sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5f0322696b3437520bd4c106c07897c003ddbf0fa9cbd7c781dda10296753faa | Python | 1,371 | 56 | import os
import shutil
class models_genesis_config:
model = "Unet3D"
suffix = "genesis_chest_ct"
exp_name = model + "-" + suffix
# data
data = "/mnt/dataset/shared/zongwei/LUNA16/Self_Learning_Cubes"
train_fold=[0,1,2,3,4]
valid_fold=[5,6]
test_fold=[7,8,9]
hu_min = -1000.0
... |
f6037e98820db072fa82c025b857313bd5228872f50682e0657245b1e3cc6d96 | Python | 1,371 | 38 | import os
import subprocess
from tests.conftest import FIRST_LINE_MODEL_PATH, MIC_PLATES_PATH, DRUG_NAME, PROJECT_ROOT, TARGET_MIC_CSV
import csv
import pytest
from os import path
@pytest.mark.assets_required
def test_main(tmp_path):
d = tmp_path / "predictions"
d.mkdir()
env = os.environ.copy()
env[... |
03047a5a13c75d4e0a37ee6320025729256108e85a552cdd529a1319a01243bc | Python | 1,374 | 51 | import numpy as np
import pandas as pd
import time
import datetime
def remove_non_expressed(df: pd.DataFrame, verbose: bool=False):
"""
Identifies and removes genes with mean 0 from the dataframe,
i.e. non-expressed genes.
Parameters
----------
df : DataFrame
Gene expression data, ... |
66f8b0e0f4aee4da973bfd954f413de231a85037a629e9a0043c2838e3966fd0 | Python | 1,374 | 30 | import numpy as np
import pandas as pd
import pytest
from pyaging.preprocess import _preprocess
def test_bigwig_signals_and_missing_regions_keep_input_order(monkeypatch, tmp_path):
pybigwig = pytest.importorskip("pyBigWig")
path = tmp_path / "sample.bw"
with pybigwig.open(str(path), "w") as bw:
b... |
707561a7f3f4ed9a37f6dbc8f6570eebe7c2bcf5d21b6a865758f0d393e07176 | Python | 1,374 | 46 | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
class Normalizer(object):
"""Normalize a Tensor and restore it later."""
def __init__(self, tensor=None, mean=None, s... |
91404e328cfcccf1683cd3f717d5c9d6f0fabe25f6247907d273288c014b0a48 | Python | 1,374 | 44 | import numpy as np
import pandas as pd
import pandas._testing as tm
class TestJoin:
def test_append_join_nondatetimeindex(self):
rng = pd.timedelta_range("1 days", periods=10)
idx = pd.Index(["a", "b", "c", "d"])
result = rng.append(idx)
assert isinstance(result[0], pd.Timedelta)... |
315143245cb500362f68c15416862062953dcb25d3d091d885f8007681130d97 | Python | 1,375 | 42 | import dask.dataframe
import pandas as pd
import os
def concat_path_elements_to_filelist(*args):
'''e.g.
args = ['str', [1,2,3], pd.Series([1,2,3])]
> [['str', 'str', 'str'], [1, 2, 3], [1, 2, 3]]
'''
if not args:
return []
args = [[arg] if isinstance(arg, (str, int, float)) else... |
8a896351264ba6044a75bf9ccb209bc381187f7405a1631cd4d9a604b60872d7 | Python | 1,375 | 39 | import importlib
import os
from PyQt5.QtWidgets import QMessageBox
from .DropDownMenu import DropDownMenu
class SelectPKLFile(DropDownMenu):
"""
A dropdown menu widget specifically designed for selecting .pkl files.
"""
def __init__(self, default_msg, normative_model_ids):
"""
Initial... |
c0596e8dd4cfcdd8c1983a7c57180612399005b027a628392870f7ab39a71e78 | Python | 1,375 | 48 |
import numpy as np
import scipy.stats as stats
def factorize_normal(samples, freedom=1) -> tuple[float, float]:
mu, sigma = stats.norm.fit(samples)
return (mu, sigma * freedom)
def factorize_halfnormal(samples, freedom=1) -> tuple[float]:
loc, scale = stats.halfnorm.fit(samples)
return (scale * fre... |
0e4db88d4babe6957abdd023e01a9c8ac0593d75210c433e4f836c58eb385a5b | Python | 1,376 | 50 | import sys
import glob
import pandas as pd
import numpy as np
import h5py
file_list = sys.argv[2:]
print(file_list)
LABEL = 'Sample_characteristics_ch1'
total_data = pd.DataFrame()
for ff in file_list:
data = pd.read_csv(ff, sep='\t', index_col=6)
total_data = total_data.append(data)
print(total_data.sh... |
8b6755098393cfa4f54290b7672306c999ef071ad518cd66c8faecb11e0109de | Python | 1,378 | 45 | from typing import List, Optional, Union
import numpy as np
import scipy.signal
import scipy.interpolate
def _findFirstPeak(signal: np.ndarray, minHeight: float = 0.3, prominence: float = 0.05) -> Optional[int]:
peaks, _ = scipy.signal.find_peaks(signal, prominence=prominence, height=minHeight)
if len(peaks)... |
046f1127da9899a30271a54be55ff96393594390d6db0c09f0314b2654e19a01 | Python | 1,379 | 32 | #! /usr/bin/env python
##############################################################################
## DendroPy Phylogenetic Computing Library.
##
## Copyright 2010-2015 Jeet Sukumaran and Mark T. Holder.
## All rights reserved.
##
## See "LICENSE.rst" for terms and conditions of usage.
##
## If you use this wo... |
3ba3d7dab4582f2828f79554b72f48e24c488dc141b3efc8c01fbdeb93770408 | Python | 1,381 | 38 | # Copyright 2018-2025 Xanadu Quantum Technologies 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 or... |
9e6456f1b395f1be3431dd27fde24c38a85504aa2e4a5097b4c86682453f583f | Python | 1,383 | 26 | # please install abagen in terminal first
# by running the following:
# pip install brainsmash
#
import numpy
import pandas as pd
from brainsmash.mapgen.base import Base
# input data
# brain map a one-dimensional scalar vector (Nx1, N = number of regions)
brain_map_file = "Y:\\projects\\jinliu5\\2021_Lon... |
0bbf29d0f083f73a3473fc5e414f26db617a69d1b8a886d59f8acb6aa9ac096a | Python | 1,384 | 39 | """Varimax rotation."""
import torch
from einops import rearrange
def varimax(phi: torch.Tensor, gamma: float = 1.0, n_iterations: int = 20) -> torch.Tensor:
"""Apply an orthogonal Varimax rotation.
Parameters
----------
phi
Input matrix of shape `(*batch, n_components, n_channels)`.
... |
53bfaf657bd23909e03fe07e3f8f186e44cc67b0a83078486a40b8b0cc6aba99 | Python | 1,384 | 40 | from .spaciotemporal_binning import time_list_from_pd
import pandas as pd
import numpy as np
import dask.dataframe as dd
import dask
import compatibility
from .temporal_binning import universal as temporal_binning
def spatial_binning(sa,
min_time = 0,
max_time = 245+50,
... |
6b7242a2af66ddcd5d85ea2ae4e57128d5c6bf308aeb879b2b3985705a2bdff8 | Python | 1,384 | 46 | import numpy as np
from modules import (
normalisation,
transformations,
transformation_utils,
)
class BaseImageArrayNormalisation(normalisation.Normalisation):
def apply_normalisation(self) -> np.ndarray:
"""
Apply the normalisation and store the relevant values for inver... |
96ba0f6bf6bb507ca6ecbc1cb46fc2461d198b86f070f9e948a9e96b60f4a9d6 | Python | 1,384 | 36 | import pandas as pd
import numpy as np
pheno_type = "multresp"
zmat_file_orig = f"zmat/{pheno_type}_orig_zmat_201217.csv.gz"
zmat_file_perm = f"zmat/{pheno_type}_perm_zmat_201217.csv.gz"
out_file_orig = f"zmat/{pheno_type}_orig_zmat_201217.filtered.csv.gz"
out_file_perm = f"zmat/{pheno_type}_perm_zmat_201217.filtered.... |
9b55730fe056d2c3d8d8a1a6b7b07670e553fc949791ef355fa163e93fd9b49e | Python | 1,384 | 36 | import tensorflow as tf
from tensorflow.keras import layers
def double_conv_block(x, n_filters):
x = layers.Conv2D(n_filters, 3, padding = "same", activation = "relu", kernel_initializer = "he_normal")(x)
x = layers.Conv2D(n_filters, 3, padding = "same", activation = "relu", kernel_initializer = "he_normal"... |
9bddf3d8dc15cce80c25b2b7da8133df19669a551b027c62448a8c1f720e75e2 | Python | 1,384 | 41 | # Copyright 2018-2023 Xanadu Quantum Technologies 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 or... |
7bff4d169fbcd165219544ff2a5d7b3ad8558959d6bba136850be5109d31c0ad | Python | 1,385 | 69 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# JN 2016-01-22
"""
Create a csv file containing downsampling information from all channels
"""
from __future__ import print_function, division, absolute_import
import os
import tables
import csv
FNAME_OUT = 'h5meta.txt'
def to_file(msg, outname):
"""
write h5in... |
fccc9cf1445f22e0339d28950204d611a44170d7ae2df32058c5ec7b2866dd86 | Python | 1,385 | 52 |
import sys
import numpy as np, pandas as pd
from matplotlib import pyplot as plt
from matplotlib import colors
from matplotlib import figure
CLONE_MAT = str(sys.argv[1]) + '.csv'
CLONE_NPY = str(sys.argv[1]) + '.npy'
CELL_BCS = str(sys.argv[2])
DATASET = str(sys.argv[3]) + '.csv'
# load csv file as pandas and nump... |
eba66a7e4e4a4c30624cd9e4e7eee4ed5fd4a78687b0e4b7d3f78f329d40b83f | Python | 1,386 | 43 | # -*- coding: utf-8 -*-
"""
Created on Wed Jun 21 20:21:56 2023
Convolutional Spiking Neural Networks for Intent Detection Based on Anticipatory
Brain Potentials Using Electroencephalogram
Authors: Nathan Lutes, V. S. S. Nadendla, K. Krishnamurthy
"""
def main():
#this function defines the paths for data loading... |
cfa53b7e6d7a0e578f1391469ce8707808f6009d3b3fc972ca316cb77e0b0644 | Python | 1,388 | 49 | import sys
import re
import storestate
UNKCOUNT = int(sys.argv[1])
WordCount = { }
for line in open(sys.argv[2]):
count,word = line.split()
WordCount[word] = int(count)
for line in sys.stdin:
wordlist = re.findall('(?<= )[^\(\) ]+',line)
for word in wordlist:
## if word not in WordCount:
## sys.std... |
52bb3d03559f5c2596869728e8f0effb91f1fade11349e83ebdcedf3efd76923 | Python | 1,390 | 41 | #
# Program to write Clebsch Gordan coefficients to text file
#
print('Program starting')
import os
import numpy as np
#from tensorfieldnetworks.utils import FLOAT_TYPE
import protein_holography.network.clebsch as clebsch
cutoff_l = 10
print('Gathering cg coefficients')
# here we implement the Clebsch Gordan coe... |
a19aa06b6f0f45fd2243e670e9f2facecf4525ae1f5186554ee4d7027837f911 | Python | 1,390 | 43 | import os
import time
import concurrent.futures
import json
# 该函数用于检查反编译后没有samli文件的apk,没有smali文件的apk就记为废物样本
path = r'E:\pythonProject\Experiment\out_benign_samples'
allapkfiles = os.listdir(path)
def check(apkfilename):
abspath = os.path.join(path, apkfilename)
files = os.listdir(abspath)
if "... |
e6c7fe1e9ca6c87f2232b51afa67bd5911f2f356fedeee66d2c37986559669c3 | Python | 1,394 | 61 | """Analysis procedure registry + dispatcher."""
from __future__ import annotations
from typing import Any, Callable
import pandas as pd
from pycaret_server.analyses.base import (
AnalysisProcedureError,
AnalysisResult,
)
from pycaret_server.analyses.procedures import (
run_anova_oneway,
run_arima,
... |
309e7220474b95940d245f1c25ed6d2ae4ef8364553aff3f7ca41e23ccf885fa | Python | 1,396 | 51 | #!/usr/bin/env python3
"""
Script to check md5 of files on S3.
"""
import argparse
import boto3
import hashlib
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--md5-file',
dest = 'md5_file')
parser.add_argument('-p', '--s3_prefix',
... |
a2628109cc350df582783caaffd20297e868cadaef5e5ee8cc383f14522569c8 | Python | 1,399 | 35 | #! /usr/bin/env python
import argparse
from os import mkdir,listdir
from shutil import copyfile
from os.path import exists,splitext,normpath,join
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-i","--indir",required=True,help="Input folder which contains all trees and alignments")
par... |
60fc1765d44150f50907371b84c371aa7dc12db55e48abac9d86d3c30acce88c | Python | 1,400 | 56 | # JN 2016-05-12
"""
Setup options.py with the correct clustering binaries
"""
from __future__ import print_function
import os
import sys
import subprocess
OPT_PATH = os.path.join('combinato', 'options.py')
DEF_OPT_PATH = os.path.join('combinato', 'default_options.py')
def setup():
if os.path.isfile(OPT_PATH):
... |
f1f73fda6f2dc92d0abaf8ef829748b0f760742c59bf77d9c346f0c52bcf3ebc | Python | 1,400 | 54 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sys
import matplotlib.ticker as ticker
import matplotlib.transforms as transforms
from pathlib import Path
from statistics import mean, stdev
import math
import sys
import argparse
sys.path.append('../')
from plotting_functions import *
#... |
c63c1855eabca79923b3dcae9acb2c29c981a80261e6fbc74b78b0fc1af939c0 | Python | 1,401 | 62 | from scipy.integrate import solve_ivp
from scipy.io import loadmat
import numpy as np
from time import time
class P:
field_locs = None
field_sizes = None
disc_step = None
Iloc = None
W = None
I = None
v = None
tau = None
T = None
dt = None
n = None
def run_cont(t, v, p):... |
6d89b0bea5a669fe7fedab54d2c19c7a4ed1784546e4b202ddffdb9e89d7635a | Python | 1,402 | 58 | from subprocess import run
from argparse import ArgumentParser
from pathlib import Path
import os
def cli():
cwd = os.getcwd()
parser = ArgumentParser()
parser.add_argument(
"--data",
default=Path(f"{cwd}/../data/norbelladine_4OMTase/boxes/4OMTase_dataset.pkl"),
type=Path,
)
... |
d310b283f72eaf9056e8f98cae5ec9216131f7e891320c4f01a0e4cad75f9768 | Python | 1,402 | 40 | class ObjectFactory:
"""General-purpose class to instantiate some other base class from rl_games. Usual use case it to instantiate algos, players etc.
The ObjectFactory class is used to dynamically create any other object using a builder function (typically a lambda function).
"""
def __init__(self):... |
397883d65e72a49cd17ea32c0ff5799897260a66e5f01ce7a6cb742d039d8d05 | Python | 1,403 | 43 | """Heads that produce (x, y) predictions for coordinate regression."""
import torch
from jaxtyping import Float
from torch import nn
# to ignore imports for sphinx-autoapidoc
__all__: list[str] = []
class LinearRegressionHead(nn.Module):
"""Linear regression head that converts 2D feature maps to a vector of (x,... |
310f6465383ffdfff9f2dc0bc40890158637c9b3755fe5e745f0f0b2eded47d9 | Python | 1,404 | 56 | import datetime
import numpy as np
import pytest
from pandas._libs.tslibs import (
Resolution,
get_resolution,
)
from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
def test_get_resolution_nano():
# don't return the fallback RESO_DAY
arr = np.array([1], dtype=np.int64)
res = get_resolution(ar... |
822101ef211a76d2dd934a46d25ec0b7c760d0177559a6081cd48757f166692c | Python | 1,404 | 34 | #! /usr/bin/env python
##############################################################################
## DendroPy Phylogenetic Computing Library.
##
## Copyright 2010-2015 Jeet Sukumaran and Mark T. Holder.
## All rights reserved.
##
## See "LICENSE.rst" for terms and conditions of usage.
##
## If you use this wo... |
684e04bb10898b2bffb6f47c730b808bc7c9422fd44f92992008196f4807924a | Python | 1,405 | 47 | #!/usr/bin/env python
import torch
from torch.utils.data import DataLoader
from rdkit import Chem
from rdkit import rdBase
import data_struct as ds
from data_struct import MolData, Vocabulary
from model import RNN
import sys
def Sample(filename, enumerate_number):
voc = Vocabulary(init_from_file="./Voc")
P... |
19cf751381513a7148cfe4b7493efa693bd653e1a38b7ffe13af4d3773a9e0c2 | Python | 1,406 | 39 | # coding: utf-8
get_ipython().run_line_magic('cd', 'proboscis_utils')
get_ipython().run_line_magic('ls', '')
#get_ipython().run_line_magic('runfile("test_proboscis_model.py")', '')
#runfile
#get_ipython().system('runfile')
get_ipython().run_line_magic('cat', 'test_proboscis_model.py')
import test_proboscis_model as pro... |
d05d04684bdb568623496ad7bca58785795b31b006e1f8dd7cde8a61c1ad8eb9 | Python | 1,406 | 44 | import os
import numpy as np
import string
from typing import Iterable
def parse_a3m(filename):
seqs = []
table = str.maketrans(dict.fromkeys(string.ascii_lowercase))
# read file line by line
for line in open(filename,"r"):
# skip labels
if line[0] != '>':
# remove lowercas... |
e5096bd5837c3e8c56a2db1eb8395c8745a6192c607f38dd6babcbc8ab1b708f | Python | 1,406 | 38 | '''
Created on Sep 16, 2016
@author: arco
'''
from model_data_base.plotfunctions._decorators import dask_to_pandas, subsequent_calls_per_line, return_figure_or_axis
@dask_to_pandas
@return_figure_or_axis #has to stand above subsequent_calls_per_line
@subsequent_calls_per_line
def average_std(mean, std,
... |
e47f7941266e24a0798573fa40394695877ca052addb3236947d1e473981f8d5 | Python | 1,408 | 49 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.tests.copy_view.util import get_array
@pytest.mark.parametrize(
"cons",
[
lambda x: pd.TimedeltaIndex(x),
lambda x: pd.TimedeltaIndex(pd.TimedeltaIndex(x)),
],
)
def test_timedeltaindex(cons):
... |
c5c5e34d3942d4f52be9ec9211ca01d29409ff7d406eda226be0a3da30ea4994 | Python | 1,412 | 37 | """
Miscellaneous util functions
"""
import random
import string
def _generate_random_code(length=10, seed=None):
"""Generate random alphanumerical code, starting with a letter for proper sorting in file browsers.
:param length: specifies length of random string, defaults to 10
:type length: int
:par... |
760464f1516bbe160881967ee44f94dedb31b24960533e0c9a4314e56ad36600 | Python | 1,413 | 53 | #from pymatgen import MPRester
from pymatgen.ext.matproj import MPRester
from pymatgen.io.cif import CifWriter
import csv
import sys
import os
print('start')
###need 2 files: csv containing materials project IDs and atom_init.json (generic)
###get API key from materials project login dashboard online
API... |
0b1c7733f1cbe4050f631ad8e581e76388c5f0ae1bd2a7aaf29421136c855c81 | Python | 1,414 | 71 | import pytest
from src.config_handler import read_yaml, get_entry
from pathlib import Path
@pytest.fixture
def pipe_config():
return {
'pipeline-test': {
'descr': 'Test Pipeline',
'steps': [
{
'name': 'scaler'
},
{
... |
cabb637bddd686cb64426eaaca7464907e13d05791726cac31027a4d4f476919 | Python | 1,414 | 33 | from isaacgymenvs.pbt.launcher.run_description import ParamGrid, RunDescription, Experiment
from isaacgymenvs.pbt.experiments.run_utils import version, default_num_frames
kuka_env = 'allegro_kuka'
_frames = default_num_frames
_pbt_num_policies = 8
_name = f'{kuka_env}_{version}_pbt_{_pbt_num_policies}p'
_wandb_acti... |
087aac9b31928fd3a80e5a5bbaaf11ec7f5201789b713ee64428616a5853c64f | Python | 1,415 | 36 | """Ellipse phantom for testing."""
import torch
from mrpro.phantoms import EllipseParameters, EllipsePhantom
class EllipsePhantomTestData:
"""Create ellipse phantom for testing.
Parameters
----------
n_y
number of voxels along y
n_x
number of voxels along x
"""
def __ini... |
3de3cafe1c81c7e346bea851a2307037eb5e28ed010a0059cf9d3b76e345bb3f | Python | 1,416 | 50 | import os
import shutil
import pytest
from pcntoolkit.dataio.norm_data import NormData
from pcntoolkit.normative_model import NormativeModel
from pcntoolkit.regression_model.test_model import TestModel
from test.fixtures.norm_data_fixtures import *
from test.fixtures.path_fixtures import *
@pytest.fixture
def test_... |
e7b11786515250d79f33eb9705ac66a6d97a9f2f54a2a8d2fb7292fa19f860c8 | Python | 1,417 | 51 | # JN 2015-01-11
# refactoring
from __future__ import division, print_function, absolute_import
import logging
import numpy as np
import scipy.stats as stats
from .. import options
logger = logging.getLogger(__name__)
def select_features(features):
"""
select the features that go into sorting
"""
fact... |
a20379d5ea31dbbe767bbd1603eb44e0b5b6be5005b581f492e7216e4ebc0aae | Python | 1,418 | 33 | from subprocess import check_output
# Hyper-parameters
table = "bow_tuning"
# target = "dspln_PSYCHIATRY_12"
target = "dspln_SOCIALWORK_12"
max_tokens = [5000] # 1000
# c_s = [0.1, 0.2, 0.3, 0.5]
# c_s = [0.6, 0.7, 0.8, 0.9, 1]
# c_s = [1, 3, 5, 10, 100]
# c_s = [0.1, 0.5, 0.7, 0.8, 0.9, 1, 1.1, 3, 5, 10]
# c_s = [0.... |
cee35a829e9ad0b29f230b8f6df7eec42034afb4cf0377da9e3568aa1d766d3d | Python | 1,418 | 55 | import time
class Timer(object):
"""docstring for Timer"""
def __init__(self, class_name=[]):
super(Timer, self).__init__()
self.duration = {}
self.start_time = {}
self.names=[]
for name in class_name:
self.duration[name] = 0.0
self.start... |
74bbd9234dfdf7f6a3575722fd16338d2b4c8f7952cfeb2ec3269db2c91b1ce2 | Python | 1,419 | 55 | # -*- coding: utf-8 -*-
#Author: https://pamelaguevara.icb.udec.cl/grupo-imagenes-medicas/
#from soma import aims
import numpy as N
import os
import shutil
import math
#from soma import aims
def read_bundle( infile ):
points = []
bun_file = infile + 'data'
os.path.getsize( bun_file )
bytes = os.path.getsiz... |
79485a1dc76d53930dfd9a433ce9fbae3708b3b611bd95ee1513ce8db64265cc | Python | 1,419 | 45 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Analysis of the power spectral density of the ROI time series: calculate PSD and export.
Vinding, M. C., Eriksson, A., Low, C. M. T., Waldthaler, J., Ferreira, D., Ingvar, M., Svenningsson, P., & Lundqvist, D. (2021). Different features of the cortical sensorimotor rh... |
097880cee21ac659f6c58d28747c3b500c41a1d49f0747cc71c4c8343ac21448 | Python | 1,420 | 49 | """Tests for Phase Operator."""
import pytest
import torch
from mrpro.operators import PhaseOp
from mrpro.utils import RandomGenerator
from tests import autodiff_test
def test_phase_operator_forward():
"""Test that PhaseOp returns angle of tensors."""
rng = RandomGenerator(seed=2)
a = rng.complex64_tens... |
09f3e86b6f6902c37f13e77f5b357eb5f05ace24cdc5b03c88a74afd9813fd76 | Python | 1,420 | 39 | import unittest
import torch
from scipy.spatial.transform import Rotation
import numpy as np
class TestCaseNeSVoR(unittest.TestCase):
@staticmethod
def assert_tensor_close(*args, **kwargs):
torch.testing.assert_close(*args, **kwargs)
@staticmethod
def assert_tensor_equal(*args, **kwargs):
... |
e557decd9cf64821cacd56f23b847fa9111e9bfb1c61af895e4b6e98bf0da2ac | Python | 1,420 | 43 | # 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... |
f28181d957e88d4261670b0970318ef6223d4c7434673d38d4cbbbf655e7d8a0 | Python | 1,421 | 50 | import numpy as np
import sys
def namesort(nameSet):
sort_name = []
for sta in ['HAFStastic','safe','kappa','phi','dihhStastic',\
'ihsStastic','nslStastic','sfsStastic','allelFrebaseStastic','hapFrebaseStastic']:
for name in nameSet:
if name.endswith(sta):
... |
74c30fd96f0dadf79c74bc85055c22dbb02dc2691581ab4b2973df0d328381b0 | Python | 1,422 | 53 | import sys
import argparse
class FQRecord:
def __init__(self):
self.seq = ""
self.qual = ""
self.anno = ""
self.name = ""
def string(self):
ret = "@" + self.name.strip() + "\n" + \
self.seq + "\n" + \
"+" + self.anno + "\n" + \
self.qual
... |
a0a13f8af76b99a65d960f38c51c18007afd19504e46d132f477e706bae29787 | Python | 1,423 | 53 | # flake8: noqa
"""phy: interactive visualization and manual spike sorting of large-scale ephys data."""
# ------------------------------------------------------------------------------
# Imports
# ------------------------------------------------------------------------------
import atexit
import logging
import os.p... |
95771833ded13ee0c4e9a26cf7024d24ca479e43f10ba4f23ea557b576e906ba | Python | 1,424 | 44 | import numpy as np
import pytest
from pandas.errors import Pandas4Warning
import pandas as pd
import pandas._testing as tm
class TestDataFrameReindexLike:
def test_reindex_like(self, float_frame):
other = float_frame.reindex(index=float_frame.index[:10], columns=["C", "B"])
tm.assert_frame_equa... |
08133a4cb52cb42255d1f1ed56e12181396898344faa9990cf8b7353ef86c3ac | Python | 1,425 | 46 | """
Implements logging functions throughout ScanOMetrics package
"""
from os import EX_USAGE # "command used incorrectly" exit code
from sys import exit
__VERBOSE_LEVEL__ = 0
def set_verbose(verbose):
"""Set global verbose level.
:param verbose: verbose level (0: hide everything, 1: warning/errors, 2: note... |
0ce97b804235bce563c12e5f902018500ba6c2e3949216561af5e9a5c9119506 | Python | 1,425 | 44 | import numpy as np
import pandas as pd
import pandas._testing as tm
class TestWhere:
def test_where_intlike_str_doesnt_cast_ints(self):
idx = pd.Index(range(3))
mask = np.array([True, False, True])
res = idx.where(mask, "2")
expected = pd.Index([0, "2", 2])
tm.assert_index... |
bb2da70d64ffd951ca05d1e43cb018791616bb94c14d511295be59701c9ffac2 | Python | 1,425 | 51 | # -*- coding: utf-8 -*-
"""
Created on Mon Jul 25 12:09:14 2022
@author: jeanbaptiste
"""
import time
import json
import sys
sys.path.insert(0, "/projectnb/dunlop/JB/deepcellcontrol")
import deepcellcontrol as dcc
models_folder = "/projectnb/dunlop/JB/deepcellcontrol/assets/models/"
# model_folder = models_folder+"... |
ea5b237c0599b58a53ada2a3f9cb6db214ea656fbb802a00c342f31111e41e7d | Python | 1,425 | 44 | import numpy as np
import pandas as pd
mask2index = lambda mask: np.flatnonzero(mask)
def index2mask(idx_arr, n):
mask = np.zeros(n, dtype=int)
mask[idx_arr] = 1
return mask.astype(np.bool)
def np_groupby(arr, groups):
"""Numpy implementation of `groupby` operation (a common method in pandas).
... |
aedd7f4a7ec8b77c5cc068af5f820168727c0d3518b77d94ca7420358473ab5d | Python | 1,426 | 61 | __all__ = [
"RetentionTimeLibraryBase",
"IndicesNonSlice",
"Indices",
"Columns",
]
import abc
from typing import List, Optional, Union
import numpy as np
import numpy.typing as npt
import pandas as pd
IndicesNonSlice = Union[List[int], npt.NDArray[np.int_], "pd.Series[int]"]
Indices = Union[slice, In... |
d61b9b4561a72a7ddb88d43fc46474b9efdd0ac4c7fd18c822819a58339541d8 | Python | 1,426 | 54 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from emcee.autocorr import AutocorrError, integrated_time
def get_chain(seed=1234, ndim=3, N=100000):
np.random.seed(seed)
a = 0.9
x = np.empty((N, ndim))
x[0] = np.zeros(ndim)
for i in range(1, N):
x[i] = x[i - 1] * a + np.random.... |
12769ee2e73e43bcfbc97658db3bf541d0da7e6f7fad04b6ca8c0b9f499407f6 | Python | 1,429 | 41 | import numpy as np
import pandas as pd
import pandas._testing as tm
class TestFactorize:
def test_factorize_period(self):
idx1 = pd.PeriodIndex(
["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"],
freq="M",
)
exp_arr = np.array([0, 0, 1, 1, 2, 2], ... |
8b4666a3760ff466ae0849c440b552f57b75709f22cf8cc96c3f2e72dd089215 | Python | 1,429 | 38 | import numpy as np
import pandas as pd
import pandas._testing as tm
def test_isocalendar_returns_correct_values_close_to_new_year_with_tz():
# GH#6538: Check that DatetimeIndex and its TimeStamp elements
# return the same weekofyear accessor close to new year w/ tz
dates = ["2013/12/29", "2013/12/30", "2... |
4b419340e6966aea8e90621cb137330da54e044be0fbfd9857f46111d35e2498 | Python | 1,430 | 40 | import os, sys
import pickle
import apnet
import argparse
import pandas as pd
import numpy as np
parser = argparse.ArgumentParser(description='Train single model with provided train and test set')
# Mandatory arguments
parser.add_argument('validation_path', help='[string] Path to validation pkl file')
parser.add_arg... |
47481b1d55c7c61a3ebe4c538ab0bada6663097a823f373df3ca999ddc58a3a9 | Python | 1,431 | 44 | """
Chromosight's kernel submodule contains each default kernel in the form of a
dictionary whith the kernel name. The items of the dictionaries are the
key-value pairs from the kernel's json file, with the kernel matrices
pre-loaded under the "kernels" key. Here is the kernel submodule can be used to
extract the first... |
651dee8a7abba16e7763aabf006da7a8d2f11c0f8815b607128d15dcfc804bf7 | Python | 1,434 | 56 | import os
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from src.models import TemporalPC
from src.utils import *
from src.get_data import *
device = "cuda" if torch.cuda.is_available() else "cpu"
print(device)
result_path... |
a6bfa1bba037f2ef7e760a8f814795133eb3cc65fd4f660d49fc0634ab632e3b | Python | 1,435 | 45 | from typing import Union
import pandas as pd
from deepglyco.chem.gpep.glycans import GlycanNode
from deepglyco.chem.gpep.nglycans import NGlycan
glycans = pd.read_csv(r"D:\GlycoDIA\data\background_glycan.txt", header=None)
glycans.columns = ["original_struct"]
def generate_entrapment_glycan(glycan: str):
new_g... |
fc060695b5cddc8e6f8d872b7caa5bbdab928473d9d347a1bd44e4acbfa585dd | Python | 1,435 | 60 | #!/usr/bin/env python
'''
Testcases for the KwargsParser
'''
from windseer.utils import KwargsParser
import unittest
class TestKwargsParser(unittest.TestCase):
def run_case(self, type, default_value, use_name=False):
kwargs = {
bool.__name__: True,
int.__name__: 1,
f... |
2973a4bf3d74e55ba04a6880e6df3a71d68a4f6239ab7dee0e5a1dc62d37bb6b | Python | 1,438 | 55 | # importing os module
import os
directory = os.getcwd()
for count, mapname in enumerate(os.listdir(directory)):
directory2 = directory+"/"+mapname
if mapname == '.DS_Store':
continue
if mapname == 'LICENSE':
continue
if mapname == 'README.md':
continue
if mapname == 'ren... |
d955cf46987d8f3ba849eb1dccfbd11c764cb24687248dfa5de9475d93c89f7c | Python | 1,438 | 48 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 23 10:57:50 2023
@author: tomas
"""
import glob
from cine import Cine
import meniscus_rate as mr
import tube2
import numpy as np
import pathlib
from skimage.io import imsave
import pandas as pd
if __name__ == '__main__':
videonames = glob.glo... |
380ccf8af95c5b412c6817f6e413cbe4e5601df66e341b4e800512526f037ac0 | Python | 1,443 | 42 | from pandas.errors import Pandas4Warning
import pandas as pd
import pandas._testing as tm
class TestFillNA:
def test_fillna_period(self):
# GH#11343
idx = pd.PeriodIndex(["2011-01-01 09:00", pd.NaT, "2011-01-01 11:00"], freq="h")
exp = pd.PeriodIndex(
["2011-01-01 09:00", "20... |
2c747cf859aa1f83d9cd99be0f4a45f107c9b987b8afe580f59ced131577a684 | Python | 1,444 | 41 | import pytest
import pandas as pd
import pandas._testing as tm
class TestPickle:
def test_pickle(self, temp_file):
# GH#4606
idx = pd.to_datetime(["2013-01-01", pd.NaT, "2014-01-06"])
idx_p = tm.round_trip_pickle(idx, temp_file)
assert idx_p[0] == idx[0]
assert idx_p[1] is... |
76ab33bfa73a7f447a6db9b086eeec33cc0bcb64a1242d7ab0ec1ce7441f7cd1 | Python | 1,444 | 36 | #! /usr/bin/env python
##############################################################################
## DendroPy Phylogenetic Computing Library.
##
## Copyright 2010-2015 Jeet Sukumaran and Mark T. Holder.
## All rights reserved.
##
## See "LICENSE.rst" for terms and conditions of usage.
##
## If you use this wo... |
33276d5f13464262b4fb4a541a45ffa4243bac1e324f7dd43ab5f35d374e7543 | Python | 1,445 | 54 | """
vision.py
Created March 2, 2021
Contains all general methods for image processing. (Try to keep all the ugly cv2 calls in here.)
"""
from ecgdigitize.image import BinaryImage, GrayscaleImage
import math
from typing import List, Tuple
import cv2
import numpy as np
def houghLines(binary: BinaryImage, threshold: i... |
e5fe62a79bd3328c458f41ac8b4419034944c6b22e032b0bd739997461d9ee5a | Python | 1,446 | 43 | # coding: utf-8
"""
Tests write output (should it return True or False or write)
"""
from atropos.commands.trim.filters import (
NContentFilter, DISCARD, KEEP, SingleWrapper, PairedWrapper
)
from atropos.io.seqio import Sequence
def test_ncontentfilter():
# third parameter is True if read should be discarded
... |
f7b1f8da773e48b91afaca7cd0b64fbf2100ba841cf3c538efb2cdb75549f064 | Python | 1,446 | 51 | import numpy as np
from numpy.linalg import slogdet, inv
class SymmetrizedKullbackLeiblerDivergenceGaussians(object):
name = "sKLD_Gaussians"
"""
sKLD = 0.5 ( KL(p || q) + KL(q || p) )
"""
def __init__(self, cov1, cov2, mean1, mean2):
"""
:param cov1: Covariance of Gaussian p1()... |
294f9b54667c1f3e8de9b9fc4e67bf569de833ee81e1ffef7c136a22a522ccb3 | Python | 1,447 | 46 | """Regression test for the legacy SIMS checkpoint format.
The shipped sims_app/checkpoint/*.ckpt files were trained against
scsims 3.x. v4 must be able to load them unchanged. This test is
*skipped* in CI (the .ckpt files are huge and live in a separate
repo) but can be run locally to verify legacy compatibility.
Run... |
1e245d406ba5bea56c593afb0ac85dfb79aa997761ec5b1e821dc0e6bb6a3b4e | Python | 1,449 | 33 | import os
import pygraphviz as pgv
# Directory containing the DOT files
directory = "./hypotheses_tmpdot_"
# Process each DOT file in the directory
for filename in os.listdir(directory):
if filename.endswith(".dot"):
filepath = os.path.join(directory, filename)
# Load the graph from the D... |
5d0345e4d76cbba9479d6c418ded54133b6b0b6106622c2897f9f348d0dc98e6 | Python | 1,449 | 57 | # Copyright 2026 Xanadu Quantum Technologies 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 or agre... |
644129ba161f736236d6013c4ad8b3386ac4ab3b32cecf6f7abc1fd956b7f759 | Python | 1,449 | 71 | #
# File to select proteins from pdb files and store them in hdf5 file
#
from argparse import ArgumentParser
import sys
import Bio.PDB as pdb
import Bio.PDB.mmtf as mmtf
import h5py
import numpy as np
from protein_holography.utils.posterity import get_metadata,
parser = ArgumentParser()
parser.add_argument(
'-... |
e3c3a8a4ebaa16d46102163f577ce6f5657602a0fe984f9a3ec93d1602950f1d | Python | 1,449 | 51 | # -*- coding: utf-8 -*-
"""
Train the linear regression model & get the results for table S1
Created on Fri Apr 22 16:16:27 2022
@author: jeanbaptiste
"""
import copy
import time
import json
import uuid
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.keras
# sys.path.in... |
3c17a6d0aa33b2f4bec7d6bb35f5f18f1595cc5737c9a32b67778b2994bea52f | Python | 1,451 | 47 | #! /usr/bin/python
import sys
import re
'''
Print warnings if predictors are not found in datatable
'''
def removez(mstr):
match = re.match("z\.\((.*)\)", mstr)
if match is not None:
return match.group(1)
else:
return mstr
def formula2predictorstr(lines):
#e.g. fdur ~ z.(myfixed) + (1 ... |
147252dfb987780c4dd7018451e99d4d5d5ed7f444bb98178368b5bc509a7f6b | Python | 1,456 | 52 | """
_
.__(.)< (KWAK)
\___)
~~~~~~~~~~~~~~~~~~~~
"""
import time
import sys
sys.path.append("../")
from HandPoseStreamer.pose_streamer import Pose_Streamer
def working_example_2():
# Working example of a TCP stream of pose data from Python to Unity
# One pose is made of 17 triplets of ... |
a7a64db2bba50ede84d01fec89a51f4234be9c8dcd0e7729ee79370953e4c340 | Python | 1,456 | 56 | import torch
def optimizer_keys(optimizer):
keys = list(optimizer.param_groups[0].keys())
keys = tuple(keys[1:])
names = ()
for name in keys:
param = optimizer.param_groups[0][name]
if isinstance(param, tuple):
names += len(param) * (name,)
else:
names ... |
41786766a93f645ca5a5a082dec1afa821598e78028fac2632b982e560fe8d61 | Python | 1,459 | 43 | # emacs: -*- mode: python-mode; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
"""
.. _metas_peaks2maps:
================================
Generate MA maps with peaks2maps
================================
.. warning::
peaks2maps has been deprecated within NiMARE and wil... |
ede603561732e383d663212653c8773caff1cf616c3ac8f8075c0e9457c97b09 | Python | 1,460 | 32 | from wordfreq import tokenize, word_frequency
def test_apostrophes():
# Test that we handle apostrophes in French reasonably.
assert tokenize("qu'un", 'fr') == ['qu', 'un']
assert tokenize("qu'un", 'fr', include_punctuation=True) == ["qu'", "un"]
assert tokenize("langues d'oïl", 'fr') == ['langues', "... |
c7017624a2698febec867170e04d45db56d2e00dea553886dc2bc416c536d03a | Python | 1,461 | 46 | # -*- coding: utf-8 -*-
import numpy as np
from .red_blue import RedBlueMove
__all__ = ["DESnookerMove"]
class DESnookerMove(RedBlueMove):
"""A snooker proposal using differential evolution.
Based on `Ter Braak & Vrugt (2008)
<http://link.springer.com/article/10.1007/s11222-008-9104-9>`_.
Credit ... |
ca67eae527e71d0acd8c59faf62771696b706ad270cd569f2e08a3219c1e717c | Python | 1,461 | 37 | import numpy as np, matplotlib.pyplot as plt
import pumpprobe as pp
plt.rc('axes',labelsize=16)
time = np.linspace(0,40,400)
ec = pp.ExponentialConvolution([0.1,0.12,3.])
y = ec.eval(time)
y_plt = np.zeros(len(y)+100)
y_plt[100:] = y
time2 = np.linspace(-10,40,500)
peak_t = ec.get_peak_time(time)
rise_t = ec.get_eff... |
9dd87dac3c8fae6323ac96c315d89b57c5566dcc9eb38526494a4ce426bf9fe2 | Python | 1,462 | 41 | """
.. _metas_peaks2maps:
================================
Generate MA maps with peaks2maps
================================
.. warning::
peaks2maps has been deprecated within NiMARE and will be removed in version 0.0.13.
"""
###############################################################################
# Start... |
a49001284de42708e0b277440728b5ecd464a44426d36308f09418c579a1c91f | Python | 1,463 | 39 | from PyQt5.QtWidgets import QLabel, QApplication, QStyle, QToolTip
from PyQt5.QtCore import Qt
class Icon(QLabel):
"""
A custom widget representing an icon with a tooltip.
"""
def __init__(self, tooltip_text, icon_name='SP_MessageBoxQuestion'):
"""
Initialize the Icon widget.
... |
ed93a6fd8fde37fb7b4cf143e11cb28dd58de8fd284f546617e32df44626c84e | Python | 1,463 | 42 | import torch
from torch import nn
import torch.nn.functional as F
class TcnnNet(nn.Module):
def __init__(self, params, **kwargs):
import tinycudann as tcnn
nn.Module.__init__(self)
self.actions_num = actions_num = kwargs.pop('actions_num')
input_shape = kwargs.pop('input_shape')
... |
013f6ea012b903841af6c6d6929d635cabc115bd7d451dba46673662e02abfdf | Python | 1,465 | 39 | #!/usr/bin/env python
import argparse
import torch
def main():
parser = argparse.ArgumentParser(
description="Release an OpenNMT-py model for inference")
parser.add_argument("--model", "-m",
help="The model path", required=True)
parser.add_argument("--output", "-o",
... |
188723e31e7d4ca340bf036dcf512e48c6a6aa4382b3161a36e64b23655bfbe0 | Python | 1,465 | 32 | import sys
import re
#remove F model features where antecedent is null and k is a pronoun k such as f[01]&Top?&X-xX:he_1
#he, him, his, she, her, they, them, their, its
#multiple models for different subtypes of coreference?
#F depth syncat kvec kvec kvec corefOFF goldfek
#fek is of format: F&E&K, where k is cat:pr... |
a9d047bc88a0d5af18fa5bfd2f11472fee872951b66587e02c43f1133cf8f67b | Python | 1,467 | 49 | """
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 pathlib import Path
from typing import Dict
import h5py
import numpy as np
def save_reconstructions(reconstructions: Dict[str, np.nda... |
1402dab2502d5a8f5958ee6f9146ada711f4f3d1848c2832ab0e0895a4832530 | Python | 1,468 | 39 | from unittest.mock import Mock
import pandas as pd
import pyaging.preprocess._preprocess_utils as preprocess_utils
def test_load_ensembl_metadata_uses_downloaded_hf_path_and_filters_chromosomes(monkeypatch, tmp_path):
metadata_path = tmp_path / "returned-by-helper.csv"
pd.DataFrame(
{
"g... |
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