sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
48869e27c87f015acf93ef4ca16285ce478d2d96cf2970de486812836fdc6c0d | Python | 2,243 | 57 | import pytest
from qtpy import PYQT5, PYQT6, PYQT_VERSION, PYSIDE2, PYSIDE6, QtNetwork
def test_qtnetwork():
"""Test the qtpy.QtNetwork namespace"""
assert QtNetwork.QAbstractNetworkCache is not None
assert QtNetwork.QNetworkCacheMetaData is not None
if not PYSIDE2:
assert QtNetwork.QHttpMult... |
567c100d23e9367b15a670f74b47380f950e387cbf7fa90e8d98b94f3aa8be5d | Python | 2,243 | 75 | #!/usr/bin/env /usr/bin/python
import sys
import numpy as np
def main():
if (len(sys.argv) < 2):
print('\n This script computes the average probability of the top 15L long+medium-range \n (i.e., |i-j|>=12) predicted distance from the npz file.\n')
print(' Please note that higher probability usual... |
5b43368ff50179983bcd6d0ac720b91c18a05247b3bdb7faa229b46dbe9622a9 | Python | 2,243 | 58 | """Tests for the shared focal loss function."""
import numpy as np
import pytest
import tensorflow as tf
from artifact_identification.losses import focal_loss_with_class_weights
class TestFocalLoss:
"""Unit tests for focal_loss_with_class_weights."""
def test_returns_callable(self):
loss_fn = focal... |
843a9fd4cd11fd453706aa933a966cb7e237a3751326fed2a4c4c23894e50d5c | Python | 2,243 | 73 | import pandas as pd
import time
import json
import content_feature as cf
import csv
import spacy
nlp = spacy.load('en_core_web_lg')
def get_content_vector(tweets):
links = cf.count_urls(tweets)
mentions = cf.count_mentions(tweets)
content_vector = {
"links_ratio":links[0],
"unique_links_rat... |
03dcdea1ffb8dc742a6679162f0a0ec20ffaa3cbc5fbc55ecb5108844028fba2 | Python | 2,245 | 96 | from rscvp.util.util_gspread import GSPREAD_SHEET_PAGE
from rscvp.visual.util import SFTF_ARRANGEMENT
__all__ = ['GENERIC_HEADERS',
'SPATIAL_HEADERS',
'PF_HEADERS',
#
'VIS_HEADERS',
'VIS_DIR_HEADERS',
'VIS_SFTF_HEADERS',
#
'var_pag... |
107b478e80452aebfbcc26a372fd2b30ab89f84c68ff6cec04c009e987dc9609 | Python | 2,245 | 66 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to convert gradient tables between FSL and MRtrix formats.
"""
import argparse
import logging
from scilpy.io.utils import (assert_gradients_filenames_valid,
assert_inputs_exist, assert_outputs_exist,
ad... |
e60311ba2db2282b438ba142873b0fb3c53d1da1af314f4dd9b84327f78b48ef | Python | 2,245 | 65 | #!/usr/bin/env python
import numpy as np
import pandas as pd
import click as ck
from sklearn.metrics import classification_report
from sklearn.metrics.pairwise import cosine_similarity
import sys
from collections import deque
import time
import logging
from sklearn.metrics import roc_curve, auc, matthews_corrcoef
from... |
f00d8a57736e6bca15f4f67b75fa730b7a28324c420d5a233e27dd637f2a4ad2 | Python | 2,245 | 57 |
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from pathlib import Path
cur_path = Path.cwd() / "data"/ "coupling_z_records_g123_fold36.txt"
# cur_path = Path.cwd() / "data"/ "coupling_z_records_g123_fold39.txt" #
# cur_path = Path.cwd() / "data"/ "coupling_z_records_g123... |
19f40fd45659a155bdef86834cd59443fb72562c62093dc0fb5144402980020e | Python | 2,246 | 56 | import argparse
import sys
from ogo.cli.ref.SidewaysFallFe import main as sideways_fall_main # Adjust import based on file structure
from ogo.cli.ref.SpineCompressionFe import main as spine_compression_main # Adjust import based on file structure
def main():
"""
Wrapper function to run the vertebra or femur F... |
38a0b5d2b7f5c87e8c37b31c0eb1bad1db0f1b3a498af8821be8a9a48b8bd979 | Python | 2,246 | 59 | #!/usr/bin/env python
"""
apply_thresholds.py
───────────────────
读取 cross_validation 生成的 fold*_val_preds.npz，再应用：
1) Beta Calibration (若提供系数)
2) 逐标签阈值
输出跨折平均 SubsetAcc 与 MCC-macro。
"""
from __future__ import annotations
import argparse, glob, json, numpy as np
from pathlib import Path
from sklearn.metrics import... |
b5d914b5799404dd0572ccdf55fa0065be6fcbe0ec8a7c48a510cdd2cc344e76 | Python | 2,246 | 66 | #!/usr/bin/env python3
#imports
import argparse
from ase import Atoms
import numpy as np
from ase.visualize import view
from ase.io import read, write
from ase.optimize import *
from ase import units
from KerNNCalculator.KerNNCalculator import *
from ase.md.langevin import Langevin
from ase.io.trajectory import Trajec... |
010d9015e89729b3e9e88f2c51e13c1fb6ebfb5ff29dd8af5eded9ebfc3adb32 | Python | 2,248 | 67 | from typing import List
import torch
from torch_geometric.data import TemporalData
class TemporalDataLoader(torch.utils.data.DataLoader):
r"""A data loader which merges succesive events of a
:class:`torch_geometric.data.TemporalData` to a mini-batch.
Args:
data (TemporalData): The :obj:`~torch_... |
cd23b0850d8fd7b5bd155d3a853099c3985f9915c9f1fdd25b1fb9b2c8a1705d | Python | 2,248 | 57 | """Run one chain, as a subprocess.
``track_dataset`` fans chains out across processes rather than threads, for two
reasons that are both ROICaT's:
* it re-extracts its 55 MB ROInet model into ``tempfile.gettempdir()`` on every
run, so concurrent runs sharing a TMPDIR overwrite the ``.pth`` while their
neighbours ... |
ddf9f6de6344f8b4c01096b163c0fe2a7d213dd18375a2b616b0a399cf6f04b3 | Python | 2,248 | 63 | #!/usr/bin/env python3
"""Print a summary of any Thalamus capture (``.tha``) file.
A handy first step when you receive a recording: see which nodes it contains, what
kind of data each produced, the analog channel names, the recording duration, and
the metadata stored at the start. Works on any ``.tha`` file, includin... |
bc16bbdd72b54fccc492c66242daddd7bf05ff8b8867df8fbd126b5d6a7c1a0e | Python | 2,249 | 63 | import os
import tempfile
import numpy as np
from ivim.constants import y
from ivim.io.base import write_bval, read_cval
from ivim.seq.lte import calc_b, calc_c, G_from_b, cval_from_bval
# Paths to data
temp_folder = tempfile.gettempdir()
# Parameter values
G = np.array([10e-3, 30e-3, 60e-3])
Delta = 20e-3
delta =... |
91bf216026bdd6e21716f4fb199e142f34c8a393d10ee48d1a74565b61f7fe18 | Python | 2,250 | 80 | # -*- coding: utf-8 -*-
#
# aodf := Asymmetric Orientation Distribution Function (aODF)
#
import numpy as np
from dipy.reconst.shm import sph_harm_ind_list
def compute_asymmetry_index(sh_coeffs, order, mask):
"""
Compute asymmetry index (ASI) (Cetin-Karayumak et al, 2018) from
asymmetric ODF volume expr... |
69042a4848bbdaa91a2a78bfa9b08de60633cc11a62107b5ccf1483822714512 | Python | 2,251 | 67 | #!/usr/bin/env python3
#imports
import argparse
from ase import Atoms
import numpy as np
from ase.visualize import view
from ase.io import read, write
from ase.optimize import *
from ase import units
from KerNNCalculator.KerNNCalculator import *
from ase.md.langevin import Langevin
from ase.io.trajectory import Trajec... |
8038a0d7ecb65e1699675bc91619acee19de48b05d71225f7f00e8466c7c8b35 | Python | 2,251 | 91 | import os.path as op
import nibabel as nib
import numpy as np
def rician_img_correct(dwiname, noisemapname, outpath=None):
"""Performs Rician correction on a dataset with a noisemap
Parameters
----------
dwiname : str
Filename of image to be corrected
noisemapname : str
Filename ... |
01acc4135da01f543c096212966a67fd041a4073b72c399abef144953b4d4476 | Python | 2,252 | 60 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(),
keys=['processing.zip', 'commit_am... |
0bdd7f9a6da4926ca7f483f44fd1917de2a67379c007dd67c93cadbba224c7da | Python | 2,252 | 84 | import numpy as np
import hipsternet.input_data as input_data
import hipsternet.neuralnet as nn
from hipsternet.solver import *
import sys
n_iter = 1000
alpha = 1e-3
mb_size = 64
n_experiment = 1
reg = 1e-5
print_after = 100
p_dropout = 0.8
loss = 'cross_ent'
nonlin = 'relu'
solver = 'adam'
def prepro(X_train, X_va... |
44ac89e0d55b654647fadab7790e4e3dc618e0a819e734e666e48dd14f3ff5c4 | Python | 2,252 | 51 | import os
import subprocess
import shutil
import nibabel as nib
import time
import numpy as np
import pandas as pd
from nipype import Node, Workflow
from nipype.interfaces.base import BaseInterface, BaseInterfaceInputSpec, TraitedSpec, File, Directory, traits, CommandLineInputSpec, File, TraitedSpec, CommandLine, Direc... |
9f28d056e4382c030f14ec46b875a9b2aa399809d6c36392b8b52ff94d97e635 | Python | 2,252 | 30 | from testsetclass import TestsetClass
class PArel(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['h2s2o71', 's4o42', 'c2h2f41', 'his3', 'sugar2', 'A2', 'his2', 'sugar3', 'sugar4', 'h4p2o71', 'aminocoohbz2', 'aminocoohbz3', 'arg2', 'c2cl43', 'T2', 'c2cl42', 'T3', 'h2s2o72', 's... |
bd377b68d9504662f5e5ece3eb2e52fd3524375de48ceedede80dadded16cdac | Python | 2,252 | 60 | """Contains constants used for the Therapeutics Data Commons Data."""
DATASET_TO_TYPE = {
"Caco2_Wang": "regression",
"PAMPA_NCATS": "classification",
"HIA_Hou": "classification",
"Pgp_Broccatelli": "classification",
"Bioavailability_Ma": "classification",
"Lipophilicity_AstraZeneca": "regressi... |
67aff2333da21c39a5f1b56b979fc3e15f972515ed625921c6387b022a54d81e | Python | 2,253 | 64 | """Merge echos into single 4D nifti.
NOTE: This script might be unnecessary if you like having the echos as separate
files. However, note that this script also exports an echo average image. Which
is convenient.
"""
import os
import nibabel as nb
import numpy as np
# Parameters
NII_ECHOS = [
"/home/faruk/DATA... |
d2d3ec220bef524e425063e62ad1d13b02607d954b97bf4f39e11dbbd2d688c8 | Python | 2,253 | 57 | from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_vision import stimulus_set_registry, data_registry
from brainscore_core.supported_data_standards.brainio.s3 import (
load_assembly_from_s3,
load_stimulus_set_from_s3,
)
BIBTEX = """@article {Maniquet2024.... |
ad0e8b0fc0931dee993b7d977f5e07601cae2e550e2b3ad355bdb586fe7289fa | Python | 2,254 | 54 | import os
import pickle
import json
import numpy as np
import sys
from ogb.lsc import WikiKG90MDataset, WikiKG90MEvaluator
import pdb
from collections import defaultdict
import torch.nn.functional as F
import torch
# python save_test_submission.py $SAVE_PATH $NUM_PROC
if __name__ == '__main__':
path = sys.argv[1]
... |
b17411ec856e7bcf25499bc53ea02290d94a907bb60a4dc729561141eeb8cfdb | Python | 2,254 | 58 | from dataclasses import dataclass, field
import torch
@dataclass
class System:
"""
A data structure representing a system with various tensors and embeddings,
used in graph-based computations.
Attributes:
batch (torch.Tensor, optional):
A tensor indicating the batch assignments o... |
bd1758839162496a63f67aaf43fc9881db72cc384f098126da8c95f7bf22f275 | Python | 2,254 | 75 | import sys
from pathlib import Path
p_dir = str(Path(__file__).absolute().parents[1])
if p_dir not in sys.path: sys.path.insert(0, p_dir)
import numpy as np
args_short_to_long = {
"-l": ("loss", 0),
"-bs": ("batch_size", 0),
"-do": ("dropout", 0),
"-wd": ("weight_decay", 0),
"-dim": ("dim", 0),
... |
e18157f38602afc03da6127f27d4ec38af383514fbd64745d633bafeee8efb29 | Python | 2,255 | 67 | import os
import pandas as pd
__author__ = "ContraxSuite, LLC; LexPredict, LLC"
__copyright__ = "Copyright 2015-2018, ContraxSuite, LLC"
__license__ = "https://github.com/LexPredict/lexpredict-legan-dictionary/blob/master/LICENSE"
__version__ = "1.0.7"
__maintainer__ = "LexPredict, LLC"
__email__ = "support@contraxsu... |
7c504f89eee496d6a46e4e30d0dfee7df04c357300e0486bb8fcf1ca68c780ba | Python | 2,256 | 63 | import numpy as np
from scilpy.denoise.asym_filtering import \
unified_filtering, cosine_filtering
from scilpy.reconst.utils import get_sh_order_and_fullness
from scilpy.tests.arrays import (
fodf_3x3_order8_descoteaux07,
fodf_3x3_order8_descoteaux07_filtered_unified,
fodf_3x3_order8_descoteaux07_f... |
8e94bbe6f3b03944c9b9808cf233576a7dc0d9dfaa0f361aa81b627ac0377689 | Python | 2,256 | 67 | #!/usr/bin/env python3
#imports
import argparse
from ase import Atoms
import numpy as np
from ase.visualize import view
from ase.io import read, write
from ase.optimize import *
from ase import units
from KerNNCalculator.KerNNCalculator import *
from ase.md.langevin import Langevin
from ase.io.trajectory import Trajec... |
a087ca2b16bdc8bb225739d80a41a90d50bc449fa30d5bec3e0abb334fd4bb5d | Python | 2,256 | 47 | import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from pathlib import Path
data_path = Path('D:\Data\Lobster\Lobster_Recording-200319-161008\EventClassifierDataset\output_IL.mat')
data = loadmat(str(data_path.absolute()))
mat_confusion = data.get('mat_confusion')
#... |
e228ea557e7fd4b602bd547d908ccb0fb41e0484d9b344946caa13f97c4edb09 | Python | 2,256 | 80 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
from abc import ABC, abstractmethod
from collections.abc import Collection, Iterator, Sequence
from typing import TYPE_CHECKING
from attrs import define, field, validators
from cvat.apps.dataset_manager import da... |
f94ce9dfa5e609ef7d9360420b6d33bd0759ae4bc60e78b7cdc999288acd6c69 | Python | 2,256 | 54 | import pytest
from qtpy import PYQT5, PYSIDE2
@pytest.mark.skipif(
not (PYQT5 or PYSIDE2),
reason="Only available in Qt5 bindings",
)
def test_qtlocation():
"""Test the qtpy.QtLocation namespace"""
from qtpy import QtLocation
if PYSIDE2:
assert QtLocation.QGeoServiceProviderFactory is no... |
de75c0ced3350b46b0c223f646a2bff8421a794d6ab9159902980df15ff56d52 | Python | 2,257 | 58 | """
Brain-Score vision plugin — Iteration 1 baseline.
ConvNeXt-Large with LAION-2B CLIP pretraining (in12k+in1k fine-tune, 384px), exposed
with FINE-GRAINED block-level candidate layers so the framework's per-region
LayerSelection has good options (existing convnext entries only expose the 4 coarse
stage outputs). reg... |
e97d454871ccd7e2fab28e943612a63bd0a27b75c9a5b8a7c22bb9d44ea4b37c | Python | 2,257 | 43 | from torchmetrics import Metric
from typing import Optional
import torch
class MeanPearsonCorrCoefPerChannel(Metric):
is_differentiable: Optional[bool] = False
higher_is_better: Optional[bool] = True
def __init__(self, n_channels:int, dist_sync_on_step=False):
"""Calculates the mean pearson correl... |
11f0f6adb045ed1650caa6ae75d387db0e4a4596628efba8960681440a7acdbe | Python | 2,258 | 51 | import os
import shutil
def collect_files_by_pattern(bids_deriv_root, subfolder_path, target_filename, have_id, output_dir):
"""
Traverse BIDS derivatives to collect specified files across all subjects and sessions.
Parameters:
- bids_deriv_root (str): Path to the BIDS derivatives root directory.
... |
35797adc2fbdac0899e174f5dbd85e9af55972b8dc0a988441e406ed5e33797a | Python | 2,258 | 64 | import torch
import torch.nn as nn
from ..modeling import Sam
from .amg import calculate_stability_score
class SamCoreMLModel(nn.Module):
"""
This model should not be called directly, but is used in CoreML export.
"""
def __init__(
self,
model: Sam,
use_stability_score: bool =... |
3b155b33ecf84dc06769717c12930cb93764312350032943dc40eadc1fa4a067 | Python | 2,258 | 75 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Compute bundle volume per label in mm3. This script supports anisotropic voxels
resolution. Volume is estimated by counting the number of voxel occupied by
each label and multiplying it by the volume of a single voxel.
The labels can be obtained by scil_bundle_label_m... |
a4bdc4a9adac99ebfa73165872b5ec69e0ada013acd3b5a7f0e1f75319a2d76c | Python | 2,258 | 67 | from scipy.spatial import cKDTree
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import scanpy as sc
import numpy as np
import pandas as pd
def logreg_epoch_score(adata, epoch):
"""
Returns the median logreg test score averaged across species.
Also... |
474e10607646e28bc16812ee778c48baa0a15e5b68906c0939f2e83ca23f32d0 | Python | 2,259 | 76 | from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn, Tensor
__all__ = ["BalancedBCEWithLogitsLoss", "balanced_binary_cross_entropy_with_logits"]
def balanced_binary_cross_entropy_with_logits(
logits: Tensor, targets: Tensor, gamma: float = 1.0, ignore_index: Optional[in... |
487ec3838cc31a1fe562cfd5dc1fea5812f9a5dc208373bef1b4e2e54a3d8698 | Python | 2,259 | 73 | import pytest
import numpy as np
from nems.layers.base import Parameter, Phi
def test_constructor():
# Name should be only required argument, shape defaults to ()
p = Parameter('test')
assert p.shape == ()
p = Parameter('test', shape=(2,3))
assert p.shape == (2,3)
assert p.prior.shape == (2,... |
492e2e68436c04f7cd646b13678a1bd984e4e4526ebdf8f6cfb9989f6db2714b | Python | 2,259 | 64 | import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from sklearn import tree
from sklearn.metrics import roc_auc_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sk... |
d68fdd852d156992fa0951650fe6a39c36b3ac4dcc76a93fef9ea615c832e3b4 | Python | 2,260 | 69 | # %%
# imports
# this notebook script expects monica's torchtrain environment
import sys
sys.path.append("/home/mthieu/Repos/emonet-py/python/myutils")
from emonet_utils import Cowen2017Dataset
import torch
from torchvision.models import resnet50, ResNet50_Weights
these_weights = ResNet50_Weights.IMAGENET1K_V2
import... |
dd9d4c6cbe94df5e44e865a930807e3a7f32b681b88b7e2b8873e4f4fb05d8ba | Python | 2,260 | 75 | import unittest
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
from sklearn.utils import Bunch
from sklearn.metrics import get_scorer
from causallib.estimation import IPW
from causallib.model_selection import GridSearchCV
from causallib.contr... |
aeaa1307714824d71416d0b4320afcdd25b597591f8cc19a147139a841d7f02e | Python | 2,261 | 64 | from monai.transforms import Compose, ToTensord, ScaleIntensityd
import torch
import numpy as np
import cv2
from torch.utils.data import DataLoader
from data_loader import BrainXLDataset
datafolder = "/home/fi5666wi/Brain_CT_MR_data/DL"
# Removed due to insufficient quality on MRI image
# 1_BN52, 2_CK79, 3_CL44, 4_J... |
7b00804b7f79746c05f2f1043d1daba030c4ec86f017f7755d7cdc24bf885235 | Python | 2,262 | 63 | import torch
import torch.nn as nn
import torchvision.models as models
class SqueezeExcitation(nn.Module):
def __init__(self, in_channels, reduction=4):
super(SqueezeExcitation, self).__init__()
self.se = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(in_channels, in_cha... |
9420c344ce631d4c66958f07064acbe2ffe34f137b3605f6fc4cb375b1277a23 | Python | 2,262 | 49 | import numpy as np
stimplotfns = {
'AudStim_10kHz': lambda timestamps,ax: [ax.axvspan(timestamp, timestamp + 0.2, **{'color': 'k', 'alpha': 1, 'lw': 0}) \
for timestamp in timestamps],\
'passive_bluenoise': lambda timestamps,ax: \
[ax.axvspan(timestamp, tim... |
180a0803f3765b3073e8559f050964224a4cd467b99b81b20368b5dea4154bc1 | Python | 2,266 | 72 | #Type something like this from within python: ARGS="2015_04_07_02"
#template: ARGS="filename"
#then type: execfile('InductionAnal.py')
#If not in python, type: python InductionAnal.py 031011_5Or
import os
#if running from windows, might need to execute the following command:
#os.getcwd()
#might need to use for... |
125caf7c18e6f7165550c040863746136f35dae1f075d819cad5c64abeefa397 | Python | 2,267 | 56 | class Test_AS_Config:
def test_config(self, dropConfig,demo_dir):
assert dropConfig.AS.getWorkdir() == f"{demo_dir}/Scripts/AberrantSplicing/pipeline"
dict_ = {
'run': True,
'groups': ['fraser', 'fraser_external'],
'recount': True,
'longRead': False,
... |
1fc75ae41d2455735b0c4449a8bfa487de79cbb50ea871c6ba836888712c6f85 | Python | 2,268 | 91 | import networkx as nx
import numpy as np
import os
from tqdm import tqdm
edge_path=r'Twibot-22/edges.npy'
edge_index=list(np.load(edge_path))
edges=[]
dataset_name='Twibot-22'
id_include=(np.load(dataset_name+'/id_include.npy',allow_pickle=True))
if not os.path.exists(dataset_name):
os.mkdir(dataset_name)
id_inclu... |
941118792cb4249537ff3c2afd86b2e1525a5dfe7514bf3c3dadf1e957ed2a66 | Python | 2,268 | 69 | """Utility functions related to the ADMET-AI web server."""
import re
from pathlib import Path
from tempfile import TemporaryDirectory
import pandas as pd
from flask import request
from werkzeug.utils import secure_filename
SVG_WIDTH_PATTERN = re.compile(r"width=['\"]\d+(\.\d+)?[a-z]+['\"]")
SVG_HEIGHT_PATTERN = re.... |
8a0ee5dfa29cd01c6c0125c028a48d5afa13547b891d9c54663e376e48149310 | Python | 2,269 | 76 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import nibabel as nb
from nipype.interfaces.freesurfer import ApplyVolTransform
# local inputs
from ..io.get_filename import get_filename
def skullstrip_refined(file_mask1, file_mask2):
"""Skullstrip refined.
The ... |
e8103c047f441308da941730afcd1a9d1c62ff6f5e6a2898ff8425cdd703505e | Python | 2,269 | 68 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from logging import Logger
from pathlib import Path
import django_rq
from django.conf import settings
from rq.exceptions import DeserializationError
from rq.job import Job
from cvat.apps.engine.log import get_migration_logger
from cvat.apps.engine.... |
14033ca2c568e92aedf8f751050df445dd1d64566c1e7c9068b7fa57a8c11e56 | Python | 2,270 | 64 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
from urllib.request import urlretrieve
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_prepro... |
53f2f9237b1c8b1505f5b59f656a682d81ed967b478d91b2b0509d1465f8a3b3 | Python | 2,270 | 68 | # %% ### import modules
from pathlib import Path
import pandas as pd
# %% read results table
results_dir = Path(__file__).resolve().parents[1] / "results"
# %% data
for analysis in ["fg"]:
df = pd.read_excel(results_dir / f"long_{analysis}_results.xlsx")
# %% round values
df["estimate"] = df["estimate"]... |
6688f898666ef122b5027447da8138fa430af700a2658bb1ea2212268a086c51 | Python | 2,270 | 63 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Daniel Abele
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Licen... |
ef29bcb0fbd6aaf0fe91988bda7d9b9203af5cd65a2ea1a6619a87c966ec938b | Python | 2,270 | 64 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
from urllib.request import urlretrieve
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_prepro... |
ade0b2ea9f665194732eb72c1db77434992b1b235faa81fac2fed0e31e868081 | Python | 2,271 | 69 | from __future__ import annotations
import importlib
import json
import math
import subprocess
import sys
from pathlib import Path
import numpy as np
import PIL.Image
import pytest
pytest.importorskip("pycocotools.mask")
labelme2coco = importlib.import_module("examples.instance_segmentation.labelme2coco")
def test_... |
395ec5c82e65ff7f24c026d1309a7866979bf4b9063039011e378102b263414b | Python | 2,272 | 72 | #!/usr/bin/env python
"""
The standard HAL dialog box.
All the dialog boxes in HAL are sub-classes of this dialog box.
Hazen 2/17
"""
from PyQt5 import QtWidgets
import storm_control.hal4000.qtWidgets.qtAppIcon as qtAppIcon
class HalDialog(QtWidgets.QDialog):
#
# In order to have the correct minimization ... |
b7ac8d8f630e97b4c06869d309a4ddd64d9aa01b69c05d676c668da338053935 | Python | 2,272 | 62 | import json
from copy import deepcopy
from pathlib import Path
from eir.experiment_io.input_object_io import get_input_serialization_path
from eir.experiment_io.input_object_io_modules.sequence_input_io import (
load_sequence_input_object,
)
from eir.experiment_io.output_object_io_modules.output_io_utils import (
... |
2897a284a095a11f552567bb1a623733ad9ba8333b38425f99bdc3c6f26df078 | Python | 2,273 | 37 | """Prepare outcome-independent memberships; frozen configurations are immutable."""
import hashlib,json
from src.acquisition.download import ROOT,sha256
PRIMARY=['HALLMARK_OXIDATIVE_PHOSPHORYLATION','HALLMARK_GLYCOLYSIS','HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY','HALLMARK_MTORC1_SIGNALING','HALLMARK_PI3K_AKT_MTOR_SIG... |
2c34003816b954ddd37568a98f6cb5692f5da06b46237ef0e65fd452cf685cc2 | Python | 2,273 | 64 | import os, sys
# set path
current_dir = os.getcwd()
sys.path.append(current_dir)
import argparse
from src.dataset.cath_dataset import CathDataset
from src.dataset.mutant_dataset import MutantDataset
from src.utils.dataset_utils import NormalizeProtein
def build_cath_dataset(args, split):
dataset = CathDataset(
... |
909f356843ae1807d90e8c21d9eaa915699f24acba5fe6e73c91f7ef510b252b | Python | 2,273 | 64 | import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import logomaker
from matplotlib.backends.backend_pdf import PdfPages
def plot_loss(history, out_fh, loss_fxn=None):
'''
plot train and val loss curves per epoch
history is original history object returned by ker... |
66fbeef6c72843eaf596014e6cbdff787eb77878f7c0913fae089d64abe62722 | Python | 2,274 | 62 | from tqdm import tqdm
import numpy as np
import torch
# def train(model, optimizer, train_loader, num_epochs, seed=1, device="cpu"):
# if device is None:
# device = torch.device(device)
# torch.manual_seed(seed)
# for epoch in range(num_epochs):
# model = model.train... |
6becd1f10a891674c629d027775f5211cbe100b1db4ef7c2512f4325271e26e4 | Python | 2,274 | 72 | import shutil
from flair.data import Dictionary
from flair.trainers.language_model_trainer import TextCorpus
def test_train_resume_language_model_training(
resources_path, results_base_path, tasks_base_path
):
# get default dictionary
dictionary: Dictionary = Dictionary.load("chars")
# get the examp... |
cbb3d810ea2d8bd46a45f522db604b43add432e2d1937f0489a56828c1df04e7 | Python | 2,274 | 73 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Validate and correct gradients from eddy outputs
With full AP-PA eddy outputs a full bvec bval (2x nb of dirs and bval)
that doesnt fit with the output dwi (1x nb of dir)
"""
import argparse
import logging
import numpy as np
from scilpy.io.utils import (add_overwrit... |
fb6cf0fe0b8235e8173a02f0b0cd3d0a312c1df9c4d4dce88814c7c2b5997a6d | Python | 2,274 | 64 | import os
import shutil
import unittest
from auxiliary.turbopath import turbopath
from brainles_preprocessing.brain_extraction import HDBetExtractor
class TestHDBetExtractor(unittest.TestCase):
def setUp(self):
test_data_dir = turbopath(__file__).parent + "/test_data"
input_dir = test_data_dir +... |
4d8680feb6bdccb716eeca0da71cc71bee861095189616b9928bdf3c2b1d1297 | Python | 2,275 | 65 | import argparse
import subprocess
import sys
import os
def list_tools(base_dir):
"""列出所有可用工具"""
print("Available tools:")
for folder in ["python", "bash", "matlab"]:
folder_path = os.path.join(base_dir, folder)
if os.path.exists(folder_path):
for script in os.listdir(folder_path... |
5cfde8efe5f29370fa07dac5fd99ccb7c54c1e70e000a34b90eea5bede5a17fc | Python | 2,275 | 85 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
import copy
# external inputs
import numpy as np
import nibabel as nb
def get_std(file_in, path_output, name_output, set_outlier=None):
"""Get std.
This function computes the standard deviation of one or more time series.
Parameter... |
7ef2c4b7029cdd1cd27901264f7a9ecd43fcd29988ac0cac49518b3e1754ec21 | Python | 2,275 | 65 | """Tests for the molecular docking tool.
Hermetic by default: candidate resolution uses a SMILES (no network), docking is
exercised via ``mock_docking`` and by binding the tool to the single-agent graph.
The real Vina path runs only when the optional ``docking`` extra (vina + meeko) is
installed.
"""
from unittest.mo... |
c8ab492244881d7f9315200dec6e758d68f7af696b742eb9e14bf9bec0225415 | Python | 2,275 | 76 | # coding=utf-8
"""
Tests for deepreg/model/loss/label.py in
pytest style
"""
from test.unit.util import is_equal_tf
from typing import List, Optional, Union
import numpy as np
import pytest
import tensorflow as tf
from deepreg.loss.label import DiceLoss, DiceScore
from deepreg.loss.util import MultiScaleMixin, separ... |
ca36e795a552c11e1f812a8c871e6f79f1d83d2a62e35c44468768505259f4f4 | Python | 2,275 | 56 | ##########################################################################
# Simple ycm_extra_conf.py example #
# Copyright (C) <2013> Onur Aslan <onur@onur.im> #
# #
# This file is loade... |
cc42c5bc9311ad2008bbf0000c528e0429fd687c4086ff548d58ef7edc0414f3 | Python | 2,275 | 60 | import os
import numpy as np
from scipy.io import wavfile
from tqdm import tqdm
from active_filter.LMSnoise_cancel import LMSNoiseCanceller
import re
def read_wav_files_from_folder(audio_dir, scale=None):
folder_list = [os.path.join(audio_dir, f) for f in os.listdir(audio_dir) if os.path.isdir(os.path.join(audio_d... |
57853bbc5cd19d3ad8a73e9452077df9843c270de9b75a87ff2dc4cfc1bb605f | Python | 2,276 | 69 | #!/usr/bin/env python3
"""
diverse_picker.py
´ÓSMILESÎļþÖÐÑ¡È¡¶àÑùÐÔ×Ó¼¯£¨Ä¬ÈÏButina¾ÛÀࣩ
Ó÷¨£º
python diverse_picker.py input.smi output.smi 100 --method butina --cutoff 0.65
"""
import sys, argparse
from rdkit import Chem, DataStructs
from rdkit.Chem import rdMolDescriptors
from rdkit.ML.Cluster impo... |
d9855d7970bf2d75aab74901c9019188975cd5035fb6347aa4e19e3dc7f1c80a | Python | 2,277 | 61 | """Data loading, winsorisation & scaling utilities (auto-rebuild scaler)."""
from __future__ import annotations
import math, os
from pathlib import Path
from typing import Tuple
import joblib, numpy as np, pandas as pd
from sklearn.preprocessing import StandardScaler
from src.config import C
def load_data(
csv_pa... |
0394e027c2b04d29838761f9bf412e90d79755170a4133d0224ebf903da6d490 | Python | 2,278 | 64 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
from urllib.request import urlretrieve
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_prepro... |
37b876f5a294536afb787be2921f5f4745c6723b34cb3736868077a6a8f1392e | Python | 2,278 | 64 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
from urllib.request import urlretrieve
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_prepro... |
4618f312d00145a77f07fdb9cfe811d066b00d552b2e6c582c6bf6dcc95ebb2a | Python | 2,278 | 64 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
from urllib.request import urlretrieve
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_prepro... |
6cede6d59c7600c262a6b42d74113d9f8162161c2304def5e7f1c4bfb7ea1816 | Python | 2,278 | 55 | """Reduce bounding box to decrease filesize.
NOTE: I have determined the ranges either in ITKSNAP or FSLEYES. The ranges
should be close to the cortex and excapsulate the whole cortex. Avoid putting
the boundaries too close (i.e. one voxel away) form the cortex. Give a bit of a
margin (e.g. 10-20 voxels).
"""
import ... |
7673a5a9e63f3138f44b8414215be0dda030abf87ab566289f70ebd48f9beecd | Python | 2,278 | 64 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
from urllib.request import urlretrieve
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_prepro... |
07022d27a71ae6b1ae3f3932ca3326042dc715a8b41e0b9811bdb2b1fc8e03f3 | Python | 2,280 | 59 | # Copyright (C) 2021-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import os.path as osp
import shutil
from datumaro.components.dataset import StreamDataset
from datumaro.plugins.data_formats.cityscapes import write_label_map
from cvat.apps.dataset_manager.bindings impor... |
1c51fd7fb26446e3e3a7b1377a5bd292729fd43ff27c19484f1681d3a75dc704 | Python | 2,280 | 53 | from PySide6 import QtGui, QtWidgets, QtCore
from PySide6.QtWidgets import QWidget, QMenuBar, QMainWindow, QVBoxLayout, QLabel, QHBoxLayout, QGridLayout, QPushButton, QCheckBox, QSlider, QTableWidget, QTableWidgetItem, QTabWidget, QGroupBox, QSpinBox, QFileDialog, QMessageBox
from PySide6.QtGui import QAction, QIcon
fr... |
5156910f8dff11cdd11eb797fff7c8835cd7bf0bad5d874f1df15d14cc60370b | Python | 2,280 | 70 | #!/usr/bin/env python3
# -*- coding: UTF-8 -*-
# Python standard library
from __future__ import print_function
from subprocess import CalledProcessError
import os, subprocess
# Local imports
from utils import fatal, err
def set_options(strict):
"""
Changes behavior of default shell and get overrides options... |
e16c7ecca2397eb90eef6c2cc2ff6d822db377a1ebc068f2ffe0c3cc1de9dcda | Python | 2,280 | 63 | # -*- coding: utf-8 -*-
"""
Apply nonlinear deformation to surface mesh
The purpose of the following script is to apply two deformations in succession
(orig -> ana -> epi) to a surface mesh using generated coordinate mappings. The
script needs an installation of freesurfer.
"""
# python standard library inputs
impor... |
e5860aea6417e2cf0cb29bda61e90c164b1bdcd653ff0f6d0eab9ee1646bc75b | Python | 2,280 | 69 | # %%
from pathlib import Path
import seaborn as sns
import pandas as pd
import bap1b.figures as figures
# %% ### set paths
data_dir = Path(__file__).resolve().parents[1] / "data"
results_dir = Path(__file__).resolve().parents[1] / "results"
# %% ### load data
df = pd.read_excel(data_dir / "cs_baseline.xlsx")
df["dia... |
1c9040e4f6b08a693ac63b6f8afeec225c75719d26bb87ebfefd692c9de03518 | Python | 2,281 | 91 | # myelinfccoupling/binning.py
from typing import Optional
import numpy as np
import pandas as pd
from .config import Config
from . import model
def bin_by_myelin(edges: pd.DataFrame, cfg: Config) -> pd.DataFrame:
"""
Add a 'my_bin' column to edges based on quantiles of myelin.
"""
edges_b = edges.c... |
bc2fc57758ef7385268ffab66001f48ac550a840bd48783c7618a232b4327cc1 | Python | 2,281 | 62 | from torch import nn, relu, dropout, cat as tcat
import torch.nn.init as init
class Block(nn.Module):
"""
A neural network block used in DLIM for phenotype prediction.
This block consists of a configurable number of hidden layers, each followed by
an activation function and optional dropout and batch ... |
77735772509ca77a0ac5b8082c8db3fb772689bf899049171a7926b357987af8 | Python | 2,282 | 61 | from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.utils.metaestimators import _safe_split
import pandas as pd
from sklearn.base import clone
def cross_fitting(estimator, X, y, n_splits=5, predict_proba=False,
return_estimator=True):
"""
Args:
estimator(object):... |
0c02cca2267567c591176a724c585c925964f36ec2680c0a09256b22130970e4 | Python | 2,283 | 80 | # Configuration file for the Sphinx documentation builder.
import os
# -- Project information -----------------------------------------------------
project = "MuscleX"
copyright = "2025, BioCAT"
author = "BioCAT"
version = "2.1"
release = "2.1.1"
# -- General configuration -----------------------------------------... |
586b6f5bc9a255532cb2321f18e18b3c6270128d5cf45bc56ebe1f4052361684 | Python | 2,283 | 71 | # The ImpedanceFitter is a package to fit impedance spectra to
# equivalent-circuit models using open-source software.
#
# Copyright (C) 2018, 2019 Leonard Thiele, leonard.thiele[AT]uni-rostock.de
# Copyright (C) 2018, 2019, 2020 Julius Zimmermann, julius.zimmermann[AT]uni-rostock.de
#
# This program is ... |
95556efb1b231e0d079576742161f90b8299fa5ed291b5706211595d22528e67 | Python | 2,283 | 64 | from matplotlib import pyplot as plt
import numpy as np
purple = [.5, 0, .5, 0.9]
purple_light = [.5,0,.5, 0.3]
green = [0, 1, 0, 0.9]
green_light = [0,1,0,.3]
blue = [1,.3,0, .9]
blue_light = [1,.3,0,.3]
grey = [.5,.5,.5, .9]
grey_light = [.5,.5,.5, .3]
colors = [purple,green, blue]
colors_light = [purple_light, green... |
9e686270b0c10f30ab839c569da9b8bb26b08a0fa1e5ce45e1d7f627d6a9020e | Python | 2,284 | 93 | import numpy as np
import numpy.typing as npt
def generate_xyz(array: npt.NDArray, path: str):
"""Generate Vesta-like XYZ file from a numpy array
Params
------
array: Numpy array containing XYZ information
path: Path+Name to save the XYZ file
"""
f = open(path, "w") # Open the file
... |
eaa3ffe47eada9c24c3daa305c06f573e574d89a10a9611b53adaeaf512c1372 | Python | 2,284 | 64 | from typing import Any
import torch
from pytorch_lightning import LightningModule
from torchmetrics.classification.accuracy import Accuracy
from {{cookiecutter.project_slug}} import utils
log = utils.get_logger(__name__)
class ClassificationTaskModule(LightningModule):
def __init__(
self,
net: t... |
4cca61be2c65a51f2841e743f6793b886c4afde72b21139c1d978254ff4da095 | Python | 2,285 | 84 | #!/usr/bin/env python3
import os, sys, re
usage = "usage: {} longest_orfs.cds.scores longest_orfs.cds cds.gff.scored\n\n".format(sys.argv[0])
if len(sys.argv) < 4:
sys.stderr.write(usage)
sys.exit(1)
def main():
cds_scores_file = sys.argv[1]
cds_fasta = sys.argv[2]
scored_orfs_file = sys.argv... |
dbcb9d93e1b8db6f7c3dabc858cf3319093d939466af060a6cbcc64ed35fa3ec | Python | 2,285 | 52 | """Tests for the pure-geometry helpers in generate_patches (no pycurv needed)."""
import numpy as np
from scipy.spatial.distance import pdist
from surface_morphometrics import generate_patches as gp
def test_nearest_triangle():
tri = np.array([[float(x), 0, 0] for x in range(11)])
part = np.array([[0.2, 0, 0... |
bb072c57d5f1c6b9fedf20d33ef6c0b52a1c9756c4930bfb968e4c66ae889689 | Python | 2,286 | 52 | from qtpy.QtCore import QAbstractTableModel, Qt, QModelIndex
class PandasModel(QAbstractTableModel):
def __init__(self, dataframe, parent=None):
super().__init__(parent)
self._dataframe = dataframe
# Initialize check states for each row, False means unchecked
self.check_states = {i... |
02d19b116a042a69343f008a941b6cf05c25100ee8d0e020b800bcf849dccdb8 | Python | 2,287 | 51 | """
Iter-4 candidate — DINOv2 ViT-L/14 WITH the official ImageNet linear classifier (dinov2_vitl14_lc).
Why this exists: the headless DINOv2 (num_classes=0) cannot do Brain-Score's label-based behavior
(Geirhos/Baker route through LabelBehavior, which asserts the model emits 1000 ImageNet logits) —
so plain DINOv2 fai... |
1791d2f4df1954fc4773ab6193fbb5ecd5f047470769a0adcfe3d5859094d156 | Python | 2,287 | 52 | from PyQt5.QtCore import QAbstractTableModel, Qt, QModelIndex
class PandasModel(QAbstractTableModel):
def __init__(self, dataframe, parent=None):
super().__init__(parent)
self._dataframe = dataframe
# Initialize check states for each row, False means unchecked
self.check_states = {... |
24cc0bb935678a7aea3dd290c47c944b83c550e64db5cfca1c306bcfe37b59b2 | Python | 2,287 | 65 | import torch
from lightning import LightningModule
from torchmetrics.classification import MultilabelAUROC
from custom.network import SETresnet50
class NPhaseClassifier(LightningModule):
def __init__(self) -> None:
super().__init__()
self.model = SETresnet50(num_class=2, n_token=48)
... |
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