sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7fe0982ff097eb6d3bd65e2ae541c5d4a1bde16b0c4ba0a7014136c2290a4273 | Python | 5,991 | 158 | from typing import Dict, List, Mapping, Optional, Sequence, Union, Any
from typing_extensions import Literal, Final
from dataclasses import dataclass
import numpy as np
from numpy.typing import NDArray
# Type tag constants
TYPE_KEY: Final[str] = "#type"
SPARSE_VECTOR_TYPE_VALUE: Final[str] = "sparse_vector"
@datacla... |
d35311fe4aff85a1194f13da5e812e13cc1556c30f050fb9526002c42a0d99a2 | Python | 5,994 | 182 |
import os
import random
import numpy as np
from tqdm import tqdm
import torch
from torchvision import transforms
from skimage.metrics import (
mean_squared_error,
structural_similarity,
peak_signal_noise_ratio
)
from pytorch_radon import Radon, IRadon
from pytorch_radon.filters import LearnableFilter
f... |
a1b095b48d05ff9a79a007188e608ef6cc9ca4302a454ec6303e51234054c2be | Python | 5,997 | 168 | # -*- coding: utf-8 -*-
# /usr/bin/env python3
#
# This software and supporting documentation are distributed by
# Institut Federatif de Recherche 49
# CEA/NeuroSpin, Batiment 145,
# 91191 Gif-sur-Yvette cedex
# France
#
# This software is governed by the CeCILL license version 2 under
# French law... |
4d0803f282ba74b18eda881a6bc809c8daa5c44399a337ca341b4178312bede2 | Python | 5,998 | 203 | """
Serial-MQTT Bridge.
author: Tal Eisenberg <eisental@gmail.com>
Run `python main.py --help` for more information.
"""
import importlib
import json
import logging
import sys
import subprocess
import argparse
import platform
import traceback
from serial.tools import list_ports
from serial_mqtt import SerialMQTTBridg... |
3e980725b05b4e51759724bdddf4ab6c4c1a710819b0e1eebe31fc387729eae5 | Python | 6,001 | 136 | import os
import pyedflib
import numpy as np
from sleeprnn.data import utils
PATH_REC = "register"
PATH_MARKS = os.path.join("label", "spindle")
PATH_STATES = os.path.join("label", "state")
KEY_FILE_EEG = "file_eeg"
KEY_FILE_STATES = "file_states"
KEY_FILE_MARKS = "file_marks"
IDS_INVALID = [4, 8, 15, 16]
IDS_TEST = ... |
7963a568a1d698acda2f24ef104351e1fcb4c681c4c21145d407930213242567 | Python | 6,003 | 144 | import time
from watchdog.observers import Observer
from watchdog.events import PatternMatchingEventHandler
import nibabel as nib
from monai.inferers import SlidingWindowInferer
from unest_base_patch_4 import UNesT
import shutil
import nibabel.processing as nip
import numpy as np
import torch
import monai.transforms as... |
32361446e504c66644e3d0f9dc44b45bc5ed6ee7357cc75fad48be39abfb6af3 | Python | 6,007 | 116 | import tensorflow as tf
def process_metadata(name_to_metadata):
for variable_name in name_to_metadata.keys():
numpy_shape = name_to_metadata[variable_name]["numpy_shape"]
variable_type = name_to_metadata[variable_name]["variable_type"]
if variable_type == "x":
tfrecords_type =... |
849d7e707633c902644a9bad4b2543dc74c019bf3b07ff6bec964a498224d014 | Python | 6,007 | 195 | """
Base class for sound delivery.
@author: Mathieu Scheltienne
"""
from abc import ABC, abstractmethod
import numpy as np
import sounddevice as sd
from scipy.io import wavfile
from ... import logger
class _Sound(ABC):
"""
Base audio stimulus class.
Parameters
----------
volume : list | int | ... |
3a51921c07405efec7894035e38400c308bda84f1a3bab7d5d73f271cd2474a3 | Python | 6,010 | 152 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from joblib import delayed, Parallel
import numpy as np
from sleeprnn.common import checks, constants, pkeys
from sleeprnn.data.stamp_correction import filter_duration_stamps
from sleeprnn.data.stamp_correcti... |
e86c2882ac2452dab85e1b587e8d9b9dc5f960a872e54c38448765770503134a | Python | 6,011 | 117 | import numpy as np
import high_lvl_build_network_funcs as bnf
if __name__ == "__main__":
NET_TYPE_VAR_INP = input('WHICH_DATASET_TO_GENERATE_NETWORK_FOR? [TYPE M FOR MNIST/ N FOR NMNIST]: ')
NET_TYPE_VAR = str(NET_TYPE_VAR_INP)
NET_NUM_VAR_INP = input('NETWORK_NUMBER_TO_CREATE? [int]: ')
... |
7e41cf46933abd6028f03cf6d8b41a03d891bf3dbd137256fd79898a6782fed9 | Python | 6,013 | 167 | "Identification of individual fragments given the predictions generated by the idCNN"
import json
import logging
from collections.abc import Sequence
from pathlib import Path
import numpy as np
import torch
from idtrackerai import Fragment, ListOfFragments
from idtrackerai.utils import track
from ..network import I... |
1c9a8fdac2feff9a411b524e1948a70aedff8e8eeeba6c56e9b7afeb8bf61d15 | Python | 6,014 | 208 | import copy
import inspect
import itertools
from geomstats.vectorization import repeat_point
KNOWN_POINT_NAMES = (
"point",
"base_point",
"point_a",
"point_b",
"fiber_point",
"mat",
)
KNOWN_VECTOR_NAMES = (
"vec",
"vector",
"tangent_vec",
"tangent_vec_a",
"tangent_vec_b",
... |
8794c23c6612e4b0831d4bbdff5de0603ec3fa7540e9206986cfb07ea82eb9ff | Python | 6,014 | 176 | import pytest
import geomstats.backend as gs
from geomstats.numerics.finite_differences import forward_difference
from geomstats.test.random import get_random_times
from geomstats.test.vectorization import generate_vectorization_data
from geomstats.test_cases.geometry.fiber_bundle import FiberBundleTestCase
from geoms... |
49a194e2d2ea7e7bd03ed616974493472bcaf129afb6c20df1e81030bb948c4b | Python | 6,018 | 159 | from typing import List, Tuple
import torch
from torch import nn as tnn
from reinvent.models import meta_data
from reinvent.models.linkinvent.model_vocabulary.paired_model_vocabulary import (
PairedModelVocabulary,
)
from reinvent.models.linkinvent.networks import EncoderDecoder
from reinvent.models.model_mode_en... |
ac1bf5224d4806110e7e610f1a44a6869c2768fcb11f516c211c4b5ccf0afa49 | Python | 6,019 | 192 | import sys
import multiprocessing as mp
from neurodecode import logger
from neurodecode.utils.timer import Timer
from neurodecode.triggers import Trigger, TriggerDef
from neurodecode.gui.streams import redirect_stdout_to_queue
import neurodecode.utils.io as io
def check_config(cfg):
'''
Check the variables c... |
d617bceedee9fd14ffe826f7bb4ac9ff740c2a2e57885bdabb6311daff49112b | Python | 6,020 | 154 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... |
90f4570e77003ceb29ef145f2192ad039eb423e828ac873684efbff64ddb3f9d | Python | 6,021 | 158 | import tensorflow as tf
from tensorflow import keras as k
tf.config.list_physical_devices()
import os
os.environ['CONDA_DLL_SEARCH_MODIFICATION_ENABLE'] = '1' # needed due to dll bug in my environment - might be unnecessary for other ppl
from survival_model import SurvivalModel
import numpy as np
import pandas as pd
f... |
e040032826b37c76efa294159814c144ca66144560f495e6d1916d6ca048e819 | Python | 6,021 | 177 | from pathlib import Path
import ase.io
import numpy as np
import pytest
from tests.helpers import REPO_ROOT
import yaml
from ase.atoms import Atoms
from tests.helpers import preprocess_data, run_mace_train
pytest_mace_dir = REPO_ROOT
# NOTE: this file keeps its own sample_configs fixture (only 10 rattled
# configs... |
e20b84a671463b2b0b13c591c382464f608013acde05ac24803448eb8529daf4 | Python | 6,021 | 187 | import asyncio
import os
from functools import wraps
from enum import Enum
from typing import Any, Callable, Dict, Optional, Sequence, Union, TypeVar
from opentelemetry import trace
from opentelemetry.sdk.resources import SERVICE_NAME, Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.... |
4e93d0dfe0f00923da63ec112afed2b9b18b63f1c479962b5b25224c08b91743 | Python | 6,022 | 141 | from __future__ import absolute_import, division, print_function
import argparse
import os
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from tqdm import tqdm
from data.data_loader import TrainDataset,TestDataset
from models.models import Brainmodels
... |
b37e0c41cc3123a607fc003e78a077c625c0c0fa9e3c17bd85484dced8617a07 | Python | 6,022 | 143 | # Copyright 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Federated Bootstrap Aggregation for XGBoost module."""
import json
import numpy as np
from openfl.interface.aggregation_functions.core import AggregationFunction
def get_global_model(iterator, target_round):
"""
Retrieves the glo... |
374be4802c7c2da6675ca7a998e5095d308fe354e6a92a6f982808b38c507b75 | Python | 6,025 | 164 | import os
import shutil
import tarfile
from typing import Optional, Sequence, Union, Dict
import config
import numpy as np
import torch
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
from torchvision.transforms.transforms import Compose, Normalize
class ImgAndTargetPlotter:
def... |
f0195ac6bfdf2e750805c88aa1572f1b7bca53daa04ed5e2258f13c6a7e0be07 | Python | 6,026 | 186 | from __future__ import annotations
import argparse
import re
import sys
from argparse import ArgumentParser, Namespace
from pathlib import Path
import more_itertools as itx
import pytest
from hypothesis import given
from hypothesis import strategies as st
from pathvalidate import Platform, is_valid_filename
from pyte... |
3ac669c0f6e18fa744546ac48f9819394f0043f7266e6a4d069b3dd4f03e1528 | Python | 6,033 | 212 | import numpy as np
from sleeprnn.helpers import misc
from sleeprnn.common import constants
def print_available_ckpt(optimal_thr_for_ckpt_dict, filter_dates, file=None):
if filter_dates[0] is None:
filter_dates[0] = -1
if filter_dates[1] is None:
filter_dates[1] = 1e12
print("Available ckp... |
f98370d396b4d31da03a7718a5a753437e9f5e93c9696eb47195a56a17a59212 | Python | 6,033 | 159 | """
Do the tail approximations do what they are for?
The point of both fits is to resolve a p-value that counting cannot reach, so the
question is never "is the fit exact" -- extrapolating a tail from a hundred points
cannot be -- but "does it beat what it replaces". Both checks below therefore
build a large reference... |
49dafe42705d2ec3c2235c55f44cd07ef9aed22bff8734b32bbdbef6aa31ddfa | Python | 6,034 | 146 | """
The GOOD-HIV dataset adapted from `MoleculeNet
<https://pubs.rsc.org/en/content/articlehtml/2018/sc/c7sc02664a>`_.
"""
import os
import os.path as osp
import gdown
import torch
from munch import Munch
from torch_geometric.data import InMemoryDataset, extract_zip
class GOODHIV(InMemoryDataset):
r"""
The G... |
5e60b787d56c36c1826d5d925947ef5af1f666d8385b94d60273004c11152599 | Python | 6,034 | 130 | """The interactive dashboard: that it is built from the verified data and that it is one file."""
from __future__ import annotations
import sys
from pathlib import Path
import polars as pl
import pytest
pytest.importorskip("plotly", reason="needs the `summary` extra")
sys.path.insert(0, "summary")
from vdjdb.summar... |
237f7d4744f4589ea1783de389b2b7a1750bbae76fb22255a24781fc9b78ef9a | Python | 6,037 | 163 | from typing import Any, Dict, Mapping, Optional, TypeVar
from urllib.parse import quote, urlparse, urlunparse
import logging
import orjson as json
import httpx
import chromadb.errors as errors
from chromadb.config import Component, Settings, System
logger = logging.getLogger(__name__)
# inherits from Component so t... |
fab3b6346711515f9a9e9739895e763be417daad3740f99557823e8f8b1f8e70 | Python | 6,037 | 173 | """Generates moda_preprocessed_segments.npz (~160 MB) and metadata.csv (~50 kB).
The file is a pre-processed (cropped and filtered) MODA dataset that includes only the
annotated portions.
Before running this script, run generate_moda_npz_files.py because it uses the files
generated by that script.
The generated file ... |
1af3de7404f47a8d940fc44f38f012de8041df437d1bea0f55776f128bebd36f | Python | 6,046 | 187 | import json
import pandas as pd
# Path to the Bandit JSON report
json_report_path = 'bandit_results.json'
html_report_path = 'results.html'
def read_json_report(json_report_path):
"""
Read the JSON report from the specified path.
Args:
json_report_path (str): Path to the JSON report file.
Re... |
093bb07156af68a4488556e6e4daa7e9f115e7eb51a43172f190be37f4da893b | Python | 6,049 | 199 | import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import StepLR,MultiStepLR
import math
import torch.nn.functional as F
from torch.utils import data
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset
from preproces... |
ef57148fa58a9c0d7fd70f1f93231084f14cd45085cd0562e9a12662334dfe7d | Python | 6,050 | 196 | import pandas as pd
from scipy.io import loadmat
import numpy as np
import matplotlib.pyplot as plt
import xml.etree.ElementTree as ET
import re
from pyimzml.ImzMLWriter import ImzMLWriter
from pyImagingMSpec.inMemoryIMS import inMemoryIMS
import os
def loadBrukerFIDs(file_path, fid_length, read_length, fi... |
02f53b3cb9fd2adaabede54616aed7fb6c7d96e062113f5b30ab0606d47cd3bb | Python | 6,052 | 213 | import collections
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import predictive_coding as pc
import analysis_utils as au
def plot(df, plot="Reward"):
df = au.nature_pre(df)
groups = ['Env', 'Rule', 'pc_learning_rate']
df = au.add_metric_per_gr... |
cf83600759c297648677807cc240b2e80c1d62df1b3ecf767c32977ef521c52e | Python | 6,053 | 157 | """
Created on Sat September 16 21:15:06 2023
Description: Implementation of different shuffling methods of neural datasets for
Paper url: https://www.biorxiv.org/content/10.1101/2023.09.11.557230v2
@author: Ekaterina Kuzmina, ekaterina.kuzmina@skoltech.ru
@author: Dmitrii Kriukov, dmitrii.kriukov@skoltech.ru
"""
im... |
14ee92273c34263394117d6a2dba38b53a4006674d4174dae7966b9df1a2aa54 | Python | 6,054 | 110 | # Copyright 2020 Forschungszentrum Jülich GmbH and Aix-Marseille Université
# "Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements; and to You under the Apache License, Version 2.0. "
import numpy as np
from neo.core import SpikeTrain, AnalogSignal
from quantities import m... |
66bd454261c372afc4fc954dca47888fa34515d4d84ac79055805ad9d8e0b1f4 | Python | 6,056 | 126 | import os
import pandas as pd
import warnings
import torch
from torch.utils.data import DataLoader,TensorDataset
from modelClasses import *
from utilityFunctions import *
#torch.backends.cudnn.deterministic = True
warnings.filterwarnings("ignore")
# torch.backends.cudnn.benchmark = True
# os.environ['CUDA_LAUNCH_BLOC... |
2d7c6ce1432551e5fc804ac7b3baba50cfe487b49fa19e5d2a41e3a57e491969 | Python | 6,060 | 146 | """
The GOOD-PCBA dataset adapted from `MoleculeNet
<https://pubs.rsc.org/en/content/articlehtml/2018/sc/c7sc02664a>`_.
"""
import os
import os.path as osp
import gdown
import torch
from munch import Munch
from torch_geometric.data import InMemoryDataset, extract_zip
class GOODPCBA(InMemoryDataset):
r"""
The... |
32705c70073ad2ac015f016d6f29effa8af3daf25da1faf340cb44d3245f3751 | Python | 6,060 | 179 | import pytest
import torch
from e3nn import o3
from nequip.utils import torch_default_dtype, dtype_from_name
from nequip.utils.versions.torch_versions import _TORCH_GE_2_4
from nequip.nn._tp_scatter_base import TensorProductScatter
_CUEQ_INSTALLED = False
_OEQ_INSTALLED = False
# test data dimensions
NUM_NODES = 8
... |
43f626043aafa7064a984ba3da0ecb54ebe2638ea30323ea57b2fbc4d9c86e27 | Python | 6,070 | 187 | from chromadb.api.types import EmbeddingFunction, Space
from chromadb.utils.embedding_functions.schemas import validate_config_schema
from chromadb.api.types import (
Document,
Documents,
Embedding,
Embeddings,
Image,
Images,
is_document,
is_image,
Embeddable,
)
from typing import Li... |
6867127c7234800b946783a61f5519c4050ae69d961a2a1b6a62b879df1a2c6c | Python | 6,075 | 177 | import numpy as np
import random
import rdkit.Chem as Chem
from rdkit.Chem.Scaffolds import MurckoScaffold
import csv
import pickle
def read_data(smilefile):
fp = open(smilefile, 'r')
smile_list = []
for smile in fp:
smile = smile.strip()
smile_list.append(smile)
fp.close()
return ... |
cd12c32d6296c2ead7ee814f70604905235936a65af1869bcfca0ed85ae3d3b6 | Python | 6,075 | 160 | #!/usr/bin/env python3
# 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.
import argparse
import collections
import os
import re
import torch
from fairseq.file_io import PathManager
def aver... |
051392df32552a0407e44eab51221b3cc06f710f1262547a3ac82cf6ba736c77 | Python | 6,077 | 158 | import os
import torch
import numpy as np
from tqdm import tqdm
from torch_geometric.data import DataLoader
class RegTrainer():
def __init__(self, train_dataset, val_dataset, configs, device):
self.configs = configs
self.train_dataset = train_dataset
self.val_dataset = val_dataset
... |
0c7e2f3b379dee09746790d5d75a5950671ab9cf3844f1257f5145167ebda38c | Python | 6,077 | 164 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 12 15:25:17 2018
@author: ngritti
"""
# from csbdeep.models import CARE
# from deeprest.rawDataClass import rawData
# from deeprest.modelClass import modelRest
# from deeprest.timeLapseClass import sampleTimeLapse
import time, os, glob
import shutil... |
41203f4b8c16fc3a6cda2ce091a189e703ce0fa75d416058c88319b66ab95d3b | Python | 6,080 | 190 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 25 12:13:31 2019
@author: Qian Zhu
"""
import scipy
import scipy.stats
import sys
import re
import os
import numpy as np
import math
from operator import itemgetter
from scipy.spatial.distance import squareform, pdist
from scipy.stats import percen... |
d0b9cb44e0af4862bb252b272934cb9b4569913dc3f9824bcf86efea49dd3e0c | Python | 6,080 | 156 | '''
Example showing hardware-timed continuous analog output with two channels without using a callback function
pynidaqmx.ao.hardwareContinuousVoltageNoCallback_twoChannels
Purpose
Demonstrates how to do hardware-timed continuous analog output without a callback function.
To achieve this we use sample regenerat... |
31f0939d1aeccfd6442e606f77c302c9a77bf9f0bf95530e660219aa9493d553 | Python | 6,085 | 195 | """Demonstrates active learning molecular dynamics with constant temperature."""
import argparse
import os
import time
import ase.io
import numpy as np
from ase import units
from ase.md.langevin import Langevin
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from mace.calculators.mace import MAC... |
fca60de70be163ebc7b9a5f0b73bb48f62d3af26897dbbc754d0b439b6272a70 | Python | 6,085 | 190 | import random
import pytest
import geomstats.backend as gs
from geomstats.geometry.hypersphere import Hypersphere, HypersphereMetric
from geomstats.test.parametrizers import DataBasedParametrizer
from geomstats.test.random import HypersphereIntrinsicRandomDataGenerator
from geomstats.test_cases.geometry.hypersphere i... |
cd589af1db418eefdc1776054e968fefdc286f556a8b720cf31cda9a54ba78a8 | Python | 6,086 | 164 | """
Based on the U-Net implementation from https://github.com/jvanvugt/pytorch-unet,
released under the MIT License, copied below:
MIT License
Copyright (c) 2018 Joris
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to... |
00dc1e9ad74e34d72c7d470238b72099e53069e8770bf420fc3b8322366e6e9a | Python | 6,087 | 162 | import pytest
import geomstats.backend as gs
from geomstats.geometry.symmetric_matrices import SymmetricMatrices
from geomstats.test_cases.information_geometry.dirichlet import (
DirichletDistributionsTestCase,
DirichletMetricTestCase,
)
# TODO: add againts numpy test case for pdf?
def sectional_curvature(... |
575af19c2c4b495af3ffc5602b6243b31d07b2e220c2191caadad6a025d2971d | Python | 6,089 | 198 | import torch as T
import torch.nn as nn
from Networks.network import Network
class UNet_concat_student(Network):
def __init__(self, base, expansion):
super(UNet_concat_student, self).__init__()
self._build(base, expansion)
def forward(self, image, segbox = None, eval_mode = True):
lay... |
6a3948638d2a7e664a0009a29434ddf4125c1207a6463ef208d346d15232f57a | Python | 6,090 | 164 | """Example experiment for a linear baseline method."""
import glob
import json
import logging
import os
# set gpu private
os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
import tensorflow as tf
tf.config.run_functions_eagerly(True)
import sys
# add base path to sys
sys.path.append(os.path.join(os.path.dirname(__file... |
5e01aaf48af94b79212e09f8cc654c92f96f84a39de9acc754bce2324b49632d | Python | 6,091 | 151 | """The argument vocabulary every algorithms family shares.
`tail` is spoken by inference, alignment and regression alike, and by the two
facade methods that expose a permutation test (`Roc.summary`,
`Adjacency.ttest`), so its validator and the p-value computation built on it
live here rather than inside any one family... |
b19b39f7283738f1b13a75cba36ea32c4cd2b3da6d6b3433e7de409760901cf8 | Python | 6,097 | 205 | import pytest
import torch
from nequip.nn.embedding.utils import (
cutoff_partialdict_to_fulldict,
cutoff_fulldict_to_tensor,
cutoff_tensor_to_str,
cutoff_str_to_fulldict,
cutoff_partialdict_to_tensor,
)
from nequip.utils.global_dtype import _GLOBAL_DTYPE
def test_round_trip_two_types():
"""T... |
754917b44ed62939f452e2846807be33592f90b2d7ebde5fed041629ba869e6c | Python | 6,100 | 154 | import numpy as np
import pandas as pd
import pytest
from nltools.cross_validation import KFoldStratified
def check_valid_split(train, test, n_samples=None):
"""Helper: Check that train/test split is valid."""
train, test = set(train), set(test)
# Train and test split should not overlap
assert train.... |
4fd433d3077bf8285a453c5bde930ef5eba67b026bf68e555ed5ba7912f65cec | Python | 6,102 | 142 | import random
import numpy as np
import torch
import gpytorch
class GreedySearch(object):
"""
Algorithm class for greedy search. In greedy search, one dimension or direction is optimized
at any given time. For example, if you have a 2D rectangle search space with width A and
length B, this algorithm w... |
ffadd45a3c2e38939ac2cfe10423564c7e1d1673263d08cfcd20b83acdc9279c | Python | 6,109 | 183 | from typing import Optional
import torch
from torch import Tensor
import torch.nn.functional as F
from torch.nn import ModuleList, Linear, BatchNorm1d
from torch_sparse import SparseTensor
from torch_geometric.nn import GCNConv
from ..models import ScalableGNN
from torch_geometric.typing import Adj, OptTensor
class... |
398f968ff06c5f6a1fe377cdb18f6235ecd836979e823e8d048ac5cdffb547cf | Python | 6,111 | 173 | import argparse
import json
import os
import re
import sys
from pydfc.dfc_utils import TR_intersection, rank_norm
from pydfc.ml_utils import find_available_subjects, load_dFC
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from helper_functions import ( # pyright: ignore[reportMissingImports]
build_e... |
462097e7a0dc48b4780b67ade28474e3ce8ed1b660c368da751036e364078929 | Python | 6,116 | 160 | # -*- coding: utf-8 -*-
"""Provides utility functions for visualization."""
from warnings import warn
from math import ceil
import matplotlib.pyplot as plt
# import mpl_toolkits.axes_grid.axes_size as Size
# from mpl_toolkits.axes_grid import Divider
import numpy as np
def plot_image(x, fig=None, ax=None, **kwargs):
... |
cb4cd678500c8ca6330fc05c3d1555a92fb3929e43d17f18bdfe830437aa63d6 | Python | 6,118 | 198 | import boto3
import json
import subprocess
import os
import re
def b64text(txt):
"""Generate Base 64 encoded CF json for a multiline string, subbing in values where appropriate"""
lines = []
for line in txt.splitlines(True):
if "${" in line:
lines.append({"Fn::Sub": line})
else... |
e2f3512378b329058f07bbb647d62dbe58742b99e598a32c1124bc39426ddad6 | Python | 6,119 | 168 | import math
import numpy as np
import torch
import torch_geometric
from torch_cluster import radius_graph
from torch_scatter import scatter
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from utils import get_iso_dataset
from e3nn.nn.models.gate_points_2101 import Network
from e3nn import ... |
5b0463e570c7a7ffd75624471c04ce3aa062334938f031c4e748fa015caa91fb | Python | 6,120 | 187 | import sys
import csv
import numpy as np
import scipy as sp
from scipy import optimize
import math
def readdata(filename):
with open(filename, newline='') as csvfile:
reader = csv.reader(csvfile, delimiter=' ', quotechar='|')
fs = []
psd = []
for idx,row in enumerate(reader):
... |
531d18155b20b46e922c8b3f14875de69ff11cd76dd0dcd7460bb2e1b545040c | Python | 6,122 | 165 | # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... |
3621a072ab20cbff8dc5ebd2aa145dc8c171bfbb4eede4da38b6c7697ab179db | Python | 6,123 | 117 | import os
aff = os.sched_getaffinity(0)
print('**********************before import torch******************************'+str(aff))
import torch
print('**********************after import torch******************************'+str(os.sched_getaffinity(0)))
os.sched_setaffinity(0, aff)
from DeepPurpose import utils, dataset... |
b1a90723522607fd7e0cb5fe5e61b6271ff5bf5b941cc5d2fdbe8631b1e8af16 | Python | 6,123 | 210 | import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as dsets
import torch.nn.functional as F
import numpy as np
from torch.autograd import Variable
from torch.optim.lr_scheduler import StepLR,MultiStepLR
import matplotlib.pyplot as plt
import math
from sklearn.util... |
ff77c2f41301f67f373215a70c9b6f86237a2b2824dec0ef63db698ee8d0b380 | Python | 6,130 | 144 | import numpy as np
class ion_datacube():
# a class for holding a datacube from an MSI dataset and producing ion images
def __init__(self, step_size=[]): # define mandatory parameters (todo- validator to check all present)
self.xic = [] # 'xtracted ion chromatogram (2d array of intensities(vector of ... |
3739a78d474b1419c0b236baa4d20c10aff7abf3c24a803e90faff6be0565f12 | Python | 6,131 | 210 | """
Implementation of dFC methods.
Created on Jun 29 2023
@author: Mohammad Torabi
"""
import time
from copy import deepcopy
import numpy as np
from ..dfc import DFC
from ..time_series import TIME_SERIES
from .base_dfc_method import BaseDFCMethod
from .sliding_window_clustr import SLIDING_WINDOW_CLUSTR
###########... |
fd0985d6b8596111528993292a26852a82232ea94fdecd743527bd3c8ccbf45c | Python | 6,131 | 184 | """
Run MDS on structures to create an embedding visualization
Coloring options:
* training TM similarity
* scTM
* helix/beta strand annotations
* length
"""
import os
import json
import logging
from glob import glob
import argparse
import pandas as pd
from sklearn.manifold import MDS
import umap
from matplotlib im... |
54410d6f5863d77f428fabcb18aa7572f3747140c8d3a8cbbe38032feb00234d | Python | 6,132 | 142 |
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 10 18:14:19 2020
@author: Tom
"""
import tensorflow as tf
from tensorflow import keras as k
tf.config.list_physical_devices()
import os
os.environ['CONDA_DLL_SEARCH_MODIFICATION_ENABLE'] = '1' # needed due to dll bug in my environment - might be unnecessary for other pp... |
936547ae3674ff45781fa9dee0a2c776a7d622d6b7aa2f7299565b5107673bd5 | Python | 6,134 | 140 | r"""DeepMSI reconstruction of the high-res MSI transients by predicting the encodings of
the high-res data. Autoencoder and the encoding regressor are trained on existing high-res
dataset from a few tissue sections of a 3D volume. Encodings can then be predicted from
low-res data (short transients) of the remaining... |
6b43f9c2e9d2224ce1b95621db477da132e6dce5efe64f7af5cabc2652550dd1 | Python | 6,143 | 199 | # coding=utf-8
"""
Vocabulary helper class
"""
import re
import numpy as np
class Vocabulary:
"""Stores the tokens and their conversion to one-hot vectors."""
def __init__(self, tokens=None, starting_id=0):
"""
Instantiates a Vocabulary instance.
:param tokens: A list of tokens (st... |
119cd794d0b042404876ab6475d0921d6c1c34b10583e51cd28848d62f04a781 | Python | 6,151 | 125 | """The promiscuity table: the contract of the committed file, not the predictor behind it.
`mhcmatch` fetches its reference data from HuggingFace, so nothing here scores anything -- a test that
fails when HuggingFace is slow is a test that gets disabled. What is asserted is the part that goes
wrong without anybody not... |
de86261e10074a8f567ff8e79d937d3b743ee3edbaff4e925300d84ddbaafed4 | Python | 6,151 | 202 | import sys
import multiprocessing as mp
import neurodecode.utils.io as io
from neurodecode import logger
from neurodecode.utils.timer import Timer
from neurodecode.triggers import Trigger, TriggerDef
from neurodecode.stream_receiver import StreamReceiver
from neurodecode.gui.streams import redirect_stdout_to_queue
#... |
ecc84cdde2aa5994990b1e86f35e54c1b833fc8175136af23deef0f88c5932b3 | Python | 6,153 | 202 | """Varifolds related machinery.
General framework is introduced in [KCC2017]_.
See [CCGGR2020]_ for details about kernels.
Implementation is based in pykeops (https://www.kernel-operations.io/keops/).
In particular, see
https://www.kernel-operations.io/keops/_auto_tutorials/surface_registration/plot_LDDMM_Surface.html... |
7389bb745156b975039642900334e6928947f7b47282cccda676e7c6b428e5b0 | Python | 6,154 | 179 | from collections import namedtuple
from typing import Dict, List, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor
from model_angelo.utils.fasta_utils import FASTASequence
from model_angelo.utils.residue_constants import parse_sequence_string
def point_cloud_to_s... |
6ed8286ea1a4f250c3b7a73fb550d2f8f24d9da6317f769859926d6979370a89 | Python | 6,155 | 103 | """``method.frequency``: three independent columns, none derived from another (#696).
One free-text column holds at least three different things. Measured on the corpus: **63,532
non-blank cells over 7,111 distinct values**, of which 3,548 distinct are a ratio written `x/X`
(`12/163`, `52/27589`, `5719/33921`), others... |
cdaeb18ca5cf8a153fb87e5e1a5576ddca835ff4dc90cb6de03ca7c00cee11a4 | Python | 6,156 | 158 | import copy
import itertools as it
from typing import Any, Dict, List
import more_itertools as itx
import pytest
from hypothesis import assume, given
from hypothesis import strategies as st
from snakebids.core.datasets import BidsComponent, BidsDataset
from snakebids.tests import strategies as sb_st
from snakebids.te... |
8e982f226f9ab90a24d3ab83ccda47b544397e53d5e1084b1293ff8459219964 | Python | 6,158 | 171 | from chromadb.api.types import (
Documents,
Embeddings,
Images,
Embeddable,
EmbeddingFunction,
)
from chromadb.utils.embedding_functions.schemas import validate_config_schema
from typing import List, Dict, Any, Union, cast, Sequence
import numpy as np
def create_langchain_embedding(
langchain_... |
47756cbe0b82e8736eb579dd0b5da8810530d29db978fe3b7655f7be5b231238 | Python | 6,162 | 196 | from dataclasses import dataclass
from typing import Tuple
from unittest import TestCase
from unittest.mock import MagicMock
from sniffles.cluster import Cluster, resplit_bnd
from sniffles.leadprov import Lead
from sniffles.sv import SVCall, SVCallBNDInfo, resolve_bnd
@dataclass
class TestInfo:
id: str
conti... |
f74de9df3c17490dd492413d8e6196aa6893bb30ea01b41ccc195f559ba5f9ff | Python | 6,162 | 154 | from utils_ds import *
from utils import mol_to_dgl_graph
from data_utils.merge_data import split_line
from rdkit.Chem import AllChem
import pickle
import json
from tqdm import tqdm
from rdkit import RDLogger
RDLogger.DisableLog('rdApp.warning')
vocab_file = os.path.join("data", "pretraining_data", "merge_vocab_freque... |
e88346a1ca0ef46f0c0d6a012240084d154f257b8c06be7b43b397d9169782c8 | Python | 6,165 | 154 | import numpy as np
import itertools
import warnings
from scipy.ndimage import convolve as conv
from types import SimpleNamespace
simple_3x3 = SimpleNamespace(
x=np.array([[-0, 0, 0],
[-1, 0, 1],
[-0, 0, 0]], dtype=float),
y=np.array([[-0, -1, ... |
d4258269ccf5a5720b8cd6992e73ac36b5525426d6f48f152d7af754d2c1514f | Python | 6,166 | 176 | """Tests for Plot Scatter marker and hue rendering."""
from typing import Any
import numpy as np
import pandas as pd
import pytest
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure
from qtpy import QtCore, QtWidgets
from sanpy.bAnalysisResults import get_plot_result_def... |
a47751c95b81c960e8f40a7a4d135a436640c7cb95fff841926ffb39e28cd2d4 | Python | 6,167 | 169 | import os, glob, tqdm
from skimage.io import imread, imsave
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
import matplotlib as mpl
import scipy.ndimage as ndi
from skimage.metrics import structural_similarity as ssim
from skimage.metrics import normalized_root_mse as nrmse
import skimage.... |
70f0400455875d77db30dfeca0fbba52ff6a27eb8c5327f32e2892d406b797dd | Python | 6,168 | 138 | # Author: Youzhi Luo (yzluo@tamu.edu)
# Updated by: Anmol Anand (aanand@tamu.edu)
import os
import torch
import torch.nn.functional as F
from torch_geometric.loader import DataLoader
from torch_geometric.datasets import TUDataset
from .model import RewardGenModel
from dig.auggraph.dataset.aug_dataset import DegreeTran... |
41b3978850b90b379c9fe05be83d3f04020bc16f44e645e49d818bb54640b332 | Python | 6,173 | 142 | #*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* ... |
a498be41ebc941f6719eeb47c7d169d1a0eaf67478403afcb27a8d43d3e8f317 | Python | 6,174 | 169 | import os
from typing import Generator, cast
import numpy as np
import pytest
import chromadb
from chromadb.api.types import (
Embeddable,
EmbeddingFunction,
Embeddings,
Image,
Document,
)
from chromadb.test.property.strategies import hashing_embedding_function
from chromadb.test.property.invariants... |
04e69e0740256c99c65c96ba0fc2c5efd076eabd64784cf538f0494695f21f53 | Python | 6,175 | 154 | import numpy as np
import itertools
import warnings
from scipy.ndimage import convolve as conv
from types import SimpleNamespace
simple_3x3 = SimpleNamespace(
x=np.array([[-0, 0, 0],
[-1, 0, 1],
[-0, 0, 0]], dtype=float),
y=np.array([[-0, -1, ... |
c83cb4aa598693aff6b2953d968e6cedc433c5def5fea53385d2f4ca3ecd8451 | Python | 6,176 | 166 | # This file is a part of the `nequip` package. Please see LICENSE and README at the root for information on using it.
import torch
from nequip.data import AtomicDataDict, compute_neighborlist_
from nequip.data._nl import DEFAULT_NEIGHBORLIST_BACKEND
from nequip.data._key_registry import get_field_type
from typing impor... |
cafa80d885379c4a8a9005773bf013866fff6a031232eb7678d66f747d3cd0f3 | Python | 6,176 | 191 |
import os
import random
import numpy as np
from tqdm import tqdm
import torch
from torchvision import transforms
from skimage.metrics import (
mean_squared_error,
structural_similarity,
peak_signal_noise_ratio
)
from skimage.transform import iradon, iradon_sart
from pytorch_radon import Radon, IRadon
fr... |
cf1e96c754583437cf7ec62eab7f63d82fc3f174315221f5c00db7f421d1d390 | Python | 6,176 | 179 | # 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.
import torch
from fairseq import utils
from fairseq.dataclass.utils import gen_parser_from_dataclass
class FairseqOptimizer(object):
def... |
7a260185ce0a85bf32a529702ab9e7b80423dc2dd83da456284904aad79635be | Python | 6,179 | 126 | """Assemble a release: the zips, the manifest, the checksums, and ``latest-version.txt``.
Six steps:
1. **plan** -- derive the version from the tag, assert the tag does not already exist;
2. **prepare** -- rewrite ``latest-version.txt`` in the working tree, idempotently;
3. **build** -- write every bundle, all embedd... |
e4d30a9d6b6c259a3873d1ffb747b1211a8b64d9735ac23ca9108c04527bd7e9 | Python | 6,182 | 164 | # Scripts to modify the spacing of OCT images/labels and rescale sparse landmarks
# 2023 Peng Liu Email: firstname.lastname@nct-dresden.de
import os
import nrrd
import numpy as np
import json
import pickle
import argparse
import glob
with open("funcs/header_original_scale.pkl", 'rb') as f:
header_original_scale ... |
e4984b34c194a2620987413fd5abfaa1f887cc56008de6dc81d31478e45f3124 | Python | 6,183 | 200 | """Learning embedding of graph using Poincare Ball Model."""
import logging
import matplotlib.pyplot as plt
import geomstats.backend as gs
import geomstats.visualization as visualization
from geomstats.datasets.utils import load_karate_graph
from geomstats.geometry.poincare_ball import PoincareBall
def log_sigmoid... |
33750b43f834aa0332aa80a3f13c31c1b9e9507f31790ac05de13dd7d5ae087f | Python | 6,184 | 152 | # ==============================================================================
# Copyright 2025 Technical University of Denmark
# Author: Nikolas Borrel-Jensen
#
# All Rights Reserved.
#
# Licensed under the MIT License.
#
# 2D: When running MATLAB in parallel with multiple threads, each source position is written to... |
649d5777247759d1831b210b042becddb2e8ef605123a65a50706e445c81e44a | Python | 6,189 | 157 | ## loading package
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import pylab
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from statannot import add_stat_annotation
import matplotlib as mpl
from scipy import stats, cluster
import glob
import re... |
14032a04179e957f6951473f4f5110b07ea04d93817fcba5884e33bb1507ebd7 | Python | 6,190 | 202 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 12 15:25:17 2018
@author: ngritti
"""
import time, os, glob
from tqdm import tqdm
import numpy as np
from skimage.io import imread
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from skimage.metrics import structural_simil... |
4da219a724b27e89f5b8b235ab112e5ee0dbd71f159fcceea9d73dc272f2ed35 | Python | 6,193 | 191 | import numpy as np
import renderapi
from scipy.linalg import block_diag
from scipy.sparse import csr_matrix
from .utils import aff_matrix, aff_matrices, AlignerTransformException
__all__ = ['AlignerRotationModel']
class AlignerRotationModel(renderapi.transform.AffineModel):
"""
Object for implementing rotat... |
9122f8fb421a07e928a00e18955f0c54bba0426a4bec1e3e5c5cdcd9544c162a | Python | 6,201 | 173 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 12 15:25:17 2018
@author: ngritti
"""
# from csbdeep.models import CARE
# from deeprest.rawDataClass import rawData
# from deeprest.modelClass import modelRest
# from deeprest.timeLapseClass import sampleTimeLapse
import time, os, glob
from tqdm imp... |
86bd8517201bc857db495f32bb643a2df2794f7f9e40a3415929175b714e4ead | Python | 6,202 | 253 | import os
import nengo
import torch
import argparse
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from brian2 import *
from nest import *
from time import time as t
from experiments import ROOT_DIR
import brian2genn
from bindsnet.network import Network
from bindsnet.network.topology import C... |
8a2cdcd0117cbbe915f8f7b3f250aaef7ddb1a24cfe776e9b285ce93d77aa982 | Python | 6,202 | 63 | REACTION_SUZUKI = [
"[*;$(c2aaaaa2),$(c2aaaa2):1]-!@[*;$(c2aaaaa2),$(c2aaaa2):2]>>[#6;H0;D3;$([#6](~[#6])~[#6]):1]B(O)O.[#6;H0;D3;$([#6](~[#6])~[#6]):2][Cl]"
]
SCAFFOLD_SUZUKI = "[*:0]c1ccc2c(c1)cc(cc2)c3ccccc3"
DECORATION_SUZUKI = "[*]c1ncncc1"
DECORATION_NO_SUZUKI = "[*]C"
FRAGMENT_REACTIONS = [
"[#6;$(C[C;$(... |
89165e056b4dfbd5dd2629aa8d978169c48a1ecd0478e0ae771db269fabc29b4 | Python | 6,206 | 177 | import json
import os
import tempfile
from typing import Tuple
import pandas as pd
import yaml
from reinvent_plugins.components.synthsense.parameters import ComponentLevelParameters
__ENV_NAME = "CAZP_PROFILES"
CAZP_PROFILE = __ENV_NAME if __ENV_NAME in os.environ else "HITINVENT_PROFILES"
def mergedicts(a: dict,... |
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