sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
40728d7d31bc5241f618392c5dfd9b50a18124f95ba216f098ebe20ecf6cfde6 | Python | 775 | 26 | from pathlib import Path
import cv2
from neuralib.typing import PathLike
from rscvp.util.util_sort import anatomical_sort_key
def make_dir_animation(directory: PathLike,
output: PathLike,
suffix: str = '*.png',
fps: int = 20):
"""make animatio... |
fabf36483579315fd758c50004dd7d0b12803da8ba974a5bfc23487877eafad4 | Python | 776 | 31 | # -*- coding: utf-8 -*-
from flair.parser.modules.dropout import SharedDropout
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, n_in, n_hidden, dropout=0, identity = False):
super(MLP, self).__init__()
self.linear = nn.Linear(n_in, n_hidden)
self.identity = identity
... |
50c0d50c26c7ed7a2ec5a02897c211f5ebf9336336c542093addbb66f8098db8 | Python | 777 | 13 | 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.... |
12bc248228df9c0095aade2e249f92223ad647e0fac6a814e69d9352a6e8f19b | Python | 778 | 29 |
"""
SETUP:
Ensure the drive is mapped on the linux worker system
sudo mount -t drvfs '\\10.99.66.32\Team Spider' /mnt/z
Start a scheduler:
dask-scheduler --port 9999 --dashboard-address 9998
Start worker(s) (on linux) *in the directory containing this repository code*
dask-wor... |
52c0ca4f904763dce7f70f32e3dd2ecd28629ad4d428a1ed6e22d3c5338a8497 | Python | 778 | 26 | # -----------------------------------------------------------------------------
# Copyright © 2009- The Spyder Development Team
#
# Licensed under the terms of the MIT License
# (see LICENSE.txt for details)
# -----------------------------------------------------------------------------
"""Provides QtQuickWidgets clas... |
5488df71b92d7f16c9b08125d733327bbfa505bd234299048ec79840a3e214a5 | Python | 778 | 30 | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
class ScalarMix(nn.Module):
def __init__(self, n_layers, dropout=0):
super(ScalarMix, self).__init__()
self.n_layers = n_layers
self.dropout = dropout
self.weights = nn.Parameter(torch.zeros(n_layers))
self.gamm... |
4a1dfdf0ecc52e4e91c380e31adca6b4666a539bd454a68975f3da3c431db4b1 | Python | 779 | 27 | # -----------------------------------------------------------------------------
# Copyright © 2014-2015 Colin Duquesnoy
# Copyright © 2009- The Spyder Development Team
#
# Licensed under the terms of the MIT License
# (see LICENSE.txt for details)
# ----------------------------------------------------------------------... |
5645038f7fc4b979bb30270f4a292f24b94c5c2e4268d065b4101c506af316fb | Python | 780 | 31 | ''' A collection of general python utilities '''
import time
class Timer(object):
"""
modified from:
http://stackoverflow.com/questions/5849800/tic-toc-functions-analog-in-python
a helper class for timing
use:
with Timer('foo_stuff'):
# do some foo
# do some stuff
as an alternative... |
90ece7183661ff17cfd0450147c201fa8de3792e77a70dc74c6bb335b749ce8c | Python | 780 | 30 | from functools import cached_property
import numpy as np
import AFQ.recognition.utils as abu
class PreprocPlan:
def __init__(self, tg):
self.tg = tg
@cached_property
def fgarray(self):
return np.asarray(abu.resample_tg(self.tg, 20), dtype=np.float32)
@cached_property
def crosse... |
9491618f87669e5397bc6d22ad21b0e0915137ca029cac89bb255ee7d109f15e | Python | 780 | 26 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import requests
from django.conf import settings
from health_check.backends import BaseHealthCheckBackend
from health_check.exceptions import HealthCheckException
from cvat.utils.http import make_requests_session
class OPAHealthCheck(BaseHealthChe... |
ac2f21e6ed9f15b1574e0841901c884caccb3b16fcf2a9d3e7794fe75e9934fb | Python | 780 | 29 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import importlib
from django.apps import AppConfig
class AccessTokensConfig(AppConfig):
default_auto_field = "django.db.models.BigAutoField"
name = "cvat.apps.access_tokens"
verbose_name = "API Access Tokens"
def ready(self):
... |
330c259273f9e2443a5190b7733a61c233e194bee0e04a1f971616b44935d9c5 | Python | 781 | 19 | """
Pytest configuration for the comet test suite.
teneto (a test-only reference implementation used to cross-check comet's output) still imports
`from nilearn.input_data import NiftiLabelsMasker`. That module was deprecated in nilearn 0.11
and removed in 0.13+. When CI installs a fresh env against comet's `nilearn >=... |
80c8d51fb11f9c625cfb3eaeeaf9d232559239d44c390c5047c97d92d4dfadbb | Python | 781 | 31 | import sys
import pandas as pd
import numpy as np
def get_coords(index):
coords = pd.DataFrame(index=index)
coords['x'] = index.str.split('x').str.get(0).map(float)
coords['y'] = index.str.split('x').str.get(1).map(float)
return coords
def main():
counts = pd.read_csv('data/adata_heart_9_data.cs... |
94cb989b0773bcdefcd78f8f8fae83585e20fa809c5e236eb968a0a639a11dc1 | Python | 781 | 23 | from .base import (load_conte69, load_sc, load_fc, load_fsa,
load_fsa5, load_subcortical, structural_covariance,
fetch_ahba, risk_genes, load_example_data,
load_summary_stats, load_fc_as_one, load_sc_as_one,
nfaces, getaffine, load_mask, write_... |
afee2a04d7f2f6205b6b2ff16f5ad27ae43f7e2356a0997444bc458edaa52fc0 | Python | 781 | 32 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
# Generated by Django 5.2.16 on 2026-09-04 04:10
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("iam", "0003_move_user_content_type"),
]
operations = [
migrations.Add... |
b3f4b92c0c39789ef3abf170e75346300872eb59754f02c73ddb05e5dd1a948c | Python | 781 | 29 | import numpy as np
from pathlib import Path
from typing import Dict, Tuple
def inspect_npz(filepath: Path) -> Dict[str, Tuple[int, ...]]:
"""
加载 .npz 文件并返回其中所有数组的名称及形状。
参数
----
filepath : Path
.npz 文件路径
返回
----
Dict[str, Tuple[int, ...]]
键为数组名称，值为对应的 shape 元组
"""
... |
dc2f9c74d81c5afa0c848d19e2ad9e52eac0ab9a341ed20c154c9e3e06cab1fe | Python | 781 | 29 | import json
from subprocess import Popen, PIPE
def test_cli():
process = Popen(['mmsplice', 'run'], stdin=PIPE, stdout=PIPE)
options = {}
process.stdin.write((json.dumps(options) + '\n').encode())
process.stdin.flush()
assert process.stdout.readline().decode() == 'MMSPLICE-RESPONSE:1\n'
vari... |
ea1379ea074d7c0ed8f1cc9edd63f7e7e787691d24b5169162e3d28bd2ff7dca | Python | 781 | 18 | ###### Visualization configs ################################################################
from matplotlib.colors import LinearSegmentedColormap, BoundaryNorm, ListedColormap
colors = [(1, 1, 1),
(0, 1, 0),
(1.0, 0, 0.0)] # Color for 1 (red)
# Create a ListedColormap
incmap = ListedColormap(c... |
d2f314d8b43bfa9113d8469fa5d0ad6d1729f7081e1917334372f3154700e6ce | Python | 782 | 23 | #!/usr/bin/env python3
import pandas as pd
import matplotlib.pyplot as plt
import sqlite3
conn = sqlite3.connect("data2.db")
df = pd.read_sql_query("""SELECT
SUM(CASE typ WHEN 5 THEN volume ELSE 0 END) AS volume5,
SUM(CASE typ WHEN 6 THEN volume ELSE 0 END) AS volume6,
SUM(CASE typ WHEN 7 THEN volume ELSE 0 END... |
0c7af2e77d05be9c1b1fe63728840d3624c21acdde5cc411c22b0ad5c37b52a8 | Python | 783 | 28 | # Generated by Django 4.2.6 on 2023-11-21 19:52
import django.db.models.deletion
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("engine", "0076_remove_storages_that_refer_to_deleted_cloud_storages"),
]
operations = [
migrations.AlterFiel... |
a4dfc160e60f932546e14821ff09d1c9abeee30bc279d075675b601fde8ffd90 | Python | 783 | 28 | import pytest
from qtpy import PYQT5, PYQT_VERSION
from tests.utils import pytest_importorskip
def test_qtwebenginecore():
"""Test the qtpy.QtWebEngineCore namespace"""
QtWebEngineCore = pytest_importorskip("qtpy.QtWebEngineCore")
assert QtWebEngineCore.QWebEngineHttpRequest is not None
@pytest.mark.s... |
18adf738c387aa53c3fc60d6f27cd95370ac26ec1b07ddf66ca57391e3b9c304 | Python | 784 | 33 | ###############################################################################
CNtoColor = {}
CNtoColor[0]= '224,224,224'
CNtoColor[1]= '160,160,160'
CNtoColor[2]= '0,0,0'
CNtoColor[3]= '0,0,153'
CNtoColor[4]= '51,51,255'
CNtoColor[5]= '0,255,255'
CNtoColor[6]= '0,153,0'
CNtoColor[7]= '255,255,0'
CNtoC... |
4f62362978560b7236fa30b44c6a60b631fe75d83fcf2421e77f5aa5a06f73c8 | Python | 784 | 26 | import os
from glob import glob
# Adjust import based on your package structure
from stack_corr.warp import warp_image #type: ignore
# Configuration
input_folder = '/path/to/input_images'
output_folder = '/path/to/output_images'
reference_image = '/path/to/reference_image.tif'
os.makedirs(output_folder, exist_ok=Tr... |
660bde497c0442d198de167540ade63c77dbbd0d69cf346ce7142a8e265e319c | Python | 784 | 33 | from qtpy.QtWidgets import (
QVBoxLayout,
QLabel, QTextEdit,
QPushButton,
QWidget,
)
class LogWidget(QWidget):
def __init__(self):
super().__init__()
layout = QVBoxLayout()
# TextEdit: log window widget
self.log_box = QTextEdit()
self.l... |
9d07438b1199b905f121877a247ad22307a462ff33c42a3b5c25bb7b301a8a96 | Python | 784 | 30 | """ Repairs self intersections """
import SVMTK as svm
from pathlib import Path
if __name__ == "__main__":
print("Start ",__file__)
outdir = Path("results")
outdir.mkdir(exist_ok=True)
pial = svm.Surface("../Data/lh-pial.stl")
print("Number of self intersections", pial.num_self_intersections()... |
c6c781bb6006957646754a36af4a598388dee9a51e42876d3daeac4ac91c1179 | Python | 784 | 25 | # Copyright (C) 2021-2022 Intel Corporation
#
# SPDX-License-Identifier: MIT
from allauth.account import app_settings as allauth_settings
from allauth.account.models import EmailAddress
from rest_framework import exceptions
from rest_framework.authentication import BasicAuthentication
class BasicAuthenticationEx(Ba... |
ee062c9b0c1a95092f33b8e81cd7166f52f9f708776295563a10c23b95714688 | Python | 784 | 7 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, get_layers
model_registry['alexnet_w1_mlp4096_simclr_baseline_64715_probe0'] = lambda: ModelCommitment(identifier='alexnet_w1_mlp4096_simclr_baseline_64715_probe0'... |
22f5c99babd23e302d2c37cd8acb0580a6981f475c3b3cc1d332b357f015dec6 | Python | 785 | 28 | # loss function
import tensorflow as tf
import tensorflow.keras.backend as K
# the loss function
def loss_function(y_true, y_pred):
# mse
squared_diff = tf.square(y_true - y_pred)
mse_loss = tf.reduce_mean(squared_diff)
# fft
label_fft1 = tf.signal.rfft2d(y_true)
pred_fft1 = tf.signal.rff... |
d0f16f409c529ec5433fca648dadff8706a02445050b2a9576b52214eba7f8cb | Python | 785 | 31 | # Copyright 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 unittest
from nab.util import getProbationPeri... |
db33b2df54d80a1c26afbf1dc00a0ad181a3fe2eec302e15813d28ed3b88c653 | Python | 785 | 26 | #!/usr/bin/env python3
"""Train and evaluate the eye movement artifact detector."""
import logging
from artifact_identification.detectors.eye_movement import EyeMovementDetector
def main():
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = loggin... |
036029c17a33c45b4c7db91de88a6533fa9f3dd36a8787860d296afd49dae146 | Python | 786 | 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=['others.zip'])
tmp_dir = tempfile.Tempo... |
4cdd499049f043785b6750b21c982bc4255bbeacb82cad86ebbc46c8229c3069 | Python | 786 | 38 | #!/usr/bin/env python
"""
Read data in UCSC wiggle format and write it to an "array tree" file.
usage: %prog array_length output.array_tree < input.wig
"""
import sys
from bx.arrays.array_tree import (
array_tree_dict_from_reader,
FileArrayTreeDict,
)
from bx.arrays.wiggle import WiggleReader
def main():
... |
57c88c4320d11fe327264cc675afaad9307f62e674cfdb66966f6b41c6645c30 | Python | 786 | 22 | import torch
import numpy as np
import pandas as pd
from torch_geometric.data import Data, HeteroData
from datetime import datetime as dt
# dataset
# from dataset import fast_merge,df_to_mask
from tqdm import tqdm
import os
print(os.getcwd())
# node=pd.read_json("/data2/whr/czl/TwiBot22-baselines/datasets/cresci-2015/... |
1ba9a697de07ba2724b33a8b8315debd91bb5a4ff3ea3d6c11e7a2c04340dd2b | Python | 787 | 31 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import PIL.Image
import cvat_sdk.auto_annotation as cvataa
import cvat_sdk.models as models
from ._torchvision import TorchvisionFunction
class _TorchvisionClassificationFunction(TorchvisionFunction):
_label_type = "tag"
def detect(
... |
27a22aae23b6a3fd0cfd19ca91c0d017cd2cf4b4d03402356b74ebea374bfad6 | Python | 787 | 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=['others.zip'])
tmp_dir = tempfile.Tempo... |
39244064dd83bb466314b0fd1e6fce72f45854fb4d2cdfd8d9f6edc675a73f83 | Python | 787 | 30 | # -*- coding: utf-8 -*-
"""
Created on Mon Sep 2 19:22:50 2019
@author: czz
"""
from gluoncv import data, utils
from mxnet import gluon
import mxnet as mx
import cv2
classes = ['hat', 'person']
ctx = mx.gpu()
frame = '1.jpg'
img = cv2.imread(frame)
x, img = data.transforms.presets.yolo.load_test(f... |
71a36c1602ee23b6942dcaefacb284b2274a3254cec17746546f6e8f162387b6 | Python | 787 | 37 | #!/usr/bin/env python
"""
Read a maf from stdin and print the chromosome number for each alignment. It
searches for 'chr' in each alignment block src, and may not be robust if other
src formats are used.
NOTE: See 'align_print_template.py' for a more general variation of this
program.
usage: %prog refindex [op... |
899032871185e1bcf6b334f393f8c3ed396523e8eec882362b82501b92e942c3 | Python | 787 | 29 | from nibabel import load as load_mri, save as save_mri, Nifti1Image
from multiprocessing import Pool
import numpy as np
mri_paths = None # to define
mask_paths = None # to define
dest_paths = None # to define
N_PROCS = None # to define
MEDIAN = 500
def process_mri(mri_path, mask_path, dest_path):
print(f"Start ... |
9a2acb10e703eb214977d48226bfe576ebbe8afc934f9513a81728d5bdfd8fbc | Python | 787 | 26 | #!/usr/bin/env python3
"""Train and evaluate the muscle artifact detector."""
import logging
from artifact_identification.detectors.muscle import MuscleArtifactDetector
def main():
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogg... |
c4141a62e3067bad74705330ec1843dbaf796ad8fc89d56ef28ea93fe582587a | Python | 788 | 28 | # define utility functions
import torch
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, chunks=2, dim=1)
x = x[0] + 1j * ... |
0c3fbfb2b612b0bd0c37683de32dfdcc2d8c59d60d141e6b29dea24e8cbcdaf0 | Python | 789 | 23 | #!/usr/bin/env python3
from setuptools import setup
setup(
name='featurevis',
version='0.2.1',
description='Visualize features cells are responsive to via gradient ascent',
author='Erick Cobos',
author_email='ecobos@bcm.edu',
license='MIT',
url='https://github.com/cajal/featurevis',
key... |
d6fa9304cee23f14977dcd33a9da0a33f731a4be48b36897a36f35b074f1dee6 | Python | 790 | 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 = 'C:/Users/axthi/simnibs/memoslap_utils/'
sys.path.append(path_to_utils)
from simnibs_memoslap_utils.ut... |
e709c0b5ee6f42d5fc12d3a3e0948b25a2c922693240826d032511492a54b61e | Python | 790 | 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=['tracking.zip'])
tmp_dir = tempfile.Tem... |
1d676f8a4ad828455e90b8fcb033bc8a2e29269d3c9676f50db3c7f73b60ab6f | Python | 791 | 28 | # -*- coding: utf-8 -*-
"""
Created on Thu Jun 18 15:21:44 2020
@author: Yoshi
"""
import numpy as np
def polar_coordinates_calculator(responsive_locations,corner):
# Converts cartesian coordinates to polar coordinates
polar_coordinates = []
for cell_location in responsive_locations:
x_cor... |
7228b6c5695ec93c42945dab9f65f4362236af7a475522fe0084f87c52592776 | Python | 792 | 23 | import dtiplayground.dmri.tractography as base
import dtiplayground.dmri.common as common
import yaml
logger=common.logger.write
class @MODULENAME@(base.modules.DTITractographyModule):
def __init__(self,config_dir,*args,**kwargs):
super().__init__(config_dir)
def generateDefaultProtocol(self,... |
ae803cf7fcf490bc3b9d094d6f95e803d374c8c6c0eaf57d33535759fced7c3f | Python | 792 | 34 | import matplotlib
# color the depth, kitti magma_r, nyu jet
#def colorize(value, cmap='magma_r', vmin=None, vmax=None):
def colorize(value, cmap='magma_r', vmin=None, vmax=None):
# TODO: remove hacks
# for abs
# vmin=1e-3
# vmax=80
# for relative
# value[value<=vmin]=vmin
# vmin=None
... |
fe9ccc147b89efaf691d3f7d68bf175cbb90107d0093d3e334645dfe34383c10 | Python | 792 | 23 | import dtiplayground.dmri.fiberprofile as base
import dtiplayground.dmri.common as common
import yaml
logger=common.logger.write
class @MODULENAME@(base.modules.DTIFiberProfileModule):
def __init__(self,config_dir,*args,**kwargs):
super().__init__(config_dir)
def generateDefaultProtocol(self,... |
9e4b8f902ce84cb70552a59b7188193a7bc8eb1fc354fd6d603be8341a576ba8 | Python | 793 | 28 | # data_loaders/spectra_inference_dataset_loader.py
import torch
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
class class_ls(Dataset):
def __init__(self, x, transform=None):
df = pd.read_pickle(x)
self.X = df['coo_format_data'].tolist()
self.N = df['Precursor'... |
c17dff88070de0af11f31aa9ba10d34235524f4a6529e3f9aee1c7b90c2d8736 | Python | 793 | 28 | from dataclasses import fields
import numpy as np
def array_safe_eq(a, b) -> bool:
"""Check if a and b are equal, even if they are numpy arrays"""
if a is b:
return True
if isinstance(a, np.ndarray) and isinstance(b, np.ndarray):
return a.shape == b.shape and (a == b).all()
try:
... |
45be4e4465751bb7910b3fdc5f8702ad1d5cfc6f271bdb024f258bd809f0c85c | Python | 794 | 32 | from impedancefitter.utils import get_equivalent_circuit_model
import numpy as np
from impedancefitter.cpe import cpe_ct_model, cpe_ct_w_model
omega = 2. * np.pi * np.logspace(1, 8, num=20)
def test_cpe_RCT():
k = 1e-7
alpha = 0.9
Rct = 100
model = 'parallel(CPE, R)'
m = get_equivalent_circuit_... |
49d08b87e413a349ab0bd13d8edf0963762f6f317708290a324c4830476b84d5 | Python | 794 | 29 | import sys
import torch
from model.GeminiMol import GeminiMol
if __name__ == '__main__':
# check GPU
print('CUDA available:', torch.cuda.is_available()) # Should be True
print('CUDA capability:', torch.cuda.get_arch_list())
print('GPU number:', torch.cuda.device_count()) # Should be > 0
# read d... |
66873351624cc1b163ae4d82f9c63625bf7916bf79d0b1806f590ed7338799c6 | Python | 794 | 31 | '''Test ogo setup version is correct'''
import unittest
from packaging import version
import re
import ogo
class TestVersionIsCompliant(unittest.TestCase):
'''Test the ogo version is PEP compliant'''
def test_get_version(self):
'''Can access __version__'''
ogo.__version__
def test_get_v... |
8ebd64dfd11acb8b0718d6c307840decdfdfeb2ca3c5224766830ac569c6cefa | Python | 794 | 17 | def diceFunction(pred, ground):
if pred.shape != ground.shape:
raise ValueError("Shape mismatch: pred and ground must have the same shape.")
intersection = np.logical_and(pred, ground)
if ((pred.sum() == 0) and (ground.sum() == 0)):
return 1.0
return (2.0 * intersection.sum()) / (pred.s... |
8ee57bb772db82bcc3eb44ab25b34ae922582ce466849bcf84714b83c69e1f3b | Python | 795 | 22 | """
Plot the impedance measured with a frequency response analyser.
For that, we first convert the data measured with a MokuGo and
a Rohde&Schwarz oscilloscpe.
Both devices have an input impedance of 1MOhm.
We wanted to measure a 47Ohm resistor.
Thus, we connected a 100Om resistor in series with the 47Ohm resistor.
Thi... |
08da5936d245afc80b3db3aa160a4c257caf3b51e50faf76a416dd7ee40da3a5 | Python | 796 | 32 | from typing import Dict
import torch
from skoots.validate.stats import get_volume, get_surface_area
from torch import Tensor
def stats_per_instance(x: Tensor, anisotropy: Tensor) -> Dict[str, Tensor]:
"""
:param x:
:param anisotropy:
:return:
"""
summary = {}
object_id = torch.unique(x)... |
831b8af5da87d485bce1c9774bf6671e477decf5e2e4478ff489bf3e57b2de90 | Python | 796 | 23 | import torch
from batchgeneratorsv2.transforms.base.basic_transform import BasicTransform
class RandomTransform(BasicTransform):
def __init__(self, transform: BasicTransform, apply_probability: float = 1):
super().__init__()
self.transform = transform
self.apply_probability = apply_probab... |
8fdc4f702536fb653dafdf08ca04221a9d8551525585f6a90cc3bdadf58b08df | Python | 796 | 23 | import dtiplayground.dmri.fiberprofile as base
import dtiplayground.dmri.common as common
import yaml
logger=common.logger.write
class IDENTITY_Process(base.modules.DTIFiberProfileModule):
def __init__(self,config_dir,*args,**kwargs):
super().__init__(config_dir)
def generateDefaultProtocol(s... |
3ae568e9e9e15934d5c52e1150af8099e10bb3efdbea570ef9fc7f3bea2e91cc | Python | 797 | 29 | from __future__ import annotations
from typing import Final
import imgviz
import numpy as np
from numpy.typing import NDArray
_LABEL_COLORMAP: Final[NDArray[np.uint8]] = imgviz.label_colormap()
def resolve_shape_color(
*, config: dict, label: str, label_index: int
) -> tuple[int, int, int]:
mode = config["... |
ba4c3d43a22d5b0fee55f60eb4ec876ad83f27a4eccef683e4cf93206d32e916 | Python | 797 | 37 | #!/usr/bin/env python
"""
Read a PAIRWISE maf from stdin and print the percent identity of each
alignment, where percent identity is defined as the number of matching columns
over the number of aligned (non-gap) columns.
TODO: Generalize for more than two species
usage: %prog < maf > out
"""
import sys
from bx.ali... |
bc3bb4a395da187cfdd9aab407e60c67d07109aa7cefcd2efbee7f58c0410823 | Python | 797 | 29 | import pytest
import subprocess
import logging
CORES = "2"
LOGGER = logging.getLogger(__name__)
def run(cmd, dir_path, report_stdout=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs):
shell = isinstance(cmd, str)
response = subprocess.run(
cmd, cwd=dir_path, shell=shell,
stdout=... |
cc2925ae29f6f7966d59e8ea5cc3bbe6b5f59a3fad265f36c485fe40f62e5979 | Python | 797 | 31 | from scipy import stats
import numpy as np
from .base import Distribution
class Beta(Distribution):
"""Beta prior.
Parameters
----------
alpha : scalar or ndarray
Parameter of distribution
beta : scalar or ndarray
Parameter of distribution
"""
def __init__(self, alpha, ... |
e0cdc453f2b77810e3954e2fb47a533dfd955b65304513207866b4e0d3839a28 | Python | 797 | 35 | """Example script for calculating static power/coherence spectra.
"""
print("Setting up")
import os
import numpy as np
from osl_dynamics.analysis import static
from osl_dynamics.data import Data
# Make a directory to hold output files
os.makedirs("spectra", exist_ok=True)
# Load data
data = Data(
[
f"/w... |
110fc0d03bb42379752c676dbdf01507a9d3c7fcfde6827553f99931528aba97 | Python | 798 | 29 | import pytest
from qtpy import PYQT5, PYQT_VERSION
def test_qtsensors():
"""Test the qtpy.QtSensors namespace"""
QtSensors = pytest.importorskip("qtpy.QtSensors")
assert QtSensors.QAccelerometer is not None
assert QtSensors.QAccelerometerFilter is not None
assert QtSensors.QAccelerometerReading ... |
25b990abe41cbc8ed8db11327a7f5c79aa8cddb85f6e75cdcf584b174d777e3d | Python | 800 | 26 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from contextlib import contextmanager
from django.db import connection, transaction
__all__ = ("transaction_with_repeatable_read",)
@contextmanager
def transaction_with_repeatable_read():
with transaction.atomic():
match connection.ve... |
40aa26c0abafe9b50b588301b024ae7e4106bb635ad480d14057669c8f8a9012 | Python | 800 | 24 | import argparse
import yaml
from wandb_utils import WandbLogger
WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
def create_dataset_artifact(opt):
with open(opt.data) as f:
data = yaml.safe_load(f) # data dict
logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation')
if __name__ == '__ma... |
4a15a735ad2870562b01c705814ab4fab02761ed3ba75671094e51782aa7d5e7 | Python | 800 | 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
fetch_data(get_testing_files_dict(), keys=['processing.zip'])
tmp_dir = tempfile.TemporaryDirectory()
def test_help_option(script_runner):
ret... |
5d44325ecd2dea6281684393c6e8122bfc67cdd8698316c6376bf4a50f6b6127 | Python | 800 | 23 | #!/usr/bin/env python3
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
# 1. 读取净获益数据
# 假设已有 CSV: threshold, net_benefit, treat_all, treat_none
df = pd.read_csv(Path(__file__).parents[1] / "results" / "decision_curve_data.csv")
# 2. 绘图
plt.figure(figsize=(6, 4))
plt.plot(df["threshold"], df... |
9daa37ce7531a45c5fe773b3db3083c001fd0f28cd298d7dc824a7208ec66152 | Python | 800 | 26 | from dataclasses import dataclass
from enum import Enum, auto
class AxonModificationMode(Enum):
KEEP_AXON = auto()
MYELINATED_AXON = auto()
REPLACE_WITH_STRAIGHT_AXON = auto()
REPLACE_WITH_MYELINATED_STRAIGHT_AXON = auto()
@dataclass
class CellModificationParameters:
"""A dataclass with paramete... |
c05d137aa30580b963a956ecf242946eb47f7b6983ca4e5b52679af206bb6ee7 | Python | 800 | 12 | from brainscore_vision import benchmark_registry
from .benchmark import Hebart2023fmri
benchmark_registry['Hebart2023_fmri.V1-ridge'] = lambda: Hebart2023fmri('V1', 'ridge')
benchmark_registry['Hebart2023_fmri.V2-ridge'] = lambda: Hebart2023fmri('V2', 'ridge')
benchmark_registry['Hebart2023_fmri.V4-ridge'] = lambda: H... |
cd2aa970d03c348479f9b9414a8a81940934baa8dffca71fd5cc212d27a740c3 | Python | 801 | 35 | #!/usr/bin/env python
import os
import sys
sys.path.append('.')
import click as ck
import numpy as np
import pandas as pd
import gzip
from collections import Counter
import logging
logging.basicConfig(level=logging.INFO)
@ck.command()
@ck.option(
'--data-file', '-df', default='data/swissprot_exp.pkl',
help=... |
dbdb512b8db7b3eceb679be7def6708ec0f26495df397989de7a4a3e7885f0a2 | Python | 801 | 18 | import logging
import sys
import csv
import argparse
import hierarchy
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='extend a tree using node mapping.')
parser.add_argument('hierarchy_file', type=file, help='path to hierarchy file')
parser.add_argument('mapping_file', type=file, h... |
e215783e646299b359f76437c28571c68a436ef249f76ad753d6a6b0dfaf2d7f | Python | 801 | 16 | from testsetclass import TestsetClass
class ADIM6(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['AM5', 'AD3', 'AM2', 'AD4', 'AM7', 'AD6', 'AM3', 'AD5', 'AD2', 'AD7', 'AM6', 'AM4']
self.exp_values = {'0': 1.34, '1': 1.99, '2': 2.89, '3': 3.78, '4': 4.60, '5': 5.55}
... |
144ccca4f6ff8f45199716a1f0cbb3a6982279d134bcc8f6db01ff1ff0f84a14 | Python | 802 | 28 | """test_fast_knn.py"""
import functools
import numpy as np
import impyute as impy
from impyute.ops.testing import return_na_check
# pylint:disable=invalid-name
SHAPE = (5, 5)
def test_return_type(knn_test_data):
imputed = impy.fast_knn(knn_test_data)
return_na_check(imputed)
def test_impute_value(test_data... |
080554d4642e4907bc11aadf35c31cd101a0ee758de642477fe938b0d87bd2a0 | Python | 803 | 33 | import subprocess
import pytest
from deepreg import log
logger = log.get(__name__)
def execute_commands(cmds):
for cmd in cmds:
try:
logger.info(f"Running {cmd}")
out = subprocess.check_output(cmd, shell=True).decode("utf-8")
logger.info(out)
except subproces... |
1ff7d9d9dfde19f18b9eedbcf641ecaf61f1fa8d2ef3b1b5b581dcfa3b3becae | Python | 803 | 7 | highlevel={}
highlevel["SOCIAL_IQ_COVAREP"]="http://immortal.multicomp.cs.cmu.edu/Social-IQ/acoustic/SOCIAL_IQ_COVAREP.csd"
highlevel["SOCIAL-IQ_QA_BERT_LASTLAYER_BINARY_CHOICE"]="http://immortal.multicomp.cs.cmu.edu/Social-IQ/qa/SOCIAL-IQ_QA_BERT_LASTLAYER_BINARY_CHOICE.csd"
highlevel["SOCIAL-IQ_QA_BERT_MULTIPLE_CHOIC... |
39541bc1890c0f2de4795b07bb90d50746ac003851385fe35f2c56f1994ab58b | Python | 803 | 42 | '''
Abstract class for controlling a hardware valve. Function names were derived from original valveChain and hamilton classes.
George Emanuel
2/16/2018
'''
from abc import ABC, abstractmethod
class AbstractValve(ABC):
@abstractmethod
def changePort(self, valve_ID, port_ID, direction = 0):
pass
... |
5ba542627b728eb921cc50ae0e4455c1e2ddd6820aae29813cba6ac6681d0b83 | Python | 803 | 29 | import platform
from importlib.util import find_spec
"""
Adapted from:
https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/utilities/imports.py
"""
def _module_available(module_path: str) -> bool:
"""Check if a path is available in your environment.
>>> _module_available(... |
5f126e464dc4387d2f87a25d6307ee91a339b8127addfd8dc38cfb73ef2533fe | Python | 804 | 27 | # Generated by Django 3.2.18 on 2023-03-13 21:34
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("engine", "0066_auto_20230319_1252"),
]
operations = [
migrations.AlterField(
model_name="cloudstorage",
name="crede... |
c7e4392d02701ceb03b5ce9eed3d68c9d7c7e6bdb2fe341a8423430108daf737 | Python | 804 | 29 | from dataclasses import dataclass
from typing import Literal
from eir.models.output.tabular.linear import LinearOutputModuleConfig
from eir.models.output.tabular.mlp_residual import ResidualMLPOutputModuleConfig
from eir.models.output.tabular.shared_mlp_residual import (
SharedResidualMLPOutputModuleConfig,
)
@d... |
2405fd9705b17937f397b8f8fb857c70044d951085541743668d235106ec16fa | Python | 805 | 44 | #!/usr/bin/env python.
"""
Read a maf and print the text as a fasta file.
usage: %prog < maf > fasta
"""
import sys
from bx.align import maf
def __main__():
maf_reader = maf.Reader(sys.stdin)
# Confusing since maf_to_concat_fasta takes names.
# if len( sys.argv ) > 1:
# comps = map( int, sys... |
65241891e93ba866eb84dc3215212a2a0a71497dac444f3c250a3a24d1af17ef | Python | 805 | 30 | import sys
from argparse import ArgumentParser
from connectomics.utils.evaluate import adapted_rand
from connectomics.data.utils.data_io import readvol
def evaluate(seg, gt):
arand, prec, rec = adapted_rand(seg, gt, all_stats=True)
print(f"Adapted Rand Error: {arand}")
print(f"Precision: {prec}")
prin... |
711a6de6059cb6391e8a805794d6a285c324ca4c230f343258ab23d837e4762b | Python | 805 | 31 | import base64
import requests
def encode_image_to_base64(file_path: str) -> str:
with open(file_path, "rb") as image_file:
image_bytes = image_file.read()
return base64.b64encode(image_bytes).decode("utf-8")
def send_request(url: str, payload: list[dict]) -> list[dict]:
response = requests.... |
763745fbfee3dcd02b75a0e8a5f11d1093b4e5dc179414cb3af2314db4ed7958 | Python | 805 | 33 |
from setuptools import setup
import string
import os.path as op
from setuptools_scm import get_version
def local_version(version):
"""
Patch in a version that can be uploaded to test PyPI
"""
scm_version = get_version()
if "dev" in scm_version:
gh_in_int = []
for char in version.n... |
d7c972b42a005055c5495e2d261e02b9ab1139d2ccb8a20073130f3547798e3c | Python | 805 | 26 | #!/usr/bin/env python
"""
Test correlation lock.
Hazen 09/19
"""
import numpy
import storm_analysis.simulator.draw_gaussians_c as dg
import storm_control.sc_hardware.utility.corr_lock_c2dg as cl2DG
def test_c2dg():
clf = cl2DG.CorrLockFitter(roi_size = 8, sigma = 1.0, threshold = 0.1)
for i in range(10):
... |
fee77f73fc765216d78c5a6c0c8a8223d36ebbdad587cd16adcd14bf40d5bd96 | Python | 805 | 37 | # Train a Linear Regressor
# Imports
import numpy.typing as npt
from sklearn.linear_model import LinearRegression
from modules.tools.types_abbrv import LINEAR_REG_TYPE
def train(
X: npt.NDArray[float], y: npt.NDArray[float]
) -> tuple[LINEAR_REG_TYPE, dict]:
"""Train a linear regression predictor
Para... |
8823825ee4c3f389468a83cf92bb304af9cfc41546445415cdfa7c730e00874b | Python | 806 | 31 | """SFCN with a softmax output for categorical classification."""
from tensorflow import Tensor
from tensorflow.keras.layers import Dense
from .sfcn import SFCN
class CategoricalSFCN(SFCN):
"""SFCN for multi-class classification tasks.
The prediction head is a Dense layer with softmax activation,
produc... |
a9a7f34ec00feb8cf22f0e501799863e41c533c60c2374c799bf15f5cced9939 | Python | 806 | 37 | import pandas as pd
import glob
import os
import sys
ChiThresh = 9
# Step 1: Find all your CSV files
csv_files = glob.glob("results/batch_*/output_ID_*.csv")
print(f"Found {len(csv_files)} result files.")
# Step 2: Collect transformed rows
rows = []
for file in csv_files:
df = pd.read_csv(file)
# Drop 'St... |
2d5572af4297391c70e5cd48c1252ba6a6b52fb77dde504e941542a7b123bc08 | Python | 807 | 37 | import pandas as pd
import glob
import os
import sys
ChiThresh = 12
# Step 1: Find all your CSV files
csv_files = glob.glob("results/batch_*/output_ID_*.csv")
print(f"Found {len(csv_files)} result files.")
# Step 2: Collect transformed rows
rows = []
for file in csv_files:
df = pd.read_csv(file)
# Drop 'S... |
7a407acd58489609c7a4eb9e412854dbfc5a6aeeba973ec4935fa2a611eb900d | Python | 807 | 27 | import skimage.io as io
import numpy as np
import torch
from torch import Tensor
from typing import Optional
def imread(image_path: str,
pin_memory: Optional[bool] = False) -> Tensor:
"""
Imports an image from file and returns in torch format
:param image_path: path to image
:param pin_memo... |
7eeddcd99d7d74d2fa89cfe641c22274822151677f9bb043fb1972eccfbef40b | Python | 807 | 27 | from EIANN import utils as ut
network_pkl_name = '20231129_EIANN_2_hidden_mnist_van_bp_relu_SGD_config_G_complete_optimized_66049_257.pkl'
path = f'/scratch/yc1376/data/eiann/{network_pkl_name}'
net = ut.load_network(path)
name = path.split('/')[-1].split('.')[0]
print(f'Network Name: {name}')
print(f'Final Val Accur... |
b62429fbaf2800cef198f270808349c00550d79a128a8cc5c294cd03bc46c92d | Python | 807 | 37 | import pandas as pd
import glob
import os
import sys
ChiThresh = 11
# Step 1: Find all your CSV files
csv_files = glob.glob("results/batch_*/output_ID_*.csv")
print(f"Found {len(csv_files)} result files.")
# Step 2: Collect transformed rows
rows = []
for file in csv_files:
df = pd.read_csv(file)
# Drop 'S... |
13cae19bf6f8180723710e4d6d01e1cad1feed8deb5e6bf290d7a5240e36ee00 | Python | 808 | 34 | """Print free energy.
"""
from sys import argv
if len(argv) != 2:
print("Please pass the number of states, e.g. python 3_print_free_energy.py 8")
exit()
n_states = int(argv[1])
output_dir = f"results/models/{n_states:02d}_states"
import pickle
import numpy as np
def get_best_run(min_, max_):
best_fe ... |
2598774fa91247a77c30f6f67845eda081f3c1e824c93b767a819de46b81edac | Python | 808 | 28 | # -----------------------------------------------------------------------------
# Copyright © 2009- The Spyder Development Team
#
# Licensed under the terms of the MIT License
# (see LICENSE.txt for details)
# -----------------------------------------------------------------------------
"""Provides QtPurchasing classe... |
32e9bb02602ef503f0c494dc5de083a313ade85e78739bf86c8aaa7eef8998bb | Python | 808 | 37 | import pandas as pd
import glob
import os
import sys
ChiThresh = 6.4
# Step 1: Find all your CSV files
csv_files = glob.glob("results/batch_*/output_ID_*.csv")
print(f"Found {len(csv_files)} result files.")
# Step 2: Collect transformed rows
rows = []
for file in csv_files:
df = pd.read_csv(file)
# Drop '... |
4702235e6e52fa4f7ef3af20480a87ceed10101065274dcafbc8714e84eb8451 | Python | 808 | 26 | #!/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... |
969de2a851f5772244e28aaca964d01263d9e3574f3a27c33fc934f63526629c | Python | 808 | 25 | # Created by David Coggan on 2024 06 26
import brainscore_vision
import numpy as np
def test_Coggan2024_fMRI_stimuli():
stimulus_set = brainscore_vision.load_stimulus_set('Coggan2024_fMRI')
assert len(stimulus_set) == 24
assert len(set(stimulus_set['object_name'])) == 8
assert len(set(stimulus_set['o... |
68ebe56d6faf0cc4dbf0fa845d9e43a35dcb6e91ac2664cc7defb23b403b280f | Python | 810 | 31 | """ Create a mesh with multiple surfaces """
import SVMTK as svm
import numpy as np
from pathlib import Path
if __name__ == "__main__":
print("Start ",__file__)
surf = svm.Surface();
outdir = Path("results")
outdir.mkdir(exist_ok=True)
sphere1 = svm.Surface()
sphere2 = svm.Surface()
spher... |
aacf55eaef10a77d2c41f8e16f32666957c92ae1e5ba15dfecb955f10679d3c9 | Python | 810 | 31 | import torch
from torch import nn, Tensor
class CustomEmbedding(nn.Module):
# A custom embedding class to circumvent error of default implementation.
def __init__(self, num_classes, embedding_dim):
super(CustomEmbedding, self).__init__()
self.num_classes = num_classes
self.embeddin... |
cce9f7960ba7fbd0244853fa22a180dc4b2858d789bd5e7bdd5dc585e80230d9 | Python | 810 | 35 | #!/usr/bin/env python
"""
For each interval in `bed1` count the number of intersecting regions in `bed2`.
usage: %prog bed1 bed2
"""
import sys
from bx.intervals import (
Intersecter,
Interval,
)
bed1, bed2 = sys.argv[1:3]
ranges = {}
for line in open(bed2):
fields = line.strip().split()
chrom = fi... |
218476e690a6d8fd799c1fcd86b35fa49e34492e07477c62423584cfc5d3c6b5 | Python | 811 | 21 | from matplotlib import pyplot as plt, rcParams
FIG_WIDTH = 7.5 # width of figure in inches
BASE_FONT_SIZE = 6
RATIO = FIG_WIDTH / BASE_FONT_SIZE
TINY_SIZE = 6
SMALL_SIZE = TINY_SIZE + 2
MEDIUM_SIZE = SMALL_SIZE + 2
BIGGER_SIZE = MEDIUM_SIZE + 2
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('... |
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