sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
9bce116e7136a85cc0526a18b2b838b3d3b0fb9b6b2065e77526ad77b33fcbd6 | Python | 687 | 25 | import sys
bed_fname1 = sys.argv[1]
bed_fname2 = sys.argv[2]
def load_bed(bed_fname):
data = []
with open(bed_fname, "r") as f:
for line in f:
tokens = line.strip().split()
chrom, start, stop = tokens[0:3]
data.append((chrom, int(start), int(stop)))
return data
... |
e597869457023952a3578e615fed7c3b65b235df7451906f444aa0d42f6887a8 | Python | 687 | 23 | import os;
import sys;
my_path=os.path.dirname(os.path.realpath(__file__));
root=os.path.abspath(my_path+'/../..')
sys.path.insert(0,root);
from biswebpython.core.bis_objects import *
from biswebpython.modules.smoothImage import *
# change this
fname1= root +'/test/testdata/indiv/prep.nii.gz';
input=bisImage();
inp... |
faeec4e7204247c0d3765a591e3a80255a6b374b1f09d6bfc64c9dfe81949614 | Python | 687 | 21 | from PIL import Image
from lightly.transforms.simclr_transform import SimCLRTransform, SimCLRViewTransform
from .. import helpers
def test_view_on_pil_image() -> None:
single_view_transform = SimCLRViewTransform(input_size=32)
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
... |
255c6cc3c7eb3cf9464b8b3fff28a6febfc9a389372c335abc72e7f15962717f | Python | 688 | 25 | from sys import argv
from msp import MSP
from msp_fly import fly_am
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
fly_am(msp)
msp.architecture["propagate_sparsity_spec"] = dict(type="NoSparsityPropagation")
msp.architecture["final_processor_spec"].update(
dict(
bottleneck_spec=dict(type="First... |
90d323c3f0419b81d03c7ed5a367d35a178d7cbaad9872a2cb0b0e63577daf3c | Python | 688 | 27 | """
This module define a
"""
# pylint: disable=W0223
import torch
import logging
from utils.utils import select_output_lm
import torch
class SumLossJustExpWeights(
torch.nn.modules.loss._Loss
): # pylint: disable=protected-access
"""
This class define a specific loss :
"""
def __init__(self):
... |
11f3521c310a9f31c5c11dc2d526d9f8a48b3ab32794dc2d46e1ff79429a19f9 | Python | 689 | 23 | from neuron import h, gui
# Since a double is 64-bits and a pointer is also 32 or 64-bits, we can store
# the bits of a pointer in the memory that was allocated for use as doubles.
# Concretely, we, using VERBATIM, safe pointers in ASSIGNED or RANGE variables.
#
# The pattern is found in the builtin mod file: `apcount... |
9901524a702109d878adb100ca8841b75f05f3523d8b344ccebfc40c1af33489 | Python | 689 | 17 | """Script for export pre-trained models in GluonCV model zoo."""
from __future__ import print_function
import argparse
import gluoncv as gcv
gcv.utils.check_version('0.6.0')
def parse_args():
parser = argparse.ArgumentParser("Export model helper.")
parser.add_argument('--model', '-m', required=True, type=str, ... |
263051ee39876e568e2d5d5b39f8f6940fa90739f9d0ff3d76ea91a16eedd692 | Python | 690 | 26 | import pytest
from rpy2 import robjects
@pytest.fixture(scope='module')
def set_class_A():
robjects.r('methods::setClass("A", representation(a="numeric", b="character"))')
yield
robjects.r('methods::removeClass("A")')
def test_slotnames(set_class_A):
ainstance = robjects.r('new("A", a=1, b="c")'... |
58d71775afb05ac23b9f5c92c6376ac0a859e3975fb1f661e1baca0dc7b09f5d | Python | 690 | 15 | # Copyright 2018 The TensorFlow 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 applica... |
bbfba43b03ea9308c9fdef0292650dd573ecf419caf2eb8a87d2075c0a8e5d0f | Python | 690 | 24 | """
Simulate creating old GUIDs from new onew
"""
import os
import sys
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/")
sys.path.insert(0, PATH_SRC)
import pyabf
def oldIncorrectGUID(GUID):
""... |
66f261562ffe09167b284a29077614a54eaabf316c69ee912de4ecfa39c56458 | Python | 691 | 37 | import numpy as np
from sklearn.preprocessing import normalize
np.set_printoptions(precision=3)
np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
data = np.array([
[100, 100],
[300, 100],
[200, 100]]);
data2= np.array([
[ 10,8 ],
[ -10,8 ],
[ 10,8 ]]);
norm_data=normalize(data,ax... |
a9de86ca022907fb40d644c2561f6d7ac78567e9c0cefdc9d3584ca80f764c35 | Python | 691 | 36 | import gc
from neuron import h, gui
def allocate_props(n_instances, mech_name):
s = h.Section()
s.nseg = 1
for _ in range(n_instances):
getattr(h, mech_name)(s(0.5))
def assert_equal(actual, expected):
assert actual == expected, f"{actual} != {expected}"
def check_ctor(n_instances, mech_... |
1a1ff9ced3aa54ea934c84365798eceaee52d729369f48110d63e7737d45bc76 | Python | 692 | 24 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
import logging
logger = logging.getLogger()
def init_logger(log_file=None, log_file_level=logging.NOTSET):
log_format = logging.Formatter("[%(asctime)s %(levelname)s] %(message)s")
logger = logging.getLogger()
logger.setLevel(logging.INFO)
... |
38cc9a734b74a371e3f22a2186ca301fa9a50f9991bf724913ffb30e6df47c6d | Python | 692 | 22 | from ethoscope.tracking.roi_builders import TubeMonitorWithTargetROIBuilder
import cv2
def draw_rois(im, all_rois):
for roi in all_rois:
x,y = roi.offset
y += roi.rectangle[3]/2
x += roi.rectangle[2]/2
cv2.putText(im, str(roi.idx), (x,y), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255,255... |
45062e647b88cf1842e3acabc484e2173c7521a105397c2e8f45a6df12b0fdd2 | Python | 692 | 23 | """
This module define a converter
"""
import torch
from converter.generic_converter import GenericConverter
from utils.utils import square_pad_sequence
class ConverterInverseWeightToPred(GenericConverter):
"""
This class convert proba output to prediction
"""
def convert_output_and_target(self, outp... |
4f71f6d732c19be1d4707ce45f0ae3a292fd1cc1dc12dd225008316d00b75e73 | Python | 692 | 29 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="SpliceaiAsDownstream",
motif_model_spec=dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
),
spliceai_spec=dict(t... |
681fc52fe73d7a9b0498a05e62c35e228b6254ba61b9468765abe74cb80c7dd3 | Python | 692 | 21 | from PIL import Image
from lightly.transforms.simsiam_transform import SimSiamTransform, SimSiamViewTransform
from .. import helpers
def test_view_on_pil_image() -> None:
single_view_transform = SimSiamViewTransform(input_size=32)
sample = Image.new("RGB", (100, 100))
output = single_view_transform(samp... |
ba05503314badf903dc0a3cdeb7ffaf57b1d9d2d366bac9d425798a5e2d372f2 | Python | 692 | 29 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="SpliceaiAsDownstream",
motif_model_spec=dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
),
spliceai_spec=dict(t... |
dc36595571d08466f5b10e22d6862789c8cf95cff1738fc4a55b75c5e352574f | Python | 692 | 24 | import os
from matplotlib import image
import numpy as np
import nibabel as nib
import itk
import pandas as pd
import tarfile
import shutil
from scripts.preprocess_utils import find_file_in_path
input_path = "data/pseudolabels_mris"
# +
for id_patient in os.listdir("data/registered/all_mris"):
filepath = "data/re... |
f51658d65c0bdb5044c90f85239fa4a4829b1202b54dd38142ef55c604771de4 | Python | 692 | 21 | """
See how fast just the protocol can be read from ABF files
"""
import os
import sys
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/")
sys.path.insert(0, PATH_SRC)
import pyabf
import time
demoAbf... |
f692a153ec29e96fa9d7a9c8405cc8aa1149ada389490512628c6e55101761e5 | Python | 692 | 19 | import numpy as np
def perm_test_two_tailed( a, b ):
nsamples = 10000
permutation_a_b = np.zeros( [np.size(a), 2] )
over_a_b = 1
delta_a_b = np.nanmean(a) - np.nanmean(b)
if np.isnan(delta_a_b):
p_value_a_b = 1
else:
for k in range(nsamples):
for i in range(0,np... |
7911c42a9f03c82e7b20db7f36c529605263fafb469eaf3382086c9cc960afc4 | Python | 694 | 18 | import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
root = "/home/luke/PycharmProjects/NeuralPred"
def build_dataset(dataset, ntau, nlat, nspan, scale, model, layer, n_pca):
os.system(f"python fit.py --root={root} --dataset={dataset} --ntau={ntau} --nlat={nlat} --nspan={nspan} --scale={scale} --model={model} --la... |
7a02455fc8b54fb1dffb23a4831d6da9e779bf4743aed79df2ebaf7578c867c0 | Python | 694 | 29 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="SpliceaiAsDownstream",
motif_model_spec=dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
),
spliceai_spec=dict(t... |
93917e00517bc38939c5c32e3da84805dfec4214d898f9a67fa508aa6ed84bcc | Python | 694 | 27 | import pickle
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np
all_features = []
for i in range(0,300000,10000):
f_name = '../preprocessed_pickles/all_features_'+ str(i)
with open(f_name, "rb") as f:
temp = pickle.load(f)
all_features.extend(temp... |
9853ce251fd0076f37166f2079f3875fdbf97d2b2190b3b2ffe144672c624102 | Python | 694 | 33 | #!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from scipy.sparse import *
from scipy.io import mmwrite
nb_pre = 100
nb_post = nb_pre//4
sigma = 20.0 # Width of diagonal in channels
mat = np.empty((nb_pre,nb_post))
x = np.arange(nb_pre)
for i,c in enumerate(np.linspace(0,nb_pre,nb_post))... |
f395f7468ce8ecff5e88b7565b569fe9d504fd27844a452ea58d30caf27f5b85 | Python | 694 | 29 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="SpliceaiAsDownstream",
motif_model_spec=dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
),
spliceai_spec=dict(t... |
fbc7b17baa4d4e103bd4141506e30e4a29aae26ccc354946ffc9fec44c0ad08f | Python | 694 | 29 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="SpliceaiAsDownstream",
motif_model_spec=dict(
type="PSAMMotifModel",
motif_spec=dict(type="rbns"),
exclude_names=("3P", "5P"),
),
spliceai_spec=dict(t... |
00859b73fdb590bab79cc5579fe9a8ed2c5d690dc245f4a3c173046a8291c424 | Python | 695 | 38 | """
This file is a part of BdPy
"""
__all__ = ['add_bias']
import numpy as np
def add_bias(x, axis=0):
"""
Add bias terms to x
Parameters
----------
x : array_like
Data matrix
axis : 0 or 1, optional
Axis in which bias terms are added (default: 0)
Returns
-------
... |
07c132eba2d0eb3462048673b4102c1d41c0fccabca2fedfa0b4310d13f3572b | Python | 695 | 39 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""A range of utility functions for xcp_d interfaces and workflows."""
from xcp_d.utils import (
atlas,
bids,
boilerplate,
concatenation,
confounds,
doc,
execsummary,
filema... |
d5094a581065523d1c9050f961d073593b77da7e2238b50eb3fc9693f7add887 | Python | 695 | 18 | from review_comparison.compute_metric import compute_metric
from review_comparison.generate_all_predictions import generate_all_predictions
from review_comparison.parse_and_create_the_different_dataset import (
create_the_different_dataset,
)
import logging
logging.getLogger().setLevel(logging.INFO)
# Create the... |
65e6381260619acec4138e8402fdf7fa5ac6c1996d3909fcbe48b6a8e8089ac3 | Python | 696 | 24 | 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"] = dict(
type="AdjustedMotifModel",
model_to_adjust_spec=dict(
... |
7b3439d07e52e39d0faaa264ec5ae6ff43ff7be2391b2d5da944b989584acfd6 | Python | 696 | 18 | import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
root = "/home/luke/PycharmProjects/NeuralPred"
def build_dataset(dataset, ntau, nlat, nspan, scale, model, layer, n_pca):
os.system(f"python fit.py --root={root} --dataset={dataset} --ntau={ntau} --nlat={nlat} --nspan={nspan} --scale={scale} --model={model} --la... |
88a8659e31cc26e2f9fb0ce044c93db37cf1a7b53ba55abe06a9da2349889d60 | Python | 696 | 29 | from logging import getLogger
import torchvision.datasets as datasets
logger = getLogger()
class MoCoDataset(datasets.ImageFolder):
def __init__(
self,
data_path,
tau_g,
):
super(MoCoDataset, self).__init__(data_path)
self.tau_g = tau_g # set of global transforms
... |
4e76c218d70d7bdefe38614f8aa4d92da0da4000554bb06633c3e4fd4d9ebdf0 | Python | 697 | 25 | import sqlite3
from .db import DB
class ContactTestingInformation(DB):
def __init__(self):
super().__init__()
self.table_name = 'TG_contact_testing'
self.table_desc = 'Telangana primary and secondary contact testing'
self.cols = self.getcolumns()
def getcolumns(self):
... |
6bd3a731e37e1f6933cd3b50ae8a7f5525980df43e0d502d58cba659e51a5626 | Python | 697 | 24 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 146
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"] = dict(
type="AdjustedMotifModel",
model_to_adjust_spec=dict(
... |
a11e4114b53f353cd1f777a3299bf7c6384033b0be33b2d0eb00a8ecc473a5eb | Python | 697 | 24 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 146
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"] = dict(
type="AdjustedMotifModel",
model_to_adjust_spec=dict(
... |
a7bd7a4d18afb4052a349296228fd06294ff1ec33779b04e42fcccd098f569ad | Python | 697 | 27 | import unittest
import hydra
import pytest
from hydra.experimental import compose
try:
from hydra import initialize
except ImportError:
from hydra.experimental import initialize
from lightly.cli.version_cli import version_cli
class TestCLIVersion(unittest.TestCase):
def setUp(self):
with initia... |
0bc5be48cc6c743cdac975bdc2253e0f4ad7e4cfe7393c2f973c40d8054d785d | Python | 698 | 25 | from sys import argv
from msp import MSP
from msp_fly import fly_am
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
fly_am(msp)
msp.architecture["propagate_sparsity_spec"] = dict(type="OnlyAllowInfluenceOnSplicesites")
msp.architecture["final_processor_spec"].update(
dict(
bottleneck_spec=dict(t... |
3364327efa77be67809852a16788c381779b519abe21ad1ffad9bf43f6825913 | Python | 698 | 38 | import io
import os
from setuptools import setup, find_packages
def read(
fname,
):
with io.open(
os.path.join(
os.path.dirname(__file__),
fname,
),
encoding="utf-8",
) as f:
return f.read()
setup(
name="XASNet-XAI",
packages=find_packages("s... |
5dc83781efd45154341379efa7962f4e274b037024fe4a59f2d6f8f998a81383 | Python | 698 | 24 | 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"] = dict(
type="AdjustedMotifModel",
model_to_adjust_spec=dict(
... |
cdcffa1b726166f2ab14b24093650948e1d9c0deec1a88c2f1b5d309b99a531e | Python | 698 | 23 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""tests for neighbor sampler"""
import unittest
from sknetwork.data.test_graphs import test_graph
from sknetwork.gnn.neighbor_sampler import *
from sknetwork.utils import get_degrees
class TestNeighSampler(unittest.TestCase):
def setUp(self) -> None:
"""T... |
d29412652597dac81a9fb456e9c97ec125baff7119e057d0c95e92b0a912b967 | Python | 698 | 24 | import sys
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._colorbar import ColorBar
from ._hoverlabel import Hoverlabel
from ._legendgrouptitle import Legendgrouptitle
from ._textfont import Textfont
from . import colorbar
from . import hoverlabel
from . import legendgrouptitle
el... |
d3db0e327b68f973de77c90a0605706720b9a0f702ab7c86ea6661ac3679c827 | Python | 698 | 36 | project = "minimal-basis"
copyright = "2023, Sudarshan Vijay"
author = "Sudarshan Vijay"
release = "0.0.1"
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join("..", "..", "src")))
extensions = ["myst_parser", "sphinx.ext.autodoc", "sphinx.ext.autosummary"]
myst_enable_extensions = [
"amsmath",
... |
5bb7b05487a5d20e39e5890684af778a780fca7c0bd05134d535cba2f202289f | Python | 699 | 24 | """
This module define a converter
"""
import logging
import torch
from converter.generic_converter import GenericConverter
from utils.utils import square_pad_sequence
class ConverterProbaToPred(GenericConverter):
"""
This class convert proba output to prediction
"""
def convert_output_and_target(sel... |
77d0687d446e35c0102493f3b0f9ec0c4075707d4bd63e3e3503d4b4339ca192 | Python | 699 | 30 | """Add public tag to project
Revision ID: f3a10c95079f
Revises: 9d5ff5377b63
Create Date: 2025-01-14 22:01:12.694786
"""
import sqlalchemy as sa
from alembic import op
# revision identifiers, used by Alembic.
revision = "f3a10c95079f"
down_revision = "54d9cae546ad"
branch_labels = None
depends_on = None
def upgra... |
b43bdb31ef30aabfddaf94d7884c31ac0c92a6f11b649769f74f5c9db34e4799 | Python | 699 | 24 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 146
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"] = dict(
type="AdjustedMotifModel",
model_to_adjust_spec=dict(
... |
c6fca231fe31f35542424f4fbe7ce698134fbf08db0259abf830c60d3121c3cf | Python | 699 | 27 | 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... |
f5017bd6de083140d91f5247845cd9c37dd0b28f17f6b3b9538cdcefab3876ef | Python | 699 | 25 | # Set default logging handler to avoid "No handler found" warnings.
import logging
import os
import subprocess
try: # Python 2.7+
from logging import NullHandler
except ImportError:
class NullHandler(logging.Handler):
def emit(self, record):
pass
current_path = os.getcwd()
try:
GIT_V... |
058baef796d49e5de517aef6e305b06dad454248f860d9bc9487a038a221dc3b | Python | 700 | 27 | 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"] = 146
msp.arc... |
3d31cbcb5b1b6eb9eade7abe9d379920385fb8650cc9d8bcedb63fa85aee8a69 | Python | 700 | 16 | from keras.src.backend.tensorflow import distribution_lib
from keras.src.backend.tensorflow import ops
from keras.src.backend.tensorflow import random
from keras.src.backend.tensorflow import rnn
from keras.src.backend.tensorflow import tensorboard
from keras.src.backend.tensorflow.ops.core import Variable
from keras.s... |
937e1890c4405a7c358e04649df3e648a32c394c10dbcb9c858bd613f94ca657 | Python | 700 | 24 | import time
from ethoscope.hardware.interfaces.interfaces import BaseInterface
from ethoscope.stimulators.odour_stimulators import DynamicOdourSleepDepriver
from ethoscope.tests.integration_api_tests.old.utils import test_stimulator
class MockSDInterface(BaseInterface):
def send(self, channel, stimulus_duration)... |
cdc775fac5e790fdc31cce7ad9073dcda3aac7e7aa69dab5ab36cea1277b4161 | Python | 700 | 22 | import numpy as np
def gabor(t, s, t0, f0, phase=0.0, segment_length_s=20.0):
"""
Generates values for Gabor atom with unit amplitude.
t0 - global, t - also global,
"""
t_segment = t % segment_length_s
t0_segment = t0 % segment_length_s
result = np.exp(-np.pi*((t_segment-t0_segment)/s)**2)... |
10b0a5e41a4a4453ccd62e064e0f02b3c7a78bb7b33393a0504be767016d770c | Python | 701 | 21 | #!/usr/bin/env python3
import sys
import subprocess
import os
##Script to estimate the illumina coverage before running hypo.
##Author: Jèssica Gómez Garrido
##email: jessica.gomez@cnag.crg.eu
fasta = sys.argv[1]
bam = sys.argv[2]
dir=os.path.dirname(os.path.realpath(__file__))
glen_out = subprocess.check_output(di... |
4a9fc478af99744fb19c4dc7d7adef485dc152f2a43ece12cab1893fd20a19ee | Python | 701 | 15 | import unittest
from modular_splicing.motif_perturbations.summarize_effect import avg_blur, radial_sum
class TestSum(unittest.TestCase):
def test_sum_basic(self):
self.assertEqual(radial_sum([1, 2, 3, 4, 5], 0).tolist(), [1, 2, 3, 4, 5])
self.assertEqual(radial_sum([1, 2, 3, 4, 5], 1).tolist(), [... |
5d5d1e947b91b66008daeaa3bd4769b3bb984018dc50184a64c1d07a7a07f449 | Python | 701 | 25 | import sqlite3
from .db import DB
class VaxInfoTable(DB):
def __init__(self):
self.table_name = 'WB_vaccination'
self.table_desc = 'West Bengal vaccination information'
self.cols = self.getcolumns()
def getcolumns(self):
cols = {
'date': 'DATE NOT NULL PRIMARY KEY... |
9460f7416a043d2b9f0454a38e3d430fb24c13295d9e896593c5b5cf32f97b85 | Python | 701 | 17 | # 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... |
b54f66042d837c9c7133b46b6eaff36dfb897b1f71f894958c1913c5caae909d | Python | 701 | 26 | from .adds.add_deconvolution import add_deconvolution
from .adds.add_loupe_clusters import add_loupe_clusters
from .adds.add_mask import add_mask, apply_mask
from .adds.annotation import annotation
from .adds.image import image
from .adds.labels import labels
from .adds.lr import lr
from .adds.parsing import parsing
fr... |
1f8a5318fe1b9fd08f7ec5c00810348bf0c60cb835dc4e7292234d60c94b2b8c | Python | 702 | 27 | #/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup
requirements = [
]
setup(
name="encoding",
version="0.1.0",
description="Code for fitting fMRI encoding",
author="Amanda LeBel",
author_email="amanda_lebel@berkeley.edu",
packages=[
'encoding',
],
package_d... |
2570c504c0436015121823202b53867ed78ed0ab13e3e982184506d5b96c4760 | Python | 702 | 23 | import numpy as np
import pytest
import gym
from gym import spaces
from gym.wrappers import FlattenObservation
@pytest.mark.parametrize("env_id", ["Blackjack-v1"])
def test_flatten_observation(env_id):
env = gym.make(env_id, disable_env_checker=True)
wrapped_env = FlattenObservation(env)
obs, info = env... |
3c7284905309e2d1cf30610a6706a4fdaaab7d81dcd30271a1ff54ade66da269 | Python | 702 | 16 | from bioconfpred.model import ComENetModel
from bioconfpred.ranker import ModelRanker
from bioconfpred.params import COMENET_CONFIG
from rdkit import Chem
from rdkit.Chem.rdDistGeom import EmbedMultipleConfs
config = COMENET_CONFIG
config['data_split'] = None
checkpoint_path = "/home/bb596/hdd/conf_ranking/data/lightni... |
30eb429d9af0f8bef47750fcec60c0f5391ccbac30c919d8f74ea8b496dee315 | Python | 703 | 18 | """
Normalizing flow models for PyTorch.
All the layers defined in this module are invertible and implement an
``inverse()`` method (not to be comfused with the ``Tensor``'s ``backward()``
method which backpropagate the gradients).
The forward propagation of the modules here return both the transformation
of the inpu... |
8cf2c928960496d169636b9cb74272244903d6168ec8d53c87fd4ac222cc8193 | Python | 703 | 26 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""tests for postprocessing"""
import unittest
import numpy as np
from sknetwork.ranking.postprocess import top_k
class TestPostprocessing(unittest.TestCase):
def test_top_k(self):
scores = np.arange(10)
index = top_k(scores, 3)
self.asser... |
8f9a88b4f2c4426d9479ec782201fe5ff39551ce29162beddd789f4d46621811 | Python | 703 | 30 | # %%
import sys
import argparse
import random
# %%
def main():
parser = argparse.ArgumentParser()
parser.add_argument('beds', nargs='+')
parser.add_argument('-n', type=int)
args = parser.parse_args()
if args.n < 1:
exit(1)
lines = []
for bed in args.beds:
with open... |
a0fc4fe475186973d8663f1712f723b10841e73410846e196c0f274dd43fb804 | Python | 703 | 34 |
__author__ = 'quentin'
from ethoscope_node.utils.helpers import generate_new_device_map
from ethoscope_node.utils.mysql_backup import MySQLdbToSQlite, DBNotReadyError
import logging
import optparse
import time
import multiprocessing
import traceback
import os
import subprocess
import re
db_credentials = {
"n... |
25a96c750043cf22f961d76e1bc6e012c9e068b5c4543f2d55b2657c985a98f6 | Python | 704 | 19 | # flake8: noqa: F401
import pyqtgraph as pg
from . import mask
from . import shapes
def pg_roi_to_impose_shape(roi, tr, point_um):
"""Convenience method for converting a pyqtgraph ROI to an impose shape"""
if isinstance(roi, pg.CircleROI):
return shapes.Circle.from_pg_roi(roi, tr, point_um)
elif ... |
1868ee23f78c0ac3a97ac058166a6599fcda84a670394c88343c96cd8798e2a4 | Python | 705 | 30 | from abc import ABC, abstractmethod
class PostProcessor(ABC):
@abstractmethod
def post_process(self, element):
"""
Post process the element, producing a sequence of elements
"""
pass
class FlattenerPostProcessor(PostProcessor):
def __init__(self, *, indices):
self... |
1d5734d62188f3e83812cb9cea1e6123ffc97a564b5fd6442a62ec996b0c304b | Python | 705 | 20 | from __future__ import annotations
def detect_quorum_queues(app, driver_type: str) -> tuple[bool, str]:
"""Detect if any of the queues are quorum queues.
Returns:
tuple[bool, str]: A tuple containing a boolean indicating if any of the queues are quorum queues
and the name of the first quorum ... |
ecc60d1c6ee00aac6fe6285b50146e13225c86fa3c57bd2a046b157bb6117984 | Python | 706 | 30 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture = dict(
type="PreprocessedSpliceAI",
preprocessor_spec=dict(type="Identity"),
spliceai_spec=dict(type="SpliceAIModule", output_size=6),
)
msp.data_dir = "../data/canonical_and_gtex_dataset/"
... |
478704c9a00f76d6f3fd9580f9afcf493a36f134ecaf478d0fe108766c8a7cb8 | Python | 707 | 22 | import tqdm.auto as tqdm
import numpy as np
def mcmc_step(state, current_score, rng, *, report, temperature=1, **kwargs):
mutation = state.sample_mutation(rng, **kwargs)
state.perform(mutation)
new_score = state.score()
log_ratio = temperature * (new_score - current_score)
if np.log(rng.rand()) < ... |
a1fb7d07883b498b942181a44d5b4213514f53dc32ecab346a37801ed2596220 | Python | 707 | 27 | """
This module define a
"""
# pylint: disable=W0223
import torch
import logging
from utils.utils import select_output_lm
import torch
class SquaredSumLossJustExpWeights(
torch.nn.modules.loss._Loss
): # pylint: disable=protected-access
"""
This class define a specific loss :
"""
def __init__(s... |
a4cf1ea17ea9037df36254296411771431de841f771dbfabf2b5628fee6ed3be | Python | 707 | 24 | import sys
import logging
class LoggerWriter(object):
def __init__(self, writer):
self._writer = writer
self._msg = ''
def write(self, message):
self._msg = self._msg + message
while '\n' in self._msg:
pos = self._msg.find('\n')
self._writer(self._msg[:p... |
04127a031f4f11ff8980a4676e7e0e7c4aa8326739da70603e40c38f7246db34 | Python | 708 | 24 | import numpy as np
import math
import pdb
# ------------------------------------------------------
# Set default radius to 0.5
def set_default_radius(swc_matrix):
updated_swc_matrix = swc_matrix
if (swc_matrix.loc[:, "Radius"].isnull().values.any()):
updated_swc_matrix["Radius"] = updated_swc_matri... |
251992b90100d95e4023f151a65eb56cd56db9642352499ea0ac2b07ff84bfed | Python | 708 | 22 | from time import sleep
from celery import shared_task
from t.integration.tasks import LEGACY_TASKS_DISABLED
@shared_task
def waitfor(seconds: int) -> None:
print(f"Waiting for {seconds} seconds...")
for i in range(seconds):
sleep(1)
print(f"{i+1} seconds passed")
print("Done waiting")
i... |
bb36b2d2104f6e6f5f73fabfe50002f99167d01e03dd04e1591d48fee16d69c3 | Python | 708 | 19 | import numpy as np
import torch
import opt_einsum as oe
from torch.autograd.functional import jacobian
from src.MODEL import *
#============================calculate the energy===================================
class Property(torch.nn.Module):
def __init__(self,density,nnmod):
super(Property,self).__init_... |
9f7c440e268ed2bd78d8ce4704a7c7a82213c5e122d7048458f0e582b09e9f17 | Python | 709 | 16 | # Copyright (c) 2024 PaddleHelix Authors. All Rights Reserved.
#
# Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0
# International License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://creativecommo... |
a42b25505c3f994b13ea2ba251dd3bad3618a73b65db698d1ab88c1f08c9d572 | Python | 709 | 23 | from sys import argv
from msp import MSP, amm_mean40v1
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"] = dict(
type="AdjustedMotifModel",
model_to_adjust... |
ffc7a780bdccf606861241baf70267cc9f5d1ccd7b941b1308bbcaa8db18dfc8 | Python | 709 | 15 | # Copyright (c) 2024 PaddleHelix Authors. All Rights Reserved.
#
# Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0
# International License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://creativecommo... |
c69d687aa02b522494364d26f4cd2ac2ebd4e51abc0b1f62db2bf0d20265784d | Python | 710 | 30 | import numpy as np
from neurogym.core import TrialEnv
from neurogym.utils import spaces
class Null(TrialEnv):
"""Null task."""
def __init__(self, dt=100) -> None:
super().__init__(dt=dt)
self.action_space = spaces.Discrete(1)
self.observation_space = spaces.Box(
-np.inf,
... |
02904cb99efd2237b91536485b742f052fd9d83713400019a39b80853be75a92 | Python | 711 | 36 | from mongoengine import *
import datetime
import time
class Event(EmbeddedDocument):
name = StringField()
created_at = DateTimeField()
meta = {'allow_inheritance': True}
def now(self, event):
self.created_at = datetime.datetime.now()
class SessionEvent(Event):
user = StringField... |
95c99370ddea64d44d49861f3cf26e0483f5b5f6ff5d33833cccf25cbda3a00d | Python | 711 | 21 | from PIL import Image
from lightly.transforms.swav_transform import SwaVTransform, SwaVViewTransform
from .. import helpers
def test_view_on_pil_image() -> None:
single_view_transform = SwaVViewTransform()
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
assert output.sha... |
2749a17454a9aa8a610a2213d739046488af06fe742da1c06802d00ab28a3bf8 | Python | 712 | 30 | import flask
def test_aborting(app):
class Foo(Exception):
whatever = 42
@app.errorhandler(Foo)
def handle_foo(e):
return str(e.whatever)
@app.route("/")
def index():
raise flask.abort(flask.redirect(flask.url_for("test")))
@app.route("/test")
def test():
... |
9ca6075bcadabbf31f8aae1f40a41532c8f760814c54a9f23230c25b5a859b4a | Python | 712 | 31 | """add index workflow_request_step_states__workflow_invocation_id
Revision ID: 3a2914d703ca
Revises: c39f1de47a04
Create Date: 2023-03-07 15:06:55.682273
"""
from galaxy.model.database_object_names import build_index_name
from galaxy.model.migrations.util import (
create_index,
drop_index,
)
# revision iden... |
6ea29a79dd7b2d04784b5342942e573ca823b2f927fe2d8b70b4fdedf62866de | Python | 713 | 27 | # -*- coding: utf-8 -*-
"""
Created on Thu Mar 2 10:25:14 2023
@author: fede_
"""
import pickle
import matplotlib.pyplot as plt
# define full path to the results of the permutations test
results = '/path_to_output_directory/permutations.pkl'
# define full path where to save the image
image = 'path_to_output_director... |
7bb501084b1640ce30f620a13e9f5b7d49b72a31396ea69f1954d7e124d7ce7c | Python | 713 | 29 | #!/usr/bin/env python
"""Convert GlimmerHMM gene predictions into protein sequences.
This works with both the GFF and the costumn Tabular Output.
And is only a wrapper to call the appropiate scripts.
Usage:
glimmerhmm_to_sequence.py <glimmer output> <ref fasta> <output file> <format> <protein>
"""
import sys
im... |
bde721f36012475ad8f28d241ca2c66cba84c74904473992892bf597887dc09f | Python | 713 | 21 | from pl_bolts.models.rl.advantage_actor_critic_model import AdvantageActorCritic
from pl_bolts.models.rl.double_dqn_model import DoubleDQN
from pl_bolts.models.rl.dqn_model import DQN
from pl_bolts.models.rl.dueling_dqn_model import DuelingDQN
from pl_bolts.models.rl.noisy_dqn_model import NoisyDQN
from pl_bolts.models... |
06328de19a9782fc284691a7bb66948c6d41c01f673e727c1828182e9349433c | Python | 714 | 15 | # Copyright (c) 2024 PaddleHelix Authors. All Rights Reserved.
#
# Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0
# International License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://creativecommo... |
a1429251698f6fa1c9fc3b3effd50e43ad154f6cd9464af3584ba68888062ce0 | Python | 714 | 23 |
"""
Code here makes it easy to glance at a header item from every demo ABF file.
"""
from imports import *
if __name__=="__main__":
for fname in sorted(glob.glob(PATH_DATA+"/*.abf")):
abf = pyabf.ABF(fname)
if abf.abfFileFormat==1:
print(abf.abfID, abf._headerV1.nTelegraphEnable)
... |
c1a5b39ea0fac792372228ce49acff413981923c5436529c7cb19508d8442ffe | Python | 714 | 23 | #!/usr/bin/env python
"""
Wrapper and analysis for simulations ran with PLUMED.
"""
# =============================================================================
# UTILITY FUNCTIONS
# =============================================================================
def check_plumed_is_installed():
"""Check that th... |
26d593435b6d5b49e9da91cbd711183cf0f2e2f0f1b1228b90a6589ef88d9e7e | Python | 715 | 17 | """
Gathers all splitters in one place for convenient imports
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
# TODO(rbharath): Get rid of * import
# from deepchem.splits.splitters import *
# from deepchem.splits.splitters import ScaffoldSplitter
# from... |
9587250fab4b787aa3e48b86e75c4a5e85683a96f13d96ed58cf5659cd600577 | Python | 715 | 24 | 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"] = dict(
type="AdjustedMotifModel",
model_to_adjust_spec=dict(
... |
2931d3861c1de27f1e34fa9490b9af87f4eb0c25e10ca79c4cb3ef6043d31409 | Python | 716 | 34 | """add display_name column to galaxy_user
Revision ID: 64d49ad328d4
Revises: a4c3f2e1b790
Create Date: 2026-08-13 10:00:00.000000
"""
from sqlalchemy import Column
from galaxy.model.custom_types import TrimmedString
from galaxy.model.migrations.util import (
add_column,
drop_column,
)
# revision identifier... |
5564c4306c2232503819125d0c49845ad64c282042f34b9c4d26bc7cfa9d00ab | Python | 716 | 25 | """
This module define a CrossEntropyPerToken
"""
# pylint: disable=W0223
import torch
import logging
from utils.utils import select_output_lm
class CrossEntropyPerProtWeighted(
torch.nn.modules.loss._Loss
): # pylint: disable=protected-access
"""
This class define a specific loss : crossentropy loss for... |
5e81939b250dbb055a002ba32e3bec3bd27d16122eacdce7fb9a83ec6e871372 | Python | 716 | 27 | # Generated by Django 2.2.2 on 2019-06-12 17:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('biomedisa_app', '0042_upload_shift_size'),
]
operations = [
migrations.RemoveField(
model_name='upload',
name='shift... |
c78861e14ffd122de3a2b6d061d48852079abb0ed660baccd24b4c0f2f2e6e22 | Python | 716 | 25 | import os
import numpy as np
from PIL import Image
abs = np.load('abs_trainSR.npy')
angle = np.load('angle_trainSR.npy')
save_path = 'train_cat_tif'
if os.path.exists(save_path):
print('save path existed')
else:
os.makedirs(save_path)
def transform_val(array):
array = 255 * (array - np.... |
da6f82591613613805ff54f193b66dad0d1f97f275651caf85098c3ec6358a1a | Python | 716 | 29 | import pytest
import rpy2.rlike.functional as rlf
def test_tapply_sumbystring():
seq = ( 1, 2, 3, 4, 5, 6)
tags = ('a', 'b', 'a', 'c', 'b', 'a')
expected = {'a': 1+3+6,
'b': 2+5,
'c': 4}
with pytest.deprecated_call():
res = rlf.tapply(seq, tags, sum)... |
8aed1716e8fcc634af0b955ef2c33ce31d77bd030676e607b8bc6f4fd831586e | Python | 717 | 23 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["motif_model_spec"]["motif_width"] = 9
msp.architecture["motif_model_spec"]["motif_feature_extractor_spec"] = dict(
type="FCMotifFeatureExtractor", extra_compute=10, num_compute_layers=1
)
msp.architec... |
5d1e1ab015a28075ab486cfa10d8a4501ada75de9f66850e418f1ead112c85c8 | Python | 718 | 22 | """ Main entry point of the bioseq2seq library """
from __future__ import division, print_function
import bioseq2seq.inputters
import bioseq2seq.encoders
import bioseq2seq.decoders
import bioseq2seq.models
import bioseq2seq.utils
import bioseq2seq.modules
import bioseq2seq.bin
from bioseq2seq.trainer import Trainer
im... |
473cba46b7a47462e67bafa269a5b8a4d056b1ba87bfc4ed29b543e26da81228 | Python | 719 | 19 | import unittest
import numpy as np
from carbontracker import predictor
class TestPredictor(unittest.TestCase):
def test_predict_energy(self):
total_epochs = 10
epoch_energy_usages = np.array([10, 20, 30, 40, 50])
expected_result = total_epochs * np.mean(epoch_energy_usages)
result =... |
b4b17e8cbb1f336385a9bc951cad68f635d55bfd4f6d390c7319005ee1a330ef | Python | 720 | 24 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Tests for hierarchy API"""
import unittest
from sknetwork.data.test_graphs import *
from sknetwork.hierarchy import *
class TestHierarchyAPI(unittest.TestCase):
def test_undirected(self):
adjacency = test_graph()
n = adjacency.shape[0]
f... |
697581f4feed5a25a9bb57974b5113cbf2f390ca153085e79ef2e2933fb02c3b | Python | 721 | 21 | from PIL import Image
from lightly.transforms.smog_transform import SMoGTransform, SmoGViewTransform
from .. import helpers
def test_view_on_pil_image() -> None:
single_view_transform = SmoGViewTransform(crop_size=32)
sample = Image.new("RGB", (100, 100))
output = single_view_transform(sample)
asser... |
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