sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d4b517df6612a5af8daddc47bbd0ddc04f64d0b48a40220111ce147bcfbfa101 | Python | 742 | 27 | from setuptools import setup, find_packages
setup(name='HD_BET',
version='1.0',
description='Tool for brain extraction',
url='https://github.com/MIC-DKFZ/hd-bet',
python_requires='>=3.5',
author='Fabian Isensee',
author_email='f.isensee@dkfz.de',
license='Apache 2.0',
zi... |
1c1c06ef82c0fc9850ca39a11c0829ba4541c05be2b1654449eaa2654d395934 | Python | 743 | 24 | import numpy as np
from . import utils
def trials_are_same_length(trial_data, ref_field=None):
"""
Check if all trials of a dataset have the same length
Parameters
----------
trial_data : pd.DataFrame
data in trial_data format
ref_field : str (optional)
time-varying field to us... |
85cc6ec071f0b4d8bd8fb4eb95cb46ac821999eaa909e33d3a9d336fe40cae8f | Python | 743 | 31 | from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from codecarbon import EmissionsTracker
digits = datasets.load_digits()
# flatten the images
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
# Create a clas... |
a5df8b87d598957d83dc5f6f2af99c65b10dd9f4094f275ee4444718d5feef4e | Python | 743 | 29 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 82
msp.architecture["splicepoint_model_spec"] = dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepoint-model-donor-1",
)
msp.arch... |
baf104c12ed010da15cca45238fa59da0e099a4b46976814faf803882c67dc72 | Python | 743 | 27 | import sqlite3
from .db import DB
class FatalitiesTable(DB):
def __init__(self):
self.table_name = 'KA_individual_fatalities'
self.table_desc = 'Karnataka individual fatality diagnosis'
self.cols = self.getcolumns()
def getcolumns(self):
cols = {
'date': 'DATE NOT... |
e5062181f1ad86da2032cb2321d3b4a8f3bffdcbafa8e3b2ca4a7bfa0bca1ca5 | Python | 743 | 21 | from mirp._data_import.dicom_multi_frame import ImageDicomMultiFrameStack, ImageDicomMultiFrameIndividual
from mirp._data_import.dicom_file_mr import ImageDicomFileMR
class ImageDicomFileMRDCE(ImageDicomFileMR):
def __init__(self, **kwargs):
super().__init__(**kwargs)
class ImageDicomFileMRDCEMultiFrame... |
2e166065b2f7a4c3e1941ce973ce5c9170c304f837a8e481b0c8143f1c6a85ab | Python | 744 | 16 | # Copyright 2019 Novartis Institutes for BioMedical Research Inc. 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... |
6ad234072ca967a32e71bc0dbf777138cf570f698ff983b30341cdbdaaa9a516 | Python | 744 | 28 | import sqlite3
from .db import DB
class DistrictCaseInformation(DB):
def __init__(self):
super().__init__()
self.table_name = 'TG_district_cases_info'
self.table_desc = 'Telangana daily new cases in districts'
self.cols = self.getcolumns()
def getcolumns(self):
cols... |
b742ec7e60b46feea80d62e68994db8e42237d6768c8259bdb7991d3ae0b2138 | Python | 744 | 20 | # Copyright (c) OpenMMLab. All rights reserved.
from .avgpool2d_neck import AvgPool2dNeck
from .cae_neck import CAENeck
from .densecl_neck import DenseCLNeck
from .linear_neck import LinearNeck
from .mae_neck import MAEPretrainDecoder
from .mocov2_neck import MoCoV2Neck
from .nonlinear_neck import NonLinearNeck
from .o... |
54a98f19f6a416d17c347255dac0482b8979c09c941b22c6aa358273328389a0 | Python | 745 | 26 | import numpy as np
from F01 import Feature
class F08(Feature):
def get_map_float32(self, img, roi):
b, g, r = img[:, :, 0], img[:, :, 1], img[:, :, 2]
# Prevent div by 0
b[b == 0] = 1
g[g == 0] = 1
# Prepare "redness" map
red = np.minimum(r / (1.0 * b), r / (1.0 ... |
bc8b861fde78a631d9f44adf1c7cc4487a514681e57acfb27efb30c050127668 | Python | 745 | 41 | from .docs import deprecated # noqa: F401
from .mean import (
mean_covariance,
mean_ale,
mean_alm,
mean_euclid,
mean_harmonic,
mean_identity,
mean_kullback_sym,
mean_logdet,
mean_logeuclid,
mean_power,
mean_riemann,
mean_wasserstein,
maskedmean_riemann,
nanmean_r... |
bfe5af8b2e98aff20ebaad76b3f478d3c900bbb7efbfd9d2faf06481d9045b62 | Python | 745 | 24 | import numpy as np
from scipy import stats
from sklearn.metrics import mean_squared_error
def calculate_mse(y_true, y_score):
vals = []
for class_index in range(y_true.shape[-1]):
vals.append(mean_squared_error(y_true[:, class_index], y_score[:, class_index]))
return np.array(vals)
def calculate... |
c3c5ae22592d0d4dae35e2206d6c08c02049113e9375914f323af99191d22969 | Python | 745 | 22 | import sys
import os
homedir = sys.argv[1]
tmpdir = sys.argv[2]
network = sys.argv[3]
phenotype_name = sys.argv[4]
"""
this makes the features and targets in tmpdir
"""
os.system('python /{0}/preprocess.py {1} {2}'.format(homedir,network,tmpdir))
working_dir = homedir + '/results_matchedsamples_null_081822/{0}_netwo... |
cbbb50186b2f97ac48d23dbd81c136d751a5d8beb238910b5365766a204f6f6a | Python | 745 | 30 | """
Refactor the AP detection module to provide a clean interface.
You can always refactor the core later.
"""
import os
import sys
PATH_HERE = os.path.abspath(os.path.dirname(__file__))
PATH_DATA = os.path.abspath(PATH_HERE+"../../../data/abfs/")
PATH_SRC = os.path.abspath(PATH_HERE+"../../../src/")
sys.path.insert(0... |
f900da9b74ab2a792f9046154969965adc8b50852a21656c9f251bf7a0319ea1 | Python | 745 | 22 | import sys
import os
homedir = sys.argv[1]
tmpdir = sys.argv[2]
network = sys.argv[3]
phenotype_name = sys.argv[4]
"""
this makes the features and targets in tmpdir
"""
os.system('python /{0}/preprocess_SepSampPCA.py {1} {2}'.format(homedir,network,tmpdir))
working_dir = homedir + '/results_SepSampPCA_null/{0}_netwo... |
362d0f16d761c712f3bb5504aa444988e39d9d525cd7ce0854588f3a89398447 | Python | 746 | 34 | """Short example for ``tl.debug`` helpers."""
from __future__ import annotations
import torch
from torch import nn
import torchlens as tl
class DebugModel(nn.Module):
"""Tiny model with one numerically unstable operation."""
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Create a non-finit... |
77b3575916980df655651525890e6702f00994b23818bf3dcea90d41f7b9caec | Python | 746 | 28 | #!/usr/bin/python
from distutils.core import setup
classifiers=[
'Development Status :: 4 - Beta',
'Environment :: X11 Applications',
'Intended Audience :: End Users/Desktop',
'License :: OSI Approved :: GNU General Public License (GPL)',
'Operating System :: POSIX :: Linux',
'Prog... |
72f722d8fe24ff64ef94e5e4fe6e0b966b875b9e87b331dcc297599b48738ca9 | Python | 747 | 26 | import sqlite3
from .db import DB
class DailySummary(DB):
def __init__(self):
super().__init__()
self.table_name = 'KL_daily_summary'
self.table_desc = 'Summary of new COVID-19 cases (last 24 hours)'
self.cols = self.getcolumns()
def getcolumns(self):
cols = {
... |
20786cf2fb0a797d6d71a346c36b79484b8d2011500cddf0f4faaa78e331b54d | Python | 748 | 21 | """Tests for specparam.algorithms.definitions."""
from specparam.algorithms.algorithm import Algorithm
from specparam.algorithms.definitions import *
###################################################################################################
###################################################################... |
362b15179020c5f630ad9eaa5e61fcc5861ac016bd649220ce6cba364d61d1d4 | Python | 748 | 28 | import sqlite3
from .db import DB
class ActiveCases(DB):
def __init__(self):
super().__init__()
self.table_name = 'GA_active_cases'
self.table_desc = 'BREAKUP OF ACTIVE POSITIVE CASES'
self.cols = self.getcolumns()
def getcolumns(self):
cols = {
'date': '... |
52d57fe271e9542a9ed7a06a1bee487e4d0feb119576a37196f274e58c4aea7a | Python | 748 | 27 | import numpy as np
def rep(x, each=1, times=1, use_inversion=False):
""""This functions replicates the R rep function for tiling and repeating vectors"""
each = int(each)
times = int(times)
if each > 1:
x = np.repeat(x, repeats=each)
if times > 1:
if use_inversion:
le... |
cb80626821d4c397357493ce48903abe09014253f14366dea4bee10d5d5eb395 | Python | 748 | 24 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in July 2022
@author: Thomas Bonald <thomas.bonald@telecom-paris.fr>
"""
import unittest
import numpy as np
from sknetwork.utils.membership import get_membership, from_membership
class TestMembership(unittest.TestCase):
def test_membership(self):
... |
7ae6b9e28adcc799fa6380fab33bccfaa51c9b04e65d32a850089072bb631469 | Python | 749 | 23 | import torch
from collections import OrderedDict
class script_pes():
def __init__(self,pes,name):
self.pes=pes
self.name=name
def __call__(self,state_dict):
new_state_dict = OrderedDict()
for k, v in state_dict['reannparam'].items():
if k[0:7]=="module.":
... |
d91bed40565ab3b30da0532930c2101195b7678f89f5058391138bd75b42ec05 | Python | 749 | 19 | """Specific tasks based on the same base task."""
import gymnasium as gym # using ngym.make would lead to circular import
from neurogym import wrappers
from neurogym.utils import scheduler
def roitman02(**kwargs):
env = gym.make("PerceptualDecisionMaking-v0", disable_env_checker=True, **kwargs)
return wrap... |
08223d2d789c3255a28d760676e96b7d4fdab72a1cc29d2ea2e9cb74f9dd8677 | Python | 750 | 22 | import sys
import os
homedir = sys.argv[1]
tmpdir = sys.argv[2]
network = sys.argv[3]
phenotype_name = sys.argv[4]
"""
this makes the features and targets in tmpdir
"""
os.system('python /{0}/preprocess.py {1} {2}'.format(homedir,network,tmpdir))
working_dir = homedir + '/results_matchedsamples_boot_081822/{0}_netwo... |
442273be2282c5b3988f0f51fb87c909d6bfb6dd6991d71d983a9fc3bfad8a91 | Python | 750 | 27 | import sys
import pathlib
import matplotlib.pyplot as plt
import numpy as np
from scipy import signal
try:
PATH_HERE = pathlib.Path(__file__).parent
PATH_ABFS = PATH_HERE.joinpath("../../data/abfs/").resolve()
PATH_SRC = PATH_HERE.joinpath("../../src/").resolve()
print(PATH_SRC)
sys.path.insert(0, ... |
720b9d321dcd9f9273bf1b993ef0f333b5bd3a23523d4364debf584075fee3f6 | Python | 750 | 24 | """Base class for XLA SavedModel archive."""
class XLASavedModelArchive:
def __init__(self):
raise NotImplementedError(
"XLASavedModelArchive is an abstract class. "
"Use a subclass such as SavedModelExportArchive."
)
def track(self, resource):
raise NotImpleme... |
b63d5fd94eb8051e3c6754a63d4fa76818ab12c699d2f75bd2060992badb4fb3 | Python | 751 | 27 | import subprocess
from subprocess import run
import numpy as np
# spring: 974 summer: 610 fall: 1015 winter: 506 sar2opt: 587/ 627
num_test = 627
if __name__ == '__main__':
cmd = "bash Image_translation_codes/pytorch-CycleGAN-and-pix2pix/run_time.sh"
results = subprocess.Popen(cmd, stdout=subpro... |
28a825f5cdecaec0154611fb0aedb98df0ca6c8e0b8b08b5ab3169e6e756d173 | Python | 752 | 32 | """Convert coco to bdd100k format."""
import argparse
from scalabel.common.parallel import NPROC
from scalabel.label.from_coco import run
def parse_arguments() -> argparse.Namespace:
"""Parse the arguments."""
parser = argparse.ArgumentParser(description="coco to bdd100k")
parser.add_argument(
"... |
77b51bc849b9a5af79b8b1acec3fd7dcd5f6c7c770dc2931696493f3a15cea49 | Python | 752 | 25 | import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
def plot_transition_matrix(hsmm, ordered_states, size, path):
ordering = [hsmm.states.index(i) for i in ordered_states]
trans_matr = hsmm.transition_matrix[ordering][:, ordering]
first, second = np.where(trans_matr)
plt.figure... |
d7603bbfb9415cf5638de2de580de7134e25d9fa1e19dc9b1cd7e38dccd458e8 | Python | 752 | 20 | from tensorflow.keras.layers import BatchNormalization
import tensorflow as tf
# https://stackoverflow.com/questions/65195956/keras-custom-batch-normalization-layer-with-an-extra-variable-that-can-be-change
class AccumBatchNormalization(BatchNormalization):
def __init__(self, **kwargs):
super(AccumBatchNo... |
037f6e4fce5bee7cbfd9f9f9fc379f3cee7382f22ec88e0cb8c8e2019e3a2640 | Python | 753 | 19 | from datasets.basic import MultiSiteDataset
from datasets.fundus import FundusDataset
from datasets.mediastinum_tumor import MediastinumTumorDataset
from datasets.midline import MidlineDataset
__all__ = ['all_datasets', 'get_dataset']
all_datasets = {
'mediastinum-tumor': MediastinumTumorDataset,
'fundus': Fu... |
060fb5ab92dbade2e9f1a3d56fef71630baa02b79e5d3a0f84d8174fba4cc2c8 | Python | 753 | 27 | import torch
from torch import nn
class SEModule(nn.Module):
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.fc1 = nn.Conv3d(channels, channels // reduction, kernel_size=1)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv3d(channels // reduction, ... |
15f1258395263d76fe13b7f31ced827553e827d4c839b5aa1ca324f8be7f3c60 | Python | 753 | 33 | import numpy as np
from .annotation import Annotation
class Null(Annotation):
"""
Represents a site that should never be included in a transcript.
"""
@classmethod
def find_patterns(cls, sites, usable, skippable):
return super().find_patterns(sites, np.ones_like(usable), skippable)
... |
78eaba3418a3d8744fb6e07630ca33a90fcf71bf07fdf14743979595579f9831 | Python | 753 | 13 | """Module defining various utilities."""
from bioseq2seq.utils.misc import split_corpus, aeq, use_gpu, set_random_seed
from bioseq2seq.utils.alignment import make_batch_align_matrix
from bioseq2seq.utils.report_manager import ReportMgr, build_report_manager
#from bioseq2seq.utils.raytune_utils import RayTuneReportMgr
f... |
677cc5e2e849b295a7aabd6c03a1714a89667b87a957a81658fecfe752c46351 | Python | 754 | 30 | from sys import argv
from msp import MSP
from msp_fly import fly_am
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
fly_am(msp)
msp.architecture["final_processor_spec"].update(
dict(
bottleneck_spec=dict(type="ConvolutionalBottleneck", width=5),
post_bottleneck_lsmrp_spec=dict(type="Early... |
6b3904aad544e8629c69ccf83aa0ad56f63fcc1edc02dad45a29741fbf6544c2 | Python | 754 | 28 | # Generated by Django 3.2.6 on 2022-05-19 15:05
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0087_processeddata_repository_specimen_tomographicdata'),
]
operations = [
migrations.AddField(
model_name='rep... |
13b8dd964c54405e3da26d60ce4249dd3e50b405373ddf20ecb6dd1b589c4e24 | Python | 755 | 25 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
import ast
from typing import Set
FLOAT_PATTERN = r"^[-+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?$"
POS_FLOAT_PATTERN = r"^[+]?[0-9]*\.?[0-9]+([eE][-+]?[0-9]+)?$"
INT_PATTERN = r"^-?\d+$"
# matches float as well as NaN:
FLOAT_NAN_PATTERN = r"^\s*(([Nn][Aa][Nn])|([-+]... |
25b32c9a58323e4d69ccd022014025d874c49ffda80b50b6023c0e4fb18e6543 | Python | 755 | 19 | from pyrho import VERSION
from distutils.core import setup
setup(
name='pyrho',
version=VERSION,
description='Inference of fine-scale recombination maps using composite '
'likelihoods and fused-LASSO.',
author='Jeffrey P. Spence, Yun S. Song',
author_email='spence.jeffrey@berkeley.ed... |
5b7aa0115e8bc2bc68552a113f2318ef196a6fe5d829c2e1d95a4a4c4c9f3177 | Python | 755 | 27 | import subprocess
from subprocess import run
import numpy as np
# spring: 974 summer: 610 fall: 1015 winter: 506 sar2opt: 587/ 627
num_test = 627
if __name__ == '__main__':
cmd = "bash Image_translation_codes/contrastive-unpaired-translation/run_time.sh"
results = subprocess.Popen(cmd, stdout=su... |
72affc8149becd43cdec8b4f9ed02013d7cf7fc686915f874592256e65ccbc7d | Python | 755 | 26 | import sys
import collections
from collections import Counter
d = collections.defaultdict(list)
def get_matrix(seq_fa):
with open(seq_fa) as f:
for line in f:
line = line.strip()
if line.startswith('>'):
continue
else:
for i in range(len(line)):
if i ==5:
base = 'I'
else:
base... |
bc34e18bfac0187c64f82798bbd37835193e9cedffcedd1f612920d2243329cb | Python | 756 | 29 | import sys
import pathlib
import matplotlib.pyplot as plt
import numpy as np
try:
PATH_HERE = pathlib.Path(__file__).parent
PATH_ABFS = PATH_HERE.joinpath("../../data/abfs/").resolve()
PATH_SRC = PATH_HERE.joinpath("../../src/").resolve()
print(PATH_SRC)
sys.path.insert(0, str(PATH_SRC))
import... |
e93b5fc0b083e3e63dedbc790795b0b8af8b1095183e206402131e1f1d192288 | Python | 756 | 30 | import argparse
import torch
from src.datasets import util
# obtain mean and std of dataset
def main(args):
dataset = util.get_dataset(args.root, name=args.dataset, split="train", ntau=args.ntau, idx=0, normalise=False)
frames_list = []
for x, y in dataset:
frames_list.append(x)
movie_tens... |
72abdc7404dc17e5d4077ac6121a5d7fefa917040df3393bc79b2720e0efcc33 | Python | 757 | 45 | from typing import List, Tuple, Any, TypedDict
class TimeSeries(TypedDict):
data: List[Tuple]
class TableSchema(TypedDict):
title: str = None
columns: Tuple[str]
class DataTable(TypedDict):
title: str = None
columns: Tuple[str]
data: TimeSeries
class StateSchema(TypedDict)... |
5fca23fa657679c5d7c621758ccb6502ab2f24fac5d433178ed4665754c63d37 | Python | 758 | 23 | #### Modified from TAURUS Lee et al., Nat Methods 2019 (https://github.com/dixonlab/Taurus-MH)
import pysam
import sys
import os
os.system('sort -k1n '+sys.argv[1]+'_multi_split_aligned.txt_man_dedupped.txt > '+sys.argv[1]+'_multi_split_aligned.txt_man_dedupped_queryname_sorted.txt')
bam=sys.argv[1]
dfh=pysam.Alignm... |
ebef1c95322db70862f020ebbd689e04c902462788b8e809f158dae3223e4019 | Python | 758 | 32 | """ Distribution configuration for morphsynthesis
"""
import imp
from setuptools import find_packages, setup
VERSION = imp.load_source("tns.version", "tns/version.py").VERSION
setup(
version=VERSION,
description='TNS= synthesis of artificial neurons using their topological profiles package',
author='Li... |
403442e166ded51df83ff6124097ee7778f8b64429a942ef437f021e7a504cfc | Python | 759 | 29 | from typing import Optional, Union
from anndata import AnnData
from matplotlib import pyplot as plt
from pathlib import Path
import os
def convert_scanpy(
adata: AnnData,
use_quality: str = "hires",
) -> Optional[AnnData]:
adata.var_names_make_unique()
library_id = list(adata.uns["spatial"].keys())[... |
a1b241a1cba43b01cfbeaac53d87a42444cc2dc8300ad90fdb01005455733461 | Python | 759 | 29 | from setuptools import setup
setup(name='Phys_Seg',
version='0.1',
description='Tool for physics-based MR segmentation',
url='https://github.com/TBD',
python_requires='>=3.6',
author='Pedro Borges',
author_email='p.borges.17@ucl.ac.uk',
license='Apache 2.0',
zip_safe=Fal... |
e2693bdc2d69d8c362c68f1f2c34d3c0f6d67c76d837ba65d11f0c4b992de257 | Python | 759 | 24 | """Tests for specparam.plts.time."""
from pytest import raises
from specparam import SpectralTimeModel
from specparam.modutils.errors import NoModelError
from specparam.tests.tutils import plot_test
from specparam.tests.tsettings import TEST_PLOTS_PATH
from specparam.plts.time import *
############################... |
4dbfe1ad7d2fcb307d9418d589e75cf5da3fafcd83efe2aca72b19daaa650c9a | Python | 760 | 22 | import sys
import os
homedir = sys.argv[1]
tmpdir = sys.argv[2]
network = sys.argv[3]
phenotype_name = sys.argv[4]
"""
this makes the features and targets in tmpdir
"""
os.system('python /{0}/preprocess.py {1} {2}'.format(homedir,network,tmpdir))
working_dir = homedir + '/results_matchedsamples_boot_null_081822/{0}_... |
4e12358a4afd055dfe6e5cc4136dcab3e6f0353220ee6dd178afac4874bf0b48 | Python | 760 | 30 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="SpliceaiAsDownstream",
motif_model_spec=dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
),
spliceai_spec=dict(t... |
a40b95552b72f6aca443f34e5099fcc7edd83abe5f6833a28ec6266f9d6550ee | Python | 760 | 29 | import sqlite3
from .db import DB
class VaccinationCoverage(DB):
def __init__(self):
super().__init__()
self.table_name = 'MP_vaccination_coverage'
self.table_desc = 'Vaccination Coverage'
self.cols = self.getcolumns()
def getcolumns(self):
cols = {
'date... |
174167bc1b0468a81ede1392306ba05331de9b1c6e6a7936af27b2e642aad3a0 | Python | 761 | 24 | """Train Mask-RCNN end to end."""
import os
# disable autotune
os.environ['MXNET_CUDNN_AUTOTUNE_DEFAULT'] = '0'
os.environ['MXNET_GPU_MEM_POOL_TYPE'] = 'Round'
os.environ['MXNET_GPU_MEM_POOL_ROUND_LINEAR_CUTOFF'] = '26'
os.environ['MXNET_EXEC_BULK_EXEC_MAX_NODE_TRAIN_FWD'] = '999'
os.environ['MXNET_EXEC_BULK_EXEC_MAX_... |
1c29222d8726cd8094f6954eb72d8a66c49ebb09ed134c79cce689c69719752a | Python | 761 | 26 | #!/usr/bin/env python
import sys,re, collections
'''convert the provided example file to only have a single positive affinity
for the best rmsd example'''
#first identify best rmsd
bestval = dict()
bestlig = dict()
for line in open(sys.argv[1]):
# 0 1a30/1a30_rec.gninatypes 1a30/1a30_ligand_0.gninatypes # 8.46937 ... |
d1cb297d90005ff6f24b1a2e9af4e643bd5b85870c442f7ddb4fb9b8f7bfb428 | Python | 761 | 25 | from __future__ import annotations
from typing import Dict, Callable
import panel as pn
def main() -> None:
from .model_inspector import model_inspector_app
from .module_inspector import module_inspector_app
from .track_viewer import track_viewer_app
from .experiment_viewer import experiment_viewer_a... |
0ebfe4c0cfc5804a6e472fea7b1c4e333c103ce23500024cd876cda7033821f4 | Python | 762 | 16 | """
VAE Template
============
This is a basic template for implementing a Variational Autoencoder in PyTorch Lightning.
A default encoder and decoder have been provided but can easily be replaced by custom models.
This template uses the CIFAR10 dataset but image data of any dimension can be fed in as long as the ima... |
27f7ff7a1ff37d3c23a08938feb6f6d4572868b1bedbf5732b03db3a3c9b14ed | Python | 762 | 28 | """
Average visual model vectors over instances of the same concept
"""
import numpy as np
import pandas as pd
import unidecode
from scipy.io import savemat
labels = pd.read_csv("stimuli.csv")
for layer in ["V1","V2","V4","IT","decoder"]:
vectors = []
words = []
cornet = np.load(f"CORnet-S_{layer}_outp... |
48acdbbc62c9b001e811ba527a3e279186ef7a2a2b6f96ec33102689ccbfc18c | Python | 762 | 24 | # -*- coding: utf-8 -*-
# Copyright (c) 2016-2018 Braintech Sp. z o.o. [Ltd.] <http://www.braintech.pl>
# All rights reserved.
"""
Module providing constants for data proxy.
Author:
Mateusz Kruszyński <mateusz.kruszynski@gmail.com>
"""
import numpy
SAMPLE_SIZES = {'DOUBLE': 8, 'FLOAT': 4}
SAMPLE_STRUCT_TYPES = ... |
61d9ca82b6b87eaa40638cf98b1b5df593d9117d4e6c7cecc4958e25b5b8e916 | Python | 762 | 20 | # Copyright (c) 2020 PaddlePaddle Authors. 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 app... |
cb68a4bd05a0efc8be9afd23a97bf4855b7e7830cefb1bf2a580872c7a7a2ccb | Python | 762 | 26 | """
Test ABF1 tags
"""
import os
import sys
PATH_HERE = os.path.abspath(os.path.dirname(__file__))
PATH_DATA = os.path.abspath(PATH_HERE+"../../../data/abfs/")
PATH_SRC = os.path.abspath(PATH_HERE+"../../../src/")
sys.path.insert(0, PATH_SRC)
import pyabf
import glob
import numpy as np
import matplotlib.pyplot as plt
... |
28254a168163dab34c4bbc9aa67766bef97fe8e3a95e289f548f39bfea89f0e2 | Python | 763 | 28 | import sqlite3
from .db import DB
class BedCapacity(DB):
def __init__(self):
super().__init__()
self.table_name = 'GA_bed_capacity'
self.table_desc = 'BED CAPACITY IN COVID CARE CENTRES'
self.cols = self.getcolumns()
def getcolumns(self):
cols = {
'date':... |
50f6f3761394f88e34a454b62dfda50623e9c15ac7ffb2624f469afacb923623 | Python | 763 | 37 | """
Perform coregistration for RSA.
"""
import sys
sys.path.append('..')
import mne
from config import fname
subject = 20
trans_tsss = fname.tsss(subject=subject, session=2, run=2)
info = mne.io.read_info(trans_tsss)
#%% coregister
coreg = mne.gui.coregistration(
subject=f'sub-{subject:02d}',
subjects_dir=fna... |
82ff0bf348f6d39c59ef39784df2e9ac0dbd8be55e86992a7238d77ed2ceef41 | Python | 763 | 29 | import sys
import pytest
import datetime
import inspect
import numpy as np
import glob
try:
# this ensures pyABF is imported from this specific path
sys.path.insert(0, "src")
import pyabf
except:
raise ImportError("couldn't import local pyABF")
@pytest.mark.parametrize("atfPath", glob.glob("data/abfs... |
83424eb3e3b57bdb59a16013095da8975b8e09a5a67363440ca804de9f5f621e | Python | 763 | 1 | import numpy as np
import pandas as pd
import time
import random
# [1] data preprocessing #
df = pd.read_csv('./data/GSE72056_melanoma_single_cell_revised_v2.csv', index_col=0) # dimension = (genes x cells)
#df1 = df.iloc[0:3,:]
print("starting preprocessing:")
n1 = df.shape[0] # row genes
n2 = df.shape[1] # col ce... |
dc82547b57c561a202b218dbbea7b822d9da003986226c6f2ce2a8046edba427 | Python | 763 | 25 | """
Fixed bin size and fixed bin number algorithms are already fully covered in ibsi_1_test.py and elsewhere.
"""
import os
from mirp import extract_features
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
def test_pyradiomics_fbs():
data = extract_features(
write_features=False,
export... |
ef59fa514a0b45885b17493db3a402d281ecb776492490e6865667bf99cffc2c | Python | 763 | 24 | import os.path
import json
import numpy as np
import pandas as pd
from input_parameters import input_root_path, processed_metadata_filepath
def summarize(df, class_col_name = "category"):
table = df.groupby([class_col_name]).size().reset_index(name='counts')
print(table)
print(table.to_markdown())
... |
32d36df2ee5a25114fedc7774ead7de66308d4b86db4b1a0cadbc223e832c8ac | Python | 764 | 27 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._dimension import Dimension
from ._domain import Domain
from ._labelfont import Labelfont
from ._legendgrouptitle import Legendgrouptitle
from ._line import Line
from ._tickfont import Tickfont
from . import legendgrouptitl... |
5e4dd4924711ce3e98e74e62d96479295093f6f379c638f256588dc43c8cd174 | Python | 764 | 25 | import os
import joblib
import yaml
def preprocess_data(dataset, config):
X = dataset[config['features']]
y = dataset[[config['outcome']['time'], config['outcome']['event']]].values
y[:, 1] = 1 - y[:, 1] # inverse to censored
return X, y
def save(save_dir, model, c_index, params, data):
os.make... |
7626c99c6b3722b522fe86c08280848e3ad4b19063032433b31348e58a154205 | Python | 764 | 32 | __author__ = 'quentin'
from ethoscope.tracking.roi_builders import ImgMaskROIBuilder
from ethoscope.tracking.cameras import MovieVirtualCamera
from ethoscope.tracking.monitor import Monitor
from ethoscope.tracking.trackers import AdaptiveBGModel
from ethoscope.tracking.interactors import SleepDepInteractor
from ethos... |
898b1813595af3709eb7d0a040277bbbb4e0628d3ff19d7de01f4d5d154cecad | Python | 764 | 23 | from PIL import Image
from lightly.transforms.msn_transform import MSNTransform, MSNViewTransform
from .. import helpers
def test_view_on_pil_image() -> None:
single_view_transform = MSNViewTransform(crop_size=32)
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
assert ou... |
c64dd3e11469cfd7cdbb0bb8986a07fc8ba14275369782a4103aecbc661368b8 | Python | 764 | 30 | '''Util functions useful for general purposes'''
import numpy as np
from numpy import sqrt
def get_random_point(D=1.0):
'''
Get 3-d coordinates of a new random point.
The distance between the produced point and (0,0,0)
is given by the value D.
'''
# pylint: disable=assignment-from-no-return
... |
b74da03f302192218a698c0a7f71a0fdb1f2af5554acb7f094e8d34c8fc1bc81 | Python | 765 | 29 | """The module describes the ``hostname`` job metrics plugin."""
import logging
from . import InstrumentPlugin
log = logging.getLogger(__name__)
class HostnamePlugin(InstrumentPlugin):
"""Gather hostname"""
plugin_type = "hostname"
def __init__(self, **kwargs):
pass
def pre_execute_instru... |
5ff9f111f07929d30115085d41317a0893dfec74594912cc51efb29d5d97a7f5 | Python | 766 | 26 | from setuptools import setup
def read_requirements(path):
with open(path, "r") as f:
return [line.strip() for line in f if not line.isspace()]
with open("README.md", "r", encoding="UTF-8") as fh:
long_description = fh.read()
setup(
name="spaco-release",
version="0.2.0",
python_requires... |
07a8d712c17e5666287d42658552be71e9344437ac0a3cc54d1551c1592c67fa | Python | 767 | 27 | import os
import tns
def run():
# Cell to use as input for the diameter model
dir_path = os.path.dirname(os.path.realpath(__file__))
input_cell = os.path.join(dir_path, '../test_data/bio/C220197A-P2.h5')
# Outputcell to be saved
output_cell_name = os.path.join(dir_path, './local/Neuron_diam')
... |
92d813b8088d60beaaa888af11218478ef31c23af82a93cf69557c67aee4061f | Python | 767 | 29 | import sqlite3
from .db import DB
class DBProperties(DB):
def __init__(self):
super().__init__()
self.table_name = 'Metadata_DB_Properties'
self.table_desc = 'Tracks key metadata for the database'
self.cols = self.getcolumns()
def getcolumns(self):
cols = {
... |
31ea1e6eee286a0f30069fc68c2a2d7de9add7bd66384be32eacf736b647002b | Python | 768 | 26 | """
This module is the main script to launch the training process
"""
import argparse
import logging
import torch
from training_manager.training_factory import TrainingFactory
from config_manager.config_manager import ConfigManager
if torch.cuda.is_available():
logging.getLogger().setLevel(logging.INFO)
else:
... |
c6fe01f5944b78b06b46da4031b5f27f0b3123db118633f2f1be2bac79be5a11 | Python | 768 | 28 | # Generated by Django 3.0.4 on 2020-03-24 15:27
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0066_auto_20200324_1626'),
]
operations = [
migrations.AlterField(
model_name='mushroomspot',
name=... |
f21e1c6d28e93e659a14b006b89e91d1149aab0ab5b63a56bd2cd17ca0c1b4d1 | Python | 768 | 25 | import os
import nibabel as nib
import matplotlib.pyplot as plt
import shutil
from tqdm import tqdm
dp = 'PATH_TO_KITS_DATA_FOLDER' # should be .../kits21/kits21/data
tp = os.path.join(os.environ['OV_DATA_BASE'],
'raw_data', 'kits21')
timp = os.path.join(tp, 'images')
tlbp = os.path.join(tp, 'labels... |
f6cd620b3a7a6e59999b3e6e548c0cbd7ff00630e27a994ef4f8252ad7d5a912 | Python | 768 | 35 | """Base class of classifier for the number detection
"""
import torch
from torch import nn
class Classifier(nn.Module):
"""Base classifier class
"""
def __init__(self, params):
super().__init__()
@staticmethod
def getDataLoaders():
raise NotImplementedError()
def forward(sel... |
16b1b2846676a041e4e2262b3d2df7f440a6eea1e761671704f32d1a39601eba | Python | 769 | 32 | from shapely.geos import geos_version_string as shapely_geos_version
from pygeos import geos_capi_version_string
import warnings
# shapely has something like: "3.6.2-CAPI-1.10.2 4d2925d6"
# pygeos has something like: "3.6.2-CAPI-1.10.2"
if not shapely_geos_version.startswith(geos_capi_version_string):
PYGEOS_SHAPE... |
19cf4e1339ce002c447ec138995d0cdc51fc52950c151bd5dd69c4360af276dc | Python | 769 | 25 | import os
import random
import pickle as pkl
import imodelsx
import json
from typing import List
import tqdm
if __name__ == '__main__':
distribution_pairs = json.load(open('benchmark.json'))
all_h2score = []
for i, d in enumerate(tqdm.tqdm(distribution_pairs)):
print('examples', len(d['positive_sa... |
c315ae1afc0aec484abffa6c30454967a2dc27c382b3f4cc2459744255a0a7e8 | Python | 769 | 22 | import lammps.PES as PES
from collections import OrderedDict
import torch
def jit_pes():
init_pes=PES.PES()
state_dict = torch.load("EANN.pth",map_location='cpu')
new_state_dict = OrderedDict()
for k, v in state_dict['eannparam'].items():
if k[0:7]=="module.":
name = k[7:] # remove `... |
faee2f62a4c51cb6a97055f92bc0f9816b3eb665ca3583d28d4226d564e1804c | Python | 769 | 23 | import torch.nn as nn
import torch.nn.functional as F
class BackwardsOnlyLeakyReLU(nn.Module):
"""
Acts like a ReLU in the forwards direction, but in the backwards
direction, it acts like a leaky ReLU with a negative slope of
`slope_on_negatives`.
"""
def __init__(self, slope_on_negatives):
... |
002ea03f2e9c24c998454783e72e1182046f9bad58a51df4e0a70a5b20325921 | Python | 770 | 1 | import numpy as np
import pandas as pd
import time
import random
# [1] data preprocessing #
df = pd.read_csv('./data/GSE72056_melanoma_single_cell_revised_v2_gene_filtering.csv', index_col=0) # dimension = (genes x cells)
print("starting preprocessing:")
n1 = df.shape[0] # row genes
n2 = df.shape[1] # col cells
ce... |
080b94ba584650b2e27d7d5f509adbad501f00233d4628941cd752efe7ebfb6a | Python | 770 | 28 | import unittest
import numpy as np
from scipy import integrate as scipy_integrate
from spitfire.time.integrator import odesolve
class TestComparisonToSciPyIntegrate(unittest.TestCase):
def test_lotka_volterra(self):
a = 1.
b = 0.1
c = 1.5
d = 0.75
def rhs(t, x):
... |
1ef7486dd9fdb8423ebe058a87a2284205d1f15da32978fcf10fd811d9e217e6 | Python | 770 | 37 | import seaborn as sns
from matplotlib import pyplot as plt
sns.set_style('whitegrid')
stimes = """27:09.25
23:05.32
15:00.63
13:05.17
11:26.52
12:06.25
11:37.36
11:35.57
11:38.39"""
def to_secs(t):
m, s = map(float, t.split(":"))
return s + 60 * m
times = [to_secs(t) / 60. for t in stimes.strip().split()]
... |
8fde7468eeb2a415c298f4a29ceb4d9fe108019105878105edf358fbfb577ced | Python | 770 | 24 | """Tests for specparam.modes.paramdef."""
from collections import OrderedDict
from specparam.modes.paramdef import *
###################################################################################################
###################################################################################################
... |
16c3b67376c8bc7844a41d0dc81039ee8513720f23f9f0365ac32d9067d30e02 | Python | 771 | 27 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._cells import Cells
from ._domain import Domain
from ._header import Header
from ._hoverlabel import Hoverlabel
from ._legendgrouptitle import Legendgrouptitle
from . import cells
from . import header
from . import hove... |
3cdfa13edb5506814a676709477a3cd9d2ce7456f75129ba230924ae1c248983 | Python | 772 | 27 | """Manipulate Python warnings."""
import logging
import warnings
_wlog = logging.getLogger('py.warnings')
_wlog.addHandler(logging.NullHandler())
def _warn(message, category=None, stacklevel=1, source=None, **kwargs):
"""Redefine the warning function."""
if category is not None:
category = type(cate... |
2df9eadd42278907e3532cf0708d2c4ca3ecdc51bc731d55589ad9e38f45367b | Python | 773 | 35 | """Private registry kernel package (architecture memo 6.1; never public API).
One private kernel; many typed public domain doors; no public universal
registry. Domain doors import from here; users import the doors.
"""
from __future__ import annotations
from .kernel import (
TORCHLENS_PROVIDER,
MissingProvid... |
fdbd149ee5049422830e69359b44e7a5fafd73b15ec139744fabf4189cebefbb | Python | 773 | 30 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
fraction_of_standard_epoch = 5509 / 162706
msp.decay_per_epoch = 0.9 ** fraction_of_standard_epoch
msp.architecture["num_motifs"] = 82
msp.architecture["motif_model_spec"] = dict(
type="PSAMMotifModel",
motif_spe... |
30b599ff9f2bff1410b4b4bd4db4efb566fc8dca9396470de13e3e2b7b104701 | Python | 774 | 28 | import json
def fix_ids(objs):
objects_list = json.loads(objs.to_json().replace('\"_id\"', '\"id\"'))
return objects_list
def td_format(td_object):
seconds = int(td_object.total_seconds())
periods = [
('year', 60*60*24*365),
('month', 60*60*24*30),
('day', ... |
3a5a704d99150927112c1df0de696f021c77ce05b0ef916b95c1e5df3a89e21f | Python | 774 | 36 | #!/usr/bin/env python
"""
Convert from pileup file to interval index file.
usage: %prog <options> in_file out_file
"""
import optparse
from bx.interval_index_file import Indexes
def main():
# Read options, args.
parser = optparse.OptionParser()
options, args = parser.parse_args()
input_fname, outp... |
a73d4894df45bbcb6e5ca3518f04bd821195c82e678b7bf6f32c2093fe80d708 | Python | 774 | 24 | """Tests for specparam.plts.event."""
from pytest import raises
from specparam import SpectralTimeEventModel
from specparam.modutils.errors import NoModelError
from specparam.tests.tutils import plot_test
from specparam.tests.tsettings import TEST_PLOTS_PATH
from specparam.plts.event import *
#####################... |
1d3d82676c8a075ca569205416d7349f2165a213f39a373f46e6af0e7dd473b2 | Python | 775 | 23 | import argparse
import pandas as pd
from statsmodels.stats.multitest import fdrcorrection
parser = argparse.ArgumentParser()
parser.add_argument("--coords", action = "store",
help = 'table with dapeak results')
parser.add_argument("--out", action = "store", help = "output file")
parser.add_argument("--alpha", action... |
2745c1c6a1229aae06af2c2b9d56ba13af14e2aa621f61ef21cc3e751024354c | Python | 775 | 30 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
fraction_of_standard_epoch = 64764 / 162706
msp.decay_per_epoch = 0.9 ** fraction_of_standard_epoch
msp.architecture["num_motifs"] = 82
msp.architecture["motif_model_spec"] = dict(
type="PSAMMotifModel",
motif_sp... |
5a3afe85bbc5fa143b0bd2e1c6c9300db34170d7a05d26c6f57501487453e9e3 | Python | 775 | 35 | from modular_splicing.models_for_testing.models_for_testing import (
EndToEndModelsForTesting,
)
def create_series(name, path, donor_seed, model_seeds, density):
return EndToEndModelsForTesting(
name=f"{name} d={donor_seed}",
path_prefix=path,
seeds=[x * 1000 + donor_seed for x in mode... |
664f86e67d46bb8891f62a7af0f2466e7487be623861ada9fff66e603b3f6356 | Python | 775 | 26 | import sqlite3
from .db import DB
class TMinusOneCumulativeSummary(DB):
def __init__(self):
super().__init__()
self.table_name = 'KL_cumulative_summary_t_minus_one'
self.table_desc = 'Summary of COVID-19 cases till T Minus One'
self.cols = self.getcolumns()
def getcolumns(se... |
ce1595da4cbbe325897146cbe831d799bcbad9e73e3e1b7bef062f9bda9cbf0a | Python | 776 | 33 | import sys
import unittest
sys.path.append("..")
from llspy import parse
class FilenameTests(unittest.TestCase):
def setUp(self):
self.example_name = (
"cell5_ch0_stack0006_488nm_" "0005280msec_0020936553msecAbs.tif"
)
self.dict = {
"abstime": 20936553,
... |
fffeb716a5c5aa13534b24c6623e32631f190e87b0d3fb821c75a1265ac84680 | Python | 776 | 31 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in May 2023
"""
import numpy as np
from scipy import sparse
from sknetwork.path.distances import get_distances
def breadth_first_search(adjacency: sparse.csr_matrix, source: int):
"""Breadth-first ordering starting from some node.
Parameters
---... |
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