sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
78b226a3ca3a9ad9e01a5335c912b7eb7d77298c9fe7ea1d0c0047161e0c1ad7 | Python | 4,075 | 112 | from __future__ import annotations
import json
import logging
import re
from re import Pattern
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from pydantic import Field
from typing_exte... |
264e419ddcb450fbccca8eb879154c05aa53566e2f04e6f483efd77a0e7f558e | Python | 4,076 | 107 | import math
from glob import glob
import h5py
import nibabel as nib
import numpy as np
import SimpleITK as sitk
import torch
import torch.nn.functional as F
from medpy import metric
from tqdm import tqdm
def test_single_case(net, image, stride_xy, stride_z, patch_size, num_classes=1):
w, h, d = image.shape
... |
a9f1daae702fbd612200a438abc936ec5e44592494b81b60e96dc4002482b282 | Python | 4,076 | 144 | # Adapted from OpenFold
# 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/l... |
3b64127095e8b318e188ec8d08e6c1cd4ad8522586764bb85cc5bb92dcf5bbee | Python | 4,077 | 75 | import pandas as pd
import params
def summarise_flies_from_data_summary(path=None, headless=False, predictions=False):
if path is None and not headless and not predictions:
path = params.data_summary_csv_dir
elif path is None and headless:
path = params.data_headless_summary_csv_dir
elif ... |
f55a07f6d2a34130e960d5f029c28bf88394677a779eded41424e7cb7422234e | Python | 4,078 | 108 | import json
import warnings
class AcqNotification:
class Acquisition:
ACQ_STARTED = "acq_started"
ACQ_EVENTS_FINISHED = "acq_events_finished"
@staticmethod
def to_string():
return "global"
class Hardware:
PRE_HARDWARE = "pre_hardware"
PRE_Z_DRIVE ... |
939feb03b6f89d9e8dd53d7758fda48acc012cb05c67e850f0092dd662c7c6d5 | Python | 4,082 | 116 | import uncertainpy as un
import numpy as np
import matplotlib.pyplot as plt
from prettyplot import *
import scipy.integrate as integrate
import os
### FIGURE5 ###
data = un.Data()
data.load(filename='../data/simulation_outputs/UQSA_physiological/K_se_5/K_se.h5')
K_se_mean = data['K_se']['mean']
K_se_p5 = data['K_se'][... |
2b26dbaa797c9d777b6a2ee30d6a35ad1de107cea311bdd831239958cc8ab66e | Python | 4,083 | 91 | '''
read channel info for OpenEphys data recorded by BT
9/20/2022 jz103
'''
import os
import gc
import warnings
from copy import deepcopy
from time import time
import re
import datetime
import json
import xml.etree.ElementTree as ET
import numpy as np
import matplotlib.pyplot as plt
VALID_SESSION_LAMBDA = lambda x: ... |
b0f1da78b04365a62ac585c6c52b9c8e4e11992ed60b11810ae347e647554ea4 | Python | 4,085 | 157 | ########################################################################
# Stochastic second-order irreversible reaction on a surface.
# RESTORE
# AIMS: to verify checkpointing and restoring of the well-mixed stochastic
# solver 'Wmdirect' in the context of the Second-Order Irreversible
# Surface Reaction model (se... |
ce5c3c1c78bb616afca83525884525fd3bf4c9136e96dbea6eac00311d59774e | Python | 4,087 | 107 | import math
import torch
import torch.nn.functional as F
class DynamicPool3d(torch.nn.Module):
def __init__(self, scale, steps, mode, irreps):
super().__init__()
self.scale = scale #in physical units
self.steps = steps
self.mode = mode
self.kernel_size = tuple([math.floor(s... |
2ebe0b4e2ba546682247e8b7934739b7d2999e8f6cb66f20b880ec577b9b39ba | Python | 4,092 | 134 | from typing import Any
from typing import Callable
import torch
from torch import nn
from torchvision.models import resnet50
from ptrnets.utils.config import load_state_dict_from_model_name
__all__ = [
"resnet50_cifar10",
"resnet50_cifar10_corrupt0",
"resnet50_cifar10_corrupt0_2",
"resnet50_cifar10_c... |
d88910abc9a077d7c3a33a04f57c438a5a4832865905998ff28ff3ff8d9073f7 | Python | 4,092 | 114 | from scipy.stats import gamma, uniform
import math
import numpy as np
# this is an unfortunately hard-coded dictionary of the parameters of prior distributions.
param_dists = {
"tau_e": {
"type": "gamma",
"alpha": 2,
"loc": 0.004492887509221829,
"scale": 0.0032375996670863947,
}... |
d915940a47ca4f0be649a38354be85d8e27244f90f3223624fe63d1a7cf13348 | Python | 4,092 | 146 | import torch
from abc import ABC, abstractmethod
class ImageModel(ABC):
"""
Defines the structure of an image model object used to offer interpolation on an image.
Attributes
----------
data : torch.Tensor (size m)
Original image data.
omega : torch.Tensor (size # of dimensions x 2)
Image domain.
m : torc... |
4cf02a420b6e05c71432b07895f4f1aea91b30ee26f9ac30d7c2f31d7cf3665c | Python | 4,093 | 94 | import argparse
from collections import namedtuple
import json
import pathlib
import time
import dataclasses
from flax.metrics import tensorboard
import jax
from chemoxrl import rppo
from chemoxrl.cell import EnvParams
parser = argparse.ArgumentParser(description="Script to run experiment.")
# ExperimentConfig par... |
58c732eb6caba3c88263102353898217c6f1b4a88a527c38a28d2d7b3d03a4ef | Python | 4,097 | 130 | """Tracers that call listeners."""
from collections.abc import Awaitable, Callable
from typing import TYPE_CHECKING
from langchain_core.runnables.config import (
RunnableConfig,
acall_func_with_variable_args,
call_func_with_variable_args,
)
from langchain_core.tracers.base import AsyncBaseTracer, BaseTrac... |
433d12a4cc25a42871c3770a975ef7046716b4181251406d3cb7112f9d031881 | Python | 4,098 | 64 | # 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... |
30369de77e69ca84f782c18202501ea41a5ac8bf4381812034b66327427c1f7b | Python | 4,102 | 157 | ########################################################################
# Stochastic second-order irreversible reaction on a surface.
# RESTORE
# AIMS: to verify checkpointing and restoring of the well-mixed stochastic
# solver 'Wmdirect' in the context of the Second-Order Irreversible
# Surface Reaction model (se... |
40e5a048df7f981124e150fe7a3ff5e5b3ddafe8a37efad9109856a9a2719e8e | Python | 4,103 | 108 | # This extension template provides instructions to add new causal discovery algorithms to pgmpy.
#
# Please follow the following steps:
# 1. Copy this file to `pgmpy/causal_discovery` and rename the file as `[MyCausalDiscoveryAlgo].py`
# 2. Go through the file and address all the TODOs.
# 3. Add an import statement in ... |
73cf38a2a1ed5fa2946ff7ee441e5e1c5422457c3e4e7ce0b1306548c8bd33f0 | Python | 4,103 | 88 | import torch.nn
from models.GFNO import *
from models.FNO import *
import torch.nn as nn
# ----------------------------------------------------------------------------------------------------------------------
# Ghybrid2d
# ----------------------------------------------------------------------------------------------... |
f364fe7a775ae172e88661868db4fe377a864907c17e7297e5a2e695695da505 | Python | 4,103 | 72 | # *=========================================================================
# *
# * Copyright Erasmus MC Rotterdam and contributors
# *
# * Licensed under the GNU GENERAL PUBLIC LICENSE Version 3;
# * you may not use this file except in compliance with the License.
# *
# * Unless required by applicable law or agre... |
91c975d88b70f6e3043dfe982eab0b5c46883cfe9f1ed4667a59145eafb87680 | Python | 4,106 | 124 | # -- coding: utf-8 --
# @Time : 2022/10/15 16:11
# @Author : Biao Zhang
# @Email : littlebiao@outlook.com
# @File : heco_config.py
from yacs.config import CfgNode as CN
import time
# --------------------------------------------------------------
# Config of model
# -----------------------------------------------------... |
2d63489d917a7ca27afe4e0a0e39f88abf255506148846515117e8375b001bd4 | Python | 4,107 | 111 | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@Description: :
@Date : 2022/06/17 12:22:32
@Author : Zilan Yu
@version : 1.0
'''
import torch
import torch.nn.functional as F
import torchmetrics
import pandas as pd
import sys, os
from scipy.stats import spearmanr
from HeteroModel import *
from d... |
0746f53ea11435d0006c466e87a1a700aa7d15d61c76c018eaab5256f187ade0 | Python | 4,108 | 105 | # 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... |
6b8b74622080bc0f0b71b4df27ce35ea64e640a6e672469d26ee2822cb4cd770 | Python | 4,108 | 107 | # Copyright 2024 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... |
54e06ae0ddac71a9dea7a7bad37d8d4a6de48ea7a9644c10f764f7eb0a243fb1 | Python | 4,111 | 131 | import argparse
from typing import Iterable, Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from bindsnet.encoding import bernoulli, poisson
from bindsnet.environment import GymEnvironment
from bindsnet.network import Network
from bindsnet.network.nodes import... |
be3589834caf4e423cfc078b94560f3b0d19d4add5afb6efefb4f7792f05b8b5 | Python | 4,114 | 113 | from collections import defaultdict
import logging
from typing import Dict, List
from sklearn.metrics import (accuracy_score, precision_score, precision_recall_curve, recall_score)
from .predict import predict
from HMSA.data import MoleculeDataLoader, StandardScaler
from HMSA.models import HMSAModel
from HMSA.utils imp... |
5f9b4d9151b567c9fe6157139a48f4b6e35d66989b5cb5728186fdf5e368f3c4 | Python | 4,115 | 70 | # 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"
#... |
a3f0e1a90e7a397d62bf540e2b821349ab5909372fde4ac031220b7a44bb8f3d | Python | 4,119 | 117 | from collections.abc import Callable, Sequence
from langchain_core.agents import AgentAction
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePas... |
ede965ca5bdc9e8f2cda0b7d3cb71deccabcea417fdd9155a5a855f08fc49891 | Python | 4,120 | 115 | import torch
import argparse
import torch.optim as optim
import torch.nn.functional as F
import sys
sys.path.append("../../")
from TCN.adding_problem.model import TCN
from TCN.adding_problem.utils import data_generator
parser = argparse.ArgumentParser(description='Sequence Modeling - The Adding Problem')
parser.add_ar... |
7b348729284429679c2db1a7ed5671ff11403bc2532d0b61f65fd725f5e64f5a | Python | 4,122 | 105 | import sys
import os
from matplotlib.pyplot import axis
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from processing.constants import *
from processing.functions import *
from os.path import join as pjoin
import numpy as np
import pickle
from sklearn.model_selection import KFold
import pathlib
pathlib... |
6547e5d84ebf85f781ad1dcf3f3b0668e90221cfe003151d6ef41411434c59f8 | Python | 4,123 | 129 | ####################################################################################
#
# 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... |
ac1ba433da7af1b1da32ba820f49bc54531ec2ade884ae18f649467593118760 | Python | 4,123 | 108 | # 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... |
ee6032da03332ac19a442e892a1fd85a2e1d989d3f090fe07e0233488087f2c7 | Python | 4,123 | 127 | import types
import numpy as np
import matplotlib.pyplot as plt
from fastweights_numpy import FastWeights, FeedforwardNet
from dt_utils import Timer
from data_utils import generate_recognition_data
from neural_net_utils import Sigmoid, CrossEntropy, plot_W, plot_B, maxabs
#%% Generate data
d = 50 #len... |
074403155a04d5d481d280c60597aeb397269933715b5b456f8375bb477cd9f6 | Python | 4,124 | 129 | ####################################################################################
#
# 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... |
6b5142d80262e0b5664842f2c6a9635975ff4ba7ebcc7c6250ebb83fa976a579 | Python | 4,124 | 107 | import os
import torch
import pickle
import collections
import math
import json
import pandas as pd
import numpy as np
import networkx as nx
from rdkit import Chem
from rdkit.Chem import Descriptors
from rdkit.Chem import AllChem
from rdkit import DataStructs
from rdkit.Chem.rdMolDescriptors import GetMorganFingerprint... |
dbdd0ad59d63aeb5450b3c261af97a14648ebbff78948a2d084ad166d5e683d5 | Python | 4,126 | 99 | """
Implementation of the reduced Bayesian model learner algorithm as described
in Nassar et al. (2012) online methods.
"""
import numpy as np
import scipy.stats
P_C = 1 / 10.
STD_GEN = 10 / 300.
B_0 = 0.5
TAU_0 = 0.5
def run_inference(outcome,
p_c=P_C,
std_gen=STD_GEN,
b_0=B_0,
tau_0=TAU_0):
"""
... |
06304409a09d9e78aa97af9611d294cd225b5d41f43941af3c89228d0c8ab920 | Python | 4,127 | 99 | import os
import glob
from PIL import Image
help_msg = """
The dataset can be downloaded from https://cityscapes-dataset.com.
Please download the datasets [gtFine_trainvaltest.zip] and [leftImg8bit_trainvaltest.zip] and unzip them.
gtFine contains the semantics segmentations. Use --gtFine_dir to specify the path to th... |
7a6ca0438f1517966799663661c619d9e7646fd13c95df590a8f84fc2a366303 | Python | 4,130 | 111 | from sklearn.manifold import MDS
import mne
import mne_rsa
import os.path as op
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skimage import io, color
from config_FaceName import MEG_data_path,group_name,Ids,stim_folder,stimuli_list,MRI_data_path
src_fname = MRI_data_path+'/fsaverage/bem/f... |
8b64d5a2f71ab2ca637d20525cb095d99053b7cee64e78bdac6c6b91c4ca10fc | Python | 4,130 | 120 | """Define tests for ensemble networks such as Snapshot ensembling."""
import pytest
import numpy as np
import torch
from vulcanai.models.cnn import ConvNet
from vulcanai.models.dnn import DenseNet
from vulcanai.models.ensemble import SnapshotNet
from torch.utils.data import TensorDataset, DataLoader
torch.manual_seed(... |
923a0d955490fedd5b48bc1442746ba9c3a7d3f82d77823a35a26af78bcd4c25 | Python | 4,131 | 107 | from collections.abc import Hashable
from math import lgamma
import numpy as np
from scipy.special import gammaln
from pgmpy.structure_score._base import BaseStructureScore
from pgmpy.utils import encode_columns, get_state_counts_array
class BDeu(BaseStructureScore):
r"""
BDeu structure score for discrete B... |
adccdfbbe3f572a62fb15b93b8780273e745beaf1af08ad2550fddf5f74a347f | Python | 4,132 | 114 | import uncertainpy as un
import numpy as np
import matplotlib.pyplot as plt
from prettyplot import *
import scipy.integrate as integrate
import os
### FIGURE8 ###
data = un.Data()
data.load(filename='../data/simulation_outputs/UQSA_pathological/K_se_5/K_se.h5')
K_se_mean = data['K_se']['mean']
K_se_p5 = data['K_se']['... |
6e43b810f361e5d57d79465abb5e59edbfd14b81c8452ceeba0c23202e800767 | Python | 4,133 | 111 | # Training procedure for LeNet-5 CIFAR-100.
#Code base from https://github.com/BIGBALLON/cifar-10-cnn/blob/master/1_Lecun_Network/LeNet_dp_da_wd_keras.py
import keras
import numpy as np
from keras import optimizers
from keras.datasets import cifar100
from keras.models import Sequential
from keras.layers import Conv2D... |
fbd65ac1a7874448f391ed194fe543c3b1114fc4e9eddbdcfc66e0e2782257cd | Python | 4,133 | 109 | ''' Test module for sae models.
:Version:
1.0
:Date:
08.02.2016
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2016
This program is free software: you can redistribute it and/or modify
it under the terms o... |
71fea952c5e7b43fda4cd1ac05ac90c9c9564c60f8d05e99635dfb35628f74bc | Python | 4,137 | 123 | ######################################################################
######################################################################
# HOW TO USE:
# Make sure that llvm-cov is installed (check shell command "llvm-cov --version")
# Compile RAYX in DEBUG mode with Clang (Ideally 14)
# Run this script from the r... |
8ca6192076f646959cf688490805bf3d86244703cf176fee39ccedc36a77b138 | Python | 4,137 | 111 | # Libraries
import numpy as np
from tensorflow.keras import backend as K
smooth = 1
def jaccard_distance_loss(y_true, y_pred, smooth=100, backend=K):
y_true_f = backend.flatten(y_true)
y_pred_f = backend.flatten(y_pred)
intersection = backend.sum(backend.abs(y_true_f * y_pred_f))
sum_ = backend.sum(b... |
582c423840ad9648352c02538993b81ae703dcd84339affd4a96c6b142f05022 | Python | 4,140 | 79 | %matplotlib qt
import mne
from config_FaceName import Results_data_path,MRI_data_path,stats_comparisions
import matplotlib.pyplot as plt
import numpy as np
from custom_functions import read_stats_list,print_sig_stats
cluster_method='tfce'
bands = dict(theta=[4, 7],alpha=[8, 13],beta=[14, 30],gamma=[40, 90])
tfce_p_sig... |
d26139d0ea3d3767c072d6e886a08056e87298ec85e9c6109491fedaede3047c | Python | 4,145 | 134 | """Helpers for Materials Project (MP) charge-density benchmarks (GPWNO splits)."""
from __future__ import annotations
import json
import os
from pathlib import Path
from typing import Iterable
# Crystal families in GPWNO / data_splits/mp_splits/
MP_LATTICE_FAMILIES = (
"cubic",
"hexagonal",
"monoclinic",... |
cf54dac9922c3c6b93fa0e26014c482594fe27b8d7f2dcb686df0b43dfa26623 | Python | 4,148 | 115 | """
Conducts gene term enrichment analysis with GO database for
max predictive and redundant subsets from dRFE.
"""
import numpy as np
import pandas as pd
import functools, os
import collections as cx
from pybiomart import Dataset
from scipy.stats import fisher_exact
from statsmodels.stats.multitest import multipletes... |
711a3d6001b772ce04302b25833881b70b97aa700cafbe5954fb633ca7cc1d58 | Python | 4,149 | 112 | ####################################################################################
#
# 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... |
a28d2f537b4b300440af5a045cbeb3b60bf91cd5f57a0a1888f68e1e833d6d1e | Python | 4,149 | 65 | """Shared figure style for the manuscript (matplotlib, 300 dpi, vector + raster export).
Palette: Okabe-Ito (colour-blind safe), validated with the dataviz palette checker (adjacent-pair order chosen to
keep the weakest pair, pink/green, non-adjacent). Identity is never colour-alone: datasets also get fixed markers
an... |
023f62957546664395c7c85e2d389f073edafdedb6746eb7e7d65fd1bdf55c90 | Python | 4,150 | 136 | """Shared retry utilities for agent middleware.
This module contains common constants, utilities, and logic used by both
model and tool retry middleware implementations.
"""
from __future__ import annotations
import random
from collections.abc import Callable
from typing import Literal
from langchain_core.exception... |
4eb55fbb65812a9e7128abd720f6ba802542ce3593371c8869bc07636787caad | Python | 4,152 | 109 | """
Predict tool usage to weigh the predicted tools
"""
import os
import numpy as np
import warnings
import csv
import collections
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from scripts import utils
warnings.filterwarnings("ignore")
main_path... |
f22f47f31e30628027057369a57c753e6ce8f45bc1c1e701ec3bfa68b460faa7 | Python | 4,153 | 123 | """
Tabulate feature weight data into a single CSV for
further analysis using other tools. This produces
a CSV with header. The features themselves are not
included.
Marco Lui, February 2013
"""
import argparse, os, csv, sys
import numpy as np
import bz2, base64
from cPickle import loads
from langid.train.common imp... |
5404b0d0748d545ed147f0c592a4bad22aff9c4c6a4da989541ac82ad763e0bd | Python | 4,155 | 97 | from .utils.collate_fn import BatchMasking, BatchSubstructContext, BatchSubstructContext3D, BatchMaskingGraph, \
BatchDenseMatrix
from torch.utils.data import DataLoader
class DataLoaderMasking(DataLoader):
"""Data loader which merges data objects from a
:class:`torch_geometric.data.dataset` to a mini-batc... |
306bbbc7fa76d6338afd0eec991bf4ef86817ecc6d84efd80c6eac0d4af30a1d | Python | 4,157 | 98 | import os
from core.loss.contrastive_loss import inter_intra_modality_loss
from core.network import network_register, MIGA
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
import warnings
warnings.filterwarnings('ignore')
import torch
import torch.nn as nn
import torch.nn.functional as F
fr... |
60859eca667aac596235b143b5534a53a43d785800ffcbff422ab4af32cde5fe | Python | 4,157 | 113 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 6 21:13:59 2021
@author: athanasiadis
"""
import numpy as np
from joblib import Parallel, delayed
import multiprocessing
import mkl
def eigendecomposition(cov_mat):
"""
eigendecomposition
______________________
This is a... |
9f4a38de264b06677f00098fc1b562218d7991fa9e2ddbb24bd88011301a119a | Python | 4,157 | 114 | """
This file implements a class that uses the weights from a delayedPLIER run to transform gene
expression data into LV space
"""
import random
from typing import List
import numpy as np
import pandas as pd
class PlierTransform():
def __init__(self, weight_file: str, lambda_file: str, debug: bool = False):
... |
17e901596a7a9a6efffdb66dcec62c9251217c91d504c22ae403cc4b9d446e36 | Python | 4,159 | 109 | import logging
import os
import socket
import sys
from fcest.helpers.inference import run_adam_svwp
from fcest.models.wishart_process import SparseVariationalWishartProcess
import gpflow
from gpflow.ci_utils import ci_niter
from configs.configs import get_config_dict
from helpers.evaluation import leave_every_other_o... |
601bbf0d90d23323883c3da0f9dcada22301a1be38cdf1a0e2a2becf825ee0df | Python | 4,160 | 120 | from typing import Any
from typing import Callable
import torch
from torch import nn
from torchvision.models import alexnet
from torchvision.models import resnet50
from torchvision.models import vgg16
from ..utils.config import load_state_dict_from_model_name
__all__ = [
"resnet50_trained_on_sin",
"resnet50_... |
7640d75a048dabf5c9e14b208b489760004bc12ddabb9449509ff390359dc8e8 | Python | 4,160 | 123 | # 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... |
057bef85bd14d70e61291dae9bbeceacccf90ee5228b81b9d6d4dea712cb418e | Python | 4,162 | 112 | # -*- coding: utf-8 -*-
"""
Created on Sun Mar 21 11:36:41 2021
@author: gritti
"""
import sys, time, tqdm, copy, os, glob
from skimage.io import imread, imsave
import numpy as np
import PyQt5.QtWidgets
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings
warnings.filterwarnings("ignore")
from mor... |
dfc78ada731c310f54f2ce14fb791aa641ef3db647706a6c9963c24ee04132fd | Python | 4,162 | 133 | '''
run_function.py - run a function within a python module remotely
=================================================================
Purpose
-------
This script allows a specificied function from a specified python module
to be executed on the cluster with specified parameters.
Usage
-----
A statement is specifi... |
07d2ea67c92bf0bc4b02adf04f0a36a17e08afeebec082e21fbb8a5a9b1f4b99 | Python | 4,165 | 112 | ####################################################################################
#
# 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... |
396d7903f0836ef067c9b3855eaaac0cb632500b47037e4c99770f4c6a1faeeb | Python | 4,165 | 140 | """Peptide spectrum match dataclass."""
import dataclasses
from typing import Iterable, Optional, Tuple
import spectrum_utils.proforma
@dataclasses.dataclass
class PepSpecMatch:
"""
Peptide Spectrum Match (PSM) dataclass
Parameters
----------
sequence : str
The amino acid sequence of th... |
2a8e739ad69cae9de816e9a8911c634222f0d0c1686b6a8d4174a4a936e1502a | Python | 4,169 | 95 | import pytest
import os
import filecmp
def compare_all_but_volume(fname1, fname2,equal=True):
n=0
nEqual=0
with open(fname1) as f1:
with open(fname2) as f2:
for line1, line2 in zip(f1, f2):
if("olume" not in line1):
if line1==line2:
... |
2fc2857501aa262c8c18626389f91a03d8fd3edc90e491bfccf540e3dede549d | Python | 4,169 | 139 | """Compare models."""
# pylint: skip-file
import copy
import numpy as np
from sklearn.linear_model import (
ARDRegression, BayesianRidge, LinearRegression, RidgeCV, LassoCV
)
from embanded.embanded_numpy import EMBanded
from embanded.embanded_torch import EMBanded as EMBanded_torch
from himalaya.ridge import GroupR... |
fb1aafceba9741e9997df56e6617635b39e654b59a8234af18a78af52b1a8982 | Python | 4,176 | 98 | import logging
import os
import socket
import sys
import pandas as pd
from configs.configs import get_config_dict
from helpers.data import load_data
from helpers.evaluation import leave_every_other_out_split, get_test_log_likelihood, get_test_location_estimated_covariance_structure
if __name__ == "__main__":
d... |
0bb0661bd22372cb7b34de481f7d43e3a86308109edae2fcda2993bda7bb8011 | Python | 4,177 | 113 | import pandas as pd
import numpy as np
import cv2
import torch
from torchvision import transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader,Dataset
from joblib import Parallel,delayed
from tqdm import tqdm
from engine import utils
from constants import LUNG_DIS,PROCESSED_MEL_DIR
... |
cf28e75303844d62ab06266d43b0555133900f496cd1ad0555848432c5e43ee0 | Python | 4,178 | 88 | import copy
import warnings
warnings.filterwarnings("ignore")
class Enrichmentscope():
from .EnrichmentVisualization import dotplot
from .EnrichmentVisualization import enrichment_map
from .EnrichmentVisualization import enrichment_network
from .EnrichmentVisualization import gsea_heatmap
from .E... |
70c0498dd49c349d6d1f883dd16b32f580d5d4a2111795c5b600e62e2325a2ef | Python | 4,181 | 125 | import torch
#from plyfile import PlyData
from torch_geometric.data import Data, Dataset
from torch_geometric.utils import from_networkx
from torch_geometric.transforms import FaceToEdge, Cartesian
from sklearn.cluster import AgglomerativeClustering
from multiprocessing import Pool
from rdkit import Chem
import pandas... |
80567cb5f03757d3d743eb31f4ec25e6e6f916a5ebdfda2088cbc18dda7d4a61 | Python | 4,181 | 153 | import torch
import hienet._keys as KEY
class LossDefinition:
"""
Base class for loss definition
weights are defined in outside of the class
"""
def __init__(
self, name=None, unit=None, criterion=None, ref_key=None, pred_key=None
):
self.name = name
self.unit = unit
... |
b6e4eb6ee4a4b773bafabd10009639957d275a014c736e1ffb84bce3664d9997 | Python | 4,181 | 128 | from __future__ import annotations
import json
from typing import Any
from langchain_core._api import deprecated
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_text_splitt... |
0aedf9799cef6f9a458a6f1a5b572c0ae3dd34021d1b7d1b0966c535986c04ea | Python | 4,183 | 129 | ########################################################################
# Stochastic degradation-diffusion process.
# AIMS: to verify if zero order reactions are inserted correctly to the SSA
# after bugfix https://github.com/CNS-OIST/HBP_STEPS/pull/146
# Note:
# 1. This is not validated against analytical result, ... |
3271abc7152d8f3e28ad6ef19e647df7bdb2282dfaf86d45f18842525544ba1e | Python | 4,183 | 127 | import os
import numpy as np
import string
import tensorflow as tf
# read A3M and convert letters into
# integers in the 0..20 range
def parse_a3m(filename):
seqs = []
table = str.maketrans(dict.fromkeys(string.ascii_lowercase))
# read file line by line
for line in open(filename,"r"):
# skip l... |
034e4b5d950dc8ba7daa1e4cf6f4bbf01419100f37ec1a87127740dfef2b2388 | Python | 4,185 | 98 | 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
da... |
8480f6dd47e276e085f09f78ad5e9ec520adfecfebe7b62cab62128f232705a4 | Python | 4,185 | 128 | # 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.
# model settings
model = dict(
type='MaskRCNN',
pretrained=None,
backbone=dict(
type='ConvNeXt',
... |
ee2f7bfd35e33d198a08221d574ef7dd28b8f939e8556a11ad69c1b8fdebb68d | Python | 4,185 | 90 | import sys
import os
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import numpy as np
sys.path.insert(1, os.path.join(sys.path[0], '../../../../'))
from Articles.Sequential_Learning_Striatum.Figures.FigureSequential import set_blank_axis
import Articles.Sequential_Learning_Striatum.Figures.cfg_p... |
193eb273404b9a4ad9edf03668b37256a204087c6bc13ba8a68adf8ff530ba20 | Python | 4,186 | 160 | """Shared utilities for benchmarks: timing, metadata, and formatting."""
import json
import platform
import subprocess
import time
from contextlib import contextmanager
from dataclasses import dataclass, field
from importlib.resources import as_file, files
from pathlib import Path
import numpy as np
import torch
# -... |
494cb061e0916a3badde3c7ee0cf46add84fcb26b0b6606a8417443b4fbccab3 | Python | 4,190 | 112 | """ Componets of the model
"""
import torch.nn as nn
import torch
import torch.nn.functional as F
def xavier_init(m):
if type(m) == nn.Linear:
nn.init.xavier_normal_(m.weight) #xavier初始化方法中服从正态分布
if m.bias is not None:
m.bias.data.fill_(0.0)
class GraphConvolution(nn.Module... |
4bd30eb748f3042553f15992adf5a2f466b78b456c8827e4a909a29f2488be63 | Python | 4,190 | 114 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 24 15:38:10 2020
@author: gritti
"""
import tqdm
import os
from skimage.io import imread
import numpy as np
import time
# import sys
# sys.path.append(os.path.join('..'))
from orgseg.DatasetTools import io as ioDT
from orgseg.MLModel import io as ioML
from orgseg.MLMode... |
8116ccf81d7c31613be967c8a607bc584e1e109c3c960bc8494e8e2ce11c8024 | Python | 4,190 | 155 | import os
import numpy as np
def onehot_encoding(seqs):
residues = ['A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K',
'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V']
encoding_map = np.eye(len(residues))
residues_map = {}
for i, r in enumerate(residues):
residues... |
53e85688bb6efa0103455dcef1fa95f11d58ad5124c376b66fbd7cde1180040f | Python | 4,192 | 133 | # Utils for getting model layers
from collections import OrderedDict
from typing import Any
from typing import Callable
from typing import List
from typing import Optional
import torch
from torch import nn
def clip_model(model: nn.Module, layer_name: str) -> nn.Sequential:
"""
Returns a copy of the model up ... |
6b05201fb058f2104c0940fd4aa31068d6cd987de36e1ae00b77b66f9a621174 | Python | 4,193 | 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
from skimage.morphology import erosion, closing
# from sklearn.cluster import MiniBatchKMeans
import skfuzzy as fuzz
... |
de45aa48c6e5d3387c5c88a8f51dc65b0c59d653aedca121a1c4d661f227d9ac | Python | 4,193 | 128 | # pylint: skip-file
# 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:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup ---------------------------------------------------... |
449b29ee4be75619abf5a363ae5981e41904cb44cac29699995e4bcd9bb0385c | Python | 4,194 | 133 | from collections import OrderedDict
from typing import Optional
from typing import Tuple
from typing import Union
import torch
from torch import nn
class Flatten(nn.Module):
"""
Helper module for flattening input tensor to 1-D for the use in Linear modules
"""
def forward(self, x: torch.Tensor) -> ... |
a10db18f365f98f9d9d0c3bc72ee135f9dbaa818b055e5179a69cfa2c3dec486 | Python | 4,195 | 110 | import os
import scanpy as sc
import pandas as pd
import numpy as np
# dictionaries shortening datasets' descriptions
shorten_dicts = {
'hepatoblast': {'hepatoblast/hepatocyte': 'h', 'cholangiocyte': 'c',},
'marrow': {'(Bone_Marrow_Mesenchyme)': ''},
'thymus': {'(Thymus)': '', 'T cell_': '', 'T cell': '... |
b34efd674ab0b01ecbf6b0db5a201bf713ffa7022890b342bdf88ca190c07c45 | Python | 4,195 | 79 | # 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... |
a1bf8571a4ab3955e9f8cb2f3492535730efb689c56686b5f46223334e8323f4 | Python | 4,196 | 125 | import numpy as np
import pandas as pd
import pingouin as pg
def compute_icc_scores_pingouin(observations: np.array, icc_type: str):
"""
Computes ICC scores using third-party library.
:param observations:
:param icc_type: "ICC1", "ICC2", "ICC3"
:return:
"""
icc_df = _get_pingouin_icc_scor... |
45a041e332422d83064ec1fde126a3ea62dcdecede8c6838e688d63ad624b354 | Python | 4,198 | 141 | from math import pi, sqrt, atan
# cached constants
sqrt_2 = sqrt(2)
four_thirds = 4./3.
five_thirds_sqrt2 = 5./3. * sqrt_2
# volume of cylinder of height `(b - a) / K` and radius `r`
def Vc(r, a, b, one_over_K):
return pi * one_over_K * (b - a) * r*r
# volume of truncated square pyramid with radii `a` and `b` ... |
e3ecf35b4c8857990560306062d0745aff7f10d8b37f3c73f8787bc2888e3e53 | Python | 4,198 | 118 | """Unit tests specifically for the model_runner module."""
import pytest
import torch
from casanovo.config import Config
from casanovo.denovo.model_runner import ModelRunner
def test_initialize_model(tmp_path):
"""Test that"""
config = Config()
config.train_from_scratch = False
ModelRunner(config=con... |
af12b50868b07ca61b654e9108eaf9febe5da6723119539171690a4593ffff04 | Python | 4,200 | 122 |
# -*- coding: utf-8 -*-
"""
An PyTorch implementation of the DeepIGeoS paper:
Wang, Guotai and Zuluaga, Maria A and Li, Wenqi and Pratt, Rosalind and Patel, Premal A and Aertsen, Michael and Doel, Tom and David, Anna L and Deprest, Jan and Ourselin, S{\'e}bastien and others:
DeepIGeoS: a deep interactive geo... |
b9b60a9c1f8ea5e47e921cf4750b3da444495cb484bf82d99648aa8b2f4a3a9e | Python | 4,200 | 129 | ########################################################################
# Stochastic degradation-diffusion process.
# AIMS: to verify if zero order reactions are inserted correctly to the SSA
# after bugfix https://github.com/CNS-OIST/HBP_STEPS/pull/146
# Note:
# 1. This is not validated against analytical result, ... |
3d1675fde58fe48b83fc5c315780c5fbc2619124c891f2e562c0a991666ad324 | Python | 4,201 | 138 | import numpy as np
def load_flatmap(file):
from voxcell import VoxelData
vd = VoxelData.load_nrrd(file)
# mask for mapped values
fx_msk = vd.raw[:,:,:,0] > -1
fy_msk = vd.raw[:,:,:,1] > -1
assert(np.all(fx_msk == fy_msk)) # must coincide
return vd, fx_msk
def discretize(pos, fxlen):
... |
6ac879c19d9550943fabb6ca7ecbf63cc1d97d6ccc0552e9c172f3941090cea6 | Python | 4,201 | 125 | # third party modules
import numpy as np
# pnabind modules
from pnabind.structure import StructureData
from pnabind.structure import getAtomKDTree
from pnabind.structure.data import data
from pnabind.structure import mapPointFeaturesToStructure, getResidueDistance
def smoothResidueLabels(residues, labels, nc=2, dista... |
d10be4b62b1aa35001d2bf2ec5c18bec8629b0aca4d45f9f474d5d0037bb4a34 | Python | 4,207 | 86 | """The compatibility check must catch changed morphometry and atlas labels."""
from pathlib import Path
import shutil
import tempfile
import unittest
import nibabel as nib
import numpy as np
import trimesh
from postprocessing.core.compare import MORPH, SURFACES, compare
class CoreComparisonTests(unittest.TestCase):... |
8e836474a7d9c615cd0b96b06312125c90d78a85b754a4790e1f7f702d2f6d1f | Python | 4,209 | 143 | import yaml
from typing import List
from animalai.communicator_objects import (
ArenasConfigurationsProto,
ArenaConfigurationProto,
ItemToSpawnProto,
VectorProto,
)
yaml.Dumper.ignore_aliases = lambda *args: True
class Vector3(yaml.YAMLObject):
yaml_tag = u"!Vector3"
def __init__(self, x: f... |
3554f1dfbb1dfa28c20bbe2a3cafcffd433d526cdad86f61b9809c645ed30e6d | Python | 4,212 | 112 |
# This function is used to collect the metadata of the GSV panoramas based on the sample point shapefile
# Copyright(C) Xiaojiang Li, Ian Seiferling, Marwa Abdulhai, Senseable City Lab, MIT
def GSVpanoMetadataCollector(samplesFeatureClass,num,ouputTextFolder):
'''
This function is used to call the Google AP... |
ad84cd5bee6b0675004d8eec9aff1c4f7086fe4072fa09ed1345af1f363efbbb | Python | 4,212 | 122 | import pytest
from langchain_classic.evaluation.string_distance import (
PairwiseStringDistanceEvalChain,
StringDistance,
StringDistanceEvalChain,
)
@pytest.mark.requires("rapidfuzz")
@pytest.mark.parametrize("distance", list(StringDistance))
def test_zero_distance(distance: StringDistance) -> None:
... |
c1b79a91c1de450618c3ec0442c2a829d20bc44d1b11ba6ea90b8b554a987b27 | Python | 4,214 | 94 | import numpy as np
import scipy as sp
import scipy.stats
import scipy.spatial
import scipy.spatial.distance
def relative_entropy_analysis(features_a, features_b, all_data_a, all_data_b,
bin_width=None, bin_num=10, verbose=True, override_name_check=False):
"""
Calculates the Jense... |
8481d0560a574ddde6d01d68b75c1e65738486588c36238026258345fcc78a3e | Python | 4,215 | 87 | import jsonargparse
from torch.utils.data import Dataset
from CEL.utils.register import register
from CEL.data.datasets import *
from torch.utils.data import DataLoader
from typing import List, Dict
from CEL.data.datasets.meta_dataset import DatasetRegistry
# from CEL.data.datasets.pendulum import DampledPendulum, Inte... |
214835f263e152530f80c0b85480a6c357c558016439a22b6a7eaa0561a0ac36 | Python | 4,217 | 107 | import numpy as np
import pandas as pd
import os, tqdm
from scipy.ndimage import map_coordinates
from skimage.io import imread
from scipy.ndimage import label
from skimage import measure, img_as_bool
import multiprocessing
from itertools import repeat
if __name__ == '__main__':
import sys
sys.path.append(os.pa... |
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