sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
3c51303cd4c9cfbe71b5725841053bdccc17efe4e31694a8be5b7f59a773ea37 | Python | 1,389 | 45 | """Dynamic graph coverage for repeated fastlog recording."""
from __future__ import annotations
from collections import Counter
import pytest
import torch
import torch.nn.functional as F
from torch import nn
import torchlens as tl
class DynamicMLP(nn.Module):
"""MLP with input-dependent control flow."""
... |
4c223044fcb43bb6f1f9c536c31fb865afd83948e912f0b79a7976625ac2dfd9 | Python | 1,389 | 44 | from __future__ import annotations
from typing import Dict
import torch
from .core import Domain
_FeatureType = Dict[str, torch.Tensor]
def _lnd2nld(feature: torch.Tensor) -> torch.Tensor:
"""Convert features having the shape of (L, N, D) to (N, L, D)."""
return feature.permute(1, 0, 2)
def _nld2lnd(feat... |
7c2c0fdc2d5f69360ca0cb03dd766ffe7ccb647446641417fb80fd2bcb680f44 | Python | 1,389 | 49 | import numpy as np
from sklearn import linear_model, svm, metrics
class OrdinalRidge(linear_model.Ridge):
"""
Overwrite Ridge from scikit-learn to use
the (minus) squared error as score function.
(see https://github.com/scikit-learn/scikit-learn/issues/3848
on why this cannot be accomplished usin... |
eb03c2fa27bc887d117f58ac718e9289b86f5201e82fd5f6c01bf1926d86bb78 | Python | 1,390 | 57 | import pytest
import rpy2.rinterface as ri
ri.initr()
def test_init():
rgetattr = ri.baseenv.find('::')
formula = rgetattr('stats', 'formula')
f = formula(ri.StrSexpVector(['y ~ x', ]))
assert f.typeof == ri.RTYPES.LANGSXP
def test_init_invalid():
seq = True
with pytest.raises(TypeError):
... |
fda7dcd907bbfd44c1e358f4265d77eee278e88e4ade3e4e2ad24319683611f7 | Python | 1,390 | 49 | #!/usr/bin/env python
from Bio import Seq
from Bio.Alphabet import generic_dna
def check_gff(gff_iterator):
"""
Check GFF files before feeding to SeqIO to be sure they have sequences.
"""
for rec in gff_iterator:
if isinstance(rec.seq, Seq.UnknownSeq):
print("Warning: FASTA sequen... |
1c3af236aa30f68cfdeb062ee8ba3ff19262ae1b2ba49e2f2c205921d75fab65 | Python | 1,391 | 30 | from unittest import result
import pandas as pd
import matplotlib.pyplot as plt
import utils
from prodict import Prodict
import numpy as np
from scipy import stats
def explore_encondings(cfg: Prodict):
encodings_path = f"{cfg.DATA.OUTPUT_DIR}/{cfg.TEST.RUN_ID}/e{cfg.TEST.EPOCH_TO_TEST}/tables/encodings/epoch-{cf... |
56d3a8ae148ed90b9882051e62c65269ba09e430d64ce18ceeb55a38501fb997 | Python | 1,391 | 36 | """Test model with expression parameters"""
import numpy as np
import pyjibe.head
from helpers import MockModelModuleExpr, make_directory_with_data
def test_model_expr_base(qtbot):
"""Expressions sanity check
This test is just to make sure that parameters that have an
expression are not overridden by t... |
c42a3f3fd11842e121df90bf80f240dbde651431cc3036a1c92302175324d93d | Python | 1,391 | 46 | # coding=utf-8
# Copyright 2023 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... |
e803c446fea13b8a916ebdeaeb1c89758edaac2608b85baf635bc751c650102e | Python | 1,391 | 43 | import tensorflow as tf
from tensorflow import keras
import os
import numpy as np
import nibabel as nib
import matplotlib.pyplot as plt
# Jayse Weaver, weaverjaysem@gmail.com
# Evaluating CNN described in https://doi.org/10.1162/imag_a_00023
# Below example processes two individual diffusion directions
# For a 4D NIf... |
e9910633d76738accf887c8df0364d94e80496b3af25eb568feea707abc1279f | Python | 1,391 | 48 | import os
import utils
import yaml
import joblib
from sklearn.feature_selection import SelectKBest
from sklearn.pipeline import Pipeline
from sksurv.datasets import get_x_y
from sksurv.ensemble import RandomSurvivalForest
from skopt.space import Real, Categorical, Integer
def preprocess_data(dataset, config):
X =... |
1ecbc65630b35474b775657e361a792604de83b6d8e1ec1194d4e651791e47b1 | Python | 1,392 | 40 | """Tests for specparam.algorthms.settings."""
from specparam.algorithms.settings import *
###################################################################################################
###################################################################################################
def test_settings_values():... |
16aeca1c35597f3e9b1c3142a28acfb7e1125633dd8449247a1037f48dbbc8e6 | Python | 1,393 | 32 | '''
Forming the features vector with the fukui indices.
'''
import numpy as np
def add_fukuis(df_row, h, longest_molecule):
'''
Adding in the fukui indices for each atom in features vector.
'''
h = np.array(h).reshape([longest_molecule, -1])
padded_electro_fukuis, padded_nucleo_fukuis, padded_rad_f... |
3a1cbabf245e1920d740acfb92acdacc1af03dd6a9c59cfefaffeb1ba6adf750 | Python | 1,393 | 57 | import numpy as np
import scanpy as sc
from anndata import AnnData
from scipy import sparse as sp
from sklearn.neighbors import kneighbors_graph
from utils import dopca
def get_adj(count, k=160, pca=30, mode="connectivity"):
if pca:
countp = dopca(count, dim=pca)
else:
countp = count
A = ... |
70e66fad135156dd2bfe7c3fe15ecdd4fa10bc36979b1faae6dcc8732dd4ac74 | Python | 1,393 | 48 | 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_... |
b29311f345d42c1220a86d389c97448bcece07c05296282f5b0208d235a7d6b9 | Python | 1,393 | 39 | # copyright (c) 2024 PaddleHelix Authors. All Rights Reserve.
#
# 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://creativecommons.o... |
bab8d87a8f0b745e3513e2ac3abc53133faf0f2bff29e352500f6946b06e2674 | Python | 1,393 | 48 | 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_... |
cff6a4e928fdab59d7b4b44c8d21e7178512284c82589de5895164d14c5a97d2 | Python | 1,393 | 47 | #!/usr/bin/env python
"""
A generic wrapper to access nmodl binaries from a python installation
Please create a softlink with the binary name to be called.
"""
import os
import stat
import sys
if sys.version_info >= (3, 9):
from importlib.metadata import metadata, PackageNotFoundError
from importlib.resources ... |
303a09cc790b1fb556bf64e6dbea5e1a1daaac9ba3069ca89c18faba4127203f | Python | 1,394 | 48 | 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_... |
3aabe36567e2d0878b99dd025ea468540a3ad82f4e3fe22a5c65f2e07b595279 | Python | 1,394 | 46 | #!/usr/bin/env python
#=========================================================================
#
# Copyright NumFOCUS
#
# 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://... |
78579f218695bf779610b5f0cf020ccc0916704a59cba4d6d2170980d2dc719c | Python | 1,394 | 32 | import setuptools
from setuptools.command.install import install
from io import open
import os
install_requires = ["numpy", "matplotlib", "biom-format", "rpy2"]
setuptools.setup(
name='lefse',
version='1.1.2',
author='Nicola Segata',
author_email='nicola.segata@unitn.it',
url='http://github.com/Seg... |
ce30dcace32c7146526079c4cd2314de9417f2cfd6d4a414d4e1e5b3c4b70917 | Python | 1,394 | 44 | from celery.contrib.abortable import AbortableAsyncResult, AbortableTask
class test_AbortableTask:
def setup_method(self):
@self.app.task(base=AbortableTask, shared=False)
def abortable():
return True
self.abortable = abortable
def test_async_result_is_abortable(self):
... |
26a398d6505419c1af2728e87835bc105ff8c0c116c7b93f555258eb52f7a8ca | Python | 1,395 | 49 | #!/usr/bin/env python
import os
import sys
from xml.sax.saxutils import escape
def make_table(directory):
ret = ['<table class="fileList">\n']
for root, dirs, files in os.walk(directory):
root = root.rstrip("/")
ret.append(
' <tr><td class="directory">%s</td></tr>\n'
... |
337f5913843ffe1b72eb233114b16a0bfb9f631f1b32878652794935efd8ee25 | Python | 1,395 | 81 | from datetime import datetime
import numpy as np
import torch
import os
import time
from constants import get_args
from spliceai_torch import (
SpliceAI,
load_model,
save_model,
SpliceAIDataset,
evaluate_model,
)
from load_psam_motif import read_motifs
from map_protein_name import get_map_protei... |
cf74bbda37eaff872a853d652aea026e6e42824a7ff2f536004c182f4ae763db | Python | 1,395 | 48 | 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(
... |
2acf628ba41383ac7ddfa7276300c7a08440313598b6ac77399539b5ddfdd82b | Python | 1,396 | 40 | #!/usr/bin/env python
from distutils.core import setup
import wyrm
setup(
name='Wyrm',
version=wyrm.__version__,
description='Toolbox for Brain Computer Interfacing Experiments.',
long_description='A Python toolbox for on-line BCI experiments and off-line BCI data analysis.',
author='Bastian Ve... |
d12336492c8cdde0788c8acdc9face30072273ba225b847357301ada2620d674 | Python | 1,396 | 37 | from __future__ import annotations
import os
import unittest
import warnings
from coco_froc_analysis.count import count_point
class TestFrocCurve(unittest.TestCase):
def setUp(self):
self.gt_ann_path = f'{os.getcwd()}/tests/example_coco_data/face-mask.json'
self.pr_prefect = (
f'{os.... |
8456f079ddea0f9e2142815669994be24b380a8f4c18a333dfcf235b7f04734a | Python | 1,397 | 43 | from typing import List, Optional, Tuple, Union
class Field:
"""Object to specify requirements for a field provided in configuration file."""
def __init__(self, name: str, types: List, default = None, requirements: Optional[List] = None, key: Optional[str] = None):
"""
Class constructor.
... |
d45b777761c22719d5206b18d1da3a5227860999ed24e797c79269240fcdab39 | Python | 1,397 | 54 | #!/usr/bin/env python
"""
Input: Molecule file
Output: Molecule file with hydrogen atoms added at the target pH.
"""
import argparse
import sys
from openbabel import openbabel, pybel
openbabel.obErrorLog.StopLogging()
def parse_command_line(argv):
parser = argparse.ArgumentParser()
parser.add_argum... |
0b27981f691f0ca48649446805f5adc7eb137196503fe6cad7bd0d98a6259ac8 | Python | 1,398 | 41 | import logging
from pip._internal.cli.base_command import Command
from pip._internal.operations.check import (
check_package_set, create_package_set_from_installed,
)
logger = logging.getLogger(__name__)
class CheckCommand(Command):
"""Verify installed packages have compatible dependencies."""
name = 'c... |
5c6dfe2584fac9f86277d071ea9a26d2fa458ca1c92411b177ba627c5eab86e0 | Python | 1,398 | 32 | from angorapy.analysis.investigators import Predictability
import tensorflow as tf
from angorapy.common.policies import BasePolicyDistribution
import tensorflow_probability as tfp
class Correlation(Predictability):
def __init__(self, network: tf.keras.Model, distribution: BasePolicyDistribution):
super... |
1cb057d8db31de0a45cc6a9d9733f6c6779bd1cc3f5ec67412f5ac5b7596c785 | Python | 1,399 | 47 | import pytest
from pytest_celery import CeleryTestSetup, CeleryTestWorker
from t.smoke.tests.stamping.workers.legacy import * # noqa
from t.smoke.tests.stamping.workers.legacy import LegacyWorkerContainer
from t.smoke.workers.dev import SmokeWorkerContainer
@pytest.fixture
def default_rabbitmq_broker_image() -> str... |
4bd9ed89de3550d23930be75ca1189ce2b6d1a102fde897e2db74e5c0022d251 | Python | 1,399 | 47 | # Copyright 2022 Deepfold Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... |
7a78bca864c67949a1be74eb7731c29112051af4d42af0c1b0ab0defb46f81da | Python | 1,399 | 44 | """GluonCV Utility functions."""
# pylint: disable=wildcard-import,exec-used,wrong-import-position
from __future__ import absolute_import
import types
def import_dummy_module(code, name):
# create blank module
module = types.ModuleType(name)
# populate the module with code
exec(code, module.__dict__)
... |
9dcf78a752d1bb6ba6f16499673c1028749ccf9b8445858144f0899c7cba6a18 | Python | 1,399 | 25 | from __future__ import annotations
from .bootstrap_froc_curve import generate_bootstrap_froc_curves # noqa: F401, E501
from .froc_curve import froc_point # noqa: F401
from .froc_curve import generate_froc_curve # noqa: F401
documentation = None
"""
This submodule provides functionality for evaluating Free-Response... |
cf28e480c140d85bc9992f31cba7d1b9655280a3902b9b23bcc4f75bcca42866 | Python | 1,400 | 60 | import os
import hydra
import hydra.experimental
import numpy as np
import pytest
import torch
pytest_plugins = ["helpers_namespace"]
hydra.experimental.initialize(
os.path.join(os.path.dirname(__file__), "../pointnet2/config")
)
@pytest.helpers.register
def build_cfg(overrides=[]):
return hydra.experiment... |
d18a3e8c097f13d8dda6279153d40f203dfe6ceff001b8029d3764f62f54e083 | Python | 1,401 | 46 | """Phase 4 miscellaneous capture tests."""
from __future__ import annotations
import json
from pathlib import Path
import torch
import torchlens as tl
import torchlens.utils as utils
def test_register_op_rule_affects_flops() -> None:
"""Custom op rules are accepted by the utility registry."""
utils.regis... |
f0745c9c725c68d49f83f6fe0e0f752910230d70b1fb2ed91e063c203d16cdef | Python | 1,401 | 68 | #!/usr/bin/env python
import sys
import pysam
import numpy as np
import collections
from collections import Counter
values = 12
def get_site_arround(sites_path, fasta_path, values):
fasta = pysam.FastaFile(fasta_path)
sequences = []
with open(sites_path) as f:
for line in f:
line = line.strip().split('\t')
... |
1d6ee2cc29cf7ea393664415923bd4e462207d58ecd48d9b6af5e69befd5d611 | Python | 1,402 | 82 | from ._alphabets import DNA, AminoAlphabet, NucleotideAlphabet
# NOTE the "*" character is termination i.e. STOP codon
canonical_codons_to_aas = {
"TTT": "F",
"TTC": "F",
"TTA": "L",
"TTG": "L",
"CTT": "L",
"CTC": "L",
"CTA": "L",
"CTG": "L",
"ATT": "I",
"ATC": "I",
"ATA": "... |
2bf180ded399ca05ae29a99076353c50ebfa22adbc0fe4b73952ac47e03aa2c0 | Python | 1,402 | 40 | """
core.feature_spec — Canonical feature pool definitions.
The classifier consumes 15 features in a fixed order:
- 5D state-geometry channels (`CHANNELS_5D`):
I_A, org, I_M, I_R, rel_alpha
- 10 established / candidate EEG-AD markers (`FEATURES = AP_TOPO + CONN_ALPHA`):
AP_TOPO (7): iaf, dfa, fooo... |
393fc60cd399cb650c7a872094864e629307abb3de09b1bcd20dd8ec0df9ef5d | Python | 1,402 | 42 | import pytest
from rpy2.robjects import packages
try:
from rpy2.robjects.lib import tidyr
has_tidyr = True
msg = ''
except packages.PackageNotInstalledError as error:
has_tidyr = False
msg = str(error)
from rpy2 import rinterface
from rpy2.robjects import vectors
datasets = packages.importr('data... |
56ecc2ee32e11d6f5eef4266b838f14a669b8a6ad3eb79fe534c92a4dd198505 | Python | 1,402 | 41 | #!/usr/bin/env python3
import sys
import os
import argparse
def read_params(args):
parser = argparse.ArgumentParser(description='Convert LEfSe output to '
'Circlader input')
parser.add_argument( 'inp_f', metavar='INPUT_FILE', nargs='?',
default=None, typ... |
9c697e33b67cdac5607df0ccbdc9ac53aeb9cd3dec3d50826f96bb9ac37381da | Python | 1,402 | 47 | # Directional sensory input and synaptic weights
from os.path import join
import numpy as np
import os
import pandas as pd
from library.comput_utils import cal_hd_np
from library.params import ParamsHolder
from library.shared_vars import sim_results_dir
from library.utils import save_pickle
from library.simulation impo... |
146f82a410382723a22da94ba93cfc8f0d055fdb93e8db6a1eb5b51a0192e979 | Python | 1,403 | 36 | """Test model with expression parameters"""
import numpy as np
import pyjibe.head
from helpers import MockModelModuleExpr, make_directory_with_data
def test_model_expr_base(qtbot):
"""Expressions sanity check
This test is just to make sure that parameters that have an
expression are not overridden by t... |
3ae53296c06c7b53f5d9d715a39f2ff32884c477ee353aa9f67b2f06d400196f | Python | 1,403 | 44 | #!/usr/bin/env python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Simple volume-preserving MAF on cartesian coordinates.
"""
# =================================================... |
9991be52f839e65342bfc82f8b684afa3a7291e2b7e729665e7e0e5ca82ee40d | Python | 1,403 | 45 | import attr
import numpy as np
from working.synthetic_data.mutation.codon_mutation import CodonMutation
@attr.s
class CodonAddress:
unchangable_positions_mask = attr.ib()
all_codon_indices = attr.ib()
is_exon = attr.ib()
index_map = attr.ib()
@classmethod
def of(cls, eci, ici, unchangable_p... |
1cfe67750570ad16b36ba812d6bc775e2d7953893e58724f414dc527c4631f00 | Python | 1,404 | 39 | import pycistarget
import pyranges as pr
import pandas as pd
from scenicplus.wrappers.run_pycistarget import run_pycistarget
import glob
import os
from pyscistarget.utils import region_names_to_coordinates
import pyprojroot
proj_dir = str(pyprojroot.here())
nvolp_peak_fnm = os.path.join(proj_dir, "18.snap2_peakcalli... |
4fe0985a6a808fa7260b38ed1b2d1db82c9efbbb8a49c97d90070cef4a5b2ee2 | Python | 1,404 | 53 | from os import path
from threading import Thread
import traceback
import logging
import time
try:
import picamera
except:
logging.warning("Could not load picamera module")
class RecordVideo(Thread):
def __init__(self, data=None, name="myvideo", ETHOSCOPE_DIR = "/ethoscope_data/results"):
#TOD... |
2585fbb837849710930694e1e327c44240fb3ded3f119f6495e6e49646f5ec0b | Python | 1,405 | 42 | import pandas as pd
import glob
import os
from statsmodels.stats.multitest import fdrcorrection
import seaborn as sns
import matplotlib.pyplot as plt
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--sample", action = "store",
help = "name of sample")
parser.add_argument("--results", action = ... |
83d0ba13a9cc4bd47e3a1bbc5c1f15c5fc024f14c16cedc4a642ce02911ea4db | Python | 1,405 | 33 | import numpy as np
from sklearn import metrics
class Metrics(object):
"""Define metrics to be use when training/evaluating models.
"""
@classmethod
def flat_accuracy(cls, y_true, y_pred):
""" Function to calculate the accuracy of our predictions vs labels
"""
return np.sum(y_pr... |
a8aa7b7c678c58220d399610aff5a9443b4da28cbe169efd11704c779e8882b6 | Python | 1,405 | 46 | import pymc as pm
from pmsurv.models import weibull_linear
import numpy as np
import time
def main():
model = weibull_linear.WeibullModelLinear()
model.load('data/model.yaml')
print(model.trace)
x_predict = np.asarray([[2 * 10, 2, 3, 4, 0, 0, 0]])
start = time.time()
cached_model = model.cre... |
cc44165ea4444bc114aedba76cf2f15451f182df97629317589d46902742f118 | Python | 1,406 | 48 | import os
from flask import Flask
def create_app(test_config=None):
"""Create and configure an instance of the Flask application."""
app = Flask(__name__, instance_relative_config=True)
app.config.from_mapping(
# a default secret that should be overridden by instance config
SECRET_KEY="de... |
d7f0aae4fd4b23f310dcdd9af2c1c29e9f57c96e5793da0af4fc9845750cefb5 | Python | 1,407 | 40 | import pytest
import contextlib
import os
import tempfile
from rpy2.robjects.conversion import localconverter
import rpy2.robjects as robjects
from rpy2.robjects.lib import grid
from rpy2.robjects.vectors import FloatVector, StrVector
@pytest.mark.parametrize(
'constructor,args,kwargs,res_cls',
((grid.unit, ... |
fe43ecbda424db27383d1961e35604df431b5e4954f1e0610fe3982ddec09c03 | Python | 1,407 | 52 | import numpy as np
from neuron import gui
from neuron import h
from neuron.units import ms
def test_cvode(rtol):
nseg = 1
mech = "scalar"
s = h.Section()
cvode = h.CVode()
cvode.active(True)
cvode.atol(1e-10)
s.insert(mech)
s.nseg = nseg
t_hoc = h.Vector().record(h._ref_t)
va... |
825085e96b03a166006a835cb8754003525edb60b4a627d4b60dc9f2351fbee4 | Python | 1,408 | 48 | """Benchmarking module for AlignedSegment functionality"""
import os
import array
import pysam
from TestUtils import BAM_DATADIR
def set_binary_tag():
read = pysam.AlignedSegment()
read.set_tag('FZ', array.array('H', range(1000)))
return len(read.get_tag('FZ'))
def read_binary_tag(fn):
with pysam.... |
d839834724983ccc2928ce7e2842f6d3a0ba56fae6d19a339af43e92262d86b6 | Python | 1,408 | 45 | # code from berak taken from https://answers.opencv.org/question/134248/how-to-define-the-lower-and-upper-range-of-a-color/
import cv2
import numpy as np
image_hsv = None # global ;(
pixel = (20,60,80) # some stupid default
# mouse callback function
def pick_color(event,x,y,flags,param):
if event == cv2.EVENT_L... |
dc07bde268ed38ed4902695a8d1475e066dee6710da5425aa45b5da31efdd115 | Python | 1,408 | 38 | import json
import pandas as pd
from tqdm import tqdm
def levenshteinDistance(s1, s2):
if len(s1) > len(s2):
s1, s2 = s2, s1
distances = range(len(s1) + 1)
for i2, c2 in enumerate(s2):
distances_ = [i2+1]
for i1, c1 in enumerate(s1):
if c1 == c2:
distan... |
4829f27323af65a5653c810bac2404141b307c6adaa1a1f039a2b4647b547043 | Python | 1,409 | 49 | import numpy as np
import pandas as pd
from nfp.preprocessing.mol_preprocessor import SmilesPreprocessor
from tensorflow.keras.utils import get_file
from tqdm.auto import tqdm
from spektral.data import Dataset, Graph
from spektral.utils import sparse
# Load the YSI dataset and split into train and validation sets
ysi... |
638495e9d3d3c8b48e533f5a9861f050d746d19d78ff8ef7a498ca7cd49b9a26 | Python | 1,409 | 28 | import os
import slicer
from slicer.ScriptedLoadableModule import *
# Source: https://github.com/lassoan/SlicerSegmentEditorExtraEffects
class SegmentEditorBiomedisaPrediction(ScriptedLoadableModule):
"""Uses ScriptedLoadableModule base class, available at:
https://github.com/Slicer/Slicer/blob/main/Base/Pytho... |
6fb1cbb463aba283c1b84ccf5027671808497ea0836c81e7eade7764a0a10b9c | Python | 1,409 | 50 | import numpy as np
from .conf_ranker import ConfRanker
from rdkit import Chem
from rdkit.Chem.rdFreeSASA import CalcSASA, classifyAtoms
from rdkit.Chem import Mol
from typing import List, Any, Sequence
class SASARanker(ConfRanker):
"""Rank conformers using the Solvent Accessible Surface Area (SASA)
:param n... |
c967e7dccc035a3b80382a061a16da81bc38bb9bb81aa292dff596c3a6a6c87e | Python | 1,409 | 48 | 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(
... |
795f4b8505c159e2eada20bd9dfef6357b8acac5ddd1922c2d8f07c94ad214b9 | Python | 1,410 | 52 | import os
from pathlib import Path
import sys
import traceback
from typing import Dict
import snapatac2 as sa2
import pyprojroot
code_root_dir = str(pyprojroot.here())
pack_dir = f"{code_root_dir}/package/python"
sys.path.insert(0, pack_dir)
import utils #pyright: ignore # noqa: E402
# * log
logger = utils.set_file_... |
493a90cdf8782d53508b4ae219f3e6ab4e9579287934bc8ce70e734e7c6a5d03 | Python | 1,412 | 40 | import numpy as np
from pyrates import CircuitTemplate, NodeTemplate, clear_frontend_caches
clear_frontend_caches()
# node definition
li = NodeTemplate.from_yaml("model_templates.base_templates.tanh_node")
N = 5
nodes = [f"p{i+1}" for i in range(N)]
net = CircuitTemplate(name="li_coupled", nodes={key: li for key in no... |
dfc59da4750225a74f37586637579fc7ada3cc3b4dd7aa5606fe8df4bed9d6b9 | Python | 1,412 | 47 | # -*- coding: utf-8 -*-
# @Author: gviejo
# @Date: 2022-01-17 13:34:41
# @Last Modified by: gviejo
# @Last Modified time: 2022-01-17 22:56:34
from setuptools import setup, find_packages
#with open('README.md') as readme_file:
# readme = readme_file.read()
requirements = [
'pynapple',
'scikit-learn',
'seabo... |
adf607ad7bc2d7e2e0f86331d248890a8ac5223786b9c6dfa53943133ddd77b4 | Python | 1,413 | 52 | """Email column unique constraint
Revision ID: 1cf595475b58
Revises: d619fdfa6168
Create Date: 2024-07-03 19:53:22.443016
"""
from alembic import op
from galaxy.model.database_object_names import build_index_name
from galaxy.model.migrations.data_fixes.user_table_fixer import EmailDeduplicator
from galaxy.model.migr... |
b08c119f6ffe388ded28572c32e58211d215a97dc94ee59db789b9ecd565d05a | Python | 1,414 | 36 | def fly_am(msp):
fraction_of_standard_epoch = 15294 / 162706
msp.decay_per_epoch = 0.9 ** fraction_of_standard_epoch
msp.architecture["num_motifs"] = 53
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"] = dict(
type="AdjustedMo... |
cbe6cfe86f4d17e52aca17a7344132b8f769b0b6484801c241746afd4fb68b7f | Python | 1,414 | 38 | import argparse
import glob
import os
from tqdm import tqdm
def main():
parser = argparse.ArgumentParser(description="Compare tags")
parser.add_argument('folder_tags', metavar='file', help="folder with human marked tags")
parser.add_argument('-s', '--snr', action='store', type=str, default=None, help="sn... |
cc3f084a7ece7bb4d609b1690992763acb91115f24387afed6f3b08f6e2152f8 | Python | 1,414 | 50 | # ruff: noqa -- harvest-time provenance script, committed as run (lint header added at import)
"""Write a genuine artifact using whatever torchlens is on PYTHONPATH.
Usage: write_artifact.py <outdir> <level>
Real model: torchvision resnet18 IMAGENET1K_V1 (cached), 32x32 input (small artifact).
"""
import json
import o... |
41e506f037d33defa1944ae67541e23af95f29337dc4a8ecb9b7b6edd5a434ff | Python | 1,415 | 46 | """
Utility functions used for classic control environments.
"""
from typing import Optional, SupportsFloat, Tuple
def verify_number_and_cast(x: SupportsFloat) -> float:
"""Verify parameter is a single number and cast to a float."""
try:
x = float(x)
except (ValueError, TypeError):
raise ... |
6bd40e6a0d480269927d31abe536932b29a6e370a508bd921f853f01ff021fb3 | Python | 1,416 | 39 | import importlib
import numpy as np
from keras.src.testing import test_case
from keras.src.utils import module_utils
class LazyModuleTest(test_case.TestCase):
def test_missing_required_attr(self):
lm = module_utils.LazyModule("numpy", required_attr="dummy_attribute")
self.assertFalse(lm.availabl... |
be0cb0d3c68dc12200e0105ebf1c1f77cde60e12f13a571d30e1917f5f9f0cb4 | Python | 1,416 | 50 | import base64
import logging
from galaxy.files.models import (
BaseFileSourceConfiguration,
BaseFileSourceTemplateConfiguration,
FilesSourceRuntimeContext,
)
from . import (
DefaultBaseFilesSource,
PluginKind,
)
log = logging.getLogger(__name__)
class Base64FilesSource(DefaultBaseFilesSource):
... |
e246c3a7f29479f9dda888ad17ba87d15c506131189798a68ad3ad2e85f7be08 | Python | 1,417 | 44 | from setuptools import setup
import versioneer
requirements = [
'tensorflow>=2.0', 'tensorflow-probability[tf]', # tfp for the Batch Norm (covariance)
# 'tensorflow-addons',
'numpy', 'six', 'packaging',
'pandas', 'scipy', # Data
'colorlog', 'openpyxl', # Logging
... |
0818b70d690f17f47b3bbe022aa2d45041f9b99daed46b018ca79d60876ed7ba | Python | 1,418 | 40 | from setuptools import setup
setup(
name='pyoperant',
version='0.1.2',
author='Justin Kiggins',
author_email='justin.kiggins@gmail.com',
maintainer='Timothy Gentner',
maintainer_email='tgentner@ucsd.edu',
description='hardware interface and controls for operant conditioning',
long_descr... |
1e95dd9a7f94b5d797ba6a48d93c319be1a8a858c0d7595b6706a418d77d013d | Python | 1,418 | 31 | # Define a set of distributions from which to sample
# Currently the available sampling features are:
# 1. soma_size
# 2. n_neurites
# 3. trunk_orientation_deviations
from distributions import sample
distributions_exemplar = {
"soma_size": {"norm": {"mean": 8, "std": 1.18}},
"n_neurites": {"norm": {"mean": 6.7... |
f3f1419fd9dc666817ee173ab0902ef19cd9806b1d2e5d90bc6ca5836b6ae1ec | Python | 1,418 | 34 | """R65: two runnable-load parse-time exception classes were plain ValueError
subclasses invisible to ``except TorchLensError`` handling, with no branchable
``fields["code"]``. They now subclass ``TorchLensError`` (keeping ``ValueError``
via MRO) and carry their code on ``fields``.
"""
from __future__ import annotation... |
1d2cb556f6d7a8f8ba6f8a066e0c73d726ee439cdf5e7036384836590aa80ebe | Python | 1,419 | 43 | from os.path import dirname, realpath, exists
from setuptools import setup, find_packages
import sys
author = u"Paul Müller"
authors = [author]
description = 'Graphical user interface for Bio-AFM analysis'
name = 'pyjibe'
year = "2019"
sys.path.insert(0, realpath(dirname(__file__))+"/"+name)
from _version import ver... |
1ff26481620864d788256b7622c2b6f701126ae48ad3c7c42ff931debfecf198 | Python | 1,419 | 39 | """Widget containing preprocessing plot"""
from nanite import preproc
from PyQt5 import QtWidgets
from .mpl_preproc import MPLPreproc
class WidgetPlotPreproc(QtWidgets.QWidget):
def __init__(self, *args, **kwargs):
"""Display preprocessing graph with navigation"""
super(WidgetPlotPreproc, self)._... |
4460b558aeffca1e4593d0eb684985b525a048595f8ff71879dca5ed77f32e37 | Python | 1,419 | 38 | from __future__ import annotations
import torch
from PIL import Image
from lightly.transforms import CAPITransform
from lightly.transforms.utils import IMAGENET_NORMALIZE
def test_view_on_pil_image() -> None:
transform = CAPITransform(input_size=32)
output = transform(Image.new("RGB", (100, 100)))
asser... |
72ea3e6c0166274972f55cb8a63acc6fee79aa66fa1d593174f9bc76f42374bd | Python | 1,419 | 39 | """Widget containing preprocessing plot"""
from nanite import preproc
from PyQt6 import QtWidgets
from .mpl_preproc import MPLPreproc
class WidgetPlotPreproc(QtWidgets.QWidget):
def __init__(self, *args, **kwargs):
"""Display preprocessing graph with navigation"""
super(WidgetPlotPreproc, self)._... |
871d61742e8ee3d067058327b45af36e5a2260e25d71b6eefa602c522a1f5414 | Python | 1,419 | 51 | import pydeface.utils as pdu
from shutil import which
import tempfile
import os
import pytest
from pathlib import Path
import urllib.request
def test_cleanup_files():
files = []
for p in range(3):
files.append(tempfile.mkstemp()[1])
pdu.cleanup_files(files[0])
pdu.cleanup_files(*files[1:])
... |
9a3630774af361aa05af0bdac85bdc93a2ee0a4d7931b447ea826adaee1318bb | Python | 1,419 | 38 | '''
Standard levels for Asteroid Impact
This script generates the standard levels and exports them as json files, including a level list.
'''
import json
from makelevel import make_level
from os import path
def export_levels(levelprefix='hard'):
leveldirectory = 'levels'
levels = [
# crazy town
# beatable in 10... |
686f15c49a6ee73b4ae81e42890b711fff1e9c1e000b6a82993553a4a238160a | Python | 1,420 | 55 | from sys import argv
from msp import MSP
msp = MSP()
msp.file = __file__
msp.seed = int(argv[1])
msp.architecture["num_motifs"] = 80
msp.architecture["sparse_spec"] = dict(type="SpatiallySparseAcrossChannels")
msp.architecture["motif_model_spec"] = dict(type="NoMotifModel")
msp.architecture["affine_sparsity_enforcer"... |
cead471c3fdc612e580ea5118e4f04538547c87a8f90ddb3a9c7b4d934c75e56 | Python | 1,420 | 47 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import unicode_literals
from __future__ import division
from __future__ import print_function
import math
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.nn.parameter import Parameter
from torch.nn.modules.module... |
026def5c794a4bc6625fd633fa428cd7b039a4e6d1ada3e5db435c78edf03473 | Python | 1,421 | 36 | from os.path import abspath, join
import numpy as np
def run():
from spitfire.chemistry.mechanism import ChemicalMechanismSpec
from spitfire.chemistry.tabulation import build_adiabatic_eq_library, apply_mixing_model, PDFSpec
import spitfire.chemistry.analysis as sca
test_xml = abspath(join('tests', '... |
392fc17e4d8e2818a4251acbec2a738b0c59ab16c0e1d09c1c5f9a916e1bdcf2 | Python | 1,421 | 56 | # to prepare for computeing the distance matrix based on blosum62
# following the formula in NetMHCpan paper,
# first compute the alignment scores of the HLA pseudo sequences
import numpy as np
import pandas as pd
from collections import defaultdict
from collections import Counter
# load blosum62_X
blosum62_X = pd.r... |
b2e52f3ae449a0680acebe26b0e4f506728165314000d0cd8d6693a688a869d0 | Python | 1,421 | 50 | from lightly.transforms.byol_transform import (
BYOLTransform,
BYOLView1Transform,
BYOLView2Transform,
)
class TiCoTransform(BYOLTransform):
"""Implements the transformations for TiCo[0]. These are the same as BYOL[1].
Input to this transform:
PIL Image or Tensor.
Output of this tran... |
5a07d5e51f4fc579b8cfb2d7b2e96c8cf9cba11e67c16134c3696b556f782f61 | Python | 1,422 | 50 | import os
import sys
import glob
import pdb
try:
import cPickle as pickle
except:
import pickle
import numpy as np
# import matplotlib.pyplot as plt
def mergetraces(ftr, outswcfile, logfiles=None):
ftr_minus_traces = os.path.split(ftr)[-1]
ftr_minus_traces = ftr_minus_traces[:-7]
... |
aa614286edabd1a8b11a5cc8169880736561efd5591fbe78a37bbac9dd5d79da | Python | 1,422 | 51 | """GCP Batch runner utilities and templates."""
from string import Template
from galaxy.util.resources import resource_string
from .helpers import (
compute_gpu_machine_type,
compute_machine_type,
convert_cpu_to_milli,
convert_duration_to_seconds,
convert_memory_to_mib,
DEFAULT_CPU_MILLI,
... |
f6ebc2b09360a4fc17a10b1d916000ae0b675ff45a89fd082f0b5dfd618a90ce | Python | 1,422 | 53 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
import sys
def stop_err(msg):
sys.stderr.write("%s\n" % msg)
sys.exit(1)
def get_col_index(col_str):
if isinstance(col_str, str) and col_str[0] == "c":
col_str = col_str[1:]
return int(col_str) - 1
def main(blast_tabular_file, outfile, score_c... |
1e5079c2425b09e9023f8f34ef365a630c96dbf7d4fad2419271f3ea815edc7a | Python | 1,423 | 55 | import os
import hydra
import omegaconf
import pytorch_lightning as pl
import torch
from pytorch_lightning.loggers import TensorBoardLogger
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
def hydra_params_to_dotdict(hparams):
def _to_dot_dict(cfg):
res = {}
for k, v in ... |
33ecf405a076fd41a980c18230b83926d89fa3d2e8bc2da680fbb29b08bd51ea | Python | 1,424 | 51 | 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=4),
)
msp.data_spec = dict(
type="H5Dataset",
datap... |
f1314775c0c87c1c9f0d02b18a27369abdd0f983e6aab1cff5489e210823013d | Python | 1,425 | 49 | from __future__ import annotations
import json
import os
from datetime import datetime
from typing import TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from bream4.device_interfaces.devices.base_device import BaseDeviceInterface
# Native numpy types are not json serialisable
# This causes weird type issue... |
3ecd5b3e143ecf752a95ad77bf77a71729fd4ac85e05841394eff276e436d35e | Python | 1,426 | 65 | from collections.abc import Sequence
import numpy as np
from ._utils import StrSeqType, cast_seqs, check_axes
def remove_N_seqs(seqs: Sequence[str]):
"""Removes sequences containing 'N' from a list of sequences.
Parameters
----------
seqs
List of sequences to be filtered.
Returns
-... |
6a8cfeceaf33f3af8be24c46995e58cd6cb69aef3785ba3b8eac30953fa32c82 | Python | 1,426 | 47 | from typing import List
import snapatac2 as sa2
def modify_obs_name(sds: sa2.AnnData | sa2.AnnDataSet,
obs_key = "sample") -> List[str]:
obs_names: List[str] = [f"{i}.{j}"
for i, j in zip(
sds.obs[obs_key].to_list(), sds.obs_names)]
return obs_names
def... |
bc5ebdec3d1c4dfc782e299c99badee4990970b8b5ad45f4b52fc9be25f5d598 | Python | 1,426 | 41 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on December 2020
@author: Thomas Bonald <bonald@enst.fr>
"""
import unittest
import numpy as np
from numpy.linalg import norm
from sknetwork.data import karate_club, painters
from sknetwork.utils import get_neighbors, get_degrees, get_weights
class TestNeig... |
3a99524d98eb69c44c331eb36f1b6221397e9b4817612fafedce14f23d7f0a30 | Python | 1,428 | 44 | # overcoming the problem of SHAPE data working with a single line.
# creating multiple multiple files containg SHAPE data for a single sequence and running RNAfold for every
# single sequence.
import re
import sys
from subprocess import PIPE, Popen
from Bio import SeqIO
params_list = sys.argv[1:]
param_list_no_shape... |
4e5baec07f00baf5f28258046086b86e75545962ceaec64b641dfdae25ffffa1 | Python | 1,428 | 43 | import sqlite3
from .db import DB
class DistrictVaccinationTable(DB):
def __init__(self):
super().__init__()
self.table_name = 'UK_vaccination'
self.table_desc = 'Uttarakhand district wise vaccination snapshot'
self.cols = self.getcolumns()
def getcolumns(self):
cols... |
cc97b38570a6425f5e2a44763959ff49ce05314b1d45b29b2b2e91f94b9d77e2 | Python | 1,429 | 48 | import copy
from typing import List
import numpy as np
from pymatgen.core.structure import Molecule
def perturb_along_eigenmode(
ts_molecule: Molecule, eigenmode: List[float], scaling: float
) -> Molecule:
"""Perturn the molecule along the eigen modes based on a scaling factor.
Args:
ts_molecul... |
41d7efeaab7add610163c3bee58aaa2ef6f78191a034699a4e55a67b14e6cc8f | Python | 1,430 | 46 | 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 pyabf.tools.sweep
import matplotlib.pyplot as plt
if __name__ == "__main__":
... |
9c1503be3a006094daf906c8a3dec9b656211c8325c72399f6f67290aea2706e | Python | 1,430 | 50 | #!/usr/bin/env python3
"""
Run this script to get ThermoNet ddG predictions.
"""
# import required modules
from tensorflow.keras.models import load_model
import numpy as np
from argparse import ArgumentParser
def parse_cmd_args():
"""
Parse command-line arguments for running ThermoNet.
Returns
... |
2b4f38bbe837339617562045d234a098f41af05d8c512b3441286ac38c24f755 | Python | 1,431 | 50 | 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(
... |
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