sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b6066d26e67055234413c72d7e195fc3d9a197b9475882a1f37e5f9b294b85b6 | Python | 1,508 | 38 | import os
import numpy as np
import nibabel as nib
import pandas as pd
import tarfile
import shutil
#extracted up to 8841
path_from = '/mnt/aertslab/USERS/Ben/BRAIN AGE/ABCD/Package_1200046/fmriresults01/abcd-mproc-release4/' #"/Volumes/aertslab/USERS/Anna/ModelsGenesis/keras/data/ngz/"
extract_to_path = '/mnt/aertsla... |
9b32320a5998da48adb3649296f29a1c8a585268d67874a571d6dafb03032be1 | Python | 1,510 | 53 | 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["num_motifs"] = 82
msp.arch... |
2fb643986cbf9ea22f388ee9f6983aa09914e24092c5e96cdda16e8c7e481bce | Python | 1,512 | 57 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
selected_splicepoints = [
dict(
type="FromLoadedSplicepointModel",
splicepoint_model_path=f"model/msp-216a1_{i}",
splicepoint_index=2,
)
for i in (2, 3, 4, 5)
]
msp.architecture["num_... |
7a76121c1b4db7d32c49c0f9392b5db7c51a797ee98c8d25173d1278992d552e | Python | 1,512 | 45 | import torch
from survival_plus_x.models.vit import vit_output_head
from survival_plus_x.models.cox_lightning import metrics_from_step_outputs
from survival_plus_x.losses.cindex_boosting import cindex_approx
from survival_plus_x.models.model_head import SurvivalModelHead
class CindexHead(SurvivalModelHead):
def ... |
cdd1e3d888b63b06386245a7493002c7e76f5d27029eef4593e8a60b33f7b385 | Python | 1,512 | 51 | #!/usr/bin/env python
"""
Convert from interval file to interval index file.
usage: %prog <options> in_file out_file
-c, --chr-col: chromosome column, default=1
-s, --start-col: start column, default=2
-e, --end-col: end column, default=3
"""
import optparse
from bx.interval_index_file import Indexes
... |
e826a42607b3e66ffaa161b1e8983ebf42a4d255cab8fca33ba9b1d83b1e92dc | Python | 1,512 | 40 | import numpy as np
def eclips_from_onehot(all_eclip_motifs):
"""
Produce a list of eclip peaks from a one-hot encoding of eclip peaks.
Parameters
----------
all_eclip_motifs: a matrix of shape (N, L, M, R, 2), where M is the number of motifs,
R is the number of replicates, and 2 is the on... |
0f6317c01a4b36b0e27c6f781ca64d30914c5cb2751c4239bdc38d6355a23c1b | Python | 1,513 | 55 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
Change the title from a molecule file to metadata
value of a given-id of the same molecule file.
"""
import argparse
import random
import string
from openbabel import openbabel, pybel
openbabel.obErrorLog.StopLogging()
def main():
parser = argparse.Arg... |
618bc143897b90aa10b69862af5e888d0f70a91662f9078b2c394212f34521f3 | Python | 1,513 | 58 | #!/usr/bin/python
import sys
import os
import re
import pandas as pd
import numpy as np
import math
import json
import glob
import ConfigParser
def main():
##### check argument #####
if len(sys.argv)<2:
sys.exit("ERROR: not enough arguments\nUSAGE ./magma_expPlot.py <filedir>")
##### get command line arguments #... |
1f769252ea0eb98bab34231e887c93223d718d2971e764ff93bb1f6d362e534f | Python | 1,514 | 41 | from io import open
from os import path
from setuptools import find_packages, setup
import versioneer
here = path.abspath(path.dirname(__file__))
# Get the long description from the README file
with open(path.join(here, "README.md"), encoding="utf-8") as f:
long_description = f.read()
# Arguments marked as "Re... |
cf805a4a072f134cbcc572374664dc560cc8541cd63a876d4893dc232701329a | Python | 1,515 | 48 | import cv2 as cv
import F01, F02, F03, F04, F05, F06, F07, F08, F09
class Features(object):
def __init__(self):
self.features = {
"F01": F01.F01(),
"F02": F02.F02(),
"F03": F03.F03(),
"F04": F04.F04(),
"F05": F05.F05(),
"F06": F06.F0... |
9740d8054771b0a2358f2fca3c27298ba19885e87e1784c424fdde6cba9b20e1 | Python | 1,516 | 54 | '''bdpy.mri.image'''
from itertools import product
import numpy as np
import nibabel
from bdpy.mri import load_mri
def export_brain_image(brain_data, template, xyz=None, out_file=None):
'''Export a brain data array as a brain image.
Parameters
----------
brain_data : array
Brain data arra... |
6bfc083603e2397f8c5bd2e6f1edaa8749115da293946548cb458d7a1a50d643 | Python | 1,517 | 53 | """ANTS interfaces."""
import logging
import os
from nipype.interfaces.base import isdefined, traits
from niworkflows.interfaces.fixes import (
FixHeaderApplyTransforms,
_FixTraitApplyTransformsInputSpec,
)
LOGGER = logging.getLogger('nipype.interface')
class _ApplyTransformsInputSpec(_FixTraitApplyTransfo... |
6ced96b91f2ddb97fbf10900ced74951642217270a7ef5a0f0d17d729bba43c4 | Python | 1,517 | 61 | import re
import sys
shape_file = sys.argv[1]
pattern = re.compile("^>.*$")
toWrite = ""
count_for_id = 1
seq_counter = 0
new_id = ""
seq_id = []
seq_string = []
orig_id = []
name_file = "FASTA/data.names"
array_all_chunks = []
with open(name_file, "r") as f:
for line in f:
if len(line.strip()) == 0:
... |
b2ea7a4f0413033e438fc59a1e0507d7305ff1c79dc5e400f60dbfaaee1d1cee | Python | 1,517 | 57 | import configparser
import os
try:
import pathlib as plib
plib.Path()
except (ImportError, AttributeError):
import pathlib2 as plib
except (ImportError, AttributeError):
raise ImportError("no pathlib detected. For python2: pip install pathlib2")
thisDirectory = os.path.dirname(os.path.abspath(__file... |
4edfd8866cfb359e43419fd1762e098ec7542479897779b01e450e7170ddd8dd | Python | 1,518 | 53 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["splicepoint_model_spec"] = dict(
type="BothLSSIModels",
acceptor="model/splicepoint-model-acceptor-1",
donor="model/splicepoint-model-donor-1",
)
msp.architecture["num_motifs"] = 82
msp.arch... |
9525127b9b784af2867127a04c36a1b19b93f5267f5c62a24f9c8401858dc52a | Python | 1,518 | 60 | """myapp.py
Usage::
# The worker service reacts to messages by executing tasks.
(window1)$ python myapp.py worker -l INFO
# The beat service sends messages at scheduled intervals.
(window2)$ python myapp.py beat -l INFO
# XXX To diagnose problems use -l debug:
(window2)$ python myapp.py beat -l de... |
064941bd065a807b887bf7b3e741baea11d57a7dac54b3f8459c7530e5ca7182 | Python | 1,519 | 39 | import pathlib
import sys
import unittest
from asmc.asmc import DecodingParams, DecodingMode
data_dir = pathlib.Path(__file__).resolve().parent.parent / 'ASMC_data'
if not data_dir.exists():
print(f'ERROR. {data_dir} does not exist. Did you clone ASMC recursively with submodules?')
sys.exit()
class TestDeco... |
48fddccac8ab4326601ae1cd825c528347effcf3144d5d0a77a2435207fec677 | Python | 1,519 | 57 | """
Graph Neural Network training utility methods
:author: Afan Secic (main developer), Anna Saranti (corrections and refactoring)
:copyright: © 2020 HCI-KDD (ex-AI) group
:date: 2020-12-01
"""
import networkx as nx
import copy
import numpy as np
import torch
#class gnn_training_utils:
def check_if_... |
71d05eb2f02d7cb462aafe4b877c7a7c9bfc619539097f899f580bc223c3cb96 | Python | 1,519 | 53 | from unittest.mock import MagicMock, Mock
from click import Option
from celery import Celery
# This module defines a mocked Celery application to replicate
# the behavior of Pyramid-Celery's configuration by preload options.
# Preload options should propagate to commands like shell and purge etc.
#
# The Pyramid-Cel... |
dfeafba1898af5d6b1ca1bd52727db974ca0dd54615295a2f80f450a56d8bf1b | Python | 1,519 | 45 | import numpy as np
import math
import pdb
# ------------------------------------------------------
# ParentIndex Check
def parentindex_check(swc_matrix, soma_rows, tree_rows, check_list_df):
set_root_flag = False
invalidparent_to_root_flag = False
# check root ParentIndex is -1
check_list_df.loc[3... |
c14f0af8d74f701fd1e018f7b3346ab9bb9412f3e0c89c16cc6ef899cfd3aa38 | Python | 1,520 | 46 | #!/usr/bin/env python
# Created on: October 30, 2013
__author__ = "Sven Kreiss, Kyle Cranmer"
__version__ = "0.1"
import optparse
parser = optparse.OptionParser(version=__version__)
parser.add_option("-i", "--input", dest="input", default="atlas_counting_model/standard_combined_meas_2ph_model.root", help="Input ro... |
aa53fc40c697a4e74ebfd511d68d5b4191b9b6d3a92234542fe5c3b65c794b63 | Python | 1,521 | 49 | import warnings
import numpy as np
from sklearn import preprocessing
from sklearn.pipeline import Pipeline
import copy
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
class LogStandardScaler:
def __init__(self, selected_columns):
su... |
2f708433fa526b91a35a9bbeda1562b225d1c3ead302f19c86774c412f0b548d | Python | 1,522 | 58 | from .bulletin import Bulletin
from bs4 import BeautifulSoup
class WestBengal(Bulletin):
def __init__(self, basedir):
statename = 'WB'
super().__init__(basedir, statename)
self.bulletin_url = 'https://www.wbhealth.gov.in/pages/corona/bulletin'
self.match_text = '2019 ncov bullet... |
57e5ab84630a64a29e28f0159afb97de83e462463560edb091ae1201119275a6 | Python | 1,522 | 49 | """Options shared by the test suite.
Tests marked `acceptance` take minutes, so pyproject.toml deselects them by
default; select them with `pytest -m acceptance`.
"""
import pytest
def pytest_addoption(parser: pytest.Parser) -> None:
group = parser.getgroup("acceptance", "acceptance tests (pytest -m acceptance)... |
9bf69b13670497f7849cab9d3fbd3f244c3989d7fd1b60d92c597582a0c2c973 | Python | 1,522 | 43 | import argparse
import os
import shutil
import sys
THIS_DIRECTORY = os.path.dirname(__file__)
parser = argparse.ArgumentParser(
prog="search_for_new_screenshots",
description="Searches a Selenium screenshot directory for screenshots used in the admin docs and grabs new ones if available",
)
SEARCH_FOR = [
... |
663ce4d9d37a52d3472a861429ef81f1b26030a898609fa0f2ea9bd852cd113d | Python | 1,524 | 48 | from unittest import TestCase
import numpy as np
from scipy import stats
import spateo.segmentation.bp as bp
from ..mixins import TestMixin
class TestBP(TestMixin, TestCase):
def test_create_neighbor_offsets(self):
neighborhood = np.zeros((3, 3), dtype=bool)
neighborhood[0, 1] = True
ne... |
cb50a4a50e8bdfd3f9b1a218cd86d9bd7075e9dde445025a1084257af9706366 | Python | 1,524 | 48 | """
Verify that we use the correct trained models. This test should
FAIL if we use re-trained models, and it will have to be updated.
"""
import unittest
import joblib
from pathlib import Path
from metal_auxiliaries import MD5_HASH_CODES
class TestTrainedModels(unittest.TestCase):
def test_correct_hash(... |
222722b7becc1b1548f044cc3d304ae63fab1d61cd464550fe694dbb9987e509 | Python | 1,526 | 49 | import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from gradient_accumulator import GradientAccumulateModel
def create_model():
input = tf.keras.layers.Input(shape=(10,))
x = Dense(32, input_shape=(10,), activation="relu")(input)
x = Dense(16,... |
870e26cc48c61579bebf1bacd8305f2855b49caa06d226cbe521fa4a17dc86b6 | Python | 1,526 | 49 | import os
import subprocess
# base directory
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# pip installation
if not os.path.exists(os.path.join(BASE_DIR,'biomedisa/settings.py')):
# metadata
import importlib_metadata
metadata = importlib_metadata.metadata("biomedisa")
__all... |
134f3de5208e61ccb2b790e8872f3deb75f99cef6bb1b0e011df22b080f88023 | Python | 1,527 | 40 | import json
from tabulate import tabulate
# results = json.load(open("output/output.json","r"))
def write_fancy_output(results, out_file: str = 'output.md'):
output_md = ""
output_md += f'## PeptideCRF - Results\n'
output_md += f'### Summary of {results["INFO"]["size"]} predicted sequences\n'
output... |
716c21104a477d88cfa8981ada2d257a3347b222309c991b90adf92d1935d023 | Python | 1,527 | 58 | """
Code here relates to measurements performed on ABF sweeps.
"""
import numpy as np
import pyabf
def getMeanSweep(abf, baseline=None):
assert isinstance(abf, pyabf.ABF)
meanSweep = np.zeros(len(abf.sweepY))
for sweepNumber in abf.sweepList:
abf.setSweep(sweepNumber, baseline=baseline)
m... |
114aaba6f73c99470fcae81900793a809c4212ea1effc22d23590b3a1fbc5f2c | Python | 1,528 | 49 | """
This file tests retrieval of values from the user list.
The user list replaces one cell in the waveform editor for episodic files.
Instead of using a value and a delta, the user can provide a list of values to apply by sweep.
"""
import sys
import pytest
import numpy as np
try:
# this ensures pyABF is import... |
11ecf5ec49e0722ad51d5568c9b0a55d2e77d78fd8cf9e091735e576e6c47608 | Python | 1,528 | 38 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Tests for clustering API"""
import unittest
from sknetwork.clustering import *
from sknetwork.data.test_graphs import *
class TestClusteringAPI(unittest.TestCase):
def setUp(self):
self.algos = [Louvain(return_aggregate=True), Leiden(return_aggregate=Tru... |
48a06027b2b1318b4ad8c1cf70281929447d3062991c154668b1c50e1fa638f9 | Python | 1,528 | 34 | from pyrosm.data_manager import get_osm_data
from pyrosm.frames import prepare_geodataframe
from pyrosm.utils import validate_custom_filter
import warnings
def get_poi_data(nodes, node_coordinates, way_records, relations, tags_as_columns,
custom_filter, bounding_box):
# Validate filter
custom... |
6385d8616825043dce50920f799ffd7628175ab4db49c185fef3a3b973ff3cd1 | Python | 1,528 | 32 | import pysam.utils
annotate = pysam.utils.PysamDispatcher('bcftools', 'annotate')
call = pysam.utils.PysamDispatcher('bcftools', 'call')
cnv = pysam.utils.PysamDispatcher('bcftools', 'cnv')
concat = pysam.utils.PysamDispatcher('bcftools', 'concat')
consensus = pysam.utils.PysamDispatcher('bcftools', 'consensus')
conve... |
64d796cf8382e44744ecdc85eec870fc6bfe69d44ae79f4178feeb2288641036 | Python | 1,528 | 37 | from os.path import abspath, join
def run(num_procs):
from spitfire.chemistry.mechanism import ChemicalMechanismSpec
from spitfire.chemistry.tabulation import build_nonadiabatic_defect_transient_slfm_library
import spitfire.chemistry.analysis as sca
from spitfire.data.get import datafile
import nu... |
481de19f501dd54b5b6fcdc9c97af6e23b64cb1378ab6e6dcb2e07137c96283b | Python | 1,530 | 54 | """Tests for specparam.modes.mode."""
from collections import OrderedDict
import numpy as np
from specparam.modes.definitions import ap_powerlaw
from specparam.modes.paramdef import ParamDefinition
from specparam.modes.mode import *
##################################################################################... |
d043124a91296abcbb0f3ab3f0be64d4c09a3c5963d4121429a28a1fd3299250 | Python | 1,530 | 49 | import torch
import torch.nn as nn
import math
import numpy as np
import torch.nn.functional as F
from torch.autograd import Function
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
ctx.alpha = alpha
return x.view_as(x)
@staticmethod
def backward(ctx, grad_out... |
51b0c141b42e8041b72b1b3bae97d906de114a4fc3910f14cc7d485a26c3a458 | Python | 1,531 | 64 | import os
import tempfile
import subprocess
import json
import tqdm.auto as tqdm
def load_architecture(path):
overall_path = f"experiments/{path}"
if not path.endswith(".py"):
return None
if path in {"msp-266a1.py", "msp.py"}:
return None
if not os.path.exists(overall_path):
re... |
7b4a8879717cf00d849490e75f1c52e421f18a3893d542aa27f49d127d561b31 | Python | 1,531 | 57 | """Phase 5a tests for site collections."""
from __future__ import annotations
import torch
import torchlens as tl
from torchlens.intervention.sites import SiteCollection, sites as _sites
def test_sites_builds_structured_sweep() -> None:
"""``sites()`` should compose layer, op, and mode sweep dimensions."""
... |
e83a3844042d1040b4b91dc3b033dcaea2fddb44c901dfccb084fa30a351e755 | Python | 1,531 | 45 | # Import the necessary modules and functions
from pathlib import Path
import matplotlib.pyplot as plt
from BM3D.src.denoise import bm3d
from Loader.src.utils import load_nii
from NLM.src.denoise import nlm
if __name__ == "__main__":
# Get the base path of the current file
base_path = Path(__file__).parent
... |
85a37c58b7688885e6da412120f522ec88f4321da1af487588614aaa00b5e001 | Python | 1,532 | 62 | import sys
sys.path.insert(1, '/u/home/r/ryo10244/Xiao_lab/dsRNA_pred/script/utils')
import re
import pysam
import argparse as ap
import pandas as pd
import numpy as np
from utils import *
def main(args):
argp = ap.ArgumentParser(description="concatanate train and null file",
... |
e0cef8b0bd6b9cc1fe165b26bdb560117d100766d52162597d326160a2b6c710 | Python | 1,532 | 70 | """Replace a module output with a raw PyTorch forward hook.
Run with:
``python examples/intervention/17_raw_forward_hook_replacement.py``
"""
from __future__ import annotations
import torch
import torchlens as tl
class HookedMlp(torch.nn.Module):
"""Tiny MLP with a raw-hook replacement site."""
def __in... |
a943d76c4a1f16cd77d52c5ae26910d1609315944469b256eb57e0c72982f27d | Python | 1,533 | 55 | import json
import os
import appdirs
import logging
from score_behavior import appauthor, appname
"""
the system will search for config files:
1) in the default user application data folder (changes by OS), under score_config.json
2) in the current directory, under score_config.json
3) the command line argument give... |
9c345bcf53ce5168ed6d28fe8d22d10a3295db8c458d230c8df8dbd06557046f | Python | 1,534 | 48 | import os
import warnings
import h5py
import numpy as np
import scanpy as sc
from scbig import run_scbig, preprocess, setup_seed
warnings.filterwarnings('ignore')
for dataset in ['DPT', 'YAN', 'Deng']:
print('---------------- data: {} ----------------- '.format(dataset))
setup_seed(0)
method = 'scBiG'
... |
fda3a48de6a49d14470b06429e17a42dc71f97011ff72b44a57e59db0ad45f3c | Python | 1,534 | 40 | from setuptools import setup, find_packages
with open('README.md', 'r') as fh:
long_description = fh.read()
# Read install requirements from requirements.txt
REQUIREMENTS = [i.strip() for i in open("requirements.txt").readlines()]
setup(
name='rcc-xcorr', # the name used for pip install
version='1.0.14'... |
a1b69e7f2fda09eeec0c5d58546cfa26ad85503fe6eab5e52925e1e0b5cc2cd8 | Python | 1,535 | 65 | import cv2
import numpy as np
def contrast_adjust(image, CLAHE_grid, CLAHE_clip):
"""contrast adjust images
Parameters
----------
image: numpy.ndaray
image to pre-process. CV_8UC1 (uint8) or CV_16UC1 (uint16)
CLAHE_grid : int
passed as tileGridSize to cv2.createCLAHE. If -1, skipp... |
eaa44f0fecdd93a28bfc93a84e7e13e844b9ba394b0f518c5094c2bd04efad64 | Python | 1,535 | 42 | """
Demonstrate how to load many ABFs and use a cached stimulus waveform
from an ATF file which only gets loaded once. Since the ATF file is kept
in memory, this is stimulus waveform caching.
NOTE: this type of caching is not actually used!
The Stimulus class is used for this!
"""
import os
import sys
import glob
im... |
69fb095e9353e1e5a014494635aac46f32babbef3a1ee60416b4f737ff15c7fc | Python | 1,536 | 41 | """Prepare ADE20K dataset"""
import os
import shutil
import argparse
import zipfile
from gluoncv.utils import download, makedirs
_TARGET_DIR = os.path.expanduser('~/.mxnet/datasets/ade')
def parse_args():
parser = argparse.ArgumentParser(
description='Initialize ADE20K dataset.',
epilog='Example:... |
a90d04c248d726e16b15f9bd189421d0e5d09f9d9c576928687ddd85a41da51c | Python | 1,536 | 50 | """
Keycloak OpenID Connect backend for Galaxy.
This backend extends Galaxy's base OIDC implementation with Keycloak-specific features.
"""
from galaxy.authnz.oidc import GalaxyOpenIdConnect
class KeycloakOpenIdConnect(GalaxyOpenIdConnect):
"""
Keycloak OIDC backend for Galaxy.
Inherits PKCE support, l... |
cee2069efbba1f2703815a249c5c11ff7ec714913ed19658bd23c8aa3b0e3dfe | Python | 1,536 | 39 | import sys
from ij import IJ
# Fiji Jython interpreter implements Python 2.5 which does not
# provide support for argparse.
input_file = sys.argv[-8]
image_datatype = sys.argv[-7]
filter = sys.argv[-6]
radius = sys.argv[-5]
mask = sys.argv[-4]
light_background = sys.argv[-3]
dont_substract = sys.argv[-2]
tmp_output_p... |
833557b323e481c0d9479352d0636380cba221f37e5a138aaa9a5c524e919272 | Python | 1,537 | 60 | class LLSpyError(Exception):
"""
Generic exception indicating anything relating to the execution
of LLSpy. A string containing an error message should be supplied
when raising this exception.
"""
class ParametersError(LLSpyError):
"""
Exception indicating something is wrong with the parame... |
e4143f1fb17f467e294480f29e5e4f527aabfa7e68f4224164281a137da13910 | Python | 1,537 | 47 | """Add workflow_source and workflow_source_type and public column
Revision ID: a99a5b52ccb8
Revises: 7ffd33d5d144
Create Date: 2024-10-10 14:23:29.485113
"""
import sqlalchemy as sa
from galaxy.model.migrations.util import (
add_column,
column_exists,
drop_column,
transaction,
)
# revision identifi... |
18649f3f0eb5892a838542aaf4a45b8591eca46ceb568c675fa1c999c4785a64 | Python | 1,538 | 45 | import numpy as np
import tqdm.auto as tqdm
from permacache import permacache, stable_hash
from modular_splicing.gtex_data.annotation.compute_optimal_sequence import (
compute_optimal_sequences_all,
)
def compute_psis_from_annotation(sites, index_juncs, tpm_junc_each, annotations_chosen):
psis = np.zeros((l... |
67637c8db1ada42dc7985fdeea5e7bc345b12e70e93a11902cf96bec2809a821 | Python | 1,538 | 29 | import os
import slicer
from slicer.ScriptedLoadableModule import *
# Source: https://github.com/lassoan/SlicerSegmentEditorExtraEffects
class SegmentEditorBiomedisa(ScriptedLoadableModule):
"""Uses ScriptedLoadableModule base class, available at:
https://github.com/Slicer/Slicer/blob/main/Base/Python/slicer/S... |
828e95ab5eb6397b24df3f774aa360ea0f4c46ca11d0b7c57f0c99b91a7e2e8d | Python | 1,538 | 54 | 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["num_motifs"] = 82
msp.arch... |
b8b1a6441063a09eed4528cedf4316ce1c23e815959a3869e111a74f1b85c40d | Python | 1,540 | 54 | from typing import Optional, Union
import numpy as np
from anndata import AnnData
import logging as logg
import scipy.spatial as spatial
def disk(
adata: AnnData,
use_data: str = "X_umap",
radius: float = 10.0,
rates: int = 1,
method: str = "mean",
copy: bool = False,
) -> Optional[AnnData]:
... |
4d31c81171370f279cc47759d15850f96c0662736d4b435ba5f03b2eeb0fa1c4 | Python | 1,543 | 50 | """
Test ABF with variable length sweeps.
"""
import glob
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
try:
PATH_HERE = os.path.abspath(os.path.dirname(__file__))
PATH_DATA = os.path.abspath(PATH_HERE+"../../../data/abfs/")
PATH_SRC = os.path.abspath(PATH_HERE+"../../../src/")
... |
8cc3e28adda3da33f4059b1398c47e984d94ce12376f48de0e3fdca485472285 | Python | 1,543 | 56 | import pytest
from . import utils
import rpy2.rinterface as rinterface
rinterface.initr()
def _just_pass(x):
pass
@pytest.fixture(scope='module')
def silent_console_print():
with utils.obj_in_module(rinterface.callbacks,
'consolewrite_print',
_just_... |
a6c6730c928ff15e56c5d27a39aacc14cd4e3cc48c6d390472c7aaab2c7ac596 | Python | 1,545 | 53 | import json
import tns.validator as tested
from jsonschema.exceptions import ValidationError
from nose.tools import assert_raises
from pathlib import Path
DATA = Path(__file__).parent.resolve() / 'data'
def dummy_params():
with (DATA / 'params2.json').open() as f:
data = json.load(f)
return data
de... |
d9bae89f1337738ac8b0dd0b2468a52cf47311f866f96684d7d3540c18f9b8e0 | Python | 1,545 | 51 | import os
import pytest
from mirp._images.digital_xray_image import DXImage
from mirp._masks.base_mask import BaseMask
from mirp.extract_features_and_images import extract_features_and_images, extract_images
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
@pytest.mark.ci
def test_basic_dx_feature_extractio... |
63005718af7b23e6a3214369cc79b1efd0e7596f1d10adcc125f36e5add9dd17 | Python | 1,546 | 57 | import pathlib
import shutil
import tempfile
import h5py
import numpy as np
import pytest
from impose import formats
data_path = pathlib.Path(__file__).parent / "data"
def test_load_brillouin():
path = data_path / "brillouin.h5"
data, meta = formats.load(path)
# check metadata
assert np.isnan(met... |
68e3d930144949b3c48ab744c9499520b209b111027f1aab938d3f4f75b1dfc3 | Python | 1,546 | 50 | import os
import numpy as np
from modular_splicing.data_pipeline.create_dataset import create_dataset
def select_to_match(lengths, small, large):
[large_idxs] = np.where(large)
np.random.RandomState(0).shuffle(large_idxs)
lengths_ordered = lengths[large_idxs]
lengths_cumulative = np.cumsum(lengths_ord... |
bf62aeb3edd21743d3aa0698476003ee260741bdebdbd91bfb882e8d873ed2a5 | Python | 1,546 | 33 | from tensorflow.keras.callbacks import TensorBoard
from tensorflow import GradientTape
import tensorflow as tf
# This extends TensorBoard to save gradients as histogram
# ExtendedTensorBoard can then be used in replace of tf.keras.callbacks.TensorBoard.
class ExtendedTensorBoard(TensorBoard):
def _log_gradients(s... |
01b854b54851a6ce426dd6b2d8d564954873344cfa0d9206f447340910a5bdf1 | Python | 1,547 | 42 | import torch
import numpy as np
import torch.distributed as dist
# define the strategy of weight decay
class Scheduler():
def __init__(self,end_lr,decay_factor,checkpoint,lr_scheduler,restart,optim,model,swa_model,save_pes):
self.best_loss = 1e30
self.rank=dist.get_rank()
self.end_lr=end_lr... |
33347c30a126ae3cb9ac7b80cdf0802cb7adba31457adf90e9728503f1bfc1f3 | Python | 1,547 | 52 | 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="ParallelSpatiallySparse",
sparse_specs=[
dict(type="SpatiallySparseAcrossChannels"),
dict(type="SpatiallySparseAcros... |
6239f18d461db50843128a977affdb253b2cd8c9e9c39fa38bbacda64192a368 | Python | 1,547 | 36 | """Prepare DIV2K - bicubic downscaling x4 competition datasets"""
import os
import argparse
import zipfile
from gluoncv.utils import download, makedirs
def parse_args():
parser = argparse.ArgumentParser(
description='Initialize DIV2K - bicubic downscaling x4 competition dataset.',
epilog='Example: ... |
0663e8131f41d9cc74303d2412c82a6f633b585cb9e1bcaa61ecd924293fab44 | Python | 1,548 | 48 | import numpy as np
from keras.models import load_model
from Bio import SeqIO
from load2data import onehot
def Pred_Genomic(model_path, chrom_path,result_path,step):
"""
:param model_path:
:param chrom_path:
:param result_path:
:return:
"""
model = load_model(model_path.encode(... |
21e50e22b3c7cd2d0df3e1c0aaa4bec65f941b44ced6731826dd5f06a0aecb9c | Python | 1,548 | 48 | # Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
import pickle
import numpy as np
from numpy.testing import assert_array_equal
from eelbrain import datasets
from eelbrain._stats.spm import LM, LMGroup
def test_lm():
ds = datasets.get_uts()
model = ds.eval("A*B*Y")
coeffs = ds['uts'].ols(model)
... |
70d247c799a3ea0c8a619f6065a5658dc451fcf6526d2ba77d89be261b1b2694 | Python | 1,549 | 42 | """
This module create TransformerClassifPerProt
"""
# pylint: disable=W0223
import torch.nn as nn
class TransformerClassifPerProtMultipleLayer(nn.Module):
def __init__(self, originalModel):
super(TransformerClassifPerProtMultipleLayer, self).__init__()
# Partie multi level embedding
NB_LA... |
89da2f6f81cc472c28f11dabb19d49d3f67926ec565738448c9e384220894aa6 | Python | 1,549 | 49 | import sys
import torch
from src.datasets.base import BaseDataset
class CadenaDataset(BaseDataset):
def __init__(self, root, split, ntau, normalise=True, scale=1):
super().__init__(normalise, scale, False)
# Load dependencies
sys.path.append(f"{root}/dependencies/Cadena2019PlosCB")
... |
e4cc133a0c071876d06e50aaf3bd00bafe7cd4ae784a4869fab862609c6543d5 | Python | 1,549 | 39 | def entire_model_types():
from modular_splicing.models.lssi import SplicePointIdentifier
from .preprocessed_spliceai import (
PreprocessedSpliceAI,
)
from .spliceai_as_downstream import SpliceaiAsDownstream
from .modified import (
DroppedMotifFromModel,
DiscretizeMotifModel,
... |
5adf9c333f42682a9e7bf5b404bcdaae7dffed62b2cc97773888e66a25798ac9 | Python | 1,550 | 48 | import itertools
import numpy as np
import pandas as pd
import tqdm.auto as tqdm
from modular_splicing.utils.io import model_steps
from modular_splicing.evaluation import standard_e2e_eval
pattern = "model/msp-266a1-{splice},{left},{right}_{seed}"
CL_VALUES = list(range(0, 1 + 20, 2))
def accuracy_results(splice, ... |
bcef3e1acd0825656445851f46e402300a226180c61e554d69818311dfff807f | Python | 1,550 | 49 | from setuptools import setup #, find_packages
CLASSIFIERS = """
Development Status :: 1 - Planning
Intended Audience :: Science/Research
Intended Audience :: Developers
License :: OSI Approved :: MIT License
Programming Language :: Python
Programming Language :: Python :: 3
Programming Language :: Python :: 3.6
Progra... |
12f6b4fa6ce412739970acc61f610ff76a076bbe92c1e5e47ee12541e862e237 | Python | 1,551 | 39 | """ Conv2d w/ Same Padding
Hacked together by Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional
from .padding import pad_valid_time, get_padding_value
def conv2d_valid_time(
x, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, str... |
42db69f6e400e2e0a2e4a934cf6a3e4e0b068c7758a9f7bf3c86e6fd625771c8 | Python | 1,551 | 43 | #!/usr/bin/env python
import datetime
import os
import sys
def extract_datetime_from_line(line, year):
# Expected format: I0210 13:39:22.381027 25210 solver.cpp:204] Iteration 100, lr = 0.00992565
line = line.strip().split()
month = int(line[0][1:3])
day = int(line[0][3:])
timestamp = line[1]
p... |
4a2d898051c6e7f2cb4c4a1b9bb3684d1d479b938b59db305e55cbbd544f809c | Python | 1,551 | 1 | import numpy as np
import pandas as pd
import time
import random
# [1] data preprocessing #
df = pd.read_csv('./data/GSE75688_GEO_processed_Breast_Cancer_raw_TPM_matrix_gene_filtering_log.csv', index_col=0) # dimension = (genes x cells)
sample_inf = pd.read_csv('./data/GSE75688_final_sample_information.csv', index_col... |
9316ddec8c57c790cc22a82f8c1a2d342be50de318f09817df8d18a44cc643f5 | Python | 1,551 | 44 | import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="gradient-accumulator",
version="0.5.2",
author="André Pedersen and David Bouget and Javier Pérez de Frutos and Tor-Arne Schmidt Nordmo",
author_email="andrped94@gmail.com",
... |
964a4b9ef1f8a5e3cabf16c2a267fd6fcf82314d4cf7fb89aac38ff54f3b6b10 | Python | 1,551 | 62 | from .models_for_testing import EndToEndModelsForTesting, STANDARD_DENSITY, TEN_EPOCHS
FM = EndToEndModelsForTesting(
name="FM",
path_prefix="model/msp-273.665a3",
seeds=(1,),
density=STANDARD_DENSITY,
binarized_seeds=(1,),
binarized_step=TEN_EPOCHS[10],
)
# extra FMs just for the e2e tests
#... |
da0b3b9bfc9d146754ca99a33a1457b64311f2c074df3e2cee9027395488eeb7 | Python | 1,551 | 51 | import os
import pytest
from mirp._images.digital_xray_image import MGImage
from mirp._masks.base_mask import BaseMask
from mirp.extract_features_and_images import extract_features_and_images, extract_images
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
@pytest.mark.ci
def test_basic_mg_feature_extractio... |
fee15fca2b3f3e6efed1631dac7cfa9a92da9975b1c8a99033d7e474f4e88864 | Python | 1,551 | 65 | # -*- coding: utf-8 -*-
"""
Created on Fri Aug 26 09:15:06 2022
@author: LABadmin
"""
import numpy as np
from matplotlib import pyplot as plt
import random
import sklearn
import seaborn as sns
import scipy as sp
from matplotlib import rc
import matplotlib.ticker as mtick
import matplotlib as mpl
from scipy import s... |
58350c9982dd3f262cfc61e11bf258092582c65f9e77ba904876b544a47c31b9 | Python | 1,552 | 83 | #!/usr/bin/python
import sys
import os
import pandas as pd
import numpy as np
import math
import re
filedir = sys.argv[1]
if re.match(".+\/$", filedir) is None:
filedir += '/'
width = 300 #px
height = 300 #px
obsP = []
f = open(filedir+"input.snps", 'r')
f.readline()
for l in f:
l = l.rstrip()
l = l.split("\t")... |
cf60ec3adfea85685394a9430e5b0b21b0d3ffb070791328804b32226d3f2a7a | Python | 1,552 | 44 | from anndata import AnnData
from .logging import logger_manager as lm
def copy_adata(adata: AnnData) -> AnnData:
"""wrapper for deep copy adata and log copy operation since it is memory intensive.
Args:
adata: An adata object that will be deep copied.
logger: Whether to report logging info
... |
a9e129212b9f468fd688ecf4873dce8b6410db080b1862dcacc51a6a1b00f781 | Python | 1,553 | 38 | """Prepare ILSVRC 2015 DET dataset
=========================================
`ILSVRC DET dataset <http://image-net.org/>`_ is
a dataset for object localization/detection and image/scene classification task.
It contains two hundred full labeled categories for detection and 1000 categories for Object localization
This ... |
eb30aa0054067dbee3b9326dc80f14fa6a80ddb1524090453084373e4d7ed4eb | Python | 1,554 | 44 | """Tests for specparam.plts.model."""
import numpy as np
from specparam.tests.tutils import plot_test
from specparam.tests.tsettings import TEST_PLOTS_PATH
from specparam.plts.model import *
###################################################################################################
#########################... |
3b88a4ea7532a3e7ee5f7227aa4a44d4bdd71d1ae1d1e09694dcf97d793f354e | Python | 1,555 | 55 | import torch
from typing import List
from tqdm import tqdm
import numpy as np
@torch.jit.script
def spectrum(E: torch.Tensor,
osc: torch.Tensor,
sigma: torch.Tensor,
x: torch.Tensor) -> torch.Tensor:
"""
Function for Gaussian broadening of the XAS spectra so
that ... |
7b00819ce16226506f51fa8a15568efd2aa7e669aecbbfca39f8f5bbd339817a | Python | 1,555 | 58 | from permacache import permacache, stable_hash
import tqdm.auto as tqdm
from modular_splicing.legacy.remapping_pickle import permacache_with_remapping_pickle
from modular_splicing.utils.arrays import Sparse
from modular_splicing.utils.run_batched import run_batched
def _predict_motifs_raw(m, xs, bs):
"""
Pre... |
8f0bf012a8464fd7253a76d3b635eebfc0b1b2416381fe86459282c4c4e62ec4 | Python | 1,555 | 48 | from __future__ import annotations
from pathlib import Path
from typing import Optional
from grpc import RpcError
from bream4.device_interfaces.devices.base_device import BaseDeviceInterface
def get_run_output_path(device: BaseDeviceInterface, filename: str, subdirs: Optional[list[str]] = None) -> Path:
"""Ret... |
abc5129a520ea21b656b451ec1f591652935951048aef63cd81dce50bacec90c | Python | 1,555 | 54 | """
Flow analysis datatypes.
"""
import logging
from galaxy.datatypes.binary import Binary
from galaxy.datatypes.protocols import DatasetProtocol
from galaxy.datatypes.sniff import (
build_sniff_from_prefix,
FilePrefix,
)
from . import data
log = logging.getLogger(__name__)
@build_sniff_from_prefix
class F... |
b7bb888863c692772da6d820ab2f1455f50414e76811579af5394144f03b158d | Python | 1,555 | 54 | import exrex
import pandas as pd
import re
import vampire.xcr_vector_conversion as xcr_vector_conversion
# We don't take the gap character.
aas = xcr_vector_conversion.AA_LIST[:20]
# This is how we specify the "tcregex" format.
replacement_dict = {
# . means any amino acid
'\.': '[' + ''.join(aas) + ']',
... |
155a0fda095f7e90123a12984844e59013bcff45d544d91d4168b9465053d1a3 | Python | 1,556 | 40 | import pathlib
from PyQt6 import QtCore, QtGui, QtWidgets
class DirectoryDialogMultiSelect(QtWidgets.QFileDialog):
def __init__(self, *args):
"""A dialog that lets the user select multiple directories"""
QtWidgets.QFileDialog.__init__(self, *args)
self.setOption(self.Option.DontUseNativeD... |
e42f7cdb0fa47fa024df21a3e42ff77698db5668bf7c75dd0a509efc5aedff46 | Python | 1,556 | 51 | import os
import pytest
from mirp._images.digital_xray_image import CRImage
from mirp._masks.base_mask import BaseMask
from mirp.extract_features_and_images import extract_features_and_images, extract_images
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
@pytest.mark.ci
def test_basic_cr_feature_extractio... |
e9d27a70727493982d6d8b84b3192cb6e9de58382cad30c583224e4b3ac90b82 | Python | 1,556 | 54 | #!/usr/bin/env python
"""The setup script."""
from setuptools import setup, find_packages
with open("README.md", encoding="utf8") as readme_file:
readme = readme_file.read()
with open("HISTORY.rst") as history_file:
history = history_file.read()
with open("requirements.txt") as f:
requirements = f.read... |
fd043df9566c07381f9ce6567b73aa5267a704df79f8ef203d85becc99afd505 | Python | 1,556 | 53 | from unittest import mock
import torch
from pytest import CaptureFixture
from lightly.utils import dist
class TestDist:
def test_eye_rank_undist(self):
assert torch.all(dist.eye_rank(3) == torch.eye(3))
def test_eye_rank_dist(self):
n = 3
zeros = torch.zeros((n, n)).bool()
e... |
91ae525d66bf42182bb2a72344488761239213bc7ca2b0c5661d2ff767e7d4bb | Python | 1,557 | 52 | import itertools
import os
import random
##########################################
# main setting finetuned archs
##########################################
GLOBAL_PARAMS = {
'ngrams': [1, 2, 3, 4, 5, 6, 7],
'layer': ['last_hidden_state_mean'], # 'pooler_output'
}
PARAMS_LIST = [
{
'datas... |
9fd5493bc9ef984fc000171a52b58d1ee69c8648e6505b4fe09d7b571936233c | Python | 1,557 | 36 | import numpy as np
def run_2D(pace=100, nsteps=100000, sigma=0.1, height=0.5, biasfactor=10, ipos=np.array([-1,-1]),tag=1):
with open("plumed.dat","w") as f:
print("""p: DISTANCE ATOMS=1,2 COMPONENTS
ff: MATHEVAL ARG=p.x,p.y PERIODIC=NO FUNC=(7*x^4-23*x^2+7*y^4-23*y^2)
bb: BIASVALUE ARG=ff
METAD ARG=p.x,p.... |
cef5fabc29bc90bac5df20d5fa84ba18c76a5d468086f78f9f4c2d993c8f7e64 | Python | 1,557 | 42 | import torch
import numpy as np
import torch.distributed as dist
# define the strategy of weight decay
class Scheduler():
def __init__(self,end_lr,decay_factor,checkpoint,lr_scheduler,restart,optim,model,swa_model,save_pes):
self.best_loss = 1e30
self.rank=dist.get_rank()
self.end_lr=end_lr... |
7c0421ed28efeadbbc9f31b6a7048cea45ea8b595943fbc2a93e726307102b36 | Python | 1,558 | 53 | 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... |
985e78718563852682268a69dfa26e3608f97b8086b24e8a36c58690ddd9cc76 | Python | 1,558 | 58 | # skip test: data unavailable
"""Compute TRFs with boosting"""
from eelbrain import *
# load the data
ds = load.fiff.events('Data/R2290_HAYO_P3H_1-8-raw.fif')
ds = ds.sub("trigger == 167")
ds['meg'] = load.fiff.epochs(ds, 0, tstop=60)
# load the saved DSS
todss, fromdss = load.unpickle('Data/R2290_HAYO_P3H_DSS.pickl... |
c86baf510560a5b92fa56f482d270a9e4832996cc693502f1f17eb29c9f76c10 | Python | 1,558 | 61 | """add user defined file sources
Revision ID: c14a3c93d66b
Revises: c14a3c93d66a
Create Date: 2023-04-01 17:25:37.553039
"""
from sqlalchemy import (
Boolean,
Column,
DateTime,
ForeignKey,
Integer,
String,
Text,
)
from galaxy.model.custom_types import (
JSONType,
UUIDType,
)
from... |
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