sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2625da041885e2bf38951a564569bc5b7366e4c5a697232f1762a245bfe70271 | Python | 4,485 | 121 | # coding=utf-8
# Copyright 2026 The Google Research 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 applicab... |
ab66498e7b176d67e7e57e5280e323cd21de43bc1e60866e817243ab86f184cb | Python | 4,485 | 143 | # coding=utf-8
# Copyright 2026 The Google Research 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 applicab... |
5b1ec6abf1cdffb51a1a4fd3dcf6556d22ac46d44bafa896635c6dadf6fd951e | Python | 4,486 | 71 | import argparse
import scripts.train.few_shot
from scripts.train.few_shot.train import main
parser = argparse.ArgumentParser(description='Train prototypical networks')
# data args
default_dataset = 'geometry'
default_split = 'sable-meyer'
parser.add_argument('--data.dataset', type=str, default=default_dataset, metav... |
11f600e1cd2103c5fed1676949f1bf68c41244e37d356a0cde559707ab5619a5 | Python | 4,487 | 141 | import rdkit
import rdkit.Chem as Chem
import copy
from .chemutils import get_clique_mol, tree_decomp, get_mol, get_smiles, set_atommap, enum_assemble, decode_stereo
def get_slots(smiles):
mol = Chem.MolFromSmiles(smiles)
return [(atom.GetSymbol(), atom.GetFormalCharge(), atom.GetTotalNumHs()) for atom in mol.... |
a44f078c505efff6e47fcdc814f8fe0854c355893847c8b11561f4a4493d530b | Python | 4,487 | 104 |
#
#
#from poseConfig import aliceConfig as conf
#
#import PoseUNet
#import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
#conf.batch_size = 16
#name = 'pose_unet_increasing_filter_32_bsize16'
## self = PoseUNet.PoseUNet(conf,name=name,for_training=False)
## val_dist, val_ims, val_preds, val_predlocs, val_locs, val_info... |
eb03983ee15e821888ad4c8f321ffaba8b9346f3d09f6624ab4597d200dcc9ce | Python | 4,487 | 135 | #!/usr/bin/python2
# MIT License.
# Copyright (c) 2016 William Skellenger
#
# 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, including without limitation
# the rights to... |
1a8d21a54addf9e9f260a12a87ab5c284273ecf01d86e41457bf3923c99bbd1b | Python | 4,488 | 100 | import torch
from torch.optim.optimizer import Optimizer, required
class PIDOptimizer(Optimizer):
r"""Implements stochastic gradient descent (optionally with momentum).
Nesterov momentum is based on the formula from
`On the importance of initialization and momentum in deep learning`__.
Args:
p... |
27ae68c9b86843fb189e11c2f987f62f7885dc24304f1b888da46fd8cb166f27 | Python | 4,488 | 99 | from typing import Optional, Union
from segmentation_models_pytorch.encoders import get_encoder
from segmentation_models_pytorch.base import (
SegmentationModel,
SegmentationHead,
ClassificationHead,
)
from .decoder import PSPDecoder
class PSPNet(SegmentationModel):
"""PSPNet_ is a fully convolution ... |
5bb9a00386a5545e7c03e417bf0063c6c6831d932e17ecc9df21905c43364881 | Python | 4,488 | 95 |
# Collect predictions
from glob import glob
from pathlib import Path
import sys
from unittest import skip
import pandas as pd
import numpy as np
# sample used
pipe = sys.argv[1]
#opts = '_averaged-source_Schaefer400x17_WM+CSF+GS_hcpaging_695_zscored-beh_'
opts = '_averaged-source_Schaefer400x17_WM+CSF+GS_hcpya_771_... |
e51628601b72f26e60b613cd28759a645b5f2b71660095aadaa823145b0c48ee | Python | 4,488 | 159 | # Node-group asset "Color OKLab Offset" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
import math
from typing imp... |
d91f5d55db9b9cc222da231ecf8c234060ae9aab6411b47f0c5465472fd2b382 | Python | 4,489 | 124 | """Community detection algorithms.
Supported models:
- "infomap": Infomap
- "flatsbm": Degree-corrected SBM
- "bp": Belief propagation
"""
import sys
import numpy as np
import pandas as pd
from scipy import sparse
from scipy.sparse.csgraph import connected_components
# ===============================================... |
3c9a16429d4101b84cc54f12513b4a36a590dda7e6031fde77ba8b45f65f6acf | Python | 4,490 | 147 | """ The layer thickness click command """
from collections import defaultdict
import configparser
import glob
import pathlib
import os
import click
import numpy as np
import pandas as pd
from layer_recognition.utilities import get_s1hl_corners
from layer_recognition.utilities import (
get_animal_by_image_id,
... |
a4700021893acf453ec85595043364ea2639ff3eced2364f5220590e9159556c | Python | 4,490 | 122 | """
Generates plot with custom accuracy metric.
The custom accuracy is plotted for each machine learning model trained: 'spikes30', 'signal30', and 'signal100',
as a function of training epoch. For more than one training instance for each model, the average custom accuracy is
plotted as a solid line, accompanied by a ... |
4c6a72dcecf6f7002336e71b1c0c2eb4a05d2029100b273dcc2f3fcd4dec479d | Python | 4,491 | 132 | # https://github.com/AdeelH/pytorch-multi-class-focal-loss/blob/master/focal_loss.py
from typing import Optional, Sequence
import torch
from torch import Tensor
from torch import nn
from torch.nn import functional as F
class FocalLoss(nn.Module):
""" Focal Loss, as described in https://arxiv.org/abs/1708.02002.... |
be85649c4d35402fe4a1aa8593011a3c0baf6a1f4b68e1b020e1e933473d852d | Python | 4,491 | 109 | """Verify graph registration against bundled real data, not generated layouts."""
import csv
import gzip
import json
import re
import shutil
import struct
from pathlib import Path
import numpy as np
import pytest
from liquidaion_fly.anatomy import Anatomy, decode_skeleton, ensure_anatomy
ROOT = Path(__file__).resolv... |
4e5ad6c4bce02a3518e11b5f71b135f7cecec314b2a2332652f546bab079a711 | Python | 4,492 | 158 | #!/usr/bin/env python
import time
import boto3
from lambda_function import get_user_data
client = boto3.client('autoscaling')
response = client.delete_auto_scaling_group(
AutoScalingGroupName='chunkflow-inference',
ForceDelete=True
)
print('response of deleting auto scaling group: %s' % response)
time.sleep(... |
ee4703d13da7de0daf2ae08dea8c4dddb4755a317ea7ce54f656a7466bda78f1 | Python | 4,492 | 127 | # coding=utf-8
# Copyright 2026 The Google Research 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 applicab... |
01cdc32d24c3455c9a10cb07a634cae0429e67a9004f52e32b904701e6a505fb | Python | 4,493 | 100 | from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
from anndata import AnnData
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from pertpy._types import _sklearn_random_state, cast_frame, cast_matrix
from... |
191c28bde483384b3f64281817f9e65b6445bafd8ee38a5dc70e00c088b3f612 | Python | 4,496 | 145 | # coding=utf-8
# Copyright 2026 The Google Research 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 applicab... |
4f041d9d8f6d7150d5024302f307ae05d0e3235f68be958bcc0873c9625c43cd | Python | 4,496 | 193 | import random
import pandas as pd
import numpy as np
import torch
import dgl
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import DataStructs
from tdc.single_pred import ADME
from tdc.single_pred import HTS
from tdc.single_pred import Tox
ATOM_VOCAB = [
'C', 'N', 'O', 'S', 'F',
'H', 'Si',... |
005d8cdc42fe8754e2986db24aed921d50e5a77f1c0352e51ffd63f9fe6788ca | Python | 4,497 | 163 | # This code calculates the particle level gr for a 3d system, next the particle level caging potential and coarse-grains it over up-to the 2nd nearest neighbours.
# Inputs : linked trajectories of the particles
# Outputs: local gr, Caging potential of each particle at different time frame, Coarse-grained caging pote... |
664f2388917da56820832e930a94e725065f98cd042757eedbee769ed8f76545 | Python | 4,497 | 121 | from copy import deepcopy
import numpy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from albumentations import HorizontalFlip, VerticalFlip, Rotate, Compose, ShiftScaleRotate, HueSaturationValue, \
RandomBrightnessContrast, ImageCompression, GaussianBlur
class AugmentedEncoding(object):... |
7b4baed28e6bc2ee5988136bae3420785ff4e63062a60b790edd7043b44350fc | Python | 4,498 | 112 | """Test Robust Reference."""
# Authors: The PyPREP developers
# SPDX-License-Identifier: MITs
from unittest import mock
import mne
import numpy as np
import pytest
from pyprep.reference import Reference
@pytest.mark.usefixtures("raw", "montage")
def test_basic_input(raw, montage):
"""Test Reference output dat... |
4dbac0fa58d9f941a886077584402ed4637788615302f21fa3216d1b52361702 | Python | 4,499 | 148 | """
Based on previously selected data, containing the location of a 5 seconds signal window,
and the optimal max interval parameters, generates a trainset.h5 file with only the
5 seconds slice of the signal (as a float array), and corresponding spikes and bursts
(binary array)
Input
----------
- "data/trainset... |
e442bf69b3d9ae2f5e2280ff3721fbaf7c77231169ea2417b5501768845a41f4 | Python | 4,500 | 143 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Atchuth Naveen
Code developed at UC Irvine.
command line executable to convert a directory of tif images
to zarr format that can be visualized on neuroglancer
call as: python tif_to_ome.py img_dir {--out_dir} {--channel channel}
(append optional arguments to... |
f645caf5b28be67430364b0ab060b5434c618c2ebfbfd804da41a3b69d642d8a | Python | 4,500 | 106 | from os import SEEK_END
from neuron import h as nrn, vec2numpy
from neuron import gui
from matplotlib import pyplot as plt
import numpy
# Neuron units -----------------------------------------------------------
# Category Variable Units
# Time t [ms]
# Voltage ... |
149183f32e0aba5f1b7297ef5c31070ecb1e75a50f5e4f719333aa5e3e2b6e5f | Python | 4,501 | 120 | """Inference: an LFA strip image -> positive / negative.
image --[segmentation: unet | MnUV3 @ 256x256]--> band mask
mask --[classifier @ 256x256]-------------------> P(positive)
python -m src.predict --image strip.jpg \
--seg-weights weights/mnuv3_seg_train_no_test_no.pth \
--cls-weights... |
809a44af06ec66b47fe8dd038cfb22c95f7d3baf495a1318f5bf0589a52de555 | Python | 4,502 | 136 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
def set_requires_grad(nets, requires_grad=False):
"""Set requires_grad for all the networks.
Args:
nets (nn.Module | list[nn.Module]): A list of networks or a single
network.
requires_grad (bool): Whet... |
beea3a68f00cafa79ad7b3212b9cc98fd3d98a36c55178d5eae427ea978ea691 | Python | 4,503 | 179 | import numpy as np
from moabb.datasets import (
Zhou2016,
BNCI2014004,
BNCI2014001,
MunichMI,
Weibo2014,
Cho2017,
)
from burst_space import BurstSpace
from burst_features import TfFeatures
from preprocess import (
load_sub,
apply_preprocessing,
Dreyer2023,
)
from help_funcs import ... |
93432f58daef0e18d947ee85402a6b0f438e09735d9f162e775b9d008236e17f | Python | 4,504 | 107 | #!/usr/bin/env python
from scipy.sparse import csr_matrix
import time
from multiprocessing import Pool
import itertools
from itertools import chain
import scipy.sparse
from SCanSNP.classifyUtils import *
from SCanSNP.VCFUtils import *
from SCanSNP.DBLsutils import *
from SCanSNP.ComputeLLK import *
from SCanSNP.GenUt... |
a39d6d3f686956da213f7de0498c809063692df60306ac7162c69dca24598b51 | Python | 4,504 | 170 | from contextlib import suppress
from io import TextIOWrapper
from . import abc
class SpecLoaderAdapter:
"""
Adapt a package spec to adapt the underlying loader.
"""
def __init__(self, spec, adapter=lambda spec: spec.loader):
self.spec = spec
self.loader = adapter(spec)
def __get... |
fe6461a70e766b81efcafa7d706c9c2550f43aba42006109f054e460f11f0ce2 | Python | 4,504 | 137 | # Node-group asset "Sample Mix Float" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECK... |
93aa2b87d2067355b184feb309a6931c0275d3b9bdebac1eabc95df3cfebb83f | Python | 4,506 | 120 | ###############################################################################
# This code generates the Supplementary Figure 6 of the paper "Longitudinal #
# single-subject neuroimaging study reveals effects of daily environmental, #
# physiological and lifestyle factors on functional brain connectivity" #
... |
b3d01146c21c8747deaace860b38780cac5798780583e009d1a69e05e1ce8900 | Python | 4,506 | 129 | # coding=utf-8
# Copyright 2026 The Google Research 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 applicab... |
1dcdd9ac20d27105843928267c8d2867ffae15bcab6ca05439e151693f983892 | Python | 4,509 | 103 | ##############################################################################################
# Testing if the evoked response across subjects is significant
##############################################################################################
import matplotlib.pyplot as plt
import mne
import numpy as np
imp... |
eb6ef3b49b3dcce2fedfc1c9ee45c17ab47e813f0a05f602f14cc4c0c243618a | Python | 4,509 | 154 | import re
from functools import lru_cache
from typing import Callable, List
from ._cell_widths import CELL_WIDTHS
# Regex to match sequence of the most common character ranges
_is_single_cell_widths = re.compile("^[\u0020-\u006f\u00a0\u02ff\u0370-\u0482]*$").match
@lru_cache(4096)
def cached_cell_len(text: str) -> ... |
9519865a202a40c33d45a10c41c4cf0313e62b7d533d8c4a75b2ad54ba08c194 | Python | 4,510 | 121 | # Copyright (c) 2022, Ko Sugawara
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and t... |
afe500163a72e0be179c42407d82a1ee543b9b075ac6bb1effc1c2cb44b68a96 | Python | 4,511 | 150 | import numpy as np
from pyspi.data import Data
import warnings, copy
"""
Some parsing functions for decorating so that we can either input the time series directly or use the data structure
"""
def parse_univariate(function):
def parsed_function(self,data,i=None,inplace=True):
if not isinstance(data,Data):... |
efa84c120bb6906cc61ddb7c97a6ef0c99742dfc37e5602e935713f5d666a57e | Python | 4,511 | 140 | #!/usr/bin/env python
from __future__ import print_function
import colorsys
import glob
import os
import pybullet as p
import random
import sys
import numpy as np
from lxml import etree
from pybullet_tools.pr2_utils import DRAKE_PR2_URDF, set_group_conf
from pybullet_tools.utils import STATIC_MASS, CLIENT, connect,... |
35e198cbe6ee9104fef149dc4f0065e4ef135095e9cf2ae225d4bfa619dae6c1 | Python | 4,512 | 117 | """
RELEASE_ID_SPACE_CHECK.py - written 2026-09-17.
WHY THIS FILE EXISTS.
The published record (Zenodo 10.5281/zenodo.22795740, v1.0) ships one table whose first
column is documented as:
"root_id | int64 | FlyWire segment identifier, materialisation 783.
Use this as the join key."
and the README's wo... |
a89f5d8230da18dcd5e468ab922bc3c8deeb0ad9c12e41a5d4436d7b7a86c68f | Python | 4,512 | 114 | import torch
from denoising_diffusion_pytorch import Unet, GaussianDiffusion
from torch import nn
from einops import rearrange, reduce, repeat
import torch.nn.functional as F
from collections import namedtuple
ModelPrediction = namedtuple('ModelPrediction', ['pred_noise', 'pred_x_start'])
def exists(x):
return x ... |
72354e1fa3f21d1d139a5881595b3989c34e95cf20049d409e15c898fb70afdb | Python | 4,513 | 148 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 13 13:23:17 2024
@author: Dustin Garner
"""
import datetime
import numpy as np
import pandas as pd
from fafbseg import flywire
import flycode.utils as utils
import flycode.reduction as reduction
import flycode.readfiles as readfiles
client = fly... |
837c1c845cea109cfd28e2b35281f1c4298d5bc6fa9b2ab9d7ed0142213fd78d | Python | 4,513 | 116 | """Multi-monitor compare-window tests for the CCF Boundary Editor."""
import numpy as np
import pytest
@pytest.fixture
def qapp():
from PySide6.QtWidgets import QApplication
app = QApplication.instance() or QApplication([])
yield app
def _two_region_volumes(tmp_path, shape=(20, 20, 30)):... |
b79081fb03f3ff57f8b988f06ce703d5ef1d456b77fac994ab08fffd225e5b4d | Python | 4,513 | 139 | """Round-trip tests for the --reg_effort + advanced ANTs override CLI flags.
Headless: only argparse + the _build_ants_overrides helper are exercised.
No registration runs.
"""
import pytest
from regtools.registration.cli import (
build_parser,
_build_ants_overrides,
_resolve_annotation_overri... |
c66892822acf41d8218270c04eb6aad967173d297943192314a77792f5b0c636 | Python | 4,513 | 99 | import os
import pandas as pd
import numpy as np
import unittest
import sys
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/../../src")
from hornet.input import prepare_inputs
class TestPrepareInputs(unittest.TestCase):
def setUp(self):
self.path = '.data_test'
# Create temporary ... |
ff6ccf28940fa644fd8fe6b1386de5095be2f346d3e040942c2286b3f8f74b34 | Python | 4,516 | 155 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
import ... |
118be5723a1e9b51d8a1d0064dce2a9e06b92bcc8710d3487b524c6a540da0f1 | Python | 4,518 | 118 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# 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, including without limitation the rights
# to use, copy, modify, merge,... |
4223c6f3f4f181e17fdc5bb5ba73dc413b55b02f37b5c568066b12a335c92fb2 | Python | 4,518 | 142 | """Tests for the Transform + Metric dropdown split in the registration GUI."""
import os
import sys
PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if PROJECT_ROOT not in sys.path:
sys.path.insert(0, PROJECT_ROOT)
os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
impor... |
f54e7d588ee2a5d090d78a0bdb8e74e725ef40e30682c8d5022a8cabc1d672c7 | Python | 4,518 | 140 | """Perform and save time frequency analysis of given files."""
from __future__ import annotations
from pathlib import Path
from typing import Literal
import pandas as pd
import pte_decode
from matplotlib import pyplot as plt
from numpy import True_
import motor_intention.plotting_settings
import motor_intention.proj... |
2b7fb4c9519f34b2455aa0c47c45e998adc5565cba9010236d5472febecac1da | Python | 4,519 | 143 | """Utility functions for molecular dynamics."""
from __future__ import annotations
from typing import TYPE_CHECKING, Literal
from ase.md.andersen import Andersen
from ase.md.bussi import Bussi
from ase.md.langevin import Langevin
from ase.md.nose_hoover_chain import MTKNPT, IsotropicMTKNPT, NoseHooverChainNVT
from a... |
7175c999ffa601eaddcae5f473c80858b3d8c8a8a9f94b7691657c547f17d264 | Python | 4,520 | 99 | ''' This file generates the first-level GLM analysis for the supplementary
analysis for nback tasks regressing the threshold values'''
import os, sys
import numpy as np
import pandas as pd
from glob import glob
import nibabel as nib
from nilearn.glm.first_level.design_matrix import make_first_level_design_matrix... |
b8fa7b4e65b9325bda578103d2c77fd7cd5320b2d5c141938b514c0e03ae7fb9 | Python | 4,520 | 84 | import numpy as np
import matplotlib.pyplot as plt;
import matplotlib.patches as patches;
import matplotlib.colors as colors
import seaborn as sns
def addNetColors(fcMatrix):
""" A function to generate a heatmap figure with CAB-NP colors added along axes of FC matrix; python 3
INPUT
fcMatrix... |
27047b3efd43053b1739e925c0939e58b23a2cc81bf07c68b0d3eb1c1a6a964d | Python | 4,521 | 154 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import font_manager
import numpy as np
from sklearn.preprocessing import MinMaxScaler, StandardScaler
CB91_Blue = '#1C2385'
CB9... |
f442a7a8f4fb8da2474d8ed524062d557b7f059b505ddfbe3ef36a667cd20ccd | Python | 4,521 | 124 | import os
import sys
sys.path.append(os.path.abspath(os.path.dirname(__file__)))
import torch
import argparse
torch.serialization.add_safe_globals([argparse.Namespace])
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.callbacks import ModelCheckpoint, ModelSummary, RichProgressBar
from pyto... |
fcfaef4908b65c23909f80ce12421fc0e158654c0fd6f41ba2f084d5e4474ca6 | Python | 4,521 | 101 | import numpy as np
from skimage.filters import threshold_otsu
def ispadding(x):
# identify the padding in array
return np.abs(x - 15213) < 1e-6
def get_otsu_regions(out, labels, args_params = None):
""" Identify DeepSIF source region using otsu_threshould, run on CPU
:param out: np.arrry; the output... |
3f08ca5d8b0fa1276fcaa7febb8af2be180c67918a15f65ed1a475613ebe8bc0 | Python | 4,522 | 109 | # coding=utf-8
# Copyright 2026 The Google Research 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 applicab... |
f1cc320cfd8fbec25ccadbacc072b707cf0140de85678e81492b487a0617f4fb | Python | 4,522 | 142 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
create remove motion-corrupted files from concatenated echo files
python remove_corrupted_dynamics.py [file location] [sequence num] [number of echos] [dyn to exclude]
output: concat files e1, e2, e3 in folder 'files for recon' with the corrupted
motion dynamics rem... |
76aad742a5e0706d130b2a535ee680a6aaa9baade6d91b9d9221019e18b6f848 | Python | 4,523 | 122 | # coding=utf-8
# Copyright 2026 The Google Research 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 applicab... |
e24fdc1acc0c3fdbdd5eb766f690c261cfa4cdc796113ef8c525765a598bd5ef | Python | 4,523 | 141 | # Node-group asset "Atoms to Curves" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKI... |
eb38566497c752ee4d23e4a94f7d022f235635b05dff642f145251e194bdc1fd | Python | 4,523 | 146 | """Plot UPDRS Med. OFF vs ON"""
from __future__ import annotations
import csv
from enum import Enum
from pathlib import Path
from typing import Annotated
import numpy as np
import pandas as pd
import pte_decode
import scipy.stats
from pytask import Product
import motor_intention.plotting_settings
import motor_intent... |
0a3c10eebd5e0b4718e3d18b1f93b5a8a9b715ca45128b06724785ac76b93ead | Python | 4,526 | 123 | # coding=utf-8
# Copyright 2026 The Google Research 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 applicab... |
16203b57c81483d8bdf8b5391e4a7f0da86c97dc5760b83ad1ea9e8fef9922b9 | Python | 4,526 | 132 | import numpy as np
from scipy.stats import ttest_ind
try:
from numba import njit
use_numba = True
except ImportError:
use_numba = False
def _binarize(W):
"""
Binarizes a matrix
Parameters
----------
W : (N, N) array_like
Matrix to be binarized
Returns
-------
binari... |
e66fa3f683f3e74018bec14f700c6381fdf8ca968057681dd21e359561c2805d | Python | 4,526 | 107 | from math import pi as PI
from control_tools.ik.pr2_ik import arm_difference_fn
import numpy as np
# NOTE(caelan): these are LTAMP-specific utility functions (separate from utils.py)
BASE_FRAME = 'base_link'
PR2_JOINT_SAFETY_LIMITS = {
# Taken from /ltamp-pr2/models/pr2_description/pr2.urdf
# safety_contro... |
a5a5ed9e39e105894b1baa6797d0f76fe1d149bf19dedb7a75ab9f496db37557 | Python | 4,527 | 112 | # Librairies
import os
import sys
code_path = os.path.split(os.path.split(os.getcwd())[0])[0]
sys.path.append(code_path)
import numpy as np
import random
from sklearn.metrics import balanced_accuracy_score, accuracy_score
from joblib import dump
import time
import argparse
from setting import *
from utils import *
from... |
c6d47fb5427aa49c7f6eb808e7f110b704a1b56b017eafb5e7475d6ccd8cfad0 | Python | 4,527 | 130 | from itertools import chain
from unittest import TestCase
from elasticai.creator_plugins.grouped_filter.src.index_generators import (
generate_deinterleaved_indices,
sliding_window,
)
class InterleaveChannelsAfterGroupwiseOrdering(TestCase):
def select(self, filtered, indices):
filtered = tuple(f... |
90d85f511b86339332e954ad4a0e3bdef404cf30d8d5a4302123be9880b3a52e | Python | 4,528 | 136 | """Tests for E3FP fingerprints.
Author: Seth Axen
E-mail: seth.axen@gmail.com
"""
import pytest
class TestFingerprintIO:
def test_fprint_from_indices(self):
from e3fp.fingerprint.fprint import (
Fingerprint,
CountFingerprint,
FloatFingerprint,
)
for fp_... |
a57b9a0aad57b4c268fab501cd19b11efcbe2d7baf995c96c601631d85c9a945 | Python | 4,528 | 145 | # %%
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
import math
import torch.optim as optim
import torch
import torch.nn as nn
from torch_geometric.data import DataLoader
import argparse
from metrics import get_cindex
from dataset import *
from model import MGraphDTA
from utils import *
from log.train_logger impor... |
ae530a5cb378e50d5d5d185d363e90ad745586cfebde1d310f6c51b8d7ed181c | Python | 4,528 | 141 | import torch
from torch import nn
from elasticai.creator.nn.integer.design_creator_module import DesignCreatorModule
from elasticai.creator.nn.integer.MPQ.ffn.design import FFN as FFNDesign
from elasticai.creator.nn.integer.MPQ.linear import Linear
from elasticai.creator.nn.integer.MPQ.relu import ReLU
from elasticai.... |
b3a094bd1eb6ef9cd58704c5eb973b368b3083158f63359e0a843e4a5b2837ee | Python | 4,529 | 133 | import sys
import argparse
from pathlib import Path
import dolfin as d
import numpy as np
from smart import mesh_tools
from mech_parser_args import (
add_preprocess_mech_mesh_arguments,
Shape,
shape2symfraction,
)
# Define expressions to be used for geometry definitions.
outerExpr15 = "(1 - z**4/(1000+... |
db36d06207bd6cfbaa8dcb38d517a8158ba7946b26c42ec827de169b17d7cc8e | Python | 4,529 | 99 | # C. Cocuzza, 2021
# A function to take a vector of 360 (Glasser et al, 2016; MMP Atlas) node's data (e.g., activation estimates)
# that have been sorted/ordered into CAB-NP assignments (Ji et al., 2019; https://github.com/ColeLab/ColeAnticevicNetPartition) and
# un-order back into original MMP ordering
# Main use:... |
18a2dbc20bf08d5faa663a597cf7e937aea3faa8fdfcaef6fe1c2e32844e9f6c | Python | 4,530 | 134 | """Test the configuration of pyprep's logger."""
# Authors: The PyPREP developers
# SPDX-License-Identifier: MIT
import io
import logging
import subprocess
import sys
import pytest
import pyprep
from pyprep._logging import LOGGER_NAME, _StreamHandler
@pytest.fixture
def logger():
"""Hand out the package logge... |
b52bdf53e17797502f66d20fc8aa746510266f2c0fc705743a20538de7d4c646 | Python | 4,530 | 104 | from ..main_analysis import *
def get_model_metadata(model_string, sample_image_paths, convert_to_dataframe=True):
model_option = get_model_options()[model_string]
model_option['model_string'] = model_string
model = eval(model_option['call'])
model = model.eval()
if torch.cuda.is_available():
... |
bcfca962b720d0685fb67e330c31f0c26eb1c7d887a13ebe6dd79774f6c37b77 | Python | 4,530 | 123 | """
This file can be used to configure the project. The different
attributes of the "Settings" class can be accessed as follows:
from fidnet import config.
A typical use case for this file is to keep track of paths
to different files in the project.
This is using pydantic-settings. In order to read more about how
to... |
e7c3ddcede6d88a8dcdff02b40fecf3fc01c4e409c8bb99db83f1bc6d5efdc47 | Python | 4,532 | 101 | # Script for additionally preprocess for DeepH-hybrid
# Coded by ZC Tang @ Tsinghua Univ for DeepH-hybrid support. e-mail: az_txycha@126.com
# Please follow the instructions in "README" to use this script
import numpy as np
import h5py
import json
import argparse
import os
Bohr2Ang = 0.52918
def modify_h5(h5_in, h5... |
518fc505d96cf1422bc25612fb25d800c15f08b9e4e1b83a4bd05f39eb3328b9 | Python | 4,533 | 101 | import os
import pandas as pd
import pytorch_lightning as pl
from statistics import mean
from typing import Any, Callable, List
import torch
from torchmetrics import Accuracy
# local imports
from shapelearningtheory.networks import make_resnet50, make_vit_b_16, \
make_vgg_19, \
make_mlp_small, make_convnet_smal... |
640d9b7fa8948773dc8cc9ede5b189cb2beb526008b9c0cb8a6edcd0a8d9d39e | Python | 4,535 | 148 | import os
import argparse
import glob
from typing import Generator
import pandas as pd
from language import load_input_language, Language
from learning_experiment import LearningExp
import openai
openai.organization = "org-MHqlKDvTANBEASvMZSUJGnvZ"
openai.api_key = os.getenv("OPENAI_API_KEY")
# Globals
PARTICIPA... |
2db07051dcd1bd8736db1f0bfe5ea434851fe93613d186ad1f2c370faa117656 | Python | 4,536 | 141 | ''' Script to apply Maxwell filtering to Eleka Neuromag data '''
from os.path import join, basename
from stormdb.access import Query
from stormdb.process import Maxfilter
from mne.io import Raw
from mne.bem import fit_sphere_to_headshape
import warnings
import os
import re
import numpy as np
from sys import argv
from... |
c2f40b90f57e36353a3434847d85fe7a5b0e88bf68b19df5293d0a4c51c33eda | Python | 4,536 | 94 | """
Train a DGCNN Model on the AMIGOS Dataset
=========================================
In this tutorial, we'll walk through how to leverage TorchEEG for training a Dynamical Graph Convolutional Neural Network (DGCNN) to classify emotions using the AMIGOS dataset. Let's walk through the essential steps—from dataset pr... |
2e6cca56be662b321483da822176412d90186cae637762395fba9f92f6ccaa60 | Python | 4,537 | 79 | """REAL_PN_DEP.py 2026-09-29. Pre-registration: 04-NOTES/PREREG-2026-09-29e-REAL-PN-DEPRESSION.md
Seki et al. 2017 real receptor input -> AL cut-out with fly-like ORN->all depression (f 0.72, tau 2.4 s, x0 = 1) -> real PNs.
usage: python REAL_PN_DEP.py [run|analyse] (workers: RD_WORKERS, default 4)"""
import sys, os,... |
334f0f95c591329ca15819ee5775e8133ca024de5fb28bf5c2010fba3c6345b4 | Python | 4,537 | 109 | # -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
from yolo_v4 import _conv2d_fixed_padding, _fixed_padding, _get_size, \
_detection_layer, _upsample
slim = tf.contrib.slim
_BATCH_NORM_DECAY = 0.9
_BATCH_NORM_EPSILON = 1e-05
_LEAKY_RELU = 0.1
_ANCHORS = [(10, 14), (23, 27), (37, 58),
... |
01c086cff04211a744086b45f4504f1f6567b77a8201332bd26ec924dd861beb | Python | 4,538 | 106 | """A single definition."""
import copy
from typing import Union
from hed.models.hed_group import HedGroup
class DefinitionEntry:
"""Stores the resolved contents of a single HED Definition.
A ``DefinitionEntry`` is created when a ``Definition/`` tag group is parsed
and stored in a :class:`~hed.models.De... |
316c0afeeedbac36886b9607d7c6bb39040e80891e2d9ee6aae7255311cd8aa2 | Python | 4,538 | 143 | """
Optuna example that demonstrates a pruner for Tensorflow (Estimator API).
In this example, we optimize the hyperparameters of a neural network for hand-written
digit recognition in terms of validation accuracy. The network is implemented by Tensorflow and
evaluated by MNIST dataset. Throughout the training of neur... |
5c1c240076c3224781351e1676ddece447f0c6ee125de72b4619970e2527f916 | Python | 4,538 | 111 | # !/usr/bin/env python
# Copyright (c) 2020 Intel Corporation.
# authors: German Ros (german.ros@intel.com)
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
Create a human readable version of the scores provided by the leaderboard.
"""
from __f... |
868680d4dd3d95686992ad4cbc2e25088f803d064873617978e1101c19c354ce | Python | 4,538 | 112 | import argparse
from loader import MoleculeDataset
from dataloader import DataLoaderAE
from util import NegativeEdge
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
import numpy as np
from model import GNN, GNN_graphpred
from sklearn.metrics impor... |
fe97ad59d9e425e19387f4c5e4b024d03aca9d22f8a1c42449d60f38c7548769 | Python | 4,538 | 120 | """ This script runs the model-estimation pipeline
1. Load data
2. Prepare analysis
3. Estimate model for enrichment experiment
4. Estimate model for Rac1-experiment
"""
if __name__ == '__main__':
import numpy as np
from scipy.stats import beta
import matplotlib
import matplotlib.pyp... |
7b7c18bea9aff540f6bc9cfafc1f6eac2d20d07bfe91f913da45bf695a52caca | Python | 4,539 | 91 | """
Main run function for getting synapse nearest neighbour distances
authors: Michael Reimann, András Ecker; last modified: 01.2023
"""
import os
import logging
from tqdm import tqdm
import numpy as np
import pandas as pd
from morphio import Morphology
from conntility.subcellular import MorphologyPathDistanceCalculat... |
0ab7c71e9abb8f67b625ca8234e75b7ae51a1d22b9fa4962b98bd86bd52bc7d3 | Python | 4,540 | 97 | import torch
from torch.optim.optimizer import Optimizer, required
class PIDAccOptimizer_Sym_Tz(Optimizer):
r"""Implements stochastic gradient descent (optionally with momentum).
Nesterov momentum is based on the formula from
`On the importance of initialization and momentum in deep learning`__.
... |
5d1d9ac70211055241c8692019e74868478a18ba27dcbfc570f66f88d2e77b86 | Python | 4,541 | 120 | from classification_models_3D import get_submodules_from_kwargs
from keras.src.legacy.backend import int_shape
def slice_tensor(x, start, stop, axis):
if axis == 4:
return x[:, :, :, :, start:stop]
elif axis == 1:
return x[:, start:stop, :, :, :]
else:
raise ValueError("Slice axis ... |
8f8f7dd8941b552a8907a0b5a162fe5d50bee6e31dafc95f4736ed5d43dcf011 | Python | 4,542 | 134 | import random
import unittest
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data.dataloader import DataLoader
from torcheeg.models import BCUNet, BUNet
from torcheeg.trainers import CDDPMTrainer, DDPMTrainer
class DummyDataset(Dataset):
def __init__(self, length: int ... |
283567017e4b8affd637c48aea7f9e2cae73bc2c2b7e627057014d9d4ac0c5af | Python | 4,543 | 115 | """AST + color helpers for theme-consistency guard tests.
Pure-stdlib (ast, re) so it imports without Qt. Used by
test_theme_consistency.py to prove no GUI module bypasses the theme engine.
"""
from __future__ import annotations
import ast
import re
# Module-level style constants that are rendered from L... |
7954a9e93510320690dcc7a0ce335c135d766271a02c5bae26984fe5b9db27c7 | Python | 4,544 | 148 | """Plot UPDRS Med. OFF vs ON"""
from __future__ import annotations
import csv
from enum import Enum
from pathlib import Path
from typing import Annotated
import numpy as np
import pandas as pd
import pte_decode
import scipy.stats
from pytask import Product
import motor_intention.plotting_settings
import motor_intent... |
7ad8085a819629729a595f2f9ba098b95f825bba551032f4a16b43ad218efd1c | Python | 4,544 | 120 | """Optimizes hyperparameters using Bayesian optimization."""
from copy import deepcopy
import json
from typing import Dict, Union
import os
from hyperopt import fmin, hp, tpe
import numpy as np
from chemprop.args import HyperoptArgs
from chemprop.constants import HYPEROPT_LOGGER_NAME
from chemprop.models import Mole... |
bea60683d1883740bd29a472b0e1965ac0b293f008057047c2eed676ed248556 | Python | 4,544 | 138 | ##
# \file template_stack_estimator.py
# \brief Class to estimate template stack automatically
#
# \author Michael Ebner (michael.ebner.14@ucl.ac.uk)
# \date January 2018
#
import os
import re
import json
import numpy as np
import skimage.measure
import SimpleITK as sitk
import pysitk.simple_itk_helpe... |
25870aeacb4e5d80ed5825e80ddcca7b32e92ba7c7fbc2d16eefe19570524d12 | Python | 4,545 | 131 | from typing import Any, Dict, Optional, Tuple
import torch
from pytorch_lightning import LightningDataModule
from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split
from torchvision.datasets import MNIST
from torchvision.transforms import transforms
class MNISTDataModule(LightningDataModule):
... |
1ae2b5d72c2bf132a8106190b7ea0b2fb6c488bcd764b19934942cca3ed36e3a | Python | 4,546 | 140 | """
Optuna example that optimizes multi-layer perceptrons using PyTorch.
In this example, we optimize the validation accuracy of fashion product recognition using
PyTorch and FashionMNIST. We optimize the neural network architecture as well as the optimizer
configuration. As it is too time consuming to use the whole F... |
3188d35d8deb97ebbf67d83aedea6e344294314e47d946f2545a18645b4cc04b | Python | 4,547 | 112 | import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
import torch.utils.data.distributed
import logging
import random
import numpy as np
from monai.transforms import \
Compose, Resize, EnsureChannelFirst, S... |
8ba9a795c4d6ff01d7a06f4a68f88174a8fd16eae597b10c333dfee5ecda12f0 | Python | 4,547 | 148 | import numba
from neurolib.control.optimal_control.oc import OC
from neurolib.models.wc.timeIntegration import compute_hx, compute_hx_nw, Duh, Dxdoth
class OcWc(OC):
"""Class for optimal control specific to neurolib's implementation of the two-population Wilson-Cowan model
("WCmodel").
:param mo... |
cc6b10dff73a0a7435eac28499fed14824f1605615298e61cedc84eb2b4b384f | Python | 4,547 | 155 | # coding=utf-8
# Copyright 2026 The Google Research 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 applicab... |
3006e7c88c75b1bb0474895cce184f9b238069d1d3877c93fd1c58ea79eac1f9 | Python | 4,548 | 146 | #!/usr/remote/bin/python -u
import numpy as np
import os
from sys import argv
import copy
import math
import re
import stat
import shutil
from optparse import OptionParser
import readline
def question(question,typefunc,default=None,autocomplete=True):
if typefunc==int or typefunc==float:
if not default==None and... |
6f4cc708cfe513d23b46049f8cff46e3e8c10cb0dd0c3cb5f25ac1b7b196ede3 | Python | 4,548 | 136 | import os
import os.path as op
import numpy as np
from tqdm import tqdm
import mne
import mne_bids
import nibabel as nib
import matplotlib.pyplot as plt
from utils import load_raw, bipolar_reference
from params import BIDS_ROOT as bids_root
from params import SUBJECTS as subjects
from params import TEMPLATE as templ... |
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