sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5001ff751210954d2e865f8bfece5c7fb70e39ee01533c36f25b03138b241997 | Python | 3,741 | 93 | import argparse
import sys
from pathlib import Path
from MRdataset import load_mr_dataset
from MRdataset.config import MRDS_EXT
from mrQA import check_compliance
from mrQA.run_merge import check_and_merge
from mrQA.run_parallel import create_script, submit_job
def main():
"""
Console script only for ABCD loc... |
80b48733c296fb2f82c5f6c69fb347478efc6f8f61e10110ec6eeed83bc00cd9 | Python | 3,741 | 89 | # 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... |
374df2dc35a16b0be75d0b0b97e39f7549a6c10049b8ed09b88cbfb238b098aa | Python | 3,742 | 103 | from torch.utils.data import Dataset
import numpy as np
from torchvision import transforms
import torch
import json
if torch.cuda.is_available() is True:
try:
import cupy as cu
from cucim.skimage.morphology import dilation, disk
cuda = True
except ImportError:
from skimage.morph... |
8d2dba745523f907a6189d00185cf7fd17c7e7cbf788ee3e20ed1a3e6b6c4ccb | Python | 3,742 | 124 | BaseDir = "R:/Temp/Threads3/Thread0"
MeanCurr = 0.150
StdCurr = 0.150
file_name = 'Cell'
durations = [9900] #ms
stopTime = [10000] #ms
rangesL = [10000] #phase calculation crop limit low
rangesH = [-10000] #phase calculation crop limit high
resultsdir = BaseDir+'/Results/Results.txt'
#Simulation
Step = 0.1 #ms
#Intr... |
4aca4d2ed48155450b203c64fc072cd06a590c14297bab02c83e554876010eb4 | Python | 3,743 | 93 | import numpy as np
import gym
from stable_baselines3 import PPO
import torch
from stable_baselines3 import A2C, PPO
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
import pickle
from stable_baselines3.common.utils... |
4d396dd345829f1c873f9da4a6ee4f58eca092d4ae67c4fd681679dbb95d4c84 | Python | 3,743 | 105 | import rdkit.Chem as Chem
from .mol_graph import bond_fdim, bond_features
import numpy as np
BOND_TYPE = ["NOBOND", Chem.rdchem.BondType.SINGLE, Chem.rdchem.BondType.DOUBLE, Chem.rdchem.BondType.TRIPLE, Chem.rdchem.BondType.AROMATIC]
N_BOND_CLASS = len(BOND_TYPE)
binary_fdim = 4 + bond_fdim
INVALID_BOND = -1
def get... |
60241b399785fcc9aeeae868c8bcc3bfb4ee51bdb95d724c45b7612c47afd52e | Python | 3,743 | 124 | BaseDir = "R:/Temp/Threads10/Thread0"
MeanCurr = 0.150
StdCurr = 0.150
file_name = 'Cell'
durations = [9900] #ms
stopTime = [10000] #ms
rangesL = [10000] #phase calculation crop limit low
rangesH = [-10000] #phase calculation crop limit high
resultsdir = BaseDir+'/Results/Results.txt'
#Simulation
Step = 0.1 #ms
#Int... |
f4b5dd124f646e6b183daa6e734410eeb350583adb4d20e1896df89a9efe7693 | Python | 3,746 | 99 | import re
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_classic.agents.agent import AgentOutputParser
from langchain_classic.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
FINAL_ANSWER_ACTION = "Final Answer:"
MISSING_ACTION_AFTER... |
1d7a380c92d5e3fd479ba88e8a5f21a9d4419c52c6a43d365671a2f7631e049f | Python | 3,747 | 121 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import functools
from typing import Optional
import h5py
import torch
# import torchdata.datapipes as dp
import os
from .common import build_datapipes
class CFDDatasetOpener(torch.utils.data.Dataset):
"""DataPipe to load Navier-Stokes data... |
f1df25108a3937fdef4acbcab9ee9b18dcdab94d36cd6f2db38822fdb15886c7 | Python | 3,747 | 82 | import os
import pickle
import argparse
from pathlib import Path
import openmm
from openmm import unit, app
from perses.app.relative_point_mutation_setup import PointMutationExecutor
from perses.utils.smallmolecules import render_protein_residue_atom_mapping
# Read args
parser = argparse.ArgumentParser(description='... |
4c642ba487360da1f68e53ddc345b81a3e08d4fd1ea684b1c373b3958233d6a1 | Python | 3,748 | 107 | import argparse
from pathlib import Path
import pandas as pd
import torch
from monai import transforms
from monai.data import CacheDataset
from torch.utils.data import DataLoader
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--run_dir", help="Directory name to generate output resu... |
c80e77de744b929a0f629a4b28aaa6fe009559f8300cf0a6d2b290e9666f8b1d | Python | 3,748 | 125 | BaseDir = "R:/Temp/Threads16/Thread0"
MeanCurr = 0.150
StdCurr = 0.150
file_name = 'Cell'
durations = [9900] #ms
stopTime = [10000] #ms
rangesL = [10000] #phase calculation crop limit low
rangesH = [-10000] #phase calculation crop limit high
resultsdir = BaseDir+'/Results/Results.txt'
#Simulation
Step = 0.1 #ms
#Int... |
fe435b6650d1a8a2004e113248999267546b6a982d0ee58ffeab9db59ae3d5e9 | Python | 3,750 | 102 | ##############################################################################
#
# Merges sample-wise TIN scores reported by tin-score-calculation.py into
# a single table.
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institute_of_Bioinformatics
# CONTACT: wsciekly.maciek@g... |
66d6c42f960ad8b92607066d1563defc590576504b446d29d4f88ede037448a9 | Python | 3,751 | 76 | """Regional surface statistics without requiring a tissue segmentation volume."""
from __future__ import annotations
import argparse
from pathlib import Path
import nibabel as nib
import numpy as np
FIELDS = ('area', 'area.pial', 'thickness', 'curv', 'white.K')
def read_measurements(subject_dir, hemi):
subjec... |
1997328b692637538b1e800f624ebb2e4aa236473309963dd958b3ff9cccb8cc | Python | 3,752 | 127 | from typing import Tuple, List
from pathlib import Path
import re
import numpy as np
import pandas as pd
import scipy.stats as stats
from scipy.fft import rfft, rfftfreq
from statsmodels.stats.outliers_influence import variance_inflation_factor
def naive_diff(x: np.ndarray, dt: float) -> np.ndarray:
stencil = np... |
e352d0f3737f0c12f07c43f7901142646ba50cbc1d43f9c7aff2f82cf324531c | Python | 3,758 | 85 | import gc
import os
import numpy as np
import pandas as pd
from keras import backend as K
import keras
import random
import tensorflow as tf
from keras.models import Sequential,load_model
from keras.layers import Dense, Dropout, Activation, Conv1D, Input, Flatten
from keras import regularizers
from scipy.stats import ... |
8c413c930a5e487d190631727653e7256c1db49ad2bdf8402644b248a73e6cb2 | Python | 3,760 | 133 | import os
import sys
import logging
import shutil
import random
import torch
import numpy as np
from datetime import datetime
def save_checkpoint(
epoch,
model,
optimizer,
scheduler,
lowest_loss,
output_dir,
device,
ema,
name="checkpoint.pt",
):
if ema is not None:
with... |
ab2cea8fcf543365485605ad19bf0cc96cb78b8597ca4d8cb6fd11124b056587 | Python | 3,760 | 99 | """
Plot histogram of raw learning rates for all trials in a group dataset.
"""
import argparse
import data_analysis_utils as dana
import matplotlib.pyplot as plt
import numpy as np
import os.path as op
import plot_utils as plut
import scipy.stats
FIGSIZE = (plut.A4_PAPER_CONTENT_WIDTH * 1/2, plut.DEFAULT_HEIGHT)
SM... |
53a59d4318109eda8d137f6fe3f09eda25a36fb17d4acf7389d62b12c060dd28 | Python | 3,761 | 83 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# 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... |
87c274b3f9ea1baca8f86e2adbff28ba9022f3ad789b0e5843b47bcf57c6bb8e | Python | 3,761 | 96 | from time import mktime, strptime
path = 'E:/Old (12mo) Q175'
#mouse = input("Enter the name of a mouse and press enter: ")
b_first_event = True
b_mouse_entered = False
time_shift_events = []
early_exit_events = []
entrance_before_exit = []
entrance_count = 0
exit_count = 0
mice = ['12QA01','12QA02','12QA... |
df50989ec45fc6087e59ea374e9ce1d7c8e1ffd1875fd25eabb966615f040af0 | Python | 3,761 | 92 | from typing import Any, Mapping, Optional, Tuple
import torch
from torch.nn import functional as F
from torch.nn import init
from torch.nn.modules import ELU, BatchNorm2d, Conv2d, Module, Sequential
from torch.nn.parameter import Parameter
from .base import Readout, Reduction
class AttentionReadout(Readout):
de... |
479275063ca63aa12d2848ef92bfa04707b5c5a6a8c1f70577ae511ee603cdbe | Python | 3,763 | 107 | from __future__ import annotations
from dataclasses import dataclass
from enum import Enum
from typing import Any
from typing import Dict
from typing import Optional
from typing import Sequence
from typing import Tuple
from pydantic import BaseModel
from ..common.config import get_config_file
from ..common.config im... |
6b67c96d3e0e6f7eac6b9e118d099719d75d3b76030bc7d19de1a216e7519655 | Python | 3,763 | 107 | import warnings
from abc import ABC
from typing import Any
from langchain_core._api import deprecated
from langchain_core.chat_history import (
BaseChatMessageHistory,
InMemoryChatMessageHistory,
)
from langchain_core.messages import AIMessage, HumanMessage
from pydantic import Field
from langchain_classic.ba... |
a8a809b854970b27695cd3778d35d264037d2c5ec385cc8c966037b084500add | Python | 3,765 | 50 | from glob import glob
# Define parameters -------------------------------
begin_time = 180 # [in sec] where to start analyzing time series
end_time = 480 # [in sec] where to stop analyzing time series
frequency = 1500 ... |
e8292f036a886b2d95d09125a438627a6cfb9ebb796fa7494558e02a1bfcfd1f | Python | 3,765 | 81 | """One explicit RAS <-> crop-voxel contract for inputs, labels and exports."""
from __future__ import annotations
from pathlib import Path
import nibabel as nib
import numpy as np
from nibabel.affines import apply_affine
from utils.files import atomic_json, sha256_file
from utils.surface_io import write_obj
MODAL... |
a98bc437149be8e2e397d6d086f0ace7b954a57e9936e92e49b1b85a3c1e4315 | Python | 3,766 | 76 | # -*- coding: utf-8 -*-
"""
################################################
MIT License
Copyright (c) 2021 L. C. Lee
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 ... |
f1e615e8919a2a572b06f66358b46363db52601186bc1102cbfbf2c38ef8b27f | Python | 3,766 | 111 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
def discrete_cmap(N, vmin=0, vmax=1, base_cmap='seismic_r', over='w', under='w', bad='w', alpha=1.0, values=None):
base = plt.cm.get_cmap(base_cmap)
if values is None:
color_list = np.array(base(np.linspace(vmin, vmax, N)))
... |
eda267a2e5fecb5e97313b9689fe9f642806959c63a0cd38594a7a488e7334ba | Python | 3,767 | 111 | from typing import Any
import torch
from torch import nn
from torchvision.models import vgg19
from ptrnets.utils.config import load_state_dict_from_model_name
__all__ = ["vgg19_original", "vgg19_norm"]
# Define vgg auxiliary class
class VGGConv(nn.Module):
def __init__(self) -> None:
super().__init__()... |
2fc62473776b9b00790112c209f798cbfa1fedb1ff919772dc976ab44dce5726 | Python | 3,768 | 83 | import torch
import torch.nn as nn
#from equivariant_attention.modules import get_basis_and_r, GSE3Res, GNormBias
#from equivariant_attention.modules import GConvSE3, GNormSE3
#from equivariant_attention.fibers import Fiber
from rfdiffusion.util_module import init_lecun_normal_param
from se3_transformer.model import ... |
59b98a552d1e7e7d766d012b716bd02755fb7ef0d3e1c2a7f9f9bcf5ca3cf79e | Python | 3,769 | 111 | import sys
from pathlib import Path
import numpy as np
from openff.toolkit.topology import Molecule
from openmmforcefields.generators import SystemGenerator
from pkg_resources import resource_filename
from simtk import openmm, unit
from simtk.openmm import app
DATA_PATH = Path(resource_filename("openforcefields", "..... |
689e113556829c2fc72e249f1ca36d8e65661e13fadf57da462c1c49e2cf8e32 | Python | 3,769 | 134 | import cv2
import numpy as np
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from random import random, choice
from io import BytesIO
from PIL import Image
from PIL import ImageFile
from scipy.ndimage.filters import gaussian_filter
Im... |
089f69a0a6f2345be31676f4bf82c36e195ee36faf8ebdd386cbdd474f937344 | Python | 3,770 | 100 | #!/usr/bin/env python
import concurrent.futures
# import warnings
import requests
from pycilium.utils.import_tools.catmaid_utils import CatmaidApiTokenAuth
from pycilium.utils.skeleton_utils import *
# warnings.simplefilter('once')
# # comment the line below out unless you need it. Seriously.
# catmaid_request_kwar... |
09ff3bb5bd79959e34e5da18522be295f0577851a586b3d465444cb1043af186 | Python | 3,770 | 120 | # 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 ... |
81c45827db8aab7f3c059ff1a2334628200135142945dd8709fd8d2ac1cb2775 | Python | 3,771 | 131 | #!/usr/bin/env python3
"""
Construct a probabilistic atlas from a set of N label images each containing
M unique labels. The final atlas will be a 4D float image (nx x ny x nz x M)
scaled from 0.0 to 1.0
Usage
----
probabilistic.py -o <Output filename> <List of N label image volumes>
probabilistic.py -h
Example
----
... |
cb1f50998e1a8406811ecb03a0e406aae383be86fdf3ce9010dfd24223e3804f | Python | 3,772 | 124 | """
##############################################################################
#
# Convert clustered events into bed format.
#
# AUTHOR: Maciej_Bak
# AFFILIATION: University_of_Basel
# AFFILIATION: Swiss_Institute_of_Bioinformatics
# CONTACT: wsciekly.maciek@gmail.com
# CREATED: 26-03-2020
# LICENSE: ... |
6225700c749319f55f8ac56c7b6fb452c257826936f20956d0ddf70f3102e245 | Python | 3,773 | 118 | import errno
import os
import re
import sys
import zipfile
from typing import Any
from typing import Dict
from typing import Optional
import gdown
import requests
import torch
from gdown.parse_url import parse_url
def _get_name(id_: str) -> str:
"""
Gets the base url name from the gdrive using the file id.
... |
a67532e63d5c03d631a3216690a7a2b1c55dc4a1c173c80a018a8f1cd7d16a30 | Python | 3,775 | 122 | ########################################################################
# Stochastic production and degradation well-mixed reactions.
# AIMS: to verify STEPS well-mixed stochastic solver 'Wmdirect'
# solves the master equation correctly when applied to a
# zero-order production and first-order degradation process... |
fb514911bf9fb9b2c9c9f5e813c28e5faa344fd3feedf14fb5c16b486774a8be | Python | 3,780 | 113 | """
Author: 2022 Anna M. Puszkarska
SPDX-License-Identifier: Apache-2.0
"""
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from pathlib import Path
from sklearn import metrics
from typing import List, Optional
def get_plot(y_test: List[np.ndarray],
y_pred: np.ndarray,
... |
dcd74ee8b539cb2dae3fefbf958864a6ff29a456834d196a4426a2dc71822da4 | Python | 3,781 | 94 | """
Plot the ranges of cpp and uncertainty values encountered in the two learning tasks.
"""
import data_analysis_utils as dana
import matplotlib.pyplot as plt
import numpy as np
import os.path as op
import plot_utils as plut
FNAME_PREFIX = "task_ranges"
def run_with_args():
output_dir = dana.get_output_dir_for_... |
e37308adf8b510fbed85c85278ccc7885ac47f6b610114f6229278f5e78c5543 | Python | 3,781 | 108 | import os
import pandas as pd
import iFeatureOmegaCLI
import torch
import numpy as np
from torch_geometric.data import Data
import warnings
warnings.filterwarnings("ignore")
import pickle
from tqdm import tqdm
import multiprocessing
import logging
from torch_geometric.utils import add_self_loops
import contextlib
impor... |
e8cc487ee94c85d3575c702cc7297ddcef08c82ccf02e66171afe3ff7c50161f | Python | 3,783 | 96 | # 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... |
4d640d1161576e7f4df28d95af429d05e0d87d9359b5ca9e0b4a01340f04a767 | Python | 3,784 | 122 | import math
from unittest import TestCase
import pytest
import torch
import torch.nn as nn
from chroma.layers.basic import (
MaybeOnehotEmbedding,
MeanEmbedding,
NodeProduct,
NoOp,
OneHot,
PeriodicPositionalEncoding,
PositionalEncoding,
PositionWiseFeedForward,
Transpose,
Trian... |
58e31771d1291148a17dc13e49972454f8d834c75117f5c0e83086f2cea8215c | Python | 3,784 | 103 | from torch.utils.data import Dataset
import numpy as np
from torchvision import transforms
import torch
import json
if torch.cuda.is_available() is True:
try:
import cupy as cu
from cucim.skimage.morphology import dilation, disk
cuda = True
except ImportError:
from skimage.morph... |
74bae4cc7b49a2980f6ebecddc04a807b9c5b735fc5501c3adf51b11820de7ed | Python | 3,785 | 98 | """Define the model utilitiy tests."""
import pytest
import numpy as np
import torch
from vulcanai.models.utils import (round_list,
get_one_hot,
pad,
set_tensor_device,
master_... |
871132fddc4d3678e1839214d7643f1403b2ee1011f75defd6c70a87bba8247d | Python | 3,785 | 129 | # 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... |
696d8ecd5bee5d18b1cb8a9a2913544e84276dc015d10cab05335e2155aa9182 | Python | 3,786 | 101 | import numpy as numx
import pydeep.misc.visualization as VIS
import pydeep.base.numpyextension as npExt
import pydeep.misc.io as IO
import pydeep.base.activationfunction as AFCT
class One_shot_learner(object):
def __init__(self,
input_dim,
hidden_dim,
output_dim,... |
f938e2da3696a3e629eab8f4fa56b7fb91a36f9fde647a28cf195dd0ea90bcb8 | Python | 3,787 | 135 | import argparse
import torch
from bindsnet.encoding import bernoulli
from bindsnet.environment import GymEnvironment
from bindsnet.learning import Hebbian
from bindsnet.network import Network
from bindsnet.network.monitors import Monitor
from bindsnet.network.nodes import Input, LIFNodes
from bindsnet.network.topolog... |
267de13009dff1c6d034e1cfbf83c723dacfb930fb6d6d6baaa2582a5c891943 | Python | 3,789 | 125 | # make sure to activate your virtual environment e.g., $ conda activate dedalus
import numpy as np
import dedalus.public as d3
import logging
import h5py, os
logger = logging.getLogger(__name__)
import matplotlib
import subprocess, glob
# Initialize lists
init_perturbs, outputs = [], []
n_samples = 150
# Simulation ... |
45e24e904ae7973a349fe98c3555c799662e78a09a9dee137d9318e283443c23 | Python | 3,791 | 75 | # 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... |
b7d96422df481b0836d88ac4ca8e71fd420479f5882c3f43231669ff38eafe21 | Python | 3,791 | 99 | # FCN model
# when tuning start with learning rate->mini_batch_size ->
# momentum-> #hidden_units -> # learning_rate_decay -> #layers
import tensorflow.keras as keras
import tensorflow as tf
import numpy as np
import time
from utils.utils import save_logs
from utils.utils import calculate_metrics
class Classifier_CNN... |
2f98088736c7b358f32897cf0895f517465aaba434285526c4ac3ec492778520 | Python | 3,792 | 122 | ########################################################################
# Stochastic production and degradation well-mixed reactions.
# AIMS: to verify STEPS well-mixed stochastic solver 'Wmdirect'
# solves the master equation correctly when applied to a
# zero-order production and first-order degradation process... |
9e42667c94df6b7f4ca72fe04b72d5173c2da2f0312f7cab3d49ae0ea169df87 | Python | 3,792 | 123 | ########################################################################
# Stochastic production and degradation well-mixed reactions.
# AIMS: to verify STEPS well-mixed stochastic solver 'Wmrssa'
# solves the master equation correctly when applied to a
# zero-order production and first-order degradation process.
... |
afd7c418987ca8113a880021c4d1825629f6926a7344664d3b5adeade40a4524 | Python | 3,792 | 118 | """ running the ntf over a range of frequencies."""
from ..forward import network_transfer as nt
import time as time
import numpy as np
def run_forward(brain, params, freqs):
"""run_forward. Function for running the forward model over the passed in range of frequencies,
for the handed set of parameters (which... |
eb3ec4f36bb5f18b95749dce4d4fd4a11098315272893dc34c8a9d19be1ccdc4 | Python | 3,792 | 135 | from pathlib import Path
import os
import shutil
import contextlib
import joblib
from tqdm import tqdm
import numpy as np
from joblib import delayed, Parallel, parallel_backend
import multiprocessing
import torch
from scipy import special
import matplotlib.pyplot as plt
def create_folder(path,clear=False):
if... |
2d94ee97bf78aac7ac199e39fbc35141f446f96b0b09f7f955fff39f3235003c | Python | 3,793 | 113 | import os
# from deepinterpolation.generic import JsonSaver, ClassLoader
class Deepinterpolation:
"""module for deepinterpolation denoising on an imaging series"""
def __init__(self):
self.generator_param = None
self.inference_param = None
self.model_path = None
self.input_pa... |
ed8d2115e79bbeabe068221b0636e10ddae964489d917116ae6bd1d8b827c135 | Python | 3,793 | 89 | """
Plot the distribution of intervals (in terms of number of outcomes, or trials)
between two estimate updates that the subjects performed.
Note: The interval between two updates corresponds to what is called "step width"
in Gallistel et al. (2014).
"""
import argparse
import data_analysis_utils as dana
import matpl... |
0370feb604e30366a6cd5fd8ca7bdde3ccec87bed004c276e8902d0d72146fcd | Python | 3,796 | 107 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import MEnet
def command_tile(args):
from .tile import tile
print(args)
tile(args)
def command_train(args):
from .train import train
train(args)
def command_predict(args):
from .predict import predict
predict(args)
#... |
7287ac4947dd1a94a6a79329b46d94262a0769ba37c71e2e47a672f3c650fc0a | Python | 3,796 | 139 | # 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... |
d9888e39a98c4d727f14ee3c7421e1ba393cd308697b88218b01608988a0c73d | Python | 3,797 | 128 | # This script is part of pymaid (http://www.github.com/navis-org/pymaid).
# Copyright (C) 2017 Philipp Schlegel
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of ... |
a90219761e5824829e9d1fd1aa6e203f4e8aeb37166761d34d9eab467323c0b1 | Python | 3,802 | 132 | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 27 09:28:41 2021
@author: pku
"""
import matplotlib.pyplot as plt
#%%
import numpy as np
import tensorflow as tf
from tensorflow.keras import applications, regularizers, optimizers
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.app... |
56bf8fd3e24fc8a740ea0a673b36276f65d14ec393394a32ae5151a96e1ad30f | Python | 3,803 | 91 | """Purpose of this script: try sorting those single sessions separately
that failed when batch run together with other sessions"""
import os
import sys
from time import time
import multiprocessing
from queue import Empty as qEmptyException
import traceback
import json
import numpy as np
sys.path.insert(0, "../")
fr... |
94e6346a4648fa323e1a997b28d77d2fcb70a8a934e8daf59d77f3c3ac095e1c | Python | 3,805 | 125 | import os
from multiprocessing import Pool
from skimage.transform import resize
from ..utils.io import *
def dist_corrcoef(image_1, image_2):
"""Return a distance between two images corresponding to 1 minus the
correlation coefficient between them. This can go from 0 to 2."""
dist = 0
for x1, x2 in ... |
62952666a0bd6a18aa7f6010bcd1c4ffa21b6f726d99eb4530ddb95a8802b02e | Python | 3,806 | 121 | import pickle as pkl
import itertools
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
from pathlib import Path
import sim_utils as util
basic_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
SAVEFIGSS = True # Global variable to enable/disable saving figures
# SAVEFIGSS = False... |
420394634679cfc95040bdfe56569f98c0fa1ddbdcd616944866b171839cb35a | Python | 3,807 | 122 | """Test the comparison chains."""
import re
import pytest
from langchain_classic.evaluation.comparison.eval_chain import (
LabeledPairwiseStringEvalChain,
PairwiseStringEvalChain,
PairwiseStringResultOutputParser,
resolve_pairwise_criteria,
)
from langchain_classic.evaluation.criteria.eval_chain impo... |
9bf7fef38b1a6220ffaa030a2b98e832d0c2e5df9e9045db1ec5dd8283283ddd | Python | 3,808 | 94 | import os
import numpy as np
import scanpy as sc
import scvelo as scv
import cellrank as cr
from scipy.io import mmwrite
os.chdir('/home/jfleck/projects/cutntag/')
adata_d158_A395 = sc.read('data/drugs/drugs_d15_d18_A395_v2velo_v1.2lines.h5ad')
sc.pp.neighbors(adata_d158_A395, use_rep='X_harmony', n_neighbors=20)
s... |
14acfa3fd91675f7a8f0d55b31470baa8d804f119f1539bbca26ba3ee865fa4b | Python | 3,809 | 102 | ####################################################################################
#
# 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... |
2b238094f91bdc13a2f0d1f363f8f51341e0f2177f9fbec5bd1f46b02bdaae69 | Python | 3,809 | 123 | ########################################################################
# Stochastic production and degradation well-mixed reactions.
# AIMS: to verify STEPS well-mixed stochastic solver 'Wmrssa'
# solves the master equation correctly when applied to a
# zero-order production and first-order degradation process.
... |
a03b9b7b06c310e8c957b8e08ec489aa4570eb8090e94fe626ac1a86437ed4b2 | Python | 3,810 | 102 | ####################################################################################
#
# 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... |
d707328075aaa63034b38e46a230abd3b2e72e73908183ca858192a3763ac207 | Python | 3,810 | 84 | import matplotlib
import matplotlib.pyplot as plt
import os
from Articles.Sequential_Learning_Striatum.Code.FigureSequential import *
font = {'size' : 16}
matplotlib.rc('font', **font)
name_STDP = ['Symmetric LTD','Anti-Hebbian','Hebbian', 'Symmetric LTP']
name='Bernstein'
P_list=[10,20]
stim_by_pattern_list=[3,4... |
119115eab931c1fd67766e5d0c343f038af7d4d8b3075bc5cd919c8849a1fe8b | Python | 3,811 | 107 | import argparse
import numpy as np
from oset.ecg.peak_detection.peak_det_simple import peak_det_simple
def peak_det_local_search(x, ff, *args):
"""
peak_det_local_search - R-peak detector based on local max/min search
peaks, peak_indexes = PeakDetection(x,f,flag, num_rounds),
inputs:
x: vector o... |
3ce7823e800bc4c74b0575b7001e7605072768c89b7d3094ee170f2920b40a1e | Python | 3,811 | 145 | import numpy as np
PRECISION= 17
np.set_printoptions(precision= PRECISION, suppress= True, floatmode= 'maxprec')
def read_bvecs(bvec_file, assume_normed= True):
with open(bvec_file, 'r') as f:
bvecs = [[float(num) for num in line.split()] for line in f.read().split('\n') if line]
# bvec_file can be 3... |
58025342fed6967cbccf181b2afca850e06129c105d51513a87d58ba80bfa4ba | Python | 3,811 | 86 | import matplotlib
import matplotlib.pyplot as plt
import os
from Articles.Sequential_Learning_Striatum.Code.FigureSequential import *
font = {'size' : 16}
matplotlib.rc('font', **font)
name_STDP = ['Symmetric LTD','Anti-Hebbian','Hebbian', 'Symmetric LTP']
name_J = ['No Inhibition','1<->2','1->2', 'Random']
name=... |
9d1400355e3dd519512e3595e8bd2897ad510b2dc1e44e996f33ef59e905190d | Python | 3,811 | 145 | #!/usr/bin/env python3
"""
Convert single Nifti-1 3D or 4D volume to set of PNG stacks.
Usage
----
nifti2png.py -i <Nifti filename> -o <PNG image stub>
nifti2png.py -h
Example
----
>>> nifti2png.py -i mynifti.nii.gz -o mypngs
Authors
----
Mike Tyszka, Caltech Brain Imaging Center
Dates
----
2016-06-28 JMT Adapt fro... |
d16e9751a2eab41791335fbb4d945eefd0fec23e9a9aa44d4aa9a3d96bec72b6 | Python | 3,811 | 125 | # 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... |
16488683c62f4285dc598bc293268755840401ffa5a99e2e3178779b822b75c8 | Python | 3,812 | 95 | import torch
from torch import nn
class Encoder(nn.Module):
"""
Base class for all encoders
"""
def regularizer(self, data_key=None, reduction="sum", average=None, detach_core=False):
reg = self.core.regularizer().detach() if detach_core else self.core.regularizer()
reg = reg + self.r... |
6c655e7e4af2840046d7daf48d27fa142025a667125f8f7ff082cf2a3f711af6 | Python | 3,813 | 112 | import unittest
from nilearn import connectome
import numpy as np
from helpers.array_operations import _check_symmetric
from helpers.array_operations import map_array, reorder_symmetric_matrix
from helpers.array_operations import reconstruct_symmetric_matrix_from_tril
from helpers.array_operations import slice_covari... |
96dff57fd125ead710373af3f68b4f6263050696d6ec0b4f3ab7cea3c978fad0 | Python | 3,813 | 135 | ########################################################################
# Stochastic first-order reversible reaction.
# RESTORE
# AIMS: to verify checkpointing and restoring of the well-mixed stochastic
# solver 'Wmdirect' in the context of the First-Order Reversible
# Reaction model (see validation/first_order_re... |
bf709ef959453ad689ed63a0c118d1a7915f3c0ae4733febd2e045b8f3cee9a1 | Python | 3,815 | 142 | """Test concurrency behavior of batch and async batch operations."""
import asyncio
import time
from threading import Lock
from typing import Any
import pytest
from langchain_core.runnables import RunnableConfig, RunnableLambda
@pytest.mark.asyncio
async def test_abatch_concurrency() -> None:
"""Test that abat... |
d8593b10b2173f9509ca3c8f929c3ed4d2c90ddff13f531ff2b524fcf94b4914 | Python | 3,818 | 126 | import os
import h5py
import numpy as np
import scipy as sp
import anndata as ad
import pandas as pd
import scanpy as sc
import scvelo as scv
import loompy as lp
import cellrank as cr
os.chdir('/home/jfleck/projects/cutntag/')
#### Join all kallisto results ####
# all_samples = os.listdir('data/individual_samples/')... |
79fcbb5c54491450bbd715ae73fb6baffe30ebce9380fb564c622d5b3064aa0a | Python | 3,820 | 123 | import os
import pickle
from warnings import warn
import torch
import MuTILs_Panoptic.utils.torchvision_transforms as tvdt
ISCUDA = torch.cuda.is_available()
def tensor_isin(arr1, arr2):
r""" Compares a tensor element-wise with a list of possible values.
See :func:`torch.isin`
Source: https://github.c... |
d93ca83077ce8a15eae4891b6f574d6fd91cf6d2782f308695ebc055d66f7604 | Python | 3,823 | 103 | import sys
gene={}
###This file is to classify EE/EP/PP/NN neoloops###########
cutoff=sys.argv[1]#############cutoff of peaks############
for i in open('/scratch/users/txie/RNA-seq/0ref/v29_all.gff'):
t=i.strip().split()
if not t[2] in gene.keys():
gene[t[2]]=[]
if t[-1]=='+':
loc=t... |
5b92129e2a59a38698e92559cad1dfeb9aea96b46a86a854ad9efc690afaacb6 | Python | 3,825 | 119 | """**Memory** maintains Chain state, incorporating context from past runs.
This module contains memory abstractions from LangChain v0.0.x.
These abstractions are now deprecated and will be removed in LangChain v1.0.0.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Any
... |
0298d8b484a7fdec6adebae554dcc9753a7d2b160cccec0867834ce31125c05e | Python | 3,827 | 132 | # %% imports
import argparse
import importlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.sparse.construct import rand
import seaborn as sns
import mne
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import Fu... |
80be3017fe89c84c246af1e6d32f7ab5713aef26d0315873643af2f7464d05eb | Python | 3,828 | 154 | import numpy as np
from encoder_utils import FeatureTargetLoader, Preparer, Encoder
def run_subcortical(
subject: str,
directory: str,
alphas: list,
tmin: float = -10e-3,
tmax: float = 30e-3,
sfreq: int = 4096,
invert: bool = False
) -> tuple[np.array, float, np.array, dict, float]:
""... |
35f0853535ca768d46be9dd54f651967ef16447424bfcbb83a541cf222976b4f | Python | 3,829 | 88 | import logging
import os
import socket
import sys
from fcest.helpers.data import to_3d_format
from nilearn import connectome
import numpy as np
import pandas as pd
from configs.configs import get_config_dict
from helpers.brain_states import compute_basis_state
from helpers.hcp import get_human_connectome_project_subj... |
097941b551348ae3b7348d62a36abe4a20005f1506afa97cc91bdeef728d61ed | Python | 3,830 | 100 | import glob
import shutil
import tempfile
from pathlib import Path
import hypothesis.strategies as st
import pytest
from hypothesis import given, settings, HealthCheck
from MRdataset import import_dataset
from MRdataset.tests.simulate import make_compliant_bids_dataset
THIS_DIR = Path(__file__).parent.resolve()
@s... |
90447c826095a7787c52c23004a6dbaa07634c2f26e37d008daad34de8cda9f7 | Python | 3,830 | 55 | #TO BE UPDATED
import numpy as np
import math
from WeightModel.WeightClass import weightClass
from InitFunctions import gaussian
class graupner(weightClass) :
parameters = {'save' : False, 'step' : None,
'Cpre': 1., 'Cpost' : 2., 'tCa' : 20., 'delay_C' : 0.,
'gammap' : 321.808,... |
fec99eb10e4d59f4144c30dc02d494ceb1d59eeb977485311c30b13c2aab7740 | Python | 3,830 | 135 | ########################################################################
# Stochastic first-order reversible reaction.
# RESTORE
# AIMS: to verify checkpointing and restoring of the well-mixed stochastic
# solver 'Wmdirect' in the context of the First-Order Reversible
# Reaction model (see validation/first_order_re... |
b94156834c7a71f0c8b9ca8eec3616cd7a9d4d3c273c47548e782d6335c69e0e | Python | 3,831 | 99 | # Copyright (c) <2021> <University of Paderborn>
# Signal and System Theory Group, Univ. of Paderborn, https://sst-group.org/
# https://github.com/SSTGroup/independent_vector_analysis
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated d... |
f805bbfd2af67b1ed9b661d10c6c4b17a7b280341f56b09d72596e48e8f4eb72 | Python | 3,833 | 137 | """Print information about the system and langchain packages for debugging purposes."""
import pkgutil
import platform
import re
import sys
from collections.abc import Sequence
from importlib import metadata, util
def _get_sub_deps(packages: Sequence[str]) -> list[str]:
"""Get any specified sub-dependencies."""
... |
40ca2445b5a0681fbcf5bf777dbcd73d24b4da941f02278943be7924cf10c34b | Python | 3,836 | 117 | '''utility functions to write and read tfrecord datasets.
'''
import os
from glob import glob
from tqdm import tqdm
import tensorflow as tf
def tfrecord_from_sampler(output_path, sampler, serialize_fn):
'''prepare tfrecords from a sampler.
Parameters
----------
output_path : string
path to s... |
1c0aabfc92a56125f5e5be69fb55f8a83b1f5faf0a733e1c16b87d2116585601 | Python | 3,838 | 56 | """Figure 1: study framework. Three data sources feed one pipeline: anchoring, protocol, estimator, calibration, audited outputs.
No equation or algorithm numbers are hard-coded (they change when the manuscript is edited); the caption carries references."""
from __future__ import annotations
import os, sys
import matpl... |
f6ad6092c19457a67d34c58e5f3a94f9d85fed9e86d6ff85988fb400e9741e82 | Python | 3,838 | 106 | """
Visualise a sample of instances (images and annotations) stored in a TFRecord dataset
"""
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
# Parse TFRecords
def parse_image(example_proto):
# Create a dictionary describing the features.
image_f... |
112a44dfa9450f1197242604d530db82008b5d63a272f79640a14ac36939a568 | Python | 3,839 | 100 | from collections import defaultdict
from pathlib import Path
def get_fasta_dict(fasta_file):
seq_dict = defaultdict(str)
with open(fasta_file) as f_fasta:
lines = f_fasta.readlines()
for line in lines:
if line.startswith(">"):
chain = line[1:].strip()
el... |
2d8de6f13435ce49513b62442d7da26b638fcd1963dd24b35c4831f0090e2dac | Python | 3,839 | 143 | ########################################################################
# Stochastic second-order irreversible reaction: [A]0 != [B]0
# RESTORE
# AIMS: to verify checkpointing and restoring of the well-mixed stochastic
# solver 'Wmdirect' in the context of the Second-Order Irreversible
# Reaction model (see valida... |
5310934c4b90771f85bf65b5bc352a93e55d76f0e72345413c08ef621bdb0a7c | Python | 3,839 | 90 | """Held-out evaluation with arrangement-ensembling, for confusion matrices & metrics.
The iterate-N runner (`runner.run`) accumulates *attribution*; this module accumulates
*predictions*. It fixes ONE stratified train/test split, trains the backbone under N
arrangements, and ensembles the softmax over arrangements int... |
688f1139e733509bb80f29a2f9961c1ea6a8f818ee9d0b2fdefe6ff841ceea8b | Python | 3,840 | 127 | # 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... |
fd1cc1cd7449c6eec7e3a853e4584a9c9320c39105741fd399fb67cdaf0cad31 | Python | 3,840 | 81 | from torch.utils.data import DataLoader
from qm9.data.args import init_argparse
from qm9.data.collate import PreprocessQM9
from qm9.data.utils import initialize_datasets
import os
def retrieve_dataloaders(cfg):
if 'qm9' in cfg.dataset:
batch_size = cfg.batch_size
num_workers = cfg.num_workers
... |
8c11010b763d74046887e4b7e849ef2f76129a8d0d96fed4a362f0a0bbac94a3 | Python | 3,841 | 114 | """Test embeddings base module."""
import pytest
from langchain_classic.embeddings.base import (
_SUPPORTED_PROVIDERS,
_infer_model_and_provider,
_parse_model_string,
)
@pytest.mark.parametrize(
("model_string", "expected_provider", "expected_model"),
[
("openai:text-embedding-3-small", ... |
71081a3edaf943424ef30b0f9bde132a44a7dc76d99853daa3fd4723c224d733 | Python | 3,842 | 102 | import hydra
from hydra.core.config_store import ConfigStore
from config import Scenario
from model import Net
import os
from utils import *
from matplotlib.ticker import FormatStrFormatter
from tqdm import tqdm
import matplotlib as mpl
from matplotlib import font_manager
from scipy.stats import pearsonr
import yaml
fr... |
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