xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

aeadf47f1566dc963f3113497ae37f7465070600

217

py

Python

django_api/cas/admin.py

LonelVino/world-week-test

73d5201564cd1f3d7ece4ccb4aa5ba6baaf03f68

[ "MIT" ]

null

django_api/cas/admin.py

LonelVino/world-week-test

73d5201564cd1f3d7ece4ccb4aa5ba6baaf03f68

[ "MIT" ]

null

django_api/cas/admin.py

LonelVino/world-week-test

73d5201564cd1f3d7ece4ccb4aa5ba6baaf03f68

[ "MIT" ]

null

from django.contrib import admin from .models import Cas # 内置的表 class OrderAdmin(admin.ModelAdmin): list_display = ['username','password','role'] list_filter = ['created', 'updated'] admin.site.register(Cas)

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py

Python

solutions/Counting_power_sets.py

AlexGameAndWebDev/CodeWars-Python

222b8244e9f248dbb4e5fabd390dd4cce446dc84

[ "MIT" ]

44

2015-05-24T13:46:22.000Z

2022-03-22T10:40:10.000Z

solutions/Counting_power_sets.py

badruu/CodeWars-Python

222b8244e9f248dbb4e5fabd390dd4cce446dc84

[ "MIT" ]

3

2016-09-10T07:14:02.000Z

2021-09-14T12:16:25.000Z

solutions/Counting_power_sets.py

badruu/CodeWars-Python

222b8244e9f248dbb4e5fabd390dd4cce446dc84

[ "MIT" ]

48

2016-04-03T04:48:33.000Z

2022-03-14T23:32:17.000Z

""" Counting power sets http://www.codewars.com/kata/54381f0b6f032f933c000108/train/python """ def powers(lst): return 2 ** len(lst)

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py

Python

tests/test_models.py

bmdefreitas/star-wars-api-python

e118843b7a304433549085f5ee6a08bf455b4d43

[ "Apache-2.0" ]

null

tests/test_models.py

bmdefreitas/star-wars-api-python

e118843b7a304433549085f5ee6a08bf455b4d43

[ "Apache-2.0" ]

null

tests/test_models.py

bmdefreitas/star-wars-api-python

e118843b7a304433549085f5ee6a08bf455b4d43

[ "Apache-2.0" ]

null

def test_a(): x = "this" assert "h" in x def test_b(): x = "hello" assert "h" in x def test_c(): x = "world" assert "w" in x

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py

Python

pycue/FileSequence/__init__.py

writetoalex/TestOpenCue

e38df23f20b5f5be37b0f2a078e6a8dd2c562fc4

[ "Apache-2.0" ]

2

2019-01-27T10:08:30.000Z

2019-01-27T10:08:32.000Z

pycue/FileSequence/__init__.py

writetoalex/TestOpenCue

e38df23f20b5f5be37b0f2a078e6a8dd2c562fc4

[ "Apache-2.0" ]

null

pycue/FileSequence/__init__.py

writetoalex/TestOpenCue

e38df23f20b5f5be37b0f2a078e6a8dd2c562fc4

[ "Apache-2.0" ]

1

2019-01-27T12:54:26.000Z

from FrameRange import FrameRange from FrameSet import FrameSet

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9d7c0d6a36e316354346cd52846f2bbad84f42c5

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py

Python

tests/observers/observer.py

sando-io/flowmancer

34e6679651b00c1e8c78e211cac493708ce9b1b7

[ "MIT" ]

null

tests/observers/observer.py

sando-io/flowmancer

34e6679651b00c1e8c78e211cac493708ce9b1b7

[ "MIT" ]

21

2022-01-07T03:14:34.000Z

2022-01-22T22:32:20.000Z

tests/observers/observer.py

natsunlee/flowmancer

34e6679651b00c1e8c78e211cac493708ce9b1b7

[ "MIT" ]

null

import pytest from flowmancer.observers.observer import Observer def test_abstract_init_error(): with pytest.raises(TypeError): Observer() # type: ignore

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py

Python

codeforces/rounds/#367/fact.py

GinugaSaketh/Codes

e934aa5652dd86231a42e3f7f84b145eb35bf47d

[ "MIT" ]

null

codeforces/rounds/#367/fact.py

GinugaSaketh/Codes

e934aa5652dd86231a42e3f7f84b145eb35bf47d

[ "MIT" ]

null

codeforces/rounds/#367/fact.py

GinugaSaketh/Codes

e934aa5652dd86231a42e3f7f84b145eb35bf47d

[ "MIT" ]

null

a=200 f=1 while a>1: f=f*a a=a-1 print f

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9dcd475a4c8eb284fdf874e63572ac12951d3b5c

259

py

Python

lib/triplet_loss/triplet_loss_op.py

aditya2592/PoseCNN

a763120ce0ceb55cf3432980287ef463728f8052

[ "MIT" ]

655

2018-03-21T19:55:45.000Z

2022-03-25T20:41:21.000Z

lib/triplet_loss/triplet_loss_op.py

yuxng/FCN

77fbb50b4272514588a10a9f90b7d5f8d46974fb

[ "MIT" ]

122

2018-04-04T13:57:49.000Z

2022-03-18T09:28:44.000Z

lib/triplet_loss/triplet_loss_op.py

yuxng/FCN

77fbb50b4272514588a10a9f90b7d5f8d46974fb

[ "MIT" ]

226

2018-03-22T01:40:04.000Z

2022-03-17T11:56:14.000Z

import tensorflow as tf import os.path as osp filename = osp.join(osp.dirname(__file__), 'triplet_loss.so') _triplet_loss_module = tf.load_op_library(filename) triplet_loss = _triplet_loss_module.triplet triplet_loss_grad = _triplet_loss_module.triplet_grad

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py

Python

pycrostates/utils/tests/test_utils.py

mscheltienne/pycrostates

be87adf69c94b2b179064f337acd8a49d01c305d

[ "BSD-3-Clause" ]

1

2021-12-14T09:58:57.000Z

pycrostates/utils/tests/test_utils.py

mscheltienne/pycrostates

be87adf69c94b2b179064f337acd8a49d01c305d

[ "BSD-3-Clause" ]

null

pycrostates/utils/tests/test_utils.py

mscheltienne/pycrostates

be87adf69c94b2b179064f337acd8a49d01c305d

[ "BSD-3-Clause" ]

null

import pytest from mne import create_info from mne.io.constants import FIFF from pycrostates.io import ChInfo from pycrostates.utils import _compare_infos from pycrostates.utils._logs import logger, set_log_level set_log_level("INFO") logger.propagate = True def test_compare_infos(caplog): """Test _compare_infos(cluster_info, inst_info).""" # minimum chinfo chinfo = ChInfo(ch_names=["Fpz", "Cz", "CPz"], ch_types="eeg") _compare_infos(chinfo, chinfo.copy()) # with montage chinfo.set_montage("standard_1020") caplog.clear() _compare_infos(chinfo, chinfo.copy()) assert "does not have the same channels montage" not in caplog.text # with MNE info without montage info = create_info(["Fpz", "Cz", "CPz"], 1, "eeg") caplog.clear() _compare_infos(chinfo, info) assert "does not have the same channels montage" in caplog.text caplog.clear() _compare_infos(info, chinfo) assert "does not have the same channels montage" in caplog.text # with MNE info with montage info = create_info(["Fpz", "Cz", "CPz"], 1, "eeg") info.set_montage("standard_1020") caplog.clear() _compare_infos(chinfo, info) assert "does not have the same channels montage" not in caplog.text caplog.clear() _compare_infos(info, chinfo) assert "does not have the same channels montage" not in caplog.text caplog.clear() # with different channels chinfo1 = ChInfo(ch_names=["Fpz", "Cz", "CPz"], ch_types="eeg") chinfo2 = ChInfo(ch_names=["Oz", "Cz", "CPz"], ch_types="eeg") with pytest.raises(ValueError, match="does not have the same channels"): _compare_infos(chinfo1, chinfo2) with pytest.raises(ValueError, match="does not have the same channels"): _compare_infos(chinfo2, chinfo1) # with subset of channels info1 = ChInfo(ch_names=["Fpz", "Cz", "CPz"], ch_types="eeg") info2 = ChInfo(ch_names=["Cz", "CPz"], ch_types="eeg") with pytest.raises(ValueError, match="does not have the same channels"): _compare_infos(cluster_info=info1, inst_info=info2) _compare_infos(cluster_info=info2, inst_info=info1) # with different kind/unit/coord_frame chinfo1 = ChInfo(ch_names=["Fpz", "Cz", "CPz"], ch_types="eeg") chinfo1.set_montage("standard_1020") chinfo2 = chinfo1.copy() chinfo2["chs"][0]["unit"] = FIFF.FIFF_UNIT_C caplog.clear() _compare_infos(chinfo1, chinfo2) assert "does not have the same channels units" in caplog.text caplog.clear() chinfo2 = chinfo1.copy() chinfo2["chs"][0]["coord_frame"] = FIFF.FIFFV_COORD_DEVICE _compare_infos(chinfo1, chinfo2) assert "does not have the same coordinate frames" in caplog.text caplog.clear() chinfo2 = chinfo1.copy() chinfo2["chs"][0]["kind"] = FIFF.FIFFV_MEG_CH _compare_infos(chinfo1, chinfo2) assert "does not have the same channels kinds" in caplog.text caplog.clear() chinfo2 = chinfo1.copy() chinfo2["chs"][0]["unit"] = FIFF.FIFF_UNIT_C chinfo2["chs"][0]["coord_frame"] = FIFF.FIFFV_COORD_DEVICE chinfo2["chs"][0]["kind"] = FIFF.FIFFV_MEG_CH _compare_infos(chinfo1, chinfo2) assert "does not have the same channels units" in caplog.text assert "does not have the same coordinate frames" in caplog.text assert "does not have the same channels kinds" in caplog.text

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py

Python

tests/__init__.py

lowcloudnine/runinside

84011e3448dd5f1e4a094307eae0ce0834026b21

[ "MIT" ]

null

tests/__init__.py

lowcloudnine/runinside

84011e3448dd5f1e4a094307eae0ce0834026b21

[ "MIT" ]

1

2021-01-24T16:20:24.000Z

tests/__init__.py

lowcloudnine/runinside

84011e3448dd5f1e4a094307eae0ce0834026b21

[ "MIT" ]

1

2021-01-20T05:45:51.000Z

"""Unit test package for runinside."""

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Python

demo/person/tests/project/resources/person/controllers/test_legal_person_controllers.py

giovannifarlley/ms--fastapi-template

5bbd6903305db07cc18330ec86fb04ca518e9dab

[ "MIT" ]

24

2021-03-07T13:00:35.000Z

2022-02-11T03:41:51.000Z

demo/person/tests/project/resources/person/controllers/test_legal_person_controllers.py

giovannifarlley/ms--fastapi-template

5bbd6903305db07cc18330ec86fb04ca518e9dab

[ "MIT" ]

2

2021-05-15T01:05:17.000Z

2021-08-13T13:53:57.000Z

demo/person/tests/project/resources/person/controllers/test_legal_person_controllers.py

giovannifarlley/ms--fastapi-template

5bbd6903305db07cc18330ec86fb04ca518e9dab

[ "MIT" ]

4

2021-04-27T12:18:33.000Z

2021-10-03T23:43:23.000Z

import pytest from datetime import datetime from http import HTTPStatus as status from httpx import AsyncClient from tests.configurations.constants import API_URL from project.routers import app resource = "/person/legal/" _birthdate = str(datetime.strptime("1985-08-01", "%Y-%m-%d")) base_data = { "status": "active", "business_name": "teste of the corp", "fantasy_name": "teste corporate dev", "sponsor_business_document_id": "12132334354", "business_document_id": "54556576010287", "email": "test2corporate4dev@teste.com", "phone": "+5534988882222", } _output_data = {} @pytest.mark.asyncio async def test_save_legal_person_200(): async with AsyncClient(app=app, base_url=API_URL) as client: response = await client.post(resource, json=base_data) assert response.status_code == status.OK _output_data["id"] = response.json()["id"] @pytest.mark.asyncio async def test_save_legal_person_400(): async with AsyncClient(app=app, base_url=API_URL) as client: response = await client.post(resource, json=base_data) assert response.status_code == status.BAD_REQUEST @pytest.mark.asyncio async def test_save_legal_person_422(): async with AsyncClient(app=app, base_url=API_URL) as client: response = await client.post(resource, json={}) assert response.status_code == status.UNPROCESSABLE_ENTITY @pytest.mark.asyncio async def test_update_legal_person_200(): async with AsyncClient(app=app, base_url=API_URL) as client: _id = _output_data["id"] response = await client.put(f"{resource}{_id}", json={"email": "carlos.neto@teste.com"}) assert response.status_code == status.OK @pytest.mark.asyncio async def test_update_legal_person_500(): async with AsyncClient(app=app, base_url=API_URL) as client: response = await client.put(f"{resource}{_birthdate}", json={"email": "carlos.neto@teste.com"}) assert response.status_code == status.INTERNAL_SERVER_ERROR @pytest.mark.asyncio async def test_get_legal_person_by_id_200(): async with AsyncClient(app=app, base_url=API_URL) as client: _id = _output_data["id"] response = await client.get(f"{resource}by/id/{_id}") assert response.status_code == status.OK @pytest.mark.asyncio async def test_get_legal_person_by_id_500(): async with AsyncClient(app=app, base_url=API_URL) as client: response = await client.get(f"{resource}by/id/{_birthdate}") assert response.status_code == status.INTERNAL_SERVER_ERROR @pytest.mark.asyncio async def test_get_legal_person_qs_200(): async with AsyncClient(app=app, base_url=API_URL) as client: response = await client.get(f"{resource}by/qs", params={}) assert response.status_code == status.OK @pytest.mark.asyncio async def test_get_legal_person_qs_204(): async with AsyncClient(app=app, base_url=API_URL) as client: response = await client.get(f"{resource}by/qs", params={"business_name": "tony sterco"}) assert response.status_code == status.NO_CONTENT @pytest.mark.asyncio async def test_delete_legal_person_200(): async with AsyncClient(app=app, base_url=API_URL) as client: _id = _output_data["id"] response = await client.delete(f"{resource}by/{_id}") assert response.status_code == status.OK @pytest.mark.asyncio async def test_delete_legal_person_500(): async with AsyncClient(app=app, base_url=API_URL) as client: response = await client.delete(f"{resource}by/{_birthdate}") assert response.status_code == status.INTERNAL_SERVER_ERROR

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py

Python

conformalmapping/unitdisk.py

TorbenFricke/cmtoolkit

f1bf1ec191fd9b20e6edcd3385c8b9fee1d638ca

[ "BSD-3-Clause" ]

16

2017-10-14T17:13:48.000Z

2022-01-11T22:19:45.000Z

conformalmapping/unitdisk.py

TorbenFricke/cmtoolkit

f1bf1ec191fd9b20e6edcd3385c8b9fee1d638ca

[ "BSD-3-Clause" ]

11

2015-05-11T08:02:42.000Z

2020-05-21T16:13:45.000Z

conformalmapping/unitdisk.py

TorbenFricke/cmtoolkit

f1bf1ec191fd9b20e6edcd3385c8b9fee1d638ca

[ "BSD-3-Clause" ]

3

2019-12-31T23:07:29.000Z

2021-03-08T02:05:38.000Z

from .disk import Disk from .circle import Circle def unitdisk(): """creates a unit disk region. d = unitdisk() Creates the unit disk region by d = disk(0, 1). """ return Disk(Circle(0, 1))

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py

Python

venv/lib/python2.7/site-packages/requests_toolbelt/exceptions.py

LockScreen/Backend

42485a997f365172c7a63527f0df3b5707fd23f9

[ "MIT" ]

1

2016-04-07T12:16:09.000Z

venv/lib/python2.7/site-packages/requests_toolbelt/exceptions.py

LockScreen/Backend

42485a997f365172c7a63527f0df3b5707fd23f9

[ "MIT" ]

null

venv/lib/python2.7/site-packages/requests_toolbelt/exceptions.py

LockScreen/Backend

42485a997f365172c7a63527f0df3b5707fd23f9

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Collection of exceptions raised by requests-toolbelt.""" class StreamingError(Exception): """Used in :mod:`requests_toolbelt.downloadutils.stream`.""" pass

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55

py

Python

python/testData/inspections/PyRedundantParenthesesInspection/YieldFrom.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/inspections/PyRedundantParenthesesInspection/YieldFrom.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/inspections/PyRedundantParenthesesInspection/YieldFrom.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def method_name(in1): return (yield from func(in1))

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06140dd897fec072f78630a9ee309548de2074b3

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py

Python

tests/contracts/interop/test_binary.py

Degget1986/neo-mamba

da7312d5027f3e9b0e5421495d5c00915bdfd786

[ "MIT" ]

null

tests/contracts/interop/test_binary.py

Degget1986/neo-mamba

da7312d5027f3e9b0e5421495d5c00915bdfd786

[ "MIT" ]

null

tests/contracts/interop/test_binary.py

Degget1986/neo-mamba

da7312d5027f3e9b0e5421495d5c00915bdfd786

[ "MIT" ]

null

import unittest from neo3 import vm from tests.contracts.interop.utils import test_engine class BinaryInteropTestCase(unittest.TestCase): def test_serialization(self): engine = test_engine() engine.push(vm.IntegerStackItem(100)) engine.invoke_syscall_by_name("System.Binary.Serialize") item = engine.pop() self.assertIsInstance(item, vm.ByteStringStackItem) self.assertEqual(b'\x21\x01\x64', item.to_array()) # Create an item with data larger than engine.MAX_ITEM_SIZE # this should fail in the BinarySerializer class engine.push(vm.ByteStringStackItem(b'\x01' * (1024 * 1024 * 2))) with self.assertRaises(ValueError) as context: engine.invoke_syscall_by_name("System.Binary.Serialize") self.assertEqual("Output length exceeds max size", str(context.exception)) def test_deserialization(self): engine = test_engine() original_item = vm.IntegerStackItem(100) engine.push(original_item) engine.invoke_syscall_by_name("System.Binary.Serialize") engine.invoke_syscall_by_name("System.Binary.Deserialize") item = engine.pop() self.assertEqual(original_item, item) engine.push(vm.ByteStringStackItem(b'\xfa\x01')) with self.assertRaises(ValueError) as context: engine.invoke_syscall_by_name("System.Binary.Deserialize") self.assertEqual("Invalid format", str(context.exception)) def test_base64(self): engine = test_engine() original_item = vm.IntegerStackItem(100) engine.push(original_item) engine.invoke_syscall_by_name("System.Binary.Base64Encode") item = engine.pop() self.assertEqual('ZA==', item.to_array().decode()) engine.push(item) engine.invoke_syscall_by_name("System.Binary.Base64Decode") item = engine.pop() self.assertEqual(original_item, vm.IntegerStackItem(item.to_array())) def test_base58(self): engine = test_engine() original_item = vm.IntegerStackItem(100) engine.push(original_item) engine.invoke_syscall_by_name("System.Binary.Base58Encode") item = engine.pop() self.assertEqual('2j', item.to_array().decode()) engine.push(item) engine.invoke_syscall_by_name("System.Binary.Base58Decode") item = engine.pop() self.assertEqual(original_item, vm.IntegerStackItem(item.to_array())) def test_itoa(self): engine = test_engine() original_item = vm.IntegerStackItem(100) base = vm.IntegerStackItem(10) engine.push(base) engine.push(original_item) engine.invoke_syscall_by_name("System.Binary.Itoa") item = engine.pop() self.assertEqual('100', item.to_array().decode('utf-8')) engine = test_engine() base = vm.IntegerStackItem(16) engine.push(base) engine.push(original_item) engine.invoke_syscall_by_name("System.Binary.Itoa") item = engine.pop() self.assertEqual('64', item.to_array().decode('utf-8')) engine = test_engine() invalid_base = vm.IntegerStackItem(2) engine.push(invalid_base) engine.push(original_item) with self.assertRaises(ValueError) as context: engine.invoke_syscall_by_name("System.Binary.Itoa") self.assertIn("Invalid base specified", str(context.exception)) def test_atoi(self): engine = test_engine() original_item = vm.ByteStringStackItem(b'100') base = vm.IntegerStackItem(10) engine.push(base) engine.push(original_item) engine.invoke_syscall_by_name("System.Binary.Atoi") item = engine.pop() self.assertEqual(vm.IntegerStackItem(100), item) engine = test_engine() original_item = vm.ByteStringStackItem(b'64') base = vm.IntegerStackItem(16) engine.push(base) engine.push(original_item) engine.invoke_syscall_by_name("System.Binary.Atoi") item = engine.pop() self.assertEqual(vm.IntegerStackItem(100), item) engine = test_engine() invalid_base = vm.IntegerStackItem(2) engine.push(invalid_base) engine.push(original_item) with self.assertRaises(ValueError) as context: engine.invoke_syscall_by_name("System.Binary.Atoi") self.assertIn("Invalid base specified", str(context.exception))

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null

0

1

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1

0

null

0

5

ae182d4feefa4d891f2560e7d044273d7cdd9cfa

30

py

Python

Compiler Design Lab/ChocoPy_LLVM_Compiler/custom_tests/whilestmt.py

Abhishek-Aditya-bs/Lab-Projects-and-Assignments

fd2681a1c7453367a4df1790e58afb312f13998c

[ "MIT" ]

null

Compiler Design Lab/ChocoPy_LLVM_Compiler/custom_tests/whilestmt.py

Abhishek-Aditya-bs/Lab-Projects-and-Assignments

fd2681a1c7453367a4df1790e58afb312f13998c

[ "MIT" ]

null

Compiler Design Lab/ChocoPy_LLVM_Compiler/custom_tests/whilestmt.py

Abhishek-Aditya-bs/Lab-Projects-and-Assignments

fd2681a1c7453367a4df1790e58afb312f13998c

[ "MIT" ]

null

while True: printf("%d",1)

15

18

0.566667

5

30

3.4

1

0

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0.2

30

2

18

15

0.666667

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0.5

1

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null

0

1

0

null

0

1

0

1

0

5

ae3c1457734fabc1f68ffe066404bdc8e7e1d777

131

py

Python

twister/accounts/admin.py

ultr4nerd/twister_project

1627e27ced781f6ea715edd178c82e00dc5e8775

[ "MIT" ]

null

twister/accounts/admin.py

ultr4nerd/twister_project

1627e27ced781f6ea715edd178c82e00dc5e8775

[ "MIT" ]

5

2021-03-18T22:29:40.000Z

2022-03-11T23:41:52.000Z

twister/accounts/admin.py

ultr4nerd/twister_project

1627e27ced781f6ea715edd178c82e00dc5e8775

[ "MIT" ]

null

"""Admin config for accounts app""" from django.contrib import admin from .models import Profile admin.site.register(Profile)

14.555556

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5.555556

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null

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1

0

1

0

1

0

5

ae86d0679d61d6125135708ca69ca32e8acb5615

32

py

Python

plugins/uptime_plugin/__init__.py

StarryPy/StarryPy-Historic

b9dbd552b8c4631a5a8e9dda98b7ba447eca59da

[ "WTFPL" ]

38

2015-02-12T11:57:59.000Z

2018-11-15T16:03:45.000Z

plugins/uptime_plugin/__init__.py

StarryPy/StarryPy-Historic

b9dbd552b8c4631a5a8e9dda98b7ba447eca59da

[ "WTFPL" ]

68

2015-02-05T23:29:47.000Z

2017-12-27T08:26:25.000Z

plugins/uptime_plugin/__init__.py

StarryPy/StarryPy-Historic

b9dbd552b8c4631a5a8e9dda98b7ba447eca59da

[ "WTFPL" ]

21

2015-02-06T18:58:21.000Z

2017-12-24T20:08:59.000Z

from uptime import UptimePlugin

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null

0

1

0

1

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5

ae9c177d3942674ac9f5e057d337bbc8b66bcb46

46

py

Python

workspace/bat_test/c.py

Anylee2142/News_Rank_System

0185cf5cdbe709e27a8ec270733bef135ce32a89

[ "MIT" ]

null

workspace/bat_test/c.py

Anylee2142/News_Rank_System

0185cf5cdbe709e27a8ec270733bef135ce32a89

[ "MIT" ]

null

workspace/bat_test/c.py

Anylee2142/News_Rank_System

0185cf5cdbe709e27a8ec270733bef135ce32a89

[ "MIT" ]

null

import time print('completed !') time.sleep(1)

15.333333

20

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1

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null

0

1

0

1

0

5

882d44aaf1ae5054b13e6943021fd581bb5286f8

2,800

py

Python

test/test_rnn/test_learner.py

DwangoMediaVillage/marltas_core

91a5caf75c2350a31d47d1b0408c817644a0d41e

[ "MIT" ]

9

2021-02-15T08:20:31.000Z

2022-01-04T09:29:35.000Z

test/test_rnn/test_learner.py

DwangoMediaVillage/marltas_core

91a5caf75c2350a31d47d1b0408c817644a0d41e

[ "MIT" ]

null

test/test_rnn/test_learner.py

DwangoMediaVillage/marltas_core

91a5caf75c2350a31d47d1b0408c817644a0d41e

[ "MIT" ]

1

2021-09-21T16:11:17.000Z

import tempfile from pathlib import Path from dqn import episodic_curiosity from dqn.learner import LearnerConfig from dqn.rnn.config import IntrinsicRewardConfig, RNNConfigBase from dqn.rnn.datum import Batch, SampleFromBuffer from dqn.rnn.learner import Learner from dqn.rnn.policy import Policy def test_vector_obs_update(): config = RNNConfigBase(obs_shape=[ 2, ], intrinsic_reward=IntrinsicRewardConfig( enable=True, episodic_curiosity=episodic_curiosity.EpisodicCuriosityConfig(enable=True))) learner = Learner(config=config) batch = Batch.from_buffer_sample(sample=SampleFromBuffer.as_random(size=3, np_defs=config.sample_from_buffer_def)) learner.update_core(batch) def test_update(): config = RNNConfigBase(learner=LearnerConfig(batch_size=3, double_dqn=True, target_sync_interval=1), intrinsic_reward=IntrinsicRewardConfig( enable=True, episodic_curiosity=episodic_curiosity.EpisodicCuriosityConfig(enable=True))) learner = Learner(config=config) batch = Batch.from_buffer_sample(sample=SampleFromBuffer.as_random(size=3, np_defs=config.sample_from_buffer_def)) learner.update_core(batch) def test_save_model(): config = RNNConfigBase(learner=LearnerConfig(batch_size=2, target_sync_interval=1), intrinsic_reward=IntrinsicRewardConfig(enable=True)) learner = Learner(config=config) with tempfile.TemporaryDirectory() as log_dir: learner.save_model(log_dir=Path(log_dir), global_step=0) def test_update_param(): config = RNNConfigBase(learner=LearnerConfig(batch_size=3, double_dqn=True, target_sync_interval=1), intrinsic_reward=IntrinsicRewardConfig( enable=True, episodic_curiosity=episodic_curiosity.EpisodicCuriosityConfig(enable=True))) learner = Learner(config=config) policy = Policy(config=config) assert learner.online_model.get_param() != policy.model.get_param() assert learner.episodic_curiosity_module.embedding_network.get_param( ) != policy.episodic_curiosity_module.embedding_network.get_param() assert learner.episodic_curiosity_module.inverse_model.get_param( ) != policy.episodic_curiosity_module.inverse_model.get_param() policy.update_model_param(learner.get_model_param(), only_online_model=False) assert learner.online_model.get_param() == policy.model.get_param() assert learner.episodic_curiosity_module.embedding_network.get_param( ) == policy.episodic_curiosity_module.embedding_network.get_param() assert learner.episodic_curiosity_module.inverse_model.get_param( ) == policy.episodic_curiosity_module.inverse_model.get_param()

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0.255319

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null

0

1

0

null

0

5

883f862cc0c20708c1aae1c0650d64555ee69dd7

203

py

Python

backend/api/admin/actions/export_label.py

james687/disfactory-backend

9129e40e5a21f1e9e16bfc149b08b3758865be86

[ "MIT" ]

35

2020-01-02T10:52:49.000Z

2022-03-18T06:01:15.000Z

backend/api/admin/actions/export_label.py

james687/disfactory-backend

9129e40e5a21f1e9e16bfc149b08b3758865be86

[ "MIT" ]

348

2019-10-09T12:58:42.000Z

2022-03-30T14:17:51.000Z

backend/api/admin/actions/export_label.py

james687/disfactory-backend

9129e40e5a21f1e9e16bfc149b08b3758865be86

[ "MIT" ]

19

2019-10-09T12:51:11.000Z

2021-12-12T01:02:32.000Z

from api.utils import set_function_attributes class ExportLabelMixin: @set_function_attributes(short_description="下載標籤及交寄執據") def export_labels_as_docx(self, request, queryset): return

25.375

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6.333333

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1

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1

0

null

0

1

0

5

8850bf212374a055c68a2770a2a9e0dcf5b7d17d

97

py

Python

main/admin.py

NikOneZ1/MarkovChainText

894f3de75c2a5781f95c95557e40fbfbe29ef051

[ "MIT" ]

1

2022-01-20T17:26:29.000Z

main/admin.py

NikOneZ1/MarkovChainText

894f3de75c2a5781f95c95557e40fbfbe29ef051

[ "MIT" ]

7

2022-01-11T16:24:12.000Z

2022-01-21T23:05:19.000Z

main/admin.py

NikOneZ1/MarkovChainText

894f3de75c2a5781f95c95557e40fbfbe29ef051

[ "MIT" ]

null

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

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1

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1

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5

8877db3ef29712d02e9772bcf5582727bc389b58

181

py

Python

storm/request/parameters/__init__.py

Rud356/Storm

18f8b7ac89babf9252da28c39c2cc84087f2afaf

[ "Apache-2.0" ]

1

2022-03-18T18:14:34.000Z

storm/request/parameters/__init__.py

Rud356/Storm

18f8b7ac89babf9252da28c39c2cc84087f2afaf

[ "Apache-2.0" ]

1

2021-12-23T19:39:27.000Z

storm/request/parameters/__init__.py

Rud356/Storm

18f8b7ac89babf9252da28c39c2cc84087f2afaf

[ "Apache-2.0" ]

null

from .base_request_parameter import BaseRequestParameter from .cookie import CookieParameter from .query import QueryParameter from .url import URLParameter, CompilableUrlParameter

36.2

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181

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181

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1

0

1

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1

0

5

887bb6ae1f2b3650ee4214e41caa5c9edfeee145

119

py

Python

sphinx/__main__.py

daobook/sphinx

ef8daca1f9a82ede9b4b0b5cde93f3414cee3dfe

[ "BSD-2-Clause" ]

null

sphinx/__main__.py

daobook/sphinx

ef8daca1f9a82ede9b4b0b5cde93f3414cee3dfe

[ "BSD-2-Clause" ]

1,662

2015-01-02T11:45:27.000Z

2015-01-03T12:21:29.000Z

sphinx/__main__.py

daobook/sphinx

ef8daca1f9a82ede9b4b0b5cde93f3414cee3dfe

[ "BSD-2-Clause" ]

null

"""The Sphinx documentation toolchain.""" import sys from sphinx.cmd.build import main sys.exit(main(sys.argv[1:]))

14.875

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4.833333

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7

42

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0.294118

0

1

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true

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0.666667

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1

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null

0

1

0

1

0

1

0

5

88b6b9d42626b0e473c809b983d97dc73b946078

27

py

Python

model/__init__.py

Impavidity/text-classification-cnn

70e4a22802c568870ecf007eae557c58c9379e03

[ "MIT" ]

null

model/__init__.py

Impavidity/text-classification-cnn

70e4a22802c568870ecf007eae557c58c9379e03

[ "MIT" ]

null

model/__init__.py

Impavidity/text-classification-cnn

70e4a22802c568870ecf007eae557c58c9379e03

[ "MIT" ]

null

from model.cnnText import *

27

0.814815

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5.5

1

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1

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0.916667

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1

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true

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null

0

1

0

null

0

1

0

1

0

5

ee20963803e059cad9c58c94839787ffec56ef90

98

wsgi

Python

app.wsgi

ahmedwaqas92/jwacademicsweb

d8f98e5bc3af7ba15bd285e1e7dab411459586da

[ "MIT" ]

2

2022-03-18T05:31:46.000Z

2022-03-19T11:27:16.000Z

app.wsgi

ahmedwaqas92/jwacademicsweb

d8f98e5bc3af7ba15bd285e1e7dab411459586da

[ "MIT" ]

null

app.wsgi

ahmedwaqas92/jwacademicsweb

d8f98e5bc3af7ba15bd285e1e7dab411459586da

[ "MIT" ]

1

2022-03-20T16:50:43.000Z

import sys sys.path.insert(0, '/var/www/html/jwacademicsweb') from app import app as application

19.6

50

0.77551

16

98

4.75

0.8125

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0.112245

98

4

51

24.5

0.862069

0

0.285714

0

1

0

true

0

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0

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null

0

1

0

1

0

1

0

5

ee24534286c141193db422290085673793296c39

149

py

Python

blog/admin.py

xk-wang/django_blog

fdaf826584e9791df8584801b250e052a993661c

[ "MIT" ]

null

blog/admin.py

xk-wang/django_blog

fdaf826584e9791df8584801b250e052a993661c

[ "MIT" ]

null

blog/admin.py

xk-wang/django_blog

fdaf826584e9791df8584801b250e052a993661c

[ "MIT" ]

null

from django.contrib import admin from .models import Post, Column # Register your models here. admin.site.register(Post) admin.site.register(Column)

24.833333

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0.805369

22

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0

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true

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0

1

0

null

0

1

0

1

0

5

c98c87ca4fbd5cdfada20281315a92b089758dba

129

py

Python

index/admin.py

ISeaTeL/ISeaTeL_Cup_Site

df22d993db2649a3eb118177bdd13358e3036e59

[ "MIT" ]

null

index/admin.py

ISeaTeL/ISeaTeL_Cup_Site

df22d993db2649a3eb118177bdd13358e3036e59

[ "MIT" ]

null

index/admin.py

ISeaTeL/ISeaTeL_Cup_Site

df22d993db2649a3eb118177bdd13358e3036e59

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from index.models import Bulletin admin.site.register(Bulletin)

16.125

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129

5.777778

0.666667

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null

0

1

0

1

0

1

0

5

c9de1bf48f37e5cb0fdf36202f56abebf8c78c48

419

py

Python

app/models/models.py

igmalta/ml-classification-api

3fd2ebc7b656ed879b126931d40b015ac8588fb7

[ "MIT" ]

null

app/models/models.py

igmalta/ml-classification-api

3fd2ebc7b656ed879b126931d40b015ac8588fb7

[ "MIT" ]

null

app/models/models.py

igmalta/ml-classification-api

3fd2ebc7b656ed879b126931d40b015ac8588fb7

[ "MIT" ]

null

# app/models/models.py # Document models from mongoengine import Document, StringField class User(Document): """User model""" # User register fields email = StringField(required=True) first_name = StringField(required=True) last_name = StringField(required=True) password = StringField(required=True) # Database alias used to connect (mongoengine format) meta = {"db_alias": "user"}

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a013fdfadc6567800c3ef6a253cb95a10354b95b

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py

Python

lib/clckwrkbdgr/test/test_sql.py

umi0451/dotfiles

c618811be788d995fe01f6a16b355828d7efdd36

[ "MIT" ]

2

2017-04-16T14:54:17.000Z

2020-11-12T04:15:00.000Z

lib/clckwrkbdgr/test/test_sql.py

clckwrkbdgr/dotfiles

292dac8c3211248b490ddbae55fe2adfffcfcf58

[ "MIT" ]

null

lib/clckwrkbdgr/test/test_sql.py

clckwrkbdgr/dotfiles

292dac8c3211248b490ddbae55fe2adfffcfcf58

[ "MIT" ]

null

import unittest unittest.defaultTestLoader.testMethodPrefix = 'should' from clckwrkbdgr import sql class TestSqlTableRow(unittest.TestCase): def should_representt_row_as_string(self): row = sql.Row([1, 2, 'foo'], ['First', 'Second', 'Third']) self.assertEqual(str(row), "{'First':1, 'Second':2, 'Third':'foo'}") self.assertEqual(repr(row), "Row((1, 2, 'foo'), ('First', 'Second', 'Third'))") def should_compare_rows_as_tuples_of_Values(self): row = sql.Row([1, 2, 'foo'], ['First', 'Second', 'Third']) same = sql.Row([1, 2, 'foo'], ['First', 'Second', 'Third']) other = sql.Row([1, 3, 'foo'], ['First', 'Second', 'Third']) self.assertEqual(row, same) self.assertNotEqual(row, other) self.assertTrue(row == same) self.assertFalse(row != same) self.assertFalse(row == other) self.assertTrue(row <= same) self.assertFalse(row < same) self.assertTrue(row <= other) self.assertTrue(row < other) def should_access_row_by_index(self): row = sql.Row([1, 2, 'foo'], ['First', 'Second', 'Third']) self.assertEqual(row[0], 1) self.assertEqual(row[1], 2) self.assertEqual(row[2], 'foo') def should_access_row_by_name(self): row = sql.Row([1, 2, 'foo'], ['First', 'Second', 'Third']) self.assertEqual(row['First'], 1) self.assertEqual(row['Second'], 2) self.assertEqual(row['Third'], 'foo') def should_access_row_by_attr(self): row = sql.Row([1, 2, 'foo'], ['First', 'Second', 'Third']) self.assertEqual(row.First, 1) self.assertEqual(row.Second, 2) self.assertEqual(row.Third, 'foo') def should_iterate_over_row(self): row = sql.Row([1, 2, 'foo'], ['First', 'Second', 'Third']) self.assertEqual(tuple(row), (1, 2, 'foo')) def should_create_sql_row(self): row = sql.Row([1, 2, 'foo'], ['First', 'Second', 'Third']) self.assertEqual(row['First'], 1) self.assertEqual(row['Second'], 2) self.assertEqual(row['Third'], 'foo') self.assertEqual(tuple(row), (1, 2, 'foo')) def should_create_sql_row_without_header(self): row = sql.Row([1, 2, 'foo']) self.assertEqual(row['0'], 1) self.assertEqual(row['1'], 2) self.assertEqual(row['2'], 'foo') self.assertEqual(tuple(row), (1, 2, 'foo')) def should_access_headers(self): row = sql.Row([1, 2, 'foo'], ['First', 'Second', 'Third']) self.assertEqual(row.get_headers(), ('First', 'Second', 'Third')) row = sql.Row([1, 2, 'foo']) self.assertEqual(row.get_headers(), ('0', '1', '2'))

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py

Python

dietr/models/pantry.py

essoplerck/dietr

982636c1fea848cf6b90c036dc6ca8a4f37f68a2

[ "MIT" ]

1

2020-09-25T03:53:46.000Z

dietr/models/pantry.py

essoplerck/dietr

982636c1fea848cf6b90c036dc6ca8a4f37f68a2

[ "MIT" ]

null

dietr/models/pantry.py

essoplerck/dietr

982636c1fea848cf6b90c036dc6ca8a4f37f68a2

[ "MIT" ]

null

class PantryModel: def get_ingredients(self, user_id): """Get all ingredients from in pantry and return a list of instances of the ingredient class. """ pass

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py

Python

geo_agent/test/class_A.py

kevjp/openstreetmap-carto

be30cfe8d73f78cb4b5ba9acaaf42a942c70270d

[ "CC0-1.0" ]

null

geo_agent/test/class_A.py

kevjp/openstreetmap-carto

be30cfe8d73f78cb4b5ba9acaaf42a942c70270d

[ "CC0-1.0" ]

null

geo_agent/test/class_A.py

kevjp/openstreetmap-carto

be30cfe8d73f78cb4b5ba9acaaf42a942c70270d

[ "CC0-1.0" ]

null

import psycopg2 from shapely import geometry, ops, wkb import time import pyproj import numpy as np def ways_tab(convert_osm_nodes_sql_2_numpy, convert_osm_ways_vertices_sql_2_numpy): # Connect to database print("I was here") conn = psycopg2.connect(dbname = "london_routing", user = "kevinryan", host = "localhost") curs = conn.cursor() t1 = time.time() start = 2448172873 end = 2372627054 curs.execute("select osm_id, attributes -> 'lon' as lon, attributes -> 'lat' as lat, svals(slice(tags, ARRAY['leisure','shop'])) from osm_nodes where tags -> 'shop' = 'supermarket' or tags -> 'leisure' = 'park';") results = curs.fetchall() osm_nodes_np_arr = convert_osm_nodes_sql_2_numpy(results) curs.execute("select osm_id, lon, lat from ways_vertices_pgr;") ways_vertices = curs.fetchall() osm_waysvertex_np_arr = convert_osm_ways_vertices_sql_2_numpy(ways_vertices) return osm_nodes_np_arr, osm_waysvertex_np_arr def convert_osm_nodes_sql_2_numpy(sql_table): """ convert sql table to numpy array """ projectlon_lat_2_utm = pyproj.Proj(proj='utm', zone=30, ellps='WGS84', preserve_units=True) rows = [] for res in sql_table: x, y = projectlon_lat_2_utm(float(res[1]), float(res[2])) res_elem = (res[0], float(res[1]), float(res[2]), y, x, res[3]) rows.append(res_elem) # convert sql table to numpy array dt = np.dtype([('osm_id', 'i4'), ('lon', 'float'), ('lat', 'float'), ('y', 'float'), ('x', 'float'), ('tags', 'U12')]) osm_nodes_np_arr = np.array(rows, dt) # sort according to location osm_nodes_np_arr = osm_nodes_np_arr[np.lexsort((osm_nodes_np_arr['y'], osm_nodes_np_arr['x']))] return osm_nodes_np_arr def convert_osm_ways_vertices_sql_2_numpy(sql_table): """ convert sql table to numpy array """ projectlon_lat_2_utm = pyproj.Proj(proj='utm', zone=30, ellps='WGS84', preserve_units=True) rows = [] for res in sql_table: x, y = projectlon_lat_2_utm(float(res[1]), float(res[2])) res_elem = (res[0], float(res[1]), float(res[2]), y, x) rows.append(res_elem) # convert sql table to numpy array # dt = np.dtype([('osm_id', 'i4'), ('lon', 'float'), ('lat', 'float'), ('y', 'float'), ('x', 'float')]) dt = np.dtype(float) osm_waysvertex_np_arr = np.array(rows, dt) # sort according to location y = osm_waysvertex_np_arr[:,3] # sort by y x = osm_waysvertex_np_arr[:,4] # sort by x osm_waysvertex_np_arr = osm_waysvertex_np_arr[np.lexsort((x,y))] return osm_waysvertex_np_arr a,b = ways_tab(convert_osm_nodes_sql_2_numpy, convert_osm_ways_vertices_sql_2_numpy) # def __init__(self): # # Connect to database # self.projectlon_lat_2_utm = pyproj.Proj(proj='utm', zone=30, ellps='WGS84', preserve_units=True) # print("I was here") # conn = psycopg2.connect(dbname = "london_routing", user = "kevinryan", host = "localhost") # curs = conn.cursor() # t1 = time.time() # start = 2448172873 # end = 2372627054 # curs.execute("select osm_id, attributes -> 'lon' as lon, attributes -> 'lat' as lat, svals(slice(tags, ARRAY['leisure','shop'])) from osm_nodes where tags -> 'shop' = 'supermarket' or tags -> 'leisure' = 'park';") # results = curs.fetchall() # self.convert_osm_nodes_sql_2_numpy(results) # curs.execute("select osm_id, lon, lat from ways_vertices_pgr;") # ways_vertices = curs.fetchall() # self.convert_osm_ways_vertices_sql_2_numpy(ways_vertices) # def convert_osm_nodes_sql_2_numpy(self,sql_table): # """ # convert sql table to numpy array # """ # rows = [] # for res in sql_table: # x, y = self.latlon_2_yx(float(res[1]), float(res[2])) # res_elem = (res[0], float(res[1]), float(res[2]), y, x, res[3]) # rows.append(res_elem) # # convert sql table to numpy array # dt = np.dtype([('osm_id', 'i4'), ('lon', 'float'), ('lat', 'float'), ('y', 'float'), ('x', 'float'), ('tags', 'U12')]) # self.osm_nodes_np_arr = np.array(rows, dt) # # sort according to location # self.osm_nodes_np_arr = self.osm_nodes_np_arr[np.lexsort((self.osm_nodes_np_arr['y'], self.osm_nodes_np_arr['x']))] # def convert_osm_ways_vertices_sql_2_numpy(self,sql_table): # """ # convert sql table to numpy array # """ # rows = [] # for res in sql_table: # x, y = self.latlon_2_yx(float(res[1]), float(res[2])) # res_elem = (res[0], float(res[1]), float(res[2]), y, x) # rows.append(res_elem) # # convert sql table to numpy array # # dt = np.dtype([('osm_id', 'i4'), ('lon', 'float'), ('lat', 'float'), ('y', 'float'), ('x', 'float')]) # dt = np.dtype(float) # self.osm_waysvertex_np_arr = np.array(rows, dt) # # sort according to location # y = self.osm_waysvertex_np_arr[:,3] # sort by y # x = self.osm_waysvertex_np_arr[:,4] # sort by x # self.osm_waysvertex_np_arr = self.osm_waysvertex_np_arr[np.lexsort((x,y))] # def latlon_2_yx(self, lat=None, lon=None): # """ # Converts WGS84 lat lon values to UTM # """ # # Project lon lat coordinates into UTM scale # return self.projectlon_lat_2_utm(lat, lon) # def yx_2_latlon(self,y,x): # """ # Converts y,x UTM values to WGS84 lat, lon # """ # # Project UTM y, x values to lon lat # return self.projectlon_lat_2_utm(lat, lon, inverse=True) # # Connect to database # conn = psycopg2.connect(dbname = "london_routing", user = "kevinryan", host = "localhost") # curs = conn.cursor() # t1 = time.time() # start = 2448172873 # end = 2372627054 # curs.execute("SELECT seq, edge, b.the_geom AS \"the_geom (truncated)\", b.name FROM pgr_dijkstra('SELECT gid as id, source_osm as source, target_osm as target, length as cost FROM ways',%s, %s, false) a INNER JOIN ways b ON (a.edge = b.gid) ORDER BY seq;", [start, end]) # __conf = { # 'route_list' : [wkb.loads(row[2], hex=True) for row in curs] # } # t2 = time.time() # total = t2 -t1 # print(total) # @staticmethod # def config(name): # return A.__conf[name]

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py

Python

library/.config/calibre/conversion/page_setup.py

funkeyfreak/calibre-drm-stripper

90813b644c86543fb423b4fd664685a02b43e525

[ "Apache-2.0" ]

null

library/.config/calibre/conversion/page_setup.py

funkeyfreak/calibre-drm-stripper

90813b644c86543fb423b4fd664685a02b43e525

[ "Apache-2.0" ]

null

library/.config/calibre/conversion/page_setup.py

funkeyfreak/calibre-drm-stripper

90813b644c86543fb423b4fd664685a02b43e525

[ "Apache-2.0" ]

1

2022-02-05T00:18:21.000Z

version https://git-lfs.github.com/spec/v1 oid sha256:a07475e365d5c7b75a938ac47a51edd365885de6f6a6e33216decf0ec2d247c4 size 43

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5

4e70dd3b203202feae01a64d17e89a8cc3a94eba

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py

Python

load_rmbg_model.py

VincentSchmid/AOE_Shirts-api

a4566809199618a5d40af5182e3009cdf275ebd5

[ "MIT" ]

1

2021-11-24T12:17:25.000Z

load_rmbg_model.py

VincentSchmid/AOE_Shirts

a4566809199618a5d40af5182e3009cdf275ebd5

[ "MIT" ]

null

load_rmbg_model.py

VincentSchmid/AOE_Shirts

a4566809199618a5d40af5182e3009cdf275ebd5

[ "MIT" ]

null

from rembg.u2net.detect import load_model load_model()

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py

Python

ciclo1_python/upb/MisionTIC_UPB_Ciclo1/Python-coding/Ejemplos-Formador/copia_para_VisualStudioCode_mision-tic-2022/07-Python-101-Formato-salida-FelipeEscallon.py

felipeescallon/mision_tic_2022

20496fc40b18d2e98114d6362928f34fde41aaae

[ "CC0-1.0" ]

7

2021-07-05T21:25:50.000Z

2021-11-09T11:09:41.000Z

ciclo1_python/upb/MisionTIC_UPB_Ciclo1/Python-coding/Ejemplos-Formador/copia_para_VisualStudioCode_mision-tic-2022/07-Python-101-Formato-salida-FelipeEscallon.py

felipeescallon/mision_tic_2022

20496fc40b18d2e98114d6362928f34fde41aaae

[ "CC0-1.0" ]

null

ciclo1_python/upb/MisionTIC_UPB_Ciclo1/Python-coding/Ejemplos-Formador/copia_para_VisualStudioCode_mision-tic-2022/07-Python-101-Formato-salida-FelipeEscallon.py

felipeescallon/mision_tic_2022

20496fc40b18d2e98114d6362928f34fde41aaae

[ "CC0-1.0" ]

null

#07-Python-101-Formato-salida-FelipeEscallon

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14d7577b38f85e4cb2bed56a69c79ece3f99f89f

52

py

Python

tracki/src/infrastructure/exceptions/shift.py

rok-povsic/Tracki

f92fec62fa66e87fa6feb509142f09cd548c570a

[ "MIT" ]

null

tracki/src/infrastructure/exceptions/shift.py

rok-povsic/Tracki

f92fec62fa66e87fa6feb509142f09cd548c570a

[ "MIT" ]

null

tracki/src/infrastructure/exceptions/shift.py

rok-povsic/Tracki

f92fec62fa66e87fa6feb509142f09cd548c570a

[ "MIT" ]

null

class NoShiftsPresentException(Exception): pass

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null

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1

0

5

093a38d3285a9e2e563e6d4d62e7577902afa955

44

py

Python

src/autoinfo/decoders/errors.py

JeyKip/autoinfo-scrapper

ca0cb87b1d9486b71928fb08df734fc1413b7967

[ "MIT" ]

null

src/autoinfo/decoders/errors.py

JeyKip/autoinfo-scrapper

ca0cb87b1d9486b71928fb08df734fc1413b7967

[ "MIT" ]

null

src/autoinfo/decoders/errors.py

JeyKip/autoinfo-scrapper

ca0cb87b1d9486b71928fb08df734fc1413b7967

[ "MIT" ]

null

class HexDecodingError(Exception): pass

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null

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1

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5

093fd533a0862c09fbc718d58b8c0088239a51ff

431

py

Python

src/pyinterp/tests/test_cf.py

CNES/pangeo-pyinterp

5f75f62a6c681db89c5aa8c74e43fc04a77418c3

[ "BSD-3-Clause" ]

67

2019-07-09T09:10:22.000Z

2022-03-01T09:46:35.000Z

src/pyinterp/tests/test_cf.py

CNES/pangeo-pyinterp

5f75f62a6c681db89c5aa8c74e43fc04a77418c3

[ "BSD-3-Clause" ]

8

2019-07-15T13:54:31.000Z

2021-06-28T05:06:34.000Z

src/pyinterp/tests/test_cf.py

CNES/pangeo-pyinterp

5f75f62a6c681db89c5aa8c74e43fc04a77418c3

[ "BSD-3-Clause" ]

7

2019-07-15T17:28:16.000Z

2022-01-19T19:43:47.000Z

# Copyright (c) 2021 CNES # # All rights reserved. Use of this source code is governed by a # BSD-style license that can be found in the LICENSE file. import pyinterp.cf def test_longitude(): assert isinstance(pyinterp.cf.AxisLongitudeUnit().units, list) def test_latitude(): assert isinstance(pyinterp.cf.AxisLatitudeUnit().units, list) def test_time(): assert isinstance(pyinterp.cf.AxisTimeUnit().units, list)

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py

Python

project/server/auth/__init__.py

InStateTeam/virtual-identities

91af9f8f9aa709a77360d608e29ae9969fc0e098

[ "MIT" ]

267

2017-01-10T09:01:55.000Z

2022-03-23T02:59:31.000Z

project/server/auth/__init__.py

InStateTeam/virtual-identities

91af9f8f9aa709a77360d608e29ae9969fc0e098

[ "MIT" ]

39

2019-03-13T05:40:40.000Z

2021-06-25T15:17:43.000Z

project/server/auth/__init__.py

InStateTeam/virtual-identities

91af9f8f9aa709a77360d608e29ae9969fc0e098

[ "MIT" ]

199

2016-12-13T20:44:01.000Z

2022-03-30T08:57:15.000Z

# project/server/auth/__init__.py

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11bd057a9cd7de84419ef0ffaf1ece4a575bf92e

302

py

Python

retriever/pipeline.py

GaiYu0/learning_to_retrieve_reasoning_paths

f83c7a8f707c62d51b749716b7afc01e9cb9d737

[ "MIT" ]

null

retriever/pipeline.py

GaiYu0/learning_to_retrieve_reasoning_paths

f83c7a8f707c62d51b749716b7afc01e9cb9d737

[ "MIT" ]

null

retriever/pipeline.py

GaiYu0/learning_to_retrieve_reasoning_paths

f83c7a8f707c62d51b749716b7afc01e9cb9d737

[ "MIT" ]

null

db = DocDB('models/hotpot_models/wiki_db/wiki_abst_only_hotpotqa_w_original_title.db') ranker = TfidfDocRanker(tfidf_path='models/hotpot_models/tfidf_retriever/wiki_open_full_new_db_intro_only-tfidf-ngram\=2-hash\=16777216-tokenizer\=simple.npz') data = json.load('data/hotpot/hotpot_train_v1.1.json')

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null

0

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1

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1

null

0

5

11cb204e4b35088a579af7dc279f2120c980082d

119

py

Python

devtask/commands/extend_process.py

vecin2/em_automation

b65bc498cc7c366d06425e51aaf04b970d581050

[ "MIT" ]

null

devtask/commands/extend_process.py

vecin2/em_automation

b65bc498cc7c366d06425e51aaf04b970d581050

[ "MIT" ]

84

2018-09-15T21:36:23.000Z

2021-12-13T19:49:57.000Z

devtask/commands/extend_process.py

vecin2/em_automation

b65bc498cc7c366d06425e51aaf04b970d581050

[ "MIT" ]

null

from devtask.extend_process import main class ExtendProcessCommand(object): def run(self): return main()

17

39

0.722689

14

119

6.071429

0.928571

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null

0

1

0

null

0

1

0

1

0

5

11cd27134b52d159bbf74c67f6e908940e49a3d0

140

py

Python

universities_api/admin.py

FahimSifnatul/world_universities

3406a1b701a0db597b5f899da7d6d84ca50d3c93

[ "MIT" ]

null

universities_api/admin.py

FahimSifnatul/world_universities

3406a1b701a0db597b5f899da7d6d84ca50d3c93

[ "MIT" ]

null

universities_api/admin.py

FahimSifnatul/world_universities

3406a1b701a0db597b5f899da7d6d84ca50d3c93

[ "MIT" ]

null

from django.contrib import admin # customs from .models import Universities # Register your models here. admin.site.register(Universities)

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140

6.333333

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null

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1

0

1

0

1

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5

11f23e87af3ef69b00b7c5c5aadb48644f51e6eb

93

py

Python

blender_bindings/material_loader/shaders/source2_shaders/complex.py

anderlli0053/SourceIO

3c0c4839939ce698439987ac52154f89ee2f5341

[ "MIT" ]

199

2019-04-02T02:30:58.000Z

2022-03-30T21:29:49.000Z

bpy_utilities/material_loader/shaders/source2_shaders/complex.py

syborg64/SourceIO

e4ba86d801f518e192260af08ef533759c2e1cc3

[ "MIT" ]

113

2019-03-03T19:36:25.000Z

2022-03-31T19:44:05.000Z

bpy_utilities/material_loader/shaders/source2_shaders/complex.py

syborg64/SourceIO

e4ba86d801f518e192260af08ef533759c2e1cc3

[ "MIT" ]

38

2019-05-15T16:49:30.000Z

2022-03-22T03:40:43.000Z

from .vr_complex import VrComplex class Complex(VrComplex): SHADER: str = 'complex.vfx'

18.6

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0.741935

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93

5.666667

0.75

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93

5

34

18.6

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null

0

1

0

1

0

1

0

5

ee9b20b886373a6805e130f92b0bc180058d00c5

227

py

Python

map_matching/map_matcher.py

paperanonymous945/MapRec

5be48b02db855ce648d2674923a15c65afa90146

[ "MIT" ]

34

2019-11-21T12:48:20.000Z

2022-03-06T11:39:08.000Z

map_matching/map_matcher.py

paperanonymous945/MapRec

5be48b02db855ce648d2674923a15c65afa90146

[ "MIT" ]

1

2021-09-08T08:53:58.000Z

map_matching/map_matcher.py

paperanonymous945/MapRec

5be48b02db855ce648d2674923a15c65afa90146

[ "MIT" ]

12

2020-06-29T13:43:12.000Z

2022-02-17T10:39:59.000Z

class MapMatcher: def __init__(self, rn, routing_weight='length'): self.rn = rn self.routing_weight = routing_weight def match(self, traj): pass def match_to_path(self, traj): pass

20.636364

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4.517241

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0.290749

227

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0.25

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null

1

0

null

0

1

0

1

0

5

ee9d78e21914794aaaf691fa3e5abbd2ff7867e8

113

py

Python

19/00/1/sub/Programmer.py

pylangstudy/201706

f1cc6af6b18e5bd393cda27f5166067c4645d4d3

[ "CC0-1.0" ]

null

19/00/1/sub/Programmer.py

pylangstudy/201706

f1cc6af6b18e5bd393cda27f5166067c4645d4d3

[ "CC0-1.0" ]

70

2017-06-01T11:02:51.000Z

2017-06-30T00:35:32.000Z

19/00/1/sub/Programmer.py

pylangstudy/201706

f1cc6af6b18e5bd393cda27f5166067c4645d4d3

[ "CC0-1.0" ]

null

import super.Human class Programmer(super.Human.Human): def programming(self): print('programming.')

22.6

36

0.707965

13

113

6.153846

0.692308

0.25

0

0.168142

113

4

37

28.25

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1

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0.25

0

0.75

0.25

1

0

null

1

0

1

0

null

0

1

0

1

0

5

eea00b0f4cdb340f8d3a838428a74222b2499a5e

120

py

Python

batproject/batapp/admin.py

JaL11/BAT

ed4bccef3c70ec01064ebd0c26933853d4f95355

[ "MIT" ]

1

2020-07-16T14:29:55.000Z

batproject/batapp/admin.py

JaL11/BAT

ed4bccef3c70ec01064ebd0c26933853d4f95355

[ "MIT" ]

63

2020-06-04T14:41:18.000Z

2020-07-29T18:06:14.000Z

batproject/batapp/admin.py

JaL11/BAT

ed4bccef3c70ec01064ebd0c26933853d4f95355

[ "MIT" ]

6

2020-06-06T13:12:35.000Z

2020-08-28T20:25:51.000Z

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

20

32

0.808333

17

120

5.705882

0.647059

0

0.125

120

5

33

24

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true

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1

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null

0

1

0

1

0

1

0

5

e1141ee496f7d25b97066abe088f08c00bcc1561

69

py

Python

rasa_exp/core/__init__.py

shfshf/rasa_exp

dd6db46c14c36f0ffe9602551836af43cebcfead

[ "Apache-2.0" ]

17

2019-07-02T05:27:33.000Z

2021-11-21T08:03:51.000Z

rasa_contrib/core/__init__.py

howl-anderson/rasa_nlu_addons

fea3b818a343f1458d7cf15a4d9063464a304b19

[ "Apache-2.0" ]

13

2019-12-23T18:15:45.000Z

2022-03-11T23:50:37.000Z

rasa_contrib/core/__init__.py

howl-anderson/rasa_nlu_addons

fea3b818a343f1458d7cf15a4d9063464a304b19

[ "Apache-2.0" ]

3

2019-09-10T08:42:33.000Z

2020-10-19T15:48:52.000Z

from rasa_contrib.core.policies import StackedBilstmTensorFlowPolicy

34.5

68

0.913043

7

69

8.857143

1

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69

1

69

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true

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1

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null

0

1

0

1

0

1

0

5

e12aff8890b9005c632edc75a06ea457afa8c429

421

py

Python

examples/no_output/just_code.py

andriyor/sphinx-gallery

cc53540162613850c5bb19fa1172a1be960b1484

[ "BSD-3-Clause" ]

309

2015-01-18T23:00:29.000Z

2022-03-24T15:27:51.000Z

examples/no_output/just_code.py

andriyor/sphinx-gallery

cc53540162613850c5bb19fa1172a1be960b1484

[ "BSD-3-Clause" ]

891

2015-01-04T19:45:44.000Z

2022-03-31T02:36:49.000Z

examples/no_output/just_code.py

andriyor/sphinx-gallery

cc53540162613850c5bb19fa1172a1be960b1484

[ "BSD-3-Clause" ]

197

2015-01-27T13:14:14.000Z

2022-03-28T20:16:39.000Z

# -*- coding: utf-8 -*- """ A short Python script ===================== This demonstrates an example ``.py`` file that is not executed when gallery is generated (see :ref:`build_pattern`) but nevertheless gets included as an example. Note that no output is capture as this file is not executed. """ # Code source: Óscar Nájera # License: BSD 3 clause from __future__ import print_function print([i for i in range(10)])

28.066667

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421

4.5

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14

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0.804533

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null

0

1

0

1

0

1

0

5

011580ab06c019bfc68258734a660d65a62f621d

98

py

Python

senko/l10n/__init__.py

thatoneolib/senko

686d768f8bc0c69a874dba180abb85049ff473b9

[ "MIT" ]

null

senko/l10n/__init__.py

thatoneolib/senko

686d768f8bc0c69a874dba180abb85049ff473b9

[ "MIT" ]

null

senko/l10n/__init__.py

thatoneolib/senko

686d768f8bc0c69a874dba180abb85049ff473b9

[ "MIT" ]

null

from .locale import Locale, NullLocale from .locales import Locales from .mixin import LocaleMixin

32.666667

38

0.836735

13

98

6.307692

0.538462

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0.122449

98

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32.666667

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0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

012e87241e7746c6eb99042c67af58d0a59a51e0

30

py

Python

deepvoice3_pytorch/version.py

faixan-khan/accent_control

24dcb1568d74228f8093245bb3268cc572b90b53

[ "MIT" ]

null

deepvoice3_pytorch/version.py

faixan-khan/accent_control

24dcb1568d74228f8093245bb3268cc572b90b53

[ "MIT" ]

null

deepvoice3_pytorch/version.py

faixan-khan/accent_control

24dcb1568d74228f8093245bb3268cc572b90b53

[ "MIT" ]

null

__version__ = '0.1.1+897f31e'

15

29

0.7

5

30

3.4

0.8

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0.296296

0.1

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30

0.333333

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0.433333

0

1

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false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

0141c8d8c09d035f19b7360ad29ac27734cf9513

171

py

Python

bin/iamonds/hexiamonds-4x10-long-hex.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

null

bin/iamonds/hexiamonds-4x10-long-hex.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

null

bin/iamonds/hexiamonds-4x10-long-hex.py

tiwo/puzzler

7ad3d9a792f0635f7ec59ffa85fb46b54fd77a7e

[ "Intel" ]

1

2022-01-02T16:54:14.000Z

#!/usr/bin/env python # $Id$ """856 solutions""" import puzzler from puzzler.puzzles.hexiamonds import Hexiamonds4x10LongHexagon puzzler.run(Hexiamonds4x10LongHexagon)

17.1

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171

7.555556

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0.058065

0.093567

171

9

65

19

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0

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true

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null

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1

0

1

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1

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5

018a475f9604a3598a4c9e551c3d0543cf048471

174

py

Python

tasks/html-decorator/task.py

dzbrozek/interview-tasks

552bf7f0652ec34b57d961cc59d0be14216b18eb

[ "MIT" ]

null

tasks/html-decorator/task.py

dzbrozek/interview-tasks

552bf7f0652ec34b57d961cc59d0be14216b18eb

[ "MIT" ]

null

tasks/html-decorator/task.py

dzbrozek/interview-tasks

552bf7f0652ec34b57d961cc59d0be14216b18eb

[ "MIT" ]

null

def html_tag(): # write body of decorator pass @html_tag('p') def foobar(): return 'foobar' if __name__ == "__main__": assert foobar() == '<p>foobar</p>'

13.384615

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4.043478

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174

12

39

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null

0

null

0

1

0

1

0

5

6d73e91230e44532ab62b4795b61b091812c9cbc

10,421

py

Python

sdk/python/pulumi_minio/s3_bucket.py

pulumi/pulumi-minio

8c95b8c5680b6e063e3e5b90365ab7a31f4733bd

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

1

2021-08-13T17:29:02.000Z

sdk/python/pulumi_minio/s3_bucket.py

pulumi/pulumi-minio

8c95b8c5680b6e063e3e5b90365ab7a31f4733bd

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

26

2021-06-30T22:17:37.000Z

2022-03-31T15:33:28.000Z

sdk/python/pulumi_minio/s3_bucket.py

pulumi/pulumi-minio

8c95b8c5680b6e063e3e5b90365ab7a31f4733bd

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['S3BucketArgs', 'S3Bucket'] @pulumi.input_type class S3BucketArgs: def __init__(__self__, *, acl: Optional[pulumi.Input[str]] = None, bucket: Optional[pulumi.Input[str]] = None, bucket_prefix: Optional[pulumi.Input[str]] = None, force_destroy: Optional[pulumi.Input[bool]] = None): """ The set of arguments for constructing a S3Bucket resource. """ if acl is not None: pulumi.set(__self__, "acl", acl) if bucket is not None: pulumi.set(__self__, "bucket", bucket) if bucket_prefix is not None: pulumi.set(__self__, "bucket_prefix", bucket_prefix) if force_destroy is not None: pulumi.set(__self__, "force_destroy", force_destroy) @property @pulumi.getter def acl(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "acl") @acl.setter def acl(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "acl", value) @property @pulumi.getter def bucket(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "bucket") @bucket.setter def bucket(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "bucket", value) @property @pulumi.getter(name="bucketPrefix") def bucket_prefix(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "bucket_prefix") @bucket_prefix.setter def bucket_prefix(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "bucket_prefix", value) @property @pulumi.getter(name="forceDestroy") def force_destroy(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "force_destroy") @force_destroy.setter def force_destroy(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "force_destroy", value) @pulumi.input_type class _S3BucketState: def __init__(__self__, *, acl: Optional[pulumi.Input[str]] = None, bucket: Optional[pulumi.Input[str]] = None, bucket_domain_name: Optional[pulumi.Input[str]] = None, bucket_prefix: Optional[pulumi.Input[str]] = None, force_destroy: Optional[pulumi.Input[bool]] = None): """ Input properties used for looking up and filtering S3Bucket resources. """ if acl is not None: pulumi.set(__self__, "acl", acl) if bucket is not None: pulumi.set(__self__, "bucket", bucket) if bucket_domain_name is not None: pulumi.set(__self__, "bucket_domain_name", bucket_domain_name) if bucket_prefix is not None: pulumi.set(__self__, "bucket_prefix", bucket_prefix) if force_destroy is not None: pulumi.set(__self__, "force_destroy", force_destroy) @property @pulumi.getter def acl(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "acl") @acl.setter def acl(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "acl", value) @property @pulumi.getter def bucket(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "bucket") @bucket.setter def bucket(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "bucket", value) @property @pulumi.getter(name="bucketDomainName") def bucket_domain_name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "bucket_domain_name") @bucket_domain_name.setter def bucket_domain_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "bucket_domain_name", value) @property @pulumi.getter(name="bucketPrefix") def bucket_prefix(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "bucket_prefix") @bucket_prefix.setter def bucket_prefix(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "bucket_prefix", value) @property @pulumi.getter(name="forceDestroy") def force_destroy(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "force_destroy") @force_destroy.setter def force_destroy(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "force_destroy", value) class S3Bucket(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, acl: Optional[pulumi.Input[str]] = None, bucket: Optional[pulumi.Input[str]] = None, bucket_prefix: Optional[pulumi.Input[str]] = None, force_destroy: Optional[pulumi.Input[bool]] = None, __props__=None): """ ## Example Usage ```python import pulumi import pulumi_minio as minio state_terraform_s3 = minio.S3Bucket("stateTerraformS3", acl="public", bucket="state-terraform-s3") pulumi.export("minioId", state_terraform_s3.id) pulumi.export("minioUrl", state_terraform_s3.bucket_domain_name) ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. """ ... @overload def __init__(__self__, resource_name: str, args: Optional[S3BucketArgs] = None, opts: Optional[pulumi.ResourceOptions] = None): """ ## Example Usage ```python import pulumi import pulumi_minio as minio state_terraform_s3 = minio.S3Bucket("stateTerraformS3", acl="public", bucket="state-terraform-s3") pulumi.export("minioId", state_terraform_s3.id) pulumi.export("minioUrl", state_terraform_s3.bucket_domain_name) ``` :param str resource_name: The name of the resource. :param S3BucketArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(S3BucketArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, acl: Optional[pulumi.Input[str]] = None, bucket: Optional[pulumi.Input[str]] = None, bucket_prefix: Optional[pulumi.Input[str]] = None, force_destroy: Optional[pulumi.Input[bool]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = S3BucketArgs.__new__(S3BucketArgs) __props__.__dict__["acl"] = acl __props__.__dict__["bucket"] = bucket __props__.__dict__["bucket_prefix"] = bucket_prefix __props__.__dict__["force_destroy"] = force_destroy __props__.__dict__["bucket_domain_name"] = None super(S3Bucket, __self__).__init__( 'minio:index/s3Bucket:S3Bucket', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, acl: Optional[pulumi.Input[str]] = None, bucket: Optional[pulumi.Input[str]] = None, bucket_domain_name: Optional[pulumi.Input[str]] = None, bucket_prefix: Optional[pulumi.Input[str]] = None, force_destroy: Optional[pulumi.Input[bool]] = None) -> 'S3Bucket': """ Get an existing S3Bucket resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _S3BucketState.__new__(_S3BucketState) __props__.__dict__["acl"] = acl __props__.__dict__["bucket"] = bucket __props__.__dict__["bucket_domain_name"] = bucket_domain_name __props__.__dict__["bucket_prefix"] = bucket_prefix __props__.__dict__["force_destroy"] = force_destroy return S3Bucket(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter def acl(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "acl") @property @pulumi.getter def bucket(self) -> pulumi.Output[str]: return pulumi.get(self, "bucket") @property @pulumi.getter(name="bucketDomainName") def bucket_domain_name(self) -> pulumi.Output[str]: return pulumi.get(self, "bucket_domain_name") @property @pulumi.getter(name="bucketPrefix") def bucket_prefix(self) -> pulumi.Output[Optional[str]]: return pulumi.get(self, "bucket_prefix") @property @pulumi.getter(name="forceDestroy") def force_destroy(self) -> pulumi.Output[Optional[bool]]: return pulumi.get(self, "force_destroy")

37.351254

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0.631513

1,177

10,421

5.282923

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0.122226

0.109682

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0.723223

0.708106

0.676584

0.649566

0.604214

0

0.004264

0.257269

10,421

278

135

37.485612

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1

0.157895

false

0.005263

0.026316

0.073684

0.278947

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null

0

1

0

1

0

1

0

null

0

5

6d923e63878cd464cdf6ea1f299ae5e0ad1032f8

206

py

Python

login_rest/api/urls.py

noctilukkas/api-login-token-drf

6f15571da8ecaf4588674b1e59dbe25c7520cc28

[ "MIT" ]

null

login_rest/api/urls.py

noctilukkas/api-login-token-drf

6f15571da8ecaf4588674b1e59dbe25c7520cc28

[ "MIT" ]

null

login_rest/api/urls.py

noctilukkas/api-login-token-drf

6f15571da8ecaf4588674b1e59dbe25c7520cc28

[ "MIT" ]

null

from django.urls import path from .views import PersonaList urlpatterns = [ path('persona/', PersonaList.as_view(), name='persona_list'), path('tuvieja/', PersonaList.as_view(), name='tuvieja'), ]

25.75

65

0.708738

25

206

5.72

0.56

0.181818

0.237762

0.293706

0

0.131068

206

7

66

29.428571

0.798883

0

0.169903

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false

0

0.333333

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0.333333

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0

1

0

1

0

null

0

1

0

5

6da0ef9209e4743e6a0a457e2dbb7c41d6f78bf0

104

py

Python

acmicpc/2935.py

juseongkr/BOJ

8f10a2bf9a7d695455493fbe7423347a8b648416

[ "Apache-2.0" ]

7

2020-02-03T10:00:19.000Z

2021-11-16T11:03:57.000Z

acmicpc/2935.py

juseongkr/Algorithm-training

8f10a2bf9a7d695455493fbe7423347a8b648416

[ "Apache-2.0" ]

1

2021-01-03T06:58:24.000Z

acmicpc/2935.py

juseongkr/Algorithm-training

8f10a2bf9a7d695455493fbe7423347a8b648416

[ "Apache-2.0" ]

1

2020-01-22T14:34:03.000Z

a = int(input()) t = input() b = int(input()) if t == '*': print(a*b) elif t == '+': print(a+b)

13

16

0.442308

18

104

2.555556

0.444444

0.347826

0.304348

0.347826

0

0.259615

104

7

17

14.857143

0.597403

0

0.019231

0

1

0

false

0

0.285714

1

0

null

1

0

1

0

null

0

5

6df3f1016ae723a7eeb3a51c4f056dbe0bed65d4

29

py

Python

Lib/test/test_import/data/circular_imports/indirect.py

sireliah/polish-python

605df4944c2d3bc25f8bf6964b274c0a0d297cc3

[ "PSF-2.0" ]

1

2018-06-21T18:21:24.000Z

Lib/test/test_import/data/circular_imports/indirect.py

sireliah/polish-python

605df4944c2d3bc25f8bf6964b274c0a0d297cc3

[ "PSF-2.0" ]

null

Lib/test/test_import/data/circular_imports/indirect.py

sireliah/polish-python

605df4944c2d3bc25f8bf6964b274c0a0d297cc3

[ "PSF-2.0" ]

null

z . zaimportuj basic, basic2

14.5

28

0.758621

4

29

5.5

1

0

0.041667

0.172414

29

1

29

0.875

0

null

0

1

null

0

1

0

null

0

1

0

null

0

1

0

1

0

5

0993c0a1e7d5e798e6b8aa5e8b02a123408ab805

59

py

Python

deep_utils/utils/utils/__init__.py

dornasabet/deep_utils

be61b36ea5b3219831e9f2a364fbd4a63858abed

[ "MIT" ]

null

deep_utils/utils/utils/__init__.py

dornasabet/deep_utils

be61b36ea5b3219831e9f2a364fbd4a63858abed

[ "MIT" ]

null

deep_utils/utils/utils/__init__.py

dornasabet/deep_utils

be61b36ea5b3219831e9f2a364fbd4a63858abed

[ "MIT" ]

null

from .main import shift_lst, dictnamedtuple, easy_argparse

29.5

58

0.847458

8

59

6

1

0

0.101695

59

1

59

0.90566

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0997d4db5bfba66e2cc1e01edfe1217ec0276a27

162

py

Python

project/main/tests.py

Dima12101/TestTravis

0bda2a7593a413e03c12343c00e1ceaae0ce3ab9

[ "Apache-2.0" ]

null

project/main/tests.py

Dima12101/TestTravis

0bda2a7593a413e03c12343c00e1ceaae0ce3ab9

[ "Apache-2.0" ]

5

2021-03-19T02:58:59.000Z

2022-02-10T08:58:41.000Z

project/main/tests.py

Dima12101/TestTravis

0bda2a7593a413e03c12343c00e1ceaae0ce3ab9

[ "Apache-2.0" ]

null

from django.test import TestCase class Test(TestCase): def setUp(self): self.value = 6 def test(self): self.assertEqual(self.value, 6)

16.2

39

0.635802

22

162

4.681818

0.545455

0.15534

0.194175

0

0.016667

0.259259

162

9

40

18

0.841667

0

0.166667

1

0.333333

false

0

0.166667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

09ac90f7e9a182461fa4fa7a877be80f0f5cb7ba

283

py

Python

tests/conftest.py

HeadHaus/Skeema

fc0faf13afad2c95b8943eaa3bfc2cc23b7de003

[ "MIT" ]

null

tests/conftest.py

HeadHaus/Skeema

fc0faf13afad2c95b8943eaa3bfc2cc23b7de003

[ "MIT" ]

null

tests/conftest.py

HeadHaus/Skeema

fc0faf13afad2c95b8943eaa3bfc2cc23b7de003

[ "MIT" ]

null

import pytest def uid(v): return f"id{v}" @pytest.fixture def id0(): return uid(0) @pytest.fixture def id1(): return uid(1) @pytest.fixture def id2(): return uid(2) @pytest.fixture def id3(): return uid(3) @pytest.fixture def id4(): return uid(4)

9.129032

19

0.614841

44

283

3.954545

0.431818

0.373563

0.45977

0

0.046296

0.236749

283

30

20

9.433333

0.759259

0

0.277778

0

0.017668

0

1

0.333333

false

0

0.055556

0.333333

0.722222

0

null

1

0

null

0

1

0

1

0

5

09ff1d4b73739294f4e39ea21cb52b7a5a441436

64

py

Python

1.py

xingjian2016/demo

923c77fdb0bbe9d449686dd05ffecde0edffff7c

[ "Apache-2.0" ]

null

1.py

xingjian2016/demo

923c77fdb0bbe9d449686dd05ffecde0edffff7c

[ "Apache-2.0" ]

null

1.py

xingjian2016/demo

923c77fdb0bbe9d449686dd05ffecde0edffff7c

[ "Apache-2.0" ]

null

#-*- coding:utf-8 -*- def display(name,age): print (name,age)

12.8

22

0.609375

10

64

3.9

0.8

0.358974

0

0.018182

0.140625

64

4

23

16

0.690909

0.3125

0

1

0.5

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

5

61ee190538925b251b5270cd4e5eb672a2c43cfa

62

py

Python

aerokit/aero/nozzle.py

PierreMignerot/aerokit

78717288d840ef5cb3939b44e967cf8f250dc270

[ "MIT" ]

null

aerokit/aero/nozzle.py

PierreMignerot/aerokit

78717288d840ef5cb3939b44e967cf8f250dc270

[ "MIT" ]

null

aerokit/aero/nozzle.py

PierreMignerot/aerokit

78717288d840ef5cb3939b44e967cf8f250dc270

[ "MIT" ]

null

# backward compatibility from aerokit.instance.nozzle import *

31

37

0.83871

7

62

7.428571

1

0

0.096774

62

2

37

31

0.928571

0.354839

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

61f6daa72eec769ddae3cc029c89e845fbc42e64

49

py

Python

main/candidate_ranking/__init__.py

wissembrdj/welink

ebc0cd4742578ad22014bd8067796e8cc1869f02

[ "MIT" ]

null

main/candidate_ranking/__init__.py

wissembrdj/welink

ebc0cd4742578ad22014bd8067796e8cc1869f02

[ "MIT" ]

null

main/candidate_ranking/__init__.py

wissembrdj/welink

ebc0cd4742578ad22014bd8067796e8cc1869f02

[ "MIT" ]

null

__all__ = ["candidate_similarities", "coherence"]

49

0.77551

4

49

8.25

1

0

0.061224

49

1

49

0.717391

0

0.62

0.44

0

1

0

false

0

1

0

null

0

1

0

1

null

0

5

11120d6590c58a95c24d151d030ecdce5218d3c3

87

py

Python

neural_pipeline/data_processor/__init__.py

pfriesch/neural-pipeline

2df4f7467a721b1fbd93f4439086c6dcee5dac2c

[ "MIT" ]

null

neural_pipeline/data_processor/__init__.py

pfriesch/neural-pipeline

2df4f7467a721b1fbd93f4439086c6dcee5dac2c

[ "MIT" ]

null

neural_pipeline/data_processor/__init__.py

pfriesch/neural-pipeline

2df4f7467a721b1fbd93f4439086c6dcee5dac2c

[ "MIT" ]

null

from .data_processor import DataProcessor, TrainDataProcessor from .model import Model

29

61

0.862069

10

87

7.4

0.7

0

0.103448

87

2

62

43.5

0.948718

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

111c6af163e2efe61fe15b1f50626044653ed560

143

py

Python

PythonClient/carla/driving_benchmark/experiment_suites/__init__.py

felipecode/CAL

bc3556097e61b69735392e310b2b0916ebeebce4

[ "MIT" ]

204

2019-01-28T13:31:53.000Z

2022-03-23T23:57:18.000Z

PythonClient/carla/driving_benchmark/experiment_suites/__init__.py

felipecode/CAL

bc3556097e61b69735392e310b2b0916ebeebce4

[ "MIT" ]

39

2019-02-02T22:14:14.000Z

2022-01-30T08:21:51.000Z

PythonClient/carla/driving_benchmark/experiment_suites/__init__.py

felipecode/CAL

bc3556097e61b69735392e310b2b0916ebeebce4

[ "MIT" ]

64

2019-02-24T10:26:04.000Z

2022-03-04T12:49:59.000Z

from .basic_experiment_suite import BasicExperimentSuite from .corl_2017 import CoRL2017 from .longcontrol_2018 import LongitudinalControl2018

35.75

56

0.895105

16

143

7.75

0.75

0

0.122137

0.083916

143

3

57

47.666667

0.824427

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

116370d280717873265e0aad450b575b75dd9ec7

11,294

py

Python

setup.py

sendx/sendx-api-python

edce9755d3718efb12cb5493da7cbac961cb1d9b

[ "Apache-2.0" ]

null

setup.py

sendx/sendx-api-python

edce9755d3718efb12cb5493da7cbac961cb1d9b

[ "Apache-2.0" ]

null

setup.py

sendx/sendx-api-python

edce9755d3718efb12cb5493da7cbac961cb1d9b

[ "Apache-2.0" ]

null

# coding: utf-8 """ SendX REST API **NOTE:** All API calls contain 2 parameters - 'api_key' and 'team_id'. These can be inferred from your settings page 'https://app.sendx.io/setting' under the sections 'Api Key' and 'Team Id' respectively. For checking language specific Clients: - [Golang](https://github.com/sendx/sendx-api-go) - [Python](https://github.com/sendx/sendx-api-python) - [Ruby](https://github.com/sendx/sendx-api-ruby) - [Java](https://github.com/sendx/sendx-api-java) - [PHP](https://github.com/sendx/sendx-api-php) - [NodeJS](https://github.com/sendx/sendx-api-nodejs) We also have a [Javascript API](http://help.sendx.io/knowledge_base/topics/javascript-api-1) for client side integrations. SendX REST API has two methods: * Identify * Track ## Identify API Method Identify API Method is used to attach data to a visitor. If a contact is not yet created then we will create the contact. In case contact already exists then we update it. **Example Request:** ```json { email: \"john.doe@gmail.com\", firstName: \"John\", lastName: \"Doe\", birthday: \"1989-03-03\", customFields: { \"Designation\": \"Software Engineer\", \"Age\": \"27\", \"Experience\": \"5\" }, tags: [\"Developer\", \"API Team\"], } ``` Note that tags are an array of strings. In case they don't exist previously then API will create them and associate them with the contact. Similarly if a custom field doesn't exist then it is first created and then associated with the contact along-with the corresponding value. In case custom field exists already then we simply update the value of it for the aforementioned contact. We don't delete any of the properties based on identify call. What this means is that if for the same contact you did two API calls like: **API Call A** ```json { email: \"john.doe@gmail.com\", firstName: \"John\", birthday: \"1989-03-03\", customFields: { \"Designation\": \"Software Engineer\" }, tags: [\"Developer\"], } ``` **API Call B** ```json { email: \"john.doe@gmail.com\", customFields: { \"Age\": \"29\" }, tags: [\"API Team\"], } ``` Then the final contact will have firstName as **John**, birthday as **1989-03-03** present. Also both tags **Developer** and **API Team** shall be present along with custom fields **Designation** and **Age**. **Properties:** * **firstName**: type string * **lastName**: type string * **email**: type string * **newEmail**: type string * **company**: type string * **birthday**: type string with format **YYYY-MM-DD** eg: 2016-11-21 * **customFields**: type map[string]string * **tags**: type array of string In case email of an already existing contact needs to be updated then specify current email under email property and updated email under newEmail property. **Response:** ```json { \"status\": \"200\", \"message\": \"OK\", \"data\": { \"encryptedTeamId\": \"CLdh9Ig5GLIN1u8gTRvoja\", \"encryptedId\": \"c9QF63nrBenCaAXe660byz\", \"tags\": [ \"API Team\", \"Tech\" ], \"firstName\": \"John\", \"lastName\": \"Doe\", \"email\": \"john.doe@gmail.com\", \"company\": \"\", \"birthday\": \"1989-03-03\", \"customFields\": { \"Age\": \"29\", \"Designation\": \"Software Engineer\" } } } ``` ## Track API Method Track API Method is used to track a contact. In the track API object you can: * **addTags**: * **removeTags**: You can have automation rules based on tag addition as well as tag removal and they will get executed. For eg: * On **user registration** tag start onboarding drip for him / her. * **Account Upgrade** tag start add user to paid user list and start account expansion drip. * On removal of **trial user** tag start upsell trial completed users drip. **Example Request:** > \\_scq.push([\"track\", { \"addTags\": [\"blogger\", \"female\"] }]); > \\_scq.push([\"track\", { \"addTags\": [\"paid user\"], \"removeTags\": [\"trial user\"] }]); **Response:** > { \"status\": \"200\", \"message\": \"OK\", \"data\": \"success\" } OpenAPI spec version: v1 Generated by: https://github.com/swagger-api/swagger-codegen.git Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import sys from setuptools import setup, find_packages NAME = "swagger_client" VERSION = "1.0.0" # To install the library, run the following # # python setup.py install # # prerequisite: setuptools # http://pypi.python.org/pypi/setuptools REQUIRES = ["urllib3 >= 1.15", "six >= 1.10", "certifi", "python-dateutil"] setup( name=NAME, version=VERSION, description="SendX REST API", author_email="", url="", keywords=["Swagger", "SendX REST API"], install_requires=REQUIRES, packages=find_packages(), include_package_data=True, long_description="""\ **NOTE:** All API calls contain 2 parameters - 'api_key' and 'team_id'. These can be inferred from your settings page 'https://app.sendx.io/setting' under the sections 'Api Key' and 'Team Id' respectively. For checking language specific Clients: - [Golang](https://github.com/sendx/sendx-api-go) - [Python](https://github.com/sendx/sendx-api-python) - [Ruby](https://github.com/sendx/sendx-api-ruby) - [Java](https://github.com/sendx/sendx-api-java) - [PHP](https://github.com/sendx/sendx-api-php) - [NodeJS](https://github.com/sendx/sendx-api-nodejs) We also have a [Javascript API](http://help.sendx.io/knowledge_base/topics/javascript-api-1) for client side integrations. SendX REST API has two methods: * Identify * Track ## Identify API Method Identify API Method is used to attach data to a visitor. If a contact is not yet created then we will create the contact. In case contact already exists then we update it. **Example Request:** ```json { email: \"john.doe@gmail.com\", firstName: \"John\", lastName: \"Doe\", birthday: \"1989-03-03\", customFields: { \"Designation\": \"Software Engineer\", \"Age\": \"27\", \"Experience\": \"5\" }, tags: [\"Developer\", \"API Team\"], } ``` Note that tags are an array of strings. In case they don't exist previously then API will create them and associate them with the contact. Similarly if a custom field doesn't exist then it is first created and then associated with the contact along-with the corresponding value. In case custom field exists already then we simply update the value of it for the aforementioned contact. We don't delete any of the properties based on identify call. What this means is that if for the same contact you did two API calls like: **API Call A** ```json { email: \"john.doe@gmail.com\", firstName: \"John\", birthday: \"1989-03-03\", customFields: { \"Designation\": \"Software Engineer\" }, tags: [\"Developer\"], } ``` **API Call B** ```json { email: \"john.doe@gmail.com\", customFields: { \"Age\": \"29\" }, tags: [\"API Team\"], } ``` Then the final contact will have firstName as **John**, birthday as **1989-03-03** present. Also both tags **Developer** and **API Team** shall be present along with custom fields **Designation** and **Age**. **Properties:** * **firstName**: type string * **lastName**: type string * **email**: type string * **newEmail**: type string * **company**: type string * **birthday**: type string with format **YYYY-MM-DD** eg: 2016-11-21 * **customFields**: type map[string]string * **tags**: type array of string In case email of an already existing contact needs to be updated then specify current email under email property and updated email under newEmail property. **Response:** ```json { \"status\": \"200\", \"message\": \"OK\", \"data\": { \"encryptedTeamId\": \"CLdh9Ig5GLIN1u8gTRvoja\", \"encryptedId\": \"c9QF63nrBenCaAXe660byz\", \"tags\": [ \"API Team\", \"Tech\" ], \"firstName\": \"John\", \"lastName\": \"Doe\", \"email\": \"john.doe@gmail.com\", \"company\": \"\", \"birthday\": \"1989-03-03\", \"customFields\": { \"Age\": \"29\", \"Designation\": \"Software Engineer\" } } } ``` ## Track API Method Track API Method is used to track a contact. In the track API object you can: * **addTags**: * **removeTags**: You can have automation rules based on tag addition as well as tag removal and they will get executed. For eg: * On **user registration** tag start onboarding drip for him / her. * **Account Upgrade** tag start add user to paid user list and start account expansion drip. * On removal of **trial user** tag start upsell trial completed users drip. **Example Request:** > \\_scq.push([\"track\", { \"addTags\": [\"blogger\", \"female\"] }]); > \\_scq.push([\"track\", { \"addTags\": [\"paid user\"], \"removeTags\": [\"trial user\"] }]); **Response:** > { \"status\": \"200\", \"message\": \"OK\", \"data\": \"success\" } """ )

205.345455

5,400

0.601381

1,435

11,294

4.720557

0.204181

0.055506

0.026867

0.033658

0.745645

0.706377

0.693239

0.670062

0.63168

0.624889

0

0.025647

0.240482

11,294

54

5,401

209.148148

0.764048

0.462458

0

0.052632

0.938501

0.167632

0

1

0

false

0

0.105263

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0.105263

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null

0

1

0

1

0

1

0

1

0

null

0

5

febfddd7198fc9c25959879a7ec64b57f2ba20f6

32

py

Python

chapter02/code03.py

ggggxiaolong/python

c73ea1ffcc6450ae13bd86b07520eb7b52c9f3c3

[ "MIT" ]

null

chapter02/code03.py

ggggxiaolong/python

c73ea1ffcc6450ae13bd86b07520eb7b52c9f3c3

[ "MIT" ]

null

chapter02/code03.py

ggggxiaolong/python

c73ea1ffcc6450ae13bd86b07520eb7b52c9f3c3

[ "MIT" ]

null

str = raw_input() print int(str)

16

17

0.71875

6

32

3.666667

0.833333

0

0.125

32

2

18

16

0.785714

0

null

0

null

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

feeeba75222b3ad1e58902cdd82165421f508fa1

88

py

Python

precession/__init__.py

DariaGangardt/precession

35c9226c78b5b73a06d26cc02e5234a93c12b1c7

[ "MIT" ]

20

2016-03-22T14:51:17.000Z

2022-02-22T14:42:31.000Z

precession/__init__.py

DariaGangardt/precession

35c9226c78b5b73a06d26cc02e5234a93c12b1c7

[ "MIT" ]

1

2017-09-21T15:07:05.000Z

precession/__init__.py

DariaGangardt/precession

35c9226c78b5b73a06d26cc02e5234a93c12b1c7

[ "MIT" ]

14

2016-05-05T06:52:04.000Z

2022-02-21T23:42:49.000Z

""" precession - description """ from .__version__ import * from .precession import *

11

26

0.704545

8

88

7.25

0.625

0

0.170455

88

7

27

12.571429

0.794521

0.272727

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3a20b1ad559e0966e29990d7bc2d26c200800309

138

py

Python

arithmetic/make-arithmetic-data.py

yaopang/TensorFlowNLP

f06301712312493e5fd52ee38b0b918ec60b91e1

[ "MIT" ]

23

2016-10-10T20:27:54.000Z

2021-01-16T05:02:01.000Z

arithmetic/make-arithmetic-data.py

yaopang/TensorFlowNLP

f06301712312493e5fd52ee38b0b918ec60b91e1

[ "MIT" ]

null

arithmetic/make-arithmetic-data.py

yaopang/TensorFlowNLP

f06301712312493e5fd52ee38b0b918ec60b91e1

[ "MIT" ]

23

2016-10-09T20:17:59.000Z

2019-10-15T12:34:31.000Z

import random for i in range(5000): a = random.randint(0,1000) b = random.randint(0,1000) print("{}+{},{}".format(a, b, a+b))

23

39

0.57971

23

138

3.478261

0.608696

0.325

0.35

0.45

0

0.126126

0.195652

138

6

39

23

0.594595

0

0.057554

0

1

0

false

0

0.2

0

0.2

1

0

null

1

0

1

0

null

0

5

3a38ce7a13d08d1c47500756912ddb735f9b7d52

59

py

Python

astropy/units.py

Ayush2007A/Code-master

fafe4a020adc3f8e78c78f6b8b2b08b5c3005613

[ "Unlicense" ]

1

2021-02-05T10:29:30.000Z

astropy/units.py

Ayush2007A/Code-master

fafe4a020adc3f8e78c78f6b8b2b08b5c3005613

[ "Unlicense" ]

null

astropy/units.py

Ayush2007A/Code-master

fafe4a020adc3f8e78c78f6b8b2b08b5c3005613

[ "Unlicense" ]

null

from astropy import units as u print(42.0 * u.kilometer)

19.666667

31

0.728814

11

59

3.909091

0.909091

0

0.0625

0.186441

59

2

32

29.5

0.833333

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3a52cc0d1545ff7743d36eed25bb93de8a44f95d

130

py

Python

__init__.py

audacious-software/Passive-Data-Kit-External-Sensors

c4781c04ce3cb485b0c1e50a9e7c6db0c92a9959

[ "Apache-2.0" ]

null

__init__.py

audacious-software/Passive-Data-Kit-External-Sensors

c4781c04ce3cb485b0c1e50a9e7c6db0c92a9959

[ "Apache-2.0" ]

null

__init__.py

audacious-software/Passive-Data-Kit-External-Sensors

c4781c04ce3cb485b0c1e50a9e7c6db0c92a9959

[ "Apache-2.0" ]

null

# pylint: disable=invalid-name default_app_config = 'passive_data_kit_external_sensors.apps.PassiveDataKitExternalSensorsConfig'

32.5

97

0.876923

14

130

7.714286

1

0

0.053846

130

3

98

43.333333

0.878049

0.215385

0

0.74

0

1

0

false

1

0

1

0

null

0

1

0

1

null

0

1

0

5

3a5330f3918a61d0f64326458d7b2dae80e6ff70

291

py

Python

tests/test_cookiecutter_pypackage_instance.py

billsioros/cookiecutter-pypackage-instance

e986b7eb20fdeefdbf229ff5d3b0e74e1f492671

[ "MIT" ]

null

tests/test_cookiecutter_pypackage_instance.py

billsioros/cookiecutter-pypackage-instance

e986b7eb20fdeefdbf229ff5d3b0e74e1f492671

[ "MIT" ]

13

2021-08-28T10:58:25.000Z

2021-09-12T17:43:34.000Z

tests/test_cookiecutter_pypackage_instance.py

billsioros/cookiecutter-pypackage-instance

e986b7eb20fdeefdbf229ff5d3b0e74e1f492671

[ "MIT" ]

1

2021-09-09T22:03:23.000Z

"""Tests concerning the `cookiecutter_pypackage_instance` module.""" from cookiecutter_pypackage_instance.cookiecutter_pypackage_instance import factorial def test_cookiecutter_pypackage_instance(): assert factorial(0) == 1 assert factorial(1) == 1 assert factorial(5) == 120

29.1

85

0.783505

33

291

6.636364

0.515152

0.383562

0.52968

0

0.031621

0.130584

291

9

86

32.333333

0.833992

0.213058

0

0.6

1

0.2

true

0

0.2

0

0.4

0

null

1

0

1

0

null

0

1

0

1

0

5

e902264ea72bcdfb78dc916dd5606671f37ffe6e

24

py

Python

vedo/version.py

marcomusy/vedo

cbf6639f2eb10491527beaf7cc3656c797f9fb42

[ "CC0-1.0" ]

836

2020-06-14T02:38:12.000Z

2022-03-31T15:39:50.000Z

vedo/version.py

marcomusy/vedo

cbf6639f2eb10491527beaf7cc3656c797f9fb42

[ "CC0-1.0" ]

418

2020-06-14T10:51:32.000Z

2022-03-31T23:23:14.000Z

vedo/version.py

marcomusy/vedo

cbf6639f2eb10491527beaf7cc3656c797f9fb42

[ "CC0-1.0" ]

136

2020-06-14T02:26:41.000Z

2022-03-31T12:47:18.000Z

_version='2021.0.6dev0'

12

23

0.75

4

24

4.25

1

0

0.304348

0.041667

24

1

24

0.434783

0

0.5

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

3a6ef78efbea6148fbc19299de4cbfa3dd781231

36

py

Python

setup.py

foxleoly/tools

f0ad8617808d2bce47bbc2f69a696590d1a606b6

[ "MIT" ]

null

setup.py

foxleoly/tools

f0ad8617808d2bce47bbc2f69a696590d1a606b6

[ "MIT" ]

null

setup.py

foxleoly/tools

f0ad8617808d2bce47bbc2f69a696590d1a606b6

[ "MIT" ]

null

#!/usr/bin/env python # setup new pc

18

21

0.694444

7

36

3.571429

1

0

0.138889

36

2

22

18

0.806452

0.916667

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

3ac077c97ffb107ef434fb0d9ac356a518f5be18

119

py

Python

generate_voice.py

DanielFlockhart/RNN-language-Model

7e4b060d4bf01560b410f43bf1ac90bb164eae16

[ "MIT" ]

null

generate_voice.py

DanielFlockhart/RNN-language-Model

7e4b060d4bf01560b410f43bf1ac90bb164eae16

[ "MIT" ]

null

generate_voice.py

DanielFlockhart/RNN-language-Model

7e4b060d4bf01560b410f43bf1ac90bb164eae16

[ "MIT" ]

null

from gtts import gTTS import os,time def save_sound(message): speech = gTTS(message) speech.save("audio.mp3")

17

28

0.714286

18

119

4.666667

0.666667

0.238095

0

0.010204

0.176471

119

6

29

19.833333

0.846939

0

0.07563

0

1

0.2

false

0

0.4

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

3ac9275b7bfe4e2c5dbe44a10888ba2cd8ef71eb

133

py

Python

league/admin.py

klauck/mettliga

a5fa9f06092274f22a69785ae9a869268c181967

[ "MIT" ]

1

2017-05-12T19:48:15.000Z

league/admin.py

klauck/mettliga

a5fa9f06092274f22a69785ae9a869268c181967

[ "MIT" ]

null

league/admin.py

klauck/mettliga

a5fa9f06092274f22a69785ae9a869268c181967

[ "MIT" ]

null

from django.contrib import admin from .models import MettEater, Metting admin.site.register(MettEater) admin.site.register(Metting)

22.166667

38

0.827068

18

133

6.111111

0.555556

0.163636

0.309091

0

0.090226

133

5

39

26.6

0.909091

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

c94dc8382a511fe00771d81b0f4053e12fbd4a76

104

py

Python

cv/educations/admin.py

vignif/django_cv

0426b47da82341e676adcf7b441a7b55a3fa2d78

[ "MIT" ]

null

cv/educations/admin.py

vignif/django_cv

0426b47da82341e676adcf7b441a7b55a3fa2d78

[ "MIT" ]

null

cv/educations/admin.py

vignif/django_cv

0426b47da82341e676adcf7b441a7b55a3fa2d78

[ "MIT" ]

null

from django.contrib import admin from educations.models import Education admin.site.register(Education)

26

39

0.855769

14

104

6.357143

0.714286

0

0.086538

104

4

40

26

0.936842

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

c9575a0147102ced0a480e51e8eed5a5a3495119

110

py

Python

edx_lint/__main__.py

eduNEXT/edx-lint

e129a8b5478469f44737cb7ba1afc93b5a994bba

[ "Apache-2.0" ]

43

2015-05-30T21:35:34.000Z

2021-09-21T07:15:05.000Z

edx_lint/__main__.py

eduNEXT/edx-lint

e129a8b5478469f44737cb7ba1afc93b5a994bba

[ "Apache-2.0" ]

106

2015-02-02T17:43:55.000Z

2021-12-20T03:05:16.000Z

edx_lint/__main__.py

eduNEXT/edx-lint

e129a8b5478469f44737cb7ba1afc93b5a994bba

[ "Apache-2.0" ]

22

2015-08-28T16:19:41.000Z

2021-09-01T10:36:54.000Z

"""edx_lint's module-callable entry point.""" import sys from edx_lint.cmd.main import main sys.exit(main())

18.333333

45

0.745455

19

110

4.210526

0.684211

0.175

0

0.109091

110

5

46

22

0.816327

0.354545

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

c95f22dfbea8b2cf6d3c1030255c5ae2a34bb5c6

162

py

Python

SE/metadatos.py

Karimx/SGBDR

e4fa3a2d066d40c4d77b5021e8f2a582c16d03fa

[ "Apache-2.0" ]

1

2016-05-12T06:34:30.000Z

SE/metadatos.py

Karimx/SGBDR

e4fa3a2d066d40c4d77b5021e8f2a582c16d03fa

[ "Apache-2.0" ]

null

SE/metadatos.py

Karimx/SGBDR

e4fa3a2d066d40c4d77b5021e8f2a582c16d03fa

[ "Apache-2.0" ]

null

class MetaDato: def __init__(self, nombreTabla, campos): pass def calcularRegistros(self): pass def getRegistro(self): pass

16.2

44

0.611111

16

162

5.9375

0.625

0.147368

0

0.314815

162

9

45

18

0.855856

0

0.428571

0

1

0.428571

false

0.428571

0

0.571429

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c979a57c1534701ef27d687eef4f1d6eacb6e948

14,746

py

Python

day3.py

venomousmoog/adventofcode2021

0111db027954ab38d6c17e0c8048df3449cb90d2

[ "Apache-2.0" ]

null

day3.py

venomousmoog/adventofcode2021

0111db027954ab38d6c17e0c8048df3449cb90d2

[ "Apache-2.0" ]

null

day3.py

venomousmoog/adventofcode2021

0111db027954ab38d6c17e0c8048df3449cb90d2

[ "Apache-2.0" ]

null

# day3 - sonar sweeps # def count_bits(bits, pos): ones = sum([int(b[pos]) for b in bits]) zeros = len(bits) - ones return (ones, zeros) def power(bits): width = len(bits[0]) gamma = [] epsilon = [] for i in range(0, width): (ones, zeros) = count_bits(bits, i) print(f"ones = {ones} zeros = {zeros}") gamma.append("1" if ones > zeros else "0") epsilon.append("0" if ones > zeros else "1") gamma_b = "".join(gamma) epsilon_b = "".join(epsilon) print(f"gamma_b = {gamma_b} epsilon_v = {epsilon_b}") gamma_v = int("".join(gamma), 2) epsilon_v = int("".join(epsilon), 2) print(f"gamma_v = {gamma_v} epsilon_v = {epsilon_v}") print(f"power = {gamma_v * epsilon_v}") def life_support(bits): width = len(bits[0]) oxygen = list(bits) scrubber = list(bits) for i in range(0, width): (ones, zeros) = count_bits(oxygen, i) obit = "1" if ones >= zeros else "0" print(f"obit = {obit}") oxygen = [x for x in oxygen if x[i] == obit] print(f"o = {oxygen}") for i in range(0, width): (ones, zeros) = count_bits(scrubber, i) sbit = "0" if zeros <= ones else "1" print(f"sbit = {sbit}") scrubber = [x for x in scrubber if x[i] == sbit] print(f"s = {scrubber}") if len(scrubber) == 1: break print(f"o = {oxygen}, s = {scrubber}") oxygen_v = int(oxygen[0], 2) scrubber_v = int(scrubber[0], 2) print(f"o = {oxygen_v}, s = {scrubber_v}, life_support = {oxygen_v*scrubber_v}") test_data = """ 00100 11110 10110 10111 10101 01111 00111 11100 10000 11001 00010 01010""".split() data = """ 010111111011 010010101110 011001001100 001000001010 111100101000 111010101100 000111101111 010011010011 100010111011 101011000111 100111101010 101101101101 110010110110 100110011100 001110011000 011000101010 001100111101 100011101111 100111011001 011100101101 111101000111 111000101011 001001000101 010110011000 110100100001 010010010011 100100100100 011011001000 111101011101 101011110011 110011101101 001001000100 100111101110 101101101010 111110101000 111011011001 111110101101 110101010100 011100110000 000010111110 111011111111 010111111110 101001110101 001111010100 001111110001 000010000000 010001101000 100001001111 101111000010 000011110001 110111101110 000000100111 010100000111 011111100001 100011110001 101000001111 101010111001 001101100100 001100111001 001000011010 001001111100 011001011001 101011001010 100101101101 000101011110 101100110111 011000010110 110101000110 011010100101 110011100110 111101000010 111110101011 110101100101 101110101010 100110001011 000110101100 010100001001 010011001110 101010010101 101010000101 100100001011 010010100101 011000111111 001010000001 000001100111 010101101111 101110000110 100101001001 010000101001 001101011001 011101111110 011110011100 110100110111 000101011010 100000010011 011100111000 001110101000 001001001000 110101011101 000011001111 101000011010 100011110010 100100010111 110001010100 111010100110 101001010100 110100111101 111000010111 010001111000 000101010110 010100000100 001000110010 100111001100 111001000100 011100001011 100110001010 000010100111 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111100000001 110100010111 000001000011 101100010011 000100011111 111000001001 101100011100 010110010001 000110000010 110000101111 011010011100 111000011110 100110110001 100000011100 101010001011 010111001100 100111110111 111100001011 010100100100 100111100101 111100000111 101001001101 111001111000 001110010100 111010011001 010100111111 110111010011 100100000001 001101111111 111101001100 000111101010 000100101001 111011011101 010001110111 111010100001 001101010100 101010011100 000011100100 100101101010 011111100000 011011001110 000101000111 010010111111 001100111011 000001101001 001110110001 010011011110 001011011000 100000111010 001000100101 010001100011 011001000111 010110101010 101000001001 110101011000 001100000101 100110011001 000011001001 100110111001 111101101001 011011001001 010001100111 001100011100 011111101111 100100111010 101111110011 110111001110 000111100000 011110000101 111110100010 001011001101 110100101011 011100001101 111001111100 111110100110 000010010100 010111110010 001110001101 011111010011 110110111010 101001110000 011110110011 000111011000 010111000110 001011100010 001110000010 010111000011 010011111110 011000010000 101111011110 101010000110 011100011101 011101101100 100110101001 000000011001 100101010010 110011010101 010100100111 011101011100 011001111001 000001001000 001000101011 010110111101 100110010010 010010010110 000000111001 011111110101 011111001000 100100011111 000001111000 111101010010 101010100100 011010111101 000001011011 101111100001 100011000000 110011001010 000100010111 100000010000 101111010110 010010010001 101011100001 101001011100 001111000011 111000100010 001101110110 001110111011 100001110010 000001110001 111100001000 100010011101 000011000110 111100100110 000111110111 000100111001 011001110001 111101010001 110110101100 010001011001 001101111110 000101111010 110100100110 111011001110 110011010100 010000100011 011000111001 110001110001 101000000001 101011000101 011010000110 000010111000 010011100011 111101101110 001111010010 100011011100 111110111010 000010110010 101000011100 010011101011 110001011000 000000010011 001101010010 110111111011 111101011100 111001010011 110111100101 111000111001 001110010010 001011101101 110100100111 101011100000 011000100101 110101010010 000110000111 111111001000 000011100110 101101110011 001110011111 110000101110 110011100101 101110100101 001000111000 101111000011 100111111011 011010101111 101101001000 000101101110 111101000011 000011000010 000101010010 101001011101 110011010011 111010010001 110100010001 010100001010 110111101010 100111000101 101001100010 011010011011 101011101101 011001100001 001000110111 000100101000 010000000111 111111110000 001011110110 100010010001 100110101111 110110010000 100110011011 001100110101 100011101100 000011011001 010110100001 011110101111 101011011000 101110111001 111000010000 011100000010 100110100101 010101011111 100000111001 011000111100 101100100111 101011111000 111101111101 101000100011 101000010101 111001010010 101110000111 101010000001 000100100111 100001011101 110010101010 000000100100 011101000101 110010001101 111011101110 111111111110 011100000111 101111110010 000101100110 000110001011 100100011000 010001101111 011110010000 100011011111 010101101010 000011100000 000101001110 001001110001 111110011001 101110000000 010001010010 110001000110 101011001110 010001101001 000000011011 101001010011 011010000100 001100001100 100010001001 100000111111 001000000000 100110110100 100111110000 101001110001 000110101101 000111001010 101100011011 110001101000 000111101110 110110010011 100010001000 001010001100 001011111110 001110000111 110010100000 100001110101 000110111101 110010011000 111110111011 101000000011 011010011110 110000001111 011000110100 111111001101 000010110100 101100001100 110000101011 010000001110 101001101000 011000000100 101101011000 100111111100 110001001010 001010101100 111010001110 101110100110 011110010001 111011111010 000001100110 010001100101 100101001111 111001001011 111101101010 011011000001 010111010101 110001111110 110100101110 111101011011 011000101011 010100111100 011000000111 101000101001 111000110001 101000110001 001111010011 100000010100 001001101101 010101110001 100110000101 011000101101 001110110010 100111000001 011001000001 000101101000 101000101110 000100001110 111110101111 011110110001 101001110010 000000111010 101111010010 011110011010 101000011111 000100001100 110001111001 110000110101 111010011100 001000111011 111110001111 011011011000 000110110001 101001010010 111010100011 000001111100 110010000001 110101111011 100011001001 010011100001 101101011111 100000001010 001011001000 000011100010 011110111000 000110111011 010111001011 011100011000 101010011011 001001011110 000110001010 111011010101 010101110010 011100101001 100000110010 111100111001 001011011101 110101101001 001000111110 101011000011 001110111111 100101010110 111011011110 010111111001 011001010001 110101111111 110100100101 111111110001 010011000101 011101101010 101111000100 011101000100 001110110110 010011011000 000011011010 110000000110 001100001101 111001111001 100001100101 100100010011 111011111000 001001111110 101101000000 000001100001 001100001011 110011011111 010101111110 001110011100 111111000100 010011000010 101100010000 000010110111 110100000101 110100010010 111001100101 010000010001 101100100110 001111101100 011100110110 000000111101 001000000100 010101101101 100101101000 001001010101 000000001111 110111110100 000010110110 001000000010 011000101000 100101001101 010110101110 100010110001 010111010000 001111011001 000011110100 100100111000 011011100110 100100010110 000001111101 010111011011 110001000101 111100010001 101001000111 110100010000 001010000111 101001110111 010010010010 100010111100 001101000011 011001110011 000100101111 010110011101 001001011010 111011101001 111111000000 111110000111 110000001011 010000110010 011100011001 011001100011 001101000110 011001011111 100111100000 110111010100 101101000001 110011101000 001010110101 100101010001 101000101100 010110001000 111011111011 000010010001 010111011010 011101101011 001000101110 101010100111 001011000111 011011010110 110111101111 100001110001 100100101111 110000010011 011110111011 000110000100 110000111111 100110101100 001101110101 011100000100 001100011110 010111010010 001110101101 100110001110 100100010000 001000001101 011101110011 011001100110 110101000011 100101001010 111111101111 010011000100 100111000111 101100101100 010110000100 111100000100 000000101001 000011110010 011011110111 011101110001 111110000001 011000011001 100001101111 110000011010 110110101010 110100110000 111111000011 100011100010 100100010010 110100011000 000110000110 001101001000 101011110111 000011110111 001001100100 100010110011 100111111000 000001111010 110001111101 100011111111 111001001101 011111101011 101111110111 100111000011 010010001010 010101101011 011011110000 110111000000 010011101111 010001010101 111110101010 001010101010 001100101101 111011000111 101001000001 000101000100 010011110010 001110100010 110111001000 111011001101 111110001010 110000100001 000000010101 000010011001 011100100111 001101111010 000001100100 011111101100 110100011111 010110000000 001010101001 000010010011 111100000110 110101000101 010110001011 111001001000 010110101111 001000000001 111100001101 001000110110 110001101001 001110001011 111101101011 011101111100 100011001011 001000001001 000001001100 100100110111 111111101101 001110100000 001001001001 001111001100 101100101111 101100110010 101000100001 101000010110 100000010101 011010011000 101101101110 011101001100 101011111100 101110101000 110010100100 011111011110 110010011101 110100100011 111111100100 011011011101 011100100001 101011110000 001100100011 000111111000 110010001010 100001001001 110010001000 111010001001 010110111111 101011001101 101010011110 011011110001 101010100011 100000110111 100011101001 101110010011 101011011011 110010010000 011110100011 010010110110 100110010111 110100000110 101011101011 001100010100 101101010000 110110001001 010010001101 111011010110 111100001110 000101111001 100001000001 101111101001 100101101110 000011001101 000001000110 111110001011 000010101111 011101101101 000000000110 111111101010 101001110110 111011000100 001011101111 110010111010 110011110001 111100011111 110110010001 100101110000 010001001011 101100010100 110100001010 001011101001 110110000001 001101001001 100000111100 001101101010 010000110011 011111101110 110001110011 011110011000 000010001010 001110000100 000110101111 100011111000 100101100010 111111110010 101100011001 011100100000 100001100011 001010011110 111100011010 010110100010 101110010010 001011110010 100010111000 001100110010 001001101001 011111101101 101100111100 011010011101 101011100100 111000001000 010111011100 001010000000 011110000110 110101101110 000111000101 101001011010 110010100010 100101110010 000101100100 010100110010 011001000010 110010011010 010100001011 011001111110 011000101001 011101011011 010010000111 000001110111 100010101001 111100011101 100010001011 111100101100 011111010101 111100101110 111101001110 111000010110 000000110110 011110000001 000111011110 111010111010 101010001101 111110101100 000111110010 110000111101 111101110101 101010101100 101110110010 110110011010 111110110001 011101111011 101011111011 100111101101 000110011001 100101101011 110101101101 010111110100 001111110010 101010101000 011001100100 101011111111 001001000001 010100100110 111100011100 011100011010 000011011110 001011101010 010010001110 101101101111 000101000010 010111100010 010010010000 111000000101 010001100010 101100001111 011110111100 100011000010 010100001111 011100101110 011000111110 111101010000 110100111000 001010001111 111100011011 101000111110 100000011011 010110000001 001111100111 001111101111 011001011100 111001000011 110011101111 001101100011 101100111111 011111101010 010000011110 001111111111 010001001001 111010101000 110100001001 111111010111 000101110100 101101100000 001101100001 001111111000 100111101001 000110010001 100011101011 000010000101 101010001100 000111110011 001010110001 111110001110 010011100010 100011010000 100001010010 100011000101 110011000100 010000010000 110100110011 100011000111 010111101010 101011000010 110000101100 111100001111 000110100001 010010110001 111000100110 000110111110 100101000000 011101001010 101111011011 111011101111 000000010100 110011101110 111001110111 011111000111 111111011100 110010011111 111111110111 001110110011 000010010010 100100100001 111010000101 001001110011 101001101111 101110101110 101000101111""".split() power(data) life_support(data)

13.691736

84

0.880646

1,261

14,746

10.275971

0.839017

0.00463

0.001389

0.002547

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1

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null

1

0

5

a323831709abf3b08a5a3c6cb19c140b718070cd

1,808

py

Python

frappe-bench/env/lib/python2.7/site-packages/gocardless_pro/resources/instalment_schedule.py

ibrahmm22/library-management

b88a2129a5a2e96ce1f945ec8ba99a0b63b8c506

[ "MIT" ]

30

2015-07-08T21:10:10.000Z

2022-02-17T10:08:55.000Z

frappe-bench/env/lib/python2.7/site-packages/gocardless_pro/resources/instalment_schedule.py

ibrahmm22/library-management

b88a2129a5a2e96ce1f945ec8ba99a0b63b8c506

[ "MIT" ]

21

2015-12-14T02:24:52.000Z

2022-02-05T15:56:00.000Z

frappe-bench/env/lib/python2.7/site-packages/gocardless_pro/resources/instalment_schedule.py

ibrahmm22/library-management

b88a2129a5a2e96ce1f945ec8ba99a0b63b8c506

[ "MIT" ]

19

2016-02-10T15:57:42.000Z

2022-02-05T10:21:05.000Z

# WARNING: Do not edit by hand, this file was generated by Crank: # # https://github.com/gocardless/crank # class InstalmentSchedule(object): """A thin wrapper around a instalment_schedule, providing easy access to its attributes. Example: instalment_schedule = client.instalment_schedules.get() instalment_schedule.id """ def __init__(self, attributes, api_response): self.attributes = attributes self.api_response = api_response @property def created_at(self): return self.attributes.get('created_at') @property def currency(self): return self.attributes.get('currency') @property def id(self): return self.attributes.get('id') @property def links(self): return self.Links(self.attributes.get('links')) @property def metadata(self): return self.attributes.get('metadata') @property def name(self): return self.attributes.get('name') @property def payment_errors(self): return self.attributes.get('payment_errors') @property def status(self): return self.attributes.get('status') @property def total_amount(self): return self.attributes.get('total_amount') class Links(object): """Wrapper for the response's 'links' attribute.""" def __init__(self, attributes): self.attributes = attributes @property def customer(self): return self.attributes.get('customer') @property def mandate(self): return self.attributes.get('mandate') @property def payments(self): return self.attributes.get('payments')

17.72549

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null

0

1

0

1

0

5

a329455af1a886af67beff6ab805c6887d7e83bf

25,026

py

Python

src/utils.py

HFooladi/lincs_processing

ac21d84bc09565a7c7e9285f6bae5557fa1617de

[ "MIT" ]

null

src/utils.py

HFooladi/lincs_processing

ac21d84bc09565a7c7e9285f6bae5557fa1617de

[ "MIT" ]

null

src/utils.py

HFooladi/lincs_processing

ac21d84bc09565a7c7e9285f6bae5557fa1617de

[ "MIT" ]

null

from __future__ import unicode_literals, print_function, division from typing import List, Union import pickle import pandas as pd from tqdm import tqdm from collections import Counter __author__ = "Hosein Fooladi" __email__ = "fooladi.hosein@gmail.com" def load_pickle(dataset_dir: str) -> List: """Loading (reading) a pickle file Parameters ---------- dataset_dir: str It must be string file that shows the directory of the dataset. Returns ------- List """ assert isinstance(dataset_dir, str), "The dataset_dir must be a string object" fp = open(dataset_dir, 'rb') return pickle.load(fp) def write_pickle(dataset_dir: str, data: List) -> None: """Writing a file (data) into a pickle file (dataset_dir) Parameters ---------- dataset_dir: str It must be string file that shows the directory for writing. data: List the object that should be written into the pickle file. """ assert isinstance(dataset_dir, str), "The dataset_dir must be a string object" fp = open(dataset_dir, 'wb') pickle.dump(data, fp) def print_statistics(data: Union[str, List]) -> None: """Print data statistics This function takes the directory of dataset and returns some useful statistics about the data. Parameters ---------- data: Union[str, List] the data can be a string which is the directory of the dataset. dataset should be a pickle file. e.g., valid argument is something like this: './Data/level3_trt_cp_landmark.pkl' or it can be a list which contains the gene expression and metadata. It must be a list of tuples with the following format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) """ print("=================================================================") print("Data Loading..") assert isinstance(data, (str, list)), "The data should be string or list object" if isinstance(data, str): with open(data, "rb") as f: train = pickle.load(f) else: assert isinstance(data, list), "The data must be a list object" train = data print("Data Statistics\n") print("Number of Train Data: {}".format(len(train))) print("Please wait while we are retriving information ...") cell_lines = [] compounds = [] doses = [] times = [] for i in range(len(train)): cell_lines.append(train[i][0][0]) compounds.append(train[i][0][1]) doses.append(train[i][0][3]) times.append(train[i][0][5]) print("Number of unique Cell Lines: {}".format(len(set(cell_lines)))) print("Number of unique Compounds: {}".format(len(set(compounds)))) print("Number of unique doses: {}".format(len(set(doses)))) print("Number of unique times: {}".format(len(set(times)))) def print_most_frequent(data: Union[str, List], n: int = 3) -> None: """Print most frequent cell line, compounds, and does. This function takes the directory of dataset (or a list object) and integer n and returns The n most frequent cell lines, compounds and doses in the dataset. Parameters ---------- data: Union[str, List] the data can be a string which is the directory of the dataset. dataset should be a pickle file. e.g., valid argument is something like this: './Data/level3_trt_cp_landmark.pkl' or it can be a list which contains the gene expression and metadata. It must be a list of tuples with the following format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) n: int, optional (default 3) An integer which determine number of frequent statistics we want to retrieve. Default=3. """ print("=================================================================") print("Data Loading..") assert isinstance(n, int), "The parameter n must be an integer" assert isinstance(data, (str, list)), "The data should be string or list object" if isinstance(data, str): with open(data, "rb") as f: train = pickle.load(f) else: assert isinstance(data, list), "The data must be a list object" train = data print("Please wait while we are retriving information ...") cell_lines = [] compounds = [] doses = [] for i in tqdm(range(len(train))): cell_lines.append(train[i][0][0]) compounds.append(train[i][0][1]) doses.append(train[i][0][3]) print("loop finished !!!") print("Most frequent Cell Lines: {}".format( Counter(cell_lines).most_common(n))) print("Most frequent Compounds: {}".format(Counter(compounds).most_common(n))) print("Most frequent Doses: {}".format(Counter(doses).most_common(n))) def cell_line_frequent(data: Union[str, List], n: int = 3) -> List: """Returns list of data belongs to most frequent cell lines This function takes the directory of dataset (or a list object) and integer n, and parse the data to keep only the data that belongs to n most frequent cell lines. Parameters ---------- data: Union[str, List] the data can be a string which is the directory of the dataset. dataset should be a pickle file. e.g., valid argument is something like this: './Data/level3_trt_cp_landmark.pkl' or it can be a list which contains the gene expression and metadata. It must be a list of tuples with the following format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) n: int, optional (default 3) An integer which determine number of frequent statistics we want to retrieve. Default=3. Returns ------- parse_data: List A list containing data that belongs to n most frequent cell lines. """ print("=================================================================") print("Data Loading..") assert isinstance(n, int), "The parameter n must be an integer" assert isinstance(data, (str, list)), "The data should be string or list object" if isinstance(data, str): with open(data, "rb") as f: train = pickle.load(f) else: assert isinstance(data, list), "The data must be a list object" train = data print("Please wait while we are retriving information ...") cell_lines = [] for i in tqdm(range(len(train))): cell_lines.append(train[i][0][0]) print("Number of unique Cell Lines: {}".format(len(set(cell_lines)))) print("Most frequent Cell Lines: {}".format( Counter(cell_lines).most_common(n))) if n > len(set(cell_lines)): import warnings warnings.warn( "n is greater than number of unique cell lines available in the dataset" ) # List of n most frequent cell lines x = list(map(lambda x: x[0], Counter(cell_lines).most_common(n))) parse_data = [line for line in train if line[0][0] in x] return parse_data def cell_line_list(data: Union[str, List], cells: List[str] = ['MCF7']) -> List: """Filter data based on desired cell line list This function takes the directory of dataset (or alist object) and a list cells, and parse the data to keep only the data that belongs to cells list. Parameters ---------- data: Union[str, List] the data can be a string which is the directory of the dataset. dataset should be a pickle file. e.g., valid argument is something like this: './Data/level3_trt_cp_landmark.pkl' or it can be a list which contains the gene expression and metadata. It must be a list of tuples with the following format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) cells: List[str] list of cell lines that we want to keep their data to retrieve. Default=['MCF7'] Returns ------- parse_data: list A list containing data that belongs to desired list. """ assert isinstance(cells, list), "The parameter cells must be a list" print("=================================================================") print("Data Loading..") assert isinstance(data, (str, list)), "The data should be string or list object" if isinstance(data, str): with open(data, "rb") as f: train = pickle.load(f) else: assert isinstance(data, list), "The data must be a list object" train = data print("Number of Train Data: {}".format(len(train))) parse_data = [line for line in train if line[0][0] in cells] print("Number of Data after parsing: {}".format(len(parse_data))) return parse_data def parse_list(data: Union[str, List], indicator: int = 0, query=['MCF7']) -> List: """Filter the data based on compound, cell line, dose or time This function takes the directory of dataset, indicator that indicates whether you want to subset the data based on cell line, compound, dose, or time and a list which shows what part of the data you want to keep. The output will be a list of desired parsed dataset. Parameters ---------- data: Union[str, List] the data can be a string which is the directory of the dataset. dataset should be a pickle file. e.g., valid argument is something like this: './Data/level3_trt_cp_landmark.pkl' or it can be a list which contains the gene expression and metadata. It must be a list of tuples with the following format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) indicator: int it must be an integer from 0 1 2 and 3 that shows whether we want to retrieve the data based on cells, compound or dose. 0: cell_lines 1:compounds 2:doses 3:time Default=0 (cell_lines) query: List list of cells or compounds or doses that we want to retrieve. The list depends on the indicator. If the indicator is 0, you should enter the list of desired cell lines and so on. Default=['MCF7'] Returns ------- parse_data: List A list containing data that belongs to desired list. """ assert isinstance(indicator, int), "The indicator must be an int object" assert indicator in [0, 1, 2, 3], "You should choose indicator from 0, 1, 2 range" assert isinstance(query, list), "The parameter query must be a list" print("=================================================================") print("Data Loading..") assert isinstance(data, (str, list)), "The data should be string or list object" if isinstance(data, str): with open(data, "rb") as f: train = pickle.load(f) else: assert isinstance(data, list), "The data must be a list object" train = data mapping = {0: 0, 1: 1, 2: 3, 3: 5} k = mapping[indicator] mapping_name = {0: 'cell_lines', 1: 'compounds', 2: 'doses', 3: 'time'} print("Number of Train Data: {}".format(len(train))) print("You are parsing the data base on {}".format(mapping_name[indicator])) parse_data = [line for line in train if line[0][k] in query] print("Number of Data after parsing: {}".format(len(parse_data))) return parse_data def parse_most_frequent(data: Union[str, List], indicator: int = 0, n: int = 3) -> List: """Returns most frequent data (based on cell line, compound, ...) This function takes the directory of dataset, indicator that indicates whether you want to subset the data based on cell line, compound, dose, or time and a n which how much frequent items you want to keep. The output will be a list of desired parsed dataset. Parameters ---------- data: Union[str, List] the data can be a string which is the directory of the dataset. dataset should be a pickle file. e.g., valid argument is something like this: './Data/level3_trt_cp_landmark.pkl' or it can be a list which contains the gene expression and metadata. It must be a list of tuples with the following format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) indicator: int, optional (default n=0) It must be an integer from 0 1 2 and 3 that shows whether we want to retrieve the data based on cells, compound or dose. 0: cell_lines 1:compounds 2:doses 3:time Default=0 n: int, optional (default n=3) number of most frequent cells or compounds or doses that we want to retrieve. The list depends on the indicator. If the indicator is 0, you should enter the number of desired cell lines and so on. Default=3 Returns ------- parse_data: List A list containing data that belongs to desired list. """ assert isinstance(indicator, int), "The indicator must be an int object" assert indicator in [0, 1, 2, 3], "You should choose indicator from 0, 1, 2, 3 range" assert isinstance(n, int), "The parameter n must be an integer" print("=================================================================") print("Data Loading..") assert isinstance(data, (str, list)), "The data should be string or list object" if isinstance(data, str): with open(data, "rb") as f: train = pickle.load(f) else: assert isinstance(data, list), "The data must be a list object" train = data mapping = {0: 0, 1: 1, 2: 3, 3: 5} k = mapping[indicator] mapping_name = {0: 'cell_lines', 1: 'compounds', 2: 'doses', 3: 'time'} mylist = [] for i in tqdm(range(len(train))): mylist.append(train[i][0][k]) print("Number of unique {}: {}".format(mapping_name[indicator], len(set(mylist)))) print("Most frequent {}: {}".format(mapping_name[indicator], Counter(mylist).most_common(n))) assert n <= len(set(mylist)), "n is out of valid range!" # List of n most frequent cell lines y = list(map(lambda x: x[0], Counter(mylist).most_common(n))) parse_data = [line for line in train if line[0][k] in y] return parse_data def parse_chunk_frequent(data: Union[str, List], indicator: int = 0, start: int = 0, end: int = 3) -> List: """ This function takes the directory of dataset, indicator that indicates whether you want to subset the data based on cell line, compound, dose, or time and a start and end which shows what chunk of data is desirable. E.g., if start=0 and end=3, you are subsetting 3 most frequent data. The output will be a list of desired parsed dataset. Parameters ---------- data: Union[str, List] the data can be a string which is the directory of the dataset. dataset should be a pickle file. e.g., valid argument is something like this: './Data/level3_trt_cp_landmark.pkl' or it can be a list which contains the gene expression and metadata. It must be a list of tuples with the following format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) indicator: int, optional (default n=0) It must be an integer from 0 1 2 and 3 that shows whether we want to retrieve the data based on cells, compound or dose. 0: cell_lines 1:compounds 2:doses 3:time Default=0 start: int indicates the start of the list you want to subset. Default=0 end: int indicates the end of the list you want to subset. Default=3 Returns ------- parse_data: List A list containing data that belongs to desired list. """ assert isinstance(indicator, int), "The indicator must be an int object" assert indicator in [0, 1, 2], "You should choose indicator from 0, 1, 2 range" assert isinstance(start, int), "The parameter start must be an integer" assert isinstance(end, int), "The parameter end must be an integer" assert start <= end, "The start should be less than the end!!" print("=================================================================") print("Data Loading..") assert isinstance(data, (str, list)), "The data should be string or list object" if isinstance(data, str): with open(data, "rb") as f: train = pickle.load(f) else: assert isinstance(data, list), "The data must be a list object" train = data mapping = {0: 0, 1: 1, 2: 3, 3: 5} k = mapping[indicator] mapping_name = {0: 'cell_lines', 1: 'compounds', 2: 'doses', 3: 'time'} mylist = [] for i in range(len(train)): mylist.append(train[i][0][k]) print("Number of unique {}: {}".format(mapping_name[indicator], len(set(mylist)))) assert end < len(set(mylist)), "end is out of valid range!" # List of n most frequent cell lines y = list(map(lambda x: x[0], Counter(mylist).most_common()))[start:end] print("Desired {}: {}".format(mapping_name[indicator], y)) parse_data = [line for line in train if line[0][k] in y] return parse_data def parse_dose_range(data: Union[str, List], dose_min: int = 0, dose_max: int = 5) -> List: """ This function takes the directory of dataset minimum and maximum dose and return a list of data that are within the desired range. Parameters ---------- data: Union[str, List] the data can be a string which is the directory of the dataset. dataset should be a pickle file. e.g., valid argument is something like this: './Data/level3_trt_cp_landmark.pkl' or it can be a list which contains the gene expression and metadata. It must be a list of tuples with the following format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) dose_min: int, optional (default dose_min=0) minimum dose. Default=0 dose_max: int, optional (default dose_max=5) maximum_dose. Default=5 Returns -------- parse_data: List A list containing data that belongs to desired list ( Desired range of doses). """ assert isinstance(dose_min, int), "The parameter dose_min must be an integer" assert isinstance(dose_max, int), "The parameter dose_max must be an integer" assert dose_min < dose_max, "The minimum dose must be less than the maximum dose !!" print("=================================================================") print("Data Loading..") assert isinstance(data, (str, list)), "The data should be string or list object" if isinstance(data, str): with open(data, "rb") as f: train = pickle.load(f) else: assert isinstance(data, list), "The data must be a list object" train = data print("Number of Train Data: {}".format(len(train))) parse_data = [ line for line in train if line[0][3] > dose_min and line[0][3] < dose_max ] print("Number of Data after parsing: {}".format(len(parse_data))) return parse_data def to_dataframe(data: Union[str, List]) -> pd.DataFrame: """This takes a list and produce a pandas datframe of data The input to this function is a list which contains metadata (such as cell lines, compounds, ..) and gene expression. this function returns a pandas dataframe where the first columns belongs to gene expression and last four columns contain metaddata cell line, compound, dose, and time in this order. Parameters ---------- data: Union[str, List] the data can be a string which is the directory of the dataset. dataset should be a pickle file. e.g., valid argument is something like this: './Data/level3_trt_cp_landmark.pkl' or it can be a list which contains the gene expression and metadata. It must be a list of tuples with the following format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) Returns ------- pd.DataFrame This is a pandas dataframe where the first columns contains gene expression (978 or 12328-dimension) and the last four columns contains cell line, pert_id, dose, and time """ assert isinstance(data, (str, list)), "The data should be string or list object" if isinstance(data, str): with open(data, "rb") as f: train = pickle.load(f) else: assert isinstance(data, list), "The data must be a list object" train = data genes = [line[1] for line in train] cell_lines = [line[0][0] for line in train] compounds = [line[0][1] for line in train] doses = [line[0][3] for line in train] times = [line[0][5] for line in train] metadata = { "cell_lines": cell_lines, "compounds": compounds, "doses": doses, "times": times } data_df = pd.concat([pd.DataFrame(genes), pd.DataFrame(metadata)], axis=1) return data_df def parse_list_v2(dataset_dir, indicator=0, query=['MCF7'], data=None): """ This function takes the directory of dataset, indicator that indicates whether you want to subset the data based on cell line, compound, dose, time, touchstone, clinical phase, MOA or target. Moreover, it takes a list which shows what part of the data you want to keep. The output will be a list of desired parsed dataset. Input: Mandatory: -:param dataset_dir (str): It must be string file that shows the directory of the dataset. dataset should be a pickle file. e.g., valid argument is something like this: './Data/level3_trt_cp_landmark_allinfo.pkl' The pickle file should be as the following: list Format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type, touchstone, clinical phase, moa, target) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) Optional: -:params indicator (int): it must be an integer from 0 1 2 3 4 5 6 7 that shows whether we want to retrieve the data based on cells, compound, dose, touchstone, clinical phase, moa or target. 0: cell_lines 1: compounds 2: doses 3: time 4: touchstone 5: clinical phase 6: moa 7: target Default=0 (cell_lines) -:params query (list): list of cells or compounds or doses or time or touchstone or clinical phase or MOA or target that we want to retrieve. The list depends on the indicator. If the indicator is 0, you should enter the list of desired cell lines and so on. Default=['MCF7'] -:param data (list): It must be a list with the following format: line[0]:(cell_line, drug, drug_type, does, does_type, time, time_type, touchstone, clinical phase, moa, target) line[1]: 978 or 12328-dimensional Vector(Gene_expression_profile) Output: -:params parse_data (list): A list containing data that belongs to desired list. Note: If you provide the data argument, the function igonres the dataset_dir argument and returns output based on the provided data. Otherwise, it returns output based on dataset_dir. """ assert isinstance(dataset_dir, str), "The dataset_dir must be a string object" assert isinstance(indicator, int), "The indicator must be an int object" assert indicator in [ 0, 1, 2, 3, 4, 5, 6, 7 ], "You should choose indicator from 0, 1, 2, 3, 4, 5, 6, 7 range" assert isinstance(query, list), "The parameter query must be a list" print("=================================================================") print("Data Loading..") if data is None: with open(dataset_dir, "rb") as f: train = pickle.load(f) else: assert isinstance(data, list), "The data must be a list object" train = data mapping = {0: 0, 1: 1, 2: 3, 3: 5, 4: 7, 5: 8, 6: 9, 7: 10} k = mapping[indicator] mapping_name = { 0: 'cell_lines', 1: 'compounds', 2: 'doses', 3: 'time', 4: 'tochstone', 5: 'clinical_phase', 6: 'moa', 7: 'target' } print("Number of Train Data: {}".format(len(train))) print("You are parsing the data base on {}".format(mapping_name[indicator])) parse_data = [] if indicator in [0, 1, 2, 3, 4]: parse_data = [line for line in train if line[0][k] in query] elif indicator in [5, 6, 7]: for line in train: tmp = line[0][k][0].split('|') for a in tmp: if a in query: parse_data.append(line) break print("Number of Data after parsing: {}".format(len(parse_data))) return parse_data

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unity/__init__.py

princeton-vl/PackIt

9894d252c5238d582cba7c3d19540f89d47e4166

[ "BSD-3-Clause" ]

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2022-01-04T15:30:52.000Z

unity/__init__.py

princeton-vl/PackIt

9894d252c5238d582cba7c3d19540f89d47e4166

[ "BSD-3-Clause" ]

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2020-07-21T20:21:08.000Z

2022-03-12T00:42:18.000Z

unity/__init__.py

princeton-vl/PackIt

9894d252c5238d582cba7c3d19540f89d47e4166

[ "BSD-3-Clause" ]

5

2020-07-27T12:35:00.000Z

2021-07-19T03:04:21.000Z

from .communicator_objects import * from .unityagents import *

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Python

uimnet/metrics/__init__.py

facebookresearch/uimnet

d7544cf5fb4c65cb262dca203afb0db4ba6c569d

[ "MIT" ]

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2021-07-28T18:40:20.000Z

2022-01-26T23:50:41.000Z

uimnet/metrics/__init__.py

facebookresearch/uimnet

d7544cf5fb4c65cb262dca203afb0db4ba6c569d

[ "MIT" ]

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2021-08-31T13:44:56.000Z

2021-08-31T14:10:12.000Z

uimnet/metrics/__init__.py

facebookresearch/uimnet

d7544cf5fb4c65cb262dca203afb0db4ba6c569d

[ "MIT" ]

1

2021-11-06T01:55:58.000Z

#!/usr/bin/env python3 # # # Copyright (c) 2021 Facebook, inc. and its affiliates. All Rights Reserved # # from uimnet.metrics.out_domain import * from uimnet.metrics.auc import * from uimnet.metrics.fused_prediction import * from uimnet.metrics.prediction import *

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Python

probnode/interface/ievent.py

medasmarathon/Probnode

7a372a5d515a2e13575690d738404c14df07c784

[ "BSD-3-Clause" ]

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2022-02-25T17:05:30.000Z

2022-02-28T15:39:58.000Z

probnode/interface/ievent.py

medasmarathon/Probnode

7a372a5d515a2e13575690d738404c14df07c784

[ "BSD-3-Clause" ]

1

2022-02-26T15:12:20.000Z

2022-02-27T08:29:44.000Z

probnode/interface/ievent.py

medasmarathon/Probnode

7a372a5d515a2e13575690d738404c14df07c784

[ "BSD-3-Clause" ]

null

class IEvent: pass

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6e646ede69755b2cf1217989d36d06fe6e0c7efa

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py

Python

imagepy/tools/Measure/distance_tol.py

dada1437903138/imagepy

65d9ce088894eef587054e04018f9d34ff65084f

[ "BSD-4-Clause" ]

1,178

2017-05-25T06:59:01.000Z

2022-03-31T11:38:53.000Z

imagepy/tools/Measure/distance_tol.py

TomisTony/imagepy

3c378ebaf72762b94f0826a410897757ebafe689

[ "BSD-4-Clause" ]

76

2017-06-10T17:01:50.000Z

2021-12-23T08:13:29.000Z

imagepy/tools/Measure/distance_tol.py

TomisTony/imagepy

3c378ebaf72762b94f0826a410897757ebafe689

[ "BSD-4-Clause" ]

315

2017-05-25T12:59:53.000Z

2022-03-07T22:52:21.000Z

from sciapp.action import DistanceTool as Plugin

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Python

game/displays/null_display.py

scooler/tic-tac-toe

8ec14c0c35dd48edc8718b478e5f4c83891a941a

[ "MIT" ]

null

game/displays/null_display.py

scooler/tic-tac-toe

8ec14c0c35dd48edc8718b478e5f4c83891a941a

[ "MIT" ]

null

game/displays/null_display.py

scooler/tic-tac-toe

8ec14c0c35dd48edc8718b478e5f4c83891a941a

[ "MIT" ]

null

class NullDisplay: def __init__(self, board=None): pass def show_results(self): pass def draw(self): pass

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py

Python

01_Python_Basico_Intermediario/Aula053/aula53.py

Joao-Inacio/Curso-de-Python3

179d85f43f77dced640ffb143a87214538254cf3

[ "MIT" ]

1

2021-07-19T12:31:49.000Z

01_Python_Basico_Intermediario/Aula053/aula53.py

Joao-Inacio/Curso-de-Python3

179d85f43f77dced640ffb143a87214538254cf3

[ "MIT" ]

null

01_Python_Basico_Intermediario/Aula053/aula53.py

Joao-Inacio/Curso-de-Python3

179d85f43f77dced640ffb143a87214538254cf3

[ "MIT" ]

null

""" Módulos padrão do Python """ import sys print(sys.platform)

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py

Python

thecampy/__init__.py

SyphonArch/thecampy

91da9fb3e4cbbcb6304ac7e8f9b52a7fcce26301

[ "MIT" ]

null

thecampy/__init__.py

SyphonArch/thecampy

91da9fb3e4cbbcb6304ac7e8f9b52a7fcce26301

[ "MIT" ]

null

thecampy/__init__.py

SyphonArch/thecampy

91da9fb3e4cbbcb6304ac7e8f9b52a7fcce26301

[ "MIT" ]

null

from . import utils from .client import client from .models import Cookie, Soldier, Message from .exceptions import ThecampyException, ThecampyValueError, ThecampyReqError

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170

py

Python

hello/chalan/admin.py

sumanbudhathoki9808/E-Challan

5ce22f8d68bee1dbabc8d3081238fd56c1a325a6

[ "MIT" ]

null

hello/chalan/admin.py

sumanbudhathoki9808/E-Challan

5ce22f8d68bee1dbabc8d3081238fd56c1a325a6

[ "MIT" ]

18

2021-04-08T08:56:58.000Z

2021-05-19T15:50:30.000Z

hello/chalan/admin.py

sumanbudhathoki9808/E-Challan

5ce22f8d68bee1dbabc8d3081238fd56c1a325a6

[ "MIT" ]

null

from django.contrib import admin # Register your models here. from .models import challan, dynamicAbout admin.site.register(challan) admin.site.register(dynamicAbout)

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py

Python

stdlib_tests/test_math.py

MajkB/voc-1

a79af19c07fa66965522d8fb97ae783541cda110

[ "BSD-3-Clause" ]

null

stdlib_tests/test_math.py

MajkB/voc-1

a79af19c07fa66965522d8fb97ae783541cda110

[ "BSD-3-Clause" ]

null

stdlib_tests/test_math.py

MajkB/voc-1

a79af19c07fa66965522d8fb97ae783541cda110

[ "BSD-3-Clause" ]

null

import math as math print(math.floor(5.2232323))

12.5

28

0.76

9

50

4.222222

0.777778

0

0.181818

0.12

50

3

29

16.666667

0.681818

0

1

0

true

0

0.5

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0.5

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null

0

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null

0

1

0

1

0

1

0

5

955ff3766b779c1a5c2527c02e0a825da76d092b

14,483

py

Python

machinelearning/test/kerasutilstest.py

hayj/MachineLearning

66a34b6776450f7d597acca05525120fb28c8deb

[ "MIT" ]

null

machinelearning/test/kerasutilstest.py

hayj/MachineLearning

66a34b6776450f7d597acca05525120fb28c8deb

[ "MIT" ]

null

machinelearning/test/kerasutilstest.py

hayj/MachineLearning

66a34b6776450f7d597acca05525120fb28c8deb

[ "MIT" ]

null

# coding: utf-8 import os import sys sys.path.append('../') import unittest import doctest from machinelearning import kerasutils from machinelearning.kerasutils import * # The level allow the unit test execution to choose only the top level test mini = 0 maxi = 9 assert mini <= maxi print("==============\nStarting unit tests...") if mini <= 0 <= maxi: class DocTest(unittest.TestCase): def testDoctests(self): """Run doctests""" doctest.testmod(kerasutils) if mini <= 1 <= maxi: class Test1(unittest.TestCase): def test1(self): esm = normalizeEarlyStopMonitor(\ { 'val_loss': {'patience': 3, 'min_delta': 0.1}, 'val_acc': {'patience': 2}, 'val_top_k_categorical_accuracy': {'patience': 2, 'min_delta': 0.1}, }) history = \ { 'val_loss': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], 'val_acc': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], 'val_top_k_categorical_accuracy': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], } self.assertTrue(not hasToEarlyStop(history, esm)) def test2(self): esm = normalizeEarlyStopMonitor(\ { 'val_loss': {'patience': 3}, 'val_acc': {'patience': 2}, 'val_top_k_categorical_accuracy': {'patience': 2}, }) history = \ { 'val_loss': [0.1, 0.1, 0.1, 0.1, 0.1, 0.09], 'val_acc': [0.1, 0.1, 0.09, 0.08, 0.07, 0.06], 'val_top_k_categorical_accuracy': [0.1, 0.1, 0.1, 0.09, 0.08, 0.07], } self.assertTrue(not hasToEarlyStop(history, esm)) def test3(self): esm = normalizeEarlyStopMonitor(\ { 'val_loss': {'patience': 3, 'min_delta': 0.01}, 'val_acc': {'patience': 2, 'min_delta': 0.01}, 'val_top_k_categorical_accuracy': {'patience': 2, 'min_delta': 0.01}, }) history = \ { 'val_loss': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], 'val_acc': [0.1, 0.1, 0.09, 0.08, 0.07, 0.06], 'val_top_k_categorical_accuracy': [0.1, 0.1, 0.1, 0.09, 0.08, 0.07], } self.assertTrue(hasToEarlyStop(history, esm)) def test3(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 2}, }) history = \ { 'val_acc': [0.12, 0.13, 0.07, 0.06, 0.05], } self.assertTrue(hasToEarlyStop(history, esm)) def test4(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 3}, }) history = \ { 'val_acc': [0.1, 0.1, 0.12, 0.13, 0.07, 0.06, 0.05], } self.assertTrue(not hasToEarlyStop(history, esm)) def test5(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 1}, }) history = \ { 'val_acc': [0.1, 0.1, 0.12, 0.13, 0.07], } self.assertTrue(not hasToEarlyStop(history, esm)) def test5(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 0}, }) history = \ { 'val_acc': [0.1, 0.1, 0.12, 0.13, 0.07], } self.assertTrue(hasToEarlyStop(history, esm)) def test7(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 4}, }) history = \ { 'val_acc': [0.1, 0.14, 0.10, 0.11, 0.12, 0.13], } self.assertTrue(not hasToEarlyStop(history, esm)) def test7(self): esm = normalizeEarlyStopMonitor(\ { 'val_loss': {'patience': 1, 'min_delta': 0.5}, 'val_acc': {'patience': 3}, }) history = \ { 'val_loss': [10, 20, 9, 8, 8, 6, 5, 6], 'val_acc': [0.1, 0.14, 0.10, 0.11, 0.12, 0.13], } self.assertTrue(not hasToEarlyStop(history, esm)) def test8(self): esm = normalizeEarlyStopMonitor(\ { 'val_loss': {'patience': 1, 'min_delta': 0.5}, 'val_acc': {'patience': 3}, }) history = \ { 'val_loss': [10, 20, 9, 8, 8, 6, 5, 6, 7], 'val_acc': [0.1, 0.14, 0.10, 0.11, 0.12, 0.13], } self.assertTrue(hasToEarlyStop(history, esm)) def test9(self): esm = normalizeEarlyStopMonitor(\ { 'val_loss': {'patience': 3}, 'val_acc': {'patience': 3}, 'val_top_k_categorical_accuracy': {'patience': 3}, }) history = \ { 'val_loss': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], 'val_acc': [0.1, 0.1, 0.09, 0.08, 0.07, 0.1, 0.1], 'val_top_k_categorical_accuracy': [0.1, 0.1, 0.1, 0.09, 0.08, 0.1], } self.assertTrue(not hasToEarlyStop(history, esm)) def test10(self): esm = normalizeEarlyStopMonitor(\ { 'val_loss': {'patience': 3}, 'val_acc': {'patience': 3}, 'val_top_k_categorical_accuracy': {'patience': 3}, }) history = \ { 'val_loss': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1], 'val_acc': [0.1, 0.1, 0.09, 0.1, 0.07, 0.09], 'val_top_k_categorical_accuracy': [0.1, 0.1, 0.1, 0.09, 0.08, 0.1], } self.assertTrue(not hasToEarlyStop(history, esm)) def test11(self): esm = normalizeEarlyStopMonitor(\ { 'val_loss': {'patience': 3}, 'val_acc': {'patience': 3}, 'val_top_k_categorical_accuracy': {'patience': 3}, }) history = \ { 'val_loss': [0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.3], 'val_acc': [0.1, 0.1, 0.09, 0.1, 0.07, 0.12, 0.13], 'val_top_k_categorical_accuracy': [0.1, 0.1, 0.1, 0.09, 0.08, 0.1, 0.12, 0.13], } self.assertTrue(not hasToEarlyStop(history, esm)) def test12(self): esm = normalizeEarlyStopMonitor(\ { 'val_loss': {'patience': 3}, 'val_acc': {'patience': 3}, 'val_top_k_categorical_accuracy': {'patience': 3}, }) history = \ { 'val_loss': [0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.3], 'val_acc': [0.1, 0.1, 0.09, 0.1, 0.07, 0.07, 0.09, 0.09], 'val_top_k_categorical_accuracy': [0.1, 0.1, 0.1, 0.09, 0.08, 0.1, 0.12, 0.13], } self.assertTrue(not hasToEarlyStop(history, esm)) def test13(self): esm = normalizeEarlyStopMonitor(\ { 'val_loss': {'patience': 2}, 'val_acc': {'patience': 3}, 'val_top_k_categorical_accuracy': {'patience': 3}, }) history = \ { 'val_loss': [0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.3, 0.4], 'val_acc': [0.1, 0.1, 0.09, 0.1, 0.07, 0.07, 0.09, 0.09], 'val_top_k_categorical_accuracy': [0.1, 0.1, 0.1, 0.09, 0.08, 0.099, 0.095, 0.06], } self.assertTrue(hasToEarlyStop(history, esm)) def test14(self): esm = normalizeEarlyStopMonitor(\ { 'val_top_k_categorical_accuracy': {'patience': 1, 'min_delta': 0.03}, }) history = \ { 'val_top_k_categorical_accuracy': [0.13, 0.1, 0.1, 0.09, 0.08, 0.099, 0.095, 0.11, 0.12], } self.assertTrue(hasToEarlyStop(history, esm)) def test15(self): esm = normalizeEarlyStopMonitor(\ { 'val_top_k_categorical_accuracy': {'patience': 1, 'min_delta': 0.03}, }) history = \ { 'val_top_k_categorical_accuracy': [0.13, 0.1, 0.1, 0.09, 0.08, 0.099, 0.095, 0.15, 0.12], } self.assertTrue(hasToEarlyStop(history, esm)) def test16(self): esm = normalizeEarlyStopMonitor(\ { 'val_top_k_categorical_accuracy': {'patience': 1, 'min_delta': 0.019}, }) history = \ { 'val_top_k_categorical_accuracy': [0.13, 0.1, 0.1, 0.09, 0.08, 0.099, 0.095, 0.15, 0.12], } self.assertTrue(not hasToEarlyStop(history, esm)) def test17(self): esm = normalizeEarlyStopMonitor(\ { 'val_top_k_categorical_accuracy': {'patience': 2, 'min_delta': 0.1}, }) history = \ { 'val_top_k_categorical_accuracy': [0.1, 0.2, 0.3, 0.4], } self.assertTrue(not hasToEarlyStop(history, esm)) def test18(self): esm = normalizeEarlyStopMonitor(\ { 'val_top_k_categorical_accuracy': {'patience': 2, 'min_delta': 0.2}, }) history = \ { 'val_top_k_categorical_accuracy': [0.1, 0.2, 0.3, 0.4], } self.assertTrue(hasToEarlyStop(history, esm)) def test19(self): esm = normalizeEarlyStopMonitor(\ { 'val_top_k_categorical_accuracy': {'patience': 2, 'min_delta': 0.1}, }) history = \ { 'val_top_k_categorical_accuracy': [0.1, 0.2, 0.3, 0.4, 0.4, 0.4, 0.4], } self.assertTrue(hasToEarlyStop(history, esm)) def test20(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 3}, 'val_top_k_categorical_accuracy': {'patience': 3}, }) history = \ { 'val_acc': [0.1, 0.2, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3], 'val_top_k_categorical_accuracy': [0.1, 0.2, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3], } self.assertTrue(not hasToEarlyStop(history, esm)) def test20(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 3}, 'val_top_k_categorical_accuracy': {'patience': 3}, }) history = \ { 'val_acc': [0.1, 0.2, 0.3, 0.4, 0.3, 0.3, 0.3, 0.3], 'val_top_k_categorical_accuracy': [0.1, 0.2, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3], } self.assertTrue(not hasToEarlyStop(history, esm)) def test20(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 3}, 'val_top_k_categorical_accuracy': {'patience': 3}, }) history = \ { 'val_acc': [0.1, 0.2, 0.3, 0.4, 0.3, 0.3, 0.3, 0.3], 'val_top_k_categorical_accuracy': [0.1, 0.2, 0.3, 0.4, 0.3, 0.3, 0.3, 0.3], } self.assertTrue(hasToEarlyStop(history, esm)) def test21(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 0}, }) history = \ { 'val_acc': [0.1], } self.assertTrue(not hasToEarlyStop(history, esm)) def test22(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 0}, }) history = \ { 'val_acc': [0.1, 0.2], } self.assertTrue(not hasToEarlyStop(history, esm)) def test23(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 0}, }) history = \ { 'val_acc': [], } self.assertTrue(not hasToEarlyStop(history, esm)) def test24(self): esm = normalizeEarlyStopMonitor(\ { 'val_acc': {'patience': 0}, }) history = \ { 'val_acc': [0.1, 0.02], } self.assertTrue(hasToEarlyStop(history, esm)) if mini <= 2 <= maxi: class Test2(unittest.TestCase): def test1(self): normalizeEarlyStopMonitor\ ( { 'val_loss': {'patience': 50, 'min_delta': 0.5555, 'mode': 'auto'}, 'val_acc': {'patience': 50, 'mode': 'auto'}, 'val_top_k_categorical_accuracy': {'patience': 50, 'min_delta': 0, 'mode': 'auto'}, }, ) if mini <= 12 <= maxi: class Test2(unittest.TestCase): def test1(self): x1 = xVal y1 = yVal x2 = iteratorToArray(asap.getTokensOnlyValidationInfiniteBatcher(), steps=asap.getValidationBatchsCount()) y2 = iteratorToArray(asap.getLabelOnlyValidationInfiniteBatcher(), steps=asap.getValidationBatchsCount()) for i in range(len(x1)): if i % 100 == 0: print("--------a") print(x1[i]) print(x2[i]) print("--------b") print(y1[i]) print(y2[i]) self.assertTrue(np.array_equal(x1[i], x2[i])) self.assertTrue(np.array_equal(y1[i], y2[i])) self.assertTrue(x1[i][1] == x2[i][1]) self.assertTrue(y1[i][1] == y2[i][1]) self.assertTrue(np.array_equal(x1, x2)) self.assertTrue(np.array_equal(y1, y2)) self.assertTrue(not np.array_equal(x1[2], x2[4])) self.assertTrue(not np.array_equal(y1[2], y2[4])) if __name__ == '__main__': unittest.main() # Orb executes it as a Python unit-test in eclipse print("Unit tests done.\n==============")

37.040921

118

0.446454

1,634

14,483

3.813341

0.085679

0.042369

0.056813

0.047504

0.839833

0.824266

0.75317

0.723158

0.689295

0.647248

0

0.104955

0.399365

14,483

391

119

37.040921

0.611335

0.010357

0

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false

0

0.015957

0

0.109043

0.021277

0

null

0

1

0

1

0

null

0

5

95d511a32b601ff55826a3bf005531ae0702a99f

56

py

Python

katas/kyu_8/multiply_the_number.py

the-zebulan/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

40

2016-03-09T12:26:20.000Z

2022-03-23T08:44:51.000Z

katas/kyu_8/multiply_the_number.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

null

katas/kyu_8/multiply_the_number.py

akalynych/CodeWars

1eafd1247d60955a5dfb63e4882e8ce86019f43a

[ "MIT" ]

36

2016-11-07T19:59:58.000Z

2022-03-31T11:18:27.000Z

def multiply(n): return n * (5 ** len(str(abs(n))))

18.666667

38

0.535714

10

56

3

0.8

0

0.022727

0.214286

56

2

39

28

0.659091

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

2519d4d502acc4da656de340df5dc2a3030f1040

289

py

Python

simplads/simplad_bundle/retu.py

Cogmob/simplads

8731c4a02273109187cfe601058ce797e32ba1ae

[ "MIT" ]

null

simplads/simplad_bundle/retu.py

Cogmob/simplads

8731c4a02273109187cfe601058ce797e32ba1ae

[ "MIT" ]

null

simplads/simplad_bundle/retu.py

Cogmob/simplads

8731c4a02273109187cfe601058ce797e32ba1ae

[ "MIT" ]

null

from simplads.simplad_monad.simplad_monad import SimpladResult from simplads import ErrorDeltaMaker def rn(i): return SimpladResult(val=i, delta_map={}) def error(value, error): print('retu') return SimpladResult(val=value, delta_map={'error': ErrorDeltaMaker.error(error)})

28.9

86

0.764706

37

289

5.864865

0.486486

0.110599

0.202765

0

0.121107

289

9

87

32.111111

0.854331

0

0.031142

0

1

0.285714

false

0

0.285714

0.142857

0.857143

0.142857

0

null

0

1

0

1

0

null

0

1

0

1

0

5

254f6239f94ec7944489d4fd57a90de98abc3e78

141

py

Python

pydistributed/event_source/exceptions.py

jaycosaur/PyDistributed

b10ad07a56b78416d09790f1faf9bc7dfd2b02ba

[ "MIT" ]

null

pydistributed/event_source/exceptions.py

jaycosaur/PyDistributed

b10ad07a56b78416d09790f1faf9bc7dfd2b02ba

[ "MIT" ]

null

pydistributed/event_source/exceptions.py

jaycosaur/PyDistributed

b10ad07a56b78416d09790f1faf9bc7dfd2b02ba

[ "MIT" ]

null

class OffsetMissingInIndex(Exception): pass class CouldNotFindOffset(Exception): pass class LogSizeExceeded(Exception): pass

12.818182

38

0.758865

12

141

8.916667

0.5

0.364486

0.336449

0

0.177305

141

10

39

14.1

0.922414

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

null

0

1

0

5

255e7a4cf07bbac4240fac105b2cf34ce4a479af

298

py

Python

ossim/ossim/views.py

devil-r/Os-simulator

afb99d57d16ebb598a66fcd967f1d67247b4efb4

[ "MIT" ]

null

ossim/ossim/views.py

devil-r/Os-simulator

afb99d57d16ebb598a66fcd967f1d67247b4efb4

[ "MIT" ]

null

ossim/ossim/views.py

devil-r/Os-simulator

afb99d57d16ebb598a66fcd967f1d67247b4efb4

[ "MIT" ]

null

from django.shortcuts import get_object_or_404, render from django.http import HttpResponseRedirect,HttpResponse from django.urls import reverse def index(request): return render(request, 'ossim/index2.html') def matindex(request): return render(request, 'mat/mainindex.html')

27.090909

58

0.768456

38

298

5.947368

0.631579

0.132743

0.168142

0.230089

0

0.015748

0.147651

298

10

59

29.8

0.874016

0

0.121528

0

1

0.285714

false

0

0.428571

0.285714

1

0

null

0

1

0

null

0

1

0

1

0

5

c2dc8b1dd7b4351785ec73649f13a3b2fc8ab78a

711

py

Python

instagram_scraper/model/__init__.py

luengwaiban/instagram-python-scraper

d3427afba9865d914a9b5daaafde9f2981ebf1c3

[ "MIT" ]

139

2019-06-02T16:19:06.000Z

2021-09-08T08:16:43.000Z

instagram_scraper/model/__init__.py

luengwaiban/instagram-python-scraper

d3427afba9865d914a9b5daaafde9f2981ebf1c3

[ "MIT" ]

4

2019-06-10T02:06:10.000Z

2020-07-07T04:45:59.000Z

instagram_scraper/model/__init__.py

luengwaiban/instagram-python-scraper

d3427afba9865d914a9b5daaafde9f2981ebf1c3

[ "MIT" ]

16

2019-06-07T10:02:49.000Z

2021-06-03T20:41:33.000Z

# # -*- coding:utf-8 -*- from instagram_scraper.model.base_model import BaseModel from instagram_scraper.model.initializer_model import InitializerModel from instagram_scraper.model.media import Media from instagram_scraper.model.account import Account from instagram_scraper.model.carousel_media import CarouselMedia from instagram_scraper.model.tag import Tag from instagram_scraper.model.location import Location from instagram_scraper.model.story import Story from instagram_scraper.model.user_stories import UserStories from instagram_scraper.model.like import Like __all__ = ["base_model", "initializer_model", "media", "account", "carousel_media", "Tag", "Location", "Story", "UserStories", "Like"]

39.5

134

0.825598

91

711

6.21978

0.263736

0.229682

0.353357

0.441696

0

0.001541

0.087201

711

17

135

41.823529

0.87057

0.028129

0

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false

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0.909091

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0.909091

0

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1

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null

0

1

0

1

0

5

6c06f3e0c2b2f146bce3a1a975b695c45c67f2f9

86

py

Python

evaluation/__init__.py

dichotomies/NeuralDiff

0f5f07e20e94368f8637f2fec1ec5a0c914524de

[ "MIT" ]

5

2022-01-28T23:31:42.000Z

2022-03-13T09:21:50.000Z

evaluation/__init__.py

yashbhalgat/NeuralDiff

a480f2103384a4f5d77eb84abd977a200e6e6405

[ "MIT" ]

2

2022-02-03T12:12:48.000Z

2022-02-18T05:07:21.000Z

evaluation/__init__.py

dichotomies/NeuralDiff

0f5f07e20e94368f8637f2fec1ec5a0c914524de

[ "MIT" ]

null

from . import video, segmentation from .segmentation import evaluate, evaluate_sample

28.666667

51

0.837209

10

86

7.1

0.6

0

0.116279

86

2

52

43

0.934211

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

6c3096e7054ff51f6b45c39984d2cd3d6e8ae973

64

py

Python

tests/test_pep542.py

nickdrozd/experimental

e4532800939935cb2ebaec12d0e0fcc63cd7ea78

[ "MIT" ]

21

2018-06-19T00:57:38.000Z

2022-02-20T22:52:53.000Z

tests/test_pep542.py

nickdrozd/experimental

e4532800939935cb2ebaec12d0e0fcc63cd7ea78

[ "MIT" ]

3

2018-07-21T14:48:15.000Z

2019-03-06T15:29:20.000Z

tests/test_pep542.py

nickdrozd/experimental

e4532800939935cb2ebaec12d0e0fcc63cd7ea78

[ "MIT" ]

4

2017-08-18T18:08:05.000Z

2018-07-21T14:04:20.000Z

from .common import experimental from .pep542_testfile import *

21.333333

32

0.828125

8

64

6.5

0.75

0

0.053571

0.125

64

2

33

32

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

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py

Python

enthought/enable/radio_group.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/enable/radio_group.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/enable/radio_group.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from enable.radio_group import *

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py

Python

vpp-api/python/vpp_papi/__init__.py

akshayanadahalli/vpp_ietf97

273c26a531bf031b3426588041bad67fe7f0a246

[ "Apache-2.0" ]

1

2019-06-12T12:13:45.000Z

vpp-api/python/vpp_papi/__init__.py

akshayanadahalli/vpp_ietf97

273c26a531bf031b3426588041bad67fe7f0a246

[ "Apache-2.0" ]

null

vpp-api/python/vpp_papi/__init__.py

akshayanadahalli/vpp_ietf97

273c26a531bf031b3426588041bad67fe7f0a246

[ "Apache-2.0" ]

1

2020-11-09T10:43:08.000Z

__import__('pkg_resources').declare_namespace(__name__) from . vpp_papi import *

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py

Python

src/sharkradar/Util/sharkradarDbutils.py

bmonikraj/shark-radar

61dc685cd41041fc3b6d92de76c211cb0d23c6cf

[ "BSD-3-Clause" ]

8

2019-08-15T21:43:37.000Z

2022-03-13T06:49:54.000Z

src/sharkradar/Util/sharkradarDbutils.py

bmonikraj/shark-radar

61dc685cd41041fc3b6d92de76c211cb0d23c6cf

[ "BSD-3-Clause" ]

3

2019-08-25T11:33:05.000Z

2022-02-27T15:34:19.000Z

src/sharkradar/Util/sharkradarDbutils.py

bmonikraj/shark-radar

61dc685cd41041fc3b6d92de76c211cb0d23c6cf

[ "BSD-3-Clause" ]

null

""" DB Utils functions for the project """ import sys from os.path import dirname as opd, realpath as opr import os import time import sqlite3 basedir = opd(opd(opd(opr(__file__)))) sys.path.append(basedir) from sharkradar.Config.Config import Config def createTableIfNotExists(): """ Creates the SERVICE_RD, SERVICE_LOGS table in SQLite3 file mode, if table doesn't exist Columns of Table KEYS: VALUES: --------- ------------- i) ip ip address associated with micro-service ii) port port associated with micro-service iii) service_name unique name of the micro-service iv) status status (up/down) sent from the micro-service v) mem_usage Current memory usage vi) cpu_usage Current CPU usage vii) network_throughput Current network throughput viii) req_active No. of requests currently being processed by the instance ix) success_rate Fraction of requests successfully served x) health_interval The time interval specified by the micro-service at which it will send health report to service R/D continuously xi) status Status of the discovery log xii) retry_id Retry ID in discovery log """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) conn.execute('''CREATE TABLE IF NOT EXISTS SERVICE_RD (SERVICE_NAME TEXT NOT NULL, IP TEXT NOT NULL, PORT TEXT NOT NULL, MEM_USAGE REAL NOT NULL, CPU_USAGE REAL NOT NULL, NW_TPUT_BW_RATIO REAL NOT NULL, REQ_ACTIVE_RATIO REAL NOT NULL, SUCCESS_RATE REAL NOT NULL, TIME_STAMP BIGINT NOT NULL, HEALTH_INTERVAL BIGINT NOT NULL);''') conn.execute('''CREATE TABLE IF NOT EXISTS SERVICE_LOGS (SERVICE_NAME TEXT NOT NULL, IP TEXT NOT NULL, PORT TEXT NOT NULL, MEM_USAGE REAL NOT NULL, CPU_USAGE REAL NOT NULL, NW_TPUT_BW_RATIO REAL NOT NULL, REQ_ACTIVE_RATIO REAL NOT NULL, SUCCESS_RATE REAL NOT NULL, TIME_STAMP BIGINT NOT NULL, HEALTH_INTERVAL BIGINT NOT NULL);''') conn.execute('''CREATE TABLE IF NOT EXISTS DISCOVERY_LOGS (SERVICE_NAME TEXT NOT NULL, IP TEXT NOT NULL, PORT TEXT NOT NULL, TIME_STAMP BIGINT NOT NULL, STATUS TEXT NOT NULL, RETRY_ID TEXT NOT NULL);''') conn.commit() conn.close() def findServiceByNameAndIpAndPort(service_name, ip, port): """ Find services by service name, IP address and port number @params:service_name: A string, representing the service name @params:ip: IP address of the service @params:port : Port number of the service @return: List of the query results from DB """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) response = conn.execute( """SELECT * FROM SERVICE_RD WHERE SERVICE_NAME = ? AND IP = ? AND PORT = ?""", (service_name, ip, port)).fetchall() conn.close() return response def findServiceByName(service_name): """ Find services by service name @params:service_name: A string, representing the service name @return: List of the query results from DB """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) service_instances = conn.execute( """SELECT * from SERVICE_RD WHERE SERVICE_NAME = ?""", (service_name, )).fetchall() conn.close() return service_instances def getAllService(): """ Find all services at current time @return: List of the query results from DB """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) service_instances = conn.execute( """SELECT * from SERVICE_RD""").fetchall() conn.close() return service_instances def updateServiceByAll( current_time_stamp, health_interval, mem_usage, cpu_usage, nw_tput_bw_ratio, req_active_ratio, success_rate, service_name, ip, port): """ Update services details @params:current_time_stamp: Current time stamp @params:health_interval: Health interval frequency in secs, the maximum threshold after which if health status is not received, the service will be de-registered @params:mem_usage: Memory usage in % of service @params:cpu_usage: CPU usage in % of service @params:nw_tput_bw_ratio: Ratio of current network throughput with maximum capacity (bandwidth) in % @params:req_active_ratio: Ratio of current requests being handled with maximum requests limit in % @param:success_rate: Ratio of successful response by total requests in % @params:service_name: A string, representing the service name @params:ip: IP address of the service @params:port : Port number of the service @return: Total number of rows updated """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) conn.execute( """UPDATE SERVICE_RD SET TIME_STAMP = ?, HEALTH_INTERVAL = ?, MEM_USAGE = ?, CPU_USAGE = ?, NW_TPUT_BW_RATIO = ?, REQ_ACTIVE_RATIO = ?, SUCCESS_RATE = ? WHERE SERVICE_NAME = ? AND IP = ? AND PORT = ?""", (current_time_stamp, health_interval, mem_usage, cpu_usage, nw_tput_bw_ratio, req_active_ratio, success_rate, service_name, ip, port)) conn.commit() total_changes = conn.total_changes conn.close() return total_changes def insertServiceByAll( service_name, ip, port, current_time_stamp, health_interval, mem_usage, cpu_usage, nw_tput_bw_ratio, req_active_ratio, success_rate): """ Insert services details @params:service_name: A string, representing the service name @params:ip: IP address of the service @params:port : Port number of the service @params:current_time_stamp: Current time stamp @params:health_interval: Health interval frequency in secs, the maximum threshold after which if health status is not received, the service will be de-registered @params:mem_usage: Memory usage in % of service @params:cpu_usage: CPU usage in % of service @params:nw_tput_bw_ratio: Ratio of current network throughput with maximum capacity (bandwidth) in % @params:req_active_ratio: Ratio of current requests being handled with maximum requests limit in % @param:success_rate: Ratio of successful response by total requests in % """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) conn.execute( """INSERT INTO SERVICE_RD (SERVICE_NAME, IP, PORT, TIME_STAMP, HEALTH_INTERVAL, MEM_USAGE, CPU_USAGE, NW_TPUT_BW_RATIO, REQ_ACTIVE_RATIO, SUCCESS_RATE) \ VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""", (service_name, ip, port, current_time_stamp, health_interval, mem_usage, cpu_usage, nw_tput_bw_ratio, req_active_ratio, success_rate)) conn.commit() conn.close() def insertServiceByAllPersist( service_name, ip, port, current_time_stamp, health_interval, mem_usage, cpu_usage, nw_tput_bw_ratio, req_active_ratio, success_rate): """ Insert services details persistant @params:service_name: A string, representing the service name @params:ip: IP address of the service @params:port : Port number of the service @params:current_time_stamp: Current time stamp @params:health_interval: Health interval frequency in secs, the maximum threshold after which if health status is not received, the service will be de-registered @params:mem_usage: Memory usage in % of service @params:cpu_usage: CPU usage in % of service @params:nw_tput_bw_ratio: Ratio of current network throughput with maximum capacity (bandwidth) in % @params:req_active_ratio: Ratio of current requests being handled with maximum requests limit in % @param:success_rate: Ratio of successful response by total requests in % """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) conn.execute( """INSERT INTO SERVICE_LOGS (SERVICE_NAME, IP, PORT, TIME_STAMP, HEALTH_INTERVAL, MEM_USAGE, CPU_USAGE, NW_TPUT_BW_RATIO, REQ_ACTIVE_RATIO, SUCCESS_RATE) \ VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""", (service_name, ip, port, current_time_stamp, health_interval, mem_usage, cpu_usage, nw_tput_bw_ratio, req_active_ratio, success_rate)) conn.commit() conn.close() def getServicePersist(limit=250): """ Fetch service log records by limit @params:limit: latest n records @return: List of the query results from DB """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) service_instances = conn.execute( """SELECT * from SERVICE_LOGS ORDER BY TIME_STAMP DESC LIMIT ?""", (limit,)).fetchall() conn.close() return service_instances def insertDiscoveryPersist( service_name, ip, port, current_time_stamp, status, retryid): """ Insert discovery details persistant @params:service_name: A string, representing the service name @params:ip: IP address of the service @params:port : Port number of the service @params:current_time_stamp: Current time stamp @params:status: Status of the service @params:retryid: Retry ID for the discovery log """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) conn.execute( """INSERT INTO DISCOVERY_LOGS (SERVICE_NAME, IP, PORT, TIME_STAMP, STATUS, RETRY_ID) \ VALUES (?, ?, ?, ?, ?, ?)""", (service_name, ip, port, current_time_stamp, status, retryid)) conn.commit() conn.close() def getDiscoveryPersist(limit=250): """ Fetch service log records by limit @params:limit: latest n records @return: List of the query results from DB """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) service_instances = conn.execute( """SELECT * from DISCOVERY_LOGS ORDER BY TIME_STAMP DESC LIMIT ?""", (limit,)).fetchall() conn.close() return service_instances def updateDiscoveryPersist( status, retryid): """ Update discovery details persistant @params:service_name: A string, representing the service name @params:ip: IP address of the service @params:port : Port number of the service @params:current_time_stamp: Current time stamp @params:status: Status of the service @params:retryid: Retry ID for the discovery log """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) conn.execute( """UPDATE DISCOVERY_LOGS SET STATUS = ? WHERE RETRY_ID = ?""", (status, retryid)) conn.commit() conn.close() def getLatestRecordsDiscoveryLogs(service_name, ip, port, latest_records): """ Fetch latest n records from discovery logs corresponding to service name, IP and port @params:service_name: A string representing service name @params:ip: IP address @params:port: Port numbers @params:latest_records: Limit @return: List of records in ordered by desc timestamp """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) discovery_instances = conn.execute( """SELECT STATUS FROM DISCOVERY_LOGS WHERE SERVICE_NAME = ? AND IP = ? AND PORT = ? ORDER BY TIME_STAMP DESC LIMIT ?""", (service_name, ip, port, latest_records)).fetchall() conn.close() return discovery_instances def deleteServiceByNameAndIpAndPort(service_name, ip, port): """ Delete services by service name, Ip and port @params:service_name: A string, representing the service name @params:ip: IP Address of the service @params:port: Port number of the service """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) conn.execute( """DELETE FROM SERVICE_RD WHERE SERVICE_NAME = ? AND IP = ? AND PORT = ?""", (service_name, ip, port)) conn.commit() conn.close() def deleteServiceByNameAndTimestampDifferenceWithHealthInterval(service_name): """ Find services by service name, based on services who are declared dead Dead -> Services who haven't sent health status post their health interval @params:service_name: A string, representing the service name """ DB_PATH = Config.getDbPath() conn = sqlite3.connect(DB_PATH) current_time_epoch = time.time() conn.execute( """DELETE FROM SERVICE_RD WHERE SERVICE_NAME = ? AND ? - TIME_STAMP > HEALTH_INTERVAL""", (service_name, current_time_epoch)) conn.commit() conn.close()

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