sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
a0164f3dab2ead960390fac72f9a076b0916c09d8daf75ad39cb8e520997cae3 | Python | 15,334 | 354 | import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import json
def make_onset_timeseries(time, length):
x = np.zeros(length)
x[time] = 1
return x
def make_kernels(tau_rise, tau_fall, timestep):
"""Make the kernel to convolve the onset timeseries."""
max_time = int(8... |
39c974d7b50c9dc6b78ae92f4c83be3304bdcd0db19fe100dc7f96fc2d7a4043 | Python | 15,338 | 289 | from zigzag.classes.hardware.architecture.memory_hierarchy import MemoryHierarchy
from zigzag.classes.hardware.architecture.memory_level import MemoryLevel
from zigzag.classes.hardware.architecture.operational_unit import Multiplier
from zigzag.classes.hardware.architecture.operational_array import MultiplierArray
from... |
cda1f4282283e2d4c05d81295f7e26b83869af37de12b337980f1bb5abc790f8 | Python | 15,341 | 317 | import h5py
import numpy as np
from ..exceptions import PipelineException
from .eye_tracking import ANALOG_PACKET_LEN
from .signal import mirrconv, spaced_max
def read_behavior_file(filename):
""" Reads hdf5 file with timestamps and analog signals.
:param filename: path of the file. Needs a %d where multiple... |
c6be042d47baac0734835ea13fbd3167d66391ca6eec1f666165a4a44d1a508e | Python | 15,344 | 410 | """Tests for mcmlnet.experiments.data_loaders modules."""
import numpy as np
import pytest
import torch
from torch.utils.data import DataLoader
from mcmlnet.experiments.data_loaders.aggregation import (
aggregate_data_image_level,
aggregate_data_subject_level,
)
from mcmlnet.experiments.data_loaders.config im... |
877cc34f5ed55d8eceac9efbf1a432313e34eb8eabc9537bae05aa77b93bcc6e | Python | 15,354 | 374 | # 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 numpy as np
import torch
from torchvision.ops.boxes import batched_nms, box_area # type: ignore
from typing impor... |
f4f7a9fa7f61880e165cc86ae3f803717c10558987d88c970440a60760a477e8 | Python | 15,365 | 356 | import torch
import torchvision
import torchvision.transforms as transforms
import random
import numpy as np
from torchvision.datasets import ImageFolder
import os
from torch.utils.data import Dataset
from PIL import Image
import glob
from tqdm import tqdm
datapath = './datafile'
class SelectiveImageFol... |
398d6bb0ce81f5327fed4afaed1d13be9f0b99e870c42c66ab7686e652be250b | Python | 15,368 | 481 | #!/usr/bin/env python
import argparse
import os
import os.path as op
import numpy as np
import glob
import nibabel as nib
from pydesigner.fitting.dwipy import DWI
from pydesigner.system.utils import writeNii
from pydesigner.tractography import odf
'''
python run_pydesigner_fit.py \
--dwi /Volumes/Flashy/HIE_FBI_... |
e6e50defaa796d57b86b307c0c5e9b9155f06a61da27de7e18afe8846010ab55 | Python | 15,371 | 368 | #!/usr/bin/python3
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import json
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader
from model import KGEModel
from dataloader impo... |
1b14bfafe8bcdeead010b99f1a58adb675bf35efd4ad298611bafd3eb419e584 | Python | 15,386 | 495 | # -*- coding: utf-8 -*-
import itertools
import multiprocessing
from math import cos, radians
from dipy.data import get_sphere
import numpy as np
from dipy.direction import peak_directions
from dipy.reconst.shm import sh_to_sf_matrix
from scilpy.reconst.utils import get_sh_order_and_fullness
# Constants
NB_PARAMS ... |
c8d48ee096dc68048b7b51e173651fd586b498d46a2b5f9638c6ed70c2a5c766 | Python | 15,392 | 491 | # Copyright (C) 2019-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import inspect
import os
import os.path as osp
import re
import tempfile
import zipfile
from collections.abc import Generator, Iterable
from contextlib import contextmanager
from copy import deepcopy
from d... |
c3e1b688a6fd0a75ba2cd13257ccfd995010bb0d46fc8a6e29d4dfa0f4a72cf9 | Python | 15,399 | 488 | """
Plotting functionality based on VTK.
"""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
import os
import warnings
from collections import defaultdict
import numpy as np
from numpy.lib.stride_tricks import as_strided
from vtk import vtkCommand
import vtk.qt as vtk_qt
from brainsp... |
597aa804a70e2f6f0d94a2681ecf99e32f6fe777217b6c6f1f31f2ce3fa58e13 | Python | 15,407 | 394 | """Script to extract laminar resting state activity from digit ROIs"""
import nibabel as nb
import numpy as np
import glob
from nilearn.connectome import ConnectivityMeasure
from nilearn import plotting
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
plt.style.use('dark_background')
ROOT =... |
40f504d4ef5e097faf54f8fbbe2c291e7d925a6171d2c01ab7f5e349a5315bce | Python | 15,412 | 364 | import tkinter as tk
import numpy as np
import math
import json
from collections import defaultdict
from tkinter import messagebox
import argschema as ags
class DrawingEditSchema(ags.ArgSchema):
input_file = ags.fields.InputFile(
description="Path to input json file"
)
output_file = ags... |
a119982564983048282069f6b0f734f486508ce1d1b1c57c668a636aa6cb47d6 | Python | 15,414 | 453 | # Copyright 2024 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
303e7f7dfc9ff6bfd4d4ab85f84b632956f37444764c7a3d31b728ac22c0dfc3 | Python | 15,415 | 390 | import sys
from pathlib import Path
import os
sys.path.append(str(Path(os.path.abspath(__file__)).parents[3]))
import argparse
import time
from shutil import copy
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
from sk... |
754ed2382f3477d5691a60e1c92458a9b6fa30366827cd16edcaa1cbd5e0c57b | Python | 15,423 | 712 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Segment, mask and bin 'MB-KCc' files
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import mbflim.utils as ut
import mbflim.visualization as vis
import mbflim.image.image_processing as pro
import mbflim.image.image_set_processing as setpro
import ... |
7b2586d1c37f008022ab93c7bb17655d9f3be0364f4cda95dbcaddb77fd112c8 | Python | 15,423 | 363 | """Pack a finished output folder into a ``.meanap`` bundle.
Express mode writes a bundle because it never drew the figures. A normal run
writes an output folder and nothing else — 111 MB and several hundred PNGs on a
two-recording batch, against the ~3 MB of numbers every one of them was drawn
from. So the question co... |
bc0b3a4f91a1b250dfeee8e5b79e11dcebb72e61d01fb81ac948804d08671f00 | Python | 15,423 | 461 | from __future__ import annotations
from pathlib import Path
import os
import polars as pl
import re
import shutil
import sqlclz
from typing import NamedTuple, Annotated, Type, Literal, overload
from neuralib.tools.gspread import upload_dataframe_to_spreadsheet, SpreadSheetName
from neuralib.util.verbose import fprin... |
4b5c6fc3b97e1c6e97d638374bfb4e04192547140358c3785793583a2eae3b6c | Python | 15,428 | 372 | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # 只为触发3D后端
import os
def load_base_data(InputFolderName,graph_index,is_single_cell = True,fine_GT = False,coarse_GT = False):
"""
Load spatial coordinate and optional annotatio... |
2fcf0e17b42f85d67ba1add107b13ab6a105c111d6dbf85b0cd3b919a1edfc4e | Python | 15,429 | 469 | """
Monte Carlo physical data generation script to test surrogate model generalization.
This script generates physical parameter datasets for forward surrogate model
evaluation on unseen distributions, using a hand-crafted sampling strategy
for a three-layer tissue model.
Sampling Strategies:
- Three layer model with... |
761c932eea6b052e90e56ad33eec46167870d3bcb863b6668c7ff5bbb08c8dad | Python | 15,433 | 712 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Segment, mask and bin 'MB-KCc-cond' files
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import mbflim.utils as ut
import mbflim.visualization as vis
import mbflim.image.image_processing as pro
import mbflim.image.image_set_processing as setpro
im... |
2955951ae8daf1144425ce6ac584fc6da5c575944fef7ed950013ab75be81b60 | Python | 15,439 | 378 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script to compute all of the Diffusion Tensor Imaging (DTI) metrics.
By default, will output all available metrics, using default names. Specific
names can be specified using the metrics flags that are listed in the "Metrics
files flags" section.
If --not_all is set,... |
c2ecb4ede4a1092a864260e604caa9f55e14e84ed6a19812750b8b5dc3a7af29 | Python | 15,441 | 302 | import json
import os
from mako.template import Template
from sympy.parsing.sympy_parser import parse_expr
from sympy.printing import cxxcode
def remove_parentheses(expr):
if '(' in expr and ')' in expr:
return expr[:expr.index('(')]
else:
return expr
def post_process(curr, json_dir):
un... |
ea7bff627703a8f950a7e23e3e3abeccd63f720a4cd5ddfdc985444f745ad878 | Python | 15,442 | 409 | """The opt-in real-data tier.
Skipped unless ``CCF_STREAMLINES_TEST_DATA`` names the directory holding the
real assets, in the published layout::
<dir>/streamlines/surface_paths_10_v3.h5
<dir>/streamlines/closest_surface_voxel_lookup.h5
<dir>/view_lookup/*.h5
<dir>/cortical_metrics/cortical_layers_10_... |
d76e9a333a90ee91b0090f5b63ffe08c3c0ee930debc049d2f3e4d8e17c108df | Python | 15,449 | 428 | import logging
import numpy as np
import pandas as pd
from typing import Union
from ..stats import stats
from ..testing import correction
from ..testing.det import _DifferentialExpressionTest
class RefSets:
"""
Class for a list of gene sets.
Input:
1. Read gene sets from file.
2. Give a list wit... |
e4f26b10cd2c73f08772969dfd8d974ab6cc7ab76b1549036a615a93a44a06fd | Python | 15,451 | 416 | """
This script makes a plot of the rate differences between Actor (self) and
Observer (other) conditions for 1R, 1NR, 2NR, and xNR (1NR + 2NR) trials.
It first identifies neurons with a significant rate difference between Actor
and Observer conditions (via rank-sum test) for each history type.
The plot and subsequen... |
aec0c62224e72377565c9b29149ecc5d53051308536396f5224a07ec0cd59ad0 | Python | 15,455 | 475 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
import sys
def main(gene_familly):
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="... |
102e2d8c0cb8b16573c11121ee4e8b723acbe3cc652a870f98ba55035f551c13 | Python | 15,460 | 499 | # -*- coding: utf-8 -*-
"""
Create some statistics to test significant changes
in voxelized and labeled data
TODO: cleanup / make generic
"""
#:copyright: Copyright 2015 by Christoph Kirst, The Rockefeller University, New York City
#:license: GNU, see LICENSE.txt for details.
import sys
self = sys.modules[__name__];... |
f70542fde86b031906d69c6918f2c2b7c950cc6e8dfd1b5a63dcd4d10ae89dd3 | Python | 15,468 | 320 | import os
import os.path as osp
import torch
import random
import numpy as np
from tqdm import tqdm
import pandas as pd
from glob import glob
from torch_geometric.data import (Data, InMemoryDataset, download_url,
extract_gz, extract_tar, extract_zip)
from torch_geometric.data.makedirs... |
8dbb5c9035a94b239556e7e1adb0f676c860060f69a4d62eac30cd3d1ee2b0a4 | Python | 15,469 | 283 | #
# fiberprofile/analysis/detect_flip.py
#
# Find the correction of the tensor frame that makes a diffusion tensor NRRD correctly oriented: a flip of x, y, z
# (and their combinations) of the stored components, in the measurement frame of the header or in the frame of the
# voxel axes (components estimated in v... |
a262a2456135f46117eb0ec4fcf948b2157ed3c808c016ac24668c3f971fbbde | Python | 15,470 | 352 | """
(C) Copyright 2021 IBM Corp.
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
... |
9693b60b6f43187e828cc37d9c8de85f292c3c37763ee031097a612c9856e79b | Python | 15,471 | 266 | """
If you use this code, please cite the first SynthSeg paper:
https://github.com/BBillot/lab2im/blob/master/bibtex.bib
Copyright 2020 Benjamin Billot
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 ... |
a56cfb47c5d7e41a4dcb22e56941598ab68ecb9cd6f2d32a2112eb098601712c | Python | 15,476 | 416 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import io
import threading
from logging import Logger
from pathlib import Path
import cvat_sdk.datasets as cvatds
import PIL.Image
import pytest
from cvat_sdk import Client, models
from cvat_sdk.core.proxies.annotations import AnnotationUpdateAction... |
c390c0d6ef9a31c06821b6ddeac8c43a433b4736a109ae719043c3955a2fcead | Python | 15,479 | 325 | from typing import List
import numpy as np
from math import prod
from zigzag.classes.hardware.architecture.memory_hierarchy import MemoryHierarchy
from zigzag.classes.hardware.architecture.memory_level import MemoryLevel
from zigzag.classes.mapping.temporal.temporal_mapping import TemporalMapping
from zigzag.classes.o... |
0358126953e6b34229283977c2757c3ac79691136b727ea311afa66c4efff712 | Python | 15,480 | 417 | #pylint: skip-file
#type: ignore
"""
Implementation of the simple task
"""
import time
import math
import typing
import asyncio
import logging
import datetime
import numpy as np
import os
import stl
from ..qt import *
from . import task_context
from .widgets import Form, ListAsTabsWidget
from .util import wait_for, ... |
c345993f06cdf62777fc608902f2402290fd0ec2a0ada8807277686c7892ebd5 | Python | 15,488 | 369 | import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
import pandas as pd
import statistics
import tensorflow as tf
def generate_filt_sequences(stride:int, filter_size:int, feature_map_size:int) -> list:
"""Return a list of indices that the filter covers in one step. e.g. if the filter is
... |
d9ed87956a99406e9334a8df3fef7c9bad6b0034330da284c1f970c98310892a | Python | 15,491 | 409 | import os
from collections import OrderedDict
from math import ceil
import numpy as np
import torch
import h5py
from scipy.spatial.distance import cdist
from openbabel import pybel, openbabel
from skimage.segmentation import clear_border
from skimage.morphology import closing
from skimage.measure import label
from p... |
1f50947ac590c1e03592bab6911f1dc9c54406df29fb2101a655b1ef7865c48d | Python | 15,497 | 570 | # 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 ... |
0fee5e928b306c174992d5eaf21adf9a82d8d9674635c2b2f296b17d0d3fb12f | Python | 15,498 | 431 | import random
import torch
import pdb
from pytorch_transformers import (
BertTokenizer,)
import re
def convert_dictionary_to_dict(char_map):
return char_map.item2idx
class BatchedData(list):
def __init__(self,input):
super().__init__(input)
self.features = {}
self.teacher_features = {}
pass
# features = ... |
4c1dc9186de64b97564519a0d9bbbd866f76386fa926bbfc90e99efcb92cb6b2 | Python | 15,499 | 227 | import pytest
from pathlib import Path
import tempfile
import shutil
from datetime import datetime
from chemgraph.models.ase_input import ASEOutputSchema
from chemgraph.tools.report_tools import generate_html
# Sample ASE output data from the test file
sample_ase_output = {
'converged': True,
'final_structure'... |
bff837196d3c687ffcbc5c2d79c17ec8da63ae116427fcb15e0f6f7e372e0867 | Python | 15,499 | 321 | #!/usr/bin/env python
from __future__ import print_function
import argparse
import sys
import os
import json
import csv
import re
import errno
from collections import OrderedDict
# Prints to stderr
def eprint(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs)
def conditionInSamples(condition):
for s i... |
546cc726516969c156a4e86a196687d36b91941afcb7a54f456fe9b7d1e2fa7b | Python | 15,500 | 318 | #!/bin/python3
# Plot functions for neuronal and synaptic variables as well as spike raster plots
# Copyright 2021-2024 Jannik Luboeinski
# License: Apache-2.0 (http://www.apache.org/licenses/LICENSE-2.0)
# Contact: mail[at]jlubo.net
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import json
im... |
7958d63374d3ede601e205f573bfe95de65c3f50e5464fec0fee3dece61b6e53 | Python | 15,502 | 314 | import os
import glob
import seaborn as sns
from scipy.stats import kendalltau, wilcoxon
import numpy as np
import pandas as pd
import nibabel as nib
import matplotlib.pyplot as plt
from sklearn.feature_selection import VarianceThreshold
from nilearn.maskers import NiftiMasker
from sklearn.metrics import pairwise_dista... |
c7efdbbf579fbd7c38161d223e1bb1acf8b3b1a6dd6f45bd391562301237441c | Python | 15,505 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
dae016360cf7edc61ea023a8ace29c79a069deb9b7c41d42b13eca8ae1de2e3c | Python | 15,508 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
12baca6d3e5e90027439e72ced204341780cb16014a6b3b7c26f7c6c03dfd6ce | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
499915c54da49132f464fc1d3d1454de2ae98b083eae95a16471b90aa61fadcf | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
61d33e7ac43cd301de28ea30684c0e7cfcb2f1291a52ae456cbc691fb0e6cca5 | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
6633a89c7e84c327b7f30aaaa01384867cf48050186f62ffe5ff0a5c209d56fa | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
7c523c2f2166835e4fa5212eadd842de88379cbc8bb77ec9bde42f0525836b1e | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
86f82cf6f68bdd8b798caba8e21259c873fd366e8adafac61b2c6733b19196f0 | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
8834d0e4e16af43cdea6cc3e6b744ab2d580a0621a796a882a1da6ef1e1cca26 | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
8edade34b20be7fb40df5950243cb01c276d30748aa7710a7b6b80ec5003c429 | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
ad81ecea6374cb494f4ce04f1d5ae4e43af6b4956f37e62264936cf24ca07176 | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
b7fc462b8903bbc3381429b1b207e775dff47514be31eef189f89fb74c3efa4c | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
c2fe62a45accfbaecbecf31c4f9eb42dcd397057555bdab29677b39c0dcabbb4 | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
d5964bed8a79ab5d39306ccaceb4b86a7c2932562fb314892f644b12458eb633 | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
db5d5635685f533473d9cdf92f43aebab4c91065b839d1be28e9350ac9515ddb | Python | 15,509 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
35c408fcb6fb4f17a492f7685e09a34939922d8afe66f74c70b427fde3fe6eaf | Python | 15,510 | 327 | #import base trainer so I can inherit from it
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
#import custom transform
# from batchgeneratorsv2.transforms.custom.missing_modality_transform import MissingModalityTransform
from batchgeneratorsv2.transforms.custom.missing_modality_transform_per_i... |
90d8a96243686994d78248e9a54753ed7b2c721342684c3f4066c8c9aec4b206 | Python | 15,510 | 345 | #!/usr/bin/env python
# author: Jannes Spangenberg
# e-mail: jannes.spangenberg@uni-jena.de
# github: https://github.com/JannesSP
# website: https://jannessp.github.io
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import read5_ont
import pysam
import seaborn as sns
from argparse import Argumen... |
1f74182a37e92b5228c5165610f3f578ebb32abd81959774aa56173e2f20ee2a | Python | 15,511 | 402 | """ Loader Factory, Fast Collate, CUDA Prefetcher
Prefetcher and Fast Collate inspired by NVIDIA APEX example at
https://github.com/NVIDIA/apex/commit/d5e2bb4bdeedd27b1dfaf5bb2b24d6c000dee9be#diff-cf86c282ff7fba81fad27a559379d5bf
Hacked together by / Copyright 2019, Ross Wightman
"""
import logging
import random
from... |
618796f02cdb8946d188407df590ab08ac223bcb9ba7247e109654c0f274459c | Python | 15,512 | 456 | # Copyright 2014-2015 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
import datetime
import itertools
import numpy
imp... |
c20f6572353f3052a517b56b140073cfbf69397e7c497839ad29d47bfda6cb4b | Python | 15,512 | 413 | #!/usr/bin/env python
# Copyright (c) 2026 Rohit Jena. All rights reserved.
#
# This file is part of FireANTs, distributed under the terms of
# the FireANTs License version 1.0. A copy of the license can be found
# in the LICENSE file at the root of this repository.
#
# IMPORTANT: This code is part of FireANTs and its... |
00167fcac52bc15c0c2ec575fe33d9fb5dd7ceafdae1afce204a58130648bf5c | Python | 15,517 | 456 | # Copyright 2014-2015 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
import datetime
import itertools
import numpy
imp... |
4c440a90b9da608c4d4bd9bfcec4e340c87c90c3f6e36f05f48d29e1ec8a1db3 | Python | 15,520 | 375 | import torch
from torch import Tensor
import torch.nn as nn
#from .._internally_replaced_utils import load_state_dict_from_url
from typing import Type, Any, Callable, Union, List, Optional
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8... |
d67d06e8aa98941a75acee256f1c7515093e5f0267ab3e963b10408c5ef4deff | Python | 15,523 | 299 | import itertools
import logging
from collections import Counter
import numpy as np
import scipy.stats
import xarray
from numpy.random.mtrand import RandomState
from scipy.stats import pearsonr
from brainscore_core.supported_data_standards.brainio.assemblies import walk_coords, DataAssembly
from brainscore_core.metric... |
7d0d71464baa5afc3c61aa5312decc3c3024ff683334b0b3da25253a22c2e1d2 | Python | 15,526 | 417 | from typing import Any, Literal, Union
import torch
from eir.predict_modules.predict_tabular_input_setup import (
ComputedPredictTabularInputInfo,
)
from eir.serve_modules.serve_schemas import ComputedServeTabularInputInfo
from eir.setup.input_setup import al_input_objects_as_dict
from eir.setup.input_setup_modul... |
8219546b2c521a84af482923d5e80792d5c95083043047480630252d0683ec15 | Python | 15,526 | 343 | #!/usr/bin/env python
"""
(single) focus lock.
Hazen 04/17
"""
import importlib
from PyQt5 import QtCore, QtWidgets
import storm_control.sc_library.parameters as params
import storm_control.hal4000.halLib.halDialog as halDialog
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_control.hal400... |
f54289758fb1987a5a5ee2e676f1d2b564774f2fa447de3297d9de3bea251304 | Python | 15,526 | 450 | import shutil
import numpy as np
import pandas as pd
import hail as hl
# from memory_profiler import profile
from hail.linalg import BlockMatrix
from scipy.sparse import csr_matrix, save_npz, load_npz
from heig.utils import log_execution_time
from heig.wgs.utils import *
"""
TODO:
1. Merge multiple sparse genotype dat... |
e783efaa84215001aa54f3ee463c18754971f95524def229ac3a3cb4ceb2dcbb | Python | 15,531 | 293 | from qtpy.QtGui import QAction
from qtpy.QtWidgets import QMainWindow, QMenu, QFileDialog, QApplication
from napari_dmc_brainmap.results.probe_vis.probe_vis.view.MainWidget import MainWidget
# from napari_dmc_brainmap.preprocessing.preprocessing_tools import adjust_contrast, do_8bit
from napari_dmc_brainmap.utils.atl... |
005f9242f787d81089c9f80170ad4e90bec744a467d2bf25619d31006ea7b531 | Python | 15,534 | 425 | """
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... |
748bf794c848625b12d6d5fc5cac8d0d53d38ea96620157c8bf64533a0a3b045 | Python | 15,534 | 472 | """
This script makes behavior plots for the paper, for the two-player monkey task.
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.ticker as... |
fb7956daf56bdf85859579983df7435c3d55958ce267f077cb2f5088c291a1ea | Python | 15,537 | 433 | import torch
import torch.nn as nn
import torch.nn.functional as F
import file_util
import rangezero_statis
# Model for RM-ResNet
thresh = 0.5 # neuronal threshold
lens = 0.5 # hyper-parameters of approximate function
decay = 0.25 # decay constants
num_classes = 1000
time_window = 4
device = torch.device("cuda:0" ... |
77692584b769a284251dc61633e284f09b3843ece03043f0c3a5e7013d25645d | Python | 15,538 | 437 | # -*- coding: utf-8 -*-
"""Compute metrics from fMRI time series."""
import datetime
import os
import nibabel as nb
import numpy as np
from scipy.stats import zscore
from ..io.filename import get_filename
from ..matlab import MatlabCommand
from ..processing.behavior import get_onset_vols
__all__ = ["compute_psc", "... |
b4bf82e315e50656e1173549566eb37ce45a30680cb696ddc8f96315c8293b87 | Python | 15,542 | 391 | import glob
import os
import sys
from argparse import ArgumentParser
import numpy as np
import torch
import torch.utils.data as Data
from Functions import generate_grid, Dataset_epoch, transform_unit_flow_to_flow_cuda, \
generate_grid_unit
from miccai2020_model_stage import Miccai2020_LDR_laplacian_unit_add_lvl1,... |
edc15fb6dd0604c3450305d975c98c9e1cf2fbd6fd9d2cd05d64b6e731acda09 | Python | 15,543 | 453 | #!/usr/bin/env python
import os
import subprocess
import ruamel_yaml as yaml
import configure_seaborn as cs
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import seaborn as sns
import utils as utils
from matplotlib.gridspec import GridSpec
from matplotlib.p... |
47c4d898ff901be8d4335a975a3705a3d6049b6247bc950b870340d2bfce0b69 | Python | 15,545 | 389 | # Copyright 2024 BDP 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 applicab... |
3e9f0efd6a6b5a020b281bed09e4df5b92834dc64a8af41a79d49494359e2986 | Python | 15,560 | 393 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# *************************************************************************** #
# Copyright © 2022, UChicago Argonne, LLC #
# All Rights Reserved #
# Softwar... |
c0646beb82423e969b860c65fb75fe68ac3c4cd2ca79017635fd1afb5b72d1c6 | Python | 15,563 | 375 | import unittest
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.exceptions import NotFittedError
from causallib.positivity import (
Trimming, UnivariateBoundingBox, Matching
)
from causallib.positivity.univariate_bbox import (
QuantileContinuousSupport, Support, Continuous... |
1e33661c5d47810831e7e5e07038018c13069aaa1f6250d7750a1e619654f8da | Python | 15,565 | 343 | # import matplotlib.pyplot as plt
import numpy
import os
import SimpleITK as sitk
import yaml
import copy
import traceback
from dipy.core.gradients import gradient_table
from dipy.data import load_nifti, get_sphere, default_sphere
from dipy.direction import peaks_from_model
from dipy.io.gradients import read_bvals_bv... |
63d2d9ae55de5423fd42e6536775ac9bbfbb44cb07f7e0ce0581b0541246c644 | Python | 15,570 | 344 | #!/usr/bin/env python
"""
(single) focus lock.
Hazen 04/17
"""
import importlib
from PyQt5 import QtCore, QtWidgets
import storm_control.sc_library.parameters as params
import storm_control.hal4000.halLib.halDialog as halDialog
import storm_control.hal4000.halLib.halMessage as halMessage
import storm_control.hal400... |
b2048f5c1a9b87b003642b78ce4676d740b3f552836f72fb4e9b7b41e048d27a | Python | 15,570 | 433 | import random
import torch
import pdb
from pytorch_transformers import (
BertTokenizer,)
import re
def convert_dictionary_to_dict(char_map):
return char_map.item2idx
class BatchedData(list):
def __init__(self,input):
super().__init__(input)
self.features = {} # token features
self.img_features = {}
self.... |
9e10c6ddd1bea91b14aeb50a9d0530ae09e7ef2d7e0ca6dff3abbc8240d48da3 | Python | 15,572 | 351 | import unittest
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
from causallib.estimation import IPW
from causallib.estimatio... |
1e0dec8d5aa0014a7d941505181d19c30f95a3f93254b75abec51f40b9c4a8e8 | Python | 15,577 | 416 | from typing import Any, Literal, Union
import torch
from eir.predict_modules.predict_tabular_input_setup import (
ComputedPredictTabularInputInfo,
)
from eir.serve_modules.serve_schemas import ComputedServeTabularInputInfo
from eir.setup.input_setup import al_input_objects_as_dict
from eir.setup.input_setup_modul... |
f76b1d811dc1830c93bd3500cd6f8ccd271d2c1e52b74ed7d4e03d57f7c797dd | Python | 15,580 | 324 | #Type something like this from within python: ARGS="2015_04_07_02 M 30 none 10.5 DM -1"
#template: ARGS="filename sex age drugs theta region estradiol"
#then type: execfile('PopSpikeAnal.py')
#If not in python, type: python PopSpikeAnal.py 031011_5Or M 30 none 10.5 DM
import os
#if running from windows, might ne... |
d77676f7ce29e297783db134e80ad82054200f6adc012200fdb6a56413231189 | Python | 15,583 | 480 | # This is to store functions for visualisation and plotting.
import numpy as np
import matplotlib.pyplot as plt
from lapy.plot import _get_color_levels, _map_z2color
import plotly
import plotly.graph_objs as go
def _get_colorscale(vmin, vmax):
"""Put together a colorscale map depending on the range of v-values. A... |
96e5c7b8ee45b042ee3578bb314c56b2c2c1129b171c69594ea6ad292a1697d7 | Python | 15,592 | 550 | import math
from typing import Union
import tensorflow as tf
"""
An Exchange-Correlation (XC) functional class collecting ingredients for training
neural-network-LMF based XC models in density functional theory.
This class implements:
1. PBE exchange (with optional Hirao range-separation).
2. B95 or B97 corr... |
362a876beaf46afa3997fcce221f59ab6adc24c5e4bd559a5270e0dba98564f5 | Python | 15,593 | 308 | '''
embeddings::plot.py
General purpose plotting functions that may be useful for working with
the embeddings we are creating. Available as embedding.plot.*
'''
from .internal import *
from .func import compute_dissimilarity_matrix
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn
... |
4ff581f2fc73853a81bded10eb2abe7ec0c788b2de44a5d60cb792ee0b71974a | Python | 15,593 | 369 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Visualize 2-dimensional fODF slice loaded from disk.
Given an image of SH coefficients, this script displays a slice in a
given orientation. The user can also add a background on top of which the
fODF are to be displayed. Using a full SH basis, the script can be used ... |
268a7030bc086c5388331ed40b1d14f0b2121062dd24054c3debf9ac3e112cf3 | Python | 15,594 | 399 | # fmt: off
# flake8: noqa
"""Track Every Thing Accuracy metric."""
import numpy as np
from scipy.optimize import linear_sum_assignment
from .. import _timing
from ._base_metric import _BaseMetric
EPS = np.finfo("float").eps # epsilon
class TETA(_BaseMetric):
"""TETA metric."""
def __init__(self, exhaust... |
65bae8c5d4e2584dddf770a7db3eb81eaa57af61cdfc1ad042fdac8e0248cffc | Python | 15,604 | 352 | #!/usr/bin/env python3
"""
Main manuscript - Figure 5: Axon packing models with different volume fractions
First row: Axon model compartmentalization masks showing intra-axonal, myelin, and extra-axonal spaces
Second row: Fieldmaps for θ=0° (fibers parallel to B0)
Third row: Fieldmaps for θ=90° (fibers perpendicular t... |
bc709732f3cdb65344bd222eeb9abe4d73cd0b06d043a3a2fa0de321471056be | Python | 15,605 | 397 | ##################################################################################
### Module : dmriatlasbuilder.py
### Description : Atlasbuilder api
###
###
###
### Written by : scalphunter@gmail.com , 2022/10/08
### Copyrights reserved by NIRAL
#######################################################################... |
19df650df34c530d75dca4f5feb655482e7bc6a0102ed815578ee7f91b1c430e | Python | 15,606 | 435 | """
tensorflow/keras utilities for the neuron project
If you use this code, please cite
Dalca AV, Guttag J, Sabuncu MR
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation,
CVPR 2018
Contact: adalca [at] csail [dot] mit [dot] edu
License: GPLv3
"""
import sys
# third party
import n... |
87fcb51dbaf05bdd452cb82f01c3c693c3e85774aab602580a81221c2e3b0efc | Python | 15,610 | 384 | import torch
from torch import Tensor
import torch.nn.functional as F
import torch.nn as nn
import math
from math import floor, ceil
from torch.hub import load_state_dict_from_url
from typing import Type, Any, Callable, Union, List, Optional
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
... |
f720fa55be3e3088ba56aa9efc2f55018846b516ceb646e474a1e5ce03e7598d | Python | 15,611 | 416 | """Rich-based display helpers for the ChemGraph CLI.
This module handles all terminal rendering: banners, tables,
response formatting, and API-key status display.
"""
from __future__ import annotations
import json
import os
import re
from difflib import unified_diff
from typing import Any
from rich.align import Ali... |
8533ca65dd2b641175fa233aa4bf72dffa506f9f40987bdef2645fa7633e08a0 | Python | 15,612 | 435 | # Configuration file for the Sphinx documentation builder.
# This file uses modern Sphinx 9.0+ configuration with PyData Sphinx Theme
#
# For the full list of built-in configuration values, see:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------... |
38234095e1a0fa2578e51cc8bfffb6705f4242b5ba925ce9a27ef7f06698d228 | Python | 15,617 | 380 | from __future__ import division
import numpy.ma as ma
import numpy as np
import scipy as s
import math
from mofapy2.core.utils import *
from mofapy2.core import gpu_utils
from time import time
# Import manually defined functions
from .variational_nodes import BernoulliGaussian_Unobserved_Variational_Node
from .variat... |
1b9b45fc73b0ab846d204998f8c007f4443a9da68741524a062d39d94559f02d | Python | 15,618 | 346 | """
This file contains functions to handle keras/tensorflow tensors.
- blurring_sigma_for_downsampling
- gaussian_kernel
- resample_tensor
- expand_dims
If you use this code, please cite the first SynthSeg paper:
https://github.com/BBillot/lab2im/blob/master/bibtex.bib
Copyright 2020 Benjamin Billot... |
41195a5b3c4ed31604922a7e7d7ffab41dfa9a7e6bf45f4a0e2bd177b6e274ff | Python | 15,618 | 349 | """Packing a finished output folder into a bundle, after the run.
A bundle used to be reachable only through express mode, which is a decision
made *before* the analysis starts. So a full run that turned out to be worth
emailing — 111 MB of PNGs against the ~3 MB they were drawn from — had no route
to the shareable fi... |
f0acaec347aa87da0583ecde2022b2ec0ba5c2076861cc20a0970cb055b47dce | Python | 15,621 | 401 | from brainscore_core.supported_data_standards.brainio.assemblies import BehavioralAssembly
from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set
from brainscore_core.supported_data_standards.brainio.s3 import load_assembly_from_s3, load_stimulus_set_from_s3
BIBTEX = """TBD"""
# circle_... |
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