sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
51b3066f3c27435a57c1f48e27dd51f370645d794e43e01f92fbc3c3fd0063cd | Python | 3,525 | 138 | """===========================
Pipeline template
===========================
.. Replace the documentation below with your own description of the
pipeline's purpose
Overview
========
This pipeline computes the word frequencies in the configuration
files :file:``pipeline.yml` and :file:`conf.py`.
Usage
=====
See ... |
59bcf9a4eb6b1365c964ccee5c8a892357674f0e152cbb5cbae2f5b79fc4ef29 | Python | 3,526 | 125 | """
Test cwas.preparation
"""
import random
from multiprocessing import cpu_count
from pathlib import Path
from typing import Tuple
import pytest
from cwas.env import Env
from cwas.preparation import Preparation
import cwas.cli
import sys
class PreparationMock(Preparation):
"""Mocking the Preparation class"""
... |
e1b1e0a8043a46c5652b04929a7c7b5cdcef2468332b0553cfb765e8080e5454 | Python | 3,527 | 81 | import matplotlib.pyplot as plt
import torch
from skimage.io import imread, imsave
import os
import numpy as np
import cupy as cu
from cucim.skimage.morphology import dilation, ball
"""
3D patches generation from Svetlana annotations for visualisation
"""
binary = torch.load("/mnt/86e98852-2345-4dcb-ae92-58406694998... |
81f46ba84b706b09aa2fb9249b474509d537a4fe953c6541f2091c56cfe2cac5 | Python | 3,532 | 75 |
# 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://... |
97b2df2c5a0617c9baa5c48db0b9b7bce1423f95661473bd7a9e9df9aaba26a6 | Python | 3,533 | 81 | # Imports
import sys
import os
import argparse
import inspect
from datetime import datetime
import numpy as np
from tqdm import tqdm
from math import ceil
from time import perf_counter, process_time
import torch
import torch.optim as optim
try:
from torchdiffeq.__init__ import odeint_adjoint as odeint
except Impo... |
bdda867ce1329486b139493314e1cb09c64036858439193a581d04c2a76e8209 | Python | 3,533 | 103 | import numpy as numx
import pydeep.base.numpyextension as numxExt
import DBM3LayerBinary.model as MODEL
import DBM3LayerBinary.trainer as TRAINER
import DBM3LayerBinary.estimator as ESTIMATOR
from pydeep.base.activationfunction import Sigmoid
import pydeep.misc.io as IO
import pydeep.misc.toyproblems as TOY
import pyde... |
26c4ab7bda36ec82fc5eee8a2b0c5533d3981c836f5b1a50dd27332429bbd2a2 | Python | 3,534 | 103 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.openapi.planner_prompt import (
API_CONTROLLER_PROMPT,
API_CONTROLLER_TOOL_DESCRIPTION,
API_CONTROLLER_TOOL_NAME,
API_ORCHESTRATOR_PROM... |
10ef948fdedb4a4dca49a94fe91443b8cf76c5163e4da8e1ae53300418ac9cc6 | Python | 3,538 | 94 | import os
os.nice(19)
import numpy as numx
import pydeep.rbm.model as MODEL
import pydeep.rbm.trainer as TRAINER
import pydeep.rbm.estimator as ESTIMATOR
import pydeep.misc.visualization as VISUALIZATION
import pydeep.misc.io as IO
import pydeep.misc.numpyextension as npExt
import mkl
import scipy.io as sio
mkl.set_nu... |
aac5e7d83dafdecb7081e468491ac1cbde6487ba62f92083423390f7ab2c962f | Python | 3,539 | 93 | # coding=utf-8
import torch
import torch.nn.functional as F
from alg.algs.ERM import ERM
class MMD(ERM):
def __init__(self, args):
super(MMD, self).__init__(args)
self.args = args
self.kernel_type = "gaussian"
def my_cdist(self, x1, x2):
print('---------MMD my_cdist()--------... |
f4243152ab800f7437cc432a0165dd1fdd662d9d3916fec75bf7d26faee73743 | Python | 3,539 | 97 | # Copyright Generate Biomedicines, Inc.
#
# 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 ... |
b15e9020698194c52fea94042e800e9706dffdc5e8aa47398050d9cceba164e4 | Python | 3,542 | 93 | import sys
import os
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
sys.path.insert(1, os.path.join(sys.path[0], '../../../../'))
import Articles.Sequential_Learning_Striatum.Figures.cfg_pdf as cfg
from NetworkModel.StriatumNeurons import find_params_neuron
def step_AP(t, beg, end, I_value):
... |
e3e0ea3da56eceec74d5e563b1fa22da3e1579aa7d86c9628ab246679aea7eb6 | Python | 3,543 | 112 | """
##############################################################################
#
# Select columns with pas expression table for the latter MARA model script
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institute_of_Bioinformatics
# CONTACT: wsciekly.maciek@gmail.com
# C... |
b27732befc23bdf95e93c1870eb6795bc7aec2b6f2d0f03f40a67d3d8815b686 | Python | 3,545 | 116 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Adapted from https://github.com/florentmeyniel/TransitionProbModel
"""
from . import Inference_ChangePoint as io_hmm
import numpy as np
from scipy.stats import dirichlet
def run_inference(seq, options=None):
"""
run_inference is a wrapper that computes the p... |
ec62a3221bf94356b6c660c169dcb69103a58419434fbd147cd26b0ccbcc852a | Python | 3,545 | 109 | """Triton GTO pair density: same math as ``(R[:,rad] * Y[:,sph] * c).sum(-1)``.
Spherical harmonics stay in e3nn so Y matches the original kernel exactly.
The kernel never materializes ``phi[N, n_orb]``.
"""
from __future__ import annotations
import torch
try:
import triton
import triton.language as tl
... |
13dc6ffb3ba1f87b6676b03a997bce5eb386e05c65fdfa3ce0f115f4a5f78935 | Python | 3,546 | 112 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division, print_function
import argparse
from time import time
# import pylab as pl
import nibabel as nib
import numpy as np
from dipy.data import get_sphere
from dipy.reconst.shm import real_sh_tournier, real_sh_descoteaux, order_from_ncoef
fro... |
31ca6eba6a21436549b25d2adc4b7460b06c63bce05e79773f78942f1155f27e | Python | 3,546 | 113 | import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import torchvision
import numpy as np
import os
import cv2 as cv
class SampleDataset(Dataset):
def __init__(self, sample_dir, target_dir, transform=None):
self.sample_dir = sample_dir
se... |
8e5a454060565e78baf86a09a5d1d9012ae7601e657e09269961e476c758e2f3 | Python | 3,546 | 85 | # 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... |
3fdd5c4ee928e307b100d7fd8c4558842fce46881bc8f3bb2cbbf1ea2c7a87b5 | Python | 3,547 | 113 | import os.path as osp
import numpy as np
import ase.io
from tqdm import tqdm
import torch
from torch_geometric.data import InMemoryDataset, Data
from torch_geometric.loader import DataLoader
class Pyg3BPA(InMemoryDataset):
def __init__(
self,
root="dataset/",
name="train_300K",
tra... |
edd7ab7795df09809082c502e46fa2ab902f09aa4e35591863f80b990f53d0df | Python | 3,547 | 113 | import os.path as osp
import numpy as np
import ase.io
from tqdm import tqdm
import torch
from torch_geometric.data import InMemoryDataset, Data
from torch_geometric.loader import DataLoader
class PygAcAc(InMemoryDataset):
def __init__(
self,
root="dataset/",
name="train_300K",
tra... |
78903cad14de781be49ff7f8d2371170821a95da49cca52bdcfb239ddf8d0069 | Python | 3,548 | 134 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import division, print_function
import argparse
from time import time
# import pylab as pl
import nibabel as nib
import numpy as np
import skfuzzy as fuzz
DESCRIPTION = """
Compute fuzzy segmentation from an arbitrary amount of images.
"""
EPILOG = ... |
d90f9663affe6d358f9a38702056f421b6ecf0f6b6454053066d2923e3e32e57 | Python | 3,549 | 90 | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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
#
# U... |
096c60f55ac717e8a3eaabd46837b751df8abe4cd4a6ef148024a42747906c6f | Python | 3,550 | 99 | """
In this file the functions to create particle, swarm,
and the main BPSO algorithm is defined
"""
import time
import pandas as pd
from model_functions import*
No_switches = n_actions
#----- BPSO parameters
N_particles = 80
itermax = 400
# Particle parameters
particle_dims=No_switches #dimension ... |
dd5047e040da522882424fb717906cbc7f2394488353cd6d740d02d369baad28 | Python | 3,555 | 70 | import pandas as pd
import numpy as np
import re
import argparse
def get_data(reference_path,rawdata_path,save_path,cancer):
data_CNV = pd.read_csv(rawdata_path+'/'+cancer+'.CNV_masked_seg.csv', sep=',')
data_CNV['ID'] = 'chr' + data_CNV['Chromosome'].astype(str) + '_' + data_CNV['Start'].astype(str) + '_' + d... |
36ce73052011fca2979dcb6d435def9e0bfb69f1ebe1656282e0da75f278c4cb | Python | 3,557 | 82 | import os
import pytest
from imagingplus.processing.paq import PaqData
from imagingplus.processing.microscopes import PrairieViewMetadata
from imagingplus.workflows.TwoPhotonImaging import TwoPhotonImaging
@pytest.mark.skip
def test_TwoPhotonImagingTrial(twophoton_imaging_multitrial_noPreDoneSuite2p_fixture, exist... |
8c4dd99e5b3c1794f9f6f39f23143b44a248f6e10ae69d005a1e90ebd8ce3775 | Python | 3,557 | 96 | # Copyright Generate Biomedicines, Inc.
#
# 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 ... |
442606d75de5e06817a57ca3b6046d809d78ac0dd6b4bfccfc13646577e5be21 | Python | 3,558 | 114 | import numpy as np
import torch
import sys; sys.path.append('/Users/danil/My')
from dt_utils import Timer
from data_utils import generate_recognition_data, data_to_tensor, chance
from plastic import PlasticNet, LearnedHebb
from torch_net_utils import plot_hebb
from fastweights_numpy import FastWeights as Fa... |
55323decdc84d646fab35a7edf54d7a0d2dfa08c8762aebc907e0d74c50f7f3a | Python | 3,558 | 111 | import glob
import os
import SimpleITK as sitk
import numpy as np
import argparse
from medpy.metric import binary
def read_nii(path):
return sitk.GetArrayFromImage(sitk.ReadImage(path))
def new_dice(pred,label):
tp_hard = np.sum((pred == 1).astype(np.float) * (label == 1).astype(np.float))
fp_hard = np.su... |
e28c2bfecc9ac4586f812b844d1d69d481107efa78cc5118de983d8228492540 | Python | 3,558 | 97 | # Fine-tuning procedure for Stanford Cars using ResNet 50
import keras
import pickle
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.models import Model
from keras.optimizers import SGD
from sklearn.model_selection import train_test_split
from load_data_cars import load_data_cars
from image_gen_... |
2f7a7efb57ab7002c828dedd656f10e2915be94e686aba83978a8e70697bc161 | Python | 3,559 | 67 | # 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... |
47f52625958a70de61c9f54a20b3f292d637dc91d36c93dea332f041b68513e7 | Python | 3,560 | 117 | #############################################################################
#
#Updated by: Yiqin Wang
#
##############################################################################
import sys, os
#set probe names
probe_all = []
#46 mtDNA probes
for i,n in [("A",12),("B",12),("C",12),("D",10)]:
for j in range(1,n... |
6fa40606390148af76e5c86297e8e259b34e2fdcafcd7a8935108c32c987cf8b | Python | 3,560 | 74 | # Creation of binary visual-semantic mapping (no semantic embedding) and further analyses
# Copyright (c) 2021 Laurent Caplette
import numpy as np
import pickle
ncomps_feat = 213
nperms = 1000
# load visual features
all_im_words = np.load('all_im_words_final.npy', allow_pickle=True)
all_feats = np.load('all_feats_fi... |
85d6cda67f5dde87362498ea7c102fab0411d7f1db48096b2e09ef0665fe28ce | Python | 3,560 | 111 | import numpy
import pandas
def vtxs_edges_to_linesegments(vtxs, edges):
return [(vtxs[i], vtxs[j]) for i, j in edges]
def vtxs_edges_rootid_read_compact_detail(compactdetail, **kwargs):
nodes, conns, tags, _something, annos = compactdetail
if conns:
raise NotImplementedError("Connectors are not... |
d6f98b9d12c99c961c5f3dbd989c6c3b9be419b18c5fa47259bde8d74e58db00 | Python | 3,560 | 85 | # Sconda conda activate ghmc1; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/GhmCN/reviewer_questions/distribution_of_genes_and_distances_activ72B; python
import pandas as pd
import numpy as np
import torch
import torch_geometric
from scipy.sparse import load_npz
device = torch.device("... |
a28249b138251967665a594cdf6c7e1eff8f097a90eca4b6745b814f7315b622 | Python | 3,561 | 97 | import os, tqdm
import numpy as np
import matplotlib as mpl
from textwrap import wrap
import matplotlib.pyplot as plt
from skimage.io import imread
from matplotlib import rc
rc('pdf', fonttype=42)
from itertools import repeat
import multiprocessing
from morgana.DatasetTools import io
# from orgseg import ImageTools
d... |
c50651758aed47ba58ac8daa2f063dfcb7897ad5a01b88e987301cb5d23ab671 | Python | 3,561 | 135 | from typing import Tuple
import cv2
import gradio as gr
import numpy as np
import torch
import torchvision
from PIL import Image
from src.data.utils import CLASS_COLOR, CLASS_ID_REVERSED
from src.models.smp.model import HistologySegmentationModel
model = HistologySegmentationModel.load_from_checkpoint(
'models/u... |
f1b9107f6ee76ef1713d3f2ca4e9e1f7612a59756c3b7d53df37536c1511992a | Python | 3,561 | 152 | # -*- coding: utf-8 -*-
"""
Created on Mon Nov 28 23:11:35 2022
@author: Agus
"""
'''
Imports
'''
#matplotlib.use('Qt5Agg')
import os
import numpy as np
import glob
'''
FUNCTIONS
'''
def deltaf_f(traces):
#calculate delta F/F for every trace
#traces is array containing traces for all neurons in one mouse
... |
48b9d25dbd1fb8317e5a1574918e898c4261b285ccff719c1d77533920bbb4df | Python | 3,564 | 73 | # 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... |
4edbbd81c230fc9d275641bd00067c185b639586af60539c7d30102e84108565 | Python | 3,564 | 116 | """
##############################################################################
#
# Collapse result tables from distinct windows
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institute_of_Bioinformatics
# CONTACT: wsciekly.maciek@gmail.com
# CREATED: 18-04-2020
# LICENS... |
7894ceac90ea5fa60d1dc61ea6593af39c2cbdd81d0a824f0cfef8e5488c94a1 | Python | 3,566 | 95 | import os
import numpy as np
import random
from PIL import Image
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
import torch.utils.data as data
import torch
class featuresdataset_inference(data.Dataset):
def __init__(self, data_path, data_frame, raw_images=False, transform=None):
# opens... |
2029c150cce97d981200c0e01aaa992795e19a29e0b67a66560f675c32309cac | Python | 3,568 | 79 | # Copyright (c) 2008-2026 the MRtrix3 contributors.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Covered Software is provided under this License on an "as is"
#... |
821d0a656b2b64a3e53c1bb507400edb9586015849dfabc471cd6f574fc5d4f7 | Python | 3,568 | 100 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
9fe82cfa25076e0a261f01f4a58919092e82e7e510d001db4693f587428190ff | Python | 3,568 | 123 | #!/usr/bin/env python3
"""
Calculate probability-weighted metrics given a metric image and a probabilistic atlas
Authors
----
Mike Tyszka, Caltech Brain Imaging Center
License
----
This file is part of atlaskit.
atlaskit is free software: you can redistribute it and/or modify
it under the terms of the GNU Ge... |
570b28962d0106bf8066e2be4a122feed157c3f0eaf0968266b5ff05b1f6b7b9 | Python | 3,569 | 100 | ####################################################################################
#
# 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... |
6a51a9141eb1fc6468299ae1028dae9097ef916879aae8ce670d66208aa3c174 | Python | 3,569 | 93 | import argparse
import numpy as np
from pensa.features import \
read_structure_features
from pensa.dimensionality import \
calculate_pca, \
pca_eigenvalues_plot, \
sort_trajs_along_common_pc, \
compare_projections
from pensa.comparison import \
pca_feature_correlation
# -------------#
# --- MA... |
a9b70c144450760d7dc9ab9e35a4c9252847537c97ee9375213784eb2b2e0429 | Python | 3,570 | 62 | """Generate LaTeX tables from final result files (rerun after every result update).
tables/table4_conformal.tex : PICP / MPIW / Winkler / late-life PICP by method under P3 and P4 (GBDT anchored, 5 seeds)
tables/table5_fewcell.tex : few-cell curve (adapted_split and references)
"""
from __future__ import annotatio... |
c9094dfc417c275c647cda95d0a877865311d85670b72e37263a3acf3f9ebbfd | Python | 3,570 | 156 | import re
def parse_task(log):
if re.search('ihm_C', log):
return 'multitask'
if re.search('partition', log):
return 'los'
if re.search('deep_supervision', log):
return 'decomp'
if re.search('ave_auc_micro', log):
return 'pheno'
if re.search('AUC of ROC', log):
... |
c740a0b5134c2fc7043cd2cbb9b8b24a52a77b52be0ce20c595d57d846842ecd | Python | 3,571 | 107 | # standard packages
import os
import subprocess
# third party packages
import numpy as np
# pnabind packages
from pnabind.utils import moveFile
from .mesh import Mesh
def runMSMS(atoms, file_prefix='mesh', basedir='.',
clean=True, quiet=True, hydrogens=True, area_only=False, mesh_kwargs={}, **kwargs
):
... |
51ad565b1f2f7a6140d3822900b940fd3650b0a43f2080d65161850d460ae18b | Python | 3,572 | 116 | """
##############################################################################
#
# Collapse result tables from distinct windows
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institute_of_Bioinformatics
# CONTACT: wsciekly.maciek@gmail.com
# CREATED: 09-04-2020
# LICENS... |
8f151fd84bcfbc0e42e602cd59b56ad03efb2127d21fb4346460882c3a19c6cb | Python | 3,573 | 36 | """Generate main_2col.tex from main.tex: two-column elsarticle (5p) with wide floats promoted to starred environments.
Single-column floats keep [!htbp]; starred floats use [!tp] (two-column LaTeX cannot place them here or at the bottom).
Float barriers come from placeins in main.tex, so no float drifts past a section ... |
1f5ef4c4ed627fe18f7a66c7cab2f34d657ac4c71fbd9a6e993f0391a644b641 | Python | 3,575 | 138 | import os
from typing import Tuple, Optional
import numpy as np
from scipy import stats
import torch
import torch.utils.data as td
from sklearn.utils.class_weight import compute_class_weight
class ImbalancedDataSampler(td.Sampler):
def __init__(self, targets: np.ndarray): # [len(data),]
self.num_samples... |
952c213f992791dcf0ac5e520f4aece26abca656e1dc1a9f54c53f588cf7dd0b | Python | 3,575 | 116 | import logging
import os
import socket
import sys
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from configs.configs import get_config_dict
from helpers.rockland import get_rockland_subjects, load_rockland_data
def plot_static_functional_connectivity_estimate(
config_dict: dict,
c... |
ce6fb5667f735080e8696de71b0b8618becb720adfca798ea8293bbb5c3d4a84 | Python | 3,575 | 79 | """
Domain boundary distance score, as defined in CASP 7 paper:
Assessment of predictions submitted for the CASP7 domain prediction category (2007)
https://onlinelibrary.wiley.com/doi/10.1002/prot.21675
Under our scoring scheme all predictions within 8 residues of the correct boundary
will score, but predictions that ... |
efaaf7b5b4be0975f214afca74f4c8e01c83349de20edfa155900121096d7591 | Python | 3,576 | 104 | from proto_net.protonet.model import Protonet
from proto_net.utils.engine import Engine
from proto_net.protonet.dataLoader import ProtoNetDataLoader,ProtoNetDataSet
from processing.constants import *
from processing.data_structure import data_structure
from sklearn.model_selection import KFold
import numpy as np
... |
168adcf95019b79a007629441301fd2fa4eb8b256fdd51bb50d1fe79647b5e84 | Python | 3,577 | 68 | import mne
from config_FaceName import Results_data_path,stats_comparisions,MRI_data_path,Results_data_path
from custom_functions import read_stats_list, print_sig_stats
cluster_method='tfce'
tfce_p_sig=0.05/2.0 # adjust for sensor and source stat tests
#sensor stats
for comparision in stats_comparisions:
print(c... |
fa83b6f82edd76ea43a93d460236a3ed4031880c84eed8c542c3fafb636c00a2 | Python | 3,579 | 93 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class inter_att(nn.Module):
def __init__(self, hidden_dim, attn_drop):
super(inter_att, self).__init__()
self.fc = nn.Linear(hidden_dim, hidden_dim, bias=True)
nn.init.xavier_normal_(self.fc.weight, gain=... |
f947702fb8e8f109baa3c599e2115970176f660e0d6b72f206138962bc41617c | Python | 3,580 | 86 | import logging
import os
import socket
import sys
from fcest.helpers.array_operations import get_all_lower_triangular_indices_tuples
import numpy as np
import pandas as pd
import pingouin
from configs.configs import get_config_dict
if __name__ == "__main__":
data_split = 'LEOO' # leave-every-other-out
exp... |
d36c81669f346940e08ecc445e365658fe01f0efb50b91231d706009df5e2fcf | Python | 3,581 | 129 | from typing import Any
import pytest
from langchain_tests.integration_tests.base_store import (
BaseStoreAsyncTests,
BaseStoreSyncTests,
)
from typing_extensions import override
from langchain_core.stores import InMemoryStore
# Check against standard tests
class TestSyncInMemoryStore(BaseStoreSyncTests[Any]... |
6cc7a426d7a89d625352bc6f8184d237945ff840fa7efa150db79f8873fc85de | Python | 3,583 | 97 | """PyTorch Hub models
Usage:
import torch
model = torch.hub.load('repo', 'model')
"""
from pathlib import Path
import torch
from models.yolo import Model
from utils.general import check_requirements, set_logging
from utils.google_utils import attempt_download
from utils.torch_utils import select_device
dep... |
a7754fb14e730ddb314930471750478f43ccc8c709b4e714ed535f036c5add39 | Python | 3,584 | 89 | from mimic3models.metrics import print_metrics_multilabel, print_metrics_binary
import sklearn.utils as sk_utils
import numpy as np
import pandas as pd
import argparse
import json
import os
def main():
parser = argparse.ArgumentParser()
parser.add_argument('prediction', type=str)
parser.add_argument('--te... |
aa3c58b3f6222ad934c07186672a2492fe957eac7215d8706724048f0051e894 | Python | 3,586 | 116 | from langchain_core.messages import AIMessage, ToolCall, ToolMessage
from langchain_classic.agents.format_scratchpad.openai_tools import (
format_to_openai_tool_messages,
)
from langchain_classic.agents.output_parsers.openai_tools import (
parse_ai_message_to_openai_tool_action,
)
def test_calls_convert_agen... |
84b93a05f4358b6e11e38cf54a223ddac44ee1b804343d96edc113f897ab3ce6 | Python | 3,587 | 104 | import numpy as np
import pytest
import torch
import torch.nn.functional as F
from chroma.data import Protein
from chroma.layers.structure.backbone import RigidTransformer
from chroma.layers.structure.protein_graph import ProteinFeatureGraph
def test_protein_feature_graph():
torch.manual_seed(10)
dim_nodes,... |
a2e80d6248421c7e9b54da017a46fb7c80d27a29e0ea8edebb15404f0597bb2a | Python | 3,588 | 91 | # Prepare images and labels for validation #3
# Copyright (c) 2023 Laurent Caplette
import numpy as np
import os, csv, h5py
from PIL import Image
np.random.seed()
homedir = '' ### path of home directory
imSize = 224
nRecon = 25 # number of concepts / recons
nppim = 3 # number of perm images per recon
nIms = int((nR... |
bc5495eb1197f9d981df7469bf8b9168c4dbbf2d87aa822e7c43647aed2e90f9 | Python | 3,588 | 90 | from torch.utils.data import Dataset
import pandas as pd
import torch
from pathlib import Path
import numpy as np
device = "cuda:0" if torch.cuda.is_available() else "cpu"
smi_char = {'<MASK>': 0,'C': 1, ')': 2, '(': 3, 'c': 4, 'O': 5, ']': 6, '[': 7,
'@': 8, '1': 9, '=': 10, 'H': 11, 'N': 12, '... |
495613c759e57061552f26c1bb392986b7af53b9b3217a9eeb78671fd05e9668 | Python | 3,589 | 54 | """Figure 3: schematic of the five evaluation protocols. Each panel shows a grid of cells or vehicles (rows) x cycles or
charges (columns); train in blue, test in orange, calibration in green. Datasets are grouped by colour bands on the left;
P5 adds a grey band of field vehicles that only ever appears as test data."""... |
c786f26db6bd11df7a9f19d0673a8667ec3afd1f2faa266b2169595c41da71f1 | Python | 3,589 | 114 | import sys
import os
import subprocess
import re
########################################################################
########################################################################
# Import setuptools
# Use existing setuptools, otherwise try ez_setup.
try:
import setuptools
except ImportError:
# ... |
634838f0ed319219d4c078a6a657c00afeda85bf4a4c037dd27c64841791dc9b | Python | 3,590 | 147 | """
Common functions
Marco Lui, January 2013
"""
from itertools import islice
import marshal
import tempfile
import gzip
class Enumerator(object):
"""
Enumerator object. Returns a larger number each call.
Can be used with defaultdict to enumerate a sequence of items.
"""
def __init__(self, start=0):
s... |
bc639082347b3848cefb6939b3b4a6658a39421be38d08c5e51e2b7755cfa4aa | Python | 3,590 | 147 | # use threshold > 100 if not training until threshold reward
# 2 Bit XOR
cur_1 = {
'name' : 'cur_1',
'folder' : 'src/configs/AAI_tasks/2_bit_XOR/',
'thresholds' : [
200
],
'train_files' : [
'2_bit_XOR-all.yml'
],
'test_files' : [
'2_bit_XOR-0.yml',
'2_bit_XOR... |
d6e70547d889f0c6fc71e1c71cec7e0c8d435e784f8fb3d5c66fb1c7de4b6da7 | Python | 3,591 | 109 | import os
import nibabel as nib
import numpy as np
from scipy.ndimage.morphology import binary_erosion
def erode_mask(mask_data, iterations=1):
'''Erode a mask N times using scipy binary_erosion.'''
mask_data = (mask_data > 0)*1
mask_data[0,:,:] = 0
mask_data[:,0,:] = 0
mask_data[:,:,0] = 0
... |
16277ebe43fa9d1ed69c95de439ff4733011403378feb28366ef520b658643be | Python | 3,592 | 122 | import math
from collections import Counter
# ==================================================
# Statistical tools
# ==================================================
# efficient combinatorial function to handle extremely large numbers
def log_choose(n, k):
r = 0.0
# swap for efficiency if k is more than h... |
42a9971e6e2a760a0a6fd3dd5b1c471d9cbc7de54341d47639b00734e82816a3 | Python | 3,592 | 103 | """
Preform gene term enrichment analysis with GOATools.
"""
import session_info
import pandas as pd
import collections as cx
from pybiomart import Dataset
from functools import lru_cache
from statsmodels.stats.multitest import multipletests
# GO analysis
from goatools.obo_parser import GODag
from goatools.base import... |
48eb59cfd53227fb072d69445a3d38464f6feb69e99c12250a3bb6d29d7fcb77 | Python | 3,592 | 114 | #!/usr/bin/env python3
import argparse
import os
import re
import shutil
import subprocess
import yaml
import collections.abc
def update_dict_recursively(d, u):
for k, v in u.items():
if isinstance(v, collections.abc.Mapping):
d[k] = update_dict_recursively(d.get(k, {}), v)
else:
... |
25bfb130b0fcb92a6d8166d75e95de68569d84030c8431cc94fce90d039aa538 | Python | 3,594 | 71 | import argparse
import os
import h5py
import numpy as np
def get_metric(h5df, metric, key):
# A function to calculate the average or median coverage of each h5 file for each key present.
metrics = []
n = 1000
for i in range(0, len(h5df["data/X"]), n): # len(h5df["data/X"])
array = np.array(h... |
ee7814b027e835ca28c52a4786eaa8fc6246d0d552a93ce1ebb2681e687d0f52 | Python | 3,595 | 84 | import numpy as np
import pandas as pd
from utils.features.spatial import get_spatial_features
from utils.features.morph import get_inst_stat
def update_with_affix(dict, name):
new_dict = {}
for k in dict.keys():
new_dict[name + ": " + k] = dict[k]
return new_dict
def get_morph_features(dict, pa... |
241562221a4378b3aeb1d24b0a40a4f86eb4ec88a12ad17daa26221ea0a717ed | Python | 3,596 | 74 | from config_FaceName import group_name,Ids,RSA_data_path,MRI_data_path,Results_data_path
import mne
import os.path as op
from custom_functions import print_sig_stats,read_stats_list
from scipy import stats as stats
import numpy as np
from scipy import stats as stats
import matplotlib.pyplot as plt
from mne_rsa.source_l... |
8c708cadb26bfe18be5053dda094de28ac98c2cd37c4539df2350dc9422ad13b | Python | 3,597 | 75 | #Built-in and external imports
import math
import torch
from torch.utils.data import TensorDataset
import torch.nn.functional as F
#My imports
from data import generate_recog_data_batch, generate_recog_data
from networks import HebbNet
from plotting import plot_train_perf, plot_recog_generalization, plot_reco... |
b7b4aa7d0b5bbe7e3dea0e7871eb327ed468a1f13a8526d41375dfa83c2a2e6c | Python | 3,598 | 118 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup --------------------------------------------------------------
# If extensions (or module... |
f2f4251c5bf1799b4a4ee5f43c547b3d663899d30e825c1fbcc0f456e7da1fab | Python | 3,598 | 95 | import torch
def compute_mean_mad(dataloaders, properties, dataset_name):
if dataset_name == 'qm9':
return compute_mean_mad_from_dataloader(dataloaders['train'], properties)
elif dataset_name == 'qm9_second_half' or dataset_name == 'qm9_second_half':
return compute_mean_mad_from_dataloader(dat... |
8f5ef294a7c1f22205bc2e5c1edab883019695cdf646aec427ea4552488f0307 | Python | 3,599 | 75 | import os
import socket
import sys
from configs.configs import get_config_dict
from helpers.slurm import send_slurm_job_to_queue, save_slurm_job_to_file
if __name__ == "__main__":
hostname = socket.gethostname()
assert hostname == 'hivemind'
data_set_name = sys.argv[1] # 'd2', 'd3d', or 'd{%d}s'
... |
e9d36c36e799c831ab632f774e190f0fa1bafbdd48fc02f284ef9e94e200d734 | Python | 3,599 | 95 | import numpy as numx
import scipy as sc
def seperateDataByLabelsForMNISTandCIFAR10(data, label, status = False):
zeros = None
ones = None
twos = None
threes = None
fours = None
fives = None
sixes = None
sevens = None
eights = None
nines = None
for i in range(data.shape[0])... |
cd98446b9ee165c9a5fa6f8fe7d7aa883bdad734542bdb00a20cfeb7a2cce536 | Python | 3,600 | 88 | # MIT License
# Copyright (c) 2021 Yuyang Wang
# 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, pu... |
3d932b60a94231c03c13f3c6f4c321484b95c2a59021d47aef7abe633154ba74 | Python | 3,602 | 69 | # Copyright (c) 2008-2026 the MRtrix3 contributors.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Covered Software is provided under this License on an "as is"
#... |
e5480b72aaa896ef4489ff15ef66099680cf2c1ff399d8e86d5bf45f3aa1b9cb | Python | 3,603 | 86 | from typing import Callable, List, Union
import numpy as np
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem
Molecule = Union[str, Chem.Mol]
FeaturesGenerator = Callable[[Molecule], np.ndarray]
FEATURES_GENERATOR_REGISTRY = {}
def register_features_generator(features_generator_name: str) -> Cal... |
dc45829f258de1da29fbc0f856a9654b2f76b3d098cafe025335166cfcf53839 | Python | 3,604 | 45 | #! /bin/env python
#
# Michael Gibson 23 April 2015
# Modified Adrian Foy Sep 2018
import sys, struct
import numpy as np
def read_one_data_block(data, header, indices, fid):
"""Reads one 60 or 128 sample data block from fid into data, at the location indicated by indices."""
# In version 1.2, we moved from s... |
04e4e3e920e7235bbc2395b6cfcdcbb8dbcd7813d3fee7c072bf43451d9bc605 | Python | 3,605 | 105 | import os
import utils.hhblits_search as hh
import utils.psiblast_search as psi
from tqdm.std import trange
import yaml
import argparse
import time
# Load the paths of tools and databases
with open("config.yaml", 'r') as f:
cfg = yaml.load(f, Loader=yaml.FullLoader)
psiblast = cfg['psiblast']
rosett... |
3f779fda63004947e2aba326e20d155e91474f1402122fb0dac0c0b55a1f959b | Python | 3,607 | 96 | # Fine-tuning procedure for Birds using ResNet 50
import keras
import pickle
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.models import Model
from keras.optimizers import SGD
from sklearn.model_selection import train_test_split
from load_data_birds import load_data_birds
from image_gen_extend... |
17bd0830e0a3d7414b41d2b6f648892622e93ec30f4c30d03d88dee9bca54477 | Python | 3,608 | 72 |
pdb_protein = {
'amino_acid_names': [
'ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLU', 'GLN', 'GLY', 'HIS', 'ILE', 'LEU', 'LYS', 'MET', 'PHE', 'PRO', 'SER', 'THR', 'TRP', 'TYR', 'VAL'],
'NON_STANDARD_SUBSTITUTIONS': {
'2AS':'ASP', '3AH':'HIS', '5HP':'GLU', 'ACL':'ARG', 'AGM':'ARG', 'AIB':'ALA', 'ALM... |
28d1d25ea86b0d42dcf67cbf6859cde779292eaf25139ab1f85ac1fe773ad9fa | Python | 3,608 | 110 | # Training procedure for CIFAR-10 using DenseNet 40 with growth rate 12.
from __future__ import print_function
import os.path
import densenet
import numpy as np
import sklearn.metrics as metrics
from keras.datasets import cifar10
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenera... |
2409a63e23a5f1ba369b297607ac83906d5df03d4a5d832a4063fbbb226f13c5 | Python | 3,609 | 116 | # 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... |
93e28cda94883ccacf6cddda56808dae40e919ee3cfeea5ca371c1d2068b6e8b | Python | 3,611 | 87 | import cv2
import numpy as np
import pandas as pd
from glob import glob
from tqdm import tqdm
import matplotlib.pyplot as plt
from joblib import Parallel,delayed
import librosa
import librosa.display
from engine import utils
from constants import JUST_DIR,ICBHI_DIR,BASE_DATA_DIR
from constants import MEL_SPEC_SZ
LUNG... |
6ae5946954dc5160de6d6db3afe046efdf2e37a516f4bac8a6df74602a90d94e | Python | 3,612 | 92 | """tests for registration/rigid.py.
test_landmark_align_matches_legacy checks against the old picking.py math
(tests/fixtures/landmark_align_baseline.json). test_procrustes_icp_recovers_known_transform
is just a correctness check on made-up data.
"""
import json
from pathlib import Path
import numpy as np
import pyt... |
721e55a8ff0c8ad5483d0a2c0ca8deab5457c3a6484a2479813e8b2344512d63 | Python | 3,613 | 88 | import torch
import torch.nn.functional as F
from torch_geometric.nn import EdgePooling as EdgePoolingBase
from torch_geometric.data import Data
class EdgePooling(EdgePoolingBase):
def __init__(self, in_channels,
edge_dim=None, edge_score_method=None, dropout=0,
add_to_edge_score=0... |
ac582ee1995e5eaa37879226630e7e91123d16b54873ff1f2d80a327b98097c2 | Python | 3,614 | 88 | # Copyright (c) 2008-2026 the MRtrix3 contributors.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Covered Software is provided under this License on an "as is"
#... |
c16e318b67edfb9a10b4906e77a6e01b5ce486c313d135ec2ae4a7055c7ff1f8 | Python | 3,615 | 120 | import json
import pickle
import numpy as np
import torch
import utils # noqa: E402
from dopp import FeedForwardCell
from sim import (_compute_input_potentials, _compute_input_rates, # noqa: E402
_compute_preferred_angles)
def sim(params, model, *, angle_1, angle_2):
angle_1 = torch.Tensor([... |
acdd888194b7eab8e6752bdab8ec1934ab55ef6e0c1ac828b9971160157dd21a | Python | 3,619 | 114 | import os.path as osp
import numpy as np
import ase.io
from tqdm import tqdm
import torch
from torch_geometric.data import InMemoryDataset, Data
from torch_geometric.loader import DataLoader
class rMD17(InMemoryDataset):
def __init__(
self,
root="dataset/",
name="benzene",
mode="tr... |
b242544d804617346609a1b2d79cab957a852f1e50bd737d625198db2bcb6f15 | Python | 3,619 | 105 | try:
from rdkit import Chem
from eval_follow_edm.rdkit_functions import BasicMolecularMetrics
use_rdkit = True
except ModuleNotFoundError:
use_rdkit = False
import torch
import matplotlib
matplotlib.use('Agg')
import numpy as np
from eval_follow_edm import bond_analyze
############################
# V... |
665ceff184005d2521b2a977d6fcf5a75f95e92cd7b9a0677cbade4bf2d79900 | Python | 3,621 | 100 | # -*- coding: utf-8 -*-
"""
An implementation of the 3D U-Net paper:
Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, Olaf Ronneberger:
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation.
MICCAI (2) 2016: 424-432
Note that there are some modifications from the origina... |
e518b4c61f5b81a327deda1c69fa2f776a9716bc506420d6143c1233bcd7edda | Python | 3,621 | 109 | """
Virtual node generation methods.
"""
import numpy as np
import torch
from ase import Atoms
from pymatgen.io.ase import AseAtomsAdaptor
from pymatgen.analysis import local_env
from pymatgen.analysis.graphs import StructureGraph
from scdp.common.utils import compute_bonds
CrystalNN = local_env.CrystalNN(
dista... |
3317ba1c3e14568417a9cf2bebc3229e90f2549e753ae5ada35e435726bfd003 | Python | 3,622 | 90 | ## Imports
import numpy as np
from chromograph.pipeline import config
from chromograph.preprocessing.utils import *
from cytograph.visualization.scatter import *
from cytograph.visualization.colors import Colorizer
import torch
from torch import nn
from twobitreader import TwoBitFile
from deeplift.dinuc_shuffle impor... |
5e32463f0f5330ac0eee0b1f95e5c9b81cdb5d1981907e008352ff382bda86ec | Python | 3,622 | 113 | from collections.abc import Sequence
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils.function_calling import con... |
7bfe580a527b420adb894ca6fe3d117a560df9148438dbb932c262bc0150fe0d | Python | 3,622 | 93 | """This package includes all the modules related to data loading and preprocessing
To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
You need to implement four functions:
-- <__init__>: ... |
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