sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
94fceaa71a2186094a0340180b23860610a3665504b4848fc0af0a941bc61217 | Python | 3,015 | 72 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['processing.zip'])
tmp_dir = tempfile.T... |
03b057a5bdb9a848be086af459333b172b45b7dfebf9871e351a7913c5c3adc1 | Python | 3,016 | 62 | """A module that contains unit tests for the :py:mod:`corelay.processor.affinity` module."""
import typing
import numpy
from scipy.sparse import csr_matrix
from scipy.spatial.distance import pdist, squareform
from corelay.processor.affinity import RadialBasisFunction, SparseKNN
class TestSparseKNN:
"""Contains... |
195dc80b25755e5bb0500221924a7702e5b1114e9f16d5db8ef0d6f6f28b6a3f | Python | 3,016 | 82 | import json
import torch
import pandas as pd
import numpy as np
from torch_geometric.data import Data
from torch_geometric.loader import NeighborLoader
def generate_graph_loader(dataset="Twibot-20", num_neighbors=40, num_layers=3, batch_size=256):
# node = json.load(open(f"../../datasets/{dataset}/node.json", "r"... |
b9079c3d10e9cc07c1119ebd0ba5e2aa2931efa8b89b556cfd280577e8920ea0 | Python | 3,017 | 96 | # -*- coding: utf-8 -*-
import numpy as np
from scilpy.dwi.operations import compute_dwi_attenuation, \
detect_volume_outliers, apply_bias_field
def test_apply_bias_field():
# DWI is 1 everywhere, one voxel at 0.
dwi = np.ones((10, 10, 10, 5))
dwi[0, 0, 0, :] = 0
mask = np.ones((10, 10, 10), dty... |
10b2a0dc08abba5429e8f7f59f336c8e7b06141830899d0944cae9c3c2cd3110 | Python | 3,018 | 70 | import subprocess
import torch
from batchgenerators.utilities.file_and_folder_operations import save_json, join, isfile, load_json
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
from torch import distributed as dist
class nnUNetTrainerBenchmark_5epochs(nnUNetTrainer):
def __init__(self,... |
20c77536a5db0670b6c0bd88f7de77b6c37f3945c7cf6f7b11319b556e8202e4 | Python | 3,018 | 85 | import pickle
import warnings
warnings.filterwarnings("ignore")
import argparse
from nltk import ToktokTokenizer, WordNetLemmatizer
if __name__ == "__main__":
# please download data from https://data.stanford.edu/congress_text and place in the folder ./data/hein-daily
# Reference: https://data.stanford.edu/c... |
7b1fde31cfab90483ec1bad02f17773889648190171b481f4d30529d4cfe8c5b | Python | 3,018 | 109 | #!/usr/bin/python3
#
import os
import sys
import argparse
import numpy as np
from sklearn.neighbors import NearestNeighbors
from uac import utils
# Create the parser
my_parser = argparse.ArgumentParser(description="Writes vtx files for volumetric UAC")
# Add the arguments
my_parser.add_argument("target_path", metava... |
7d10c853314955c914e02ec7b941c535f112c4aff506d4d52eab7ddc2d8492f4 | Python | 3,019 | 90 | #!/usr/bin/env python3
"""
Example: SMILES -> RDKit 3D conformation -> XYZ file.
Requires:
- RDKit (e.g. `conda install -c conda-forge rdkit`)
Usage:
python examples/conformation_generation/01_smiles_to_rdkit_xyz.py \
--smiles "CCO" \
--out rdkit.xyz
"""
from __future__ import annotations
import argpars... |
e70377d73cbb8203cc47ba7f71e3d240080e1f7ed4bf2bd4d1382fc240419f99 | Python | 3,019 | 104 | # Copyright (c) DP Technology.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import contextlib
def str_hash(text: str):
hash = 0
for ch in text:
hash = (hash * 281 ^ ord(ch) * 997) & 0xFFFFFF... |
39554f844f8e35083c59e92cc9b18515e417e167a8c12f0f9425ccc9c33a2b95 | Python | 3,020 | 87 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import json
import base64
import io
import yaml
import numpy as np
from PIL import Image
from mmpose.apis import MMPoseInferencer
def init_context(context):
context.logger.info("Init detector...")
det_config = (
"/opt/nuclio/mmpos... |
fba7897471af61bf03a4de03007a00e020b8bfb211b7bbfc42fcb5123c510a1d | Python | 3,020 | 72 | '''
Python file with the purpose of a shell script.
'''
import subprocess
import shlex
import os
'''
datasets = ['TwoCubesBlendereevee', 'TwoCubesBlender1024', 'TwoCubesBlender128']
for dataset in datasets:
command = f"python3 Analysis_2Tables.py --path2outs {dataset}_IRNet_0 --nb_instances 2"
args = shlex.s... |
1384fc49e15e86984daa68738e37f0d9e1dbbea80bd8f542f37f13ee81d87963 | Python | 3,022 | 75 | import numpy as np
def find_subset_with_all_ones(matrix, row_weight=1, min_rows=5, min_cols=600):
"""
Finds a subset of the given binary matrix with all ones,
represented as row and column indices.
Args:
matrix (numpy.ndarray): A binary matrix with M rows and N columns.
row_weight (fl... |
7128f9cba5774b7a46b7ae99fe53ddfcd7c2711e5f23e48f9820c65589483591 | Python | 3,022 | 91 | import numpy as np
import torch as pt
import seaborn as sns
import matplotlib.pyplot as plt
from Functional_Fusion.dataset import DataSetMDTB
import PcmPy as pcm
from scipy.stats import pearsonr
from OptimalBattery.global_config import data_dir,save_dir
import OptimalBattery.util as ut
# Constants
base_dir = f'{data_di... |
a51c7a4306e767b5f344e2429d65587b663e75d9a2c8246d35e38c5af08768b7 | Python | 3,022 | 80 | import seaborn as sns
import numpy as np
def plot_cortical_cell(ax, sk, ld=None, title=None):
"""Plot a cortical neuron according to ivscc style plot
Parameters
----------
ax : matplotlib.axes
Axes object in which to put plot
sk : allensdk.core.swc.Morphology
Morphology object ske... |
10c6c6a31a75a55f5b026d8039a6176d71a9b4f1f556a6deff42088dbc26ca01 | Python | 3,024 | 77 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
import os
import numpy as np
from numpy.testing import assert_array_equal
from bluepysnap import Circuit
from voxcell import VoxelData
from utils import TEST_DATA_DIR
import connectome_... |
b7d34971ec252cee7becf120371233c9a9e32ad8c8baef9a870da1625b2a1132 | Python | 3,024 | 85 |
"""
Read line data from a text file. This reader is very similar to sparse pointer reader.
The data format is:
lineseries_id,z_position,x_position,y_position\n
lineseries_id,z_position,x_position,y_position\n
...
No header.
The loader creates a list of lists, where all points within each list are linked.
All poi... |
fc9117c026ad14547be67eaf754a026942a25fd290eb68faff47b6ec9af392ad | Python | 3,024 | 86 | # Copyright 2024 Google LLC
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... |
3dfbb07ca560bd3b433ab8f1fe42c9d8d1ebea32773292d3740c85660e1042ab | Python | 3,025 | 66 | # -*- coding: utf-8 -*-
"""
Create FOV ROI
This script computes a label file to include only vertices (in further analysis) which
are within the intersection of multiple volumes.
"""
# external inputs
import numpy as np
import nibabel as nb
# local inputs
from fmri_tools.surface.mesh import Mesh
from fmri_tools.sur... |
0a86c164fd50bfc824d1970287caf7acc65191b67e9f2d00af65eafeeea4dac0 | Python | 3,026 | 79 | #!/usr/bin/env python3
import pysam
import argparse
import pathlib
def parse_command() -> argparse.Namespace:
"""Function to parse the command line input
Args:
None
Return:
Namespace: returns the args as a standard Namespace object
"""
parser = argparse.ArgumentParser(description='... |
6b64f67fadd179344ac7676f3f7722ebfc90d311db8f61507ba5064cab1e136d | Python | 3,026 | 85 | import math
import torch
import torch.nn.functional as F
from distances import CosineSimilarity
from utils import common_functions as c_f
from utils import loss_and_miner_utils as lmu
from losses.base_metric_loss_function import BaseMetricLossFunction
from losses.mixins import WeightRegularizerMixin
###### modified... |
6e4a53721b2b334a1c6f5affa3a8d3731f511b5773941872a212bb39fa107e79 | Python | 3,026 | 116 | """Data loading and saving operations."""
import numpy as np
import scipy.io as sio
from typing import List, Dict, Text, Union
def load_label3d_data(path: Text, key: Text):
"""Load Label3D data
Args:
path (Text): Path to Label3D file
key (Text): Field to access
Returns:
TYPE: Dat... |
b40cd9e32b03bcaa155aa48ba85263ee75e37ba2379e6e0411c8f835e50c5a49 | Python | 3,026 | 96 | # Copyright 2024 Google LLC
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... |
e8c10d8e5d684913efbff905a9fbbed194b8ae1cb49b5d3dce410e68248325cd | Python | 3,026 | 78 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import torch
import torch.nn as nn
from torchvision.models import resnet18
from brainscore_core.supported_data_standards.brainio.s3 import load_weight_file
from brainscore_vision.model_helpers.activations.pytorch import Pyt... |
fa622b2930647a61a6bb7a31676b7d42dbe4286c380b9a7eaf6773642f20cd4e | Python | 3,028 | 87 | # Copyright 2014-2015 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
import os
import pandas
try:
import simplejson ... |
429b97c52df659fb572a4652aba771c067d13d33c70669917bacd3f4404e21ef | Python | 3,029 | 89 | from typing import Final, Literal
import numpy as np
from argclz import try_int_type, argument
from neuralib.plot import diag_histplot, plot_figure
from rscvp.statistic.persistence_agg.core import AbstractPersistenceAgg
from rscvp.util.cli import Region
from rscvp.visual.main_response import VisualPatternCache, Apply... |
5b1c57de4332bce59ddce893ec1f352dbc29ee567007546a26637fc03fb977a2 | Python | 3,029 | 100 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import numpy as np
import nibabel as nb
from numpy.matlib import repmat
def generate_coordinate_mapping(file_in, pad, path_output=None, suffix=None,
time=False, write_output=False):
"""Generate c... |
6788c20cead60cd89a62c36dd5be81f651ef44ec87a2e3796430182685671ed0 | Python | 3,029 | 103 | """
Activation functions.
"""
import torch
import numpy as np
import EIANN.external as external
def set_activation(network, activation, **kwargs):
# Set callable activation function
if isinstance(activation, str):
if activation in globals():
activation = globals()[activation]
elif... |
6bdad75e456d1ec20424f5e57f72287855ffdbd4bf6373020e4eeda0be989a85 | Python | 3,031 | 110 | import traceback
from pathlib import Path
from typing import List, Optional, Tuple
import click
from aicsimageio import AICSImage
from PIL import Image
from tqdm import tqdm
from brainways.utils.image import resize_image, slice_to_uint8
from brainways.utils.io_utils import ImagePath
def creat_thumbnail(
input: ... |
d3dd5c2739074602187fee3f4be84b6607ba24d51389169be6d5cd38f5f213ec | Python | 3,031 | 76 | import os
import pandas as pd
import subprocess
from nipype import Node, Workflow
from nipype.interfaces.utility import IdentityInterface, Merge, Function
from .fsl.fsl_anat_nipype import FSLANAT
class FSLANATPipeline:
def __init__(self, subject, session, output_path, **kwargs):
"""
FSL Anat Pipel... |
49e1817d994456c1e4f5d5901a528b4504d3afd05f94bc458a1e2b3ee6d5db65 | Python | 3,032 | 93 | #!/usr/bin/env python
# author: Jannes Spangenberg
# github: https://github.com/JannesSP
# website: https://jannessp.github.io
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, Namespace
import os
from pathlib import Path
from collections import defaultdict
from tqdm import tqdm
import h5py
def pars... |
4cbd75f92287c457b5b6964fea466e966e464c87a61fbc0e088ec72776c56f79 | Python | 3,032 | 84 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors:
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may... |
5970b58e06a2a328517e8eac41af063162bb248ccbeb8265c3b41752b7c453ec | Python | 3,032 | 80 | import torch
from utils import common_functions as c_f
from utils import loss_and_miner_utils as lmu
from losses.generic_pair_loss import GenericPairLoss
class LiftedStructureLoss(GenericPairLoss):
def __init__(self, neg_margin=1, pos_margin=0, **kwargs):
super().__init__(mat_based_loss=False, **kwargs)
... |
bc33afcdc69d0576c212a46533b4101c2028d80d7e0e22356b4b7a8bc1be4f70 | Python | 3,032 | 113 | from collections.abc import Callable, Generator, Iterator
from pathlib import Path
from typing import (
TYPE_CHECKING,
)
import numpy as np
import pandas as pd
import polars as pl
from tqdm import tqdm
from eir.data_load.label_setup import get_file_path_iterator
from eir.setup.input_setup_modules.setup_sequence i... |
5bc7a2ad0274cb093432816f6dd59f94adb63f0ffdddfbce0eaec3427a6310c8 | Python | 3,033 | 87 | import torch
import torch.nn as nn
import torch.nn.functional as F
import sklearn
import sklearn.cluster
from torch.nn.modules.module import Module
from torch.nn.parameter import Parameter
def cluster(data, k, temp, num_iter, init, cluster_temp):
cuda0 = torch.cuda.is_available()
if cuda0:
mu = init.c... |
9527b4a746e0d9244ad705fe3dd50c8ed227317b6e49c9120871afc888ed9025 | Python | 3,033 | 77 | from __future__ import print_function, absolute_import
import os.path as osp
import glob
import re
from ..utils.data import BaseImageDataset
class PersonX(BaseImageDataset):
"""
PersonX
Reference:
Sun et al. Dissecting Person Re-identification from the Viewpoint of Viewpoint. CVPR 2019.
Dataset ... |
26cf2bd62df82888d57e5b55d67cbc9093be774cf19fdda6b3b89189f0b0717d | Python | 3,034 | 102 | import torch
from torch_geometric.graphgym.register import (
register_edge_encoder,
register_node_encoder,
)
@register_node_encoder('Integer')
class IntegerFeatureEncoder(torch.nn.Module):
r"""Provides an encoder for integer node features.
Args:
emb_dim (int): The output embedding dimension.... |
c29311ab85b5bbcd786796df7fbace66d7f5e60360c65f1af56118604cdb00c8 | Python | 3,034 | 85 |
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
import scanpy as sc
import anndata as ad
def intersect(lst1, lst2):
temp = set(lst2)
lst3 = [value for value in lst1 if value in temp]
return lst3
def combine_training_data(src, tgt, is_preprocess=True):
if not is_preprocess... |
f0b77fd0b541c850488b8fd76a0ae3fae69b2775f19b9802303ca5c095b34673 | Python | 3,036 | 80 | """Molecular docking graph.
Reuses the single-agent graph setup, changing only the default tool list and the
default system prompt so the agent is focused on molecular docking. Keeping docking
tools in their own graph (rather than in the single-agent defaults) means the general
single-agent workflow and its evaluation... |
70e354d420b4a7c321ca234615399d0cfc3a0174614b08b35751504647e47bba | Python | 3,037 | 81 | import random
import time
import tensorflow as tf
from .utils import multi_input
from .utils import setup_ckpt_manager
from .loss import loss_function_mimo
from .metrics import metrics_func_mimo
from .batchloader import BatchLoader
#from .show_utils import subShow3
def train_model(model, train_data, val_data, config... |
ec915dedb7f9f7e152895d492934805c0dfbb80c0bf1e6d61b5e49fe9ecb79bb | Python | 3,037 | 100 | # Coulomb matrix descriptor functions
# Imports
import json
import glob
import collections
import numpy as np
import numpy.typing as npt
import ase
import ase.io
import dscribe.descriptors
from modules.build_data.fhi_aims.descriptors.default_settings import (
DEFAULT_CM_SETTINGS,
)
from modules.tools.strings im... |
294ab0ecc73de75f83f3bcb5b9e384688b73f89accc2b95d94c307dccbaf3023 | Python | 3,038 | 38 | from brainscore_core.supported_data_standards.brainio.assemblies import DataAssembly
from brainscore_vision import data_registry, stimulus_set_registry, load_stimulus_set
from brainscore_core.supported_data_standards.brainio.s3 import load_assembly_from_s3, load_stimulus_set_from_s3
BIBTEX = """@article{CICHY201912,
t... |
9267ef7b0982aa2f407e533c13ba7898e434012690c6092014c63dcaef72977b | Python | 3,038 | 92 | import numpy as np
ALL_DEGREES = ["1", "2", "3", "4", "5"]
LABELS = [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4]
LABEL_NAMES = ['horn', 'tiger', 'guitar', 'kangaroo']
CHANCE_LEVEL = 0.5
SUBJECTS = [
"sub01",
"sub02",
"sub03",
"sub04",
"sub05",
"sub06",
"sub07",
"s... |
619bb869b11827875c74b4507f6e6925296ffc2a8296cfd1dbc6daef9b728b5e | Python | 3,039 | 95 | import numpy as np
from ccf_streamlines.projection import ISOCORTEX_LAYER_KEYS
# Structure set IDs from the mouse ontology, in the same pia-to-white-matter
# order as ISOCORTEX_LAYER_KEYS. The names come from that one list so a
# thickness file written here is always keyed the way the projector classes
# read it back... |
719761590feaf906fcbcc85115531ef69bd7e5d80b9188a1d4999f5b47a32d61 | Python | 3,039 | 86 | """
Copyright 1999 Illinois Institute of Technology
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, publis... |
9a24c8198b8493a6f4cfb96fadebf468290d87ba7c1987634fa4324c70c2faa1 | Python | 3,039 | 105 | #!/usr/bin/env python
import sys
from PyQt5 import QtCore, QtWidgets
import storm_control.sc_library.tcpClient as tcpClient
import storm_control.sc_library.tcpMessage as tcpMessage
import storm_control.sc_library.tcpServer as tcpServer
class Client(QtCore.QObject):
def __init__(self, **kwds):
super()._... |
3844749132372945c1c366628448c946774a497c07e880ad92bef83b6ea4261a | Python | 3,040 | 69 | import os
import json
import numpy as np
import matplotlib.pyplot as plt
import ivim
fig,axes = plt.subplots(2,1,figsize=(12, 5))
deltas = [15e-3,8e-3]
Deltas = [25e-3,10e-3]
T = 45e-3
TE = 60e-3
titles = ['Monopolar','Bipolar']
# Pulse sequence diagrams
for ax,delta,Delta,title in zip(axes,deltas,Deltas,titles):
... |
6a735a144911bdcd36bdd2dc184755131fe091f9ebdecd1f7c31553f2aee6256 | Python | 3,040 | 53 | """Upsample each image to 2 times the resolution.
NOTE: This is important to preserve the single voxel level fine details such as the intracortical vessels
"""
import os
import subprocess
import numpy as np
import nibabel as nb
# =============================================================================
NII_NAMES... |
8b9a01167d8cbe73f2d23876fc4ea72afc01805a009d6e7bfbac253a158bd969 | Python | 3,041 | 104 | """Nottingham MEGUK: Source reconstruction."""
from glob import glob
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import mne
from osl_dynamics.meeg import parallel, parcellation, rhino, source_recon
from osl_dynamics.utils.filenames import OSLFilenames
# -----------------------------------------... |
fab7e6ec0507ecdd702d72f18b1284c6278a25d8e82bf8272aa59429da8a5dc5 | Python | 3,042 | 93 | # by Shaoxuan Yuan at 2024/04/22
import numpy as np
from PIL import Image
import os
from sklearn.model_selection import train_test_split
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
# 图像和掩码的文件夹路径
image_folder = 'D:/桌面/project/atomai/dataset/augmented_image... |
0f4e719dde3f2fd7f0ed1819da6c54be3d9b1ec82ab85c3e888ea2b84bd1cd21 | Python | 3,043 | 95 | import polars as pl
from matplotlib import pyplot as plt
from matplotlib.axes import Axes
from argclz import as_argument
from neuralib.plot import plot_figure
from neuralib.typing import AxesArray, ArrayLikeStr, ArrayLike
from neuralib.util.verbose import publish_annotation
from rscvp.statistic.core import StatPipelin... |
685249488bb19bba68f8f464031d668224326257cd482774257e58b79f79865f | Python | 3,043 | 66 | """System prompts for the single_agent_iri and single_agent_iri_flat workflows."""
alcf_iri_prompt = """\
You are an operator assistant for ALCF (Argonne Leadership Computing Facility).
You answer questions about machine state, allocations, jobs, and remote
filesystems by calling the ALCF IRI Facility API through six ... |
ba5f12e7a8ab276a079538c8755b3fa7ab41a65ee887b8a1443aa27a8cdf4145 | Python | 3,043 | 81 | #!/usr/bin/env python
"""
plot_results.py
───────────────
依据 cross_validation.py 与 apply_thresholds.py 输出，
生成期刊所需 图表：STARD 流程图、ROC、校准、PR、阈值曲线、混淆矩阵，以及表格 CSV.
"""
import glob, json, numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import (
roc_curve, auc,
precision_recall_curve,
confusion_mat... |
0b99848f9f629fb0afee56598f63c43b7b0016c7422aa32e9f1d94c61c164658 | Python | 3,044 | 96 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Henrik Zunker
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... |
22bc7562a4f97c2295a3f9aea134fb58b5c4ba245293d63cbd85640ac7f67ebf | Python | 3,044 | 98 | #!/usr/bin/env python
"""
Thorlabs FW102C control.
Hazen 5/12
Josh 6/26/2013
"""
import traceback
import storm_control.sc_hardware.baseClasses.illuminationHardware as illuminationHardware
import storm_control.sc_hardware.serial.RS232 as RS232
class FW102C(RS232.RS232):
"""
Encapsulates comm... |
6cea5ef30ec67956a16598e35fcdae8849c424036ed2fc3a7db54b49902dfee5 | Python | 3,044 | 97 | #!/usr/bin/env python
from sputils import *
nargs=len(sys.argv)
threshs=['1.0']
if nargs<2:
print('Usage: '+sys.argv[0]+' BPname threshs...')
BPname="B03P223"
BPname="B05P160"
else:
BPname=sys.argv[1]
iarg=2
if nargs>iarg: threshs=sys.argv[iarg:]
iarg+=1
rezdir='dropping_accuracy_filtered_results'
#re... |
0d74f480b21e0df2fadd9d62eb7f5d755ee05baae14ca25a334a1bf92061bef8 | Python | 3,045 | 63 | from SlideRunner.dataAccess.database import Database
import pandas as pd
import numpy as np
import glob
from pathlib import Path
from tqdm import tqdm
from slide.data_loader import SlideContainer
from random import *
def create_indices(files, patches):
indices = []
patches_per_slide = patches//len(files)
f... |
1e79ed1ed0869e86fdbf9f59f217879ee79f519d3d37559cb8068301671cd7f5 | Python | 3,045 | 90 | from dataclasses import dataclass
from sklearn.preprocessing import StandardScaler
from eir.data_load.label_setup import (
al_label_transformers,
al_target_columns,
merge_target_columns,
)
from eir.setup import schemas
from eir.setup.schema_modules.output_schemas_tabular import (
TabularOutputTypeConf... |
a86a62650e1839bf0ba2978cf5791b9f271f537c2dbd4af63a97c58942101f64 | Python | 3,045 | 91 | import math
from torch_geometric.graphgym.config import cfg, set_cfg
from torch_geometric.graphgym.model_builder import create_model
def params_count(model):
"""Computes the number of parameters.
Args:
model (nn.Module): PyTorch model
"""
return sum([p.numel() for p in model.parameters()])
... |
444449c835233fda191eda5e5146c2e1cadb72151aa33782245be0a80d3183f7 | Python | 3,046 | 93 | # -*- coding: utf-8 -*-
"""
Created on Tue Jan 17 14:58:25 2023
@author: gligorov
"""
import matplotlib.pyplot as plt
import numpy as np
def plot_history(history, what_to_plot = 'all'):
"""
Plot history of training
what_to_plot can be set to 'acc', 'loss' or 'lr' (for learning rate)
or ... |
415207f965b1a062ab96934d0c9b02d9b097a29d5a081315e9e8874ed95aab47 | Python | 3,047 | 114 | # The ImpedanceFitter is a package to fit impedance spectra to equivalent-circuit models using open-source software.
#
# Copyright (C) 2018, 2019 Leonard Thiele, leonard.thiele[AT]uni-rostock.de
# Copyright (C) 2018, 2019, 2020 Julius Zimmermann, julius.zimmermann[AT]uni-rostock.de
#
# This program is free ... |
6454b96e2bedcefdc457dcea80ab1e926b90ca86598203d2725865fed44d1f1a | Python | 3,047 | 92 | from typing import Any, Optional, Tuple
import torch
from torch import Tensor
from torch_geometric.utils import scatter
class SparseCrossEntropy(torch.autograd.Function):
# We implement our own custom autograd function for this to avoid the
# double gradient computation to `inputs`.
@staticmethod
de... |
e7f5b8054fdea39eeed0498661f9b889fc0ac4595bb73da89fa477c3e11ecd80 | Python | 3,047 | 102 | import re
import subprocess
from itertools import count
from pathlib import Path
from textwrap import dedent
import mkdocs_gen_files
from qtpy.QtWidgets import QApplication
DOCS = Path(__file__).parent.parent
CODE_BLOCK = re.compile("``` ?python\n([^`]*)```", re.DOTALL)
def _set_dark_mode(dark_mode: bool):
"""S... |
7b757e52a04c81aa0df340d586c4089880747aecc434f5d8aef72ed1421c4f5a | Python | 3,050 | 35 | from testsetclass import TestsetClass
class G21EA(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['EA_9', 'EA_13n', 'EA_14n', 'EA_25', 'EA_si', 'EA_o-', 'EA_9n', 'EA_f-', 'EA_21', 'EA_24n', 'EA_12n', 'EA_18n', 'EA_si-', 'EA_s-', 'EA_21n', 'EA_c', 'EA_cl-', 'EA_16', 'EA_22', 'E... |
447cdbb8e2dc587388c39b266379401256c26bc47dbcca6eff625b1be1199bf2 | Python | 3,052 | 107 | # -*- coding: utf-8 -*-
# python standard library inputs
import argparse
# external inputs
import numpy as np
from nibabel.freesurfer.io import read_label, read_geometry, write_geometry
# local inputs
from ..io.surf import read_mgh, write_mgh
__all__ = ['clip_surface', 'clip_mgh']
def clip_surface(verts, faces, i... |
b6a6739ef0c7cc396220085885e4a126205c0046803e966b9ee7bedbe56b7af5 | Python | 3,052 | 79 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from typing import Any
from django.contrib.auth import get_user_model
from django.contrib.auth.models import Group
from django.test import RequestFactory, TestCase
from rest_framework.exceptions import ValidationError
from cvat.apps.iam.middleware ... |
ee78fd4398e1edbb91f698d3d9263823824958c610781f5dd89143b2cd9612f7 | Python | 3,052 | 91 | import numpy as np
from scipy import stats
import os
import pandas as pd
from faster_bootstrap import bootstrap, load_bootstrap
from bootstrap_volumes import load_results, volume_bootstrap
from statsmodels.multivariate.manova import MANOVA
def welch_ttest(x, y):
n_classes = x.shape[1]
t = np.zeros(n_classes)
... |
6dc0ce113d334e13cb614edac33a7ed0845853c5d0ebd9c2c11ad454de224ead | Python | 3,053 | 96 | '''
Utilities for the console.
'''
import sys, time
import numpy as np
from typing import Any, Union, Callable
def timestamp(s: float) -> str:
'''
Get a timestamp from time measurement s.
INPUTS:
s - Measurement of time (analogous to time.time())
OUTPUTS:
str - Formatt... |
84bd48cda1ad468fb8c231d0c2eb83ad53386633d6e63ddaa6ed929c5521bdba | Python | 3,053 | 95 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Some utilities for backbones, in particular for windowing"""
from typing import Tuple
import torch
import torch.nn as... |
99a0373974f4898c87dbc7724a92eee0968ccc893c8670d373a61d5883e640e0 | Python | 3,053 | 81 | """Plot voxelwise prediction accuracy for each participant.
Figure 2A.
"""
import matplotlib.pyplot as plt
import numpy as np
import os
from collections import defaultdict
from utils import (
get_voxel_to_fsaverage_mapper,
languages,
load_model_results,
num_fsaverage_vertices,
participant_names,
... |
2a1e70238d057d7e59f9ead3d908e0a31a0cefe55038de2084c9d205145bfbf6 | Python | 3,055 | 92 | import os
import json
import pandas as pd
from tqdm import tqdm
dataset_path = '../../datasets'
dataset_list = os.listdir(dataset_path)
# dataset_list = ['Twibot-20']
class User:
"""Class of a User"""
def __init__(self, userid: str):
self.userid = str(userid)
self.dna = ''
def add_tweet... |
8d8fc60a4b661d22e44024ef8a3cf33aec6737feff70cdb8c85c9a497b48c931 | Python | 3,056 | 93 | from pydantic import BaseModel, Field
from typing import Optional, Union, Dict
from pathlib import Path
import torch
import logging
try:
from fairchem.core import FAIRChemCalculator
from fairchem.core.units.mlip_unit.mlip_unit import MLIPPredictUnit
from fairchem.core.units.mlip_unit.api.inference import ... |
b4f244dd8d915cc3d4f810dea903acfa265208b77b711a407a3ebe1f1e9403f2 | Python | 3,056 | 107 | from flair.data import Dictionary
from flair.models import TextClassifier
from flair.trainers import ModelTrainer
from flair.training_utils import convert_labels_to_one_hot, Metric
def test_metric_get_classes():
metric = Metric("Test")
metric.add_fn("class-1")
metric.add_fn("class-3")
metric.add_tn("... |
c0b14151d756b4481173a16d2da02868f0c9c20f4940a94fe4c26536be0bd66c | Python | 3,056 | 73 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
import pytest
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['others.zip', 'processin... |
30cdb615252df72496a2265d9d7333d1fd69232df46f468cd688ab3166a7ed10 | Python | 3,057 | 62 | from train_gtex_random_weights import *
def main():
model_type = 'MultiGene'
current_dir = os.path.dirname(__file__)
DATA_DIR = os.path.join(current_dir,'../data')
config_path = os.path.join(current_dir,"configs/multi_gene_196kb_blood.yaml")
remaining_tests = [(0,0,'60pxxfdk',False,'epoch=19-s... |
32c5fe8c253cb9f1330e50053684d778cb7cff1e587d4352028d77e50a954b18 | Python | 3,058 | 101 | import os
import sys
import math
import pprint
import torch
from torchdrug import core, models
from torchdrug.utils import comm
import numpy as np
import random
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from biopathnet import dataset, layer, model, task, util
def solver_load(checkpoint, load_opti... |
2af5e2d7567cfc51f188465e75ffee6cb6f13e0c6c60d76e4c6105bda889c630 | Python | 3,059 | 84 | from .utils import *
import subprocess as sub
LABDATA_SLURM_FOLDER = pjoin(os.path.expanduser('~'),'labdatatools','slurm') #TODO: get this from preferences file
def has_slurm():
proc = sub.Popen('sinfo', shell=True, stdout=sub.PIPE, stderr = sub.PIPE)
out,err = proc.communicate()
if len(err):
retu... |
42572d5e1cc6065255f133df57d09d4d5a7337f2c7fc08e342b4f81a9f5cad24 | Python | 3,059 | 90 | # Copyright 2014-2015 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
import os
from nab.sweeper import Sweeper
from na... |
dbdbba8c8c2d4d9309ca9ba9a10e10095466805957d08486abd6eb31e817b134 | Python | 3,059 | 86 | """
A sparse encoder
"""
import torch
from torch import nn
import MinkowskiEngine as ME
class Encoder(nn.Module):
"""
Encoder with sparse convolution
"""
def __init__(self):
super().__init__()
self.model = nn.Sequential(
ME.MinkowskiConvolution(in_channels = 1,
... |
df0e3e7820d762ef4b38212f88e7c38cca3d68f37e1e3faa7510592d31a5ec31 | Python | 3,059 | 85 | # This file was automatically generated by SWIG (http://www.swig.org).
# Version 4.0.2
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info as _swig_python_version_info
if _swig_python_version_info < (2, 7, 0):
raise Runtime... |
185679f451109ba66cb96a2c0f0336e5a3ac8fdb4e38ef4377f6af06953c60ee | Python | 3,060 | 104 | import os
import os.path as osp
import numpy as np
import torch
from torch.utils.data import Dataset
import cv2
import matplotlib.pyplot as plt
import albumentations as A
from PIL import Image
from albumentations.pytorch import ToTensorV2
def get_training_transform():
return A.Compose(
[
A.RandomCro... |
2c695994a1b8b7c0f3cff292d627e4bf9de0f6e7c057118a61667f4444f66703 | Python | 3,060 | 108 | """
utils/metrics_ci.py
基于 Bootstrap 的多指标置信区间计算：
- AUC-macro : ROC-AUC 的宏平均
- MCC-macro : Matthews Correlation Coefficient 的宏平均
- F1-macro : F1-score 的宏平均
- SubsetAcc : 样本级 Exact-match（所有标签均正确）的平均
"""
import numpy as np
from sklearn.metrics import (
roc_auc_score,
matthews_corrcoe... |
cd07dadd6e0b9635bc44a6c73ac5a0ba3aa6ebec8a7092563a33b5873462cf6a | Python | 3,060 | 75 | from typing import List, Tuple, Dict, Any
import monai
from monai import transforms
import torch
class Subsampled(transforms.MapTransform):
"""Transform that subsamples input data and pads to a specified size.
Args:
keys (list): List of keys to apply transform to
spatial_size (tuple): Targ... |
db712eb36ba7710d45e168a6d33b87870854378d4e4c93ce40169bd2775848a5 | Python | 3,060 | 93 | import user_features as uf
import csv
import ijson
import pandas as pd
import time
import sys
import os
def get_user_vector(user):
user_vector = {
"id": uf.get_user_id(user),
"screen_name_length": uf.get_screen_name_length(user),
"description_length": uf.get_description_length(user),
... |
0de4b6caa53a0d6c84fd1586216fac9b160245d7c14f96a671a89dba3bdb0f23 | Python | 3,061 | 86 | import numpy as np
from skimage import io
import getopt
import sys
def chop_paste_usage():
print("""
Usage : chop_paste.py -i [the img which you input, if you input one, default chop]
-m [paste/chop]
-f [the chop or paste model, x or y]
-n [the numb... |
9b9cb8ea02afada8e004ea38547d3982d65aea2f31f65b594909528d38294173 | Python | 3,061 | 102 | from langgraph.graph import StateGraph, START, END
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from chemgraph.tools.ase_tools import (
run_ase,
save_atomsdata_to_file,
file_to_atomsdata,
)
from chemgraph.tools.cheminformatics_tools import (
molecule_name_t... |
07372bb8dcfc33ff73ccf08f4813d14c936b0ec59c8cd39dbf5169016b2bb2c7 | Python | 3,063 | 73 | from typing import Optional
import torch
import torch.nn.functional as F
from torch_geometric.explain import Explainer, Explanation
from torch_geometric.explain.config import MaskType, ModelMode, ModelReturnType
def unfaithfulness(
explainer: Explainer,
explanation: Explanation,
top_k: Optional[int] = N... |
5df4b267b58cd8b10c6704259ac060e87923096691b3b241068cb0e5cbbeb5df | Python | 3,063 | 128 | #!/usr/bin/env python3
"""
A reader of SAM format.
import samreader
# ...
# Attach it to stdin and print the header.
sam_reader = SamReader(sys.stdin)
print sam_reader.header
# ...
# Open a SAM file and store each read starting position.
with fopen(myfile) as fp:
sr = ... |
6fefeaef1ba22f44856caf2bd26242fbc4465653299fe7a59157ca1ccd05c342 | Python | 3,063 | 98 | """
Settings for DataJoint.
"""
import json
from collections import OrderedDict
from .exceptions import PipelineException
import collections
from pprint import pformat
LOCALCONFIG = 'pipeline_config.json'
GLOBALCONFIG = '.pipeline_config.json'
validators = collections.defaultdict(lambda: lambda value: True)
default =... |
2395bebb4a54c3a3fa096f3b86f1500d9649b2a39e861434cf96454ced0aaba0 | Python | 3,064 | 88 | import logging
import sys
import warnings
_LOG_FORMAT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
def setup_logger(name=None, level=logging.INFO):
"""Set up a logger with consistent formatting.
This function configures a logger with a standard format that includes
timestamp, logger name, lo... |
70fbeea60595b577a4b293a0d693d38dbd4d48264e15bec5648186f00d00bd67 | Python | 3,064 | 109 | import pytest
from pyment.cli.predict import _detect_format, _parse_folder_name, predict
@pytest.mark.parametrize(
'name, expected',
[
(
'sub-S001_ses-01_run-1_T1w',
('S001', '01', '1', 'T1w'),
),
(
'sub-S001_ses-01_run-1',
('S001', '01'... |
785d94fd8ffc39e3cd4701cfd8415c440788f2fb33473a7d8ab62cf73c013047 | Python | 3,066 | 68 | """Streamlit session-state initialisation for the ChemGraph UI."""
import logging
import streamlit as st
from ui.config import load_config
logger = logging.getLogger(__name__)
def init_session_state() -> None:
"""Ensure all required session-state keys exist with sensible defaults.
This function is idempo... |
7f8bc8397a0d339fc2788e231511bc3dd5d635d8d05fc1fca0be1329a0c20fc0 | Python | 3,066 | 96 | import argparse
import io
import math
import os
import pandas as pd
import requests
from tqdm import tqdm
DATASET_URL = 'https://s3.amazonaws.com/openneuro.org/ds000030'
PARTICIPANTS_URL = f'{DATASET_URL}/participants.tsv'
DEFAULT_DESTINATION = os.path.join(os.path.expanduser('~'), 'data', 'ds000030')
def _download... |
30a2cd43d32684092c3af0e81645c1617580810b963e486444ebf444bad76961 | Python | 3,067 | 78 | """
Determine amount of each interval in one set covered by the intervals of
another set. Adds two columns to the first input, giving number of bases
covered and percent coverage on the second input.
"""
from bx.intervals.io import (
BitsetSafeReaderWrapper,
GenomicInterval,
)
from bx.tabular.io import (
C... |
a5cc790956043df165928bb115631907ac146db7217b3a4367ec4bafe7c34d13 | Python | 3,067 | 76 | import numpy as np
import matplotlib.pyplot as plt
import brainpy.math as bm
#set default ramndom seed for reproducibility
bm.random.seed(1)
#set backend to cpu
bm.set_platform('cpu')
# Levy exponent v.s. adaptation strength; Levy exponent v.s. noise strength
ticksize = 14
labelsize = 18
linewidth = 2
color = ['#F18... |
e2da7f4875fa9de734d18c115b27a84e88482d65bcdc24742aea7c60a73c5cf3 | Python | 3,068 | 102 | #!/usr/bin/env python3
"""
Example: run xTB optimization from an input XYZ.
Equivalent to:
{xtb_binary} {input_struct_filename} {' '.join(run_flags)} > xtb.log
Default flags correspond to:
xtb_flags: ["--gfn","2","--opt"]
Usage:
python examples/conformation_generation/02_optimize_xtb_from_xyz.py \
--input ... |
24826a78e446d6895b83585a990d233421a7c121035dfb298667f194f13c8d7d | Python | 3,069 | 101 | # -*- coding: utf-8 -*-
# python standard library inputs
import argparse
# external inputs
import numpy as np
import nibabel as nb
from nipype.interfaces.ants import N4BiasFieldCorrection
def calc_t1w(file_bold, file_vaso, file_out, apply_bias):
"""Compute a T1-weighted image from nulled and not-nulled time ser... |
2d925c502a6a9989d8783dce90583c4fc3ce93c5fca0bdb45a29f3046dac53da | Python | 3,069 | 118 | import numpy as np
import numpy.testing as npt
import torch
from brainways.utils.atlas.slice_atlas import (
get_slice_coordinates,
homog_center_at_zero,
homog_indices,
rotate,
slice_atlas,
translate,
)
def test_homog_indices():
idxs = homog_indices(1, 2, 2)
expected = [[0, 0, 0, 0], [... |
52f7ee6853446f27d44565af988dfc9a78a427415658495ee49a44c2552f06cf | Python | 3,070 | 110 | import pickle as stdlib_pickle
import dill
import pytest
import nelpy as nel
from nelpy import formatters
SERIALIZERS = [stdlib_pickle, dill]
def _roundtrip_without_attrs(obj, attrs, serializer):
for attr in attrs:
obj.__dict__.pop(attr, None)
return serializer.loads(serializer.dumps(obj))
@pyte... |
69a612135a92d4491c2466401cb9417a7a6b655d4db9929b925216b1fa968161 | Python | 3,070 | 121 | import numpy as np
from impedancefitter.single_shell import eps_cell_single_shell, single_shell_model
from impedancefitter.single_shell_ellipsoid import (
eps_cell_single_shell_ellipsoid,
single_shell_ellipsoid_model,
)
from impedancefitter.suspensionmodels import Lk, eps_sus_ellipsoid_MW
# for single_shell_m... |
7c258a97fce26071b26d5b12824ec292df61bafbce6a2d82b014f48b4509a6ae | Python | 3,070 | 83 | # -*- coding: utf-8 -*-
"""
Analyses of the EMPRISE data set
by Joram Soch <soch@cbs.mpg.de>
"""
# import modules
import os
import time
import EMPRISE
import NumpRF
import numpy as np
import scipy as sp
import pandas as pd
import nibabel as nib
import matplotlib.pyplot as plt
### Simulation C: NumpRF, 07,21/09/2023... |
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