sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
3f8a463852dd0cdea076f61954ec6d32af5a97f24754eb28e02e97c6931ddfee | Python | 2,363 | 51 | from pddlstream.algorithms.downward import add_predicate, make_predicate, get_literals, fact_from_fd, conditions_hold, \
apply_action, get_derived_predicates
from pddlstream.language.constants import And, Not
from pddlstream.language.conversion import evaluation_from_fact
from pddlstream.language.function import Fu... |
d1469ec673395fca661a75c4e58fc5021a72d5a5a1558863bb5d8a703fbb319b | Python | 2,363 | 63 | """Command Line Interface for TGAN."""
import argparse
from tgan.research.experiments import run_experiments
def get_train_parser():
"""Build the ArgumentParser for CLI."""
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argum... |
0bb238e22e27fcefe5181f5c8600af30dc02f52af1ae102b9367602d6f1ba514 | Python | 2,364 | 46 | """Example A: register an alternative neural interface and an experiment that compares it.
Enable: Plugins -> Manage plugins... (then restart), or --plugin example_normalized_interface,
or LIQUIDAION_PLUGINS=example_normalized_interface. Select it in the Components panel, or run
the experiment from the Plugins panel. ... |
33fecba07af557369c219e57b92d905ee08aabc39fb2fdcc8b27f38004e35a2d | Python | 2,364 | 46 | # 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... |
3ca270acc1e70f89b4bd931d9872779be3ab2c46e829c601a583488b9429b88a | Python | 2,364 | 80 | # Node-group asset "Atom ID" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKING
from ... |
6bce410fd82afb050e3b101b49f5383fde62c3dc472348db10377dbdfabb0d22 | Python | 2,364 | 68 | from torch.utils.data import Dataset, DataLoader
import torch
import numpy as np
from rocf_scoring.data_preprocessing.loading_data import load_raw_data
from rocf_scoring.data_preprocessing.preprocess import preprocess, BIN_LOCATIONS
from rocf_scoring.helpers.helpers import check_if_in_range
class ROCFDataset(Dataset):... |
b96f1a2ccd95a9dac7e19297dff0e4fc90fe6b154c1b966fa3dbdcaa60b3ba51 | Python | 2,364 | 43 | # 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... |
730f2d1824c0b2cd95728be6c8111bb6887107c1cd7d8b7937567e87d52efc6d | Python | 2,365 | 87 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
def _test_esm1b(alphabet):
import torch
batch_converter = alphabet.get_batch_converter()
data = [
("protein1", "MKTVRQG"... |
1613fd2d8f2e65a31d63b53475b6a948002c48525655595f5f6c60880ffaed7f | Python | 2,366 | 77 | import seaborn as sbn
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import pyplot as plt
# ========================================
# Matplotlib settings
params = {
"legend.fontsize": 16,
"legend.frameon": False,
"ytick.labelsize": 15,
"xtick.labelsize": 15,
# "figure.dpi... |
5de5710fa5ec4745f7175882625d0d4f078bd789d7e9cebf208e0ac6e701f561 | Python | 2,366 | 61 | import nibabel as nib
import glob
import argparse
import matplotlib.cm as cm
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
# Argument parsing
parser = argparse.ArgumentParser(description='Plot lesion frequency maps.')
parser.add_argument('--t... |
1b989b8b483715679e515895668b3d43942281380e454d38cfb6f3a60b4a5e85 | Python | 2,367 | 47 | import unittest
from hed.schema.hed_schema_entry import HedTagEntry
class MockEntry:
def __init__(self, attributes, parent=None):
self.attributes = attributes
self.takes_value_child_entry = False
self._parent_tag = parent
_check_inherited_attribute = HedTagEntry._check_inherited_attr... |
85e933f0a6683a2ff1fcd923ffb790eeb8f32d980850fc0ffffb18b7b67bc8c6 | Python | 2,368 | 80 | #!/usr/bin/python3
"""
Convert SWC files to connected points.
SWC nodes are combined into section lines and saved as Python dictionary in
JSON format.
"""
import multiprocessing as mp
import numpy as np
import argparse
import json
import os
from treem import Morph, SWC
from treem.io import TreemEncoder
examples = ... |
b1932e322a7b63eeb7ff605c9a7bc977a01019c504c918551cbf3e89ee4b53f4 | Python | 2,368 | 79 | """Spin-echo spectrum."""
# %%
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
import console
from console.interfaces.acquisition_data import AcquisitionData
from console.spcm_control.acquisition_control import AcquisitionControl
from console.utilities import sequences
# Create... |
f599266806a5c9c96ea0326b412aea485e9a1452f5f93976539c5816f9b9aa3a | Python | 2,368 | 75 | import argparse
import numpy as np
import sys
def generate():
parser = argparse.ArgumentParser()
parser.add_argument('--vocab_file', default='vocab.txt', type=str)
parser.add_argument('--vectors_file', default='vectors.csv', type=str)
args = parser.parse_args()
with open(args.vocab_file, 'r') as f... |
bb35070253ccea1432e00575b27c0f7057ef296e4b3abe745e9f3caa6dc840aa | Python | 2,369 | 95 | HEADERS_TO_CONVERT = [
'Sex',
'Location3',
'Diagnosis',
'Chemo (Neoadjuvant)',
]
PRE_OP_PREDICTORS = [
'Sex', # need to one hot encode
'Age',
'Location3', # need to one hot encode
'Diagnosis', # OHE
'Chemo (Neoadjuvant)', #OHE
'TumorSize',
'TumorVolume (cm^3)',... |
ebe0c3ab148ed26da4da6460f82686ec0c2fccab653fb5b8824706635455dc42 | Python | 2,369 | 101 | """Helpers
----------
"""
import numpy as np
import json
import operator
import hashlib
import pickle
import parameters as ps
def merge_dictionaries(main_dict, new_dict, path=None):
"""
Merges new dictionary recursively into main dictionary.
Parameters
----------
main_dict
Main dictionar... |
4e067de8b261479ac1e8a3c1bb9d98adfdb92e31b9ce618c695fb1c77fb61885 | Python | 2,370 | 69 | import os
import torch
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.ProteinNet")
@utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",))
class ProteinNet(data.ProteinDataset):
"""
A set of prot... |
525a9735e6cb620d981998ae6b98418ba6ae0e9c5606b9b9ed49712bb47e520b | Python | 2,370 | 66 | # checking over the command line params
import os
def check_params(args):
# avoid overwriting saved weights or other output files
if args.train is True:
if os.path.exists(
args.out + "/Train/disperseNN2_" + str(args.seed) + ".weights.h5"
) and args.force is False:
prin... |
223bb7c4ebdfdace9091c1ad5dafe50ae618af82e9a309eedf66c69c0881bc07 | Python | 2,371 | 66 | import matplotlib
matplotlib.use("Agg")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import yaml, io
with open("./config/MNIST_config.yaml", 'r') as stream:
config= yaml.safe_load(stream)
config["default_path"]= str(config["default_path"])
config["cuda"]= str(config["cud... |
8eca9c0e7b0e90e5364ee0fbe4780290a87d1e5fb74c9993100ff06e3c458cdf | Python | 2,371 | 69 | # This file is part of Eigen, a lightweight C++ template library
# for linear algebra.
#
# Copyright (C) 2012 Keir Mierle <mierle@gmail.com>
#
# This Source Code Form is subject to the terms of the Mozilla
# Public License v. 2.0. If a copy of the MPL was not distributed
# with this file, You can obtain one at http://m... |
ad8626ec1606b63d87fb211995d8af921d977de1278420d9884f0f6c02cdca5c | Python | 2,371 | 77 | import os
import tempfile
import pandas as pd
from tests.mock_data import MockCvOutput, MockVisData
from cuppa.visualization.visualization import CuppaVisDataBuilder, CuppaVisPlotter, CuppaVisData
class TestCuppaVisData:
EXPECTED_COLUMNS = pd.Series((
"sample_id", "data_type",
"clf_group", "clf_... |
ba59bda71a8a840f290f64e1c70b899952b5b839c769dc38e736df471b136bae | Python | 2,371 | 90 | # ---
# jupyter:
# jupytext:
# cell_metadata_filter: -all
# custom_cell_magics: kql
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.11.2
# kernelspec:
# display_name: Python 3 (ipykernel)
# language: python
# ... |
d02614eccf28b40670cadad87782797afbc16e9e72d4c63d56b8d543c590a8a1 | Python | 2,371 | 72 | ##
# \file image_similarity_evaluator_test.py
# \brief Test ImageSimilarityEvaluator class
#
# \author Michael Ebner (michael.ebner.14@ucl.ac.uk)
# \date February 2018
import os
import unittest
import numpy as np
import re
import SimpleITK as sitk
import pysitk.python_helper as ph
import niftymic.base.stack as ... |
626a6037d63b1c6947f7b536e2fbeafd859be5d79a2b8fc36e20fc66e166cbe1 | Python | 2,372 | 71 | # Copyright 2016 Julien Danjou
# Copyright 2016 Joshua Harlow
# Copyright 2013-2014 Ray Holder
#
# 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
#... |
d97d88c1f9f1eaa4356f599db581bdcfaa79ae16d8da2c7551130b6072e18d9c | Python | 2,373 | 66 | from ruamel.yaml import YAML
import sys
import pandas as pd
from parser_dicts_variables import dict_manifest_to_version
def main():
# Read the information from the manifest
with open('manifest.bioimage.io.yaml', 'r', encoding='utf8') as f:
yaml = YAML()
yaml.preserve_quotes = True
mani... |
ea7bc49df0fe7c62066ae71965ebb025e032ae00ee897ee0c324406ac1db7ab4 | Python | 2,373 | 94 | """
Basic example of CCG learning in a CLEVR-like domain.
"""
from pyccg.chart import WeightedCCGChartParser, printCCGDerivation
from pyccg.model import Model
from pyccg.lexicon import Lexicon
from pyccg.logic import TypeSystem, Ontology, Expression
from pyccg.word_learner import WordLearner
########
# Ontology: def... |
463beb8f180cb2a002835762dde11e135faae9ebbcf6d9da68d5f9cd9d486171 | Python | 2,374 | 72 | # 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... |
812b9a7c6c1501442b642df222dd6da3164e249f6f078a4abd120856b0fae847 | Python | 2,375 | 69 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class DotProduct(nn.Module):
def __init__(self, num_classes=1000, feat_dim=2048, bias=True):
super(DotProduct, self).__init__()
# print('<DotProductClassifier> contains bias: {}'.format(bias))
self.fc = nn.Linear... |
ec01c8291f3634b26ed4f838aa3464ec7638f5fd7764120d7cd736319ad758bc | Python | 2,375 | 55 | #! /usr/bin/env python
from lab.parser import Parser
parser = Parser()
parser.add_pattern('hc_iterations', 'iPDB: iterations = (\d+)', required=False, type=int)
parser.add_pattern('hc_num_patters', 'iPDB: number of patterns = (\d+)', required=False, type=int)
parser.add_pattern('hc_size', 'iPDB: size = (\d+)', requir... |
f6905ea2a24ad2c58d61cc20dbdff28eefb6da81d10818f4fbd427000a467ce0 | Python | 2,375 | 116 | """Common slab workflows."""
from __future__ import annotations
from typing import TYPE_CHECKING
from quacc import subflow
from quacc.atoms.slabs import make_adsorbate_structures, make_slabs_from_bulk
if TYPE_CHECKING:
from typing import Any
from ase.atoms import Atoms
from quacc import Job
@subflow... |
d7d6751be6aeef8f4e4ffa7ad23469d0168d2f5bfcce4eaf6a8d10976dcaae55 | Python | 2,376 | 73 |
#import tensorflow as tf
import tensorflow.keras.layers as klayers
import tensorflow.keras.regularizers as kreg
import tensorflow.keras.initializers as kinit
'''
VGG backbone (truncated) for Convolutional Pose Machines,
Shih-En Wei, Varun Ramakrishna, Takeo Kanade, Yaser Sheikh
https://arxiv.org/abs/1602.00134
'... |
32a94b981ba65d471e43ca1b56d933db0763ea38bcdc7815c3802515aae4983f | Python | 2,377 | 54 | import os
import sys
import time
datadir = sys.argv[1] # should point to the directory where the fast5 & reds dir exist
model_name = 'sigmap'
exedir = "../run_scripts/" # the path to HierarchyCall's test/run_scripts dir
filtered_bonito_script_path = "../targetcall/run_bonito_params.py "
# File Paths
fast5_path = ... |
6ae22b52e6c6216da1c23c8b4b2460e31625adfdd6d6244ca5a3647f642b17e1 | Python | 2,377 | 67 | import os
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.HumanPPI")
@utils.copy_args(data.ProteinPairDataset.load_lmdbs, ignore=("sequence_field", "target_fields"))
class HumanPPI(data.ProteinPairDataset):
"""
Binar... |
7fa5c7eb32ae1930292af6d267b973062809efc1bf765ebef89b25c7736bc237 | Python | 2,377 | 67 | import os
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.YeastPPI")
@utils.copy_args(data.ProteinPairDataset.load_lmdbs, ignore=("sequence_field", "target_fields"))
class YeastPPI(data.ProteinPairDataset):
"""
Binar... |
0888f0900af2249a12b4286ef6f0677621a3a51398164213d614e9f2f83df47a | Python | 2,378 | 64 | # 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... |
f19292e5dee9498746d431449ac437b57d8c6943c61bfc9ff9dfebba0464e2ea | Python | 2,378 | 75 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge,... |
f5567c655d5c80cfef21a1292b6f57c641d7a4f1fff0079d1e42ca256f99b543 | Python | 2,379 | 63 | #!/usr/bin/env python
#
# Copyright 2015 Google Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of... |
e95c50d6eb2907d290d31c23c63fd119c0322101d1ede6a353c6731f9cca4342 | Python | 2,380 | 67 | import os
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.PPIAffinity")
@utils.copy_args(data.ProteinPairDataset.load_lmdbs, ignore=("sequence_field", "target_fields"))
class PPIAffinity(data.ProteinPairDataset):
r"""
... |
1816167540977900b083e9265187947a20204d7fae8dd87935f561ef37d27498 | Python | 2,382 | 104 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
def alias_method(idx, probs, nsyn):
"""
Alias method for drawing random numbers from a discrete probability
distribution. See http://www.keithschwarz.com/darts-dice-coins/
Parameters
----------
idx: np.ndarray
compartmen... |
2644163031ade5ad3047bd35fbf22d2031a47723f34a46740db72f0c06a1e2cf | Python | 2,382 | 68 | """Test remove trend."""
# Authors: The PyPREP developers
# SPDX-License-Identifier: MIT
import numpy as np
import pytest
import pyprep.removeTrend as removeTrend
def test_highpass():
"""Test for checking high pass filters."""
srate = 100
t = np.arange(0, 30, 1 / srate)
lowfreq_signal = np.sin(2 * ... |
7bab25519f673b450f343c4535a70f601d19354e8613cdc67df94a09cfce5880 | Python | 2,382 | 74 | from torch.utils.data import Dataset
import numpy as np
import pandas as pd
from Bio.Seq import Seq
from torch.utils.data import Dataset
from utils import reverse_complement, encode_seq
def read_coverage(asb_path: str, pos_fdr_upper: float=0.05, neg_fdr_lower: float = 0.5, for_eval: bool=False, eps: float=1e-10):
... |
11bf178f4de1e841d5f0b603f255b8e4ec8f863e4aeeea2b9d6cff2a98aaf6b8 | Python | 2,383 | 78 |
import datetime
import os
import random
import binutil
from dreamcoder.dreamcoder import commandlineArguments, ecIterator
from dreamcoder.grammar import Grammar
from dreamcoder.program import Primitive
from dreamcoder.task import Task
from dreamcoder.type import arrow, tint
from dreamcoder.utilities import numberOfC... |
57dd116a9d4f49c6b6e39da9950af01ff73eaefbafc9c13acb0c50e6ba0258d2 | Python | 2,383 | 57 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from lab.reports import Attribute
from common_setup import IssueConfig, IssueExperiment
REVS = ["issue648-base", "issue648-v2"]
SUITE=suites.suite_optimal_strips()
SUITE.extend(suites.suite_ipc14_opt_strips())
CONFIGS = [
# Test label re... |
947bdfeffd89f6dd06d9ab5b64fc1ac16600ac03be13c76d0e3aa6926616a9bb | Python | 2,383 | 73 | import numpy as np
from .timeIntegration import simulateBOLD
class BOLDModel:
"""
Balloon-Windkessel BOLD simulator class.
BOLD activity is downsampled to 0.5 Hz by default.
BOLD simulation results are saved in t_BOLD, BOLD instance attributes.
"""
def __init__(self, N, dt, normalize_input=... |
c62f4d33535aabbe76206f754cfa9b98aa26ca7beb3ab9fd49e87c7ac07c9c7b | Python | 2,383 | 83 | # 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... |
d61a25ad2b1d631a7512ca1adb27d11cb8e26250918b78d8672db25a6eb66155 | Python | 2,383 | 78 | from distutils.errors import DistutilsOptionError
from setuptools.command.setopt import edit_config, option_base, config_file
def shquote(arg):
"""Quote an argument for later parsing by shlex.split()"""
for c in '"', "'", "\\", "#":
if c in arg:
return repr(arg)
if arg.split() != [arg... |
3ab918f00534286f7d1253174db9709c0a7cfc47353ec22f2c396cef3b1ce2c9 | Python | 2,384 | 76 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_DI... |
8fd42376adb14dcf0a8464b0bdf594276e201dcc5de38243602bb1d4897473e1 | Python | 2,384 | 83 | # 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... |
e1e7479fa935891bdd1c18d5ae626a0b3a85836c2d1c19ec7272bc47ac72a8ae | Python | 2,384 | 70 | from typing import Callable
from torch import Tensor
from torch.nn import Module
from .quantize_to_fixed_point import quantize_to_fxp_hte, quantize_to_fxp_stochastic
from .two_complement_fixed_point_config import FixedPointConfigV2
def get_quantize_to_fixed_point(
func: Callable[[Tensor, Tensor, Tensor, Tensor]... |
2fcc3086baed21635152c5cbc160d7ccc19eacb84c62be22c5b9276a04634b46 | Python | 2,385 | 85 | import networkx as nx
import numpy as np
from copy import copy
import random
import logging
from hippo.environments.w_maze import get_wmaze
from hippo.agents.replay_agent import ReplayAgent
from hippo.environments.action_perception import action_perception_loop
from hippo.environments.sequences import (
create_tra... |
ddfb0b225d9a1d55c818210c7fa22509ea44b3e71d48afd0c6e8f36ea21ac833 | Python | 2,386 | 58 | import torch
import torch.nn.functional as F
class DCS_multi_lay_resolution(torch.nn.Module):
def __init__(
self,
input_channels: int = 128,
output_channels: int = 10,
num_blocks: int = 3,
dilations: list = [3, 6, 9],
kernel_size:int = 3,
mode = "concat"
... |
e2e62ea253c26b9ac27bd112543ea25949b2d0dc16aa384a2ae045ade4053237 | Python | 2,386 | 86 | import torch
import torch.nn as nn
import torch.nn.functional as F
def get_predictor(input_dim, p_type="elu", vi=True):
if p_type == "elu":
return EluPredictor(input_dim, vi)
elif p_type == "identity":
return IdentiyPredictor(input_dim)
else:
raise ValueError("Function type not kno... |
87a0cf0bfeb9ec1ff451ea8b23dce69c7d2940acc9c707c540660b99b8c7130e | Python | 2,387 | 95 | """
.. _recording_example:
Record from connected devices
=============================
This example shows how to record video streams from a connected Pupil Core
headset.
.. note::
This example requires the dependencies for
:ref:`streaming<streaming_dependencies>` and
:ref:`recording<recording_dependenc... |
3b9c79ffe2f8773a91d31682293ae4bf0c1b38350c32d37656baa034e58029d2 | Python | 2,388 | 68 | import matplotlib
matplotlib.use("Agg")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import yaml, io
with open("./config/DryBean_config.yaml", 'r') as stream:
config= yaml.safe_load(stream)
config["default_path"]= str(config["default_path"])
config["split"]= float(config... |
3f592c52afe09f0441f462ef80149f3d0ce4c391cf78ee1d851c5773b0ac423a | Python | 2,389 | 77 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_DI... |
54188581a48e8d004d3d704268e76fe447596b974f25b8e082f1e690b4d41706 | Python | 2,389 | 73 | import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from localization import Localization
import mayavi.mlab
import sys
def load_surface(path_to_surface):
"""
Loads a surface file from disk with nibabel functionality,
and returns a list of the points and faces in that file
... |
6a8a3a9c76a5944e9502e808de4116e73c5c75328d2b22d5e1f5e2b3753515b4 | Python | 2,391 | 66 | """
Optuna example that demonstrates a pruner for LightGBM.
In this example, we optimize the validation accuracy of cancer detection using LightGBM.
We optimize both the choice of booster model and their hyperparameters. Throughout
training of models, a pruner observes intermediate results and stop unpromising trials.... |
acfc850a5e76758f62f23e5b7c36d49f62eba007452cb1b499628339759efc07 | Python | 2,391 | 68 | extensions = dict(
required_params=['x', 'event_column', 'training_frame'],
validate_required_params="""
# If x is missing, then assume user wants to use all columns as features.
if (missing(x)) {
if (is.numeric(event_column)) {
x <- setdiff(col(training_frame), event_column)
} else {
x <- s... |
3719b6773ab2f711b387594b6661c2cbadd544d59552b765130ea5cf3a12e906 | Python | 2,392 | 68 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
384ad84aa2d6a86583ddc4041563820e8de94083fcc88561124cc5528f12ef4b | Python | 2,392 | 62 | import numpy as np
import torch
class BerPossionLoss():
def __init__(self, num_nodes, num_edges):
self.num_nodes = num_nodes
self.num_edges = num_edges
self.num_possible_edges = num_nodes**2 - num_nodes
self.num_nonedges = self.num_possible_edges - self.num_edges
edge_prob... |
d33f4360dc5381c72be5936e75183226519d70a266a06a24a183a8370d835c26 | Python | 2,392 | 98 | # Node-group asset "Integer Distance" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECK... |
dc82aa7cfb705ec55b3a0a59eb58d2850985bc18f66b58ba86418ae61c5038d3 | Python | 2,392 | 70 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge,... |
eca03564716189c29f08266d7feeb69517220f96e9752e078882dad85e13b1d4 | Python | 2,392 | 54 | # MEGIT model inference batch processing script
# This script will process ALL subfolders
import sys
import os
sys.path.append(os.getcwd())
import argparse
from keras.models import load_model
from megit.model.pred import default_model, inference
import warnings
# CLI inputs parser ----------------------... |
425913a94fb601cb801781dffb81e4d58379d380f363c1c41de04d7f05cf566f | Python | 2,393 | 74 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import itertools
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from downward.reports.compare import ComparativeReport
import common_setup
from common_setup import IssueConfig, IssueExperiment, RelativeScatterPlotReport
DIR = os.path.dirname... |
2c215e4a25fe7cd42e03ce214a1a63fed595f199c64ff2807941545d8d8e9098 | Python | 2,394 | 77 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment, get_algo_nick, get_repo_base
from relativescatter import RelativeScatterPl... |
88daa8e949790b14b67e19b7ad56ef01e6756db962ac09c0066509356a2e5669 | Python | 2,394 | 61 | from ase.io import read
import numpy as np
import torch
from torch.utils.data import DataLoader
from ..data import ASEData, ASEDBData, atoms_collate_fn
from tqdm import tqdm
def eval(model, data_loader, properties, device):
output = {prop: [] for prop in properties}
target = {prop: [] for prop in properties}
... |
98e72453e56cceacfecf9eb7a69a89adf5b6cc6114cee2aba34a3083116cc004 | Python | 2,394 | 74 | """CLI for extracting and preprocessing a subset of data in sleeplab format."""
import argparse
import json
import logging
from pathlib import Path
from sleeplab_format.extractor import config, preprocess
from sleeplab_format import reader, writer
logger = logging.getLogger(__name__)
def extract(src_dir: Path, dst... |
a33c1fc15ae844661bc583dadedcb3a3e1637cfb83da87c3054f79434a768fc4 | Python | 2,394 | 68 | """Hub header badge: background loading + update-available indicator."""
import pytest
@pytest.fixture(scope="module")
def _qapp():
try:
from PySide6.QtWidgets import QApplication
except Exception as exc: # pragma: no cover
pytest.skip(f"PySide6 unavailable: {exc}")
return QAp... |
23de562c131719cd5a5ecabc12c5d427413dce9d2226ba76cfec5953a74a15fe | Python | 2,395 | 95 | from collections.abc import Callable
from typing import Any, cast
import torch
from torch import nn
import elasticai.creator.ir as ir
from elasticai.creator import function_dispatch as FD
dgraph_handlers: list[Any] = []
node_handlers: dict[str, Any] = {}
def _register_dgraph(fn: Any) -> Any:
dgraph_handlers.ap... |
6a4c5ff56ef3bbbb04984dc371bea42079476581bf47597df9fac5c6eeb8ba9a | Python | 2,395 | 73 | import os
import torch
import pytest
import numpy as np
@pytest.fixture(scope="session", autouse=True)
def set_seed():
np.random.seed(42) # noqa: NPY002
import logging
from ase.io import read
from quacc import get_settings
from pathlib import Path
from ts_workflow_examples.geodesic_ts_without_hessian.using_ne... |
efe7609818ddbae971f7a477977eb14527bf2d846b0aa6ff165cf921a9a67c66 | Python | 2,395 | 92 | # 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... |
d4263cef607ea5d07f230fa6eebd0d4547bbf07d66f7a06b978fb5a895df49be | Python | 2,396 | 80 | # Import libraries
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import torch.nn.functional as F
import matplotlib.pyplot as plt
import os
import pickle
import numpy as np
from sklearn import metrics
from sklearn.model_... |
07ae76bd4863a099e05e5ebc88c342e993082e0dd0173d22631693c7e86c8c10 | Python | 2,397 | 68 | # Copyright (c) 2022, Ko Sugawara
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and t... |
4004b4c634ddb9099d0cb4c64d856484aa51f925a3879eee5bc0ccea5d7fd7e3 | Python | 2,397 | 52 | """
toposampling - Topology-assisted sampling and analysis of activity data
Copyright (C) 2020 Blue Brain Project / EPFL & University of Aberdeen
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation... |
55e6081b29e41854faccdb23df07a7d8b12e69a1ba4e8d56501dee3363cdf845 | Python | 2,397 | 81 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
c1f9a8784c58ff8439729347581c3f17df3d90cd9a84a99ac167a951b9cde6bb | Python | 2,397 | 80 | # 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... |
1f4fdfe2e4305c932984a89261a71d3ea6384423ba88932d1ec26f0dee243924 | Python | 2,398 | 77 | # 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... |
88a410528488297a3bced36ab218f10dc5bd18512ef3a01418a830605548e5c4 | Python | 2,398 | 66 | # Copyright (C) 2017 Sarah Parisot <s.parisot@imperial.ac.uk>, , Sofia Ira Ktena <ira.ktena@imperial.ac.uk>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
... |
162e9050cdeafcbc6efcf4ee1a08254c75f95589df9370c92101e1290cddcda9 | Python | 2,400 | 74 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_DI... |
29e3a78d7258abcffe1ad740eee9b7dff10d6263de172595e93ad8934047fe71 | Python | 2,400 | 59 | import pandas as pd
import numpy as np
import os
celltype_list = ['ASC', 'EX', 'IN', 'ODC', 'OPC', 'MG']
Trans_df = pd.read_csv('../chromVAR/Motif_Trans.txt', sep='\t', header=None)
Trans_dict = dict(zip(Trans_df[0], Trans_df[1]))
# Peak Motif colocation Information (based on hg38)
Motif_Peak_df = pd.read_csv('Motif... |
69c1c693955a42210a53e7c3832df86add796ec9d3405d6bead9ff3fa33ace61 | Python | 2,400 | 80 | import argparse
import os
import sys
from enum import Enum
import coglet
class Mode(Enum):
PREDICT = "predict"
TRAIN = "train"
def __str__(self) -> str:
return str(self.value)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Cog HTTP server")
parser.add_argument... |
a057bd7b695f8667ef4daf0ee06c6472727fde67e748de666f4b097219c95a7c | Python | 2,401 | 57 | from typing import Dict, Union, List
from ..base_transform import EEGTransform
class BaselineRemoval(EEGTransform):
r'''
A transform method to subtract the baseline signal (the signal recorded before the emotional stimulus),
the nosie signal is removed from the emotional signal unrelated to the emotiona... |
45ac13ca543cc1ca3eddca41dafad8e62035386d2835e732028bbe5de5363230 | Python | 2,402 | 85 | """Class to run generic commands in a subprocess."""
from __future__ import annotations
import os
from contextlib import ExitStack
from pathlib import Path
from shlex import split
from subprocess import PIPE, CompletedProcess, run
from typing import TYPE_CHECKING, ClassVar, Final
from quacc.runners._base import Base... |
92e733be4ba79090fdc71c0e83948ce4162bfc751f88878cf507df94a5e026e2 | Python | 2,402 | 63 | import sys
import os
# Add the parent directory to the sys.path
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(parent_dir)
# Now you can import the function from the script
from PyUtilities.setupFunctions import read_config_file
import pandas as pd
import os
# open expor... |
afe1171d50a4af5b22f4592a9af512c8761abde80c1d5e882dceadb33e2502e5 | Python | 2,402 | 61 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from common_setup import IssueConfig, IssueExperiment, is_test_run
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["issue535-v1", "issue535-v2"]
CONFIGS = [
IssueConfig(
... |
e91d9f50c1c7d4dad68ab6a2505433b7847f39f36e2b5bb670c92f7da4d9268b | Python | 2,402 | 61 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from common_setup import IssueConfig, IssueExperiment, is_test_run
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["issue535-v2", "issue535-v3"]
CONFIGS = [
IssueConfig(
... |
fcf29bdcee083edb2b80751aa3688f18b056a17849b95a9fdc613fc10cc18405 | Python | 2,402 | 61 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from common_setup import IssueConfig, IssueExperiment, is_test_run
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["issue535-v3", "issue535-v4"]
CONFIGS = [
IssueConfig(
... |
3352fca9457bedc38bbfcdda812061c94034f36f7e966a29fd30771a2acbff55 | Python | 2,403 | 86 | #!/usr/bin/env python
# Copyright (c) 2019 Intel Corporation
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
This module provides the key configuration parameters for an XML-based scenario
"""
import carla
class ActorConfigurationData(objec... |
88304f6235a39eb83dad805d30175e4d97ef8a695eabf032303d6536a1db0fae | Python | 2,403 | 101 |
import random
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
def set_global_seed(seed: int) -> None:
"""
Sets random seed into PyTorch, TensorFlow, Numpy and Random.
Args:
seed: random seed
"""
torch.manual_seed(seed)
torch.cud... |
3ac5c2860e3706853229ecc3b55d4b4c6bd7504431a3719a972bfee0c8124051 | Python | 2,404 | 75 | import os
import sys
sys.path.append(os.path.abspath('../'))
from multiprocessing import Lock
from model.KuramotoClassFor import Kuramoto
import concurrent.futures
import itertools
import gc
import numpy as np
import scipy.io as sio
import csv
import matplotlib.pyplot as plt
import time
import model.parserConfig as p... |
5b1f18b24283a7b2c8b0374faa6102ce427f1c8128b986d01ee539446d361c4a | Python | 2,404 | 61 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from common_setup import IssueConfig, IssueExperiment, is_test_run
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["issue535-base", "issue535-v1"]
CONFIGS = [
IssueConfig(
... |
66a2f0bd2cf14152b1da9e29d962a8ada5d8d1aae039856307237556c3adaa7e | Python | 2,404 | 68 | """Qt-free completeness checks for the registration help content."""
import pytest
# The option keys each GUI control offers (must match the combo `data` keys in
# registration_tab.py / batch_2d_tab.py). If a combo gains/loses an option,
# update HELP and this expected set together.
EXPECTED_KEYS = {
"met... |
96665b1c13d4489180bbbb1ce68f1611a05371955bca3f85313aec683a0f593d | Python | 2,404 | 76 | import pandas as pd
import pytest
from cuppa.sample_data.sample_metadata import SampleMetadata, TrainingSampleSelector
metadata_df = pd.DataFrame(
[
[0, "Breast", "Breast: TNBC", 0],
[1, "Breast", "Breast: TNBC", 76],
[2, "Breast", "Breast: TNBC", 151],
[3, "Breast", "Breast: TNBC... |
070d663c684bf176ca18bcab775fcb22a8bd57b6f1f056d3370f9f763d6ac189 | Python | 2,405 | 94 | from __future__ import annotations
from importlib.util import find_spec
from pathlib import Path
from typing import TYPE_CHECKING
from monty.dev import requires
from quacc.runners.ase import BaseRunner
has_phonopy = bool(find_spec("phonopy"))
has_seekpath = bool(find_spec("seekpath"))
if TYPE_CHECKING:
from c... |
1d9a22e50ffe8941f1d998091397bcdf4f86f631aeae52b0f3f19f7a890944ee | Python | 2,406 | 41 | import pandas as pd
import seaborn as sns
import numpy as np
import sys
sys.path.insert(0, r"C:\Users\ICN_guest\Desktop\ui")
import gaussianize
def gaussianize_data(data, column_name):
out = gaussianize.Gaussianize(strategy="brute", max_iter=200, verbose=True)
out.fit(data[column_name])
data[f"{column_na... |
293b59ac075e5b2b16d84156ebc5c314ae1f6de96d15021bb6c8ea16562903c4 | Python | 2,406 | 69 | import torch
import pytorch_lightning as pl
from typing import Any, Callable, Tuple
class TrainingWrapper(pl.LightningModule):
def __init__(self, net: torch.nn.Module,
loss_fun: Callable, metric: Callable,
lr: float=0.01, weight_decay: float=1e-4,
momentum: float=0.9, gamma: flo... |
e15c0d0d87b0b925ac2c951399b28e81a354c24b29bbe018c6ffba127800e13b | Python | 2,406 | 69 | 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
""",
module="""
#' Extracts the best R2 values for all predictor subset size.
#'
#' @param model is a H2OModel wit... |
3076d3bcc5c58f1f4abb8c0670f7d42e6bac292e7459e9c541e9a3e3608002cd | Python | 2,407 | 63 | from ..neuralnodes import utils, func
import numpy as np
from .params import Params
def run_model(stim: np.ndarray, p: Params, fs: float = 1_000, verbose_output: bool = False, return_dict: bool = False):
# --- STIMULUS -----------------------------------------------------------
try:
p = Params.from_li... |
ccab2e895e5e991a6dd4d33dd36338131427ade6b9dc00810031ea54479bd2d5 | Python | 2,407 | 78 | from __future__ import annotations
import re
from typing import Any
from wikibaseintegrator.datatypes.basedatatype import BaseDataType
from wikibaseintegrator.wbi_enums import WikibaseSnakType
class URL(BaseDataType):
"""
Implements the Wikibase data type for URL strings
"""
DTYPE = 'ur... |
519c89c650e12684b04796c25f6dc8f8d7810b6ad9b83fd14fb20befbb115c21 | Python | 2,408 | 84 |
import tensorflow as tf
import math
#KL(N_0|N_1) = tr(\sigma_1^{-1} \sigma_0) +
# (\mu_1 - \mu_0)\sigma_1^{-1}(\mu_1 - \mu_0) - k +
# \log( \frac{\det \sigma_1}{\det \sigma_0} )
def all_pairs_gaussian_kl(mu, sigma, add_third_term=False):
sigma_sq = tf.square(sigma) + 1e-8
#mu is [batchsize x dim_z]
#... |
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