xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

1f24058abc9302e5904e57a55566ba52d6e04283

44

py

Python

src/apps/accounts/models/__init__.py

dieisabel/proggy

9e1428e5d1d5ba0217e34f86800a7d783a3673cd

[ "MIT" ]

1

2021-03-13T20:59:25.000Z

src/apps/accounts/models/__init__.py

dieisabel/proggy

9e1428e5d1d5ba0217e34f86800a7d783a3673cd

[ "MIT" ]

69

2021-03-09T11:17:26.000Z

2021-07-22T15:05:34.000Z

src/apps/accounts/models/__init__.py

dieisabel/proggy

9e1428e5d1d5ba0217e34f86800a7d783a3673cd

[ "MIT" ]

null

from accounts.models.profile import Profile

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1f46792984eca51b2cb50ba40fa64122830351a3

157

py

Python

asyncio_redis/__init__.py

vtheno/asyncio-redis

a57a528d1bdf14be12953f8bf96df2f3ed24b840

[ "BSD-2-Clause-FreeBSD" ]

4

2015-06-10T13:11:46.000Z

2016-03-15T16:56:34.000Z

asyncio_redis/__init__.py

vtheno/asyncio-redis

a57a528d1bdf14be12953f8bf96df2f3ed24b840

[ "BSD-2-Clause-FreeBSD" ]

1

2015-06-10T12:50:44.000Z

2015-06-10T20:16:27.000Z

asyncio_redis/__init__.py

vtheno/asyncio-redis

a57a528d1bdf14be12953f8bf96df2f3ed24b840

[ "BSD-2-Clause-FreeBSD" ]

2

2017-06-12T09:13:26.000Z

2018-03-05T01:07:55.000Z

""" Redis protocol implementation for asyncio (PEP 3156) """ from .connection import * from .exceptions import * from .pool import * from .protocol import *

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6

1f8de059d58fc58c493088494737c40769f26cfb

125

py

Python

app/handlers/__init__.py

s-klimov/meal_bo

f74898c179a8551c8ec8df147aabc659496c610e

[ "MIT" ]

1

2022-02-20T06:16:01.000Z

app/handlers/__init__.py

s-klimov/meal_bot

f74898c179a8551c8ec8df147aabc659496c610e

[ "MIT" ]

null

app/handlers/__init__.py

s-klimov/meal_bot

f74898c179a8551c8ec8df147aabc659496c610e

[ "MIT" ]

null

from loguru import logger from .errors import * from .private import * logger.info("Handlers are successfully configured")

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6

2f875b35c1388088cae1701c13a92db35df6cb5a

47

py

Python

src/package/__init__.py

sudosubin/bins

821385f005180c9bbff803f819a498e59fbe27c8

[ "MIT" ]

null

src/package/__init__.py

sudosubin/bins

821385f005180c9bbff803f819a498e59fbe27c8

[ "MIT" ]

null

src/package/__init__.py

sudosubin/bins

821385f005180c9bbff803f819a498e59fbe27c8

[ "MIT" ]

null

from package.base import Package # noqa: F401

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1

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6

85ecb9e8ebb52305e5263d1ceec8f461373c9c81

121

py

Python

ProjectEuler/problem_13.py

aaditkamat/competitive-programming

d0b8f30d3cb3411d2467b98363c12d75d852e245

[ "MIT" ]

null

ProjectEuler/problem_13.py

aaditkamat/competitive-programming

d0b8f30d3cb3411d2467b98363c12d75d852e245

[ "MIT" ]

3

2019-02-24T11:42:28.000Z

2019-06-03T14:15:46.000Z

ProjectEuler/problem_13.py

aaditkamat/online-judge-submissions

d0b8f30d3cb3411d2467b98363c12d75d852e245

[ "MIT" ]

null

import fileinput def solution(): return str(sum([int(num) for num in fileinput.input()]))[0: 10] print(solution())

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0.677686

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121

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1

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1

0

6

c837176d46f85b35920c8391d0d95326e1850d94

62

py

Python

lang/Python/factorial-8.py

ethansaxenian/RosettaDecode

8ea1a42a5f792280b50193ad47545d14ee371fb7

[ "MIT" ]

1

2021-05-05T13:42:20.000Z

lang/Python/factorial-8.py

ethansaxenian/RosettaDecode

8ea1a42a5f792280b50193ad47545d14ee371fb7

[ "MIT" ]

null

lang/Python/factorial-8.py

ethansaxenian/RosettaDecode

8ea1a42a5f792280b50193ad47545d14ee371fb7

[ "MIT" ]

null

def factorial(n): return n * factorial(n - 1) if n else 1

20.666667

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0.629032

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0

1

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null

0

1

0

1

0

6

c8506ceab30f9a6cf194cd2ffc0faf21bfd8d502

23

py

Python

ieeemac/__init__.py

Goggin/ieeemac

135f3905af850a9e76be5f5eb6404a975c2ffdeb

[ "MIT" ]

null

ieeemac/__init__.py

Goggin/ieeemac

135f3905af850a9e76be5f5eb6404a975c2ffdeb

[ "MIT" ]

null

ieeemac/__init__.py

Goggin/ieeemac

135f3905af850a9e76be5f5eb6404a975c2ffdeb

[ "MIT" ]

null

from .ieeemac import *

11.5

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5.666667

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null

0

1

0

1

0

1

0

6

c85c8d28d69de3e2b988c2c43803a670d3d657b3

170

py

Python

python/problem0001.py

kosmos-zhang/projecteulernet

616279ba7ced61b882383a8d33ce3e7ccddc98e1

[ "Apache-2.0" ]

null

python/problem0001.py

kosmos-zhang/projecteulernet

616279ba7ced61b882383a8d33ce3e7ccddc98e1

[ "Apache-2.0" ]

null

python/problem0001.py

kosmos-zhang/projecteulernet

616279ba7ced61b882383a8d33ce3e7ccddc98e1

[ "Apache-2.0" ]

null

print (sum(range(3, 1000, 3)) + sum(range(5, 1000, 5)) - sum(range(15, 1000, 15))) #233168 print (sum({x for x in range(1000) if x % 3 == 0 or x % 5 == 0})) #233168

42.5

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170

2.794118

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0

0.300752

0.217647

170

3

92

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null

0

1

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1

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6

c08fd8f84401138cabeeeacd4599da7d42b90978

196

py

Python

bbgateway/__init__.py

lexotero/bbgateway

9cac7aaeb972037ef6509728dd97eef81995c4aa

[ "MIT" ]

null

bbgateway/__init__.py

lexotero/bbgateway

9cac7aaeb972037ef6509728dd97eef81995c4aa

[ "MIT" ]

null

bbgateway/__init__.py

lexotero/bbgateway

9cac7aaeb972037ef6509728dd97eef81995c4aa

[ "MIT" ]

null

from bbgateway.Order import Order from bbgateway.Shipping import Shipping from bbgateway.Billing import Billing from bbgateway.CreditCard import CreditCard from bbgateway.Merchant import Merchant

32.666667

43

0.872449

25

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196

5

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1

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6

c096302cc73830f102ca085c7633fc8d777c5350

106

py

Python

tests/test_version.py

RCheese/deplodocker

1b562e0a18efcffdcbd89f0176c08241ca526c94

[ "MIT" ]

5

2020-11-08T16:37:59.000Z

2021-02-19T22:44:55.000Z

tests/test_version.py

RCheese/deplodocker

1b562e0a18efcffdcbd89f0176c08241ca526c94

[ "MIT" ]

null

tests/test_version.py

RCheese/deplodocker

1b562e0a18efcffdcbd89f0176c08241ca526c94

[ "MIT" ]

null

import pytest def test_version(): import deplodocker assert deplodocker.__version__ == "0.2.1"

13.25

45

0.707547

13

106

5.384615

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0

0.035294

0.198113

106

7

46

15.142857

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true

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1

0

1

0

6

c0c4bd0b6203a39911eb3fd6a80c9b386d9ad1d7

977

py

Python

actor/map_obj/stairs.py

tamamiyasita/Roguelike-Tutorial-2020

db4d4e5369010567bc39bdd404c4f3a7998670fd

[ "MIT" ]

null

actor/map_obj/stairs.py

tamamiyasita/Roguelike-Tutorial-2020

db4d4e5369010567bc39bdd404c4f3a7998670fd

[ "MIT" ]

null

actor/map_obj/stairs.py

tamamiyasita/Roguelike-Tutorial-2020

db4d4e5369010567bc39bdd404c4f3a7998670fd

[ "MIT" ]

null

from data import IMAGE_ID from constants import * from actor.actor import Actor class Up_Stairs(Actor): def __init__(self, x=0, y=0, image=None): super().__init__( # texture_number=31, image=IMAGE_ID["up_stairs"], x=x, y=y, # scale=SPRITE_SCALE*2, color=COLORS.get("black"), visible_color=COLORS.get("light_wall"), not_visible_color=COLORS.get("dark_wall") ) self.tag = [Tag.map_obj, Tag.up_stairs] class Down_Stairs(Actor): def __init__(self, x=0, y=0, image=None): super().__init__( # texture_number=31, image=IMAGE_ID["down_stairs"], x=x, y=y, # scale=SPRITE_SCALE*2, color=COLORS.get("black"), visible_color=COLORS.get("light_wall"), not_visible_color=COLORS.get("dark_wall") ) self.tag = [Tag.map_obj, Tag.down_stairs]

27.914286

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6

c0c7be2ff0fc7a8eca9950919ca6f50ada1f8b67

1,868

py

Python

transformers_sklearn/utils/features_utils.py

victor-lozhnikov/transformers_sklearn

993e38155ff112f85d805b4e87c150e6a7d0daa2

[ "Apache-2.0" ]

52

2019-12-12T07:06:12.000Z

2022-02-20T01:31:01.000Z

transformers_sklearn/utils/features_utils.py

victor-lozhnikov/transformers_sklearn

993e38155ff112f85d805b4e87c150e6a7d0daa2

[ "Apache-2.0" ]

2

2020-05-25T08:15:29.000Z

2022-02-12T16:09:24.000Z

transformers_sklearn/utils/features_utils.py

victor-lozhnikov/transformers_sklearn

993e38155ff112f85d805b4e87c150e6a7d0daa2

[ "Apache-2.0" ]

4

2020-09-23T11:52:12.000Z

2022-02-19T06:57:50.000Z

import torch.nn as nn from transformers import BertPreTrainedModel,BertModel class BertForSequenceVector(BertPreTrainedModel): def __init__(self,config): super().__init__(config) self.num_labels = config.num_labels self.bert = BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.init_weights() def forward(self,input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None): outputs = self.bert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) return pooled_output class BertForTokenVector(BertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.bert = BertModel(config) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.init_weights() def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, labels=None): outputs = self.bert( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) return sequence_output

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1,868

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6

c0fcab7f24f7e553ceeab14d4b40d66433e22880

151

py

Python

iplib3/constants/__init__.py

Diapolo10/iplib

001479b2095fd8008f9db726b1bd9c0b0ee16eac

[ "MIT" ]

6

2021-04-18T19:46:40.000Z

2021-06-28T22:03:25.000Z

iplib3/constants/__init__.py

Diapolo10/iplib

001479b2095fd8008f9db726b1bd9c0b0ee16eac

[ "MIT" ]

10

2021-05-01T19:46:35.000Z

2021-07-04T08:39:35.000Z

iplib3/constants/__init__.py

Diapolo10/iplib

001479b2095fd8008f9db726b1bd9c0b0ee16eac

[ "MIT" ]

4

2021-05-01T22:04:24.000Z

2021-06-13T14:29:20.000Z

"""Various constant values used by iplib3""" from .ipv4 import * from .ipv6 import * from .address import * from .subnet import * from .port import *

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6

9b255b60d1cc22ced35a5cb60cac6d7316dfb813

135

py

Python

baseline/__init__.py

LLLjun/learn-to-cluster

3b834589923baf72523e288cc462e0df591b99c1

[ "MIT" ]

620

2019-04-16T01:06:59.000Z

2022-03-27T15:15:45.000Z

baseline/__init__.py

LLLjun/learn-to-cluster

3b834589923baf72523e288cc462e0df591b99c1

[ "MIT" ]

83

2019-04-29T08:55:16.000Z

2022-03-11T09:27:16.000Z

baseline/__init__.py

LLLjun/learn-to-cluster

3b834589923baf72523e288cc462e0df591b99c1

[ "MIT" ]

141

2019-04-16T08:53:02.000Z

2022-03-14T08:49:37.000Z

from .sklearn_cluster import * from .aro import (aro, knn_aro) from .chinese_whispers import (chinese_whispers, chinese_whispers_fast)

33.75

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9e2b29ee4eac8381ced0a4896861c6973bb149c7

38,358

py

Python

tests/test_unary_operators.py

gf712/onnxruntime-numpy

752ecb90e97295384c96ff339165c461ba4caf87

[ "MIT" ]

2

2021-04-24T07:50:31.000Z

2021-09-07T18:56:51.000Z

tests/test_unary_operators.py

gf712/onnxruntime-numpy

752ecb90e97295384c96ff339165c461ba4caf87

[ "MIT" ]

null

tests/test_unary_operators.py

gf712/onnxruntime-numpy

752ecb90e97295384c96ff339165c461ba4caf87

[ "MIT" ]

null

import onnxruntime_numpy as onp from onnxruntime_numpy.types import ( float_types, integer_types, is_unsigned_int, all_types, is_bool, numeric_types, bool_types) import pytest import numpy as np from .utils import expect import itertools def argmax_use_numpy(data, axis=0, keepdims=1): result = np.argmax(data, axis=axis) if (keepdims == 1): result = np.expand_dims(result, axis) return result.astype(np.int64) def argmax_use_numpy_select_last_index(data, axis=0, keepdims=True): data = np.flip(data, axis) result = np.argmax(data, axis=axis) result = data.shape[axis] - result - 1 if keepdims: result = np.expand_dims(result, axis) return result.astype(np.int64) def argmin_use_numpy(data, axis=0, keepdims=1): result = np.argmin(data, axis=axis) if (keepdims == 1): result = np.expand_dims(result, axis) return result.astype(np.int64) def argmin_use_numpy_select_last_index(data, axis=0, keepdims=True): data = np.flip(data, axis) result = np.argmin(data, axis=axis) result = data.shape[axis] - result - 1 if keepdims: result = np.expand_dims(result, axis) return result.astype(np.int64) @pytest.mark.parametrize("type_a", [*float_types, *integer_types]) def test_abs(type_a): if is_unsigned_int(type_a): # it is invalid to use unsigned int type with negative values a = onp.array([1, 2, 3], dtype=type_a) else: a = onp.array([-1, -2, -3], dtype=type_a) expected = onp.array([1, 2, 3], dtype=type_a) result = onp.absolute(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_acos(type_a): a = onp.array([1., .5, .1], dtype=type_a) expected = onp.array([0., 1.04719755, 1.47062891], dtype=type_a) result = onp.acos(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_acosh(type_a): a = onp.array([1., 2., 3.], dtype=type_a) expected = onp.array([0., 1.3169579, 1.76274717], dtype=type_a) result = onp.acosh(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmax_default_axes_keepdims(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) keepdims = True expected = argmax_use_numpy(x, keepdims=keepdims) result = onp.argmax(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmax_default_axes_keepdims_select_last_index(type_a): x = np.array([[2, 2], [3, 10]], dtype=type_a) keepdims = True expected = argmax_use_numpy_select_last_index(x, keepdims=keepdims) result = onp.argmax(onp.array(x), select_last_index=True) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmax_keepdims(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = 1 keepdims = True expected = argmax_use_numpy(x, axis=axis, keepdims=keepdims) result = onp.argmax(onp.array(x), axis=axis, keepdims=keepdims) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmax_keepdims_select_last_index(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = 1 keepdims = True expected = argmax_use_numpy_select_last_index( x, axis=axis, keepdims=keepdims) result = onp.argmax( onp.array(x), axis=axis, keepdims=keepdims, select_last_index=True) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmax_negative_axis_keepdims(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = -1 keepdims = True expected = argmax_use_numpy( x, axis=axis, keepdims=keepdims) result = onp.argmax( onp.array(x), axis=axis, keepdims=keepdims) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmax_negative_axis_keepdims_select_last_index(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = -1 keepdims = True expected = argmax_use_numpy_select_last_index( x, axis=axis, keepdims=keepdims) result = onp.argmax( onp.array(x), axis=axis, keepdims=keepdims, select_last_index=True) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmax_no_keepdims(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = 1 keepdims = False expected = argmax_use_numpy( x, axis=axis, keepdims=keepdims) result = onp.argmax( onp.array(x), axis=axis, keepdims=keepdims) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmax_no_keepdims_select_last_index(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = 1 keepdims = False expected = argmax_use_numpy_select_last_index( x, axis=axis, keepdims=keepdims) result = onp.argmax( onp.array(x), axis=axis, keepdims=keepdims, select_last_index=True) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmin_default_axes_keepdims(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) keepdims = True expected = argmin_use_numpy(x, keepdims=keepdims) result = onp.argmin(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmin_default_axes_keepdims_select_last_index(type_a): x = np.array([[2, 2], [3, 10]], dtype=type_a) keepdims = True expected = argmin_use_numpy_select_last_index(x, keepdims=keepdims) result = onp.argmin(onp.array(x), select_last_index=True) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmin_keepdims(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = 1 keepdims = True expected = argmin_use_numpy(x, axis=axis, keepdims=keepdims) result = onp.argmin(onp.array(x), axis=axis, keepdims=keepdims) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmin_keepdims_select_last_index(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = 1 keepdims = True expected = argmin_use_numpy_select_last_index( x, axis=axis, keepdims=keepdims) result = onp.argmin( onp.array(x), axis=axis, keepdims=keepdims, select_last_index=True) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmin_negative_axis_keepdims(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = -1 keepdims = True expected = argmin_use_numpy( x, axis=axis, keepdims=keepdims) result = onp.argmin( onp.array(x), axis=axis, keepdims=keepdims) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmin_negative_axis_keepdims_select_last_index(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = -1 keepdims = True expected = argmin_use_numpy_select_last_index( x, axis=axis, keepdims=keepdims) result = onp.argmin( onp.array(x), axis=axis, keepdims=keepdims, select_last_index=True) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmin_no_keepdims(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = 1 keepdims = False expected = argmin_use_numpy( x, axis=axis, keepdims=keepdims) result = onp.argmin( onp.array(x), axis=axis, keepdims=keepdims) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int32]) def test_argmin_no_keepdims_select_last_index(type_a): x = np.array([[2, 1], [3, 10]], dtype=type_a) axis = 1 keepdims = False expected = argmin_use_numpy_select_last_index( x, axis=axis, keepdims=keepdims) result = onp.argmin( onp.array(x), axis=axis, keepdims=keepdims, select_last_index=True) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_asin(type_a): a = onp.array([1., .2, .3], dtype=type_a) expected = onp.array([1.57079633, 0.20135792, 0.30469265], dtype=type_a) result = onp.asin(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_asinh(type_a): a = onp.array([1., .2, .3], dtype=type_a) expected = onp.array([0.88137359, 0.19869011, 0.29567305], dtype=type_a) result = onp.asinh(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_atan(type_a): a = onp.array([1., .2, .3], dtype=type_a) expected = onp.array([0.78539816, 0.19739556, 0.29145679], dtype=type_a) result = onp.atan(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_atanh(type_a): a = onp.array([0., .2, .3], dtype=type_a) expected = onp.array([0., 0.20273255, 0.3095196], dtype=type_a) result = onp.atanh(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [*all_types]) @pytest.mark.parametrize("type_b", [*all_types]) def test_cast(type_a, type_b): a = onp.array([0, 1, 2], dtype=type_a) if is_bool(type_b) or is_bool(type_a): expected = onp.array([0, 1, 1], dtype=type_b) else: expected = onp.array([0, 1, 2], dtype=type_b) result = onp.cast(a, type_b) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_ceil(type_a): a = onp.array([-1.5, 2.49, -3.99], dtype=type_a) expected = onp.array([-1., 3., -3], dtype=type_a) result = onp.ceil(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [*numeric_types]) def test_clip(type_a): if type_a in [np.int16, np.int32, np.uint16, np.uint32]: return a = onp.array([0, 1, 2], dtype=type_a) expected = onp.array([0, 1, 1], dtype=type_a) result = onp.clip(a, minimum=0, maximum=1) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_cos(type_a): a = onp.array([1, 2, 3], dtype=type_a) expected = onp.array([0.54030231, -0.41614684, -0.9899925], dtype=type_a) result = onp.cos(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_cosh(type_a): a = onp.array([1, 2, 3], dtype=type_a) expected = onp.array([1.54308063, 3.76219569, 10.067662], dtype=type_a) result = onp.cosh(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_det(type_a): a = onp.array([[1., 2.], [3., 4.]], dtype=type_a) expected = onp.array(-2, dtype=type_a) result = onp.det(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_det_nd(type_a): a = onp.array([[[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]]], dtype=type_a) expected = onp.array([-2., -3., -8.], dtype=type_a) result = onp.det(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_erf(type_a): a = onp.array([[1, 2, 3], [-1, -2, 0]], dtype=type_a) expected = onp.array([[0.84270079, 0.99532227, 0.99997791], [-0.84270079, -0.99532227, 0.]], dtype=type_a) result = onp.erf(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [*float_types]) def test_exp(type_a): a = onp.array([-1, 0, 1], dtype=type_a) expected = onp.array([0.36787945, 1., 2.71828175], dtype=type_a) result = onp.exp(a) expect(expected.numpy(), result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.uint64, np.int32, np.int64]) @pytest.mark.parametrize("type_b", [*float_types, np.uint64, np.int32, np.int64]) def test_eyelike_populate_off_main_diagonal(type_a, type_b): shape = (4, 5) off_diagonal_offset = 1 if type_a in integer_types: x = np.random.randint(0, 100, size=shape, dtype=type_a) elif type_a in float_types: x = np.random.randn(*shape).astype(type_a) else: raise ValueError(f"Invalid type {type_a}") expected = np.eye(shape[0], shape[1], k=off_diagonal_offset, dtype=type_b) result = onp.eye_like(onp.array(x, dtype=type_a), dtype=type_b, k=off_diagonal_offset) assert result.dtype == type_b expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.uint64, np.int32, np.int64]) @pytest.mark.parametrize("type_b", [*float_types, np.uint64, np.int32, np.int64]) def test_eyelike_with_dtype(type_a, type_b): shape = (3, 4) if type_a in integer_types: x = np.random.randint(0, 100, size=shape, dtype=type_a) elif type_a in float_types: x = np.random.randn(*shape).astype(type_a) else: raise ValueError(f"Invalid type {type_a}") expected = np.eye(shape[0], shape[1], dtype=type_b) result = onp.eye_like(onp.array(x, dtype=type_a), dtype=type_b) assert result.dtype == type_b expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.uint64, np.int32, np.int64]) def test_eyelike_without_dtype(type_a): shape = (4, 4) if type_a in integer_types: x = np.random.randint(0, 100, size=shape, dtype=type_a) elif type_a in float_types: x = np.random.randn(*shape).astype(type_a) else: raise ValueError(f"Invalid type {type_a}") expected = np.eye(shape[0], shape[1], dtype=type_a) result = onp.eye_like(onp.array(x, dtype=type_a)) assert result.dtype == type_a expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.uint64, np.int32, np.int64]) def test_eyelike_with_3d_tensor(type_a): shape = (4, 4, 1) if type_a in integer_types: x = np.random.randint(0, 100, size=shape, dtype=type_a) elif type_a in float_types: x = np.random.randn(*shape).astype(type_a) else: raise ValueError(f"Invalid type {type_a}") with pytest.raises(ValueError): _ = onp.eye_like(onp.array(x, dtype=type_a)) def test_eyelike_unsupported_type(): shape = (4, 4) x = np.random.randint(0, 100, size=shape, dtype=np.int32) with pytest.raises(TypeError): _ = onp.eye_like(onp.array(x), dtype=np.str_) @pytest.mark.parametrize("type_a", all_types) def test_flatten(type_a): shape = (2, 3, 4, 5) a = np.random.random_sample(shape).astype(type_a) for i in range(len(shape)): new_shape = (1, -1) if i == 0 else (np.prod(shape[0:i]).astype(int), -1) expected = np.reshape(a, new_shape) result = onp.flatten(onp.array(a, dtype=type_a), axis=i) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_flatten_negativate_axis(type_a): shape = (2, 3, 4, 5) a = np.random.random_sample(shape).astype(type_a) for i in range(-len(shape), 0): new_shape = (np.prod(shape[0:i]).astype(int), -1) expected = np.reshape(a, new_shape) result = onp.flatten(onp.array(a, dtype=type_a), axis=i) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_flatten_with_default_axis(type_a): shape = (5, 4, 3, 2) a = np.random.random_sample(shape).astype(type_a) new_shape = (5, 24) expected = np.reshape(a, new_shape) result = onp.flatten(onp.array(a)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_floor(type_a): x = np.random.randn(3, 4, 5).astype(type_a) expected = np.floor(x) result = onp.floor(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", float_types) def test_identity(type_a): x = np.array([[[ [1, 2], [3, 4], ]]], dtype=type_a) expected = x result = onp.identity(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", float_types) def test_isinf_infinity(type_a): x = np.array([-1.2, np.nan, np.inf, 2.8, np.NINF, np.inf], dtype=type_a) expected = np.isinf(x) result = onp.isinf(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", float_types) def test_isinf_negative_infinity_only(type_a): x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf], dtype=type_a) expected = np.isneginf(x) result = onp.isneginf(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", float_types) def test_isinf_positive_infinity_only(type_a): x = np.array([-1.7, np.nan, np.inf, -3.6, np.NINF, np.inf], dtype=type_a) expected = np.isposinf(x) result = onp.isposinf(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_isnan(type_a): x = np.array([3.0, np.nan, 4.0, np.nan], dtype=type_a) expected = np.isnan(x) result = onp.isnan(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", float_types) def test_log(type_a): x = np.array([1, 10], dtype=type_a) expected = np.log(x) result = onp.log(onp.array(x)) expect(expected, result.numpy()) x = np.exp(np.random.randn(3, 4, 5).astype(type_a)) expected = np.log(x) result = onp.log(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_mean_variance_normalization(type_a): input_data = np.array([[[[0.8439683], [0.5665144], [0.05836735]], [[0.02916367], [0.12964272], [0.5060197]], [[0.79538304], [0.9411346], [0.9546573]]], [[[0.17730942], [0.46192095], [0.26480448]], [[0.6746842], [0.01665257], [0.62473077]], [[0.9240844], [0.9722341], [0.11965699]]], [[[0.41356155], [0.9129373], [0.59330076]], [[0.81929934], [0.7862604], [0.11799799]], [[0.69248444], [0.54119414], [0.07513223]]]], dtype=type_a) data_mean = np.mean(input_data, axis=(0, 2, 3), keepdims=1) data_mean_squared = np.power(data_mean, 2) data_squared = np.power(input_data, 2) data_squared_mean = np.mean(data_squared, axis=(0, 2, 3), keepdims=1) std = np.sqrt(data_squared_mean - data_mean_squared) expected = ((input_data - data_mean) / (std + 1e-9)).astype(type_a) result = onp.mean_variance_normalization(onp.array(input_data)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int8, np.int32, np.int64]) def test_negative(type_a): x = np.array([-4, 2]).astype(type_a) expected = np.negative(x) result = onp.negative(onp.array(x)) expect(expected, result.numpy()) result = -onp.array(x) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [np.float32, np.uint8, np.int32, np.int64]) def test_nonzero(type_a): x = np.array([[1, 0], [1, 1]], dtype=type_a) expected = np.array(np.nonzero(x), dtype=np.int64) result = onp.nonzero(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*bool_types]) def test_not(type_a): x = (np.random.randn(3, 4) > 0).astype(type_a) expected = np.logical_not(x) result = onp.not_(onp.array(x)) expect(expected, result.numpy()) x = (np.random.randn(3, 4, 5) > 0).astype(type_a) expected = np.logical_not(x) result = onp.not_(onp.array(x)) expect(expected, result.numpy()) x = (np.random.randn(3, 4, 5, 6) > 0).astype(type_a) expected = np.logical_not(x) result = onp.not_(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types]) def test_reciprocal(type_a): x = np.array([-4, 2]).astype(type_a) expected = np.reciprocal(x) result = onp.reciprocal(onp.array(x)) expect(expected, result.numpy()) x = np.random.rand(3, 4, 5).astype(type_a) + 0.5 expected = np.reciprocal(x) result = onp.reciprocal(onp.array(x)) expect(expected, result.numpy()) def reshape_reference_implementation(data, shape, allowzero=0): # replace zeros with corresponding dim size # we need to do this because np.reshape doesn't support 0 by default unless # 'allowzero' is set new_shape = np.copy(shape) if allowzero == 0: zeros_index = np.where(shape == 0) new_shape[zeros_index] = np.array(data.shape)[zeros_index] reshaped = np.reshape(data, new_shape) return reshaped @pytest.mark.parametrize("type_a", all_types) def test_reshape_reordered_all_dims(type_a): original_shape = [2, 3, 4] expected_shape = [4, 2, 3] x = np.random.uniform(size=original_shape).astype(type_a) expected = reshape_reference_implementation(x, expected_shape) result = onp.array(x).reshape(expected_shape) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_reshape_reordered_last_dims(type_a): original_shape = [2, 3, 4] expected_shape = [2, 4, 3] x = np.random.uniform(size=original_shape).astype(type_a) expected = reshape_reference_implementation(x, expected_shape) result = onp.array(x).reshape(expected_shape) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_reshape_reduced_dims(type_a): original_shape = [2, 3, 4] expected_shape = [2, 12] x = np.random.uniform(size=original_shape).astype(type_a) expected = reshape_reference_implementation(x, expected_shape) result = onp.array(x).reshape(expected_shape) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_reshape_extended_dims(type_a): original_shape = [2, 3, 4] expected_shape = [2, 3, 2, 2] x = np.random.uniform(size=original_shape).astype(type_a) expected = reshape_reference_implementation(x, expected_shape) result = onp.array(x).reshape(expected_shape) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_reshape_one_dim(type_a): original_shape = [2, 3, 4] expected_shape = [24] x = np.random.uniform(size=original_shape).astype(type_a) expected = reshape_reference_implementation(x, expected_shape) result = onp.array(x).reshape(expected_shape) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_reshape_negative_dim(type_a): original_shape = [2, 3, 4] expected_shape = [2, -1, 2] x = np.random.uniform(size=original_shape).astype(type_a) expected = reshape_reference_implementation(x, expected_shape) result = onp.array(x).reshape(expected_shape) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_reshape_negative_extended_dims(type_a): original_shape = [2, 3, 4] expected_shape = [-1, 2, 3, 4] x = np.random.uniform(size=original_shape).astype(type_a) expected = reshape_reference_implementation(x, expected_shape) result = onp.array(x).reshape(expected_shape) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", float_types) def test_round(type_a): x = np.array([0.1, 0.5, 0.9, 1.2, 1.5, 1.8, 2.3, 2.5, 2.7, -1.1, -1.5, -1.9, -2.2, -2.5, -2.8]).astype(type_a) expected = np.array([0., 0., 1., 1., 2., 2., 2., 2., 3., -1., -2., -2., -2., -2., -3.]).astype(type_a) result = onp.round(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_shape(type_a): x = np.array([ [1, 2, 3], [4, 5, 6], ]).astype(type_a) expected = np.array([ 2, 3, ]).astype(np.int64) result = onp.shape(onp.array(x)) expect(expected, result.numpy()) x = np.random.randn(3, 4, 5).astype(type_a) expected = np.array(x.shape).astype(np.int64) result = onp.shape(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", numeric_types) def test_sign(type_a): x = np.array(range(-5, 6)).astype(type_a) expected = np.sign(x) result = onp.sign(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", float_types) def test_sin(type_a): x = np.array([-1, 0, 1]).astype(type_a) expected = np.sin(x) result = onp.sin(onp.array(x)) expect(expected, result.numpy()) x = np.random.randn(3, 4, 5).astype(type_a) expected = np.sin(x) result = onp.sin(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_sinh(type_a): x = np.array([-1, 0, 1]).astype(type_a) expected = np.sinh(x) result = onp.sinh(onp.array(x)) expect(expected, result.numpy()) x = np.random.randn(3, 4, 5).astype(type_a) expected = np.sinh(x) result = onp.sinh(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_size(type_a): x = np.array([ [1, 2, 3], [4, 5, 6], ]).astype(type_a) expected = np.array(6).astype(np.int64) result = onp.size(onp.array(x)) expect(expected, result.numpy()) x = np.random.randn(3, 4, 5).astype(type_a) expected = np.array(x.size).astype(np.int64) result = onp.size(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", float_types) def test_sqrt(type_a): x = np.array([1, 4, 9]).astype(type_a) expected = np.sqrt(x) result = onp.sqrt(onp.array(x)) expect(expected, result.numpy()) x = np.abs(np.random.randn(3, 4, 5).astype(type_a)) expected = np.sqrt(x) result = onp.sqrt(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_transpose_all_permutations(type_a): shape = (2, 3, 4) x = np.random.uniform(0, 1, size=shape).astype(type_a) permutations = list(itertools.permutations(np.arange(len(shape)))) for i in range(len(permutations)): expected = np.transpose(x, permutations[i]) result = onp.transpose(onp.array(x), permutations[i]) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_transpose_default(type_a): shape = (2, 3, 4) x = np.random.uniform(0, 1, size=shape).astype(type_a) expected = x.T result = onp.array(x).T expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_tan(type_a): x = np.array([-1, 0, 1]).astype(type_a) expected = np.tan(x) result = onp.tan(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [np.float32]) def test_tanh(type_a): x = np.array([-1, 0, 1]).astype(type_a) expected = np.tanh(x) result = onp.tanh(onp.array(x)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_squeeze(type_a): x = np.random.randn(1, 3, 4, 5).astype(type_a) axes = np.array([0], dtype=np.int64) expected = np.squeeze(x, axis=0) result = onp.squeeze(onp.array(x), onp.array(axes)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_squeeze_negative_axes(type_a): x = np.random.randn(1, 3, 1, 5).astype(type_a) axes = np.array([-2], dtype=np.int64) expected = np.squeeze(x, axis=-2) result = onp.squeeze(onp.array(x), onp.array(axes)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_squeeze_lazy(type_a): x = np.random.randn(1, 3, 1, 5).astype(type_a) axes = np.array([-1], dtype=np.int64) axes += axes # -2 expected = np.squeeze(x, axis=-2) result = onp.squeeze(onp.array(x), onp.array(axes)) expect(expected, result.numpy()) # TODO: update this when onnxruntime release ONNX opset 14 support # @pytest.mark.parametrize("type_a", all_types) # def test_trilu_lower(type_a): # x = np.random.randint(10, size=(4, 5)).astype(type_a) # expected = np.tril(x, 0) # result = onp.tril(onp.array(x)) # expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int64]) def test_topk(type_a): axis = 1 largest = True k = 3 X = np.array([ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=type_a) K = np.array([k], dtype=np.int64) values_expected = np.array([[3, 2, 1], [7, 6, 5], [11, 10, 9]], dtype=type_a) indices_expected = np.array([[3, 2, 1], [3, 2, 1], [3, 2, 1]], dtype=np.int64) values, indices = onp.topk( onp.array(X), onp.array(K), axis=axis, largest=largest) expect(values_expected, values.numpy()) expect(indices_expected, indices.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int64]) def test_topk_negative_axis(type_a): axis = -1 largest = True k = 3 X = np.array([ [0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11], ], dtype=type_a) K = np.array([k], dtype=np.int64) values_expected = np.array([[3, 2, 1], [7, 6, 5], [11, 10, 9]], dtype=type_a) indices_expected = np.array([[3, 2, 1], [3, 2, 1], [3, 2, 1]], dtype=np.int64) values, indices = onp.topk( onp.array(X), onp.array(K), axis=axis, largest=largest) expect(values_expected, values.numpy()) expect(indices_expected, indices.numpy()) @pytest.mark.parametrize("type_a", [*float_types, np.int64]) def test_topk_smallest(type_a): axis = 1 largest = False sorted = True k = 3 X = np.array([ [0, 1, 2, 3], [4, 5, 6, 7], [11, 10, 9, 8], ], dtype=type_a) K = np.array([k], dtype=np.int64) values_expected = np.array([[0, 1, 2], [4, 5, 6], [8, 9, 10]], dtype=type_a) indices_expected = np.array([[0, 1, 2], [0, 1, 2], [3, 2, 1]], dtype=np.int64) values, indices = onp.topk( onp.array(X), onp.array(K), axis=axis, largest=largest, sorted=sorted) expect(values_expected, values.numpy()) expect(indices_expected, indices.numpy()) @pytest.mark.parametrize("type_a", [np.float32, np.int8, np.int64]) def test_unique_not_sorted_without_axis(type_a): x = np.array([2, 1, 1, 3, 4, 3], dtype=type_a) y, indices, inverse_indices, counts = np.unique( x, True, True, True) # prepare index mapping from sorted to unsorted argsorted_indices = np.argsort(indices) inverse_indices_map = {i: si for i, si in zip( argsorted_indices, np.arange(len(argsorted_indices)))} indices = indices[argsorted_indices] y_expected = np.take(x, indices, axis=0) inverse_indices = np.asarray([inverse_indices_map[i] for i in inverse_indices], dtype=np.int64) counts = counts[argsorted_indices] indices_expected = indices.astype(np.int64) inverse_indices_expected = inverse_indices.astype(np.int64) counts_expected = counts.astype(np.int64) y, indices, inverse_indices, counts = onp.unique(onp.array( x), return_index=True, return_inverse=True, return_counts=True, sorted=False) expect(y_expected, y.numpy()) expect(indices_expected, indices.numpy()) expect(inverse_indices_expected, inverse_indices.numpy()) expect(counts_expected, counts.numpy()) @pytest.mark.parametrize("type_a", [np.float32, np.int8, np.int64]) def test_unique_sorted_with_axis(type_a): x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]], dtype=type_a) y_expected, indices, inverse_indices, counts = np.unique( x, True, True, True, axis=0) indices_expected = indices.astype(np.int64) inverse_indices_expected = inverse_indices.astype(np.int64) counts_expected = counts.astype(np.int64) y, indices, inverse_indices, counts = onp.unique( onp.array(x), return_index=True, return_inverse=True, return_counts=True, sorted=True, axis=0) expect(y_expected, y.numpy()) expect(indices_expected, indices.numpy()) expect(inverse_indices_expected, inverse_indices.numpy()) expect(counts_expected, counts.numpy()) @pytest.mark.parametrize("type_a", [np.float32, np.int8, np.int64]) def test_unique_sorted_with_axis_3d(type_a): x = np.array([[[1, 1], [0, 1], [2, 1], [0, 1]], [[1, 1], [0, 1], [2, 1], [0, 1]]], dtype=type_a) y_expected, indices, inverse_indices, counts = np.unique( x, True, True, True, axis=1) indices_expected = indices.astype(np.int64) inverse_indices_expected = inverse_indices.astype(np.int64) counts_expected = counts.astype(np.int64) y, indices, inverse_indices, counts = onp.unique( onp.array(x), return_index=True, return_inverse=True, return_counts=True, sorted=True, axis=1) expect(y_expected, y.numpy()) expect(indices_expected, indices.numpy()) expect(inverse_indices_expected, inverse_indices.numpy()) expect(counts_expected, counts.numpy()) @pytest.mark.parametrize("type_a", [np.float32, np.int8, np.int64]) def test_unique_negative_axis(type_a): x = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 3]], dtype=type_a) y_expected, indices, inverse_indices, counts = np.unique( x, True, True, True, axis=-1) indices_expected = indices.astype(np.int64) inverse_indices_expected = inverse_indices.astype(np.int64) counts_expected = counts.astype(np.int64) y, indices, inverse_indices, counts = onp.unique( onp.array(x), return_index=True, return_inverse=True, return_counts=True, sorted=True, axis=-1) expect(y_expected, y.numpy()) expect(indices_expected, indices.numpy()) expect(inverse_indices_expected, inverse_indices.numpy()) expect(counts_expected, counts.numpy()) @pytest.mark.parametrize("type_a", [np.float32, np.int8, np.int64]) def test_unique_without_axis(type_a): x = np.array([2, 1, 1, 3, 4, 3], dtype=type_a) y_expected, indices, inverse_indices, counts = np.unique( x, True, True, True) indices_expected = indices.astype(np.int64) inverse_indices_expected = inverse_indices.astype(np.int64) counts_expected = counts.astype(np.int64) y, indices, inverse_indices, counts = onp.unique( onp.array(x), return_index=True, return_inverse=True, return_counts=True, sorted=True) expect(y_expected, y.numpy()) expect(indices_expected, indices.numpy()) expect(inverse_indices_expected, inverse_indices.numpy()) expect(counts_expected, counts.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_unsqueeze(type_a): x = np.random.randn(1, 3, 4, 5).astype(type_a) axes = np.array([0], dtype=np.int64) expected = np.expand_dims(x, axis=0) result = onp.unsqueeze(onp.array(x), onp.array(axes)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_unsqueeze_negative_axes(type_a): x = np.random.randn(1, 3, 1, 5).astype(type_a) axes = np.array([-2], dtype=np.int64) expected = np.expand_dims(x, axis=-2) result = onp.unsqueeze(onp.array(x), onp.array(axes)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_unsqueeze_lazy(type_a): x = np.random.randn(1, 3, 1, 5).astype(type_a) axes = np.array([-1], dtype=np.int64) axes += axes # -2 expected = np.expand_dims(x, axis=-2) result = onp.unsqueeze(onp.array(x), onp.array(axes)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_unsqueeze_one_axis(type_a): x = np.random.randn(3, 4, 5).astype(np.float32) for i in range(x.ndim): axes = np.array([i]).astype(np.int64) expected = np.expand_dims(x, axis=i) result = onp.unsqueeze(onp.array(x), onp.array(axes)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_unsqueeze_two_axis(type_a): x = np.random.randn(1, 3, 1, 5).astype(type_a) axes = np.array([1, 4], dtype=np.int64) expected = np.expand_dims(x, axis=1) expected = np.expand_dims(expected, axis=4) result = onp.unsqueeze(onp.array(x), onp.array(axes)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_unsqueeze_three_axis(type_a): x = np.random.randn(3, 4, 5).astype(type_a) axes = np.array([2, 4, 5]).astype(np.int64) expected = np.expand_dims(x, axis=2) expected = np.expand_dims(expected, axis=4) expected = np.expand_dims(expected, axis=5) result = onp.unsqueeze(onp.array(x), onp.array(axes)) expect(expected, result.numpy()) @pytest.mark.parametrize("type_a", all_types) def test_unsqueeze_unsorted(type_a): x = np.random.randn(3, 4, 5).astype(type_a) axes = np.array([5, 4, 2]).astype(np.int64) expected = np.expand_dims(x, axis=2) expected = np.expand_dims(expected, axis=4) expected = np.expand_dims(expected, axis=5) result = onp.unsqueeze(onp.array(x), onp.array(axes)) expect(expected, result.numpy())

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null

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6

7b6ffbbc1e2020cdf87b57063f5a92c4b4e8cfb0

5,355

py

Python

resources/messaging/index.py

cdklabs/cdk-amazon-chime-resources

7a356b8cf4d98dbd0e9733e8b2d7712699c7fe3d

[ "Apache-2.0" ]

8

2022-02-04T21:11:43.000Z

2022-03-28T01:25:28.000Z

resources/messaging/index.py

cdklabs/cdk-amazon-chime-resources

7a356b8cf4d98dbd0e9733e8b2d7712699c7fe3d

[ "Apache-2.0" ]

5

2022-02-17T00:24:05.000Z

2022-03-28T17:41:32.000Z

resources/messaging/index.py

cdklabs/cdk-amazon-chime-resources

7a356b8cf4d98dbd0e9733e8b2d7712699c7fe3d

[ "Apache-2.0" ]

1

2022-03-27T22:43:43.000Z

import app_instance import channel_flow import instance_admin import instance_user import streaming_config import data_retention def handler(event, context): print(event) responseData = {} properties = event["ResourceProperties"]["properties"] uid = event["ResourceProperties"]["uid"] resource_type = event["ResourceProperties"]["resourceType"] if event["RequestType"] == "Create": if resource_type == "AppInstance": try: responseData["appInstanceArn"] = app_instance.create_messaging_app_instance(uid, **properties) return {"PhysicalResourceId": uid, "Data": responseData} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) if resource_type == "ChannelFlow": try: responseData["channelFlowArn"] = channel_flow.create_channel_flow(uid, **properties) return {"PhysicalResourceId": uid, "Data": responseData} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) if resource_type == "AppInstanceUser": try: responseData["appInstanceUser"] = instance_user.create_app_instance_user(uid, **properties) return {"PhysicalResourceId": uid, "Data": responseData} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) if resource_type == "AppInstanceAdmin": try: app_instance_admin = instance_admin.create_app_instance_admin(uid, **properties) responseData["AppInstanceAdminArn"] = app_instance_admin["Arn"] responseData["AppInstanceAdminName"] = app_instance_admin["Name"] return {"PhysicalResourceId": uid, "Data": responseData} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) if resource_type == "StreamingConfig": try: streaming_config.add_app_instance_streaming(uid, **properties) return {"PhysicalResourceId": uid} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) if resource_type == "DataRetention": try: data_retention.add_data_retention_policy(uid, **properties) return {"PhysicalResourceId": uid} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) elif event["RequestType"] == "Update": if resource_type == "StreamingConfig": try: streaming_config.add_app_instance_streaming(uid, **properties) return {"PhysicalResourceId": uid} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) if resource_type == "DataRetention": try: data_retention.add_data_retention_policy(uid, **properties) return {"PhysicalResourceId": uid} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) elif event["RequestType"] == "Delete": if resource_type == "AppInstance": try: responseData["appInstanceArn"] = app_instance.delete_messaging_app_instance(uid) return {"Data": responseData} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) if resource_type == "ChannelFlow": try: responseData["channelFlowArn"] = channel_flow.delete_channel_flow(uid) return {"Data": responseData} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) if resource_type == "AppInstanceUser": try: instance_user.delete_app_instance_user(uid) responseData["appInstanceUser"] = "Deleted" return {"Data": responseData} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) if resource_type == "AppInstanceAdmin": try: instance_admin.delete_app_instance_admin(uid) responseData["appInstanceAdmin"] = "Deleted" return {"Data": responseData} except Exception as e: error = {"error": f"Exception thrown: {e}"} print(error) raise Exception(error) else: responseData = {"Message": "Update is no-op. Returning success status."} return {"PhysicalResourceId": uid, "Data": responseData}

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0.55817

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0.723098

0.708362

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5,355

123

111

43.536585

0.826395

0

0.754237

0

0.189169

0

1

0.008475

false

0

0.050847

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0.110169

0

null

0

1

0

1

0

null

0

6

7bcae73d057998202417086caa3aa1fe8af90229

536

py

Python

Configuration/StandardSequences/python/MagneticField_38T_UpdatedMap_cff.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

3

2018-08-24T19:10:26.000Z

2019-02-19T11:45:32.000Z

Configuration/StandardSequences/python/MagneticField_38T_UpdatedMap_cff.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

7

2016-07-17T02:34:54.000Z

2019-08-13T07:58:37.000Z

Configuration/StandardSequences/python/MagneticField_38T_UpdatedMap_cff.py

NTrevisani/cmssw

a212a27526f34eb9507cf8b875c93896e6544781

[ "Apache-2.0" ]

5

2018-08-21T16:37:52.000Z

2020-01-09T13:33:17.000Z

from __future__ import print_function print("""#################################################################### # WARNING: the module # # Configuration.StandardSequences.MagneticField_38T_UpdatedMap_cff # # is deprecated. Please use # # Configuration.StandardSequences.MagneticField_cff.py # ####################################################################""") from Configuration.StandardSequences.MagneticField_38T_cff import *

44.666667

77

0.462687

31

536

7.645161

0.612903

0.379747

0.544304

0.388186

0

0.009756

0.235075

536

11

78

48.727273

0.568293

0

0.773408

0.47191

0

1

0

true

0

0.25

0

0.25

0

null

1

0

1

null

0

1

0

6

cdbec087d87ba470075cf20ef07e6fede82d7b57

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py

Python

gimp_be/utils/__init__.py

J216/gimp_be

02cc0e9627bee491cf1e6d5102ce0a3f07f1043e

[ "MIT" ]

3

2017-02-05T08:12:19.000Z

2019-08-02T14:31:56.000Z

gimp_be/utils/__init__.py

J216/gimp_be

02cc0e9627bee491cf1e6d5102ce0a3f07f1043e

[ "MIT" ]

1

2017-01-11T05:54:51.000Z

2019-01-08T03:48:57.000Z

gimp_be/utils/__init__.py

J216/gimp_be

02cc0e9627bee491cf1e6d5102ce0a3f07f1043e

[ "MIT" ]

null

from string_tools import *

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a830a363ac15bfd8bdd0293b3c256799a8fee085

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py

Python

CodeWars/8 Kyu/Pole Vault Starting Marks.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/8 Kyu/Pole Vault Starting Marks.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/8 Kyu/Pole Vault Starting Marks.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

A = (10.67-9.45) / (1.83-1.52) B = 9.45 - A*1.52 def starting_mark(height): return round(A * height + B, 2)

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a96455db97763aa5a0a58e43ac33033820fa2eb3

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py

Python

wickes_tools/__init__.py

1wickes/wickes-tools

ab8135c80183c2a3958cc84cf1a4a2edb3688c7b

[ "MIT" ]

null

wickes_tools/__init__.py

1wickes/wickes-tools

ab8135c80183c2a3958cc84cf1a4a2edb3688c7b

[ "MIT" ]

null

wickes_tools/__init__.py

1wickes/wickes-tools

ab8135c80183c2a3958cc84cf1a4a2edb3688c7b

[ "MIT" ]

null

from wickes_tools import cal1, cal4

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a983be22264144bfc63e8df9f666ebe6e37d8a36

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py

Python

nnfs/datasets/synthetic/__init__.py

akshaykurmi/neural-networks-from-scratch

54d62d9f5adb102d14267a922a515fa79bf52bd6

[ "MIT" ]

2

2019-09-13T22:31:21.000Z

2020-11-28T18:51:14.000Z

nnfs/datasets/synthetic/__init__.py

akshaykurmi/neural-networks-from-scratch

54d62d9f5adb102d14267a922a515fa79bf52bd6

[ "MIT" ]

null

nnfs/datasets/synthetic/__init__.py

akshaykurmi/neural-networks-from-scratch

54d62d9f5adb102d14267a922a515fa79bf52bd6

[ "MIT" ]

null

from .two_moons import TwoMoons from .two_spirals import TwoSpirals

22.666667

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6

5703c734755f1d5b86e6d43ade299d08e5a332f4

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py

Python

tests/system/test_runner.py

paulocoutinhox/pygemstones

79397ee187670dc78746a3b3f64ca6118cd3a86c

[ "MIT" ]

2

2021-11-28T11:13:07.000Z

2022-02-02T02:26:47.000Z

tests/system/test_runner.py

paulocoutinhox/pygemstones

79397ee187670dc78746a3b3f64ca6118cd3a86c

[ "MIT" ]

4

2022-01-04T22:22:09.000Z

2022-01-21T06:44:03.000Z

tests/system/test_runner.py

paulocoutinhox/pygemstones

79397ee187670dc78746a3b3f64ca6118cd3a86c

[ "MIT" ]

null

import os import pytest import pygemstones.io.file as f import pygemstones.system.platform as p import pygemstones.system.runner as r # ----------------------------------------------------------------------------- def test_run(capsys): if p.is_windows(): r.run(["dir"]) else: r.run(["ls"]) captured = capsys.readouterr() assert captured.out == "" # ----------------------------------------------------------------------------- def test_run_program_not_exists(): with pytest.raises(SystemExit) as info: if p.is_windows(): r.run(["dir", "---"]) else: r.run(["ls", "---"]) assert info.value.args[0] == 10 # ----------------------------------------------------------------------------- def test_run_shell(capsys): if p.is_windows(): r.run_as_shell(["dir"]) else: r.run_as_shell(["ls"]) captured = capsys.readouterr() assert captured.out == "" # ----------------------------------------------------------------------------- def test_run_as_shell_program_not_exists(): with pytest.raises(SystemExit) as info: r.run_as_shell(["xyz-program"]) assert info.value.args[0] == 10 # ----------------------------------------------------------------------------- def test_external(tmp_path): external_path = os.path.join(tmp_path, "external") f.create_dir(external_path) external_file_path = os.path.join(external_path, "mod1.py") file_content = "def run(args):\n print(123)\n" f.set_file_content(external_file_path, file_content) r.run_external( external_path, "mod1", "run", [], show_error_log=True, show_log=True, ) # ----------------------------------------------------------------------------- def test_external_with_error(tmp_path): external_path = os.path.join(tmp_path, "external") f.create_dir(external_path) external_file_path = os.path.join(external_path, "mod1.py") file_content = "def run(args):\n xyz()\n" f.set_file_content(external_file_path, file_content) with pytest.raises(SystemExit) as info: r.run_external( external_path, "mod1", "run", [], show_error_log=True, show_log=True, ) assert info.value.args[0] == 10 # ----------------------------------------------------------------------------- def test_external_with_error_but_silent(tmp_path): external_path = os.path.join(tmp_path, "external") f.create_dir(external_path) external_file_path = os.path.join(external_path, "mod1.py") file_content = "def run(args):\n xyz()\n" f.set_file_content(external_file_path, file_content) r.run_external(external_path, "mod1", "run", [])

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5738170a5087009b4b48c050048c8e1fbc649f0c

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py

Python

python/gigasecond/gigasecond.py

jca/exercism

ae420f38529644fe60d2b3d764766499e7ece8b6

[ "MIT" ]

null

python/gigasecond/gigasecond.py

jca/exercism

ae420f38529644fe60d2b3d764766499e7ece8b6

[ "MIT" ]

1

2021-05-11T22:46:46.000Z

python/gigasecond/gigasecond.py

jca/exercism

ae420f38529644fe60d2b3d764766499e7ece8b6

[ "MIT" ]

null

from datetime import timedelta def add_gigasecond(birth_date, _gigasecond = 1e9): return birth_date + timedelta(seconds=_gigasecond)

27.6

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93e8dcd243b3a934acd0d1e12880c0db4a795144

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py

Python

src/routes/__init__.py

kirill-kundik/CinemaChallengeBackend

aea4ac801a9a5c907f36f07b67df162b4bd85044

[ "MIT" ]

null

src/routes/__init__.py

kirill-kundik/CinemaChallengeBackend

aea4ac801a9a5c907f36f07b67df162b4bd85044

[ "MIT" ]

null

src/routes/__init__.py

kirill-kundik/CinemaChallengeBackend

aea4ac801a9a5c907f36f07b67df162b4bd85044

[ "MIT" ]

null

from .users import USER_BLUEPRINT from .achievement import ACHIEVEMENT_BLUEPRINT from .trivia import TRIVIA_BLUEPRINT

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f5627c70ba251ac2aea725c2d3653250f898e787

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py

Python

resources/dot_PyCharm/system/python_stubs/cache/2528a849a5456e787494763a21786d1d4631453f09d8dec771fc5410559afa11/numpy/core/_multiarray_tests.py

basepipe/developer_onboarding

05b6a776f8974c89517868131b201f11c6c2a5ad

[ "MIT" ]

1

2020-04-20T02:27:20.000Z

resources/dot_PyCharm/system/python_stubs/cache/2528a849a5456e787494763a21786d1d4631453f09d8dec771fc5410559afa11/numpy/core/_multiarray_tests.py

basepipe/developer_onboarding

05b6a776f8974c89517868131b201f11c6c2a5ad

[ "MIT" ]

null

resources/dot_PyCharm/system/python_stubs/cache/2528a849a5456e787494763a21786d1d4631453f09d8dec771fc5410559afa11/numpy/core/_multiarray_tests.py

basepipe/developer_onboarding

05b6a776f8974c89517868131b201f11c6c2a5ad

[ "MIT" ]

null

# encoding: utf-8 # module numpy.core._multiarray_tests # from C:\Python27\lib\site-packages\numpy\core\_multiarray_tests.pyd # by generator 1.147 # no doc # no imports # functions def array_indexing(*args, **kwargs): # real signature unknown pass def extint_add_128(*args, **kwargs): # real signature unknown pass def extint_ceildiv_128_64(*args, **kwargs): # real signature unknown pass def extint_divmod_128_64(*args, **kwargs): # real signature unknown pass def extint_floordiv_128_64(*args, **kwargs): # real signature unknown pass def extint_gt_128(*args, **kwargs): # real signature unknown pass def extint_mul_64_64(*args, **kwargs): # real signature unknown pass def extint_neg_128(*args, **kwargs): # real signature unknown pass def extint_safe_binop(*args, **kwargs): # real signature unknown pass def extint_shl_128(*args, **kwargs): # real signature unknown pass def extint_shr_128(*args, **kwargs): # real signature unknown pass def extint_sub_128(*args, **kwargs): # real signature unknown pass def extint_to_128(*args, **kwargs): # real signature unknown pass def extint_to_64(*args, **kwargs): # real signature unknown pass def format_float_OSprintf_g(val, precision): # real signature unknown; restored from __doc__ """ format_float_OSprintf_g(val, precision) Print a floating point scalar using the system's printf function, equivalent to: printf("%.*g", precision, val); for half/float/double, or replacing 'g' by 'Lg' for longdouble. This method is designed to help cross-validate the format_float_* methods. Parameters ---------- val : python float or numpy floating scalar Value to format. precision : non-negative integer, optional Precision given to printf. Returns ------- rep : string The string representation of the floating point value See Also -------- format_float_scientific format_float_positional """ pass def getset_numericops(*args, **kwargs): # real signature unknown pass def get_buffer_info(*args, **kwargs): # real signature unknown pass def get_c_wrapping_array(*args, **kwargs): # real signature unknown pass def get_fpu_mode(): # real signature unknown; restored from __doc__ """ get_fpu_mode() Get the current FPU control word, in a platform-dependent format. Returns None if not implemented on current platform. 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f5914ce03d87df13e0d6b0781a9456ea2c2c3044

2,433

py

Python

tests.py

Rhadow/python-crawler

4da2e1027900bedfe892e57a6e149a532dc62f3e

[ "MIT" ]

null

tests.py

Rhadow/python-crawler

4da2e1027900bedfe892e57a6e149a532dc62f3e

[ "MIT" ]

null

tests.py

Rhadow/python-crawler

4da2e1027900bedfe892e57a6e149a532dc62f3e

[ "MIT" ]

null

import unittest import collections from src.url_manager import UrlManager class TestUrlManager(unittest.TestCase): def setUp(self): self.manager = UrlManager('www.google.com') def tearDown(self): self.manager = None def test_initial(self): self.assertEqual( self.manager.urls_to_crawl, collections.deque(['www.google.com'])) def test_add_url(self): self.assertEqual( self.manager.urls_to_crawl, collections.deque(['www.google.com'])) self.manager.add_url('www.facebook.com') self.assertEqual( self.manager.urls_to_crawl, collections.deque(['www.google.com', 'www.facebook.com'])) self.manager.add_url('www.facebook.com') self.assertEqual( self.manager.urls_to_crawl, collections.deque(['www.google.com', 'www.facebook.com'])) google_url = self.manager.get_url() self.assertEqual( self.manager.urls_to_crawl, collections.deque(['www.facebook.com'])) self.manager.add_url('www.google.com') self.assertEqual( self.manager.urls_to_crawl, collections.deque(['www.facebook.com'])) def test_add_urls(self): self.assertEqual( self.manager.urls_to_crawl, collections.deque(['www.google.com'])) self.manager.add_urls(['www.facebook.com', 'www.twitter.com']) self.assertEqual( self.manager.urls_to_crawl, collections.deque( ['www.google.com', 'www.facebook.com', 'www.twitter.com']) ) def test_get_url(self): self.assertEqual( self.manager.urls_to_crawl, collections.deque(['www.google.com'])) self.assertEqual(self.manager.has_url(), True) google_url = self.manager.get_url() self.assertEqual(google_url, 'www.google.com') self.assertEqual( self.manager.urls_to_crawl, collections.deque([])) def test_has_url(self): self.assertEqual( self.manager.urls_to_crawl, collections.deque(['www.google.com'])) self.assertEqual(self.manager.has_url(), True) google_url = self.manager.get_url() self.assertEqual(self.manager.has_url(), False) if __name__ == '__main__': url_manager_test_suite = unittest.TestLoader().loadTestsFromTestCase( TestUrlManager) unittest.TextTestRunner(verbosity=2).run(url_manager_test_suite)

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0

null

0

1

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1

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null

0

6

f5aa1ce13ea362436d63973dcf612e6adcab4597

1,851

py

Python

IntelligenceInput/src/loaddict.py

SportsTHU/NLP_and_AI_Intro

b83f0181a891ae93684017f4829a4597b6c2aec9

[ "MIT" ]

1

2020-06-02T08:47:37.000Z

IntelligenceInput/src/loaddict.py

YijiaShaw/NLP_and_AI_Intro

b83f0181a891ae93684017f4829a4597b6c2aec9

[ "MIT" ]

null

IntelligenceInput/src/loaddict.py

YijiaShaw/NLP_and_AI_Intro

b83f0181a891ae93684017f4829a4597b6c2aec9

[ "MIT" ]

null

import json from config import config def load_dict(triple=False): pinyin_dict = dict() freq_dict = dict() trans_dict = dict() emit_dict = dict() with open(config['DATA_PATH'] + 'pinyin.txt', 'r') as f: for line in f.readlines(): pinyin_dict[line.split()[0]] = line.split()[1:] with open(config['DATA_PATH'] + 'freq_dic.json', 'r') as f: freq_dict = json.load(f) with open(config['DATA_PATH'] + 'trans_dic.json', 'r') as f: trans_dict = json.load(f) with open(config['DATA_PATH'] + 'emit_dic.json', 'r') as f: emit_dict = json.load(f) if not triple: return [pinyin_dict, freq_dict, trans_dict, emit_dict] else: with open(config['DATA_PATH'] + 'bi_freq_dic.json', 'r') as f: bi_freq_dict = json.load(f) with open(config['DATA_PATH'] + 'trip_dic.json', 'r') as f: trip_dict = json.load(f) return [pinyin_dict, freq_dict, trans_dict, emit_dict, bi_freq_dict, trip_dict] ''' def load_dict(): pinyin_dict = dict() freq_dict = dict() trans_dict = dict() emit_dict = dict() with open('../data/pinyin.txt', 'r') as f: for line in f.readlines(): pinyin_dict[line.split()[0]] = line.split()[1:] with open('../data/freq_dic.json', 'r') as f: freq_dict = json.load(f) with open('../data/trans_dic.json', 'r') as f: trans_dict = json.load(f) with open('../data/emit_dic.json', 'r') as f: emit_dict = json.load(f) with open('../data/bi_freq_dic.json', 'r') as f: bi_freq_dict = json.load(f) with open('../data/trip_dic.json', 'r') as f: trip_dict = json.load(f) return [pinyin_dict, freq_dict, trans_dict, emit_dict, bi_freq_dict, trip_dict] '''

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5fe35fa3785b1f6d3cc00f0b80d4596a09251372

96

py

Python

venv/lib/python3.8/site-packages/packaging/markers.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/packaging/markers.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/packaging/markers.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/96/9c/15/2fb9d9c3eb892684a7d2505feb22caf3d822021c5e119bf182726797da

96

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py

Python

tests/__init__.py

fossabot/superstructure

f4ab5cac269fb3dedfbd3a54c441af23edf3840b

[ "MIT" ]

null

tests/__init__.py

fossabot/superstructure

f4ab5cac269fb3dedfbd3a54c441af23edf3840b

[ "MIT" ]

null

tests/__init__.py

fossabot/superstructure

f4ab5cac269fb3dedfbd3a54c441af23edf3840b

[ "MIT" ]

null

from .test_bewusstsein import * from .test_logik import *

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py

Python

tokendealer/tokendealer/__init__.py

PacktPublishing/Python-Microservices-Development-2nd-Edition

bbd0ed0f2f26e91cf589e539a70666057dc880eb

[ "MIT" ]

16

2021-08-28T13:46:53.000Z

2022-03-21T18:09:57.000Z

tokendealer/tokendealer/__init__.py

saibaldas/Python-Microservices-Development-2nd-Edition

bbd0ed0f2f26e91cf589e539a70666057dc880eb

[ "MIT" ]

null

tokendealer/tokendealer/__init__.py

saibaldas/Python-Microservices-Development-2nd-Edition

bbd0ed0f2f26e91cf589e539a70666057dc880eb

[ "MIT" ]

15

2021-08-19T03:49:17.000Z

2022-03-23T13:53:33.000Z

from tokendealer.app import app # NOQA

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py

Python

BSpline/b_spline.py

NovemberChopin/GuideLine

d49b3b527a5e54f3ee734c8d5245efb89150d594

[ "MIT" ]

null

BSpline/b_spline.py

NovemberChopin/GuideLine

d49b3b527a5e54f3ee734c8d5245efb89150d594

[ "MIT" ]

null

BSpline/b_spline.py

NovemberChopin/GuideLine

d49b3b527a5e54f3ee734c8d5245efb89150d594

[ "MIT" ]

1

2022-02-28T11:58:47.000Z

import parameter_selection as ps import numpy as np import bspline_curve as bc import bspline_surface as bs import matplotlib.pyplot as plt ''' 通过给出的一些轨迹点，反求控制点，画出B样条曲线 ''' def curve_inter_figure(): ''' Input: Data points ''' D_X = [1, 1, 0, -0.5, 1.5, 3, 4, 4.2, 4] D_Y = [0, 1, 2, 3, 4, 3.5, 3, 2.5, 2] D = [D_X, D_Y] D_N = len(D_X) k = 3 # degree ''' Step 1. Calculate parameters ''' # p_uniform = ps.uniform_spaced(D_N) # print(p_uniform) # p_chord_length = ps.chord_length(D_N, D) # print(p_chord_length) p_centripetal = ps.centripetal(D_N, D) # print(p_centripetal) ''' Step 2. Calculate knot vector ''' knot = ps.knot_vector(p_centripetal, k, D_N) print(knot) ''' Step 3. Calculate control points ''' P_inter = bc.curve_interpolation(D, D_N, k, p_centripetal, knot) # print(P_inter) fig = plt.figure() for i in range(D_N): plt.scatter(D[0][i], D[1][i], color='r') plt.scatter(P_inter[0][i], P_inter[1][i], color='b') for i in range(D_N - 1): tmp_x = [P_inter[0][i], P_inter[0][i+1]] tmp_y = [P_inter[1][i], P_inter[1][i+1]] plt.plot(tmp_x, tmp_y, color='b') ''' Step 4. Calculate the points on the b-spline curve ''' piece_num = 80 p_piece = np.linspace(0, 1, piece_num) P_piece = bc.curve(P_inter, D_N, k, p_piece, knot) # print(P_piece) for i in range(piece_num - 1): tmp_x = [P_piece[0][i], P_piece[0][i+1]] tmp_y = [P_piece[1][i], P_piece[1][i+1]] plt.plot(tmp_x, tmp_y, color='g') plt.show() def curve_approx_figure(D_X , D_Y): # D_X = [1, 1, 0, -0.5, 1.5, 3, 4, 4.2, 4] # D_Y = [0, 1, 2, 3, 4, 3.5, 3, 2.5, 2] D = [D_X, D_Y] D_N = len(D_X) k = 4 # degree H = 8 # the number of control points ''' Step 1. Calculate the parameters ''' p_centripetal = ps.centripetal(D_N, D) ''' Step 2. Calculate the knot vector ''' knot = ps.knot_vector(p_centripetal, k, D_N) ''' Step 3. Calculate the control points ''' P_control = bc.curve_approximation(D, D_N, H, k, p_centripetal, knot) # print(P_control) fig = plt.figure() for i in range(H): plt.scatter(P_control[0][i], P_control[1][i], color='b') for i in range(D_N): plt.scatter(D[0][i], D[1][i], color='r') # for i in range(H - 1): # tmp_x = [P_control[0][i], P_control[0][i+1]] # tmp_y = [P_control[1][i], P_control[1][i+1]] # plt.plot(tmp_x, tmp_y, color='b') ''' Step 4. Calculate the points on the b-spline curve ''' piece_num = 80 p_piece = np.linspace(0, 1, piece_num) p_centripetal_new = ps.centripetal(H, P_control) knot_new = ps.knot_vector(p_centripetal_new, k, H) P_piece = bc.curve(P_control, H, k, p_piece, knot_new) # print(P_piece) for i in range(piece_num - 1): tmp_x = [P_piece[0][i], P_piece[0][i+1]] tmp_y = [P_piece[1][i], P_piece[1][i+1]] plt.plot(tmp_x, tmp_y, color='g') plt.show() # plt.savefig("./test.png") def surface_inter_figure(): D_X = [[0.0, 3, 6, 7], [0.0, 3, 6, 7], [0.0, 3, 6, 7]] D_Y = [[2, 2, 2, 2], [5, 5, 5, 5], [10, 10, 10, 10]] D_Z = [[0, -2, -5, -8], [0, -3, -5, -9], [0, -2, -5, -8]] D = [D_X, D_Y, D_Z] k = 2 q = 2 ''' Step 1. Calculate control surface's control points ''' P_control, knot_uv = bs.surface_interpolation(D, k, q) ''' Step 2. Calculate the points on the b-spline surface ''' piece_uv = [20, 30] P_piece = bs.surface(P_control, k, q, piece_uv, knot_uv) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') for i in range(len(D_X)): for j in range(len(D_X[0])): ax.scatter(D_X[i][j], D_Y[i][j], D_Z[i][j], color='r') ax.scatter(P_control[0][i][j], P_control[1][i][j], P_control[2][i][j], color='b') for i in range(len(D_X)): for j in range(len(D_X[0]) - 1): tmp_x = [P_control[0][i][j], P_control[0][i][j + 1]] tmp_y = [P_control[1][i][j], P_control[1][i][j + 1]] tmp_z = [P_control[2][i][j], P_control[2][i][j + 1]] ax.plot(tmp_x, tmp_y, tmp_z, color='b') for i in range(len(D_X)-1): for j in range(len(D_X[0])): tmp_x = [P_control[0][i][j], P_control[0][i + 1][j]] tmp_y = [P_control[1][i][j], P_control[1][i + 1][j]] tmp_z = [P_control[2][i][j], P_control[2][i + 1][j]] ax.plot(tmp_x, tmp_y, tmp_z, color='b') for i in range(len(P_piece[0])-1): for j in range(len(P_piece[0][0])): tmp_x = [P_piece[0][i][j], P_piece[0][i+1][j]] tmp_y = [P_piece[1][i][j], P_piece[1][i+1][j]] tmp_z = [P_piece[2][i][j], P_piece[2][i+1][j]] ax.plot(tmp_x, tmp_y, tmp_z, color='g') for i in range(len(P_piece[0])): for j in range(len(P_piece[0][0])-1): tmp_x = [P_piece[0][i][j], P_piece[0][i][j+1]] tmp_y = [P_piece[1][i][j], P_piece[1][i][j+1]] tmp_z = [P_piece[2][i][j], P_piece[2][i][j+1]] ax.plot(tmp_x, tmp_y, tmp_z, color='g') plt.show() def surface_approx_figure(): D_X = [[0.0, 3, 6, 7, 9, 15], [0.0, 3, 6, 7, 9, 15], [0.0, 3, 6, 7, 9, 15], [0.0, 3, 6, 7, 9, 15], [0.0, 3, 6, 7, 9, 15]] D_Y = [[2, 2, 2, 2, 2, 2], [5, 5, 5, 5, 5, 5], [10, 10, 10, 10, 10, 10], [15, 15, 15, 15, 15, 15], [20, 20, 20, 20, 20, 20]] D_Z = [[0, -2, -5, -8, -10, -14], [0, -3, -5, -9, -12, -15], [0, -2, -5, -8, -11, -16], [-1, -4, -6, -8, -11.5, -15], [1, -2, -4, -8, -11, -16]] D = [D_X, D_Y, D_Z] k = 2 q = 3 E = len(D_X) - 1 F = len(D_X[0]) - 1 ''' Step 1. Calculate control surface's control points ''' P_control = bs.surface_approximation(D, k, q, E, F) ''' Step 2. Calculate the points on the b-spline surface ''' piece_uv = [20, 30] knot_uv =[[], []] tmp_param = np.zeros((1, E)) for i in range(F): D_col_X = [x[i] for x in P_control[0]] D_col_Y = [y[i] for y in P_control[1]] D_col = [D_col_X, D_col_Y] tmp_param = tmp_param + np.array(ps.centripetal(E, D_col)) param_u = np.divide(tmp_param, F).tolist()[0] param_v = [] tmp_param = np.zeros((1, F)) for i in range(E): D_row_X = P_control[0][i] D_row_Y = P_control[1][i] D_row = [D_row_X, D_row_Y] tmp_param = tmp_param + np.array(ps.centripetal(F, D_row)) # param_v.append(np.average(np.array(tmp_param))) param_v = np.divide(tmp_param, E).tolist()[0] knot_uv[0] = ps.knot_vector(param_u, k, E) knot_uv[1] = ps.knot_vector(param_v, q, F) P_piece = bs.surface(P_control, k, q, piece_uv, knot_uv) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') for i in range(len(D_X)): for j in range(len(D_X[0])): ax.scatter(D_X[i][j], D_Y[i][j], D_Z[i][j], color='r') for i in range(len(P_control[0])): for j in range(len(P_control[0][0])): ax.scatter(P_control[0][i][j], P_control[1][i][j], P_control[2][i][j], color='b') for i in range(len(P_control[0])): for j in range(len(P_control[0][0]) - 1): tmp_x = [P_control[0][i][j], P_control[0][i][j + 1]] tmp_y = [P_control[1][i][j], P_control[1][i][j + 1]] tmp_z = [P_control[2][i][j], P_control[2][i][j + 1]] ax.plot(tmp_x, tmp_y, tmp_z, color='b') for i in range(len(P_control[0]) - 1): for j in range(len(P_control[0][0])): tmp_x = [P_control[0][i][j], P_control[0][i + 1][j]] tmp_y = [P_control[1][i][j], P_control[1][i + 1][j]] tmp_z = [P_control[2][i][j], P_control[2][i + 1][j]] ax.plot(tmp_x, tmp_y, tmp_z, color='b') for i in range(len(P_piece[0]) - 1): for j in range(len(P_piece[0][0])): tmp_x = [P_piece[0][i][j], P_piece[0][i + 1][j]] tmp_y = [P_piece[1][i][j], P_piece[1][i + 1][j]] tmp_z = [P_piece[2][i][j], P_piece[2][i + 1][j]] ax.plot(tmp_x, tmp_y, tmp_z, color='g') for i in range(len(P_piece[0])): for j in range(len(P_piece[0][0]) - 1): tmp_x = [P_piece[0][i][j], P_piece[0][i][j + 1]] tmp_y = [P_piece[1][i][j], P_piece[1][i][j + 1]] tmp_z = [P_piece[2][i][j], P_piece[2][i][j + 1]] ax.plot(tmp_x, tmp_y, tmp_z, color='g') plt.show() def get_control_point(D_X , D_Y): # D_X = [1, 1, 0, -0.5, 1.5, 3, 4, 4.2, 4] # D_Y = [0, 1, 2, 3, 4, 3.5, 3, 2.5, 2] D = [D_X, D_Y] D_N = len(D_X) k = 4 # degree H = 8 # the number of control points ''' Step 1. Calculate the parameters ''' p_centripetal = ps.centripetal(D_N, D) ''' Step 2. Calculate the knot vector ''' knot = ps.knot_vector(p_centripetal, k, D_N) ''' Step 3. Calculate the control points ''' P_control = bc.curve_approximation(D, D_N, H, k, p_centripetal, knot) print(P_control) fig = plt.figure() for i in range(H): plt.scatter(P_control[0][i], P_control[1][i], color='b') # for i in range(D_N): # plt.scatter(D[0][i], D[1][i], color='r') # for i in range(H - 1): # tmp_x = [P_control[0][i], P_control[0][i+1]] # tmp_y = [P_control[1][i], P_control[1][i+1]] # plt.plot(tmp_x, tmp_y, color='b') ''' Step 4. Calculate the points on the b-spline curve ''' # piece_num = 80 # p_piece = np.linspace(0, 1, piece_num) # p_centripetal_new = ps.centripetal(H, P_control) # knot_new = ps.knot_vector(p_centripetal_new, k, H) # P_piece = bc.curve(P_control, H, k, p_piece, knot_new) # # print(P_piece) # for i in range(piece_num - 1): # tmp_x = [P_piece[0][i], P_piece[0][i+1]] # tmp_y = [P_piece[1][i], P_piece[1][i+1]] # plt.plot(tmp_x, tmp_y, color='g') plt.show() return P_control curve_inter_figure() # # # curve_approx_figure() # # surface_inter_figure() # # surface_approx_figure()

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27bdda156a5ae3c14222f65263d069e7a5575ddb

46

py

Python

backend/app/models/__init__.py

adriangb/fastapi-blog

e4db152065648470ad7c2116b84edcdf289f3efc

[ "MIT" ]

57

2021-02-22T02:21:57.000Z

2022-03-25T07:30:09.000Z

backend/app/models/__init__.py

adriangb/fastapi-blog

e4db152065648470ad7c2116b84edcdf289f3efc

[ "MIT" ]

5

2021-05-30T22:21:55.000Z

2022-01-22T23:41:51.000Z

backend/app/models/__init__.py

adriangb/fastapi-blog

e4db152065648470ad7c2116b84edcdf289f3efc

[ "MIT" ]

5

2021-10-09T20:14:43.000Z

2022-02-21T07:31:54.000Z

from .post import Post from .user import User

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py

Python

Driver/drivers/LabBrick_LMS_Synthesizer/__init__.py

Lagikna/QuLab-drivers

badf3f975e38fbf79c5bdd4be16ff9e02c26e74f

[ "MIT" ]

16

2018-03-16T12:08:31.000Z

2022-03-20T08:53:35.000Z

Driver/drivers/LabBrick_LMS_Synthesizer/__init__.py

Lagikna/QuLab-drivers

badf3f975e38fbf79c5bdd4be16ff9e02c26e74f

[ "MIT" ]

148

2018-03-18T09:33:18.000Z

2022-03-21T16:00:15.000Z

qulab/Driver/drivers/LabBrick_LMS_Synthesizer/__init__.py

feihoo87/QuLab

cc16f4777e5523fca327f7f0a9725fd13f9b057f

[ "MIT" ]

14

2018-03-18T08:00:12.000Z

2020-10-21T12:39:42.000Z

from .LabBrick_LMS_Synthesizer import Driver

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py

Python

meracanapi/apitelemac/__init__.py

meracan/meracan-api

aff04f3d9d0dce46fe0b8ce89394ec22823a0ea4

[ "MIT" ]

null

meracanapi/apitelemac/__init__.py

meracan/meracan-api

aff04f3d9d0dce46fe0b8ce89394ec22823a0ea4

[ "MIT" ]

null

meracanapi/apitelemac/__init__.py

meracan/meracan-api

aff04f3d9d0dce46fe0b8ce89394ec22823a0ea4

[ "MIT" ]

null

from .apitelemac import ApiTelemac

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py

Python

__init__.py

RyouZhang/py_es_dsl

1564ffdaf6da5b00b20eca87db5781279301ab18

[ "MIT" ]

1

2017-08-28T02:53:38.000Z

__init__.py

RyouZhang/py_es_dsl

1564ffdaf6da5b00b20eca87db5781279301ab18

[ "MIT" ]

null

__init__.py

RyouZhang/py_es_dsl

1564ffdaf6da5b00b20eca87db5781279301ab18

[ "MIT" ]

null

from util.elasticsearch.dsl import * from util.elasticsearch.aggr import *

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py

Python

eod/models/__init__.py

Helicopt/EOD

b5db36f4ce267bf64d093b8174bde2c4097b4718

[ "Apache-2.0" ]

1

2021-11-24T09:32:27.000Z

eod/models/__init__.py

jinfagang/EOD

a45b74430070d82d9248a10fb5e1116bb7ababe1

[ "Apache-2.0" ]

null

eod/models/__init__.py

jinfagang/EOD

a45b74430070d82d9248a10fb5e1116bb7ababe1

[ "Apache-2.0" ]

null

from .backbones import * # noqa from .heads import * # noqa from .necks import * # noqa from .model_helper import * # noqa from .utils import * # noqa from .postprocess import * # noqa

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8b7af415bdb496f6e0ee8e0c29770e5f09d15cbc

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py

Python

anomaly_detection/networks/stack_conv_net.py

leggedrobotics/anomaly_navigation

e4d87a1b67904e0537de3fa4b3e53bc4932d1681

[ "MIT" ]

21

2020-01-14T19:05:14.000Z

2022-02-03T11:12:26.000Z

anomaly_detection/networks/stack_conv_net.py

leggedrobotics/anomaly_navigation

e4d87a1b67904e0537de3fa4b3e53bc4932d1681

[ "MIT" ]

1

2020-01-15T10:00:05.000Z

2020-01-15T10:55:44.000Z

anomaly_detection/networks/stack_conv_net.py

leggedrobotics/anomaly_navigation

e4d87a1b67904e0537de3fa4b3e53bc4932d1681

[ "MIT" ]

9

2020-01-14T19:04:46.000Z

2021-11-14T13:42:56.000Z

import torch import torch.nn as nn import torch.nn.functional as F from anomaly_detection.base.base_net import BaseNet class StackConvNet(BaseNet): def __init__(self, in_channels=3, use_bn=False, use_dropout=True): super().__init__() self.rep_dim = 128 self.pool = nn.MaxPool2d(2, 2) self.use_bn = use_bn self.use_dropout = use_dropout if use_dropout: self.drop = nn.Dropout2d(p=0.05) self.conv1 = nn.Conv2d(in_channels, 32, 5, bias=False) self.conv2 = nn.Conv2d(32, 64, 5, bias=False) self.conv3 = nn.Conv2d(64, 128, 5, bias=False) self.fconv1 = nn.Conv2d(128, self.rep_dim, 1, bias=False) if use_bn: self.bn2d1 = nn.BatchNorm2d(32, eps=1e-04, affine=False) self.bn2d2 = nn.BatchNorm2d(64, eps=1e-04, affine=False) self.bn2d3 = nn.BatchNorm2d(128, eps=1e-04, affine=False) def forward(self, x): x = self.conv1(x) if self.use_bn: x = self.bn2d1(x) x = F.leaky_relu(x) if self.use_dropout: x = self.drop(x) x = self.pool(x) x = self.conv2(x) if self.use_bn: x = self.bn2d2(x) x = F.leaky_relu(x) if self.use_dropout: x = self.drop(x) x = self.pool(x) x = self.conv3(x) if self.use_bn: x = self.bn2d3(x) x = F.leaky_relu(x) if self.use_dropout: x = self.drop(x) x = self.fconv1(x) return x class StackConvNet_Autoencoder(BaseNet): def __init__(self, in_channels=3, use_bn=False, use_dropout=True): super().__init__() self.rep_dim = 128 self.pool = nn.MaxPool2d(2, 2) self.use_bn = use_bn self.use_dropout = use_dropout if use_dropout: self.drop = nn.Dropout2d(p=0.05) # Encoder (must match the network above) self.conv1 = nn.Conv2d(in_channels, 32, 5, bias=False) nn.init.xavier_uniform_(self.conv1.weight, gain=nn.init.calculate_gain('leaky_relu')) self.conv2 = nn.Conv2d(32, 64, 5, bias=False) nn.init.xavier_uniform_(self.conv2.weight, gain=nn.init.calculate_gain('leaky_relu')) self.conv3 = nn.Conv2d(64, 128, 5, bias=False) nn.init.xavier_uniform_(self.conv3.weight, gain=nn.init.calculate_gain('leaky_relu')) self.fconv1 = nn.Conv2d(128, self.rep_dim, 1, bias=False) if use_bn: self.bn2d1 = nn.BatchNorm2d(32, eps=1e-04, affine=False) self.bn2d2 = nn.BatchNorm2d(64, eps=1e-04, affine=False) self.bn2d3 = nn.BatchNorm2d(128, eps=1e-04, affine=False) self.bn2d = nn.BatchNorm2d(self.rep_dim, eps=1e-04, affine=False) # Decoder self.deconv1 = nn.ConvTranspose2d(self.rep_dim, 128, 1, bias=False) nn.init.xavier_uniform_(self.deconv1.weight, gain=nn.init.calculate_gain('leaky_relu')) self.deconv2 = nn.ConvTranspose2d(128, 64, 5, bias=False) nn.init.xavier_uniform_(self.deconv2.weight, gain=nn.init.calculate_gain('leaky_relu')) self.deconv3 = nn.ConvTranspose2d(64, 32, 5, bias=False) nn.init.xavier_uniform_(self.deconv3.weight, gain=nn.init.calculate_gain('leaky_relu')) self.deconv4 = nn.ConvTranspose2d(32, in_channels, 5, bias=False) nn.init.xavier_uniform_(self.deconv4.weight, gain=nn.init.calculate_gain('leaky_relu')) if use_bn: self.bn2d4 = nn.BatchNorm2d(128, eps=1e-04, affine=False) self.bn2d5 = nn.BatchNorm2d(64, eps=1e-04, affine=False) self.bn2d6 = nn.BatchNorm2d(32, eps=1e-04, affine=False) def forward(self, x): x = self.conv1(x) if self.use_bn: x = self.bn2d1(x) x = F.leaky_relu(x) if self.use_dropout: x = self.drop(x) x = self.pool(x) x = self.conv2(x) if self.use_bn: x = self.bn2d2(x) x = F.leaky_relu(x) if self.use_dropout: x = self.drop(x) x = self.pool(x) x = self.conv3(x) if self.use_bn: x = self.bn2d3(x) x = F.leaky_relu(x) if self.use_dropout: x = self.drop(x) x = self.fconv1(x) if self.use_bn: x = self.bn2d(x) x = F.leaky_relu(x) if self.use_dropout: x = self.drop(x) x = self.deconv1(x) if self.use_bn: x = self.bn2d4(x) x = F.leaky_relu(x) if self.use_dropout: x = self.drop(x) x = self.deconv2(x) if self.use_bn: x = self.bn2d5(x) x = F.leaky_relu(x) if self.use_dropout: x = self.drop(x) x = F.interpolate(x, scale_factor=2) x = self.deconv3(x) if self.use_bn: x = self.bn2d6(x) x = F.leaky_relu(x) if self.use_dropout: x = self.drop(x) x = F.interpolate(x, scale_factor=2) x = self.deconv4(x) x = torch.tanh(x) return x

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1

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null

0

6

8b879a0327379ab25a17d9ea55eb7f0350e0aa5c

3,602

py

Python

test/utils.py

gelijergensen/PermutationImportance

7a09a407e42745c223055e0597c5226ff64b2f3c

[ "MIT" ]

4

2019-02-01T17:49:14.000Z

2020-06-25T15:09:56.000Z

test/utils.py

gelijergensen/PermutationImportance

7a09a407e42745c223055e0597c5226ff64b2f3c

[ "MIT" ]

42

2018-09-27T19:35:32.000Z

2020-10-09T17:56:57.000Z

test/utils.py

gelijergensen/PermutationImportance

7a09a407e42745c223055e0597c5226ff64b2f3c

[ "MIT" ]

4

2018-09-27T19:34:33.000Z

2021-02-12T19:41:31.000Z

"""These are just a handful of functions which are useful only for helping run certain tests Note: These tests aren't automatically run by pytest, but can be manually run by calling pytest on this file""" import numpy as np def make_test_data(): """This is a useful tool to help with making a dataset where the relative ranks of the variable importances is known""" count = 750 class_0 = np.random.normal(size=(count, 3)) * \ np.array([4, 2, 1]) + np.array([0, 11, 3]) class_1 = np.random.normal(size=(count, 3)) * \ np.array([4, 2, 1]) + np.array([-3, 8, 0]) training_inputs = np.concatenate((class_0[:-50], class_1[:-50]), axis=0) scoring_inputs = np.concatenate((class_0[-50:], class_1[-50:]), axis=0) training_outputs = np.array([(0 if i < count-50 else 1) for i in range(2*count - 100)]) scoring_outputs = np.array([(0 if i < 50 else 1) for i in range(100)]) indices = np.random.permutation(2*count - 100) training_inputs = training_inputs[indices] training_outputs = training_outputs[indices] indices = np.random.permutation(100) scoring_inputs = scoring_inputs[indices] scoring_outputs = scoring_outputs[indices] return (training_inputs, training_outputs), (scoring_inputs, scoring_outputs) def test_make_test_data(): (training_inputs, training_outputs), (scoring_inputs, scoring_outputs) = make_test_data() assert len(training_inputs) == len(training_outputs) assert len(training_inputs) == 400 assert len(scoring_inputs) == len(scoring_outputs) assert len(scoring_inputs) == 100 assert training_inputs.shape[1] == scoring_inputs.shape[1] assert len(np.unique(scoring_outputs)) == len(np.unique(training_outputs)) assert len(np.unique(scoring_outputs)) == 2 def make_proba_test_data(): """This is a useful tool to help with making a dataset where the relative ranks of the variable importances is known""" class_0 = np.random.uniform(size=(250, 3)) * \ np.array([4, 2, 1]) + np.array([-4, 9, 1]) class_1 = np.random.uniform(size=(250, 3)) * \ np.array([4, 2, 1]) + np.array([-5, 7, -1]) training_inputs = np.concatenate((class_0[:200], class_1[:200]), axis=0) scoring_inputs = np.concatenate((class_0[200:], class_1[200:]), axis=0) training_outputs = np.array([(0 if i < 200 else 1) for i in range(400)]) scoring_outputs = np.array([(0 if i < 50 else 1) for i in range(100)]) indices = np.random.permutation(400) training_inputs = training_inputs[indices] training_outputs = np.stack( (training_outputs[indices], 1 - training_outputs[indices]), axis=-1) indices = np.random.permutation(100) scoring_inputs = scoring_inputs[indices] scoring_outputs = np.stack( (scoring_outputs[indices], 1 - scoring_outputs[indices]), axis=-1) return (training_inputs, training_outputs), (scoring_inputs, scoring_outputs) def test_make_proba_test_data(): (training_inputs, training_outputs), (scoring_inputs, scoring_outputs) = make_proba_test_data() assert len(training_inputs) == len(training_outputs) assert len(training_inputs) == 400 assert len(scoring_inputs) == len(scoring_outputs) assert len(scoring_inputs) == 100 assert training_inputs.shape[1] == scoring_inputs.shape[1] assert len(np.unique(scoring_outputs)) == len(np.unique(training_outputs)) assert len(np.unique(scoring_outputs)) == 2 assert scoring_outputs.shape[1] == 2

43.926829

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3,602

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0.182879

0.108062

0.056604

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null

0

1

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null

0

6

47386f424134c12bf1632376029f3fb9c2af5711

34

py

Python

illuminate/__init__.py

donowsolutions/illuminate

6e425363d22bbc2b24966321872ab6eb6936c68c

[ "MIT" ]

null

illuminate/__init__.py

donowsolutions/illuminate

6e425363d22bbc2b24966321872ab6eb6936c68c

[ "MIT" ]

null

illuminate/__init__.py

donowsolutions/illuminate

6e425363d22bbc2b24966321872ab6eb6936c68c

[ "MIT" ]

null

from .illuminate import Illuminate

34

0.882353

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34

7.5

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34

0.967742

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null

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null

0

1

0

1

0

1

0

6

473cdaa58ce6a54be59c6e0ee48947de2b2721b2

201

py

Python

heksher/api/v1/__init__.py

biocatchltd/Heksher

b50b3659a606cb188437adb1f95747efb3ba7b59

[ "MIT" ]

3

2021-01-21T11:41:06.000Z

2021-10-20T06:51:53.000Z

heksher/api/v1/__init__.py

biocatchltd/Heksher

b50b3659a606cb188437adb1f95747efb3ba7b59

[ "MIT" ]

18

2021-02-01T06:38:53.000Z

2022-02-14T13:46:33.000Z

heksher/api/v1/__init__.py

biocatchltd/Heksher

b50b3659a606cb188437adb1f95747efb3ba7b59

[ "MIT" ]

null

import heksher.api.v1.context_features # noqa: F401 import heksher.api.v1.rules # noqa: F401 import heksher.api.v1.settings # noqa: F401 from heksher.api.v1.util import router __all__ = ['router']

28.714286

52

0.756219

31

201

4.741935

0.451613

0.272109

0.326531

0.367347

0.353742

0

0.074286

0.129353

201

6

53

33.5

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0.8

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1

0

1

0

null

0

1

0

1

0

6

473da47703546ae7fc724a9b57b964921f7119d2

96

py

Python

privugger/distributions/__init__.py

itu-square/reident

a9f2a2cfb43ea0adeccbbed7ef119f5eae243bf5

[ "Apache-2.0" ]

2

2021-12-10T13:45:37.000Z

2021-12-15T08:32:01.000Z

privugger/distributions/__init__.py

itu-square/reident

a9f2a2cfb43ea0adeccbbed7ef119f5eae243bf5

[ "Apache-2.0" ]

39

2021-03-24T10:08:50.000Z

2022-03-29T22:02:24.000Z

privugger/distributions/__init__.py

itu-square/privugger

9b57605dbd1ed072feaedc17ca0cd688dbf2459a

[ "Apache-2.0" ]

null

from privugger.distributions.discrete import * from privugger.distributions.continuous import *

32

48

0.854167

10

96

8.2

0.6

0.317073

0.634146

0

0.083333

96

2

49

48

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1

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true

0

1

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0

1

0

null

0

1

0

1

0

6

47b4f893b0d381de0c08cd50693b0d7ba2df916b

107

py

Python

controller/storage/src/const.py

urabe0225/ogc-poc1

5185d55b7df8d2ca1d0d6625a28763c6903ed0f4

[ "Apache-2.0" ]

null

controller/storage/src/const.py

urabe0225/ogc-poc1

5185d55b7df8d2ca1d0d6625a28763c6903ed0f4

[ "Apache-2.0" ]

2

2018-10-16T04:19:19.000Z

2018-10-22T07:33:09.000Z

controller/storage/src/const.py

urabe0225/ogc-poc1

5185d55b7df8d2ca1d0d6625a28763c6903ed0f4

[ "Apache-2.0" ]

2

2018-08-25T12:10:43.000Z

2019-12-27T01:47:13.000Z

# -*- coding: utf-8 -*- # environment variable name FACE_UPLOAD_DIR_FULLPATH = 'FACE_UPLOAD_DIR_FULLPATH'

21.4

53

0.757009

14

107

5.357143

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0.266667

0.346667

0.56

0

0.010638

0.121495

107

4

54

26.75

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0.439252

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0.421053

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false

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null

1

0

1

0

1

null

0

6

9a54628f383130542636e8dc8acf901525f41e0b

5,795

py

Python

app/app/calculation_service/tests/test_restful_api.py

alasdair-macleod/demoappback

fedd555279a1e3b91172face7cec41eb158cbf0f

[ "MIT" ]

null

app/app/calculation_service/tests/test_restful_api.py

alasdair-macleod/demoappback

fedd555279a1e3b91172face7cec41eb158cbf0f

[ "MIT" ]

1

2019-01-16T15:45:31.000Z

app/app/calculation_service/tests/test_restful_api.py

alasdair-macleod/demoappback

fedd555279a1e3b91172face7cec41eb158cbf0f

[ "MIT" ]

1

2017-10-30T14:34:41.000Z

import main import unittest from json import loads class MainTestCase(unittest.TestCase): def setUp(self): main.app.testing = True self.app = main.app.test_client() def test_Hello_World(self): """Should response Hello World!""" get_test = self.app.get('/') self.assertEqual(b'Hello World!', get_test.data) def test_calculate_fail(self): """Should response fail message and with one warning message.""" json_content = """{"tolerance":100, "_isuFactors":{"uMatrix":{"name":"","logger":null,"_values":{"mathjs":"DenseMatrix","data":[],"size":[0]},"_type":"All mean differences zero"},"variables":[{"valueNames":[],"name":"1","inHypothesis":false,"isuFactorNature":"All mean differences zero","nature":"Within","origin":"Outcome","standardDeviation":1},{"valueNames":[],"name":"2","inHypothesis":false,"isuFactorNature":"All mean differences zero","nature":"Within","origin":"Outcome","standardDeviation":1},{"valueNames":["1","2"],"inHypothesis":true,"isuFactorNature":"All mean differences zero","nature":"Between","origin":"Between ISU Predictor","name":"p1","type":"ORDINAL","units":"","child":{"valueNames":["3","4"],"inHypothesis":true,"isuFactorNature":"All mean differences zero","nature":"Between","origin":"Between ISU Predictor","name":"p2","type":"ORDINAL","units":"","child":null}},{"valueNames":["3","4"],"inHypothesis":true,"isuFactorNature":"All mean differences zero","nature":"Between","origin":"Between ISU Predictor","name":"p2","type":"ORDINAL","units":"","child":null}],"betweenIsuRelativeGroupSizes":[{"_tableId":null,"dimensions":[{"order":0,"factorName":"p1","factorType":"Between ISU Predictor","value":"1"},{"order":0,"factorName":"p2","factorType":"Between ISU Predictor","value":"3"}],"_table":[[{"value":1,"id":[{"order":0,"factorName":"p1","factorType":"Between ISU Predictor","value":"1"},{"order":0,"factorName":"p2","factorType":"Between ISU Predictor","value":"3"}]},{"value":1,"id":[{"order":0,"factorName":"p1","factorType":"Between ISU Predictor","value":"1"},{"order":1,"factorName":"p2","factorType":"Between ISU Predictor","value":"4"}]}],[{"value":1,"id":[{"order":1,"factorName":"p1","factorType":"Between ISU Predictor","value":"2"},{"order":0,"factorName":"p2","factorType":"Between ISU Predictor","value":"3"}]},{"value":1,"id":[{"order":1,"factorName":"p1","factorType":"Between ISU Predictor","value":"2"},{"order":1,"factorName":"p2","factorType":"Between ISU Predictor","value":"4"}]}]]}],"marginalMeans":[{"_tableId":{"value":1,"id":[{"order":0,"factorName":"1","factorType":"Outcome","value":""}]},"_table":[[{"value":1,"id":[{"order":0,"factorName":"1","factorType":"Outcome","value":""},{"order":0,"factorName":"p1","factorType":"Between ISU Predictor","value":"1"},{"order":0,"factorName":"p2","factorType":"Between ISU Predictor","value":"3"}]}],[{"value":1,"id":[{"order":0,"factorName":"1","factorType":"Outcome","value":""},{"order":0,"factorName":"p1","factorType":"Between ISU Predictor","value":"1"},{"order":1,"factorName":"p2","factorType":"Between ISU Predictor","value":"4"}]}],[{"value":1,"id":[{"order":0,"factorName":"1","factorType":"Outcome","value":""},{"order":1,"factorName":"p1","factorType":"Between ISU Predictor","value":"2"},{"order":0,"factorName":"p2","factorType":"Between ISU Predictor","value":"3"}]}],[{"value":1,"id":[{"order":0,"factorName":"1","factorType":"Outcome","value":""},{"order":1,"factorName":"p1","factorType":"Between ISU Predictor","value":"2"},{"order":1,"factorName":"p2","factorType":"Between ISU Predictor","value":"4"}]}]]},{"_tableId":{"value":1,"id":[{"order":0,"factorName":"2","factorType":"Outcome","value":""}]},"_table":[[{"value":1,"id":[{"order":0,"factorName":"2","factorType":"Outcome","value":""},{"order":0,"factorName":"p1","factorType":"Between ISU Predictor","value":"1"},{"order":0,"factorName":"p2","factorType":"Between ISU Predictor","value":"3"}]}],[{"value":1,"id":[{"order":0,"factorName":"2","factorType":"Outcome","value":""},{"order":0,"factorName":"p1","factorType":"Between ISU Predictor","value":"1"},{"order":1,"factorName":"p2","factorType":"Between ISU Predictor","value":"4"}]}],[{"value":1,"id":[{"order":0,"factorName":"2","factorType":"Outcome","value":""},{"order":1,"factorName":"p1","factorType":"Between ISU Predictor","value":"2"},{"order":0,"factorName":"p2","factorType":"Between ISU Predictor","value":"3"}]}],[{"value":1,"id":[{"order":0,"factorName":"2","factorType":"Outcome","value":""},{"order":1,"factorName":"p1","factorType":"Between ISU Predictor","value":"2"},{"order":1,"factorName":"p2","factorType":"Between ISU Predictor","value":"4"}]}]]}],"smallestGroupSize":[3],"theta0":[[0,0]],"outcomeCorrelationMatrix":{"_values":{"mathjs":"DenseMatrix","data":[[1,0],[0,1]],"size":[2,2]}}},"_targetEvent":"REJECTION","_solveFor":"POWER","_ciwidth":1,"_power":[0.5],"_selectedTests":["Wilks Likelihood Ratio"],"_typeOneErrorRate":[0.01],"_gaussianCovariate":null,"_scaleFactor":[1],"_varianceScaleFactors":[1,2],"_powerCurve":{"_confidenceInterval":{"assumptions":"Beta Fixed","lowerTailProbability":0,"upperTailProbability":1,"betaSamplesize":10,"betasigmaRank":1},"_xAxis":"DesiredPower","_dataSeries":[]}}""" post_test = self.app.post('/api/calculate', data=json_content, content_type='application/json') result = loads(post_test.data) self.assertEqual(result['results'][0]['power'], 'Your hypothesis and means have been chosen such that there is no difference. As such power can be no greater than your type one error rate. Please change either your hypothesis or your means. ') self.assertEqual(result['status'], 200) if __name__ == '__main__': unittest.main()

193.166667

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0.664528

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5,795

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4,768

199.827586

0.658702

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0.15

false

0

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0.35

0

null

0

1

0

1

0

1

0

1

null

0

6

9a5b46fbec6b6a61cd074af4647b3151bee45fde

38

py

Python

rick_db/conn/sqlite/__init__.py

oddbit-project/rick_db

02910c071f3ad58fdd88b2a27bfdd2bc61497d42

[ "MIT" ]

null

rick_db/conn/sqlite/__init__.py

oddbit-project/rick_db

02910c071f3ad58fdd88b2a27bfdd2bc61497d42

[ "MIT" ]

null

rick_db/conn/sqlite/__init__.py

oddbit-project/rick_db

02910c071f3ad58fdd88b2a27bfdd2bc61497d42

[ "MIT" ]

null

from .sqlite import Sqlite3Connection

19

37

0.868421

4

38

8.25

1

0

0.029412

0.105263

38

1

38

0.941176

0

1

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true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

7bdf6cfa4265debf7d9a64b94bdfc4982837b743

164

py

Python

xrayreader/images/china.py

tb-brics/dorothy-data-reader

918d23c11099134f90939903d0b35288e0492c5c

[ "MIT" ]

null

xrayreader/images/china.py

tb-brics/dorothy-data-reader

918d23c11099134f90939903d0b35288e0492c5c

[ "MIT" ]

null

xrayreader/images/china.py

tb-brics/dorothy-data-reader

918d23c11099134f90939903d0b35288e0492c5c

[ "MIT" ]

null

""" Get data of the images from China DataSet. """ from .reader import ReaderBase class Reader(ReaderBase): """Get data of the images from image dataset."""

16.4

52

0.70122

23

164

5

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Python

src/vbr/tableclasses/associations/__init__.py

a2cps/python-vbr

9d5d4480386d0530450d59157e0da6937320f928

[ "BSD-3-Clause" ]

1

2021-05-26T19:08:29.000Z

src/vbr/tableclasses/associations/__init__.py

a2cps/python-vbr

9d5d4480386d0530450d59157e0da6937320f928

[ "BSD-3-Clause" ]

7

2021-05-04T13:12:39.000Z

2022-03-09T21:04:33.000Z

src/vbr/tableclasses/associations/__init__.py

a2cps/python-vbr

9d5d4480386d0530450d59157e0da6937320f928

[ "BSD-3-Clause" ]

2

2021-04-20T14:46:52.000Z

2021-06-07T20:28:28.000Z

from .describe import * from .event import * from .hierarchy import * from .self_from import * from .self_in import *

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Python

gryphon/wizard/__init__.py

vittorfp/labskit_cli

28e109b4a9f36a03d499eb953e04a4fb787632fe

[ "MIT" ]

null

gryphon/wizard/__init__.py

vittorfp/labskit_cli

28e109b4a9f36a03d499eb953e04a4fb787632fe

[ "MIT" ]

1

2022-03-08T14:54:26.000Z

2022-03-08T15:02:52.000Z

gryphon/wizard/__init__.py

vittorfp/labskit_cli

28e109b4a9f36a03d499eb953e04a4fb787632fe

[ "MIT" ]

null

from .add import add from .init import init from .generate import generate from .about import about from .exit_program import exit_program from .settings import settings

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py

Python

dlpipe/schemas/__init__.py

j-o-d-o/accident_predictor

d193eacfa9451015c184914e7e244becd99aa890

[ "MIT" ]

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dlpipe/schemas/__init__.py

j-o-d-o/accident_predictor

d193eacfa9451015c184914e7e244becd99aa890

[ "MIT" ]

null

dlpipe/schemas/__init__.py

j-o-d-o/accident_predictor

d193eacfa9451015c184914e7e244becd99aa890

[ "MIT" ]

null

from .experiment import ExperimentSchema

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py

Python

app/dao/communication_item_dao.py

department-of-veterans-affairs/notification-api

698bc98d8e78a13a0b2cfc432cfc718ff1016b06

[ "MIT" ]

10

2020-05-04T14:11:06.000Z

2022-02-22T19:06:36.000Z

app/dao/communication_item_dao.py

department-of-veterans-affairs/notification-api

698bc98d8e78a13a0b2cfc432cfc718ff1016b06

[ "MIT" ]

554

2020-05-07T21:56:24.000Z

2022-03-31T23:04:51.000Z

app/dao/communication_item_dao.py

department-of-veterans-affairs/notification-api

698bc98d8e78a13a0b2cfc432cfc718ff1016b06

[ "MIT" ]

4

2020-08-27T16:43:29.000Z

2021-02-17T22:17:27.000Z

import uuid from typing import List from app import db from app.models import CommunicationItem def dao_create_communication_item(communication_item: CommunicationItem): communication_item.id = communication_item.id if communication_item.id else uuid.uuid4() db.session.add(communication_item) def get_communication_items() -> List[CommunicationItem]: return CommunicationItem.query.all() def get_communication_item(communication_item_id) -> CommunicationItem: return CommunicationItem.query.filter_by(id=communication_item_id).one()

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Python

tests/functional/dpg_events/test_dpg_reconfiguration_invalid_vlan.py

atsgen/tf-vcenter-fabric-manager

bb2cf0a0f80464457e1b884847df77a11259077c

[ "Apache-2.0" ]

1

2022-03-13T06:31:49.000Z

tests/functional/dpg_events/test_dpg_reconfiguration_invalid_vlan.py

atsgen/tf-vcenter-fabric-manager

bb2cf0a0f80464457e1b884847df77a11259077c

[ "Apache-2.0" ]

null

tests/functional/dpg_events/test_dpg_reconfiguration_invalid_vlan.py

atsgen/tf-vcenter-fabric-manager

bb2cf0a0f80464457e1b884847df77a11259077c

[ "Apache-2.0" ]

1

2020-08-25T12:44:56.000Z

import pytest from tests import utils from pyVmomi import vim from vnc_api import vnc_api from cvfm import models @pytest.fixture def vmware_dpg_invalid_vlan(): net_data = { "key": "dvportgroup-1", "name": "dpg-1", "type": vim.DistributedVirtualPortgroup, "dvs-name": "dvs-1", "vlan": 0, } return utils.create_vmware_net(net_data) @pytest.fixture def vmware_dpg_valid_vlan(): net_data = { "key": "dvportgroup-1", "name": "dpg-1", "type": vim.DistributedVirtualPortgroup, "dvs-name": "dvs-1", "vlan": 5, } return utils.create_vmware_net(net_data) @pytest.fixture def vmware_vm_1_invalid_dpg(vmware_dpg_invalid_vlan): return utils.create_vmware_vm("vm-1", "esxi-1", [vmware_dpg_invalid_vlan]) @pytest.fixture def vmware_vm_2_invalid_dpg(vmware_dpg_invalid_vlan): return utils.create_vmware_vm("vm-2", "esxi-2", [vmware_dpg_invalid_vlan]) @pytest.fixture def vmware_vm_1_valid_dpg(vmware_dpg_valid_vlan): return utils.create_vmware_vm("vm-1", "esxi-1", [vmware_dpg_valid_vlan]) @pytest.fixture def vmware_vm_2_valid_dpg(vmware_dpg_valid_vlan): return utils.create_vmware_vm("vm-2", "esxi-2", [vmware_dpg_valid_vlan]) def test_dpg_reconfiguration_from_invalid_vlan( topology_with_two_nodes, vnc_test_client, vcenter_api_client, vmware_controller, vmware_dpg_invalid_vlan, vmware_vm_1_invalid_dpg, vmware_vm_2_invalid_dpg, ): # dpg-1 created in dvs-1 with invalid VLAN 0 dpg_created_update = vcenter_api_client.create_dpg(vmware_dpg_invalid_vlan) vmware_controller.handle_update(dpg_created_update) # vm-1 created on host esxi-1 with single interface in (dvs-1, dpg-1) vm_created_update_1 = vcenter_api_client.create_vm(vmware_vm_1_invalid_dpg) vmware_controller.handle_update(vm_created_update_1) # vm-2 created on host esxi-2 with single interface in (dvs-1, dpg-1) vm_created_update_2 = vcenter_api_client.create_vm(vmware_vm_2_invalid_dpg) vmware_controller.handle_update(vm_created_update_2) # No created objects in VNC API for invalid DPG vmis = vnc_test_client.read_all_vmis() assert len(vmis) == 0 with pytest.raises(vnc_api.NoIdError): vnc_test_client.read_vn(models.generate_uuid("dvportgroup-1")) # dpg-1 VLAN reconfigured from 0 to 5 dpg_reconfigured_update = vcenter_api_client.reconfigure_dpg( vmware_dpg_invalid_vlan, 5 ) vmware_controller.handle_update(dpg_reconfigured_update) vnc_vn = vnc_test_client.read_vn(models.generate_uuid("dvportgroup-1")) assert vnc_vn.name == "dvs-1_dpg-1" vmis = vnc_test_client.read_all_vmis() assert len(vmis) == 2 created_vmi = vmis["esxi-1_dvs-1_dpg-1"] utils.verify_vnc_vmi( vnc_vmi=created_vmi, vmi_name="esxi-1_dvs-1_dpg-1", vpg_name="esxi-1_dvs-1", vn_name="dvs-1_dpg-1", vlan=5, ) created_vmi = vmis["esxi-2_dvs-1_dpg-1"] utils.verify_vnc_vmi( vnc_vmi=created_vmi, vmi_name="esxi-2_dvs-1_dpg-1", vpg_name="esxi-2_dvs-1", vn_name="dvs-1_dpg-1", vlan=5, ) def test_dpg_reconfiguration_to_invalid_vlan( topology_with_two_nodes, vnc_test_client, vcenter_api_client, vmware_controller, vmware_dpg_valid_vlan, vmware_vm_1_valid_dpg, vmware_vm_2_valid_dpg, ): # dpg-1 created in dvs-1 with invalid VLAN 0 dpg_created_update = vcenter_api_client.create_dpg(vmware_dpg_valid_vlan) vmware_controller.handle_update(dpg_created_update) # vm-1 created on host esxi-1 with single interface in (dvs-1, dpg-1) vm_created_update_1 = vcenter_api_client.create_vm(vmware_vm_1_valid_dpg) vmware_controller.handle_update(vm_created_update_1) # vm-2 created on host esxi-2 with single interface in (dvs-1, dpg-1) vm_created_update_2 = vcenter_api_client.create_vm(vmware_vm_2_valid_dpg) vmware_controller.handle_update(vm_created_update_2) vnc_vn = vnc_test_client.read_vn(models.generate_uuid("dvportgroup-1")) assert vnc_vn.name == "dvs-1_dpg-1" vmis = vnc_test_client.read_all_vmis() assert len(vmis) == 2 created_vmi = vmis["esxi-1_dvs-1_dpg-1"] utils.verify_vnc_vmi( vnc_vmi=created_vmi, vmi_name="esxi-1_dvs-1_dpg-1", vpg_name="esxi-1_dvs-1", vn_name="dvs-1_dpg-1", vlan=5, ) created_vmi = vmis["esxi-2_dvs-1_dpg-1"] utils.verify_vnc_vmi( vnc_vmi=created_vmi, vmi_name="esxi-2_dvs-1_dpg-1", vpg_name="esxi-2_dvs-1", vn_name="dvs-1_dpg-1", vlan=5, ) # dpg-1 VLAN reconfigured from 5 to 0 dpg_reconfigured_update = vcenter_api_client.reconfigure_dpg( vmware_dpg_valid_vlan, 0 ) vmware_controller.handle_update(dpg_reconfigured_update) vmis = vnc_test_client.read_all_vmis() assert len(vmis) == 0 with pytest.raises(vnc_api.NoIdError): vnc_test_client.read_vn(models.generate_uuid("dvportgroup-1"))

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py

Python

plugins/statsd/komand_statsd/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

46

2019-06-05T20:47:58.000Z

2022-03-29T10:18:01.000Z

plugins/statsd/komand_statsd/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

386

2019-06-07T20:20:39.000Z

2022-03-30T17:35:01.000Z

plugins/statsd/komand_statsd/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

43

2019-07-09T14:13:58.000Z

2022-03-28T12:04:46.000Z

# GENERATED BY KOMAND SDK - DO NOT EDIT from .decr.action import Decr from .gauge.action import Gauge from .incr.action import Incr from .set.action import Set from .timing.action import Timing

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py

Python

clr/__init__.py

manhhv87/densenet_bottleneck

fd08eb88514dacaff1bcec8bc52a77ea56ab72c7

[ "MIT" ]

null

clr/__init__.py

manhhv87/densenet_bottleneck

fd08eb88514dacaff1bcec8bc52a77ea56ab72c7

[ "MIT" ]

null

clr/__init__.py

manhhv87/densenet_bottleneck

fd08eb88514dacaff1bcec8bc52a77ea56ab72c7

[ "MIT" ]

null

from clr.clr_callback import CyclicLR

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ef316f810781d0c4d669448f5b91c7ab5f29c802

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py

Python

src/ml_ner.py

ncbi-nlp/PhenoTagger

e2857068def2580a4c3048682787ce7ae9a8d126

[ "MIT" ]

32

2020-09-29T21:17:19.000Z

2022-03-22T14:06:41.000Z

src/ml_ner.py

ncbi-nlp/PhenoTagger

e2857068def2580a4c3048682787ce7ae9a8d126

[ "MIT" ]

9

2021-03-09T06:04:43.000Z

2022-01-10T13:20:08.000Z

src/ml_ner.py

ncbi-nlp/PhenoTagger

e2857068def2580a4c3048682787ce7ae9a8d126

[ "MIT" ]

4

2021-02-01T19:44:55.000Z

2022-03-03T04:20:22.000Z

# -*- coding: utf-8 -*- """ Created on Fri Jun 12 16:41:54 2020 @author: luol2 """ import io import time import numpy as np def ml_intext(infile): fin=open(infile,'r',encoding='utf-8') alltexts=fin.read().strip().split('\n\n') fin.close() data_list=[] label_list=[] for sents in alltexts: lines=sents.split('\n') temp_sentece=[] label=lines[0].split('\t')[0] label_list.append(label) for i in range(1,len(lines)): seg=lines[i].split('\t') temp_sentece.append(seg) data_list.append(temp_sentece) return data_list,label_list def ml_intext_fn(ml_input): fin=io.StringIO(ml_input) alltexts=fin.read().strip().split('\n\n') fin.close() data_list=[] label_list=[] for sents in alltexts: lines=sents.split('\n') temp_sentece=[] label=lines[0].split('\t')[0] label_list.append(label) for i in range(1,len(lines)): seg=lines[i].split('\t') temp_sentece.append(seg) data_list.append(temp_sentece) return data_list,label_list def pun_filter(temp_entity): pun_list=[',','.','!',';',':','?','(',')','[',']','{','}'] filter_flag=0 for ele in temp_entity: if ele in pun_list: filter_flag=1 break return filter_flag def pos_filter(temp_pos,temp_entity): pos_list_l=['PRP'] pos_list=['IN','DT','CC','O','MD','EX','POS','WDT','WP','WP$','WRB','TO','PRP$'] verb_word=['is','are','was','were','had','have','has','be','been','also'] filter_flag=0 if (temp_entity[0] in verb_word) or (temp_entity[-1] in verb_word): filter_flag=1 if (temp_pos[0] in pos_list) or (temp_pos[-1] in pos_list) or (temp_pos[0] in pos_list_l): filter_flag=1 return filter_flag def build_ngram_testset_filted(conll_input,Ngram=8): fin_genia=io.StringIO(conll_input) fout_context=io.StringIO() fout_txt=io.StringIO() index_dict={} allentity=[] alltext=fin_genia.read().strip().split('\n\n') fin_genia.close() num_total=0 for i in range(0,len(alltext)): lines=alltext[i].split('\n') ori_txt=[] for ele in lines: seg=ele.split('\t') ori_txt.append(seg[0]) fout_txt.write(' '.join(ori_txt)+'\n') if Ngram>len(lines): Ngram=len(lines) fout_context_list=[] temp_entity=[] temp_pos=[] for ngram in range(2,Ngram+1): if ngram==1: for j in range(0, len(lines)): sid=0 eid=0 for m in range(0,len(lines)): if m==j: sid=m eid=m fout_context_list.append(lines[m]+'\tO\tB') temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[3]) else: pass # print(sentence[m]) # fout_context_list.append(lines[m]+'\tO\tO') if pun_filter(temp_entity)==0 and pos_filter(temp_pos,temp_entity)==0: num_total+=1 if ' '.join(temp_entity) not in allentity: allentity.append(' '.join(temp_entity)) fout_context.write('HP:None\t'+' '.join(temp_entity)+'\n') fout_context.write('\n'.join(fout_context_list)+'\n\n') index_dict[str(num_total)]=[i,sid,eid] temp_entity=[] temp_pos=[] fout_context_list=[] elif ngram==2: for j in range(0, len(lines)-1): sid=0 eid=0 for m in range(0,len(lines)): if m==j: fout_context_list.append(lines[m]+'\tO\tB') sid=m temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[3]) elif m==j+1: fout_context_list.append(lines[m]+'\tO\tB') eid=m temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[3]) else: pass # fout_context_list.append(lines[m]+'\tO\tO') if pun_filter(temp_entity)==0 and pos_filter(temp_pos,temp_entity)==0: num_total+=1 if ' '.join(temp_entity) not in allentity: allentity.append(' '.join(temp_entity)) fout_context.write('HP:None\t'+' '.join(temp_entity)+'\n') fout_context.write('\n'.join(fout_context_list)+'\n\n') index_dict[str(num_total)]=[i,sid,eid] temp_entity=[] temp_pos=[] fout_context_list=[] else : for j in range(0, len(lines)-ngram+1): sid=0 eid=0 for m in range(0,len(lines)): if m==j: fout_context_list.append(lines[m]+'\tO\tB') sid=m temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[3]) elif m>j and m<j+ngram-1: fout_context_list.append(lines[m]+'\tO\tB') temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[2]) elif m==j+ngram-1: fout_context_list.append(lines[m]+'\tO\tB') eid=m temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[3]) else: pass # fout_context_list.append(lines[m]+'\tO\tO') if pun_filter(temp_entity)==0 and pos_filter(temp_pos,temp_entity)==0: num_total+=1 if ' '.join(temp_entity) not in allentity: allentity.append(' '.join(temp_entity)) fout_context.write('HP:None\t'+' '.join(temp_entity)+'\n') fout_context.write('\n'.join(fout_context_list)+'\n\n') index_dict[str(num_total)]=[i,sid,eid] temp_entity=[] temp_pos=[] fout_context_list=[] return fout_context.getvalue(),fout_txt.getvalue(),index_dict def build_all_ngram_testset_filted(conll_input,Ngram=8): fin_genia=io.StringIO(conll_input) fout_context=io.StringIO() fout_txt=io.StringIO() index_dict={} allentity=[] alltext=fin_genia.read().strip().split('\n\n') fin_genia.close() num_total=0 for i in range(0,len(alltext)): lines=alltext[i].split('\n') ori_txt=[] for ele in lines: seg=ele.split('\t') ori_txt.append(seg[0]) fout_txt.write(' '.join(ori_txt)+'\n') if Ngram>len(lines): Ngram=len(lines) fout_context_list=[] temp_entity=[] temp_pos=[] for ngram in range(1,Ngram+1): if ngram==1: for j in range(0, len(lines)): sid=0 eid=0 for m in range(0,len(lines)): if m==j: sid=m eid=m fout_context_list.append(lines[m]+'\tO\tB') temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[3]) else: pass # print(sentence[m]) # fout_context_list.append(lines[m]+'\tO\tO') if pun_filter(temp_entity)==0 and pos_filter(temp_pos,temp_entity)==0: num_total+=1 if ' '.join(temp_entity) not in allentity: allentity.append(' '.join(temp_entity)) fout_context.write('HP:None\t'+' '.join(temp_entity)+'\n') fout_context.write('\n'.join(fout_context_list)+'\n\n') index_dict[str(num_total)]=[i,sid,eid] temp_entity=[] temp_pos=[] fout_context_list=[] elif ngram==2: for j in range(0, len(lines)-1): sid=0 eid=0 for m in range(0,len(lines)): if m==j: fout_context_list.append(lines[m]+'\tO\tB') sid=m temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[3]) elif m==j+1: fout_context_list.append(lines[m]+'\tO\tB') eid=m temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[3]) else: pass # fout_context_list.append(lines[m]+'\tO\tO') if pun_filter(temp_entity)==0 and pos_filter(temp_pos,temp_entity)==0: num_total+=1 if ' '.join(temp_entity) not in allentity: allentity.append(' '.join(temp_entity)) fout_context.write('HP:None\t'+' '.join(temp_entity)+'\n') fout_context.write('\n'.join(fout_context_list)+'\n\n') index_dict[str(num_total)]=[i,sid,eid] temp_entity=[] temp_pos=[] fout_context_list=[] else : for j in range(0, len(lines)-ngram+1): sid=0 eid=0 for m in range(0,len(lines)): if m==j: fout_context_list.append(lines[m]+'\tO\tB') sid=m temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[3]) elif m>j and m<j+ngram-1: fout_context_list.append(lines[m]+'\tO\tB') temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[2]) elif m==j+ngram-1: fout_context_list.append(lines[m]+'\tO\tB') eid=m temp_seg=lines[m].split('\t') temp_entity.append(temp_seg[0]) temp_pos.append(temp_seg[3]) else: pass # fout_context_list.append(lines[m]+'\tO\tO') if pun_filter(temp_entity)==0 and pos_filter(temp_pos,temp_entity)==0: num_total+=1 if ' '.join(temp_entity) not in allentity: allentity.append(' '.join(temp_entity)) fout_context.write('HP:None\t'+' '.join(temp_entity)+'\n') fout_context.write('\n'.join(fout_context_list)+'\n\n') index_dict[str(num_total)]=[i,sid,eid] temp_entity=[] temp_pos=[] fout_context_list=[] return fout_context.getvalue(),fout_txt.getvalue(),index_dict def output_result(result,label_2_index,Top_N=5): fout=io.StringIO() hpo_label={} for key in label_2_index.keys(): hpo_label[label_2_index[key]]=key for line in result: #Top_index=line.argsort()[-1*Top_N:][::-1] index_top_unsort=np.argpartition(line,-Top_N)[-Top_N:] values_top=line[index_top_unsort] Top_index=index_top_unsort[np.argsort(-values_top)] temp_list=[] for max_index in Top_index: hpo_id=hpo_label[max_index] hpo_id_value=round(line[max_index],5) temp_list.append(str(hpo_id)+'|'+str(hpo_id_value)) fout.write('\t'.join(temp_list)+'\n') return fout.getvalue() def decode_tsv(test_score, ml_input_index, ml_input_txt, T=0.8): fin_predict=io.StringIO(test_score) fin_text=io.StringIO(ml_input_txt) fout=io.StringIO() test_txt=fin_text.read().strip().split('\n') test_index=ml_input_index test_pre=fin_predict.read().strip().split('\n') fin_text.close() fin_predict.close() sent_result={} for i in range(0,len(test_pre)): seg_pre=test_pre[i].split('\t')[0].split('|') #print(seg_pre,T) if float(seg_pre[1])>T and seg_pre[0]!='HP:None': term_id=str(i+1) pre_result=[test_index[term_id][1],test_index[term_id][2],seg_pre[0],seg_pre[1]] sent_id=str(test_index[term_id][0]) if sent_id not in sent_result.keys(): sent_result[sent_id]=[pre_result] else: sent_result[sent_id].append(pre_result) for i in range(0,len(test_txt)): fout.write(test_txt[i]+'\n') if str(i) in sent_result.keys(): temp_result={} for ele in sent_result[str(i)]: temp_line=str(ele[0])+'\t'+str(ele[1])+'\t'+' '.join(test_txt[i].split()[ele[0]:ele[1]+1])+'\t'+ele[2]+'\t'+ele[3] temp_result[temp_line]=[ele[0],ele[1]] if len(temp_result)>=1: temp_result=sorted(temp_result.items(), key=lambda d: (d[1][0],d[1][1]), reverse=False) for ent in temp_result: fout.write(ent[0]+'\n') fout.write('\n') return fout.getvalue() def score_filter(temp_entity, T=0.1): result_list=[] for i in range(0,len(temp_entity)): if float (temp_entity[i][-1])>T: result_list.append(temp_entity[i]) return(result_list) def find_max_entity_nest(nest_list): temp_result_list={} for i in range(0, len(nest_list)): hpoid=nest_list[i][-2] score=float(nest_list[i][-1]) if hpoid not in temp_result_list.keys(): temp_result_list[hpoid]=nest_list[i] else: if score>float(temp_result_list[hpoid][-1]): temp_result_list[hpoid]=nest_list[i] new_list=[] for hpoid in temp_result_list.keys(): new_list.append(temp_result_list[hpoid]) return new_list def duplicate_filter(temp_entity): result_list=[] if len(temp_entity)>1: first_entity=temp_entity[0] nest_list=[first_entity] max_eid=int(first_entity[1]) for i in range(1,len(temp_entity)): segs=temp_entity[i] if int(segs[0])> max_eid: if len(nest_list)==1: result_list.append(nest_list[0]) nest_list=[segs] if int(segs[1])>max_eid: max_eid=int(segs[1]) else: result_list.extend(find_max_entity_nest(nest_list)) nest_list=[segs] if int(segs[1])>max_eid: max_eid=int(segs[1]) else: nest_list.append(segs) if int(segs[1])>max_eid: max_eid=int(segs[1]) if nest_list!=[]: if len(nest_list)==1: result_list.append(nest_list[0]) else: result_list.extend(find_max_entity_nest(nest_list)) else: result_list=temp_entity return result_list def combine_strategy(test_decode_temp, T=0.8): fin=io.StringIO(test_decode_temp) fout=io.StringIO() documents=fin.read().strip().split('\n\n') fin.close() for doc in documents: lines=doc.split('\n') context=lines[0] final_entity_list=[] if len(lines)>1: # all entity candidates temp_entity=[] for i in range(1,len(lines)): temp_entity.append(lines[i].split('\t')) #print('all entity condidates: ',len(temp_entity)) # 将阈值低于T的候选过滤 filter1=score_filter(temp_entity,T) # print('filter1:', len(filter1)) filter2=duplicate_filter(filter1) #print('filter2:', filter2) final_entity_list=filter2 fout.write(context+'\n') for ele in final_entity_list: fout.write('\t'.join(ele)+'\n') fout.write('\n') return fout.getvalue() def model_predict(ml_input,nn_model,ml_input_txt,ml_input_index,Threshold): if nn_model.model_type=='cnn': test_set,test_label = ml_intext_fn(ml_input) test_x, test_y = nn_model.rep.represent_instances_all_feas(test_set,test_label,word_max_len=nn_model.hyper['sen_max'],char_max_len=nn_model.hyper['word_max']) input_test = [] if nn_model.fea_dict['word'] == 1: input_test.append(test_x[0]) if nn_model.fea_dict['char'] == 1: input_test.append(test_x[1]) if nn_model.fea_dict['lemma'] == 1: input_test.append(test_x[2]) if nn_model.fea_dict['pos'] == 1: input_test.append(test_x[3]) test_pre = nn_model.model.predict(input_test,batch_size=256) elif nn_model.model_type=='bert': test_set,test_label = ml_intext_fn(ml_input) test_x,test_y=nn_model.rep.load_data(test_set,test_label,word_max_len=nn_model.maxlen) test_pre = nn_model.model.predict(test_x,batch_size=128) test_score=output_result(test_pre, nn_model.rep.label_2_index,Top_N=3) #print('test_score:',test_score) test_decode_temp=decode_tsv(test_score, ml_input_index, ml_input_txt, T=Threshold) #print('decode_temp:\n',test_decode_temp) # test_pre_tsv=combine_strategy(test_decode_temp,T=Threshold) return test_decode_temp def model_predict_old(ml_input,nn_model,ml_input_txt,ml_input_index,Threshold): if nn_model.model_type=='cnn': test_set,test_label = ml_intext_fn(ml_input) test_x, test_y = nn_model.rep.represent_instances_all_feas(test_set,test_label,word_max_len=nn_model.hyper['sen_max'],char_max_len=nn_model.hyper['word_max']) input_test = [] if nn_model.fea_dict['word'] == 1: input_test.append(test_x[0]) if nn_model.fea_dict['char'] == 1: input_test.append(test_x[1]) if nn_model.fea_dict['lemma'] == 1: input_test.append(test_x[2]) if nn_model.fea_dict['pos'] == 1: input_test.append(test_x[3]) test_pre = nn_model.model.predict(input_test,batch_size=256) elif nn_model.model_type=='bert': test_set,test_label = ml_intext_fn(ml_input) test_x,test_y=nn_model.rep.load_data(test_set,test_label,word_max_len=nn_model.maxlen) test_pre = nn_model.model.predict(test_x,batch_size=128) test_score=output_result(test_pre, nn_model.rep.label_2_index,Top_N=3) #print('test_score:',test_score) test_decode_temp=decode_tsv(test_score, ml_input_index, ml_input_txt, T=0.0) #print('decode_temp:\n',test_decode_temp) test_pre_tsv=combine_strategy(test_decode_temp,T=Threshold) return test_pre_tsv def output_txt(ml_input_txt): fin_text=io.StringIO(ml_input_txt) fout=io.StringIO() test_txt=fin_text.read().strip().split('\n') fin_text.close() for i in range(0,len(test_txt)): fout.write(test_txt[i]+'\n') fout.write('\n') return fout.getvalue() def ml_tagging(ssplit_token,ml_model,Threshold): ml_input, ml_input_txt,ml_input_index=build_ngram_testset_filted(ssplit_token) #print('ml_input:') #print(ml_input) if len(ml_input_index)>0: ml_pre_tsv=model_predict(ml_input,ml_model,ml_input_txt,ml_input_index,Threshold) else: ml_pre_tsv=output_txt(ml_input_txt) return ml_pre_tsv def ml_tagging_allngram(ssplit_token,ml_model,Threshold): ml_input, ml_input_txt,ml_input_index=build_all_ngram_testset_filted(ssplit_token) #print('ml_input:') #print(ml_input) if len(ml_input_index)>0: ml_pre_tsv=model_predict_old(ml_input,ml_model,ml_input_txt,ml_input_index,Threshold) else: ml_pre_tsv=output_txt(ml_input_txt) return ml_pre_tsv

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0.073084

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1

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null

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6

325b9593f69d56bfb9ede516a4b9fcf52a45c9a5

38

py

Python

pyTrivialFTP/__init__.py

roberto-reale/pyTrivialFTP

b54500570456eafcf8315608831cd65a47757a6b

[ "MIT" ]

null

pyTrivialFTP/__init__.py

roberto-reale/pyTrivialFTP

b54500570456eafcf8315608831cd65a47757a6b

[ "MIT" ]

null

pyTrivialFTP/__init__.py

roberto-reale/pyTrivialFTP

b54500570456eafcf8315608831cd65a47757a6b

[ "MIT" ]

null

from pyTrivialFTP import pyTrivialFTP

19

37

0.894737

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0.105263

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38

1

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true

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1

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null

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1

0

null

0

1

0

1

0

1

0

6

326d9ff3c66f54430b1b8250821c3efda501e15b

48

py

Python

backend/products/views/__init__.py

MaCkRage/optimize_sql

346011fbda5ddf96a3c34357820452e165b7767c

[ "MIT" ]

null

backend/products/views/__init__.py

MaCkRage/optimize_sql

346011fbda5ddf96a3c34357820452e165b7767c

[ "MIT" ]

null

backend/products/views/__init__.py

MaCkRage/optimize_sql

346011fbda5ddf96a3c34357820452e165b7767c

[ "MIT" ]

null

from .update_products import update_values_view

24

47

0.895833

7

48

5.714286

0.857143

0

0.083333

48

1

48

0.909091

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0

null

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1

0

1

0

1

0

6

3ef8272f07e321bd69f8000babbd6d2c895bb7b7

104

py

Python

problems/flip-bit/flip.py

vidyadeepa/the-coding-interview

90171b77b6884176a6c28bdccb5d45bd6929b489

[ "MIT" ]

1,571

2015-12-09T14:08:47.000Z

2022-03-30T21:34:36.000Z

problems/flip-bit/flip.py

vidyadeepa/the-coding-interview

90171b77b6884176a6c28bdccb5d45bd6929b489

[ "MIT" ]

117

2015-10-22T05:59:19.000Z

2021-09-17T00:14:38.000Z

problems/flip-bit/flip.py

vidyadeepa/the-coding-interview

90171b77b6884176a6c28bdccb5d45bd6929b489

[ "MIT" ]

452

2015-10-21T23:00:58.000Z

2022-03-18T21:16:50.000Z

def flip(integer, bit): return integer ^ (1 << (bit - 1)) print flip(8, 3) # 12 print flip(8, 4) # 0

17.333333

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0.576923

19

104

3.157895

0.631579

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0.333333

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0.1125

0.230769

104

5

36

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0.6375

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null

0

1

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1

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6

411e01b5280c22d9c7acd4751f3c8ecfc365251e

95

py

Python

Tree/redBlackTree.py

amal029/DataStructuresAndAlgorithmsInPython

ccf36ae9e6d1ab8c2be09315f4ad6ac715e222fd

[ "MIT" ]

null

Tree/redBlackTree.py

amal029/DataStructuresAndAlgorithmsInPython

ccf36ae9e6d1ab8c2be09315f4ad6ac715e222fd

[ "MIT" ]

null

Tree/redBlackTree.py

amal029/DataStructuresAndAlgorithmsInPython

ccf36ae9e6d1ab8c2be09315f4ad6ac715e222fd

[ "MIT" ]

null

from binarySearchTree import BinarySearchTree class RedBlackTree(BinarySearchTree): pass

15.833333

45

0.831579

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95

9.875

0.75

0

0.136842

95

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null

0

1

0

1

0

6

f5d3cbeeb942890d09e4abb17916cc6ed314a31c

5,071

py

Python

shadertest/type_map.py

Kupoman/shadertest

79b959e0ff00ac8c30918e83e0751f25bcc447ae

[ "MIT" ]

7

2018-11-10T20:49:56.000Z

2021-08-31T04:34:56.000Z

shadertest/type_map.py

Kupoman/shadertest

79b959e0ff00ac8c30918e83e0751f25bcc447ae

[ "MIT" ]

null

shadertest/type_map.py

Kupoman/shadertest

79b959e0ff00ac8c30918e83e0751f25bcc447ae

[ "MIT" ]

2

2018-12-10T03:01:05.000Z

2018-12-10T12:51:11.000Z

import ctypes import OpenGL.GL as gl TYPE_MAP = { 'float': { 'uniform': gl.glUniform1f, 'ctype': ctypes.c_float, 'buffer_type': gl.GL_R32F, 'shader_layout': 'r32f', 'shader_buffer': 'imageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, vec4({name}({args}), vec3(0.0)));' }, 'vec2': { 'uniform': gl.glUniform2f, 'ctype': (ctypes.c_float * 2), 'buffer_type': gl.GL_RG32F, 'shader_layout': 'rg32f', 'shader_buffer': 'imageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, vec4({name}({args}), vec2(0.0)));' }, 'vec3': { 'uniform': gl.glUniform3f, 'ctype': (ctypes.c_float * 4), 'buffer_type': gl.GL_RGBA32F, 'shader_layout': 'rgba32f', 'shader_buffer': 'imageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, vec4({name}({args}), 0.0));' }, 'vec4': { 'uniform': gl.glUniform4f, 'ctype': (ctypes.c_float * 4), 'buffer_type': gl.GL_RGBA32F, 'shader_layout': 'rgba32f', 'shader_buffer': 'imageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0,{name}({args}));' }, 'int': { 'uniform': gl.glUniform1i, 'ctype': ctypes.c_int, 'buffer_type': gl.GL_R32I, 'shader_layout': 'r32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, ivec4({name}({args}), ivec3(0)));' }, 'ivec2': { 'uniform': gl.glUniform2i, 'ctype': (ctypes.c_int * 2), 'buffer_type': gl.GL_RG32I, 'shader_layout': 'rg32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, ivec4({name}({args}), ivec2(0)));' }, 'ivec3': { 'uniform': gl.glUniform3i, 'ctype': (ctypes.c_int * 4), 'buffer_type': gl.GL_RGBA32I, 'shader_layout': 'rgba32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, ivec4({name}({args}), 0));' }, 'ivec4': { 'uniform': gl.glUniform4i, 'ctype': (ctypes.c_int * 4), 'buffer_type': gl.GL_RGBA32I, 'shader_layout': 'rgba32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, {name}({args}));' }, 'uint': { 'uniform': gl.glUniform1ui, 'ctype': ctypes.c_uint, 'buffer_type': gl.GL_R32UI, 'shader_layout': 'r32ui', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, ivec4({name}({args}), ivec3(0)));' }, 'ivec2': { 'uniform': gl.glUniform2i, 'ctype': (ctypes.c_int * 2), 'buffer_type': gl.GL_RG32I, 'shader_layout': 'rg32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, ivec4({name}({args}), ivec2(0)));' }, 'ivec3': { 'uniform': gl.glUniform3i, 'ctype': (ctypes.c_int * 4), 'buffer_type': gl.GL_RGBA32I, 'shader_layout': 'rgba32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, ivec4({name}({args}), 0));' }, 'ivec4': { 'uniform': gl.glUniform4i, 'ctype': (ctypes.c_int * 4), 'buffer_type': gl.GL_RGBA32I, 'shader_layout': 'rgba32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, {name}({args}));' }, 'bool': { 'uniform': gl.glUniform1i, 'ctype': ctypes.c_int, 'buffer_type': gl.GL_R32I, 'shader_layout': 'r32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, ivec4({name}({args}), ivec3(0)));' }, 'bvec2': { 'uniform': gl.glUniform2i, 'ctype': (ctypes.c_int * 2), 'buffer_type': gl.GL_RG32I, 'shader_layout': 'rg32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, ivec4({name}({args}), ivec2(0)));' }, 'bvec3': { 'uniform': gl.glUniform3i, 'ctype': (ctypes.c_int * 4), 'buffer_type': gl.GL_RGBA32I, 'shader_layout': 'rgba32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, ivec4({name}({args}), 0));' }, 'bvec4': { 'uniform': gl.glUniform4i, 'ctype': (ctypes.c_int * 4), 'buffer_type': gl.GL_RGBA32I, 'shader_layout': 'rgba32i', 'shader_buffer': 'iimageBuffer', 'shader_store': lambda name, args: f'imageStore(result, 0, ivec4({name}({args})));' }, }

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6

eb2be2725bbd3d95769e35cf26a3a8ef6416095a

1,029

py

Python

Script's/00 - Otros/Metodos de Cadenas.py

CamiloBallen24/Python-PildorasInformaticas

a734ac064e34b01a2f64080d5391625a5de77f54

[ "Apache-2.0" ]

null

Script's/00 - Otros/Metodos de Cadenas.py

CamiloBallen24/Python-PildorasInformaticas

a734ac064e34b01a2f64080d5391625a5de77f54

[ "Apache-2.0" ]

null

Script's/00 - Otros/Metodos de Cadenas.py

CamiloBallen24/Python-PildorasInformaticas

a734ac064e34b01a2f64080d5391625a5de77f54

[ "Apache-2.0" ]

1

2019-06-04T19:51:05.000Z

#TEMA: METODOS DE CADENA ################################################################ print("Ejemplo #1") miCadena = "Hola" print(miCadena.upper()) #Pasa a mayusculas tooo print() print() print() ################################################################ ################################################################ print("Ejemplo #2") miCadena = "Hola" print(miCadena.lower()) #Pasa todo a minuscula print() print() print() ################################################################ ################################################################ print("Ejemplo #3") miCadena = "hOlA cOmo ESTAS" print(miCadena.capitalize()) #primera letra en mayuscula y resto en minuscula print() print() print() ################################################################ ################################################################ print("Ejemplo #4") edad = "123" print(edad.isdigit()) #Comprueba si es un digito print() print() print() ################################################################

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null

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1

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null

0

1

0

1

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6

de1d21282f94d3b187b228f65673209a25d24930

35

py

Python

bsp/stm32f103c8/pikascript/main.py

ccccmagicboy2022/pikascript

154ccd8e90e0d50e1551536d32bd2a3648e194d2

[ "MIT" ]

228

2021-09-11T06:09:43.000Z

2022-03-30T08:09:01.000Z

bsp/stm32f103c8/pikascript/main.py

ccccmagicboy2022/pikascript

154ccd8e90e0d50e1551536d32bd2a3648e194d2

[ "MIT" ]

48

2021-09-25T01:23:43.000Z

2022-03-31T07:34:43.000Z

bsp/stm32f103c8/pikascript/main.py

ccccmagicboy2022/pikascript

154ccd8e90e0d50e1551536d32bd2a3648e194d2

[ "MIT" ]

31

2021-09-17T12:06:45.000Z

2022-03-19T16:10:11.000Z

import STM32F1 import PikaStdLib

7

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1

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1

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6

de306001c6697fe19f17f283c47c5fababa4f6f0

32,904

py

Python

metadatas/taskaug.py

erprashu/Metal_erning

79d1a6a457be37258df50a9194946caeb86845a2

[ "MIT" ]

null

metadatas/taskaug.py

erprashu/Metal_erning

79d1a6a457be37258df50a9194946caeb86845a2

[ "MIT" ]

null

metadatas/taskaug.py

erprashu/Metal_erning

79d1a6a457be37258df50a9194946caeb86845a2

[ "MIT" ]

null

from __future__ import print_function import numpy as np import random import math import multiprocessing import pdb from PIL import Image import torch import torch.utils.data as data import torchvision.transforms.functional as TranF from .utils import ProtoData class DualCategories(data.Dataset): def __init__(self, dataset, p=0.5, std=0.1, batch_size_down=4e4): self.dataset = dataset self.std = std self.batch_num = multiprocessing.Value("d", -1.) self.batch_size_down = batch_size_down self.phase = self.dataset.phase self.num_cats_new = self.dataset.num_cats * (self.dataset.num_cats - 1) // 2 self.num_cats = self.dataset.num_cats + self.num_cats_new if p == -1: self.p = float(self.num_cats_new) / self.num_cats else: self.p = p def sampleCategories(self, sample_size): self.batch_num.value += 1 p = self.p * (self.batch_size_down - self.batch_num.value) / self.batch_size_down sample1_size = np.sum(np.random.rand(sample_size) > p) sample2_size = sample_size - sample1_size sample1 = np.random.choice(self.dataset.num_cats, sample1_size, replace=False) sample2 = np.random.choice(self.num_cats_new, sample2_size, replace=False) + self.dataset.num_cats return list(sample1) + list(sample2) def sampleImageIdsFrom(self, cat_id, sample_size=1): """ Samples `sample_size` number of unique image ids picked from the category `cat_id` (i.e., self.dataset.label2ind[cat_id]). Args: cat_id: a scalar with the id of the category from which images will be sampled. sample_size: number of images that will be sampled. Returns: image_ids: a list of length `sample_size` with unique image ids. Each id is a 2-elements tuples. The 1st element of each tuple is the augment process mark and the 2nd element is the image loading related information.. """ if cat_id < self.dataset.num_cats: return [(False, d_id) for d_id in self.dataset.sampleImageIdsFrom( self.dataset.labelIds[cat_id], sample_size)] else: for cat_id in range(self.dataset.num_cats, self.dataset.num_cats + self.num_cats_new): cat_id = cat_id - self.dataset.num_cats cat1_id = int((-1 + math.sqrt(1 + 8 * cat_id)) / 2) cat2_id = int(cat_id - cat1_id * (cat1_id + 1) / 2) cat1_id += 1 ids1 = self.dataset.sampleImageIdsFrom(self.dataset.labelIds[cat1_id], sample_size) ids2 = self.dataset.sampleImageIdsFrom(self.dataset.labelIds[cat2_id], sample_size) return [(True, d_id) for d_id in zip(ids1, ids2)] def createExamplesTensorData(self, examples): """ Creates the examples image and label tensor data. Args: examples: a list of 2-element tuples, each representing a train or test example. The 1st element of each tuple is the image id of the example and 2nd element is the category label of the example, which is in the range [0, nK - 1], where nK is the total number of categories (both novel and base). Returns: images: a tensor of shape [nExamples, Height, Width, 3] with the example images, where nExamples is the number of examples (i.e., nExamples = len(examples)). labels: a tensor of shape [nExamples] with the category label of each example. """ dataset = self.dataset def get_image(img_idx): if img_idx[0]: img_idx = img_idx[1] image1 = dataset[img_idx[0]][0] image2 = dataset[img_idx[1]][0] shift = np.random.normal(loc=0, scale=self.std) return image1 * (shift / 2.) + image2 * ((1 - shift) / 2.) else: return dataset[img_idx[1]][0] images = torch.stack( [get_image(img_idx) for img_idx, _ in examples], dim=0) labels = torch.LongTensor([label for _, label in examples]) return images, labels def __repr__(self): return self.__class__.__name__ + '(' \ + 'p=' + str(self.p) + ', ' \ + 'std=' + str(self.std) + ', ' \ + 'phase=' + str(self.phase) + ', ' \ + 'num_cats_new=' + str(self.num_cats_new) + ', ' \ + 'num_cats=' + str(self.num_cats) + ', ' \ + 'batch_size_down=' + str(self.batch_size_down) + ')' class PermuteChannels(data.Dataset): def __init__(self, dataset, p=-1, ): self.dataset = dataset self.phase = self.dataset.phase self.num_cats_new = self.dataset.num_cats * 5 self.num_cats = self.dataset.num_cats + self.num_cats_new self.orders = (torch.LongTensor([0, 1, 2]), torch.LongTensor([1, 2, 0]), torch.LongTensor([2, 0, 1]), torch.LongTensor([1, 0, 2]), torch.LongTensor([0, 2, 1]), torch.LongTensor([2, 1, 0])) if p == -1: self.p = 5./6. else: self.p = p def sampleCategories(self, sample_size): sample1_size = np.sum(np.random.rand(sample_size) > self.p) sample2_size = sample_size - sample1_size sample1 = np.random.choice(self.dataset.num_cats, sample1_size, replace=False) sample2 = np.random.choice(self.num_cats_new, sample2_size, replace=False) + self.dataset.num_cats return list(sample1) + list(sample2) def sampleImageIdsFrom(self, cat_id, sample_size=1): """ Samples `sample_size` number of unique image ids picked from the category `cat_id` (i.e., self.dataset.label2ind[cat_id]). Args: cat_id: a scalar with the id of the category from which images will be sampled. sample_size: number of images that will be sampled. Returns: image_ids: a list of length `sample_size` with unique image ids. Each id is a 2-elements tuples. The 1st element of each tuple is the augment process mark and the 2nd element is the image loading related information.. """ perm_id = cat_id // self.dataset.num_cats cat_id = cat_id % self.dataset.num_cats return [(perm_id, d_id) for d_id in self.dataset.sampleImageIdsFrom( self.dataset.labelIds[cat_id], sample_size)] def createExamplesTensorData(self, examples): """ Creates the examples image and label tensor data. Args: examples: a list of 2-element tuples, each representing a train or test example. The 1st element of each tuple is the image id of the example and 2nd element is the category label of the example, which is in the range [0, nK - 1], where nK is the total number of categories (both novel and base). Returns: images: a tensor of shape [nExamples, Height, Width, 3] with the example images, where nExamples is the number of examples (i.e., nExamples = len(examples)). labels: a tensor of shape [nExamples] with the category label of each example. """ dataset = self.dataset images = torch.stack( [dataset[img_idx[1]][0][self.orders[img_idx[0]]] for img_idx, _ in examples], dim=0) labels = torch.LongTensor([label for _, label in examples]) return images, labels def __repr__(self): return self.__class__.__name__ + '(' \ + 'p=' + str(self.p) + ', ' \ + 'phase=' + str(self.phase) + ', ' \ + 'num_cats_new=' + str(self.num_cats_new) + ', ' \ + 'num_cats=' + str(self.num_cats) + ')' class DropChannels(data.Dataset): def __init__(self, dataset, p=-1): self.dataset = dataset self.phase = self.dataset.phase self.num_cats_new = self.dataset.num_cats * 6 self.num_cats = self.dataset.num_cats + self.num_cats_new self.orders = (torch.LongTensor([1, 1, 1]), torch.LongTensor([0, 1, 0]), torch.LongTensor([1, 0, 0]), torch.LongTensor([1, 1, 0]), torch.LongTensor([1, 0, 1]), torch.LongTensor([0, 1, 1]), torch.LongTensor([0, 0, 1])) if p == -1: self.p = 5./6. else: self.p = p def sampleCategories(self, sample_size): sample1_size = np.sum(np.random.rand(sample_size) > self.p) sample2_size = sample_size - sample1_size sample1 = np.random.choice(self.dataset.num_cats, sample1_size, replace=False) sample2 = np.random.choice(self.num_cats_new, sample2_size, replace=False) + self.dataset.num_cats return list(sample1) + list(sample2) def sampleImageIdsFrom(self, cat_id, sample_size=1): """ Samples `sample_size` number of unique image ids picked from the category `cat_id` (i.e., self.dataset.label2ind[cat_id]). Args: cat_id: a scalar with the id of the category from which images will be sampled. sample_size: number of images that will be sampled. Returns: image_ids: a list of length `sample_size` with unique image ids. Each id is a 2-elements tuples. The 1st element of each tuple is the augment process mark and the 2nd element is the image loading related information.. """ ch_id = cat_id // self.dataset.num_cats cat_id = cat_id % self.dataset.num_cats return [(ch_id, d_id) for d_id in self.dataset.sampleImageIdsFrom( self.dataset.labelIds[cat_id], sample_size)] def createExamplesTensorData(self, examples): """ Creates the examples image and label tensor data. Args: examples: a list of 2-element tuples, each representing a train or test example. The 1st element of each tuple is the image id of the example and 2nd element is the category label of the example, which is in the range [0, nK - 1], where nK is the total number of categories (both novel and base). Returns: images: a tensor of shape [nExamples, Height, Width, 3] with the example images, where nExamples is the number of examples (i.e., nExamples = len(examples)). labels: a tensor of shape [nExamples] with the category label of each example. """ dataset = self.dataset images = torch.stack( [dataset[img_idx[1]][0] * self.orders[img_idx[0]].view(3,1,1) for img_idx, _ in examples], dim=0) labels = torch.LongTensor([label for _, label in examples]) return images, labels def __repr__(self): return self.__class__.__name__ + '(' \ + 'p=' + str(self.p) + ', ' \ + 'phase=' + str(self.phase) + ', ' \ + 'num_cats_new=' + str(self.num_cats_new) + ', ' \ + 'num_cats=' + str(self.num_cats) + ')' class Rot90(data.Dataset): def __init__(self, dataset, p=-1, batch_size_down=8e4): self.dataset = dataset self.batch_num = multiprocessing.Value("d", -1.) self.batch_size_down = batch_size_down self.phase = self.dataset.phase self.num_cats_new = self.dataset.num_cats * 3 self.num_cats = self.dataset.num_cats + self.num_cats_new if p == -1: self.p = float(self.num_cats_new) / self.num_cats else: self.p = p def sampleCategories(self, sample_size): self.batch_num.value += 1. sample1_size = np.sum(np.random.rand(sample_size) > self.p) sample2_size = sample_size - sample1_size sample1 = np.random.choice(self.dataset.num_cats, sample1_size, replace=False) sample2 = np.random.choice(self.num_cats_new, sample2_size, replace=False) + self.dataset.num_cats return list(sample1) + list(sample2) def sampleImageIdsFrom(self, cat_id, sample_size=1): """ Samples `sample_size` number of unique image ids picked from the category `cat_id` (i.e., self.dataset.label2ind[cat_id]). Args: cat_id: a scalar with the id of the category from which images will be sampled. sample_size: number of images that will be sampled. Returns: image_ids: a list of length `sample_size` with unique image ids. Each id is a 2-elements tuples. The 1st element of each tuple is the augment process mark and the 2nd element is the image loading related information.. """ rot90_id = int(cat_id // self.dataset.num_cats) cat_id = cat_id % self.dataset.num_cats return [(rot90_id, d_id) for d_id in self.dataset.sampleImageIdsFrom( self.dataset.labelIds[cat_id], sample_size)] def createExamplesTensorData(self, examples): """ Creates the examples image and label tensor data. Args: examples: a list of 2-element tuples, each representing a train or test example. The 1st element of each tuple is the image id of the example and 2nd element is the category label of the example, which is in the range [0, nK - 1], where nK is the total number of categories (both novel and base). Returns: images: a tensor of shape [nExamples, Height, Width, 3] with the example images, where nExamples is the number of examples (i.e., nExamples = len(examples)). labels: a tensor of shape [nExamples] with the category label of each example. """ dataset = self.dataset images = torch.stack( [torch.rot90(dataset[img_idx[1]][0], img_idx[0], [1, 2]) for img_idx, _, _ in examples], dim=0) labels = torch.LongTensor([label for _, label, _ in examples]) dc_labels = torch.LongTensor([label for _, _, label in examples]) return images, labels, dc_labels def __repr__(self): return self.__class__.__name__ + '(' \ + 'p=' + str(self.p) + ', ' \ + 'phase=' + str(self.phase) + ', ' \ + 'num_cats_new=' + str(self.num_cats_new) + ', ' \ + 'num_cats=' + str(self.num_cats) + ', ' \ + 'batch_size_down=' + str(self.batch_size_down) + ')' class AddNoise(data.Dataset): def __init__(self, dataset, p=-1, batch_size_down=8e4): self.dataset = dataset self.batch_num = multiprocessing.Value("d", -1.) self.batch_size_down = batch_size_down self.phase = self.dataset.phase self.num_cats = self.dataset.num_cats if p == -1: self.p = float(self.num_cats_new) / self.num_cats else: self.p = p def sampleCategories(self, sample_size): self.batch_num.value += 1. sample1_size = np.sum(np.random.rand(sample_size) > self.p) sample2_size = sample_size - sample1_size sample1 = np.random.choice(self.dataset.num_cats, sample1_size, replace=False) sample2 = np.random.choice(self.dataset.num_cats, sample2_size, replace=False) + self.dataset.num_cats return list(sample1) + list(sample2) def sampleImageIdsFrom(self, cat_id, sample_size=1): """ Samples `sample_size` number of unique image ids picked from the category `cat_id` (i.e., self.dataset.label2ind[cat_id]). Args: cat_id: a scalar with the id of the category from which images will be sampled. sample_size: number of images that will be sampled. Returns: image_ids: a list of length `sample_size` with unique image ids. Each id is a 2-elements tuples. The 1st element of each tuple is the augment process mark and the 2nd element is the image loading related information.. """ delta = random.uniform(0, 0.5) add_id = int(cat_id // self.dataset.num_cats) cat_id = cat_id % self.dataset.num_cats return [(add_id * delta, d_id) for d_id in self.dataset.sampleImageIdsFrom( self.dataset.labelIds[cat_id], sample_size)] def createExamplesTensorData(self, examples): """ Creates the examples image and label tensor data. Args: examples: a list of 2-element tuples, each representing a train or test example. The 1st element of each tuple is the image id of the example and 2nd element is the category label of the example, which is in the range [0, nK - 1], where nK is the total number of categories (both novel and base). Returns: images: a tensor of shape [nExamples, Height, Width, 3] with the example images, where nExamples is the number of examples (i.e., nExamples = len(examples)). labels: a tensor of shape [nExamples] with the category label of each example. """ dataset = self.dataset images = torch.stack( [dataset[img_idx[1]][0] + torch.randn_like(dataset[img_idx[1]][0]) * np.sqrt(img_idx[0]) for img_idx, _ in examples], dim=0) labels = torch.LongTensor([label for _, label in examples]) return images, labels def __repr__(self): return self.__class__.__name__ + '(' \ + 'p=' + str(self.p) + ', ' \ + 'phase=' + str(self.phase) + ', ' \ + 'num_cats=' + str(self.num_cats) + ', ' \ + 'batch_size_down=' + str(self.batch_size_down) + ')' class TaskAug(data.Dataset): def __init__(self, dataset, method, p=-1, batch_size_down=8e4): self.dataset = dataset self.batch_num = multiprocessing.Value("d", -1.) self.batch_size_down = batch_size_down self.phase = self.dataset.phase self.num_cats = self.dataset.num_cats self.method = method self.test = np.random.randint(50) if p == -1: self.p = float(self.num_cats_new) / self.num_cats else: self.p = p @staticmethod def rand_bbox(size, lam=0.5): W = size[0] H = size[1] cut_rat = np.sqrt(1. - lam) cut_w = np.int(W * cut_rat) cut_h = np.int(H * cut_rat) # uniform cx = np.random.randint(W) cy = np.random.randint(H) bbx1 = np.clip(cx - cut_w // 2, 0, W) bby1 = np.clip(cy - cut_h // 2, 0, H) bbx2 = np.clip(cx + cut_w // 2, 0, W) bby2 = np.clip(cy + cut_h // 2, 0, H) return bbx1, bby1, bbx2, bby2 def sampleCategories(self, sample_size): self.batch_num.value += 1. p = self.p #* min(1., self.batch_num.value / self.batch_size_down) sample1_size = np.sum(np.random.rand(sample_size) > p) sample2_size = sample_size - sample1_size sample1 = np.random.choice(self.dataset.num_cats, sample_size, replace=False) mix_sample = np.random.choice(sample_size, sample2_size, replace=False) for ms in mix_sample: sample1[ms] += self.dataset.num_cats sample2 = np.random.choice(self.dataset.num_cats, sample2_size, replace=False) + self.dataset.num_cats * 2 return list(sample1) + list(sample2) def sampleImageIdsFrom(self, cat_id, sample_size=1): """ Samples `sample_size` number of unique image ids picked from the category `cat_id` (i.e., self.dataset.label2ind[cat_id]). Args: cat_id: a scalar with the id of the category from which images will be sampled. sample_size: number of images that will be sampled. Returns: image_ids: a list of length `sample_size` with unique image ids. Each id is a 2-elements tuples. The 1st element of each tuple is the augment process mark and the 2nd element is the image loading related information.. """ group_id = int(cat_id // self.dataset.num_cats) cat_id = cat_id % self.dataset.num_cats if self.method == "Mix" or self.method == "Combine": return [(group_id, d_id) for d_id in self.dataset.sampleImageIdsFrom( self.dataset.labelIds[cat_id], sample_size)] elif self.method == "CutMix": lam = np.random.beta(2., 2.) rbbx = self.rand_bbox(self.dataset.img_size, lam) return [(group_id, d_id, rbbx) for d_id in self.dataset.sampleImageIdsFrom( self.dataset.labelIds[cat_id], sample_size)] def createExamplesTensorData(self, examples): """ Creates the examples image and label tensor data. Args: examples: a list of 2-element tuples, each representing a train or test example. The 1st element of each tuple is the image id of the example and 2nd element is the category label of the example, which is in the range [0, nK - 1], where nK is the total number of categories (both novel and base). Returns: images: a tensor of shape [nExamples, Height, Width, 3] with the example images, where nExamples is the number of examples (i.e., nExamples = len(examples)). labels: a tensor of shape [nExamples] with the category label of each example. """ dataset = self.dataset images = torch.stack([dataset[img_idx[1]][0] for img_idx, _ in examples if img_idx[0] <= 1], dim=0) labels = torch.LongTensor([label for img_idx, label in examples if img_idx[0] <= 1]) labels_2 = torch.LongTensor([label for img_idx, label in examples if img_idx[0] == 1]) labels_3 = torch.LongTensor([label for img_idx, label in examples if img_idx[0] == 2]) assert(len(labels_2) == len(labels_3)) if len(labels_2) > 0: images_2 = torch.stack([dataset[img_idx[1]][0] for img_idx, _ in examples if img_idx[0] == 1], dim=0) images_3 = torch.stack([dataset[img_idx[1]][0] for img_idx, _ in examples if img_idx[0] == 2], dim=0) uni_l2 = torch.unique(labels_2) n2 = len(uni_l2) uni_l = torch.unique(labels) if self.method == "Mix": for i, l2 in enumerate(uni_l2): images[labels == l2] = (images_2[labels_2 == l2] + images_3[labels_3 == len(uni_l) + i])/2 elif self.method == "CutMix": labels_all = torch.LongTensor([label for img_idx, label in examples]) for i, l2 in enumerate(uni_l2): lam = np.random.beta(2., 2.) for j in range(len(images[labels == l2])): bbx1, bby1, bbx2, bby2 = self.rand_bbox(self.dataset.img_size, lam) images[labels == l2][j][:, bbx1:bbx2, bby1:bby2] = images_3[labels_3 == len(uni_l) + i][j][:, bbx1:bbx2, bby1:bby2] elif self.method == "Combine": for i, l2 in enumerate(uni_l2): new_images = torch.cat((images_2[labels_2 == l2], images_3[labels_3 == len(uni_l) + i])) new_order = torch.randperm(len(new_images))[:len(new_images)//2] images[labels == l2] = new_images[new_order] return images, labels def __repr__(self): return self.__class__.__name__ + '(' \ + 'p=' + str(self.p) + ', ' \ + 'phase=' + str(self.phase) + ', ' \ + 'num_cats=' + str(self.num_cats) + ', ' \ + 'batch_size_down=' + str(self.batch_size_down) + ')' class RE(data.Dataset): def __init__(self, dataset, p=1): self.dataset = dataset self.phase = self.dataset.phase self.num_cats = self.dataset.num_cats self.p = p @staticmethod def rand_bbox(size, sl = 0.02, sh = 0.4, r1 = 0.3, mean=[0.4914, 0.4822, 0.4465]): for attempt in range(100): area = size[0] * size[1] target_area = random.uniform(sl, sh) * area aspect_ratio = random.uniform(r1, 1/r1) h = int(round(math.sqrt(target_area * aspect_ratio))) w = int(round(math.sqrt(target_area / aspect_ratio))) if w < size[0] and h < size[1]: x1 = random.randint(0, size[1] - h) y1 = random.randint(0, size[0] - w) x2 = x1 + h y2 = y1 + w return x1, y1, x2, y2, mean def sampleCategories(self, sample_size): sample1_size = np.sum(np.random.rand(sample_size) > self.p) sample2_size = sample_size - sample1_size sample1 = np.random.choice(self.dataset.num_cats, sample1_size, replace=False) sample2 = np.random.choice(self.dataset.num_cats, sample2_size, replace=False) + self.dataset.num_cats return list(sample1) + list(sample2) def sampleImageIdsFrom(self, cat_id, sample_size=1): """ Samples `sample_size` number of unique image ids picked from the category `cat_id` (i.e., self.dataset.label2ind[cat_id]). Args: cat_id: a scalar with the id of the category from which images will be sampled. sample_size: number of images that will be sampled. Returns: image_ids: a list of length `sample_size` with unique image ids. Each id is a 2-elements tuples. The 1st element of each tuple is the augment process mark and the 2nd element is the image loading related information.. """ group_id = int(cat_id // self.dataset.num_cats) cat_id = cat_id % self.dataset.num_cats #rbbx = self.rand_bbox(self.dataset.img_size, lam) return [(group_id, d_id) for d_id in self.dataset.sampleImageIdsFrom( self.dataset.labelIds[cat_id], sample_size)] def createExamplesTensorData(self, examples): """ Creates the examples image and label tensor data. Args: examples: a list of 2-element tuples, each representing a train or test example. The 1st element of each tuple is the image id of the example and 2nd element is the category label of the example, which is in the range [0, nK - 1], where nK is the total number of categories (both novel and base). Returns: images: a tensor of shape [nExamples, Height, Width, 3] with the example images, where nExamples is the number of examples (i.e., nExamples = len(examples)). labels: a tensor of shape [nExamples] with the category label of each example. """ dataset = self.dataset images = torch.stack([dataset[img_idx[1]][0] for img_idx, _ in examples if img_idx[0] <= 1], dim=0) labels = torch.LongTensor([label for img_idx, label in examples if img_idx[0] <= 1]) labels_2 = torch.LongTensor([label for img_idx, label in examples if img_idx[0] == 1]) if len(labels_2) > 0: uni_l2 = torch.unique(labels_2) for i, l2 in enumerate(uni_l2): for j in range(len(images[labels == l2])): bbx1, bby1, bbx2, bby2, mean = self.rand_bbox(self.dataset.img_size) images[labels == l2][j][0, bbx1:bbx2, bby1:bby2] = mean[0] images[labels == l2][j][1, bbx1:bbx2, bby1:bby2] = mean[1] images[labels == l2][j][2, bbx1:bbx2, bby1:bby2] = mean[2] return images, labels def __repr__(self): return self.__class__.__name__ + '(' \ + 'p=' + str(self.p) + ', ' \ + 'phase=' + str(self.phase) + ', ' \ + 'num_cats=' + str(self.num_cats) + ')' class Solarize(data.Dataset): def __init__(self, dataset, p=1): self.dataset = dataset self.phase = self.dataset.phase self.num_cats = self.dataset.num_cats self.p = p @staticmethod def random_solarize(data): solarize = K.RandomSolarize(0.1, 0.1, same_on_batch=True) out = solarize(data.view([-1] + list(data.shape[-3:]))) return out.view(data.shape) def sampleCategories(self, sample_size): sample1_size = np.sum(np.random.rand(sample_size) > self.p) sample2_size = sample_size - sample1_size sample1 = np.random.choice(self.dataset.num_cats, sample1_size, replace=False) sample2 = np.random.choice(self.dataset.num_cats, sample2_size, replace=False) + self.dataset.num_cats return list(sample1) + list(sample2) def sampleImageIdsFrom(self, cat_id, sample_size=1): """ Samples `sample_size` number of unique image ids picked from the category `cat_id` (i.e., self.dataset.label2ind[cat_id]). Args: cat_id: a scalar with the id of the category from which images will be sampled. sample_size: number of images that will be sampled. Returns: image_ids: a list of length `sample_size` with unique image ids. Each id is a 2-elements tuples. The 1st element of each tuple is the augment process mark and the 2nd element is the image loading related information.. """ group_id = int(cat_id // self.dataset.num_cats) cat_id = cat_id % self.dataset.num_cats return [(group_id, d_id) for d_id in self.dataset.sampleImageIdsFrom( self.dataset.labelIds[cat_id], sample_size)] def createExamplesTensorData(self, examples): """ Creates the examples image and label tensor data. Args: examples: a list of 2-element tuples, each representing a train or test example. The 1st element of each tuple is the image id of the example and 2nd element is the category label of the example, which is in the range [0, nK - 1], where nK is the total number of categories (both novel and base). Returns: images: a tensor of shape [nExamples, Height, Width, 3] with the example images, where nExamples is the number of examples (i.e., nExamples = len(examples)). labels: a tensor of shape [nExamples] with the category label of each example. """ dataset = self.dataset images = torch.stack([dataset[img_idx[1]][0] for img_idx, _ in examples if img_idx[0] <= 1], dim=0) labels = torch.LongTensor([label for img_idx, label in examples if img_idx[0] <= 1]) labels_2 = torch.LongTensor([label for img_idx, label in examples if img_idx[0] == 1]) if len(labels_2) > 0: uni_l2 = torch.unique(labels_2) for i, l2 in enumerate(uni_l2): images[labels == l2] = self.random_solarize(images[labels == l2]) return images, labels def __repr__(self): return self.__class__.__name__ + '(' \ + 'p=' + str(self.p) + ', ' \ + 'phase=' + str(self.phase) + ', ' \ + 'num_cats=' + str(self.num_cats) + ')'

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4,306

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4.211333

0.053646

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0.040146

0.051616

0.892908

0.879674

0.868203

0.85916

0.843443

0.841017

0

0.023617

0.328258

32,904

768

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42.84375

0.796815

0.297806

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0.11399

false

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0.028497

0.020725

0.264249

0.002591

0

null

0

1

0

null

0

6

de36ec245d78334480c438d1a63f442ed0d8517a

153

py

Python

hijack/signals.py

HarryRybacki/django-hijack

6368284f3763da282aae3a3807fcce7b2372d7bb

[ "MIT" ]

null

hijack/signals.py

HarryRybacki/django-hijack

6368284f3763da282aae3a3807fcce7b2372d7bb

[ "MIT" ]

null

hijack/signals.py

HarryRybacki/django-hijack

6368284f3763da282aae3a3807fcce7b2372d7bb

[ "MIT" ]

1

2019-09-29T04:50:23.000Z

from django.dispatch import Signal post_superuser_login = Signal(providing_args=['user_id']) post_superuser_logout = Signal(providing_args=['user_id'])

30.6

58

0.816993

21

153

5.571429

0.619048

0.222222

0.324786

0.393162

0.42735

0

0.071895

153

5

58

30.6

0.823944

0

0.090909

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

6

de3a47ea18963dbf563c062f58f2d481cb785d7b

70

py

Python

coopy/symbolic/types/__init__.py

abarreal/coopy

af2c42ab20e534d7790d7f591d39ea9e6c727c35

[ "MIT" ]

null

coopy/symbolic/types/__init__.py

abarreal/coopy

af2c42ab20e534d7790d7f591d39ea9e6c727c35

[ "MIT" ]

null

coopy/symbolic/types/__init__.py

abarreal/coopy

af2c42ab20e534d7790d7f591d39ea9e6c727c35

[ "MIT" ]

null

from .primitives import * from .function import * from .sorts import *

23.333333

25

0.757143

9

70

5.888889

0.555556

0.377358

0

0.157143

70

3

26

23.333333

0.898305

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

de64b2b0e32dfa6945c6bf19ea3789d41df74666

26

py

Python

ioc_toolkit/__init__.py

fhightower/ioc-toolkit

2ddce3e2794d982906bb57fd0dd62d0cf09b9319

[ "MIT" ]

null

ioc_toolkit/__init__.py

fhightower/ioc-toolkit

2ddce3e2794d982906bb57fd0dd62d0cf09b9319

[ "MIT" ]

14

2018-01-13T13:14:52.000Z

2018-07-31T15:24:50.000Z

ioc_toolkit/__init__.py

fhightower/ioc-toolkit

2ddce3e2794d982906bb57fd0dd62d0cf09b9319

[ "MIT" ]

null

from . import ioc_toolkit

13

25

0.807692

4

26

5

1

0

0.153846

26

1

26

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

de7a02bf2bfc24ed89938de16127ff7d4be6bece

5,054

py

Python

pex/exe/dll.py

EntySec/pex

370f1b96d2e54609fe335c6f20179e56a1266a58

[ "MIT" ]

null

pex/exe/dll.py

EntySec/pex

370f1b96d2e54609fe335c6f20179e56a1266a58

[ "MIT" ]

null

pex/exe/dll.py

EntySec/pex

370f1b96d2e54609fe335c6f20179e56a1266a58

[ "MIT" ]

null

#!/usr/bin/env python3 # # MIT License # # Copyright (c) 2020-2022 EntySec # # 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. # import struct class Dll: magic = [ b"\x4d\x5a" ] headers = { 'x86': ( b'\x4d\x5a\x90\x00\x03\x00\x00\x00\x04\x00\x00\x00\xff\xff\x00\x00\xb8\x00\x00\x00\x00\x00\x00\x00' b'\x40\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80\x00\x00\x00\x0e\x1f\xba\x0e\x00\xb4\x09\xcd' b'\x21\xb8\x01\x4c\xcd\x21\x54\x68\x69\x73\x20\x70\x72\x6f\x67\x72\x61\x6d\x20\x63\x61\x6e\x6e\x6f' b'\x74\x20\x62\x65\x20\x72\x75\x6e\x20\x69\x6e\x20\x44\x4f\x53\x20\x6d\x6f\x64\x65\x2e\x0d\x0d\x0a' b'\x24\x00\x00\x00\x00\x00\x00\x00\x50\x45\x00\x00\x4c\x01\x03\x00\x9e\xa7\xb6\x58\x00\x00\x00\x00' b'\x00\x00\x00\x00\xe0\x00\x0e\x23\x0b\x01\x02\x1b\x00\x02\x00\x00\x00\x06\x00\x00\x00\x00\x00\x00' b'\x00\x10\x00\x00\x00\x10\x00\x00\x00\x00\x00\x00\x00\x00\x00\x10\x00\x10\x00\x00\x00\x02\x00\x00' b'\x04\x00\x00\x00\x01\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x00\x40\x00\x00\x00\x04\x00\x00' b'\xe2\x9e\x00\x00\x03\x00\x00\x00\x00\x00\x20\x00\x00\x10\x00\x00\x00\x00\x10\x00\x00\x10\x00\x00' b'\x00\x00\x00\x00\x10\x00\x00\x00\x00\x20\x00\x00\xff\x0e\x00\x00\x00\x30\x00\x00\x14\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x2e\x74\x65\x78\x74\x00\x00\x00' b'\x54\x01\x00\x00\x00\x10\x00\x00\x00\x02\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' b'\x00\x00\x00\x00\x20\x00\x50\x60\x2e\x65\x64\x61\x74\x61\x00\x00\xff\x0e\x00\x00\x00\x20\x00\x00' b'\x00\x04\x00\x00\x00\x06\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x40\x00\x30\x40' b'\x2e\x69\x64\x61\x74\x61\x00\x00\x14\x00\x00\x00\x00\x30\x00\x00\x00\x02\x00\x00\x00\x0a' ) } def pack_dll(self, arch, data, dll_inj_funcs=[], filename='kernel32'): if arch in self.headers.keys(): pe = self.headers[arch] + b'\x00' * 546 + data if arch == 'x86': pe += b'\xff\xff\xff\xff\x00\x00\x00\x00\xff\xff\xff\xff' content = pe.ljust(1536, b'\x00') content += b'\x00' * 16 content += b'\x01\x00\x00\x00' content += struct.pack('<I', len(dll_inj_funcs)) * 2 content += b'\x28\x20\x00\x00' content += struct.pack('B', 0x28 + len(dll_inj_funcs) * 4) + b'\x20\x00\x00' content += struct.pack('B', 0x28 + len(dll_inj_funcs) * 8) + b'\x20\x00\x00' content += b'\x00\x10\x00\x00' * len(dll_inj_funcs) base = 0x2100 + len(filename) - 1 content += struct.pack('<H', base) + b'\x00\x00' for func_name in dll_inj_funcs[:-1]: base += len(func_name) + 1 content += struct.pack('<H', base) + b'\x00\x00' for i in range(len(dll_inj_funcs)): content += struct.pack('<H', i) content += filename.encode() + b'.dll\x00' for func_name in dll_inj_funcs: content += func_name + b'\x00' content = content.ljust(3072, b'\x00') else: raise RuntimeError("DLL header corrupted!") return content raise RuntimeError("Failed to find compatible DLL header!")

52.103093

111

0.621488

880

5,054

3.545455

0.242045

0.540385

0.631731

0.676923

0.440064

0.405449

0.375641

0.321474

0.272756

0.25609

0

0.255416

0.214484

5,054

96

112

52.645833

0.530479

0.214879

0

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de8dd6a223d5dc885ff7925e98265c90246bc91b

186

py

Python

preacher/compilation/extraction/__init__.py

ymoch/preacher

ae68170d14c72791884e91b20054bd13a79b52d0

[ "MIT" ]

3

2019-08-01T03:14:49.000Z

2020-01-31T08:55:22.000Z

preacher/compilation/extraction/__init__.py

ymoch/preacher

ae68170d14c72791884e91b20054bd13a79b52d0

[ "MIT" ]

353

2019-04-14T14:53:28.000Z

2022-03-11T03:26:08.000Z

preacher/compilation/extraction/__init__.py

ymoch/preacher

ae68170d14c72791884e91b20054bd13a79b52d0

[ "MIT" ]

1

2020-08-01T06:23:08.000Z

"""Extraction compilation.""" from .extraction import ExtractionCompiler from .factory import create_extraction_compiler __all__ = ["ExtractionCompiler", "create_extraction_compiler"]

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6

dec2156cbffc7609e2abf1ba7286e33bae289b11

9,700

py

Python

models/attention.py

Schwartz-Zha/My-invertible-resnet

5415975bb0d640f3bf3ef4a7b986563e84109270

[ "MIT" ]

null

models/attention.py

Schwartz-Zha/My-invertible-resnet

5415975bb0d640f3bf3ef4a7b986563e84109270

[ "MIT" ]

null

models/attention.py

Schwartz-Zha/My-invertible-resnet

5415975bb0d640f3bf3ef4a7b986563e84109270

[ "MIT" ]

null

import torch import torch.nn as nn from torch.nn import Parameter, Softmax class PAM_Module(nn.Module): """ Position attention module""" # paper: Dual Attention Network for Scene Segmentation def __init__(self, in_dim): super(PAM_Module, self).__init__() self.chanel_in = in_dim self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1) self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1) self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1) self.gamma = Parameter(torch.zeros(1)) self.softmax = Softmax(dim=-1) def forward(self, x): """ inputs : x : input feature maps( B X C X H X W) returns : out : attention value + input feature ( B X C X H X W) attention: B X (HxW) X (HxW) """ m_batchsize, C, height, width = x.size() proj_query = self.query_conv(x).view(m_batchsize, -1, width * height).permute(0, 2, 1) # [B, HW, C] proj_key = self.key_conv(x).view(m_batchsize, -1, width * height) # [B, C, HW] energy = torch.bmm(proj_query, proj_key) # Batch matrix multiplication, [B, HW, HW] attention = self.softmax(energy) # [B, HW, HW] proj_value = self.value_conv(x).view(m_batchsize, -1, width * height) # [B, C, HW] out = torch.bmm(proj_value, attention.permute(0, 2, 1)) # batch matrix multiplication, out = out.view(m_batchsize, C, height, width) out = self.gamma * out + x return out class PAM_Module_v2(nn.Module): ''' Little Bit Simplified Positional Attention Module ''' def __init__(self, input_channel_num): super(PAM_Module_v2, self).__init__() self.c_in = input_channel_num self.query_conv = nn.Conv2d(in_channels=self.c_in, out_channels=self.c_in // 8, kernel_size=1) self.key_conv = nn.Conv2d(in_channels=self.c_in, out_channels=self.c_in // 8, kernel_size=1) self.value_conv = nn.Conv2d(in_channels=self.c_in, out_channels=self.c_in, kernel_size=1) self.gamma = Parameter(torch.zeros(1)) self.softmax = Softmax(dim=1) def forward(self, x): B, C, H, W = x.size() proj_query = self.query_conv(x).view(B, -1, H * W).permute(0, 2, 1) # [B, HW, C//8] proj_key = self.key_conv(x).view(B, -1, H * W) # [B, C//8, HW] energy = torch.bmm(proj_query, proj_key) # Batch matrix multiplication, [B, HW, HW] attention = self.softmax(energy) proj_value = self.value_conv(x).view(B, -1, H * W) # [B, C, HW] out = torch.bmm(proj_value, attention).view(B, C, H, W) out = self.gamma * out + x return out class PAM_Module_v3(nn.Module): ''' Dot Product Positional Attention Module ''' def __init__(self, in_c): super(PAM_Module_v3, self).__init__() self.in_c = in_c self.query_conv = nn.Conv2d(in_channels=self.in_c, out_channels=self.in_c // 8, kernel_size=1) self.key_conv = nn.Conv2d(in_channels=self.in_c, out_channels=self.in_c // 8, kernel_size=1) self.value_conv = nn.Conv2d(in_channels=self.in_c, out_channels=self.in_c, kernel_size=1) self.gamma = Parameter(torch.zeros(1)) def forward(self, x): B, C, H, W = x.size() proj_query = self.query_conv(x).view(B, -1, H * W).permute(0, 2, 1) proj_key = self.key_conv(x).view(B, -1, H * W) energy = torch.bmm(proj_query, proj_key) attention = energy / float(H * W) proj_value = self.value_conv(x).view(B, -1, H * W) # [B, C, HW] out = torch.bmm(proj_value, attention).view(B, C, H, W) out = self.gamma * out + x return out class PAM_Module_v4(nn.Module): ''' Concatenation Style PAM ''' def __init__(self, in_c): super(PAM_Module_v4, self).__init__() self.in_c = in_c self.inter_c = in_c // 8 self.query_conv = nn.Conv2d(in_channels=in_c, out_channels=self.inter_c, kernel_size=1) self.key_conv = nn.Conv2d(in_channels=in_c, out_channels=self.inter_c, kernel_size=1) self.concat_conv = nn.Conv2d(in_channels=self.inter_c * 2, out_channels=1, kernel_size=1, bias=False) self.value_conv = nn.Conv2d(in_channels=in_c, out_channels=in_c, kernel_size=1) self.gamma = Parameter(torch.zeros(1)) def forward(self, x): B, C, H, W = x.size() proj_query = self.query_conv(x).view(B, self.inter_c, -1, 1) # [B, inter_c, HW, 1] proj_key = self.key_conv(x).view(B, self.inter_c, 1, -1) # [B, inter_c, 1, HW] proj_query.repeat(1, 1, 1, H * W) proj_key.repeat(1, 1, H * W, 1) concat_feature = torch.cat([proj_query, proj_key], dim=1) # [B, 2*inter_c, HW, HW] energy = self.concat_conv(concat_feature).squeeze() # [B, HW, HW] attention = energy / float(H * W) proj_value = self.value_conv(x).view(B, -1, H * W) out = torch.bmm(proj_value, attention).view(B, -1, H, W) out = self.gamma * out + x return out class PAM_Module_v5(nn.Module): ''' Deepmind proposed attention with Lipschitz constant ''' def __init__(self, in_c): super(PAM_Module_v5, self).__init__() self.in_c = in_c self.inter_c = in_c // 8 self.query_conv = nn.Conv2d(in_channels=in_c, out_channels=self.inter_c, kernel_size=1) self.key_conv = nn.Conv2d(in_channels=in_c, out_channels=self.inter_c, kernel_size=1) self.softmax = nn.Softmax(dim=-1) self.value_conv = nn.Conv2d(in_channels=in_c, out_channels=in_c, kernel_size=1) self.gamma = Parameter(torch.zeros(1)) def forward(self, x): B, C, H, W = x.size() proj_query = self.query_conv(x).view(B, H * W, -1) # [B, HW, inter_c] proj_key = self.key_conv(x).view(B, H * W, -1) # [B, HW, inter_c] energy = -torch.cdist(proj_query, proj_key) / float(H * W) # [B, HW, HW] attention = self.softmax(energy) proj_value = self.value_conv(x).view(B, -1, H * W) out = torch.bmm(proj_value, attention).view(B, -1, H, W) out = self.gamma * out + x return out class PAM_Module_v6(nn.Module): ''' Concatenation Style PAM, with non-lin bound ''' def __init__(self, in_c): super(PAM_Module_v6, self).__init__() self.in_c = in_c self.inter_c = in_c // 8 self.query_conv = nn.Conv2d(in_channels=in_c, out_channels=self.inter_c, kernel_size=1) self.key_conv = nn.Conv2d(in_channels=in_c, out_channels=self.inter_c, kernel_size=1) self.concat_conv = nn.Conv2d(in_channels=self.inter_c * 2, out_channels=1, kernel_size=1, bias=False) self.value_conv = nn.Conv2d(in_channels=in_c, out_channels=in_c, kernel_size=1) self.gamma = Parameter(torch.zeros(1)) self.nonlin = nn.Tanh() def forward(self, x): B, C, H, W = x.size() proj_query = self.query_conv(x).view(B, self.inter_c, -1, 1) # [B, inter_c, HW, 1] proj_key = self.key_conv(x).view(B, self.inter_c, 1, -1) # [B, inter_c, 1, HW] proj_query.repeat(1, 1, 1, H * W) proj_key.repeat(1, 1, H * W, 1) concat_feature = torch.cat([proj_query, proj_key], dim=1) # [B, 2*inter_c, HW, HW] energy = self.concat_conv(concat_feature).squeeze() # [B, HW, HW] attention = energy / float(H * W) proj_value = self.value_conv(x).view(B, -1, H * W) proj_value = self.nonlin(proj_value) out = torch.bmm(proj_value, attention).view(B, -1, H, W) out = self.gamma * out + x return out class PAM_Module_v7: ''' Concatenation Style PAM, with turbulance ''' def __init__(self, in_c): super(PAM_Module_v7, self).__init__() self.in_c = in_c self.inter_c = in_c // 8 self.query_conv = nn.Conv2d(in_channels=in_c, out_channels=self.inter_c, kernel_size=1) self.key_conv = nn.Conv2d(in_channels=in_c, out_channels=self.inter_c, kernel_size=1) self.concat_conv = nn.Conv2d(in_channels=self.inter_c * 2, out_channels=1, kernel_size=1, bias=False) self.value_conv = nn.Conv2d(in_channels=in_c, out_channels=in_c, kernel_size=1) self.gamma = Parameter(torch.zeros(1)) def forward(self, x): B, C, H, W = x.size() proj_query = self.query_conv(x).view(B, self.inter_c, -1, 1) # [B, inter_c, HW, 1] proj_key = self.key_conv(x).view(B, self.inter_c, 1, -1) # [B, inter_c, 1, HW] proj_query.repeat(1, 1, 1, H * W) proj_key.repeat(1, 1, H * W, 1) concat_feature = torch.cat([proj_query, proj_key], dim=1) # [B, 2*inter_c, HW, HW] energy = self.concat_conv(concat_feature).squeeze() # [B, HW, HW] attention = energy / float(H * W) proj_value = self.value_conv(x).view(B, -1, H * W) out = torch.bmm(proj_value, attention).view(B, -1, H, W) out = self.gamma * out + x return out def turbulance_hook(module, inputs): with torch.no_grad(): res = module.forward(inputs) turbu_res = module.forward(inputs * 1.0000001) lip = torch.dist(turbu_res, res) / torch.dist(inputs, inputs * 1.0000001) if lip > 0.9: module.gamma = module.gamma * (0.9 / lip) else: pass if __name__ == '__main__': demo_input = torch.randn(32, 12, 16, 16) layer = nn.Conv2d(in_channels=12, out_channels=12, kernel_size=3, padding=1) layer.register_forward_pre_hook(turbulance_hook) layer.forward(demo_input)

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null

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1

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null

0

6

a0d14d8390b6613f8687bcfc4ef009916a8b324e

12,803

py

Python

stellarobservatory/centralities.py

andrenarchy/stellar-observatory

0e1f6af4cdacae19248353f902686d8192130436

[ "MIT" ]

14

2019-05-29T09:45:00.000Z

2021-04-22T20:11:15.000Z

stellarobservatory/centralities.py

andrenarchy/stellar-observatory

0e1f6af4cdacae19248353f902686d8192130436

[ "MIT" ]

10

2019-05-29T09:47:01.000Z

2020-09-15T20:34:55.000Z

stellarobservatory/centralities.py

andrenarchy/stellar-observatory

0e1f6af4cdacae19248353f902686d8192130436

[ "MIT" ]

5

2019-05-29T07:33:02.000Z

2021-11-24T18:46:03.000Z

"""Centralities""" # pylint: disable=invalid-name from itertools import combinations from typing import Callable, Dict, FrozenSet, List, Set import numpy from scipy.linalg import eig, expm from .intactness import get_intact_nodes from .quorums import enumerate_quorums from .quorum_slice_definition import Definitions, get_is_slice_contained, get_trust_graph from .utils.graph import Graph, get_adjacency_matrix, get_dependencies, \ get_transpose_graph, Node, Nodes from .utils.hypergraph import get_hypergraph_adjacency_matrix, get_hypergraph_incidence_matrix from .utils.scc import get_strongly_connected_components from .utils.sets import powerset def get_eigenvector_centralities(nodes: List[Node], definitions: Definitions) -> numpy.array: """Compute trust graph eigenvector centralities""" trust_graph = get_trust_graph(definitions) adjacency_matrix = get_adjacency_matrix(nodes, trust_graph) eigenvalues, eigenvectors = eig(adjacency_matrix, left=True, right=False) index = numpy.argsort(numpy.real(eigenvalues))[-1] centralities = numpy.abs(eigenvectors[:, index]) return centralities / numpy.max(centralities) def get_subgraph_centralities(nodes: List[Node], definitions: Definitions) -> numpy.array: """Compute trust graph subgraph centralities""" trust_graph = get_trust_graph(definitions) adjacency_matrix = get_adjacency_matrix(nodes, trust_graph) exp_adjacency_matrix = expm(adjacency_matrix) centralities = numpy.diag(exp_adjacency_matrix) return centralities / numpy.max(centralities) def get_quorum_eigenvector_centralities(nodes: List[Node], definitions: Definitions) -> numpy.array: """Compute quorum eigenvector centralities""" fbas = (get_is_slice_contained(definitions), set(nodes)) hyperedge_list = list(enumerate_quorums(fbas)) incidence_matrix = get_hypergraph_incidence_matrix(nodes, hyperedge_list) MMT = incidence_matrix.dot(incidence_matrix.T) eigenvalues, eigenvectors = eig(MMT) index = numpy.argsort(numpy.real(eigenvalues))[-1] centralities = numpy.abs(eigenvectors[:, index]) return centralities / numpy.max(centralities) def get_quorum_subgraph_centralities(nodes: List[Node], definitions: Definitions) -> numpy.array: """Compute quorum subgraph centralities""" fbas = (get_is_slice_contained(definitions), set(nodes)) hyperedge_list = list(enumerate_quorums(fbas)) adjacency_matrix = get_hypergraph_adjacency_matrix(nodes, hyperedge_list) exp_adjacency_matrix = expm(adjacency_matrix) centralities = numpy.diag(exp_adjacency_matrix) return centralities / numpy.max(centralities) def get_quorum_intersection_eigenvector_centralities(nodes: List[Node], definitions: Definitions) -> numpy.array: """Compute quorum intersection eigenvector centralities""" fbas = (get_is_slice_contained(definitions), set(nodes)) quorums = list(enumerate_quorums(fbas)) hyperedge_list = list([a.intersection(b) for a, b in combinations(quorums, 2)]) incidence_matrix = get_hypergraph_incidence_matrix(nodes, hyperedge_list) MMT = incidence_matrix.dot(incidence_matrix.T) eigenvalues, eigenvectors = eig(MMT) index = numpy.argsort(numpy.real(eigenvalues))[-1] centralities = numpy.abs(eigenvectors[:, index]) return centralities / numpy.max(centralities) def get_quorum_intersection_subgraph_centralities(nodes: List[Node], definitions: Definitions) -> numpy.array: """Compute quorum intersection subgraph centralities""" fbas = (get_is_slice_contained(definitions), set(nodes)) quorums = list(enumerate_quorums(fbas)) hyperedge_list = list([a.intersection(b) for a, b in combinations(quorums, 2)]) adjacency_matrix = get_hypergraph_adjacency_matrix(nodes, hyperedge_list) exp_adjacency_matrix = expm(adjacency_matrix / numpy.linalg.norm(adjacency_matrix, 2)) centralities = numpy.diag(exp_adjacency_matrix) return centralities / numpy.max(centralities) def get_intactness_matrix(nodes: List[Node], definitions: Definitions, get_ill_behaved_weight: Callable[[Set[Node]], float]) -> numpy.array: """Compute matrix for intactness-based centralities""" fbas = (get_is_slice_contained(definitions), set(nodes)) node_to_index = {node: index for index, node in enumerate(nodes)} M = numpy.zeros((len(nodes), len(nodes))) for ill_behaved_nodes in powerset(nodes): if ill_behaved_nodes == set() or ill_behaved_nodes == set(nodes): continue intact_nodes = get_intact_nodes(fbas, ill_behaved_nodes) befouled_nodes = set(nodes).difference(intact_nodes) induced_befouled_nodes = befouled_nodes.difference(ill_behaved_nodes) ill_behaved_weight = get_ill_behaved_weight(ill_behaved_nodes) for ill_behaved_node in ill_behaved_nodes: for induced_befouled_node in induced_befouled_nodes: M[node_to_index[ill_behaved_node], \ node_to_index[induced_befouled_node]] += ill_behaved_weight return M def get_intactness_eigenvector_centralities(nodes: List[Node], definitions: Definitions, get_ill_behaved_weight: Callable[[Set[Node]], float] ) -> numpy.array: """Compute intactness eigenvector centralities""" M = get_intactness_matrix(nodes, definitions, get_ill_behaved_weight) eigenvalues, eigenvectors = eig(M) index = numpy.argsort(numpy.real(eigenvalues))[-1] centralities = numpy.abs(eigenvectors[:, index]) return centralities / numpy.max(centralities) def get_intactness_ls_centralities(nodes: List[Node], definitions: Definitions, get_ill_behaved_weight: Callable[[Set[Node]], float], get_mu: Callable[[numpy.array], float]) -> numpy.array: """Compute intactness linear system centralities""" M = get_intactness_matrix(nodes, definitions, get_ill_behaved_weight) A = numpy.eye(len(nodes)) - get_mu(M) * M centralities = numpy.linalg.solve(A, numpy.ones(len(nodes))) return centralities / numpy.max(centralities) def get_scc_dependencies(sccs: List[Nodes], scc_graph: Graph, scc_index: Node): """Get SCC dependencies""" scc_dependencies = get_dependencies(scc_graph, scc_index) dependencies: Set[Node] = set() for dependency in scc_dependencies: dependencies.update(sccs[dependency]) return dependencies def get_scc_dependents(sccs: List[Nodes], scc_graph: Graph, scc_index: Node): """Get SCC dependents""" scc_graph_transpose = get_transpose_graph(scc_graph) scc_dependents = get_dependencies(scc_graph_transpose, scc_index) dependents: Set[Node] = set() for dependent in scc_dependents: dependents.update(sccs[dependent]) return dependents def get_hierarchical_intactness_matrix(nodes: List[Node], definitions: Definitions, get_ill_behaved_weight: Callable[[Set[Node]], float] ) -> numpy.array: """Compute matrix for hierarchical intactness-based centralities""" # pylint: disable=too-many-locals fbas = (get_is_slice_contained(definitions), set(nodes)) node_to_index = {node: index for index, node in enumerate(nodes)} M = numpy.zeros((len(nodes), len(nodes))) trust_graph = get_trust_graph(definitions) sccs, scc_graph = get_strongly_connected_components(trust_graph) for scc_index, _ in scc_graph.items(): dependencies = get_scc_dependencies(sccs, scc_graph, scc_index) dependents = get_scc_dependents(sccs, scc_graph, scc_index) for ill_behaved_nodes in powerset(dependencies.union(sccs[scc_index])): if ill_behaved_nodes == set() or ill_behaved_nodes == set(nodes): continue befouled_nodes = set(nodes) - get_intact_nodes(fbas, ill_behaved_nodes) affected_befouled_nodes = (befouled_nodes - ill_behaved_nodes) & \ (sccs[scc_index] | dependents) ill_behaved_weight = get_ill_behaved_weight(ill_behaved_nodes) for ill_behaved_node in ill_behaved_nodes: for affected_befouled_node in affected_befouled_nodes: M[node_to_index[ill_behaved_node], \ node_to_index[affected_befouled_node]] += ill_behaved_weight return M def get_hierarchical_intactness_eigenvector_centralities( nodes: List[Node], definitions: Definitions, get_ill_behaved_weight: Callable[[Set[Node]], float] ) -> numpy.array: """Compute hierarchical intactness eigenvector centralities""" M = get_hierarchical_intactness_matrix(nodes, definitions, get_ill_behaved_weight) eigenvalues, eigenvectors = eig(M) index = numpy.argsort(numpy.real(eigenvalues))[-1] centralities = numpy.abs(eigenvectors[:, index]) return centralities / numpy.max(centralities) def get_hierarchical_intactness_ls_centralities( nodes: List[Node], definitions: Definitions, get_ill_behaved_weight: Callable[[Set[Node]], float], get_mu: Callable[[numpy.array], float] ) -> numpy.array: """Compute hierarchical intactness linear system centralities""" M = get_hierarchical_intactness_matrix(nodes, definitions, get_ill_behaved_weight) A = numpy.eye(len(nodes)) - get_mu(M) * M centralities = numpy.linalg.solve(A, numpy.ones(len(nodes))) return centralities / numpy.max(centralities) def get_minimal_intactness_matrix( nodes: List[Node], definitions: Definitions, get_ill_behaved_weight: Callable[[Set[Node]], float] ) -> numpy.array: """Compute matrix for minimal intactness-based centralities""" fbas = (get_is_slice_contained(definitions), set(nodes)) node_to_index = {node: index for index, node in enumerate(nodes)} M = numpy.zeros((len(nodes), len(nodes))) # note: this assumes that powerset() iterates from smallest to largest subset def get_induced_befouled_nodes(ill_behaved_nodes: Nodes): if ill_behaved_nodes == set() or ill_behaved_nodes == set(nodes): return set() intact_nodes = get_intact_nodes(fbas, ill_behaved_nodes) befouled_nodes = set(nodes).difference(intact_nodes) return befouled_nodes.difference(ill_behaved_nodes) ill_behaved_to_induced_befouled: Dict[FrozenSet[Node], Set[Node]] = {} def is_minimal_befouling(ill_behaved_nodes: Nodes, induced_befouled_nodes: Nodes): for ill_behaved_node in ill_behaved_nodes: smaller_induced_befouled_nodes = ill_behaved_to_induced_befouled[ frozenset(ill_behaved_nodes.difference({ill_behaved_node})) ] difference = smaller_induced_befouled_nodes.difference({ill_behaved_node}) if difference >= induced_befouled_nodes: return False return True for ill_behaved_nodes in powerset(nodes): induced_befouled_nodes = get_induced_befouled_nodes(ill_behaved_nodes) ill_behaved_to_induced_befouled[frozenset(ill_behaved_nodes)] = induced_befouled_nodes if not is_minimal_befouling(ill_behaved_nodes, induced_befouled_nodes): continue ill_behaved_weight = get_ill_behaved_weight(ill_behaved_nodes) for ill_behaved_node in ill_behaved_nodes: for induced_befouled_node in induced_befouled_nodes: M[node_to_index[ill_behaved_node], \ node_to_index[induced_befouled_node]] += ill_behaved_weight return M def get_minimal_intactness_eigenvector_centralities( nodes: List[Node], definitions: Definitions, get_ill_behaved_weight: Callable[[Set[Node]], float] ) -> numpy.array: """Compute minimal intactness eigenvector centralities""" M = get_minimal_intactness_matrix(nodes, definitions, get_ill_behaved_weight) eigenvalues, eigenvectors = eig(M) index = numpy.argsort(numpy.real(eigenvalues))[-1] centralities = numpy.abs(eigenvectors[:, index]) return centralities / numpy.max(centralities) def get_minimal_intactness_ls_centralities( nodes: List[Node], definitions: Definitions, get_ill_behaved_weight: Callable[[Set[Node]], float], get_mu: Callable[[numpy.array], float] ) -> numpy.array: """Compute minimal intactness linear system centralities""" M = get_minimal_intactness_matrix(nodes, definitions, get_ill_behaved_weight) A = numpy.eye(len(nodes)) - get_mu(M) * M centralities = numpy.linalg.solve(A, numpy.ones(len(nodes))) return centralities / numpy.max(centralities)

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209

py

Python

backend/settings/__init__.py

co-demos/apiviz-backend

8a86b92dce728e81c1c935427b890da590edd720

[ "MIT" ]

1

2019-12-17T22:31:00.000Z

backend/settings/__init__.py

co-demos/apiviz-backend

8a86b92dce728e81c1c935427b890da590edd720

[ "MIT" ]

10

2019-05-28T19:57:28.000Z

2021-06-01T23:46:00.000Z

backend/settings/__init__.py

co-demos/apiviz-backend

8a86b92dce728e81c1c935427b890da590edd720

[ "MIT" ]

null

# -*- encoding: utf-8 -*- # from app_settings import * from .app_languages import * from .app_files import * from .app_choices import * # from .app_nomenclature_tags import * from .app_auth_modif import *

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1

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6

9d10265704ca457e68984aef4770f67a4db7908e

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py

Python

testing/test_lock.py

xpenalosa/Degree-Final-Project

5d6c1a6aa034c41bf88ae0c8b16d7c0ddb0c3eec

[ "Unlicense" ]

null

testing/test_lock.py

xpenalosa/Degree-Final-Project

5d6c1a6aa034c41bf88ae0c8b16d7c0ddb0c3eec

[ "Unlicense" ]

null

testing/test_lock.py

xpenalosa/Degree-Final-Project

5d6c1a6aa034c41bf88ae0c8b16d7c0ddb0c3eec

[ "Unlicense" ]

null

from kazoo.recipe.lock import Lock, ReadLock, WriteLock from kazoo.exceptions import LockTimeout, NoNodeError from basetest import BaseTest class LockTest(BaseTest): def __init__(self, client_1, client_2): self.client_1 = client_1 self.client_2 = client_2 self.testpath = "/test/lock" @BaseTest.make_test def test_no_lock(self): self.client_1.ensure_path(self.testpath) self.client_2.ensure_path(self.testpath) path = self.client_1.create(self.testpath + "/no_lock", b"Info") data, stats = self.client_1.get(path) assert data == b"Info", "Data mismatch for client_1" data, stats = self.client_2.get(path) assert data == b"Info", "Data mismatch for client_2" @BaseTest.make_test def test_lock_delete(self): self.client_1.ensure_path(self.testpath) path = self.client_1.create( self.testpath + "/lock_delete", b"Info") lock = self.client_1.Lock(path, "client_1") lock_1_acquired = False with lock: lock_1_acquired = True try: self.client_1.delete(path, recursive=True) except Exception as e: print(type(e)) raise Exception(e) else: try: data, stats = self.client_1.get(path) except NoNodeError: pass else: raise Exception("Did not delete node") assert lock_1_acquired == True, "Did not acquire lock" @BaseTest.make_test def test_dual_lock_single(self): self.client_1.ensure_path(self.testpath) path = self.client_1.create(self.testpath + "/single", b"Info") lock = self.client_1.Lock(path, "client_1") lock_1_acquired = False with lock: lock_1_acquired = True assert True, "Lock not acquired but passed check" assert lock.contenders() is not None, "No contenders" data, stats = self.client_1.get(path) assert data == b"Info", "Data mismatch" assert lock_1_acquired == True, "Did not acquire lock" @BaseTest.make_test def test_dual_lock_and_get(self): self.client_1.ensure_path(self.testpath) path = self.client_1.create(self.testpath + "/block", b"Info") lock = self.client_1.Lock(path, "client_1") lock_1_acquired = False with lock: lock_1_acquired = True data, stats = self.client_2.get(path) assert data is not None, "Data is none" assert data == b"Info", "Data mismatch" assert lock_1_acquired == True, "Did not acquire lock" @BaseTest.make_test def test_dual_locks(self): self.client_1.ensure_path(self.testpath) path = self.client_1.create(self.testpath + "/2_locks", b"Info") lock_1 = self.client_1.Lock(path, "client_1") lock_1_acquired = False with lock_1: lock_1_acquired = True lock_2 = self.client_2.Lock(path, "client_2") lock_2_acquired = False try: lock_2_acquired = lock_2.acquire(timeout=0.1) except LockTimeout: # Expected behaviour assert lock_2_acquired == False else: lock_2.cancel() assert False, "Did not throw LockTimeout" assert lock_1_acquired == True, "Did not acquire lock 1" @BaseTest.make_test def test_read_then_read(self): self.client_1.ensure_path(self.testpath) path = self.client_1.create(self.testpath + "/r_locks", b"Info") lock_1 = self.client_1.ReadLock(path, "client_1") lock_1_acquired = False with lock_1: lock_1_acquired = True lock_2 = self.client_2.ReadLock(path, "client_2") lock_2_acquired = False with lock_2: lock_2_acquired = True data, stats = self.client_2.get(path) assert data == b"Info", "Data mismatch" assert lock_2_acquired == True, "Did not acquire lock 2" assert lock_1_acquired == True, "Did not acquire lock 1" @BaseTest.make_test def test_read_then_write(self): self.client_1.ensure_path(self.testpath) path = self.client_1.create( self.testpath + "/rw_locks", b"Info") lock_1 = self.client_1.ReadLock(path, "client_1") lock_1_acquired = False with lock_1: lock_1_acquired = True lock_2 = self.client_2.WriteLock(path, "client_2") try: lock_2.acquire(timeout=0.1) except LockTimeout: pass else: assert False, "Lock 2 was acquired" assert lock_1_acquired == True, "Did not acquire lock 1" @BaseTest.make_test def test_write_then_read(self): self.client_1.ensure_path(self.testpath) path = self.client_1.create( self.testpath + "/wr_locks", b"Info") lock_1 = self.client_1.WriteLock(path, "client_1") lock_1_acquired = False with lock_1: lock_1_acquired = True lock_2 = self.client_2.ReadLock(path, "client_2") lock_2_acquired = False try: lock_2_acquired = lock_2.acquire(timeout=0.1) except LockTimeout: # Expected behaviour pass else: assert False, "Lock 2 was acquired" assert lock_2_acquired == False, "Acquired lock 2" assert lock_1_acquired == True, "Did not acquire lock 1" @BaseTest.make_test def test_write_then_write(self): self.client_1.ensure_path(self.testpath) path = self.client_1.create(self.testpath + "/w_locks", b"Info") lock_1 = self.client_1.WriteLock(path, "client_1") lock_1_acquired = False with lock_1: lock_1_acquired = True lock_2 = self.client_2.WriteLock(path, "client_2") lock_2_acquired = False try: lock_2_acquired = lock_2.acquire(timeout=0.1) except LockTimeout: # Expected behaviour pass else: assert False, "Lock 2 was acquired" assert lock_2_acquired == False, "Acquired lock 2" assert lock_1_acquired == True, "Did not acquire lock 1"

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null

0

1

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null

0

6

19b7d4a6098b98fd1608988acb4f69bbd83bc753

2,628

py

Python

Deep_Learning_from_Scratch/ch04/LossFunctions.py

H-BlackGom/Study-HR

1dc5ab6ac3b382765b342b7bb35c2c5c69ba23e5

[ "Apache-2.0" ]

null

Deep_Learning_from_Scratch/ch04/LossFunctions.py

H-BlackGom/Study-HR

1dc5ab6ac3b382765b342b7bb35c2c5c69ba23e5

[ "Apache-2.0" ]

null

Deep_Learning_from_Scratch/ch04/LossFunctions.py

H-BlackGom/Study-HR

1dc5ab6ac3b382765b342b7bb35c2c5c69ba23e5

[ "Apache-2.0" ]

null

import numpy as np import torch import torch.nn def sum_of_squares_error(y, t): return 0.5 * torch.sum((y-t)**2) t = torch.tensor([0, 0, 1, 0, 0, 0, 0, 0, 0, 0]) y = torch.tensor([0.1, 0.05, 0.6, 0.0, 0.05, 0.1, 0.0, 0.1, 0.0, 0.0]) print(sum_of_squares_error(y, t), "'2'일 확률이 가장 높은 경우") y = torch.tensor([0.1, 0.05, 0.1, 0.0, 0.05, 0.1, 0.0, 0.6, 0.0, 0.0]) print(sum_of_squares_error(y, t), "'7'일 확률이 가장 높은 경우") print("---------------------------------------------------") def default_cross_entropy_error(y, t): delta = 1e-7 return -torch.sum(t * torch.log(y + delta)) t = torch.tensor([0, 0, 0, 0, 0, 0, 0, 1, 0, 0]) y = torch.tensor([0.1, 0.05, 0.6, 0.0, 0.05, 0.1, 0.0, 0.1, 0.0, 0.0]) print(default_cross_entropy_error(y, t)) y = torch.tensor([0.1, 0.05, 0.1, 0.0, 0.05, 0.1, 0.0, 0.6, 0.0, 0.0]) print(default_cross_entropy_error(y, t)) print("---------------------------------------------------") def numpy_cross_entropy_error(y, t): if y.ndim == 1: y = y.reshape(1, y.size) t = t.reshape(1, t.size) batch_size = y.shape[0] return -np.sum(t * np.log(y + 1e-7)) / batch_size t = np.array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0]) y = np.array([0.1, 0.05, 0.6, 0.0, 0.05, 0.1, 0.0, 0.1, 0.0, 0.0]) print(numpy_cross_entropy_error(y, t)) def torch_cross_entropy_error(y, t): if y.dim() == 1: y = y.view(1, y.size()[0]) t = t.view(1, t.size()[0]) batch_size = y.shape[0] return -torch.sum(t * torch.log(y + 1e-7)) / batch_size t = torch.tensor([0, 0, 0, 0, 0, 0, 0, 1, 0, 0]) y = torch.tensor([0.1, 0.05, 0.6, 0.0, 0.05, 0.1, 0.0, 0.1, 0.0, 0.0]) print(torch_cross_entropy_error(y, t)) def numpy_cross_entropy_error_1(y, t): if y.ndim == 1: y = y.reshape(1, y.size) t = t.reshape(1, t.size) if t.size == y.size: t = t.argmax(axis=1) batch_size = y.shape[0] return -np.sum(np.log(y[np.arange(batch_size), t] + 1e-7)) / batch_size t = np.array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0]) y = np.array([0.1, 0.05, 0.6, 0.0, 0.05, 0.1, 0.0, 0.1, 0.0, 0.0]) print(numpy_cross_entropy_error_1(y, t)) def cross_entropy_error_1(y, t): if y.dim() == 1: y = y.view(1, y.size()[0]) t = t.view(1, t.size()[0]) if t.size() == y.size(): t = t.argmax(axis=1) batch_size = y.shape[0] return -torch.sum(torch.log(y[torch.arange(batch_size), t] + 1e-7)) / batch_size t = torch.tensor([0, 0, 0, 0, 0, 0, 0, 1, 0, 0]) y = torch.tensor([0.1, 0.05, 0.6, 0.0, 0.05, 0.1, 0.0, 0.1, 0.0, 0.0]) print(cross_entropy_error_1(y, t)) print("---------------------------------------------------")

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0.800604

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0

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2,628

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false

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1

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1

0

1

0

null

0

6

c22595614e5fe6acd1a0ed3c6bb42abe9924ccf2

167

py

Python

mellow/services.py

stett/django-mellow-auth

f83c0c7e6509d16c8344039d075b1f8ba0c7b761

[ "MIT" ]

null

mellow/services.py

stett/django-mellow-auth

f83c0c7e6509d16c8344039d075b1f8ba0c7b761

[ "MIT" ]

null

mellow/services.py

stett/django-mellow-auth

f83c0c7e6509d16c8344039d075b1f8ba0c7b761

[ "MIT" ]

null

import random import string def make_activation_key(): return ''.join(random.choice(string.ascii_letters + string.digits) for i in range(40))

20.875

70

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22

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0.233533

167

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0

1

0

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6

dfc7ed7e6548d96fcd217a81431ffc87c51a3b54

5,605

py

Python

src/sentry/testutils/fixtures.py

Casecommons/sentry

b69a2373a658c5c775671fe9985c3fa4f2eafcfd

[ "BSD-3-Clause" ]

null

src/sentry/testutils/fixtures.py

Casecommons/sentry

b69a2373a658c5c775671fe9985c3fa4f2eafcfd

[ "BSD-3-Clause" ]

null

src/sentry/testutils/fixtures.py

Casecommons/sentry

b69a2373a658c5c775671fe9985c3fa4f2eafcfd

[ "BSD-3-Clause" ]

null

""" sentry.testutils.fixtures ~~~~~~~~~~~~~~~~~~~~~~~~~ :copyright: (c) 2010-2014 by the Sentry Team, see AUTHORS for more details. :license: BSD, see LICENSE for more details. """ from exam import fixture from sentry.models import Activity, Event, Group, Project, Team, User from sentry.utils.compat import pickle from sentry.utils.strings import decompress # an example data blog from Sentry 5.4.1 (db level) LEGACY_DATA = pickle.loads(decompress("""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""")) class Fixtures(object): @fixture def projectkey(self): return self.project.key_set.get_or_create(user=self.user)[0] @fixture def user(self): return self.create_user('admin@localhost', username='admin') @fixture def team(self): return Team.objects.create( name='foo', slug='foo', owner=self.user, ) @fixture def project(self): return Project.objects.create( owner=self.user, name='Bar', slug='bar', team=self.team, ) @fixture def group(self): return self.create_group() @fixture def event(self): return self.create_event(event_id='a' * 32) @fixture def activity(self): return Activity.objects.create( group=self.group, event=self.event, project=self.project, type=Activity.NOTE, user=self.user, data={} ) def create_user(self, email, **kwargs): kwargs.setdefault('username', email) kwargs.setdefault('is_staff', True) kwargs.setdefault('is_superuser', True) user = User(email=email, **kwargs) user.set_password('admin') user.save() return user def create_event(self, event_id, **kwargs): if 'group' not in kwargs: kwargs['group'] = self.group kwargs.setdefault('project', kwargs['group'].project) kwargs.setdefault('message', 'Foo bar') kwargs.setdefault('data', LEGACY_DATA) return Event.objects.create( event_id=event_id, **kwargs ) def create_group(self, project=None, **kwargs): return Group.objects.create( message='Foo bar', project=project or self.project, **kwargs )

62.977528

3,342

0.812489

374

5,605

12.128342

0.508021

0.015432

0.012346

0.013228

0

0.102823

0.115076

5,605

88

3,343

63.693182

0.811694

0.039964

0

0.171875

0

0.015625

0.634469

0.613438

0

1

0

1

0.15625

false

0.015625

0.0625

0.125

0.390625

0

1

null

0

1

0

1

null

1

0

1

0

6

dfeed4648eafc4bf3070fdd102f7abd159803cec

46

py

Python

exercise/ch03/identity.py

HFTshoon/deep-learning-from-scratch

c7c85abb33fbb710f055daec6d2c31322401fa02

[ "MIT" ]

null

exercise/ch03/identity.py

HFTshoon/deep-learning-from-scratch

c7c85abb33fbb710f055daec6d2c31322401fa02

[ "MIT" ]

null

exercise/ch03/identity.py

HFTshoon/deep-learning-from-scratch

c7c85abb33fbb710f055daec6d2c31322401fa02

[ "MIT" ]

null

import numpy as np def identity(x): return x

11.5

18

0.73913

9

46

3.777778

0.888889

0

0.195652

46

4

19

11.5

0.918919

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

5f2fda1dcf88056baa24480cc78fea20e4a5ac90

36

py

Python

class1/test1.py

sbyount/pyaut3

2fcf19851487db49d76d5b6996ee0f9194d90816

[ "Apache-2.0" ]

1

2019-04-17T02:49:58.000Z

class1/test1.py

sbyount/pyaut3

2fcf19851487db49d76d5b6996ee0f9194d90816

[ "Apache-2.0" ]

null

class1/test1.py

sbyount/pyaut3

2fcf19851487db49d76d5b6996ee0f9194d90816

[ "Apache-2.0" ]

null

print ("test") print ("test again")

12

20

0.638889

5

36

4.6

0.6

0.782609

0

0.138889

36

2

21

18

0.741935

0

0.388889

0

1

0

true

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

a04a88017340754ea7be4c0d1c8a0d55aded3b9f

6,688

py

Python

bin/bin_SMHMr/compute_logN_logS.py

JohanComparat/nbody-npt-functions

a034db4e5a9b2f87dc42eeb6059c4dd280589e4a

[ "CC0-1.0" ]

4

2017-11-07T02:15:46.000Z

2022-03-03T01:35:53.000Z

bin/bin_SMHMr/compute_logN_logS.py

JohanComparat/nbody-npt-functions

a034db4e5a9b2f87dc42eeb6059c4dd280589e4a

[ "CC0-1.0" ]

null

bin/bin_SMHMr/compute_logN_logS.py

JohanComparat/nbody-npt-functions

a034db4e5a9b2f87dc42eeb6059c4dd280589e4a

[ "CC0-1.0" ]

2

2020-08-12T14:26:38.000Z

2021-09-14T06:08:58.000Z

from os.path import join import os import glob import time import cPickle import fileinput import astropy.io.fits as fits import numpy as n import healpy #ras = n.array([healpy.pix2ang(NSIDE,pix_id)[1]*180./n.pi for pix_id in pix_ids ]) #decs = n.array([ (healpy.pix2ang(NSIDE,pix_id)[0]-n.pi/2.)*180./n.pi for pix_id in pix_ids ]) #n.savetxt("px_ra_Dec.txt", n.transpose([pix_ids, ras, decs])) NSIDE = 16 pix_ids_16 = n.arange(healpy.nside2npix(NSIDE)) area_per_pixel_16 = 129600./n.pi/healpy.nside2npix(NSIDE) print("pixel area considered=",area_per_pixel_16,"deg2") NSIDE = 32 pix_ids_32 = n.arange(healpy.nside2npix(NSIDE)) area_per_pixel_32 = 129600./n.pi/healpy.nside2npix(NSIDE) print("pixel area considered=",area_per_pixel_32,"deg2") path_2_light_cone = os.path.join(os.environ["MD10"], 'light-cone', 'MDPL2_FluxProj000_ClustersCombinedModel_bias0.6_with_header.fits') hd = fits.open(path_2_light_cone) log_f_05_20 = n.log10(hd[1].data['F_05_20']) HEALPIX_16 = healpy.ang2pix(16, hd[1].data['galactic_latitude_deg']*n.pi/180. , hd[1].data['galactic_longitude_deg']*n.pi/180. ) HEALPIX_32 = healpy.ang2pix(32, hd[1].data['galactic_latitude_deg']*n.pi/180. , hd[1].data['galactic_longitude_deg']*n.pi/180. ) out = n.histogram(log_f_05_20, bins = n.arange(-18, -8., 0.2)) per_pixel_out_16 = n.array([ n.histogram(log_f_05_20[HEALPIX_16==hp_i], bins = n.arange(-18, -8., 0.2))[0] for hp_i in pix_ids[:1000] ]) per_pixel_out_c_16 = n.array([[ n.sum(el[ii:]) for ii in range(len(el)) ] for el in per_pixel_out_16 ]) frac_err_13deg2 = n.std(per_pixel_out_c_16, axis=0)/n.mean(per_pixel_out_c_16, axis=0) per_pixel_out_32 = n.array([ n.histogram(log_f_05_20[HEALPIX_32==hp_i], bins = n.arange(-18, -8., 0.2))[0] for hp_i in pix_ids[:1000] ]) per_pixel_out_c_32 = n.array([[ n.sum(el[ii:]) for ii in range(len(el)) ] for el in per_pixel_out_32 ]) frac_err_3deg2 = n.std(per_pixel_out_c, axis=0)/n.mean(per_pixel_out_c_32, axis=0) # cumulative number density per square degrees x_out = 0.5*(out[1][1:] + out[1][:-1]) c_out = n.array([n.sum(out[0][ii:]) for ii in range(len(out[0])) ])*n.pi/129600. path_2_light_cone = os.path.join(os.environ["MD10"], 'light-cone', 'MDPL2_FluxProj000_ClustersCombinedModel_bias0.6_with_header.fits') hd = fits.open(path_2_light_cone) log_f_05_20 = n.log10(hd[1].data['F_05_20']) out = n.histogram(log_f_05_20, bins = n.arange(-18, -8., 0.2)) # cumulative number density per square degrees x_out = 0.5*(out[1][1:] + out[1][:-1]) c_out = n.array([n.sum(out[0][ii:]) for ii in range(len(out[0])) ])*n.pi/129600. path_2_light_cone = os.path.join(os.environ["MD10"], 'light-cone', 'MDPL2_FluxProj000_ClustersCombinedModel_with_header.fits') hd = fits.open(path_2_light_cone) log_f_05_20 = n.log10(hd[1].data['F_05_20']) out_0 = n.histogram(log_f_05_20, bins = n.arange(-18, -8., 0.2)) # cumulative number density per square degrees x_out_0 = 0.5*(out_0[1][1:] + out_0[1][:-1]) c_out_0 = n.array([n.sum(out_0[0][ii:]) for ii in range(len(out_0[0])) ])*n.pi/129600. path_2_logNlogS_data = os.path.join(os.environ["DARKSIM_DIR"], 'observations', 'logNlogS', 'logNlogS_Finoguenov_07_15_clusters.data') x_data, y_data = n.loadtxt(path_2_logNlogS_data, unpack=True) import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as p plotDir = os.path.join(os.environ['HOME'], 'wwwDir', "eRoMok", "logNlogS") p.figure(1, (6,6)) p.plot(x_out, n.log10(c_out), 'k', lw=2, rasterized = True, label = 'Planck mock v0.6' ) p.plot(x_out, n.log10(c_out*(1-frac_err_13deg2)), 'k--', lw=1, rasterized = True, label = 'v0.6, 13.3deg2 scatter' ) p.plot(x_out, n.log10(c_out*(1+frac_err_13deg2)), 'k--', lw=1, rasterized = True) p.plot(x_out, n.log10(c_out*(1-frac_err_3deg2)), 'r--', lw=1, rasterized = True, label = 'v0.6, 3.5deg2 scatter' ) p.plot(x_out, n.log10(c_out*(1+frac_err_3deg2)), 'r--', lw=1, rasterized = True) p.plot(x_out_0, n.log10(c_out_0), 'm--', rasterized = True, label = 'Planck mock v0.0' ) path_2_logNlogS_data = os.path.join(os.environ["DARKSIM_DIR"], 'observations', 'logNlogS', 'logNlogS_Finoguenov_cosmos_2007_clusters.data') x_data, y_data, y_min, y_max = n.loadtxt(path_2_logNlogS_data, unpack=True) p.fill_between(n.log10(x_data), y1 = n.log10(y_max), y2=n.log10(y_min), color='b' , rasterized = True, alpha=0.5) p.plot(n.log10(x_data), n.log10(y_data), color='b', label = 'COSMOS Finoguenov 2007' ) path_2_logNlogS_data = os.path.join(os.environ["DARKSIM_DIR"], 'observations', 'logNlogS', 'logNlogS_Finoguenov_ecdfs_2015_clusters.data') x_data, y_data, y_min, y_max = n.loadtxt(path_2_logNlogS_data, unpack=True) p.fill_between(n.log10(x_data), y1 = n.log10(y_max), y2=n.log10(y_min) , rasterized = True, alpha=0.5, color='g' ) p.plot(n.log10(x_data), n.log10(y_data), color='g', label = 'ECDFS Finoguenov 2015' ) p.axhline(7, ls='dashed') p.xlabel('log(F[0.5-2 keV])') p.ylabel('log(>F) [/deg2]') p.legend(frameon=False, loc=0) #p.yscale('log') p.xlim((-16, -12)) p.ylim((-2, 3.1)) p.title('full sky cluster mock') p.grid() p.savefig(os.path.join(plotDir, "logN_logS_clusters.jpg")) p.clf() p.figure(1, (6,6)) p.plot(10**x_out, c_out, 'k', lw=2, rasterized = True, label = 'Planck mock v0.6' ) p.plot(10**x_out, c_out*(1-frac_err_13deg2), 'k--', lw=1, rasterized = True, label = 'v0.6, 13.3deg2 scatter' ) p.plot(10**x_out, c_out*(1+frac_err_13deg2), 'k--', lw=1, rasterized = True) p.plot(10**x_out, c_out*(1-frac_err_3deg2), 'r--', lw=1, rasterized = True, label = 'v0.6, 3.5deg2 scatter' ) p.plot(10**x_out, c_out*(1+frac_err_3deg2), 'r--', lw=1, rasterized = True) p.plot(10**x_out_0, c_out_0, 'm--', rasterized = True, label = 'Planck mock v0.0' ) path_2_logNlogS_data = os.path.join(os.environ["DARKSIM_DIR"], 'observations', 'logNlogS', 'logNlogS_Finoguenov_cosmos_2007_clusters.data') x_data, y_data, y_min, y_max = n.loadtxt(path_2_logNlogS_data, unpack=True) p.fill_between(x_data, y1 = y_max, y2=y_min, color='b' , rasterized = True, alpha=0.5) p.plot(x_data, y_data, color='b', label = 'COSMOS Finoguenov 2007' ) path_2_logNlogS_data = os.path.join(os.environ["DARKSIM_DIR"], 'observations', 'logNlogS', 'logNlogS_Finoguenov_ecdfs_2015_clusters.data') x_data, y_data, y_min, y_max = n.loadtxt(path_2_logNlogS_data, unpack=True) p.fill_between(x_data, y1 = y_max, y2=y_min , rasterized = True, alpha=0.5, color='g' ) p.plot(x_data, y_data, color='g', label = 'ECDFS Finoguenov 2015' ) p.axhline(7, ls='dashed') p.xlabel('F[0.5-2 keV]') p.ylabel('>F [/deg2]') p.legend(frameon=False, loc=0) p.yscale('log') p.xscale('log') p.xlim((1e-16, 1e-12)) p.ylim((1e-2, 2e3)) p.title('full sky cluster mock') p.grid() p.savefig(os.path.join(plotDir, "logN_logS_clusters_loglog.jpg")) p.clf()

44

139

0.709031

1,285

6,688

3.454475

0.140856

0.025681

0.02478

0.038297

0.882181

0.874071

0.859203

0.82879

0.789367

0.755576

0

0.079536

0.097638

6,688

151

140

44.291391

0.656007

0.057566

0

0.336634

0

0.195033

0.085655

0

1

0

false

0

0.108911

0

0.108911

0.019802

0

null

0

1

0

null

0

6

a04cd59194d102a5ac006758175a011f50d43897

6,270

py

Python

tests/test_time_zones.py

danielwardega141196/unittests-with-pytest

8dbedbe87fbfc5786856872dff6873395e6f4726

[ "MIT" ]

null

tests/test_time_zones.py

danielwardega141196/unittests-with-pytest

8dbedbe87fbfc5786856872dff6873395e6f4726

[ "MIT" ]

null

tests/test_time_zones.py

danielwardega141196/unittests-with-pytest

8dbedbe87fbfc5786856872dff6873395e6f4726

[ "MIT" ]

null

from unittest.mock import MagicMock, patch, call from pytest import raises from application import time_zones as TIME_ZONE def test_init_TimeZonesClass(): first_time_zone = 'UTC' first_instance = TIME_ZONE.TimeZonesClass() assert first_instance._time_zone == first_time_zone second_time_zone = 'Europe/Warsaw' second_instance = TIME_ZONE.TimeZonesClass(second_time_zone) assert second_instance._time_zone == second_time_zone third_time_zone = 'XYZ' expected_ValueError_message = '%s is not a valid time zone format in Python' % (third_time_zone) with raises(ValueError, match=expected_ValueError_message): third_time_zone = TIME_ZONE.TimeZonesClass(third_time_zone) @patch.object(TIME_ZONE, "datetime") @patch.object(TIME_ZONE, "get_time_zone_info") @patch.object(TIME_ZONE.TimeZonesClass, "__init__") def test_instance_time_zone_date(class_init, get_time_zone_info, datetime): class_init.return_value = None get_time_zone_info.return_value = 1 result_datetime_now = MagicMock() datetime.now = MagicMock(return_value=result_datetime_now) result_datetime_now.strftime = MagicMock(return_value=2) example_instance = TIME_ZONE.TimeZonesClass() example_instance._time_zone = 3 expected_result = 2 received_result = example_instance.instance_time_zone_date() assert expected_result == received_result class_init.assert_called_once_with() get_time_zone_info.assert_called_once_with(3) datetime.now.assert_called_once_with(1) result_datetime_now.strftime.assert_called_once_with('%H:%M %d-%m-%Y') @patch.object(TIME_ZONE, 'time_zone_list', [1, 2, 3]) @patch.object(TIME_ZONE, "datetime") @patch.object(TIME_ZONE, "get_time_zone_info") def test_another_time_zone_date(get_time_zone_info, datetime): get_time_zone_info.return_value = 1 result_datetime_now = MagicMock() datetime.now = MagicMock(return_value=result_datetime_now) result_datetime_now.strftime = MagicMock(return_value=2) first_time_zone = 3 expected_result = 2 received_result = TIME_ZONE.TimeZonesClass.another_time_zone_date(first_time_zone) assert expected_result == received_result get_time_zone_info.assert_called_once_with(3) datetime.now.assert_called_once_with(1) result_datetime_now.strftime.assert_called_once_with('%H:%M %d-%m-%Y') second_time_zone = 4 expected_ValueError_message = '%s is not a valid time zone format in Python' % (second_time_zone) with raises(ValueError, match=expected_ValueError_message): TIME_ZONE.TimeZonesClass.another_time_zone_date(second_time_zone) @patch.object(TIME_ZONE, 'time_zone_list', [3, 4]) @patch.object(TIME_ZONE, "datetime") @patch.object(TIME_ZONE, "get_time_zone_info") @patch.object(TIME_ZONE.TimeZonesClass, "__init__") def test_time_zones_difference(class_init, get_time_zone_info, datetime): class_init.return_value = None datetime.utcnow = MagicMock(return_value=1) result_get_time_zone_info = MagicMock() get_time_zone_info.return_value = result_get_time_zone_info result_utcoffset = MagicMock() result_get_time_zone_info.utcoffset = MagicMock(return_value=result_utcoffset) result_utcoffset.total_seconds = MagicMock(return_value=2) first_time_zone = 3 second_time_zone = 4 example_instance = TIME_ZONE.TimeZonesClass() expected_result = 0 received_result = example_instance.time_zones_difference(first_time_zone, second_time_zone) assert expected_result == received_result class_init.assert_called_once_with() assert datetime.utcnow.call_count == 1 assert get_time_zone_info.call_count == 2 expected_calls = [call(3), call(4)] assert get_time_zone_info.call_args_list == expected_calls result_get_time_zone_info.utcoffset.call_count == 2 expected_calls = [call(1), call(1)] assert result_get_time_zone_info.utcoffset.call_args_list == expected_calls assert result_utcoffset.total_seconds.call_count == 2 @patch.object(TIME_ZONE, 'time_zone_list', [2, 3]) @patch.object(TIME_ZONE, "datetime") @patch.object(TIME_ZONE, "get_time_zone_info") @patch.object(TIME_ZONE.TimeZonesClass, "__init__") def test_time_zones_difference_one_time_zone(class_init, get_time_zone_info, datetime): class_init.return_value = None datetime.utcnow = MagicMock(return_value=1) result_get_time_zone_info = MagicMock() get_time_zone_info.return_value = result_get_time_zone_info result_utcoffset = MagicMock() result_get_time_zone_info.utcoffset = MagicMock(return_value=result_utcoffset) result_utcoffset.total_seconds = MagicMock(return_value=2) example_instance = TIME_ZONE.TimeZonesClass() example_instance._time_zone = 2 example_time_zone = 3 expected_result = 0 received_result = example_instance.time_zones_difference(example_time_zone) assert expected_result == received_result class_init.assert_called_once_with() assert datetime.utcnow.call_count == 1 assert get_time_zone_info.call_count == 2 expected_calls = [call(3), call(2)] assert get_time_zone_info.call_args_list == expected_calls result_get_time_zone_info.utcoffset.call_count == 2 expected_calls = [call(1), call(1)] assert result_get_time_zone_info.utcoffset.call_args_list == expected_calls assert result_utcoffset.total_seconds.call_count == 2 @patch.object(TIME_ZONE, 'time_zone_list', [1, 2]) @patch.object(TIME_ZONE.TimeZonesClass, "__init__") def test_time_zones_difference_raises(class_init): class_init.return_value = None first_time_zone = 3 second_time_zone = 1 example_instance = TIME_ZONE.TimeZonesClass() first_expected_ValueError_message = '%s is not a valid time zone format in Python' % (first_time_zone) with raises(ValueError, match=first_expected_ValueError_message): example_instance.time_zones_difference(first_time_zone, second_time_zone) third_time_zone = 1 fourth_time_zone = 4 second_expected_ValueError_message = '%s is not a valid time zone format in Python' % (fourth_time_zone) with raises(ValueError, match=second_expected_ValueError_message): example_instance.time_zones_difference(third_time_zone, fourth_time_zone)

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a0b0916be861bf53b026c5f062f14134a971c4ac

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py

Python

venv/Lib/site-packages/docutils/parsers/rst/include/isoamso.txt.py

roshanba/mangal

f7b428811dc07214009cc33f0beb665ead402038

[ "bzip2-1.0.6", "MIT" ]

null

venv/Lib/site-packages/docutils/parsers/rst/include/isoamso.txt.py

roshanba/mangal

f7b428811dc07214009cc33f0beb665ead402038

[ "bzip2-1.0.6", "MIT" ]

null

venv/Lib/site-packages/docutils/parsers/rst/include/isoamso.txt.py

roshanba/mangal

f7b428811dc07214009cc33f0beb665ead402038

[ "bzip2-1.0.6", "MIT" ]

null

XX XXXX XXXX XXXX XXX XXXX XXXXXX XX XXX XXXXXX XXXXXXX XX XXXXXXX XXXX XXX XXXXXXX XXXXXXXXX XXXXXXXX XXXXXXXXX XXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXX XX XXXXXXXXXXXXXXXXXXX XXXX XX XXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XX XXXXX XXXXXXXXX XXXXXXX XX XXXXX XX XXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXX XXXXXXXX XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXXX XXXX XXX XXXXXX XX XXXXX XXXXXXXX XX XXX XXXXX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXXX XXXX XXX XXXXXX XX XXXXX XXXXXXXX XX XXX XXXX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXXX XXXX XXX XXXXXX XX XXXXX XXXXXXXX XXXX XXX XXXXX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXXX XXXX XXX XXXXXX XX XXXXX XXXXXXXX XXXX XXX XXXX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXXX XXXX XXX XXXXXX XX XXXXX XXXXXXXX XXXXX XXX XX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXXX XXXX XXX XXXXXX XX XXXXX XXXXXXXX XXXX XXX XX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXXX XXXX XXX XXXXXX XX XXXXX XXXXXXXX XXXXX XXX XXXX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXXX XXXX XXX XXXXXX XX XXXXX XXXXXXXX XXXX XXX XXXX XX XXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXXX XXXX XXX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXXXX XXXX XXX XX XXXXXX XXXXXXXXX XXXXXXX XX XXXXXX XXXXXX XXXXXXX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXX XXXXXX XXXXXXX XXXX XXX XXXXXX XXXXXXX XX XXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXX XX XXXXXX XXXXXXXXX XXXXXXX XX XXX XXXXXX XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXX XXXXXX XXXXXXX XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXXXXXX XX XXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXX XXXX XXXXX XXXXXX XXXXX XX XXXXXX XXXXXXXXX XXXXXXX XX XXXXXX XXXX XXX XXXXXXXXXX XXXXXXX XX XXX XXXXX XX XXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXX XXXX XXXXX XXXXXX XX XXX XXXXX XX XXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXXXX XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXXXX XX XXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXX XXXX XXXXXXX XX XXXXX XXXXXXXXX XXXXXXX XX XXXXXX XXXXX X XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXX XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXX XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXXXX XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXXX XXXXX XXXXX XXXXXX XXXX XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXXXXXX XXXXXXX X XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXXX XXXXXX XXXXXXX X XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXXX XXXXXX XXXXXXX X XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXXX XXXXXX X XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXXX XXXXXX X XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXX XXXX XXXX XXXXX XXXXX XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXX XXXXXXXX XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXXXXXX XX XXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXX XXXX XX XXXXXX XXXXXXXXX XXXXXXX XXXXXXX XX XXXXX XXXX XXXXXXXX XXXX XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXX XXX XXXXX XX XXXX XXXXXXXXX XXXXXXX XX XXXXXXX XXXXX XXXXXXX XXXXXX X XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXX XXXXXXXX XXXX XXX XX XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXX XXXXXXXX XXXX XXX XX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXX XXXX XXXXX XXXXX XXXXX XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXX XXXX XXXXXXXX XX XXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXXXXXX XXXXXXX X XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXXXXX XXXXXXX XX XXXXXX XXXXXXXXX XXXXXXX XX XXX XXXXXX XXXXXXX XX XXXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXXXXX XXXXXXXXX XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXX XXXXXXXX XX XXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XXXXX XXXXXXXX XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXX XX XXXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXXXX XXXXXX XXXX XX XXXXXXXX XXXXXXXXX XXXXXXX XX XXXXXX XXXXXXX X

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39f7d242de78174b9570817e95f83d72facdbed5

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py

Python

linepipe/test/test_version.py

painengine/linepipe

9c2447f6a430fe7e83aaa41af3fea68b343d2555

[ "BSD-3-Clause" ]

null

linepipe/test/test_version.py

painengine/linepipe

9c2447f6a430fe7e83aaa41af3fea68b343d2555

[ "BSD-3-Clause" ]

null

linepipe/test/test_version.py

painengine/linepipe

9c2447f6a430fe7e83aaa41af3fea68b343d2555

[ "BSD-3-Clause" ]

null

from .. import __version__ def test_version_is_string(): assert(isinstance(__version__, str))

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26609fe5e7baa03cd2aa2460262e23c46a8fa986

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py

Python

tests/test_page_urls.py

lfernandez55/Tutor_App_Icarus

b0de87f3c6a66e8e503fc8ed0ecde40d1c4e22cd

[ "BSD-2-Clause" ]

null

tests/test_page_urls.py

lfernandez55/Tutor_App_Icarus

b0de87f3c6a66e8e503fc8ed0ecde40d1c4e22cd

[ "BSD-2-Clause" ]

null

tests/test_page_urls.py

lfernandez55/Tutor_App_Icarus

b0de87f3c6a66e8e503fc8ed0ecde40d1c4e22cd

[ "BSD-2-Clause" ]

1

2021-01-13T05:19:54.000Z

# Copyright 2014 SolidBuilds.com. All rights reserved # # Authors: Ling Thio <ling.thio@gmail.com> from __future__ import print_function # Use print() instead of print from flask import url_for def test_page_urls(client): # Visit home page response = client.get(url_for('main.home_page'), follow_redirects=True) assert response.status_code==200 # Login as user and visit User page response = client.post(url_for('user.login'), follow_redirects=True, data=dict(email='user@example.com', password='Password1')) assert response.status_code==200 response = client.get(url_for('main.member_page'), follow_redirects=True) assert response.status_code==200 # Edit User Profile page response = client.get(url_for('main.user_profile_page'), follow_redirects=True) assert response.status_code==200 response = client.post(url_for('main.user_profile_page'), follow_redirects=True, data=dict(first_name='User', last_name='User')) response = client.get(url_for('main.member_page'), follow_redirects=True) assert response.status_code==200 # Logout response = client.get(url_for('user.logout'), follow_redirects=True) assert response.status_code==200 # Login as admin and visit Admin page response = client.post(url_for('user.login'), follow_redirects=True, data=dict(email='admin@example.com', password='Password1')) assert response.status_code==200 response = client.get(url_for('admin.admin_page'), follow_redirects=True) assert response.status_code==200 # users response = client.get(url_for('admin.admin_list_users'), follow_redirects=True) assert response.status_code==200 response = client.get(url_for('admin.admin_create_tutor'), follow_redirects=True) assert response.status_code==200 # editing the admin. . . response = client.get(url_for('admin.admin_edit_user', user_id=1), follow_redirects=True) assert response.status_code==200 # creating a tutor. . . . response = client.post(url_for('admin.admin_create_tutor'), follow_redirects=True, data=dict(first_name='joe', last_name='mo', email='joen@example.com', password='Password1', tutor_phone='801-540-7777')) assert response.status_code==200 # editing the tutor. . .. response = client.get(url_for('admin.admin_edit_tutor', user_id=3), follow_redirects=True) assert response.status_code==200 # roles response = client.get(url_for('admin.admin_list_roles'), follow_redirects=True) assert response.status_code==200 # courses response = client.get(url_for('admin.admin_list_courses'), follow_redirects=True) assert response.status_code==200 # languages response = client.get(url_for('admin.admin_list_languages'), follow_redirects=True) assert response.status_code==200 # Logout response = client.get(url_for('user.logout'), follow_redirects=True) assert response.status_code==200

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py

Python

venv/lib/python3.8/site-packages/numpy/tests/test_reloading.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/numpy/tests/test_reloading.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/numpy/tests/test_reloading.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/6e/3c/8f/891c7700c3d672ef84c606eb960997e03c5102cc3053f722fd19778419

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null

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6

cd3b47af606bb378cf32388e6646c338081abc3b

46

py

Python

call_to_hook/__init__.py

dextertechnology/bitbucket_build_webhook

f00b0399fb60c56969ec84dcbec06f77b081c41e

[ "Apache-2.0" ]

null

call_to_hook/__init__.py

dextertechnology/bitbucket_build_webhook

f00b0399fb60c56969ec84dcbec06f77b081c41e

[ "Apache-2.0" ]

null

call_to_hook/__init__.py

dextertechnology/bitbucket_build_webhook

f00b0399fb60c56969ec84dcbec06f77b081c41e

[ "Apache-2.0" ]

null

from .response_handler import ValidateResponse

46

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1

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6

cd407ac26d60f7b87f183c2fb73a65c50bfe7222

153

py

Python

bauh/api/user.py

DN-debug/bauh

83aeccae87d7fe26f6c5bf24be005288d5d54d84

[ "Zlib" ]

null

bauh/api/user.py

DN-debug/bauh

83aeccae87d7fe26f6c5bf24be005288d5d54d84

[ "Zlib" ]

null

bauh/api/user.py

DN-debug/bauh

83aeccae87d7fe26f6c5bf24be005288d5d54d84

[ "Zlib" ]

null

import os from typing import Optional def is_root(user_id: Optional[int] = None): return user_id == 0 if user_id is not None else os.getuid() == 0

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6

cd5b25aa0b0316cd7c56978778a1a5aab48ab9be

7,800

py

Python

cloze/rnn.py

ecalder6/MT-HW2

1356aeb374a6e4d0b0ae819684bf314039948c56

[ "MIT" ]

null

cloze/rnn.py

ecalder6/MT-HW2

1356aeb374a6e4d0b0ae819684bf314039948c56

[ "MIT" ]

null

cloze/rnn.py

ecalder6/MT-HW2

1356aeb374a6e4d0b0ae819684bf314039948c56

[ "MIT" ]

null

from __future__ import print_function import numpy as np import argparse import torch import torch.utils.data import torch.nn as nn import torch.optim as optim from torch.autograd import Variable from torchvision import datasets, transforms import torch.nn.functional as F import cv2 as cv import random def logsumexp(value, dim=None, keepdim=True): m, _ = torch.max(value, dim=dim, keepdim=True) value0 = value - m return m + torch.log(torch.sum(torch.exp(value0), dim=dim, keepdim=True)) class RNN(nn.Module): def __init__(self, vocab_size, hidden_size = 16, embedding_size=32): super(RNN, self).__init__() self.vocab_size = vocab_size self.hidden_size = hidden_size self.embedding_size = embedding_size self.embeddings = Variable(torch.randn(vocab_size, embedding_size), requires_grad=True) self.W_x = Variable(torch.randn(embedding_size, hidden_size), requires_grad=True) self.b_x = Variable(torch.randn(hidden_size), requires_grad=True) self.W_h = Variable(torch.randn(hidden_size, hidden_size), requires_grad=True) self.b_h = Variable(torch.randn(hidden_size), requires_grad=True) self.output = Variable(torch.randn(hidden_size, vocab_size), requires_grad=True) def forward(self, x): encode = self.embeddings[x.data,:] seq_length = x.size()[0] batch_size = x.size()[1] h = self.init_hidden(batch_size) total_h = Variable(torch.FloatTensor(seq_length, batch_size, self.hidden_size)) for t, step in enumerate(encode): print(t) a = step.matmul(self.W_x) + self.b_x b = h.matmul(self.W_h) + self.b_h c = a + b h = self.sigmoid(c) total_h[t] = h a = total_h.matmul(self.output) return self.logsoftmax(a) def logsoftmax(self, a): return a - logsumexp(a, 2).expand_as(a) def sigmoid(self, c): return 1. / (1. + c.mul(-1).exp()) def init_hidden(self, batch_size): return Variable(torch.zeros(batch_size, self.hidden_size)) class BiRNN(nn.Module): def __init__(self, vocab_size, hidden_size = 8, embedding_size=32): super(BiRNN, self).__init__() self.vocab_size = vocab_size self.hidden_size = hidden_size self.embedding_size = embedding_size self.embeddings = Variable(torch.randn(vocab_size, embedding_size), requires_grad=True) self.W_x1 = Variable(torch.randn(embedding_size, hidden_size), requires_grad=True) self.b_x1 = Variable(torch.randn(hidden_size), requires_grad=True) self.W_h1 = Variable(torch.randn(hidden_size, hidden_size), requires_grad=True) self.b_h1 = Variable(torch.randn(hidden_size), requires_grad=True) self.W_x2 = Variable(torch.randn(embedding_size, hidden_size), requires_grad=True) self.b_x2 = Variable(torch.randn(hidden_size), requires_grad=True) self.W_h2 = Variable(torch.randn(hidden_size, hidden_size), requires_grad=True) self.b_h2 = Variable(torch.randn(hidden_size), requires_grad=True) self.output = Variable(torch.randn(2*hidden_size, vocab_size), requires_grad=True) def forward(self, x): encode = self.embeddings[x.data,:] seq_length = x.size()[0] batch_size = x.size()[1] h = self.init_hidden(batch_size) total_h1 = Variable(torch.FloatTensor(seq_length, batch_size, self.hidden_size)) for t, step in enumerate(encode): total_h1[t] = h print(t) if t == seq_length - 1: break a = step.matmul(self.W_x1) + self.b_x1 b = h.matmul(self.W_h1) + self.b_h1 c = a + b h = self.sigmoid(c) h = self.init_hidden(batch_size) total_h2 = Variable(torch.FloatTensor(seq_length, batch_size, self.hidden_size)) for t, step in enumerate(reversed(encode)): print(seq_length-t-1) total_h2[t] = h if t == seq_length - 1: break a = step.matmul(self.W_x2) + self.b_x2 b = h.matmul(self.W_h2) + self.b_x2 c = a + b h = self.sigmoid(c) total_h = torch.cat((total_h1, total_h2), 2) a = total_h.matmul(self.output) return self.logsoftmax(a) def logsoftmax(self, a): return a - logsumexp(a, 2).expand_as(a) def sigmoid(self, c): return 1. / (1. + c.mul(-1).exp()) def init_hidden(self, batch_size): return Variable(torch.zeros(batch_size, self.hidden_size)) class BiGRU(nn.Module): def __init__(self, vocab_size, hidden_size = 8, embedding_size=32, dropout=None): super(BiGRU, self).__init__() self.vocab_size = vocab_size self.hidden_size = hidden_size self.embedding_size = embedding_size self.dropout = dropout self.embeddings = Variable(torch.randn(vocab_size, embedding_size), requires_grad=True) self.W_z1 = nn.Linear(embedding_size + hidden_size, 1) self.W_r1 = nn.Linear(embedding_size + hidden_size, 1) self.W_h1 = nn.Linear(embedding_size + hidden_size, hidden_size) self.W_z2 = nn.Linear(embedding_size + hidden_size, 1) self.W_r2 = nn.Linear(embedding_size + hidden_size, 1) self.W_h2 = nn.Linear(embedding_size + hidden_size, hidden_size) self.output = nn.Linear(2*hidden_size, vocab_size) self.sigmoid = nn.Sigmoid() # self.softmax = nn.LogSoftmax() self.tanh = nn.Tanh() def forward(self, x): encode = self.embeddings[x.data,:] seq_length = x.size()[0] batch_size = x.size()[1] h = self.init_hidden(batch_size) total_h1 = Variable(torch.FloatTensor(seq_length, batch_size, self.hidden_size)) for t, step in enumerate(encode): total_h1[t] = h print(t) if self.dropout and self.training: step_mask = Variable(torch.bernoulli( torch.Tensor(batch_size, self.embedding_size).fill_(1. - self.dropout)), requires_grad=False) / self.dropout h_mask = Variable(torch.bernoulli( torch.Tensor(batch_size, self.hidden_size).fill_(1. - self.dropout)), requires_grad=False) / self.dropout step = step * step_mask h = h * h_mask if t == seq_length - 1: break z_t = self.sigmoid(self.W_z1(torch.cat((h, step),1))).expand_as(h) r_t = self.sigmoid(self.W_r1(torch.cat((h, step),1))).expand_as(h) h_t1 = self.tanh(self.W_h1(torch.cat((r_t*h, step), 1))) h = (1. - z_t) * h + z_t * h_t1 h = self.init_hidden(batch_size) total_h2 = Variable(torch.FloatTensor(seq_length, batch_size, self.hidden_size)) for t, step in enumerate(reversed(encode)): print(seq_length-t-1) total_h2[t] = h if t == seq_length - 1: break z_t = self.sigmoid(self.W_z2(torch.cat((h, step),1))).expand_as(h) r_t = self.sigmoid(self.W_r2(torch.cat((h, step),1))).expand_as(h) h_t2 = self.tanh(self.W_h2(torch.cat((r_t*h, step), 1))) h = (1. - z_t) * h + z_t * h_t2 total_h = torch.cat((total_h1, total_h2), 2) a = self.output(total_h) return self.logsoftmax(a) def logsoftmax(self, a): return a - logsumexp(a, 2).expand_as(a) def init_hidden(self, batch_size): return Variable(torch.zeros(batch_size, self.hidden_size)) model = BiRNN(5, 4, 8) model.train() word_idx = Variable(torch.LongTensor([[0, 3], [1, 3], [2, 3]])) model(word_idx)

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0

null

0

1

0

null

0

6

cd6122d5be924b9396decbda478b90d9f45c1f32

74

py

Python

dsp_python_imp/Ch18/speech_synthesis.py

xrick/Lcj-DSP-in-Python

f27ee7036dc0df41b96e0b06ed13bb8fd874a714

[ "MIT" ]

null

dsp_python_imp/Ch18/speech_synthesis.py

xrick/Lcj-DSP-in-Python

f27ee7036dc0df41b96e0b06ed13bb8fd874a714

[ "MIT" ]

null

dsp_python_imp/Ch18/speech_synthesis.py

xrick/Lcj-DSP-in-Python

f27ee7036dc0df41b96e0b06ed13bb8fd874a714

[ "MIT" ]

null

import os os.system("espeak \"Hello World\"") os.system("espeak \"你好嗎\"")

18.5

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11

74

4.454545

0.636364

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0

null

0

1

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6

cd617a73f67a749e9636afae0b22a146d30b2516

44

py

Python

test.py

drohrbaugh9/Minecraft

ddc4737ac7965c5098d0c3d5e6e4e1ec16d8ef61

[ "MIT" ]

2

2015-02-18T04:43:57.000Z

2016-01-05T02:51:40.000Z

test.py

drohrbaugh9/Minecraft

ddc4737ac7965c5098d0c3d5e6e4e1ec16d8ef61

[ "MIT" ]

null

test.py

drohrbaugh9/Minecraft

ddc4737ac7965c5098d0c3d5e6e4e1ec16d8ef61

[ "MIT" ]

2

2017-04-22T16:21:11.000Z

2021-11-09T19:17:04.000Z

import mc world = mc.World() mc.run(world)

8.8

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0.681818

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44

3.75

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null

0

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6

cd69fbaab173ebc30b2af08c15669c02d7aaa7b0

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py

Python

algorithms/__init__.py

hcchengithub/genetic-algorithms

c1d10bc154288cb7927ca708523f7db62efb7649

[ "MIT" ]

74

2020-07-21T09:34:30.000Z

2022-03-26T13:56:51.000Z

algorithms/__init__.py

hcchengithub/genetic-algorithms

c1d10bc154288cb7927ca708523f7db62efb7649

[ "MIT" ]

null

algorithms/__init__.py

hcchengithub/genetic-algorithms

c1d10bc154288cb7927ca708523f7db62efb7649

[ "MIT" ]

47

2020-09-22T03:05:20.000Z

2022-03-20T10:49:53.000Z

from algorithms.bruteforce import bruteforce

22.5

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6

cd7ef5c335a00afc9884f037e767f6eac0e54caf

87

py

Python

src/am_information_model/model/__init__.py

augmentedfabricationlab/additive_manufacturing_information_model

135ba9e1c15693358f1e19db6c0d590b2009f6d8

[ "MIT" ]

1

2021-08-12T07:20:05.000Z

src/am_information_model/model/__init__.py

augmentedfabricationlab/am_information_model

135ba9e1c15693358f1e19db6c0d590b2009f6d8

[ "MIT" ]

null

src/am_information_model/model/__init__.py

augmentedfabricationlab/am_information_model

135ba9e1c15693358f1e19db6c0d590b2009f6d8

[ "MIT" ]

null

from .model import * from .node import * from .layer import * from .utilities import *

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1

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1

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1

0

6

26aa73806dd51a326bc582039cf0b628ecc7fc7a

32

py

Python

Imaobong Tom/Phase 1/Python Basic 1/Day 2/2.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

6

2020-05-23T19:53:25.000Z

2021-05-08T20:21:30.000Z

Imaobong Tom/Phase 1/Python Basic 1/Day 2/2.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

8

2020-05-14T18:53:12.000Z

2020-07-03T00:06:20.000Z

Imaobong Tom/Phase 1/Python Basic 1/Day 2/2.py

CodedLadiesInnovateTech/-python-challenge-solutions

430cd3eb84a2905a286819eef384ee484d8eb9e7

[ "MIT" ]

39

2020-05-10T20:55:02.000Z

2020-09-12T17:40:59.000Z

import sys print(sys.version)

6.4

18

0.75

5

32

4.8

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0

0.15625

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4

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1

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6

26cb805a3ed73274af197bc712b230bcaa234f53

94

py

Python

src/frobs_rl/wrappers/__init__.py

jmfajardod/gym_gazebo_sb3

72afcd4943c2c145e7e01bfce842f2d09b5b7978

[ "MIT" ]

67

2022-02-09T03:05:40.000Z

2022-03-20T17:54:53.000Z

src/frobs_rl/wrappers/__init__.py

Jovergara/frobs_rl

72afcd4943c2c145e7e01bfce842f2d09b5b7978

[ "MIT" ]

6

2021-09-27T20:32:36.000Z

2022-02-11T02:22:22.000Z

src/frobs_rl/wrappers/__init__.py

Jovergara/frobs_rl

72afcd4943c2c145e7e01bfce842f2d09b5b7978

[ "MIT" ]

21

2022-03-03T14:47:05.000Z

2022-03-17T10:06:39.000Z

from frobs_rl.wrappers import NormalizeActionWrapper, NormalizeObservWrapper, TimeLimitWrapper

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1

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1

0

6

26e72398f731c7b30673317fc458984ea5963047

177

py

Python

resnest/torch/models/build.py

mohitktanwr/Improved-Inverse-ResNest-Isprs

8463d7be0f67c398c91241f47cd7d9e0d235d799

[ "Apache-2.0" ]

3,168

2020-04-04T01:22:28.000Z

2022-03-31T12:14:50.000Z

resnest/torch/models/build.py

mohitktanwr/Improved-Inverse-ResNest-Isprs

8463d7be0f67c398c91241f47cd7d9e0d235d799

[ "Apache-2.0" ]

138

2020-04-04T02:12:30.000Z

2022-03-21T03:20:52.000Z

resnest/torch/models/build.py

mohitktanwr/Improved-Inverse-ResNest-Isprs

8463d7be0f67c398c91241f47cd7d9e0d235d799

[ "Apache-2.0" ]

527

2020-04-04T05:17:26.000Z

2022-03-31T06:15:34.000Z

from fvcore.common.registry import Registry RESNEST_MODELS_REGISTRY = Registry('RESNEST_MODELS') def get_model(model_name): return RESNEST_MODELS_REGISTRY.get(model_name)

25.285714

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5.791667

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0.096045

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6

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1

0

6

26eaca18156e7e0a5ea8944896887ae962bf7578

335

py

Python

data/datasets/__init__.py

khoehlein/CNNs-for-Wind-Field-Downscaling

eb8418d4d893fcb2beb929abb241281b7a9b6a95

[ "MIT" ]

5

2021-05-05T06:08:52.000Z

2022-03-24T04:57:52.000Z

data/datasets/__init__.py

khoehlein/CNNs-for-Wind-Field-Downscaling

eb8418d4d893fcb2beb929abb241281b7a9b6a95

[ "MIT" ]

null

data/datasets/__init__.py

khoehlein/CNNs-for-Wind-Field-Downscaling

eb8418d4d893fcb2beb929abb241281b7a9b6a95

[ "MIT" ]

2

2021-08-07T05:18:05.000Z

2022-03-31T03:48:37.000Z

from .LowResHighResDataset import LowResHighResDataset from .PatchData import PatchData from .PatchDataSection import PatchDataSection from .DataSection import DataSection from .DataCollection import DataCollection from .RandomShufflingDataSection import RandomShufflingDataSection from .NearestNeighborData import NearestNeighborData

41.875

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10.714286

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7

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1

0

1

0

1

0

6

f837b985374fc4197197d2e8b54d43bb139c591c

185

py

Python

kubernetes_manager/apps.py

breimers/Django-Kubernetes-Manager

cb78b51e6aeabcd4037166fa8e7c565180fb7d93

[ "MIT" ]

13

2020-04-03T08:33:51.000Z

2022-02-22T07:30:28.000Z

kubernetes_manager/apps.py

breimers/Django-Kubernetes-Manager

cb78b51e6aeabcd4037166fa8e7c565180fb7d93

[ "MIT" ]

27

2020-04-03T06:51:38.000Z

2022-01-21T13:12:28.000Z

kubernetes_manager/apps.py

breimers/Django-Kubernetes-Manager

cb78b51e6aeabcd4037166fa8e7c565180fb7d93

[ "MIT" ]

6

2020-09-14T18:22:37.000Z

2022-03-04T09:08:25.000Z

from django.apps import AppConfig class KubernetesManagerConfig(AppConfig): name = "kubernetes_manager" verbose_name = "Kubernetes Manager" def ready(self): pass

18.5

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0.718919

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6.894737

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1

0

6

f85d10c8a7a8e0c2ec652d6f45cd63f8e612368c

86

py

Python

settings/channel_archiver/NIH.ENSEMBLE_settings.py

bopopescu/Lauecollect

60ae2b05ea8596ba0decf426e37aeaca0bc8b6be

[ "MIT" ]

null

settings/channel_archiver/NIH.ENSEMBLE_settings.py

bopopescu/Lauecollect

60ae2b05ea8596ba0decf426e37aeaca0bc8b6be

[ "MIT" ]

1

2019-10-22T21:28:31.000Z

2019-10-22T21:39:12.000Z

settings/channel_archiver/NIH.ENSEMBLE_settings.py

bopopescu/Lauecollect

60ae2b05ea8596ba0decf426e37aeaca0bc8b6be

[ "MIT" ]

2

2019-06-06T15:06:46.000Z

2020-07-20T02:03:22.000Z

values[4].filename = '//mx340hs/data/anfinrud_1906/Archive/NIH.ENSEMBLE.values[4].txt'

86

0.77907

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86

5.076923

0.846154

0.212121

0

0.107143

0.023256

86

1

86

0.678571

0

0.724138

0

1

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true

0

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null

1

0

1

0

1

null

0

1

0

6

3e06d9a4729baa2fc267d59d44e2141dc1778445

25

py

Python

Modules/vms/libclidef/libclidef.py

vmssoftware/cpython

b5d2c7f578d33963798a02ca32f0c151c908aa7c

[ "0BSD" ]

2

2021-10-06T15:46:53.000Z

2022-01-26T02:58:54.000Z

Modules/vms/libclidef/libclidef.py

vmssoftware/cpython

b5d2c7f578d33963798a02ca32f0c151c908aa7c

[ "0BSD" ]

null

Modules/vms/libclidef/libclidef.py

vmssoftware/cpython

b5d2c7f578d33963798a02ca32f0c151c908aa7c

[ "0BSD" ]

null

from _libclidef import *

12.5

24

0.8

3

25

6.333333

1

0

0.16

25

1

25

0.904762

0

1

0

true

0

1

0

1

0

1

0

null

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0

null

0

1

0

1

0

1

0

6

3e0e56d82a2587cc7d3e383f360e7d832678d9ca

131

py

Python

cli_stryket/system_exception.py

mile95/cli-stryket

3c4ea10c1937a179a17881b0b235b5daa3d6de91

[ "MIT" ]

null

cli_stryket/system_exception.py

mile95/cli-stryket

3c4ea10c1937a179a17881b0b235b5daa3d6de91

[ "MIT" ]

null

cli_stryket/system_exception.py

mile95/cli-stryket

3c4ea10c1937a179a17881b0b235b5daa3d6de91

[ "MIT" ]

null

from __future__ import annotations class InvalidSystemException(Exception): pass class FetchException(Exception): pass

13.1

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8.25

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0.167939

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9

41

14.555556

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true

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3e8ed7a4395d9e685d3bf512535962c678885a75

13,344

py

Python

tests/test_experiment/test_result_package/test_abstract_result_packages.py

mv1388/AIToolbox

c64ac4810a02d230ce471d86b758e82ea232a7e7

[ "MIT" ]

3

2019-10-12T12:24:09.000Z

2020-08-02T02:42:43.000Z

tests/test_experiment/test_result_package/test_abstract_result_packages.py

mv1388/aitoolbox

1060435e6cbdfd19abcb726c4080b663536b7467

[ "MIT" ]

3

2020-04-10T14:07:07.000Z

2020-04-22T19:04:38.000Z

tests/test_experiment/test_result_package/test_abstract_result_packages.py

mv1388/aitoolbox

1060435e6cbdfd19abcb726c4080b663536b7467

[ "MIT" ]

null

import unittest from tests.utils import * from aitoolbox.experiment.result_package.abstract_result_packages import MultipleResultPackageWrapper, PreCalculatedResultPackage class TestAbstractResultPackage(unittest.TestCase): def test_basic(self): result_pkg = DummyResultPackageExtend() result_pkg.prepare_result_package([10] * 100, [11] * 100, {}) self.assertEqual(result_pkg.results_dict, {'dummy': 111, 'extended_dummy': 1323123.44}) self.assertEqual(result_pkg.get_results(), {'dummy': 111, 'extended_dummy': 1323123.44}) self.assertEqual(result_pkg.get_hyperparameters(), {}) self.assertEqual(str(result_pkg), 'dummy: 111.0\nextended_dummy: 1323123.44') self.assertEqual(len(result_pkg), 2) def test_get_additional_results_dump_paths(self): paths_1 = [['filename', 'file/path/filename']] result_pkg_1 = DummyResultPackageExtendV2(paths_1) self.assertEqual(result_pkg_1.get_additional_results_dump_paths(), paths_1) self.assertEqual(result_pkg_1.additional_results_dump_paths, paths_1) paths_2 = [['filename', 'file/path/filename'], ['fafafdfa', 'ewqewq/eqwq/rrrrrr/fafafdfa']] result_pkg_2 = DummyResultPackageExtendV2(paths_2) self.assertEqual(result_pkg_2.get_additional_results_dump_paths(), paths_2) self.assertEqual(result_pkg_2.additional_results_dump_paths, paths_2) def test_format_enforcement_get_additional_results_dump_paths(self): # Test wrong format catching with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2('file/path/string/not/list') result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2(['file/path/string/not/list']) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2(['file/path/string/not/list', 'another/string/']) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2([['file/path/string/not/list/not2/elements/insublist']]) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2([['file/path/string/not/list/not2/'], ['still/not/2elements']]) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2([['file/path/string/not/list/not2/', 2332]]) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2([[2332, 'file/path/string/not/list/not2/']]) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2([[{'ddasd': 223}, 2332]]) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2([['file/path/not2/', 'dad'], ['weaeew']]) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2([['file/path/not2/', 'dad'], ['weaeew', 2]]) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2([[['file/path/not2/', 'dad'], ['weaeew', 'wadas']]]) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2([['file/path/not2/', 'dad'], 2323244]) result_pkg_2.get_additional_results_dump_paths() with self.assertRaises(ValueError): result_pkg_2 = DummyResultPackageExtendV2([['file/path/not2/', 'dad'], 'dpasppsa']) result_pkg_2.get_additional_results_dump_paths() def test_combine_packages(self): result_d_1 = {'metric1': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) pkg_1.prepare_result_package([10] * 100, [11] * 100, {'dddd': 222}) result_d_2 = {'metric3': 1, 'metric4': 2} pkg_2 = DummyResultPackageExtendVariable(result_d_2) pkg_2.prepare_result_package([10] * 100, [11] * 100, {'qqq': 445}) combo_pkg_1_2 = pkg_1 + pkg_2 self.assertEqual(type(combo_pkg_1_2), MultipleResultPackageWrapper) self.assertEqual(len(combo_pkg_1_2.result_packages), 2) self.assertNotEqual(combo_pkg_1_2.result_packages[0], pkg_1) self.assertEqual(combo_pkg_1_2.result_packages[0].results_dict, pkg_1.results_dict) self.assertEqual(combo_pkg_1_2.result_packages[0].get_results(), pkg_1.get_results()) self.assertEqual(combo_pkg_1_2.result_packages[0].get_hyperparameters(), pkg_1.get_hyperparameters()) self.assertNotEqual(combo_pkg_1_2.result_packages[1], pkg_2) self.assertEqual(combo_pkg_1_2.result_packages[1].results_dict, pkg_2.results_dict) self.assertEqual(combo_pkg_1_2.result_packages[1].get_results(), pkg_2.get_results()) self.assertEqual(combo_pkg_1_2.result_packages[1].get_hyperparameters(), pkg_2.get_hyperparameters()) self.assertEqual(combo_pkg_1_2.results_dict, {pkg_1.pkg_name: result_d_1, f'{pkg_2.pkg_name}1': result_d_2}) self.assertEqual(combo_pkg_1_2.y_true['DummyPackage1'].tolist(), [10] * 100) self.assertEqual(combo_pkg_1_2.y_predicted['DummyPackage1'].tolist(), [11] * 100) self.assertEqual(combo_pkg_1_2.hyperparameters, {'dddd': 222}) def test_combine_package_w_dict(self): result_d_1 = {'metric1': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) pkg_1.prepare_result_package([10] * 100, [11] * 100, {'dddd': 222}, ) pkg_2_dict = {'metric3': 11111} combo_pkg_1_2 = pkg_1 + pkg_2_dict self.assertEqual(type(combo_pkg_1_2), MultipleResultPackageWrapper) self.assertEqual(len(combo_pkg_1_2.result_packages), 2) self.assertNotEqual(combo_pkg_1_2.result_packages[0], pkg_1) self.assertEqual(combo_pkg_1_2.result_packages[0].results_dict, pkg_1.results_dict) self.assertEqual(combo_pkg_1_2.result_packages[0].get_results(), pkg_1.get_results()) self.assertEqual(combo_pkg_1_2.result_packages[0].get_hyperparameters(), pkg_1.get_hyperparameters()) self.assertEqual(type(combo_pkg_1_2.result_packages[1]), PreCalculatedResultPackage) self.assertEqual(combo_pkg_1_2.result_packages[1].results_dict, pkg_2_dict) self.assertEqual(combo_pkg_1_2.result_packages[1].get_results(), pkg_2_dict) self.assertEqual(combo_pkg_1_2.result_packages[1].get_hyperparameters(), {}) self.assertEqual(combo_pkg_1_2.results_dict, {pkg_1.pkg_name: result_d_1, combo_pkg_1_2.result_packages[1].pkg_name: pkg_2_dict}) self.assertEqual(combo_pkg_1_2.results_dict, {pkg_1.pkg_name: result_d_1, 'PreCalculatedResult': pkg_2_dict}) def test_combine_package_metric_name_clash(self): result_d_1 = {'metricSAME': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) pkg_1.prepare_result_package([10] * 100, [11] * 100, {'dddd': 222}) result_d_2 = {'metricSAME': 33232, 'metric3': 1000} pkg_2 = DummyResultPackageExtendVariable(result_d_2) pkg_2.prepare_result_package([10] * 100, [11] * 100, {'qqq': 445}) combo_pkg_1_2 = pkg_1 + pkg_2 self.assertEqual(combo_pkg_1_2.results_dict, {pkg_1.pkg_name: result_d_1, f'{pkg_2.pkg_name}1': result_d_2}) def test_combine_metric_dict_name_clash(self): result_d_1 = {'metricSAME': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) pkg_1.prepare_result_package([10] * 100, [11] * 100, {'dddd': 222}) pkg_2_dict = {'metricSAME': 33232, 'metric3': 1000} combo_pkg_1_2 = pkg_1 + pkg_2_dict self.assertEqual(combo_pkg_1_2.results_dict, {pkg_1.pkg_name: result_d_1, combo_pkg_1_2.result_packages[1].pkg_name: pkg_2_dict}) self.assertEqual(combo_pkg_1_2.results_dict, {pkg_1.pkg_name: result_d_1, 'PreCalculatedResult': pkg_2_dict}) def test_fail_dict_not_defined(self): result_d_1 = {'metric1': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) with self.assertRaises(ValueError): pkg_2_dict = {'metric3': 11111} combo_pkg_1_2 = pkg_1 + pkg_2_dict with self.assertRaises(ValueError): pkg_2_dict = {'metric3': 11111} combo_pkg_1_2 = pkg_2_dict + pkg_1 def test_fail_dict_not_defined_pkg(self): result_d_1 = {'metricSAME': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) result_d_2 = {'metricSAME': 33232, 'metric3': 1000} pkg_2 = DummyResultPackageExtendVariable(result_d_2) pkg_2.prepare_result_package([10] * 100, [11] * 100, {'qqq': 445}) with self.assertRaises(ValueError): combo_pkg_1_2 = pkg_1 + pkg_2 def test_append_packages(self): result_d_1 = {'metric1': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) pkg_1.prepare_result_package([10] * 100, [11] * 100, {'dddd': 222}) result_d_2 = {'metric3': 1, 'metric4': 2} pkg_2 = DummyResultPackageExtendVariable(result_d_2) pkg_2.prepare_result_package([100] * 100, [110] * 100, {'qqq': 445}) pkg_1 += pkg_2 self.assertEqual(type(pkg_1), DummyResultPackageExtendVariable) self.assertEqual(pkg_1.results_dict, {**result_d_1, **result_d_2}) self.assertEqual(pkg_1.results_dict, {'metric1': 33232, 'metric2': 1000, 'metric3': 1, 'metric4': 2}) self.assertEqual(pkg_1.y_true.tolist(), [10] * 100) self.assertEqual(pkg_1.y_predicted.tolist(), [11] * 100) self.assertEqual(pkg_1.hyperparameters, {'dddd': 222}) self.assertEqual(pkg_1.get_hyperparameters(), {'dddd': 222}) def test_append_dict_packages(self): result_d_1 = {'metric1': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) pkg_1.prepare_result_package([10] * 100, [11] * 100, {'dddd': 222}) pkg_dict_2 = {'metric3': 1, 'metric4': 2} pkg_1 += pkg_dict_2 self.assertEqual(type(pkg_1), DummyResultPackageExtendVariable) self.assertEqual(pkg_1.results_dict, {**result_d_1, **pkg_dict_2}) self.assertEqual(pkg_1.results_dict, {'metric1': 33232, 'metric2': 1000, 'metric3': 1, 'metric4': 2}) self.assertEqual(pkg_1.y_true.tolist(), [10] * 100) self.assertEqual(pkg_1.y_predicted.tolist(), [11] * 100) self.assertEqual(pkg_1.hyperparameters, {'dddd': 222}) self.assertEqual(pkg_1.get_hyperparameters(), {'dddd': 222}) def test_fail_append_packages_name_clash_val_fail(self): result_d_1 = {'metricSAME': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) pkg_1.prepare_result_package([10] * 100, [11] * 100, {'dddd': 222}) result_d_2 = {'metricSAME': 1, 'metric4': 2} pkg_2 = DummyResultPackageExtendVariable(result_d_2) pkg_2.prepare_result_package([100] * 100, [110] * 100, {'qqq': 445}) with self.assertRaises(ValueError): pkg_1 += pkg_2 with self.assertRaises(ValueError): pkg_1 += [23323] with self.assertRaises(ValueError): pkg_1 += 33121 def test_fail_append_dict_packages_name_clash(self): result_d_1 = {'metricSAME': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) pkg_1.prepare_result_package([10] * 100, [11] * 100, {'dddd': 222}) pkg_dict_2 = {'metricSAME': 1, 'metric4': 2} with self.assertRaises(ValueError): pkg_1 += pkg_dict_2 def test_package_contains(self): result_d_1 = {'metric1': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) with self.assertRaises(ValueError): res = 'metric1' in pkg_1 pkg_1.prepare_result_package([10] * 100, [11] * 100, {'dddd': 222}) self.assertTrue('metric1' in pkg_1) self.assertTrue('metric2' in pkg_1) self.assertFalse('metricMissing' in pkg_1) def test_package_get_item(self): result_d_1 = {'metric1': 33232, 'metric2': 1000} pkg_1 = DummyResultPackageExtendVariable(result_d_1) with self.assertRaises(ValueError): res = pkg_1['metric1'] pkg_1.prepare_result_package([10] * 100, [11] * 100, {'dddd': 222}) self.assertEqual(pkg_1['metric1'], result_d_1['metric1']) self.assertEqual(pkg_1['metric2'], result_d_1['metric2']) with self.assertRaises(KeyError): res = pkg_1['metricMissing']

49.058824

129

0.683453

1,704

13,344

4.96831

0.071009

0.052918

0.040397

0.044885

0.857075

0.845618

0.808174

0.759272

0.743444

0.742854

0

0.081813

0.194844

13,344

271

130

49.239852

0.706162

0.001948

0

0.622549

0

0.088916

0.019976

0

0.392157

1

0.073529

false

0

0.014706

0

0.093137

0

null

0

1

0

null

0

6

e426986d25b4108553f318bc0b9c08f4b32541f1

157

py

Python

panoptes_aggregation/__init__.py

amyrebecca/aggregation-for-caesar

5f0d884932312010f9caeb8ebfcfe358f490e41f

[ "Apache-2.0" ]

null

panoptes_aggregation/__init__.py

amyrebecca/aggregation-for-caesar

5f0d884932312010f9caeb8ebfcfe358f490e41f

[ "Apache-2.0" ]

null

panoptes_aggregation/__init__.py

amyrebecca/aggregation-for-caesar

5f0d884932312010f9caeb8ebfcfe358f490e41f

[ "Apache-2.0" ]

null

from . import extractors from . import reducers from . import running_reducers from . import scripts from . import version __version__ = version.__version__

22.428571

33

0.808917

19

157

6.210526

0.368421

0.423729

0.305085

0

0.146497

157

6

34

26.166667

0.880597

0

1

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false

0

0.833333

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0.833333

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1

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null

0

1

0

1

0

6

e42e3cfc42c4a0820e3f94e404d7300bf4d4f72f

149

py

Python

nepali/exceptions.py

surajrisal/nepali

e0b7b4413fd18720290a547baf2425d9eb7469e6

[ "MIT" ]

null

nepali/exceptions.py

surajrisal/nepali

e0b7b4413fd18720290a547baf2425d9eb7469e6

[ "MIT" ]

null

nepali/exceptions.py

surajrisal/nepali

e0b7b4413fd18720290a547baf2425d9eb7469e6

[ "MIT" ]

null

""" Exceptions for nepali """ class InvalidDateFormatException(Exception): pass class InvalidNepaliDateTimeObjectException(Exception): pass

16.555556

54

0.785235

11

149

10.636364

0.727273

0.222222

0

0.134228

149

9

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16.555556

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0.14094

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true

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null

1

0

1

0

null

0

1

0

6

e44987f8bf228db5ed011668fe529601e1b30537

29,737

py

Python

src/plot.py

zyh1999/new_MARL

2abc361a3f2c5844bad57318cd31413af3fdbc8f

[ "Apache-2.0" ]

null

src/plot.py

zyh1999/new_MARL

2abc361a3f2c5844bad57318cd31413af3fdbc8f

[ "Apache-2.0" ]

null

src/plot.py

zyh1999/new_MARL

2abc361a3f2c5844bad57318cd31413af3fdbc8f

[ "Apache-2.0" ]

null

import seaborn as sns import matplotlib.pyplot as plt from matplotlib.ticker import FuncFormatter import os import json import numpy as np import pandas as pd sns.set() sns.set_style("darkgrid", {"axes.facecolor": "#f0f0f7"}) linestyle = ['-', '--', ':', '-.'] fontsize = 20 EXP_PATH = os.path.join(os.environ['NFS_HOME'], 'code/pymarl') total_timesteps = {'sc2': 2000000, 'sc2mt': 10000000, 'particle': 1000000} def smooth(data, window=20): y = np.ones(window) for idx in range(len(data)): x = np.asarray(data[idx]) z = np.ones(len(x)) smoothed_x = np.convolve(x, y, 'same') / np.convolve(z, y, 'same') data[idx] = smoothed_x return data def json_to_list(data_json): data_list = [] for data in data_json: data_list.append(data['value']) return data_list def check_original_data(env_name, map_list, algo_list, seed_idx_list): original_data = dict() result_dir = os.path.join(EXP_PATH, 'results', 'exp_v2', env_name) error_algo_path = [] error_state_path = [] error_data_path = [] error_timestep_path = [] for map_name in map_list: original_data[map_name] = dict() for algo_id in algo_list[map_name]: map_dir = map_name if 'to' in map_name: map_dir = map_name.split('to')[1][1:] algo_path = os.path.join(result_dir, map_dir, algo_id) if not os.path.exists(algo_path): error_algo_path.append(algo_path) continue original_data[map_name][algo_id] = dict() seed_list = os.listdir(algo_path) if ".DS_Store" in seed_list: seed_list.remove(".DS_Store") seed_list.sort() tmp_seed_list = [] # if algo_id != 'qmix_latent_scale': if algo_id not in seed_idx_list[map_name]: for i in range(len(seed_list)): tmp_seed_list.append(seed_list[i]) else: for i in seed_idx_list[map_name][algo_id]: tmp_seed_list.append(seed_list[i]) seed_list = tmp_seed_list for seed_id, seed_path in enumerate(seed_list): state_path = os.path.join(algo_path, seed_path, '1', 'run.json') if not os.path.exists(state_path) or os.path.getsize(state_path) == 0: error_state_path.append(state_path) continue with open(state_path) as json_file: state = json.load(json_file) if state['status'] != "RUNNING": error_state_path.append(state_path) continue original_data[map_name][algo_id][seed_id] = None data_path = os.path.join(algo_path, seed_path, '1', 'info.json') if not os.path.exists(data_path) or os.path.getsize(data_path) == 0: error_data_path.append(data_path) del original_data[map_name][algo_id][seed_id] continue with open(data_path) as json_file: data = json.load(json_file) if env_name == 'sc2' or env_name == 'sc2mt': data_y = data['test/battle_won_mean'] data_x = np.array(data['test/battle_won_mean_T']) elif env_name == 'particle': data_y = json_to_list(data['test/return_mean']) data_x = np.array(data['test/return_mean_T']) if 'to' in map_name: data_x = data_x - total_timesteps[env_name] if len(data_y) != len(data_x) or data_x[-1] < total_timesteps[env_name]: error_timestep_path.append(data_path) del original_data[map_name][algo_id][seed_id] continue original_data[map_name][algo_id][seed_id] = pd.DataFrame({'y': data_y, 'x': data_x}) if original_data[map_name][algo_id] == dict(): del original_data[map_name][algo_id] continue original_data[map_name][algo_id]['x'] = pd.concat([ original_data[map_name][algo_id][seed]['x'] for seed in original_data[map_name][algo_id] ], axis=0, ignore_index=True).drop_duplicates().sort_values(ignore_index=True) original_data[map_name][algo_id]['y'] = [] for seed in original_data[map_name][algo_id]: if seed == 'x' or seed == 'y': continue original_data[map_name][algo_id]['y'].append(pd.merge(original_data[map_name][algo_id]['x'], original_data[map_name][algo_id][seed].loc[:, ['x', 'y']], how='left', left_on='x', right_on='x').interpolate(method='linear').fillna(0)['y']) original_data[map_name][algo_id]['x'] = np.array(original_data[map_name][algo_id]['x']) original_data[map_name][algo_id]['y'] = np.array(original_data[map_name][algo_id]['y']) # original_data[map_name][algo_id]['y'] = smooth(original_data[map_name][algo_id]['y']) print(error_state_path) print(error_data_path) print(error_timestep_path) return original_data def get_original_data(env_name, map_list, algo_list, seed_idx_list): original_data = dict() result_dir = os.path.join(EXP_PATH, 'results', 'exp_v2', env_name) for map_name in map_list: original_data[map_name] = dict() for algo_id in algo_list[map_name]: original_data[map_name][algo_id] = dict() map_dir = map_name if 'to' in map_name: map_dir = map_name.split('to')[1][1:] algo_path = os.path.join(result_dir, map_dir, algo_id) seed_list = os.listdir(algo_path) seed_list.sort() tmp_seed_list = [] # if algo_id != 'qmix_latent_scale': if algo_id not in seed_idx_list[map_name]: for i in range(len(seed_list)): tmp_seed_list.append(seed_list[i]) else: for i in seed_idx_list[map_name][algo_id]: tmp_seed_list.append(seed_list[i]) seed_list = tmp_seed_list if ".DS_Store" in seed_list: seed_list.remove(".DS_Store") # assert len(seed_list) >= 5, "Not enough seeds" # if len(seed_list) > 5: # del seed_list[5:] # del seed_list[:-5] for seed_id, seed_path in enumerate(seed_list): original_data[map_name][algo_id][seed_id] = dict() data_path = os.path.join(algo_path, seed_path, '1', 'info.json') with open(data_path) as json_file: data = json.load(json_file) if env_name == 'sc2': data_y = data['test/battle_won_mean'] data_x = np.array(data['test/battle_won_mean_T']) elif env_name == 'particle': data_y = json_to_list(data['test/return_mean']) data_x = np.array(data['test/return_mean_T']) # original_data[map_name][algo_id][seed_id] = pd.DataFrame({'y': data['test/battle_won_mean'], 'x': data['test/battle_won_mean_T']}) if 'to' in map_name: original_data[map_name][algo_id][seed_id] = pd.DataFrame({'y': data_y, 'x': data_x - 2000000}) else: original_data[map_name][algo_id][seed_id] = pd.DataFrame({'y': data_y, 'x': data_x}) original_data[map_name][algo_id]['x'] = pd.concat([ original_data[map_name][algo_id][seed_id]['x'] for seed_id in range(len(seed_list)) ], axis=0, ignore_index=True).drop_duplicates().sort_values(ignore_index=True) original_data[map_name][algo_id]['y'] = [] for seed_id in range(len(seed_list)): original_data[map_name][algo_id]['y'].append(pd.merge(original_data[map_name][algo_id]['x'], original_data[map_name][algo_id][seed_id].loc[:, ['x', 'y']], how='left', left_on='x', right_on='x').interpolate(method='linear').fillna(0)['y']) original_data[map_name][algo_id]['x'] = np.array(original_data[map_name][algo_id]['x']) original_data[map_name][algo_id]['y'] = np.array(original_data[map_name][algo_id]['y']) # original_data[map_name][algo_id]['y'] = smooth(original_data[map_name][algo_id]['y']) return original_data def changex(temp, position): return int(temp/100000) def plot_reward_results(original_data, algo_list, map_name, env_name, type): filename = env_name + '_' + type + '_' + map_name + '.pdf' plt.figure(figsize=(10, 7)) # plt.figure(figsize=(10, 4)) plt.gca().xaxis.set_major_formatter(FuncFormatter(changex)) gap = 200 for idx, algo_id in enumerate(original_data[map_name]): sns.tsplot(time=original_data[map_name][algo_id]['x'][0::gap], data=original_data[map_name][algo_id]['y'][:, 0::gap], linestyle=linestyle[0], condition=algo_id, color=sns.color_palette(n_colors=12)[idx]) # for idx, algo_id in enumerate(algo_list[map_name]): # sns.tsplot(time=original_data[map_name][algo_id]['x'][0::gap], data=original_data[map_name][algo_id]['y'][:, 0::gap], # linestyle=linestyle[0], condition=algo_id[4:] if 'updet' in algo_id else algo_id, color=sns.color_palette()[idx]) # plt.legend(loc='upper left', ncol=1, fontsize=14) # plt.legend(loc='upper center', ncol=3, mode="expand", fontsize=14) plt.legend(loc='upper center', ncol=2, handlelength=2, mode="expand", borderaxespad=0.1, prop={'size': 14}) plt.title(map_name, fontsize=fontsize) plt.xlabel(r'Total timesteps ($\times 10^5$)', fontsize=fontsize) if env_name == 'sc2': plt.xlim((-10000, 2000000 + 20000)) plt.ylim((-0.1, 1.6)) plt.xticks([0, 500000, 1000000, 1500000, 2000000], fontsize=fontsize) plt.yticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0], fontsize=fontsize) plt.ylabel('Median Test Win %', fontsize=fontsize, labelpad=10) elif env_name == 'sc2mt': plt.xlim((-10000, 10000000 + 20000)) plt.ylim((-0.1, 1.6)) plt.xticks([0, 2000000, 4000000, 6000000, 8000000, 10000000], fontsize=fontsize) plt.yticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0], fontsize=fontsize) plt.ylabel('Median Test Win %', fontsize=fontsize, labelpad=10) elif env_name == 'particle': plt.xlim((-10000, 1000000 + 20000)) plt.ylim((-0.1, 20)) plt.xticks([0, 200000, 400000, 600000, 800000, 1000000], fontsize=fontsize) plt.yticks([0.0, 2.0, 4.0, 6.0, 8.0, 10.0], fontsize=fontsize) plt.ylabel('Average Return', fontsize=fontsize, labelpad=10) # plt.xticks([0, 500000, 1000000, 1500000, 2000000, 2500000, 3000000, 3500000, 4000000], fontsize=fontsize) plt.savefig(os.path.join(EXP_PATH, 'results', 'fig', filename), format='pdf', bbox_inches='tight') plt.show() def plot_attention_map(): attn_mix_data = [] # n_blocks * timesteps * n_heads * n_units * n_units attn_mix_data.append(np.load(os.path.join(EXP_PATH, 'results', 'fig', 'attn_mix', 'block_0', 'attn.npy'))) attn_mix_data.append(np.load(os.path.join(EXP_PATH, 'results', 'fig', 'attn_mix', 'block_1', 'attn.npy'))) attn_mix_data = np.array(attn_mix_data) attn_mix_data = attn_mix_data.reshape(2, -1, 3, attn_mix_data.shape[3], attn_mix_data.shape[4]) attn_agent_data = [] # n_blocks * timesteps * n_agents * n_heads * n_units * n_units attn_agent_data.append(np.load(os.path.join(EXP_PATH, 'results', 'fig', 'attn_agent', 'block_0', 'attn.npy'))) attn_agent_data.append(np.load(os.path.join(EXP_PATH, 'results', 'fig', 'attn_agent', 'block_1', 'attn.npy'))) attn_agent_data = np.array(attn_agent_data)[:, -71:-1, :, :-1, :-1] attn_agent_data = attn_agent_data.reshape(2, attn_agent_data.shape[1], -1, 3, attn_agent_data.shape[3], attn_agent_data.shape[4]) # 一行是一个 attention，每行的列元素求和为 0 filename = 'attn_mix.pdf' plt.figure(figsize=(10, 3)) n_heads = attn_mix_data.shape[2] for i in range(1, n_heads + 1): plt.subplot(1, n_heads, i) # sns.heatmap(1 - attn_mix_data[0, 0, i - 1], vmin=0.5, vmax=1, cmap='rocket', linewidths=.5) sns.heatmap(attn_mix_data[0, 0, i - 1], cmap=sns.cubehelix_palette(as_cmap=True, gamma=0.8), linewidths=.5) plt.xticks([]) # plt.yticks([]) # plt.axis('off') plt.tight_layout() plt.savefig(os.path.join(EXP_PATH, 'results', 'fig', filename), format='pdf', bbox_inches='tight') plt.show() filename = 'attn_agent.pdf' plt.figure(figsize=(10, 15)) n_heads = attn_agent_data.shape[3] n_agents = attn_agent_data.shape[2] for i in range(1, n_heads + 1): for j in range(1, n_agents + 1): plt.subplot(n_agents, n_heads, (j - 1) * n_heads + i) # sns.heatmap(1 - attn_agent_data[0, 0, 0, i - 1], vmin=0.5, vmax=1, cmap='rocket', linewidths=.5) sns.heatmap(attn_agent_data[0, 0, j - 1, i - 1], cmap=sns.cubehelix_palette(as_cmap=True, gamma=0.8), linewidths=.5) plt.xticks([]) # plt.yticks([]) # plt.axis('off') plt.tight_layout() plt.savefig(os.path.join(EXP_PATH, 'results', 'fig', filename), format='pdf', bbox_inches='tight') plt.show() # seed_idx_list = { # '3m': {'vdn_updet': [0, 1, 2, 3, 4], 'qmix_ext': [0, 1, 2, 3, 4], 'qmix_ext_scale': [0, 1, 2, 3, 4], 'qmix_latent': [0, 1, 2, 3, 4], 'qmix_latent_scale': [1, 2, 3, 4, 5]}, # '5m_vs_6m': {'vdn_updet': [0, 1, 2, 3, 4], 'qmix_ext': [0, 2, 3, 4, 5], 'qmix_ext_scale': [0, 1, 2, 3, 4], 'qmix_latent': [1, 2, 4, 5, 6], 'qmix_latent_scale': [0, 1, 2, 4, 5]}, # '8m_vs_9m': {'vdn_updet': [0, 1, 2, 3, 4], 'qmix_ext': [1, 2, 3, 4, 5], 'qmix_ext_scale': [2, 3, 5, 7, 8], 'qmix_latent': [1, 2, 3, 4, 5], 'qmix_latent_scale': [0, 1, 2, 3, 4]}, # '10m_vs_11m': {'vdn_updet': [0, 1, 2, 3, 4], 'qmix_ext': [0, 4, 5, 6, 7], 'qmix_ext_scale': [0, 1, 2, 4, 5], 'qmix_latent': [1, 3, 4, 5, 6], 'qmix_latent_scale': [0, 1, 2, 3, 4]}, # '2s3z': {'vdn_updet': [1, 2, 3, 4, 5], 'qmix_ext': [0, 1, 2, 3, 4], 'qmix_ext_scale': [0, 1, 2, 3, 4], 'qmix_latent': [0, 1, 3, 4, 5], 'qmix_latent_scale': [0, 1, 2, 3, 4]}, # '3s5z': {'vdn_updet': [0, 1, 2, 3, 4], 'qmix_ext': [0, 2, 4, 5, 6], 'qmix_ext_scale': [0, 1, 2, 3, 4], 'qmix_latent': [0, 2, 4, 5, 6], 'qmix_latent_scale': [1, 7, 9, 10, 11], 'qmix': [0, 1, 2, 4, 6]}, # '3s_vs_3z': {'vdn_updet': [0, 1, 2, 3, 4], 'qmix_ext': [0, 1, 2, 3, 4], 'qmix_ext_scale': [0, 1, 2, 3, 4], 'qmix_latent': [0, 1, 2, 3, 4], 'qmix_latent_scale': [0, 1, 2, 3, 4]}, # '3s_vs_5z': {'vdn_updet': [0, 1, 2, 3, 4], 'qmix_ext': [0, 1, 2, 3, 4], 'qmix_ext_scale': [0, 1, 2, 3, 4], 'qmix_latent': [0, 1, 2, 3, 4], 'qmix_latent_scale': [0, 3, 4, 9, 13], 'qmix': [1, 2, 4, 5, 6], 'vdn': [0, 1, 3, 4, 10]}, # '3m_to_5m_vs_6m': {}, # '5m_vs_6m_to_3m': {'qmix_latent_scale_5m_vs_6m': [1, 2, 3, 4, 5]}, # '8m_vs_9m_to_10m_vs_11m': {}, # '10m_vs_11m_to_8m_vs_9m': {'qmix_latent_scale_10m_vs_11m': [0, 1, 2, 4, 5]}, # '3m_to_10m_vs_11m': {'qmix_latent_scale_3m': [0, 1, 2, 4, 5]}, # '10m_vs_11m_to_3m': {}, # '2s3z_to_3s5z': {'qmix_latent_scale_2s3z': [0, 1, 3, 7, 8]}, # '3s_vs_3z_to_3s_vs_5z': {'qmix_latent_scale_3s_vs_3z': [4, 0, 3, 5, 8]}, # '3s5z_to_3s_vs_5z': {}, # '3s_vs_5z_to_3s5z': {}, # '3m_to_8m_vs_9m': {'qmix_latent_scale_3m': [0, 1, 2, 4, 5]}, # '5m_vs_6m_to_8m_vs_9m': {}, # 'tag_4_4_2': {}, # 'tag_8_8_2': {}, # 'tag_16_16_2': {}, # 'htag_8_4_2': {}, # 'htag_16_8_2': {}, # '25m': {}, # } seed_idx_list = { '3m': {}, '5m_vs_6m': {}, '8m_vs_9m': {}, '10m_vs_11m': {}, '2s3z': {}, '3s5z': {}, '3s_vs_3z': {}, '3s_vs_5z': {}, '3m_to_5m_vs_6m': {}, '5m_vs_6m_to_3m': {}, '8m_vs_9m_to_10m_vs_11m': {}, '10m_vs_11m_to_8m_vs_9m': {}, '3m_to_10m_vs_11m': {}, '10m_vs_11m_to_3m': {}, '2s3z_to_3s5z': {}, '3s_vs_3z_to_3s_vs_5z': {}, '3s5z_to_3s_vs_5z': {}, '3s_vs_5z_to_3s5z': {}, '3m_to_8m_vs_9m': {}, '5m_vs_6m_to_8m_vs_9m': {}, 'tag_4_4_2': {}, 'tag_8_8_2': {}, 'tag_16_16_2': {}, 'htag_8_4_2': {}, 'htag_16_8_2': {}, '25m': {}, '3-8m_symmetric': {}, '3-8sz_symmetric': {}, '3-8MMM_symmetric': {}, '3-8csz_symmetric': {}, } def plot_sc2_normal_all(): map_list = ['3m', '5m_vs_6m', '8m_vs_9m', '10m_vs_11m', '2s3z', '3s5z', '3s_vs_3z', '3s_vs_5z'] algo_list = { '3m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '5m_vs_6m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '8m_vs_9m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '10m_vs_11m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '2s3z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '3s5z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '3s_vs_3z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '3s_vs_5z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'] } original_data = get_original_data('sc2', map_list, algo_list, seed_idx_list) for map_name in map_list: plot_reward_results(original_data, algo_list, map_name, 'sc2', 'normal_all') def plot_sc2_normal_sota(): map_list = ['3m', '5m_vs_6m', '8m_vs_9m', '10m_vs_11m', '2s3z', '3s5z', '3s_vs_3z', '3s_vs_5z'] # map_list = ['3m', '5m_vs_6m', '8m_vs_9m', '10m_vs_11m', '2s3z', '3s5z', '3s_vs_3z', '3s_vs_5z', '25m'] algo_list = { '3m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'qplex', 'token_qmix_wise_attn', 'token_qmix_wise_trans', 'token_vdn_wise_attn', 'token_vdn_wise_trans', 'token_qmix_updet', 'token_vdn_updet'], '5m_vs_6m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'qplex', 'token_qmix_wise_attn', 'token_qmix_wise_trans', 'token_vdn_wise_attn', 'token_vdn_wise_trans', 'token_qmix_updet', 'token_vdn_updet'], '8m_vs_9m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'qplex', 'token_qmix_wise_attn', 'token_qmix_wise_trans', 'token_vdn_wise_attn', 'token_vdn_wise_trans', 'token_qmix_updet', 'token_vdn_updet'], '10m_vs_11m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'qplex', 'token_qmix_wise_attn', 'token_qmix_wise_trans', 'token_vdn_wise_attn', 'token_vdn_wise_trans', 'token_qmix_updet', 'token_vdn_updet'], '2s3z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'qplex', 'token_qmix_wise_attn', 'token_qmix_wise_trans', 'token_vdn_wise_attn', 'token_vdn_wise_trans', 'token_qmix_updet', 'token_vdn_updet'], '3s5z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'qplex', 'token_qmix_wise_attn', 'token_qmix_wise_trans', 'token_vdn_wise_attn', 'token_vdn_wise_trans', 'token_qmix_updet', 'token_vdn_updet'], '3s_vs_3z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'qplex', 'token_qmix_wise_attn', 'token_qmix_wise_trans', 'token_vdn_wise_attn', 'token_vdn_wise_trans', 'token_qmix_updet', 'token_vdn_updet'], '3s_vs_5z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'qplex', 'token_qmix_wise_attn', 'token_qmix_wise_trans', 'token_vdn_wise_attn', 'token_vdn_wise_trans', 'token_qmix_updet', 'token_vdn_updet'], '25m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'qplex', 'token_qmix_wise_attn', 'token_qmix_wise_trans', 'token_vdn_wise_attn', 'token_vdn_wise_trans', 'token_qmix_updet', 'token_vdn_updet'] } original_data = check_original_data('sc2', map_list, algo_list, seed_idx_list) for map_name in map_list: plot_reward_results(original_data, algo_list, map_name, 'sc2', 'normal_sota') def plot_sc2mt_normal_sota(): map_list = ['3-8m_symmetric', '3-8sz_symmetric', '3-8MMM_symmetric', '3-8csz_symmetric'] algo_list = { '3-8m_symmetric': ['entity_qmix_attn', 'entity_qmix_trans', 'entity_vdn_attn', 'entity_vdn_trans', 'entity_qmix_refil_attn', 'entity_vdn_refil_attn', 'entity_qmix_refil_imagine', 'entity_qmix_refil_imagine_parallel'], '3-8sz_symmetric': ['entity_qmix_attn', 'entity_qmix_trans', 'entity_vdn_attn', 'entity_vdn_trans', 'entity_qmix_refil_attn', 'entity_vdn_refil_attn', 'entity_qmix_refil_imagine', 'entity_qmix_refil_imagine_parallel'], '3-8MMM_symmetric': ['entity_qmix_attn', 'entity_qmix_trans', 'entity_vdn_attn', 'entity_vdn_trans', 'entity_qmix_refil_attn', 'entity_vdn_refil_attn', 'entity_qmix_refil_imagine', 'entity_qmix_refil_imagine_parallel'], '3-8csz_symmetric': ['entity_qmix_attn', 'entity_qmix_trans', 'entity_vdn_attn', 'entity_vdn_trans', 'entity_qmix_refil_attn', 'entity_vdn_refil_attn', 'entity_qmix_refil_imagine', 'entity_qmix_refil_imagine_parallel'], } original_data = check_original_data('sc2mt', map_list, algo_list, seed_idx_list) for map_name in map_list: plot_reward_results(original_data, algo_list, map_name, 'sc2mt', 'normal_sota') def plot_sc2_normal_baseline(): map_list = ['5m_vs_6m', '8m_vs_9m', '3s_vs_5z', '2s3z'] # map_list = ['3m', '5m_vs_6m', '8m_vs_9m', '10m_vs_11m', '2s3z', '3s5z', '3s_vs_3z', '3s_vs_5z', '25m'] algo_list = { '3m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_latent_scale'], '5m_vs_6m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_latent_scale', 'qmix_sparse'], '8m_vs_9m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_latent_scale', 'qmix_sparse'], '10m_vs_11m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_latent_scale'], '2s3z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_latent_scale', 'qmix_sparse'], '3s5z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_latent_scale'], '3s_vs_3z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_latent_scale'], '3s_vs_5z': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_latent_scale', 'qmix_sparse'], '25m': ['coma', 'iql', 'vdn', 'qmix', 'qtran', 'vdn_updet', 'qmix_latent_scale'] } original_data = get_original_data('sc2', map_list, algo_list, seed_idx_list) for map_name in map_list: plot_reward_results(original_data, algo_list, map_name, 'sc2', 'normal_baseline') def plot_sc2_normal_ablation(): map_list = ['3m', '5m_vs_6m', '8m_vs_9m', '10m_vs_11m', '2s3z', '3s5z', '3s_vs_3z', '3s_vs_5z'] algo_list = { '3m': ['vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '5m_vs_6m': ['vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '8m_vs_9m': ['vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '10m_vs_11m': ['vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '2s3z': ['vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '3s5z': ['vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '3s_vs_3z': ['vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], '3s_vs_5z': ['vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'] } original_data = get_original_data('sc2', map_list, algo_list, seed_idx_list) for map_name in map_list: plot_reward_results(original_data, algo_list, map_name, 'sc2', 'normal_ablation') def plot_sc2_transfer_all(): # map_list = ['3s_vs_3z_to_3s_vs_5z'] map_list = ['3m_to_5m_vs_6m', '5m_vs_6m_to_3m', '8m_vs_9m_to_10m_vs_11m', '10m_vs_11m_to_8m_vs_9m', '3m_to_10m_vs_11m', '10m_vs_11m_to_3m', '2s3z_to_3s5z', '3s_vs_3z_to_3s_vs_5z', '3s5z_to_3s_vs_5z', '3s_vs_5z_to_3s5z', '3m_to_8m_vs_9m', '5m_vs_6m_to_8m_vs_9m'] algo_list = { '3m_to_5m_vs_6m': ['vdn_updet_3m', 'qmix_ext_3m', 'qmix_ext_scale_3m', 'qmix_latent_3m', 'qmix_latent_scale_3m'], '5m_vs_6m_to_3m': ['vdn_updet_5m_vs_6m', 'qmix_ext_5m_vs_6m', 'qmix_ext_scale_5m_vs_6m', 'qmix_latent_5m_vs_6m', 'qmix_latent_scale_5m_vs_6m'], '8m_vs_9m_to_10m_vs_11m': ['vdn_updet_8m_vs_9m', 'qmix_ext_8m_vs_9m', 'qmix_ext_scale_8m_vs_9m', 'qmix_latent_8m_vs_9m', 'qmix_latent_scale_8m_vs_9m'], '10m_vs_11m_to_8m_vs_9m': ['vdn_updet_10m_vs_11m', 'qmix_ext_10m_vs_11m', 'qmix_ext_scale_10m_vs_11m', 'qmix_latent_10m_vs_11m', 'qmix_latent_scale_10m_vs_11m'], '3m_to_10m_vs_11m': ['vdn_updet_3m', 'qmix_ext_3m', 'qmix_ext_scale_3m', 'qmix_latent_3m', 'qmix_latent_scale_3m'], '10m_vs_11m_to_3m': ['vdn_updet_10m_vs_11m', 'qmix_ext_10m_vs_11m', 'qmix_ext_scale_10m_vs_11m', 'qmix_latent_10m_vs_11m', 'qmix_latent_scale_10m_vs_11m'], '2s3z_to_3s5z': ['vdn_updet_2s3z', 'qmix_ext_2s3z', 'qmix_ext_scale_2s3z', 'qmix_latent_2s3z', 'qmix_latent_scale_2s3z'], '3s_vs_3z_to_3s_vs_5z': ['vdn_updet_3s_vs_3z', 'qmix_ext_3s_vs_3z', 'qmix_ext_scale_3s_vs_3z', 'qmix_latent_3s_vs_3z', 'qmix_latent_scale_3s_vs_3z'], '3s5z_to_3s_vs_5z': ['vdn_updet_3s5z', 'qmix_ext_3s5z', 'qmix_ext_scale_3s5z', 'qmix_latent_3s5z', 'qmix_latent_scale_3s5z'], '3s_vs_5z_to_3s5z': ['vdn_updet_3s_vs_5z', 'qmix_ext_3s_vs_5z', 'qmix_ext_scale_3s_vs_5z', 'qmix_latent_3s_vs_5z', 'qmix_latent_scale_3s_vs_5z'], '3m_to_8m_vs_9m': ['vdn_updet_3m', 'qmix_ext_3m', 'qmix_ext_scale_3m', 'qmix_latent_3m', 'qmix_latent_scale_3m'], '5m_vs_6m_to_8m_vs_9m': ['vdn_updet_5m_vs_6m', 'qmix_ext_5m_vs_6m', 'qmix_ext_scale_5m_vs_6m', 'qmix_latent_5m_vs_6m', 'qmix_latent_scale_5m_vs_6m'], } original_data = get_original_data('sc2', map_list, algo_list, seed_idx_list) for map_name in map_list: plot_reward_results(original_data, algo_list, map_name, 'sc2', 'transfer_all') def plot_sc2_transfer_baseline(): # map_list = ['3s_vs_3z_to_3s_vs_5z'] map_list = ['3m_to_5m_vs_6m', '5m_vs_6m_to_3m', '8m_vs_9m_to_10m_vs_11m', '10m_vs_11m_to_8m_vs_9m', '3m_to_10m_vs_11m', '10m_vs_11m_to_3m', '2s3z_to_3s5z', '3s_vs_3z_to_3s_vs_5z', '3s5z_to_3s_vs_5z', '3s_vs_5z_to_3s5z', '3m_to_8m_vs_9m', '5m_vs_6m_to_8m_vs_9m'] algo_list = { '3m_to_5m_vs_6m': ['vdn_updet_3m', 'qmix_latent_scale_3m'], '5m_vs_6m_to_3m': ['vdn_updet_5m_vs_6m', 'qmix_latent_scale_5m_vs_6m'], '8m_vs_9m_to_10m_vs_11m': ['vdn_updet_8m_vs_9m', 'qmix_latent_scale_8m_vs_9m'], '10m_vs_11m_to_8m_vs_9m': ['vdn_updet_10m_vs_11m', 'qmix_latent_scale_10m_vs_11m'], '3m_to_10m_vs_11m': ['vdn_updet_3m', 'qmix_latent_scale_3m'], '10m_vs_11m_to_3m': ['vdn_updet_10m_vs_11m', 'qmix_latent_scale_10m_vs_11m'], '2s3z_to_3s5z': ['vdn_updet_2s3z', 'qmix_latent_scale_2s3z'], '3s_vs_3z_to_3s_vs_5z': ['vdn_updet_3s_vs_3z', 'qmix_latent_scale_3s_vs_3z'], '3s5z_to_3s_vs_5z': ['vdn_updet_3s5z', 'qmix_latent_scale_3s5z'], '3s_vs_5z_to_3s5z': ['vdn_updet_3s_vs_5z', 'qmix_latent_scale_3s_vs_5z'], '3m_to_8m_vs_9m': ['vdn_updet_3m', 'qmix_latent_scale_3m'], '5m_vs_6m_to_8m_vs_9m': ['vdn_updet_5m_vs_6m', 'qmix_latent_scale_5m_vs_6m'], } original_data = get_original_data('sc2', map_list, algo_list, seed_idx_list) for map_name in map_list: plot_reward_results(original_data, algo_list, map_name, 'sc2', 'transfer_baseline') def plot_particle_normal_all(): map_list = ['tag_4_4_2', 'tag_8_8_2', 'tag_16_16_2', 'htag_8_4_2', 'htag_16_8_2'] algo_list = { 'tag_4_4_2': ['vdn', 'qmix', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], 'tag_8_8_2': ['vdn', 'qmix', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], 'tag_16_16_2': ['vdn', 'qmix', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], 'htag_8_4_2': ['vdn', 'qmix', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'], 'htag_16_8_2': ['vdn', 'qmix', 'vdn_updet', 'qmix_ext', 'qmix_ext_scale', 'qmix_latent', 'qmix_latent_scale'] } seed_idx_list = { 'tag_4_4_2': {}, 'tag_8_8_2': {}, 'tag_16_16_2': {}, 'htag_8_4_2': {}, 'htag_16_8_2': {}, } original_data = get_original_data('particle', map_list, algo_list, seed_idx_list) for map_name in map_list: plot_reward_results(original_data, algo_list, map_name, 'particle', 'normal_all') if __name__ == '__main__': # plot_sc2_normal_all() # plot_sc2_normal_baseline() # plot_sc2_normal_ablation() # plot_sc2_transfer_all() # plot_sc2_transfer_baseline() # plot_particle_normal_all() # plot_attention_map() plot_sc2_normal_sota() # plot_sc2mt_normal_sota()

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