sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
33ea060e7d5c1bdd4dd06b424ac2ed0cc6b40ef85afc39ba623d076262664106 | Python | 4,082 | 128 | # Taken from a PyTorch Lightning tutorial
# https://pytorch-lightning.readthedocs.io/en/stable/notebooks/lightning_examples/cifar10-baseline.html
import os
import argparse
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from pl_bolts.datamodules import CIFAR10DataMo... |
70bcb90df18d8b21c48599f4b4d1b0b393c2adad0163153b4f4069adacc0d94d | Python | 4,082 | 96 | '''
airPLS.py Copyright 2014 Renato Lombardo - renato.lombardo@unipa.it
Baseline correction using adaptive iteratively reweighted penalized least squares
This program is a translation in python of the R source code of airPLS version 2.0
by Yizeng Liang and Zhang Zhimin - https://code.google.com/p/airpls
Reference:
Z.... |
8d5caa833285160a756e888d36c81df37ac875dea4c88c2bfe66cf99a21afe10 | Python | 4,083 | 120 | import os
import numpy as np
import requests
from collections import Counter
from rdkit import Chem
from tqdm import tqdm
from time import sleep
import logging
import matplotlib.pyplot as plt
import seaborn as sns
def seq2rat(sequence):
# dict count stores information for each sequence
dict_count = dict(Cou... |
ab36e767dbe01711fde264601dbc03dd09a5b99458925012c9b2ffb4b8a5dc1d | Python | 4,084 | 119 |
# -*- coding: utf-8 -*-
#
#
# PyRates software framework for flexible implementation of neural
# network model_templates and simulations. See also:
# https://github.com/pyrates-neuroscience/PyRates
#
# Copyright (C) 2017-2018 the original authors (Richard Gast and
# Daniel Rose), the Max-Planck-Institute for Human ... |
11f64c921cd5605c12648b3586f9f9864ef050aa99d446e5cf5757c677322984 | Python | 4,085 | 116 | """I/O functions for Adjacency objects."""
from pathlib import Path
import numpy as np
import polars as pl
from nltools.io.h5 import _read_polars_frame, _reject_legacy_h5, _require_h5
def _write(adj, file_name, method="long"):
"""Write an Adjacency to a `.csv` or `.h5` file.
HDF5 is the round-trip format:... |
a1e2e6285c0184a83a9ff5b9c66df4548bc322aef9654c0e60f8e0122eebcfb9 | Python | 4,085 | 111 | """
The GOOD-Arxiv dataset adapted from `OGB
<https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html>`_ benchmark.
"""
import os
import os.path as osp
import gdown
import torch
from munch import Munch
from torch_geometric.data import InMemoryDataset, extract_zip
class GOODArxi... |
a776a65449bf41991656c8b28ff49403100546448ad7158c2f7caf617b109d3a | Python | 4,086 | 122 | """
Is the max-tree really the exact TFCE integral?
This is the claim the whole transform rests on, so it is checked against an
*independent* implementation: the obvious one, which steps the height over a grid
and labels connected components with ``scipy.ndimage.label`` at each step. That
reference has a discretisatio... |
3dede757eacc989588f0028cce46c4ba43f3cb5108bddad032c6547b1395f45e | Python | 4,087 | 84 | import os
import tarfile
from os import PathLike
from pathlib import Path
from tempfile import TemporaryDirectory
from urllib.request import urlretrieve
from .utils import _print_progress
DATASET_URLS = {
"AFM-ice-Cu111": "https://zenodo.org/records/10047850/files/AFM-ice-Cu111.tar.gz?download=1",
"AFM-ice-Au... |
9623b5abd22de2ceea31bb142b696cdce7e80d818ee39fbc22ab72abe35cc013 | Python | 4,088 | 117 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... |
0d6313ba5ba60509d5087ec4c54821f7dd5d7c41720e923ca5a52ed4cf9d2b00 | Python | 4,089 | 145 | #!/usr/bin/env python3
"""Create the build metadata bundled with a packaged SanPy application."""
from __future__ import annotations
import argparse
import json
import platform
import subprocess
from datetime import datetime
from importlib.metadata import PackageNotFoundError, version
from pathlib import Path
PACKA... |
719686c2dbcde661537e4cef7e6f91f12605a7bdea934e2fed097d2b9348424c | Python | 4,089 | 128 | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Ke Sun (sunk@mail.ustc.edu.cn)
# ------------------------------------------------------------------------------
from ... |
0ee4d1678bbcdbc3b14a3c3843481e7f2790a0cfa1f65d5410f7df7b0c67ad41 | Python | 4,091 | 97 | # Copyright 2020-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from enum import Enum
# Define the model names. This is a non exhaustive list of models that can be used in the tests
class ModelName(Enum):
"""
Enum class to define the model names.
"""
# IMP - The model name must be same (... |
31029431f63a832c781f79b3de9e1e26b24290dbf276a6448e16b3be49bde41c | Python | 4,092 | 101 | """Keep proprietary held-out data out of the repository and out of every release.
TCRvdb / MATCHMAKERS (Messemaker et al., doi:10.1101/2025.04.28.651095) is licensed for academic,
non-commercial use with no redistribution, in whole or in part. So TCRvdb may be read during
validation and must never be written anywhere ... |
d16a5b6fb8c7e939863f67274ecd875ee8ba1dc1e71f292a420a2d1181b68a2f | Python | 4,092 | 121 | import sys
import os
import numpy as np
import LFPy
import json
from cortical_neuron import cortical_neuron as init_neuron
import csv
from neuron import h, load_mechanisms
from scipy.stats import poisson
from math import ceil
def load_spike_times(spikingFile):
""" Load spike times from the file specified
by th... |
663e7e7c17a3fcee0693a5ce2a5d1bfb89aa1951074a3e5b3d12aa2d00375455 | Python | 4,093 | 93 | """
Detect waves by clustering triggers that are close to each other in time and space.
"""
import neo
import numpy as np
import quantities as pq
import argparse
from sklearn.cluster import DBSCAN
from utils.io_utils import load_neo, write_neo
from utils.neo_utils import remove_annotations
CLI = argparse.ArgumentPars... |
95028dbb32b55a0507a88d751c719e7dedcbe94263d70d822a3902765741bcdb | Python | 4,093 | 112 | import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
df... |
e01dc9adeb6d4bb3fa842062f4aad6b23c24c4d3e21b13574173a235e49b64fe | Python | 4,096 | 142 | # Author: Guoqing Bao
# School of Computer Science, The University of Sydney
# Date: 2019-12-12
# GitHub Project Link: https://github.com/guoqingbao/Pathofusion
# Please cite our work if you found it is useful for your research or clinical practice
# from IPython import get_ipython
import os
# os.environ["CUDA_VISIBLE... |
359a73c4633a9a2183df8dd281fa49977380aa2a7cea01ec0c54f4fd7df44f0a | Python | 4,097 | 114 | # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... |
393fc8c01c1e9a0a5e2614596eed72cfcbc49d755c1cabaa2febf8079bbbddf5 | Python | 4,099 | 94 | """
Detect waves by clustering triggers that are close to each other in time and space.
"""
import neo
import numpy as np
import quantities as pq
import argparse
from pathlib import Path
from sklearn.cluster import DBSCAN
from utils.io_utils import load_neo, write_neo
from utils.neo_utils import remove_annotations
CL... |
4555048736ac548b89f696396625b2ad11f1f90881bd2c3e8f1cfbd75efa8fcd | Python | 4,099 | 112 | import torch
import torch.nn.functional as F
import torch.nn as nn
import pytorch_lightning as pl
import torchvision
class VisionTransformer(pl.LightningModule):
def __init__(self,classes,multiLabel=False,LR=1e-3,optim='SGD',momentum=0.99,pretrained=False):
#LR:learning rate
#optim: SGD or Adam
... |
00578af9341688334dfd133e0668d6ec258087fd07337bc596f5a4fbbd987ff2 | Python | 4,100 | 108 | # Copyright 2020-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from openfl.experimental.workflow.interface import FLSpec
from openfl.experimental.workflow.placement import aggregator, collaborator
import logging
logging.basicConfig(level=logging.INFO)
log = logging.getLogger(__name__)
class TestFlowIn... |
0cb079592df9676697a7048897e87a0edd9bc9ff09e8baa3ef664d4033e6e394 | Python | 4,100 | 87 | #Import the Libraries
import numpy as np
from scipy.io import loadmat # this is the SciPy module that loads mat-files
import matplotlib.pyplot as plt
from datetime import datetime, date, time
import pandas as pd
import os
from natsort import natsorted
import re
#Make a list of the files
#Mat_Beh_dir='/ZPOOL/data/proj... |
54d8978f515932db9c7ce8dcab12ce8974ae586231c365f5973f1650b6c5595a | Python | 4,100 | 82 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script for training a CNN model, parameters are currently set to values used in
final model.
"""
import numpy as np
import functions as f
import tensorflow as tf
import matplotlib.pyplot as plt
runs = 50 #Number of training runs to pe... |
5af307e7df5e9a7acbff9d7be566d58b07dadebfd3dac766a75c895b3e978a20 | Python | 4,102 | 114 | import numpy as np
import xarray as xr
from numpy.testing import assert_allclose
from xarray.testing import assert_allclose as assert_xr_allclose
from gemmr.sample_analysis.postproc import *
def test_power():
ds = xr.Dataset(dict(
between_assocs=xr.DataArray(np.linspace(0, 1, 10).reshape(1, -1), dims=('... |
ee64b1abc8b575bb4a1c4f0ceedd9a4d15a7c8d2a4c2de4187ea6ecaf0781b9e | Python | 4,103 | 122 | import pickle
from typing import cast
import numpy as np
Value = float | str | np.float32 | np.float64 | np.ndarray | None
class History:
def __init__(self, csvpath: str | None = None, warmup: int = 0):
"""
warmup: `int`
Write only if `self.count > warmup`.
Useful if unkn... |
78a04c74f5a9ca3d933a37e0445616d9be01ade768741b550f638f669507cea1 | Python | 4,104 | 98 | """
Remove detected triggers (state transitions) when the corresponding Up and Down
states are shorter than a minimum duration.
"""
import numpy as np
import neo
import argparse
import quantities as pq
from utils.io_utils import load_neo, write_neo
from utils.parse import str_to_bool
CLI = argparse.ArgumentParser()
... |
200c3d9446b39379fe24be3604901aa9787827b1dee8b19525501b2a04b88121 | Python | 4,105 | 130 | import pytest
from corgie.helpers import BoolFn
from corgie.cli.create_segmentation_masks import (
DetectSlipMisalignmentsJob,
DetectStepMisalignmentsJob,
DetectConsecutiveMasksJob,
)
def misalignment_evaluation(stack, result, exp):
bf = BoolFn(exp)
mask = [False] * len(stack)
def stack_simi... |
9c53ebbd99e954ce1e73a3578d76a23cf13c7e92307dba3b075207fda47ecde3 | Python | 4,106 | 106 | import numpy as np
def generate_stim(duration, dt=1e-4, stim_on=.2, I_stim=[1.], nr_of_trains=5, nr_of_pulses=4, stim_freq=5, pulse_width=[.2e-3], pulse_freq=100, ipi=.1e-3):
''' Generate a pulse train to be used as the stimulation waveform.
Parameters:
----------------------------------------------------... |
03d9a6bb92152f4c7915729e3315451f24cdb8508f61a0541a917e35723afc5b | Python | 4,107 | 134 | from .objparser import load_obj, save_obj, WavefrontOBJ
import numpy as np
from scipy.spatial.transform import Rotation
class ObjHandler:
"""
Handler for .obj files.
"""
@classmethod
def read(cls, filename):
"""Load an .obj file.
:param filename: The path of the file.
:ty... |
65033131a26296a04a09ce403e4c18030f4640f74d1c9c62e85fc9040917a46c | Python | 4,107 | 105 | """Module that defines optimizers to train a model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from sleeprnn.common import constants
from sleeprnn.nn import adam_w
def generic_optimizer_fn(optimizer, loss, clip_norm):
... |
c91d68e85e8b4a4d96fc9c82810f97a07ec4217d8d980008e68b0f71ce89a11c | Python | 4,107 | 114 | """`--keep_checkpoints`, `--save_all_checkpoints` and `--clip_grad`.
Three flags whose effect is a file that is or is not deleted, and a gradient that
is or is not shortened. All three are invisible in a passing run, which is why
none of them had a test: the training finishes either way.
`--keep_checkpoints` is the o... |
ce37a7401313bd05003d323bf3c49680dc731f633766d116d45e35dafb7915b6 | Python | 4,107 | 113 | from video_stream import ImageSource, AcquireException
import cv2
import time
from pathlib import Path
class VideoImageSource(ImageSource):
"""
VideoImageSource - an image source that reads images from a video file or a camera using openCV.
"""
default_params = {
**ImageSource.default_params,... |
2da95d37e2fefa63dd409b997013be490f6c4ab8a9b6bc113a7656222c2157d7 | Python | 4,111 | 93 | """
Detect waves by clustering triggers that are close to each other in time and space.
"""
import neo
import numpy as np
import quantities as pq
import argparse
from sklearn.cluster import DBSCAN
from utils.io_utils import load_neo, write_neo
from utils.neo_utils import remove_annotations
CLI = argparse.ArgumentPars... |
15cf50edcded580f9920a61766d8774a3ba5736fa1fbb0a0fda61723d126d587 | Python | 4,113 | 80 | """No metric may depend on the order of the rows it is handed.
CLAUDE.md hard rule 7: same inputs, same bytes, in another process, on another host, at another core
count. A metric that reads a tie off the incoming row order breaks that without raising, and it
breaks it *selectively* -- purity survived because it sums ... |
beb6654d76fd8b12a8d8b3f7e20cd2e9f9ba4c5ad45a39f2ffe432d10be58f91 | Python | 4,114 | 127 | import json
import os
from pathlib import Path
from typing import List, Optional
import pandas as pd
import pytest
import torch
from ase import build
from mace import data as mace_data
from mace.calculators.foundations_models import mace_mp
from mace.tools import AtomicNumberTable, torch_geometric, torch_tools
def ... |
3f49e03dbded7797434af8fe6d34bc81f36a53b567911902719e8183e9c4126e | Python | 4,116 | 106 | # this script reads in a csv file containing smiles (in "smiles_column") and enumerates ring systems and their precedence,
# resulting in a .json database that can be used in the "comp_ringprecedence" scoring component
# example use
# python compute_database_precedence.py --dataset [CSV_FILE] --smiles_column SMILES --o... |
6c6f2ed56a6d3c460c550764a3e3370c0afbb298287906bf2a3898006611b473 | Python | 4,117 | 126 | import argparse
from tqdm import tqdm
import torch.nn as nn
import torch.nn.functional as F
import torch
from bindsnet.network import Network
from bindsnet.pipeline import EnvironmentPipeline
from bindsnet.encoding import bernoulli, poisson
from bindsnet.network.topology import Connection
from bindsnet.environment im... |
72000a41503aae3baf7e1cde547d1e86f89a0f9656c50a09e5352fd5ee923335 | Python | 4,117 | 110 | import torch
import numpy as np
from torch_geometric.utils import to_dense_adj, dense_to_sparse, subgraph
from torch_geometric.data import Batch, Data
class UniformSample():
r"""Uniformly node dropping on the given graph or batched graphs.
Class objects callable via method :meth:`views_fn`.
Args:
... |
b13d63caaa719c4e922dedfc0db6e8e98212b678cdc5385a94a10e2f13d73aa7 | Python | 4,118 | 115 | from PymoNNto import *
import time
from globparams import *
import scipy as sp
settings = {'dtype': float32, 'synapse_mode': SxD}
def segIndex(inp_list, size):
import numpy as np
arr = [[] for _ in range(size)]
for idx,value in enumerate(inp_list):
arr[value].append(idx)
arr = [np.... |
c77e8c83716e6069dec60c852b63821bad3be33633ea4f39d0a315998bbb7b9c | Python | 4,118 | 98 | """
Remove detected triggers (state transitions) when the corresponding Up and Down
states are shorter than a minimum duration.
"""
import numpy as np
import neo
import argparse
import quantities as pq
from distutils.util import strtobool
from utils.io_utils import load_neo, write_neo
CLI = argparse.ArgumentParser()... |
167427400af6a4a2df98b83acc343921b2ab30cbf31a78176298eb9a0517f800 | Python | 4,120 | 106 | from typing import Dict, List
import numpy as np
from rdkit.Chem.rdChemReactions import ChemicalReaction
from reinvent.chemistry.library_design import FragmentReactions
from reinvent.chemistry.library_design.reaction_filters.base_reaction_filter import (
BaseReactionFilter,
)
from reinvent.chemistry.library_desi... |
e17be70758a743430383fd1de6501544de9e79d81eabc92f109dbed8760a650e | Python | 4,120 | 94 | """
Detect waves by clustering triggers that are close to each other in time and space.
"""
import neo
import numpy as np
import quantities as pq
import argparse
from pathlib import Path
from sklearn.cluster import DBSCAN
from utils.io_utils import load_neo, write_neo
from utils.neo_utils import remove_annotations
CL... |
1eb89abf30aa64c875724f0983805dfa6513e89d3ab1506ac0450f2ad8796643 | Python | 4,126 | 115 | import torch as T
import torch.nn as nn
from Networks.network import Network
class UNet4(Network):
def __init__(self, base, expansion):
super(UNet4, self).__init__()
self._build(base, expansion)
def forward(self, image, segbox=None):
layer_0, layer_1, layer_2 = self._analysis(image)
... |
c81b12e6b4c020aa23aacadc1f20ad7ff40e1e04120cabf7359f586b918a90f6 | Python | 4,128 | 91 | # -*- coding: utf-8 -*-
import functions.rate_coding
import numpy as np
import functions.Graphs
import matplotlib.pyplot as plt
import time_locked_functions
import functions.glm_to_predict_speed_from_neuronal_activity as glm
import functions.glm_tst_50_neurons_no_iterations as glm_50
import functions.statis... |
18b10e61d57afe29f11e04eb695e4430bc526591c399d7323051d1016e601b02 | Python | 4,129 | 119 | import os, argparse, logging
from tqdm.auto import tqdm, trange
from pathlib import Path
import numpy as np
import torch
from model import SpiceMix
from util import config_logger
logger = config_logger(logging.getLogger(__name__))
def parse_arguments():
parser = argparse.ArgumentParser()
# dataset
parser.add_... |
4d7e56d3df4b00c88b6a113f104bcc7595b5c9f0674a786618d48de90a2b2d08 | Python | 4,130 | 112 | import numpy as np
import os,glob
import shutil
from tifffile import imsave, imread
from PyQt5.QtWidgets import QFileDialog, QApplication
def get_meta(paramFile):
param_list={}
with open(paramFile) as f:
lines = f.readlines()
for l in lines:
try:
(val,p)=l.split(":")... |
852d2260378dd50b4bbf68c741f2ad2e0bdb863b2d2078e7a769696361354675 | Python | 4,130 | 99 | """
Remove detected triggers (state transitions) when the corresponding Up and Down
states are shorter than a minimum duration.
"""
import numpy as np
import neo
import argparse
from pathlib import Path
import quantities as pq
from utils.io_utils import load_neo, write_neo
from utils.parse import str_to_bool
CLI = ar... |
6bd117295401df24fd65c47e4e8e2f69ecf2a1837cca346c9abdb1cff9911306 | Python | 4,131 | 99 | """
Remove detected triggers (state transitions) when the corresponding Up and Down
states are shorter than a minimum duration.
"""
import numpy as np
import neo
import argparse
from pathlib import Path
import quantities as pq
from utils.io_utils import load_neo, write_neo
from utils.parse import str_to_bool
CLI = a... |
a77a59aed61a758106b5fa76c501bbae238a5c9421fb2f7ffac573e00fe0bdcd | Python | 4,133 | 107 | """The tiny AtomicDipolesMACE anchor and its reference.
Its own family, not part of ``references``: the dipole anchor emits no
energy, so an ase calculator cannot be driven for it at all, and its channels
reach no calculator's results dict. It is snapshotted through the model
forward instead.
"""
from __future__ impo... |
77039c415ac946f31172c4e84a426a21d7532ba1784fed5ad946c69285a3b025 | Python | 4,134 | 126 | import math
import pytest
import geomstats.backend as gs
from .base import ComplexVectorSpaceOpenSetTestData
from .complex_riemannian_metric import ComplexRiemannianMetricTestData
LN_3 = math.log(3.0)
EXP_4 = math.exp(4.0)
SQRT_2 = math.sqrt(2.0)
SQRT_8 = math.sqrt(8.0)
class ComplexPoincareDiskTestData(ComplexVe... |
cd95cd0787cdc7eed5a1495521bb591c87730e1e6136ea83882826040377f027 | Python | 4,135 | 123 | """Internal module for console introspection.
Module modified from [pandas](https://github.com/pandas-dev/pandas)
`pandas.io.formats.console` under BSD 3-Clause License
LICENSE
=======
BSD 3-Clause License
Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData \
Development Team
All r... |
68d8b5863323ac9a4a838ff8c25309e79b5bedf3b08aa2d1d411d741d9ad25eb | Python | 4,136 | 171 | """Class for Stratified Spaces.
Lead authors: Anna Calissano and Jonas Lueg
"""
from abc import ABC, abstractmethod
import geomstats.backend as gs
class Point(ABC):
"""Class for points of a set."""
@abstractmethod
def equal(self, point, atol=gs.atol):
"""Check equality against another point.
... |
ee44f49e196b4d30433d808bcefc4899e928e430be847b329c993ae2050012bc | Python | 4,137 | 97 | import torch
import numpy as np
def collect_bond_dists(mols_dict, valid_list, con_mat_list):
"""
Collect the lengths for each type of chemical bond in given valid molecular geometries.
Args:
mol_dicts (dict): A python dict where the key is the number of atoms, and the value indexed by that key is... |
fb0ed655fc8a7c48f10b56f66ffbeb30af99df331d6be92247714c7400e4306b | Python | 4,138 | 123 | import csv
import os
import pytest
from reinvent.runmodes.scoring.run_scoring import run_scoring
SCORING_CONFIG = {
"type": "custom_sum",
"parallel": 1,
"component": [
{
"custom_alerts": {
"endpoint": [
{
"name": "Unwanted SMA... |
8b4415876839f09dede521ef106bb140b56d34719082be6e152b83c2964e9369 | Python | 4,146 | 112 |
# -*- coding: utf-8 -*-
#
#
# PyRates software framework for flexible implementation of neural
# network models and simulations. See also:
# https://github.com/pyrates-neuroscience/PyRates
#
# Copyright (C) 2017-2018 the original authors (Richard Gast and
# Daniel Rose), the Max-Planck-Institute for Human Cognitiv... |
1bf9681aeb1a48c5e2d88bdc535ca3a058faeb8d17f1d258484dbf892fb68fef | Python | 4,149 | 132 | """
Partially noise the structures and reconstruct them using the trained model
"""
import os
import argparse
import logging
from pathlib import Path
import json
from typing import *
import numpy as np
import torch
from huggingface_hub import snapshot_download
from foldingdiff import datasets, sampling, utils, mode... |
2838bb98f45c6bf72f52dd7c38592a658a480464f09d9a36b6c44f6c55ba9e62 | Python | 4,151 | 151 | """Compute scores with Therapeutics Data Commons
Example TOML config (staged learning):
[[stage.scoring.component]]
[stage.scoring.component.TDCommons]
[[stage.scoring.component.TDCommons.endpoint]]
name = "tdc_jnk3"
weight = 1
[stage.scoring.component.TDCommons.endpoint.params]
type = "JNK3"
"""
from __future__ ... |
81a2408f5e62342272081af1ad7c51bd90cf40fad6c60f812f5b1fa8a31bc0fc | Python | 4,152 | 131 | """
Does the accelerated GLM return what the unaccelerated one returns?
The whole point of :class:`tfce.glm.PermutedGLM` is that it never forms the
permuted dataset and never forms the residual matrix. That is an algebraic
rearrangement, not an approximation, so it must agree with an honest refit to
floating-point pre... |
b545a7437908d848b1071f7998799c076e8ef970d2fa555e12fb2785afc17e66 | Python | 4,153 | 135 | """Machinery to handle global control of dtypes.
Notes
-----
Functions starting with "_pre" are shared functions that just need access to
specific backend functions. e.g. `_pre_set_default_dtype` requires access to
`as_dtype`. `set_default_dtype` can then be created in each backend by doing
`set_default_dtype = _pre_s... |
9186f2941378665c33531ec0de119e28c0dcdf26986be604f86a7ac1ff87f160 | Python | 4,154 | 130 |
import random
import time
from pathlib import Path
import numpy as np
import ppafm.ml.AuxMap as aux
import ppafm.ocl.field as FFcl
import ppafm.ocl.oclUtils as oclu
import ppafm.ocl.relax as oclr
import torch
from ppafm.ml.Generator import InverseAFMtrainer
from ppafm.ocl.AFMulator import AFMulator
import mlspm.prep... |
9c1125ee8f230f300b1e8349ed9a4ae899e4cf40a3a82497d1cfede716c6b74e | Python | 4,155 | 139 | #!/usr/bin/env python
"""Tests for `pymonntorch` package."""
import pytest
# from click.testing import CliRunner
# from pymonntorch import cli
import pymonntorch
def test_network_cpu_annotated():
from pymonntorch import (
Network,
Behavior,
NeuronGroup,
SynapseGroup,
Re... |
5c38f0f6928dfb3aeb9789efaf50597a393ff9ee39f4c13f1c614285a5eabbf8 | Python | 4,156 | 93 | from DeepPurpose import utils, dataset
from DeepPurpose import DTI as models
from DeepPurpose.utils import *
import pandas as pd
import wandb
from tqdm import tqdm
import sys
import warnings
warnings.filterwarnings("ignore")
import argparse
def main(run_id, cuda_id, wandb_project_name, general_architecture_version):
... |
8c2386682079752567335524b1bdd23f0646b57c0b93f9cae2b6ab07ca446cb9 | Python | 4,156 | 115 | # Copyright (C) 2024 Intel Corporation
# Licensed subject to the terms of the separately executed evaluation license agreement between
# Intel Corporation and you.
import numpy as np
from typing import Iterator, Tuple
from openfl.federated import PyTorchTaskRunner
from openfl.utilities import Metric
class Template... |
05284a14f6320fc0d01e63442552e54c65bbc8d01e0ef6e2eb8cf483057a8548 | Python | 4,157 | 135 | import os
import pytest
import numpy as np
from chromadb.utils.embedding_functions.morph_embedding_function import (
MorphEmbeddingFunction,
)
def test_morph_embedding_function_with_api_key() -> None:
"""Test Morph embedding function when API key is available."""
if os.environ.get("MORPH_API_KEY") is None... |
de1804fbe7a38566e45655a6e7661310aa8db3dabbb78977a9d723de4fd6a26e | Python | 4,159 | 115 | """ICA/PCA component viewer — interactive decomposition explorer."""
import matplotlib.pyplot as plt
import numpy as np
import sklearn
from numpy.fft import fft, fftfreq
from nilearn.plotting import plot_stat_map
from nltools.utils import _attempt_to_import
# Optional dependencies
ipywidgets = _attempt_to_import(
... |
5cdb26a58c0959bb2de9690a53acd47d9647562a384fe317972336511baa5415 | Python | 4,160 | 90 | import tensorflow as tf
flags = tf.app.flags
FLAGS = flags.FLAGS
class OptimizerAE(object):
"""
Optimizer for GAE
"""
def __init__(self, preds, labels, degree_matrix, num_nodes, pos_weight, norm, clusters_distance):
preds_sub = preds
labels_sub = labels
# Reconstruction t... |
621a418f2716f3ba052325cd6f2fa1bbaf05e7ee316592cff976562dd3a70f26 | Python | 4,160 | 106 | """
Plot snapshots of the input data showing the detected waves.
"""
import os
import numpy as np
import quantities as pq
import argparse
import matplotlib.pyplot as plt
from utils.io_utils import load_neo, save_plot
from utils.neo_utils import analogsignal_to_imagesequence
CLI = argparse.ArgumentParser()
CLI.add_arg... |
e7b523c41fc2c9935bb24801f0f52fb0249e3002638d6c604ad7a851d8b0b38c | Python | 4,160 | 147 | from numba import deferred_type, int64, boolean, types, float64, int32, optional
from numba.experimental import jitclass
import numpy as np
TreeType = deferred_type()
spec = [
('children', optional(TreeType)),
('dataIndex', int64[:]),
('dataClass', int64[:]),
('dataLabel', int64[:]),
... |
c987dbcb799983a140d94aa340c66ae726c6216a0ea8e6b6e72d9e13fcffe3fd | Python | 4,162 | 104 | """`--foundation_model_kwargs`, and the one path that reads it.
The flag is a string holding a Python dict literal, parsed with
`ast.literal_eval` at the very top of `run()` -- before any data is read, and
whether or not a foundation model was asked for. So the parse is what every run
pays for, and it had no test.
Wh... |
c9c0d22903b0f3d8d9a40f9572ba8817bcd701efe5357a924f0dcc91866340ac | Python | 4,162 | 130 | r"""Compute and visualize a geodesic regression on the sphere.
The generative model of the data is:
:math:`Z = Exp_{\beta_0}(\beta_1.X)` and :math:`Y = Exp_Z(\epsilon)`
where:
- :math:`Exp` denotes the Riemannian exponential,
- :math:`\beta_0` is called the intercept,
- :math:`\beta_1` is called the coefficient,
- :ma... |
5ffa51b0a9e1ea231b0a7ea5d811de64dca519c58e2cf5b8c1ec568204a1d2e9 | Python | 4,163 | 134 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
import subprocess
import tempfile
import zipfile
import pytest
from conftest import _check_and_print
@pytest.fixture(scope="session")
def package_from_training(fake_model_training_session, tmp_path_... |
5c309207e64e06357502bf31b6e5bfb5fb0742c2874673b23475da60a762c5d7 | Python | 4,166 | 108 | import time
import torch
from torch import Tensor
from torch_sparse import SparseTensor
from torch.utils.data import DataLoader
from torch_geometric.data import Data
from typing import NamedTuple, List, Tuple
from dig_ext.relabel import relabel_one_hop
relabel_fn = relabel_one_hop
class SubData(NamedTuple):
dat... |
cbe3e4cabd4b00806fdeaaa499d4a8e1bbdd300be63996e6bdf18462092c0d24 | Python | 4,167 | 117 | """
Script to add oxygen to backbone. This is not strictly included in the original
set of dihedral/bond angles, and it is not technically a part of the backbone,
but is required for some downstream tools to work (e.g. pymol). This script is
meant to be run directly on generated backbones, that have not yet have side
c... |
f37c8dace4224321b8bb54272bab224c46b6ac9c13df5e7fa1b8c8ea31e5c8b4 | Python | 4,167 | 109 | from typing import NamedTuple, List, Tuple
import time
import torch
from torch import Tensor
from torch.utils.data import DataLoader
from torch_sparse import SparseTensor
from torch_geometric.data import Data
from dig_ext.relabel import relabel_one_hop
relabel_fn = relabel_one_hop
class SubData(NamedTuple):
da... |
e8c039b1fa3f336faef667fcb584c779ecc5583d612bf4d278fdcb2aadb9a7d9 | Python | 4,170 | 127 | # 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 torch
import torch.nn as nn
from fairseq.modules import Fp32GroupNorm
class KmeansVectorQuantizer(nn.Module):
def __init__(
... |
1a87c1a5a6e6a7414fcad3ae4c36d6343e6f60540e370414768b78c276664b8c | Python | 4,171 | 160 | """Resolve the SanPy source identity used for spike detection."""
from __future__ import annotations
import json
import subprocess
from dataclasses import dataclass
from importlib.resources import files
from pathlib import Path
from typing import Any
import sanpy
@dataclass(frozen=True)
class SanPyProvenance:
... |
0fd3ace4604cca63650021e93d18bfe63ca3762d1526c34477f76a4630dca8f8 | Python | 4,173 | 143 | import deep_convolutional_representation as dcr
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
from sklearn.metrics import mean_absolute_error, r2_score, max_error
from sklearn.metrics import explained_variance_score, mean_squared_error
import matplotlib.pyplot as plt
import ... |
961d1cd1458e2e41ab511471f3190a4c17e2656c0d7341e609f37e5da4601a4a | Python | 4,174 | 91 | """AIRR conformance and the legacy-vs-tables property, on a real build.
Marked ``release``: needs a built directory (``VDJDB_TABLES``, default ``out/tables``) and the legacy
projection beside it. The unit tests cover the mappings; these two properties only mean something at
corpus scale.
"""
from __future__ import ann... |
626d655c3f90524a1bb05af574da2394f007b9f366ff39ace4e096629cfb6f42 | Python | 4,175 | 98 | # Author: Youzhi Luo (yzluo@tamu.edu)
# Updated by: Anmol Anand (aanand@tamu.edu)
import torch
from torch_geometric.data import Batch, Data
from torch.nn import Linear, Sequential, ReLU, Softmax, BatchNorm1d as BN
from torch.distributions import Categorical
from .aug_encoder import AugEncoder
from .node_fm import Node... |
0cc780d6f95ab0d91b8d1361303f2962965325d412598639e0310e3a92bc6bac | Python | 4,176 | 108 | import argparse
import os
import re
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import torch.nn as nn
from models.models import Brainmodels
from data.data_loader import *
from rsa import *
# Define model names and their cor... |
813259104efe520057c56149cfcd65de6af51a24bb2539ec5611f5f793e3ec67 | Python | 4,179 | 144 | #!/usr/bin/env python3
#
# Sniffles2
# A fast structural variant caller for long-read sequencing data
#
# Created: 14.01.2024
# Author: Hermann Romanek
# Maintainer: Hermann Romanek
# Contact: sniffles@romanek.at
#
"""
Resource monitoring utilities (currently memory) for Sniffles
"""
import logging
import... |
ec7b941308aa8182cd3c2aba8d28000ea15fea482f2809833f15f953ff755bfd | Python | 4,183 | 115 | import unittest
import numpy.testing as npt
import pytest
import torch
from reinvent.models import ReinventAdapter
from reinvent.models.reinvent.models.model import Model
from reinvent.runmodes.utils.helpers import set_torch_device
from tests.test_data import PROPANE, BENZENE, METAMIZOLE
@pytest.mark.integration
@p... |
bade3819c78daf2ca62cbe08fa1f539d393563875228266eb52c6054a8cf0ac7 | Python | 4,189 | 113 | from pathlib import Path
import mne
from .. import io
from ... import logger
def set_eeg_reference(inst, ref_channels, ref_old=None, **kwargs):
"""
Reference to new channels. MNE raw object is modified in-place for
efficiency.
Parameters
----------
inst : mne.io.Raw | mne.io.RawArray | mne.... |
197ee435365f50a1ec33e9709a117079f22d524621cde6b147602768547831c1 | Python | 4,190 | 132 | from __future__ import annotations
import argparse
import re
from pathlib import Path
from typing import Any
import attrs
from snakebids import bidsapp
from snakebids.exceptions import ConfigError
from snakebids.io.yaml import get_yaml_io
def _make_underscore_dash_aliases(name: str) -> set[str]:
"""Generate --... |
88d49763e973528ade234686ea909dba884f13792bc10922e2540f02feb57050 | Python | 4,190 | 121 | import torch
from mace.tools import torch_geometric
def _zeros_with_size0(tensor: torch.Tensor, size0: int) -> torch.Tensor:
if tensor.dim() == 0:
return torch.zeros_like(tensor)
shape = list(tensor.shape)
shape[0] = size0
return torch.zeros(shape, dtype=tensor.dtype, device=tensor.device)
... |
0eff85fefd87e4e5e7d94e983bfba12df60a226702de97c20d3e38ee5a49daa1 | Python | 4,193 | 122 | import random
import pytest
from geomstats.metric_geometry.bhv_space import Tree, TreeSpace, TreeTopology
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test.test_case import TestCase, np_only
from geomstats.test_cases.metric_geometry.point_set import (
PointSetMetricTestCase,
P... |
322b11fc513a60601e2edf65fa29c1c57abee9580577ff3e7b9d1cfcd44e409f | Python | 4,194 | 145 | """Filter SMILES with regular expressions
Assumes correct SMILES syntax e.g. that elements other than the basic ones are
in brackets.
"""
from __future__ import annotations
__all__ = ["RegexFilter", "SMILES_TOKENS_REGEX"]
import re
from collections import Counter
from typing import Optional, TYPE_CHECKING
from . im... |
350eb6e54bca6be1376e67127d2768bbcc3d0275819c538ee72aed71494f6f48 | Python | 4,194 | 90 | import os, sys, argparse, logging, warnings
import numpy as np
from configs import configs
from train_eval import train_eval, evaluate, predict
from metrics import RMSE, MAE, ROC, PRC
from datasets import get_dataset, get_splitted
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=str, default="trai... |
f20961aef037493ccac9f585364a7e8f3897f203652cf1d194be8e63290eb63b | Python | 4,194 | 147 | import torch
class TaggableObject(torch.nn.Module):
"""This is the base class for all taggable objects.
This class is used to add tags to objects and to search for objects with a specific tag.
It is a torch.nn.Module, so that any object class inheriting from this class can be used
as a torch.nn.Modul... |
86456b81ab2c0b21ed4752cbf6807919565b60fa6cd69891d87affc88e7f50a6 | Python | 4,195 | 96 | # -*- coding: utf-8 -*-
#
#
# PyRates software framework for flexible implementation of neural
# network model_templates and simulations. See also:
# https://github.com/pyrates-neuroscience/PyRates
#
# Copyright (C) 2017-2018 the original authors (Richard Gast and
# Daniel Rose), the Max-Planck-Institute for Human Cogn... |
afdefff0cfd6684dfa2284fcf4230b22dd1f22fb34a00e5b41f5bbf7b80b7c09 | Python | 4,195 | 141 | # Copyright 2020-2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Module with auxiliary CLI helper functions."""
import os
import re
from itertools import islice
from os import environ
from pathlib import Path
from sys import argv
from click import echo, style
from yaml import FullLoader, load
FX = ... |
9f60ca0b851fe7789c4f95c5f906fc2b6f111fb02c8f47b7ba1bb11a1219e358 | Python | 4,196 | 97 | from collections.abc import Sequence
from qtpy.QtCore import Qt, Signal # type: ignore[reportPrivateImportUsage]
from qtpy.QtWidgets import QComboBox, QHBoxLayout, QLabel, QWidget
from idtrackerai.GUI_tools import InvertibleSlider, LabelRangeSlider
class IntensityThresholds(QWidget):
newValue = Signal(tuple)
... |
3a1e1e4607fd3532a8cf34104cad41f73e823a14451d1e8cb0d30c0cd7ab01c2 | Python | 4,198 | 136 | from PymoNNto.Exploration.StorageManager.StorageManager import *
class StorageManager2:
# %nr %rnd
def __init__(self, folder, subfolder='folder+"_"+id', mainfolder='StorageManager', datafolder=get_data_folder(),
print_path=True):
if os.path.exists(folder):
path = folder
... |
80ca03159400271654b3a3e874990ccffa3ad85680ce242cc5a17c05f5a70b2f | Python | 4,198 | 105 | # Copyright 2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""This module contains the LocalDataSource class."""
import hashlib
import os
from hashlib import sha384
from pathlib import Path
from typing import Callable, Generator
from openfl.federated.data.sources.data_source import DataSource, DataSourc... |
d8af023ebf00e686a71e9ba1f1b26960087c6f1232423c54ad7f613c0f3968cd | Python | 4,200 | 139 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 14 11:55:56 2023
@author: egemo
"""
from torch.cuda import is_available
from sys import stdout
def get_device(p=False):
"""
Simple helper that returns "cuda" if a cuda-available device is present,
else "cpu"
Parameters
------... |
0c7f9e2a8d5fdaa0c97ba4866db4658069f3c0035b00f3dfce66aa4c73d28134 | Python | 4,201 | 109 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
import torch
from e3nn.o3._tensor_product._tensor_product import TensorProduct
from .utils import scatter
from .model_modifier_utils import replace_submodules, model_modifier
class TensorProductScatt... |
6571425eaf590d0bab046fbf63d2a980e4f66199514db708d479e65c4574258f | Python | 4,202 | 126 |
from pathlib import Path
import random
import time
import numpy as np
import ppafm.ml.AuxMap as aux
import ppafm.ocl.field as FFcl
import ppafm.ocl.oclUtils as oclu
import ppafm.ocl.relax as oclr
import torch
from ppafm.ml.Generator import InverseAFMtrainer
from ppafm.ocl.AFMulator import AFMulator
import mlspm.prep... |
72e6ba60bff89869f170d0623cab924242ca440952042349937d8e26881335c9 | Python | 4,203 | 117 | """Canonical kwarg vocabulary for the ISC family (v0.6.0 follow-up to #474).
Historically the isc engine used `metric` for the mean/median central-tendency
choice and `sim_metric` for the actual similarity metric, while the public
`isc()` wrapper spoke the canonical names (`summary`, `metric`) and translated.
The cons... |
e15836af820871dc78d929ff703a2378dd46ee1d9eb2e864f228bee3be5fa2b8 | Python | 4,204 | 136 | # Copyright 2020-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import logging
from openfl.experimental.workflow.interface import FLSpec, Aggregator, Collaborator
from openfl.experimental.workflow.placement import aggregator, collaborator
from openfl.databases import TensorDB
from openfl.utilities import... |
686343f56180ff75e9186dd9ea792e2d7ec21d4d4634ce9590f2c3110954b014 | Python | 4,206 | 86 | """The mature variable domain of a chain, for AIRR ``Receptor``.
AIRR's ``receptor_variable_domain_{1,2}_aa`` is the complete mature variable domain --
*"from and including the first AA after the signal peptide to and including the last AA that is
completely encoded by the J gene"* -- and it is non-nullable. VDJdb has... |
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