sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
dded7452adf5c4739bf782ab809d4db9904369f0101b904f83a0fcfd7c91ecd4 | Python | 559 | 15 | # 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.di... |
e33087d4e2ddbdce429d08bc193b803e969f1503ede1b9b0fc5ad878bf836858 | Python | 559 | 19 | import numpy as np
import torch
import atom3d.util.results as res
import atom3d.util.metrics as met
# Define the training run
name = 'logs/lep_test/lep'
print(name)
# Load training results
rloader = res.ResultsGNN(name, reps=[0,1,2])
results = rloader.get_all_predictions()
# Calculate and print results
summary = me... |
9a7945168d4644c6369c499298b7c57e01b1ed573d8cace31504a77304d16bcf | Python | 560 | 15 | # 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... |
e776d37dd577ac017ca48b39f941f288a64509f1b604c1b6504ccc9d551f8a91 | Python | 560 | 12 | from pathlib import Path
from yarl import URL
# local path where the package stores its data
CACHE_PATH = Path.home() / ".genomic_benchmarks"
# local path where the references are stored
REF_CACHE_PATH = CACHE_PATH / "fasta"
# where the interval datasets are stored (if read from local)
DATASET_DIR_PATH = (Path(__file... |
439982db47d7d9d95b6decbac9925e62d5ae7d6bdc9944f47f908bdb82bd7e50 | Python | 562 | 29 | """
Stacked histogram on a log scale
================================
_thumb: .5, .45
"""
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
sns.set_theme(style="ticks")
diamonds = sns.load_dataset("diamonds")
f, ax = plt.subplots(figsize=(7, 5))
sns.despine(f)
sns.histplot(
diamon... |
41b3c8e81835f7c3a315912d6c09f3447612a47e726ce7765bd08f55cc2a1550 | Python | 563 | 15 | from setuptools import setup
setup(name='pymtrf',
version='0.0.1a0',
description='Translation of the matlab mtrf toolbox, see http://www.mee.tcd.ie/lalorlab/resources.html',
url='http://github.com/storborg/funniest',
author='Simon R. Steinkamp',
author_email='simon.steinkamp@googlemail.co... |
119c8368806091b30003e5d89282d725a460d151ce92d6ff211bc48243f6beac | Python | 564 | 24 | # 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 OpRunUnaryNum
class Hardmax(OpRunUnaryNum):
def _run(self, x, axis=None):
axis = axis or self.axis
if x.size == 0:
... |
63d43824628ed5741a47ba1fcde857c8781733e2ac26a28209c45fcf38edbc29 | Python | 565 | 23 | """Training data streams used by Tabby-Pretrain."""
from .real_data import (
BlastRealIterableDataset,
concat_mixed_batches,
discover_blast_shards,
)
from .synthetic_data import (
ArrowCauKerSCMIterableDataset,
ArrowSyntheticIterableDataset,
OnlineCauKerIterableDataset,
discover_arrow_files... |
a4e83134ddb0b5edaaf74ed3a11578185b8af23e3bae8b30674942af4d9784b3 | Python | 565 | 27 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 3 14:59:25 2022
@author: sam
"""
import argparse
from tqdm import tqdm
import cv2
if __name__ == '__main__':
p = argparse.ArgumentParser('write frame')
p.add_argument('--frames', nargs="+", type=int)
p.add_argument('--video')
a... |
0166b9e587e00614f175d7290f0284167c17d667876c1b9949e6fc5cb3892396 | Python | 567 | 19 | from pathlib import Path
# REPO_ROOT = Path('C:\\Users\\Richard\\Desktop\\ABCD_Study\\Publication\\abcd_paper')
REPO_ROOT = Path(__file__).resolve().parent.parent
RAW_DATA_DIR = REPO_ROOT / 'data' / 'raw'
PROCESSED_DATA_DIR = REPO_ROOT / 'data' / 'processed'
RESULTS_DIR = REPO_ROOT / 'results'
# Color blind friendly ... |
3e0c0c4ed50d12dc1defb53514e80b9ec751c77723608882bff7d6a9ad2d8884 | Python | 567 | 15 | import sys
from parameterSetup import ParameterSetup
if __name__ == '__main__':
args = sys.argv
classifierIDs = args[1:]
res = []
for classifierID in classifierIDs:
paramFileName = 'params.' + classifierID + '.json'
params = ParameterSetup(paramFileName=paramFileName)
stageLabel... |
669bdb460fc7b8f396f27ed076c0417b489cfb45be0f21c126c455db836c45e5 | Python | 567 | 24 | # 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 Abs(Base):
@staticmethod
def export() -> None:
node = ... |
8fd80b8b9077dc67eb99ba0da35688c6d8b88bb0a453dbd3325fb9eb9124afb8 | Python | 568 | 11 | import os
import sys
# Append OpenSpiel (included in this package's git repository) to the system path so it can be imported.
_OPEN_SPIEL_MODULES_PATH = os.path.abspath(os.path.join(__file__, "../../../dependencies/open_spiel"))
_OPEN_SPIEL_PYBIND_PATH = os.path.join(_OPEN_SPIEL_MODULES_PATH, "build/python")
sys.path.... |
fd037bf9112b505a1ef14ab8d2ff595cce9c034b401b09db94aff9303f22479b | Python | 568 | 20 | # 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 IsInf(OpRun):
def _run(self, data, detect_negative=None, detect_positive=None):
if detect_negative:
if detect_p... |
00ffd138755587ebc9fce2ef8e980b64410a88e59d5f5789c2c688880e3b131e | Python | 569 | 20 | # from Cython.Build import cythonize
# from distutils.sysconfig import get_python_inc
# from setuptools import setup, Extension
from setuptools import setup
setup(
name='dataprovider3',
version='0.0.1',
description='DataProvider3.',
url='https://github.com/torms3/DataProvider3',
author='Kisuk Lee',... |
36beaac3f2ff7f99395388486f5eab6b649426499e20b94e84edb1a222e32b11 | Python | 569 | 23 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from typing import TYPE_CHECKING
from onnx.reference.ops._op import OpRunUnaryNum
if TYPE_CHECKING:
import numpy as np
def _leaky_relu(x: np.ndarray, alpha: float) -> np.ndarray:
sign = (x > ... |
827779d4cdf4a3e4981d89b2abfbb646ad415e2fd7d8197bc9135ec7918ac7ae | Python | 569 | 24 | # 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 Sign(Base):
@staticmethod
def export() -> None:
node =... |
f0682cbd40ec0e0bcf69cef9d7774980b4a48b5fab05255389643a7661acf37f | Python | 569 | 20 | # -*- coding: utf-8 -*-
# from __future__ import print_function
class Error_dataset_name(Exception):
def __init__(self):
super().__init__('Please check dataset name. It should be NIH_Covid')
class Error_nor(Exception):
def __init__(self):
super().__init__('Please check normalize state')
clas... |
a4fb8ae6206a47d574b3825ec2d8161b8b7b37e3e9346379bcca24c34bf7f2a0 | Python | 570 | 22 | import h5py
import numpy as np
import os
def imread(fname):
_, ext = os.path.splitext(fname)
if ext == '.h5' or ext == '.hdf5':
with h5py.File(fname, 'r') as f:
data = np.asarray(f['/main'])
else:
raise RuntimeError("only hdf5 format is supported")
return data
def imsave(... |
d1bfd8d06669c967b04788f16459aaf3b2c141dd849a51564fdf37eb88869874 | Python | 570 | 28 |
# pylint: disable=missing-docstring
import json
class Publisher(object):
"""
Publisher object is a part of Manifest.json
"""
def __init__(self, author, email):
self.author = author
self.email = email
self.pub_dict = self.__to_dict__()
def __to_dict__(self):
pub_... |
b3e93921d542ca824a34050ed87c745b10111d55284cc9d5724617de747a2777 | Python | 571 | 18 | import importlib
def create_instance(config_map):
'''Expects a string that can be imported as with a module.class name'''
module_name, class_name = config_map['import'].rsplit(".", 1)
try:
print('Importing {}.{}'.format(module_name, class_name))
somemodule = importlib.import_module(module... |
9bbf0f8e9584d846c488625d7e145f916a85b0aa216a10dd88739482ad941d5d | Python | 572 | 20 | import sciunit.models.backends as su_backends
import inspect
try:
from .storage import storage
except ImportError:
storage_backend = None
print('Could not load storage backend')
try:
from .nest import NestBackend
except ImportError:
nest_backend = None
print('Could not load Nest backend')
ava... |
1f31d8c65c43d03dc84c9f6af7e933dc489d33497ddfb0bd7e16f5a9afbaae43 | Python | 574 | 28 |
import sys
fi=open(sys.argv[1])
fo=open(sys.argv[2],'w')
fa=open('/home/disk/database/data/ICS_scHIC/f03_atlas/eQTL/LIST.txt')
i=1
old=set()
for line in fa:
seq=line.rstrip().split('\t')
input_path='/home/disk/database/data/ICS_scHIC/f03_atlas/eQTL/GTEx_Analysis_v8_eQTL/'+seq[0]+'.format/tag.bed'
if inpu... |
5f94efe5f474faa99f7b1b13e43ea7dcabc256edb4324791361cdc7077143401 | Python | 574 | 16 | import numpy as np
from sktime.dists_kernels._numba_distances import twe_alignment_path
def test_twe_alignment_path_default_bounding_matrix():
"""Test twe_alignment_path with default bounding matrix to avoid IndexError.
This test ensures that the twe_alignment_path function works correctly
when no boundi... |
90c58de764815fe0545fe6c6ebf6d5d1275248565b130576ce80142d4e056cc9 | Python | 574 | 17 | from . import lddmm # Subpackage.
from .lddmm import resample # Function.
from .transform import Transform # Class.
from .visualization import heatslices # Function.
from .file_io import save, load # Functions.
from . import preprocessing # Subpackage.
from .preprocessing import preprocess # Function.
from . impor... |
3105887f2793fec8b8bb0bca5ffe14839edaa45d54649303e01e9c7f3ff9d270 | Python | 576 | 20 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from packaging.version import parse as version
def numpy_older_than(ver: str) -> bool:
"""Returns True if the numpy version is older than the given version."""
import numpy # noqa: PLC0415
... |
97c3f01d677337fe37af0b5e04b88279f6e9eb32589c9c31037d272fa62b6186 | Python | 576 | 17 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.reference.ops._op_common_random import _CommonRandom
class Bernoulli(_CommonRandom):
def _run(self, x, dtype=None, seed=None):
if ... |
75d3ec72d40fe595de0b8158d9a9a81769feca8ab215bc21424ef6fc7dd5c853 | Python | 577 | 21 | # 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 Where(OpRun):
def _run(self, condition, x, y):
if (
x.dtype not in (y.dtype, object)
and x.dtype.ty... |
59dbc6616cff22cd688595e2367c61e41270ff2af94921fd02b243d505f936bd | Python | 578 | 17 | # 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.
import os, numpy
def configuration(parent_package='',top_path=None):... |
6df817fa0e62e1be98153588c7b5ad6c9a7035c764cb60b23210a9cffcc99986 | Python | 581 | 21 | from __future__ import print_function
from pathlib import Path
import neuronvis.swc_to_hoc as swc
SWC = swc.SWC
soma = SWC('tests/test_data/cellbody.swc', types={1:'soma', 2:'axon', 3:'dendrite'})
soma.set_type(1)
axon = SWC('tests/test_data/axonnonscaled.swc')
axon.set_type(2)
dend = SWC('tests/test_data/dendnonscale... |
9fe602e4ba50c3e2132ce2d18ceb5b02b11b4988724e27f16cb67f6406a09c86 | Python | 581 | 32 | """
test_window.py
Just tests that the major programs in the package are all compatible with
a psychopy stimulus window.
"""
from camstim import Window, SweepStim, Behavior, VisualObject
import pytest
@pytest.fixture(scope="module")
def window():
w = Window()
yield w
# teardown
w.close()
def t... |
27041afaf29ee2cd83d3ee8351c12737ee36988040176c45a4b96ac341aaf066 | Python | 582 | 26 | from setuptools import setup, find_packages
setup(
name='nlgc',
version='0.3.1',
description='Latent Causal inference for M/EEG data',
author='Behrad Soleimani',
packages=find_packages(),
python_requires='>=3.8',
install_requires=[
'numpy',
'scipy',
'matplotlib',
... |
e010cad7a0387085722a5e1f39fe659b6991203436dc7bfcba095f7672c53624 | Python | 582 | 24 | # 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 Relu(Base):
@staticmethod
def export() -> None:
node =... |
bb4c7d924316c7bff5527f14bca998839243b43afc44d81c9eb3556f1b5c1e86 | Python | 584 | 28 | import cooler,sys
COOL_FILE=sys.argv[1]
BED_FILE=sys.argv[2]
OUT_FILE=sys.argv[3]
c = cooler.Cooler(COOL_FILE)
fi=open(BED_FILE)
fo=open(OUT_FILE,'w')
for line in fi:
if line[:3]=='chr':
seq=line.rstrip().split('\t')
this_c1=seq[0]
this_s1=seq[1]
this_e1=seq[2]
this_c2=seq... |
bbe907f9c00ab63eed223ad07869aadf01e6d7695497f23ad780f1f511d67da9 | Python | 584 | 19 | from rxnebm.proposer.retrosim_config import retrosim_config
from rxnebm.proposer.retrosim_proposer import RetrosimProposer
def test():
proposer = RetrosimProposer(retrosim_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... |
37a88e657d345de29b8baf15072e730982b0e512b8455fa78b2788a12bc51ba8 | Python | 587 | 29 | """
plot_slice
==========
.. autofunction:: optuna.visualization.plot_slice
:no-index:
The following code snippet shows how to plot the parameter relationship as slice plot.
.. _visualization-plot-slice-plotly-content:
"""
import optuna
def objective(trial):
x = trial.suggest_float("x", -100, 100)
y... |
af5513694bd407adcae1511d3ae38dadec3fc7acc62dd05e4dedfeffe9259484 | Python | 588 | 21 | #!/usr/bin/env python3
import rospy
from gazebo_msgs.msg import ModelStates
from geometry_msgs.msg import Pose
def callback(data):
global pub
index = data.name.index("jackal")
pose = data.pose[index]
pub.publish(pose)
if __name__ == '__main__':
rospy.loginfo('Initializing Node')
rospy.init_n... |
c8c8485f75ff612599842df2ee747343f4d6edca2bb6c52dd0e3a9dfccf447ec | Python | 589 | 14 | """Configures the command-line arguments the programs can be called with"""
import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input',
help='input file to read the calculation details.',
required=True)
parser.add_... |
0cb10821872f34c482578045f19a9ee2b3a22de886a44044b50b501a94eec085 | Python | 590 | 25 |
from types import ModuleType
from importlib import import_module
import torch_geometric.loader
import torch_geometric.transforms
import torch_geometric.utils
import torch_geometric.profile
import molgnn.data
from molgnn.pygdata import RedDB
from molgnn.molgnn import MolGCNConv, MolGATConv
from molgnn.molfeatures impor... |
9afa956f7047201c08cc7425b5e8c605750f600970739d6d2425b3c244cf7580 | Python | 590 | 19 | # 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 OpRunUnaryNum
class LpNormalization(OpRunUnaryNum):
def _run(self, x, axis=None, p=None):
axis = axis or self.axis
p = p or se... |
d064de5a0509b0d4e1c5a5d6cea6e30f3980351a992cd20562cefa52c39c2358 | Python | 592 | 20 | # 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 Transpose(OpRun):
def _run(self, data, perm=None):
perm_ = None if (perm is None or len(perm) == 0) else perm
if pe... |
11579fba5cb3f281a4a7ee8b5d5f8cb29a95a0132b36b5733b47eeb8af39009c | Python | 594 | 24 | """
Scatterplot with continuous hues and sizes
==========================================
_thumb: .51, .44
"""
import seaborn as sns
sns.set_theme(style="whitegrid")
# Load the example planets dataset
planets = sns.load_dataset("planets")
cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True)
g = sns.relplot(
data... |
57357e8ad0e4c9cbe342e59176a447e988bf7b98b8f9642934f1604dd2ffe518 | Python | 594 | 13 | # 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... |
789bd8dd6cae07867423a1508304d40cb46e5506c6a124e515450bd74a96f80e | Python | 594 | 13 | # 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... |
99ee438da3d1221b4b90b2940df14d9891c84f22bee906343ef0823a4ff59c65 | Python | 594 | 27 | #!/usr/bin/env python
# cloud_workstation 1.0.0
import os
import re
import subprocess
import dxpy
def _run_cmd(cmd):
print (cmd)
subprocess.check_call(cmd, shell=True)
@dxpy.entry_point('main')
def main(**job_inputs):
if 'fids' in job_inputs:
for fid in job_inputs['fids']:
cmd = 'dx d... |
de821d9397fd7047fce8a29fd3352849c939dadd4574e19782a0af1d93509dba | Python | 594 | 13 | # Copyright 2026 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... |
5c9e85d1d6a0b3a55d7bad83771e099629706524537c21ef847e0d45ef8aa29f | Python | 595 | 14 | # 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... |
2d0ca0869ce0291d5d1b35a165d8581721f15221a6e9718b5cba50d3cc3b6aaa | Python | 596 | 15 | # 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... |
69c6b0ed621100c0c7fd4d4b104e4498de89c42ad8e29f57193c636f69ab50b0 | Python | 597 | 17 | # Copyright 2019-2021 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 .EmbeddingModel import EmbeddingModel
from .TransE import TransE
from .Dis... |
b054ccf987455a93fa58371ef3b0493cee51461172453eea9811f7f0719f1b3c | Python | 598 | 15 | # Copyright (c) OpenMMLab. All rights reserved.
from .cls_head import ClsHead
from .conformer_head import ConformerHead
from .deit_head import DeiTClsHead
from .linear_head import LinearClsHead
from .multi_label_head import MultiLabelClsHead
from .multi_label_linear_head import MultiLabelLinearClsHead
from .stacked_hea... |
75de36e209a913d9602fcda5de7d1cbab05f45a9af6e7f669aeffe82c79d4a48 | Python | 599 | 29 | """
plot_contour
============
.. autofunction:: optuna.visualization.plot_contour
:no-index:
The following code snippet shows how to plot the parameter relationship as contour plot.
.. _visualization-plot-contour-plotly-content:
"""
import optuna
def objective(trial):
x = trial.suggest_float("x", -100, ... |
dfab28eede2258570067dd27aa8329b38fbecce2323eaaa9eec70b5f80fb078c | Python | 600 | 15 | __all__ = ['extract', 'roiwise_stats_indiv', 'extract_multiedge', 'load_run',
'get_edge_values', 'export_to_nx', 'parcellate',
'freesurfer', 'read_freesurfer_atlas', '__version__']
from importlib.metadata import PackageNotFoundError, version
from graynet import parcellate, freesurfer
from grayne... |
45d3e7b0eadaa6860d16d054f36d18b93db4a3714778b7fe799af6fc15124046 | Python | 601 | 18 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.helper import tensor_dtype_to_np_dtype
from onnx.reference.op_run import OpRun
class Optional(OpRun):
def _run(self, x=None, type=None): # type: ignore[override] # noqa: A002
if... |
6b39b0a593deb276e46ca4989bddcf07d48eed8d2e3caacb086830cb905daac3 | Python | 601 | 15 | """Interval based time series classifiers."""
__all__ = [
"TimeSeriesForestClassifier",
"RandomIntervalSpectralEnsemble",
"SupervisedTimeSeriesForest",
"CanonicalIntervalForest",
"DrCIF",
]
from sktime.classification.interval_based._cif import CanonicalIntervalForest
from sktime.classification.int... |
3304d8f143b86d3a6809eed5af67714f35d1a176ecea931f99121b28a4cab069 | Python | 602 | 23 | # First need to import the CG dictionary
from lgn.cg_lib.cg_dict import CGDict
# First need to import the CG dictionary
from lgn.cg_lib.cg_module import CGModule
# Import tau calculation for cg_ops
from lgn.cg_lib.cg_ops_tau import cg_product_tau
# Now for your regularly scheduled imports
from lgn.cg_lib.cg_ops impo... |
81c1801272be82a6994342f4d06e28230c82d5d553353e5bc96b975e6cb3f98b | Python | 602 | 27 | __test__ = False
import imp
import numpy as np
import pyfilterbank
from pysoundfile import SoundFile
from pysoundcard import Stream
filename = r'bonsai.wav'
ofb = filterbank.OctaveFilterbank(nth_oct=3.0, start_band=-18, end_band=12, lphp_bounds=True, filterfun='cffi')
st = Stream(input_device=False)
sf = SoundFile... |
6c2aac88650866d50ed25b3e0fdfe0d78d3f4580bdcfadbeb199f2adc9edf042 | Python | 603 | 27 | from data_util import load_pdb_npz_to_static_data_and_coordinates
import os
import numpy as np
types= ['train', 'val', 'test']
for t in types:
files = os.listdir("../dataset/"+t+"/pdb_npz")
count = [0,0]
for i in files:
if not i.endswith('npz'):
continue
count[0]+=1
... |
d8168ff36b6ae0d4b9ffd1af6ded0e4fb82251e2fa3e6fa342323e9fe0cf32dc | Python | 604 | 16 | from enum import Enum, auto
class DiagnosticKeys(Enum):
"""
This enumeration serves as the centralised source for all keys used in
Redis publish/subscribe operations throughout the diagnostics package:
- RedisPublisher uses these keys to publish diagnostic data
- DiagnosticsModel uses thes... |
4eae521afd2dea13f5a7846ec998f148a346ebda506b3ed821afe83316e4f3e0 | Python | 605 | 16 | # Copyright 2019-2021 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
#
"""This module contains utility functions for neural knowledge graph embedding ... |
cbe0a52d4b52e7f69857c7afa5d076f6d93114b2a989354886353e21d0433a9d | Python | 605 | 28 | """
plot_slice
============
.. autofunction:: optuna.visualization.matplotlib.plot_slice
:no-index:
The following code snippet shows how to plot the parameter relationship as slice plot.
.. _visualization-plot-slice-matplotlib-content:
"""
import optuna
def objective(trial):
x = trial.suggest_float("x",... |
d7a011c3ebaa37b68807033e4383583b4430fb2a9c4d07462f7e52848b61ef3a | Python | 606 | 24 | import config
import os
output_FF_layers = [100, 1] #[4000, 1000, 100, 1] #[200, 200, 100, 100, 1]
cur_work_dir = os.getcwd()
d_model = 64
attention_heads = 32
attention_dropout = 0.0
n_layers = 1
k_dim = 10
add_seq_cnn = True
add_parallel_cnn = False
cnn_dropout = 0.2
attention_norm = False
attention_layer_norm = Fal... |
cbc4d053911fac4a9587fdabcde5ef8897e754032e3152a5e2de0120703b47f6 | Python | 607 | 17 | import os
import unittest
from elftools.common.exceptions import ELFError
from elftools.elf.elffile import ELFFile
class TestSectionSelfLink(unittest.TestCase):
def test_self_link(self):
test_file = os.path.join('test', 'testfiles_for_unittests', 'section_link_to_self.elf')
with open(test_file, '... |
85f22a805a5728558dfd4a2854601e47e883fc243edf71373a80f0954d3ecd8d | Python | 608 | 20 | # 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 Upsample(OpRun):
def _run(self, x, scale, mode=None):
if mode == "nearest" and scale.astype(np.int64).tolist() == scale.tol... |
d6883d2fa6bbf0425301be4e15145261eda4decb4acfe186e5dfbac4b86de9b5 | Python | 608 | 15 | # Copyright 2026 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... |
6aaf1c8e9073d53430af88e5d1455f10b5abbad1e9ec942caed355122f5ced92 | Python | 609 | 19 | """Tests for dummy detectors."""
import pandas as pd
def test_dummy_changepoints():
"""Test expected output for DummyChangePoints."""
from sktime.detection.dummy._dummy_regular_cp import DummyRegularChangePoints
y = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
d = DummyRegularChangePoints(step_size=3)... |
0b8bc389e5920d43367c2ca2598433f069acf6ce11d1ba5664878141aa499681 | Python | 610 | 26 |
"""
timeit decorator
"""
import time
from functools import wraps
def timeit(func):
"""
Use this decorator on a method to find it's execution time.
:param func:
:return:
"""
@wraps(func)
def time_and_log(*args, **kwargs):
start = time.time()
start_cpu = time.clock()
... |
63318db9f2c7da68e46280ae26a13f6ad332e67f1fa5455a4e0dff2887eda13b | Python | 611 | 13 | # Copyright 2019-2020 The ASReview Authors. 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... |
c6513584002b3528e60ff1d9f2041dd8755b7742019d2eae77b7e3dbcaa79ce7 | Python | 611 | 13 | # Copyright 2019-2021 The ASReview Authors. 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... |
6a140c9714a080b8f7b2b64992116842984ab8e7aad02eaf06bbdb03c32e4280 | Python | 612 | 14 | # 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... |
460b3242f3ec41a91b9c555342f2abc99e2f666b2a1352e585f21d9794224a1d | Python | 613 | 15 | # 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... |
9d4ea3a69478fe3e882c790219afba23d2ebc4741dd11fbf1c63f3498c4f0664 | Python | 613 | 28 | """
Read the swc soma reconstructions and genereate hoc files.
This data is not in the shared database.
"""
from pathlib import Path
import vcnmodel.util.swc_to_hoc as swc_to_hoc
from vcnmodel.util.get_data_paths import get_data_paths
config = get_data_paths()
swcPath = Path(config["disk"], config['baseMorphologyD... |
06a998133c92428cf78c3548b096272ae31b4b965250441afc0130f5f8a5d237 | Python | 615 | 28 | """
plot_contour
============
.. autofunction:: optuna.visualization.matplotlib.plot_contour
:no-index:
The following code snippet shows how to plot the parameter relationship as contour plot.
.. _visualization-plot-contour-matplotlib-content:
"""
import optuna
def objective(trial):
x = trial.suggest_fl... |
39bcf52e2e7ef38f264a6a9c412baf00085926f5c426367af250e4e2b9aa8e2e | Python | 615 | 27 | import os
import time
import yaml
from dotmap import DotMap
def get_config_from_yaml(yaml_file):
"""
Get the config from a yaml file
:param yaml_file:
:return: config(namespace) or config(dictionary)
"""
# parse the configurations from the config yaml file provided
with open(yaml_file, 'r... |
532ab324e0eab3f8926f1ac69699295d530ccc190b4737c13c1c3115dabdbe06 | Python | 617 | 23 | """
Bivariate plot with multiple elements
=====================================
"""
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="dark")
# Simulate data from a bivariate Gaussian
n = 10000
mean = [0, 0]
cov = [(2, .4), (.4, .2)]
rng = np.random.RandomState(0)
x, y = rn... |
565466e4f1085cd4b4fc13ac7525b61845e8134f031d6ecee92ec238313613bf | Python | 618 | 17 | #
# # Different template styles
#
# [Open interactive notebook in Binder](https://mybinder.org/v2/gh/wiheto/netplotbrain/main?filepath=docs/gallery/template_styles.ipynb)
#
# There are currently four different template style options to choose from: suface, filled, cloudy, glass.
# You just need to change the specifi... |
14928f44569adcf5918a1f254a8667be27c3d10edbb714652f7baad7e50489c3 | Python | 619 | 24 | import torch
class Dataset(torch.utils.data.Dataset):
'Characterizes a dataset for PyTorch'
def __init__(self, dataset,datasetf, labels):
'Initialization'
self.labels = labels
self.dataset = dataset
self.datasetf=datasetf
def __len__(self):
'Denotes the total number... |
2448eb590744ab8929e24ce9e3ef64a5dd405b9b89ca1125f391da7ccc315048 | Python | 619 | 23 | """Preprocessing utilities for AxialTAD Hi-C patch input."""
from __future__ import annotations
import numpy as np
def standardize_patch(a: np.ndarray, patch: int) -> np.ndarray:
"""Per-row standardization for Hi-C patch input.
Args:
a: Input array of shape (..., patch, patch, 1) or similar.
... |
4d234a7ba4baf3214879d361478dee9e3f4bd905c4d8b74e696e919b6ddd4ccb | Python | 619 | 18 | import os
import unittest
from elftools.common.exceptions import ELFError
from elftools.elf.elffile import ELFFile
class TestSectionHeaderEntrySizeCheck(unittest.TestCase):
def test_size_check(self):
test_file = os.path.join('test', 'testfiles_for_unittests', 'section_header_bogus_size.elf')
with... |
cf1d25e4db159d48f90e2f227e5e42e5051d59374bb61010f7983f8abc2610a0 | Python | 619 | 23 | import os
from pathlib import Path
from tempfile import gettempdir
import shutil
import pytest
from pyontutils.core import log as _log
log = _log.getChild('test')
temp_path = Path(gettempdir(), f'.pyontutils-testing-base-{os.getpid()}')
temp_path_ap = temp_path.as_posix()
def ensure_temp_path():
if temp_path.ex... |
358a8d7fc9905f729fe6ad93b7388f079c435a4f112b99b40432393c3a4aa4c9 | Python | 620 | 26 | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import math
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class Erf(Base):
@staticmethod
def export() -> None:
... |
b69631734f9a8fe99b1cd94fead6f91947e5f8378e739a55b50ca68b20a131aa | Python | 621 | 21 | import os
import unittest
from elftools.elf.elffile import ELFFile
class TestDWARFv5(unittest.TestCase):
def test_dwarfv5_parses(self):
dwarfv5_basic = os.path.join('test', 'testfiles_for_unittests', 'dwarfv5_basic.elf')
with open(dwarfv5_basic, 'rb') as f:
elf = ELFFile(f)
... |
057ef9f0fdafe2846e9dca218986bf78fc14aad516bf664adffd2e7080cba876 | Python | 622 | 13 | #Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#This program is free software;
#you can redistribute it and/or modify
#it under the terms of the MIT License.
#This program is distributed in the hope that it will be useful,
#but WITHOUT ANY WARRANTY; without even the implied warranty of
#MERCHA... |
974d2371385dcdd0e206e40c871bd655dec58d9c274bf6b78a7ccfd6fa356dbc | Python | 623 | 29 | """
plot_optimization_history
=========================
.. autofunction:: optuna.visualization.plot_optimization_history
:no-index:
The following code snippet shows how to plot optimization history.
.. _visualization-plot-optimization-history-plotly-content:
"""
import optuna
def objective(trial):
x = t... |
32e03a9fdb33423a1374ab313bd0f769fccca08e0bf588712ad929e1b7f715df | Python | 624 | 16 | # Test data constants
EXPECTED_AREA_METRICS = {
'area_sum': 3721912.0,
'max_label': 3481,
'column_sums': [231498829.0, 150952363.0, 75868924.0, 151411232.0, 82555688.0]
}
# VarBayes iteration deltas
EXPECTED_ITER_DELTAS = [
1.0, 0.8998741, 0.6707933, 0.4703735, 0.62746835, 0.58092755,
0.604991, 0.2... |
905e2d6abc05ee0f6cbf62ec5fc7fd9af9930a77472c2fecab71379339c7247c | Python | 625 | 19 | """Shapelet based time series classifiers."""
__all__ = [
"MrSEQL",
"MrSQM",
"ShapeletLearningClassifierPyts",
"ShapeletLearningClassifierTslearn",
"ShapeletTransformClassifier",
]
from sktime.classification.shapelet_based._learning_pyts import (
ShapeletLearningClassifierPyts,
)
from sktime.c... |
df3d8a0fa176efa78602734bb347e20c42dadc137f69a92be3adaca22f5ff454 | Python | 625 | 29 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from dataclasses import dataclass
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import Sequence
import numpy as np
import onnx
@dataclass
class TestCase:
na... |
fe2ec987e08147799ee2b97a18b826e07d8edd1f765670fe0b2e9ca44bb27b6f | Python | 625 | 21 | # 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 Gather(OpRun):
def _run(self, x, indices, axis=None):
if not x.flags["C_CONTIGUOUS"]:
x = np.ascontiguousarray(... |
9a1694a9b768e8d1454fdd56e63effdb6d0503caead761b2796650beda40bfbb | Python | 628 | 19 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.reference.ops._op_common_random import _CommonRandom
class RandomNormalLike(_CommonRandom):
def _run(self, x, dtype=None, mean=None, scale... |
e7f7d31838696fcdffb341f1b8d3f1a33a1356d53296761409981b5b6ca2218c | Python | 628 | 30 | """
plot_pareto_front
=================
.. autofunction:: optuna.visualization.matplotlib.plot_pareto_front
:no-index:
The following code snippet shows how to plot the Pareto front of a study.
.. _visualization-plot-pareto-front-matplotlib-content:
"""
import optuna
def objective(trial):
x = trial.sugge... |
e23440ba9ab7ecfa977667b56247b7a31479fe6316a9bcb8eaeb958acb1aeb2f | Python | 629 | 19 | """Loads NetworkUnit plot classes"""
from .correlation_matrix import correlation_matrix
from .covar_pdf_ei import covar_pdf_ei
from .covar_pdf import covar_pdf
from .eigenvalues import eigenvalues
from .eigenvector_loads import eigenvector_loads
from .mu_std_table import mu_std_table
from .power_spectral_density impor... |
157e7cd87a2186cf39c2f21c3e06995de7da166d7799a1adf0fc614d1a064a55 | Python | 630 | 19 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.helper import np_dtype_to_tensor_dtype
from onnx.reference.ops._op_common_random import _CommonRandom
class RandomUniformLike(_CommonRandom):
def _run(self, x, dtype=None, high=None, low=... |
2c45f269afff9745be18682a014089770d5971900cd809e8ccd56660875e7480 | Python | 634 | 14 | # 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
#
# ... |
532fedf13dd1290a5f4b58bc7226803e874224fbba6a6b2e62807a3491278212 | Python | 634 | 17 | """Dummy detectors for baselines and testing."""
from sktime.detection.dummy._dummy_pretrained_an import DummyPretrainedAnomalies
from sktime.detection.dummy._dummy_regular_an import DummyRegularAnomalies
from sktime.detection.dummy._dummy_regular_cp import DummyRegularChangePoints
from sktime.detection.dummy._zero_an... |
5ee6ae3df0524a824eceda8c273c6f7a5a2e2ae07ee2c00ee04e7f33cfc0d25b | Python | 634 | 14 | # 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
#
# ... |
3c135193df9e2d47e7c9971a332e8d945f9cef6a0c4b7fe7cabb2e4227017acd | Python | 635 | 15 | # 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
#
# ... |
9f36740abb1303b73d9493dd51eabe4d84318c5d8d4d189531583688ad01b291 | Python | 635 | 31 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import sys
import atheris
with atheris.instrument_imports():
import onnx
def TestOneInput(data):
try:
model = onnx.load_model_from_string(data)
_ = len(model.graph.node)
... |
a657ce7355782eb970f95fd0d2e26eec5b9023212d2b655814da6e15b70510b4 | Python | 635 | 21 | import torch
from .modules.lpips import LPIPS
def lpips(x: torch.Tensor,
y: torch.Tensor,
net_type: str = 'alex',
version: str = '0.1'):
r"""Function that measures
Learned Perceptual Image Patch Similarity (LPIPS).
Arguments:
x, y (torch.Tensor): the input tensors t... |
78b264b98bcaa8f5b0c52864e1bd976cf61613c8416680024e9ba670c22b4396 | Python | 637 | 27 | import sys
import argparse
def _parse_args():
'''Parse the input arguments.'''
ap = argparse.ArgumentParser(description='Rename contigs')
ap.add_argument('-p', '--prefix',
help='Prefix of contigs.',
default='contig',
required=False)
return ... |
9d355bda032206e90e017ecd91f4dd2036a95710d2e0df86c935583e7141c684 | Python | 637 | 23 | """
Line plots on multiple facets
=============================
_thumb: .45, .42
"""
import seaborn as sns
sns.set(style="ticks")
dots = sns.load_dataset("dots")
# Define a palette to ensure that colors will be
# shared across the facets
palette = dict(zip(dots.coherence.unique(),
sns.color_palet... |
199794d9ae4ee5b2d6379c329ce61e746d8f104eebeaa628dda8b9672f626b5d | Python | 638 | 22 | import networkx as nx
import numpy as np
from graynet.utils import warn_nan
from pathlib import Path
# <<< change the out_dir to the folder contianing the GraphML files here:
out_dir = '/Users/Reddy/dev/graynet/example_data/freesurfer/test_outputs'
out_dir = Path(out_dir).resolve()
for gml_path in out_dir.rglob('*.gr... |
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