sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
0058416ec4e6b5d64439dec733525910daedde802c45b07dd404cf159b5324bf | Python | 8,209 | 247 | import argparse
from operator import itemgetter
from pathlib import Path
import json
import typing
from functools import partial
import numpy as np
import pandas as pd
from tqdm import tqdm
BOX = typing.Union[
np.ndarray,
typing.List[typing.Union[int, float]],
typing.Tuple[typing.Union[int, float], ...],
... |
69cb69efc81894e4062c3381d84db595cf58b93c74b9eb56d60649e32f875132 | Python | 8,210 | 220 | # ---
# jupyter:
# jupytext:
# cell_metadata_filter: -all
# custom_cell_magics: kql
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.11.2
# kernelspec:
# display_name: everything
# language: python
# name: py... |
f0e4c3258b5fa0da24848a5175759f8f9efb80625c682e0278461bf0803763f7 | Python | 8,210 | 187 | #############################################################################
# C. Jarne 204 Analysis Group of D. Vidahurre @cfin #
# #
# Code to generate scatter plots of the actual ages vs. predicted age #
# where t... |
6694edf45a7afab4aa6ee4928d7a831d10d04941b9d769f06041d3cc7315f6bb | Python | 8,211 | 196 | import math
import torch
from torch import nn
from torchdrug import core, data
from torchdrug.layers import functional
from torchdrug.core import Registry as R
@R.register("layers.GraphConstruction")
class GraphConstruction(nn.Module, core.Configurable):
"""
Construct a new graph from an existing graph.
... |
d3b7f7e422c70148eb40ae6ed976e34be8c744dcd66f93ba73aa472491d498a2 | Python | 8,211 | 227 | from enum import StrEnum
from math import degrees, radians
from typing import Sequence
import bpy
import numpy as np
import numpy.typing as npt
from mathutils import Matrix, Vector
from .. import framing
class Viewpoint(StrEnum):
DEFAULT = "default"
FRONT = "front"
BACK = "back"
TOP = "top"
BOTTOM... |
9284024fbbb3e70553546cbe81db0514f1503b919f221734e895186c85f9574f | Python | 8,212 | 248 | """distutils.file_util
Utility functions for operating on single files.
"""
import os
from .errors import DistutilsFileError
from ._log import log
# for generating verbose output in 'copy_file()'
_copy_action = {None: 'copying', 'hard': 'hard linking', 'sym': 'symbolically linking'}
def _copy_file_contents(src, ds... |
136849afd8e76c1328aae3f4d29900f0c4bb9247f3fe6b5ce4aad3585b938de4 | Python | 8,214 | 236 | """Smoke tests for tissue_masking.core.io.
Per audit, this module had no dedicated test file. The MaskingPipeline
itself is tested in test_tissue_masking.py but the lower-level I/O
helpers were not exercised directly.
Public surface:
- ChannelInfo dataclass
- discover_channels filesystem disco... |
79927910fb42f4c47095eef6c516a28ab81960ebee036b6409877740706a82a2 | Python | 8,214 | 255 | """
Go through the PDB files of biounits and select
protein chain pairs that have a certain number of residues
within a threshold distance from each other.
Originally taken from GDockScore code base.
"""
from Bio import PDB
import numpy as np
import pandas as pd
from scipy.spatial import cKDTree
import os
import ite... |
9dd11bcaf6b32b36efa780cc98df1cee2bb71a91a8defe9876393a38316b6b0a | Python | 8,214 | 257 | import os
from pyclamp.dsp.iofunc import *
from pyclamp.dsp.lsfunc import *
from pyclamp.dsp.channel import chWave
class TDF:
readFunc = {'.tab':readDTFile, '.tdf':readDTFile, '.tsv':readSVFile}
dataFunc = {'.tab':readDTData, '.tdf':readDTData, '.tsv':list}
writeFunc = {'.tab':writeDTFile, '.tdf':writeDTFile, '.... |
fc2494eeb484cdd5573ba356f97775a4d33e4bb7aab5e5a2a752e6b50e9af905 | Python | 8,214 | 279 | """
Code to perform model fitting for simulations (i.e. given X and y, fit a
model to predict y from X).
"""
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV... |
47d6dc48e244a2ee0f7d85c8160b37e87e9f62c6b996cd9652c725caf8350c40 | Python | 8,220 | 271 | #!/usr/bin/env python
# encoding: utf-8
# Written by Minh Nguyen and CBIG under MIT license:
# https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
from __future__ import print_function, division
import time
from datetime import datetime
from dateutil.relativedelta import relativedelta
import numpy as np
import... |
c6bf1bbf18f818dc8b6f54a215e6bf167e6cb0c6320436256892ec8dbf4cc296 | Python | 8,221 | 232 | import torch
from torch import nn
from elasticai.creator.nn.integer.design_creator_module import DesignCreatorModule
from elasticai.creator.nn.integer.layernorm1d.design import (
LayerNorm1d as LayerNorm1dDesign,
)
from elasticai.creator.nn.integer.math_operations import MathOperations
from elasticai.creator.nn.in... |
971e98a8e983c4516e90a7d60994f4c352b871de70b89db5790a6030f0203cae | Python | 8,222 | 236 | import numpy as np
from scipy.integrate import simpson
from scipy.interpolate import CubicSpline
from .math import r_to_xyz, spbessel_transfrorm, spharm_xyz
# == radial grids ==
class GridFunc:
'''
This is the class for functions that can be separated into a radial part and angular part.
'''
def __in... |
070380e5cfb4e38c9b180d7cb4b9d7777d0f3fb4df4b8e2ff4c84f6111f98701 | Python | 8,224 | 264 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
from functools import partial
__all__ = [
'ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnet200'
]
def conv3x3x3(in_planes, out_planes, stride=1, dilatio... |
dd3b8c0a4e9dc0c88df768c2d5c12df61ef6814acae8f6788707b97a8f3355b6 | Python | 8,224 | 149 | import os
import unittest
from hed.models.sidecar import Sidecar
from hed.models.tabular_input import TabularInput
from hed.schema.hed_schema_io import load_schema_version
from hed.tools.analysis.event_manager import EventManager
from hed.tools.analysis.hed_type import HedType
from hed.tools.analysis.hed_type_factors ... |
023cca40009c06d3ab24d657f60e15de6591e62834066004ce3af1523422f287 | Python | 8,225 | 182 | from typing import Union, Dict, List
import numpy as np
from ..base_transform import EEGTransform
class MeanStdNormalize(EEGTransform):
r'''
Perform z-score normalization on the input data. This class allows the user to define the dimension of normalization and the used statistic.
.. code-block:: pytho... |
d2134022308a96b255ce020cc24be9800f06e75d28b16898f255bf6a6c58dbf2 | Python | 8,228 | 138 | import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from scipy.stats import beta
from utils.geometry import axis_angle_to_matrix, rigid_transform_Kabsch_3D_torch
from utils.torsion import modify_conformer_torsion_angles
from utils.affine import T
def t_to_sigma(t_tr, t_ro... |
1ffa11e979acdc53e2874742861c3c591b3634f5646fd3b3de195b6d4e6ab3c9 | Python | 8,230 | 205 | import argparse
from loader import BioDataset
from dataloader import DataLoaderFinetune
from splitters import random_split, species_split
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
import numpy as np
from model import GNN, GNN_graphpred
from ... |
22a9bb2c063a5af7b173f5ef0bb3afa1765d4c058046fd784665713e9e393fbd | Python | 8,232 | 220 | # -*- coding: utf-8 -*-
import logging
import os
import pickle
import sys
import time
import warnings
from argparse import ArgumentParser
from pprint import pformat, pprint
import numpy as np
import tensorflow as tf
from tfsnippet.examples.utils import MLResults, print_with_title
from tfsnippet.scaffold import Variabl... |
bd98354ebf2c6ba11cb1010355286e3cde8dee2777cb5567f4b27100887fed5b | Python | 8,235 | 190 | """
Script to run tumor purity prediction experiments for the specified holdout
cancer type(s).
"""
import sys
import argparse
import itertools as it
from pathlib import Path
import numpy as np
import pandas as pd
from tqdm import tqdm
import pancancer_evaluation.config as cfg
from pancancer_evaluation.data_models.tc... |
6d0756e10b266ad07086b519cfa066da872825dc028af7431aadbbf62dc2d5f5 | Python | 8,237 | 196 | """
Standalone GUI for the U01 registration workflow.
Andy Thai
andy.thai@uci.edu
"""
import os
import sys
import argparse
# Ensure project root is on sys.path for module imports
from regtools import get_project_root
_project_root = get_project_root()
if _project_root not in sys.path:
sys.path.ins... |
d638b78fdfb52516924fd51e0c6bb661c22ff4a52510c532cd8fb380730a6cdf | Python | 8,238 | 158 | import numpy as np
def get_patterns_of_interval(interval_values, verbose=False):
'''
Given an interval as a series of values, calculate its basic patterns.
Returns:
- mean (float) The mean value of the interval.
- std (float) The standerd deviation of the values of the inte... |
4ddfcfbcf74ab73218742326f95aa8703b1815a66676913c247140e4828d61b8 | Python | 8,240 | 162 | ##########################################################################################
# This Script
# 1) imports the preprocessed resting state data
# 2) add stimulation trigger and remove the 'stimulus artifact' iv: -1.5 to 6 ms, for ESG use -7 to 7s
# 3) downsample the signal to 100... |
cc5879bb96edfc2da85525d02634415408dcbd2d5f8033ed61aa60cee1b54351 | Python | 8,240 | 206 | """
Add segmentation labels to GraphMCF objects in a STAR file which pairs them with the segmentation
Input: - The STAR file
Output: - New graph are pickled with the segmentation information
"""
__author__ = 'Antonio Martinez-Sanchez'
# ################ Package import
import time
import pyseg as ps
... |
03b026c8397e83ca6ee1df16577a410287a7ccdd7c500faf5081e9e4e636b10d | Python | 8,241 | 139 | import matplotlib
matplotlib.use('TkAgg') # Set the backend for matplotlib
import os
import scipy.io
import pandas as pd
from Functions.f_AnalysisGraphs import *
from Functions.f_AnalysisFunctions import *
# Set flags to control the behavior of the script
s_PlotSaveBandEvolution = False
s_PsdEvolutionAllp... |
09a9a5ccf47178a8bed719d95fee8b8b76161794bbda4053e5a4da3d6fb480c9 | Python | 8,241 | 215 | """
Motion correction using calblitz
https://github.com/agiovann/CalBlitz
(c) 2016 C. Schmidt-Hieber
GPLv3
"""
import time
import sys
import os
import numpy as np
import tempfile
import multiprocessing as mp
import glob
from sima import motion
try:
sys.path.append(os.path.expanduser("~/CaImAn/"))
import caima... |
4f94e9222ec535f880ecd7f2ca8d698cc8ad67e7630bf253bdad19863a1e5cc2 | Python | 8,241 | 189 | import os
import sys
import re
import argparse
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from passlib.hash import bcrypt
from ckg import ckg_utils
from ckg.graphdb_connector import connector
from ckg.graphdb_builder import builder_utils
try:
ckg_config = ckg_utils.read_ckg_c... |
2ac730738a691a8d697cc3a1d9ee9cb096e9ec0b2e3d65bec7154b8a50607b45 | Python | 8,242 | 271 | from PyQt5.QtWidgets import (QAction,
QComboBox,
QToolBar,
QToolButton,
)
from PyQt5.QtCore import QEvent, QPointF
from PyQt5.Qt import QMouseEvent, Qt
from wonambi import Wonambi
from wonambi.trans.reje... |
9e8dbec227a273bd9188729c03d66f0d2fe89017bf241578af1adca5bfa369bd | Python | 8,242 | 245 | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
from scipy.sparse import coo_array
from ...utils import printv
class EffectiveConnectivity:
"""Linear model of the effective connectivity.
This class implements a simple linear model of ... |
13b7ff21c589df05c6ddb3dca3d3e55a31e85a479015333159eeb116efb88b43 | Python | 8,243 | 212 | import numpy as np
import torch
import torch.nn as nn
from torchvision import models
import math
def calc_coeff(iter_num, high=1.0, low=0.0, alpha=10.0, max_iter=10000.0):
return np.float(2.0 * (high - low) / (1.0 + np.exp(-alpha*iter_num / max_iter)) - (high - low) + low)
def init_weights(m):
classname = m._... |
3f3ce7a5ca44375495ad982e89d95fd5a5ed4f11974b087ab6362d7968d1ea26 | Python | 8,243 | 187 | # 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... |
f172087a75cfd6840def1e271045a1cbd07489e1b1901c400f4dfcdc13391717 | Python | 8,243 | 194 | # coding=utf-8
# Copyright 2021 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... |
822c63e84afd6712a560a9a63fac1b46033ea69c24140b2836e7eb8ee67bcb2c | Python | 8,244 | 177 | import numpy as np
from typing import List, Tuple
from sklearn.decomposition import PCA
from scipy.spatial.distance import euclidean
# Implementation of https://www.biorxiv.org/content/10.1101/2021.03.21.436284v1.full.pdf
# that computes the statistics of manifold geometry relevant for few-shot classification
# modifi... |
fa4bfb7b52bfcb5a6b450cb1e43d3b51e173bdf540302c4012d370ca35a210e5 | Python | 8,248 | 185 | """The shared tissue-threshold helpers used by both masking consumers.
The 2D registration foreground and the standalone tissue-masking tool derive a threshold the
same way and live in different packages. They share this module rather than duplicating,
because duplicated masking logic drifting apart is a failure t... |
70c6e2b0b5489744baf0835e4573d2716ecbec4c560561c83750ae7a06a12c17 | Python | 8,249 | 204 | from math import pi
import numpy as np
import scipy.special as sp
from .. import config as CFG
from ..utils.misc import slice_same
from ..utils.orbutils import LinearRGD, GridFunc
from ..from_gpaw.gaunt import gaunt
from ..matao.matao import MatAO
from ..matao.findpairs import pairs_within_cutoff
class TwoCenterIntg... |
7141953cae76141128a7701b50de9a7156e4547e18edfaeb65d77c2cf084c2fa | Python | 8,249 | 204 | # -*- coding: utf-8 -*-
import os
from typing import Dict, Iterable, Set, Union
import torch
from supar.config import Config
from supar.models.const.aj.model import AttachJuxtaposeConstituencyModel
from supar.models.const.aj.transform import AttachJuxtaposeTree
from supar.parser import Parser
from supar.utils import ... |
bce613820a012d6fe9473d691173b09ee8dce7d3d4e4fe5b13c8cccef60d4ff7 | Python | 8,250 | 268 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from siren_pytorch import SirenNet, SirenWrapper
from core.models.Embedder import *
from core.utils.Coord import Coord
from glbSettings import *
class Model:
def __init__(self, model_type:str, *args, **kwargs):
... |
30c4f9422155ca6cd8b7bd21231a0ac46a46ac8ab253f387731fadd87e311d8c | Python | 8,251 | 215 | import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pytest
from scipy.stats import CensoredData, chisquare, ecdf, kstest
from SurvivalEVAL.evaluations.calibration.distributional import (
create_censor_hist,
d_calibration,
ksd_calibration,
)
@pytest.mark.par... |
410081e326a434fb0388de964906592834c189221c9695215cde22747bfc71eb | Python | 8,253 | 324 | import os
import copy
import tempfile
import pandas as pd
from ray import tune, air
import analysis_utils as au
import utils as u
def test_analysis_v1():
if 'RAY_ADDRESS' in os.environ:
del os.environ['RAY_ADDRESS']
"""Test all neccessary functions/pipeline for analysis_v1.py
"""
local_di... |
76af69cace18410ccc181db91ff2830b6b06a74ca90b4154286a59488d38bf90 | Python | 8,253 | 231 | import pandas as pd
import numpy as np
import random
import pickle
import time
def read_data(datafile, file_key, human=True, use_10x=True):
amino_acids = [letter for letter in 'ARNDCEQGHILKMFPSTWYV']
all_pairs = []
def invalid(seq):
return pd.isna(seq) or any([aa not in amino_acids for aa in seq])... |
9e61b8ed84cdb1fb48789846344c01952086e26cf6f9762ca51de134c200ac50 | Python | 8,253 | 239 | #!/usr/bin/env python
# requires band_deeph.json and band_dft.json created by plot_bands.py
import os
import numpy as np
import json
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
min_plot_energy = -2.5
max_plot_energy = 4
trim_gap = True
trim_min = 0.1
trim_max = 1.0
fontsize = 22
legend = Fals... |
c9c83edcced979fd043502d91600c164a45bd93f4fef9105ee4be85a7da52c47 | Python | 8,257 | 232 | ##
# \file simple_itk_registration.py
# \brief Class to use registration method based on SimpleITK
#
# \author Michael Ebner (michael.ebner.14@ucl.ac.uk)
# \date Aug 2017
# Import libraries
import os
import numpy as np
import itk
import SimpleITK as sitk
# Used to parse variable arguments to SimpleITK... |
0ada866f82084fa881c338fe363493369b71e178a4db75451ae8f82af4785d7d | Python | 8,258 | 198 | #napsPrimitives.py
from dreamcoder.program import Primitive, prettyProgram
from dreamcoder.grammar import Grammar
from dreamcoder.type import tlist, tint, arrow, baseType #, t0, t1, t2
#from functools import reduce
#types
PROGRAM = baseType("PROGRAM")
RECORD = baseType("RECORD")
FUNC = baseType("FUNC")
VAR = baseT... |
09af6057ac04a1a3d0aba4f1cec432d6482e36df9bc33f6731a149d3f29683bc | Python | 8,259 | 246 | from .config_node import ConfigNode
config = ConfigNode()
config.device = 'cuda'
# cuDNN
config.cudnn = ConfigNode()
config.cudnn.benchmark = True
config.cudnn.deterministic = False
config.dataset = ConfigNode()
config.dataset.name = 'CIFAR10'
config.dataset.dataset_dir = ''
config.dataset.image_size = 32
config.dat... |
2e733ee0b946e016e2a6eb94acb658a7b46f61298884bd14d76045691aab0578 | Python | 8,260 | 303 | #!/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
#... |
17e38d6c1b88ff13c51835b890b2d47c3c2c6f4de7cb444ece439674de65cd6b | Python | 8,261 | 143 | """MARGIN_MAP.py 2026-10-01. Pre-registration: 04-NOTES/PREREG-2026-10-01c-MARGIN-MAP.md
How much stronger must every synapse be before each sensory input explodes? Rate-theory margin g* for 128 sensory types x
2 sides under the published signs (O) and the laboratory prior (F4), whole brain, with a spiking check of 10 ... |
2f8144a3b3aa43a2f3ca93b3efd566712d07f953a3b93353f770802b704b4f05 | Python | 8,261 | 175 | import time
from unittest.mock import patch, Mock, MagicMock
import pytest
from botocore.exceptions import ClientError
from pydantic import ValidationError
import NDATools
from NDATools.upload.validation.api import ValidationV2Credentials, ValidationV2Api, ValidationV2
def test_pydantic_validation_errors(monkeypatc... |
35c2ea97e922fa910e4a8023de74d6707a1dea902688bb1eab7d2c60d7cc2ea5 | Python | 8,266 | 174 | import argparse
from loader import MoleculeDataset
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tqdm import tqdm
import numpy as np
#from model import GNN
from model import GNN
from sklearn.metrics import roc_auc_score
from splitters import scaffold_split, ran... |
b86b950120ce1c3e84246242562db150bdc61bb310187aae0ab8ad0caafe626d | Python | 8,269 | 292 | # 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... |
630e5b2e043683057db8fcaa09ea7a6bb7855e4388bf98995a9726609bc180e2 | Python | 8,272 | 193 | import os
import re
from urllib.parse import quote, unquote
from typing import Iterator, List, Tuple, Union, Dict
import pandas as pd
from sklearn import model_selection
from torcheeg.datasets.module.base_dataset import BaseDataset
from .base_model_selection import BaseModelSelection
from .utils import create_subset,... |
b403b58ef9ad0c21591c1607c18e16beb14a1bfcbf5d642455b2e4d42bf8b11d | Python | 8,272 | 205 | # -*- coding: utf-8 -*-
import os
from typing import Dict, Iterable, Set, Union
import torch
from supar.config import Config
from supar.models.const.tt.model import TetraTaggingConstituencyModel
from supar.models.const.tt.transform import TetraTaggingTree
from supar.parser import Parser
from supar.utils import Datase... |
fbe3cb484cafbf1bae81328a2ae8d38b09499505554fc2fdb4f02d202b8edbcb | Python | 8,272 | 443 | from __future__ import annotations
from contextlib import contextmanager
from dataclasses import dataclass
from functools import wraps
from pathlib import Path
from time import perf_counter
import csv
import os
from threadpoolctl import (
threadpool_info,
threadpool_limits,
)
# ============================... |
336e10daecf1a214f727e568611f11d207622b54e5c01c9671b94da3b41e757c | Python | 8,273 | 251 | #
# 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,... |
fe9982db048286d58362f1e313e17655674409c159675826701eb0c78090ab16 | Python | 8,273 | 206 | from __future__ import division, print_function
import math
import torch
from torch.autograd import Variable
import scipy.io as scio
from scipy import interpolate
import numpy as np
from scipy.ndimage.filters import gaussian_filter
#%%
def to_variable(var=(), cuda=True, volatile=False):
out = []
fo... |
4dcab1e3dc930e9d0e7044cc4ae14e423e1a11c02415c067bdd47f4e1f285f24 | Python | 8,279 | 327 | """"""
import os
from pathlib import Path
from .reader import BaseReader, _load_dataset
from .reader.motion import MotionReader
from .reader.gaze import GazeReader
from .reader.video import VideoReader, OpticalFlowReader
from .device.video import VideoDeviceUVC, VideoFileDevice
from .device.flir import VideoDeviceFLI... |
7777fc83a07b166a0c003e4b25817c3d70d7e420c7c633ad49e6d783b1bc8551 | Python | 8,279 | 281 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Written by Naren Wulan and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
"""
import torch
import time
import torch.nn as nn
import numpy as np
from torch.utils.data import DataLoader
import os
import itertools
from sklearn.model_s... |
d889d9e607d636a2e2ccef4dc1ed545e3be8d68de13c1fc74e7476134963eb1e | Python | 8,279 | 235 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
June 2024 by Paulien Voorter
paulien.voorter@gmail.com
https://www.github.com/paulienvoorter
Code is uploaded as part of our paper in MRM: 'Diffusion-derived intravoxel-incoherent motion anisotropy relates to CSF and blood flow'
"""
# this loads all patient data and... |
9c4a139b4171b5320761c70903a57bc54bac352cd71264fe847d99eef89d59ae | Python | 8,281 | 266 | #!/usr/bin/env python
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
This module provides a human agent to control the ego vehicle via keyboard
"""
import time
import json
from threading import Thread
import cv2
import numpy as np
try:
im... |
c9809880e0470088ac59cbd944b96e9e9d8276ad2064cb40480542ec5e86b28a | Python | 8,281 | 179 | """First-run setup and launch for a downloaded LiquidAion release (Python standard library only).
Started by the platform launchers (LiquidAion-Windows.bat, liquidaion-linux.sh,
LiquidAion-macOS.command) through uv, which also provides Python 3.12. On the first run it
creates a private environment in .liquidaion-env/ ... |
a0ea3538804e4cc685c88e8ae277d86c7eb514ca1347b12046b19a8ae05aa597 | Python | 8,286 | 172 | # coding=utf-8
# Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... |
f815b4bf78787bba55faef3c55018c2fc5a76127da938e78a2b124446b0ed613 | Python | 8,287 | 305 | #!/usr/bin/python
# Gogulan Karunanithy, UCL, 2021
# Python code for decoupling 2D 'in-phase' CON spectra
import copy
import os
import sys
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import matplotlib.pyplot as plt
import nmrglue as ng
import numpy as np
import tensorflow as tf
from tensorflow import keras
# polic... |
a2281657dabe9f9924930f0c00c1ccd815a6d3928ae8b298c19c5984d48374bd | Python | 8,288 | 226 | # ----------------------------------------------------------------------
# Copyright (C) 2016, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or mo... |
e51bffc11c075a82df1cba591d503678ad9cdc8a8c8472eaf37569253ddd7ea2 | Python | 8,288 | 137 | import pandas as pd
from scipy.stats import mannwhitneyu
from scipy.stats import ttest_ind
def compute_pvalues(coann_file, best_model, second_best_model, standards, metric='Average Precision', alternative='greater'):
df = pd.read_csv(coann_file, sep='\t')
df['Model'] = df['Dataset'].astype(str).apply(l... |
b9863a16c8fa19e6160428bd9efea7932f995aceabac79b1ed7a97dddf210a56 | Python | 8,292 | 166 | import torch
from torch.utils.data import DataLoader
import numpy as np
from torch.autograd import Variable
import time
from src.Mice.utils import normalize_range, un_normalize_range, check_cuda
from src.Mice.src_nn.Dataset import Dataset
from src.Mice.src_nn.Network import Network
class NOEMRFNetMT:
def __init__... |
4e40676aa43b3fc11afe1f1fcdd804fb879326a569b7e2b0788b2d2eb88f4a00 | Python | 8,293 | 264 | # -*- coding: utf-8 -*-
"""
Created on Fri May 24 14:42:38 2024
@author: mpolonen
Used to pick peaks of the modeled ABRs
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from expyfun.io import write_hdf5, read_hdf5
import scipy.signal as sig
im... |
b19bb6d965e23291358e75956b3e62f1d793aa295db84a2e23dfa89fa3407a70 | Python | 8,294 | 185 | deprecated_params = dict(k='inflection_point', f='smoothing', noise_level='noise')
def update_param(name, param):
if name == 'columns_to_encode':
param['ptype'] = '[U(str, [str])]'
param['dtype'] = 'List[Union[str, List[str]]]'
return param
return None # param untouched
def class_ex... |
ff690e5ca6f69cb20f13be928a9445a21eb856655bc6cab5ee955bf0b592f328 | Python | 8,298 | 170 | # coding=utf-8
# Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... |
18adc9651be56ff443511231a845556779d080409beb54bb80ebba44ab37cbb4 | Python | 8,299 | 92 | """ONE_PAGER.py 2026-09-30. One A4 page: the headline result, one figure, the key numbers. Every number is read from an output
file listed in 04-NOTES/CLAIMS.csv. usage: python ONE_PAGER.py ["Author Name"] -> REPORT/ONE_PAGER.pdf (+ .png preview)"""
import sys, re, textwrap
import matplotlib
matplotlib.use('Agg')... |
2d4d00ebf89daebd588d49c3a88dbadbf1c4bea66492395eea519b407065d28e | Python | 8,299 | 182 | from optparse import OptionParser
from math import factorial
import sympy
### help and usage ###
usage = "usage: %prog [options] args"
description = '''Function: prepare the input table of SNP allele frequency for TreeMix'''
version = '%prog 09.29.2021'
parser = OptionParser(usage=usage,version=version, description =... |
843a2a718b30bc9f3b54e1c604670fe11d25692a03b7357105a266b457b2421b | Python | 8,300 | 178 | """
PLACEMENT_TYPE_NULL.py written 2026-09-23.
Pre-registration: 04-NOTES/PREREG-2026-09-23-PLACEMENT-TYPE-NULL.md (written before this ran).
Two attacks on Ch 6 that PLACEMENT_DECISIVE.py did not run:
B0 remove the classifier: bench `known_nt` labels only, same neuron-level in x out null.
T1 pseudo-replicati... |
3d5dc8ff05143423108d1358113fdf51a8ba6b6cb71918fa2139e7c42624ce35 | Python | 8,302 | 220 | # Some of the code is referenced from below Github files:
# https://github.com/YudengLin/memristorBDNN, which includes `BDNN_Model.py` and `memristor_model.py`
import torch.optim
from bnnsrc.base_net import *
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from optimizers import *
import torch.... |
7019ac48e11c0f15e983dbe5a7b1e34333ca74b0b532332a08dcb79c88807d6c | Python | 8,302 | 242 | from __future__ import annotations
import logging
import os
from pathlib import Path
from shutil import copytree, move
import pytest
from ase.io import read
from maggma.stores import MemoryStore
from monty.json import MontyDecoder, jsanitize
from quacc.calculators.vasp import Vasp
from quacc.schemas.vasp import Vasp... |
68f23c95ae188c708bea9bfbe1ba38f98e2eeac182a1fe63bb83efba8f5e26e3 | Python | 8,303 | 212 | """Implementation of CLI measure command."""
import json
import math
import multiprocessing as mp
import os
import numpy as np
from treem import SWC, Morph
from treem.io import TreemEncoder
from treem.morph import SEG, get_segdata
from treem.utils.geom import norm
def _measure_neurites(morph, morphometry, name, ty... |
82f802e25eaefbe5b13c3343890af1473108198c27f014b029a0802422eb90f7 | Python | 8,305 | 217 | # 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... |
9967fcb31b02d5c1fcfa713143bb476f8ba6f42114d22389b8403d7ecf8c0585 | Python | 8,310 | 219 | # Node-group asset "Style Spheres" (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... |
d02710601a8bad524e001512c51cf3dccd58fb5632f8f5b95cfd95ec887e11e8 | Python | 8,310 | 217 | from math import isclose
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
import scanpy as sc
from anndata import AnnData
import pertpy as pt
CWD = Path(__file__).parent.resolve()
ag_rfc = pt.tl.Augur("random_forest_classifier", random_state=42)
ag_lrc = pt.tl.Augur("logistic_regressio... |
e3cd7fc19006b43bf8c91e8aaf64cadeffcaecabb46a36abaebecc806f77f76c | Python | 8,310 | 188 | import numpy as np
import pandas as pd
from scipy import optimize
def get_resting_membrane_potential(voltage, current):
voltage = np.array(voltage)
current = np.array(current)
index_end_resting = None
for index in range(5000):
current_difference = current[index+1] - current[index... |
08ffcbf1305964955bd6d5cc49e47c2daecc2fcaa51ee7b3fb1543fedbdcfa49 | Python | 8,313 | 254 |
# Pyhton module for DCE-MRI postprocessing
#
# Copyright (C) 2014 David S. Smith
#
# 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 2 of the License, or
# (at your option) any... |
340898beb66a33e8ceec00fc2987a96513d87c6d033dfcaf8f191b362c1e3f1f | Python | 8,313 | 241 | # 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... |
8f7ea3bee9ddc7bde73491aae5b31cd5f905f6270b8226f18c20df238be8f2c1 | Python | 8,313 | 206 | #!/usr/bin/env python
# coding: utf-8
"""
Author:
Hermann F. Degenhardt <degenhardthf@nih.gov>
Maximilia F. S. Degenhardt <frazaodesouzam2@nih.gov>
"""
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import random, math, os, sys, json
sys.path.append("../sr... |
eeba2324a85ce83daaa6d6da703bff4afdce0e9bc043a9c5a4fa5f0fa3f37efb | Python | 8,314 | 170 | # coding=utf-8
# Copyright 2020 The Microsoft Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... |
4d7b3a5e949b702d3b9f877bad858f6ec8f5f628b42523df4525f751ba68ebfe | Python | 8,315 | 181 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.data import Batch
from torch_geometric.nn import SGConv, global_add_pool
from torch_scatter import scatter_add
from typing import Union, Tuple
def maybe_num_electrodes(index: torch.Tensor, num_electrodes: Union[int, None] = None)... |
3a1680dfc78812481c9fc67b1ee8bbdd49af7da4350808f65a0ae0019e725775 | Python | 8,316 | 235 | """BIDS utility functions for schema loading, sidecar merging, and inheritance chain resolution."""
import json
import os
import hed.schema.hed_schema_io as hed_schema_io
from hed.tools.util.io_util import get_full_extension
def get_schema_from_description(root_path):
"""Load the HED schema version declared in ... |
da72ec4166cd727fee4121a5369c482a06859ec138a60f1b855e7909655d695f | Python | 8,316 | 230 | """Modality-invariant descriptor ANTs registration drivers (MIND, SSC).
Drives the deformable (SyN) stage in modality-invariant descriptor space while
a conventional Mattes-MI affine pass handles the rigid+affine prealign. Both the
MIND (centre-to-neighbour) and SSC (neighbour-to-neighbour) descriptor families
sh... |
138eded75b40e669a8d3888fb185f4335ae36c27251f5a9a952b744be732d547 | Python | 8,317 | 223 | # Node-group asset "Style Sticks" (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,... |
635b45406401ea4e77254c0bb53f123f035e57e2812d5158cca145e7d53e5e7e | Python | 8,317 | 220 | import torch
import torch.nn as nn
from sklearn.metrics import jaccard_score, roc_auc_score, precision_score, f1_score, average_precision_score
import numpy as np
import dill
import time
import argparse
from torch.nn import CrossEntropyLoss
from torch.optim import Adam
import os
import torch.nn.functional as F
from col... |
240404fa20252df1f9a87600b5bbafb049cfdd67c73ff794573291fbd272115e | Python | 8,318 | 215 | from collections import namedtuple
from typing import Tuple, Any, Dict
import os
import torch
import torch.nn.functional as F
import numpy as np
import h5py as h5
from atlaslib.io import stage_split_hdf5
from atlaslib.files import require_directory
from plio.section import Section
from cl_3d import utils
from cl_3d... |
53d60b15ab05143fd7df36c105f8911296538906b74aafe1652610bf2381c8fe | Python | 8,320 | 324 | import argparse
import random
import math
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import mean_squared_... |
582153254612da794e6366b84d07d4f51bf84d362a1333cafe8c4faf70df0e1c | Python | 8,320 | 190 | """manages the parameter sets for all models. all parameter are given without brian units except the ones that are used
are used for brian2 simulations."""
def get_params():
params = dict()
# runtime options #
# reduced models
params["rectify_spec_models"] = True
params["uni_dt"] = 0.05 # [ms]
... |
edb3ee282f365003ceb04a5300bac42e6c24e7289984ed715babdb4859f0a1c9 | Python | 8,320 | 243 | from __future__ import division, print_function
import time
import numpy as np
from sklearn.linear_model import Ridge
def strf_correlation(strf1, strf2, max_delay=10):
"""
Computes the dot product between two STRFs across in a way that's invariant to frequency differences and time lags up to max_delay.
... |
7aaa0b08e224f62e8567cb94a7e6741df97d07b15c4ff8cd112bd2488a389de5 | Python | 8,323 | 273 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scanpy as sc
from sklearn.preprocessing import normalize
from sklearn.metrics import pairwise_distances
import torch
import scipy.sparse as sp
from numba import jit
from scipy.sparse import issparse
def rank_genes_conf(adata, power=1, powe... |
8b189ec2dfc4e7a99f422488b424a79ad36e6bc1120ff8a3efd7dbc751a33664 | Python | 8,325 | 238 |
import argparse
import os
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--vcf', dest='vcf', help='joint vcf with all mixed genotypes SNPs',required=True,type=str)
parser.add_argument('--barcodes', dest='barcodes', help='for matrixgen mode: FILTERED barcodes file in sta... |
6cf29c0cbd2a3395b56b0508886b19d92377cfc2b214a1345d45b0b5d8153983 | Python | 8,327 | 192 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICEN... |
236f91ba8990a8aa6b62cc83517b523922dadf8bb3db36c33246844b8fd0ab7f | Python | 8,328 | 223 | #
# 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,... |
8189e1a3e690b1e9c67b6794e8f6e97104c5a6a4100e61f8d7f6baead76d97d8 | Python | 8,328 | 231 | """
PointCloud class.
"""
from pathlib import Path
from typing import List, Optional
import numpy as np
import pandas as pd
from scipy import spatial
from . import utils
class PointCloud(pd.DataFrame):
"""Class for working with point cloud data.
Dev notes:
- The PointCloud class is a child class o... |
a6797b8c19bd0bccf14f1b912ed670f919bd47cdd7356e9413e1f8408cabf5b4 | Python | 8,328 | 235 | # Script to run recursive feature elimination for feature selection for
# classification of corticospinal wave preference
try:
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
nhost = comm.Get_size()
except ModuleNotFoundError:
rank = 0
nhost = 1
import sys
from os.path impo... |
ff1aa2dc14fc56f3fdb07588d45ed5fafdcd35fc480c3f829733adf87788ae5a | Python | 8,328 | 164 | import os
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from matplotlib.patches import Rectangle
import numpy as np
import seaborn as sns
from scipy.stats import rankdata
def plot_TCGA_results(results, xlabel, ylabel, save_name=None, show=True, stats=False):
plt.figure(... |
6b104d2842c71b5c6b04f5cf36f39155b5044d208175d931b16bc921f3f2a005 | Python | 8,333 | 199 | # 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... |
a0f88440f3a82984d5f69ae287e4c8780ffd3a4e0b585e62da301c4f12fd3ada | Python | 8,334 | 200 | import os, argparse, time
import pandas as pd
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch_geometric.data import DataLoader
from utils import *
from loader import PretrainDataset
from chem import *
# model
from models.siamese_network im... |
29b80e13e2e4d18235c240c08a9977db6e84ffb9afa48470164efe9bd2fd1028 | Python | 8,335 | 216 | #This is SpikeSPY V1.2.2
#------------------------------------------------------------------------------------------------------------
import bioread
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import plotly.io
... |
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