sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
63a7a8e848ccd981b9e713bff1ecbd686fb269cdc17f7e4c6c1f3afe9322267b | Python | 3,308 | 123 | """Conversion functions between RGB and other color systems.
This modules provides two functions for each color system ABC:
rgb_to_abc(r, g, b) --> a, b, c
abc_to_rgb(a, b, c) --> r, g, b
All inputs and outputs are triples of floats in the range [0.0...1.0].
Inputs outside this range may cause exceptions... |
f5bf44cc24796fedbe35a1fc1d13287f62868f4e80a02927328626e8887d87fd | Python | 3,308 | 101 | """
neuPrint
========
<!-- difficulty: intermediate -->
Query and fetch neurons and connectivity from a neuPrint server.
[NeuPrint](https://www.biorxiv.org/content/10.1101/2020.01.16.909465v1) is a service for presenting and analyzing connectomics data.
It is used to host, for example, the Janelia EM reconstructions ... |
a00d00d0d8a54c78b45d8a2e61a35c3129a928374d60fc1f4bde8536a46e4d42 | Python | 3,309 | 92 | """A Boid (bird-oid) agent for implementing Craig Reynolds's Boids flocking model.
This implementation uses numpy arrays to represent vectors for efficient computation
of flocking behavior.
"""
import numpy as np
from mesa.experimental.continuous_space import ContinuousSpaceAgent
class Boid(ContinuousSpaceAgent):
... |
2ea620765e982cffe87b46ab2054e729540b7166dd00646d1d96624bfa1fc03d | Python | 3,322 | 106 | import logging
from descriptastorus.descriptors import rdDescriptors, rdNormalizedDescriptors
import multiprocess
import numpy as np
from rdkit import Chem
from rdkit.Chem import Descriptors, Mol
from rdkit.Chem.rdFingerprintGenerator import GetMorganGenerator
from chemprop.featurizers.base import VectorFeaturizer
fr... |
4e06397f1f90cf35bdef04e6c647db81989bf02450333fff3ca0dcc7ffc28051 | Python | 3,324 | 57 | import numpy as np
import torch
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
class nnUNetTrainer_probabilisticOversampling(nnUNetTrainer):
"""
sampling of foreground happens randomly and not for the last 33% of samples in a batch
since most trainings happen with batch size 2 an... |
e0afc9d50037967a01af1990cc7fdc2c595d6064cff25f462ed3400f0cb350d4 | Python | 3,325 | 103 | import time
from pathlib import Path
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from tqdm import tqdm
from .utils.activation_manager import ActivationManager
from .utils.pgd_attack import PGDAttack, AttackParams
from .utils.attack_result import AttackResult
from .utils.data_utils i... |
2d675f7c8c11a425cd2f1d1a34b08f4a3d24b79f422a59c8cb2b04fd86d194b2 | Python | 3,332 | 122 | # This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/openfe
import gzip
import pytest
from gufe.tests.test_tokenization import GufeTokenizableTestsMixin
import openfe
from openfe.protocols.openmm_afe import (
AbsoluteBindingComplexUnit,
Abso... |
8d8c610c4e7b548c49a723a5d35e64914cd2094cd8dcc199b0c87ccb1fdca5f2 | Python | 3,335 | 97 | #!/usr/bin/env python
#
# Copyright (c) 2021 10X Genomics, Inc. All rights reserved.
#
"""A helper stage to determine method used for multiplexed data."""
from __future__ import annotations
from typing import TYPE_CHECKING
import martian
import cellranger.rna.library as rna_library
from cellranger.fast_utils import... |
9127e7c7aa473328ba15862b6dbf4d5f0e1aa2a9bad63b3517636a4bc6615ddc | Python | 3,338 | 69 | from matplotlib import pyplot as plt
import numpy as np
import os
import pickle
def get_index(T, dt):
return int(T*1000 / dt)
def plot_VNA_responses(VNA, long_stim_start_ind, long_stim_stop_ind, short_stim_start_ind, short_stim_stop_ind):
fig, ax = plt.subplots(1, 1, figsize = (8, 3))
ax.plot(VNA, color='... |
e80f661414b5949d275f04cf616ee8e437f3533883914bff98e0dd2dc0c6dabc | Python | 3,341 | 79 | import sys
from pathlib import Path
if len(sys.argv)!=2: sys.exit("REQUIRED: pandas, pathlib; tested with Python 3.8.5\n","USAGE: python diffExprNetwork.py <path to folder containing Formaldehyde_XL_Analyzer outouts like \"F:\20210118_8samples\QE\" >")
pathFiles = Path(sys.argv[1])
pathFiles = Path("F:\\20210118_8sa... |
622c0a3cd04ffa64fc4f8cf85d0e3e89f19ba39b60d0796f864ec4d042dd5d34 | Python | 3,345 | 86 | from copy import deepcopy
from matplotlib import pyplot as plt
import numpy as np
def replace_name(name):
if name == 'Sensory_relay':
return "SR"
elif name == 'KF_gate':
return 'KFg'
elif name == 'KF_phasic':
return 'KFp'
else:
return name
def get_short_name(param_to_v... |
bc59bbb687f29ee89b8f1878604827ce85f0f734279ee000b75f0dd8c9eae5fc | Python | 3,347 | 83 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# pyre-unsafe
import torch
from torch.nn import functional as F
def squared_euclidean_distance_matrix(pts1: torch.Tensor, pts2: torch.Tensor) -> torch.Tensor:
"""
Get squared Euclidean Distance Matrix
Computes pairwise squared Euclid... |
6e47e3895776b4d51d4a55d20e5fcb7974b64b084c4b9e2291f305ea9aa1635f | Python | 3,348 | 116 | import argparse
import pandas as pd
from pathlib import Path
import logging
import os
from create_pairs_from_alignments import create_genus_df
from filter_comparisons import filter_comparisons
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--data-dir",
"-d",
... |
7ddc71b346ed9f17ba029e5ccc01194c903213a8e53dd5c1088f7af1079bf69e | Python | 3,360 | 95 | #%%
import pandas as pd
import bambi as bmb
import pymc as pm
import joblib
from scipy.stats import zscore
from os.path import join
import arviz as az
from plus_slurm import Job
import sys
sys.path.append('/mnt/obob/staff/fschmidt/cardiac_1_f')
from utils.pymc_utils import coefficients2pcorrs
#%%
#a seed for repr... |
e21e000ea0d80c2854533f4b3123099e8d1a28f171c19be153b0482983ad764b | Python | 3,362 | 101 | """MolGpKa protonation backend tests.
Locks in the reviewer's litmus case: piperazine at pH 7.4 must return the
mono-cation (+1), which requires the iterative titration protocol (naive
per-site Henderson-Hasselbalch would return +2).
"""
from __future__ import annotations
import pytest
torch = pytest.importorskip("... |
6313f7ef0e26c78625029496a688217edb7ef073f08d2766804762f54e490c3a | Python | 3,363 | 119 | """
author:CBJ
"""
import sys
from pathlib import Path
import logging
import pandas as pd
import numpy as np
PROJECT_ROOT = Path(__file__).parent.parent
sys.path.insert(0, str(PROJECT_ROOT))
from src.utils import get_fwi_grade
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s... |
66a16ae5a872e9d00c93c166aa148cbd66bcbcca3f685e386c1726fb0b059adf | Python | 3,365 | 93 | import DeepLINK as dl
import numpy as np
import keras
from keras.layers import Dense
from keras.models import Sequential
from pairwise_connected_layer import PairwiseConnected
import pandas as pd
from keras.callbacks import EarlyStopping
from sklearn.linear_model import LogisticRegressionCV
dataset = ['Zeller_CRC']
d... |
54c8e6a7d148d31940ff081e5ee04aee2ea3cc4002477db13d49aa216dfdd5fc | Python | 3,366 | 123 | # This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/openfe
import functools
import importlib
import logging
from datetime import datetime
from typing import Callable, Optional
import click
def import_thing(import_string: str):
"""Obtain an ob... |
64f1f188d26edc5ef03431ea86b1b7235e9a9d1bafa6ced2f89671c45d5c1be6 | Python | 3,366 | 109 | #!/usr/bin/env python3
#
# Copyright (c) 2019 10X Genomics, Inc. All rights reserved.
#
"""Tool for converting feature-barcode matrices from sparse format to dense.
CSV format, for use by external programs.
The commands below should be preceded by '{cmd}':
Usage:
mat2csv <input_path> <output_csv> [--genome=GENO... |
8161419878bfcbecf969bf1b32dfd85ec813993d2d4b1318069bc4d7afa6f199 | Python | 3,369 | 94 | import pandas as pd
import os
from WORC.addexceptions import WORCKeyError
# All standard texture features accepted
texture_features = ['GLCM', 'GLDZM', 'GLRLM', 'GLSZM', 'NGLDM', 'NGTDM']
def convert_radiomix_features(input_file, output_folder):
'''
Convert .xlsx from RadiomiX to WORC compatible .hdf5 format... |
c3d08ce95a9a035fa3cd66c9c8dc55487dd70f29675117e444d2c2fd958e2d0b | Python | 3,369 | 114 | import numpy as np
from stabl import data
from stabl.multi_omic_pipelines import multi_omic_stabl_cv
from sklearn.model_selection import RepeatedStratifiedKFold, GroupShuffleSplit, GridSearchCV
from sklearn.linear_model import LogisticRegression
from stabl.stabl import Stabl
from stabl.adaptive import ALogitLasso
from ... |
e18ecf522d58e9b24eeaec5b19f60923a10c79e899eb5c8303e045a3b9c5dfea | Python | 3,370 | 85 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# pyre-unsafe
from typing import Any, List
import torch
from torch.nn import functional as F
from detectron2.config import CfgNode
from detectron2.structures import Instances
from .utils import resample_data
class SegmentationLoss:
"""
... |
6d400f633d5b2033597eb36434cd8f7397145b468500b6c88b0f38d09b372a95 | Python | 3,373 | 103 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# pyre-unsafe
from .catalog import MeshInfo, register_meshes
DENSEPOSE_MESHES_DIR = "https://dl.fbaipublicfiles.com/densepose/meshes/"
MESHES = [
MeshInfo(
name="smpl_27554",
data="smpl_27554.pkl",
geodists="geodists/... |
7519a549450309bdc26803a3fb0dbcab125ec95bd5322684478d0cad75d08d11 | Python | 3,375 | 92 | #!/bin/python
"""
Script for registering and processing microglia ASAP snRNA-seq samples for Amygdala (AMY).
Workflow steps:
1. Load the region-specific sample sheet and post-QC samples.
2. Filter samples to exclude non-microglia samples (e.g., "AA_ASAP111").
3. Register samples with the trusTEr `Experiment` object.
4... |
badd1a4ffb5d0a1f3695c0586631e3c2c581090dab6c20d6a17bdaf1afbbff87 | Python | 3,376 | 77 | # This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/openfe
import MDAnalysis as mda
import pytest
from openff.units import unit
from openfe.protocols.restraint_utils.geometry.flatbottom import (
FlatBottomDistanceGeometry,
get_flatbottom_di... |
1e72849487866ad9f65f3dd496f812b5d08114d75326f96c88e6707fb767995e | Python | 3,378 | 107 |
#%% Imports
# Imports from the library
from micorr.simulation import simulations, testing, plotting, transformations
from micorr.estimators import mi_estimators, corr_est
# More general imports
import numpy as np
from functools import partial
#%% Initial parameters
# Dictionary with the estimators to be tested
est... |
6a8c1bcef9fb5d56cd68343a65c6bca734d27cf2305c5137dea69ff198941f7a | Python | 3,378 | 94 | # This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/openfe
import numpy as np
import pytest
from numpy.testing import assert_, assert_allclose
from openff.utilities import skip_if_missing
from openfe.setup import perses_scorers
from ....utils.sile... |
307517d88f70e96571ecca0ee304eae01159831c30f9dadf8727086bd672c1ed | Python | 3,383 | 96 | """
Boltzmann Wealth Model
=====================
A simple model of wealth distribution based on the Boltzmann-Gibbs distribution.
Agents move randomly on a grid, giving one unit of wealth to a random neighbor
when they occupy the same cell.
"""
from mesa import Model
from mesa.datacollection import DataCollector
from... |
fb1975ce17a6e1de9de94a7a0d545f5c280b4c3d5fb95715b2481422e2fd7045 | Python | 3,384 | 100 | # Copyright (c) Facebook, Inc. and its affiliates.
# pyre-unsafe
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional
from detectron2.structures import Instances
ModelOutput = Dict[str, Any]
SampledData = Dict[str, Any]
@dataclass
class _Sampler:
"""
Sampler registry en... |
fd29e62a1b0bff11407fcae846fbbcc309f8afa48efb42a5be4297dbb624739a | Python | 3,385 | 75 | # python proteinGroupsCombine.py D:\TMPDIR\mqpar.xml_20250403_135936 4 12
# D:\TMPDIR\mqpar.xml_20250403_135936 is output of mqrun.bat
# %%setup
#python -m pip install pandas seaborn pathlib supervenn
import sys
from pathlib import Path
# %% read
if len(sys.argv) != 4: sys.exit("\n\nREQUIRED: pandas, seaborn, supervenn... |
7dde1849c7fed559dea05cc56acdd8a17d347a2a02b8ee0f782eb0b0c67c902f | Python | 3,392 | 87 | import numpy as np
import torch
import pandas as pd
from torch.utils.data import Dataset
class LFPDataset(Dataset):
def __init__(self, cfg, aligned_data, step_onset_texture, step_onset_LD, val_idx, validation=False):
"""
Args:
cfg: config file
aligned_data: aligned lfp data ... |
f700e4c1d36b80203a7f11162bb6fce3ebbfc49037559521fd26dcda59f8294f | Python | 3,394 | 106 | #!/usr/bin/env python
#
# Copyright (c) 2017 10X Genomics, Inc. All rights reserved.
#
import json
import os
import h5py as h5
import cellranger.cr_io as cr_io
import cellranger.hdf5 as cr_h5
__MRO__ = """
stage SUMMARIZE_ANALYSIS(
in h5 matrix_h5,
in h5 pca_h5,
in h5 clustering_h5,
in ... |
4551cbaa5af87580238c7b28f3ddaa551043d782bcca350041a10dad7dd8b713 | Python | 3,397 | 86 | import multiprocessing
import shutil
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
from skimage import io
from acvl_utils.morphology.morphology_helper import generic_filter_co... |
9bc94838d4316e638f2a4c22fbf06b8ea67fa1aae18bab75280d756f7ef7e639 | Python | 3,401 | 122 | import tensorflow as tf
print(tf.__version__)
#https://stackoverflow.com/a/40219528/1137129
tf.random.set_seed(42)
#vecs=tf.random.uniform(shape=[n],minval=0,maxval=n,dtype=tf.dtypes.int64)
#https://laurentlessard.com/bookproofs/mismatched-socks/
n=1000
j=0
k=[]
while j < n:
vecs=tf.range(0,j, delta=1, dtype=tf.dty... |
52db1d61478fbfcebeb51a57b5897b979218519e0703790cf05f0168dacf0952 | Python | 3,404 | 74 | from abc import abstractmethod
from torch import Tensor, nn
from chemprop.data import BatchMolGraph
from chemprop.nn.hparams import HasHParams
class MessagePassing(nn.Module, HasHParams):
"""A :class:`MessagePassing` module encodes a batch of molecular graphs
using message passing to learn vertex-level hidd... |
d4a46bc4320abbac66750b66aacd5e64fd8cd4555b45848ee9f72654f37b257d | Python | 3,406 | 118 | import warnings
import torch
import numpy as np
import pandas as pd
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from torch.autograd import Variable
import numpy as np
import pandas as pd
from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score
imp... |
bbecd534f24d3c15ab004b325248605897ea1aa37bd8f873140fc11e3be1fbd3 | Python | 3,410 | 98 | #!/usr/bin/python3
##################################################################################
#
# MIT License
#
# Copyright (c) 2025 Kevin Rockenbach, Agnieszka Golicz
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "So... |
0f606e0248f37757fa96196a5fe4cb72e9b6492791c665ba554739a2800b7cbb | Python | 3,411 | 103 | from pathlib import Path
import pytest
from unittest.mock import MagicMock
import tempfile
from WORC.validators.preflightcheck import InvalidLabelsValidator
import WORC.addexceptions as ae
"""
Test to see what happens if a valid configuration is given to the InvalidLabelsValidator.
Under normal circumstances this sh... |
67ffae59a16a9205592e47f55eb4fd8b80fa56c6dde1c6aeda854c931afe4126 | Python | 3,411 | 119 | # 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... |
9c71cc2ad9f130e466a93a1f9e7849cee6a58c50f5ff1d7fa657766ab15a2497 | Python | 3,412 | 68 | #!pip3 install pandas --user
#!pip3 install pathlib --user
import sys
from pathlib import Path
if len(sys.argv)!=2: sys.exit("REQUIRED: pandas, pathlib\nTested with Python 3.9\n","USAGE: \npython dePepFP.py <path to folder containing psm.tsv file(s) like \"Z:/20220319_IP-UCHL1_MN/\" > <Hyperscore threshold for filet... |
b54b6dc1b153f1113b2322c780d297c367a30a3c480c281df90c6e07439d057d | Python | 3,412 | 114 | import argparse
import sys
from dna_interpolation import DNAInterpolatorWrapper
from interpolated_formatter import InterpolatedDataFormatter
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--records-file",
"-r",
help="Master table of records (required).",
... |
91e04670211b52bdc008ac5f240a36835acfcfe22b4d36d2c64f81c5d5c7613a | Python | 3,413 | 74 | import torch
import os,time
from torch import nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from tqdm import tqdm
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import random
import argparse
import running_function
if __name__=='__main__':
parser = ... |
a529ef2b18a87667bbf646e8ff7c4245ec128f9fb654171ea5a510797bda7231 | Python | 3,418 | 78 | #!/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... |
2475fef1e0291548a75e22c8595840ee0a3b36dcafa3a906c789b67d62a36cb5 | Python | 3,421 | 93 | import torch
from torch import nn
class BabyUnet(nn.Module):
"""
Neural network for semantic image segmentation U-Net (PyTorch), with only three max-pooling layers
Reference: Falk, T. et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods 16,
67–70 (2019).
Paramet... |
0e5b0b87b1cc35de765d8b75572594503ffe6eea4db93be9f004869c8322107c | Python | 3,424 | 83 | #!/usr/bin/env python
# Copyright 2016-2020 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... |
63dbf7f7743fb6d59d824656007f9077b7692adbfb1eea6f3b0d02db3855eabe | Python | 3,425 | 87 | from argparse import Namespace
import pytest
from chemprop.cli.train import _process_ffn_hidden_dims
@pytest.fixture
def base_args():
return Namespace(
ffn_hidden_dim=[300],
ffn_num_layers=1,
atom_ffn_hidden_dim=[300],
atom_ffn_num_layers=1,
bond_ffn_hidden_dim=[300],
... |
737c0363f5a4fc78c6c87a8114e200c7d634e488f332548a29453bb5be2acdbc | Python | 3,433 | 85 |
############################################################################
# Copyright (c) 2022-2026 University of Helsinki
# All Rights Reserved
# See file LICENSE for details.
############################################################################
import logging
import os
import re
import shutil
from collect... |
a70704957bedda56d5a886b009f350b58c0e76b208b72fdef88938507b9d6a2c | Python | 3,446 | 118 | from composer.models import ComposerModel
import logging
from torchmetrics import Metric
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedTokenizer
from metrics_and_callbacks import BiologicalDistance
class GeneticDistanceModel(ComposerModel):
def __init__(
... |
4caa8368fc359fe2c76345d8138e234a1882ecc1b46b6ff2c79d1291db41181e | Python | 3,450 | 87 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import unittest
from densepose.data.datasets.builtin import COCO_DATASETS, DENSEPOSE_ANNOTATIONS_DIR, LVIS_DATASETS
from densepose.data.datasets.coco import load_coco_json
from densepose.data.datasets.lvis import load_lvis_json
from densepose.data... |
77fdb031f6850f17c5e377cdf458b21caacfce7b1a1ca1b2ffce6153a06e450a | Python | 3,450 | 90 | import torch
from torch.utils.data import Dataset
import logging
class ProcessedDataset(Dataset):
"""
Data structure for a pre-processed cormorant dataset. Extends PyTorch Dataset.
Parameters
----------
data : dict
Dictionary of arrays containing molecular properties.
included_speci... |
96a857b2849c3ea3e5f5c1da80e7a7575c2aaab25a70b337ed0349ea722210b9 | Python | 3,450 | 89 | ############################################################################
# Copyright (c) 2022-2026 University of Helsinki
# # All Rights Reserved
# See file LICENSE for details.
############################################################################
"""Unit tests for the shared polyA / TSS peak detector (term... |
e6efa8cb0ea6a75f237a5909c79a8e39e54233f86cd7ee89c904305d9974c0bb | Python | 3,458 | 110 | from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any
@dataclass(slots=True)
class MolecularRecord:
access_code: str
smiles: str
source_row: int
metadata: dict[str, Any] = field(default_factory=dict)
@dataclass(slots=True)
class RunReport:
input_file... |
a04be132206b414a7f0f67f545be9535305777e9519fab8044756660a2061cc4 | Python | 3,460 | 108 | import numpy as np
from pynestml.codegeneration.nest_code_generator_utils import NESTCodeGeneratorUtils
def generate_code(neuron_model, synapse_model, sname, target_path, force_syn_vars=None):
codegen_opts = {"delay_variable": {sname: "d"},
"weight_variable": {sname: "w"}}
if force_syn_va... |
da98f9da87b7b91ea91ac49b11db45d04bc662664cafe54f58b80c01a20383b9 | Python | 3,467 | 100 | """
Neuron "Barcodes"
=================
<!-- difficulty: intermediate -->
Visualize a neuron's branching pattern as a topological "barcode".
This technique turns a neuron's branching pattern into a unique "barcode" using topological sorting,
based on [Cuntz et al. (2010) :octicons-link-external-16:](https://journals.... |
bb5688579d3675789b7d0cae39601179e774429548e3687e09da119fdde26899 | Python | 3,469 | 76 | # Copyright 2021 HIP Applied Computer Vision Lab, 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... |
ef8575abdfea3aecb01d98219f02d08f299c79ea43a2b054651661f5bfaf1b53 | Python | 3,472 | 124 | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import torch
from detectron2.config import get_cfg
from detectron2.engine import default_setup
from detectron2.modeling import build_model
from densepose import add_densepose_config
_BASE_CONFIG_DIR = "configs"
_EVOLUTION_CONFIG_SUB_DIR = "evolution"
_HRN... |
80f3b9684439165f4e5b3356ce554dedcfb2a3c2ccccd1e201f6460f3029cd78 | Python | 3,473 | 76 | from nnunetv2.training.loss.compound_losses import DC_and_topk_loss
from nnunetv2.training.loss.deep_supervision import DeepSupervisionWrapper
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
import numpy as np
from nnunetv2.training.loss.robust_ce_loss import TopKLoss
class nnUNetTrainerTopk10... |
53848f4137852d817dafd6312d3dda04e2e8428513fa38696a590d114758e6a1 | Python | 3,474 | 100 | from __future__ import annotations
import argparse
import sys
from pathlib import Path
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
# Import torch before RDKit to keep the Windows DLL load order deterministic.
import src.utils.torc... |
64c11d484a105cf97d9d7d4669e1b1b1eb33e75fe2b963ae5aebeae20f408fdb | Python | 3,474 | 101 | from sklearn.cluster import KMeans
import ot
import pandas as pd
from sklearn.neighbors import NearestNeighbors
import numpy as np
import numba
@numba.njit("f4(f4[:], f4[:])")
def euclid_dist(t1, t2):
sum = 0
for i in range(t1.shape[0]):
sum += (t1[i] - t2[i]) ** 2
return np.sqrt(sum)
# 1003
@num... |
71bc84aa57c2c42d6d4c1b28b9fae77ad98dbee74b983a4a177cfea19b932e54 | Python | 3,474 | 129 | """Metric computation for model evaluation.
Provides functions for classification metrics (accuracy, balanced accuracy,
precision, recall, F1, AUROC, AUPRC), regression metrics (MAE, RMSE, R²),
and calibration analysis (calibration curve, ECE).
"""
from typing import Dict, Optional, Tuple
import numpy as np
from skle... |
c04562bdb8c181990770578dcd91a2c4b7cdc195f95edce28033cbe24cea412f | Python | 3,477 | 98 | # Copyright (c) Facebook, Inc. and its affiliates.
import os
import unittest
import tempfile
from itertools import count
from detectron2.config import LazyConfig, LazyCall as L
from omegaconf import DictConfig
class TestLazyPythonConfig(unittest.TestCase):
def setUp(self):
self.curr_dir = os.path.dirname... |
8cfea7f8f7085144a76d1054fcce58420c21d958a405bc1d82ad3e2098122f9f | Python | 3,478 | 102 | """SessionResponses — top-level container for a session's response data."""
from .unit_response import UnitResponse
from .window_config import WindowConfig
from spikeinterface import full as si
from pathlib import Path
class SessionResponses:
def __init__(self, session_path, sorting_analyzer_path, trial_df,
... |
1672c5b263b8ed400326f5122f684e0d42b3d8c24ecaf8bab0f274a68d688a8c | Python | 3,480 | 109 | """Abstract base class for visualization backends in Mesa.
This module provides the foundational interface for implementing various
visualization backends for Mesa agent-based models.
"""
from abc import ABC, abstractmethod
import mesa
from mesa.discrete_space import (
DiscreteSpace,
OrthogonalMooreGrid,
... |
ceb217c986de6db6928e8b0732d32542774acc5702e75402b34fd1c709d3d027 | Python | 3,483 | 121 | # --- Python 标准库 ---
import gc
import random
import warnings
# --- 第三方核心科学计算库 ---
import numpy as np
import pandas as pd
# --- 生物信息学与数据分析库 ---
import anndata as ad
# --- 深度学习库 (PyTorch) ---
import torch
import torch.nn as nn
import torch.nn.functional as F
# --- 脚本级别的设置 ---
warnings.filterwarnings("ignore")
gc.col... |
8f0e3a13870b5a77f9beaf76f51b3a9dcc2284626eb58eec94900e694e90f2aa | Python | 3,484 | 88 | from typing import Any, Callable, List
import numpy as np
import numpy.typing as npt
from hsnn.core.types import SpikeTrains
from hsnn import ops
from .patterns import SpatioTemporalPattern, PolyChronGroup
__all__ = ["generate_poisson_train",
"generate_uniform_train",
"generate_poisson_pattern"... |
6b282d755bf1443d793154e7159b70d43c40d02ee8e5ec27cf3bf94b3f8d91e9 | Python | 3,488 | 118 | #!/usr/bin/env python
#
# Copyright (c) 2022 10X Genomics, Inc. All rights reserved.
#
"""K-means clustering."""
from __future__ import annotations
from typing import NamedTuple
import numpy as np
import scipy.spatial.distance as sp_dist
import sklearn.cluster as sk_cluster
import cellranger.analysis.clustering as ... |
d39554157c71184a75e7ab0fa598cc88a6027c3e8ae0ceadb0b1ab269eb9b344 | Python | 3,490 | 138 | from typing import List, Tuple, Dict
import pytest
import pandas as pd
import numpy as np
import navis
from navis.connectivity import NeuronConnector
from navis import NeuronList
def test_neuron_connector():
nrns = []
for n in navis.example_neurons():
n.name = f"{n.name}_{n.id}"
nrns.append(... |
8b2dcda602bf42670763824ce50d6d1789face117998b35b236f53f867e5dc18 | Python | 3,492 | 146 | """Tests for model.py."""
import numpy as np
from mesa.agent import Agent, AgentSet
from mesa.model import Model
def test_model_set_up():
"""Test Model initialization."""
model = Model()
assert model.running is True
assert model.time == 0.0
model.step()
assert model.time == 1.0
def test_m... |
d4cc3d975057a8e9992def18008457f369d4985845fe955326eeb7f95616502a | Python | 3,498 | 118 | import gufe
import pytest
from gufe import ChemicalSystem, SolventComponent
from gufe.tests.test_protocol import DummyProtocol
from openff.units import unit
@pytest.fixture
def solv_comp():
yield SolventComponent(positive_ion="K", negative_ion="Cl", ion_concentration=0.0 * unit.molar)
@pytest.fixture
def solvat... |
3f74e32b8c820e1ea95f8abfce7dd8e5a5ce806852fabefc99f48e01879a4887 | Python | 3,501 | 102 |
""" Aggregation of voxel features at vertex locations """
__author__ = "Fabi Bongratz"
__email__ = "fabi.bongratz@gmail.com"
from typing import Tuple
import numpy as np
import torch
import torch.nn.functional as F
from pytorch3d.ops import knn_points
from utils.utils import int_to_binlist
from logger import measur... |
8e0db31f8285ca699e215055b777c23b4987da88ad763dfbfeaf48c3b2749c71 | Python | 3,501 | 107 | import inspect
from functools import wraps
from typing import List, Optional, Tuple
import torch
import torch.nn.functional as F
from loguru import logger
def top_n_stoichiometry_combinations(
logits: torch.Tensor,
n: int = 5,
class_labels: Optional[List[int]] = None,
beam_width: int = 10,
use_ra... |
ba958ae6cff6b8e94e40bc978f1b34e24eb12ce306dba6a551378b559b505be4 | Python | 3,503 | 94 | #@String(value="<html>This script retrieves nearest neighbor distances from a 2D/3D list of centroid<br>coordinates, calling another script to plot frequencies of calculated distances.<br>You will be prompted for input data in the next dialog prompt.", visibility="MESSAGE") info
#@String(label="Column heading for X-coo... |
1526bba9b8c45a11289fbd22e7fadbef6e41859bf1f8f4054c42b1aa750fd637 | Python | 3,512 | 86 | #!/usr/bin/env python
# Copyright 2017-2022 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... |
5ce87be1378c08382c39eb8a301e6c008bb1016e19f8f4d7d2a66a2322dc6107 | Python | 3,512 | 110 |
#%% Imports
# Imports from the library
from micorr.simulation import simulations, testing, plotting, transformations
from micorr.estimators import mi_estimators, corr_est
# More general imports
import numpy as np
from functools import partial
#%% Initial parameters
# Dictionary with the estimators to be tested
est... |
8d17293268b80ea7be3362108ae158d2fa6cee75016b441ea3183c0600fb64dc | Python | 3,515 | 102 | # pynbs_parallel.py (Python 2.7)
import os
# Prevent BLAS/OpenMP oversubscription when using multiprocessing
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
from pyNBS import data_import_tools as dit
from pyNBS i... |
9693eb89042820d611f4d69dbc43c8dcafb78fa9a126ac78338979c662e41b98 | Python | 3,527 | 107 | # ruff: noqa: PLR2004
import json
from pathlib import Path
import pytest
from snakebids.exceptions import RunError
from snakebids.utils import output
def dirlen(f: Path):
return len([*f.iterdir()])
@pytest.fixture
def fake_snakemake(tmp_path: Path):
app1 = tmp_path / "app1"
app1.mkdir()
(app1 / "... |
3791dd409536345dc1bd8b918142c74746de39d781e314b030a359e8131a8bee | Python | 3,528 | 109 | # 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... |
f208bc7ed47b6a3dae45788a8153c8a08d1507977d7dcfed15200142c931aee0 | Python | 3,528 | 61 | from nnunetv2.model_sharing.model_download import download_and_install_from_url
from nnunetv2.model_sharing.model_export import export_pretrained_model
from nnunetv2.model_sharing.model_import import install_model_from_zip_file
def print_license_warning():
print('')
print('####################################... |
4ef0be0d7f9fc57adc0c332c8f9547f3af512fc79269f81fb3c4d7ccb500a4cc | Python | 3,529 | 103 | """Custom visualization components."""
from __future__ import annotations
from collections.abc import Callable
from .altair_components import (
SpaceAltair,
make_altair_plot_component,
make_altair_space,
)
from .matplotlib_components import (
SpaceMatplotlib,
make_mpl_plot_component,
make_mpl... |
945061e029f54c590bfb24c8d900a82843174982ced53d841581b0508b57ba64 | Python | 3,530 | 117 | # Run with streamlit run st_app.py
import time
import altair as alt
import pandas as pd
import streamlit as st
from model import BoltzmannScenario, BoltzmannWealth
model = st.title("Boltzman Wealth Model")
num_agents = st.slider(
"Choose how many agents to include in the model",
min_value=1,
max_value=10... |
6d78c8b747d137ed256ee611a3257ee2b31feb7ac4c168ce91e8bc3517795692 | Python | 3,533 | 99 | from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
import torchvision.transforms as transforms
from .utils.activation_manager import ActivationManager
from .utils.decomposition_handler import DecompositionHandler
from .utils.pca import PCAHandler
from .utils.ica import ICAHandler... |
8a53e1a858b57bdaac6addedcf93d4c2c973aa440e3d7df618303f0d27daacde | Python | 3,535 | 76 | # Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from detectron2.structures import Boxes, BoxMode, Instances
from densepose.modeling.losses.utils import ChartBasedAnnotationsAccumulator
from densepose.structures import DensePoseDataRelative, DensePoseList
image_shape = (100, 100)
inst... |
9fd9b25c7efbc35cdbb1f7f33e58a8ffc79add3dcec1e69d226cec89e1baf647 | Python | 3,544 | 168 | #python codonusage.py CCDS_nucleotide.20221027.fna CCDS.20221027.txt
import sys
import csv
file1 = sys.argv[1]
file2 = sys.argv[2]
ftout = f"{file1}.{file2}.aa.py.fasta"
fcout = f"{file1}.{file2}.aa.py.txt"
seqh = {}
seqc = None
val = {}
cl = 3
c2a = {
'TTT': 'F', 'TTC': 'F', 'TTA': 'L', 'TTG': 'L',
'TCT'... |
d9e1b8f397024321645ffe87da74c103cb2246388466309951614bdf46bdfa4e | Python | 3,544 | 97 | #!/usr/bin/env python
# Crop T2-weighted images to a usable FOV
#
# Usage: crop_T2.py -i <input_dir> -x_min <x_min> -x_max <x_max> -y_min <y_min> -y_max <y_max> -z_min <z_min> -z_max <z_max> -o <output_dir>
from nipype.interfaces.fsl import ExtractROI
import nibabel as nii
import argparse
import pathlib
import subproc... |
1eb8b16c6c3afea0066cb507b6f785b4b96c9616908ca203bb6f37c410a4bbb5 | Python | 3,545 | 96 | # Copyright (c) Facebook, Inc. and its affiliates.
# pyre-unsafe
from typing import Any, Tuple, Type
import torch
class BaseConverter:
"""
Converter base class to be reused by various converters.
Converter allows one to convert data from various source types to a particular
destination type. Each so... |
4ce606200e73bcffbe734d3a489ddc129f06f971967eb51b7518c5072f2d84b3 | Python | 3,545 | 100 | # This code is part of OpenFE and is licensed under the MIT license.
# For details, see https://github.com/OpenFreeEnergy/gufe
import abc
import collections
import json
from typing import Dict, Tuple
from gufe.storage.errors import ChangedExternalResourceError, MissingExternalResourceError
from gufe.storage.externalre... |
a41b0f369cf8f2b9cab2414b7faecc9bd022416e443ac83ba82036012cac136a | Python | 3,554 | 64 | 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
def convert_amos_task2(amos_base_dir: str, nnunet_dataset_id: int = 219):
"""
AMOS doesn't say anything abo... |
9b9a665cd6325ae3ca87b5cf181c544923e5a11f6aeda1f3ecb179c4571abfb7 | Python | 3,555 | 108 | from __future__ import annotations
import argparse
import json
import logging
import subprocess as sp
import tempfile
from typing import Any
import attr
from snakebids import bidsapp
from snakebids.exceptions import SnakebidsPluginError
from snakebids.plugins.base import PluginBase
from snakebids.utils.utils import ... |
810d23af53f849d66b4ccc54eac928123d2236e2ca988feebb96385f487e275c | Python | 3,557 | 98 | from __future__ import annotations
import itertools as it
from argparse import ArgumentParser
from collections.abc import Iterable
from pathlib import Path
import pytest
from hypothesis import given
from hypothesis import strategies as st
from snakebids.exceptions import ConfigError
from snakebids.plugins.bidsargs i... |
f285dbe34e15a6d40a4aa013b83e7395bd6765aba3b2eef5daf18dd6d02d9046 | Python | 3,558 | 95 | from detectron2.config import LazyCall as L
from detectron2.layers import ShapeSpec
from detectron2.modeling.meta_arch import GeneralizedRCNN
from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
from detectron2.modeling.backbone.fpn import LastLevelMaxPool
from detectron2.modeling.backbone import Bas... |
1c78cbaab8e6f5bc203b5aa0d422361f6a0cf4a391fcff2311c40065e69eccb1 | Python | 3,566 | 102 | # Run a simulation in OpenMM with a trained force field
import MDAnalysis as mda
from openff.toolkit import Topology
from openff.toolkit.topology import Molecule
import networkx as nx
from openmm.app import ForceField, PDBFile, PME, CutoffPeriodic, HBonds, Simulation, StateDataReporter, DCDReporter, CheckpointReporter... |
d2b2c5ecb747147593e18c2d0ceb5bfa3fe77c582362bb1aee371d49ea7d1b8c | Python | 3,573 | 78 | import argparse
from model_utils import build_model, adapt_input_model, compile_runner
from dataset import load_data
def train(images_path, masks_path, model_path, model_str, encoder_str,
weights, in_channels, batch_size, epochs, learning_rate, fp16):
# Load data and prepare loaders
loade... |
71239e74c19ab1d40836e025753c43c27dce1e94812018ddd84616de6075508c | Python | 3,574 | 102 | # Run a simulation in OpenMM with a trained force field
import MDAnalysis as mda
from openff.toolkit import Topology
from openff.toolkit.topology import Molecule
import networkx as nx
from openmm.app import ForceField, PDBFile, PME, CutoffPeriodic, HBonds, Simulation, StateDataReporter, DCDReporter, CheckpointReporter... |
ad93ca652c1a772154fd9360505cbbc2638d8eb8d7c1e9ed2e6e17988ce81695 | Python | 3,574 | 125 | # flake8: noqa
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------... |
5714cf430329ec81cf81fb409025535d0df59b64c1fb814cbab51be1ff639b97 | Python | 3,576 | 81 | ### the network performance without variable selection (for comparison with the performance using SurvNet)
import tensorflow as tf
import numpy as np
import math
def IN(seed,
train_X,train_Y,val_X,val_Y,test_X,test_Y,
n_classes,n_hidden1,n_hidden2,
learning_rate,epochs,batch_size,num_batches,d... |
1a050616ca910a33741197f1626ee3d66f0a67a4e5647a0abeaafb0178cd20cb | Python | 3,579 | 120 | # -*- coding: utf-8 -*-
"""Functions for working with CIVET data."""
import os
import numpy as np
from neuromaps.points import get_shared_triangles, which_triangle
def read_civet_surf(fname):
"""
Read a CIVET-style .obj geometry file.
Parameters
----------
fname : str or os.PathLike
Fi... |
43ea20c7446705303105f32087dc912db6d00477d1e6e087179fdff447d5c84e | Python | 3,583 | 98 | from dataclasses import dataclass
from typing import Optional
import torch
import torch.nn as nn
from .activation_manager import ActivationManager
from .decomposition_handler import DecompositionHandler
@dataclass
class AttackParams:
alpha: float
beta: float = 0.01
num_iterations: int = 50
class PGDAt... |
25600c7b21780e225600d6b1ffa4417197a441db074e683d30c8836c1dc16553 | Python | 3,584 | 119 | import torch.nn as nn
import torch
from torch.nn.utils.parametrizations import orthogonal
import torch.nn.functional as F
class GraphConv(nn.Module):
def __init__(self, in_features, out_features):
super(GraphConv, self).__init__()
self.in_features = in_features
self.out_features = out_featu... |
b43b6f64254032c053b15de8fd4abb205f25726f402ef9ce468aee61256cfa40 | Python | 3,585 | 119 | from __future__ import annotations
from collections.abc import Iterator
from enum import Enum, auto
from typing import TypeAlias
import importlib_resources as impr
import more_itertools as itx
from typing_extensions import NotRequired, TypedDict
from snakebids.io.yaml import get_yaml_io
from snakebids.paths import r... |
bbd13c5965686d45b0ded667c34dad885eb05d9691e5a622574af6aba0d159f0 | Python | 3,590 | 125 | """Fig 4F: Longitudinal rasters of all modulated units across sessions.
All modulated units sorted by cortical depth for ICMS92, stim condition
(ch 11, 4 uA), at weeks 0, 1, 2, 3, 4. Spikes shown from -700 to 3400 ms.
Usage:
python python/fig4/longitudinal_rasters.py
"""
import sys
from pathlib import Path
sys.pa... |
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