sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2e9e1f53366b9934b78bf41ac43aa8b413578fb5bbc224cb1321aab15f38cb96 | Python | 25,120 | 585 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/esmc/modular_esmc.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# ... |
c1b8bd1ddc2ed4d9f39a211b25b9635840fafc44d887decad045a7ee499d03b6 | Python | 25,173 | 725 | """Interfaces for resampling images in a single shot"""
import asyncio
import os
from functools import partial
import nibabel as nb
import nitransforms as nt
import nitransforms.resampling
import numpy as np
from nipype.interfaces.base import (
File,
InputMultiObject,
SimpleInterface,
TraitedSpec,
... |
6623ecf10aa43d0f229998171a9b10bdca05b0f9fa7b3175e73996cacc533d6d | Python | 25,174 | 611 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/eurobert/modular_eurobert.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation... |
3f7b49609d7ddfb5842ca1f724fe2efafc9f822f236b422144df86f104beea32 | Python | 25,184 | 878 | def compare_expression_by_layer(
adata_combined_full,
n_genes=10,
layers=["L1", "L2/3", "L4", "L5", "L6", "L6b"],
show=True,
method="wilcoxon",
):
adata_temp = adata_combined_full[
np.in1d(adata_combined_full.obs["dissected_layer"], layers)
].copy()
sc.tl.rank_genes_groups(
... |
e8f9379ae857821a36e6c8802142c5b432af9a923dcc0edcb10ef99c98ad7a72 | Python | 25,192 | 570 | # Copyright 2024 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... |
1453b0c3fc68c7971a8b36effac1773b5d9dc5f9ae99fa6801d278db5b656836 | Python | 25,244 | 669 | # from trainFunc import dataObj
import pandas as pd
from pandas.core.frame import DataFrame
import tensorflow as tf
import tensorflow.keras as keras
import pathlib
import os
# import matplotlib as plt
import pandas as pd
import numpy as np
from rusTFModules import dataProcess
import json
from rusTFModules import mo... |
1358192ce17cab9aa7a1d156a4d2d19ecffc6a75d648408dbf8c79b3eda4fb3e | Python | 25,247 | 498 | # Copyright 2025 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 ... |
918626fe2f0eba041c99b7bed54f52dc7917addd3ce51bec4a7277daee112da4 | Python | 25,310 | 622 | """
Download all EEG segments from S3 for GPD, LPD, LRDA, GRDA folders.
Integrates expert vote data from list_events_20241129.xlsx.
Updates patients.csv and segments.csv.
Generates a live HTML dashboard.
"""
import os
import re
import ast
import time
import subprocess
import threading
from concurrent.futures import Th... |
31bb9974585f483391e0e85169bda1e69708aeb105e3d414b9cd919feb14c468 | Python | 25,350 | 651 | #!/usr/bin/env python3
"""
Generate Fig 2: PD Characterization Pipeline composite figure.
Three-panel horizontal layout:
Panel A (left): Input 19-channel EEG (average reference, 10s)
Panel B (center): Architecture flowchart (matplotlib boxes/arrows)
Panel C (right): Output visualization (EEG + discharge marke... |
572914cf1b8822869315dc0db0b5e406db48775d480911e8fb9a2115a165a5ca | Python | 25,369 | 704 | """Provide class for confound removal using fMRIPrep format."""
# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
# Leonard Sasse <l.sasse@fz-juelich.de>
# Synchon Mandal <s.mandal@fz-juelich.de>
# License: AGPL
import sys
from typing import Annotated
if sys.version_info < (3, 12): # pragma... |
4398e874a48e6c0582725447cfcd2c7fd6db7ba5235634e141b834ea580ab3d6 | Python | 25,381 | 640 | """
Round 5: Rich morphological, temporal, and spatial features for ML frequency prediction.
Extracts ~25 features per segment and trains Ridge regression models with LOO-CV.
Variants:
r5_rich_ridge_logfreq - Ridge on log(freq), all features, alpha=1.0
r5_rich_ridge_period - Ridge on period (1/freq), all f... |
05c3f6790dfc45fad4ba4767016948d37894beb9853b6f89f491d87a0a31bc01 | Python | 25,408 | 585 | """
Round 5: Ordinal regression -- predict frequency as ordered categories
matching expert annotation granularity.
Experts annotate frequency using discrete values:
0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.5, 3.0 Hz
Ordinal regression respects this structure.
Variants:
a) r5_ordinal_ridge: ridge o... |
880ec167b50509e8a01eb20ad18f95db41a40073708011878682e7744e3532c8 | Python | 25,445 | 523 | # -*- coding: utf-8 -*-
__author__ = 'Zhaoning Li'
from util import *
import concurrent.futures
def IMQ_MMS_job(p):
dsf_x, dsf_y, mms, data, SS, SO, OS, mms_la, mms_ra, mms_lh, mms_rh = p
result_ss_mms_la, result_ss_mms_ra, result_ss_mms_lh, result_ss_mms_rh = [], [], [], []
result_so_mms_la, result_s... |
aa116955032da6d1e060d197ebffee23777300b677aa62fa8713f52c72da5ece | Python | 25,472 | 450 | """Data-driven registry for the Brain-Folding Simulator parameter form.
Every parameter the old ``BFSimulator.py`` exposed is described here once, as a
``Field``: its label, section, the exact ``Parameters.prm`` key it writes, its
validation rule, the info image/text/link shown when it gets focus, and its
2D/3D preset... |
3c9a0c6b79670dd76aa47749b4a09d7f7e3c0bcedbecd8f0c259f83a4d43b22a | Python | 25,473 | 487 | # 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... |
7a5636189b96f976a5b1ebcff7d2f5fae1c16eb3326ee193bce0edf8a00147be | Python | 25,476 | 771 | """
================================
Visual Working Memory - Paradigm
================================
"""
import os
import json
import time
import random
import logging
from browser import timer, html, document, window
from datetime import datetime
import copy
from radiant.utils import WebSocket
from radiant.server ... |
b11c39a314f5c1367e138793ace2f108b59aef6c6e76677011f84b1f1e462d6f | Python | 25,479 | 537 | import os
import argparse
import matplotlib.pyplot as plt
from matplotlib import cm
import torch
import numpy as np
import statistics as st
import csv
import seaborn as sns
from timeseries import EchoCard
from quality_classification import predict_single as predict_acq
from quality_classification import CardioNet
fro... |
bc4e8eea81a92a0014cf4a038a6d875e50aaaa19ec721c767ebe9304a0ccbc4b | Python | 25,495 | 547 | """
Generate comprehensive comparison figures for the breakthrough DL embedding result.
Best model: Ridge on log(freq), per-expert training (3 experts),
28 features (8 signal-processing + 20 PCA of CNN backbone embeddings).
CNN backbone pretrained on 3,816 LPD/GPD classification segments.
Result: LPD Spearman 0.5... |
fc947cce6628f64d6a4da8de29ad1f9d0fefc18a02e60a74f235ef368e8f7631 | Python | 25,507 | 559 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# 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... |
6fe73c5bf6e12d388316d3bf8a44b47b85f7ec595ad6ba25b48edbf2b00dac36 | Python | 25,511 | 653 | """
.. _model-line-graph:
Line Graph Neural Network
=========================
**Author**: `Qi Huang <https://github.com/HQ01>`_, Yu Gai,
`Minjie Wang <https://jermainewang.github.io/>`_, Zheng Zhang
.. warning::
The tutorial aims at gaining insights into the paper, with code as a mean
of explana... |
6f262cf451c22d848c0af56b2cb97fcf1b78992b477482de2604f46f569200b6 | Python | 25,528 | 494 | # 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 ... |
8a1e77cfd660dcfb658115807dc78b7aedc0916f208adb677d05dfde2910ee17 | Python | 25,550 | 419 | import dill
from Utils.Settings import minimum_firing_rate_hz, presence_ratio_thr, waveform_PT_ratio_thr, isi_violations_thr, amplitude_cutoff_thr, clip_ripples_clusters, var_thr, output_folder_figures_calculations, output_folder_calculations, minimum_ripples_count_generated_in_lateral_or_medial_spike_analysis
from sci... |
0855fcab3e6ad1d3bb35b54809365a21f036ab148e1a74db1914e71bb385d5cc | Python | 25,604 | 590 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/minicpm3/modular_minicpm3.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation... |
be3ad5370ee840e56c1943331f223403eb9bc89f67d959481b59e03834dddfc8 | Python | 25,623 | 585 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/hy_v3/modular_hy_v3.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# ... |
ae786d4f8202e67d9369ed3f42e08ea689e7996669ce3f250d35f9f3289ccfbd | Python | 25,641 | 544 | #!/usr/bin/env python3
"""
R11: Comprehensive Bootstrap Statistical Analysis
==================================================
- Expert-expert pairwise Spearman with bootstrap 95% CIs
- Algorithm vs consensus Spearman with bootstrap 95% CIs
- Statistical comparison of algorithm-consensus vs expert-expert
- Power analy... |
2c113e4a8af25b1f887972cce5b3ea42344346409c7de7d839adf643eb6b9730 | Python | 25,645 | 763 | import itertools
import shutil
from copy import copy
from inspect import currentframe
from traceback import print_exc
from typing import NamedTuple, Union
import monai
import more_itertools
import numpy
import torch
import wandb
from matplotlib import pyplot
from monai.data import list_data_collate, IterableDataset, D... |
90efbb56ee34559338f538336e4494c857374b1d1bc4a726629d5284a52d827b | Python | 25,717 | 503 | # Copyright 2026 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... |
3190bef6d7099bbea16cfc630be6b589193238f85f6a6d86c35a956c23a935ba | Python | 25,762 | 678 | # Import all required modules at the top
import os
from datetime import datetime
def setup_logging(bids_dir, subject_label, session=None,
operation="bidsonymrevert"):
"""
Set up logging functionality for BIDSonym operations.
Creates a BIDS-compliant log file and returns a logging f... |
ab02a9ee85184216641d55ffcfea2783d0aa2538193c3744a71c018f29bb21c3 | Python | 25,786 | 647 | # 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 applicab... |
875d759492ef56cbf5603fde2dcac5abfcc4cf9ede5b109d5fa827bd48bdb8a4 | Python | 25,817 | 653 | """
Experiment T3: Timing benchmark for clinical deployment feasibility.
Measures per-segment processing time for:
1. Base SP feature extraction (f_B, f_peaks, f_fft, f_tkeo, f_coh)
2. Expanded SP feature extraction (base + f_hps, f_fft_car, n_detected, f_range, signal_variance)
3. Individual feature components ... |
44624301e7824c737481d418c1adb92ccc9121a8e8868594fbbe008daeac57f2 | Python | 25,853 | 649 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/gte/modular_gte.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# ... |
ec69182f2ec6e708eb4cb0e0b170d1451d5a9e485af007c3edf8dd9587620391 | Python | 25,887 | 663 | """
Round 10: Expanded dataset evaluation — 120 patients (43 original + 77 new).
For each patient, 1 segment. For original data, pick the segment with best
expert agreement (lowest pairwise std). For new data, use the single annotated
segment.
Experiments:
r10_lopo_120patients — Ridge LOPO, alpha=1.0, all 120 pa... |
88cd3d65eb915b06fbae9bd6235b310b3121c005d139ec0667ff33eca49d9074 | Python | 25,893 | 584 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 23 10:33:51 2021
@author: Yongrong Qiu
"""
import numpy as np
from sklearn.decomposition import randomized_svd
import h5py
from scipy import interpolate
from scipy import signal
from scipy import stats
import cmath
import matplotlib.pyplot as plt
def l... |
5d5c6dffe3961e7d4c45c46ac7e11fa1dd9f185461d052e649e68080afc02cde | Python | 25,932 | 552 | """
Tool Name: torch3d_segmentation_learn
=====================================
Description:
Trains a 3D UNet segmentation model using the MONAI framework on volumetric
medical imaging data (e.g., CT, MRI). Compatible with the SMImageDataset
class, which provides 4D tensors [C, D, H, W].
The pipeline ... |
0694fdf853c367732a64b63255ec5cba4e2059282fad8f1fe57e2a6530e78a8a | Python | 26,070 | 717 | """
Harvest fast-LPD candidates from seizure folders on S3.
Searches SEIZURE, IIIC, and SEIZURE_BCH folders for segments that look like
fast LPDs (>2.5 Hz periodic discharges) rather than true evolving seizures.
Filtering criteria (ALL must pass):
a. FFT peak frequency on pointiness traces: 2.0-5.0 Hz
b. Inter-pe... |
d28af159590f2a9147109a8b70d14eeac22183c6e1e61127a1400fd5e531928c | Python | 26,072 | 551 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/embedding_gemma2/modular_embedding_gemma2.py.
# Do NOT edit this file manually as any edits will be overwritten b... |
8eeb703c09d096cb61e6bb91c13152b5707349a1ea3c99ab9a70b011bb1f9e6a | Python | 26,146 | 648 | from __future__ import absolute_import, division, print_function
import csv
import os
import sys
import time
import warnings
from functools import partial
from multiprocessing.dummy import Pool as DPool
from os.path import join
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import cv2
impor... |
06d2372ddf00dd58cdcd27837df71b096b1a4761104c7d6abd362e0ce4b009ab | Python | 26,192 | 775 | """Launching tool for DGL distributed training"""
import argparse
import json
import logging
import multiprocessing
import os
import queue
import re
import signal
import subprocess
import sys
import time
from functools import partial
from threading import Thread
from typing import Optional
def cleanup_proc(get_all_re... |
3857af5cb2cf949a947b3666e5561ccdc0d92e21ee71ede6bb431723dcf349c2 | Python | 26,197 | 813 | import json
import os
from itertools import product
import dgl
import dgl.backend as F
import numpy as np
from dgl.distributed import edge_split, load_partition_book, node_split
mode = os.environ.get("DIST_DGL_TEST_MODE", "")
graph_name = os.environ.get("DIST_DGL_TEST_GRAPH_NAME", "random_test_graph")
num_part = int... |
8e982084d1ec2b0101edb18fa6b21004d8ae970d1ef63bc2084668895d5c19ca | Python | 26,259 | 669 | #!/usr/bin/env python3
"""
Generate Fig 2: PD Characterization Pipeline composite figure.
Three-panel horizontal layout:
Panel A (left): Input 19-channel EEG (average reference, 10s)
Panel B (center): Architecture flowchart (matplotlib boxes/arrows)
Panel C (right): Output visualization (EEG + discharge marke... |
23e275702bddb276ae8c11fa2e48d4d2af23df2640077b0fffadecdcdd928d99 | Python | 26,261 | 725 | #Simulate LIF neurons
import numpy as np
import numpy.random as rand
import scipy
def convolve_online(s, h, kernel, t_offset):
if len(s.shape) > 1:
n = s.shape[0]
for i in range(n):
for idx in np.nonzero(h[i,:])[0]:
st = t_offset + idx
en = min(s.shape[0]... |
77151b428dd2e457e036a172512746ba8c400b909e2963491825b608d9de1526 | Python | 26,344 | 718 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 26 15:23:52 2019
@author: kai
"""
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from rowwise_neuron_curves import *
import os
from matplotlib.ticker import FormatS... |
6cb8a402fd304c73f41592aef7bbf4af5956a28b95bcf66ca3f525e8a5a1afd5 | Python | 26,397 | 678 | # Copyright 2022 The OpenAI Team 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
#
# ... |
35997bb7d48b847f5d8de1bac2da77859af634f74e940034bd73200ffc1c58d4 | Python | 26,409 | 634 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/dinov3_vit/modular_dinov3_vit.py.
# Do NOT edit this file manually as any edits will be overwritten by the genera... |
816c6541a29ef94a5adb19f63b33eb2620f49ea5cbae43cda5cb2ea329ae94d1 | Python | 26,440 | 641 | # Copyright 2026 Tencent HunYuan Team 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
#
... |
72dd9613610793e360a69df35cb0c3b1622b74ae808cad61ef9f0f2e42d04599 | Python | 26,451 | 884 | # Copyright (c) 2025-2026 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
from dataclasses import dataclass, field
from enum import StrEnum
from typing import Any
import pytest
import yaml
from jaqmc.utils.config import (
ConfigError,
ConfigManager,
configurable_dataclass,
... |
0f86cb06fce201b6b46c075ceacb0f0a5d2d5b1503dbddfa223667b8686932df | Python | 26,511 | 662 | # Copyright 2022 KAIST 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 requir... |
211b748bac7b97ac92f73bbb49fc9f60e9b8db569a709ac22475ad100e605938 | Python | 26,513 | 774 | import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.experimental.numpy import dot
from typing import Tuple, Dict, Optional, Union
from contextlib import contextmanager
from slideflow.norm import utils as ut
from .utils import clip_size, standardize_brightness
# ------------... |
be12662d4baf9314d0f7e4a1c5f3cf4345fa697098cb7f8e9a14d6471afe286d | Python | 26,513 | 859 | """Provide tests for PatternDataGrabber."""
# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
# Leonard Sasse <l.sasse@fz-juelich.de>
# Synchon Mandal <s.mandal@fz-juelich.de>
# License: AGPL
from copy import deepcopy
from enum import StrEnum
from itertools import product
from pathlib import P... |
191a630e005ab208c064ef86c651844fbf85009c59624cf5d77a817ad11edc4c | Python | 26,541 | 698 | # -*- coding: utf-8 -*-
"""
Created on Mon Aug 10 11:01:43 2020
@author: yqiu
"""
import torch
import torch.nn as nn
from torch.nn import functional as F
import matplotlib.pyplot as plt
import numpy as np
import h5py
from sklearn.decomposition import randomized_svd
from scipy.signal import find_peaks
from scipy.stat... |
e6457689e85ec001bb843741dae4287b36dc9ce8b3db3a7e5ee868c2b2c989c9 | Python | 26,541 | 660 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/gpt_neox/modular_gpt_neox.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation... |
9bbff593678a59d0d5e639e21d9591c728f5c013e10da00ec2bd9a8b40bc36da | Python | 26,561 | 693 | # -*- coding: utf-8 -*-
"""
Created on Mon Aug 10 11:01:43 2020
@author: yqiu
"""
import torch
import torch.nn as nn
from torch.nn import functional as F
import matplotlib.pyplot as plt
import numpy as np
import h5py
from sklearn.decomposition import randomized_svd
from scipy.signal import find_peaks
from scipy.stat... |
dd5475487c038948a709014392386971d9af4c06bd852571344e2a8d6c5f3249 | Python | 26,583 | 751 | """Reader utils."""
from __future__ import absolute_import
import gzip
import io
import os
import struct
import numpy as np
import slideflow as sf
from typing import Dict, Iterable, List, Optional, Tuple, Union, TYPE_CHECKING
from slideflow.tfrecord import iterator_utils
from slideflow.util import example_pb2, extr... |
55741e2411cd56eef0acf44ce3fd4d8dd9d626a9fa328a566602bc411855580f | Python | 26,622 | 738 | import time
import rasterio
import cv2
import threading
import multiprocessing as mp
import numpy as np
import cellpose
import cellpose.models
import logging
import slideflow as sf
import zarr
import torch
import shapely.affinity as sa
from queue import Queue
from numcodecs import Blosc
from matplotlib.colors import t... |
e75c5eeb0afe051c64da1d5371ed124fcb0cc44ce654c9ebc1223a4d5e4ec457 | Python | 26,641 | 713 | #!/usr/bin/env python3
"""
Parallel DICOM session deduplication with intelligent date-based and pattern-based grouping.
Handles cases where duplicates have different acquisition dates.
"""
import sys
import hashlib
import re
import shutil
from pathlib import Path
from collections import defaultdict, Counter
import pyd... |
11e54e5d9140554eb08c4a534f974c6fe046adfb9e9710721e1a3099c4ef2545 | Python | 26,752 | 717 | """Tool to assist with embedding interpolation for a class-conditional GAN."""
from typing import (Generator, List, Optional, Tuple, Union,
TYPE_CHECKING, Any, Iterable)
import warnings
import numpy as np
import pandas as pd
import slideflow as sf
import json
from os.path import join, dirname, exis... |
7431705a3834ef784ef93b6591d4c0a93558db8a68ba83faa1b910463b847ee6 | Python | 26,787 | 599 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/kolibri1/modular_kolibri1.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation... |
3dd769214be90e75f5e10a8678560de99a6579f61395efb1fb60be268b3ce21f | Python | 26,828 | 592 | #!/gpfs/milgram/project/turk-browne/or62/conda_envs/myenv_multimem/bin/python
"""
Similarity-matrix CV multiple regression: testing multimodal representation
in the human hippocampus beyond unimodal coding.
The similarity matrices here are Fisher-z correlations (similarities, not
dissimilarities), so this is technical... |
ab6945b157ece58018cdd0cd5295f8706e59185a0943cd8eb135f60c0fcef27f | Python | 26,892 | 725 | """
Generate annotation package for Sahar: unified manifest + images + viewer.
All 336 patients that MW has labeled (202 canonical + 134 round4).
Improvements over v1:
- JPEG images (quality=70) instead of PNG — much smaller file size
- Lower DPI (100) and smaller figure (12x8) — compact but readable
- Clean EEG only:... |
91db10cf8ac0f8274f54fa5db565dfe048358d41392c071a7deb75d24ca587b2 | Python | 26,926 | 784 | # Copyright 2023 Microsoft Corporation
# Copyright (c) 2026 ByteDance Ltd. and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
#
# This file has been modified by ByteDance Ltd. and/or its affiliates. on 2026.
#
# Original file was released under MIT, with the full license text
# available at licenses/folx_MIT.... |
f03ad6be730d55c81fe69577ff03dd79210835e9afde36eee5215680712c4276 | Python | 26,952 | 674 | # Copyright 2026 Cohere Inc. 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 ... |
b52fdadfb93789d9e413ad55178cb1cbcee22e385003687ed53c46c164dd63c8 | Python | 26,968 | 627 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/fun_asr_nano/modular_fun_asr_nano.py.
# Do NOT edit this file manually as any edits will be overwritten by the ge... |
f49577150561c1e6d695bb38320024db33da7fcea18c12f397ca5c01d8c0fa3b | Python | 27,005 | 619 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/hrm_text/modular_hrm_text.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation... |
2f77e129f10119748fb64637c65a798fc95f95ad7d5b73f5e3622e43566bd591 | Python | 27,091 | 606 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/hunyuan_v1_moe/modular_hunyuan_v1_moe.py.
# Do NOT edit this file manually as any edits will be overwritten by th... |
58722b96b8272fb065ccd36b9f7d09bd2386a790f211f5f03c38d633db602b4b | Python | 27,134 | 716 | # 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... |
5d48407f31253c942021c19ce575538124006c79fbcdbef9dea83c62d0e379ce | Python | 27,158 | 614 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/dots1/modular_dots1.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# ... |
db976aaaf941e5b0dd0952d8ffcb4513846e45618412d75e95f20905b83797dc | Python | 27,198 | 638 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from examples/modular-transformers/modular_roberta.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
... |
c3ec655cc91eb2da4030afa53c1f2fcd7d6c31381e0dccb5ff1c239abea338b2 | Python | 27,206 | 628 | # Copyright 2025 Baidu and 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 b... |
a0a8aac065a9c5c910ac0329ce2b006160090122a1f974e158357260fe48d2b2 | Python | 27,263 | 708 | # Copyright (c) 2026 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
# mypy: disable-error-code="attr-defined"
"""Frequency continuation and recovery for the molecular LIT workflow."""
from __future__ import annotations
import logging
from collections.abc import Callable
from typing import... |
0d65b619340d1287c04a51203170457d6a8e18aba92ca073b73c698fc4fcfb42 | Python | 27,310 | 625 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/glm4_moe/modular_glm4_moe.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation... |
bae3f67c7a9c371dcc2db20c783906dc73a0983e77f5d62c522514e931164894 | Python | 27,310 | 646 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/audioflamingo3/modular_audioflamingo3.py.
# Do NOT edit this file manually as any edits will be overwritten by th... |
0706a369bba930fd43ad7d3a3e776dd9f970fba45ec07a8e9abf2443f1d02a1e | Python | 27,344 | 638 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from examples/modular-transformers/modular_dummy_bert.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation ... |
471276851e13a3d73162637fdfbb012b7d583fd15fcccb7827b6fdcd3e8e8301 | Python | 27,382 | 540 | # Copyright 2024 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 applic... |
88dd443a8b1f28d74942c777b67fc927b4514a3af5456443e8fde2459ec03873 | Python | 27,422 | 617 | # Copyright 2025 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 ... |
ef3bae5ed0dd2ccc3480f8b1f41450440eb5d066bb0ed75e32c85201d6cf96c2 | Python | 27,462 | 682 | # -----------------------------------------------------------------------------
# Copyright (c) Yongning Lei 2024-2025
# All rights reserved.
#
# This script is distributed under the Apache-2.0 license.
#
# run_allses_glm.py
# -----------------
# Fit ONE GLM per subject by concatenating data across ALL sessions and the... |
403f6476d5d21321d7e45c6b97c31f139f7ced78d5d20019733e891b9449fe02 | Python | 27,557 | 481 | #%% import
#Calculation of table 1 of article #1
import sys
sys.path.append('/data8/projets/ChasseM_Compass_1013663/'+\
'code/nsauthier/git_ChasseM_Compass_1013663/'+\
'Clinical_Model/time_series_model/')
import pandas as pd, numpy as np
from DataCleaning import get_ICD10
o... |
3ebe3fb074e506f032b258190793009d5516d9a47cee12828a5bfc71a3ae0b56 | Python | 27,574 | 716 | """
HemiCET+DP Optimization Swarm
Runs 7 optimization experiments + combinations in parallel where possible.
Results feed into a live dashboard at results/hemicet_optimization_dashboard.html.
Experiments:
E1: Retrain on cleaned labels
E2: DP parameter re-optimization
E3: Post-hoc filtering / evidence thresholdi... |
b6e2ae3680f00332481cc73de802ecd6133cb3e3e580ae5cde23f065be7cf3f8 | Python | 27,587 | 834 | """Synthetic graph datasets."""
import math
import os
import pickle
import random
import networkx as nx
import numpy as np
from .. import backend as F
from ..batch import batch
from ..convert import graph
from ..transforms import reorder_graph
from .dgl_dataset import DGLBuiltinDataset
from .utils import _get_dgl_url... |
b779c8b157f0fffe6348d68469a2fcff0f384b7be30f8f1dac531a95d80a7542 | Python | 27,589 | 657 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/deit/modular_deit.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# ... |
48521fe31bc862f6bbc288d1006976864187207d0c2935a7b4c6891a254ba644 | Python | 27,616 | 628 |
"""
Created Sept 2019
Last updated Tue Apr 12 2022
@author: nsauthier@gmail.com
Series of function that takes a uncleaned dataframe of labs from CITADEL
remove NaN results, remove some redundants labs, change somme litteral labs
to numeric (+-> 1, ++ -> 2, etc.).
Then merge some labs with different names (blood gas,... |
531accafb3676df0b965d67bb8f3fe99f5e602c38d8246f44a6aec6a0f88c8a7 | Python | 27,632 | 639 | #!/usr/bin/env python3
"""
Generate Fig 2: PD Characterization Pipeline composite figure.
Three-panel horizontal layout:
Panel A (left): Input 19-channel EEG (average reference, 10s)
Panel B (center): Architecture flowchart (matplotlib boxes/arrows)
Panel C (right): Output visualization (EEG + discharge marke... |
9fcdc2e4a70690eabd508e02c64574997e1ab4674629923d9bfea565578e99dc | Python | 27,644 | 533 | # Copyright 2026 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... |
2f1db9aa07eea74519fde847ba258d03f86b7e83ee49550aa1e6ca74eefacb4a | Python | 27,669 | 695 | # -*- coding: utf-8 -*-
"""
Contains helper code for running spatial nulls models
"""
from pathlib import Path
import warnings
import numpy as np
from scipy import optimize, spatial
try: # scipy >= 1.8.0
from scipy.ndimage._measurements import _stats, labeled_comprehension
except ImportError: # scipy < 1.8.0
... |
66a30efb22bc2bce998927fd3288510d2bfe8f65a2bbe77e50755248873edd8c | Python | 27,676 | 634 | import numpy as np
import numpy.typing as npt
import base64
import json
import os
import tempfile
import matplotlib
import matplotlib.pyplot as plt
if os.environ.get('DISPLAY','') == '':
# print('No display found. Using non-interactive Agg backend.')
matplotlib.use('Agg')
import matplotlib.colors as mcolors
fr... |
b76721e88fd0140aba5ba5382d771b83477fa85fcfe076cfa78b0d8b63d3f318 | Python | 27,692 | 665 | # Copyright 2025 The Nari Labs and 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 re... |
433ef7dbab18585e5866434d9054c78b4e5af9ad92f24d03550ccd58675d50e5 | Python | 27,704 | 735 | import unittest
import backend as F
import dgl
import dgl.graphbolt as gb
import pytest
import torch
from dgl.graphbolt.impl.sampled_subgraph_impl import SampledSubgraphImpl
def _assert_container_equal(lhs, rhs):
if isinstance(lhs, torch.Tensor):
assert isinstance(rhs, torch.Tensor)
assert torch... |
567260db8fc1167e14d23c3227324f0ea17ffcd167c32ded758bb868ccec66cd | Python | 27,725 | 541 | from networks.abstract_layer import AbstractLayer
import torch
class DFCLayer(AbstractLayer):
def __init__(self, in_features, out_features, size_output, bias=True,
forward_requires_grad=False, forward_activation='tanh',
initialization='orthogonal', clip_grad_norm=-1):
... |
0873e306c57c64dd92aaa34fe6137a68ad0c127102c3fe42e2688d74bf54da8f | Python | 27,728 | 679 | # Copyright 2025 Technology Innovation Institute and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
... |
7993f50d4332e287b8408c4ed0fef41c0ef1bddd5dd27c7c79343f8d9d8f479a | Python | 27,729 | 660 | ### This script could run hybrid MLP-GNN models sweeping over layer connections : (-2,-2), (-2,-3) and (-2,-4). The current setting is for (-2,-4) layer connections.
# Importing necessary packages
import time
start_time = time.time()
# Importing necessary conformer generation & graph building packages
from o... |
dca8bcaaec743545a1add95bb2f595c61d1522b671a3bf632cc81a233d7f3ef8 | Python | 27,753 | 806 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""This module provides interfaces for workbench surface commands."""
import os
from nipype import logging
from nipype.interfaces.base import (
CommandLine,
CommandLineInputSpec,
File,
Tra... |
d6189b685de129b06843d0ee026f07eae8118eae1886425fff9df3e40de37be0 | Python | 27,757 | 682 | """
Reproduce Figures 5 & 6 from Tăuțan et al. 2025 (J Neural Eng 22:066027).
Compares expert-expert IRR with expert-algorithm IRR using:
- ICC (intra-class correlation coefficient)
- PA (mean pairwise percentage agreement)
for frequency and spatial extent of LPD, GPD segments.
Methods compared:
- PD2a (Alexan... |
08ab5a408a11b96bc9401a5dc6254b4d4ecdec4f5502a44f452d16814eb46a49 | Python | 27,759 | 697 | # Copyright 2025 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 ... |
dd508399d2da78e9ae7770287fd016ecff251d6469225ee9f601d4c3946a1efe | Python | 27,782 | 675 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/biogpt/modular_biogpt.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
... |
991407f350564cd2f28b3c542298ea6fe991d19ecc5c60d1c7ce024d7029d917 | Python | 27,806 | 697 | import sys
import time
import traceback
import slideflow as sf
import imgui
import numpy as np
import json
import glfw
import csv
import re
from os.path import join, dirname, abspath, basename, exists
from tkinter.filedialog import askopenfilename
from typing import Optional
from .model import draw_tile_predictions
fr... |
0b3e7f8c617572ac1628d0f3bf806b0e0c0205dbdf1b050b373c603de14a1f63 | Python | 27,827 | 678 | # Copyright 2025 Mobile Perception Systems Lab at TU/e 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/licens... |
dea8c3d25972bca98fe4fc62591463f0e2ba39479066e69d6acb5ad97595998b | Python | 27,828 | 789 | """MovieLens dataset"""
import os
import re
import dgl
import numpy as np
import pandas as pd
import scipy.sparse as sp
import torch as th
from dgl.data.utils import download, extract_archive, get_download_dir
from utils import to_etype_name
_urls = {
"ml-100k": "http://files.grouplens.org/datasets/movielens/ml-1... |
5712548212c03f898a72a45dcc7ef7f5353e57add17eaf6da4db252a581e8ece | Python | 27,846 | 519 | #!/usr/bin/env python
"""Draw the benchmark figure and a summary from results.jsonl.
usage: plot.py [--results results.jsonl] [--out benchmark-2026-10]
Writes OUT.png, OUT.pdf and OUT-summary.md. harmonypy 0.2.0 numbers come from
the April 2026 benchmark on the same machine (harmonypy-0.2-april-2026.json
next to this... |
148660dce5c11cf00ed454addbfb4784e47401d92d20f9dc466f50d37336aa7b | Python | 27,853 | 609 | # Copyright 2026 BioHub 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 requi... |
8dd25f2173a3a434e85bd4314b9429e752e81d69ce49736659042ca1cf932581 | Python | 27,867 | 793 | """
.. _model-dgmg:
Generative Models of Graphs
===========================================
**Author**: `Mufei Li <https://github.com/mufeili>`_,
`Lingfan Yu <https://github.com/ylfdq1118>`_, Zheng Zhang
.. warning::
The tutorial aims at gaining insights into the paper, with code as a mean
of ex... |
e7999dde8b347ca6db83596ad1a09263b4abd947a0cd1cc10c601b683cc4f6fa | Python | 27,919 | 755 | import dgl
import dgl.graphbolt as gb
import pytest
import torch
relation = "A:r:B"
reverse_relation = "B:rr:A"
@pytest.mark.parametrize("indptr_dtype", [torch.int32, torch.int64])
@pytest.mark.parametrize("indices_dtype", [torch.int32, torch.int64])
def test_minibatch_representation_homo(indptr_dtype, indices_dtyp... |
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