sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
299181c52216ad8f20824515a7db127294adf112f2b0f7c7cd8bd54340eac222 | Python | 4,161 | 104 | """Region-list filter tests."""
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=(3, 20, 30)):
import nibabel as nib
import numpy as np
... |
31b52fd84a70982eaf58c8ca0fabd41653ac594b7bad81268e4e5b5a43efb503 | Python | 4,161 | 130 | """Base jobs for Onetep."""
from __future__ import annotations
from typing import TYPE_CHECKING
from ase.calculators.onetep import Onetep, OnetepProfile
from quacc import get_settings
from quacc.runners.ase import Runner
from quacc.schemas.ase import Summarize
from quacc.utils.dicts import recursive_dict_merge
if ... |
b35b0c54f82925f25c3a07736146ba8dda3786c9206d07c74c57274d2f81d60c | Python | 4,161 | 93 | """Custom-annotation LUTs must not scale with the label VALUE.
``colorize_annotation`` indexes the LUT densely by label value, so a naive LUT is
``max_label + 1`` rows tall. Custom annotations are arbitrary user input and may
carry raw Allen structure IDs (182305709) or instance-segmentation IDs, which
would dema... |
74caf8e36e3a4c71fbc688869c20c4a8384c462b7f2945f1190f837514814540 | Python | 4,162 | 117 | # 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... |
d0165da93f829f967ffef999090ab81392dbd840522dca24e8353fc76571017f | Python | 4,163 | 123 | # \file TestIntensityCorrection.py
##
# \brief Class containing unit tests for module IntensityCorrection
#
# \author Michael Ebner (michael.ebner.14@ucl.ac.uk)
# \date October 2016
import os
import unittest
import numpy as np
import SimpleITK as sitk
import pysitk.python_helper as ph
import niftymic.base.stack... |
10cf15510bc496af3595863844de871873a39ee6d284c6bf0dc96dfd572849c9 | Python | 4,164 | 125 | import networkx as nx
import numpy as np
from functools import cached_property
from hippo.environments.w_maze import get_wmaze
def transition(from_pose, action, valid_poses):
direction_to_movement = {
0: np.array([1, 0]), # right
1: np.array([0, 1]), # down
2: np.array([-1, 0]), # left
... |
4a14185e622974516fc5944933af425bda518e9378d1a976fb1ca71fd5964d69 | Python | 4,164 | 185 | #!/usr/bin/env python
#******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
#... |
cbacef92e430b0e0a6ccd2134a61e97776457f9be726f1beb8924430efe4c462 | Python | 4,165 | 151 | import tifffile
import h5py
import warnings
import os
TIFF_FORMATS = ['.tiff', '.tif']
H5_FORMATS = ['.h5', '.hdf']
LIF_FORMATS = ['.lif']
def read_tiff_voxel_size(file_path):
"""
Implemented based on information found in https://pypi.org/project/tifffile
"""
def _xy_voxel_size(tags, key):
a... |
208b98879695aa8fe6dfae4e01d97fb348afbe16f7d61d756ea92456b21c1140 | Python | 4,166 | 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... |
56af35139de5351a165816e35bd86eb9128565c285c26450253717e729ec92c6 | Python | 4,166 | 124 | #
# 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,... |
c8c47e605405602d73f21eba5010646ab2d3fb9bb1040e65c12eb06c7cf3a34a | Python | 4,166 | 140 | import platform
import serial
import serial.tools.list_ports
import time
import numpy as np
from control._def import *
# add user to the dialout group to avoid the need to use sudo
class TriggerController():
def __init__(self,serial_number):
self.serial = None
self.platform_name = platform.system... |
d6d1538996f7fbeaa5f3899fbcebbf2bfbb0556c88060881b5c738f906515c37 | Python | 4,167 | 139 | #!/usr/bin/env python
from __future__ import print_function
import cProfile
import pstats
import numpy as np
import argparse
import pddlstream.language.exogenous as exogenous
exogenous.EXOGENOUS_AXIOMS = True
exogenous.REPLACE_STREAM = False
from pddlstream.algorithms.focused import solve_focused
from pddlstream.al... |
40dcdd15100eb451143121f116f16303c299967c95996ae2386caf07572387ed | Python | 4,169 | 65 | """REAL_PN_BADEL_WB.py 2026-10-02. Pre-registration: 04-NOTES/PREREG-2026-10-02d-BADEL-WHOLE-BRAIN.md
REAL_PN_BADEL on the whole brain (Shiu et al.'s file, conditions of HEAD_TO_HEAD) instead of the antennal-lobe cut-out.
usage: python REAL_PN_BADEL_WB.py run | analyse (RW_PROCS, default 4; resumable)"""
import sys, ... |
e5a6a0d0e4f4abb742625ae184806ee286a35e7f2a1b0de086b389b8cc8a1aaf | Python | 4,171 | 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... |
93f0195a34da8ee02b569f7c5e707d94a2588bb271a677569687f2a7f3c78fd5 | Python | 4,172 | 67 | """TRANSCRIPTOME_TYPE_MAP.py 2026-09-23. Names only; no expression value is read here.
Rules: 04-NOTES/PREREG-2026-09-23b-ADDENDUM-1.md. Output: 06-OUTPUT/transcriptome_type_map.csv (+ sha256)."""
import re, gzip, hashlib, numpy as np, pandas as pd
R = 'D:/eucys'; T = f'{R}/02-DATA/transcriptome'
fz = pd.read_csv(f'{R... |
6cf04aaec012c0e668e88ea8bc9679dfd03775e734243c9240ee47a9d25ed98a | Python | 4,175 | 118 | import numpy as np
import pytest
tifffile = pytest.importorskip("tifffile")
def _write_tif(path, arr):
tifffile.imwrite(str(path), arr)
def test_load_source_tiff_dir(tmp_path):
from regtools.ome_zarr_converter.loaders import load_source
d = tmp_path / "sections"
d.mkdir()
for i ... |
e9778a2b6542502f340ce8df52a9a1a79cdbd83b001f76a9f19dc8dcec74d42e | Python | 4,175 | 124 | # Node-group asset "Select Atomic Number" (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_C... |
73808162e2f5b5616ff2ce94ff1c40138faf9081a64b52a7e2a7887d3800ff55 | Python | 4,176 | 100 | import numpy as np
import pandas as pd
import os, re
import matplotlib.pyplot as plt
import matplotlib as mpl
from os.path import join as pjoin
import matplotlib.font_manager as font_manager
import h5py
# define path and params
# make sure the dataset_path are modified based on your personal dataset download... |
1c4285e2378d0efdef291f1f3ca402faaf389c2560f0034e6a371cae03480ca1 | Python | 4,177 | 118 | # Node-group asset "Sample Mixed Vector" (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_CH... |
22ec3f09ead127d6882d57ce0c20661bc8cf38300d6a20aeac1bb0264998c500 | Python | 4,177 | 95 | """
This script computes the exact volumes where the sequences need to be cut
@author: trianaa1
"""
import os, re
import pandas as pd
path ="/m/cs/archive/networks-pm/cognitive"
days = pd.read_csv(f'/m/cs/project/networks-pm/mri/fast_prepro_bids/participants.tsv',sep='\t')
tr = 0.594
#create a dictionary that, for ... |
57d7c1a6842d30305ecf71244781daf760af7398b13d6db097a20df0cf55b23f | Python | 4,177 | 65 | """CHECK_MODEL.py 2026-10-03. The inequality as a tool. Give it the signs of a fly-brain model built on FlyWire v783 and,
without simulating, it says whether the antennal lobe works, how far the model sits from the line, which single signs it
cannot get wrong, and, if it is broken, the shortest list of corrections in t... |
b1b9df1da582374daf94477457f2879ca9b225da9765c2696755043f35fbc9db | Python | 4,177 | 149 | from wonambi.trans import frequency, math
from wonambi.utils import create_data
from numpy.testing import assert_array_almost_equal
from scipy.signal import hann
import plotly.graph_objs as go
from .utils import save_plotly_fig
def test_trans_frequency_doc_01():
# generate data
data = create_data(n_chan=2,... |
27d38bf615cb24a664ee6a1480ac298d6f4735fe16949160fe8ffe67f4bfb536 | Python | 4,178 | 124 | """
pygments.formatter
~~~~~~~~~~~~~~~~~~
Base formatter class.
:copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
:license: BSD, see LICENSE for details.
"""
import codecs
from pip._vendor.pygments.util import get_bool_opt
from pip._vendor.pygments.styles import get_style_by_nam... |
3519e1ea0d444752647544e22c24289a9cf55fc0e70489eea1b2689e51a2033a | Python | 4,178 | 124 | ##
# \file parameter_normalization_test.py
# \brief Class containing unit tests for module ParameterNormalization
#
# \author Michael Ebner (michael.ebner.14@ucl.ac.uk)
# \date Nov 2016
import os
import sys
import unittest
import numpy as np
import SimpleITK as sitk
# Import modules
import pysitk.simple_itk_help... |
d8e32d7c4b04b7fed46c5d7efaf595b97cdc4e158a6cda666c3d72dacb6d6f40 | Python | 4,178 | 105 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
from tabulate import tabulate
from src.analyze.utils import init_mpl
from src.utils import map_to_score_grid
from src.preprocessing2.process_ratings import merge_rating_files
from constants import DATA_DIR, SCORE_COLUMNS, CI... |
4478141aef3b9dfa3716182620536246ab88d91ccd833c2579e8bafb1caa736f | Python | 4,181 | 107 | '''Preprocess events from the presentation logs to the BIDS events file for the
PVT task.
Parameters
----------
path : str
path where the raw data is stored
savepath : str
BIDS folder
'''
import os, sys
import re
import pandas as pd
from glob import glob
path = sys.argv[... |
7eb1981138929e96b2d7eef4d4381fd5544d56eb9728da5660830a38240e7f2c | Python | 4,181 | 116 | # Node-group asset "Fade Geometry" (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_CHECKING... |
8b34635bea12580c8ee9a771979a8a43efadfa0d79a4eedef1f1bcbca1a58a02 | Python | 4,181 | 110 | """Regression tests for ``_aggregate_cell_csvs_to_root``: the pipeline step that
rolls per-points-file cell CSVs (``registered_points/<stem>/``) up to the run
root, where the HTML report and its unassigned-cell QC chip read them.
Guards the report-integration bug where the cell-metrics half of the report was
sile... |
010d3a308206abe29de787a96aca3e1bcd1c162c28cd6205aa888b4eaf57eb8d | Python | 4,182 | 133 | # 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... |
b2c1cae66b211a3a298a973b3954b86e2df6948f366201380162c8a06c147b90 | Python | 4,187 | 114 | # 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... |
29d31d2ab50d9e2255c7df68bfe1cc495ef9c414d41c0257f8c4060a8739467a | Python | 4,188 | 126 | # Node group ".Accumulate Domain Transform" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# Shared by several assets, which import it; not an asset itself.
# _build_group() is the source of truth: the docstring, __init__ and accessors... |
5c905b0802af2be0429291f6f0988e1772ca3b58a321d2c16d663ee64a256b18 | Python | 4,188 | 119 | import numpy as np
from sklearn.model_selection import StratifiedKFold, train_test_split
from base import BaseDatasetsManager
from utils.logger import get_logger
from .sampler import SubsetWeightedRandomSampler, SubsetSampler, BootstrapSubsetSampler
class METABRIC_Datasets_Manager(BaseDatasetsManager):
'''
A ... |
c9fe76ff2e701cb729e69d503af2040077de8db72ba5ac84403ca634a8185700 | Python | 4,188 | 136 | # 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... |
cf1ba20223a1691355169eb0b1c22b2c5edfbd2a1d81e9941da29c596695b13c | Python | 4,188 | 119 | ##################
### Parameters ###
##################
import argparse
parser = argparse.ArgumentParser(description='Using models trained with singleDeep to predict new samples phenotypes')
# Define the parameters and default values
parser.add_argument('--inPath', type=str, help='Folder with the input data (output ... |
bb8cac7cc9c4afd5f9c93ed8747ea86abb8f8f77562330b750aa74a26e702bb9 | Python | 4,189 | 127 | '''
Script that handles PLEC data input for the DNN.
NOTE: It's very important that the ML table that you use lines up with the PLECs. So each line number in the ML table should correspond to the line number in the PLECs .npy file.
'''
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Da... |
09bd81b5217853b710d319906c98f3eade278621dd552313d5b424e0a13c7806 | Python | 4,191 | 182 | from __future__ import annotations
import importlib
import platform
import sys
CHECKS = [
("numpy", "NumPy"),
("pandas", "pandas"),
("scipy", "SciPy"),
("skimage", "scikit-image"),
("sklearn", "scikit-learn"),
("cv2", "OpenCV"),
("matplotlib", "Matplotlib"),
("seaborn", "seaborn"),
... |
38a79f310bb5c4420d6ae03e945f0816331d45e003b991246a455af8c6f84f70 | Python | 4,191 | 227 | OPTIC_LOBE_INTRINSIC_TYPES = {
"Centrifugal": ["C2", "C3"],
"Distal Medulla": [
"Dm1",
"Dm10",
"Dm11",
"Dm12",
"Dm13",
"Dm14",
"Dm15",
"Dm16",
"Dm17",
"Dm18",
"Dm19",
"Dm2",
"Dm20",
"Dm3p",
"D... |
aa930b5beeba18f0c551bba4f4093fc44cd5d98d1ac3575960b81c4671bb7651 | Python | 4,192 | 106 | """Define mention classifier class."""
import logging
from pathlib import Path
from negbio.pipeline import parse, ptb2ud, negdetect
from negbio.neg import semgraph, propagator, neg_detector
from negbio import ngrex
from tqdm import tqdm
from constants import *
class ModifiedDetector(neg_detector.Detector):
"""Ch... |
0b82e9419351ff643b6c626195aa86070ff7f97e73eb777082a07060ec039988 | Python | 4,194 | 118 | import timm
import functools
import torch.utils.model_zoo as model_zoo
from .resnet import resnet_encoders
from .dpn import dpn_encoders
from .vgg import vgg_encoders
from .senet import senet_encoders
from .densenet import densenet_encoders
from .inceptionresnetv2 import inceptionresnetv2_encoders
from .inceptionv4 im... |
478833c1623657759d197c879329eb72f39e295bb45ff165f6a368e8f07dc39c | Python | 4,195 | 138 | # -*- coding: utf-8 -*-
"""(Keras) Layers for radial basis functions"""
import numpy as np
import tensorflow as tf
class CutoffFunc(tf.keras.layers.Layer):
R"""Cutoff function layer
The following types of cutoff function are implemented (all functions are
defined within $r_{ij}<r_{c}$ and decay to zero... |
9fa84fc33e2c13dd12f214b47355e4fdfe6a7b24fb45b22c04f2bf6aab4813d2 | Python | 4,195 | 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... |
f88e526ad639b96aeb71f3512e444e18e5aa88d6e01ceb2deead1d06619d40b4 | Python | 4,195 | 103 | import sys
import traceback
import numpy as np
import csv
import os
import ray
ray.init(num_cpus=16)
from joblib import Parallel, delayed
from scipy.stats.stats import pearsonr
from src.utils import parse_config, contain_substr, logger
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
@ray.remote
def gen_optical_feature... |
6d3111582182918dd6802dbf18c8bfd0ad029fab031881290b54321104719d49 | Python | 4,196 | 88 | import numpy as np
from pathlib import Path
class Kilosort:
"""Class to represent data from Kilosort2 at different levels of curation"""
KILOSORT_PATH = "/data/MEAprojects/DLSpikeSorter/{recording_name}/spikesort_matlab4/kilosort2_results"
CURATION_PATH = "/data/MEAprojects/DLSpikeSorter/{recording_name}... |
ebaf7abf0ad968270a0fc0fb3bb35dca8a4de8df40712605b16925ebb56f01a6 | Python | 4,196 | 116 | import pandas as pd
import os
from tqdm import tqdm
from typing import Tuple
from constants import N_ITEMS, USER_RATING_DATA_DIR, MAIN_LABEL_FILENAME
from src.utils import map_to_score_grid
__all__ = ['create_label_files', 'merge_rating_files']
_COLUMNS = ['assessment_id', 'prolific_pid', 'drawing_id', 'FILE', 'part... |
087aeaa41e7d64e36a780274114ce23bef51913cc86c56661b39926e6da8075b | Python | 4,197 | 127 | import logging
import warnings
from torchdrug.core import Registry as R
from torchdrug.utils import pretty
class LoggerBase(object):
"""
Base class for loggers.
Any custom logger should be derived from this class.
"""
def log(self, record, step_id, category="train/batch"):
"""
L... |
33f4922b070acdce9c44faf7eb0eceb86e374d4ea1e2dc54963b1dd19ef9e7f7 | Python | 4,197 | 123 | '''
Create union of the input newtorks such that the new network contain all the nodes and edges from the input networks.
If one edge appears in multiple netwroks then take the highest weight. (Ref: BIONIC (Methods: Obtaining integrated results))
'''
import sys
import json
from pathlib import Path
import panda... |
868c922bdae4af25b0efc41dcb00ac0d5785a7aefc84deb153ce05dd9aaa1a68 | Python | 4,199 | 125 | # Node group ".MN_topo_assign_backbone" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# Shared by several assets, which import it; not an asset itself.
# _build_group() is the source of truth: the docstring, __init__ and accessors are... |
88f9e76b8d56f96a5f564a9580917dbfc1f9f65aa8445382f0275aa118d173a7 | Python | 4,199 | 152 | import cocotb
import pytest
from cocotb.clock import Clock
from cocotb.triggers import RisingEdge
from elasticai.creator import ir2vhdl as ir
from elasticai.creator.ir import Registry, attribute
from elasticai.creator.testing.cocotb_pytest import (
CocotbTestFixture,
eai_testbench,
)
from elasticai.creator.tes... |
253566f2b24dbac5125ec0b5d706327159e86b359a7c9c33a5e08001ff17c3b1 | Python | 4,201 | 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... |
4b37ac09914296708683ac7012a1ab71058b3ee2ed91e2b9aec3812b9257d836 | Python | 4,201 | 112 | from ngclearn import resolver, Component, Compartment
from ngclearn.components.jaxComponent import JaxComponent
from jax import numpy as jnp, jit
from ngclearn.utils import tensorstats
def _run_cell(dt, targ, mu, mask):
return _run_gaussian_cell(dt, targ, mu, mask)
def _run_gaussian_cell(dt, targ, mu, mas... |
1959392608d720a692614429c9f8e218ef225d74a8d0841323a88f091ffd1f9e | Python | 4,202 | 72 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from lab.reports import Attribute, geometric_mean
from common_setup import IssueConfig, IssueExperiment, DEFAULT_OPTIMAL_SUITE, is_test_run
BENCHMAR... |
5a1548331683887c70e124ba1c52b20f0f60d0eed370b94c037dcd0d0a2dc313 | Python | 4,204 | 150 | #!/usr/bin/env python
import os
import warnings
import pandas as pd
import matplotlib.pyplot as plt
from nltools.data import Brain_Data
from nilearn import plotting as nplot
warnings.filterwarnings("ignore") # suppress warnings
# ------------------------------------------------------------------
# paths
# -------... |
7276919bf263eb42e7245ff6b025b5660f86c86552471405525b31933d98a038 | Python | 4,205 | 136 | # 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... |
ffed63c12b4adf69d0bea334d216e21542af2f42d7bdad8aed10ed786eb677bd | Python | 4,205 | 166 | import math
import matplotlib.pyplot as plt
import numpy as np
import meep as mp
# True: plot the scattered fields in the extended air region adjacent to the grating
# False: plot the diffraction spectra based on a 1d cross section of the scattered fields
field_profile = True
resolution = 50 # pixels/μm
dpml = 1... |
9c531c1954123f6735fb8282fd8afcdf8d3ccda328d1c2008b6cc3454139b7bc | Python | 4,206 | 129 | from collections.abc import Sequence
from typing import TypeAlias, cast
import numpy as np
from numpy.typing import NDArray
FloatArray: TypeAlias = NDArray[np.float64]
IntArray: TypeAlias = NDArray[np.int_]
def blocker(array: FloatArray, multi: int = 1) -> tuple[int, IntArray, list[float]]:
"""Return the sample... |
aa166bf700cc4c4b14c857067245d1e1b6bbd882999b8807e76b785b990253bd | Python | 4,206 | 95 | """
EFFECTOME_SOLVER_FLOOR.py - written 14 September 2026. VERIFY V-43 (rewritten).
WHY THIS EXISTS
---------------
V-43 used to read:
V = np.load('.../eff_base.npz')['vec']
print(sub(V, V, 20), sub(V, V, 100)) # expect 1.0000
That compares an array to ITSELF. It returns 1.0 by construction, for ... |
3cbf852dbf292d64fd3930c152a17f07bc97381fd08a9099838ed6db86f29e64 | Python | 4,207 | 105 | import numpy as np
from scipy.ndimage import zoom
from plantsegtools.utils.io import smart_load, create_h5
from skimage.segmentation import find_boundaries
from skimage.morphology import erosion
from plantsegtools.postprocess import LMC_CONFIG_PATH
import yaml
import os
import argparse
def parse():
parser = argpa... |
a2f41ce6ab1f09ebe0109fcc2626d97e90cb4e5373682a12040f4cc081287463 | Python | 4,209 | 110 | """
Consolidate all processed information.
Just copy over the matrices to directory results, in the appropiate name
Also save the region names, which is not needed for the algorithm but is
handy to have.
Adapted from CreateTVB.m to python. Original script by:
% Authors: Michael Schirner, Simon Rothmeier, Petra Ritter... |
a758f7b7d101edfb9a14fa180a2fd98d3b7a9ed1acb232e0ef398789749686ac | Python | 4,209 | 186 | """Fingerprint comparison metrics.
Author: Seth Axen
E-mail: seth.axen@gmail.com
"""
from __future__ import division
import numpy as np
from ..fprint import CountFingerprint, diff_counts_dict
def tanimoto(fp1, fp2):
"""Calculate Tanimoto coefficient between fingerprints.
Parameters
----------
fp1 :... |
9319f1614ff3cdc3738e69ef2d608b16e8f8ebf27a82ddd94ea42a45cb865203 | Python | 4,210 | 120 | # 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... |
fbfa28efe086d6e10c4c2587a0277d03cce9ae99676318290845e742ee525bcd | Python | 4,210 | 112 | import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from constants import *
from src.utils import map_to_score_grid, score_to_class
from src.dataloaders.transforms import NormalizeImage
__all__ = ['ROCFDataset']
_SCORE_COLS = [f'score_item_{i +... |
9f88ee45f18ad9fdbc630cffb851e06de87fd07262ef4aef80a7218b62ad0a57 | Python | 4,211 | 139 | from __future__ import division
import operator
import numpy as np
import matplotlib.pyplot as plt
from soundsig.plots import make_phase_image
class ComplexPCA(object):
def __init__(self, n_components=None):
self.n_components = n_components
def fit(self, Z, zscore=True):
""" Does principa... |
d0994a3abde6158e34a9c68eff1dfc34aa4351fee8f2f3660166aae5d00898f9 | Python | 4,212 | 78 | import torch
import torch.nn.functional as F
import math
def conv3D_output_shape_f(i, input_shape, kernel_size, dilation, padding, stride):
"""
Calculate the original output size assuming the convolution is nn.Conv3d based on
input size, kernel size, dilation, padding and stride.
"""
return math.... |
8bd9b40b99a9291ed7ab9b6948033931b46750f4f95a316468fd004709971398 | Python | 4,213 | 113 | import pytest
from starlette.testclient import TestClient
from sitetack.app.main import app
client = TestClient(app)
class TestMain:
def setup_method(self):
self.valid_data_one_sequence = {
"ptm": "PHOSPHORYLATION_ST",
"label": "NO_LABELS",
"organism": "HUMAN",
... |
3d1fa5f93a539123ecf1373b5044878b1a12dc95fa0d4046b7da865c11726fa6 | Python | 4,215 | 107 | """
DeepLabCut2.0 Toolbox (deeplabcut.org)
copyright A. & M. Mathis Labs
https://github.com/AlexEMG/DeepLabCut
Please see AUTHORS for contributors.
https://github.com/AlexEMG/DeepLabCut/blob/master/AUTHORS
Licensed under GNU Lesser General Public License v3.0
"""
def add_new_videos(config,videos,copy_videos=False,co... |
b8e723920f1dfb874ede27322cd2cb49f2effdda154699e5d2790a5487f6054a | Python | 4,215 | 113 | """Digital down converter (DDC) function."""
import numpy as np
from scipy.signal import decimate
def filter_moving_average(signal: np.ndarray, decimation: int = 100, overlap: int = 8) -> np.ndarray:
r"""Decimate data using a moving average filter.
$kernel = e^{\frac{-1}{1 - x^2}} * \sin{2.073 * \pi \ x} / x... |
f131e23b5d536e68309fbbb92be31ff73f42ce486d801e7d99d5ebd1349d954d | Python | 4,216 | 106 | # 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... |
4d3eb668142915726cfe2fa055ef875b54451d70287277adaa088b7eea25a1cc | Python | 4,218 | 135 | import gzip
import json
import logging
import os
import numpy as np
import pandas as pd
import scipy.sparse
from pandas import CategoricalDtype
from cirrocumulus.util import dumps
logger = logging.getLogger("cirro")
LINE_END = "\n".encode("UTF-8")
def write_jsonl(d, f, name, index, compress=False):
output = {... |
5bad2058f55642d0d758e86bc042d188d8291831e94904f53e67d943578b1d97 | Python | 4,218 | 117 | # %%
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
import numpy as np
import torch
from torch_geometric.data import Batch
import pandas as pd
from matplotlib.colors import ListedColormap
from rdkit import Chem
from rdkit.Chem.Draw import rdMolDraw2D
from rdkit.Chem import rdDepictor
rdDepictor.SetPreferCoordGen(Tr... |
d547e1dab8d0a30e8ed467455961f827e3fc6630fac6cc65f255ebfb29f2063b | Python | 4,218 | 111 | # currently only runs locally due to reading in from command line (interferes with config)
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor
from sklearn.gaussian_process import GaussianProcessRegres... |
70aea8120dd60a0d10d79a20bfa49ebfa8b1f8a8dc3f4fe8a887de117b4039aa | Python | 4,219 | 141 | # Node-group asset "Backbone Positions" (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_CHE... |
02a5db5a7c6548376a1f66953615d6f76dcde691dd7e8c8fcb926fa4cde4c909 | Python | 4,220 | 134 | """Class to import HRV RRI data in text format.
"""
from logging import getLogger
from numpy import arange, cumsum, empty, float64, interp, newaxis
from scipy.interpolate import splev, splrep
from datetime import datetime, timedelta
from .utils import DEFAULT_DATETIME
lg = getLogger(__name__)
class LyonRRI:
"""... |
524bbc1c19bf764c4e2dac4104ea7655badb0af0213056c6a850111a2390b078 | Python | 4,220 | 69 | import numpy as np
import matplotlib.pyplot as plt
import argparse
from IPython import embed
# Leser kommmandolinjeargumenter
parser = argparse.ArgumentParser()
parser.add_argument("-case", "--patient", type=str, default="C25")
parser.add_argument("-res", "--resolution", type=int, default=16)
args = parser.parse_args... |
b9418a03556ff17671138de8b99f1cc1721a6f2c3c5b7cd0b4bb418ab3a812d7 | Python | 4,220 | 109 | import torch
from torch import nn
from models import resnet
def generate_model(opt):
assert opt.model in [
'resnet'
]
if opt.model == 'resnet':
assert opt.model_depth in [10, 18, 34, 50, 101, 152, 200]
if opt.model_depth == 10:
model = resnet.resnet10(
... |
e8e743398526972a84bb00427ed70bdd584f43e351a15ca34c047aa1dd0ed0c7 | Python | 4,220 | 115 | """
This script generates two plots to check the data quality for the eye-tracking
data. The first plot is a distribution of the fixation duration. According to
Mahanama et al. 2022, the duration of fixation in one point typically lasts
between 30 and 300 ms, and may be longer depending on the cognitive load.
The ... |
ee93d78143c0e06e745f6495abd5685b8e4b96011b0a5713f422f61e4b7d845e | Python | 4,221 | 77 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from lab.reports import Attribute, gm
import common_setup
REVS = ["issue548-base", "issue548-v2"]
LIMITS = {"search_time": 1800}
SUITE = suites.suite_optimal_with_ipc11()
B_CONFIGS = {
'rl-b50k': ['--search', 'astar(merge_and_shrink(mer... |
8a9264ca20960fedb070a93509a52a61bb378acd8e9bae2f9faa8b91ae914986 | Python | 4,223 | 119 | from typing import List, Optional, Tuple
import torch
import hydra
import pyrootutils
import pytorch_lightning as pl
from omegaconf import DictConfig
from pytorch_lightning import Callback, LightningDataModule, LightningModule, Trainer
from pytorch_lightning.loggers import Logger
pyrootutils.setup_root(__file__, indic... |
59081c7a65939d5cf6028e8f035f3077a310cedae7833eedad5292aace28497f | Python | 4,225 | 99 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""check_tex.py —— main.tex 静态自查（无 LaTeX 编译器环境的替代验证）。
v6 起新增 5–7 项：占位符残留、arXiv 摘要长度上限、参考文献引用完整性——
这三类问题都曾在"已定稿"版本里漏检过。"""
import re
from collections import Counter
src = open('game/docs/arxiv/main.tex', encoding='utf-8').read()
lines = src.split('\n')
print('1) begin/en... |
00453c544e921aee5abd8ba70974b0d097d5bdf3cf4949879c6fa71ee06bc910 | Python | 4,227 | 144 | import os
import pandas as pd
import sys
from typing import Tuple
import uuid
import numpy as np
from matplotlib import pyplot as plt
import torch
def directory_setup(model_name, dataset, results_dir, train_id: int = None, resume: str = ''):
"""setup dir for training results and model checkpoints"""
if resu... |
4d867d885e3735ef3c7a22919fdb7c6225fc306c2ecfa0acedd76ab4b6e79dbe | Python | 4,227 | 106 | #############################
# Import plotting functions #
#############################
#%% plot p-values
plot_p_values_markov(save=False)
#########################################################################
#%% plot behavioral motif comparison for worm 3 with 3 cognitive states #
#############################... |
7967f2aa965b9777a232dcf9c0b2c3a18088255a5183810e0e40c2df784cec38 | Python | 4,228 | 51 | """DOMINANT_BANC.py 2026-10-02. Pre-registration: 04-NOTES/PREREG-2026-10-02m-DOMINANT-BANC.md
Are FlyWire's dominant inputs (DOMINANT_INPUTS.py) the largest inputs of the same populations in a second fly (BANC v888)?
usage: python DOMINANT_BANC.py"""
import sys, os, time, hashlib, re
sys.path.insert(0, os.path.dirname... |
ff828b3d7147e83779caedee0e36ce6f4819e88a32549c908f7f5064c3c610b0 | Python | 4,231 | 101 | #! /usr/bin/env python
import argparse
import os
def str2bool(v):
# susendberg's function
return v.lower() in ("yes", "true", "t", "1")
def read_position_file(positionfp, col_chrom=0, col_pos=1, header=False):
posstrs = set()
with open(positionfp, 'r') as rf:
if header:
... |
12c54c8ebe1ef0ac854445a9afc9688a85746430cf144339f58edfdbf03b6102 | Python | 4,232 | 98 | from __future__ import print_function
import argparse
import os
import sys
sys.path.extend([
'pddlstream',
'ss-pybullet',
])
from collections import Counter
from learn_tools.learner import FEATURE, SCORE, CATEGORIES, TRAINING, POLICIES, SKILL
from learn_tools.learnable_skill import read_data
from learn_tool... |
6355a792127708122800ed11c2a29344da4f44db18319a3611d311f2fee9419e | Python | 4,233 | 141 | #!/usr/bin/env python2.7
from __future__ import print_function
import argparse
import random
from pddlstream.algorithms.focused import solve_focused
from pddlstream.algorithms.incremental import solve_incremental
from pddlstream.language.constants import PDDLProblem, And, Exists, print_solution, Imply
from pddlstrea... |
1685a6d3f34b820a32e0721fe0ca1046584d084f8834c91a4e372d7002701d70 | Python | 4,235 | 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... |
16f1e64f9b87fbfba29ad473e611fd5426eded557e35e8b627dba96de8fa8fc8 | Python | 4,235 | 128 | r"""
The ``codes`` object defines a mapping from common names for HTTP statuses
to their numerical codes, accessible either as attributes or as dictionary
items.
Example::
>>> import requests
>>> requests.codes['temporary_redirect']
307
>>> requests.codes.teapot
418
>>> requests.codes['\o/']
... |
6a7d023d2395bf8f3f99c277676130d2e07cd8cd7426a37c02b14031d563388a | Python | 4,235 | 121 | import torch
import os
from pytorch_lightning import LightningDataModule
import numpy as np
from lib import utils
from torch.utils.data import DataLoader
class TimeSeriesDataset(torch.utils.data.Dataset):
def __init__(self, X, scaler, t_features, seq_len=1, horizon=1):
self.X = X
self.seq_len = se... |
062c6382925238b7d971be610176ad247ad14815afae25da978276d81639cc49 | Python | 4,236 | 147 | """
Optuna example that demonstrates a pruner for tf.keras.
In this example, we optimize the validation accuracy of hand-written digit recognition
using tf.keras and MNIST, where the architecture of the neural network
and the parameters of optimizer are optimized.
Throughout the training of neural networks,
a pruner o... |
68a845d1bb892dd8cc60ba87f513022562144ffa89cc2f40a39762bc36878cba | Python | 4,236 | 110 | import os
import pybullet as p
#from contextlib import contextmanager
from control_tools.common import BASE_FRAME
from control_tools.pb_controller import PBController
from perception_tools.pb_perception import PBPerception
from perception_tools.common import get_models_path
from plan_tools.common import PR2_URDF
from ... |
05feabb2d390b18734337292d57b4e2f23ea637613f4afaedd0e7f3557b5d200 | Python | 4,237 | 115 | import pandas as pd
import torch
import numpy as np
import os
from src.utils import map_to_score_grid, score_to_class
from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
from torchvision import transforms
def get_item_classification_dataloader(item_num: int, data_root: str, labels_df: pd.DataFra... |
89d896dfef0a908e56d722b02efe555194051b18e61b2c96c2d4c60699f55e14 | Python | 4,237 | 135 | from typing import Dict, List, Union
import numpy as np
from ..base_transform import LabelTransform
class Binary(LabelTransform):
r'''
Binarize the label according to a certain threshold. Labels larger than the threshold are set to 1, and labels smaller than the threshold are set to 0.
.. code-bloc... |
960e5d627c06e1f8775c6b1c47b09c6871724bfa214a91dcd91f0c2ec0abb4e7 | Python | 4,238 | 105 | from random import randint
import matplotlib as mpl
import matplotlib.pyplot as plt
import torch
import wandb
def plot_random_datapoint(model, data, device, scalers):
random_batch = next(iter(data))
random_data = random_batch["data"]
dict_flag = False
vae_flag = False
if isinstance(random_data, dic... |
dbbb5ecda987d81967bc0255303709f1888f81161e0d46f92cfdbd881e5a74f2 | Python | 4,238 | 95 |
import glob
import os
import pickle
import re
import numpy as np
import pandas as pd
from function_features import *
import sys
from choose_version import choose, chooselabel
from constants import VARIABLE
import h5py
import argparse
def main():
version = 20180409
arg_parser = argparse.ArgumentParser(
... |
e01e29cfbbeae526705f501a41b6b1bd96fa5c7bd908e556c8195996bd1f6faf | Python | 4,238 | 117 | import scanpy as sc
import numpy as np
from sklearn import metrics
from scipy.optimize import linear_sum_assignment
import torch
from torch import nn
class ZINBLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, mean, disp, pi, x=None, eps=1e-10):
if x is No... |
e12903d663f9b16d9432f4a0734d4b038076c516fa17ab9fa1cb4f2b2e632d6c | Python | 4,239 | 103 | """Bridge: turn an Allen CCF annotation coronal slice into the boolean
contour ``atlas_edge`` array the vendored mousebrain pipeline consumes.
This replaces ``mousebrain.atlas_segmentation`` (which extracts contour lines
from a printed colour atlas plate) with crisp region boundaries computed
directly from the in... |
7915df447d527003f04be8e9450caa343a1b56607362d5145c610bdc3ad991de | Python | 4,240 | 109 | """
This script reads the .asc eyetracker files in a folder and extracts information
about the fixation, saccade, and blinks for each file, following the guidelines in:
https://risoms.github.io/mdl/docs/build/manual/EyeLink%201000%20Plus%20User%20Manual%201.0.12.pdf
The script returns a dataframe with the information.... |
4d5d09a7721f50fce759e29a7394116a30e957f506ef80ed1c10513d4bf5c537 | Python | 4,241 | 104 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ht... |
61d4d8e652ba688ee73c844395129e1a4cf811057bcbf2b163b0fbfec2317fc8 | Python | 4,241 | 138 | #!/usr/bin/env python
"""
Create long-format dataframes from the full ISC similarity matrices
for each movie and Schaefer parcel.
Each output CSV contains the correlation value for each pair of subjects.
"""
import pandas as pd
from pathlib import Path
# -------------------------------------------------------------... |
dd6777e5a110e3aa1ee56cca4383e71cb4a12bafa9ce277b8e51739630716b9a | Python | 4,241 | 70 | """AT_REST.py 2026-09-28. Pre-registration: 04-NOTES/PREREG-2026-09-28c-AT-REST.md
Every ORN fires at its receptor's spontaneous rate (Hallem & Carlson 2006, DoOR row SFR). Does the model stay quiet at rest,
as fly projection neurons do (1-5 Hz; Kazama & Wilson 2009)?"""
import sys, os, glob, time, hashlib
sys.path.ins... |
27900f1c722eb32abcf2835fd95d342e1fbc9a6e49515be566b4059b0b2406ab | Python | 4,242 | 97 | import os
import csv
import math
from collections import defaultdict
from tqdm import tqdm
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.OPV")
@utils.copy_args(data.MoleculeDataset.load_smiles)
class OPV(data.MoleculeDatas... |
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