xszheng2020/the_stack_dedup_python_hits_3 · Datasets at Hugging Face

c788d631d1d237cc626cf0a634afe19d5a987c8c

109

py

Python

reference/shape/primitives_2d/arc_open.py

abhikpal/p5-examples

b7ac2f7713c4b724cf99579d1249141007a4619a

[ "MIT" ]

16

2018-03-05T07:09:28.000Z

2022-03-12T11:44:10.000Z

reference/shape/primitives_2d/arc_open.py

abhikpal/p5-examples

b7ac2f7713c4b724cf99579d1249141007a4619a

[ "MIT" ]

5

2017-08-14T07:58:30.000Z

2019-01-10T05:40:07.000Z

reference/shape/primitives_2d/arc_open.py

abhikpal/p5-examples

b7ac2f7713c4b724cf99579d1249141007a4619a

[ "MIT" ]

13

2017-08-21T10:23:01.000Z

2021-07-31T00:03:42.000Z

from p5 import * def draw(): no_loop() arc((180, 180), 251, 251, 0, PI + QUARTER_PI, 'OPEN') run()

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392

py

Python

prompts/date.py

Robert-96/py-prompts

683185aea28f589e89cc80225a7326e60131b3c0

[ "MIT" ]

null

prompts/date.py

Robert-96/py-prompts

683185aea28f589e89cc80225a7326e60131b3c0

[ "MIT" ]

null

prompts/date.py

Robert-96/py-prompts

683185aea28f589e89cc80225a7326e60131b3c0

[ "MIT" ]

null

import sys from datetime import datetime class DatePromptPS1(object): def __str__(self): return "[%s] >>> " % (datetime.now().strftime('%d/%m')) def __len__(self): return len(str(self)) class DatePromptPS2(object): def __str__(self): width = len(sys.ps1) return "... ".rjust(width, " ") sys.ps1 = DatePromptPS1() sys.ps2 = DatePromptPS2()

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py

Python

todoapp/models.py

shrionit/todo_api

2864d5521d788e8f0f160279b1486b1155be192a

[ "MIT" ]

null

todoapp/models.py

shrionit/todo_api

2864d5521d788e8f0f160279b1486b1155be192a

[ "MIT" ]

null

todoapp/models.py

shrionit/todo_api

2864d5521d788e8f0f160279b1486b1155be192a

[ "MIT" ]

null

from django.db import models from django.urls import reverse class TodoItem(models.Model): note = models.TextField(max_length=500) date = models.DateTimeField(auto_now_add=True) def __str__(self): return self.note def get_absolute_url(self): return reverse("TodoItem_detail", kwargs={"pk": self.pk})

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c7a9d44e9f378d3ce127b341cc3db17701d2edba

108

py

Python

C_Data_resources/solutions/ex2_1.py

oercompbiomed/CBM101

20010dcb99fbf218c4789eb5918dcff8ceb94898

[ "MIT" ]

7

2019-07-03T07:41:55.000Z

2022-02-06T20:25:37.000Z

C_Data_resources/solutions/ex2_1.py

oercompbiomed/CBM101

20010dcb99fbf218c4789eb5918dcff8ceb94898

[ "MIT" ]

9

2019-03-14T15:15:09.000Z

2019-08-01T14:18:21.000Z

C_Data_resources/solutions/ex2_1.py

oercompbiomed/CBM101

20010dcb99fbf218c4789eb5918dcff8ceb94898

[ "MIT" ]

11

2019-03-12T10:43:11.000Z

2021-10-05T12:15:00.000Z

def plot(k): plt.imshow(X[k].reshape(8,8), cmap='gray') plt.title(f"Number = {y[k]}") plt.show()

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c7bab59e8f10dbc3976b497427e2131399dc5b4d

655

py

Python

tests/fixtures/model.py

nickderobertis/github-secrets

43c599c012b18fd8bb98cd3c61064dfce42512bb

[ "MIT" ]

null

tests/fixtures/model.py

nickderobertis/github-secrets

43c599c012b18fd8bb98cd3c61064dfce42512bb

[ "MIT" ]

1

2021-02-21T18:50:06.000Z

tests/fixtures/model.py

nickderobertis/github-secrets

43c599c012b18fd8bb98cd3c61064dfce42512bb

[ "MIT" ]

null

import pytest from github_secrets.app import GithubSecretsApp from github_secrets.manager import SecretsManager from tests.config import CONFIG_FILE_PATH_YAML, APP_CONFIG_FILE_PATH_YAML def get_secrets_manager(**kwargs) -> SecretsManager: manager = SecretsManager(config_path=CONFIG_FILE_PATH_YAML, **kwargs) return manager def get_secrets_app(**kwargs) -> GithubSecretsApp: app = GithubSecretsApp(config_path=APP_CONFIG_FILE_PATH_YAML, **kwargs) return app @pytest.fixture(scope="function") def secrets_manager(): return get_secrets_manager() @pytest.fixture(scope="function") def secrets_app(): return get_secrets_app()

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c7d0fc847327bb3036a38063378dfc70076abeb7

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py

Python

src/z3c/formui/interfaces.py

zopefoundation/z3c.formu

978abd2d0cb28a536da59dc27377605193fe41da

[ "ZPL-2.1" ]

1

2018-11-11T14:04:11.000Z

src/z3c/formui/interfaces.py

zopefoundation/z3c.formu

978abd2d0cb28a536da59dc27377605193fe41da

[ "ZPL-2.1" ]

5

2017-12-05T14:29:28.000Z

2021-09-20T06:36:31.000Z

src/z3c/formui/interfaces.py

zopefoundation/z3c.formu

978abd2d0cb28a536da59dc27377605193fe41da

[ "ZPL-2.1" ]

2

2015-04-03T06:02:45.000Z

2017-12-05T15:13:03.000Z

############################################################################## # # Copyright (c) 2007 Zope Foundation and Contributors. # All Rights Reserved. # # This software is subject to the provisions of the Zope Public License, # Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE. # ############################################################################## """Form UI Interfaces.""" from zope.publisher.interfaces.browser import IBrowserRequest from zope.viewlet.interfaces import IViewletManager class IFormUILayer(IBrowserRequest): """A basic layer for the Form UI package.""" class IDivFormLayer(IFormUILayer): """A layer that supports forms created only using DIV elements.""" class ITableFormLayer(IFormUILayer): """A layer that supports forms created using tables.""" class ICSS(IViewletManager): """CSS viewlet manager."""

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c7ee633318b472a21ffc52b8f5190c8945ac6494

282

py

Python

plugins/commands/start.py

oktest145/TorrentSearcherBot

d68dba2bd980394baadf0c8e9b3fb19d1cc2a905

[ "MIT" ]

26

2020-08-05T06:51:23.000Z

2021-07-12T09:56:57.000Z

plugins/commands/start.py

oktest145/TorrentSearcherBot

d68dba2bd980394baadf0c8e9b3fb19d1cc2a905

[ "MIT" ]

2

2020-08-18T06:36:55.000Z

2021-02-03T10:36:50.000Z

plugins/commands/start.py

oktest145/TorrentSearcherBot

d68dba2bd980394baadf0c8e9b3fb19d1cc2a905

[ "MIT" ]

65

2020-08-17T17:43:04.000Z

2021-10-02T08:01:59.000Z

from pyrogram import Client, filters from pyrogram.types import Message from config import START_MESSAGE @Client.on_message(filters.command("start")) async def start_message(c: Client, m: Message): await m.reply_text(START_MESSAGE, reply_to_message_id=m.message_id)

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1be7beb5a2a66aa3735a3574bec2559e0dc9b2cf

681

py

Python

jesse/strategies/Test16/__init__.py

noenfugler/jesse

217a3168620a755c1a9576d9deb27105db7dccf8

[ "MIT" ]

3,999

2018-11-09T10:38:51.000Z

2022-03-31T12:29:12.000Z

jesse/strategies/Test16/__init__.py

noenfugler/jesse

217a3168620a755c1a9576d9deb27105db7dccf8

[ "MIT" ]

172

2020-04-16T16:19:08.000Z

2022-03-28T13:28:55.000Z

jesse/strategies/Test16/__init__.py

noenfugler/jesse

217a3168620a755c1a9576d9deb27105db7dccf8

[ "MIT" ]

495

2019-03-01T21:48:53.000Z

2022-03-30T15:35:19.000Z

from jesse.strategies import Strategy # test_increasing_position_size_after_opening class Test16(Strategy): def should_long(self): return self.price < 7 def go_long(self): qty = 1 self.buy = qty, 7 self.stop_loss = qty, 5 self.take_profit = qty, 15 def update_position(self): # buy 1 more at current price if self.price == 10: self.buy = 1, 10 self.take_profit = 2, 15 self.stop_loss = 2, 5 def go_short(self): pass def should_cancel(self): return False def filters(self): return [] def should_short(self): return False

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402da257fac91a1822b14500650292149c4bc858

608

py

Python

setup.py

GlennHD/py-suricataparser

6b19175b05cc2f6af67271c6f74bae5e3dd827e3

[ "Apache-2.0" ]

10

2021-03-29T21:45:37.000Z

2022-03-27T15:42:28.000Z

setup.py

GlennHD/py-suricataparser

6b19175b05cc2f6af67271c6f74bae5e3dd827e3

[ "Apache-2.0" ]

null

setup.py

GlennHD/py-suricataparser

6b19175b05cc2f6af67271c6f74bae5e3dd827e3

[ "Apache-2.0" ]

3

2021-07-22T21:28:22.000Z

2022-03-18T17:44:44.000Z

from setuptools import setup import suricataparser setup( name="suricataparser", version=suricataparser.__version__, author="Michail Tsyganov", url="https://github.com/m-chrome/py-suricataparser", description="Suricata rule parser", packages=["suricataparser"], python_requires=">=3.6", classifiers=[ "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", ] )

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4069f6be3aedc735dc09c87e11aca6aa2f6f5c12

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py

Python

applications/popart/bert/tests/unit/pytorch/full_graph_utils.py

kew96/GraphcoreExamples

22dc0d7e3755b0a7f16cdf694c6d10c0f91ee8eb

[ "MIT" ]

null

applications/popart/bert/tests/unit/pytorch/full_graph_utils.py

kew96/GraphcoreExamples

22dc0d7e3755b0a7f16cdf694c6d10c0f91ee8eb

[ "MIT" ]

null

applications/popart/bert/tests/unit/pytorch/full_graph_utils.py

kew96/GraphcoreExamples

22dc0d7e3755b0a7f16cdf694c6d10c0f91ee8eb

[ "MIT" ]

null

# Copyright (c) 2019 Graphcore Ltd. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import random import numpy as np import torch import popart import onnx from bert_model import ExecutionMode from tests.utils import run_py, copy_weights_to_torch, run_fwd_model, check_tensors, check_model from tests import torch_lamb def get_mapping(config, init=None): if init is None: init = {} if config.execution_mode == ExecutionMode.DEFAULT: embedding_proj = { "bert.embeddings.word_embeddings.weight": "Embedding/Embedding_Dict", "bert.embeddings.position_embeddings.weight": "Embedding/Positional_Dict", "bert.embeddings.token_type_embeddings.weight": "Embedding/Segment_Dict", "bert.embeddings.LayerNorm.weight": "Embedding/Gamma", "bert.embeddings.LayerNorm.bias": "Embedding/Beta", } init.update(**embedding_proj) if config.split_qkv: for i in range(config.num_layers): layer = { f"bert.encoder.layer.{i}.attention.self.query.weight": f"Layer{i}/Attention/Q", f"bert.encoder.layer.{i}.attention.self.key.weight": f"Layer{i}/Attention/K", f"bert.encoder.layer.{i}.attention.self.value.weight": f"Layer{i}/Attention/V", f"bert.encoder.layer.{i}.attention.output.dense.weight": f"Layer{i}/Attention/Out", f"bert.encoder.layer.{i}.attention.output.LayerNorm.weight": f"Layer{i}/Attention/Gamma", f"bert.encoder.layer.{i}.attention.output.LayerNorm.bias": f"Layer{i}/Attention/Beta", f"bert.encoder.layer.{i}.intermediate.dense.weight": f"Layer{i}/FF/1/W", f"bert.encoder.layer.{i}.intermediate.dense.bias": f"Layer{i}/FF/1/B", f"bert.encoder.layer.{i}.output.dense.weight": f"Layer{i}/FF/2/W", f"bert.encoder.layer.{i}.output.dense.bias": f"Layer{i}/FF/2/B", f"bert.encoder.layer.{i}.output.LayerNorm.weight": f"Layer{i}/FF/Gamma", f"bert.encoder.layer.{i}.output.LayerNorm.bias": f"Layer{i}/FF/Beta", } init.update(**layer) else: for i in range(config.num_layers): layer = { f"bert.encoder.layer.{i}.attention.self.query.weight": f"Layer{i}/Attention/QKV", f"bert.encoder.layer.{i}.attention.self.key.weight": f"Layer{i}/Attention/QKV", f"bert.encoder.layer.{i}.attention.self.value.weight": f"Layer{i}/Attention/QKV", f"bert.encoder.layer.{i}.attention.output.dense.weight": f"Layer{i}/Attention/Out", f"bert.encoder.layer.{i}.attention.output.LayerNorm.weight": f"Layer{i}/Attention/Gamma", f"bert.encoder.layer.{i}.attention.output.LayerNorm.bias": f"Layer{i}/Attention/Beta", f"bert.encoder.layer.{i}.intermediate.dense.weight": f"Layer{i}/FF/1/W", f"bert.encoder.layer.{i}.intermediate.dense.bias": f"Layer{i}/FF/1/B", f"bert.encoder.layer.{i}.output.dense.weight": f"Layer{i}/FF/2/W", f"bert.encoder.layer.{i}.output.dense.bias": f"Layer{i}/FF/2/B", f"bert.encoder.layer.{i}.output.LayerNorm.weight": f"Layer{i}/FF/Gamma", f"bert.encoder.layer.{i}.output.LayerNorm.bias": f"Layer{i}/FF/Beta", } init.update(**layer) else: embedding_proj = { "bert.embeddings.word_embeddings.weight": "BertModel/Encoder/Embeddings/Token/weight", "bert.embeddings.position_embeddings.weight": "BertModel/Encoder/Embeddings/Position/weight", "bert.embeddings.token_type_embeddings.weight": "BertModel/Encoder/Embeddings/Segment/weight", "bert.embeddings.LayerNorm.weight": "BertModel/Encoder/Embeddings/Norm/Gamma", "bert.embeddings.LayerNorm.bias": "BertModel/Encoder/Embeddings/Norm/Beta", } init.update(**embedding_proj) if config.split_qkv: for i in range(config.num_layers): layer = { f"bert.encoder.layer.{i}.attention.self.query.weight": f'BertModel/Encoder/Layer{i}/Attention/Q', f"bert.encoder.layer.{i}.attention.self.key.weight": f'BertModel/Encoder/Layer{i}/Attention/K', f"bert.encoder.layer.{i}.attention.self.value.weight": f'BertModel/Encoder/Layer{i}/Attention/V', f"bert.encoder.layer.{i}.attention.output.dense.weight": f'BertModel/Encoder/Layer{i}/Attention/Out', f"bert.encoder.layer.{i}.attention.output.LayerNorm.weight": f'BertModel/Encoder/Layer{i}/Attention/Norm/Gamma', f"bert.encoder.layer.{i}.attention.output.LayerNorm.bias": f'BertModel/Encoder/Layer{i}/Attention/Norm/Beta', f"bert.encoder.layer.{i}.intermediate.dense.weight": f'BertModel/Encoder/Layer{i}/FF/1/Dense/Weight', f"bert.encoder.layer.{i}.intermediate.dense.bias": f'BertModel/Encoder/Layer{i}/FF/1/Dense/Bias', f"bert.encoder.layer.{i}.output.dense.weight": f'BertModel/Encoder/Layer{i}/FF/2/Dense/Weight', f"bert.encoder.layer.{i}.output.dense.bias": f'BertModel/Encoder/Layer{i}/FF/2/Dense/Bias', f"bert.encoder.layer.{i}.output.LayerNorm.weight": f'BertModel/Encoder/Layer{i}/FF/Norm/Gamma', f"bert.encoder.layer.{i}.output.LayerNorm.bias": f'BertModel/Encoder/Layer{i}/FF/Norm/Beta', } init.update(**layer) else: for i in range(config.num_layers): layer = { f"bert.encoder.layer.{i}.attention.self.query.weight": f'BertModel/Encoder/Layer{i}/Attention/QKV', f"bert.encoder.layer.{i}.attention.self.key.weight": f'BertModel/Encoder/Layer{i}/Attention/QKV', f"bert.encoder.layer.{i}.attention.self.value.weight": f'BertModel/Encoder/Layer{i}/Attention/QKV', f"bert.encoder.layer.{i}.attention.output.dense.weight": f'BertModel/Encoder/Layer{i}/Attention/Out', f"bert.encoder.layer.{i}.attention.output.LayerNorm.weight": f'BertModel/Encoder/Layer{i}/Attention/Norm/Gamma', f"bert.encoder.layer.{i}.attention.output.LayerNorm.bias": f'BertModel/Encoder/Layer{i}/Attention/Norm/Beta', f"bert.encoder.layer.{i}.intermediate.dense.weight": f'BertModel/Encoder/Layer{i}/FF/1/Dense/Weight', f"bert.encoder.layer.{i}.intermediate.dense.bias": f'BertModel/Encoder/Layer{i}/FF/1/Dense/Bias', f"bert.encoder.layer.{i}.output.dense.weight": f'BertModel/Encoder/Layer{i}/FF/2/Dense/Weight', f"bert.encoder.layer.{i}.output.dense.bias": f'BertModel/Encoder/Layer{i}/FF/2/Dense/Bias', f"bert.encoder.layer.{i}.output.LayerNorm.weight": f'BertModel/Encoder/Layer{i}/FF/Norm/Gamma', f"bert.encoder.layer.{i}.output.LayerNorm.bias": f'BertModel/Encoder/Layer{i}/FF/Norm/Beta', } init.update(**layer) return init def get_transform(config, init=None): if init is None: init = {} def q_transform(arr): return arr[:, 0:config.hidden_size].T def k_transform(arr): return arr[:, config.hidden_size:config.hidden_size * 2].T def v_transform(arr): return arr[:, config.hidden_size * 2:config.hidden_size * 3].T if config.split_qkv: for i in range(config.num_layers): layer = { f"bert.encoder.layer.{i}.attention.self.query.weight": np.transpose, f"bert.encoder.layer.{i}.attention.self.key.weight": np.transpose, f"bert.encoder.layer.{i}.attention.self.value.weight": np.transpose, f"bert.encoder.layer.{i}.attention.output.dense.weight": np.transpose, f"bert.encoder.layer.{i}.intermediate.dense.weight": np.transpose, f"bert.encoder.layer.{i}.output.dense.weight": np.transpose, } init.update(**layer) else: for i in range(config.num_layers): layer = { f"bert.encoder.layer.{i}.attention.self.query.weight": q_transform, f"bert.encoder.layer.{i}.attention.self.key.weight": k_transform, f"bert.encoder.layer.{i}.attention.self.value.weight": v_transform, f"bert.encoder.layer.{i}.attention.output.dense.weight": np.transpose, f"bert.encoder.layer.{i}.intermediate.dense.weight": np.transpose, f"bert.encoder.layer.{i}.output.dense.weight": np.transpose, } init.update(**layer) return init def fwd_graph(popart_model, torch_model, mode, mapping=None, transform=None, replication_factor=1, replicated_tensor_sharding = False): # ------------------- PopART -------------------- config = popart_model.config builder = popart_model.builder sequence_info = popart.TensorInfo( "UINT32", [config.micro_batch_size * config.sequence_length]) indices = builder.addInputTensor(sequence_info) positions = builder.addInputTensor(sequence_info) segments = builder.addInputTensor(sequence_info) data = { indices: np.random.randint( 0, config.vocab_length, (replication_factor, config.micro_batch_size * config.sequence_length)).astype(np.uint32), positions: np.random.randint( 0, config.sequence_length, (replication_factor, config.micro_batch_size * config.sequence_length)).astype(np.uint32), segments: np.random.randint( 0, 2, (replication_factor, config.micro_batch_size * config.sequence_length)).astype(np.uint32) } user_options = {} if mode == ExecutionMode.PHASED: user_options = { "batchSerializationFactor": 1, "executionPhases": popart_model.total_execution_phases } output = popart_model(indices, positions, segments) ipus = 2 else: output = popart_model.build_graph(indices, positions, segments) ipus = popart_model.total_ipus proto = builder.getModelProto() outputs, _ = run_py(proto, data, output, user_options=user_options, execution_mode=mode, replication_factor=replication_factor, replicated_tensor_sharding=replicated_tensor_sharding, ipus=ipus) # ----------------- PopART -> PyTorch ---------------- proto = onnx.load_model_from_string(proto) inputs = { "input_ids": data[indices].reshape(replication_factor * config.micro_batch_size, config.sequence_length).astype(np.int32), "position_ids": data[positions].reshape(replication_factor * config.micro_batch_size, config.sequence_length).astype(np.int32), "token_type_ids": data[segments].reshape(replication_factor * config.micro_batch_size, config.sequence_length).astype(np.int32) } torch_to_onnx = get_mapping(config, init=mapping) transform_weights = get_transform(config, init=transform) # ------------------- PyTorch ------------------------- # Turn off dropout torch_model.eval() copy_weights_to_torch(torch_model, proto, torch_to_onnx, transform_weights) torch_outputs = run_fwd_model(inputs, torch_model) check_tensors(torch_outputs, outputs) def bwd_graph(popart_model, torch_model, mode, popart_loss_fn, torch_loss_fn, mapping=None, transform=None, replication_factor=1, replicated_tensor_sharding=False, opt_type="SGD"): np.random.seed(1984) random.seed(1984) torch.manual_seed(1984) # ------------------- PopART -------------------- config = popart_model.config builder = popart_model.builder sequence_info = popart.TensorInfo( "UINT32", [config.micro_batch_size * config.sequence_length]) indices = builder.addInputTensor(sequence_info) positions = builder.addInputTensor(sequence_info) segments = builder.addInputTensor(sequence_info) data = { indices: np.random.randint( 0, config.vocab_length, (replication_factor, config.micro_batch_size * config.sequence_length)).astype(np.uint32), positions: np.random.randint( 0, config.sequence_length, (replication_factor, config.micro_batch_size * config.sequence_length)).astype(np.uint32), segments: np.random.randint( 0, 2, (replication_factor, config.micro_batch_size * config.sequence_length)).astype(np.uint32) } num_reps = 5 user_options = {} if mode == ExecutionMode.PHASED: user_options = { "batchSerializationFactor": 1, "executionPhases": popart_model.total_execution_phases } output = popart_model(indices, positions, segments) ipus = 2 else: output = popart_model.build_graph(indices, positions, segments) ipus = popart_model.total_ipus loss = popart_loss_fn(output) proto = builder.getModelProto() if opt_type == "SGD": optimizer = popart.ConstSGD(1e-3) elif opt_type == "LAMB": optMap = { "defaultLearningRate": (1e-3, True), "defaultBeta1": (0.9, True), "defaultBeta2": (0.999, True), "defaultWeightDecay": (0.0, True), "maxWeightNorm": (10.0, True), "defaultEps": (1e-8, True), "lossScaling": (1.0, True), } optimizer = popart.Adam(optMap, mode=popart.AdamMode.Lamb) elif opt_type == "LAMB_NO_BIAS": optMap = { "defaultLearningRate": (1, False), "defaultBeta1": (0, False), "defaultBeta2": (0, False), "defaultWeightDecay": (0.0, False), "defaultEps": (1e-8, False), "lossScaling": (1.0, False), } optimizer = popart.Adam(optMap, mode=popart.AdamMode.LambNoBias) else: raise ValueError(f"Unknown opt_type={opt_type}") patterns = popart.Patterns() if mode == ExecutionMode.PHASED: patterns.enablePattern("TiedGatherPattern", False) patterns.enablePattern("SparseAccumulatePattern", False) outputs, post_proto = run_py(proto, data, output, loss=loss, optimizer=optimizer, user_options=user_options, execution_mode=mode, patterns=patterns, replication_factor=replication_factor, replicated_tensor_sharding=replicated_tensor_sharding, ipus=ipus, num_reps=num_reps) # ----------------- PopART -> PyTorch ---------------- proto = onnx.load_model_from_string(proto) inputs = { "input_ids": data[indices].reshape(replication_factor * config.micro_batch_size, config.sequence_length).astype(np.int32), "position_ids": data[positions].reshape(replication_factor * config.micro_batch_size, config.sequence_length).astype(np.int32), "token_type_ids": data[segments].reshape(replication_factor * config.micro_batch_size, config.sequence_length).astype(np.int32) } torch_to_onnx = get_mapping(config, init=mapping) transform_weights = get_transform(config, init=transform) # ------------------- PyTorch ------------------------- # Turn off dropout torch_model.eval() copy_weights_to_torch(torch_model, proto, torch_to_onnx, transform_weights) if opt_type == "SGD": optim = torch.optim.SGD(torch_model.parameters(), 1e-3, weight_decay=0.0, momentum=0.0) elif opt_type == "LAMB": optim = torch_lamb.Lamb(torch_model.parameters(), lr=1e-3, weight_decay=0.0, biasCorrection=True) for _ in range(num_reps): torch_outputs = torch_model( **{k: torch.from_numpy(t).long() for k, t in inputs.items()}) torch_loss = torch_loss_fn(torch_outputs) torch_loss.backward() optim.step() optim.zero_grad() check_tensors([output.detach().numpy() for output in torch_outputs], outputs, margin=1.5e-06) check_model(torch_model, post_proto, torch_to_onnx, transform_weights, margin=5e-5)

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4076bfe112fb67e01a74f2a1a5ba32ca28579bb8

82

py

Python

recipes/Python/577944_Random_Binary_List/recipe-577944.py

tdiprima/code

61a74f5f93da087d27c70b2efe779ac6bd2a3b4f

[ "MIT" ]

2,023

2017-07-29T09:34:46.000Z

2022-03-24T08:00:45.000Z

recipes/Python/577944_Random_Binary_List/recipe-577944.py

unhacker/code

73b09edc1b9850c557a79296655f140ce5e853db

[ "MIT" ]

32

2017-09-02T17:20:08.000Z

2022-02-11T17:49:37.000Z

recipes/Python/577944_Random_Binary_List/recipe-577944.py

unhacker/code

73b09edc1b9850c557a79296655f140ce5e853db

[ "MIT" ]

780

2017-07-28T19:23:28.000Z

2022-03-25T20:39:41.000Z

from random import * randBinList = lambda n: [randint(0,1) for b in range(1,n+1)]

27.333333

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408aaa995e753c508b2f0cd65c35fda7f28c70d5

126

py

Python

src/gretel_synthetics/errors.py

DLPerf/gretel-synthetics

58a820327e283ecc224de3686aa035b7e32bfaa6

[ "Apache-2.0" ]

252

2020-03-02T16:41:11.000Z

2022-03-28T20:57:15.000Z

src/gretel_synthetics/errors.py

DLPerf/gretel-synthetics

58a820327e283ecc224de3686aa035b7e32bfaa6

[ "Apache-2.0" ]

39

2020-03-16T18:33:48.000Z

2021-11-10T19:13:53.000Z

src/gretel_synthetics/errors.py

DLPerf/gretel-synthetics

58a820327e283ecc224de3686aa035b7e32bfaa6

[ "Apache-2.0" ]

36

2020-05-21T14:45:27.000Z

2022-03-01T01:32:58.000Z

""" Custom error classes """ class GenerationError(Exception): pass class TooManyInvalidError(RuntimeError): pass

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null

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409bee5c5d500a2409e68a6afd049032bab76832

650

py

Python

doc/source/image/brightness.py

ppawlak/pystacia

854053a2872c9374e2c121c4af549f6bba640116

[ "MIT" ]

9

2015-02-11T21:33:33.000Z

2021-06-14T14:55:24.000Z

doc/source/image/brightness.py

ppawlak/pystacia

854053a2872c9374e2c121c4af549f6bba640116

[ "MIT" ]

1

2016-08-01T12:31:17.000Z

doc/source/image/brightness.py

ppawlak/pystacia

854053a2872c9374e2c121c4af549f6bba640116

[ "MIT" ]

2

2015-08-21T08:23:25.000Z

2018-10-31T02:52:50.000Z

from os.path import dirname, join from pystacia import lena dest = join(dirname(__file__), '../_static/generated') image = lena(128) image.brightness(-1) image.write(join(dest, 'lena_brightness-1.jpg')) image.close() image = lena(128) image.brightness(-0.6) image.write(join(dest, 'lena_brightness-0.6.jpg')) image.close() image = lena(128) image.brightness(-0.25) image.write(join(dest, 'lena_brightness-0.25.jpg')) image.close() image = lena(128) image.brightness(0.25) image.write(join(dest, 'lena_brightness0.25.jpg')) image.close() image = lena(128) image.brightness(0.75) image.write(join(dest, 'lena_brightness0.75.jpg')) image.close()

20.967742

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40ac68f12b17c2d8d345f3fe5d1a87798ead247a

235

py

Python

project/blog/templatetags/str_to_lowercase.py

ivanprytula/django-celery-telegram-api

2668247546ce4d34a5d1054126a6314bdf72eaae

[ "MIT" ]

null

project/blog/templatetags/str_to_lowercase.py

ivanprytula/django-celery-telegram-api

2668247546ce4d34a5d1054126a6314bdf72eaae

[ "MIT" ]

null

project/blog/templatetags/str_to_lowercase.py

ivanprytula/django-celery-telegram-api

2668247546ce4d34a5d1054126a6314bdf72eaae

[ "MIT" ]

null

from django import template from django.template.defaultfilters import stringfilter register = template.Library() @register.filter(name='str_to_lowercase') @stringfilter def sting_value_to_lowercase(value): return value.lower()

21.363636

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null

0

null

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40c14dd39c1eb16c4077aa27fa4601de12eea083

322

py

Python

code/5/function_types.py

TeamLab/introduction_to_pythoy_TEAMLAB_MOOC

ebf1ff02d6a341bfee8695eac478ff8297cb97e4

[ "MIT" ]

65

2017-11-01T01:57:21.000Z

2022-02-08T13:36:25.000Z

code/5/function_types.py

TeamLab/introduction_to_pythoy_TEAMLAB_MOOC

ebf1ff02d6a341bfee8695eac478ff8297cb97e4

[ "MIT" ]

9

2017-11-03T15:05:30.000Z

2018-05-17T03:18:36.000Z

code/5/function_types.py

TeamLab/introduction_to_pythoy_TEAMLAB_MOOC

ebf1ff02d6a341bfee8695eac478ff8297cb97e4

[ "MIT" ]

64

2017-11-01T01:57:23.000Z

2022-01-19T03:52:12.000Z

# def a_calculateRectangleArea(): # print (5 * 7) # def b_calculateRectangleArea(x, y): # print (x * y) # print(b_calculateRectangleArea(4, 8)) # def c_calculateRectangleArea(): # return 5 * 7 def d_calculateRectangleArea(x, y): print("함수 안에 있습니다") return (x * y) print(d_calculateRectangleArea(5,7))

24.769231

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null

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1

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40c4aa65fc8620335c9187c1ed2bb117b9c2d02c

493

py

Python

db.py

TitusKirch/justgamingbot

acb925bd335d03c8d59ba581c9e9bc82e6ca870a

[ "MIT" ]

null

db.py

TitusKirch/justgamingbot

acb925bd335d03c8d59ba581c9e9bc82e6ca870a

[ "MIT" ]

null

db.py

TitusKirch/justgamingbot

acb925bd335d03c8d59ba581c9e9bc82e6ca870a

[ "MIT" ]

null

import os from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker from dotenv import load_dotenv load_dotenv() # setup database engine, create tables and setup session db_engine = create_engine('mysql+mysqldb://' + os.getenv('DATABASE_USER') + ':' + os.getenv('DATABASE_PASSWORD') + '@' + os.getenv('DATABASE_HOST') + '/' + os.getenv('DATABASE_NAME'), encoding="utf8", echo=False) Session = sessionmaker(bind=db_engine) db_session = Session()

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null

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40edabf9ee3467ce971b028c9d3c42fd47a3ea1f

1,084

py

Python

tests/conftest.py

nxet/skidcoinz-factory

2b6107bcae2f93bf33a3992b063131751f3e137f

[ "MIT" ]

null

tests/conftest.py

nxet/skidcoinz-factory

2b6107bcae2f93bf33a3992b063131751f3e137f

[ "MIT" ]

null

tests/conftest.py

nxet/skidcoinz-factory

2b6107bcae2f93bf33a3992b063131751f3e137f

[ "MIT" ]

null

import pytest import brownie # # utils # @pytest.fixture(scope='module') def deployer(accounts): return accounts[-1] @pytest.fixture(scope='module') def UniswapV2Pair(pm): def fn(address): uniPair = pm('Uniswap/v2-core@1.0.1').UniswapV2Pair return uniPair.at(address) return fn # # v1 # from v1.conftest import Config as _ConfigV1 from v1.conftest import deploy_fixture as deploy_fixture_v1 @pytest.fixture(scope='module') def ConfigV1(): return _ConfigV1 @pytest.fixture(scope='module') def ContractFixtureV1(GenericSkidCoinV1, deployer): return deploy_fixture_v1(GenericSkidCoinV1, deployer) # # v2 # from v2.conftest import Config as _ConfigV2 from v2.conftest import deploy_fixture as deploy_fixture_v2 @pytest.fixture(scope='module') def ConfigV2(): return _ConfigV2 @pytest.fixture(scope='module') def ContractFixtureV2(GenericSkidCoinV2, deployer): return deploy_fixture_v2(GenericSkidCoinV2, deployer) # # apply fn_isolation to all future tests # @pytest.fixture(autouse=True) def isolation(fn_isolation): pass

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1

0

3

40f28fed3ba7c1bc38261c43ceaa706323bd8d70

1,976

py

Python

src/game/parents/base_objects/exit.py

gtaylor/dott

b0dfbecc1171ed82566ecf814a73ce3dcaa468be

[ "BSD-3-Clause" ]

3

2016-01-10T09:22:01.000Z

2016-05-01T23:16:16.000Z

src/game/parents/base_objects/exit.py

gtaylor/dott

b0dfbecc1171ed82566ecf814a73ce3dcaa468be

[ "BSD-3-Clause" ]

1

2016-03-29T02:52:49.000Z

src/game/parents/base_objects/exit.py

gtaylor/dott

b0dfbecc1171ed82566ecf814a73ce3dcaa468be

[ "BSD-3-Clause" ]

1

2020-04-16T15:45:26.000Z

""" Contains exit-related stuff. """ from src.game.parents.base_objects.base import BaseObject class ExitObject(BaseObject): """ An 'Exit' is used for moving from one location to another. The command handler checks a player's location for an exit's name/alias that matches the user's input. If a match is found, the player moves to the exit's destination. """ # ## Begin properties. # def get_destination(self): """ Returns the object's destination. :rtype: BaseObject or ``None``. :returns: A reference to the exit's destination BaseObject. If no destination is set, or the destination has been destroyed, this returns ``None``. :raises: NoSuchObject if the ID can't be found in the DB. """ return self._object_store.get_object(self.destination_id) def set_destination(self, obj_or_id): """ Sets the object's destination. :type obj_or_id: int or BaseObject :param obj_or_id: The new destination for the object in ID or BaseObject instance form. """ self._generic_baseobject_to_id_property_setter('destination_id', obj_or_id) destination = property(get_destination, set_destination) @property def base_type(self): """ Returns this object's type lineage. :rtype: str :returns: ``'exit'`` """ return 'exit' # ## Begin methods # def pass_object_through(self, obj): """ Attempts to pass an object through this exit. Takes into consideration any additional locks/permissions. :param BaseObject obj: The object to attempt to pass through this exit. """ if not self.destination: obj.emit_to('That exit leads to nowhere.') return # Move the object on through to destination. obj.move_to(self.destination)

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null

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null

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1

0

1

0

1

0

3

40f3e7edb1877ad487b3c2e68ab572deb08c7971

185

py

Python

ALGORITHM/large.py

Nerdcode/NEWTOHACK

dbeb9ea773ac01bbd80b4ed951f16914c4a37d8f

[ "MIT" ]

null

ALGORITHM/large.py

Nerdcode/NEWTOHACK

dbeb9ea773ac01bbd80b4ed951f16914c4a37d8f

[ "MIT" ]

null

ALGORITHM/large.py

Nerdcode/NEWTOHACK

dbeb9ea773ac01bbd80b4ed951f16914c4a37d8f

[ "MIT" ]

null

# Python program to find largest # number in a list # list of numbers list1 = [10, 20, 4, 45, 99] # printing the maximum element print("Largest element is:", max(list1))

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3

9059f53318d3979e3448492e4923d6a40733a0ed

976

py

Python

simulation/events.py

alexpod1000/WoW-Spell-Casting-Simulation

9c3ab6f2e06eef1e1892e7f8b3663b0687bb31df

[ "Apache-2.0" ]

null

simulation/events.py

alexpod1000/WoW-Spell-Casting-Simulation

9c3ab6f2e06eef1e1892e7f8b3663b0687bb31df

[ "Apache-2.0" ]

null

simulation/events.py

alexpod1000/WoW-Spell-Casting-Simulation

9c3ab6f2e06eef1e1892e7f8b3663b0687bb31df

[ "Apache-2.0" ]

null

class Event: def __init__(self, event_name, sender, params=None): """ Defines an event. name: event name sender: who has sent the event params: dictionary of event parameters """ self.event_name = event_name self.sender = sender self.params = params @property def name(self): return self.event_name def handle(self, simulation, sender, params): """ Method that will be called when the event will need to be handled. Defined by a function: (simulation_instance, sender, params)->resulting_parameters """ pass class EventEmitter: def __init__(self, emitter): """ Emit events with some policy (for implementing automatic event generation). emitter: entity that will be the sender of the events emitted by this emitter """ self.emitter = emitter def emit(self, model): pass

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905a1f847caa21efe9f9053020db627d8b52ae53

274

py

Python

malcolm/modules/zebra/blocks/__init__.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

malcolm/modules/zebra/blocks/__init__.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

malcolm/modules/zebra/blocks/__init__.py

aaron-parsons/pymalcolm

4e7ebd6b09382ab7e013278a81097d17873fa5c4

[ "Apache-2.0" ]

null

from malcolm.yamlutil import make_block_creator, check_yaml_names zebra_driver_block = make_block_creator( __file__, "zebra_driver_block.yaml") zebra_runnable_block = make_block_creator( __file__, "zebra_runnable_block.yaml") __all__ = check_yaml_names(globals())

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905de6f46fa7aac0ec3afec8ebf0d4275da8b014

167

py

Python

democelery/security/apps.py

alexdzul/democelery

4627d155ccc3a6daf21ec4e4b8d554891446288d

[ "MIT" ]

1

2021-12-02T05:29:37.000Z

democelery/security/apps.py

alexdzul/democelery

4627d155ccc3a6daf21ec4e4b8d554891446288d

[ "MIT" ]

null

democelery/security/apps.py

alexdzul/democelery

4627d155ccc3a6daf21ec4e4b8d554891446288d

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.apps import AppConfig class SecurityConfig(AppConfig): name = 'democelery.security'

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0

1

0

null

0

1

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3

9097bdcb809cdcc6e1bc477bad5047285c4087c0

340

py

Python

filter_plugins/sets.py

maruina/ansible-playbooks

6535ece0de9bd1f1f3dc4352fe4ee440090a9ccb

[ "MIT" ]

null

filter_plugins/sets.py

maruina/ansible-playbooks

6535ece0de9bd1f1f3dc4352fe4ee440090a9ccb

[ "MIT" ]

null

filter_plugins/sets.py

maruina/ansible-playbooks

6535ece0de9bd1f1f3dc4352fe4ee440090a9ccb

[ "MIT" ]

null

"""To be deleted once https://github.com/ansible/ansible/pull/15062 has been merged""" def issubset(a, b): return set(a) <= set(b) def issuperset(a, b): return set(a) >= set(b) class FilterModule(object): def filters(self): return { 'issubset': issubset, 'issuperset': issuperset, }

20

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0

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1

0

1

0

3

9098586743dca3b1c3b3e62a1d65427ac52faef8

182

py

Python

dbt_dag_factory/__version__.py

tomasfarias/dbt-dag-factory

817af463ae00d34d48ccc16f7a9f36a97409d8f3

[ "MIT" ]

4

2021-12-13T02:50:34.000Z

2022-03-01T16:37:52.000Z

dbt_dag_factory/__version__.py

tomasfarias/dbt-dag-factory

817af463ae00d34d48ccc16f7a9f36a97409d8f3

[ "MIT" ]

null

dbt_dag_factory/__version__.py

tomasfarias/dbt-dag-factory

817af463ae00d34d48ccc16f7a9f36a97409d8f3

[ "MIT" ]

null

30.333333

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1

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1

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null

0

3

90b68606a50f35db9e513bed30d949d45fc38b13

39

py

Python

Chapter 01/Chap01_Example1.53.py

Anancha/Programming-Techniques-using-Python

e80c329d2a27383909d358741a5cab03cb22fd8b

[ "MIT" ]

null

Chapter 01/Chap01_Example1.53.py

Anancha/Programming-Techniques-using-Python

e80c329d2a27383909d358741a5cab03cb22fd8b

[ "MIT" ]

null

Chapter 01/Chap01_Example1.53.py

Anancha/Programming-Techniques-using-Python

e80c329d2a27383909d358741a5cab03cb22fd8b

[ "MIT" ]

null

a=4 b=2 # addition operator print(a+b)

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null

0

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90cb2a759065ac38f9dbf3d2135573f0c3d76926

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py

Python

day_bot/__init__.py

zenmaldives/instagram-bot

cbd0b2dcebb9b80ffdbf82af8d7b560beddcd211

[ "MIT" ]

56

2016-03-06T07:43:44.000Z

2022-01-04T16:42:40.000Z

day_bot/__init__.py

zenmaldives/instagram-bot

cbd0b2dcebb9b80ffdbf82af8d7b560beddcd211

[ "MIT" ]

7

2015-04-20T12:55:34.000Z

2016-02-18T15:49:30.000Z

day_bot/__init__.py

zenmaldives/instagram-bot

cbd0b2dcebb9b80ffdbf82af8d7b560beddcd211

[ "MIT" ]

15

2015-04-20T14:13:14.000Z

2016-02-15T06:56:59.000Z

__author__ = 'gipmon'

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0

1

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null

0

3

90d7d8b6985b978cad168847e34495f2df7f855b

505

py

Python

refinery/units/compression/bz2.py

larsborn/refinery

c8b19156b17e5fa5de5c72bc668a14d646584560

[ "BSD-3-Clause" ]

null

refinery/units/compression/bz2.py

larsborn/refinery

c8b19156b17e5fa5de5c72bc668a14d646584560

[ "BSD-3-Clause" ]

null

refinery/units/compression/bz2.py

larsborn/refinery

c8b19156b17e5fa5de5c72bc668a14d646584560

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import bz2 as bz2_ from .. import arg, Unit from ...lib.argformats import number class bz2(Unit): """ BZip2 compression and decompression. """ def __init__(self, level: arg('-l', type=number[1:9], help='compression level preset between 1 and 9') = 9): super().__init__(level=level) def process(self, data): return bz2_.decompress(data) def reverse(self, data): return bz2_.compress(data, self.args.level)

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0

1

0

1

0

3

90edaa8c8449d7fdcd5cab414d1a98a45e113140

285

py

Python

projects/admin.py

akshaya9/fosswebsite

5669a90ebb1fea2213c207a938236fba7643375c

[ "MIT" ]

369

2017-10-02T03:04:24.000Z

2022-03-26T10:54:55.000Z

projects/admin.py

akshaya9/fosswebsite

5669a90ebb1fea2213c207a938236fba7643375c

[ "MIT" ]

121

2017-10-01T14:21:48.000Z

2018-11-08T16:57:26.000Z

projects/admin.py

akshaya9/fosswebsite

5669a90ebb1fea2213c207a938236fba7643375c

[ "MIT" ]

69

2017-10-13T11:04:38.000Z

2021-12-08T06:23:19.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.contrib import admin # Register your models here. from .models import * admin.site.register(Project) admin.site.register(ProjectMembers) admin.site.register(ProjectScreenShot) admin.site.register(Language)

21.923077

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null

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1

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null

0

1

0

1

0

3

90fcd5dd50300fc402fc5b48fba437ffcb9637bc

167

py

Python

test/basic/test_geojson.py

deeplook/gdp

ca2b1a5d55136bf3ade0df8a1c77c07e425a77fc

[ "MIT" ]

null

test/basic/test_geojson.py

deeplook/gdp

ca2b1a5d55136bf3ade0df8a1c77c07e425a77fc

[ "MIT" ]

null

test/basic/test_geojson.py

deeplook/gdp

ca2b1a5d55136bf3ade0df8a1c77c07e425a77fc

[ "MIT" ]

null

def test_point(): "Test point." from geojson import Point p = Point((-115.81, 37.24)) assert p == {"coordinates": [-115.81, 37.24], "type": "Point"}

27.833333

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null

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1

0

1

0

null

0

3

291daac52e5b58c591704ae3730ca65b5d304934

278

py

Python

physiossl/utils/profile.py

larryshaw0079/PhysioLearn

6438924a1b2a0c2ce4c238f504654f9a7f993d9e

[ "MIT" ]

2

2021-12-11T15:17:47.000Z

2021-12-27T07:39:31.000Z

physiossl/utils/profile.py

larryshaw0079/PhysioSSL

6438924a1b2a0c2ce4c238f504654f9a7f993d9e

[ "MIT" ]

null

physiossl/utils/profile.py

larryshaw0079/PhysioSSL

6438924a1b2a0c2ce4c238f504654f9a7f993d9e

[ "MIT" ]

null

""" @Time : 2021/11/26 11:38 @File : profile.py @Software: PyCharm @Desc : """ import numpy as np def embedding_visualize(embeddings: np.ndarray, labels: np.ndarray = None, proj_dim: int = 3): pass def knn_monitor(): pass def logits_accuracy(): pass

13.9

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1

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1

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3

2925abae8257de8adbbe9aeff0aa8f9fad6b0f6e

51

py

Python

smtpc/__init__.py

msztolcman/smtpc

68617d52b552b2eaf0633f09460726ffbccfe54c

[ "MIT" ]

5

2021-03-14T21:17:31.000Z

2021-07-10T22:28:23.000Z

smtpc/__init__.py

msztolcman/smtpc

68617d52b552b2eaf0633f09460726ffbccfe54c

[ "MIT" ]

1

2021-03-31T20:19:19.000Z

2021-04-01T06:47:55.000Z

smtpc/__init__.py

msztolcman/smtpc

68617d52b552b2eaf0633f09460726ffbccfe54c

[ "MIT" ]

2

2021-03-24T09:22:33.000Z

2021-05-12T20:36:52.000Z

__all__ = ('__version__', ) __version__ = '0.9.2'

12.75

27

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6

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3.333333

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0

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3

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1

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null

0

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1

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null

0

3

29266d9413b347bc7e0d1e6abe7b1c199daa0acd

1,087

py

Python

rpm_package_explorer/exceptions.py

chong601/rpm-package-explorer

5a14f15f90612528b323d0bb4cdeb2005925b02b

[ "MIT" ]

1

2021-07-15T23:07:17.000Z

rpm_package_explorer/exceptions.py

chong601/rpm-package-explorer

5a14f15f90612528b323d0bb4cdeb2005925b02b

[ "MIT" ]

null

rpm_package_explorer/exceptions.py

chong601/rpm-package-explorer

5a14f15f90612528b323d0bb4cdeb2005925b02b

[ "MIT" ]

1

2021-07-15T23:07:23.000Z

# TODO: add/import version support here class InvalidState(Exception): """Used when repomd data reaches an unexpected state""" pass class UnsupportedFileListException(Exception): """Used when the file list version is unsupported""" def __init__(self, version): super().__init__(f'This file list database version is unsupported. Please raise an issue. ' f'Currently support version {version} only.') class UnsupportedPrimaryDatabaseException(Exception): """Used when the primary version is unsupported""" def __init__(self, version) -> None: super().__init__(f'This primary database version is unsupported. Please raise an issue. ' f'Currently support version {version} only.') class UnsupportedOtherDatabaseException(Exception): """Used when the other version is unsupported""" def __init__(self, version) -> None: super().__init__(f'This other database version is unsupported. Please raise an issue. ' f'Currently support version {version} only.')

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2931be39061d9d722b71afb7a556f1fba541b273

261

py

Python

jangli/test/list_of_object_test.py

AbhimanyuHK/Json_Object_Conv

601f47697ef67e8e9665d2c4183169480e33041b

[ "MIT" ]

1

2020-05-09T02:14:41.000Z

jangli/test/list_of_object_test.py

AbhimanyuHK/Json_Object_Conv

601f47697ef67e8e9665d2c4183169480e33041b

[ "MIT" ]

null

jangli/test/list_of_object_test.py

AbhimanyuHK/Json_Object_Conv

601f47697ef67e8e9665d2c4183169480e33041b

[ "MIT" ]

null

from jangli.list_of_object import ListObject class A: def __init__(self, b): self.b = b def append_test(): lt = ListObject(A) lt.append(A(7)) print(lt) def insert_test(): lt = ListObject(A) lt.insert(1, A(8)) print(lt)

13.736842

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0

null

0

1

0

3

297b770706691b8a34e01f4496192e24c3f8cc06

337

py

Python

03-sdp-inro/hanoi.py

iproduct/intro-python

8fcf682286dad3fc65f46ccff33aefab9c601306

[ "Apache-2.0" ]

3

2022-01-10T07:56:37.000Z

2022-02-14T16:37:56.000Z

03-sdp-inro/hanoi.py

iproduct/intro-python

8fcf682286dad3fc65f46ccff33aefab9c601306

[ "Apache-2.0" ]

null

03-sdp-inro/hanoi.py

iproduct/intro-python

8fcf682286dad3fc65f46ccff33aefab9c601306

[ "Apache-2.0" ]

1

2022-02-14T16:36:46.000Z

def hanoi(n, from_tower, to_tower, other_tower): if n == 1: # recursion bottom print(f'{from_tower} -> {to_tower}') else: # recursion step hanoi(n - 1, from_tower, other_tower, to_tower) print(f'{from_tower} -> {to_tower}') hanoi(n - 1, other_tower, to_tower, from_tower) hanoi(4, 1, 3, 2)

30.636364

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0

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0

null

1

0

null

0

3

297ec9262ccd2c9f2563334c49d7df9880bbcc1a

97

py

Python

package/__init__.py

shahriyardx/discord-cog-package-template

a85658153846a075501921c682263d13e9194cbd

[ "MIT" ]

null

package/__init__.py

shahriyardx/discord-cog-package-template

a85658153846a075501921c682263d13e9194cbd

[ "MIT" ]

null

package/__init__.py

shahriyardx/discord-cog-package-template

a85658153846a075501921c682263d13e9194cbd

[ "MIT" ]

null

from .cog import CogName def setup(bot): bot.add_cog(CogName(bot)) __version__ = "0.0.1"

10.777778

29

0.670103

16

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1

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null

0

1

0

null

0

1

0

3

462ebf84062bebd84d94ee7518cd884e221e55ed

601

py

Python

flute/apps/amber/const.py

gumupaier/flute

2a6816355ead2cab26a606cae304d275216eaa16

[ "Apache-2.0" ]

4

2020-10-30T12:00:28.000Z

2021-05-10T05:51:13.000Z

flute/apps/amber/const.py

gumupaier/flute

2a6816355ead2cab26a606cae304d275216eaa16

[ "Apache-2.0" ]

null

flute/apps/amber/const.py

gumupaier/flute

2a6816355ead2cab26a606cae304d275216eaa16

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # @Time : 2020/12/11 10:13 上午 # @File : const.py VERSION_IMAGE_MAP = { 'storaged': { 'nightly': 'vesoft/nebula-storaged:nightly', '1.2': 'vesoft/nebula-storaged:v1.2.0', '1.1': 'vesoft/nebula-storaged:v1.1.0' }, 'metad': { 'nightly': 'vesoft/nebula-metad:nightly', '1.2': 'vesoft/nebula-metad:v1.2.0', '1.1': 'vesoft/nebula-metad:v1.1.0' }, 'graphd': { 'nightly': 'vesoft/nebula-graphd:nightly', '1.2': 'vesoft/nebula-graphd:v1.2.0', '1.1': 'vesoft/nebula-graphd:v1.1.0' } }

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1

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0

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4630f29f361645b2935ec2cedfaa7ca1a50e4c72

6,123

py

Python

conans/test/functional/editable/graph_related_test.py

Ignition/conan

84a38590987ecb9f3011f73babc95598ea62535f

[ "MIT" ]

null

conans/test/functional/editable/graph_related_test.py

Ignition/conan

84a38590987ecb9f3011f73babc95598ea62535f

[ "MIT" ]

null

conans/test/functional/editable/graph_related_test.py

Ignition/conan

84a38590987ecb9f3011f73babc95598ea62535f

[ "MIT" ]

null

# coding=utf-8 import os import textwrap import unittest from parameterized import parameterized from conans.model.ref import ConanFileReference from conans.test.utils.tools import TestClient, TestServer conanfile_base = textwrap.dedent("""\ from conans import ConanFile class APck(ConanFile): {body} """) conanfile = conanfile_base.format(body="pass") conan_package_layout = textwrap.dedent("""\ [includedirs] src/include """) class EmptyCacheTestMixin(object): """ Will check that the cache after using the link is empty """ def setUp(self): self.servers = {"default": TestServer()} self.t = TestClient(servers=self.servers, users={"default": [("lasote", "mypass")]}, path_with_spaces=False) self.ref = ConanFileReference.loads('lib/version@user/channel') self.assertFalse(os.path.exists(self.t.cache.base_folder(self.ref))) def tearDown(self): self.t.run('editable remove {}'.format(self.ref)) self.assertFalse(self.t.cache.installed_as_editable(self.ref)) class ExistingCacheTestMixin(object): """ Will check that the cache after using the link contains the same data as before """ def setUp(self): self.servers = {"default": TestServer()} self.t = TestClient(servers=self.servers, users={"default": [("lasote", "mypass")]}, path_with_spaces=False) self.ref = ConanFileReference.loads('lib/version@user/channel') self.t.save(files={'conanfile.py': conanfile}) self.t.run('create . {}'.format(self.ref)) self.assertTrue(os.path.exists(self.t.cache.base_folder(self.ref))) self.assertListEqual(sorted(os.listdir(self.t.cache.base_folder(self.ref))), ['build', 'export', 'export_source', 'locks', 'metadata.json', 'metadata.json.lock', 'package', 'source']) def tearDown(self): self.t.run('editable remove {}'.format(self.ref)) self.assertTrue(os.path.exists(self.t.cache.base_folder(self.ref))) self.assertListEqual(sorted(os.listdir(self.t.cache.base_folder(self.ref))), ['build', 'export', 'export_source', 'locks', 'metadata.json', 'metadata.json.lock', 'package', 'source']) class RelatedToGraphBehavior(object): def test_do_nothing(self): self.t.save(files={'conanfile.py': conanfile, "mylayout": conan_package_layout, }) self.t.run('editable add . {}'.format(self.ref)) self.assertTrue(self.t.cache.installed_as_editable(self.ref)) @parameterized.expand([(True, ), (False, )]) def test_install_requirements(self, update): # Create a parent and remove it from cache ref_parent = ConanFileReference.loads("parent/version@lasote/channel") self.t.save(files={'conanfile.py': conanfile}) self.t.run('create . {}'.format(ref_parent)) self.t.run('upload {} --all'.format(ref_parent)) self.t.run('remove {} --force'.format(ref_parent)) self.assertFalse(os.path.exists(self.t.cache.base_folder(ref_parent))) # Create our project and link it self.t.save(files={'conanfile.py': conanfile_base.format(body='requires = "{}"'.format(ref_parent)), "mylayout": conan_package_layout, }) self.t.run('editable add . {}'.format(self.ref)) # Install our project and check that everything is in place update = ' --update' if update else '' self.t.run('install {}{}'.format(self.ref, update)) self.assertIn(" lib/version@user/channel from user folder - Editable", self.t.out) self.assertIn(" parent/version@lasote/channel from 'default' - Downloaded", self.t.out) self.assertTrue(os.path.exists(self.t.cache.base_folder(ref_parent))) @parameterized.expand([(True,), (False,)]) def test_middle_graph(self, update): # Create a parent and remove it from cache ref_parent = ConanFileReference.loads("parent/version@lasote/channel") self.t.save(files={'conanfile.py': conanfile}) self.t.run('create . {}'.format(ref_parent)) self.t.run('upload {} --all'.format(ref_parent)) self.t.run('remove {} --force'.format(ref_parent)) self.assertFalse(os.path.exists(self.t.cache.base_folder(ref_parent))) # Create our project and link it path_to_lib = os.path.join(self.t.current_folder, 'lib') self.t.save(files={'conanfile.py': conanfile_base.format(body='requires = "{}"'.format(ref_parent)), "mylayout": conan_package_layout, }, path=path_to_lib) self.t.run('editable add "{}" {}'.format(path_to_lib, self.ref)) # Create a child an install it (in other folder, do not override the link!) path_to_child = os.path.join(self.t.current_folder, 'child') ref_child = ConanFileReference.loads("child/version@lasote/channel") self.t.save(files={'conanfile.py': conanfile_base. format(body='requires = "{}"'.format(self.ref)), }, path=path_to_child) update = ' --update' if update else '' self.t.run('create "{}" {} {}'.format(path_to_child, ref_child, update)) child_remote = 'No remote' if update else 'Cache' self.assertIn(" child/version@lasote/channel from local cache - {}".format(child_remote), self.t.out) self.assertIn(" lib/version@user/channel from user folder - Editable", self.t.out) self.assertIn(" parent/version@lasote/channel from 'default' - Downloaded", self.t.out) self.assertTrue(os.path.exists(self.t.cache.base_folder(ref_parent))) class CreateLinkOverEmptyCache(EmptyCacheTestMixin, RelatedToGraphBehavior, unittest.TestCase): pass class CreateLinkOverExistingCache(ExistingCacheTestMixin, RelatedToGraphBehavior, unittest.TestCase): pass

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0.716544

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0.623024

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0.22489

6,123

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0.799621

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1

0

1

0

null

0

1

0

3

467e579db0cf8efc321b38a34d5b42cf4ccd3fd2

3,224

py

Python

extractstopwords.py

npedrazzini/PreModernSlavic-NLP

ab17849c5b2dfb8f4733db13d2259c97b9180974

[ "MIT" ]

1

2021-09-20T08:41:24.000Z

extractstopwords.py

npedrazzini/PreModernSlavic-NLP

ab17849c5b2dfb8f4733db13d2259c97b9180974

[ "MIT" ]

null

extractstopwords.py

npedrazzini/PreModernSlavic-NLP

ab17849c5b2dfb8f4733db13d2259c97b9180974

[ "MIT" ]

1

2021-08-07T08:34:07.000Z

import pandas as pd df = pd.read_csv('/Users/nilo/Desktop/_TOROT_/stopwords.csv') stopwordsdf = pd.DataFrame(df, columns=['lemma_id','pos','form','lemma']) C = stopwordsdf[(stopwordsdf["pos"] == 'C-') & (stopwordsdf["lemma"] != 'FIXME')] C = sorted(C["form"].unique()) print('C- =') print("set(\n\"\"\"\n" + " ".join(C)+ "\n\"\"\".split()\n)") Dq = stopwordsdf[(stopwordsdf["pos"] == 'Dq') & (stopwordsdf["lemma"] != 'FIXME')] Dq = sorted(Dq["form"].unique()) print('Dq =') print("set(\n\"\"\"\n" + " ".join(Dq)+ "\n\"\"\".split()\n)") Du = stopwordsdf[(stopwordsdf["pos"] == 'Du') & (stopwordsdf["lemma"] != 'FIXME')] Du = sorted(Du["form"].unique()) print('Du =') print("set(\n\"\"\"\n" + " ".join(Du)+ "\n\"\"\".split()\n)") G = stopwordsdf[(stopwordsdf["pos"] == 'G-') & (stopwordsdf["lemma"] != 'FIXME')] G = sorted(G["form"].unique()) print('G- =') print("set(\n\"\"\"\n" + " ".join(G)+ "\n\"\"\".split()\n)") I = stopwordsdf[(stopwordsdf["pos"] == 'I-') & (stopwordsdf["lemma"] != 'FIXME')] I = sorted(I["form"].unique()) print('I- =') print("set(\n\"\"\"\n" + " ".join(I)+ "\n\"\"\".split()\n)") Ma = stopwordsdf[(stopwordsdf["pos"] == 'Ma') & (stopwordsdf["lemma"] != 'FIXME')] Ma = sorted(Ma["form"].unique()) print('Ma =') print("set(\n\"\"\"\n" + " ".join(Ma)+ "\n\"\"\".split()\n)") Pd = stopwordsdf[(stopwordsdf["pos"] == 'Pd') & (stopwordsdf["lemma"] != 'FIXME')] Pd = sorted(Pd["form"].unique()) print('Pd =') print("set(\n\"\"\"\n" + " ".join(Pd)+ "\n\"\"\".split()\n)") Pi = stopwordsdf[(stopwordsdf["pos"] == 'Pi') & (stopwordsdf["lemma"] != 'FIXME')] Pi = sorted(Pi["form"].unique()) print('Pi =') print("set(\n\"\"\"\n" + " ".join(Pi)+ "\n\"\"\".split()\n)") Pk = stopwordsdf[(stopwordsdf["pos"] == 'Pk') & (stopwordsdf["lemma"] != 'FIXME')] Pk = sorted(Pk["form"].unique()) print('Pk =') print("set(\n\"\"\"\n" + " ".join(Pk)+ "\n\"\"\".split()\n)") Pp = stopwordsdf[(stopwordsdf["pos"] == 'Pp') & (stopwordsdf["lemma"] != 'FIXME')] Pp = sorted(Pp["form"].unique()) print('Pp =') print("set(\n\"\"\"\n" + " ".join(Pp)+ "\n\"\"\".split()\n)") Pr = stopwordsdf[(stopwordsdf["pos"] == 'Pr') & (stopwordsdf["lemma"] != 'FIXME')] Pr = sorted(Pr["form"].unique()) print('Pr =') print("set(\n\"\"\"\n" + " ".join(Pr)+ "\n\"\"\".split()\n)") Ps = stopwordsdf[(stopwordsdf["pos"] == 'Ps') & (stopwordsdf["lemma"] != 'FIXME')] Ps = sorted(Ps["form"].unique()) print('Ps =') print("set(\n\"\"\"\n" + " ".join(Ps)+ "\n\"\"\".split()\n)") Pt = stopwordsdf[(stopwordsdf["pos"] == 'Pt') & (stopwordsdf["lemma"] != 'FIXME')] Pt = sorted(Pt["form"].unique()) print('Pt =') print("set(\n\"\"\"\n" + " ".join(Pt)+ "\n\"\"\".split()\n)") Px = stopwordsdf[(stopwordsdf["pos"] == 'Px') & (stopwordsdf["lemma"] != 'FIXME')] Px = sorted(Px["form"].unique()) print('Px =') print("set(\n\"\"\"\n" + " ".join(Px)+ "\n\"\"\".split()\n)") R = stopwordsdf[(stopwordsdf["pos"] == 'R-') & (stopwordsdf["lemma"] != 'FIXME')] R = sorted(R["form"].unique()) print('R- =') print("set(\n\"\"\"\n" + " ".join(R)+ "\n\"\"\".split()\n)") V = stopwordsdf[(stopwordsdf["pos"] == 'V-') & (stopwordsdf["lemma"] != 'FIXME')] V = sorted(V["form"].unique()) print('V- =') print("set(\n\"\"\"\n" + " ".join(V)+ "\n\"\"\".split()\n)")

38.380952

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0.522333

408

3,224

4.117647

0.102941

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0.238095

0.095238

0.133333

0

0.102667

3,224

84

83

38.380952

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false

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0.014925

0.477612

0

null

1

0

null

0

1

0

3

468fd50fd813893776c245dff35b32e1ca4bf944

123

py

Python

Django_Developer/HyperJob Agency/resume/urls.py

dimk00z/JetBrains-Academy

04304e6221f464292a2687d5b0a0260f8b557da4

[ "MIT" ]

null

Django_Developer/HyperJob Agency/resume/urls.py

dimk00z/JetBrains-Academy

04304e6221f464292a2687d5b0a0260f8b557da4

[ "MIT" ]

null

Django_Developer/HyperJob Agency/resume/urls.py

dimk00z/JetBrains-Academy

04304e6221f464292a2687d5b0a0260f8b557da4

[ "MIT" ]

null

from django.urls import path from .views import ResumeListView urlpatterns = [ path("", ResumeListView.as_view()), ]

15.375

39

0.723577

14

123

6.285714

0.714286

0

0.162602

123

7

40

17.571429

0.854369

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

3

469c3c1b619806380cbea384ee1a3a2113c10a84

2,167

py

Python

src/cp_request/visitor.py

aquariumbio/experiment-request

026e3eb767c47f980a35004e9ded5e4e33553693

[ "MIT" ]

null

src/cp_request/visitor.py

aquariumbio/experiment-request

026e3eb767c47f980a35004e9ded5e4e33553693

[ "MIT" ]

null

src/cp_request/visitor.py

aquariumbio/experiment-request

026e3eb767c47f980a35004e9ded5e4e33553693

[ "MIT" ]

null

import abc from cp_request import ( Attribute, Control, ExperimentalRequest, Measurement, NamedEntity, Sample, Treatment, Unit, Value, Version ) from cp_request.design import ( DesignBlock, BlockReference, GenerateBlock, ProductBlock, ReplicateBlock, SumBlock, SubjectReference, TreatmentReference, TreatmentValueReference ) class RequestVisitor(abc.ABC): """ Abstract visitor for structured request classes. Includes stubbed visit methods for each class, with each simply returning. To create a visitor, inherit from this class, define an initializer, and each appropriate visit method. """ @abc.abstractmethod def __init__(self): pass def visit_design_block(self, block: DesignBlock): return def visit_product_block(self, block: ProductBlock): return def visit_block_reference(self, reference: BlockReference): return def visit_sum_block(self, block: SumBlock): return def visit_subject_reference(self, reference: SubjectReference): return def visit_treatment_reference(self, reference: TreatmentReference): return def visit_treatment_value_reference(self, reference: TreatmentValueReference): return def visit_replicate_block(self, block: ReplicateBlock): return def visit_generate_block(self, block: GenerateBlock): return def visit_attribute(self, attribute: Attribute): return def visit_version(self, version: Version): return def visit_treatment(self, treatment: Treatment): return def visit_sample(self, sample: Sample): return def visit_control(self, control: Control): return def visit_measurement(self, measurement: Measurement): return def visit_unit(self, unit: Unit): return def visit_value(self, value: Value): return def visit_named_entity(self, entity: NamedEntity): return def visit_experiment(self, experiment: ExperimentalRequest): return

22.112245

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0.670512

217

2,167

6.529954

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0.107269

0.177841

0.048694

0

0.265344

2,167

97

79

22.340206

0.890075

0.105215

0

0.283582

0

1

0.298507

false

0.014925

0.044776

0.283582

0.641791

0

null

0

null

0

1

0

1

0

3

469f3dd1590c6f187f62bbab790e848b4bd2722f

151

py

Python

Jan17/SimpleDictionary2.py

RoyalBiharCoders/bootcampJan2021

31d1cae15d9dca3d0a57157b3ee115c575c7c2b6

[ "Apache-2.0" ]

3

2021-01-22T09:16:34.000Z

2021-02-06T10:07:40.000Z

Jan17/SimpleDictionary2.py

RoyalBiharCoders/bootcampJan2021

31d1cae15d9dca3d0a57157b3ee115c575c7c2b6

[ "Apache-2.0" ]

null

Jan17/SimpleDictionary2.py

RoyalBiharCoders/bootcampJan2021

31d1cae15d9dca3d0a57157b3ee115c575c7c2b6

[ "Apache-2.0" ]

2

2021-01-10T15:46:35.000Z

2021-02-01T13:24:57.000Z

#Defining a simple dictionary where key is "A" and its value is a list i.e. ["apple", "animal"] myDict = {"A": ["apple", "animal"]} print(myDict["A"])

37.75

95

0.642384

25

151

3.88

0.68

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0

0.152318

151

4

96

37.75

0.757813

0.622517

0

0.22807

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0

0.5

1

0

null

0

1

0

null

0

1

0

3

46a72b918890beea76e84fb418bc485830acbc7a

208

py

Python

yunionclient/api/dnsrecords.py

tb365/mcclient_python

06647e7496b9e2c3aeb5ade1276c81871063159b

[ "Apache-2.0" ]

3

2021-09-22T11:34:08.000Z

2022-03-13T04:55:17.000Z

yunionclient/api/dnsrecords.py

xhw20190116/python_yunionsdk

eb7c8c08300d38dac204ec4980a775abc9c7083a

[ "Apache-2.0" ]

13

2019-06-06T08:25:41.000Z

2021-07-16T07:26:10.000Z

yunionclient/api/dnsrecords.py

xhw20190116/python_yunionsdk

eb7c8c08300d38dac204ec4980a775abc9c7083a

[ "Apache-2.0" ]

7

2019-03-31T05:43:36.000Z

2021-03-04T09:59:05.000Z

from yunionclient.common import base class DNSRecordManager(base.StandaloneManager): keyword = 'dnsrecord' keyword_plural = 'dnsrecords' _columns = ['ID', 'Name', 'Records', 'TTL', 'is_public']

26

60

0.706731

21

208

6.857143

0.904762

0

0.158654

208

7

61

29.714286

0.822857

0

0.211538

0

1

0

false

0

0.2

0

1

0

1

0

null

0

1

0

null

0

1

0

3

46ac2c4badf766b0620ffb58b32db74c2321e9f7

1,715

py

Python

sample/helpers.py

itsbetuliremsedef/vulnerability-analyzer

94f6c7ba4fe823fba9b814142d01a3cbde15372c

[ "BSD-2-Clause" ]

null

sample/helpers.py

itsbetuliremsedef/vulnerability-analyzer

94f6c7ba4fe823fba9b814142d01a3cbde15372c

[ "BSD-2-Clause" ]

null

sample/helpers.py

itsbetuliremsedef/vulnerability-analyzer

94f6c7ba4fe823fba9b814142d01a3cbde15372c

[ "BSD-2-Clause" ]

null

import json ### SWAGGER DOCS DETAILS __API_LABEL = "Scout24" __API_DEFINITION = "dependency vulnerability check" ### JSON KEY DETAILS __JSON_KEY_VULS = "vulnerabilities" __JSON_KEY_DEPS = "dependencies" __JSON_KEY_NAME = "name" __JSON_KEY_SEVERITY = "severity" __JSON_KEY_FILE_NAME = "fileName" __JSON_KEY_OUT_NUM_VULS = "num_vulnerabilities" __JSON_KEY_OUT_FILE_NAMES = "file_names" __JSON_KEY_INDEX_SPLITTER = "####" __JSON_KEY_OUTPUT_NAME = "vulnerability_name" ### ENDPOINT DETAILS __ENDPOINT_EXERCISE1 = "/exercise1/about-owasp" __ENDPOINT_EXERCISE2 = "/exercise2/filter-sort" __ENDPOINT_EXERCISE3 = "/exercise3/histogram" ### JSON FILE __FILEPATH = "./file/report.json" ### EXERCISE1 DEFINITION FILE IN JSON FORMAT __FILEPATH_EXERCISE1 = "./file/exercise1(owasp-def).json" ### HELPERS def api_label(): return __API_LABEL def api_def(): return __API_DEFINITION def json_key_v(): return __JSON_KEY_VULS def json_key_name(): return __JSON_KEY_NAME def json_key_severity(): return __JSON_KEY_SEVERITY def json_splitter(): return __JSON_KEY_INDEX_SPLITTER def json_key_out_name(): return __JSON_KEY_OUTPUT_NAME def json_key_out_file_names(): return __JSON_KEY_OUT_FILE_NAMES def json_key_out_num_vul(): return __JSON_KEY_OUT_NUM_VULS def json_key_file_name(): return __JSON_KEY_FILE_NAME def json_key_file_deps(): return __JSON_KEY_DEPS def json_file_path(): return __FILEPATH def json_file_exercise1_path(): return __FILEPATH_EXERCISE1 def endpoint_exercise1(): return __ENDPOINT_EXERCISE1 def endpoint_exercise2(): return __ENDPOINT_EXERCISE2 def endpoint_exercise3(): return __ENDPOINT_EXERCISE3

16.028037

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1,715

5.137168

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0.162791

0.100775

0.03876

0.078381

0

0.013005

0.148105

1,715

106

58

16.179245

0.781656

0.065889

0

0.158095

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1

0.326531

false

0

0.020408

0.326531

0.673469

0

null

0

null

0

1

0

1

0

3

46b0b7f85cd8e61cb2a9359f711fe4226d5aaed9

160

py

Python

PiWriter/config.py

GammaGames/piwriter

101a8ebec5e98a98216d553d04a63fda20f40ff7

[ "MIT" ]

null

PiWriter/config.py

GammaGames/piwriter

101a8ebec5e98a98216d553d04a63fda20f40ff7

[ "MIT" ]

null

PiWriter/config.py

GammaGames/piwriter

101a8ebec5e98a98216d553d04a63fda20f40ff7

[ "MIT" ]

null

#!/usr/bin/env python3 from configparser import ConfigParser def get_config(): config = ConfigParser() config.read('piwriter.ini') return config

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5.947368

0.736842

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0.007634

0.18125

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0.13125

0

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0

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false

0

0.2

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0.6

0

1

0

null

0

1

0

null

0

1

0

3

d3b8bde476ba1d035b74a4cba14959304b5e702e

813

py

Python

tests/test_task_processing.py

sobolevn/paasta

8b87e0b13816c09b3d063b6d3271e6c7627fd264

[ "Apache-2.0" ]

1,711

2015-11-10T18:04:56.000Z

2022-03-23T08:53:16.000Z

tests/test_task_processing.py

sobolevn/paasta

8b87e0b13816c09b3d063b6d3271e6c7627fd264

[ "Apache-2.0" ]

1,689

2015-11-10T17:59:04.000Z

2022-03-31T20:46:46.000Z

tests/test_task_processing.py

sobolevn/paasta

8b87e0b13816c09b3d063b6d3271e6c7627fd264

[ "Apache-2.0" ]

267

2015-11-10T19:17:16.000Z

2022-02-08T20:59:52.000Z

# Copyright 2015-2017 Yelp Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # We just want to test that task_processing is available in the virtualenv def test_import(): from task_processing.task_processor import TaskProcessor tp = TaskProcessor() tp.load_plugin("task_processing.plugins.mesos")

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py

Python

pyvisdk/mo/host_datastore_system.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

pyvisdk/mo/host_datastore_system.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

pyvisdk/mo/host_datastore_system.py

Infinidat/pyvisdk

f2f4e5f50da16f659ccc1d84b6a00f397fa997f8

[ "MIT" ]

null

from pyvisdk.base.managed_object_types import ManagedObjectTypes from pyvisdk.base.base_entity import BaseEntity import logging ######################################## # Automatically generated, do not edit. ######################################## log = logging.getLogger(__name__) class HostDatastoreSystem(BaseEntity): '''This managed object creates and removes datastores from the host.To a host, a datastore is a storage abstraction that is backed by one of several types of storage volumes:An ESX Server system automatically discovers the VMFS volume on attached Logical Unit Numbers (LUNs) on startup and after re-scanning the host bus adapter. Datastores are automatically created. The datastore label is based on the VMFS volume label. If there is a conflict with an existing datastore, it is made unique by appending a suffix. The VMFS volume label will be unchanged.Destroying the datastore removes the partitions that compose the VMFS volume.Datastores are never automatically removed because transient storage connection outages may occur. They must be removed from the host using this interface.See Datastore''' def __init__(self, core, name=None, ref=None, type=ManagedObjectTypes.HostDatastoreSystem): super(HostDatastoreSystem, self).__init__(core, name=name, ref=ref, type=type) @property def capabilities(self): '''Capability vector indicating the available product features.''' return self.update('capabilities') @property def datastore(self): '''List of datastores on this host.''' return self.update('datastore') def ConfigureDatastorePrincipal(self, userName, password=None): '''Configures datastore principal user for the host.Configures datastore principal user for the host. :param userName: Datastore principal user name. :param password: Optional password for systems that require password for user impersonation. ''' return self.delegate("ConfigureDatastorePrincipal")(userName, password) def CreateLocalDatastore(self, name, path): '''Creates a new local datastore. :param name: The name of a datastore to create on the local host. :param path: The file path for a directory in which the virtual machine data will be stored. ''' return self.delegate("CreateLocalDatastore")(name, path) def CreateNasDatastore(self, spec): '''Creates a new network-attached storage datastore. :param spec: The specification for creating a network-attached storage volume. ''' return self.delegate("CreateNasDatastore")(spec) def CreateVmfsDatastore(self, spec): '''Creates a new VMFS datastore. :param spec: The specification for creating a datastore backed by a VMFS. ''' return self.delegate("CreateVmfsDatastore")(spec) def ExpandVmfsDatastore(self, datastore, spec): '''Increases the capacity of an existing VMFS datastore by expanding (increasing the size of) an existing extent of the datastore. :param datastore: The datastore whose capacity should be increased. :param spec: The specification describing which extent of the VMFS datastore to expand. ''' return self.delegate("ExpandVmfsDatastore")(datastore, spec) def ExtendVmfsDatastore(self, datastore, spec): '''Increases the capacity of an existing VMFS datastore by adding new extents to the datastore. :param datastore: The datastore whose capacity should be increased. :param spec: The specification describing what extents to add to a VMFS datastore. ''' return self.delegate("ExtendVmfsDatastore")(datastore, spec) def QueryAvailableDisksForVmfs(self, datastore=None): '''Query to list disks that can be used to contain VMFS datastore extents. If the optional parameter name is supplied, queries for disks that can be used to contain extents for a VMFS datastore identified by the supplied name. Otherwise, the method retrieves disks that can be used to contain new VMFS datastores.Query to list disks that can be used to contain VMFS datastore extents. If the optional parameter name is supplied, queries for disks that can be used to contain extents for a VMFS datastore identified by the supplied name. Otherwise, the method retrieves disks that can be used to contain new VMFS datastores.Query to list disks that can be used to contain VMFS datastore extents. If the optional parameter name is supplied, queries for disks that can be used to contain extents for a VMFS datastore identified by the supplied name. Otherwise, the method retrieves disks that can be used to contain new VMFS datastores. :param datastore: The managed object reference of the VMFS datastore you want extents for. ''' return self.delegate("QueryAvailableDisksForVmfs")(datastore) def QueryUnresolvedVmfsVolumes(self): '''Get the list of unbound VMFS volumes. For sharing a volume across hosts, a VMFS volume is bound to its underlying block device storage. When a low level block copy is performed to copy or move the VMFS volume, the copied volume will be unbound. ''' return self.delegate("QueryUnresolvedVmfsVolumes")() def QueryVmfsDatastoreCreateOptions(self, devicePath, vmfsMajorVersion=None): '''Queries options for creating a new VMFS datastore for a disk.See devicePath :param devicePath: The devicePath of the disk on which datastore creation options are generated.See devicePath :param vmfsMajorVersion: major version of VMFS to be used for formatting the datastore. If this parameter is not specified, then the default VMFS version for the host is used.See devicePathvSphere API 5.0 ''' return self.delegate("QueryVmfsDatastoreCreateOptions")(devicePath, vmfsMajorVersion) def QueryVmfsDatastoreExpandOptions(self, datastore): '''Queries for options for increasing the capacity of an existing VMFS datastore by expanding (increasing the size of) an existing extent of the datastore. :param datastore: The datastore to be expanded. ''' return self.delegate("QueryVmfsDatastoreExpandOptions")(datastore) def QueryVmfsDatastoreExtendOptions(self, datastore, devicePath, suppressExpandCandidates=None): '''Queries for options for increasing the capacity of an existing VMFS datastore by adding new extents using space from the specified disk.See devicePath :param datastore: The datastore to be extended.See devicePath :param devicePath: The devicePath of the disk on which datastore extension options are generated.See devicePath :param suppressExpandCandidates: Indicates whether to exclude options that can be used for extent expansion also. Free space can be used for adding an extent or expanding an existing extent. If this parameter is set to true, the list of options returned will not include free space that can be used for expansion.See devicePathvSphere API 4.0 ''' return self.delegate("QueryVmfsDatastoreExtendOptions")(datastore, devicePath, suppressExpandCandidates) def RemoveDatastore(self, datastore): '''Removes a datastore from a host. :param datastore: The datastore to be removed. ''' return self.delegate("RemoveDatastore")(datastore) def ResignatureUnresolvedVmfsVolume_Task(self, resolutionSpec): '''Resignature an unbound VMFS volume. To safely enable sharing of the volume across hosts, a VMFS volume is bound to its underlying block device storage. When a low level block copy is performed to copy or move the VMFS volume, the copied volume will be unbound. In order for the VMFS volume to be usable, a resolution operation is needed to determine whether the VMFS volume should be treated as a new volume or not and what extents compose that volume in the event there is more than one unbound volume.Resignature an unbound VMFS volume. To safely enable sharing of the volume across hosts, a VMFS volume is bound to its underlying block device storage. When a low level block copy is performed to copy or move the VMFS volume, the copied volume will be unbound. In order for the VMFS volume to be usable, a resolution operation is needed to determine whether the VMFS volume should be treated as a new volume or not and what extents compose that volume in the event there is more than one unbound volume. :param resolutionSpec: A data object that describes what the disk extents to be used for creating the new VMFS volume. ''' return self.delegate("ResignatureUnresolvedVmfsVolume_Task")(resolutionSpec) def UpdateLocalSwapDatastore(self, datastore=None): '''Choose the localSwapDatastore for this host. Any change to this setting will affect virtual machines that subsequently power on or resume from a suspended state at this host, or that migrate to this host while powered on; virtual machines that are currently powered on at this host will not yet be affected. :param datastore: The selected datastore. If this argument is unset, then the localSwapDatastore property becomes unset. Otherwise, the host must have read/write access to the indicated datastore. ''' return self.delegate("UpdateLocalSwapDatastore")(datastore)

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py

Python

win/devkit/other/pymel/extras/completion/py/maya/app/mayabullet/Trace.py

leegoonz/Maya-devkit

b81fe799b58e854e4ef16435426d60446e975871

[ "ADSL" ]

10

2018-03-30T16:09:02.000Z

2021-12-07T07:29:19.000Z

win/devkit/other/pymel/extras/completion/py/maya/app/mayabullet/Trace.py

leegoonz/Maya-devkit

b81fe799b58e854e4ef16435426d60446e975871

[ "ADSL" ]

null

win/devkit/other/pymel/extras/completion/py/maya/app/mayabullet/Trace.py

leegoonz/Maya-devkit

b81fe799b58e854e4ef16435426d60446e975871

[ "ADSL" ]

9

2018-06-02T09:18:49.000Z

2021-12-20T09:24:35.000Z

def Trace(tag=''): pass def TracePrint(strMsg): pass _traceEnabled = False _traceIndent = 0

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315b13fc668e1d229343673d8b58e7c2598b4424

238

py

Python

src/refdoc/__init__.py

novopl/sphinx-refdoc

ca26b374bdb20db18801b8db6f909e9118a67864

[ "MIT" ]

null

src/refdoc/__init__.py

novopl/sphinx-refdoc

ca26b374bdb20db18801b8db6f909e9118a67864

[ "MIT" ]

14

2017-10-11T10:22:34.000Z

2021-06-01T22:37:14.000Z

src/refdoc/__init__.py

novopl/sphinx-refdoc

ca26b374bdb20db18801b8db6f909e9118a67864

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ This module implements python reference documentation generator for sphinx. """ from __future__ import absolute_import from .logic import generate_docs __version__ = '0.3.1' __all__ = [ 'generate_docs' ]

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null

0

1

0

3

318a53285747d99ff8546ea8d86c75cd0f1cf01b

369

py

Python

fatf/accountability/__init__.py

So-Cool/fat-forensics

6fa252a1d90fe543242ef030a5f8a3f9c9f692fe

[ "BSD-3-Clause" ]

48

2019-09-12T04:54:48.000Z

2022-02-27T01:49:55.000Z

fatf/accountability/__init__.py

So-Cool/fat-forensics

6fa252a1d90fe543242ef030a5f8a3f9c9f692fe

[ "BSD-3-Clause" ]

4

2019-11-04T00:01:15.000Z

2021-01-27T16:35:29.000Z

fatf/accountability/__init__.py

So-Cool/fat-forensics

6fa252a1d90fe543242ef030a5f8a3f9c9f692fe

[ "BSD-3-Clause" ]

11

2019-09-17T13:39:43.000Z

2021-07-27T11:04:33.000Z

""" The :mod:`fatf.accountability` module holds a range of accountability methods. This module holds a variety of techniques that can be used to assess *privacy*, *security* and *robustness* of artificial intelligence pipelines and the machine learning process: *data*, *models* and *predictions*. """ # Author: Kacper Sokol <k.sokol@bristol.ac.uk> # License: new BSD

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31b07bed3c61d5cafcb3fc73df6e8e6b7e47e032

473

py

Python

src/Selenium2Library/keywords/__init__.py

piaoransk/selenium2libForMyself

b6bb880c3aa8ab6e5ddaffdb574aab6150ae3604

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

11

2017-09-30T05:47:28.000Z

2019-04-15T11:58:40.000Z

src/Selenium2Library/keywords/__init__.py

piaoransk/selenium2libForMyself

b6bb880c3aa8ab6e5ddaffdb574aab6150ae3604

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

null

src/Selenium2Library/keywords/__init__.py

piaoransk/selenium2libForMyself

b6bb880c3aa8ab6e5ddaffdb574aab6150ae3604

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

7

2018-02-13T10:22:39.000Z

2019-07-04T07:39:28.000Z

from .alert import AlertKeywords from .browsermanagement import BrowserManagementKeywords from .cookie import CookieKeywords from .element import ElementKeywords from .formelement import FormElementKeywords from .javascript import JavaScriptKeywords from .runonfailure import RunOnFailureKeywords from .screenshot import ScreenshotKeywords from .selectelement import SelectElementKeywords from .tableelement import TableElementKeywords from .waiting import WaitingKeywords

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31b4db5426c98c266c7765d06d53ea430178b58f

1,563

py

Python

cysecuretools/targets/cyb06xx7/maps/memory_map.py

cypresssemiconductorco/cysecuretools

f27b6a7a5d5829427d746bac046c496bfe2b5898

[ "Apache-2.0" ]

9

2019-09-16T19:33:20.000Z

2020-11-05T00:56:20.000Z

cysecuretools/targets/cyb06xx7/maps/memory_map.py

Infineon/cysecuretools

f27b6a7a5d5829427d746bac046c496bfe2b5898

[ "Apache-2.0" ]

1

2021-04-16T08:17:16.000Z

2021-05-21T05:55:58.000Z

cysecuretools/targets/cyb06xx7/maps/memory_map.py

Infineon/cysecuretools

f27b6a7a5d5829427d746bac046c496bfe2b5898

[ "Apache-2.0" ]

1

2019-10-03T17:24:24.000Z

""" Copyright (c) 2019-2020 Cypress Semiconductor Corporation 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. """ from cysecuretools.core import MemoryMapBase class MemoryMap_cyb06xx7(MemoryMapBase): @property def FLASH_ADDRESS(self): return 0x10000000 @property def FLASH_SIZE(self): return 0x000E0000 @property def PROVISION_JWT_PACKET_ADDRESS(self): return 0x100FB600 @property def PROVISION_JWT_PACKET_SIZE(self): return 0x4A00 @property def SPE_IMAGE_ID(self): return 1 @property def NSPE_IMAGE_ID(self): return 16 @property def SMIF_MEM_MAP_START(self): return 0x18000000 @property def VECTOR_TABLE_ADDR_ALIGNMENT(self): return 0x400 # SFB addresses @property def TOC1_ADDRESS(self): return 0x16007800 @property def TOC1_SFB_ADDRESS_OFFSET(self): return 0x14 @property def TOC1_HASH_OBJ_OFFSET(self): return 0x08 @property def SYSCALL_TABLE_ADDR(self): return 0x16002400

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31d7b16a61e93f151f32930afc808c7133e2dceb

900

py

Python

polyaxon_schemas/specs/__init__.py

orf/polyaxon-schemas

dce55df25ae752fc3fbf465ea53add126746d630

[ "MIT" ]

null

polyaxon_schemas/specs/__init__.py

orf/polyaxon-schemas

dce55df25ae752fc3fbf465ea53add126746d630

[ "MIT" ]

null

polyaxon_schemas/specs/__init__.py

orf/polyaxon-schemas

dce55df25ae752fc3fbf465ea53add126746d630

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function from polyaxon_schemas.specs import kinds from polyaxon_schemas.specs.build import BuildSpecification from polyaxon_schemas.specs.experiment import ExperimentSpecification from polyaxon_schemas.specs.group import GroupSpecification from polyaxon_schemas.specs.job import JobSpecification from polyaxon_schemas.specs.notebook import NotebookSpecification from polyaxon_schemas.specs.pipelines import PipelineSpecification from polyaxon_schemas.specs.tensorboard import TensorboardSpecification SPECIFICATION_BY_KIND = { kinds.BUILD: BuildSpecification, kinds.EXPERIMENT: ExperimentSpecification, kinds.GROUP: GroupSpecification, kinds.JOB: JobSpecification, kinds.NOTEBOOK: NotebookSpecification, kinds.TENSORBOARD: TensorboardSpecification, kinds.PIPELINE: PipelineSpecification, }

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1

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31dc6cedee0fbc975dcb4293784c57cf774c51b7

712

py

Python

library/record_shutit_build/record_shutit_build.py

aidanhs/shutit

e8b1aa0f0df4941cc759a76837dbee3ef88e5a60

[ "MIT" ]

null

library/record_shutit_build/record_shutit_build.py

aidanhs/shutit

e8b1aa0f0df4941cc759a76837dbee3ef88e5a60

[ "MIT" ]

null

library/record_shutit_build/record_shutit_build.py

aidanhs/shutit

e8b1aa0f0df4941cc759a76837dbee3ef88e5a60

[ "MIT" ]

null

"""ShutIt module. See http://shutit.tk """ from shutit_module import ShutItModule class record_shutit_build(ShutItModule): def is_installed(self, shutit): # Always run this return False def build(self, shutit): # default the delivery to bash here shutit.add_to_bashrc('''export SHUTIT_OPTIONS="$SHUTIT_OPTIONS --delivery bash"''') return True def module(): return record_shutit_build( 'shutit.tk.record_shutit_build.record_shutit_build', 0.39952141313136, description='Module to record a shutit build. See README.md in the source folder.', maintainer='ian.miell@gmail.com', depends=['shutit.tk.setup','shutit.tk.shutit.shutit','shutit.tk.ttygif.ttygif','shutit.tk.docker.docker'] )

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1

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31e5336a533c97580ce8a988f1ba5aecae4f458d

1,223

py

Python

platform/polycommon/polycommon/options/option_manager.py

admariner/polyaxon

ba355c38166047eb11e60de4cee4d7c3b48db323

[ "Apache-2.0" ]

3,200

2017-05-09T11:35:31.000Z

2022-03-28T05:43:22.000Z

platform/polycommon/polycommon/options/option_manager.py

admariner/polyaxon

ba355c38166047eb11e60de4cee4d7c3b48db323

[ "Apache-2.0" ]

1,324

2017-06-29T07:21:27.000Z

2022-03-27T12:41:10.000Z

platform/polycommon/polycommon/options/option_manager.py

admariner/polyaxon

ba355c38166047eb11e60de4cee4d7c3b48db323

[ "Apache-2.0" ]

341

2017-01-10T23:06:53.000Z

2022-03-10T08:15:18.000Z

#!/usr/bin/python # # Copyright 2018-2021 Polyaxon, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Tuple from polyaxon.utils.manager_interface import ManagerInterface from polycommon.options.option import Option class OptionManager(ManagerInterface): def _get_state_data( # pylint:disable=arguments-differ self, option: Option ) -> Tuple[str, Option]: return option.key, option def subscribe(self, option: Option) -> None: # pylint:disable=arguments-differ """ >>> subscribe(SomeOption) """ super().subscribe(obj=option) def get(self, key: str) -> Option: # pylint:disable=arguments-differ return super().get(key=key)

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9ec3d5d30407a5cebacf33d77713d29a95453c96

5,461

py

Python

sources/app/routes.py

pablintino/Altium-DBlib-source

65e85572f84048a7e7c5a116b429e09ac9a33e82

[ "MIT" ]

1

2021-06-23T20:19:45.000Z

sources/app/routes.py

pablintino/Altium-DBlib-source

65e85572f84048a7e7c5a116b429e09ac9a33e82

[ "MIT" ]

null

sources/app/routes.py

pablintino/Altium-DBlib-source

65e85572f84048a7e7c5a116b429e09ac9a33e82

[ "MIT" ]

null

# # MIT License # # Copyright (c) 2020 Pablo Rodriguez Nava, @pablintino # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # from app import api from rest_layer.component_list_resource import ComponentListResource from rest_layer.component_resource import ComponentResource from rest_layer.footprint_component_reference_resource import FootprintComponentReferenceResource from rest_layer.footprint_data_resource import FootprintDataResource from rest_layer.footprint_element_component_reference_resource import FootprintElementComponentReferenceResource from rest_layer.footprint_list_resource import FootprintListResource from rest_layer.footprint_resource import FootprintResource from rest_layer.inventory.inventory_category_item_list_resource import InventoryCategoryItemListResource from rest_layer.inventory.inventory_category_list_resource import InventoryCategoryListResource from rest_layer.inventory.inventory_category_parent_resource import InventoryCategoryParentResource from rest_layer.inventory.inventory_category_resource import InventoryCategoryResource from rest_layer.inventory.inventory_item_category_resource import InventoryItemCategoryResource from rest_layer.inventory.inventory_item_list_resource import InventoryItemListResource from rest_layer.inventory.inventory_item_location_resource import InventoryItemLocationResource from rest_layer.inventory.inventory_item_property_element_resource import InventoryItemPropertyElementResource from rest_layer.inventory.inventory_item_property_list_resource import InventoryItemPropertyListResource from rest_layer.inventory.inventory_item_resource import InventoryItemResource from rest_layer.inventory.inventory_item_stock_location_resource import InventoryItemStockLocationResource from rest_layer.inventory.inventory_location_list_resource import InventoryLocationListResource from rest_layer.inventory.inventory_location_resource import InventoryLocationResource from rest_layer.inventory.inventory_stocks_mass_update_resource import InventoryStocksMassUpdateResource from rest_layer.metadata_api import MetadataResource from rest_layer.symbol_component_reference_resource import SymbolComponentReferenceResource from rest_layer.symbol_data_resource import SymbolDataResource from rest_layer.symbol_list_resource import SymbolListResource from rest_layer.symbol_resource import SymbolResource api.add_resource(MetadataResource, '/metadata') api.add_resource(ComponentListResource, '/components') api.add_resource(ComponentResource, '/components/<int:id>') api.add_resource(SymbolComponentReferenceResource, '/components/<int:id>/symbol') api.add_resource(FootprintComponentReferenceResource, '/components/<int:id>/footprints') api.add_resource(FootprintElementComponentReferenceResource, '/components/<int:id>/footprints/<int:id_f>') api.add_resource(SymbolListResource, '/symbols') api.add_resource(SymbolResource, '/symbols/<int:id>') api.add_resource(SymbolDataResource, '/symbols/<int:id>/data') api.add_resource(FootprintListResource, '/footprints') api.add_resource(FootprintResource, '/footprints/<int:id>') api.add_resource(FootprintDataResource, '/footprints/<int:id>/data') # Items endpoints api.add_resource(InventoryItemListResource, '/inventory/items') api.add_resource(InventoryItemResource, '/inventory/items/<int:id>') api.add_resource(InventoryItemLocationResource, '/inventory/items/<int:id>/locations') api.add_resource(InventoryItemPropertyListResource, '/inventory/items/<int:id>/properties') api.add_resource(InventoryItemPropertyElementResource, '/inventory/items/<int:id>/properties/<int:prop_id>') api.add_resource(InventoryItemStockLocationResource, '/inventory/items/<int:id>/locations/<int:id_loc>/stock') api.add_resource(InventoryItemCategoryResource, '/inventory/items/<int:id>/category') # Locations endpoints api.add_resource(InventoryLocationListResource, '/inventory/locations') api.add_resource(InventoryLocationResource, '/inventory/locations/<int:id>') # Stock management endpoints api.add_resource(InventoryStocksMassUpdateResource, '/inventory/stocks/updates') # Categories endpoints api.add_resource(InventoryCategoryListResource, '/inventory/categories') api.add_resource(InventoryCategoryResource, '/inventory/categories/<int:id>') api.add_resource(InventoryCategoryItemListResource, '/inventory/categories/<int:id>/items') api.add_resource(InventoryCategoryParentResource, '/inventory/categories/<int:id>/parent')

61.359551

112

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7.268371

0.263578

0.045714

0.074286

0.067692

0.161538

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0.018901

0

0.000789

0.071415

5,461

88

113

62.056818

0.89647

0.21681

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0.162627

0.13156

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0.509434

0

0.509434

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null

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0

1

0

1

0

3

9ec4ce9d1f53a12ca2717ff07b632bca8aae1f96

30,454

py

Python

tests/meshes.py

cbcoutinho/learn_dg

b22bf91d1a0daedb6b48590c7361c3a9c3c7f371

[ "BSD-2-Clause" ]

6

2017-03-08T09:26:10.000Z

2020-06-25T01:25:12.000Z

tests/meshes.py

cbcoutinho/learn_dg

b22bf91d1a0daedb6b48590c7361c3a9c3c7f371

[ "BSD-2-Clause" ]

null

tests/meshes.py

cbcoutinho/learn_dg

b22bf91d1a0daedb6b48590c7361c3a9c3c7f371

[ "BSD-2-Clause" ]

1

2018-01-03T05:51:10.000Z

""" All of these meshes were created using either the test1D.geo or test2D.geo files in the test directory. To create a mesh using `gmsh`, calculate the following from the main source dir: $ gmsh test/test2D.geo -order 5 -2 This will create a test/test2D.msh file. Steps are similar for other orders of quadrilaterals, etc. """ import textwrap """ Global 1D meshes """ def mesh_Linear1DAdvDiffEqual(): gmsh_buffer = """\ $MeshFormat 2.2 0 8 $EndMeshFormat $Nodes 53 1 0 0 0 2 1 0 0 3 0.005030611117855227 0 0 4 0.01028898505360888 0 0 5 0.01578543473744853 0 0 6 0.02153073886104991 0 0 7 0.0275361641320945 0 0 8 0.03381348747337311 0 0 9 0.04037502051188173 0 0 10 0.04723363071047033 0 0 11 0.05440276741538541 0 0 12 0.06189649069088329 0 0 13 0.06972949691528545 0 0 14 0.0779171446731899 0 0 15 0.08647549147831562 0 0 16 0.09542132338499346 0 0 17 0.1047721814948864 0 0 18 0.1145464039321533 0 0 19 0.1247631588183544 0 0 20 0.1354424829767921 0 0 21 0.1466053185760331 0 0 22 0.1582735583739696 0 0 23 0.1704700819318919 0 0 24 0.1832188106214766 0 0 25 0.1965447437932882 0 0 26 0.2104740155104575 0 0 27 0.2250339402607451 0 0 28 0.2402530764359224 0 0 29 0.2561612661172213 0 0 30 0.2727897036477114 0 0 31 0.2901710001687771 0 0 32 0.3083392463940801 0 0 33 0.3273300718524898 0 0 34 0.3471807106396735 0 0 35 0.3679300994701113 0 0 36 0.3896189290639822 0 0 37 0.4122897303969599 0 0 38 0.4359869623522767 0 0 39 0.4607570985513004 0 0 40 0.4866487146275841 0 0 41 0.5137125865497462 0 0 42 0.5420017871557876 0 0 43 0.5715717987934756 0 0 44 0.6024806105980948 0 0 45 0.6347888244962619 0 0 46 0.6685598190666602 0 0 47 0.7038597970735351 0 0 48 0.7407580124632287 0 0 49 0.7793268079814842 0 0 50 0.8196418280784888 0 0 51 0.8617821258089199 0 0 52 0.9058303511173761 0 0 53 0.9518728765844403 0 0 $EndNodes $Elements 54 1 15 2 0 1 1 2 15 2 0 2 2 3 1 2 0 1 1 3 4 1 2 0 1 3 4 5 1 2 0 1 4 5 6 1 2 0 1 5 6 7 1 2 0 1 6 7 8 1 2 0 1 7 8 9 1 2 0 1 8 9 10 1 2 0 1 9 10 11 1 2 0 1 10 11 12 1 2 0 1 11 12 13 1 2 0 1 12 13 14 1 2 0 1 13 14 15 1 2 0 1 14 15 16 1 2 0 1 15 16 17 1 2 0 1 16 17 18 1 2 0 1 17 18 19 1 2 0 1 18 19 20 1 2 0 1 19 20 21 1 2 0 1 20 21 22 1 2 0 1 21 22 23 1 2 0 1 22 23 24 1 2 0 1 23 24 25 1 2 0 1 24 25 26 1 2 0 1 25 26 27 1 2 0 1 26 27 28 1 2 0 1 27 28 29 1 2 0 1 28 29 30 1 2 0 1 29 30 31 1 2 0 1 30 31 32 1 2 0 1 31 32 33 1 2 0 1 32 33 34 1 2 0 1 33 34 35 1 2 0 1 34 35 36 1 2 0 1 35 36 37 1 2 0 1 36 37 38 1 2 0 1 37 38 39 1 2 0 1 38 39 40 1 2 0 1 39 40 41 1 2 0 1 40 41 42 1 2 0 1 41 42 43 1 2 0 1 42 43 44 1 2 0 1 43 44 45 1 2 0 1 44 45 46 1 2 0 1 45 46 47 1 2 0 1 46 47 48 1 2 0 1 47 48 49 1 2 0 1 48 49 50 1 2 0 1 49 50 51 1 2 0 1 50 51 52 1 2 0 1 51 52 53 1 2 0 1 52 53 54 1 2 0 1 53 2 $EndElements """ gmsh_buffer = textwrap.dedent(gmsh_buffer) return gmsh_buffer def mesh_Quad1DAdvDiffEqual(): gmsh_buffer = """\ $MeshFormat 2.2 0 8 $EndMeshFormat $Nodes 53 1 0 0 0 2 1 0 0 3 0.01028898512093517 0 0 4 0.0215307390081578 0 0 5 0.0338134875014839 0 0 6 0.04723363127339462 0 0 7 0.06189649036599121 0 0 8 0.07791714442309114 0 0 9 0.09542132322625055 0 0 10 0.1145464038838541 0 0 11 0.1354424830607478 0 0 12 0.1582735586150919 0 0 13 0.1832188114257096 0 0 14 0.2104740173701267 0 0 15 0.2402530757044003 0 0 16 0.2727897030614678 0 0 17 0.3083392459861587 0 0 18 0.3471807104480421 0 0 19 0.3896189291322947 0 0 20 0.4359869680476123 0 0 21 0.4866487219042465 0 0 22 0.5420017867318138 0 0 23 0.6024806089626876 0 0 24 0.6685598177119734 0 0 25 0.7407580114554201 0 0 26 0.8196418274935637 0 0 27 0.9058303510422514 0 0 28 0.005144492560467496 0 0 29 0.01590986206454648 0 0 30 0.02767211325482123 0 0 31 0.04052355938743926 0 0 32 0.05456506081969292 0 0 33 0.06990681739454088 0 0 34 0.08666923382467084 0 0 35 0.1049838635550523 0 0 36 0.1249944434723082 0 0 37 0.1468580208379198 0 0 38 0.1707461850204007 0 0 39 0.1968464143979182 0 0 40 0.2253635465372635 0 0 41 0.2565213893829341 0 0 42 0.2905644745238133 0 0 43 0.3277599782171004 0 0 44 0.3683998197901684 0 0 45 0.4128029485899535 0 0 46 0.4613178449759294 0 0 47 0.5143252543179917 0 0 48 0.5722411978472507 0 0 49 0.6355202133373304 0 0 50 0.7046589145836968 0 0 51 0.7801999194744919 0 0 52 0.8627360892679076 0 0 53 0.9529151755211257 0 0 $EndNodes $Elements 28 1 15 2 0 1 1 2 15 2 0 2 2 3 8 2 0 1 1 3 28 4 8 2 0 1 3 4 29 5 8 2 0 1 4 5 30 6 8 2 0 1 5 6 31 7 8 2 0 1 6 7 32 8 8 2 0 1 7 8 33 9 8 2 0 1 8 9 34 10 8 2 0 1 9 10 35 11 8 2 0 1 10 11 36 12 8 2 0 1 11 12 37 13 8 2 0 1 12 13 38 14 8 2 0 1 13 14 39 15 8 2 0 1 14 15 40 16 8 2 0 1 15 16 41 17 8 2 0 1 16 17 42 18 8 2 0 1 17 18 43 19 8 2 0 1 18 19 44 20 8 2 0 1 19 20 45 21 8 2 0 1 20 21 46 22 8 2 0 1 21 22 47 23 8 2 0 1 22 23 48 24 8 2 0 1 23 24 49 25 8 2 0 1 24 25 50 26 8 2 0 1 25 26 51 27 8 2 0 1 26 27 52 28 8 2 0 1 27 2 53 $EndElements """ gmsh_buffer = textwrap.dedent(gmsh_buffer) return gmsh_buffer def mesh_Cub1DAdvDiffEqual(): gmsh_buffer = """\ $MeshFormat 2.2 0 8 $EndMeshFormat $Nodes 79 1 0 0 0 2 1 0 0 3 0.01028898512093517 0 0 4 0.0215307390081578 0 0 5 0.0338134875014839 0 0 6 0.04723363127339462 0 0 7 0.06189649036599121 0 0 8 0.07791714442309114 0 0 9 0.09542132322625055 0 0 10 0.1145464038838541 0 0 11 0.1354424830607478 0 0 12 0.1582735586150919 0 0 13 0.1832188114257096 0 0 14 0.2104740173701267 0 0 15 0.2402530757044003 0 0 16 0.2727897030614678 0 0 17 0.3083392459861587 0 0 18 0.3471807104480421 0 0 19 0.3896189291322947 0 0 20 0.4359869680476123 0 0 21 0.4866487219042465 0 0 22 0.5420017867318138 0 0 23 0.6024806089626876 0 0 24 0.6685598177119734 0 0 25 0.7407580114554201 0 0 26 0.8196418274935637 0 0 27 0.9058303510422514 0 0 28 0.003429661706978295 0 0 29 0.006859323413956723 0 0 30 0.01403623641667604 0 0 31 0.01778348771241692 0 0 32 0.02562498850593348 0 0 33 0.02971923800370915 0 0 34 0.03828686875878747 0 0 35 0.04276025001609104 0 0 36 0.05212125097092682 0 0 37 0.05700887066845901 0 0 38 0.06723670838502425 0 0 39 0.0725769264040577 0 0 40 0.08375187069081094 0 0 41 0.08958659695853075 0 0 42 0.1017963501121184 0 0 43 0.1081713769979863 0 0 44 0.1215117636094902 0 0 45 0.1284771233351214 0 0 46 0.1430528415788625 0 0 47 0.1506632000969772 0 0 48 0.1665886428852978 0 0 49 0.1749037271555037 0 0 50 0.1923038800738486 0 0 51 0.2013889487219877 0 0 52 0.2204003701482179 0 0 53 0.2303267229263091 0 0 54 0.2510986181567562 0 0 55 0.261944160609112 0 0 56 0.2846395507030314 0 0 57 0.296489398344595 0 0 58 0.3212864008067865 0 0 59 0.3342335556274143 0 0 60 0.361326783342793 0 0 61 0.3754728562375439 0 0 62 0.4050749421040672 0 0 63 0.4205309550758398 0 0 64 0.452874219333157 0 0 65 0.4697614706187017 0 0 66 0.5050997435133822 0 0 67 0.5235507651225979 0 0 68 0.5621613941421051 0 0 69 0.5823210015523964 0 0 70 0.6245070118791162 0 0 71 0.6465334147955447 0 0 72 0.6926258822931223 0 0 73 0.7166919468742712 0 0 74 0.767052616801468 0 0 75 0.7933472221475159 0 0 76 0.8483713353431263 0 0 77 0.8771008431926888 0 0 78 0.9372202340281676 0 0 79 0.9686101170140837 0 0 $EndNodes $Elements 28 1 15 2 0 1 1 2 15 2 0 2 2 3 26 2 0 1 1 3 28 29 4 26 2 0 1 3 4 30 31 5 26 2 0 1 4 5 32 33 6 26 2 0 1 5 6 34 35 7 26 2 0 1 6 7 36 37 8 26 2 0 1 7 8 38 39 9 26 2 0 1 8 9 40 41 10 26 2 0 1 9 10 42 43 11 26 2 0 1 10 11 44 45 12 26 2 0 1 11 12 46 47 13 26 2 0 1 12 13 48 49 14 26 2 0 1 13 14 50 51 15 26 2 0 1 14 15 52 53 16 26 2 0 1 15 16 54 55 17 26 2 0 1 16 17 56 57 18 26 2 0 1 17 18 58 59 19 26 2 0 1 18 19 60 61 20 26 2 0 1 19 20 62 63 21 26 2 0 1 20 21 64 65 22 26 2 0 1 21 22 66 67 23 26 2 0 1 22 23 68 69 24 26 2 0 1 23 24 70 71 25 26 2 0 1 24 25 72 73 26 26 2 0 1 25 26 74 75 27 26 2 0 1 26 27 76 77 28 26 2 0 1 27 2 78 79 $EndElements """ gmsh_buffer = textwrap.dedent(gmsh_buffer) return gmsh_buffer """ Single 2D meshes """ def mesh_Single2D_quadquad(): gmsh_buffer = """\ $MeshFormat 2.2 0 8 $EndMeshFormat $PhysicalNames 5 1 1 "lower" 1 2 "right" 1 3 "upper" 1 4 "left" 2 5 "domain" $EndPhysicalNames $Nodes 9 1 0 0 0 2 1 0 0 3 1 1 0 4 0 1 0 5 0.5 0 0 6 1 0.5 0 7 0.5 1 0 8 0 0.5 0 9 0.5 0.5 0 $EndNodes $Elements 5 1 8 2 1 1 1 2 5 2 8 2 2 2 2 3 6 3 8 2 3 3 3 4 7 4 8 2 4 4 4 1 8 7 10 2 5 1 1 2 3 4 5 6 7 8 9 $EndElements """ gmsh_buffer = textwrap.dedent(gmsh_buffer) return gmsh_buffer def mesh_Single2D_cubquad(): gmsh_buffer = """\ $MeshFormat 2.2 0 8 $EndMeshFormat $PhysicalNames 5 1 1 "lower" 1 2 "right" 1 3 "upper" 1 4 "left" 2 5 "domain" $EndPhysicalNames $Nodes 16 1 0 0 0 2 1 0 0 3 1 1 0 4 0 1 0 5 0.3333333333333333 0 0 6 0.6666666666666667 0 0 7 1 0.3333333333333333 0 8 1 0.6666666666666667 0 9 0.6666666666666667 1 0 10 0.3333333333333333 1 0 11 0 0.6666666666666667 0 12 0 0.3333333333333333 0 13 0.3333333333333333 0.3333333333333333 0 14 0.6666666666666667 0.3333333333333333 0 15 0.6666666666666667 0.6666666666666667 0 16 0.3333333333333333 0.6666666666666667 0 $EndNodes $Elements 5 1 26 2 1 1 1 2 5 6 2 26 2 2 2 2 3 7 8 3 26 2 3 3 3 4 9 10 4 26 2 4 4 4 1 11 12 7 36 2 5 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 $EndElements """ gmsh_buffer = textwrap.dedent(gmsh_buffer) return gmsh_buffer def mesh_Single2D_quarquad(): gmsh_buffer = """\ $MeshFormat 2.2 0 8 $EndMeshFormat $PhysicalNames 5 1 1 "lower" 1 2 "right" 1 3 "upper" 1 4 "left" 2 5 "domain" $EndPhysicalNames $Nodes 25 1 0 0 0 2 1 0 0 3 1 1 0 4 0 1 0 5 0.25 0 0 6 0.5 0 0 7 0.75 0 0 8 1 0.25 0 9 1 0.5 0 10 1 0.75 0 11 0.75 1 0 12 0.5 1 0 13 0.25 1 0 14 0 0.75 0 15 0 0.5 0 16 0 0.25 0 17 0.25 0.25 0 18 0.75 0.25 0 19 0.75 0.75 0 20 0.25 0.75 0 21 0.5 0.25 0 22 0.75 0.5 0 23 0.5 0.75 0 24 0.25 0.5 0 25 0.5 0.5 0 $EndNodes $Elements 5 1 27 2 1 1 1 2 5 6 7 2 27 2 2 2 2 3 8 9 10 3 27 2 3 3 3 4 11 12 13 4 27 2 4 4 4 1 14 15 16 5 37 2 5 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 $EndElements """ gmsh_buffer = textwrap.dedent(gmsh_buffer) return gmsh_buffer """ Global 2D meshes """ def mesh_Multiple2D_biquad(): gmsh_buffer = """\ $MeshFormat 2.2 0 8 $EndMeshFormat $PhysicalNames 5 1 1 "lower" 1 2 "right" 1 3 "upper" 1 4 "left" 2 5 "domain" $EndPhysicalNames $Nodes 9 1 0 0 0 2 1 0 0 3 1 1 0 4 0 1 0 5 0.499999999998694 0 0 6 1 0.499999999998694 0 7 0.5000000000020591 1 0 8 0 0.5000000000020591 0 9 0.5000000000003766 0.5000000000003766 0 $EndNodes $Elements 12 1 1 2 1 1 1 5 2 1 2 1 1 5 2 3 1 2 2 2 2 6 4 1 2 2 2 6 3 5 1 2 3 3 3 7 6 1 2 3 3 7 4 7 1 2 4 4 4 8 8 1 2 4 4 8 1 9 3 2 5 1 1 5 9 8 10 3 2 5 1 8 9 7 4 11 3 2 5 1 5 2 6 9 12 3 2 5 1 9 6 3 7 $EndElements """ gmsh_buffer = textwrap.dedent(gmsh_buffer) return gmsh_buffer def mesh_Multiple2D_quadquad(): gmsh_buffer = """\ $MeshFormat 2.2 0 8 $EndMeshFormat $PhysicalNames 5 1 1 "lower" 1 2 "right" 1 3 "upper" 1 4 "left" 2 5 "domain" $EndPhysicalNames $Nodes 15 1 0 0 0 2 1 0 0 3 1 2 0 4 0 2 0 5 0.4999999999986718 0 0 6 1 0.999999999997388 0 7 1 0.4999999999988388 0 8 1 1.499999999998694 0 9 0.5000000000013305 2 0 10 0 1.000000000004118 0 11 0 1.500000000001978 0 12 0 0.5000000000020592 0 13 0.5 1.000000000000753 0 14 0.4999999999993359 0.500000000000449 0 15 0.5000000000006652 1.500000000000336 0 $EndNodes $Elements 8 1 8 2 1 1 1 2 5 2 8 2 2 2 2 6 7 3 8 2 2 2 6 3 8 4 8 2 3 3 3 4 9 5 8 2 4 4 4 10 11 6 8 2 4 4 10 1 12 7 10 2 5 1 1 2 6 10 5 7 13 12 14 8 10 2 5 1 10 6 3 4 13 8 9 11 15 $EndElements """ gmsh_buffer = textwrap.dedent(gmsh_buffer) return gmsh_buffer def mesh_Multiple2D_cubquad(): gmsh_buffer = """\ $MeshFormat 2.2 0 8 $EndMeshFormat $PhysicalNames 5 1 1 "lower" 1 2 "right" 1 3 "upper" 1 4 "left" 2 5 "domain" $EndPhysicalNames $Nodes 28 1 0 0 0 2 2 0 0 3 2 1 0 4 0 1 0 5 0.999999999997388 0 0 6 0.3333333333326662 0 0 7 0.6666666666650272 0 0 8 1.333333333331592 0 0 9 1.666666666665796 0 0 10 2 0.3333333333324915 0 11 2 0.6666666666657831 0 12 1.000000000004118 1 0 13 1.666666666667929 1 0 14 1.333333333336069 1 0 15 0.6666666666694121 1 0 16 0.3333333333347059 1 0 17 0 0.6666666666668164 0 18 0 0.3333333333341704 0 19 0.9999999999996314 0.3333333333333333 0 20 1.000000000001875 0.6666666666666666 0 21 0.3333333333333461 0.3333333333338913 0 22 0.6666666666664889 0.3333333333336123 0 23 0.6666666666679507 0.6666666666667167 0 24 0.333333333334026 0.6666666666667667 0 25 1.333333333333084 0.3333333333330527 0 26 1.666666666666507 0.3333333333327721 0 27 1.666666666667219 0.6666666666660778 0 28 1.333333333334577 0.6666666666663723 0 $EndNodes $Elements 8 1 26 2 1 1 1 5 6 7 2 26 2 1 1 5 2 8 9 3 26 2 2 2 2 3 10 11 4 26 2 3 3 3 12 13 14 5 26 2 3 3 12 4 15 16 6 26 2 4 4 4 1 17 18 7 36 2 5 1 1 5 12 4 6 7 19 20 15 16 17 18 21 22 23 24 8 36 2 5 1 5 2 3 12 8 9 10 11 13 14 20 19 25 26 27 28 $EndElements """ gmsh_buffer = textwrap.dedent(gmsh_buffer) return gmsh_buffer def mesh_Multiple2D_quarquad(): gmsh_buffer = """\ $MeshFormat 2.2 0 8 $EndMeshFormat $PhysicalNames 5 1 1 "lower" 1 2 "right" 1 3 "upper" 1 4 "left" 2 5 "domain" $EndPhysicalNames $Nodes 81 1 0 0 0 2 1 0 0 3 1 1 0 4 0 1 0 5 0.499999999998694 0 0 6 0.124999999999778 0 0 7 0.2499999999994167 0 0 8 0.3749999999990553 0 0 9 0.6249999999990205 0 0 10 0.749999999999347 0 0 11 0.8749999999996735 0 0 12 1 0.499999999998694 0 13 1 0.124999999999778 0 14 1 0.2499999999994167 0 15 1 0.3749999999990553 0 16 1 0.6249999999990205 0 17 1 0.749999999999347 0 18 1 0.8749999999996735 0 19 0.5000000000020591 1 0 20 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py

Python

Python/Sets/py-introduction-to-sets.py

kamleshmugdiya/Hackerrank

2dfb3689dd6cc7848e8b3d91f8674384652e5a56

[ "MIT" ]

2

2020-05-18T14:59:34.000Z

2020-05-23T15:22:55.000Z

Python/Sets/py-introduction-to-sets.py

kamleshmugdiya/Hackerrank

2dfb3689dd6cc7848e8b3d91f8674384652e5a56

[ "MIT" ]

null

Python/Sets/py-introduction-to-sets.py

kamleshmugdiya/Hackerrank

2dfb3689dd6cc7848e8b3d91f8674384652e5a56

[ "MIT" ]

1

2018-10-09T11:43:17.000Z

def average(arr): x = set(arr) return ((sum(x)/len(x)))

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Python

analysis/analyseGroundFlatfields.py

Borlaff/EuclidVisibleInstrument

73a64ad275054d7b1a26f0fe556eae222b65f613

[ "BSD-2-Clause" ]

5

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2019-12-29T05:29:00.000Z

analysis/analyseGroundFlatfields.py

Borlaff/EuclidVisibleInstrument

73a64ad275054d7b1a26f0fe556eae222b65f613

[ "BSD-2-Clause" ]

null

analysis/analyseGroundFlatfields.py

Borlaff/EuclidVisibleInstrument

73a64ad275054d7b1a26f0fe556eae222b65f613

[ "BSD-2-Clause" ]

3

2015-07-13T10:01:41.000Z

2019-05-28T13:41:47.000Z

""" A simple script to analyse ground/lab flat fields. """ import matplotlib #matplotlib.use('pdf') matplotlib.rc('text', usetex=True) matplotlib.rcParams['font.size'] = 17 matplotlib.rc('xtick', labelsize=14) matplotlib.rc('axes', linewidth=1.1) matplotlib.rcParams['legend.fontsize'] = 11 matplotlib.rcParams['legend.handlelength'] = 3 matplotlib.rcParams['xtick.major.size'] = 5 matplotlib.rcParams['ytick.major.size'] = 5 matplotlib.rcParams['image.interpolation'] = 'none' import matplotlib.pyplot as plt import pyfits as pf import numpy as np import glob as g from scipy import fftpack from scipy import ndimage from scipy import signal import cPickle from support import files as fileIO import scipy.optimize as optimize from matplotlib import animation def makeFlat(files): shape = pf.getdata(files[0]).shape summed = np.zeros(shape) for file in files: data = pf.getdata(file) prescan = data[11:2056, 9:51].mean() overscan = data[11:2056, 4150:4192].mean() Q0 = data[:, 51:2098] Q1 = data[:, 2098:4145] #subtract the bias levels Q0 -= prescan Q1 -= overscan data[:, 51:2098] = Q0 data[:, 2098:4145] = Q1 fileIO.writeFITS(data, file.replace('.fits', 'biasremoved.fits'), int=False) summed += data summed /= summed[11:2056, 56:4131].mean() #write out FITS file fileIO.writeFITS(summed, 'combined.fits', int=False) avg = np.average(np.asarray([pf.getdata(file) for file in files]), axis=0) avg /= avg[11:2056, 56:4131].mean() fileIO.writeFITS(avg, 'averaged.fits', int=False) avg = np.median(np.asarray([pf.getdata(file) for file in files]), axis=0) avg /= avg[11:2056, 56:4131].mean() fileIO.writeFITS(avg, 'median.fits', int=False) def measureNoise(data, size, file, gain=3.5, flat='combined.fits', debug=False): """ Measure average signal level and variance in several patches within a single image. .. Warning:: One must flat field the data before calculating the variance. Hence, the results are uncertain. It is better to use a pairwise analysis if at least two exposures at a given flux level is available. :param data: :param size: :return: """ #move to electrons data *= gain #means of prescan and overscan prescan = data[11:2056, 9:51].mean() overscan = data[11:2056, 4150:4192].mean() #take out pre and overscan #x should start from 55 and go to 2090 for first Q #from 2110 to 4130 for the second Q # y should range from 10 to 2055 to have clean area... Q0 = data[:, 51:2098].copy() Q1 = data[:, 2098:4145].copy() if debug: print prescan, overscan #subtract the bias levels Q0 -= prescan Q1 -= overscan #load a flat and remove-pixel-to-pixel variation due to the flat... flat = pf.getdata(flat) Q0 /= flat[:, 51:2098] Q1 /= flat[:, 2098:4145] data[:, 51:2098] = Q0 data[:, 2098:4145] = Q1 fileIO.writeFITS(data, file.replace('.fits', 'flattened.fits'), int=False) Q0 = data[11:2056, 56:2091].copy() Q1 = data[11:2056, 2111:4131].copy() #number of pixels in new areas Q0y, Q0x = Q0.shape Q1y, Q1x = Q1.shape #number of patches Q0y = int(np.floor(Q0y / size)) Q0x = int(np.floor(Q0x / size)) Q1y = int(np.floor(Q1y / size)) Q1x = int(np.floor(Q1x / size)) flux = [] variance = [] for i in range(Q0y): for j in range(Q0x): minidy = i*int(size) maxidy = minidy + int(size) minidx = j*int(size) maxidx = minidx + int(size) patch = Q0[minidy:maxidy, minidx:maxidx] avg = np.mean(patch) var = np.var(patch) #filter out stuff too close to saturation if avg < 300000 and var/avg < 2.5 and avg/var < 2.5: flux.append(avg) variance.append(var) for i in range(Q1y): for j in range(Q1x): minidy = i * int(size) maxidy = minidy + int(size) minidx = j * int(size) maxidx = minidx + int(size) patch = Q1[minidy:maxidy, minidx:maxidx] avg = np.mean(patch) var = np.var(patch) #filter out stuff too close to saturation if avg < 300000 and var/avg < 2.5 and avg/var < 2.5: flux.append(avg) variance.append(var) flux = np.asarray(flux) variance = np.asarray(variance) print file, np.mean(flux), np.mean(variance) results = dict(flux=flux, variance=variance) return results def measureNoiseRandomPositions(data, size, file, flat='combined.fits', rands=50, debug=False): """ Measure average signal level and variance in several patches within a single image :param data: :param size: :return: """ #means of prescan and overscan prescan = data[11:2056, 9:51].mean() overscan = data[11:2056, 4150:4192].mean() #take out pre and overscan #x should start from 55 and go to 2090 for first Q #from 2110 to 4130 for the second Q # y should range from 10 to 2055 to have clean area... Q0 = data[:, 51:2098].copy() Q1 = data[:, 2098:4145].copy() if debug: print prescan, overscan #subtract the bias levels Q0 -= prescan Q1 -= overscan #load a flat and remove-pixel-to-pixel variation due to the flat... flat = pf.getdata(flat) Q0 /= flat[:, 51:2098] Q1 /= flat[:, 2098:4145] data[:, 51:2098] = Q0 data[:, 2098:4145] = Q1 fileIO.writeFITS(data, file.replace('.fits', 'flattened.fits'), int=False) Q0 = data[11:2056, 56:2091].copy() Q1 = data[11:2056, 2111:4131].copy() #number of pixels in new areas Q0y, Q0x = Q0.shape Q1y, Q1x = Q1.shape h = size / 2. Q0y -= h Q0x -= h Q1y -= h Q1x -= h xpos = np.random.random_integers(h, min(Q0x, Q1x), size=rands) ypos = np.random.random_integers(h, min(Q0y, Q1y), size=rands) flux = [] variance = [] for i in xpos: for j in ypos: patch = Q0[j-h:j+h, i-h:i+h] avg = np.mean(patch) var = np.var(patch) #filter out stuff too close to saturation if avg < 62000:# and var/avg < 2.0 and avg/var < 2.0: flux.append(avg) variance.append(var) #print avg, var for i in xpos: for j in ypos: patch = Q1[j-h:j+h, i-h:i+h] avg = np.mean(patch) var = np.var(patch) #filter out stuff too close to saturation if avg < 62000:# and var/avg < 2.0 and avg/var < 2.0: flux.append(avg) variance.append(var) flux = np.asarray(flux) variance = np.asarray(variance) results = dict(flux=flux, variance=variance) return results def plotAutocorrelation(data, output='Autocorrelation.pdf'): """ :param data: :return: """ import acor fig = plt.figure() plt.subplots_adjust(left=0.15) plt.title(r'CCD273-84-2-F15, Serial number: 11312-14-01') ax = fig.add_subplot(111) for file, values in data.iteritems(): flux = np.mean(values['flux']) variance = values['variance'] tau, mean, sigma = acor.acor(variance) ax.plot(flux, mean, 'bo') ax.set_xlabel(r'$ \left < \mathrm{Signal} \right > \quad [e^{-}]$') ax.set_ylabel('mean') #plt.legend(shadow=True, fancybox=True, loc='upper left', numpoints=1) plt.savefig(output) plt.close() def plotResults(data, size, pairwise=True, output='FlatfieldFullwellEstimate.pdf'): """ :param data: :return: """ size = int(size) fig = plt.figure() plt.subplots_adjust(left=0.15) plt.title(r'CCD273-84-2-F15, Serial number: 11312-14-01') ax = fig.add_subplot(111) mf = np.asarray([]) mv = np.asarray([]) for file, values in data.iteritems(): flux = values['flux'] variance = values['variance'] mflux = np.mean(flux) mvar = np.mean(variance) mf = np.hstack((mf, flux)) mv = np.hstack((mv, variance)) ax.plot(flux, variance, 'r.', alpha=0.05) ax.plot(np.median(flux), np.median(variance), 'bo') ax.errorbar(mflux, mvar, yerr=np.std(variance), marker='s', ecolor='green', mfc='green', mec='green', ms=5, mew=1) ax.plot([-1,], [-1,], 'r.', label='data') ax.plot([-1,], [-1,], 'bo', label='median') ax.errorbar([-1,], [-1,], marker='s', ecolor='green', mfc='green', mec='green', ms=5, mew=1, label='mean') msk = mf < 220000 z = np.polyfit(mf[msk], mv[msk], 2) ev = np.poly1d(z) x = np.linspace(0, 250000, 100) #second order but no intercept fitfunc = lambda p, x: p[0]*x*(1 - p[1]*x) errfunc = lambda p, x, y: fitfunc(p, x) - y p1, success = optimize.leastsq(errfunc, [1.0, 1e-6], args=(mf[msk], mv[msk])) y2 = fitfunc(p1, x) ax.plot(x, ev(x), 'k-', lw=2, label='2nd order fit') ax.plot(x, y2, 'y:', lw=2, label='2nd order fit, no intercept') txt = r'$y = %.3e \times x^{2} + %.4f x + %.3f$' % (z[0], z[1], z[2]) txt2 = r'$y = %.4f x (1 - %.3e \times x)$' % (p1[0], p1[1]) ax.text(0.3, 0.1, txt, horizontalalignment='left', verticalalignment='center', transform=ax.transAxes, alpha=0.5, size='small') ax.text(0.3, 0.15, txt2, horizontalalignment='left', verticalalignment='center', transform=ax.transAxes, alpha=0.5, size='small') ax.plot([0, 250000], [0, 250000], 'm--', lw=1.5, label='shot noise') ax.set_xlim(0, 240000) ax.set_ylim(0, 170000) ax.set_xlabel(r'$ \left < \mathrm{Signal}_{%i \times %i} \right > \quad [e^{-}]$' % (size, size)) if pairwise: ax.set_ylabel(r'$\frac{1}{2}\sigma^{2}(\Delta \mathrm{Signal}_{%i \times %i}) \quad [(e^{-})^{2}]$' % (size, size)) else: ax.set_ylabel(r'$\sigma^{2}(\mathrm{Signal}_{%i \times %i}) \quad [(e^{-})^{2}]$' % (size, size)) plt.legend(shadow=True, fancybox=True, loc='upper left', numpoints=1) plt.savefig(output) plt.close() def plotResultsRowColumn(data, pairwise=True, output='FlatfieldFullwellEstimateRowColumn.pdf'): """ :param data: :return: """ #diff plot fig = plt.figure() plt.subplots_adjust(left=0.15) plt.title(r'CCD273-84-2-F15, Serial number: 11312-14-01') ax = fig.add_subplot(111) for file, values in data.iteritems(): rowvariance = values['rowvariance'] rowflux = values['rowflux'] rowmflux = np.mean(rowflux) rowmvar = np.mean(rowvariance) columnflux = values['columnflux'] columnmflux = np.mean(columnflux) columnvariance = values['columnvariance'] columnmvar = np.mean(columnvariance) flux = (rowmflux+columnmflux)/2. ax.plot(flux, rowmvar / columnmvar, 'bo') #ax.plot(flux, np.median(rowvariance) / np.median(columnvariance), 'rs') ax.plot([-1, ], [-1, ], 'bo', label='mean') #ax.plot([-1, ], [-1, ], 'rs', label='median') ax.plot([0, 250000], [1, 1], 'k--', lw=1.5) ax.set_xlim(0, 240000) #ax.set_ylim(0.9, 1.1) ax.set_ylim(0.99, 1.01) ax.set_ylabel(r'$\frac{\sigma^{2}_{row}}{\sigma^{2}_{column}}$') ax.set_xlabel(r'$ \left < \mathrm{Signal} \right > \quad [e^{-}]$') plt.legend(shadow=True, fancybox=True, loc='upper left', numpoints=1) plt.savefig('RowColumnDifference.pdf') plt.close() #fits fig = plt.figure() plt.subplots_adjust(left=0.15) plt.title(r'CCD273-84-2-F15, Serial number: 11312-14-01') ax = fig.add_subplot(111) rowmf = np.asarray([]) rowmv = np.asarray([]) columnmf = np.asarray([]) columnmv = np.asarray([]) for file, values in data.iteritems(): rowflux = values['rowflux'] rowvariance = values['rowvariance'] rowmflux = np.mean(rowflux) rowmvar = np.mean(rowvariance) rowmf = np.hstack((rowmf, rowflux)) rowmv = np.hstack((rowmv, rowvariance)) columnflux = values['columnflux'] columnvariance = values['columnvariance'] columnmflux = np.mean(columnflux) columnmvar = np.mean(columnvariance) columnmf = np.hstack((columnmf, columnflux)) columnmv = np.hstack((columnmv, columnvariance)) ax.plot(np.median(rowflux), np.median(rowvariance), 'ro') ax.errorbar(rowmflux, rowmvar, yerr=np.std(rowvariance), marker='s', ecolor='red', mfc='red', mec='red', ms=5, mew=1) ax.plot(np.median(columnflux), np.median(columnvariance), 'bo') ax.errorbar(columnmflux, columnmvar, yerr=np.std(columnvariance), marker='s', ecolor='blue', mfc='blue', mec='blue', ms=5, mew=1) ax.plot([-1,], [-1,], 'ro', label='row median') ax.errorbar([-1,], [-1,], marker='s', ecolor='red', mfc='red', mec='red', ms=5, mew=1, label='row mean') ax.plot([-1,], [-1,], 'bo', label='column median') ax.errorbar([-1,], [-1,], marker='s', ecolor='blue', mfc='blue', mec='blue', ms=5, mew=1, label='column mean') msk = rowmf < 170000 x = np.linspace(0, 250000, 100) #second order but no intercept fitfunc = lambda p, x: p[0]*x*(1 - p[1]*x) errfunc = lambda p, x, y: fitfunc(p, x) - y p1, success = optimize.leastsq(errfunc, [1.0, 1e-6], args=(rowmf[msk], rowmv[msk])) y2 = fitfunc(p1, x) ax.plot(x, y2, 'r-', lw=2, label='2nd order fit (row)') txt2 = r'$y_{row} = %.4f x (1 - %.3e \times x)$' % (p1[0], p1[1]) ax.text(0.3, 0.15, txt2, horizontalalignment='left', verticalalignment='center', transform=ax.transAxes, alpha=0.5, size='small') msk = columnmf < 170000 #second order but no intercept p2, success = optimize.leastsq(errfunc, [1.0, 1e-6], args=(columnmf[msk], columnmv[msk])) y3 = fitfunc(p2, x) ax.plot(x, y3, 'b--', lw=2, label='2nd order fit (column)') txt2 = r'$y_{column} = %.4f x (1 - %.3e \times x)$' % (p2[0], p2[1]) ax.text(0.3, 0.1, txt2, horizontalalignment='left', verticalalignment='center', transform=ax.transAxes, alpha=0.5, size='small') ax.plot([0, 250000], [0, 250000], 'k--', lw=1.5, label='shot noise') ax.set_xlim(0, 240000) ax.set_ylim(0, 170000) ax.set_xlabel(r'$ \left < \mathrm{Signal} \right > \quad [e^{-}]$') if pairwise: ax.set_ylabel(r'$\frac{1}{2}\sigma^{2}(\Delta \mathrm{Signal}) \quad [(e^{-})^{2}]$') else: ax.set_ylabel(r'$\sigma^{2}(\Delta \mathrm{Signal}) \quad [(e^{-})^{2}]$') plt.legend(shadow=True, fancybox=True, loc='upper left', numpoints=1) plt.savefig(output) plt.close() def findPairs(): """ :return: """ files = g.glob('05*Euclid.fits') for file in files: data = pf.getdata(file) c1 = np.average(data[1800:1810, 200:210]) print file, c1 def pairwiseNoise(pairs, gain=3.5, size=100.0, simple=False): """ Calculates the mean flux within a region of size * size and the variance from the difference image. The variance of the difference image is divided by 2, given that var(x-y) = var(x) + var(y). The calculations are performed in electrons so that variance = noise**2 should be equal to the mean counts if no correlated noise and other effects are present. This would be the case of pure shot noise. :return: """ results = {} for f1, f2 in pairs: #move from ADUs to electrons d1 = pf.getdata(f1) * gain d2 = pf.getdata(f2) * gain if simple: #pre/overscans prescan1 = d1[11:2056, 9:51].mean() overscan1 = d1[11:2056, 4150:4192].mean() prescan2 = d2[11:2056, 9:51].mean() overscan2 = d2[11:2056, 4150:4192].mean() #define quadrants and subtract the bias levels Q10 = d1[11:2051, 58:2095].copy() - prescan1 Q20 = d2[11:2051, 58:2095].copy() - prescan2 Q11 = d1[11:2051, 2110:4132].copy() - overscan1 Q21 = d2[11:2051, 2110:4132].copy() - overscan2 else: y1, x1 = d1.shape #subtract over/prescan row-by-row to minimise the any bias variation in column direction for row in range(y1): prescan1 = np.median(d1[row, 9:48]) prescan2 = np.median(d2[row, 9:48]) d1[row, :2099] -= prescan1 d2[row, :2099] -= prescan2 for row in range(y1): overscan1 = np.median(d1[row, 4152:4190]) overscan2 = np.median(d2[row, 4152:4190]) d1[row, 2100:] -= overscan1 d2[row, 2100:] -= overscan2 #define quadrants; usable image area Q0 Q10 = d1[11:2051, 58:2095].copy() Q20 = d2[11:2051, 58:2095].copy() #Q1 Q11 = d1[11:2051, 2110:4132].copy() Q21 = d2[11:2051, 2110:4132].copy() #number of pixels in new areas Q10y, Q10x = Q10.shape Q11y, Q11x = Q11.shape #number of patches Q0y = int(np.floor(Q10y / size)) Q0x = int(np.floor(Q10x / size)) Q1y = int(np.floor(Q11y / size)) Q1x = int(np.floor(Q11x / size)) flux = [] variance = [] for i in range(Q0y): for j in range(Q0x): minidy = i * int(size) maxidy = minidy + int(size) minidx = j * int(size) maxidx = minidx + int(size) patch1 = np.ravel(Q10[minidy:maxidy, minidx:maxidx]) patch2 = np.ravel(Q20[minidy:maxidy, minidx:maxidx]) avg1 = np.mean(patch1) avg2 = np.mean(patch2) diff = patch1.copy() - patch2.copy() var = np.var(diff) #filter out stuff if the averages are too far off if avg1-avg2 < 100: flux.append((avg1+avg2)/2.) variance.append(var/2.) #variance.append(var/np.sqrt(2.)) for i in range(Q1y): for j in range(Q1x): minidy = i * int(size) maxidy = minidy + int(size) minidx = j * int(size) maxidx = minidx + int(size) patch1 = np.ravel(Q11[minidy:maxidy, minidx:maxidx]) patch2 = np.ravel(Q21[minidy:maxidy, minidx:maxidx]) avg1 = np.mean(patch1) avg2 = np.mean(patch2) diff = patch1.copy() - patch2.copy() var = np.var(diff) #filter out stuff if the averages are too far off if avg1-avg2 < 100: flux.append((avg1+avg2)/2.) variance.append(var/2.) #variance.append(var/np.sqrt(2.)) flux = np.asarray(flux) variance = np.asarray(variance) results[f1] = dict(flux=flux, variance=variance) print f1, flux.mean(), variance.mean() return results def pairwiseNoiseRowColumns(pairs, gain=3.5): """ Calculates the mean flux within a row/column and the variance from the difference image. The variance of the difference image is divided by 2, given that var(x-y) = var(x) + var(y). The calculations are performed in electrons so that variance = noise**2 should be equal to the mean counts if no correlated noise and other effects are present. This would be the case of pure shot noise. :return: """ print 'file , column flux, row flux, column variance, row variance' results = {} for f1, f2 in pairs: #move from ADUs to electrons d1 = pf.getdata(f1) * gain d2 = pf.getdata(f2) * gain y1, x1 = d1.shape #subtract over/prescan row-by-row to minimise the any bias variation in column direction for row in range(y1): prescan1 = np.median(d1[row, 9:48]) prescan2 = np.median(d2[row, 9:48]) d1[row, :2099] -= prescan1 d2[row, :2099] -= prescan2 for row in range(y1): #not really an overscan but prescan to a different node overscan1 = np.median(d1[row, 4152:4190]) overscan2 = np.median(d2[row, 4152:4190]) d1[row, 2100:] -= overscan1 d2[row, 2100:] -= overscan2 #define quadrants; usable image area Q0 Q10 = d1[11:2051, 58:2095].copy() Q20 = d2[11:2051, 58:2095].copy() #Q1 Q11 = d1[11:2051, 2110:4132].copy() Q21 = d2[11:2051, 2110:4132].copy() #number of pixels in newly defined areas Q10y, Q10x = Q10.shape Q11y, Q11x = Q11.shape #data containers rowflux = [] rowvariance = [] columnflux = [] columnvariance = [] #loop over rows in Q0 for row in range(Q10y): patch1 = Q10[row, :] patch2 = Q20[row, :] avg1 = np.mean(patch1) avg2 = np.mean(patch2) diff = patch1.copy() - patch2.copy() var = np.var(diff) #filter out stuff if the averages are too far off if avg1-avg2 < 200: rowflux.append((avg1+avg2)/2.) rowvariance.append(var/2.) #loop over columns in Q0 for column in range(Q10x): patch1 = Q10[:, column] patch2 = Q20[:, column] avg1 = np.mean(patch1) avg2 = np.mean(patch2) diff = patch1.copy() - patch2.copy() var = np.var(diff) #filter out stuff if the averages are too far off if avg1-avg2 < 200: columnflux.append((avg1+avg2)/2.) columnvariance.append(var/2.) #loop over rows in Q1 for row in range(Q11y): patch1 = Q11[row, :] patch2 = Q21[row, :] avg1 = np.mean(patch1) avg2 = np.mean(patch2) diff = patch1.copy() - patch2.copy() var = np.var(diff) #filter out stuff if the averages are too far off if avg1-avg2 < 200: rowflux.append((avg1+avg2)/2.) rowvariance.append(var/2.) #loop over columns in Q1 for column in range(Q11x): patch1 = Q11[:, column] patch2 = Q21[:, column] avg1 = np.mean(patch1) avg2 = np.mean(patch2) diff = patch1.copy() - patch2.copy() var = np.var(diff) #filter out stuff if the averages are too far off if avg1-avg2 < 200: columnflux.append((avg1+avg2)/2.) columnvariance.append(var/2.) rowflux = np.asarray(rowflux) rowvariance = np.asarray(rowvariance) columnflux = np.asarray(columnflux) columnvariance = np.asarray(columnvariance) results[f1] = dict(rowflux=rowflux, rowvariance=rowvariance, columnflux=columnflux, columnvariance=columnvariance) #print f1, np.median(columnflux), np.median(rowflux), np.median(columnvariance), np.median(rowvariance) print f1, np.mean(columnflux), np.mean(rowflux), np.mean(columnvariance), np.mean(rowvariance) return results def plotDetectorCounts(): """ :return: """ #files = g.glob('05*Euclid.fits') #files = g.glob('05*Euclidflattened.fits') files = g.glob('05*Euclidbiasremoved.fits') fig = plt.figure(1) plt.title(r'CCD273-84-2-F15, Serial number: 11312-14-01') ax = fig.add_subplot(111) for file in files: data = pf.getdata(file) c1 = np.average(data[1800:1810, 200:210]) c2 = np.average(data[1000:1010, 1800:1810]) c3 = np.average(data[1000:1010, 2300:2310]) c4 = np.average(data[200:210, 3800:3810]) plt.figure(2) im = plt.imshow(data, origin='lower', extent=[0, 4100, 0, 2070]) plt.plot([205, 1805, 2305, 3805], [1805, 1005, 1005, 205], 'rs') plt.text(200, 1810, 'C1') plt.text(1800, 1010, 'C2') plt.text(2300, 1010, 'C3') plt.text(3800, 210, 'C4') plt.xlim(0, 4100) plt.ylim(0, 2070) c = plt.colorbar(im) c.set_label('Image Scale') plt.xlabel('X [pixels]') plt.ylabel('Y [pixels]') plt.savefig(file.replace('.fits', '.png')) plt.close() del data plt.plot(c1, c1 / c2, 'bo') plt.plot(c4, c4 / c3, 'rs') plt.plot(c1, c1 / c2, 'bo', label='C1 vs C1 / C2') plt.plot(c4, c4 / c3, 'rs', label='C4 vs C4 / C3') ax.set_xlim(0, 65000) ax.set_xlabel('Counts [C1 or C4] [ADU]') ax.set_ylabel('Delta Counts [C1/C2 or C4/C3] [ADU]') plt.legend(shadow=True, fancybox=True, numpoints=1) plt.savefig('gradient.pdf') def simulatePoissonProcess(max=200000, size=200): """ Simulate a Poisson noise process. :param max: :param size: :return: None """ #for non-linearity from support import VISinstrumentModel size = int(size) fluxlevels = np.linspace(1000, max, 50) #readnoise readnoise = np.random.normal(loc=0, scale=4.5, size=(size, size)) #PRNU prnu = np.random.normal(loc=1.0, scale=0.02, size=(size, size)) fig = plt.figure(1) plt.title(r'Simulation: $%i \times %s$ region' % (size, size)) plt.subplots_adjust(left=0.14) ax = fig.add_subplot(111) for flux in fluxlevels: d1 = np.random.poisson(flux, (size, size))*prnu + readnoise d2 = np.random.poisson(flux, (size, size))*prnu + readnoise fx = (np.average(d1) + np.average(d2)) / 2. ax.plot(fx, np.var(d1-d2)/2., 'bo') d1 = np.random.poisson(flux, (size, size))*prnu + readnoise d2 = np.random.poisson(flux, (size, size))*prnu + readnoise #d1nonlin = VISinstrumentModel.CCDnonLinearityModelSinusoidal(d1, 0.1, phase=0.5, multi=1.5) #d2nonlin = VISinstrumentModel.CCDnonLinearityModelSinusoidal(d2, 0.1, phase=0.5, multi=1.5) d1nonlin = VISinstrumentModel.CCDnonLinearityModel(d1) d2nonlin = VISinstrumentModel.CCDnonLinearityModel(d2) fx = (np.average(d1) + np.average(d2)) / 2. ax.plot(fx, np.var(d1nonlin-d2nonlin)/2., 'rs') d1 = np.random.poisson(flux, (size, size))*prnu*1.05 + readnoise #5% gain change d2 = np.random.poisson(flux, (size, size))*prnu + readnoise fx = (np.average(d1) + np.average(d2)) / 2. ax.plot(fx, np.var(d1 - d2) / 2., 'mD') ax.plot([-1, ], [-1, ], 'bo', label='data (linear)') ax.plot([-1, ], [-1, ], 'rs', label='data (non-linear)') ax.plot([-1, ], [-1, ], 'mD', label='data (gain change)') ax.plot([0, max], [0, max], 'k-', lw=1.5, label='shot noise') ax.set_xlim(0, max) ax.set_ylim(0, max) ax.set_xlabel(r'$ \left < \mathrm{Signal}_{%i \times %i} \right > \quad [e^{-}]$' % (size, size)) ax.set_ylabel(r'$\frac{1}{2}\sigma^{2}(\Delta \mathrm{Signal}) \quad [(e^{-})^{2}]$') plt.legend(shadow=True, fancybox=True, loc='upper left', numpoints=1) plt.savefig('Simulation.pdf') plt.close() def simulatePoissonProcessRowColumn(max=200000, size=200, short=True, Gaussian=False): """ :param max: :param size: :return: """ fluxlevels = np.linspace(1000, max, 40) #readnoise readnoise = np.random.normal(loc=0, scale=4.5, size=(size, size)) #PRNU prnu = np.random.normal(loc=1.0, scale=0.02, size=(size, size)) fig = plt.figure(1) plt.title(r'Simulation: $%i \times %s$ region' % (size, size)) plt.subplots_adjust(left=0.14) ax = fig.add_subplot(111) #correlation coefficient val = 1.455e-6 * 1.8 print val for flux in fluxlevels: d1 = np.random.poisson(flux, (size, size)) * prnu + readnoise d2 = np.random.poisson(flux, (size, size)) * prnu + readnoise #convolution if ~Gaussian: #kernel = np.array([[0,val*flux,0],[0,(1-val),0],[0,val*flux,0]]) #kernel = np.array([[0,val*flux,0],[val*flux,(1-val),val*flux],[0,val*flux,0]]) kernel = np.array([[0, val*flux/4., 0], [val*flux/4., (1-val), val*flux/4.], [0, val*flux/4., 0]]) d1 = ndimage.convolve(d1, kernel) d2 = ndimage.convolve(d2, kernel) #gaussian smoothing if Gaussian: if short: d1 = ndimage.filters.gaussian_filter(d1, [2, 0]) d2 = ndimage.filters.gaussian_filter(d2, [2, 0]) else: d2 = ndimage.filters.gaussian_filter(d2, [15, 0]) d1 = ndimage.filters.gaussian_filter(d1, [15, 0]) #change the correlation in row direction #for column in range(size): # for row in range(size-2): # d1[row+1, column] = (d1[row, column] + d1[row+1, column] + d1[row+2, column]) / 3. # d2[row+1, column] = (d2[row, column] + d2[row+1, column] + d2[row+2, column]) / 3. #calculate correlation in row/column direction rowvar = [] rowfx = [] for row1, row2 in zip(d1, d2): var = np.var(row1 - row2) / 2. fx = (np.average(row1) + np.average(row2)) / 2. rowvar.append(var) rowfx.append(fx) ax.plot(rowfx, rowvar, 'b.', alpha=0.1) ax.plot(np.average(np.asarray(rowfx)), np.average(np.asarray(rowvar)), 'bo', zorder=14) #ax.plot(np.median(np.asarray(rowfx)), np.median(np.asarray(rowvar)), 'bo') colvar = [] colfx = [] for column1, column2 in zip(d1.T, d2.T): var = np.var(column1 - column2) / 2. fx = (np.average(column1) + np.average(column2)) / 2. colvar.append(var) colfx.append(fx) ax.plot(colfx, colvar, 'r.', alpha=0.1) ax.plot(np.average(np.asarray(colfx))+2000, np.average(np.asarray(colvar)), 'rs', zorder=14) #ax.plot(np.average(np.median(colfx)), np.median(np.asarray(colvar)), 'rs') #save d1 to a FITS file... if short: fileIO.writeFITS(d1, 'correlatedNoiseShort.fits', int=False) else: fileIO.writeFITS(d1, 'correlatedNoiseLong.fits', int=False) ax.plot([0, ], [0, ], 'bo', label='row') ax.plot([0, ], [0, ], 'rs', label='column') ax.plot([0, max], [0, max], 'k-', lw=1.5, label='shot noise') #fitted curve x = np.arange(0, max+1000, 1000) y = -1.375e-6*x**2 + 0.9857*x + 1084.37 ax.plot(x, y, 'g-', label='2nd order curve') ax.set_xlim(0, max) ax.set_ylim(0, max) ax.set_xlabel(r'$ \left < \mathrm{Signal}_{%i \times %i} \right > \quad [e^{-}]$' % (size, size)) ax.set_ylabel(r'$\frac{1}{2}\sigma^{2}(\Delta \mathrm{Signal}) \quad [(e^{-})^{2}]$') plt.legend(shadow=True, fancybox=True, loc='upper left', numpoints=1) if short: plt.savefig('SimulationRowColShort.pdf') else: plt.savefig('SimulationRowColLong.pdf') plt.close() def analyseCorrelationFourier(file1='05Sep_14_35_00s_Euclid.fits', file2='05Sep_14_36_31s_Euclid.fits', gain=3.1, small=False, shift=False, interpolation='none'): """ :param file1: :param file2: :param small: :param shift: :param interpolation: :return: None """ d1 = pf.getdata(file1) * gain d2 = pf.getdata(file2) * gain #pre/overscans #prescan1 = d1[11:2056, 9:51].mean() overscan1 = d1[11:2056, 4150:4192].mean() #prescan2 = d2[11:2056, 9:51].mean() overscan2 = d2[11:2056, 4150:4192].mean() #define quadrants and subtract the bias levels #Q10 = d1[11:2051, 58:2095].copy() - prescan1 #Q20 = d2[11:2051, 58:2095].copy() - prescan2 Q11 = d1[11:2050, 2110:4131].copy() - overscan1 Q21 = d2[11:2050, 2110:4131].copy() - overscan2 #limit to 1024 Q11 = Q11[300:1324, 300:1324] Q21 = Q21[300:1324, 300:1324] q1y, q1x = Q11.shape #small region if small: Q11 = Q11[500:756, 500:756].copy() Q21 = Q21[500:756, 500:756].copy() print Q11.shape #Fourier analysis: calculate 2D power spectrum and take a log if shift: fourierSpectrum1 = np.log10(np.abs(fftpack.fftshift(fftpack.fft2(Q11))) + 1) fourierSpectrum2 = np.log10(np.abs(fftpack.fftshift(fftpack.fft2(Q21))) + 1) else: fourierSpectrum1 = np.log10(np.abs(fftpack.fft2(Q11)) + 1) fourierSpectrum2 = np.log10(np.abs(fftpack.fft2(Q21)) + 1) #difference image diff = (Q11 - Q21).copy() if shift: fourierSpectrumD = np.log10(np.abs(fftpack.fftshift(fftpack.fft2(diff))) + 1) else: fourierSpectrumD = np.log10(np.abs(fftpack.fft2(diff)) + 1) #plot images fig = plt.figure(figsize=(14.5,6.5)) plt.suptitle('Fourier Analysis of Flat-field Data') plt.suptitle(r'Original Image $[e^{-}]$', x=0.24, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.52, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.79, y=0.26) ax1 = fig.add_subplot(131) ax2 = fig.add_subplot(132) ax3 = fig.add_subplot(133) i1 = ax1.imshow(Q11, origin='lower', interpolation=interpolation) if small: plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1e', ticks=[3.1*45000, 3.1*47000, 3.1*49000]) i2 = ax2.imshow(fourierSpectrum1[0:128, 0:128], interpolation=interpolation, origin='lower', vmin=2.5, vmax=6.5, rasterized=True) i3 = ax3.imshow(fourierSpectrum1[0:128, 0:128], interpolation=interpolation, origin='lower', vmin=2.5, vmax=6.5, rasterized=True) else: plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1e', ticks=[3.1*35000, 3.1*40000, 3.1*45000, 3.1*50000]) i2 = ax2.imshow(fourierSpectrum1[0:q1y/2, 0:q1x/2], interpolation=interpolation, origin='lower', vmin=4, vmax=7, rasterized=True) i3 = ax3.imshow(fourierSpectrum1[0:q1y/2, 0:q1x/2], interpolation=interpolation, origin='lower', vmin=4, vmax=7, rasterized=True) tmpx = ax3.get_xlim() tmpy = ax3.get_ylim() ax3.set_xlim(tmpx[1] - 20, tmpx[1]) ax3.set_ylim(tmpy[1] - 20, tmpy[1]) if small: plt.colorbar(i2, ax=ax2, orientation='horizontal') plt.colorbar(i3, ax=ax3, orientation='horizontal') else: plt.colorbar(i2, ax=ax2, orientation='horizontal') plt.colorbar(i3, ax=ax3, orientation='horizontal')#, ticks=[10, 10.5, 11, 11.5, 12, 12.5]) ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax3.set_xlabel('$l_{x}$') ax1.set_ylabel('Y [pixel]') #ax2.set_ylabel('$l_{y}$') #ax3.set_ylabel('$l_{y}$') if small: plt.savefig('Fourier1.pdf') else: plt.savefig('Fourier1Full.pdf') plt.close() fig = plt.figure(figsize=(14.5,6.5)) plt.suptitle('Fourier Analysis of Flat-field Data') plt.suptitle(r'Original Image $[e^{-}]$', x=0.24, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.52, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.79, y=0.26) ax1 = fig.add_subplot(131) ax2 = fig.add_subplot(132) ax3 = fig.add_subplot(133) i1 = ax1.imshow(Q21, origin='lower', interpolation=interpolation) if small: plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1e', ticks=[3.1*45000, 3.1*47000, 3.1*49000]) i2 = ax2.imshow(fourierSpectrum2[0:128, 0:128], interpolation=interpolation, origin='lower', vmin=2, vmax=7, rasterized=True) i3 = ax3.imshow(fourierSpectrum2[0:128, 0:128], interpolation=interpolation, origin='lower', vmin=2, vmax=7, rasterized=True) else: plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1e', ticks=[3.1*35000, 3.1*40000, 3.1*45000, 3.1*50000]) i2 = ax2.imshow(fourierSpectrum2[0:q1y/2, 0:q1x/2], interpolation=interpolation, origin='lower', vmin=4, vmax=7, rasterized=True) i3 = ax3.imshow(fourierSpectrum2[0:q1y/2, 0:q1x/2], interpolation=interpolation, origin='lower', vmin=4, vmax=7, rasterized=True) tmpx = ax3.get_xlim() tmpy = ax3.get_ylim() ax3.set_xlim(tmpx[1] - 20, tmpx[1]) ax3.set_ylim(tmpy[1] - 20, tmpy[1]) if small: plt.colorbar(i2, ax=ax2, orientation='horizontal')#, ticks=[6, 7, 8, 9, 10, 11, 12]) plt.colorbar(i3, ax=ax3, orientation='horizontal') else: plt.colorbar(i2, ax=ax2, orientation='horizontal')#, ticks=[8, 9.5, 11, 13]) plt.colorbar(i3, ax=ax3, orientation='horizontal')#, ticks=[10, 10.5, 11, 11.5, 12, 12.5]) ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax3.set_xlabel('$l_{x}$') ax1.set_ylabel('Y [pixel]') #ax2.set_ylabel('$l_{y}$') #ax3.set_ylabel('$l_{y}$') if small: plt.savefig('Fourier2.pdf') else: plt.savefig('Fourier2Full.pdf') plt.close() fig = plt.figure(figsize=(14.5,6.5)) plt.suptitle('Fourier Analysis of Flat-field Data') plt.suptitle(r'Original Image $[e^{-}]$', x=0.24, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.52, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.79, y=0.26) ax1 = fig.add_subplot(131) ax2 = fig.add_subplot(132) ax3 = fig.add_subplot(133) i1 = ax1.imshow(diff, origin='lower', interpolation=interpolation, vmin=-1200, vmax=1200) plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%i', ticks=[-1200, -600, 0, 600, 1200]) if small: i2 = ax2.imshow(fourierSpectrumD[0:128, 0:128], interpolation=interpolation, origin='lower', vmin=2, vmax=6.5, rasterized=True) i3 = ax3.imshow(fourierSpectrumD[0:128, 0:128], interpolation=interpolation, origin='lower', vmin=2, vmax=6.5, rasterized=True) else: i2 = ax2.imshow(fourierSpectrumD[0:q1y/2, 0:q1x/2], interpolation=interpolation, origin='lower', vmin=2.5, vmax=7.5, rasterized=True) i3 = ax3.imshow(fourierSpectrumD[0:q1y/2, 0:q1x/2], interpolation=interpolation, origin='lower', vmin=2.5, vmax=7.5, rasterized=True) tmpx = ax3.get_xlim() tmpy = ax3.get_ylim() ax3.set_xlim(tmpx[1] - 20, tmpx[1]) ax3.set_ylim(tmpy[1] - 20, tmpy[1]) if small: plt.colorbar(i2, ax=ax2, orientation='horizontal')#, ticks=[7, 8, 9, 10, 11, 12]) plt.colorbar(i3, ax=ax3, orientation='horizontal')#, ticks=[7, 7.5, 8, 8.5, 9, 9.5]) else: plt.colorbar(i2, ax=ax2, orientation='horizontal')#, ticks=[9.5, 10, 10.5, 11, 11.5]) plt.colorbar(i3, ax=ax3, orientation='horizontal')#, ticks=[9, 9.5, 10, 10.5, 11]) ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax3.set_xlabel('$l_{x}$') ax1.set_ylabel('Y [pixel]') #ax2.set_ylabel('$l_{y}$') #ax3.set_ylabel('$l_{y}$') if small: plt.savefig('FourierDifference.pdf') else: plt.savefig('FourierDifferenceFull.pdf') plt.close() def spatialAutocorrelation(file1='05Sep_14_35_00s_Euclid.fits', file2='05Sep_14_36_31s_Euclid.fits', gain=3.1, interpolation='none', size=1024): """ Calculates a spatial 2D autocorrelation from the difference image and visualises the findings. Generates also two plots from simulated data for comparisons. :param file1: name of the first FITS file to be used in the analysis :param file2: name of the second FITS file to be used in the analysis :param gain: gain conversion between ADUs and electrons :param interpolation: whether the visualisation grid should be interpolated or not :param size: side length in pixels of the area to be used for analysis :return: None """ d1 = pf.getdata(file1) * gain d2 = pf.getdata(file2) * gain #pre/overscans overscan1 = d1[11:2056, 4150:4192].mean() overscan2 = d2[11:2056, 4150:4192].mean() #define quadrants and subtract the bias levels Q11 = d1[11:2050, 2110:4131].copy() - overscan1 Q21 = d2[11:2050, 2110:4131].copy() - overscan2 #limit to 1024 Q11 = Q11[300:1324, 300:1324] Q21 = Q21[300:1324, 300:1324] print Q11.mean(), Q21.mean() level = (np.average(Q11) + np.average(Q21)) / 2. if size > Q11.shape[0] or size > Q11.shape[1]: print 'size too large, will abort...' return None diff = Q11 - Q21 diff = diff[0:size, 0:size] #autoc = signal.correlate2d(diff, diff, mode='full') #slow autoc = signal.fftconvolve(diff, np.flipud(np.fliplr(diff)), mode='full') autoc /= np.max(autoc) autoc *= 100. fileIO.writeFITS(autoc, 'autocorrelationRealdata.fits', int=False) yc, xc = autoc.shape xc /= 2. yc /= 2. xc -= 0.5 yc -= 0.5 #plot images fig = plt.figure(figsize=(14.5, 6.5)) plt.suptitle(r'Autocorrelation of Flat Field Data ($%i \times %i$ grid)' % (size, size)) plt.suptitle(r'Difference Image $[e^{-}]$', x=0.24, y=0.26) plt.suptitle(r'Autocorrelation Interaction $[\%]$', x=0.51, y=0.26) plt.suptitle(r'Autocorrelation Interaction $[\%]$', x=0.78, y=0.26) ax1 = fig.add_subplot(131) ax2 = fig.add_subplot(132) ax3 = fig.add_subplot(133) i1 = ax1.imshow(diff, origin='lower', interpolation=interpolation) plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.0f', ticks=[-1500, -750, 0, 750, 1500]) i2 = ax2.imshow(autoc, interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) i3 = ax3.imshow(autoc, interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) ax3.set_xlim(xc - 5, xc + 5) ax3.set_ylim(yc - 5, yc + 5) plt.colorbar(i2, ax=ax2, orientation='horizontal') plt.colorbar(i3, ax=ax3, orientation='horizontal')#, ticks=[10, 10.5, 11, 11.5, 12, 12.5]) ax1.set_xlabel('X [pixel]') ax1.set_ylabel('Y [pixel]') plt.savefig('SpatialAutocorrelation.pdf') plt.close() diff = np.random.poisson(level, size=(size, size)) - np.random.poisson(level, size=(size, size)) #autoc = signal.correlate2d(diff, diff, mode='full') #slow autoc = signal.fftconvolve(diff, np.flipud(np.fliplr(diff)), mode='full') autoc /= np.max(autoc) autoc *= 100. fileIO.writeFITS(autoc, 'autocorrelationSimulated.fits', int=False) yc, xc = autoc.shape xc /= 2. yc /= 2. xc -= 0.5 yc -= 0.5 #plot images fig = plt.figure(figsize=(14.5, 6.5)) plt.suptitle(r'Autocorrelation of Simulated Data ($%i \times %i$ grid)' % (size, size)) plt.suptitle(r'Difference Image $[e^{-}]$', x=0.24, y=0.26) plt.suptitle(r'Autocorrelation Interaction $[\%]$', x=0.51, y=0.26) plt.suptitle(r'Autocorrelation Interaction $[\%]$', x=0.78, y=0.26) ax1 = fig.add_subplot(131) ax2 = fig.add_subplot(132) ax3 = fig.add_subplot(133) i1 = ax1.imshow(diff, origin='lower', interpolation=interpolation) plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.0f', ticks=[-1500, -750, 0, 750, 1500]) i2 = ax2.imshow(autoc, interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) i3 = ax3.imshow(autoc, interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) ax3.set_xlim(xc - 5, xc + 5) ax3.set_ylim(yc - 5, yc + 5) plt.colorbar(i2, ax=ax2, orientation='horizontal') plt.colorbar(i3, ax=ax3, orientation='horizontal') ax1.set_xlabel('X [pixel]') ax1.set_ylabel('Y [pixel]') plt.savefig('SpatialAutocorrelationSimulation.pdf') plt.close() size = 128 diff = np.random.poisson(level, size=(size, size)) autoc = signal.correlate2d(diff, diff, mode='full') autoc /= np.max(autoc) autoc *= 100. yc, xc = autoc.shape xc /= 2. yc /= 2. #plot images fig = plt.figure(figsize=(14.5, 6.5)) plt.suptitle(r'Autocorrelation of Simulated Data ($%i \times %i$ grid)' % (size, size)) plt.suptitle(r'Image $[e^{-}]$', x=0.24, y=0.26) plt.suptitle(r'Autocorrelation Interaction $[\%]$', x=0.51, y=0.26) plt.suptitle(r'Autocorrelation Interaction $[\%]$', x=0.78, y=0.26) ax1 = fig.add_subplot(131) ax2 = fig.add_subplot(132) ax3 = fig.add_subplot(133) i1 = ax1.imshow(diff, origin='lower', interpolation=interpolation) plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1e') i2 = ax2.imshow(autoc, interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) i3 = ax3.imshow(autoc, interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) ax3.set_xlim(xc - 5, xc + 5) ax3.set_ylim(yc - 5, yc + 5) plt.colorbar(i2, ax=ax2, orientation='horizontal') plt.colorbar(i3, ax=ax3, orientation='horizontal') ax1.set_xlabel('X [pixel]') ax1.set_ylabel('Y [pixel]') plt.savefig('SpatialAutocorrelationSimulationSingle.pdf') plt.close() def spatialAutocorrelationMovie(pairs, gain=3.1, interpolation='none', calculate=False): """ Calculates a spatial 2D autocorrelation from the difference image and visualises the findings. :param gain: gain conversion between ADUs and electrons :param interpolation: whether the visualisation grid should be interpolated or not :param calculate: whether to recalculate the autocorrelation functions or load from existing file :return: None """ if calculate: data = {} for file1, file2 in pairs: d1 = pf.getdata(file1) * gain d2 = pf.getdata(file2) * gain #pre/overscans overscan1 = d1[11:2056, 4150:4192].mean() overscan2 = d2[11:2056, 4150:4192].mean() #define quadrants and subtract the bias levels Q11 = d1[11:2050, 2110:4131].copy() - overscan1 Q21 = d2[11:2050, 2110:4131].copy() - overscan2 #limit to 1024 Q11 = Q11[500:1524, 500:1524] Q21 = Q21[500:1524, 500:1524] #difference image diff = Q11 - Q21 #check the average signal levels Q11a = np.average(Q11) Q21a = np.average(Q21) d = np.abs(Q11a - Q21a) print Q11a, Q21a, d, file1, file2 if d > 50.: print 'too large difference in the average signal level, will ignore...' #seems to be rather sensitive to this! continue autoc = signal.fftconvolve(diff, np.flipud(np.fliplr(diff)), mode='full') autoc /= np.max(autoc) autoc *= 100. level = (Q11a + Q21a) / 2. data[level] = dict(data=diff, autoc=autoc) fileIO.cPickleDumpDictionary(data, 'spatialAutocorrelationMovieData.pk') else: data = cPickle.load(open('spatialAutocorrelationMovieData.pk')) #average signal levels, sorted keys = data.keys() keys.sort() #centre of the autocorrelation yc, xc = data[keys[0]]['autoc'].shape xc /= 2. yc /= 2. xc -= 0.5 yc -= 0.5 #plot images fig = plt.figure(figsize=(14.5, 6.5)) plt.suptitle(r'Autocorrelation of Flat Field Data') plt.suptitle(r'Difference Image $[e^{-}]$', x=0.24, y=0.26) plt.suptitle(r'Autocorrelation Interaction $[\%]$', x=0.51, y=0.26) plt.suptitle(r'Autocorrelation Interaction $[\%]$', x=0.78, y=0.26) ax1 = fig.add_subplot(131) ax2 = fig.add_subplot(132) ax3 = fig.add_subplot(133) i1 = ax1.imshow(data[keys[0]]['data'], origin='lower', interpolation=interpolation, vmin=-1000, vmax=1000, rasterized=True) p1 = plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.0f', ticks=[-1000, -500, 0, 500, 1000]) i2 = ax2.imshow(data[keys[0]]['autoc'], interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) i3 = ax3.imshow(data[keys[0]]['autoc'], interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) ax3.set_xlim(xc - 5, xc + 5) ax3.set_ylim(yc - 5, yc + 5) p2 = plt.colorbar(i2, ax=ax2, orientation='horizontal') p3 = plt.colorbar(i3, ax=ax3, orientation='horizontal') ax1.set_xlabel('X [pixel]') ax1.set_ylabel('Y [pixel]') text = ax1.text(0.02, 0.9, '', transform=ax1.transAxes) def init(): i1 = ax1.imshow(data[keys[0]]['data'], origin='lower', interpolation=interpolation, vmin=-1000, vmax=1000, rasterized=True) i2 = ax2.imshow(data[keys[0]]['autoc'], interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) i3 = ax3.imshow(data[keys[0]]['autoc'], interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) return i1, p1, i2, p2, i3, p3, text def animate(i): i1 = ax1.imshow(data[keys[i]]['data'], origin='lower', interpolation=interpolation, vmin=-1000, vmax=1000, rasterized=True) i2 = ax2.imshow(data[keys[i]]['autoc'], interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) i3 = ax3.imshow(data[keys[i]]['autoc'], interpolation=interpolation, origin='lower', rasterized=True, vmin=0, vmax=5) text.set_text('Mean Signal $\sim$ %.0f $e^{-}$' % keys[i]) return i1, p1, i2, p2, i3, p3, text #note that the frames defines the number of times animate functions is being called anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(keys), interval=2, blit=True) anim.save('SpatialCorrelation.mp4', fps=1) def examples(interpolation='none'): """ This function generates 1D and 2D power spectra from simulated data. :param interpolation: :return: None """ Pois1D = np.random.poisson(100000, 1024) PowerSpectrum = np.log10(np.abs(fftpack.fft(Pois1D))) #PowerSpectrum = np.log10(np.abs(fftpack.fftshift(fftpack.fft(Pois1D)))) print '1D Poisson:' print np.mean(PowerSpectrum), np.median(PowerSpectrum), np.min(PowerSpectrum), np.max(PowerSpectrum), np.std(PowerSpectrum) fig = plt.figure(figsize=(14, 8)) plt.suptitle('Fourier Analysis of Poisson Noise') plt.suptitle('Input Data', x=0.32, y=0.93) plt.suptitle(r'Power Spectrum', x=0.72, y=0.93) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) a = plt.axes([.65, .6, .2, .2], axisbg='y') ax1.plot(Pois1D, 'bo') ax2.plot(PowerSpectrum, 'r-') a.plot(PowerSpectrum, 'r-') ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax1.set_ylabel('Input Values') ax2.set_ylabel(r'$\log_{10}$(Power Spectrum)') ax1.set_xlim(0, 1024) ax2.set_xlim(0, 1024) ax2.set_ylim(2, 7) a.set_xlim(0, 20) plt.savefig('FourierPoisson1D.pdf') plt.close() #remove mean Pois1D -= 100000 #np.mean(Pois1D) PowerSpectrum = np.abs(fftpack.fft(Pois1D)) print '1D Poisson (mean removed):' print np.mean(PowerSpectrum), np.median(PowerSpectrum), np.min(PowerSpectrum), np.max(PowerSpectrum), np.std( PowerSpectrum) fig = plt.figure(figsize=(14, 8)) plt.suptitle('Fourier Analysis of Poisson Noise (mean removed)') plt.suptitle('Input Data', x=0.32, y=0.93) plt.suptitle(r'Power Spectrum', x=0.72, y=0.93) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) a = plt.axes([.65, .6, .2, .2], axisbg='y') ax1.plot(Pois1D, 'bo') ax2.plot(PowerSpectrum, 'r-') a.hist(PowerSpectrum, bins=20) ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax1.set_ylabel('Input Values') #ax2.set_ylabel(r'$\log_{10}$(Power Spectrum)') ax2.set_ylabel('Power Spectrum') ax1.set_xlim(0, 1024) ax2.set_xlim(0, 1024) #ax2.set_ylim(10**2, 10**7) #a.set_xlim(0, 20) plt.savefig('FourierPoissonMeanRemoved1D.pdf') plt.close() Sin1D = 20.*np.sin(np.arange(256) / 10.) PowerSpectrum = np.log10(np.abs(fftpack.fft(Sin1D))) print '1D Sin:' print np.mean(PowerSpectrum), np.median(PowerSpectrum), np.min(PowerSpectrum), np.max(PowerSpectrum), np.std(PowerSpectrum) fig = plt.figure(figsize=(14, 8)) plt.suptitle('Fourier Analysis of Sine Wave') plt.suptitle('Input Data', x=0.32, y=0.93) plt.suptitle(r'Power Spectrum', x=0.72, y=0.93) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) a = plt.axes([.65, .6, .2, .2], axisbg='y') ax1.plot(Sin1D, 'bo') ax2.plot(PowerSpectrum, 'r-') a.plot(PowerSpectrum, 'r-') ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax1.set_ylabel('Input Values') ax2.set_ylabel(r'$\log_{10}$(Power Spectrum)') ax1.set_xlim(0, 256) ax2.set_xlim(0, 256) a.set_xlim(0, 20) plt.savefig('FourierSin1D.pdf') plt.close() Top1D = np.zeros(256) Top1D[100:110] = 1. PowerSpectrum = np.log10(np.abs(fftpack.fft(Top1D))) print '1D Tophat:' print np.mean(PowerSpectrum), np.median(PowerSpectrum), np.min(PowerSpectrum), np.max(PowerSpectrum), np.std(PowerSpectrum) fig = plt.figure(figsize=(14, 8)) plt.suptitle('Fourier Analysis of Tophat') plt.suptitle('Input Data', x=0.32, y=0.93) plt.suptitle(r'Power Spectrum', x=0.72, y=0.93) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) ax1.plot(Top1D, 'bo') ax2.plot(PowerSpectrum, 'r-') ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax1.set_ylabel('Input Values') ax2.set_ylabel(r'$\log_{10}$(Power Spectrum)') ax1.set_xlim(0, 256) ax2.set_xlim(0, 256) plt.savefig('FourierTophat1D.pdf') plt.close() s = 2048 ss = s / 2 Pois = np.random.poisson(100000, size=(s, s)) #fourierSpectrum1 = np.log10(np.abs(fftpack.fftshift(fftpack.fft2(Pois)))) fourierSpectrum1 = np.log10(np.abs(fftpack.fft2(Pois))) print 'Poisson 2d:', np.var(Pois) print np.mean(fourierSpectrum1), np.median(fourierSpectrum1), np.std(fourierSpectrum1), np.max(fourierSpectrum1), np.min(fourierSpectrum1) fig = plt.figure(figsize=(14.5, 6.5)) plt.suptitle('Fourier Analysis of Poisson Data') plt.suptitle('Original Image', x=0.32, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.72, y=0.26) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) i1 = ax1.imshow(Pois, origin='lower', interpolation=interpolation) plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1f', ticks=[99000, 100000, 101000]) i2 = ax2.imshow(fourierSpectrum1[0:ss, 0:ss], interpolation=interpolation, origin='lower', rasterized=True, vmin=3, vmax=7) plt.colorbar(i2, ax=ax2, orientation='horizontal') ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax1.set_ylabel('Y [pixel]') plt.savefig('FourierPoisson.pdf') ax2.set_xlim(0, 10) ax2.set_ylim(0, 10) plt.savefig('FourierPoisson2.pdf') ax2.set_xlim(ss-10, ss-1) ax2.set_ylim(ss-10, ss-1) plt.savefig('FourierPoisson3.pdf') plt.close() #Poisson with smoothing... #val = 1.455e-6 / 2. #flux = 100000 #kernel = np.array([[0, val * flux, 0], [val * flux, (1 - val), val * flux], [0, val * flux, 0]]) #kernel = np.array([[0.01, 0.02, 0.01], [0.02, 0.88, 0.02], [0.01, 0.02, 0.01]]) kernel = np.array([[0.0025, 0.01, 0.0025], [0.01, 0.95, 0.01], [0.0025, 0.01, 0.0025]]) Pois = ndimage.convolve(Pois.copy(), kernel) #Pois = ndimage.filters.gaussian_filter(Pois.copy(), sigma=0.4) fourierSp = np.log10(np.abs(fftpack.fft2(Pois))) print 'Poisson 2d Smoothed:', np.var(Pois) print np.mean(fourierSp), np.median(fourierSp), np.std(fourierSp), np.max(fourierSp), np.min(fourierSp) fig = plt.figure(figsize=(14.5, 6.5)) plt.suptitle('Fourier Analysis of Smoothed Poisson Data') plt.suptitle('Original Image', x=0.32, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.72, y=0.26) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) i1 = ax1.imshow(Pois, origin='lower', interpolation=interpolation) plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1f', ticks=[99000, 100000, 101000]) i2 = ax2.imshow(fourierSp[0:ss, 0:ss], interpolation=interpolation, origin='lower', rasterized=True, vmin=3, vmax=7) plt.colorbar(i2, ax=ax2, orientation='horizontal') ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax1.set_ylabel('Y [pixel]') plt.savefig('FourierPoissonSmooth.pdf') ax2.set_xlim(0, 10) ax2.set_ylim(0, 10) plt.savefig('FourierPoissonSmooth2.pdf') ax2.set_xlim(ss-10, ss-1) ax2.set_ylim(ss-10, ss-1) plt.savefig('FourierPoissonSmooth3.pdf') plt.close() #difference fig = plt.figure() plt.suptitle('Power Spectrum of Smoothed Poisson Data / Power Spectrum of Poisson Data') ax = fig.add_subplot(111) i = ax.imshow(fourierSp[0:ss, 0:ss] / fourierSpectrum1[0:ss, 0:ss], origin='lower', interpolation=interpolation, vmin=0.9, vmax=1.1) plt.colorbar(i, ax=ax, orientation='horizontal') plt.savefig('FourierPSDiv.pdf') ax.set_xlim(0, 10) ax.set_ylim(0, 10) plt.savefig('FourierPSDiv2.pdf') ax.set_xlim(ss-10, ss-1) ax.set_ylim(ss-10, ss-1) plt.savefig('FourierPSDiv3.pdf') plt.close() #x = np.arange(1024) #y = 10 * np.sin(x / 30.) + 20 #img = np.vstack([y, ] * 1024) x, y = np.mgrid[0:32, 0:32] #img = 10*np.sin(x/40.) * 10*np.sin(y/40.) img = 100 * np.cos(x*np.pi/4.) * np.cos(y*np.pi/4.) kernel = np.array([[0.0025, 0.01, 0.0025], [0.01, 0.95, 0.01], [0.0025, 0.01, 0.0025]]) img = ndimage.convolve(img.copy(), kernel) fourierSpectrum2 = np.abs(fftpack.fft2(img)) #fourierSpectrum2 = np.log10(np.abs(fftpack.fftshift(fftpack.fft2(img)))) print np.mean(fourierSpectrum2), np.median(fourierSpectrum2), np.std(fourierSpectrum2), np.max(fourierSpectrum2), np.min(fourierSpectrum2) fig = plt.figure(figsize=(14.5, 6.5)) plt.suptitle('Fourier Analysis of Flat-field Data') plt.suptitle('Original Image', x=0.32, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.72, y=0.26) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1e') i2 = ax2.imshow(fourierSpectrum2[0:512, 0:512], interpolation=interpolation, origin='lower', rasterized=True) plt.colorbar(i2, ax=ax2, orientation='horizontal') ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax2.set_ylim(0, 16) ax2.set_xlim(0, 16) ax1.set_ylabel('Y [pixel]') plt.savefig('FourierSin.pdf') plt.close() x, y = np.mgrid[0:1024, 0:1024] img = 10*np.sin(x/40.) * 10*np.sin(y/40.) fourierSpectrum2 = np.log10(np.abs(fftpack.fft2(img))) print np.mean(fourierSpectrum2), np.median(fourierSpectrum2), np.std(fourierSpectrum2), np.max(fourierSpectrum2), np.min(fourierSpectrum2) fig = plt.figure(figsize=(14.5, 6.5)) plt.suptitle('Fourier Analysis of Flat-field Data') plt.suptitle('Original Image', x=0.32, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.72, y=0.26) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) i1 = ax1.imshow(img, origin='lower', interpolation=interpolation) plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1e') i2 = ax2.imshow(fourierSpectrum2[0:512, 0:512], interpolation=interpolation, origin='lower', rasterized=True, vmin=-1, vmax=7) plt.colorbar(i2, ax=ax2, orientation='horizontal') ax1.set_xlabel('X [pixel]') ax2.set_xlabel('$l_{x}$') ax2.set_ylim(0, 20) ax2.set_xlim(0, 20) ax1.set_ylabel('Y [pixel]') plt.savefig('FourierSin2.pdf') plt.close() def sinusoidalExample(): interpolation = 'none' x, y = np.mgrid[0:32, 0:32] img = 100 * np.cos(x*np.pi/4.) * np.cos(y*np.pi/4.) power = np.log10(np.abs(fftpack.fft2(img.copy()))) sigma = np.linspace(0.2, 3.0, 20) fig = plt.figure(figsize=(14.5, 7)) plt.suptitle('Fourier Analysis of Sinusoidal Data') plt.suptitle('Gaussian Smoothed', x=0.32, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.72, y=0.26) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) p1 = plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1f', ticks=[-100, -50, 0, 50, 100]) i2 = ax1.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=-1, vmax=7) p2 = plt.colorbar(i2, ax=ax2, orientation='horizontal') sigma_text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) def init(): i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) i2 = ax1.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=-1, vmax=7) sigma_text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) return i1, p1, i2, p2, sigma_text def animate(i): im = ndimage.filters.gaussian_filter(img.copy(), sigma=sigma[i]) power = np.log10(np.abs(fftpack.fft2(im))) i1 = ax1.imshow(im, origin='lower', interpolation=interpolation) i2 = ax2.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=-1, vmax=7) sigma_text.set_text('sigma=%f' % sigma[i]) return i1, p1, p2, p2, sigma_text #note that the frames defines the number of times animate functions is being called anim = animation.FuncAnimation(fig, animate, init_func=init, frames=20, interval=1, blit=True) anim.save('FourierSmoothing.mp4', fps=3) def poissonExample(): interpolation = 'none' img = np.random.poisson(100000, size=(32, 32)) power = np.log10(np.abs(fftpack.fft2(img.copy()))) sigma = np.linspace(0.2, 3.0, 20) fig = plt.figure(figsize=(14.5, 7)) plt.suptitle('Fourier Analysis of Poisson Data') plt.suptitle('Gaussian Smoothed', x=0.32, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.72, y=0.26) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) p1 = plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1f', ticks=[99500, 100000, 100500]) i2 = ax1.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=7) p2 = plt.colorbar(i2, ax=ax2, orientation='horizontal') sigma_text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) def init(): i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) i2 = ax1.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=7) sigma_text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) return i1, p1, p2, p2, sigma_text def animate(i): im = ndimage.filters.gaussian_filter(img.copy(), sigma=sigma[i]) power = np.log10(np.abs(fftpack.fft2(im))) i1 = ax1.imshow(im, origin='lower', interpolation=interpolation) i2 = ax2.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=7) sigma_text.set_text('sigma=%f' % sigma[i]) return i1, p1, p2, p2, sigma_text #note that the frames defines the number of times animate functions is being called anim = animation.FuncAnimation(fig, animate, init_func=init, frames=20, interval=1, blit=True) anim.save('FourierSmoothingPoisson.mp4', fps=3) def poissonExampleLowpass(): interpolation = 'none' img = np.random.poisson(100000, size=(32, 32)) power = np.log10(np.abs(fftpack.fft2(img.copy()))) sigma = np.linspace(0.1, 100.0, 20) fig = plt.figure(figsize=(14.5, 7)) plt.suptitle('Fourier Analysis of Poisson Data (lowpass filtering)') plt.suptitle('Lowpass Filtered', x=0.32, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.72, y=0.26) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) p1 = plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1f', ticks=[99500, 100000, 100500]) i2 = ax1.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=7) p2 = plt.colorbar(i2, ax=ax2, orientation='horizontal') sigma_text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) def init(): i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) i2 = ax1.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=7) sigma_text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) return i1, p1, p2, p2, sigma_text def animate(i): kernel_low = [[1.0/sigma[i],1.0/sigma[i],1.0/sigma[i]], [1.0/sigma[i],1.0/sigma[i],1.0/sigma[i]], [1.0/sigma[i],1.0/sigma[i],1.0/sigma[i]]] im = ndimage.convolve(img.copy(), kernel_low) power = np.log10(np.abs(fftpack.fft2(im))) i1 = ax1.imshow(im, origin='lower', interpolation=interpolation) i2 = ax2.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=7) sigma_text.set_text('kernel %f' % (1./sigma[i])) return i1, p1, p2, p2, sigma_text #note that the frames defines the number of times animate functions is being called anim = animation.FuncAnimation(fig, animate, init_func=init, frames=20, interval=1, blit=True) anim.save('FourierSmoothingPoissonLowpass.mp4', fps=3) def poissonExamplePixelSharing(): interpolation = 'none' img = np.random.poisson(100000, size=(32, 32)) power = np.log10(np.abs(fftpack.fft2(img.copy()))) sigma = np.logspace(-4, 1, 100) fig = plt.figure(figsize=(14.5, 7)) plt.suptitle('Fourier Analysis of Poisson Data (kernel smoothing)') plt.suptitle('Kernel Convolved', x=0.32, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.72, y=0.26) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) p1 = plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1f', ticks=[99500, 100000, 100500]) i2 = ax1.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=7) p2 = plt.colorbar(i2, ax=ax2, orientation='horizontal') sigma_text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) def init(): i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) i2 = ax1.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=7) sigma_text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) return i1, p1, p2, p2, sigma_text def animate(i): kernel = [[0.0, sigma[i]/4., 0.0], [sigma[i]/4., 1.0 - sigma[i], sigma[i]/4.], [0.0, sigma[i]/4., 0.0]] im = ndimage.convolve(img.copy(), kernel) power = np.log10(np.abs(fftpack.fft2(im))) i1 = ax1.imshow(im, origin='lower', interpolation=interpolation) i2 = ax2.imshow(power[0:16, 0:16], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=7) sigma_text.set_text('kernel %f' % sigma[i]) return i1, p1, p2, p2, sigma_text #note that the frames defines the number of times animate functions is being called anim = animation.FuncAnimation(fig, animate, init_func=init, frames=100, interval=1, blit=True) anim.save('FourierSmoothingPoissonSharing.mp4', fps=3) def poissonExamplePixelSharing2(): interpolation = 'none' flux = 100000 size = 2**6 ss = size /2 img = np.random.poisson(flux, size=(size, size)) power = np.log10(np.abs(fftpack.fft2(img.copy()))) sigma = np.logspace(-3, -0.1, 25) fig = plt.figure(figsize=(14.5, 7)) plt.suptitle('Fourier Analysis of Poisson Data (kernel smoothing)') plt.suptitle('Kernel Convolved', x=0.32, y=0.26) plt.suptitle(r'$\log_{10}$(2D Power Spectrum)', x=0.72, y=0.26) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) p1 = plt.colorbar(i1, ax=ax1, orientation='horizontal', format='%.1f', ticks=[99500, 100000, 100500]) i2 = ax1.imshow(power[0:ss, 0:ss], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=6) p2 = plt.colorbar(i2, ax=ax2, orientation='horizontal') sigma_text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) def init(): i1 = ax1.imshow(img, origin='lower', interpolation=interpolation, rasterized=True) i2 = ax1.imshow(power[0:ss, 0:ss], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=6) sigma_text = ax1.text(0.02, 0.95, '', transform=ax1.transAxes) return i1, p1, p2, p2, sigma_text def animate(i): kernel = [[0.0, sigma[i]/4., 0.0], [sigma[i]/4., 1.0 - sigma[i], sigma[i]/4.], [0.0, sigma[i]/4., 0.0]] im = ndimage.convolve(img.copy(), kernel) print 'smoothed', sigma[i], np.var(img), np.var(im) power = np.log10(np.abs(fftpack.fft2(im))) i1 = ax1.imshow(im, origin='lower', interpolation=interpolation) i2 = ax2.imshow(power[0:ss, 0:ss], interpolation=interpolation, origin='lower', rasterized=True, vmin=2, vmax=6) sigma_text.set_text('kernel %e' % sigma[i]) return i1, p1, p2, p2, sigma_text #note that the frames defines the number of times animate functions is being called anim = animation.FuncAnimation(fig, animate, init_func=init, frames=25, interval=1, blit=True) anim.save('FourierSmoothingPoissonSharing2.mp4', fps=3) def comparePower(file1='05Sep_14_35_00s_Euclid.fits', file2='05Sep_14_36_31s_Euclid.fits', gain=3.1): d1 = pf.getdata(file1) * gain d2 = pf.getdata(file2) * gain #pre/overscans overscan1 = d1[11:2056, 4150:4192].mean() overscan2 = d2[11:2056, 4150:4192].mean() #define quadrants and subtract the bias levels Q11 = d1[11:2050, 2110:4131] - overscan1 Q21 = d2[11:2050, 2110:4131] - overscan2 #limit to 1024 Q11 = Q11[300:1324, 300:1324] Q21 = Q21[300:1324, 300:1324] #difference image diff = Q11 - Q21 fourierSpectrumD = np.abs(fftpack.fft2(diff))[0:512, 0:512] cornervalues = fourierSpectrumD[510:512, 510:512] print 'data' print cornervalues print np.log10(cornervalues) print fourierSpectrumD[511:512, 511:512] print np.log10(fourierSpectrumD[511:512, 511:512]) #simulate res = [] flux = 145000 size = 1024 ss = size / 2 #for x in xrange(20): # img1 = np.random.poisson(flux, size=(size, size)) # img2 = np.random.poisson(flux, size=(size, size)) # power = np.abs(fftpack.fft2((img1 - img2)))[0:ss, 0:ss] # res.append(power) #res = np.average(res, axis=0) img1 = np.random.poisson(flux, size=(size, size)) img2 = np.random.poisson(flux, size=(size, size)) res = np.abs(fftpack.fft2((img1 - img2)))[0:ss, 0:ss] print 'simulated' cornervalues = res[510:512, 510:512] print cornervalues print np.log10(cornervalues) print res[511:512, 511:512] print np.log10(res[511:512, 511:512]) fig = plt.figure(figsize=(15, 7)) plt.suptitle('Power Spectrum values') plt.suptitle('Difference Image', x=0.3, y=0.94) plt.suptitle('Simulated Poisson Data', x=0.72, y=0.94) ax1 = fig.add_subplot(121) ax2 = fig.add_subplot(122) fig.subplots_adjust(wspace=0.25) ax1.hist(np.ravel(fourierSpectrumD), bins=40, normed=True, range=[0, 1750000], label='power spectrum values') ax1.axvline(x=fourierSpectrumD[511, 511], c='r', ls='-', lw=2, zorder=14, label='(512, 512)') ax1.axvline(x=fourierSpectrumD[510, 510], c='g', ls=':', lw=2, zorder=14, label='(511, 511)') ax1.axvline(x=fourierSpectrumD[511, 510], c='y', ls='--', lw=2, zorder=14, label='(511, 512)') ax1.axvline(x=fourierSpectrumD[510, 511], c='m', ls='-.', lw=2, zorder=14, label='(512, 511)') ax2.hist(np.ravel(res), bins=40, normed=True, range=[0, 1750000], label='power spectrum values') ax2.axvline(x=res[511, 511], c='r', ls='-', lw=2, zorder=14, label='(512, 512)') ax2.axvline(x=res[510, 510], c='g', ls=':', lw=2, zorder=14, label='(511, 511)') ax2.axvline(x=res[511, 510], c='y', ls='--', lw=2, zorder=14, label='(511, 512)') ax2.axvline(x=res[510, 511], c='m', ls='-.', lw=2, zorder=14, label='(512, 511)') ax1.locator_params(nbins=6) ax2.locator_params(nbins=6) ax1.legend(shadow=True, fancybox=True) ax2.legend(shadow=True, fancybox=True) plt.savefig('PowerSpectrumDistributions.pdf') plt.close() if __name__ == '__main__': size = 200. #makeFlat(files) #plotDetectorCounts() #findPairs() # pairs = [('05Sep_14_57_00s_Euclid.fits', '05Sep_14_58_27s_Euclid.fits'), ('05Sep_14_41_10s_Euclid.fits', '05Sep_14_43_30s_Euclid.fits'), ('05Sep_14_25_05s_Euclid.fits', '05Sep_14_26_30s_Euclid.fits'), ('05Sep_15_00_09s_Euclid.fits', '05Sep_15_02_21s_Euclid.fits'), ('05Sep_14_45_07s_Euclid.fits', '05Sep_14_46_28s_Euclid.fits'), ('05Sep_14_27_58s_Euclid.fits', '05Sep_14_30_22s_Euclid.fits'), ('05Sep_14_09_15s_Euclid.fits', '05Sep_14_10_38s_Euclid.fits'), ('05Sep_15_03_51s_Euclid.fits', '05Sep_15_05_18s_Euclid.fits'), ('05Sep_14_47_56s_Euclid.fits', '05Sep_14_49_25s_Euclid.fits'), ('05Sep_14_31_56s_Euclid.fits', '05Sep_14_33_23s_Euclid.fits'), ('05Sep_14_13_32s_Euclid.fits', '05Sep_14_14_57s_Euclid.fits'), ('05Sep_15_06_50s_Euclid.fits', '05Sep_15_08_18s_Euclid.fits'), ('05Sep_14_50_59s_Euclid.fits', '05Sep_14_52_31s_Euclid.fits'), ('05Sep_14_35_00s_Euclid.fits', '05Sep_14_36_31s_Euclid.fits')] #examples() #sinusoidalExample() #poissonExample() #poissonExampleLowpass() #poissonExamplePixelSharing() #poissonExamplePixelSharing2() #analyseCorrelationFourier() #analyseCorrelationFourier(small=True) #analyseCorrelationFourier(shift=True) #analyseCorrelationFourier(small=True, shift=True) #comparePower() #spatialAutocorrelation() spatialAutocorrelationMovie(pairs) #simulation # simulatePoissonProcess(size=size) # simulatePoissonProcessRowColumn() # simulatePoissonProcessRowColumn(short=False) # #pixel region # output = pairwiseNoise(pairs, size=size) # fileIO.cPickleDumpDictionary(output, 'data.pk') # # output = cPickle.load(open('data.pk')) # plotAutocorrelation(output) # plotResults(output, size) # # #row-column # out = pairwiseNoiseRowColumns(pairs) # fileIO.cPickleDumpDictionary(out, 'dataRowColumn.pk') # # out = cPickle.load(open('dataRowColumn.pk')) # plotResultsRowColumn(out) # # out = {} # for file in g.glob('05*Euclid.fits'): # data = pf.getdata(file) # results = measureNoise(data, size, file) # try: # print file, np.median(results['flux']), np.median(results['variance']) # except: # print 'No useful data in ', file # continue # out[file] = results # fileIO.cPickleDumpDictionary(out, 'dataOLD.pk') # out = cPickle.load(open('dataOLD.pk')) # plotResults(out, size, pairwise=False, output='FullwellEstimateOLD.pdf')

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9ed032d61b371886f55c64a19ab95ddf65ab4e9d

77

py

Python

apps/carts/urls.py

makethedayunique/pandama-online-store

38c02809a89087f5a6c83fd6ee2c39dab8d66f6c

[ "MIT" ]

null

apps/carts/urls.py

makethedayunique/pandama-online-store

38c02809a89087f5a6c83fd6ee2c39dab8d66f6c

[ "MIT" ]

null

apps/carts/urls.py

makethedayunique/pandama-online-store

38c02809a89087f5a6c83fd6ee2c39dab8d66f6c

[ "MIT" ]

null

from django.urls import path from apps.carts import views urlpatterns = [ ]

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182

py

Python

polybar-scripts/inbox-imap-python/inbox-imap-python.py

alexshoo/polybar-scripts

ecfbaa45400d6184f73dd447313925f92c74828c

[ "Unlicense" ]

1

2019-02-15T18:42:24.000Z

polybar-scripts/inbox-imap-python/inbox-imap-python.py

alexshoo/polybar-scripts

ecfbaa45400d6184f73dd447313925f92c74828c

[ "Unlicense" ]

null

polybar-scripts/inbox-imap-python/inbox-imap-python.py

alexshoo/polybar-scripts

ecfbaa45400d6184f73dd447313925f92c74828c

[ "Unlicense" ]

1

2019-03-29T13:17:22.000Z

#!/usr/bin/python import imaplib obj = imaplib.IMAP4_SSL('imap.mail.net', 993) obj.login('userlogin', 'pass123') obj.select() print(len(obj.search(None, 'unseen')[1][0].split()))

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py

Python

project/__init__.py

sveetch/Sveetoy

02a7fca00dd2602e97ebee845f7a76eadcbcc2d0

[ "MIT" ]

1

2017-10-24T09:45:59.000Z

project/__init__.py

sveetch/Sveetoy

02a7fca00dd2602e97ebee845f7a76eadcbcc2d0

[ "MIT" ]

55

2017-01-22T16:02:53.000Z

2020-08-04T15:18:44.000Z

project/__init__.py

sveetch/Sveetoy

02a7fca00dd2602e97ebee845f7a76eadcbcc2d0

[ "MIT" ]

1

2018-06-13T15:47:29.000Z

# -*- coding: utf-8 -*- """ Sveetoy Demo project to build with Optimus ``__version__`` define the Sass library version, not the demonstration project. """ __version__ = "0.9.1"

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73539562d73737b9e59771170b650c6b923a3a03

346

py

Python

trinity/rpc/modules/main.py

shreyasnbhat/py-evm

cd31d83185e102a7cb2f11e2f67923b069ee9cef

[ "MIT" ]

1

2018-12-09T11:56:53.000Z

trinity/rpc/modules/main.py

shreyasnbhat/py-evm

cd31d83185e102a7cb2f11e2f67923b069ee9cef

[ "MIT" ]

null

trinity/rpc/modules/main.py

shreyasnbhat/py-evm

cd31d83185e102a7cb2f11e2f67923b069ee9cef

[ "MIT" ]

2

2018-12-09T15:58:11.000Z

2020-09-29T07:10:21.000Z

from lahja import ( Endpoint ) from trinity.chains.base import BaseAsyncChain class RPCModule: _chain = None def __init__(self, chain: BaseAsyncChain, event_bus: Endpoint) -> None: self._chain = chain self._event_bus = event_bus def set_chain(self, chain: BaseAsyncChain) -> None: self._chain = chain

20.352941

75

0.679191

41

346

5.439024

0.439024

0.161435

0.206278

0.161435

0

0.239884

346

16

76

21.625

0.847909

0

0.181818

0

1

0.181818

false

0

0.181818

0

0.545455

0

null

0

1

0

null

0

1

0

3

735b36f9763e24197fb1834d6ff9aa6fc5164e76

268

py

Python

project/forms.py

abrusebas1997/Activintine

1d1a0ce06284bd08cee8c46843583a37ac98dd1c

[ "MIT" ]

null

project/forms.py

abrusebas1997/Activintine

1d1a0ce06284bd08cee8c46843583a37ac98dd1c

[ "MIT" ]

null

project/forms.py

abrusebas1997/Activintine

1d1a0ce06284bd08cee8c46843583a37ac98dd1c

[ "MIT" ]

null

from django import forms from project.models import Activity class ActivityForm(forms.ModelForm): class Meta: # """ Render and process a form based on the Activity model. """ model = Activity fields = ("title", "content", "author", "image")

26.8

68

0.664179

32

268

5.5625

0.78125

0

0.227612

268

9

69

29.777778

0.859903

0.231343

0

0.112745

0

1

0

false

0

0.333333

0

0.666667

0

null

0

1

0

null

0

1

0

1

0

3

735fc86cb60a5e51b78737cd180e9731652ebdcf

219

py

Python

normflow/__init__.py

pkulwj1994/normalizing-flows

326321c4aea4a3f6ab703f82e21277a79cd7d9e4

[ "MIT" ]

96

2020-10-17T12:02:41.000Z

2022-03-31T23:53:35.000Z

normflow/__init__.py

pkulwj1994/normalizing-flows

326321c4aea4a3f6ab703f82e21277a79cd7d9e4

[ "MIT" ]

4

2020-05-05T16:39:58.000Z

2021-12-17T09:32:26.000Z

normflow/__init__.py

pkulwj1994/normalizing-flows

326321c4aea4a3f6ab703f82e21277a79cd7d9e4

[ "MIT" ]

16

2020-05-05T15:41:33.000Z

2022-03-31T09:40:28.000Z

#! /usr/bin/env python # -*- coding: utf-8 -*- from .core import * from . import flows from . import distributions from . import transforms from . import nets from . import utils from . import HAIS __version__ = '1.0'

18.25

27

0.69863

31

219

4.806452

0.612903

0.402685

0

0.01676

0.182648

219

12

28

18.25

0.815642

0.196347

0

0.017143

0

1

0

false

0

0.875

0

0.875

0

null

1

0

null

0

1

0

1

0

3

7dfdb5f977665942e820c1c3d6fe148ffeef9ba7

126

py

Python

src/test/python/a.py

xiaoma20082008/pvm

a1c3c312c362ca30c7202645b047664e36a690e7

[ "Apache-2.0" ]

null

src/test/python/a.py

xiaoma20082008/pvm

a1c3c312c362ca30c7202645b047664e36a690e7

[ "Apache-2.0" ]

null

src/test/python/a.py

xiaoma20082008/pvm

a1c3c312c362ca30c7202645b047664e36a690e7

[ "Apache-2.0" ]

null

a = {} b = 123 c = '' d = {} print(a) e = 123.456 f = [1, "2", "3.14", False, {}, [4, "5", {}, True]] g = (1, "2", True, None)

15.75

51

0.380952

24

126

2

0.791667

0.083333

0

0.191489

0.253968

126

8

52

15.75

0.319149

0

0.055118

0

1

0

false

0

0.125

1

0

null

0

1

0

1

0

null

0

3

b4056a4bd916e9711695468cb950533a190b4703

113

py

Python

utils/parsing.py

jaobernardi/roboscovid-redacted

831abbcf42781560c89c5a6782ab7de238b43aca

[ "MIT" ]

null

utils/parsing.py

jaobernardi/roboscovid-redacted

831abbcf42781560c89c5a6782ab7de238b43aca

[ "MIT" ]

null

utils/parsing.py

jaobernardi/roboscovid-redacted

831abbcf42781560c89c5a6782ab7de238b43aca

[ "MIT" ]

null

def parse_string(input, vars={}): for var in vars: input = input.replace(f"${var}$", vars[var]) return input

22.6

46

0.663717

18

113

4.111111

0.611111

0

0.150442

113

4

47

28.25

0.770833

0

0.061947

0

1

0.25

false

0

0.5

0

1

0

null

0

1

0

null

0

1

0

3

b40723909d0ed1749efd89e26284d9be69af7be0

216

py

Python

users/factories.py

Arpit8081/Phishtray_Edited_Version

9f3342e6fd2620b7f01ad91ce5b36fa8ea111bc8

[ "MIT" ]

2

2020-03-31T12:38:10.000Z

2022-01-21T22:21:06.000Z

users/factories.py

Arpit8081/Phishtray_Edited_Version

9f3342e6fd2620b7f01ad91ce5b36fa8ea111bc8

[ "MIT" ]

252

2018-05-24T14:55:24.000Z

2022-02-26T13:02:10.000Z

users/factories.py

Arpit8081/Phishtray_Edited_Version

9f3342e6fd2620b7f01ad91ce5b36fa8ea111bc8

[ "MIT" ]

11

2018-06-23T14:54:42.000Z

2021-02-19T11:33:44.000Z

import factory from users.models import User class UserFactory(factory.django.DjangoModelFactory): class Meta: model = User username = factory.Sequence(lambda n: "username{0:0=2d}".format(n + 1))

19.636364

75

0.708333

28

216

5.464286

0.714286

0

0.022727

0.185185

216

10

76

21.6

0.846591

0

0.074074

0

1

0

false

0

0.333333

0

0.833333

0

1

0

null

0

null

0

1

0

1

0

3

b40c1dd2ce522c2f2497e93d9095e9beefcec550

175

py

Python

PyZoo/validators/utils/listSecure.py

franzinBr/PyZoo

a2deab63f46fbdfa92b0602d8efbfc9f19b9fef9

[ "MIT" ]

3

2021-09-29T22:23:55.000Z

2022-02-16T13:52:56.000Z

PyZoo/validators/utils/listSecure.py

franzinBr/PyZoo

a2deab63f46fbdfa92b0602d8efbfc9f19b9fef9

[ "MIT" ]

null

PyZoo/validators/utils/listSecure.py

franzinBr/PyZoo

a2deab63f46fbdfa92b0602d8efbfc9f19b9fef9

[ "MIT" ]

null

class ListSecure(list): def get(self, index, default=None): try: return self.__getitem__(index) except IndexError: return default

21.875

42

0.588571

18

175

5.5

0.777778

0

0.331429

175

7

43

25

0.846154

0

1

0.166667

false

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

3

b410c5bd9e6cedd6d9fa28dcfb6736cb54c3ac65

231

py

Python

387-First_Unique_Character_in_a_String.py

QuenLo/leecode

ce861103949510dc54fd5cb336bd992c40748de2

[ "MIT" ]

6

2018-06-13T06:48:42.000Z

2020-11-25T10:48:13.000Z

387-First_Unique_Character_in_a_String.py

QuenLo/leecode

ce861103949510dc54fd5cb336bd992c40748de2

[ "MIT" ]

null

387-First_Unique_Character_in_a_String.py

QuenLo/leecode

ce861103949510dc54fd5cb336bd992c40748de2

[ "MIT" ]

null

class Solution: def firstUniqChar(self, s: str) -> int: count = collections.Counter(s) for indx, ch in enumerate(s): if count[ch] < 2: return indx return -1

23.1

43

0.484848

26

231

4.307692

0.769231

0

0.015038

0.424242

231

9

44

25.666667

0.827068

0

1

0.142857

false

0

0.571429

0

1

0

null

0

1

0

null

0

1

0

3

b4283ac24168066c9d1d9a6553d694458b356177

137

py

Python

WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/tempfile/tempfile_tempdir.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/tempfile/tempfile_tempdir.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/tempfile/tempfile_tempdir.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

# """ """ # end_pymotw_header import tempfile tempfile.tempdir = "/I/changed/this/path" print("gettempdir():", tempfile.gettempdir())

12.454545

45

0.693431

15

137

6.2

0.8

0

0.109489

137

10

46

13.7

0.762295

0.124088

0

0.3

0

1

0

true

0

0.333333

0

0.333333

1

0

null

0

null

0

1

0

1

0

3

b43d2b9da5ca68495129b75b7d80ef687ebe14ce

244

py

Python

blendmotion/core/__init__.py

DeepL2/BlendMotion

4db804cc47f38b51d255d84c0e4d9f951900bf2b

[ "MIT" ]

null

blendmotion/core/__init__.py

DeepL2/BlendMotion

4db804cc47f38b51d255d84c0e4d9f951900bf2b

[ "MIT" ]

2

2019-01-06T09:15:09.000Z

2019-01-06T09:16:52.000Z

blendmotion/core/__init__.py

DeepL2/BlendMotion

4db804cc47f38b51d255d84c0e4d9f951900bf2b

[ "MIT" ]

1

2019-01-06T09:12:51.000Z

from blendmotion.core import animation, effector, rigging def register(): animation.register() effector.register() rigging.register() def unregister(): rigging.unregister() effector.unregister() animation.unregister()

20.333333

57

0.717213

23

244

7.608696

0.434783

0

0.172131

244

11

58

22.181818

0.866337

0

1

0.222222

true

0

0.111111

0

0.333333

0

1

0

null

0

null

0

1

0

3

b43dd1a344a9cba9e511f20c32261cc725c7d568

98

py

Python

GRADE 9/Python/BraydenViana-Python-Video8.py

i1470s/School-Work

e00843f3506b2ad674dce5e47ce3321002cc23e5

[ "MIT" ]

null

GRADE 9/Python/BraydenViana-Python-Video8.py

i1470s/School-Work

e00843f3506b2ad674dce5e47ce3321002cc23e5

[ "MIT" ]

null

GRADE 9/Python/BraydenViana-Python-Video8.py

i1470s/School-Work

e00843f3506b2ad674dce5e47ce3321002cc23e5

[ "MIT" ]

null

foods = ['bacon', 'tuna', 'ham', 'sausages', 'beef'] for f in foods: print(f) print(len(f))

19.6

53

0.561224

15

98

3.666667

0.733333

0

0.193878

98

5

54

19.6

0.696203

0

0.252632

0

1

0

false

0

0.5

1

0

null

0

1

0

null

0

1

0

3

b4496955204178ea5c22a852367cc1c883835e5a

182

py

Python

server/fundraisers/urls.py

Techbikers/techbikers-api

f9c6ea467d1ae730e9cabe0d4785423634c044e5

[ "MIT" ]

2

2016-08-14T04:21:04.000Z

2017-05-23T22:04:48.000Z

server/fundraisers/urls.py

Techbikers/techbikers-api

f9c6ea467d1ae730e9cabe0d4785423634c044e5

[ "MIT" ]

19

2015-08-26T10:05:02.000Z

2018-06-27T20:08:54.000Z

server/fundraisers/urls.py

Techbikers/api

f9c6ea467d1ae730e9cabe0d4785423634c044e5

[ "MIT" ]

6

2015-08-19T16:49:13.000Z

2018-05-25T16:38:24.000Z

from django.conf.urls import url, include from server.fundraisers.views import FundraisersList urlpatterns = [ url(r'^$', FundraisersList.as_view(), name='fundraiser-list') ]

20.222222

65

0.747253

22

182

6.136364

0.818182

0

0.126374

182

8

66

22.75

0.849057

0

0.093407

0

1

0

false

0

0.4

0

0.4

0

1

0

null

0

1

0

null

0

1

0

3

b45509ec6d18f1e633346c9e3d8f69d71b9e1ea6

1,458

py

Python

tsvis/parser/utils/vis_logging.py

iGame-Lab/TS-VIS

b0cd8d13ac1ebc5d857597b2a373b8e51e606358

[ "Apache-2.0" ]

15

2021-08-30T09:45:27.000Z

2022-03-28T04:49:54.000Z

tsvis/parser/utils/vis_logging.py

iGame-Lab/TS-VIS

b0cd8d13ac1ebc5d857597b2a373b8e51e606358

[ "Apache-2.0" ]

1

2021-08-30T09:55:49.000Z

tsvis/parser/utils/vis_logging.py

iGame-Lab/TS-VIS

b0cd8d13ac1ebc5d857597b2a373b8e51e606358

[ "Apache-2.0" ]

1

2022-03-28T04:50:16.000Z

# -*- coding: utf-8 -*- """ Copyright 2021 Tianshu AI Platform. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ============================================================= """ from tsvis.server.command_line import get_cmd_line from pathlib import Path class VisLogging: _instance = None # 保证只有一个单例 def __new__(cls, *args, **kwargs): if cls._instance is None: cls._instance = object.__new__(cls) return cls._instance def __init__(self, cmd_line): if cmd_line.action != "migrate": self._logging_path = Path(cmd_line.args.logdir).absolute() self._cache_path = self._logging_path.parent / "__viscache__" @property def logdir(self): return self._logging_path @property def cachedir(self): return self._cache_path _logging = VisLogging(get_cmd_line()) def get_logger(): return _logging

30.375

74

0.652949

186

1,458

4.897849

0.569892

0.065862

0.049396

0.035126

0

0.008043

0.23251

1,458

47

75

31.021277

0.806077

0.462963

0

0.095238

0

0.026536

0

1

0.238095

false

0

0.095238

0.142857

0.619048

0

null

0

null

0

1

0

1

0

3

b45795316d6c8041db93ffee842df708f74f2199

669

py

Python

src/dotctl/installers/macappstore.py

wwmoraes/dotctl

104e7dc3db8ef0389f03108a589a97c6f0923692

[ "MIT" ]

null

src/dotctl/installers/macappstore.py

wwmoraes/dotctl

104e7dc3db8ef0389f03108a589a97c6f0923692

[ "MIT" ]

null

src/dotctl/installers/macappstore.py

wwmoraes/dotctl

104e7dc3db8ef0389f03108a589a97c6f0923692

[ "MIT" ]

null

from typing import List from dotctl.installers.installer import Installer from functools import cached_property class MacAppStore(Installer): @property def base_cmd(self): return ["mas"] @property def install_cmd(self): return ["install"] @property def uninstall_cmd(self): return ["uninstall"] @cached_property def list(self) -> List[str]: list_process = self.__cmd__([*self.base_cmd, "list"], capture=True) entries = list_process.stdout.splitlines() return sorted([entry.split(" ")[0] for entry in entries]) def is_installed(self, package: str, binary: str = None) -> bool: return (binary or package) in self.list

23.892857

71

0.705531

87

669

5.275862

0.45977

0.095861

0.084967

0

0.001815

0.176383

669

27

72

24.777778

0.831216

0

0.15

0

0.035874

0

1

0.25

false

0

0.15

0.2

0.7

0

null

0

null

0

1

0

1

0

3

81f208bf97700d94e58e78bea66e5f17caccdf6f

2,951

py

Python

read_data19.py

zyxwvu321/Classifer_SSL_Longtail

e6c09414c49e695b0f4221a3c6245ae3929a1788

[ "MIT" ]

null

read_data19.py

zyxwvu321/Classifer_SSL_Longtail

e6c09414c49e695b0f4221a3c6245ae3929a1788

[ "MIT" ]

null

read_data19.py

zyxwvu321/Classifer_SSL_Longtail

e6c09414c49e695b0f4221a3c6245ae3929a1788

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Spyder Editor This is a temporary script file. """ import pandas as pd import numpy as np import shutil import os from pathlib import Path from tqdm import tqdm #import cv2 #%% src_im_fd = 'D:/dataset/ISIC/ISIC_2019_Training_Input/' tar_im_fd = '../data/train19/' df = pd.read_csv('D:/dataset/ISIC/ISIC_2019_Training_GroundTruth.csv') df_v = df.values img_name = df_v[:,0] label_np = df_v[:,1:] #labels = np.zeros_like(img_name) # #for idx,v in enumerate(label_np): # labels[idx] = np.where(label_np[1]==1)[0][0] # labels = [ np.where(v==1)[0][0] for v in label_np] dict_label = dict() for i in range(8): dict_label[i] = df.columns[1:][i] for val,key in dict_label.items(): os.makedirs(tar_im_fd +key, exist_ok =True) for idx,fn in enumerate(tqdm(img_name)): src_fn = Path(src_im_fd)/(fn + '.jpg') tar_fn = Path(tar_im_fd)/ dict_label[labels[idx]]/(fn + '.jpg') if os.path.exists(str(src_fn)): shutil.copyfile(src_fn,tar_fn) else: print(f'filename {str(src_fn)} not exist') # #%% write test # df = pd.read_csv('./data/ISIC/ISIC2018_Task3_Testing_Score_imb.csv') # tar_im_fd = './data/ISIC/test18/' # src_im_fd = '/home/minjie/dataset/ISIC/ISIC2018_Task3_Test_Input/' # for val,key in dict_label.items(): # os.makedirs(tar_im_fd +key, exist_ok =True) # df_v = df.values # img_name = df_v[:,0] # label_np = df_v[:,1:] # labels = [ np.where(v==v.max())[0][0] for v in label_np] # for idx,fn in enumerate(tqdm(img_name)): # src_fn = Path(src_im_fd)/(fn + '.jpg') # tar_fn = Path(tar_im_fd)/ dict_label[labels[idx]]/(fn + '.jpg') # if os.path.exists(str(src_fn)): # shutil.copyfile(src_fn,tar_fn) # else: # print(f'filename {str(src_fn)} not exist') # #%% read ISIC19 data # src_im_fd = '/home/minjie/dataset/ISIC/ISIC_2019_Training_Input/' # tar_im_fd = './data/ISIC/train19/' # df = pd.read_csv('./data/ISIC/ISIC_2019_Training_GroundTruth.csv') # df_v = df.values # img_name = df_v[:,0] # label_np = df_v[:,1:] # #labels = np.zeros_like(img_name) # # # #for idx,v in enumerate(label_np): # # labels[idx] = np.where(label_np[1]==1)[0][0] # # # labels = [ np.where(v==1)[0][0] for v in label_np] # dict_label = dict() # n_label = len(df.columns)-2 # for i in range(n_label): # dict_label[i] = df.columns[1:][i] # dict_label[3]= 'AKIEC' # for val,key in dict_label.items(): # os.makedirs(tar_im_fd +key, exist_ok =True) # for idx,fn in enumerate(tqdm(img_name)): # src_fn = Path(src_im_fd)/(fn + '.jpg') # tar_fn = Path(tar_im_fd)/ dict_label[labels[idx]]/(fn + '.jpg') # if os.path.exists(str(src_fn)): # #img = cv2.imread(str(src_fn)) # #img_resize = cv2.resize(img,(600,450)) # #cv2.imwrite(str(tar_fn),img_resize) # shutil.copyfile(src_fn,tar_fn) # else: # print(f'filename {str(src_fn)} not exist')

26.827273

70

0.633006

506

2,951

3.456522

0.195652

0.034305

0.036021

0.04574

0.781018

0.75586

0.731275

0.672956

0

0.029938

0.185022

2,951

110

71

26.827273

0.697297

0.653338

0

0.154412

0.095588

0

1

0

false

0

0.24

0

0.24

0.04

0

null

0

1

0

1

0

null

0

3

c32f8c2cafd56b294844266bbb327e02e8f9736a

180

py

Python

todoapp/todo/urls.py

joaofranca13/to-do-django

08fc0c87ce9c3ab4c71acfe1b4a1fcd06b54427b

[ "MIT" ]

null

todoapp/todo/urls.py

joaofranca13/to-do-django

08fc0c87ce9c3ab4c71acfe1b4a1fcd06b54427b

[ "MIT" ]

null

todoapp/todo/urls.py

joaofranca13/to-do-django

08fc0c87ce9c3ab4c71acfe1b4a1fcd06b54427b

[ "MIT" ]

null

from django.urls import path from . import views urlpatterns = [ path('', views.home, name='home'), path('updatetask/<int:pk>/', views.updatetask, name='updatetask'), ]

18

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0

1

0

null

0

1

0

3

c33811d3dab0b108196f893f658efbdca66a3329

718

py

Python

src/notifier/properties.py

fmudrunek/github-slack-pr-notifier

fe0240e2ddc0d41ab3a7db9d9680ff2a13ef551e

[ "MIT" ]

null

src/notifier/properties.py

fmudrunek/github-slack-pr-notifier

fe0240e2ddc0d41ab3a7db9d9680ff2a13ef551e

[ "MIT" ]

null

src/notifier/properties.py

fmudrunek/github-slack-pr-notifier

fe0240e2ddc0d41ab3a7db9d9680ff2a13ef551e

[ "MIT" ]

null

import os import json from typing import Dict, List def __get_env(variable): if variable not in os.environ: raise ValueError(f"Environment variable '{variable}' not found") return os.environ[variable] def get_github_token() -> str: return __get_env("GITHUB_TOKEN") def get_slack_bearer_token() -> str: return __get_env("SLACK_BEARER_TOKEN") def get_github_api_url() -> str: return __get_env("GITHUB_REST_API_URL") def read_config(config_path) -> Dict[str, List[str]]: with open(config_path) as json_data_file: config = json.load(json_data_file) return {entry["slack_channel"]: entry["repositories"] for entry in config['notifications']}

25.642857

96

0.696379

100

718

4.66

0.43

0.051502

0.077253

0.096567

0.143777

0

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718

27

97

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0

null

0

null

0

1

0

1

0

3

c3489aaefa685e0eaf805688fc6c32d79700bd81

756

py

Python

cloudrail/knowledge/context/aws/resource_based_policy.py

my-devops-info/cloudrail-knowledge

b7c1bbd6fe1faeb79c105a01c0debbe24d031a0e

[ "MIT" ]

null

cloudrail/knowledge/context/aws/resource_based_policy.py

my-devops-info/cloudrail-knowledge

b7c1bbd6fe1faeb79c105a01c0debbe24d031a0e

[ "MIT" ]

null

cloudrail/knowledge/context/aws/resource_based_policy.py

my-devops-info/cloudrail-knowledge

b7c1bbd6fe1faeb79c105a01c0debbe24d031a0e

[ "MIT" ]

null

from abc import abstractmethod from typing import Optional, List from cloudrail.knowledge.context.aws.iam.policy import Policy from cloudrail.knowledge.context.aws.aws_resource import AwsResource from cloudrail.knowledge.context.aws.service_name import AwsServiceName, AwsServiceAttributes class ResourceBasedPolicy(AwsResource): def __init__(self, account: str, region: str, tf_resource_type: AwsServiceName, aws_service_attributes: AwsServiceAttributes = None): super().__init__(account, region, tf_resource_type, aws_service_attributes) self.resource_based_policy: Optional[Policy] = None @abstractmethod def get_keys(self) -> List[str]: pass @property def is_tagable(self) -> bool: return False

36

137

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756

6.325843

0.47191

0.069272

0.117229

0.154529

0.170515

0

0.150794

756

20

138

37.8

0.876947

0

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0.2

false

0.066667

0.333333

0.066667

0.666667

0

null

0

null

0

1

0

1

0

3

5edad65a3a706deeafe6386cf26e4067580d474e

825

py

Python

pptx/opc/shared.py

handwriter/python-pptx

22351c6f9fe637cadddca3461c4899af7d439711

[ "MIT" ]

1

2020-03-20T01:47:10.000Z

pptx/opc/shared.py

handwriter/python-pptx

22351c6f9fe637cadddca3461c4899af7d439711

[ "MIT" ]

null

pptx/opc/shared.py

handwriter/python-pptx

22351c6f9fe637cadddca3461c4899af7d439711

[ "MIT" ]

null

# encoding: utf-8 """ Objects shared by modules in the pptx.opc sub-package """ from __future__ import absolute_import, print_function, unicode_literals class CaseInsensitiveDict(dict): """ Mapping type that behaves like dict except that it matches without respect to the case of the key. E.g. cid['A'] == cid['a']. Note this is not general-purpose, just complete enough to satisfy opc package needs. It assumes str keys for example. """ def __contains__(self, key): return super(CaseInsensitiveDict, self).__contains__(key.lower()) def __getitem__(self, key): return super(CaseInsensitiveDict, self).__getitem__(key.lower()) def __setitem__(self, key, value): return super(CaseInsensitiveDict, self).__setitem__(key.lower(), value)

31.730769

80

0.688485

104

825

5.163462

0.644231

0.039106

0.167598

0.189944

0.1527

0

0.001541

0.213333

825

25

81

33

0.825886

0.380606

0

1

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0

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1

0.125

0

null

0

1

0

null

0

1

0

1

0

3

5edc841872c5752fdbe75bbc4fd42360406f04b5

450

py

Python

lebanese_channels/services/nbn.py

blazeinmedia/Lebanese-Channels

f314868ac3da69ce5a27f6f953145096be1c31eb

[ "MIT" ]

1

2020-04-09T19:39:35.000Z

lebanese_channels/services/nbn.py

blazeinmedia/Lebanese-Channels

f314868ac3da69ce5a27f6f953145096be1c31eb

[ "MIT" ]

null

lebanese_channels/services/nbn.py

blazeinmedia/Lebanese-Channels

f314868ac3da69ce5a27f6f953145096be1c31eb

[ "MIT" ]

null

from lebanese_channels.channel import Channel from lebanese_channels.services.utils import stream class NBN(Channel): def get_name(self) -> str: return 'NBN' def get_logo(self) -> str: return 'https://nbntv.me/wp-content/uploads/2018/08/cropped-nbn-logo-512-192x192.jpg' def get_stream_url(self) -> str: return stream.fetch_from('http://player.l1vetv.com/nbn') def get_epg_data(self): return None

26.470588

93

0.691111

65

450

4.646154

0.584615

0.07947

0.129139

0

0.043836

0.188889

450

16

94

28.125

0.783562

0

0.090909

0.237778

0

1

0.363636

false

0

0.181818

0.363636

1

0

null

0

null

0

1

0

1

0

3

5ee55b9c983b2f94bd320f586932371216e6a6f4

591

py

Python

inclusive/inclusive.py

numpde/inclusive

63f637473272f7af66ffe8d1d3fcd4ddf1c22a72

[ "MIT" ]

null

inclusive/inclusive.py

numpde/inclusive

63f637473272f7af66ffe8d1d3fcd4ddf1c22a72

[ "MIT" ]

null

inclusive/inclusive.py

numpde/inclusive

63f637473272f7af66ffe8d1d3fcd4ddf1c22a72

[ "MIT" ]

null

import builtins from collections.abc import Iterable class Template: def __init__(self, builtin_function): self.builtin_function = builtin_function def __call__(self, *args): return self.builtin_function(*args) def __getitem__(self, args): if isinstance(args, Iterable): old = self.builtin_function(*args) new = self.builtin_function(old.start, old.stop + 1, old.step) else: old = self.builtin_function(args) new = self.builtin_function((old.start or 0) + 1, old.stop + 1, old.step) return new range = Template(builtins.range) slice = Template(builtins.slice)

24.625

76

0.737733

82

591

5.073171

0.365854

0.288462

0.319712

0.165865

0.341346

0.269231

0

0.007968

0.150592

591

23

77

25.695652

0.820717

0

1

0.176471

false

0

0.117647

0.058824

0.470588

0

null

1

0

null

0

3

5ef746ab4cc0103069cd4eb908a678e8c53ceca1

71

py

Python

pdbprocessor/appinfo.py

igik20/pdbprocessor

2d385dd895019e5703508915599d35db2df4bcb2

[ "MIT" ]

null

pdbprocessor/appinfo.py

igik20/pdbprocessor

2d385dd895019e5703508915599d35db2df4bcb2

[ "MIT" ]

null

pdbprocessor/appinfo.py

igik20/pdbprocessor

2d385dd895019e5703508915599d35db2df4bcb2

[ "MIT" ]

null

class AppInfo: VERSION = "Alpha 0.2" AUTHOR = "Igor Trujnara"

17.75

28

0.619718

9

71

4.888889

1

0

0.038462

0.267606

71

4

29

17.75

0.807692

0

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0

1

0

false

0

1

0

1

0

null

0

1

0

null

0

1

0

3

6f11356e6c06cab1bad7698ae514a58841aa063b

5,709

py

Python

HVAE/Modules.py

omiethescientist/HyperbolicDeepLearning

33554d3bf4668fdd1945df5be69ab38a1e7db686

[ "MIT" ]

1

2020-01-10T20:35:05.000Z

HVAE/Modules.py

omiethescientist/HyperbolicDeepLearning

33554d3bf4668fdd1945df5be69ab38a1e7db686

[ "MIT" ]

null

HVAE/Modules.py

omiethescientist/HyperbolicDeepLearning

33554d3bf4668fdd1945df5be69ab38a1e7db686

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F import geoopt from geoopt.manifolds import PoincareBall from RiemannLayers import GeodesicLayer, MobiusLayer # Code Inspired by Emile Mathieu from Microsoft Research and Oxford Stats # Paper: https://arxiv.org/pdf/1901.06033.pdf # Define Encoder and Decoder Modules for the VAE class WrappedEncoder(nn.Module): def __init__(self, input_dim, latent_dim, n_hlayers, hlayer_size, activation, dropout, manifold): super(WrappedEncoder, self).__init__() self.x = input_dim self.z = latent_dim self.n = n_hlayers self.n_size = hlayer_size self.activation = activation self.dropout = dropout self.manifold = manifold #Create custom encoder architechture layers = [] layers.extend([nn.Linear(self.x, self.n_size), self.activation, nn.Dropout(p=self.dropout)]) for i in range(self.n - 1): layers.extend([nn.Linear(self.n_size, self.n_size), self.activation, nn.Dropout(p=self.dropout)]) self.enc = nn.Sequential(*layers) self.learned_param = nn.Linear(self.n_size, self.z) def forward(self, inputs): e = self.enc(inputs) param = self.learned_param(e) mu = self.manifold.expmap0(param) log_Sigma = F.softplus(mu) return mu, log_Sigma class WrappedDecoder(nn.Module): def __init__(self, latent_dim, output_dim, n_hlayers, hlayer_size, activation, dropout, manifold): super(WrappedDecoder, self).__init__() self.z = latent_dim self.x = output_dim self.n = n_hlayers self.n_size = hlayer_size self.activation = activation self.dropout = dropout self.manifold = manifold #Create Custom Encoder Architechture layers = [] layers.extend([nn.Linear(self.z, self.n_size), self.activation, nn.Dropout(p=self.dropout)]) for i in range(self.n - 1): layers.extend([nn.Linear(self.n_size, self.n_size), self.activation, nn.Dropout(p=self.dropout)]) self.dec = nn.Sequential(*layers) self.output_layer = nn.Linear(self.n_size, self.x) def forward(self, embeddings): emb = self.manifold.logmap0(embeddings) emb = self.dec(emb) recon = self.output_layer(emb) return recon class MobiusEncoder(nn.Module): def __init__(self, input_dim, latent_dim, n_hlayers, hlayer_size, activation, dropout, manifold): super(MobiusEncoder, self).__init__() self.x = input_dim self.z = latent_dim self.n = n_hlayers self.n_size = hlayer_size self.activation = activation self.dropout = dropout self.manifold = manifold layers = [] layers.extend([nn.Linear(self.x, self.n_size), self.activation, nn.Dropout(p=self.dropout)]) for i in range(self.n - 1): layers.extend([nn.Linear(self.n_size, self.n_size), self.activation, nn.Dropout(p=self.dropout)]) self.enc = nn.Sequential(*layers) self.sigma_out = nn.Linear(self.n_size, self.z) self.output_layer = MobiusLayer(self.n_size, self.z, self.manifold) def forward(self, inputs): e = self.enc(inputs) mu = self.output_layer(e) mu = self.manifold.expmap0(mu) log_Sigma = F.softplus(self.sigma_out(e)) return mu, log_Sigma class GeodesicDecoder(nn.Module): def __init__(self, latent_dim, output_dim, n_hlayers, hlayer_size, activation, dropout, manifold): super(GeodesicDecoder, self).__init__() self.z = latent_dim self.x = output_dim self.n = n_hlayers self.n_size = hlayer_size self.activation = activation self.dropout = dropout self.manifold = manifold input_layer = GeodesicLayer(self.z, self.n_size, self.manifold) layers = [input_layer] layers.extend([self.activation, nn.Dropout(p=self.dropout)]) for i in range(self.n - 1): layers.extend([nn.Linear(self.n_size, self.n_size), self.activation, nn.Dropout(p=self.dropout)]) self.dec = nn.Sequential(*layers) self.output_layer = nn.Linear(self.n_size, self.x) def forward(self, embeddings): decode = self.dec(embeddings) recon = self.output_layer(decode) return recon class MobiusDecoder(nn.Module): def __init__(self, latent_dim, output_dim, n_hlayers, hlayer_size, activation, dropout, manifold): super(MobiusDecoder, self).__init__() self.z = latent_dim self.x = output_dim self.n = n_hlayers self.n_size = hlayer_size self.activation = activation self.dropout = dropout self.manifold = manifold layers = [] layers.extend([MobiusLayer(self.z, self.n_size, self.manifold), self.activation, nn.Dropout(p=self.dropout)]) for i in range(self.n - 1): layers.extend([nn.Linear(self.n_size, self.n_size), self.activation, nn.Dropout(p=self.dropout)]) self.dec = nn.Sequential(*layers) self.output_layer = nn.Linear(self.n_size, self.x) def forward(self, embeddings): emb = self.dec(embeddings) recon = self.output_layer(emb) return recon #Debugging Code #if __name__ == '__main__': # inputs = torch.randn(64, 6000).double() # x = inputs.shape[1] # z = 2 # n = 2 # n_size = 256 # activ = nn.LeakyReLU() # drop_rate = 0.2 # manifold = PoincareBall(c=1) # Encoders = [WrappedEncoder(x, z, n, n_size, activ, drop_rate, manifold), # MobiusEncoder(x, z, n, n_size, activ, drop_rate, manifold)] # for e in Encoders: # e = e.double() # print(e(inputs)) # embeddings = torch.randn(64, 2).double() # Decoders = [GeodesicDecoder(z, x, n, n_size, activ, drop_rate, manifold), # MobiusDecoder(z, x, n, n_size, activ, drop_rate, manifold)] # for d in Decoders: # d = d.double() # print(d(embeddings))

33.19186

111

0.678753

805

5,709

4.637267

0.141615

0.048219

0.062684

0.073132

0.733994

0.710956

0.704795

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0.627645

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5,709

171

112

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0

null

0

1

0

1

0

null

0

3

6f29e4984cd54995362e3f86984d2e51409eef2b

189

py

Python

etc/gunicorn.conf.py

NestorMonroy/BlogTemplate

82dfc7eb26e8a8ff0d51f29176c3b4d537092be7

[ "MIT" ]

null

etc/gunicorn.conf.py

NestorMonroy/BlogTemplate

82dfc7eb26e8a8ff0d51f29176c3b4d537092be7

[ "MIT" ]

8

2020-07-22T02:06:35.000Z

2021-09-22T19:22:27.000Z

etc/gunicorn.conf.py

NestorMonroy/BlogTemplate

82dfc7eb26e8a8ff0d51f29176c3b4d537092be7

[ "MIT" ]

null

workers = 2 bind = '127.0.0.1:8000' workers = 1 timeout = 60 errorlog = '/usr/local/apps/blog-nestor/nblog.gunicorng.error' accesslog = '/usr/local/apps/blog-nestor/nblog.gunicorng.access'

27

64

0.73545

30

189

4.633333

0.666667

0.115108

0.172662

0.230216

0.517986

0

0.081871

0.095238

189

6

65

31.5

0.730994

0

0.597884

0.52381

0

1

0

false

0

null

0

1

0

1

0

1

null

0

3

6f305c939d18f7600fecc97812393e8050ecf030

2,616

py

Python

catalog/model/plain_models.py

eoss-cloud/madxxx_catalog_api

ef37374a36129de4f0a6fe5dd46b5bc2e2f01d1d

[ "MIT" ]

null

catalog/model/plain_models.py

eoss-cloud/madxxx_catalog_api

ef37374a36129de4f0a6fe5dd46b5bc2e2f01d1d

[ "MIT" ]

null

catalog/model/plain_models.py

eoss-cloud/madxxx_catalog_api

ef37374a36129de4f0a6fe5dd46b5bc2e2f01d1d

[ "MIT" ]

null

#-*- coding: utf-8 -*- """ EOSS catalog system catalog objects and fixed data structures used for the serialization/deserialization process """ __author__ = "Thilo Wehrmann, Steffen Gebhardt" __copyright__ = "Copyright 2016, EOSS GmbH" __credits__ = ["Thilo Wehrmann", "Steffen Gebhardt"] __license__ = "GPL" __version__ = "1.0.0" __maintainer__ = "Thilo Wehrmann" __email__ = "twehrmann@eoss.cloud" __status__ = "Production" from datetime import datetime class ResourcesURLS(object): def __init__(self): self.metadata_url = None self.resource_url = None self.quicklook_url = None class Catalog_Dataset(object): def __init__(self): self.entity_id = None self.acq_time = None self.sensor = None self.tile_identifier = None self.clouds = None self.level = None self.daynight = None self.time_registered = datetime.utcnow() def __hash__(self): return hash(self.entity_id) ^ hash(self.tile_identifier) ^ hash(self.acq_time) class S3PrivateContainer(object): def __init__(self): self.region = None self.bucket = None self.filename = None def to_dict(self): return dict(s3privat=self.__dict__) class S3PublicContainer(object): def __init__(self): self.http = None self.bucket = None self.prefix = None def to_dict(self): return dict(s3public=self.__dict__) class SentinelS3Container(object): def __init__(self): self.zip = None self.bucket = None self.tile = None self.product = None self.quicklook = None def to_dict(self): return dict(s3public=self.__dict__) class CopernicusSciHubContainer(object): def __init__(self): self.http = None def to_dict(self): return dict(scihub=self.__dict__) class USGSOrderContainer(object): def __init__(self): self.link = None def to_dict(self): return dict(usgs=self.__dict__) class GoogleLandsatContainer(object): supported_sensors = {'OLI_TIRS': 'L8', 'LANDSAT_ETM_SLC_OFF': 'L7', 'LANDSAT_ETM': 'L7', 'LANDSAT_TM': 'L5', 'TIRS': 'L8', 'OLI': 'L8'} base = 'http://storage.googleapis.com/earthengine-public/landsat/%s/%03d/%03d/%s.tar.bz' def __init__(self): self.link = None def to_dict(self): return dict(google=self.__dict__) class PlanetContainer(object): def __init__(self): self.analytic = None self.visual = None def to_dict(self): return dict(planet=self.__dict__)

24.222222

92

0.647171

309

2,616

5.074434

0.365696

0.091837

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0.086097

0.316327

0.205995

0.119898

0

0.01214

0.244266

2,616

107

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24.448598

0.780981

0.051223

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0.342466

0

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false

0

0.013699

0.109589

0.506849

0

null

0

null

0

1

0

1

0

3

6f47fee45670779fff046e3ea197a15ae1be3db4

38

py

Python

tests/src/industry/framework/__init__.py

rpeach-sag/apama-industry-analytics-kit

a3f6039915501d41251b6f7ec41b0cb8111baf7b

[ "Apache-2.0" ]

3

2019-09-02T18:21:22.000Z

2020-04-17T16:34:57.000Z

tests/src/industry/framework/__init__.py

rpeach-sag/apama-industry-analytics-kit

a3f6039915501d41251b6f7ec41b0cb8111baf7b

[ "Apache-2.0" ]

null

tests/src/industry/framework/__init__.py

rpeach-sag/apama-industry-analytics-kit

a3f6039915501d41251b6f7ec41b0cb8111baf7b

[ "Apache-2.0" ]

null

__all__ = [ "BaseTest", "Correlator" ]

38

0.657895

3

38

7

1

0

0.131579

38

1

38

0.636364

0

0.461538

0

1

0

false

0

1

0

null

0

1

0

null

0

3

6f54330a15167a0071b3b689ba003037c296ab29

98

py

Python

output/models/ms_data/wildcards/wild_o012_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

1

2021-08-14T17:59:21.000Z

output/models/ms_data/wildcards/wild_o012_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

4

2020-02-12T21:30:44.000Z

2020-04-15T20:06:46.000Z

output/models/ms_data/wildcards/wild_o012_xsd/__init__.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

null

from output.models.ms_data.wildcards.wild_o012_xsd.wild_o012 import Foo __all__ = [ "Foo", ]

16.333333

71

0.744898

15

98

4.333333

0.8

0.246154

0

0.071429

0.142857

98

5

72

19.6

0.702381

0

0.030612

0

1

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false

0

0.25

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0.25

0

1

0

null

1

0

1

0

null

0

3

6f56837109e174665ba1d7bb7e0ec728829ee7c2

116

py

Python

seismiqb/__init__.py

gazprom-neft/seismiqb

d4906d41c79407c99cfa6f91d6005c0e453d1138

[ "Apache-2.0" ]

73

2019-10-08T08:50:12.000Z

2022-03-23T20:18:02.000Z

seismiqb/__init__.py

gazprom-neft/seismiqb

d4906d41c79407c99cfa6f91d6005c0e453d1138

[ "Apache-2.0" ]

69

2019-09-06T14:00:57.000Z

2022-03-30T13:02:54.000Z

seismiqb/__init__.py

gazprom-neft/seismiqb

d4906d41c79407c99cfa6f91d6005c0e453d1138

[ "Apache-2.0" ]

28

2019-11-04T18:40:07.000Z

2022-03-23T16:18:54.000Z

"""Init file""" from . import batchflow from .src import * # pylint: disable=wildcard-import __version__ = '0.1.0'

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py

Python

tests/beacon/types/test_fork_data.py

shreyasnbhat/py-evm

cd31d83185e102a7cb2f11e2f67923b069ee9cef

[ "MIT" ]

1

2018-12-09T11:56:53.000Z

tests/beacon/types/test_fork_data.py

shreyasnbhat/py-evm

cd31d83185e102a7cb2f11e2f67923b069ee9cef

[ "MIT" ]

null

tests/beacon/types/test_fork_data.py

shreyasnbhat/py-evm

cd31d83185e102a7cb2f11e2f67923b069ee9cef

[ "MIT" ]

2

2018-12-09T15:58:11.000Z

2020-09-29T07:10:21.000Z

from eth.beacon.types.fork_data import ( ForkData, ) def test_defaults(sample_fork_data_params): fork_data = ForkData(**sample_fork_data_params) assert fork_data.pre_fork_version == sample_fork_data_params['pre_fork_version'] assert fork_data.post_fork_version == sample_fork_data_params['post_fork_version'] assert fork_data.fork_slot == sample_fork_data_params['fork_slot']

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6f7942f138265d9b78e71c0374638965bbb6e088

184

py

Python

stats/queries.py

TravelChain/golos-ql

a2acad0b56d349f3811b2bd0fc8ec1ce3257156c

[ "MIT" ]

5

2018-08-28T20:54:54.000Z

2022-02-09T21:21:53.000Z

stats/queries.py

TravelChain/golos-ql

a2acad0b56d349f3811b2bd0fc8ec1ce3257156c

[ "MIT" ]

null

stats/queries.py

TravelChain/golos-ql

a2acad0b56d349f3811b2bd0fc8ec1ce3257156c

[ "MIT" ]

2

2018-09-26T06:28:34.000Z

2018-11-20T20:14:00.000Z

import graphene from stats.types import Stats class StatsQuery(graphene.ObjectType): stats = graphene.Field(Stats) def resolve_stats(self, context): return Stats()

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4895029808bc2d4e25becca7179cc71558bcb3c0

433

py

Python

AC_TD3_code/utils/commons.py

Jiang-HB/AC_CDQ

4b4ec2d611c4481ad0b99cf7ea79eb23014a0325

[ "MIT" ]

7

2021-05-03T05:50:14.000Z

2022-03-24T15:35:59.000Z

AC_TD3_code/utils/commons.py

Jiang-HB/AC_CDQ

4b4ec2d611c4481ad0b99cf7ea79eb23014a0325

[ "MIT" ]

null

AC_TD3_code/utils/commons.py

Jiang-HB/AC_CDQ

4b4ec2d611c4481ad0b99cf7ea79eb23014a0325

[ "MIT" ]

1

2022-03-25T02:24:53.000Z

import pickle def load_data(path): file = open(path, "rb") data = pickle.load(file) file.close() return data def save_data(path, data): file = open(path, "wb") pickle.dump(data, file) file.close() def chunker_list(seq, size): return [seq[pos: pos + size] for pos in range(0, len(seq), size)] def chunker_num(num, size): return [list(range(num))[pos: pos + size] for pos in range(0, num, size)]

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48a22edbd76757826317bab349c3dda7fe2d8fb3

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py

Python

web/transiq/restapi/tests/employee_roles_functionality_mapping_tests.py

manibhushan05/transiq

763fafb271ce07d13ac8ce575f2fee653cf39343

[ "Apache-2.0" ]

null

web/transiq/restapi/tests/employee_roles_functionality_mapping_tests.py

manibhushan05/transiq

763fafb271ce07d13ac8ce575f2fee653cf39343

[ "Apache-2.0" ]

14

2020-06-05T23:06:45.000Z

2022-03-12T00:00:18.000Z

web/transiq/restapi/tests/employee_roles_functionality_mapping_tests.py

manibhushan05/transiq

763fafb271ce07d13ac8ce575f2fee653cf39343

[ "Apache-2.0" ]

null

import json from django.contrib.auth.models import User from django.urls import reverse from model_mommy import mommy from rest_framework import status from rest_framework.test import APITestCase from authentication.models import Profile from employee.models import Employee from restapi.models import TaskDashboardFunctionalities, EmployeeRoles, EmployeeRolesFunctionalityMapping from utils.models import AahoOffice class ErfmTests(APITestCase): def setUp(self): self.login_url = reverse('login') self.logout_url = reverse('logout') self.erfmlist_url = reverse('employee_roles_functionalities_mapping_list/') self.erfmcreate_url = reverse('employee_roles_functionalities_mapping_create/') self.user = User.objects.create_user(username='john_doe', email='harshadasawant89@gmail.com', password='abc12345') Profile.objects.create( user=self.user, name='John_Doe', phone='9619125174', ) self.login_data = self.client.post(self.login_url, {'username': 'john_doe', 'password': 'abc12345'}).content self.login_data = json.loads(self.login_data.decode('utf8')) self.token = 'Token {}'.format(self.login_data['token']) self.client.credentials(HTTP_AUTHORIZATION=self.token) self.aaho_office = mommy.make(AahoOffice) self.employee = mommy.make(Employee, office=self.aaho_office) self.employee_id = self.employee.id self.employee_roles = mommy.make(EmployeeRoles) self.employeeroles_id = self.employee_roles.id self.taskdf = mommy.make(TaskDashboardFunctionalities) self.functionality = self.taskdf.functionality self.tdid = self.taskdf.id self.erfm = mommy.make(EmployeeRolesFunctionalityMapping, td_functionality=self.taskdf, employee_role=self.employee_roles, caption="Bharat") self.erfm_id = self.erfm.id self.caption = self.erfm.caption class ErfmCreateTests(ErfmTests): """ Test ID:TS02RQ00006 Created By:Hari Created On:11/12/2018 Scenario:req-quotes-create/ Status:failure Message:wrong content type Status code:415 """ def test_erfm_create_415_header_with_wrong_content_type(self): # Negative test case of req quotes create with HTTP Header Authorization token with wrong content type self.client.credentials(HTTP_AUTHORIZATION=self.token) response = self.client.post(self.erfmcreate_url, json.dumps({"td_functionality_id": self.tdid, "employee_role_id": self.employeeroles_id, "caption": self.caption }), content_type='application/pdf') self.assertEqual(response.status_code, status.HTTP_415_UNSUPPORTED_MEDIA_TYPE) """ Test ID:TS02RQ00007 Created By:Hari Created On:11/12/2018 Scenario:req-quotes-create/ Status:failure Message:invalid method header Status code:401 """ def test_erfm_create_401_no_header(self): # Negative test case of req quotes create with no HTTP Header Authorization token self.client.credentials() response = self.client.post(self.erfmcreate_url, {"td_functionality_id": self.tdid, "employee_role_id": self.employeeroles_id, "caption": self.caption }) self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) self.assertEqual(response.data['detail'], "Authentication credentials were not provided.") """ Test ID:TS02RQ00008 Created By:Hari Created On:11/12/2018 Scenario:req-quotes-create/ Status:failure Message:expired header Status code:401 """ def test_erfm_create_401_expired_header(self): # Negative test case of req quotes create with expired/logged out HTTP Header Authorization token self.client.credentials(HTTP_AUTHORIZATION=self.token) response = self.client.delete(self.logout_url) self.assertEqual(response.status_code, status.HTTP_200_OK) response = self.client.post(self.erfmcreate_url, json.dumps({"td_functionality_id": self.tdid, "employee_role_id": self.employeeroles_id, "caption": self.caption }), content_type='application/json') self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) self.assertEqual(response.data['detail'], "Invalid token.") """ Test ID:TS02RQ00008 Created By:Hari Created On:11/12/2018 Scenario:req-quotes-create/ Status:failure Message:wrong token Status code:401 """ def test_erfm_create_401_wrong_token(self): # Negative test case of req quotes create with wrong HTTP Header Authorization token token = 'Token 806fa0efd3ce26fe080f65da4ad5a137e1d056ff' self.client.credentials(HTTP_AUTHORIZATION=token) response = self.client.post(self.erfmcreate_url, json.dumps({"td_functionality_id": self.tdid, "employee_role_id": self.employeeroles_id, "caption": self.caption }), content_type='application/json') self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED) self.assertEqual(response.data['detail'], "Invalid token.") """ Test ID:TS02RQ00009 Created By:Hari Created On:11/12/2018 Scenario:req-quotes-create/ Status:failure Message:wrong vehicle number Status code:400 """ def test_erfm_create_400_emptybody(self): # Negative test case of req quotes create with HTTP Header Authorization token and wrong vehicle_no self.client.credentials(HTTP_AUTHORIZATION=self.token) response = self.client.post(self.erfmcreate_url, json.dumps({}), content_type='application/json') self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.data['td_functionality_id'][0], "This field is required.") self.assertEqual(response.data['employee_role_id'][0], "This field is required.") self.assertEqual(response.data['caption'][0], "This field is required.") """ Test ID:TS02RQ00010 Created By:Hari Created On:11/12/2018 Scenario:req-quotes-create/ Status:failure Message:wrong requirement Status code:400 """ def test_erfm_create_400_fields_empty(self): # Negative test case of req quotes create with HTTP Header Authorization token and wrong requirement_id self.client.credentials(HTTP_AUTHORIZATION=self.token) response = self.client.post(self.erfmcreate_url, json.dumps({"td_functionality_id": "", "employee_role_id": "", "caption": "" }), content_type='application/json') self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.data['td_functionality_id'][0], "A valid integer is required.") self.assertEqual(response.data['employee_role_id'][0], "A valid integer is required.") self.assertEqual(response.data['caption'][0], "This field may not be blank.") """ Test ID:TS02RQ00011 Created By:Hari Created On:11/12/2018 Scenario:req-quotes-create/ Status:failure Message:wrong supplier Status code:400 """ def test_erfm_create_400_corrupt_fields(self): # Negative test case of req quotes create with HTTP Header Authorization token and wrong broker_id self.client.credentials(HTTP_AUTHORIZATION=self.token) response = self.client.post(self.erfmcreate_url, json.dumps({"td_functionality_id": "jhg", "employee_role_id": "dsfy", "caption": "jhgfq" }), content_type='application/json') self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) self.assertEqual(response.data['td_functionality_id'][0], "A valid integer is required.") self.assertEqual(response.data['employee_role_id'][0], "A valid integer is required.") def test_erfm_create_400_non_existent_tdid(self): # Negative test case of req quotes create with HTTP Header Authorization token and wrong broker_id self.client.credentials(HTTP_AUTHORIZATION=self.token) response = self.client.post(self.erfmcreate_url, json.dumps({"td_functionality_id": 324223, "employee_role_id": self.employee_roles, "caption": self.caption }), content_type='application/json') self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_erfm_create_400_non_existent_employeeid(self): # Negative test case of req quotes create with HTTP Header Authorization token and wrong broker_id self.client.credentials(HTTP_AUTHORIZATION=self.token) response = self.client.post(self.erfmcreate_url, json.dumps({"td_functionality_id": self.tdid, "employee_role_id": 7643645, "caption": "jhgfq" }), content_type='application/json') self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) """ Test ID:TS02RQ00012 Created By:Hari Created On:11/12/2018 Scenario:req-quotes-create/ Status:failure Message:rate not integer Status code:400 """ def test_erfm_create_uniquefield(self): # Negative test case of tdf create with HTTP Header Authorization token and functionality not unique self.client.credentials(HTTP_AUTHORIZATION=self.token) response = self.client.post(self.erfmcreate_url, json.dumps({"td_functionality_id": self.tdid, "employee_role_id": self.employeeroles_id, "caption": "Inward Entry" }), content_type='application/json') self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) """ Test ID:TS02RQ00014 Created By:Hari Created On:11/12/2018 Scenario:req-quotes-create/ Status:success Message:requirement quote created Status code:201 """ def test_erfm_create_201(self): # Positive test case of req quotes create with HTTP Header Authorization token self.taskdf = mommy.make(TaskDashboardFunctionalities) self.functionality = self.taskdf.functionality self.tdid = self.taskdf.id self.erfm = mommy.make(EmployeeRolesFunctionalityMapping, td_functionality=self.taskdf, employee_role=self.employee_roles, caption="Bharat") self.erfm_id = self.erfm.id self.caption = self.erfm.caption self.client.credentials(HTTP_AUTHORIZATION=self.token) response = self.client.post(self.erfmcreate_url, json.dumps({"td_functionality_id": self.tdid, "employee_role_id": self.employeeroles_id, "caption": self.caption }), content_type='application/json') self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(response.data['status'], "success") self.assertEqual(response.data['msg'], "Employee Roles Functionalities Mapping Created")

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48a98d83a1203cf9dcf76c6ba1e1621f7c41c711

206

py

Python

Project_Codev0.1/Class-diagram_Classes/Wallpaper.py

cyberseihis/Wallsource

4bd981e75c3ebf97c9673ffb80147ef2bdf7d61a

[ "MIT" ]

null

Project_Codev0.1/Class-diagram_Classes/Wallpaper.py

cyberseihis/Wallsource

4bd981e75c3ebf97c9673ffb80147ef2bdf7d61a

[ "MIT" ]

null

Project_Codev0.1/Class-diagram_Classes/Wallpaper.py

cyberseihis/Wallsource

4bd981e75c3ebf97c9673ffb80147ef2bdf7d61a

[ "MIT" ]

null

Class Wallpaper: def setWallpaperName(self, WallpaperName: str): self.WallpaperName = WallpaperName def remove(self): del self def pick(self) return WallpaperName

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48dc87bfcd88a717eec42697f4eb5eaca3859450

81

py

Python

monosi/scheduler/__init__.py

LaudateCorpus1/monosi

67c24c7cf9d645b2c3d80a83efbd3837e14b8c7f

[ "Apache-2.0" ]

1

2022-02-20T21:42:16.000Z

monosi/scheduler/__init__.py

LaudateCorpus1/monosi

67c24c7cf9d645b2c3d80a83efbd3837e14b8c7f

[ "Apache-2.0" ]

null

monosi/scheduler/__init__.py

LaudateCorpus1/monosi

67c24c7cf9d645b2c3d80a83efbd3837e14b8c7f

[ "Apache-2.0" ]

null

from monosi.scheduler.base import MonosiScheduler scheduler = MonosiScheduler()

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48de51ff8e5859d98ea0e54420963113fb408370

883

py

Python

third-party/corenlp/third-party/stanza/test/slow_tests/text/test_senna.py

arunchaganty/odd-nails

d3667ea666c02b7a71af1c26c4b22b9f4ab4c7c0

[ "Apache-2.0" ]

5

2020-03-19T07:19:49.000Z

2021-09-29T06:33:47.000Z

third-party/stanza/test/slow_tests/text/test_senna.py

arunchaganty/django-corenlp

4cda142d375bdac84057cedc3d08b525b1e2d498

[ "Apache-2.0" ]

3

2015-12-03T00:30:26.000Z

2016-01-05T22:07:20.000Z

third-party/corenlp/third-party/stanza/test/slow_tests/text/test_senna.py

arunchaganty/hypatia

d3667ea666c02b7a71af1c26c4b22b9f4ab4c7c0

[ "Apache-2.0" ]

3

2020-03-19T07:19:50.000Z

2021-03-30T13:42:27.000Z

__author__ = 'victor' import numpy as np from unittest import TestCase from stanza.text.vocab import SennaVocab class TestSenna(TestCase): def test_get_embeddings(self): v = SennaVocab() v.add("!") E = v.get_embeddings() e_exclamation = np.array([float(e) for e in """ -1.03682 1.77856 -0.693547 1.5948 1.5799 0.859243 1.15221 -0.976317 0.745304 -0.494589 0.308086 0.25239 -0.1976 1.26203 0.813864 -0.940734 -0.215163 0.11645 0.525697 1.95766 0.394232 1.27717 0.710788 -0.389351 0.161775 -0.106038 1.14148 0.607948 0.189781 -1.06022 0.280702 0.0251156 -0.198067 2.33027 0.408584 0.350751 -0.351293 1.77318 -0.723457 -0.13806 -1.47247 0.541779 -2.57005 -0.227714 -0.817816 -0.552209 0.360149 -0.10278 -0.36428 -0.64853 """.split()]) self.assertTrue(np.allclose(e_exclamation, E[v["!"]]))

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48ff1c1bb077a0cec584e9e40158a71e6705fed2

383

py

Python

p4gen/__init__.py

siddhantbajaj1/Whispersnapper_p4benchmark_with_p4-version16_Compatibility

093dea92c6419ddaf9126903b5275646f89fda37

[ "Apache-2.0" ]

15

2017-03-13T03:09:49.000Z

2021-11-12T15:31:29.000Z

p4gen/__init__.py

AbhinavJindl/Whippersnapper_P4_benchmark

9b06aeeebaf763a45c643b5a0901a36b94343759

[ "Apache-2.0" ]

1

2017-05-06T09:55:57.000Z

2017-05-06T11:59:50.000Z

p4gen/__init__.py

AbhinavJindl/Whippersnapper_P4_benchmark

9b06aeeebaf763a45c643b5a0901a36b94343759

[ "Apache-2.0" ]

13

2016-12-07T01:56:30.000Z

2021-06-04T08:08:32.000Z

""" A python module that generates P4 benchmarking programs .. moduleauthor:: Tu Dang <huynh.tu.dang@usi.sh> """ from subprocess import call from pkg_resources import resource_filename def copy_scripts(output_dir): call(['cp', resource_filename(__name__, 'template/run_switch.sh'), output_dir]) call(['cp', resource_filename(__name__, 'template/run_test.py'), output_dir])

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null

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null

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48ffc4550f0db067a00eca3191231180875b9c83

87

py

Python

emplog/theapi/apps.py

tsitsiflora/newapi

2f1c85b6b529c246fa1c890303f40b7308177d73

[ "Apache-2.0" ]

null

emplog/theapi/apps.py

tsitsiflora/newapi

2f1c85b6b529c246fa1c890303f40b7308177d73

[ "Apache-2.0" ]

null

emplog/theapi/apps.py

tsitsiflora/newapi

2f1c85b6b529c246fa1c890303f40b7308177d73

[ "Apache-2.0" ]

null

from django.apps import AppConfig class TheapiConfig(AppConfig): name = 'theapi'

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5b048e0da32688964dcb9b81de946d6a5a2ccf61

893

py

Python

src/azure-firewall/azext_firewall/_client_factory.py

blackchoey/azure-cli-extensions

bbfd80ba164c4605dbdbe5e2b8dc26c3aa0f29e4

[ "MIT" ]

1

2021-09-16T09:13:38.000Z

src/azure-firewall/azext_firewall/_client_factory.py

blackchoey/azure-cli-extensions

bbfd80ba164c4605dbdbe5e2b8dc26c3aa0f29e4

[ "MIT" ]

null

src/azure-firewall/azext_firewall/_client_factory.py

blackchoey/azure-cli-extensions

bbfd80ba164c4605dbdbe5e2b8dc26c3aa0f29e4

[ "MIT" ]

null

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- def network_client_factory(cli_ctx, aux_subscriptions=None, **_): from azure.cli.core.commands.client_factory import get_mgmt_service_client from .profiles import CUSTOM_FIREWALL return get_mgmt_service_client(cli_ctx, CUSTOM_FIREWALL, aux_subscriptions=aux_subscriptions, api_version='2019-04-01') def cf_firewalls(cli_ctx, _): return network_client_factory(cli_ctx).azure_firewalls def cf_firewall_fqdn_tags(cli_ctx, _): return network_client_factory(cli_ctx).azure_firewall_fqdn_tags

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5b0a59a3025b923d22c3bbeda256f8b2d00022d3

321

py

Python

python/tests/test_ie.py

coleve27/simple_sauce

aae43a5402e111a6ca94520110760f3ae4b7b8d6

[ "MIT" ]

null

python/tests/test_ie.py

coleve27/simple_sauce

aae43a5402e111a6ca94520110760f3ae4b7b8d6

[ "MIT" ]

null

python/tests/test_ie.py

coleve27/simple_sauce

aae43a5402e111a6ca94520110760f3ae4b7b8d6

[ "MIT" ]

null

from simplesauce.options import SauceOptions class TestInternetExplorer(object): def test_defaults(self): sauce = SauceOptions('internet explorer') assert sauce.browser_name == 'internet explorer' assert sauce.browser_version == 'latest' assert sauce.platform_name == 'Windows 10'

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