sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c38d21c6bd1227ddafb070c8bac94cc44de7ca6be76c302efb7f9ded1dbb4d5a | Python | 1,010 | 15 | from __future__ import absolute_import
from . import utils
from . import paths
from . import setup_hd_glio
print("\n########################")
print("If you are using HD-GLIO, please cite the following papers:\n")
print("Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, Brugnara G, Schell M... |
e8c4ef776244dc9a619693ac5b0c93fbdd28f3c40fb941ca3d2b36d2bcb09a85 | Python | 1,010 | 27 | """Test functions for specparam.params.converter."""
from specparam.params.converter import *
###################################################################################################
###################################################################################################
def test_base_param_con... |
a50b9e17ea7e47a5578479d28a78ec11dc3c1b702299b66c96930b262bd93745 | Python | 1,011 | 33 | import os
import argparse
import ont_fast5_api.multi_fast5 as ml
import sys
def main():
parser = argparse.ArgumentParser(description="you should add those parameter")
parser.add_argument('-i', required=True, type=str, help='fast5 data dir')
parser.add_argument('-o', default=None, type=str, help='save id fi... |
2cd0cec90e559e4dccbd4755da6e8e3f11fc8185c37ce3f3d2bc7f713a06f63f | Python | 1,012 | 40 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
import os.path
import sys
import pandas as pd
import argparse
DEFAULT_THRESHOLD = 12.
def main():
parser = argparse.ArgumentParser(description='Selects transients based on amplitude criterium.')
parser.add_argument('files', nargs='+',... |
b097715b9c4c2f2e246ba4403363b90a33372543d7d3ed066eae5a0c226f02c3 | Python | 1,012 | 36 | 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["motif_model_spec"] = dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
)
msp.architecture["sparse_spec"] = dict(type="S... |
d087e502d776c3abad2cb42379f63cd12d69122dbbee8ff23e06863dacf0d81e | Python | 1,012 | 26 | from set_transformer_modules import *
class SetTransformer(nn.Module):
def __init__(self, dim_input, num_outputs, dim_output, num_inds=32, dim_hidden=128, num_heads=4, ln=False, num_sabs=2):
super(SetTransformer, self).__init__()
if num_sabs == 2:
self.enc = nn.Sequential(
... |
03ad6157a1bb897e6276202fe2e99a33880252ada1fa881c40d2babd9071ec41 | Python | 1,013 | 46 | #!/usr/bin/env python
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.sparse import *
from scipy.io import mmread
import argparse
parser = argparse.ArgumentParser(description='Plot weight histogram.')
parser.add_argument('--input', type=str, default='lastrun/input_con.0.wmat', he... |
ba7b53be42bcb7cce008a448e3cc3c45c7d86e614a24a2f3b757a2373f52a9d5 | Python | 1,013 | 43 | #!/usr/bin/env python
import sys
from Bio import SeqIO
def main():
input_file = open(sys.argv[1], "rU")
output_file = open(sys.argv[2], "w")
filtered_records = []
output_file.write("##gff-version 3\n")
for record in SeqIO.parse(input_file, "fasta"):
seq_start = 1
seq_id = recor... |
ff22a628f1d95d52b0879028999b8ea9004fe0a324aa5f0a446d96f11364c76a | Python | 1,013 | 26 | '''
Copyright 2021 Twente Medical Systems international B.V., Oldenzaal The Netherlands
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 requir... |
034ddf8c7cd6e3b8732010b05dbe376c0a9472f85d822529a5ebf89effe1499a | Python | 1,014 | 30 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
This script:
Loads the labeled dataset from labels.pkl.
Splits images into training and testing datasets.
Moves one image per person (if at least 4 exist) to the testing dataset.
"""
import pickle
import os
# Load the labeled dataset
with open('pickle_labels/labels.... |
0dea743f839d131f9788c916945546e65207ca9f9ee78acd02e957cbe7af0e41 | Python | 1,014 | 36 | import glob
import sys
import os
from setuptools import setup, find_packages, Extension
from Cython.Distutils import build_ext
import pysam
test_module_suffix = os.path.dirname(os.path.abspath(__file__)).split(os.sep)[-1]
test_module_name = "PysamTestModule_{}".format(test_module_suffix)
pysam_libraries = pysam.get... |
7a6ed64d893e71aecbe765327420ed75aef97a7d2b9195c3a6214d04b80247c8 | Python | 1,014 | 17 | from .base_options import BaseOptions
class TestOptions(BaseOptions):
def initialize(self, parser):
BaseOptions.initialize(self, parser)
parser.add_argument('--results_dir', type=str, default='../results/', help='saves results here.')
parser.add_argument('--phase', type=str, default='test'... |
ba9403791ae763a49e195d81415f83c5f032277649f8014f20728caf4cab9ca9 | Python | 1,014 | 39 | from os import path
import setuptools
path_to_repo = path.abspath(path.dirname(__file__))
with open(path.join(path_to_repo, "readme.md"), encoding="utf-8") as f:
long_description = f.read()
required_pypi = [
"numpy",
"scikit-learn",
"pandas",
"tqdm",
"dict_hash", # required for caching
"... |
f51c2234fb89f94edd67500f9f0d2bd84ab377e11a71254abde5f9fe6cde19c7 | Python | 1,014 | 28 | import unittest
from os.path import join, abspath
from numpy.testing import assert_allclose
from tests.tabulation.adiabatic_burke_schumann.rebless import run
from spitfire.chemistry.library import Library
from spitfire.chemistry.ctversion import check as cantera_version_check
if cantera_version_check('atleast', 2, ... |
e0c65f2b291461236b27e949f98e13e0522c772e7ccb8c5c37a4a21ec04a7dd7 | Python | 1,015 | 26 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from ultralytics.models.yolo.detect import DetectionValidator
__all__ = ["NASValidator"]
class NASValidator(DetectionValidator):
"""Ultralytics YOLO NAS Validator for object detection.
Extends DetectionValidator from the Ultralytics models... |
4c5d8aa26be804484965fc69b17f4146511d82fb9caba6ed19a035e94a64b752 | Python | 1,016 | 27 | __author__ = 'cenwan'
#-----Please download the 'GOTerm_List.txt' file via http://bioinfadmin.cs.ucl.ac.uk/downloads/FFPredGAN/
with open(".../GOTerm_List.txt") as ScriptFile:
GOTermsRaw=ScriptFile.readlines()
GOTerms=[]
for index in range(0,len(GOTermsRaw)):
GOTerms.append(GOTermsRaw[index].strip())
#-----... |
4b02880032538f20f691530da8115d2543c48631d83dd024dc69ce93d27c3801 | Python | 1,017 | 27 | # 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... |
a541a81bcfecef58f33e02483b6a5355859b59413250d9bdc8c9362b3a4b2090 | Python | 1,017 | 38 | import random
def create_sublists(l, size: int):
"""
Divides list into sublists with given size, e.g. [1, 2, 3, 4, 5] --> [[1, 2], [3, 4], [5]]
Last sublist might conatin less eleents, if len(l) not dividable by size.
:param l: list
:param size: size of sublists
:return: list with sublists
... |
c68da5bb2373bd7bdda14761fdf5a69800c3f26435976f5eca7ccee3bf483357 | Python | 1,017 | 29 | import os
from shutil import copy
def check_dir_exists(path):
if not os.path.exists(path):
os.mkdir(path)
def split_by_name(dir_path, real_save_path, fake_save_path):
check_dir_exists(real_save_path)
check_dir_exists(fake_save_path)
if not os.path.exists(real_save_path):
os.mkdir(real... |
47b7e754933806ae0c08be0daa85b30c790af10e6b59ecbe9975fc08943c4c4f | Python | 1,018 | 30 | import argparse
import os
import torch
import numpy as np
from transformers import *
from src.data.Batcher import Batcher
from src.utils.Config import Config
from src.utils.util import device
from src.adapet import adapet
from src.eval.eval_model import test_eval
if __name__ == "__main__":
parser = argparse.Argum... |
91d44cd18032c3245113d641be22a1247020fafa9d067c683bed29fa4928cf66 | Python | 1,018 | 38 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="PreprocessedSpliceAI",
preprocessor_spec=dict(type="Identity"),
spliceai_spec=dict(type="SpliceAIModule", output_size=3),
)
msp.data_spec = dict(
type="H5Dataset",
datapo... |
e5a17b08843011d2f6ed4ee827d007c79ab2b1beaa0ad65eab28af4c482582d9 | Python | 1,018 | 29 | import argparse
import os
import torch
import numpy as np
from transformers import *
from src.data.Batcher import Batcher
from src.utils.Config import Config
from src.utils.util import device, ParseKwargs
from src.adapet import adapet
from src.eval.eval_model import dev_eval
if __name__ == "__main__":
parser = ar... |
3d6d0c4b509c982e76439ec08f73fbaa6c58f099774cfd006681e0c9e8ba5359 | Python | 1,019 | 35 | import pandas as pd
def format_time(minutes):
days = int(minutes // (24*60))
remainder = minutes % (24*60)
hours = int(remainder // (60))
remainder2 = remainder % (60)
minutes = int(round(remainder2))
return f'{days}D:{hours}Hr:{minutes}m'
def estimate(test_ism_time,train_mdig_time):
... |
5ba54932decd9288b9eb0ad91d04c1299d2537b753c226a93e87518869d6ac87 | Python | 1,021 | 35 | 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["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature... |
863268995a9b36770f63d02bfadfd501bbb94047e9ed8a2b522741b1b480733c | Python | 1,021 | 30 |
# -*- coding: utf-8 -*-
#
#
# RectiPy software framework for recurrent neural network training in
# Python. See also: https://github.com/pyrates-neuroscience/RectiPy
#
# Copyright (C) 2022 Richard Gast.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Pub... |
b5caa82fe75d1a7d4e9a1006ded76afef405816e0c406c034d3d8a6b116e1743 | Python | 1,021 | 33 | #!/usr/bin/env python
import os
import sys
import pandas
from deepspeech_training.util.downloader import maybe_download
def _download_and_preprocess_data(data_dir):
# Conditionally download data
LDC93S1_BASE = "LDC93S1"
LDC93S1_BASE_URL = "https://catalog.ldc.upenn.edu/desc/addenda/"
local_file = ma... |
ead446d4a870c580b9606cc1844a4f3bef4fef1122fb39dcfcaa6a0512a74570 | Python | 1,022 | 34 | import numpy as np
from .hsmm_model import HSMM
def fill_gaps_in_probability(p):
[nonzero_indices] = np.nonzero(p)
starts, ends = nonzero_indices[:-1], nonzero_indices[1:]
gap_size = ends - starts > 1
starts, ends = starts[gap_size], ends[gap_size]
pnew = p.copy()
for start, end in zip(starts... |
136b509287874ae224181de418d1cd6b6f75a11a1587d08af3f19cb46362197d | Python | 1,023 | 36 | #!/usr/bin/env python
"""Tests for `stlearn` package."""
import unittest
import numpy.testing as npt
from stlearn.classes import Spatial
from .utils import read_test_data
adata = read_test_data()
class TestSpatial(unittest.TestCase):
"""Tests for `stlearn` package."""
def test_setup_spatial(self):
... |
d227cf7333b679aab714114ff05d7f06d2a64aafb9d8df64adaa6f859c54cc72 | Python | 1,023 | 29 | # 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... |
236d2c202af0c70b7b8a60f0b7b762eb5dafb941880474abe5e420967fa92355 | Python | 1,024 | 39 | from celery import Celery
from celery import Task
from flask import Flask
from flask import render_template
def create_app() -> Flask:
app = Flask(__name__)
app.config.from_mapping(
CELERY={
"broker_url": "redis://localhost",
"result_backend": "redis://localhost",
"... |
51aaa0b9f496cb1e44c0c8e3e56c4375546f623b17a73224013161dd8e09aea9 | Python | 1,024 | 25 | from setuptools import setup, find_packages
from Cython.Build import cythonize
import numpy as np
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setup(
name='biss',
version='1.0.0',
author='Martin Weigl, Adrian Slowak, Emma Kiemeyer, Ines Langthallner, Daniel Ritzber... |
d719f1354be6df49beea97fc2a2e924e5648949eab0b1b170243aec3f3f5730e | Python | 1,024 | 52 | # Create your tasks here
from demoapp.models import Widget
from celery import shared_task
@shared_task
def add(x, y):
return x + y
@shared_task
def mul(x, y):
return x * y
@shared_task
def xsum(numbers):
return sum(numbers)
@shared_task
def count_widgets():
return Widget.objects.count()
@sha... |
edf42e96f2b317dc840652e27bb7cbe46d59772886e030056506782e2c7deaab | Python | 1,024 | 32 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""tests for regression API"""
import unittest
from sknetwork.data.test_graphs import test_bigraph, test_graph, test_digraph
from sknetwork.regression import *
class TestAPI(unittest.TestCase):
def test_basic(self):
methods = [Diffusion(), Dirichlet()]
... |
0fcb667d2b426a1eb187d38b847f6658bafbeeae1a921839f3460724c2a9ab76 | Python | 1,025 | 33 | import codecs
import locale
import re
import sys
BOMS = [
(codecs.BOM_UTF8, 'utf8'),
(codecs.BOM_UTF16, 'utf16'),
(codecs.BOM_UTF16_BE, 'utf16-be'),
(codecs.BOM_UTF16_LE, 'utf16-le'),
(codecs.BOM_UTF32, 'utf32'),
(codecs.BOM_UTF32_BE, 'utf32-be'),
(codecs.BOM_UTF32_LE, 'utf32-le'),
]
ENCOD... |
1ff536341c06fc42c0f76af8580619b15d91a878c0d215d4565410bd5dc86837 | Python | 1,025 | 42 | import logging
from abc import (
ABCMeta,
abstractmethod,
)
from collections.abc import (
Iterable,
Mapping,
)
from typing import (
TYPE_CHECKING,
Union,
)
from galaxy import exceptions
if TYPE_CHECKING:
from galaxy.model import (
DatasetCollection,
DatasetCollectionElement... |
67909aaf02fc3f54f2b7cb2cadda33348cd2e71ccd5685454461114dd7164fa3 | Python | 1,025 | 24 | import argparse
from src import datasets
def main(args):
pc_builder = datasets.PCBuilder(args.root, args.model, args.layer, args.dataset, args.ntau, args.nlat, args.nspan, args.scale, args.n_pca)
pc_builder.fit_and_transform(args.batch)
if __name__ == "__main__":
parser = argparse.ArgumentParser(descrip... |
ca586bbcfb948e113a0b90add7d5242e8c4212972697eff23a0b13f45485c45f | Python | 1,025 | 34 | import sqlite3
from .db import DB
class Overview(DB):
def __init__(self):
super().__init__()
self.table_name = 'GA_overview'
self.table_desc = 'CASE INFO'
self.cols = self.getcolumns()
def getcolumns(self):
cols = {
'date': 'DATE NOT NULL PRIMARY KEY',
... |
92c04076506fbc96f68e781c9c779982be5d38817b4a49c8f2ea3d14dd53b1c8 | Python | 1,026 | 31 | from numpy.testing import assert_array_equal
from pyriemann.channelselection import ElectrodeSelection, FlatChannelRemover
def test_electrodeselection(get_covmats, get_labels):
"""Test transform of channelselection."""
n_matrices, n_channels, n_classes = 10, 30, 2
covmats = get_covmats(n_matrices, n_chann... |
a6b2abcdf43965958e359e31bc5c373b22050fbf2e470efed72e6fd8677356aa | Python | 1,026 | 29 | import sys
import os.path as op
import mne
import pandas as pd
import scipy
import numpy as np
from zapline_iter import zapline_until_gone
try:
base_dir = sys.argv[1]
pipeline = sys.argv[2]
step_path = sys.argv[3]
except:
print("incorrect arguments")
sys.exit()
subjects=pd.read_csv(op.join(base_d... |
ebb2ad0ba3624f7156a203da8602065008ce81a0a790fb79f21e3f1d7463176c | Python | 1,026 | 43 | """Util functions."""
import cPickle
import pandas as pd
__author__ = "Gianluca Corrado"
__copyright__ = "Copyright 2016, Gianluca Corrado"
__license__ = "MIT"
__maintainer__ = "Gianluca Corrado"
__email__ = "gianluca.corrado@unitn.it"
__status__ = "Production"
def feature_size(store_name):
"""Number of featur... |
c1a9dca168497c6b7d7f86f418aba3f34edc2113821abf05b47b3674b882eac6 | Python | 1,027 | 29 | # -*- coding: utf-8 -*-
'''
Copyright 2021 Twente Medical Systems international B.V., Oldenzaal The Netherlands
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/LI... |
5021a8eba0fa4b388bc200f9449a1642dbd05d42d61fbe73fa232d6a435d3267 | Python | 1,028 | 29 | # -*- coding: utf-8 -*-
'''
Copyright 2021 Twente Medical Systems international B.V., Oldenzaal The Netherlands
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/LI... |
259d63467a357aa67d187de050e3b2dffa02e7578cbeb223f0c6f933aa590343 | Python | 1,029 | 38 | import json
import os
from caper.caper_labels import CaperLabels
def test_create_file(tmp_path):
cl = CaperLabels()
backend = 'my_backend'
custom_labels = tmp_path / 'my_custom_labels.json'
custom_labels_dict = {'hello': 'world', 'good': {'bye': 'bro'}}
custom_labels.write_text(json.dumps(custo... |
3fb78644fe19faf05f4fe6c45ec39c41da14602937d5a927c4f4020bffeddf9f | Python | 1,029 | 33 | import sys
sys.path.append("models_architectures/")
from wrapper_prot_bert_BFD import WrapperProtBertBFD
from transformers import AutoTokenizer, AutoModel
import os
import torch
Model_name_on_hugging_face = "Rostlab/prot_bert"
d_model = 1024
saving_directory = "data/models/pre_trained_models/"
model_directory = "Prot... |
4d2058d87b22bb430b1b968c8e09b972436db3514fd40b19e4ccf89d8ba0ba72 | Python | 1,029 | 40 | import numpy as np
def specificity_index(img_sem, img_syn, masker, threshold=4):
""" Compute the sensitivity score which is the ratio: np.log10(x_sem/s_syn).
Args:
- img_sem:
- img_syn:
Returns:
- sensitivity_score: float
"""
def f(a, b):
a = max(a, 0)
b = ma... |
4d558d38ab70ae7bdbed669c5ce958d392da1952ae883d66d6332ed9731ae6a8 | Python | 1,029 | 27 | """Post-build smoke test run by cibuildwheel against each built wheel.
Imports the installed wheel (cibuildwheel runs this from outside the source
tree, so ``import pyrosm`` resolves to the wheel, not the repo) and parses the
bundled ``test_pbf`` extract end to end, exercising the compiled Cython
pipeline. Kept depend... |
628f334b4cedd031510933613e5207a542d35961d5a543eb3183eaea4ed74144 | Python | 1,029 | 39 | #!/usr/bin/env python
"""Tests for `stlearn` package."""
import unittest
import stlearn as st
import scanpy as sc
from .utils import read_test_data
import numpy as np
global adata
adata = read_test_data()
class TestPSTS(unittest.TestCase):
"""Tests for `stlearn` package."""
def test_PSTS(self):
... |
a77aed2374c736c5621f510c77feedf5b99d4b1b9ca8e2c4d5add9244a3aed7f | Python | 1,030 | 29 | import numpy as np
import seqpro.rag as r
from seqpro.rag import Ragged
def test_concatenate_ragged_axis_prepend_regular():
# prepend a size-1 pad per group (the prepend_pad_itv use case)
base = Ragged.from_offsets(
np.array([10, 11, 12], np.int32), (2, None), np.array([0, 2, 3], np.int64)
) # [[... |
e50dc47bef7536588523d40c6bba797bcc2b7a39a2c3e66efd42b478b3a4605d | Python | 1,030 | 29 | """
This module create TransformerClassifContact
"""
# pylint: disable=W0223
import torch.nn as nn
class TransformerClassifContact(nn.Module):
"""
This class define a Transformer model that classify each couple of amino acid of a protein
"""
def __init__(self, transformer_embedder, dropout):
... |
080941c8fc90430165d3fa204e83c00220cf4e60af6c7632e856dac072a490fc | Python | 1,031 | 31 | from setuptools import setup, find_packages
import os
setup(
# Needed to silence warnings (and to be a worthwhile package)
name='ovseg',
url='https://https://github.com/ThomasBudd/ovseg',
author='Thomas Buddenkotte',
author_email='thomasbuddenkotte@googlemail.com',
# Needed to actually package ... |
221bece523e22572575b3451ea79ffcf4b81a6a8c863081ff2d1d26588b00a7d | Python | 1,031 | 34 | #!/usr/bin/env python
import sys
from zipfile import ZipFile
from pathlib import Path
suffix_out = '.contents'
def create_contents(f):
file_zip = Path(f)
# remove suffix_out if present
if len(file_zip.suffixes) > 0 and file_zip.suffixes[-1] == suffix_out:
file_zip = Path(file_zip.stem)
file_o... |
5576a43ca545f5d24c54c007ab85379f17c489ef2183ff36e660ec39250009ef | Python | 1,031 | 35 | import sqlite3
from .db import DB
class DistrictCasesTable(DB):
def __init__(self):
super().__init__()
self.table_name = 'WB_district_cases'
self.table_desc = 'West Bengal district wise case info'
self.cols = self.getcolumns()
def getcolumns(self):
cols = {
... |
63720f02cf0ea52da6917688bfc7e2601f486d4848374752c6c2f386c58676c9 | Python | 1,031 | 33 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
import wx
import wx.html # wx.html2 causes ImportError on some Ubuntu installations
from .frame import EelbrainFrame
class TextFrame(EelbrainFrame):
"Read-only text frame, shows itself"
def __init__(self, parent, title, text, *args, **kwargs):
... |
b6ce97d1213f389126e73f7402b1c58ac29e4dc19eca5048ed57b6e9ce1669df | Python | 1,031 | 33 | class Tokenizer(object):
"""A class to turn arbitrary inputs into integer classes."""
def __init__(self):
# the default class for an unseen entry during test-time
self._data = {"unk": 1}
self.num_classes = 1
self.train = True
self.unknown = []
def __call__(self, ite... |
b75746ece5fea28a4b54b72005069d2c5d554549ddb2cae04177a452d07aa1e8 | Python | 1,031 | 36 | # -*- coding: utf-8 -*-
# @Author : Yue Liu
# @Email : yueliu19990731@163.com
# @Time : 2021/11/25 11:11
import torch
import random
import numpy as np
from .kmeans_gpu import kmeans
from sklearn.cluster import KMeans
from .evaluation import evaluation
def k_means(embedding, k, y_true, device="cpu"):
"""
... |
fa32257c97c1dfd009c74ee4b2e227df8f267b2038d7f482adc1838536a68b86 | Python | 1,031 | 28 | import morphio, neurom, tmd
from tns import extract_input
from tns.generate import diametrizer
k0 = neurom.load_neuron('../test_data/bio/C220197A-P2.h5')
model0 = extract_input.from_diameter.model(k0)
n0 = morphio.mut.Morphology('../test_data/bio/C220197A-P2.h5')
diametrizer.build(n0, diam_method='M1') # Constant ... |
94e1a9c21bba5f4d1dd9955dc1fbfa4ef3f300755b8999df8b6c3492eb4eb56c | Python | 1,032 | 35 | import sys
from ij import IJ
# Fiji Jython interpreter implements Python 2.5 which does not
# provide support for argparse.
input = sys.argv[-7]
iterations = int(sys.argv[-6])
count = int(sys.argv[-5])
black_background = sys.argv[-4] == "yes"
pad_edges_when_eroding = sys.argv[-3] == "yes"
tmp_output_path = sys.argv[-... |
c19f9062dbf24f0e831459022b29c086ef5922bb8f50ddd722efcb7aa335def3 | Python | 1,032 | 30 | """End-to-end Python-level benchmarks for the Rust tokenize/translate port.
Run: pixi run -e dev pytest benches/bench_tokenize_translate.py --benchmark-only
Measures the public API (including PyO3 marshalling), which Rust-only criterion
misses and which decides the small-array regime. To compare against the Numba
bas... |
34c7037a53b9cbdb5e15fbd9e6091b449cdf7f308aced9533b43ead32f29f77a | Python | 1,033 | 29 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import av
def get_video_container(path_to_vid, multi_thread_decode=False, backend="pyav"):
"""
Given the path to the video, return the pyav video container.
Args:
path_to_vid (str): path to the video.
... |
d2d326323bc74d2c6ec940df37858b26ad3a1c0fb5a3de041c338ead556c66cb | Python | 1,033 | 40 | import torch.nn as nn
from modular_splicing.models.modules.sparsity.sparsity import SparseLayer
class NoSparsity(SparseLayer, nn.Module):
"""
Acts like a sparse layer but does not actually enforce sparsity in any way.
Pretends to have sparsity to keep code that interacts with it happy.
"""
def ... |
27d2e1226f33c3b07fd98d0d919b64e4743cdaed791b9b78f094bfe3f5a72ab9 | Python | 1,034 | 28 | # ruff: noqa -- harvest-time provenance script, committed as run (lint header added at import)
import warnings
p = "/tmp/eco_r3/art_v2.16.0_portable"
import torchlens as tl
print("reader", tl.__version__, end=" ")
try:
from torchlens.io import detect_tlspec_format
print("| detect:", detect_tlspec_format(p))
... |
290952819b6b847b466514cd3c4ab63baa7e2078146dc1955383c6536a63b698 | Python | 1,034 | 34 | import sqlite3
from .db import DB
class DistrictMucormycosisCasesTable(DB):
def __init__(self):
super().__init__()
self.table_name = 'UK_mucormycosis_cases'
self.table_desc = 'Uttarakhand mucormycosis case info'
self.cols = self.getcolumns()
def getcolumns(self):
col... |
3b71ed4355020f86582c18b4449f02173dff7aa45d60979505df7409dad9af0d | Python | 1,034 | 35 | import sys
from ij import IJ
# Fiji Jython interpreter implements Python 2.5 which does not
# provide support for argparse.
input = sys.argv[-4]
black_background = sys.argv[-3] == "yes"
tmp_output_path = sys.argv[-2]
output_datatype = sys.argv[-1]
# Open the input image file.
input_image_plus = IJ.openImage(input)
... |
d5593a7d0f2f34a77beabb88fa3f09c87000d40ee808f46b95c6a44665cf9dba | Python | 1,034 | 34 | """ Barcode utils
"""
import logging
import numpy as np
from tmd.Topology.transformations import tmd_scale
from tmd.Topology.statistics import get_lengths
L = logging.getLogger(__name__)
def scale_barcode(ph, target_distance):
""" Given a target distance, scale the persistence homology
in order to make sure... |
02284951ae814ef9cc020d7282ca41bae400acef50b3366d7cab3f3764955d7f | Python | 1,035 | 33 | import torch.nn as nn
from models.mn.model import get_model as get_mobilenet
from models.dymn.model import get_model as get_dymn
from helpers.utils import NAME_TO_WIDTH
class EnsemblerModel(nn.Module):
def __init__(self, models):
super(EnsemblerModel, self).__init__()
self.models = nn.ModuleList(... |
34d9bde7916107adb4e92f04582f5e945e9b2a48600f10d39bcc8b0d9cc8ec44 | Python | 1,035 | 43 | """
Widget for editing an arena's dimensions and geometry.
"""
from __future__ import annotations
import param
from .widget_base import (
collapsible_family_box,
numeric_tuple_param_control,
param_control,
)
__all__ = ["build_area_widget"]
def build_area_widget(area: param.Parameterized) -> object:
... |
5b71f7d05bd19fb680b662565618a88d1cc94dd904f501040b96c468da8b33ed | Python | 1,035 | 38 | from functools import wraps
import time
import numpy as np
_profilerDict = {}
def timeit(func):
@wraps(func)
def timeit_wrapper(*args, **kwargs):
start_time = time.perf_counter()
result = func(*args, **kwargs)
end_time = time.perf_counter()
total_time = end_time - start_time
... |
73df79e2727cfeca90c584fb72a4411ebeb7a9033c7734370f48f2f80f8046fd | Python | 1,035 | 24 | import subprocess
import boto3
batch = boto3.client('batch', region_name='us-east-1')
response = batch.describe_job_definitions(status='ACTIVE')['jobDefinitions']
instance_type_info = {}
for res in response:
jobDefinition = res['jobDefinitionName'] # example: gluon-cv-p2_8xlarge:1
instance = jobDefinition.spli... |
a04572a9e47f3cfe0835fe8cb39b5ba415fd23c3bd415d7243c1b93010124a00 | Python | 1,035 | 37 | 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["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.arch... |
d6b3caca99e4037f9c351aa7814905f4a625ae435edd234a509a5297f75104e4 | Python | 1,035 | 31 | import encodings.idna # noqa: F401
import os
import socket
import pytest
from pyjibe.head import update
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
try:
s.connect(("www.python.org", 80))
NET_AVAILABLE = True
except socket.gaierror:
# no internet
NET_AVAILABLE... |
2ded5b73f5446350cc18ba58fdb7a2352548a8340904e623626686e8b2193520 | Python | 1,036 | 29 | # coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
494aae6fe60814835bdbf82d2830159c0b6547ea6ebb1d1adaf6a7c6bcaef7bd | Python | 1,036 | 31 | from pyabf.abf2.section import Section
class UserListSection(Section):
"""
Contains elements of the ABF2 user list.
The user list allows custom values to be used as part of the epoch table.
"""
def __init__(self, fb):
Section.__init__(self, fb, 172)
self.nULEnable = [None]*self._... |
a2b973f6c953910485fbf41543142b317e4c726289ac3f09d7fd42d4617e60d4 | Python | 1,036 | 33 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._domain import Domain
from ._hoverlabel import Hoverlabel
from ._insidetextfont import Insidetextfont
from ._legendgrouptitle import Legendgrouptitle
from ._marker import Marker
from ._outsidetextfont import Outsidetextfont
... |
acdef38ce17e04582842d2c01e13c875d1b14a92721aa46a128ad77d79922078 | Python | 1,036 | 31 | import numpy as np
import cv2
from tqdm import tqdm
from glob import glob
def main():
for f in tqdm(glob("/home/rassman/bone2gene/data/masks/unet/*/*.png")):
if "vis" in f or "bone_age" in f:
continue
raw_prediction = cv2.imread(f, cv2.IMREAD_GRAYSCALE)
binary = (raw_predictio... |
5fcea8002ba9aa95cf782c235757040a7c0e5b4668652c47d8d2d7fb9306641b | Python | 1,037 | 37 | 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["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.arc... |
6aed9e1803926eb27cb2dec1596846d164983cd9dcf2f583f03158326eb7eae7 | Python | 1,037 | 43 | import torch
def cindex_approx(labels, risk, sigma=1.e-3, reduction_fn=torch.sum):
"""
According to https://www.medrxiv.org/content/10.1101/2021.10.11.21264761v1.full.pdf
section 3.4
"""
t = labels[:, 0]
e = labels[:, 1]
n = len(t)
# has the times as row vector repeated along rows
... |
745446921b7bd55e93631e3fcc13a5651ec6348829d9f32b1293b01c750956e4 | Python | 1,037 | 42 | from sys import argv
from msp import MSP, rbnsp_split_90
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.batch_size = msp.batch_size // 3
msp.lr = float(msp.lr) / 3
msp.architecture["num_motifs"] = 172
msp.architecture["channels"] = 200
psam_model = dict(
type="PSAMMotifModel",
motif_spec=dict(t... |
cee2efc910660f5fe41aeabac0856b7e9ab30d42d8a2290e4309428cc61812a6 | Python | 1,037 | 43 | #### over lap for sanple sites
#### usage : python overlap_sites.py <sample 1> <sample 2> ...<sample n> > overlap.txt
### modify parameter : value to get minimal overlap times
import sys
import collections
from collections import Counter
value = 2
dict_c = collections.defaultdict(list)
c = collections.defaultdict(lam... |
e57c135cffc4708c9810ac33773e27144415f3ac867689fc68977d9c420427ab | Python | 1,037 | 37 | import setuptools
with open('README.md', 'r') as fh:
long_description = fh.read()
setuptools.setup(
name = "adpred",
version="1.3.2",
author="Ariel Erijman",
author_email="aerijman@fredhutch.org, aerijman@neb.com",
description="Prediction of Transcription Activation Domains from protein seque... |
393c0c70f6aea3d02210a657c5ac9334c179fed27b69cb908a8e46a2d6ac5ff4 | Python | 1,039 | 18 | # https://stackoverflow.com/questions/24100558/how-can-i-split-a-module-into-multiple-files-without-breaking-a-backwards-compa/24100645
from cvnn.layers.pooling import ComplexMaxPooling2D, ComplexAvgPooling2D, ComplexAvgPooling3D, ComplexPolarAvgPooling2D
from cvnn.layers.pooling import ComplexUnPooling2D, ComplexMaxPo... |
0bc931164b8176cb7780380e1da28493cf8c0d062735b5fe05648c6a14027706 | Python | 1,040 | 27 | '''
Copyright 2021 Twente Medical Systems international B.V., Oldenzaal The Netherlands
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 requir... |
3a9e524ef20cdf9ed4918532e4d62bf698b776ed82d07688a7dec1288d7e5e17 | Python | 1,040 | 30 |
# -*- coding: utf-8 -*-
#
#
# RectiPy software framework for recurrent neural network training in
# Python. See also: https://github.com/pyrates-neuroscience/RectiPy
#
# Copyright (C) 2022 Richard Gast.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Pub... |
4940503277974ebaf008b08c3ada78c7d960e5ac4e89d8670a2207b6018fd8c6 | Python | 1,040 | 48 | import adolc
import numpy as np
def tapeFunction(x, rowno):
adolc.trace_on(rowno)
ax = adolc.as_adouble(x)
# marking the x variables as independent
for item in iter(ax):
item.declareIndependent()
L = ax[0]
Inp = ax[1]
Out = ax[2]
P = ax[3]
ay = adolc.as_adouble(0)
if rowno ==... |
a2e42fef47b36b4b24dcbf3f20cf3f1b6dfd79b6d1d087d9b5c039afc861e273 | Python | 1,041 | 37 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
fraction_of_standard_epoch = 9241 / 162706
msp.decay_per_epoch = 0.9 ** fraction_of_standard_epoch
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.arch... |
0a76c390612bec5d2188b771962968a616a546c1c2f8c6f8a97f34998733f53c | Python | 1,042 | 39 | from os import path
import setuptools
path_to_repo = path.abspath(path.dirname(__file__))
with open(path.join(path_to_repo, 'readme.md'), encoding='utf-8') as f:
long_description = f.read()
required_pypi = [
'transformers[torch]',
'numpy',
'datasets',
'scikit-learn',
'pandas',
'spacy',
... |
0d0619491863bd27434c06892cb0a90edc409f54bffafffc9e5363bf84f68f06 | Python | 1,042 | 40 | """Utilities for defining models
"""
import operator
class KeyBasedCompareMixin(object):
"""Provides comparision capabilities that is based on a key
"""
def __init__(self, key, defining_class):
self._compare_key = key
self._defining_class = defining_class
def __hash__(self):
... |
11a48920d058d77113505f49e042dac6ca86cfe4410cae917f5209661eea4f56 | Python | 1,042 | 29 | import sqlite3
from .db import Database
from .UK_tables import UK_district_wise_cases, UK_district_wise_mucormycosis_cases, \
UK_district_wise_testing, UK_district_wise_vaccination, UK_hospital_wise_deaths, \
UK_case_info
class UttarakhandDB(Database):
def __init__(self, datadir):
super().__... |
70dba6d9510fe82886e37d72d2fb8edb6727c10a9f10325e4bc1aaa26af79daf | Python | 1,042 | 26 | from .plotting.gene_plot import gene_plot
from .plotting.gene_plot import gene_plot_interactive
from .plotting.feat_plot import feat_plot
from .plotting.cluster_plot import cluster_plot
from .plotting.cluster_plot import cluster_plot_interactive
from .plotting.subcluster_plot import subcluster_plot
from .plotting.non_s... |
929bf081e70c87a64e299138d7e3553929545a5fd6fafa53e12821deff641d0d | Python | 1,042 | 29 | # -*- coding: utf-8 -*-
'''
Copyright 2021 Twente Medical Systems international B.V., Oldenzaal The Netherlands
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/LI... |
e126df5b5fa2fec0b2e7f488a5fc2cbcec33c6cb3022634c71246ff47344902e | Python | 1,042 | 34 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._cluster import Cluster
from ._hoverlabel import Hoverlabel
from ._legendgrouptitle import Legendgrouptitle
from ._line import Line
from ._marker import Marker
from ._selected import Selected
from ._textfont import Textfont... |
1e7970603dd18b2214f66e68e5adcc98ef288b188331212260cf5306465cd837 | Python | 1,043 | 25 | import os
import numpy as np
import sqlite3
def read_db_atoms(atom_db_path):
if os.path.exists(atom_db_path) and atom_db_path.endswith('db'):
cursor = sqlite3.connect(atom_db_path).cursor()
# need to create a txt compatable numpy array
# channel, iteration, modulus, amplitude (not p2p), po... |
228dfc8c7d94d330b484c6b0456735681eda3d2a1d37b7101ded2d693563192e | Python | 1,043 | 30 |
# -*- coding: utf-8 -*-
#
#
# RectiPy software framework for recurrent neural network training in
# Python. See also: https://github.com/pyrates-neuroscience/RectiPy
#
# Copyright (C) 2022 Richard Gast.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Pub... |
56f6aea5063ac1289fed1ee2835398c4e66e233637cb5b1af340e03cf416a24c | Python | 1,043 | 39 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.batch_size = 2 * msp.batch_size // 3
msp.lr = float(msp.lr) * 2 / 3
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec... |
6feacd73923571b48e8fdd61549b18f1dfc5f59b6f0077abc67c0b436bcec4b8 | Python | 1,043 | 30 |
# -*- coding: utf-8 -*-
#
#
# RectiPy software framework for recurrent neural network training in
# Python. See also: https://github.com/pyrates-neuroscience/RectiPy
#
# Copyright (C) 2022 Richard Gast.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Pub... |
80056ce4eecfc54412f49af2557558c3799affe57cacd6dc2c89ae9ddcc88835 | Python | 1,043 | 38 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._colorbar import ColorBar
from ._hoverlabel import Hoverlabel
from ._legendgrouptitle import Legendgrouptitle
from ._marker import Marker
from ._selected import Selected
from ._unselected import Unselected
from . import col... |
a7dc729e90091203c8713afae79d5ea112ac06a9215f51b24063fb5a0292a305 | Python | 1,043 | 33 | import json
import os
from matplotlib import pyplot as plt
from angorapy.common.const import PATH_TO_EXPERIMENTS, QUALITATIVE_COLOR_PALETTE
experiment_ids = ['1653053413', '1655284851', '1654708464']
reward_developments = {}
for id in experiment_ids:
with open(os.path.join("../../../", PATH_TO_EXPERIMENTS, str(... |
abe961eaf3b877aec9d6c11dc0a91174819c7ea31a0aed8e877f6fa3c286e399 | Python | 1,043 | 31 | import unittest
import numpy as np
import torch
from parameterized import parameterized
from modular_splicing.models.modules.activations.backward_only_leaky_relu import (
BackwardsOnlyLeakyReLU,
)
class TestDensityForSameEntropy(unittest.TestCase):
@parameterized.expand([(seed,) for seed in range(100)])
... |
c5e06e7cae0c3f0f111735e0ce469b7c93abdd4fec85feaf90105c8b7ae853fc | Python | 1,043 | 36 | 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["propagate_sparsity_spec"] = dict(
type="OnlyAllowInfluenceOnSplicesites"
)
msp.architecture["final_processor_spec"].update(
dict(
bottleneck_spec=... |
e55aa5a19958d431629f100dcafb10f92ee1ada9932c52a1ba2600593e910379 | Python | 1,043 | 42 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 28 10:57:11 2022
@author: fcheng
"""
import numpy as np
def cal_orientation_index(theta, R):
"""
calculate OI of unit for theta, using unit responses to orientations R
(larger OI, higher selectivity)
1st dimension of R and the leng... |
f472f07da2d6758d405ffafda411403313e6ba74c82d0c252fe9f5568b009c3d | Python | 1,043 | 39 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.batch_size = 2 * msp.batch_size // 3
msp.lr = float(msp.lr) * 2 / 3
msp.architecture["num_motifs"] = 82
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec... |
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