xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

21806b4d25e1fdbe294b272bdd0091d6f0990d01

71

py

Python

App/MainPage.py

tartaruswh/SaaSCyberWaterSupplyGWAuto

07b43c67e059a5b602957d94e9f441e74d12bde1

[ "Apache-2.0" ]

null

App/MainPage.py

tartaruswh/SaaSCyberWaterSupplyGWAuto

07b43c67e059a5b602957d94e9f441e74d12bde1

[ "Apache-2.0" ]

null

App/MainPage.py

tartaruswh/SaaSCyberWaterSupplyGWAuto

07b43c67e059a5b602957d94e9f441e74d12bde1

[ "Apache-2.0" ]

null

from App.BasePage import BasePage class MainPage(BasePage): pass

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py

Python

password generator 2.py

Arjitg450/Python-Programs

0630422c9002632a91b5ccf75f6cd02308c6e929

[ "MIT" ]

null

password generator 2.py

Arjitg450/Python-Programs

0630422c9002632a91b5ccf75f6cd02308c6e929

[ "MIT" ]

null

password generator 2.py

Arjitg450/Python-Programs

0630422c9002632a91b5ccf75f6cd02308c6e929

[ "MIT" ]

null

# generate a password with length "passlen" with no duplicate characters in the password import random s = "abcdefghijklmnopqrstuvwxyz01234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*()?" lengthinput=int(input("Enter the length of password you want : ")) p="".join(random.sample(s,lengthinput)) print(p) s = "abcdefghijklmnopqrstuvwxyz01234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*()?" lengthinput=int(input("Enter the length of password you want : ")) p="".join(random.choice(s) for _ in range(lengthinput)) print(p) # with duplicate characters import random s = "abcdefghijklmnopqrstuvwxyz01234567890ABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*()?" lengthinput=int(input("Enter the length of password you want : ")) for i in range(lengthinput): p="".join(random.choices(s)) print(p,end="")

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Python

rdr_service/alembic/versions/9cbaee181bc9_modify_gc_validation_metrics.py

all-of-us/raw-data-repository

d28ad957557587b03ff9c63d55dd55e0508f91d8

[ "BSD-3-Clause" ]

39

2017-10-13T19:16:27.000Z

2021-09-24T16:58:21.000Z

rdr_service/alembic/versions/9cbaee181bc9_modify_gc_validation_metrics.py

all-of-us/raw-data-repository

d28ad957557587b03ff9c63d55dd55e0508f91d8

[ "BSD-3-Clause" ]

312

2017-09-08T15:42:13.000Z

2022-03-23T18:21:40.000Z

rdr_service/alembic/versions/9cbaee181bc9_modify_gc_validation_metrics.py

all-of-us/raw-data-repository

d28ad957557587b03ff9c63d55dd55e0508f91d8

[ "BSD-3-Clause" ]

19

2017-09-15T13:58:00.000Z

2022-02-07T18:33:20.000Z

"""modify gc validation metrics Revision ID: 9cbaee181bc9 Revises: 8cda4ff4eba7 Create Date: 2020-03-12 15:12:20.131031 """ from alembic import op import sqlalchemy as sa from sqlalchemy.dialects import mysql # revision identifiers, used by Alembic. revision = '9cbaee181bc9' down_revision = '8cda4ff4eba7' branch_labels = None depends_on = None def upgrade(engine_name): globals()["upgrade_%s" % engine_name]() def downgrade(engine_name): globals()["downgrade_%s" % engine_name]() def upgrade_rdr(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('genomic_gc_validation_metrics', sa.Column('chipwellbarcode', sa.String(length=80), nullable=True)) op.add_column('genomic_gc_validation_metrics', sa.Column('idat_green_received', sa.SmallInteger(), nullable=False)) op.add_column('genomic_gc_validation_metrics', sa.Column('idat_red_received', sa.SmallInteger(), nullable=False)) op.add_column('genomic_gc_validation_metrics', sa.Column('tbi_received', sa.SmallInteger(), nullable=False)) op.add_column('genomic_gc_validation_metrics', sa.Column('vcf_received', sa.SmallInteger(), nullable=False)) op.drop_column('genomic_gc_validation_metrics', 'biobank_id') op.drop_column('genomic_gc_validation_metrics', 'sample_id') # ### end Alembic commands ### # Change datatypes op.execute('ALTER TABLE genomic_gc_validation_metrics MODIFY `call_rate` VARCHAR(10);') op.execute('ALTER TABLE genomic_gc_validation_metrics MODIFY `mean_coverage` VARCHAR(10);') op.execute('ALTER TABLE genomic_gc_validation_metrics MODIFY `genome_coverage` VARCHAR(10);') op.execute('ALTER TABLE genomic_gc_validation_metrics MODIFY `contamination` VARCHAR(10);') op.execute('ALTER TABLE genomic_gc_validation_metrics MODIFY `site_id` VARCHAR(80);') def downgrade_rdr(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('genomic_gc_validation_metrics', sa.Column('sample_id', mysql.VARCHAR(length=80), nullable=True)) op.add_column('genomic_gc_validation_metrics', sa.Column('biobank_id', mysql.VARCHAR(length=80), nullable=False)) op.drop_column('genomic_gc_validation_metrics', 'vcf_received') op.drop_column('genomic_gc_validation_metrics', 'tbi_received') op.drop_column('genomic_gc_validation_metrics', 'idat_red_received') op.drop_column('genomic_gc_validation_metrics', 'idat_green_received') op.drop_column('genomic_gc_validation_metrics', 'chipwellbarcode') # ### end Alembic commands ### # Change datatypes op.execute('ALTER TABLE genomic_gc_validation_metrics MODIFY `call_rate` INTEGER;') op.execute('ALTER TABLE genomic_gc_validation_metrics MODIFY `mean_coverage` INTEGER;') op.execute('ALTER TABLE genomic_gc_validation_metrics MODIFY `genome_coverage` INTEGER;') op.execute('ALTER TABLE genomic_gc_validation_metrics MODIFY `contamination` INTEGER;') op.execute('ALTER TABLE genomic_gc_validation_metrics MODIFY `site_id` INTEGER;') def upgrade_metrics(): # ### commands auto generated by Alembic - please adjust! ### pass # ### end Alembic commands ### def downgrade_metrics(): # ### commands auto generated by Alembic - please adjust! ### pass # ### end Alembic commands ###

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py

Python

venv/lib/python3.8/site-packages/numpy/f2py/f90mod_rules.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/numpy/f2py/f90mod_rules.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/numpy/f2py/f90mod_rules.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/a0/b6/6d/27f4f540d0ab6b7080096f2850b9908a8614e1b3957215ad810a75ccbf

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800c06f0694a07708de19340d33999ea14363b71

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py

Python

src/spaceone/inventory/connector/aws_kinesis_data_stream_connector/__init__.py

jean1042/plugin-aws-cloud-services

1cf192557b03478af33ae81f40b2a49f735716bb

[ "Apache-2.0" ]

4

2020-06-22T01:48:07.000Z

2020-08-24T00:51:09.000Z

src/spaceone/inventory/connector/aws_kinesis_data_stream_connector/__init__.py

jean1042/plugin-aws-cloud-services

1cf192557b03478af33ae81f40b2a49f735716bb

[ "Apache-2.0" ]

2

2020-07-20T01:58:32.000Z

2020-08-04T07:41:37.000Z

src/spaceone/inventory/connector/aws_kinesis_data_stream_connector/__init__.py

jean1042/plugin-aws-cloud-services

1cf192557b03478af33ae81f40b2a49f735716bb

[ "Apache-2.0" ]

6

2020-06-22T09:19:40.000Z

2020-09-17T06:35:37.000Z

from spaceone.inventory.connector.aws_kinesis_data_stream_connector.connector import KinesisDataStreamConnector

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8041dee96ba15fef4742a0d4164b7065957c5b50

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py

Python

meerschaum/connectors/sql/_tools.py

bmeares/Meerschaum

37bd7a9923efce53e91c6a1d9c31f9533b9b4463

[ "Apache-2.0" ]

32

2020-09-14T16:29:19.000Z

2022-03-08T00:51:28.000Z

meerschaum/connectors/sql/_tools.py

bmeares/Meerschaum

37bd7a9923efce53e91c6a1d9c31f9533b9b4463

[ "Apache-2.0" ]

3

2020-10-04T20:03:30.000Z

2022-02-02T21:04:46.000Z

meerschaum/connectors/sql/_tools.py

bmeares/Meerschaum

37bd7a9923efce53e91c6a1d9c31f9533b9b4463

[ "Apache-2.0" ]

5

2021-04-22T23:49:21.000Z

2022-02-02T12:59:08.000Z

#! /usr/bin/env python3 # -*- coding: utf-8 -*- # vim:fenc=utf-8 """ Import everything from `meerschaum.connectors.sql.tools` for backwards compatability. """ from meerschaum.connectors.sql.tools import *

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Python

linlearn/estimator/llm.py

LinLearn/linlearn

de5752d47bbe8e2fb62d41b0dcf2526f87545e1c

[ "BSD-3-Clause" ]

null

linlearn/estimator/llm.py

LinLearn/linlearn

de5752d47bbe8e2fb62d41b0dcf2526f87545e1c

[ "BSD-3-Clause" ]

null

linlearn/estimator/llm.py

LinLearn/linlearn

de5752d47bbe8e2fb62d41b0dcf2526f87545e1c

[ "BSD-3-Clause" ]

null

# Authors: Stephane Gaiffas <stephane.gaiffas@gmail.com> # Ibrahim Merad <imerad7@gmail.com> # License: BSD 3 clause """ This module implement the ``LLM`` class for the Lecué - Lerasle - Mathieu robust estimator. ``StateLLM`` is a place-holder for the LLM estimator containing: gradient: numpy.ndarray A numpy array of shape (n_weights,) containing gradients computed by the `grad` function returned by the `grad_factory` factory function. TODO: fill the missing things """ from collections import namedtuple import numpy as np from numba import jit from ._base import Estimator, jit_kwargs from .._utils import np_float # Better implementation of argmedian ?? @jit(**jit_kwargs) def argmedian(x): med = np.median(x) id = 0 for a in x: if a == med: return id id += 1 raise ValueError("Failed argmedian") # return np.argpartition(x, len(x) // 2)[len(x) // 2] StateLLM = namedtuple( "StateLLM", [ "block_means", "sample_indices", "gradient", "loss_derivative", "partial_derivative", "n_grad_calls", "n_pderiv_calls", ], ) class LLM(Estimator): def __init__(self, X, y, loss, n_classes, fit_intercept, n_blocks): # assert n_blocks % 2 == 1 super().__init__(X, y, loss, n_classes, fit_intercept) # n_blocks must be uneven self.n_blocks = n_blocks + ((n_blocks + 1) % 2) if self.n_blocks >= self.n_samples: self.n_blocks = self.n_samples - (self.n_samples % 2 + 1) self.n_samples_in_block = max(1, self.n_samples // n_blocks) # no last block size, the remaining samples are just ignored # self.last_block_size = self.n_samples % self.n_samples_in_block # if self.last_block_size > 0: # self.n_blocks += 1 def get_state(self): return StateLLM( block_means=np.empty(self.n_blocks, dtype=np_float), sample_indices=np.arange(self.n_samples, dtype=np.uintp), gradient=np.empty( (self.n_features + int(self.fit_intercept), self.n_classes), dtype=np_float, ), loss_derivative=np.empty(self.n_classes, dtype=np_float), partial_derivative=np.empty(self.n_classes, dtype=np_float), n_grad_calls=0, n_pderiv_calls=0, ) def partial_deriv_factory(self): X = self.X y = self.y n_samples_in_block = self.n_samples_in_block n_blocks = self.n_blocks loss = self.loss n_classes = self.n_classes value_loss = loss.value_factory() deriv_loss = loss.deriv_factory() if self.fit_intercept: @jit(**jit_kwargs) def partial_deriv(j, inner_products, state): sample_indices = state.sample_indices n_calls = state.n_pderiv_calls n_calls += 1 block_means = state.block_means np.random.shuffle(sample_indices) # Cumulative sum in the block objectives_sum_block = 0.0 # Block counter counter = 0 for i, idx in enumerate(sample_indices[:n_blocks*n_samples_in_block]): objectives_sum_block += value_loss(y[idx], inner_products[idx]) if ((i != 0) and ((i + 1) % n_samples_in_block == 0)) or n_samples_in_block == 1: block_means[counter] = objectives_sum_block / n_samples_in_block counter += 1 objectives_sum_block = 0.0 argmed = argmedian(block_means) deriv = state.loss_derivative partial_derivative = state.partial_derivative for k in range(n_classes): partial_derivative[k] = 0.0 if j == 0: for i in sample_indices[ argmed * n_samples_in_block : (argmed + 1) * n_samples_in_block ]: deriv_loss(y[i], inner_products[i], deriv) for k in range(n_classes): partial_derivative[k] += deriv[k] else: for i in sample_indices[ argmed * n_samples_in_block : (argmed + 1) * n_samples_in_block ]: deriv_loss(y[i], inner_products[i], deriv) for k in range(n_classes): partial_derivative[k] += deriv[k] * X[i, j - 1] for k in range(n_classes): partial_derivative[k] /= n_samples_in_block return partial_deriv else: # Same function without an intercept @jit(**jit_kwargs) def partial_deriv(j, inner_products, state): sample_indices = state.sample_indices n_calls = state.n_pderiv_calls n_calls += 1 block_means = state.block_means np.random.shuffle(sample_indices) # Cumulative sum in the block objectives_sum_block = 0.0 # Block counter counter = 0 for i, idx in enumerate(sample_indices[:n_blocks*n_samples_in_block]): objectives_sum_block += value_loss(y[idx], inner_products[idx]) if ((i != 0) and ((i + 1) % n_samples_in_block == 0)) or n_samples_in_block == 1: block_means[counter] = objectives_sum_block / n_samples_in_block counter += 1 objectives_sum_block = 0.0 argmed = argmedian(block_means) deriv = state.loss_derivative partial_derivative = state.partial_derivative for k in range(n_classes): partial_derivative[k] = 0.0 for i in sample_indices[ argmed * n_samples_in_block : (argmed + 1) * n_samples_in_block ]: deriv_loss(y[i], inner_products[i], deriv) for k in range(n_classes): partial_derivative[k] += deriv[k] * X[i, j] for k in range(n_classes): partial_derivative[k] /= n_samples_in_block return partial_deriv def grad_factory(self): X = self.X y = self.y loss = self.loss value_loss = loss.value_factory() deriv_loss = loss.deriv_factory() n_samples_in_block = self.n_samples_in_block n_blocks = self.n_blocks n_classes = self.n_classes n_features = self.n_features if self.fit_intercept: @jit(**jit_kwargs) def grad(inner_products, state): sample_indices = state.sample_indices n_calls = state.n_grad_calls n_calls += 1 block_means = state.block_means gradient = state.gradient # for i in range(n_samples): # sample_indices[i] = i np.random.shuffle(sample_indices) # Cumulative sum in the block objectives_sum_block = 0.0 # Block counter counter = 0 for i, idx in enumerate(sample_indices[:n_blocks*n_samples_in_block]): objectives_sum_block += value_loss(y[idx], inner_products[idx]) if ((i != 0) and ((i + 1) % n_samples_in_block == 0)) or n_samples_in_block == 1: block_means[counter] = objectives_sum_block / n_samples_in_block counter += 1 objectives_sum_block = 0.0 argmed = argmedian(block_means) for j in range(n_features + 1): for k in range(n_classes): gradient[j, k] = 0.0 deriv = state.loss_derivative for i in sample_indices[ argmed * n_samples_in_block : (argmed + 1) * n_samples_in_block ]: deriv_loss(y[i], inner_products[i], deriv) for k in range(n_classes): gradient[0, k] += deriv[k] for j in range(n_features): gradient[j + 1, k] += ( deriv[k] * X[i, j] ) # np.outer(X[i], deriv) for j in range(n_features + 1): for k in range(n_classes): gradient[j, k] /= n_samples_in_block return 0 return grad else: @jit(**jit_kwargs) def grad(inner_products, state): sample_indices = state.sample_indices n_calls = state.n_grad_calls n_calls += 1 block_means = state.block_means gradient = state.gradient # for i in range(n_samples): # sample_indices[i] = i np.random.shuffle(sample_indices) # Cumulative sum in the block objectives_sum_block = 0.0 # Block counter counter = 0 for i, idx in enumerate(sample_indices[:n_blocks*n_samples_in_block]): objectives_sum_block += value_loss(y[idx], inner_products[idx]) if ((i != 0) and ((i + 1) % n_samples_in_block == 0)) or n_samples_in_block == 1: block_means[counter] = objectives_sum_block / n_samples_in_block counter += 1 objectives_sum_block = 0.0 argmed = argmedian(block_means) for j in range(n_features): for k in range(n_classes): gradient[j, k] = 0.0 deriv = state.loss_derivative for i in sample_indices[ argmed * n_samples_in_block : (argmed + 1) * n_samples_in_block ]: deriv_loss(y[i], inner_products[i], deriv) for j in range(n_features): for k in range(n_classes): gradient[j, k] += ( deriv[k] * X[i, j] ) # np.outer(X[i], deriv) for j in range(n_features): for k in range(n_classes): gradient[j, k] /= n_samples_in_block return 0 return grad

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Python

trec2015/cuttsum/classifiers/__init__.py

kedz/cuttsum

992c21192af03fd2ef863f5ab7d10752f75580fa

[ "Apache-2.0" ]

6

2015-09-10T02:22:21.000Z

2021-10-01T16:36:46.000Z

trec2015/cuttsum/classifiers/__init__.py

kedz/cuttsum

992c21192af03fd2ef863f5ab7d10752f75580fa

[ "Apache-2.0" ]

null

trec2015/cuttsum/classifiers/__init__.py

kedz/cuttsum

992c21192af03fd2ef863f5ab7d10752f75580fa

[ "Apache-2.0" ]

2

2018-04-04T10:44:32.000Z

2021-10-01T16:37:26.000Z

from cuttsum.classifiers._nugget_classifier import NuggetClassifier from cuttsum.classifiers._nugget_regressor import NuggetRegressor __all__ = ["NuggetClassifier", "NuggetRegressor"]

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py

Python

plasmapy_nei/eigen/tests/test_eigen.py

StanczakDominik/PlasmaPy-NEI

1137689c0e5b2d1e1147ead64a02b9848a1b123d

[ "BSD-2-Clause-Patent", "BSD-3-Clause" ]

null

plasmapy_nei/eigen/tests/test_eigen.py

StanczakDominik/PlasmaPy-NEI

1137689c0e5b2d1e1147ead64a02b9848a1b123d

[ "BSD-2-Clause-Patent", "BSD-3-Clause" ]

null

plasmapy_nei/eigen/tests/test_eigen.py

StanczakDominik/PlasmaPy-NEI

1137689c0e5b2d1e1147ead64a02b9848a1b123d

[ "BSD-2-Clause-Patent", "BSD-3-Clause" ]

null

"""Tests for eigentables""" def test_import(): """Test that the subpackage can be imported.""" import plasmapy_nei.eigen

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1d4b6b61ed95e5aea75a94fbf6c005592e9bec74

58

py

Python

V0.2 RELEASE STABLE/smartphish.py

patrol114/TheSmartool

7103015f1b2ccfbbc8b8aa6e7977e19d70c15ac0

[ "MIT" ]

31

2020-12-02T20:10:51.000Z

2022-03-22T16:22:54.000Z

V0.2 RELEASE STABLE/smartphish.py

patrol114/TheSmartool

7103015f1b2ccfbbc8b8aa6e7977e19d70c15ac0

[ "MIT" ]

null

V0.2 RELEASE STABLE/smartphish.py

patrol114/TheSmartool

7103015f1b2ccfbbc8b8aa6e7977e19d70c15ac0

[ "MIT" ]

6

2021-01-10T01:06:23.000Z

2021-09-30T23:17:49.000Z

import os import sys def connect(): print

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6

1d85a72f8435dfedb891e99a59e399348e9648ec

17,252

py

Python

backend/tests/integration/service/test_product_service.py

willrp/willstores-ws

62c4f400f40fed1aef4f316c7e73dfecba98d026

[ "MIT" ]

null

backend/tests/integration/service/test_product_service.py

willrp/willstores-ws

62c4f400f40fed1aef4f316c7e73dfecba98d026

[ "MIT" ]

null

backend/tests/integration/service/test_product_service.py

willrp/willstores-ws

62c4f400f40fed1aef4f316c7e73dfecba98d026

[ "MIT" ]

null

import pytest from elasticsearch_dsl import Index, Search from uuid import uuid4 from backend.service import ProductService from backend.model import Product from backend.tests.factories import ProductFactory from backend.errors.no_content_error import NoContentError from backend.errors.not_found_error import NotFoundError from backend.errors.request_error import ValidationError @pytest.fixture(scope="session") def service(): service = ProductService() return service def test_product_service_products_count(service, es_object): prod_list = ProductFactory.create_batch(2) [prod_obj.save(using=es_object.connection) for prod_obj in prod_list] Index("store", using=es_object.connection).refresh() result = service.products_count() assert result > 0 def test_product_service_super_discounts(service, es_object): prod_list = ProductFactory.create_batch(2) [prod_obj.save(using=es_object.connection) for prod_obj in prod_list] Index("store", using=es_object.connection).refresh() results = service.super_discounts() assert len(results) > 0 assert type(results[0]) == Product test_alt_id = "I_test_product_service_super_discounts" ProductFactory.create(gender=test_alt_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.super_discounts(gender=test_alt_id) assert len(results) == 1 assert type(results[0]) == Product with pytest.raises(NoContentError): results = service.super_discounts(gender=str(uuid4())) def test_product_service_search(service, es_object): search = service._ProductService__search() assert type(search) == Search service._ProductService__search(query="query", gender="gender", sessionid="sessionid", sessionname="sessionname", brand="brand", kind="kind", pricerange={"min": 1.0, "max": 100.0}) assert type(search) == Search def test_product_service_select_pricerange(service, es_object): ProductFactory.create(price={"outlet": 10.0, "retail": 100.0}).save(using=es_object.connection) ProductFactory.create(price={"outlet": 20.0, "retail": 120.0}).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_pricerange() for key in ["min", "max"]: assert key in results assert len(results.keys()) == 2 assert results["min"] <= results["max"] test_alt_id = "I_test_product_service_select_pricerange" test_id = str(uuid4()) ProductFactory.create(gender=test_alt_id, price={"outlet": 10.0, "retail": 100.0}).save(using=es_object.connection) ProductFactory.create(gender=test_alt_id, price={"outlet": 20.0, "retail": 120.0}).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_pricerange(gender=test_alt_id) assert results["min"] == 10.0 assert results["max"] == 20.0 with pytest.raises(NoContentError): service.select_pricerange(gender=str(uuid4())) ProductFactory.create(sessionid=test_id, price={"outlet": 10.0, "retail": 100.0}).save(using=es_object.connection) ProductFactory.create(sessionid=test_id, price={"outlet": 20.0, "retail": 120.0}).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_pricerange(sessionid=test_id) assert results["min"] == 10.0 assert results["max"] == 20.0 with pytest.raises(NoContentError): service.select_pricerange(sessionid=str(uuid4())) ProductFactory.create(sessionname=test_id, price={"outlet": 10.0, "retail": 100.0}).save(using=es_object.connection) ProductFactory.create(sessionname=test_id, price={"outlet": 20.0, "retail": 120.0}).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_pricerange(sessionname=test_id) assert results["min"] == 10.0 assert results["max"] == 20.0 with pytest.raises(NoContentError): service.select_pricerange(sessionname=str(uuid4())) ProductFactory.create(brand=test_id, price={"outlet": 10.0, "retail": 100.0}).save(using=es_object.connection) ProductFactory.create(brand=test_id, price={"outlet": 20.0, "retail": 120.0}).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_pricerange(brand=test_id) assert results["min"] == 10.0 assert results["max"] == 20.0 with pytest.raises(NoContentError): service.select_pricerange(brand=str(uuid4())) ProductFactory.create(kind=test_id, price={"outlet": 10.0, "retail": 100.0}).save(using=es_object.connection) ProductFactory.create(kind=test_id, price={"outlet": 20.0, "retail": 120.0}).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_pricerange(kind=test_id) assert results["min"] == 10.0 assert results["max"] == 20.0 with pytest.raises(NoContentError): service.select_pricerange(kind=str(uuid4())) results = service.select_pricerange(query=test_id) assert results["min"] == 10.0 assert results["max"] == 20.0 results = service.select_pricerange(query=test_alt_id) assert results["min"] == 10.0 assert results["max"] == 20.0 with pytest.raises(NoContentError): service.select_pricerange(query=str(uuid4())) def test_product_service_get_total(service, es_object): ProductFactory.create().save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.get_total() assert results > 0 test_alt_id = "I_test_product_service_get_total" test_id = str(uuid4()) ProductFactory.create(gender=test_alt_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.get_total(gender=test_alt_id) assert results == 1 results = service.get_total(gender=str(uuid4())) assert results == 0 ProductFactory.create(sessionid=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.get_total(sessionid=test_id) assert results == 1 results = service.get_total(sessionid=str(uuid4())) assert results == 0 ProductFactory.create(sessionname=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.get_total(sessionname=test_id) assert results == 1 results = service.get_total(sessionname=str(uuid4())) assert results == 0 ProductFactory.create(brand=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.get_total(brand=test_id) assert results == 1 results = service.get_total(brand=str(uuid4())) assert results == 0 ProductFactory.create(kind=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.get_total(kind=test_id) assert results == 1 results = service.get_total(kind=str(uuid4())) assert results == 0 results = service.get_total(pricerange={"min": 1.0, "max": 100.0}) assert results > 0 results = service.get_total(pricerange={"min": 10000.0, "max": 20000.0}) assert results == 0 results = service.get_total(query=test_id) assert results == 2 results = service.get_total(query=test_alt_id) assert results == 1 results = service.get_total(query=str(uuid4())) assert results == 0 def test_product_service_select_brands(service, es_object): ProductFactory.create().save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_brands() assert len(results) > 0 test_alt_id = "I_test_product_service_select_brands" test_id = str(uuid4()) ProductFactory.create(gender=test_alt_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_brands(gender=test_alt_id) assert len(results) == 1 for key in ["brand", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_brands(gender=str(uuid4())) ProductFactory.create(sessionid=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_brands(sessionid=test_id) assert len(results) == 1 for key in ["brand", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_brands(sessionid=str(uuid4())) ProductFactory.create(sessionname=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_brands(sessionname=test_id) assert len(results) == 1 for key in ["brand", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_brands(sessionname=str(uuid4())) ProductFactory.create(brand=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_brands(brand=test_id) assert len(results) == 1 for key in ["brand", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_brands(brand=str(uuid4())) ProductFactory.create(kind=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_brands(kind=test_id) assert len(results) == 1 for key in ["brand", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_brands(kind=str(uuid4())) results = service.select_brands(pricerange={"min": 1.0, "max": 100.0}) assert len(results) > 0 with pytest.raises(NoContentError): service.select_brands(pricerange={"min": 10000.0, "max": 20000.0}) results = service.select_brands(query=test_id) assert len(results) == 2 for key in ["brand", "amount"]: assert key in results[0] results = service.select_brands(query=test_alt_id) assert len(results) == 1 for key in ["brand", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_brands(query=str(uuid4())) def test_product_service_select_kinds(service, es_object): ProductFactory.create().save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_kinds() assert len(results) > 0 test_alt_id = "I_test_product_service_select_kinds" test_id = str(uuid4()) ProductFactory.create(gender=test_alt_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_kinds(gender=test_alt_id) assert len(results) == 1 for key in ["kind", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_kinds(gender=str(uuid4())) ProductFactory.create(sessionid=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_kinds(sessionid=test_id) assert len(results) == 1 for key in ["kind", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_kinds(sessionid=str(uuid4())) ProductFactory.create(sessionname=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_kinds(sessionname=test_id) assert len(results) == 1 for key in ["kind", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_kinds(sessionname=str(uuid4())) ProductFactory.create(brand=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_kinds(brand=test_id) assert len(results) == 1 for key in ["kind", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_kinds(brand=str(uuid4())) ProductFactory.create(kind=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select_kinds(kind=test_id) assert len(results) == 1 for key in ["kind", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_kinds(kind=str(uuid4())) results = service.select_kinds(pricerange={"min": 1.0, "max": 100.0}) assert len(results) > 0 with pytest.raises(NoContentError): service.select_kinds(pricerange={"min": 10000.0, "max": 20000.0}) results = service.select_kinds(query=test_id) assert len(results) == 2 for key in ["kind", "amount"]: assert key in results[0] results = service.select_kinds(query=test_alt_id) assert len(results) == 1 for key in ["kind", "amount"]: assert key in results[0] with pytest.raises(NoContentError): service.select_kinds(query=str(uuid4())) def test_product_service_select(service, es_object): ProductFactory.create().save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select() assert len(results) > 0 test_alt_id = "I_test_product_service_select" test_id = str(uuid4()) ProductFactory.create(gender=test_alt_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select(gender=test_alt_id) assert len(results) == 1 assert type(results[0]) == Product with pytest.raises(NoContentError): service.select(gender=str(uuid4())) ProductFactory.create(sessionid=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select(sessionid=test_id) assert len(results) == 1 assert type(results[0]) == Product with pytest.raises(NoContentError): service.select(sessionid=str(uuid4())) ProductFactory.create(sessionname=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select(sessionname=test_id) assert len(results) == 1 assert type(results[0]) == Product with pytest.raises(NoContentError): service.select(sessionname=str(uuid4())) ProductFactory.create(brand=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select(brand=test_id) assert len(results) == 1 assert type(results[0]) == Product with pytest.raises(NoContentError): service.select(brand=str(uuid4())) ProductFactory.create(kind=test_id).save(using=es_object.connection) Index("store", using=es_object.connection).refresh() results = service.select(kind=test_id) assert len(results) == 1 assert type(results[0]) == Product with pytest.raises(NoContentError): service.select(kind=str(uuid4())) results = service.select(pricerange={"min": 1.0, "max": 100.0}) assert len(results) > 0 assert type(results[0]) == Product with pytest.raises(NoContentError): service.select(pricerange={"min": 10000.0, "max": 20000.0}) results = service.select(query=test_id) assert len(results) == 2 assert type(results[0]) == Product results = service.select(query=test_alt_id) assert len(results) == 1 assert type(results[0]) == Product with pytest.raises(NoContentError): service.select(query=str(uuid4())) def test_product_service_select_by_id(service, es_object): obj = ProductFactory.create() obj.save(using=es_object.connection) Index("store", using=es_object.connection).refresh() obj_id = obj.meta["id"] results = service.select_by_id(obj_id) assert type(results) == Product assert results.meta["id"] == obj_id fake_id = str(uuid4()) with pytest.raises(NotFoundError): service.select_by_id(fake_id) def test_product_service_select_by_item_list(service, es_object): price = {"outlet": 10.0, "retail": 20.0} item_list = [] for i in range(3): obj = ProductFactory.create(price=price) obj.save(using=es_object.connection) item_list.append({"item_id": obj.meta["id"], "amount": i+1}) Index("store", using=es_object.connection).refresh() results, total = service.select_by_item_list(item_list) assert len(results) == len(item_list) item_id_list = [item["item_id"] for item in item_list] for obj in results: assert type(obj) == Product assert obj.meta["id"] in item_id_list assert total["outlet"] == 60.0 assert total["retail"] == 120.0 fake_item_list = [{"item_id": str(uuid4()), "amount": 2} for x in range(2)] over_item_list = item_list + fake_item_list with pytest.raises(ValidationError): service.select_by_item_list(over_item_list)

34.43513

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2,263

17,252

5.199735

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0.058469

0.083964

0.148551

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0.05949

0

null

0

1

0

null

0

6

d54c2d68c64b9d8ec38ca041918d09a87ea4a433

229

py

Python

snakegame/entities/__init__.py

vedard/SnakeGame

828f3e892084848a45d72a8ca62385e94cf96adb

[ "MIT" ]

null

snakegame/entities/__init__.py

vedard/SnakeGame

828f3e892084848a45d72a8ca62385e94cf96adb

[ "MIT" ]

null

snakegame/entities/__init__.py

vedard/SnakeGame

828f3e892084848a45d72a8ca62385e94cf96adb

[ "MIT" ]

null

from snakegame.entities.entity import Entity from snakegame.entities.drawable import Drawable from snakegame.entities.moveable import Moveable from snakegame.entities.fruit import Fruit from snakegame.entities.snake import Snake

38.166667

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229

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null

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1

0

1

0

6

d550243211f5daba2c41bfbe7b3b18e766b7ef7e

125

py

Python

numsim/__init__.py

ffernandoalves/NumSim

44544cfa6a451835efafbc847780fdcb8ad9081c

[ "MIT" ]

1

2021-05-26T07:14:21.000Z

numsim/__init__.py

ffernandoalves/NumSim

44544cfa6a451835efafbc847780fdcb8ad9081c

[ "MIT" ]

null

numsim/__init__.py

ffernandoalves/NumSim

44544cfa6a451835efafbc847780fdcb8ad9081c

[ "MIT" ]

null

""" source package """ from .computer import * from .utils import load_data_generated from .animation import start_animation

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null

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1

0

1

0

6

d550eb81ba376a2ac0d1f38cb80c6e08b626d5b2

30

py

Python

Hello.py

LucaViolin/MB215Lab1

d07e01fde189c53ed8b1b1f5266206a455fd9a50

[ "MIT" ]

null

Hello.py

LucaViolin/MB215Lab1

d07e01fde189c53ed8b1b1f5266206a455fd9a50

[ "MIT" ]

null

Hello.py

LucaViolin/MB215Lab1

d07e01fde189c53ed8b1b1f5266206a455fd9a50

[ "MIT" ]

null

print("Hello world from Luca")

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0.766667

5

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0

6

d5772e94e5cd6fef97e00f69f3bc2b7e309250d7

124

py

Python

mlcvs/tica/__init__.py

luigibonati/mlcvs

6567fb0774dc354f9cf3472dc356fdcf10aba6f2

[ "BSD-3-Clause" ]

1

2022-02-14T10:06:42.000Z

mlcvs/tica/__init__.py

luigibonati/mlcvs

6567fb0774dc354f9cf3472dc356fdcf10aba6f2

[ "BSD-3-Clause" ]

9

2021-10-31T09:28:09.000Z

2022-03-23T15:13:21.000Z

mlcvs/tica/__init__.py

luigibonati/mlcvs

6567fb0774dc354f9cf3472dc356fdcf10aba6f2

[ "BSD-3-Clause" ]

null

__all__ = ["linear_tica","tica"] from .linear_tica import TICA_CV from .deep_tica import DeepTICA_CV from .tica import TICA

24.8

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638ee73b957def88f9ab6ba4fd829aafadd99b00

92

py

Python

lib/gateway/ctpGateway/__init__.py

myron0330/metatrade

b0358ad3dce6ba50e4801b6af557d7883d8a5d9a

[ "MIT" ]

1

2018-06-28T09:49:08.000Z

lib/gateway/ctpGateway/__init__.py

myron0330/metatrade

b0358ad3dce6ba50e4801b6af557d7883d8a5d9a

[ "MIT" ]

null

lib/gateway/ctpGateway/__init__.py

myron0330/metatrade

b0358ad3dce6ba50e4801b6af557d7883d8a5d9a

[ "MIT" ]

null

from . market_gateway import CTPMarketGateway from . trader_gateway import CtpTraderGateway

30.666667

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92

7.8

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0.333333

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1

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1

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1

0

null

0

1

0

1

0

1

0

6

639954b6db6959a843b2ca6b9283315569d93fa5

40

py

Python

toolcraft/gui/widget/__init__.py

SpikingNeurons/_toolcraft_

070c79ea9248e4082bd69b5344f7b532e57f7730

[ "BSD-3-Clause" ]

null

toolcraft/gui/widget/__init__.py

SpikingNeurons/_toolcraft_

070c79ea9248e4082bd69b5344f7b532e57f7730

[ "BSD-3-Clause" ]

1

2021-09-20T22:22:05.000Z

toolcraft/gui/widget/__init__.py

SpikingNeurons/_toolcraft_

070c79ea9248e4082bd69b5344f7b532e57f7730

[ "BSD-3-Clause" ]

null

from .core import * from .plot import *

13.333333

19

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40

4.666667

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40

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20

0.875

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0

6

63b3e8c01272f40661044ef1534bd15cbad17a27

7,153

py

Python

test/test_getpy.py

atom-moyer/getpy

8d5a846d030d345408a4dc71793d5918521180c4

[ "MIT" ]

83

2019-04-14T05:39:34.000Z

2022-03-28T18:18:03.000Z

test/test_getpy.py

atom-moyer/getpy

8d5a846d030d345408a4dc71793d5918521180c4

[ "MIT" ]

5

2019-04-30T16:23:10.000Z

2021-03-26T12:15:52.000Z

test/test_getpy.py

atom-moyer/getpy

8d5a846d030d345408a4dc71793d5918521180c4

[ "MIT" ]

5

2019-04-29T19:15:15.000Z

2022-03-04T19:08:50.000Z

import pytest import numpy as np import getpy as gp def test_getpy_methods(): key_type = np.dtype('u8') value_type = np.dtype('u8') keys = np.random.randint(1, 1000, size=10**2, dtype=key_type) values = np.random.randint(1, 1000, size=10**2, dtype=value_type) gp_dict = gp.Dict(key_type, value_type) gp_dict[keys] = values p_dict = {key : value for key, value in zip(keys, values)} assert len(gp_dict) == len(np.unique(keys)) assert all([gp_dict[key] == p_dict[key] for key in keys]) def test_getpy_methods_with_multidim(): key_type = np.dtype('u8') value_type = np.dtype('u8') keys = np.random.randint(1, 1000, size=10**2, dtype=key_type).reshape(10,10) values = np.random.randint(1, 1000, size=10**2, dtype=value_type).reshape(10,10) gp_dict = gp.Dict(key_type, value_type) gp_dict[keys] = values p_dict = {key : value for key, value in zip(keys.flat, values.flat)} assert len(gp_dict) == len(np.unique(keys)) assert all([gp_dict[key] == p_dict[key] for key in keys.flat]) def test_getpy_methods_with_strings(): key_type = np.dtype('S8') value_type = np.dtype('S8') keys = np.array([np.random.bytes(8) for i in range(10**2)], dtype=key_type) values = np.array([np.random.bytes(8) for i in range(10**2)], dtype=value_type) gp_dict = gp.Dict(key_type, value_type) gp_dict[keys] = values p_dict = {key : value for key, value in zip(keys, values)} assert len(gp_dict) == len(np.unique(keys)) assert all([gp_dict[key] == p_dict[key] for key in keys]) def test_getpy_methods_with_multidim_and_strings(): key_type = np.dtype('S8') value_type = np.dtype('S8') keys = np.array([np.random.bytes(4) for i in range(10**2)], dtype=key_type).reshape(10,10) values = np.array([np.random.bytes(4) for i in range(10**2)], dtype=value_type).reshape(10,10) gp_dict = gp.Dict(key_type, value_type) gp_dict[keys] = values p_dict = {key : value for key, value in zip(keys.flat, values.flat)} assert len(gp_dict) == len(np.unique(keys.flat)) assert all([gp_dict[key] == p_dict[key] for key in keys.flat]) def test_getpy_methods_with_default(): key_type = np.dtype('u8') value_type = np.dtype('u8') keys = np.random.randint(1, 1000, size=10**2, dtype=key_type) values = np.random.randint(1, 1000, size=10**2, dtype=value_type) default_value = 4242 gp_dict = gp.Dict(key_type, value_type, default_value=default_value) gp_dict[keys] = values random_keys = np.random.randint(1, 1000, size=500, dtype=key_type) random_values = gp_dict[random_keys] assert np.all(random_values[np.where(gp_dict.contains(random_keys))] != default_value) assert np.all(random_values[np.where(np.logical_not(gp_dict.contains(random_keys)))] == default_value) def test_getpy_methods_with_default_and_strings(): key_type = np.dtype('S8') value_type = np.dtype('S8') keys = np.array([np.random.bytes(8) for i in range(10**2)], dtype=key_type) values = np.array([np.random.bytes(8) for i in range(10**2)], dtype=value_type) default_value = np.random.bytes(8) gp_dict = gp.Dict(key_type, value_type, default_value=default_value) gp_dict[keys] = values random_keys = np.array([np.random.bytes(8) for i in range(10**3)], dtype=key_type) random_values = gp_dict[random_keys] assert np.all(random_values[np.where(gp_dict.contains(random_keys))] != default_value) assert np.all(random_values[np.where(np.logical_not(gp_dict.contains(random_keys)))] == default_value) def test_getpy_types(): for key_type, value_type in gp.dict_types: gp_dict = gp.Dict(key_type, value_type) keys = np.array(range(256), dtype=key_type) values = np.array(range(256), dtype=value_type) gp_dict[keys] = values values = gp_dict[keys] def test_getpy_dump_load(): key_type = np.dtype('u8') value_type = np.dtype('u8') keys = np.random.randint(1, 1000, size=10**1, dtype=key_type) values = np.random.randint(1, 1000, size=10**1, dtype=value_type) gp_dict_1 = gp.Dict(key_type, value_type) gp_dict_1[keys] = values gp_dict_1.dump('test/test.bin') gp_dict_2 = gp.Dict(key_type, value_type) gp_dict_2.load('test/test.bin') assert len(gp_dict_1) == len(gp_dict_2) def test_getpy_big_dict_u4_u4(): key_type = np.dtype('u4') value_type = np.dtype('u4') gp_dict = gp.Dict(key_type, value_type) values = np.random.randint(10**9, size=10**4, dtype=value_type) for i in range(10**2): keys = np.random.randint(10**9, size=10**4, dtype=key_type) gp_dict[keys] = values def test_getpy_big_dict_u8_u8(): key_type = np.dtype('u8') value_type = np.dtype('u8') gp_dict = gp.Dict(key_type, value_type) values = np.random.randint(10**15, size=10**4, dtype=value_type) for i in range(10**2): keys = np.random.randint(10**15, size=10**4, dtype=key_type) gp_dict[keys] = values def test_getpy_big_dict_u8_S8(): key_type = np.dtype('u8') value_type = np.dtype('S8') gp_dict = gp.Dict(key_type, value_type) values = np.array([np.random.bytes(8) for i in range(10**4)], dtype=value_type) for i in range(10**2): keys = np.random.randint(10**15, size=10**4, dtype=key_type) gp_dict[keys] = values def test_getpy_big_dict_u8_u8_lookup(): key_type = np.dtype('u8') value_type = np.dtype('u8') gp_dict = gp.Dict(key_type, value_type) keys = np.random.randint(10**15, size=10**5, dtype=key_type) values = np.random.randint(10**15, size=10**5, dtype=value_type) gp_dict[keys] = values for i in range(10**2): values = gp_dict[keys] def test_getpy_very_big_dict_u4_u4(): key_type = np.dtype('u4') value_type = np.dtype('u4') gp_dict = gp.Dict(key_type, value_type) values = np.random.randint(10**9, size=10**5, dtype=value_type) for i in range(10**2): keys = np.random.randint(10**9, size=10**5, dtype=key_type) gp_dict[keys] = values def test_getpy_very_big_dict_u8_u8(): key_type = np.dtype('u8') value_type = np.dtype('u8') gp_dict = gp.Dict(key_type, value_type) values = np.random.randint(10**15, size=10**5, dtype=value_type) for i in range(10**2): keys = np.random.randint(10**15, size=10**5, dtype=key_type) gp_dict[keys] = values def test_getpy_very_big_dict_u8_S8(): key_type = np.dtype('u8') value_type = np.dtype('S8') gp_dict = gp.Dict(key_type, value_type) values = np.array([np.random.bytes(8) for i in range(10**5)], dtype=value_type) for i in range(10**2): keys = np.random.randint(10**15, size=10**5, dtype=key_type) gp_dict[keys] = values def test_getpy_very_big_dict_u8_S16(): key_type = np.dtype('u8') value_type = np.dtype('S16') gp_dict = gp.Dict(key_type, value_type) values = np.array([np.random.bytes(16) for i in range(10**5)], dtype=value_type) for i in range(10**2): keys = np.random.randint(10**15, size=10**5, dtype=key_type) gp_dict[keys] = values

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0.095068

0.073644

0.064271

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0

null

0

1

0

null

0

6

63c219c2fe082a3e8cb86d1432a6f0d8338c3173

4,383

py

Python

util/data/gen/ntmarta.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/ntmarta.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/ntmarta.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

symbols = [] exports = [{'type': 'function', 'name': 'AccConvertAccessMaskToActrlAccess', 'address': '0x7ffb3a7b28a0'}, {'type': 'function', 'name': 'AccConvertAccessToSD', 'address': '0x7ffb3a7b2a20'}, {'type': 'function', 'name': 'AccConvertAccessToSecurityDescriptor', 'address': '0x7ffb3a7b2be0'}, {'type': 'function', 'name': 'AccConvertAclToAccess', 'address': '0x7ffb3a7b2d40'}, {'type': 'function', 'name': 'AccConvertSDToAccess', 'address': '0x7ffb3a7b2de0'}, {'type': 'function', 'name': 'AccFreeIndexArray', 'address': '0x7ffb3a7c10c0'}, {'type': 'function', 'name': 'AccGetAccessForTrustee', 'address': '0x7ffb3a7b30c0'}, {'type': 'function', 'name': 'AccGetExplicitEntries', 'address': '0x7ffb3a7b31b0'}, {'type': 'function', 'name': 'AccGetInheritanceSource', 'address': '0x7ffb3a7c11f0'}, {'type': 'function', 'name': 'AccLookupAccountName', 'address': '0x7ffb3a7b3280'}, {'type': 'function', 'name': 'AccLookupAccountSid', 'address': '0x7ffb3a7b3600'}, {'type': 'function', 'name': 'AccLookupAccountTrustee', 'address': '0x7ffb3a7b3a40'}, {'type': 'function', 'name': 'AccProvCancelOperation', 'address': '0x7ffb3a7bb5e0'}, {'type': 'function', 'name': 'AccProvGetAccessInfoPerObjectType', 'address': '0x7ffb3a7bb670'}, {'type': 'function', 'name': 'AccProvGetAllRights', 'address': '0x7ffb3a7bb720'}, {'type': 'function', 'name': 'AccProvGetCapabilities', 'address': '0x7ffb3a7a6480'}, {'type': 'function', 'name': 'AccProvGetOperationResults', 'address': '0x7ffb3a7bb930'}, {'type': 'function', 'name': 'AccProvGetTrusteesAccess', 'address': '0x7ffb3a7bbab0'}, {'type': 'function', 'name': 'AccProvGrantAccessRights', 'address': '0x7ffb3a7bbbd0'}, {'type': 'function', 'name': 'AccProvHandleGetAccessInfoPerObjectType', 'address': '0x7ffb3a7bbd60'}, {'type': 'function', 'name': 'AccProvHandleGetAllRights', 'address': '0x7ffb3a7bbe60'}, {'type': 'function', 'name': 'AccProvHandleGetTrusteesAccess', 'address': '0x7ffb3a7bc000'}, {'type': 'function', 'name': 'AccProvHandleGrantAccessRights', 'address': '0x7ffb3a7baf60'}, {'type': 'function', 'name': 'AccProvHandleIsAccessAudited', 'address': '0x7ffb3a7bc080'}, {'type': 'function', 'name': 'AccProvHandleIsObjectAccessible', 'address': '0x7ffb3a7bc120'}, {'type': 'function', 'name': 'AccProvHandleRevokeAccessRights', 'address': '0x7ffb3a7bc290'}, {'type': 'function', 'name': 'AccProvHandleRevokeAuditRights', 'address': '0x7ffb3a7bc370'}, {'type': 'function', 'name': 'AccProvHandleSetAccessRights', 'address': '0x7ffb3a7bc450'}, {'type': 'function', 'name': 'AccProvIsAccessAudited', 'address': '0x7ffb3a7bc550'}, {'type': 'function', 'name': 'AccProvIsObjectAccessible', 'address': '0x7ffb3a7bc680'}, {'type': 'function', 'name': 'AccProvRevokeAccessRights', 'address': '0x7ffb3a7bcb30'}, {'type': 'function', 'name': 'AccProvRevokeAuditRights', 'address': '0x7ffb3a7bcc80'}, {'type': 'function', 'name': 'AccProvSetAccessRights', 'address': '0x7ffb3a7bcdd0'}, {'type': 'function', 'name': 'AccRewriteGetExplicitEntriesFromAcl', 'address': '0x7ffb3a7b91e0'}, {'type': 'function', 'name': 'AccRewriteGetHandleRights', 'address': '0x7ffb3a7a2ea0'}, {'type': 'function', 'name': 'AccRewriteGetNamedRights', 'address': '0x7ffb3a7c1890'}, {'type': 'function', 'name': 'AccRewriteSetEntriesInAcl', 'address': '0x7ffb3a7a4180'}, {'type': 'function', 'name': 'AccRewriteSetHandleRights', 'address': '0x7ffb3a7a22a0'}, {'type': 'function', 'name': 'AccRewriteSetNamedRights', 'address': '0x7ffb3a7a24b0'}, {'type': 'function', 'name': 'AccSetEntriesInAList', 'address': '0x7ffb3a7b3b60'}, {'type': 'function', 'name': 'AccTreeResetNamedSecurityInfo', 'address': '0x7ffb3a7a11d0'}, {'type': 'function', 'name': 'EventGuidToName', 'address': '0x7ffb3a7abaa0'}, {'type': 'function', 'name': 'EventNameFree', 'address': '0x7ffb3a7abb40'}, {'type': 'function', 'name': 'GetExplicitEntriesFromAclW', 'address': '0x7ffb3a7b1d10'}, {'type': 'function', 'name': 'GetMartaExtensionInterface', 'address': '0x7ffb3a7a5e60'}, {'type': 'function', 'name': 'GetNamedSecurityInfoW', 'address': '0x7ffb3a7a3000'}, {'type': 'function', 'name': 'GetSecurityInfo', 'address': '0x7ffb3a7a2df0'}, {'type': 'function', 'name': 'SetEntriesInAclW', 'address': '0x7ffb3a7a4170'}, {'type': 'function', 'name': 'SetNamedSecurityInfoW', 'address': '0x7ffb3a7a2180'}, {'type': 'function', 'name': 'SetSecurityInfo', 'address': '0x7ffb3a7a2210'}]

2,191.5

4,370

0.701346

302

4,383

10.178808

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0.195185

0.260247

0

0.083354

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4,383

2

4,370

2,191.5

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0

0.697993

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0.159672

0

1

0

false

0

1

null

0

1

0

1

0

1

0

null

0

6

8954a09b0f1137fa6660a8283930ea0464aee22e

39

py

Python

100 Curso aulapharos/008 BibliotecasModulos/ImportarSoloUnaParte.py

malcabaut/AprendiendoPython

b1e8731f1614b08b5ace1b7d1ecbeb041b21f28b

[ "MIT" ]

null

100 Curso aulapharos/008 BibliotecasModulos/ImportarSoloUnaParte.py

malcabaut/AprendiendoPython

b1e8731f1614b08b5ace1b7d1ecbeb041b21f28b

[ "MIT" ]

null

100 Curso aulapharos/008 BibliotecasModulos/ImportarSoloUnaParte.py

malcabaut/AprendiendoPython

b1e8731f1614b08b5ace1b7d1ecbeb041b21f28b

[ "MIT" ]

null

from math import sqrt print(sqrt(16))

13

22

0.74359

7

39

4.142857

0.857143

0

0.060606

0.153846

39

2

23

19.5

0.818182

0

1

0

true

0

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null

0

1

0

1

0

1

0

6

9865e15bf3a1d78dcc619a788a0b10f26ab08200

185

py

Python

Python/PythonOOP/packages_test/script.py

JosephAMumford/CodingDojo

505be74d18d7a8f41c4b3576ca050b97f840f0a3

[ "MIT" ]

2

2018-08-18T15:14:45.000Z

2019-10-16T16:14:13.000Z

Python/PythonOOP/packages_test/script.py

JosephAMumford/CodingDojo

505be74d18d7a8f41c4b3576ca050b97f840f0a3

[ "MIT" ]

null

Python/PythonOOP/packages_test/script.py

JosephAMumford/CodingDojo

505be74d18d7a8f41c4b3576ca050b97f840f0a3

[ "MIT" ]

6

2018-05-05T18:13:05.000Z

2021-05-20T11:32:48.000Z

from test_modules import arithmetic print arithmetic.add(5,8) print arithmetic.subtract(10,5) print arithmetic.multiply(12,6) print arithmetic.divide(6,2) print arithmetic.divide(6,0)

23.125

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0.281879

0.295302

0

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185

7

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null

0

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1

0

6

986b7e3da19417abfe8c4dad830d126cdfb4ac53

94

py

Python

phonescrubber/numbers/__init__.py

RagtagOpen/phone-number-validator

762122efa03c3e6057204c8b5a7e3bdc468c94e4

[ "MIT" ]

1

2019-06-04T14:40:09.000Z

phonescrubber/numbers/__init__.py

RagtagOpen/phone-number-validator

762122efa03c3e6057204c8b5a7e3bdc468c94e4

[ "MIT" ]

7

2019-02-05T16:31:37.000Z

2019-06-17T12:18:25.000Z

phonescrubber/numbers/__init__.py

RagtagOpen/phone-number-validator

762122efa03c3e6057204c8b5a7e3bdc468c94e4

[ "MIT" ]

1

2021-03-12T04:18:21.000Z

from flask import Blueprint numbers_bp = Blueprint('numbers', __name__) from . import views

15.666667

43

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12

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94

5

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null

0

1

0

1

0

6

7f42ed137187799aee134f2dd3ceb282d8d8f882

13,772

py

Python

phennyfyxata/scores/tests.py

Venefyxatu/phennyfyxata

cc4031c8483166c1eb77a2e14c3f0074ace58374

[ "BSD-2-Clause" ]

2

2015-05-18T13:49:42.000Z

2015-05-18T14:16:45.000Z

phennyfyxata/scores/tests.py

Venefyxatu/phennyfyxata

cc4031c8483166c1eb77a2e14c3f0074ace58374

[ "BSD-2-Clause" ]

2

2015-11-03T16:48:45.000Z

2015-11-19T08:49:04.000Z

phennyfyxata/scores/tests.py

Venefyxatu/phennyfyxata

cc4031c8483166c1eb77a2e14c3f0074ace58374

[ "BSD-2-Clause" ]

null

import json import datetime from django.test import TestCase from django.test.client import Client from scores.models import War from scores.models import Writer from scores.models import WarParticipants from scores.models import ParticipantScore class ParticipationHelper: def participate(self, war_id, writer_nick): response = Client().post('/api/war/participate/', {'id': war_id, 'writer': writer_nick}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code def withdraw(self, war_id, writer_nick): response = Client().post('/api/war/withdraw/', {'id': war_id, 'writer': writer_nick}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code class ParticipantTests(TestCase): def setUp(self): starttime = datetime.datetime.now() + datetime.timedelta(0, seconds=300) endtime = starttime + datetime.timedelta(0, seconds=600) self.ph = ParticipationHelper() self.war = War(starttime=starttime, endtime=endtime) self.war.save() self.writer = Writer(nick='TestWriter') self.writer.save() self.c = Client() def test_participate_war(self): self.ph.participate(self.war.id, self.writer.nick) participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 1, 'Should have 1 participant, not %s' % len(participants) def test_withdraw_war(self): self.ph.participate(self.war.id, self.writer.nick) participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 1, 'Should have 1 participant, not %s' % len(participants) self.ph.withdraw(self.war.id, self.writer.nick) participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 0, 'Should have 0 participants, not %s' % len(participants) def test_add_new_participant(self): self.ph.participate(self.war.id, 'NewWriter') participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 1, 'Should have 1 participant, not %s' % len(participants) def test_add_same_participant(self): self.ph.participate(self.war.id, self.writer.nick) participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 1, 'Should have 1 participant, not %s' % len(participants) self.ph.participate(self.war.id, self.writer.nick) participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 1, 'Should have 1 participant, not %s' % len(participants) def test_withdraw_unparticipating_writer(self): self.ph.withdraw(self.war.id, 'NewWriter') participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 0, 'Should have 0 participants, not %s' % len(participants) def test_participate_nonexistant_war(self): response = self.c.post('/api/war/participate/', {'id': 9, 'writer': self.writer.nick}) assert response.status_code == 404, 'Response status should be 404, not %s' % response.status_code def test_list_participants(self): self.ph.participate(self.war.id, self.writer.nick) participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 1, 'Should have 1 participant, not %s' % len(participants) response = self.c.post('/api/war/listparticipants/', {'id': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code expected_response = [self.writer.nick] assert json.loads(response.content) == expected_response, 'Response should be "%s", not %s' % (expected_response, json.loads(response.content)) self.ph.participate(self.war.id, 'NewWriter') participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 2, 'Should have 1 participant, not %s' % len(participants) response = self.c.post('/api/war/listparticipants/', {'id': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code expected_response = [self.writer.nick, 'NewWriter'] assert json.loads(response.content) == expected_response, 'Response should be "%s", not %s' % (expected_response, json.loads(response.content)) self.ph.withdraw(self.war.id, self.writer.nick) participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 1, 'Should have 1 participant, not %s' % len(participants) response = self.c.post('/api/war/listparticipants/', {'id': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code expected_response = ['NewWriter'] assert json.loads(response.content) == expected_response, 'Response should be "%s", not %s' % (expected_response, json.loads(response.content)) self.ph.withdraw(self.war.id, 'NewWriter') participants = WarParticipants.objects.filter(war__id=self.war.id) assert len(participants) == 0, 'Should have 0 participants, not %s' % len(participants) response = self.c.post('/api/war/listparticipants/', {'id': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code assert json.loads(response.content) == [], 'Response should be [], not %s' % json.loads(response.content) class WarTests(TestCase): def setUp(self): self.c = Client() self.starttime = datetime.datetime.now() + datetime.timedelta(0, seconds=300) self.starttime = self.starttime - datetime.timedelta(0, seconds=self.starttime.second, microseconds=self.starttime.microsecond) self.endtime = self.starttime + datetime.timedelta(0, seconds=600) self.endtime = self.endtime - datetime.timedelta(0, seconds=self.endtime.second, microseconds=self.endtime.microsecond) def test_war_info(self): response = self.c.post('/api/war/new/', {'starttime': self.starttime.strftime('%s'), 'endtime': self.endtime.strftime('%s')}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code response = self.c.post('/api/war/info/', {'id': 1}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code expected_response = {'id': '1', 'starttime': self.starttime.strftime('%s'), 'endtime': self.endtime.strftime('%s')} assert json.loads(response.content) == expected_response, 'Response should be %s, not %s' % (expected_response, json.loads(response.content)) def test_create_war(self): response = self.c.post('/api/war/new/', {'starttime': self.starttime.strftime('%s'), 'endtime': self.endtime.strftime('%s')}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code assert len(War.objects.all()) == 1, 'There should be 1 war, not %s' % len(War.objects.all()) def test_no_active_wars(self): response = self.c.post('/api/war/new/', {'starttime': self.starttime.strftime('%s'), 'endtime': self.endtime.strftime('%s')}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code response = self.c.get('/api/war/active/') assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code assert json.loads(response.content) == [], 'Response should be [], not %s' % json.loads(response.content) def test_active_wars(self): starttime = datetime.datetime.now() starttime = starttime - datetime.timedelta(0, seconds=starttime.second, microseconds=starttime.microsecond) response = self.c.post('/api/war/new/', {'starttime': starttime.strftime('%s'), 'endtime': self.endtime.strftime('%s')}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code response = self.c.get('/api/war/active/') assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code expected_response = [{'id': 1, 'starttime': starttime.strftime('%s'), 'endtime': self.endtime.strftime('%s')}] assert json.loads(response.content) == expected_response, 'Response should be "%s", not %s' % (expected_response, json.loads(response.content)) def test_planned_wars(self): response = self.c.post('/api/war/new/', {'starttime': self.starttime.strftime('%s'), 'endtime': self.endtime.strftime('%s')}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code response = self.c.get('/api/war/planned/') assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code expected_response = [{'id': 1, 'starttime': self.starttime.strftime('%s'), 'endtime': self.endtime.strftime('%s')}] assert json.loads(response.content) == expected_response, 'Response should be %s, not %s' % (expected_response, json.loads(response.content)) def test_no_planned_wars(self): response = self.c.get('/api/war/planned/') assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code expected_response = [] assert json.loads(response.content) == expected_response, 'Response should be %s, not %s' % (expected_response, json.loads(response.content)) class ScoreTests(TestCase): def setUp(self): starttime = datetime.datetime.now() + datetime.timedelta(0, seconds=300) endtime = starttime + datetime.timedelta(0, seconds=600) self.war = War(starttime=starttime, endtime=endtime) self.war.save() self.writer = Writer(nick='TestWriter') self.writer.save() self.c = Client() def test_get_score_for_war(self): response = self.c.post('/api/score/register/', {'writer': self.writer.nick, 'score': 200, 'war': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code response = self.c.post('/api/writer/getscore/', {'writer': self.writer.nick, 'war': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code expected_response = {'war': str(self.war.id), 'writer': self.writer.nick, 'score': 200} assert json.loads(response.content) == expected_response, 'Response should be %s, not %s' % (expected_response, json.loads(response.content)) def test_register_score(self): response = self.c.post('/api/score/register/', {'writer': self.writer.nick, 'score': 200, 'war': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code assert len(ParticipantScore.objects.all()) == 1, 'There should be one ParticipantScore object' ps = ParticipantScore.objects.all()[0] assert ps.writer == self.writer, 'ParticipantScore writer is not as expected' assert ps.score == 200, 'ParticipantScore score should be 200, not %s' % ps.score assert ps.war == self.war, 'ParticipantScore war is not as expected' def test_deregister_score(self): response = self.c.post('/api/score/register/', {'writer': self.writer.nick, 'score': 200, 'war': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code assert len(ParticipantScore.objects.all()) == 1, 'There should be one ParticipantScore object' ps = ParticipantScore.objects.all()[0] assert ps.writer == self.writer, 'ParticipantScore writer is not as expected' assert ps.score == 200, 'ParticipantScore score should be 200, not %s' % ps.score assert ps.war == self.war, 'ParticipantScore war is not as expected' response = self.c.post('/api/score/deregister/', {'writer': self.writer.nick, 'war': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code assert len(ParticipantScore.objects.all()) == 0, 'There should be no ParticipantScore objects' def test_update_score(self): response = self.c.post('/api/score/register/', {'writer': self.writer.nick, 'score': 200, 'war': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code assert len(ParticipantScore.objects.all()) == 1, 'There should be one ParticipantScore object' ps = ParticipantScore.objects.all()[0] assert ps.score == 200, 'ParticipantScore score should be 200, not %s' % ps.score response = self.c.post('/api/score/register/', {'writer': self.writer.nick, 'score': 400, 'war': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code ps = ParticipantScore.objects.all()[0] assert ps.score == 400, 'ParticipantScore score should be 400, not %s' % ps.score response = self.c.post('/api/score/register/', {'writer': self.writer.nick, 'score': 100, 'war': self.war.id}) assert response.status_code == 200, 'Response status should be 200, not %s' % response.status_code ps = ParticipantScore.objects.all()[0] assert ps.score == 100, 'ParticipantScore score should be 100, not %s' % ps.score

54.434783

151

0.678405

1,786

13,772

5.149496

0.054311

0.114168

0.097858

0.0411

0.894205

0.879743

0.856584

0.851147

0.841905

0

0.023584

0.181019

13,772

252

152

54.650794

0.791826

0

0.653409

0

0.220375

0.013724

0

0.340909

1

0.125

false

0

0.045455

0

0.193182

0

null

0

1

0

null

0

6

7f5bd7ccfce6c2251e7369d0afb6c544acc3318f

42

py

Python

code/abc111_b_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

3

2019-08-16T16:55:48.000Z

2021-04-11T10:21:40.000Z

code/abc111_b_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

code/abc111_b_01.py

KoyanagiHitoshi/AtCoder

731892543769b5df15254e1f32b756190378d292

[ "MIT" ]

null

n=int(input()) print((((n-1)//111)+1)*111)

21

27

0.547619

9

42

2.555556

0.666667

0.347826

0

0.195122

0.02381

42

2

27

21

0.365854

0

1

0

false

0

0.5

1

0

null

1

0

1

0

1

0

null

0

1

0

6

7f815a8e673246786acfcd4eb086250939cd3d44

2,108

py

Python

5lab/inverse_test.py

vhall415/ee144_robotics

090a8da4da682b1175790f7d8d3c655fca9831cb

[ "MIT" ]

null

5lab/inverse_test.py

vhall415/ee144_robotics

090a8da4da682b1175790f7d8d3c655fca9831cb

[ "MIT" ]

null

5lab/inverse_test.py

vhall415/ee144_robotics

090a8da4da682b1175790f7d8d3c655fca9831cb

[ "MIT" ]

1

2021-10-07T18:35:29.000Z

from __future__ import division import Arm from Arm import position, makeVector from math import pi import unittest link1 = 1.0 link2 = 0.5 class InverseTestCase(unittest.TestCase): def test_case1(self): arm = Arm.Arm(link1=link1, link2=link2) end_effector = position(link1+link2, 0) joints = arm.inverse_kinematics(end_effector) self.assertAlmostEqual(joints.theta1, 0.0) self.assertAlmostEqual(joints.theta2, 0.0) end_effector = position(link1, link2) joints = arm.inverse_kinematics(end_effector) self.assertAlmostEqual(joints.theta1, 0.0) self.assertAlmostEqual(joints.theta2, pi / 2) def test_case2(self): arm = Arm.Arm(link1=link1, link2=link2) end_effector = position(link2, -link1) joints = arm.inverse_kinematics(end_effector) self.assertAlmostEqual(joints.theta1, -pi / 2) self.assertAlmostEqual(joints.theta2, pi / 2) end_effector = position(0, link1+link2) joints = arm.inverse_kinematics(end_effector) self.assertAlmostEqual(joints.theta1, pi / 2) self.assertAlmostEqual(joints.theta2, 0.0) def test_case3(self): arm = Arm.Arm(link1=link1, link2=link2, origin=makeVector(1,1)) end_effector = position(1+link2, 1-link1) joints = arm.inverse_kinematics(end_effector) self.assertAlmostEqual(joints.theta1, -pi / 2) self.assertAlmostEqual(joints.theta2, pi / 2) end_effector = position(1, 1+link1+link2) joints = arm.inverse_kinematics(end_effector) self.assertAlmostEqual(joints.theta1, pi / 2) self.assertAlmostEqual(joints.theta2, 0.0) def test_unreachable(self): arm = Arm.Arm(link1=link1, link2=link2) end_effector = position(1+link1+link2, 0) with self.assertRaises(ValueError): joints = arm.inverse_kinematics(end_effector) end_effector = position(0, 0) with self.assertRaises(ValueError): joints = arm.inverse_kinematics(end_effector) if __name__ == '__main__': unittest.main()

34

71

0.675996

256

2,108

5.410156

0.160156

0.127076

0.233935

0.150181

0.790614

0.769675

0.766065

0.742238

0

0.048721

0.221063

2,108

61

72

34.557377

0.794762

0

0.520833

0

0.003795

0

0.291667

1

0.083333

false

0

0.104167

0

0.208333

0

null

0

1

0

1

0

null

0

6

7f9dbc3c6d7c747d4e033dda4f2de05b67da71bd

28

py

Python

cilva/__init__.py

GoodhillLab/CILVA

322e48b197044312296be507d9f06e1f4440739a

[ "MIT" ]

8

2019-07-06T09:25:27.000Z

2022-03-11T15:30:16.000Z

cilva/__init__.py

GoodhillLab/CILVA

322e48b197044312296be507d9f06e1f4440739a

[ "MIT" ]

null

cilva/__init__.py

GoodhillLab/CILVA

322e48b197044312296be507d9f06e1f4440739a

[ "MIT" ]

4

2019-07-03T01:55:26.000Z

2020-11-22T06:38:47.000Z

from . import core, analysis

28

0.785714

4

28

5.5

1

0

0.142857

28

1

28

0.916667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f6ae258fabc4343eeda2c6f83b7f59fd1fd40f40

31,729

py

Python

assignment 0/python_lab/submit_python_lab.py

dhruvgairola/linearAlgebra-coursera

20109133b9e53a7a38cbd17d8ca1fa1316bbf0d3

[ "MIT" ]

6

2015-09-18T02:07:21.000Z

2020-04-22T17:05:11.000Z

submit_python_lab.py

tri2sing/LinearAlgebraPython

f3dde94f02f146089607eb520ebd4467becb5f9e

[ "Apache-2.0" ]

null

submit_python_lab.py

tri2sing/LinearAlgebraPython

f3dde94f02f146089607eb520ebd4467becb5f9e

[ "Apache-2.0" ]

10

2015-09-05T03:54:00.000Z

2020-04-21T12:56:40.000Z

######## ######## # Hi there, curious student. # # # # This submission script runs some tests on your # # code and then uploads it to Coursera for grading. # # # # Changing anything in this script might cause your # # submissions to fail. # ######## ######## import io, os, sys, doctest, traceback, importlib, urllib.request, urllib.parse, urllib.error, base64, hashlib, random, ast URL = 'matrix-001' part_friendly_names = ['Minutes in a Week', 'Remainder', 'Divisibility', 'Assign y', 'Squares Comprehension', 'Powers of 2 Comprehension', 'Nine Element Set', 'Five Element Set', 'Base 10 Three Digit Numbers', 'Intersection of Sets', 'Average', 'Sum of Three Lists', 'Cartesian-Product Lists', 'Three Element Tuples', 'Remove (0,0,0)', 'First Element', 'List and Set Differences', 'Odd Numbers', 'Range and Zip', 'Zip Sum', 'Generate Dictionary', 'Modify Missing Key', 'Range Squared', 'Identity', 'List Integers', 'Names to Salaries', 'Next Ints', 'Cubes', 'dict2list', 'list2dict'] groups = [[('fFZfuj5BSf0z9vju', 'Minutes in a Week', '>>> print(test_format(minutes_in_week))\n')], [('RCTi7BRCbNnUGLrE', 'Remainder', '>>> print(test_format(remainder_without_mod))\n>>> print(test_format(line_contains_substr("remainder_without_mod", "%")))\n')], [('3ngJR6j6I6xGkMb6', 'Divisibility', '>>> print(test_format(divisible_by_3))\n')], [('SqKKP5oqXdJNQR54', 'Assign y', '>>> print(test_format(statement_val))\n')], [('LUxaUjsmC7dF6lFR', 'Squares Comprehension', '>>> print(test_format(first_five_squares))\n>>> print(test_format(use_comprehension("first_five_squares")))\n')], [('OO0ctDZZKI72zUu7', 'Powers of 2 Comprehension', '>>> print(test_format(first_five_pows_two))\n>>> print(test_format(use_comprehension("first_five_pows_two")))\n')], [('6jcpLOvzpLmsTSVm', 'Nine Element Set', '>>> nine_elements_set = {x*y for x in X1 for y in Y1}\n>>> print(test_format(len(nine_elements_set)))\n>>> print(test_format(len(X1)))\n>>> print(test_format(len(Y1)))\n')], [('Yc7Syvika5HHSjWY', 'Five Element Set', '>>> five_elements_set = {x*y for x in X2 for y in Y2}\n>>> print(test_format(len(five_elements_set)))\n>>> print(test_format(len(X2)))\n>>> print(test_format(len(Y2)))\n>>> print(test_format(len(X2 & Y2)))\n')], [('q4059GW7SFmhlnuV', 'Base 10 Three Digit Numbers', '>>> digits = {0,1,2,3,4,5,6,7,8,9}\n>>> base = 10\n>>> print(test_format(three_digits_set == set(range(1000))))\n>>> print(test_format(use_comprehension("three_digits_set")))\n>>> print(test_format(line_contains_substr("three_digits_set", "base")))\n')], [('apLtHTyCikXLtyrF', 'Intersection of Sets', '>>> print(test_format(S_intersect_T))\n>>> print(test_format(use_comprehension("S_intersect_T")))\n')], [('FnRTEV9wumMHDSRk', 'Average', '>>> print(test_format(L_average))\n')], [('xvF1K4mjiljgnWFv', 'Sum of Three Lists', '>>> print(test_format(LofL_sum))\n>>> print(test_format(use_comprehension("LofL_sum")))\n')], [('cXHf573AUNrEoFxH', 'Cartesian-Product Lists', '>>> print(test_format(set(map(tuple, cartesian_product))))\n>>> print(test_format(use_comprehension("cartesian_product")))\n')], [('Y4tuZGBVfLcQt8lN', 'Three Element Tuples', '>>> print(test_format(zero_sum_list == [(0, 0, 0), (0, 2, -2), (0, -2, 2), (1, 1, -2), (1, -2, 1), (2, 0, -2), (2, 2, -4), (2, -4, 2), (2, -2, 0), (-4, 2, 2), (-2, 0, 2), (-2, 1, 1), (-2, 2, 0)]))\n>>> print(test_format(use_comprehension("zero_sum_list")))\n')], [('S8w9l4cmOKXbgCfe', 'Remove (0,0,0)', '>>> print(test_format(exclude_zero_list))\n>>> print(test_format(use_comprehension("exclude_zero_list")))\n')], [('Qnq4vQ5vW6mORoqE', 'First Element', '>>> print(test_format(first_of_tuples_list == (0,0,0)))\n>>> print(test_format(len(first_of_tuples_list)))\n>>> print(test_format(sum(first_of_tuples_list)))\n>>> print(test_format(use_comprehension("first_of_tuples_list")))\n')], [('yoiJUxauMefcohSi', 'List and Set Differences', '>>> print(test_format(len(L1) == len(list(set(L1))) ))\n>>> L2_new = list(set(L2)) \n>>> print(test_format(len(L2) == len(L2_new)))\n>>> print(test_format(L2 == L2_new))\n')], [('3I5KzAun0uzowUp4', 'Odd Numbers', '>>> print(test_format(odd_num_list_range))\n>>> print(test_format(use_comprehension("odd_num_list_range")))\n')], [('D8ygxQYLerbhMhMX', 'Range and Zip', '>>> print(test_format(range_and_zip))\n>>> print(test_format(use_comprehension("range_and_zip")))\n')], [('klnmJrHwxgXXLTYT', 'Zip Sum', '>>> print(test_format(list_sum_zip))\n>>> print(test_format(use_comprehension("list_sum_zip")))\n')], [('FdPpEhCqyxB5Bt8S', 'Generate Dictionary', '>>> print(test_format(set(value_list)))\n>>> print(test_format(use_comprehension("value_list")))\n')], [('14cqsN8TvrYGNLVu', 'Modify Missing Key', '>>> print(test_format(use_comprehension("value_list_modified_1")))\n>>> print(test_format(use_comprehension("value_list_modified_2")))\n>>> print(test_format(value_list_modified_1))\n>>> print(test_format(value_list_modified_2))\n')], [('C5kNoKaPB4ApgbT7', 'Range Squared', '>>> print(test_format(square_dict == {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81, 10: 100, 11: 121, 12: 144, 13: 169, 14: 196, 15: 225, 16: 256, 17: 289, 18: 324, 19: 361, 20: 400, 21: 441, 22: 484, 23: 529, 24: 576, 25: 625, 26: 676, 27: 729, 28: 784, 29: 841, 30: 900, 31: 961, 32: 1024, 33: 1089, 34: 1156, 35: 1225, 36: 1296, 37: 1369, 38: 1444, 39: 1521, 40: 1600, 41: 1681, 42: 1764, 43: 1849, 44: 1936, 45: 2025, 46: 2116, 47: 2209, 48: 2304, 49: 2401, 50: 2500, 51: 2601, 52: 2704, 53: 2809, 54: 2916, 55: 3025, 56: 3136, 57: 3249, 58: 3364, 59: 3481, 60: 3600, 61: 3721, 62: 3844, 63: 3969, 64: 4096, 65: 4225, 66: 4356, 67: 4489, 68: 4624, 69: 4761, 70: 4900, 71: 5041, 72: 5184, 73: 5329, 74: 5476, 75: 5625, 76: 5776, 77: 5929, 78: 6084, 79: 6241, 80: 6400, 81: 6561, 82: 6724, 83: 6889, 84: 7056, 85: 7225, 86: 7396, 87: 7569, 88: 7744, 89: 7921, 90: 8100, 91: 8281, 92: 8464, 93: 8649, 94: 8836, 95: 9025, 96: 9216, 97: 9409, 98: 9604, 99: 9801}))\n>>> print(test_format(use_comprehension("square_dict")))\n')], [('tShbyCCphwno07CP', 'Identity', '>>> print(test_format(identity_dict))\n>>> print(test_format(use_comprehension("identity_dict")))\n')], [('pRtDJVxpnw3d5lFi', 'List Integers', '>>> print(test_format(representation_dict[135]))\n>>> print(test_format(representation_dict[291]))\n>>> print(test_format(use_comprehension("representation_dict")))\n>>> print(test_format(line_contains_substr("representation_dict", "base")))\n')], [('lZLRqgDYLYKTNaEx', 'Names to Salaries', '>>> print(test_format(listdict2dict))\n>>> print(test_format(use_comprehension("listdict2dict")))\n')], [('tGNwBWZTFRhlJgVe', 'Next Ints', '>>> print(test_format(nextInts([1, 5, 7])))\n>>> print(test_format(nextInts([0, 0, 0, 0, 0])))\n>>> print(test_format(nextInts([570, 968, 723, 179, 762, 377, 845, 320, 475, 952, 680, 874, 708, 493, 901, 896, 164, 165, 404, 147, 917, 936, 205, 615, 518, 254, 856, 584, 287, 336, 452, 551, 914, 706, 558, 842, 52, 593, 733, 398, 119, 874, 769, 585, 572, 261, 440, 404, 293, 176, 575, 224, 647, 241, 319, 974, 5, 373, 367, 609, 661, 691, 47, 64, 79, 744, 606, 205, 424, 88, 648, 419, 165, 399, 594, 760, 348, 638, 385, 754, 491, 284, 531, 258, 745, 634, 51, 557, 346, 577, 375, 979, 773, 523, 441, 952, 50, 534, 641, 621, 813, 511, 279, 565, 228, 86, 187, 395, 261, 287, 717, 989, 614, 92, 8, 229, 372, 378, 53, 350, 936, 654, 74, 750, 20, 978, 506, 793, 148, 944, 23, 962, 996, 586, 404, 216, 148, 284, 797, 805, 501, 161, 64, 608, 287, 127, 136, 902, 879, 433, 553, 366, 155, 763, 728, 117, 300, 990, 345, 982, 767, 279, 814, 516, 342, 291, 410, 612, 961, 445, 472, 507, 251, 832, 737, 62, 384, 273, 352, 752, 455, 216, 731, 7, 868, 111, 42, 190, 841, 283, 215, 860, 628, 835, 145, 97, 337, 57, 791, 443, 271, 925, 666, 452, 601, 571, 218, 901, 479, 75, 912, 708, 33, 575, 252, 753, 857, 150, 625, 852, 921, 178, 832, 126, 929, 16, 427, 533, 119, 256, 937, 107, 740, 607, 801, 827, 667, 776, 95, 940, 66, 982, 930, 825, 878, 512, 961, 701, 657, 584, 204, 348, 564, 505, 303, 562, 399, 415, 784, 588, 2, 729, 478, 396, 314, 130, 493, 947, 724, 540, 608, 431, 107, 497, 68, 791, 521, 583, 359, 221, 713, 683, 945, 274, 568, 666, 517, 241, 401, 437, 958, 572, 561, 929, 342, 149, 971, 762, 249, 538, 277, 761, 489, 728, 372, 131, 366, 702, 73, 382, 58, 223, 423, 642, 628, 6, 158, 946, 710, 232, 211, 747, 215, 579, 396, 521, 597, 966, 401, 749, 546, 310, 786, 691, 333, 817, 162, 961, 674, 132, 235, 481, 410, 477, 311, 932, 352, 64, 771, 837, 609, 654, 535, 530, 346, 294, 441, 532, 824, 422, 912, 99, 894, 246, 99, 111, 806, 360, 652, 753, 489, 735, 996, 8, 742, 793, 341, 498, 790, 402, 542, 892, 573, 78, 994, 676, 225, 675, 904, 196, 156, 819, 959, 501, 554, 381, 525, 608, 401, 937, 875, 373, 803, 258, 530, 901, 175, 656, 533, 91, 304, 497, 321, 906, 893, 995, 238, 51, 419, 70, 673, 479, 852, 864, 143, 224, 911, 207, 41, 603, 824, 764, 257, 653, 521, 28, 673, 333, 536, 748, 92, 98, 951, 655, 278, 437, 167, 253, 849, 343, 554, 313, 333, 556, 919, 636, 21, 841, 854, 550, 993, 291, 324, 224, 48, 927, 784, 387, 276, 652, 860, 100, 386, 153, 988, 805, 419, 75, 365, 920, 957, 23, 592, 280, 814, 800, 154, 776, 169, 635, 379, 919, 742, 145, 784, 201, 711, 209, 36, 317, 718, 84, 974, 768, 518, 884, 374, 447, 160, 295, 29, 23, 421, 384, 104, 123, 40, 945, 765, 32, 243, 696, 603, 129, 650, 957, 659, 863, 582, 165, 681, 33, 738, 917, 410, 803, 821, 636, 162, 662, 231, 75, 799, 591, 258, 722, 131, 805, 600, 704, 995, 793, 502, 624, 656, 43, 597, 353, 867, 116, 568, 26, 16, 251, 78, 764, 799, 287, 575, 190, 718, 619, 377, 465, 267, 688, 772, 359, 451, 459, 139, 71, 821, 312, 334, 988, 929, 797, 830, 26, 3, 90, 450, 715, 174, 910, 258, 229, 325, 517, 37, 260, 950, 20, 881, 156, 231, 114, 670, 287, 631, 982, 855, 841, 72, 561, 368, 289, 829, 428, 815, 207, 844, 68, 143, 707, 259, 669, 362, 943, 550, 133, 367, 900, 233, 109, 504, 803, 985, 333, 318, 680, 952, 408, 268, 890, 101, 423, 261, 641, 500, 389, 885, 76, 682, 811, 941, 142, 552, 401, 429, 973, 287, 472, 630, 383, 569, 630, 135, 823, 49, 507, 433, 550, 660, 403, 88, 879, 697, 571, 790, 896, 252, 172, 911, 485, 30, 657, 821, 412, 204, 801, 763, 329, 199, 315, 940, 515, 29, 22, 66, 221, 63, 678, 368, 545, 560, 301, 292, 987, 673, 573, 399, 148, 326, 418, 687, 85, 167, 774, 657, 754, 168, 113, 412, 353, 234, 923, 720, 691, 319, 711, 1000, 188, 969, 123, 547, 127, 69, 782, 533, 898, 574, 214, 848, 599, 112, 833, 26, 750, 462, 480, 511, 644, 929, 725, 310, 41, 559, 961, 399, 527, 960, 352, 468, 755, 732, 944, 115, 408, 642, 888, 922, 780, 727, 459, 473, 122, 716, 908, 576, 498, 196, 647, 912, 275, 238, 79, 75, 427, 299, 470, 347, 792, 969, 21, 424, 596, 88, 98, 475, 917, 683, 47, 843, 742, 673, 702, 983, 996, 430, 53, 327, 769, 666, 453, 93, 498, 942, 299, 200, 968, 202, 193, 508, 706, 247, 51, 721, 327, 484, 855, 565, 777, 33, 816, 827, 36, 962, 235, 297, 666, 111, 453, 445, 111, 653, 690, 325, 36, 187, 633, 854, 829, 74, 840, 744, 375, 124, 694, 236, 222, 88, 449, 134, 542, 812, 325, 373, 975, 131, 78, 390, 114, 969, 633, 57, 110, 635, 396, 947, 913, 148, 215, 465, 72, 463, 830, 885, 532, 728, 701, 31, 541, 54, 411, 916, 268, 596, 72, 971, 907, 856, 65, 55, 108, 222, 24, 482, 150, 864, 768, 332, 40, 961, 80, 745, 984, 170, 424, 28, 442, 146, 724, 32, 786, 985, 386, 326, 840, 416, 931, 606, 746, 39, 295, 355, 80, 663, 463, 716, 849, 606, 83, 512, 144, 854, 384, 976, 675, 549, 318, 893, 193, 562, 419, 444, 427, 612, 362, 567, 529, 273, 807, 381, 120, 66, 397, 738, 948, 99, 427, 560, 916, 283, 722, 111, 740, 156, 942, 215, 67, 944, 161, 544, 597, 468, 441, 483, 961, 503, 162, 706, 57, 37, 307, 142, 537, 861, 944]) ))\n>>> print(test_format(use_comprehension("nextInts")))\n')], [('gbXfwMbLBL6ySr10', 'Cubes', '>>> print(test_format(cubes([0, 0, 0, 0])))\n>>> print(test_format(cubes([4, 5, 6])))\n>>> print(test_format(cubes([0.5, 1.5, 2.5, 3.5])))\n>>> print(test_format(cubes([768, 275, 645, 106, 332, 836, 109, 268, 721, 711, 642, 393, 671, 263, 480, 211, 819, 735, 797, 394, 625, 199, 308, 937, 552, 435, 70, 316, 987, 188, 291, 387, 844, 939, 781, 329, 484, 678, 223, 598, 135, 717, 444, 650, 40, 740, 799, 315, 933, 321, 81, 410, 512, 651, 471, 867, 910, 769, 657, 588, 769, 174, 347, 759, 222, 904, 248, 547, 158, 254, 966, 47, 980, 948, 461, 234, 266, 976, 105, 125, 468, 612, 468, 521, 828, 93, 562, 135, 751, 160, 159, 812, 212, 553, 456, 704, 683, 849, 529, 795])))\n>>> print(test_format(use_comprehension("cubes")))\n')], [('HikXLgYM3rySDfdy', 'dict2list', '>>> dct1 = {}\n>>> keylist1 = []\n>>> print(test_format(dict2list(dct1, keylist1)))\n>>> dct2 = {\'a\':\'A\', \'b\':\'B\', \'c\':\'C\'}\n>>> keylist2 = [\'b\',\'c\',\'a\']\n>>> print(test_format(dict2list(dct2, keylist2)))\n>>> dct3 = {3: 395, 8: 816, 9: 370, 10: 102, 11: 746, 18: 477, 20: 284, 26: 783, 27: 55, 35: 108, 43: 621, 45: 225, 46: 56, 51: 503, 54: 24, 55: 742, 62: 491, 64: 317, 66: 739, 70: 972, 71: 372, 74: 312, 76: 826, 77: 215, 78: 507, 80: 970, 87: 966, 90: 798, 91: 353, 94: 358, 101: 880, 102: 730, 105: 514, 106: 867, 108: 723, 117: 412, 120: 870, 124: 511, 126: 904, 127: 196, 128: 758, 130: 89, 131: 631, 133: 45, 137: 345, 138: 246, 139: 141, 142: 963, 143: 583, 146: 626, 148: 615, 149: 581, 150: 889, 154: 662, 155: 993, 157: 765, 158: 7, 160: 67, 162: 862, 172: 212, 174: 493, 175: 676, 176: 915, 177: 220, 179: 7, 180: 362, 186: 586, 191: 632, 194: 755, 196: 537, 198: 398, 201: 330, 202: 337, 207: 767, 212: 41, 214: 341, 223: 84, 224: 651, 226: 898, 227: 926, 231: 801, 232: 751, 235: 216, 236: 234, 238: 445, 243: 534, 244: 81, 246: 860, 248: 478, 249: 659, 250: 107, 254: 609, 255: 488, 256: 108, 257: 497, 260: 649, 264: 684, 267: 964, 268: 294, 269: 327, 271: 621, 276: 713, 278: 195, 281: 559, 283: 858, 287: 931, 289: 89, 293: 850, 294: 277, 295: 537, 298: 430, 301: 244, 302: 950, 305: 594, 306: 98, 307: 438, 308: 564, 310: 643, 311: 363, 314: 109, 315: 295, 316: 604, 317: 268, 324: 166, 331: 853, 336: 123, 346: 46, 348: 186, 349: 404, 350: 426, 352: 34, 353: 741, 355: 385, 356: 115, 357: 613, 366: 369, 367: 513, 369: 36, 370: 755, 375: 77, 377: 780, 378: 57, 380: 123, 381: 914, 384: 575, 386: 866, 387: 377, 389: 915, 393: 36, 398: 895, 399: 215, 404: 317, 406: 711, 411: 490, 412: 752, 413: 879, 414: 344, 417: 723, 419: 431, 421: 279, 422: 518, 425: 346, 427: 992, 429: 758, 433: 48, 435: 66, 436: 349, 437: 429, 438: 616, 439: 186, 449: 917, 452: 807, 457: 916, 458: 548, 459: 601, 463: 891, 464: 897, 465: 404, 467: 241, 469: 510, 471: 66, 472: 688, 473: 797, 475: 252, 476: 408, 479: 79, 484: 307, 485: 462, 494: 492, 497: 841, 499: 200, 501: 451, 502: 494, 504: 754, 505: 56, 506: 234, 507: 849, 509: 984, 511: 902, 512: 156, 516: 721, 517: 905, 518: 728, 521: 505, 523: 29, 534: 256, 537: 179, 539: 820, 540: 199, 543: 358, 545: 626, 547: 21, 549: 456, 550: 447, 553: 316, 559: 997, 560: 513, 561: 171, 563: 231, 565: 913, 573: 330, 575: 697, 579: 682, 581: 92, 584: 65, 590: 393, 591: 258, 592: 0, 593: 978, 597: 407, 598: 497, 599: 420, 601: 13, 603: 460, 611: 710, 614: 228, 623: 837, 626: 98, 629: 363, 630: 510, 634: 339, 635: 625, 637: 787, 639: 774, 642: 401, 643: 187, 644: 35, 646: 183, 647: 872, 651: 901, 652: 399, 654: 635, 659: 762, 660: 358, 661: 537, 664: 639, 665: 49, 672: 121, 675: 909, 676: 369, 679: 901, 680: 409, 685: 694, 688: 979, 690: 604, 692: 212, 695: 856, 696: 722, 697: 493, 699: 340, 700: 706, 701: 549, 702: 129, 708: 222, 709: 433, 710: 872, 711: 874, 713: 197, 714: 109, 715: 463, 716: 47, 717: 5, 718: 639, 719: 900, 722: 467, 723: 785, 725: 993, 726: 89, 727: 428, 729: 47, 731: 178, 732: 74, 735: 82, 736: 68, 737: 953, 739: 490, 740: 399, 744: 489, 747: 83, 751: 178, 756: 982, 758: 343, 759: 346, 762: 600, 775: 424, 776: 669, 781: 214, 785: 438, 789: 616, 790: 852, 791: 444, 795: 671, 796: 909, 797: 331, 798: 534, 800: 782, 803: 570, 804: 638, 807: 535, 808: 852, 809: 424, 812: 75, 816: 303, 818: 730, 824: 501, 827: 138, 828: 700, 829: 475, 834: 858, 844: 814, 846: 269, 848: 258, 851: 144, 856: 585, 857: 427, 858: 136, 862: 59, 864: 981, 868: 591, 870: 754, 871: 778, 874: 77, 875: 809, 877: 198, 878: 712, 880: 699, 881: 978, 882: 301, 883: 51, 885: 453, 888: 881, 889: 758, 890: 786, 891: 329, 893: 280, 894: 594, 897: 410, 898: 567, 901: 764, 902: 528, 904: 191, 909: 664, 911: 582, 912: 945, 915: 1000, 917: 818, 922: 165, 924: 111, 925: 624, 928: 215, 930: 304, 931: 625, 933: 621, 935: 625, 937: 685, 938: 477, 939: 857, 940: 471, 941: 720, 944: 516, 945: 27, 947: 216, 948: 926, 950: 78, 954: 391, 957: 260, 958: 461, 961: 415, 962: 374, 965: 516, 968: 832, 970: 121, 975: 181, 984: 834, 987: 517, 988: 752, 989: 241, 993: 7, 997: 523}\n>>> keylist3 = [3, 8, 9, 10, 11, 18, 20, 26, 27, 35, 43, 45, 46, 51, 54, 55, 62, 64, 66, 70, 71, 74, 76, 77, 78, 80, 87, 90, 91, 94, 101, 102, 105, 106, 108, 117, 120, 124, 126, 127, 128, 130, 131, 133, 137, 138, 139, 142, 143, 146, 148, 149, 150, 154, 155, 157, 158, 160, 162, 172, 174, 175, 176, 177, 179, 180, 186, 191, 194, 196, 198, 201, 202, 207, 212, 214, 223, 224, 226, 227, 231, 232, 235, 236, 238, 243, 244, 246, 248, 249, 250, 254, 255, 256, 257, 260, 264, 267, 268, 269, 271, 276, 278, 281, 283, 287, 289, 293, 294, 295, 298, 301, 302, 305, 306, 307, 308, 310, 311, 314, 315, 316, 317, 324, 331, 336, 346, 348, 349, 350, 352, 353, 355, 356, 357, 366, 367, 369, 370, 375, 377, 378, 380, 381, 384, 386, 387, 389, 393, 398, 399, 404, 406, 411, 412, 413, 414, 417, 419, 421, 422, 425, 427, 429, 433, 435, 436, 437, 438, 439, 449, 452, 457, 458, 459, 463, 464, 465, 467, 469, 471, 472, 473, 475, 476, 479, 484, 485, 494, 497, 499, 501, 502, 504, 505, 506, 507, 509, 511, 512, 516, 517, 518, 521, 523, 534, 537, 539, 540, 543, 545, 547, 549, 550, 553, 559, 560, 561, 563, 565, 573, 575, 579, 581, 584, 590, 591, 592, 593, 597, 598, 599, 601, 603, 611, 614, 623, 626, 629, 630, 634, 635, 637, 639, 642, 643, 644, 646, 647, 651, 652, 654, 659, 660, 661, 664, 665, 672, 675, 676, 679, 680, 685, 688, 690, 692, 695, 696, 697, 699, 700, 701, 702, 708, 709, 710, 711, 713, 714, 715, 716, 717, 718, 719, 722, 723, 725, 726, 727, 729, 731, 732, 735, 736, 737, 739, 740, 744, 747, 751, 756, 758, 759, 762, 775, 776, 781, 785, 789, 790, 791, 795, 796, 797, 798, 800, 803, 804, 807, 808, 809, 812, 816, 818, 824, 827, 828, 829, 834, 844, 846, 848, 851, 856, 857, 858, 862, 864, 868, 870, 871, 874, 875, 877, 878, 880, 881, 882, 883, 885, 888, 889, 890, 891, 893, 894, 897, 898, 901, 902, 904, 909, 911, 912, 915, 917, 922, 924, 925, 928, 930, 931, 933, 935, 937, 938, 939, 940, 941, 944, 945, 947, 948, 950, 954, 957, 958, 961, 962, 965, 968, 970, 975, 984, 987, 988, 989, 993, 997]\n>>> print(test_format(dict2list(dct3, keylist3)))\n>>> print(test_format(use_comprehension("dict2list")))\n')], [('SgCJwpfcAVel9YfA', 'list2dict', '>>> L1 = []\n>>> keylist1 = []\n>>> print(test_format(list2dict(L1, keylist1)))\n>>> L2 =[\'A\',\'B\',\'C\']\n>>> keylist2 = [\'a\',\'b\',\'c\']\n>>> print(test_format(list2dict(L2, keylist2)))\n>>> L3 = [395, 816, 370, 102, 746, 477, 284, 783, 55, 108, 621, 225, 56, 503, 24, 742, 491, 317, 739, 972, 372, 312, 826, 215, 507, 970, 966, 798, 353, 358, 880, 730, 514, 867, 723, 412, 870, 511, 904, 196, 758, 89, 631, 45, 345, 246, 141, 963, 583, 626, 615, 581, 889, 662, 993, 765, 7, 67, 862, 212, 493, 676, 915, 220, 7, 362, 586, 632, 755, 537, 398, 330, 337, 767, 41, 341, 84, 651, 898, 926, 801, 751, 216, 234, 445, 534, 81, 860, 478, 659, 107, 609, 488, 108, 497, 649, 684, 964, 294, 327, 621, 713, 195, 559, 858, 931, 89, 850, 277, 537, 430, 244, 950, 594, 98, 438, 564, 643, 363, 109, 295, 604, 268, 166, 853, 123, 46, 186, 404, 426, 34, 741, 385, 115, 613, 369, 513, 36, 755, 77, 780, 57, 123, 914, 575, 866, 377, 915, 36, 895, 215, 317, 711, 490, 752, 879, 344, 723, 431, 279, 518, 346, 992, 758, 48, 66, 349, 429, 616, 186, 917, 807, 916, 548, 601, 891, 897, 404, 241, 510, 66, 688, 797, 252, 408, 79, 307, 462, 492, 841, 200, 451, 494, 754, 56, 234, 849, 984, 902, 156, 721, 905, 728, 505, 29, 256, 179, 820, 199, 358, 626, 21, 456, 447, 316, 997, 513, 171, 231, 913, 330, 697, 682, 92, 65, 393, 258, 0, 978, 407, 497, 420, 13, 460, 710, 228, 837, 98, 363, 510, 339, 625, 787, 774, 401, 187, 35, 183, 872, 901, 399, 635, 762, 358, 537, 639, 49, 121, 909, 369, 901, 409, 694, 979, 604, 212, 856, 722, 493, 340, 706, 549, 129, 222, 433, 872, 874, 197, 109, 463, 47, 5, 639, 900, 467, 785, 993, 89, 428, 47, 178, 74, 82, 68, 953, 490, 399, 489, 83, 178, 982, 343, 346, 600, 424, 669, 214, 438, 616, 852, 444, 671, 909, 331, 534, 782, 570, 638, 535, 852, 424, 75, 303, 730, 501, 138, 700, 475, 858, 814, 269, 258, 144, 585, 427, 136, 59, 981, 591, 754, 778, 77, 809, 198, 712, 699, 978, 301, 51, 453, 881, 758, 786, 329, 280, 594, 410, 567, 764, 528, 191, 664, 582, 945, 1000, 818, 165, 111, 624, 215, 304, 625, 621, 625, 685, 477, 857, 471, 720, 516, 27, 216, 926, 78, 391, 260, 461, 415, 374, 516, 832, 121, 181, 834, 517, 752, 241, 7, 523]\n>>> keylist3 = [3, 8, 9, 10, 11, 18, 20, 26, 27, 35, 43, 45, 46, 51, 54, 55, 62, 64, 66, 70, 71, 74, 76, 77, 78, 80, 87, 90, 91, 94, 101, 102, 105, 106, 108, 117, 120, 124, 126, 127, 128, 130, 131, 133, 137, 138, 139, 142, 143, 146, 148, 149, 150, 154, 155, 157, 158, 160, 162, 172, 174, 175, 176, 177, 179, 180, 186, 191, 194, 196, 198, 201, 202, 207, 212, 214, 223, 224, 226, 227, 231, 232, 235, 236, 238, 243, 244, 246, 248, 249, 250, 254, 255, 256, 257, 260, 264, 267, 268, 269, 271, 276, 278, 281, 283, 287, 289, 293, 294, 295, 298, 301, 302, 305, 306, 307, 308, 310, 311, 314, 315, 316, 317, 324, 331, 336, 346, 348, 349, 350, 352, 353, 355, 356, 357, 366, 367, 369, 370, 375, 377, 378, 380, 381, 384, 386, 387, 389, 393, 398, 399, 404, 406, 411, 412, 413, 414, 417, 419, 421, 422, 425, 427, 429, 433, 435, 436, 437, 438, 439, 449, 452, 457, 458, 459, 463, 464, 465, 467, 469, 471, 472, 473, 475, 476, 479, 484, 485, 494, 497, 499, 501, 502, 504, 505, 506, 507, 509, 511, 512, 516, 517, 518, 521, 523, 534, 537, 539, 540, 543, 545, 547, 549, 550, 553, 559, 560, 561, 563, 565, 573, 575, 579, 581, 584, 590, 591, 592, 593, 597, 598, 599, 601, 603, 611, 614, 623, 626, 629, 630, 634, 635, 637, 639, 642, 643, 644, 646, 647, 651, 652, 654, 659, 660, 661, 664, 665, 672, 675, 676, 679, 680, 685, 688, 690, 692, 695, 696, 697, 699, 700, 701, 702, 708, 709, 710, 711, 713, 714, 715, 716, 717, 718, 719, 722, 723, 725, 726, 727, 729, 731, 732, 735, 736, 737, 739, 740, 744, 747, 751, 756, 758, 759, 762, 775, 776, 781, 785, 789, 790, 791, 795, 796, 797, 798, 800, 803, 804, 807, 808, 809, 812, 816, 818, 824, 827, 828, 829, 834, 844, 846, 848, 851, 856, 857, 858, 862, 864, 868, 870, 871, 874, 875, 877, 878, 880, 881, 882, 883, 885, 888, 889, 890, 891, 893, 894, 897, 898, 901, 902, 904, 909, 911, 912, 915, 917, 922, 924, 925, 928, 930, 931, 933, 935, 937, 938, 939, 940, 941, 944, 945, 947, 948, 950, 954, 957, 958, 961, 962, 965, 968, 970, 975, 984, 987, 988, 989, 993, 997]\n>>> print(test_format(list2dict(L3, keylist3)))\n>>> print(test_format(use_comprehension("dict2list")))\n')]] source_files = ['python_lab.py'] * len(sum(groups,[])) try: import python_lab as solution test_vars = vars(solution).copy() except Exception as exc: print(exc) print("!! It seems like you have an error in your stencil file. Please fix before submitting.") sys.exit(1) def find_lines(varname): return list(filter(lambda l: varname in l, list(open("python_lab.py")))) def find_line(varname): ls = find_lines(varname) return ls[0] if len(ls) else None def use_comprehension(varname): lines = find_lines(varname) for line in lines: try: if "comprehension" in ast.dump(ast.parse(line)): return True except: pass return False def double_comprehension(varname): line = find_line(varname) return ast.dump(ast.parse(line)).count("comprehension") == 2 def line_contains_substr(varname, word): lines = find_line(varname) for line in lines: if word in line: return True return False def test_format(obj, precision=6): tf = lambda o: test_format(o, precision) delimit = lambda o: ', '.join(o) otype = type(obj) if otype is str: return "'%s'" % obj elif otype is float: fstr = '%%.%dg' % precision return fstr % obj elif otype is set: if len(obj) == 0: return 'set()' return '{%s}' % delimit(sorted(map(tf, obj))) elif otype is dict: return '{%s}' % delimit(sorted(tf(k)+': '+tf(v) for k,v in obj.items())) elif otype is list: return '[%s]' % delimit(map(tf, obj)) elif otype is tuple: return '(%s%s)' % (delimit(map(tf, obj)), ',' if len(obj) is 1 else '') elif otype.__name__ in ['Vec','Mat']: entries = delimit(map(tf, sorted(filter(lambda o: o[1] != 0, obj.f.items())))) return '<%s %s {%s}>' % (otype.__name__, test_format(obj.D), entries) else: return str(obj) def output(tests): dtst = doctest.DocTestParser().get_doctest(tests, test_vars, 0, '<string>', 0) runner = ModifiedDocTestRunner() runner.run(dtst) return runner.results test_vars['test_format'] = test_vars['tf'] = test_format test_vars['find_lines'] = find_lines test_vars['find_line'] = find_line test_vars['use_comprehension'] = use_comprehension test_vars['double_comprehension'] = double_comprehension test_vars['line_contains_substr'] = line_contains_substr base_url = '://class.coursera.org/%s/assignment/' % URL protocol = 'https' colorize = False verbose = False class ModifiedDocTestRunner(doctest.DocTestRunner): def __init__(self, *args, **kwargs): self.results = [] return super(ModifiedDocTestRunner, self).__init__(*args, checker=OutputAccepter(), **kwargs) def report_success(self, out, test, example, got): self.results.append(got) def report_unexpected_exception(self, out, test, example, exc_info): exf = traceback.format_exception_only(exc_info[0], exc_info[1])[-1] self.results.append(exf) class OutputAccepter(doctest.OutputChecker): def check_output(self, want, got, optionflags): return True def submit(parts_string, login, password): print('= Coding the Matrix Homework and Lab Submission') if not login: login = login_prompt() if not password: password = password_prompt() if not parts_string: parts_string = parts_prompt() parts = parse_parts(parts_string) if not all([parts, login, password]): return for sid, name, part_tests in parts: sys.stdout.write('== Submitting "%s"' % name) if 'DEV' in os.environ: sid += '-dev' (login, ch, state, ch_aux) = get_challenge(login, sid) if not all([login, ch, state]): print(' !! Error: %s\n' % login) return # to stop Coursera's strip() from doing anything, we surround in parens results = output(part_tests) prog_out = '(%s)' % ''.join(map(str.rstrip, results)) token = challenge_response(login, password, ch) src = source(sid) feedback = submit_solution(login, token, sid, prog_out, src, state, ch_aux) if len(feedback.strip()) > 0: if colorize: good = 'incorrect' not in feedback.lower() print(': \033[1;3%dm%s\033[0m' % (2 if good else 1, feedback.strip())) else: print(': %s' % feedback.strip()) if verbose: for t, r in zip(part_tests.split('\n'), results): sys.stdout.write('%s\n%s' % (t, r)) sys.stdout.write('\n\n') def login_prompt(): return input('Login email address: ') def password_prompt(): return input("One-time password from the assignment page (NOT your own account's password): ") def parts_prompt(): print('These are the assignment parts that you can submit:') for i, name in enumerate(part_friendly_names): print(' %d) %s' % (i+1, name)) return input('\nWhich parts do you want to submit? (Ex: 1, 4-7): ') def parse_parts(string): def extract_range(s): s = s.split('-') if len(s) == 1: return [int(s[0])] else: return list(range(int(s[0]), 1+int(s[1]))) parts = map(extract_range, string.split(',')) flat_parts = sum(parts, []) return sum(list(map(lambda p: groups[p-1], flat_parts)),[]) def get_challenge(email, sid): """Gets the challenge salt from the server. Returns (email,ch,state,ch_aux).""" params = {'email_address': email, 'assignment_part_sid': sid, 'response_encoding': 'delim'} challenge_url = '%s%schallenge' % (protocol, base_url) data = urllib.parse.urlencode(params).encode('utf-8') req = urllib.request.Request(challenge_url, data) resp = urllib.request.urlopen(req) text = resp.readall().decode('utf-8').strip().split('|') if len(text) != 9: print(' !! %s' % '|'.join(text)) sys.exit(1) return tuple(text[x] for x in [2,4,6,8]) def challenge_response(email, passwd, challenge): return hashlib.sha1((challenge+passwd).encode('utf-8')).hexdigest() def submit_solution(email_address, ch_resp, sid, output, source, state, ch_aux): b64ize = lambda s: str(base64.encodebytes(s.encode('utf-8')), 'ascii') values = { 'assignment_part_sid' : sid , 'email_address' : email_address , 'submission' : b64ize(output) , 'submission_aux' : b64ize(source) , 'challenge_response' : ch_resp , 'state' : state } submit_url = '%s%ssubmit' % (protocol, base_url) data = urllib.parse.urlencode(values).encode('utf-8') req = urllib.request.Request(submit_url, data) response = urllib.request.urlopen(req) return response.readall().decode('utf-8').strip() def source(sid): src = [] for fn in set(source_files): with open(fn) as source_f: src.append(source_f.read()) return '\n\n'.join(src) if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() env = os.environ helps = [ 'numbers or ranges of tasks to submit' , 'the email address on your Coursera account' , 'your ONE-TIME password' , 'use ANSI color escape sequences' , 'show the test\'s interaction with your code' , 'use an encrypted connection to Coursera' , 'use an unencrypted connection to Coursera' ] parser.add_argument('tasks', default=env.get('COURSERA_TASKS'), nargs='*', help=helps[0]) parser.add_argument('--email', default=env.get('COURSERA_EMAIL'), help=helps[1]) parser.add_argument('--password', default=env.get('COURSERA_PASS'), help=helps[2]) parser.add_argument('--colorize', default=False, action='store_true', help=helps[3]) parser.add_argument('--verbose', default=False, action='store_true', help=helps[4]) group = parser.add_mutually_exclusive_group() group.add_argument('--https', dest="protocol", const="https", action="store_const", help=helps[-2]) group.add_argument('--http', dest="protocol", const="http", action="store_const", help=helps[-1]) args = parser.parse_args() if args.protocol: protocol = args.protocol colorize = args.colorize verbose = args.verbose submit(','.join(args.tasks), args.email, args.password)

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Python

data/scripts/radials/blue_frog.py

anhstudios/swganh

41c519f6cdef5a1c68b369e760781652ece7fec9

[ "MIT" ]

20

2015-02-23T15:11:56.000Z

2022-03-18T20:56:48.000Z

data/scripts/radials/blue_frog.py

anhstudios/swganh

41c519f6cdef5a1c68b369e760781652ece7fec9

[ "MIT" ]

null

data/scripts/radials/blue_frog.py

anhstudios/swganh

41c519f6cdef5a1c68b369e760781652ece7fec9

[ "MIT" ]

20

2015-04-04T16:35:59.000Z

2022-03-24T14:54:37.000Z

import swgpy from swgpy.object import * from swgpy.sui import * from swgpy.utility import vector3, quat from swgpy.combat import * from swgpy.gamesystems import * from swgpy import ACTION import random class PyRadialMenu(RadialMenu): def buildRadial(self, owner, target, radials): radial_list = RadialOptionsList() radial_list.append(RadialOptions(0, RadialIdentifier.itemUse, 1, 'Hack Universe')) radial_list.append(RadialOptions(0, RadialIdentifier.examine, 1, '')) radial_list.append(RadialOptions(1, RadialIdentifier.serverMenu1, 3, 'items')) radial_list.append(RadialOptions(1, RadialIdentifier.serverMenu2, 3, 'Weapon Pack')) radial_list.append(RadialOptions(1, RadialIdentifier.serverMenu3, 3, 'Armor Pack')) radial_list.append(RadialOptions(1, RadialIdentifier.serverMenu4, 3, 'Structures Pack')) radial_list.append(RadialOptions(1, RadialIdentifier.serverMenu5, 3, 'Pets Pack')) radial_list.append(RadialOptions(1, RadialIdentifier.serverMenu6, 3, 'Instrument Pack')) radial_list.append(RadialOptions(1, RadialIdentifier.serverMenu7, 3, 'Ham Options')) radial_list.append(RadialOptions(1, RadialIdentifier.serverMenu8, 3, 'Professions')) return radial_list levels = ('None', 'Light', 'Medium', 'Heavy') damage_types = ('Energy', 'Kinetic', 'Acid', 'Cold', 'Electricity', 'Heat') def defaultPostProcess(self, item): pass def weaponPostProcess(self, item): item.max_condition = random.randint(100, 10000) item.setStringAttribute('wpn_armor_pierce_rating', random.choice(self.levels)) item.setFloatAttribute('wpn_attack_speed', random.uniform(0.1, 5)) item.setStringAttribute('cat_wpn_damage.wpn_damage_type', random.choice(self.damage_types)) min_damage = random.randint(1, 1000) max_damage = random.randint(min_damage, 1000) item.setIntAttribute('cat_wpn_damage.wpn_damage_min', min_damage) item.setIntAttribute('cat_wpn_damage.wpn_damage_max', max_damage) item.setFloatAttribute('cat_wpn_damage.wpn_wound_chance', random.uniform(0, 100)) item.setIntAttribute('cat_wpn_rangemods.wpn_range_zero', 0) item.setIntAttribute('cat_wpn_rangemods.wpn_range_mid', 40) item.setIntAttribute('cat_wpn_rangemods.wpn_range_max', -80) item.setIntAttribute('cat_wpn_attack_cost.wpn_attack_cost_health', random.randint(1, 200)) item.setIntAttribute('cat_wpn_attack_cost.wpn_attack_cost_action', random.randint(1, 200)) item.setIntAttribute('cat_wpn_attack_cost.wpn_attack_cost_mind', random.randint(1, 200)) def armorPostProcess(self, item): item.max_condition = random.randint(100, 10000) item.setStringAttribute('armor_rating', random.choice(self.levels)) item.setFloatAttribute('cat_armor_special_protection.armor_eff_kinetic', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_special_protection.armor_eff_energy', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_special_protection.armor_eff_blast', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_special_protection.armor_eff_stun', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_special_protection.armor_eff_elemental_heat', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_special_protection.armor_eff_elemental_cold', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_special_protection.armor_eff_elemental_acid', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_special_protection.armor_eff_elemental_electrical', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_special_protection.armor_eff_restraint', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_effectiveness.armor_eff_restraint', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_effectiveness.armor_eff_energy', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_effectiveness.armor_eff_blast', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_effectiveness.armor_eff_stun', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_effectiveness.armor_eff_elemental_heat', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_effectiveness.armor_eff_elemental_cold', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_effectiveness.armor_eff_elemental_acid', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_effectiveness.armor_eff_elemental_electrical', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_effectiveness.armor_eff_restraint', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_vulnerability.armor_eff_kinetic', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_vulnerability.armor_eff_energy', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_vulnerability.armor_eff_blast', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_vulnerability.armor_eff_stun', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_vulnerability.armor_eff_elemental_heat', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_vulnerability.armor_eff_elemental_cold', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_vulnerability.armor_eff_elemental_acid', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_vulnerability.armor_eff_elemental_electrical', random.uniform(0, 100) if random.random()>0.7 else 0) item.setFloatAttribute('cat_armor_vulnerability.armor_eff_restraint', random.uniform(0, 100) if random.random()>0.7 else 0) item.setIntAttribute('cat_armor_encumbrance.armor_health_encumbrance', random.randint(20, 300)) item.setIntAttribute('cat_armor_encumbrance.armor_action_encumbrance', random.randint(20, 300)) item.setIntAttribute('cat_armor_encumbrance.armor_mind_encumbrance', random.randint(20, 300)) item.setStringAttribute('crafter', 'Blue Frog, Inc.') def giveItems(self, owner, list, postProcess): sim = self.getKernel().serviceManager().simulationService() inv = self.getKernel().serviceManager().equipmentService().getEquippedObject(owner, "inventory") for name in list: item = sim.createObject(name, swgpy.ContainerPermission.DEFAULT) if item is not None: postProcess(item) inv.add(owner, item) def displaySUIList(self, owner, list, callbackName): sui = self.getKernel().serviceManager().suiService() #if sui.getSUIWindowByScriptName(owner, 'Script.listBox') != None: #return options = EventResultList() for option in list: options.append(option) window = sui.createListBox(ListBoxType.OK_CANCEL, '0xDEADBEEF', '...00010011010001...\n\n...[OVERRIDE]...\n\nWELCOME, JOHN SMEDLEY', options, owner) results = ResultList() results.append('List.lstList:SelectedRow') callback = PythonCallback(self, callbackName) window.subscribeToEventCallback(0, '', InputTrigger.OK, results, callback) window.subscribeToEventCallback(1, '', InputTrigger.CANCEL, results, callback) sui.openSUIWindow(window) def professionCallback(self, owner, event_id, results): if event_id == 0: if int(results[0]) == 0: self.displaySUIList(owner, ['grant entertainer_novice'], 'entertainerCallback') return True def entertainerCallback(self, owner, event_id, results): if event_id == 0: if int(results[0]) == 0: creature = owner.toCreature() GameSytems = self.getKernel().serviceManager().gamesystemsService() GameSytems.grantSkill(creature, "social_entertainer_novice") return True def itemCallback(self, owner, event_id, results): if event_id == 0: if int(results[0]) == 0: self.giveItems(owner, self.vehicleDeeds, self.defaultPostProcess) if int(results[0]) == 1: self.giveItems(owner, self.droidDeeds, self.defaultPostProcess) return True def weaponCallback(self, owner, event_id, results): if event_id == 0: self.giveItems(owner, self.weapons[int(results[0])], self.weaponPostProcess) return True def armorCallback(self, owner, event_id, results): if event_id == 0: self.giveItems(owner, self.armor[int(results[0])], self.armorPostProcess) return True def structureCallback(self, owner, event_id, results): if event_id == 0: self.giveItems(owner, self.structureDeeds[int(results[0])], self.defaultPostProcess) return True def hamCallback(self, owner, event_id, results): print('result : ' + "{0} : {1}".format(event_id, results[0])) if event_id == 0: if int(results[0]) == 0: self.displaySUIList(owner, ['Health Wounds', 'heal Health Wounds','Action Wounds', 'heal Action Wounds', 'Mind Wounds', 'heal Mind Wounds'], 'woundCallback') if int(results[0]) == 1: self.displaySUIList(owner, ['Health Damage', 'heal Health Damage', 'Action Damage', 'Mind Damage'], 'damageCallback') return True def woundCallback(self, owner, event_id, results): if event_id == 0: combat = self.getKernel().serviceManager().combatService() ham = combat.getHamManager() creature = owner.toCreature() if int(results[0]) == 0: a = ham.applyWound(creature,0,25) if int(results[0]) == 1: ham.removeWound(creature,0,25) if int(results[0]) == 2: ham.applyWound(creature,3,25) if int(results[0]) == 3: ham.removeWound(creature,3,25) if int(results[0]) == 4: ham.applyWound(creature,6,25) if int(results[0]) == 5: ham.removeWound(creature,6,25) return True def damageCallback(self, owner, event_id, results): combat = self.getKernel().serviceManager().combatService() ham = combat.getHamManager() creature = owner.toCreature() if event_id == 0: if int(results[0]) == 0: ham.updateCurrentHitpoints(creature, 0, -75) if int(results[0]) == 1: ham.updateCurrentHitpoints(creature, 0, 75) if int(results[0]) == 2: ham.updateCurrentHitpoints(creature, 3, -75) if int(results[0]) == 3: ham.updateCurrentHitpoints(creature, 3, 75) if int(results[0]) == 4: ham.updateCurrentHitpoints(creature, 6, -75) if int(results[0]) == 5: ham.updateCurrentHitpoints(creature, 6, 75) return True def handleRadial(self, owner, target, action): if action == RadialIdentifier.serverMenu1: self.displaySUIList(owner, ['vehicles', 'droids'], 'itemCallback') elif action == RadialIdentifier.serverMenu2: self.displaySUIList(owner, ['Melee Weapons', 'Ranged Weapons', 'Misc Weapons'], 'weaponCallback') elif action == RadialIdentifier.serverMenu3: self.displaySUIList(owner, ['Bone', 'Bounty Hunter', 'Chitin', 'Composite', 'Ithorian Defender', 'Ithorian Guardian', 'Ithorian Sentinel', 'Mandalorian', 'Marine', 'Padded', 'Ris', 'Stormtrooper', 'Tantel', 'Ubese'], 'armorCallback') elif action == RadialIdentifier.serverMenu4: self.displaySUIList(owner, ['Crafting Structures', 'Housing Structures', 'Corellia Civic Structures', 'Naboo Civic Structures', 'Tatooine Civic Structures', 'Guild Structures', 'Faction Structures'], 'structureCallback') elif action == RadialIdentifier.serverMenu5: self.giveItems(owner, self.petDeeds, self.defaultPostProcess) elif action == RadialIdentifier.serverMenu6: self.giveItems(owner, self.instruments, self.defaultPostProcess) elif action == RadialIdentifier.serverMenu7: self.displaySUIList(owner, ['Wounds', 'Damage'], 'hamCallback') elif action == RadialIdentifier.serverMenu8: self.displaySUIList(owner, ['entertainer'], 'professionCallback') vehicleDeeds = ('object/tangible/deed/vehicle_deed/shared_jetpack_deed.iff', 'object/tangible/deed/vehicle_deed/shared_landspeeder_av21_deed.iff', 'object/tangible/deed/vehicle_deed/shared_landspeeder_x31_deed.iff', 'object/tangible/deed/vehicle_deed/shared_landspeeder_x34_deed.iff', 'object/tangible/deed/vehicle_deed/shared_speederbike_flash_deed.iff', 'object/tangible/deed/vehicle_deed/shared_speederbike_swoop_deed.iff') weapons = [('object/weapon/melee/2h_sword/shared_2h_sword_battleaxe.iff', 'object/weapon/melee/2h_sword/shared_2h_sword_blacksun_hack.iff', 'object/weapon/melee/2h_sword/shared_2h_sword_cleaver.iff', 'object/weapon/melee/2h_sword/shared_2h_sword_katana.iff', 'object/weapon/melee/2h_sword/shared_2h_sword_maul.iff', 'object/weapon/melee/2h_sword/shared_2h_sword_scythe.iff', 'object/weapon/melee/axe/shared_axe_heavy_duty.iff', 'object/weapon/melee/axe/shared_axe_vibroaxe.iff', 'object/weapon/melee/baton/shared_baton_gaderiffi.iff', 'object/weapon/melee/baton/shared_baton_stun.iff', 'object/weapon/melee/baton/shared_victor_baton_gaderiffi.iff', 'object/weapon/melee/knife/shared_knife_dagger.iff', 'object/weapon/melee/knife/shared_knife_donkuwah.iff', 'object/weapon/melee/knife/shared_knife_janta.iff', 'object/weapon/melee/knife/shared_knife_stone.iff', 'object/weapon/melee/knife/shared_knife_stone_noob.iff', 'object/weapon/melee/knife/shared_knife_survival.iff', 'object/weapon/melee/knife/shared_knife_vibroblade.iff', 'object/weapon/melee/polearm/shared_lance_nightsister.iff', 'object/weapon/melee/polearm/shared_lance_staff_janta.iff', 'object/weapon/melee/polearm/shared_lance_staff_metal.iff', 'object/weapon/melee/polearm/shared_lance_staff_wood_s1.iff', 'object/weapon/melee/polearm/shared_lance_staff_wood_s2.iff', 'object/weapon/melee/polearm/shared_lance_vibrolance.iff', 'object/weapon/melee/polearm/shared_polearm_vibro_axe.iff', 'object/weapon/melee/special/shared_blacksun_razor.iff', 'object/weapon/melee/special/shared_vibroknucler.iff'), ('object/weapon/ranged/carbine/shared_carbine_cdef.iff', 'object/weapon/ranged/carbine/shared_carbine_cdef_corsec.iff', 'object/weapon/ranged/carbine/shared_carbine_dh17.iff', 'object/weapon/ranged/carbine/shared_carbine_dh17_black.iff', 'object/weapon/ranged/carbine/shared_carbine_dh17_snubnose.iff', 'object/weapon/ranged/carbine/shared_carbine_dxr6.iff', 'object/weapon/ranged/carbine/shared_carbine_e11.iff', 'object/weapon/ranged/carbine/shared_carbine_ee3.iff', 'object/weapon/ranged/carbine/shared_carbine_elite.iff', 'object/weapon/ranged/carbine/shared_carbine_laser.iff', 'object/weapon/ranged/carbine/shared_carbine_nym_slugthrower.iff', 'object/weapon/ranged/heavy/shared_heavy_acid_beam.iff', 'object/weapon/ranged/heavy/shared_heavy_lightning_beam.iff', 'object/weapon/ranged/heavy/shared_heavy_particle_beam.iff', 'object/weapon/ranged/heavy/shared_heavy_rocket_launcher.iff', 'object/weapon/ranged/pistol/shared_pistol_cdef.iff', 'object/weapon/ranged/pistol/shared_pistol_cdef_corsec.iff', 'object/weapon/ranged/pistol/shared_pistol_cdef_noob.iff', 'object/weapon/ranged/pistol/shared_pistol_d18.iff', 'object/weapon/ranged/pistol/shared_pistol_de_10.iff', 'object/weapon/ranged/pistol/shared_pistol_dh17.iff', 'object/weapon/ranged/pistol/shared_pistol_dl44.iff', 'object/weapon/ranged/pistol/shared_pistol_dl44_metal.iff', 'object/weapon/ranged/pistol/shared_pistol_dx2.iff', 'object/weapon/ranged/pistol/shared_pistol_fwg5.iff', 'object/weapon/ranged/pistol/shared_pistol_geonosian_sonic_blaster_loot.iff', 'object/weapon/ranged/pistol/shared_pistol_launcher.iff', 'object/weapon/ranged/pistol/shared_pistol_power5.iff', 'object/weapon/ranged/pistol/shared_pistol_republic_blaster.iff', 'object/weapon/ranged/pistol/shared_pistol_scatter.iff', 'object/weapon/ranged/pistol/shared_pistol_scout_blaster.iff', 'object/weapon/ranged/pistol/shared_pistol_scout_blaster_corsec.iff', 'object/weapon/ranged/pistol/shared_pistol_srcombat.iff', 'object/weapon/ranged/pistol/shared_pistol_striker.iff', 'object/weapon/ranged/pistol/shared_pistol_striker_noob.iff', 'object/weapon/ranged/pistol/shared_pistol_tangle.iff', 'object/weapon/ranged/rifle/shared_rifle_acid_beam.iff', 'object/weapon/ranged/rifle/shared_rifle_beam.iff', 'object/weapon/ranged/rifle/shared_rifle_berserker.iff', 'object/weapon/ranged/rifle/shared_rifle_bowcaster.iff', 'object/weapon/ranged/rifle/shared_rifle_cdef.iff', 'object/weapon/ranged/rifle/shared_rifle_dlt20.iff', 'object/weapon/ranged/rifle/shared_rifle_dlt20a.iff', 'object/weapon/ranged/rifle/shared_rifle_e11.iff', 'object/weapon/ranged/rifle/shared_rifle_ewok_crossbow.iff', 'object/weapon/ranged/rifle/shared_rifle_flame_thrower.iff', 'object/weapon/ranged/rifle/shared_rifle_jawa_ion.iff', 'object/weapon/ranged/rifle/shared_rifle_laser.iff', 'object/weapon/ranged/rifle/shared_rifle_laser_noob.iff', 'object/weapon/ranged/rifle/shared_rifle_lightning.iff', 'object/weapon/ranged/rifle/shared_rifle_sg82.iff', 'object/weapon/ranged/rifle/shared_rifle_spraystick.iff', 'object/weapon/ranged/rifle/shared_rifle_t21.iff', 'object/weapon/ranged/rifle/shared_rifle_tenloss_dxr6_disruptor_loot.iff', 'object/weapon/ranged/rifle/shared_rifle_tusken.iff', 'object/weapon/ranged/rifle/shared_rifle_victor_tusken.iff'), ()] armor = [ ('object/tangible/wearables/armor/bone/shared_armor_bone_s01_bicep_l.iff', 'object/tangible/wearables/armor/bone/shared_armor_bone_s01_bicep_r.iff', 'object/tangible/wearables/armor/bone/shared_armor_bone_s01_boots.iff', 'object/tangible/wearables/armor/bone/shared_armor_bone_s01_bracer_l.iff', 'object/tangible/wearables/armor/bone/shared_armor_bone_s01_bracer_r.iff', 'object/tangible/wearables/armor/bone/shared_armor_bone_s01_chest_plate.iff', 'object/tangible/wearables/armor/bone/shared_armor_bone_s01_gloves.iff', 'object/tangible/wearables/armor/bone/shared_armor_bone_s01_helmet.iff', 'object/tangible/wearables/armor/bone/shared_armor_bone_s01_leggings.iff',), ('object/tangible/wearables/armor/bounty_hunter/shared_armor_bounty_hunter_belt.iff', 'object/tangible/wearables/armor/bounty_hunter/shared_armor_bounty_hunter_bicep_l.iff', 'object/tangible/wearables/armor/bounty_hunter/shared_armor_bounty_hunter_bicep_r.iff', 'object/tangible/wearables/armor/bounty_hunter/shared_armor_bounty_hunter_boots.iff', 'object/tangible/wearables/armor/bounty_hunter/shared_armor_bounty_hunter_bracer_l.iff', 'object/tangible/wearables/armor/bounty_hunter/shared_armor_bounty_hunter_bracer_r.iff', 'object/tangible/wearables/armor/bounty_hunter/shared_armor_bounty_hunter_chest_plate.iff', 'object/tangible/wearables/armor/bounty_hunter/shared_armor_bounty_hunter_gloves.iff', 'object/tangible/wearables/armor/bounty_hunter/shared_armor_bounty_hunter_helmet.iff', 'object/tangible/wearables/armor/bounty_hunter/shared_armor_bounty_hunter_leggings.iff'), ('object/tangible/wearables/armor/chitin/shared_armor_chitin_s01_bicep_l.iff', 'object/tangible/wearables/armor/chitin/shared_armor_chitin_s01_bicep_r.iff', 'object/tangible/wearables/armor/chitin/shared_armor_chitin_s01_boots.iff', 'object/tangible/wearables/armor/chitin/shared_armor_chitin_s01_bracer_l.iff', 'object/tangible/wearables/armor/chitin/shared_armor_chitin_s01_bracer_r.iff', 'object/tangible/wearables/armor/chitin/shared_armor_chitin_s01_chest_plate.iff', 'object/tangible/wearables/armor/chitin/shared_armor_chitin_s01_gloves.iff', 'object/tangible/wearables/armor/chitin/shared_armor_chitin_s01_helmet.iff', 'object/tangible/wearables/armor/chitin/shared_armor_chitin_s01_leggings.iff'), ('object/tangible/wearables/armor/composite/shared_armor_composite_bicep_l.iff', 'object/tangible/wearables/armor/composite/shared_armor_composite_bicep_r.iff', 'object/tangible/wearables/armor/composite/shared_armor_composite_boots.iff', 'object/tangible/wearables/armor/composite/shared_armor_composite_bracer_l.iff', 'object/tangible/wearables/armor/composite/shared_armor_composite_bracer_r.iff', 'object/tangible/wearables/armor/composite/shared_armor_composite_chest_plate.iff', 'object/tangible/wearables/armor/composite/shared_armor_composite_gloves.iff', 'object/tangible/wearables/armor/composite/shared_armor_composite_helmet.iff', 'object/tangible/wearables/armor/composite/shared_armor_composite_leggings.iff'), ('object/tangible/wearables/armor/ithorian_defender/shared_ith_armor_s01_bicep_l.iff', 'object/tangible/wearables/armor/ithorian_defender/shared_ith_armor_s01_bicep_r.iff', 'object/tangible/wearables/armor/ithorian_defender/shared_ith_armor_s01_boots.iff', 'object/tangible/wearables/armor/ithorian_defender/shared_ith_armor_s01_bracer_l.iff', 'object/tangible/wearables/armor/ithorian_defender/shared_ith_armor_s01_bracer_r.iff', 'object/tangible/wearables/armor/ithorian_defender/shared_ith_armor_s01_chest_plate.iff', 'object/tangible/wearables/armor/ithorian_defender/shared_ith_armor_s01_gloves.iff', 'object/tangible/wearables/armor/ithorian_defender/shared_ith_armor_s01_helmet.iff', 'object/tangible/wearables/armor/ithorian_defender/shared_ith_armor_s01_leggings.iff'), ('object/tangible/wearables/armor/ithorian_guardian/shared_ith_armor_s02_bicep_l.iff', 'object/tangible/wearables/armor/ithorian_guardian/shared_ith_armor_s02_bicep_r.iff', 'object/tangible/wearables/armor/ithorian_guardian/shared_ith_armor_s02_boots.iff', 'object/tangible/wearables/armor/ithorian_guardian/shared_ith_armor_s02_bracer_l.iff', 'object/tangible/wearables/armor/ithorian_guardian/shared_ith_armor_s02_bracer_r.iff', 'object/tangible/wearables/armor/ithorian_guardian/shared_ith_armor_s02_chest_plate.iff', 'object/tangible/wearables/armor/ithorian_guardian/shared_ith_armor_s02_gloves.iff', 'object/tangible/wearables/armor/ithorian_guardian/shared_ith_armor_s02_helmet.iff', 'object/tangible/wearables/armor/ithorian_guardian/shared_ith_armor_s02_leggings.iff'), ('object/tangible/wearables/armor/ithorian_sentinel/shared_ith_armor_s03_bicep_l.iff', 'object/tangible/wearables/armor/ithorian_sentinel/shared_ith_armor_s03_bicep_r.iff', 'object/tangible/wearables/armor/ithorian_sentinel/shared_ith_armor_s03_boots.iff', 'object/tangible/wearables/armor/ithorian_sentinel/shared_ith_armor_s03_bracer_l.iff', 'object/tangible/wearables/armor/ithorian_sentinel/shared_ith_armor_s03_bracer_r.iff', 'object/tangible/wearables/armor/ithorian_sentinel/shared_ith_armor_s03_chest_plate.iff', 'object/tangible/wearables/armor/ithorian_sentinel/shared_ith_armor_s03_gloves.iff', 'object/tangible/wearables/armor/ithorian_sentinel/shared_ith_armor_s03_helmet.iff', 'object/tangible/wearables/armor/ithorian_sentinel/shared_ith_armor_s03_leggings.iff'), ('object/tangible/wearables/armor/mandalorian/shared_armor_mandalorian_belt.iff', 'object/tangible/wearables/armor/mandalorian/shared_armor_mandalorian_bicep_l.iff', 'object/tangible/wearables/armor/mandalorian/shared_armor_mandalorian_bicep_r.iff', 'object/tangible/wearables/armor/mandalorian/shared_armor_mandalorian_bracer_l.iff', 'object/tangible/wearables/armor/mandalorian/shared_armor_mandalorian_bracer_r.iff', 'object/tangible/wearables/armor/mandalorian/shared_armor_mandalorian_chest_plate.iff', 'object/tangible/wearables/armor/mandalorian/shared_armor_mandalorian_gloves.iff', 'object/tangible/wearables/armor/mandalorian/shared_armor_mandalorian_helmet.iff', 'object/tangible/wearables/armor/mandalorian/shared_armor_mandalorian_leggings.iff', 'object/tangible/wearables/armor/mandalorian/shared_armor_mandalorian_shoes.iff'), ('object/tangible/wearables/armor/marine/shared_armor_marine_backpack.iff', 'object/tangible/wearables/armor/marine/shared_armor_marine_bicep_l.iff', 'object/tangible/wearables/armor/marine/shared_armor_marine_bicep_r.iff', 'object/tangible/wearables/armor/marine/shared_armor_marine_boots.iff', 'object/tangible/wearables/armor/marine/shared_armor_marine_chest_plate.iff', 'object/tangible/wearables/armor/marine/shared_armor_marine_chest_plate_rebel.iff', 'object/tangible/wearables/armor/marine/shared_armor_marine_helmet.iff', 'object/tangible/wearables/armor/marine/shared_armor_marine_leggings.iff'), ('object/tangible/wearables/armor/padded/shared_armor_padded_s01_belt.iff', 'object/tangible/wearables/armor/padded/shared_armor_padded_s01_bicep_l.iff', 'object/tangible/wearables/armor/padded/shared_armor_padded_s01_bicep_r.iff', 'object/tangible/wearables/armor/padded/shared_armor_padded_s01_boots.iff', 'object/tangible/wearables/armor/padded/shared_armor_padded_s01_bracer_l.iff', 'object/tangible/wearables/armor/padded/shared_armor_padded_s01_bracer_r.iff', 'object/tangible/wearables/armor/padded/shared_armor_padded_s01_chest_plate.iff', 'object/tangible/wearables/armor/padded/shared_armor_padded_s01_gloves.iff', 'object/tangible/wearables/armor/padded/shared_armor_padded_s01_helmet.iff', 'object/tangible/wearables/armor/padded/shared_armor_padded_s01_leggings.iff'), ('object/tangible/wearables/armor/ris/shared_armor_ris_bicep_l.iff', 'object/tangible/wearables/armor/ris/shared_armor_ris_bicep_r.iff', 'object/tangible/wearables/armor/ris/shared_armor_ris_boots.iff', 'object/tangible/wearables/armor/ris/shared_armor_ris_bracer_l.iff', 'object/tangible/wearables/armor/ris/shared_armor_ris_bracer_r.iff', 'object/tangible/wearables/armor/ris/shared_armor_ris_chest_plate.iff', 'object/tangible/wearables/armor/ris/shared_armor_ris_gloves.iff', 'object/tangible/wearables/armor/ris/shared_armor_ris_helmet.iff', 'object/tangible/wearables/armor/ris/shared_armor_ris_leggings.iff'), ('object/tangible/wearables/armor/stormtrooper/shared_armor_stormtrooper_bicep_l.iff', 'object/tangible/wearables/armor/stormtrooper/shared_armor_stormtrooper_bicep_r.iff', 'object/tangible/wearables/armor/stormtrooper/shared_armor_stormtrooper_boots.iff', 'object/tangible/wearables/armor/stormtrooper/shared_armor_stormtrooper_bracer_l.iff', 'object/tangible/wearables/armor/stormtrooper/shared_armor_stormtrooper_bracer_r.iff', 'object/tangible/wearables/armor/stormtrooper/shared_armor_stormtrooper_chest_plate.iff', 'object/tangible/wearables/armor/stormtrooper/shared_armor_stormtrooper_gloves.iff', 'object/tangible/wearables/armor/stormtrooper/shared_armor_stormtrooper_helmet.iff', 'object/tangible/wearables/armor/stormtrooper/shared_armor_stormtrooper_leggings.iff', 'object/tangible/wearables/armor/stormtrooper/shared_armor_stormtrooper_utility_belt.iff'), ('object/tangible/wearables/armor/tantel/shared_armor_tantel_skreej_boots.iff', 'object/tangible/wearables/armor/tantel/shared_armor_tantel_skreej_chest_plate.iff', 'object/tangible/wearables/armor/tantel/shared_armor_tantel_skreej_helmet.iff'), ('object/tangible/wearables/armor/ubese/shared_armor_ubese_bandolier.iff', 'object/tangible/wearables/armor/ubese/shared_armor_ubese_boots.iff', 'object/tangible/wearables/armor/ubese/shared_armor_ubese_bracer_l.iff', 'object/tangible/wearables/armor/ubese/shared_armor_ubese_bracer_r.iff', 'object/tangible/wearables/armor/ubese/shared_armor_ubese_gloves.iff', 'object/tangible/wearables/armor/ubese/shared_armor_ubese_helmet.iff', 'object/tangible/wearables/armor/ubese/shared_armor_ubese_jacket.iff', 'object/tangible/wearables/armor/ubese/shared_armor_ubese_pants.iff', 'object/tangible/wearables/armor/ubese/shared_armor_ubese_shirt.iff')] structureDeeds = [('object/tangible/deed/factory_deed/shared_factory_clothing_deed.iff', 'object/tangible/deed/factory_deed/shared_factory_food_deed.iff', 'object/tangible/deed/factory_deed/shared_factory_item_deed.iff', 'object/tangible/deed/factory_deed/shared_factory_structure_deed.iff', 'object/tangible/deed/generator_deed/shared_generator_fusion_deed.iff', 'object/tangible/deed/generator_deed/shared_generator_photo_bio_deed.iff', 'object/tangible/deed/generator_deed/shared_generator_solar_deed.iff', 'object/tangible/deed/generator_deed/shared_generator_wind_deed.iff', 'object/tangible/deed/harvester_deed/shared_harvester_creature_deed.iff', 'object/tangible/deed/harvester_deed/shared_harvester_flora_deed.iff', 'object/tangible/deed/harvester_deed/shared_harvester_flora_deed_heavy.iff', 'object/tangible/deed/harvester_deed/shared_harvester_flora_deed_medium.iff', 'object/tangible/deed/harvester_deed/shared_harvester_gas_deed.iff', 'object/tangible/deed/harvester_deed/shared_harvester_gas_deed_heavy.iff', 'object/tangible/deed/harvester_deed/shared_harvester_gas_deed_medium.iff', 'object/tangible/deed/harvester_deed/shared_harvester_liquid_deed.iff', 'object/tangible/deed/harvester_deed/shared_harvester_liquid_deed_heavy.iff', 'object/tangible/deed/harvester_deed/shared_harvester_liquid_deed_medium.iff', 'object/tangible/deed/harvester_deed/shared_harvester_moisture_deed.iff', 'object/tangible/deed/harvester_deed/shared_harvester_moisture_deed_heavy.iff', 'object/tangible/deed/harvester_deed/shared_harvester_moisture_deed_medium.iff', 'object/tangible/deed/harvester_deed/shared_harvester_ore_heavy_deed.iff', 'object/tangible/deed/harvester_deed/shared_harvester_ore_s1_deed.iff', 'object/tangible/deed/harvester_deed/shared_harvester_ore_s2_deed.iff'), ('object/tangible/deed/player_house_deed/shared_corellia_house_large_deed.iff', 'object/tangible/deed/player_house_deed/shared_corellia_house_large_style_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_corellia_house_medium_deed.iff', 'object/tangible/deed/player_house_deed/shared_corellia_house_medium_style_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_corellia_house_small_deed.iff', 'object/tangible/deed/player_house_deed/shared_corellia_house_small_floor_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_corellia_house_small_style_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_corellia_house_small_style_02_floor_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_generic_house_large_deed.iff', 'object/tangible/deed/player_house_deed/shared_generic_house_large_style_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_generic_house_medium_deed.iff', 'object/tangible/deed/player_house_deed/shared_generic_house_medium_style_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_generic_house_small_deed.iff', 'object/tangible/deed/player_house_deed/shared_generic_house_small_floor_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_generic_house_small_style_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_generic_house_small_style_02_floor_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_merchent_tent_style_01_deed.iff', 'object/tangible/deed/player_house_deed/shared_merchent_tent_style_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_merchent_tent_style_03_deed.iff', 'object/tangible/deed/player_house_deed/shared_naboo_house_large_deed.iff', 'object/tangible/deed/player_house_deed/shared_naboo_house_medium_deed.iff', 'object/tangible/deed/player_house_deed/shared_naboo_house_medium_style_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_naboo_house_small_deed.iff', 'object/tangible/deed/player_house_deed/shared_naboo_house_small_style_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_tatooine_house_large_deed.iff', 'object/tangible/deed/player_house_deed/shared_tatooine_house_medium_deed.iff', 'object/tangible/deed/player_house_deed/shared_tatooine_house_medium_style_02_deed.iff', 'object/tangible/deed/player_house_deed/shared_tatooine_house_small_deed.iff', 'object/tangible/deed/player_house_deed/shared_tatooine_house_small_style_02_deed.iff'), ('object/tangibe/deed/city_deed/shared_bank_corellia_deed.iff', 'object/tangibe/deed/city_deed/shared_cantina_corellia_deed.iff', 'object/tangibe/deed/city_deed/shared_cantina_corellia_deed.iff', 'object/tangibe/deed/city_deed/shared_cityhall_corellia_deed.iff', 'object/tangibe/deed/city_deed/shared_cloning_corellia_deed.iff', 'object/tangibe/deed/city_deed/shared_garage_corellia_deed.iff', 'object/tangibe/deed/city_deed/shared_cantina_corellia_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_lrg_01_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_lrg_02_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_lrg_03_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_lrg_04_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_lrg_05_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_med_01_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_med_02_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_med_03_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_med_04_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_med_05_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_sml_01_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_sml_02_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_sml_03_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_sml_04_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_corellia_sml_05_deed.iff', 'object/tangibe/deed/city_deed/shared_hospital_corellia_deed.iff', 'object/tangibe/deed/city_deed/shared_shuttleport_corellia_deed.iff', 'object/tangibe/deed/city_deed/shared_theater_corellia_deed.iff'), ('object/tangibe/deed/city_deed/shared_bank_naboo_deed.iff', 'object/tangibe/deed/city_deed/shared_cantina_naboo_deed.iff', 'object/tangibe/deed/city_deed/shared_cantina_naboo_deed.iff', 'object/tangibe/deed/city_deed/shared_cityhall_naboo_deed.iff', 'object/tangibe/deed/city_deed/shared_cloning_naboo_deed.iff', 'object/tangibe/deed/city_deed/shared_garage_naboo_deed.iff', 'object/tangibe/deed/city_deed/shared_cantina_naboo_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_lrg_01_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_lrg_02_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_lrg_03_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_lrg_04_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_lrg_05_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_med_01_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_med_02_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_med_03_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_med_04_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_med_05_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_sml_01_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_sml_02_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_sml_03_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_sml_04_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_naboo_sml_05_deed.iff', 'object/tangibe/deed/city_deed/shared_hospital_naboo_deed.iff', 'object/tangibe/deed/city_deed/shared_shuttleport_naboo_deed.iff', 'object/tangibe/deed/city_deed/shared_theater_naboo_deed.iff'), ('object/tangibe/deed/city_deed/shared_bank_tatooine_deed.iff', 'object/tangibe/deed/city_deed/shared_cantina_tatooine_deed.iff', 'object/tangibe/deed/city_deed/shared_cantina_tatooine_deed.iff', 'object/tangibe/deed/city_deed/shared_cityhall_tatooine_deed.iff', 'object/tangibe/deed/city_deed/shared_cloning_tatooine_deed.iff', 'object/tangibe/deed/city_deed/shared_garage_tatooine_deed.iff', 'object/tangibe/deed/city_deed/shared_cantina_tatooine_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_lrg_01_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_lrg_02_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_lrg_03_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_lrg_04_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_lrg_05_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_med_01_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_med_02_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_med_03_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_med_04_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_med_05_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_sml_01_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_sml_02_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_sml_03_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_sml_04_deed.iff', 'object/tangibe/deed/city_deed/shared_garden_tatooine_sml_05_deed.iff', 'object/tangibe/deed/city_deed/shared_hospital_tatooine_deed.iff', 'object/tangibe/deed/city_deed/shared_shuttleport_tatooine_deed.iff', 'object/tangibe/deed/city_deed/shared_theater_tatooine_deed.iff'), ('object/tangible/deed/guild_deed/shared_corellia_guild_deed.iff', 'object/tangible/deed/guild_deed/shared_generic_guild_deed.iff', 'object/tangible/deed/guild_deed/shared_naboo_guild_deed.iff', 'object/tangible/deed/guild_deed/shared_tatooine_guild_deed.iff', 'object/tangible/deed/guild_deed/shared_tatooine_guild_style_02_deed.iff'), ('object/tangible/deed/faction_perk/covert_detector/shared_detector_32m_deed.iff', 'object/tangible/deed/faction_perk/hq/shared_hq_s01.iff', 'object/tangible/deed/faction_perk/hq/shared_hq_s01_pvp.iff', 'object/tangible/deed/faction_perk/hq/shared_hq_s02.iff', 'object/tangible/deed/faction_perk/hq/shared_hq_s02_pvp.iff', 'object/tangible/deed/faction_perk/hq/shared_hq_s03.iff', 'object/tangible/deed/faction_perk/hq/shared_hq_s03_pvp.iff', 'object/tangible/deed/faction_perk/hq/shared_hq_s04.iff', 'object/tangible/deed/faction_perk/hq/shared_hq_s04_pvp.iff', 'object/tangible/deed/faction_perk/hq/shared_hq_s05.iff', 'object/tangible/deed/faction_perk/hq/shared_hq_s05_pvp.iff', 'object/tangible/deed/faction_perk/minefield/shared_field_1x1_deed.iff', 'object/tangible/deed/faction_perk/turret/shared_block_lg_deed.iff', 'object/tangible/deed/faction_perk/turret/shared_block_med_deed.iff', 'object/tangible/deed/faction_perk/turret/shared_block_sm_deed.iff', 'object/tangible/deed/faction_perk/turret/shared_dish_lg_deed.iff', 'object/tangible/deed/faction_perk/turret/shared_dish_sm_deed.iff', 'object/tangible/deed/faction_perk/turret/shared_tower_lg_deed.iff', 'object/tangible/deed/faction_perk/turret/shared_tower_med_deed.iff', 'object/tangible/deed/faction_perk/turret/shared_tower_sm_deed.iff')] petDeeds = ('object/tangible/deed/pet_deed/shared_angler_deed.iff', 'object/tangible/deed/pet_deed/shared_bageraset_deed.iff', 'object/tangible/deed/pet_deed/shared_bantha_deed.iff', 'object/tangible/deed/pet_deed/shared_bearded_jax_deed.iff', 'object/tangible/deed/pet_deed/shared_blurrg_deed.iff', 'object/tangible/deed/pet_deed/shared_boar_wolf_deed.iff', 'object/tangible/deed/pet_deed/shared_bocatt_deed.iff', 'object/tangible/deed/pet_deed/shared_bol_deed.iff', 'object/tangible/deed/pet_deed/shared_bolle_bol_deed.iff', 'object/tangible/deed/pet_deed/shared_bolma_deed.iff', 'object/tangible/deed/pet_deed/shared_bordok_deed.iff', 'object/tangible/deed/pet_deed/shared_brackaset_deed.iff', 'object/tangible/deed/pet_deed/shared_carrion_spat_deed.iff', 'object/tangible/deed/pet_deed/shared_choku_deed.iff', 'object/tangible/deed/pet_deed/shared_cu_pa_deed.iff', 'object/tangible/deed/pet_deed/shared_dalyrake_deed.iff', 'object/tangible/deed/pet_deed/shared_dewback_deed.iff', 'object/tangible/deed/pet_deed/shared_dune_lizard_deed.iff', 'object/tangible/deed/pet_deed/shared_durni_deed.iff', 'object/tangible/deed/pet_deed/shared_eopie_deed.iff', 'object/tangible/deed/pet_deed/shared_falumpaset_deed.iff', 'object/tangible/deed/pet_deed/shared_fambaa_deed.iff', 'object/tangible/deed/pet_deed/shared_gnort_deed.iff', 'object/tangible/deed/pet_deed/shared_graul_deed.iff', 'object/tangible/deed/pet_deed/shared_gronda_deed.iff', 'object/tangible/deed/pet_deed/shared_gualama_deed.iff', 'object/tangible/deed/pet_deed/shared_guf_drolg_deed.iff', 'object/tangible/deed/pet_deed/shared_gurnaset_deed.iff', 'object/tangible/deed/pet_deed/shared_gurrcat_deed.iff', 'object/tangible/deed/pet_deed/shared_gurreck_deed.iff', 'object/tangible/deed/pet_deed/shared_hermit_spider_deed.iff', 'object/tangible/deed/pet_deed/shared_huf_dun_deed.iff', 'object/tangible/deed/pet_deed/shared_huurton_deed.iff', 'object/tangible/deed/pet_deed/shared_ikopi_deed.iff', 'object/tangible/deed/pet_deed/shared_kaadu_deed.iff', 'object/tangible/deed/pet_deed/shared_kahmurra_deed.iff', 'object/tangible/deed/pet_deed/shared_kima_deed.iff', 'object/tangible/deed/pet_deed/shared_kimogila_deed.iff', 'object/tangible/deed/pet_deed/shared_kliknik_deed.iff', 'object/tangible/deed/pet_deed/shared_krahbu_deed.iff', 'object/tangible/deed/pet_deed/shared_kusak_deed.iff', 'object/tangible/deed/pet_deed/shared_kwi_deed.iff', 'object/tangible/deed/pet_deed/shared_langlatch_deed.iff', 'object/tangible/deed/pet_deed/shared_malkloc_deed.iff', 'object/tangible/deed/pet_deed/shared_mawgax_deed.iff', 'object/tangible/deed/pet_deed/shared_marek_deed.iff', 'object/tangible/deed/pet_deed/shared_mott_deed.iff', 'object/tangible/deed/pet_deed/shared_narglatch_deed.iff', 'object/tangible/deed/pet_deed/shared_piket_deed.iff', 'object/tangible/deed/pet_deed/shared_pugoriss_deed.iff', 'object/tangible/deed/pet_deed/shared_rancor_deed.iff', 'object/tangible/deed/pet_deed/shared_roba_deed.iff', 'object/tangible/deed/pet_deed/shared_ronto_deed.iff', 'object/tangible/deed/pet_deed/shared_sand_panther_deed.iff', 'object/tangible/deed/pet_deed/shared_sharnaff_deed.iff', 'object/tangible/deed/pet_deed/shared_shear_mite_deed.iff', 'object/tangible/deed/pet_deed/shared_slice_hound_deed.iff', 'object/tangible/deed/pet_deed/shared_snorbal_deed.iff', 'object/tangible/deed/pet_deed/shared_squall_deed.iff', 'object/tangible/deed/pet_deed/shared_swirl_prong_deed.iff', 'object/tangible/deed/pet_deed/shared_thune_deed.iff', 'object/tangible/deed/pet_deed/shared_torton_deed.iff', 'object/tangible/deed/pet_deed/shared_tybis_deed.iff', 'object/tangible/deed/pet_deed/shared_veermok_deed.iff', 'object/tangible/deed/pet_deed/shared_verne_deed.iff', 'object/tangible/deed/pet_deed/shared_vesp_deed.iff', 'object/tangible/deed/pet_deed/shared_vir_vur_deed.iff', 'object/tangible/deed/pet_deed/shared_woolamander_deed.iff', 'object/tangible/deed/pet_deed/shared_zucca_boar_deed.iff') droidDeeds = ( 'object/tangible/deed/pet_deed/shared_deed_3p0_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_3p0_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_binary_load_lifter_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_binary_load_lifter_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_dz70_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_dz70_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_le_repair_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_le_repair_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_mse_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_mse_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_power_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_power_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_probot_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_probot_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_r2_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_r2_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_r3_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_r3_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_r4_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_r4_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_r5_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_r5_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_surgical_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_surgical_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_treadwell_advanced_basic.iff', 'object/tangible/deed/pet_deed/shared_deed_treadwell_basic.iff', ) instruments = ( 'object/tangible/instrument/shared_bandfill.iff', 'object/tangible/instrument/shared_fanfar.iff', 'object/tangible/instrument/shared_fizz.iff', 'object/tangible/instrument/shared_flute_droopy.iff', 'object/tangible/instrument/shared_instrument_kloo_horn.iff', 'object/tangible/instrument/shared_mandoviol.iff', 'object/tangible/instrument/shared_nalargon.iff', 'object/tangible/instrument/shared_ommni_box.iff', 'object/tangible/instrument/shared_slitherhorn.iff', 'object/tangible/instrument/shared_traz.iff')

64.637363

161

0.787105

6,446

47,056

5.40785

0.084549

0.119539

0.14923

0.105425

0.860495

0.841304

0.819301

0.747239

0.633667

0.532259

0

0.017141

0.094972

47,056

728

162

64.637363

0.801395

0.001509

0

0.084465

0

0.705368

0.686085

0

0.000213

0

1

0.024133

false

0.001508

0.012066

0

0.066365

0.001508

0

null

0

1

0

1

null

0

6

f6e30ade2a441765ef5ceda6874b4a54b908e65b

298

py

Python

chia/knowledge/messages.py

cabrust/chia

3eaf815b261dc8a85d64fd698e0079515ec0dde9

[ "BSD-3-Clause" ]

null

chia/knowledge/messages.py

cabrust/chia

3eaf815b261dc8a85d64fd698e0079515ec0dde9

[ "BSD-3-Clause" ]

2

2021-10-06T13:19:09.000Z

2021-10-20T17:32:36.000Z

chia/knowledge/messages.py

cabrust/chia

3eaf815b261dc8a85d64fd698e0079515ec0dde9

[ "BSD-3-Clause" ]

null

from chia import instrumentation class ConceptChangeMessage(instrumentation.Message): def __init__(self, sender: str): super().__init__(sender=sender) class RelationChangeMessage(instrumentation.Message): def __init__(self, sender: str): super().__init__(sender=sender)

24.833333

53

0.744966

30

298

6.866667

0.466667

0.213592

0.242718

0.281553

0.61165

0

0.154362

298

11

54

27.090909

0.81746

0

0.571429

0

1

0.285714

false

0

0.142857

0

0.714286

0

null

1

0

1

0

null

0

1

0

1

0

6

120fe14e7b70a2e4e47442edfd2f60c3ad4033b7

131

py

Python

tests/shared.py

mutalyzer/client

b425a6391dc131069b5b7b7803dd55e8f16affe5

[ "MIT" ]

1

2021-01-13T21:42:18.000Z

tests/shared.py

mutalyzer/client

b425a6391dc131069b5b7b7803dd55e8f16affe5

[ "MIT" ]

null

tests/shared.py

mutalyzer/client

b425a6391dc131069b5b7b7803dd55e8f16affe5

[ "MIT" ]

1

2018-10-30T14:58:52.000Z

from hashlib import md5 from io import StringIO def md5_check(data, md5sum): return md5(data.encode()).hexdigest() == md5sum

18.714286

51

0.732824

19

131

5

0.684211

0

0.045455

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6

12582dd380f02bfeb830f8f74c60f04ecf4ae9dc

19

py

Python

__init__.py

n-hachi/raytrace

aebdffba70002b6ed8f798f8b206d5d21617a4eb

[ "CC0-1.0" ]

null

__init__.py

n-hachi/raytrace

aebdffba70002b6ed8f798f8b206d5d21617a4eb

[ "CC0-1.0" ]

null

__init__.py

n-hachi/raytrace

aebdffba70002b6ed8f798f8b206d5d21617a4eb

[ "CC0-1.0" ]

null

from . import objs

9.5

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1260fb10eb80b1eebeb38f4ff8b8fe7fb3bdf554

612

py

Python

cmt/converter/__init__.py

IceflowRE/cmt

ec85642f5317bf70f7fa203f68a096edd2f190be

[ "MIT" ]

null

cmt/converter/__init__.py

IceflowRE/cmt

ec85642f5317bf70f7fa203f68a096edd2f190be

[ "MIT" ]

47

2019-05-16T21:35:56.000Z

2020-01-19T17:15:42.000Z

cmt/converter/__init__.py

IceflowRE/cmt

ec85642f5317bf70f7fa203f68a096edd2f190be

[ "MIT" ]

2

2019-07-09T18:31:54.000Z

2019-09-05T05:26:40.000Z

from cmt.converter.cmap_v0 import Converter as Converter_cmap_0 from cmt.converter.cmap_v1 import Converter as Converter_cmap_1 from cmt.converter.cmap_v2 import Converter as Converter_cmap_2 from cmt.converter.ecmap_v0 import Converter as Converter_ecmap_0 from cmt.converter.ecmap_v1 import Converter as Converter_ecmap_1 from cmt.converter.ecmap_v2 import Converter as Converter_ecmap_2 from cmt.converter.ecmap_v4 import Converter as Converter_ecmap_4 __all__ = ["Converter_cmap_0", "Converter_cmap_1", "Converter_cmap_2", "Converter_ecmap_0", "Converter_ecmap_1", "Converter_ecmap_2", "Converter_ecmap_4"]

61.2

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0.854575

99

612

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0.161616

0.347107

0.231405

0.376033

0.557851

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null

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1

0

6

1264b0b7c2e032d6fedb11cd1951fbe53e6a7525

179

py

Python

nipype/workflows/dmri/mrtrix/__init__.py

carlohamalainen/nipype

0c4f587946f48277de471b1801b60bd18fdfb775

[ "BSD-3-Clause" ]

1

2018-04-18T12:13:37.000Z

nipype/workflows/dmri/mrtrix/__init__.py

ito-takuya/nipype

9099a5809487b55868cdec82a719030419cbd6ba

[ "BSD-3-Clause" ]

null

nipype/workflows/dmri/mrtrix/__init__.py

ito-takuya/nipype

9099a5809487b55868cdec82a719030419cbd6ba

[ "BSD-3-Clause" ]

1

2020-02-19T13:47:05.000Z

from diffusion import create_mrtrix_dti_pipeline from connectivity_mapping import create_connectivity_pipeline from group_connectivity import (create_group_connectivity_pipeline)

44.75

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22

179

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0.454545

0.232258

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179

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null

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0

1

0

6

126e37d6a860db38bbfe15efe6f80a10571e7268

47

py

Python

d1lod/d1lod/metadata/__init__.py

DataONEorg/slinky

1f0f774b7b5556126d75524ac9fd328ad0fc1ba2

[ "Apache-2.0" ]

2

2019-03-07T21:14:27.000Z

2021-03-30T00:24:13.000Z

d1lod/d1lod/metadata/__init__.py

DataONEorg/slinky

1f0f774b7b5556126d75524ac9fd328ad0fc1ba2

[ "Apache-2.0" ]

64

2021-03-11T22:28:45.000Z

2022-03-17T18:41:08.000Z

d1lod/d1lod/metadata/__init__.py

DataONEorg/slinky

1f0f774b7b5556126d75524ac9fd328ad0fc1ba2

[ "Apache-2.0" ]

2

2018-09-05T16:38:42.000Z

2021-03-12T18:07:20.000Z

import eml import dryad import fgdc import iso

9.4

12

0.829787

8

47

4.875

0.625

0

0.170213

47

4

13

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null

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1

0

1

0

6

89e0484b812cfd0d131cfb56e8a181705ed1faa5

48,478

py

Python

Synthetic_InducedCycle/CodeZip_ST.py

GKAT-NeurIPS2021/GKAT_Experiments

38eda546bfedfaf5a86999309c30595d9fe83cc7

[ "MIT" ]

3

2021-07-29T05:20:45.000Z

2022-01-07T01:24:44.000Z

Synthetic_InducedCycle/CodeZip_ST.py

GKAT-NeurIPS2021/GKAT_Experiments

38eda546bfedfaf5a86999309c30595d9fe83cc7

[ "MIT" ]

null

Synthetic_InducedCycle/CodeZip_ST.py

GKAT-NeurIPS2021/GKAT_Experiments

38eda546bfedfaf5a86999309c30595d9fe83cc7

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import networkx as nx import matplotlib.pyplot as plt import time import numpy as np import pickle from tqdm.notebook import tqdm, trange import random import dgl import dgl.function as fn import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from dgl.data import MiniGCDataset from dgl.nn.pytorch import * from torch.utils.data import DataLoader from tqdm.notebook import tqdm, trange import seaborn as sns from random import shuffle from multiprocessing import Pool import multiprocessing from functools import partial from networkx.generators.classic import cycle_graph import networkx as nx import matplotlib.pyplot as plt import seaborn as sns from deepwalk import OnlyWalk import os, sys class HiddenPrints: def __enter__(self): self._original_stdout = sys.stdout sys.stdout = open(os.devnull, 'w') def __exit__(self, exc_type, exc_val, exc_tb): sys.stdout.close() sys.stdout = self._original_stdout colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] #### Graph Generations def shuffle_two_lists(list_1, list_2): c = list(zip(list_1, list_2)) random.shuffle(c) return zip(*c) #%% ##### Spanning Tree Synthetic Graph from collections import deque # method return farthest node and its distance from node u def BFS(graph_in, u): # marking all nodes as unvisited visited = [False for i in range(len(graph_in.nodes) + 1)] # mark all distance with -1 distance = [-1 for i in range(len(graph_in.nodes) + 1)] # distance of u from u will be 0 distance[u] = 0 # in-built library for queue which performs fast oprations on both the ends queue = deque() queue.append(u) # mark node u as visited visited[u] = True while queue: # pop the front of the queue(0th element) front = queue.popleft() # loop for all adjacent nodes of node front for i in [x for x in graph_in.neighbors(front)]: if not visited[i]: # mark the ith node as visited visited[i] = True # make distance of i , one more than distance of front distance[i] = distance[front]+1 # Push node into the stack only if it is not visited already queue.append(i) maxDis = 0 # get farthest node distance and its index for i in range(len(graph_in.nodes) ): if distance[i] > maxDis: maxDis = distance[i] nodeIdx = i return nodeIdx, maxDis def FindLongestNodePairs(graph_in): # first DFS to find one end point of longest path node, Dis = BFS(graph_in, 0) # second DFS to find the actual longest path node_2, LongDis = BFS(graph_in, node) return node, node_2 #%% def generate_tree(num_nodes, num_graphs): all_tree_graphs = [] print(f'Start generating tree graphs') for i in trange(num_graphs): all_tree_graphs.append(nx.generators.trees.random_tree(num_nodes)) return all_tree_graphs def generate_tree_adding_edges(num_nodes, num_graphs, num_edges = 3): all_tree_graphs_adding_edges = [] print(f'Start generating tree graphs with edges') for i in trange(num_graphs): tree = nx.generators.trees.random_tree(num_nodes) for j in range(num_edges): while True: vertices = random.sample(range(num_nodes), 2) if ((vertices[0], vertices[1]) not in tree.edges) and ((vertices[1], vertices[0]) not in tree.edges): tree.add_edge(vertices[0], vertices[1]) break else: continue all_tree_graphs_adding_edges.append(tree) return all_tree_graphs_adding_edges def generate_tree_adding_edges_with_longest_distance(num_nodes, num_graphs, num_edges = 3): #num_edges = 1 all_tree_graphs_adding_edges = [] print(f'Start generating tree graphs with edges with longest distance') for i in trange(num_graphs): tree = nx.generators.trees.random_tree(num_nodes) for j in range(num_edges): #while True: vertices = FindLongestNodePairs(tree) #vertices = random.sample(range(num_nodes), 2) #if ((vertices[0], vertices[1]) not in tree.edges) and ((vertices[1], vertices[0]) not in tree.edges): tree.add_edge(vertices[0], vertices[1]) # break #else: # continue all_tree_graphs_adding_edges.append(tree) return all_tree_graphs_adding_edges def generate_tree_adding_edges_with_shortest_distance(num_nodes, num_graphs, num_edges = 3): #num_edges = 1 all_tree_graphs_adding_edges = [] print(f'Start generating tree graphs with edges with longest distance') for i in trange(num_graphs): while True: tree = nx.generators.trees.random_tree(num_nodes) vertices = FindLongestNodePairs(tree) longest_len = nx.shortest_path_length(tree,source=vertices[0],target=vertices[1]) source = np.random.choice(len(tree.nodes)) target = np.random.choice(len(tree.nodes)) if source != target: if nx.shortest_path_length(tree,source=source,target=target) >= 2: if nx.shortest_path_length(tree,source=source,target=target) < longest_len: tree.add_edge(source, target) all_tree_graphs_adding_edges.append(tree) break #for j in range(num_edges): # source = np.random.choice(len(tree.nodes)) # for target in tree.nodes: # if nx.shortest_path_length(tree,source=source,target=target) == 2: # tree.add_edge(source, target) # break #all_tree_graphs_adding_edges.append(tree) return all_tree_graphs_adding_edges #%% def generate_graphs_labels(num_nodes, num_train_tree, num_train_edge_tree, num_val_tree, num_val_edge_tree, num_test_tree, num_test_edge_tree, num_edges, is_dgl_type = False, path_length = 10, num_random_walk= 50, p=1e3, q=1, stopping_prob = 0.0): tree_train_graphs = generate_tree_adding_edges_with_shortest_distance(num_nodes, num_train_tree, num_edges) tree_train_labels = list(np.zeros(num_train_tree)) edge_tree_train_graphs = generate_tree_adding_edges_with_longest_distance(num_nodes, num_train_edge_tree, num_edges) edge_tree_train_labels = list(np.ones(num_train_edge_tree)) tree_val_graphs = generate_tree_adding_edges_with_shortest_distance(num_nodes, num_val_tree, num_edges) tree_val_labels = list(np.zeros(num_val_tree)) edge_tree_val_graphs = generate_tree_adding_edges_with_longest_distance(num_nodes, num_val_edge_tree, num_edges) edge_tree_val_labels = list(np.ones(num_val_edge_tree)) tree_test_graphs = generate_tree_adding_edges_with_shortest_distance(num_nodes, num_test_tree, num_edges) tree_test_labels = list(np.zeros(num_test_tree)) edge_tree_test_graphs = generate_tree_adding_edges_with_longest_distance(num_nodes, num_test_edge_tree, num_edges) edge_tree_test_labels = list(np.ones(num_test_edge_tree)) all_train_graphs = tree_train_graphs + edge_tree_train_graphs all_train_labels = tree_train_labels + edge_tree_train_labels all_val_graphs = tree_val_graphs + edge_tree_val_graphs all_val_labels = tree_val_labels + edge_tree_val_labels all_test_graphs = tree_test_graphs + edge_tree_test_graphs all_test_labels = tree_test_labels + edge_tree_test_labels all_train_graphs_shuffled, all_train_labels_shuffled = \ shuffle_two_lists(all_train_graphs, all_train_labels) all_val_graphs_shuffled, all_val_labels_shuffled = \ shuffle_two_lists(all_val_graphs, all_val_labels) all_test_graphs_shuffled, all_test_labels_shuffled = \ shuffle_two_lists(all_test_graphs, all_test_labels) all_train_graphs_shuffled = list(all_train_graphs_shuffled) all_train_labels_shuffled = list(all_train_labels_shuffled) all_val_graphs_shuffled = list(all_val_graphs_shuffled) all_val_labels_shuffled = list(all_val_labels_shuffled) all_test_graphs_shuffled = list(all_test_graphs_shuffled) all_test_labels_shuffled = list(all_test_labels_shuffled) return all_train_graphs_shuffled, all_train_labels_shuffled,\ all_val_graphs_shuffled, all_val_labels_shuffled,\ all_test_graphs_shuffled, all_test_labels_shuffled def networkx_to_dgl_graphs(all_train_graphs_shuffled, all_val_graphs_shuffled, all_test_graphs_shuffled): for i in range(len(all_train_graphs_shuffled)): all_train_graphs_shuffled[i] = dgl.from_networkx(all_train_graphs_shuffled[i]) for i in range(len(all_val_graphs_shuffled)): all_val_graphs_shuffled[i] = dgl.from_networkx(all_val_graphs_shuffled[i]) for i in range(len(all_test_graphs_shuffled)): all_test_graphs_shuffled[i] = dgl.from_networkx(all_test_graphs_shuffled[i]) return all_train_graphs_shuffled, all_val_graphs_shuffled, all_test_graphs_shuffled def dgl_to_networkx_graphs(all_train_graphs_shuffled, all_val_graphs_shuffled, all_test_graphs_shuffled): for i in range(len(all_train_graphs_shuffled)): all_train_graphs_shuffled[i] = nx.Graph(all_train_graphs_shuffled[i].to_networkx()) for i in range(len(all_val_graphs_shuffled)): all_val_graphs_shuffled[i] = nx.Graph(all_val_graphs_shuffled[i].to_networkx()) for i in range(len(all_test_graphs_shuffled)): all_test_graphs_shuffled[i] = nx.Graph(all_test_graphs_shuffled[i].to_networkx()) return all_train_graphs_shuffled, all_val_graphs_shuffled, all_test_graphs_shuffled #GWK_masking = generate_masking_GWK(all_train_graphs_shuffled, all_val_graphs_shuffled, all_test_graphs_shuffled, path_length, num_random_walk, stopping_prob, p, q) #GAT_masking = generate_masking_GAT(all_train_graphs_shuffled, all_val_graphs_shuffled, all_test_graphs_shuffled) #%% ##### Generate masking def generate_masking_GAT(train_graphs, val_graphs, test_graphs): train_masking = [] val_masking = [] test_masking = [] print('Start generating GAT masking') for graph in train_graphs: adj = nx.linalg.graphmatrix.adjacency_matrix(graph).todense() np.fill_diagonal(adj, 1) train_masking.append(torch.from_numpy(adj)) for graph in val_graphs: adj = nx.linalg.graphmatrix.adjacency_matrix(graph).todense() np.fill_diagonal(adj, 1) val_masking.append(torch.from_numpy(adj)) for graph in test_graphs: adj = nx.linalg.graphmatrix.adjacency_matrix(graph).todense() np.fill_diagonal(adj, 1) test_masking.append(torch.from_numpy(adj)) return train_masking, val_masking, test_masking def generate_masking_GWK(train_graphs, val_graphs, test_graphs, num_random_walk, path_length, stopping_prob, p, q, ignore_start = False): train_masking = [] val_masking = [] test_masking = [] print('Start generating GWK masking') for i in tqdm(range(len(train_graphs))): graph = (train_graphs[i]) n2v = OnlyWalk.Node2vec_onlywalk(graph = graph, path_length=path_length, num_paths=num_random_walk, p=p, q=q, stop_prob = stopping_prob, with_freq_mat = True) counting_atten = torch.from_numpy(n2v.walker.freq_mat) if ignore_start: counting_atten -= np.eye(len(counting_atten))*num_random_walk train_masking.append(MinMaxScaler(counting_atten).float()) for i in tqdm(range(len(val_graphs))): graph = (val_graphs[i]) n2v = OnlyWalk.Node2vec_onlywalk(graph = graph, path_length=path_length, num_paths=num_random_walk, p=p, q=q, stop_prob = stopping_prob, with_freq_mat = True) counting_atten = torch.from_numpy(n2v.walker.freq_mat) if ignore_start: counting_atten -= np.eye(len(counting_atten))*num_random_walk val_masking.append(MinMaxScaler(counting_atten).float()) for i in tqdm(range(len(test_graphs))): graph = (test_graphs[i]) n2v = OnlyWalk.Node2vec_onlywalk(graph = graph, path_length=path_length, num_paths=num_random_walk, p=p, q=q, stop_prob = stopping_prob, with_freq_mat = True) counting_atten = torch.from_numpy(n2v.walker.freq_mat) if ignore_start: counting_atten -= np.eye(len(counting_atten))*num_random_walk test_masking.append(MinMaxScaler(counting_atten).float()) return train_masking, val_masking, test_masking #%% ##### Better Version def counting_attn(node, epsilon, adj_mat, discount_factor, rand_seed=None): if rand_seed: np.random.seed(rand_seed) else: np.random.seed() counting_vector = np.zeros(adj_mat.shape[1]) counting_vector[node] = 1 step_length = 0 visited_nodes = [node] loop_count = 0 while True: continue_or_not = np.random.choice([0, 1], p = [epsilon, 1 - epsilon]) if continue_or_not: while True: next_available_nodes = np.where(adj_mat[node, :] != 0)[0] if len(set(next_available_nodes) - set(visited_nodes))>0: next = np.random.choice(list(set(next_available_nodes) - set(visited_nodes))) step_length += 1 counting_vector[next] += discount_factor**step_length visited_nodes.append(next) node = next else: return counting_vector ''' if next in visited_nodes: loop_count +=1 if loop_count > 1000: #print('exceed loop count') return counting_vector else: continue else: ''' else: return counting_vector def cal_counting_attn(adj, num_random_walks, stopping_prob, discounting_fact, seed = 666): np.random.seed(seed) nb_nodes = adj.shape[0] #counting_attn vector_dict = [] for node in range(nb_nodes): all_vectors = [] for i in range(num_random_walks): try: vector = counting_attn(node, stopping_prob, np.array(adj), discounting_fact, i+1) except: vector = np.zeros(np.array(adj).shape[1]) vector[node] = 1 all_vectors.append(vector) vector_dict.append(all_vectors) return vector_dict def collate(samples): # The input `samples` is a list of pairs # (graph, label). graphs, labels = map(list, zip(*samples)) batched_graph = dgl.batch(graphs) return batched_graph, torch.tensor(labels) def MinMaxScaler(data): diff = data.transpose(0,1) - torch.min(data, axis = 1)[0] range = torch.max(data, axis = 1)[0] - torch.min(data, axis = 1)[0] return (diff / (range + 1e-7)).transpose(0,1) #%% # Graph NN ##### Attention Model definition class GWKLayer(nn.Module): def __init__(self, in_dim, out_dim, feat_drop=0., attn_drop=0., alpha=0.2, agg_activation=F.elu): super(GWKLayer, self).__init__() self.feat_drop = nn.Dropout(feat_drop) self.fc = nn.Linear(in_dim, out_dim, bias=False) torch.nn.init.xavier_uniform_(self.fc.weight) #torch.nn.init.zeros_(self.fc.bias) self.attn_l = nn.Parameter(torch.ones(size=(out_dim, 1))) self.attn_r = nn.Parameter(torch.ones(size=(out_dim, 1))) self.attn_drop = nn.Dropout(attn_drop) self.activation = nn.LeakyReLU(alpha) self.softmax = nn.Softmax(dim = 1) self.agg_activation=agg_activation def clean_data(self): ndata_names = ['ft', 'a1', 'a2'] edata_names = ['a_drop'] for name in ndata_names: self.g.ndata.pop(name) for name in edata_names: self.g.edata.pop(name) def forward(self, feat, bg, counting_attn): #with HiddenPrints(): # prepare, inputs are of shape V x F, V the number of nodes, F the dim of input features self.g = bg h = self.feat_drop(feat) #print('h shape is \n') #print(h.shape) head_ft = self.fc(h).reshape((h.shape[0], -1)) #print('ft shape is \n') #print(head_ft.shape) a1 = torch.mm(head_ft, self.attn_l) # V x 1 a2 = torch.mm(head_ft, self.attn_r) # V x 1 a = self.attn_drop(a1 + a2.transpose(0, 1)) a = self.activation(a) #print('a shape is \n') #print(a.shape) #maxes = torch.max(a, 1, keepdim=True)[0] a_ = a #- maxes a_nomi = torch.mul(torch.exp(a_), counting_attn.float()) a_deno = torch.sum(a_nomi, 1, keepdim=True) a_nor = a_nomi/(a_deno+1e-9) ret = torch.mm(a_nor, head_ft) #print('ret shape is \n') #print(ret.shape) if self.agg_activation is not None: ret = self.agg_activation(ret) return ret class GWKClassifier(nn.Module): def __init__(self, in_dim, hidden_dim, num_heads, n_classes, feat_drop_=0., attn_drop_=0.,): super(GWKClassifier, self).__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.layers = nn.ModuleList([ nn.ModuleList([GWKLayer(in_dim, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads)]), nn.ModuleList([GWKLayer(hidden_dim * num_heads, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(1)]), ]) self.classify = nn.Linear(hidden_dim, n_classes) self.softmax = nn.Softmax(dim = 1) def forward(self, bg, counting_attn, normalize = 'normal'): # For undirected graphs, in_degree is the same as # out_degree. h = bg.in_degrees().view(-1, 1).float() #print('input shape is \n') #print(h.shape) num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) #if std_: # h = (h - mean_)/std_ #else: # h = h/np.max(features) for i, gnn in enumerate(self.layers): all_h = [] for j, att_head in enumerate(gnn): all_h.append(att_head(h, bg, counting_attn)) h = torch.squeeze(torch.cat(all_h, dim=1)) #print('Output shape is \n') #print(h.shape) bg.ndata['h'] = h hg = dgl.mean_nodes(bg, 'h') #return self.softmax(self.classify(hg)) return self.classify(hg) class GWKClassifier_2hid(nn.Module): def __init__(self, in_dim, hidden_dim, num_heads, n_classes, feat_drop_=0., attn_drop_=0.,): super(GWKClassifier_2hid, self).__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.layers = nn.ModuleList([ nn.ModuleList([GWKLayer(in_dim, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads)]), nn.ModuleList([GWKLayer(hidden_dim * num_heads, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(1)]) ]) self.classify = nn.Linear(hidden_dim * 1, n_classes) self.softmax = nn.Softmax(dim = 1) def forward(self, bg, counting_attn, normalize = 'normal'): h = bg.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) for i, gnn in enumerate(self.layers): all_h = [] for j, att_head in enumerate(gnn): all_h.append(att_head(h, bg, counting_attn)) h = torch.squeeze(torch.cat(all_h, dim=1)) bg.ndata['h'] = h hg = dgl.mean_nodes(bg, 'h') return self.classify(hg) class GWKClassifier_3hid(nn.Module): def __init__(self, in_dim, hidden_dim, num_heads, n_classes, feat_drop_=0., attn_drop_=0.,): super(GWKClassifier_3hid, self).__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.layers = nn.ModuleList([ nn.ModuleList([GWKLayer(in_dim, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[0])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[0], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[1])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[1], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(1)]) ]) self.classify = nn.Linear(hidden_dim , n_classes) self.softmax = nn.Softmax(dim = 1) def forward(self, bg, counting_attn, normalize = 'normal'): h = bg.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) for i, gnn in enumerate(self.layers): all_h = [] for j, att_head in enumerate(gnn): all_h.append(att_head(h, bg, counting_attn)) h = torch.squeeze(torch.cat(all_h, dim=1)) bg.ndata['h'] = h hg = dgl.mean_nodes(bg, 'h') return self.classify(hg) class GWKClassifier_4hid(nn.Module): def __init__(self, in_dim, hidden_dim, num_heads, n_classes, feat_drop_=0., attn_drop_=0.,): super(GWKClassifier_4hid, self).__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.layers = nn.ModuleList([ nn.ModuleList([GWKLayer(in_dim, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[0])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[0], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[1])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[1], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[2])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[2], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(1)]) ]) self.classify = nn.Linear(hidden_dim, n_classes) self.softmax = nn.Softmax(dim = 1) def forward(self, bg, counting_attn, normalize = 'normal'): h = bg.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) for i, gnn in enumerate(self.layers): all_h = [] for j, att_head in enumerate(gnn): all_h.append(att_head(h, bg, counting_attn)) h = torch.squeeze(torch.cat(all_h, dim=1)) bg.ndata['h'] = h hg = dgl.mean_nodes(bg, 'h') return self.classify(hg) class GWKClassifier_5hid(nn.Module): def __init__(self, in_dim, hidden_dim, num_heads, n_classes, feat_drop_=0., attn_drop_=0.,): super(GWKClassifier_5hid, self).__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.layers = nn.ModuleList([ nn.ModuleList([GWKLayer(in_dim, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[0])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[0], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[1])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[1], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[2])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[2], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[3])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[3], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(1)]) ]) self.classify = nn.Linear(hidden_dim, n_classes) self.softmax = nn.Softmax(dim = 1) def forward(self, bg, counting_attn, normalize = 'normal'): h = bg.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) for i, gnn in enumerate(self.layers): all_h = [] for j, att_head in enumerate(gnn): all_h.append(att_head(h, bg, counting_attn)) h = torch.squeeze(torch.cat(all_h, dim=1)) bg.ndata['h'] = h hg = dgl.mean_nodes(bg, 'h') return self.classify(hg) class GWKClassifier_6hid(nn.Module): def __init__(self, in_dim, hidden_dim, num_heads, n_classes, feat_drop_=0., attn_drop_=0.,): super(GWKClassifier_6hid, self).__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.layers = nn.ModuleList([ nn.ModuleList([GWKLayer(in_dim, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[0])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[0], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[1])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[1], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[2])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[2], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[3])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[3], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[4])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[4], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(1)]) ]) self.classify = nn.Linear(hidden_dim, n_classes) self.softmax = nn.Softmax(dim = 1) def forward(self, bg, counting_attn, normalize = 'normal'): h = bg.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) for i, gnn in enumerate(self.layers): all_h = [] for j, att_head in enumerate(gnn): all_h.append(att_head(h, bg, counting_attn)) h = torch.squeeze(torch.cat(all_h, dim=1)) bg.ndata['h'] = h hg = dgl.mean_nodes(bg, 'h') return self.classify(hg) class GWKClassifier_7hid(nn.Module): def __init__(self, in_dim, hidden_dim, num_heads, n_classes, feat_drop_=0., attn_drop_=0.,): super(GWKClassifier_7hid, self).__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.layers = nn.ModuleList([ nn.ModuleList([GWKLayer(in_dim, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[0])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[0], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[1])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[1], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[2])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[2], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[3])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[3], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[4])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[4], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[5])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[5], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(1)]) ]) self.classify = nn.Linear(hidden_dim, n_classes) self.softmax = nn.Softmax(dim = 1) def forward(self, bg, counting_attn, normalize = 'normal'): h = bg.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) for i, gnn in enumerate(self.layers): all_h = [] for j, att_head in enumerate(gnn): all_h.append(att_head(h, bg, counting_attn)) h = torch.squeeze(torch.cat(all_h, dim=1)) bg.ndata['h'] = h hg = dgl.mean_nodes(bg, 'h') return self.classify(hg) class GWKClassifier_8hid(nn.Module): def __init__(self, in_dim, hidden_dim, num_heads, n_classes, feat_drop_=0., attn_drop_=0.,): super(GWKClassifier_8hid, self).__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.layers = nn.ModuleList([ nn.ModuleList([GWKLayer(in_dim, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[0])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[0], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[1])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[1], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[2])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[2], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[3])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[3], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[4])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[4], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[5])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[5], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[6])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[6], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(1)]) ]) self.classify = nn.Linear(hidden_dim, n_classes) self.softmax = nn.Softmax(dim = 1) def forward(self, bg, counting_attn, normalize = 'normal'): h = bg.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) for i, gnn in enumerate(self.layers): all_h = [] for j, att_head in enumerate(gnn): all_h.append(att_head(h, bg, counting_attn)) h = torch.squeeze(torch.cat(all_h, dim=1)) bg.ndata['h'] = h hg = dgl.mean_nodes(bg, 'h') return self.classify(hg) class GWKClassifier_9hid(nn.Module): def __init__(self, in_dim, hidden_dim, num_heads, n_classes, feat_drop_=0., attn_drop_=0.,): super(GWKClassifier_9hid, self).__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.layers = nn.ModuleList([ nn.ModuleList([GWKLayer(in_dim, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[0])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[0], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[1])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[1], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[2])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[2], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[3])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[3], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[4])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[4], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[5])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[5], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[6])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[6], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[7])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[7], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(1)]) ]) self.classify = nn.Linear(hidden_dim, n_classes) self.softmax = nn.Softmax(dim = 1) def forward(self, bg, counting_attn, normalize = 'normal'): h = bg.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) for i, gnn in enumerate(self.layers): all_h = [] for j, att_head in enumerate(gnn): all_h.append(att_head(h, bg, counting_attn)) h = torch.squeeze(torch.cat(all_h, dim=1)) bg.ndata['h'] = h hg = dgl.mean_nodes(bg, 'h') return self.classify(hg) class GWKClassifier_10hid(nn.Module): def __init__(self, in_dim, hidden_dim, num_heads, n_classes, feat_drop_=0., attn_drop_=0.,): super(GWKClassifier_10hid, self).__init__() self.num_heads = num_heads self.hidden_dim = hidden_dim self.layers = nn.ModuleList([ nn.ModuleList([GWKLayer(in_dim, hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[0])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[0], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[1])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[1], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[2])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[2], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[3])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[3], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[4])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[4], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[5])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[5], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[6])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[6], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[7])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[7], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(num_heads[8])]), nn.ModuleList([GWKLayer(hidden_dim * num_heads[8], hidden_dim, feat_drop = feat_drop_, attn_drop = attn_drop_, agg_activation=F.elu) for _ in range(1)]) ]) self.classify = nn.Linear(hidden_dim, n_classes) self.softmax = nn.Softmax(dim = 1) def forward(self, bg, counting_attn, normalize = 'normal'): h = bg.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) for i, gnn in enumerate(self.layers): all_h = [] for j, att_head in enumerate(gnn): all_h.append(att_head(h, bg, counting_attn)) h = torch.squeeze(torch.cat(all_h, dim=1)) bg.ndata['h'] = h hg = dgl.mean_nodes(bg, 'h') return self.classify(hg) ##### Convolutional model definition class GCNClassifier(nn.Module): def __init__(self, in_dim, hidden_dim, n_classes): super(GCNClassifier, self).__init__() self.conv1 = GraphConv(in_dim, hidden_dim) self.conv2 = GraphConv(hidden_dim, hidden_dim) self.classify = nn.Linear(hidden_dim, n_classes) def forward(self, g, normalize = 'normal'): # Use node degree as the initial node feature. For undirected graphs, the in-degree # is the same as the out_degree. h = g.in_degrees().view(-1, 1).float() #print('input shape is \n') #print(h.shape) num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) #if std_: # h = (h - mean_)/std_ #else: # h = h/np.max(features) # Perform graph convolution and activation function. h = F.relu(self.conv1(g, h)) h = F.relu(self.conv2(g, h)) g.ndata['h'] = h # Calculate graph representation by averaging all the node representations. hg = dgl.mean_nodes(g, 'h') return self.classify(hg) # 1 hidden layer class GCNClassifier_1hid(nn.Module): def __init__(self, in_dim, hidden_dim, n_classes): super(GCNClassifier_1hid, self).__init__() self.conv1 = GraphConv(in_dim, hidden_dim[0]) self.classify = nn.Linear(hidden_dim[-1], n_classes) def forward(self, g, normalize = 'normal'): h = g.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) # Perform graph convolution and activation function. h = F.relu(self.conv1(g, h)) g.ndata['h'] = h hg = dgl.mean_nodes(g, 'h') return self.classify(hg) # 2 hidden layers class GCNClassifier_2hid(nn.Module): def __init__(self, in_dim, hidden_dim, n_classes): super(GCNClassifier_2hid, self).__init__() self.conv1 = GraphConv(in_dim, hidden_dim[0]) self.conv2 = GraphConv(hidden_dim[0], hidden_dim[1]) self.classify = nn.Linear(hidden_dim[-1], n_classes) def forward(self, g, normalize = 'normal'): h = g.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) # Perform graph convolution and activation function. h = F.relu(self.conv1(g, h)) h = F.relu(self.conv2(g, h)) g.ndata['h'] = h hg = dgl.mean_nodes(g, 'h') return self.classify(hg) # 3 hidden layers class GCNClassifier_3hid(nn.Module): def __init__(self, in_dim, hidden_dim, n_classes): super(GCNClassifier_3hid, self).__init__() self.conv1 = GraphConv(in_dim, hidden_dim[0]) self.conv2 = GraphConv(hidden_dim[0], hidden_dim[1]) self.conv3 = GraphConv(hidden_dim[1], hidden_dim[2]) self.classify = nn.Linear(hidden_dim[-1], n_classes) def forward(self, g, normalize = 'normal'): h = g.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) # Perform graph convolution and activation function. h = F.relu(self.conv1(g, h)) h = F.relu(self.conv2(g, h)) h = F.relu(self.conv3(g, h)) g.ndata['h'] = h hg = dgl.mean_nodes(g, 'h') return self.classify(hg) # 4 hidden layers class GCNClassifier_4hid(nn.Module): def __init__(self, in_dim, hidden_dim, n_classes): super(GCNClassifier_4hid, self).__init__() self.conv1 = GraphConv(in_dim, hidden_dim[0]) self.conv2 = GraphConv(hidden_dim[0], hidden_dim[1]) self.conv3 = GraphConv(hidden_dim[1], hidden_dim[2]) self.conv4 = GraphConv(hidden_dim[2], hidden_dim[3]) self.classify = nn.Linear(hidden_dim[-1], n_classes) def forward(self, g, normalize = 'normal'): h = g.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) # Perform graph convolution and activation function. h = F.relu(self.conv1(g, h)) h = F.relu(self.conv2(g, h)) h = F.relu(self.conv3(g, h)) h = F.relu(self.conv4(g, h)) g.ndata['h'] = h hg = dgl.mean_nodes(g, 'h') return self.classify(hg) # 5 hidden layers class GCNClassifier_5hid(nn.Module): def __init__(self, in_dim, hidden_dim, n_classes): super(GCNClassifier_5hid, self).__init__() self.conv1 = GraphConv(in_dim, hidden_dim[0]) self.conv2 = GraphConv(hidden_dim[0], hidden_dim[1]) self.conv3 = GraphConv(hidden_dim[1], hidden_dim[2]) self.conv4 = GraphConv(hidden_dim[2], hidden_dim[3]) self.conv5 = GraphConv(hidden_dim[3], hidden_dim[4]) self.classify = nn.Linear(hidden_dim[4], n_classes) def forward(self, g, normalize = 'normal'): h = g.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) # Perform graph convolution and activation function. h = F.relu(self.conv1(g, h)) h = F.relu(self.conv2(g, h)) h = F.relu(self.conv3(g, h)) h = F.relu(self.conv4(g, h)) h = F.relu(self.conv5(g, h)) g.ndata['h'] = h hg = dgl.mean_nodes(g, 'h') return self.classify(hg) # 6 hidden layers class GCNClassifier_6hid(nn.Module): def __init__(self, in_dim, hidden_dim, n_classes): super(GCNClassifier_6hid, self).__init__() self.conv1 = GraphConv(in_dim, hidden_dim[0]) self.conv2 = GraphConv(hidden_dim[0], hidden_dim[1]) self.conv3 = GraphConv(hidden_dim[1], hidden_dim[2]) self.conv4 = GraphConv(hidden_dim[2], hidden_dim[3]) self.conv5 = GraphConv(hidden_dim[3], hidden_dim[4]) self.conv6 = GraphConv(hidden_dim[4], hidden_dim[5]) self.classify = nn.Linear(hidden_dim[5], n_classes) def forward(self, g, normalize = 'normal'): h = g.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) # Perform graph convolution and activation function. h = F.relu(self.conv1(g, h)) h = F.relu(self.conv2(g, h)) h = F.relu(self.conv3(g, h)) h = F.relu(self.conv4(g, h)) h = F.relu(self.conv5(g, h)) h = F.relu(self.conv6(g, h)) g.ndata['h'] = h hg = dgl.mean_nodes(g, 'h') return self.classify(hg) # 7 hidden layers class GCNClassifier_7hid(nn.Module): def __init__(self, in_dim, hidden_dim, n_classes): super(GCNClassifier_7hid, self).__init__() self.conv1 = GraphConv(in_dim, hidden_dim[0]) self.conv2 = GraphConv(hidden_dim[0], hidden_dim[1]) self.conv3 = GraphConv(hidden_dim[1], hidden_dim[2]) self.conv4 = GraphConv(hidden_dim[2], hidden_dim[3]) self.conv5 = GraphConv(hidden_dim[3], hidden_dim[4]) self.conv6 = GraphConv(hidden_dim[4], hidden_dim[5]) self.conv7 = GraphConv(hidden_dim[5], hidden_dim[6]) self.classify = nn.Linear(hidden_dim[-1], n_classes) def forward(self, g, normalize = 'normal'): h = g.in_degrees().view(-1, 1).float() num_nodes = h.shape[0] features = h.numpy().flatten() if normalize == 'normal': mean_ = np.mean(features) std_ = np.std(features) h = (h - mean_)/std_ elif normalize == 'minmax': h = h/np.max(features) # Perform graph convolution and activation function. h = F.relu(self.conv1(g, h)) h = F.relu(self.conv2(g, h)) h = F.relu(self.conv3(g, h)) h = F.relu(self.conv4(g, h)) h = F.relu(self.conv5(g, h)) h = F.relu(self.conv6(g, h)) h = F.relu(self.conv7(g, h)) g.ndata['h'] = h hg = dgl.mean_nodes(g, 'h') return self.classify(hg)

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Python

projects/ex48/test-ex48_convert.py

ba-nyar-naing/python-excercises

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projects/ex48/test-ex48_convert.py

ba-nyar-naing/python-excercises

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projects/ex48/test-ex48_convert.py

ba-nyar-naing/python-excercises

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[ "MIT" ]

3

2018-06-10T17:19:05.000Z

2018-06-26T13:49:33.000Z

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epytope/Data/pssms/smm/mat/B_58_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/smm/mat/B_58_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/smm/mat/B_58_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

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test/test_contrib.py

tina300399/torchgeometry

48d8026f0a5f3d4ac5567b7b2738390892b3cc8d

[ "Apache-2.0" ]

null

test/test_contrib.py

tina300399/torchgeometry

48d8026f0a5f3d4ac5567b7b2738390892b3cc8d

[ "Apache-2.0" ]

null

test/test_contrib.py

tina300399/torchgeometry

48d8026f0a5f3d4ac5567b7b2738390892b3cc8d

[ "Apache-2.0" ]

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2019-10-04T05:05:28.000Z

import pytest import torch import torchgeometry as tgm from torch.autograd import gradcheck import utils class TestExtractTensorPatches: def test_smoke(self): input = torch.arange(16.).view(1, 1, 4, 4) m = tgm.contrib.ExtractTensorPatches(3) assert m(input).shape == (1, 4, 1, 3, 3) def test_b1_ch1_h4w4_ws3(self): input = torch.arange(16.).view(1, 1, 4, 4) m = tgm.contrib.ExtractTensorPatches(3) patches = m(input) assert patches.shape == (1, 4, 1, 3, 3) assert utils.check_equal_torch(input[0, :, :3, :3], patches[0, 0]) assert utils.check_equal_torch(input[0, :, :3, 1:], patches[0, 1]) assert utils.check_equal_torch(input[0, :, 1:, :3], patches[0, 2]) assert utils.check_equal_torch(input[0, :, 1:, 1:], patches[0, 3]) def test_b1_ch2_h4w4_ws3(self): input = torch.arange(16.).view(1, 1, 4, 4) input = input.expand(-1, 2, -1, -1) # copy all channels m = tgm.contrib.ExtractTensorPatches(3) patches = m(input) assert patches.shape == (1, 4, 2, 3, 3) assert utils.check_equal_torch(input[0, :, :3, :3], patches[0, 0]) assert utils.check_equal_torch(input[0, :, :3, 1:], patches[0, 1]) assert utils.check_equal_torch(input[0, :, 1:, :3], patches[0, 2]) assert utils.check_equal_torch(input[0, :, 1:, 1:], patches[0, 3]) def test_b1_ch1_h4w4_ws2(self): input = torch.arange(16.).view(1, 1, 4, 4) m = tgm.contrib.ExtractTensorPatches(2) patches = m(input) assert patches.shape == (1, 9, 1, 2, 2) assert utils.check_equal_torch(input[0, :, 0:2, 1:3], patches[0, 1]) assert utils.check_equal_torch(input[0, :, 0:2, 2:4], patches[0, 2]) assert utils.check_equal_torch(input[0, :, 1:3, 1:3], patches[0, 4]) assert utils.check_equal_torch(input[0, :, 2:4, 1:3], patches[0, 7]) def test_b1_ch1_h4w4_ws2_stride2(self): input = torch.arange(16.).view(1, 1, 4, 4) m = tgm.contrib.ExtractTensorPatches(2, stride=2) patches = m(input) assert patches.shape == (1, 4, 1, 2, 2) assert utils.check_equal_torch(input[0, :, 0:2, 0:2], patches[0, 0]) assert utils.check_equal_torch(input[0, :, 0:2, 2:4], patches[0, 1]) assert utils.check_equal_torch(input[0, :, 2:4, 0:2], patches[0, 2]) assert utils.check_equal_torch(input[0, :, 2:4, 2:4], patches[0, 3]) def test_b1_ch1_h4w4_ws2_stride21(self): input = torch.arange(16.).view(1, 1, 4, 4) m = tgm.contrib.ExtractTensorPatches(2, stride=(2, 1)) patches = m(input) assert patches.shape == (1, 6, 1, 2, 2) assert utils.check_equal_torch(input[0, :, 0:2, 1:3], patches[0, 1]) assert utils.check_equal_torch(input[0, :, 0:2, 2:4], patches[0, 2]) assert utils.check_equal_torch(input[0, :, 2:4, 0:2], patches[0, 3]) assert utils.check_equal_torch(input[0, :, 2:4, 2:4], patches[0, 5]) def test_b1_ch1_h3w3_ws2_stride1_padding1(self): input = torch.arange(9.).view(1, 1, 3, 3) m = tgm.contrib.ExtractTensorPatches(2, stride=1, padding=1) patches = m(input) assert patches.shape == (1, 16, 1, 2, 2) assert utils.check_equal_torch(input[0, :, 0:2, 0:2], patches[0, 5]) assert utils.check_equal_torch(input[0, :, 0:2, 1:3], patches[0, 6]) assert utils.check_equal_torch(input[0, :, 1:3, 0:2], patches[0, 9]) assert utils.check_equal_torch(input[0, :, 1:3, 1:3], patches[0, 10]) def test_b2_ch1_h3w3_ws2_stride1_padding1(self): batch_size = 2 input = torch.arange(9.).view(1, 1, 3, 3) input = input.expand(batch_size, -1, -1, -1) m = tgm.contrib.ExtractTensorPatches(2, stride=1, padding=1) patches = m(input) assert patches.shape == (batch_size, 16, 1, 2, 2) for i in range(batch_size): assert utils.check_equal_torch( input[i, :, 0:2, 0:2], patches[i, 5]) assert utils.check_equal_torch( input[i, :, 0:2, 1:3], patches[i, 6]) assert utils.check_equal_torch( input[i, :, 1:3, 0:2], patches[i, 9]) assert utils.check_equal_torch( input[i, :, 1:3, 1:3], patches[i, 10]) def test_b1_ch1_h3w3_ws23(self): input = torch.arange(9.).view(1, 1, 3, 3) m = tgm.contrib.ExtractTensorPatches((2, 3)) patches = m(input) assert patches.shape == (1, 2, 1, 2, 3) assert utils.check_equal_torch(input[0, :, 0:2, 0:3], patches[0, 0]) assert utils.check_equal_torch(input[0, :, 1:3, 0:3], patches[0, 1]) def test_b1_ch1_h3w4_ws23(self): input = torch.arange(12.).view(1, 1, 3, 4) m = tgm.contrib.ExtractTensorPatches((2, 3)) patches = m(input) assert patches.shape == (1, 4, 1, 2, 3) assert utils.check_equal_torch(input[0, :, 0:2, 0:3], patches[0, 0]) assert utils.check_equal_torch(input[0, :, 0:2, 1:4], patches[0, 1]) assert utils.check_equal_torch(input[0, :, 1:3, 0:3], patches[0, 2]) assert utils.check_equal_torch(input[0, :, 1:3, 1:4], patches[0, 3]) # TODO: implement me def test_jit(self): pass def test_gradcheck(self): input = torch.rand(2, 3, 4, 4) input = utils.tensor_to_gradcheck_var(input) # to var assert gradcheck(tgm.contrib.extract_tensor_patches, (input, 3,), raise_exception=True) class TestSoftArgmax2d: def _test_smoke(self): input = torch.zeros(1, 1, 2, 3) m = tgm.contrib.SpatialSoftArgmax2d() assert m(input).shape == (1, 1, 2) def _test_top_left(self): input = torch.zeros(1, 1, 2, 3) input[..., 0, 0] = 10. coord = tgm.contrib.spatial_soft_argmax2d(input, True) assert pytest.approx(coord[..., 0].item(), -1.0) assert pytest.approx(coord[..., 1].item(), -1.0) def _test_top_left_normalized(self): input = torch.zeros(1, 1, 2, 3) input[..., 0, 0] = 10. coord = tgm.contrib.spatial_soft_argmax2d(input, False) assert pytest.approx(coord[..., 0].item(), 0.0) assert pytest.approx(coord[..., 1].item(), 0.0) def _test_bottom_right(self): input = torch.zeros(1, 1, 2, 3) input[..., -1, 1] = 10. coord = tgm.contrib.spatial_soft_argmax2d(input, True) assert pytest.approx(coord[..., 0].item(), 1.0) assert pytest.approx(coord[..., 1].item(), 1.0) def _test_bottom_right_normalized(self): input = torch.zeros(1, 1, 2, 3) input[..., -1, 1] = 10. coord = tgm.contrib.spatial_soft_argmax2d(input, False) assert pytest.approx(coord[..., 0].item(), 2.0) assert pytest.approx(coord[..., 1].item(), 1.0) def _test_batch2_n2(self): input = torch.zeros(2, 2, 2, 3) input[0, 0, 0, 0] = 10. # top-left input[0, 1, 0, -1] = 10. # top-right input[1, 0, -1, 0] = 10. # bottom-left input[1, 1, -1, -1] = 10. # bottom-right coord = tgm.contrib.spatial_soft_argmax2d(input) assert pytest.approx(coord[0, 0, 0].item(), -1.0) # top-left assert pytest.approx(coord[0, 0, 1].item(), -1.0) assert pytest.approx(coord[0, 1, 0].item(), 1.0) # top-right assert pytest.approx(coord[0, 1, 1].item(), -1.0) assert pytest.approx(coord[1, 0, 0].item(), -1.0) # bottom-left assert pytest.approx(coord[1, 0, 1].item(), 1.0) assert pytest.approx(coord[1, 1, 0].item(), 1.0) # bottom-right assert pytest.approx(coord[1, 1, 1].item(), 1.0) # TODO: implement me def _test_jit(self): pass def _test_gradcheck(self): input = torch.rand(2, 3, 3, 2) input = utils.tensor_to_gradcheck_var(input) # to var assert gradcheck(tgm.contrib.spatial_soft_argmax2d, (input), raise_exception=True) def test_run_all(self): self._test_smoke() self._test_top_left() self._test_top_left_normalized() self._test_bottom_right() self._test_bottom_right_normalized() self._test_batch2_n2() self._test_gradcheck()

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Python

nbdev_sphinx/_modidx.py

fastai/nbdev-stdlib

8a956e40ee31c32170ab96f832fc8e0c9510c83e

[ "Apache-2.0" ]

2

2020-10-15T14:59:56.000Z

2020-10-15T17:29:18.000Z

nbdev_sphinx/_modidx.py

fastai/nbdev-stdlib

8a956e40ee31c32170ab96f832fc8e0c9510c83e

[ "Apache-2.0" ]

3

2020-10-17T05:05:21.000Z

2020-10-19T21:19:01.000Z

nbdev_sphinx/_modidx.py

fastai/nbdev-stdlib

8a956e40ee31c32170ab96f832fc8e0c9510c83e

[ "Apache-2.0" ]

null

# Autogenerated by get_module_idx.py d = { 'syms': { 'docutils.parsers': {'docutils.parsers.rst': 'https://www.sphinx-doc.org/en/stable/extdev/markupapi.html#module-docutils.parsers.rst'}, 'sphinx': { 'sphinx.addnodes': 'https://www.sphinx-doc.org/en/stable/extdev/nodes.html#module-sphinx.addnodes', 'sphinx.application': 'https://www.sphinx-doc.org/en/stable/extdev/appapi.html#module-sphinx.application', 'sphinx.builders': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders', 'sphinx.directives': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#module-sphinx.directives', 'sphinx.domains': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#module-sphinx.domains', 'sphinx.environment': 'https://www.sphinx-doc.org/en/stable/extdev/envapi.html#module-sphinx.environment', 'sphinx.errors': 'https://www.sphinx-doc.org/en/stable/extdev/appapi.html#module-sphinx.errors', 'sphinx.parsers': 'https://www.sphinx-doc.org/en/stable/extdev/parserapi.html#module-sphinx.parsers'}, 'sphinx.builders': { 'sphinx.builders.changes': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.changes', 'sphinx.builders.dirhtml': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.dirhtml', 'sphinx.builders.dummy': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.dummy', 'sphinx.builders.epub3': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.epub3', 'sphinx.builders.gettext': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.gettext', 'sphinx.builders.html': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.html', 'sphinx.builders.latex': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.latex', 'sphinx.builders.linkcheck': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.linkcheck', 'sphinx.builders.manpage': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.manpage', 'sphinx.builders.singlehtml': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.singlehtml', 'sphinx.builders.texinfo': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.texinfo', 'sphinx.builders.text': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.text', 'sphinx.builders.xml': 'https://www.sphinx-doc.org/en/stable/usage/builders/index.html#module-sphinx.builders.xml', 'sphinx.builders.Builder': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder', 'sphinx.builders.Builder.build': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.build', 'sphinx.builders.Builder.build_all': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.build_all', 'sphinx.builders.Builder.build_specific': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.build_specific', 'sphinx.builders.Builder.build_update': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.build_update', 'sphinx.builders.Builder.finish': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.finish', 'sphinx.builders.Builder.get_outdated_docs': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.get_outdated_docs', 'sphinx.builders.Builder.get_relative_uri': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.get_relative_uri', 'sphinx.builders.Builder.get_target_uri': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.get_target_uri', 'sphinx.builders.Builder.init': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.init', 'sphinx.builders.Builder.prepare_writing': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.prepare_writing', 'sphinx.builders.Builder.write_doc': 'https://www.sphinx-doc.org/en/stable/extdev/builderapi.html#sphinx.builders.Builder.write_doc'}, 'sphinx.domains': { 'sphinx.domains.python': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#module-sphinx.domains.python', 'sphinx.domains.Domain': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain', 'sphinx.domains.Index': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Index', 'sphinx.domains.ObjType': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.ObjType', 'sphinx.domains.Domain.add_object_type': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.add_object_type', 'sphinx.domains.Domain.check_consistency': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.check_consistency', 'sphinx.domains.Domain.clear_doc': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.clear_doc', 'sphinx.domains.Domain.directive': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.directive', 'sphinx.domains.Domain.get_enumerable_node_type': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.get_enumerable_node_type', 'sphinx.domains.Domain.get_full_qualified_name': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.get_full_qualified_name', 'sphinx.domains.Domain.get_objects': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.get_objects', 'sphinx.domains.Domain.get_type_name': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.get_type_name', 'sphinx.domains.Domain.merge_domaindata': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.merge_domaindata', 'sphinx.domains.Domain.process_doc': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.process_doc', 'sphinx.domains.Domain.process_field_xref': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.process_field_xref', 'sphinx.domains.Domain.resolve_any_xref': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.resolve_any_xref', 'sphinx.domains.Domain.resolve_xref': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.resolve_xref', 'sphinx.domains.Domain.role': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.role', 'sphinx.domains.Domain.setup': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Domain.setup', 'sphinx.domains.Index.generate': 'https://www.sphinx-doc.org/en/stable/extdev/domainapi.html#sphinx.domains.Index.generate'}, 'sphinx.environment': { 'sphinx.environment.collectors': 'https://www.sphinx-doc.org/en/stable/extdev/collectorapi.html#module-sphinx.environment.collectors', 'sphinx.environment.BuildEnvironment': 'https://www.sphinx-doc.org/en/stable/extdev/envapi.html#sphinx.environment.BuildEnvironment', 'sphinx.environment.BuildEnvironment.doc2path': 'https://www.sphinx-doc.org/en/stable/extdev/envapi.html#sphinx.environment.BuildEnvironment.doc2path', 'sphinx.environment.BuildEnvironment.new_serialno': 'https://www.sphinx-doc.org/en/stable/extdev/envapi.html#sphinx.environment.BuildEnvironment.new_serialno', 'sphinx.environment.BuildEnvironment.note_dependency': 'https://www.sphinx-doc.org/en/stable/extdev/envapi.html#sphinx.environment.BuildEnvironment.note_dependency', 'sphinx.environment.BuildEnvironment.note_reread': 'https://www.sphinx-doc.org/en/stable/extdev/envapi.html#sphinx.environment.BuildEnvironment.note_reread', 'sphinx.environment.BuildEnvironment.relfn2path': 'https://www.sphinx-doc.org/en/stable/extdev/envapi.html#sphinx.environment.BuildEnvironment.relfn2path'}, 'sphinx.ext': { 'sphinx.ext.autodoc': 'https://www.sphinx-doc.org/en/stable/usage/extensions/autodoc.html#module-sphinx.ext.autodoc', 'sphinx.ext.autosectionlabel': 'https://www.sphinx-doc.org/en/stable/usage/extensions/autosectionlabel.html#module-sphinx.ext.autosectionlabel', 'sphinx.ext.autosummary': 'https://www.sphinx-doc.org/en/stable/usage/extensions/autosummary.html#module-sphinx.ext.autosummary', 'sphinx.ext.coverage': 'https://www.sphinx-doc.org/en/stable/usage/extensions/coverage.html#module-sphinx.ext.coverage', 'sphinx.ext.doctest': 'https://www.sphinx-doc.org/en/stable/usage/extensions/doctest.html#module-sphinx.ext.doctest', 'sphinx.ext.duration': 'https://www.sphinx-doc.org/en/stable/usage/extensions/duration.html#module-sphinx.ext.duration', 'sphinx.ext.extlinks': 'https://www.sphinx-doc.org/en/stable/usage/extensions/extlinks.html#module-sphinx.ext.extlinks', 'sphinx.ext.githubpages': 'https://www.sphinx-doc.org/en/stable/usage/extensions/githubpages.html#module-sphinx.ext.githubpages', 'sphinx.ext.graphviz': 'https://www.sphinx-doc.org/en/stable/usage/extensions/graphviz.html#module-sphinx.ext.graphviz', 'sphinx.ext.ifconfig': 'https://www.sphinx-doc.org/en/stable/usage/extensions/ifconfig.html#module-sphinx.ext.ifconfig', 'sphinx.ext.imgconverter': 'https://www.sphinx-doc.org/en/stable/usage/extensions/imgconverter.html#module-sphinx.ext.imgconverter', 'sphinx.ext.imgmath': 'https://www.sphinx-doc.org/en/stable/usage/extensions/math.html#module-sphinx.ext.imgmath', 'sphinx.ext.inheritance_diagram': 'https://www.sphinx-doc.org/en/stable/usage/extensions/inheritance.html#module-sphinx.ext.inheritance_diagram', 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7f1d5ecb573bdeada480e6a00f837bf28096e216

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py

Python

models/model_mgn/__init__.py

qychen13/ClusterAlignReID

9dca1a39b7f1035c9579d80bbb73aa45480a616c

[ "MIT" ]

15

2020-08-24T22:47:39.000Z

2021-04-19T07:51:32.000Z

models/model_mgn/__init__.py

qychen13/ClusterAlignReID

9dca1a39b7f1035c9579d80bbb73aa45480a616c

[ "MIT" ]

1

2021-10-14T03:07:12.000Z

2021-11-05T13:59:55.000Z

models/model_mgn/__init__.py

qychen13/ClusterAlignReID

9dca1a39b7f1035c9579d80bbb73aa45480a616c

[ "MIT" ]

1

2020-08-26T02:48:40.000Z

from .mgn import MGN

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613861477fbd06d5f97125a23e94bff4149e190a

116

py

Python

start.py

FujiMakoto/discordpy-bot-template

6a3eff598f3db93e45059c43154c24217d33de3e

[ "MIT" ]

1

2020-09-21T14:09:50.000Z

start.py

FujiMakoto/discordpy-bot-template

6a3eff598f3db93e45059c43154c24217d33de3e

[ "MIT" ]

null

start.py

FujiMakoto/discordpy-bot-template

6a3eff598f3db93e45059c43154c24217d33de3e

[ "MIT" ]

null

from discordbot.config import config from discordbot.discordbot import bot bot.run(config.get('Discord', 'token'))

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6

619702d55f16e696718246c7df95d978fcefc6ce

1,138

py

Python

test_code/turtle_test.py

AJ-Gonzalez/Axolotl_Studio

8975b6bf0c393409cb8ee6ec67cdf46e00be6542

[ "MIT" ]

null

test_code/turtle_test.py

AJ-Gonzalez/Axolotl_Studio

8975b6bf0c393409cb8ee6ec67cdf46e00be6542

[ "MIT" ]

null

test_code/turtle_test.py

AJ-Gonzalez/Axolotl_Studio

8975b6bf0c393409cb8ee6ec67cdf46e00be6542

[ "MIT" ]

null

import turtle import tkinter as tk def do_stuff(): for color in ["red", "yellow", "green"]: my_lovely_turtle.color(color) my_lovely_turtle.right(120) def press(): do_stuff() if __name__ == "__main__": screen = turtle.Screen() screen.bgcolor("cyan") canvas = screen.getcanvas() button = tk.Button(canvas.master, text="Press me", command=press) canvas.create_window(-200, -200, window=button) my_lovely_turtle = turtle.Turtle(shape="turtle") turtle.done() 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 import turtle import tkinter as tk def do_stuff(): my_lovely_turtle.setpos(100,200) for color in ["red", "yellow", "green"]: my_lovely_turtle.color(color) my_lovely_turtle.right(120) def press(): do_stuff() if __name__ == "__main__": screen = turtle.Screen() screen.bgcolor("cyan") canvas = screen.getcanvas() button = tk.Button(canvas.master, text="Press me", command=press) canvas.create_window(-200, -200, window=button) my_lovely_turtle = turtle.Turtle(shape="turtle") turtle.done()

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61a2e56a9912390631992ba4b7a75a7366082962

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py

Python

FusionIIIT/applications/programme_curriculum/views.py

Draco-D/Fusion

065f5f9939d6f736b6b42c2650e5a05aef5dab52

[ "bzip2-1.0.6" ]

1

2021-08-05T10:31:35.000Z

FusionIIIT/applications/programme_curriculum/views.py

Draco-D/Fusion

065f5f9939d6f736b6b42c2650e5a05aef5dab52

[ "bzip2-1.0.6" ]

null

FusionIIIT/applications/programme_curriculum/views.py

Draco-D/Fusion

065f5f9939d6f736b6b42c2650e5a05aef5dab52

[ "bzip2-1.0.6" ]

null

from django.http import request from django.shortcuts import render, HttpResponse from django.http import HttpResponse, HttpResponseRedirect import itertools from django.contrib import messages from django.contrib.auth.decorators import login_required from django.contrib.auth.models import User from .models import Programme, Discipline, Curriculum, Semester, Course, Batch, CourseSlot from .forms import ProgrammeForm, DisciplineForm, CurriculumForm, SemesterForm, CourseForm, BatchForm, CourseSlotForm # from applications.academic_information.models import Student from applications.globals.models import (DepartmentInfo, Designation, ExtraInfo, Faculty, HoldsDesignation) # ------------module-functions---------------# @login_required(login_url='/accounts/login') def programme_curriculum(request): """ This function is used to Differenciate acadadmin and all other user. @param: request - contains metadata about the requested page @variables: user_details - Gets the information about the logged in user. des - Gets the designation about the looged in user. """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : return HttpResponseRedirect('/programme_curriculum/admin_mainpage') # ------------all-user-functions---------------# def main_page(request): """ This function is used to display the main page of programme_curriculum @param: request - contains metadata about the requested page """ return render(request, 'programme_curriculum/mainpage.html') def view_all_programmes(request): """ This function is used to display all the programmes offered by the institute. @variables: ug - UG programmes pg - PG programmes phd - PHD programmes """ ug = Programme.objects.filter(category='UG') pg = Programme.objects.filter(category='PG') phd = Programme.objects.filter(category='PHD') return render(request, 'programme_curriculum/view_all_programmes.html', {'ug': ug, 'pg': pg, 'phd': phd}) def view_curriculums_of_a_programme(request, programme_id): """ This function is used to Display Curriculum of a specific Programmes. @param: programme_id - Id of a specific programme @variables: curriculums - Curriculums of a specific programmes batches - List of batches for curriculums working_curriculum - Curriculums that are affective past_curriculum - Curriculums thet are obsolete """ program = Programme.objects.get(id=programme_id) curriculums = Programme.get_curriculums_objects(program) batches = [] for curriculum in curriculums: batches.append([Curriculum.get_batches(curriculum)]) working_curriculums = curriculums.filter(working_curriculum=1) past_curriculums = curriculums.filter(working_curriculum=0) return render(request,'programme_curriculum/view_curriculums_of_a_programme.html', {'program': program, 'past_curriculums': past_curriculums, 'working_curriculums': working_curriculums}) def view_all_working_curriculums(request): """ views all the working curriculums offered by the institute """ curriculums = Curriculum.objects.filter(working_curriculum=1) return render(request,'programme_curriculum/view_all_working_curriculums.html',{'curriculums':curriculums}) def view_semesters_of_a_curriculum(request, curriculum_id): """ This function is used to Display all Semester of a Curriculum. @param: curriculum_id - Id of a specific curriculum @variables: transpose_semester_slots - semester_slots 2D list is transpose for viewing in HTML <table>. semester_credits - Total Credits for each semester. """ curriculum = Curriculum.objects.get(id=curriculum_id) semesters = Curriculum.get_semesters_objects(curriculum) semester_slots = [] for sem in semesters: a = list(Semester.get_courseslots_objects(sem)) semester_slots.append(a) max_length = 0 for course_slots in semester_slots: max_length = max(max_length, len(course_slots)) for course_slots in semester_slots: course_slots += [""] * (max_length - len(course_slots)) semester_credits = [] for semester in semesters: credits_sum = 0 for course_slot in semester.courseslots: max_credit = 0 courses = course_slot.courses.all() for course in courses: max_credit = max(max_credit, course.credit) credits_sum = credits_sum + max_credit semester_credits.append(credits_sum) transpose_semester_slots = list(zip(*semester_slots)) return render(request, 'programme_curriculum/view_semesters_of_a_curriculum.html', {'curriculum': curriculum, 'semesters': semesters, 'semester_slots': transpose_semester_slots, 'semester_credits': semester_credits}) def view_a_semester_of_a_curriculum(request, semester_id): """ views a specfic semester of a specfic curriculum """ semester = Semester.objects.get(id=semester_id) course_slots = Semester.get_courseslots_objects(semester) return render(request, 'programme_curriculum/view_a_semester_of_a_curriculum.html', {'semester': semester, 'course_slots': course_slots}) def view_a_courseslot(request, courseslot_id): """ view a course slot """ course_slot = CourseSlot.objects.get(id=courseslot_id) return render(request, 'programme_curriculum/view_a_courseslot.html', {'course_slot': course_slot}) def view_all_courses(request): """ views all the course slots of a specfic semester """ courses = Course.objects.all() return render(request, 'programme_curriculum/view_all_courses.html', {'courses': courses}) def view_a_course(request, course_id): """ views the details of a Course """ course = Course.objects.get(id=course_id) return render(request, 'programme_curriculum/view_a_course.html', {'course': course}) def view_all_discplines(request): """ views the details of a Course """ disciplines = Discipline.objects.all() return render(request, 'programme_curriculum/view_all_disciplines.html', {'disciplines': disciplines}) def view_all_batches(request): """ views the details of a Course """ batches = Batch.objects.all() return render(request, 'programme_curriculum/view_all_batches.html', {'batches': batches}) # ------------Acad-Admin-functions---------------# @login_required(login_url='/accounts/login') def admin_main_page(request): """ This function is used to display the main page of programme_curriculum for acadadmin @param: request - contains metadata about the requested page """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass return render(request, 'programme_curriculum/acad_admin/admin_mainpage.html') @login_required(login_url='/accounts/login') def admin_view_all_programmes(request): """ This function is used to display all the programmes offered by the institute. @variables: ug - UG programmes pg - PG programmes phd - PHD programmes """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass ug = Programme.objects.filter(category='UG') pg = Programme.objects.filter(category='PG') phd = Programme.objects.filter(category='PHD') return render(request, 'programme_curriculum/acad_admin/admin_view_all_programmes.html', {'ug': ug, 'pg': pg, "phd": phd}) @login_required(login_url='/accounts/login') def admin_view_curriculums_of_a_programme(request, programme_id): """ This function is used to Display Curriculum of a specific Programmes. @param: programme_id - Id of a specific programme @variables: curriculums - Curriculums of a specific programmes batches - List of batches for curriculums working_curriculum - Curriculums that are affective past_curriculum - Curriculums thet are obsolete """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass program = Programme.objects.get(id=programme_id) curriculums = Programme.get_curriculums_objects(program) working_curriculums = curriculums.filter(working_curriculum=1) past_curriculums = curriculums.filter(working_curriculum=0) return render(request,'programme_curriculum/acad_admin/admin_view_curriculums_of_a_programme.html', {'program': program, 'past_curriculums': past_curriculums, 'working_curriculums': working_curriculums}) @login_required(login_url='/accounts/login') def admin_view_all_working_curriculums(request): """ views all the working curriculums offered by the institute """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass curriculums = Curriculum.objects.filter(working_curriculum=1) return render(request,'programme_curriculum/acad_admin/admin_view_all_working_curriculums.html',{'curriculums':curriculums}) @login_required(login_url='/accounts/login') def admin_view_semesters_of_a_curriculum(request, curriculum_id): """ gets all the semesters of a specfic curriculum """ curriculum = Curriculum.objects.get(id=curriculum_id) semesters = Curriculum.get_semesters_objects(curriculum) semester_slots = [] for sem in semesters: a = list(Semester.get_courseslots_objects(sem)) semester_slots.append(a) max_length = 0 for course_slots in semester_slots: max_length = max(max_length, len(course_slots)) for course_slots in semester_slots: course_slots += [""] * (max_length - len(course_slots)) semester_credits = [] for semester in semesters: credits_sum = 0 for course_slot in semester.courseslots: max_credit = 0 courses = course_slot.courses.all() for course in courses: max_credit = max(max_credit, course.credit) credits_sum = credits_sum + max_credit semester_credits.append(credits_sum) print (semester_credits) transpose_semester_slots = list(zip(*semester_slots)) return render(request, 'programme_curriculum/acad_admin/admin_view_semesters_of_a_curriculum.html', {'curriculum': curriculum, 'semesters': semesters, 'semester_slots': transpose_semester_slots, 'semester_credits': semester_credits}) @login_required(login_url='/accounts/login') def admin_view_a_semester_of_a_curriculum(request, semester_id): """ This function is used to Display all Semester of a Curriculum. @param: curriculum_id - Id of a specific curriculum @variables: transpose_semester_slots - semester_slots 2D list is transpose for viewing in HTML <table>. semester_credits - Total Credits for each semester. """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass semester = Semester.objects.get(id=semester_id) course_slots = Semester.get_courseslots_objects(semester) return render(request, 'programme_curriculum/acad_admin/admin_view_a_semester_of_a_curriculum.html', {'semester': semester, 'course_slots': course_slots}) @login_required(login_url='/accounts/login') def admin_view_a_courseslot(request, courseslot_id): """ view a course slot """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass course_slot = CourseSlot.objects.get(id=courseslot_id) return render(request, 'programme_curriculum/acad_admin/admin_view_a_courseslot.html', {'course_slot': course_slot}) @login_required(login_url='/accounts/login') def admin_view_all_courses(request): """ views all the course slots of a specfic semester """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass courses = Course.objects.all() return render(request, 'programme_curriculum/acad_admin/admin_view_all_courses.html', {'courses': courses}) @login_required(login_url='/accounts/login') def admin_view_a_course(request, course_id): """ views the details of a Course """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass course = Course.objects.get(id=course_id) return render(request, 'programme_curriculum/acad_admin/admin_view_a_course.html', {'course': course}) @login_required(login_url='/accounts/login') def admin_view_all_discplines(request): """ views the details of a Course """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass disciplines = Discipline.objects.all() return render(request, 'programme_curriculum/acad_admin/admin_view_all_disciplines.html', {'disciplines': disciplines}) @login_required(login_url='/accounts/login') def admin_view_all_batches(request): """ views the details of a Course """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass batches = Batch.objects.all() return render(request, 'programme_curriculum/acad_admin/admin_view_all_batches.html', {'batches': batches}) @login_required(login_url='/accounts/login') def add_discipline_form(request): user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass form = DisciplineForm() submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = DisciplineForm(request.POST) if form.is_valid(): form.save() messages.success(request, "Added Discipline successful") return HttpResponseRedirect('/programme_curriculum/mainpage/') return render(request, 'programme_curriculum/acad_admin/add_discipline_form.html',{'form':form}) @login_required(login_url='/accounts/login') def edit_discipline_form(request, discipline_id): user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass discipline = Discipline.objects.get(id=discipline_id) form = DisciplineForm(instance=discipline) submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = DisciplineForm(request.POST, instance=discipline) if form.is_valid(): form.save() messages.success(request, "Updated "+ discipline.name +" successful") return HttpResponseRedirect("/programme_curriculum/admin_disciplines/") return render(request, 'programme_curriculum/acad_admin/add_discipline_form.html',{'form':form}) @login_required(login_url='/accounts/login') def add_programme_form(request): user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass form = ProgrammeForm() submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = ProgrammeForm(request.POST) if form.is_valid(): form.save() messages.success(request, "Added successful") return HttpResponseRedirect('/programme_curriculum/admin_mainpage') return render(request,'programme_curriculum/acad_admin/add_programme_form.html',{'form':form, 'submitbutton': submitbutton}) @login_required(login_url='/accounts/login') def edit_programme_form(request, programme_id): user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass programme = Programme.objects.get(id=programme_id) form = ProgrammeForm(instance=programme) submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = ProgrammeForm(request.POST, instance=programme) if form.is_valid(): form.save() messages.success(request, "Updated "+ programme.name +" successful") return HttpResponseRedirect("/programme_curriculum/admin_programmes/") return render(request, 'programme_curriculum/acad_admin/add_programme_form.html',{'form':form, 'submitbutton': submitbutton}) @login_required(login_url='/accounts/login') def add_curriculum_form(request): """ This function is used to add Curriculum and Semester into Curriculum and Semester table. @variables: no_of_semester - Get number of Semesters from form. NewSemester - For initializing a new semester. """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass form = CurriculumForm() submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = CurriculumForm(request.POST) if form.is_valid(): form.save() no_of_semester = int(form.cleaned_data['no_of_semester']) # print(form) # print(no_of_semester) curriculum = Curriculum.objects.all().last() for semester_no in range(1, no_of_semester+1): NewSemester = Semester(curriculum=curriculum,semester_no=semester_no) NewSemester.save() messages.success(request, "Added successful") return HttpResponseRedirect('/programme_curriculum/admin_mainpage') return render(request, 'programme_curriculum/acad_admin/add_curriculum_form.html',{'form':form, 'submitbutton': submitbutton}) @login_required(login_url='/accounts/login') def edit_curriculum_form(request, curriculum_id): """ This function is used to edit Curriculum and Semester into Curriculum and Semester table. @variables: no_of_semester - Get number of Semesters from form. OldSemester - For Removing dropped Semester. NewSemester - For initializing a new semester. """ user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass curriculum = Curriculum.objects.get(id=curriculum_id) form = CurriculumForm(instance=curriculum) submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = CurriculumForm(request.POST, instance=curriculum) if form.is_valid(): form.save() no_of_semester = int(form.cleaned_data['no_of_semester']) old_no_of_semester = Semester.objects.filter(curriculum=curriculum).count() if(old_no_of_semester != no_of_semester): if(old_no_of_semester > no_of_semester): for semester_no in range(no_of_semester+1, old_no_of_semester+1): try: OldSemester = Semester.objects.filter(curriculum=curriculum).filter(semester_no=semester_no) OldSemester.delete() except: print("Failed to remove old semester") elif(old_no_of_semester < no_of_semester): for semester_no in range(max(1, old_no_of_semester), no_of_semester+1): try: NewSemester = Semester(curriculum=curriculum,semester_no=semester_no) NewSemester.save() except: print("Failed to add new semester") print("Old No of Semesters - " + str(old_no_of_semester)) print("Entered No of Semesters - " + str(no_of_semester)) print("Current No of Semesters (after operation) - " + str(Semester.objects.filter(curriculum=curriculum).count())) messages.success(request, "Updated "+ curriculum.name +" successful") return HttpResponseRedirect('/programme_curriculum/admin_mainpage') return render(request, 'programme_curriculum/acad_admin/add_curriculum_form.html',{'form':form, 'submitbutton': submitbutton}) @login_required(login_url='/accounts/login') def add_course_form(request): user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass form = CourseForm() submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = CourseForm(request.POST) if form.is_valid(): form.save() messages.success(request, "Added successful") return HttpResponseRedirect("/programme_curriculum/admin_course/") return render(request,'programme_curriculum/acad_admin/add_course_form.html',{'form':form}) @login_required(login_url='/accounts/login') def update_course_form(request, course_id): user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass course = Course.objects.get(id=course_id) form = CourseForm(instance=course) submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = CourseForm(request.POST, instance=course) if form.is_valid(): form.save() messages.success(request, "Updated "+ course.name +" successful") return HttpResponseRedirect("/programme_curriculum/admin_course/" + str(course_id) + "/") return render(request,'programme_curriculum/acad_admin/add_course_form.html',{'course':course, 'form':form, 'submitbutton': submitbutton}) @login_required(login_url='/accounts/login') def add_courseslot_form(request): user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass form = CourseSlotForm() submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = CourseSlotForm(request.POST) if form.is_valid(): form.save() messages.success(request, "Added Course Slot successful") return HttpResponseRedirect('/programme_curriculum/admin_mainpage/') return render(request, 'programme_curriculum/acad_admin/add_courseslot_form.html',{'form':form, 'submitbutton': submitbutton}) @login_required(login_url='/accounts/login') def edit_courseslot_form(request, courseslot_id): user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass courseslot = CourseSlot.objects.get(id=courseslot_id) form = CourseSlotForm(instance=courseslot) submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = CourseSlotForm(request.POST, instance=courseslot) if form.is_valid(): form.save() messages.success(request, "Updated"+ courseslot.name +"successful") return HttpResponseRedirect("/programme_curriculum/admin_courseslot/" + str(courseslot.id) + "/") return render(request,'programme_curriculum/acad_admin/add_courseslot_form.html',{'courseslot':courseslot, 'form':form, 'submitbutton':submitbutton}) @login_required(login_url='/accounts/login') def add_batch_form(request): user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass form = BatchForm() submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = BatchForm(request.POST) if form.is_valid(): form.save() messages.success(request, "Added Batch successful") return HttpResponseRedirect('/programme_curriculum/admin_batches/') return render(request, 'programme_curriculum/acad_admin/add_batch_form.html',{'form':form, 'submitbutton': submitbutton}) @login_required(login_url='/accounts/login') def edit_batch_form(request, batch_id): user_details = ExtraInfo.objects.get(user = request.user) des = HoldsDesignation.objects.all().filter(user = request.user).first() if str(des.designation) == "student" or str(des.designation) == "Associate Professor" or str(des.designation) == "Professor" or str(des.designation) == "Assistant Professor" : return HttpResponseRedirect('/programme_curriculum/mainpage/') elif str(request.user) == "acadadmin" : pass batch = Batch.objects.get(id=batch_id) form = BatchForm(instance=batch) submitbutton= request.POST.get('Submit') if submitbutton: if request.method == 'POST': form = BatchForm(request.POST, instance=batch) if form.is_valid(): form.save() messages.success(request, "Updated "+ batch.name +" successful") return HttpResponseRedirect("/programme_curriculum/admin_batches/") return render(request,'programme_curriculum/acad_admin/add_batch_form.html',{'batch':batch, 'form':form, 'submitbutton':submitbutton})

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null

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1

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null

0

6

61afd7e4dd1c6956da8feaf408d83a72419f12d1

39

py

Python

simbench/test/__init__.py

navin3011/Seminar-Energy-economy

ddff1bf28f445d5a447fab119d7a6192f231d9c3

[ "BSD-3-Clause" ]

51

2019-05-13T15:33:35.000Z

2022-03-09T06:43:11.000Z

simbench/test/__init__.py

johanneshiry/simbench

59019645d917d2fd4539f7dfb5565a617492e556

[ "BSD-3-Clause" ]

22

2020-04-02T12:46:11.000Z

2022-02-14T16:20:55.000Z

simbench/test/__init__.py

johanneshiry/simbench

59019645d917d2fd4539f7dfb5565a617492e556

[ "BSD-3-Clause" ]

18

2019-11-02T19:03:38.000Z

2022-02-23T21:42:33.000Z

from simbench.test.run_tests import *

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6

4ee01fae453ddc6805c6b9d56b900b5b1ac86c19

3,147

py

Python

functions/aou/tests/unit_tests.py

broadinstitute/wfl

1e5691100330a9afa0270fb4bab0a7d0a7d3bdc2

[ "BSD-3-Clause" ]

15

2020-03-04T17:30:25.000Z

2022-03-09T14:57:26.000Z

functions/aou/tests/unit_tests.py

broadinstitute/wfl

1e5691100330a9afa0270fb4bab0a7d0a7d3bdc2

[ "BSD-3-Clause" ]

184

2020-03-06T20:55:15.000Z

2022-03-15T18:24:57.000Z

functions/aou/tests/unit_tests.py

broadinstitute/wfl

1e5691100330a9afa0270fb4bab0a7d0a7d3bdc2

[ "BSD-3-Clause" ]

2

2020-07-08T19:16:26.000Z

2020-07-10T18:47:30.000Z

import mock from google.cloud import storage, exceptions from aou import main bucket_name = "test_bucket" file_name = "dev/chip_name/chipwell_barcode/analysis_version/arrays/metadata/file.txt" event_data = {'bucket': bucket_name, 'name': file_name} def test_get_manifest_path_from_uploaded_file_with_environment_prefix(): uploaded_file = "dev/chip_name/chipwell_barcode/analysis_version/arrays/metadata/file.txt" manifest_file = "dev/chip_name/chipwell_barcode/analysis_version/ptc.json" result = main.get_manifest_path(uploaded_file) assert result == manifest_file def test_get_manifest_path_from_uploaded_file(): uploaded_file = "chip_name/chipwell_barcode/analysis_version/arrays/metadata/file.txt" manifest_file = "chip_name/chipwell_barcode/analysis_version/ptc.json" result = main.get_manifest_path(uploaded_file) assert result == manifest_file @mock.patch("aou.main.update_workload", return_value=["workflow_uuid"]) @mock.patch("aou.main.get_or_create_workload", return_value="workload_uuid") @mock.patch.object(storage.Blob, 'download_as_string') @mock.patch("aou.main.get_auth_headers") def test_manifest_file_not_uploaded(mock_headers, mock_download, mock_get_workload, mock_update_workload): client = mock.create_autospec(storage.Client()) mock_download.side_effect = exceptions.NotFound('Error') main.submit_aou_workload(event_data, None) assert not mock_get_workload.called assert not mock_update_workload.called @mock.patch("aou.main.update_workload", return_value=["workflow_uuid"]) @mock.patch("aou.main.get_or_create_workload", return_value="workload_uuid") @mock.patch.object(storage.Bucket, 'get_blob') @mock.patch.object(storage.Blob, 'download_as_string') @mock.patch("aou.main.get_auth_headers") def test_input_file_not_uploaded(mock_headers, mock_download, mock_get_blob, mock_get_workload, mock_update_workload): client = mock.create_autospec(storage.Client()) mock_download.return_value = '{"notifications": [{"file": "gs://test_bucket/file.txt", "environment": "dev"}]}' mock_get_blob.return_value = None main.submit_aou_workload(event_data, None) assert not mock_get_workload.called assert not mock_update_workload.called @mock.patch("aou.main.update_workload", return_value=["workflow_uuid"]) @mock.patch("aou.main.get_or_create_workload", return_value="workload_uuid") @mock.patch.object(storage.Bucket, 'get_blob') @mock.patch.object(storage.Blob, 'download_as_string') @mock.patch("aou.main.get_auth_headers") def test_wfl_called_when_sample_upload_completes(mock_headers, mock_download, mock_get_blob, mock_get_workload, mock_update_workload): client = mock.create_autospec(storage.Client()) mock_download.return_value = '{"executor": "http://cromwell.broadinstitute.org", ' \ '"sample_alias": "test_sample", ' \ '"notifications": [{"file": "gs://test_bucket/file.txt", "environment": "dev"}]}' mock_get_blob.return_value = "blob" main.submit_aou_workload(event_data, None) assert mock_get_workload.called assert mock_update_workload.called

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3,147

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0

0.153846

0

null

0

1

0

null

0

6

f62c130b5216b68bb5b3b452b405d876039ac144

109

py

Python

bunsen/py/typing.py

castorini/bunsen

6fe9b8b71ca37631974e96889e1a72d35fd5a437

[ "MIT" ]

null

bunsen/py/typing.py

castorini/bunsen

6fe9b8b71ca37631974e96889e1a72d35fd5a437

[ "MIT" ]

null

bunsen/py/typing.py

castorini/bunsen

6fe9b8b71ca37631974e96889e1a72d35fd5a437

[ "MIT" ]

null

from typing import Callable __all__ = ['check_input_types'] def check_input_types(fn: Callable): pass

13.625

36

0.752294

15

109

4.933333

0.733333

0.27027

0.405405

0

0.165138

109

8

37

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null

1

0

1

0

null

0

1

0

1

0

6

f62eda1ab76b71ab679542814b4c9f340aa45036

120

py

Python

model_and_testcode/model_4.py

YunqiuXu/DecentralizedAlgorithmTradingPlatform

4138f9c267272eb8c4a9d3e13e94c3ec20a52af1

[ "MIT" ]

null

model_and_testcode/model_4.py

YunqiuXu/DecentralizedAlgorithmTradingPlatform

4138f9c267272eb8c4a9d3e13e94c3ec20a52af1

[ "MIT" ]

null

model_and_testcode/model_4.py

YunqiuXu/DecentralizedAlgorithmTradingPlatform

4138f9c267272eb8c4a9d3e13e94c3ec20a52af1

[ "MIT" ]

null

class Model(): def __init__(self): self.result = 9500 def show_result(self): return self.result

20

26

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4.533333

0.6

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120

5

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false

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0

1

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null

1

0

1

0

null

0

1

0

1

0

6

f657b9d83b52bca53cb17e9e05f37115cc1fbd51

17,745

py

Python

seg-part/dataloaders/datasets/chaos.py

QIU023/LifeLong-Segmentation

f479d1641f461e9344dcf661d0ada7484fb80896

[ "MIT" ]

1

2022-02-25T10:39:47.000Z

seg-part/dataloaders/datasets/chaos.py

QIU023/LifeLong-Segmentation

f479d1641f461e9344dcf661d0ada7484fb80896

[ "MIT" ]

null

seg-part/dataloaders/datasets/chaos.py

QIU023/LifeLong-Segmentation

f479d1641f461e9344dcf661d0ada7484fb80896

[ "MIT" ]

null

from __future__ import print_function, division import os import random from ipdb import set_trace from PIL import Image import numpy as np from torch.utils.data import Dataset from mypath import Path from dataloaders.utils import encode_segmap from torchvision import transforms import dataloaders.custom_transforms3 as tr import pydicom import imageio def get_file_name(current_dir): return [f.split('.')[0] for f in os.listdir(current_dir) if f.endswith(".jpg")] def get_dir0(base_dir): return [f for f in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir,f)) and f.startswith("Patient")] def get_dir(base_dir): return [f for f in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir,f))] class Chaos1Test(Dataset): NUM_CLASSES = 2 def __init__(self, args): super().__init__() self._base_dir = args.tdpath self.args = args self.images = [] self.file_names = [] base_dir = '/data/weishizheng/QiuYiqiao/CHAOS_Test_Sets/Test_Sets/CT' self.pseudo_labels = [] dir1s = get_dir0(base_dir) for dir1 in dir1s: dir1 = os.path.join(base_dir, dir1) assert os.path.isdir(dir1) dir2s = get_dir(dir1) for dir2 in dir2s: dir2 = os.path.join(base_dir, dir1, dir2) assert os.path.isdir(dir2) dir3s = get_dir(dir2) for dir3 in dir3s: dir3 = os.path.join(base_dir, dir1, dir2, dir3) assert os.path.isdir(dir3) file_name = get_file_name(dir3) for name in file_name: assert os.path.isfile(os.path.join(base_dir, dir1, dir2, dir3, name + ".jpg")) self.images.append(os.path.join(base_dir,dir1,dir2,dir3,name + ".jpg")) self.file_names.append(name) self.pseudo_labels.append(os.path.join('/data/weishizheng/QiuYiqiao/Segmentation-codes/result_chaos1_addval/', name + "_segmentation.jpg")) print("num of test images:{}".format(len(self.images))) def __len__(self): return len(self.images) def __getitem__(self, index): image = Image.open(self.images[index]) # print(image.size) return self.transform_test(image),self.file_names[index], image.size def transform_test(self, images): composed_transforms = transforms.Compose([ FixedResize(size=self.args.crop_size), Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), ToTensor() ]) return composed_transforms(images) class Chaos2Test(Dataset): ''' Chaos Dataset ''' NUM_CLASSES = 5 def __init__(self, args, base_dir=Path.db_root_dir('chaos2'), split='test', ): super().__init__() #self._base_dir = base_dir self.args = args self.split = split self.images = [] self.confidence_map = [] self.pseudo_labels = [] self.file_names = [] self.base_dir = '/data/weishizheng/QiuYiqiao/CHAOS_Test_Sets/Test_Sets/MR' dir1s = get_dir0(base_dir) for dir1 in dir1s: dir1 = os.path.join(base_dir, dir1) assert os.path.isdir(dir1) dir2s = get_dir(dir1) for dir2 in dir2s: dir2 = os.path.join(base_dir, dir1, dir2) assert os.path.isdir(dir2) dir3s = get_dir(dir2) for dir3 in dir3s: dir3 = os.path.join(base_dir, dir1, dir2, dir3) assert os.path.isdir(dir3) file_name = get_file_name(dir3) for name in file_name: assert os.path.isfile(os.path.join(base_dir, dir1, dir2, dir3, name + ".jpg")) self.images.append(os.path.join(base_dir,dir1,dir2,dir3,name + ".jpg")) self.file_names.append(name) self.pseudo_labels.append(os.path.join('/data/weishizheng/QiuYiqiao/Segmentation-codes/result_chaos2_test_label/', name + "_segmentation.jpg")) self.confidence_map.append(os.path.join('/data/weishizheng/QiuYiqiao/Segmentation-codes/result_chaos2_test_confidence/', name + "_segmentation.jpg")) #assert (len(self.images) == len(self.labels)) print('Number of images in {}: {:d}'.format(split, len(self.images))) def __len__(self): return len(self.images) def transform_test(self, image): composed_transforms = transforms.Compose([ FixedResize(size=self.args.crop_size), Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), ToTensor()]) return composed_transforms(image) def __getitem__(self, item): _img = Image.open(self.images[item]) return self.transform_test(_img), self.file_names[item], _img.size def __str__(self): return 'Chaos2_2019(split=' + str(self.split) + ')' class ChaosSegmentation1(Dataset): ''' Chaos Dataset ''' NUM_CLASSES = 2 def __init__(self, args, base_dir=Path.db_root_dir('chaos1'), split='train', ): super().__init__() self._base_dir = base_dir self.args = args self.split = split self.train_percent = self.args.train_percent self.images = [] self.labels = [] self.id_dir = [1, 2, 5, 6, 8, 10, 14, 16, 18, 19] self.ids_dir2 = [1, 5, 6, 7, 8, 9, 10, 11, 12, 2] for i in self.id_dir: _image_dir = os.path.join(self._base_dir,"Patient-CHAOS CT_SET_" + str(i), "Study_" + str(i) + "_CT[]", str(i)) _label_dir = os.path.join(self._base_dir, str(i), "Ground") _num_image = len([lists for lists in os.listdir(_image_dir) if os.path.isfile(os.path.join(_image_dir, lists))]) for j in range(_num_image): if j < 10: _image = os.path.join(_image_dir, "i000" + str(j) + ",0000b.jpg") _label = os.path.join(_label_dir, "liver_GT_00" + str(j) + ".png") elif j < 100: _image = os.path.join(_image_dir, "i00" + str(j) + ",0000b.jpg") _label = os.path.join(_label_dir, "liver_GT_0" + str(j) + ".png") else: _image = os.path.join(_image_dir, "i0" + str(j) + ",0000b.jpg") _label = os.path.join(_label_dir, "liver_GT_" + str(j) + ".png") assert os.path.isfile(_image) assert os.path.isfile(_label) self.images.append(_image) self.labels.append(_label) for i in range(21,31): _image_dir = os.path.join(self._base_dir,"Patient-CHAOS CT_SET_" + str(i), "Study_" + str(i) + "_CT[]", str(i)) _label_dir = os.path.join(self._base_dir, str(i), "Ground") _num_image = len([lists for lists in os.listdir(_image_dir) if os.path.isfile(os.path.join(_image_dir, lists))]) for j in range(_num_image): _file = "IMG-00" if self.ids_dir2[i - 21] < 10: _file += "0" _file += str(self.ids_dir2[i - 21]) _file += "-00" if j < 9: _file += "00" elif j < 99: _file += "0" _file += str(j+1) + ".jpg" _image = os.path.join(_image_dir, _file) if j < 10: _label = os.path.join(_label_dir, "liver_GT_00" + str(j) + ".png") elif j < 100: _label = os.path.join(_label_dir, "liver_GT_0" + str(j) + ".png") else: _label = os.path.join(_label_dir, "liver_GT_" + str(j) + ".png") assert os.path.isfile(_image) assert os.path.isfile(_label) self.images.append(_image) self.labels.append(_label) self.train_num = int(len(self.images)*self.train_percent) self.val_num = int(len(self.images)*(1-self.train_percent)) random.shuffle( list(zip(self.images,self.labels))) self.train_images = self.images[0:self.train_num] self.train_labels = self.labels[0:self.train_num] self.val_images = self.images[self.train_num+1:] self.val_labels = self.labels[self.train_num+1:] pseudo_set = Chaos1Test(self.args) self.test_images = pseudo_set.images self.pseudo_labels = pseudo_set.pseudo_labels print('Number of images in {}:'.format(split)) if self.split == 'train': print('{:d}'.format(self.train_num+len(self.pseudo_labels))) else: print('{:d}'.format(self.val_num)) def __len__(self): if self.split == 'train': return self.train_num + len(self.pseudo_labels) elif self.split == 'val': return self.val_num def _make_img_gt_point_pair(self, item): _pseudo = False if self.split == 'train': if item >= self.train_num: _pseudo = True _img = Image.open(self.test_images[item-self.train_num]).convert("RGB") _target = Image.open(self.pseudo_labels[item-self.num_of_train]) else: _img = Image.open(self.train_images[item]).convert("RGB") _target = Image.open(self.train_labels[item]) else: _img = Image.open(self.val_images[item]).convert("RGB") _target = Image.open(self.val_labels[item]) return _img, _target, _pseudo def transform_tr(self, sample): composed_transforms = transforms.Compose([ tr.RandomHorizontalFlip(), tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size), tr.RandomGaussianBlur(), tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), tr.ToTensor()]) return composed_transforms(sample) def transform_val(self, sample): composed_transforms = transforms.Compose([ tr.FixScaleCrop(crop_size=self.args.crop_size), tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), tr.ToTensor()]) return composed_transforms(sample) def __getitem__(self, item): _img, _target, _pseudo =self._make_img_gt_point_pair(item) sample = {'image': _img, 'label': _target, 'pseudo': _pseudo} if self.split == 'train': return self.transform_tr(sample) elif self.split == 'val': return self.transform_val(sample) def __str__(self): return 'Chaos2019(split=' + str(self.split) + ')' class ChaosSegmentation2(Dataset): ''' Chaos Dataset ''' NUM_CLASSES = 5 def __init__(self, args, base_dir=Path.db_root_dir('chaos2'), split='train', ): super().__init__() self._base_dir = base_dir self.args = args self.split = split self.train_percent = self.args.train_percent self.images = [] self.labels = [] self.id_dir = [1, 2, 3, 5, 8, 10, 13, 15, 19, 20, 21, 22, 31, 32, 33, 34, 36, 37, 38, 39] self.ids_dir2 = [4, 10, 4, 16, 34, 46, 64, 75, 22, 27, 4, 5, 29, 31, 37, 5, 13, 17, 23, 27]#T1DUAL label self.ids_dir3 = [2, 7, 2, 14, 31, 43, 61, 73, 24, 29, 1, 8, 26, 30, 34, 8, 16, 20, 22, 26]#T2SPIR label p = 0 for i in self.id_dir: _image_dir = os.path.join(self._base_dir, "Patient-CHAOS MR_SET_" + str(i), "Study_" + str(i) + "_MR[]", str(i) + "1") _label_dir = os.path.join(self._base_dir, str(i), "T2SPIR", "Ground") _num_image = len([lists for lists in os.listdir(_image_dir) if os.path.isfile(os.path.join(_image_dir, lists))]) for j in range(_num_image): _file = "IMG-" if self.ids_dir3[p] < 10: _file += "000" else: _file += "00" _file += str(self.ids_dir3[p]) _file += "-00" if j < 9: _file += "00" elif j < 99: _file += "0" _file += str(j+1) _image = os.path.join(_image_dir, _file + ".jpg") _label = os.path.join(_label_dir, _file + ".png") assert os.path.isfile(_image) assert os.path.isfile(_label) self.images.append(_image) self.labels.append(_label) p += 1 p = 0 for i in self.id_dir: _image_dir = os.path.join(self._base_dir, "Patient-CHAOS MR_SET_" + str(i), "Study_" + str(i) + "_MR[]", str(i) + "2") _label_dir = os.path.join(self._base_dir, str(i), "T1DUAL", "Ground") _num_label = len([lists for lists in os.listdir(_label_dir) if os.path.isfile(os.path.join(_label_dir, lists))]) for j in range(_num_label): _file = "IMG-" if self.ids_dir2[p] < 10: _file += "000" else: _file += "00" _file += str(self.ids_dir2[p]) _file += "-00" _file2 = _file if 2*j < 8: _file += "00" elif 2*j < 98: _file += "0" _file += str(2*j+2) _image = os.path.join(_image_dir, _file + ".jpg") _label = os.path.join(_label_dir, _file + ".png") assert os.path.isfile(_image) assert os.path.isfile(_label) self.images.append(_image) self.labels.append(_label) p += 1 self.num_of_train = int(len(self.images)*self.train_percent) self.num_of_val = int(len(self.images)*(1-self.train_percent)) random.shuffle(list(zip(self.images,self.labels))) self.train_images=self.images[0:self.num_of_train] self.train_labels=self.labels[0:self.num_of_train] self.val_images=self.images[self.num_of_train+1:] self.val_labels=self.labels[self.num_of_train+1:] pseudo_set=Chaos2Test(self.args) self.test_images=pseudo_set.images self.pseudo_labels=pseudo_set.pseudo_labels print('Number of images in {}:'.format(split)) if self.split == 'train': print('{:d}'.format(self.num_of_train)) else: print('{:d}'.format(self.num_of_val)) def __len__(self): if self.split == 'train': return self.num_of_train + len(self.test_images) elif self.split == 'val': return self.num_of_val def _make_img_gt_point_pair(self, item): #set_trace() _pseudo = False _confidence = None if self.split == 'train': if item >= self.num_of_train: _pseudo = True _img = Image.open(self.test_images[item-self.num_of_train]).convert("RGB") _confidence = Image.open(self.confidence_map[item-self.num_of_train]) _target = Image.open(self.pseudo_labels[item-self.num_of_train]) else: _img = Image.open(self.train_images[item]).convert("RGB") _target = Image.open(self.train_labels[item]) else: _img = Image.open(self.val_images[item]).convert("RGB") _target = Image.open(self.val_labels[item]) _target = np.asarray(_target) _label = Image.fromarray(encode_segmap(_target,'chaos2').astype('uint8')) #_target = Image.open(self.labels[item]) return _img, _label, _confidence, _pseudo def transform_tr(self, sample): composed_transforms = transforms.Compose([ tr.RandomHorizontalFlip(), tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size), tr.RandomGaussianBlur(), tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), tr.ToTensor() tr.Labsize()]) return composed_transforms(sample) def transform_val(self, sample): composed_transforms = transforms.Compose([ tr.FixScaleCrop(crop_size=self.args.crop_size), tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), tr.ToTensor()]) return composed_transforms(sample) def __getitem__(self, item): _img, _target, _confidence, _pseudo = self._make_img_gt_point_pair(item) sample = {'image': _img, 'label': _target, 'confidence': _confidence, 'pseudo': _pseudo } if self.split == 'train': return self.transform_tr(sample) elif self.split == 'val': return self.transform_val(sample) def __str__(self): return 'Chaos2_2019(split=' + str(self.split) + ')' def __main__(): dataset1=Chaos2Segmentation('train') if __name__ == '__main__': main()

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6

9ca5826f9a87adc5d2dfe2d7b3e791b4cad6a186

146

py

Python

code/deploy/score.py

lyh01/hlazmlworkspace1

c562699d9ce77f943ae32fda61f538c2baead62b

[ "MIT" ]

null

code/deploy/score.py

lyh01/hlazmlworkspace1

c562699d9ce77f943ae32fda61f538c2baead62b

[ "MIT" ]

null

code/deploy/score.py

lyh01/hlazmlworkspace1

c562699d9ce77f943ae32fda61f538c2baead62b

[ "MIT" ]

1

2021-02-04T20:53:30.000Z

import logging def init(): logging.info(f"pseudo init") def run(data): logging.info(f"pseudo score") return { "predict": ["None"])

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143e42b9c4b3e056e56da0f5dad4bf8050480258

208

py

Python

quiz.py

Sami-ul/UnitCirclePracticer

c6a28d4cbe794438a23cacc52bdd66bea2e166de

[ "CC0-1.0" ]

2

2022-02-03T04:36:01.000Z

2022-02-04T03:20:24.000Z

quiz.py

Sami-ul/UnitCirclePracticer

c6a28d4cbe794438a23cacc52bdd66bea2e166de

[ "CC0-1.0" ]

null

quiz.py

Sami-ul/UnitCirclePracticer

c6a28d4cbe794438a23cacc52bdd66bea2e166de

[ "CC0-1.0" ]

null

from unitCircle import unitCircle circle = unitCircle() quiz = circle.generateQuiz(10, "degradtocoor") # quiz = circle.generateQuiz(10, "degtorad") print(quiz[0]) # quiz print("\n\n\n") print(quiz[1]) # key

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14421f94d5313f9f9de384160a37f97b21d72667

455

py

Python

tests/test_challenge25_pytest.py

stevenliu216/challenges

a8991fc3cc2309f8ef0ba6d189be001377153583

[ "MIT" ]

null

tests/test_challenge25_pytest.py

stevenliu216/challenges

a8991fc3cc2309f8ef0ba6d189be001377153583

[ "MIT" ]

14

2018-09-18T02:00:28.000Z

2019-07-08T15:59:56.000Z

tests/test_challenge25_pytest.py

stevenliu216/challenges

a8991fc3cc2309f8ef0ba6d189be001377153583

[ "MIT" ]

7

2018-09-17T14:52:24.000Z

2020-10-02T21:55:20.000Z

from challenges.challenge25 import max_profit def test_max_profit(): assert max_profit([7, 1, 5, 3, 6, 4]) == 5 assert max_profit([2, 1, 4, 5, 2, 9, 7]) == 8 def test_no_profit(): assert max_profit([7, 6, 4, 3, 1]) == 0 def test_buy_at_end(): assert max_profit([2, 1, 2, 1, 0, 1, 2]) == 2 def test_min_at_right(): assert max_profit([2, 4, 1]) == 2 def test_profit_not_at_min(): assert max_profit([3, 2, 6, 5, 0, 3]) == 4

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0.604396

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1

0

1

0

6

147e9e9c78980dc80e21c003f2b9c430aaa38491

159

py

Python

pctiler/tests/conftest.py

gadomski/planetary-computer-apis

53a04c0b24b9ccc06812bfb8ac2961bbfe58a108

[ "MIT" ]

null

pctiler/tests/conftest.py

gadomski/planetary-computer-apis

53a04c0b24b9ccc06812bfb8ac2961bbfe58a108

[ "MIT" ]

null

pctiler/tests/conftest.py

gadomski/planetary-computer-apis

53a04c0b24b9ccc06812bfb8ac2961bbfe58a108

[ "MIT" ]

null

import pytest from fastapi.testclient import TestClient from pctiler.main import app @pytest.fixture def client() -> TestClient: return TestClient(app)

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null

0

1

0

null

0

1

0

1

0

6

1ae8880be88869ae9ad57c3ba94ba4e4fbffe062

71

py

Python

PyBASC/tests/test_smoke.py

AkiNikolaidis/BASC

07ac80c1a22df84db8bdd30b09b881cecc8caf1d

[ "MIT" ]

24

2017-09-22T07:47:27.000Z

2021-09-10T07:04:59.000Z

PyBASC/tests/test_smoke.py

AkiNikolaidis/BASC

07ac80c1a22df84db8bdd30b09b881cecc8caf1d

[ "MIT" ]

19

2017-10-24T17:52:32.000Z

2019-10-02T17:51:04.000Z

PyBASC/tests/test_smoke.py

AkiNikolaidis/BASC

07ac80c1a22df84db8bdd30b09b881cecc8caf1d

[ "MIT" ]

4

2017-11-17T00:47:32.000Z

2020-11-02T17:56:14.000Z

def test_smoke(): assert "Testing TravisCI" == "Testing TravisCI"

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71

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1

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6

0d3d5d0a550bc839c6836e0ef2e515425f8fb6e2

46

py

Python

mlcollection/lib/bayesian/__init__.py

posborne/mlcollection

65e1d0902ad0a3e5a53d98fb68432ce98ff970a3

[ "MIT" ]

2

2015-07-24T23:53:18.000Z

2015-08-18T10:35:16.000Z

mlcollection/lib/bayesian/__init__.py

posborne/mlcollection

65e1d0902ad0a3e5a53d98fb68432ce98ff970a3

[ "MIT" ]

null

mlcollection/lib/bayesian/__init__.py

posborne/mlcollection

65e1d0902ad0a3e5a53d98fb68432ce98ff970a3

[ "MIT" ]

null

# TODO: implement set of bayesian classifiers

23

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1

46

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null

0

1

0

1

0

6

b4a400d1f331cd10333aa9bf143efe8f0bc8b204

34

py

Python

easytorch/data/__init__.py

sraashis/quenn

4bc6b7aca7ed13bed3502d0d5f2ea1a4c839bb41

[ "MIT" ]

20

2020-07-30T16:46:57.000Z

2021-12-11T21:24:19.000Z

easytorch/data/__init__.py

sraashis/quenn

4bc6b7aca7ed13bed3502d0d5f2ea1a4c839bb41

[ "MIT" ]

4

2020-12-13T15:03:28.000Z

2022-03-12T00:59:06.000Z

easytorch/data/__init__.py

sraashis/quenn

4bc6b7aca7ed13bed3502d0d5f2ea1a4c839bb41

[ "MIT" ]

3

2021-06-06T00:23:01.000Z

2021-11-08T14:21:13.000Z

from easytorch.data.data import *

17

33

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5.4

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1

0

6

b4b0e61430e34fb953486f0d967e182a7e83c709

47

py

Python

casbin_sqlalchemy_adapter/__init__.py

yyellowsun/sqlalchemy-adapter

d1ce6302dd4bdf483e0e10ca1b304ad25add8191

[ "Apache-2.0" ]

54

2019-03-04T11:19:16.000Z

2022-03-30T12:48:20.000Z

casbin_sqlalchemy_adapter/__init__.py

yyellowsun/sqlalchemy-adapter

d1ce6302dd4bdf483e0e10ca1b304ad25add8191

[ "Apache-2.0" ]

49

2019-04-26T20:00:44.000Z

2022-03-10T04:06:00.000Z

casbin_sqlalchemy_adapter/__init__.py

yyellowsun/sqlalchemy-adapter

d1ce6302dd4bdf483e0e10ca1b304ad25add8191

[ "Apache-2.0" ]

35

2019-04-19T21:50:58.000Z

2022-02-03T12:40:50.000Z

from .adapter import CasbinRule, Adapter, Base

23.5

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1

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1

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6

b4b59363e3b438bc469b98834a56ae90e72a6e58

43

py

Python

optimal_fantasy/models/__init__.py

Sphunt2005/HIOGJFSDIOJFGLK

bd8ca4d7fce53abc7d0ab61f79406f5d8c06f669

[ "MIT" ]

null

optimal_fantasy/models/__init__.py

Sphunt2005/HIOGJFSDIOJFGLK

bd8ca4d7fce53abc7d0ab61f79406f5d8c06f669

[ "MIT" ]

null

optimal_fantasy/models/__init__.py

Sphunt2005/HIOGJFSDIOJFGLK

bd8ca4d7fce53abc7d0ab61f79406f5d8c06f669

[ "MIT" ]

null

import optimal_fantasy.models.mip_complete

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0.902439

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0

null

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1

0

1

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1

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6

b4b8308fe774ee5cca9f1854763c68eef6e040b8

265

py

Python

01-logica-de-programacao-e-algoritmos/Aula 02/exercicio 04 aula-02.py

rafaelbarretomg/Uninter

1f84b0103263177122663e991db3a8aeb106a959

[ "MIT" ]

null

01-logica-de-programacao-e-algoritmos/Aula 02/exercicio 04 aula-02.py

rafaelbarretomg/Uninter

1f84b0103263177122663e991db3a8aeb106a959

[ "MIT" ]

null

01-logica-de-programacao-e-algoritmos/Aula 02/exercicio 04 aula-02.py

rafaelbarretomg/Uninter

1f84b0103263177122663e991db3a8aeb106a959

[ "MIT" ]

null

C = float(input('Digite a temperatura em Celsius: ')) F = ((9 * C)/5) + 32 # Maneira Classica print('A temperatura de %.2f graus Celsius em Fahrenheit é de %.2f' %(C, F)) # Maneira Moderna print('A temperatura {} graus Celsius em Fahrenheit é de {}' .format(C, F))

44.166667

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265

4.116279

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0.19209

0.271186

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265

6

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44.166667

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1

0

null

0

1

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6

b4e61123f7514a964f719aa0414103aeb7726e63

31

py

Python

tests/operators/__init__.py

VoiSmart/airflow

c8b67f2ade5a165d46677b59620f782b7e4d1983

[ "Apache-2.0" ]

null

tests/operators/__init__.py

VoiSmart/airflow

c8b67f2ade5a165d46677b59620f782b7e4d1983

[ "Apache-2.0" ]

null

tests/operators/__init__.py

VoiSmart/airflow

c8b67f2ade5a165d46677b59620f782b7e4d1983

[ "Apache-2.0" ]

null

from .docker_operator import *

15.5

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0.806452

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1

31

0.888889

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null

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1

0

1

0

1

0

6

37070678228c06d099555f024b5b33d479cfcb1d

163

py

Python

tests/test_propertypro.py

Ifyokoh/End-to-End-Machine-Learning

f23f8034aa3fc02c6dd834de50a25d5603adc8d2

[ "MIT" ]

null

tests/test_propertypro.py

Ifyokoh/End-to-End-Machine-Learning

f23f8034aa3fc02c6dd834de50a25d5603adc8d2

[ "MIT" ]

null

tests/test_propertypro.py

Ifyokoh/End-to-End-Machine-Learning

f23f8034aa3fc02c6dd834de50a25d5603adc8d2

[ "MIT" ]

null

import pytest from propertypro.propertypro import Propertypro def test_scrape_data() -> None: assert len(Propertypro().scrape_data(100, ["enugu"])) == 105

20.375

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0.736196

20

163

5.85

0.7

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0

0.042857

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163

7

65

23.285714

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0

0.030675

0

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0.25

true

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1

0

1

0

6

3718c4d3cfc72a0f332f2d88d403f1a8bca91e30

38

py

Python

pulsectl_asyncio/__init__.py

mbrea-c/pulsectl-asyncio

9755fd30b8fe3538192cf00e92a2b54586893c6b

[ "MIT" ]

7

2021-02-26T08:21:38.000Z

2021-11-18T11:51:54.000Z

pulsectl_asyncio/__init__.py

mbrea-c/pulsectl-asyncio

9755fd30b8fe3538192cf00e92a2b54586893c6b

[ "MIT" ]

6

2021-02-26T16:01:44.000Z

2022-03-22T21:36:24.000Z

pulsectl_asyncio/__init__.py

mbrea-c/pulsectl-asyncio

9755fd30b8fe3538192cf00e92a2b54586893c6b

[ "MIT" ]

3

2021-09-17T13:28:22.000Z

2022-03-17T03:53:25.000Z

from .pulsectl_async import PulseAsync

38

0.894737

5

38

6.6

1

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1

38

0.942857

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1

0

1

0

1

0

6

2ec35fd5524593053fb0dad5e0c4cf96216b387d

107

py

Python

cpf_cnpj/__init__.py

elcidon/cpf_cnpj

71db003669c5e75f62fd6347c5d50d8ae15a1c1a

[ "MIT" ]

null

cpf_cnpj/__init__.py

elcidon/cpf_cnpj

71db003669c5e75f62fd6347c5d50d8ae15a1c1a

[ "MIT" ]

null

cpf_cnpj/__init__.py

elcidon/cpf_cnpj

71db003669c5e75f62fd6347c5d50d8ae15a1c1a

[ "MIT" ]

null

from .cpf_cnpj import Cpf, Cnpj, CpfCnpj, BaseCpfCnpj __all__ = ["Cpf", "Cnpj", "CpfCnpj", "BaseCpfCnpj"]

26.75

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false

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null

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2eee2de1b6a1981df4667d4594c79b6f02a71ad0

235

py

Python

ariadne/tracknet_v2_1/__init__.py

t3hseus/ariadne

b4471a37741000e22281c4d6ff647d65ab9e1914

[ "MIT" ]

6

2020-08-28T22:44:07.000Z

2022-01-24T20:53:00.000Z

ariadne/tracknet_v2_1/__init__.py

t3hseus/ariadne

b4471a37741000e22281c4d6ff647d65ab9e1914

[ "MIT" ]

1

2021-02-20T09:38:46.000Z

ariadne/tracknet_v2_1/__init__.py

t3hseus/ariadne

b4471a37741000e22281c4d6ff647d65ab9e1914

[ "MIT" ]

2

2021-10-04T09:25:06.000Z

2022-02-09T09:09:09.000Z

from . import model from . import model_small from . import model_big from . import dataset from . import processor from . import processor_with_model from . import processor_for_validating from . import loss from . import data_loader

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0

0.153191

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2c162048e46ac6ab33261c0c1de3721d3319b43e

187

py

Python

References/Geovana Neves/TCC_Geovana_Neves_GitHub/SUAVE_modifications/SUAVE-feature-constant_throttle_EAS/trunk/SUAVE/Attributes/Atmospheres/Earth/__init__.py

Vinicius-Tanigawa/Undergraduate-Research-Project

e92372f07882484b127d7affe305eeec2238b8a9

[ "MIT" ]

null

References/Geovana Neves/TCC_Geovana_Neves_GitHub/SUAVE_modifications/SUAVE-feature-constant_throttle_EAS/trunk/SUAVE/Attributes/Atmospheres/Earth/__init__.py

Vinicius-Tanigawa/Undergraduate-Research-Project

e92372f07882484b127d7affe305eeec2238b8a9

[ "MIT" ]

null

References/Geovana Neves/TCC_Geovana_Neves_GitHub/SUAVE_modifications/SUAVE-feature-constant_throttle_EAS/trunk/SUAVE/Attributes/Atmospheres/Earth/__init__.py

Vinicius-Tanigawa/Undergraduate-Research-Project

e92372f07882484b127d7affe305eeec2238b8a9

[ "MIT" ]

null

# classes from Constant_Temperature import Constant_Temperature from US_Standard_1976 import US_Standard_1976 from International_Standard import International_Standard # packages # ...

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8

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0

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25997f4e8860292816eb547663bd2610eeed4142

47

py

Python

torchvision/models/quantization/__init__.py

fsavard-eai/vision

7d509c5daccb436e53c52a477b1ab214f34df4ac

[ "BSD-3-Clause" ]

null

torchvision/models/quantization/__init__.py

fsavard-eai/vision

7d509c5daccb436e53c52a477b1ab214f34df4ac

[ "BSD-3-Clause" ]

null

torchvision/models/quantization/__init__.py

fsavard-eai/vision

7d509c5daccb436e53c52a477b1ab214f34df4ac

[ "BSD-3-Clause" ]

1

2020-02-11T02:03:07.000Z

from .mobilenet import * from .resnet import *

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47

5.833333

0.666667

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0.170213

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true

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null

0

1

0

1

0

1

0

6

25fc5fd13dc3277363ca898cf7727837fe54cdc9

345

py

Python

stores/apps/shops/admin_urls.py

diassor/CollectorCity-Market-Place

892ad220b8cf1c0fc7433f625213fe61729522b2

[ "Apache-2.0" ]

135

2015-03-19T13:28:18.000Z

2022-03-27T06:41:42.000Z

stores/apps/shops/admin_urls.py

dfcoding/CollectorCity-Market-Place

e59acec3d600c049323397b17cae14fdcaaaec07

[ "Apache-2.0" ]

null

stores/apps/shops/admin_urls.py

dfcoding/CollectorCity-Market-Place

e59acec3d600c049323397b17cae14fdcaaaec07

[ "Apache-2.0" ]

83

2015-01-30T01:00:15.000Z

2022-03-08T17:25:10.000Z

from django.conf.urls.defaults import * #TODO: ask to martin and delete this file # #urlpatterns = patterns('', # url(r'^customers/$', 'lots.views.home_admin', name='customers_admin'), # url(r'^inventary/$', 'lots.views.home_admin', name='inventory_admin'), # url(r'^account/$', 'lots.views.home_admin', name='shop_account_admin'), #)

34.5

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4.893617

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0.169565

0.234783

0.286957

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0.113043

345

10

77

34.5

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null

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1

0

1

0

1

0

1

0

6

d305fb0f3a7e16058edcbe615936e47dd9059004

6,434

py

Python

tests/test_plotting.py

feilong/brainplotlib

9b65394a42ae816c7a9282adf5d79ae698fdd74e

[ "BSD-3-Clause" ]

12

2022-02-01T16:33:09.000Z

2022-02-10T10:56:50.000Z

tests/test_plotting.py

feilong/brainplotlib

9b65394a42ae816c7a9282adf5d79ae698fdd74e

[ "BSD-3-Clause" ]

null

tests/test_plotting.py

feilong/brainplotlib

9b65394a42ae816c7a9282adf5d79ae698fdd74e

[ "BSD-3-Clause" ]

null

import os import numpy as np import importlib.util from brainplotlib import brain_plot class TestPlotting: def test_icoorder5_masked(self, tmp_path): values = np.arange(9372), np.arange(9370) img = brain_plot(*values, vmax=18741, vmin=0, cmap=None) assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_icoorder5_masked.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]]) def test_icoorder5_masked_random(self, tmp_path): rng = np.random.default_rng() values = rng.random((9372, )), rng.random((9370, )) img = brain_plot(*values, vmax=1, vmin=0, cmap=None) assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_icoorder5_masked_random.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]]) def test_icoorder5_nonmasked(self, tmp_path): values = np.arange(10242), np.arange(10242) img = brain_plot(*values, vmax=20483, vmin=0, cmap=None) assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_icoorder5_nonmasked.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]]) def test_icoorder5_nonmasked_random(self, tmp_path): rng = np.random.default_rng() values = rng.random((10242, )), rng.random((10242, )) img = brain_plot(*values, vmax=1, vmin=0, cmap=None) assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_icoorder5_nonmasked_random.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]]) def test_icoorder3_masked(self, tmp_path): values = np.arange(588), np.arange(587) img = brain_plot(*values, vmax=1174, vmin=0, cmap=None) assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_icoorder3_masked.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]]) def test_icoorder3_masked_random(self, tmp_path): rng = np.random.default_rng() values = rng.random((588, )), rng.random((587, )) img = brain_plot(*values, vmax=1, vmin=0, cmap=None) assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_icoorder3_masked_random.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]]) def test_icoorder3_nonmasked(self, tmp_path): values = np.arange(642), np.arange(642) img = brain_plot(*values, vmax=1283, vmin=0, cmap=None) assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_icoorder3_nonmasked.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]]) def test_icoorder3_nonmasked(self, tmp_path): rng = np.random.default_rng() values = rng.random((642, )), rng.random((642, )) img = brain_plot(*values, vmax=1, vmin=0, cmap=None) assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_icoorder3_nonmasked_random.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]]) class TestColormaps: def test_bwr_cmap(self, tmp_path): rng = np.random.default_rng() values = rng.random((588, )), rng.random((587, )) img = brain_plot(*values, vmax=1, vmin=0, cmap='bwr') assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_bwr_cmap.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]]) def test_jet_cmap(self, tmp_path): rng = np.random.default_rng() values = rng.random((588, )), rng.random((587, )) img = brain_plot(*values, vmax=1, vmin=0, cmap='jet') assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_jet_cmap.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]]) class TestScale: def test_color_scale(self, tmp_path): rng = np.random.default_rng() values = rng.random((588, )), rng.random((587, )) img, scale = brain_plot(*values, vmax=1, vmin=0, cmap='viridis', return_scale=True) from matplotlib import cm assert isinstance(scale, cm.ScalarMappable) assert img.shape in [(1560, 1728, 4), (1560, 1728, 3)] assert img.dtype == np.float64 assert np.all(img <= 1) assert np.all(img >= 0) if importlib.util.find_spec('cv2'): import cv2 cv2.imwrite(os.path.join(tmp_path, 'test_colorscale.png'), np.round(img * 255).astype(np.uint8)[:, :, [2, 1, 0, 3]])

45.631206

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0.58968

943

6,434

3.914104

0.084836

0.041723

0.065565

0.083446

0.899756

0.868599

0.842861

0.835004

0

0.101986

0.248679

6,434

140

145

45.957143

0.661564

0

0.65873

0

0.052689

0.037302

0

0.357143

1

0.087302

false

0

0.214286

0

0.325397

0

null

0

1

0

null

0

6

d3127ab34dcec0d795089ba25587e35faf757639

249

py

Python

lib/__init__.py

acordonez/ENSO_metrics

6b5a3c33ec89a5c6de147bf6dc03872a89b9da90

[ "BSD-3-Clause" ]

11

2020-07-09T01:03:59.000Z

2022-03-18T11:39:06.000Z

lib/__init__.py

acordonez/ENSO_metrics

6b5a3c33ec89a5c6de147bf6dc03872a89b9da90

[ "BSD-3-Clause" ]

17

2020-07-09T00:42:14.000Z

2022-03-21T23:14:33.000Z

lib/__init__.py

acordonez/ENSO_metrics

6b5a3c33ec89a5c6de147bf6dc03872a89b9da90

[ "BSD-3-Clause" ]

4

2020-09-24T05:42:15.000Z

2022-03-15T16:17:53.000Z

from .EnsoCollectionsLib import * from .EnsoComputeMetricsLib import * from .EnsoErrorsWarnings import * from .EnsoMetricsLib import * from .EnsoToolsLib import * from .EnsoUvcdatToolsLib import * from .EnsoPlotLib import * from .KeyArgLib import *

27.666667

36

0.807229

24

249

8.375

0.416667

0.348259

0

0.128514

249

8

37

31.125

0.926267

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d37faf6da04807045bd3c9e11c76826693d48906

50

py

Python

webdriver/tests/support/__init__.py

shs96c/web-platform-tests

61acad6dd9bb99d32340eb41f5146de64f542359

[ "BSD-3-Clause" ]

4

2020-09-09T15:28:01.000Z

2021-12-01T00:59:56.000Z

webdriver/tests/support/__init__.py

shs96c/web-platform-tests

61acad6dd9bb99d32340eb41f5146de64f542359

[ "BSD-3-Clause" ]

1

2021-03-31T20:23:55.000Z

webdriver/tests/support/__init__.py

shs96c/web-platform-tests

61acad6dd9bb99d32340eb41f5146de64f542359

[ "BSD-3-Clause" ]

1

2021-04-06T20:06:58.000Z

from merge_dictionaries import merge_dictionaries

25

49

0.92

6

50

7.333333

0.666667

0.772727

0

0.08

50

1

50

0.956522

0

1

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true

0

1

0

1

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null

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1

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1

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1

0

6

d39f5fecd3d8efddec7769b22b9bd3abc7e6204b

79

py

Python

sonata/loggers/__init__.py

sergevkim/sonata

2250b60174628ee76fb7d54bf50e4b8b07b505d5

[ "MIT" ]

1

2021-03-15T19:01:43.000Z

sonata/loggers/__init__.py

sergevkim/sonata

2250b60174628ee76fb7d54bf50e4b8b07b505d5

[ "MIT" ]

null

sonata/loggers/__init__.py

sergevkim/sonata

2250b60174628ee76fb7d54bf50e4b8b07b505d5

[ "MIT" ]

null

from .base_logger import BaseLogger from .neptune_logger import NeptuneLogger

19.75

41

0.860759

10

79

6.6

0.7

0.363636

0

0.113924

79

3

42

26.333333

0.942857

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

d3a264cd68c46cc5f70b27ecb402c72485be8f75

71

py

Python

crepes/__init__.py

translational-informatics/crepes

5a9a088ccfe61fbd48118ca7ffc90ac524834df4

[ "MIT" ]

null

crepes/__init__.py

translational-informatics/crepes

5a9a088ccfe61fbd48118ca7ffc90ac524834df4

[ "MIT" ]

null

crepes/__init__.py

translational-informatics/crepes

5a9a088ccfe61fbd48118ca7ffc90ac524834df4

[ "MIT" ]

null

from .omopReader import read_omop_data from .generate import generate

17.75

38

0.84507

10

71

5.8

0.7

0

0.126761

71

3

39

23.666667

0.935484

0

1

0

1

0

true

0

1

0

1

0

1

0

null

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1

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1

0

null

0

1

0

1

0

1

0

6

6c8f44c564d3ef0690096b26c795c6bc22b7f12a

35

py

Python

textattack/datasets/translation/__init__.py

fighting41love/TextAttack

24e48f0022dc3a7bdcd5cbb3430f1c72cfcb522d

[ "MIT" ]

2

2020-07-08T08:55:37.000Z

2020-09-03T00:57:38.000Z

textattack/datasets/translation/__init__.py

SatoshiRobatoFujimoto/TextAttack

a809a9bddddff9f41750949e26edde26c8af6cfa

[ "MIT" ]

null

textattack/datasets/translation/__init__.py

SatoshiRobatoFujimoto/TextAttack

a809a9bddddff9f41750949e26edde26c8af6cfa

[ "MIT" ]

null

from .translation_datasets import *

35

0.857143

4

35

7.25

1

0

0.085714

35

1

35

0.90625

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6ca6473211e545ce9b160c9567941350880957cf

111

py

Python

app/osm_observer/model/__init__.py

grischard/osm-observer

9e833e98696abc4a2aab942c8899aaf039166fc1

[ "MIT" ]

4

2018-04-24T17:55:08.000Z

2021-02-18T00:52:04.000Z

app/osm_observer/model/__init__.py

grischard/osm-observer

9e833e98696abc4a2aab942c8899aaf039166fc1

[ "MIT" ]

1

2021-02-08T20:30:42.000Z

app/osm_observer/model/__init__.py

grischard/osm-observer

9e833e98696abc4a2aab942c8899aaf039166fc1

[ "MIT" ]

2

2019-09-27T23:57:11.000Z

2020-09-19T19:01:37.000Z

from .user import * from .coverage import * from .changes import * from .review import * from .filter import *

18.5

23

0.72973

15

111

5.4

0.466667

0.493827

0

0.18018

111

5

24

22.2

0.89011

0

1

0

true

0

1

0

1

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1

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1

0

null

0

1

0

1

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1

0

6

6cead22c7055416edbdf49b241c9f2804dcbc565

2,539

py

Python

epytope/Data/pssms/smm/mat/B_35_01_10.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/smm/mat/B_35_01_10.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/smm/mat/B_35_01_10.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

B_35_01_10 = {0: {'A': 0.075, 'C': 0.34, 'E': 0.274, 'D': 0.45, 'G': 0.33, 'F': -0.917, 'I': -0.282, 'H': -0.319, 'K': 0.455, 'M': -0.841, 'L': -0.281, 'N': 0.176, 'Q': -0.069, 'P': 1.066, 'S': 0.103, 'R': 0.444, 'T': 0.036, 'W': -0.03, 'V': 0.064, 'Y': -1.074}, 1: {'A': -0.427, 'C': 0.0, 'E': 0.45, 'D': 0.0, 'G': -0.241, 'F': 0.35, 'I': -0.059, 'H': -0.011, 'K': 0.518, 'M': -0.052, 'L': 0.014, 'N': 0.488, 'Q': 0.268, 'P': -1.098, 'S': 0.197, 'R': 0.105, 'T': 0.08, 'W': -0.136, 'V': -0.441, 'Y': -0.004}, 2: {'A': -0.078, 'C': 0.02, 'E': 0.008, 'D': 0.041, 'G': 0.221, 'F': -0.233, 'I': -0.261, 'H': -0.055, 'K': 0.265, 'M': -0.246, 'L': -0.045, 'N': 0.053, 'Q': -0.056, 'P': 0.219, 'S': 0.047, 'R': 0.209, 'T': 0.026, 'W': 0.044, 'V': -0.155, 'Y': -0.024}, 3: {'A': 0.022, 'C': -0.165, 'E': -0.003, 'D': -0.044, 'G': -0.071, 'F': 0.029, 'I': 0.002, 'H': 0.07, 'K': 0.144, 'M': 0.051, 'L': -0.023, 'N': 0.062, 'Q': -0.016, 'P': -0.046, 'S': -0.031, 'R': -0.015, 'T': 0.029, 'W': 0.026, 'V': 0.026, 'Y': -0.047}, 4: {'A': 0.017, 'C': 0.144, 'E': 0.037, 'D': -0.186, 'G': 0.057, 'F': -0.048, 'I': -0.148, 'H': 0.042, 'K': 0.129, 'M': -0.255, 'L': 0.047, 'N': 0.043, 'Q': -0.062, 'P': 0.067, 'S': 0.06, 'R': 0.21, 'T': -0.044, 'W': 0.02, 'V': -0.004, 'Y': -0.127}, 5: {'A': 0.001, 'C': 0.01, 'E': 0.001, 'D': 0.015, 'G': -0.028, 'F': -0.007, 'I': 0.026, 'H': 0.006, 'K': 0.03, 'M': -0.012, 'L': -0.031, 'N': -0.003, 'Q': 0.009, 'P': -0.016, 'S': 0.009, 'R': 0.02, 'T': -0.015, 'W': -0.0, 'V': -0.008, 'Y': -0.004}, 6: {'A': 0.058, 'C': -0.012, 'E': 0.096, 'D': -0.18, 'G': -0.112, 'F': -0.026, 'I': 0.066, 'H': 0.1, 'K': 0.108, 'M': 0.011, 'L': -0.029, 'N': 0.162, 'Q': -0.224, 'P': -0.109, 'S': 0.031, 'R': 0.254, 'T': -0.217, 'W': -0.022, 'V': 0.096, 'Y': -0.052}, 7: {'A': -0.172, 'C': -0.19, 'E': 0.06, 'D': 0.023, 'G': 0.195, 'F': 0.026, 'I': -0.104, 'H': 0.039, 'K': 0.265, 'M': -0.053, 'L': -0.016, 'N': 0.068, 'Q': 0.035, 'P': -0.001, 'S': 0.021, 'R': 0.137, 'T': -0.062, 'W': -0.1, 'V': -0.031, 'Y': -0.14}, 8: {'A': -0.196, 'C': 0.188, 'E': -0.149, 'D': 0.07, 'G': 0.188, 'F': 0.021, 'I': -0.258, 'H': 0.138, 'K': 0.518, 'M': -0.127, 'L': 0.089, 'N': -0.058, 'Q': 0.082, 'P': -0.221, 'S': -0.038, 'R': 0.015, 'T': -0.099, 'W': 0.199, 'V': -0.237, 'Y': -0.125}, 9: {'A': 0.029, 'C': 0.007, 'E': 0.344, 'D': 0.23, 'G': -0.067, 'F': -0.889, 'I': 0.344, 'H': 0.088, 'K': 0.335, 'M': -0.518, 'L': -0.105, 'N': 0.09, 'Q': 0.4, 'P': -0.281, 'S': -0.061, 'R': 0.161, 'T': 0.273, 'W': 0.1, 'V': 0.469, 'Y': -0.947}, -1: {'con': 4.6849}}

2,539

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0.020202

0.010101

0.012121

0.078788

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0.163056

2,539

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1

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0

1

0

1

0

null

0

6

9f270531128adb484cae5ac12daf3fc9ff79fdd5

292

py

Python

The Last Comit.py

aash-gates/aash-python-babysteps

cb88b02b0d33ac74acb183d4f11f6baad0ad3db9

[ "Unlicense" ]

7

2020-11-16T18:23:21.000Z

2021-12-18T14:08:54.000Z

The Last Comit.py

aash-gates/aash-python-babysteps

cb88b02b0d33ac74acb183d4f11f6baad0ad3db9

[ "Unlicense" ]

null

The Last Comit.py

aash-gates/aash-python-babysteps

cb88b02b0d33ac74acb183d4f11f6baad0ad3db9

[ "Unlicense" ]

1

2020-12-21T15:59:44.000Z

#A Last Commit on this Year 31/12/2021 print("Good Bye 2021 it was a Great Year had Lots of fun, and this is the last Commit for the year") print(".") print(".") print(".") print(".") print(".") print(".") print(".") print(".") print("This is the Last Comit for 2021") #end of the program

18.25

100

0.64726

51

292

3.705882

0.509804

0.42328

0.555556

0.634921

0.238095

0

0.066116

0.171233

292

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101

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0

1

0

null

0

1

0

1

0

6

9f6b86ace42a9ccdca95c654beb7d1cce5d865af

31

py

Python

training/__init__.py

awagot/CNN-POD

ebee234831ff58609563a925b7a47e0f4c30a16e

[ "CC0-1.0" ]

2

2021-04-08T10:30:58.000Z

2021-08-18T11:23:05.000Z

training/__init__.py

awagot/CNN-POD

ebee234831ff58609563a925b7a47e0f4c30a16e

[ "CC0-1.0" ]

1

2021-04-07T21:28:59.000Z

training/__init__.py

awagot/CNN-POD

ebee234831ff58609563a925b7a47e0f4c30a16e

[ "CC0-1.0" ]

2

2021-04-09T09:41:32.000Z

2021-04-16T13:09:43.000Z

from training.training import *

31

0.83871

4

31

6.5

0.75

0

0.096774

31

1

31

0.928571

0

1

0

true

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1

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1

0

null

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1

0

null

0

1

0

1

0

1

0

6

9f8b12673847116ad8a0d39897b52a94b5ff3752

21

py

Python

pycascades/utils/__init__.py

vishalbelsare/pycascades

61ac44f7db3093451f9778d2f6dee5f1390ce38a

[ "BSD-3-Clause" ]

11

2020-11-05T09:50:07.000Z

2022-03-30T18:34:07.000Z

pycascades/utils/__init__.py

vishalbelsare/pycascades

61ac44f7db3093451f9778d2f6dee5f1390ce38a

[ "BSD-3-Clause" ]

1

2021-11-08T15:10:26.000Z

2021-11-08T15:11:36.000Z

pycascades/utils/__init__.py

vitusbenson/pycascades

961f3e3cca43fcf75983ddf72821533f183e3a09

[ "BSD-3-Clause" ]

3

2021-09-11T09:03:30.000Z

2021-11-05T06:37:09.000Z

from . import plotter

21

0.809524

3

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5.666667

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0.142857

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1

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0.944444

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1

0

true

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0

null

0

1

0

null

0

1

0

1

0

1

0

6

9fad6af7615588e71b44c953892540525cedfef7

93

py

Python

test/main.py

sveneberth/pypi-test

d05e628476cfcd1c223e1ff547daeb2fd2e1c973

[ "MIT" ]

null

test/main.py

sveneberth/pypi-test

d05e628476cfcd1c223e1ff547daeb2fd2e1c973

[ "MIT" ]

null

test/main.py

sveneberth/pypi-test

d05e628476cfcd1c223e1ff547daeb2fd2e1c973

[ "MIT" ]

null

from first_package_xyz567 import bar print(bar.make_random_args()) print(bar.get_version())

18.6

36

0.817204

15

93

4.733333

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0.225352

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0.034884

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6

9fb2128ab8087e7d6575043c119defc3235841fc

46

py

Python

__init__.py

rhuard/PyTextDecorator

3ace385ba49bdcd1acb8f8033b9f25ed779eea97

[ "MIT" ]

null

__init__.py

rhuard/PyTextDecorator

3ace385ba49bdcd1acb8f8033b9f25ed779eea97

[ "MIT" ]

5

2016-08-16T00:38:54.000Z

2016-08-17T05:49:48.000Z

__init__.py

rhuard/PyTextDecorator

3ace385ba49bdcd1acb8f8033b9f25ed779eea97

[ "MIT" ]

null

from PyTextDecorator.pytextdecorator import *

23

45

0.869565

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10

0.75

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1

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true

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null

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0

null

0

1

0

1

0

1

0

6

4cd93f07708f652db8e94848852b5be4f44560ac

13,100

py

Python

refinery/bnpy/bnpy-dev/tests/suffstats/TestParamBag.py

csa0001/Refinery

0d5de8fc3d680a2c79bd0e9384b506229787c74f

[ "MIT" ]

103

2015-01-13T00:48:14.000Z

2021-11-08T10:53:22.000Z

refinery/bnpy/bnpy-dev/tests/suffstats/TestParamBag.py

csa0001/Refinery

0d5de8fc3d680a2c79bd0e9384b506229787c74f

[ "MIT" ]

7

2015-02-21T04:03:40.000Z

2021-08-23T20:24:54.000Z

refinery/bnpy/bnpy-dev/tests/suffstats/TestParamBag.py

csa0001/Refinery

0d5de8fc3d680a2c79bd0e9384b506229787c74f

[ "MIT" ]

27

2015-01-23T00:54:31.000Z

2020-12-30T14:30:50.000Z

''' Unit-tests for ParamBag ''' from bnpy.suffstats.ParamBag import ParamBag import numpy as np import unittest class TestParamBag(unittest.TestCase): def shortDescription(self): return None def test_setAllFieldsToZero_K1_D1(self, K=1, D=1): A = ParamBag(K=K, D=D) s = 123 N = np.ones(K) x = np.ones((K,D)) xxT = np.ones((K,D,D)) W = np.ones((K,K)) A.setField('s', s) A.setField('N', N, dims='K') A.setField('x', x, dims=('K','D')) A.setField('xxT', xxT, dims=('K','D','D')) A.setField('W', W, dims=('K','K')) A.setAllFieldsToZero() assert np.allclose(A.s, 0.0) assert np.allclose(A.N, np.zeros(K)) assert np.allclose(A.x, np.zeros(K)) assert np.allclose(A.xxT, np.zeros(K)) assert np.allclose(A.xxT, np.zeros((K,K))) ######################################################### insertEmptyComps def test_insertEmptyComps_K1_D1(self, K=1, D=1): A = ParamBag(K=K, D=D) s = 123 N = np.zeros(K) x = np.zeros((K,D)) xxT = np.zeros((K,D,D)) W = np.zeros((K,K)) A.setField('s', s) A.setField('N', N, dims='K') A.setField('x', x, dims=('K','D')) A.setField('xxT', xxT, dims=('K','D','D')) A.setField('W', W, dims=('K','K')) A.insertEmptyComps(2) assert np.allclose(A.s, 123) assert np.allclose(A.N, np.zeros(K+2)) assert np.allclose(A.x, np.zeros(K+2)) assert np.allclose(A.xxT, np.zeros(K+2)) assert np.allclose(A.W, np.zeros((K+2,K+2))) def test_insertEmptyComps_K1_D1(self, K=1, D=2): A = ParamBag(K=K, D=D) s = 123 N = np.zeros(K) x = np.zeros((K,D)) xxT = np.zeros((K,D,D)) W = np.zeros((K,K)) A.setField('s', s) A.setField('N', N, dims='K') A.setField('x', x, dims=('K','D')) A.setField('xxT', xxT, dims=('K','D','D')) A.setField('W', W, dims=('K','K')) A.insertEmptyComps(2) assert np.allclose(A.s, 123) assert np.allclose(A.N, np.zeros(K+2)) assert np.allclose(A.x, np.zeros((K+2,D))) assert np.allclose(A.xxT, np.zeros((K+2,D,D))) assert np.allclose(A.W, np.zeros((K+2,K+2))) def test_insertEmptyComps_K3_D1(self, K=3, D=1): A = ParamBag(K=K, D=D) s = 123 N = np.zeros(K) x = np.zeros((K,D)) xxT = np.zeros((K,D,D)) W = np.zeros((K,K)) A.setField('s', s) A.setField('N', N, dims='K') A.setField('x', x, dims=('K','D')) A.setField('xxT', xxT, dims=('K','D','D')) A.setField('W', W, dims=('K','K')) A.insertEmptyComps(2) assert np.allclose(A.s, 123) assert np.allclose(A.N, np.zeros(K+2)) assert np.allclose(A.x, np.zeros(K+2)) assert np.allclose(A.xxT, np.zeros(K+2)) assert np.allclose(A.W, np.zeros((K+2,K+2))) def test_insertEmptyComps_K3_D3(self, K=3, D=3): A = ParamBag(K=K, D=D) s = 123 N = np.zeros(K) x = np.zeros((K,D)) xxT = np.zeros((K,D,D)) W = np.zeros((K,K)) A.setField('s', s) A.setField('N', N, dims='K') A.setField('x', x, dims=('K','D')) A.setField('xxT', xxT, dims=('K','D','D')) A.setField('W', W, dims=('K','K')) A.insertEmptyComps(2) assert np.allclose(A.s, 123) assert np.allclose(A.N, np.zeros(K+2)) assert np.allclose(A.x, np.zeros((K+2,D))) assert np.allclose(A.xxT, np.zeros((K+2,D,D))) assert np.allclose(A.W, np.zeros((K+2,K+2))) ######################################################### Verify insert def test_insertComps_K1_D1(self): A = ParamBag(K=1,D=1) s = 123.456 A.setField('scalar', s, dims=None) A.setField('N', [1], dims='K') A.setField('x', [[1]], dims=('K','D')) A.setField('xxT', [[[1]]], dims=('K','D','D')) Abig = A.copy() Abig.insertComps(A) assert Abig.K == 2 assert np.allclose(Abig.N, np.hstack([A.N, A.N])) assert Abig.scalar == 2*s Abig.insertComps(A) assert Abig.K == 3 assert np.allclose(Abig.N, np.hstack([A.N, A.N, A.N])) assert Abig.scalar == 3*s A.insertComps(Abig) assert A.K == 4 assert A.scalar == 4*s assert np.allclose(A.N, np.hstack([1,1,1,1])) def test_insertComps_K1_D3(self, K=1, D=3): A = ParamBag(K=K,D=D) s = 123.456 A.setField('scalar', s, dims=None) A.setField('N', [1.0], dims='K') A.setField('x', np.random.rand(K,D), dims=('K','D')) A.setField('xxT', np.random.rand(K,D,D), dims=('K','D','D')) Abig = A.copy() Abig.insertComps(A) assert Abig.K == 2 assert np.allclose(Abig.N, np.hstack([A.N, A.N])) assert Abig.scalar == 2*s assert Abig.xxT.shape == (2,3,3) assert np.allclose(Abig.xxT[0], A.xxT) assert np.allclose(Abig.xxT[1], A.xxT) Abig.insertComps(A) assert Abig.K == 3 assert np.allclose(Abig.N, np.hstack([A.N, A.N, A.N])) assert Abig.scalar == 3*s assert Abig.xxT.shape == (3,3,3) assert np.allclose(Abig.xxT[0], A.xxT) assert np.allclose(Abig.xxT[1], A.xxT) A.insertComps(Abig) assert A.K == 4 assert A.scalar == 4*s assert np.allclose(A.N, np.hstack([1,1,1,1])) ######################################################### Verify remove def test_removeComp_K1_D1(self): A = ParamBag(K=1,D=1) A.setField('N', [1], dims='K') A.setField('x', [[1]], dims=('K','D')) with self.assertRaises(ValueError): A.removeComp(0) def test_removeComp_K3_D1(self): A = ParamBag(K=3,D=1) A.setField('N', [1,2,3], dims='K') A.setField('x', [[4],[5],[6]], dims=('K','D')) A.setField('W', np.ones((3,3)), dims=('K','K')) Aorig = A.copy() A.removeComp(1) assert Aorig.K == A.K + 1 assert A.N[0] == Aorig.N[0] assert A.N[1] == Aorig.N[2] assert np.allclose( A.x, [[4],[6]]) assert np.allclose(A.W, np.ones((2,2))) def test_remove_K3_D2(self, K=3, D=2): A = ParamBag(K=K, D=D) s = 123 N = np.random.rand(K) x = np.random.rand(K,D) xxT = np.random.randn(K,D,D) A.setField('s', s) A.setField('N', N, dims='K') A.setField('x', x, dims=('K','D')) A.setField('xxT', xxT, dims=('K','D','D')) Abig = A.copy() # First remove a few fields for k in range(K-1): A.removeComp(0) assert A.K == K - k - 1 assert A.s == s assert np.allclose(A.getComp(0).x, x[k+1]) assert np.allclose(A.getComp(0).xxT, xxT[k+1]) ######################################################### Verify get def test_getComp_K1_D1(self): A = ParamBag(K=1,D=1) A.setField('scalar', 1, dims=None) A.setField('N', [1], dims='K') A.setField('x', [[1]], dims=('K','D')) c = A.getComp(0) assert c.K == 1 assert c.N == A.N assert c.x == A.x assert id(c.scalar) != id(A.scalar) assert id(c.N) != id(A.N) assert id(c.x) != id(A.x) def test_getComp_K3_D1(self): A = ParamBag(K=3,D=1) A.setField('N', [1,2,3], dims='K') A.setField('x', [[4],[5],[6]], dims=('K','D')) c = A.getComp(0) assert c.K == 1 assert c.N == A.N[0] assert c.x == A.x[0] assert id(c.N) != id(A.N) assert id(c.x) != id(A.x) ######################################################### Verify add/subtract def test_add_K1_D1(self): A = ParamBag(K=1,D=1) B = ParamBag(K=1,D=1) C = A + B assert C.K == A.K and C.D == A.D A.setField('N', [1], dims='K') B.setField('N', [10], dims='K') C = A + B assert C.N[0] == 11.0 def test_add_K3_D2(self, K=3, D=2): A = ParamBag(K=K,D=D) A.setField('xxT', np.random.randn(K,D,D), dims=('K','D','D')) B = ParamBag(K=K,D=D) B.setField('xxT', np.random.randn(K,D,D), dims=('K','D','D')) C = A + B assert np.allclose(C.xxT, A.xxT + B.xxT) def test_sub_K3_D2(self, K=3, D=2): A = ParamBag(K=K,D=D) A.setField('xxT', np.random.randn(K,D,D), dims=('K','D','D')) B = ParamBag(K=K,D=D) B.setField('xxT', np.random.randn(K,D,D), dims=('K','D','D')) C = A - B assert np.allclose(C.xxT, A.xxT - B.xxT) def test_iadd_K3_D2(self, K=3, D=2): A = ParamBag(K=K,D=D) A.setField('xxT', np.random.randn(K,D,D), dims=('K','D','D')) A.setField('x', np.random.randn(K,D), dims=('K','D')) B = ParamBag(K=K,D=D) B.setField('x', np.random.randn(K,D), dims=('K','D')) B.setField('xxT', np.random.randn(K,D,D), dims=('K','D','D')) origID = hex(id(A)) A += B newID = hex(id(A)) assert origID == newID A = A + B newnewID = hex(id(A)) assert newnewID != origID def test_isub_K3_D2(self, K=3, D=2): A = ParamBag(K=K,D=D) A.setField('xxT', np.random.randn(K,D,D), dims=('K','D','D')) A.setField('x', np.random.randn(K,D), dims=('K','D')) B = ParamBag(K=K,D=D) B.setField('x', np.random.randn(K,D), dims=('K','D')) B.setField('xxT', np.random.randn(K,D,D), dims=('K','D','D')) origID = hex(id(A)) A -= B newID = hex(id(A)) assert origID == newID A = A - B newnewID = hex(id(A)) assert newnewID != origID ######################################################### Dim 0 parsing def test_parseArr_dim0_passes(self): PB1 = ParamBag(K=1, D=1) x = PB1.parseArr(1.23, dims=None) assert x.ndim == 0 and x.size == 1 x = PB1.parseArr([1.23], dims=('K')) assert x.ndim == 1 and x.size == 1 PB2 = ParamBag(K=2, D=1) x = PB2.parseArr(1.23, dims=None) assert x.ndim == 0 and x.size == 1 PB5 = ParamBag(K=5, D=40) x = PB5.parseArr(1.23, dims=None) assert x.ndim == 0 and x.size == 1 def test_parseArr_dim0_fails(self): ''' Verify fails for 0-dim input when K > 1 ''' PB2 = ParamBag(K=2, D=1) with self.assertRaises(ValueError): x = PB2.parseArr(1.23, dims=('K')) with self.assertRaises(ValueError): x = PB2.parseArr(1.23, dims='K') ######################################################### Dim 1 parsing def test_parseArr_dim1_passes(self): # K = 1, D = 1 PB1 = ParamBag(K=1, D=1) x = PB1.parseArr([1.23], dims='K') assert x.ndim == 1 and x.size == 1 x = PB1.parseArr([[1.23]], dims=('K','D')) assert x.ndim == 2 and x.size == 1 # K = *, D = 1 PB2 = ParamBag(K=2, D=1) x = PB2.parseArr([1.,2.], dims='K') assert x.ndim == 1 and x.size == 2 x = PB2.parseArr([[1.],[2.]], dims=('K','D')) assert x.ndim == 2 and x.size == 2 # K = 1, D = * PB3 = ParamBag(K=1, D=3) x = PB3.parseArr([[1., 2., 3.]], dims=('K','D')) assert x.ndim == 2 and x.size == 3 # K = *, D = * PB2 = ParamBag(K=4, D=1) x = PB2.parseArr([[1.],[2.],[3.],[4.]], dims=('K','D')) assert x.ndim == 2 and x.size == 4 N = PB2.parseArr([1.,2.,3.,4.], dims='K') assert N.ndim == 1 and N.size == 4 def test_parseArr_dim1_fails(self): PB1 = ParamBag(K=1, D=1) with self.assertRaises(ValueError): x = PB1.parseArr([1.23], dims=('K','D')) PB2 = ParamBag(K=2, D=1) with self.assertRaises(ValueError): x = PB2.parseArr([1.23], dims=('K')) with self.assertRaises(ValueError): x = PB2.parseArr([1.23], dims=('K','D')) PB3 = ParamBag(K=1, D=3) with self.assertRaises(ValueError): x = PB3.parseArr([1.,2.], dims=('K','D')) PB3 = ParamBag(K=2, D=3) with self.assertRaises(ValueError): x = PB3.parseArr([1.,2.,3.,4.,5.,6.], dims=('K','D')) ######################################################### Dim 2 parsing def test_parseArr_dim2_passes(self): PB2 = ParamBag(K=2, D=2) x = PB2.parseArr(np.eye(2), dims=('K','D')) assert x.ndim == 2 and x.size == 4 PB31 = ParamBag(K=3, D=1) x = PB31.parseArr([[10],[11],[12]], dims=('K','D')) assert x.ndim == 2 and x.size == 3 def test_parseArr_dim2_fails(self): PB2 = ParamBag(K=2, D=2) with self.assertRaises(ValueError): x = PB2.parseArr([[1.,2]], dims=('K')) with self.assertRaises(ValueError): x = PB2.parseArr([[1.,2]], dims=('K','D')) with self.assertRaises(ValueError): x = PB2.parseArr(np.eye(3), dims=('K','D')) PB1 = ParamBag(K=1, D=2) with self.assertRaises(ValueError): # should be 1x2x2, not 2x2 x = PB1.parseArr(np.eye(2), dims=('K','D','D')) ######################################################### Dim 3 parsing def test_parseArr_dim3_passes(self): K=2 D=2 PB = ParamBag(K=K, D=D) x = PB.parseArr(np.random.randn(K,D,D), dims=('K','D','D')) assert x.ndim == 3 and x.size == K*D*D K=1 D=2 PB = ParamBag(K=K, D=D) x = PB.parseArr(np.random.rand(K,D,D), dims=('K','D', 'D')) assert x.ndim == 3 and x.size == K*D*D K=3 D=1 PB = ParamBag(K=K, D=D) x = PB.parseArr(np.random.rand(K,D,D), dims=('K','D', 'D')) assert x.ndim == 3 and x.size == K*D*D def test_parseArr_dim3_fails(self): PB = ParamBag(K=2, D=2) with self.assertRaises(ValueError): x = PB.parseArr([[[1.,2]]], dims=('K')) with self.assertRaises(ValueError): x = PB.parseArr([[[1.,2]]], dims=('K','D')) with self.assertRaises(ValueError): x = PB.parseArr(np.random.randn(3,3,3), dims=('K','D')) with self.assertRaises(ValueError): x = PB.parseArr(np.random.randn(3,3,3), dims=('K','D','D'))

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981b33f85196fab8723b0323f91f04b14ff682b7

75

py

Python

test_data/examples/class_def_in_multi_files/level_two/k2.py

mosckital/python-mro-language-server

de5b7dd8d94ab2c94543c55a7c5e5691e664b4a4

[ "MIT" ]

null

test_data/examples/class_def_in_multi_files/level_two/k2.py

mosckital/python-mro-language-server

de5b7dd8d94ab2c94543c55a7c5e5691e664b4a4

[ "MIT" ]

7

2020-09-18T22:55:50.000Z

2020-10-07T22:50:40.000Z

tests/examples/class_def_in_multi_files/level_two/k2.py

mosckital/vscode_python_mro

7a61a4a4dc1e4bc73fcdc9d5242a5fee500d6b1d

[ "MIT" ]

null

from ..level_one.intermediate_defs import B, D, E class K2(D, B, E): pass

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py

Python

src/funchain/__init__.py

eugenma/funchain

53c9c71a10eaa587d8d7a7e9adc3e13046f2bf14

[ "MIT" ]

null

src/funchain/__init__.py

eugenma/funchain

53c9c71a10eaa587d8d7a7e9adc3e13046f2bf14

[ "MIT" ]

null

src/funchain/__init__.py

eugenma/funchain

53c9c71a10eaa587d8d7a7e9adc3e13046f2bf14

[ "MIT" ]

null

from .funchain import Chain

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e26d821cc27fa0591735af46c74542a630b4182e

184

py

Python

lunzi/serialization.py

roosephu/boots

2f4f500f54feb95cf36abd863f3de4510d6f4950

[ "MIT" ]

13

2019-10-15T10:43:39.000Z

2021-03-20T06:27:15.000Z

lunzi/serialization.py

roosephu/boots

2f4f500f54feb95cf36abd863f3de4510d6f4950

[ "MIT" ]

null

lunzi/serialization.py

roosephu/boots

2f4f500f54feb95cf36abd863f3de4510d6f4950

[ "MIT" ]

6

2020-01-21T06:51:18.000Z

2021-05-27T20:25:35.000Z

from typing import Union, IO, Any import numpy as np def save(obj: Any, file: Union[str, IO]): np.save(file, obj) def load(file: Union[str, IO]): return np.load(file)[()]

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e2a613d42012d3909e347ab788f02d69784ab4cf

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py

Python

lang/Python/literals-string-2.py

ethansaxenian/RosettaDecode

8ea1a42a5f792280b50193ad47545d14ee371fb7

[ "MIT" ]

5

2021-01-29T20:08:05.000Z

2022-03-22T06:16:05.000Z

lang/Python/literals-string-2.py

ethansaxenian/RosettaDecode

8ea1a42a5f792280b50193ad47545d14ee371fb7

[ "MIT" ]

null

lang/Python/literals-string-2.py

ethansaxenian/RosettaDecode

8ea1a42a5f792280b50193ad47545d14ee371fb7

[ "MIT" ]

1

2021-04-13T04:19:31.000Z

r'\x20' == '\\x20'

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py

Python

number_alt.py

takanoriyanagitani/row2columnar

ce6474227a0ace55762a9d3a92b76581e8dd3bd9

[ "MIT" ]

null

number_alt.py

takanoriyanagitani/row2columnar

ce6474227a0ace55762a9d3a92b76581e8dd3bd9

[ "MIT" ]

null

number_alt.py

takanoriyanagitani/row2columnar

ce6474227a0ace55762a9d3a92b76581e8dd3bd9

[ "MIT" ]

null

import math def number_alt_float_none(f=0.0, alt=0.0): return alt if None == f else f def number_alt_float_nan(f=0.0, alt=0.0): return alt if math.isnan(f) else f def number_alt_float_nn(f=0.0, alt=0.0): return number_alt_float_nan( number_alt_float_none(f, alt), alt )

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e2fae4c67334343112ceb3c28d8e06f09aed5cf1

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py

Python

sources/simulators/multiprocessing_simulator/__init__.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

sources/simulators/multiprocessing_simulator/__init__.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

sources/simulators/multiprocessing_simulator/__init__.py

M4rukku/impact_of_non_iid_data_in_federated_learning

c818db03699c82e42217d56f8ddd4cc2081c8bb1

[ "MIT" ]

null

from .multiprocessing_simulator import MultiprocessingBasedSimulator

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py

Python

models/__init__.py

SJTU-lqiu/QA4IE

5b58612ad7a423279e0c6852750fcec3b3fde321

[ "MIT" ]

28

2018-05-02T01:37:41.000Z

2021-06-13T04:21:15.000Z

models/__init__.py

SJTU-lqiu/QA4IE

5b58612ad7a423279e0c6852750fcec3b3fde321

[ "MIT" ]

1

2019-08-21T09:55:38.000Z

2019-08-26T01:15:37.000Z

models/__init__.py

SJTU-lqiu/QA4IE

5b58612ad7a423279e0c6852750fcec3b3fde321

[ "MIT" ]

6

2018-05-06T13:58:04.000Z

2021-08-24T05:25:18.000Z

from .qa4ie import QA4IESS, QA4IEQA, QA4IEAT

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1a54a282a4e02fee15b483d99f74eea570df3f33

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py

Python

Python/8 kyu/Century From Year/solution.py

Hsins/CodeWars

7e7b912fdd0647c0af381d8b566408e383ea5df8

[ "MIT" ]

1

2020-01-09T21:47:56.000Z

Python/8 kyu/Century From Year/solution.py

Hsins/CodeWars

7e7b912fdd0647c0af381d8b566408e383ea5df8

[ "MIT" ]

1

2020-01-20T12:39:03.000Z

Python/8 kyu/Century From Year/solution.py

Hsins/CodeWars

7e7b912fdd0647c0af381d8b566408e383ea5df8

[ "MIT" ]

null

# [8 kyu] Century From Year # # Author: Hsins # Date: 2019/12/21 import math def century(year): return math.ceil(year / 100)

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py

Python

app/repository/events.py

maestro-server/data-app

cde6479cc84fe410220b34742772d5017571e3d3

[ "Apache-2.0" ]

null

app/repository/events.py

maestro-server/data-app

cde6479cc84fe410220b34742772d5017571e3d3

[ "Apache-2.0" ]

1

2019-11-21T17:06:31.000Z

app/repository/events.py

maestro-server/data-app

cde6479cc84fe410220b34742772d5017571e3d3

[ "Apache-2.0" ]

null

from .model import Model class Events(Model): pass

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204d72987c9530a046e99b4624bc1892ca9faf39

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py

Python

Streamlit/pages/page1.py

jhockx/server-configuration

106bc6c0a57eaa582486701c80aac4f968ef0ba0

[ "MIT" ]

1

2021-04-28T06:15:14.000Z

Streamlit/pages/page1.py

jhockx/server-configuration

106bc6c0a57eaa582486701c80aac4f968ef0ba0

[ "MIT" ]

null

Streamlit/pages/page1.py

jhockx/server-configuration

106bc6c0a57eaa582486701c80aac4f968ef0ba0

[ "MIT" ]

null

import streamlit as st def main(): st.title('Page 1 -- TITLE')

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6

645d2d79a46aad626c48e854ad7e5dcefb97dc2a

100

py

Python

testsuite/E71.py

dpursehouse/pep8

8d658692345e6866741719595f14a144337b3b9f

[ "MIT" ]

1

2015-08-04T11:47:25.000Z

testsuite/E71.py

dpursehouse/pep8

8d658692345e6866741719595f14a144337b3b9f

[ "MIT" ]

null

testsuite/E71.py

dpursehouse/pep8

8d658692345e6866741719595f14a144337b3b9f

[ "MIT" ]

null

#: E712 if res == True: pass #: E712 if res != False: pass #: E711 if res == None: pass

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100

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null

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646cfc1949f60de4e3efd6f01431e2bc12c6ce95

174

py

Python

Django-Projects/djangoprojects/rootapp/views.py

Pratyush-Avi/Django-Project

e1173d87bab57dd165814589f0f64af20fc41049

[ "MIT" ]

null

Django-Projects/djangoprojects/rootapp/views.py

Pratyush-Avi/Django-Project

e1173d87bab57dd165814589f0f64af20fc41049

[ "MIT" ]

null

Django-Projects/djangoprojects/rootapp/views.py

Pratyush-Avi/Django-Project

e1173d87bab57dd165814589f0f64af20fc41049

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpResponse # Create your views here. def root(request): return HttpResponse("The server has started")

21.75

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0.764368

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6

648f1ca9e67a6b1b4b7dc5735a2d858453c25c4e

150

py

Python

pattern/8.py

itspuneet/itspuneet

d44f78afcff275aa56f03bba738ac3e4f2c30843

[ "bzip2-1.0.6" ]

null

pattern/8.py

itspuneet/itspuneet

d44f78afcff275aa56f03bba738ac3e4f2c30843

[ "bzip2-1.0.6" ]

null

pattern/8.py

itspuneet/itspuneet

d44f78afcff275aa56f03bba738ac3e4f2c30843

[ "bzip2-1.0.6" ]

null

for i in range(5): for j in range(i): print(' ',end='') for j in range((2*5-2*i)-1): print('*',end='') print()

18.75

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7

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false

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0

null

1

0

1

0

null

0

1

0

6

3754ad274835a492f5cbab68c4857816cfc43a1c

67

py

Python

testchild.py

stephaniecorwin/CTDSwk2

5f6cabfab98314c72a6ecb9f0470f68ca712fcb0

[ "Unlicense" ]

null

testchild.py

stephaniecorwin/CTDSwk2

5f6cabfab98314c72a6ecb9f0470f68ca712fcb0

[ "Unlicense" ]

null

testchild.py

stephaniecorwin/CTDSwk2

5f6cabfab98314c72a6ecb9f0470f68ca712fcb0

[ "Unlicense" ]

null

## Adding a new file in child branch print ("Inside child branch")

22.333333

36

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11

67

4.454545

0.818182

0.44898

0

0.179104

67

2

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33.5

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0.492537

0

0.612903

0

1

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true

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1

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0

1

0

null

0

1

0

1

0

6

377d96c7df5de2e4bb3457c3cfe98f19c6bd3c8f

27

py

Python

admin/__init__.py

drnasmith/flask-ispyb-admin

eebf7ee9489e22265aa7cd23263a3bb74efa9a86

[ "Apache-2.0" ]

null

admin/__init__.py

drnasmith/flask-ispyb-admin

eebf7ee9489e22265aa7cd23263a3bb74efa9a86

[ "Apache-2.0" ]

null

admin/__init__.py

drnasmith/flask-ispyb-admin

eebf7ee9489e22265aa7cd23263a3bb74efa9a86

[ "Apache-2.0" ]

null

from admin import init_app

13.5

26

0.851852

5

27

4.4

1

0

0.148148

27

1

27

0.956522

0

1

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1

0

null

0

1

0

1

0

1

0

6

37893131ee38c3cc761c697d0fecc850e4358835

120

py

Python

Ascending-binary-sorting.py

chandrikadeb7/LinkedIn-SDE-CodingSolutions

8c9ef219a08e030c99f53f52db57d327550a0367

[ "MIT" ]

3

2021-03-12T08:14:23.000Z

2021-07-16T06:47:40.000Z

Ascending-binary-sorting.py

chandrikadeb7/LinkedIn-SDE-CodingSolutions

8c9ef219a08e030c99f53f52db57d327550a0367

[ "MIT" ]

null

Ascending-binary-sorting.py

chandrikadeb7/LinkedIn-SDE-CodingSolutions

8c9ef219a08e030c99f53f52db57d327550a0367

[ "MIT" ]

1

2021-04-17T18:06:24.000Z

def rearrange(elements): # Write your code here return sorted(elements, key=lambda x:(str(bin(x)).count('1'),x))

40

68

0.675

19

120

4.263158

0.842105

0

0.009804

0.15

120

3

68

40

0.784314

0.166667

0

0.010101

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0.333333

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1

0.5

false

0

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1

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null

0

1

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1

0

1

0

6

37998a05ca0d09a8a0b7a0f248a0053e58cc97c0

89

py

Python

application/views/doctor/__init__.py

mrpoor/heart_telehealth

74f6ea9400e0691207d42e9987cb60b3a4b1681c

[ "MIT" ]

null

application/views/doctor/__init__.py

mrpoor/heart_telehealth

74f6ea9400e0691207d42e9987cb60b3a4b1681c

[ "MIT" ]

null

application/views/doctor/__init__.py

mrpoor/heart_telehealth

74f6ea9400e0691207d42e9987cb60b3a4b1681c

[ "MIT" ]

null

from flask import Blueprint doctor = Blueprint('doctor', __name__) from . import views

14.833333

38

0.764045

11

89

5.818182

0.636364

0.46875

0

0.157303

89

5

39

17.8

0.853333

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1

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false

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0.666667

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0.666667

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null

1

0

1

0

null

0

1

0

1

0

6

80895058b0f82ecfe56051254c8769ab8fdf2339

30,272

py

Python

guidance_plugin_validator/src/guidance_plugin_validator/guidance_plugin_validator.py

usdot-fhwa-stol/carma-platform

d45a1afbf1efdb0b8cd62fcec5a3033b7306df33

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

112

2020-04-27T17:06:46.000Z

2022-03-31T15:27:14.000Z

guidance_plugin_validator/src/guidance_plugin_validator/guidance_plugin_validator.py

usdot-fhwa-stol/carma-platform

d45a1afbf1efdb0b8cd62fcec5a3033b7306df33

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

982

2020-04-17T11:28:04.000Z

2022-03-31T21:12:19.000Z

guidance_plugin_validator/src/guidance_plugin_validator/guidance_plugin_validator.py

usdot-fhwa-stol/carma-platform

d45a1afbf1efdb0b8cd62fcec5a3033b7306df33

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

57

2020-05-07T15:48:11.000Z

2022-03-09T23:31:45.000Z

#!/usr/bin/env python """ * Copyright (C) 2021 LEIDOS. * * 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 rospy import rosnode import guidance_plugin_components from cav_msgs.msg import Plugin from cav_msgs.msg import SystemAlert class GuidancePluginValidator: """ Primary class for the guidance_plugin_validator node. Conducts the validation of each of the Guidance Plugins (of type 'Strategic', 'Tactical', or 'Control-Wrapper') as provided by this node's configuration parameters. """ def __init__(self): """Default constructor for GuidancePluginValidator""" # Create plugin_discovery subscriber self.plugin_discovery_sub = rospy.Subscriber("plugin_discovery", Plugin, self.plugin_discovery_cb) self.system_alert_sub = rospy.Subscriber("system_alert", SystemAlert, self.system_alert_cb) # Read in config params self.validation_duration = rospy.get_param('~validation_duration', 300) # Maximum time (sec) that node will spend conducting validation before results are considered final self.strategic_plugin_names = rospy.get_param('~strategic_plugins_to_validate', []) self.tactical_plugin_names = rospy.get_param('~tactical_plugins_to_validate', []) self.control_plugin_names = rospy.get_param('~control_plugins_to_validate', []) # Write config params to log file rospy.loginfo("Config params for guidance_plugin_validator:") rospy.loginfo("Validation Duration: " + str(self.validation_duration) + " seconds") rospy.loginfo("Strategic Guidance Plugins: " + str(self.strategic_plugin_names)) rospy.loginfo("Tactical Guidance Plugins: " + str(self.tactical_plugin_names)) rospy.loginfo("Control Guidance Plugins: " + str(self.control_plugin_names)) # Boolean flag to indicate whether drivers are ready (this indicates that plugin node validation checks can begin) self.has_startup_completed = False # Boolean flag to indicate whether each guidance plugin's node has been validated self.has_node_validation_completed = False # Boolean flag to indicate whether final results have been written to log file self.has_logged_final_results = False # Set spin rate self.spin_rate = rospy.Rate(10) # 10 Hz # Initialize empty dicts that will be populated with a <plugin-type>PluginResults object for each Guidance Plugin self.strategic_plugin_validation_results = {} # Key is plugin's name; Value is plugin's StrategicPluginResults object self.tactical_plugin_validation_results = {} # Key is plugin's name; Value is plugin's TacticalPluginResults object self.control_plugin_validation_results = {} # Key is plugin's name; Value is plugin's ControlPluginResults object # Call member function to populate the 'validation results' dicts self.populate_results_dicts(self.strategic_plugin_names, self.tactical_plugin_names, self.control_plugin_names) def populate_results_dicts(self, strategic_plugin_names, tactical_plugin_names, control_plugin_names): """Initialize the the 'validation results' lists and 'index by name' dicts for this object""" # Populate validation results dict for Strategic Plugins for plugin_name in strategic_plugin_names: self.strategic_plugin_validation_results[plugin_name] = guidance_plugin_components.StrategicPluginResults(plugin_name) # Populate validation results dict for Tactical Plugins for plugin_name in tactical_plugin_names: self.tactical_plugin_validation_results[plugin_name] = guidance_plugin_components.TacticalPluginResults(plugin_name) # Populate validation results dict for Control Plugins for plugin_name in control_plugin_names: self.control_plugin_validation_results[plugin_name] = guidance_plugin_components.ControlPluginResults(plugin_name) return def spin(self): """ Function to ensure node spins at configured spin rate. """ while not rospy.is_shutdown(): if self.has_startup_completed: # Conduct node validation if it has not yet occurred if not self.has_node_validation_completed: self.conduct_node_validation() self.has_node_validation_completed = True # If time has surpassed the configured validation duration, the current results are considered final. Write to log file. seconds_since_startup_completed = rospy.get_time() - self.start_time_seconds if (seconds_since_startup_completed >= self.validation_duration): if not self.has_logged_final_results: self.log_final_results_for_each_plugin() self.has_logged_final_results = True self.spin_rate.sleep() return def log_final_results_for_each_plugin(self): """ Calls appropriate function for each plugin's 'results' object in order to write all final validation results to the log file for this node. """ rospy.loginfo("**********************************************************") rospy.loginfo("******Final Validation Results for Strategic Plugins******") rospy.loginfo("**********************************************************") # Write final validation results to log file for Guidance Strategic Plugins for plugin_name, plugin_results_object in self.strategic_plugin_validation_results.items(): plugin_results_object.write_strategic_final_results_to_logs() rospy.loginfo("**********************************************************") rospy.loginfo("******Final Validation Results for Tactical Plugins*******") rospy.loginfo("**********************************************************") # Write final validation results to log file for Guidance Tactical Plugins for plugin_name, plugin_results_object in self.tactical_plugin_validation_results.items(): plugin_results_object.write_tactical_final_results_to_logs() rospy.loginfo("**********************************************************") rospy.loginfo("*******Final Validation Results for Control Plugins*******") rospy.loginfo("**********************************************************") # Write final validation results to log file for Guidance Control Plugins for plugin_name, plugin_results_object in self.control_plugin_validation_results.items(): plugin_results_object.write_control_final_results_to_logs() rospy.loginfo("**********************************************************") rospy.loginfo("*******End of Final Validation Results for Plugins********") rospy.loginfo("**********************************************************") return def system_alert_cb(self, msg): """ Callback function for the system_alert topic. The Guidance Plugin Validator Node will begin conducting validation checks on each plugin's node after a 'DRIVERS_READY' alert has been received. """ # Startup has completed when drivers are ready if msg.type == SystemAlert.DRIVERS_READY: rospy.loginfo("DRIVERS_READY message received. Beginning node validation.") self.start_time_seconds = rospy.get_time() self.has_startup_completed = True return def plugin_discovery_cb(self, msg): """ Callback function for the plugin_discovery topic. Processes the first received message for each guidance plugin (as specified by this node's configuration parameters), and updates the plugin's 'results' object accordingly. """ # Get the name of this plugin based on the message plugin_name = msg.name # Validate the plugin_discovery message based on the plugin's type (Strategic, Tactical, or Control) if plugin_name in self.strategic_plugin_names: # Do not process message if this plugin has already had its plugin_discovery message validated # Note: Assumption is that once one message is received and processed for a plugin, the rest will be identical if self.strategic_plugin_validation_results[plugin_name].has_had_plugin_discovery_message_validated: return # Process the message and log appropriate messages rospy.loginfo("Processing plugin_discovery message for " + str(plugin_name) + " (Strategic Plugin)") self.strategic_plugin_validation_results[plugin_name].has_had_plugin_discovery_message_validated = True expected_capability = self.strategic_plugin_validation_results[plugin_name].requirement_results.correct_plugin_discovery_capability if msg.capability == expected_capability: self.strategic_plugin_validation_results[plugin_name].requirement_results.has_correct_plugin_discovery_capability = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery capability " + str(expected_capability)) else: rospy.logerr("ERROR: " + str(plugin_name) + " plugin_discovery capability == " + str(msg.capability) + " (expected + " + str(expected_capability) + ")") expected_type = self.strategic_plugin_validation_results[plugin_name].requirement_results.correct_plugin_discovery_type if msg.type == expected_type: self.strategic_plugin_validation_results[plugin_name].requirement_results.has_correct_plugin_discovery_type = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery type " + str(expected_type)) else: rospy.logerr("ERROR: " + str(plugin_name) + " plugin_discovery type == " + str(msg.type) + " (expected + " + str(expected_type) + ")") if msg.available == True: self.strategic_plugin_validation_results[plugin_name].optional_results.has_correct_plugin_discovery_available = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery availabile == True") else: rospy.logwarn("WARNING: " + str(plugin_name) + " plugin_discovery available == " + str(msg.available) + " (expected True)") if msg.activated == True: self.strategic_plugin_validation_results[plugin_name].optional_results.has_correct_plugin_discovery_activated = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery activated == True") else: rospy.logwarn("WARNING: " + str(plugin_name) + " plugin_discovery activated == " + str(msg.activated) + " (expected True)") elif plugin_name in self.tactical_plugin_names: # Do not process message if this plugin has already had its plugin_discovery message validated # Note: Assumption is that once one message is received and processed for a plugin, the rest will be identical if self.tactical_plugin_validation_results[plugin_name].has_had_plugin_discovery_message_validated: return # Process the message and log appropriate messages rospy.loginfo("Processing plugin_discovery message for " + str(plugin_name) + " (Tactical Plugin)") self.tactical_plugin_validation_results[plugin_name].has_had_plugin_discovery_message_validated = True expected_capability = self.tactical_plugin_validation_results[plugin_name].requirement_results.correct_plugin_discovery_capability if msg.capability == expected_capability: self.tactical_plugin_validation_results[plugin_name].requirement_results.has_correct_plugin_discovery_capability = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery capability " + str(expected_capability)) else: rospy.logerr("ERROR: " + str(plugin_name) + " plugin_discovery capability == " + str(msg.capability) + " (expected + " + str(expected_capability) + ")") expected_type = self.tactical_plugin_validation_results[plugin_name].requirement_results.correct_plugin_discovery_type if msg.type == expected_type: self.tactical_plugin_validation_results[plugin_name].requirement_results.has_correct_plugin_discovery_type = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery type " + str(expected_type)) else: rospy.logerr("ERROR: " + str(plugin_name) + " plugin_discovery type == " + str(msg.type) + " (expected + " + str(expected_type) + ")") if msg.available == True: self.tactical_plugin_validation_results[plugin_name].optional_results.has_correct_plugin_discovery_available = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery availabile == True") else: rospy.logwarn("WARNING: " + str(plugin_name) + " plugin_discovery availability == " + str(msg.available) + " (expected True)") if msg.activated == True: self.tactical_plugin_validation_results[plugin_name].optional_results.has_correct_plugin_discovery_activated = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery activated == True") else: rospy.logwarn("WARNING: " + str(plugin_name) + " plugin_discovery activated == " + str(msg.activated) + " (expected True)") elif plugin_name in self.control_plugin_names: # Do not process message if this plugin has already had its plugin_discovery message validated # Note: Assumption is that once one message is received and processed for a plugin, the rest will be identical if self.control_plugin_validation_results[plugin_name].has_had_plugin_discovery_message_validated: return # Process the message and log appropriate messages rospy.loginfo("Processing plugin_discovery message for " + str(plugin_name) + " (Control Plugin)") self.control_plugin_validation_results[plugin_name].has_had_plugin_discovery_message_validated = True expected_capability = self.control_plugin_validation_results[plugin_name].requirement_results.correct_plugin_discovery_capability if msg.capability == expected_capability: self.control_plugin_validation_results[plugin_name].requirement_results.has_correct_plugin_discovery_capability = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery capability " + str(expected_capability)) else: rospy.logerr("ERROR: " + str(plugin_name) + " plugin_discovery capability == " + str(msg.capability) + " (expected + " + str(expected_capability) + ")") expected_type = self.control_plugin_validation_results[plugin_name].requirement_results.correct_plugin_discovery_type if msg.type == expected_type: self.control_plugin_validation_results[plugin_name].requirement_results.has_correct_plugin_discovery_type = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery type " + str(expected_type)) else: rospy.logerr("ERROR: " + str(plugin_name) + " plugin_discovery type == " + str(msg.type) + " (expected + " + str(expected_type) + ")") if msg.available == True: self.control_plugin_validation_results[plugin_name].optional_results.has_correct_plugin_discovery_available = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery availabile == True") else: rospy.logwarn("WARNING: " + str(plugin_name) + " plugin_discovery availability == " + str(msg.available) + " (expected True)") if msg.activated == True: self.control_plugin_validation_results[plugin_name].optional_results.has_correct_plugin_discovery_activated = True rospy.loginfo("Success: " + str(plugin_name) + " has plugin_discovery activated == True") else: rospy.logwarn("WARNING: " + str(plugin_name) + " plugin_discovery activated == " + str(msg.activated) + " (expected True)") return def conduct_node_validation(self): """ Call appropriate member functions to conduct validation of node communication interfaces. """ rospy.loginfo("Beginning validation checks for node subscriptions, publications, and advertised services") self.validate_strategic_plugins() self.validate_tactical_plugins() self.validate_control_plugins() rospy.loginfo("Completed validation checks for node subscriptions, publications, and advertised services") return def validate_strategic_plugins(self): """ Conduct validation checks for each strategic plugin's node (as specified by this node's configuration parameters) for proper publications, subscriptions, and advertised services. Based on the results, this function updates each strategic plugin's StrategicPluginResults object accordingly. """ for plugin_name, plugin_results_object in self.strategic_plugin_validation_results.items(): plugin_node_name = plugin_results_object.node_name rospy.loginfo("Processing publishers, subscribers, and services for " + str(plugin_name) + " (Strategic Plugin)") # Check whether the node has been created if rosnode.rosnode_ping(plugin_node_name, max_count = 5): plugin_results_object.requirement_results.has_node = True rospy.loginfo("Success: Node " + str(plugin_node_name) + " exists.") else: rospy.logerr("ERROR: No node response for " + str(plugin_node_name) + ". Node does not exist.") # Obtain string that includes information regarding a node's publications, subscriptions, and services rosnode_info_string = (rosnode.get_node_info_description(plugin_node_name)) # Get substring from rosnode info that contains 'Subscriptions' information sub_index_start = rosnode_info_string.index("Subscriptions:") sub_index_end = rosnode_info_string.index("Services:") subscriptions_string = rosnode_info_string[sub_index_start:sub_index_end] # Check for required and optional subscriptions if plugin_results_object.optional_results.current_pose_topic in subscriptions_string: plugin_results_object.optional_results.has_current_pose_sub = True rospy.loginfo("Success: " + str(plugin_node_name) + " subscribes to " + str(plugin_results_object.optional_results.current_pose_topic)) else: rospy.logwarn("WARNING: " + str(plugin_node_name) + " does not subscribe to " + str(plugin_results_object.optional_results.current_pose_topic)) if plugin_results_object.optional_results.current_speed_topic in subscriptions_string: plugin_results_object.optional_results.has_current_speed_sub = True rospy.loginfo("Success: " + str(plugin_node_name) + " subscribes to " + str(plugin_results_object.optional_results.current_speed_topic)) else: rospy.logwarn("WARNING: " + str(plugin_node_name) + " does not subscribe to " + str(plugin_results_object.optional_results.current_speed_topic)) # Get substring from rosnode info that contains 'Publications' information pub_index_start = rosnode_info_string.index("Publications:") pub_index_end = rosnode_info_string.index("Subscriptions:") publications_string = rosnode_info_string[pub_index_start:pub_index_end] # Check for required and optional publications if plugin_results_object.requirement_results.plugin_discovery_topic in publications_string: plugin_results_object.requirement_results.has_plugin_discovery_pub = True rospy.loginfo("Success: " + str(plugin_node_name) + " publishes to " + str(plugin_results_object.requirement_results.plugin_discovery_topic)) else: rospy.logerr("ERROR: " + str(plugin_node_name) + " does not publish to " + str(plugin_results_object.requirement_results.plugin_discovery_topic)) # Get substring from rosnode info that contains 'Services' information serv_index_start = rosnode_info_string.index("Services:") services_string = rosnode_info_string[serv_index_start:] # Check for required and optional servers if plugin_results_object.requirement_results.plan_maneuvers_service in services_string: plugin_results_object.requirement_results.has_plan_maneuvers_service = True rospy.loginfo("Success: " + str(plugin_node_name) + " advertises service " + str(plugin_results_object.requirement_results.plan_maneuvers_service)) else: rospy.logerr("ERROR: " + str(plugin_node_name) + " does not advertise service " + str(plugin_results_object.requirement_results.plan_maneuvers_service)) return def validate_tactical_plugins(self): """ Conduct validation checks for each tactical plugin's node (as specified by this node's configuration parameters) for proper publications, subscriptions, and advertised services. Based on the results, this function updates each tactical plugin's TacticalPluginResults object accordingly. """ for plugin_name, plugin_results_object in self.tactical_plugin_validation_results.items(): plugin_node_name = plugin_results_object.node_name rospy.loginfo("Processing publishers, subscribers, and services for " + str(plugin_name) + " (Tactical Plugin)") # Check whether the node has been created if rosnode.rosnode_ping(plugin_node_name, max_count = 5): plugin_results_object.requirement_results.has_node = True rospy.loginfo("Success: Node " + str(plugin_node_name) + " exists.") else: rospy.logerr("ERROR: No node response for " + str(plugin_node_name) + ". Node does not exist.") # Obtain string that includes information regarding a node's publications, subscriptions, and services rosnode_info_string = (rosnode.get_node_info_description(plugin_node_name)) # Get substring from rosnode info that contains 'Subscriptions' information sub_index_start = rosnode_info_string.index("Subscriptions:") sub_index_end = rosnode_info_string.index("Services:") subscriptions_string = rosnode_info_string[sub_index_start:sub_index_end] # Check for required and optional subscriptions if plugin_results_object.optional_results.current_pose_topic in subscriptions_string: plugin_results_object.optional_results.has_current_pose_sub = True rospy.loginfo("Success: " + str(plugin_node_name) + " subscribes to " + str(plugin_results_object.optional_results.current_pose_topic)) else: rospy.logwarn("WARNING: " + str(plugin_node_name) + " does not subscribe to " + str(plugin_results_object.optional_results.current_pose_topic)) if plugin_results_object.optional_results.current_speed_topic in subscriptions_string: plugin_results_object.optional_results.has_current_speed_sub = True rospy.loginfo("Success: " + str(plugin_node_name) + " subscribes to " + str(plugin_results_object.optional_results.current_speed_topic)) else: rospy.logwarn("WARNING: " + str(plugin_node_name) + " does not subscribe to " + str(plugin_results_object.optional_results.current_speed_topic)) # Get substring from rosnode info that contains 'Publications' information pub_index_start = rosnode_info_string.index("Publications:") pub_index_end = rosnode_info_string.index("Subscriptions:") publications_string = rosnode_info_string[pub_index_start:pub_index_end] # Check for required and optional publications if plugin_results_object.requirement_results.plugin_discovery_topic in publications_string: plugin_results_object.requirement_results.has_plugin_discovery_pub = True rospy.loginfo("Success: " + str(plugin_node_name) + " publishes to " + str(plugin_results_object.requirement_results.plugin_discovery_topic)) else: rospy.logerr("ERROR: " + str(plugin_node_name) + " does not publish to " + str(plugin_results_object.requirement_results.plugin_discovery_topic)) # Get substring from rosnode info that contains 'Services' information serv_index_start = rosnode_info_string.index("Services:") services_string = rosnode_info_string[serv_index_start:] # Check for required and optional servers if plugin_results_object.requirement_results.plan_trajectory_service in services_string: plugin_results_object.requirement_results.has_plan_trajectory_service = True rospy.loginfo("Success: " + str(plugin_node_name) + " advertises service " + str(plugin_results_object.requirement_results.plan_trajectory_service)) else: rospy.logerr("ERROR: " + str(plugin_node_name) + " does not advertise service " + str(plugin_results_object.requirement_results.plan_trajectory_service)) return def validate_control_plugins(self): """ Conduct validation checks for each control plugin's node (as specified by this node's configuration parameters) for proper publications, subscriptions, and advertised services. Based on the results, this function updates each control plugin's ControlPluginResults object accordingly. """ for plugin_name, plugin_results_object in self.control_plugin_validation_results.items(): plugin_node_name = plugin_results_object.node_name rospy.loginfo("Processing publishers, subscribers, and services for " + str(plugin_name) + " (Control Plugin)") # Check whether the node has been created if rosnode.rosnode_ping(plugin_node_name, max_count = 5): plugin_results_object.requirement_results.has_node = True rospy.loginfo("Success: Node " + str(plugin_node_name) + " exists.") else: rospy.logerr("ERROR: No node response for " + str(plugin_node_name) + ". Node does not exist.") # Obtain string that includes information regarding a node's publications, subscriptions, and services rosnode_info_string = (rosnode.get_node_info_description(plugin_node_name)) # Get substring from rosnode info that contains 'Subscriptions' information sub_index_start = rosnode_info_string.index("Subscriptions:") sub_index_end = rosnode_info_string.index("Services:") subscriptions_string = rosnode_info_string[sub_index_start:sub_index_end] # Check for required and optional subscriptions if plugin_results_object.requirement_results.plan_trajectory_topic in subscriptions_string: plugin_results_object.requirement_results.has_plan_trajectory_sub = True rospy.loginfo("Success: " + str(plugin_node_name) + " subscribes to " + str(plugin_results_object.requirement_results.plan_trajectory_topic)) else: rospy.logerr("ERROR: " + str(plugin_node_name) + " does not subscribe to " + str(plugin_results_object.requirement_results.plan_trajectory_topic)) # Get substring from rosnode info that contains 'Publications' information pub_index_start = rosnode_info_string.index("Publications:") pub_index_end = rosnode_info_string.index("Subscriptions:") publications_string = rosnode_info_string[pub_index_start:pub_index_end] # Check for required and optional publications if plugin_results_object.requirement_results.plugin_discovery_topic in publications_string: plugin_results_object.requirement_results.has_plugin_discovery_pub = True rospy.loginfo("Success: " + str(plugin_node_name) + " publishes to " + str(plugin_results_object.requirement_results.plugin_discovery_topic)) else: rospy.logerr("ERROR: " + str(plugin_node_name) + " does not publish to " + str(plugin_results_object.requirement_results.plugin_discovery_topic)) if plugin_results_object.requirement_results.final_waypoints_topic in publications_string: plugin_results_object.requirement_results.has_final_waypoints_pub = True rospy.loginfo("Success: " + str(plugin_node_name) + " publishes to " + str(plugin_results_object.requirement_results.final_waypoints_topic)) else: rospy.logerr("ERROR: " + str(plugin_node_name) + " does not publish to " + str(plugin_results_object.requirement_results.final_waypoints_topic)) return

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809cb1571b61cb14de4143b75d6cc3c36478794b

34

py

Python

utils/models/pykao/__init__.py

bhklab/ptl-oar-segmentation

354c3ee7f042a025f74e210a7b8462beac9b727d

[ "Apache-2.0" ]

3

2022-01-18T19:25:46.000Z

2022-02-05T18:53:24.000Z

utils/models/pykao/__init__.py

bhklab/ptl-oar-segmentation

354c3ee7f042a025f74e210a7b8462beac9b727d

[ "Apache-2.0" ]

null

utils/models/pykao/__init__.py

bhklab/ptl-oar-segmentation

354c3ee7f042a025f74e210a7b8462beac9b727d

[ "Apache-2.0" ]

null

from .model import Modified3DUNet

17

33

0.852941

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0

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1

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6

809e2ab2119e26a91e93865ccb20a6e0b3f8c2c0

30

py

Python

CADMium/inverter/__init__.py

VHchavez/CADMium

39f3bd63ca69502a80c677855da72f9e691b57e2

[ "BSD-3-Clause" ]

null

CADMium/inverter/__init__.py

VHchavez/CADMium

39f3bd63ca69502a80c677855da72f9e691b57e2

[ "BSD-3-Clause" ]

1

2021-04-23T20:38:38.000Z

CADMium/inverter/__init__.py

VHchavez/CADMium

39f3bd63ca69502a80c677855da72f9e691b57e2

[ "BSD-3-Clause" ]

2

2020-10-07T20:48:56.000Z

2021-04-22T19:06:18.000Z

from .inverter import Inverter

30

0.866667

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