sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
51820c9d38e314fdfd4d8b9c78692c2e68655528a2a8689395c26b94f5975cf4 | Python | 811 | 28 | # Generated by Django 4.2.13 on 2024-07-12 19:06
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("engine", "0080_alter_trackedshape_track"),
]
operations = [
migrations.AddField(
model_name="job",
name="assignee_u... |
a93e33ec6aabc1b111a021a1eee828219d6b4d0aca0cb9c09e1dccaeb67e4e1d | Python | 811 | 25 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['tractometry.zip'])
tmp_dir = tempfile.... |
dee6400ae3fd6abac40d23f3795e5d3d942407abe4688c850b474f9a6368c72a | Python | 811 | 35 | #!/usr/bin/env python
"""
Filter maf blocks for presence of wildcard columns. Blocks must meet the
criteria of having at least `min_good` columns, each of which has more than
`min_species` rows that are NOT wildcard bases ('*').
TODO: Allow specifying the character of the wildcard base.
usage: %prog min_good min_spe... |
b9844526838d602f9961d5335cd5794c0ee28ce0e569802be8e1f975e7b363e9 | Python | 812 | 32 | # -----------------------------------------------------------------------------
# Copyright © 2014-2015 Colin Duquesnoy
#
# Licensed under the terms of the MIT License
# (see LICENSE.txt for details)
# -----------------------------------------------------------------------------
"""Provides QtDesigner classes and func... |
e893296addfb60b0e1e8b706403b2c83039279e6e6e79eebce9309a6b60f7631 | Python | 812 | 27 | """ Create a cube mesh with perserved sharp edges """
import SVMTK as svm
from pathlib import Path
if __name__ == "__main__":
print("Start ",__file__)
outdir = Path("results")
outdir.mkdir(exist_ok=True)
surf1 = svm.Surface()
# Creates a cube surfaces by specific two corners of the cube
# Corn... |
0f760cb94fcf0765aea29ae5421cef37b8df0698b5178d6571a61346cce5bf81 | Python | 814 | 28 | import numpy as np
def calculate_gncc(fixed, moving):
mu_f = np.mean(fixed)
mu_m = np.mean(moving)
a = np.sum(abs(fixed - mu_f) * abs(moving - mu_m))
b = np.sqrt(np.sum((fixed - mu_f) ** 2) * np.sum((moving - mu_m) ** 2))
return a / b
def calculate_ncc(fixed, moving):
assert fixed.shape == ... |
af53505356fea7d412099446c92ca955f6f9169d477c5e3621f8040ea0658b1b | Python | 814 | 26 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys='surface_vtk_fib.zip')
tmp_dir = tempfil... |
00623c60067e4a305d5d633c81c47979a4ea8b3a53234845f1300c0fe2ff590f | Python | 815 | 18 | from brainscore_vision import stimulus_set_registry
from brainscore_core.supported_data_standards.brainio.s3 import load_stimulus_set_from_s3
BIBTEX = """@article{islam2021shape,
title={Shape or texture: Understanding discriminative features in cnns},
author={Islam, Md Amirul and Kowal, Matthew and Esser, Patrick ... |
0224dba26ea72e720c85cba45a5d25efe6cfae09ef528ffd464117ee5ca0a967 | Python | 815 | 18 | '''
Collects commonly-used Modules for easier import.
Ex: `from nems.modules import WeightChannels, FIR, DoubleExponential`
'''
from .nonlinearity import LevelShift, DoubleExponential, RectifiedLinear, ReLU, \
Sigmoid, Hinge, PReLU, LogCompress, SoftRelu
from .filter import FiniteImpulseResponse, FIR, STRF
from ... |
7037fb991f0a1aba839bff987fe8ebb50f475097c853509053c283b1d14037b4 | Python | 815 | 42 | #!/usr/bin/env python
"""
Read a set of ranges and a nib file, print portions of nib overlapping
those ranges to stdout
usage: %prog range_file nib_file
"""
import bx.seq.nib
from bx.cookbook import doc_optparse
def __main__():
options, args = doc_optparse.parse(__doc__)
try:
range_file = open(arg... |
918c11cb6e7ab56d2255a8d31d8e9804f77de50625f7f5891401aeb2a288e86a | Python | 816 | 37 | import os
import sys
import pickle
import lmdb
def wirte_to_lmbd(filename, outfilename):
try:
os.remove(outfilename)
except:
pass
env_new = lmdb.open(
outfilename,
subdir=False,
readonly=False,
lock=False,
readahead=False,
meminit=False,
... |
e13c9fec356c397b4917baa4f84f4299341022d06ea788771f894186834658e3 | Python | 816 | 37 | import numpy as np
import torch
import torch.testing as ptt
import unittest
from deepdwi.recons import linsub
from deepdwi.models import bloch
if __name__ == 'main':
unittest.main()
devices = [torch.device('cpu')]
if torch.cuda.is_available():
devices.append(torch.device('cuda'))
class testLinsub(unittest.... |
00ba4bbcd1163ebd67fa582dee5b1ded15378b6781dcb525ca9094cd04bcc99f | Python | 817 | 32 | # Copyright (c) DP Technology.
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import BaseWrapperDataset
class NumelDataset(BaseWrapperDataset):
... |
333e970d82c962cefc6d8c018b292b053bd34fc1e42d915dc84a84dc8096959a | Python | 817 | 33 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
import os
import sys
import shutil
import tempfile
from contextlib import contextmanager
from types import ModuleType
TEST_DIR = os.path.dirname(os.path.abspath(__file__))
TEST_DATA_DIR ... |
35cb3f487f2b2deaf23537ce180bbc434ae74f17494787ee0a991a312f777379 | Python | 817 | 28 | # -*- coding: utf-8 -*-
"""
*ClearMap* is a python toolbox for the analysis and registration of volumetric
data from cleared tissues obtained with Light Sheet microscopy.
The ClearMap code package is structured into four main modules:
* **IO** for reading and writing images and data
* **Alignment** for resampl... |
2892bf9194b90509605c65126955c2985dd5700c4d2d017d9707db678d263e96 | Python | 818 | 28 | from pathlib import Path
from eir.experiment_io.label_transformer_io import load_transformers
from eir.experiment_io.output_object_io_modules.output_io_utils import (
load_output_config_from_yaml,
)
from eir.setup.output_setup_modules.tabular_output_setup import (
ComputedTabularOutputInfo,
set_up_tabular_... |
554b8ab1976964ff698fb5b407ae40d385ea12df69c2da04fcd2a5c26b01bf46 | Python | 818 | 23 | import dtiplayground.dmri.preprocessing as base
import dtiplayground.dmri.common as common
import yaml
logger=common.logger.write
color=common.Color
class @MODULENAME@(base.modules.DTIPrepModule):
def __init__(self,config_dir,*args,**kwargs):
super().__init__(config_dir,*args,**kwargs)
def ge... |
eeb04e9baa3e24e1c42bd24c6e0f55e958e470db9b1703ce459bb7e8d69f8d0c | Python | 818 | 31 | from torch import Tensor
from typing import Tuple, List
from torch import Tensor
def get_adjacent_labels(x: Tensor, y: Tensor) -> List[Tuple[int, int]]:
"""
calculates which masks of a signle object have two labels (due to the border)
:param x:
:param y:
:return:
"""
# Could ideally use... |
f65358fd75411ed172d3c5fa35ed2d25e5b23bed0bf1f9e8089345a8aab75741 | Python | 818 | 33 | import pandas as pd
label = pd.read_csv('/data2/whr/czl/TwiBot22-baselines/datasets/gilani-2017/label.csv')
main_user = pd.read_csv('./user_feature.csv')
user = main_user.loc[0:len(main_user)-1]
user_list = list(user['user_id'])
del_list = []
bool_label = []
cnt = 0
new = []
for i,r in label.iterrows():
if r[1]... |
cfa46e769edee3cf425a83b0dd35c1306d00d66cd74a01815b3e4ca6d9cf016d | Python | 819 | 15 | from .auto_augment import RandAugment, AutoAugment, rand_augment_ops, auto_augment_policy,\
rand_augment_transform, auto_augment_transform
from .config import resolve_data_config, resolve_model_data_config
from .constants import *
from .dataset import ImageDataset, IterableImageDataset, AugMixDataset
from .dataset_... |
26d2aa572ab5d6e13d24f4f7f2b988a16c95f3494ac87a3887a3ae6235af4eb0 | Python | 820 | 27 | import csv
from cv2 import IMREAD_REDUCED_GRAYSCALE_2
import numpy as np
from pyparsing import lineStart
from tqdm import tqdm
import networkx as nx
dataset_name='Twibot-22'
edges=[]
id_list=np.load('/data2/whr/lyh/twibot22_baseline/'+dataset_name+'/id.npy')
id_list=list(id_list)
id_include=set()
G=nx.Graph()
with open... |
e59bb07bf55ecff7167f6de6d9c9766497fa9b28b489605bdc1f9848930393af | Python | 820 | 32 | import torch
from torch import sin, cos, atan2, acos
def rot_z(gamma):
return torch.tensor([
[cos(gamma), -sin(gamma), 0],
[sin(gamma), cos(gamma), 0],
[0, 0, 1]
], dtype=gamma.dtype)
def rot_y(beta):
return torch.tensor([
[cos(beta), 0, sin(beta)],
[0, 1, 0],
... |
045559e621053a5bb47081d2ebe7c991171b19d3a5f6490f542e7c52ba5d22f7 | Python | 821 | 29 | import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from LSTHM import LSTHM
import numpy
#in=40, 3 modalities,4 attentions
full_in=numpy.array(numpy.zeros([32,40]))
inputx=Variable(torch.Tensor(full_in),requires_grad=True)
full_in=numpy.array(numpy.zeros([32,12]))
inp... |
59cc7142e1fe4cc4ee1bd84a34c48b31c49544102b9f467f2da2e9731ecb3e18 | Python | 821 | 29 | import numpy as np
from nose.tools import eq_
from fancyimpute import SimilarityWeightedAveraging
def test_similarity_weighted_column_averaging():
X = np.array([
[0.1, 0.9, 0.2],
[0.8, 0.1, 0.01],
[0.95, 0.2, 0.3],
[0.14, 0.85, 0.3],
])
X_incomplete = X.copy()
X_incomp... |
d735139bebbec1558fb2443117e89530ce9e7870cad67e9d80593b948c683912 | Python | 821 | 22 | # 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 use, reproduction, or... |
2008665d60489adaff7f1f52b2423f7879440d68829a2980486cfdff9a89e291 | Python | 823 | 30 | # define utility functions
import torch
import random
import numpy as np
from torch.fft import *
from torch.optim.lr_scheduler import LambdaLR
def ifft2c(k):
return ifftshift(ifft2(fftshift(k)))
def fft2c(x):
return fftshift(fft2(ifftshift(x)))
def view_as_complex(x):
x = torch.chunk(x, chu... |
5dc4c740069f157be4fc22c9b47aa2c482d6b0b8d2c2cc4fcb2df9b6125c815f | Python | 823 | 28 | import pytest
from qtpy import PYQT5, PYQT6, PYQT_VERSION, PYSIDE6, QtTest, _parse_version
def test_qttest():
"""Test the qtpy.QtTest namespace"""
assert QtTest.QTest is not None
if PYQT5 or PYQT6 or PYSIDE6:
assert QtTest.QSignalSpy is not None
if (
(PYQT5 and _parse_versio... |
02566f4e1f86b4a228cbfb06c2c6ed1180c9241a1c791907ae8f8fcc243548e1 | Python | 824 | 24 | # TODO
# From supplement doc:
# "align and combine datasets for fitting a single, large model"
# So, for example, combining PEG and A1 data to fit with a single pop model.
from nems.tools.arrays import concatenate_dicts
def combine_neurons(*datasets, neurons_keys='response', axes=-1, newaxis=False):
"""Merge mu... |
c20bfb2e055ceda3cadf4cdd0c8d44d8078cb28908a687b4ae4f94e9ec20ad4d | Python | 824 | 25 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['processing.zip'])
tmp_dir = tempfile.T... |
c3130d2a52fc9261aa8ff10d0f0e83c8b601d557c2ebc8fc95de65b4151bae93 | Python | 824 | 37 | # -*- coding: utf-8 -*-
"""TODO"""
import os
import core.core_global as core
from core.device import Device
class Monitor(Device):
"""TODO"""
def __init__(self, filename):
"""TODO"""
super().__init__()
self.active = True
self.filename = filename
try:
os.... |
ff939029bf2d270325befb74a9338c06690a60077d8b14bc6ea50f3459fd3a84 | Python | 824 | 29 | #!/usr/bin/env python
import warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
import pandas as pd
import numpy as np
from sklearn.externals import joblib
from PAINTeR import global_vars
import PAINTeR.connectivity as con... |
af2224678f760e204886050799f577e00c6dfa577f281b03df2990907d7f5460 | Python | 825 | 28 | import subprocess
import os
import platform
path_to_file = os.path.join(os.getcwd(), "bannotator")
path_to_app = os.path.join(path_to_file, "app.py")
pc_path_to_icon = os.path.join(path_to_file, "resources", "icon.ico")
mac_path_to_icon = os.path.join(path_to_file, "resources", "icon.icns")
if platform.system() == "Wi... |
d4406f36be4d2d2a3b0ab28691c95901deee210cee72793e7a76d678be4ecae1 | Python | 825 | 19 | from brainscore_vision import metric_registry
from .metric import ConfusionSimilarity, TasksConsistency
BIBTEX = """@article {Maniquet2024.04.02.587669,
author = {Maniquet, Tim and de Beeck, Hans Op and Costantino, Andrea Ivan},
title = {Recurrent issues with deep neural network models of visual recogn... |
eaebe9d8774d96761c37e9fc87c7bcdbc40227f5f37e8f4c616156271d782c25 | Python | 825 | 22 | # 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 use, reproduction, or... |
182fb9c873db585c1cc76d128a8145cca9bea032223d6ba1defb98df6ff13612 | Python | 826 | 35 | #!/usr/bin/env python
"""
Merge any overlapping regions of bed files. Bed files can be provided on the
command line or on stdin. Merged regions are always reported on the '+'
strand, and any fields beyond chrom/start/stop are lost.
usage: %prog bed files ...
"""
import fileinput
import sys
from typing import (
T... |
22eaae4eb1aab84500b582b098af711bb48d0d72f71a30f46d3662dce62726d4 | Python | 826 | 28 | # -*- coding: UTF-8 -*-
from __future__ import unicode_literals
import sys
import os
sys.path.append("../")
from whoosh.index import create_in,open_dir
from whoosh.fields import *
from whoosh.qparser import QueryParser
from jieba.analyse import ChineseAnalyzer
analyzer = ChineseAnalyzer()
schema = Schema(title=TEXT... |
2fcedd81c73b7479bf300fc733f3c42daefa29c276f0220e90b52417baeb999e | Python | 826 | 31 | import sys
import logging
logger = logging.getLogger('.'.join(__name__.split('.')[:-1]))
_is_interactive = bool(getattr(sys, 'ps1', sys.flags.interactive))
_hander = None
def unconfigure_logging():
if _hander is not None:
logger.removeHandler(_hander)
logger.setLevel(logging.NOTSET)
def enable_l... |
58eb929b00073d286060743ce4eff5be0bb226d09be0ba03f171ad9df7e4ab10 | Python | 826 | 31 | # -----------------------------------------------------------------------------
# Copyright © 2009- The Spyder Development Team
#
# Licensed under the terms of the MIT License
# (see LICENSE.txt for details)
# -----------------------------------------------------------------------------
"""Provides Linux-specific util... |
c12dfd9e3d8b93392a3a8143a5d7781854299f536ace00cb1a241acf8d83364b | Python | 826 | 27 |
class ShapeError(Exception):
def __init__(self, layer, **shapes):
"""Raise in Layer.evaluate if data or parameter shapes are misaligned.
Parameters
----------
layer : .layer.Layer
Layer that the exception was raised from.
shapes : dict
Keys ... |
0ec954904a99747d5e751b9604f0e29923bbb0153b3aa0f1b4c0f1d880a2e72a | Python | 827 | 36 | import subprocess
import os
def git_init():
print("Initializing git")
subprocess.run(["git", "init"])
def dvc_init():
print("Initializing dvc")
subprocess.run(["dvc", "init"])
subprocess.run(["git", "commit", "-qm", "build: initialize DVC"])
def git_commit():
print("Staging files")
sub... |
4ca93c2cff0a384115909e3716ac020c5f54c2e75d3b9e73d6403c67f49d0a08 | Python | 827 | 29 | import base64
import numpy as np
import requests
def encode_array_to_base64(file_path: str) -> str:
array_np = np.load(file_path)
array_bytes = array_np.tobytes()
return base64.b64encode(array_bytes).decode("utf-8")
def send_request(url: str, payload: list[dict]) -> dict:
response = requests.post(u... |
6489fa5ed7489a3dcb8ce30f78ea59631e073327a1b834e842b015bf5c5caeac | Python | 827 | 35 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
from cvat_sdk.api_client import apis, models
from cvat_sdk.core.proxies.model_proxy import (
ModelDeleteMixin,
ModelListMixin,
ModelRetrieveMixin,
ModelUpdateMixin,
build_model_bases,
)
_UserEn... |
03b25dce8769a377af5b51501afeebbea165cbae0e2f05024b0cd6a78e08c4a5 | Python | 828 | 23 | import os
import pandas as pd
def drosophila_data(datadate='20241119'):
'''Prepare the drosophila VNC connectome and metadata'''
dm_datadir = f'../data/{datadate}_dm_data'
if not os.path.isdir(dm_datadir):
os.mkdir(dm_datadir)
if not os.path.isdir(dm_datadir + '/processed'):
os.mkdi... |
ac600f325346f9dd52c63516d301f3dd9f733ce0d265839893f7dd6ed29c5ff4 | Python | 828 | 31 | # -----------------------------------------------------------------------------
# Copyright © 2009- The Spyder Development Team
#
# Licensed under the terms of the MIT License
# (see LICENSE.txt for details)
# -----------------------------------------------------------------------------
"""Provides Windows-specific ut... |
e3193159fa8d7373e787078873c8b1f272c4bf6f022e3dd0fed9a33e8b46bee3 | Python | 828 | 27 | # Generated by Django 4.2.6 on 2023-11-17 10:10
from django.db import migrations, models
from cvat.apps.engine.models import Location
def manually_remove_outdated_relations(apps, schema_editor):
Storage = apps.get_model("engine", "Storage")
CloudStorage = apps.get_model("engine", "CloudStorage")
Storage... |
eea8ada3bf3f648f8688e9875be13fa656f6a330fb659bcd6dec48a389e33d3e | Python | 828 | 27 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
import torch
@torch.library.custom_op("flash::flash_attn_func", mutates_args=())
def flash_attn_func_op(
q: torch.Tensor, k: torch.Tensor, v: torch.Tensor
) -> torch.Tensor:
from flash_attn_interface import flash_attn_func as fa3
r... |
498619e533fd191b40edd49cd4ba32e7f93abb10094628c2b6211b4cabc6c3f3 | Python | 829 | 29 | from .cnn import CNN6, CNN10, CNN14
from .resnet import ResNet10, ResNet18, ResNet34, ResNet50, ResNet101
from .efficientnet import EfficientNet_B0, EfficientNet_B1, EfficientNet_B2
from .ast import ASTModel
from .ssast import SSASTModel
from .projector import Projector
_backbone_class_map = {
'cnn6': CNN6,
'c... |
18857f6073dae96ced204af16f286a5db3091beadc3e9c60823cfbed66240c8b | Python | 830 | 29 | #encoding=utf-8
import sys
import threading
sys.path.append("../")
import jieba
class Worker(threading.Thread):
def run(self):
seg_list = jieba.cut("我来到北京清华大学",cut_all=True)
print("Full Mode:" + "/ ".join(seg_list)) #全模式
seg_list = jieba.cut("我来到北京清华大学",cut_all=False)
print("Defau... |
e839d53c557c5fed3602f5392e46eaa12d3b9f3b1032847abc44ae00a4422af2 | Python | 830 | 15 | import os
import ClearMap.Settings as settings
filename = os.path.join(settings.ClearMapPath, 'Test/Data/ImageAnalysis/cfos-substack.tif');
import ClearMap.Visualization.Plot as plt
import ClearMap.IO as io
data = io.readData(filename, z = (0,26));
import ClearMap.ImageProcessing.BackgroundRemoval as bgr
dataBGR = bgr.... |
10de94a9fa998d01eed191843f723bc2bb0f655f306757726d67746492bbaaee | Python | 831 | 37 | import glob
from sys import argv
import numpy as np
import os
dir1 = argv[1]
dir2 = argv[2]
files1 = sorted(glob.glob(dir1+"/V_t*"), key=os.path.getmtime)
files2 = sorted(glob.glob(dir2+"/V_t*"), key=os.path.getmtime)
data_final = []
for f1, f2 in zip(files1, files2):
data1 = open(f1, "r")
data2 = open(f2, "... |
f56f1adef4cbea9e73699e6ca0f18db6d8536ca01ccb6df3d0512afe8a36d45b | Python | 831 | 30 | import sys
from setuptools import setup
from setuptools import find_packages
exec(open('mofapy2/version.py').read())
def setup_package():
install_requires = ['pandas', 'scipy', 'numpy', 'sklearn', 'argparse', 'h5py']
metadata = dict(
name = 'mofapy2',
version = __version__,
description = 'Multi-... |
18f9b2d2b615ad7d94733089f6adf5f4b212e79391aad1f76d530a0d0edf4d81 | Python | 832 | 26 | """Submit jobs to the BMRC cluster.
"""
import os
def write_job_script(n_states, run):
with open("job.sh", "w") as file:
name = f"cc-{n_states}-{run}"
file.write("#!/bin/bash\n")
file.write(f"#SBATCH -J {name}\n")
file.write(f"#SBATCH -o logs/{name}.out\n")
file.write(f"#S... |
dbb4740465120b3187ad563083e55b81979dface9e17020b47063c1d883bc8ad | Python | 832 | 29 | import json
from pathlib import Path
from eir.experiment_io.input_object_io_modules.input_io_utils import (
load_input_config_from_yaml,
)
from eir.setup.input_setup_modules.setup_bytes import ComputedBytesInputInfo
def load_bytes_input_object(
serialized_input_folder: Path,
) -> ComputedBytesInputInfo:
... |
fd14da70f0edaa18e2ce9d0a401a2208f4f100c047de923f40e9f438fe3fe52a | Python | 832 | 20 | from torch.optim.lr_scheduler import _LRScheduler
# Polynomial learning rate scheduler
class PolyLRScheduler(_LRScheduler):
def __init__(self, optimizer, initial_lr: float, max_steps: int, exponent: float = 0.9, current_step: int = None):
self.optimizer = optimizer
self.initial_lr = initial_lr
... |
4318a3108979d1a851cb3bbf10b7f43d8645dd6b08934f407971b0c7c7174bf8 | Python | 833 | 24 | import numpy as np
import statsmodels.stats.multitest
def correct(pvals, method="fdr_bh", alpha=0.05):
"""
Performs multiple testing corrections available in statsmodels.stats.multitest.multipletests().
:param pvals: uncorrected p-values. Must be 1-dimensional.
:param method: Multiple testing correct... |
ee4da90ec458bd994ab92e2b28d7c33939706ed9a6af57d1470b2a08c6594356 | Python | 833 | 27 | from pyment.utils.strip_extension import strip_extension
def test_strip_extension_nii_gz():
assert strip_extension('sub-01.nii.gz') == 'sub-01', (
'Expected strip_extension to strip .nii.gz as a single unit'
)
def test_strip_extension_single_extension():
assert strip_extension('sub-01.mgz') == '... |
fd46443ae4545cc996ba3fcd1161ac787108312892f0bf00785a4a9a25d85c0e | Python | 833 | 30 | #!/usr/bin/env python
"""
Test that software starts in various configurations.
"""
from storm_control.test.hal.standardHalTest import halTest
def test_hal_starts_1():
halTest(config_xml = "none_classic_config.xml",
test_module = "storm_control.hal4000.testing.testing")
def test_hal_starts_2():
ha... |
30ac2ff3b54e38330a866533eae8fe7f5306f87793e302085a6d28d1cae028d3 | Python | 834 | 33 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import os
import re
from http import HTTPStatus
from http.server import BaseHTTPRequestHandler, HTTPServer
class RequestHandler(BaseHTTPRequestHandler):
def do_POST(self):
TARGET_URL_PATTERN = re.compile(r"/" + os.getenv("PAYLOAD_ENDPOI... |
34614492b263a67688d54914019416c71fdb1942a34af79154d2ca876aa0c036 | Python | 834 | 25 | import pandas as pd
import dask.dataframe as dd
import csv
labels = pd.read_csv("/data2/whr/czl/TwiBot22-baselines/datasets/Twibot-20/label.csv")
user_final = []
for row in labels.iterrows():
user_final.append(row[1][0])
df = dd.read_csv("/data2/whr/czl/TwiBot22-baselines/datasets/Twibot-20/edge.csv")
user_to_pos... |
cca11e50b449d4c6b08051d528a758e5dca2688af6c52797b07f0a9084676c20 | Python | 834 | 32 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 11 19:03:57 2025
@author: axthi
"""
import sys
# --------------------------------------------------
# NOTE: Update this path to fit to your installation
path_to_utils = '/mrhome/axelt/memoslap_utils/memoslap'
sys.path.append(path_to_utils)
from simnibs_memoslap_utils.uti... |
7138fb5db84112c66965f08c8da32dfbd5a1679d70d0f64d1e131441e978b17d | Python | 835 | 39 | import argparse
import os.path
import sys
import warnings
import torch
import bism.eval.affinities
import bism.eval.lsd
from yacs.config import CfgNode
torch.set_float32_matmul_precision('high')
def main():
parser = argparse.ArgumentParser(description='SKOOTS Training Parameters')
parser.add_argument('--... |
9d0cf4e72be20d84fb1778e123a08940a012a28463f00b2f6b7fe66c9765cc0e | Python | 835 | 26 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['processing.zip'])
tmp_dir = tempfile.T... |
bb1557e7ef90388e2c9895792221af5c4a906f7453f7ef37e3bcced5802af0ee | Python | 835 | 26 | from sklearn.ensemble import RandomForestClassifier
import numpy as np
import torch as th
import sys
sys.path.append("..")
sys.path.append("../..")
from utils import set_global_seed
from utils.eval import evaluate_on_all_metrics
def train():
train_data = np.array(th.load("train_data.pt"))
train_label = th.load... |
2e56d0771b8a03856f163d71b2cb3d5ee76d6e4b68bcc0a9bf4778ab91f5508b | Python | 836 | 32 | # Script to extract one data row for every countx each to generate test
# data for test_epidata_get_Case_Data_read.py which is not to large
import pandas as pd
from memilio.epidata import defaultDict as dd
from memilio.epidata import getDataIntoPandasDataFrame as gd
# Put any CaseDataFull.json file or Update file ha... |
5c5803a69d04d436db74e4248833bd472e32ce685a60775efe7b589408f2aa40 | Python | 837 | 27 | from setuptools import setup, find_packages
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setup(name='ecgxai',
version='0.1.1',
description='Neatly packaged AI methods for ECG analysis',
author='Rutger van de Leur',
author_email='r.r.vandeleur@umcutrecht... |
82e410cda4a6fc75f55119a7471f0b5f269612dcf457d0472bb070e8e0410e39 | Python | 837 | 24 | import os
import glob
import pandas as pd
import scanpy as sc
deconv_results = []
for afile in glob.glob(os.path.join('.', '*', '*_cell2loc_deconv.h5ad')):
adata = sc.read_h5ad(afile)
'''
# add 5% quantile, representing confident cell abundance, 'at least this amount is present'
deconv_q5 = adata... |
d1a7660ba1399d2775ea900da1fe177b2341ef05efcdad850f8167f8dceb068b | Python | 837 | 31 | import shutil
from pathlib import Path
def get_saved_model_path(run_folder: Path) -> str:
model_path = next((run_folder / "saved_models").iterdir())
assert model_path.suffix == ".pt"
return str(model_path)
def zip_folder(target_folder: str, destination_zip: str | None = None) -> None:
if destinati... |
f9e9e2c2abdbbf05cdad8c3c3e620c0fde6e31c3ccaba1c4e8e04eca3cd1f959 | Python | 837 | 47 | """ Common operations on matrices
*Look into whether it's worth writing these in raw c*
"""
import numpy as np
def nan_indices(data):
""" Finds the indices of all missing values.
Parameters
----------
data: numpy.ndarray
Returns
-------
List of tuples
Indices of all missing value... |
00f87c2af940d91e5b1a3ef4490fd9482c7ffaadc280c0d9e6c83a38f6cbca69 | Python | 838 | 27 | from __future__ import annotations
import numpy as np
import PIL.Image
from pytestqt.qtbot import QtBot
from labelme._utils._image import img_qt_to_arr
from labelme._widgets._brightness_contrast_dialog import BrightnessContrastDialog
def test_combined_adjustment_preserves_clipped_brightness_pivot(
*, qtbot: QtB... |
641ee2f5a5b5e2ba7c9ab762dc7ba20f12c0f4c655642d8aa55b7d38ffb01817 | Python | 838 | 24 | import scanpy as sc
import pandas as pd
h5ad_file = '/path/to/RNA.E.MATRIX.h5ad'
csv_file = '/path/to/magmasiggenes.csv' #Format of csv is the default MAGMA output
output_file = '/path/to/output_dir/RNA.E.MATRIX_siggenes.h5ad'
adata = sc.read_h5ad(h5ad_file)
print(f'Original dataset has {adata.n_vars} genes and {adat... |
844509048560ae5ff68b524fdb1f0b722ae4bd75608e7f26aa34d8f0f29da3f7 | Python | 839 | 27 | import pytest
from tests.utils import pytest_importorskip
def test_sip():
"""Test the qtpy.sip namespace"""
sip = pytest_importorskip("qtpy.sip")
assert sip.assign is not None
assert sip.cast is not None
assert sip.delete is not None
assert sip.dump is not None
assert sip.enableautoconve... |
94b8189e2b4175da746082dd5c655c3c3a87ad4b19b035679c974458007a0d1c | Python | 839 | 27 | """Get training data.
This script will download source reconstructed task MEG data collected using
an Elekta scanner.
The data is from the Wakeman-Henson dataset: https://www.nature.com/articles/sdata20151
It consists of 19 subjects performing a visual perception task.
Each subject performed 6 runs.
"""
import os
... |
73189ee03760182eaa75ca5401232c5b26df87885878c5070a2c34cdd72afc96 | Python | 840 | 35 | #!/usr/bin/env python
from pathlib import Path
import numpy as np
import PIL.Image
here = Path(__file__).resolve().parent
def main() -> None:
label_png = here / "apc2016_obj3/label.png"
print("Loading:", label_png)
print()
lbl = np.asarray(PIL.Image.open(label_png))
labels = np.unique(lbl)
... |
e06e439e0149ec3916be3b0c31528239d2057d42ac774a250d1ddc8647bf3dc9 | Python | 840 | 30 | # by Shaoxuan Yuan at 2024/04/22
from PIL import Image
import numpy as np
def image_to_numpy(image_path):
# 使用PIL库打开图片
image = Image.open(image_path)
# 将图片转换为NumPy数组
image_array = np.array(image)
return image_array
def save_numpy_as_npy(numpy_array, save_path):
# 保存为.npy格式
np... |
5388ec50469246a703ca7716e42de5d61a8499a81eaaf88e265954963f44cab7 | Python | 841 | 17 | from brainscore_vision import benchmark_registry
from .benchmarks.sanghavi2020_benchmark import DicarloSanghavi2020V4PLS, DicarloSanghavi2020ITPLS
benchmark_registry['Sanghavi2020.V4-pls'] = DicarloSanghavi2020V4PLS
benchmark_registry['Sanghavi2020.IT-pls'] = DicarloSanghavi2020ITPLS
from .benchmarks.sanghavijozwik2... |
f62abaa1df66c420d6febc5bc6fe1e181db138c9207685eb0c85c4d926a81104 | Python | 841 | 31 | # update_ml_input.py
import sys
import re
def update_ml_input(file_path, dataset_name):
with open(file_path, 'r') as file:
lines = file.readlines()
for i, line in enumerate(lines):
if line.strip().startswith("N;"):
lines[i] = f"N;{dataset_name}\n"
break
wit... |
2c29e21498a9fe43fb54cc6e11cebaf81d8e1129517d275f0fc69d47a37dc887 | Python | 842 | 29 | import subprocess
import sys
import yaml
script = 'flatmaps/plot_flatmap_countstats.py'
scripts = [script]#[script_calc]
pathpath = 'PATHS/filepaths_carlen.yml'
with open(pathpath, 'r') as myfile: pathdict = yaml.safe_load(myfile)
sys.path.append(pathdict['code']['workspace'])
from pfcmap.python import settings as S
... |
7602bc87baca013cc2a0fcc8d54fabcc3ecc1957248b361a862982d231bd4807 | Python | 843 | 36 | import numpy as np
def create_rank_k_dataset(
n_rows=5,
n_cols=5,
k=3,
fraction_missing=0.1,
symmetric=False,
random_seed=0):
np.random.seed(random_seed)
x = np.random.randn(n_rows, k)
y = np.random.randn(k, n_cols)
XY = np.dot(x, y)
if symmetric:
... |
77d353293627631f4592f1fd692f290b9007ab17da336b7035c260601efa3419 | Python | 843 | 26 | import torch
from ..util.util import asnumpy
__all__ = ['Inference']
class Inference:
def __init__(self, model, device=None, amp=False, transforms=None):
self.transforms = transforms
self.device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
self.model = model
self.m... |
a0ff32f1ce29cd3320b58bf866ab2911e227d422f951c2ffe2ac71c255d4b308 | Python | 843 | 26 | from datetime import timedelta
from kaapana.operators.KaapanaBaseOperator import KaapanaBaseOperator
from kaapana.blueprints.kaapana_global_variables import DEFAULT_REGISTRY, KAAPANA_BUILD_VERSION
class DicomToNiftiOperator(KaapanaBaseOperator):
def __init__(
self,
dag,
name="dicom-to-nifti... |
0ebe62c76ea8e5754bc4c12c19d76cbd0401ad3ddd377166a7f345867c1a5b3d | Python | 844 | 28 | """
Bypass the required registration step for higher level analyses in FSL FEAT
"""
import subprocess
import glob
ROOT = '/Users/sebastiandresbach/data/s1Anfunco/Nifti'
subs = ['sub-12']
for sub in subs:
funcDir = f'{ROOT}/derivatives/{sub}/func'
featFolders = sorted(glob.glob(f'{funcDir}/*.feat'))
... |
242f7ee618f31f4fb5ddaefef1a584a549a14620223eb27e2a1cce6b1b234d54 | Python | 845 | 21 | class Test_MAE_Config:
def test_config(self,dropConfig,demo_dir):
assert dropConfig.MAE.getWorkdir() == f"{demo_dir}/Scripts/MonoallelicExpression/pipeline"
dict_ = {
'qcVcf': f'{demo_dir}/Data/qc_vcf_1000G.vcf.gz',
'groups': ['mae'],
'qcGroups': ['mae'],
... |
42cc727143e5aa0e0718bd31cdfdd854b32cc943f80b3991e9c0103da04a3f45 | Python | 845 | 29 | import glob
import os
def replace_in_file(path, old, new):
with open(path, 'r', encoding='utf-8') as f:
text = f.read()
if old in text:
text = text.replace(old, new)
with open(path, 'w', encoding='utf-8') as f:
f.write(text)
print(f"Reverted: {path}")
def batch_rep... |
53f83ea07f87902772d3a22e537066428c73493426ce796f2febefa0315a1147 | Python | 845 | 28 | """
Interaction tools for image manipulation in MuscleX.
Tools are pure logic handlers that process mouse events on matplotlib axes.
They are NOT Qt widgets - they handle behavior, not UI.
"""
from .interaction_tool import InteractionTool
from .tool_manager import ToolManager
from .chords_center_tool import ChordsCen... |
0648a0db2c1ae36e5586a69bbdcd0ac46af7e6d422af75905c2701f403265e1f | Python | 846 | 25 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
# Inherit parent config
from cvat.settings.production import * # pylint: disable=wildcard-import
# We use MD5 password hasher instead of default PBKDF2 here to speed up REST API tests,
# because the current implementation of the tests requires auth... |
45515347e27bdfdbe079485351ba2f18fe56de4550e158c7dae50df38a831fd7 | Python | 846 | 26 | #!/usr/bin/env python3
"""Train and evaluate the non-physiological artifact detector."""
import logging
from artifact_identification.detectors.non_physiological import NonPhysiologicalDetector
def main():
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
... |
6cf514bc3102acd9ec458035b4f9f56a8e213ac3ee86fb453543794541e02bd9 | Python | 846 | 41 | from cProfile import label
import numpy as np
import pandas as pd
import json
import torch
from pathlib import Path
def str_to_int(s):
if s == 'human':
return 1
else:
return 0
path1 = Path('datasets/Twibot-20')
data = pd.read_csv(path1 / 'label.csv')
data_label = {}
for id in data['id']:
... |
afe9a013bb003986ad14462381d5d7287d118ce2ef439147d2b731bfcfa0351d | Python | 846 | 16 | from .agc import adaptive_clip_grad
from .attention_extract import AttentionExtract
from .checkpoint_saver import CheckpointSaver
from .clip_grad import dispatch_clip_grad
from .cuda import ApexScaler, NativeScaler
from .decay_batch import decay_batch_step, check_batch_size_retry
from .distributed import distribute_bn,... |
bc1da179878dc5ac3618f565a23d35571d94ffa255c2a2bac1e826db9606d86e | Python | 846 | 28 | import pandas as pd
import numpy as np
import sys
from util import read_list, read_cluster_gene
from filter_gene import *
if __name__ == '__main__':
wt_expr = pd.read_csv(sys.argv[1])
pcgs = read_list(sys.argv[2])
genes = list(wt_expr.columns)
print(len(genes))
genes = remove_marker(genes)
ge... |
46b591cc4a2ab51de5b0eda50336176e82cf6f7b98117db3ded2f18812155a37 | Python | 847 | 31 | from .reshape import Reshape
from .sequential import Sequential
from .data_parallel import DataParallel
from .to_hetero_transformer import to_hetero
from .to_hetero_with_bases_transformer import to_hetero_with_bases
from .to_fixed_size_transformer import to_fixed_size
from .encoding import PositionalEncoding, TemporalE... |
4e7647a0bca5285000a4d439a3f30982d77f298fd192d83da2be615febaa51b5 | Python | 847 | 26 | import nilearn as nl
def get_template(template="T1", resolution=2, threshold=None):
## case MNI152 grey matter template or mask
if template in ["gm", "GM", "gmv", "GMV"]:
if threshold is None:
temp = nl.datasets.load_mni152_gm_template(resolution=resolution)
else:
t... |
6a0b154cdca70be99e7a0945991dc39bcf100bb35878cd7aac02f5691cfdb44a | Python | 847 | 25 | from urllib.parse import urlparse
def stream_batch(batch_path: str, temp_dir: str = 'tmp') -> str:
"""Dry version of `count.stream_batch`.
"""
with open(batch_path, 'r') as f:
for line in f:
if line.isspace() or line.startswith('#'):
continue
sep = '\t' if '... |
a5cee9e06ef13c53d1de8f1241cdc15875bb7ab8ca6940a3d9f66e544c504cff | Python | 847 | 26 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy.io.fetcher import get_testing_files_dict, fetch_data
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['others.zip'])
tmp_dir = tempfile.TemporaryDirectory()
def test_hel... |
b5395c85e3eaef75cf5bde9ce55584435ab261676cbdcd5856770fa65acf2d7a | Python | 847 | 38 | """
Model utilities for flync EBM classifier.
This module provides schema validation and prediction utilities for the
EBM-based lncRNA classification model.
"""
from .ebm_predictor import EBMPredictor, EBMPredictorWithScaler
from .schema_extractor import (
ModelSchema,
extract_and_save_schema,
extract_sch... |
c731ab8844980b1a6f518454db0c81f3493f66f3cfbd6b1bdcbdc54d181f7403 | Python | 847 | 33 | from setuptools import setup, find_packages
with open("README.md", "r") as f:
long_description = f.read()
with open("cellseg/__init__.py", "r") as f:
init = f.readlines()
for line in init:
if '__author__' in line:
__author__ = line.split("'")[-2]
if '__email__' in line:
__email__ = ... |
b194da1ddb790c811018a89c6f6f75e3fb8b442919cdba773b490eacf9fa762e | Python | 848 | 20 | from testsetclass import TestsetClass
class C60ISO(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['2', '9', '3', '10',
#'7',
'8', '1', '4', '5']
self.exp_values = {'0': 37.40, '1': 56.87, '2': 56.89, '3': 69.89, '5': 111.72, '6': 142.... |
bb119f3b6a589c13f80ed23b0fbf873f8ac5479d497c376f64d902422ecd1a41 | Python | 848 | 27 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['tractometry.zip'])
tmp_dir = tempfile.... |
cef9a52779793799b093b6dce0507d231f3fd3ce7fc20f06b52c7f385f299d7f | Python | 848 | 34 | import base64
import numpy as np
import requests
def load_and_encode_data(data_pointer: str) -> str:
arr = np.fromfile(data_pointer, dtype="uint8")
arr_bytes = arr.tobytes()
return base64.b64encode(arr_bytes).decode("utf-8")
def send_request(url: str, payload: list[dict]) -> dict:
response = reques... |
a7a4991fc72036f1435e2835f89973c14a58c15c66a442bd1a8e03add676861b | Python | 849 | 26 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys='surface_vtk_fib.zip')
tmp_dir = tempfil... |
1c03b2c13a26bee4203b771e835e6edecbb695eab5454668ef7f6a2b7a1af532 | Python | 850 | 17 | # Copyright (c) OpenMMLab. All rights reserved.
from .compose import Compose
from .formating import (Collect, ImageToTensor, ToDataContainer, ToTensor,
Transpose, to_tensor)
from .test_time_aug import MultiScaleFlipAug
from .loading import DepthLoadAnnotations, DisparityLoadAnnotations, LoadIma... |
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