sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c425ce7e0f2b97123ec3715d6bc78f950f47ed755a4308a4bca1852e6c92fa4a | Python | 716 | 37 |
"""
Dimension class for model server metrics
"""
class Dimension(object):
"""
Dimension class defining key value pair
"""
def __init__(self, name, value):
"""
Constructor for Dimension class
Parameters
----------
name: str
NAme of dimension
... |
08e19c99e93fbec420836bf4667f8b14dde3a0e6390a5d95feb0ee13e7125373 | Python | 717 | 28 | """Plaid dataset."""
from sktime.datasets._single_problem_loaders import load_plaid
from sktime.datasets.classification._base import _ClassificationDatasetFromLoader
class PLAID(_ClassificationDatasetFromLoader):
"""PLAID time series classification problem.
Example of a univariate problem with unequal lengt... |
482dd8e750699266f31624df7515ce916cdc70ef5b69df96c0f3ca1f4cc892a9 | Python | 717 | 23 | from src.cajal.sample_mesh import (
compute_icdm_all,
_connect_helper,
cell_generator,
get_geodesic,
sample_vertices,
)
import os
def test():
for b in [True, False]:
for s in ["euclidean", "geodesic"]:
outcsv = "tests/mesh_icdm_" + str(b) + s + "heat" + ".csv"
... |
bc9db2d79db45cb6d61636bd8fd3a13fd2ae555269a19632ac5db1f1e2f40ad6 | Python | 717 | 19 | import numpy as np
import atom3d.util.results as res
import atom3d.util.metrics as met
cutoff='06'
maxnumat='600'
for id_split in ['30','60']:
name = 'lba-id'+id_split+'-siamese_cutoff-'+cutoff+'_maxnumat-'+maxnumat
print(name)
rloader = res.ResultsENN(name, reps=[1,2,3])
results = rloader.get_all_pre... |
4a71a4796232a3b5cbd400c8a5ecb9bfe48a2b385ff6a37f12bf3c238efe2ed7 | Python | 719 | 30 | """Custom exceptions for ParaDime.
"""
class NotTrainedError(Exception):
"""Method of DR instance called before training."""
class RelationsNotComputedError(Exception):
"""Attempted to access relation data before computation."""
class NoDatasetRegisteredError(Exception):
"""Attempted to access the dat... |
edc5c1499b47adff10eaa9b2b77d31baff9fc3dfce2d63ac25bc90fadbc43400 | Python | 720 | 24 | import torch
import numpy as np
import atom3d.datasets as da
from pathlib import Path
class MockDataset(torch.utils.data.Dataset):
def __init__(self):
self.data = np.arange(30)
self.year = 2020-np.arange(30)
self.scaf = [0]*10 + [1]*5 + [2]*3 + [3,4,5]*2 + [6,7,8,9,10,11]
np.random.... |
4cbc8f2a59c8fd664e43e4b8937ae845787aa280a684ff0cb1f2fe5a126d9c5e | Python | 721 | 18 | # coding=utf-8
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... |
90892ae69baa3880bd5df54a803feef0c1f6e8233bb12b962d53379950f1ec48 | Python | 721 | 28 | """Tests for base change point detection class."""
import numpy as np
import pytest
from sktime.tests.test_switch import run_test_module_changed
@pytest.mark.skipif(
not run_test_module_changed("sktime.detection"),
reason="module not changed",
)
def test_fit_transform_numpy():
"""Test that numpy input w... |
ad12530e4f6434fd1c1cd787633ccf9ef338a64f9c1e596ce3e3d7994ef44d50 | Python | 721 | 24 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.op_run import OpRun
class Concat(OpRun):
def _preprocess(self, a: np.ndarray, axis: int) -> np.ndarray:
rank = len(a.shape)
if rank == 0:
... |
ef76d52dafe8037a89c3dc073196b6018ac3b58e533c56fc6d4e7823e546a27b | Python | 722 | 19 | # coding=utf-8
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... |
fc83b3f611598422d23cf0230de2509ba775ecaf975bbdc3d0445f4f827200f2 | Python | 722 | 22 | """Dictionary based time series classifiers."""
__all__ = [
"IndividualBOSS",
"BOSSEnsemble",
"BOSSVSClassifierPyts",
"ContractableBOSS",
"TemporalDictionaryEnsemble",
"IndividualTDE",
"WEASEL",
"MUSE",
]
from sktime.classification.dictionary_based._boss import BOSSEnsemble, Individual... |
1f264ef89fbdfc7e53ee7d5be7ff7f91a1626f69203f9146b1caefe2612941d9 | Python | 723 | 26 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class NonZero(Base):
@staticmethod
def export() -> None:
nod... |
2a392f29c23fb8cc6c4acc59159af5636690b91a8ceb2514dfb2051cfce1e602 | Python | 723 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Atan(Base):
@staticmethod
def export() -> None:
node =... |
cfc673511186ca69df042bc59ca7fb6f37ca15de3d79b926276d782b56612f3d | Python | 723 | 20 | # coding=utf-8
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... |
e11ecadee8962e0f8bf794aad808526daafe719b9d227602b84df699e199c646 | Python | 723 | 19 | # coding=utf-8
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... |
e74bf8ed5ab64de8316fe001a8e4345134d682f1fea42fa6f05298c548a31fc4 | Python | 723 | 20 | # coding=utf-8
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... |
d281c383b3ddb67d96b107629186fe3bdd2b75365fc8a4d50f01ba18bc67c2ad | Python | 724 | 17 | # coding=utf-8
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... |
423455760b5f855a1b6d5b264ce44622bb16f16229e52da4094375d40013538c | Python | 725 | 20 | # coding=utf-8
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... |
3826e20ddad8c8a51eb5014828ab382f6b42b818761401e21642700a974caa8d | Python | 726 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Acos(Base):
@staticmethod
def export() -> None:
node =... |
d7540d273890d015adb94e97e6302cbc41ece689433f2cc3b71e320c1aa411d1 | Python | 726 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Asin(Base):
@staticmethod
def export() -> None:
node =... |
c607edcdb1fd49ba4581b41c0d175e07cd2a489e7429d64b94deacb5ad4db74c | Python | 727 | 17 | # from src.configs.atacseq import atacseq_config
# from src.configs.atacseq_classify_only import atacseq_clonly_config
# from src.configs.atacseq_no_alternating import atacseq_all_config
#
# from src.configs.atacseq import atacseq_config
# from src.configs.default import default_config
# from src.configs.dlpfc im... |
899629df970611d3cbba387871fc930824aa1214cb7e36fa886616cf9d0f1e3f | Python | 728 | 19 | # coding = utf-8
# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... |
6b351ea2c39356726cd637dddd86e274d2c012addd02512cdb639c8affca7223 | Python | 729 | 25 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNumpy
class Max(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.maximum, o... |
dcd8571997d5f8320676d4d8a4b45beb8b041264d6e17fb8d7d93b4308d8058b | Python | 729 | 25 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
from onnx.reference.ops._op import OpRunBinaryNumpy
class Min(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.minimum, o... |
393799199107aab7cd393e806bc9aac28b16621b07ed1472859e19f1b7089a42 | Python | 730 | 40 |
from itertools import combinations, product
def subset_generator(s):
"""
a generator to generate all subset of s, also contains Ø.
Parameters
----------
s: iterable
a set of nodes
"""
for i in range(len(s) + 1):
if i == 0:
yield set()
else:
... |
8e69cd2a821728da7fb326c181b9b09377ef8b2c5ccea475a3107082f00adbf8 | Python | 730 | 17 | def train_model(config, DataHandler, model, callback_list, verbose=0):
if verbose > 0:
print('Start training')
train_input, y_train = [DataHandler['X_train'], DataHandler['dg_train']], DataHandler['y_train']
valid_input, y_val = [DataHandler['X_valid'], DataHandler['dg_valid']], DataHandler['y_valid... |
9ceb50c57020a25300fb000837023c2d0a87fd8df01a612d069380e8ae017cd8 | Python | 734 | 30 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
def hardswish(x: np.ndarray) -> np.ndarray:
alfa = float(1 / 6)
beta... |
ab6b875bcb1ed8ddc0fc323e42fe756d20283ed01be2ce8b3fe20b11869e8684 | Python | 734 | 27 | import numpy as np
from scipy import ndimage as ndi
def NCC_3D(I, J, ws):
"""
I, J: np.ndarray (3D)
ws: local window size
"""
# Use float64
I = I.astype(np.float64)
J = J.astype(np.float64)
kernel = np.ones((ws,)*I.ndim)
I_bar = I - ndi.convolve(I, kernel, mode="reflect") / kernel... |
f45e29ff59adc2625404c1f12d4485e19bf13926da0011e43cea1b0d9b801fa9 | Python | 738 | 21 | # Copyright 2023 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
845b0561357e35bdd0cd9afb972bc0304bfc5432eeef6224ec9b965fea410cb5 | Python | 739 | 24 | import sys
import numpy as np
import torch
import atom3d.util.results as res
import atom3d.util.metrics as met
seqid = sys.argv[1]
# Define the training run
name = f'logs/lba_test_withH_{seqid}/lba'
print(name)
# Load training results
rloader = res.ResultsGNN(name, reps=[0,1,2])
results = rloader.get_all_prediction... |
8b40b30d8186fb8194cf0fee36ac71a7c43343eef02f641e16ddbfbb588aea74 | Python | 739 | 29 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 29 15:08:27 2019
@author: wanglab
"""
import numpy as np, os, tifffile
src = "/home/wanglab/mounts/wang/pisano/tracing_output/retro_4x/20180313_jg_bl6f_prv_23/full_sizedatafld/20180313_jg23_4x_647_008na_1hfds_z7d5um_300msec_10povlp_ch00"
#trying ... |
a18c2e81a000f52345133488eabc760ef057da15c99d2ede103ca0c65ec64888 | Python | 739 | 38 | """
plot_timeline
=============
.. autofunction:: optuna.visualization.plot_timeline
:no-index:
The following code snippet shows how to plot the timeline of a study.
Timeline plot can visualize trials with overlapping execution time
(e.g., in distributed environments).
.. _visualization-plot-timeline-plotly-con... |
324d9c21ffb94f62aa34464d399fef267f38013640cafab3008ee61b78a0a8c0 | Python | 741 | 27 | """
FacetGrid with custom projection
================================
_thumb: .33, .5
"""
import numpy as np
import pandas as pd
import seaborn as sns
sns.set()
# Generate an example radial datast
r = np.linspace(0, 10, num=100)
df = pd.DataFrame({'r': r, 'slow': r, 'medium': 2 * r, 'fast': 4 * r})
# Convert the d... |
c0ff53eaeed4de50213d7f4132699d3dd1ce9d071d7276929ba863a825a70ae6 | Python | 741 | 28 | """
Different cubehelix palettes
============================
_thumb: .4, .65
"""
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="dark")
rs = np.random.RandomState(50)
# Set up the matplotlib figure
f, axes = plt.subplots(3, 3, figsize=(9, 9), sharex=True, sharey=True)
# Rota... |
e640bdb305c42dbdbac47d13473d58665bd64c222bcb0624b9175a1ab8b07cca | Python | 741 | 22 | # SPDX-License-Identifier: LGPL-3.0-or-later
"""Check style registration without requiring a model or inference backend.
CI loads the runtime plugin through LAMMPS_PLUGIN_PATH. A standalone plugin
can instead be loaded explicitly with DEEPMD_TEST_PLUGIN.
"""
import os
from lammps import (
lammps,
)
def test_dp... |
183ac09203f2fa2a149ab7fa8ed9f89c2ab63a06393f4ce899b0c5f3e72aa5a5 | Python | 742 | 33 | '''
@author: Nicholas Browning
'''
# #SUPPORTED OPERATOR TYPES#
import evaluators as evals
# terrible version of Enum struct as python 2.7 doesn't have it
# crossover types
SBX = 'sbx'
UNI_X = "uniform_crossover"
# mutator types
POLY_M = 'polynomial_mutation'
UNI_M = 'uniform_mutation'
# replacer types
SWAP = 'swap'... |
f6fafd6b815d9fc52ad1b323ffe0ae97ec82e0a6764ae49f4e3f78ed9c90aba0 | Python | 743 | 31 | """
Horizontal boxplot with observations
====================================
_thumb: .7, .37
"""
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="ticks")
# Initialize the figure with a logarithmic x axis
f, ax = plt.subplots(figsize=(7, 6))
ax.set_xscale("log")
# Load the example planets ... |
a0600d18a09be97841684be5e01629ea83bdf5d9901ee8fd63f15fc54103c096 | Python | 744 | 24 | from rxnebm.proposer.gln_config import gln_config
from rxnebm.proposer.gln_proposer import GLNProposer
def test():
proposer = GLNProposer(gln_config)
product_smiles = [
"[Br:1][CH2:2]/[CH:3]=[CH:4]/[C:5](=[O:6])[O:7][Si:8]([CH3:9])([CH3:10])[CH3:11]",
"[Br:1][c:12]1[cH:11][cH:10][c:9]([OH:15])... |
f2542f524fa7d4f4c7e334a3963b734ed6354588f2d81395feabf70c33403667 | Python | 744 | 20 | import numpy as np
import tensorflow as tf
import constants
import dataset
es = constants.ExperimentSettings()
model_prefix = constants.model_name(es)
for fold in range(constants.n_folds):
# Load de encoder
filename = constants.classifier_filename(model_prefix, es, fold)
model = tf.keras.models.load_model(... |
3836a0c0835ed2e512d495aff64f1e85b5b9023ca039aa6a65ad121b71b70dea | Python | 746 | 37 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
def _get_indices(i, shape):
res = np.empty((len(shape),), dtype=np.int64)
k = len(shape) - 1
while k > 0:
m = i % shape[k]
res[k] = m
i -= m
... |
7215b00e828b516773b02119eb71b6433ee02a9a0302f69f920463fa72d04f11 | Python | 747 | 25 | # Visualize integrated datasets
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import anndata as ad
import scanpy as sc
from matplotlib.colors import ListedColormap
def main():
sns.set_context("poster")
sns.set_palette("Set2")
data_dir = "./data/anndata/"
fig_dir = "./figures... |
8e45f3f54da93efbed594330176d91f6fc445f65067f7bc38ba61e03066fdbb9 | Python | 747 | 27 | """
FacetGrid with custom projection
================================
_thumb: .33, .5
"""
import numpy as np
import pandas as pd
import seaborn as sns
sns.set_theme()
# Generate an example radial datast
r = np.linspace(0, 10, num=100)
df = pd.DataFrame({'r': r, 'slow': r, 'medium': 2 * r, 'fast': 4 * r})
# Convert... |
d2c6378ab9aa1c2e3d0b04ed146953dd712067ecdd220f5e3006a892ae8a5e6c | Python | 747 | 28 | import ast
import configparser
from collections.abc import Mapping
class Settings(Mapping):
def __init__(self, setting_file='settings.ini'):
config = configparser.ConfigParser()
config.read(setting_file)
self.settings_dict = _parse_values(config)
def __getitem__(self, key):
re... |
72196798daad68feb3d914b2aa44485be8ec987a480696f10e150639c1c81680 | Python | 748 | 22 | # -*-coding:Utf-8 -*
import os
import nipype.interfaces.ants as ants
import bcblib.tools.constants as cst
proba = os.path.join(cst.get_ants_priors_folder(), "brainPrior.nii.gz")
temp = os.path.join(cst.get_ants_priors_folder(), "brainWithSkullTemplate.nii.gz")
def skull_strip(dim, anatomical, brain_proba=proba, te... |
a9157f3f4df7e5be0b9d232238d469482e1e946724a741579ad273fd042e54b0 | Python | 748 | 17 | # Copyright 2018-2025
# Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICE... |
407bc19b0ddc93c07627ff073fdbf351ec9dc900850085bc88d7a3e26d57c1d2 | Python | 749 | 20 | # Copyright 2007 Zachary Pincus
# This file is part of CellTool.
#
# CellTool is free software; you can redistribute it and/or modify
# it under the terms of version 2 of the GNU General Public License as
# published by the Free Software Foundation.
def configuration(parent_package='',top_path=None):
from numpy.d... |
e558df00736ff8e9ff17b3f1b802cfa29eec1dd19d652d4e0b485854525d7700 | Python | 749 | 21 | # Import seaborn objects
from .rcmod import * # noqa: F401,F403
from .utils import * # noqa: F401,F403
from .palettes import * # noqa: F401,F403
from .relational import * # noqa: F401,F403
from .regression import * # noqa: F401,F403
from .categorical import * # noqa: F401,F403
from .distributions import * # noqa... |
fe7a8fe8b3be6eff0a6b4a8c9a16ed27a05b70dc7ccd212a282f992537805990 | Python | 749 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Ceil(Base):
@staticmethod
def export() -> None:
node =... |
948370ce9d60b8803a31e559efd3470081a1bcb1cab3ed0a4f1dafac6557f08e | Python | 750 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Neg(Base):
@staticmethod
def export() -> None:
node = ... |
8f4b543b4e926361b785cc07fb850b484ab09f802565767ac1cc3b0f4f2560e7 | Python | 751 | 30 | """
Horizontal boxplot with observations
====================================
_thumb: .7, .37
"""
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="ticks")
# Initialize the figure with a logarithmic x axis
f, ax = plt.subplots(figsize=(7, 6))
ax.set_xscale("log")
# Load the example planets datase... |
02ba90fc1f02fceeb5280e05937868f24b1c91b8cbbbe08e3a53c6897c9ff0b1 | Python | 752 | 17 | # Copyright 2018-2025
# Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICE... |
4350dc47b775668a518c059d7f5dc788bef9c1a247bc8aad4623c160c796a966 | Python | 752 | 28 | import numpy as np
import poisson_disc as pd
import nibabel as nib
import pyvista
if __name__ == '__main__':
file = 'estimated_cortex.nii.gz'
img = nib.load(file).get_fdata()
volume = np.sum(img)
dims3d = np.array(img.shape)
box_volume = np.prod(dims3d + 1)
n = 30000
box_n = int(n * box_v... |
72fb445db760b2a16c8d9cd87c1e13fa8a51a98eb8d3baf64b68798420951d50 | Python | 753 | 33 | from pyhanlp import HanLP
from snownlp import SnowNLP
import pkuseg
# Chinese segmentation
def zh_segmentator(line):
return " ".join(pkuseg.pkuseg().cut(line))
# Chinese simplify -> Chinese traditional standard
def zh_traditional_standard(line):
return HanLP.convertToTraditionalChinese(line)
# Chinese sim... |
b8cbe01a47894209c6cf4caae4b3891d146f6cb8bb1711fc7e8ce5260c2a0196 | Python | 754 | 22 | # Copyright (c) OpenMMLab. All rights reserved.
import random
import string
import tempfile
from mmcls.datasets.utils import check_integrity, rm_suffix
def test_dataset_utils():
# test rm_suffix
assert rm_suffix('a.jpg') == 'a'
assert rm_suffix('a.bak.jpg') == 'a.bak'
assert rm_suffix('a.bak.jpg', su... |
c9104e17a1bd51611e9ea283e2cbe8bca8112092e8c8103280a5926b4d0a36c3 | Python | 754 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Log(Base):
@staticmethod
def export() -> None:
node = ... |
cf5fd279ca4a7250debbbe90ae385e4c0e77d74d91861332b5b430f5b33c99dd | Python | 754 | 27 | import unittest
from neurondm import OntTerm as OntTerm_
from neurondm.core import OntTermOntologyOnly
from pyontutils.core import OntGraph
from pyontutils.namespaces import partOf
from .common import skipif_no_net
@skipif_no_net
class TriplesSimple:
OntTerm = None
def test_part_of(self):
eeeee = self... |
e198f92d6620c8a7fa6ba18dc93a61aa351dec264b4ca49d0fcbf16675dfafdb | Python | 754 | 33 | """
plot_hypervolume_history
========================
.. autofunction:: optuna.visualization.matplotlib.plot_hypervolume_history
:no-index:
The following code snippet shows how to plot optimization history.
.. _visualization-plot-hypervolume-history-matplotlib-content:
"""
import optuna
import matplotlib.pypl... |
f7f8ef3529d4a993fedfd40eaa5cd1d848259397d42561818ebad32d3155577d | Python | 754 | 22 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.op_run import OpRun
class ReverseSequence(OpRun):
def _run(self, data, sequence_lens, batch_axis=None, time_axis=None):
index = [slice(0, s) for s in data.shape]
... |
2ba2312e3fc4e626fc69637f7837c50663e1af89c08360a3dcb87b51a3961f7b | Python | 755 | 21 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import onnx
import onnx.onnx_cpp2py_export.printer as C # noqa: N812
def to_text(
proto: onnx.ModelProto | onnx.FunctionProto | onnx.GraphProto | onnx.NodeProto,
) -> str:
if isinstance(proto... |
34d871f342f65594ac8e4a6b8fc592811c4c514fb250c6f64810667af8a34201 | Python | 755 | 40 | # SPDX-License-Identifier: LGPL-3.0-or-later
from .dipole_fitting import (
DipoleFitting,
)
from .dos_fitting import (
DOSFittingNet,
)
from .dpa4_ener import (
SeZMEnergyFittingNet,
)
from .ener_fitting import (
EnergyFittingNet,
)
from .invar_fitting import (
InvarFitting,
)
from .latent_charge im... |
7227af1b7e0785a5806c1f3e940ca89b14553becc369be953043ece6112805c3 | Python | 756 | 29 | import sys
fi=open(sys.argv[1])
fo=open(sys.argv[1]+'.ucscInter.bed','w')
i=1
for line in fi:
seq=line.rstrip().split('\t')
c1=seq[0]
s1=seq[1]
e1=seq[2]
c2=seq[3]
s2=seq[4]
e2=seq[5]
if c1==c2:
signal=str(min([int(seq[6]),1000]))
this_chr=c1
this_start=str(m... |
b7b0cb1a3715787e8eca88850ed11c864deef77295f43cc63978690cfc70142e | Python | 756 | 23 | import concurrent.futures
import multiprocessing
def get_process_pool_executor(max_workers=multiprocessing.cpu_count()):
return concurrent.futures.ProcessPoolExecutor(max_workers)
def execute_task_in_parallel(
task_fn, inputs, max_workers=multiprocessing.cpu_count()
):
results = {}
with get_process_... |
c15099c9cf056cbfb708d337e6d3f97dfc48a3cb4d8c9a89c78df7dc5feb1403 | Python | 757 | 17 | # Copyright 2019 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
e27c1fa76de073e01d2d958e4bd1165ff638eb5018a4888c5503d78320e8affe | Python | 757 | 28 | import torch
from torch import nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0... |
1a65ce0f01fdd1f4e749b62c3cf0ed098dbb7b48dc8b4faddad3adaad0f55923 | Python | 758 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Sqrt(Base):
@staticmethod
def export() -> None:
node =... |
44190a5ea9c99028fda5a37fbab130d6fbc59200cd07df573575fd285d7dc73e | Python | 758 | 24 | import os
from translate_common import translation, detokenize_translation
from translate_from_transliteration import translate_transliteration_base
def translate_transliteration(sentence):
tmp_file = "transliteration.tmp"
with open(tmp_file, "w") as f:
f.write(sentence)
raw_result = translate_tr... |
4708a7c3fab8b858d7fa91bb10ccfbcb142396944f70bd6ffe9d5b63664318be | Python | 761 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Exp(Base):
@staticmethod
def export() -> None:
node = ... |
a74b72c2cc9cfe4814c7052b05171e36983b137572e7bc5504b68b017042cf96 | Python | 761 | 28 | from q2_matchmaker._stats import hotelling_ttest, spherical_test
import numpy as np
import unittest
class TestStats(unittest.TestCase):
def setUp(self):
np.random.seed(1)
n, p = 100, 50
self.Xrand = np.random.randn(n, p)
self.Xreal = np.random.randn(n, p)
self.Xreal[:, 0] +... |
b804944936037fb543f2b4de625afa3088d64fb39674a3541fd7c8010fd75fbf | Python | 762 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Floor(Base):
@staticmethod
def export() -> None:
node ... |
7806994d424f62872f10c3aecebe4d8e953302514b13ec543d9eafdf0b9fceb3 | Python | 766 | 28 | """Arsenal test code."""
import pytest
from sktime.classification.kernel_based import Arsenal
from sktime.datasets import load_unit_test
from sktime.tests.test_switch import run_test_for_class
@pytest.mark.skipif(
not run_test_for_class(Arsenal),
reason="run test only if softdeps are present and incremental... |
cd6f3ced21f6b7122651fbd4f3493cdcd6f1c6794795b77306376bc98fb7fb8f | Python | 766 | 16 | # Copyright 2018-2025
# Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICE... |
fa4c6f6dcb32ae6340de1ff8e9aa20a027fa6eaa7aad658d2bc39cf82d5ef9c6 | Python | 766 | 21 | # coding=utf-8
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... |
0777f5186c02d5816f684795c87531ece4dfbac53a653b6770fb7dc91b602d52 | Python | 768 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Tanh(Base):
@staticmethod
def export() -> None:
node =... |
2543abeb5291ad6c8669fa2d63bd4306be37d51892a77b65e53c79721f26375b | Python | 769 | 25 | """
Scatterplot with multiple semantics
===================================
_thumb: .45, .5
"""
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="whitegrid")
# Load the example diamonds dataset
diamonds = sns.load_dataset("diamonds")
# Draw a scatter plot while assigning point colors and si... |
26c4969b8040f1d601793643afcbc4a0552bf3ee4dd0732868c9388b5752a287 | Python | 769 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Cosh(Base):
@staticmethod
def export() -> None:
node =... |
f27e23471ea9304402cbff127b93259998d4b59afd09799d16220736fdd48460 | Python | 769 | 24 | from pathlib import Path
from setuptools import find_packages, setup
required = Path("requirements.txt").read_text(encoding="utf-8").split("\n")
setup(
name="flair",
version="0.15.1",
description="A very simple framework for state-of-the-art NLP",
long_description=Path("README.md").read_text(encoding... |
4ceaa5d41f1a8738a10ab307268b8178f81780c0a35f89270941a08ec0500342 | Python | 770 | 18 | from __future__ import print_function
import numpy as np
from featureExtractor import FeatureExtractor
from featureDownSampler import FeatureDownSampler
class FeatureExtractorRealFourierDownSampled(FeatureExtractor):
def __init__(self, params):
self.params = params
self.extractorType = 'realFourie... |
7bf286d8d6fbb69ec9df285005b2700773d6e313631ff60211376acf3878149b | Python | 770 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Sinh(Base):
@staticmethod
def export() -> None:
node =... |
7c6aed1f2186fb0347f4fd36ff39f4bb2b582095d1d1e6439e75e09cd903acf6 | Python | 773 | 27 | """Collections of estimators, datasets, and metrics."""
from sktime.catalogues.classification import (
BakeOffCatalogue,
DummyClassificationCatalogue,
)
from sktime.catalogues.forecasting import (
DummyForecastingCatalogue,
M4CompetitionCatalogueDaily,
M4CompetitionCatalogueHourly,
M4Competitio... |
7d08a48847e8557dc6b8125c382cabf13d19b38a025640abb19866281e98c902 | Python | 773 | 29 | """ContinuousIntervalTree test code."""
import numpy as np
import pytest
from sktime.classification.sklearn import ContinuousIntervalTree
from sktime.tests.test_switch import run_test_for_class
@pytest.mark.skipif(
not run_test_for_class(ContinuousIntervalTree),
reason="run test only if softdeps are present... |
a532a292f66f696b2c155db62e0f662bc15459a311f6beb7003630be1b39538b | Python | 773 | 36 | import numpy as np
a = np.load('056_500probs_test.npy')
b = a[:, :, 1]
c = a[:, :, 2]
d = a[:, :, 3]
probs = b+c+d
probs = np.reshape(probs, (56,5852))
edges_train= probs/56
results=np.zeros((5852))
for i in range(56):
results=results+edges_train[i,:]
index=results<(0.5)
results[index]= 0
genes=77
edges_resul... |
9d85bcbf2fd85bc9296d7f6b91572848a890cc6d4ccdc89dc7af447340fcd9ad | Python | 775 | 25 | # ----------------------------------------------------------------------------
# Copyright (c) 2017-2026, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... |
7f85ea9f0f1f17adf1c40ad02e310b051181123901eea160d666159618e17b82 | Python | 776 | 33 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed May 11 09:24:34 2022
@author: nagehan
"""
import matplotlib.pyplot as plt
import numpy as np
import scipy
import scipy.stats
gi = np.array([1.03, 1.69, 1.85, 1.83, 1.77, 2.11, 2.33, 2.21, 2.53])
gi_zilles = np.array([1.17, 1.60, 1.75, 1.97, 1.87, 2.31... |
10aa5378ccce6567674b6d1557ff675fc9f7b55b033005dfe26a430655716408 | Python | 777 | 17 | """M4 forecasting competition catalogue."""
from sktime.catalogues.forecasting.M4._daily import M4CompetitionCatalogueDaily
from sktime.catalogues.forecasting.M4._hourly import M4CompetitionCatalogueHourly
from sktime.catalogues.forecasting.M4._monthly import M4CompetitionCatalogueMonthly
from sktime.catalogues.foreca... |
2446b70df995664bc47e89544b930feb1f89480136357168d01c597046fb55e9 | Python | 777 | 17 | # Copyright 2019 DeepMind Technologies Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
bc945b2c84e1d7eb95bd30f95e149c625c411d35ec66a94a8456dc35cd862199 | Python | 777 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Softsign(Base):
@staticmethod
def export() -> None:
no... |
4c93f898be7ddb9dd284261aaf88679aabd796dcbfaabf4d5e3359730894bbb1 | Python | 778 | 21 | from __future__ import print_function
import sys
import json
from os import listdir
from parameterSetup import ParameterSetup
from classifierTrainer import trainClassifier
args = sys.argv
optionType = args[1] if len(args) > 2 else ''
optionVals = args[2:] if len(args) > 2 else [0]
print('optionType =', optionType, ', ... |
8826a1215824438a067116d9e28eb4b62b72937914790cdad24e9b1d468eacbd | Python | 778 | 21 | # Copyright 2019-2026 The AmpliGraph Authors. All Rights Reserved.
#
# This file is Licensed under the Apache License, Version 2.0.
# A copy of the Licence is available in LICENCE, or at:
#
# http://www.apache.org/licenses/LICENSE-2.0
#
from ampligraph.latent_features.layers.corruption_generation import Corruption... |
b6a6d13c9c03bc8b0fcab6619f22df107f2b1ea8b9f13c9183b555ca643e4ef7 | Python | 780 | 31 | #!/usr/bin/env python
# cloud_workstation 1.0.0
# Generated by dx-app-wizard.
#
# Basic execution pattern: Your app will run on a single machine from
# beginning to end.
#
# See https://wiki.dnanexus.com/Developer-Portal for documentation and
# tutorials on how to modify this file.
#
# DNAnexus Python Bindings (dxpy) d... |
bc6280555a16d123e9f957be2b056987447dabbc2a74cd6359b78a61fa9bc046 | Python | 780 | 28 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Asinh(Base):
@staticmethod
def export() -> None:
node ... |
d5c6b0195044650b5a38a01e9e57f514fbe59b515ef48d2b64c848aac49cfcad | Python | 780 | 40 |
"""
Collect process memory usage metrics here
Pass a json, collection of pids and gpuID
"""
import logging
import psutil
def get_cpu_usage(pid):
"""
use psutil for cpu memory
:param pid: str
:return: int
"""
try:
process = psutil.Process(int(pid))
except psutil.Error:
... |
12008536be3c94a4534ed63c38e38f61641a9c6a8b8066b54a2445197a9908c2 | Python | 781 | 28 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.reference.ops.aionnxml.op_binarizer import compute_binarizer
class ... |
5c101e626e16dc795b5ac2adb7c80ec2a13f1629c8118ed1740e91838636ef90 | Python | 781 | 17 | # Copyright 2018-2025
# Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICE... |
f1fdbe2ed22d7447f524553c811d5a41d332a27ee55347c2d67d686c3ed478e6 | Python | 781 | 32 | import pytest
import os
from pathlib import Path
import atom3d.util.file as fi
pdb_path = 'tests/test_data/pdb'
test_file_list = [pdb_path+'/103l.pdb',
pdb_path+'/117e.pdb',
pdb_path+'/11as.pdb',
pdb_path+'/2olx.pdb']
def test_find_files():
file_lis... |
2fe651c65c3020323537ba36303eb40a5dfe4f271bf4e1646760f4990b185560 | Python | 783 | 26 | import pickle as pkl
import numpy as np
import os
def main():
FOLDER = "data/tl_train/U6T7/xu2015TrainHl60/set0/10_fold/0_fold/"
FOLDER_2 = "data/tl_train/U6T7/xu2015TrainHl60/set1/10_fold/0_fold/"
FILE_NAME = "X_biofeat_train.pkl"
with open(os.path.join(FOLDER, FILE_NAME), "rb") as fp:
X_bio... |
5c7a24b1de8783cd574cd02ef2053a6230b7e56bd3b6b89c455b5fae81050178 | Python | 784 | 25 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
import logging
from logging.handlers import RotatingFileHandler
logger = logging.getLogger()
def init_logger(log_file=None, log_file_level=logging.NOTSET):
log_format = logging.Formatter("[%(asctime)s %(levelname)s] %(message)s")
logger = logging... |
c3901f3012bbba297e3f9fa7e08f560c4169717093d6de03ea48f5c2a5bed1f3 | Python | 784 | 28 | from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import argparse
import logging
import numpy as np
import torch
import torch.nn as nn
t_float = torch.float32
np_float = np.float32
str_float = "float32"
opts = argparse.ArgumentParser(description='gpu option... |
1ac7e7dcadaed8598f71e77f38bdf9d36903ec558c24f91fad2d0702ddc55feb | Python | 786 | 27 | from collections.abc import Iterable
from typing import Union
from flair.data import Corpus, MultiCorpus
from flair.models import MultitaskModel
from flair.nn import Classifier, Model
def make_multitask_model_and_corpus(
mapping: Iterable[Union[tuple[Classifier, Corpus], tuple[Classifier, Corpus, float]]]
) -> t... |
1c322edbb7c2d35148c30b855e1eb6c4e1d0102d0d9ee3222f3b120c6dae4282 | Python | 787 | 25 | import os
from translate_common import translation, detokenize_translation
from translate_from_cuneiform import translate_cuneiform_base
def translate_cuneiform(sentence):
tmp_file = "cuneiform.tmp"
with open(tmp_file, "w") as f:
f.write(sentence)
raw_result = translate_cuneiform_base(tmp_file, T... |
cae4844e0ffbf270b14b9732d0c43615293c963c56947882173b52ad75cbca5f | Python | 789 | 21 | import pandas as pd
import numpy as np
import os
file_names = [i for i in os.listdir('results/') if i.endswith('_cellclass_2.txt')]
timepoints = [i.split('_')[1] for i in file_names]
fdr_vals = [i.split('_')[3] for i in file_names]
sources = ['Engineered' if 'Engineered' in i else 'Donor' for i in file_names]
df_comb... |
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