sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
9809362f58c200be50f75d3edf5a98dcf2f845b28a58f03244500c254d2f5f80 | Python | 2,532 | 95 | import typing
from pathlib import Path
from operator import itemgetter
import cv2
import numpy as np
def processing(image: np.ndarray, boxes: np.ndarray) \
-> np.ndarray:
backgrounds = is_bg(image, boxes)
result = boxes[np.where(~backgrounds)]
return result
def is_bg(image: np.ndarray, boxes: n... |
79923371ea2196866f6df8b58e7fb7317b9aa6ffc868ae3db33d245e1f0f9c69 | Python | 2,533 | 78 |
def replace_infs_nans(array):
array[np.isinf(array) | np.isnan(array)] = 0
return array
data=h5py.File('G:/noise_celling_data/ZUIXINDATA/sub04_DATA/TEST_DATA/noise_celling_hebing/chongfuwumean/sub04_test_mean.mat', 'r')
dataset = data['averagedData'] # 根据数据集的名称获取数据集的引用
Y_test = dataset[:]
d... |
7aed0cf7289129e1920beba82a92b52ac0d4eab4309cfa4be262b2902d96315f | Python | 2,533 | 90 | import torch
import lightning.pytorch as pl
from datamodule import SeqDataModule
from test_predict import save_predict
from trainer import LitModel, TrainingConfig
from utils import set_global_seed
from lightning.pytorch.callbacks import ModelCheckpoint
from pathlib import Path
import glob
import argparse
par... |
906a8f12b224e21b0fbbd8c3217ebcf2b6ee940c0b764617bce0ae1b90e07a63 | Python | 2,533 | 59 | # -*- coding: utf-8 -*-
OPTIMAL = False
CONFIGS = [
# eager_greedy_ff
(330, ["--evaluator", "h=ff(transform=H_COST_TRANSFORM)",
"--search",
"eager_greedy([h],preferred=[h],cost_type=S_COST_TYPE,bound=BOUND)"]),
# lazy_greedy_ff
(411, ["--evaluator", "h=ff(transform=H_COST_TRANSFORM... |
9b97940da835ae19b72f949d608c325696701955ae9942d7ffa6680d21ce09b2 | Python | 2,533 | 79 | from .equiformer_v2_oc20 import EquiformerV2_OC20 as eqv2
import torch
import torch.nn as nn
import torch_geometric as tg
import e3nn.o3 as o3
from torch_scatter import scatter_mean
class EuBERT(nn.Module):
def __init__(
self,
num_layers=3,
max_neighbors=200,
max_radius=16,
... |
b878dc3a48817032c3c532df628c260553390f7e40910ce946de87200aba81b9 | Python | 2,534 | 72 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''setup file for the hybridLFPy Python module'''
import os
from setuptools import setup, Extension
import numpy
from Cython.Distutils import build_ext
with open('README.md') as file:
long_description = file.read()
# read version file
d = {}
exec(open(os.path.join('hy... |
ca9d9e4f42671350e0d3625460f91155ede83f499158ba3ca9d13b6ca8f66e4d | Python | 2,534 | 85 | # Mancy Chen 25/04/2023
# Convert the Matlab code from Cheima
import os
import nibabel as nib
import numpy as np
import pandas as pd
from scipy.ndimage import binary_dilation
import matplotlib.pyplot as plt
# Set the main directory
maindir = '.../Matlab_ROI_to_Python/stats/'
Output = '.../Matlab_ROI_to_Python/Output/'... |
3a93a585855fffa04864afd863094637e17a66ddd1617c2c6d814659c5aa005a | Python | 2,535 | 93 | import numpy as np
from scipy.stats import pearsonr, spearmanr
from PIL import Image
import random
def load_images(image_paths):
images = []
for path in image_paths:
img = Image.open(path).convert('L')
img_array = np.array(img)
images.append(img_array)
return images
... |
4598f5bfa03c1b0e9f08470c44759ffd17d85c6f372249ccf9cf15ab101e7ee0 | Python | 2,536 | 83 | # Prediction interface for Cog ⚙️
# https://github.com/replicate/cog/blob/main/docs/python.md
import asyncio
import logging
import time
from opentelemetry import trace
from cog import (
AsyncConcatenateIterator,
BaseRunner,
Input,
__version__,
concurrent,
current_scope,
)
logging.basicConfig... |
b596094485f29fcafc1d3e1b87c9afef7d5d2b28d2414a06a0f90603d43924c2 | Python | 2,536 | 44 | # +
import torch
class categorical_crossentropy_2d:
def __init__(self, weights=None,mask=False):
self.weights = weights
self.mask = mask
self.eps = torch.finfo(torch.float32).eps
def loss(self,y_pred,y_true):
if self.mask:
loss_sum = torch.sum(self.weights[0... |
f6a9d058f6bb954596ce577339411d18ea92184b439e3353645bd6f6b88d3c5e | Python | 2,536 | 79 | import numpy as np
import torch
import torch.utils.data as data
import torch.nn.functional as F
import torch.nn as nn
L = 32
# convolution window size in residual units
W = np.asarray([11, 11, 11, 11, 11, 11, 11, 11,
21, 21, 21, 21, 41, 41, 41, 41])
# atrous rate in residual units
AR = np.asarray([1, 1,... |
a3108e284564a4f2330c1e6a6c52d03d2108b9dd47e56b20bfce1a0bf0432d56 | Python | 2,537 | 79 | from .equiformer_v2_oc20_v2 import EquiformerV2_OC20 as eqv2
import torch
import torch.nn as nn
import torch_geometric as tg
import e3nn.o3 as o3
from torch_scatter import scatter_mean
class EuBERT(nn.Module):
def __init__(
self,
num_layers=3,
max_neighbors=200,
max_radius=16,
... |
e45aa5830e97fa63f0e1bbcdf388e98ac7e72afe5e9395be50ae5a9d8a28078e | Python | 2,537 | 90 | import json
import sys
import torch.nn as nn
from model import LegNet
from dataclasses import dataclass, asdict
from pathlib import Path
from dataclasses import InitVar
@dataclass
class TrainingConfig:
stem_ch: int
stem_ks: int
ef_ks: int
ef_block_sizes: list[int]
resize_factor: int
pool_siz... |
6370312a8de4baccfca3751923c3926f7a38495bffb6256d1706cb275a54539c | Python | 2,538 | 66 | from typing import Dict, List, Union
import numpy as np
import torch
from ..base_transform import EEGTransform
class Contrastive(EEGTransform):
r'''
To allow efficient training, we need to prepare the data loading such that we sample two different, random augmentations for each EEG in the batch. The easiest... |
7e4e501f8c16e4adaa93551398bfdf2ea6965d339303cabdae20ac01a20f0487 | Python | 2,538 | 103 | from SurvivalEVAL.evaluations.calibration import (
aj_calibration,
aj_calibration_per_event,
coverage_ic,
cr_d_calibration,
cr_d_calibration_per_event,
d_cal_ic,
d_calibration,
integrated_calibration_index,
integrated_calibration_index_cr,
km_calibration,
ksd_cal_ic,
ksd_... |
01de0d1fea1bfdf3a644e20d90f5bacd12bdacf71f2b1dafedf6bc85cddaac9e | Python | 2,539 | 78 | ## USER INPUT #############################################
files ='/gpfs/Home/cuk476/GeneExpression/20231027_all_coronal_batch/1_subjects_reconstructed_resampled/*_section_resampled.nii.gz'
###########################################################
axis = 2 # which axis to iterate through (Default: 2 for z axis)
over... |
131c93b0576e2caad60dfb28e5d677dd81f985d04653c0a86079bfc3439460f1 | Python | 2,539 | 79 | # -*- coding: utf-8 -*-
#
# Implementation of Granger-Geweke causality
#
#
# Builtin/3rd party package imports
import numpy as np
def granger(CSD, Hfunc, Sigma):
"""
Computes the pairwise Granger-Geweke causalities
for all (non-symmetric!) channel combinations
according to Equation 8 in [1]_.
The... |
3e3f7bfb8d6b42cb0b12f388adc0a1da522c72be47de4b82ab60b6072271d0b6 | Python | 2,539 | 51 | import numpy as np
import pytest
import regtools.geometry_registration.pipeline as pl
def test_safe_image_warping_installed_on_live_call_site():
# Import submodules directly (the package __init__ re-exports the functions,
# not the submodule objects, so we must reach into the submodules themselves).
... |
626edf9026b26c56283c4665d1f147e2e6418e832b97cae63a90bad7bbfd12c0 | Python | 2,539 | 84 | import importlib.util
import sys
class VendorImporter:
"""
A PEP 302 meta path importer for finding optionally-vendored
or otherwise naturally-installed packages from root_name.
"""
def __init__(self, root_name, vendored_names=(), vendor_pkg=None):
self.root_name = root_name
self.... |
75959f53fa4b6a27ff687a8877602c1f6ddc8e840f07ce083fc4cd437bc3882c | Python | 2,540 | 86 | # -*- coding: utf-8 -*-
"""Some simple test that dataset can be loaded"""
import pytest, tempfile
import tensorflow as tf
import numpy as np
from helpers import *
def test_numpy():
dataset = get_trivial_numpy_ds()
data = get_trivial_numpy()
iterator = iter(dataset)
out = next(iterator)
for k in o... |
7cac1a4e6b99af225f4d10a22d9372f45ab4f8756967e267080485a107cb2f08 | Python | 2,540 | 56 | #!/usr/bin/env python
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import math
import torch
from torch import nn
from torch.autograd import Function
from torch.nn.modules.utils import _triple
from torch.autograd.function import once_differentiable
import... |
feb5e58cb4810748a1d5f43314f430b4a16bd48a0e55521ef30feb14f4b62829 | Python | 2,540 | 76 | import numpy as np
def _ecdf(x):
"""No frills empirical cdf used in fdrcorrection."""
nobs = len(x)
return np.arange(1, nobs + 1) / float(nobs)
def fdrcorrection(pvals, alpha=0.05, method="indep", is_sorted=False):
"""Pvalue correction for false discovery rate.
This covers Benjamini/Hochberg fo... |
1c6aa9812ef4cc0e1bed9b7be866aa615b1ca841edd03a2a38c3458e184f67f9 | Python | 2,541 | 79 | """ The quPath project to metadata convert click command """
import os
import click
import pandas as pd
from layer_recognition.io import get_qpproject_images_metadata
@click.command()
@click.option(
"--qupath-project-path",
required=True,
help="qupath project path that contains images metadata",
)
@cli... |
4d7aec3d9ac7f1c72253f7847fbb8b94af67c1394c9191170351b73f7d8e8506 | Python | 2,541 | 73 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 1 07:32:52 2022
@author: haotian teng
"""
import os
import sys
import toml
import numpy as np
import argparse
def split(args):
collections = {}
fs = os.listdir(args.input)
for f in fs:
if f.endswith(".npy"):
try:
... |
903e15177075798e1df6aad0d092d2432fd2d2db8bf970aa55a8775d509435c8 | Python | 2,541 | 49 | """Tilted (oblique) CCF cross-section extraction — pure numpy, headless."""
import numpy as np
from scipy.ndimage import map_coordinates
def rotation_matrix(pitch_deg, yaw_deg):
"""Rotation for volume axes (z=AP, y=DV, x=ML). Public: the viewer imports it.
pitch = rotation in the (z, y) plane about... |
dfa3599e35ac0ae136f90127f14245b8eb80e71d5eaf818b4c373a56ac3dffba | Python | 2,541 | 79 | from .equiformer_v2_oc20_v2_new import EquiformerV2_OC20 as eqv2
import torch
import torch.nn as nn
import torch_geometric as tg
import e3nn.o3 as o3
from torch_scatter import scatter_mean
class EuBERT(nn.Module):
def __init__(
self,
num_layers=3,
max_neighbors=200,
max_radius=16,
... |
f8ffcb40489bf814ec1c426314d3ac5a44bf6b759c41cef0fc51a22864ef63d5 | Python | 2,541 | 80 | """
Bonito CRF basecalling
"""
import torch
import numpy as np
from koi.decode import beam_search, to_str
from bonito.multiprocessing import thread_iter
from bonito.util import chunk, stitch, batchify, unbatchify, half_supported
def stitch_results(results, length, size, overlap, stride, reverse=False):
"""
... |
4a4444cba6d5ca7d812b8c0db8986cb0249198f566d45105cf9b85c8ed1c15fe | Python | 2,542 | 63 | def update_param(name, param):
if name == 'distribution':
values = param['values']
param['values'] = [v for v in values if v not in ['custom', 'ordinal', 'quasibinomial']]
return param
elif name == 'stopping_metric':
param['values'].remove('anomaly_score')
param['values']... |
736a5fe16d69994162a4d9cb769509052c460906c46a742c174ed01c601c5759 | Python | 2,542 | 51 | # -*- coding: utf-8 -*-
#--------------------------------------------------------------------#
# #
# Copyright (C) 2019 HOLOEYE Photonics AG. All rights reserved. #
# Contact: https://holoeye.com/contact/ #
# ... |
e2b142c1a388e3e4324c62a2fe5d041e0d5a7758e822fd4abac65d1eb8bec2c5 | Python | 2,542 | 34 | """posthoc_margin_map_check_2026-10-03.py — POST HOC (after MARGIN_MAP's pre-registered spiking check returned NO, 0/10).
The rate theory counted >= 2,000 neurons firing at g* + 0.1 for the ten checked inputs; spiking counted 594-1,957 (one input,
ENS1, exploded on both sides of g*). Is the theory wrong about which inp... |
9977666092e61cfc38992d7ad5eeef1433c99dabdf79e5ef5861694ececbfa28 | Python | 2,545 | 75 | from .virtual_volume import VirtualVolume
import struct
from .config import *
class Segm:
def __init__(self, d0, d1, ind0, ind1):
self.D0 = d0
self.D1 = d1
self.ind0 = ind0
self.ind1 = ind1
class Block(VirtualVolume):
def __init__(self, container, row_index, col_index, file):... |
12ae73a42fe33ca4900d09f6e17db2f86b6a4f1798620f67aa2b4f7cb617c41b | Python | 2,546 | 98 | import os
import numpy as np
import random
import math
import sys
outdir = sys.argv[1]
observations = sys.argv[2]
def addIndividuals(np_array):
indexes = range(np_array.shape[0])
list_array_added = []
for j in indexes:
if (j % 2) != 0:
array_temp = np_array[j, :] + np_array[j-1, :]
... |
6060e9fe0124d47b71a3b35deca0ec42dc1d306009148e6a195b189fca733e22 | Python | 2,546 | 65 | import sys, os
import numpy as np
import pandas as pd
import pickle
from scipy.io import loadmat, savemat
import concurrent.futures
sys.path.append('/m/cs/scratch/networks-pm/effects_externalfactors_on_functionalconnectivity/src/analysis')
from utils import get_behav_data_15days
#####################################... |
92c1640265b8d60e4745c9d509695d743a99138163708a7e9dc5b4ebb3e90972 | Python | 2,546 | 79 | import os
import time
import datetime
import pickle
import yaml
import threading
import logging
import sys
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
from src.server import Server
from src.utils import launch_tensor_board
if __name__ == "__main__":
# read configuration file
confi... |
3c4275fe010f6d6815654bc76fc1d8b0415f53fe49c01abd917e39ed5aa82dba | Python | 2,547 | 70 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
89e0ae60bb686a1bf40e33c1c5f63bb1d6a96672aada96b2404601710972c7e0 | Python | 2,547 | 97 | import os
import pandas as pd
from calvados.cfg import Config, Job, Components
import subprocess
import numpy as np
from argparse import ArgumentParser
#parser = ArgumentParser()
#parser.add_argument('--name',nargs='?',required=True,type=str)
#args = parser.parse_args()
cwd = os.getcwd()
sysname = 'md'
## set the si... |
91abd32a21e5ec9e8c67fe3a96461b6f959ea77b60294f894a4264bd8a754258 | Python | 2,549 | 76 | ##Exp varified in all four
import pandas as pd
import os
import numpy as np
import csv
import json
df=pd.read_csv('Exp_Pl_protID_to_OGID.csv')
values_ath = set(df['ath'])
values_zma = set(df['zma'])
values_ppp = set(df['ppp'])
values_cre = set(df['cre'])
Plastid_Exp_all_Four = list(values_ath & values_zma & values... |
7e1d4c9fed7d585d86ff336237435545638a76ef8773c51a5e569e7a8c30570a | Python | 2,550 | 69 | import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import seaborn as sns
def heatmap_cycle_data (roi_cycle_data, freq_band, roi_names, ax=None,
center=0, cmap_y0 = 0, cmap='RdBu',
cbar_kws=None, vmin=None, vmax=None):
'... |
d423f935db0a3ef2b419665f7b5b0887e86cf2c99ea01c55909e3bd058523576 | Python | 2,550 | 79 | from .equiformer_v2_oc20_v2_new_afscores import EquiformerV2_OC20 as eqv2
import torch
import torch.nn as nn
import torch_geometric as tg
import e3nn.o3 as o3
from torch_scatter import scatter_mean
class EuBERT(nn.Module):
def __init__(
self,
num_layers=3,
max_neighbors=200,
max_ra... |
3a7f40cc20416cd5456fd045159065cf1e47f09a0ab0d9572bd96c4fe813f5fe | Python | 2,551 | 78 | # \file TestBrainStripping.py
# \brief Class containing unit tests for module BrainStripping
#
# \author Michael Ebner (michael.ebner.14@ucl.ac.uk)
# \date December 2015
import os
import unittest
import numpy as np
import SimpleITK as sitk
import pysitk.simple_itk_helper as sitkh
import niftymic.utilities.brain... |
43fbed3b1add8cd5a2f8a12ac5d71a55332d52b642b1cc2ca2f412cf68d3cdb6 | Python | 2,551 | 63 | import os
import argparse
import json
import pandas as pd
import sys
from config_train import config as cfg_train
from config_eval import config as cfg_eval
import hyperparameters_multilabel
from constants import REYMULTICLASSIFIER, DATA_DIR
from src.analyze.performance_measures import PerformanceMeasures
from src.tr... |
c23bea53b5b3f0034ed5b40a2ba99432c11da0f801c9fe6e3dd96085fd016fc8 | Python | 2,551 | 105 | from pathlib import Path
from numpy import ndarray
from .utils import _match, get_tsv, get_json
from ..dataset import Dataset
BIDS_ENTITIES = (
'sub',
'ses',
'task',
'acq',
'ce',
'rec',
'dir',
'run',
'mod',
'echo',
'recording',
'proc',
'space',
)
class BIDSNa... |
1c83ebab25eb9173bbe24ce947ee71fbf447d6553b71895e2d7e6a5c900446a9 | Python | 2,552 | 83 | # Node-group asset "Frame ID" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKING
from... |
7adab9ab67460dd8fdd593a05a10e3a84540704a3ec44cf334ab9988772db215 | Python | 2,552 | 80 | #!/bin/env python3
'''
Define a pool of particles, i.e. a dictionary that keeps track of how many time
each quasi-particle shows up.
'''
import re
from frozendict import frozendict
from typing import Callable
from .yamlrepr import YAML_String_Representer
class ParticlePool(dict, YAML_String_Representer):
@property... |
839de8399b1dd0bb34a813778f97b619074a3788fbd477019697b97af8055e6d | Python | 2,552 | 103 | from __future__ import annotations
import warnings
from pathlib import Path
import pytest
from emmet.core.tasks import TaskDoc
FILE_DIR = Path(__file__).parent
PSEUDO_DIR = FILE_DIR / "fake_pseudos"
with warnings.catch_warnings():
warnings.simplefilter("ignore")
MOCK_METALLIC_TASKDOC = TaskDoc.from_director... |
8823f82d767d15453ce8922762f522fdc9eb42a0c69776979930cff4b50280e4 | Python | 2,552 | 69 | from NAB.nab.detectors.base import AnomalyDetector
import numpy as np
import math
class KnncadDetector(AnomalyDetector):
def __init__(self, *args, **kwargs):
super(KnncadDetector, self).__init__(*args, **kwargs)
self.buf = []
self.training = []
self.calibration = []
self.... |
ef7c6c1f552587f32b25792b88ecbf9f5b8cc70d02079fc46c471b0fa2fbdbb7 | Python | 2,552 | 88 | # -*- coding: utf-8 -*-
import numpy as np
import os
import glob
import random
import argparse
import itertools
import nibabel as nib
from nilearn import plotting
from tqdm import tqdm
from sklearn.cross_decomposition import PLSRegression
from sklearn.linear_model import LinearRegression
from sklearn.linear_model impor... |
4de2ae6ea99e693f2bcc0fe84482016d43d553350c1997445a07fe8e482ad756 | Python | 2,553 | 71 | import os
from collections import defaultdict
from tqdm import tqdm
from rdkit import Chem, RDLogger
import torch
from torchdrug import data, utils
from torchdrug.data import feature
from torchdrug.core import Registry as R
@R.register("datasets.QM9")
@utils.copy_args(data.MoleculeDataset.load_csv, ignore=("smiles... |
518014f6d883b9beb608ee342a38da24b3a80243d49e144a1d555f3a6da8387b | Python | 2,553 | 90 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
68cb19196cccb84ae56bdc2baa8d5627399a41b86418f15c5cccc5ac3ed2e0df | Python | 2,553 | 88 | #!/usr/bin/env python3
import os
import pandas as pd
out_dir = "/project/3011157.03/Simon/proj_2022_CABB_movie/MRI/TimingsHPC7TMovies"
os.makedirs(out_dir, exist_ok=True)
movie_names = {
"1.1": "Two_Men",
"1.2": "Welcome_To_Bridgeville",
"1.3": "Pockets",
"1.4": "Inside_the_Human_Body",
"1.5": "Vi... |
b0d748474b191e4b9fa6b2cd7cae38ca4cad7956f27c080ae5723047f6a6e7fc | Python | 2,554 | 70 | import matplotlib
matplotlib.use("Agg")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import yaml, io
with open("./config/DryBean_config.yaml", 'r') as stream:
config= yaml.safe_load(stream)
config["default_path"]= str(config["default_path"])
config["split"]= float(config... |
ed3d01eef335cd3bc434d92ff8643fd029d8de705f3991e842b3c637b69095dc | Python | 2,554 | 82 | import seaborn
from matplotlib import pyplot as plt
from merlin.plots._base import AbstractPlot
class OptimizationScaleFactorsPlot(AbstractPlot):
def __init__(self, analysisTask):
super().__init__(analysisTask)
def get_required_tasks(self):
return {'optimize_task': 'all'}
def get_requi... |
66f0924c763a74cc5592841d419292fef514db4f038a10b3c0a78024e87112e2 | Python | 2,555 | 96 |
"""
Graphical Interface used to generate burst_quality metric.
The program takes as input images generated by "generate_pred_vs_def_bursts.py", stored in
""data/burst_quality/images"", and show them, together with three buttons: 'Red', 'Blue', 'Equal'.
When one of the button is clicked, the information about (image,... |
cb336ab60711b27f18ceaca3fdf3ebd8bb4d02778618c2e298eaf4bddd906356 | Python | 2,555 | 70 | # coding=utf-8
# Copyright 2021 The Eleuther AI and HuggingFace Inc. team.
#
# 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 requir... |
45088f8b5778155336071934e1d4215d9d8faa47a58c42f67d967d498a8843bf | Python | 2,556 | 81 | import functools
import os
import site
import sys
import sysconfig
import typing
from pip._internal.exceptions import InstallationError
from pip._internal.utils import appdirs
from pip._internal.utils.virtualenv import running_under_virtualenv
# Application Directories
USER_CACHE_DIR = appdirs.user_cache_dir("pip")
... |
5e9e45b91b0bbe5bd76dc5699007ea73b8e4a42b5ce409d6f3f09c8233e326d9 | Python | 2,556 | 87 | class NboDataPoint:
"""Abstract class as template for NBO single and double data classes."""
def __init__(self, nbo_id, nbo_type, atom_indices, energy, occupation, orbital_occupations, contributions):
"""Constructor"""
# check for equal length of involved atoms and contributions
asse... |
6546bc825684cd9c99ae61924ffeffab158f83b79c433d8cb2ce53347f525fe7 | Python | 2,557 | 83 | from pyclbr import Function
from dolfin import *
import numpy as np
import matplotlib.pyplot as plt
import sys
import os
from IPython import embed
import argparse
# Command line arguments checking for different simulation experiments
parser = argparse.ArgumentParser()
parser.add_argument("--order", type=int, default=1... |
d75fb6a2f02a4a8eb8d5f284d54dc0a20549558a9d8dec447b2bbf3648b21c4c | Python | 2,557 | 84 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
9ab237a408c8c8de85cf2676d3bf0b4fe0e52500d1e0e055187ee585bf0c64bf | Python | 2,558 | 109 | import torch
from elasticai.creator_plugins.quantized_grads.fixed_point import FixedPointConfigV2
from elasticai.creator_plugins.quantized_grads.fixed_point.quantize_to_fixed_point import (
_clamp,
_round_to_fixed_point_hte,
quantize_to_fxp_hte,
)
def test_round_determinstic_to_fixed_point():
conf = ... |
cf90a029c502117a8ee4aac4a8d789f04578403d709bdec03a7b05185051924b | Python | 2,558 | 70 | import time
import os
import threading
class Logger:
def __init__(self, dir_name, is_values, ought_values):
self.dir_name = dir_name
self.is_values = is_values
self.ought_values = ought_values
# Check if directory exists
if not os.path.isdir(self.dir_name):... |
d291adefd1555579c44539873e4b426ae4e2a35f78468c5149a62afeb78b70cb | Python | 2,558 | 78 | import itertools
import os
import shutil
from pathlib import Path
import bpy
SUBFOLDER = "Molecular Nodes"
def list_templates():
paths = bpy.utils.app_template_paths()
t_paths = list(paths) if paths is not None else []
names = list(
itertools.chain.from_iterable(
[[folder.stem for fol... |
1a24ede57c2292040127edbe89d550128eb5585a82ff05a56f19305e87b53a3f | Python | 2,559 | 70 | import matplotlib
matplotlib.use("Agg")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import yaml, io
with open("./config/MNIST_config.yaml", 'r') as stream:
config= yaml.safe_load(stream)
config["default_path"]= str(config["default_path"])
config["cuda"]= str(config["cud... |
a04fd82dcf833057ad89f825b078c5875db1044f1ec9593cf493a56b3775e604 | Python | 2,559 | 74 | import os
from rdkit import Chem
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.PDBBind")
@utils.copy_args(data.ProteinLigandDataset.load_lmdbs, ignore=("sequence_field", "smiles_field", "target_fields"))
class PDBBind(dat... |
a16161757b9103b56c5bcb2b7aaa3262e3256895c1f100e6c4818d377e3a144d | Python | 2,559 | 106 | import torch
from torch import nn
__all__ = [
'_CONV_DICT',
'_CONV_TRANS_DICT',
'_AVG_POOL_DICT',
'_MAX_POOL_DICT',
'_NORM_DICT',
'_REFLECTION_PAD_DICT',
'_CENTER_CROP_DICT',
'_ACTIVATION_DICT',
'activation_from_str',
]
def center_crop_1d(layer: torch.Tensor, target: torch.Tensor) ... |
ae106d36a9334a5bf7bbbc1f906bfdce67721f7fa1f367ed4077284dede32650 | Python | 2,559 | 101 | # -*- coding: utf-8 -*-
"""Participant class"""
from os.path import join as opj
from dcm2bids.utils.utils import DEFAULT
class Participant(object):
""" Class representing a participant
Args:
name (str): Label of your participant
session (str): Optional label of a session
"""
def _... |
b862ff4b61f0fb4c4b9eb46725697a96e84e38406de3e0f9c6484c99337af25e | Python | 2,560 | 65 | from matplotlib import pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix as cm
def confusion_matrix(all_labels, all_predictions, metadata, num_clusters, unsupervised, target_fp = None):
# confusion matrix for single experiment. If unsupervised, plots the contingency matrix of clusters aga... |
d4f3989c043f10eef8b7536276e92f1b298f4eeecc9444fc56558fbdb2738833 | Python | 2,560 | 92 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
dc47c852c0a5ade20aa53d4e16d0da9de8ff0c753465b26e8a1c588bdc4dd5ba | Python | 2,560 | 87 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Docs here"""
__author__ = "Mitchell R. Vollger"
__credits__ = ["Mitchell R. Vollger"]
__maintainer__ = "Mitchell R. Vollger"
__email__ = "mrvollger@gmail.com"
__status__ = "Development"
import logging
import argparse
import pysam
import sys
from tqdm... |
74ba694ec0e70502ccbb631c462bf1cf46a65f8c9813f2becbb8fcea53799198 | Python | 2,561 | 83 | """
Molecular Dynamics recipes for EMT.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
from ase.calculators.emt import EMT
from ase.md.verlet import VelocityVerlet
from ase.units import bar, fs
from quacc import Remove, job
from quacc.runners.ase import Runner
from quacc.schemas.ase import ... |
88aeecb7d37ad16e33d6fab4a9d01961ef663a1b8cac13700ece2f1daf428fa2 | Python | 2,561 | 78 | import numpy as np
import sys
import copy
class ParamStorage(object):
"""
Minimal Parameter Storage Unit with some Checks
Inspired by: Michael Hughes's ParamBag
"""
def __init__(self, K=0, doCollapseK1=False, **kwargs):
self.K = K
self.D = 0
if sys.version_info > (3, 0):
... |
d2f18ae34f4c32b57f981db152bfdd43edfd948eb6f07ad21eae5cd9c6876cec | Python | 2,561 | 121 | import numpy as np
import math
print("To estimate m(X,Y) please provide (in micrometers):")
print("- the reciprocal of the density of cell-type X:")
dx = input()
print("- the reciprocal of the density of cell-type Y:")
dy = input()
print("- the larger of the mean radii of cell types X and Y")
mx=input()
print("---... |
4bed47c9dceffd02d30fac45ca9a79994f099edab229bfd8755b0cc785ba845f | Python | 2,562 | 51 | import numpy as np
import matplotlib.pyplot as plt
import os
from pulse_train import PulseTrain_Sinusoid, PulseTrain_TI
####################################################################################
###################### Plotting Sinusoidal Waveforms ###############################
#############################... |
52619b837907a0225fc68009b1c88e7cd13b34b20650c8608e87b1a1fa739fe7 | Python | 2,562 | 108 | from __future__ import annotations
import pytest
ray = pytest.importorskip("ray")
from quacc import flow, job, subflow
def test_ray_decorators(tmp_path, monkeypatch):
monkeypatch.chdir(tmp_path)
@job
def add(a, b):
return a + b
@job
def mult(a, b):
return a * b
@job
d... |
7d295dfae901b10f909b8064e428a6eceabb4a49b7dd27bec35bcdfee3005460 | Python | 2,562 | 59 | """
Reconvolution events
====================
This tutorial shows the basic building block of the reconvolution approach (see [1]_ and [2]_) that is implemented in
the PyntBCI library for analysing code-modulated responses. This tutorial generates an arbitrary code (here an
m-sequence) and shows different kinds of eve... |
88e0bc19d58b1cbbc27278fab48e6dff671ab4032a4f76af93276ecfbf0df0ee | Python | 2,562 | 88 | # pylint: disable=invalid-name, missing-function-docstring, no-member
""" MeanTransformer model module """
from random import uniform, randint
import torch
from torch import nn
from omegaconf import OmegaConf, DictConfig
def get_model(cfg: DictConfig, model_cfg: DictConfig):
return MeanTransformer(model_cfg)
... |
0dd832585bb0ff3abd339078eb1ca4440076dd54ab566aaf0f0aaf78198e0c67 | Python | 2,566 | 64 | from make_outer_surface import make_smoothed_surface
import os.path as osp
from config import paths
import argparse
from nibabel import freesurfer as nf
from scipy.spatial import distance as dist
import json
def test_make_smoothed_surface(subject, db_root):
for hemi in ['lh','rh']:
old_coords,new_coords = ... |
35554eff5597c84c50eabcb3a014b55da9fdea5d1bcabf3b55f51ee3adfea262 | Python | 2,566 | 72 | import os
from rdkit import Chem
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.BindingDB")
@utils.copy_args(data.ProteinLigandDataset.load_lmdbs, ignore=("sequence_field", "smiles_field", "target_fields"))
class BindingDB... |
735d994bd4f53aa5136aa013ac37389a389cae0ec733d29ec53e757caefe5f3c | Python | 2,566 | 68 | from typing import Dict, List, Union
import numpy as np
import torch
from ..base_transform import EEGTransform
class Contrastive(EEGTransform):
r'''
To allow efficient training, we need to prepare the data loading such that we sample two different, random augmentations for each EEG in the batch. The easiest... |
d1fe32fc83760b77eeafade1e603f240eb1722c2568753975bf07ad8567bd7a8 | Python | 2,566 | 62 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ht... |
6ad7ec4c5d6d919000a1e2fb593ecb024cf66995908b970c57af71cae11fc6ee | Python | 2,567 | 83 | # Node-group asset "Assembly ID" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKING
f... |
76fe1599112b2e198b595cac5eb4897ce3a17fd5ebcfd63fb7aedb757afd5f8a | Python | 2,567 | 85 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Written by Minh Nguyen and CBIG under MIT license:
# https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
from __future__ import print_function
import argparse
import numpy as np
import cbig.Nguyen2020.misc as misc
def get_args():
parser = argparse.Argument... |
3dab8413ec5e5517cb6a25b3c6a36f5082ca6d82ef9765941a1fe75a10750631 | Python | 2,568 | 89 | from sklearn.model_selection import KFold, StratifiedKFold
from torch.utils.data import DataLoader, Subset
from engine_real import *
from model.resinf_foreal import ResInf
import torch.nn as nn
import torch.optim as optim
import wandb
import pickle
from sklearn.model_selection import train_test_split
from dataloaders.d... |
5f33e81fc2fd5fd6df5896f38077c291233f2c051ebbe581a3f32e0a65f4de2f | Python | 2,568 | 76 | import tensorflow as tf
import pandas as pd
import numpy as np
from tqdm import tqdm
import tensorflow_addons as tfa
r_square = tfa.metrics.r_square.RSquare(dtype=tf.float32, y_shape=(1,))
if tf.config.list_physical_devices('GPU'):
strategy = tf.distribute.MirroredStrategy()
print("All devices: ", tf.config.l... |
280f5ed2c60f482b77a670b93e87e32bda95b6f5013df59f47773acb920feba9 | Python | 2,569 | 90 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
6bde6fdb7cb405261ca32bad0e5bfc0d3966ef5a1acdca25544edb34e6e8211e | Python | 2,569 | 94 | from .ds_utils import _get_clio_client
FLYWIRE_MINIMAL_SCENE = {
"layers": [
{
"source": "graphene://https://prod.flywire-daf.com/segmentation/1.0/fly_v31",
"type": "segmentation_with_graph",
"selectedAlpha": 0.14,
"segments": [],
"name": "flywir... |
4db156f1fa795262446704f2f4eb3883076aabe7620b8bce64c125c4275b249c | Python | 2,570 | 104 | from __future__ import annotations
import warnings
from pathlib import Path
import pytest
from emmet.core.tasks import TaskDoc
FILE_DIR = Path(__file__).parent
PSEUDO_DIR = FILE_DIR / "fake_pseudos"
with warnings.catch_warnings():
warnings.simplefilter("ignore")
MOCK_METALLIC_TASKDOC = TaskDoc.from_director... |
b3ce051de0012e3ad2d2a3a7168c7b043e88aba0f260af8649d41ded4ece01dc | Python | 2,571 | 90 | import time
from functools import wraps
from contextlib import contextmanager
import matplotlib.pyplot as plt
import torch
import numpy as np
"""
basic tools
"""
# --------------------------------------------
# dict to string for logger by recursion
# --------------------------------------------
def dict2str(opt, inde... |
6066eaa1eab3e88f24d48293ce7feda46998974063b2d97b657273ffe2940e1d | Python | 2,572 | 83 | # Node-group asset "Symmetry ID" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKING
f... |
ace372069dfea4d7b110b47a3ee08860b4b174364696767f7834a3a89308891b | Python | 2,572 | 64 | import argparse
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
sns.set_context("paper")
sns.set_theme(style='white')
# Read data
parser = argparse.ArgumentParser()
parser.add_argument('--filename', type=str, default='SGA1-2_summary2.xlsx', nargs='?')
parser.add_... |
cedd68afbfa7a5ab01df86c6c1ebb24bd34e91a90bf68b8b1d5dd3e738e6f3d1 | Python | 2,572 | 75 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge,... |
dfba378be591bb582fb3caf5b6c3bccbaae3da8e9c3ec4c8f2b7078ada3b247c | Python | 2,572 | 53 | """Publication figure: reservoir dynamics over thousands of tokens, from saved continuous runs."""
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
from liquidaion_fly.continuous import load_run, repetition_metrics
root = Path("results/continuous")
o... |
c4d2f130d02c4d9764120ef6dc8035fff28859cd86b85405ff342551a49d8fd2 | Python | 2,573 | 79 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
87be6375da4f4018c81a8aeec17042d9775a05d1f2f8da24b93be7eccb82c593 | Python | 2,574 | 70 | """Visualize atomic attention and save similarity map."""
from typing import Union
import numpy as np
from tqdm import tqdm
from chemprop.args import PredictArgs, TrainArgs
from chemprop.data import get_data, MoleculeDataLoader, MoleculeDataset
from chemprop.utils import load_args, load_checkpoint, update_prediction_a... |
db53d4272c6241f1daf73b3ff7aa91079477697b42af9e4a08734a44c4064e9a | Python | 2,574 | 70 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Nov 8 09:07:07 2019
@author: adele
"""
import os
from os.path import join as joinP
import logging
import cPickle as cpk
import pandas as pd
from utils_objet import MetabolicGraph
#Setting logging preferences
logging.basicConfig(level=logging.INFO)
... |
f39ff44279e7100e932747befa33bfbbf7f3c2ddf17e41498f56276a196198cc | Python | 2,574 | 68 | """Test diagnostics interpretation with QC threshold color cues."""
import json
import re
from regtools.registration.report.metrics import _render_diagnostics
from regtools.registration.report import build_html_report
def test_bad_folding_gets_warn_class(tmp_path):
with open(tmp_path / "registration_dia... |
61c233c3250a650d64bf45fac0c3e9417c1b1106418d08ecb53d60c2df20cfa3 | Python | 2,575 | 68 | import shutil
from batchgenerators.utilities.file_and_folder_operations import *
import SimpleITK as sitk
from nnunet.paths import nnUNet_raw_data
if __name__ == '__main__':
#data is available at http://medicalsegmentation.com/covid19/
download_dir = '/home/fabian/Downloads'
task_id = 69
t... |
fa848a6ef17fc0aa78d86e947f7845fb12a6862a926b261e077b79a7773a2091 | Python | 2,575 | 64 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from common_setup import IssueConfig, IssueExperiment, is_test_run
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["issue535-base", "issue535-v5"]
CONFIGS = [
IssueConfig(
... |
d11dc67ad86fc6cd07444cf44096ef658de82d895ab223439767cc34bc7915ef | Python | 2,576 | 49 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ht... |
d3fff64d0053428aa46a362634fb751f11117117804fa6e854a240df6f29af4e | Python | 2,576 | 106 | """
Interface adapters for low-level readers.
"""
import abc
import io
import itertools
from typing import BinaryIO, List
from .abc import Traversable, TraversableResources
class SimpleReader(abc.ABC):
"""
The minimum, low-level interface required from a resource
provider.
"""
@property
@ab... |
612c54a4737bcffb74f94b5e03b34e96083bd6be063fee6f3e2ab9d604f4b980 | Python | 2,577 | 71 | import torch
import torch.nn as nn
import torch.distributed as dist
import torch
import torch.distributed as dist
class GatherLayer(torch.autograd.Function):
"""Gather tensors from all process, supporting backward propagation."""
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input... |
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