sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a199f4a163604d71fb0397a86187d648d01a2add5000692687f98de54abdec05 | Python | 17,507 | 380 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/hyperclovax_vision_v2/modular_hyperclovax_vision_v2.py.
# Do NOT edit this file manually as any edits will be ove... |
bed2014c7f170200bf92bcab3e2374340a5d9dc2cd9e0188a06e73527489a56b | Python | 17,512 | 384 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/fast_vlm/modular_fast_vlm.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation... |
1457a42a189c0fa409c94aac6723d10e26bbb24bdc99d6a5274342f6e6578eba | Python | 17,516 | 503 | import os
import sys
import struct
import math
import matplotlib.pyplot as plt
import numpy as np
import random
import pandas as pd
import seaborn as sns
from sklearn.linear_model import Lasso, LassoCV
from sklearn.model_selection import train_test_split
from sklearn.utils import parallel_backend
from vtk import vtkX... |
a8a9f41f9a91bdd71e6eec80eefafb01c048c887ad5919f7faae968d5b7c27f3 | Python | 17,524 | 445 | """
Build annotation_viewer.html for round 3 with inlined base64 images.
"""
import json, base64
import pandas as pd
from pathlib import Path
BASE = Path(__file__).resolve().parent.parent
R3_DIR = BASE / 'data' / '_archive' / 'pd_round3'
IMG_DIR = R3_DIR / 'images'
def main():
manifest = pd.read_csv(R3_DIR / 'ma... |
acd1905bf0b583f2dbf9fe04827242d107763aeae5545d5d4fb41fe285ecbc40 | Python | 17,547 | 461 | # harmonypy - A data alignment algorithm.
# Copyright (C) 2018 Ilya Korsunsky
# 2019 Kamil Slowikowski <kslowikowski@gmail.com>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, ... |
737bf23291969e9a81208c86d27a6c4d9280720b3a439b9f2027a54f31b2461b | Python | 17,548 | 517 | # repo originally forked from https://github.com/Confusezius/Deep-Metric-Learning-Baselines
##################################### LIBRARIES ###########################################
import warnings
warnings.filterwarnings("ignore")
import csv
import pickle as pkl
import time
import auxiliaries as aux
import matp... |
d6436c80f602a2ac3799b3ac1d36ed06a7cbcb49f0800b7b0193278e3bacdf43 | Python | 17,552 | 641 | # Use python 3.5 or higher
# obsolete: conda install -c auto cbor=0.1.4
from __future__ import print_function
import cbor
import itertools
import typing
PageId = str
PageName = str
class Page(object):
"""
The name and skeleton of a Wikipedia page.
.. attribute:: page_name
:rtype: PageName
... |
a90ece1916b8e6370626d6a114e942da371adece20bc0dea80e0595f46012261 | Python | 17,568 | 425 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/glm5_next/modular_glm5_next.py.
# Do NOT edit this file manually as any edits will be overwritten by the generati... |
1b40e150eca244e11d86cecd24b50eb4086ff219fefab5a07d608042d6ca7498 | Python | 17,599 | 533 | # repo originally forked from https://github.com/Confusezius/Deep-Metric-Learning-Baselines
"""to do:
clean all of the files - particularly the main.py and also the losses and dataset files and the file for doing the dataloading
-- fast loading etc
need to change all of the copyrights at the top of all of the files... |
c4f8a005dd7bcb725fd88e0a9a7641ec2170024b1165678d32e47c78e8f2746f | Python | 17,599 | 475 | """
===========================
Visuospatial working memory
===========================
"""
from bci_framework.extensions.stimuli_delivery import StimuliAPI
from bci_framework.extensions.stimuli_delivery.utils import keypress
from bci_framework.extensions.stimuli_delivery.utils import Widgets as w
from bci_framework.... |
55cfffcd563fa7fe2a41866b89cc2743812db0420c4bb83a93f4f692649b0cc8 | Python | 17,606 | 573 | # Copyright (c) 2026 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
import json
from pathlib import Path
from typing import Any, cast
import numpy as np
import pyscf.gto
import pyscf.lib
import pyscf.scf
import pytest
from click.testing import CliRunner
from jax import numpy as jnp
from up... |
859a1e00b619ec77c0dc182e8fbe15c6659ad021e3e75f80a6400fa04844a957 | Python | 17,606 | 525 | """
=============
Binary to EEG
=============
A transformer for Kafka that reads binary data and stream EEG data.
Binary -> Kafka-Transformer -> EEG
For examples and descriptions refers to documentation:
`Data storage handler <../A1-raw_cleaning.ipynb>`_
"""
import sys
import pickle
import struct
from functools imp... |
501f2eb4bae19f3a3f546d43c1db69e8b50d2f0702c8ec433bbc0cc8ce7b3e22 | Python | 17,610 | 482 | """
This script demonstrates node classification with GraphSAGE on large graphs,
merging GraphBolt (GB) and PyTorch Geometric (PyG). GraphBolt efficiently manages
data loading for large datasets, crucial for mini-batch processing. Post data
loading, PyG's user-friendly framework takes over for training, showcasing s... |
3db00790735894ebf8268752de0126842cf011757037dece68a01ba7db27c29d | Python | 17,620 | 600 | def distance_to_adult(
adata_adult,
adata_embryo,
top_genes,
title_self,
title_other,
category_adult="our_groups",
category_embryo="leiden",
scale=1,
ylim=[-5, 30],
color_adult=["#D61B1A", "#F9C061", "#E4007F"],
color_embryo=["#00FF00", "#FF5500", "#0000FF"],
):
groups = ... |
8bd947a75dfe5ade0c2905146cb80cb92a9da547e052ef1ff22aa4cf5516049f | Python | 17,629 | 481 | """
T4: Spike-based frequency estimation experiments.
Tests a spike-interval frequency approach for low-frequency PDs where
FFT/Ridge fail due to only 2-5 discharges in a 10-second window.
Experiments:
t4_spike_only - f_spike standalone
t4_ridge_with_spike_a5 - Ridge(9 feats + f_spike), alpha=5
t4_ri... |
8d96022e810cb39316c7d147f725b54f3cb4c498cd2cd3a60dab7765c023583c | Python | 17,646 | 549 | """Functions for IO of Triangle Meshes.
Should be called via the TriaMesh member functions.
"""
import logging
from typing import TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from .tria_mesh import TriaMesh
logger = logging.getLogger(__name__)
def read_fssurf(filename: str) -> "TriaMesh":
"""Load tr... |
ef5dfc6077c3b6d87b580cbe5b95527ecc1453fed4fccd132e00fcb89a325af7 | Python | 17,647 | 461 | """
Improvement #3: Iterative frequency refinement.
After initial detection with CNN frequency, compute IPI from detected discharges,
use that as a refined frequency estimate, and re-run the DP.
Also tests combining CNN freq with ACF freq for a better initial estimate.
Usage:
conda run -n foe_dl python code/cet_... |
c9e03022fdddfb01c91226254f7dc618aa275f349bdfad91b6cd5909ac2b2025 | Python | 17,650 | 450 | """
Round 4 Combiner: Type-specific approaches + matched-filter envelope FFT + ML.
Combinations tested:
1. r4_type_specific_best: LPD=ACF+peakcount+Bayesian, GPD=FFT_pointiness+peakcount
2. r4_type_specific_v2: LPD=matched-filter envelope FFT, GPD=FFT+peakcount
3. r4_type_specific_v3: LPD=median(envelope_fft, ac... |
22d273c827ab73e027a5e89ca89249065584da19844873a2459b9cca3186077d | Python | 17,651 | 519 | """
Auto-label RDA wave peaks using bandpass filtering + peak detection.
For each of the 549 RDA cases with expert-verified frequency annotations:
1. Load 18-channel bipolar EEG segment
2. Bandpass filter around the known frequency (f ± bandwidth)
3. Average filtered signal across involved channels per hemispher... |
f998946e4418779102891b9dd0c730d8d686e095e94638beb2b454248b14b240 | Python | 17,673 | 438 | """
================================================
:mod:`replacers` -- Survivor replacement methods
================================================
This module provides pre-defined replacers for evolutionary computations.
All replacer functions have the following arguments:
- *... |
121af33044b9abb3ff5bb1d57558c9abf8cff5370fdb13ca8d800045e18ca484 | Python | 17,689 | 549 | import datetime
import math
import os
import sys
import time
from collections import defaultdict
import numpy as np
import torch
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
from numpy import random
from torch.nn.parallel import DistributedDataParallel
from torch.nn.paramete... |
66fddcd68a60f236836e73e34067336d4b0f6f26404f7812a8d14519fd1b2f34 | Python | 17,695 | 376 | # Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
718885a8ca12603c906feaa0021659e7cb9d24b8038fe0930aef3b11ba6908e0 | Python | 17,697 | 491 | """Polygon class for open and closed polygon paths.
This module provides a Polygon class for processing 2D and 3D polygon paths with
various geometric operations including resampling, smoothing, and metric computations.
"""
import logging
import sys
import numpy as np
from scipy import sparse
logger = logging.getLog... |
19941a61264f8ddb97aeecda081968a961eb072d10f6e76d0e674eb62ca862f5 | Python | 17,718 | 496 | # import libraries and packages
import os
import pandas as pd
import numpy as np
import deepdish as dd
import scipy
import h5py
from SUITPy import flatmap
import nibabel as nib
# Import module as such - no need to make them a class
import connectivity.constants as const
import connectivity.io as cio
import connectivit... |
d89366e4f751ccae2231a14eb11da85dddb288846abab02e1267e4e749d975ac | Python | 17,719 | 389 | #!/usr/bin/env python3
"""Build the GROND reproducibility data bank: grond_data.h5.
Self-contained .h5 file pairing each EEG segment with all of its labels,
algorithm predictions, cohort flags, and metadata.
Schema:
/segments/{segment_id}/
eeg (19, 2000) float32 monopolar referential EEG at 200 ... |
19e8a5d6111cb264cd75fb37824e77597fd971822907eca9c1dd6809ce56c34d | Python | 17,721 | 393 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/lighton_ocr/modular_lighton_ocr.py.
# Do NOT edit this file manually as any edits will be overwritten by the gene... |
d8b137e6be3667ed0885dc8c3b503461992043e17d408437be92522fdb444f1e | Python | 17,722 | 502 | # Copyright (c) 2026 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
# mypy: disable-error-code="operator"
"""Numerical stability tests for the source-sampled LIT error factor."""
from functools import reduce
from operator import itemgetter
import jax
import numpy as np
from jaqmc_contrib_... |
30c5bab3388cdc82f6dcc9e49e027d95f209ddb6a40b461584e09a8646252a30 | Python | 17,725 | 387 | # Copyright 2026 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... |
3e66eeeba23882a4544c45a3baaf69ac87844dd9afd2b24c8aad4809be08548e | Python | 17,731 | 559 | # This program predicts Ki values in the form of pCHEMBL/MW values of molecules for the target protein coagulation factor F11a with Shannon entropies, fractional Shannon entropy, ligand BEI and MW as descriptors
# import the necessary packages
import os
import numpy as np
import pandas as pd
from KiNet_mlp import KiN... |
09ca9fc4f2745032665fc9ed920b2ede89fd04062a1e3cb8b02cb50035b8b445 | Python | 17,747 | 360 | import dgl
import dgl.graphbolt as gb
import dgl.sparse as dglsp
import torch
def test_integration_link_prediction():
torch.manual_seed(926)
indptr = torch.tensor([0, 0, 1, 3, 6, 8, 10])
indices = torch.tensor([5, 3, 3, 3, 3, 4, 4, 0, 5, 4])
matrix_a = dglsp.from_csc(indptr, indices)
seeds = tor... |
251b4c1fb47ee74c63a54fd2dfa932e4b412a97568c469fa060553ab4a608bbe | Python | 17,765 | 539 | import itertools
import time
from collections import defaultdict as ddict
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from catboost import CatBoostClassifier, CatBoostRegressor, Pool, sum_models
from sklearn import preprocessing
from sklearn.metrics import r2_score
from tqdm imp... |
59962b0b19023d78e2fa19fb10d560a7ee821b39380b009b663ff1850caef1fa | Python | 17,779 | 500 | """
Experiment: New laterality feature engineering for laterality classification.
Computes 4 new asymmetry features from raw EEG segments:
- ve_asymmetry: Variance-explained asymmetry (narrowband 0.5-3 Hz / total)
- peak_amp_asymmetry: Peak amplitude asymmetry (lowpassed signal)
- coherence_asy... |
1f3ca78470de3e3dcd9ebcf6266a6a5b68fb8f4672150ebfb7f4d9922a93a868 | Python | 17,782 | 488 | # Copyright (c) 2025-2026 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
"""Tests for Hall wavefunctions and their building blocks."""
from typing import cast
import jax
import numpy as np
import pytest
from jax import numpy as jnp
from jaqmc.app.hall.data import HallData
from jaqmc.app.... |
08a6bec28e732a8dcb0a56ed66cb99c3f440630c15d3ad5118eb0b38713667f5 | Python | 17,803 | 558 | import mxnet as mx
import numpy as np
from mxnet import nd
from ..._sparse_ops import (
_bwd_segment_cmp,
_csrmask,
_csrmm,
_csrsum,
_gsddmm,
_gspmm,
_scatter_add,
_segment_reduce,
)
from ...base import ALL, dgl_warning, is_all
from ...heterograph_index import create_unitgraph_from_csr... |
aa8d9a285c8818466195f89a0439f84c3bd62e354d8eb90f6744225130ea4715 | Python | 17,819 | 456 | # Copyright 2026 The HuggingFace Inc. team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... |
9994b9bdddcda46f2d81b11e24b151e8c5cd1302512c759eaa5853e06894ef97 | Python | 17,844 | 466 | # Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... |
b7364d185400556c782290cb11a5a2dd5031aca8debabdd44d2bfd558a9f6f3d | Python | 17,857 | 350 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 16 2021
@author: martin
"""
import numpy as np
import pandas as pd
import os
#import json
import re
import glob
import time
import nibabel as nib
from nilearn import image
#from nilearn import plotting
from nilearn.input_data import NiftiLabelsMas... |
bc873242a84ed9b028b36fcaa1bc79eb1f8ef094229f48ead942d0804214b500 | Python | 17,874 | 447 | #!/usr/bin/env python3
"""
Implementation of Brzonko et al. Elife (2017) using NEST.
The code structure is adapted from Microcircuit example
on NEST website authored by Hendrik Rothe, Hannah Bos,
Sacha van Albada; May 2016
Code author: Mohammad M. Nejad
"""
import os
import json
from copy import deepcopy
import nest
... |
bab8a71623c225c0da6d2fa09ce2299422f4f2dda01a6f2edb96603708649b66 | Python | 17,880 | 310 | """
Script to analyse and collect data from sensitivity analysis simulations
"""
__author__ = "Nils A. Koch"
__copyright__ = "Copyright 2022, Nils A. Koch"
__license__ = "MIT"
import numpy as np
import h5py
import pandas as pd
import os
from ast import literal_eval
import json
import scipy.stats as stats
# folder
to... |
77b4c57c8356fc55c52dc03ce13f817b4defc12f36e7747a08e15aa89e29c92a | Python | 17,889 | 339 | # Copyright 2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
4945d97df36de31e11737c600775cfda995e774d2680246990dc72f6932baf55 | Python | 17,893 | 486 | # Copyright 2019 The Open AI Team Authors and The HuggingFace Inc. team.
#
# 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... |
e783c73588b87f789643744210f3cc0002596c0dd9f38d56577aee0d50f5fcba | Python | 17,898 | 406 | # Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... |
ed6c12f5155882a5cb0a68d2eb6b51d92818dafad67bf3dad33a88fcb7fe82e1 | Python | 17,905 | 568 | from __future__ import division, print_function
from repli1d.expeData import replication_data
import pandas as pd
import numpy as np
from scipy import stats
# %load ./../functions/detect_peaks.py
"""Detect peaks in data based on their amplitude and other features."""
import numpy as np
__author__ = "Marcos Duarte... |
51d9a23ba95ee7b45d75451017964c0f782932463f59896cd879b01b4c288d5f | Python | 17,908 | 453 | """
Generate Fig 5: Spatial Agreement (4 rows: LPD, GPD, LRDA, GRDA).
LPD/GPD rows: full inter-rater analysis (3 raters + model).
LRDA/GRDA rows: model vs single rater (SZ only).
Layout: 4 rows × 3 columns
Col 1: Jaccard matrix heatmap
Col 2: Bar chart (rater-rater vs model-rater)
Col 3: All pairwise agreements... |
a14e284015f3f3d1c0cab7a1ac378afae258a8bede3739e377ccb7603c27d1d6 | Python | 17,912 | 529 | # Generated by the protocol buffer compiler. DO NOT EDIT!
# source: example.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pb2
from google.protobuf import message as _message
from... |
6dc3a4509d4688524a8e4896bc5f070ff9c5a0bda0b621b8c7fe4c88988e558e | Python | 17,919 | 449 | # Copyright 2026 SK Telecom and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
e533f35931a02cf270f69fbf3bbde91375fef8aba0676ca090649e518430549a | Python | 17,929 | 426 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/ijepa/modular_ijepa.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# ... |
7f4df894b34b94e528b4e842acddac8afbfe7ddabcd01d8524bddfa0d91713db | Python | 17,932 | 497 | #!/usr/bin/env python3
"""
Data cleanup script for IIIC Frequency Functions project.
Steps A-G as specified.
"""
import os
import json
import shutil
import pandas as pd
from collections import defaultdict
BASE = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
LABELS = os.path.join(BASE, '... |
f39a9db34838a7f83eaad461060019d728213e6e0a5de954cb2e459bf4dd7737 | Python | 17,958 | 370 | # Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... |
f7eb4ca6bd8a7be1552f88af90707bd262270b4cea24815fa008cf2ad825d6a9 | Python | 17,976 | 363 | import ast
import os
import unittest
import numpy as np
import pandas as pd
from automatchnames import get_genus_from_full_name
from logan_metabolite_vars import logan_alkaloid_hits_output_csv
from metabolite_vars import rub_apoc_alkaloid_hits_output_csv
from pkg_resources import resource_filename
from import_trait_d... |
857aedb8f565377c8ff8995a198def6d95a5c4c9c3086e143b72d7c771ee6de2 | Python | 17,979 | 484 | # Copyright (c) 2026 ByteDance Ltd. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Structured sparse Jacobian storage types for Forward Laplacian.
Theory-to-storage overview
==========================
The dense Jacobian layout in this package has shape
``(prod(input_shape), *result_shape)``. The lea... |
f153a98ce66fcef724b667025f4b5f0e6efb87368520933e33d57a644f50167b | Python | 17,984 | 349 | # Copyright 2026 The HuggingFace Inc. team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... |
425eecd9cc9ee89ef6b03c1efae89ef4400f40469201e70eefb057748c4e9d53 | Python | 17,993 | 486 | # scientific library imports
import dask.array as da
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import seaborn as sns
from matplotlib.collections import LineCollection
# neuro base class imports
from .base.ts_data import _TsData
from .base.utils.base import _is_iterable
from .base.utils.... |
81121afac48ea2cc5ba630db8c25b0af84f1c98569cbc2f4f88d820cd591f8a6 | Python | 18,010 | 505 | """Training GCMC model on the MovieLens data set by mini-batch sampling.
The script loads the full graph in CPU and samples subgraphs for computing
gradients on the training device. The script also supports multi-GPU for
further acceleration.
"""
import argparse
import logging
import os, time
import random
import stri... |
93fd6a1e53bea1326c0aa097c1270c891e6db094ed8ff01f92bf3ef02610e06f | Python | 18,055 | 427 | #!/usr/bin/env python3
"""Build segment_labels.csv — the single canonical label file.
One row per EEG file on disk, consolidating all label sources:
- segments.csv (file registry — metadata merged into output)
- annotations.csv (per-segment per-rater labels)
- patients.csv (patient-level labels — laterality, exclusion... |
84f041bdc3333f58cb9566a3ae615b8e7b18c753ff1930287888b2ccfd858639 | Python | 18,056 | 410 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/minicpm_ocr/modular_minicpm_ocr.py.
# Do NOT edit this file manually as any edits will be overwritten by the gene... |
4f05be86390b1bff4d9343d57a2ba945385ab80b23d32a6230bfdda8cf15e00c | Python | 18,062 | 447 | """
Build a channel-level PD (periodic discharge) dataset.
For each patient, loads first segment, extracts each of 18 bipolar channels,
and assigns a binary PD label per channel based on subtype and laterality.
Label logic:
- GPD: all 18 channels = positive (1), subsampled to max 6 per patient
- LPD with laterali... |
fc013a2f4b61164e39422e62083d74049b215298aea865de7cd3784cfb1dfb3b | Python | 18,072 | 423 | # Copyright 2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
4bb3d627a6d3efa04ac4a41d01e745a3c4f21887411d0f974c932f2ac9635a69 | Python | 18,074 | 511 | """Spatial Localization Contest Harness — data loading, evaluation, leaderboard."""
import sys
import json
import time
import warnings
import numpy as np
import pandas as pd
import scipy.io as sio
from pathlib import Path
from collections import defaultdict
from sklearn.metrics import f1_score, jaccard_score, roc_auc_s... |
6efe9d7c17850ffca5dbb3b39d0ac028304c87f74fa246aa6334d1a36f9d6c53 | Python | 18,078 | 507 | #!/usr/bin/env python3
"""Generate LRDA side-labeling viewer.
Selects LRDA cases that are confirmed by our best model, plus additional IIIC
cases with strong LRDA vote majority. Generates an HTML viewer for MW to
label each case as Left or Right dominant.
Usage:
conda run -n morgoth python code/lateralization_con... |
2095bb6f2bc8955694026c37ccd70e1b7d341ab709a1e0bb132a63f70de4bbc8 | Python | 18,103 | 499 | """CPU cached feature for GraphBolt."""
from typing import Dict, Optional, Union
import torch
from ..base import get_device_to_host_uva_stream, get_host_to_device_uva_stream
from ..feature_store import (
bytes_to_number_of_items,
Feature,
FeatureKey,
wrap_with_cached_feature,
)
from .cpu_feature_cach... |
f8a00683d6077c50b9c55e42bb36529a1886685ae772ddb0b95db61b7b16cb68 | Python | 18,117 | 540 | """
This example references examples/graphbolt/pyg/labor/node_classification.py
"""
import argparse
import time
from copy import deepcopy
import dgl.graphbolt as gb
import dgl.nn as dglnn
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
def accuracy(out, labels):
assert ... |
dd8baa13b47f6355a30dde1a2ccd7b061dad6db32c66f93e716f4cdbc5c0c1f3 | Python | 18,119 | 438 | """
Controller: upload_listen
=================================
Description:
A template controller for upload + listen workflows.
It accepts a csv file containing paths to files to be uploaded to the Blackboard.
It listens for results and processes them.
This file is expected to be customiz... |
c61135a38e3b65e3742fa55efff5ccbee017b50e455a757139b4815024332a1a | Python | 18,140 | 463 | # Copyright 2025 HuggingFace Inc. team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... |
7965f8b339005d3e39eeeee7aed89705a10e6abc282f38a1ef946a9cce0a4bc5 | Python | 18,146 | 447 | # Copyright 2023 Alibaba Research and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... |
5640a689708b3268103d379c53bf986f149dd4cf4f2ca1464cbc9b20d98a643a | Python | 18,151 | 433 | #!/usr/bin/env python3
"""Independent-expert IRR bar-plot figure (Fig 5): paired EE vs EA with CIs + significance.
Two-row layout:
Row 1: Canonical 4-rater independent-expert cohort (MW, SZ, TZ, AS) under
the canonical majority-accept consensus rule (V14 algorithm).
Panel A: Frequency Spearman rho ... |
672772dccb3193bf0ffc3ece56c80facc5a937aabbfef38a5e0babeb0724f274 | Python | 18,151 | 493 | """
This script trains and tests a GraphSAGE model for link prediction on
large graphs using efficient and tailor-made neighbor sampling.
Paper: [Inductive Representation Learning on Large Graphs]
(https://arxiv.org/abs/1706.02216)
While node classification predicts labels for nodes based on their
local neighborhoods... |
d55b1ed51a00c3a700dfa904c7bf4927d37dd63ee057f4fdcc2588385104bbce | Python | 18,172 | 556 | """Utilities for pytorch NN package"""
# pylint: disable=no-member, invalid-name
import torch as th
import torch.nn.functional as F
from torch import nn
from ... import DGLGraph, function as fn
from ...base import dgl_warning
def matmul_maybe_select(A, B):
"""Perform Matrix multiplication C = A * B but A could ... |
4cb57da86adca1c39ca2d324efb6d023c7ad29b9f4d7a7e8e4594834e24fcfd7 | Python | 18,175 | 426 | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
2e060e8fe4e6b7187e2eb16b673d9c312c405f12ccf311bc4744a9bc5b03f276 | Python | 18,195 | 341 | # Copyright 2026 The HuggingFace Inc. team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... |
6eea51094375ebef4120b30b7c5d2334fcfa39a03fcae3c7b1462c51d2adfeb9 | Python | 18,213 | 532 | from __future__ import absolute_import
import os
import numpy as np
from .. import backend as F
from ..convert import graph as dgl_graph
from .dgl_dataset import DGLBuiltinDataset
from .utils import load_graphs, load_info, loadtxt, save_graphs, save_info
class LegacyTUDataset(DGLBuiltinDataset):
r"""LegacyTUD... |
36973e9c0d7240f4d2b1841b4db3df6398eb84aa2ff299f5da82dcdcff819d03 | Python | 18,215 | 588 | """Provide tests for MultipleDataGrabber."""
# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
# License: AGPL
from copy import deepcopy
from pathlib import Path
import pytest
from pydantic import AnyUrl
from junifer.datagrabber import (
BaseDataGrabber,
MultipleDataGrabber,
PatternDataladDataGrab... |
1ae443f473e87dae7b5208fe32ca8d4c026062dde4d99cd34791a079432f73b6 | Python | 18,220 | 413 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 10 01:18:52 2023
@author: nelab
"""
import os
import scipy.io as scio
import numpy as np
import random
from skimage.util import random_noise
import pytorch_lightning as pl
from torch.utils.data import Dataset, DataLoader
import torch.utils.data as ... |
db16c6c3685ef22fad24dd3919490db9a40214de9022a3f0661d544bc9dfd5ab | Python | 18,228 | 333 | """
Script to analyse and collect data from simulations
"""
__author__ = "Nils A. Koch"
__copyright__ = "Copyright 2022, Nils A. Koch"
__license__ = "MIT"
import pandas as pd
import h5py
import json
import os
import numpy as np
from ast import literal_eval
import string
import matplotlib.cm as cm
# %% rheo_{}_ex.csv... |
441e1550f238af1b1b8a2b5ef4381437254c3006c7c64bdd6749311c24ac83d4 | Python | 18,230 | 569 | import argparse
import time
from copy import deepcopy
from functools import partial
import dgl.graphbolt as gb
import torch
# For torch.compile until https://github.com/pytorch/pytorch/issues/121197 is
# resolved.
import torch._inductor.codecache
torch._dynamo.config.cache_size_limit = 32
import torch.nn as nn
imp... |
f5e77b9658b60542266e34ee56fe41d34b11d3851e77080387eb6daa855403ba | Python | 18,242 | 407 | """
================================================
:mod:`observers` -- Algorithm monitoring methods
================================================
This module provides pre-defined observers for evolutionary computations.
All observer functions have the following arguments:
- *... |
e0679cbc2f8c2c287653b204227084d20e178b64c6ff5b78b92d7fba95c28600 | Python | 18,249 | 553 | import math
from itertools import product
from typing import Tuple
import numpy as np
import pandas as pd
from numba import jit
from scipy import linalg, signal
def merge_bouts(bouts, min_dist):
bouts = list(bouts)
i = 1
while i < len(bouts) - 1:
current_dist = bouts[i + 1][0] - bouts[i][1]
... |
d01eec1a581a897d20817c1753a65c2a749d991b5ec57ed3eba7d541de7a29d6 | Python | 18,285 | 416 | import numpy
import matplotlib.pyplot as plt
def avg_and_rms(x):
N = len(x)
avgx = numpy.mean(x)
rmsx = 0
for i in range(N):
rmsx += (avgx-x[i])**2
rmsx = numpy.sqrt(rmsx/(N-1))
return avgx,rmsx
def manual(filename,idelay,idur,spikedurat):
"""Manual approach"""
... |
263f09b384084a513b085c4ecaa1f4c70ad3dbf21fcc93d4d28c31db47c13b6a | Python | 18,291 | 501 | """
LPD Laterality Labeling Tool.
For each unlabeled LPD case:
- Runs CNN+Attention PD detector to predict laterality
- Shows EEG with predicted side in blue, other side in black
- Channel layout: left hemi | right hemi | midline (same as BIPD viewer)
Controls:
Enter = accept model prediction, advance
Space... |
f56df2723aeac85ff25fa4a7b58a849b586b29b77ae5da9dd1621e4380497bc4 | Python | 18,298 | 492 | import numpy as np
from multiprocessing import Pool # Process pool
from multiprocessing import sharedctypes
from itertools import groupby
import sys
import ctypes as ct
RED = "\033[1m\033[91m"
GREEN = "\033[1m\033[92m"
END = "\033[0m"
# # # #
# # # ... |
488d9ea91145570e61a3c33f43b506faa3e17dcc59075081f93804e0758e9c1e | Python | 18,301 | 565 | import unittest
import backend as F
import dgl
import pytest
from dgl.base import ALL
from utils import check_graph_equal, get_cases, parametrize_idtype
def check_equivalence_between_heterographs(
g1, g2, node_attrs=None, edge_attrs=None
):
assert g1.ntypes == g2.ntypes
assert g1.etypes == g2.etypes
... |
063f3a92c0e2e04a79380f0873bb3efc344e66163ceeac37abe5c38cc945d540 | Python | 18,325 | 493 | import numpy as np
from multiprocessing import Pool # Process pool
from multiprocessing import sharedctypes
from itertools import groupby
import sys
import ctypes as ct
RED = "\033[1m\033[91m"
GREEN = "\033[1m\033[92m"
END = "\033[0m"
# # # #
# # # ... |
31eb483b1cd7f56458dcc1ff453b6164c8b4a6d2f4b61a270477dbbe32cf5759 | Python | 18,327 | 492 | """
Distributed Node Classification
===============================
In this tutorial, we will walk through the steps of performing distributed GNN training
for a node classification task. To understand distributed GNN training, you need to
read the tutorial of multi-GPU training first. This tutorial is developed on to... |
121307e137d2b4c184aa3aa71e673e709b51d7c341d64564ac17262ec6d27fc0 | Python | 18,336 | 521 | # Copyright (c) 2026 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
"""Adaptive continuation and durable-state helpers for LIT."""
from __future__ import annotations
import hashlib
import json
import logging
from collections.abc import Callable, Mapping
from dataclasses import asdict
from... |
16c8e37b877216527f84e2c79bfe7f14222dd752f14deb47f7b16bbd5395a649 | Python | 18,336 | 490 | """PyTorch model utility functions."""
import types
from types import SimpleNamespace
from typing import Dict, Generator, Iterable, List, Tuple, Union, Optional
import torch
import numpy as np
import slideflow as sf
import contextlib
from packaging import version
from pandas.core.frame import DataFrame
from scipy.spe... |
16b5b07d3db05fe81826581ed07dd91f769cbd4bc10465106bf2aa023838b892 | Python | 18,342 | 489 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/hgnet_v2/modular_hgnet_v2.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation... |
c36c7223562a63c14d6779034b9d771fe809583583d07ce83e0083ec7bfc4ec0 | Python | 18,343 | 489 | import argparse
import json
import logging
import os
from typing import Any
import torch
from huggingface_hub import hf_hub_download, upload_folder
from safetensors.torch import load_file, save_file
from tokenizers import Tokenizer, decoders, pre_tokenizers, processors
from tokenizers.models import BPE
from transform... |
db9e192f4514bd6a2fa9a6f5f748144c6a19036f387aa1c5292298e32fcbbd53 | Python | 18,351 | 469 | import abc
import copy
import random
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from rl_nav import constants
from rl_nav.environments import base_env
class EscapeEnv(base_env.BaseEnvironment, abc.ABC):
"""Grid world environment with multiple rooms.
Between each room is a door, t... |
c7285a911c68070606cea8be3057ceb0f4d50bc4b664d386ec92a3fc4c6298fe | Python | 18,354 | 387 | import sys
import argparse
import logging
import os
import numpy as np
import torch
from datetime import datetime
from torch import float32
from sklearn.metrics import f1_score
from tensorboardX import SummaryWriter
from torchvision.transforms import Normalize, Compose, Resize, RandomAffine
from torch.utils.data impor... |
9548632784628926c6ef9bcc7f3451bc9e65240fb13104962c0ffd48ca1e556d | Python | 18,366 | 431 | from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier, RandomForestClassifier, RandomForestRegressor
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression, SGDRegressor, LinearRegression
from sklearn.neural_network import MLPClas... |
760c9da9588f4905fcf18634bdd30563fe5b5bc70a5210a8b05206a89f1f3b75 | Python | 18,367 | 423 | #!/usr/bin/env python3
"""
Reproduce the key results from:
Tautan et al. 2025, J. Neural Eng. 22, 066027
This script reproduces:
- Table 1: MAE between mean expert annotations and algorithm outputs
- Figures 5 & 6: ICC (expert-expert and expert-algorithm) for segments
with 100% agreement on pattern classification
""... |
22258a65dfe6a19299b5a2a748ea9df019d974001e2caaab9d84ee2a56206733 | Python | 18,370 | 533 | """
Discharge timing detection for LPDs and GPDs.
Detects individual discharge times on each channel using:
1. Per-channel event evidence (pointiness + TKEO)
2. Aggregate evidence by subtype (GPD vs LPD)
3. Dynamic programming for optimal discharge sequence
Usage:
from discharge_timing import detect_discharge_tim... |
7b66716676b7c837fc4c717358e07bc9620d9cc78429807910d12129de7ab05e | Python | 18,376 | 518 | # coding=utf-8
# Copyright 2020 The SimCLR Authors.
#
# 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... |
e108d58e9e5a3230d4a3d4200b172d4d079ba488dd63168e3fa39ec2a34a6fcb | Python | 18,388 | 497 | """
=================
:mod:`crossovers`
=================
.. Copyright 2012 Aaron Garrett
.. Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction,... |
88bf55d09bf68b2f990e88179007664642e674343e7b737401dee482b70490c6 | Python | 18,390 | 416 | import numpy as np
import pandas as pd
# Add progress bar to apply method
from getting_logan_malarial_regions import logan_taxa_in_malarial_countries_csv
from logan_climate_vars import logan_compiled_climate_vars_csv
from logan_common_name_vars import output_logan_common_names_csv
from logan_manually_collected_data imp... |
88bbbcc449261bbe47b29dab37c86464e41fc339af614470f6c155ddb8d656a0 | Python | 18,411 | 507 | """
===============
Stream handlers
===============
"""
import os
import sys
import json
import pickle
from datetime import datetime
from typing import Optional, List, Callable, TypeVar
from collections.abc import Sequence
from PySide6.QtUiTools import QUiLoader
from PySide6.QtCore import QTimer, Qt, QRect
from PySid... |
a94fbc6683fb3e187599f5e6ead41124bdd36f91e0871982ccc43b71525ba874 | Python | 18,413 | 510 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
# change the default font family
plt.rcParams.update({'font.family':'Arial'})
plt.rc('xtick',labelsize=14)
plt.rc('ytick',labelsize=14)
plt.rc('legend', fontsize=14)
def reldiffs(fother,f1,iother,i1):
iout = []
... |
6b82d481f4aefd8e18406be024dee391c9084bbd4634f154a3ca152e093cf92c | Python | 18,443 | 484 | #!/usr/bin/env python3
import typer
from pathlib import Path
from datetime import datetime, timedelta
import scipy.io as sio
import json
from rich.console import Console
from rich.table import Table
from launchcontainers.utils import atomic_rename_pairs, parse_subses_list
app = typer.Typer()
console = Console()
# --... |
542092abc116e7ff2386b921c6683df20520db8de3f48416bb1611ce5b7309e5 | Python | 18,444 | 409 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/aya_vision/modular_aya_vision.py.
# Do NOT edit this file manually as any edits will be overwritten by the genera... |
e2c33dfd6c2bb97046d213b113ece280111862936e059570a0364180e6c3a2a1 | Python | 18,449 | 520 |
# This file helps to compute a version number in source trees obtained from
# git-archive tarball (such as those provided by githubs download-from-tag
# feature). Distribution tarballs (built by setup.py sdist) and build
# directories (produced by setup.py build) will contain a much shorter file
# that just contains t... |
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