sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
9833115043c55fe97e71f58199bf6b07c17c6ac7cc777be45ced4709d34839d0 | Python | 3,280 | 92 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import numpy as np
import torch
import os
import scipy.io as sio
import CBIG_pMFM_basic_functions as fc
def CBIG_mfm_validation_desikan_main(gpu_index=0):
'''
This func... |
98d2db50c9a417cbe4d0be786ad122e8cd5d23a8b1eeaf2f43ca2bc73d35dc9c | Python | 3,280 | 78 | # Copyright (C) 2025 ETH Zurich, Moritz Thürlemann, and other AMP contributors
import json
import os
import numpy as np
import yaml
from pathlib import Path
def write_qm_mm_json(path:str):
parameters = {}
parameters["qm_zone_resnames"] = ["ACE", "ASN","LEU","TYR","ILE","GLN","TRP","LEU","LYS","ASP","GLY","G... |
d5ca32e76a7368a25d44bfd811727575f99b1270ef5dd3f6b92e37ea9c3d6043 | Python | 3,280 | 92 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import numpy as np
import torch
import os
import scipy.io as sio
import CBIG_pMFM_basic_functions as fc
def CBIG_mfm_validation_desikan_main(gpu_index=0):
'''
This func... |
6007bda798affaaa4bd92485b49a50c1d7b85ef9b67ba7016653440b327c814e | Python | 3,282 | 89 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import numpy as np
import torch
import os
import scipy.io as sio
import CBIG_pMFM_basic_functions as fc
def CBIG_mfm_validation_desikan_main(gpu_index=0, weight=1):
'''
... |
a1c1913004895b6fde967b5988f16741b73f51bbb39ad26cd9af7a02c84d9b49 | Python | 3,282 | 95 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import numpy as np
import torch
import CBIG_pMFM_basic_functions as fc
import warnings
def CBIG_mfm_validation_desikan_main(gpu_index=0):
'''
This function is... |
0ca31b2a9fd0644760022c8f2f31370a6cb4b7f47b3f4f319f35154f76bf70ac | Python | 3,283 | 78 | import os
import time
import argparse
import pandas as pd
import scanpy as sc
from os.path import join as pjoin
from gears import PertData, GEARS
def main(parser):
args = parser.parse_args()
# get data
pert_data = PertData(args.data_dir)
# load dataset in paper: norman, adamson, dixit.
try:
... |
b0e3853cba28589fd6647b1faad09cca9a2ef506323854f63f95f5688b57f19d | Python | 3,283 | 99 | from pathlib import Path
import rich
import rich.syntax
import rich.tree
from beartype.typing import Sequence
from hydra.core.hydra_config import HydraConfig
from lightning_utilities.core.rank_zero import rank_zero_only
from omegaconf import DictConfig, OmegaConf, open_dict
from rich.prompt import Prompt
from src.uti... |
c5356c685cf7582da8133bc44cb9559343ab0b109830d68e9741f80eab582dfb | Python | 3,283 | 80 | from model.builders.prostate_models import build_pnet2
task = 'classification_binary'
selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes.csv'
# selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes_and_memebr_of_reactome.csv'
data_base = {'id': 'ALL', 'type': 'prostate_paper',
'pa... |
800610836b5259e45be5514739a520a4d00d30280a32e8aee5dfe048b016b892 | Python | 3,284 | 90 | import argparse
import json
from collections import defaultdict
import os
import soundfile as sf
from tqdm import tqdm
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Example argument parser')
parser.add_argument('--wavs_tsv', type=str)
parser.add_argument('--lid_preds', type=str)
... |
8a483c2e91981a4263e038c66ccf3fa305d2ceea901e0b7b07108098a7bcd2f7 | Python | 3,285 | 116 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import logging
from pathlib import Path
import random
import soundfile as sf
import torch
from tqdm import tqdm
... |
fe6a92ef64799b3eebcc5765296d17f4c96462d9d4a105c327a2ccf5954f899a | Python | 3,285 | 104 | import pytest
import torch
def test_poisson_gene_selection():
import numpy as np
import pytest
from scvi.data import poisson_gene_selection, synthetic_iid
n_top_genes = 10
adata = synthetic_iid()
poisson_gene_selection(adata, batch_key="batch", n_top_genes=n_top_genes)
keys = [
"... |
6db73958873e1d6d1d5a4b60cd6e04412e2cc151521e685046b16f212bbea741 | Python | 3,287 | 102 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
BLEU scoring of generated translations against reference translations.
"""
import argparse
import os
import sys
fr... |
80c48a279d3b9a685c01b8464b9a71b1df962573f3010dbf22b5d84e7f687c9a | Python | 3,287 | 86 | #!/usr/bin/env python3
"""
Compute overlap between Xu and Rynard et al. transcripts and ENCODE4
brain-expressed transcripts by intron chain identity.
Outputs encode4_overlap.parquet with columns:
isoform - SFARI transcript ID
type - "known" (FSM) or "novel" (all other categories)
in_encode4 - True if... |
7b4c453dcb170a6e842f66a5f5e6e2bcc4151958a24e1d2329fecf1a6a15f87a | Python | 3,288 | 84 | # Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... |
2c5c025d40c6cf03765118a906e57c96d7db4d9394dcc1f52a6f02b2e5b5dd5a | Python | 3,289 | 84 | from typing import Union, Optional
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from transformers.trainer_utils import SchedulerType
def get_linear_schedule_with_warmup(
optimizer: Optimizer,
num_training_steps: int,
num_lm_warmup_steps: int,
last_epoch=-1
):
"""... |
083e6a2c3d8d935cbcee6f82db1c97147b7e25828e31a8d949c9edf385e19b40 | Python | 3,290 | 106 | import torch
def compute_average_triplet_loss(
model,
anchor_encode,
pos_list,
neg_list,
loss,
device
):
"""
Compute the average triplet loss for a given anchor encoding, positive and negative samples.
"""
num_samples = pos_list.shape[1]
# Flatten the (B, S, ...) sample li... |
d626dffb405da8ad0ea28d8d0b41e7c99a0e7dcedea1563bfa7810698a8b64b0 | Python | 3,290 | 91 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import numpy as np
import torch
import os
import scipy.io as sio
import CBIG_pMFM_basic_functions as fc
def CBIG_mfm_validation_desikan_main(gpu_index=0):
'''
This func... |
a44c115108f56a75bfeadac7a06fb33036dac6f0e7cc9ed3ce75c1c7558575ee | Python | 3,292 | 106 | # isort: skip_file
# else segmentation fault when importing concept
import pyarrow # noqa: F401
from pathlib import Path
import matplotlib.pyplot as plt
import scanpy as sc
from anndata import AnnData
from concept import scConcept
import logging
log = logging.getLogger(__name__)
validation = False
BLAMPEYQ = Path... |
5e477f9bc33eb526e004e039cad594ca64bce491bdeb8a7e5c73cdcbce9c8009 | Python | 3,295 | 114 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from fairseq import utils
class TestUtils(unittest.TestCase):
def test_convert_padding_direction(self):
... |
a574d756e3723b9de421656c1322f8b7204629784b170583b362bf76be23995e | Python | 3,295 | 98 | import unittest
from typing import Iterable
from unittest.mock import patch
from pyecharts import options as opts
from pyecharts.charts import Bar, Line, Tab
from pyecharts.commons.utils import OrderedSet
from pyecharts.components import Table
from pyecharts.faker import Faker
from pyecharts.globals import Th... |
34d9fc3668b39cdf80bb0f98c7a8c930161bcb2cfefa3c7c9a8b1f6ad004eb03 | Python | 3,296 | 94 | """Simulation dataset: NPZ patch loader with Min-Max normalization for synthetic spatial data."""
import os
import glob
import numpy as np
import torch
from torch.utils.data import Dataset
class ImagePatchDataset(Dataset):
def __init__(self, npz_dir):
self.file_list = glob.glob(os.path.join(... |
98a3b7ca298f5341d71acbe96b8eef885b69824a9f941b248ec4351f820023b6 | Python | 3,296 | 83 | import vtk, slicer
import numpy as np
def getGridDefinition(node):
if isinstance(node, slicer.vtkMRMLScalarVolumeNode) or isinstance(node, slicer.vtkMRMLLabelMapVolumeNode):
directionMatrix = vtk.vtkMatrix4x4()
node.GetIJKToRASDirectionMatrix(directionMatrix)
grid = node.GetImageData()
size = np.arr... |
ff1e1ecf8523a236595547eb1811f6f420c2ab7d77f6f30e66b1b0e8d397ab81 | Python | 3,296 | 87 | import argparse
import copy
import neuroglancer
import neuroglancer.cli
import numpy as np
from example import add_example_layers
def _make_viewport_adjust_command(adjustments):
def handler(s):
with viewer.txn() as s:
for i, amount in adjustments:
s.partial_viewport[i] += amou... |
5b4784e6640907b59d54d9a21e2cd4dd2f2732d70db140cb17b56f1edd8f3f67 | Python | 3,300 | 91 | """xtb GFN-FF runner with PBC support for periodic crystals.
xtb GFN2 does NOT support PBC ("Multipoles not available with PBC"); GFN-FF
is the universal force field that handles 3D periodic cells via xtb 6.x.
We use POSCAR (VASP) input format because xtb auto-detects PBC from it.
For perovskites and heavy-element pe... |
1ed9455b3bdba3e88a8a74e2447bf0fb7f9a8b1912a7e363fbcfec1bc2f7b49c | Python | 3,302 | 89 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
from typing import Optional, List
from omegaconf import II
from fairseq.dataclass import FairseqData... |
c1b8ffed16aa2ecb0b71bcf5442d1e244745ed0f9ad9bfaeb4c092da2db7e668 | Python | 3,302 | 88 | import streamlit as st
import numpy as np
import cv2
import os
import joblib
from collections import defaultdict
import cmapy
import importlib
import time
import msi_visual.app.utils.viewer
importlib.reload(msi_visual.app.utils.viewer)
from msi_visual.app.utils.viewer import display_comparison, get_stats, ge... |
5d1265d9875024a5654ee591237e8a514d8d35017621b66c5ec86efa4aec23f4 | Python | 3,303 | 83 | from model.builders.prostate_models import build_pnet2
task = 'classification_binary'
selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes.csv'
selected_samples = 'samples_with_fusion_data.csv'
data_base = {'id': 'ALL', 'type': 'prostate_paper',
'params': {
'data_type': ['mut... |
9d19438fd653ed6077e8f2857ad76c418b3e8fb6e939c070fba85dd3b5d327b9 | Python | 3,303 | 92 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import numpy as np
import torch
import CBIG_pMFM_basic_functions as fc
import os
def CBIG_mfm_validation_desikan_main(gpu_index=0):
'''
This function is to validate the... |
6f1dba2b771a5e1814e06305dc27439248cbd88f7ebbeb28494e9a6e8540c717 | Python | 3,306 | 87 | import pandas as pd
import networkx as nx
from neuron import h
def get_external_connections():
ref_external = []
par_external = []
ri_external = []
for sec in h.allsec():
counter = 0
for seg in sec:
if counter < 1:
ref_external.append(seg)
p... |
2cceab4d46272c5380f0dfb2eb43c67e097370da2dcd52bc487ca754c47b017a | Python | 3,308 | 70 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import unittest
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.models.transformer import Tran... |
e82b53235af1ccbd495bc6745bee026e60d4bc6b2d411cf239d210b03e414002 | Python | 3,308 | 79 | import json
import pathlib
import pytest
import vak.datapipes.frame_classification
ARGNAMES = 'dataset_csv_filename, input_type, frame_dur'
ARGVALS = [
(pathlib.Path('bird1_prep_230319_115852.csv'), 'spect', 0.002),
(pathlib.Path('bird1_prep_230319_115852.csv'), 'spect', 0.001),
(pathlib.Path('bird1_pre... |
819846dd390e3d8951f2113b8e7fddd89379d7a54a3937bc1bc928c1cc0e2384 | Python | 3,310 | 97 | import pytest
import torch
from moove.models.ConvMLP import ConvMLP
from moove.models.CNN import CNN
class TestConvMLP:
"""Tests for the binary segmentation model."""
def test_output_shape_default(self):
model = ConvMLP(input_size=192)
model.eval()
x = torch.randn(4, 192)
out ... |
957bb2fb42ced13dbc5337c5978a13875dd8d9a9470b75c5795c7102b86c9a60 | Python | 3,310 | 132 |
import torch
from pathlib import Path
import joblib
import copy
import numpy as np
from tqdm.auto import tqdm
from utils.training import train_forecasting_model, predict_forecasting_model
from utils.measures import compute_eta_gauss
from models.transformer import transformer_model_generator
from models.lstm import a... |
8fc5e53ea15f5e0619f9b24de557e71b7c4cc6d881eecfb9613fe8dabb475d46 | Python | 3,312 | 85 | import copy
from .test_definition import TweetyNetDefinition
import pytest
import torch
import vak.models
class TestFrameClassificationModel:
MODEL_DEFINITION_MAP = {
'TweetyNet': TweetyNetDefinition,
}
@pytest.mark.parametrize(
'model_name',
[
'TweetyNet',
... |
e2cd048e39df1530eee5e5e49ff38e3e74026cd828128fd0442d374cca9b9a87 | Python | 3,312 | 120 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import ast
import argparse
import json
import logging
from pathlib import Path
import soundfile as sf
import torch
from tqdm import tqdm
fro... |
7fd495cc4821189501d5b7c4463fb2444de9a47a0406a33e5db1c5674b1a0cd8 | Python | 3,314 | 90 | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import Conv2d, Module, Linear, BatchNorm2d, ReLU
from torch.nn.modules.utils import _pair
__all__ = ['SplAtConv2d']
class SplAtConv2d(Module):
"""Split-Attention Conv2d
"""
def __init__(self, in_channels, channels, kernel_size... |
9e3aba3a5f17a59016186f16f2e395d062fccf1491b632f1155175920b880c51 | Python | 3,314 | 76 | import os
import argparse
from bs4 import BeautifulSoup
import warnings
def format_input(input_path, formatted_input_path):
"""Formats the input XML files by unescaping the escaped special characters.
Replaces the HTML encoding of the special characters to its original form.
The conversion is as follows:
... |
5f733d4fa9c14b950efb955dadc543ed57101bb890a61c560917aaad48ff980e | Python | 3,315 | 74 | import unittest
import re
from rdflib import Graph
from parse_csv import CSVParser
from parser import Parser
class TestCases(unittest.TestCase):
def setUp(self):
self.parser = Parser("/home/ubuntu/workspace/whatizit/code/Config.yaml")
self.csv_parser = CSVParser('/home/ubuntu/workspace/whatiz... |
61c5932a4ce26cc6ed91641eb5a37a80fc3347a827c07bae5e6cb4974193a5fb | Python | 3,315 | 85 | from pathlib import Path
import os
from tqdm import tqdm
import numpy as np
import torchvision.transforms as T
def _save_tile_and_label(tile, label, wsi_name, roi_name, coords, save_path):
#create a name for the tile based on coordinates
fname = f"{wsi_name}_{roi_name}_y-{coords[0]}_x-{coords[1]}"
tilepath... |
a428f4355909a8c8acba20b14ca1f34aedf6a7f13bec7d3ba1956dfcd2328515 | Python | 3,315 | 95 | from typing import List
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.collections import LineCollection
from matplotlib.colors import colorConverter
from sklearn.neighbors import NearestNeighbors
import loompy
from .colors import colors75
def manifold(ds: loompy.LoomConnection, out_file: str, ... |
0a857c4ee12c246063fa8d8bf201a8ac17f3a9749a4074029e64f96838604374 | Python | 3,316 | 77 | """
Merge the recovered supplementary datasets with the original 6-dataset global matrix,
then re-run neuroCombat and Limma DGE on the combined super-matrix.
"""
import os
import pandas as pd
import numpy as np
from neuroCombat import neuroCombat
from scipy.stats import ttest_ind
from statsmodels.stats.multitest import... |
a3348b34a567a81ff7abf808a3bc34bc77835a4b5e8e3c301869ee7e304fc26b | Python | 3,317 | 120 | import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
import pytest
from oneqmc import Molecule
from oneqmc.density_models.base import DensityModel
from oneqmc.density_models.score_matching import (
ScoreMatchingBatchFactory,
ScoreMatchingDensityTrainer,
)
from oneqmc.device_utils import rep... |
1c2059ded60513a21bab81e269a0ec1b1cc5689f996f8c6361b17e256574702b | Python | 3,318 | 116 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Utils for analysis"""
from math import sqrt
from typing import Literal
import numpy as np
from skimage.segmentation import find_boundaries
def pixels_in_radius(
img: np.ndarray, center: tuple[int, int], r: float
) -> tuple[np.ndarray, np.ndarray]:
"""List al... |
46abe69fcfbb66a05611ed9cf264170e2214f445dee69ee8e602cb9c139e5342 | Python | 3,319 | 81 | from model.builders.prostate_models import build_pnet2_account_for
task = 'classification_binary'
# selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes.csv'
selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes_and_memebr_of_reactome.csv'
data_base = {'id': 'ALL', 'type': 'prostate_paper',
... |
e032836fcd8511ea8ee9a9dbf1b0a8f0ae178f66a6d9d933ebb3c7f4eabfd63d | Python | 3,319 | 69 | 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... |
1c4e07589d5d5d550dd4fd8dac3e3379e99aa3dbde5c0fd57cb0e92de98a1fba | Python | 3,320 | 97 | """
This code contains a wrapper class for the distribution analysis method.
Source: https://github.com/zbmed-semtec/medline-preprocessing/tree/main/code/Distribution_Analysis
docs: https://github.com/zbmed-semtec/medline-preprocessing/tree/main/docs/Distribution_Analysis
author: Vishnu Vardhan Dadi
copyright: GENERA... |
552202af17b4af36d30dfd6a0216a503d88a83def7f27f6c0599698714e82711 | Python | 3,320 | 116 | import torch
import torch.nn as nn
from .fast_resnet3d import *
from .slow_resnet3d import *
__all__ = ["SlowFastNetwork", "slow_fast_resnet18", "slow_fast_resnet50"]
class SlowFastNetwork(nn.Module):
"""
Construction of the SlowFast architecture of Feichtenhofer et al., 2019.
https://arxiv.org/pdf/1812... |
8c670800b5027d4e580f0ec5c2fdfa67f3438ddd6d774eff3dadd8cb3d31f39d | Python | 3,321 | 83 | from collections.abc import Hashable
import numpy as np
from pgmpy.structure_score._base import BaseStructureScore
from pgmpy.utils import encode_columns, get_state_counts_array
class LogLikelihood(BaseStructureScore):
r"""
Log-likelihood structure score for discrete Bayesian networks.
This score evalu... |
96e0aaab9c313fc327b124488c33a1f2ba2c67934203c0cf9f7d828db67c4578 | Python | 3,325 | 85 | # Copyright (C) 2025 ETH Zurich, Moritz Thürlemann, and other AMP contributors
import torch
import numpy as np
@torch.jit.script
class Graph:
def __init__(
self,
Z,
nodes,
coords_qm,
mm_monos_esp,
mm_monos_pol,
mol_charge,
mol_size,
R1,
... |
1386634ecbba2e88d800eb8a38d971fb60b1492a8aec18174ca72f1c9a1e72b3 | Python | 3,329 | 107 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
import os
import joblib
import numpy as np
from examples.textless_nlp.gslm.speech2unit.clustering.utils impor... |
86fd63b30fe992eb8b00fd65462249b15620563ce1eb6d259b981c01b9bf1161 | Python | 3,329 | 71 | # Align genotypes to reference file, e.g.
# - Extract SNPs from the reference (merge by CHR:POS --- require data to be on the same genomic build)
# - Extract subset of individuals (for example, european population)
# - Merge SNPs together (for example if input data is split by chromosome)
#
# To run this tool:
# - Down... |
d88a19578e614d1347119f98696c0de1c2c89bcb710b09cf433d2519fe63d1ed | Python | 3,330 | 105 | import vtk, qt, slicer
from math import sqrt, cos, sin
from .CircleEffect import AbstractCircleEffect
class AbstractPointToPointEffect(AbstractCircleEffect):
def __init__(self, sliceWidget):
# keep a flag since events such as sliceNode modified
# may come during superclass construction, which will
# ... |
c81053551fc2c85c95a80b54be00ef38e1b868ea002104347fc16f29d84f8ba1 | Python | 3,332 | 91 | #!/usr/bin/env python
# Author: Jin Lee (leepc12@gmail.com)
import sys
import os
import argparse
from encode_lib_common import (
assert_file_not_empty, log, ls_l, mkdir_p)
from encode_lib_genomic import (
peak_to_bigbed, peak_to_hammock, get_region_size_metrics, get_num_peaks)
from encode_lib_blacklist_filter imp... |
09977a2251a04277715379d7266625c5c4ccc15c9d57e97cf859031b11a0bba0 | Python | 3,333 | 98 | import os
from pathlib import Path
BASE_DIR = Path(".")
INDEX_2016_PATH = BASE_DIR / "dataset" / "index" / "v2016" / "INDEX_general_PL_data.2016"
INDEX_2020_PATH = BASE_DIR / "dataset" / "index" / "v2020.R1" / "INDEX_general_PL.2020R1.lst"
CASF_2013_DIR = BASE_DIR / "dataset" / "coreset_CASF-2013"
CASF_2016_DIR = BA... |
335c980f784297aea7f0478f5309664ed62a4913746ca49c857acbfe60a0e12f | Python | 3,333 | 95 | #!/usr/bin/env python
import unittest
import sys
import shutil
import os
import re
import gzip
if "DEBUG" in sys.argv:
sys.path.insert(0, "..")
sys.path.insert(0, "../../")
sys.path.insert(0, ".")
sys.argv.remove("DEBUG")
import metax.Formats as Formats
from M01_covariances_correlations import Proces... |
a5f67f5158fbc447b61e4bf4e65af954e87a5d0487d270dc6a34454290f140ad | Python | 3,333 | 100 | import gzip
import shutil
import pandas as pd
from pathlib import Path
from mne.preprocessing.eyetracking import read_eyelink_calibration
root = "/egor2/egor/MovieProject2/bids_data/sourcedata"
results = []
for gz_path in sorted(Path(root).rglob("*task-backtothefuture*.asc.gz")):
gz_path = gz_path.resolve()
... |
2f59053972e0339976f306359c3052f0fe3996a4e648461496cedb98f08792bf | Python | 3,335 | 109 | from os.path import join
import matplotlib.gridspec as gridspec
import pandas as pd
from adjustText import adjust_text
from matplotlib import pyplot as plt
# https://stackoverflow.com/questions/32185411/break-in-x-axis-of-matplotlib
from config_path import PROSTATE_DATA_PATH
from setup import saving_dir
def run():
... |
51ed5b8957a76c4223c9a45b50427350e5f040ab58aaeac45ad433403c16b52e | Python | 3,335 | 83 | from model.builders.prostate_models import build_pnet2
task = 'classification_binary'
selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes.csv'
# selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes_and_memebr_of_reactome.csv'
data_base = {'id': 'ALL', 'type': 'prostate_paper',
'p... |
a4f463907fb1a75e61c9053999bebe718fe16f82be3f0f3a100dd05d5d0d86de | Python | 3,335 | 83 | from model.builders.prostate_models import build_pnet2
task = 'classification_binary'
selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes.csv'
# selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes_and_memebr_of_reactome.csv'
data_base = {'id': 'ALL', 'type': 'prostate_paper',
'p... |
e61b78920fa20f71c087251d195e8a6d5f99db52cb034d4985bd1b302ac5e476 | Python | 3,335 | 112 | #### ::: DNABERT-viz find motifs ::: ####
import os
import pandas as pd
import numpy as np
import argparse
import motif_utils as utils
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help="The inp... |
b24914d2622217c79eb9a10922afe7b28c29ae74d3535e64c07d5be27635bf2f | Python | 3,337 | 124 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Classes to organize information for runML.py"""
# import packages
from dataclasses import dataclass, asdict
from typing import Optional
import numpy as np
import pandas as pd
import pickle
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from s... |
c7d673574d882d5812c6fe4e5653c5dfc47c416639027bd03b481190684ec5c1 | Python | 3,337 | 103 | from __future__ import annotations
from dataclasses import dataclass
from typing import TYPE_CHECKING
from xarray import DataArray
if TYPE_CHECKING:
from collections.abc import Callable, Iterable
from typing import Literal
@dataclass(frozen=True)
class MRVIReduction:
"""Reduction dataclass for :meth:`~... |
eaafcb90907faf8e1a8223cf009bdf8a86de8667fee6ebc2c5090140ed93a89b | Python | 3,337 | 102 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
from itertools import zip_longest
def replace_oovs(source_in, target_in, vocabulary, source_out, targ... |
596e3efb756971373b7f53c6fc3f035a20ee92e827e0f21164ffb5d3a210e71b | Python | 3,339 | 83 | """
Training of Random Forest based Segmentation
============================================
This module provides functionality for training a Random Forest-based segmentation model
using multiscale features. Leveraging traditional machine learning methods rather
than deep learning.
Key Features:
- Reads train... |
b7d570b3bf30beaa14e90113ebf0d13e6eab1bfbaeb6733f73b924187ab3afbf | Python | 3,339 | 81 | from model.builders.prostate_models import build_pnet2
task = 'classification_binary'
# selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes.csv'
selected_genes = 'tcga_prostate_expressed_genes_and_cancer_genes_and_memebr_of_reactome.csv'
data_base = {'id': 'ALL', 'type': 'prostate_paper',
'pa... |
6ff345b46c8dea33315bce2bbae0f640e44cd00308cee48ae169e40ab717a42e | Python | 3,341 | 102 | import numpy as np
from mdt import CompositeModelTemplate
__author__ = 'Robbert Harms'
__date__ = "2015-06-22"
__maintainer__ = "Robbert Harms"
__email__ = "robbert@xkls.nl"
class CHARMED_r1(CompositeModelTemplate):
"""The CHARMED model with 1 restricted compartments"""
model_expression = '''
S0 * (... |
3e5f5ad251506b17ae0219b8ca9cba75d0cb589e98573cfbd915a40080ef1618 | Python | 3,342 | 101 | # This extension template provides instructions to add new Conditional Independence (CI) tests to pgmpy.
# Please follow the following steps:
# 1. Copy this file to `pgmpy/ci_tests` and rename it as `your_ci_test.py`.
# 2. Go through this file and address all the TODOs.
# 3. Add an import in `pgmpy/ci_tests/__init__.p... |
d2c2cf4a38d1781d320774afa04a4d051e7f3af42ec6fcca1bcc6d68ae1e199a | Python | 3,343 | 100 | import numpy as np
import torch
from torch.autograd import Function
from pytorch_grad_cam.utils.find_layers import replace_all_layer_type_recursive
class GuidedBackpropReLU(Function):
@staticmethod
def forward(self, input_img):
positive_mask = (input_img > 0).type_as(input_img)
output = torch.... |
6074df4922384d041fb67730e6a388cfade577d1b04c2ad7dc6d6c40047e1cb7 | Python | 3,344 | 73 | # 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... |
e39f36bf3137c863d59e5c2dcc3471efe430ce97b9285a094880af313aeb81a5 | Python | 3,345 | 91 | # Copyright (C) 2025 ETH Zurich, Moritz Thürlemann, and other AMP contributors
import os
import torch
import torch.nn as nn
import numpy as np
from torch import Tensor
from typing import Final
from utilities.Scatter import scatter_sum as scatter
from datastructures.Graphs import Graph
"""
computes D4 dispersion ene... |
01ecad6cf248afc3a4cf54676a8d23d32ed360d9194b5cc0abbc9de8fc70e363 | Python | 3,347 | 103 | # 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 --------------------------------------------------------------
# If ex... |
a26ec4ba8c601b25bf99839b47695497c0d4768972f6deb49bd39ea64c2c9f79 | Python | 3,348 | 96 | """Unit tests for packaging/contributors.py (pure text helpers).
The script lives in ``packaging/`` (which is *not* a package and would shadow the PyPI ``packaging``
distribution if imported by name), so it is loaded directly from its file path.
"""
import importlib.util
from pathlib import Path
import pytest
_MOD_... |
491c7ab8405eaa72dceeaabcc9c3861e358d0d770692ab8fd8a7eb32ef55f2dc | Python | 3,349 | 75 | import math
import logging
import torch
from torch import optim
from torch.optim.lr_scheduler import LambdaLR
# def get_cosine_schedule_with_warmup(optimizer: optim.Optimizer,
# num_warmup_steps: int,
# num_training_steps: int,
# ... |
c5bd9ad10ad569679abda0a65726e47af95997214b0ee1fc4fec73eebc85ef7c | Python | 3,350 | 117 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Performs basic image processing. Filters and masks images."""
from math import sqrt
import numpy as np
from skimage import morphology
from reader import nd2_img_reader, get_stain
from .filters import apply_gaussian_filter, remove_baseline, binarize
from utils.resampl... |
70c594f4cad3374a448d40e09543bfdd945f0dc052deba1154db7e7364bedcab | Python | 3,353 | 63 | # arxiv: https://arxiv.org/abs/2007.06191
# source: https://github.com/d-li14/PSConv/blob/fefe40d998/mmdet/models/utils/psconv.py
import torch
import torch.nn as nn
class PSConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, parts=4, bias=False):
... |
db186060947b2a101579774d98821fc9c17e6547752ffa6fb50101f01f22c220 | Python | 3,353 | 107 | from transformers import AutoTokenizer
from nucleotide_transformer.chatNT.gpt_decoder import GptConfig, RotaryEmbeddingConfig
from nucleotide_transformer.chatNT.model import build_chat_nt_fn
from nucleotide_transformer.chatNT.multi_modal_perceiver_projection import (
PerceiverResamplerConfig,
)
from nucleotide_tra... |
d3d9c5dc1ece4a408b33f343e77ba173f215dba7d2f53f84f6f92009351544e3 | Python | 3,354 | 96 | """Module to use CaLM as a pretrained model."""
import os
import pickle
import requests
from typing import Optional, Union, List
import torch
from .alphabet import Alphabet
from .sequence import CodonSequence
from .model import ProteinBertModel
class ArgDict:
def __init__(self, d):
self.__dict__ = d
_A... |
9e1573370ae74fe48b2cce4676d10723be187d9d5b3c6bf128240924578cede4 | Python | 3,355 | 88 | from ... import options as opts
from ... import types
from ...charts.chart import Chart
from ...globals import ChartType
class Gauge(Chart):
"""
<<< Gauge >>>
The gauge displays a single key business measure.
"""
def add(
self,
series_name: str,
data_pair:... |
a09a25f84235caee42b3a0afc92cde509572c8790d91aeeab5ee56ce5de44cf2 | Python | 3,356 | 93 | #
# makeCC.py
# Copyright (c) 2020 Daisuke Endo
# This software is released under the MIT License.
# http://opensource.org/licenses/mit-license.php
#
#----------------------------------
# 作成者：Daisuke Endo
# 連絡先:daisuke.endo96@gmail.com
# 最終更新日 2020/5/17
#----------------------------------
# ここには2つの関数を記述している。
# 1. あるペアの... |
91279a961b20f790e1e3a77258a7e99ab975689c8c9aea4ae5e52c999d9b4c15 | Python | 3,357 | 101 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from scipy.interpolate import interp1d
import torchaudio
from fairseq.tasks.text_to_speech import (
batch... |
f6a87a13f4be0994dc2274e9c064e7e806302e1994ac1087380fad113635813d | Python | 3,357 | 84 | # -*- coding: utf-8 -*-
"""
.. _tutorial06_ref:
Tutorial 6: Regions and Parcellations
=====================================
This tutorial demonstrates how to plot brain regions.
Regions and parcellations can be plotted with ``brainplot`` as one or more
layers, and it's possible to add region outlines by simply addi... |
0f71c7f2738aec0fceba5bef0bd022fd9633965419eb7f8b0adc484ad7c3e2cb | Python | 3,358 | 83 | # _*_ coding: UTF-8 _*_
# Version information START --------------------------------------------------
VERSION_INFO = \
"""
Author: ZHANG YUBO
Topology weighting using twisst2
"""
# Version information END ----------------------------------------------------
import argparse
import os
from multiproce... |
12915d212052df2f5acf54e779e0338d7053a42445e29f8e8bb759224ae61b83 | Python | 3,358 | 91 | import sys
import numpy as np
import pandas as pd
from argparse import ArgumentParser, SUPPRESS
def calculate_metrics(confusion_matrix):
num_classes = confusion_matrix.shape[0]
precision = np.zeros(num_classes)
recall = np.zeros(num_classes)
f1 = np.zeros(num_classes)
for i in range(n... |
32bd8295e729973d3d588f10546f3628b934ca01c01f2388bc157d4bfc1f5e98 | Python | 3,360 | 82 | import pubchempy as pcp
import os
import multiprocessing
from rdkit import Chem
from rdkit.Chem import AllChem
from openbabel import pybel
import logging
class CompoundConverter:
def __init__(self, output_dir='./lig_pdbqt'):
self.output_dir = output_dir
if not os.path.exists(output_dir):
... |
077568168f6c0f7bf2291e47826991361a166f9ba99c68e6b2912221c118b1de | Python | 3,363 | 80 | """Full SIESTA-PBE production run for the 100-structure BaTiO3 case study.
Variable-cell relaxation of every polymorph, MPI-parallel over k-points
(Diag.ParallelOverK) + coarser k-mesh (kdens=0.35) — ~7x faster than the
serial OMP=4 baseline (validated by benchmark: hex 30-atom 43 min -> ~6 min).
Scheduler: jobs sort... |
1ac0a64fcb82a7dd7a299ad78349085153daf8b4ca3e1ae27ebb059520a864a2 | Python | 3,363 | 91 | import torch
from torch import nn
class ContextBlock(nn.Module):
def __init__(self,inplanes,ratio,pooling_type='att',
fusion_types=('channel_add', )):
super(ContextBlock, self).__init__()
valid_fusion_types = ['channel_add', 'channel_mul']
assert pooling_type in ['avg', 'a... |
d86b7aeecaca2ae9a79871ecfa33af7a04251eb3e8c2a3168533905e6c2677da | Python | 3,363 | 127 | import argparse
from datetime import datetime
from pathlib import Path
import h5py
import numpy as np
from spacestream.analyses.hvm_fitter import HvmFitter
from spacestream.core.paths import HVM_PATH
from spacestream.utils.general_utils import log
from spacestream.utils.get_utils import get_mapping
def main(
ta... |
22decc2f097911b038b1a27927d38fc9d5153fad855ac90252e9f0f0d89e56cb | Python | 3,365 | 103 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import collections
import unittest
import numpy as np
from fairseq.data import ListDataset, ResamplingDataset
class TestResamplingDataset(u... |
4c1c1e50188fd813608d5854f9f72a680951e1d1c485c2adb377a99a99fe9dde | Python | 3,365 | 97 | from collections.abc import Hashable
import numpy as np
from pgmpy.structure_score._base import BaseStructureScore
class LogLikelihoodGauss(BaseStructureScore):
r"""
Log-likelihood structure score for Gaussian Bayesian networks.
This score evaluates a continuous Bayesian network structure by fitting a ... |
816f87914bfbab3a3fc5acc95486296fdc6dc56a340fdc08e91b02978fd4ffc4 | Python | 3,366 | 118 | import pyliftover
import logging
BASEPAIR = {
"A": "T",
"C": "G",
"G": "C",
"T": "A"
}
def try_reverse_compelement(str_):
'''
If reverse complement fails, return empty string
'''
res = ''
for i in str_:
if i in BASEPAIR:
res = BASEPAIR[i] + res
else:
... |
8d06dcbb0a98b98948a987746cbadccb866e2852e90eb78adc1f80785c819f7a | Python | 3,370 | 115 | import copy
from typing import Optional
import h5py
import numpy as np
import torch
import torchvision
from PIL import Image
from spacestream.core.paths import HVM_PATH
from spacestream.datasets.imagenet import NORM_CFG
from spacestream.utils.dataloader_utils import duplicate_channels
HVM_TRANSFORMS = torchvision.tr... |
7f3f194d6ccf851b80313cc554e31e4d1d72514aefe4c0e04fb491b73c8c4d65 | Python | 3,371 | 78 | import gradio as gr
import os
from .blocks import upload_pdb_button
from utils.downloader import download_pdb, download_af2
root_dir = __file__.rsplit("/", 3)[0]
structure_types = ["AlphaFoldDB", "PDB"]
def upload_structure(file: str):
return file
def get_structure_path(structure: str, structure_type: str) -... |
d5b7a00b1217b84c9e25facdf6f2ea39e1fe45b49694f180932eff35b930c76f | Python | 3,372 | 91 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from dataclasses import dataclass
import torch.nn.functional as F
from fairseq import utils
from fairseq.logging import metrics
f... |
62b827e4a8ca8efb254d6cbccdd9c48cda85fc1e0a40a0e88af83b5502d20741 | Python | 3,374 | 122 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2020 Daisuke Matsuyoshi
# Released under the GNU AGPLv3
# https://opensource.org/licenses/AGPL-3.0
"""
Calculate DTI-NODDI parameters
"""
__author__ = "Daisuke Matsuyoshi @dicemt"
import numpy as np
from scipy import sqrt
from scipy import special
from ... |
6b0fb096afec83f03bc09eeadf0c1c6bfa53716cea201e39dde177c16e5e693b | Python | 3,375 | 80 | import torch
import torch.nn as nn
import torch.nn.functional as F
# This code originally comes from https://github.com/ha-ha-ha-han/UKBiobank_deep_pretrain
# Original Paper:
# Peng H, Gong W, Beckmann CF, Vedaldi A, Smith SM.
# Accurate brain age prediction with lightweight deep neural networks.
# Medical image ana... |
4c36fda5cbe11584cfd85ba8da3022abb349c7fd836fc2a241ae872dd216b36f | Python | 3,376 | 102 | #!/usr/bin/env python
"""Provide functions to merge multiple versions.yml files."""
import platform
from textwrap import dedent
import yaml
def _make_versions_html(versions):
"""Generate a tabular HTML output of all versions for MultiQC."""
html = [
dedent(
"""\\
<style>
... |
d88e30762c88ca5f915e381e365371f4f78ca66441d47957bf7fbf1b92620b5b | Python | 3,378 | 90 | """Tests for ``aestetik.utils.utils_grid``.
Covers the NaN-imputation axis bug (issue #9) directly on
``_create_spot`` and on the public ``create_st_grid`` entry point.
"""
import numpy as np
import pytest
from aestetik.utils.utils_grid import (
_build_trees,
_compute_offsets_flat,
_create_spot,
creat... |
0a5414588e9d6b4c49afba813d45d4f53774396fa4e915119251bfc5a9cff71e | Python | 3,379 | 81 | from ... import options as opts
from ... import types
from ...charts.chart import RectChart
from ...globals import ChartType
class PictorialBar(RectChart):
"""
<<< PictorialBar Chart >>>
PictorialBar is a histogram that can set various figurative graphic
elements (such as images, SVG PathD... |
2770a0a4878318cd526fcb3e7ca41c5ad20cc9586e68b60f8e5a197661b6d370 | Python | 3,380 | 96 | import os
from collections import namedtuple # use for easy to read parametrization
from glob import glob
import matplotlib.pyplot as plt
import pandas as pd
import yaml
from dotenv import load_dotenv
from IPython.display import display # noqa: F401
from joblib import Parallel, delayed
from mne import set_log_level
... |
1d3f89daaa831250530c96c7113f211ba7e0be70ccfe39436a5690d0d94128ff | Python | 3,381 | 91 | """Default settings values for NeuroSyncApp."""
from __future__ import annotations
from copy import deepcopy
DEFAULT_SETTINGS: dict[str, str | bool] = {
"selected_trace_color": "green",
"selected_sem_color": "grey",
"selected_bar_sem_color": "grey",
"selected_bar_fill_color": "blue",
"selected_ba... |
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