sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
32f800abefe2bd0a58e4278fdd58ded9623bc5865646d2ef48f381306f4b8b41 | Python | 21,495 | 393 | # 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... |
45064db40ee273994b1583451bd055cfd8838572bf55ab1970a4297d7d234281 | Python | 21,496 | 468 | import sys
import tensorflow as tf
# ModuleNotFoundError is new in 3.6; older versions will throw SystemError
if sys.version_info < (3, 6):
ModuleNotFoundError = SystemError
try:
from . import layers
from . import model_inputs
from .sampling_utils import SamplingUtils
except (ModuleNotFoundError, Impo... |
22c63e545252496ac05116669a5e2f34d38f727ea32c30a8939e62c45c6a4026 | Python | 21,497 | 503 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/ministral/modular_ministral.py.
# Do NOT edit this file manually as any edits will be overwritten by the generati... |
9d9aae8a27c933376dff9f58dc26eb92c2136298b6d2b4fe644c509c68f4a1f5 | Python | 21,528 | 571 | from __future__ import annotations
import copy
import json
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Any
import joblib
import numpy as np
import pandas as pd
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier
from sklearn.f... |
963e979411c15cf78dc1f49061af26c39d577a87dab84aadf796efc5e48b8d3c | Python | 21,534 | 403 | # Copyright 2022 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 ... |
684c1c30b8695f4276ab280d264adf80b71e510b7fd1889a56495057234ff711 | Python | 21,542 | 613 | # Copyright (c) 2025-2026 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
"""Tests for molecular wavefunction implementations.
Backbone-specific tests are in backbone_test.py.
"""
from dataclasses import replace
from typing import Any, cast
import h5py
import jax
import jax.numpy as jnp
i... |
c282ae62316e9891e520e82c75633124b28673999e290b41fb26b743086c5b8c | Python | 21,555 | 499 | # Copyright 2026 Reflection AI 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
#
# Unles... |
c7c45875526e65a956dc2f1fbd12978adb30c9abb700e34e110043b3a7a9ce6c | Python | 21,566 | 529 | from PyQt5.QtWidgets import QWidget, QHBoxLayout, QMainWindow, QAction, QStackedLayout, QApplication, QMessageBox
from PyQt5.QtGui import QIcon
from PyQt5.QtCore import QThread, QObject, pyqtSignal as Signal
import subprocess
import threading
import time
import signal
import os
import sys
import json
import re
import n... |
3d6c52bec2ca0d2bc446818e6fe47b47b0c1e515236b41c5c3661353745db9b1 | Python | 21,652 | 488 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/cohere2/modular_cohere2.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation o... |
0319f57d1bb230d1efff19a8c2221f734346a2ca2b733dc233049842c876e2cf | Python | 21,664 | 543 | # Copyright (c) 2025-2026 ByteDance Ltd. and/or its affiliates
# SPDX-License-Identifier: Apache-2.0
import math
from dataclasses import field
from typing import Literal
import jax
import numpy as np
from flax import linen as nn
from jax import numpy as jnp
from jaqmc.array_types import Params
from jaqmc.geometry.p... |
32b7ba55490d21d0a7b6f2a6628449cf2050c82527103bbbfca63e1457d884bb | Python | 21,672 | 409 | # 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 t... |
4b12e88c139682301924240e42217358e512af68d73f3b2c02b7bd23f160be51 | Python | 21,691 | 654 | #!/usr/bin/env python
from optparse import OptionParser
import copy, os, pdb, random, shutil, subprocess, time
import h5py
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import spearmanr
import seaborn as sns
from sklearn import preproc... |
08291d59c5b7e684aff9d6f7ae0aaf78977baffe9c97f8f34244471954e486dc | Python | 21,707 | 574 | # Copyright 2018 DPR Authors, The Hugging Face 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 o... |
f2d51e7e9ce6ad7f0024321292a97b0c367ca5331a921d9403da92531bb11cc0 | Python | 21,713 | 501 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/granite/modular_granite.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation o... |
a8d73305c2292530901724950fcfb05c63a0f0596d78fba81c774f6014db333d | Python | 21,716 | 565 | #!/usr/bin/env python3
"""Brain-Folding Simulator — Adaptive Bento Scientific Dashboard (PySide6).
A single lightweight PySide6 app: a slim header, a responsive bento grid of the
six parameter categories (reflowing 3 → 2 → 1 columns), a contextual Parameter
Guide (right side on wide windows, docked below on narrow one... |
befe74a4a396a5dab31a575d6317d1f2622f9b9931c239665ba8d37d6e874ae6 | Python | 21,717 | 585 | from turtle import title
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd
import seaborn as sns
import torch
import os
from matplotlib.colors import LogNorm
folder = '../data/'
models_name = {
'30K': "f2_30k",
'100K': "f2_100k",
'Local': "f1_local_... |
d2d960d1d81dcd42e51bcb57211f6d65053c487693c447be53447dbfa867aa40 | Python | 21,717 | 555 | """
Dataset utilities for MST classifier training and inference.
`MSTDataset` adapts a list of `SMImage` objects into the dictionary format
expected by the MST data modules. Optional TorchIO preprocessing and
augmentation steps can be applied before tensors are returned.
TorchIO preprocessing reference:
https://docs.... |
7989723cd5c2ec1e0072cdebedfb212e18e29c82e47f0ffcf38239dbc2180bd2 | Python | 21,742 | 562 | """
Evaluate CET FP reduction improvements:
Stage A: Threshold CET before max() combination
Stage B: PD-weighted channel selection for evidence aggregation
Uses parameter_sweep infrastructure for parameterized DP.
Usage:
conda run -n foe_dl python code/cet_model/eval_improvements.py
"""
import sys
import jso... |
bdd71046bc3e34c02e4b4eedc86822a45b97e08f9be127347d29a3e6cf97f940 | Python | 21,743 | 593 | import json
import logging
import os
import dgl
import numpy as np
import torch
from distpartitioning import array_readwriter
from distpartitioning.array_readwriter.parquet import ParquetArrayParser
from files import setdir
def _chunk_numpy_array(arr, fmt_meta, chunk_sizes, path_fmt, vector_rows=False):
paths = ... |
11714ea1148d470811ea64f6804581ea49fe7738d4e72c575feca55317619426 | Python | 21,754 | 710 | from matplotlib.widgets import Button
from pathlib import Path
from scipy import ndimage
from skimage.registration import phase_cross_correlation
from matplotlib.patches import Rectangle
import numpy as np
import tifffile
import pandas as pd
import matplotlib.pyplot as plt
import re
def to_uint8(a):
"""
Dete... |
5ed70ac0fefa1f88afcff3fb2b7dbada583509c18a79c43138f232cd4ed76781 | Python | 21,765 | 485 | import gc
import json
import logging
import os
import pathlib
import re
import tempfile
import time
from datetime import datetime
from queue import Queue
from typing import Any
import torch
from datasets import Dataset
from huggingface_hub import HfApi
from tqdm import trange
from transformers import (
AutoModelF... |
da1d3e0c181b183d2cbbde6646730b1222ffa4ac3c86875b327cb8b2e32e7ec7 | Python | 21,765 | 460 | """Adapted from Nematode: https://github.com/demelin/nematode """
# TODO: Add an attention visualization component - very important (~easy)
""" Layer implementations. """
import sys
import numpy as np
import tensorflow as tf
from tensorflow.python.ops.init_ops import glorot_uniform_initializer
try:
from . impor... |
3273eb9c1b74099033230adcd457f28431919b08ebfbf644f1086aab67ada28a | Python | 21,785 | 534 | # Copyright 2025 Meta AI 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 requ... |
acce6aa7f84bc0db0d8b0287cd256f244288fffe4d5a5ec4169b1cd11d9351df | Python | 21,816 | 626 | ### This script could run MLP only model for pchembl/MW prediction (of IC50 of Tissue Factor Pathway Inhibitor) using combination of Shannon entropies (SMILES Shannon, SMARTS Shannon, InChiKey Shannon and SMILES partial/ fractional Shannon) and MW as descriptors
# import the necessary packages
from imutils import pat... |
1f814580b3df2574be5ded5af7b0c1061805a5021d30d31c9608dc931fa2358e | Python | 21,837 | 511 | # Copyright 2022 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 ... |
ff002a14ab0150041104df19c3678aff187100b3dedf9809bf1bf0dcc9119fda | Python | 21,838 | 503 | # Copyright 2023 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... |
9a8678f7134f3e4941df83037bad88ce143b0a5d499fab55bcca62968fd5f0e1 | Python | 21,842 | 549 | import zarr
import slideflow as sf
import imgui
import numpy as np
import cellpose
import cellpose.models
from os.path import join, dirname, abspath
from functools import partial
from threading import Thread
from slideflow.slide.utils import draw_roi
from slideflow.cellseg import segment_slide, Segmentation
from ._uti... |
599d24996f5ad3fa21d9fdfcaaed14de87d0572b310c2d075d287263e5a14288 | Python | 21,887 | 533 | import argparse
import os
import shutil
import sys
import time
import warnings
from random import sample
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn import metrics
from torch.autograd import Variable
from torch.optim.lr_scheduler import MultiStepLR
from cgcnn_ads_onl... |
93e61306fd74127bc7cc1cf15823596608e983df2c4f664a9811110eb2ec6d76 | Python | 21,894 | 533 | import argparse
import os
import shutil
import sys
import time
import warnings
from random import sample
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn import metrics
from torch.autograd import Variable
from torch.optim.lr_scheduler import MultiStepLR
from cgcnn_ads_onl... |
b2ef7a474974488f432ca62fd9c519d6e62037eccda29af72a9c9169851a9875 | Python | 21,906 | 629 | """
Module for Approximate Bayesian Computations
"""
from abc import ABCMeta, abstractmethod # Abstract Base Classes
import multiprocessing as mp
import numpy as np
from scipy import stats
from numpy.lib.recfunctions import stack_arrays
from numpy.testing import assert_almost_equal
import time
class ABCProcess(mp.Pro... |
08fbf4d31b7279e0204d9173bc5e37966d8a91ddbd26713f7ee5a69e65c8c7fb | Python | 21,916 | 480 | """
Calculate a set of outcomes summarizing the quality of a set of generated
molecules.
"""
import argparse
import os
import numpy as np
import pandas as pd
import scipy.stats
import sys
from fcd_torch import FCD
from itertools import chain
from rdkit import Chem
from rdkit.Chem import Descriptors
from rdkit.Chem imp... |
0310ab8c921b14294b61dbf0a7d6e393eca4f86df612686afab91e7e4300b284 | Python | 21,976 | 477 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py.
# Do NOT edit this file manually as any edits will be overwritt... |
d775791656a5805dbb68e4287f720b8aa3d170fc92809142006bc3d81b8f31bd | Python | 22,007 | 469 | # 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... |
bf1b086e79d2c86c9521e0501e8b43cf3aa14fd8b05ee7f49999bd805da98be9 | Python | 22,023 | 520 | #!/usr/bin/env python3
"""
Render publication-quality EEG characterization figures from JSON data.
Self-contained: only requires matplotlib, numpy, scipy, json.
Reads figure_*_examples_data.json and produces figure_*_examples.png at 300 DPI.
"""
import json
import os
import sys
import numpy as np
import matplotlib
ma... |
57e438f2f1cf1305d34e578d75e37ddb8e06e9baca03ee3eba87ded432f63fba | Python | 22,055 | 624 | #!/usr/bin/env python3
"""
Build Fig 2 composite: swap Panel B (with real topoplot) into the figure.
Renders draw_panel_b.py with a real Laplacian topoplot computed from the
EEG segment, then pastes the updated Panel B into the existing composite
figure (preserving Panels A and C untouched).
To change the example seg... |
4321346738f0e3a808642e381d92e0703306da501e17beec730e83660aeb6b55 | Python | 22,060 | 570 | """CycleGAN-based stain normalization.
Modified from: https://github.com/Boehringer-Ingelheim/stain-transfer
Pretrained weights from: https://osf.io/byf27/
Original license:
BSD 2-Clause License
Copyright (c) 2023, Boehringer Ingelheim
Redistribution and use in source and binary forms, with or without modification,... |
eed8fdabb4d39d5243b0aff5d017c5ae6e5f5cab2c29d79a706f3dee9c2c17ce | Python | 22,069 | 472 | # 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... |
a514a3d47e0159ce56fffe4eb0c01e5ba7d0e29dfadda17f6839f798a52eb0dd | Python | 22,074 | 665 | """
CET+HPP pipeline: CNN Evidence Trace + HPP Dynamic Programming.
Replaces the handcrafted pointiness+TKEO evidence in HPP with learned
CNN evidence, then runs the same DP algorithm for discharge detection.
Evaluates three methods:
1. HPP-only (handcrafted evidence)
2. CET+HPP (old CETModel, no skip connections... |
f05fdd28773f61693332bbd51760d58d1f62560c56ecca30a155efe16ca545ef | Python | 22,087 | 675 | """
This script is a GraphBolt counterpart of
``/examples/core/rgcn/hetero_rgcn.py``. It demonstrates how to use GraphBolt
to train a R-GCN model for node classification on the Open Graph Benchmark
(OGB) dataset "ogbn-mag" and "ogb-lsc-mag240m". For more details on "ogbn-mag",
please refer to the OGB website: (https://... |
51efc39d9f5575574d15c8094e7b0773adf8cb154c6d70e6301d291bda328c24 | Python | 22,090 | 602 | import os
import sys
import json
import numpy as np
import matplotlib.pyplot as plt
from .PlotHelper import add_goal_zone, add_obstacles, add_tmaze
class RepresentationVisualization:
"""Plotting a representation of firing maps."""
def __init__(self,
data_path,
flname,
... |
4c5ab416799701d9ffe5452cd53fbe6a2bb66909640c4de58b7bf22f7369c268 | Python | 22,130 | 478 | """
Generate HTML viewer for reviewing cases where HemiCET disagrees with GT labels.
Prioritizes high-yield cases:
1. Cases where HemiCET finds many more discharges than GT (possible label FNs)
2. Cases where HemiCET finds fewer discharges (possible label FPs)
3. Cases with large frequency disagreement
4. hpp_labeler ... |
eac2dd5ca58562039f42c1aa36d2b69449b0baaa11636e0c536717690eea0988 | Python | 22,153 | 683 | """Provide API functions."""
# 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 atexit
import importlib
import importlib.util
import os
import shutil
import sys
from pathlib import Path
imp... |
4ab7e6271afb4149cfc75f94749c63f437be62a54c0711c8b57c2c6f4a88c9b9 | Python | 22,188 | 539 | # 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... |
9737d1ca5ac93da8d9f50021388c2752019804469d59eb9dcf38549e54a943f2 | Python | 22,220 | 624 | #!/usr/bin/env python3
# -----------------------------------------------------------------------------
# Copyright (c) Yongning Lei 2025-2026
# Apache-2.0 license
# -----------------------------------------------------------------------------
"""
compute_tsnr.py
Compare tSNR between ME_denoised, ME_optcom, and SE data... |
35ff153574007321f8fbdd338b2579279131ef8eef7f60382087a0b6cb18aad5 | Python | 22,233 | 586 | # Copyright 2018 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 ... |
6aeaab74090a0cda394b7fbf1f6a8a45cf955a2fc72fbd51d43309b5e98f7f3f | Python | 22,275 | 581 | #!/usr/bin/env python3
"""PDProfiler Optimization Contest — preprocessing/postprocessing variants.
Runs PDProfiler with different pre- and post-processing on all labeled LPD
segments. No retraining — uses existing trained weights. Evaluates laterality,
frequency, and discharge timing metrics. Generates an HTML leaderb... |
b48c4bbd94402369126456e5d00d60c6b81379517cb5882676035ce7d879cc10 | Python | 22,291 | 445 | # 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 app... |
fb6deb188c2b484be9ac2e00d8296d6dda68e3d7bbd34ce212b8204fc37b834d | Python | 22,308 | 510 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/cohere/modular_cohere.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
... |
d3f887ff14e15d9c7842b36e3d25436ed4dbfbb7f1e3a59d2ed1cc456a62de3b | Python | 22,314 | 683 | """Dataset utilities."""
from __future__ import absolute_import
import errno
import hashlib
import os
import pickle
import sys
import warnings
import networkx.algorithms as A
import numpy as np
import requests
from tqdm.auto import tqdm
from .. import backend as F
from .graph_serialize import load_graphs, load_labe... |
d06015b0a529a2a8556378284292183d8dec21e3de9a57c23f8f228388bffe20 | Python | 22,321 | 641 | # -*- coding: utf-8 -*-
"""
Functionality for transforming files between spaces
"""
import os
from pathlib import Path
import nibabel as nib
from nilearn import image as nimage
import numpy as np
from scipy.interpolate import interpn
from neuromaps.datasets import (ALIAS, DENSITIES, fetch_atlas,
... |
4383aad8b47d4be63bb36cbb78c44ce82cea61c54df4c0e033063ad18b76a692 | Python | 22,357 | 662 | import argparse
import math
import os
import random
import sys
import time
import dgl
import numpy as np
import torch
import torch.nn.functional as F
from dgl.dataloading import DataLoader, Sampler
from dgl.nn import GraphConv, SortPooling
from dgl.sampling import global_uniform_negative_sampling
from ogb.linkproppre... |
4c7e950f4c2ecf333f7ed7be94b8c4c1172d077bbd1f81fa3b4404db2aa67343 | Python | 22,366 | 531 | import os
from sched import scheduler
import shutil
import sys
import random
import numpy as np
import torch
from torch import nn
from torch.optim.lr_scheduler import ReduceLROnPlateau
from e3nn.o3 import Irreps, Irrep
from .from_nequip.tp_utils import tp_path_exists
from .tr_modules import TRIrreps
def set_random... |
05229945d63763431491142ef341dea0f117d5ec33fdbfe025bd299f1e9503a8 | Python | 22,387 | 703 | ### This script could run a MLP-based deep neural network model comparing SEF, Morgan Fingerprint and SHED descriptors
# import the necessary packages
import numpy as np
import pandas as pd
from KiNet_mlp import KiNet_mlp
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.optimizers import SGD
from... |
c7045eea8cbc50a3c58ca23fafeb3caae4b55dace26fb6b05ef377140d8c40aa | Python | 22,389 | 547 | # Copyright 2025 MiniMaxAI and HuggingFace Inc. teams. 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 req... |
790cfbad9e84637602be42741425f88b260e72e26464a6d624c7acd8a5d31496 | Python | 22,424 | 628 | """
Generate laterality annotation viewer for LPD cases.
For each LPD patient:
1. Estimate frequency using pre-trained Ridge model
2. Auto-detect laterality using variance-explained per hemisphere
3. Generate clean EEG image
4. Build HTML viewer sorted by confidence (ambiguous first)
Must run with: conda run -n foe p... |
ecb4e4e529f650bee63b04d108109906f59175a3b466c6ca97badad8440e4b0b | Python | 22,445 | 472 | # Copyright 2018 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 ... |
b42bfaab97f7d257069ad1e5e4499bec4964e99d481af9cc7058e88cd63b9c5b | Python | 22,462 | 488 | import time
import webbrowser
from collections import namedtuple
from pathlib import Path
from typing import Tuple, List, Optional, Set, Union, NamedTuple
import numpy as np
from PyQt5.QtWidgets import QApplication
from napari.utils.colormaps import low_discrepancy_image
from numpy import ndarray
from seg2link.seg2dl... |
17f45608362ae91e571ea613613ea623a7800323cdaebb654fc2f19d63b0d9f5 | Python | 22,468 | 626 | """Provide concrete implementation for feature storage via SQLite."""
# Authors: Federico Raimondo <f.raimondo@fz-juelich.de>
# Synchon Mandal <s.mandal@fz-juelich.de>
# License: AGPL
import json
from enum import StrEnum
from typing import TYPE_CHECKING, Any, Optional
import numpy as np
import pandas as pd
... |
bfa69b1acbd011bd2d9b2dfeee7ca6e73ea77b6f0289e3e8f9a080ff51cc0330 | Python | 22,475 | 562 | #!/usr/bin/env python
## Program: VMTK
## Module: $RCSfile: vmtkimageinitialization.py,v $
## Language: Python
## Date: $Date: 2006/05/31 10:51:21 $
## Version: $Revision: 1.19 $
## Copyright (c) Luca Antiga, David Steinman. All rights reserved.
## See LICENSE file for details.
## This software... |
b6802fbeb734119238d8634ad430ab094f4fde30740801d32e1033ffa8846854 | Python | 22,483 | 644 | """
Module for Approximate Bayesian Computations
"""
from abc import ABCMeta, abstractmethod # Abstract Base Classes
import multiprocessing as mp
import numpy as np
from scipy import stats
from numpy.lib.recfunctions import stack_arrays
from numpy.testing import assert_almost_equal
import time
class ABCProcess(mp.Pro... |
79c401913edebd9a192da3da40d7666b96e44750d62eae77f0d777982aa9f88c | Python | 22,498 | 566 | #!/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... |
15c8c0e9f0728f045f6f61775ee99f3ff7898935e98b1ba41f79d74c29c74627 | Python | 22,524 | 635 | """
Core OpenBCI object for handling connections and samples from the board.
EXAMPLE USE:
def handle_sample(sample):
print(sample.channel_data)
board = OpenBCIBoard()
board.print_register_settings()
board.start_streaming(handle_sample)
NOTE: If daisy modules is enabled, the callback will occur every two samples, ... |
6589cb0cfbb97874271cfe8721d745483e4d914718a674ee5339af44d0347d56 | Python | 22,528 | 508 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/hyperclovax/modular_hyperclovax.py.
# Do NOT edit this file manually as any edits will be overwritten by the gene... |
0857d1b6ad9d4b937e3bfa81295f9b8d615c31fff4a8922ab99c0eddc80cc0ab | Python | 22,531 | 518 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/hunyuan_v1_dense/modular_hunyuan_v1_dense.py.
# Do NOT edit this file manually as any edits will be overwritten b... |
9516b3eb647ad937c766109bfe1792aa936326414ec88787447f43a10c52271e | Python | 22,542 | 563 | # Copyright 2025 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... |
a1ccace101c445b3c5e3e4efb8fbd4b0448d694f81d14fe6d0597e7c0bbe2ea3 | Python | 22,566 | 599 | from PyQt5.QtWidgets import QVBoxLayout, QWidget
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.animation import FuncAnimation
import matplotlib.pyplot as plt
import socket
import threading
import pickle
import os
import time
import ... |
ffc1bccd31e4c0868cbefb13428cf03f0cbcc38a40f67cda179e59ffe29e7b8f | Python | 22,566 | 474 | # 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... |
58c9e0ed97fc4217f77a7f43e4acab25e425d0af60a568e0a5d77b19870cdb5f | Python | 22,624 | 530 | #!/usr/bin/env python3
"""ML-based LRDA vs GRDA classification — unconstrained approaches.
Balanced data (subsample GRDA to match LRDA), 10-fold stratified CV.
Tries multiple feature sets × classifiers, reports AUC for each.
"""
import sys
import json
import time
import warnings
import numpy as np
import pandas as pd
... |
e849ed4dfeeda6f2d6e1ea532df58baf562673cff7bca55b5bd11cab30d7bf9a | Python | 22,626 | 578 | # Copyright 2025 Baidu Inc 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 by applicable ... |
0222a23c1e07c6068b52e71e2751cdc19f2ed07f4502b092ba15565a9a829f6b | Python | 22,637 | 653 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 19 10:27:05 2019
@author: Kai Sandbrink
"""
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.model_selection import train_test_split
from scipy.optimize import minimize
import numpy as np
import pandas as pd
import pickle, os,... |
2798be6413d5bb99de695ab90ae48634a57b764ae09363366124af82e562c98c | Python | 22,662 | 522 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/exaone4/modular_exaone4.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation o... |
6714e45b131695f18720b45425e44d53e2d40fa57e481ae0debb0b829dca67b8 | Python | 22,677 | 701 | # Written by Alexandria D'Souza and Birgit Schilling
#
# All publications that utilize this software MS1Probe
# should provide acknowledgement to the Buck Institute for Research on Aging,
# the Gibson Laboratory/Chemistry Core
# and refer to the following website link
# http://www.gibsonproteomics.org/resources/MS1Prob... |
3a781497f7d1e648fbec2cfbcee3c252b4628a981f9dd0afdd34c8122ceb3e56 | Python | 22,689 | 587 | #!/usr/bin/env python3
# -----------------------------------------------------------------------------
# Copyright (c) Yongning Lei 2025
# Apache-2.0 license
# -----------------------------------------------------------------------------
"""
run_tedana.py
Run tedana multi-echo ICA on fMRIprep per-echo T1w nifti output... |
52764f5cd92019629c4ae173487e213358970f7aa6166eac920b15aa73891d02 | Python | 22,741 | 778 | # import packages
import os
from pathlib import Path
import nibabel as nib
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from matplotlib.colors import LinearSegmentedColormap
from SUITPy import flatmap
from SUITPy import atlas as catlas
from n... |
76f03ac909f50c8f3119b117cca960b5428cc4d3adb5cf80055d42ad381e1341 | Python | 22,787 | 686 | """
This script, `hetero_rgcn.py`, trains and tests a Relational Graph
Convolutional Network (R-GCN) model for node classification on the
Open Graph Benchmark (OGB) dataset "ogbn-mag". For more details on
"ogbn-mag", please refer to the OGB website:
(https://ogb.stanford.edu/docs/linkprop/)
Paper [Modeling Relational ... |
e5456d3a4ccec925f18914414177491d125669db028c605d5e3447a20d6e848d | Python | 22,787 | 646 | """MovieLens dataset"""
import os
import numpy as np
import pandas as pd
from torch import LongTensor, Tensor
from ..base import dgl_warning
from ..convert import heterograph
from .dgl_dataset import DGLDataset
from .utils import (
_get_dgl_url,
download,
extract_archive,
load_graphs,
load_info,... |
a49c92d428061a648e63f0da09bc4a08c0fa4ef2681ce27b8e83da72edb4d3cf | Python | 22,795 | 636 | """
BIPD vs GPD Reviewer — Review model predictions and relabel cases.
Shows all GPD + BIPD cases sorted by model BIPD probability (highest first).
For each case, displays:
- EEG with channels grouped: L-lateral, L-parasag, midline, R-parasag, R-lateral
- Left hemisphere discharge times (red vertical lines)
- Ri... |
183772fd4134a6735ca787e40576ef89ecf10a7760395766248605e3fac6d7bb | Python | 22,870 | 652 | """
Train CNN+Attention v3: Use v1 CURATED dataset (815 patients, 9310 channels)
with UPDATED expert frequency labels from segment_labels.csv.
Changes from v1:
- Loads channel_dataset.npz from pd_channel_cache_backup_v1/ (curated 815 patients)
- Updates frequency labels from current segment_labels.csv expert_freq_... |
bb2b2079e93bf65a01d40341982dc708c9217c32689db186b766fdc5583735bf | Python | 22,881 | 572 | """
Tool Name: torch2d_point_heatmap_learn
======================================
Train a Gaussian-heatmap Carina localizer on the legacy trachea-centered crop.
"""
from __future__ import annotations
import asyncio
import csv
import json
import os
import random
import matplotlib
matplotlib.use("Agg")
import matplot... |
53b7fff985d6bf3b4898397ccda70257cc8624175ffd4cea9f77c3485aff0aab | Python | 22,887 | 593 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 15 14:16:41 2020
This module provides a set of functions to create non-randomized
alternating sequences of events and add realisitic components
similar to the data obtained from a fMRI experiment. It also
provides tools to calcuate optimality ... |
2b64931fa89739c2c14e7ea4baa565b63ac233d43638c05b33284090fb3db2f9 | Python | 22,888 | 669 | import argparse
import datetime
import os
import sys
import time
import dgl
import torch
from dgl.data.utils import load_graphs, save_graphs
from dgl.dataloading import GraphDataLoader
from ogb.linkproppred import DglLinkPropPredDataset, Evaluator
from torch.nn import BCEWithLogitsLoss
from torch.utils.data import Dat... |
4982ac0bf8ce42efa44ffb35f87d50c3f9e3b1fba105e96541a1054b8ebaba0e | Python | 22,898 | 629 | import sys
import os
sys.path.append("../utils")
import time
from datetime import timedelta
from contextlib import redirect_stdout
from datetime import datetime
from sklearn.svm import SVC
from WDKernel import wdkernel_gram_matrix, get_K_value, parallel_wdkernel_gram_matrix
from sklearn.metrics import roc_curve, auc, r... |
2a56ffe9a3cec3ea228c936cc845f34ff1e979c5a2c12053b2742bcd22b9f023 | Python | 22,913 | 629 | """Provide JuniferNiftiSpheresMasker class."""
# Authors: Synchon Mandal <s.mandal@fz-juelich.de>
# License: AGPL
from collections.abc import Callable
from typing import TYPE_CHECKING, Union
import numpy as np
from nilearn import image, masking
from nilearn._utils.niimg import img_data_dtype, safe_get_data
from nile... |
b6fdb42338357530e73c54943679c7e13a65fc246a7cf0c49aa36a5d5c433949 | Python | 22,915 | 548 | #!/usr/bin/env python3
"""
Render publication-quality EEG characterization figures from JSON data.
Self-contained: only requires matplotlib, numpy, scipy, json.
Reads figure_*_examples_data.json and produces figure_*_examples.png at 300 DPI.
"""
import json
import os
import sys
import numpy as np
import matplotlib
ma... |
0196e6807de8e6f87b9f3bf19961fd96c2855a68c2a9a59315fc61f79dcdabcd | Python | 22,926 | 633 | """Utility functions for MIL."""
import os
import inspect
import slideflow as sf
import numpy as np
from os.path import exists, join, isdir
from typing import Optional, Tuple, Union, Dict, List, Any, TYPE_CHECKING
from slideflow import errors, log
from slideflow.util import path_to_name
from slideflow.model.torch_uti... |
1f6589fb939ae4d152d51a58c96dc8ae7ac5b660a911d40b9ab36e2e0ef37cd0 | Python | 22,929 | 729 | """Functions for plotting Tet and Tria Meshes with overlays.
For visualizing results in a juypter notebook use this::
.. code-block:: python
import plotly
plotly.offline.init_notebook_mode(connected=True)
...
"""
import re
from bisect import bisect
import numpy as np
import plotly
impor... |
54e0cb2c155ed6dcd1365ef4c3226c3b40a32fd923d2a6e4a045f6d516c4c089 | Python | 22,933 | 568 | #!/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... |
e153c9b4e9b9ac9c94cb1510c22696829a5e1134f9d0a199304286e41d834f2a | Python | 22,935 | 575 | #!/usr/bin/env python3
"""Train a whole-image Carina heatmap model with a trachea-probability channel."""
from __future__ import annotations
import argparse
import csv
import json
import re
import sys
import traceback
from pathlib import Path
import matplotlib
import numpy as np
import SimpleITK as sitk
import torch... |
9e43f64e047a62707d7b1a1bc1362a1c2c179c1f4e537ac913e0f6e08e3b9f28 | Python | 22,945 | 571 | #!/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... |
1cd743c526c2693433815576f10917296edbd0655d7beea496d6f3d39562a350 | Python | 22,957 | 568 | """Utility functions for sampling."""
from collections import defaultdict
from typing import Dict, List, Optional, Tuple, Union
import torch
from ..base import CSCFormatBase, etype_str_to_tuple, expand_indptr
def unique_and_compact(
nodes: Union[
List[torch.Tensor],
Dict[str, List[torch.Tensor]... |
f7daca8a6a0209511f837a3ecd10692ec6b033ceb25c29e35aa58d5a467bd1d1 | Python | 22,978 | 687 | """
Copyright 2020 Google LLC. This software is provided as-is, without warranty or
representation for any use or purpose. Your use of it is subject to your
agreement with Google.
"""
import pandas as pd
import string
from collections import Counter
from .AutoViz_Utils import save_image_data
pd.set_optio... |
1fcadc5991c9f44a9184e8149b1fd0c2338f160568b788a283b35d303a7a16bd | Python | 22,988 | 608 | import numpy as np
import numba # with NUMBA to make it faster !!!
import argparse
import matplotlib.pyplot as plt
### ================================================
### ========== Reformat parameters ================
### ======= for single cell simulation =============
### =================================... |
bd67622123e365b7dc801380d3a5fa856050c4b226a46ab257e4715a0c310fd7 | Python | 22,999 | 675 | #!/usr/bin/env python
from optparse import OptionParser
import copy, os, pdb, random, shutil, subprocess, time
import h5py
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import spearmanr
import seaborn as sns
from sklearn import preproc... |
83d8cf90abda66427eb00c58896d1c231f995cb4b09adc4de32f49e037f7661d | Python | 23,177 | 677 | """
Visualize frame-level discharge detections on EEG plots.
For each patient:
1. Load segment(s), run each channel through DischargeDetector
2. Peak-pick discharge times per channel
3. Generate EEG plot (morgoth style) with overlays:
- Standard black EEG traces
- RED DOTS at detected discharge times p... |
d3c5634af3bcdd5c9b899f6704acc770fb218aefed5be6c53bb8c14d9eff0291 | Python | 23,177 | 518 | """Adapted from Nematode: https://github.com/demelin/nematode """
import sys
import tensorflow as tf
# ModuleNotFoundError is new in 3.6; older versions will throw SystemError
if sys.version_info < (3, 6):
ModuleNotFoundError = SystemError
try:
from . import exception
from . import rnn_inference
from... |
377427ee6c72c985a009f60b60ff5ece6088c77774a1f51fd861cc47afa9d26c | Python | 23,201 | 449 | # Copyright 2022 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 ... |
616f4d3340b986499ce43d0823190baecfa39fb7e47ee1d31c0d54557c73a79c | Python | 23,224 | 623 | # Copyright 2022 Microsoft 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
#
# ... |
b5e2ec79864a1155ef4b6ec860d887d4a78d1d413cc7aeb3555ac62fdf93ec6b | Python | 23,224 | 540 | #!/usr/bin/env python3
"""
Render publication-quality EEG characterization figures from JSON data.
Self-contained: only requires matplotlib, numpy, scipy, json.
Reads figure_*_examples_data.json and produces figure_*_examples.png at 300 DPI.
"""
import json
import os
import sys
import numpy as np
import matplotlib
ma... |
9d3ac67224d6f19ec8fcd66498749d0d2f202b71c4bd0b23b73c1be44df880ea | Python | 23,361 | 550 | # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/gemma2/modular_gemma2.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
... |
e0997f1d02637e59a42820b0700732a71b9374bf6c83de43b7cb204040914366 | Python | 23,410 | 600 | # Copyright 2025 The HuggingFace Inc. team and Google LLC. 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... |
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