xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

1ec5acb6cddde1e1c45143866f925a23902a44c9

261

py

Python

basic_grammar/list_usecase.py

OnoYuta/python_programing

5d191bef5666c0a826f6daa0bd45bc9dd6603d59

[ "MIT" ]

null

basic_grammar/list_usecase.py

OnoYuta/python_programing

5d191bef5666c0a826f6daa0bd45bc9dd6603d59

[ "MIT" ]

null

basic_grammar/list_usecase.py

OnoYuta/python_programing

5d191bef5666c0a826f6daa0bd45bc9dd6603d59

[ "MIT" ]

null

seat = [] min = 0 max = 5 print(min <= len(seat) < max) # True seat.append('person') seat.append('person') seat.append('person') seat.append('person') seat.append('person') print(min <= len(seat) < max) # False seat.pop() print(min <= len(seat) < max) # True

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py

Python

connect_extras/tests/__init__.py

lpatmo/actionify_the_news

998d8ca6b35d0ef1b16efca70f50e59503f5a62d

[ "MIT" ]

null

connect_extras/tests/__init__.py

lpatmo/actionify_the_news

998d8ca6b35d0ef1b16efca70f50e59503f5a62d

[ "MIT" ]

null

connect_extras/tests/__init__.py

lpatmo/actionify_the_news

998d8ca6b35d0ef1b16efca70f50e59503f5a62d

[ "MIT" ]

null

"""Tests for 3rd party Connect helpers"""

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94aa1923e155c54770574e5edede898b7a79083b

95

py

Python

mmdet3d/models/utils/__init__.py

gopi231091/mmdetection3d

1b2e64cd75c8d1c238c61a3bc1e3c62a7d403b53

[ "Apache-2.0" ]

217

2021-12-10T09:44:33.000Z

2022-03-31T16:17:35.000Z

mmdet3d/models/utils/__init__.py

gopi231091/mmdetection3d

1b2e64cd75c8d1c238c61a3bc1e3c62a7d403b53

[ "Apache-2.0" ]

22

2021-12-29T08:57:31.000Z

2022-03-31T11:21:53.000Z

mmdet3d/models/utils/__init__.py

gopi231091/mmdetection3d

1b2e64cd75c8d1c238c61a3bc1e3c62a7d403b53

[ "Apache-2.0" ]

23

2021-12-13T06:56:38.000Z

2022-03-28T02:02:13.000Z

from .clip_sigmoid import clip_sigmoid from .mlp import MLP __all__ = ['clip_sigmoid', 'MLP']

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94f0e815742b183791270712c5a45d7f720eedea

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py

Python

Deque_Hanoi_&_DelimiterChecks/DSQ_Test.py

rockgard3n/Data-Structures

93b6b5aaf74cb0f39be233d0f80fd881e27969af

[ "MIT" ]

null

Deque_Hanoi_&_DelimiterChecks/DSQ_Test.py

rockgard3n/Data-Structures

93b6b5aaf74cb0f39be233d0f80fd881e27969af

[ "MIT" ]

null

Deque_Hanoi_&_DelimiterChecks/DSQ_Test.py

rockgard3n/Data-Structures

93b6b5aaf74cb0f39be233d0f80fd881e27969af

[ "MIT" ]

null

import unittest from Deque_Generator import get_deque, LL_DEQUE_TYPE, ARR_DEQUE_TYPE from Stack import Stack from Queue import Queue class DSQTester(unittest.TestCase): def setUp(self): # Run your tests with each deque type to ensure that # they behave identically. self.__deque = get_deque(LL_DEQUE_TYPE) self.__stack = Stack() self.__queue = Queue() #Empty Tests def test_empty_deque(self): self.assertEqual('[ ]', str(self.__deque), 'Empty deque should print as "[ ]"') def test_empty_stack(self): self.assertEqual('[ ]', str(self.__stack), 'Empty stack should print as "[ ]"') def test_empty_queue(self): self.assertEqual('[ ]', str(self.__queue), 'Empty queue should print as "[ ]"') #Deque Tests ##push tests starting with empty deque up to two, tests to make sure push ##functions create proper deques with proper string format and len def test_push_front_empty_deque(self): self.__deque.push_front('Victory') self.assertEqual('[ Victory ]', str(self.__deque)) self.assertEqual(1, len(self.__deque)) def test_push_front_empty_deque_length(self): self.__deque.push_front('Victory') self.assertEqual(1, len(self.__deque)) def test_push_front_single_deque(self): self.__deque.push_front('Victory') self.__deque.push_front('have') self.assertEqual('[ have, Victory ]', str(self.__deque)) def test_push_front_single_deque_length(self): self.__deque.push_front('Victory') self.__deque.push_front('have') self.assertEqual(2, len(self.__deque)) def test_push_front_double_deque(self): self.__deque.push_front('Victory') self.__deque.push_front('have') self.__deque.push_front('we') self.assertEqual('[ we, have, Victory ]', str(self.__deque)) self.assertEqual(3, len(self.__deque)) def test_push_front_double_deque_length(self): self.__deque.push_front('Victory') self.__deque.push_front('have') self.__deque.push_front('we') self.assertEqual(3, len(self.__deque)) ##pop front tests starting with empty deque working up to two, tests make ##sure pop functions create proper deques with proper string format and len def test_pop_front_empty_deque(self): with self.assertRaises(IndexError): pop = self.__deque.pop_front() self.assertEqual('[ ]', str(self.__deque)) def test_pop_front_single_deque_string(self): self.__deque.push_front('Liam') pop = self.__deque.pop_front() self.assertEqual('[ ]', str(self.__deque)) def test_pop_front_single_deque_popvalue(self): self.__deque.push_front('Liam') pop = self.__deque.pop_front() self.assertEqual('Liam', pop) def test_pop_front_single_deque_length(self): self.__deque.push_front('Liam') pop = self.__deque.pop_front() self.assertEqual(0, len(self.__deque)) def test_pop_front_double_deque_string(self): self.__deque.push_front('Liam') self.__deque.push_front('Coolguy') pop = self.__deque.pop_front() self.assertEqual('[ Liam ]', str(self.__deque)) def test_pop_front_double_deque_popvalue(self): self.__deque.push_front('Liam') self.__deque.push_front('Coolguy') pop = self.__deque.pop_front() self.assertEqual('Coolguy', pop) def test_pop_front_double_deque_length(self): self.__deque.push_front('Liam') self.__deque.push_front('Coolguy') pop = self.__deque.pop_front() self.assertEqual(1, len(self.__deque)) ##peek front tests starting with empty deque working up to deque with 2 ##entries. Test makes sure peek function working correctly without affecting ##entries in deque def test_peek_front_empty_deque(self): with self.assertRaises(IndexError): peek = self.__deque.peek_front() self.assertEqual('[ ]', str(self.__deque)) def test_peek_front_single_deque_string(self): self.__deque.push_front('Liam') peek = self.__deque.peek_front() self.assertEqual('[ Liam ]', str(self.__deque)) def test_peek_front_single_deque_peekvalue(self): self.__deque.push_front('Liam') peek = self.__deque.peek_front() self.assertEqual('Liam', peek) def test_peek_front_single_deque_length(self): self.__deque.push_front('Liam') peek = self.__deque.peek_front() self.assertEqual(1, len(self.__deque)) def test_peek_front_double_deque_string(self): self.__deque.push_front('Victory') self.__deque.push_front('have') peek = self.__deque.peek_front() self.assertEqual('[ have, Victory ]', str(self.__deque)) def test_peek_front_double_deque(self): self.__deque.push_front('Liam') self.__deque.push_front('Coolguy') peek = self.__deque.peek_front() self.assertEqual('Coolguy', peek) def test_peek_front_double_deque(self): self.__deque.push_front('Liam') self.__deque.push_front('Coolguy') self.assertEqual(2, len(self.__deque)) ##push back tests starting with empty deque working up to deque with 2 ##entries. Test ensures push back function creating correct deque structure ##with proper str format and len value. def test_push_back_on_empty_deque_string(self): self.__deque.push_back('Liam') self.assertEqual('[ Liam ]', str(self.__deque)) def test_push_back_on_empty_deque_length(self): self.__deque.push_back('Liam') self.assertEqual(1, len(self.__deque)) def test_push_back_twice_on_deque_string(self): self.__deque.push_back('Coolguy') self.__deque.push_back('Liam') self.assertEqual('[ Coolguy, Liam ]', str(self.__deque)) def test_push_back_twice_on_deque_length(self): self.__deque.push_back('Coolguy') self.__deque.push_back('Liam') self.assertEqual(2, len(self.__deque)) def test_push_back_thrice_on_deque_string(self): self.__deque.push_back('Coolguy') self.__deque.push_back('Liam') self.__deque.push_back('duh') self.assertEqual('[ Coolguy, Liam, duh ]', str(self.__deque)) def test_push_back_thrice_on_deque_length(self): self.__deque.push_back('Coolguy') self.__deque.push_back('Liam') self.__deque.push_back('duh') self.assertEqual(3, len(self.__deque)) ##pop back tests starting with empty deque working up to deque with 3 ##entries. Test ensures pop back function creates correct deque structure ##with proper str format and len value def test_pop_back_empty_deque(self): with self.assertRaises(IndexError): pop = self.__deque.pop_back() self.assertEqual('[ ]', str(self.__deque)) def test_pop_back_single_deque_string(self): self.__deque.push_back('Liam') pop = self.__deque.pop_back() self.assertEqual('[ ]', str(self.__deque)) def test_pop_back_single_deque_popvalue(self): self.__deque.push_back('Liam') pop = self.__deque.pop_back() self.assertEqual('Liam', pop) def test_pop_back_single_deque_length(self): self.__deque.push_back('Liam') pop = self.__deque.pop_back() self.assertEqual(0, len(self.__deque)) def test_pop_back_double_deque_string(self): self.__deque.push_back('Coolguy') self.__deque.push_back('Liam') pop = self.__deque.pop_back() self.assertEqual('[ Coolguy ]', str(self.__deque)) def test_pop_back_double_deque_popvalue(self): self.__deque.push_back('Coolguy') self.__deque.push_back('Liam') pop = self.__deque.pop_back() self.assertEqual('Liam', pop) def test_pop_back_double_deque_length(self): self.__deque.push_back('Coolguy') self.__deque.push_back('Liam') pop = self.__deque.pop_back() self.assertEqual(1, len(self.__deque)) ##peek back tests starting with empty deque up to deque with 2 entries. ##Tests to ensure peek back function is pulling correct data without ##affecting entries in deque. def test_peek_back_empty_deque_string(self): with self.assertRaises(IndexError): peek = self.__deque.peek_back() self.assertEqual(0, len(self.__deque)) def test_peek_back_empty_deque_length(self): with self.assertRaises(IndexError): peek = self.__deque.peek_back() self.assertEqual(0, len(self.__deque)) def test_peek_back_single_deque_string(self): self.__deque.push_back('Coolguy') peek = self.__deque.peek_back() self.assertEqual('[ Coolguy ]', str(self.__deque)) def test_peek_back_single_deque_peekvalue(self): self.__deque.push_back('Coolguy') peek = self.__deque.peek_back() self.assertEqual('Coolguy', peek) def test_peek_back_single_deque_length(self): self.__deque.push_back('Coolguy') peek = self.__deque.peek_back() self.assertEqual(1, len(self.__deque)) def test_peek_back_double_deque_string(self): self.__deque.push_back('Coolguy') self.__deque.push_back('Liam') peek = self.__deque.peek_back() self.assertEqual('[ Coolguy, Liam ]', str(self.__deque)) def test_peek_back_double_deque_peekvalue(self): self.__deque.push_back('Coolguy') self.__deque.push_back('Liam') peek = self.__deque.peek_back() self.assertEqual('Liam', peek) def test_peek_back_double_deque_length(self): self.__deque.push_back('Coolguy') self.__deque.push_back('Liam') peek = self.__deque.peek_back() self.assertEqual(2, len(self.__deque)) #Stack Tests ##push tests starting with empty stack up to stack with 2 entries def test_push_empty_stack_string(self): self.__stack.push('Liam') self.assertEqual('[ Liam ]', str(self.__stack)) def test_push_empty_stack_length(self): self.__stack.push('Liam') self.assertEqual(1, len(self.__stack)) def test_push_single_stack_string(self): self.__stack.push('Liam') self.__stack.push('Coolguy') self.assertEqual('[ Coolguy, Liam ]', str(self.__stack)) def test_push_single_stack_length(self): self.__stack.push('Liam') self.__stack.push('Coolguy') self.assertEqual(2, len(self.__stack)) def test_push_multiple_stack_string(self): self.__stack.push('Liam') self.__stack.push('Coolguy') self.__stack.push('here') self.assertEqual('[ here, Coolguy, Liam ]', str(self.__stack)) def test_push_multiple_stack_length(self): self.__stack.push('Liam') self.__stack.push('Coolguy') self.__stack.push('here') self.assertEqual(3, len(self.__stack)) ##pop tests def test_pop_empty_stack(self): with self.assertRaises(IndexError): pop = self.__stack.pop() self.assertEqual('[ ]', str(self.__stack)) def test_pop_single_stack_string(self): self.__stack.push('Liam') self.__stack.pop() self.assertEqual('[ ]', str(self.__stack)) def test_pop_single_stack_length(self): self.__stack.push('Liam') self.__stack.pop() self.assertEqual(0, len(self.__stack)) def test_pop_double_stack_string(self): self.__stack.push('Liam') self.__stack.push('Coolguy') pop = self.__stack.pop() self.assertEqual('[ Liam ]', str(self.__stack)) def test_pop_double_stack_popvalue(self): self.__stack.push('Liam') self.__stack.push('Coolguy') pop = self.__stack.pop() self.assertEqual('Coolguy', pop) def test_pop_double_stack_length(self): self.__stack.push('Liam') self.__stack.push('Coolguy') pop = self.__stack.pop() self.assertEqual(1, len(self.__stack)) ##peek tests def test_peek_empty_stack(self): with self.assertRaises(IndexError): peek = self.__stack.peek() self.assertEqual('[ ]', str(self.__stack)) def test_peek_single_stack_string(self): self.__stack.push('Liam') peek = self.__stack.peek() self.assertEqual('[ Liam ]', str(self.__stack)) def test_peek_single_stack_peekvalue(self): self.__stack.push('Liam') peek = self.__stack.peek() self.assertEqual('Liam', peek) def test_peek_single_stack_length(self): self.__stack.push('Liam') peek = self.__stack.peek() self.assertEqual(1, len(self.__stack)) def test_peek_double_stack_string(self): self.__stack.push('Coolguy') self.__stack.push('Liam') peek = self.__stack.peek() self.assertEqual('[ Liam, Coolguy ]', str(self.__stack)) def test_peek_double_stack_peekvalue(self): self.__stack.push('Coolguy') self.__stack.push('Liam') peek = self.__stack.peek() self.assertEqual('Coolguy', peek) def test_peek_double_stack_length(self): self.__stack.push('Coolguy') self.__stack.push('Liam') peek = self.__stack.peek() self.assertEqual(2, len(self.__stack)) #Queue Tests ##Enqueue tests def test_enq_empty_string(self): self.__queue.enqueue('Liam') self.assertEqual('[ Liam ]', str(self.__queue)) def test_enq_empty_length(self): self.__queue.enqueue('Liam') self.assertEqual(1, len(self.__queue)) def test_enq_multiple_string(self): self.__queue.enqueue('Liam') self.__queue.enqueue('Coolguy') self.assertEqual('[ Liam, Coolguy ]', str(self.__queue)) def test_enq_multiple_length(self): self.__queue.enqueue('Liam') self.__queue.enqueue('Coolguy') self.assertEqual(2, len(self.__queue)) if __name__ == '__main__': unittest.main()

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bf9ff674e5be999babc1d3d67faf8f670827ac5f

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py

Python

python/packages/isce3/geometry/__init__.py

isce3-testing/isce3-circleci-poc

ec1dfb6019bcdc7afb7beee7be0fa0ce3f3b87b3

[ "Apache-2.0" ]

null

python/packages/isce3/geometry/__init__.py

isce3-testing/isce3-circleci-poc

ec1dfb6019bcdc7afb7beee7be0fa0ce3f3b87b3

[ "Apache-2.0" ]

1

2021-12-23T00:00:31.000Z

python/packages/isce3/geometry/__init__.py

isce3-testing/isce3-circleci-poc

ec1dfb6019bcdc7afb7beee7be0fa0ce3f3b87b3

[ "Apache-2.0" ]

1

2021-12-02T21:10:11.000Z

from isce3.ext.isce3.geometry import * from .rdr2rdr import rdr2rdr

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bfa1c24e323ab6dcab1e4f1477dd8e3ae5599e93

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py

Python

lg_nav_to_device/src/lg_nav_to_device/__init__.py

FuriousJulius/lg_ros_nodes

15a84c5022ab2f5b038d11a5589cd4a34010b1d6

[ "Apache-2.0" ]

16

2015-10-10T11:55:37.000Z

2022-02-24T22:47:48.000Z

lg_nav_to_device/src/lg_nav_to_device/__init__.py

FuriousJulius/lg_ros_nodes

15a84c5022ab2f5b038d11a5589cd4a34010b1d6

[ "Apache-2.0" ]

292

2015-09-29T21:59:53.000Z

2022-03-31T15:59:31.000Z

lg_nav_to_device/src/lg_nav_to_device/__init__.py

constantegonzalez/lg_ros_nodes

1c7b08c42e90205922602c86805285508d1b7971

[ "Apache-2.0" ]

5

2017-05-03T06:22:43.000Z

2021-08-19T16:54:14.000Z

from .device_writer import DeviceWriter from .background_stopper import BackgroundStopper

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py

Python

mooncake_utils/everything.py

ericyue/mooncake_utils

e6809f0e4e0153a1cbc7de150813806ab394f7eb

[ "Apache-2.0" ]

1

2019-01-02T10:18:07.000Z

mooncake_utils/everything.py

ericyue/mooncake_utils

e6809f0e4e0153a1cbc7de150813806ab394f7eb

[ "Apache-2.0" ]

1

2017-07-16T16:32:43.000Z

mooncake_utils/everything.py

ericyue/mooncake_utils

e6809f0e4e0153a1cbc7de150813806ab394f7eb

[ "Apache-2.0" ]

null

from mooncake_utils.date import * from mooncake_utils.cmd import * from mooncake_utils.file import * from mooncake_utils.data import * from mooncake_utils.log import * from mooncake_utils.alert import * from mooncake_utils.hadoop import * #from mooncake_utils.cython_build import * from mooncake_utils.config import * from mooncake_utils.network import *

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py

Python

ably/util/unicodemixin.py

abordeau/ably-python

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[ "Apache-2.0" ]

null

ably/util/unicodemixin.py

abordeau/ably-python

3fc0c1b99148005e5784384a331a24ac41a8207c

[ "Apache-2.0" ]

null

ably/util/unicodemixin.py

abordeau/ably-python

3fc0c1b99148005e5784384a331a24ac41a8207c

[ "Apache-2.0" ]

null

import six class UnicodeMixin(object): if six.PY3: def __str__(self): return self.__unicode__() else: def __str__(self): return self.__unicode__().encode('utf8')

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py

Python

resources.py

geometalab/QGisLayerStyleLoader

8cf23106ae31014001dd762b7841d129bb45cc9b

[ "MIT" ]

5

2017-03-03T19:03:10.000Z

2022-01-26T18:50:49.000Z

resources.py

geometalab/QGisLayerStyleLoader

8cf23106ae31014001dd762b7841d129bb45cc9b

[ "MIT" ]

4

2016-09-19T17:26:04.000Z

2021-07-26T12:05:14.000Z

resources.py

geometalab/QGisLayerStyleLoader

8cf23106ae31014001dd762b7841d129bb45cc9b

[ "MIT" ]

2

2017-01-04T15:52:50.000Z

2021-07-20T06:32:18.000Z

# -*- coding: utf-8 -*- # Resource object code # # Created by: The Resource Compiler for PyQt5 (Qt v5.11.2) # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore qt_resource_data = b"\ \x00\x00\x02\x98\ \x89\ \x50\x4e\x47\x0d\x0a\x1a\x0a\x00\x00\x00\x0d\x49\x48\x44\x52\x00\ \x00\x00\x1a\x00\x00\x00\x1a\x08\x06\x00\x00\x00\xa9\x4a\x4c\xce\ \x00\x00\x00\x09\x70\x48\x59\x73\x00\x00\x0e\xc4\x00\x00\x0e\xc4\ \x01\x95\x2b\x0e\x1b\x00\x00\x02\x4a\x49\x44\x41\x54\x48\x89\xbd\ \xd6\xbf\x8f\x4c\x51\x18\xc6\xf1\xcf\x11\xc4\xaf\xd9\x59\x09\x8d\ \xca\xe8\xa9\xb7\xa2\xd5\xd1\x48\xfc\x0b\xaa\xa5\xd0\x68\xd8\x46\ \xa3\xa1\x60\x35\x0a\x8d\xc6\x16\xa8\x44\x23\x0a\x89\x4e\x24\x68\ \x6c\xc1\x6e\x76\x65\x83\x5d\xbf\x12\x22\x92\x57\x31\xef\xdd\xbd\ \x7b\xdd\x59\x33\xd9\xc4\x49\xde\x62\xce\x79\xde\xe7\x3b\xe7\x3d\ \xf7\xbc\xf7\x96\x88\xf0\x3f\xc6\x96\x51\xc4\xa5\x94\x4e\x29\xe5\ \x56\x46\x67\x24\x52\x44\xfc\x33\x30\x86\xeb\x58\xc4\xab\x8c\xc5\ \x9c\x1b\x1b\xc6\xa3\x6c\x54\xba\x52\x4a\x17\x97\x71\x12\x9f\xf1\ \x13\x55\x42\xc1\x0e\x8c\xe3\x1e\x2e\x44\xc4\x97\x81\x5e\x6d\xa0\ \x1a\xe0\x04\xbe\xe2\x47\x0d\xf0\x97\x1c\x3b\x73\xd7\xf7\x07\x01\ \xd7\x81\x46\x04\x8c\x04\x2c\x11\xd1\x04\x7c\xb1\xbe\x44\xa3\x8e\ \xaa\xa4\xdd\x3a\xb0\x60\x12\x57\xf0\x16\xdf\x36\x01\x68\x03\x76\ \x70\x10\xe7\xb7\x44\xc4\x35\x1c\xc5\x02\xf6\x63\x5b\x8a\x36\x03\ \xd8\x96\x5e\x0b\x38\x1a\x11\xd7\x9a\x67\x34\x81\x29\x1c\xc0\x2f\ \xa3\xef\xae\x60\xbb\xfe\xa3\x7f\x31\x22\x9e\xad\x2e\x34\x9f\xba\ \xbc\x88\x0f\xb1\x0f\x5b\xf1\x1d\xbf\x37\x80\x96\xd4\xed\x49\xdd\ \x47\x1c\x8f\x88\x6f\x75\xd1\x6a\x67\x28\xa5\xf4\x4a\x29\x33\x98\ \x47\x0f\xb7\x71\x1a\xcb\xd6\x4a\xda\x1c\x55\x89\x96\x53\x7b\x3b\ \x73\xe7\x4b\x29\x33\xa5\x94\x5e\x5d\x7c\x18\x0f\x72\xbb\xaf\x71\ \xae\xa5\x33\x4c\xe0\x09\xe6\xf0\x22\x63\x2e\xe7\x26\x5a\xf4\xe7\ \xd2\x6b\x31\xbd\x0f\x0f\xd5\xeb\x22\xe2\x59\x44\x1c\xc3\x29\xfd\ \x52\x7e\xc7\xa9\x88\x38\x56\x3f\x87\x7f\x99\x54\xff\xa2\x87\x19\ \xfd\x56\xb3\x88\x4b\xc3\xf4\xb0\xc6\x4e\x2e\x65\xee\xe7\xf4\xea\ \xad\xae\xb5\x88\x3b\x78\x8a\x59\x2c\x0d\x03\x4c\xc0\x52\xe6\x3c\ \x45\xe7\x2f\x4d\xcb\x59\x3c\xc2\x3b\xbc\xc4\x1d\xac\xa4\xc9\x54\ \x0b\x60\x2a\xd7\x56\x52\xfb\x32\x73\x1f\x35\xcf\xae\x0e\x78\xac\ \x7f\xc1\xde\xe0\x4c\xcd\xec\x10\xee\x66\x39\x96\x70\x3e\x63\x29\ \xe7\xee\xe2\x50\x4d\x7f\x26\x3d\x16\xd2\x73\xa2\xba\x42\x93\xfa\ \x97\x73\x16\xcf\xb1\x6b\x40\x79\x2a\xe0\x4a\xc6\x3a\x40\x43\xbb\ \x2b\xbd\x66\xd3\x7b\xb2\x5a\xe8\x5a\x7b\xb1\x7d\xc0\x4d\xec\x1d\ \x60\x72\x04\x47\x06\xac\xed\xcd\xdc\x0f\xd6\x5e\x8c\xdd\xb6\x33\ \xea\xe2\x06\x3e\x65\x4c\x0f\x02\xb6\x00\xa6\x6b\x79\x37\x2a\xc0\ \xc0\xa7\xae\xb1\xc3\x4f\xfa\xb7\x7e\x1a\xe3\x2d\xba\xf1\x5c\x5b\ \x4e\xed\xf5\x26\x60\x43\x50\x0b\x70\xb9\x06\xdc\x9d\x31\x5d\x9b\ \x1f\x08\x18\x0a\x54\x03\x8e\xd5\x80\xef\x33\x2a\xc0\xe6\x3f\x4e\ \x9a\x23\x3b\xfb\xd5\xfc\x79\x36\x1a\x1d\x7a\xc3\xdc\x51\x40\x9b\ \x19\x7f\x00\x65\xdb\xac\x69\xcb\x5e\x68\x6f\x00\x00\x00\x00\x49\ \x45\x4e\x44\xae\x42\x60\x82\ " qt_resource_name = b"\ \x00\x07\ \x07\x3b\xe0\xb3\ \x00\x70\ \x00\x6c\x00\x75\x00\x67\x00\x69\x00\x6e\x00\x73\ \x00\x10\ \x02\xd4\x99\x62\ \x00\x4c\ \x00\x61\x00\x79\x00\x65\x00\x72\x00\x53\x00\x74\x00\x79\x00\x6c\x00\x65\x00\x4c\x00\x6f\x00\x61\x00\x64\x00\x65\x00\x72\ \x00\x08\ \x0a\x61\x5a\xa7\ \x00\x69\ \x00\x63\x00\x6f\x00\x6e\x00\x2e\x00\x70\x00\x6e\x00\x67\ " qt_resource_struct_v1 = b"\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x01\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x02\ \x00\x00\x00\x14\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\ \x00\x00\x00\x3a\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\ " qt_resource_struct_v2 = b"\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x01\ \x00\x00\x00\x00\x00\x00\x00\x00\ \x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x02\ \x00\x00\x00\x00\x00\x00\x00\x00\ \x00\x00\x00\x14\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\ \x00\x00\x00\x00\x00\x00\x00\x00\ \x00\x00\x00\x3a\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\ \x00\x00\x01\x7a\xa4\x20\xaa\x38\ " qt_version = [int(v) for v in QtCore.qVersion().split('.')] if qt_version < [5, 8, 0]: rcc_version = 1 qt_resource_struct = qt_resource_struct_v1 else: rcc_version = 2 qt_resource_struct = qt_resource_struct_v2 def qInitResources(): QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data) def qCleanupResources(): QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data) qInitResources()

42.556604

121

0.726225

1,009

4,511

3.209118

0.280476

0.194565

0.197344

0.151946

0.191785

0.18252

0.161828

0

0.300371

0.042784

4,511

105

122

42.961905

0.449514

0.033695

0

0.123596

0

0.58427

0.00023

0

1

0

1

0.022472

false

0

0.011236

0

0.033708

0

null

0

1

0

1

0

1

0

1

0

null

1

0

5

44a6a949923f82c74bc82bab830bb9912bad3e6c

561

py

Python

src/forecastability/cov.py

PacktPublishing/Modern-Time-Series-Forecasting-with-Python-

391ae9c8c8c5b2fba20a8ada8e48e68eb46f118a

[ "MIT" ]

10

2021-08-09T11:06:28.000Z

2022-03-07T14:47:36.000Z

src/forecastability/cov.py

PacktPublishing/Modern-Time-Series-Forecasting-with-Python-

391ae9c8c8c5b2fba20a8ada8e48e68eb46f118a

[ "MIT" ]

null

src/forecastability/cov.py

PacktPublishing/Modern-Time-Series-Forecasting-with-Python-

391ae9c8c8c5b2fba20a8ada8e48e68eb46f118a

[ "MIT" ]

null

import warnings import numpy as np def calc_norm_sd(x, original): if (len(x) <= 2) and np.all(x==0): warnings.warn("Array should not be all zeroes or should atleast more than 1 datapoint. COV will be NaN") cov = np.nan else: cov = np.std(x) / np.mean(original) return cov def calc_cov(x): if (len(x) <= 2) and np.all(x==0): warnings.warn("Array should not be all zeroes or should atleast more than 1 datapoint. COV will be NaN") cov = np.nan else: cov = np.std(x) / np.mean(x) return cov

31.166667

112

0.613191

98

561

3.479592

0.367347

0.058651

0.035191

0.041056

0.739003

0

0.01467

0.270945

561

18

113

31.166667

0.819071

0

0.625

0

0.309609

0

1

0.125

false

0

0.125

0

0.375

0

null

0

1

0

null

0

5

44b925732a824addb50f17e3e2d647eff6025a50

90

py

Python

square_table.py

hashimas/learning_python

a6eba00031c79c5793e5bdc651374a41a7c3545f

[ "Apache-2.0" ]

null

square_table.py

hashimas/learning_python

a6eba00031c79c5793e5bdc651374a41a7c3545f

[ "Apache-2.0" ]

null

square_table.py

hashimas/learning_python

a6eba00031c79c5793e5bdc651374a41a7c3545f

[ "Apache-2.0" ]

null

for x in range(1,11): print(repr(x).rjust(2), repr(x*x).rjust(3),repr(x*x*x).rjust(4))

45

68

0.611111

22

90

2.5

0.545455

0.272727

0.218182

0

0.074074

0.1

90

2

68

45

0.604938

0

1

0

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

5

44d47e5543c7fb18b6e8c2bee85e85248b85e8cb

134

py

Python

examples/SimpleERC20/test/__init__.py

IC3Hydra/Hydra

ba42d62108bb1a374bc2e2290d6535c22062b2d3

[ "MIT" ]

78

2017-11-02T19:25:04.000Z

2022-03-20T01:03:36.000Z

examples/SimpleERC20/test/__init__.py

IC3Hydra/Hydra

ba42d62108bb1a374bc2e2290d6535c22062b2d3

[ "MIT" ]

2

2018-02-01T16:07:35.000Z

2018-08-29T14:57:03.000Z

examples/SimpleERC20/test/__init__.py

IC3Hydra/Hydra

ba42d62108bb1a374bc2e2290d6535c22062b2d3

[ "MIT" ]

6

2017-11-02T16:27:58.000Z

2021-05-08T00:12:22.000Z

PATH_TO_HEADS = 'examples/SimpleERC20/heads/' META_CONTRACT = 'examples/SimpleERC20/Hydra.sol' SPEC = 'examples/SimpleERC20/Spec.sol'

33.5

48

0.798507

17

134

6.117647

0.588235

0.548077

0

0.048

0.067164

134

4

49

33.5

0.784

0

0.641791

0

1

0

false

0

1

0

null

1

0

1

0

1

null

0

5

781bf6b9c7753e124c1e9e6c749e51440a335224

199

py

Python

src/controllers/delivery.py

MaksonViini/Flask-Delivery-App

de131a08d7dd0b42ddd3ffd7dd395d9dc177d5fb

[ "MIT" ]

null

src/controllers/delivery.py

MaksonViini/Flask-Delivery-App

de131a08d7dd0b42ddd3ffd7dd395d9dc177d5fb

[ "MIT" ]

null

src/controllers/delivery.py

MaksonViini/Flask-Delivery-App

de131a08d7dd0b42ddd3ffd7dd395d9dc177d5fb

[ "MIT" ]

null

from flask_restx import Resource from server.instance import server delivery_ns = server.delivery_name_space class Delivery(Resource): def get(self, ): return {'hello': 'world'}

16.583333

40

0.713568

25

199

5.52

0.72

0.202899

0

0.201005

199

11

41

18.090909

0.867925

0

0.050251

0

1

0.166667

false

0

0.333333

0.166667

0.833333

0

1

0

null

1

0

null

0

1

0

5

7847af6bde3ef1a5424189ec30988c33acba35ee

1,214

py

Python

4-25/8.py

tonyyzy/ProjectEuler

f52de2f931ebd4df2020e32d12062866b1586e72

[ "MIT" ]

null

4-25/8.py

tonyyzy/ProjectEuler

f52de2f931ebd4df2020e32d12062866b1586e72

[ "MIT" ]

null

4-25/8.py

tonyyzy/ProjectEuler

f52de2f931ebd4df2020e32d12062866b1586e72

[ "MIT" ]

null

number = 7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450 list = [] for i in str(number): list.append(int(i)) a = [] for i in range(0,988): b = 1 for j in range(0, 13): b *= list[i+j] a.append(b) a.sort(reverse=True) print(a)

93.384615

1,010

0.912685

40

1,214

27.7

0.525

0.00722

0.01083

0

0.879581

0.056013

1,214

13

1,011

93.384615

0.08726

0

1

0

1

0

false

0

0.083333

0

1

null

0

1

0

1

0

1

0

null

1

0

5

786dbd03b63c3402a3bbf979c36b94bb4258d6f6

74

py

Python

tests/__init__.py

icaropires/pdf2dataset

b070d656fa446c296458512515fc68fc43d949e1

[ "Apache-2.0" ]

11

2020-06-30T03:22:57.000Z

2021-11-16T03:35:50.000Z

tests/__init__.py

icaropires/pdf2dataset

b070d656fa446c296458512515fc68fc43d949e1

[ "Apache-2.0" ]

23

2020-07-21T19:03:37.000Z

2020-11-01T15:53:03.000Z

tests/__init__.py

icaropires/pdf2dataset

b070d656fa446c296458512515fc68fc43d949e1

[ "Apache-2.0" ]

4

2020-07-15T20:16:28.000Z

2021-04-13T18:38:22.000Z

import pytest pytest.register_assert_rewrite('tests.testing_dataframe')

14.8

57

0.851351

9

74

6.666667

0.888889

0

0.067568

74

4

58

18.5

0.869565

0

0.310811

0

0.5

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

787957a4d081235ae2e4c79c2fe142e10dc5a006

24

py

Python

django_common_utils/libraries/models/mixins/helpers/__init__.py

Myzel394/django-common-utils

038bc4481bd44d67545ce307170f530a464f678b

[ "MIT" ]

2

2021-02-08T11:10:53.000Z

2021-03-14T15:34:21.000Z

django_common_utils/libraries/models/mixins/helpers/__init__.py

Myzel394/django-common-utils

038bc4481bd44d67545ce307170f530a464f678b

[ "MIT" ]

null

django_common_utils/libraries/models/mixins/helpers/__init__.py

Myzel394/django-common-utils

038bc4481bd44d67545ce307170f530a464f678b

[ "MIT" ]

1

2021-02-18T15:34:14.000Z

from .queryset import *

12

23

0.75

3

24

6

1

0

0.166667

24

1

24

0.9

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

787b1ac18aa3055f6f4aa187ae40919628127873

41

py

Python

api/tests/seed.py

ShubhamGG/Anubis

2c538ef258a1edf5463596a33bc66caa2ef7e35b

[ "MIT" ]

65

2021-06-27T07:18:27.000Z

2021-09-17T16:58:24.000Z

api/tests/seed.py

efaraz27/Anubis

40a12933877df7f39dd75ca26148858774fcda7b

[ "MIT" ]

114

2021-06-27T08:37:43.000Z

2021-10-24T00:51:01.000Z

api/tests/seed.py

efaraz27/Anubis

40a12933877df7f39dd75ca26148858774fcda7b

[ "MIT" ]

15

2021-06-27T07:26:51.000Z

2021-10-06T18:42:39.000Z

from anubis.rpc.seed import seed seed()

10.25

32

0.756098

7

41

4.428571

0.714286

0

0.146341

41

3

33

13.666667

0.885714

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

78ac9fb91ae2ed231cd29ccc16e74d8716e7a797

319

py

Python

configs/gdrn/lmoSingleObj/resnest50d_online_AugCosyAAEGray_mlBCE_DoubleMask_lmoRealNBPbr_100e/resnest50d_online_AugCosyAAEGray_mlBCE_DoubleMask_lmoRealNBPbr_100e_12_holepuncher_bop_test.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

33

2021-12-15T07:11:47.000Z

2022-03-29T08:58:32.000Z

configs/gdrn/lmoSingleObj/resnest50d_online_AugCosyAAEGray_mlBCE_DoubleMask_lmoRealNBPbr_100e/resnest50d_online_AugCosyAAEGray_mlBCE_DoubleMask_lmoRealNBPbr_100e_12_holepuncher_bop_test.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

3

2021-12-15T11:39:54.000Z

2022-03-29T07:24:23.000Z

configs/gdrn/lmoSingleObj/resnest50d_online_AugCosyAAEGray_mlBCE_DoubleMask_lmoRealNBPbr_100e/resnest50d_online_AugCosyAAEGray_mlBCE_DoubleMask_lmoRealNBPbr_100e_12_holepuncher_bop_test.py

THU-DA-6D-Pose-Group/self6dpp

c267cfa55e440e212136a5e9940598720fa21d16

[ "Apache-2.0" ]

null

_base_ = "./resnest50d_online_AugCosyAAEGray_mlBCE_DoubleMask_lmoRealNBPbr_100e_01_ape_bop_test.py" OUTPUT_DIR = ( "output/gdrn/lmoRealPbrSO/resnest50d_online_AugCosyAAEGray_mlBCE_DoubleMask_lmoRealNBPbr_100e_SO/holepuncher" ) DATASETS = dict(TRAIN=("lmo_pbr_holepuncher_train", "lmo_NoBopTest_holepuncher_train"))

53.166667

113

0.868339

38

319

6.605263

0.657895

0.12749

0.239044

0.278884

0.486056

0

0.039604

0.050157

319

5

114

63.8

0.788779

0

0.786834

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0

null

0

1

0

1

0

1

null

0

5

152867b2061f6833c56aae302dc6f88839e144ea

1,385

py

Python

scripts/spring 2020/test_compute_route.py

ronan-keane/hav-sim

0aaf9674e987822ff2dc90c74613d5e68e8ef0ce

[ "Apache-2.0" ]

null

scripts/spring 2020/test_compute_route.py

ronan-keane/hav-sim

0aaf9674e987822ff2dc90c74613d5e68e8ef0ce

[ "Apache-2.0" ]

null

scripts/spring 2020/test_compute_route.py

ronan-keane/hav-sim

0aaf9674e987822ff2dc90c74613d5e68e8ef0ce

[ "Apache-2.0" ]

2

2020-09-30T22:44:37.000Z

2021-05-09T07:36:28.000Z

""" Test that the compute_route() function in the havsim.simulation.road package works correctly. """ from havsim.simulation.road import Road from havsim.simulation.road import compute_route def test1(): road1 = Road(num_lanes=2, length=4, name='road1') road2 = Road(num_lanes=1, length=4, name='road2') road3 = Road(num_lanes=1, length=3, name='road3') road4 = Road(num_lanes=1, length=2, name='road4') road5 = Road(num_lanes=1, length=24, name='road5') road1.connect(road2, [1], [0]) road1.connect(road5, [0], [0]) road2.connect(road3) road3.connect(road4) road4.merge(road5, 0, 0, (1, 2), (23, 24)) road5.connect('exit', is_exit=True) assert compute_route(road1, 0, 'exit') == ['road2', 'road3', 'road4', 'road5', 'exit'] def test2(): road1 = Road(num_lanes=2, length=4, name='road1') road2 = Road(num_lanes=1, length=4, name='road2') road3 = Road(num_lanes=1, length=3, name='road3') road4 = Road(num_lanes=1, length=2, name='road4') road5 = Road(num_lanes=1, length=8, name='road5') road1.connect(road2, [1], [0]) road1.connect(road5, [0], [0]) road2.connect(road3) road3.connect(road4) road4.merge(road5, 0, 0, (1, 2), (7, 8)) road5.connect('exit', is_exit=True) assert compute_route(road1, 0, 'exit') == ['road5', 'exit'] def test_all(): test1() test2() test_all()

30.777778

93

0.638267

209

1,385

4.143541

0.205742

0.080831

0.138568

0.120092

0.794457

0.725173

0

0.08589

0.176173

1,385

44

94

31.477273

0.673094

0.067148

0

0.5625

0

0.077103

0

0.0625

1

0.09375

false

0

0.0625

0

0.15625

0

null

0

1

0

null

0

5

153dd62a028aebd33d87c7a739508d9a08947a25

730

py

Python

2_animal_printer.py

ak0029/Zookeeper

ad61bafb57078602c5ce0d1dec1ac56217f6d4ab

[ "MIT" ]

null

2_animal_printer.py

ak0029/Zookeeper

ad61bafb57078602c5ce0d1dec1ac56217f6d4ab

[ "MIT" ]

null

2_animal_printer.py

ak0029/Zookeeper

ad61bafb57078602c5ce0d1dec1ac56217f6d4ab

[ "MIT" ]

null

print(r"""Switching on camera from habitat with camels... ___.-''''-. /___ @ | ',,,,. | _.'''''''._ ' | / \ | \ _.-' \ | '.-' '-. | ', | '', ',,-, ':; ',,| ;,, ,' ;; ! ; !'',,,',',,,,'! ; ;: : ; ! ! ! ! ; ; :; ; ; ! ! ! ! ; ; ;, ; ; ! ! ! ! ; ; ; ; ! ! ! ! ; ; ;,, !,! !,! ;,; /_I L_I L_I /_I Yey, our little camel is sunbathing!""")

38.421053

58

0.126027

21

730

3.761905

0.809524

0.050633

0.075949

0

0.663014

730

19

59

38.421053

0.321138

0

0.105263

0

0.978962

0

1

0

true

0

0.052632

1

0

null

0

1

0

1

0

1

0

null

0

1

0

5

ec72798e9996cbe6100a0aa377f790750337d843

105

py

Python

clouds/io/__init__.py

jchen42703/understanding-clouds-kaggle

6972deb25cdf363ae0d9a9ad26d538280613fc94

[ "Apache-2.0" ]

1

2019-10-26T16:33:40.000Z

clouds/io/__init__.py

jchen42703/understanding-clouds-kaggle

6972deb25cdf363ae0d9a9ad26d538280613fc94

[ "Apache-2.0" ]

1

2019-11-08T02:50:25.000Z

2019-11-19T03:36:54.000Z

clouds/io/__init__.py

jchen42703/understanding-clouds-kaggle

6972deb25cdf363ae0d9a9ad26d538280613fc94

[ "Apache-2.0" ]

null

from .dataset import CloudDataset, ClassificationCloudDataset, \ ClfSegCloudDataset

35

64

0.695238

6

105

12.166667

1

0

0.266667

105

2

65

52.5

0.948052

0

1

0

true

0

0.5

0

0.5

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

eca736137690d10a1b35603e34eeba3d325c180b

443

py

Python

plugins/trello/komand_trello/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

46

2019-06-05T20:47:58.000Z

2022-03-29T10:18:01.000Z

plugins/trello/komand_trello/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

386

2019-06-07T20:20:39.000Z

2022-03-30T17:35:01.000Z

plugins/trello/komand_trello/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

43

2019-07-09T14:13:58.000Z

2022-03-28T12:04:46.000Z

# GENERATED BY KOMAND SDK - DO NOT EDIT from .deactivate_user.action import DeactivateUser from .deactivated_list.action import DeactivatedList from .get_boards_by_member.action import GetBoardsByMember from .member_list.action import MemberList from .remove_member_from_board.action import RemoveMemberFromBoard from .remove_member_from_cards.action import RemoveMemberFromCards from .remove_member_from_org.action import RemoveMemberFromOrg

49.222222

66

0.880361

57

443

6.578947

0.491228

0.224

0.128

0.16

0

0.083521

443

8

67

55.375

0.923645

0.083521

0

1

0

1

0

true

0

1

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

5

ecd636977f371e720da80f3fc621bd3ffb8b202d

5,164

py

Python

Binance/Trades.py

awaismemon26/BinanceRESTAPI

8aab315fd6ae5e25e526af2ed9a7c3f8dfd0fea2

[ "MIT" ]

2

2022-01-30T19:26:56.000Z

2022-02-26T17:36:41.000Z

Binance/Trades.py

awaismemon26/BinanceRESTAPI

8aab315fd6ae5e25e526af2ed9a7c3f8dfd0fea2

[ "MIT" ]

null

Binance/Trades.py

awaismemon26/BinanceRESTAPI

8aab315fd6ae5e25e526af2ed9a7c3f8dfd0fea2

[ "MIT" ]

null

from Binance.BinanceClient import awais26_client, awais100_client, sarah12_client import pandas as pd from pandas import DataFrame as dataframe def get_ticker_usd_value(ticker): pd.set_option('precision', 8) ticker = ticker + 'USDT' usdt_price = awais26_client.get_symbol_ticker(symbol=ticker) cleanDF = dataframe(usdt_price, index=[0]) cleanDF.set_index('symbol', inplace=True) cleanDF = float(cleanDF['price'][0]) return cleanDF def get_awais26_aggregated_trades(ticker): json_data = awais26_client.get_aggregate_trades(symbol=ticker) tradesDF = pd.json_normalize(json_data) tradesDF.columns=['Id', 'Price', 'Quantity', 'FirstTradeId', 'LastTradeId', 'Date', 'IsBuyerMaker', 'IsBestPriceMatch'] return tradesDF def get_awais26_historical_trades(ticker): json_data = awais26_client.get_historical_trades(symbol=ticker, limit=1000, fromId='329135120') tradesDF = pd.json_normalize(json_data) tradesDF['time'] = pd.to_datetime(tradesDF['time'], unit='ms') tradesDF['time'] = tradesDF.time.dt.tz_localize('UTC').dt.tz_convert('Europe/Berlin') # tradesDF.columns=['Id', 'Price', 'Quantity', 'FirstTradeId', 'LastTradeId', 'Date', 'WasBuyerMaker', 'WasBestPriceMatch'] return tradesDF def get_awais26_recent_trades(ticker, count): json_data = awais26_client.get_my_trades(symbol=ticker, limit=count) tradesDF = pd.json_normalize(json_data) cols = ['symbol', 'time', 'qty', 'price', 'quoteQty' ,'commission', 'commissionAsset', 'isBuyer', 'isMaker', 'isBestMatch'] tradesDF = tradesDF[cols] return tradesDF def get_awais26_recent_trade_sum(ticker): json_data = awais26_client.get_my_trades(symbol=ticker, limit=500) tradesDF = pd.json_normalize(json_data) if tradesDF.empty: return 'No Trades Found!' tradesDF['time'] = pd.to_datetime(tradesDF['time'], unit='ms') tradesDF['time'] = tradesDF['time'].dt.strftime('%Y-%m-%dT%H:%M:%S').astype(str) cols = ['symbol', 'time', 'qty', 'price', 'quoteQty', 'commission', 'commissionAsset', 'isBuyer', 'isMaker'] groupbyCols = ['time','isBuyer', 'isMaker'] tradesDF = tradesDF[cols] tradesDF[['qty', 'quoteQty', 'price', 'commission']] = tradesDF[['qty', 'quoteQty', 'price', 'commission']].apply(pd.to_numeric, errors='ignore') tradesDF['fees_usd'] = tradesDF.apply(lambda row: row['commission'] if row['commissionAsset'] == 'USDT' else (get_ticker_usd_value(row['commissionAsset']) * row['commission']), axis=1) tradesDF = tradesDF.groupby(groupbyCols, as_index=False)[['qty','price', 'quoteQty', 'fees_usd']].agg( { 'qty': 'sum', 'price' : 'mean', 'quoteQty': 'sum', 'fees_usd': 'sum' } ) return tradesDF def get_awais100_recent_trade_sum(ticker): json_data = awais100_client.get_my_trades(symbol=ticker, limit=500) tradesDF = pd.json_normalize(json_data) if tradesDF.empty: return 'No Trades Found!' tradesDF['time'] = pd.to_datetime(tradesDF['time'], unit='ms') tradesDF['time'] = tradesDF['time'].dt.strftime('%Y-%m-%dT%H:%M:%S').astype(str) cols = ['symbol', 'time', 'qty', 'price', 'quoteQty', 'commission', 'commissionAsset', 'isBuyer', 'isMaker'] groupbyCols = ['time','isBuyer', 'isMaker'] tradesDF = tradesDF[cols] tradesDF[['qty', 'quoteQty', 'price', 'commission']] = tradesDF[['qty', 'quoteQty', 'price', 'commission']].apply(pd.to_numeric, errors='ignore') tradesDF['fees_usd'] = tradesDF.apply(lambda row: row['commission'] if row['commissionAsset'] == 'USDT' else (get_ticker_usd_value(row['commissionAsset']) * row['commission']), axis=1) tradesDF = tradesDF.groupby(groupbyCols, as_index=False)[['qty','price', 'quoteQty', 'fees_usd']].agg( { 'qty': 'sum', 'price' : 'mean', 'quoteQty': 'sum', 'fees_usd': 'sum' } ) return tradesDF def get_sarah12_recent_trade_sum(ticker): json_data = sarah12_client.get_my_trades(symbol=ticker, limit=500) tradesDF = pd.json_normalize(json_data) if tradesDF.empty: return 'No Trades Found!' tradesDF['time'] = pd.to_datetime(tradesDF['time'], unit='ms') tradesDF['time'] = tradesDF['time'].dt.strftime('%Y-%m-%dT%H:%M:%S').astype(str) cols = ['symbol', 'time', 'qty', 'price', 'quoteQty', 'commission', 'commissionAsset', 'isBuyer', 'isMaker'] groupbyCols = ['time','isBuyer', 'isMaker'] tradesDF = tradesDF[cols] tradesDF[['qty', 'quoteQty', 'price', 'commission']] = tradesDF[['qty', 'quoteQty', 'price', 'commission']].apply(pd.to_numeric, errors='ignore') tradesDF['fees_usd'] = tradesDF.apply(lambda row: row['commission'] if row['commissionAsset'] == 'USDT' else (get_ticker_usd_value(row['commissionAsset']) * row['commission']), axis=1) tradesDF = tradesDF.groupby(groupbyCols, as_index=False)[['qty','price', 'quoteQty', 'fees_usd']].agg( { 'qty': 'sum', 'price' : 'mean', 'quoteQty': 'sum', 'fees_usd': 'sum' } ) return tradesDF

44.904348

188

0.651046

599

5,164

5.445743

0.175292

0.05886

0.034335

0.042305

0.808706

0.800429

0.749234

0.708155

0.673207

0

0.014768

0.173896

5,164

115

189

44.904348

0.749883

0.023431

0

0.615385

0

0.229059

0

1

0.076923

false

0

0.032967

0

0.21978

0

null

0

1

0

1

0

null

0

5

01a4b5914d226c49a4b71dd7335ab2e8094358fc

32

py

Python

carrots/carrots.py

chrisgzf/kattis

7b66474c040a31cfc997863141f57a7c81f6ebab

[ "MIT" ]

null

carrots/carrots.py

chrisgzf/kattis

7b66474c040a31cfc997863141f57a7c81f6ebab

[ "MIT" ]

null

carrots/carrots.py

chrisgzf/kattis

7b66474c040a31cfc997863141f57a7c81f6ebab

[ "MIT" ]

null

a, b = input().split() print(b)

10.666667

22

0.5625

6

32

3

0.833333

0

0.15625

32

2

23

16

0.666667

0

1

0

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

01c8465df2c322efda9679660b609148a6ba86b0

99

py

Python

z_tests/a_gym_info.py

linklab/minimal_rl

382d99ca355ea405414c4ed1077fb4e8ed3532a9

[ "MIT" ]

null

z_tests/a_gym_info.py

linklab/minimal_rl

382d99ca355ea405414c4ed1077fb4e8ed3532a9

[ "MIT" ]

null

z_tests/a_gym_info.py

linklab/minimal_rl

382d99ca355ea405414c4ed1077fb4e8ed3532a9

[ "MIT" ]

1

2021-10-17T14:09:05.000Z

from gym import envs for idx, env_name in enumerate(envs.registry.all()): print(idx, env_name)

24.75

52

0.737374

17

99

4.176471

0.764706

0.169014

0.28169

0

0.151515

99

3

53

33

0.845238

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

01e6f0e2ab79f1b7be4b30cacf5aec3b191421f8

154

py

Python

Wrapping/Python/SIMPL/__init__.py

v7t/SIMPL

41c941ac957960ec17d067ffe5c566390c4a2553

[ "NRL" ]

null

Wrapping/Python/SIMPL/__init__.py

v7t/SIMPL

41c941ac957960ec17d067ffe5c566390c4a2553

[ "NRL" ]

2

2019-02-23T20:46:12.000Z

2019-07-11T15:34:13.000Z

Wrapping/Python/SIMPL/__init__.py

v7t/SIMPL

41c941ac957960ec17d067ffe5c566390c4a2553

[ "NRL" ]

null

""" Some Description """ # This imports the python module from . import dream3d_py #from . import utils #__all__ = ['simpl_common','simpl_test_dirs']

15.4

45

0.714286

20

154

5.1

0.85

0.196078

0

0.007752

0.162338

154

9

46

17.111111

0.782946

0.727273

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

bf192ca49ae1720cf8ce2e9b65791fbe97067479

1,113

py

Python

pypeln/sync/api/flat_map_sync_test.py

quarckster/pypeln

f4160d0f4d4718b67f79a0707d7261d249459a4b

[ "MIT" ]

1,281

2018-09-20T05:35:27.000Z

2022-03-30T01:29:48.000Z

pypeln/sync/api/flat_map_sync_test.py

webclinic017/pypeln

5231806f2cac9d2019dacbbcf913484fd268b8c1

[ "MIT" ]

78

2018-09-18T20:38:12.000Z

2022-03-30T20:16:02.000Z

pypeln/sync/api/flat_map_sync_test.py

webclinic017/pypeln

5231806f2cac9d2019dacbbcf913484fd268b8c1

[ "MIT" ]

88

2018-09-24T10:46:14.000Z

2022-03-28T09:34:50.000Z

import hypothesis as hp from hypothesis import strategies as st import time import pypeln as pl import cytoolz as cz MAX_EXAMPLES = 10 @hp.given(nums=st.lists(st.integers())) @hp.settings(max_examples=MAX_EXAMPLES) def test_flat_map_square(nums): def _generator(x): yield x yield x + 1 yield x + 2 nums_py = map(lambda x: x ** 2, nums) nums_py = cz.mapcat(_generator, nums_py) nums_py = list(nums_py) nums_pl = pl.sync.map(lambda x: x ** 2, nums) nums_pl = pl.sync.flat_map(_generator, nums_pl) nums_pl = list(nums_pl) assert nums_pl == nums_py @hp.given(nums=st.lists(st.integers())) @hp.settings(max_examples=MAX_EXAMPLES) def test_flat_map_square_workers(nums): def _generator(x): yield x yield x + 1 yield x + 2 nums_py = map(lambda x: x ** 2, nums) nums_py = cz.mapcat(_generator, nums_py) nums_py = list(nums_py) nums_pl = pl.sync.map(lambda x: x ** 2, nums) nums_pl = pl.sync.flat_map(_generator, nums_pl, workers=3) nums_pl = list(nums_pl) assert sorted(nums_pl) == sorted(nums_py)

24.195652

62

0.665768

186

1,113

3.747312

0.209677

0.1033

0.05165

0.063128

0.76901

0.705882

0

0.012702

0.221923

1,113

45

63

24.733333

0.792148

0

0.647059

0

0.058824

1

0.117647

false

0

0.147059

0

0.264706

0

null

0

1

0

null

0

5

bf240f6cfc336066dc7c1506c7651e020100a6e8

135

py

Python

osp/citations/models/__init__.py

davidmcclure/open-syllabus-project

078cfd4c5a257fbfb0901d43bfbc6350824eed4e

[ "Apache-2.0" ]

220

2016-01-22T21:19:02.000Z

2022-01-25T04:33:55.000Z

osp/citations/models/__init__.py

davidmcclure/open-syllabus-project

078cfd4c5a257fbfb0901d43bfbc6350824eed4e

[ "Apache-2.0" ]

14

2016-01-23T14:34:39.000Z

2016-09-19T19:58:37.000Z

osp/citations/models/__init__.py

davidmcclure/open-syllabus-project

078cfd4c5a257fbfb0901d43bfbc6350824eed4e

[ "Apache-2.0" ]

14

2016-02-03T13:47:48.000Z

2019-03-27T13:09:05.000Z

from .text import Text from .citation import Citation from .text_index import Text_Index from .citation_index import Citation_Index

16.875

42

0.82963

20

135

5.4

0.25

0.148148

0

0.140741

135

7

43

19.285714

0.931034

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

bf3899fc7413db1583661e51609f4eefb0563c61

97

py

Python

open-codegen/opengen/functions/is_symbolic.py

elsuizo/optimization-engine

c264264dd2ca035670ccd3d5d87e53c2f98b7d35

[ "Apache-2.0", "MIT" ]

253

2019-03-02T03:47:54.000Z

2022-03-25T01:00:41.000Z

open-codegen/opengen/functions/is_symbolic.py

elsuizo/optimization-engine

c264264dd2ca035670ccd3d5d87e53c2f98b7d35

[ "Apache-2.0", "MIT" ]

157

2019-03-23T15:13:24.000Z

2022-03-04T19:13:22.000Z

open-codegen/opengen/functions/is_symbolic.py

elsuizo/optimization-engine

c264264dd2ca035670ccd3d5d87e53c2f98b7d35

[ "Apache-2.0", "MIT" ]

26

2019-03-05T01:48:35.000Z

2022-03-18T15:31:27.000Z

import casadi.casadi as cs def is_symbolic(u): return isinstance(u, (cs.SX, cs.MX, cs.DM))

16.166667

47

0.680412

18

97

3.611111

0.722222

0

0.175258

97

5

48

19.4

0.8125

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

173995c2a0bd922f5a15dcf4c5d138e39f75d76f

920

py

Python

scripts/pac/crlpac/__init__.py

mkarim2017/insarzd

e7d05f836e7ca044166e38bad549629ed00d71f1

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

28

2019-10-04T01:18:29.000Z

2022-02-15T11:18:18.000Z

scripts/pac/crlpac/__init__.py

mkarim2017/insarzd

e7d05f836e7ca044166e38bad549629ed00d71f1

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

null

scripts/pac/crlpac/__init__.py

mkarim2017/insarzd

e7d05f836e7ca044166e38bad549629ed00d71f1

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

11

2019-10-04T08:36:54.000Z

2021-06-21T08:47:28.000Z

#!/usr/bin/env python3 #class from .funcs import Dummy #functions from .funcs import getWidth from .funcs import getLength from .funcs import getInfo from .funcs import get_content from .funcs import changeXmlName from .funcs import writeSARconfig_ALOS2 from .funcs import create_xml from .funcs import renderParXml from .funcs import runCmd from .funcs import run_record_cmd from .funcs import writeOffset from .funcs import meanOffset from .funcs import cullOffsetbyMean from .funcs import cullOffset from .funcs import getOffset from .funcs import cal_coherence from .funcs import overlapFrequency from .funcs import gaussian from .funcs import create_multi_index from .funcs import create_multi_index2 from .funcs import fit_surface from .funcs import cal_surface from .funcs import read_param_for_checking_overlap from .funcs import check_overlap from .funcs import read_insar_arg from .funcs import set_filename

27.878788

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null

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1

0

1

0

1

0

5

1755f08b19ae9c1f60c4396357585c837264ec56

27

py

Python

aliyun-python-sdk-dts/aliyunsdkdts/__init__.py

bricklayer-Liu/aliyun-openapi-python-sdk

20da2554de22679fc7c5462c483663e4d79512aa

[ "Apache-2.0" ]

1

2021-03-08T02:59:17.000Z

aliyun-python-sdk-dts/aliyunsdkdts/__init__.py

bricklayer-Liu/aliyun-openapi-python-sdk

20da2554de22679fc7c5462c483663e4d79512aa

[ "Apache-2.0" ]

null

aliyun-python-sdk-dts/aliyunsdkdts/__init__.py

bricklayer-Liu/aliyun-openapi-python-sdk

20da2554de22679fc7c5462c483663e4d79512aa

[ "Apache-2.0" ]

null

__version__ = '5.0.78.19.4'

27

0.666667

6

27

2.333333

1

0

0.28

0.074074

27

1

27

0.28

0

0.392857

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

1772f04f6d1101d31d26cc4926a830a698fe0caa

93

py

Python

lumin/inference/__init__.py

choisant/lumin

c039136eb096e8f3800f13925f9325b99cf7e76b

[ "Apache-2.0" ]

43

2019-02-11T16:16:42.000Z

2021-12-13T15:35:20.000Z

lumin/inference/__init__.py

choisant/lumin

c039136eb096e8f3800f13925f9325b99cf7e76b

[ "Apache-2.0" ]

48

2020-05-21T02:40:50.000Z

2021-08-10T11:07:08.000Z

lumin/inference/__init__.py

choisant/lumin

c039136eb096e8f3800f13925f9325b99cf7e76b

[ "Apache-2.0" ]

14

2019-05-02T15:09:41.000Z

2022-01-12T21:13:34.000Z

# from .summary_stat import * # noqa F304 # __all__ = [*summary_stat.__all__] # noqa F405

23.25

48

0.688172

12

93

4.5

0.666667

0.407407

0

0.08

0.193548

93

3

49

31

0.64

0.913978

0

null

0

null

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null

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null

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1

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null

1

0

1

0

1

0

null

0

1

0

5

178c95c6914ba12d27bcf351df63da0162c398e6

96

py

Python

mica/archive/aca_dark/__init__.py

sot/mica

136a9b0d9521efda5208067b51cf0c8700b4def3

[ "BSD-3-Clause" ]

null

mica/archive/aca_dark/__init__.py

sot/mica

136a9b0d9521efda5208067b51cf0c8700b4def3

[ "BSD-3-Clause" ]

150

2015-01-23T17:09:53.000Z

2022-01-10T00:50:54.000Z

mica/archive/aca_dark/__init__.py

sot/mica

136a9b0d9521efda5208067b51cf0c8700b4def3

[ "BSD-3-Clause" ]

null

# Licensed under a 3-clause BSD style license - see LICENSE.rst from .dark_cal import * # noqa

32

63

0.739583

16

96

4.375

0.9375

0

0.012821

0.1875

96

2

64

48

0.884615

0.6875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

179cc507e6ce988530edd377750f8fff5e4dbeda

138

py

Python

jgf/__init__.py

filipinascimento/jgf

6558ba152937bdb4814190e4c1a89c7ade5bdfaf

[ "MIT" ]

null

jgf/__init__.py

filipinascimento/jgf

6558ba152937bdb4814190e4c1a89c7ade5bdfaf

[ "MIT" ]

1

2021-03-22T21:36:19.000Z

jgf/__init__.py

filipinascimento/jgf

6558ba152937bdb4814190e4c1a89c7ade5bdfaf

[ "MIT" ]

1

2020-08-03T15:54:16.000Z

#!/usr/bin/python # -*- coding: <utf-8> -*- from .core import load,save from . import igraph from . import conmat __version__ = "0.2.2"

15.333333

27

0.652174

21

138

4.095238

0.761905

0.232558

0

0.034783

0.166667

138

8

28

17.25

0.713043

0.289855

0

0.052632

0

1

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false

0

0.75

0

0.75

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null

1

0

1

0

null

0

1

0

1

0

5

bdb1a57cff3db8332ef70e5c3a7166cf920104ca

708

py

Python

trigonometry.py

Coder4OO/trigonometry

4f3e0ad32e2a5cbc5590acd139b88dcfc99838bf

[ "MIT" ]

null

trigonometry.py

Coder4OO/trigonometry

4f3e0ad32e2a5cbc5590acd139b88dcfc99838bf

[ "MIT" ]

null

trigonometry.py

Coder4OO/trigonometry

4f3e0ad32e2a5cbc5590acd139b88dcfc99838bf

[ "MIT" ]

null

import math def get_sine_angle(o, h): return math.degrees(math.asin(o/h)) def get_cosine_angle(a, h): return math.degrees(math.acos(a/h)) def get_tangent_angle(o, a): return math.degrees(math.atan(o/a)) def get_opposite_from_sine(h, ang): return math.degrees(math.sin(ang))*h def get_hypotenuse_from_sine(o, ang): return o/math.degrees(math.sin(ang)) def get_adjacent_from_cosine(h, ang): return math.degrees(math.cos(ang))*h def get_hypotenuse_from_cosine(a, ang): return a/math.degrees(math.cos(ang)) def get_opposite_from_tangent(a, ang): return math.degrees(math.tan(ang))*a def get_adjacent_from_tangent(o, ang): return o/math.degrees(math.tan(ang)) print(get_sine_angle(4,5))

23.6

39

0.748588

131

708

3.847328

0.206107

0.107143

0.267857

0.25

0.521825

0.297619

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0

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708

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0.95

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1

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null

0

1

0

1

0

5

bdeabd44a5b6e3fc302a3f44e936da7a6314482f

74

py

Python

yadialogs/urls.py

idlesign/django-yadialogs

e3964b3dd296c5b379f83287bf94cc50c4f4cfdd

[ "BSD-3-Clause" ]

null

yadialogs/urls.py

idlesign/django-yadialogs

e3964b3dd296c5b379f83287bf94cc50c4f4cfdd

[ "BSD-3-Clause" ]

null

yadialogs/urls.py

idlesign/django-yadialogs

e3964b3dd296c5b379f83287bf94cc50c4f4cfdd

[ "BSD-3-Clause" ]

null

from .utils import get_yadialogs_urls urlpatterns = get_yadialogs_urls()

18.5

37

0.837838

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74

5.8

0.7

0.413793

0.551724

0

0.108108

74

3

38

24.666667

0.878788

0

1

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false

0

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null

1

0

1

0

null

0

1

0

5

da148ee8d9c091d98a86c3d78dbb095e933fe651

422

py

Python

mythx_models/request/version.py

ConsenSys/mythx-models

e912c2fc6e7d18041310d3b9f0f95085db47ed9b

[ "MIT" ]

2

2019-08-26T13:42:28.000Z

2019-11-13T15:44:16.000Z

mythx_models/request/version.py

ConsenSys/mythx-models

e912c2fc6e7d18041310d3b9f0f95085db47ed9b

[ "MIT" ]

22

2019-08-26T13:14:55.000Z

2021-04-18T14:22:52.000Z

mythx_models/request/version.py

ConsenSys/mythx-models

e912c2fc6e7d18041310d3b9f0f95085db47ed9b

[ "MIT" ]

6

2019-08-29T15:51:38.000Z

2021-04-05T11:41:34.000Z

"""This module contains the VersionRequest domain model.""" from pydantic import BaseModel class VersionRequest(BaseModel): @property def endpoint(self): return "v1/version" @property def method(self): return "GET" @property def payload(self): return {} @property def headers(self): return {} @property def parameters(self): return {}

16.230769

59

0.606635

42

422

6.095238

0.571429

0.214844

0.140625

0.164063

0

0.003367

0.296209

422

25

60

16.88

0.858586

0.125592

0

0.470588

0

0.035813

0

1

0.294118

false

0

0.058824

0.294118

0.705882

0

null

1

0

1

0

null

0

1

0

1

0

5

da575c3aa3c5bcc23b6fc35bc33b8dfe0f1a4d82

304

py

Python

keycache/__init__.py

psytron/keycache

0b69e21719dbe76908476c01e3e487aae2612fd2

[ "Apache-2.0" ]

2

2020-04-27T07:48:54.000Z

2020-10-21T17:47:54.000Z

keycache/__init__.py

psytron/keycache

0b69e21719dbe76908476c01e3e487aae2612fd2

[ "Apache-2.0" ]

null

keycache/__init__.py

psytron/keycache

0b69e21719dbe76908476c01e3e487aae2612fd2

[ "Apache-2.0" ]

null

# github.com/psytron/keycache # USAGE : # keycache.set_system_encrypt_key( sysfinger.generate() ) # keycache.get('healthity' , alias='healthity') # future usage # levels.set( 'alias L 0' ,'domain L 1' ) # def set( self , 'level_0' , 'level_1'): # print(' next level ') from .keycache import Keycache

25.333333

57

0.6875

41

304

4.97561

0.658537

0

0.015504

0.151316

304

12

58

25.333333

0.775194

0.835526

0

1

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

e505c8e2e1631d9e04edaf9ead93f01702af971a

204

py

Python

sip/examples/flask_processing_controller/app/tests/conftest.py

SKA-ScienceDataProcessor/integration-prototype

5875dc0489f707232534ce75daf3707f909bcd15

[ "BSD-3-Clause" ]

3

2016-11-08T02:27:05.000Z

2018-01-22T13:26:11.000Z

sip/examples/flask_processing_controller/app/tests/conftest.py

SKA-ScienceDataProcessor/integration-prototype

5875dc0489f707232534ce75daf3707f909bcd15

[ "BSD-3-Clause" ]

87

2016-11-24T11:09:01.000Z

2021-03-25T22:23:59.000Z

sip/examples/flask_processing_controller/app/tests/conftest.py

SKA-ScienceDataProcessor/integration-prototype

5875dc0489f707232534ce75daf3707f909bcd15

[ "BSD-3-Clause" ]

10

2016-05-18T09:41:36.000Z

2019-07-04T10:19:24.000Z

# pylint: disable=unused-import # coding=utf-8 """Pytest configuration""" import pytest from .rest_api_fixtures import (get_test_app, get_db_client, init_db, set_root_url)

29.142857

69

0.671569

27

204

4.740741

0.814815

0

0.00641

0.235294

204

6

70

34

0.814103

0.313725

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e5355a644266f3bcfa8bf7cb259666b6c8987205

75

py

Python

PYTHON/Py3_Mundo1_Fundamental/exercicios/exer.30.py

Marciobroficial/CURSO-EM-VIDEO

37b10c26336a9744236603282af77661fdf8c61a

[ "MIT" ]

1

2021-10-09T18:11:20.000Z

PYTHON/Py3_Mundo1_Fundamental/exercicios/exer.35.py

Coppini21/CURSO-EM-VIDEO

37b10c26336a9744236603282af77661fdf8c61a

[ "MIT" ]

1

2021-09-15T04:18:34.000Z

2022-03-02T23:16:26.000Z

PYTHON/Py3_Mundo1_Fundamental/exercicios/exer.35.py

Coppini21/CURSO-EM-VIDEO

37b10c26336a9744236603282af77661fdf8c61a

[ "MIT" ]

3

2021-12-15T17:19:51.000Z

2022-03-29T02:19:00.000Z

# 1 "PREPARANDO O AMBIENTE". #-------------------------------------------#

25

45

0.266667

4

75

5

1

0

0.014493

0.08

75

2

46

37.5

0.275362

0.92

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

e56ce1dc1b0f10e7eab6c733c49847aee69068c2

93

wsgi

Python

flask/recomenda.wsgi

ttm/pnud4

89e1fd866dbdea7afcb3d1020816370e303f258c

[ "Unlicense" ]

1

2015-05-12T17:03:26.000Z

flask/recomenda.wsgi

ttm/pnud4

89e1fd866dbdea7afcb3d1020816370e303f258c

[ "Unlicense" ]

null

flask/recomenda.wsgi

ttm/pnud4

89e1fd866dbdea7afcb3d1020816370e303f258c

[ "Unlicense" ]

null

import sys sys.path.insert(0, '/disco/pnud4/flask') from recomenda import app as application

23.25

40

0.784946

15

93

4.866667

0.866667

0

0.024096

0.107527

93

3

41

31

0.855422

0

0.193548

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e574230c7749ff95fa41ae51fd055ecd1078e267

84

py

Python

env/Lib/site-packages/countdowntimer_model/constants.py

gtkacz/fantasytrashtalk

24ed8ba6c4fae2eca5b15f66b62338a8c87debd2

[ "MIT" ]

4

2021-03-29T07:35:41.000Z

2022-01-12T09:54:55.000Z

env/Lib/site-packages/countdowntimer_model/constants.py

gtkacz/fantasytrashtalk

24ed8ba6c4fae2eca5b15f66b62338a8c87debd2

[ "MIT" ]

4

2020-08-06T14:51:06.000Z

2021-09-22T18:53:50.000Z

env/Lib/site-packages/countdowntimer_model/constants.py

gtkacz/fantasytrashtalk

24ed8ba6c4fae2eca5b15f66b62338a8c87debd2

[ "MIT" ]

3

2020-04-20T18:54:10.000Z

2021-03-29T07:35:13.000Z

import pytz TIMEZONE_CHOICES = tuple(zip(pytz.all_timezones, pytz.all_timezones))

16.8

69

0.809524

12

84

5.416667

0.666667

0.215385

0.492308

0

0.095238

84

4

70

21

0.855263

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

e574c1a519b08425c19ca005f1f7cf9a60998555

27,898

py

Python

python/pyxir/frontend/tvm/relay_tools/relay_l2_convolution.py

anilmartha/pyxir

0972aed63748afd82ef414b67a6cceaedd738b38

[ "Apache-2.0" ]

25

2020-06-17T22:41:13.000Z

2022-03-22T16:28:22.000Z

python/pyxir/frontend/tvm/relay_tools/relay_l2_convolution.py

anilmartha/pyxir

0972aed63748afd82ef414b67a6cceaedd738b38

[ "Apache-2.0" ]

25

2021-03-16T06:26:44.000Z

2022-03-18T11:28:33.000Z

python/pyxir/frontend/tvm/relay_tools/relay_l2_convolution.py

anilmartha/pyxir

0972aed63748afd82ef414b67a6cceaedd738b38

[ "Apache-2.0" ]

19

2020-07-30T10:03:02.000Z

2021-06-29T01:18:16.000Z

# Copyright 2020 Xilinx Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Module for transforming Relay L2 operators to XLayer objects L2: Convolution related operators """ import math import logging import numpy as np import pyxir as px from typing import Dict, List, Callable import tvm from tvm.relay.expr import Expr from pyxir import graph from pyxir.graph.layer import XLayer from pyxir.graph.layer import xlayer_factory as xlf from .util import Schedule from .relay_2_xlayer_registry import register_relay_2_xlayer_converter,\ register_relay_2_xlayer_converter_base logger = logging.getLogger("pyxir") @register_relay_2_xlayer_converter('nn.avg_pool2d') def nn_avg_pool2d(expr: Expr, params: Dict[str, np.ndarray], schedule: Schedule, net: Dict[Expr, Expr], op_idx: Dict[str, int], RELAY_2_XLAYER: Dict[str, Callable], **kwargs) -> XLayer: """ TVM Avg Pool2d to XLayer Relay ----- Type: tvm.relay.op.nn.nn.avg_pool2d Ref: https://docs.tvm.ai/api/python/relay/nn.html Parameters: - data (tvm.relay.Expr) The input data to the operator. - strides (tuple of int, optional) The strides of pooling. - padding (tuple of int, optional) The padding for pooling. - layout (str, optional) Layout of the input. - ceil_mode (bool, optional) To enable or disable ceil while pooling. - count_include_pad (bool, optional) To include padding to compute the average. """ if expr in net: logger.debug("MEMORY: NN AVG POOL2D") # This expressions is already transformed so we reuse that one return net[expr] pool_size = [int(e) for e in list(expr.attrs.pool_size)] strides = [int(e) for e in list(expr.attrs.strides)] padding = [int(e) for e in list(expr.attrs.padding)] data_layout = str(expr.attrs.layout) ceil_mode = bool(expr.attrs.ceil_mode) count_include_pad = bool(expr.attrs.count_include_pad) # if count_include_pad: # logger.debug("Padding: {}".format(padding)) # raise NotImplementedError("Including padding in avg pool2d # " computation" # " is not supported") data_expr, data_expr_class = expr.args[0], expr.args[0].__class__.__name__ data_layer = RELAY_2_XLAYER[data_expr_class](data_expr, params, schedule, net, op_idx, RELAY_2_XLAYER, **kwargs) logger.debug("nn_avg_pool2d: {}".format(hash(expr))) # Update schedule with input data layer if data_expr not in net: schedule.append(data_expr) net[data_expr] = data_layer # Create XLayer pool_type = 'Avg' # Convert NHWC -> NCHW TODO: remove data layout if data_layout == 'NHWC': t_name = 'nn_avg_pool2d_NHWC>NCHW-' + str(hash(expr)) data_layer.tops.append(t_name) data_layer = xlf.get_xop_factory_func('Transpose', internal=True)( t_name, data_layer, [0, 3, 1, 2]) schedule.append(t_name) net[t_name] = data_layer # Create name op_name = 'nn_avg_pool2d-' + str(hash(expr)) X = xlf.get_xop_factory_func('Pooling')( op_name, data_layer, pool_type, pool_size, strides, padding, 'NCHW', ceil_mode, count_include_pad, relay_id=[hash(expr)]) logger.debug("-- outshape: {}".format(list(X.shapes))) # !Important: set input layer tops data_layer.tops.append(X.name) # Convert to NCHW -> NHWC TODO: remove data layout if data_layout == 'NHWC': schedule.append(X.name) net[X.name] = X t_name = 'nn_avg_pool2d_NCHW>NHWC-' + str(hash(expr)) X.tops.append(t_name) res_X = xlf.get_xop_factory_func('Transpose', internal=True)( t_name, X, [0, 2, 3, 1]) else: res_X = X return res_X @register_relay_2_xlayer_converter_base('nn.batch_flatten') def nn_batch_flatten(op_name: str, expr: Expr, in_xlayers: List[XLayer]) -> XLayer: """ TVM NN batch_flatten to XLayer Relay ----- Type: tvm.relay.op.nn.nn.batch_flatten Ref: https://docs.tvm.ai/api/python/relay/nn.html Parameters: - data (tvm.relay.Expr) The input data to the operator. """ X = px.ops.batch_flatten(op_name, in_xlayers, relay_id=[hash(expr)]) return X @register_relay_2_xlayer_converter('nn.conv2d') def nn_conv2d(expr: Expr, params: Dict[str, np.ndarray], schedule: Schedule, net: Dict[Expr, Expr], op_idx: Dict[str, int], RELAY_2_XLAYER: Dict[str, Callable], **kwargs) -> XLayer: """ TVM Convolution to XLayer Relay ----- Type: tvm.relay.op.nn.nn.conv2d Ref: https://docs.tvm.ai/api/python/relay/nn.html Parameters: - data (tvm.relay.Expr) The input data to the operator. - weight (tvm.relay.Expr) The weight expressions. - strides (tuple of int, optional) The strides of convolution. - padding (tuple of int, optional) The padding of convolution on both sides of inputs before convolution. - dilation (tuple of int, optional) Specifies the dilation rate to be used for dilated convolution. - groups (int, optional) Number of groups for grouped convolution. - channels (int, optional) Number of output channels of this convolution. - kernel_size (tuple of int, optional) The spatial of the convolution kernel. - data_layout (str, optional) Layout of the input. - kernel_layout (str, optional) Layout of the weight. - out_layout (str, optional) Layout of the output, by default, out_layout is the same as data_layout - out_dtype (str, optional) Specifies the output data type for mixed precision conv2d. """ if expr in net: logger.debug("MEMORY: CONV2D") # This expressions is already transformed so we reuse that one return net[expr] data_expr, data_expr_class = \ expr.args[0], expr.args[0].__class__.__name__ weights_expr, weights_expr_class = \ expr.args[1], expr.args[1].__class__.__name__ data_layer = RELAY_2_XLAYER[data_expr_class](data_expr, params, schedule, net, op_idx, RELAY_2_XLAYER, **kwargs) weights_layer = RELAY_2_XLAYER[weights_expr_class](weights_expr, params, schedule, net, op_idx, RELAY_2_XLAYER, **kwargs) weights_shape = weights_layer.shapes logger.debug("nn_conv2d: {}".format(hash(expr))) data_layout = str(expr.attrs.data_layout) kernel_layout = str(expr.attrs.kernel_layout) h_index, w_index = kernel_layout.index('H'), kernel_layout.index('W') o_index = kernel_layout.index('O') # HW kernel_size = [int(e) for e in list(expr.attrs.kernel_size)] \ if expr.attrs.kernel_size is not None \ else [weights_shape[h_index], weights_shape[w_index]] strides = [int(e) for e in list(expr.attrs.strides)] padding = [int(e) for e in list(expr.attrs.padding)] dilation = [int(e) for e in list(expr.attrs.dilation)] groups = int(expr.attrs.groups) if expr.attrs.groups is not None else 1 channels = int(expr.attrs.channels) if expr.attrs.channels is not None \ else weights_shape[o_index] # out_layout = str(expr.attrs.out_layout) # out_dtype = str(expr.attrs.out_dtype) assert len(data_layer.shapes) == 4 assert weights_layer.data is not None # Update schedule with child layers # ! We don't add weights layer as this weight is precomputed # TODO What if weights layer can't be precomputed # TODO WHat if weights layer is shared if data_expr not in net: schedule.append(data_expr) net[data_expr] = data_layer # Create XLayer # Convert NHWC -> NCHW TODO: remove data layout if data_layout == 'NHWC': t_name = 'nn_conv2d_NHWC>NCHW-' + str(hash(expr)) data_layer.tops.append(t_name) data_layer = \ xlf.get_xop_factory_func('Transpose', internal=True)( t_name, data_layer, [0, 3, 1, 2]) schedule.append(t_name) net[t_name] = data_layer # Create name op_name = 'nn_conv2d-' + str(hash(expr)) # [pad_h, pad_w] or [pad_h_top, pad_h_bottom, pad_w_left, pad_w_right] xpadding = padding if len(padding) == 2\ else [padding[i] for i in [0, 2, 1, 3]] X = xlf.get_xop_factory_func('Convolution')( op_name, data_layer, weights_layer, kernel_size, strides, xpadding, dilation, groups, channels, 'NCHW', kernel_layout, relay_id=[hash(expr)]) logger.debug("--outshape: {}".format(list(X.shapes))) # !Important: set input layer tops data_layer.tops.append(X.name) # Convert to NCHW -> NHWC TODO: remove data layout if data_layout == 'NHWC': schedule.append(X.name) net[X.name] = X t_name = 'nn_conv2d_NCHW>NHWC-' + str(hash(expr)) X.tops.append(t_name) res_X = xlf.get_xop_factory_func('Transpose', internal=True)( t_name, X, [0, 2, 3, 1]) else: res_X = X return res_X @register_relay_2_xlayer_converter('nn.conv2d_transpose') def nn_conv2d_transpose(expr: Expr, params: Dict[str, np.ndarray], schedule: Schedule, net: Dict[Expr, Expr], op_idx: Dict[str, int], RELAY_2_XLAYER: Dict[str, Callable], **kwargs) -> XLayer: """ Convert Relay nn.conv2d_transpose to Conv2DTranspose XLayer Relay ----- Type: tvm.relay.nn.conv2d_transpose Ref: https://docs.tvm.ai/langref/relay_op.html Parameters: - data (tvm.relay.Expr) The input data to the operator. - weight (tvm.relay.Expr) The weight expressions. - strides (Tuple[int], optional) The strides of convolution. - padding (Tuple[int], optional) The padding of convolution on both sides of inputs. - dilation (Tuple[int], optional) Specifies the dilation rate to be used for dilated convolution. - channels (int, optional) Number of output channels of this convolution. - kernel_size (tuple of int, optional) The spatial of the convolution kernel. - groups (int, optional) Number of groups for grouped convolution. - data_layout (str, optional) Layout of the input. - kernel_layout (str, optional) Layout of the weight. - out_layout (Optional[str]) Layout of the output, by default, out_layout is the same as data_layout - output_padding (Tuple[int], optional) Additional zero-padding to be added to one side of the output. - out_dtype (str, optional) Specifies the output data type for mixed precision conv2d. """ if expr in net: logger.debug("MEMORY: CONV2D_TRANSPOSE") return net[expr] # HW # kernel_size = [int(e) for e in list(expr.attrs.kernel_size)] # strides = [int(e) for e in list(expr.attrs.strides)] # padding = [int(e) for e in list(expr.attrs.padding)] # dilation = [int(e) for e in list(expr.attrs.dilation)] # groups = int(expr.attrs.groups) if expr.attrs.groups is not None else 1 # channels = int(expr.attrs.channels) if expr.attrs.channels is not None \ # else None # data_layout = str(expr.attrs.data_layout) # kernel_layout = str(expr.attrs.kernel_layout) data_expr, data_expr_class = \ expr.args[0], expr.args[0].__class__.__name__ weights_expr, weights_expr_class = \ expr.args[1], expr.args[1].__class__.__name__ data_layer = RELAY_2_XLAYER[data_expr_class](data_expr, params, schedule, net, op_idx, RELAY_2_XLAYER, **kwargs) weights_layer = RELAY_2_XLAYER[weights_expr_class](weights_expr, params, schedule, net, op_idx, RELAY_2_XLAYER, **kwargs) weights_shape = weights_layer.shapes logger.debug("nn_conv2d_transpose") data_layout = str(expr.attrs.data_layout) kernel_layout = str(expr.attrs.kernel_layout) # NOTE TVM uses different kernel layout description than we do and we have to switch O and I kernel_layout = ''.join(({'O': 'I', 'I': 'O', 'H': 'H', 'W': 'W'}[c] for c in kernel_layout)) h_index, w_index = kernel_layout.index('H'), kernel_layout.index('W') o_index = kernel_layout.index('O') # HW kernel_size = [int(e) for e in list(expr.attrs.kernel_size)] \ if expr.attrs.kernel_size is not None \ else [weights_shape[h_index], weights_shape[w_index]] strides = [int(e) for e in list(expr.attrs.strides)] padding = [int(e) for e in list(expr.attrs.padding)] dilation = [int(e) for e in list(expr.attrs.dilation)] groups = int(expr.attrs.groups) if expr.attrs.groups is not None else 1 channels = int(expr.attrs.channels) if expr.attrs.channels is not None \ else weights_shape[o_index] # out_layout = str(expr.attrs.out_layout) # out_dtype = str(expr.attrs.out_dtype) logger.debug("-- kernel_size {}".format(kernel_size)) logger.debug("-- strides {}, {}".format(strides, type(strides[0]))) logger.debug("-- padding {}".format(padding)) logger.debug("-- dilation {}".format(dilation)) logger.debug("-- groups {}, {}".format(groups, type(groups))) logger.debug("-- channels {}".format(channels)) logger.debug("-- data_layout {}".format(data_layout)) logger.debug("-- kernel_layout {}".format(kernel_layout)) assert len(data_layer.shapes) == 4 assert weights_layer.data is not None # Update schedule with child layers # ! We don't add weights layer as this weight is precomputed # TODO What if weights layer can't be precomputed # TODO WHat if weights layer is shared if data_expr not in net: schedule.append(data_expr) net[data_expr] = data_layer # Create XLayer # Initialize relay idx with relay idx of weights relay_idx = weights_layer.attrs['relay_id'][:] # Relay converts a NHWC conv2d_transpose layer into a # transpose -> conv2d_transpose (NCHW) -> transpose. For partitioning we # keep track of those relay ids inside the conv2d_transpose operation if 'Transpose' in data_layer.type: relay_idx.append(data_layer.attrs['relay_id'][0]) # TODO: NHWC op_name = 'nn_conv2d_transpose-' + str(hash(expr)) relay_idx.append(hash(expr)) # [pad_h, pad_w] or [pad_h_top, pad_h_bottom, pad_w_left, pad_w_right] xpadding = padding if len(padding) == 2\ else [padding[i] for i in [0, 2, 1, 3]] X = xlf.get_xop_factory_func('Conv2DTranspose')( op_name, data_layer, weights_layer, kernel_size, strides, xpadding, dilation, groups, channels, data_layout, kernel_layout, relay_id=relay_idx ) logger.debug("--outshape: {}".format(list(X.shapes))) # !Important: set input layer tops: data_layer.tops.append(op_name) return X @register_relay_2_xlayer_converter('nn.global_avg_pool2d') def nn_global_avg_pool2d(expr: Expr, params: Dict[str, np.ndarray], schedule: Schedule, net: Dict[Expr, Expr], op_idx: Dict[str, int], RELAY_2_XLAYER: Dict[str, Callable], **kwargs) -> XLayer: """ TVM global Avg pooling to XLayer Relay ----- Type: tvm.relay.op.nn.nn.global_avg_pool2d Ref: https://docs.tvm.ai/api/python/relay/nn.html Parameters: - data (tvm.relay.Expr) The input data to the operator. - layout (str, optional) Layout of the input. """ if expr in net: logger.debug("MEMORY: GLOBAL AVG POOL2D") # This expressions is already transformed so we reuse that one return net[expr] data_layout = str(expr.attrs.layout) data_expr, data_expr_class = expr.args[0], expr.args[0].__class__.__name__ data_layer = RELAY_2_XLAYER[data_expr_class](data_expr, params, schedule, net, op_idx, RELAY_2_XLAYER, **kwargs) logger.debug("nn_global_avg_pool2d") # Update schedule with input data layer if data_expr not in net: schedule.append(data_expr) net[data_expr] = data_layer # Create XLayers # Convert NHWC -> NCHW TODO: remove data layout if data_layout == 'NHWC': t_name = 'nn_global_avg_pool2d_NHWC>NCHW-' + str(hash(expr)) data_layer.tops.append(t_name) data_layer = \ xlf.get_xop_factory_func('Transpose', internal=True)( t_name, data_layer, [0, 3, 1, 2]) schedule.append(t_name) net[t_name] = data_layer # Create name op_name = 'nn_global_avg_pool2d-' + str(hash(expr)) pool_type = 'Avg' X = xlf.get_xop_factory_func('GlobalPooling')( op_name, data_layer, pool_type, 'NCHW', relay_id=[hash(expr)]) logger.debug("-- outshape: {}".format(list(X.shapes))) # !Important: set input layer tops data_layer.tops.append(X.name) # Convert to NCHW -> NHWC TODO: remove data layout if data_layout == 'NHWC': schedule.append(X.name) net[X.name] = X t_name = 'nn_global_avg_pool2d_NCHW>NHWC-' + str(hash(expr)) X.tops.append(t_name) res_X = xlf.get_xop_factory_func('Transpose', internal=True)( t_name, X, [0, 2, 3, 1]) else: res_X = X return res_X @register_relay_2_xlayer_converter('nn.global_max_pool2d') def nn_global_max_pool2d(expr: Expr, params: Dict[str, np.ndarray], schedule: Schedule, net: Dict[Expr, Expr], op_idx: Dict[str, int], RELAY_2_XLAYER: Dict[str, Callable], **kwargs) -> XLayer: """ TVM global max pool to XLayer TODO Overlap with globale_avg_pool2d Relay ----- Type: tvm.relay.op.nn.nn.global_max_pool2d Ref: https://docs.tvm.ai/api/python/relay/nn.html Parameters: - data (tvm.relay.Expr) The input data to the operator. - layout (str, optional) Layout of the input. """ if expr in net: logger.debug("MEMORY: GLOBAL MAX POOL2D") # This expressions is already transformed so we reuse that one return net[expr] data_layout = str(expr.attrs.layout) data_expr, data_expr_class = expr.args[0], expr.args[0].__class__.__name__ data_layer = RELAY_2_XLAYER[data_expr_class](data_expr, params, schedule, net, op_idx, RELAY_2_XLAYER, **kwargs) logger.debug("nn_global_max_pool2d") # Update schedule with input data layer if data_expr not in net: schedule.append(data_expr) net[data_expr] = data_layer # Create XLayers # Convert NHWC -> NCHW TODO: remove data layout if data_layout == 'NHWC': t_name = 'nn_global_max_pool2d_NHWC>NCHW-' + str(hash(expr)) data_layer.tops.append(t_name) data_layer = xlf.get_xop_factory_func('Transpose', internal=True)( t_name, data_layer, [0, 3, 1, 2]) schedule.append(t_name) net[t_name] = data_layer # Create name op_name = 'nn_global_max_pool2d-' + str(hash(expr)) pool_type = 'Max' X = xlf.get_xop_factory_func('GlobalPooling')( op_name, data_layer, pool_type, 'NCHW', relay_id=[hash(expr)]) logger.debug("-- outshape: {}".format(list(X.shapes))) # !Important: set input layer tops: data_layer.tops.append(op_name) # Convert to NCHW -> NHWC TODO: remove data layout if data_layout == 'NHWC': schedule.append(X.name) net[X.name] = X t_name = 'nn_global_max_pool2d_NCHW>NHWC-' + str(hash(expr)) X.tops.append(t_name) res_X = xlf.get_xop_factory_func('Transpose', internal=True)( t_name, X, [0, 2, 3, 1]) else: res_X = X return res_X @register_relay_2_xlayer_converter('nn.max_pool2d') def nn_max_pool2d(expr: Expr, params: Dict[str, np.ndarray], schedule: Schedule, net: Dict[Expr, Expr], op_idx: Dict[str, int], RELAY_2_XLAYER: Dict[str, Callable], **kwargs) -> XLayer: """ TVM max pool to XLayer Relay ----- Type: tvm.relay.op.nn.nn.max_pool2d Ref: https://docs.tvm.ai/api/python/relay/nn.html Parameters: - data (tvm.relay.Expr) The input data to the operator. - strides (tuple of int, optional) The strides of pooling. - padding (tuple of int, optional) The padding for pooling. - layout (str, optional) Layout of the input. - ceil_mode (bool, optional) To enable or disable ceil while pooling. """ if expr in net: logger.debug("MEMORY: MAX POOL2D") return net[expr] pool_size = [int(e) for e in list(expr.attrs.pool_size)] strides = [int(e) for e in list(expr.attrs.strides)] padding = [int(e) for e in list(expr.attrs.padding)] data_layout = str(expr.attrs.layout) ceil_mode = bool(expr.attrs.ceil_mode) data_expr, data_expr_class = expr.args[0], expr.args[0].__class__.__name__ data_layer = RELAY_2_XLAYER[data_expr_class](data_expr, params, schedule, net, op_idx, RELAY_2_XLAYER, **kwargs) logger.debug("nn_max_pool2d") # Update schedule with input data layer if data_expr not in net: schedule.append(data_expr) net[data_expr] = data_layer # Create XLayers pool_type = 'Max' # Convert NHWC -> NCHW TODO: remove data layout if data_layout == 'NHWC': t_name = 'nn_max_pool2d_NHWC>NCHW-' + str(hash(expr)) data_layer.tops.append(t_name) data_layer = xlf.get_xop_factory_func('Transpose', internal=True)( t_name, data_layer, [0, 3, 1, 2]) schedule.append(t_name) net[t_name] = data_layer # Create name op_name = 'nn_max_pool2d-' + str(hash(expr)) logger.debug("-- name: {}".format(op_name)) X = xlf.get_xop_factory_func('Pooling')( op_name, data_layer, pool_type, pool_size, strides, padding, 'NCHW', ceil_mode, False, relay_id=[hash(expr)]) logger.debug("-- outshape: {}".format(list(X.shapes))) # !Important: set input layer tops data_layer.tops.append(X.name) # Convert to NCHW -> NHWC TODO: remove data layout if data_layout == 'NHWC': schedule.append(X.name) net[X.name] = X t_name = 'nn_max_pool2d_NCHW>NHWC-' + str(hash(expr)) X.tops.append(t_name) res_X = xlf.get_xop_factory_func('Transpose', internal=True)( t_name, X, [0, 2, 3, 1]) else: res_X = X return res_X @register_relay_2_xlayer_converter_base('nn.pad') def pad(op_name: str, expr: Expr, in_xlayers: List[XLayer]) -> XLayer: """ TVM padding layer to XLayer Relay ----- Type: tvm.relay.op.nn.pad Ref: https://docs.tvm.ai/api/python/relay/nn.html Parameters: - data (tvm.relay.Expr) The input data to the operator - pad_width (tuple of <tuple of <int>>, required) Number of values padded to the edges of each axis, in the format of ((before_1, after_1), …, (before_N, after_N)) - pad_value (float, optional, default=0.0) The value used for padding """ pad_width = [[int(e) for e in t] for t in expr.attrs.pad_width] if hasattr(expr.attrs, "pad_value"): pad_value = float(expr.attrs.pad_value) else: # For tvm>=v0.8.dev0 assert len(in_xlayers) > 1, "If pad_value is not a Relay operation attribute, it is expected"\ " as an input expression" assert in_xlayers[1].type[0] == "Constant", "Only static padding is supported." pad_value = float(in_xlayers[1].data[0]) logger.debug("nn_pad: {}".format(hash(expr))) logger.debug("-- pad width: {}".format(pad_width)) logger.debug("-- pad value: {}".format(pad_value)) X = px.ops.pad(op_name, in_xlayers[0], pad_width, pad_value, relay_id=[hash(expr)]) return X @register_relay_2_xlayer_converter_base('nn.upsampling') def nn_upsampling(op_name: str, expr: Expr, in_xlayers: List[XLayer]) -> XLayer: """ TVM 2D Upsampling to XLayer Relay ----- Type: tvm.relay.split Desc: Upsampling. This operator takes data as input and does 2D scaling to the given scale factor. In the default case, where the data_layout is NCHW with data of shape (n, c, h, w) out will have a shape (n, c, h*scale_h, w*scale_w) method indicates the algorithm to be used while calculating the out value and method can be one of (bilinear, nearest_neighbor, bicubic) Ref: https://docs.tvm.ai/langref/relay_op.html Parameters: - data (tvm.relay.Expr) The input data to the operator. - scale_h (tvm.relay.Expr) The scale factor for height upsampling. - scale_w (tvm.relay.Expr) The scale factor for width upsampling. - layout (str, optional) Layout of the input. - method (str, optional) Scale method to used [nearest_neighbor, bilinear, bicubic]. - align_corners (bool, optional) Whether to keep corners in proper place. """ scale_h = float(expr.attrs.scale_h) scale_w = float(expr.attrs.scale_w) layout = str(expr.attrs.layout) method = str(expr.attrs.method) align_corners = bool(expr.attrs.align_corners) X = px.ops.upsampling2d( op_name, in_xlayers, scale_h=scale_h, scale_w=scale_w, data_layout=layout, method=method, align_corners=align_corners, relay_id=[hash(expr)] ) logger.debug("-- outshape: {}".format(list(X.shapes))) return X

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py

Python

misc/server.py

fabriciocgf/IOT_LoRa_Dashboard

ba6eb168c47f57f682a84a50ad366319d12e5126

[ "MIT" ]

22

2017-05-02T21:23:27.000Z

2022-01-31T20:11:32.000Z

misc/server.py

fabriciocgf/IOT_LoRa_Dashboard

ba6eb168c47f57f682a84a50ad366319d12e5126

[ "MIT" ]

6

2017-05-04T08:17:13.000Z

2017-06-28T09:39:22.000Z

misc/server.py

fabriciocgf/IOT_LoRa_Dashboard

ba6eb168c47f57f682a84a50ad366319d12e5126

[ "MIT" ]

8

2017-05-04T09:03:40.000Z

2021-09-07T14:43:38.000Z

#!/usr/bin/python import os, sys, requests, json from datetime import datetime import time import random myCMD = sys.argv[2]; def callWebservicePost(entity, jsonString): #set up the url and headers# fast server urlF = "http://35.163.116.152:8080/SensorThingsServer-1.0/v1.0/" + entity# slow server urlS = "http://35.162.114.82:8080/SensorThingsServer-1.0/v1.0/" + entity# slow server urlVM = "http://192.168.56.101:8080/SensorThingsServer-1.0/v1.0/" + entity userURL = "http://" + sys.argv[1] + "/SensorThingsServer-1.0/v1.0/" + entity headers = { "Content-Type": "application/json", "Accept": "application/json" } #Use requests module to send a POST request request = requests.post(userURL, data = json.dumps(jsonString), headers = headers) # print the status code and the response as a json or text. print(request.status_code) # status - code should be 201, to let us know the entity has been created print("Content-Length: " + request.headers["Content-Length"]) print(request.headers) if request.status_code != 201: #print error print(request.text) print("Error") else :#do something with response print("Success: " + str(random.randrange(0, 100, 2))) # Observation if myCMD == "o" and __name__ == '__main__': dsID = int(sys.argv[3]) while (True): #Create a json string dtNow = datetime.now() currentTime = dtNow.isoformat() value = random.randrange(0, 101, 2) print("Pushing to server: " + str(value)) jsonString = {"phenomenonTime": currentTime,"resultTime": currentTime,"result": value,"Datastream": {"@iot.id": dsID}} #Call method in order to perform a POST request to the webservice callWebservicePost("Observations", jsonString) time.sleep(5) # Thing if myCMD == "t" and __name__ == '__main__': myName = sys.argv[3] # Create a json string coord = [-117.123, 54.123] properties = {"property1": "it's waterproof","property2": "it glows in the dark","property3": "it repels insects"} location = {"type": "Point","coordinates": coord} locations = [{"name": myName + " location","description": myName + "location description","encodingType": "application/vnd.geo+json","location": location}] sensor = {"name": myName + " Sensor","description": myName + " Sensor Description","encodingType": "http://schema.org/description","metadata": "Calibration date: Jan 11, 2015"} uoM = {"name": "Celsius","symbol": "C","definition": "http://www.qudt.org/qudt/owl/1.0.0/unit/Instances.html#Celsius"} observedP = {"name": "Temperature","definition": "http://www.qudt.org/qudt/owl/1.0.0/quantity/Instances.html#Temperature","description": "Temperature of the camping site"} obsType = "http://www.opengis.net/def/observationType/OGC-OM/2.0/OM_Measurement" datastreams = [{"Sensor": sensor,"unitOfMeasurement": uoM,"name": myName + " Datastream","description": myName + " Datastream Description","observationType": obsType,"ObservedProperty": observedP}] jsonString = {"name": myName + "Thing","description": myName + "description","properties": properties,"Locations": locations,"Datastreams": datastreams} #Call method in order to perform a POST request to the webservice callWebservicePost("Things", jsonString) # Thing Wit Location if myCMD == "tl" and __name__ == '__main__': myName = sys.argv[3] # Create a json string coord = [float(sys.argv[4]), float(sys.argv[5])] properties = {"property1": "it's waterproof","property2": "it glows in the dark","property3": "it repels insects"} location = {"type": "Point","coordinates": coord} locations = [{"name": myName + " location","description": myName + "location description","encodingType": "application/vnd.geo+json","location": location}] sensor = {"name": myName + " Sensor","description": myName + " Sensor Description","encodingType": "http://schema.org/description","metadata": "Calibration date: Jan 11, 2015"} uoM = {"name": "Celsius","symbol": "C","definition": "http://www.qudt.org/qudt/owl/1.0.0/unit/Instances.html#Celsius"} observedP = {"name": "Temperature","definition": "http://www.qudt.org/qudt/owl/1.0.0/quantity/Instances.html#Temperature","description": "Temperature of the camping site"} obsType = "http://www.opengis.net/def/observationType/OGC-OM/2.0/OM_Measurement" datastreams = [{"Sensor": sensor,"unitOfMeasurement": uoM,"name": myName + " Datastream","description": myName + " Datastream Description","observationType": obsType,"ObservedProperty": observedP}] jsonString = {"name": myName + "Thing","description": myName + "description","properties": properties,"Locations": locations,"Datastreams": datastreams} #Call method in order to perform a POST request to the webservice callWebservicePost("Things", jsonString) # Location if myCMD == "l" and __name__ == '__main__': myName = sys.argv[3] thingID = sys.argv[4] while (True): #Create a json string coord = [8.4 + random.randrange(0, 100, 2) / 1000, 49 + random.randrange(0, 100, 2) / 1000] jsonString = {"name": myName + "Location","description": myName + "description","encodingType": "application/vnd.geo+json","location": {"type": "Point","coordinates": coord}} #Call method in order to perform a POST request to the webservice callWebservicePost("Things(" + thingID + ")/Locations", jsonString) time.sleep(5) # Location if myCMD == "lv" and __name__ == '__main__': myName = sys.argv[3] thingID = sys.argv[4] coord = [float(sys.argv[5]), float(sys.argv[6])] jsonString = {"name": myName + "Location","description": myName + "description","encodingType": "application/vnd.geo+json","location": {"type": "Point","coordinates": coord}} #Call method in order to perform a POST request to the webservice callWebservicePost("Things(" + thingID + ")/Locations", jsonString) # all if myCMD == "a" and __name__ == '__main__': myName = sys.argv[3] thingID = sys.argv[4] while (True): #Create a json string coord = [8.4 + random.randrange(0, 100, 2) / 1000, 49 + random.randrange(0, 100, 2) / 1000] jsonString = {"name": myName + "Location","description": myName + "description","encodingType": "application/vnd.geo+json","location": {"type": "Point","coordinates": coord}} #Call method in order to perform a POST request to the webservice callWebservicePost("Things(" + thingID + ")/Locations", jsonString) dsID = int(sys.argv[5])# Create a json string dtNow = datetime.now() currentTime = dtNow.isoformat() jsonString = {"phenomenonTime": currentTime,"resultTime": currentTime,"result": random.randrange(0, 101, 2),"Datastream": {"@iot.id": dsID}} #Call method in order to perform a POST request to the webservice callWebservicePost("Observations", jsonString) time.sleep(5)

53.555556

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0.139449

6,748

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53.984

0.764078

0.135892

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null

0

1

0

null

0

5

e5ac0c186805f80c0d7b1b074089d80ca81860db

113,736

py

Python

src/NER/corpus/taxonomy_2021_for_NLP.py

ohikendoit/cbi_mrc_analytics

e2f4b8bfa60d032275543fe0e5c61f0c96444b8b

[ "MIT" ]

null

src/NER/corpus/taxonomy_2021_for_NLP.py

ohikendoit/cbi_mrc_analytics

e2f4b8bfa60d032275543fe0e5c61f0c96444b8b

[ "MIT" ]

null

src/NER/corpus/taxonomy_2021_for_NLP.py

ohikendoit/cbi_mrc_analytics

e2f4b8bfa60d032275543fe0e5c61f0c96444b8b

[ "MIT" ]

null

emergency = ['conflict', 'violence', 'displacement', 'drought', 'earthquake', 'fire', 'flooding', 'freeze', 'health emergency', 'dengue', 'pneumonic plague', 'measles', 'landslide', 'tropical storm', 'typhoon', 'cyclone', 'hurricane', 'tsunami', 'urban disaster', 'volcanic eruption', 'volcano', 'refugee', 'terrorist attack', 'cold wave', 'complex emergency', 'epidemic', 'extratropical cyclone', 'flash flood', 'flood', 'heat wave', 'insect infestation', 'land slide', 'mud slide', 'severe local storm', 'snow avalanche', 'storm surge', 'technological disaster', 'tropical cyclone', 'volcano', 'wild fire', 'flood crisis', 'victims', 'flood victims', 'flood powerful', 'powerful storms', 'hoisted flood', 'explosion', 'flood cost', 'affected tornado', 'tornado relief', 'photos flood', 'water rises', 'flood waters', 'flood appeal', 'victims explosion', 'bombing suspect', 'massive explosion', 'affected areas','flood relief', 'flood affected', 'tornado victims', 'explosions running', 'evacuated', 'relief', 'flood death', 'deaths confirmed', 'affected flooding', 'people killed', 'dozens', 'footage', 'survivor finds', 'flood worsens', 'flood damage', 'major flood', 'rubble', 'another explosion', 'confirmed dead', 'rescue','flood warnings', 'tornado survivor', 'damage', 'devastating', 'flood toll', 'affected hurricane', 'prayers families', 'crisis', 'text donation', 'redcross give', 'recede', 'bombing', 'massive', 'bombing victims', 'explosion ripped', 'gets donated', 'donated victims', 'relief efforts', 'news flood', 'flood emergency', 'fire flood', 'huge explosion', 'bushfire', 'torrential rains', 'affected explosion', 'disaster', 'tragedy','twister', 'blast', 'fatalities', 'dead explosion', 'survivor', 'death', 'explosion reported', 'evacuees', 'large explosion', 'firefighters', 'morning flood', 'praying', 'public safety', 'destroyed', 'displaced', 'fertilizer explosion', 'donate tornado', 'retweet donate', 'flood tornado', 'casualties', 'climate change', 'financial donations', 'stay strong', 'dead hundreds', 'major explosion', 'bodies recovered', 'waters recede', 'response disasters', 'victims donate','fire fighters', 'explosion victims', 'prayers city', 'torrential', 'bomber', 'explosion registered', 'missing flood', 'brought hurricane', 'relief fund', 'help tornado', 'explosion fire', 'tragic', 'enforcement official', 'dealing hurricane', 'flood recovery', 'dead torrential', 'flood years', 'massive tornado', 'crisis rises', 'flood peak', 'flood ravaged','missing explosion', 'floods kill', 'tornado damage', 'cross tornado', 'facing flood', 'deadly explosion', 'dead missing', 'floods force', 'flood disaster', 'tornado disaster', 'medical examiner', 'fire explosion', 'storm', 'flood hits', 'floodwaters', 'emergency', 'flood alerts', 'crisis unfolds', 'daring rescue', 'tragic events', 'medical office', 'deadly tornado', 'people trapped', 'lives hurricane', 'bombings reports', 'breaking suspect', 'bombing investigation', 'praying affected', 'surging floods', 'explosion injured', 'injured explosion', 'responders killed', 'explosion caught', 'city tornado', 'damaged hurricane', 'suspect bombing', 'massive manhunt', 'releases images', 'shot killed', 'rains severely', 'house flood', 'live coverage', 'devastating tornado', 'lost lives', 'reportedly dead', 'following explosion', 'remember lives', 'tornado flood', 'want help', 'seconds bombing', 'reported dead', 'safe hurricane','dead floods', 'flood threat', 'flood situation', 'thousands homes', 'risk running', 'dying hurricane', 'bombing shot','police people', 'terrible explosion', 'prayers involved', 'reported injured', 'seismic', 'victims waters', 'flood homeowners', 'flood claims', 'homeowners reconnect', 'reconnect power', 'power supplies', 'rescuers help', 'free hotline', 'hotline help', 'saddened loss', 'identified suspect', 'bombings saddened','reported explosion', 'prepare hurricane', 'landfall', 'bombing case','communities damaged', 'destruction', 'levy', 'tornado', 'hurricane coming', 'toxins flood', 'release toxins', 'toxins', 'supplies waters', 'crisis found', 'braces major', 'government negligent', 'terror', 'memorial service', 'terror attack', 'coast hurricane', 'terrified hurricane', 'hurricane category', 'devastating fire', 'disaster area', 'disaster preparedness', 'disaster recovery', 'disaster relief', 'disaster response', 'disaster site', 'disaster situation', 'emergency response', 'flood control', 'flood damage', 'flood relief', 'flooded', 'flooding', 'heavy rainfall'] emergency_event = ['haiti earthquake', 'earthquake in haiti', 'tropical storm grace', 'coronavirus', 'corona virus', 'coronavirus disease', 'covid-19', 'typhoon goni', 'typhoon rolly', 'typhoon vamco', 'typhoon ulysses', 'beirut port explosions', 'beirut explosion', 'beirut blast', 'cyclone harold', 'hurricane dorian', 'cyclone kenneth', 'cyclone idai', 'indonesia tsunami', 'indian ocean earthquake', 'indian ocean tsunami', 'typhoon manghut'] humanitarian_theme = ['accountability to affected people', 'business continuity', 'civil military coordination', 'climate change', 'community engagement', 'conflict and fragility', 'disaster risk reduction', 'disaster risk', 'early warning', 'early warning system', 'gender', 'humanitarian development index', 'humanitarian development nexus', 'humanitarian development', 'human rights', 'impact measurement', 'innovation and new technologies', 'innovation and technologies', 'forced displacement', 'peace', 'preparedness', 'prevention', 'public private partnership', 'recovery', 'response', 'MSME', 'small and medium sized enterprise', 'small and medium sized enterprises', 'sustainable development', 'sustainable development goals', 'affected families', 'affected regions', 'aid agencies', 'aids', 'collapsed'] humanitarian_action_clusters = ['food security', 'health', 'logistic', 'logistics', 'nutrition', 'protection', 'shelter', 'water sanitation', 'water hygiene', 'hygiene', 'camp coordination', 'early recovery', 'education', 'emergency telecommunication', 'emergency telecommunications'] #disaster_management_theme = ['caution and advice', 'injured People', 'dead people', 'infrastructure damage', 'supplies needed or offered', 'services needed or offered', 'or trapped people', 'displaced and evacuated people', 'animal management', 'personal updates', 'sympathy', 'children and education', 'food and nutrition', 'logistic and transportation', 'camp and shelter', 'water', 'sanitation', 'and hygiene', 'safety and security', 'telecommunications', 'weather', 'response agencies in place', "witnesse's accounts", 'impact of the Crisis', 'million children effected', 'schools next to fertilizer plant', 'savethechildren', 'adoption', 'affected families', 'authorities advising children', 'babies', 'babies born months', 'babies rescued','baby born in tent', 'baby needs milk', 'can help children', 'cancelled due', 'cancelled due to strong winds', 'children', 'classes are canceled because of', 'classes are suspended', 'collapse families search trucks hauling debris', 'displaced families', 'families hit by floods', 'families hit by typhoon', 'family center', 'family killed', 'family needs rescue', 'family receive emergency shelter kit', 'hundreds of children', 'hundreds of families', 'hungry children', 'injured are children', 'kids', 'kids displaced', 'kids lessons', 'kids movies', 'national high school', 'no class tomorrow', 'no classes', 'no classes and office', 'no classes for both private and public schools', 'no school', 'nursery', 'private and public schools', 'public schools', 'relief assistance to families', 'relief assistance to families affected','schools are closed', 'schools are now accepting donations', 'schools extend class suspension until', 'schools football team', 'search for family & friends', 'sem break', 'students now', 'students return', 'teachers', 'the children', 'the families', 'the kids', 'the sandy hook kids', 'thousands of families', "victim's families", ' bags of rice', 'relief food packs', 'relief needs food and water', 'rescue trapped no food water', 'army providing mres', 'care and share food bank', 'distributes food', 'donate any supplies', 'donate/distribute food', 'donating binalot meals', 'donation pickup', 'donations especially rice', 'donations like canned goods', 'drop off donations', 'emergency food packages', 'family food packs', 'food', 'food packets and health services to affected people', 'food packs', 'food supplies', 'hospital food shortage', 'hospital is running out of food', 'hospital needs food', 'need canned', 'needs food', 'no food', 'non-food', "not confident it will last but maybe i won't lose all the food", 'packaged food', 'provided food', 'starving to death', 'victims identified', 'well-fed', 'working to buy million meal', 'allergies', 'another storm', 'army doctors treating patients', 'at least hurt', 'at least people injured', 'at least people injured by glass shards', 'at least people were injured', 'breathing problems', 'carcinogens', 'critical gunshot victim', 'dengue', 'dengue cases', 'doctors and patients', 'donate blood', 'donate blood following explosion', 'donate medicines', 'dozens are injured', 'dozens badly hurt', 'dozens hurt', 'dozens injured', 'dozens of injuries', 'emergency services working tonight', 'epidemics of communicable infectious diseases', 'female patients', 'haze', 'haze prevention', 'haze situation', 'health advisories', 'health advisory', 'help children recover from trauma', 'highway hypnosis', 'hospital', 'hospital address', 'hospital appeals', 'hospital ceo on west explosion', 'hospital director', 'hospital needs help', 'hospital patients', 'hospital staff', 'hundreds injured', 'hundreds likely injured after massive explosion at fertiliser plant', 'hundreds more injured', 'leaves injured', 'medical', 'medical assistance', 'medical camps', 'medical centers', 'medical facility', 'medical supplies', 'medical supplies/prescriptions', 'multiple injuries', 'multiple injuries reported', 'multiple injuries reported at', 'need to provide first aid while waiting on emergency services', 'no surgery', 'no surgery staff present', 'nurse among victims', 'officials fear increased risk of west nile virus', 'patients', 'people hospitalized', 'people hurt', 'people injured', 'people injured by falling', 'people injured by glass shards from shattered', 'people really suck', 'people who were injured', 'red crescent society', 'red cross', 'red cross emergency numbers', 'red cross flood relief', 'respiratory problems', 'search healthcare', 'snake bite vaccine', 'still aching this morning', 'suffering', 'suffering from', 'surgical capacity', 'sustaining burns', 'the headache', 'the hospital', 'the patients', 'those still recovering', 'those suffering', 'tons of people giving blood', 'took painkiller', 'trauma', 'victims treated', 'victims treated at', 'victims were treated', 'wildfire smoke chokes', 'without serious injury', ' boats washed', ' chopper', ' eastbound lanes', ' miles of roads damaged -no flush order', ' suspended', ' times over speed limit', ' trucks', 'a brief delay', 'a brief suspension', 'a huge gash', 'a huge gash through the road', 'a runaway train carrying light crude oil', 'a train', 'a transportation official', 'a vehicle', 'access opening up along highway ', 'aid chopper targeted', 'aid chopper targeted by militant rockets', 'aircraft', 'aircraft carrier heads to', 'aircraft flying over area', 'airport devastated by fire', 'airport terminal still closed', 'airstrip', 'airways', 'amtrak empire service', 'are cut off', 'army launched steel foot-bridge', 'army trucks', 'arrival section', 'arrival/departure roads closed at', 'at least flights canceled', 'at least terminal', 'boats', 'bridge', 'bridges destroyed', 'brief halt', 'building a bridge', 'bus services', 'buses', 'cargo', 'cars abandoned to flood', 'chopper', 'church and roads impacted', 'closed after the flood', 'coach crash', 'convoy', 'copters', 'costa fleet', 'cracks roads', 'cruise', 'cruise line', 'cruise ship', 'damaged', 'delayed', 'delayed after shooting at los angeles airport', 'delayed flights at lax', 'delayed for', 'departures area', 'destruction at the national airport', 'devastating train explosion', 'disaster may get worse as ship appears', 'displaced baggage', 'disruptions', 'dozens of cars', 'dozens of fire trucks', 'emergency unit vehicles', 'family needs boat', 'ferries', 'fire has shut', 'fire ravages international arrivals', 'firefighting aircraft crash', 'flash floods cause traffic', 'flash floods cause traffic chaos', 'flights to', 'flood damage to area roads', 'flood strands airline passengers', 'flooded at queen station', 'flooded street', 'flooded streets make driving a nightmare', 'flooded track', 'forces closure', 'grounds flights', 'has closed', 'heavy damage', "helicopter's rotor blades", 'help build an emergency airport to replace', 'huge fire closes', 'huge fire shuts', 'hundreds of tourists stranded', 'impassable roads', 'inspected', 'inundated', 'landslides destroyed roads to towns', 'large blocks of cement ripped off roads', 'large fire shuts', 'los angeles airport', 'major delays', 'major fire closes', 'major fire shuts', 'major victims of car accidents', 'many rows of semi-trailers stranded waiting', 'massive fire closes', "massive fire closes kenya's international", 'massive fire engulfs major', 'massive fire shuts', 'metro-north crash', 'metro-north passenger train', 'military convoy', 'military truck', 'motorists', 'multiple tsa agents', 'navy ship', 'nearest passable road', 'no firetrucks', 'no flights', 'no trains tomorrow morning', 'no visibility', 'not functioning properly', 'not passable', 'not safe for light vehicles', 'over fire brigade vehicles', 'over flights canceled', 'over roads damaged', 'passengers evacuated', 'planes', 'police', 'police car', 'police chopper', 'police cruiser abandoned off memorial drive', 'police helicopter', 'police helicopter crash', 'police helicopter crash landed', 'police helicopter going', 'police helicopter lifted', 'police helicopter winched', 'police van yesterday', 'racing my bike', 'rail line carrying fracked oil', 'rail safety', 'rail safety study', 'rail shipment', 'rail tanker', "railway's", 'relief goods stuck', 'relief helicopter', 'relief supplies flown to', 'remote area', 'rescue boats', 'resume normal operations', 'resume normal service', 'river walkway', 'road', 'road closure', 'roads', 'roads (destroyed)', 'roads are shredded', 'roads closed', 'roads damaged', 'roads now', 'roads torn up', 'roads turn into rivers', 'roads washed away', 'rope bridge', 'runaway train', 'search trucks hauling debris', 'sends chopper', 'service resumes', 'severe congestion', 'spanish train', 'stranded', 'stranded car', 'stranded communities', 'stranded passengers', 'stranded pilgrims', 'stranded traffic', 'stranded train', 'strands ', 'strands commuters', 'struggling to reach', 'struggling to reach remote areas worst hit', 'submerged towns', 'suspends vessels', 'taxis', 'terminals evacuated', 'the train', 'trains', 'trains today', 'updates travel advisory', 'vessels', 'vessels out-bound', 'wagons', ' flee homes', ' flee wildfire', ' homeless', ' homeless people', ' homes', ' homes are damaged', ' homes confirmed lost', ' homes destroyed', ' homes feared destroyed', ' homes have now been inundated', ' homes left uninhabitable', ' relief camps', ' tents', ' to flee', ' to flee homes', 'shelter has been established', 'army offering their camps', 'army rescue base camp', "army's special medical camp", 'at least homes destroyed', 'being moved to community centre', 'building housing', 'camps', 'can you help with long-term housing', 'collapsing homes', 'destroying homes', 'destroys homes', 'destroys more homes', 'destroys homes', 'estimated homes', 'evacuate homes', 'flood-relief camp', 'flooded house', 'flooding', 'force from homes', 'force people to leave their homes', 'homes at risk', 'homes burned', 'homes destroyed after flash floods', 'homes evacuated', 'homes lost', 'homes lost due', 'hotel', 'house destroyed', 'house washed away by flooding', 'houses damaged by power . earthquake', 'housing', 'hundreds homes', 'hundreds homes lost', 'hundreds of homes', 'installed', 'many lost house', 'massive flooding has destroyed multiple homes', "navigators ' camps", "navigators' camps", 'nearest shelter', 'need shelter?', 'not full', 'not threatening homes', 'offering housing', 'over shelters', 'people losing homes', 'people use shelter kits distributed', 'relief camp', 'residents allowed to return to damaged homes', 'searching for housing', 'shelter', 'shelter material', 'shelter team', 'shelter team arrived', 'shelter to people', 'tents', 'those who have tragically lost their homes', 'those who lost their homes', 'thousands flee homes', 'thousands homeless', 'to house', 'without shelter', ' rivers converged', 'aid for private water infrastructure', 'bottled water', 'exceeds gallons', 'flash floods hit open-air fracking waste water ponds', 'flood', 'flood water', 'flood waters', 'flooded', 'floods recede', 'full of water', "half of india's rivers", 'have dried up', 'mostly contaminated', 'muddy torrents', 'need clean water', 'no concern over drinking water quality at', 'no drinking water', 'no flush order', 'no water', 'no water and electricity', 'polluting rivers', 'pollution', 'rain harvesting', 'release of flood waters', 'release of waters', 'releases more water', 'releasing more water', 'restoring water', 'river level', 'river pollution', 'river water', 'river waters', 'serious flooding', 'sewage water', 'tainted', 'the disposal', 'undrinkable', 'washing', 'water', 'water filtration systems', 'water purification material', 'water usage', 'webpage', 'arrests', ' girls charged', ' held over deadly brazil nightclub fire', ' more arrests made', ' people shot', ' shooter', 'suspect', 'fireworks', 'stay safe guys', 'a gunman', 'airport shooting', 'another shooting', 'army', 'army confirms dead', 'army deployed', 'army gunship helicopters are bombing the area where the earthquake wreaked havoc', 'army helpline no', 'army helpline numbers', 'army is doing', 'army leads rescue operations', 'army medical emergency helpline', 'army official', 'army operations', 'army personnel', 'army rescue operation', 'arrested', 'arsonist', 'arsonists', 'at least we are safe', 'authorities begin investigation', 'authorities got shooter immediately', 'be safe everyone', 'being shot', 'binding building safety agreement', 'bomb-making', 'building owner arrested', 'building safety agreement', 'checking car fuel tanks', 'claims first life', 'congressmen request classified briefing', 'considered armed and dangerous', 'coroner', 'criminal inquiry', 'criminal investigation', "dead suspect's", 'detained', 'disaster preparedness not only for high level security zones', 'driver suspected of', 'fatal shooting rampage', 'federal officer', 'federal source', 'fireworks', 'flood-triggered oil/gas spills', 'guns', 'had more guns to protect it', 'i hope everyone is ok !', 'investigators', 'investigators ask', 'investigators focus', 'investigators say military exercise may be cause', 'investigators search', "investigators search railway's offices", 'keeping safe', 'killing of man', 'lapd confirmed they shot the suspect', 'lawmakers debate gun control', 'lax shooter', 'lax shooter opened fire with assault rifle', 'lax shooter pulled assault rifle', 'lax shooting suspect', 'lax shooting suspect charged with murder', 'lax suspect', 'lax suspect shot', 'looting homes', 'making sure were all safe', 'military communities threatened', 'military live ordinance exercises', 'no alarms', 'no suspect in custody', 'no threat', 'not feeling safe to go anywhere anymore', 'not negotiable with rebels', 'not safe anyone', 'officials confirm more bombs', 'officials investigating', 'officials launch criminal investigation', 'officials say the threat', 'officials to conduct expedited 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fine', 'think positive', 'thinking about everyone', 'thinking of', 'thinking of a lot of families', 'thinking of all', 'thinking of all my friends', 'thinking of all our friends', 'thinking of everyone helping to', 'thinking of my friends and everyone', 'thinking of you', 'this is not good', 'this is over my house', 'this too shall pass', 'thnx god', 'those individuals', 'those who passed so suddenly', 'to my family & friends', 'to pray', 'to pray for', 'tragic event', 'tragic scene', 'traumatic time', 'uplifting', 'upsetting', 'urgh', 'we are asking all of you', "we are hoping that they're safe", 'we are praying for you', 'we getting slammed with your flood calls !?', 'we have to pray more', 'we pray', "we're staying", 'what did just happend', 'who matters the most', 'without parole', 'would be sweet', 'you texted', 'your prayers'] search_term = ['conflict', 'violence', 'displacement', 'drought', 'earthquake', 'fire', 'flooding', 'freeze', 'health emergency', 'dengue', 'pneumonic plague', 'measles', 'landslide', 'tropical storm', 'typhoon', 'cyclone', 'hurricane', 'tsunami', 'urban disaster', 'volcanic eruption', 'refugee', 'terrorist attack', 'cold wave', 'complex emergency', 'epidemic', 'extratropical cyclone', 'flash flood', 'flood', 'heat wave', 'insect infestation', 'land slide', 'mud slide','snow avalanche', 'storm surge', 'technological disaster', 'tropical cyclone', 'volcano', 'wild fire', 'flood crisis', 'explosion', 'affected tornado', 'affected', 'death toll', 'tornado relief', 'flood appeal', 'massive explosion', 'affected areas', 'praying victims', 'injured', 'lurches fire', 'flood relief', 'flood affected', 'tornado victims', 'deadly', 'evacuated', 'relief', 'flood death', 'deaths confirmed', 'affected flooding', 'people killed', 'flood damage', 'people dead', 'major flood', 'rubble', 'another explosion', 'flood warnings', 'tornado survivor', 'damage', 'devastating', 'flood toll', 'affected hurricane', 'crisis', 'relief efforts', 'flood emergency', 'fire flood', 'huge explosion', 'bushfire', 'torrential rains', 'affected explosion', 'disaster', 'twister', 'blast', 'injuries reported', 'fatalities', 'large explosion', 'destroyed', 'displaced', 'casualties', 'climate change', 'major explosion', 'response disasters', 'explosion victims', 'tragic', 'dealing hurricane', 'flood recovery', 'dead torrential', 'flood years', 'massive tornado', 'buried alive', 'alive rubble', 'crisis rises', 'flood ravaged', 'killed injured', 'killed people', 'people died', 'floods kill', 'tornado damage', 'facing flood', 'deadly explosion', 'flood disaster', 'tornado disaster', 'help victims', 'hundreds homes', 'severe flooding', 'magnitude', 'firefighters police', 'fire explosion', 'storm', 'flood hits', 'floodwaters', 'emergency', 'flood alerts', 'crisis unfolds', 'tragic events', 'deadly tornado', 'people trapped', 'surging floods', 'city tornado', 'damaged hurricane', 'rains severely', 'house flood', 'devastating tornado', 'lost lives', 'reportedly dead', 'following explosion', 'tornado flood', 'early warninig', 'warning', 'dead floods', 'flood threat', 'flood situation', 'risk running', 'loss life', 'thoughts victims', 'terrible explosion', 'seismic', 'flood homeowners', 'flood claims', 'power supplies', 'free hotline', 'hotline help', 'registered magnitude', 'prepare hurricane', 'landfall', 'crisis worsens', 'communities damaged', 'destruction', 'tornado', 'hurricane coming', 'toxins flood', 'release toxins', 'toxins', 'supplies waters', 'crisis found', 'braces major', 'government negligent', 'attack', 'waiting hurricane', 'terror', 'memorial service', 'terror attack', 'coast hurricane', 'terrified hurricane', 'hurricane category', 'disaster relief', 'cleanup', 'troops lend', 'effected hurricane', 'time hurricane', 'saying hurricane', 'praying families', 'dramatic', 'path hurricane']

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104,677

0.729057

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1

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null

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5

e5b142ce1fe5c6d48f707e90d204987e528da2f3

9,319

py

Python

tests/integration/test_join.py

flcong/dask-sql

39980fd40f49ddf3c1910c8e36e8a88bd78b82de

[ "MIT" ]

null

tests/integration/test_join.py

flcong/dask-sql

39980fd40f49ddf3c1910c8e36e8a88bd78b82de

[ "MIT" ]

null

tests/integration/test_join.py

flcong/dask-sql

39980fd40f49ddf3c1910c8e36e8a88bd78b82de

[ "MIT" ]

null

import numpy as np import pandas as pd import pytest from pandas.testing import assert_frame_equal def test_join(c): df = c.sql( "SELECT lhs.user_id, lhs.b, rhs.c FROM user_table_1 AS lhs JOIN user_table_2 AS rhs ON lhs.user_id = rhs.user_id" ) df = df.compute() expected_df = pd.DataFrame( {"user_id": [1, 1, 2, 2], "b": [3, 3, 1, 3], "c": [1, 2, 3, 3]} ) assert_frame_equal( df.sort_values(["user_id", "b", "c"]).reset_index(drop=True), expected_df, ) def test_join_inner(c): df = c.sql( "SELECT lhs.user_id, lhs.b, rhs.c FROM user_table_1 AS lhs INNER JOIN user_table_2 AS rhs ON lhs.user_id = rhs.user_id" ) df = df.compute() expected_df = pd.DataFrame( {"user_id": [1, 1, 2, 2], "b": [3, 3, 1, 3], "c": [1, 2, 3, 3]} ) assert_frame_equal( df.sort_values(["user_id", "b", "c"]).reset_index(drop=True), expected_df, ) def test_join_outer(c): df = c.sql( "SELECT lhs.user_id, lhs.b, rhs.c FROM user_table_1 AS lhs FULL JOIN user_table_2 AS rhs ON lhs.user_id = rhs.user_id" ) df = df.compute() expected_df = pd.DataFrame( { # That is strange. Unfortunately, it seems dask fills in the # missing rows with NaN, not with NA... "user_id": [1, 1, 2, 2, 3, np.NaN], "b": [3, 3, 1, 3, 3, np.NaN], "c": [1, 2, 3, 3, np.NaN, 4], } ) assert_frame_equal( df.sort_values(["user_id", "b", "c"]).reset_index(drop=True), expected_df ) def test_join_left(c): df = c.sql( "SELECT lhs.user_id, lhs.b, rhs.c FROM user_table_1 AS lhs LEFT JOIN user_table_2 AS rhs ON lhs.user_id = rhs.user_id" ) df = df.compute() expected_df = pd.DataFrame( { # That is strange. Unfortunately, it seems dask fills in the # missing rows with NaN, not with NA... "user_id": [1, 1, 2, 2, 3], "b": [3, 3, 1, 3, 3], "c": [1, 2, 3, 3, np.NaN], } ) assert_frame_equal( df.sort_values(["user_id", "b", "c"]).reset_index(drop=True), expected_df, ) def test_join_right(c): df = c.sql( "SELECT lhs.user_id, lhs.b, rhs.c FROM user_table_1 AS lhs RIGHT JOIN user_table_2 AS rhs ON lhs.user_id = rhs.user_id" ) df = df.compute() expected_df = pd.DataFrame( { # That is strange. Unfortunately, it seems dask fills in the # missing rows with NaN, not with NA... "user_id": [1, 1, 2, 2, np.NaN], "b": [3, 3, 1, 3, np.NaN], "c": [1, 2, 3, 3, 4], } ) assert_frame_equal( df.sort_values(["user_id", "b", "c"]).reset_index(drop=True), expected_df, ) def test_join_complex(c): df = c.sql( "SELECT lhs.a, rhs.b FROM df_simple AS lhs JOIN df_simple AS rhs ON lhs.a < rhs.b", ) df = df.compute() df_expected = pd.DataFrame( {"a": [1, 1, 1, 2, 2, 3], "b": [1.1, 2.2, 3.3, 2.2, 3.3, 3.3]} ) assert_frame_equal(df.sort_values(["a", "b"]).reset_index(drop=True), df_expected) df = c.sql( """ SELECT lhs.a, lhs.b, rhs.a, rhs.b FROM df_simple AS lhs JOIN df_simple AS rhs ON lhs.a < rhs.b AND lhs.b < rhs.a """ ) df = df.compute() df_expected = pd.DataFrame( {"a": [1, 1, 2], "b": [1.1, 1.1, 2.2], "a0": [2, 3, 3], "b0": [2.2, 3.3, 3.3],} ) assert_frame_equal(df.sort_values(["a", "b0"]).reset_index(drop=True), df_expected) def test_join_complex_2(c): df = c.sql( """ SELECT lhs.user_id, lhs.b, rhs.user_id, rhs.c FROM user_table_1 AS lhs JOIN user_table_2 AS rhs ON rhs.user_id = lhs.user_id AND rhs.c - lhs.b >= 0 """ ) df = df.compute() df_expected = pd.DataFrame( {"user_id": [2, 2], "b": [1, 3], "user_id0": [2, 2], "c": [3, 3]} ) assert_frame_equal(df.sort_values("b").reset_index(drop=True), df_expected) def test_join_literal(c): df = c.sql( """ SELECT lhs.user_id, lhs.b, rhs.user_id, rhs.c FROM user_table_1 AS lhs JOIN user_table_2 AS rhs ON True """ ) df = df.compute() df_expected = pd.DataFrame( { "user_id": [2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3], "b": [1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], "user_id0": [1, 1, 2, 4, 1, 1, 2, 4, 1, 1, 2, 4, 1, 1, 2, 4], "c": [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4], } ) assert_frame_equal( df.sort_values(["b", "user_id", "user_id0"]).reset_index(drop=True), df_expected, ) df = c.sql( """ SELECT lhs.user_id, lhs.b, rhs.user_id, rhs.c FROM user_table_1 AS lhs JOIN user_table_2 AS rhs ON False """ ) df = df.compute() df_expected = pd.DataFrame({"user_id": [], "b": [], "user_id0": [], "c": []}) assert_frame_equal(df.reset_index(), df_expected.reset_index(), check_dtype=False) def test_join_lricomplex(c): # ---------- Panel data (equality and inequality conditions) # Correct answer dfcorrpn = pd.DataFrame( [ [0, 1, pd.NA, pd.NA, pd.NA, pd.NA], [1, 5, 32, 2, pd.NA, 112], [1, 5, 32, 4, 13, 113], [2, 1, 33, pd.NA, pd.NA, pd.NA], ], columns=["ids", "dates", "pn_nullint", "startdate", "lk_nullint", "lk_int",], ) change_types = { "pn_nullint": "Int32", "lk_nullint": "Int32", "startdate": "Int64", "lk_int": "Int64", } for k, v in change_types.items(): dfcorrpn[k] = dfcorrpn[k].astype(v) # Left Join querypnl = """ select a.*, b.startdate, b.lk_nullint, b.lk_int from user_table_pn a left join user_table_lk b on a.ids=b.id and b.startdate<=a.dates """ dftestpnl = ( c.sql(querypnl) .compute() .sort_values(["ids", "dates", "startdate"]) .reset_index(drop=True) ) assert_frame_equal(dftestpnl, dfcorrpn, check_dtype=False) # Right Join querypnr = """ select b.*, a.startdate, a.lk_nullint, a.lk_int from user_table_lk a right join user_table_pn b on b.ids=a.id and a.startdate<=b.dates """ dftestpnr = ( c.sql(querypnr) .compute() .sort_values(["ids", "dates", "startdate"]) .reset_index(drop=True) ) assert_frame_equal(dftestpnr, dfcorrpn, check_dtype=False) # Inner Join querypni = """ select a.*, b.startdate, b.lk_nullint, b.lk_int from user_table_pn a inner join user_table_lk b on a.ids=b.id and b.startdate<=a.dates """ dftestpni = ( c.sql(querypni) .compute() .sort_values(["ids", "dates", "startdate"]) .reset_index(drop=True) ) assert_frame_equal( dftestpni, dfcorrpn.dropna(subset=["startdate"]) .assign( startdate=lambda x: x["startdate"].astype("int64"), lk_int=lambda x: x["lk_int"].astype("int64"), ) .reset_index(drop=True), check_dtype=False, ) # ---------- Time-series data (inequality condition only) # # Correct answer dfcorrts = pd.DataFrame( [ [1, 21, pd.NA, pd.NA, pd.NA], [3, pd.NA, 2, pd.NA, 112], [7, 23, 2, pd.NA, 112], [7, 23, 4, 13, 113], ], columns=["dates", "ts_nullint", "startdate", "lk_nullint", "lk_int",], ) change_types = { "ts_nullint": "Int32", "lk_nullint": "Int32", "startdate": "Int64", "lk_int": "Int64", } for k, v in change_types.items(): dfcorrts[k] = dfcorrts[k].astype(v) # Left Join querytsl = """ select a.*, b.startdate, b.lk_nullint, b.lk_int from user_table_ts a left join user_table_lk2 b on b.startdate<=a.dates """ dftesttsl = ( c.sql(querytsl) .compute() .sort_values(["dates", "startdate"]) .reset_index(drop=True) ) assert_frame_equal(dftesttsl, dfcorrts, check_dtype=False) # Right Join querytsr = """ select b.*, a.startdate, a.lk_nullint, a.lk_int from user_table_lk2 a right join user_table_ts b on a.startdate<=b.dates """ dftesttsr = ( c.sql(querytsr) .compute() .sort_values(["dates", "startdate"]) .reset_index(drop=True) ) assert_frame_equal(dftesttsr, dfcorrts, check_dtype=False) # Inner Join querytsi = """ select a.*, b.startdate, b.lk_nullint, b.lk_int from user_table_ts a inner join user_table_lk2 b on b.startdate<=a.dates """ dftesttsi = ( c.sql(querytsi) .compute() .sort_values(["dates", "startdate"]) .reset_index(drop=True) ) assert_frame_equal( dftesttsi, dfcorrts.dropna(subset=["startdate"]) .assign( startdate=lambda x: x["startdate"].astype("int64"), lk_int=lambda x: x["lk_int"].astype("int64"), ) .reset_index(drop=True), check_dtype=False, )

27.984985

127

0.534607

1,385

9,319

3.419495

0.094585

0.046875

0.057432

0.064611

0.802576

0.761402

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0.730785

0.696579

0.672086

0

0.042441

0.307222

9,319

332

128

28.069277

0.69114

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0

null

0

1

0

1

0

null

0

5

e5b2788ca138945d25146a5a065db8adb0b4c8a0

158

py

Python

ransomcare/config/__init__.py

Happyholic1203/ransomcare

81a8dd1e0ed2dee6549321624e8e311a69e727d9

[ "MIT" ]

16

2016-07-20T15:57:14.000Z

2021-10-16T07:54:21.000Z

ransomcare/config/__init__.py

Happyholic1203/ransomcare

81a8dd1e0ed2dee6549321624e8e311a69e727d9

[ "MIT" ]

null

ransomcare/config/__init__.py

Happyholic1203/ransomcare

81a8dd1e0ed2dee6549321624e8e311a69e727d9

[ "MIT" ]

7

2016-07-22T09:30:16.000Z

2020-04-07T06:59:08.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import os if os.environ.get('RANSOMECARE_ENV') == 'dev': from .dev import * else: from .prod import *

15.8

46

0.607595

23

158

4.130435

0.73913

0

0.007937

0.202532

158

9

47

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1

0

null

0

1

0

1

0

1

0

5

f90c7db435a8d979ca6e193315d763ed475ff576

176

py

Python

features/steps/data/src/fails_isort.py

ldamewood/ly-python-tools

4c6e17357ddd0ce68dae539f4527ab3a40e58698

[ "MIT" ]

null

features/steps/data/src/fails_isort.py

ldamewood/ly-python-tools

4c6e17357ddd0ce68dae539f4527ab3a40e58698

[ "MIT" ]

null

features/steps/data/src/fails_isort.py

ldamewood/ly-python-tools

4c6e17357ddd0ce68dae539f4527ab3a40e58698

[ "MIT" ]

null

# pylint: disable=all # flake8: noqa import logging import collections from typing import DefaultDict foo: DefaultDict[str, str] = collections.defaultdict() logging.info(foo)

19.555556

54

0.789773

22

176

6.318182

0.636364

0

0.006452

0.119318

176

8

55

22

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0.181818

0

1

0

true

0

0.6

0

0.6

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0

null

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0

null

0

1

0

1

0

1

0

5

0054c4655f1169c4430788ca1c7a5fe5e19de471

5,874

py

Python

test_code.py

Oliver-Chalkley/whole_cell_modelling_suite

dc5896635b88398210d0fd1d7bc3065ba716351a

[ "MIT" ]

null

test_code.py

Oliver-Chalkley/whole_cell_modelling_suite

dc5896635b88398210d0fd1d7bc3065ba716351a

[ "MIT" ]

null

test_code.py

Oliver-Chalkley/whole_cell_modelling_suite

dc5896635b88398210d0fd1d7bc3065ba716351a

[ "MIT" ]

null

import os from connections import Karr2012Bg as karr_conn from analysis.genome import Genes as anal bc3_conn = karr_conn('oc13378', 'bg', 'Oliver', 'Chalkley', 'o.chalkley@bristol.ac.uk', None, None, None) all_genes = ('MG_001', 'MG_002', 'MG_003', 'MG_004', 'MG_005', 'MG_006', 'MG_007', 'MG_008', 'MG_009', 'MG_010', 'MG_011', 'MG_012', 'MG_013', 'MG471', 'MG472', 'MG_014', 'MG_015', 'MG_018', 'MG_019', 'MG_020', 'MG_021', 'MG_022', 'MG_023', 'MG_024', 'MG_025', 'MG_026', 'MG_027', 'MG_028', 'MG_029', 'MG_030', 'MG_031', 'MG_032', 'MG_033', 'MG_034', 'MG_035', 'MG_036', 'MG_037', 'MG_038', 'MG_039', 'MG_040', 'MG_041', 'MG_042', 'MG_043', 'MG_044', 'MG_045', 'MG_046', 'MG_047', 'MG_048', 'MG_049', 'MG_050', 'MG_051', 'MG_052', 'MG_053', 'MG_054', 'MG_055', 'MG_473', 'MG_474', 'MG_056', 'MG_057', 'MG_058', 'MG_059', 'MG_060', 'MG_061', 'MG475', 'MG_062', 'MG_063', 'MG_064', 'MG_065', 'MG_066', 'MG_067', 'MG_068', 'MG_069', 'MG_070', 'MG_071', 'MG_072', 'MG_073', 'MG_074', 'MG_075', 'MG_076', 'MG_077', 'MG_078', 'MG_079', 'MG_080', 'MG_081', 'MG_082', 'MG_083', 'MG_084', 'MG_085', 'MG_086', 'MG_087', 'MG_088', 'MG_089', 'MG_090', 'MG_091', 'MG_092', 'MG_093', 'MG_094', 'MG_095', 'MG_096', 'MG_097', 'MG_098', 'MG_099', 'MG_100', 'MG_101', 'MG_102', 'MG_103', 'MG_476', 'MG_104', 'MG_105', 'MG_106', 'MG_107', 'MG_108', 'MG_109', 'MG_110', 'MG_111', 'MG_112', 'MG_113', 'MG_114', 'MG_115', 'MG_116', 'MG_117', 'MG_118', 'MG_119', 'MG_120', 'MG_121', 'MG_122', 'MG_123', 'MG_124', 'MG_125', 'MG_126', 'MG_127', 'MG_128', 'MG_129', 'MG_130', 'MG_131', 'MG_132', 'MG_133', 'MG_134', 'MG_135', 'MG_136', 'MG_137', 'MG_138', 'MG_139', 'MGrrnA16S', 'MGrrnA23S', 'MGrrnA5S', 'MG_140', 'MG_141', 'MG_477', 'MG_142', 'MG_143', 'MG_144', 'MG_145', 'MG_146', 'MG_147', 'MG_148', 'MG_149', 'MG_478', 'MG_150', 'MG_151', 'MG_152', 'MG_153', 'MG_154', 'MG_155', 'MG_156', 'MG_157', 'MG_158', 'MG_159', 'MG_160', 'MG_161', 'MG_162', 'MG_163', 'MG_164', 'MG_165', 'MG_166', 'MG_167', 'MG_168', 'MG_169', 'MG_170', 'MG_171', 'MG_172', 'MG_173', 'MG_174', 'MG_175', 'MG_176', 'MG_177', 'MG_178', 'MG_179', 'MG_180', 'MG_181', 'MG_182', 'MG_183', 'MG_184', 'MG_185', 'MG_186', 'MG_187', 'MG_188', 'MG_189', 'MG_190', 'MG_191', 'MG_192', 'MG_194', 'MG_195', 'MG_196', 'MG_197', 'MG_198', 'MG_199', 'MG_200', 'MG_201', 'MG_202', 'MG479', 'MG_203', 'MG_204', 'MG_205', 'MG_206', 'MG_207', 'MG_208', 'MG_209', 'MG_210', 'MG_480', 'MG_481', 'MG_211', 'MG_482', 'MG_212', 'MG_213', 'MG_214', 'MG_215', 'MG_216', 'MG483', 'MG484', 'MG485', 'MG486', 'MG487', 'MG488', 'MG489', 'MG490', 'MG_217', 'MG_218', 'MG_491', 'MG_219', 'MG_220', 'MG492', 'MG_221', 'MG_222', 'MG_223', 'MG_224', 'MG_225', 'MG_226', 'MG_227', 'MG_228', 'MG_229', 'MG_230', 'MG_231', 'MG_232', 'MG_233', 'MG_234', 'MG_235', 'MG_236', 'MG_237', 'MG_238', 'MG_239', 'MG_240', 'MG_241', 'MG_242', 'MG_243', 'MG_244', 'MG_245', 'MG_246', 'MG_247', 'MG_248', 'MG_249', 'MG_250', 'MG_251', 'MG_252', 'MG_253', 'MG_254', 'MG493', 'MG_255', 'MG_494', 'MG495', 'MG496', 'MG_256', 'MG_257', 'MG_258', 'MG_259', 'MG_260', 'MG497', 'MG_261', 'MG_262', 'MG_498', 'MG_263', 'MG_264', 'MG_265', 'MG_266', 'MG_267', 'MG_268', 'MG_0001', 'MG_269', 'MG_270', 'MG_271', 'MG_272', 'MG_0002', 'MG_273', 'MG_274', 'MG_275', 'MG_276', 'MG_277', 'MG_278', 'MG_279', 'MG_280', 'MG_281', 'MG499', 'MG500', 'MG501', 'MG502', 'MG503', 'MG_282', 'MG_283', 'MG_284', 'MG_285', 'MG_286', 'MG_287', 'MG504', 'MG_288', 'MG_289', 'MG_290', 'MG_291', 'MG_505', 'MG_292', 'MG_293', 'MG_294', 'MG_295', 'MG_296', 'MG_297', 'MG_298', 'MG_299', 'MG_300', 'MG_301', 'MG_302', 'MG_303', 'MG_304', 'MG_305', 'MG_306', 'MG_307', 'MG_308', 'MG_309', 'MG_310', 'MG_311', 'MG_312', 'MG_313', 'MG_314', 'MG_315', 'MG_316', 'MG_317', 'MG_318', 'MG_319', 'MG_320', 'MG506', 'MG507', 'MG_321', 'MG508', 'MG509', 'MG510', 'MG511', 'MG512', 'MG513', 'MG514', 'MG_322', 'MG_323', 'MG_0003', 'MG_0004', 'MG_515', 'MG_324', 'MG_325', 'MG_326', 'MG_327', 'MG_328', 'MG_329', 'MG_330', 'MG_331', 'MG_332', 'MG_333', 'MG_334', 'MG_335', 'MG_516', 'MG_517', 'MG_336', 'MG_337', 'MG_338', 'MG_339', 'MG_340', 'MG_341', 'MG_342', 'MG_343', 'MG_344', 'MG_345', 'MG_346', 'MG_347', 'MG518', 'MG_348', 'MG519', 'MG_349', 'MG_350', 'MG520', 'MG_521', 'MG_351', 'MG_352', 'MG_353', 'MG_354', 'MG_355', 'MG_356', 'MG_357', 'MG_358', 'MG_359', 'MG_360', 'MG_361', 'MG_362', 'MG_363', 'MG_522', 'MG_364', 'MG_365', 'MG_366', 'MG_367', 'MG_368', 'MG_369', 'MG_370', 'MG_371', 'MG_372', 'MG_373', 'MG_374', 'MG_375', 'MG_376', 'MG_377', 'MG_378', 'MG_379', 'MG_380', 'MG_381', 'MG523', 'MG_382', 'MG_383', 'MG_384', 'MG_524', 'MG_385', 'MG_386', 'MG_387', 'MG_388', 'MG_389', 'MG_390', 'MG_391', 'MG_392', 'MG_393', 'MG_394', 'MG_395', 'MG_396', 'MG_397', 'MG_398', 'MG_399', 'MG_400', 'MG_401', 'MG_402', 'MG_403', 'MG_404', 'MG_405', 'MG_406', 'MG_407', 'MG_408', 'MG_409', 'MG_410', 'MG_411', 'MG_412', 'MG_414', 'MG_525', 'MG_417', 'MG_418', 'MG_419', 'MG_421', 'MG_422', 'MG_423', 'MG_424', 'MG_425', 'MG_426', 'MG_427', 'MG_428', 'MG_429', 'MG_430', 'MG_431', 'MG_432', 'MG_433', 'MG_434', 'MG_435', 'MG_437', 'MG_438', 'MG_439', 'MG_440', 'MG_441', 'MG_442', 'MG_443', 'MG_444', 'MG_445', 'MG_446', 'MG_447', 'MG_448', 'MG_449', 'MG_450', 'MG_451', 'MG_452', 'MG_453', 'MG_454', 'MG_455', 'MG_456', 'MG_457', 'MG_458', 'MG_459', 'MG_460', 'MG_461', 'MG_462', 'MG_463', 'MG_464', 'MG_465', 'MG_466', 'MG_467', 'MG_468', 'MG_526', 'MG_469', 'MG_470') genomes = anal(bc3_conn, all_genes) genomes.appendGenomeFromDb('/home/oli/git/published_libraries/whole_cell_modelling_suite/whole_cell_modelling_suite/analysis/ko.sqll', 'select gen.genome from mgKarr2012Genome as gen join growthAndDivision as gad on gad.genome_id = gen.id join batch on batch.id = gad.batch_id join experiment as exp on exp.id = batch.experiment_id where experiment_id = 17') print(genomes.genomes.head())

451.846154

5,238

0.61985

1,112

5,874

2.821942

0.518885

0.005099

0.007011

0.014659

0

0.301458

0.100953

5,874

12

5,239

489.5

0.292748

0

0.125

0.595335

0.021791

0

1

0

false

0

0.375

0

0.375

0.125

0

null

0

1

0

1

0

null

0

1

0

5

0097f88f2ffb9faa4f5c43b91f05774d3a135151

133

py

Python

cinema_system/userAccount/admin.py

SJPark94/E-Cinema-Booking-System

dbb92f615a3c5f63def2cc7247183555176d79ef

[ "MIT" ]

1

2019-04-22T19:55:25.000Z

cinema_system/userAccount/admin.py

SJPark94/E-Cinema-Booking-System

dbb92f615a3c5f63def2cc7247183555176d79ef

[ "MIT" ]

null

cinema_system/userAccount/admin.py

SJPark94/E-Cinema-Booking-System

dbb92f615a3c5f63def2cc7247183555176d79ef

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from userAccount.models import UserInfo admin.site.register(UserInfo)

22.166667

39

0.827068

18

133

6.111111

0.666667

0

0.112782

133

6

40

22.166667

0.932203

0.195489

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

00d2895ce3bafd0e526da27212a435152a511b44

137

py

Python

tests/pipeline_example.py

jbn/vaquero

83d913c4f72f67cf0a48061de752134a44facb87

[ "MIT" ]

1

2017-03-16T14:41:03.000Z

tests/pipeline_example.py

jbn/vaquero

83d913c4f72f67cf0a48061de752134a44facb87

[ "MIT" ]

5

2016-11-04T16:10:46.000Z

2016-11-04T16:19:58.000Z

tests/pipeline_example.py

jbn/vaquero

83d913c4f72f67cf0a48061de752134a44facb87

[ "MIT" ]

null

def f(items): items.append(100) def g(items): items.append(items.pop() * 2) def h(items): items.extend([items.pop()] * 10)

15.222222

36

0.59854

22

137

3.727273

0.5

0.365854

0.390244

0

0.054545

0.19708

137

8

37

17.125

0.690909

0

1

0.5

false

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

9706c66218ea7265b0ed3711d87100ad7ec5169a

355

py

Python

pydiscordbio/exceptions.py

awersli99/pydiscordbio

adbe6853594f1ee700043f9520dfd9a893fa44f0

[ "MIT" ]

7

2020-08-29T15:56:24.000Z

2021-02-21T22:30:37.000Z

pydiscordbio/exceptions.py

awersli99/pydiscordbio

adbe6853594f1ee700043f9520dfd9a893fa44f0

[ "MIT" ]

null

pydiscordbio/exceptions.py

awersli99/pydiscordbio

adbe6853594f1ee700043f9520dfd9a893fa44f0

[ "MIT" ]

null

class APIError(Exception): """Raised when there is an API error""" pass class NotFound(Exception): """Raised on a 404 status code""" pass class UserNotFound(Exception): """Raised when a specified user cannot be found by the API""" pass class InvalidSearch(Exception): """Raised when a search query is invalid""" pass

18.684211

65

0.670423

47

355

5.06383

0.617021

0.252101

0.239496

0.168067

0

0.010949

0.228169

355

18

66

19.722222

0.857664

0.43662

0

0.5

0

1

0

true

0.5

0

0.5

0

null

1

0

null

0

1

0

5

974ae41712d756f8ccc9ce875b61abceaf433da9

780

py

Python

test/test_field_length.py

smalbadger/kommander

eefa145cdabce8c8e3afdb1d6d857ebfff7a0735

[ "MIT" ]

1

2021-03-18T00:20:53.000Z

test/test_field_length.py

smalbadger/kommander

eefa145cdabce8c8e3afdb1d6d857ebfff7a0735

[ "MIT" ]

65

2021-02-14T03:36:57.000Z

2022-03-04T08:33:10.000Z

test/test_field_length.py

smalbadger/pymessagelib

eefa145cdabce8c8e3afdb1d6d857ebfff7a0735

[ "MIT" ]

null

import unittest from pymessagelib import Field, Bit, Bits class TestFieldLength(unittest.TestCase): def testNonDWordAlignedField(self): field = Bits(7) self.assertTrue(field.length_as_format(Field.Format.Hex), 2) self.assertTrue(field.length_as_format(Field.Format.Dec), 1) self.assertTrue(field.length_as_format(Field.Format.Oct), 3) self.assertTrue(field.length_as_format(Field.Format.Bin), 7) def testDWordAlignedField(self): field = Bits(8) self.assertTrue(field.length_as_format(Field.Format.Hex), 2) self.assertTrue(field.length_as_format(Field.Format.Dec), 1) self.assertTrue(field.length_as_format(Field.Format.Oct), 3) self.assertTrue(field.length_as_format(Field.Format.Bin), 8)

41.052632

68

0.721795

104

780

5.259615

0.259615

0.204753

0.277879

0.365631

0.698355

0

0.015314

0.162821

780

18

69

43.333333

0.822358

0

0.4

0

0.533333

1

0.133333

false

0

0.133333

0

0.333333

0

null

1

0

1

0

null

0

1

0

5

97535751b1842cb083a73e6acb48d5eb9267d5eb

82

py

Python

High School/9th Grade APCSP (Python)/Unit 1/Unit 01.02 (The Basics)/01.02.04.py

SomewhereOutInSpace/Computer-Science-Class

f5d21850236a7a18dc53b4a650ecbe9a11781f1d

[ "Unlicense" ]

null

High School/9th Grade APCSP (Python)/Unit 1/Unit 01.02 (The Basics)/01.02.04.py

SomewhereOutInSpace/Computer-Science-Class

f5d21850236a7a18dc53b4a650ecbe9a11781f1d

[ "Unlicense" ]

null

High School/9th Grade APCSP (Python)/Unit 1/Unit 01.02 (The Basics)/01.02.04.py

SomewhereOutInSpace/Computer-Science-Class

f5d21850236a7a18dc53b4a650ecbe9a11781f1d

[ "Unlicense" ]

null

# 01.02.04 ASCII KAT print("(---)") print("(o o)") print("(=Y=)") print(" ( ) ")

11.714286

20

0.45122

12

82

3.083333

0.666667

0

0.086957

0.158537

82

6

21

13.666667

0.449275

0.219512

0

0.322581

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

9769cd30baf99cacd5c6e81462a1193666c13796

197

py

Python

ajax/conf.py

joestump/django-ajax

b71619d5c00d8e0bb990ddbea2c93cf303dc2c80

[ "BSD-3-Clause" ]

62

2015-01-09T23:02:06.000Z

2020-12-27T19:44:58.000Z

ajax/conf.py

joestump/django-ajax

b71619d5c00d8e0bb990ddbea2c93cf303dc2c80

[ "BSD-3-Clause" ]

7

2015-03-26T21:52:54.000Z

2016-06-20T20:53:43.000Z

ajax/conf.py

joestump/django-ajax

b71619d5c00d8e0bb990ddbea2c93cf303dc2c80

[ "BSD-3-Clause" ]

12

2015-02-23T11:58:44.000Z

2020-10-26T22:32:58.000Z

from __future__ import absolute_import from django.conf import settings from appconf import AppConf class AjaxAppConf(AppConf): AJAX_AUTHENTICATION = 'ajax.authentication.BaseAuthentication'

24.625

66

0.837563

22

197

7.227273

0.590909

0.226415

0

0.116751

197

7

67

28.142857

0.913793

0

0.192893

0

1

0

false

0

0.6

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

9772efc4d2bba0fa82ae4cb3be4801fb89e754ba

59

py

Python

test_framework/fixtures/__init__.py

Gliger13/bdo_daily_bot

d569405fcae1978c2bb1ac34d1f75936040a3552

[ "MIT" ]

null

test_framework/fixtures/__init__.py

Gliger13/bdo_daily_bot

d569405fcae1978c2bb1ac34d1f75936040a3552

[ "MIT" ]

null

test_framework/fixtures/__init__.py

Gliger13/bdo_daily_bot

d569405fcae1978c2bb1ac34d1f75936040a3552

[ "MIT" ]

null

from test_framework.fixtures.database.collections import *

29.5

58

0.864407

7

59

7.142857

1

0

0.067797

59

1

59

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

97844b09085e3762495848bd7867fa91a2955270

139

py

Python

tests/some_python_package/module3.py

rmorshea/sphinx-resolve-py-references

c0a1d6ebee0582d7a9ac5c3661f4d18405c6607f

[ "BSD-2-Clause" ]

1

2021-11-02T19:12:05.000Z

tests/some_python_package/module3.py

rmorshea/sphinx-resolve-py-references

c0a1d6ebee0582d7a9ac5c3661f4d18405c6607f

[ "BSD-2-Clause" ]

1

2021-11-02T19:19:01.000Z

2021-11-15T03:44:45.000Z

tests/some_python_package/module3.py

rmorshea/sphinx-resolve-py-references

c0a1d6ebee0582d7a9ac5c3661f4d18405c6607f

[ "BSD-2-Clause" ]

1

2021-08-21T22:39:38.000Z

GLOBAL_3 = "global 3" """Docs for global 3""" class Class3: """Docs for class 3""" def function_3(): """Docs for function 3"""

12.636364

29

0.589928

21

139

3.809524

0.380952

0.2625

0.2

0

0.065421

0.230216

139

10

30

13.9

0.682243

0.258993

0

0.115942

0

1

0.333333

false

0

0.666667

0

1

0

null

1

0

null

0

1

0

1

0

5

979dc3ecd80cdfb2f22305be4708fbe2145f1fd4

121

py

Python

ploopsnapshot/__init__.py

nexcess/python-ploopsnapshot

774ca48844b6edb5e1d900f30f7433534bacd9e4

[ "Apache-2.0" ]

null

ploopsnapshot/__init__.py

nexcess/python-ploopsnapshot

774ca48844b6edb5e1d900f30f7433534bacd9e4

[ "Apache-2.0" ]

null

ploopsnapshot/__init__.py

nexcess/python-ploopsnapshot

774ca48844b6edb5e1d900f30f7433534bacd9e4

[ "Apache-2.0" ]

null

17.285714

40

0.719008

17

121

5.117647

0.764706

0

0.039604

0.165289

121

6

41

20.166667

0.821782

0.553719

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

97aa2119b1c1f4a56ff8051bf19d763a097dbc77

21

py

Python

__init__.py

gunnarpope/pyjson

467e1fcceabe05d0703ce583bd46359a754e7352

[ "MIT" ]

null

__init__.py

gunnarpope/pyjson

467e1fcceabe05d0703ce583bd46359a754e7352

[ "MIT" ]

null

__init__.py

gunnarpope/pyjson

467e1fcceabe05d0703ce583bd46359a754e7352

[ "MIT" ]

null

from pyjson import *

10.5

20

0.761905

3

21

5.333333

1

0

0.190476

21

1

21

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

97bc2532cfac05983c8f300297a239de7e1d5601

166

py

Python

kube_hunter/conf/__init__.py

mormamn/kube-hunter

14d73e201eda58eef6d873f023e39df13a9464fa

[ "Apache-2.0" ]

2

2022-02-09T18:05:46.000Z

2022-03-11T06:39:01.000Z

kube_hunter/conf/__init__.py

mormamn/kube-hunter

14d73e201eda58eef6d873f023e39df13a9464fa

[ "Apache-2.0" ]

null

kube_hunter/conf/__init__.py

mormamn/kube-hunter

14d73e201eda58eef6d873f023e39df13a9464fa

[ "Apache-2.0" ]

null

from kube_hunter.conf.parser import parse_args from kube_hunter.conf.logging import setup_logger config = parse_args() setup_logger(config.log) __all__ = [config]

18.444444

49

0.813253

25

166

5

0.56

0.128

0.224

0.288

0

0.108434

166

8

50

20.75

0.844595

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

5

8af7f5f832074326bcddb5b7006dba3c35f6c751

305

py

Python

Leetcode/0111. Minimum Depth of Binary Tree/0111.py

Next-Gen-UI/Code-Dynamics

a9b9d5e3f27e870b3e030c75a1060d88292de01c

[ "MIT" ]

null

Leetcode/0111. Minimum Depth of Binary Tree/0111.py

Next-Gen-UI/Code-Dynamics

a9b9d5e3f27e870b3e030c75a1060d88292de01c

[ "MIT" ]

null

Leetcode/0111. Minimum Depth of Binary Tree/0111.py

Next-Gen-UI/Code-Dynamics

a9b9d5e3f27e870b3e030c75a1060d88292de01c

[ "MIT" ]

null

class Solution: def minDepth(self, root: Optional[TreeNode]) -> int: if not root: return 0 if not root.left: return self.minDepth(root.right) + 1 if not root.right: return self.minDepth(root.left) + 1 return min(self.minDepth(root.left), self.minDepth(root.right)) + 1

30.5

71

0.655738

45

305

4.444444

0.377778

0.24

0.32

0.22

0

0.016878

0.222951

305

9

72

33.888889

0.827004

0

1

0.111111

false

0

0.666667

0

null

1

0

1

0

null

0

1

0

5

8aff1f68ca51c0e05cbe1c3bb79d898a7c541e4f

62

py

Python

src/core/transactions/models/__init__.py

arnulfojr/simple-pos

119c4c52bf62f52004f4b2b031098ed71890d250

[ "MIT" ]

1

2018-09-11T19:32:25.000Z

src/core/transactions/models/__init__.py

arnulfojr/simple-pos

119c4c52bf62f52004f4b2b031098ed71890d250

[ "MIT" ]

null

src/core/transactions/models/__init__.py

arnulfojr/simple-pos

119c4c52bf62f52004f4b2b031098ed71890d250

[ "MIT" ]

null

from transactions import Transaction from items import Item

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c1221c14a3108b548a1cbf9f50a1b93c9161ccb0

258

py

Python

src/mlsafari/exceptions.py

THargreaves/machine-learning-safari

500f47531c424bb5df494c6db94bf63c578e9932

[ "MIT" ]

1

2021-07-16T23:39:50.000Z

src/mlsafari/exceptions.py

THargreaves/machine-learning-safari

500f47531c424bb5df494c6db94bf63c578e9932

[ "MIT" ]

6

2021-07-15T22:24:56.000Z

2021-10-10T09:20:36.000Z

src/mlsafari/exceptions.py

THargreaves/machine-learning-safari

500f47531c424bb5df494c6db94bf63c578e9932

[ "MIT" ]

null

"""Package-specific exceptions.""" class NotFittedError(Exception): """Exception raised when predicting using an unfitted estimator.""" pass class ConvergenceError(Exception): """Exception raised when a model fails to converge.""" pass

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c130e12a8cf58886043c4e259a7512b1bab540ed

244

py

Python

src/2_exercises/3_BehavioralPatterns/1_ChainOfResponsibility/exercise_chain_of_responsibility.py

MilovanTomasevic/Python-Design-Patterns

2a30e24e062bc623e390f8ced9c228c3ff038e54

[ "MIT" ]

2

2020-12-01T21:06:29.000Z

2022-01-11T12:40:06.000Z

src/2_exercises/3_BehavioralPatterns/1_ChainOfResponsibility/exercise_chain_of_responsibility.py

MilovanTomasevic/Python-Design-Patterns

2a30e24e062bc623e390f8ced9c228c3ff038e54

[ "MIT" ]

null

src/2_exercises/3_BehavioralPatterns/1_ChainOfResponsibility/exercise_chain_of_responsibility.py

MilovanTomasevic/Python-Design-Patterns

2a30e24e062bc623e390f8ced9c228c3ff038e54

[ "MIT" ]

1

2022-01-11T12:39:51.000Z

class Goblin(Creature): def __init__(self, game, attack=1, defense=1): # todo class GoblinKing(Goblin): def __init__(self, game): # todo class Game: def __init__(self): self.creatures = []

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null

0

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5

c14ed16ac4e1198d13f9ac042e8cdbfc7c1823bf

194

py

Python

src/graph_transpiler/webdnn/backend/webgl/kernels/sinh.py

steerapi/webdnn

1df51cc094e5a528cfd3452c264905708eadb491

[ "MIT" ]

1

2021-04-09T15:55:35.000Z

src/graph_transpiler/webdnn/backend/webgl/kernels/sinh.py

steerapi/webdnn

1df51cc094e5a528cfd3452c264905708eadb491

[ "MIT" ]

null

src/graph_transpiler/webdnn/backend/webgl/kernels/sinh.py

steerapi/webdnn

1df51cc094e5a528cfd3452c264905708eadb491

[ "MIT" ]

null

from webdnn.backend.webgl.kernels.elementwise import register_elementwise_kernel from webdnn.graph.operators.sinh import Sinh register_elementwise_kernel(Sinh, "y = (exp(x0) - exp(-x0))/2.0;")

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null

0

1

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1

0

5

c15f8e2ae5410b1fdb19115991fe5b5d438a1823

2,948

py

Python

ability-python/app/service/ability_service.py

corner4world/cubeai

ffe3ded358a70cd0a512420f7df2bb931eaae1c9

[ "Apache-2.0" ]

null

ability-python/app/service/ability_service.py

corner4world/cubeai

ffe3ded358a70cd0a512420f7df2bb931eaae1c9

[ "Apache-2.0" ]

null

ability-python/app/service/ability_service.py

corner4world/cubeai

ffe3ded358a70cd0a512420f7df2bb931eaae1c9

[ "Apache-2.0" ]

null

import requests from app.service import umm_client from app.global_data.global_data import g def forward_request(prev_request): path = prev_request.path if path.startswith('/model/'): deployment_uuid = path[7:] res = umm_client.find_ability(deployment_uuid) if res['status'] != 'ok': raise Exception('未找到部署实例') deployment = res['value'] k8s_port = deployment.get('k8sPort') if k8s_port is None: raise Exception('k8s实例端口停止服务') internal_ip = g.get_central_config()['kubernetes']['ability']['internalIP'] url = 'http://{}:{}/api/model'.format(internal_ip, k8s_port) res = requests.post(url=url, data=prev_request.body, headers=prev_request.headers) return { 'response': res, } if path.startswith('/file/'): deployment_uuid, method = path[6:].split('/') res = umm_client.find_ability(deployment_uuid) if res['status'] != 'ok': raise Exception('未找到部署实例') deployment = res['value'] k8s_port = deployment.get('k8sPort') if k8s_port is None: raise Exception('k8s实例端口停止服务') internal_ip = g.get_central_config()['kubernetes']['ability']['internalIP'] url = 'http://{}:{}/api/file/{}'.format(internal_ip, k8s_port, method) res = requests.post(url=url, data=prev_request.body, headers=prev_request.headers) return { 'response': res, } if path.startswith('/stream/'): deployment_uuid, method = path[8:].split('/') res = umm_client.find_ability(deployment_uuid) if res['status'] != 'ok': raise Exception('未找到部署实例') deployment = res['value'] k8s_port = deployment.get('k8sPort') if k8s_port is None: raise Exception('k8s实例端口停止服务') internal_ip = g.get_central_config()['kubernetes']['ability']['internalIP'] url = 'http://{}:{}/api/stream/{}'.format(internal_ip, k8s_port, method) res = requests.post(url=url, data=prev_request.body, headers=prev_request.headers) return { 'response': res, } if path.startswith('/web/'): path = path[5:] i = path.find('/') deployment_uuid = path[:i] filename = path[i+1:] res = umm_client.find_ability(deployment_uuid) if res['status'] != 'ok': raise Exception('未找到部署实例') deployment = res['value'] k8s_port = deployment.get('k8sPort') if k8s_port is None: raise Exception('k8s实例端口停止服务') internal_ip = g.get_central_config()['kubernetes']['ability']['internalIP'] url = 'http://{}:{}/web/{}'.format(internal_ip, k8s_port, filename) res = requests.get(url=url, headers=prev_request.headers) return { 'response': res, } raise Exception('unsupported API name')

29.48

90

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2,948

5.1

0.19697

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0.761141

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0

null

0

1

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null

0

5

c18a1cdf42fbb77ee1da8b2d9c8836265da88292

2,718

py

Python

tethysapp/modflow/tests/unit_tests/cli/link_databases.py

Aquaveo/tethysapp-modflow

5e662d8346f2ffd414ac912a531eef06c5ae79d9

[ "BSD-3-Clause" ]

null

tethysapp/modflow/tests/unit_tests/cli/link_databases.py

Aquaveo/tethysapp-modflow

5e662d8346f2ffd414ac912a531eef06c5ae79d9

[ "BSD-3-Clause" ]

null

tethysapp/modflow/tests/unit_tests/cli/link_databases.py

Aquaveo/tethysapp-modflow

5e662d8346f2ffd414ac912a531eef06c5ae79d9

[ "BSD-3-Clause" ]

null

""" ******************************************************************************** * Name: init_command * Author: nswain * Created On: July 26, 2018 * Copyright: (c) Aquaveo 2018 ******************************************************************************** """ import mock import unittest import tethysapp.modflow.cli.link_databases as ld try: from StringIO import StringIO except ImportError: from io import StringIO class LinkDatabasesTests(unittest.TestCase): def setUp(self): pass def tearDown(self): pass @mock.patch('tethysapp.modflow.cli.link_databases.create_engine') @mock.patch('sys.stdout', new_callable=StringIO) def test_link_databases_no_models(self, mock_print, mock_create_engine): mock_result = mock.MagicMock() mock_result.__iter__.return_value = [] mock_create_engine().execute.return_value = mock_result mock_arguments = mock.MagicMock(service_name="test_db") ld.link_databases(mock_arguments) print_value = mock_print.getvalue() self.assertIn('No results found', print_value) mock_result.close.assert_called() mock_create_engine().execute.assert_called() @mock.patch('tethys_apps.utilities.create_ps_database_setting') @mock.patch('tethysapp.modflow.cli.link_databases.create_engine') @mock.patch('sys.stdout', new_callable=StringIO) def test_link_databases_create_success(self, mock_print, mock_create_engine, mock_cpds): mock_result = mock.MagicMock() mock_result.__iter__.return_value = ['eggs'] mock_create_engine().execute.return_value = mock_result mock_cpds.return_value = True mock_arguments = mock.MagicMock(service_name="test_db") ld.link_databases(mock_arguments) print_value = mock_print.getvalue() self.assertNotIn('No results found', print_value) self.assertIn('Successfully linked Modflow DB', print_value) @mock.patch('tethys_apps.utilities.create_ps_database_setting') @mock.patch('tethysapp.modflow.cli.link_databases.create_engine') @mock.patch('sys.stdout', new_callable=StringIO) def test_link_databases_create_failure(self, mock_print, mock_create_engine, mock_cpds): mock_result = mock.MagicMock() mock_result.__iter__.return_value = ['eggs'] mock_create_engine().execute.return_value = mock_result mock_cpds.return_value = False mock_arguments = mock.MagicMock(service_name="test_db") ld.link_databases(mock_arguments) print_value = mock_print.getvalue() self.assertNotIn('No results found', print_value) self.assertIn('Could not link the database', print_value)

37.75

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2,718

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5

c19e0c5a8d5b28b5e539fde8decc9b5cb138a181

162

py

Python

cli/src/dolbyio_rest_apis_cli/__init__.py

dolbyio-samples/dolbyio-rest-apis-client-python

37354dc10f967c4656776f9e2651a2284a11f530

[ "MIT" ]

1

2021-12-23T17:55:06.000Z

client/src/dolbyio_rest_apis/__init__.py

dolbyio-samples/dolbyio-rest-apis-client-python

37354dc10f967c4656776f9e2651a2284a11f530

[ "MIT" ]

null

client/src/dolbyio_rest_apis/__init__.py

dolbyio-samples/dolbyio-rest-apis-client-python

37354dc10f967c4656776f9e2651a2284a11f530

[ "MIT" ]

null

"""Versioning""" import importlib.metadata try: __version__ = importlib.metadata.version(__name__) except importlib.metadata.PackageNotFoundError: pass

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null

1

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1

0

null

0

1

0

5

c19fc136336de932459c56981c8af99f9204df91

4,595

py

Python

test/python/test_select.py

slyalin/openvino_tensorflow

37a2e5b6ff1e60217d31340ad3975b41faa39da0

[ "Apache-2.0" ]

null

test/python/test_select.py

slyalin/openvino_tensorflow

37a2e5b6ff1e60217d31340ad3975b41faa39da0

[ "Apache-2.0" ]

null

test/python/test_select.py

slyalin/openvino_tensorflow

37a2e5b6ff1e60217d31340ad3975b41faa39da0

[ "Apache-2.0" ]

1

2021-05-12T07:35:34.000Z

# ============================================================================== # Copyright (C) 2021 Intel Corporation # SPDX-License-Identifier: Apache-2.0 # ============================================================================== """Openvino Tensorflow floor operation test """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import pytest import numpy as np import tensorflow as tf tf.compat.v1.disable_eager_execution() from common import NgraphTest class TestSelect(NgraphTest): def test_select_scalar(self): a = [1.5] p = tf.compat.v1.placeholder(dtype=tf.bool) out = tf.where(p, x=[1], y=[0]) def run_test(sess): return sess.run(out, feed_dict={p: a}) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_select_sameshape(self): a = [True, False, True, True] p = tf.compat.v1.placeholder(dtype=tf.bool) out = tf.where(p, x=[1] * 4, y=[0] * 4) def run_test(sess): return sess.run(out, feed_dict={p: a}) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_select_diffrank(self): a = [1, 1] x = [[0, 0], [2, 2]] y = [[2, 2], [1, 1]] p = tf.compat.v1.placeholder(dtype=tf.bool) out = tf.where(p, x, y) def run_test(sess): return sess.run(out, feed_dict={p: a}) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_select_complexshape1(self): a = np.random.random(size=[7]).astype(np.float32) x = np.random.random(size=[7, 3, 2, 1]).astype(np.float32) p = tf.compat.v1.placeholder(dtype=tf.bool) out = tf.where(p, x, x) def run_test(sess): return (sess.run(out, feed_dict={p: a})) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_select_complexshape2(self): a = np.random.random(size=[7]).astype(np.float32) x = np.random.random(size=[7, 3, 2, 7]).astype(np.float32) p = tf.compat.v1.placeholder(dtype=tf.bool) out = tf.where(p, x, x) def run_test(sess): return (sess.run(out, feed_dict={p: a})) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_select_complexshape3(self): a = np.random.random(size=[5]).astype(np.float32) x = np.random.random(size=[5, 3, 1]).astype(np.float32) p = tf.compat.v1.placeholder(dtype=tf.bool) out = tf.where(p, x, x) def run_test(sess): return (sess.run(out, feed_dict={p: a})) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() class TestWhere(NgraphTest): env_map = None def setup_method(self): self.env_map = self.store_env_variables( ['OPENVINO_TF_CONSTANT_FOLDING']) self.set_env_variable('OPENVINO_TF_CONSTANT_FOLDING', '1') def teardown_method(self): self.restore_env_variables(self.env_map) def test_where(self): a = np.array([1.1, 3.0], [2.2, 4.4]).astype(np.float32) p = tf.compat.v1.placeholder(dtype=tf.float32, shape=(2, 2)) out = tf.where(tf.equal(p, 3.0)) def run_test(sess): return sess.run(out, feed_dict={p: a}) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_where_scalar(self): a = [1.5] p = tf.compat.v1.placeholder(dtype=tf.bool) out = tf.where(p) def run_test(sess): return sess.run(out, feed_dict={p: a}) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_where_bool(self): a = [True, False, False, True, False] p = tf.compat.v1.placeholder(dtype=tf.bool) out = tf.where(p) def run_test(sess): return sess.run(out, feed_dict={p: a}) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all() def test_where_complexshape1(self): a = np.random.random(size=[7]).astype(np.float32) p = tf.compat.v1.placeholder(dtype=tf.bool) out = tf.where(p) def run_test(sess): return (sess.run(out, feed_dict={p: a})) assert ( self.with_ngraph(run_test) == self.without_ngraph(run_test)).all()

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c1e5e85284059d6aaef3993a41e3b7f6d6224cc2

99

py

Python

src/mathenjeu/apps/__init__.py

sdpython/mathenjeu

97fc9140ef89ac9c3c6ba46803121fd5d23eb8d1

[ "MIT" ]

1

2019-10-12T00:48:35.000Z

src/mathenjeu/apps/__init__.py

sdpython/mathenjeu

97fc9140ef89ac9c3c6ba46803121fd5d23eb8d1

[ "MIT" ]

8

2019-01-13T11:52:55.000Z

2020-11-19T01:27:28.000Z

src/mathenjeu/apps/__init__.py

sdpython/mathenjeu

97fc9140ef89ac9c3c6ba46803121fd5d23eb8d1

[ "MIT" ]

null

""" @file @brief Shortcut to *apps*. """ from .qcm import QCMApp from .staticapp import StaticApp

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de08d1aeee706c880dd50b180b53482e7adb433b

231

py

Python

lib/__init__.py

JoshOrndorff/snippets

ef06e03de09897014f88d89a84b597aabde7edaa

[ "Unlicense" ]

null

lib/__init__.py

JoshOrndorff/snippets

ef06e03de09897014f88d89a84b597aabde7edaa

[ "Unlicense" ]

null

lib/__init__.py

JoshOrndorff/snippets

ef06e03de09897014f88d89a84b597aabde7edaa

[ "Unlicense" ]

null

from .Cocurricular import Cocurricular from .LetterGrade import LetterGrade from .GeneralSchoolCourse import GeneralSchoolCourse from .Cname import Cname # Trying to make a better structured namespace here. #from . import ctyENGE

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1

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5

de0fa734b8a22b55814931cf787643e117d0322a

38

py

Python

reviser/tests/deploying/__init__.py

rocketboosters/reviser

03ee5eadd35db78cf122e48fac4d48981518af11

[ "MIT" ]

null

reviser/tests/deploying/__init__.py

rocketboosters/reviser

03ee5eadd35db78cf122e48fac4d48981518af11

[ "MIT" ]

null

reviser/tests/deploying/__init__.py

rocketboosters/reviser

03ee5eadd35db78cf122e48fac4d48981518af11

[ "MIT" ]

null

"""Tests for deploying subpackage."""

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0

5

de161aac762f73302d271978f27251dd6d34d2a7

99

py

Python

.eggs/py2app-0.14-py3.6.egg/py2app/recipes/matplotlib_prescript.py

stfbnc/mtsa_py

0dd14f0e51e3251f10b3da781867fbc7173608eb

[ "MIT" ]

17

2018-08-28T04:40:07.000Z

2021-12-15T06:19:31.000Z

.eggs/py2app-0.14-py3.6.egg/py2app/recipes/matplotlib_prescript.py

stfbnc/mtsa_py

0dd14f0e51e3251f10b3da781867fbc7173608eb

[ "MIT" ]

4

2019-05-17T09:35:30.000Z

2022-03-13T03:50:20.000Z

.eggs/py2app-0.14-py3.6.egg/py2app/recipes/matplotlib_prescript.py

stfbnc/mtsa_py

0dd14f0e51e3251f10b3da781867fbc7173608eb

[ "MIT" ]

3

2019-01-15T07:13:53.000Z

2020-03-29T00:48:39.000Z

import os os.environ['MATPLOTLIBDATA'] = os.path.join( os.environ['RESOURCEPATH'], 'mpl-data')

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0

0.111111

99

3

45

33

0.784091

0

0.343434

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

a9c0e776f1e7b634c239e9a0381a3419946cbc60

57

py

Python

aat/ui/__init__.py

mthomascarcamo/aat

fd86f513ccf79625516d2236be655498b24ec742

[ "Apache-2.0" ]

305

2020-02-24T02:25:43.000Z

2022-03-26T22:53:43.000Z

aat/ui/__init__.py

mthomascarcamo/aat

fd86f513ccf79625516d2236be655498b24ec742

[ "Apache-2.0" ]

79

2020-02-20T21:00:58.000Z

2022-03-27T14:06:26.000Z

aat/ui/__init__.py

mthomascarcamo/aat

fd86f513ccf79625516d2236be655498b24ec742

[ "Apache-2.0" ]

71

2020-05-10T11:52:25.000Z

2022-03-29T07:51:48.000Z

from .application import ServerApplication # noqa: F401

28.5

56

0.807018

6

57

7.666667

1

0

0.061224

0.140351

57

1

57

0.877551

0.175439

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

e727f4f9620a374cf23f59d8d68ab0adec19ec67

225

py

Python

python/8kyu/repeat_it.py

Sigmanificient/codewars

b34df4bf55460d312b7ddf121b46a707b549387a

[ "MIT" ]

3

2021-06-08T01:57:13.000Z

2021-06-26T10:52:47.000Z

python/8kyu/repeat_it.py

Sigmanificient/codewars

b34df4bf55460d312b7ddf121b46a707b549387a

[ "MIT" ]

null

python/8kyu/repeat_it.py

Sigmanificient/codewars

b34df4bf55460d312b7ddf121b46a707b549387a

[ "MIT" ]

2

2021-06-10T21:20:13.000Z

2021-06-30T10:13:26.000Z

"""Kata url: https://www.codewars.com/kata/557af9418895e44de7000053.""" from typing import Optional def repeat_it(string: Optional[str], n: int) -> str: return string * n if isinstance(string, str) else 'Not a string'

28.125

71

0.72

32

225

5.03125

0.75

0

0.098446

0.142222

225

7

72

32.142857

0.735751

0.288889

0

0.077922

0

1

0.333333

false

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

5

e7305163c22fc37fd055e2cf4375d7b83785e03c

255

py

Python

psi/context/api.py

bburan/psiexperiment

9b70f7f0b4a4379d8c3fc463e1df272153afd247

[ "MIT" ]

5

2016-05-26T13:46:00.000Z

2020-03-03T13:07:47.000Z

psi/context/api.py

bburan/psiexperiment

9b70f7f0b4a4379d8c3fc463e1df272153afd247

[ "MIT" ]

2

2018-04-17T15:06:35.000Z

2019-03-25T18:13:10.000Z

psi/context/api.py

bburan/psiexperiment

9b70f7f0b4a4379d8c3fc463e1df272153afd247

[ "MIT" ]

1

2016-05-28T19:36:38.000Z

from .context_item import ( BoolParameter, ContextGroup, ContextMeta, EnumParameter, Expression, FileParameter, OrderedContextMeta, Parameter, Result, UnorderedContextMeta ) from .selector import CartesianProduct, SingleSetting, SequenceSelector

36.428571

78

0.823529

20

255

10.45

0.9

0

0.117647

255

6

79

42.5

0.928889

0

1

0

true

0

0.4

0

0.4

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

e764d73557a58b76166ed9cf6b44749ca62b8e68

144

py

Python

WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/sys/package_dir_b/__init__.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/sys/package_dir_b/__init__.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/sys/package_dir_b/__init__.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

11.076923

57

0.534722

14

144

5.357143

0.785714

0.24

0.453333

0

0.042105

0.340278

144

12

58

12

0.747368

0.8125

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

1

0

1

0

null

0

1

0

5

e778cca89246c70fd08a4c9b98e8d471a1c6c386

181

py

Python

src/autograph/views.py

RobetSlovev39/AutoGraph

c8bdb358b95143ab0d8c6f7c475a6c21f7a76b95

[ "MIT" ]

null

src/autograph/views.py

RobetSlovev39/AutoGraph

c8bdb358b95143ab0d8c6f7c475a6c21f7a76b95

[ "MIT" ]

null

src/autograph/views.py

RobetSlovev39/AutoGraph

c8bdb358b95143ab0d8c6f7c475a6c21f7a76b95

[ "MIT" ]

null

from .services.core import update_devices from django.http import HttpResponse, HttpRequest def index_view(request: HttpRequest) -> HttpResponse: return HttpResponse('works')

25.857143

53

0.801105

21

181

6.809524

0.761905

0

0.121547

181

6

54

30.166667

0.899371

0

0.027624

0

1

0.25

false

0

0.5

0.25

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

e7831086c6096e715b8c0c4e2716db7db6e35fa5

466

py

Python

Layers/uni_lstm_layer.py

KaiQiangSong/Structure-Infused-Copy-Mechanism

da159ea47516894829d34d3db05bd87b0398bb02

[ "BSD-3-Clause" ]

33

2018-05-31T00:58:07.000Z

2021-12-10T06:51:12.000Z

Layers/uni_lstm_layer.py

KaiQiangSong/Structure-Infused-Copy-Mechanism

da159ea47516894829d34d3db05bd87b0398bb02

[ "BSD-3-Clause" ]

3

2018-10-31T15:55:16.000Z

2021-08-29T12:50:14.000Z

Layers/uni_lstm_layer.py

KaiQiangSong/Structure-Infused-Copy-Mechanism

da159ea47516894829d34d3db05bd87b0398bb02

[ "BSD-3-Clause" ]

3

2018-05-30T22:03:08.000Z

2019-07-22T21:04:10.000Z

import theano import theano.tensor as T import numpy as np from utility.utility import * from lstm_layer import * def uni_lstm_init(prefix, params, layer_setting): return lstm_init(prefix+'_forward', params, layer_setting) def uni_lstm_calc(prefix, params, layer_setting,state_below, h_init = None, c_init = None, mask = None, training = True): return lstm_calc(prefix+'_forward', params, layer_setting, state_below, h_init, c_init, mask, training = True)

38.833333

121

0.772532

72

466

4.736111

0.388889

0.129032

0.211144

0.140762

0.322581

0.193548

0

0.135193

466

12

122

38.833333

0.846154

0

0.034261

0

1

0.222222

false

0

0.555556

0.222222

1

0

null

0

1

0

null

0

1

0

1

0

5

e78e4f9ae652756ae223cca8f8d396788f6a0c19

168

py

Python

agents/utils.py

tlbai/atari-agents

31ec79180a8a9c070b811984e06888bd5da8baf2

[ "MIT" ]

3

2019-01-28T14:44:30.000Z

2019-05-07T06:07:03.000Z

agents/utils.py

happybai/atari-agents

31ec79180a8a9c070b811984e06888bd5da8baf2

[ "MIT" ]

null

agents/utils.py

happybai/atari-agents

31ec79180a8a9c070b811984e06888bd5da8baf2

[ "MIT" ]

1

2019-03-18T00:46:50.000Z

import numpy as np # https://en.wikipedia.org/wiki/Grayscale#Converting_color_to_grayscale def rgb2gray(image): return np.dot(image[...,:3], [0.299, 0.587, 0.114])

33.6

71

0.720238

28

168

4.214286

0.821429

0

0.092715

0.10119

168

5

72

33.6

0.688742

0.404762

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

e793fbc5a06e88c8c137cbb67e83bf012036208f

2,753

py

Python

demo/tests/test_pages.py

SocialGouv/ecollecte

1bfce2e0700b563c111c11452356b46ecb2630e4

[ "MIT" ]

9

2018-11-28T07:36:37.000Z

2022-02-04T12:56:11.000Z

demo/tests/test_pages.py

betagouv/e-controle

b6f790ca2590ac257a47930a1e521b86ce3edb29

[ "MIT" ]

154

2018-11-22T14:41:17.000Z

2022-02-12T08:48:57.000Z

demo/tests/test_pages.py

betagouv/e-controle

b6f790ca2590ac257a47930a1e521b86ce3edb29

[ "MIT" ]

10

2018-11-13T06:57:10.000Z

2022-03-21T13:04:49.000Z

import pytest from django.shortcuts import reverse from django.urls.exceptions import NoReverseMatch from tests import factories, utils pytestmark = pytest.mark.django_db def test_demo_user_is_logged_in_when_requesting_demo_page(client, settings): settings.DEBUG = True settings.ALLOW_DEMO_LOGIN = True settings.DEMO_INSPECTOR_USERNAME = 'inspector@test.com' utils.reload_urlconf() user = factories.UserFactory(username='inspector@test.com') user.is_superuser = False user.is_staff = False user.save() response = client.get(reverse('demo-inspector'), follow=True) assert response.status_code == 200 session_user = response.context['user'] assert session_user.is_authenticated def test_login_as_demo_user_is_not_available_if_debug_mode_if_off(client, settings): settings.DEBUG = False settings.ALLOW_DEMO_LOGIN = True settings.DEMO_INSPECTOR_USERNAME = 'inspector@test.com' utils.reload_urlconf() with pytest.raises(NoReverseMatch): reverse('demo-inspector') def test_login_as_demo_user_is_not_available_if_setting_prevents(client, settings): settings.DEBUG = True settings.ALLOW_DEMO_LOGIN = False settings.DEMO_INSPECTOR_USERNAME = 'inspector@test.com' utils.reload_urlconf() with pytest.raises(NoReverseMatch): reverse('demo-inspector') response = client.get('/demo-controleur/') assert response.status_code == 404 response = client.get('/demo/') assert response.status_code == 404 def test_demo_user_is_not_logged_in_if_superuser(client, settings): settings.DEBUG = True settings.ALLOW_DEMO_LOGIN = True settings.DEMO_INSPECTOR_USERNAME = 'inspector@test.com' utils.reload_urlconf() user = factories.UserFactory(username='inspector@test.com') user.is_superuser = True user.is_staff = False user.save() response = client.get(reverse('demo-inspector'), follow=True) assert response.status_code != 200 def test_demo_user_is_not_logged_in_if_staff(client, settings): settings.DEBUG = True settings.ALLOW_DEMO_LOGIN = True settings.DEMO_INSPECTOR_USERNAME = 'inspector@test.com' utils.reload_urlconf() user = factories.UserFactory(username='inspector@test.com') user.is_superuser = False user.is_staff = True user.save() response = client.get(reverse('demo-inspector'), follow=True) assert response.status_code != 200 def test_demo_user_is_not_logged_in_if_username_not_in_setting(client, settings): settings.DEBUG = True settings.ALLOW_DEMO_LOGIN = True settings.DEMO_INSPECTOR_USERNAME = None utils.reload_urlconf() response = client.get(reverse('demo-inspector'), follow=True) assert response.status_code != 200

33.573171

84

0.752997

357

2,753

5.515406

0.179272

0.039614

0.085323

0.097511

0.787202

0.751143

0.708989

0

0.007725

0.153651

2,753

81

85

33.987654

0.837339

0

0.676923

0

0.092626

0

0.107692

1

0.092308

false

0

0.061538

0

0.153846

0

null

0

1

0

null

0

5

e799da227391770e4722f65f64e95ca3940bc5e7

41

py

Python

tests/__init__.py

lfpratik/python_sbom

2129a0f551a9ed7e4859018011c33e2008d076f5

[ "Apache-2.0" ]

1

2021-06-07T16:00:54.000Z

tests/__init__.py

lfpratik/python_sbom

2129a0f551a9ed7e4859018011c33e2008d076f5

[ "Apache-2.0" ]

null

tests/__init__.py

lfpratik/python_sbom

2129a0f551a9ed7e4859018011c33e2008d076f5

[ "Apache-2.0" ]

1

2021-06-07T16:00:58.000Z

"""Unit test package for python_sbom."""

20.5

40

0.707317

6

41

4.666667

1

0

0.121951

41

1

41

0.777778

0.829268

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

82095a36c546695d809c85e4b6818394c6433a92

37

py

Python

sq.py

darkdebo/python_codes

5644482b7a7cb4d775de0194bae84024e24bfcaf

[ "MIT" ]

null

sq.py

darkdebo/python_codes

5644482b7a7cb4d775de0194bae84024e24bfcaf

[ "MIT" ]

1

2019-09-03T10:15:36.000Z

sq.py

darkdebo/python_codes

5644482b7a7cb4d775de0194bae84024e24bfcaf

[ "MIT" ]

null

print(sum([i**2 for i in range(11)]))

37

0.621622

9

37

2.555556

0.888889

0

0.090909

0.108108

37

1

37

0.606061

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

823bdd7765cb8136008a8c54947dec23d4d51c24

280

py

Python

crestdsl/model/api/__init__.py

stklik/CREST

7fd97c50b0c6c923e1c477105bed4f0ea032bb99

[ "MIT" ]

14

2019-08-06T10:17:46.000Z

2022-03-13T12:50:59.000Z

crestdsl/model/api/__init__.py

stklik/CREST

7fd97c50b0c6c923e1c477105bed4f0ea032bb99

[ "MIT" ]

16

2018-01-20T00:54:24.000Z

2019-07-24T15:43:42.000Z

crestdsl/model/api/__init__.py

stklik/CREST

7fd97c50b0c6c923e1c477105bed4f0ea032bb99

[ "MIT" ]

1

2021-02-01T15:33:24.000Z

# TODO: make this more beautiful by using the __all__ in the entity module, # rather than importing everything here from .api import get_parent, get_name, get_current, get_root, get_children, get_sources, get_targets from .convenienceAPI import pullup, relay, add, dependencies

40

100

0.807143

42

280

5.119048

0.785714

0

0.139286

280

6

101

46.666667

0.892116

0.396429

0

0.166667

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

1

0

5

41517b39849f7d892f4ec3a1e8aecb3557836ee2

85

py

Python

7_kyu/All_Star_Code_Challenge_1.py

UlrichBerntien/Codewars-Katas

bbd025e67aa352d313564d3862db19fffa39f552

[ "MIT" ]

null

7_kyu/All_Star_Code_Challenge_1.py

UlrichBerntien/Codewars-Katas

bbd025e67aa352d313564d3862db19fffa39f552

[ "MIT" ]

null

7_kyu/All_Star_Code_Challenge_1.py

UlrichBerntien/Codewars-Katas

bbd025e67aa352d313564d3862db19fffa39f552

[ "MIT" ]

null

def sum_ppg(player_one, player_two): return player_one['ppg'] + player_two['ppg']

42.5

48

0.729412

14

85

4.071429

0.5

0.315789

0

0.117647

85

2

48

42.5

0.76

0

0.069767

0

1

0.5

false

0

0.5

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

415fa2aaddf6a5244523209b3983e9c11e5afbc6

70

py

Python

Day8/ex3/package/subpackage2/module.py

ash2shukla/Python101-ABESIT

f6460fed42b3076ce2cb510e4bc09db758a81a0d

[ "MIT" ]

36

2018-06-19T14:08:54.000Z

2020-01-06T14:58:03.000Z

Day8/ex3/package/subpackage2/module.py

ash2shukla/Python101-ABESIT

f6460fed42b3076ce2cb510e4bc09db758a81a0d

[ "MIT" ]

null

Day8/ex3/package/subpackage2/module.py

ash2shukla/Python101-ABESIT

f6460fed42b3076ce2cb510e4bc09db758a81a0d

[ "MIT" ]

19

2018-06-11T19:31:07.000Z

2020-10-05T12:42:34.000Z

def module_method(arg): print('Module Method inside subpackage2',arg)

35

46

0.8

10

70

5.5

0.7

0.436364

0

0.015625

0.085714

70

2

46

35

0.84375

0

0.450704

0

1

0.5

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

5

4168c696cee489bfdfd5086c577e841027fca447

62

py

Python

tests/guinea-pig/lab_assistant.py

carocad/daVinci

4045bc7af9f0900d42d2576f7bdab98ab47d7ac2

[ "Apache-2.0" ]

2

2016-04-17T11:20:26.000Z

2018-05-24T22:20:24.000Z

tests/guinea-pig/lab_assistant.py

carocad/daVinci

4045bc7af9f0900d42d2576f7bdab98ab47d7ac2

[ "Apache-2.0" ]

16

2015-05-19T21:20:04.000Z

2015-06-27T12:41:19.000Z

tests/guinea-pig/lab_assistant.py

carocad/CodeInk

4045bc7af9f0900d42d2576f7bdab98ab47d7ac2

[ "Apache-2.0" ]

null

from cage1 import pig1 from cage2.pig2 import eat import os

10.333333

26

0.790323

11

62

4.454545

0.727273

0

0.08

0.193548

62

5

27

12.4

0.9

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

4177ce77dc6e423eb65e4787f4658d1a62eb0746

45

py

Python

addition.py

subhankarbehera/python

25c57abda91775fc60140e14a9a9621e4bd898c7

[ "MIT" ]

null

addition.py

subhankarbehera/python

25c57abda91775fc60140e14a9a9621e4bd898c7

[ "MIT" ]

null

addition.py

subhankarbehera/python

25c57abda91775fc60140e14a9a9621e4bd898c7

[ "MIT" ]

null

x=19 y=20.35 print(x); print(y); print(x+y);

7.5

11

0.6

12

45

2.25

0.5

0.444444

0

0.15

0.111111

45

5

12

9

0.525

0

1

0

false

0

0.6

1

0

null

1

0

1

0

null

0

1

0

5

41aad2b714bb01bcf7e57da5192f0407d2025a22

276

py

Python

swarmdjango/core/models/User.py

YCP-Swarm-Robotics-Capstone-2020-2021/swarm-website-backend

081d1930cc9283ee299d373f91f7c127f466c104

[ "MIT" ]

null

swarmdjango/core/models/User.py

YCP-Swarm-Robotics-Capstone-2020-2021/swarm-website-backend

081d1930cc9283ee299d373f91f7c127f466c104

[ "MIT" ]

51

2020-08-31T16:50:09.000Z

2021-05-10T03:04:18.000Z

swarmdjango/core/models/User.py

YCP-Swarm-Robotics-Capstone-2020-2021/swarm-website-backend

081d1930cc9283ee299d373f91f7c127f466c104

[ "MIT" ]

null

from django.db import models class User(models.Model): username = models.TextField() password = models.TextField() email = models.EmailField() firstName = models.TextField() lastName = models.TextField() accountLevel = models.IntegerField(default=0);

27.6

50

0.710145

29

276

6.758621

0.655172

0.306122

0

0.004405

0.177536

276

9

51

30.666667

0.859031

0

1

0

false

0.125

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

68dbbb00a7cfbda21af01002eb1510ee58cd606f

28

py

Python

graph-tool/doc/sphinxext/__init__.py

johankaito/fufuka

32a96ecf98ce305c2206c38443e58fdec88c788d

[ "Apache-2.0" ]

1

2015-08-04T19:41:53.000Z

graph-tool/doc/sphinxext/__init__.py

johankaito/fufuka

32a96ecf98ce305c2206c38443e58fdec88c788d

[ "Apache-2.0" ]

null

graph-tool/doc/sphinxext/__init__.py

johankaito/fufuka

32a96ecf98ce305c2206c38443e58fdec88c788d

[ "Apache-2.0" ]

null

from .numpydoc import setup

14

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null

0

1

0

1

0

5

68e2875068044e9c7bffef8433d0d649bf604c66

7,322

py

Python

hyperparameters/generator.py

ZHENTAN007/Chem-Graph-Kernel-Machine

963fdad2d0d6f2bfd962778caee0591b44ecbdcd

[ "MIT" ]

7

2021-02-28T11:44:12.000Z

2021-12-13T07:17:05.000Z

hyperparameters/generator.py

ZHENTAN007/Chem-Graph-Kernel-Machine

963fdad2d0d6f2bfd962778caee0591b44ecbdcd

[ "MIT" ]

2

2021-05-13T14:08:48.000Z

2021-06-14T12:28:20.000Z

hyperparameters/generator.py

ZHENTAN007/Chem-Graph-Kernel-Machine

963fdad2d0d6f2bfd962778caee0591b44ecbdcd

[ "MIT" ]

4

2021-03-05T08:20:47.000Z

2021-12-13T07:17:39.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from typing import Dict, Iterator, List, Optional, Union, Literal, Tuple class HyperJsonGenerator: def tMGR(self, k: float = 0.9, k_bounds: Tuple[float, float] = (0.75, 1.0), k_an=0.75): return { 'Normalization': [10000, (1000, 30000)], 'a_type': ['Tensorproduct', 'fixed'], 'atom_AtomicNumber': {'kDelta': [k_an, k_bounds, 0.05]}, 'atom_AtomicNumber_list_1': {'kConv': [k, k_bounds, 0.05]}, 'atom_AtomicNumber_list_2': {'kConv': [k, k_bounds, 0.05]}, 'atom_AtomicNumber_list_3': {'kConv': [k, k_bounds, 0.05]}, 'atom_AtomicNumber_list_4': {'kConv': [k, k_bounds, 0.05]}, 'atom_MorganHash': {'kDelta': [k, k_bounds, 0.05]}, 'atom_Ring_count': {'kDelta': [k, k_bounds, 0.05]}, 'atom_RingSize_list': {'kConv': [k, k_bounds, 0.05]}, 'atom_Hcount': {'kDelta': [k, k_bounds, 0.05]}, 'atom_AtomicNumber_count_1': {'kDelta': [k, k_bounds, 0.05]}, 'atom_AtomicNumber_count_2': {'kDelta': [k, k_bounds, 0.05]}, 'atom_Chiral': {'kDelta': [k, k_bounds, 0.05]}, 'b_type': ['Tensorproduct', 'fixed'], 'bond_Order': {'kDelta': [k, k_bounds, 0.05]}, 'bond_Stereo': {'kDelta': [k, k_bounds, 0.05]}, 'bond_RingStereo': {'kDelta': [k, k_bounds, 0.05]}, 'p_type': ['Additive_p', 'fixed'], 'probability_AtomicNumber': {'Const_p': [1.0, 'fixed']}, 'q': [0.01, [0.01, 0.5], 0.01], } def add(self, k: float = 0.9, k_bounds: Tuple[float, float] = (0.75, 1.0), c: float = 1.0, c_bounds: Tuple[float, float] = (1.0, 10.0)): return { 'Normalization': [10000, (1000, 30000)], 'a_type': ['Additive', 'fixed'], 'atom_AtomicNumber': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds]}, 'atom_AtomicNumber_list_1': {'Const': [c, c_bounds], 'kConv': [k, k_bounds]}, 'atom_AtomicNumber_list_2': {'Const': [c, c_bounds], 'kConv': [k, k_bounds]}, 'atom_AtomicNumber_list_3': {'Const': [c, c_bounds], 'kConv': [k, k_bounds]}, 'atom_AtomicNumber_list_4': {'Const': [c, c_bounds], 'kConv': [k, k_bounds]}, 'atom_MorganHash': {'Const': [c, c_bounds], 'kDelta': [k, k_bounds]}, 'atom_Ring_count': {'Const': [c, c_bounds], 'kDelta': [k, k_bounds]}, 'atom_RingSize_list': {'Const': [c, c_bounds], 'kConv': [k, k_bounds]}, 'atom_Hcount': {'Const': [c, c_bounds], 'kDelta': [k, k_bounds]}, 'atom_AtomicNumber_count_1': {'Const': [c, c_bounds], 'kDelta': [k, k_bounds]}, 'atom_AtomicNumber_count_2': {'Const': [c, c_bounds], 'kDelta': [k, k_bounds]}, 'atom_Chiral': {'Const': [c, c_bounds], 'kDelta': [k, k_bounds]}, 'b_type': ['Additive', 'fixed'], 'bond_Order': {'Const': [c, c_bounds], 'kDelta': [k, k_bounds]}, 'bond_Stereo': {'Const': [c, c_bounds], 'kDelta': [k, k_bounds]}, 'bond_RingStereo': {'Const': [c, c_bounds], 'kDelta': [k, k_bounds]}, 'p_type': ['Additive_p', 'fixed'], 'probability_AtomicNumber': {'Const_p': [1.0, "fixed"]}, 'q': [0.01, (0.001, 0.5)], } def general(self, k: float = 0.9, k_bounds: Tuple[float, float] = (0.75, 1.0), c: float = 1.0, c_bounds: Tuple[float, float] = (1.0, 10.0), p: float = 1.0, p_bounds: Tuple[float, float] = (1.0, 10.0)): k_uniform = 0.05 p_uniform = 1.0 return { 'Normalization': [10000, (1000, 50000), 1000], 'a_type': ['Additive', 'fixed'], 'atom_AtomicNumber': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'atom_AtomicNumber_list_1': {'Const': [c, c_bounds, 1.0], 'kConv': [k, k_bounds, k_uniform]}, 'atom_AtomicNumber_list_2': {'Const': [c, c_bounds, 1.0], 'kConv': [k, k_bounds, k_uniform]}, 'atom_AtomicNumber_list_3': {'Const': [c, c_bounds, 1.0], 'kConv': [k, k_bounds, k_uniform]}, 'atom_AtomicNumber_list_4': {'Const': [c, c_bounds, 1.0], 'kConv': [k, k_bounds, k_uniform]}, 'atom_MorganHash': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'atom_Ring_count': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'atom_RingSize_list': {'Const': [c, c_bounds, 1.0], 'kConv': [k, k_bounds, k_uniform]}, 'atom_Hcount': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'atom_AtomicNumber_count_1': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'atom_AtomicNumber_count_2': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'atom_Chiral': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'b_type': ['Additive', 'fixed'], 'bond_Order': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'bond_Stereo': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'bond_RingStereo': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'bond_Conjugated': {'Const': [c, c_bounds, 1.0], 'kDelta': [k, k_bounds, k_uniform]}, 'p_type': ['Additive_p', 'fixed'], 'probability_AtomicNumber': {'Const_p': [1.0, "fixed"]}, 'probability_group_an5': {'Assign_p': [p, p_bounds, p_uniform]}, 'probability_group_an6': {'Assign_p': [p, p_bounds, p_uniform]}, 'probability_group_an7': {'Assign_p': [p, p_bounds, p_uniform]}, 'probability_group_an8': {'Assign_p': [p, p_bounds, p_uniform]}, 'probability_group_an9': {'Assign_p': [p, p_bounds, p_uniform]}, 'probability_group_an14': {'Assign_p': [p, p_bounds, p_uniform]}, 'probability_group_an15': {'Assign_p': [p, p_bounds, p_uniform]}, 'probability_group_an16': {'Assign_p': [p, p_bounds, p_uniform]}, 'probability_group_an17': {'Assign_p': [p, p_bounds, p_uniform]}, 'probability_group_an35': {'Assign_p': [p, p_bounds, p_uniform]}, 'probability_group_an53': {'Assign_p': [p, p_bounds, p_uniform]}, 'q': [0.01, (0.01, 0.5), 0.01], } hyper_json = HyperJsonGenerator() product_hyper = hyper_json.tMGR() open('tMGR.json', 'w').write( json.dumps(product_hyper, indent=1, sort_keys=False)) product_hyper['Normalization'] = [True, 'fixed'] open('tMGR-Norm.json', 'w').write( json.dumps(product_hyper, indent=1, sort_keys=False)) product_hyper['Normalization'] = [False, 'fixed'] open('tMGR-non-Norm.json', 'w').write( json.dumps(product_hyper, indent=1, sort_keys=False)) additive_hyper = hyper_json.general() open('additive.json', 'w').write( json.dumps(additive_hyper, indent=1, sort_keys=False)) additive_hyper['Normalization'] = [True, 'fixed'] open('additive-Norm.json', 'w').write( json.dumps(additive_hyper, indent=1, sort_keys=False)) additive_hyper['Normalization'] = [False, 'fixed'] open('additive-non-Norm.json', 'w').write( json.dumps(additive_hyper, indent=1, sort_keys=False))

60.512397

107

0.566375

993

7,322

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7,322

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0.080357

0

null

0

1

0

null

0

5

6b7c75e2791a351df267d546fb8fe8519bdc217c

13,516

py

Python

tests/nlu_core_tests/namespace_tests.py

milyiyo/nlu

d209ed11c6a84639c268f08435552248391c5573

[ "Apache-2.0" ]

480

2020-08-24T02:36:40.000Z

2022-03-30T08:09:43.000Z

tests/nlu_core_tests/namespace_tests.py

milyiyo/nlu

d209ed11c6a84639c268f08435552248391c5573

[ "Apache-2.0" ]

28

2020-09-26T18:55:43.000Z

2022-03-26T01:05:45.000Z

tests/nlu_core_tests/namespace_tests.py

milyiyo/nlu

d209ed11c6a84639c268f08435552248391c5573

[ "Apache-2.0" ]

76

2020-09-25T22:55:12.000Z

2022-03-17T20:25:52.000Z

import unittest from tests.test_utils import get_sample_pdf_with_labels, get_sample_pdf, get_sample_sdf, get_sample_pdf_with_extra_cols, get_sample_pdf_with_no_text_col ,get_sample_spark_dataframe from nlu import * class TestNameSpace(unittest.TestCase): def test_tokenize(self): df = nlu.load('en.tokenize').predict('What a wonderful day!') print(df) df = nlu.load('tokenize').predict('What a wonderful day!') print(df) def test_pos(self): df = nlu.load('pos', verbose=True).predict('What a wonderful day!') print(df) # # def test_embed(self): # # df = nlu.load('en.embed').predict('What a wonderful day!') # # # # print(df) # # df = nlu.load('embed').predict('What a wonderful day!') # print(df) # # # def test_embed_glove(self): # df = nlu.load('en.embed.glove').predict('What a wonderful day!') # # print(df) # # df = nlu.load('embed.glove').predict('What a wonderful day!') # print(df) # df = nlu.load('glove').predict('What a wonderful day!') # print(df) # def test_sentiment_twitter_out(self): # res=nlu.load('en.sentiment.twitter',verbose=True).predict('@elonmusk Tesla stock price is too high imo') # ifninite loop ?? res = nlu.load('en.sentiment.imdb',verbose=True).predict('The Matrix was a pretty good movie') print(res) print(res.columns) def test_output_levels(self): print('token test') df = nlu.load('sentiment',verbose=True).predict('What a wonderful day!', output_level='token') print(df) print('document test') df = nlu.load('sentiment',verbose=True).predict('What a wonderful day!', output_level='document') print(df) print('sentence test') df = nlu.load('sentiment',verbose=True).predict('What a wonderful day!', output_level='sentence') print(df) print('chunk test') df = nlu.load('sentiment',verbose=True).predict('I like peanut butter and jelly!', output_level='chunk') print(df) def test_ner_multilingual(self): df = nlu.load('ner',verbose=True).predict('New York is a great place and America aswell') print(df) def test_sentiment(self): df = nlu.load('en.sentiment').predict('What a wonderful day!') def test_emotion(self): df = nlu.load('en.classify.emotion').predict('What a wonderful day!') print(df) def test_spell(self): df = nlu.load('spell').predict('What a wonderful day!') print(df) # def test_dependency(self): df = nlu.load('dep', verbose=True).predict('What a wonderful day!') print(df) def test_dependency_untyped(self): df = nlu.load('dep.untyped', verbose=True).predict('What a wonderful day!') print(df) def test_bert(self): df = nlu.load('bert').predict('What a wonderful day!') print(df) def test_lang(self): df = nlu.load('lang', verbose=True).predict('What a wonderful day!') print(df) print(df.columns) print(df['language_de']) print(df['language_fr']) print(len(df['language_de'][0])) # df = nlu.load('xx.classify.lang').predict('What a wonderful day!') # print(df) # df = nlu.load('classify.lang').predict('What a wonderful day!') # print(df) # print(df) def test_explain(self): df = nlu.load('en.explain').predict('What a wonderful day!') print(df) df = nlu.load('explain').predict('What a wonderful day!') print(df) def test_match(self): df = nlu.load('match.date',verbose=True).predict('What a wonderful day!') print(df) # df = nlu.load('en.match.date').predict('What a wonderful day!') # print(df) def test_clean_stop(self): # df = nlu.load('clean.stop').predict('What a wonderful day!') # print(df) df = nlu.load('en.clean.stop').predict('What a wonderful day!') print(df) def test_spell(self): df = nlu.load('spell').predict('What a wonderful day!') print(df) df = nlu.load('en.spell').predict('What a wonderful day!') print(df) # def test_all_spell(self): # df = nlu.load('en.spell.symmetric').predict('What a wonderful day!') # # print(df) # # df = nlu.load('en.spell.context').predict('What a wonderful day!') # print(df) # df = nlu.load('en.spell.norvig').predict('What a wonderful day!') # # print(df) # df = nlu.load('spell').predict('What a wonderful day!') # # print(df) # # df = nlu.load('en.spell').predict('What a wonderful day!') # # print(df) # def test_biobert(self): # df = nlu.load('biobert').predict('What a wonderful day!') # # print(df) # # df = nlu.load('en.embed.biobert').predict('What a wonderful day!') # print(df) # # def test_elmo(self): # df = nlu.load('en.embed.elmo').predict('What a wonderful day!') # print(df) # df = nlu.load('elmo').predict('What a wonderful day!') # print(df) # # def test_use(self): # df = nlu.load('en.embed.use').predict('What a wonderful day!') # # print(df) # # df = nlu.load('use').predict('What a wonderful day!') # print(df) # # def test_albert(self): # df = nlu.load('en.embed.albert').predict('What a wonderful day!') # # print(df) # # df = nlu.load('albert').predict('What a wonderful day!') # print(df) # # def test_xlnet(self): # df = nlu.load('en.embed.xlnet').predict('What a wonderful day!') # # print(df) # # df = nlu.load('xlnet').predict('What a wonderful day!') # print(df) def test_lemma(self): df = nlu.load('lemma').predict('What a wonderful day!') print(df) df = nlu.load('en.lemma').predict('What a wonderful day!') print(df) # def test_norm(self): # df = nlu.load('lemma').predict('What a wonderful day!') # # print(df) # df = nlu.load('en.lemma').predict('What a wonderful day!') # # print(df) # # def test_use(self): # df = nlu.load('en.embed_sentence.use').predict('What a wonderful day!') # print(df) # # def test_glove(self): # df = nlu.load('nl.ner.wikiner.glove_6B_300').predict('What a wonderful day!') # # print(df) def test_sentence_detector(self): df = nlu.load('sentence_detector', verbose=True).predict('What a wonderful day! Tomorrow will be even better!') print(df) def test_stopwords(self): df = nlu.load('match.chunk').predict('What a wonderful day!') print(df) def test_classify_lang(self): df = nlu.load('xx.classify.wiki_7').predict('What a wonderful day!') print(df) def test_sentiment_on_datasets(self): df = nlu.load('sentiment.twitter').predict('What a wonderful day!') print(df) # df = nlu.load('sentiment.imdb').predict('What a wonderful day!') # print(df) def test_multiple_nlu_references(self): # df = nlu.load('elmo bert').predict('What a wonderful day!') df = nlu.load('elmo').predict('What a wonderful day!') print(df) # df = nlu.load('sentiment.imdb').predict('What a wonderful day!') # print(df) def test_sentiment_output(self): res = nlu.load('sentiment',verbose=True).predict('Your life is the sum of a remainder of an unbalanced equation inherent to the programming of the matrix. You are the eventuality of an anomaly, which despite my sincerest efforts I have been unable to eliminate from what is otherwise a harmony of mathematical precision. While it remains a burden assiduously avoided, it is not unexpected, and thus not beyond a measure of control. Which has led you, inexorably, here.', output_level='sentence') # res = nlu.load('bert',verbose=True).predict('@Your life is the sum of a remainder of an unbalanced equation inherent to the programming of the matrix. You are the eventuality of an anomaly, which despite my sincerest efforts I have been unable to eliminate from what is otherwise a harmony of mathematical precision. While it remains a burden assiduously avoided, it is not unexpected, and thus not beyond a measure of control. Which has led you, inexorably, here.', output_level='sentence') print(res) print(res['sentiment']) print(res.dtypes) def test_stem(self): pdf = get_sample_pdf() res = nlu.load('stem',verbose=True).predict(pdf ) print(res) res = nlu.load('en.stem',verbose=True).predict(pdf) print(res) def test_norm(self): pdf = get_sample_pdf() res = nlu.load('norm',verbose=True).predict(pdf, output_positions=True ) print(res) # res = nlu.load('en.norm',verbose=True).predict(pdf) # print(res) def test_chunk(self): res = nlu.load('chunk',verbose=True).predict('I like peanut butter and jelly!' ) print(res) def test_ngram(self): pdf = get_sample_pdf() # res = nlu.load('ngram',verbose=True).predict(pdf ) pipe = nlu.load('ngram',verbose=True) # print(res['ngrams']) print("PIPE", pipe) res = nlu.load('en.ngram',verbose=True).predict(pdf) print(res['ngrams']) def test_chunk_embeds(self): pdf = get_sample_pdf() res = nlu.load('embed_chunk',verbose=True).predict("What a wondful day!" ) print(res) res = nlu.load('en.embed_chunk',verbose=True).predict(pdf) print(res) def test_regex_matcher(self): pdf = get_sample_pdf() res = nlu.load('match.regex',verbose=True).predict(pdf ) print(res) def test_text_matcher(self): pdf = get_sample_pdf() res = nlu.load('match.text',verbose=True).predict(pdf ) print(res) def test_auto_sentence_embed_bert(self): # TODO WIP pdf = get_sample_pdf() res = nlu.load('embed_sentence.bert',verbose=True).predict(pdf ) print(res) def test_auto_sentence_embed_elmo(self): # TODO WIP pdf = get_sample_pdf() res = nlu.load('embed_sentence.elmo',verbose=True).predict(pdf ) print(res) # def test_bad_pandas_column_datatype(self): # sdf = get_sample_spark_dataframe() # res = nlu.load('asdasj.asdas',verbose=True).predict(sdf, output_level='sentence') # # res = nlu.load('bert',verbose=True).predict('@Your life is the sum of a remainder of an unbalanced equation inherent to the programming of the matrix. You are the eventuality of an anomaly, which despite my sincerest efforts I have been unable to eliminate from what is otherwise a harmony of mathematical precision. While it remains a burden assiduously avoided, it is not unexpected, and thus not beyond a measure of control. Which has led you, inexorably, here.', output_level='sentence') # # print(res) # # def test_bad_pandas_dataframe_datatype(self): # sdf = get_sample_spark_dataframe() # res = nlu.load('asdasj.asdas',verbose=True).predict(sdf, output_level='sentence') # # res = nlu.load('bert',verbose=True).predict('@Your life is the sum of a remainder of an unbalanced equation inherent to the programming of the matrix. You are the eventuality of an anomaly, which despite my sincerest efforts I have been unable to eliminate from what is otherwise a harmony of mathematical precision. While it remains a burden assiduously avoided, it is not unexpected, and thus not beyond a measure of control. Which has led you, inexorably, here.', output_level='sentence') # # print(res) #2.6 test def test_electra(self): pdf = get_sample_pdf() res = nlu.load('en.embed.electra',verbose=True).predict(pdf ) print(res) def test_embed_sentence_bert(self): pdf = get_sample_pdf() res = nlu.load('en.embed_sentence.small_bert_L2_128',verbose=True).predict(pdf ) print(res) def test_embed_sentence_bert(self): pdf = get_sample_pdf() res = nlu.load('en.embed_sentence.biobert.pubmed_base_cased',verbose=True).predict(pdf ) print(res) def test_toxic(self): pdf = get_sample_pdf() res = nlu.load('en.classify.toxic',verbose=True).predict(pdf ) print(res) def test_e2e(self): pdf = get_sample_pdf() res = nlu.load('en.classify.e2e',verbose=True).predict(pdf ) print(res) def test_labse(self): pdf = get_sample_pdf() res = nlu.load('xx.embed_sentence.labse',verbose=True).predict(pdf ) print(res) def test_xx_bert(self): pdf = get_sample_pdf() res = nlu.load('xx.embed_sentence',verbose=True).predict(pdf ) print(res) def test_26_bert(self): res = nlu.load('en.ner.bert',verbose=True).predict('The NLU library is a machine learning library, simmilar to Tensorflow and Keras') print(res) if __name__ == '__main__': unittest.main()

35.382199

503

0.610462

1,831

13,516

4.403605

0.117422

0.077267

0.065856

0.148456

0.807516

0.748729

0.718467

0.709537

0.637356

0.566414

0

0.001586

0.253625

13,516

381

504

35.475066

0.79768

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0.005848

0.227236

0.011806

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0.002625

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1

0.233918

false

0

0.017544

0

0.25731

0.350877

0

null

0

1

0

null

0

1

0

5

6b975006473f3a1a27b8beec65cd2b431119ce42

20

py

Python

hello_word.py

codegenin/djangocourse-profiles-rest-api

9f26b27dd82593ed1de88d3c54fb6e97c7747c05

[ "MIT" ]

null

hello_word.py

codegenin/djangocourse-profiles-rest-api

9f26b27dd82593ed1de88d3c54fb6e97c7747c05

[ "MIT" ]

null

hello_word.py

codegenin/djangocourse-profiles-rest-api

9f26b27dd82593ed1de88d3c54fb6e97c7747c05

[ "MIT" ]

null

print('Hellow Word')

20

0.75

3

20

5

1

0

0.05

20

1

20

0.789474

0

0.52381

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

6bea389593d18f144baf635df00e828a46352027

425

py

Python

python/tests/test_counting_bits.py

kellyjoe256/coding-challenge-solutions

357e9c8e94cdcb7a3384ec8f4072c2e1facc9833

[ "MIT" ]

1

2021-04-15T22:25:55.000Z

python/tests/test_counting_bits.py

kellyjoe256/coding-challenge-solutions

357e9c8e94cdcb7a3384ec8f4072c2e1facc9833

[ "MIT" ]

null

python/tests/test_counting_bits.py

kellyjoe256/coding-challenge-solutions

357e9c8e94cdcb7a3384ec8f4072c2e1facc9833

[ "MIT" ]

1

2021-04-15T22:26:10.000Z

from assertpy import assert_that from counting_bits import count_bits def test_correct_count(): assert_that(count_bits(0)).is_equal_to(0) assert_that(count_bits(4)).is_equal_to(1) assert_that(count_bits(7)).is_equal_to(3) assert_that(count_bits(9)).is_equal_to(2) assert_that(count_bits(10)).is_equal_to(2) assert_that(count_bits(15)).is_equal_to(4) assert_that(count_bits(1234)).is_equal_to(5)

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d42a6390040bc6db3f0f5c3d631103c318c73921

106

py

Python

example/backers/admin.py

feinheit/zipfelchappe

1bbe56c910d2f33a047f8215ed432aac2988b70e

[ "BSD-3-Clause" ]

25

2015-05-17T22:13:37.000Z

2021-05-31T17:17:57.000Z

example/backers/admin.py

feinheit/zipfelchappe

1bbe56c910d2f33a047f8215ed432aac2988b70e

[ "BSD-3-Clause" ]

16

2015-03-12T09:19:27.000Z

2020-11-23T08:41:37.000Z

example/backers/admin.py

feinheit/zipfelchappe

1bbe56c910d2f33a047f8215ed432aac2988b70e

[ "BSD-3-Clause" ]

5

2015-04-27T15:17:53.000Z

2020-08-11T10:38:40.000Z

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

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0.839623

13

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6.846154

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0

0.103774

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null

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5

d444bc4ec7af475d3fdfa1292231dc720c884591

72

py

Python

authlib/specs/rfc7516/models.py

tk193192/authlib

4c60a628f64c6d385a06ea55e416092726b94d07

[ "BSD-3-Clause" ]

2

2021-04-26T18:17:37.000Z

2021-04-28T21:39:45.000Z

authlib/specs/rfc7516/models.py

tk193192/authlib

4c60a628f64c6d385a06ea55e416092726b94d07

[ "BSD-3-Clause" ]

4

2021-03-19T08:17:59.000Z

2021-06-10T19:34:36.000Z

authlib/specs/rfc7516/models.py

tk193192/authlib

4c60a628f64c6d385a06ea55e416092726b94d07

[ "BSD-3-Clause" ]

2

2021-05-24T20:34:12.000Z

2022-03-26T07:46:17.000Z

from authlib.jose import JWEAlgorithm, JWEEncAlgorithm, JWEZipAlgorithm

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0.954545

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5

d4468459c4213f0ec3625d6ce3bfc629b8faa6b1

4,514

py

Python

tests/unit/system_status/status_group_unit_test.py

BMeu/Orchard

cd595c9942e4e1ad0032193059f2b39fdf3bcfba

[ "MIT" ]

2

2016-10-06T21:19:32.000Z

2016-10-06T21:58:04.000Z

tests/unit/system_status/status_group_unit_test.py

BMeu/Orchard

cd595c9942e4e1ad0032193059f2b39fdf3bcfba

[ "MIT" ]

392

2016-10-06T17:13:30.000Z

2021-01-15T04:15:38.000Z

tests/unit/system_status/status_group_unit_test.py

BMeu/Orchard

cd595c9942e4e1ad0032193059f2b39fdf3bcfba

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Unit Test: orchard.system_status.status_group """ import unittest import orchard.system_status class StatusGroupUnitTest(unittest.TestCase): def setUp(self): app = orchard.create_app('Testing') self.app_context = app.app_context() self.app_context.push() self.client = app.test_client(use_cookies = True) def tearDown(self): self.app_context.pop() def test_initialization(self): status_group = orchard.system_status.StatusGroup('Group 1') self.assertEqual(status_group._label, 'Group 1') self.assertListEqual(status_group._items, []) self.assertFalse(status_group._has_subgroups) self.assertFalse(status_group._is_subgroup) def test_label(self): status_group = orchard.system_status.StatusGroup('Group 1') self.assertEqual(status_group.label, 'Group 1') def test_append(self): status_item_1 = orchard.system_status.StatusItem('Item 1', str) status_item_2 = orchard.system_status.StatusItem('Item 2', str) status_item_3 = orchard.system_status.StatusItem('Item 3', str) status_group_1 = orchard.system_status.StatusGroup('Group 1') status_group_2 = orchard.system_status.StatusGroup('Group 2') status_group_3 = orchard.system_status.StatusGroup('Group 3') self.assertListEqual(status_group_1._items, []) success = status_group_1.append(status_item_2) self.assertListEqual(status_group_1._items, [status_item_2]) self.assertTrue(success) success = status_group_1.append(status_item_3) self.assertListEqual(status_group_1._items, [status_item_2, status_item_3]) self.assertTrue(success) success = status_group_1.append(status_item_1) self.assertListEqual(status_group_1._items, [status_item_2, status_item_3, status_item_1]) self.assertTrue(success) success = status_group_1.append(status_item_3) self.assertListEqual(status_group_1._items, [status_item_2, status_item_3, status_item_1, status_item_3]) self.assertTrue(success) status_group_1._items = [] success = status_group_1.append(status_group_2) self.assertListEqual(status_group_1._items, [status_group_2]) self.assertTrue(success) self.assertTrue(status_group_1._has_subgroups) self.assertFalse(status_group_1._is_subgroup) self.assertFalse(status_group_2._has_subgroups) self.assertTrue(status_group_2._is_subgroup) success = status_group_2.append(status_group_3) self.assertListEqual(status_group_2._items, []) self.assertFalse(success) self.assertFalse(status_group_2._has_subgroups) self.assertTrue(status_group_2._is_subgroup) self.assertFalse(status_group_3._has_subgroups) self.assertFalse(status_group_3._is_subgroup) success = status_group_3.append(status_group_1) self.assertListEqual(status_group_3._items, []) self.assertFalse(success) self.assertFalse(status_group_3._has_subgroups) self.assertFalse(status_group_3._is_subgroup) self.assertTrue(status_group_1._has_subgroups) self.assertFalse(status_group_1._is_subgroup) success = status_group_1.append(status_group_3) self.assertListEqual(status_group_1._items, [status_group_2, status_group_3]) self.assertTrue(success) self.assertTrue(status_group_1._has_subgroups) self.assertFalse(status_group_1._is_subgroup) self.assertFalse(status_group_3._has_subgroups) self.assertTrue(status_group_3._is_subgroup) def test_iterator(self): status_item_1 = orchard.system_status.StatusItem('Item 1', str) status_item_2 = orchard.system_status.StatusItem('Item 2', str) status_item_3 = orchard.system_status.StatusItem('Item 3', str) status_group = orchard.system_status.StatusGroup('Group 1') items = [] for status_item in status_group: items.append(status_item.label) self.assertEqual(items, []) status_group.append(status_item_2) status_group.append(status_item_3) status_group.append(status_item_1) status_group.append(status_item_3) items = [] for status_item in status_group: items.append(status_item.label) self.assertEqual(items, ['Item 2', 'Item 3', 'Item 1', 'Item 3'])

39.946903

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0.089522

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0.85351

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0.703967

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4,514

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null

0

1

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5

d453702fe5955b8c7f1bde6f60ded7a799dc2c7f

1,939

py

Python

tpa/src/ognjen/typed_priority_array.py

savara94/TypedPriorityArray

8486184a21f767496978272a4861b9f3f7fd7495

[ "MIT" ]

null

tpa/src/ognjen/typed_priority_array.py

savara94/TypedPriorityArray

8486184a21f767496978272a4861b9f3f7fd7495

[ "MIT" ]

null

tpa/src/ognjen/typed_priority_array.py

savara94/TypedPriorityArray

8486184a21f767496978272a4861b9f3f7fd7495

[ "MIT" ]

null

""" Module containing TypedPriorityArray """ class TypedPriorityArray(object): """ Typed data structure that keeps elements in order. """ def __init__(self, *args, **kwargs): self.arg = list(args) self.kwarg = kwargs #self.my_list = list(self.arg) #raise NotImplementedError @property def length(self): return self.__len__() #return len(self.my_list) #raise NotImplementedError @property def array_type(self): return type(self.arg) #raise NotImplementedError @property def reversed(self): raise NotImplementedError @reversed.setter def reversed(self, descending): raise NotImplementedError def insert(self, element): raise NotImplementedError def pop(self, index): #self.arg = self.arg[:index] + self.arg[index+1:] return self.arg.pop(index) def contains(self, element): return self.__contains__(element) #raise NotImplementedError def index_of(self, element): return self.__getitem__(element) #ne vraca -1 #raise NotImplementedError def __contains__(self, element): cont = element in self.arg return cont #raise NotImplementedError def __iter__(self): i = self.arg.__iter__() return i #raise NotImplementedError def __getitem__(self, key): index = self.arg.index(key) return index # ne vraca -1 #raise NotImplementedError def __len__(self): return self.arg.__len__() #raise NotImplementedError def __str__(self): if reversed == True: return '<='.join(str(x) for x in self.arg) else: return '=>'.join(str(x) for x in self.arg) #raise NotImplementedError def __repr__(self): return self.__str__() #raise NotImplementedError

23.938272

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null

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5

2e0ccafb9ccfefea9bacac37872be87a641998c5

39

py

Python

gui/__init__.py

skylogic004/pylinkcloud

018535058d508a36c1ea5acf906bcb7114cd914c

[ "BSD-3-Clause" ]

2

2017-02-02T04:39:32.000Z

2017-12-11T07:09:18.000Z

gui/__init__.py

skylogic004/pylinkcloud

018535058d508a36c1ea5acf906bcb7114cd914c

[ "BSD-3-Clause" ]

null

gui/__init__.py

skylogic004/pylinkcloud

018535058d508a36c1ea5acf906bcb7114cd914c

[ "BSD-3-Clause" ]

null

# from .gui_controls import GuiControls

39

0.846154

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6.4

1

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1

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1

0

null

0

1

0

5

2e1785242c49aa97ac355e9f58271dec8e156fd1

264

py

Python

src/pandas_profiling/report/presentation/flavours/html/collapse.py

anurag-gandhi/pandas-profiling

2373f3a299264f7b312dbe4b92edc14d36e8140e

[ "MIT" ]

76

2020-07-06T14:44:05.000Z

2022-02-14T15:30:21.000Z

src/pandas_profiling/report/presentation/flavours/html/collapse.py

anurag-gandhi/pandas-profiling

2373f3a299264f7b312dbe4b92edc14d36e8140e

[ "MIT" ]

11

2020-08-09T02:30:14.000Z

2022-03-12T00:50:14.000Z

src/pandas_profiling/report/presentation/flavours/html/collapse.py

anurag-gandhi/pandas-profiling

2373f3a299264f7b312dbe4b92edc14d36e8140e

[ "MIT" ]

11

2020-07-12T16:18:07.000Z

2022-02-05T16:48:35.000Z

from pandas_profiling.report.presentation.core import Collapse from pandas_profiling.report.presentation.flavours.html import templates class HTMLCollapse(Collapse): def render(self): return templates.template("collapse.html").render(**self.content)

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