sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6e2ceb54f4c035330679b5c629cebd14ef8f6edf63a3f73c3b7ca37bda99a5c7 | Python | 5,406 | 153 | """Microsaccade event representation and post-detection rules.
The post-detection rules implement the variants Nouri et al. 2025 apply on
top of canonical Engbert-Kliegl detection:
1. Refractory merge: events whose onset falls within `refractory_merge_s`
of the previous event's offset are merged into the previous ... |
4ab4bc4f136b89ee8e677e4596dc7d30e58096fe3ca1b85678ba139ca1093d10 | Python | 5,407 | 213 | # 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... |
75f5ee8b9765166ac51d07d7d675b74124b26bba002149f547b6d7e30f4aeb76 | Python | 5,407 | 142 | #!/usr/bin/env python
# coding: utf-8
import os
from pydoc import classname
import numpy as np
import pandas as pd
import argparse
import numpy as np
import pandas as pd
import sys
from tabulate import tabulate
from constants import *
from src.training.train_utils import Logger
from src.models import get_classifier
fr... |
844f5a8afe395e1e74277de332adf2321c8167cf9c3dbdaccdb4b8dbb44e315c | Python | 5,407 | 150 | # coding: utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = n... |
1296d8048ad6e104d8ac0e86f2e50920a65a2e68fcc384b92969d7ce6afa850a | Python | 5,408 | 156 | """distutils.command.bdist
Implements the Distutils 'bdist' command (create a built [binary]
distribution)."""
import os
import warnings
from ..core import Command
from ..errors import DistutilsPlatformError, DistutilsOptionError
from ..util import get_platform
def show_formats():
"""Print list of available fo... |
29b8bcc080b928e704e7b42a20d6936538285a41807319efe5407d39555386e5 | Python | 5,408 | 135 | # 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... |
d10335fe702fbb6536602474a861425e3356ba0baa503b9eaadf59d8e9a5614d | Python | 5,408 | 200 | # Node-group asset "Find Bonded Atom" (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... |
023f6ae9d322e1bc7b6b6233e9ea80efb37f9231f2644378351590648b292b92 | Python | 5,409 | 117 | from elasticai.creator.file_generation.savable import Path
from elasticai.creator.file_generation.template import (
InProjectTemplate,
module_to_package,
)
from elasticai.creator.vhdl.auto_wire_protocols.port_definitions import create_port
from elasticai.creator.vhdl.design.design import Design
from elasticai.c... |
f9b85fa05c1c184e5418034510fbc95334efce8756a85cad03cf4ddabf28c5d6 | Python | 5,409 | 105 | # SPDX-FileCopyrightText: 2024 Blue Brain Project / EPFL
#
# SPDX-License-Identifier: AGPL-3.0-or-later
"""
Functions that calculate coupling coefficent and normalize them
(Pearson correlation of binned spikes/calcium traces with the mean centered population average)
authors: András Ecker, Daniela Egas Santander, Mich... |
4adc2ce96fa4574d273ca86c3256528fefe40702270e609183cc612b07afe604 | Python | 5,410 | 110 | import numpy as np
import pandas as pd
import sys
def get_row_at_region(df, key):
"""For use with count_df and region_df (cell_region_count.csv and region_counts.csv)"""
return df[df["region"] == key]
def get_row_at_parcellation_idx(df, idx):
"""For use only with CCF_all_regions / 1_adult_mou... |
3f8a0e15e858bebb7d1bdefc4597dad6590ea0c1fc81f3c697970e6c94a01c4e | Python | 5,411 | 143 | import json
import databpy
import numpy as np
import pytest
import starfile
from nodebpy.nodes.geometry import StoreNamedAttribute
from pandas import DataFrame
from scipy.spatial.transform import Rotation
import molecularnodes as mn
from tests.utils import GeometrySet
from .constants import data_dir
@pytest.mark.para... |
5423e8484998003708b2849306f6c2df8fb4d0cd0887c58bde20d4d7e963a5ea | Python | 5,412 | 181 | """Methods for reading and splitting sleeplab format data to tf.data.Dataset."""
import logging
import numpy as np
import sleeplab_format as slf
import sleeplab_tf_dataset as sds
import tensorflow as tf
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
component_config = {
'c... |
a5620100be9345911c8d0e0b78d7137d4f7e44440b21bfe8b85c8c4494460a24 | Python | 5,413 | 149 | import matplotlib
matplotlib.use("Agg")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import yaml, io
with open("./config/MNIST_config.yaml", 'r') as stream:
config= yaml.safe_load(stream)
config["default_path"]= str(config["default_path"])
config["cuda"]= str(config["cud... |
a9c7160d646f79bb1d94def8411f6fdce81f880be09244b3537a0d0e8b67a328 | Python | 5,414 | 182 | """Common workflows for phonons."""
from __future__ import annotations
from importlib.util import find_spec
from typing import TYPE_CHECKING
import numpy as np
from ase.atoms import Atoms
from monty.dev import requires
from quacc import job, subflow
from quacc.atoms.phonons import (
get_atoms_supercell_by_phono... |
dc87c81ae514640b08001f06c2d0b51ac0bf42ee07bdb0cd7aa880ff25f280b5 | Python | 5,414 | 184 | # 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... |
173c1925d99ee0d16373a5872660ca2b4206ffde3487c3ffa00bf0c204eb0cbf | Python | 5,416 | 154 | #
# 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,... |
76e1814a4cec7ab46284c0439bc0670651fa57a72ae5b2dfa423fbf5cf51bed5 | Python | 5,416 | 127 | """Implementation of the python API for the cell count of the flagser C++
library."""
from ._utils import _extract_unweighted_graph, _extract_weighted_graph
from .modules.flagser_count_pybind import compute_cell_count
def flagser_count_unweighted(adjacency_matrix, directed=True):
"""Compute the cell count per di... |
12357d2faa2da266b32fafce0a76bf22272c1837394b2384188bb5629d6499be | Python | 5,419 | 171 | from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt
import numpy as np
import math as m
from mpl_toolkits.mplot3d import Axes3D
import torch
import torch.optim as optim
from Six_hump_camel import Six_hump_camel_func
def set_para(lr):
# equivalent_momentum = 0.5
# momentum = 1 *... |
d51a6533784289676b0747a72f88a36d3a5a6e391574c10171adf5294c6ab72f | Python | 5,419 | 147 | # 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... |
d880b71bc2d47d7ef608621011a6221e5e7952b7ef8aae998b66f50f0caf4e5b | Python | 5,419 | 121 | import unittest
import torch
import numpy as np
from torcheeg.transforms import ToTensor, Resize, RandomNoise, RandomMask, RandomWindowSlice, RandomWindowWarp, RandomPCANoise, RandomFlip, RandomShift, RandomChannelShuffle, RandomFrequencyShift, RandomSignFlip, Contrastive, RandomMaskGrid, RandomMask2D
class TestTorch... |
0bcdbe9bbc3d37508c1e380d51718ae50692810c6b90be2fcbb6a4c7ea19bd94 | Python | 5,420 | 133 | import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import random
from dataloaders.data_loader_hyper import NetDatasetEpidemic
from torch.utils.data import DataLoader
import datetime
from model.resinf import ResInf
from engine import *
parser = argparse.ArgumentParser()
pa... |
2f7b7cff020ea2ff1e9bebd958e71b91db2bc1ee3737afe0a8d879a47ed63dde | Python | 5,420 | 147 | ######################## BEGIN LICENSE BLOCK ########################
# The Original Code is Mozilla Universal charset detector code.
#
# The Initial Developer of the Original Code is
# Netscape Communications Corporation.
# Portions created by the Initial Developer are Copyright (C) 2001
# the Initial Developer. All R... |
b6d7e9eb097569d49d589e668b9f994cab8fff2d34dd920fc967c1ff5a8026f6 | Python | 5,420 | 144 | import json
import uuid
from pathlib import Path
from unittest.mock import MagicMock
import pytest
import NDATools
from NDATools.clientscripts.vtcmd import validate
from NDATools.upload.cli import ValidatedFile
from NDATools.upload.validation.api import ValidationV2Credentials, ValidationV2
from NDATools.upload.valid... |
f02f41b60ed77b35f9941feb4d3c19bdd9693df3115a4f72238bc40d5c41293a | Python | 5,420 | 180 | from argparse import ArgumentParser
from pathlib import Path
import pickle
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from bed_reader import to_bed
from simulations.wrapper import simulate_agg_risk_and_dis_outc
from simul... |
3ea803eb5987d0a5c27a09a977e1c7466c057fff24e2b4b83241368d971ab81e | Python | 5,421 | 138 | #!/usr/bin/env python
# Copyright 2025, Felix Krueger (fkrueger@altoslabs.com)
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later ... |
70ec933b6259acba959d514d2534c937ca1387d1f0d5618854b8a07a21842162 | Python | 5,421 | 104 | """
Consensus assembly botany
last modified: András Ecker 02.2023
"""
import os
import numpy as np
import pandas as pd
from assemblyfire.config import Config
import assemblyfire.utils as utils
from assemblyfire.topology import AssemblyTopology, simplex_counts_consensus_instantiations
import assemblyfire.plots as plot... |
8baf9e81d45c786fa9833da04adbe65f3cbcd79b36e031e4b5ebbfd903465e06 | Python | 5,421 | 175 | """Optuna example that optimizes the hyperparameters of
a reinforcement learning agent using A2C implementation from Stable-Baselines3
on a Gymnasium environment.
This is a simplified version of what can be found in https://github.com/DLR-RM/rl-baselines3-zoo.
You can run this example as follows:
$ python sb3_sim... |
2e3ec60df8b61a254fc7044408d2dd3c8d0b919baad55e5f103821bf06648878 | Python | 5,422 | 154 | # -*- coding: utf-8 -*-
"""Dcm2niix class"""
import logging
import os
import shlex
import shutil
import tarfile
import zipfile
from glob import glob
from dcm2bids.utils.io import valid_path
from dcm2bids.utils.utils import DEFAULT, run_shell_command
class Dcm2niixGen(object):
""" Object to handle dcm2niix exec... |
3427c0ac48174434c93dcd918c9a5b5db596b9e9a27d1ddd1a472a38c292eb28 | Python | 5,422 | 146 | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import pathlib
import torch
from esm import Alphabet, FastaBatchedDataset, ProteinBertModel, pretr... |
c0d79800b2489e4f9f310cccfdc3a7c7c6aea75551bd1e69312005d086e3f703 | Python | 5,422 | 121 | '''
Description:
Run MIOFlow on single-cell data.
'''
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy
import torch
import sys
sys.path.append("MIOFlow_model_revised")
from baseline.MIOFlow_model_revised.running import trainModel, makeSimulation
from benchmark.BenchmarkUtils im... |
4ea0c7cbd2fd27fac448c4792104a1e6c96fbe1f48aa695323f92b216ac2971d | Python | 5,424 | 73 | """SIGN_PATCH.py 2026-09-30. Writes a small, standalone sign patch for Shiu et al.'s Connectivity_783.parquet:
every antennal-lobe local neuron inhibitory except those a laboratory has shown to be cholinergic (the 'lab prior', F4 of
HEAD_TO_HEAD: 50/50 glomeruli healthy, real-fly r 0.736, taste unchanged, the only cond... |
ff180071d14aaf440d63087f8bdf0053d86f7cfdd7db0024844945711445dd4a | Python | 5,424 | 158 | """
pygments.formatters
~~~~~~~~~~~~~~~~~~~
Pygments formatters.
:copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
:license: BSD, see LICENSE for details.
"""
import re
import sys
import types
import fnmatch
from os.path import basename
from pip._vendor.pygments.formatters._mapp... |
96af92c131b7e37c5ecbd9ce6d184e4f69d3ab610c8853554826f605b91b4e91 | Python | 5,425 | 176 | """Module to filter the data.
"""
from logging import getLogger
from itertools import product
from numpy import arange, empty, ix_, expand_dims, squeeze
from scipy.signal import (iirfilter,
iirnotch,
filtfilt,
get_window,
... |
c6a9db6833d1a3415cb64ef88e8bcee28c62dc208dbe997c04cb123a1a534fef | Python | 5,425 | 196 | """Get E3FP default parameters and read parameters from files.
Author: Seth Axen
E-mail: seth.axen@gmail.com
"""
import os
import copy
import ast
from configparser import (
ConfigParser,
NoSectionError,
DuplicateSectionError,
)
CONFIG_DIR = os.path.dirname(os.path.realpath(__file__))
DEF_PARAM_FILE = os.... |
c7fd242e5fa25f5ef98424310ef43709f25a93aff68d6c39fd161d7d9a0f19af | Python | 5,425 | 145 | import torch
from torch.optim import Adam
import yaml
from combust.layers import get_activation_by_string
from combust.models import NewtonNet
from combust.train import Trainer
from combust.data import parse_ani1x
# torch.autograd.set_detect_anomaly(True)
torch.set_default_tensor_type(torch.DoubleTensor)
# settings... |
ffb94f8c7378bca48b057f09924563cb1bae15646670ce7473d2b6fe307f0908 | Python | 5,425 | 131 | import os
import logging
import numpy as np
from typing import List, Optional
from lightning.pytorch import Trainer
from lightning.pytorch.callbacks.model_checkpoint import ModelCheckpoint
from lightning.pytorch.strategies import DDPStrategy
from cellmincer.datasets import build_datamodule, wipe_temp_files
from cel... |
402020da42da7e56e6b923ec72684880b135803c93481b1aa2848a12a6d861c6 | Python | 5,426 | 136 | import os
from scipy.io import loadmat
import numpy as np
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from datasets.SequenceDatasets import dataset
from datasets.sequence_aug import *
from tqdm import tqdm
#Digital data was collected at 12,000 samples per second
signal_size = ... |
c6ec86e17bc51904f25b4bc0d2c2e397665d9d72f939e07923b0828761935ce8 | Python | 5,426 | 108 | import pickle as pk
from os.path import exists, join as joinP
from copy import deepcopy
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics.pairwise import manhattan_distances
from utils_general import fitting_tsne, sim_matrix
def plot_ppty(df_info_val, X_tsne, colours, alph=... |
e4d7bf0bd2c8680bc1a376f0229cc1eb543e59fa676297f13708dc32f57aae4f | Python | 5,427 | 138 | """
======================
Run PREP in stages
======================
:meth:`~pyprep.PrepPipeline.fit` runs the whole PREP pipeline in one call, but
sometimes you want to drive the individual stages yourself, for example to log
progress, plot intermediate results, or decide what to do with bad channels
*before* commit... |
f8bee1896a98c6c0aa51556a79254b557ef63ee478fafbeb02225e308eed34a4 | Python | 5,427 | 115 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2024 Clinic for Diagnositic and Interventional Radiology, University Hospital Bonn, 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 ... |
5d47d9819ad823eb8d3bb94ea8a0814fffc110ee71d1bcb9c5b799c01c50e826 | Python | 5,430 | 138 | """Write the report's tabular data as spreadsheet CSVs under ``<output>/report_csv/``.
The interactive report is self-contained HTML, so its derived numbers (per-region
counts/density/enrichment, hemisphere split, regional volume change, QC checks,
solver convergence) otherwise live only inside the page. This modu... |
c7b6386c6dfec3a225da895f04a7f312cebfc853a13200e6ecac8ff65a49f850 | Python | 5,431 | 170 | import warnings
from collections.abc import Callable, Iterable, Sequence
from dataclasses import dataclass
from itertools import starmap
from typing import Any, Protocol, TypeAlias, override
import elasticai.creator.function_dispatch as FD
from elasticai.creator import ir
from elasticai.creator import plugin as _pl
fr... |
2ae9f20c27c1d1f6c491dfe9f64427cf987920b2b210ff649a2b61f8c9470764 | Python | 5,433 | 115 | import argparse
import collections
import pandas as pd
from mode import *
from utils import set_random_seed
import logging
from parse_config import ConfigParser
SEED = 1126
set_random_seed(SEED)
#run main using a gridsearch
def main(config):
logging.info('Using Random Seed {} for this experiment'.format(SEED))
... |
6cf950aaaae1aa346e886e67c09e898b78c4d5c437834501d5400a24707ace30 | Python | 5,434 | 181 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from cellsim16popsParams_modified_regular_input import multicompartment_params
from plot_methods import network_sketch, plot_population, plot_signal_sum
import analysis_params
from hybridLFPy import CachedNetwork
import plotting_helpers as phlp
import os
from matplotlib imp... |
0cbcd2caa2935eac7f32da5b737175fdecc40605a00d770912f37a199fca98d1 | Python | 5,436 | 182 | """Core recipes for the tblite code."""
from __future__ import annotations
from importlib.util import find_spec
from typing import TYPE_CHECKING
from monty.dev import requires
from quacc import job
from quacc.runners.ase import Runner
from quacc.schemas.ase import Summarize, VibSummarize
from quacc.utils.dicts impo... |
323d214c6ed8ec1577e23be17b2561aff9bb90d8147d8bc1b8bb418428c0b24b | Python | 5,436 | 139 | import numpy as np
import pandas as pd
import pytest
from cuppa.classifier.cuppa_prediction import CuppaPrediction
from cuppa.constants import PREDICT_NA_FILL_VALUE, CLF_GROUPS
from tests.mock_data import MockTrainingData, MockCuppaClassifier
from cuppa.classifier.cuppa_classifier import CuppaClassifier
from cuppa.cla... |
26b6b2545e692c2ed3d34a884f16df580c218711c5d7f4a5c43be146e7268222 | Python | 5,437 | 72 | """ORN_COUNTERFACTUAL.py 2026-09-23. Pre-registration: 04-NOTES/PREREG-2026-09-23c-ORN-COUNTERFACTUAL.md"""
import os, re, numpy as np, pandas as pd, scipy.sparse as sp
from scipy.sparse.linalg import eigs
R = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
L = []
def p(m=''): L.append(str(m)); print(m, fl... |
41e920ec30458f32896384d25e5423ae0981dc41687b276756f44adccb71453f | Python | 5,437 | 151 | # 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... |
8a66e676f35d4807ce4bc145e1c6e6561e22cc362ebff891f3f06346d023ac63 | Python | 5,438 | 149 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 15 15:28:55 2024
@author: felipe
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import scipy.io as sio
import sys
import os
sys.path.append(os.path.abspath('../../'))
sys.path.append(os.path.abspath('.... |
f7ce6fb5ae178a202640ccd32c95c2258dd49ba64451263623c0f69b827d20e5 | Python | 5,438 | 153 | # EyeNED OCT-ONH segmentation and feature extraction
# Copyright (C) 2024 Erasmus MC
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your optio... |
e975729ee7d744604d2597e62c993619d92475f7ef8070a0b082cd437c60f633 | Python | 5,439 | 108 | #!/usr/bin/python
from __future__ import print_function
import socket
import sys
import os
import subprocess
use_multiprocessing = True
if use_multiprocessing:
import multiprocessing
max_cpus = 2 # We might want to not run on the full number of cores, as Rosetta take about 2 Gb of memory per instance
######... |
5560d0343cf607494e38a296a937437b9e176e99b4ae346ee7c4ee304500f915 | Python | 5,440 | 156 | #!/bin/env python
import tensorflow
import deepexplain
import click
import csv
import logging
import gzip
import numpy as np
# options
@click.command()
@click.option('--sequence',
'sequence_file',
required=True,
type=click.Path(exists=True, readable=True),
he... |
2dc003ef1389bb3162355456cfc79c02f416f40842454b61be005af57f09839b | Python | 5,442 | 168 | import numpy as np
import jax.numpy as jnp
import jax
from netket.stats import Stats
from tre_tvmc.driver.utils import print_mpi
class TrackBestModel:
def __init__(self, monitor: str = "mean"):
self.monitor = monitor
self.best_loss_value = np.inf
self.best_loss_stats = None
self.b... |
d96fb3998a76145195fb112b7132aef1b9d3307a3a4427547b07ebc386cf0b3d | Python | 5,442 | 125 | options = dict(
verbose=True,
model_extensions=[
'h2o.model.extensions.VariableImportance',
],
)
def update_param(name, param):
if name == 'stopping_metric':
param['values'] = ['AUTO', 'AUUC', 'ATE', 'ATT', 'ATC', 'qini']
return param
if name == 'distribution':
para... |
814e568ce0111f855d9e277cad6cb36d51d8c8cc7c7b935b404bce1d910c456b | Python | 5,443 | 131 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 12 11:32:51 2018
@author: Carlos Atico Ariza, PhD
"""
# Creation of pandas DF with features for each image at botleneck
# imports the FeatureGen class found in the generate_features.py module
import os
from generate_features import FeatureGen
from p... |
f78c52f5e92ca17248a89c5fa2fe1f1bd2ee5cec7bc9a07f6a92c26e0160e449 | Python | 5,443 | 127 | # set QT_API environment variable
import os
os.environ["QT_API"] = "pyqt5"
import qtpy
# qt libraries
from qtpy.QtCore import *
from qtpy.QtWidgets import *
from qtpy.QtGui import *
# app specific libraries
import control.widgets as widgets
import control.camera as camera
import control.camera_TIS as camera_tis
impo... |
dc3fd8a216eaa2d411fe41bf47e2fb76d7563bdc3c00db4e80f52af1f4baee75 | Python | 5,444 | 157 |
"""
@Mariellapanag
Created on Feb 2023
@author: mariella
Functions for computing abnormalities from continuous iEEG data.
Includes
power signal : compute power of signal
energy_range : compute energy range for each signal
get_cycles: obtain cycles using bandpass filter
TODO:
"""
# internal modules
i... |
9b016f6c20051312f99f9728bb88f3c00729530e3ebdc711e23cfd84353df488 | Python | 5,445 | 154 | # Node-group asset "Dihedral Angle" (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_CHECKIN... |
12796366b4ac72aa7705e9982ce2d613f54e94f1a470d494485e4527e9ba9561 | Python | 5,446 | 120 | """Source order of get_library_data: GitHub first, then the cached copy, then the packaged copy.
The registry URL is pointed at a real local file (a file:// URL, which urllib serves like any other) so these
tests never reach the network, and each test uses its own cache folder. The module constant is set directly
in e... |
27ca0c5e7a232921126dcf8162b03b3ca08a721595030d5b7760a98c4750af1b | Python | 5,446 | 169 | # 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... |
ca3a693119a13b73cf5bb23a001984554ea977e4dee1dddf93e09ecbd4b3a702 | Python | 5,446 | 152 | import os
import argparse
import json
import xml.etree.ElementTree as ET
from typing import Dict, List
from tqdm import tqdm
import re
def get_label2id(labels_path: str) -> Dict[str, int]:
"""id is 1 start"""
with open(labels_path, 'r') as f:
labels_str = f.read().split()
labels_ids = list(range(1... |
76c96d60f4fe169f8502b4d85d4b0abf7d551b228a3e80270c5efc566d1d22fc | Python | 5,447 | 132 | import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import random
from dataloaders.data_loader_general_para import NetTrainDataset, NetTestDataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from pathlib import Path
from model.resinf import ResInf
from eng... |
b58b0843112c285d023bbc754c820a0c0c8a43a395955f19cea17654c8502e09 | Python | 5,450 | 156 | """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
Attributes:
_out_channels (list of int): specify number of channels for each encoder feature tensor
_depth (int): specify number of stages in decoder (in other words number of downsampling operations)
_... |
eb142e82f03050ea8fd423e1eb33ba966f61a0e34b6b448750420ae0cfebb378 | Python | 5,450 | 133 | import numpy as np
import pandas as pd
import h5py
import os
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
from collections import Counter
import seaborn as sns
fig = plt.figure(figsize=(10, 5), dpi=500)
plt.rcParams.update({'font.family': 'Arial','font.size': 12, 'font.weight': 'normal'})
gs ... |
2e5e6d59e6dbdf3fb32feaaa7b49021756d90a92af30112876a8abcfa333f594 | Python | 5,452 | 161 | # Plots probabilities/decision boundary to classify preferential parameters
# for Fig 7B
from os.path import join
from get_paths import *
import classification
import numpy as np
import matplotlib.pyplot as plt
from TVATAnalysis import TVATAnalysis
from colors_paultol import cmap_iridescent as cmap
import ... |
73e562a34050636a8053df6f77420e2314192ab0dde37634e5c0c49eea6baaf5 | Python | 5,452 | 138 | import librosa
import numpy as np
import os
import random
import pickle
from tqdm import trange
def process_google_sample(sample_path,frame_length = 0.04,frame_shift = 0.02,n_mfcc = 40):
if(type(sample_path)==str):
signal, sr = librosa.load(sample_path,sr=16000)
else:
signal=sample_p... |
bbfd33bc86d098ca61cd59bcfbfad74b079fe85b808ff2e4d56c96526b428399 | Python | 5,454 | 118 | import torch
import torchvision
from torch import Tensor
from typing import Callable, List, Literal
from .trainingwrapper import TrainingWrapper
class SpatialSoftmax2d(torch.nn.Module):
"""Non-linearity like layer that applies a softmax in a local neighborhood.
Shape:
- Input: `(N, C, H_{in}, W_{i... |
cc79c44617ee6e6bcac8cde481d00eb34758162a947f3291f8302de32d0d58e2 | Python | 5,454 | 165 | from pip._vendor.packaging.specifiers import SpecifierSet
from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
from pip._internal.req.req_install import InstallRequirement
from .base import Candidate, CandidateLookup, Requirement, format_name
class ExplicitRequirement(Requirement):
def __in... |
0440a20fc4cc29824ba2b9a2539d57b9672fa9dc957a13fd4d2d81fedbc31248 | Python | 5,455 | 130 | import numpy as np
import filenames
import read_files
import write_files
import main_config as conf
import optimiser
import printer
def prepare_procedure(mode: str, algorithm: str, dataset: str, test_id: str):
"""
Offline preparation for real-time VMD-based mode removal and pre-processing
:param mode: 'I... |
a61d763be6adb63480678e7a8d537a2a08a1fe0e4697a859207a93d18219e471 | Python | 5,456 | 117 | import scanpy as sc
import anndata as ad
import numpy as np
import pandas as pd
import Cell_BLAST as cb
from time import time
import random
from memory_profiler import memory_usage
cb.config.N_JOBS = 4
cb.config.RANDOM_SEED = random.randint(0, 10000)
def my_func(adata):
sc.pp.filter_genes(adata, min_cells=3)
... |
fce01f790f211b8e3f14873d9fef69f83501a49ce367eb540134d336861c8035 | Python | 5,456 | 114 | import os
from typing import Tuple, Union
import pandas as pd
from sklearn import model_selection
from torcheeg.datasets.module.base_dataset import BaseDataset
from ..utils import get_random_dir_path
from .utils import create_subset, ensure_cache, read_indices, write_indices
def train_test_split_per_subject_cross_t... |
8d20b8c5f2dd70c35bb5b587b69cdb16435ad16ee4bdffff9ec627d780bf0045 | Python | 5,457 | 207 | import os
import pathlib
import tempfile
import functools
import contextlib
import types
import importlib
import inspect
import warnings
import itertools
from typing import Union, Optional, cast
from .abc import ResourceReader, Traversable
from ._compat import wrap_spec
Package = Union[types.ModuleType, str]
Anchor ... |
626b20c309c42938d26b44d50a730a55b9c087e70f090a4d1e0085fe64e61ed7 | Python | 5,458 | 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... |
81b86b05622ddd4a754a620e5fdfb5537b964e7299b0f0f515ff5a5047599c83 | Python | 5,458 | 131 | """Tests for damped-FPI inversion of the Laplacian pull field at points.
The Laplacian PDE produces a *pull* field ``d`` such that the volume
warp is ``refined[T] = reg_vol[T + d(T)]``. To move a point ``P`` in
registered space through the same warp we need ``F`` with
``F + d(F) = P``.
Plain FPI (3 iterations)... |
77729139593fa5c9453c843d67f9f41bbfa22db7785c73c473920c83ea4c713b | Python | 5,459 | 168 | """
From a mask with sources and sinks computes the max flow network within a GraphMCF
Input: - GraphMCF (pickle file)
- Mask (1-sources, 2-sinks, othwise-background)
Output: - Max flow network stored as edge GraphMCF property
"""
__author__ = 'Antonio Martinez-Sanchez'
# ################... |
087638c824dbb894c24c1ad76ceafa19e31e55e7d9433f70d8c2889c5a4be73f | Python | 5,460 | 126 | import logging
from typing import Callable
import torch
from torch.utils.tensorboard import SummaryWriter
import torch.nn as nn
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
from tqdm import tqdm
from .nt_xent import NT_Xent
from chemprop.args import TrainArgs
from chemprop.data i... |
0dee9ed4df54c5cdc0f6e0aad14ced74f1418d3bde2accbe920ff63974f2ce34 | Python | 5,460 | 202 | import numpy as np
from autograd.builtins import SequenceBox
from autograd.extend import Box, primitive
from autograd.tracer import trace_primitives_map
from . import numpy_wrapper as anp
Box.__array_priority__ = 90.0
class ArrayBox(Box):
__slots__ = []
__array_priority__ = 100.0
@primitive
def __... |
a3062fc6615378fbe858f74904d986935bbce9f1eeda9a711261175e946b8068 | Python | 5,460 | 166 | models_to_run = [
'alexnet_classification',
'vgg16_classification',
'resnet18_classification',
'resnet50_classification',
'resnet101_classification',
'resnet152_classification',
'squeezenet1_0_classification',
'densenet121_classification',
'googlenet_classification',
'shufflenet_v2_x1_0_classification',
'mob... |
c0c9fe9b8523780c2073800d530eb56584b00c86efb33bdff32c31a26bfcb0e7 | Python | 5,460 | 128 | import torch
from .decode_strategy import DecodeStrategy
def sample_with_temperature(logits, sampling_temp, keep_topk):
"""Select next tokens randomly from the top k possible next tokens.
Samples from a categorical distribution over the ``keep_topk`` words using
the category probabilities ``logits / samp... |
d07eaf052d3465ce5ff6cd67d0bf4d152336d5abbbfd630602282ff40afb2bc4 | Python | 5,460 | 135 | import os
import imageio.v2 as imageio
import numpy as np
import pytest
from regtools.atlas.registry import TEMPLATE_IMAGE, ANNOTATION_IMAGE
from regtools.geometry_registration import pipeline
from regtools.geometry_registration.types import RunParams, RunResult
from regtools.utils.io.volume_io import load_n... |
52eafb4a5d2d3830b852ded6119380e13d22865fd3f07497a859cd0a5cf2abd7 | Python | 5,461 | 185 | import pandas as pd
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from utils import Seq2Tensor, reverse_complement
class TrainSeqDatasetProb(Dataset):
""" Sequence dataset. """
def __init__(self,
ds: pd.DataFrame,
use_re... |
c092585d34a8b49f3cbc2315b19de675b98eafa270610c91cc9cc3460299a22a | Python | 5,461 | 163 | # 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... |
185a0458733e189c9556bdcd7c3a03202d8375979698943aab9011ad0973d54b | Python | 5,462 | 160 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''This script performs statistical analyses on decoding coefficients transformed into patterns of activity
using cluster based permutations.'''
# Setup directories
proj_name = 'MINDLAB2020_MEG-AuditoryPatternRecognition'
wdir = '/projects/' + proj_name + '/scratch/work... |
c1087c7b92e33cc1902ea527d79ca08071d43b4e8feab9d49b0bf09d8111222f | Python | 5,462 | 140 | import anndata as ad
import numpy as np
import pandas as pd
import pertpy as pt
from pertpy.tools._perturbation_efficacy._mixscape import MixscapeGaussianMixture, _mixscape_gmm_predict_proba
NUM_CELLS_PER_GROUP = 10
ACCURACY_THRESHOLD = 0.8
def test_mixscape(adata):
adata.layers["X_pert"] = adata.X
mixscape... |
24ac43cfa61497719757485c7eb5be4cd6e7c606a76c804468b6a3ffd73b50da | Python | 5,463 | 131 | """
Copright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import patches
from fig_utils import *
def fig5(root, psths, rts, gts, save_figure=True):
il = 0
nk, nn, nt2 = psths.s... |
dee23dce8c87095a761fec1115b87802a1518dad9886790f62bca1a127e0f364 | Python | 5,463 | 165 | '''ShuffleNet in PyTorch.
See the paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def conv_bn(inp, oup, stride):
return nn.Sequential(
nn... |
df3b994de4e9dd5518a716cf7b3db97eda428bb403249215b06e89d0b452053d | Python | 5,463 | 178 | from __future__ import annotations
import gzip
import logging
import os
from pathlib import Path
import pytest
from quacc.utils.files import check_logfile, copy_decompress_files, make_unique_dir
LOGGER = logging.getLogger(__name__)
LOGGER.propagate = True
def test_make_unique_dir(tmp_path, monkeypatch):
monke... |
12e32d455dc4e2271b2fb5b945eb9be2615c37a69250dc8c3e3d4dbeb36eeecc | Python | 5,464 | 189 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICU... |
376800b6eec1fda3f70db402db73a027fc41290e6fed5c5b865d47b36ff618db | Python | 5,464 | 189 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICU... |
4ce5ebfc5c3e47cd7daa2ba4e9cdf1b70c769d6286286fd2739059e717edd99d | Python | 5,464 | 189 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICU... |
848855b987a1352f8c380164bdf7fe4665214b827ec778f9215eb0b8e4991c48 | Python | 5,464 | 189 | #!/usr/bin/env python
"""Python script to run cell model"""
"""
/* Copyright (c) 2015 EPFL-BBP, All rights reserved.
THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS''
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICU... |
a492ccd46d9075618bcaffc5ffee8e0a6f1c441d46bc1992f61c58706156c9f8 | Python | 5,464 | 125 | import json
import os
import pickle
import argparse
import numpy as np
import ramanspy as rp
def run(
image_type,
num_endmembers=5,
spectral_size=1000,
mixture_mode='linear',
image_size=100,
realistic_endmembers=False,
noise=False,
noise_amplitude=0.1,
... |
b405514bb10d3381286eaf08dffcabb416e20f6e3955e6ef6041884e6f293c0f | Python | 5,464 | 138 | """The 2D foreground masker: GrabCut is the default for foreground_init + metrics.
Wires the benchmark-winning seeded entropy GrabCut (``compute_entropy_grabcut`` with its
default config = percentile 1-99.5 + Multi-Otsu-4 seed) in as the default foreground
silhouette for 2D ``foreground_init`` (scale + centroid) a... |
3acff8ba22235d669a6cff6e188bc88a692e7db8ea14fe9b3e4ab79bc9c4beec | Python | 5,465 | 112 | """
STABILITY_BOUND.py written 2026-09-07
FROM MEASUREMENT TO THEOREM. The placement conjecture says real inhibition sits where it
suppresses excitatory recurrence. That is an observation. This asks whether it is FORCED.
THE THEOREM PART, and it is classical two-population algebra, provable on a napkin.
Mean-field... |
f31c1d65f8ae98cd9d738acc359bf08d622dfa818a2d2cfd5f159d4253a5af08 | Python | 5,465 | 171 | import control.RCM_API as RCM_API
import json
class NL5:
def __init__(self):
self.rcm = RCM_API.RCM_API()
self.rcm.initialize_device(simulated=False)
self.load_settings()
def set_scan_amplitude(self,amplitude):
self.scan_amplitude = amplitude
self.rcm.set_float_pa... |
4f9639ce6a4517789e1d04cd64ba5d90513908330c01fb45cd2b5f6049cce6c2 | Python | 5,466 | 159 | # -*- coding: utf-8 -*-
#
# Short-time Fourier transform, uses np.fft as backend
#
# Builtin/3rd party package imports
import numpy as np
import scipy.signal as sci_sig
import logging
import platform
# local imports
from ._norm_spec import _norm_spec
def stft(
dat,
fs=1.0,
window=None,
nperseg=256,
... |
836232444514263a391e4bd7daa9e7c0458fae6450f38662af1963f768b9e297 | Python | 5,466 | 157 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Example plot for LFPy: Single-synapse contribution to the potential
through head
Copyright (C) 2017 Computational Neuroscience Group, NMBU.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as pub... |
9127c8be8567ac5bf84f4f5ec13afecfb8042fdcb56a9b94770e80b57f69df9e | Python | 5,466 | 154 | from typing import cast
import bpy
import numpy as np
import pytest
from bpy.types import Camera, SpaceView3D
import molecularnodes as mn
from molecularnodes.utils import frame_mapper
from .constants import codes, data_dir
def _viewport_spaces():
return [
cast(SpaceView3D, space)
for screen in bpy... |
ad539af134fdd553db8d8720752344a569fba2e2cff0d952866456d666fbd746 | Python | 5,466 | 128 | import numpy as np
from .io import writeH5, readH5, readNd2, readXlsx
from .image import imAdjust, imTrimBlack
import matplotlib.pyplot as plt
class tilesData:
# input: nd2 and xlsx file
# output: on-demand volume at given downsample ratio
# assume zyx order
def __init__(self, tiles_vol, tiles_info, ti... |
fa0555c6b1030a2c3fe1ad289e2b9202e5c920d8900e6772e409db2fe4492836 | Python | 5,467 | 145 | #
# 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,... |
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