sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b0bc0009598264fe744d32987a697d2ad522171a248beae458783987158d8649 | Python | 552 | 26 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from common_setup import IssueConfig, IssueExperiment
REVS = ["issue592-base", "issue592-v2"]
SUITE = suites.suite_satisficing()
CONFIGS = [
IssueConfig("seq-sat-lama-2011", [], driver_options=["--alias", "seq-sat-lama-2011"]),
Issu... |
f9172eef7be94c7fba93430bbe6918c1872bb53c662b34c55031390924491388 | Python | 552 | 26 | import numpy as np
class BatchLoader:
def __init__(self, batch_size: int = 4):
self.batch_size = batch_size
self.items = []
@property
def full(self) \
-> bool:
return len(self.items) >= self.batch_size
@property
def empty(self) \
-> bool:
r... |
6fae73c05778a9b92113d41e87c302f8e74d7906bd2dba3d3c1f26cf557745c7 | Python | 553 | 18 | """ Authored by: Neel Kanwal (neel.kanwal0@gmail.com)"""
# This file creates histograms from prediction to better understand ROC plots.
# Update paths to processed datasets
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
from utils import plot_histogram
# Give path to ex... |
2340fb2c69a06715548bc254eec970f82573d02ce0bf2ae784fe07fc007056da | Python | 554 | 29 | import sys
import subprocess
from setuptools import setup
from setuptools.command.build_ext import build_ext
from setuptools.command.build_py import build_py
class Build(build_ext):
def run(self):
if subprocess.call(["make"]) != 0:
sys.exit(-1)
super().run()
class BuildPy(build_py):... |
614ac7aca0060deefe04eb8e9bfedae30efbbf714711705093ab27cea336843f | Python | 554 | 22 | # Metaparameters for optimization
params = {}
# Base 2 power to generate Sobol samples
params["base2_power"] = 9 # ec.py
# Number of particles
params["n_particles"] = 2 ** params["base2_power"] # run_pso.py
# Number of iterations
params["n_gen"] = 1000 # run_pso.py
# Total number of models evaluated
params["max_ev... |
e74b7e5e33b1614db9dbb0405ba55fc63239f36cc71c57d63e2e38caea9452e1 | Python | 554 | 21 | from elasticai.creator_plugins.grouped_filter import FilterParameters
def build_af_parameters() -> list[tuple[FilterParameters, FilterParameters]]:
current_channels = 1
params = []
def filter(w, o, s=1):
nonlocal current_channels
params.append(
FilterParameters(
... |
6c977fed4ced16bba57d96d7a640acf07871d13df19e32f0d157d13859f76649 | Python | 555 | 38 | from . import (
assets,
blender,
color,
converters,
download,
material,
nodes,
session,
ui,
utils,
)
from .assets import template
from .entities import Molecule
from .ui.addon import register, unregister
try:
from .scene import Canvas
except ModuleNotFoundError:
pass
__... |
fb530a736ca155d8f773adda71071519f3d3ba9e35f485aecd2aa3f017f24937 | Python | 555 | 24 | import os
import torch
import numpy as np
def list_of_distances(X, Y):
return torch.sum((torch.unsqueeze(X, dim=2) - torch.unsqueeze(Y.t(), dim=0)) ** 2, dim=1)
def make_one_hot(target, target_one_hot):
target = target.view(-1,1)
target_one_hot.zero_()
target_one_hot.scatter_(dim=1, index=target, valu... |
aa2e49c102766d857b29d43a7df3cd9ba9b8e0fec86499b39ce60d9debbbe5c0 | Python | 557 | 15 | import APT_interface as apt
import PoseUNet_resnet as PoseURes
import tensorflow as tf
class Pose_mdn_unet(PoseURes.PoseUMDN_resnet):
def __init__(self,conf,**kwargs):
conf.mdn_use_unet_loss = True
PoseURes.PoseUMDN_resnet.__init__(self,conf,**kwargs)
if self.name == 'deepnet':
... |
21dc43cd1bd363632128731d554bae402d592098b2b74280ad333f2e00e87184 | Python | 558 | 22 | import shutil
from setuptools import setup, find_packages
from codecs import open
from os import path
here = path.abspath(path.dirname(__file__))
def read_requirements_file(filename):
req_file_path = path.join(path.dirname(path.realpath(__file__)), filename)
with open(req_file_path) as f:
return [li... |
5ccebfd63cb73d1920d740d67aa463f1e59a2de75eb6680c1e16085bd6af1183 | Python | 558 | 17 |
import os
from argparse import ArgumentParser
import torch
from tqdm import tqdm
parser = ArgumentParser()
parser.add_argument('--esm_embeddings_path', type=str, default='data/embeddings_output', help='')
parser.add_argument('--output_path', type=str, default='data/esm2_3billion_embeddings.pt', help='')
args = pars... |
9cb62519ae042e90aafb50a2cea651b28aa9d28312fcbe52d40325da50fc8a4d | Python | 558 | 17 | # /usr/bin/python2.7
from otscore import calcMitScore
for tech in ["5gRNA","12gRNA"]:
namelst = ['sgID', 'gRNA', 'OTS', 'Mismatch','Prediction']
f = open('OT/{0}.tab'.format(tech) , 'w')
f.write('{0}\n'.format('\t'.join(namelst)))
f1 = open('../Data/{0}/POTseq.tab'.format(tech),'r')
f1.readline()
... |
cba09fc67b8811aed14292527511d66568844b5e8f2637675d9f71d7682464b8 | Python | 559 | 21 | import nibabel as nib
import pickle
def save_dict(di_, filename_):
with open(filename_, 'wb') as f:
pickle.dump(di_, f)
def load_dict(filename_):
with open(filename_, 'rb') as f:
u = pickle._Unpickler(f)
u.encoding = 'latin1'
ret_di = u.load()
#print(p)
#ret_di ... |
d3aa8379a8af872c8b1a872947d07a0cb9637f42d604729f036eafad8db5cabd | Python | 559 | 18 | from wikibaseintegrator.datatypes.string import String
class MusicalNotation(String):
"""
Implements the Wikibase data type 'musical-notation'
"""
DTYPE = 'musical-notation'
PTYPE = 'http://wikiba.se/ontology#MusicalNotation'
def set_value(self, value: str | None = None):
a... |
5456142eb2b31fc04d2630c5d2c63f4bd0d416b096d25ee27ef09e1048f4e6ae | Python | 560 | 24 | import sys
def main(f):
zic1 = '206373_at'
probesets = get_probesets()
fh = open(f, 'r')
for line in fh:
if line.startswith("Scan"):
print line.rstrip()
if line.startswith(zic1):
print line.rstrip()
for probeset in probesets:
if line.startswit... |
ff7732a5e7c09d55ac42cc425cdc1f2e88fdcd497b2f034623782b7e8ae3144c | Python | 561 | 19 | import torch
from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
def test_unetdiscriminatorsn():
"""Test arch: UNetDiscriminatorSN."""
# model init and forward (cpu)
net = UNetDiscriminatorSN(num_in_ch=3, num_feat=4, skip_connection=True)
img = torch.rand((1, 3, 32, 32), dtype=torch.... |
9785680f5fc450c6741cbbfff19ba06128c5ec27b3d6e6b3f4e8b49c59f505a7 | Python | 562 | 24 | #! /usr/bin/env python
from lab.parser import Parser
class CustomParser(Parser):
def __init__(self):
Parser.__init__(self)
self.add_pattern(
"num_sga_patterns",
"Found (\d+) SGA patterns.",
required=False,
type=int)
self.add_pattern(
... |
ccec0cf788caaaa186b1c6e37b3a9dcd2f6e8870819ef1cd0107f43ff3f351a9 | Python | 562 | 23 | """
Metrics for quantifying stimulus selectivity
"""
import numpy as np
def compute_osi(stimuli, R):
"""
Orientation Selectivity Index (OSI)
Arguments:
stimuli (n_stim, ): angles in rad.
R (n_stim, ): response/rates
Returns:
float
"""
pref = np.where(stimuli == 0)[0][... |
d2f8605a70dc27001af047354febc96c91ce95d40c58bf757d24419d1440cbc3 | Python | 562 | 25 | from __future__ import annotations
from pathlib import Path
import pytest
FILE_DIR = Path(__file__).parent
MRCC_DIR = Path(FILE_DIR, "mrcc_run")
def mock_execute(self, directory, *args, **kwargs):
import gzip
with (
gzip.open(MRCC_DIR / "mrcc.out.gz", "rb") as f,
open(Path(directory, "mrcc... |
e142111a98428f2883fb22ffc6875e2300186efa9465b4e0201e7e7b67804a13 | Python | 563 | 21 | def disable_logfile_if_none(optimizer, logfile) -> None:
if logfile is not None:
return
current = getattr(optimizer, 'logfile', None)
close = getattr(current, 'close', None)
if close is not None:
close()
optimizer.logfile = None
def flush_logfile(logfile) -> None:
flush = getat... |
307369f446ccfc1861a5253fa0529fdc26b0dc291fa23f1074b884438ff3f396 | Python | 564 | 22 | # Stitching core module - provides stitching algorithms and utilities
from .stitcher import Stitcher
from .tile import Tile
from .run_tissuecyte_stitching_classic import (
create_section_json,
get_section_data,
stitch_section,
generate_tiles,
generate_avg_tiles,
get_section_avg,
... |
fc1ad5a2770c1cfcf05f2a8edc10f8fbdb8ccb482660818e60861c419d72c07e | Python | 564 | 27 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
REVS = ["issue546-base", "issue546-v1"]
LIMITS = {"search_time": 1800}
SUITE = suites.suite_optimal_with_ipc11()
CONFIGS = {
"seq_opt_fdss_1": ["--alias", "seq-opt-fdss-1"],
"seq_opt_fdss_2": ["--alias", "seq-opt... |
9f990f054865a2d249e1d51f41bf0898489eee3ed7487686fb97e6f79d1d8ea8 | Python | 565 | 23 | import torch.nn as nn
class LSTM(nn.Module):
def __init__(self, cfg):
super(LSTM, self).__init__()
self.lstm = nn.LSTM(
input_size=cfg.WORD_EMBED_SIZE,
hidden_size=cfg.HIDDEN_SIZE, # int(hidden_size / num_directions),
num_layers=cfg.NUM_LAYERS,
... |
3cbf190023c8d2cf1b1808dcaa8c221ba7124d13ceed43506dbcee4113599918 | Python | 566 | 5 | from .option import sir_parse2class, class2dict, OptionClass, dict2str, print_dict, infer_json_from_name, AI_parse, save_json, sir_parse2dict, dict2class
from .tools import timeblock, WriteOutputTxt, mkdir, mkdirs, mkdir_with_time, get_timestamp, product_of_tuple_elements, set_seed
from .image_process import F_cal_aver... |
2eb05f03925f563a0e0f5ee29f351a19f7d5cf5979b1e3b47d171f45fbc7655f | Python | 567 | 13 | from unittest import TestCase
from elasticai.creator_plugins.grouped_filter import FilterParameters
class GetInChannelsByGroup(TestCase):
def test_two_in_channels_in_two_groups_gets_0_1(self):
p = FilterParameters(kernel_size=1, in_channels=2, out_channels=2, groups=2)
self.assertEqual(((0,), (1,... |
78e48dfd0a8f31cae6ad5c1f8858db85b4a7daee49b2f9faadf4269b03e174c8 | Python | 567 | 20 | import glob
from tqdm import tqdm
def flatten_list(list_of_lists):
flat_list = [item for sublist in list_of_lists for item in sublist]
return flat_list
def get_files(path, extension=".wav"):
filenames = []
for filename in tqdm(glob.iglob(f"{path}/**/*{extension}", recursive=True)):
print(filen... |
18e8f8267d52bff9f246a1f22be5ad826f5e21097061ae7c9646dd96533a70ab | Python | 568 | 27 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
REVS = ["issue546-base", "issue546-v1"]
LIMITS = {"search_time": 1800}
SUITE = suites.suite_satisficing_with_ipc11()
CONFIGS = {
"seq_sat_fdss_1": ["--alias", "seq-sat-fdss-1"],
"seq_sat_fdss_2": ["--alias", "seq... |
1d28ee8ab886eee34a9329df274b5e6c7d7aec387925b08c623e68c45bb22225 | Python | 568 | 24 | import tensorflow as tf
import numpy as np
def create_rot_mat(theta):
return tf.constant(
[
[1.0, 0.0, 0.0],
[0.0, np.cos(theta), -np.sin(theta)],
[0.0, np.sin(theta), np.cos(theta)],
],
dtype=tf.float32,
)
def rotate(x, theta):
ndim = x.ndim -... |
3e2c8ec16002230a4d2b3bdf6d624bf334325547d8879143eddcbe414156167f | Python | 568 | 17 | import json
def create_custom_nab_windows_file(dataset: str, full_windows_file_path: str, export_windows_file_path: str):
with open(full_windows_file_path) as full_wfile:
full_windows = json.load(full_wfile)
win_to_export = None
for key in full_windows.keys():
if dataset in key:
... |
1ed3ecd0bbe7ce3bd08b5679ac60ab8f325e4c32eac80c553a0992af69f6927c | Python | 569 | 28 | def filter_opt(opt, tag):
ret = { }
for k,v in opt.items():
tokens = k.split('.')
if tokens[0] == tag:
ret['.'.join(tokens[1:])] = v
return ret
def format_opts(d):
ret = []
for k,v in d.items():
if isinstance(v, bool) and v == True:
ret = ret + ["--... |
c7d966d2827a69539e58ea32ddbe3425756418ef0dfd87f6f48291550168f82a | Python | 569 | 16 | import APT_interface as apt
import os
# Alice's dataset
name = 'alice'
val_ratio = 0.1
lbl_file = '/home/kabram/Dropbox (HHMI)/temp/multitarget_bubble_expandedbehavior_20180425_FxdErrs_OptoParams20181126_dlstripped.lbl'
nviews = 1
for view in range(nviews):
conf = apt.create_conf(lbl_file,0,'tfds','/home/kabram/t... |
eb4085a7679561d073abc09107311abb259175fcec670435f6f4c7e05939ad08 | Python | 569 | 26 | from setuptools import setup, find_packages
setup(
name='deepsramp',
version='0.1',
packages=find_packages(),
install_requires=[
'torch',
'pandas',
'scikit-learn',
'joblib',
'tqdm',
'matplotlib',
'seaborn',
],
entry_points={
'conso... |
c9e8bb41adfea481712bdb06c0338ba5c0aab59285f6f6b2a30bd07cac62d15e | Python | 571 | 19 | class NTXent(nn.Module):
"""
Normalized Temperature-scaled Cross Entropy Loss
"""
def __init__(self, temperature=1.0):
super().__init__()
self.sim = nn.CosineSimilarity(dim=-1)
self.xent = nn.CrossEntropyLoss()
self.temperature = temperature
def forward(self, z_candi... |
55b66765b7eb1e2eb05f1d011fdbff0e2ac530302cc1c2372b2956735a7a3b33 | Python | 572 | 18 | from elasticai.creator_plugins.quantized_grads.base_modules import BatchNorm2d
from elasticai.creator_plugins.quantized_grads.fixed_point import (
FixedPointConfigV2,
QuantizeForwHTE,
QuantizeParamToFixedPointHTE,
QuantizeParamToFixedPointStochastic,
)
def test_batchnorm_fxp_init():
conf = FixedPo... |
958a7bfeef6085149d5143b6503ebc6b96f16bc1114341c353f322a6f0b46474 | Python | 573 | 24 | import unittest
class TestLogoMain(unittest.TestCase):
def test_imports(self):
try:
from dreamcoder.domains.logo.main import (
animateSolutions,
dreamFromGrammar,
list_options,
outputDreams,
enumerateDreams,
... |
07702f0b56916158fbee32100e808da1cd19d8cb991cd96bfef80437a869b33d | Python | 574 | 28 | import sys
from pybioshell.core.data.sequence import read_ASN1_checkpoint
if len(sys.argv) < 2 :
print("""
Converts a sequence profile (in ASN.1 format) produced by psiblast to a flat tabular format
USAGE:
python asn1_to_profile.py input.asn1
EXAMPLE:
python asn1_to_profile.py d1or4A_.asn1
CATEGORIES... |
17e4a3fca3d1b5593d0c2790e2f1566d99b15919432879292729ae9c5eac6031 | Python | 574 | 22 | # -*- coding: utf-8 -*-
# @Author: Sadamori Kojaku
# @Date: 2022-11-04 00:14:13
# @Last Modified by: Sadamori Kojaku
# @Last Modified time: 2022-12-11 21:03:06
# %%
import belief_propagation
import networkx as nx
import numpy as np
from scipy import sparse
# Create a karate club network.
A = nx.to_scipy_sparse_mat... |
e09c4ad85035a66fa337bd30c5d5477df13d32a319fcd04c145a35ac0f0dd3c3 | Python | 574 | 23 | """Survival precision-recall, calibration-slope, and sharpness diagnostics."""
from .area_under_pr_curve import (
auprc_ic,
auprc_right_censor,
auprc_right_censored_grid,
auprc_uncensored_grid,
)
from .calibration_slope import (
calibration_slope_interval_censor,
calibration_slope_right_censor,... |
e708b45fd4cd58bd8fd0977178fdfe6268da339edcbf62a360eefa749bc27d35 | Python | 574 | 29 | from elasticai.creator.vhdl.design.signal import Signal
def x(width: int) -> Signal:
return Signal(name="x", width=width)
def done() -> Signal:
return Signal(name="done", width=0)
def enable() -> Signal:
return Signal(name="enable", width=0)
def clock() -> Signal:
return Signal(name="clock", wid... |
3d53e8a01d233f986464450b482c02d3be39df65056d1d8fb60bb4239cf0982b | Python | 575 | 24 | #!/usr/bin/env python
"""
requests.certs
~~~~~~~~~~~~~~
This module returns the preferred default CA certificate bundle. There is
only one — the one from the certifi package.
If you are packaging Requests, e.g., for a Linux distribution or a managed
environment, you can change the definition of where() to return a s... |
8809debc06fa0291ba804663215703430dcac5bddf138824caa5d27b29d461f9 | Python | 575 | 24 | """Error handling module for HED."""
from .error_reporter import (
ErrorHandler,
separate_issues,
get_printable_issue_string,
get_printable_issue_string_html,
check_for_any_errors,
sort_issues,
iter_errors,
)
from .error_types import (
DefinitionErrors,
TemporalErrors,
SchemaErr... |
ab8ffaf6ba41aeaab859122b56bbc982726222debc7215c35fdc86a8e84ad072 | Python | 576 | 13 | from unittest import TestCase
from codex.data.neurotransmitters import lookup_nt_type, NEURO_TRANSMITTER_NAMES
class NtTest(TestCase):
def test_lookup_nt_type(self):
for nt in NEURO_TRANSMITTER_NAMES.keys():
self.assertEqual(nt, lookup_nt_type(nt.lower()))
self.assertEqual(nt, look... |
b2629417c9568d5b264a66767fe1c94dfd7fc4f2244a9188b031da017292928d | Python | 576 | 15 | import Pose_openpose
import open_pose4 as op
class Pose_multi_openpose(Pose_openpose.Pose_openpose):
def __init__(self,conf,name='deepnet',**kwargs):
super(Pose_multi_openpose,self).__init__(conf,name=name,**kwargs)
self.conf.is_multi = True
def get_pred_fn(self,model_file=None,max_n=None,imsz... |
b2d884862cba49b192281adff5a2d6510eaef6b66f5dd65809bf4303e643878e | Python | 576 | 26 | """
Adjoint-based sensitivity-analysis module for pymeep.
Authors: Homer Reid <homer@homerreid.com>, Alec Hammond <alec.hammond@gatech.edu>, Ian Williamson <iwill@google.com>
"""
from .objective import *
from . import utils
from .utils import DesignRegion
from .basis import BilinearInterpolationBasis
from .optimiza... |
e857afbb07f4725c0118083d403189d5d3e00295468d29895eb16f65f7a17e0c | Python | 576 | 26 | """
Set of utilities for segmentation and analysis of n-dimensional images.
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 02.04.14
"""
__author__ = 'martinez'
__version__ = "$Revision: 001 $"
from . import pexceptions
from . import disperse_io
from . import graph
from . import vt... |
51ea3a0e5e587951444affebcf6c89c1afbd703bcc6a8b23dc2834a3de8c5797 | Python | 577 | 22 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 5 11:32:39 2023
@author: benozzo
"""
import numpy as np
from scipy import stats, linalg
def computeS(A, sigma2):
#from Casti et al., 2023 (CDC2023)
nS = A.shape[0]
Q = sigma2 * np.diag(np.ones(nS))
Sigma = - linalg.solve_continuous... |
5539929a66c99db7c1ceaab04154a1cc328487b83da0de3c7bccc33ecf449133 | Python | 577 | 26 | # Logistic Regression
from sklearn.linear_model import LogisticRegression
# Decision Trees
from sklearn.tree import DecisionTreeClassifier
# Random Forest
from sklearn.ensemble import RandomForestClassifier
# Support Vector Machines
from sklearn.svm import SVC
# Imbalance Random Forest
from imblearn.ensemble import... |
9b97be149286eda4b9190e03ac024bef2252d46f3aa98ef3fcd76cb6f9a251df | Python | 579 | 19 | '''
python setup.py build_ext -i
to compile
'''
from distutils.core import setup, Extension
from Cython.Build import cythonize
from Cython.Distutils import build_ext
import numpy
setup(
name='Sim3DR_Cython', # not the package name
cmdclass={'build_ext': build_ext},
ext_modules=[Extension("Sim3DR_Cython",... |
a717fa5d80886839b289ffb1cd34bf3e998154d6e2318a78b0bc3224196555cc | Python | 579 | 26 | #!/bin/env python3
'''
Draw Feynman diagrams for the quasi-particles in the 2D patterns of cardiac
arrhythmia
'''
import matplotlib.pyplot as plt
import sys
import yaml
from pdlfeynman.diagram import *
diagramfile = sys.argv[1]
posfile = sys.argv[2]
plotfile = sys.argv[3]
with open(diagramfile) as f:
diagram = ya... |
2fb28b16d5a3621a777d4898c571570c6fe289a9ee68c2cb7c6dda593204a5d3 | Python | 580 | 21 | """Test measure functions defined like SNR."""
import pytest
from console.utilities.snr import signal_to_noise_ratio
@pytest.mark.parametrize("noise_snr", [
(0.2, 41.1),
(0.5, 32.7),
(1.0, 26.8)
])
def test_snr_calculation(noise_snr, test_spectrum):
"""Test snr calculation."""
num_samples = 400
... |
9bc3296fb2359f447d6a3187e2dcd312738388f2fc4ea4eef4e9dee604010751 | Python | 580 | 23 | #Path hack.
import sys; import os
sys.path.insert(0, os.path.abspath('..'))
from volume import *
from mysql import db
class task:
def __init__(self, id):
self.id = id
self.local = True
self.channel_id , self.segmentation_id = db.query("select channel_id, segmentation_id from tasks where id="+str(... |
f17e75b4c36f560df99bbce16b889a275235630cf5f9eb18628ac682d8d4b20b | Python | 580 | 26 | import numpy as np
from median.median import Median
def test_init():
buff = np.array([0, 1, 2, 3, 3, 4, 4, 5, 6, 7, 7, 7, 7, 8, 9])
print(len(buff))
assert len(buff) % 2 == 1
buff = Median(initB=buff)
def test_int():
data = np.random.default_rng().integers(0, 5, size=30)
# data = np.array([... |
69c81fb1e382e7974dad50336812a95221f767a57b43509ac6c890dcaee90be1 | Python | 581 | 23 | # -*- coding: utf-8 -*-
#
# Copyright (C) 2012-2022 Vinay Sajip.
# Licensed to the Python Software Foundation under a contributor agreement.
# See LICENSE.txt and CONTRIBUTORS.txt.
#
import logging
__version__ = '0.3.6'
class DistlibException(Exception):
pass
try:
from logging import NullHandler
except Impor... |
8850c9e61b7e3c79cd0aef3bcf90e1501056255c31ddd97f7019d1e0796236e6 | Python | 581 | 22 | """
Set of processes for being in mult-processor mode within spatial package
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 11.01.17
"""
__author__ = 'martinez'
####### HELPING FUNCTIONS
####### PROCESSES
# Radial Averaged Fourier Transform alone process
# points: array with poi... |
9aafb43130d72ce094a4543de787498fd4fe7f19ba63fc9485c23da8c289755c | Python | 581 | 25 | import unittest
import torch
from torcheeg.models import GRU, LSTM
class TestRNN(unittest.TestCase):
def test_gru(self):
model = GRU(num_electrodes=32, hid_channels=64, num_classes=2)
eeg = torch.randn(2, 32, 128)
pred = model(eeg)
self.assertEqual(tuple(pred.shape), (2, 2))
... |
d8e849414b4ec6ca1658d1ea07bccba03fa50424c67d1d38e5a14973ab00aa8f | Python | 581 | 21 | import numpy as np
def get_label(layer, event):
cords = np.round(layer.coordinates).astype(np.int16)
val = layer.get_value()
if val is None:
return
if val != 0:
if 'Shift' in event.modifiers:
layer.selected_label = val
elif layer.mode =='pan_zoom':
data =... |
fd9159859a260c156fa3111d57ec683485c871ee9580f1e8eb2399dbd601727c | Python | 581 | 17 | import unittest
import torch
# local imports
from shapelearningtheory.analysis.gradients import *
class TestJacobian(unittest.TestCase):
def test_identity(self):
net = torch.nn.Identity()
batchsize = 4
height = 8
width = 6
channels = 3
x = torch.randn(batchsize, chan... |
cbe80c1fdcdd76c2821f3e76ccf92342be9415b858e1033f31b3b30401715bb5 | Python | 582 | 29 | # external
import pytest
# project
import textdistance
ALG = textdistance.MLIPNS
@pytest.mark.parametrize('left, right, expected', [
('', '', 1),
('a', '', 0),
('', 'a', 0),
('a', 'a', 1),
('ab', 'a', 1),
('abc', 'abc', 1),
('abc', 'abcde', 1),
('abcg', 'abcdeg', 1),
('abcg', 'a... |
aa59a1df6e520557ef1ba31ef6073936c879b1dc07070cc706ae9a117b4ab0b0 | Python | 583 | 20 | from typing import Callable, List, Optional
from pip._internal.req.req_install import InstallRequirement
from pip._internal.req.req_set import RequirementSet
InstallRequirementProvider = Callable[
[str, Optional[InstallRequirement]], InstallRequirement
]
class BaseResolver:
def resolve(
self, root_r... |
e495c18db3417e107ce2cb7f0ed9335961fc8b8645ca3e2706ebdae2c29b6c8f | Python | 583 | 25 | """Package containing Qt widgets.
The program is organized in widgets.
Roughly one widget is stored in one module.
A module should start by importing:
- packages that are in the Python library
- additional packages (in this order: numpy, scipy, PyQt5)
- relative imports
Leave an empty line between each group of ... |
356cf0d354b3eb486b91a7f8ffcdb6cfbdcb8b7ace3cb89c7c5ea9ee65ccb8d9 | Python | 584 | 28 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from downward import suites
from common_setup import IssueConfig, IssueExperiment
REVS = ["issue551-base", "issue551-v3"]
BENCHMARKS = os.path.expanduser('~/downward-benchmarks')
SUITE = suites.suite_optimal_strips()
CONFIGS = [
IssueConfig("seq-opt-bjolp... |
356ef1dacfec4ecf3b8a068765bb7a77d58ef42975c784fef1e19eea86115b72 | Python | 584 | 25 | import pandas as pd
import numpy as np
import h5py
df = pd.read_csv('test_sequences.txt', delimiter = "\t", header=None)
print(df.head())
df.columns = ['sequences', 'expression']
df = df.drop(['expression'], axis=1)
with h5py.File(('test_seq_onehot.h5'), 'r') as hf:
X = hf['test_seq_onehot'][:]
from tensorflow i... |
44a98869a21c5243869b5d39af2ca9f8ff8f3831b05948b33cc870d2932423e6 | Python | 584 | 28 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from downward import suites
from common_setup import IssueConfig, IssueExperiment
REVS = ["issue551-base", "issue551-v1"]
BENCHMARKS = os.path.expanduser('~/downward-benchmarks')
SUITE = suites.suite_optimal_strips()
CONFIGS = [
IssueConfig("seq-opt-bjolp... |
f7bbb79976a7e71b60079609f860f2887cd10d738e73b3a8ca00801c461cbd26 | Python | 584 | 28 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from downward import suites
from common_setup import IssueConfig, IssueExperiment
REVS = ["issue551-base", "issue551-v2"]
BENCHMARKS = os.path.expanduser('~/downward-benchmarks')
SUITE = suites.suite_optimal_strips()
CONFIGS = [
IssueConfig("seq-opt-bjolp... |
1454e7b813487f49136ab276d8b1897d8b6a22ce04a925872cddbd2dcb276a92 | Python | 585 | 22 | #!/bin/env python
"""
OXASL - Brain extraction
Copyright (c) 2008 University of Nottingham
"""
import fsl.wrappers as fsl
def brain(wsp, img, thresh=0.5):
"""
Extract brain from wholehead image
"""
bet_result = fsl.bet(img, seg=True, mask=False, fracintensity=thresh, output=fsl.LOAD, log=wsp.fsllog)
... |
b36c8863922892bdd33759b7f07ada486db02b36871bd158fa559c94fb43db4e | Python | 585 | 19 | import os
import sys
sam_path = sys.argv[1]
modified_sam_path = sys.argv[2]
sam_file = open(sam_path)
modified_sam_file = open(modified_sam_path, 'w')
for line in sam_file:
if line.startswith('@'):
line = line.strip()
modified_sam_file.write(line + '\n')
else:
fields = line.strip().sp... |
78fb6c6feaa9960ad1d9facc4842ebd4459f4aac2ecd84e9d43f56870b18e174 | Python | 586 | 18 | # -*- coding: utf-8 -*-
from tempfile import TemporaryDirectory
def test_help_option(script_runner):
ret = script_runner.run(['dcm2bids_helper', '--help'])
assert ret.success
def test_helper(script_runner):
i_tmpDir = TemporaryDirectory()
o_tmpDir = TemporaryDirectory()
ret = script_runner.run(... |
4a80a243b5c4f5e682be7057a8bbb78dafc093fc169fa606301ea9ad6c382fd6 | Python | 587 | 30 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
REVS = ["issue67-v1-base", "issue67-v1"]
SUITE = suites.suite_optimal_with_ipc11()
CONFIGS = {
"astar_blind": [
"--search",
"astar(blind())"],
"astar_lmcut": [
"--search",
"astar(lm... |
b54759d49a4b5c7318dfe6ea0513c1720fb737929d9fafe81198b26d5b4c90c2 | Python | 587 | 17 | from ._version import __version__
from .flagio import load_unweighted_flag, load_weighted_flag, \
save_unweighted_flag, save_weighted_flag
from .flagser import flagser_unweighted, flagser_weighted
from .flagser_count import flagser_count_unweighted, \
flagser_count_weighted
__all__ = ['load_unweighted_flag',
... |
6e1c6768373da2ab6254f6e1ac2cd6f4492c11bbdbe4e404d89a4010691a9e61 | Python | 588 | 12 | import bsub
import os
nconfigs = 19
outdir = '/groups/branson/home/bransonk/tracking/code/APT_develop/deepnet/bsubout'
cwd = os.path.dirname(os.path.abspath(__file__))
for cfgi in range(4,5):
jobname = f'mem{cfgi:02d}'
logfile = os.path.join(outdir,f'{jobname}.log')
errfile = os.path.join(outdir,f'{jobname... |
1113d0ecf9e93da85b94e24f28ec8ef51a81e4d5b7d060a7147e75caa301371e | Python | 590 | 22 | from elasticai.creator.hdl_ir import (
IrFactory,
collect_transitive_implementation_closure,
)
from elasticai.creator.ir import Registry
factory = IrFactory()
def test_can_remove_dead_implementations_from_reg():
g = factory.graph()
reg = Registry(
main=g.add_nodes(
factory.node("a... |
69e3758c342712904b8881d5d39be0f7b5c2b1715e7d20c8f0db583267a01033 | Python | 590 | 26 | """Utility methods and class for fingerprinting-related functions.
Author: Seth Axen
E-mail: seth.axen@gmail.com
"""
from ..util import E3FPError
class E3FPInvalidFingerprintError(E3FPError, TypeError):
"""Fingerprint is incorrectly formatted."""
class E3FPMolError(E3FPError, TypeError):
"""Mol is of incor... |
a7d7244bbf83728be29539addc9e7e98a20d0aba326bc75a935550492630a3ef | Python | 590 | 10 | # Copyright (c) 2023-2024 Blue Brain Project/EPFL
# 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 ... |
f21f05f86d5b02feea360369bf11a4e33da45cc10203d16992494088f383bd85 | Python | 591 | 25 | import meep as mp
cell_size = mp.Vector3(6, 6, 0)
geometry1 = [
mp.Cylinder(center=mp.Vector3(), radius=1.0, material=mp.Medium(index=3.5))
]
sim1 = mp.Simulation(cell_size=cell_size, geometry=geometry1, resolution=20)
sim1.init_sim()
geometry2 = [
mp.Cylinder(center=mp.Vector3(1, 1), radius=1.0, material=... |
aceb71e05536dfa87495e03e80ea480b6d2cf2e0254368c261140d5d011ef4db | Python | 592 | 14 | """ Task class: This class specifies the task variables """
class Task:
""" This class specifies the instance variables and methods of the object-based memory task """
def __init__(self):
""" This function defines the instance variables unique to each instance """
self.n_days = 21 # number ... |
39fbb11c0e296d8c6258573af507c7eb44f524e32e0598073e28e2c4cc46c16c | Python | 593 | 21 | from sklearn.feature_selection import VarianceThreshold
def low_variance_feature_selection(X, threshold):
"""
This function implements the low_variance feature selection (existing method in scikit-learn)
Input
-----
X: {numpy array}, shape (n_samples, n_features)
input data
p:{float}
... |
a11f5f01bb59ceada7f4f010f8a8434babb4b77b2dd9732a548a08804ace6b59 | Python | 593 | 25 | from .features import OptopatchGlobalFeatureExtractor
from .utils import \
crop_center, \
generate_optimizer, \
generate_lr_scheduler, \
get_nn_spatio_temporal_mean, \
get_nn_spatial_mean, \
pad_images_torch
from .ws import \
OptopatchBaseWorkspace, \
OptopatchDenoisingWorkspace
__all... |
2c9803722a2e70f656dded0ecc5097cd3c8554da1b258cf37e026f93d15988d4 | Python | 594 | 26 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import downward.suites
import common_setup
import configs
CONFIGS = configs.default_configs_optimal(ipc=False, extended=False)
print(sorted(CONFIGS.keys()))
print(len(CONFIGS))
SUITE = downward.suites.suite_optimal_with_ipc11()
SCATTER_ATTRIBUTES = ["total_time"]
exp... |
5dc529acc772bef71e61a65307cf20c0836451dfa529d6ae624088085cbd4603 | Python | 594 | 24 | NEURO_TRANSMITTER_NAMES = {
"DA": "dopamine",
"SER": "serotonin",
"GABA": "gabaergic",
"GLUT": "glutamate",
"ACH": "acetylcholine",
"OCT": "octopamine",
}
def lookup_nt_type(txt):
if txt:
if txt.upper() in NEURO_TRANSMITTER_NAMES:
return txt.upper()
txt_low = tx... |
9f0d35861796bcccb93b9e1407db9e2990ad28a9260bb9c3a8f8fda408446204 | Python | 594 | 21 | import uuid
from queso.sensors.pennylane.sensor import brick_wall_probe
from queso.sensors.pennylane.sensor import Interaction, Measurement, Sensor
from queso.sensors.pennylane.sensor import Phase, U3
def test_calculate_state():
n_layers = 2
n = 4
probe = brick_wall_probe(n=n, d=2, n_layers=n_layers)
... |
10e985e8bff5a23c7b04f104ae1beb6365d0280a33b1e3fbe20806956219db4d | Python | 595 | 21 | from .cross_validate import chemprop_train, cross_validate, TRAIN_LOGGER_NAME
from .evaluate import evaluate, evaluate_predictions
from .make_predictions import chemprop_predict, make_predictions
from .molecule_fingerprint import chemprop_fingerprint
from .predict import predict
from .run_training import run_training
f... |
2398948b51875e075c7d16b4c2ec72bcc2b020250b404a8eb54df16d8361a2bc | Python | 595 | 21 | from __future__ import annotations
import pytest
pytest.importorskip("tblite.ase")
pytest.importorskip("phonopy")
pytest.importorskip("seekpath")
from ase.build import bulk
from quacc.recipes.tblite.phonons import phonon_flow
def test_phonon_flow(tmp_path, monkeypatch):
monkeypatch.chdir(tmp_path)
atoms =... |
951ac2c57466a8158419e63fea0bc97f36683c0ffef214100e4ea97416d06a69 | Python | 595 | 29 | import sys
from pybioshell.core.data.io import read_scorefile
if len(sys.argv) < 2 :
print("""
Simple example that parses a score file (Rosetta output)
USAGE:
python3 read_scorefile.py score-file
EXAMPLE:
python3 read_scorefile.py scores.sf
CATEGORIES: core/data/io/read_scorefile
KEYWORDS: scorefile i... |
9f713fb17558d7d206285e107a0994415f6ce5f24918ca7a3774223d831906b4 | Python | 595 | 24 | # SPDX-FileCopyrightText: 2024 Blue Brain Project / EPFL
#
# SPDX-License-Identifier: AGPL-3.0-or-later
'''
Compute basic topological network properites of a connectome and its controls
Author(s): Daniela Egas S.
Last updated: 12.2023
'''
import json
import sys
sys.path.append('../../../library')
from structural_basi... |
ac2f73a0cec006d670d7f8abfbf63f66e114287e722300825ce09946ff389319 | Python | 595 | 27 | from setuptools import setup
requirements = [
"numpy",
"scanpy",
"numba",
"pandas",
"scikit-learn",
"scipy",
"seaborn",
"matplotlib",
"pytest",
"torch"
]
setup(
name="Annotatability",
version="0.0.5",
packages=["Annotatability"],
url="https://github.com/nitzanla... |
c460eb5d810f4b4ebcecd876386323475dd15a6927bad5e5b16d8024acf738cf | Python | 595 | 16 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from plot_methods import os, plt, plotMorphologyTable
from cellsim16popsParams_default import multicompartment_params
import analysis_params
if __name__ == '__main__':
params = multicompartment_params()
ana_params = analysis_params.params()
ana_params.set_PLOS_... |
e86ae88ae488a7e889f4aab504fd89b7f68bb25075b079dbeee1c2054a1f3df0 | Python | 595 | 28 | """Software for fitting HMP on EEG/MEG data."""
from importlib.metadata import PackageNotFoundError, version
from . import (
basedata,
crossvalidation,
distributions,
estimators,
io,
models,
patterndata,
patterns,
projectors,
utils,
visu,
)
try:
__version__ = version("... |
b7b659c6cc1df3a092a4a5df68e9ae25f137765744d4c49604beec9bc3993e6d | Python | 599 | 14 | # Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .builder import (BACKBONES, CLASSIFIERS, HEADS, LOSSES, NECKS,
build_backbone, build_classifier, build_head, build_loss,
build_neck)
from .classifiers import * # noqa: F401,F403
... |
701744b709be2312a292f0710a47872fabf014f564b5bebfe348102622ac6c4d | Python | 600 | 22 | # This document contains plot utilities
def latex_plt(matplotlib):
""" This function updates the matplotlib library to use Latex and changes some default plot parameters
:param matplotlib: matplotlib instance
:return: Updated matplotlib instance
"""
pgf_with_latex = {
"axes.labelsize": 6,... |
ef33e695b0a870ceb618c9b996262e60350a31293b9de9c19263864e492906b3 | Python | 601 | 22 | """Setup pyprep."""
import sys
from setuptools import setup
import versioneer
# Give setuptools a hint to complain if it's too old a version
# 30.3.0 allows us to put most metadata in setup.cfg
# 38.3.0 contains most setup.cfg bugfixes
# Should match pyproject.toml
SETUP_REQUIRES = ["setuptools >= 38.3.0"]
# This e... |
28cc315766cf7a1dfc9da394c6ed4ca787be52dc747f24a7767d4b88e2e1a754 | Python | 602 | 16 | from .resnet import *
from .mobilenet_v2 import *
from config import visual_backbone_list
def get_visual_backbone(args):
if args.visual_backbone in visual_backbone_list:
model = eval(args.visual_backbone)()
if args.pretrain_vis_path !="":
model.load_model(pretrain=args.pretrain_vis_pat... |
3e2678573374406cee85967c0d1c37a3805e1512ca1cda0d27e8cb9374a16991 | Python | 602 | 24 | import numpy as np
from typing import Tuple
"""
This module contains Matlab functions that do not have equivalents in
python libraries.
"""
def matlab_gauss2D(shape: Tuple[int, int]=(3, 3), sigma: float=0.5
) -> np.array:
"""
2D gaussian mask - should give the same result as MATLAB's
... |
67d3a86c13f11fe4011d55d76b9cd50580f9b297db1e0057fce8122a7495bc62 | Python | 602 | 18 | import torch
import torch.nn.functional as F
def gram_schmidt(vectors):
orthogonalized = []
for vector in vectors:
for ortho_vector in orthogonalized:
projection = torch.dot(vector, ortho_vector) / torch.dot(ortho_vector, ortho_vector)
vector = vector - projection * ortho_vector... |
8328270005ddb59b86573f0587a1cfa9ce50bb2971f846f0e4ba05767fbfd357 | Python | 602 | 21 | from numpy import array
def read_gene_indices(path: str):
with open(path, "r") as f:
lines = f.readlines()
assert (
len(lines) % 3 == 0
), f"number of lines in {path} needs to be a multiple of 3"
triples = [lines[i * 3 : (i + 1) * 3] for i in range(int(len(lines) / 3))]
gene_indi... |
c0a3fa784b78aa5700799c1f554fc96ca51cadf4902a45b84a05239469b9d132 | Python | 602 | 20 | from typing import Protocol
import torch
from torch import Tensor, nn
from elasticai.creator.base_modules.math_operations import Quantize
class MathOperations(Quantize, Protocol): ...
class LeakyReLU(torch.nn.LeakyReLU):
def __init__(self, mathops: MathOperations, init: float = 1e-2) -> None:
super().... |
ca38ea85d2de714501f53ec6797ccc65b0164cc4d901799d97dc479f93782332 | Python | 602 | 26 | import torch
from elasticai.creator_plugins.quantized_grads.base_modules import Linear
from elasticai.creator_plugins.quantized_grads.fixed_point import (
FixedPointConfigV2,
QuantizeForwHTE,
QuantizeParamToFixedPointHTE,
QuantizeParamToFixedPointStochastic,
)
def test_conv1d_fxp_init():
conf = F... |
eaa3a90e01008653cbe059e421e2bd9772b0b9278673d3b140a0ba00e243f71a | Python | 602 | 19 | import torch
from .conv1d import MathOperations as Conv1dOps
from .linear import MathOperations as LinearOps
from .lstm_cell import MathOperations as LSTMOps
class TorchMathOperations(LinearOps, Conv1dOps, LSTMOps):
def quantize(self, a: torch.Tensor) -> torch.Tensor:
return a
def add(self, a: torch... |
f7f7b6a5c3c92344af7f2293d6947564e7fac844120577b16c7516f50768f1e5 | Python | 602 | 20 | from typing import Any
import torch
class Binarize(torch.autograd.Function):
@staticmethod
def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
if len(args) == 0:
raise TypeError
x = args[0]
out_of_range = torch.logical_or(torch.gt(x, 1.0), torch.lt(x, -1.0))
c... |
5c02cebd70c1d5ef4c5f5113115909a09c4430823191e12d492d8d7b6de2eb37 | Python | 604 | 19 | '''
Adopted: DeeperCut by Eldar Insafutdinov
https://github.com/eldar/pose-tensorflow
'''
def pose_net(cfg):
net_type = cfg.net_type
if 'mobilenet' in net_type:
print("Initializing MobileNet")
from deeplabcut.pose_estimation_tensorflow.nnet.pose_net_mobilenet import PoseNet
cls = PoseNe... |
240cc958fadcb25673ca5eabb25676c11afd4eea06a621718d3f07dbd61cb5d4 | Python | 605 | 22 | from math import sqrt
# Spherical harmonics
SPHERICAL_HARMONICS_LMAX = 10 # Maximum angular momentum for spherical harmonics
# pairs info
BASE_TRANSLATIONS = 20 # Assume all primitive translations are within [-BASE//2, BASE//2)
# two-center integrals
TWOCENTER_RGRID_DEN = 100. / 12. # 100 points at 12 bohr
AOFT_QGR... |
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