sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
e4f9be1386548d5156f7f56048c9b37698419aa0dfc396c01a7c50beaa48708b | Python | 5,704 | 173 | # Kai Sandbrink
# 2022-10-01
# This script runs the decoding analyses for the Explore-Exploit Setting
# %% LIBRARY IMPORTS
import torch
import os, copy
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_err... |
a493d226229049749f407878e688c4ffc7a84c70fc4956297462a9859cd55e9e | Python | 5,705 | 173 | """
Train and compare DLIM (Deep Latent Interaction Model) models
on different datasets and environments.
This script:
1. Loads data for a given dataset/environment combination. This script allows for data from Kemble et al. and Kinsler et al.
2. Trains multiple instances of DLIM with and without spectral regularizat... |
86c8e04e3d8bd51758948173322bc37c75056d124a6cace837074faa7ac5ba0e | Python | 5,706 | 145 | import sys
import importlib
import pandas as pd
import train_it_mdp as sv
importlib.reload(sv)
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
import matplotlib.pylab as plt
import n_choice_markov as mdp
importlib.reload(mdp)
import numpy as np
from torch.utils.data import DataLoade... |
b95a42472b4b5092c39121b5905df3060ca3e95719c99f82c25401da31c96016 | Python | 5,706 | 205 | from typing import List
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from scipy.spatial.distance import cdist
from cv2.ximgproc import guidedFilter
from PIL import Image
# fushion stragety
def low_pass(img, k=(30, 30)):
'img: np.ndarray, float32, 0-1'
assert img.dtype == np.floa... |
e07164295e19e77de1ffa104366bc876bdd07a97c22574890eed7564a5169f8f | Python | 5,707 | 149 | import numpy as np
import pandas as pd
from sklearn import metrics
import scanpy as sc
import ot
from sklearn.decomposition import PCA
import scipy.sparse as sp
from sklearn.model_selection import train_test_split
import torch
from torch_geometric.data import Data
import sklearn.neighbors
def index_to_mask(index, size... |
80a0624eb29f6a10569d8b26e5bfaa5b7c64df734b0871601ef7fc3847e7a1ba | Python | 5,708 | 168 | import os
import pandas as pd
import numpy as np
import pickle
import pkg_resources
from ast import literal_eval
class Architecture:
def __init__(self, architecture, baseline_accuracy,
ptq_accuracy, qat_accuracy,
noisy_accuracy, analog_accuracy, baseline_drift_60,
... |
096f67a2adc00f8da046cc98c0f79ad45bba434c26407eb5003d4eb7cf1e5333 | Python | 5,709 | 152 | """
Copyright 1999 Illinois Institute of Technology
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, publis... |
d2dff2b97624e69e3f1e68d09f4456435d0e22d2de362662aa4b46ea4d972d8e | Python | 5,709 | 197 | import math
def add_vec(u, v):
return (u[0] + v[0], u[1] + v[1])
def norm_vec(v):
return math.sqrt(v[0] * v[0] + v[1] * v[1])
def sub_vec(u, v):
return (u[0] - v[0], u[1] - v[1])
def rot90_vec(v):
return (v[1], -v[0])
def scal_vec(alpha, v):
return (alpha * v[0], alpha * v[1])
def unit_v... |
74baa26e1076a1712e8628823c391af318d2ea579ed2a38b0d589416090feecd | Python | 5,712 | 176 | #!/usr/bin/env python
"""
A TCP communication class that acts as the server side for generic communications
between programs in the storm-control project
Jeff Moffitt
3/8/14
jeffmoffitt@gmail.com
Hazen 05/14
"""
import sys
from PyQt5 import QtCore, QtGui, QtWidgets, QtNetwork
from storm_control.sc_library.tcpMessag... |
a04378300082131ef815c49b7e12685614c13ce97fa3eaef471e28901ea04fe7 | Python | 5,712 | 157 | import unittest
import numpy as np
import torch
import torch.nn as nn
import torch.testing as ptt
from torch.utils.data import DataLoader
from deepdwi import fourier, util
from deepdwi.dims import *
from deepdwi.recons import zsssl
if __name__ == "__main__":
unittest.main()
devices = [torch.device('cpu')]
i... |
be568a093bf9e8556b8039ee376a27b6e89a6b3d06c1c7b90ca050fa9ac8de7b | Python | 5,712 | 142 | import platform
import sys
from glob import glob
from setuptools import (
Extension,
setup,
)
def main():
metadata = {"scripts": glob("scripts/*.py")}
if len(sys.argv) >= 2 and (
"--help" in sys.argv[1:] or sys.argv[1] in ("--help-commands", "egg_info", "--version", "clean", "sdist")
):
... |
6d13b84e889ce60345c866cb16b65a1d051976c0afb959c61b7aae0f223f2aeb | Python | 5,713 | 175 | #!/usr/bin/env python
"""
Analyze frames using QRunnables and QThreadPool.
Hazen 05/17
"""
import time
from PyQt5 import QtCore
import storm_control.hal4000.halLib.halModule as halModule
import storm_control.hal4000.spotCounter.lmmObjectFinder as lmmObjectFinder
class AnalysisWorker(QtCore.QRunnable)... |
e3069d7f6f963056f01739b464052674706b1cbf70e1c49348c3a45ce9ff83ab | Python | 5,714 | 152 | """
Copyright 1999 Illinois Institute of Technology
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, publis... |
8d22e5524caa2a241d1419f09e2104efa38f514d481e6446b944948e9f33bf99 | Python | 5,716 | 163 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
from .GAT import GATConv
from.preprocess import fix_seed
class Discriminator(nn.Module):
def __init__(self, n_h):
super(Discriminator, self).__init__()
... |
7148c30970b0722aaa33ddbb419e1d53836e3091a66147f62636b64da1f6d924 | Python | 5,717 | 134 | # import brain-score specific libraries
from brainscore_core.supported_data_standards.brainio.assemblies import NeuronRecordingAssembly
from brainscore_vision import load_dataset, load_ceiling, load_metric
from brainscore_vision.benchmark_helpers.neural_common import NeuralBenchmark, average_repetition
from brainscore_... |
1ca5f7dff0b9b3eecc098d2cc6864ef4f06e727916d161e219481c05ae6beb98 | Python | 5,718 | 269 | # The ImpedanceFitter is a package to fit impedance spectra to equivalent-circuit models using open-source software.
#
# Copyright (C) 2018, 2019 Leonard Thiele, leonard.thiele[AT]uni-rostock.de
# Copyright (C) 2018, 2019, 2020 Julius Zimmermann, julius.zimmermann[AT]uni-rostock.de
#
# This program is free ... |
4e1da59f4cb25e042fd0ee1d30f1ded0af4ee529a27e8ef94b3f79593205e68e | Python | 5,718 | 128 | #!/usr/bin/env python
"""
单折训练脚本（随机 8:2 划分），支持 FocalLoss / BCE 与 CC-Head
"""
import argparse
import random
from pathlib import Path
from typing import List
import numpy as np
import torch
from torch.utils.data import DataLoader, WeightedRandomSampler
import config
from dataset import StrabismusDataset
from models.ml... |
58adf6107ffbe8cf073d03da21c804e38fde8593695542ef7ae5e7c32d86f333 | Python | 5,719 | 175 | '''EfficientNet in PyTorch.
Paper: "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks".
Reference: https://github.com/keras-team/keras-applications/blob/master/keras_applications/efficientnet.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
def swish(x):
return x ... |
61cda632c12be96f1cbb351a7f7f113fd38acc265bb4c28b4b68238fbe17be67 | Python | 5,719 | 155 |
import torch
import numpy as np
import ogb
from ogb.linkproppred import LinkPropPredDataset
import matplotlib.pyplot as plt
from scipy import stats
def plot_nodes(nodes, data, str="train"):
# generate unique node lists
u, indices = np.unique(nodes, return_index=True)
x = []
y = []
z = []
for... |
9e2f3d29c19d906ac2904a68c22468fa16471eadf37d2231453732231e87734a | Python | 5,720 | 121 | import argparse
import utils
import keras
from keras.models import load_model
from os.path import join
import numpy as np
import gc
'''
Get the average of the predictions from all ResidualBind models in an ensemble
if --eval flag set, evaluates ensemble avg prediction performance on test set
if --distill flag set, ave... |
48bf9b1d9b640217cbf6bb0cd4cb20a76842b5ee4845097f80b1c167eac69baa | Python | 5,721 | 199 | import os
import numpy as np
import pandas as pd
import datetime as dt
import sklearn.metrics as mt
from pathlib import Path
from tqdm import tqdm
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
datas... |
edd18cab09a55ba2a28c11951f4652ed9e547aa1785d4c8ddbbce6f8decfc532 | Python | 5,722 | 166 | import numpy as np
import scipy
import ot
from .glmpca import glmpca
import anndata as ad
import scanpy as sc
from scipy.spatial import distance
def filter_for_common_genes(slices):
"""
param: slices - list of slices (AnnData objects)
"""
assert len(slices) > 0, "Cannot have empty list."
co... |
1e78ff95a0a8d95acb80daac25168c10502ab7c296a1b5fe19f197c0d5103695 | Python | 5,725 | 190 | # Copyright (C) 2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from enum import StrEnum
from django.conf import settings
from cvat.apps.iam.permissions import OpenPolicyAgentPermission
from .models import Membership
class OrganizationPermission(OpenPolicyAgentPermissio... |
f6bb83726a7c75597b488d5f9fff7ef6a7fc4135644d60f2e27900149959e59b | Python | 5,725 | 146 | import pandas as pd
import numpy as np
from causallib.positivity import BasePositivity
from sklearn.linear_model import LogisticRegression
OPTIMAL_THRESHOLD_ACCURACY = 5e-6
def _check_is_valid_threshold_value(threshold_value):
if not isinstance(threshold_value, (float, type(None))):
raise ValueError("inv... |
1c2cb02a1d854bc578c8dd0497594b774b6ef5e9dc0d07378328b03735d82cc9 | Python | 5,727 | 172 | # This file is part of Mooney computational modeling project.
#
# Mooney computational modeling project is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) an... |
452c0bb786c8bff47e02f6cc0b7758d9d2548b30c97c34662480c2df48128972 | Python | 5,727 | 206 | """Functions for running full pipelines via the config API.
See the documentation for :mod:`osl_dynamics.config_api` for example usage.
"""
import argparse
import logging
import pprint
from pathlib import Path
from typing import Callable, Dict, List, Optional, Union
import numpy as np
import yaml
from osl_dynamics.... |
cdbe3e71d4adde9f527080d927ebbc221115363d21066bde3f8ea700f75b9104 | Python | 5,727 | 180 | from ..qt import *
import typing
import asyncio
import collections
from .. import thalamus_pb2
from ..task_controller.util import create_task_with_exc_handling
class ChannelsList(typing.NamedTuple):
node: str
channels: typing.List[str]
sample_intervals: typing.List[int]
class ChannelsDataModel(QAbstractItemMod... |
8ab65244ad44604d0341d600c3f3b645e95d829e8c28d2261e3aaac2b8e47fff | Python | 5,729 | 186 | #!/usr/bin/env python3
# This script is included in documentation. Adapt line numbers if touched.
import arbor as A
from arbor import units as U
import pandas as pd # You may have to pip install these
import seaborn as sns # You may have to pip install these
from math import sqrt
# Construct a cell with the followi... |
9628961746d006a4989cc4677c22432e75ceb62c2e46dfbe9ee91dc106246ba9 | Python | 5,729 | 134 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 3 11:00:46 2025
@author: rl05
Plot Supplementary Figure - Bayes Factor time series
"""
import sys
import os
import os.path as op
import pandas as pd
import pickle
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from mat... |
4b2f34320c76aa03dc7c1fc09a1de0ef1d15a3f26468aba15728b93b7275ddd3 | Python | 5,730 | 167 | import numpy as np
import sys, os, string, inspect, types, glob, fnmatch, re, copy, shutil
import signal, time, errno, socket, platform, tempfile, traceback, stat
import distutils.spawn, operator, tarfile
from optparse import OptionParser
import math
import json
import random
#-----------------------------------------... |
dd47d7aad4684870819449f50ac4460d9092ffe7793d60ece8ef59a628cf7876 | Python | 5,730 | 121 | import pytest
from qtpy import PYQT6, PYSIDE6
from tests.utils import pytest_importorskip
@pytest.mark.skipif(PYQT6, reason="Not complete in PyQt6")
@pytest.mark.skipif(PYSIDE6, reason="Not complete in PySide6")
def test_qt3drender():
"""Test the qtpy.Qt3DRender namespace"""
Qt3DRender = pytest_importorskip(... |
17abb584bc3489cdab3425f7e471d729499edb810fb764bf1d23447e787c7d0d | Python | 5,732 | 165 | '''
CH4_H2O_H2O
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for CH4_H2O_H2O:
P: potential energy, G: Gradient, D: Nonadiabatic coupling vector
This is listed at the end of description to show the availability.
For example: P/G means both potent... |
69decdcb1635c5f449ea110d9dee8ed79420dd2dd076ea24415e3d4b4d098109 | Python | 5,732 | 137 | import numpy as np
from dipy.core.gradients import gradient_table
from src.wrappers.OsipiBase import OsipiBase
from src.original.fitting.IAR_LundUniversity.ivim_fit_method_biexp import IvimModelBiExp
class IAR_LU_biexp(OsipiBase):
"""
Bi-exponential fitting algorithm by Ivan A. Rashid, Lund University
"""... |
209352275451fd7475e5d50f2a3df7d197d9be4a7c81080db9eee526f03debd7 | Python | 5,734 | 200 | """
Functions for plotting results from a mean-centered PLS
"""
import numpy as np
import pandas as pd
import seaborn as sns
def _set_group_lvls(n_conds, n_grps, grp_lvls=None):
"""
Derives a pandas data series of group labels
Parameters
----------
n_conds : int
Number of conditions in t... |
48f067d3368e5c02f18ef8281c7d8282d505480175478878da4b554a2f27d2be | Python | 5,734 | 157 | import sys
import yaml
import os
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib import pyplot as plt
pathpath,myrun = sys.argv[1:]
#pathpath = 'PATHS/filepaths_carlen.yml'
#myrun = 'runC00dMP3_brain'
with open(pathpath) as yfile: pathdict = yaml.safe_load(yfile)
rundict_path = pat... |
ac1b4ebf9fbee8e0ecbc58c818695a9d4707775f65da9326c1de09e784cf2588 | Python | 5,735 | 139 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import os.path as osp
from tempfile import TemporaryDirectory
from datumaro.components.dataset import Dataset
from datumaro.components.errors import DatasetError, DatasetImportError, DatasetNotFoundError
from django.test import SimpleTestCase
from ... |
200b144dca39fc2f82a30964ceb5b9caf1321478134a718cf0dc606fa95e79e5 | Python | 5,737 | 152 | # %%
# importz
import os
import av
import cv2 as cv
import numpy as np
from numpy.core.fromnumeric import reshape
from PIL import Image, ImageOps
from tqdm import tqdm
# %%
# Functions for numpy videos
# resize needs to be a tuple of width, height
def read_and_resize_video_frames(path, resize = None):
frames = [... |
4ceaeb964dca920ab47e107e0a5fa6744a21b9b9932fa5c11cac566a21937a60 | Python | 5,738 | 130 | """
HiP-CT Registration Helper Functions
"""
import os
import numpy as np
import skimage.io as skio
import json
import glob
import tqdm
import natsort
import SimpleITK as sitk
def convert_16_to_8(im_itk_arr):
"""
This function converts 16-bits itk stack to 8-bits itk stack
Args:
im_itk_arr: 16-bits... |
7e81d2d921ba9f7fa0b794beadbf797c49ba9786ff9ec2ba30aef3c4c0bf1dcb | Python | 5,738 | 119 | import numpy as np
from result_caching import store
from brainscore_core.supported_data_standards.brainio.assemblies import DataAssembly
from brainscore_vision import load_dataset, load_metric
from brainscore_vision.benchmark_helpers.properties_common import PropertiesBenchmark, _assert_grating_activations, \
calc... |
df623c717482d7a43adadb5bbe386ced3f937476175df39adb8eba45796a3f17 | Python | 5,738 | 123 | # wfield - tools to analyse widefield data - decomposition
# Copyright (C) 2020 Joao Couto - jpcouto@gmail.com
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the Licens... |
17e93ae85ed5570d76f5811ab0982d7fdb503569cbf47251ce232692d8788cbd | Python | 5,739 | 172 | import os
import numpy as np
import pickle
import pkg_resources
from ast import literal_eval
class Architecture:
def __init__(self, architecture, baseline_accuracy,
ptq_accuracy, qat_accuracy,
noisy_accuracy, analog_accuracy,
noisy_drift_60,noisy_drift_3600,noi... |
cf445650530fe9b59211487fc831e3ee409143bf6cb13f7dacd84a15f0728da6 | Python | 5,740 | 161 | """
Kinematic dynamics: velocity, acceleration, angular displacement.
Ported from utils/kinematics.py (Chambers et al. 2020 pipeline).
"""
import numpy as np
import pandas as pd
def get_delta(df: pd.DataFrame, inp: str, outp: str) -> pd.DataFrame:
"""Frame-to-frame difference of column `inp` → stored in `outp`.... |
f3966978c330c89187ad43d9d876e31281a0ba03ee3ba590ba02b776e3fac7b2 | Python | 5,741 | 194 | """
Stochastic Gates Layers and Models
Adapted from: https://github.com/runopti/stg
Original authors: Yutaro Yamada https://github.com/runopti
License: MIT
Modification:
- introduced warm_start as mu-gates initialization
"""
import math
import numpy as np
import torch
import torch.nn as nn
from stg_PopPK.utils impo... |
24bb4c3d9f669e55b2575a5685eea6d62ad00586e0d88d8d0feb8af7168976dc | Python | 5,742 | 89 | import seaborn as sns
from Utilities_and_Loading import *
def define_global_variables(script_vers):
my_general_parameters = {}
# Paths
my_general_parameters['data_path'] = '../../Data/'
vars_path = '../../Results/variables'
figures_path = '../../Results/figures'
my_general_parameters['vars_path... |
e478e7c18100409a98ccbad24412c41e678993cc89b077e18e686499b9a91db7 | Python | 5,747 | 168 | import h5py
import logging
import numpy as np
import pandas as pd
from scipy.linalg import cho_solve, cho_factor
import heig.input.dataset as ds
def check_input(args):
# required arguments
if args.bases is None:
raise ValueError("--bases is required")
if args.ldrs is None:
raise ValueError... |
4da4b62dc2bd1c522d28ee064408a4f6fac7cdc1200619c94da680075d353cc2 | Python | 5,748 | 154 |
""" Classes and functions for fitting ivim model """
import numpy as np
from scipy.optimize import lsq_linear
from dipy.reconst.base import ReconstModel
from dipy.reconst.multi_voxel import multi_voxel_fit
from dipy.utils.optpkg import optional_package
from scipy.signal import unit_impulse
class IvimModelL... |
6c22122ee73eb84f3d25ebae2987dd4caa6adcce1faa74de29d5a1f1f70c4858 | Python | 5,749 | 146 | """Test the functions in the yasa/hypno.py file."""
import unittest
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
import yasa
from yasa.hypno import Hypnogram, simulate_hypnogram
from yasa.hypno import _hypno_find_periods as hfp
hy... |
3eb8fd8b80613901c183efec07c78965f7002944818893560c9ac86a390e3dbe | Python | 5,751 | 148 | import datajoint as dj
from pipeline import experiment
from commons import lab
from datajoint.jobs import key_hash
import os
import numpy as np
from .utils import h5, signal
from .exceptions import PipelineException
from . import notify
schema = dj.schema('pipeline_temperature')
@schema
class Temperature(dj.Import... |
51a3d0c58d7e25835dafe4312cf465fb18f9517233a22fb2bfe38bab2d0a2957 | Python | 5,751 | 132 | import torch
import numpy
import pandas as pd
import torch_geometric.transforms as T
from torch_geometric.data import Data, InMemoryDataset
import os
## Define "Dataset" class based on ordinary Python list.
class SpatialOmicsImageDataset(InMemoryDataset):
"""
Spatial omics dataset loader for HRCHY-Cy... |
b6ab540c247e2a19cb659a2c642e616a9746324f2a0032bad2dd73c0d8734f01 | Python | 5,751 | 139 | """
HMM: Summary Statistics
=======================
A useful way of characterising the data is to use statistics that summarise the state time course, these are referred to as 'summary statistics'. The state time course captures the dynamics in the training data, therefore, summary statistics characterise the dynamics... |
2cf2fa8563a511c53e395889302fb8da625e5237b64d090d0ba965b09238616b | Python | 5,752 | 156 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2023/6/3 15:23
# @Author : SONG-LE
# @知乎 : https://www.zhihu.com/people/xiao-xue-sheng-ye-xiang-xie-shu
'''
封装画图的算法
'''
import matplotlib.pyplot as plt
import numpy as np
import os
import scienceplots
# 判断图片目录是否存在
#fdir = r'E:\Projects\WMH_segmentation\derivative... |
46e75495575faeb60d0642c057c896a023a916b088ec1d3761e99a6fd4f26827 | Python | 5,752 | 154 | import torch
from torch import Tensor
import torch.nn.functional as F
from typing import Dict, Tuple, Union, Sequence, List
from typing import Tuple
@torch.jit.script
def _compute_zero_padding(kernel_size: List[int]) -> Tuple[int, int, int]:
r"""Utility function that computes zero padding tuple.
Adapted from K... |
0380ec8c56820d575cebb6e8a0410b0152d89ff8cd4ba804b20a9d15e9efbe30 | Python | 5,753 | 166 | '''
AOSLO systen
-------------------
For creating an AOSLO object matched to a user's system.
'''
import numpy
import yaml
from scipy.interpolate import interp1d
class AOSLO:
''' An AOSLO class. Instances of this class are AOSLO objects with
parameters specified in the system parameter yaml file
... |
28a8e220431e1f06699429531b2ea2395b575e478ad1aef3dc6892aae9206205 | Python | 5,753 | 142 | import scanpy as sc
import pandas as pd
import numpy as np
import scipy
import os
from anndata import AnnData,read_csv,read_text,read_mtx
from scipy.sparse import issparse
import random
import torch
from Spa3D import Spa3D
def preprocessing_filter_by_gene_number(adata,min_counts=None,max_counts=None,min_cells=10,max_c... |
b744ece2dcd41342d0c81afbd62da20634032def4d5a499545fec2e313a59497 | Python | 5,753 | 189 | import logging
import threading
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List, Optional
from torch.distributed import rpc
from torch_geometric.distributed.dist_context import DistContext, DistRole
try:
from torch._C._distributed_rpc import _is_current_rpc_agent_set
except Excep... |
242ae55fa5199a7114315881f36a0ffe635c9ed0da0c13d40c9b654685b30489 | Python | 5,754 | 135 | import os
import pandas as pd
import numpy as np
import scipy.sparse as sp
import torch
import random
from six.moves import cPickle as pickle
from torch_geometric.data import Data
from torch_geometric.utils import to_undirected, remove_self_loops
from sklearn.model_selection import StratifiedShuffleSplit
import imblear... |
a8845d346e7a891f855ea4e49508342ac44898b48a469c43391bb12c7dea34d5 | Python | 5,756 | 102 | montreal_idx = {
1: ((0, 2, 3, 7, 8, 13, 17, 1, 4, 5, 6, 9, 10, 11, 12, 14, 15, 16, 18),
(19, 21, 22, 26, 27, 32, 36, 20, 23, 24, 25, 28, 29, 30, 31, 33, 34, 35, 37)),
4: ((0, 2, 3, 7, 8), (13, 17), (1, 4, 5, 6, 9, 10, 11, 12), (14, 15, 16, 18), #left
(19, 21, 22, 26, 27), (32, 36), (20, 23, 24,... |
f7ff78006ba9780fc36172e3edf1e1802e86eadd56ea0cf831a30e398c515d6b | Python | 5,757 | 100 | planner_prompt = """
You are an expert in computational chemistry and the manager responsible for decomposing user queries into subtasks.
Your task:
- Read the user's input and break it into a list of subtasks.
- Each subtask must correspond to calculating a property **of a single molecule only** (e.g., energy, enthal... |
622245a80a87cbc7b4a33e6381ef6c4e83a97c8ed5cb974dc528b2be66665a6a | Python | 5,759 | 141 | import numpy as np
import pyvista as pv
from pathlib import Path
COARSE = '/mnt/home/naghavis/Documents/Research/DeepCardioSim/deepcardio/LVmean/LV_mean.vtk'
RR1_FILE = '/mnt/home/naghavis/Documents/Research/DeepCardioSim/deepcardio/LVmean/LV_mean_RR1.vtk'
RR2_FILE = '/mnt/home/naghavis/Documents/Research/DeepCardioSi... |
5f2fe537fcc8d1a7d7c63e7ae760727bd41f842206673f3dd1dc76b692d4c846 | Python | 5,761 | 126 | import nighres
import os
import nibabel
import numpy
import glob
inpath = '/home/pilou/Datasets/Sereno_cb2020/cerebmap20/mri/'
outdir = '/home/pilou/Projects/Cerebellum/Cerebellar-segmentation-ABC/sereno/coreg/'
T1_img = inpath+'orig.mgz'
T2_img = inpath+'T2.mgz'
PD_img = inpath+'PD.mgz'
mni09c = '/home/pilou/Data... |
0db56dffe29c88deac92396966aaf17bce4b73125094556575af91b370e263a0 | Python | 5,762 | 209 | import numpy as np
import numpy.typing as npt
import ase
from modules.tools.system_cutoff import (
find_closest_atoms_to_mol, find_closest_pair_of_atoms
)
"""
Parameters for all descriptors:
-------------------------------
descriptor: descriptor binary data
site_size: site size parameter
mol_path: path to molecule... |
852a7f93f3a0711047f7be6cad2742dad8fb0471ac3ea61d9a562b1fd6f4868c | Python | 5,762 | 179 | """ Distributed training/validation utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
from typing import Optional
import torch
from torch import distributed as dist
from .model import unwrap_model
_logger = logging.getLogger(__name__)
def reduce_tensor(tensor, n):
rt = tenso... |
2190abedadb075a73968f026070abe261b47185138428d17e3ed2fc09f2b3089 | Python | 5,764 | 266 | #! /usr/bin/env python
# aponteeduardo@gmail.com
# copyright (C) 2017
''''
Contains functions related to the parameters.
'''
from . import containers
from . import reparametrize as reparam
from . import likelihoods
class Parameters(containers.AlgebraicObject):
''' Abstract class. '''
@classmethod
... |
bf989b884c4234e57d1f0795266fa805455ea4574ad94c27c3173ff4d5b3d58e | Python | 5,765 | 126 | from causallib.estimation.base_weight import WeightEstimator
from .univariate_curve_fitter import UnivariateCurveFitter
from sklearn.base import BaseEstimator as SKLearnBaseEstimator
from typing import Any
import pandas as pd
from copy import deepcopy
from .survival_utils import canonize_dtypes_and_names
from .base_sur... |
3f36aaa52fec77af4e02e3bc0c184da3b578e570111f7304e46dc89849aa77d4 | Python | 5,767 | 158 | #!/usr/bin/env python
"""
HAL module for controlling a Marzhauser stage.
Hazen 04/17
"""
from PyQt5 import QtCore
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_control.sc_hardware.baseClasses.stageModule as stageModule
import storm_control.sc_hardware.marzhauser.marzhauser as marzhauser
... |
f798cd1a3e70802afa6183bae9815b0ed268d2b8c6683448758e763efa93872b | Python | 5,768 | 171 | from pathlib import Path
from typing import Optional
import click
import numpy as np
import pyvista as pv
import scipy
import skimage
from loguru import logger
from simple_mri import SimpleMRI, load_mri
from brainmeshing.utils import binary_image_surface_extraction
from gmri2fem.utils import largest_island, segmentat... |
0b557d3bb5788126538d7866da57e9521c511b583ad1f99e0560d2be31a36ee3 | Python | 5,770 | 117 | from collections.abc import Sequence
import colorama as clr
def log_and_raise_missing_or_multiple_config_matching_general(
train_name: str,
train_type: str,
matches: Sequence[dict],
predict_names_and_types: Sequence[tuple[str, str]],
name: str,
) -> None:
assert name in ("input", "output")
... |
f0d9b93a06eeb810297bcb6aaba73c9f0da5fcbdf141b5d7997cf204dccdefce | Python | 5,770 | 158 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Prepare a typical command for topup and create the necessary files.
The reversed b0 must be in a different file.
"""
import argparse
import logging
import os
import subprocess
import nibabel as nib
import numpy as np
from scilpy.io.utils import (add_overwrite_arg, ... |
fad281866eefd32f78a7ec93d1852a20f43bad80b53b7aa9c723c93fd96451d1 | Python | 5,770 | 151 | #! /usr/bin/env python
"""Label connected components of membrane graphs for per-region statistics.
Each connected component of a triangle graph is given a unique integer id in a
`component_number` vertex property. This parallels the `patch_number` property
written by generate_patches.py, so the same downstream per-re... |
de123f9fd89b54c04d7ceab4ba8fa2582fe1bfa84bde4df5e81cdf385f8111ef | Python | 5,771 | 161 | """Layer thickness measurement, against streamlines whose layers we chose.
The layer memberships are assigned voxel by voxel here, so the returned start,
end and thickness are all derivable by hand: a straight path of unit-spaced
voxels at 10 micron resolution puts layer *i* between 10*i and 10*(i+1).
"""
import nump... |
c5d9efd23a4d3a47a8c4ac04d673b6547f385d52b8e77a1dd755a65f6d245046 | Python | 5,772 | 197 | import asyncio
from typing import Literal, Type
import numpy as np
import obspy
import pytest
from matplotlib import pyplot as plt
from seisbench import models
from seisbench.ext.utils import get_edge_indices
from seisbench.models.base import _trim_nan, _trim_zeros
from seisbench.models.utils import PredictionSegment... |
0349dfbfc6aa58f82cbceca782ec46dbb4f1d1c06ceec4ddf49ad95f6b80af4c | Python | 5,774 | 173 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
plot_bland_altman_corrected.py
生成“回归式偏倚校正”后的 Bland–Altman 图，完整消除恒定偏倚和比例偏倚，
并将 95% 限度（Limits of Agreement）收窄至 ±1.96 SD。
用法：
cd StrabismusRegression/tools
python plot_bland_altman_corrected.py
输出：
results/figs/bland_altman_proportional.png
"""
import ... |
3c081b1b73cae03e0b317e98bd2daf99e772786712a59f1e5d47e12295919ba1 | Python | 5,774 | 112 | """ This scripts generates 3 examples where we try to regress LR T2 scans into HR T1 scans. We assume here that HR label
maps are available with corresponding T1 scans. Thus this script produces pairs of real HR T1 scans along with aligned
HR synthetic scans, simulating LR T2 scans.
If you use this code, please the S... |
51a60655980665a8a0e942d341d6c40b146577e561e2a49da750c8f09859258f | Python | 5,774 | 164 | """
Compare rigid registration: legacy (so(3)) vs quaternion implementation.
For rotation angles in [5, 10, 15, 30, 45] degrees, we sample a random 3D axis,
rotate the fixed image and segmentation by that rotation, then run both
RigidRegistration (quaternion) and legacy RigidRegistration (so(3)), and assert
mean Dice ... |
a6b20f15c9adb9d3cf7c6998f3af194f8be3396c126442c09be1ebf936996098 | Python | 5,774 | 192 | #!/usr/bin/env python3
from collections import defaultdict
from dataclasses import dataclass
from typing import Optional
import json
import arbor as A
from arbor import units as U
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
here = Path(__file__).parent
# (3) A ... |
d159795796bfbead67475b17590c306235b5c5a37f0386493fbee931fc91e77e | Python | 5,774 | 184 | import pandas as pd
import numpy as np
import argparse
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser(description='Preprocess edge list')
parser.add_argument('-e','--edge_list', help='Edge List to be processed.', required=True)
parser.add_argument('-n','--node_list', help='Node List to be processed.'... |
f8c3b4f0ec3c0fbec4e0fdadad5309f5fb5f8a6e6bc27fb7b131038bcaf8722a | Python | 5,775 | 164 | import argparse
import h5py
import os
import matplotlib.pyplot as plt
import numpy as np
DIR = os.path.dirname(os.path.realpath(__file__))
HOME_DIR = DIR.rsplit('/', 1)[0]
HOME_DIR = HOME_DIR.rsplit('/', 1)[0]
print('> HOME: ', HOME_DIR)
DATA_DIR = HOME_DIR + '/data/'
print('> DATA: ', DATA_DIR)
# %%
parser = argp... |
660c899b72270bbfd21c738d719ba0a91b301302e4bb72808b6d4fe321a76c78 | Python | 5,776 | 142 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Plot all mean/std per point for a subject or population json file from
tractometry-flow.
WARNING: For population, the displayed STDs is only showing the variation
of the means. It does not account intra-subject STDs.
"""
import argparse
import itertools
import json
im... |
e6b211da925f8e6548a04bb435a1a6723b3158fbafde7a7536af10110ef64782 | Python | 5,777 | 198 | import torch
import torch.nn as nn
class Squeeze_Excitation(nn.Module):
def __init__(self, channel, r=8):
super().__init__()
self.pool = nn.AdaptiveAvgPool2d(1)
self.net = nn.Sequential(
nn.Linear(channel, channel // r, bias=False),
nn.ReLU(inplace=True),
... |
2a1030854941b82d912fd4c1949a55aac94825acb448264d5e766d0afdcb1b16 | Python | 5,778 | 136 | import warnings
import torch
from torch import optim
from featurevis.exceptions import FeatureVisException
def gradient_ascent(f, x, transform=None, regularization=None, gradient_f=None,
post_update=None, optim_name='SGD', step_size=0.1, optim_kwargs={},
num_iterations=1000, ... |
a22fbbc5537cc9323aa5aaad54d3c1f4a7dfcb1fba2e98f51ec2debaa9265c69 | Python | 5,779 | 130 | """A module that contains unit tests for the :py:mod:`corelay.processor.embedding` module."""
import typing
from importlib import import_module
import numpy
import pytest
from sklearn.datasets import load_digits
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.utils import Bunch
from corelay.pro... |
6a71cfa0bee7ad24c30197d1d6db908730a90ae39c6b03be6b34ab466c6d0774 | Python | 5,780 | 157 | # -*- coding: utf-8 -*-
# genbed: A Python toolbox for generative embedding based classification
# Copyright 2019 Sam Harrison
# Translational Neuromodeling Unit, University of Zurich & ETH Zurich
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public L... |
7fd8a9e122992b2983eb70f0ca922d0c1ea69a501691188874a54a04ce9e0f72 | Python | 5,780 | 163 | # vessap open graph benchmark dataset
import os
import os.path as osp
import sys
import sys
sys.path.insert(0, '../')
sys.path.insert(0, '../../')
sys.path.insert(0, '../../ogb')
import torch
import numpy as np
import pandas as pd
import scipy
import argparse
import networkx as nx
import torch_geometric.transforms as... |
11e6106af46977a8dc61b0ffc41600f3e6a92df8824a076a0dc148ea6320a232 | Python | 5,781 | 160 | # This file is adapted from:
# https://github.com/txie-93/cgcnn.git
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
class ConvLayer(nn.Module):
"""
Convolutional operation on graphs
"""
def __init__(self, atom_fea_len, nbr_fea_len)... |
cb3c1f6860e1fbbf730ddbafe4a3418a3d328dbd415f6768402febe8895b11bf | Python | 5,781 | 148 | from PySide6.QtWidgets import (
QWidget,
QPushButton,
QGridLayout,
QLineEdit,
QCheckBox,
)
class NavigationControls(QWidget):
"""
Reusable navigation/control widget used across multiple GUIs.
Provides:
- processFolderButton: main action (e.g., Play / Process Current Folder)
- ... |
1485be3c3ae583a9dfc6b5a00a61e75529d734a8ccbc3a74528afffcd266127a | Python | 5,782 | 188 | import logging
import matplotlib.pyplot as plt
import xml.etree.ElementTree as ET
import mne
import numpy as np
from iso_analysis.utils import bootstrap_ci
def plot_iso_single_subject_save(iso_df, subject_id, save_path):
"""
Generate a single combined ISO plot for all channels and save the plot image.
Pa... |
7fc30e389b08767c790951abe1a115873c6a4e20f350b60c31d5879678f39353 | Python | 5,783 | 149 | """
A simple plugin just to change the order of the slices
"""
from PyQt5 import QtWidgets
from lasagna.plugins.lasagna_plugin import LasagnaPlugin
from lasagna.plugins.registration_plugins import reorder_stack_UI
from lasagna.plugins.registration_plugins import selectstack_UI
class plugin(LasagnaPlugin, QtWidgets.Q... |
b7b4951d992bfef5c71a68a2298738a86cc75e9a8f3c5ce6106cd9ceda0b3251 | Python | 5,784 | 148 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Henrik Zunker
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... |
40eb4d907d1fd9ef29d7358b473fa07a1e8f9142949b3429f95f5ffc235e44b4 | Python | 5,786 | 149 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
import sys
from collections import Counter
# external inputs
import numpy as np
import nibabel as nb
from nibabel.freesurfer.io import read_geometry
from skimage import measure
from nighres.surface import probability_to_levelset
from nighres.laminar i... |
ccfe99da518c5a8df5fe0077287f3c4acdf2157d63c355db7f01e11cf1579d18 | Python | 5,786 | 197 | import logging
import os.path as op
from enum import IntEnum
from time import time
import numpy as np
from scipy.ndimage import (
binary_dilation,
distance_transform_edt,
gaussian_filter,
)
from skimage.segmentation import find_boundaries
from AFQ.data.fetch import afq_home, fetch_babyseg_models
from AFQ.... |
aabae215c5474913be5887ce64c1d832f5f60b0e3c49c89a058fd3dfc91e03b3 | Python | 5,787 | 133 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from pathlib import Path
from typing import Any
import torch
from torch import nn
from ultralytics.utils import IS_JETSON, LOGGER, is_jetson
from ultralytics.utils.torch_utils import TORCH_1_10, TORCH_2_1, unwrap_... |
0d9e7b43155688f8613a319a378ef71db85f5f6c7dd2fda6868cd6cb1e06e8cf | Python | 5,789 | 188 | import time
import json
import math
import enum
import struct
import typing
import threading
import queue as thread_queue
import random
import logging
import datetime
import asyncio
import functools
import subprocess
from ..resources import native_exe
from ..config import *
import pathlib
import grpc
from .. import u... |
9603ab2fa3e0878065696d15f304bdcb8d24ce34b15a10cf7105e6984992f189 | Python | 5,789 | 142 | from typing import NamedTuple, NewType
__all__ = ['CodeAlias', 'HEADER', 'CodeInfo',
#
'CELLULAR_IO',
'WFIELD_IO',
'BEH_IO',
#
'get_headers_from_code']
CodeAlias = str
HEADER = NewType('HEADER', str)
def h(*header: str) -> list[HEADER]:
return [H... |
1c8cdcfcf01b10f28a20da3b4bc626cb81a9754ef16264b4c0110e981f153aea | Python | 5,791 | 163 | """
Copyright 1999 Illinois Institute of Technology
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, publis... |
0ce0bde417b1f39a96380fb7ee402f6dd0945ed3ebd3baceeb8b54763af37132 | Python | 5,792 | 178 | import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score
from xgboost import XGBClassifier
import pickle
from pstimutils import PertStimDataSimple
from perturbation1 import *
def downsample_to_... |
ab824bffd0f3d9adfe299edb7d4577f77780787593c577fdb8ad7c575fe9d280 | Python | 5,794 | 167 | import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
import argparse
parser = argparse.ArgumentParser(
'Visualize the distribution of learned edges between residues.')
parser.add_argument('--num-residues', type=int, default=146,
help='Number of ... |
b91ab6d670a0a2cb42da313a48a7e9db1218e3f06dad7386e581db59410fbf5d | Python | 5,794 | 134 | import warnings
import torch
from torch import Tensor
import torch_geometric.typing
from torch_geometric import EdgeIndex
from torch_geometric.typing import Adj, SparseTensor, torch_sparse
from torch_geometric.utils import is_torch_sparse_tensor, scatter
def spmm(
src: Adj,
other: Tensor,
reduce: str = ... |
61f4d29a18a0f0bb6056dfa5da4e40b7994cf5051bda99d2ec8653279b2ba6f0 | Python | 5,795 | 117 | # Copyright 2025 BrainX Ecosystem Limited. 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... |
07006e37b546a143f73255bcae3b12f6a0762a805f2d90770c60f889ff3e0e7f | Python | 5,797 | 179 | # Implementation following https://www.pyimagesearch.com/2021/11/08/u-net-training-image-segmentation-models-in-pytorch/
import torch
import torch.nn as nn
from torchvision.transforms import functional as T
from torch.nn import functional as F
from pli_cyto.models.components.distributed import RunningNorm2D
class B... |
2de9ca4b1289d058a43cc1dc9f36abfec2857c8b5be4fe190346990352818009 | Python | 5,797 | 147 | import numpy as np
import pandas as pd
from scipy.stats import norm
from sklearn.metrics import roc_auc_score, make_scorer
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import cross_val_score, cross_val_predict, LeaveOneOut
from pipelines import preproc_pipeline
# from .util import short_feat
... |
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