sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
f7916f6cc661fe0faef259b959df96c4d99718e0bd1b5f3f2b7972e9faf8da1b | Python | 2,247 | 59 | import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
def load_image_masked(img_filename, im_threshold = 15):
orig = nib.load(img_filename)
zoom = orig.header.get_zooms()
if len(zoom) == 4:
zoom = zoom[:-1]
orig_data = orig.get_fdata()
mask = orig_data > im_threshold
... |
64010bcf485b69b9158b3f979f585c654b4ab664d386d42d7abc1ad006c9cab8 | Python | 2,249 | 61 | """PyTorch Dataset wrappers for non-graph molecular representations."""
from typing import List, Optional, Tuple
import numpy as np
import torch
from torch.utils.data import Dataset
class FingerprintDataset(Dataset):
"""Dataset wrapping fingerprint arrays and target values.
Shape contract
--------------... |
cb993b205ec6812312fa7c48a70a7569fe0e6d93247984690456aaf1028d9129 | Python | 2,252 | 95 | """This tests the CLI functionality of predicting with a callback.
"""
import json
import pytest
from chemprop.cli.main import main
pytestmark = pytest.mark.CLI
@pytest.fixture
def data_path(data_dir):
return str(data_dir / "test_smiles.csv")
@pytest.fixture
def model_path_classification(data_dir):
retur... |
ad1a0f1012b6d3801ea757f09dd7e7252b387357a4c39a5985a08ef3e8a6dc4f | Python | 2,256 | 79 | # 'grep'
import regex
from regex_syntax import *
opt_show_where = 0
opt_show_filename = 0
opt_show_lineno = 1
def grep(pat, *files):
return ggrep(RE_SYNTAX_GREP, pat, files)
def egrep(pat, *files):
return ggrep(RE_SYNTAX_EGREP, pat, files)
def emgrep(pat, *files):
return ggrep(RE_SYNTAX... |
c540f7e0b7d7684910596a749c85c5395be45eca2c7943ed4346d6b948f745ad | Python | 2,257 | 53 | import os
import unittest
from tempfile import TemporaryDirectory
from nnunetv2.utilities.file_path_utilities import copy_file_if_newer
class TestCopyFileIfNewer(unittest.TestCase):
def _write(self, path: str, content: str, mtime: float) -> None:
with open(path, 'w') as f:
f.write(content)
... |
3e1cd315602c0221f303ea95a12b44527a2b5de181ce085ec66397c92ebc2ae5 | Python | 2,260 | 64 | import pandas as pd
import numpy as np
import glob
import random
import numpy as np
import json
def softmax(x, T):
return np.exp(x/T)/np.sum(np.exp(x/T), -1, keepdims=True)
def parse_pssm(path):
data = pd.read_csv(path, skiprows=2)
floats_list_list = []
for i in range(data.values.shape[0]):
... |
aec21edeb35166971d34e95e7f7841e14519b8216735e8656a642cf705280558 | Python | 2,261 | 71 | ################################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
# Modified the original code so that it also loads images from the current
# directory as well as the subdirectories
###############################... |
417b2f49efbc2236b0fe610df9d344769f33db17b71ca47bcf2607577adb7af4 | Python | 2,267 | 49 | #Comprehensive mapping of mutations to the SARS-CoV-2 receptor-binding domain that affect recognition by polyclonal human serum antibodies #https://www.biorxiv.org/content/10.1101/2020.12.31.425021v1.full.pdf
#download https://github.com/saketkc/pysradb
#!pip install -U pysradb
#!pysradb metadata SAMN17185313 #https://... |
d8dfbf3b07210fdb98316d88211347e34bd41a98e3e73565cc8550e9de7bc9fc | Python | 2,268 | 75 | #!/usr/bin/env python
#
# Copyright (c) 2020 10X Genomics, Inc. All rights reserved.
#
"""A helper stage to determine whether or not to disable the legacy bam file (holding all reads)."""
from __future__ import annotations
from typing import TYPE_CHECKING
import martian
if TYPE_CHECKING:
import cellranger.mro_t... |
2a87fd626f373cda0701eac081b8e99aae8e4368e337ebdfff9c791bedde677a | Python | 2,269 | 50 | from matplotlib import pyplot as plt
import numpy as np
import os
import sys
import pickle
from rCPGswCPG.utils.gen_utils import get_project_root, create_dir_if_not_exist
W_Insp_Sw1 = 0.005
data_path = os.path.join(get_project_root(), "data", "experiments", f"KF_lesioning_different_amplitude_stim_{W_Insp_Sw1}", "runs"... |
8e37bc8c94f02ec3aca034215e96b70dba538ff0214222c3fc0b84795b7aa2eb | Python | 2,270 | 72 | # Copyright 2020-2020 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# 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 Licen... |
fae0584222c9ba8cc5800fc1c224775daa6ae6edb2f96a8d8aeda630db201312 | Python | 2,270 | 67 | import scanpy as sc
import anndata
import numpy as np
import scipy.sparse as sp
import pandas as pd
import time
import scanorama
import matplotlib.pyplot as plt
a_adata = sc.read_h5ad("PATH_TO_a_H5AD")
b_adata = sc.read_h5ad("PATH_TO_b_H5AD")
ortholog_table_path = "PATH_TO_ORTHOLOG_TABLE.csv"
ortholog_table = pd.read... |
a2481ba6aee9bee5d1baeb745b46f453feb794407562d7cb5758d7934de572ee | Python | 2,273 | 89 | import pandas as pd
import numpy as np
from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
np.random.seed(42)
# -----------------------------
# 1. Load data
# -----------------------------
X = pd.read_csv("./data/data_0.csv",... |
9548c7abd9a78c0bc4c2f73313f6541c969164a2abdd69f8e94238b6d05facd3 | Python | 2,278 | 60 | # This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/gufe
import warnings
from typing import ClassVar
from gufe.storage.errors import ChangedExternalResourceError, MissingExternalResourceError
class ResultServer:
"""Class to manage communicatio... |
8fa506548adc49dd93c471d0da1a9cf5a2e64b54aed69267e6f54b59c4e59b3f | Python | 2,279 | 50 | from matplotlib import pyplot as plt
import numpy as np
import os
import sys
import pickle
from rCPGswCPG.utils.gen_utils import get_project_root, create_dir_if_not_exist
W_Insp_Sw1 = 0.0
data_path = os.path.join(get_project_root(), "data", "experiments", f"Intact_network_different_amplitude_stim_{W_Insp_Sw1}", "runs"... |
8c787863b6cdf6397655d9222819b56e906537d761e78891974a35046555d31f | Python | 2,282 | 72 | from pathlib import Path
import click
import click_log
import networkx as nx
from ... import io
from ..._cli.utils import catch_exception, logger
from ..._steinbock import SteinbockException
from ..._steinbock import logger as steinbock_logger
from .. import graphs
@click.command(name="graphs", help="Export neighbo... |
fb58f5ba9a74af175aca94297fab99a100af7fe55e7c844082980de25ba1ba84 | Python | 2,282 | 80 | import numpy as np
import tifffile
from PIL import Image
from torch import nn
import torch
def save_as_tif(imgs, filename, normalize=False):
"""
Save numpy array as tif file
Parameters
----------
imgs : np.array
Data array
filename : str
Filepath to save result
normalize :... |
43e3165de23e3cd672eea0142d4837634a8cd9280a073ff236d6b7918c8db197 | Python | 2,283 | 43 | if len(sys.argv)!=2: sys.exit("\n\nREQUIRED: pandas, pathlib; tested with Python 3.7.9 \n\nUSAGE: python proteinGroupsCombinePD.py <path to folder containing *Protein.txt file(s) like \"L:\promec\Animesh\Samah\mqpar.xml.1623227664.results\" >\n\nExample\n\npython proteinGroupsCombine.py L:\promec\Animesh\Samah\mqpar.xm... |
1f39fd1011cff4762b74d929e64005deee8fb99e6c5351e5698633b534df8a73 | Python | 2,285 | 93 | import torch
def batch_stack(props):
"""
Stack a list of torch.tensors so they are padded to the size of the
largest tensor along each axis.
Parameters
----------
props : list of Pytorch Tensors
Pytorch tensors to stack
Returns
-------
props : Pytorch tensor
Stack... |
7accd8b8484319342792c57161f6f13343fc8b3564d225023bbc0e9ba60406f9 | Python | 2,286 | 72 | from typing import Optional, Union
import numpy as np
import pandas as pd
import ggetrs as gg
def run_gsea(
genes: Union[list, np.ndarray],
library: str = "BP",
threshold: Optional[float] = 0.05,
background: Optional[Union[list, np.ndarray]] = None,
use_pvalue: bool = False,
) -> pd.DataFrame:
... |
4d7c9b93dc95f86e1ff7528a87fb65d041bcb7557b982656831a54ed6dc2dacc | Python | 2,289 | 81 | """Training loops, cross-validation, and metric computation."""
from nfml.train.engine import (
classifier_val_step,
classifier_train_step,
predict_batch,
predictor_val_step,
predictor_train_step,
vae_loss,
vae_val_step,
vae_train_step,
)
from nfml.train.kfold import evaluate_holdout, t... |
28cef1ee23aae39ec9f4e16f5c0a3497270cc1e05a814a174cff74c9a0354781 | Python | 2,294 | 45 | #python pepQuanProtMap.py "L:/promec/TIMSTOF/LARS/2026/260908_Moreforsk/combined/txtLen/peptides.txt" "A0A8C5CNW5"
#python pepQuanProtMap.py "L:/promec/TIMSTOF/LARS/2026/260908_Moreforsk/trypsin/combined/txtLen/peptides.txt" "A0A8C5CNW5"
import sys
if len(sys.argv)!=3:sys.exit("USAGE: python pepQuanProtMap.py <tab-sep-... |
595cdd9c0ab8306ba8f9e9fee5cbaccb0e411efece2cdb8d4083579597ea36ca | Python | 2,295 | 65 | from dataclasses import dataclass
@dataclass(frozen=True)
class WindowConfig:
"""Configuration for pre/post-stimulus analysis windows.
Two modes:
Train mode (symmetric=False): Standard analysis with pre-stim baseline,
full stim train, and post-stim window. Used for 700ms, 2700ms.
... |
647adeb3356e467c48b93092556783c25e7ef3b014103f43089145b3157da836 | Python | 2,296 | 71 | """Data filtering utilities for electrophysiology analyses.
Standard filters for modulated, pulse-locked, and non-pulse-locked units.
"""
import numpy as np
import pandas as pd
def filter_modulated(df, max_z_score=50, min_spikes=50):
"""Filter to modulated units (both PL and NPL)."""
for ch in df['stim_chann... |
e738f39859693cd5f176c873a652c12a8ddecf21562ac80498dd49e2aa767f6b | Python | 2,297 | 79 | from __future__ import annotations
import os
import tempfile
from pathlib import Path
import bids.layout
import pytest
from hypothesis import database, settings
from pyfakefs.fake_filesystem import FakeFilesystem
import snakebids.paths.resources as specs
from snakebids import resources, set_bids_spec
## Hypothesis ... |
74e2e2f23cd7844bab47b60117e64977153ac2b3db3bcda6dc8ccd9172d7a3cd | Python | 2,301 | 52 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 31 11:22:59 2024
@author: Ehsan.Sayyah
"""
import numpy as np
from rdkit import Chem
import os
import argparse
import glob
import shutil
import time
import subprocess
import os
import glob
import shutil
import multiprocessing
base_folder_path = '/... |
191b7a2610e200a4ed6bc487615e76e0293997bc03d76fa8acd09537d15c6c80 | Python | 2,302 | 61 | from pathlib import Path
from steinbock import io
class TestIO:
def test_read_panel(self, imc_test_data_steinbock_path: Path):
io.read_panel(imc_test_data_steinbock_path / "panel.csv") # TODO
def test_write_panel(self, imc_test_data_steinbock_path: Path):
pass # TODO
def test_list_ima... |
8bcb9201e633e180f272f9fe159ee69608bb653e5508d19eeecbc8bba4180e91 | Python | 2,303 | 69 | from __future__ import division
from ldscore.irwls import IRWLS
import unittest
import numpy as np
import nose
from numpy.testing import assert_array_equal, assert_array_almost_equal
from nose.tools import assert_raises
class Test_IRWLS_2D(unittest.TestCase):
def setUp(self):
self.x = np.vstack([np.ones(... |
aa10cc5e2cbda926a59f319a55ae0a8ca5199d74a2e4b297ebaa0906255346b5 | Python | 2,307 | 84 | import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegressionCV
from sklearn.preprocessing import StandardScaler
# -----------------------------
# 1. Load data
# -----------------------------
X = pd.read_csv("./data/data_0.csv", header=None)
features = pd.read_csv("./data/features_0.csv", ... |
3322fd1e36828a90536f32122e4e59edebd5bc58f61e79f29930891112c8c084 | Python | 2,313 | 63 | #!/usr/bin/env python
#
# Copyright (c) 2022 10X Genomics, Inc. All rights reserved.
#
"""Figures out if gDNA stages should be run."""
import martian
import cellranger.csv_io as cr_csv_io
__MRO__ = """
stage DISABLE_TARGETED_STAGES(
in csv probe_set,
in bool is_visium_hd,
out bool disable_targeted_gd... |
d3543929781ef95082c34bd83c91ec81d753f8fb51ba1f9502f4d13f0a7656f4 | Python | 2,315 | 69 | """Parser module to parse gear config.json."""
from typing import Tuple
from flywheel_gear_toolkit import GearToolkitContext
# from utils.curate_output import demo
import sys
import os
from shared.utils.curate_output import demo
import warnings
import logging
log = logging.getLogger(__name__)
def parse_config(
... |
12fc4f4ca43fb8c38edfbe87306925b049890ea16359f29b84c9887647964d9e | Python | 2,316 | 70 | """Simplified EEG Signal Classification Execution Script"""
import os
from pathlib import Path
import torch
import torch.backends.cudnn as cudnn
from eeg_visual_classification.utils.lib import (
create_parser, extract_model_options, get_dataloaders, get_model_hash, load_checkpoint
)
from ..models import MODEL_REGI... |
881e1c821edcef08d11dc76fba933a874a93dbe413ad4727c6b84cdadf11fbc2 | Python | 2,316 | 57 | #!/usr/bin/env python
# Copyright 2017-2026 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obt... |
2ffb9796f8e2dcffdd9d8811fd7c24c9154356b0f81b64cfdff67d3fa33bf64c | Python | 2,317 | 65 | '''
Created on 08.04.2019
Updated: 26.09.2020
@author: Niklas Pallast and Markus Aswendt
process all DTI data
'''
import glob
import os
import numpy as np
def findData(path):
regAtlas_list = []
fileALL = glob.iglob(path + '/GV*/DTI/DSI_studio/*StrokeMask.nii.gz', recursive=True)
for filename in fileAL... |
eacbe2ed8804f87d554217b25d6c5f01858abb6d844bb362e212b4b589f04be0 | Python | 2,320 | 74 | import math
import torch
import torch.distributed as dist
from detectron2.modeling.roi_heads import FastRCNNConvFCHead, MaskRCNNConvUpsampleHead
from detectron2.utils import comm
from fvcore.nn.distributed import differentiable_all_gather
def concat_all_gather(input):
bs_int = input.shape[0]
size_list = comm... |
57018d8baf6c64f481d11d2f1cafd82381798812778892cb01b79640a064246c | Python | 2,321 | 68 | import numpy as np
import pandas as pd
import pytest
import xarray as xr
from hsnn.analysis.png import stats
@pytest.fixture
def labels() -> pd.DataFrame:
return pd.DataFrame({
"image_id": ["a", "b", "c", "d"],
"left": [1, 1, 0, 0],
"top": [1, 0, 1, 0],
})
@pytest.fixture
def occ_ar... |
cdd3f43227da346fc9741cd1a2e779fdcf20a4c58bc5c8eca15bb5bba383d28e | Python | 2,321 | 60 | import argparse
from pathlib import Path
import joblib
def baseline_predict(text, model_path):
model = joblib.load(model_path)
return str(model.predict([text])[0])
def bert_predict(text, checkpoint_path, pretrained_model=None):
try:
import torch
from transformers import AutoTokenizer
... |
9a102ab767cd702886e3359dab4b08320dde48962ec5f33ae542283c6eef9d07 | Python | 2,325 | 73 | ############################################################################
# Copyright (c) 2022-2026 University of Helsinki
# Copyright (c) 2019-2022 Saint Petersburg State University
# # All Rights Reserved
# See file LICENSE for details.
############################################################################
... |
ed8ed3859c88bd5b8e4759e40c9a6c7496cc6858eed1fd7974b54b73b384d78f | Python | 2,326 | 65 | '''
Created on 08.04.2019
Updated: 26.09.2020
@author: Niklas Pallast and Markus Aswendt
process all DTI data
'''
import glob
import os
import numpy as np
def findData(path):
regAtlas_list = []
fileALL = glob.iglob(path + '/GV*/DTI/DSI_studio/*mod_peri_scaled.nii.gz', recursive=True)
for filename in f... |
a1b90ef48bfa96b6a4cec8dd91c5bdac93c154f071777ce9fcce3c20ab59e8a3 | Python | 2,328 | 56 |
import torch
import numpy as np
import os
import glob
import tqdm
import torch.nn as nn
from protein_mpnn_utils import ProteinMPNN, tied_featurize, parse_PDB
import subprocess as sb
import sys
def get_protein_mpnn(version='v_48_020.pt'):
"""Loading Pre-trained ProteinMPNN model for structure embeddings"""
hid... |
8677fd4d56f7efa4d1f6ce63cb9675ce082a2af03c4e45d5ab4dd37015475fe1 | Python | 2,331 | 92 | from mesa.examples.advanced.epstein_civil_violence.agents import (
Citizen,
CitizenState,
Cop,
)
from mesa.examples.advanced.epstein_civil_violence.model import EpsteinCivilViolence
from mesa.visualization import (
Slider,
SolaraViz,
SpaceRenderer,
make_plot_component,
)
from mesa.visualizat... |
ace333cdcd8a1b21d4c66e48241d11e4ed89b6736a3242b719e17275bc3d3770 | Python | 2,331 | 65 | '''
Created on 08.04.2019
Updated: 26.09.2020
@author: Niklas Pallast and Markus Aswendt
process all DTI data
'''
import glob
import os
import numpy as np
def findData(path):
regAtlas_list = []
fileALL = glob.iglob(path + '/GV*/DTI/DSI_studio/*_rsfMRISplit_scaled.nii.gz', recursive=True)
for filename ... |
0eb49cf7f3deda35004ef58415c2302d106b86fabc03ecdbfa2c578a2e904702 | Python | 2,334 | 110 | """
Functions for surface creation.
"""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
from vtk import (vtkPolyData, vtkCellArray, vtkTriangleFilter,
vtkVertexGlyphFilter)
from .mesh_elements import get_edges
from ..vtk_interface.wrappers import BSPolyData
from ..vtk_... |
e9bfd856748e1fa1f2aaae39bc5b5ed02061ff3e7db5b1be2aa159debd3a24ef | Python | 2,334 | 54 | # Copyright (c) Facebook, Inc. and its affiliates.
import torch
from detectron2.layers import nonzero_tuple
__all__ = ["subsample_labels"]
def subsample_labels(
labels: torch.Tensor, num_samples: int, positive_fraction: float, bg_label: int
):
"""
Return `num_samples` (or fewer, if not enough found)
... |
74fdf85e7030af8c6cce417f93512f3cd14194790fb719da8dc5769ddb139bd8 | Python | 2,335 | 72 | # Copyright (c) Facebook, Inc. and its affiliates.
# pyre-unsafe
from dataclasses import dataclass
from typing import Union
import torch
@dataclass
class DensePoseChartPredictorOutput:
"""
Predictor output that contains segmentation and inner coordinates predictions for predefined
body parts:
* coa... |
bddcb38817e3b984158ca13ec9fb6ad0361de08516bdf02c91a9b135642ce458 | Python | 2,335 | 75 | #
# Copyright (c) 2024 10X Genomics, Inc. All rights reserved.
#
"""A helper stage to disable stages in SC_MULTI_CORE."""
from __future__ import annotations
import martian
__MRO__ = """
stage DISABLE_MULTI_CORE_STAGES(
in bool is_pd,
in bool disable_gex,
in bool disable_multi_count,
in... |
5162eca0cc678058168efdf0c58fadf55ad0b8976af3c34ecf248c49ac1b6cb5 | Python | 2,337 | 80 | """
core/connectivity.py
--------------------
Pearson correlation-based functional connectivity.
"""
import numpy as np
import pandas as pd
def compute_connectivity(rate_df: pd.DataFrame,
zscore: bool = True):
"""
Compute the pairwise Pearson correlation matrix from per-channel firin... |
6f43738892045307bd9c8a8c63f3f684c4b6521c84a50c17337b7775558285cf | Python | 2,338 | 87 | """
Converted from MATLAB script at http://billauer.co.il/peakdet.html
Returns two arrays
function [maxtab, mintab]=peakdet(v, delta, x)
%PEAKDET Detect peaks in a vector
% [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local
% maxima and minima ("peaks") in the vector V.
% MAXTAB and MINTAB cons... |
21c58ffd8ba11fb1a031fcc4b670ad40abba78eaaff527a6e8cdc5ca2af37e02 | Python | 2,339 | 55 | import matplotlib.pyplot as plt
from mne.report import Report
# topomap scale
ch_types = ['mag', 'grad', 'eeg']
scale = {'mag': 1200, 'grad': 400, 'eeg': 40}
chan_toplot = {'mag': ['MEG2111'], 'grad': ['MEG2112'], 'eeg': ['EEG070']}
col_cond = {'trav': 'crimson', 'STANDING': 'dodgerblue', 'TRAV_OUT': 'crimson... |
a38c86da87271b2aae739a423a90114a1c5ec764b440ff9d9319dc3f39240032 | Python | 2,350 | 83 | import matplotlib.pyplot as plt
import networkx as nx
import solara
from matplotlib.figure import Figure
from mesa.examples.advanced.alliance_formation.model import (
AllianceScenario,
MultiLevelAllianceModel,
)
from mesa.visualization import SolaraViz
from mesa.visualization.utils import update_counter
model... |
7c251df706b373cecb886ae338c7364804f170e8bfe38e42b7a08f739ae001a9 | Python | 2,351 | 112 | # -*- coding: utf-8 -*-
"""For testing neuromaps.transforms functionality."""
import pytest
from neuromaps import transforms
@pytest.mark.xfail
def test__regfusion_project():
"""Test projecting a volume to a surface."""
assert False
@pytest.mark.xfail
def test__vol_to_surf():
"""Test projecting a volu... |
b6f0488a2f244227219049232877cc19dc3d8e2b44f32cef6a8cb1e44e7d1c6f | Python | 2,351 | 70 | from batchgenerators.utilities.file_and_folder_operations import *
from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json
from nnunetv2.paths import nnUNet_raw
import SimpleITK as sitk
if __name__ == '__main__':
"""
"""
# extracted training.zip file is here
base = '/home/... |
ff187fe91d61dee211029ff6b1c8f151391775f4910ac21cf78dd4b01ea9b16f | Python | 2,351 | 75 | #
# Copyright (c) 2024 10X Genomics, Inc. All rights reserved.
#
"""Replace low UMI cells from cell_types.csv."""
import csv
import martian
import numpy as np
import cellranger.matrix as cr_matrix
from cellranger.cell_typing.broad_tenx.cas_postprocessing import (
LOW_UMI_BARCODE_KEY,
MIN_CELL_TYPE_UMI,
)
_... |
58caa1333213a842b7fb8be31a8534b6514ae668968e794c7d7e1bb84913355f | Python | 2,356 | 80 | #!/usr/bin/env python
#
# Copyright (c) 2020 10X Genomics, Inc. All rights reserved.
#
"""A helper stage to build CS outs tailored to the multiplexing strategy."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any
import martian
from cellranger.multi.build_per_sample_outs import build_sample_... |
425ced6c8ce6333f83b201dc78b81a27b73ea59bf53522db524ef05246f3ee12 | Python | 2,357 | 67 | #@UIService ui
# BARlib.py
# IJ BAR: https://github.com/tferr/Scripts
#
# Template BAR library (http://imagej.net/BAR#BAR_lib) to be placed in BAR/lib. This file
# demonstrates how functions/methods in a common file can be shared across your scripts.
# To load such scripting additions, append the following to your Jyt... |
fabe780eaf565fc753bce77ab385b982a88508f9358d4a16b71bbd5af0f63475 | Python | 2,360 | 69 | import sys
import os
sys.path.append(os.path.abspath(
os.path.join(os.path.dirname(__file__), '../../')))
from src.hyperparameter_search.search_space.search_space import SearchSpace
from src.hyperparameter_search.search_space.optuna_search_space import OptunaSearchSpace
from src.hyperparameter_search.multitasking i... |
57485e8afd169f0ea80e0f36d9430eb2c4a4f762d9b78c92dc1e1b10c28827e3 | Python | 2,363 | 58 | # This script is part of navis (http://www.github.com/navis-org/navis).
# Copyright (C) 2018 Philipp Schlegel
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of... |
15faa3a9ce3639623381e1551f17fcd350b607048bc335ae94e4412ade48424a | Python | 2,365 | 88 | """
Created on 10/08/2017
@author: Niklas Pallast
Neuroimaging & Neuroengineering
Department of Neurology
University Hospital Cologne
"""
import os, sys
import changSNR as ch
import brummerSNR as bm
import sijbersSNR as sj
import numpy as np
import glob
import nibabel as nii
def snrCalclualtor(input_file, method... |
f83032e393f718a46ba90c6a364994f92b8cd3a5bf9131459a0bb9cc28effae3 | Python | 2,374 | 60 | from batchgenerators.utilities.file_and_folder_operations import *
import shutil
from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json
if __name__ == '__main__':
"""
How to train our submission to the JHU benchmark
1. Execute this script here to convert the dataset into nnU-... |
6e4716758f6990916081cbb65a5829b0648c09479e29ed257f1eaba245e41e5e | Python | 2,375 | 76 | """Test if namespsaces importing work better."""
import pytest
def test_import():
"""This tests the new, simpler Mesa namespace.
See https://github.com/mesa/mesa/pull/1294.
"""
import mesa # noqa: PLC0415
from mesa.datacollection import DataCollector # noqa: PLC0415
_ = DataCollector
... |
da4125c0c292e0777ea158ac7e99b7c8156993d211c34527e4a0f8fbe96ab36b | Python | 2,383 | 76 | # %%
from __future__ import annotations
import json
import os
import os.path as op
import pickle
import sys
from wasabi import msg
from src.preprocessing import info_extraction as ie
from src.preprocessing import prep_eeg as prep
# helper pakagee
sys.path.append(op.abspath('..'))
# %%
RAW_ROOT = '../../NOD-MEEG_up... |
5d2fa44cd6c848c88d00f548db0cffce89dd21fcd7111f78dba83456243e628b | Python | 2,385 | 62 | # Copyright (c) Facebook, Inc. and its affiliates.
import math
from typing import List
import torch
from detectron2.solver.lr_scheduler import LRScheduler, _get_warmup_factor_at_iter
# NOTE: PyTorch's LR scheduler interface uses names that assume the LR changes
# only on epoch boundaries. We typically use iteration b... |
d855405eba9a613896f3c4b86a81a6d5af02e5e89727ce771b69dd9d3ec79390 | Python | 2,385 | 53 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
4469367579feb974bd815da44f996594f75da83e83763ae7d805f978e9eaf6de | Python | 2,386 | 90 | """This integration test is designed to ensure that the chemprop model can _overfit_ the training
data. A small enough dataset should be memorizable by even a moderately sized model, so this test
should generally pass."""
from lightning import pytorch as pl
import pytest
import torch
from torch.utils.data import DataL... |
bd18ddf8f2126fb7fee2638bec5a40f7b895727e3f92c4d380285fe55ee6e849 | Python | 2,387 | 70 | import csv
from lightning import pytorch as pl
import numpy as np
import pytest
from chemprop.conf import LIGHTNING_26_COMPAT_ARGS
from chemprop.data.dataloader import build_dataloader
from chemprop.data.datapoints import MoleculeDatapoint
from chemprop.data.datasets import MoleculeDataset
from chemprop.featurizers.a... |
252d5e3fb6057da21bcdf139a6eece0037382bebb8977506e3943f187ae73754 | Python | 2,388 | 108 | # %%
from __future__ import annotations
import os
import os.path as op
import sys
import joblib
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import mne
import numpy as np
import pandas as pd
from matplotlib import font_manager as fm
from matplotlib.gridspec import GridSpec
from scipy.stats import ttest_... |
af2c482007a94463dd9e7dc371e171d7ebd8919f4ae476f96d8bff04586aebd9 | Python | 2,392 | 74 | import pytest
from chemprop.utils import make_mol
def test_no_keep_h():
mol = make_mol("[H]C", keep_h=False)
assert mol.GetNumAtoms() == 1
def test_keep_h():
mol = make_mol("[H]C", keep_h=True)
assert mol.GetNumAtoms() == 2
def test_add_h():
mol = make_mol("[H]C", add_h=True)
assert mol.G... |
0d2e37b5631d2629f4760e08efa2467808e1ccf79fb6529c9d0cf381300861d0 | Python | 2,395 | 67 | import os
import re
from pathlib import Path
# Directory where script module files are located
SCRIPTS_DIR = Path('../micaflow/scripts')
# Descriptions for each script
descriptions = {
"bet": "Brain extraction using HD-BET.",
"synthseg": "Deep learning segmentation with SynthSeg.",
"coregister": "Image co... |
4673fe61397f2c3a766454441bf766d53db9d97ad8e7fcbf67821471af2698fe | Python | 2,395 | 75 | import numpy as np
from DeepKnockoffs import KnockoffMachine
from DeepKnockoffs import GaussianKnockoffs
import data
import parameters
from sklearn import preprocessing
for i in range(25):
seed=i+1
print('No.',seed,'\n')
X=np.loadtxt('../../Model-X/Dataset1/data/X_64_'+str(seed)+'.txt')
X=preproc... |
1edde89b5d94b0d4cd3848122686781d8453f2d75724ac4c8a5c5ab19c130ee9 | Python | 2,397 | 67 | #!/usr/bin/env python
# Copyright 2016-2019 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obt... |
749e784724aa85b09624f1fe54aec1dfe3c3ab1890d17d423eaec28fac802cf5 | Python | 2,403 | 70 | # -*- coding: utf-8 -*-
"""
Created on Tue Sep 5 15:54:11 2023
This script simulates traveling waves in V1, project the activity onto MEG and EEG sensors,
and compare the predicted activity in sensors to empirical data.
@author: laeti
"""
from toolbox.simulation import create_sim_from_entry
from toolbox... |
0a82a488dc6554493fa2b51e865e7e65ebfaecf96efbf263987db52bef8a83fd | Python | 2,404 | 59 | #!/usr/bin/env python
# Copyright 2017-2020 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obt... |
77745db4c9e86313cc9cd4840a68df685fdfb60ca7caf34ad109eb15bbd727e7 | Python | 2,404 | 67 | from __future__ import annotations
from pathlib import Path
import sys
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from rdkit import Chem
from src.protonation.openbabel_adapter import OpenBabelError, OpenBabelProtonator
from src.... |
51ad69f2b3b2a8baddaf4c7380f0f9a9ccb34323130319e6692b8509afbf80a7 | Python | 2,405 | 64 | import argparse
import pandas as pd
from sklearn.model_selection import KFold
import config
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--dataset', type=str, required=True, help='dataset file path')
parser.add_argument('-f', '--fold', type=int, default=10, help='number of fold... |
41209b45ce2ec0576e172029c5a43287af31475d40efe4b8cb149e0cb77b0089 | Python | 2,406 | 61 | import shutil
from pathlib import Path
from nnunetv2.dataset_conversion.Dataset027_ACDC import make_out_dirs
from nnunetv2.dataset_conversion.generate_dataset_json import generate_dataset_json
def copy_files(src_data_dir: Path, src_test_dir: Path, train_dir: Path, labels_dir: Path, test_dir: Path):
"""Copy files... |
bb34fc440e4a3143e512ae061161fe3c780aa190beed8c46fb1c1d2b1fbfc352 | Python | 2,406 | 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... |
d315ba44ed4180c3d76bf9511c4c367b74b681d5d881e0e7338a74e72ad659fe | Python | 2,406 | 67 | """Move carbon-centred anions onto adjacent heteroatoms after (de)protonation.
MolGpKa (and rule-based backends) can deprotonate an acidic C-H directly, giving
a carbanion, for example the enol(ate)-forming central C-H of a 1,3-diketone,
which is returned as ``CC(=O)[CH-]C(C)=O``. The chemically dominant form is the
e... |
09817e2ea5acada9fdc61dadcc753cd6a1fb100f76cb10bcc0bd8e40b69819ac | Python | 2,412 | 65 | import os.path
import torchvision.transforms as transforms
from data.dataset.base_dataset import BaseDataset
from data.image_folder import make_dataset
from PIL import Image
import numpy as np
import torch
# from IPython import embed
class TwoAFCDataset(BaseDataset):
def initialize(self, dataroots, load_size=64):
... |
c912b824d2d08c09bfa7bf14ed7027565cbd76fc8def8a1b68148ad4464c5f8c | Python | 2,415 | 73 | import time
import altair as alt
import numpy as np
import pandas as pd
import streamlit as st
from mesa.examples.basic.conways_game_of_life.model import ConwaysGameOfLife
model = st.title("Conway's Game of Life")
num_ticks = st.slider("Select number of Steps", min_value=1, max_value=100, value=50)
height = st.slide... |
08f7fd58f56bfb232860b1bbf560c7f40884a25cf4f95df990179ed55d9dbcc2 | Python | 2,417 | 89 | from pathlib import Path
import click
import click_log
from ... import io
from ..._cli.utils import catch_exception, logger
from ..._steinbock import SteinbockException
from ..._steinbock import logger as steinbock_logger
from ..neighbors import NeighborhoodType, try_measure_neighbors_from_disk
_neighborhood_types =... |
d1628ec602c6b4c72f5062f3f3e34b280c93a00357a4483b5d19694f3fddc0d7 | Python | 2,419 | 74 | # %%
from __future__ import annotations
import json
import os
import os.path as op
import pickle
import sys
from wasabi import msg
from src.preprocessing import info_extraction as ie
from src.preprocessing import prep_meg as prep
# helper pakage
sys.path.append(op.abspath('..'))
# %%
RAW_ROOT = '../../NOD-MEEG_upl... |
74156866b0cb6ad6551b03641541ed4b3631dbd5b2859b80ec911d423275dc5c | Python | 2,420 | 80 | """
Wrappers for VTK lookup tables.
"""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
import vtk
from vtk.util.vtkConstants import VTK_STRING, VTK_UNSIGNED_CHAR
from vtk.util.numpy_support import numpy_to_vtk
from .base import BSVTKObjectWrapper
from ..decorators import unwrap_input
... |
886b79058e166e7f49903f19deee0ff8f7eaacbbbf354ce12ed8ef10399f6639 | Python | 2,420 | 60 | from collections.abc import Iterable
import numpy as np
import torch
def recursive_fix_for_json_export(my_dict: dict):
# json is ... a very nice thing to have
# 'cannot serialize object of type bool_/int64/float64'. Apart from that of course...
keys = list(my_dict.keys()) # cannot iterate over keys() if... |
41ef9044af2244265e328aca2c222af87b870629783c05c0873dca31faaae478 | Python | 2,425 | 67 | import torch
def calc_mean_std(feat, eps=1e-5):
# eps is a small value added to the variance to avoid divide-by-zero.
size = feat.data.size()
assert (len(size) == 4)
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat... |
e35c7f8f95c42891397dedefbcca042fa8a1227bc4124a6980d483a32b34d70b | Python | 2,426 | 93 | import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
import shap
np.random.seed(42)
# -----------------------------
# 1. Load data
# -----------------------------
X = pd.read_csv("./data/data_0.csv", header=None)
features = pd.read_csv("./data/features_0.csv", header=None)[0].val... |
cfe3996ce41e78ae7ce4f78bff629268c5f0b33b0e2379543382d1c3b65c2f57 | Python | 2,427 | 62 | import tobii_research as tr
import time
import numpy as np
found_eyetrackers = tr.find_all_eyetrackers()
my_eyetracker = found_eyetrackers[0]
print("Address: " + my_eyetracker.address)
print("Model: " + my_eyetracker.model)
print("Name (It's OK if this is empty): " + my_eyetracker.device_name)
print("Serial number: "... |
3f96932ded49e62df7fedc07e48f19cd70c50e2627516a4a4f67817592f05940 | Python | 2,428 | 90 | from __future__ import annotations
import re
import subprocess as sp
import sys
from pathlib import Path
import jinja2.parser
from jinja2 import nodes
from jinja2.ext import Extension
from typing_extensions import override
class GitConfigExtension(Extension):
"""Retrieve settings from git configuration.
Sh... |
493b6c3a1e493554cd2db12d41b9fe5887c0f3d719dddd98d54a97089252f655 | Python | 2,430 | 74 | #!/usr/bin/env python
#
# Copyright (c) 2022 10X Genomics, Inc. All rights reserved.
#
"""Gets the sufficient statistics from rust stage and generate plotly plot."""
import json
import numpy as np
import tenkit.safe_json as tk_safe_json
from cellranger.targeted.targeted_constants import GDNA_PLOT_NAME
from cellran... |
a850981b0d3cc3fe33271db04d8bbf9859342f70bf07a86d15f099f68b3d1380 | Python | 2,430 | 61 | #!/usr/bin/env python3
#
############################################################################
# Copyright (c) 2022-2026 University of Helsinki
# Copyright (c) 2020-2022 Saint Petersburg State University
# # All Rights Reserved
# See file LICENSE for details.
#####################################################... |
090558187035ac51a20e054e3ec8eb9028a1293c7d9f546685e2b5806cdbd6f3 | Python | 2,440 | 67 | #!/bin/python
"""
Script for registering and processing microglia ASAP snRNA-seq samples for Putamen (PUT).
Workflow steps and notes are identical to PFC script.
"""
import truster
import pandas as pd
import os
# Paths to references and configs
cellranger_index = "/scale/gr01/shared/common/genome/10Xindexes/cellrang... |
59a094b05f0a21587f7e676863659f2285584d718e7b45b3e52986ca3f6eaa66 | Python | 2,446 | 88 | """EEG datasets with
class for dataset dataloader
"""
import torch
# Dataset class
class EEGDataset:
"""EEG dataset
Returns:
EEG dataset: some data source
"""
# Constructor
def __init__(self, opt):
# Load EEG signals
loaded = torch.load(opt["eeg_dataset"]) # signals_pat... |
80d4b07eb904475cb72dd83fc7044ae6899c3e2a6c8dc59a3413819e59974361 | Python | 2,448 | 71 | """The MFA timeout must actually stop a run that overruns.
An alignment was observed hanging for ~55 minutes with a 30-minute timeout in
force. The cause is a standard subprocess trap: MFA starts workers, and when
the parent is killed those workers keep the stdout/stderr pipes open, so the
cleanup read blocks forever.... |
0596f53162a6c9689bd14744be2ef8ec945611c35a32a34be27b4efa31c0f2d2 | Python | 2,449 | 106 | # compate the distribution of the semantic labels in the training set and the test set
import torch
import argparse
import numpy as np
parser = argparse.ArgumentParser(description="Template")
parser.add_argument(
"-id",
"--input-dataset",
help="input EEG dataset path",
)
parser.add_argument(
"-sp1",
... |
238f0f4f92df7e15f8f261c55acf367cc085fa083c01ce71a278d25f0b67d8ea | Python | 2,454 | 77 | #!/usr/bin/env python
"""
Functionality to preprocess a directory of images:
Try reading them, crop etc. to desired size, ...
And if corrupt (error), move them to different directory.
This serves to speed up training by doing preprocessing beforehand.
"""
import os
import shutil
from PIL import Image
from adain im... |
c5801d91aadb66a8e7214514b662d51a386f6ec7477ab0b7af7301b66cb9a06a | Python | 2,454 | 68 | from pkg_resources import resource_filename
from pangolin.model import *
# Change this to the desired models. The model that each number corresponds to is listed below.
model_nums = [0]
# 0 = Heart, P(splice)
# 1 = Heart, usage
# 2 = Liver, P(splice)
# 3 = Liver, usage
# 4 = Brain, P(splice)
# 5 = Brain, usage
# 6 = T... |
ec36123abd64f1fde58afc3ba7ffaa865a7e9c8bdf2f898a5433d5e0b446f9fd | Python | 2,454 | 67 | import torch
from typing import Optional
try:
from emle.models import ANI2xEMLE, MACEEMLE
from emle._units import (
_NANOMETER_TO_ANGSTROM,
_HARTREE_TO_KJ_MOL,
)
except ImportError:
ANI2xEMLE = None
MACEEMLE = None
_NANOMETER_TO_ANGSTROM = None
_HARTREE_TO_KJ_MOL = None
cla... |
63443f7e76981ee4eed59649ab8b6cef09bddd63da8d9ccace5d836afb82532f | Python | 2,455 | 45 | from typing import Tuple, Union, List
from batchgenerators.transforms.abstract_transforms import AbstractTransform
class Convert3DTo2DTransform(AbstractTransform):
def __init__(self, apply_to_keys: Union[List[str], Tuple[str]] = ('data', 'seg')):
"""
Transforms a 5D array (b, c, x, y, z) to a 4D ... |
0bdfd6a05e85d994209c4e41f5e05d5c3eb3d269dce8554a7df9bbfa858d0efc | Python | 2,457 | 88 | import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegressionCV
from sklearn.preprocessing import StandardScaler
np.random.seed(42)
# -----------------------------
# 1. Load data
# -----------------------------
X = pd.read_csv("./data/data_0.csv", header=None)
features = pd.read_csv("./dat... |
ac20d925eca2659440db64790ecb3939552f625774bb50dc7c1115b92f728b74 | Python | 2,458 | 70 | #
# Copyright (c) 2022 10X Genomics, Inc. All rights reserved.
#
"""A file to read and ingest cell_barcodes.json files produced by PARSE_MULTI_CONFIG."""
from __future__ import annotations
import json
from cellranger.barcodes.utils import load_barcode_whitelist
from cellranger.chemistry import get_whitelist_name_fro... |
95e6776bf5fd03d7da20ca240c73e4bc570702c23981757e9c27f450b36c05c8 | Python | 2,459 | 81 |
""" Put module information here """
__author__ = "Fabi Bongratz"
__email__ = "fabi.bongratz@gmail.com"
import numpy as np
def read_obj(filepath):
""" Read an .obj file in a way that separate mesh objects/structures
are not merged
"""
vertices = []
faces = []
normals = []
vertices_struct... |
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