sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
afb28bf72cec30c531e7100a081c7caffd04dd32df4f71747ce18b17a024946d | Python | 326 | 16 | """
Contains commong functions useful throughout the application
"""
import os
def create_if_not(path):
"""
Creates a folder at the given path if one doesnt exist before
===
:param path: destination to check for existense
:return: None
"""
if not os.path.exists(path):
os.makedirs(... |
2c7b4b918b57eaf36adfaa10c27abcd35d2d70ec0b6954102043bc765a3c8c60 | Python | 328 | 12 | """
Linear regression with marginal distributions
=============================================
_thumb: .65, .65
"""
import seaborn as sns
sns.set(style="darkgrid")
tips = sns.load_dataset("tips")
g = sns.jointplot("total_bill", "tip", data=tips, kind="reg",
xlim=(0, 60), ylim=(0, 12), color="m", he... |
e9cc65b31f29d1c2b790768f449c5921d1b2495663c542456967ce0a6afdf2b5 | Python | 328 | 14 | # for analyzers
# from . import analyze_run
# from . import analyze_PSCVariations
# from . import sac
# from . import sac_cython
# from . import analysis
# from . import analyze_run
# from . import isi_cv
# from . import reverse_correlation
# from . import spikestatistics
# from . import sttc
# from . import vector_str... |
efaad8ab81fb4fabab1b25fd76918eebc98065c6827713b6810d9e6a56c3e20c | Python | 328 | 11 | """Abstract base class for deep learning neural network classifiers."""
__all__ = [
"BaseDeepClassifier",
"BaseDeepClassifierPytorch",
]
from sktime.classification.deep_learning.base._base_tf import BaseDeepClassifier
from sktime.classification.deep_learning.base._base_torch import (
BaseDeepClassifierPyt... |
13c09ee5e258c32153f6c26430d9fcd871c5e2f9a014969645f6448df6535844 | Python | 329 | 14 | """
Facetting histograms by subsets of data
=======================================
_thumb: .33, .57
"""
import seaborn as sns
sns.set_theme(style="darkgrid")
df = sns.load_dataset("penguins")
sns.displot(
df, x="flipper_length_mm", col="species", row="sex",
binwidth=3, height=3, facet_kws=dict(margin_titles=... |
203e4f453944dd2d516ad780d4c085e67563321c8cd00b2a5f8fd4a3933a6cc9 | Python | 329 | 13 | # 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 Selu(OpRun):
def _run(self, x, alpha=None, gamma=None):
return ((np.where(x > 0, x, alpha * np.expm1(x)) * gamma).astype(x.... |
a2cd82403ed99e4793827f54f078687cb96c7b247130e3188f72711cffa5fbd3 | Python | 329 | 21 |
fi=open('ics_out/UNIQ_LOOP.txt')
fo=open('scATAC_RefLoop.bed','w')
fi.readline()
i=1
for line in fi:
#seq=line.rstrip().split('\t')
info=line.split('\t')
p1=info[0]
p2=info[1]
out=p1.split('-')+p2.split('-')
#out.append('PE'+str(i))
fo.write('\t'.join(out)+'\n')
i=i+1
fo.close()
fi.... |
0fe646bf83546f8544b1397a6a3c9f6477c7dc9cba27ef3e8cd89611f37597a4 | Python | 331 | 4 | SAC_ARCH1_MODEL_CONFIG_KEY = "population_server/model_configs/sac_arch1.json"
SAC_ARCH1P_MODEL_CONFIG_KEY = "population_server/model_configs/sac_arch1P.json"
SAC_ARCH1P_FIVES_MODEL_CONFIG_KEY = "population_server/model_configs/sac_arch1p_fives.json"
POKER_ARCH1_MODEL_CONFIG_KEY = "population_server/model_configs/poker_... |
9a9d8316f42844a08bfff64f55745f944d0d147681701b896b1063b3cb16ad0b | Python | 331 | 19 | # Preprocess turtle data:
# raw to h5ad
import numpy as np
import anndata as ad
import pandas as pd
def main():
datadir = "./data/raw/tosches/"
savedir = "./data/anndata/"
# Load the data
anndata = ad.read_h5ad(datadir + "turtle.h5ad")
print(anndata)
return 0
if __name__ == "__main__":... |
1177010c5887cacedec0a2568f67d275c9eded4052dea1595b2295287e7efbf8 | Python | 332 | 13 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.op_run import OpRun
class OptionalGetElement(OpRun):
def _run(self, x):
if x is None:
raise ValueError("The requested optional input has no value.")
... |
7088bee19873b3ef70428581cee2ef75345bcacfc2e1cf91bc83ecfdaa4fff9a | Python | 332 | 10 | # 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 .EmbeddingLookupLayer import EmbeddingLookupLayer
__all__ = ["EmbeddingLo... |
8fffe1b52bc9fdeddc549f4588fb49bcbd46e4ce2659b61e1135db2e2e913b84 | Python | 332 | 16 | from pathlib import Path
import time
import datetime
from dataclasses import dataclass, field
from typing import Union, List
import operator
import model_params
import toml
@dataclass
class SIM:
name:Union[Path, str, None] = None
date:float = 0.0
datestr:str = ''
dendscaling: bool=False
somascaling... |
2701e3a9038d68a7b801689e3d752fb14312575387b4ce9310b0f0d3c3957c41 | Python | 333 | 17 |
times = 10
with open('src-train-aug-err-old.txt') as f:
srcs = f.readlines()
with open('tgt-train-aug-err-old.txt') as f:
tgts = f.readlines()
with open('src-train-aug-err.txt', 'w') as f:
for t in range(times):
f.writelines(srcs)
with open('tgt-train-aug-err.txt', 'w') as f:
for t in range(times):
f.writ... |
1b1becc8dc15dfbfe2efac9bac2d858e7f40acb2e6c43e14a7fbc47eeebf3d5c | Python | 335 | 16 | from setuptools import find_packages, setup
REQUIRED_PACKAGES = [
"graspologic",
]
setup(
name="pkg",
packages=find_packages(),
version="0.1.0",
description="Local package for bilateral matching paper",
author="Neurodata",
license="MIT",
install_requires=REQUIRED_PACKAGES,
dependen... |
7f34e93df9dfcaa4fc6ee696c62ef0f514c59d4dc2a04f40fff3de6135f9be00 | Python | 336 | 19 | import sys
fi=open(sys.argv[1])
fo=open(sys.argv[1]+'.TSS.bed','w')
for line in fi:
seq=line.rstrip().split('\t')
if seq[5]=='+':
loc=int(seq[1])+1
if seq[5]=='-':
loc=int(seq[2])
this_out=[seq[0],str(loc-1),str(loc),seq[3],seq[4],seq[5]]
fo.write('\t'.join(this_out)+'\n')
fi.close... |
80ede76e31e84c74d532d195d0828e6af25e23beb76b7918aa02451e5474b060 | Python | 336 | 13 | from .input_preprocessing import (
inverse_asinh_normalize,
make_cpm_prediction_mask,
mask_aware_normalize_for_inference,
mask_aware_normalize_for_training,
)
__all__ = [
"inverse_asinh_normalize",
"make_cpm_prediction_mask",
"mask_aware_normalize_for_inference",
"mask_aware_normalize_f... |
de23d212d0224d53ef0571de723d77858d984dd52a271d1d226c9afc36ef37ff | Python | 336 | 9 | '''
This module contains trained models reported in the paper "Quo Vadis,
Action Recognition? A New Model and the Kinetics
Dataset" (https://arxiv.org/abs/1705.07750) by Joao Carreira and Andrew
Zisserman.
The code of the private module '_pytorch_i3d' is based on Deepmind's Kinetics-I3D
(https://github.com/deepmind/ki... |
3223a7a76ef8539e6b261bee265bd663bf8683f5c8cfbea8cc1a09b8c0523d48 | Python | 337 | 16 | #!/usr/bin/env python3
"""
Code to systematically find the most stable geometry for molecules on surfaces
"""
from src.dockonsurf.dos_input import read_input
from src.dockonsurf.screening import run_screening
def dockonsurf(file):
inp_vars = read_input(file)
surf_ads_list = run_screening(inp_vars)
ret... |
3337957c3e528b6a1b6578353ef42de20789ab5aa385ba50a3d249ebcb6ce3ff | Python | 337 | 13 | import distutils.core
packages = '''
celltool
celltool.command_line
celltool.command_line.plugins
celltool.contour
celltool.numerics
celltool.plot
celltool.utility
'''.strip().split()
distutils.core.setup(name='celltool', version='2.1', description='celltool package',
packages=packages, scripts=['celltool/command_... |
6b874101297859043b54bc2d0612048deac3c5da97f3f1174970e4b5aedae4e5 | Python | 338 | 16 | import torch
import transformers
import flair
def main():
print("#### Versions:")
print(f"##### Flair\n{flair.__version__}")
print(f"##### Pytorch\n{torch.__version__}")
print(f"##### Transformers\n{transformers.__version__}")
print(f"#### GPU\n{torch.cuda.is_available()}")
if __name__ == "__ma... |
9aff44ba6c903e8086d1b015816ed3f2208ae4909280f1fefb2dc5be76091d57 | Python | 338 | 12 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.ops.aionnxml._op_run_aionnxml import OpRunAiOnnxMl
class Scaler(OpRunAiOnnxMl):
def _run(self, x, offset=None, scale=None):
dx = x - offset
return ((dx * scale).... |
f0386134ea282d937e8873d6f309583887d72320fce1e39ac21b990d57eeef4a | Python | 338 | 13 | # 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 Add(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.add, onnx_... |
f0e1aef3a232e92f51fe85309581bbf220ed6c4db4e641419d3b181d65399a2e | Python | 338 | 8 | #!/usr/bin/env python3 -u
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
"""Implements base classes for detection tasks in sktime."""
__all__ = ["BaseDetector", "BaseIntervalScorer"]
from sktime.detection.base._base import BaseDetector
from sktime.detection.base._base_interval_scorer import B... |
0cfc493fcdef97a254f8f9478eb2c650dd8f3a1a9d2b474c9ad2e147144c6363 | Python | 339 | 6 | # ruff: noqa: F401
# Legacy re-export module; imported names are part of the public API.
from .binary import bin_to_int, decode_bin, encode_bin, int_to_bin, swap_bytes
from .bitstream import BitStreamReader, BitStreamWriter
from .container import Container, FlagsContainer, LazyContainer, ListContainer
from .hex import ... |
774a3e3e8acce23a97024d60b1a57596bbc732b0a8f934a001652e9e334e4b41 | Python | 340 | 16 | # 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 Softsign(OpRunUnaryNum):
def _run(self, X):
tmp = np.abs(X)
tmp += 1
np.divide(X, tmp, out=tmp)
... |
536a869ebc4a47c7ff63fbf7ea10c8d58dfeb37fffd12d86f4c0df10393e7634 | Python | 341 | 12 | # vcnmodel.util
from . import combine_pdfs
from . import hoc_swc_sectionmap
from . import mpl_colormaps
from . import readmodel
from . import params
# from . import render
# from . import show_swc
# from . import swc_to_hoc # this belongs in neuronvis, and so there it is...
# from . import vcn_cell_hoc_rename
# from ... |
548ecfa872459553620ab7c4bce16ada6c796a64a92e352b2e7a08b70a8c5a19 | Python | 341 | 13 | """GRU deep learning classifiers.
This subpackage provides GRU and GRU-FCN classifiers implemented in
PyTorch.
"""
__all__ = [
"GRUClassifier",
"GRUFCNNClassifier",
]
from sktime.classification.deep_learning.gru._gru_fcnn_torch import GRUFCNNClassifier
from sktime.classification.deep_learning.gru._gru_torch ... |
4f238133c9c81bb082f3607bc63505aeb054a960818386fd56fd6c377aec5d4d | Python | 342 | 14 | # 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 Reciprocal(OpRunUnaryNum):
def _run(self, x):
with np.errstate(divide="ignore"):
return (np.reciprocal... |
9409bae4c77af72e57bf50f16ddc491197200a9b35031345e22420154f38fd19 | Python | 342 | 14 | #from pathlib import Path
#ROOT_DIR = Path(__file__).resolve()
from setuptools import find_packages, setup
setup(
name='src',
packages=find_packages(),
version='0.1.0',
description='Explore the predictability of various psychiatric diagnoses based on neuroimaging features',
author='Richard Gaus',... |
358df012dc21f53fa864f79b279fba14acda21582dca0d59d875a37a1a05f638 | Python | 343 | 13 | # 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 Mul(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.multiply, ... |
64dc9d8727c6747833e3ed938592e26077a9fa2d7bd66bc91586baada180c879 | Python | 343 | 13 | # 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 Sub(OpRunBinaryNumpy):
def __init__(self, onnx_node, run_params):
OpRunBinaryNumpy.__init__(self, np.subtract, ... |
cf54e2f929e647639fe28d787ab8443f0339ed42642b9fe53a6294791d9bacb7 | Python | 345 | 14 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.op_run import OpRun
class Mean(OpRun):
def _run(self, *args):
res = args[0].copy()
for m in args[1:]:
res = res + m
return ((res / len(args))... |
39e5e065b18c719f9a9bfbd691bae12519e2baab32a697eb80d8abc5482a80ae | Python | 346 | 11 | retrosim_config = {
"topk": 200,
"max_prec": 200,
"similarity_type": 'Tanimoto',
"fp_type": "Morgan2Feat",
# try to use absolute path if possible
"input_data_folder": "./rxnebm/data/cleaned_data",
"input_data_file_prefix": "50k_clean_rxnsmi_noreagent_allmapped",
"output_folder": None,
... |
5ea07aaf15992281bf8220b85a46aaa6753743d636f53a3d0a979534a4c26058 | Python | 348 | 11 | """Composition for outlier, changepoint, segmentation estimators."""
# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
from sktime.detection.compose._as_transform import DetectorAsTransformer
from sktime.detection.compose._pipeline import DetectorPipeline
__all__ = [
"DetectorAsTransformer"... |
8fea4644a8a2b8c5480a8b8b6e7f5098b1b4c0d02d397a1509e7831d211aacb3 | Python | 348 | 14 | """Execute the scripts that comprise the example gallery in the online docs."""
from glob import glob
import matplotlib.pyplot as plt
if __name__ == "__main__":
fnames = sorted(glob("examples/*.py"))
for fname in fnames:
print(f"- {fname}")
with open(fname) as fid:
exec(fid.read(... |
9474da9d659feb9758275b9dd742f4bac9ea99f9194117f645bf7d7bfe7dd0da | Python | 348 | 15 | import os
from glob import glob
import pyrosetta
from pyrosetta import pose_from_pdb
pyrosetta.init()
pdbs = glob('../cleaned/*.pdb')
for f in pdbs:
p = pose_from_pdb(f)
# print(os.path.join('../cleaned_pyro', os.path.basename(f)))
p.dump_pdb(os.path.join('../cleaned_pyro', os.path.basename(f)))
pri... |
07802c36bb224d64108798bd2576e4536f45dde942efb37048155917cf944abb | Python | 349 | 7 | # ----------------------------------------------------------------------------
# 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.
# ------------------------------------------------... |
50e3c36b64dbfcf1d0de199667555af13b0b64b1ea3ebb27c09c27ac5988cf92 | Python | 349 | 13 | # __init__
# IMPORTANT: evaluation has to be imported first since energyflow must be imported before torch
# See https://github.com/pkomiske/EnergyFlow/issues/24
import jetnet.evaluation
import jetnet.datasets
import jetnet.losses
import jetnet.utils
import jetnet.datasets.utils
import jetnet.datasets.normalisations
... |
1fcb9f965ea93f9077f1218755d6dd89e06344e72f1fe71bf630a3e916db6013 | Python | 350 | 10 | # 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 .ScoringBasedEmbeddingModel import ScoringBasedEmbeddingModel
__all__ = [... |
160af652b67b4d9f75ad9d01576b5b173b2f57dd1efca5685f9e5f7503b2eadf | Python | 352 | 16 | """
Plotting model residuals
========================
"""
import numpy as np
import seaborn as sns
sns.set(style="whitegrid")
# Make an example dataset with y ~ x
rs = np.random.RandomState(7)
x = rs.normal(2, 1, 75)
y = 2 + 1.5 * x + rs.normal(0, 2, 75)
# Plot the residuals after fitting a linear model
sns.residplo... |
9c4a0751676d0bb2c288493b2dc7bd23d5e0d0bf275cd5081827a8197b169f53 | Python | 353 | 13 | """CNN deep learning classifiers.
This subpackage provides CNN based classifiers implemented in
TensorFlow and PyTorch backends.
"""
__all__ = [
"CNNClassifier",
"CNNClassifierTorch",
]
from sktime.classification.deep_learning.cnn._cnn_tf import CNNClassifier
from sktime.classification.deep_learning.cnn._cnn... |
f8dc9f805645226c825fe5e5490ca2925cf10c4a010dc98d7134f65afc68c2f0 | Python | 353 | 11 | from __future__ import print_function
from featureExtractor import FeatureExtractor
class FeatureExtractorRawData(FeatureExtractor):
def __init__(self, params):
self.params = params
self.extractorType = 'rawData'
def getFeatures(self, eegSegment, timeStampSegment, time_step, local_mu, local_s... |
179f5f0ed4d431b9dfcf4e47979132e247edd52b07ea9ca86db43dc3638dabe3 | Python | 355 | 12 | """Pretrain state logic shared by estimator base classes."""
from sktime.base._pretrain._clone_plugin import _PretrainedCloner
from sktime.base._pretrain._mixin import _PretrainMixin
from sktime.base._pretrain._state_at import _clone_at, _set_params_at
__all__ = [
"_clone_at",
"_PretrainedCloner",
"_Pretr... |
33405686c236a55351a0264b7aa6e682c72571cd5173a97d498a301ba82dc345 | Python | 355 | 14 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from scipy.spatial.distance import correlation
from sklearn.metrics import mean_squared_error
def Pearson(pred, label):
coef = 1 - correlation(pred, label)
return coef
def RMSE(pred, label):
rmse = np.sqrt(mean_squared_e... |
4ad2d86f422163a58bb2dee88fe4722597f2059721180c0e4fb79cfe0cf71562 | Python | 355 | 15 | """
Trivariate histogram with two categorical variables
===================================================
_thumb: .32, .55
"""
import seaborn as sns
sns.set_theme(style="dark")
diamonds = sns.load_dataset("diamonds")
sns.displot(
data=diamonds, x="price", y="color", col="clarity",
log_scale=(True, False), ... |
5c1eb4c4fa691d487b706773b35594bc89b559bb053c5c92d6414ce5d0885e3e | Python | 355 | 12 | # SPDX-License-Identifier: LGPL-3.0-or-later
from deepmd.dpmodel.fitting.latent_charge import (
LatentChargeFitting as LatentChargeFittingDP,
)
from deepmd.pt_expt.common import (
torch_module,
)
@torch_module
class LatentChargeFitting(LatentChargeFittingDP):
"""Per-atom latent charges predicted from the ... |
7d9c5ae89b5c6ee60225ef0c2ba8a84d173211ffc766c6b5f1a9c4be28aeb4f9 | Python | 355 | 15 | """
Plotting large distributions
============================
"""
import seaborn as sns
sns.set_theme(style="whitegrid")
diamonds = sns.load_dataset("diamonds")
clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
sns.boxenplot(
diamonds, x="clarity", y="carat",
color="b", order=clarity... |
a921225ecbbf4db6b2295683679e9b8b1432fb235544d27f0539c0d59bf0e8ba | Python | 355 | 12 | # for vcnmodel plotters
# from . import displayresult_model_run
# from . import displayresult
# from . import displayRevCorrResult
# from . import displaySACResult
# from . import IVPlots
# from . import plot_allsecs
# from . import plot_ANsims
# from . import plot_gbc_ANresults_2
# from . import plot_gbc_ivresults_2
... |
18bd76fbfa40cbf0c7a43a51a18a90f91570c1598a20efb60ebc31c26ae4f59e | Python | 356 | 15 | from .toy import get_toy_palette, sample_toy_networks
from .wrangle import (
create_node_data,
ensure_connected,
get_hemisphere_indices,
get_paired_inds,
get_paired_subgraphs,
get_seeds,
remove_group,
select_lateral_nodes,
split_connectome,
split_nodes,
to_largest_connected_c... |
7d142946ca01feda3559433980e16de4bfbb92eba7c2176f4c10111b2f25e49b | Python | 356 | 16 | """Top-level package for fnnls."""
# Do not edit this string manually, always use bumpversion
# Details in CONTRIBUTING.md
__version__ = "1.0.0"
__author__ = "Joshua Vendrow"
__email__ = "jvendrow@gmail.com"
__license__ = "MIT"
__copyright__ = "Copyright (c) 2020, Joshua Vendrow"
from .fnnls import fnnls
from .fnn... |
10e1c3631c72332c6f929f70710e05f332f61735a0c0e1cef6a7f5ba0531f0b8 | Python | 357 | 13 | """Multivariate time series transformer deep learning classifiers.
This subpackage provides multivariate time series transformer classifiers
implemented in PyTorch.
"""
__all__ = [
"MVTSTransformerClassifier",
]
from sktime.classification.deep_learning.mvts_transformer._mvts_transformer_torch import ( # noqa: E... |
8739373b5998f9c9fc282ce87db97289cf098affbcf699f34098f7018a5bab2b | Python | 357 | 14 | # 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 ThresholdedRelu(OpRunUnaryNum):
def _run(self, x, alpha=None):
alpha = alpha or self.alpha
return (np.wher... |
502321ede2fd0cd0d50be245ded460cf326860aa19b80698bdcd0a33a66c4637 | Python | 359 | 14 | import timeit
class Timer(object):
def __init__(self, name=None):
self.name = name
def __enter__(self):
self.tstart = timeit.default_timer()
def __exit__(self, type, value, traceback):
if self.name:
print("[{}]".format(self.name))
print("Elapsed: {:g}".format(... |
8d7ab05a5bbe37e790082d8905d4f55e20ce00a48cb077b5b1c243e43eda468d | Python | 359 | 14 | # 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 Elu(OpRunUnaryNum):
def _run(self, x, alpha=None):
alpha = alpha or self.alpha
return (np.where(x > 0, x, ... |
8f6999995c0a0ffb86b544360d650695b6bef53c5b2445ee1270cfc2731c2803 | Python | 359 | 16 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from warnings import catch_warnings
import numpy as np
from onnx.reference.op_run import OpRun
class Pow(OpRun):
def _run(self, a, b):
with catch_warnings(action="ignore"):
re... |
097650fe883e31578b2b9f88c7410a84edb20c4533320899d02fd39416a64d17 | Python | 362 | 10 | # 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 .CorruptionGenerationLayerTrain import CorruptionGenerationLayerTrain
__a... |
2b74acc9f376fcc6a4bcc153b381c26521089c57e33dbf8b693b90e2ef2fa2a6 | Python | 362 | 14 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import sys
from onnx.backend.test.case.base import Snippets
from onnx.backend.test.case.utils import import_recursive
def collect_snippets() -> dict[str, list[tuple[str, str]]]:
import_recursive(s... |
5ff75f77e0c613f7f2da0e5f5724ecea23f03d58ffb558dfa265677e811ece13 | Python | 362 | 12 | import subprocess
# Path to your requirements file
requirements_file = "requirements.txt"
# Open the file and install each package one by one
with open(requirements_file, 'r') as file:
for line in file:
package = line.strip()
if package:
print(f"Installing {package}...")
su... |
62c0a021e312c01286a62030ed104ae87f8f2fd012f5599fc884151815e49b17 | Python | 362 | 18 | import sys
fi=open(sys.argv[1])
fo1=open(sys.argv[2]+'.1.bed','w')
fo2=open(sys.argv[2]+'.2.bed','w')
i=1
for line in fi:
seq=line.rstrip().split('\t')
out1=[seq[0],seq[1],seq[2],'L'+str(i)+'P1']
out2=[seq[3],seq[4],seq[5],'L'+str(i)+'P2']
fo1.write('\t'.join(out1)+'\n')
fo2.write('\t'.join(out2)+... |
eae6724825c22240ccb4fd8a18f04aa29c38de00dc2d43938ea815534a73a0d9 | Python | 362 | 16 | """
Plotting model residuals
========================
"""
import numpy as np
import seaborn as sns
sns.set_theme(style="whitegrid")
# Make an example dataset with y ~ x
rs = np.random.RandomState(7)
x = rs.normal(2, 1, 75)
y = 2 + 1.5 * x + rs.normal(0, 2, 75)
# Plot the residuals after fitting a linear model
sns.re... |
b4f74bb10c0305d007123d59d5b194ed9d8c5a5038dd5dfcddea85f680701861 | Python | 363 | 18 | import time
from pprint import pprint
import ray
print(ray.__file__)
ray.init(address="localhost:10000")
pprint(ray.cluster_resources())
@ray.remote
def f():
time.sleep(0.01)
return ray.services.get_node_ip_address()
# Get a list of the IP addresses of the nodes that have joined the cluster.
pprint(set(ra... |
b815dcf003f451a0d906fea8590fb647e72d8cf7f5f66a1ed2d299920041b8dd | Python | 363 | 13 | """FCN time series classifiers.
This subpackage provides FCN based time series classifiers implemented
in TensorFlow and PyTorch backends.
"""
__all__ = [
"FCNClassifier",
"FCNClassifierTorch",
]
from sktime.classification.deep_learning.fcn._fcn_tf import FCNClassifier
from sktime.classification.deep_learnin... |
be6342f4b1f6d27a710076f32b87ec634ec1b0853d3baf478a2aa17a3f63ae80 | Python | 363 | 15 | from setuptools import setup, find_packages
setup(
name="evoaug",
version="1.0.6",
packages=find_packages(),
description = "A Python package that trains models with evolution-inspired data augmentations. ",
python_requires=">=3.6",
install_requires=[
"lightning>=2.0.0",
"torch>... |
a685d3e94c42bb67d3f0a94985a6db15ac95e5f8288af6742dc9fc307f536378 | Python | 364 | 14 | """
Plotting large distributions
============================
"""
import seaborn as sns
sns.set(style="whitegrid")
diamonds = sns.load_dataset("diamonds")
clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
sns.boxenplot(x="clarity", y="carat",
color="b", order=clarity_ranking,
... |
65f50c96058d794274ef239e64aa641875a66e5d723c0f26c5e39e9520c38009 | Python | 365 | 11 | """Classification foundation models."""
__all__ = [
"MantisClassifier",
"MomentFMClassifier",
"TSPulseClassifier",
]
from sktime.classification.foundation_models.mantis import MantisClassifier
from sktime.classification.foundation_models.momentfm import MomentFMClassifier
from sktime.classification.founda... |
088b93312ff6397d67fce4fff88ed1c77af87cef2d47c46739d2a0b7ff97143b | Python | 366 | 12 | try:
from setuptools_scm import get_version
__version__ = get_version(root="..", relative_to=__file__)
del get_version
except (LookupError, ImportError):
try:
from importlib_metadata import version # Python < 3.8
except:
from importlib.metadata import version # Python = 3.8
__... |
cfffa271a5dfeaa0d8d9579dd6910002bec5365b1f8edf06e5944c5a322234ee | Python | 366 | 15 | from .enums import ColumnSettings, Mode, RowSettings, Units, Verbosity
from .model_statistics import ModelStatistics
from .torchinfo import clear_cached_forward_pass, summary
__all__ = (
"ColumnSettings",
"Mode",
"ModelStatistics",
"RowSettings",
"Units",
"Verbosity",
"clear_cached_forward_... |
1283cbec798e1b96b281b919444c59261905bad1d6eb6bf39d3af094fcdd3cde | Python | 367 | 15 | import cv2
import functools
def rolling_frame(func):
@functools.wraps(func)
def wrapper(frame):
frame = frame.to_ndarray(format="rgb24")
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
ret = func(frame, wrapper.last_frame)
wrapper.last_frame = frame.copy()
return ret
... |
cecdcab992833364d8eb45d92bc2a98d8ff3c84a788444acf0eb1753e3197184 | Python | 367 | 10 | """IsoPrism: short-read expression to within-gene isoform proportions.
The current method is the terminal-aware Competitive-TA implementation.
It is not the historical IsoVAE or IsoBudget-v2 model.
"""
from short2long.competitive_model import IsoPrism
from short2long.competitive_predict import infer_h5ad
__version__ ... |
6e961bc830d61b90bcaecbdde3389055e20ea058daa26a8d3b8e4c8c8f22ae36 | Python | 368 | 16 | # -*- coding: utf-8 -*-
from parameterSetup import ParameterSetup
from dumpServer import DumpServer
class OnlineApplication:
def __init__(self):
self.params = ParameterSetup()
def start(self):
self.server = DumpServer(self.params)
self.server.serve()
if __name__ == '__main__':
ma... |
4531be9a81a7bc02e7823cdad59a490f140f34a12dc0bc444bcb1cbc23cd3864 | Python | 369 | 15 | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
class BaseHead(BaseModule, metaclass=ABCMeta):
"""Base head."""
def __init__(self, init_cfg=None):
super(BaseHead, self).__init__(init_cfg)
@abstractmethod
def forward_... |
bcf75c07db72df8ed0dd5f787d2a4ea8d792e42ccbcffc57f12fe3182c76a5a1 | Python | 369 | 17 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from onnx.reference.op_run import OpRun
class SequenceErase(OpRun):
def _run(self, S, ind=None):
if ind is None:
ind = -1
else:
ind = int(ind)
S2 = S... |
29f258903d2c987044e4018a51bd5bef7f239a492a1e6620e7882fa91a5d0dfc | Python | 370 | 16 | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from warnings import catch_warnings
import numpy as np
from onnx.reference.ops._op import OpRunUnaryNum
class Sqrt(OpRunUnaryNum):
def _run(self, x):
with catch_warnings(action="ignore"):... |
824a4ecc1effaa5e93a015ac8b1b7ba2739fd52eaa910c0c4ce721388f42bc03 | Python | 372 | 9 | from torchvision.models.detection.faster_rcnn import FasterRCNN
from torchvision.models.detection.backbone_utils import resnet_fpn_backbone
class FRCNNObjectDetector(FasterRCNN):
def __init__(self, num_classes=91, **kwargs):
backbone = resnet_fpn_backbone('resnet50', True)
super(FRCNNObjectDetecto... |
1cd979d03332769e1dda03c62107cb3965055f0330f0dd7f1719651095ab670e | Python | 373 | 13 | """MACNN deep learning classifiers.
This subpackage provides MACNN based classifiers implemented in
TensorFlow and PyTorch backends.
"""
__all__ = [
"MACNNClassifier",
"MACNNClassifierTorch",
]
from sktime.classification.deep_learning.macnn._macnn_tf import MACNNClassifier
from sktime.classification.deep_lea... |
3e76a613075075fa80500abf5d17de842892a8fdd99d7d2876c9393cd2faed75 | Python | 373 | 9 | from torchvision.models.detection.mask_rcnn import MaskRCNN
from torchvision.models.detection.backbone_utils import resnet_fpn_backbone
class MASKRCNNObjectDetector(MaskRCNN):
def __init__(self, num_classes=91, **kwargs):
backbone = resnet_fpn_backbone('resnet50', False)
super(MASKRCNNObjectDetect... |
b849cf97a9019e7499000cbb2d926f2dd804221ceb9242556c01a8ccae5427dc | Python | 374 | 16 | # 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 Mod(OpRun):
def _run(self, a, b, fmod=None):
fmod = self.fmod if fmod is None else fmod
if fmod == 1:
r... |
4f0f2c2cdd9a2b3ade9450c495ce272cc200dd4c9a47f3f63369db1991373e38 | Python | 375 | 14 | from .decoder import DeepNCMDecoder, LabelVerbalizerDecoder, PrototypicalDecoder
from .dropout import LockedDropout, WordDropout
from .model import Classifier, DefaultClassifier, Model
__all__ = [
"Classifier",
"DeepNCMDecoder",
"DefaultClassifier",
"LabelVerbalizerDecoder",
"LockedDropout",
"M... |
f680fbb7ddea4cd92364d7f836fbd27bf9a0a2ddc3246e4df59fdff26699f896 | Python | 375 | 14 | # 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 Swish(OpRunUnaryNum):
def _run(self, x, alpha=None):
alpha = self.alpha if alpha is None else alpha
return... |
0585cfb198f37b51fe07faf445213dacbd48fe2c7e59d69eb67e7033cc8935f1 | Python | 376 | 9 | import pyarrow.dataset as ds
from tqdm import tqdm
def stream(dataset_lis, file_format="parquet", batch_size=20000, columns=["idnumber", "standard_smiles", "achiral_fp"]):
dataset = ds.dataset(dataset_lis, format=file_format)
num_files = len(dataset.files)
return tqdm(dataset.to_batches(columns=["idnumber... |
0da93f8529aab110a2d44838343a842ef18178eb2beb7fd147319bb8164b7b53 | Python | 376 | 16 | import time
import functools
def collect_run_time(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
last = time.perf_counter()
ret = func(*args, **kwargs)
now = time.perf_counter()
elapsed_run_time = now - last
wrapper.samples.append(elapsed_run_time)
... |
319242e965ef142116bf16a114e85905204370dd26d8abeba961f9130ba45c34 | Python | 377 | 13 | """CNTC Deep Learning Classifier.
This submodule provides the CNTC deep learning classifier
implemented in both TensorFlow and PyTorch backends.
"""
__all__ = [
"CNTCClassifier",
"CNTCClassifierTorch",
]
from sktime.classification.deep_learning.cntc._cntc_tf import CNTCClassifier
from sktime.classification.d... |
633f39114da87af1f0009b3b09f973f219d8bcc654e2e0b60666644b1eccb539 | Python | 377 | 13 | """RNN deep learning classifiers.
This subpackage provides RNN based classifiers implemented in
TensorFlow and PyTorch backends.
"""
__all__ = [
"SimpleRNNClassifier",
"SimpleRNNClassifierTorch",
]
from sktime.classification.deep_learning.rnn._rnn_tf import SimpleRNNClassifier
from sktime.classification.deep... |
88a522e94e9e0a8dbe90c6745c20a92fad310480ce458cf701f279048ae9bba4 | Python | 377 | 16 | # -*- coding: utf-8 -*-
from parameterSetup import ParameterSetup
from networkServer import NetworkServer
class OnlineApplication:
def __init__(self):
self.params = ParameterSetup()
def start(self):
self.server = NetworkServer(self.params)
self.server.serve()
if __name__ == '__main__... |
249896d509fc75ab9ef15ab4a7abbfcceb7f0383fad282b81e57802187d75b6f | Python | 378 | 8 | """Basic implementations of some optimization algorithms namely
- `purecma.fmin` and `purecma.CMAES`: a basic and readible CMA-ES implementation
where the core algorithm is coded in under 40 lines (in the method `CMAES.tell`)
- `compact_ga.CompactGA`: compact GA which is the most logical abstraction of an
genetic ... |
b12fdd3e77aa3fc2be9266f5f6db3bb059c4c022679d2c7d318303e910f5b0ac | Python | 378 | 18 | #! /home/miro/miniconda/bin/python
import sys
filename=sys.argv[1]
atom_num=int(sys.argv[2])
f=open(filename,'a+')
curr_num=1
padding_count=1
padding_limit=8
for i in range(atom_num):
if (padding_count == padding_limit):
f.write (' 0\n')
padding_count=1
else:
f.write (' ... |
9eeef5cb099481a79703fde8e113f6662268763aa4dde73141bc05ed610cb955 | Python | 379 | 11 | # Define the DIR to the data, model, logs, results, and colors
#
import os
ROOT_PATH = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
DATA_PATH = os.path.join(ROOT_PATH, "data")
RESULT_PATH = os.path.join(ROOT_PATH, "results")
MODEL_PATH = os.path.join(ROOT_PATH, "model")
LOG_PATH = os.path.join(ROOT_PAT... |
531a7034d11bb7efe0005445dab834f48fde88123e464b8c557db0bdb07406fa | Python | 380 | 10 | from distutils.core import setup
setup(
name='SpikeShip',
version='1.0',
description='Fast, unsupervised discovery of high-dimensional neural spiking patterns based on optimal transport theory',
author='Boris Sotomayor-Gomez',
author_email='bsotomayor92@gmail.com',
py_modules=['spikeship.spikes... |
712f48a19fd6ff6d1bf319a2d8641f4537fc0a3cd0c4a9fa8f207f1b567db3b1 | Python | 381 | 11 | import datetime
import re
def presentTimeEscaped():
presentTime = str(datetime.datetime.now())
presentTime = presentTime.replace(' ', '_')
# presentTime = presentTime.replace('/', '-')
# presentTime = '-'.join(presentTime.split('\/'))
presentTime = re.sub(r'\:', '-', presentTime)
presentTime = ... |
93d9b4fb1866f2be605de6f9ee07b2fb8560535548c8cb66c4e0e676aa8b9900 | Python | 381 | 17 | """
Timeseries plot with error bands
================================
_thumb: .48, .45
"""
import seaborn as sns
sns.set(style="darkgrid")
# Load an example dataset with long-form data
fmri = sns.load_dataset("fmri")
# Plot the responses for different events and regions
sns.lineplot(x="timepoint", y="signal",
... |
cf8af6b4ccd1f12765bddf088de2e84455b3573ca4d2dae5de753bbf2d762b30 | Python | 381 | 18 | """
Joint kernel density estimate
=============================
_thumb: .6, .4
"""
import seaborn as sns
sns.set_theme(style="ticks")
# Load the penguins dataset
penguins = sns.load_dataset("penguins")
# Show the joint distribution using kernel density estimation
g = sns.jointplot(
data=penguins,
x="bill_len... |
e5c84cc8ef3b374c14039abcc4bc52ede80fe9d96fa6d8653f80c87d68229f30 | Python | 381 | 17 | """
Facetted ECDF plots
===================
_thumb: .30, .49
"""
import seaborn as sns
sns.set_theme(style="ticks")
mpg = sns.load_dataset("mpg")
colors = (250, 70, 50), (350, 70, 50)
cmap = sns.blend_palette(colors, input="husl", as_cmap=True)
sns.displot(
mpg,
x="displacement", col="origin", hue="model_year... |
3117a5405d75ef398e2137a2ee36234a6cc0643c9a6a74c656cad3697af3f8bb | Python | 383 | 16 | """
ASGI config for web project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/4.1/howto/deployment/asgi/
"""
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_... |
9d0086d8167fdac406131daefb4654a8ea4288116c37b0d65a23de82229d7e9d | Python | 383 | 15 | import pandas as pd
import atom3d.datasets as da
import atom3d.filters.filters as filters
import atom3d.filters.scop as scop
dataset = da.load_dataset('tests/test_data/lmdb', 'lmdb')
def test_scop_filter():
level = 'class'
filter_fn = scop.form_scop_filter(level, allowed=None, excluded=None)
for i,d ... |
b6809a0c25df2d837270058b2549bdabb80788120a3b17c5521f35c7ca522d0a | Python | 383 | 16 | """
WSGI config for web project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/4.1/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_... |
4f22c896e22c91cb2791b798fda54c12893a5793b57dfa4e2876c6934bfd1467 | Python | 384 | 18 | """
Grouped boxplots
================
_thumb: .66, .45
"""
import seaborn as sns
sns.set(style="ticks", palette="pastel")
# Load the example tips dataset
tips = sns.load_dataset("tips")
# Draw a nested boxplot to show bills by day and time
sns.boxplot(x="day", y="total_bill",
hue="smoker", palette=["m",... |
54763a3f7cbef4b2a6fd3cada4e827f2389c66619665c9fe308afcf3f8a4bc37 | Python | 385 | 16 | # 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 Trilu(OpRun):
def _run(self, x, k=None, upper=None):
k = 0 if k is None else k.item()
if upper:
return ... |
949f83d9b337e6b1ed1ce839f7a1ab8fd428244ac37dbf15a3110797a7163692 | Python | 385 | 21 | from pathlib import Path
import pandas as pd
HERE = Path(__file__).parent
def haber() -> pd.DataFrame:
"""
Tabularized counts of cell types in the
small intestinal epithelium of mice with different conditions.
Haber et al. 2017
Returns
-------
data matrix as pandas data frame.
"""
filena... |
3d146d8b25453b7672b2284a5dd9e858f35a1351db96339a0a07c39ed0d51154 | Python | 386 | 16 | """
Scatterplot with categorical variables
======================================
_thumb: .45, .45
"""
import seaborn as sns
sns.set_theme(style="whitegrid", palette="muted")
# Load the penguins dataset
df = sns.load_dataset("penguins")
# Draw a categorical scatterplot to show each observation
ax = sns.swarmplot(da... |
e8d32f99db0684408fd5a9e1155bb3dd2305f1e7e267233c3ee4d5fec851c6c0 | Python | 386 | 12 | from setuptools import find_packages, setup
setup(
name="gradient_correction",
version="0.1",
description="Gradient correction method",
author="Antonio Majdandzic",
author_email="antem@bu.edu",
platforms=["any"], # or more specific, e.g. "win32", "cygwin", "osx"
url="https://github.com/Ant... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.