sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5b36ad5df27da6102174f484b93df168057c5b00bb85f0bb95b94dcb4c6f65a3 | Python | 1,431 | 27 | import numpy as np
import pkg_resources
from vampire.germline_cdr3_aa_tensor import aa_encoding_tensors
def test_aa_encoding_tensors():
aa_order = 'ABC'
v_gene_list = ['TCRBV01-01', 'TCRBV02-01']
j_gene_list = ['TCRBJ01-01', 'TCRBJ01-02', 'TCRBJ01-03']
germline_cdr3_csv = pkg_resources.resource_filen... |
ba9e6970cdbda491ae8e2cab16c3e07787c3ae3afcf13ccf9f65540468ab39b3 | Python | 1,431 | 56 | """
Modeling of the energetics/metabolism based on the Dynamic Energy Budget theory
"""
from __future__ import annotations
from typing import Any
__displayname__ = "Dynamic Energy Budget"
__all__: list[str] = [
# expose primary public classes/functions explicitly if needed later
]
_LOADED: bool = False
def _... |
e2eed7a7eac5818742d79de218dd47d3d460dfa9af98dfc84cfd579844513362 | Python | 1,431 | 35 | import os
import sys
import numpy as np
import pandas as pd
# EUGENE
from seqproing import random_seqs, reverse_complement, encodeDNA
# Simple script tp generate commonly used file types for testing EUGENE models
if __name__ == "__main__":
args = sys.argv
num_seqs, seq_len = int(args[1]), int(args[2])
out... |
c624551f45df3bce82531951b0541502a4a591566404a5f1be5d545359cdd146 | Python | 1,432 | 39 | #!/usr/bin/env python
from distutils.core import setup
import libmushu
setup(
name = 'Mushu',
version = libmushu.__version__,
description = 'Brain Computer Interfacing signal acquisition software.',
long_description = 'A Python library for BCI signal acquisition. It can be used as a stand-alone appl... |
e84c5e05cdb7e6d2f27de2716e205c3f63e10d1ffc69b5a87445d8111722631d | Python | 1,432 | 49 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["splicepoint_model_spec"] = dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepoint-model-donor-1",
)
msp.architecture["motif_model_spec"]["motif_... |
fa2f7a14a73e215519964cbdf2a625cb6132c71b7f659831a441fddadde2d028 | Python | 1,432 | 49 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["splicepoint_model_spec"] = dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepoint-model-donor-1",
)
msp.architecture["motif_model_spec"]["motif_... |
04d16c2ae20046b38d090bde4caac37271be25edd6cf83cc5f2a12d97253b84b | Python | 1,433 | 47 | # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
1e30856cc3c295ce1b590a2ee5b9f02d48343e293ccbb5b973383e4376654306 | Python | 1,433 | 1 | import numpy as np
import pandas as pd
import time
import random
# [1] data preprocessing #
df = pd.read_csv('./data/GSE76312_Single_cell_HSCs_expression_RPKM_table_gene_filtering_log.csv', index_col=0) # dimension = (genes x cells)
sample_inf = pd.read_csv('./data/GSE76312_Giustacchini-Thongjuea_et.al_Cell_Annotation... |
c1c53311305af6392598fbc4c14a14fd764dadab6f20a99f6cfaefd2a8c54d04 | Python | 1,433 | 49 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["splicepoint_model_spec"] = dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepoint-model-donor-1",
)
msp.architecture["motif_model_spec"]["motif_... |
1c346fd2273ddd866d2e773f19795d13fe1664687f558de36a289e663b282e3e | Python | 1,434 | 43 | import sys
import socket
marcos_computer_path = "/home/nhattm/ToMoDL/ToMoDL/"
# marcos_computer_path_datasets = "/home/obanmarcos/Balseiro/DeepOPT/datasets/"
marcos_computer_path_datasets = "/home/nhattm/ToMoDL/datasets/"
marcos_computer_path_metrics = "/home/nhattm/ToMoDL/ToMoDL/metrics/"
marcos_computer_path_models ... |
c618eee0b5ee9cfe2c71c1973373469c97aac9b19e2910f7230305367cd8a2bb | Python | 1,434 | 38 | import sys
import subprocess
ref_con_fname = sys.argv[1]
cells_list_fname = sys.argv[2]
mode = sys.argv[3]
def compute_density_ratio_for_cell(cell_name, sex):
# NOTE: the results change a little bit when you use impute versus contacts
if mode == "impute":
con_fname = "../merged." + cell_name + ".ca."... |
d34abe4cb254f6cd0779219eacae4e86e7e6bcd8225e4b2b6897998e89b59f0b | Python | 1,434 | 47 | import mord
from mord.datasets.base import load_housing
from sklearn import linear_model, metrics, preprocessing
data = load_housing()
features = data.data
le = preprocessing.LabelEncoder()
le.fit(data.target)
data.target = le.transform(data.target)
features.loc[features.Infl == 'Low', 'Infl'] = 1
features.loc[featu... |
4df9a007b5a4ff2b7af8373305bceddc54a0645579111989073703fc35bc203c | Python | 1,435 | 45 | from PyQt5 import QtCore
import logging
logger = logging.getLogger(__name__)
class TrialDialogController(QtCore.QObject):
dialog_done_signal = QtCore.pyqtSignal(name="TrialDialogController.dialog_done_signal")
def __init__(self, caller, locations, object_files=None, parent=None):
super(TrialDialogC... |
747da16a8e019552aee14f52ee3a46955cde39d7ab21dc00d4b9ed02956c09aa | Python | 1,437 | 39 | """
This module define a converter
"""
import torch
from converter.generic_converter import GenericConverter
from utils.utils import select_output_lm
class ConverterL5MostLikelyMediumAndLong(GenericConverter):
"""
This class select L/5 most likely contact pred
"""
def convert_output_and_target(self, ... |
b1ca1fa4444debdf5908687a7e9a6c189d36663f3e28caa52c62ccee58369875 | Python | 1,437 | 44 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import tensorflow as tf
import pandas as pd
import argparse
import os
import sys
import pwd
import pdb
import csv
import re
import math
import time
df... |
d06ba1435ea2e56599fb60debc9b8f135f9225f5892d7d24ba71401f7238d7b1 | Python | 1,437 | 47 | from .bulletin import Bulletin
import datetime
class Haryana(Bulletin):
def __init__(self, basedir):
statename = 'HR'
super().__init__(basedir, statename)
self.bulletin_url_2020 = 'http://www.nhmharyana.gov.in/WriteReadData/userfiles/file/CoronaVirus/Daily Bulletin of COVID 19 as on {:... |
71ba910ba05828ee3b9927205f724f01ff38af32c2115f593e2a40effdeb63ef | Python | 1,438 | 42 | """Run ``celery upgrade settings`` as a real command on a real settings file."""
import subprocess
import sys
import pytest
SETTINGS = (
b'BROKER_URL = "memory://"\r\n'
b'CELERY_ALWAYS_EAGER = True\r\n'
b'# \xc3\xa9t\xc3\xa9\n'
)
def upgrade_settings(*args):
# Deprecation warnings raised by the upg... |
e5da12830e83b2b9563515594879fb5add9acea7d93d574b6c2c8bfbf03ffe59 | Python | 1,438 | 33 | import numpy as np
import math
import pdb
## ------------------------------------------------------
##-- remove bifurication TypeIDs 5 and 6 from NeuronStudio SWCs
def remove_bifuc_neuronstudio(swc_matrix):
updated_swc_matrix = swc_matrix
for i in range(0,len(updated_swc_matrix)):
if updated_swc_mat... |
2216ac49122362281cd1f89bb1b26abf6117098e17dd5a0002be794d46dfc33e | Python | 1,439 | 50 | import sys,re,os
import subprocess
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from utils import getLongestORF,parse_config, setup_fonts
from tqdm import tqdm
def parse(testfile,filename):... |
ac4585b78ad427acfed6d6e2a10668a1f5e4d21b835a8759d2125bb260490c8b | Python | 1,439 | 43 | import sys
import logging
class StreamToLogger(object):
"""
Fake file-like stream object that redirects writes to a logger instance.
Ref:
https://stackoverflow.com/questions/19425736/how-to-redirect-stdout-and-stderr-to-logger-in-python
"""
def __init__(self, logger, level):
self.logger ... |
755c76b71a05d74dd3b2abd03fad7f4e65863f9357ca109aaaa0c2b10190156d | Python | 1,440 | 43 | import pytest
import numpy as np
from causal_optoconnectics.tools import compute_trials
def test_compute_trials_from_spike_times():
s = np.array([1, 2, 3, 4, 5])
a = np.array([
.1, .99, # before 1 (Z True)
1.01, 1.02, # after 1 (X True)
2, # before 2 (Z True)
2.13, # after 2 (X... |
ce8971dcbebdd24531ea9e61ba29d65aff5b2f4e8884a8fb3110518fc8a656ae | Python | 1,440 | 56 | """Dueling DQN."""
import argparse
from pytorch_lightning import Trainer
from pl_bolts.models.rl.common.networks import DuelingCNN
from pl_bolts.models.rl.dqn_model import DQN
class DuelingDQN(DQN):
"""PyTorch Lightning implementation of `Dueling DQN <https://arxiv.org/abs/1511.06581>`_
Paper authors: Ziyu... |
c5868265a79624c061cf2dc98d267bde614c18e103dc472df49cff0ccec34055 | Python | 1,441 | 48 | from __future__ import annotations
import numpy as np
from larvaworld.lib.model.envs.arena import Arena
class _DummyAgent:
def __init__(self, pos, radius: float):
self.pos = np.array(pos, dtype=float)
self.radius = radius
class _DummyAgents(list):
@property
def pos(self):
retur... |
c882d9e2f074aa98916955091b996d44da364b5a7b0b99dc4796c1c161ff327b | Python | 1,441 | 44 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._cumulative import Cumulative
from ._error_x import ErrorX
from ._error_y import ErrorY
from ._hoverlabel import Hoverlabel
from ._insidetextfont import Insidetextfont
from ._legendgrouptitle import Legendgrouptitle
from ._... |
f137e1f71edfb66cbb2bf59333d223707679c92105d9116d9b37a39fb2831901 | Python | 1,441 | 58 | import re
path = f"binary_all_acc_tmp.txt"
psam_idx_acc_dict = dict()
with open(path, 'r') as f:
i = 0
for line in f:
if i % 3 == 0:
idx = int(line[:-1])
if i % 3 == 2:
content = float(line[:-1].split("; ")[0].split(": ")[1])
psam_idx_acc_dict[idx] = content... |
106a6ed8854b868650cb1b2ca0ac770941296107a1bfaaddc8ebf00a810db189 | Python | 1,442 | 66 | import h5py
from map_protein_name import get_map_protein
import random
import numpy as np
map_index = get_map_protein()
data = h5py.File("tiny_binary_dataset/protein_47/rbns_train.h5", "r")
# L = data["L"][:
# print(max(L))
X = data["X"][:]# .astype(int)
L = data["L"][:]# .astype(int)
Y = data["Y"][:]# .astype(int)
t... |
32580b4ddc96ad49517b34cf8a2292bb91bc5f10c25f3e946406875ff42f7049 | Python | 1,442 | 70 | import numpy as np
from neuron import h, gui
from neuron.units import ms
# Send a message from `spiker` to `receiver` using a
# `net_event` in a NET_RECEIVE block.
#
# Since the NET_RECEIVE block in the `spiker` needs to
# be triggered we add a `stim`.
def simulate():
nseg = 1
s1 = h.Section()
s1.nseg ... |
432ca2dba282b682a0d60439c45359f15d7b9cf7ecf8f9e3aa575472f2685957 | Python | 1,442 | 46 | """
More thorough sweep value validator created in June 2020.
Checks EVERY value of EVERY sweep of EVERY channel of EVERY ABF
to ensure it matches what we expect and does not change.
"""
try:
import sys
import pytest
import datetime
import inspect
import numpy as np
import glob
# this ensu... |
85471d1c1ce90df694606a499bfc25478c66beed5e1edc72d7c63fcc89b3a7aa | Python | 1,442 | 54 | import h5py
from permacache import permacache
@permacache("modules/additional_inputs/gene_element_counts")
def gene_element_counts(datafile_path, index, cl_max):
"""
Get the counts of each nucleotide in a gene.
Parameters
----------
datafile_path : str
Path to the datafile.
index : i... |
87d61081adadd695919c11f9a64342ca6e291fdf51ccc0217c3086f7ba781535 | Python | 1,442 | 44 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Example script demonstrating how to use import and pre-process the eyelink data.
@author: Florent Meyniel
"""
import re
import numpy as np
import matplotlib.pyplot as plt
from preprocess import pupil
# initialize the class
data = pupil()
# import the recording in p... |
381f30a70a8b6647a4a400798c33e9a722e48df18b374718f454b3340524e92a | Python | 1,443 | 75 | """
Diverging color scales are appropriate for continuous data that has a natural midpoint \
other otherwise informative special value, such as 0 altitude, or the boiling point
of a liquid. The color scales in this module are \
mostly meant to be passed in as the `color_continuous_scale` argument to various \
functions... |
4597c1e013a38f7913882bd83b2ea4c4376df8219ce191f9cfe9aeb33d6bacb3 | Python | 1,443 | 42 | import unittest
from argparse import Namespace
from deepspeech_training.util.importers import validate_label_eng, get_validate_label
from pathlib import Path
def from_here(path):
here = Path(__file__)
return here.parent / path
class TestValidateLabelEng(unittest.TestCase):
def test_numbers(self):
... |
4dd1631a20fe00d00d3e24a94967f1d07f6502e6f575bbee864a844dc8fab148 | Python | 1,443 | 52 | """
This script creates the histones folder
for deepregfinder
"""
import os
import subprocess
################## Edit the following ##################
histone_marks = ['H3K9ac', 'H3K27me3']
cell_line = "K562"
########################################################
os.mkdir("histones")
os.chdir("histones")
for hist... |
1279f1a6f0675e9782537753cfa248f4ee37f70ba43f3a4f7c496095108af1dd | Python | 1,444 | 39 | import cv2, os, math, operator, sys, time
import numpy as np
from scipy.signal import find_peaks
def imRescale2uint8(im):
'''Rescale and convert to 8 bit ([min max] to [0 255])'''
imOffset = im - np.min(im)
imOut = 255 * (imOffset / np.max(imOffset))
return(imOut.astype('uint8'))
def imLevels(im,low,h... |
aa76ef0b8498e287509ec5f3a65c220b3543e9129c57e7f5b4069049d6bfcea3 | Python | 1,444 | 52 | """
This example demonstrates how to use CodeCarbon with command line tools.
⚠️ IMPORTANT LIMITATION:
CodeCarbon tracks emissions at the MACHINE level when monitoring external commands
via subprocess. It measures total system power during the command execution, which
includes the command itself AND all other system p... |
b5355702d40d09316c9c5240d46423f4ee26799ecace736886501b9279d20865 | Python | 1,445 | 55 | import argparse
import pandas as pd
import numpy as np
import os, sys, shutil, importlib, glob
from tqdm.notebook import tqdm
import celloracle as co
from celloracle import motif_analysis as ma
from genomepy import Genome
import scanpy as sc
import pyarrow.parquet as pq
import pyprojroot
packdir = f"{pyprojroot.here()}... |
f77cee28d084ee970c0f72b9b4ce4731e54243995f251ff486d169a2db6039e5 | Python | 1,445 | 52 | """Task Annotations.
Annotations is a nice term for monkey-patching task classes
in the configuration.
This prepares and performs the annotations in the
:setting:`task_annotations` setting.
"""
from celery.utils.functional import firstmethod, mlazy
from celery.utils.imports import instantiate
_first_match = firstmet... |
118f013418ff47c1cacae1233d2442a312eab29f1a7f39220b93f270011332a5 | Python | 1,446 | 34 | import mne
import pandas as pd
import numpy as np
from surfer import Brain
data_fpath = 'data/zmap_feature_importance_full.txt'
subjects_dir = 'data/subjects'
df = pd.read_csv(data_fpath, sep='\t', index_col=0, header=None, names=['feature', 'score'])
df['score'] = df['score'].str.split(' ').str[0].astype(float)
pri... |
670b937339948a3cf1cc37520dcba80cefef339e0a2e4734085714010ed0d117 | Python | 1,446 | 47 | __all__ = [
"gmean",
"mean_ale",
"mean_alm",
"mean_euclid",
"mean_harmonic",
"mean_kullback_sym",
"mean_logdet",
"mean_logeuclid",
"mean_power",
"mean_riemann",
"mean_wasserstein",
"maskedmean_riemann",
"nanmean_riemann",
"median_euclid",
"median_riemann",
]
... |
ee738f6483dc6fc2fcdbd2c1b6a81bcb77451b5f29ef98507ee75ddb541e0b86 | Python | 1,446 | 26 | """Oracles wave-0 harness: registry, censuses, surface walk, static lints.
This package is the wrongness-defense machinery commissioned by the oracles
panel (plan of record: megaplan MEMO.md row P02; normative spec: the oracles
MEMO.md section 10 items 1-5). Doctrine in force here:
- Every defense is a predicate plus... |
985d15208b22916caaa73758171b5a329986e82b35ed67ad2e64322bd4d635d3 | Python | 1,447 | 48 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
import logging
from eelbrain import datasets, testnd
from eelbrain.fmtxt import html
from eelbrain.report._auto import result_report
from eelbrain.testing import requires_framework_build
@requires_framework_build
def test_result_report():
"Test result_repo... |
e0704cd3210961f4d12fd6c82ad525ff8080c778af4e7aa0c6f516dfed02806a | Python | 1,447 | 40 | import numpy
import os
import sys
import voxcell
from projection_voxels import BinarySplitVoxelArray, BinarySplitModel
from projection_voxels.binary_split_model import RootRegion
if __name__ == "__main__":
this_root = os.path.split(__file__)[0]
sub_path = "input/configuration"
if not os.path.isdir(os.path... |
1de6e72985bd807510bdb1035362d6cd86400c4e0adae82462fd3d6f917d883a | Python | 1,448 | 61 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 32
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"] = dict(
type="AdjustedMotifModel",
model_to_adjust_spec=dict(
... |
8ffec739aa3ec7e48b31e54b93c70e5b72d268a03755bcdfb70b42c4c6ee5db9 | Python | 1,448 | 43 | from unittest.mock import Mock
import pytest
from celery.backends.consul import ConsulBackend
pytest.importorskip('consul')
class test_ConsulBackend:
def setup_method(self):
self.backend = ConsulBackend(
app=self.app, url='consul://localhost:800')
def test_supports_autoexpire(self):
... |
66f5855c4f685d58923057e8bcfc974656ef4b1123b854de731655ea51787106 | Python | 1,449 | 32 | from __future__ import generators
import logging
import glob, os, functools
import gc
import pandas as pd
import numpy as np
import sys
import math
from dateutil import parser
sys.path.append('../TM2_segmentation')
df_albumin = pd.read_csv('/media/sda/Anna/TM2_segmentation/itmt_v2.0/Albumin.csv')
df_scan_date = pd.rea... |
cc8d58ffa5a75ff0f8608dfa5151867342c9544b89af3f61016385f0bfdb9f01 | Python | 1,449 | 50 | """Add reason column on invocation table
Revision ID: 3100452fa030
Revises: 518c8438a91b
Create Date: 2023-01-13 16:13:09.578391
"""
from sqlalchemy import (
Column,
ForeignKey,
Integer,
String,
)
from galaxy.model.custom_types import TrimmedString
from galaxy.model.migrations.util import (
crea... |
45ad4b0224d0f517670cd93d57e049db8862ea30a5e12bf6d90ce2095a55b91f | Python | 1,450 | 34 | import numpy as np
import pandas as pd
def create_edge_network_representation(adjMatrixBootstrap, weight_matrix, gene_names):
"""
CreateEdgeNetwork - Create Edge file.
This is needed before hypothesis test q-value derived adjacency matrix
can be consumed by R network analysis code.
... |
03559dc9eb0fb9dc0770610a5df27cda384a3ac43295201b129835971ff72af2 | Python | 1,451 | 51 | #!/usr/bin/env python
"""
Input: fasta, int
Output: tabular
Return titles with lengths of corresponding seq
"""
import sys
def compute_fasta_length(fasta_file, out_file, keep_first_char, keep_first_word=False):
infile = fasta_file
keep_first_char = int(keep_first_char)
fasta_title = ""
seq_len = 0
... |
0ea23b447aef3b6ed60fbd1ff8d745fe81edda90e79959181c7214449b1295ff | Python | 1,451 | 37 | """Test to_scalabel.py."""
import copy
import os
import unittest
from scalabel.label.io import load
from bdd100k.common.utils import load_bdd100k_config
from bdd100k.label.to_scalabel import IGNORED, bdd100k_to_scalabel
class TestBDD100KToScalabel(unittest.TestCase):
"""Test cases for bdd100k to scalabel conver... |
15f6d0523015714163a4838fb6f881c7d67ce3f81d82b7760780f11bed8f8709 | Python | 1,451 | 45 | # this may not work
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from input_parameters import dataset_name, channel_count, thickness_channel, \
processed_metadata_filepath, processed_imagedata_filepath, saved_model_filepath
from stratify import index_split
df = pd.read_json(processed_met... |
688e4aab82a6eba158dd2bc6b309d9d4ebaf8e1824e35b4f60af94da1c472195 | Python | 1,451 | 50 | from conftest import requires_matplotlib
import numpy as np
import pytest
from pyriemann.utils.viz import (
plot_embedding,
plot_cospectra,
plot_waveforms
)
@requires_matplotlib
def test_embedding(get_covmats):
"""Test ."""
n_matrices, n_channels = 5, 3
covmats = get_covmats(n_matrices, n_cha... |
758d862ce003984c53a3403011dd6dd5329841674c6beb0475616eb2370c098a | Python | 1,451 | 39 | from spacy.lang.en import English
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from imodelsx.util import generate_ngrams_list
def test_ngram_list():
nlp = English()
def spacy_tokenizer(doc):
return [str(x) for x in nlp(doc)]
for x in [
'here is a sample m... |
c6fe54424a9f41b69a4a1c8f6107ac694e1bdbafc86ee50dd7df7df1ff218f3d | Python | 1,451 | 49 | 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... |
350e465d06f033bcd56b1e42ca7b9c9cfb6eca5eef48846346cdc0c57beb1bb7 | Python | 1,452 | 58 | #!/usr/bin/python
import sys
import os
import re
import numpy as np
import ConfigParser
import json
def main():
##### check argument #####
if len(sys.argv)<2:
sys.exit("ERROR: not enough arguments\nUSAGE ./celltypePlotData.py <filedir>")
##### get command line arguments #####
filedir = sys.argv[1]
##### add '... |
630f037c7f4d1074e7ef4062eef9d3afbbd15eeeb58c04ecf08e445bff6e03c9 | Python | 1,452 | 52 | #!/usr/bin/env python
"""
Connects to the URL specified and outputs builds available at that
DSN in tabular format. UCSC Main gateway is used as default.
build description
"""
import sys
import xml.etree.ElementTree as ElementTree
from galaxy.util import requests
def getbuilds(url):
try:
text = reque... |
809e3d751098d39263292cc6b12e3b4c77443c9b54c2350709d33bde0215523b | Python | 1,452 | 45 | from typing import List, Tuple, Union
from warnings import warn
import torch
from torch import Tensor
from lightly.data.multi_view_collate import MultiViewCollate
def test_empty_batch() -> None:
multi_view_collate = MultiViewCollate()
batch: List[Tuple[List[Tensor], int, str]] = []
views, labels, fnames... |
8ddff8302d29d77b688ee712c123b3c070d16e28a69fa1f89e2810cd89b46db1 | Python | 1,452 | 41 | # test_triggerprecision.py
# Copyright (C) 2013 Bastian Venthur
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# T... |
9ea87cbae50ac6ebff36f31ebf15c3e3405639345fbb3bff5076fe3683a59e10 | Python | 1,452 | 62 | """utility functions"""
from functools import reduce
import numpy as np
def chunks_of(length, list_):
"""
Splits list into sublists, each of length n. If the length of the list is
not divisible by n, the last sublist will have length len(list) % n. The
result is provided as a generator.
>>> li... |
c33c1c87540364ea35a6b3a27fbf621c70cb5ee1f2debced9186d28d55b3913c | Python | 1,452 | 51 | import os
import random
from typing import List
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import tensorflow as tf
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from analysis.visualization import Visualizer
from utilities.plotting import transparent_cmap
os.chdir("../../... |
4a0ed059475f6e1a73e0395bf0e5d8a81a6a13c9afed225f9818ebf83f733779 | Python | 1,453 | 58 | """Util functions for FASTA format."""
__author__ = "Gianluca Corrado"
__copyright__ = "Copyright 2016, Gianluca Corrado"
__license__ = "MIT"
__maintainer__ = "Gianluca Corrado"
__email__ = "gianluca.corrado@unitn.it"
__status__ = "Production"
def import_fasta(fasta_file):
"""Import a fasta file as a dictionary.... |
87288d87a0711a9d49e49efcf74774de8985c695c25aaab250da933dd0e53ca1 | Python | 1,453 | 58 | from tensorflow import keras
from tensorflow.keras import layers
from keras.utils.vis_utils import plot_model
import tensorflow as tf
import numpy as np
import pandas as pd
from load2data import extraData
import random
import os
import random
import torch
data = pd.read_csv("D:\JWJ\PycharmWorkplace\Data\\n... |
2778b7c035d917bb0909ec6ee2f9d61ccba030ff8ae635ff65a52c133ec5afca | Python | 1,454 | 62 | import os
import tempfile
import pytest
from flaskr import create_app
from flaskr.db import get_db
from flaskr.db import init_db
# read in SQL for populating test data
with open(os.path.join(os.path.dirname(__file__), "data.sql"), "rb") as f:
_data_sql = f.read().decode("utf8")
@pytest.fixture
def app():
"... |
755509531fc2b191c6f39a4ab9abab5a3fe1c0ffef48f30864c0232a846f65b3 | Python | 1,454 | 55 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 32
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature... |
f64f04ea03eef8e1080f255abfc4ca489e6880b3788e2164019216bca1484f98 | Python | 1,454 | 55 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 32
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"]["motif_width"] = 13
msp.architecture["motif_model_spec"]["motif_feature... |
668b117c3695b48ea77e4b2d9eacaf09d737e6cdeb6e27e9f767cd080de57928 | Python | 1,455 | 49 | import pytest
# In case pytest-nunit is not installed, defines a dummy fixture
try:
import pytest_nunit
except ModuleNotFoundError:
@pytest.fixture
def add_nunit_attachment(request):
def noop(file, description):
pass
return noop
# Command line options for pytest must be adde... |
755b1fe684b35efd41546bfc4d002de03a5a31e8e24db7bc6b8e89657d06f2b7 | Python | 1,455 | 35 | from __future__ import generators
import logging
import glob, os, functools
import gc
import pandas as pd
import numpy as np
import sys
import math
from dateutil import parser
sys.path.append('../TM2_segmentation')
path_to_labs = '/media/sda/Anna/TM2_segmentation/itmt_v2.0/labs_csv/WBC.csv'
df_albumin = pd.read_csv(pa... |
cccab142f2b2bcbca574baf8e82482a69058d2d5ee0a32cdbb153651a86fd4d6 | Python | 1,455 | 36 | import argparse
from models.mn.model import get_model
from helpers.utils import NAME_TO_WIDTH
from helpers.receptive_field import receptive_field_cnn
def calc_receptive_field(args):
# model
if args.model_width:
# manually specified settings
width = args.model_width
model_name = "mn{}".... |
68cd3c895a389877472767d998add54f7b990fdd62c74e86d7cead8e5445c362 | Python | 1,456 | 47 | """Guard a release tag against the packaged version.
Used by the release workflow: given the pushed tag (e.g. ``v0.7.0``), confirm
its version matches ``setup.py`` and refuse to publish on a mismatch (which
would otherwise upload the wrong version under the tag). Also reports whether
the version is a PEP 440 pre-relea... |
db9c1a0c09be6312fb15e85e851db6f329a4bffd18700067decbe7c2ac941c5e | Python | 1,456 | 43 | # fix problems with pythons terrible import system
import os
import sys
file_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.join(file_dir, '../bin/'))
sys.path.append(os.path.join(file_dir, '..'))
# useful imports
from prob2020.python.gene_sequence import GeneSequence
import prob2020.python.... |
0a2bc82338cb5871097fd11dd101524c23b135a5a9ba71434f30b68f3c37c84d | Python | 1,457 | 48 | """Pool implementation abstract factory, and alias definitions."""
import os
# Import from kombu directly as it's used
# early in the import stage, where celery.utils loads
# too much (e.g., for eventlet patching)
from kombu.utils.imports import symbol_by_name
__all__ = ('get_implementation', 'get_available_pool_name... |
0af04a9eda4380a31583212e2eefd9daceb9f717760718ebd99d8c4f515ed1ab | Python | 1,457 | 50 | import sys
import re
if __name__ == '__main__':
texfile = sys.argv[1]
bibfile = sys.argv[2]
if not texfile.endswith('.tex') or not bibfile.endswith('.bib'):
print('Use: filterrefs.py document.tex bibliography.bib')
exit()
keys = []
pattern = r'\\citep?\{([^\}]+)\}'
with open(t... |
277ce7e9ed375858312c8d1115b181cf9c3f1c447776d4f22783007fc92b7c7c | Python | 1,457 | 52 | import numpy as np
import pytest
import gym
from gym.wrappers import FrameStack
try:
import lz4
except ImportError:
lz4 = None
@pytest.mark.parametrize("env_id", ["CartPole-v1", "Pendulum-v1", "CarRacing-v2"])
@pytest.mark.parametrize("num_stack", [2, 3, 4])
@pytest.mark.parametrize(
"lz4_compress",
... |
4582cc16a49b1a3ef21b66217214a29a17d4d8886b60b2ed238d5a98ae778617 | Python | 1,457 | 42 | import logomaker as lm
import matplotlib.pyplot as plt
from ._motifs import pfm_to_df, pfms_to_df_dict
def plot_filter_logo(
mtx,
mtx_type="counts",
vocab="DNA",
title=None
):
df = pfm_to_df(mtx, vocab=vocab)
plot_df = lm.transform_matrix(df, from_type=mtx_type, to_type="information", pseudocou... |
60b58bc5ea1ca2e0b6d3bb93c5ed1fe3cbd96ac85bb17b442d7e47e547342542 | Python | 1,457 | 46 | from pathlib import Path
from typing import Tuple, Union
import nibabel as nib
import numpy as np
def load_norm_cest_data(
cest_file: Union[Path, str], mask_file: Union[Path, str, None] = None
) -> Tuple[np.ndarray, np.ndarray]:
"""
Load and normalize CEST data from the given file paths.
Args:
... |
c76015a5c8daa7decd02d81f2b4ee42071b669e478be64eed92c4817ee55e607 | Python | 1,457 | 47 | import json
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from dexterity.design.common import QUALITATIVE_COLOR_PALETTE
AGENT_ID = 1581809147
with open(f"../../monitor/static/experiments/{AGENT_ID}/progress.json", "r") as f:
data = json.load(f)
vloss = data["vloss"]
ploss = data["ploss"]... |
d314b555007b020c52223aa0ce79a276467b0aa512424ce40f1e60459acbee5d | Python | 1,457 | 40 | from __future__ import annotations
import json
from pytest_celery import RESULT_TIMEOUT, CeleryTestWorker
from celery.canvas import StampingVisitor
from t.integration.tasks import add, identity
class test_stamping_visitor:
def test_callback(self, dev_worker: CeleryTestWorker):
on_signature_stamp = {"on... |
4f82e5797ab3e9b07772978741bcd8cbe68aeb41668d91f1bfa9a4c116e09285 | Python | 1,458 | 53 | # 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... |
e903fc0ea966a52226a0ab09f092a24b22866ef8c37380b42969295e17183460 | Python | 1,458 | 41 | """Handles the PlotOut Class"""
# License: GNU General Public License v3.0
import ipywidgets as widgets
from ipywidgets import Widget
from gui import plots_widget, AlphaLevelWidget, DropdownSelectionWidget
from gui import constants
class PlotOut(Widget):
"""Handles plotting functionality"""
def __init__(self,... |
7e590d57cad208eac0e8a4abe6d92bad83073341e236c8872a7dd80d093d44fe | Python | 1,459 | 18 | #==============================================================================#
# Author: Dominik Müller #
# Copyright: 2024 IT-Infrastructure for Translational Medical Research, #
# University of Augsburg ... |
fbb32676a5e61fe5d084050d05bc81298592e49d1ca04b0628189ec1c0a4e87d | Python | 1,459 | 69 | import pytest
import numpy as np
import jax.numpy as jnp
from cvsx.unit_conversions import Converter, convert
def test_conversion_pa_to_kpa():
pa = 1000
kpa = convert(pa, "Pa", "kPa")
assert kpa == 1
def test_default_to_ml():
l = 1
ml = convert(l, to="ml")
assert ml == 1000
def test_defau... |
3dd2cf31b06ad3a51a7dff91fd5686db2274e803748a2bb709419067423a7925 | Python | 1,460 | 50 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["splicepoint_model_spec"] = dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepoint-model-donor-1",
)
msp.architecture["motif_model_spec"]["motif_... |
7f02359aa56bef8b481a140b34c9542aed9bcbc4daa48c842e5144390303c5af | Python | 1,460 | 38 | import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt
from torch.autograd import Variable
def make_model(args, parent=False):
return DRRN(args)
class DRRN(nn.Module):
def __init__(self, args):
super(DRRN, self).__init__()
self.scale = args.scale[0]
s... |
b2e3306810c0a73fd4c6aa8b3c89905b58f64e14b0e7098b747357eb0d1ed848 | Python | 1,460 | 41 | from PyQt5.QtWidgets import QDialog, QVBoxLayout, QPushButton, QListWidget, QHBoxLayout
from PyQt5.QtCore import pyqtSignal
class ListSelectionDialog(QDialog):
dialogClosed = pyqtSignal(str) # Custom signal
def __init__(self, items, parent=None):
super(ListSelectionDialog, self).__init__(parent)
... |
20fe4d7866b4923860c415c2dd34bca54439c57fe22aec8306c02f197541f7d7 | Python | 1,461 | 48 | import numpy as np
import torch
from torch import nn
from torchvision.transforms import transforms
np.random.seed(0)
class GaussianBlur(object):
"""blur a single image on CPU"""
def __init__(self, kernel_size):
radias = kernel_size // 2
kernel_size = radias * 2 + 1
self.blur_h = nn.Co... |
cb540d4f8ef4ccf1776919e6dc8f2c56e90cbf87c8781ec8e2d543f0a9d31fbc | Python | 1,461 | 46 | import os
import logging
import torch
from transformers import ViTImageProcessor
from transformers import ViTForImageClassification
def load_models(confidence, device):
#YOLO Model
obj_model = torch.hub.load('ultralytics/yolov5', 'custom',
path = "Weights/YOLO_weights... |
9149598f3183de35ede376177d3b90a6c038a15aa37fec31907f6356f2cf49bd | Python | 1,463 | 36 | from imodelsx import submit_utils
from os.path import dirname
import os.path
repo_dir = dirname(dirname(os.path.abspath(__file__)))
# python /home/chansingh/llm-tree/experiments/01_train_model.py --use_cache 0 --subsample_frac 1 --model_name llm_tree --max_depth 3 --use_llm_refine 1
# python /home/chansingh/llm-tree/... |
c647e01e24020d3469f2c79609d06f4051b038da0ce95627ea0f71a7714920eb | Python | 1,463 | 43 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""tests for svd"""
import unittest
import numpy as np
from sknetwork.data import star_wars
from sknetwork.embedding import GSVD, SVD, PCA
from sknetwork.linalg import LanczosSVD
class TestSVD(unittest.TestCase):
def test_options(self):
biadjacency = star... |
e4a8ad80531103b8365b3000f5c0d8d156bfb9b0c5cba19d03ae5484c5b2d924 | Python | 1,463 | 50 | import unittest
from numpy import array, matrix
from numpy.testing import assert_array_equal
from pandas import DataFrame
from .context import helpers
class BasicTestSuite(unittest.TestCase):
"""Tests for Helper Functions."""
def test_is_numeric(self):
x = 1
y = 1.0
z = "1"
se... |
5e27dfb0f820e724a9b5d6146bdc90831f893bc25c769b2e41b18ecb5af27877 | Python | 1,464 | 38 | import community as community_louvain
import networkx as nx
import numpy as np
# Compute cluster centroids, which is the mean of all points in one cluster.
def computeCentroids(data, labels):
n_clusters = len(np.unique(labels))
return np.array([data[labels == i].mean(0) for i in range(n_clusters)])
class lo... |
68570c088631a674f3a91163c4ad1755b759fa5f12af615a927fc68732d81968 | Python | 1,465 | 63 | import numpy as np
class Feature(object):
def get_map(self, img, roi):
map_float32 = self.get_map_float32(img, roi)
return np.uint8(255 * map_float32)
def get_map_float32(self, img, roi):
raise NotImplementedError()
@staticmethod
def norm(img, a, b):
res = img.copy()... |
be869de013850434818b5615e9325cfe30de715bc63a3e356b0c9b9fe2341dfb | Python | 1,466 | 44 | from ethoscope.core.monitor import Monitor
from ethoscope.trackers.adaptive_bg_tracker import AdaptiveBGModel
from ethoscope.roi_builders.target_roi_builder import SleepMonitorWithTargetROIBuilder
from ethoscope.hardware.input.cameras import MovieVirtualCamera
from ethoscope.drawers.drawers import DefaultDrawer
from et... |
d70dd24c5cc67d2f57060b802ee31ee1c400b4ce4d008ed2d46b6c00ae1fdff2 | Python | 1,466 | 45 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from __future__ import annotations
from pathlib import Path
import torch
from ultralytics.utils.checks import check_requirements
from .base import BaseBackend
class TritonBackend(BaseBackend):
"""NVIDIA Triton Inference Server backend for re... |
20a48d4e646a56d5095610142ac000d90c5586c1b0ec7bf4a79a4437acdadc32 | Python | 1,467 | 59 | 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["motif_model_spec"] = dict(... |
a98937de4f86adfc3b38e408218548d7928c12c8821ae3fb032698cc7eb007dc | Python | 1,467 | 40 | '''
Basic test installation is working
'''
import collections
import numpy as np
import pytest
from OscopeBootstrap import SyntheticDataset
from OscopeBootstrap.oscope_tf import bootstrap_hypothesis_test
DataParameters = collections.namedtuple('DataParameters', 'ngroups NG N G noiselevel')
@pytest.fixture
def data... |
cccc2fe0b9869e64c9bad1336a89bc13ed6f4f962ba83f46930e6b52d13bf67c | Python | 1,467 | 55 | import numpy as np
from .misc import ifnot
def DFoSD(vec, normL=None, th=1e-6):
"Subtract mean value along first axis and normalize to S.D."
normL = ifnot(normL, len(vec))
m, x = np.mean, vec[:normL]
sdx = np.std(x, 0)
out = np.zeros(vec.shape, vec.dtype)
if np.ndim(sdx) == 0:
if np.ab... |
d96177bd6e3b9af7f6907215b987ce6d8f91bcc596f65e54982d97465cb65526 | Python | 1,467 | 48 | import argparse
import time
import gluoncv as gcv
gcv.utils.check_version('0.4.0')
from gluoncv.utils import try_import_cv2
cv2 = try_import_cv2()
import mxnet as mx
parser = argparse.ArgumentParser(description="Webcam object detection script",
formatter_class=argparse.ArgumentDefault... |
8b3fc06542d787f51d047b1edb262991114dee35fe845664be07bc1f9fae41fb | Python | 1,468 | 44 | """Validate GPU binding buffers before entering CUDA, without requiring a GPU."""
import numpy as np
import pytest
@pytest.mark.parametrize("invalid", ["dtype", "shape", "readonly", "strided"])
def test_gpu_buffer_validation(invalid):
module = pytest.importorskip("shap_cuda._cext_gpu")
out = np.zeros((2, 2, ... |
8b61b9d5502cd9e9e09b2e0638b65cac7d08c331b93fe586bf3e030bd1b570ed | Python | 1,468 | 48 | """
Package to interface with R, with a focus on interactive usage
(REPL approach to code-writing), although the package will work
in non-interactive settings.
The package aims at being simple rather than exhaustively complete
(rpy2.robjects, or rpy2.rinterface, can be used if needed), providing
a comfortable experien... |
995cb8a6591eeb79b19aebefd967d6963a4ea0d7dec3a8f03285a9f207d89588 | Python | 1,468 | 50 | #!/usr/bin/env python3
"""
Created on Fri May 31 12:07:44 2019
@author: Bian Li
@email: bian.li@yale.edu
"""
from pymol import cmd
from argparse import ArgumentParser
def parse_cmd_args():
"""Set up a command line argument parser
Returns
-------
A set of command line arguments.
"""
pars... |
bd95c964e58505295755c78d7e60b8c22918acf682044f69841a87d20d43679b | Python | 1,468 | 41 | import setuptools
import os
print('OS=',os.name);
if (os.name == 'nt'):
print('In Windows');
with open("README.md", "r") as fh:
long_description = fh.read()
with open("biswebpython/requirements.txt", "r") as fh:
install_requires = list(fh.read().splitlines())
else:
print('In UNIX');
with open("REA... |
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