sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a044a392a297bed83a9e47115c716d6d6bae58d69a443498d9bd6c2bfb935004 | Python | 913 | 22 | #!/usr/bin/env python3.9
# train an AI model, track runtime for epochs and save the best models (loss/accuracy)
from ownutil import ParseArgs, PrepareData
# read command line arguments (data set, epoch number, train/validation size)
args = ParseArgs()
#model specific wired setup
args.modelnum = 3 # model 3
arg... |
b08e981fa9ae9c9c4d0c80362d647d3c2b35e81897b3b327802d3a3ac42ff461 | Python | 913 | 50 | import numpy as np
import math
import matplotlib.pyplot as plt
x = np.arange(-90, 90, 0.1)
vhm = -12.2
ka = 4.0
vhh = -52.5
ki = 5.6
atm = 1.5
btm = -8.2
ctm = 9.1
dtm = 15.4
etm = 0.1
ath = 83.8
bth = 52.9
cth = -3.5
dth = 72.1
eth = 23.9
fth = -3.6
y1 = []
y2 = []
y3 = []
y4 = []
for v in x:
min... |
d441741ce983060e8af83f9c1a6adcc79d255a86af7222c31320e2802d1e768f | Python | 913 | 26 | class TestLogging:
def _test_log_output_manually(self):
from cuppa.logger import initialize_logging
initialize_logging("/Users/lnguyen/Desktop/train.log")
from cuppa.runners.training_runner import TrainingRunner
from cuppa.constants import DEFAULT_FUSION_OVERRIDES_PATH
from... |
22ae2f8d8f3abb1551f0c66ac6c1178a73b49714c270c5e6e414294ca03bca84 | Python | 914 | 29 | import sys
from pathlib import Path
import h5py
from bifrost.io import read_affine, read_image
def main():
transform_path = Path(sys.argv[1])
with h5py.File(str(transform_path), "r") as h5_handle:
# writes aff.mat to transform_path parent dir
read_affine(h5_handle, "/syn/affine", directory=t... |
9b9f14a968c1f2153a83acc6013db2bc62d9340fd65c37546c85e8974fee039c | Python | 914 | 31 | __all__ = [
"QmData",
"Graph",
"GraphGenerator",
"GraphGeneratorSettings",
"ElementLookUpTable",
"FileHandler",
]
def __getattr__(name):
if name == "GraphGeneratorSettings":
from .graph_generator_settings import GraphGeneratorSettings
return GraphGeneratorSettings
if nam... |
a06b8a3d726cbe9cf81d7b01e3da062c7951db8a04df5fbb5ba06d3a28546d02 | Python | 914 | 30 | import pytest
from pupil_recording_interface.packet import Packet
class TestPacket:
def test_constructor(self):
""""""
packet = Packet("stream_name", "device_uid", 1.0)
assert packet.stream_name == "stream_name"
assert packet.device_uid == "device_uid"
assert packet.timest... |
ad57f65156e6930e2a0003981130bd921761c7ede065137a32d0f6600fc6baca | Python | 914 | 33 | import json
import h5py
import numpy as np
from cirrocumulus.abstract_backed_dataset import AbstractBackedDataset
class H5ADDataset(AbstractBackedDataset):
def __init__(self):
super().__init__()
def get_suffixes(self):
return ["h5ad"]
def is_group(self, node):
return isinstance... |
41b9844a404b399b3fcdf5d9f7c417ee6443ba3e7208b1a0207e25994a86083a | Python | 915 | 33 | import numpy as np
import sklearn.preprocessing
def data_discretization(X, n_bins):
"""
This function implements the data discretization function to discrete data into n_bins
Input
-----
X: {numpy array}, shape (n_samples, n_features)
input data
n_bins: {int}
number of bins to... |
5e5888da36f8ef7b3b5cc1e50c4ef407df47d5360a39dbea5ca3d7b4a7b2bd9e | Python | 915 | 31 | """ test the configuration parser"""
from pathlib import Path
import layer_recognition.utilities as tested
CONFIG_DIR = Path(__file__).resolve().parent.parent / "Config"
def test_converter_config():
config_file_path = CONFIG_DIR / "batch_convert.ini"
print(f'DEBUG config_file_path {config_file_path}')
... |
b9bf3b99c053d61de3f187db400a2c163c66517b117c46402af73c175530b6d7 | Python | 915 | 22 | import os
import pandas as pd
import numpy as np
import pickle
from tqdm import tqdm
from argparse import ArgumentParser, Namespace, FileType
parser = ArgumentParser()
parser.add_argument('--config', type=FileType(mode='r'), default=None)
parser.add_argument('--out_dir', type=str, default='./data/', help='gpu id')
arg... |
be37f465de70fbc24d91b81f0c593ea5d1063ddc44d53a0a79b6d7c9cca81da2 | Python | 915 | 29 | # Written by Minh Nguyen and CBIG under MIT license:
# https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
import unittest
from datetime import datetime
from dateutil.relativedelta import relativedelta
import numpy as np
import cbig.Nguyen2020.misc as misc
def inefficient_month_between(end, start):
""" ... |
c1b161fecf1938dc3069ed8a71c96220208543a32777b92d1920a07790f1dadc | Python | 915 | 31 | ## Inset the "TER" expression between two chains in PDB
## Usage: python3 chain_ter.py pdb_file
## output pdb filename is extended by the term "_fmt"
import sys
if len( sys.argv ) < 2:
print("provide pdbfile")
sys.exit()
pdb_file = sys.argv[1]
out_file = pdb_file.split(".")[0]+"_fmt.pdb"
## First find the fir... |
30237fd92b2fa2eaafb100ecadd2e4e96cb9c6a5e13c052635b4071ddf26df58 | Python | 916 | 32 | """Generate the code reference pages and navigation."""
from __future__ import annotations
from pathlib import Path
import mkdocs_gen_files
nav = mkdocs_gen_files.Nav()
for path in sorted(Path("src").rglob("*.py")):
module_path = path.relative_to("src").with_suffix("")
doc_path = path.relative_to("src").wi... |
37a8b749626406dc910c37619e1d4bc6d6a3bafe5ae4f707171a481c8f763cb8 | Python | 916 | 34 | import os
import torch
import pandas as pd
import matplotlib.pyplot as plt
from run import *
# 1e-3 ,4, 32
LR = 1e-3
input_size = 280
output_sizes = [2]
batch_size_label = 128
hidden_sizes = [2048, 2048]
Epoch = 8
K = 1
accs= []
accs_inclass=[]
Permute = 1
for perm in range(Permute):
prediction = []
for k... |
ca6cdaee6e21c0cbdbefa16ecdf2b3322beb056b17f59cae0dbaff8faba10172 | Python | 916 | 22 | #!/usr/bin/env python3.9
# train an AI model, track runtime for epochs and save the best models (loss/accuracy)
from ownutil import ParseArgs, PrepareData
# read command line arguments (data set, epoch number, train/validation size)
args = ParseArgs()
#model specific wired setup
args.modelnum = 5 # model 5
arg... |
991bea16a44d9f0ccd90942befed9345d896c820e6d369d8ab4799c7ed079cdb | Python | 917 | 37 | import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
def simulate_outcome(
dfG: pd.DataFrame,
dfe: pd.DataFrame,
v_caus: float,
v_hp: float,
frac_hp_snps: float = 0.1,
) -> pd.DataFrame:
num_snps = dfG.shape[1]
hpr_indices = np.random.choice(
r... |
43cd120600f1e71d2b3721b5effb958cfc0422280962f3a4151f6486f61708d8 | Python | 919 | 38 | import numpy as np
import pytest
from SurvivalEVAL.evaluations.others import cov
def test_cov_computes_event_time_coefficient_of_variation():
cdf = np.array(
[
[0.0, 0.5, 1.0],
[0.0, 1.0, 1.0],
]
)
time_grid = np.array([0.0, 2.0, 4.0])
mean_cov, cov_values = c... |
b8848531fdc1b8d65e07179e3c5d56f2d435f1be68625f355a7d97d6da8a8109 | Python | 919 | 34 | from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
from bonito.cli import basecaller, train, evaluate, view, convert, download, export
modules = [
'basecaller', 'train', 'evaluate', 'view', 'convert', 'download', 'export'
]
__version__ = '0.5.0'
def main():
parser = ArgumentParser(
'... |
dea87e603adb05c7807ce27d8341d159bc53007598d29f7d36d6df4b7d03d2de | Python | 919 | 42 | from numpy import isnan
from wonambi import Dataset
from .paths import (eeglab_1_file,
eeglab_2_file,
eeglab_hdf5_1_file,
eeglab_hdf5_2_file,
)
def test_ioeeg_eeglab():
d1 = Dataset(eeglab_1_file)
d2 = Dataset(eeglab_2_file)
... |
36e599891b254fb81565ace1e4d805ca141fe1c79e1d4bed04f876c9fc7ae93c | Python | 920 | 35 | from pickle import load, dump
from tempfile import NamedTemporaryFile
from numpy.testing import assert_array_equal
from wonambi.trans import math
from wonambi.utils import create_data
def test_pickle_01():
data = create_data()
tmpfile = NamedTemporaryFile(delete=False)
with tmpfile as f:
dump(da... |
c641d5c2aa2b3c1c1170948ecc48a16803ee8fa8bdbf15f57dfc8c33f797edd8 | Python | 920 | 38 | """SWC data format defintion and services."""
import json
import numpy as np
class SWC():
"""Definitions of the data format."""
TYPES = (SOMA, AXON, DEND, APIC) = range(1, 5)
COLS = (I, T, X, Y, Z, R, P) = range(7) # noqa: E741
XY = slice(2, 4)
XZ = slice(2, 5, 2)
YZ = slice(3, 5)
XYZ =... |
3b21cc4b11db2501fdb863512e77304b7a1008d66a72201488287bc1cfeb2bdd | Python | 921 | 30 | import pytest
import elasticai.creator.nn.fixed_point as nn_creator
from elasticai.creator.arithmetic import FxpArithmetic, FxpParams
from tests.integration_tests.nn.fixed_point.precomputed_routine import (
routine_testing_precomputed_module,
)
@pytest.mark.ghdl
@pytest.mark.parametrize(
"total_bits, frac_bi... |
3e86cb43238b20a6832c44451341305dbf93d658455c9b7862fa20970464b4ef | Python | 921 | 33 | """
This script will copy the files from the archive folder to the BIDS folder
@author: trianaa1
"""
import os, glob, sys
import shutil
import pandas as pd
path = '/m/cs/archive/networks-pm/mri'
savepath = '/m/cs/project/networks-pm/mri/fast_prepro_bids'
#search all eye-tracker files
search_str = os.path.join(path,... |
5ad1a1a207f0097c7d48ae75afc678eabbc41b8f5e0fc14069ad2c866664251b | Python | 921 | 26 | import os
import re
class QSSHelper:
def __init__(self):
pass
@staticmethod
def open_qss(path):
"""
opens a Qt stylesheet with a path relative to the project
Note: it changes the urls in the Qt stylesheet (in memory), and makes these urls relative to the project
W... |
78f22bdfdde631c209134103a33ac1bbbf34636d6cf58e787011c0aeb53cdd1b | Python | 922 | 32 | import os
import requests
from codex import logger
def download(url: str, dest_folder: str):
logger.debug(f"Downloading {url} to {os.path.abspath(dest_folder)}")
if not os.path.exists(dest_folder):
logger.debug(f"Creating folder: {dest_folder}")
os.makedirs(dest_folder)
filename = url.... |
c2ff33160ac01c32689389dd7d50abab21a95b3f2501aa82e04763db9d81efab | Python | 922 | 31 | r"""Test OpenBabel Python bindings
On Windows or Linux, you can run these tests at the commandline
in the build folder with:
"C:\Program Files\CMake 2.6\bin\ctest.exe" -C CTestTestfile.cmake
-R pybindtest -VV
You could also "chdir" into build/test and run the test file direc... |
12884cabc0ba04caeaf11d2ce49a18b004eb94b6f2fd01e8547d9c4c370f98cc | Python | 923 | 41 | # From the Meep tutorial: plotting permittivity and fields of a bent waveguide
import meep as mp
cell = mp.Vector3(16, 16, 0)
geometry = [
mp.Block(
mp.Vector3(12, 1, mp.inf),
center=mp.Vector3(-2.5, -3.5),
material=mp.Medium(epsilon=12),
),
mp.Block(
mp.Vector3(1, 12, mp.in... |
29e7a5e777f2deff0ea1df47c2555643620d7f0c2251d95c306f60fdce429a83 | Python | 923 | 23 | #! /usr/bin/env python
from lab.parser import Parser
def calculate_old_state_size(content, props):
if 'bytes_per_state' not in props and 'preprocessor_variables' in props and 'state_var_t_size' in props:
props['bytes_per_state'] = props['preprocessor_variables'] * props['state_var_t_size']
class StateSi... |
8d955aeb352555e118674fbb49f5ac85c8b53fc861eea72202c584197bce0e1d | Python | 923 | 25 | import hashlib, numpy as np
from regtools.registration.run_provenance import collect_environment, image_stats, file_sha256_short
def test_collect_environment_keys():
env = collect_environment()
assert "python" in env and "platform" in env and "packages" in env
assert isinstance(env["packages"], dic... |
e0137ae8c4fab49add5aef9de0bbd4f6388848caec8757bfb306ac40100de636 | Python | 923 | 38 | """Computes the number of missing datapoints
Parameters
----------
file : str
path where the raw data is stored
"""
import os
import sys
import datetime as dt
import pandas as pd
import numpy as np
file = sys.argv[1]
data = pd.read_csv(file, index_col="date")
data["date"] = pd.... |
4444c78487c9fe2238b16004087adf488d350d9ef20dfb67f4b97daac5114484 | Python | 924 | 30 | """Schema validation subpackage for HED schema compliance checking.
This package contains all schema validation, compliance checking, and
attribute validation functionality.
"""
from .compliance import check_compliance, SchemaValidator, DOMAIN_TO_SECTION, SECTION_TO_DOMAIN, CONTENT_SECTIONS
from .compliance_summary i... |
f608ff355a8174d35d78f9006c2ef362a4b5b8e52a465f75d685a3483f46940c | Python | 924 | 25 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
''' Script to submit to cluster the estimation of inverse solution '''
import os
from warnings import filterwarnings
from stormdb.cluster import ClusterBatch
from stormdb.access import Query
project = 'MINDLAB2020_MEG-AuditoryPatternRecognition'
os.environ['MINDLABPROJ']=... |
1fc227bfd00756b166cbb6bd670c441b5bd0f4d9b9c3f3c7690a20eac1df8f1d | Python | 925 | 28 | # Parameters used in analysis of simulations
params = {}
# Window size for analysis of I-waves (ms)
params["trange_iwaves"] = 50 # objective.py
# Voltage value above which simulation will terminate (mV)
params["vthresh_error"] = 1000 # simulation.py
# Offset before time of I1-wave to remaining I-waves (ms)
params["... |
2993579ca8e054650816fb5a2025a14e9858ecb6d4936f6343a74e09384a4078 | Python | 925 | 25 | extensions = dict(
set_required_params="""
parms$training_frame <- training_frame
args <- .verify_dataxy(training_frame, x, y)
parms$ignored_columns <- args$x_ignore
parms$response_column <- args$y
"""
)
doc = dict(
preamble="""
H2O ANOVAGLM is used to calculate Type III SS which is used to evaluate the co... |
75f011911560e048d07f35ed79aedd180da8dd148a4fac2cdd3bc3aa0f1c5a76 | Python | 925 | 45 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import argparse
import re
import subprocess
import sys
STD_REGEX = r"(^|\s+)std::"
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("cc_file", nargs="+")
return parser.parse_args()
def check_... |
540c951ed3524aea43b88f0105826cba853a1299f6da1984c09767ec71d5e195 | Python | 926 | 23 | from cuppa.sample_data.cuppa_features import CuppaFeaturesLoader
from cuppa.classifier.cuppa_classifier import CuppaClassifier
from cuppa.constants import DEFAULT_CUPPA_CLASSIFIER_PATH
from cuppa.visualization.visualization import CuppaVisDataBuilder, CuppaVisPlotter
import importlib_resources as impresources
INPUT_PA... |
6944f627a491c240c67fbd7e3635a404e9757d1c294b2ebfa21f36e1cb0dae9f | Python | 927 | 33 | # coding=utf-8
# Copyright 2026 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... |
b6e98c8a70c013690b1ca5c08ee4158aedd0cbd98102b183388e697bc87649a0 | Python | 928 | 30 | # -*- coding: utf-8 -*-
#
# Populate namespace with datatype routines and classes
#
# Import __all__ routines from local modules
from . import base_data, continuous_data, discrete_data, methods
from .base_data import *
from .continuous_data import *
from .discrete_data import *
from .methods.definetrial import *
from ... |
8ba83870957a624ee3eca0d033a3fdb2e294c98a6d5e350c94141c60d8bf0e1a | Python | 929 | 34 | import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import pdb
import seaborn as sns #Strange I know, but sns box plots look better
df = pd.read_csv('JPSTHTable.csv')
dfstim = pd.read_csv('JPSTHTableStim.csv')
#pdb.set_trace()
dist = df['dist']
figBox = sns.boxplot(y=df['nElecJ'])
... |
6fe60a586defd861a95ca8b8cb7ddcceb220e6ee8d4168e253c6e5902d1ea36e | Python | 930 | 28 | # coding=utf-8
# Copyright 2026 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... |
a6b4ed8e98b18674e54f8b8f7626652ad09835fa504c5ab3e3f8f9ab2c9c3994 | Python | 930 | 32 | from typing import List, Tuple
import numpy as np
import torch
import yacs.config
def mixup(
batch: Tuple[torch.Tensor, torch.Tensor], alpha: float
) -> Tuple[torch.Tensor, Tuple[torch.Tensor, torch.Tensor, float]]:
data, targets = batch
indices = torch.randperm(data.size(0))
shuffled_data = data[ind... |
456ad68cc96502cc51b59c6717eeee7333e39a4e44d6054ba3c2c8222aa25163 | Python | 931 | 23 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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
#
# ht... |
b4529e179fd18dc77ddbd1a496b8e8085d2e170ef4cf74a679e49dca971ab90a | Python | 931 | 32 | import os
import subprocess
from jinja2 import Template
import pandas as pd
import numpy as np
submission = Template("""#!/bin/bash
#PBS -W group_list=ku_10001 -A ku_10001
#PBS -N {{dirname}}_{{ionic}}
#PBS -m n
#PBS -l nodes=1:ppn=1:thinnode
#PBS -l mem=50gb
#PBS -l walltime=48:00:00
#PBS -e {{dirname}}_{{ionic}}_err... |
4627eed3293ea882e3c3534191a98990b906d365354590b602c685f6ae4c48a1 | Python | 932 | 21 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""check_us.py —— 扫描 main.tex 中 \texttt 与数学模式之外的裸下划线。"""
import re
src = open('game/docs/arxiv/main.tex', encoding='utf-8').read()
# 先抠掉 \texttt{...}（允许一层花括号嵌套）
stripped = re.sub(r'\\texttt\{(?:[^{}]|\{[^{}]*\})*\}', '', src)
bad = []
for i, l in enumerate(stripped.split(... |
f94d400246346e698c452c7dc2f41e09417d348de0a29cfcd141e0aa3374daae | Python | 932 | 9 | """POST HOC (not pre-registered), 1 Oct 2026: excitatory share of local-neuron output at the break point of each BREAK_IT
order (rows saved by BREAK_IT.py in tmp/break_it_rows.csv). output: 06-OUTPUT/posthoc_break_share.txt"""
import pandas as pd
d = pd.read_csv('D:/eucys/tmp/break_it_rows.csv'); r = d[d.order != 'W'... |
fc24f7e1fc1676965193e5d73c7eca059383d9d853f56388fb6b6a95a49e3107 | Python | 932 | 31 | import setuptools
from os import path
this_directory = path.abspath(path.dirname(__file__))
with open(path.join(this_directory, "README.md"), encoding="utf-8") as f:
long_description = f.read()
with open("requirements.txt") as f:
requirements = f.read().splitlines()
setuptools.setup(
name="neurolib",
... |
de8d489d53d9ced2f963453c9357582369ab17a3d27d2c7d34bdd328ba36ce0f | Python | 933 | 33 | import torch
import torch.nn as nn
def loss_selector(loss_type):
if loss_type == "mse":
return nn.MSELoss(reduction="mean")
elif loss_type == "mse_with_mask":
return mse_with_mask
elif loss_type == "mae":
return nn.L1Loss(reduction="mean")
elif loss_type == "mae_with_mask":
... |
f27beb791ceaf1ddc74b97f332b833750e834a748b7006f0910da0787086c0be | Python | 935 | 36 | class Token(object):
def __init__(self, token, categ, semantics=None, weight=0.001):
self._token = token
self._categ = categ
self._weight = weight
self._semantics = semantics
def categ(self):
return self._categ
def weight(self):
return self._weight
def semantics(self):
return sel... |
36eddcd7f8f8a6cb5824b73d70cff0f9d853516d3541428d8cb9c3d401f6ace8 | Python | 936 | 27 | #54121 -+sycu 6y
from psychopy import parallel
import platform
PLATFORM = platform.platform()
if 'Linux' in PLATFORM:
port = parallel.ParallelPort(address='/dev/parport0') # on MEG stim PC
else: # on Win this will work, on Mac we catch error below
try:
port = parallel.setPortAddress(address='0xDFF8'... |
4fecf8860fc10ec81080bc5464445f10b027606b08b3a992f74d88adffffb917 | Python | 936 | 24 | from distutils.dep_util import newer_group
# yes, this is was almost entirely copy-pasted from
# 'newer_pairwise()', this is just another convenience
# function.
def newer_pairwise_group(sources_groups, targets):
"""Walk both arguments in parallel, testing if each source group is newer
than its corresponding ... |
7d662cfc5bc0f77a6047a42006cb021e9c0c5b96aef0f2b9278e74d6a520d2c5 | Python | 936 | 24 | #----Figure_S01_preparation-----------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code which has to be run in preparation for Figure S01
# for the connectivity analysis for Reinhard and Fukuda et al. 2024
#---------------... |
f8fe82ca93d406218e40fce5b45f640fb3b10b1e42894d4c5f79222236badd89 | Python | 936 | 29 | '''
Description:
Differential equation solver.
Author:
Jiaqi Zhang <jiaqi_zhang2@brown.edu>
'''
import torch
import torchdiffeq
import torch.nn as nn
# ===========================================
class ODE(nn.Module):
'''
Ordinary differential equation (ODE) solver.
'''
def __init__(self, inp... |
dd0e9565b9d64c73b492249ed9fd77d236d20099543ff0d4b79a874e1b0cbccc | Python | 937 | 26 | ################################################################################
# Copyright (c) 2021-2024, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory.
# Written by D. Jones <djones@llnl.gov> and UCSD collaborators in listed in CONTRIB.md
#
# All rights reserved.... |
9b7be475c8531894770fe6654f96eba1d9d52d0cf35e70fc38c54ead838ab1dd | Python | 938 | 30 | import unittest
import os
import random
import shutil
from torcheeg.model_selection import train_test_split_cross_trial
from torcheeg.datasets import DEAPDataset
class TestTrainTestSplitCrossTrial(unittest.TestCase):
def setUp(self):
shutil.rmtree('./tmp_out/')
os.mkdir('./tmp_out/')
def tes... |
5ae020798138545a23e18901bcc3da0d09ce02c1c9ed767a75fd2b889788a877 | Python | 939 | 26 | """Top-level package for pertpy."""
import warnings
from anndata._core.aligned_df import ImplicitModificationWarning
from matplotlib import MatplotlibDeprecationWarning
from numba import NumbaDeprecationWarning
warnings.filterwarnings("ignore", category=NumbaDeprecationWarning)
warnings.filterwarnings("ignore", cate... |
71d8758ca021e268380c52659e665e352eaf237eec04e3b84cf44854cb7f6c9b | Python | 939 | 36 | """SWC data format defintion and services."""
import json
import numpy as np
class SWC(): # pylint: disable=too-few-public-methods
"""Definitions of the data format."""
TYPES = (SOMA, AXON, DEND, APIC) = range(1, 5)
COLS = (I, T, X, Y, Z, R, P) = range(7)
XYZ = slice(2, 5)
XYZR = slice(2, 6)
... |
cdb314111e28d9d4f52717058695535b2c9951d13c95d0951baacaed75949973 | Python | 939 | 36 | from collections.abc import Iterable
from datetime import datetime
from elasticai.creator.file_generation.resource_utils import read_text
from elasticai.creator.hdl_ir import DataGraph
from elasticai.creator.ir2verilog import (
Code,
Registry,
TemplateDirector,
type_handler_iterable,
)
@type_handler_... |
8faccf94d992852284d31192dae900467267ddccfa5763ac1aca35265b20cca4 | Python | 940 | 36 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
import common_setup
REVS = ["issue540-base", "issue540-v1"]
LIMITS = {"search_time": 300}
SUITE = suites.suite_satisficing_with_ipc11()
CONFIGS = {
"blind": ["--search", "astar(blind())"],
"lama-first": [
"--if-unit-cost",
... |
ca85e41e5e65030ff35271d4e169c59454db298f19f6ebe450adf106c6c3c420 | Python | 940 | 27 | """
TensorLayer provides rich layer implementations trailed for
various benchmarks and domain-specific problems. In addition, we also
support transparent access to native TensorFlow parameters.
For example, we provide not only layers for local response normalization, but also
layers that allow user to apply ``tf.nn.lrn... |
30844f0b732d3e5fa3f5a2db26cf3386900fb453228805e01ae9d7a061afa0d5 | Python | 941 | 22 | # -*- coding: utf-8 -*-
OPTIMAL = True
CONFIGS = [
(1, ["--search",
"astar(merge_and_shrink(merge_strategy=merge_precomputed(merge_tree=linear(variable_order=reverse_level)),"
"shrink_strategy=shrink_bisimulation(greedy=true),"
"label_reduction=exact(before_shrinking=true,before_m... |
5ec472dd66690940e5d3f7270e1003779e5b1094b8472738c437e88590906cb7 | Python | 941 | 42 | from __future__ import annotations
from dataclasses import dataclass
@dataclass
class SegmentationConfig:
"""
Configuration for canonical instance establishment.
connectivity
Connected-component connectivity used for internally generated
instances.
None uses full spatial connect... |
01c642df0d95a750ab2946320808899f81a125c62ca3e66d03661fe80d898cea | Python | 942 | 20 |
import sys
sys.path.insert(0, '/projects/f_mc1689_1/TaskFCActflow/docs/scripts/final_analyses/')
sys.path.insert(0, '/projects/f_mc1689_1/TaskFCActflow/docs/scripts/final_analyses/ActflowToolbox/network_definitions/')
import ActflowToolbox as actflow
from calc_removal_coords import *
#First run dilateParcels.py, to ... |
ddc507aef66ffc544a2973b1c9be1a22270fb8760937921c651f69aa0b9e705a | Python | 942 | 18 | from src.model import ModelSpikeSorter
from src.data import RecordingCrossVal
from src.utils import random_seed
from src.plot import set_dpi
set_dpi(400)
random_seed(123)
model = ModelSpikeSorter.load(r"E:\KosikLab\Deep-Learning-for-Realtime-Spike-Sorting\data\models\v0_2\2950")
recording_cross_val = RecordingCrossVal... |
113893d261b0a945e460478c7c06cd435f22524a7eb6f20da83d8d5c1939d6fc | Python | 943 | 39 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward.suites import suite_optimal_with_ipc11
from downward.configs import default_configs_optimal
import common_setup
REVS = ["issue386-base", "issue386-v1"]
CONFIGS = {'astar_ipdb': ['--search', 'astar(ipdb)']} # default_configs_optimal()
TEST_RUN = True
if ... |
61174be8b7adfb62f4c41b6d29536683949da3eb410d7c8f3a61931ed2e880a0 | Python | 943 | 36 | from collections.abc import Iterable
from datetime import datetime
from elasticai.creator.file_generation.resource_utils import read_text
from elasticai.creator.hdl_ir import DataGraph
from elasticai.creator.ir2verilog import (
Code,
Registry,
TemplateDirector,
type_handler_iterable,
)
@type_handler_... |
ab71cd6d4f96a1e56a8b9b32d03ba514db9ff5410679b465c62525df9b33173a | Python | 943 | 36 | from collections.abc import Iterable
from datetime import datetime
from elasticai.creator.file_generation.resource_utils import read_text
from elasticai.creator.hdl_ir import DataGraph
from elasticai.creator.ir2verilog import (
Code,
Registry,
TemplateDirector,
type_handler_iterable,
)
@type_handler_... |
dd8c16da8aced082828aeafbf2c90222f120df5d7eadf422c3264aaee25f3359 | Python | 943 | 36 | """
Wrapper for avscale command
"""
import six
import fsl.utils.assertions as asrt
from fsl.wrappers import wrapperutils as wutils
def extract_avscale_output(fn):
def _wrapper(*args, **kwargs):
capture = six.StringIO()
log = kwargs.pop("log", {})
old_stdout = log.get("stdout", None)
... |
5df55d483a90415dfcab09695cf1c8bdb5751dfc15911538710a6fc70f986ed0 | Python | 944 | 31 | """"""
from warnings import warn
class Session:
""" Context manager for devices, streams and processes. """
def __init__(self, *instances):
""" Constructor.
Parameters
----------
instances: iterable of any
Any object(s) with a start and a stop method. The objects'... |
89fb9807ecd8b05718425f708312f851b86176389125c5d930ddc02d31160e0a | Python | 944 | 30 |
##get charge and phospho from first 20AAs
import pandas as pd
def get_NTS_features (file):
ProtID=[]
Charge=[]
Phospho=[]
TwentyAA=[]
f1=open(file)
f2=file.replace('.faa','')
f2=f2.replace('.csv','_')
for ln in f1:
if ln.startswith(">"):
ProtID.append(ln.spli... |
aec5fda9c988581b53b11001c3aca6c641a3ad84c8bd9a9e404f899585cbdf35 | Python | 944 | 30 | import cocotb
import pytest
from cocotb.triggers import Timer
from elasticai.creator.testing import CocotbTestFixture, eai_testbench
def adder_model(a: int, b: int, cin: int) -> tuple[int, int]:
return a ^ b ^ cin, 1 if a + b + cin > 1 else 0
@cocotb.test()
@eai_testbench
async def adder_truthtable(dut):
i... |
dc038ea54dfc20ef9174247cc8360a30690532b260f5863857747e2999532520 | Python | 944 | 35 |
import argparse
import sys
import os
import glob
import numpy as np
import pandas as pd
from nilearn import image as img
import nibabel
# Usage: crop.py <input nifti file to be cropped> <filename to save the cropped data> <events file with info necessary for cropping>
# Main function
if __name__ == '__main__':
inp... |
111fea90d4f8fff40b89606b3d75136315a681fc682d14e0173bbe5142810647 | Python | 946 | 30 | import unittest
import os
import random
import shutil
from torcheeg.model_selection import train_test_split_groupby_trial
from torcheeg.datasets import DEAPDataset
class TestTrainTestSplitGroupbyTrial(unittest.TestCase):
def setUp(self):
shutil.rmtree('./tmp_out/')
os.mkdir('./tmp_out/')
def... |
34c287b80a930823961bd80355bb9b6696cf7f07bee6dc3a76ae811389e29486 | Python | 946 | 32 | import warnings
warnings.filterwarnings("ignore")
import numpy as np
from pinn.io import load_numpy, sparse_batch
from ase import Atoms
import pytest
def mock_data(n_data, n_batch):
from ase.calculators.lj import LennardJones
atoms = Atoms('H3', positions=[[0, 0, 0], [0, 1, 0], [1, 1, 0]])
atoms.calc = Le... |
641d5cfe0486281b262aa5115ea2bed5bb1830544d36fd302109ddc8dad724bc | Python | 946 | 35 | from setuptools import setup, Extension
import numpy
setup(
name='pylib',
version='',
packages=['v3d', 'v3d.terafly', 'graph', 'anatomy', 'morph_topo', 'neuron_quality'],
url='',
license='MIT',
author='Yufeng Liu',
author_email='',
description='',
ext_modules=[
Extension(
... |
2209077e4843cbf24f1342b923af94bd284c7aea5b007bf8f0939cd627790632 | Python | 947 | 35 | # pylint: disable=too-many-function-args, invalid-name
""" FBIRN ICA dataset (with time direction labels) loading script"""
import numpy as np
from omegaconf import DictConfig
from src.datasets.fbirn import load_data as load_original_data
def load_data(cfg: DictConfig):
"""
Return FBIRN normal + inversed ti... |
9356baa0107f2750849e8694b84cb33a551a1e289a7e643bc3611430936ea9fc | Python | 947 | 36 | from collections.abc import Iterable
from datetime import datetime
from elasticai.creator.file_generation.resource_utils import read_text
from elasticai.creator.hdl_ir import DataGraph
from elasticai.creator.ir2verilog import (
Code,
Registry,
TemplateDirector,
type_handler_iterable,
)
@type_handler_... |
1883ba94751b859cfae60eb3907fbbf1640afbafbc479540fdf7c9cd75f526a4 | Python | 948 | 35 | """Testing module morph."""
import numpy as np
from treem import Node, Morph
def test_node_str():
"""Tests for Node string representation."""
node = Node(value=1)
assert str(node) == '1'
def test_node_point():
"""Tests for Node data point."""
node = Node(value=np.array([1, 1, 0, 0, 0, 1, -1]))... |
d1bd1779b4eb4297a45722b4651eca7bc4786cdbe22a1b18b8a10966591d96ff | Python | 948 | 40 | from torch import Tensor, nn
from torch.nn.utils.parametrize import register_parametrization
from elasticai.creator.nn.binary import MathOperations
class _Bin(nn.Module):
def __init__(self):
super().__init__()
self._ops = MathOperations()
def forward(self, input: Tensor) -> Tensor:
r... |
0d347499726a5b03c0ab57a8d33e46d8840a7f65ee15740f38ef9943e01854b7 | Python | 950 | 27 | import logging
from logging import handlers
class Logger(object):
level_relations = {
'debug':logging.DEBUG,
'info':logging.INFO,
'warning':logging.WARNING,
'error':logging.ERROR,
'crit':logging.CRITICAL
}
def __init__(self, filename, rank, level='info', when='D', b... |
e2102384d47dbe47f2cd16f10c45b89916a0b97e24e08666c16a854262105a5e | Python | 950 | 31 | # import os
# import random
# import shutil
# from tqdm import tqdm
#
#
# def random_split(raw_data_dir, data_dir):
# source_folder = raw_data_dir
# train_folder = os.path.join(data_dir, 'train/')
# test_folder = os.path.join(data_dir, 'test/')
#
# if not os.path.exists(train_folder):
# os.maked... |
3b994eb9a068ce87e32559e0009d39559bcc440d74ae35aa8b9c2f93f39c3a54 | Python | 951 | 35 | """
Optuna example that optimizes a classifier configuration using OptunaSearchCV.
In this example, we optimize a classifier configuration for Iris dataset using OptunaSearchCV.
Classifier is from scikit-learn.
"""
import optuna
from sklearn.datasets import load_iris
from sklearn.svm import SVC
if __name__ == "__m... |
2aa97c9944bc4e2926f40aab4f2688618dbde66b05ca38ae98d1e679290ab777 | Python | 952 | 26 | import numpy as np
import pylab as pl
def plot_means_and_stds(t, means, stds, color=None):
if color is None:
color = [0.5,0.5,1.0]
color = np.array(color)
c_offs = np.ones(3) - color
pl.fill_between( t, means+0.1*stds, means-0.1*stds, color=color+0.1*c_offs )
for r in np.arange(0.2,1.0,0.... |
37d16c8bc9d5627ed87a160854e226a372f31171eddacbc6d7e1fc9fe10d9ee5 | Python | 952 | 20 | import numpy as np
from bayes_opt import BayesianOptimization
from bayes_opt import acquisition
def f(x):
return np.exp(-(x - 2) ** 2) + np.exp(-(x - 6) ** 2 / 10) + 1/ (x ** 2 + 1)
if __name__ == '__main__':
acq = acquisition.UpperConfidenceBound(kappa=5) # kappa determines explore/Exploitation ratio
... |
4546c76c5e562dfd84128f4982a65317513737a62441f64c9b7ff56635fd1a1a | Python | 952 | 29 | import numpy as np
from utils import max_stats, pickle_dump
from joblib import Parallel, delayed
from joblib.externals.loky import set_loky_pickler
set_loky_pickler('pickle')
import os
os.environ['OPENBLAS_NUM_THREADS'] = "1"
os.environ['MKL_NUM_THREADS'] = "1"
NNULLS = 100
conns = {}
for res in ['125', '500']:
... |
5bcfbadc8fc224b8a5818e211f69460220bef170c9c8da54b5dd4ff5e8bc6116 | Python | 952 | 33 | import pytest
import elasticai.creator.nn.fixed_point as nn_creator
from elasticai.creator.arithmetic import FxpArithmetic, FxpParams
from tests.integration_tests.nn.fixed_point.precomputed_routine import (
routine_testing_precomputed_module,
)
@pytest.mark.ghdl
@pytest.mark.parametrize(
"total_bits, frac_bi... |
a79828d0abe1990b376b0142705356da9739215c039246680934224ab95b7f9e | Python | 952 | 33 | # -*- coding: utf-8 -*-
#
# Test proper functionality of Syncopy's `ContinuousData` class + subclasses
#
# Builtin/3rd party package imports
import os
import tempfile
# Local imports
from syncopy.datatype.util import get_dir_size
class TestDirSize:
def test_dirsize(self):
with tempfile.TemporaryDirector... |
0c595c11afa57bda1246d9c4dcf396f4e72123855294577569d70e399b915a0e | Python | 953 | 32 | from __future__ import print_function, division
import sys
import pandas as pd
import numpy as np
def parse_args():
import argparse
parser = argparse.ArgumentParser('Script for filtering an image list to only contain images with particles listed in a targets file.')
parser.add_argument('file')
parser.... |
7b9262307877815c5c0f70428b69c1e3a117b8cf7bf66ba90de3a9c36d0e94fa | Python | 953 | 25 | # coding=utf-8
# Copyright 2026 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... |
6aee9b905e7f87f03bec0fd03489af05303871288b0a64061d90498d6438d2b0 | Python | 955 | 30 | """
Set of specific utilities for spatial analysis
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 12.06.15
"""
__author__ = 'Antonio Martinez-Sanchez'
# Analysis from coordinates DEPRECATED
from .mb import CMCAnalyzer
from .plane import SetClouds
from .plane import SetClusters
fro... |
737ea90f9b12b1fedb26caf96591913051e980528d28d5a6654c97cccb1c9e40 | Python | 955 | 38 | from torch import nn
class SimpleDenseNet(nn.Module):
def __init__(
self,
input_size: int = 784,
lin1_size: int = 256,
lin2_size: int = 256,
lin3_size: int = 256,
output_size: int = 10,
):
super().__init__()
self.model = nn.Sequential(
... |
1054e11d5362d2b77a789541b1453a865b90c1be850d24b3b78179fb64ef63d0 | Python | 956 | 29 | import heapq
import math
import numpy as np
import torch
import dgl
from dgl.data.utils import load_graphs
from copy import deepcopy
from tqdm import tqdm
def get_summary_tensor(G_dataset, Labels, device, merge_classes=False):
num_G = len(G_dataset)
Labels = Labels.tolist()
node_num = G_dataset[0].ndata['... |
68142193185a8d394491b5f666585108cc884900c9e1711b012113464aeb03a2 | Python | 956 | 33 | import torch
import torch.nn as nn
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv1d(
in_channels=80, # input height
out_channels=96, # n_filters
kernel_size=480, # filte... |
0efdac0a4487b7a7467e1652848f2a52668a1ab7240c0dd3af0bc1f71dbf89dd | Python | 957 | 28 | import sys
# Make sure we are running python3.5+
if 10 * sys.version_info[0] + sys.version_info[1] < 35:
sys.exit("Sorry, only Python 3.5+ is supported.")
from setuptools import setup
def readme():
with open('README.rst') as f:
return f.read()
setup(
name = 'med2image',
ve... |
1cd77950e4f3f3a71e6bcd2e7d348e098e931c92fba35c673da8199cbf0eb835 | Python | 957 | 31 | from __future__ import annotations
import pytest
torch = pytest.importorskip("torch")
pytest.importorskip("matgl")
pytest.importorskip("phonopy")
from ase.build import bulk
from quacc.recipes.mlp.phonons import phonon_flow
def test_phonon_flow(tmp_path, monkeypatch):
monkeypatch.chdir(tmp_path)
atoms = bul... |
238d7ccb900d436bdff904275ff99b7dfce22bd0d93f0bb39384f1ea96520436 | Python | 957 | 27 | import sys
import os
data_dir = sys.argv[1]
viral_list = ['AC_000007.1' ,'NC_001526.4', 'NC_001731.1' ,'NC_009334.1', 'NC_045512.2' ,'NC_055231.1', 'NC_063383.1']
list_final = []
list_final = viral_list
# Calculate the counts in the full dataset
print('default')
abundances_dir = data_dir + use_case + '/default/filte... |
cb43fdc8e1cd767a566630115aff5985f3e060b8528c8de926edd77267132e6c | Python | 957 | 32 | import torch
import torch.nn as nn
import math
class lap_spatial_only_module(torch.nn.Module):
def __init__(self):
super(lap_spatial_only_module, self).__init__()
@staticmethod
def get_module_name():
return "lap_spatial_only"
def forward(self, x):
epsilon = 1e-8 # Small numb... |
71b5fb3666f1b8010d162907d18eb709e81a34c639f8d4a0616f2633e0a3bee9 | Python | 958 | 32 |
# import the necessary packages
from tensorflow.keras.models import load_model, save_model
import argparse
import tf2onnx
import onnx
def model2onnx():
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", type=str,
default="mask_detector.model",
... |
bf2c5f066abef29de219184815ee1b0c2f0e4f65373091406d8bff18068b9f1d | Python | 958 | 31 | # -*- coding: utf-8 -*-
import sys
import getopt
import shutil
from distutils.core import setup
import os
setup(
# data_files=[('optimizer/)],
name = "optimizer",
version = '0.0.1alpha',
packages = ['optimizer'],
package_data = {'optimizer': ['*.png']},
author = "Optimizer authors and contributo... |
1cacef853f0b690dfa5b3415b2656f65fc6d0d481875c4b5b5d4aea5eb467471 | Python | 959 | 36 | """This script is intended as a test case for mprofile"""
import time
@profile
def test1(l):
"""test1 docstring"""
a = [1] * l
time.sleep(1)
return a
@profile
def test2(l):
b = [1] * l
time.sleep(1)
return b
def test3(l):
"""test3 docstring"""
return l
if __name__ == "__main__":... |
8416090f2d66da5e9321641c1b617744e176c5579eccb56db20234bb1d225f70 | Python | 959 | 28 | import os
import json
import subprocess
from protonets.utils import format_opts, merge_dict
from protonets.utils.log import load_trace
def main(opt):
result_dir = os.path.dirname(opt['model.model_path'])
# get target training loss to exceed
trace_file = os.path.join(result_dir, 'trace.txt')
trace_val... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.