Beta', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3', ) with open('README.md', 'r') as f: readme = f.read() setup( name='tepracli', version=__version__, license=__license__, author=__author__, author_email=__author_email__, url='https://github.com/puhitaku/tepra-lite-esp32/tree/master/client', description='An example of tepra-lite-esp32 client / CLI', long_description=readme, long_description_content_type='text/markdown', classifiers=__classifiers__, packages=find_packages(), package_data={'': ['assets/ss3.ttf']}, include_package_data=True, install_requires=['click', 'pillow', 'qrcode[pil]', 'requests'], )

29.764706

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0.051005

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0

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0.166008

1,012

33

75

30.666667

0.754739

0

0.389328

0

1

0

false

0

0.033333

0

0.033333

0

null

0

null

0

1

0

9e210cf9cae77591487ca0d70ca7341aca8bd44a

16,303

py

Python

src/colorpredicate.py

petrusmassabki/color-predicate

828f62b50985cb795aa5b5743e4f7e5c305d2175

[ "MIT" ]

null

src/colorpredicate.py

petrusmassabki/color-predicate

828f62b50985cb795aa5b5743e4f7e5c305d2175

[ "MIT" ]

null

src/colorpredicate.py

petrusmassabki/color-predicate

828f62b50985cb795aa5b5743e4f7e5c305d2175

[ "MIT" ]

null

#!/usr/bin/env python3 import os import colorsys import cv2 import numpy as np from scipy.stats import multivariate_normal from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D class ColorPredicate: def __init__(self, name, images_path, n_max=10): self.name = name self._total_pixel_count = 0 self.images = self.load_images(images_path, n_max) self.masks = [255 * np.ones(image.shape[:2], np.uint8) for image in self.images] self._histogram_channels = None self._histogram_color_space = None self._bins = None self._grid = None self._ch_indexes = None self._target_histogram = None self._background_histogram = None self._gaussian_smoothed_histogram = None self._color_predicate = None self.color_spaces = { 'hsv': cv2.COLOR_BGR2HSV } self.ch_ranges = { 'b': (0, 256), 'g': (0, 256), 'r': (0, 256), 'h': (0, 180), 's': (0, 256), 'v': (0, 256) } def load_images(self, path, n_max): """Load and return a list of up to `n_max` images from `path`.""" images_list = [] n_max = min(n_max, len(os.listdir(path))) for filename in sorted(os.listdir(path))[:n_max]: image = cv2.imread(os.path.join(path, filename)) if image is not None: images_list.append(image) self._total_pixel_count += image.shape[0] * image.shape[1] return images_list def load_masks(self, path): """Load and return a list of image masks from path.""" masks_list = [] n_images = len(self.images) n_masks = len(os.listdir(path)) if n_masks >= len(self.images): for filename in sorted(os.listdir(path))[:n_images]: mask_gray = cv2.imread(os.path.join(path, filename), 0) ret, mask = cv2.threshold(mask_gray, 127, 255, cv2.THRESH_BINARY) if mask is not None: masks_list.append(mask) self.masks = masks_list else: print(f'Directory must contain at least {n_images} image files, ' f'but only {n_masks} were provided. Masks will be ignored.') @staticmethod def sample_pixels(target_pixels, bg_pixels, target_sr, bg_rate): """Take a random sample of target and background pixels. Parameters ---------- target_pixels : numpy.ndarray Array of pixels from target region. bg_pixels : numpy.ndarray Array of pixels from background region. target_sr : int or float Target pixels sample rate (percentage of total target pixels). bg_rate : int or float Ratio of background to target pixels. A value of 1.0 means equivalent distribution. Returns ------- target_pixels_sample : numpy.ndarray Array of random samples from target region. bg_pixels_sample : numpy.ndarray Array of random samples from background region. """ n_target_pixels, n_bg_pixels = len(target_pixels), len(bg_pixels) target_samples = n_target_pixels * target_sr if n_bg_pixels > 0: n_bg_samples = target_samples * bg_rate target_bg_ratio = n_target_pixels / n_bg_pixels if n_bg_samples > n_bg_pixels: target_sr = n_bg_pixels / (n_target_pixels * bg_rate) bg_sr = target_bg_ratio * target_sr * bg_rate indexes_bg_samples = np.random.choice([0, 1], size=n_bg_pixels, p=[(1 - bg_sr), bg_sr]) bg_pixels_sample = bg_pixels[indexes_bg_samples == 1] else: bg_pixels_sample = bg_pixels indexes_target_samples = np.random.choice([0, 1], size=n_target_pixels, p=[1 - target_sr, target_sr]) target_pixels_sample = target_pixels[indexes_target_samples == 1] return target_pixels_sample, bg_pixels_sample def create_multidimensional_histogram(self, color_space='bgr', ch_indexes=(0, 1, 2), bins=(8, 8, 8), target_sr=1.0, bg_rate=1.0): """Create a multidimensional histogram of instance's images. Color space can be either RGB or HSV. Dimension is set according to `ch_indexes` length. Sampling can be specified. Parameters ---------- color_space : str, optional Histogram color space. Accepts `bgr` (default) or `hsv`. ch_indexes : tuple, optional Sequence of histogram channel indexes. Values refer to `color_space` string order. E.g, use (0, 2) to create a 2D histogram of channels b and r. bins : tuple, optional Sequence of histogram bins. Must be of same length of `ch_indexes`. target_sr : int or float Target pixels sample rate (percentage of total target pixels). bg_rate : int or float Ratio of background to target pixels. A value of 1.0 means equivalent distribution. Returns ------- self._target_histogram : numpy.ndarray 2D or 3D histogram of sampled target pixels self._bg_histogram : numpy.ndarray 2D or 3D histogram of samples background pixels """ print('Computing histogram...', end=' ') target_pixels_per_image, bg_pixels_per_image = [], [] if sorted(ch_indexes) in ([0, 1], [0, 2], [1, 2], [0, 1, 2]): self._histogram_channels = [color_space[i] for i in ch_indexes] hist_range = [self.ch_ranges[ch] for ch in self._histogram_channels] else: raise ValueError('Parameter "ch_indexes" must be a sequence ' 'of unique integers between 0 and 2') for image, mask in zip(self.images, self.masks): if color_space != 'bgr': image = cv2.cvtColor(image, self.color_spaces[color_space]) target_pixels_per_image.append(image[mask > 0]) bg_pixels_per_image.append(image[~mask > 0]) target_pixels = np.concatenate(target_pixels_per_image) bg_pixels = np.concatenate(bg_pixels_per_image) target_samples, bg_samples = self.sample_pixels(target_pixels, bg_pixels, target_sr, bg_rate) self._target_histogram, _ = np.histogramdd(target_samples[:, ch_indexes], bins=bins, range=hist_range) self._background_histogram, _ = np.histogramdd(bg_samples[:, ch_indexes], bins=bins, range=hist_range) self._bins = bins self._histogram_color_space = color_space self._ch_indexes = ch_indexes print('Done!') return self._target_histogram, self._background_histogram def pdf(self, mean, cov, domain): """Multidimensional probability density function.""" pdf = multivariate_normal.pdf(domain, mean=mean, cov=cov) pdf = pdf.reshape(self._bins) return pdf def create_gaussian_smoothed_histogram(self, t_amp=1.0, t_cov=0.05, bg_amp=1.0, bg_cov=0.025, threshold=0.01, norm=True): """Create a 2D or 3D gaussian-smoothed histogram. A gaussian-smoothed histogram is built from target and background pixels according to [1]: for each pixel in target region, a normal distribution centered at its position is added to the histogram; similarly, for each pixel at background, a normal distribution is subtracted. Finally, thresholding is applied: color frequencies below threshold times maximum frequency are set to zero. [1] `Finding skin in color images`, R. Kjeldsen and J. Kender. Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, 1996. DOI:10.1109/AFGR.1996.557283 Parameters ---------- t_amp : float, optional Amplitude of target's normal distribution. Default is 1.0. t_cov : float, optional Covariance of target's normal distribution. Default is 0.05. bg_amp : float, optional Amplitude of background's normal distribution. Default is 1.0. bg_cov : float, optional Covariance of background's normal distribution. Default is 0.025. threshold : float, optional Color frequencies below threshold times maximum frequency are set to zero. Default is 0.01. norm : bool, optional When True, histogram is normalized by maximum frequency. Default is True. Returns ------- self._gaussian_smoothed_histogram : numpy.ndarray 2D or 3D gaussian-smoothed histogram. """ print('Generating gaussian-smoothed histogram...', end=' ') self._grid = np.mgrid[tuple([slice(0, b) for b in self._bins])] domain = np.column_stack([axis.flat for axis in self._grid]) gauss_sum = np.zeros(self._bins, dtype=np.float32) t_cov = t_cov * min(self._bins) bg_cov = bg_cov * min(self._bins) t_hist = self._target_histogram bg_hist = self._background_histogram for pos in np.argwhere(t_hist): pdf = self.pdf(pos, t_cov, domain) * t_amp gauss_sum += pdf * t_hist[tuple(pos)] for pos in np.argwhere(bg_hist): pdf = - self.pdf(pos, bg_cov, domain) * bg_amp gauss_sum += pdf * bg_hist[tuple(pos)] gauss_sum[gauss_sum < threshold * np.max(gauss_sum)] = 0 if norm: gauss_sum = gauss_sum / np.max(gauss_sum) self._gaussian_smoothed_histogram = gauss_sum print('Done!') return self._gaussian_smoothed_histogram def create_color_predicate(self, threshold=0, save=False, filename='color_predicate'): """Create a color predicate from gaussian-smoothed histogram. Parameters ---------- threshold : int or float, optional Histogram frequencies above threshold are set to one; frequencies below threshold are set to zero. Default is 0. save : bool, optional If true, color predicate is saved as a numpy array. Default is False. filename : str, optional Color predicate file name. Default is `color_predicate` Returns ------- color_predicate : numpy.ndarray Color predicate with the same dimension as the histogram. """ color_predicate = self._gaussian_smoothed_histogram.copy() color_predicate[color_predicate > threshold] = 1 color_predicate[color_predicate <= threshold] = 0 if save: np.save(filename, color_predicate) return color_predicate def plot_gaussian_smoothed_histogram(self, figsize=(8, 8), dpi=75, save=False): """Plot a 2D or 3D gaussian-smoothed histogram. When 2D, creates a pseudocolor histogram; when 3D, each bin is represented by a circle with size proportional to its frequency. Parameters ---------- figsize : tuple, optional Matplotlib's `figsize` parameter. Default is (8, 8). dpi : int, optional Matplotlib's `dpi` parameter. Default is 75. save : bool, optional When true, saves the plot as a png file. """ print('Plotting gaussian smoothed histogram...', end=' ') grid = self._grid ranges = self.ch_ranges bins = self._bins channels = self._histogram_channels histogram = self._gaussian_smoothed_histogram color_space = self._histogram_color_space axis = [(ranges[ch][1] / bins[i]) * grid[i] + (ranges[ch][1] / bins[i]) / 2 for i, ch in enumerate(channels)] if histogram.ndim == 3: colors = np.vstack((axis[0].flatten() / ranges[channels[0]][1], axis[1].flatten() / ranges[channels[1]][1], axis[2].flatten() / ranges[channels[2]][1])).T colors = colors[:, tuple([channels.index(ch) for ch in color_space])] if color_space == 'hsv': colors = np.array([colorsys.hsv_to_rgb(color[0], color[1], color[2]) for color in colors]) elif color_space == 'bgr': colors = colors[:, ::-1] fig = plt.figure(figsize=figsize, dpi=dpi) ax = fig.add_subplot(111, projection='3d') ax.title.set_position([0.5, 1.1]) ax.set_title(f'3D Color Histogram - ' f'{channels[0].title()} x ' f'{channels[1].title()} x ' f'{channels[2].title()}', fontsize=16) ax.xaxis.set_tick_params(labelsize=8) ax.yaxis.set_tick_params(labelsize=8) ax.zaxis.set_tick_params(labelsize=8) ax.set_xlim(ranges[channels[0]][0], ranges[channels[0]][1]) ax.set_ylim(ranges[channels[1]][0], ranges[channels[1]][1]) ax.set_zlim(ranges[channels[2]][0], ranges[channels[2]][1]) ax.set_xlabel(channels[0].title(), fontsize=12) ax.set_ylabel(channels[1].title(), fontsize=12) ax.set_zlabel(channels[2].title(), fontsize=12) ax.view_init(azim=45) ax.scatter(axis[0], axis[1], axis[2], s=histogram * 1000, c=colors) if save: ch_str = channels[0] + channels[1] + channels[2] plt.savefig(f'{self.name}_3d_{ch_str}_histogram.png') plt.show() if self._gaussian_smoothed_histogram.ndim == 2: fig = plt.figure(figsize=figsize, dpi=dpi) ax = fig.add_subplot(111) ax.set_aspect('equal') ax.set_title(f'2D Color Histogram - ' f'{channels[0].title()} x ' f'{channels[1].title()}') ax.set_xlabel(channels[0].title(), fontsize=12) ax.set_ylabel(channels[1].title(), fontsize=12, rotation=0) h = ax.pcolormesh(axis[0], axis[1], histogram) fig.colorbar(h, ax=ax) if save: ch_str = channels[0] + channels[1] plt.savefig(f'{self.name}_2d_{ch_str}_histogram.png') plt.show() print('Done!') @property def total_pixel_count(self): return self._total_pixel_count @property def gaussian_smoothed_histogram(self): return self._gaussian_smoothed_histogram @property def true_pixels_histogram(self): return self._target_histogram @property def false_pixels_histogram(self): return self._background_histogram @property def color_predicate(self): return self._color_predicate def __str__(self): description = f''' {self.name.title()} color predicate. Images: {len(self.images)} Bins: {self._bins} Color Space: {self._histogram_color_space} Channels: {self._ch_indexes} ''' return description

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0

null

0

null

0

1

0

9e23d085a14f192cef141c0732be27df361cf10b

4,456

py

Python

tests/test_basic_train.py

maxwellmckinnon/fastai

b67bf7184ac2be1825697709051c5bcba058a40d

[ "Apache-2.0" ]

1

2019-04-08T09:52:28.000Z

tests/test_basic_train.py

maxwellmckinnon/fastai

b67bf7184ac2be1825697709051c5bcba058a40d

[ "Apache-2.0" ]

null

tests/test_basic_train.py

maxwellmckinnon/fastai

b67bf7184ac2be1825697709051c5bcba058a40d

[ "Apache-2.0" ]

1

2020-05-19T12:56:20.000Z

""" module: basic_train.py - Model fitting methods docs : https://docs.fast.ai/train.html """ import pytest, fastai from fastai.vision import * from utils.fakes import * from utils.text import * from utils.mem import * from fastai.utils.mem import * from math import isclose torch_preload_mem() @pytest.fixture(scope="module") def data(): path = untar_data(URLs.MNIST_TINY) data = ImageDataBunch.from_folder(path, ds_tfms=([], []), bs=2) return data # this is not a fixture on purpose - the memory measurement tests are very sensitive, so # they need to be able to get a fresh learn object and not one modified by other tests. def learn_large_unfit(data): learn = create_cnn(data, models.resnet18, metrics=accuracy) return learn @pytest.fixture(scope="module") def learn(data): return learn_large_unfit(data) def test_get_preds(): learn = fake_learner() with CaptureStdout() as cs: a = learn.get_preds() assert learn.data.batch_size == len(a[1]) def test_save_load(learn): name = 'mnist-tiny-test-save-load' # testing that all these various sequences don't break each other model_path = learn.save(name, return_path=True) learn.load(name, purge=True) learn.data.sanity_check() assert 709 == len(learn.data.train_ds) learn.purge() learn.load(name) learn.load(name) model_path = learn.save(name, return_path=True) learn.load(name, purge=True) # basic checks #assert learn.recorder assert learn.opt assert 709 == len(learn.data.train_ds) # XXX: could use more sanity checks if os.path.exists(model_path): os.remove(model_path) def check_mem_expected(used_exp, peaked_exp, mtrace, abs_tol=2, ctx=None): used_received, peaked_received = mtrace.data() ctx = f" ({ctx})" if ctx is not None else "" assert isclose(used_exp, used_received, abs_tol=abs_tol), f"used mem: expected={used_exp} received={used_received}{ctx}" assert isclose(peaked_exp, peaked_received, abs_tol=abs_tol), f"peaked mem: expected={peaked_exp} received={peaked_received}{ctx}" def report_mem_real(used_exp, peaked_exp, mtrace, abs_tol=2, ctx=None): ctx = f" ({ctx})" if ctx is not None else "" print(f"{mtrace}{ctx}") #check_mem_expected = report_mem_real #@pytest.mark.skip(reason="WIP") @pytest.mark.cuda def test_save_load_mem_leak(data): learn = learn_large_unfit(data) name = 'mnist-tiny-test-save-load' #learn.fit_one_cycle(1) # A big difficulty with measuring memory consumption is that it varies quite # wildly from one GPU model to another. # # Perhaps we need sets of different expected numbers per developer's GPUs? # override check_mem_expected above with report_mem_real to acquire a new set # # So for now just testing the specific card I have until a better way is found. dev_name = torch.cuda.get_device_name(None) if dev_name != 'GeForce GTX 1070 Ti': pytest.skip(f"currently only matched for mem usage on specific GPU models, {dev_name} is not one of them") # save should consume no extra used or peaked memory with GPUMemTrace() as mtrace: model_path = learn.save(name, return_path=True) check_mem_expected(used_exp=0, peaked_exp=0, mtrace=mtrace, abs_tol=10, ctx="save") # load w/ purge still leaks some the first time it's run with GPUMemTrace() as mtrace: learn.load(name, purge=True) # XXX: very different numbers if done w/o fit first 42 8, w/ fit 24 16 check_mem_expected(used_exp=42, peaked_exp=8, mtrace=mtrace, abs_tol=10, ctx="load") # subsequent multiple load w/o purge should consume no extra used memory with GPUMemTrace() as mtrace: learn.load(name, purge=False) learn.load(name, purge=False) check_mem_expected(used_exp=0, peaked_exp=20, mtrace=mtrace, abs_tol=10, ctx="load x 2") # subsequent multiple load w/ purge should consume no extra used memory with GPUMemTrace() as mtrace: learn.load(name, purge=True) learn.load(name, purge=True) check_mem_expected(used_exp=0, peaked_exp=20, mtrace=mtrace, abs_tol=10, ctx="load x 2 2nd time") # purge + load w/ default purge should consume no extra used memory with GPUMemTrace() as mtrace: learn.purge() learn.load(name) check_mem_expected(used_exp=0, peaked_exp=20, mtrace=mtrace, abs_tol=10, ctx="purge+load") if os.path.exists(model_path): os.remove(model_path)

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null

0

null

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1

0

f508079561b7a2a57df3ea9bb24da6c3cf24ed29

13,454

py

Python

examples/cartpole_example/test/cartpole_PID_MPC_sim.py

marcosfelt/sysid-neural-structures-fitting

80eda427251e8cce1d2a565b5cbca533252315e4

[ "MIT" ]

17

2019-11-15T06:27:05.000Z

2021-10-02T14:24:25.000Z

examples/cartpole_example/test/cartpole_PID_MPC_sim.py

marcosfelt/sysid-neural-structures-fitting

80eda427251e8cce1d2a565b5cbca533252315e4

[ "MIT" ]

null

examples/cartpole_example/test/cartpole_PID_MPC_sim.py

marcosfelt/sysid-neural-structures-fitting

80eda427251e8cce1d2a565b5cbca533252315e4

[ "MIT" ]

4

2020-09-03T17:01:34.000Z

2021-11-05T04:09:24.000Z

import numpy as np import scipy.sparse as sparse from scipy.integrate import ode from scipy.interpolate import interp1d import time import control import control.matlab import numpy.random import pandas as pd from ltisim import LinearStateSpaceSystem from pendulum_model import * from pyMPC.mpc import MPCController # Reference model default parameters k_def = 5.0 tau_def = 120e-3 Acl_c_def = np.array([[0,1,0], [0, 0, k_def], [0, 0, -1/tau_def]]) Bcl_c_def = np.array([[0], [k_def], [1/tau_def] ]) # PID default parameters Ts_PID = 1e-3 # Reference trajectory t_ref_vec = np.array([0.0, 5.0, 10.0, 20.0, 25.0, 30.0, 40.0, 100.0]) p_ref_vec = np.array([0.0, 0.0, 0.8, 0.8, 0.0, 0.0, 0.8, 0.8]) rp_fun = interp1d(t_ref_vec, p_ref_vec, kind='linear') def xref_cl_fun_def(t): return np.array([rp_fun(t), 0.0, 0.0]) # MPC parameters Ts_MPC_def = 10e-3 Qx_def = 1.0 * sparse.diags([1.0, 0, 10.0]) # Quadratic cost for states x0, x1, ..., x_N-1 QxN_def = Qx_def Qr_def = 0.0 * sparse.eye(1) # Quadratic cost for u0, u1, ...., u_N-1 QDr_def = 1e-1 / (Ts_MPC_def ** 2) * sparse.eye(1) # Quadratic cost for Du0, Du1, ...., Du_N-1 # Defaults DEFAULTS_PENDULUM_MPC = { 'xref_cl_fun': xref_cl_fun_def, 'uref': np.array([0.0]), # N 'std_npos': 0*0.001, # m 'std_nphi': 0*0.00005, # rad 'std_dF': 0.05, # N 'w_F':20, # rad 'len_sim': 40, #s 'Acl_c': Acl_c_def, 'Bcl_c': Bcl_c_def, 'Ts_MPC': Ts_MPC_def, 'Np': 100, 'Nc': 50, 'Qx': Qx_def, 'QxN': QxN_def, 'Qr': Qr_def, 'QDr': QDr_def, 'Q_kal': np.diag([0.1, 10, 0.1, 10]), 'R_kal': 1*np.eye(2), 'QP_eps_abs': 1e-3, 'QP_eps_rel': 1e-3, 'seed_val': None } def get_parameter(sim_options, par_name): return sim_options.get(par_name, DEFAULTS_PENDULUM_MPC[par_name]) def get_default_parameters(sim_options): """ Which parameters are left to default ??""" default_keys = [key for key in DEFAULTS_PENDULUM_MPC if key not in sim_options] return default_keys def simulate_pendulum_MPC(sim_options): seed_val = get_parameter(sim_options,'seed_val') if seed_val is not None: np.random.seed(seed_val) # In[Sample times] Ts_MPC = get_parameter(sim_options, 'Ts_MPC') ratio_Ts = int(Ts_MPC // Ts_PID) # In[Real System] Cc = np.array([[1., 0., 0., 0.], [0., 0., 1., 0.]]) Cd = np.copy(Cc) nx, nu = 4,1 ny = 2 # In[initialize simulation system] t0 = 0 phi0 = -0.0 * 2 * np.pi / 360 # initial angle x0 = np.array([0, 0, phi0, 0]) # initial state #system_dyn = ode(f_ODE_wrapped).set_integrator('vode', method='bdf') # dopri5 system_dyn = ode(f_ODE_wrapped).set_integrator('dopri5') # dopri5 # system_dyn = ode(f_ODE_wrapped).set_integrator('dopri5') system_dyn.set_initial_value(x0, t0) system_dyn.set_f_params(0.0) #In[MPC params --model] Acl_c = get_parameter(sim_options, 'Acl_c') Bcl_c = get_parameter(sim_options, 'Bcl_c') Ccl_c = np.array([[1., 0., 0], [0., 0., 1]]) Dcl_c = np.zeros((2, 1)) ncl_x, ncl_u = Bcl_c.shape # number of states and number or inputs #ncl_y = np.shape(Ccl_c)[0] #In[MPC matrices discretization] Acl_d = np.eye(ncl_x) + Acl_c*Ts_MPC Bcl_d = Bcl_c*Ts_MPC Ccl_d = Ccl_c Dcl_d = Dcl_c x0_cl = np.array([0,0,phi0]) M_cl = LinearStateSpaceSystem(A=Acl_d, B=Bcl_d, C=Ccl_d, D=Dcl_d, x0=x0_cl) # MPC parameters Np = get_parameter(sim_options, 'Np') Nc = get_parameter(sim_options, 'Nc') Qx = get_parameter(sim_options, 'Qx') QxN = get_parameter(sim_options, 'QxN') Qr = get_parameter(sim_options, 'Qr') QDr = get_parameter(sim_options, 'QDr') # Constraints #xmin = np.array([-1.5, -100, -100]) #xmax = np.array([1.5, 100.0, 100]) #umin = np.array([-10]) #umax = np.array([10]) #Dumin = np.array([-100 * Ts_MPC_def]) #Dumax = np.array([100 * Ts_MPC_def]) QP_eps_rel = get_parameter(sim_options, 'QP_eps_rel') QP_eps_abs = get_parameter(sim_options, 'QP_eps_abs') # Emergency exit conditions EMERGENCY_STOP = False EMERGENCY_POS = 2.0 EMERGENCY_ANGLE = 30 * DEG_TO_RAD # Reference input and states xref_cl_fun = get_parameter(sim_options, 'xref_cl_fun') # reference state xref_cl_fun_v = np.vectorize(xref_cl_fun, signature='()->(n)') t0 = 0 xref_MPC = xref_cl_fun(t0) uref = get_parameter(sim_options, 'uref') uminus1 = np.array([0.0]) # input at time step negative one - used to penalize the first delta u at time instant 0. Could be the same as uref. kMPC = MPCController(Acl_d, Bcl_d, Np=Np, Nc=Nc, x0=x0_cl, xref=xref_MPC, uminus1=uminus1, Qx=Qx, QxN=QxN, Qu=Qr, QDu=QDr, eps_feas=1e3, eps_rel=QP_eps_rel, eps_abs=QP_eps_abs) try: kMPC.setup(solve=True) # setup initial problem and also solve it except: EMERGENCY_STOP = True if not EMERGENCY_STOP: if kMPC.res.info.status != 'solved': EMERGENCY_STOP = True # In[initialize PID] # Default controller parameters - P = -100.0 I = -1 D = -20 N = 100.0 kP = control.tf(P,1, Ts_PID) kI = I*Ts_PID*control.tf([0, 1], [1,-1], Ts_PID) kD = D*control.tf([N, -N], [1.0, Ts_PID*N - 1], Ts_PID) PID_tf = kP + kD + kI PID_ss = control.ss(PID_tf) k_PID = LinearStateSpaceSystem(A=PID_ss.A, B=PID_ss.B, C=PID_ss.C, D=PID_ss.D) # In[initialize noise] # Standard deviation of the measurement noise on position and angle std_npos = get_parameter(sim_options, 'std_npos') std_nphi = get_parameter(sim_options, 'std_nphi') # Force disturbance std_dF = get_parameter(sim_options, 'std_dF') # Disturbance power spectrum w_F = get_parameter(sim_options, 'w_F') # bandwidth of the force disturbance tau_F = 1 / w_F Hu = control.TransferFunction([1], [1 / w_F, 1]) Hu = Hu * Hu Hud = control.matlab.c2d(Hu, Ts_PID) N_sim_imp = tau_F / Ts_PID * 20 t_imp = np.arange(N_sim_imp) * Ts_PID t, y = control.impulse_response(Hud, t_imp) y = y[0] std_tmp = np.sqrt(np.sum(y ** 2)) # np.sqrt(trapz(y**2,t)) Hu = Hu / (std_tmp) * std_dF N_skip = int(20 * tau_F // Ts_PID) # skip initial samples to get a regime sample of d t_sim_d = get_parameter(sim_options, 'len_sim') # simulation length (s) N_sim_d = int(t_sim_d // Ts_PID) N_sim_d = N_sim_d + N_skip + 1 e = np.random.randn(N_sim_d) te = np.arange(N_sim_d) * Ts_PID _, d, _ = control.forced_response(Hu, te, e) d = d.ravel() # Simulate in closed loop len_sim = get_parameter(sim_options, 'len_sim') # simulation length (s) nsim = int(len_sim // Ts_MPC) #int(np.ceil(len_sim / Ts_MPC)) # simulation length(timesteps) # watch out! +1 added, is it correct? t_vec = np.zeros((nsim, 1)) status_vec = np.zeros((nsim,1)) x_vec = np.zeros((nsim, nx)) x_ref_vec = np.zeros((nsim, ncl_x)) y_vec = np.zeros((nsim, ny)) y_meas_vec = np.zeros((nsim, ny)) u_vec = np.zeros((nsim, nu)) x_model_vec = np.zeros((nsim,3)) nsim_fast = int(len_sim // Ts_PID) t_vec_fast = np.zeros((nsim_fast, 1)) x_vec_fast = np.zeros((nsim_fast, nx)) # finer integration grid for performance evaluation ref_phi_vec_fast = np.zeros((nsim_fast, 1)) y_meas_vec_fast = np.zeros((nsim_fast, ny)) x_ref_vec_fast = np.zeros((nsim_fast, nx)) # finer integration grid for performance evaluatio u_vec_fast = np.zeros((nsim_fast, nu)) # finer integration grid for performance evaluatio Fd_vec_fast = np.zeros((nsim_fast, nu)) # t_int_vec_fast = np.zeros((nsim_fast, 1)) emergency_vec_fast = np.zeros((nsim_fast, 1)) # t_step = t0 x_step = x0 u_PID = None t_pred_all = t0 + np.arange(nsim + Np + 1) * Ts_MPC Xref_MPC_all = xref_cl_fun_v(t_pred_all) for idx_fast in range(nsim_fast): ## Determine step type: fast simulation only or MPC step idx_MPC = idx_fast // ratio_Ts run_MPC_controller = (idx_fast % ratio_Ts) == 0 y_step = Cd.dot(x_step) # y[i] from the system ymeas_step = np.copy(y_step) ymeas_step[0] += std_npos * np.random.randn() ymeas_step[1] += std_nphi * np.random.randn() y_meas_vec_fast[idx_fast,:] = ymeas_step # Output for step i # Ts_MPC outputs if run_MPC_controller: # it is also a step of the simulation at rate Ts_MPC if idx_MPC < nsim: t_vec[idx_MPC, :] = t_step y_vec[idx_MPC,:] = y_step y_meas_vec[idx_MPC,:] = ymeas_step u_vec[idx_MPC, :] = u_PID x_model_vec[idx_MPC, :] = M_cl.x.ravel() xref_MPC = xref_cl_fun(t_step) x_ref_vec[idx_MPC,:] = xref_MPC.ravel() if not EMERGENCY_STOP: phi_ref_MPC, info_MPC = kMPC.output(return_status=True) # u[i] = k(\hat x[i]) possibly computed at time instant -1 else: phi_ref_MPC = np.zeros(nu) # PID angle CONTROLLER ref_phi = phi_ref_MPC.ravel() error_phi = ref_phi - ymeas_step[1] u_PID = k_PID.output(error_phi) u_PID[u_PID > 10.0] = 10.0 u_PID[u_PID < -10.0] = -10.0 u_TOT = u_PID # Ts_fast outputs t_vec_fast[idx_fast,:] = t_step x_vec_fast[idx_fast, :] = x_step #system_dyn.y u_vec_fast[idx_fast,:] = u_TOT Fd_vec_fast[idx_fast,:] = 0.0 ref_phi_vec_fast[idx_fast,:] = ref_phi ## Update to step i+1 k_PID.update(error_phi) # Controller simulation step at rate Ts_MPC if run_MPC_controller: M_cl.update(ref_phi) if not EMERGENCY_STOP: x_cl = np.array([x_step[0], x_step[1], x_step[2]]) Xref_MPC = Xref_MPC_all[idx_MPC:idx_MPC + Np + 1] xref_MPC = Xref_MPC_all[idx_MPC] kMPC.update(x_cl, phi_ref_MPC, xref=xref_MPC) # update with measurement and reference # System simulation step at rate Ts_fast time_integrate_start = time.perf_counter() system_dyn.set_f_params(u_TOT) system_dyn.integrate(t_step + Ts_PID) x_step = system_dyn.y t_int_vec_fast[idx_fast,:] = time.perf_counter() - time_integrate_start # Time update t_step += Ts_PID simout = {'t': t_vec, 'x': x_vec, 'u': u_vec, 'y': y_vec, 'y_meas': y_meas_vec, 'x_ref': x_ref_vec, 'status': status_vec, 'Fd_fast': Fd_vec_fast, 't_fast': t_vec_fast, 'x_fast': x_vec_fast, 'x_ref_fast': x_ref_vec_fast, 'u_fast': u_vec_fast, 'y_meas_fast': y_meas_vec_fast, 'emergency_fast': emergency_vec_fast, 'PID_tf': PID_tf, 'Ts_MPC': Ts_MPC, 'ref_phi_fast': ref_phi_vec_fast, 'x_model': x_model_vec, 't_int_fast': t_int_vec_fast } return simout if __name__ == '__main__': import matplotlib.pyplot as plt import matplotlib plt.close('all') simopt = DEFAULTS_PENDULUM_MPC time_sim_start = time.perf_counter() simout = simulate_pendulum_MPC(simopt) time_sim = time.perf_counter() - time_sim_start t = simout['t'] x = simout['x'] u = simout['u'] y = simout['y'] y_meas = simout['y_meas'] x_ref = simout['x_ref'] x_fast = simout['x_fast'] y_meas_fast = simout['y_meas_fast'] u_fast = simout['u_fast'] x_model = simout['x_model'] t_fast = simout['t_fast'] x_ref_fast = simout['x_ref_fast'] F_input = simout['Fd_fast'] status = simout['status'] ref_phi_fast = simout['ref_phi_fast'] uref = get_parameter(simopt, 'uref') nsim = len(t) nx = x.shape[1] ny = y.shape[1] y_ref = x_ref[:, [0, 2]] fig,axes = plt.subplots(4,1, figsize=(10,10), sharex=True) axes[0].plot(t, y_meas[:, 0], "b", label='p_meas') axes[0].plot(t_fast, x_fast[:, 0], "k", label='p') axes[0].plot(t, x_model[:, 0], "r", label='p model') axes[0].plot(t, x_ref[:, 0], "k--", label='p reference') axes[0].set_ylim(-2.0,2.0) axes[0].set_title("Position (m)") axes[1].plot(t_fast, x_fast[:, 1], "k", label='v') axes[1].plot(t, x_model[:, 1], "r", label='v model') axes[1].set_ylim(-3,3.0) axes[1].set_title("Speed (m/s)") axes[2].plot(t, y_meas[:, 1]*RAD_TO_DEG, "b", label='phi_meas') axes[2].plot(t_fast, x_fast[:, 2]*RAD_TO_DEG, 'k', label="phi") axes[2].plot(t, x_model[:, 2]*RAD_TO_DEG, "r", label='phi model') axes[2].plot(t_fast, ref_phi_fast[:,0]*RAD_TO_DEG, "k--", label="phi_ref") axes[2].set_ylim(-20,20) axes[2].set_title("Angle (deg)") axes[3].plot(t, u[:,0], label="F") axes[3].plot(t_fast, F_input, "k", label="Fd") axes[3].plot(t, uref*np.ones(np.shape(t)), "r--", label="F_ref") axes[3].set_ylim(-20,20) axes[3].set_title("Force (N)") for ax in axes: ax.grid(True) ax.legend() X = np.hstack((t_fast, x_fast, u_fast, y_meas_fast, F_input)) COL_T = ['time'] COL_X = ['p', 'v', 'theta', 'omega'] COL_U = ['u'] COL_D = ['d'] COL_Y = ['p_meas', 'theta_meas'] COL = COL_T + COL_X + COL_U + COL_Y + COL_D df_X = pd.DataFrame(X, columns=COL) df_X.to_csv("pendulum_data_PID.csv", index=False)

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f50910b14f5b09655a9e1eaecc696a5cfe950b0f

4,923

py

Python

settings.py

msetzu/data-mining

9e01d00964004dea4a2aea88dfe855f785302ef1

[ "MIT" ]

1

2018-10-09T14:41:59.000Z

settings.py

msetzu/data-mining

9e01d00964004dea4a2aea88dfe855f785302ef1

[ "MIT" ]

null

settings.py

msetzu/data-mining

9e01d00964004dea4a2aea88dfe855f785302ef1

[ "MIT" ]

null

import pandas as pd from matplotlib.colors import LinearSegmentedColormap # Dataset data = pd.read_csv("./hr.csv") entries = len(data) bins = 10 # Data analysis analysis = { "bins": 10, "balance_threshold": 0.1 } # Plot labels labels = ["satisfaction_level", "average_montly_hours", "last_evaluation", "time_spend_company", "number_project", "Work_accident", "left", "promotion_last_5years", "sales", "salary"] pretty_prints = ["Self-reported satisfaction", "AVG Monthly hours", "Time since last valuation, in years", "Time in company, in years", "Projects", "Accidents", "Left", "Promoted (last 5 years)", "Department", "Salary"] short_pretty_prints = ["Injuries", "Work hours", "Last evaluation", "Left", "Projects", "Promotion", "Wage", "Satisfaction", "Years in company", "Dpt."] departments_pretty_prints = ["Information Technology", "R&D", "Accounting", "Human Resources", "Management", "Marketing", "Product Management", "Sales", "Support", "Technical"] labels_pretty_print = {k: v for k, v in zip(labels, pretty_prints)} short_labels_pretty_print = {k: v for k, v in zip(labels, short_pretty_prints)} labels_pretty_print["salary_int"] = "Salary" continuous_labels = labels[0:2] discrete_labels = labels[2:5] categorical_labels = labels[5:-1] ordinal_labels = labels[-1:] correlated_labels = continuous_labels + discrete_labels + ["salary_int"] categorical_labels_pretty_prints = { "Work_accident": ("Not Injured", "Injured"), "left": ("Stayed", "Left"), "promotion_last_5years": ("Not promoted", "Promoted"), "sales": tuple(departments_pretty_prints) } ordinal_labels_pretty_prints = { "salary": ("Low", "Medium", "High"), } ordered_ordinal_vars = { "salary": ["low", "medium", "high"] } departments = set(data["sales"]) # Scatter plot scatter = { "sampling_size": 100, # size of each sample "samples": 5, # number of samples to extract "edge_bins": 1, # edge bins possibly containing outliers "bins": 10, "replace": True } clusetering_types = ["normal", "discrete", "raw"] # Graphs palette = { "main": "#FE4365", "complementary": "#FC9D9A", "pr_complementary": "#F9CDAD", "sc_complementary": "#C8C8A9", "secondary": "#83AF9B" } round_palette = { "main": palette["secondary"], "secondary": palette["complementary"], "pr_complementary": palette["sc_complementary"], "sc_complementary": palette["secondary"] } large_palette = { "navy": "#001f3f", "blue": "#0074D9", "green": "#2ECC40", "olive": "#3D9970", "orange": "#FF851B", "yellow": "#FFDC00", "red": "#FF4136", "maroon": "#85144b", "black": "#111111", "grey": "#AAAAAA" } large_palette_full = { "navy": "#001f3f", "blue": "#0074D9", "aqua": "#7FDBFF", "teal": "#39CCCC", "olive": "#3D9970", "green": "#2ECC40", "lime": "#01FF70", "yellow": "#FFDC00", "orange": "#FF851B", "red": "#FF4136", "maroon": "#85144b", "fuchsia": "#F012BE", "purple": "#B10DC9", "black": "#111111", "grey": "#AAAAAA", "silver": "#DDDDDD" } large_palette_stacked = { "navy": "#001f3f", "blue": "#0074D9", "olive": "#3D9970", "orange": "#FF851B", "green": "#2ECC40", "yellow": "#FFDC00", "red": "#FF4136", "maroon": "#85144b", "black": "#111111", "grey": "#AAAAAA", "stack": large_palette["orange"] } cmap_pale_pink = LinearSegmentedColormap.from_list("Pale pink", [palette["pr_complementary"], palette["main"]], N=1000000) cmap_pale_pink_and_green = LinearSegmentedColormap.from_list("Pale pink&green", [palette["main"], palette["complementary"], palette["pr_complementary"], palette["sc_complementary"], palette["secondary"]], N=1000000)

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f509ca15e0e12b426c5e187595364f7eea92a920

397

py

Python

GCD - Euclidean (Basic)/Python3/gcdEuclid.py

i-vishi/ds-and-algo

90a8635db9570eb17539201be29ec1cfd4b5ae18

[ "MIT" ]

1

2021-03-01T04:15:08.000Z

GCD - Euclidean (Basic)/Python3/gcdEuclid.py

i-vishi/ds-and-algo

90a8635db9570eb17539201be29ec1cfd4b5ae18

[ "MIT" ]

null

GCD - Euclidean (Basic)/Python3/gcdEuclid.py

i-vishi/ds-and-algo

90a8635db9570eb17539201be29ec1cfd4b5ae18

[ "MIT" ]

null

# Author: Vishal Gaur # Created: 17-01-2021 20:31:34 # function to find GCD using Basic Euclidean Algorithm def gcdEuclid(a, b): if a == 0: return b else: return gcdEuclid(b % a, a) # Driver Code to test above function a = 14 b = 35 g = gcdEuclid(a, b) print("GCD of", a, "&", b, "is: ", g) a = 56 b = 125 g = gcdEuclid(a, b) print("GCD of", a, "&", b, "is: ", g)

17.26087

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0

null

0

null

0

1

0

f50f1a90c240661a8974cc7923b38f46dce70bae

29,856

py

Python

views.py

milos-korenciak/2018.ossconf.sk

f121dde4f313a207e39c2f2e187bdad046b86592

[ "MIT" ]

7

2017-07-16T05:59:07.000Z

2018-01-22T09:35:21.000Z

views.py

milos-korenciak/2018.ossconf.sk

f121dde4f313a207e39c2f2e187bdad046b86592

[ "MIT" ]

17

2017-07-31T20:35:24.000Z

2018-02-26T22:00:12.000Z

views.py

milos-korenciak/2018.ossconf.sk

f121dde4f313a207e39c2f2e187bdad046b86592

[ "MIT" ]

13

2017-08-01T17:03:40.000Z

2021-11-02T13:24:30.000Z

#!/usr/bin/python # -*- coding: utf8 -*- import os import re import textwrap import requests import unicodedata from datetime import datetime, timedelta from flask import Flask, g, request, render_template, abort, make_response from flask_babel import Babel, gettext from jinja2 import evalcontextfilter, Markup app = Flask(__name__, static_url_path='/static') app.config['BABEL_DEFAULT_LOCALE'] = 'sk' app.jinja_options = {'extensions': ['jinja2.ext.with_', 'jinja2.ext.i18n']} babel = Babel(app) EVENT = gettext('PyCon SK 2018') DOMAIN = 'https://2018.pycon.sk' API_DOMAIN = 'https://api.pycon.sk' LANGS = ('en', 'sk') TIME_FORMAT = '%Y-%m-%dT%H:%M:%S+00:00' NOW = datetime.utcnow().strftime(TIME_FORMAT) SRC_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__))) LOGO_PYCON = 'logo/pycon_logo_square.svg' LDJSON_SPY = { "@type": "Organization", "name": "SPy o. z.", "url": "https://spy.pycon.sk", "logo": "https://spy.pycon.sk/img/logo/spy-logo.png", "sameAs": [ "https://facebook.com/pyconsk", "https://twitter.com/pyconsk", "https://www.linkedin.com/company/spy-o--z-", "https://github.com/pyconsk", ] } LDJSON_PYCON = { "@context": "http://schema.org", "@type": "Event", "name": EVENT, "description": gettext("PyCon will be back at Slovakia in 2018 again. PyCon SK is a community-organized conference " "for the Python programming language."), "startDate": "2018-03-09T9:00:00+01:00", "endDate": "2018-03-11T18:00:00+01:00", "image": DOMAIN + "/static/img/logo/pycon_long_2018.png", "location": { "@type": "Place", "name": "FIIT STU", "address": { "@type": "PostalAddress", "streetAddress": "Ilkovičova 2", "addressLocality": "Bratislava 4", "postalCode": "842 16", "addressCountry": gettext("Slovak Republic") }, }, "url": DOMAIN, "workPerformed": { "@type": "CreativeWork", "name": EVENT, "creator": LDJSON_SPY } } # calendar settings ICAL_LEN = 70 # length of a calendar (ical) line ICAL_NL = '\\n\n' # calendar newline IGNORE_TALKS = ['Break', 'Coffee Break'] TYPE = { 'talk': gettext('Talk'), 'workshop': gettext('Workshop'), } TAGS = { 'ai': gettext('Machine Learning / AI'), 'community': gettext('Community / Diversity / Social'), 'data': gettext('Data Science'), 'devops': 'DevOps', 'docs': gettext('Documentation'), 'edu': gettext('Education'), 'generic': gettext('Python General'), 'security': gettext('Security'), 'softskills': gettext('Soft Skills'), 'hardware': gettext('Hardware'), 'web': gettext('Web Development'), 'other': gettext('Other'), } FRIDAY_START = datetime(2018, 3, 9, hour=9) SATURDAY_START = datetime(2018, 3, 10, hour=9) SUNDAY_START = datetime(2018, 3, 11, hour=10, minute=15) FRIDAY_TRACK1 = ( {"pause": 5, 'title': gettext("Conference Opening"), 'duration': 25, 'flag': 'other', 'type': 'talk'}, {"pause": 15, 'title': gettext("FaaS and Furious - Zero to Serverless in 60 seconds - Anywhere")}, {"pause": 15, 'title': gettext("Docs or it didn't happen")}, {"pause": 5, 'title': gettext("GraphQL is the new black")}, {"pause": 60, 'title': gettext("To the Google in 80 Days")}, {"pause": 5, 'title': gettext("Unsafe at Any Speed")}, {"pause": 15, 'title': gettext("Protecting Privacy and Security — For Yourself and Your Community")}, {"pause": 5, 'title': gettext("ZODB: The Graph database for Python Developers.")}, {"pause": 15, 'title': gettext("Differentiable programming in Python and Gluon for (not only medical) image analysis")}, {"pause": 5, 'title': gettext("Vim your Python, Python your Vim")}, ) FRIDAY_TRACK2 = ( {"pause": 5, 'title': gettext("Conference Opening in Kiwi.com Hall"), 'duration': 25}, {"pause": 5, 'title': gettext("Python Days in Martin and follow-up activities")}, {"pause": 15, 'title': gettext("Python programming till graduation")}, {"pause": 5, 'title': gettext("Open educational resources for learning Python")}, {"pause": 60, 'title': gettext("About Ninjas and Mentors: CoderDojo in Slovakia")}, {"pause": 5, 'title': gettext("Community based courses")}, {"pause": 15, 'title': gettext("How do we struggle with Python in Martin?")}, {"pause": 5, 'title': gettext("Why hardware attracts kids and adults to IT")}, {"pause": 5, 'title': gettext("Panel discussion: Teaching IT in Slovakia - where is it heading?")}, {"pause": 5, 'title': gettext("EDU Talks"), 'duration': 30, 'language': 'SK', 'flag': 'edu', 'type': 'talk'}, ) FRIDAY_WORKSHOPS1 = ( {"pause": 10, 'title': gettext("How to create interactive maps in Python / R")}, {"pause": 60, 'title': gettext("Working with XML")}, {"pause": 5, 'title': gettext("Managing high-available applications in production")}, ) FRIDAY_WORKSHOPS2 = ( {"pause": 40, 'title': gettext("Workshop: An Introduction to Ansible")}, {"pause": 5, 'title': gettext("Introduction to Machine Learning with Python")}, ) FRIDAY_HALLWAY = ( {"pause": 0, 'title': gettext("OpenPGP key-signing party"), 'duration': 30, 'link': 'https://github.com/pyconsk/2018.pycon.sk/tree/master/openpgp-key-signing-party', 'flag': 'security'}, ) SATURDAY_TRACK1 = ( {"pause": 5, 'title': gettext("Conference Opening"), 'duration': 25, 'flag': 'other', 'type': 'talk'}, {"pause": 5, 'title': gettext("Solutions Reviews")}, {"pause": 15, 'title': gettext("Campaign Automation & Abusing Celery Properly")}, {"pause": 5, 'title': gettext("The Truth about Mastering Big Data")}, {"pause": 5, 'title': gettext("Industrial Machine Learning: Building scalable distributed machine learning pipelines with Python")}, {"pause": 25, 'title': gettext("Programming contest Semi finale"), 'duration': 30, 'flag': 'other', 'link': 'https://app.pycon.sk'}, {"pause": 5, 'title': gettext("Pythonic code, by example")}, {"pause": 15, 'title': gettext("Our DevOps journey, is SRE the next stop?")}, {"pause": 5, 'title': gettext("Implementing distributed systems with Consul")}, {"pause": 15, 'title': gettext("Designing fast and scalable Python MicroServices with django")}, {"pause": 5, 'title': gettext("When your wetware has too many threads - Tips from an ADHDer on how to improve your focus")}, {"pause": 5, 'title': gettext("Programming Python as performance: live coding with FoxDot")}, {"pause": 5, 'title': gettext("Programming Contest Grand Finale"), 'duration': 30, 'flag': 'other', 'type': 'talk', 'language': 'EN'}, {"pause": 5, 'title': gettext("Lightning Talks"), 'duration': 45, 'flag': 'other', 'type': 'talk'}, ) SATURDAY_TRACK2 = ( {"pause": 5, 'title': gettext("Conference Opening in Kiwi.com Hall"), 'duration': 25}, {"pause": 5, 'title': gettext("Meteo data in Python. Effectively.")}, {"pause": 15, 'title': gettext("Around the World in 30 minutes")}, {"pause": 5, 'title': gettext("LOCKED SHIELDS: What a good cyber testing looks like")}, {"pause": 60, 'title': gettext("Kiwi.com in ZOO")}, {"pause": 5, 'title': gettext("Keynote in Kiwi.com Hall"), 'duration': 30, 'flag': 'generic', 'type': 'talk'}, {"pause": 15, 'title': gettext("Skynet your Infrastructure with QUADS")}, {"pause": 5, 'title': gettext("Automated network OS testing")}, {"pause": 15, 'title': gettext("Tools to interact with Bitcoin and Ethereum")}, {"pause": 5, 'title': gettext("7 Steps to a Clean Issue Tracker")}, {"pause": 5, 'title': gettext("The Concierge Paradigm")}, ) SATURDAY_WORKSHOPS1 = ( {"pause": 55, 'title': gettext("Effectively running python applications in Kubernetes/OpenShift")}, {"pause": 5, 'title': gettext("Roboworkshop")}, ) SATURDAY_WORKSHOPS2 = ( {"pause": 55, 'title': gettext("Microbit:Slovakia")}, {"pause": 5, 'title': gettext("Coding in Python: A high-school programming lesson")}, ) SATURDAY_HALLWAY1 = ( {"pause": 0, 'title': gettext("Pandas documentation sprint"), 'duration': 360, 'link': 'https://python-sprints.github.io/pandas/', 'flag': 'docs'}, ) SATURDAY_HALLWAY2 = ( {"pause": 145, 'title': gettext("Programming contest"), 'duration': 95, 'flag': 'other', 'link': 'https://app.pycon.sk'}, {"pause": 5, 'title': gettext("Conference organizers meetup"), 'duration': 30, 'flag': 'community'}, ) SUNDAY_TRACK1 = ( {"pause": 5, 'title': gettext("Charon and the way out from a pickle hell")}, {"pause": 15, 'title': gettext("Making Python Behave")}, {"pause": 5, 'title': gettext("“Secret” information about the code we write")}, {"pause": 60, 'title': gettext("How to connect objects with each other in different situations with Pythonic ways - association, aggregation, composition and etc.")}, {"pause": 5, 'title': gettext("APIs: Gateway to world's data")}, {"pause": 15, 'title': gettext("Getting started with HDF5 and PyTables")}, {"pause": 5, 'title': gettext("Real-time personalized recommendations using embeddings")}, {"pause": 5, 'title': gettext("Quiz"), 'duration': 30, 'flag': 'other', 'type': 'talk'}, ) SUNDAY_WORKSHOPS1 = ( {"pause": 40, 'title': gettext("Real-time transcription and sentiment analysis of audio streams; on the phone and in the browser")}, {"pause": 5, 'title': gettext("Learn MongoDB by modeling PyPI in a document database")}, ) SUNDAY_WORKSHOPS2 = ( {"pause": 15, 'title': gettext("Testing Essentials for Scientists and Engineers")}, {"pause": 5, 'title': gettext("Cython: Speed up your code without going insane")}, ) SUNDAY_WORKSHOPS3 = ( {"pause": 15, 'title': gettext("Meet the pandas")}, {"pause": 5, 'title': gettext("Serverless with OpenFaaS and Python")}, ) SUNDAY_WORKSHOPS4 = ( {"pause": 5, 'title': gettext("Django Girls"), 'duration': 540, 'flag': 'web', 'type': 'workshop'}, ) SUNDAY_HALLWAY = ( {"pause": 5, 'title': gettext("Documentation clinic/helpdesk")}, ) AULA1 = { 'name': gettext('Kiwi.com Hall'), 'number': '-1.61', } AULA2 = { 'name': gettext('Python Software Foundation Hall'), 'number': '-1.65', } AULA3 = { 'name': gettext('SPy - Hall A'), 'number': '-1.57', } AULA4 = { 'name': gettext('SPy - Hall B'), 'number': '-1.57', } AULA5 = { 'name': gettext('Django Girls Auditorium'), 'number': '+1.31', } HALLWAY = { 'name': gettext('Hallway'), 'number': '', } def get_conference_data(url='', filters=''): """Connect to API and get public talks and speakers data.""" url = API_DOMAIN + url if filters: url = url + '&' + filters r = requests.get(url) return r.json() API_DATA_SPEAKERS = get_conference_data(url='/event/2018/speakers/') API_DATA_TALKS = get_conference_data(url='/event/2018/talks/') @app.before_request def before(): if request.view_args and 'lang_code' in request.view_args: g.current_lang = request.view_args['lang_code'] if request.view_args['lang_code'] not in LANGS: return abort(404) request.view_args.pop('lang_code') @babel.localeselector def get_locale(): # try to guess the language from the user accept # header the browser transmits. The best match wins. # return request.accept_languages.best_match(['de', 'sk', 'en']) return g.get('current_lang', app.config['BABEL_DEFAULT_LOCALE']) @app.template_filter() @evalcontextfilter def linebreaks(eval_ctx, value): """Converts newlines into <p> and <br />s.""" value = re.sub(r'\r\n|\r|\n', '\n', value) # normalize newlines paras = re.split('\n{2,}', value) paras = [u'<p>%s</p>' % p.replace('\n', '<br />') for p in paras] paras = u'\n\n'.join(paras) return Markup(paras) @app.template_filter() @evalcontextfilter def linebreaksbr(eval_ctx, value): """Converts newlines into <p> and <br />s.""" value = re.sub(r'\r\n|\r|\n', '\n', value) # normalize newlines paras = re.split('\n{2,}', value) paras = [u'%s' % p.replace('\n', '<br />') for p in paras] paras = u'\n\n'.join(paras) return Markup(paras) @app.template_filter() @evalcontextfilter def strip_accents(eval_ctx, value): """Strip non ASCII characters and convert them to ASCII.""" return unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode("utf-8") def _get_template_variables(**kwargs): """Collect variables for template that repeats, e.g. are in body.html template""" lang = get_locale() variables = { 'title': EVENT, 'logo': LOGO_PYCON, # TODO: Do we need this? 'ld_json': LDJSON_PYCON } variables['ld_json']['url'] = DOMAIN + '/' + lang + '/' variables.update(kwargs) if 'current_lang' in g: variables['lang_code'] = g.current_lang else: variables['lang_code'] = app.config['BABEL_DEFAULT_LOCALE'] return variables def generate_track(api_data, track_data, start, flag=None): """Helper function to mix'n'match API data, with schedule order defined here, to generate schedule dict""" template_track_data = [] for talk in track_data: # Check if talk is in API talk_api_data = next((item for item in api_data if item['title'] == talk['title']), None) # If talk is not in API data we'll use text from track_data dict == same structure for template generation if not talk_api_data: talk_api_data = talk if not flag or ('flag' in talk_api_data and flag == talk_api_data['flag']): # Store data to be displayed in template template_track_data.append({ "start": start, "talk": talk_api_data }) start = start + timedelta(minutes=talk_api_data.get('duration', 0)) # start = start + timedelta(minutes=talk_api_data['duration']) if not flag: # Generate break break_name = gettext('Break') if talk['pause'] in (40, 60): break_name = gettext('Lunch 🍱') if talk['pause'] in (15, 20): break_name = gettext('Coffee Break ☕') template_track_data.append({ 'start': start, 'talk': {'title': break_name}, 'css': 'break' }) start = start + timedelta(minutes=talk['pause']) # break time does not comes from API always defined in track return template_track_data def generate_schedule(api_data, flag=None): return [ { 'room': AULA1, 'start': FRIDAY_START, 'schedule': generate_track(api_data, FRIDAY_TRACK1, FRIDAY_START, flag=flag), 'day': 'friday', 'block_start': True, }, { 'room': AULA2, 'start': FRIDAY_START, 'schedule': generate_track(api_data, FRIDAY_TRACK2, FRIDAY_START, flag=flag), 'day': 'friday' }, { 'room': AULA3, 'start': FRIDAY_START, 'schedule': generate_track(api_data, FRIDAY_WORKSHOPS1, FRIDAY_START+timedelta(minutes=30), flag=flag), 'day': 'friday' }, { 'room': AULA4, 'start': FRIDAY_START, 'schedule': generate_track(api_data, FRIDAY_WORKSHOPS2, FRIDAY_START+timedelta(minutes=30), flag=flag), 'day': 'friday', }, { 'room': HALLWAY, 'start': FRIDAY_START+timedelta(minutes=395), 'schedule': generate_track(api_data, FRIDAY_HALLWAY, FRIDAY_START+timedelta(minutes=395), flag=flag), 'day': 'saturday', 'block_end': True, }, { 'room': AULA1, 'start': SATURDAY_START, 'schedule': generate_track(api_data, SATURDAY_TRACK1, SATURDAY_START, flag=flag), 'day': 'saturday', 'block_start': True, }, { 'room': AULA2, 'start': SATURDAY_START, 'schedule': generate_track(api_data, SATURDAY_TRACK2, SATURDAY_START, flag=flag), 'day': 'saturday' }, { 'room': AULA3, 'start': SATURDAY_START, 'schedule': generate_track(api_data, SATURDAY_WORKSHOPS1, SATURDAY_START+timedelta(minutes=30), flag=flag), 'day': 'saturday' }, { 'room': AULA4, 'start': SATURDAY_START, 'schedule': generate_track(api_data, SATURDAY_WORKSHOPS2, SATURDAY_START+timedelta(minutes=30), flag=flag), 'day': 'saturday' }, { 'room': HALLWAY, 'start': SATURDAY_START+timedelta(minutes=60), 'schedule': generate_track(api_data, SATURDAY_HALLWAY1, SATURDAY_START+timedelta(minutes=60), flag=flag), 'day': 'saturday', }, { 'room': HALLWAY, 'start': SATURDAY_START+timedelta(minutes=30), 'schedule': generate_track(api_data, SATURDAY_HALLWAY2, SATURDAY_START+timedelta(minutes=30), flag=flag), 'day': 'saturday', 'block_end': True, }, { 'room': AULA1, 'start': SUNDAY_START, 'schedule': generate_track(api_data, SUNDAY_TRACK1, SUNDAY_START, flag=flag), 'day': 'sunday', 'block_start': True, }, { 'room': AULA2, 'start': SUNDAY_START, 'schedule': generate_track(api_data, SUNDAY_WORKSHOPS1, SUNDAY_START, flag=flag), 'day': 'sunday' }, { 'room': AULA3, 'start': SUNDAY_START, 'schedule': generate_track(api_data, SUNDAY_WORKSHOPS2, SUNDAY_START, flag=flag), 'day': 'sunday' }, { 'room': AULA4, 'start': SUNDAY_START, 'schedule': generate_track(api_data, SUNDAY_WORKSHOPS3, SUNDAY_START, flag=flag), 'day': 'sunday' }, { 'room': AULA5, 'start': SUNDAY_START, 'schedule': generate_track(api_data, SUNDAY_WORKSHOPS4, SUNDAY_START-timedelta(minutes=135), flag=flag), 'day': 'sunday', }, { 'room': HALLWAY, 'start': SUNDAY_START, 'schedule': generate_track(api_data, SUNDAY_HALLWAY, SUNDAY_START+timedelta(minutes=45), flag=flag), 'day': 'sunday', 'block_end': True, }, ] def _timestamp(dt=None): if dt is None: dt = datetime.now() fmt = '%Y%m%dT%H%M%S' return dt.strftime(fmt) def _ignore_talk(title, names=IGNORE_TALKS): # yes, we can paste unicode symbols, but if we change the symbol this test will still work max_appended_symbols = 2 return any((title == name or title[:-(_len+1)] == name) for _len in range(max_appended_symbols) for name in names) def _hash_event(track, slot): room = track.get('room') name = room.get('name') ts = _timestamp(slot.get('start')) _hash = str(hash('{name}:{ts}'.format(name=name, ts=ts))) _hash = _hash.replace('-', '*') return '-'.join(_hash[i*5:(i+1)*5] for i in range(4)) def _normalize(text, tag=None, subsequent_indent=' ', **kwargs): # tag must be always included to determine amount of space left in the first line if tag: max_width = ICAL_LEN - len(tag) - 1 else: max_width = ICAL_LEN text = text.strip().replace('\n', ICAL_NL) return '\n'.join(textwrap.wrap(text, width=max_width, subsequent_indent=subsequent_indent, **kwargs)) # CALENDAR FUNCTIONS def generate_event(track, slot): room = track.get('room') location = '{name} ({number})'.format(**room) talk = slot.get('talk') summary = talk.get('title', 'N/A') transp = 'OPAQUE' if _ignore_talk(summary): # skip breaks # alternatively we can include breaks into talks (duration=duration+pause) return {} summary = _normalize(summary, 'SUMMARY') start = slot.get('start') duration = talk.get('duration', 0) # TODO add missing duration handling (nonzero default duration? title based dictionary? dtend = _timestamp(start + timedelta(minutes=duration)) dtstart = _timestamp(start) dtstamp = created = modified = _timestamp() # event_uuid caused the event not to be imported to calendar # this creates hash of name:start and split with dashes by 5 uid = _hash_event(track, slot) author = '' main_description = '' tags = '' speaker = talk.get('primary_speaker') if speaker: name = ' '.join([speaker.get(n, '') for n in ['first_name', 'last_name']]) author = '{name}{nl} {nl}'.format(name=name, nl=ICAL_NL) # this is to determine how many chars do we have in the first line # if author is used we start at position 1, otherwise it will be prefixed with tag: desc_tag = 'DESCRIPTION' if not author else '' abstract = talk.get('abstract', '') if abstract: main_description = _normalize(abstract, desc_tag, initial_indent=' ') + ICAL_NL if 'flag' in talk: tags = ' {nl} TAGS: {flag}'.format(nl=ICAL_NL, **talk) description = author + main_description + tags status = 'CONFIRMED' sequence = 0 # number of revisions, we will use default zero even if event changed return {'dtstart': dtstart, 'dtend': dtend, 'dtstamp': dtstamp, 'created': created, 'last-modified': modified, 'uid': uid, 'location': location, 'sequence': sequence, 'description': description, 'status': status, 'summary': summary, 'transp': transp, } @app.route('/<lang_code>/calendar.ics') def generate_ics(): # https://tools.ietf.org/html/rfc5545#section-2.1 # https://en.wikipedia.org/wiki/ICalendar#Technical_specifications omni_schedule = generate_schedule(API_DATA_TALKS) events = [] uids = set() for track in omni_schedule: schedule = track.get('schedule') for slot in schedule: evt = generate_event(track, slot) if evt and evt.get('uid') not in uids: events.append(evt) uids.update([evt.get('uid')]) calendar_ics = render_template('calendar.ics', events=events) response = make_response(calendar_ics.replace('\n', '\r\n')) response.headers["Content-Type"] = "text/calendar" return response @app.route('/<lang_code>/index.html') def index(): return render_template('index.html', **_get_template_variables(li_index='active')) @app.route('/<lang_code>/tickets.html') def tickets(): return render_template('tickets.html', **_get_template_variables(li_tickets='active')) @app.route('/<lang_code>/<flag>/<day>/schedule.html') def schedule_day_filter(flag, day): variables = _get_template_variables(li_schedule_nav='active', li_schedule='active') variables['flag'] = flag variables['day'] = day variables['tags'] = TAGS variables['all'] = {**TYPE, **TAGS} variables['data'] = api_data = API_DATA_TALKS variables['schedule'] = generate_schedule(api_data, flag=flag) return render_template('schedule.html', **variables) @app.route('/<lang_code>/<filter>/schedule.html') def schedule_filter(filter): variables = _get_template_variables(li_schedule_nav='active', li_schedule='active') if filter in ('friday', 'saturday', 'sunday'): variables['day'] = filter variables['flag'] = None else: variables['flag'] = filter variables['tags'] = TAGS variables['all'] = {**TYPE, **TAGS} variables['data'] = api_data = API_DATA_TALKS variables['schedule'] = generate_schedule(api_data, flag=variables['flag']) return render_template('schedule.html', **variables) @app.route('/<lang_code>/schedule.html') def schedule(): variables = _get_template_variables(li_schedule_nav='active', li_schedule='active') variables['tags'] = TAGS variables['all'] = {**TYPE, **TAGS} variables['data'] = api_data = API_DATA_TALKS variables['schedule'] = generate_schedule(api_data) variables['disable_last'] = True return render_template('schedule.html', **variables) @app.route('/<lang_code>/<flag>/talks.html') def talks_filter(flag): variables = _get_template_variables(li_schedule_nav='active', li_talks='active') variables['tags'] = TAGS variables['all'] = {**TYPE, **TAGS} variables['data'] = get_conference_data(url='/event/2018/talks/?flag=' + flag) return render_template('talks.html', **variables) @app.route('/<lang_code>/talks.html') def talks(): variables = _get_template_variables(li_schedule_nav='active', li_talks='active') variables['tags'] = TAGS variables['all'] = {**TYPE, **TAGS} variables['data'] = API_DATA_TALKS return render_template('talks.html', **variables) @app.route('/<lang_code>/speakers.html') def speakers(): variables = _get_template_variables(li_schedule_nav='active', li_speakers='active') variables['data'] = API_DATA_SPEAKERS variables['tags'] = TAGS variables['all'] = {**TYPE, **TAGS} return render_template('speakers.html', **variables) @app.route('/<lang_code>/speakers/<last_name>.html') def profile(last_name): variables = _get_template_variables(li_schedule_nav='active') variables['tags'] = TAGS variables['all'] = {**TYPE, **TAGS} for speaker in API_DATA_SPEAKERS: if speaker['last_name'] == last_name: variables['speaker'] = speaker break variables['talks'] = [] for track in generate_schedule(API_DATA_TALKS): for talk in track['schedule']: if ('primary_speaker' in talk['talk'] or 'secondary_speaker' in talk['talk']) and \ talk['talk']['primary_speaker']['last_name'] == variables['speaker']['last_name'] or ( 'secondary_speaker' in talk['talk'] and talk['talk']['secondary_speaker']['last_name'] == variables['speaker']['last_name']): variables['talks'].append((track, talk)) break return render_template('profile.html', **variables) @app.route('/<lang_code>/cfp.html') def cfp(): return render_template('cfp.html', **_get_template_variables(li_cfp='active')) @app.route('/<lang_code>/coc.html') def coc(): return render_template('coc.html', **_get_template_variables(li_coc='active')) @app.route('/<lang_code>/hall-of-fame.html') def hall_of_fame(): return render_template('hall-of-fame.html', **_get_template_variables(li_hall_of_fame='active')) @app.route('/<lang_code>/venue.html') def venue(): return render_template('venue.html', **_get_template_variables(li_venue='active')) @app.route('/<lang_code>/sponsoring.html') def sponsoring(): return render_template('sponsoring.html', **_get_template_variables(li_sponsoring='active')) def get_mtime(filename): """Get last modification time from file""" mtime = datetime.fromtimestamp(os.path.getmtime(filename)) return mtime.strftime(TIME_FORMAT) SITEMAP_DEFAULT = {'prio': '0.1', 'freq': 'weekly'} SITEMAP = { 'sitemap.xml': {'prio': '0.9', 'freq': 'daily', 'lastmod': get_mtime(__file__)}, 'index.html': {'prio': '1', 'freq': 'daily'}, 'schedule.html': {'prio': '0.9', 'freq': 'daily'}, 'speakers.html': {'prio': '0.9', 'freq': 'daily'}, 'hall_of_fame.html': {'prio': '0.5', 'freq': 'weekly'}, 'tickets.html': {'prio': '0.5', 'freq': 'weekly'}, } def get_lastmod(route, sitemap_entry): """Used by sitemap() below""" if 'lastmod' in sitemap_entry: return sitemap_entry['lastmod'] template = route.rule.split('/')[-1] template_file = os.path.join(SRC_DIR, 'templates', template) if os.path.exists(template_file): return get_mtime(template_file) return NOW @app.route('/sitemap.xml', methods=['GET']) def sitemap(): """Generate sitemap.xml. Makes a list of urls and date modified.""" pages = [] # static pages for rule in app.url_map.iter_rules(): if "GET" in rule.methods: if len(rule.arguments) == 0: indx = rule.rule.replace('/', '') sitemap_data = SITEMAP.get(indx, SITEMAP_DEFAULT) pages.append({ 'loc': DOMAIN + rule.rule, 'lastmod': get_lastmod(rule, sitemap_data), 'freq': sitemap_data['freq'], 'prio': sitemap_data['prio'], }) elif 'lang_code' in rule.arguments: indx = rule.rule.replace('/<lang_code>/', '') for lang in LANGS: alternate = [] for alt_lang in LANGS: if alt_lang != lang: alternate.append({ 'lang': alt_lang, 'url': DOMAIN + rule.rule.replace('<lang_code>', alt_lang) }) sitemap_data = SITEMAP.get(indx, SITEMAP_DEFAULT) pages.append({ 'loc': DOMAIN + rule.rule.replace('<lang_code>', lang), 'alternate': alternate, 'lastmod': get_lastmod(rule, sitemap_data), 'freq': sitemap_data['freq'], 'prio': sitemap_data['prio'], }) sitemap_xml = render_template('sitemap_template.xml', pages=pages) response = make_response(sitemap_xml) response.headers["Content-Type"] = "text/xml" return response if __name__ == "__main__": app.run(debug=True, host=os.environ.get('FLASK_HOST', '127.0.0.1'), port=int(os.environ.get('FLASK_PORT', 5000)), use_reloader=True)

36.81381

190

0.612775

3,584

29,856

4.967913

0.202288

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0.027183

0.044482

0.322101

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29,856

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0.00159

0

null

0

null

0

1

0

f510f358811538f9c09860ccdb42030579e71a1a

928

py

Python

scripts/fishvalidate.py

justinbois/fishactivity

6c6ac06c391b75b2725e2e2a61dd80afc34daf31

[ "MIT" ]

null

scripts/fishvalidate.py

justinbois/fishactivity

6c6ac06c391b75b2725e2e2a61dd80afc34daf31

[ "MIT" ]

null

scripts/fishvalidate.py

justinbois/fishactivity

6c6ac06c391b75b2725e2e2a61dd80afc34daf31

[ "MIT" ]

null

#!/usr/bin/env python import argparse import fishact if __name__ == '__main__': parser = argparse.ArgumentParser( description='Validate data files.') parser.add_argument('activity_fname', metavar='activity_file', type=str, help='Name of activity file.') parser.add_argument('gtype_fname', metavar='genotype_file', type=str, help='Name of genotype file.') args = parser.parse_args() print('------------------------------------------------') print('Checking genotype file...') fishact.validate.test_genotype_file(args.gtype_fname) print('------------------------------------------------\n\n\n') print('------------------------------------------------') print('Checking activity file...') fishact.validate.test_activity_file(args.activity_fname, args.gtype_fname) print('------------------------------------------------')

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null

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null

0

1

0

f513b5c28a4eaca8eb08a50fccfcd5204171dfdc

1,682

py

Python

scripts/rescale.py

danydoerr/spp_dcj

1ab9dacb1f0dc34a3ebbeed9e74226a9a53c297a

[ "MIT" ]

2

2021-08-24T16:03:30.000Z

2022-03-18T14:52:43.000Z

scripts/rescale.py

danydoerr/spp_dcj

1ab9dacb1f0dc34a3ebbeed9e74226a9a53c297a

[ "MIT" ]

null

scripts/rescale.py

danydoerr/spp_dcj

1ab9dacb1f0dc34a3ebbeed9e74226a9a53c297a

[ "MIT" ]

null

#!/usr/bin/env python3 from sys import stdout,stderr,exit from optparse import OptionParser from newick_parser import parse_tree_iterator, Branch from tree_span import calculateSpan from copy import deepcopy def rescale_absolute(tree, max_length): span = calculateSpan(tree) s = max_length/float(span) return rescale(tree, s) def rescale(tree, scale_factor): res = deepcopy(tree) stack = list() stack.append(res.subtree) while stack: x = stack.pop() if x.length: x.length *= scale_factor if type(x) == Branch: stack.extend(x.subtrees) return res if __name__ == '__main__': usage = 'usage: %prog [options] <NEWICK FILE>' parser = OptionParser(usage=usage) parser.add_option('-s', '--scale_factor', dest='scale_factor', help='Scale factor of distances in tree', type=float, default=0, metavar='FLOAT') parser.add_option('-a', '--absolute_length', dest='absolute', help='Absolute length of maximal distance in tree', type=float, default=0, metavar='FLOAT') (options, args) = parser.parse_args() if len(args) != 1: parser.print_help() exit(1) if not ((options.absolute > 0) ^ (options.scale_factor > 0)): print('!! Specify either scale factor or absolute length with ' + \ 'strictly positive number', file = stderr) exit(1) for tree in parse_tree_iterator(open(args[0])): if options.absolute > 0: print(rescale_absolute(tree, options.absolute), file = stdout) else: print(rescale(tree, options.scale_factor), file = stdout)

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false

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0.214286

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0

null

0

null

0

1

0

f51915e704bb43425413f02d24086079a01a04be

743

py

Python

mytest/playsnake.py

mrzhuzhe/stable-baselines3

6c3bc5fa4c3faba951099e3ccb5c74b763134b38

[ "MIT" ]

null

mytest/playsnake.py

mrzhuzhe/stable-baselines3

6c3bc5fa4c3faba951099e3ccb5c74b763134b38

[ "MIT" ]

null

mytest/playsnake.py

mrzhuzhe/stable-baselines3

6c3bc5fa4c3faba951099e3ccb5c74b763134b38

[ "MIT" ]

null

from stable_baselines3 import PPO import os from setup_gym_env import SnakeEnv import time #models_dir = "./models/1644408901/" + "40000" #models_dir = "./models/1644462865/" + "120000" #models_dir = "./models/1644466638/" + "100000" models_dir = "./models/1644485414/" + "100000" env = SnakeEnv() env.reset() model = PPO.load(models_dir) episodes = 10 # snake doesn't known where itself for episode in range(episodes): done = False obs = env.reset() #while True:#not done: while not done: action, _states = model.predict(obs) #print("action",action) obs, reward, done, info = env.step(action) #print('reward',reward) if done == True: print(done) env.render()

22.515152

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743

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0

0.055556

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1

0

false

0

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0.222222

0.055556

0

null

0

null

0

1

0

f519a4dd8609848cb4fec6b2221b463e32b9ae3b

13,105

py

Python

yoda2h5.py

iamholger/yodf5

79ad8d77fd2b48e1b71403339e2502b42a5435c8

[ "MIT" ]

4

2020-04-22T11:00:13.000Z

2020-12-16T17:49:47.000Z

yoda2h5.py

iamholger/yodf5

79ad8d77fd2b48e1b71403339e2502b42a5435c8

[ "MIT" ]

4

2020-12-17T16:26:16.000Z

2020-12-17T16:30:34.000Z

yoda2h5.py

iamholger/yodf5

79ad8d77fd2b48e1b71403339e2502b42a5435c8

[ "MIT" ]

2

2020-05-06T17:30:05.000Z

2020-12-16T17:58:23.000Z

#!/usr/bin/env python3 import yoda, sys import h5py import numpy as np def chunkIt(seq, num): avg = len(seq) / float(num) out = [] last = 0.0 while last < len(seq): out.append(seq[int(last):int(last + avg)]) last += avg # Fix size, sometimes there is spillover # TODO: replace with while if problem persists if len(out) > num: out[-2].extend(out[-1]) out = out[0:-1] if len(out) != num: raise Exception("something went wrong in chunkIt, the target size differs from the actual size") return out def createDatasets(f, binids, variations, depth=1, compression=4): """ Create data sets in the HDF5 file. """ nbins=len(binids) nvars=len(variations) # The fundamental moments/elements of yoda objecs floats = [ "sumw", "sumw2", "sumwx", "sumwx2", "sumwy", "sumwy2", "sumwxy", "numEntries", "xval", "xerr-", "xerr+", "yval", "yerr-", "yerr+", "xmin", "xmax", "ymin", "ymax" ] # The datasets have 3 axes: binid, weight variation, point in parameter space for df in floats: f.create_dataset(df, (nbins,nvars,depth), maxshape=(None,None,None), dtype='f' , chunks=True, compression=compression) # Lookups --- helps when reading data and reconstucting YODA objects f.create_group("Histo1D") f.create_group("Histo2D") f.create_group("Profile1D") f.create_group("Counter") f.create_group("Scatter1D") f.create_group("Scatter2D") # This is the one that works well with hdf5 when reading std::string in C++ dt = h5py.special_dtype(vlen=str) # We use these simple lists as lookup tables to associate the elements of the datasets ^^^ with # the actual YODA Analysis objects import numpy as np f.create_dataset("binids", data=np.array(binids, dtype=dt)) f.create_dataset("variations", data=np.array(variations, dtype=dt)) def dbn0ToArray(dbn): return np.array([dbn.sumW(), dbn.sumW2(), dbn.numEntries()]) def dbn1ToArray(dbn): """ The try except block deals with the underflow things not having xmin, xmax """ try: return np.array([dbn.sumW(), dbn.sumW2(), dbn.sumWX(), dbn.sumWX2(), dbn.numEntries(), dbn.xMin(), dbn.xMax()]) except: return np.array([dbn.sumW(), dbn.sumW2(), dbn.sumWX(), dbn.sumWX2(), dbn.numEntries(), 0, 0]) def H2dbn2ToArray(dbn): """ The try except block deals with the underflow things not having xmin, xmax """ try: return np.array([dbn.sumW(), dbn.sumW2(), dbn.sumWX(), dbn.sumWX2(), dbn.sumWY(), dbn.sumWY2(), dbn.sumWXY(), dbn.numEntries(), dbn.xMin(), dbn.xMax(), dbn.yMin(), dbn.yMax()]) except: return np.array([dbn.sumW(), dbn.sumW2(), dbn.sumWX(), dbn.sumWX2(), dbn.sumWY(), dbn.sumWY2(), dbn.sumWXY(), dbn.numEntries(), 0, 0, 0, 0]) def dbn2ToArray(dbn): try: return np.array([dbn.sumW(), dbn.sumW2(), dbn.sumWX(), dbn.sumWX2(), dbn.sumWY(), dbn.sumWY2(), dbn.numEntries(), dbn.xMin(), dbn.xMax()]) except: return np.array([dbn.sumW(), dbn.sumW2(), dbn.sumWX(), dbn.sumWX2(), dbn.sumWY(), dbn.sumWY2(), dbn.numEntries(), 0, 0]) def point2DToArray(pnt): return np.array([pnt.val(1), pnt.errMinus(1), pnt.errPlus(1), pnt.val(2), pnt.errMinus(2), pnt.errPlus(2)]) def point1DToArray(pnt): return np.array([pnt.val(1), pnt.errMinus(1), pnt.errPlus(1)]) def mkSafeHname(hname): return hname.replace("/","|") def mkBinids(hdict): binids= [] for num, hname in enumerate(sorted(list(hdict.keys()))): if hname.endswith("]"): continue ao = hdict[hname] base = ao.path().split("[")[0].replace("/","|") if ao.type()=="Scatter1D" or ao.type()=="Scatter2D": temp = ["{}#{}".format(base, i) for i in range(len(ao))] elif ao.type()=="Counter": temp = ["{}#{}".format(base, 0)] else: suffixes = ["T", "O", "U"] if ao.type() == "Counter": suffixes.append(0) else: suffixes.extend([i for i in range(len(ao))]) temp = ["{}#{}".format(base, s) for s in suffixes] binids.extend(temp) return binids def mkIndexDict(datadict, allbinids): ret = {'Histo1D':{}, 'Histo2D':{}, 'Profile1D':{}, 'Scatter1D':{}, 'Scatter2D':{}, 'Counter':{}} for hname, v in datadict.items(): _hname=mkSafeHname(hname) try: ret[datadict[hname].type()][_hname] = [num for num, binid in enumerate(allbinids) if binid.startswith("{}#".format(_hname))] except Exception as e: print("oops: ", e) return ret def createIndexDS(f, d_idx): for dtype, objects in d_idx.items(): for _hname, binIdx in objects.items(): f.create_dataset("{}/{}".format(dtype, _hname), data=binIdx , chunks=True) def fillDatasets(f, binIdx, variations, ddict, hname, depth=0): if len(binIdx) ==0: print("Warning, no matching binid for {} --- is this one of the raw ratios maybe???".format(hname)) return if ddict[hname].type()=='Histo1D': nFields=7 fdbn = dbn1ToArray elif ddict[hname].type()=='Histo2D': nFields=12 fdbn = H2dbn2ToArray elif ddict[hname].type()=='Profile1D': fdbn = dbn2ToArray nFields=9 elif ddict[hname].type()=='Scatter2D': fdbn = point2DToArray nFields=6 elif ddict[hname].type()=='Scatter1D': fdbn = point1DToArray nFields=3 elif ddict[hname].type()=='Counter': nFields=3 else: raise Exception("type {} Not implemented".format(ddict[hname].type())) # Empty array to be filled and written to datasets temp = np.zeros((len(binIdx), len(variations), nFields)) hids = [hname] for v in variations[1:]: hids.append("{}[{}]".format(hname, v)) # Iterate over variations for col, hn in enumerate(hids): # Iterate over bins H=ddict[hn] if H.type() == "Counter": temp[0][col] = np.array([H.sumW(), H.sumW2(), H.numEntries()]) # Things with under/overflow first elif H.type() not in ["Scatter1D", "Scatter2D", "Histo2D"]: temp[0][col] = fdbn(H.totalDbn()) temp[1][col] = fdbn(H.overflow()) temp[2][col] = fdbn(H.underflow()) for i in range(len(binIdx)-3): temp[3+i][col] = fdbn(H.bin(i)) elif H.type() =="Histo2D": temp[0][col] = fdbn(H.totalDbn()) temp[1][col] = 0.0 # Future proofing temp[2][col] = 0.0 # for i in range(len(binIdx)-3): temp[3+i][col] = fdbn(H.bin(i)) else: for i in range(len(binIdx)): temp[i][col] = fdbn(H.point(i)) if ddict[hname].type()=='Histo1D': f["sumw"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,0] f["sumw2"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,1] f["sumwx"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,2] f["sumwx2"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,3] f["numEntries"][binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,4] f["xmin"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,5] f["xmax"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,6] # elif ddict[hname].type()=='Histo2D': # f["sumw"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,0] # f["sumw2"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,1] # f["sumwx"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,2] # f["sumwx2"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,3] # f["sumwy"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,4] # f["sumwy2"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,5] # f["sumwxy"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,6] # f["numEntries"][binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,7] # f["xmin"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,8] # f["xmax"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,9] # f["ymin"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,10] # f["ymax"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,11] # elif ddict[hname].type()=='Profile1D': # f["sumw"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,0] # f["sumw2"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,1] # f["sumwx"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,2] # f["sumwx2"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,3] # f["sumwy"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,4] # f["sumwy2"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,5] # f["numEntries"][binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,6] # f["xmin"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,7] # f["xmax"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,8] # elif ddict[hname].type()=='Scatter1D': # f["xval"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,0] # f["xerr-"][binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,1] # f["xerr+"][binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,2] # elif ddict[hname].type()=='Scatter2D': # f["xval"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,0] # f["xerr-"][binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,1] # f["xerr+"][binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,2] # f["yval"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,3] # f["yerr-"][binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,4] # f["yerr+"][binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,5] # elif ddict[hname].type()=='Counter': # f["sumw"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,0] # f["sumw2"][ binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,1] # f["numEntries"][binIdx[0]:binIdx[-1]+1,:,depth] = temp[:,:,2] # else: # raise Exception("yikes") if __name__=="__main__": import sys import optparse, os, sys op = optparse.OptionParser(usage=__doc__) op.add_option("-v", "--debug", dest="DEBUG", action="store_true", default=False, help="Turn on some debug messages") op.add_option("-o", dest="OUTPUT", default="analysisobjects.h5", help="Output HDF5 file (default: %default)") opts, args = op.parse_args() YODAFILES = args from mpi4py import MPI comm = MPI.COMM_WORLD size = comm.Get_size() rank = comm.Get_rank() binids, VVV, aix, aix_flat, central = None, None, None, None, None if rank==0: # TODO if len(args)==1 and os.path.isdir(args[0]) --- hierarchical reading with pnames finding etc # Let's assume they are all consistent TODO add robustness DATA0 = yoda.readYODA(args[0]) L = sorted(list(DATA0.keys())) names = [x for x in L ]# if not "/RAW" in x] central = [x for x in names if not x.endswith("]")] variations = [x for x in names if x.endswith("]")] # TODO In principle one probably should check that all variations are always the # same, we assume this is the case here var = [] for c in central: var.append([x for x in variations if x.startswith(c+"[")]) ## Thats the weight and weight variation order we store the data in VVV = ["CentralWeight"] import re p=re.compile("\[(.*?)\]") for x in var[0]: try: VVV.append(p.findall(x)[0]) except Exception as e: print(x, e) binids = mkBinids(DATA0) # Hierarchical, i.e. top layer is the AnalysisObject type aix = mkIndexDict(DATA0, binids) # Object name as keys and lists of indices as values aix_flat = {} for k, v in aix.items(): aix_flat.update(v) binids = comm.bcast(binids, root=0) VVV = comm.bcast(VVV, root=0) aix = comm.bcast(aix, root=0) aix_flat = comm.bcast(aix_flat, root=0) central = comm.bcast(central, root=0) # NOTE dataset operations are collective # This require h5py to use and H5 that is build with MPI try: f = h5py.File(opts.OUTPUT, "w", driver='mpio', comm=MPI.COMM_WORLD) except: f = h5py.File(opts.OUTPUT, "w") createDatasets(f, binids, VVV, depth=len(YODAFILES)) createIndexDS(f, aix) rankwork = chunkIt([i for i in range(len(YODAFILES))], size) if rank==0 else None rankwork = comm.scatter(rankwork, root=0) # This part is MPI trivial for num, findex in enumerate(rankwork): DATA = yoda.readYODA(YODAFILES[findex]) for hname in central: _hname=mkSafeHname(hname) fillDatasets(f, aix_flat[_hname], VVV, DATA, hname, depth=findex) if rank==0: print("[{}] --- {}/{} complete".format(rank, num, len(rankwork))) sys.stdout.flush() f.close()

37.766571

184

0.546814

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13,105

4.084911

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0.060465

false

0

0.037209

0.018605

0.162791

0.018605

0

null

0

null

0

1

0

f51c993aef58b3c9c160f8b68cd78fc8daf5ff42

1,703

py

Python

main.py

RichezA/UnRecurZipper

dffe16811e3d79fdc20e0aada0f2ffe9c77da9a1

[ "MIT" ]

null

main.py

RichezA/UnRecurZipper

dffe16811e3d79fdc20e0aada0f2ffe9c77da9a1

[ "MIT" ]

null

main.py

RichezA/UnRecurZipper

dffe16811e3d79fdc20e0aada0f2ffe9c77da9a1

[ "MIT" ]

null

import zipfile import os import glob import sys # Actual directory that we could find somewhere class Folder: def __init__(self, path): self.path = path print("Current working folder is: " + self.path) self.checkForZippedFile() self.checkForDirectories() def checkForZippedFile(self): self.filesToUnzip = list() self.filesToUnzip = glob.glob(os.path.join(self.path,'*.zip'), recursive=True) # If we find a .zip file in the current directory for fileToUnzip in self.filesToUnzip: print("new ZipFile found at: " + fileToUnzip) zip_ref = zipfile.ZipFile(fileToUnzip, 'r') # We prepare to unzip zipFilePath = fileToUnzip.split('.zip')[0] # Reformating the path to remove the .zip at the end print("Current zip is at: " + zipFilePath) zip_ref.extractall(zipFilePath) # Extracting .zip content zip_ref.close() # Closing extraction flow os.remove(zipFilePath + '.zip') # Removing the zip files Folder(zipFilePath) # Calling Folder again def checkForDirectories(self): with os.scandir(self.path) as listOfDirectories: for entry in listOfDirectories: # We check if the actual file is a directory and if it isn't the .git one if not entry.is_file() and entry.name != '.git': entry = Folder(os.path.join(self.path, entry.name)) # Reading the first arg written in the console (program name not included) fileTest = Folder(sys.argv[1])

47.305556

116

0.593658

197

1,703

5.091371

0.436548

0.047856

0.023928

0.027916

0.035892

0

0.001736

0.323547

1,703

36

117

47.305556

0.868924

0.235467

0

0.066563

0

1

0.107143

false

0

0.142857

0

0.285714

0.107143

0

null

0

null

0

1

0

f51fe70db140c3154b176531ad8f28b9ef267b5a

1,974

py

Python

predict_CNN.py

slimtomatillo/toxic_waste_dump

4bc820f0b31f4420e789af11a9338c475c068889

[ "MIT" ]

2

2018-07-13T16:44:24.000Z

2019-10-14T21:31:02.000Z

predict_CNN.py

slimtomatillo/toxic_waste_dump

4bc820f0b31f4420e789af11a9338c475c068889

[ "MIT" ]

null

predict_CNN.py

slimtomatillo/toxic_waste_dump

4bc820f0b31f4420e789af11a9338c475c068889

[ "MIT" ]

null

# Imports import pandas as pd import pickle from keras.models import load_model from preprocess import preprocess from preprocess import prep_text #Logging import logging logging.getLogger().setLevel(logging.INFO) logging.info('Loading comments to classify...') # Enter comment to be classified below comment_to_classify = '' def return_label(predicted_probs): """ Function that takes in a list of 7 class probabilities and returns the labels with probabilities over a certain threshold. """ threshold = 0.4 labels = [] classes = ['clean', 'toxic', 'severe toxic', 'obscene', 'threat', 'insult', 'identity hate'] i = 0 while i < len(classes): if predicted_probs[i] > threshold: labels.append(classes[i]) i += 1 return (labels) def predict_label(comment_str): """ Function that takes in a comment in string form and returns the predicted class labels: not toxic, toxic, severe toxic, obscene, threat, insults, identity hate. May output multiple labels. """ data = pd.DataFrame(data=[comment_str], columns=['comment_text']) logging.info('Comments loaded.') # Preprocess text X_to_predict = preprocess(data) # Identify data to make predictions from X_to_predict = X_to_predict['model_text'] # Format data properly X_to_predict = prep_text(X_to_predict) logging.info('Loading model...') # Load CNN from disk cnn = load_model('model/CNN/binarycrossentropy_adam/model-04-0.9781.hdf5') logging.info('Model loaded.') logging.info('Making prediction(s)...') # Make predictions preds = cnn.predict(X_to_predict) for each_comment, prob in zip(data['comment_text'], preds): print('COMMENT:') print(each_comment) print() print('PREDICTION:') print(return_label(prob)) print() logging.info('Finished.') predict_label(comment_to_classify)

24.675

78

0.670719

250

1,974

5.16

0.412

0.05969

0.046512

0.029457

0.075969

0

0.008486

0.223911

1,974

79

24.987342

0.833551

0.241135

0

0.05

0

0.187326

0.037604

0

1

0.05

false

0

0.15

0

0.225

0.15

0

null

0

null

0

1

0

f5209853412f11170538a00e749d5b0ede34e2eb

797

py

Python

299. Bulls and Cows.py

fossabot/leetcode-2

335f1aa3ee785320515c3d3f03c2cb2df3bc13ba

[ "MIT" ]

2

2018-02-26T09:12:19.000Z

2019-06-07T13:38:10.000Z

299. Bulls and Cows.py

fossabot/leetcode-2

335f1aa3ee785320515c3d3f03c2cb2df3bc13ba

[ "MIT" ]

1

2018-12-24T07:03:34.000Z

299. Bulls and Cows.py

fossabot/leetcode-2

335f1aa3ee785320515c3d3f03c2cb2df3bc13ba

[ "MIT" ]

2

2018-12-24T07:01:03.000Z

2019-06-07T13:38:07.000Z

class Solution(object): def getHint(self, secret, guess): """ :type secret: str :type guess: str :rtype: str """ dic = {} countA = 0 setA = set() for i in range(len(secret)): if secret[i] == guess[i]: countA += 1 setA.add(i) elif secret[i] not in dic: dic[secret[i]] = 1 else: dic[secret[i]] += 1 countB = 0 for i in range(len(guess)): if i not in setA: if guess[i] in dic: countB += 1 dic[guess[i]] -= 1 if dic[guess[i]] == 0: del dic[guess[i]] return str(countA)+"A"+str(countB)+"B"

28.464286

46

0.385194

92

797

3.336957

0.347826

0.09772

0.087948

0.071661

0.091205

0

0.019704

0.49059

797

27

47

29.518519

0.736453

0.057716

0

0.002813

0

1

0.045455

false

0

0.136364

0

null

0

null

0

1

0

f528bf891d405b1631574286911aea9a15dea4b2

1,566

py

Python

codesmith/CloudFormation/CogCondPreAuthSettings/cog_cond_pre_auth_settings.py

codesmith-gmbh/forge

43c334d829a727b48f8e21e273017c51394010f9

[ "Apache-2.0" ]

null

codesmith/CloudFormation/CogCondPreAuthSettings/cog_cond_pre_auth_settings.py

codesmith-gmbh/forge

43c334d829a727b48f8e21e273017c51394010f9

[ "Apache-2.0" ]

null

codesmith/CloudFormation/CogCondPreAuthSettings/cog_cond_pre_auth_settings.py

codesmith-gmbh/forge

43c334d829a727b48f8e21e273017c51394010f9

[ "Apache-2.0" ]

null

import json import logging import boto3 from box import Box from crhelper import CfnResource from schema import Optional import codesmith.common.naming as naming from codesmith.common.cfn import resource_properties from codesmith.common.schema import encoded_bool, non_empty_string, tolerant_schema from codesmith.common.ssm import put_string_parameter, silent_delete_parameter_from_event helper = CfnResource() logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) properties_schema = tolerant_schema({ 'UserPoolId': non_empty_string, 'UserPoolClientId': non_empty_string, Optional('All', default=False): encoded_bool, Optional('Domains', default=[]): [str], Optional('Emails', default=[]): [str] }) ssm = boto3.client('ssm') def validate_properties(props): return Box(properties_schema.validate(props), camel_killer_box=True) @helper.create @helper.update def create(event, _): p = validate_properties(resource_properties(event)) parameter_name = naming.cog_cond_pre_auth_parameter_name(p.user_pool_id, p.user_pool_client_id) parameter_value = json.dumps({'All': p.all, 'Domains': p.domains, 'Emails': p.emails}) put_string_parameter(ssm, parameter_name, value=parameter_value, description='Forge Cognito Pre Auth Settings Parameter') return parameter_name @helper.delete def delete(event, _): return silent_delete_parameter_from_event(ssm, event) def handler(event, context): logger.info('event: %s', event) helper(event, context)

29.54717

99

0.751596

198

1,566

5.686869

0.353535

0.053286

0.050622

0.044405

0.053286

0

0.001502

0.149425

1,566

52

100

30.115385

0.843844

0

0.070881

0

1

0.102564

false

0

0.25641

0.051282

0.435897

0

null

0

null

0

1

0

f52d16005d54fc06009e6a33b0d9fa26ef35fd47

2,093

py

Python

dl_tutorials/torch_neural_networks.py

learnerzhang/AnalyticsVidhya

697689a24a9d73785164512cab8ac4ee5494afe8

[ "Apache-2.0" ]

1

2018-07-04T09:14:26.000Z

dl_tutorials/torch_neural_networks.py

learnerzhang/AnalyticsVidhya

697689a24a9d73785164512cab8ac4ee5494afe8

[ "Apache-2.0" ]

null

dl_tutorials/torch_neural_networks.py

learnerzhang/AnalyticsVidhya

697689a24a9d73785164512cab8ac4ee5494afe8

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2019-01-02 16:44 # @Author : zhangzhen # @Site : # @File : torch_neural_networks.py # @Software: PyCharm import torch import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() # 1 input image channel, 6 output channels, 5 * 5 square convolution # kernel self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5) self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5) # an affine operation: y = Wx + b self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, *input): # Max pooling over a (2, 2) window x = F.max_pool2d(F.relu(self.conv1(input[0])), (2, 2)) # if the size is a square you can only specify a single number x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2)) x = x.view(-1, self.num_flat_features(x)) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x def num_flat_features(self, x): size = x.size()[1:] num_features = 1 for s in size: num_features *= s return num_features if __name__ == '__main__': net = Net() criterion = nn.MSELoss() print(net) params = list(net.parameters()) print("参数个数:", len(params)) for param in params: print(param.size()) input = torch.randn(1, 1, 32, 32) target = torch.randn(10) out = net(input) loss = criterion(out, target) print(100 * "=") print(out, target) print("Loss:", loss) print(loss.grad_fn) # MSELoss print(loss.grad_fn.next_functions[0][0]) # Linear print(loss.grad_fn.next_functions[0][0].next_functions[0][0]) # ReLU net.zero_grad() print('conv1.bias.grad before backward') print(net.conv1.bias.grad) loss.backward() print('conv1.bias.grad after backward') print(net.conv1.bias.grad)

26.493671

77

0.592929

313

2,093

3.837061

0.373802

0.006661

0.029975

0.037469

0.149875

0.131557

0.083264

0.049958

0

0.055087

0.262781

2,093

78

26.833333

0.723266

0.180602

0

0.040816

0

0.047114

0

1

0.061224

false

0

0.061224

0

0.183673

0.265306

0

null

0

null

0

1

0

f52ec88be52c378180af93ce81749dca618e2061

2,577

py

Python

shldn/leonard.py

arrieta/shldn

8335aaeb1bfe91698bd9dfb83487393ede9225e6

[ "MIT" ]

null

shldn/leonard.py

arrieta/shldn

8335aaeb1bfe91698bd9dfb83487393ede9225e6

[ "MIT" ]

null

shldn/leonard.py

arrieta/shldn

8335aaeb1bfe91698bd9dfb83487393ede9225e6

[ "MIT" ]

null

""" Leonard always DRIVES Sheldon (this module is the __main__ driver for Sheldon) """ import argparse import sys import os try: from cooper import Sheldon except: from .cooper import Sheldon # Extensions for python source files EXTENSIONS = [".py", ".mpy"] def parse_commandline(): parser = argparse.ArgumentParser( description="Find divisions in Python code") parser.add_argument("-u", "--human_readable", help="Display friendlier output", action="store_true") parser.add_argument("-r", "--recursive", help="Scan subdirectories recursively", action="store_true") parser.add_argument("path", type=str, help="Path to the target file or directory") return parser.parse_args() def process_files(files, divs_found, readable, path=""): for filename in files: fname = os.path.join(path, filename) with open(fname) as f: pysource = f.read() s = Sheldon(pysource) try: s.analyze() except SyntaxError: exc_type, exc_obj, exc_tb = sys.exc_info() print(f"{fname} {exc_tb.tb_lineno} SyntaxError") continue divs_found += len(s.divisions) s.printdivs(fname, s.divisions, readable) return divs_found def main(): args = parse_commandline() if args.human_readable: def readableprint(*args, **kwargs): print(*args, **kwargs) else: readableprint = lambda *a, **k: None # do - nothing function files_checked = 0 divs_found = 0 # Directory path if os.path.isdir(args.path): for path, dirs, files in os.walk(args.path): files = [f for f in os.listdir(path) if f.endswith(tuple(EXTENSIONS))] files_checked += len(files) divs_found = process_files(files, divs_found, args.human_readable, path=path) if not args.recursive: exit(0) readableprint(f"{files_checked} files checked") readableprint(f"{divs_found} divisions found") # File path elif os.path.isfile(args.path): files =[f for f in [args.path] if args.path.endswith(tuple(EXTENSIONS))] divs_found = process_files(files, divs_found, args.human_readable) readableprint(f"{divs_found} divisions found") # Error else: sys.exit(f"{args.path} doesn't exist!") if __name__ == "__main__": main()

28.633333

89

0.592549

303

2,577

4.887789

0.376238

0.06077

0.037812

0.042539

0.207968

0.190412

0.097232

0.070223

0

0.001664

0.300349

2,577

89

90

28.955056

0.819745

0.064804

0

0.129032

0

0.141785

0

1

0.064516

false

0

0.080645

0

0.177419

0.129032

0

null

0

null

0

1

0

f52efbe88e2653ae5d1fd37a74f972d83828b114

40,749

py

Python

Lib/site-packages/cherrypy/test/test_core.py

raychorn/svn_Python-2.5.1

425005b1b489ba44ec0bb989e077297e8953d9be

[ "PSF-2.0" ]

null

Lib/site-packages/cherrypy/test/test_core.py

raychorn/svn_Python-2.5.1

425005b1b489ba44ec0bb989e077297e8953d9be

[ "PSF-2.0" ]

null

Lib/site-packages/cherrypy/test/test_core.py

raychorn/svn_Python-2.5.1

425005b1b489ba44ec0bb989e077297e8953d9be

[ "PSF-2.0" ]

null

"""Basic tests for the CherryPy core: request handling.""" from cherrypy.test import test test.prefer_parent_path() import cherrypy from cherrypy import _cptools, tools from cherrypy.lib import http, static import types import os localDir = os.path.dirname(__file__) log_file = os.path.join(localDir, "test.log") log_access_file = os.path.join(localDir, "access.log") favicon_path = os.path.join(os.getcwd(), localDir, "../favicon.ico") defined_http_methods = ("OPTIONS", "GET", "HEAD", "POST", "PUT", "DELETE", "TRACE", "CONNECT", "PROPFIND") def setup_server(): class Root: def index(self): return "hello" index.exposed = True favicon_ico = tools.staticfile.handler(filename=favicon_path) def andnow(self): return "the larch" andnow.exposed = True def global_(self): pass global_.exposed = True def delglobal(self): del self.__class__.__dict__['global_'] delglobal.exposed = True def defct(self, newct): newct = "text/%s" % newct cherrypy.config.update({'tools.response_headers.on': True, 'tools.response_headers.headers': [('Content-Type', newct)]}) defct.exposed = True def upload(self, file): return "Size: %s" % len(file.file.read()) upload.exposed = True root = Root() class TestType(type): """Metaclass which automatically exposes all functions in each subclass, and adds an instance of the subclass as an attribute of root. """ def __init__(cls, name, bases, dct): type.__init__(name, bases, dct) for value in dct.itervalues(): if isinstance(value, types.FunctionType): value.exposed = True setattr(root, name.lower(), cls()) class Test(object): __metaclass__ = TestType class URL(Test): _cp_config = {'tools.trailing_slash.on': False} def index(self, path_info, relative=None): return cherrypy.url(path_info, relative=bool(relative)) def leaf(self, path_info, relative=None): return cherrypy.url(path_info, relative=bool(relative)) class Params(Test): def index(self, thing): return repr(thing) def ismap(self, x, y): return "Coordinates: %s, %s" % (x, y) def default(self, *args, **kwargs): return "args: %s kwargs: %s" % (args, kwargs) class Status(Test): def index(self): return "normal" def blank(self): cherrypy.response.status = "" # According to RFC 2616, new status codes are OK as long as they # are between 100 and 599. # Here is an illegal code... def illegal(self): cherrypy.response.status = 781 return "oops" # ...and here is an unknown but legal code. def unknown(self): cherrypy.response.status = "431 My custom error" return "funky" # Non-numeric code def bad(self): cherrypy.response.status = "error" return "bad news" class Redirect(Test): class Error: _cp_config = {"tools.err_redirect.on": True, "tools.err_redirect.url": "/errpage", "tools.err_redirect.internal": False, } def index(self): raise NameError("redirect_test") index.exposed = True error = Error() def index(self): return "child" def by_code(self, code): raise cherrypy.HTTPRedirect("somewhere else", code) by_code._cp_config = {'tools.trailing_slash.extra': True} def nomodify(self): raise cherrypy.HTTPRedirect("", 304) def proxy(self): raise cherrypy.HTTPRedirect("proxy", 305) def stringify(self): return str(cherrypy.HTTPRedirect("/")) def fragment(self, frag): raise cherrypy.HTTPRedirect("/some/url#%s" % frag) def login_redir(): if not getattr(cherrypy.request, "login", None): raise cherrypy.InternalRedirect("/internalredirect/login") tools.login_redir = _cptools.Tool('before_handler', login_redir) def redir_custom(): raise cherrypy.InternalRedirect("/internalredirect/custom_err") class InternalRedirect(Test): def index(self): raise cherrypy.InternalRedirect("/") def relative(self, a, b): raise cherrypy.InternalRedirect("cousin?t=6") def cousin(self, t): assert cherrypy.request.prev.closed return cherrypy.request.prev.query_string def petshop(self, user_id): if user_id == "parrot": # Trade it for a slug when redirecting raise cherrypy.InternalRedirect('/image/getImagesByUser?user_id=slug') elif user_id == "terrier": # Trade it for a fish when redirecting raise cherrypy.InternalRedirect('/image/getImagesByUser?user_id=fish') else: # This should pass the user_id through to getImagesByUser raise cherrypy.InternalRedirect('/image/getImagesByUser?user_id=%s' % user_id) # We support Python 2.3, but the @-deco syntax would look like this: # @tools.login_redir() def secure(self): return "Welcome!" secure = tools.login_redir()(secure) # Since calling the tool returns the same function you pass in, # you could skip binding the return value, and just write: # tools.login_redir()(secure) def login(self): return "Please log in" login._cp_config = {'hooks.before_error_response': redir_custom} def custom_err(self): return "Something went horribly wrong." def early_ir(self, arg): return "whatever" early_ir._cp_config = {'hooks.before_request_body': redir_custom} class Image(Test): def getImagesByUser(self, user_id): return "0 images for %s" % user_id class Flatten(Test): def as_string(self): return "content" def as_list(self): return ["con", "tent"] def as_yield(self): yield "content" def as_dblyield(self): yield self.as_yield() as_dblyield._cp_config = {'tools.flatten.on': True} def as_refyield(self): for chunk in self.as_yield(): yield chunk class Error(Test): _cp_config = {'tools.log_tracebacks.on': True, } def custom(self): raise cherrypy.HTTPError(404, "No, <b>really</b>, not found!") custom._cp_config = {'error_page.404': os.path.join(localDir, "static/index.html")} def noexist(self): raise cherrypy.HTTPError(404, "No, <b>really</b>, not found!") noexist._cp_config = {'error_page.404': "nonexistent.html"} def page_method(self): raise ValueError() def page_yield(self): yield "howdy" raise ValueError() def page_streamed(self): yield "word up" raise ValueError() yield "very oops" page_streamed._cp_config = {"response.stream": True} def cause_err_in_finalize(self): # Since status must start with an int, this should error. cherrypy.response.status = "ZOO OK" cause_err_in_finalize._cp_config = {'request.show_tracebacks': False} def rethrow(self): """Test that an error raised here will be thrown out to the server.""" raise ValueError() rethrow._cp_config = {'request.throw_errors': True} class Ranges(Test): def get_ranges(self, bytes): return repr(http.get_ranges('bytes=%s' % bytes, 8)) def slice_file(self): path = os.path.join(os.getcwd(), os.path.dirname(__file__)) return static.serve_file(os.path.join(path, "static/index.html")) class Expect(Test): def expectation_failed(self): expect = cherrypy.request.headers.elements("Expect") if expect and expect[0].value != '100-continue': raise cherrypy.HTTPError(400) raise cherrypy.HTTPError(417, 'Expectation Failed') class Headers(Test): def default(self, headername): """Spit back out the value for the requested header.""" return cherrypy.request.headers[headername] def doubledheaders(self): # From http://www.cherrypy.org/ticket/165: # "header field names should not be case sensitive sayes the rfc. # if i set a headerfield in complete lowercase i end up with two # header fields, one in lowercase, the other in mixed-case." # Set the most common headers hMap = cherrypy.response.headers hMap['content-type'] = "text/html" hMap['content-length'] = 18 hMap['server'] = 'CherryPy headertest' hMap['location'] = ('%s://%s:%s/headers/' % (cherrypy.request.local.ip, cherrypy.request.local.port, cherrypy.request.scheme)) # Set a rare header for fun hMap['Expires'] = 'Thu, 01 Dec 2194 16:00:00 GMT' return "double header test" def ifmatch(self): val = cherrypy.request.headers['If-Match'] cherrypy.response.headers['ETag'] = val return repr(val) class HeaderElements(Test): def get_elements(self, headername): e = cherrypy.request.headers.elements(headername) return "\n".join([unicode(x) for x in e]) class Method(Test): def index(self): m = cherrypy.request.method if m in defined_http_methods: return m if m == "LINK": raise cherrypy.HTTPError(405) else: raise cherrypy.HTTPError(501) def parameterized(self, data): return data def request_body(self): # This should be a file object (temp file), # which CP will just pipe back out if we tell it to. return cherrypy.request.body def reachable(self): return "success" class Divorce: """HTTP Method handlers shouldn't collide with normal method names. For example, a GET-handler shouldn't collide with a method named 'get'. If you build HTTP method dispatching into CherryPy, rewrite this class to use your new dispatch mechanism and make sure that: "GET /divorce HTTP/1.1" maps to divorce.index() and "GET /divorce/get?ID=13 HTTP/1.1" maps to divorce.get() """ documents = {} def index(self): yield "<h1>Choose your document</h1>\n" yield "<ul>\n" for id, contents in self.documents.iteritems(): yield (" <li><a href='/divorce/get?ID=%s'>%s</a>: %s</li>\n" % (id, id, contents)) yield "</ul>" index.exposed = True def get(self, ID): return ("Divorce document %s: %s" % (ID, self.documents.get(ID, "empty"))) get.exposed = True root.divorce = Divorce() class Cookies(Test): def single(self, name): cookie = cherrypy.request.cookie[name] cherrypy.response.cookie[name] = cookie.value def multiple(self, names): for name in names: cookie = cherrypy.request.cookie[name] cherrypy.response.cookie[name] = cookie.value class ThreadLocal(Test): def index(self): existing = repr(getattr(cherrypy.request, "asdf", None)) cherrypy.request.asdf = "rassfrassin" return existing cherrypy.config.update({ 'log.error_file': log_file, 'environment': 'test_suite', 'server.max_request_body_size': 200, 'server.max_request_header_size': 500, }) appconf = { '/': {'log.access_file': log_access_file}, '/method': {'request.methods_with_bodies': ("POST", "PUT", "PROPFIND")}, } cherrypy.tree.mount(root, config=appconf) # Client-side code # from cherrypy.test import helper class CoreRequestHandlingTest(helper.CPWebCase): def testParams(self): self.getPage("/params/?thing=a") self.assertBody("'a'") self.getPage("/params/?thing=a&thing=b&thing=c") self.assertBody("['a', 'b', 'c']") # Test friendly error message when given params are not accepted. ignore = helper.webtest.ignored_exceptions ignore.append(TypeError) try: self.getPage("/params/?notathing=meeting") self.assertInBody("index() got an unexpected keyword argument 'notathing'") finally: ignore.pop() # Test "% HEX HEX"-encoded URL, param keys, and values self.getPage("/params/%d4%20%e3/cheese?Gruy%E8re=Bulgn%e9ville") self.assertBody(r"args: ('\xd4 \xe3', 'cheese') " r"kwargs: {'Gruy\xe8re': 'Bulgn\xe9ville'}") # Make sure that encoded = and & get parsed correctly self.getPage("/params/code?url=http%3A//cherrypy.org/index%3Fa%3D1%26b%3D2") self.assertBody(r"args: ('code',) " r"kwargs: {'url': 'http://cherrypy.org/index?a=1&b=2'}") # Test coordinates sent by <img ismap> self.getPage("/params/ismap?223,114") self.assertBody("Coordinates: 223, 114") def testStatus(self): self.getPage("/status/") self.assertBody('normal') self.assertStatus(200) self.getPage("/status/blank") self.assertBody('') self.assertStatus(200) self.getPage("/status/illegal") self.assertStatus(500) msg = "Illegal response status from server (781 is out of range)." self.assertErrorPage(500, msg) self.getPage("/status/unknown") self.assertBody('funky') self.assertStatus(431) self.getPage("/status/bad") self.assertStatus(500) msg = "Illegal response status from server ('error' is non-numeric)." self.assertErrorPage(500, msg) def testLogging(self): f = open(log_access_file, "wb") f.write("") f.close() f = open(log_file, "wb") f.write("") f.close() self.getPage("/flatten/as_string") self.assertBody('content') self.assertStatus(200) self.getPage("/flatten/as_yield") self.assertBody('content') self.assertStatus(200) data = open(log_access_file, "rb").readlines() host = self.HOST if not host: # The empty string signifies INADDR_ANY, # which should respond on localhost. host = "127.0.0.1" intro = '%s - - [' % host if not data[0].startswith(intro): self.fail("%r doesn't start with %r" % (data[0], intro)) haslength = False for k, v in self.headers: if k.lower() == 'content-length': haslength = True line = data[-2].strip() if haslength: if not line.endswith('] "GET %s/flatten/as_string HTTP/1.1" 200 7 "" ""' % self.prefix()): self.fail(line) else: if not line.endswith('] "GET %s/flatten/as_string HTTP/1.1" 200 - "" ""' % self.prefix()): self.fail(line) if not data[-1].startswith(intro): self.fail("%r doesn't start with %r" % (data[-1], intro)) haslength = False for k, v in self.headers: if k.lower() == 'content-length': haslength = True line = data[-1].strip() if haslength: self.assert_(line.endswith('] "GET %s/flatten/as_yield HTTP/1.1" 200 7 "" ""' % self.prefix())) else: self.assert_(line.endswith('] "GET %s/flatten/as_yield HTTP/1.1" 200 - "" ""' % self.prefix())) ignore = helper.webtest.ignored_exceptions ignore.append(ValueError) try: # Test that tracebacks get written to the error log. self.getPage("/error/page_method") self.assertInBody("raise ValueError()") data = open(log_file, "rb").readlines() self.assertEqual(data[0].strip().endswith('HTTP Traceback (most recent call last):'), True) self.assertEqual(data[-3].strip().endswith('raise ValueError()'), True) finally: ignore.pop() def testSlashes(self): # Test that requests for index methods without a trailing slash # get redirected to the same URI path with a trailing slash. # Make sure GET params are preserved. self.getPage("/redirect?id=3") self.assertStatus(('302 Found', '303 See Other')) self.assertInBody("<a href='%s/redirect/?id=3'>" "%s/redirect/?id=3</a>" % (self.base(), self.base())) if self.prefix(): # Corner case: the "trailing slash" redirect could be tricky if # we're using a virtual root and the URI is "/vroot" (no slash). self.getPage("") self.assertStatus(('302 Found', '303 See Other')) self.assertInBody("<a href='%s/'>%s/</a>" % (self.base(), self.base())) # Test that requests for NON-index methods WITH a trailing slash # get redirected to the same URI path WITHOUT a trailing slash. # Make sure GET params are preserved. self.getPage("/redirect/by_code/?code=307") self.assertStatus(('302 Found', '303 See Other')) self.assertInBody("<a href='%s/redirect/by_code?code=307'>" "%s/redirect/by_code?code=307</a>" % (self.base(), self.base())) # If the trailing_slash tool is off, CP should just continue # as if the slashes were correct. But it needs some help # inside cherrypy.url to form correct output. self.getPage('/url?path_info=page1') self.assertBody('%s/url/page1' % self.base()) self.getPage('/url/leaf/?path_info=page1') self.assertBody('%s/url/page1' % self.base()) def testRedirect(self): self.getPage("/redirect/") self.assertBody('child') self.assertStatus(200) self.getPage("/redirect/by_code?code=300") self.assertMatchesBody(r"<a href='(.*)somewhere else'>\1somewhere else</a>") self.assertStatus(300) self.getPage("/redirect/by_code?code=301") self.assertMatchesBody(r"<a href='(.*)somewhere else'>\1somewhere else</a>") self.assertStatus(301) self.getPage("/redirect/by_code?code=302") self.assertMatchesBody(r"<a href='(.*)somewhere else'>\1somewhere else</a>") self.assertStatus(302) self.getPage("/redirect/by_code?code=303") self.assertMatchesBody(r"<a href='(.*)somewhere else'>\1somewhere else</a>") self.assertStatus(303) self.getPage("/redirect/by_code?code=307") self.assertMatchesBody(r"<a href='(.*)somewhere else'>\1somewhere else</a>") self.assertStatus(307) self.getPage("/redirect/nomodify") self.assertBody('') self.assertStatus(304) self.getPage("/redirect/proxy") self.assertBody('') self.assertStatus(305) # HTTPRedirect on error self.getPage("/redirect/error/") self.assertStatus(('302 Found', '303 See Other')) self.assertInBody('/errpage') # Make sure str(HTTPRedirect()) works. self.getPage("/redirect/stringify", protocol="HTTP/1.0") self.assertStatus(200) self.assertBody("(['%s/'], 302)" % self.base()) if cherrypy.server.protocol_version == "HTTP/1.1": self.getPage("/redirect/stringify", protocol="HTTP/1.1") self.assertStatus(200) self.assertBody("(['%s/'], 303)" % self.base()) # check that #fragments are handled properly # http://skrb.org/ietf/http_errata.html#location-fragments frag = "foo" self.getPage("/redirect/fragment/%s" % frag) self.assertMatchesBody(r"<a href='(.*)\/some\/url\#%s'>\1\/some\/url\#%s</a>" % (frag, frag)) loc = self.assertHeader('Location') assert loc.endswith("#%s" % frag) self.assertStatus(('302 Found', '303 See Other')) def test_InternalRedirect(self): # InternalRedirect self.getPage("/internalredirect/") self.assertBody('hello') self.assertStatus(200) # Test passthrough self.getPage("/internalredirect/petshop?user_id=Sir-not-appearing-in-this-film") self.assertBody('0 images for Sir-not-appearing-in-this-film') self.assertStatus(200) # Test args self.getPage("/internalredirect/petshop?user_id=parrot") self.assertBody('0 images for slug') self.assertStatus(200) # Test POST self.getPage("/internalredirect/petshop", method="POST", body="user_id=terrier") self.assertBody('0 images for fish') self.assertStatus(200) # Test ir before body read self.getPage("/internalredirect/early_ir", method="POST", body="arg=aha!") self.assertBody("Something went horribly wrong.") self.assertStatus(200) self.getPage("/internalredirect/secure") self.assertBody('Please log in') self.assertStatus(200) # Relative path in InternalRedirect. # Also tests request.prev. self.getPage("/internalredirect/relative?a=3&b=5") self.assertBody("a=3&b=5") self.assertStatus(200) # InternalRedirect on error self.getPage("/internalredirect/login/illegal/extra/vpath/atoms") self.assertStatus(200) self.assertBody("Something went horribly wrong.") def testFlatten(self): for url in ["/flatten/as_string", "/flatten/as_list", "/flatten/as_yield", "/flatten/as_dblyield", "/flatten/as_refyield"]: self.getPage(url) self.assertBody('content') def testErrorHandling(self): self.getPage("/error/missing") self.assertStatus(404) self.assertErrorPage(404, "The path '/error/missing' was not found.") ignore = helper.webtest.ignored_exceptions ignore.append(ValueError) try: valerr = '\n raise ValueError()\nValueError' self.getPage("/error/page_method") self.assertErrorPage(500, pattern=valerr) self.getPage("/error/page_yield") self.assertErrorPage(500, pattern=valerr) self.getPage("/error/page_streamed") # Because this error is raised after the response body has # started, the status should not change to an error status. self.assertStatus(200) self.assertBody("word upUnrecoverable error in the server.") # No traceback should be present self.getPage("/error/cause_err_in_finalize") msg = "Illegal response status from server ('ZOO' is non-numeric)." self.assertErrorPage(500, msg, None) finally: ignore.pop() # Test custom error page. self.getPage("/error/custom") self.assertStatus(404) self.assertBody("Hello, world\r\n" + (" " * 499)) # Test error in custom error page (ticket #305). # Note that the message is escaped for HTML (ticket #310). self.getPage("/error/noexist") self.assertStatus(404) msg = ("No, <b>really</b>, not found!<br />" "In addition, the custom error page failed:\n<br />" "[Errno 2] No such file or directory: 'nonexistent.html'") self.assertInBody(msg) if (hasattr(self, 'harness') and "modpython" in self.harness.__class__.__name__.lower()): pass else: # Test throw_errors (ticket #186). self.getPage("/error/rethrow") self.assertInBody("raise ValueError()") def testRanges(self): self.getPage("/ranges/get_ranges?bytes=3-6") self.assertBody("[(3, 7)]") # Test multiple ranges and a suffix-byte-range-spec, for good measure. self.getPage("/ranges/get_ranges?bytes=2-4,-1") self.assertBody("[(2, 5), (7, 8)]") # Get a partial file. if cherrypy.server.protocol_version == "HTTP/1.1": self.getPage("/ranges/slice_file", [('Range', 'bytes=2-5')]) self.assertStatus(206) self.assertHeader("Content-Type", "text/html") self.assertHeader("Content-Range", "bytes 2-5/14") self.assertBody("llo,") # What happens with overlapping ranges (and out of order, too)? self.getPage("/ranges/slice_file", [('Range', 'bytes=4-6,2-5')]) self.assertStatus(206) ct = self.assertHeader("Content-Type") expected_type = "multipart/byteranges; boundary=" self.assert_(ct.startswith(expected_type)) boundary = ct[len(expected_type):] expected_body = ("\r\n--%s\r\n" "Content-type: text/html\r\n" "Content-range: bytes 4-6/14\r\n" "\r\n" "o, \r\n" "--%s\r\n" "Content-type: text/html\r\n" "Content-range: bytes 2-5/14\r\n" "\r\n" "llo,\r\n" "--%s--\r\n" % (boundary, boundary, boundary)) self.assertBody(expected_body) self.assertHeader("Content-Length") # Test "416 Requested Range Not Satisfiable" self.getPage("/ranges/slice_file", [('Range', 'bytes=2300-2900')]) self.assertStatus(416) # "When this status code is returned for a byte-range request, # the response SHOULD include a Content-Range entity-header # field specifying the current length of the selected resource" self.assertHeader("Content-Range", "bytes */14") elif cherrypy.server.protocol_version == "HTTP/1.0": # Test Range behavior with HTTP/1.0 request self.getPage("/ranges/slice_file", [('Range', 'bytes=2-5')]) self.assertStatus(200) self.assertBody("Hello, world\r\n") def testExpect(self): e = ('Expect', '100-continue') self.getPage("/headerelements/get_elements?headername=Expect", [e]) self.assertBody('100-continue') self.getPage("/expect/expectation_failed", [('Content-Length', '200'), e]) self.assertStatus(417) def testHeaderElements(self): # Accept-* header elements should be sorted, with most preferred first. h = [('Accept', 'audio/*; q=0.2, audio/basic')] self.getPage("/headerelements/get_elements?headername=Accept", h) self.assertStatus(200) self.assertBody("audio/basic\n" "audio/*;q=0.2") h = [('Accept', 'text/plain; q=0.5, text/html, text/x-dvi; q=0.8, text/x-c')] self.getPage("/headerelements/get_elements?headername=Accept", h) self.assertStatus(200) self.assertBody("text/x-c\n" "text/html\n" "text/x-dvi;q=0.8\n" "text/plain;q=0.5") # Test that more specific media ranges get priority. h = [('Accept', 'text/*, text/html, text/html;level=1, */*')] self.getPage("/headerelements/get_elements?headername=Accept", h) self.assertStatus(200) self.assertBody("text/html;level=1\n" "text/html\n" "text/*\n" "*/*") # Test Accept-Charset h = [('Accept-Charset', 'iso-8859-5, unicode-1-1;q=0.8')] self.getPage("/headerelements/get_elements?headername=Accept-Charset", h) self.assertStatus("200 OK") self.assertBody("iso-8859-5\n" "unicode-1-1;q=0.8") # Test Accept-Encoding h = [('Accept-Encoding', 'gzip;q=1.0, identity; q=0.5, *;q=0')] self.getPage("/headerelements/get_elements?headername=Accept-Encoding", h) self.assertStatus("200 OK") self.assertBody("gzip;q=1.0\n" "identity;q=0.5\n" "*;q=0") # Test Accept-Language h = [('Accept-Language', 'da, en-gb;q=0.8, en;q=0.7')] self.getPage("/headerelements/get_elements?headername=Accept-Language", h) self.assertStatus("200 OK") self.assertBody("da\n" "en-gb;q=0.8\n" "en;q=0.7") def testHeaders(self): # Tests that each header only appears once, regardless of case. self.getPage("/headers/doubledheaders") self.assertBody("double header test") hnames = [name.title() for name, val in self.headers] for key in ['Content-Length', 'Content-Type', 'Date', 'Expires', 'Location', 'Server']: self.assertEqual(hnames.count(key), 1) if cherrypy.server.protocol_version == "HTTP/1.1": # Test RFC-2047-encoded request and response header values c = "=E2=84=ABngstr=C3=B6m" self.getPage("/headers/ifmatch", [('If-Match', '=?utf-8?q?%s?=' % c)]) self.assertBody("u'\\u212bngstr\\xf6m'") self.assertHeader("ETag", '=?utf-8?b?4oSrbmdzdHLDtm0=?=') # Test a *LONG* RFC-2047-encoded request and response header value self.getPage("/headers/ifmatch", [('If-Match', '=?utf-8?q?%s?=' % (c * 10))]) self.assertBody("u'%s'" % ('\\u212bngstr\\xf6m' * 10)) self.assertHeader("ETag", '=?utf-8?b?4oSrbmdzdHLDtm3ihKtuZ3N0csO2beKEq25nc3Ryw7Zt4oSrbmdzdHLDtm0=?=' '=?utf-8?b?4oSrbmdzdHLDtm3ihKtuZ3N0csO2beKEq25nc3Ryw7Zt4oSrbmdzdHLDtm0=?=' '=?utf-8?b?4oSrbmdzdHLDtm3ihKtuZ3N0csO2bQ==?=') # Test that two request headers are collapsed into one. # See http://www.cherrypy.org/ticket/542. self.getPage("/headers/Accept-Charset", headers=[("Accept-Charset", "iso-8859-5"), ("Accept-Charset", "unicode-1-1;q=0.8")]) self.assertBody("iso-8859-5, unicode-1-1;q=0.8") # If we don't pass a Content-Type header, it should not be present # in cherrypy.request.headers self.getPage("/headers/Content-Type", headers=[]) self.assertStatus(500) # If Content-Type is present in the request, it should be present in # cherrypy.request.headers self.getPage("/headers/Content-Type", headers=[("Content-type", "application/json")]) self.assertBody("application/json") def testHTTPMethods(self): helper.webtest.methods_with_bodies = ("POST", "PUT", "PROPFIND") # Test that all defined HTTP methods work. for m in defined_http_methods: self.getPage("/method/", method=m) # HEAD requests should not return any body. if m == "HEAD": self.assertBody("") elif m == "TRACE": # Some HTTP servers (like modpy) have their own TRACE support self.assertEqual(self.body[:5], "TRACE") else: self.assertBody(m) # Request a PUT method with a form-urlencoded body self.getPage("/method/parameterized", method="PUT", body="data=on+top+of+other+things") self.assertBody("on top of other things") # Request a PUT method with a file body b = "one thing on top of another" h = [("Content-Type", "text/plain"), ("Content-Length", str(len(b)))] self.getPage("/method/request_body", headers=h, method="PUT", body=b) self.assertStatus(200) self.assertBody(b) # Request a PUT method with no body whatsoever (not an empty one). # See http://www.cherrypy.org/ticket/650. # Provide a C-T or webtest will provide one (and a C-L) for us. h = [("Content-Type", "text/plain")] self.getPage("/method/reachable", headers=h, method="PUT") self.assertBody("success") # Request a custom method with a request body b = ('<?xml version="1.0" encoding="utf-8" ?>\n\n' '<propfind xmlns="DAV:"><prop><getlastmodified/>' '</prop></propfind>') h = [('Content-Type', 'text/xml'), ('Content-Length', str(len(b)))] self.getPage("/method/request_body", headers=h, method="PROPFIND", body=b) self.assertStatus(200) self.assertBody(b) # Request a disallowed method self.getPage("/method/", method="LINK") self.assertStatus(405) # Request an unknown method self.getPage("/method/", method="SEARCH") self.assertStatus(501) # For method dispatchers: make sure that an HTTP method doesn't # collide with a virtual path atom. If you build HTTP-method # dispatching into the core, rewrite these handlers to use # your dispatch idioms. self.getPage("/divorce/get?ID=13") self.assertBody('Divorce document 13: empty') self.assertStatus(200) self.getPage("/divorce/", method="GET") self.assertBody('<h1>Choose your document</h1>\n<ul>\n</ul>') self.assertStatus(200) def testFavicon(self): # favicon.ico is served by staticfile. icofilename = os.path.join(localDir, "../favicon.ico") icofile = open(icofilename, "rb") data = icofile.read() icofile.close() self.getPage("/favicon.ico") self.assertBody(data) def testCookies(self): import sys if sys.version_info >= (2, 5): self.getPage("/cookies/single?name=First", [('Cookie', 'First=Dinsdale;')]) self.assertHeader('Set-Cookie', 'First=Dinsdale') self.getPage("/cookies/multiple?names=First&names=Last", [('Cookie', 'First=Dinsdale; Last=Piranha;'), ]) self.assertHeader('Set-Cookie', 'First=Dinsdale') self.assertHeader('Set-Cookie', 'Last=Piranha') else: self.getPage("/cookies/single?name=First", [('Cookie', 'First=Dinsdale;')]) self.assertHeader('Set-Cookie', 'First=Dinsdale;') self.getPage("/cookies/multiple?names=First&names=Last", [('Cookie', 'First=Dinsdale; Last=Piranha;'), ]) self.assertHeader('Set-Cookie', 'First=Dinsdale;') self.assertHeader('Set-Cookie', 'Last=Piranha;') def testMaxRequestSize(self): self.getPage("/", headers=[('From', "x" * 500)]) self.assertStatus(413) # Test for http://www.cherrypy.org/ticket/421 # (Incorrect border condition in readline of SizeCheckWrapper). # This hangs in rev 891 and earlier. lines256 = "x" * 248 self.getPage("/", headers=[('Host', '%s:%s' % (self.HOST, self.PORT)), ('From', lines256)]) # Test upload body = """--x Content-Disposition: form-data; name="file"; filename="hello.txt" Content-Type: text/plain %s --x-- """ b = body % ("x" * 96) h = [("Content-type", "multipart/form-data; boundary=x"), ("Content-Length", len(b))] self.getPage('/upload', h, "POST", b) self.assertBody('Size: 96') b = body % ("x" * 200) h = [("Content-type", "multipart/form-data; boundary=x"), ("Content-Length", len(b))] self.getPage('/upload', h, "POST", b) self.assertStatus(413) def testEmptyThreadlocals(self): results = [] for x in xrange(20): self.getPage("/threadlocal/") results.append(self.body) self.assertEqual(results, ["None"] * 20) def testDefaultContentType(self): self.getPage('/') self.assertHeader('Content-Type', 'text/html') self.getPage('/defct/plain') self.getPage('/') self.assertHeader('Content-Type', 'text/plain') self.getPage('/defct/html') def test_cherrypy_url(self): # Input relative to current self.getPage('/url/leaf?path_info=page1') self.assertBody('%s/url/page1' % self.base()) self.getPage('/url/?path_info=page1') self.assertBody('%s/url/page1' % self.base()) # Input is 'absolute'; that is, relative to script_name self.getPage('/url/leaf?path_info=/page1') self.assertBody('%s/page1' % self.base()) self.getPage('/url/?path_info=/page1') self.assertBody('%s/page1' % self.base()) # Single dots self.getPage('/url/leaf?path_info=./page1') self.assertBody('%s/url/page1' % self.base()) self.getPage('/url/leaf?path_info=other/./page1') self.assertBody('%s/url/other/page1' % self.base()) self.getPage('/url/?path_info=/other/./page1') self.assertBody('%s/other/page1' % self.base()) # Double dots self.getPage('/url/leaf?path_info=../page1') self.assertBody('%s/page1' % self.base()) self.getPage('/url/leaf?path_info=other/../page1') self.assertBody('%s/url/page1' % self.base()) self.getPage('/url/leaf?path_info=/other/../page1') self.assertBody('%s/page1' % self.base()) # Output relative to current path or script_name self.getPage('/url/?path_info=page1&relative=True') self.assertBody('page1') self.getPage('/url/leaf?path_info=/page1&relative=True') self.assertBody('../page1') self.getPage('/url/leaf?path_info=../page1&relative=True') self.assertBody('../page1') self.getPage('/url/?path_info=other/../page1&relative=True') self.assertBody('page1') if __name__ == '__main__': setup_server() helper.testmain()

38.370056

104

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4.888419

0.167261

0.052622

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0.016766

0.374869

0.331678

0.270582

0.214042

0.208711

0.182195

0

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40,749

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false

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null

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0

f52efca4ad0dbdcec53aee2fa61bc784274e7d40

1,036

py

Python

day4/solution1.py

zirne/aoc19

98feea895f0113ef60738723ca976dcbef0629b9

[ "MIT" ]

null

day4/solution1.py

zirne/aoc19

98feea895f0113ef60738723ca976dcbef0629b9

[ "MIT" ]

null

day4/solution1.py

zirne/aoc19

98feea895f0113ef60738723ca976dcbef0629b9

[ "MIT" ]

null

# Solution 1 def readInputFile(filename): f = open(filename, "r") inputString = f.read() f.close() return inputString input = readInputFile("input.txt").strip() print(input) lowest = input.split("-")[0] highest = input.split("-")[1] current = int(input.split("-")[0]) print(lowest) print(highest) def checkNeverDecreaseRule(n): n = str(n) l = len(n) i = 0 while i < l - 1: # print("comparing " + n[i] + " with " + n[i + 1] + "...") if int(n[i]) > int(n[i + 1]): return False i += 1 return True def checkHasAdjacentSame(n): n = str(n) l = len(n) i = 0 adjCount = 0 while i < l - 1: # print("comparing " + n[i] + " with " + n[i + 1] + "...") if n[i] == n[i + 1]: adjCount += 1 i += 1 if adjCount >= 1: return True else: return False resultArr = [] while current <= int(highest): if checkNeverDecreaseRule(current) and checkHasAdjacentSame(current): resultArr.append(current) #print(checkNeverDecreaseRule(lowest)) #print(checkHasAdjacentSame(lowest)) current += 1 print(len(resultArr))

18.836364

70

0.621622

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Python

e2e/Tests/Transactions/Verify.py

rikublock/Meros

7a3ae9c78af388eb523bc8a2c840018fc058ef44

[ "CC0-1.0" ]

null

e2e/Tests/Transactions/Verify.py

rikublock/Meros

7a3ae9c78af388eb523bc8a2c840018fc058ef44

[ "CC0-1.0" ]

null

e2e/Tests/Transactions/Verify.py

rikublock/Meros

7a3ae9c78af388eb523bc8a2c840018fc058ef44

[ "CC0-1.0" ]

1

2021-02-08T23:46:35.000Z

#Transactions classes. from e2e.Classes.Transactions.Transaction import Transaction from e2e.Classes.Transactions.Transactions import Transactions #TestError Exception. from e2e.Tests.Errors import TestError #RPC class. from e2e.Meros.RPC import RPC #Sleep standard function. from time import sleep #Verify a Transaction. def verifyTransaction( rpc: RPC, tx: Transaction ) -> None: if rpc.call("transactions", "getTransaction", [tx.hash.hex()]) != tx.toJSON(): raise TestError("Transaction doesn't match.") #Verify the Transactions. def verifyTransactions( rpc: RPC, transactions: Transactions ) -> None: #Sleep to ensure data races aren't a problem. sleep(2) for tx in transactions.txs: verifyTransaction(rpc, transactions.txs[tx])

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