sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
32e013918b9584244a70622935a86fcb948747d3a7ca4af1e8ff5fcf2cef50b5 | Python | 3,360 | 73 | import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.check_submission import check_models
from brainscore_core.supported_data_standards.brainio.s3 import load_weight_file
from brainscore_vision.models.mobilenet_v2_0_5_192.model impo... |
4b3a3be0c4e51ce762eeb1d3e5e5b7de82f858e438a1d24ad91716f6ea213dab | Python | 3,360 | 73 | import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.check_submission import check_models
from brainscore_core.supported_data_standards.brainio.s3 import load_weight_file
from brainscore_vision.models.mobilenet_v2_0_5_192.model impo... |
8dcc591532f7d5ea97dfac212ef5ef40f1c02ba986f581d61eec361f27a78d80 | Python | 3,360 | 92 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from django.core.management.base import CommandError
from django.db import migrations
OLD_APP_LABEL = "auth"
NEW_APP_LABEL = "iam"
CONFLICTING_USER_CONTENT_TYPE_ERROR = """\
Cannot re-label the user content type: the "{db_table}" table holds both
"... |
d313d58ec57a5da73e0652a26bd03257d86350cf908d420a517b635225842b09 | Python | 3,360 | 73 | import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.check_submission import check_models
from brainscore_core.supported_data_standards.brainio.s3 import load_weight_file
from brainscore_vision.models.mobilenet_v2_0_5_192.model impo... |
dc39a4e69d6fb2ecfae6b27db7c95fb33c0695f942419bb48e999b7301ab39ab | Python | 3,360 | 73 | import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.check_submission import check_models
from brainscore_core.supported_data_standards.brainio.s3 import load_weight_file
from brainscore_vision.models.mobilenet_v2_0_5_192.model impo... |
df1f3c49123037792f0ba846baa84c0f60806a6b1c172316577acb57ef5477c1 | Python | 3,360 | 103 | from torch.utils.data import DataLoader
from geoseg.losses import *
from geoseg.datasets.vaihingen_dataset import *
from geoseg.models.DCSwin import dcswin_small
from catalyst.contrib.nn import Lookahead
from catalyst import utils
# training hparam
max_epoch = 70
ignore_index = len(CLASSES)
train_batch_size = 8
val_ba... |
a41db6106a0b468c5018e913f8280b451c601d4a74d29624a5347ba3ba4638e3 | Python | 3,361 | 113 | """Functions and routines to get ZBrain, brain and regions, contours."""
import os
import sys
import h5py
import numpy as np
def get_vals_from_ref(file, ref):
"""Handle h5 file referencing system.
:file: <HDF5 file> .h5 file opened with h5py.
:ref: <HDF5 object reference> hdf5 reference.
return
... |
bf2f1b91935fab75a784f4a41d1da493d606966fd2f9b3e0dad6db65d7900fc4 | Python | 3,361 | 102 | #!/usr/bin/env python
"""
Peak finder for use by the camera based focus locks. This
version requires the storm-analysis project.
Hazen 11/17
"""
import numpy
import tifffile
import storm_analysis.sa_library.dao_fit_c as daoFitC
import storm_analysis.sa_library.fitting as fitting
import storm_analysis.sa_library.ia_u... |
00f9d8c1f687f1fb2fdb10ffce83bb25093dee92284f1368959d1e2e84feea73 | Python | 3,362 | 73 | import torch
import torch.nn as nn
import copy
def generate_train_id(num_class, test_ids):
train_idxs = list(range(num_class))
for test_id in test_ids:
train_idxs.remove(test_id)
return train_idxs
def generate_negative_samp_id(id_list, test_ids):
neg_samp_id = copy.deepcopy(id_lis... |
61252778ccfe1c57d6223fd660324483044a03cdaacda2d99416676eff72aa53 | Python | 3,362 | 84 | import pytest
from bism.backends.unet import UNet_2D, UNet_3D
from bism.backends.unext import UNeXT_2D, UNeXT_3D
from bism.backends.r_unet import RUNeT_2D, RUNeT_3D
from bism.backends.cellpose_net import CPnet_2D, CPnet_3D
from bism.modules.runet_block import RBlock2D, RBlock3D
from bism.modules.residual_block import... |
c8d1538d925b4947ff3887b68e515cc99ce7b7100967acf30878bf2011f86818 | Python | 3,362 | 107 | import torch
import numpy as np
import glob
import os
from deepcardio.neuralop_core.transforms import UnitGaussianNormalizer
from data_processing import CustomDataProcessorGINO
from torch_geometric.loader import DataLoader
from torch_geometric.data import Data
DataScaler = UnitGaussianNormalizer
def create_grid(mi... |
e7735af64d9e5e11fcd349a233a859c30d49aef68575396ff1e22ea69af3f21b | Python | 3,362 | 90 | """Flat @tool variant of the ALCF IRI Facility API integration.
Comparison baseline for :mod:`chemgraph.tools.alcf_iri_tools`. Instead
of exposing 7 category-dispatcher tools (each with a discovery
protocol), this module generates one @tool per action -- ~43 tools
bound to the LLM upfront.
Purpose: benchmark the cate... |
3c14392c3affd12e02b6e3364c6a26b78e58ca7f4f9814afd5a2a38fe32157b7 | Python | 3,363 | 113 | # -*- coding: utf-8 -*-
"""
Created on Fri Aug 18 18:23:26 2023
@author: DELINTE Nicolas
"""
import numpy as np
from tqdm import tqdm
from itertools import combinations
from unravel.core import get_microstructure_map, get_weighted_mean
def get_metric_along_trajectory(fixel_weights, metric_maps, roi_sections,
... |
da67e37500c73297bb7692065b0e907074e9d8203747befc20c44c837f83d21c | Python | 3,364 | 74 | import sys
sys.path.append('..')
import torch
import torch.nn as nn
from mace_layer import MACE_layer
from layers.predictor import Predictor, MixtureDensityNetwork
from models.graph_models_pyg import mdn_message
from layers.pooling_pyg import SumPoolingPyg, WeightAndSumPyG, WeightAndMeanPyG, AttentiveFPPoolingPyG, Wei... |
960fe6435c315f907f9d4d2bd399ff9738c80591bf19041a567c5885d23c66c8 | Python | 3,365 | 76 | """ Code to perform 3d stack segmentations. """
from bl3d import models, utils
import torch
import numpy as np
from os import path
#torch.backends.cudnn.benchmark=True # time efficient but memory inefficient
def segment(stack, method='ensemble', pad_mode='reflect', seg_threshold=0.8,
min_voxels=65, max... |
076cbdf99c8c47fbb4552d6ff3b811cb38c9f2c6be44eef4cc1c1e0a75dfc21d | Python | 3,366 | 74 | import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.check_submission import check_models
from brainscore_core.supported_data_standards.brainio.s3 import load_weight_file
from brainscore_vision.models.mobilenet_v2_0_5_192.model impo... |
5b2c7dec7b6a9f7305a76f4de936abb2cd4514f529d522b1a036c0eb1b948a1a | Python | 3,366 | 96 | import bism.utils.cropping
import waterz
import os.path
import os
import torch
import torch.nn as nn
from torch.cuda.amp import GradScaler, autocast
import torch.optim.swa_utils
from bism.models.construct import cfg_to_bism_model
from bism.utils.io import imread
from tqdm import tqdm
import fastremap
import numpy a... |
3c68e98e91ceba49a5806672e8276a301ad9cf3371b3cbddc1f1626997183584 | Python | 3,367 | 96 | import os
import numpy as np
import nibabel as nib
from scipy import ndimage
from nipype.interfaces.base import (
BaseInterface,
CommandLine,
CommandLineInputSpec,
BaseInterfaceInputSpec,
TraitedSpec,
File,
Directory,
)
from traits.api import Enum, Str, Bool, File
import hipsta
from cvdpro... |
ef392d54cd52c88fabaf1d7d99e9b8ab54c1e7d2907f82c691df183e66000dd4 | Python | 3,367 | 122 | import unittest
import warnings
from unittest.mock import patch
from rscvp.statistic.sql.main_osds_pie import OriDirPieStat
from rscvp.visual.main_polar import VisualPolarOptions
from rscvp.visual.main_reliability import VisualReliabilityOptions
from rscvp.visual.main_sftf_pref import VisualSFTFPrefOptions
from rscvp.... |
ddcb47cbc9ed959b3d662776ddeab2899be1d22682f544fdbcc0b16ec8fb7533 | Python | 3,369 | 79 | import os
import pandas as pd
import matplotlib.pyplot as plt
from importtdt07 import data_estractor
import sys
import re
lista_dataframes = []
errors = [] # Inicializa la lista para almacenar errores
def buscar_subdirectorios(directorio_raiz):
patron = re.compile(r'\b[1-9][0-9]?-[1-9][0-9]?-.*\b(ODD|ASC|DESC).*... |
fdd0ffa61538b825006579b64a8686f0fa5940ba3b4a9346eede8356066cbb34 | Python | 3,369 | 121 | import inspect
import re
__all__ = ["parse_numpy_docstring"]
def _white_space(text):
if text is None:
return None
else:
return re.sub(r"[\n\t\s]+", " ", text).strip()
def _split_parameter_blocks(docstring):
# First extract just the Parameters section
params_section = re.search(
... |
0260550d224390b20b7fca0b8d43de50801c2fce1dc973b71e20eb76bb4dbee5 | Python | 3,370 | 99 | import os.path as op
from typing import Union
import matplotlib.pyplot as plt
import nibabel as nib
import numpy as np
from matplotlib.ticker import AutoMinorLocator
def plot(
input: str,
output: str,
bval: Union[str, None] = None,
mask: Union[str, None] = None,
) -> None:
"""Plots and saves the ... |
79da7815ab06ef2a973402c80ddab9179c8371ccfab7008232bf21f6b3396c0b | Python | 3,370 | 62 | #!/usr/bin/env python3
# Author: Francois Aguet
import argparse
import subprocess
import os
from datetime import datetime
import gzip
import pyBigWig
parser = argparse.ArgumentParser(description='Computes coverage in bigWig and/or bedGraph format from input BAM.')
parser.add_argument('bam_file', help='Input BAM file'... |
2953460f26169a891074c1d9806ba2d7c526d5135ec47ea92f415270518ee8b2 | Python | 3,371 | 74 |
import faiss
import torch
import logging
import numpy as np
from tqdm import tqdm
from typing import Tuple
from argparse import Namespace
from torch.utils.data.dataset import Subset
from torch.utils.data import DataLoader, Dataset
import visualizations
# Compute R@1, R@5, R@10, R@20
RECALL_VALUES = [1, 5, 10, 20]
... |
a93a68010c4a41b3512cfd47793258d1eccf13442962657818dd1a44dc189228 | Python | 3,371 | 92 | # 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... |
b5fe2fabc6938848980507ac8fe4f621ed6fbf8d1c1b2bfc11bae78b77c5f4c6 | Python | 3,371 | 110 | '''
HO3
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for HO3:
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 potential energy and g... |
25f137027a7c4d13702124339b4bfa40b562c80ac386825a13cbb8b05dc568f9 | Python | 3,372 | 61 | """Average motion corrected (aka registered) echos."""
import os
import numpy as np
import nibabel as nb
NII_NAMES = [
[
"/home/faruk/DATA-PT35_SLABS/derived/sub-10_WB_crop/01_mag/02_upsample/sub-10_ses-01_run-01_dir-def_part-mag_ME3DEPI_crop_echo-1_ups2X.nii.gz",
"/home/faruk/DATA-PT35_SLABS/derived/su... |
6a0a8c0515fa9f999dca1eb055e5e164b82db00a7411549bd66cbfeaea88ae99 | Python | 3,372 | 82 | """Joystick action space for controlling agent avatars."""
from moog import action_spaces
from dm_env import specs
import numpy as np
class Joystick(action_spaces.AbstractActionSpace):
"""Joystick action space."""
def __init__(self, scaling_factor=1., action_layers='agent',
constrained_to_x... |
6a9c312626722e7a9efa6279e153cd282037d3ccb7005a872fe15b49d4cb8895 | Python | 3,372 | 98 | from deepreg.callback import build_checkpoint_callback
from deepreg.registry import REGISTRY
from deepreg.util import build_dataset
import deepreg.model.optimizer as opt
import deepreg.train as train
import tensorflow as tf
def set_GPU(device):
gpus = tf.config.list_physical_devices('GPU')
if gpus:
tr... |
e8be55af1b750493274aeb8cfac90f1a2306dfd167726652d52fb44d6d0628ce | Python | 3,372 | 48 | # NOTE timm.models.layers is DEPRECATED, please use timm.layers, this is here to reduce breakages in transition
from timm.layers.activations import *
from timm.layers.adaptive_avgmax_pool import \
adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
from timm.layers.attention_p... |
284bd3737e000d8a62106d9f2716d76e46182095a3f3ea7eeece46751fbd2aae | Python | 3,373 | 110 | from ..qt import *
from .. import thalamus_pb2_grpc
from ..observable_item_models import FlatObservableCollectionModel
from ..config import ObservableDict
from ..task_controller.util import create_task_with_exc_handling
import datetime
import asyncio
import time
import bisect
import logging
LOGGER = logging.getLogger(... |
61f9147214627f88a508d241af288746fdb8f0ca6c33e4d0dabdb8ad19fe5cb6 | Python | 3,373 | 73 | # Copyright 2021 HIP Applied Computer Vision Lab, 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... |
67005b3a14d7cb48902c9204c45004428d4b48454ad53e699fd92ac04300ed40 | Python | 3,374 | 94 | from pathlib import Path
import click
import dolfin as df
import meshio
import numpy as np
import panta_rhei as pr
import pyvista as pv
def mesh_to_pyvista_grid(mesh: meshio.Mesh, dim: int):
geometry = mesh_to_geometry(mesh, dim)
cells = cell_matrix_to_vtk_cells(geometry["polytopes"]["tetra"])
celltypes ... |
dbc22e0360158d3d880fb637f7985f1667ea358c0f48cae274c9d7728cc2cf25 | Python | 3,374 | 79 | """Axion-event-driven stimulation times (partial port of ``axionStimEventsTool.m``).
For the ``axionStimEvents`` detection method, stimulation times come from the
Axion `.raw` file's ``StimulationEvents`` (selected via a CSV that maps
``rawName, well, electrode`` rows to recordings), **not** from the voltage trace.
*... |
e60c9db6080ef6676c2267f2240bc3a53c9b9900b99757e48e250220d530c818 | Python | 3,374 | 116 | #!/usr/bin/env python
# author: Jannes Spangenberg
# github: https://github.com/JannesSP
# website: https://jannessp.github.io
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, Namespace
import os
def parse() -> Namespace:
parser = ArgumentParser(
formatter_class=ArgumentDefaultsHelpForm... |
89f324bc7d1b2e31e54f67637f3ff36bd98f7a99688278828c92a9cb05ff36c0 | Python | 3,375 | 80 | import numpy as np
from sklearn.metrics import roc_auc_score, f1_score
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import pandas as pd
'''链路预测'''
path1 = r'E:\毕业论文\对比方法\sigat\tw1.npy'
path2 = r'E:\毕业论文\数据集\台湾选举\tw_zf1.csv'
# path2 = r'E:\毕业论文\数据集\哇哈哈\Wahaha... |
996b9c380d2d70f5d9eb60aa11420cd2370ed04a390a15a4915854bb7338f63f | Python | 3,375 | 103 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import nibabel as nb
from nipype.interfaces.freesurfer import MRIConvert
from nighres.surface import probability_to_levelset
from nighres.laminar import volumetric_layering
def calc_equivol3(path, sub, n_layers, path_output):
"... |
2a054e76adbcb22f3e48a4e8f43dfb8523bbf5cca6f2257e189c9d3ed5be3696 | Python | 3,376 | 110 | import numpy as np
from matplotlib.axes import Axes
from typing import Optional
from argclz import AbstractParser, argument, int_tuple_type
from neuralib.plot import plot_figure
from neuralib.plot.colormap import insert_colorbar
from neuralib.suite2p import get_neuron_signal
from rscvp.util.cli.cli_output import DataO... |
91d9b4e94e71abd88b235383b90d258f2b11ecc9296b9365c00f7d3a07ebc91e | Python | 3,376 | 98 | from typing import Type
from neuron import h
from ..cells import NeuronCell
from .simulation import Simulation, WaveformType
from neuron.units import ms, mV
class SpikeSimulation(Simulation):
"""A NEURON simulation with the functionality to run simulations and record action potentials in all compartments
for... |
4a0da46ef4276376eb85ece3697a95adb0eab6125ae2dfbf65e55dcc02ba0f45 | Python | 3,378 | 124 | # SPSOAP descriptor functions
# Imports
import json
import glob
import numpy as np
import numpy.typing as npt
import ase
import ase.io
import dscribe.descriptors
from modules.build_data.fhi_aims.descriptors.default_settings import (
DEFAULT_SPSOAP_SETTINGS,
)
from modules.tools.strings import xyz2formula
from ... |
9b65a1782ccf6f5c5289c474504caebde1b07dea705e8845e54ec9de4ce0035e | Python | 3,379 | 85 | import torch
from tqdm import tqdm
from dataset_tool import fast_merge
import numpy as np
from transformers import pipeline
import os
import pandas as pd
import json
user=pd.read_json('../datasets/Twibot-22/user.json')
user_text=list(user['description'])
each_user_tweets=json.load(open("processed_data/id_tweet.json",... |
e8e0b228f04023cc4da25d70f12947ae02aad3c9e585e52d117500d9856ca30c | Python | 3,379 | 81 | import os
import sys
from pathlib import Path
p_dir = str(Path(__file__).absolute().parents[1])
if p_dir not in sys.path: sys.path.insert(0, p_dir)
import torch
from classifier.utils_hparams import args_short_to_long
def _get_ckpt_path_from_dir(exp_path: Path):
version = "version_0"
# If running all folds... |
eb845ba2f9b613b486f788a98abc14578fcaa04c7a89ecdf4669ae815b7e2805 | Python | 3,379 | 109 | '''
H3Cl
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for H3Cl:
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 potential energy and... |
5bffe153d13a6c3e4feae9bd04dc77f13032fcc3cce69e1d73cd3e225ec58ff1 | Python | 3,380 | 116 | #!/usr/bin/env python3
# tifffile/tiffcomment.py
# Copyright (c) Christoph Gohlke
# SPDX-License-Identifier: BSD-3-Clause
"""Print or replace ImageDescription in first page of TIFF file."""
from __future__ import annotations
import argparse
import contextlib
import sys
try:
from .tifffile import tiffcomment
ex... |
8be40bd5e63fa436aaa7ae8ce0ae02bfaffad270d88ac1db2deef88c06c9a9cc | Python | 3,380 | 81 | import nibabel as nib
import numpy as np
from loguru import logger
from nibabel.orientations import apply_orientation, axcodes2ornt, inv_ornt_aff, io_orientation, ornt_transform
def pad_volume_to_shape(volume, target_shape=(256,256,256)):
"""
Given a volume, adds necessary constant padding to yield desired sh... |
222c71f5e83963b237f57039bd9932def69022b4ef74affb400cb6669f0936f5 | Python | 3,381 | 81 | import numpy as np
from numpy.typing import NDArray
def correlation_max(a, b, sample_index) -> tuple[int, float, NDArray, NDArray, NDArray]:
"""
Calculates the correlation (np.correlate(a, b, 'full') between a and b, and returns the index of
maximum correlation, the maximum correlation, and the correlatio... |
9d5596c58a1a1d6ba1f4a28d5d90e91fb3124b4f90f0a991488483267e4715c1 | Python | 3,381 | 88 | from typing import Callable, Union
import torch
from batchgeneratorsv2.helpers.scalar_type import RandomScalar, sample_scalar
from batchgeneratorsv2.transforms.base.basic_transform import ImageOnlyTransform
class GammaTransform(ImageOnlyTransform):
def __init__(self, gamma: RandomScalar, p_invert_image: float, ... |
9f2539cf6aa314efa39dd92a56afb4788ac00717b84d886030757a332fe66dac | Python | 3,382 | 127 | from types import SimpleNamespace
import numpy as np
import pytest
from napari_dmc_brainmap.results.results_helpers.slice_handle import SliceHandle
def _make_slice_handle(location_mm):
handle = SliceHandle.__new__(SliceHandle)
handle.regData = {"atlasLocation": {"0": [0, 0, location_mm]}}
handle.regi_di... |
0b1bfdf119cb836c099d072dd27ca96f21845ef933d0838e3c82e81d9176d22a | Python | 3,383 | 83 | import torch
from tqdm import tqdm
from dataset_tool import fast_merge
import numpy as np
from transformers import pipeline
import os
user,tweet=fast_merge(dataset="Twibot-20")
user_text=list(user['description'])
tweet_text = [text for text in tweet.text]
each_user_tweets=torch.load('./processed_data/each_user_tweets... |
19d0c72e0841eb38a4e2208c94069e564fbcabbb41852d86ab9ac04896b5d071 | Python | 3,384 | 133 | '''Code to convert RGB to other color spaces
'''
#
# Color transformations from
# /auto/k1/shinji/matlab/colorspace/colorspace2.m
# /auto/k1/shinji/matlab/strflab_adds/preprocColorSpace.m
#
# Code originally by Pascal Getreuer (https://getreuer.info/) 2005-2006:
# https://www.mathworks.com/matlabcentral/fileexchange/28... |
7d1de6b4ce9567b99b5ef7b081614b2d8eed461c729e29b756693983bb1bd9a9 | Python | 3,384 | 112 | #!/usr/bin/python
#
## @file
#
# Handles "communication" with the THUM temperature reader.
#
# Hazen 3/09
#
import time
## Thum
#
# "Communicates" with a THUM temperature reader. The THUM reader periodically
# updates a file with the current temperature, etc. reading. This just reads
# that file every ... |
827f6b96a9ca6242f2281df1de7e60e1d8231bb2d8efe64e77e5e692552383f7 | Python | 3,384 | 116 | import os
import subprocess
import time
from collections.abc import Sequence
from pathlib import Path
from docs.doc_modules.experiments import AutoDocExperimentInfo, run_capture_and_save
from eir.setup.config_setup_modules.config_setup_utils import load_yaml_config
from eir.utils.logging import get_logger
logger = ge... |
900b5fd5d466fec4155bbb254d080c3ebe53252ec0b691387d93b17aadd79abe | Python | 3,384 | 106 | import sys
import os
import time
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import random
from sklearn.preprocessing import normalize
import statsmodels.api as sm
from statsmodels.stats.multitest import multipletests
from sklearn.decomposition import PCA
inpt = "mock_genom... |
d145903b4e295efd959bffeb37bc573c8c0734fa90756f76b44482729d2d54d8 | Python | 3,384 | 95 | # -*- coding: utf-8 -*-
import numpy as np
from scilpy.gradients.gen_gradient_sampling import generate_gradient_sampling
from scilpy.gradients.optimize_gradient_sampling import (
add_b0s_to_bvecs, correct_b0s_philips, compute_bvalue_lin_b,
compute_bvalue_lin_q, compute_min_duty_cycle_bruteforce,
compute_pe... |
e446a058241613616232eccfeb87e580192687d4c7e797dcaf4ae666526cbb73 | Python | 3,384 | 96 | import sys
import getopt
import numpy as np
# functions
def get_trackEM(param :str) -> np.matrix:
"""
handle '-0.010963829,-0.999939895,0.999939895,-0.010963829,-129.2603788,1664.628308'
@return reverse affine matrix.
"""
affine = np.zeros((3,3))
in_data = np.array(param.split(',')).as... |
6fe74b1cf075c7d87be0de5de81b67c325ade5beb8e3ca054b3e556af9327029 | Python | 3,385 | 83 | import torch
from tqdm import tqdm
from dataset_tool import fast_merge
import numpy as np
from transformers import pipeline
import os
user,tweet=fast_merge(dataset="cresci-2015")
user_text=list(user['description'])
tweet_text = [text for text in tweet.text]
each_user_tweets=torch.load('./processed_data/each_user_twee... |
ba4ac8db86659ee6f629118bcfd63fe780088cb3534372f7d05f08eecf4b185d | Python | 3,386 | 38 | from testsetclass import TestsetClass
class HEAVY28(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['pbh4_2', 'bih3_hcl', 'pbh4_hbr', 'hi', 'teh2_2', 'sbh3', 'bih3_nh3', 'hcl', 'teh2', 'pbh4_teh2', 'sbh3_nh3', 'sbh3_hi', 'bih3_hbr', 'pbh4_h2o', 'bih3_h2s', 'h2o', 'teh2_nh3', '... |
987049d17fb1166fa9d60fe396781730b8a2fe9652f5bb91991d6dbeae181946 | Python | 3,387 | 62 |
import os
import markdown
import dtiplayground.dmri.preprocessing as prep
class MANUAL_Exclude(prep.modules.DTIPrepModule):
def __init__(self,config_dir,*args,**kwargs):
super().__init__(config_dir,*args,**kwargs)
global logger
logger = self.logger.write
def generateDefaultPro... |
b654e260cbb6b89487d71f0956ea4f0e10e01998e7a29f59577a1463184b7722 | Python | 3,387 | 108 | '''
CH5p
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for CH5p:
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 potential energy and... |
e5d1a1829e3b7a385e60ee9068d1003a3012b0fe9df0685969b74ccc716c70ee | Python | 3,387 | 112 | # The ImpedanceFitter is a package to fit impedance spectra to
# equivalent-circuit models using open-source software.
#
# Copyright (C) 2021 Julius Zimmermann,
# julius.zimmermann[AT]uni-rostock.de
#
# This program is free software: you can redistribute it and/or modify
# ... |
b162284bfea5803111298e46f971be02b7081f51b430ebb45537ff160a24f648 | Python | 3,388 | 73 | ##################################################################################################
### Script that averages over trials of Q and MI data, employing 'extractParamsQMIfromSpikes.py' #
##################################################################################################
### Copyright 2023 Jann... |
4ad11b6858bfff8dd1cbf717116cfa2bc722ea3739a2bb0a6e1b40dd97f62f1e | Python | 3,389 | 91 | import torch
import torch.nn as nn
import torch.nn.functional as F
import skimage.io as skio
import numpy as np
import config
def get_dice(pred, organ_target):
'''
Tversky loss
:param pred: (B, C, 32, 256, 256)
:param organ_target: (B, C, 32, 256, 256)
'''
# the organ_target should... |
e38e952fc15cd2777610efd7af51eb21b41a2b2e0d2e482a7ff5ac669053eada | Python | 3,389 | 193 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
"""Ultralytics neural network modules.
This module provides access to various neural network components used in Ultralytics models, including convolution
blocks, attention mechanisms, transformer components, and detection/segmentation heads.
Examples... |
f4aa3e491e2140f5f7f2505f2b1729c40fd7464d47f627471ea2f20896503f2f | Python | 3,389 | 98 | from os import listdir
import os
import sys
import gzip
import subprocess
import time
import sys
from os.path import expanduser
import json
### FILE PATHS ###
ASSEMBLED_DIRECTORY = sys.argv[1]
FILTERED_DIRECTORY = 'filtered/'
### FILTER OPTIONS ###
MIN_LENGTH = 300
F_ANNEALING_REGION = "ACTCCTACGGGAGGCAGCAGT"
R_ANNEA... |
4b5afa0428b56662ebf95c10ee598cc0652df746bc7a796fafa7b7cc80b5caeb | Python | 3,390 | 81 | #!/usr/bin/env python
"""
Subtract one set of genomic intervals from another (base-by-base or whole
intervals). The returned GenomicIntervals will be in the order
of the first set of intervals passed in, with the corresponding
meta-data.
"""
from warnings import warn
from bx.intervals.io import (
BitsetSafeReader... |
8d355801c78f4befcdc71ce1b5fa333bef6fd89b906a8d8024eb20a12d10a85a | Python | 3,390 | 85 | """
Save nuisiance regressors so that they can be used in a FSL GLM
"""
import glob
import pandas as pd
import os
import numpy as np
from scipy import interpolate
import nibabel as nb
# subs = ['sub-15', 'sub-16', 'sub-17', 'sub-18']
subs = ['sub-12']
ROOT = '/Users/sebastiandresbach/data/s1Anfunco/Nifti'
modaliti... |
925a38ccd252744b3598e9f8ebacfdace51e49476304c2f4afba5b27c94a1998 | Python | 3,390 | 102 | from __future__ import annotations
import numpy as np
def hedges_g(x: np.ndarray, y: np.ndarray) -> float:
"""Hedges' g for two independent samples (x - y)."""
x = np.asarray(x, dtype=float)
y = np.asarray(y, dtype=float)
nx = x.size
ny = y.size
if nx < 2 or ny < 2:
raise ValueError('... |
bc6fafe695a124502d6c34e4fb03e52bc559e03930bf7bb4f3a59716de3167e8 | Python | 3,390 | 87 | # Copyright (C) 2019-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import zipfile
from pathlib import Path
from typing import BinaryIO
from datumaro.components.dataset import StreamDataset
from cvat.apps.dataset_manager.bindings import (
GetCVATDataExtractor,
NoM... |
0294354d49c3267cd34c67aeb4505636a94726341338d310155717d11b2763e4 | Python | 3,391 | 140 | """Example script for running DyNeStE on simulated HMM-MVN data.
This script should take less than a couple minutes to run and
achieve a dice coefficient of ~0.99.
"""
print("Importing packages")
import os
import pickle
import numpy as np
from osl_dynamics.simulation import HMM_MVN
from osl_dynamics.data import Dat... |
67f3fae560b711947292dbb8e10d06e9ba05f7dff800bd03cf9f259b7720a582 | Python | 3,391 | 124 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from functools import partial
from .modeling import ImageEncoderViT, MaskDecoder, PromptEncoder, Sam, TwoWa... |
c3238c702c46d43a9650f5945795b3b0d00ffb9125fc2d17962b634f956669f3 | Python | 3,391 | 94 | import os
import numpy as np
from matplotlib import path
import matplotlib.pyplot as plt
import h5py
import yaml
import sys
from glob import glob
pathpath = 'PATHS/filepaths_carlen.yml'
figdir_gen = pathdict['figdir_root']+'flatmap_rasterization'
fformat = 'png'#png to avoid too many dots
dpi = 300
with open(path... |
90c1648a4b2639aab15a86163eee4d2108e087f4e971dce21f0b3fd7f6f8d1f9 | Python | 3,392 | 111 | from ..qt import *
import asyncio
import dataclasses
import inspect
import typing
import logging
from ..config import ObservableCollection
from ..observable_item_models import FlatObservableCollectionModel, TreeObservableCollectionModel, TreeObservableCollectionDelegate
LOGGER = logging.getLogger(__name__)
WAVE_PROP... |
15f56b0352439b447e923ed81103e36b98141a029a467907c4cf3db9c516c884 | Python | 3,393 | 82 | from __future__ import annotations
import multiprocessing
import os
from typing import List
from pathlib import Path
from warnings import warn
import numpy as np
from batchgenerators.utilities.file_and_folder_operations import isfile, subfiles
from nnunetv2.configuration import default_num_processes
def _convert_to_... |
7288bb10f5dfa2c920b3738c83f9e834275580d7c8bc302ae123042d204689ea | Python | 3,393 | 104 | import pandas as pd
import pyfocus as pf
import numpy as np
from pandas_plink import read_plink
class ExprRef(object):
def __init__(self, geno, sample_info, var_info, pheno=None, covar=None, gene_info=None):
self._geno = geno # IID x SNP
self._sample_info = sample_info # info on ... |
e16b65fa77490d507b35764e1a2e4912d2d8cce241d9ae4670789affafe53a25 | Python | 3,393 | 98 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import math
from collections.abc import Sequence
import numpy as np
from numpy.typing import ArrayLike, NDArray
def encode_mask(bitmap: ArrayLike, /, bbox: Sequence[float] | None = None) -> list[float]:
"""
Encodes an image mask into an ar... |
b6663126b60965afc23f9a02d42bb6d15c3089c42d9fedfc319c0dda79b632f8 | Python | 3,394 | 110 | import pytest
from nems import Model
from nems.registry import layer
from nems.layers.base import Layer
class KeywordTester(Layer):
@layer('tester')
def from_keyword(keyword):
options = keyword.split('.')
kw_head = options[0]
layer = KeywordTester()
layer._kw_head = kw_head
... |
f0b54db703815e4d0e19beced1c5bfc5bbd26ac209f29f2cfba7d851d52baad1 | Python | 3,394 | 83 | import sys, os, gzip, argparse
import numpy as np
import pandas as pd
from scipy.sparse import *
parser = argparse.ArgumentParser()
parser.add_argument('--input_csv', type=str, help='')
parser.add_argument('--dge_path', type=str, help='')
parser.add_argument('--output', type=str, help='')
parser.add_argument('--key',... |
46b709f2d7087d1bb4ab42ca3cd16549af1e2ea31752070a0485087378b3be64 | Python | 3,395 | 103 | '''
'''
# Anwar O. Nunez-Elizalde (Apr, 2020)
#
# Updates:
#
#
#
import numpy as np
from .core import (mk_spatiotemporal_gabor,
generate_3dgabor_array,
dotspatial_frames,
dotdelay_frames,
mk_3d_gabor,
... |
a32fdbc364fc4191e20d9d3098d49a236aff8e23a4b538d536cd2be9077aafb4 | Python | 3,395 | 90 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from torchvision.models import resnet50 # 使用 ResNet 结构
from model.coatnet import conv_3x3_bn, MBConv # 你之前的 CoatNet 部分
class MoCoatResNet(nn.Module):
def __init__(self, num_classes=6):
super(MoCoatResNet, self)._... |
bbf082568e3709717ab02db18d0c2763fe525ca4e9ac29fe11c0d417ff35ede4 | Python | 3,395 | 96 | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import Sequential
from torch import nn as nn
class ResLayer(Sequential):
"""ResLayer to build ResNet style backbone.
Args:
block (nn.Module): block used to build ResLayer.
... |
f9bb161ad84fcac3ceb2d53bb551a1da2c034fb6f2341f53ed8f384c069f94d5 | Python | 3,395 | 90 | # This file was automatically generated by SWIG (http://www.swig.org).
# Version 4.0.2
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info as _swig_python_version_info
if _swig_python_version_info < (2, 7, 0):
raise Runtime... |
1fdb3950fa6c82aec4a4142c71c51e6b3dcfdaca34a9f4d52b3dd50526551851 | Python | 3,396 | 94 | from seisbench.data.base import WaveformBenchmarkDataset
class PiSDL(WaveformBenchmarkDataset):
"""
A dataset for induced seismicity from different regions in Canada, Switzerland,
Germany, and France. Induced seismic events are caused by hydraulic-fracturing
based fluid injection, geothermal power pla... |
4abe24d822174dc1cdbf055446179e2468d1f04adadbf3ef7212a9f3cdbda443 | Python | 3,397 | 75 | from brainscore_vision.model_helpers.check_submission import check_models
from .helpers.resnet_helpers import res2net50_26w_4s
import functools
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
def get... |
5c912ed3db743074b4d22428961b8d1eb8b2836128a2fe59516d766a2272f311 | Python | 3,397 | 111 | '''
K2Rb2
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for K2Rb2:
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 potential energy a... |
7936f000426b223ee0b97e09b8384fcb1e4d9bec7af75fc7887ca1db4fc68d36 | Python | 3,397 | 114 | import numpy as np
from matplotlib import pyplot as plt
from matplotlib.axes import Axes
from neuralib.plot.colormap import insert_colorbar
from neuralib.typing import ArrayLike, ArrayLikeStr
__all__ = ['selective_pie',
'dir_hist',
'mismatch_hist']
def selective_pie(n: ArrayLike, labels: Array... |
ac3c5cf0af85484c451c96d3944ac7f304291f5ed392dc59b469e6f5fd88e939 | Python | 3,397 | 96 | import os
import pandas as pd
import logging
from typing import Tuple
from util.config_vars import config
class FilterDataset:
"""
Filters the overview CSV-file according to inclusion criteria and returns tuples of
subject-, session-, and scan-ids for each MRI-scan.
"""
def __init__(self, csv_pat... |
aa4d5486e085ee88b1ef8d91b98bc6d19d39bd5fc5a693a661bbe53e64ebe4c1 | Python | 3,398 | 83 | """
Creates a plugin that is composed of a new window with a text field (QLabel)
The plugin hooks into updateMainWindowOnMouseMove and gets the X and Y
position of the mouse in the current axes each time this method is run.
This information is then displayed on the screen.
"""
from PyQt5 import QtGui, QtWidgets
from ... |
acbddddde9ab8e9c1c82d4fc5bf9e264394603175833487b1726e78e1d7f1fdf | Python | 3,398 | 80 | import numpy as np
from sklearn.metrics import f1_score, precision_score, average_precision_score, recall_score, accuracy_score, roc_curve
from sklearn.metrics import roc_auc_score, precision_recall_curve
from sklearn.utils import shuffle
import pylab as pl
from chinn.common import col1Width, colors
import xgboost as x... |
456cca6e32742e3ce95ab69bbe658917a80e96002c9c09140d07f14a816b7da3 | Python | 3,399 | 80 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from copy import copy
import torch
from ultralytics.models import yolo
from ultralytics.nn.tasks import OBBModel
from ultralytics.utils import DEFAULT_CFG, RANK
class OBBTrainer(yolo.detect.DetectionTrainer):
... |
ea9060046795c4908f01f825b7140817436835cabcfdfb719715876d071c35ea | Python | 3,399 | 131 | # Copyright 2022 Andrew Lehr
### License: Apache-2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import numpy as np
import os.path as path
'''
Paths to required directories and files
'''
# raw data paths
root = path.abspath(path.join(__file__ ,"../.."))
raw_data_dir = root + '/raw_data/'
spike_data_dir = raw_data_d... |
b3b29c3e1c2ee48d9263ae7637f1c2ad91b5eb6b58e59fae95fb71857a479952 | Python | 3,400 | 68 | """Check published metadata joins, configuration/asset integrity, and local links.
Uses only release files. It does not download images or inspect a local dataset.
"""
from __future__ import annotations
import csv
import hashlib
import json
from pathlib import Path
import re
import sys
from urllib.parse import unquot... |
ca8c6574d1cc90c2acc3998c7549178a3fe37885dc64df23f5f0dbdeff0b64fc | Python | 3,401 | 125 | # -*- coding: utf-8 -*-
# python standard library inputs
import re
# external inputs
import numpy as np
import matplotlib.pyplot as plt
from numpy.fft import fft, fftshift
def get_rf_pulse_bw(file_in, npad=1000, ninterp=1000000):
"""Get RF pulse BW.
This function computes the bandwidth in Hz of an RF pulse... |
dd1f2f5ed7914a7fa78e17aea8831551d011bce1945a55acc10d05662f78edfb | Python | 3,401 | 98 | #!/usr/bin/env python
"""
Motorized or piezo Z stage functionality (software control, see
voltageZModule for DAQ control).
The convention is that down is lowering the objective and up
is raising the objective (on an inverted microscope).
Hazen 04/18
"""
from PyQt5 import QtCore
import storm_control.hal4000.halLib.... |
e677a4035d979227d96cdb975ce307bc43be241075dbdbe29354b7f47b369225 | Python | 3,401 | 98 | import os
import torch
from torch.utils.data import random_split, Dataset
import torch_geometric.utils
from dgd.datasets.abstract_dataset import AbstractDataModule, AbstractDatasetInfos
class SpectreGraphDataset(Dataset):
def __init__(self, data_file):
""" This class can be used to load the comm20, sbm ... |
764b299821526a57f280e42585cb8f40f897f6f0a20320e33f61de54f86616e7 | Python | 3,402 | 94 | import yaml
#def check_filexists(ymlfilepath):
import logging
import logging.config
import os
import importlib.util
import sys
import numpy as np
import pandas
import collections
def iter_paths(d):
def iter1(d, path):
paths = []
for k, v in d.items():
if isinstance(v, dict):
... |
10726213af9425cfdb84b2a5716fe94b78ea3fe355715677c507c6bf65e3a4da | Python | 3,403 | 120 | import simplejson
import numpy as np
import pandas as pd
import networkx as nx
from biothings_client import get_client
mg = get_client('gene')
# Change to new DOI if updates to DrugMechDB
DOI = 3708278
# set to metapaths_biolink to use biolink model
metagraph_sheet = 'metapaths'
def read_spreadsheet(location):
... |
49f02e63a8b2a32c77aa1c867b226701f8f21fc69c8ac8313ae0437d9d526462 | Python | 3,404 | 82 | # Copyright (c) DP Technology.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numbers
from torch.nn.parameter import Parameter
from torch.nn import init
from torch.nn import functional as F
try:
import unicore_fused_l... |
8725be4e4e7bd500f01fcc2042d9c129da97c1acf2df7dc8d86d480b0f2221f5 | Python | 3,406 | 98 | """
Registering statistical maps to anatomical data
"""
import glob
import subprocess
subs = ['sub-05', 'sub-07', 'sub-09', 'sub-10', 'sub-12', 'sub-14', 'sub-15', 'sub-16', 'sub-17', 'sub-18']
subs = ['sub-06']
ROOT = '/Users/sebastiandresbach/data/s1Anfunco/Nifti'
for sub in subs:
funcDir = f'{ROOT}/derivat... |
fac8f9148495077a03d10ae6d2c9646b967b4d48c2e8c0476533f6c20522731a | Python | 3,407 | 94 | import argparse
def parameter_parser():
"""
A method to parse up command line parameters.
By default it gives an embedding of the Bitcoin OTC dataset.
The default hyperparameters give a good quality representation without grid search.
Representations are sorted by node ID.
"""
parser = argp... |
48753e4cd3f7cbdc1bf27fb0c65feea14e60f1f0f711e4b887a57927c20df4df | Python | 3,408 | 111 | # coding=utf-8
"""
Tests for deepreg/model/backbone/local_net.py
"""
from typing import Tuple
import pytest
import tensorflow as tf
from deepreg.model.backbone.local_net import AdditiveUpsampling, LocalNet
def test_additive_up_sampling():
"""
Test AdditiveUpsampling.
"""
batch = 3
filters = 4
... |
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