sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
55c10bc8e9ea72c658f73ade7d3cad8012154a2da23e860d686ce2c52866b16a | Python | 2,405 | 67 | import torch
class ActivationsAndGradients:
""" Class for extracting activations and
registering gradients from targetted intermediate layers """
def __init__(self, model, target_layers, reshape_transform, detach=True):
self.model = model
self.gradients = []
self.activations = []
... |
27cb6d278f4fecf9f2861110e5cbcd611062e79653b2a073d901c9f3a70356fd | Python | 2,407 | 69 | import numpy as np
import pandas as pd
from pgmpy.ci_tests import GeneralizedCov
from pgmpy.tests.test_ci_tests import _multivariate_fixtures
pillai_data = _multivariate_fixtures.pillai_data
def _manual_generalized_cov_statistic(res_x, res_y):
if isinstance(res_x, pd.Series):
res_x = res_x.to_frame()
... |
28b65f587471d7e052984fe3cea24c3d77ca1a0630e2998187a084e73274a429 | Python | 2,407 | 91 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
586e24e0a7b6d3df527d17f6256aae60b28e8b31a5dbad4ccce14894933849b1 | Python | 2,407 | 91 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
6fe033e72b5538646b0f8d01be16dba89f3df9a05e5d6f676cf931be57f34fe4 | Python | 2,408 | 79 | import json
import os
import numpy as np
class OEContinuousFile:
"""
Stores information about an Open Ephys .dat file, including:
- the number of channels
- the sample rate
- the bit volts value for each channel.
"""
def __init__(self, json_file, file_num = 0):
... |
8a0af9c17ce446215587eb3110ad49700d334f9101de52f66cef54cfc9d2288a | Python | 2,408 | 91 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
ec34b307e42e281a126ac05d796c26ab4f9ffbb1b1e0a5156e70626b4d5b485b | Python | 2,408 | 64 | # @license
# Copyright 2020 Google Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... |
f0a3d75b5edb56755b2ea1618454e17a4bc616d5810da189c1be426ae804db68 | Python | 2,408 | 85 | #!/usr/bin/env python3 -u
import argparse
import fileinput
import logging
import os
import sys
from fairseq.models.transformer import TransformerModel
logging.getLogger().setLevel(logging.INFO)
def main():
parser = argparse.ArgumentParser(description="")
parser.add_argument("--en2fr", required=True, help=... |
cdf82f5c642d2e35f95364cadf50dbeca660643d76443a707518f1fbca180d0b | Python | 2,409 | 66 | import random
from train.TrainPglcn import *
from train.TrainML import *
from train.TrainSglcn import *
from train.TrainNodeClassifier import *
def train_model(model, args, log=None, dataset=None):
if args.method == "pglcn":
if args.project in ["pretrain_stad", "pretrain_coad", "pretrain_ucec"]:
... |
9c56a62d9c8481de015787ce457fc2fdaddcdd9ccb7bf1c0c8d7e380a86707d8 | Python | 2,410 | 55 | from batchgenerators.utilities.file_and_folder_operations import *
import shutil
from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json
from nnunetv2.paths import nnUNet_raw
if __name__ == '__main__':
"""
Download the dataset from huggingface:
https://huggingface.co/datasets/Ab... |
4d9033319d58f6e35fd683346ee2268747b4ad664e077424134186f5b8a9fce6 | Python | 2,411 | 65 | import numpy as np
import os
from vina import Vina
def extract_first_energy_from_file(file_path):
with open(file_path, 'r') as file:
for line in file:
if "REMARK VINA RESULT:" in line:
# 假设能量值紧跟在'REMARK VINA RESULT:'之后
energy = float(line.split()[3])
... |
a72c4769533fa9b64905315eff296561b28127df9429dcd4399ccbe01d132db8 | Python | 2,411 | 75 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import copy
import unittest
import torch
from torch.cuda.amp import GradScaler, autocast
from fairseq.optim import build_opt... |
e9202568813d42fccd94c6d21fe8a9972a18e00e76183201539e94668df74997 | Python | 2,411 | 81 | from .anchored_validation import (
AnchoredModelSpec,
anchor_auc_summary,
anchor_calibration_summary,
anchor_retention_summary,
available_proxy_columns,
available_score_columns,
best_score_per_proxy_sample,
build_model_summary,
collect_anchored_validation_tables,
load_anchor_obse... |
8cdeff4d4489f342abb8bf600473fb22c3d99531bfc3e71d83010068479c57cd | Python | 2,412 | 79 | """tests for vak.cli.learncurve module"""
from unittest import mock
import pytest
import vak.config
import vak.common.constants
import vak.cli.learncurve
from . import cli_asserts
def test_learncurve(specific_config_toml_path, tmp_path, trainer_table):
root_results_dir = tmp_path.joinpath("test_learncurve_root... |
9ae1c57622b69ddccad94a387e9d563a4a6efbd914a85c264b1e444e42b1127b | Python | 2,413 | 67 | """
Usage: python -m scripts.generate \
--model-name evo-1-131k-base \
--prompt ACGT \
--n-samples 10 \
--n-tokens 100 \
--temperature 1. \
--top-k 4 \
--device cuda:0
Generates a sequence given a prompt. Also enables the user to specify vari... |
6c585e03824bb49e4bba69977ffdb77e61f22263831423091b4510cc2d7c856f | Python | 2,418 | 74 | from argschema import ArgSchemaParser
import os
import logging
import time
import numpy as np
from .depth_estimation import compute_channel_offsets, find_surface_channel
from ...common.utils import write_probe_json
def run_depth_estimation(args):
print('ecephys spike sorting: depth estimation module\n')
st... |
e956845a34996d650f1e22a0e752540a2994fd576ea1c1a9a742fe8e0cb1b1fd | Python | 2,418 | 93 | #!/usr/bin/env python
# Author_and_contribution: Niklas Mueller-Boetticher; created script
import argparse
# TODO adjust description
parser = argparse.ArgumentParser(description="HVG selection using scanpy")
parser.add_argument(
"-c", "--coordinates", help="Path to coordinates (as tsv).", required=True
)
parser... |
8bd731701f16b0ae4797be10b40ca671dc16fd82f8432b5842b6603033650ee6 | Python | 2,420 | 53 | # Download reference data from http://ctg.cncr.nl/software/magma (for example g1000_eur)
# Then you can run the tool as follows:
# python make_maf_vector.py --ref 2558411_ref.bim --bfile merged --savemat mafvec.mat
import pandas as pd
import numpy as np
import argparse
import sys
import scipy.io as sio
def parse... |
86590edbabc08233326fb31e89fec5efadbdbd77d56f8bf70fa02c36b130b81e | Python | 2,423 | 54 | # Please refer to https://github.com/SPLab-aviyente/scSGL/blob/main/notebooks/demo.ipynb
import os
import argparse
import pandas as pd #to load read GSD dataset
import numpy as np
import sys
sys.path.append('scSGL') #to add a path to search for the requested module
from pysrc.graphlearning import learn_signe... |
3977e9432de4432c1984a08d9fc7f60bf0133b18c75462bbd5d977ff99175d85 | Python | 2,424 | 70 | #! /usr/bin/env python
#
# Copyright (C) 2017-2023 Phillip Alday <me@phillipalday.com>
import os
from os import path as op
from setuptools import setup
# get the version (don't import mne here, so dependencies are not needed)
version = None
with open(os.path.join('philistine', '__init__.py'), 'r') as fid:
for li... |
d90a368fdca36dd06903880ce0f24deff868e6335345eb6c70e65306555d593e | Python | 2,424 | 70 | from PIL import Image
from sklearn.decomposition import PCA
import cv2
import numpy as np
from matplotlib import pyplot as plt
import joblib
from msi_visual.normalization import spatial_total_ion_count, total_ion_count, median_ion
from msi_visual.visualizations import visualizations_from_explanations
from msi... |
56938a29b948e279ca7c562419f7959a796a41712f895415563c7046db0e3408 | Python | 2,425 | 73 | #!/usr/bin/env python3
from __future__ import annotations
from numbers import Number
import numpy as np
from sklearn.preprocessing import OrdinalEncoder
from pgmpy.factors.base import factor_product
from pgmpy.factors.discrete import DiscreteFactor
class FactorDict(dict):
@classmethod
def from_dataframe(cl... |
b22a0084ee7b9c7ca5737cdb42e1088989fc3225d0329d61093394629733610b | Python | 2,425 | 62 | """
You can use this script to compute the accuracy given a dataset like
* generations_p=0.96.jsonl
and also a numpy array of machine/human probabilities that is the same size as the val and test sets.
"""
import json
import numpy as np
import os
import pandas as pd
# Load in the dataset
set_to_info = {'val': [], 'te... |
b23f0ac101671ce8bbd04769df8a2c69ac8c75d2c38a0dc501e6564a75193519 | Python | 2,425 | 51 | import logging
from beartype.typing import Mapping, Optional
from lightning_utilities.core.rank_zero import rank_prefixed_message, rank_zero_only
class RankedLogger(logging.LoggerAdapter):
"""A multi-GPU-friendly python command line logger."""
def __init__(
self,
name: str = __name__,
... |
d056d567eed4bfd44d22da29a25b2461c81825768d8b8419591682c6d158e55d | Python | 2,428 | 84 | # written by Nathan Boley, from https://github.com/nboley/GGR_code
import sys
import gzip
VERBOSE = False
adapters = {
'Illumina': b'AGATCGGAAGAGC',
'Nextera ': b'CTGTCTCTTATA',
'smallRNA': b'TGGAATTCTCGG'
}
def open_gz(fname):
return gzip.open(fname) if fname.endswith('.gz') else open(fname, 'rb')... |
4401e55a9db3b05df91c2c2a187ddbe555508a59441ff11ad9629d5cf3b012ec | Python | 2,429 | 69 | from __future__ import annotations
import functools
import threading
from typing import TYPE_CHECKING
from typing import TypeVar
from typing import overload
from weakref import WeakKeyDictionary
if TYPE_CHECKING:
from collections.abc import Callable
from typing import Any
T = TypeVar("T")
C = TypeVar("C",... |
1c1f07d7e9462445901577c399541492d52b6f156ac0ead1ec65ec0378bbe12a | Python | 2,430 | 75 | """
CLI entry that resolves project root and forwards to :mod:`dlbase.train` (Hydra).
**SegJointGene-CID standalone — Simulation · UNet · dynamic_segmentation_CID**.
python3 main.py --sub_path basic
Layout: raw data under ``${paths.data_dir}/Simulation_raw/``; NPZ patches under
``${paths.data_dir}/Simulation/{trai... |
686e07e811d81a004ffa79295454d5646656e49f1f1c53d9629c992fc8c0c639 | Python | 2,430 | 68 | import os
from os.path import join, basename, dirname
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from config_path import PLOTS_PATH, PROSTATE_LOG_PATH
base_dir = join(PROSTATE_LOG_PATH, 'review/fusion')
files = []
files.append(dict(Model='Fusion', file=join(base_dir, 'onsplit_average... |
289b63e644732bac9325edf49922a52352dc0d5463318c563318845794923c48 | Python | 2,431 | 97 | from __future__ import annotations
import os
from typing import TYPE_CHECKING
import pytest
from poetry.utils.env import EnvManager
if TYPE_CHECKING:
from collections.abc import Iterator
from pathlib import Path
from tests.helpers import PoetryTestApplication
@pytest.fixture
def venv_name(app: Poet... |
44ca1408a5858fc966e1858f61f1e5e7fc73f9b2f45a5047d2333ba8f41cab34 | Python | 2,432 | 73 | import os
import subprocess
import glob
from create_input_json import createInputJson
mice = ['412804']
source_path = r'\\10.128.50.151\sd4'
dest_path = r'\\10.128.50.77\sd5.3\RE-SORT'
local_data_path = r'C:\data'
probe_type = '3A'
json_directory = r'C:\Users\svc_neuropix\json_files'
for mouse in mice:
remo... |
c5f25f33405f05f530307e6c73f0122c45b0e6eff9156194924b1dadedb8e8e0 | Python | 2,432 | 76 | """Manages print format for GOEA results."""
__copyright__ = "Copyright (C) 2016-2018, DV Klopfenstein, H Tang, All rights reserved."
__author__ = "DV Klopfenstein"
# import collections as cx
class PrtFmt(object):
"""Manages print format for GOEA results."""
# Default Excel table column widths for GOEA res... |
7eaaf25c2adcffd3770efaae8d377db089bf275c112e30afdce13d62250e5877 | Python | 2,434 | 65 | import anndata as ad
import numpy as np
from st_risk.data.validate import validate_ready_directory, validate_ready_manifest
def test_validate_ready_manifest_requires_expected_keys(tmp_path):
manifest = tmp_path / "manifest.json"
manifest.write_text('{"dataset": "Human DLPFC"}', encoding="utf-8")
errors =... |
064fd332b0724d3ac28a39ae3e3f2ba92284b8f6504c67433ffb070cf8215b79 | Python | 2,435 | 74 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
from os.path import join
import nibabel as nib
import numpy as np
from tractseg.libs import fiber_utils
from tractseg.libs import exp_utils
def main():
args = sys.argv[1:]
out_dir = arg... |
071f4b0d2ab993379ef3797618f9f2252d79611177f1791485be3de37ab969a4 | Python | 2,435 | 74 | import torch
from torchvision import models
class resnet18(torch.nn.Module):
def __init__(self, pretrained=True):
super().__init__()
self.features = models.resnet18(pretrained=False)
self.conv1 = self.features.conv1
self.bn1 = self.features.bn1
self.relu = self.features.rel... |
b6705281ae42b5335b89376834aeab5f14a1a45da3a03e4f316f8ac3038fb16c | Python | 2,437 | 78 | import json
import os
from pathlib import Path
import sys
import numpy as np
import transistor_extract as TE
root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
config_path = os.path.join(root_dir, "config.json")
working_dir = os.path.dirname(os.path.abspath(sys.argv[0]))
with open(config_path, "r... |
d597603d4bf25c703bffe67ce091e265697a99d7ea41eec3e9e16fbb318f0104 | Python | 2,437 | 70 | from collections.abc import Hashable
from math import log
from pgmpy.structure_score.log_likelihood import LogLikelihood
class BIC(LogLikelihood):
r"""
BIC structure score for discrete Bayesian networks.
BIC, also known as the MDL score, balances discrete log-likelihood against model complexity. The loc... |
6bb10f62b0f077d264fce028b279171af8953a05e65db175107589a968bb773e | Python | 2,438 | 84 | import logging
import rich.table
from anndata import AnnData
from scvi.data import _constants
from ._base_field import BaseAnnDataField
logger = logging.getLogger(__name__)
class BaseUnsField(BaseAnnDataField):
"""An abstract AnnDataField for .uns attributes in the AnnData data structure.
Parameters
... |
0719878468f61614f5e65994e690b16b151681606e249f1a0441a6c5a7540c9c | Python | 2,439 | 68 | #!/usr/bin/env python3
import polars as pl
import pyBigWig
import argparse
def add_phyloP(df, pbw_path):
"""
Add phyloP scores to the splice sites DataFrame.
Args:
df (pl.DataFrame): DataFrame containing splice sites with 'chrom' and 'coord' columns.
Returns:
pl.DataFrame:... |
9ed34fa876db457d29e8e86e8593aff3a8ee9cb622763d371b87048e3d7f98bb | Python | 2,439 | 90 | """ from https://github.com/keithito/tacotron """
'''
Cleaners are transformations that run over the input text at both training and eval time.
Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
hyperparameter. Some cleaners are English-specific. You'll typically want to use... |
e370356a629b9ef4836af639afbee7969d0ab3f2b059388f9f2264d0bea4eb1f | Python | 2,441 | 82 | # scSGL - a python package for fene regulatory network inference using graph signal processing based
# signed graph learning
# Copyright (C) 2021 Abdullah Karaaslanli <evdilak@gmail.com>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as pu... |
148358b36d0342f05289f6cb31f144adbbe343cf94273c354f690db29b21314f | Python | 2,442 | 98 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
282286243cec3f314b1409eb80971b02b131dc129f76729322e48cf3d4f58f76 | Python | 2,443 | 98 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
3f5c7ed24d7dd0068900bfc2a7042378b55674a26d2988b4b3757831c772848c | Python | 2,443 | 64 | import unittest
from types import SimpleNamespace
from unittest.mock import Mock, patch
from GMXMMPBSA.make_top import CheckMakeTop
class GromacsRadiusCompatibilityTest(unittest.TestCase):
@staticmethod
def _checker(pbradii, igb=8, gbrun=True):
checker = object.__new__(CheckMakeTop)
checker.I... |
77e38333e22bb64cc0cc3f58f24fcc2d641d10ba58ca95586b1752b7caffcc8a | Python | 2,443 | 78 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from fairseq.data import data_utils
from . import BaseWrapperDataset
class TruncateDataset(BaseWrapperDataset):
"""T... |
c72681e677c6ed9ce471d9b3e3c0572f40cdff5f4bfa40656536398198a85bbf | Python | 2,444 | 69 | """Tests for ``aestetik.data_modules.data_module.AESTETIKDataModule``.
Covers issue #8: ``train_dataloader`` must use the *split*
``train_dataset`` when ``validation_split > 0``.
"""
from aestetik.data_modules.data_module import AESTETIKDataModule
def _build_datamodule(adata, validation_split: float):
return AE... |
15208fec32c495c364c7e01301152d648f32c3b3534995b912b7e55287cf2a55 | Python | 2,449 | 52 | #!/usr/bin/env python3
# MIT License
#
# Copyright 2025 Broad Institute
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to u... |
5ff4ceb42265bbf6a7ba9edda5cd83b01843b794f3a16c587e0ef53bafa57b90 | Python | 2,449 | 63 | """Manages a user-specified subset of a GO DAG."""
from __future__ import print_function
__copyright__ = "Copyright (C) 2016-present, DV Klopfenstein, H Tang, All rights reserved."
__author__ = "DV Klopfenstein"
from goatools.godag.consts import RELATIONSHIP_SET
def get_go2reldepth(goterms, relationship_set):
... |
7b3dd7468ea047522adb9deb62d1a7dcb6149dab01bc2182eebd614f8a656e6a | Python | 2,449 | 81 | import torch
import torch.utils.data as Data
import torchvision
from lib.network import Network
from torch import nn
import time
train_data = torchvision.datasets.MNIST(root='./mnist', train=True,
transform=torchvision.transforms.ToTensor(),
... |
d7c2d715b1e5577e628255e209a8c55c130d0237bfc050d6c5df56054429558e | Python | 2,449 | 98 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
aecaf10a4f870c04a13d60934ec9e1041b1a5fbc2fa301fdc9b4d18c2e0d1988 | Python | 2,450 | 58 | # -*- coding: UTF-8 -*-
"""Runs Fisher's exact test, as well as multiple corrections for each of: BP MF CC"""
__copyright__ = "Copyright (C) 2010-2019, H Tang et al., All rights reserved."
__author__ = "various"
import itertools
from goatools.go_enrichment import GOEnrichmentStudy
class GOEnrichmentStudyNS:
"""... |
2bf10543abdb881a21a53a7b26908cee8b6f25ebf195e4783e5cfabbdef6d362 | Python | 2,451 | 67 | import os
import h5py
import pickle
import numpy as np
import torch
from torchvision import datasets, transforms
import sys
gen_path=sys.argv[1]
monkey_name=sys.argv[2]
roi_name=sys.argv[3]
filename = gen_path + monkey_name + '/data_THINGS_array_'+ roi_name +'_v1.pkl'
#data_path = gen_path + monkey_name +'/THINGS_ex... |
c08255d7e4abdc8f3631f38ff1fbaa433b2b00a7614d5996761b7ee941b8de63 | Python | 2,451 | 77 | """Distances, CI table, feature extraction and classifier factory."""
import numpy as np
import pytest
from crossda.pipelines.pipeline_utils import (
session_distance,
distance_ci,
scale_to_target,
compute_distance_ci_table,
get_classifier,
extract_features_train,
extract_features_test,
)
... |
3b8e03d4bdc5642984200fa0ca314099e5c06d2faba45619d5fb42820a2277ea | Python | 2,452 | 78 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
import unittest
import torch
from fairseq.optim.adam import FairseqAdam
from fairseq.optim.fp16_optimizer impo... |
9d7fb918d6660ddbba50d8cbfdaf5be5b534a7222ab1456bc78dfc5899ec94de | Python | 2,452 | 70 | from os.path import join
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from config_path import PROSTATE_DATA_PATH
from setup import saving_dir
# tips = sns.load_dataset("tips")
# print tips.head()
# Custom function to draw the diff bars
# https://stackoverflow.com/questions/11517986/i... |
563a3322c84e63de6f61311e438ac0b2b5d355153b329734e279f1b2d48ed081 | Python | 2,453 | 77 | """Smoke tests for scripts/symm_net/main_salnet.py.
Must run from scripts/symm_net/ so that `from load_utils import ...` resolves
and `../datasets` points to the cached dataset directory (scripts/datasets/).
Results are written to --output-dir, which the tests point at tmp_path so
pytest handles cleanup automatically... |
dd030874da5cf2f44bf621f3de15a31e046e18576158b14ae9ffd8e5101efd99 | Python | 2,453 | 71 | import torch
def init_edge_rot_mat(edge_distance_vec):
edge_vec_0 = edge_distance_vec
edge_vec_0_distance = torch.sqrt(torch.sum(edge_vec_0**2, dim=1))
# Make sure the atoms are far enough apart
#assert torch.min(edge_vec_0_distance) < 0.0001
if torch.min(edge_vec_0_distance) < 0.0001:
pr... |
3682ccfe84ddb276a9fa4cb3625cb9b26bd6fc97836ea28a7d2373c2e9d1a73b | Python | 2,454 | 62 | #!/usr/bin/env python
from __future__ import print_function
from collections import OrderedDict
import re
from os import path
# TODO nf-core: Add additional regexes for new tools in process get_software_versions
regexes = {
'Pipeline': ['v_pipeline.txt', r"(\S+)"],
'Nextflow': ['v_nextflow.txt', r"(\S+)"],
... |
f4908c4241ce2b1b87263ed4459b94a4afa85f8be2cb5c0d387b6db508464715 | Python | 2,454 | 37 | import csv, json, re, sys
from pathlib import Path
import numpy as np
from scipy import sparse as sp
from sklearn.model_selection import GroupKFold
SD = Path("/home/david/atual/new-methods/data/perovskites_siesta")
sys.path.insert(0, str(SD.parent / "mofs_qmof_v2"))
from sweep_targets import ridge_alpha_and_fit, _predi... |
358637d9db0297773cb9ad6d2f145ca7826e8cff24bb63cf71a1ec1b618cd386 | Python | 2,456 | 74 | import os
from pathlib import Path
import numpy as np
import pytest
import torch
from neurovfm.data.cache import CacheManager
def test_cache_init_without_metadata_raises(tmp_path):
empty_dir = tmp_path / "no_metadata"
empty_dir.mkdir()
with pytest.raises(FileNotFoundError):
_ = CacheManager(empty_dir)
def te... |
c995c5cd244443350f0e869c4491e85b3f6d27c663606e5c695f70223b120c14 | Python | 2,456 | 67 | import numpy as np
import pandas as pd
import pytest
from pgmpy.base import DAG, PDAG
from pgmpy.causal_discovery import R2Sort
@pytest.fixture
def causal_chain_data():
rng = np.random.default_rng(seed=42)
n = 1000
x = rng.normal(0, 1, n)
y = x + rng.normal(0, 1.5, n)
z = y + rng.normal(0, 2.0, n... |
292c4c163a430ea64ca105ea27770868ef542247fc79fffa1610df3c853b88af | Python | 2,457 | 76 | import glob
import numpy as np
import os
from joblib import Parallel, delayed
import argparse
import nibabel as nib
import nilearn
from nilearn import image
from nilearn import masking
from nilearn.glm import threshold_stats_img
def resample_correction(f):
f_name = f.split('/')[-1]
img = nib.load(f)
out_f... |
6996e09c57f9e16e189355ee41a2384cd83dd6b416061a8c108dfbb1cd9575cc | Python | 2,458 | 89 |
def generate_anisotropic_unit_vectors(n, scale=(1.0, 1.0, 1.0), orient_axis=None):
"""
Generate anisotropic unit vectors on a sphere,
optionally oriented to a hemisphere.
Parameters
----------
n : int
Number of vectors.
scale : tuple of 3 floats
Scaling factors for (x, y,... |
125c12d712e3bac9b7d4c7c70723730e06c59bd105ec91df449b308822affc52 | Python | 2,459 | 70 | # scSGL - a python package for fene regulatory network inference using graph signal processing based
# signed graph learning
# Copyright (C) 2021 Abdullah Karaaslanli <evdilak@gmail.com>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as pu... |
31584c1df2b0488ff7fe4675623bf7f48c65694c8f325e02b25cbdfa9647ed96 | Python | 2,461 | 98 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
9552692b324cc3134de37929560a8954f45bdb33e6e68192e93f21d26e601046 | Python | 2,461 | 72 | from __future__ import annotations
import logging
from PySide6.QtWidgets import QMessageBox, QWidget
logger = logging.getLogger(__name__)
def describe_exception(error: BaseException) -> str:
"""Translate common technical exceptions into useful user-facing text."""
detail = str(error).strip()
if isinst... |
eb66a345d3dc17540661f903a3ecfe7d29e51d442ed7676164639312f3080435 | Python | 2,461 | 71 | """Tests for vak.train.train function."""
from unittest import mock
import pytest
import vak.config
import vak.common.constants
import vak.common.paths
import vak.train
@pytest.mark.parametrize(
"audio_format, spect_format, annot_format, model_name, train_function_to_mock",
[
("cbin", None, "notmat"... |
34e6fdceac0291e0fd6fcee34b479bebc86c90b8bdea52838c6af7fe67e6557a | Python | 2,465 | 79 | #!/usr/bin/env python
# Author_and_contribution: Niklas Mueller-Boetticher; created template
# Author_and_contribution: Thomas Chartrand; created script
import argparse
parser = argparse.ArgumentParser(description="Calculate Matthew's correlation coefficient (scikit-learn)")
parser.add_argument(
"-l", "--labels... |
a783fd223ba33d53eae11fad47d8d34b84cdf9d352246d659295a8b7ac61921b | Python | 2,465 | 80 | import itertools
import joblib
import numpy as np
import os
import unittest
from numpy import testing
from sklearn import datasets, preprocessing
#from matplotlib import pyplot
from mentor import _cluster as cluster
from mentor import _datasets as datasets
from mentor import _metrics as metrics
from mentor import _rw... |
94d2af3b6695593545cf19f0fef508f67aa1a9375d64416976f70aa4eff836bb | Python | 2,466 | 100 | from pgmpy.datasets._base import BaseDataset
BASE_URL = "real/wine-quality"
EXPERT_URL = "ground.truth/wine.quality.knowledge.txt"
class WineQualityRed(BaseDataset):
"""
References
----------
- :footcite:t:`peerj_blue_driver`
"""
_tags = {
"name": "wine_quality_red",
"n_varia... |
98802d1ae5cd8fb11091aaa9c123234098a636fcb30a34e7f37e043244170057 | Python | 2,469 | 98 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
293c492cb3f59df2cd7e38d8b220240d8c9ac620ff51fa7c34c0e0dc70b5b381 | Python | 2,470 | 64 | # Copyright 2020 The Lucent 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 applicable ... |
8ea7fe30af519c91d9716c5576b54c07174d4785e976599f5a35de18d7bdc3d1 | Python | 2,472 | 63 | import io
import logging
import unittest
from types import SimpleNamespace
from unittest.mock import patch
from GMXMMPBSA.exceptions import GMXMMPBSA_ERROR, InputError, MMPBSA_Error
class Phase9ErrorReportingTest(unittest.TestCase):
def test_fatal_helper_logs_one_concise_record_and_exception(self):
with ... |
c78f6bdc1821dd1397c6cd6f7c867ae59ac48f74ab3e0bced8f114caa42e9675 | Python | 2,473 | 77 | import torch.nn.functional as F
import torch
from torch import nn
import numpy as np
class Siren(nn.Module):
"""This is a dense neural network with sine activation functions.
Arguments:
layers -- ([*int]) amount of nodes in each layer of the network, e.g. [2, 16, 16, 1]
gpu -- (boolean) use GPU when ... |
5ed68d6460c96de5cb271e28ca606f93925ca623ea1a3cd8e14b50161a41add8 | Python | 2,474 | 61 | from pathlib import Path
import msi_visual
import json
import glob
import cmapy
import cv2
from PIL import Image
from msi_visual.nmf_segmentation import NMFSegmentation
from msi_visual.auto_colorization import AutoColorizeRandom
from msi_visual.utils import normalize_image_grayscale, image_histogram_equalizati... |
837e6b521512a809e7d3378af49254e1cc7353168bca14e575c93d26f08f8b1a | Python | 2,474 | 71 | import numpy as np
def Ent_xSamp(x,y,m,R,norm):
"""
xSE = Ent_xSamp20180320(x,y,m,R,norm)
Inputs - x, first data series
- y, second data series
- m, vector length for matching (usually 2 or 3)
- R, R tolerance to find matches (as a proportion of the average
... |
388e31adecf8a7f3d93dda798064183d014070777701d062949ae4af4d11d85a | Python | 2,475 | 81 | import neuroglancer
import numpy as np
import pytest
def test_numpy_data():
shape = (8, 16, 32)
data = np.zeros(shape)
local_volume = neuroglancer.LocalVolume(data)
assert local_volume.rank == len(shape)
assert local_volume.shape == shape
dimensions = neuroglancer.CoordinateSpace(
name... |
4176d0f38cdbc308c6bf72dd742c2af6c7aa45188fcfe1491035056d8fdcff9c | Python | 2,475 | 83 | import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock1D(nn.Module):
def __init__(self, planes, downsample=True):
super(ResidualBlock1D, self).__init__()
self.c1 = nn.Conv1d(planes, planes, kernel_size=1, stride=1, bias=False)
self.b1 = nn.BatchNorm1d(p... |
01079bd7e3a328d76da51e2dd5d0ec05b896a3fa3d602cf90f29dd1d39323040 | Python | 2,477 | 63 | #
# Copyright 2017-2023 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000 with
# Sandia Corporation, the U.S. Government retains certain rights in this software.
#
# See LICENSE for full license details
#
from ..idevice import EmptyDevice
from ..generic_device import NormalError
from ...backend import ... |
31a49b28ace2a7b726bc85d372d0dfe9a2672d9f4d223a58093f21eab37f5009 | Python | 2,477 | 64 | # @license
# Copyright 2021 Google Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... |
42569736bb66ab94c7cc237416d28b45de3cb81b6729a21a35488ffcb33e0671 | Python | 2,477 | 72 | """The package `__init__` must not make a non-BRENDA import pay for BRENDA.
`d3text.datasets.__init__` used to import `d3text.datasets.brenda` eagerly,
so importing any submodule of the package pulled in the whole BRENDA data
layer regardless of what was actually wanted. `BRENDA_SCHEMA` and
`brenda_dataset` now resolv... |
f831364b9173dfdd4dc3a8eca6f9134c26f4d741ea3fa00eb3e9bb35f2d9e332 | Python | 2,477 | 89 | import GeoJax as gj
def generate_anisotropic_unit_vectors(n, scale=(1.0, 1.0, 1.0), orient_axis=None):
"""
Generate anisotropic unit vectors on a sphere,
optionally oriented to a hemisphere.
Parameters
----------
n : int
Number of vectors.
scale : tuple of 3 floats
Scaling... |
2bc825611afa95c7160130c2850471e4a248ad62a8c1a8b317e3e7c5dc534c70 | Python | 2,478 | 90 | import GeoJax as gj
def generate_anisotropic_unit_vectors(n, scale=(1.0, 1.0, 1.0), orient_axis=None):
"""
Generate anisotropic unit vectors on a sphere,
optionally oriented to a hemisphere.
Parameters
----------
n : int
Number of vectors.
scale : tuple of 3 floats
Scalin... |
3d2b265d53b1a8622b9a71d1185d93252cb6104694f6e46c4f5610648e2c9d58 | Python | 2,478 | 60 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import io
import numpy as np
import argparse
from utils import *
parser = argpa... |
fe5628448556e1f4881c446dfb8ff6aa5393e26c6283eb92e393847ea7804491 | Python | 2,478 | 62 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from collections import defaultdict
from typing import TypeVar
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import Callback
from tqdm.auto import tqdm
from mattergen.denoiser import GemNetTDenoiser
from mattergen... |
1a7e816b6d5fb2d88890c7619edc9fef51d642d1598609e6fa3f64ecd54f5a55 | Python | 2,480 | 86 | import pathlib
import pytest
import vak.datasets
@pytest.mark.parametrize(
'dataset_config, split',
[
(
{
'name': 'CMACBench',
'params': {
'window_size': 2000,
'target_type': 'multi_fr... |
da8e6ce34930753e36fc1dfa2c3e20e48d02bda2a27d3d03a07364312c5f3bd9 | Python | 2,480 | 70 | #!/usr/bin/env python
#
# Copyright 2008, Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list... |
f1fd3c1bdacb108b52572ec0a64e3a2ef2584373d1c8a0410ac74d767c029295 | Python | 2,481 | 90 | import GeoJax as gj
imp
def generate_anisotropic_unit_vectors(n, scale=(1.0, 1.0, 1.0), orient_axis=None):
"""
Generate anisotropic unit vectors on a sphere,
optionally oriented to a hemisphere.
Parameters
----------
n : int
Number of vectors.
scale : tuple of 3 floats
Sca... |
14dcdfb309c780b2d4ef0ba1938e03c3c715c11e13bd704a61e79e0b37b59672 | Python | 2,483 | 98 | import numpy as np
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
[
"#1f77b4",
"#9edae5",
"#98df8a",
"#bcbd22",
"#d6... |
4a12c1fc68b7f6b16db6c411a2e53a40ade2aa427fce7325436f604a8cc92f60 | Python | 2,483 | 83 | import argparse
import sqlite3
import neuroglancer.equivalence_map
import numpy as np
import pandas
def load_edges2(path, include_agglo_id=False):
dtype = {
"segment_a": np.uint64,
"segment_b": np.uint64,
"score": np.float64,
"x": np.int64,
"y": np.int64,
"z": np.i... |
518b1cc56db861fdad1df70b54fb233a8286045cdbdbd8485bd49ae1d5570132 | Python | 2,484 | 71 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import numpy as np
import torch
import CBIG_pMFM_basic_functions as fc
import warnings
def CBIG_mfm_test_desikan_main_different_window(gpu_index=0,
... |
a59fbc7ffc0f4203fc4c96c8b3da6aa1819c9e3de97e66b87567c3ea5b8685b2 | Python | 2,487 | 79 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
def main():
"""
Create code file with the following format:
{'audio': 'file1', 'unitA': 'file1_chnl1_units',... |
3eeceafa57b331fa13bd7c87af15600a54ac7b634ab46dc1f0c0e72ecd068146 | Python | 2,488 | 68 | import matplotlib.pyplot as plt
from pathlib import Path
from BLPlot.plotter import (
Plotter,
iter_datasets_with_runs,
load_dataset_metric,
make_box_figure,
)
class PlotEPR(Plotter):
"""
Plotter that produces one early precision box plot per dataset.
For each dataset, reads EarlyPrecisi... |
2d48df62ed24fe3c642cb977aceddb64fcaa71bb8e1fc21a437c977920d40c7e | Python | 2,492 | 71 | import torch
def R_set(x):
'''Create an indicator matrix of risk sets, where T_j >= T_i.
Note that the input data have been sorted in descending order.
Input:
x: a PyTorch tensor that the number of rows is equal to the number of samples.
Output:
indicator_matrix: an indicator ma... |
5bda85f6698982b71b27c83941c4f33e26f96fd97be6f3bc103e1abc18757d68 | Python | 2,492 | 98 | import logging
import os
import sys
from logging.config import fileConfig
def configure_logging(config_file, verbose):
if config_file:
print(f'Configure logging using {config_file}')
fileConfig(config_file)
else:
print(f'Configure logging using basic config - verbose: {verbose}')
... |
e9c1b7301270a0bfb870bd0b949715bd5f8aceb576a4b171ac00453c5f851073 | Python | 2,492 | 65 | import numpy as np
import matplotlib.pyplot as plt
from collections import defaultdict
import matplotlib
matplotlib.use("TkAgg")
import logomaker as lm
import pandas as pd
from collections import defaultdict
def get_tcr_seqs(filename, target_epi, target_len=16):
all_tcrs = set()
data = pd.read_csv... |
06c99aeff61572e0ca2a29dd85b35eac8e3b118c7ddf0928903c41474700f35f | Python | 2,495 | 54 | import argparse
import random
import numpy as np
import torch
def config():
parser = argparse.ArgumentParser('Configuration File of CPS')
# system configuration
parser.add_argument('--gpu', default=0, type=int)
parser.add_argument('--seed', type=int, default=42)
# data preparation
parser... |
e764183b2d4b5d64441cef108b5fc388ee5b90c9df2f1dacc936c02e25823e0b | Python | 2,495 | 53 | import qt
class ImporterDialogBase():
def __init__(self):
self.importerName = None
self.fileSelectTitle = None
self.fileSelectExt = None
self.selectedFile = None
self.computeReferenceToFrame = False
self.importACPCCoordinates = False
self.importDICOM = False... |
f9890fc296d7c74a4aaf77f4b6f1b91d8f0a95d44bb169936abba1bc9bad56d6 | Python | 2,495 | 74 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import sys
import soundfile as sf
import torch
import torchaudio
from feature_utils import get_path_iterator, dump_... |
5c22613944f8d3e9ca73b509cb4db28a75a90fb26bb21107d3474424e64ddd56 | Python | 2,496 | 76 | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as osp
import math
import numpy as np
import tqdm
import torch
from shutil ... |
e40bf13173627f0650ee1697cb0d2aedbca7a2279ff4cbd194fd94b8dbefb414 | Python | 2,496 | 76 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import json
from pathlib import Path
from typing import Literal
import fire
import numpy as np
from pymatgen.entries.compatibility import MaterialsProject2020Compatibility
from mattergen.common.utils.eval_utils import load_structures
from matte... |
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