sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
185124cd2a0eabe00d982dcd6c979d2b461608f85ea69fbcc2ca6fa94cfaf0a3 | Python | 11,835 | 366 | import math
import torch
from torch import nn
from torch_geometric.nn import MessagePassing
import math
EPS = 1e-8
class VecLayerNorm(nn.Module):
def __init__(self, hidden_channels, trainable, norm_type="max_min"):
super().__init__()
self.weight = nn.Parameter(torch.empty(hidden_channels), require... |
08b74dd129088002395f695222a073ef8834a06230f89c56aaf44affeadd3971 | Python | 11,837 | 282 | # %%
"""
==============================================================
Attempt to plot ROC curves from ATM/ImCoh classification from MEG data in the source space
===============================================================
"""
# Authors: Marie-Constance Corsi <marie.constance.corsi@gmail.com>
#
# License: BSD (3-c... |
8046a50600b264fecec6146fe77d593be1051e93fd3e612229faea5f4cb8c00b | Python | 11,840 | 248 | import os
import torch
import hydra
import logging
from models import QHNet
from torch_geometric.loader import DataLoader
from torch_scatter import scatter_sum
from datasets import QH9Stable, QH9Dynamic
from torch_ema import ExponentialMovingAverage
from transformers import get_polynomial_decay_schedule_wi... |
f67f1cf1e459e37689eb8e1c641bfacf78028af98823fdbbb079b98f551111e0 | Python | 11,845 | 183 |
qm9_with_h = {
'name': 'qm9',
'atom_encoder': {'H': 0, 'C': 1, 'N': 2, 'O': 3, 'F': 4},
'atom_decoder': ['H', 'C', 'N', 'O', 'F'],
'n_nodes': {22: 3393, 17: 13025, 23: 4848, 21: 9970, 19: 13832, 20: 9482, 16: 10644, 13: 3060,
15: 7796, 25: 1506, 18: 13364, 12: 1689, 11: 807, 24: 539, 1... |
30c59161b7108b240241fb22cf4af376acfd8ef8fe0cbb6bc4e3d5832ca13fe5 | Python | 11,846 | 294 | """Combining documents by mapping a chain over them first, then combining results."""
from __future__ import annotations
from typing import Any
from langchain_core._api import deprecated
from langchain_core.callbacks import Callbacks
from langchain_core.documents import Document
from langchain_core.runnables.config ... |
2460c2bf0d5abc990e4f121f09859ce5ae1d9ca39051e0ec68c016aac0b90e9a | Python | 11,849 | 285 | from __future__ import division
from __future__ import print_function
import os
import time
import h5py
import random
import argparse
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from copy import deepcopy
from torch_geometric.data import Data
from models import *
def see... |
d66da852536dc96922801580ba99535dae67e2a070f9d66ac335f2c1d4fb5689 | Python | 11,849 | 424 | import itertools
from typing import Union
import matplotlib.pylab as plt
import numpy as np
import networkx as nx
from . import plotlying, stats
def _master_plot(
x: np.ndarray,
y: np.ndarray,
title: str = "",
xerr: Union[list, None] = None,
yerr: Union[list, None] = None,
method_name: str = "... |
095ff4103c5d12485920099a38c9c04c5d48811121ade1cb64b0dc91f63fdff5 | Python | 11,855 | 283 | import pandas as pd
import argparse
from utils import set_seed
import numpy as np
import wandb
import os
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.nn import functional as F
from torch.nn.utils.rnn import pad_sequence
from torch.cuda.amp import GradScaler
# from transformers i... |
85bf6aff639b56e83b185478af348a0cb3f2e1240add56917d251ebe250d7193 | Python | 11,856 | 240 | import os
from copy import deepcopy
from collections import OrderedDict
from itertools import groupby
import warnings
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.font_manager
from matplotlib.colors import ListedColormap
from matplotlib.cm import get_cmap
import scipy.stats as... |
829bcccd72f8ff27214ac19a7a26be2fb02b595ea4414d56f7e821295c76e537 | Python | 11,857 | 356 | # Copyright 2024 DeepMind Technologies Limited
#
# AlphaFold 3 source code is 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... |
1d038516e4b12dc5b80ee1afb7679beb2a40765da6fdada5cb0ca6d8954587b0 | Python | 11,858 | 302 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 16 11:53:52 2021
@author: athanasiadis
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import attack_foolbox as fool
class LinearNet(nn.Module):
"""
linearnet
______________________
... |
2c4fc2185d6be81a07206bee0775b3b85d8b685fc9085c9515009a1e594de639 | Python | 11,859 | 272 | """
In this file the functions to evaluate the state, reward are defined and also the action is implemented
"""
import math
import warnings
import numpy as np
from DSS_Initialize import*
# To get the details of switches and their status
def switchInfo(DSSCktobj):
#Input: DSS Circuit Object
#Returns: A list o... |
dfda53abd76bd615d982f1d6bf20b335eb528b64d3b2e019cf4aab8b8d02cc5f | Python | 11,862 | 227 | import collections
import sys
import torch
import warnings
from .solvers import FixedGridODESolver
from .misc import _compute_error_ratio, _linf_norm
from .rk_common import rk4_alt_step_func
_BASHFORTH_COEFFICIENTS = [
[], # order 0
[11],
[3, -1],
[23, -16, 5],
[55, -59, 37, -9],
[1901, -2774,... |
1a2d5a200ef8fff5c0dcec8d51aa498e47af57ce14ad886068b3b75e46d7910c | Python | 11,864 | 282 | """
Prepare the workflow paths to be used by downstream
machine learning algorithm. The paths are divided
into the test and training sets
"""
import os
import collections
import numpy as np
import random
from scripts import predict_tool_usage
from scripts import utils
main_path = os.getcwd()
class PrepareData:
... |
248a77c1d886240ba37d7132e71a417965c98377fac6ba1113d05ef51d76af8e | Python | 11,868 | 355 | ########################################################################
# 1D diffusion in a finite tube with fully-reflective boundary.
# Circular "point source" from one face.
# RESTORE
# AIMS: to verify checkpointing and restoring of the spatial stochastic
# solver 'Tetvesicle' in the context of the Bounded Diff... |
b7331015189a3c37241020a4c6bed5e1fda31e94e6efb3714348254f4b0144a8 | Python | 11,869 | 195 | # 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
#
# http://w... |
b2632769052490639106309e080d1a85f5a95bb878533fa5cc9e5f2e34477843 | Python | 11,872 | 292 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this sof... |
c36249c1721b2ca0ba33c4ba82697a155c3f31e4280940888c5abb7b6d349904 | Python | 11,877 | 306 | """
This file contains helper functions for building the model and for loading model parameters.
These helper functions are built to mirror those in the official TensorFlow implementation.
"""
import re
import math
import collections
from functools import partial
import torch
from torch import nn
from torch.nn import ... |
aa042f7f31f2f3f4fa7aaeee3fa04c0c2e5ea0ef00a2d772966af07714d53cd9 | Python | 11,904 | 271 | """
In this file the functions to evaluate the state, reward are defined and also the action is implemented
"""
import math
import warnings
import numpy as np
from Environments.DSSdirect_13bus_loadandswitching.DSS_Initialize import*
# To get the details of switches and their status
def switchInfo(DSSCktobj):
#Inpu... |
7be22445837214feee4ae77907829708bbbd1d1c5b545e1e888429374f698c10 | Python | 11,910 | 325 | """Methods to evaluate peptide-spectrum predictions."""
import re
from typing import Dict, Iterable, List, Tuple
import numpy as np
from spectrum_utils.utils import mass_diff
def aa_match_prefix(
peptide1: List[List[str]],
peptide2: List[List[str]],
aa_dict: Dict[str, float],
cum_mass_threshold: flo... |
a1a51651d10c5f4cbc3547a384dff6b51b9c6908af93f077082fe0b67cd75ba8 | Python | 11,915 | 355 | ########################################################################
# 1D diffusion in a finite tube with fully-reflective boundary.
# Circular "point source" from one face.
# RESTORE
# AIMS: to verify checkpointing and restoring of the spatial stochastic
# solver 'Tetvesicle' in the context of the Bounded Diff... |
7c6c26d81d51eec293d6dde280c3cfd65058c7b9c2a2921261bf45e3b280974f | Python | 11,917 | 362 | import numpy as np
import pandas as pd
import cooler
import cooltools
import bioframe as bf
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.colors import LinearSegmentedColormap, LogNorm
from matplotlib import ticker
##################################
# Neuronal dots-related func... |
ac599b931fcc41a1c66985730cdf8cfe42bd431f00c0ecd009ba0cf791207942 | Python | 11,920 | 322 | import shlex
import subprocess
import sys
import tempfile
from pathlib import Path
from time import sleep
import pytest
from MRdataset import load_mr_dataset
from hypothesis import given, settings, assume
from mrQA.cli import cli
from mrQA.config import DATE_SEPARATOR
from mrQA.monitor import cli as monitor_cli
from ... |
727bd4b939048f86d9a38540f9717a57577e1e8543acd7aa3d930f2ecd90517e | Python | 11,954 | 312 | import os
import subprocess
import slicer
import argparse
import DICOMScalarVolumePlugin
try:
import pandas as pd
except:
slicer.util.pip_install('pandas')
import pandas as pd
try:
from tqdm import tqdm
except:
slicer.util.pip_install('tqdm')
from tqdm import tqdm
import numpy as np
try:
... |
b3f6680bfe70aba9dd3811fb1dbdbcdac7342574a723c8aac574a3cf7f150b5b | Python | 11,959 | 360 | """
Cell Deconvolutional Network (scaden) class
"""
import os
import logging
import sys
import gc
import tensorflow as tf
import numpy as np
import pandas as pd
from anndata import read_h5ad
import collections
from .functions import sample_scaling
from rich.progress import Progress, BarColumn
logger = logging.getLogge... |
82186724b82702857257f59e72ef7e8e1fc7faae9d368f96616775d6725e6dce | Python | 11,962 | 303 | import itertools
from typing import TYPE_CHECKING
import networkx as nx
from pgmpy import logger
from pgmpy.base._base import _CoreGraph
from pgmpy.utils._warnings import _warn_external
if TYPE_CHECKING:
from pgmpy.base import DAG
class PDAG(_CoreGraph):
"""
Class for representing PDAGs (also known as ... |
2922287b4c8e8549348bd2a7d64a38f6cf85331a8baa90fc79601027933bc56c | Python | 11,963 | 299 | import os
import numpy as np
import json
import h5py
import random
from numpy.random import choice
from keras import backend as K
def read_file(file_path):
"""
Read a file
"""
with open(file_path, "r") as json_file:
file_content = json.loads(json_file.read())
return file_content
def wri... |
9fc1fe06ed503cea222178b4f660601226d1a3c8cbcd314c82eead5961e43f71 | Python | 11,969 | 375 | import logging
import os
import socket
import sys
import matplotlib.pyplot as plt
import matplotlib.colors
import numpy as np
import pandas as pd
import seaborn as sns
from configs.configs import get_config_dict
from helpers.data import reorder_ica_components
def plot_icc_scores_per_edge_joint(
config_dict: dic... |
1058d2fbc62aa81d1cef9aefd39e1c0f72008111d7926bdea45b0a57a3e2f980 | Python | 11,977 | 233 | # 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
#
# http://w... |
aed3690f999469384deedc25382a62c1f7fed5133a7c885f64a589aa6da4d3d6 | Python | 11,978 | 297 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
c43211c512f534ec44d1b42fd4cdb45e5b2efb8f252386dbb053f8a1d79a6a7d | Python | 11,986 | 328 | import torchvision
import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
"""
医学图像分割已通过 Transformer 模型取得了显著的进步，该模型擅长掌握深远的上下文和全局上下文信息。然而，这些模型的计算需求与 token 数量的平方成正比，限制了它们的深度和分辨率能力。
大多数当前方法都是逐片（称为伪 3D）处理 3D 体积图像数据，缺少关键的切片间信息，从而降低了模型的整体性能。
为了应对这些挑战，我们引入了可变形大核注意力 (D-LKA Attention) 的概念，这是一种... |
e163101488b38487c861e548ae09b19da2822776f3b3a16a89a9fb78be7ca5cf | Python | 11,989 | 309 | from src.data import get_sim_data
import torch
import numpy as np
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
model = torch.load(os.path.join("../model", f"model_sim"))
x_seqs, x_statics, y, _, seq_features, static_features = get_sim_d... |
5be012d29fcf5123e6856fe0fbd369c7cd019109e40beb6220b04e83f2f29d2a | Python | 11,990 | 326 | """3D CMUNeXt building blocks used throughout DDSurfer.
This module houses the lightweight convolutional primitives and attention
mechanisms that underpin the volumetric feature extractors used by the dual
stream networks. The implementation keeps the original numerical behaviour of
the release while providing clearer... |
908c69c3383d92fda96bd763cde4929897f1860829de81f9137be3c2d4a3d67f | Python | 11,990 | 290 | # 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
#
# http://w... |
76696ea7866588dfcd3ae424795f0a3a7189fb0fe2f2b349de9009204e63fc9c | Python | 11,991 | 297 | # This file is a part of the `allegro` package. Please see LICENSE and README at the root for information on using it.
import math
import torch
from e3nn.o3._irreps import Irreps
from e3nn.o3._wigner import wigner_3j
from nequip.nn import replace_submodules, model_modifier
from nequip.utils.dtype import torch_default_d... |
4e7819b6900b487c60da0baf7de48f636175b09991be4df06719b78aa887bcd1 | Python | 11,993 | 347 | import logging
import numpy as np
def get_ground_truth_covariance_structure(
covs_type: str,
num_samples: int,
signal_to_noise_ratio,
data_set_name: str,
) -> np.array:
"""
Return full covariance structure of shape (N, D, D).
These are the 'synthetic', 'simulated', or 'oracle' covariance... |
12b1a3ae112edb2b5121233e7063a99c83bf01ffc1461da72cbdbde6d78a1634 | Python | 11,994 | 290 | import csv
import json
import mlflow
import numpy as np
import itertools
import argparse
from pathlib import Path
def setup_results_dir(exp_name, exp_id):
"""Create and return path to results directory for this experiment."""
results_dir = Path('results') / f"{exp_name}_{exp_id}"
results_dir.mkdir(parents... |
b4dbbdabaf53962e09778ccb8e7d4dc86a7a41d7783f984af5b7ba92db7af8c9 | Python | 12,010 | 242 | import os
import glob
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from utils.constants import ALL_MODELS
from scipy.stats import iqr, linregress
from paths import TRAINING_DATA_PATH, TEST_DATA_PATH
from utils.distribution_distance import compute_jsd, compute_kld
baseline_p... |
637f9f84f190f39e8ee3245c42c22989febb539987a81897a03ee25057870c97 | Python | 12,018 | 332 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import dash_bio as dashbio
from .base import *
representation_options = [
{"label": "backbone", "value": "backbone"},
{"label": "ball+stick", "value": "ball+stick"},
{"label": "cartoon", "value": "cartoon"},
{"label": "hyperball", "value": "hyperball"},
... |
cc1d5a035133acbe0b05c2000e16784175c904e3c8e02ad6c4058f77ac627c32 | Python | 12,018 | 341 | import logging
import os
from pprint import pprint
import socket
from fcest.helpers.data import to_3d_format
import matplotlib.pyplot as plt
from nilearn.glm.first_level import FirstLevelModel, make_first_level_design_matrix, run_glm
from nilearn.plotting import plot_design_matrix
import numpy as np
import pandas as p... |
9c332311d94e303549c079b1d8bba7ed844c7b2235de369fed249cf5b3d538a6 | Python | 12,020 | 368 | # coding=utf-8
"""Define utilities for all networks."""
from math import ceil, floor
import torch
import torch.nn as nn
import torch.nn.functional as f
from torch.utils.data import DataLoader, TensorDataset
import numpy as np
import math
from sklearn.preprocessing import LabelBinarizer
from collections import OrderedD... |
1ddbed0cb349b3c32d0ddde9e3c7acc2560de4db6cf3a5b748decc6b75ae0bd6 | Python | 12,023 | 272 | import gc
import os
import argparse
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from utils.utils import current_time
from utils.visualization import draw_roc
from node_embed.featurization import load_seqs
import numpy as np
from train import train
from test import inde... |
92afd1c25eeaa4d9d1aa89da35bdb4e77c628e0ec81eab81838f695d3ab31479 | Python | 12,033 | 291 | import os
import glob
import numpy as np
import matplotlib.pyplot as plt
from utils.constants import ALL_MODELS
from paths import TRAINING_DATA_PATH, TEST_DATA_PATH
from utils.distribution_distance import compute_jsd
import matplotlib.gridspec as gridspec
from matplotlib_scalebar.scalebar import ScaleBar
from scipy.ndi... |
a92a4b480c338edd8cc9f7c953cfca475019bee15f3ca6860a80e23c26cc22cb | Python | 12,041 | 295 | """
Author: Anna M. Puszkarska
SPDX-License-Identifier: Apache-2.0
"""
import time
from pathlib import Path
from typing import Tuple, List, Optional
import numpy as np
from peptide_models.aminoacids import AMINOACIDS
from peptide_models.peptide import Peptide
from peptide_models.utils_data import fasta2df, get_pepti... |
0f3c3ea9db82c5e4b765c20b2647384fb7a03c4171029aa7d378f37f1e78e27b | Python | 12,042 | 302 | import matplotlib
# matplotlib.use('agg')
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sys
from scipy.stats import pearsonr
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import svm
from sklearn.metrics import roc_cu... |
3892ebf785070d0a4c671f34fe74d84a03d59a1fa0c380a2a8c63838b3f807ae | Python | 12,046 | 400 | import os
import tempfile
import numpy as np
import pytest
from skbase.utils.dependencies import _check_soft_dependencies
from pgmpy import config
from pgmpy.example_models import load_model
from pgmpy.factors.discrete import TabularCPD
from pgmpy.models import DiscreteBayesianNetwork
from pgmpy.readwrite import NETR... |
8c0ec44a630edf0b4e2335922585119b050e42936095d88f2905a64808600537 | Python | 12,060 | 353 | ### #################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for deta... |
3c039ab98cc6ffbeb8587aa92819905370f17bf3618d8f1b3e7f04536f1a14d0 | Python | 12,062 | 310 | import os
import numpy as np
from dival.reconstructors.odl_reconstructors import FBPReconstructor
from dliplib.reconstructors.dip import DeepImagePriorReconstructor
from dliplib.reconstructors.dip_initial import DeepImagePriorInitialReconstructor
from dliplib.reconstructors.fbpunet import FBPUNetReconstructor
from dl... |
f9531c8e3093e5aa45b26a1629d32a99269d112ecd2dab131d73a1644fc6d2be | Python | 12,065 | 290 | # Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, so... |
4c2a58ff58d5e327467877bb5147a2971ea16f6ebe07517dd6a4b13969d65b5e | Python | 12,070 | 283 | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import B... |
c69223dbc8cfc29f7c07a27845df504c3e5b7b0c9891be3db4c3d3a1df622bef | Python | 12,071 | 321 | import os, sys
try:
user_paths = os.environ['PYTHONPATH'].split(os.pathsep)
except KeyError:
user_paths = []
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import re
import yaml
from pathlib import Path
from shutil import copyfile
import pandas as pd
from random import randint
import string
import ntpath
class Config:
... |
4c93e854686e501778e301807bb274bcd8fc17e6b02e49ed5c3e2840e59c4aaa | Python | 12,076 | 351 | import numpy as np
import math
from sklearn.preprocessing import minmax_scale
from matplotlib.colors import ListedColormap
def mag2db(y):
"""Convert magnitude response to decibels for a simple array.
Args:
y (numpy array): Power spectrum, raw magnitude response.
Returns:
dby (numpy array... |
2ec7361249d543c7b99922c24ae6fa7fe19776bbe0e52e5963528ba8b6be65cc | Python | 12,090 | 296 | """Construction tests derived from the examples/ notebooks.
Covers building effectors and environments, inspecting their configuration,
initial state values after reset, custom subclassing, and policy save/load —
none of these tests advance the simulation with step().
"""
import json
import os
import tempfile
import ... |
f31a7449fb729acf79588b0adb415ba2c1c55ff4e6d51ec60cff412e47185f0c | Python | 12,090 | 378 | """
This file contains various stand-alone functions used throughout the correction process.
"""
import torch
from torch import cuda
import numpy as np
from scipy.io import loadmat
import nibabel as nib
def load_data(im1, im2=None, phase_encoding_direction=1, n=None, device='cpu', dtype=torch.float64):
"""
Load or... |
1d541c22da517484d96bfc50f788721a6a7ad0710a4603d060c60ac6567ab937 | Python | 12,093 | 315 | """
Storage layer for perses automated molecular design.
TODO
----
* Add write_sampler_state(modname, sampler_state, iteration)
* Generalize write_quantity to handle units
* Add data access routines for reading to isolate low-level storage layer
"""
__author__ = 'John D. Chodera'
###################################... |
f51689a1707c1ee31a173b8d8c9aaea33d994f4d95a79e6d51fe7369b2247142 | Python | 12,097 | 322 | """Structured tool."""
from __future__ import annotations
import functools
import textwrap
from collections.abc import Awaitable, Callable
from typing import (
TYPE_CHECKING,
Annotated,
Any,
Literal,
)
from pydantic import Field, PlainSerializer, SkipValidation
from typing_extensions import override
... |
639fd5a330f0d6585f6821228d900dafab85d4f1354ee9cdea38ef92267a442d | Python | 12,098 | 379 | import argparse
import os
from time import time as t
import matplotlib.pyplot as plt
import numpy as np
import torch
from torchvision import transforms
from tqdm import tqdm
from bindsnet import ROOT_DIR
from bindsnet.analysis.plotting import (
plot_assignments,
plot_input,
plot_performance,
plot_spik... |
ca5e9bf607dea47a530709cad464b3a49b9a615bfce78822ea2a8c2dc9105da4 | Python | 12,100 | 377 | UNET = {
"Unetmod-cond1-v2-64_4_1": {
"class_path": "pdearena.models.unet.time_cond_LN.twod_unet_cond_v2.Unet",
"init_args": {
"hidden_channels": 64,
"norm": True,
"ch_mults": (2, 2, 2, 2),
"is_attn": (False, False, False, False),
"... |
2717a3ef046d48c01b271ce3ed263c1a7ea3f2997fdffdd28ab644fdba3da8f9 | Python | 12,110 | 350 | import argparse
from pathlib import Path
from datetime import datetime
import logging
import quantities as pq
from quantities import Quantity
import neo
from neo.utils import get_events, cut_segment_by_epoch, add_epoch
from elephant.signal_processing import butter
from elephant.spectral import welch_psd
import nump... |
9fbdba0157654d4065f8e02314fd034bca651357332a05a771390370c7101056 | Python | 12,116 | 248 | import sys
import os
import random
import pandas as pd
import numpy as np
from einops import rearrange, repeat, reduce
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import Adam, SGD, AdamW,RMSprop
import torch.utils.data as Data
from torch.utils.data import Dataset, Dat... |
7fee7fee4823b246c088ed2343734256aea8ce9a69d639825ea151fea3adf039 | Python | 12,121 | 386 | from uuid import UUID
import pytest
from langchain_core.language_models import FakeListLLM
from langchain_core.tools import Tool
from langchain_core.tracers.context import collect_runs
from langchain_classic.agents import (
AgentExecutor,
AgentExecutorIterator,
AgentType,
initialize_agent,
)
from lang... |
926ea4be4dc48c645b07dea651d0f79239a25f018d36429b5da7d8a1f0e940ee | Python | 12,130 | 214 | # 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
#
# http://w... |
3f9d1a8aebff0a77dfa406e83614b1b9d7909c27ca07d2ae1fa68ecd028b49a6 | Python | 12,139 | 295 | """
Generic interface for nonlinear least-squares minimization.
"""
from warnings import warn
import numbers
import torch
from .trf import trf
from .common import EPS, in_bounds, make_strictly_feasible
__all__ = ['least_squares']
TERMINATION_MESSAGES = {
-1: "Improper input parameters status returned from `leas... |
65f728cd93caabfd549fea5d53895e80c10ecb52d8a7feda004937612336d408 | Python | 12,146 | 250 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
POCIF-BAT R2 — H-R2-1 : test de non-commutativité des opérateurs de dégradation.
Question réfutable : à dose strictement égale et jeu de conditions identique,
l'ORDRE d'application modifie-t-il l'état de santé final au-delà de la
variabilité inter-réplicats ?
Corpus ... |
b6164c6e24f97d8722c3f577d894711538e6a0f3443a543105248cb06a699851 | Python | 12,147 | 236 | # 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
#
# http://w... |
463c565e8b53c8e5f7276a028a7d51f62fea8e51c16711d787fbbb14f9f7ba7a | Python | 12,153 | 294 | "Create the PICNN model" # pylint: disable=invalid-name
import numpy as np
from omegaconf import OmegaConf
import torch
from src.PICNN.layers import (
weight,
bias,
apply_linear_layer,
get_act_func,
)
class PICNN(
torch.nn.Module
): # pylint: disable=invalid-name,too-many-insta... |
81429fb4295fa5857eb99b3184729bb3ad5cb62bea040228ad59e9256578e549 | Python | 12,156 | 337 | # coding: utf-8
""" MIT License """
'''
Project: Parenchymal Attention Network
Authors: Axel Masquelin
Description: Image manipulation and visualization function
'''
# Libraries
# ---------------------------------------------------------------------------- #
# from PIL import Image
import matplotl... |
f22cea1323af76810686e4d8901827b8c277a120994c1a5c6f8cbd7f2c3321ee | Python | 12,177 | 214 | """Tests for raw-DWI inputs, output layout and disposable intermediate cache."""
import json
import os
from pathlib import Path
import subprocess
import tempfile
import unittest
from unittest.mock import patch
import nibabel as nib
import numpy as np
import SimpleITK as sitk
from preprocessing.inputs import ensure_re... |
f89585619c376b0296ecb51663dff9117a97671afc25bfd13de045f4073f4dc9 | Python | 12,178 | 328 | from h3xsemble.utils.rigid_utils import Rigid, Rotation
import torch
import pickle
import random
def _read_pickle_select(
fn,
structure_mode, # igfold, immunebuilder, etc...
model_index,
select_feature_s=["rigidgroups_gt_frames"],
use_crystal=True,
# check_consistency=False,
# select_feat... |
9664a19d821368a38dc7c5dbde39a9bb4953d1c9e4922115fce958271642ca06 | Python | 12,179 | 325 | import numpy as np
import pyemma
from pyemma.util.contexts import settings
import MDAnalysis as mda
import matplotlib.pyplot as plt
from pensa.preprocessing import sort_coordinates, merge_and_sort_coordinates
from .visualization import project_on_eigenvector, sort_traj_along_projection
# --- METHODS FOR PRINCIPAL COM... |
81628f8af7a64c77cda5a6cfe85902639a390845d5c6d9a9005d1c08d38b95ac | Python | 12,183 | 289 | import matplotlib
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import numpy as np
import pandas as pd
import os
from scipy import stats
from plot_brains import brain_regions, prefixes, prefix_idx
from collections import defaultdict
import copy
import seaborn as sns
from statsmodels.stats.multites... |
2057eff1fe4f3c86a26a49cf1f21831717e078f72b941a4b498e2794dfff8664 | Python | 12,186 | 390 | """
Generate R codes for hBayesDM using model information defined in YAML files.
.. moduleauthor:: Jethro Lee <dlemfh96@snu.ac.kr>
.. moduleauthor:: Jaeyeong Yang <jaeyeong.yang1125@gmail.com>
"""
import argparse
import re
import sys
from collections import OrderedDict
from pathlib import Path
import yaml
try:
... |
a122999af7fb1cebec7736fceb2e1cd0f6103657641ca930eba8d10c0840ee52 | Python | 12,187 | 342 | # Copyright 2024 DeepMind Technologies Limited
#
# AlphaFold 3 source code is 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... |
8e210bbf35acb21256312d0e543395860fe215c21bf7520695bd82958125dcae | Python | 12,189 | 362 | """Tests for Google GenAI block translator."""
from copy import deepcopy
from typing import Any
from langchain_core.messages import AIMessage, AIMessageChunk
from langchain_core.messages.block_translators.google_genai import (
translate_grounding_metadata_to_citations,
)
from langchain_core.messages.tool import t... |
4f873e26e7cc5bf12b6da7e1c7c23fb521926db33bb8378dc462b66dbe9d09c4 | Python | 12,196 | 261 | from typing import Sequence, Union, Any
from collections import OrderedDict
from numpy import inf, exp
import pandas as pd
from hbayesdm.base import TaskModel
from hbayesdm.preprocess_funcs import pstRT_preprocess_func
__all__ = ['pstRT_rlddm6']
class PstrtRlddm6(TaskModel):
def __init__(self, **kwargs):
... |
7d1c2fe52045c89a2c7bfa7623efb3427d480c3ca2fdc49d8b98f2d416893602 | Python | 12,199 | 283 | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import B... |
54dc92d381b45027bd7a0ba2c61577c6eac4e1255ba7688faecacd725fdc35d0 | Python | 12,201 | 243 | """
Optimal Switching Decision taken for network
"""
import time
from timeit import default_timer as timer
from gurobipy import *
#----------------Constant Values------------------------------
M=100 #large value for optimization
Vmax=1.05 # max pu voltage
Vmin=0.95 # min pu voltage
# Squaring the v... |
9a1d827b2265b16fad602ab2e30c7dfd9fcdb5e1a090da3c1fa1299a35424948 | Python | 12,201 | 304 | import numpy as np
import deeptime
import matplotlib.pyplot as plt
from pensa.preprocessing import merge_and_sort_coordinates
from .visualization import project_on_eigenvector_tica, sort_traj_along_projection
# --- METHODS FOR TIME-LAGGED INDEPENDENT COMPONENT ANALYSIS ---
# https://deeptime-ml.github.io/latest/api/... |
f12fdacf7fc769a0427e6d2364dfaad6a4c323799ac92a936c0fd0051c35a54d | Python | 12,205 | 272 |
def get_data(dataname, suffix='NA'):
gt=[]
data_dir = '/mnt/data2/aanchal/data/'+dataname+'/'
if (dataname=='IMC_T1D'):
subfolder_name ='raw_data' # sub-folder containing raw data
raw_data_file = pd.read_csv(data_dir+'/'+subfolder_name+'/mgDF.csv', index_col=0) # contains "cells" on rows a... |
3d82c0a097b3726106057f4992acee393fa55743dd11d37d8659d3b7678aefec | Python | 12,210 | 282 | """
This module provides utility functions for aligning Markov states between
different training attempts (different models trained for a same system)
and different systems (aligning the ensembles of models for different systems).
"""
import logging
logging.basicConfig(encoding='utf-8', level=logging.INFO)
import nump... |
9a068ee67feb3526a45d59bd5f7fc59ddf51be6296c340a553c940f61129981b | Python | 12,214 | 399 | ########################################################################
# Stochastic degradation-diffusion process.
# RESTORE
# AIMS: to verify checkpointing and restoring of the spatial stochastic
# solver 'Tetvesicle' in the context of the Degradation-Diffusion model
# (see validation/kisilevich.py)
##########... |
3ad95b841cc37a0ca670a69cc0b36f27091790050c1840be137ffab04b2e6e9b | Python | 12,223 | 280 | import os
import argparse
import torch
import torch.optim as optim
import torch.nn.functional as F
import torch_geometric.loader as geom_data
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.callba... |
be9df77c2b380e5b8d110106d2d4deaef699ab26cc956b78e2408a4d337b79d5 | Python | 12,226 | 363 | import copy
import importlib
import ml_collections as mlc
def set_inf(c, inf):
for k, v in c.items():
if isinstance(v, mlc.ConfigDict):
set_inf(v, inf)
elif k == "inf":
c[k] = inf
def enforce_config_constraints(config):
def string_to_setting(s):
path = s.split... |
646c55337a889045114649327d8e6893b27d28c1367b9d55d3e3f5d8c527d868 | Python | 12,228 | 216 | # 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
#
# http://w... |
c9c8b51e96b2273a30bd572247e6bfc433880c0bb3c3ba95f6c5790607b698ab | Python | 12,231 | 399 | ########################################################################
# Stochastic degradation-diffusion process.
# RESTORE
# AIMS: to verify checkpointing and restoring of the spatial stochastic
# solver 'Tetvesicle' in the context of the Degradation-Diffusion model
# (see validation/kisilevich.py)
##########... |
bf8c7b69060c7b5bdbd1a3393d5419cb5d62d12bc85d23131c962273317b2cd9 | Python | 12,237 | 342 | import argparse
import shutil
import time
import numpy as np
import os
from os.path import exists, split, join, splitext
import sys
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
im... |
d9c6bcd1662e4d242f5fea626e70be665ac1226b737c771d4fe6cb020e0d58ca | Python | 12,237 | 356 | # Copyright 2024 DeepMind Technologies Limited
#
# AlphaFold 3 source code is 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... |
fc8d7c878247e348232e7bd87ce52563439541da230618493f7fb670ba0f4aff | Python | 12,241 | 273 | # 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
#
# http://w... |
908d0fa0ac22561a096d43e0d9080d7b1069e473fd64e6aec5bd6ea215bc5a77 | Python | 12,245 | 291 | from __future__ import division
from __future__ import print_function
import os
import time
import h5py
import random
import argparse
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from copy import deepcopy
from torch_geometric.data import Data
from models import *
def see... |
bee8e7d2c129a2392b2b24dfa8383ce39d17c854e3e577fb717bbca9f15c6c7a | Python | 12,251 | 314 | from PyQt5.QtWidgets import (QApplication, QComboBox, QGridLayout, QGroupBox, QLabel, QPushButton,
QFileDialog, QMessageBox, QWidget, QSizePolicy, QCheckBox, QTableWidget, QVBoxLayout,
QTableWidgetItem, QLineEdit)
from PyQt5.QtGui import QDoubleValidator, QIntValidator
from matplotlib.backends.backend_q... |
357142dd195386f375e903f48a6ae059bc16bf1eac6b418fdab1a0791e33c796 | Python | 12,260 | 409 | """
Polyphen prediction pipeline
=============================
:Author: Andreas Heger
:Release: $Id: pipeline_snps.py 2870 2010-03-03 10:20:29Z andreas $
:Date: |today|
:Tags: Python
Purpose
-------
Input:
Indels in pileup format.
Usage
-----
Type::
python <script_name>.py --help
for command line help.
Code... |
a5e7e3ed40b5de3a44a5508dc6b3010b3eb158e52f86b014ea808f0015cf6563 | Python | 12,264 | 207 | import os
import pickle
import logging
import tensorflow as tf
import numpy as np
import pandas as pd
from dipgnn.utils.register import registers
from dipgnn.tasks.base_training_task import BaseTrainingTask
from dipgnn.utils.regression_metrics import RegressionMetrics
@registers.task.register("regression_t... |
b1248e4fe226d52605e3bc728a62b3ea540cca89db9c6cba11355e739018ffc6 | Python | 12,274 | 277 | import argparse
import logging
import os
import random
import shutil
import sys
import time
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
from torch.nn import BCEWithLogitsLos... |
85fe4809c480e44d3ad5e33e39aef512a56b9c097c6705f99977ba0d86129fff | Python | 12,278 | 133 | """Tables and the number ledger for protocol P5 (Changan field vehicles).
tables/table_field_data.tex field corpus and label model
tables/table_p5.tex SOH MAPE under F1 / F3 / P5 by feature set (+ label-noise reference)
tables/table_p5_conformal.tex P5 interval quality with twelve laboratory sources a... |
f506ebb1dc17a835b6f272c402f97d4bdd881d7d2cf63bcd077e97dfb3b8a96f | Python | 12,280 | 292 | from PyQt5.QtWidgets import (QApplication, QComboBox, QGridLayout, QGroupBox, QLabel, QPushButton,
QFileDialog, QMessageBox, QWidget, QSizePolicy, QCheckBox, QLineEdit)
from matplotlib.backends.backend_qt5agg import FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationT... |
2116d23ac353362f69f963cdc06eba66a15e66f3234ce33da84bd6ff50d93776 | Python | 12,281 | 271 | import sys
import os
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import itertools
sys.path.insert(1, os.path.join(sys.path[0], '../../../../'))
from Simulator.SimulatorPattern import simulatorPattern, simulatorPatternDual
import Articles.Sequential_Learning_Striatum.Figures.cfg_pdf as cfg
fr... |
dc724f4fc5cb37720e406fe6ac27f500284d6bb9deff5b03f4cc17bcb4954cca | Python | 12,283 | 298 | from itertools import chain
import numpy as np
import openpyxl
import pandas as pd
import xlrd
def load_pathway():
pathway = "c2.cp.kegg.v7.2.symbols.gmt"
gene_in_pathways = []
for line in open(pathway):
line = line.strip()
split_result = line.split('\t')
gene_in_pat... |
61bff00919940950489d2af5d0ea791f6a31c635a9e5ba3e9262873af1483c8c | Python | 12,291 | 318 | from typing import List, Optional
import pandas as pd
import numpy as np
from rdkit import Chem
import polyfingerprints as pfp
REPEATING_UNIT_COLUMN_PREFIX = "SMILES_repeating_unit"
MOLPERCENT_RU_COLUMN_PREFIX = "molpercent_repeating_unit"
ADDITIVE_COLUMN_PREFIX = "additive"
ADDITIVE_WP_COLUMN_POSTFIX = "_weight_pe... |
be1d7b14d13b9ff2d390ecfff36e76d3baa6b9224fa6a18aff7f77c6c55399ba | Python | 12,291 | 250 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 17 21:22:12 2022
@author: Shencong Ni
"""
'''
Calculate the phase locking value (PLV) between the MUA in area 1 and area 2.
Run the onoff_detection.py first before running this script.
'''
import matplotlib as mpl
mpl.use('Agg')
import scipy.stats... |
2ded714673b86672634976af12a85b80c44cab15a0d443a1ba7a51632ab982eb | Python | 12,294 | 274 | from torch import nn
import torch
import math
class GCL(nn.Module):
def __init__(self, input_nf, output_nf, hidden_nf, normalization_factor, aggregation_method,
edges_in_d=0, nodes_att_dim=0, act_fn=nn.SiLU(), attention=False):
super(GCL, self).__init__()
input_edge = input_nf * 2
... |
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