sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
08858e93289bad69b2f3af001988e51c5efebbca768c9f0e99f7607e3a2d6ccc | Python | 433 | 17 | """Optimizers."""
from mrpro.algorithms.optimizers.OptimizerStatus import OptimizerStatus
from mrpro.algorithms.optimizers.adam import adam
from mrpro.algorithms.optimizers.cg import cg
from mrpro.algorithms.optimizers.lbfgs import lbfgs
from mrpro.algorithms.optimizers.pdhg import pdhg
from mrpro.algorithms.optimizer... |
4046a5b628e589bb839fe39a0f587819cb17716cd1738e3a573d4e06ebb8d791 | Python | 435 | 15 | import json
json_file = r"E:\experiment\samples\api.json"
count_file = r"E:\experiment\frequencydata\all_api.json"
entitylist = []
with open(json_file, 'r') as f0:
data = json.load(f0)
for i in data:
info = list(i.values())[0]
for s_info in info:
if s_info not in entitylist:
... |
497f24412ce2718b09ea4d098daed5a918ca59f292a70d223f6424618bcabca6 | Python | 435 | 12 | """PyTest fixtures for reconstruction tests."""
import pytest
from mrpro.data import KData
from mrpro.data.traj_calculators import KTrajectoryCartesian
from tests.data import IsmrmrdRawTestData
@pytest.fixture(scope='session')
def cartesian_kdata(ismrmrd_cart: IsmrmrdRawTestData) -> KData:
"""Create Cartesian KD... |
901d265d5fe47129070e2ffda6460dda84bdf403a7bd8513fdef530c0ea2cc34 | Python | 435 | 15 | #!/opt/local/bin/python2.7
import sys
from Common import diffeo
def main():
if not (len(sys.argv) == 3):
print 'Usage: computeJacobianFromLDDMM LDDMM_map_file.vtk result.vtk'
sys.exit(0)
v = diffeo.Diffeomorphism(filename=sys.argv[1])
u = diffeo.jacobianDeterminant(v.data, resol = v.resol,... |
feda99c39f86f959925a98f10145405222a3a90d78448d4b78eef476dceec320 | Python | 435 | 10 | """Removed — use the litpose CLI instead."""
raise SystemExit(
"scripts/predict_multi_view.py has been removed.\n"
"Please use the command line interface instead:\n\n"
" To predict on a folder of video files:\n"
" litpose predict <model_dir> <video_files_dir>\n\n"
"For more information, visit:\... |
e6bd67f43a7ec0ebf455487dde5a1047e94764e80ea4638c54d2a316ed6ab0af | Python | 437 | 19 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Feb 26 17:41:51 2023
@author: tomas
"""
import glob
import pathlib
csvnames = glob.glob("framedata/**/*.csv", recursive=True)
for name in csvnames:
csvpath = pathlib.Path(name)
txtpath = csvpath.with_suffix(".txt")
with open(csvpath) as cs... |
0d99665ed3117b0936fbe9b36185d9a8bf2024a785dc7501268018e1a879ce05 | Python | 438 | 22 | from dataknead import Knead
import csv
import json
def vanilla():
data = []
with open("input/cities.csv") as f:
reader = csv.DictReader(f)
for row in reader:
data.append(row)
with open("output/lead-cities-vanilla.json", "w") as f:
json.dump(data, f)
def knead():
... |
6c862343de554241931e4ea176ecf69d3c5002af9fc02a2731f2abad37276692 | Python | 439 | 9 | from biophysics_fitting.setup_stim import setup_apical_epsp_injection as setup_apical_epsp_injection_
def apical_epsp_injection(cell, dist = None, amplitude = None, delay = None, rise = 1.0, decay = 5):
'''
This injects epsp shaped current at a certain distance from the soma.
'''
setup_apical_ep... |
4266561d29a118c6c4d1b01e919bf3137e233b5fe8b3040bb3e46e7e37bae2ba | Python | 440 | 10 | from biophysics_fitting.setup_stim import setup_soma_step
def apical_current_injection(cell, amplitude = None, delay = None, duration = None, dist = None):
# note: setup_soma_step has been extended to support a dist parameter
setup_soma_step(cell,
amplitude = amplitude,
... |
837125ea0b12bd51de6430f9b891e80eaa011e3e358d4f27ea019a301128fc09 | Python | 442 | 17 | # -*- coding: utf-8 -*-
try:
from ptemcee import Sampler as PTSampler
except ImportError:
class PTSampler(object):
def __init__(self, *args, **kwargs):
raise ImportError(
"The PTSampler from emcee has been forked to "
"https://github.com/willvousden/ptemcee... |
943e194e54183ffd3c8870b95e6519ee4399b400a0fb0910b57b82ff86f835df | Python | 442 | 20 | import torch
import numpy as np
class AvgMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
self.losses = []
def update(self, val, n=1):
self.val = val
... |
e69adff026e6b07313d814be7dcb7ccd3c68ff9388e6cc5266efd76e83981ac7 | Python | 444 | 15 | import sys
punc = ["-LRB-", "-RRB-", ",", "-", ";", ":", "\'", "\'\'", '\"', "`", "``", ".", "!", "?", "*FOOT*", "-RRB-", ",", "-", ";", ":", ".", "!", "?"]
for line in sys.stdin:
if line.strip() != '':
wrds_in = line.strip().split()
wrds_out = []
for w in wrds_in:
if w in punc... |
6548b77e913bc28792bc05d1cf305fa7ea565c82a030aaf470c60854fd132122 | Python | 445 | 14 | # JN 2016-01-22
from __future__ import absolute_import
DATE_FNAME = 'start_stop_datetime.txt'
from .helper.create_attrs import make_attrs
from .helper.helper import parse_datetime
from .manager.tools import debug
from .manager.man_continuous import H5Manager
from .manager.tools import expandts
from .helper.helper imp... |
e28bc12984f99d3011941206e322ba3575372219d7adab80b8646a0f353b132a | Python | 445 | 14 | """Tests for simulation of coil sensitivities."""
from mrpro.data import SpatialDimension
from mrpro.phantoms.coils import birdcage_2d
def test_birdcage_sensitivities_shape():
n_z = 1
n_y = 200
n_x = 150
n_coils = 4
image_dimension = SpatialDimension(z=n_z, y=n_y, x=n_x)
simulated_coil_sensit... |
29555d5e8944a6c948216e965edca382f2445479171f492f6920737084cda3f9 | Python | 446 | 22 | # JN 2016-06-01
"""
given dropfiles, create a common one (lines contained in all files)
"""
def common_dropfile(fnames):
sets = []
for fname in fnames:
with open(fname, 'r') as fid:
sets.append(set([l.strip() for l in fid.readlines()]))
return sorted(sets[0].intersection(*(sets[... |
9c4995f78e976a2a3e5a4bfab7e727b80b150ee2582b5f58e079fc0825542bf5 | Python | 446 | 19 | import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
import tree
t = tree.Tree()
left = '(1 '
right = ') '
for line in sys.stdin:
line = line.strip()
line = line.lower().split()
string = ''
for word in line[::-1]:
w = left+word+right
... |
be83c5c2a2205778eb5cfd24db1a1de2c965833b67263ad044cebf3d9805fbd3 | Python | 446 | 14 | #!/usr/bin/env python
from setuptools import find_packages, setup
setup(
name="GCPNet",
version="0.0.1",
description="A PyTorch implementation of Geometry-Complete SE(3)-Equivariant Perceptron Networks (GCPNets)",
author="Alex Morehead",
author_email="acmwhb@umsystem.edu",
url="https://github.... |
e0295aec36c215415db518339ff493bafa55f07e186386a7daa502933b857286 | Python | 446 | 15 | # from os import wait
from OperationSystem.AnalysisProcess.BasicAnalysisProcess import BasicAnalysisProcess
import time
class WaitAnalysisProcess(BasicAnalysisProcess):
def run(self):
while (self.messenager.state.current_detect_state != 'STRD') and (self.messenager.state.control_state != 'EXI... |
ede857ba78b9dc30c4c40594ce54de3305db00cb37d673c0daeddaa376bdf3b1 | Python | 446 | 20 | """Dataset and trained model downloading functions."""
from . import utils
from .extract import (
download_abstracts,
download_cognitive_atlas,
download_nidm_pain,
download_peaks2maps_model,
fetch_neuroquery,
fetch_neurosynth,
)
__all__ = [
"download_nidm_pain",
"download_cognitive_atla... |
30fd156e177d32c654ffcc3f2fc57372b260085a79225279f966a472c8c1df94 | Python | 447 | 23 | """
Public toolkit API.
"""
from pandas._libs.lib import infer_dtype
from pandas.core.dtypes.api import * # noqa: F403
from pandas.core.dtypes.concat import union_categoricals
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
IntervalDtype,
PeriodDtype,
)
__all__ = [
"Ca... |
74fa4d631df3cbe153eef38f3903badc4decf138676f3d3a2f036842e0d2143b | Python | 447 | 22 | name = "CELLEX"
### GLOBAL CONSTANTS
# These must be defined first, as they are required in sub-packages.
ES_METRICS = ["det", "ep", "ges", "nsi"]
__author__ = ", ".join([
"Tobias O. Stannius",
])
__email__ = ', '.join([
'stannius@sund.ku.dk',
])
# Load CELLEX modules
from . import metrics
from . impor... |
c965d7c78cbcfa448e19f9ff84af00182950ca9d5e455041145770d7e9f46ec3 | Python | 447 | 14 | import numpy as np
def mod(x, y, decimals=8):
"""
Computes the remainder of the division x/y without floating point errors. The result of the division is rounded to
the given number of decimals.
:param float x: Dividend
:param float y: Divisor
:param int decimals: Number of decimals the divisi... |
2e9b779a9874871b3b13fdb3326bd8ad7b58fe308b743ee44f2a0ee7c5e02335 | Python | 448 | 14 | import sys, argparse, pandas as pd
argparser = argparse.ArgumentParser('''
Concatenates two or more space-delimited data tables.
''')
argparser.add_argument('tables', type=str, nargs='+', help='Paths to tables to concatenate')
args, unknown = argparser.parse_known_args()
data = []
for t in args.tables:
data.appen... |
15b144a0924d90abc096c47838ee62f7d4293a5c24180c88f72aabe190de2e4a | Python | 450 | 23 | from pandas.core.window.ewm import (
ExponentialMovingWindow,
ExponentialMovingWindowGroupBy,
)
from pandas.core.window.expanding import (
Expanding,
ExpandingGroupBy,
)
from pandas.core.window.rolling import (
Rolling,
RollingGroupBy,
Window,
)
__all__ = [
"Expanding",
"ExpandingGr... |
22739085f07e6d3d5b84093b23cd44d9022025aac06971ffcc9cc34dfa8cf4a2 | Python | 451 | 17 | from math import isclose
import numpy as np
import mantel
np.random.seed(117)
def test_stochasic_hundred_thousand():
n_objects = 27
n_distances = (n_objects**2 - n_objects) // 2
dists1 = np.random.random(n_distances)
dists2 = np.random.random(n_distances)
result = mantel.test(dists1, dists2, per... |
360e0f5eec7047689634b2b8bcaf67d93b2759e545b266c80d66e9580623e898 | Python | 451 | 23 | # -*- coding: utf-8 -*-
from .de import DEMove
from .de_snooker import DESnookerMove
from .gaussian import GaussianMove
from .kde import KDEMove
from .mh import MHMove
from .move import Move
from .red_blue import RedBlueMove
from .stretch import StretchMove
from .walk import WalkMove
__all__ = [
"Move",
"MHMo... |
3be45c2221cb0266bcd9622ac74808b29aa087718e0bf357eee4f65a097de75e | Python | 451 | 10 | """Removed — use the litpose CLI instead."""
raise SystemExit(
"scripts/converters/sleap2lp.py has been removed.\n"
"Please use the command line interface instead:\n\n"
" To convert a SLEAP .pkg.slp project to a Lightning Pose project:\n"
" litpose convert <project>.pkg.slp --lp_dir <lp_dir>\n\n"
... |
863dd66c86bd4e23f7c650f5ec515d387da7b4c6f9c9506d4ccf400e13b7d676 | Python | 452 | 12 | """PyTest fixtures for denoiser tests."""
import pytest
from mrpro.data import IData, SpatialDimension
@pytest.fixture
def idata_single_coil(ellipse_phantom, random_kheader) -> IData:
"""Create single-coil image."""
image_dimensions = SpatialDimension(z=1, y=ellipse_phantom.n_y, x=ellipse_phantom.n_x)
im... |
1159415cff5dd8250416847a146a11c10f8f310a44096302dde1229e96a4bb53 | Python | 454 | 18 | from .quality_control_connectivity import (
qcfc,
partial_correlation,
significant_level,
calculate_median_absolute,
)
from .distance_dependency import get_atlas_pairwise_distance, get_centroid
from .network_modularity import louvain_modularity
__all__ = [
"qcfc",
"significant_level",
"part... |
00ddc8d7a3d69a9ba5b71c49abb6d08f8de92246bc3826ee1b31e9136f971377 | Python | 455 | 13 | from setuptools import setup, find_packages
setup(
name='tnn',
version='0.0.1',
packages=find_packages(),
url='https://github.com/elizabethnewman/tnn',
license='MIT',
author='Elizabeth Newman',
author_email='elizabeth.newman@emory.edu',
description='Matrix-Mimetic Tensor Neural Networks... |
b41e9d66559bb1d2d16d687c814ca0ca08e7bcb6bcb87d4712912952bb08ad6d | Python | 455 | 13 | """Phase 6: this submodule was the deep import path for the deleted
legacy ``TSForecastingExperiment``. Kept as an empty namespace for
back-compat — anything that imports from
``pycaret.time_series.forecasting`` should migrate to
``pycaret.time_series`` (or ``pycaret.tasks``):
from pycaret.time_series import TimeS... |
dd51a9cc57d580eaf1196be3d260181b7eaf187c2dbda7281d4fc5394452471b | Python | 455 | 20 | import csv
import gzip
import sys
snum = sys.argv[1]
sampleid = 0
for fname in sys.argv[2:]:
with gzip.open(fname) as f:
reader = csv.reader(f)
header = True
for row in reader:
if header:
header = False
if sampleid != 0:
#only print header for first line
continue... |
e2b4fb46b6b63ad46efdd00d9f4e36fb504821ee401599ab59a931781af5919f | Python | 455 | 16 | from sklearn import linear_model
class Ridge:
def __init__(self, alpha):
self.alpha = alpha
self.model = linear_model.Ridge(self.alpha)
def fit(self, dstims_train_val, eeg_train_val):
self.model.fit(dstims_train_val, eeg_train_val)
self.coefs = self.m... |
f2c1d088c4cc658d9766ba49354dbced9ebf8bb446ba3f1072b85c6dad727e4a | Python | 455 | 12 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .base import BaseModel
from .dimenet_plus_plus import DimeNetPlusPlusWrap as DimeNetPlusPlus
from .gemnet.gemnet import GemNetT
from .pai... |
3ae4bbb1d5a73980d09abf348a7be50416d84319563c31f845dfcbb00b35f956 | Python | 456 | 13 | import pandas as pd
import argparse
parser = argparse.ArgumentParser(description='Remove "sentences" column and change delimiter to space.')
parser.add_argument('filename', help='The CSV file name to read from and write to.')
args = parser.parse_args()
df = pd.read_csv(args.filename)
# delete the sentences col
if 'se... |
e4171c2c71ce141d389ba3c4b7bef8329f0b104781fe8f185e4577d16e74e45b | Python | 457 | 21 | project = "hadge"
copyright = "2023, Fabiola Curion, Xichen Wu, Lukas Heumos"
author = "Fabiola Curion, Xichen Wu, Lukas Heumos"
release = "0.2.0"
extensions = ["myst_parser", "nbsphinx"]
source_suffix = {
".rst": "restructuredtext",
".txt": "markdown",
".md": "markdown",
}
templates_path = ["_templates"... |
4c914117ecc9abeda38420351b663265d1e5e308e730a84d39c1a752e31dcc78 | Python | 459 | 16 | # License: see [LICENSE, LICENSES/legged_gym/LICENSE]
from setuptools import find_packages
from distutils.core import setup
setup(
name='legged_gym',
version='1.0.0',
author='Nikita Rudin',
license="BSD-3-Clause",
packages=find_packages(),
author_email='rudinn@ethz.ch',
description='Isaac G... |
0fc85246cec2402d598cc8370fdb162ff9d776bd2c3415025269bf024e36c50a | Python | 460 | 14 | from datetime import datetime
import sys
sys.path.append("..")
from app import db
class AuthKeys(db.Model):
__tablename__ = 'auth_keys'
id = db.Column(db.Integer, primary_key=True)
designation = db.Column(db.String(150))
guid = db.Column(db.String(255))
deleted_at = db.Column(db.DateTime, default=... |
3cb745bb028c5f62fefcc9fd2818b84a045b9e0571df54c0a79c58ef42e62f72 | Python | 462 | 14 | __author__ = 'etseng@pacb.com'
from Bio import SeqIO
def check_ids_unique(fa_or_fq_filename, is_fq=False):
"""
Confirm that a FASTA/FASTQ file has all unique IDs
(used probably by collapse or fusion finding script)
"""
seen = set()
for r in SeqIO.parse(open(fa_or_fq_filename), 'fastq' if is_fq... |
0a2c77827c414a2d80e426bf7376c1a07315a93c785b22ec92b0b54bf7967dd7 | Python | 464 | 21 | """Look for unreachable code."""
import argparse
import sys
from vulture import Vulture
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("files", nargs="*")
args = parser.parse_args()
v = Vulture()
v.scavenge(args.files)
ret = 0
for item in v.get_unused_c... |
2e1d0d9980860703aea98f00021fe886838820b28308b2670819c153845564ae | Python | 464 | 21 | import numpy as np
import pandas as pd
def test_nunique():
# basics.rst doc example
series = pd.Series(np.random.default_rng(2).standard_normal(500))
series[20:500] = np.nan
series[10:20] = 5000
result = series.nunique()
assert result == 11
def test_nunique_categorical():
# GH#18051
... |
6069e1da95283bbf55ef7f0e684a49dbbc6ca1ed9bf92e9817d4f24f834b407b | Python | 464 | 17 | #!/usr/bin/python3
#
# filter-noisy-assembler-warnings.py
# Author: Stuart Berg
# <https://stackoverflow.com/a/41515691>
import sys
for line in sys.stdin:
# If line is a 'noisy' warning, don't print it or the following two lines.
if ('warning: section' in line and 'is deprecated' in line
or 'note: change... |
1d99e0586070ced445ef79b2ce53589792b32908c4f12ac3e6518ad5e942f099 | Python | 465 | 14 | # coding: utf-8
from atropos.commands.trim.qualtrim import nextseq_trim_index
from atropos.io.seqio import Sequence
def test_nextseq_trim():
s = Sequence('n', '', '')
assert nextseq_trim_index(s, cutoff=22) == 0
s = Sequence(
'n',
'TCTCGTATGCCGTCTTATGCTTGAAAAAAAAAAGGGGGGGGGGGGGGGGGNNNNNNNN... |
208e7ded53ca5cf583b68afcc0323832988c1ebaee648c606258608ae6b33adc | Python | 467 | 21 | import matplotlib.pyplot as plt
plt.rcParams.update({
'xtick.labelsize': 15,
'xtick.major.size': 10,
'ytick.labelsize': 15,
'ytick.major.size': 10,
'font.size': 12,
'axes.labelsize': 15,
'axes.titlesize': 20,
'axes.titlepad' : 30,
'legend.fontsize': 15,
# 'figure.subplot.wspace... |
e90830379daf2b76aa327d1fbde1387e651cf371f56e96d06a7829ca1a0fd08d | Python | 467 | 15 | import numpy as np
import pandas as pd
import pandas._testing as tm
class TestToSeries:
def test_to_series(self):
naive = pd.DatetimeIndex(["2013-1-1 13:00", "2013-1-2 14:00"], name="B")
idx = naive.tz_localize("US/Pacific")
expected = pd.Series(np.array(idx.tolist(), dtype="object"), na... |
5c9ef626aed9a74de926c0f63b2fbb7fe66a162054d9e48e916f4c01bcf3583f | Python | 469 | 16 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
class TestInsert:
@pytest.mark.parametrize("na", [np.nan, pd.NaT, None])
def test_insert(self, na):
# GH#18295 (test missing)
expected = pd.PeriodIndex(
["2017Q1", pd.NaT, "2017Q2", "2017Q3", "2017Q4... |
a6940c5342c0aa417205cf143e88728ae402f4e40418268d1c4b7bf1683315c2 | Python | 471 | 17 | # -*- coding: utf-8 -*-
# Add classes from sub directory #
from sdna.sim.core.detection.gc_content import *
from sdna.sim.core.detection.homopolymers import *
from sdna.sim.core.detection.kmers import *
from sdna.sim.core.detection.undesired_sequences import *
__all__ = ['GCContent',
'Homopolymers',
... |
af30719a061fe9e3b5315cf8cf88b5c20431acc5fc251de8289b389d37d729a5 | Python | 471 | 23 | # -*- coding: utf-8 -*-
# JN 2016-01-12
from __future__ import print_function, division, absolute_import
import numpy as np
DEBUG = True
def debug(msg):
if DEBUG:
print(msg)
def expandts(ts, timestep, q=1):
"""
expands time stamps by inserting 512/q interpolated
time points after each tim... |
2df60780cb70964905ba585f898be1524ab8604e418fdf3ce2dfe775794c21e3 | Python | 473 | 14 | import numpy as np
from pandas.core.dtypes.cast import dict_compat
import pandas as pd
def test_dict_compat():
data_datetime64 = {np.datetime64("1990-03-15"): 1, np.datetime64("2015-03-15"): 2}
data_unchanged = {1: 2, 3: 4, 5: 6}
expected = {pd.Timestamp("1990-3-15"): 1, pd.Timestamp("2015-03-15"): 2}
... |
8ef1843d8f5f5d612d768f62035fbbfacae04c2708ef8dfcd9736eedc1a67f08 | Python | 473 | 9 | import sys
import re
for line in sys.stdin:
for preterm,term in re.findall( '([^\(\)]+) ([^\(\)]+)', line ):
term = term.lower()
for _,formprefix,formsuffix,_,_,lemmaprefix,lemmasuffix in re.findall( '-x([^:|]*:|[^%:|]*)([^%:|]*?)%([^%:|]*)[|]([^%]?)([^-:|]*:|[^-%:|]*)([^-%:|]*?)%([^-%:|]*)', preterm ):
... |
f078668090b06c61ff32ec4370d3df9a1b3a00ba8b05a45ded18e2a38282d033 | Python | 473 | 12 | """Calling cards data analysis in Python"""
from ._Chipseq import calculate_signal, signal_heatmap, signal_plot
from ._dotplots import dotplot_bulk, dotplot_sc, dotplot_sc_mu
from ._GWAS import GWAS_adata_bulk, GWAS_adata_sc
from ._heatmap_ccrna import heatmap
from ._pair import plot_matched
from ._peaks import draw_... |
fe46cc0aee112622f403592ca557a1576524900847553d486424288167b3cb59 | Python | 474 | 16 | from setuptools import setup
setup(
name='patchwork',
version='1.0',
description='Hierarchical CNNs for segmentations',
license='MIT',
keywords='CNN medical imaging',
author='Marco Reisert',
url='https://bitbucket.org/reisert/patchwork',
download_url='https://bitbucket.org/reisert/patc... |
a006ab4987a93461b771ca72de0f7e44b0d65cd9f73e17f48fc95d639c006b76 | Python | 477 | 20 | import numpy as np
from model.pointcloud import PointCloud
from mayavi import mlab
from traits.api import HasTraits, Instance, on_trait_change
from traitsui.api import View, Item
from mayavi.core.ui.api import MayaviScene, MlabSceneModel, \
SceneEditor
from pyface.qt import QtGui, QtCore
import random
__author... |
cc948cb221e6e6cd2b7fb944e495cc40ff4a322987029d7679f0ee07ff1c2326 | Python | 477 | 13 | # MOFTransformer version 2.0.0
import os
from moftransformer import __root_dir__
example_path = {
"root_cif": os.path.join(__root_dir__, "examples/raw"),
"root_dataset": os.path.join(__root_dir__, "examples/dataset"),
"data_root": os.path.join(__root_dir__, "examples/dataset"),
"downstream": ... |
2332992ce141ca743f83687e8808591f2bbf95b6aab787d179ef16cfd733fe99 | Python | 479 | 12 | '''For collecting global values'''
# section A: general -----------------------------------------------------------
NUM_SNPS = 36 # number of seg sites, should be divisible by 4
L = 50000 # heuristic to get enough SNPs for simulations (50,000 or fifty-thousand)
DEFAULT_SEED = 1833
DEFAULT_SAMPLE_SIZE =... |
c74a71fe4dfded14f7307ff9baf82f442a47cc69e25e9ca8eccf2afb573a2e03 | Python | 480 | 24 | TYPE_ALL = -3
TYPE_NON_NOISE = -2
TYPE_ART = -1
TYPE_NO = 0
TYPE_MU = 1
TYPE_SU = 2
CLID_UNMATCHED = 0
TYPE_NAMES = {TYPE_ART: 'Artifact',
TYPE_NO: 'Not assigned',
TYPE_MU: 'Multi-Unit',
TYPE_SU: 'Single-Unit',
TYPE_ALL: 'All spikes',
TYPE_NON_NOIS... |
f30d5bebe48d2f9f9084b28abb76abcbab9fa11ba2e1cca9a5cfecd5380c3409 | Python | 481 | 18 | # Run this script as "panel serve 3_vizualize.py"
# %%
from pathlib import Path
import pandas as pd
from julearn.viz import plot_scores
output_dir = Path(__file__).parent.parent / "results" / "1_brain_age"
output_file_prefix = "ixi.S4_R8_scores"
models = ["gauss", "rvr", "svm"]
all_scores = []
for t_model in models:... |
136d1d211fc10bfa3e39affce6466b44f6e9261f08635e3156d461c0e3e69154 | Python | 482 | 20 | import os
if 'PYTHONPATH' not in os.environ:
print("Error: Run `source env.sh` to be able to run `/scripts/*.py`")
exit(1)
from pathlib import Path
from time import time
from typing import Iterable
import ecgdigitize
from ecgdigitize.otsu import otsuThreshold
import ecgdigitize.visualization as viz
from ecgdi... |
802005d127ff32069174aea3982a2bdca6ba0b8929e67b31cb7beea04753958e | Python | 485 | 17 | try:
from pandas._libs.tslibs.parsing import _does_string_look_like_datetime
except ImportError:
# Avoid whole benchmark suite import failure on asv (currently 0.4)
pass
class DoesStringLookLikeDatetime:
params = (["2Q2005", "0.0", "10000"],)
param_names = ["value"]
def setup(self, value):
... |
d15cdfcf170b2b77fd7dd89c597f1b0558127a89b20bdd718ff11ca5bfc3d774 | Python | 485 | 18 | import sys, os, re
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
import tree
trace = re.compile('\*')
t = tree.Tree()
for line in sys.stdin:
if (line.strip() !='') and (line.strip()[0] != '%'):
t.read(line)
all_words = t.words()
out = ''
... |
5b390921a558900d2dc5bb4622dc6d01c2c1c5b3f416e7de03484541fea8d47d | Python | 486 | 9 | import os
kcsd_loc = os.path.dirname(os.path.abspath(__file__))
sample_data_path = os.path.join(kcsd_loc, 'data')
from .KCSD import KCSD1D, KCSD2D, KCSD3D, MoIKCSD, oKCSD1D, oKCSD2D, oKCSD3D
from .sKCSD import sKCSD, sKCSDcell
from .validation import csd_profile
from .validation.ValidateKCSD import ValidateKCSD, Valid... |
5c4af594dc67c5d7f432df5e8960a64adc6a6f0a9a68fac10da573350adf2dd5 | Python | 486 | 19 | from pandas.io.excel._base import (
ExcelFile,
ExcelWriter,
read_excel,
)
from pandas.io.excel._odswriter import ODSWriter as _ODSWriter
from pandas.io.excel._openpyxl import OpenpyxlWriter as _OpenpyxlWriter
from pandas.io.excel._util import register_writer
from pandas.io.excel._xlsxwriter import XlsxWrite... |
97bd602d3a59af577cf672432337cd1da91bcb1bfe2934466ac63c149b8523b3 | Python | 487 | 14 | import setuptools
setuptools.setup(
name = "scNovel",
version = "1.2.0",
description = "scNovel, a neural network framework, "
"provides a fast and reliable tools for scRNA-seq novel rare cell detection.",
license = "MIT Licence",
python_requires=">=3.5.0",
packages=s... |
a16299bf557e381b55f1f75bd0e8708c2dd5c6c74b7ec6c76218dd53ad53f636 | Python | 487 | 31 | #!/usr/bin/env python
"""
File: test_load
Date: 5/4/18
Author: Jon Deaton (jdeaton@stanford.edu)
"""
import io
import BraTS
import timeit
import cProfile, pstats
BraTS.set_root("/Users/jonpdeaton/Datasets/BraTS")
brats = BraTS.DataSet(year=2017)
def load():
subset = brats.train.subset(brats.train.ids[:10])
... |
8c2fd3e934a77d1fe849bbeabeb8d6fe59c641c5e100f848bc61c7e9ff3ddf36 | Python | 488 | 20 | import os
from pathlib import Path
def calculate_difumo_centroids():
from fmriprep_denoise.features.distance_dependency import get_difumo_centroids
for d in [64, 128, 256, 512, 1024]:
get_difumo_centroids(d)
def main():
tf_dir = Path(__file__).parents[1] / "data" / \
"fmriprep-denoise-b... |
b209d3bb8a46e9b3268530217bf7a9d77f86fa7a48f5a8c78c6d6a87f839468b | Python | 488 | 10 | # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and relat... |
cf4a17b961b3923208a063439a714d3b3ee6a3bc0ed57e277d1e89e28013c767 | Python | 489 | 17 | from setuptools import setup, find_packages
setup(name='rsl_rl',
version='1.0.2',
packages=find_packages(),
author='Nikita Rudin',
author_email='rudinn@ethz.ch',
license="BSD-3-Clause",
# packages=find_packages(),
description='Fast and simple RL algorithms implemented in pytor... |
622e485ff67e1934fdee48ad620fcc9de72a73b9922dcc15f39e238e4adfeda3 | Python | 490 | 23 | from flask import Blueprint
from flask_restful import Api
from app.auth import auth
#from .person_study_resource import PersonStudyResource
api = Blueprint('api_v1', __name__, url_prefix='/api/v1')
@api.before_request
@auth.login_required
def before_request():
"""All routes in this blueprint require authentica... |
6ffc484bfbf82011cbfaf8b3ceaf0a373f936bd248094158159e41fbfdda630d | Python | 490 | 15 | from __future__ import division
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.tools.sm_exceptions import PerfectSeparationError
from statsmodels.distributions.empirical_distribution import ECDF
from scipy import stats
from collections import Counter
from itertools import chain
from utils ... |
6959371bd0d449dda35f5d5b630176006a35e47f2711f718305c7a16529e09d6 | Python | 491 | 19 | import pandas as pd
import pandas._testing as tm
def test_array_setitem_nullable_boolean_mask():
# GH 31446
ser = pd.Series([1, 2], dtype="Int64")
result = ser.where(ser > 1)
expected = pd.Series([pd.NA, 2], dtype="Int64")
tm.assert_series_equal(result, expected)
def test_array_setitem():
# ... |
791034e118971199d6470b6c3f21ccdc40c273474917fd53e0b1c704402371cb | Python | 491 | 18 | # -*- coding: utf-8 -*-
# JN 2014-10-21
# script creates a clinRecConv.py from ncs files
import os
import numpy as np
from combinato import NcsFile
from matplotlib.dates import date2num
if __name__ == "__main__":
if os.path.exists('clinRecConv.py'):
print('File exists, doing nothing')
else:
fi... |
3f69040050c9eb9b893a4b3ddf138aea696c17e62c3ad44d60f979d69bcefa8c | Python | 492 | 20 | import pytest
import pandas as pd
@pytest.mark.parametrize(
"data, index, expected",
[
([1, 2, 3], None, 3),
({"a": 1, "b": 2, "c": 3}, None, 3),
([1, 2, 3], ["x", "y", "z"], 3),
([1, 2, 3, 4, 5], ["x", "y", "z", "w", "n"], 5),
([1, 2, 3, 4], ["x", "y", "z", "w"], 4),
... |
831b0bf2a92ec0c4fdcdddf198c4fae8753199aae68798195af5c97355a1c95d | Python | 492 | 20 |
import meniscus
from pathlib import Path
import cine
import numpy as np
datapath = Path("data2/")
files = sorted(datapath.glob("*.cine"))
for file in files:
try:
video = cine.Cine(str(file))
data = meniscus.find_meniscus(video, 0, video.image_count - 1)
out = Path("men_data/")
out... |
e4fa8292e5e64e459492c059a7bc5c526618bfe80ddac3e1e48fef0c9c146aa2 | Python | 495 | 16 | from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
ext_modules = [#Extension("BioReaders", ["BioReaders.pyx"]), \
Extension("c_branch", ["c_branch.pyx"]),\
Extension("intersection_unique", ["intersection_unique.pyx"])]
setup(
name = 'tof... |
377dc66bff30de98cff94d19dadfecc9f630efa347c591db2f8a9b0134304210 | Python | 496 | 15 | '''this module contains base settings of the model data base'''
#from __future__ import absolute_import
#import dask, dask.multiprocessing
import os##
# dask schedulers
#from .compatibility import synchronous_scheduler
#scheduler = dask.multiprocessing.get
#multiprocessing_scheduler = dask.multiprocessing.get#schedu... |
3ed86dbcb6e576c7b994da11eb6a31f6f9b0571c7717e980172841f32f0224bc | Python | 496 | 20 | from __future__ import print_function # makes this work for python2 and 3
import vegas
# compile cfcn_cython, if needed, at import
import pyximport
pyximport.install(inplace=True)
import cfcn_cython
def main():
integ = vegas.Integrator(4 *[[0,1]])
print(integ(cfcn_cython.batch_f, neval=1e5, nitn=10).summa... |
435b54c757d7262d313cfa5222041e00a245287d788adc3f9503a1e66f1b4174 | Python | 496 | 21 | from .sci_ci import CoincidenceIndex
from .sci_sccg import Smoothed_CCG
from .sci_dsttc import directed_STTC
from .sci_pyinform import TE_PyInform
from .sci_idtxl import TE_IDTXL
from .sci_glmpp import GLMPP
from .sci_glmcc import GLMCC
from .sci_ensemble import NNEnsemble
from spycon.coninf import setup_ensemble
__al... |
a50bf70b70894a024b3c5f614904d3a756c328a221df46da659fc8e7900cae89 | Python | 496 | 24 | import sys
import re
import pickle
labelD = {}
num = 0
for line in sys.stdin:
m = re.match("^([^ ]+) : ([^ ]+)$", line)
if m != None:
feat = m.group(1).strip()
label = m.group(2).strip()
if label not in labelD:
num += 1
labelD[label] = num
feat = feat.replace("=1", "")
... |
bcc4eab0333947fb682e4145976c84fdad03a455edc429e98cd8a7f24eab84a1 | Python | 496 | 16 | sourcedir = 'V:\\\\cvs\\qwtplot3d\\'
stext=""$(QTDIR)"\\bin\\"
rtext="$(QTDIR)\\bin\\"
import os, string, sys
from os.path import join, getsize
for root, dirs, files in os.walk(sourcedir):
for name in files:
if '.vcproj' in name:
f = file(join(root,name),'r')
s =... |
ac17cecb3f50de9b36549592024197ec6e465ca51da6de3bc32c01edee85d806 | Python | 497 | 19 | from kcsd.sKCSD_utils import check_estimated_shape
import os
import unittest
import numpy as np
class testCheckEstimatedShape(unittest.TestCase):
def test_unchanged(self):
array = np.ones((1, 5))
out = check_estimated_shape(array)
self.assertEqual(array.shape, out.shape)
def test_chang... |
d312daf06910151e667f539d2d2bcccdd0ab0fa30470db43a1596aefc755507a | Python | 498 | 26 | from dclab.rtdc_dataset import feat_anc_core
#: integer-valued features
INTEGER_FEATURES = [
"fl1_max",
"fl1_npeaks",
"fl2_max",
"fl2_npeaks",
"fl3_max",
"fl3_npeaks",
"frame",
"index",
"ml_class",
"nevents",
]
#: features that by definition trend over the course of a measureme... |
487c80e2073e69156c1a1974e6828a27ed0b84db9aaefa46e1bedf0fef7883a3 | Python | 499 | 13 | import sys
import pandas
import re
X = pandas.read_csv(sys.stdin,sep='\t')
for disc in X['item'].unique():
print( '!ARTICLE' )
## Concatenate all words in the same discourse...
txt = ' '.join( X['word'][ X['item']==disc ].tolist() )
## Segment sentences using rules with task-dependent exceptions for cases lik... |
57c58021328c96537398b78a036fee2c8f786e21024f2adfca9caa86a812664c | Python | 502 | 10 | import tempfile
from esmologs import foldseek2fasta
import helpers
def test_foldseek2fasta_1(shared_datadir):
with tempfile.TemporaryDirectory() as output_dir:
#output_dir = "tmp"
foldseek2fasta.main(["-i", str(shared_datadir / "af2_examples"), "-o", output_dir + "/out"])
helpers.compare_fi... |
ffbfb164fe5c9e9b4993f3c911c3ab67f106fda4f3b264cbff74261dc7fdf0b9 | Python | 502 | 14 | from .tools import (get_node_parameter_value, get_parameter_value,
initializeRedisFromYAML, get_node_parameter_dump,
get_redis_info, main, get_node_io, unpack_string,
node_stage)
from .node import BRANDNode
from .supervisor import Supervisor
... |
72ed9da2260688a1bb4f6a2a6ccebba152bcf7c81b610c705188ade4e652d271 | Python | 503 | 17 | import sys
import re
import lex
DEBUG = False
for a in sys.argv:
if a=='-d': DEBUG = True
## For each line...
for line in sys.stdin:
## For each paired preterminal and terminal...
for preterm,term in re.findall( '\(([^\(\) ]+) ([^\(\) ]+)\)', line ):
term = term.lower()
out = lex.getFn( preterm, term )... |
7cd47facbabb0e313374f014f9ffa3fc5a9465437b80ed8a93f1c17a199e3c14 | Python | 505 | 25 | import numpy as np
import os
import csv
import ase.db
import ase.io
if not os.path.exists('2D_data'):
os.mkdir('2D_data')
# Connect to database
db = ase.db.connect('c2db.db')
rows = db.select(selection='workfunction')
count=0
out=[]
for row in rows:
atoms=row.toatoms()
out.append(row.workfunction)
ase.io.wri... |
863f27e2804b5667062f92f391c87c39e1b8979db57589e012c2448dd998c47a | Python | 507 | 19 | #python pull_surp.py > output
import scipy.io as io
import sys
data = io.loadmat('genmodel/ESNnew.mat')
sents = data['possents']
surps = data['rawsurp_esn']
sents = sents.ravel()
for size in xrange(6):
for variant in xrange(3):
print 'esnsurp{:d}{:d}'.format(size+1,variant),
print
for senti in xrange(len(sents... |
ade459886cf76bcb3057cbc8b15ed62e94c043f6807976a95f15f20402d8305c | Python | 508 | 14 | """
BASALT: A modular metagenomic binning and refinement pipeline.
This package exposes the high-level CLI entry point in ``BASALT.py``
and a number of internal modules implementing individual steps of
the pipeline (autobinning, dereplication, outlier removal, contig
retrieval, reassembly, and model utilities).
Typic... |
1936a927524a1278a8609c30063b25edbdf10f9414b8fab8d89ba48a36293e97 | Python | 509 | 24 | # MOFTransformer version 2.2.0
import os
__version__ = "2.2.0"
__root_dir__ = os.path.dirname(__file__)
from moftransformer import visualize, utils, modules, libs, gadgets, datamodules, assets
from moftransformer.run import run
from moftransformer.predict import predict
from moftransformer.test import test
... |
ec4099719d053412a76a8c264ceb1bd9e58a906fbfdc0b4e112146eeaa4ff45b | Python | 509 | 24 | import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'resource-gcg', 'scripts'))
from model import Model
lexicon = Model('L')
sfile = open(sys.argv[1],'r')
opts = sys.argv[2:]
unkhold = [o for o in opts if '-u' in o]
if unkhold != []:
unkhold = int(unkhold[0][2:])
else:
unkhold = 0
... |
75dffd647e8108516799c6035dc56e488c8d83db521463a1db87ed5c69340d76 | Python | 510 | 23 | import sys
depth = 0
curStr = ''
for line in sys.stdin:
for c in line:
curStr += c
if c == '(':
depth += 1
elif c == ')':
depth -= 1
if depth == 0:
print curStr
curStr = ''
elif depth < 0:
print ... |
6654b4d5c73b7bc6fc23171c9ad1d3c1cfda168660852fb6185b0ec0543b3cde | Python | 511 | 10 | """Print the mean displacement of a greedy warp and its mean error against the
synthetic warp, inside the box shown in the logo."""
import sys
import numpy as np
import nibabel as nib
c = (slice(15, 63), slice(31, 49), slice(2, 64))
s = nib.load(sys.argv[1]).get_fdata().squeeze()[c]
w = nib.load(sys.argv[2]).get_fdata(... |
a9b002d9ca895dd685f73b36d7a7463b375cc72ee1f32154091649b5f2238d1e | Python | 511 | 20 | import numpy as np
from values import *
from defs import *
def AE1(cell,memb_id,xAE1,area):
bpp = cell.conc[3,2]
cpp = cell.conc[2,2]
betapp = bpp/dKbpp
gampp = cpp/dKcpp
bp = cell.conc[3,memb_id[1]]
cp = cell.conc[2,memb_id[1]]
betap = bp/dKbp
gamp = cp/dKcp
xT = xAE1/(1+bpp/172.0)
summ = (1+betap+gamp)*(... |
b574ace3ef966fdb74e5ae7c049c8ef88cf72af29e3cbf8d8b7847624ef9bb15 | Python | 512 | 21 | import sys
print('word sentid sentpos discid discpos')
# 'id's start from 0, 'pos'es start from 1
discid = 0
sentid = 0
discpos = 1
for line in sys.stdin:
line = line.strip()
if line == "!ARTICLE":
discid += 1
discpos = 1
continue
if line:
sentpos = 1
... |
45841976778d71f3a99810800d3c154f368d6abb8e5fef740e9ea65a2ef781f7 | Python | 514 | 16 | import sys
import random
import configparser
#undersample_rate = .2 #print only 20% of negative coref data
#undersample_rate = 1.0 #off
config = configparser.ConfigParser(allow_no_value=True)
config.read(sys.argv[1])
undersample_rate = config["NModel"].getfloat("UndersampleNRate")
for line in sys.stdin:
if line.e... |
3c8a30c6a0ace7ae4dd686949178a962744f64cebaa19bbffa02837b0284b3e1 | Python | 516 | 15 | from __future__ import division
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.tools.sm_exceptions import PerfectSeparationError
from statsmodels.distributions.empirical_distribution import ECDF
from scipy import stats
from collections import Counter
from itertools import chain
import sys;... |
78a457502dfd52faa29c860d692b5d223e6ce454565af0ef513b192f9ed6de5e | Python | 516 | 17 | import sys, pandas as pd
def main():
data = pd.read_csv(sys.stdin, sep=' ', skipinitialspace=True)
sys.stderr.write('Removing NaN fdurs.\n')
fdur = None
for name in ['fdur', 'fdurFP', 'fdurGP']:
if name in data.columns.values:
fdur = name
break
if fdur:
data ... |
4e4068b67925e1bcc5f7d3d4681403420cd94c94de396d49f09ea51232e734d0 | Python | 518 | 20 | """
Tests for Indexes backed by arbitrary ExtensionArrays.
"""
import pandas as pd
class BaseIndexTests:
"""Tests for Index object backed by an ExtensionArray"""
def test_index_from_array(self, data):
idx = pd.Index(data)
assert data.dtype == idx.dtype
def test_index_from_listlike_with_... |
1e27d43a8cd324ae1e66b9ccc82822f0f32094089307ccddf1fbb3a766b3c42e | Python | 519 | 20 | def get_inputtype(input):
"""
Get input types based on input file format
:param input:input file
:return: type
"""
type = "Wrong Type"
if input.split("/")[-1].split(".")[-1] == "mol2":
type = "mol2"
elif input.split("/")[-1].split(".")[-1] == "pdb":
type = "pdb"
... |
16c4ff3dac35742763af98db9324959184a827d6faaa02f4bc9f83caaab0fb28 | Python | 520 | 17 | import sys, pandas as pd
def main():
data = pd.read_csv(sys.stdin, sep=' ', skipinitialspace=True)
sys.stderr.write('Removing unfixated tokens.\n')
fdur = None
for name in ['fdur', 'fdurFP', 'fdurGP']:
if name in data.columns.values:
fdur = name
break
if fdur:
... |
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