sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
408a93bed59840012464f12a0ebb059958dddf120295c53e4106d62b80b9f7fe | Python | 8,756 | 231 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 12 11:32:51 2018
@author: Carlos A Ariza, PhD
"""
'''This python script extract Image Features'''
import cv2
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from skimage import measure
from pair_... |
d992eb00c801ff697d556cc034595dc7bb1edaa803591d012a957296bb5b15d3 | Python | 8,756 | 245 | import random
import os
import numpy as np
import torch
import scanpy as sc
import pandas as pd
from scipy import stats
import scipy.stats as st
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import os
import scanpy as sc
def make_average(df):
dfT = df.T
dfT['Index'] = dfT.i... |
81f99f31335ee2ad075baddc905d1b2add9e9d18416ae5e5c2f9f5a18276fbd2 | Python | 8,757 | 254 | # -*- coding: utf-8 -*-
#
# Update trialdefintions/time axis of Syncopy data objects
# akin to ft_redefinetrial
#
# Builtin/3rd party package imports
import numpy as np
from numbers import Number
# Local imports
import syncopy as spy
from syncopy.shared.kwarg_decorators import unwrap_cfg
from syncopy.shared.parsers i... |
e38d83f35ede424f90b05a0148bce5a01ac9aa089522c424d04c6bdd6549087b | Python | 8,757 | 216 | #!/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 ... |
088b0f7d32e02b982fbbf7cb104f9d7ffe9c91c5d6e606d7db4cab146003949a | Python | 8,758 | 244 | # Copyright (C) 2019 Computational Science Lab, UPF <http://www.compscience.org/>
# Copying and distribution is allowed under AGPLv3 license
import torch
import rdkit
from rdkit import Chem
from rdkit.Chem import AllChem
from gPPMol.utils import *
import numpy as np
import multiprocessing
import math
import random
imp... |
19cb80bc2e5b5502ddb2c540bd5b1bd2c6b75020d2a74731fd0a6228d97d1109 | Python | 8,758 | 267 | from abc import ABCMeta, abstractmethod
import numpy as np
from scipy import sparse
import meep as mp
ABC = ABCMeta("ABC", (object,), {"__slots__": ()}) # compatible with Python 2 and 3
# ----------------------------------------------------------------------
# Basis is the abstract base class from which classes d... |
bb2b434abbae246ea5af3f1425121c24a4b695bb69b625bffab7d096fb87b8d5 | Python | 8,762 | 258 | import numpy as np
import numba
@numba.njit
def accuracy_cost(x, target_timeseries, weights, cost_matrix, dt, interval=(0, None)):
"""Total cost related to the accuracy, weighted sum of contributions.
:param x: State of dynamical system.
:type x: np.ndarray
:param target_... |
cace53245a725b21b42d34029ba3d083406582793ada7a0aa563a13bce682002 | Python | 8,762 | 234 | import math
import numpy as np
import torch
from torch import nn
from typing import List, Optional, Tuple
from .functions import *
from .blocks import \
UNetConvBlock, \
UNetUpBlock
class GUNet(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
... |
fa21d68d38a43daf04e40c0c4c043cfbb562ec8df82031eb98d3015f7382ab2f | Python | 8,762 | 253 | # 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... |
0d90b0c6a4c0766208648a0afddf56db52478b88831e4f7ee3210b2b60e46d52 | Python | 8,767 | 147 | """
Created on Sun Feb 21 05:22:32 2021
@author: Haotian Teng
"""
# from bwapy import BwaAligner
# from bwapy.libbwa import Alignment
import sys
if 'bwapy' in sys.modules:
raise ImportError("Loading bwapy again will cause error, please restart " \
"the process.")
import bwapy
import re
import... |
5f60cf0370b04e59113b3f467ab40e4192955b3997d6dba9da7aaa70ec09029b | Python | 8,768 | 233 | # 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... |
0eff9d1b89a13cb64b74d421ea8a82d15a7c047600471a35f3626ea020c698d7 | Python | 8,769 | 248 | """
Unit tests for regtools/registration/core/io_utils.py
Covers:
- extractNiftiMetadata
- loadNiiImages
- getMutualInformation / getNormalizedMutualInformation
- getDiceCoefficient / getMSE / getPSNR
"""
import os
import tempfile
import shutil
import numpy as np
import nibabel as nib
import ... |
2a9ee05affc8e9b89cea8c6a9e60184bc2be60c21b2761d562796cc0943bad2d | Python | 8,771 | 199 | 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... |
51ff90a81f303b6be59b0fed1f0b507075afc81f8c1d4521de685d12b74d26af | Python | 8,771 | 257 | # 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 applicable law or agreed to in writing, software
# distributed under th... |
f79cbaf6215b49b8bfab8060ca53f5b8ce4d424bdb6c87ae9cd95f05de496085 | Python | 8,771 | 333 | # Authors: Jose C. Garcia Alanis <alanis.jcg@gmail.com>
# Rhys Hobbs <iskandarrhobbs@gmail.com>
#
# License: BSD-3-Clause
from tqdm import tqdm
from joblib import Parallel, delayed
import numpy as np
from scipy.linalg import pinv
from scipy.stats import t
from mne.utils import logger
from mne.stats.cluster_... |
cdb48d2bbc118eaab953427b5f73d612d7e59721ed744536fa2310a0c0c84ac8 | Python | 8,772 | 223 | #!/usr/bin/env python3
# Diana Giraldo, Jan 2024
# Last update: April 2024
import os, sys
from pathlib import Path
import argparse
import torch
import numpy as np
import nibabel as nib
repo_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(0, repo_dir)
from prettier.srr_mri import reconstruct_volum... |
890703f17e616ec8a61f09835643d1f06151dff7c325c8e9a0d7b7943c0fe9e2 | Python | 8,773 | 269 | import sys
import time
import timeit
#import distutils # prefer packaging in future
import packaging
import h5py
#from scipy import stats
import scipy.io as sio
import skimage.measure
import numpy as np
import PoseTools
# prefer packaging module in future
## MK 20200608 -- Fixing this based on documentation.
## TEST ... |
4bc9cd9a384bbd5128025820f7ef3d48cde949af8cafff0f93cc75917f478f02 | Python | 8,774 | 210 | """Regression tests for the post-merge RegistrationTab bug sweep.
Covers four verified bugs in ``registration_tab.py``:
(a) Cancelling a run that used an auto-generated output dir must still surface
the "Open Output Folder" button (the pending path was never transferred to
the completed slot, leaving th... |
cd6a2204f219a79b25e26fb8c2a96a1067c6ffab9f791d803c2da8b0e73bee25 | Python | 8,775 | 212 | from typing import List, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .mmd_like import _MMDLikeTrainer
class WalkerLoss(nn.Module):
def forward(self, P_aba, y):
equality_matrix = torch.eq(y.reshape(-1, 1), y).float()
p_target = equality_matrix / equality_matrix.s... |
1ff98cb73e0bb938e734379207489aec18aae096564ba5466f99ccced439dcc0 | Python | 8,777 | 171 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import scipy.stats as stats
import scipy.signal as signal
import matplotlib.pyplot as plt
import matplotlib.axes as axes
#Scatter plots
def bivariable_std(datax,datay,ax,meanx_zero=0,meany_zero=0,stdx_zero=1,stdy_zero=1,mean_axis=1,cmap=plt.cm.Blues,ma... |
cfa5158057103a9dc3711993ef7011fb42c746d126d7bc9abf638a52e0b9075d | Python | 8,778 | 245 | import io
import os
import time
import torch.distributed as dist
import torch.utils.data as data
from PIL import Image
from .zipreader import is_zip_path, ZipReader
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a fi... |
32c3b9dd84370260f6c31517ecc03bd5f69ce0af784ebd8e839c2e2833bff227 | Python | 8,780 | 207 | # trace generated using paraview version 5.5.1
#### import the simple module from the paraview
from paraview.simple import *
#### disable automatic camera reset on 'Show'
paraview.simple._DisableFirstRenderCameraReset()
# create a new 'EnSight Reader'
heart_remeshed_3D_MAPSensicase = EnSightReader(CaseFileName='heart... |
a1a29a69386206b39c58ddb1ef353dbc294971556526af0417ef5f5170436ffd | Python | 8,783 | 194 | import io
import os
import shutil
import unittest
from hed import schema
from hed.errors import ErrorContext, ErrorHandler, HedFileError
from hed.models import Sidecar, TabularInput
class Test(unittest.TestCase):
@classmethod
def setUpClass(cls):
base_output_folder = os.path.join(os.path.dirname(os.p... |
a4a41886dceeadef5c4302088dd1e25d68ca74464946d4faeb7b6e5a22954669 | Python | 8,784 | 237 | from __future__ import annotations
import copy
from functools import cached_property
from typing import Iterable, TYPE_CHECKING
import numpy as np
import pandas as pd
from sklearn.compose import make_column_selector
from cuppa.components.passthrough import PassthroughTransformer
from cuppa.constants import SUB_CLF_N... |
da2a515d6e4e64a24f00fff3b775a5dfbf102f32212b65361e2d1eb88d371885 | Python | 8,785 | 232 | # \file transform_initializer.py
# \brief Class to obtain transform estimate to align fixed with moving
# image
#
# \author Michael Ebner (michael.ebner.14@ucl.ac.uk)
# \date Feb 2019
#
import os
import numpy as np
import SimpleITK as sitk
import pysitk.python_helper as ph
import pysitk.sim... |
4921eb14f98153057cffc04e8a919d57d0a961f7c216852205e75e49f8b4b5bb | Python | 8,787 | 216 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ht... |
b66860f0c31f9e4e88befc7f0c9d76a864d8f38d57cefc97e33843644fbb3196 | Python | 8,788 | 185 | import numpy as np
import pandas as pd
import pickle as pkl
from joblib import Parallel, delayed
from sklearn.decomposition import PCA
from src.utils import parse_config, logger, DictObj
class PCAComputer:
"""
This class loads individual features and compute PCA matrices for these features, based on all activ... |
c6dee4f441bbd77a9615d27c4408385964164efcf45469066c32f4c6c9918db0 | Python | 8,788 | 180 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 23 18:43:14 2023
@author: daltonm
"""
import dill
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import itertools
import os
from scipy.stats import binomtest, ttest_rel
from pathlib import Path
from imp... |
8320915e71f524c7026d20af3ebaaed71c4e5379b9e89da1dfe69f7fcfb96901 | Python | 8,791 | 197 | # -*- coding: utf-8 -*-
# !/usr/bin/env python3
"""
Compute log-odds of answers house for the different generative prior values and the different levels of sensory likelihood.
It plots a barplot for the selected experiments (args --experiments).
Whether subjects' behavior or ideal observer behavior or both are plotted... |
cee8fd79e113e87820ee540c5230bedfd214106da884dc86892089fc59733dd3 | Python | 8,792 | 196 | """Registry of deprecated items and the major release that removes each one.
Every deprecated item in hedtools is listed in SCHEDULED_REMOVALS with a check that tells whether
it still exists. The first test fails as soon as the package's major version reaches an item's
removal release while the item is still present, ... |
c2e9f66be85d48b1f8ee00edc62e0ab07a33bd3424e519d76ae272e6f18ab1b0 | Python | 8,794 | 293 | # 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... |
decb7d9d571a289a8069a391b67f2d297132be0f1229f55c92de0867b0aa73c0 | Python | 8,796 | 211 | import argparse
import cProfile
import os
import json
import sys
import snakemake
import time
import requests
from typing import TextIO
from typing import Dict
import merlin as m
from merlin.core import dataset
from merlin.core import executor
from merlin.util import snakewriter
def build_parser():
parser = argp... |
fdb05c48e976dd7b5542b9c7656da7dba36ac6c6f2938017f991f5f7ac15289e | Python | 8,802 | 214 | """
DeepLabCut2.0 Toolbox (deeplabcut.org)
copyright A. & M. Mathis Labs
https://github.com/AlexEMG/DeepLabCut
Please see AUTHORS for contributors.
https://github.com/AlexEMG/DeepLabCut/blob/master/AUTHORS
Licensed under GNU Lesser General Public License v3.0
"""
import numpy as np
import matplotlib.pyplot as plt
fro... |
654b9ea3bffd6ac5c8409ee49c3cb949118defd61085b52fcae1f198fe949074 | Python | 8,804 | 182 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 23 18:43:14 2023
@author: daltonm
"""
import dill
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import itertools
import os
from scipy.stats import binomtest, ttest_rel
from pathlib import Path
from imp... |
4c78c6bec351231f3641288d8778a349fcf5c0037ab90310fb1b1314968dfa6e | Python | 8,806 | 284 | # 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... |
db626147c658d1a2f14950859caadce9fd62cfa1afe362b6e036a3eda4e37d28 | Python | 8,809 | 223 | """Unix."""
from __future__ import annotations
import os
import sys
from configparser import ConfigParser
from pathlib import Path
from .api import PlatformDirsABC
if sys.platform == "win32":
def getuid() -> int:
msg = "should only be used on Unix"
raise RuntimeError(msg)
else:
from os impo... |
dd79d0236cde7f587481d830977ab27c9d2b4a5aded964960195617c163fccd0 | Python | 8,810 | 332 | from __future__ import annotations
import pytest
jf = pytest.importorskip("jobflow")
from ase.build import bulk, molecule
from quacc import flow, job
from quacc.recipes.emt.core import relax_job, static_job # skipcq: PYL-C0412
from quacc.recipes.emt.slabs import bulk_to_slabs_flow
def test_tutorial1a(tmp_path, m... |
4ebf7faa59f9a9c594d8d85efaa2d9f04e0d2529813f854aeda6e9784357f393 | Python | 8,812 | 257 | from dreamcoder.program import *
from dreamcoder.differentiation import *
import signal
class EvaluationTimeout(Exception):
pass
EVALUATIONTABLE = {}
class Task(object):
def __init__(self, name, request, examples, features=None, cache=False, test_examples=None):
'''request: the type of this task
... |
8ed8c4816a539b7beba73f0ce7b9d153f14c40833f021b54ef631f05485011b6 | Python | 8,813 | 226 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
"""
Created on Mon Jun 24 16:04:07 2019
@author: Hannah Choi
"""
"""
This code is called in main run files to refine hierarchy scores via iterations.
"""
# In[]: Cortico-cortical iterations
def iterativeCC(dfi,dfV,n_iter):
... |
b99f36d4f06a42ba248546f07bb134528744ada307ab3a12813bdf27c308977d | Python | 8,813 | 238 | import collections
import contextlib
import functools
import os
import re
import sys
import warnings
from typing import Dict, Generator, Iterator, NamedTuple, Optional, Tuple
from ._elffile import EIClass, EIData, ELFFile, EMachine
EF_ARM_ABIMASK = 0xFF000000
EF_ARM_ABI_VER5 = 0x05000000
EF_ARM_ABI_FLOAT_HARD = 0x000... |
1d1f0317e00c069f2ad4bfe0fbb53bd04d4d111d4e0c997135343c3b77b9be35 | Python | 8,814 | 245 |
import numpy as np
import monai.transforms as transforms
import json
import math
import os
import pickle
import nibabel
from joblib import Parallel, delayed
from subprocess import run
# inspired by the NVIDIA nnU-Net GitHub repository available at:
# https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorc... |
2dab6bf778898ae64c1f8d22ba927d0b6ac9dc45ead945a5a1a5c8783971a480 | Python | 8,816 | 192 | import os
from pathlib import Path
from typing import Any, Callable, Dict, Tuple, Union
import mne
import numpy as np
from torcheeg.io import EEGSignalIO, MetaInfoIO
from .base_dataset import BaseDataset
def default_read_fn(file_path, **kwargs):
# Load EEG file
raw = mne.io.read_raw(file_path)
# Conver... |
6257ad4929b9a0a595326e170b0459894826dc5cb00664b6e38fdb58d42cc19f | Python | 8,816 | 215 | import os, sys
sys.path.append('../../4K_HETU/app')
import arrayexp
import globals.global_func as gf
import numpy as np
import arrayparams
import torch
import math
import time
import logging
if __name__ == '__main__':
logname = './logs/log'
logger = logging.getLogger()
logger.setLevel(logging... |
ac20881ccc8cbad1285716241afc87417c6359520438343ffdb030831ed8887b | Python | 8,816 | 346 |
# coding: utf-8
# In[1]:
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
use_cuda = torch.cuda.is_available()
# In[2]:
class E2EBlock(torch.nn.Module):
'''E2Eblock.'''
def __init__(self, in_... |
1b4911fcae864aaf1643015dd1286da370d874c277585c728cea033647c906af | Python | 8,817 | 214 | from collections.abc import Sequence
import torch
from torch import nn
from torch.nn import functional as F
from torchdrug import core, layers
from torchdrug.layers import functional
from torchdrug.core import Registry as R
@R.register("models.ProteinResNet")
class ProteinResNet(nn.Module, core.Configurable):
"... |
4816f6895d5cadbf3d30345310a63ce91e00fd43960294d09fd55055c3033a51 | Python | 8,821 | 257 | """Utilities related archives.
"""
import logging
import os
import shutil
import stat
import tarfile
import zipfile
from typing import Iterable, List, Optional
from zipfile import ZipInfo
from pip._internal.exceptions import InstallationError
from pip._internal.utils.filetypes import (
BZ2_EXTENSIONS,
TAR_EXT... |
40218ebc0b97457839fa4615783f820e43a31cb60bff39c9466c79a62097ada0 | Python | 8,823 | 212 | from __future__ import print_function
import os
from pddlstream.algorithms.reorder import get_partial_orders
from pddlstream.language.constants import EQ, get_prefix, get_args, str_from_plan, is_parameter, \
partition_facts
from pddlstream.language.conversion import str_from_fact, evaluation_from_fact
from pddlst... |
289777aa15e357cdcb00e1cd58558bd431060a6c5c0224c01b1de0e05d94b07c | Python | 8,827 | 291 | import math
from typing import Literal, Tuple, Union
Padding2D = Union[int, Tuple[int, int], Literal["same", "valid"]]
IntOr2 = Union[int, Tuple[int, int]]
Padding = Union[int, Literal["same", "valid"]]
def linear_output_shape(N: int, out_features: int) -> tuple[int, int]:
return N, out_features
def conv1d_out... |
9517100cbc08e21365efdfcdb347a338ba806237a517da474815939d9a988815 | Python | 8,830 | 302 | #!/usr/bin/env python3
"""
m6a_supervised_cnn.py
Author: Anupama Jha <anupamaj@uw.edu>
This module predicts whether an
adenine is methylated or not. The
model is trained with Fiber-seq
HiFi read sequence, inter-pulse
distance and pulse width signal
from pacbio. The model is a
convolution neural network.
"""
import tor... |
0aea5937b07611b9fd3248e716fe6aed8b397fb97f9f11af5e7198a4cb14c36b | Python | 8,831 | 231 | #
# 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,... |
df841bb5b6c4a3ac4123246489dc741d53ffdd97e75db0b455533ee192b97a42 | Python | 8,831 | 219 | """Smoke tests for regtools/registration/compose_deformations.py.
The full composition flow invokes ANTs ``apply_transforms``, which is too
heavy for a unit test. These tests mock ``rg.antsTransformation`` so the
helper logic, shape resolution, and Laplacian path can be exercised on
tiny synthetic data.
"""
... |
09b7e00531fcc0d1b89ec32fe14f79875d832a1df7e4b0fdad3359b78b422b8a | Python | 8,833 | 197 | """User preference persistence for the CCF boundary editor.
Editor settings (mode toggles, brush radius default, per-region radius
memory) used to be five separate ``self._foo`` bools cluttering ``__init__``
and resetting to defaults on every launch. This module collects them into
one dataclass and round-trips th... |
51da52da3b7d8991a167f18efdefc42eae2e0c4ee7fdcdb2a1d12327eb2199b0 | Python | 8,833 | 186 | """
Created on Fri May 20 14:53:39 2022
@author: Haotian Teng
"""
import os
import sys
import toml
import torch
import argparse
import itertools
import numpy as np
from tqdm import tqdm
from typing import Dict,List
from xron.utils.seq_op import Methylation_DP_Aligner
from xron.nrhmm.hmm import GaussianEmissions, RHMM... |
4df5586e022a184db049835b672ea5c1f1cea823914dd13e664cb1483352c77d | Python | 8,838 | 241 | """Testing CLI command find."""
import subprocess
import os
def test_all_nodes():
"""Tests for returning all nodes."""
os.chdir(os.path.dirname(__file__) + '/data')
proc = subprocess.Popen(['swc', 'find', 'pass_simple_branch.swc'],
stdout=subprocess.PIPE,
... |
52f4b140bd29e2ef2ada425776a28df60ece660683be456936834b0f508f36b0 | Python | 8,838 | 253 | # 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... |
b5aa6d47b00d4270c05b3e055aff2f9fcfc0b2096485d92dc61cf076048bf45f | Python | 8,838 | 85 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from lab.reports import Attribute, geometric_mean
from common_setup import IssueConfig, IssueExperiment, DEFAULT_OPTIMAL_SUITE, is_test_run
BENCHMAR... |
0cbf1af9f12f37599ad999ca06f6d70697140d568b077903f9df9c294775b0ec | Python | 8,842 | 256 | from string import Template as _pyTemplate
from typing import Any, Mapping, Protocol, Self, cast
from elasticai.creator import template as tpl
class Template(Protocol):
def substitute(self, mapping: dict[str, Any] = {}, /, **kwargs) -> str: ...
class _Definition(tpl.TemplateParameter):
def __init__(self, t... |
459c9a989e166e831660fb27d0c0924570cde9b0662b8e2748898db36da46788 | Python | 8,842 | 235 | import os
import torch
import h5py
# from network.D2NNet import D2NNet
from network.ddnn import ddnn
from network.led_nn import led_nn
from network.basic_nn import basic_nn
from utils.loaders import dataset_loader, grab_testdata, save_object, net_load
from datetime import datetime
def train(model, optimizer,... |
01213e4308c521e1914d8931d40f8e4b0d4102eec75727a226f9a4a0db929943 | Python | 8,843 | 189 | # 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... |
2040fe217ebf48345114281c5ccd1a7e7431e07eef3460732806c24c3d7ba980 | Python | 8,844 | 213 | from typing import Optional
from netket.operator import AbstractOperator, Adjoint
from netket.vqs import VariationalState, FullSumState
from netket.utils.types import DType
from .overlap import InfidelityOperatorUPsi, InfidelityUPsi
import warnings
def InfidelityOperator(
target: VariationalState,
*,
U... |
242be133ad5db48022a5c79c00f243991292cef86b3fe68a03bd7b6878ce390f | Python | 8,844 | 223 | from __future__ import annotations
import pandas as pd
from sklearn.compose import make_column_selector
from cuppa.constants import SUB_CLF_NAMES, META_CLF_NAMES
from cuppa.components.calibration import RollingAvgCalibration
from cuppa.components.feature_selection import Chi2FeatureSelector, MaxScaledChi2FeatureSelec... |
c3084186995d60b1124fcad5446183cee74acc9612a56f6312549a86ca6b311b | Python | 8,848 | 339 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
### Connectivity matrix utilities
import numpy as np
import numpy.linalg as linalg
from scipy.io import loadmat
import networkx as nx
def loadConnectome(No_nodes, filename='../input_data/AAL_matrices.mat', field='C'):
"""
Load a square connectivity matrix
Pa... |
ca1ead66d7c22ef6fd321cde5628ba4e11bb2fdeae1aeee80327b12f274d7032 | Python | 8,850 | 257 | """
eCCA
====
This script shows how to use the reference pipeline for c-VEP decoding (see [1]_), which learns templates by averaging
repeated trials and spatial filters by using canonical correlation analysis (CCA). This method is implemented as eCCA in
PyntBCI.
References
----------
.. [1] Martínez-Cagigal, V., Thiel... |
f59b8432e87aeff69650985021048bbb4f3a1bc162a02b943ccdfccddc3c4c3c | Python | 8,850 | 266 | """Radiation pattern of an on-axis dipole in cylindrical coordinates.
Tutorial Reference:
https://meep.readthedocs.io/en/latest/Python_Tutorials/Near_to_Far_Field_Spectra/#radiation-pattern-of-an-antenna-in-cylindrical-coordinates
"""
import argparse
import cmath
import math
from typing import Tuple
import matplotl... |
51281b522ca7f3cf5beacdf9afe16d91f32b6e30712733930dfc0db5d48d8711 | Python | 8,852 | 275 | """
=============================================
Plot results of multivariate pattern analysis
=============================================
Creates figures to show classifier performance at multiple time points of
the EEG epoch.
Authors: José C. García Alanis <alanis.jcg@gmail.com>
License: BSD (3-clause)
"""
import... |
174c26d78c59e5c4f49c6bb723adc3ac1c75ca336e93ac0fd13961e8a4b7529b | Python | 8,853 | 236 | import json
import logging
import os
import pathlib
import time
from threading import RLock
from typing import Union, List
import boto3
import requests
from botocore.exceptions import ClientError
from pydantic import BaseModel, Field, ValidationError
from NDATools import exit_error
from NDATools.Utils import get_requ... |
feb922dd30567071021125d3619525c80dc4460a31d2e1a1527b6fb4345965c0 | Python | 8,853 | 241 | """Testing CLI command find."""
import subprocess
import os
def test_all_nodes():
"""Tests for returning all nodes."""
os.chdir(os.path.dirname(__file__) + '/data')
proc = subprocess.Popen(['swc', 'find', 'pass_simple_branch.swc'],
stdout=subprocess.PIPE,
... |
25da90af2e4e53c819bb170fa3543321912f0dc1b9deb6049597dea98ed29e20 | Python | 8,855 | 241 | """Testing CLI command find."""
import os
import subprocess
def test_all_nodes():
"""Tests for returning all nodes."""
os.chdir(os.path.dirname(__file__) + '/data')
proc = subprocess.Popen(['swc', 'find', 'pass_simple_branch.swc'],
stdout=subprocess.PIPE,
... |
f9391c9c7cf03ef0379c052ceb4a6109ab2177bbffed9ae433d8ef4e137a6976 | Python | 8,855 | 347 | #!/usr/bin/env python
# coding: utf-8
# ## Visualization of f-statistic distributions for selected features
# In[1]:
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pancancer_evaluation.config as cfg
import pancancer_evaluation.utilities.ana... |
c256ee767a4cbdf6ce8aea26ae13760bdfa20d785689001c5ce8486775fe2105 | Python | 8,858 | 189 | # coding=utf-8
# Copyright 2020, The RAG 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
#
# Unless r... |
d3c12dc37a85a4575e40ce62941fa45ca18067002cfe16b418de54df1d2bd6e9 | Python | 8,858 | 258 | """
Handling of Core Metadata for Python packages (including reading and writing).
See: https://packaging.python.org/en/latest/specifications/core-metadata/
"""
import os
import stat
import textwrap
from email import message_from_file
from email.message import Message
from tempfile import NamedTemporaryFile
from typin... |
424f5f3e9199a9ff129058b1e02c4df209afa34221897e4da909f9782b494687 | Python | 8,860 | 237 | #
# 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,... |
a930f661b01b25df7214088415cf84ce9f9c1462f2d93d24fd84a68142c1599d | Python | 8,860 | 230 | from os import path as op
from stormdb.access import Query
import pickle
import numpy as np
from scipy import stats
from mne.stats import spatio_temporal_cluster_1samp_test, spatio_temporal_cluster_test, fdr_correction, ttest_1samp_no_p
from sklearn import linear_model
from sklearn.impute import SimpleImputer as Impute... |
d907cd8d29aa1cff0cc6fb7b2020bdc15c862593153494b5065ef7339eca3345 | Python | 8,861 | 282 | #!/usr/bin/env python
# coding: utf-8
# ### Comparison of optimizers for LASSO parameter range experiments
#
# sklearn has 2 ways to fit logistic regression models with a LASSO penalty: [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) (using the libl... |
e80c1718ded4fed517931313795b0a58303f8f0fef0f47a82260f0c2ccea949d | Python | 8,861 | 231 | # 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... |
f2a7d537846ac75dd306e7484ec87996aca6e52a523c3f76d8817be5f509d4d6 | Python | 8,861 | 217 | #!/usr/bin/env python
# Copyright (c) 2018-2019 Intel Corporation
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
This module contains the result gatherer and write for CARLA scenarios.
It shall be used from the ScenarioManager only.
"""
from... |
db9047dbba76fe0f266a2d082bfbe7f9684d20ce36a00020e8065887db8a5832 | Python | 8,862 | 181 | # Dominance analysis, following math here: https://github.com/dominance-analysis/dominance-analysis
# C. Cocuzza, 2021
# This version is flexible to inputting difference sources
# Multiple regression model to test how each network influences the explained variance (accuracy) of the act-flow prediction.
# So, the IVs... |
839895f7f374aaba1b4697aca3b5adcee6462e22c242971ea2a59f3f96b5e61c | Python | 8,864 | 227 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from _negative_binomial import ZeroInflatedNegativeBinomial
from dataclasses import dataclass, field
import numpy as np
import pdb
@dataclass
class ModelConfig:
"""
Model configuration for crispAI models
-... |
4ffe37a2c788c7dca357460f89ea91c673f193631fbdf94109a49031cde57af2 | Python | 8,868 | 250 | #!/usr/bin/env python3
# coding: utf-8
from __future__ import division
"""
Creates a MobileNet Model as defined in:
Andrew G. Howard Menglong Zhu Bo Chen, et.al. (2017).
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.
Copyright (c) Yang Lu, 2017
Modified By cleardusk
"""
import... |
826922a582f2f7157928730ffaab8fd843c9a46c1c1c2d8dbc33c377be25a6d0 | Python | 8,868 | 229 | import os
import numpy as np
import h5py
from ...utils.collections import dotdict
def loadDefaultParams(Cmat=None, Dmat=None, lookupTableFileName=None, seed=None):
"""Load default parameters for a network of aLN nodes.
:param Cmat: Structural connectivity matrix (adjacency matrix) of coupling strengths, will... |
238651869dc884844a50e0bd4e0c5876cc9e0f8b6aa092ce33594ebcdc0ca620 | Python | 8,869 | 231 |
import torch.nn as nn
import torch.nn.functional as F
from layers import GraphConvolution, GraphConvolution2, InecptionGCNBlock, GraphConvolutionBS, snowball_layer,GCN
from GCN import snowball, truncated_krylov, graph_convolutional_network
import numpy as np
from torch.nn.parameter import Parameter
import torch
import... |
9bfbdb28c2fc3ccc70b034df98e3c5a10b2dcc3c901dce7cf93661709a70cfff | Python | 8,869 | 499 | from __future__ import annotations
import re
from collections.abc import Iterable, Mapping, Sequence
_NUMBER_RE = re.compile(
r"[-+]?(?:\d+(?:\.\d*)?|\.\d+)"
)
def normalize_metadata_field(
field_name: object,
) -> str:
"""
Normalize a metadata field name for semantic matching.
Examples
--... |
dc8e04e784213108715a23222c736e069752b10ecb8d6fe9994d12ea1f5c4f4c | Python | 8,871 | 269 | import warnings
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch.distributions import Distribution, Gamma, Poisson, constraints
from torch.distributions.utils import (
broadcast_all,
lazy_property,
logits_to_probs,
probs_to_logits,
)
# The functions her... |
51a26e22b456fd5364db1f081b00bbf4999b374c5301181458d85c73024f1995 | Python | 8,872 | 266 | import sys
from collections import defaultdict as d
import re
from optparse import OptionParser, OptionGroup
#Author: Martin Kapun
#Modified by: Siyuan Feng
######################################################### HELP #########################################################################
usage="""python %pro... |
2fe1bf0b9eaa59f5c7de9163bde3c5948aad4cc87f762fc265e83b24fae54c7c | Python | 8,873 | 233 | import warnings
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pytest
from SurvivalEVAL import CompetingRisksEvaluator, one_calibration_cr
from SurvivalEVAL.evaluations.calibration.single_time import one_calibration
@pytest.fixture
def grouped_data() -> tuple[np.... |
e4842594caf1e9b0ef40e5ca5f10f39c39edb2d91892b5b82b3b6ca63a23a77e | Python | 8,874 | 251 | ##
# \file export_side_by_side_simulated_vs_original_slice_comparison.py
# \brief Script to generate a pdf holding all side-by-side comparisons.
#
# This function takes the result of simulate_stacks_from_reconstruction.py as
# input. The script relies on ImageMagick.
# \author Michael Ebner (michael.ebner.14@u... |
b4669a367474d37070c25aeb4cbb5633dc8277277a0b167034a1ce5b39c9835f | Python | 8,877 | 184 | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from skimage.transform import resize
import matplotlib
import numpy as np
import comparison_datasets
import torch
plt.rcParams.update({'font.size': 20})
color_map = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","red"])
normal_c... |
e835acaaef11ce35f94ceabd164f32d2a18f42c8117f71675e40789058410af2 | Python | 8,877 | 225 | # Functions for performing classification of prefential parameters and
# preferred corticospinal wave
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.svm import SVC
np.random.seed(149)
# Identify nodes that were s... |
56004e5eb906c78711b60ec9df99459122beaac7a3e84e38359ac9c770f87c44 | Python | 8,878 | 220 | #!/usr/bin/env python
from irescue._version import __version__
from irescue._genomes import __genomes__
from irescue.misc import writerr, versiontuple, run_shell_cmd
from irescue.misc import check_requirement, check_tags
from irescue.map import makeRmsk, getRefs, prepare_whitelist, isec, chrcat
from irescue.map import... |
b56803df44188e67cbabc6c3c192b77d89143b82f9d7157edd4c64ddaae9d37c | Python | 8,879 | 221 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn as skl
from sklearn import metrics as sklm
def convert_to_numeric(data):
df = data.copy()
columns = df.columns
df_new = pd.DataFrame(columns = columns)
for i in columns:
new_values = pd.t... |
0dbafeecde786244e5a1f0f7c99f4e30c3ad2ac89897ce9ec62653113355cd8e | Python | 8,881 | 213 | import pandas as pd
import numpy as np
import random
from sklearn.impute import SimpleImputer
from sklearn.impute import KNNImputer
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
from sklearn.linear_model import LinearRegression
from impyute.imputation.cs import ... |
46d8e68468d9529c0d34ae5d0d1b38bc3aaf308e5eb7d4380d4150c79e03c1e3 | Python | 8,881 | 172 | import os
import pandas as pd
import numpy as np
from nilearn.glm.first_level import make_first_level_design_matrix
from nipype.interfaces.fsl.model import SmoothEstimate
from glob import glob
def smooth_estimate(img_paths, mask_path, out_path):
"""
Estimate smoothness parameters for a Z-statistic image.
... |
c310273e7823a0dead752764f8bf23ed0e5ea769ade8f5e098257d50699cf847 | Python | 8,881 | 267 | from contextlib import contextmanager
from pathlib import Path
import bmesh
import bpy
import databpy as db
import numpy as np
def path_resolve(path: str | Path) -> Path:
if isinstance(path, str):
return Path(bpy.path.abspath(path))
elif isinstance(path, Path):
return Path(bpy.path.abspath(str... |
07c80aa3302fe50c670809584501f1b39494cd59fb1d9228f1baba6df05e38e3 | Python | 8,891 | 191 | import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torchvision.transforms.functional as VF
from torchvision import transforms
import sys, argparse, os, copy, itertools
import pandas as pd
import numpy as np
from sklearn.utils import shuffle
from sklear... |
13ad0cdf40f0cac796ccda4005310a06b1919b83c0299a2e5784f3a1eb0212fa | Python | 8,894 | 220 | import sys
import APT_interface as apt
import TrkFile
import link_trajectories as lnk
from poseConfig import conf
import argparse
from pathlib import Path
import numpy as np
import tempfile
from TrkFile import to_mat
import hdf5storage
from scipy import io as sio
def parse_args(argv: list):
parser = argparse.Arg... |
9b1b9f99d043426eeedba41344a939e9d3a96670f906f83957cc80207b2c09cd | Python | 8,894 | 288 | """"""
from collections import deque
from queue import Queue
import logging
import warnings
import cv2
import numpy as np
from pupil_recording_interface.decorators import process
from pupil_recording_interface.process import BaseProcess
from pupil_recording_interface.externals.gaze_mappers import (
Binocular_Gaze... |
5ded06e039c287e27c87f081a5868aca7e8c6ca4e9e78db70e37720c7b8fc46c | Python | 8,895 | 226 | """
Bonito train
"""
import os
import re
from glob import glob
from functools import partial
from time import perf_counter
from collections import OrderedDict
from datetime import datetime
from bonito.schedule import linear_warmup_cosine_decay
from bonito.util import accuracy, decode_ref, permute, concat, match_names... |
6e843608c197cbc218480dabb3beb1fd411fb3df663ceff055b5cbf60fb093df | Python | 8,895 | 184 | """
INVARIANCE_LADDER.py - written 2026-09-14.
Rewritten the same day after the first version's own prediction was FALSIFIED.
The falsification is kept in section 0, because it is where the result came from.
THE POINT OF THIS FILE
----------------------
Everything else in this project MEASURES a disagreement. Th... |
afa5cb22bfcfdd47f8072e327bdde247d099d9e60e037fd9390b48f80803f940 | Python | 8,895 | 286 | # 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... |
871231f5961ad527890a41af3192d08d052ac82d4422771795c4b0a011da2839 | Python | 8,896 | 253 | #!/usr/bin/env python
#
# Copyright 2009, Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list... |
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