sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
be42c032b936120be26846f27db95f0b85ec3bf653ecd3c0349a9f84ea3ac081 | Python | 2,329 | 46 | # 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... |
8d1338d12dcc34533bff52eca142734c790767925e0cf9cb8389ffd3b1743053 | Python | 2,330 | 69 | # Copyright 2023 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... |
f77fd95c62e43badf1dd0b3a8c482e83ef8e2ee67bd1d298b24358617257db71 | Python | 2,334 | 52 | """Pinned source revisions and locations for locally compiled surface tools."""
import hashlib
import json
import os
from pathlib import Path
import platform
ROOT = Path(__file__).resolve().parent
FS_REVISION = '7eb846079b0dc0c92e8313205a3d2387b5c7a354'
FS_URL = 'https://github.com/freesurfer/freesurfer.git'
COMMANDS... |
b6a378368d45481361dd8f0f5e0afcfd45b487cba4927c267a06382d2718029f | Python | 2,335 | 68 | from torch import nn, transpose
def elu():
return nn.ELU(inplace=True)
def instance_norm(filters, eps=1e-6, **kwargs):
return nn.InstanceNorm2d(filters, affine=True, eps=eps, **kwargs)
def conv2d(in_chan, out_chan, kernel_size, dilation=1, **kwargs):
padding = dilation * (kernel_size - 1) // 2
ret... |
a1731e30aee49dc529f9cf845f3a8ec0183fec1e17d9f84eacec94635bdeed1c | Python | 2,336 | 65 | import numpy as np
import pytest
import torch
from neurovisfit.data.dataset import DataSplit
from neurovisfit.data.dataset import ImageResponseDataset
from neurovisfit.data.dataset import InputResponseSelector
from neurovisfit.data.dataset import NamedDataSplit
@pytest.fixture
def mock_input_response_selector():
... |
df5366953b335db103d70553a38b9026d156b4e110ef6a2560267108a06d0738 | Python | 2,339 | 67 | 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/')
#### Read h5ad ####
rna = sc.read('data/RNA/RNA_all_v3.2cellrank.h5ad')
rna_meta = pd.read... |
4c8c5fef4b695c4345fcac1b1e041751c06dd759fc6318edde235f7fd1f0724f | Python | 2,340 | 30 | """Writing audit of paper/main.tex prose: per-section sentence-length stats, sentences > 40 words, AI-vocabulary hits,
first-person pronouns, em/en dashes, and non-ASCII/invisible characters."""
import re, os, statistics, unicodedata
ROOT = os.path.join(os.path.dirname(__file__), "..")
s = open(os.path.join(ROOT, "pape... |
efbd62aa03ba70276bfd7273a2e9bdcfba23f6b9d13c49c603d6e8103ea49e05 | Python | 2,341 | 45 | # 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... |
b54e846dbf02c15aecb2a6f9b3e561bd128f191714e64ebc3d9622d88321fd7b | Python | 2,342 | 61 | # 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... |
0405ca7aaea78fa90f8fe437a0929d0351ddb97dcd853df39bfaa37dfee99b58 | Python | 2,345 | 69 | """
ZephOD network uses all functions listed below as input channels to combine/select from.
This file may be edited by a user to fit their particular use case,
or to add new State-of-the-Art segmentation/detection algorithms,
but each edit will require retraining of the weights.
Each function needs to take only the vo... |
533eb6efb1fc0de5932879544d881738600b56b99f4939e0205666de5a664671 | Python | 2,345 | 42 | # Sconda conda activate abc; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/fcdnn_most_variable_genes; python
# NOTE: Run /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/fcdnn_most_variable_genes/fcdnn_most_variable_genes.sh before this... |
5ba46c2402afa6a43b52bd50519e1567df12e65802f8fad8ae6c2ad875f672d5 | Python | 2,345 | 42 | # Sconda conda activate abc; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/fcdnn_variable_genes; python
# NOTE: Run /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/fcdnn_variable_genes/fcdnn_variable_genes.sh before this code
import pa... |
73422f3bd24fafad0cbf545766a49ed922165b278f67ab782857b174e026455f | Python | 2,345 | 75 | from langchain_core.prompts.chat import (
ChatPromptTemplate,
)
from langchain_core.prompts.prompt import PromptTemplate
from langchain_classic.chains.prompt_selector import (
ConditionalPromptSelector,
is_chat_model,
)
DEFAULT_REFINE_PROMPT_TMPL = (
"The original question is as follows: {question}\n"... |
0d56b71c64f4c35e4961f6d9b074fbfe0f167fec381692413c46307f26edd93d | Python | 2,347 | 104 | """Module with generic useful functions such as to return main dir path, and
writing hdf5 files."""
import os
import deepdish as dd
from pathlib import Path
def get_file_path(filename):
"""Find filename in the relative directory `../data/` .
Args:
filename (str): file we're looking for in the ./dat... |
e43cd03cf7b9044726d68d437de64b2e0fdfac1f9c1056f191e0ccedfcbe47e2 | Python | 2,347 | 70 | #! /usr/local/bin/python
#'#################################################################################
#'#################################################################################
#' Train TCGA network
#'#################################################################################
#'#################... |
b0568d7567f3117b055a2e6ec6bddbe33c838dd18a56a59f67dd8cb2dcf0490e | Python | 2,348 | 49 | from NeuronModel.Integrate_and_Fire import integrate_and_fire
import numpy as np
import matplotlib.pyplot as plt
hsv = plt.get_cmap('hsv')
def colors(n):
return hsv(np.linspace(0., 1.0, len(n)+1))
def R_func(x, params):
return params['RI_slow'] + (params['RI_fast'] - params['RI_slow']) / \
(1. + n... |
0cf4c1d194c9210154fc95fba2ef58193339421640945f5d22167acaff6203e7 | Python | 2,349 | 60 | import argparse
from pathlib import Path
from tempfile import TemporaryDirectory
from alpaca import ProvenanceGraph, AlpacaProvDocument
def add_suffix_to_gexf_file(source, suffix):
return source.parent / (source.stem + f"_{suffix}.gexf")
def merge_graphs(merged_file, *source_files):
doc = AlpacaProvDocumen... |
30cdf9f0b45b20a106cff73bd4e5653c7c09cb6dd95265c969bb43750907393d | Python | 2,350 | 70 | import os
import numpy as np
from audio_utils import WaveProcessor
from tqdm import tqdm
import mne
def create(wav_folder: str, tg_folder: str, out_folder: str, segments_list: list, config: dict) -> None:
""" Create feature from audio snips.
This function creates a feature from audio snips. The feature is a... |
c752ed2bfe7b4472c591ea04a4a5f76d4c37d3de8db2ebe905642ad0a0994a8b | Python | 2,351 | 71 | # -- 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
# --------------------------------------------------------------
# Config of model
# --------------------------------------------------------------
_... |
e33ed129dc8f4d41e2e3a8a5f6e38b0486900286b33d0a30de9111a0f8e4601a | Python | 2,351 | 96 | """Example03."""
# pylint: skip-file
import numpy as np
import torch
import compare_models
import pandas as pd
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dtype', help='dtype', default='float64')
args = parser.parse_args()
keys = ['EMB1 (PyTorch)', 'EMB2 (PyTorch)', 'EMB3 (PyTorch)', 'E... |
392ddcf14301b09c8df07852566781f03b1f03279f79a822cf0c67c05740198a | Python | 2,353 | 48 | #!/usr/bin/env python
import os
import tempfile
from ..constants.constants import HUMAN_SOMATIC_CHROMOSOMES, PLINK_1000G_URL
from ..utilities.utilities import untar
class Plink:
SUFFIXES = [ ".fam", ".bim", ".bed" ]
@staticmethod
def validateDirectory(directory, chromosomes = HUMAN_SOMATIC_CHROMOSOMES,... |
2c4f8f3519ca01829a62ee762f2b8f6491f2b48905fb9c0f741268323185dea2 | Python | 2,354 | 65 | from typing import Literal
from langchain_core.documents import Document
from langchain_core.indexing.api import _get_document_with_hash
def test_hashed_document_hashing() -> None:
document = Document(
uid="123", page_content="Lorem ipsum dolor sit amet", metadata={"key": "value"}
)
hashed_docume... |
48547ad88224c0f1d85cd4049494253c66c38fa989c0faab0619a79e770c2f0e | Python | 2,354 | 71 | from collections import OrderedDict
import copy
import torch
import torch.nn as nn
from h3xsemble.utils.tensor_utils import tensor_tree_map
class ExponentialMovingAverage:
"""
Maintains moving averages of parameters with exponential decay
At each step, the stored copy `copy` of each parameter `param` is... |
e5bb0310c3c886330cd8fe5b13c76a3b1cba35ea02004e6252914c01ed03c6a0 | Python | 2,355 | 59 | import os
from core.network import network_register, MIGA
from utils.my_containers import deep_dict_get
from ..head import get_head
from ...loss import get_loss
from utils.tensor_operator import tensor2array
from ...loss.utils import merge_loss_dict
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = ... |
1b518f025b5eb3cda04e51e6f7a60d0feb0a3158571b236b6dd475f71e69de7f | Python | 2,357 | 71 | from io import StringIO
from cwas.core.categorization import parser
from pandas import Series
def test_parse_info_field():
csq_field_names = ["Test1", "Test2", "Test3"]
info_field = (
f'##INFO=<ID=CSQ,Number=.,Type=String,Description="'
f"Consequence annotations from Ensembl VEP. Format: "
... |
b7c97ec6a9881d1bbbe8a3b6a4d3ad18086907214d6d77c0d964923de3a2b012 | Python | 2,357 | 76 | # Adapted from OpenFold
# Copyright 2021 AlQuraishi Laboratory
#
# 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 appl... |
bb64f5edf7f9e41eb8fa7eeba76bcb3cebc7fa5d9538f5524b46a123bdaf66ca | Python | 2,357 | 50 | from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.callbacks import EvalCallback
from animalai.envs.arena_config import ArenaConfig
from src import utils
from src.animalai import AAI_utils
from src.models.actor_critic import NetActorCriticPol... |
053d8df763e1d2669be9187a407435ebf18d01c39882867089da8a1df534a071 | Python | 2,358 | 65 | import sys
import torch.nn.functional as F
import numpy as np
import joblib
from dt_utils import Timer
from plastic import PlasticRecall
from data_utils import generate_delayed_recall_data, recall_chance
from neural_net_utils import random_weight_init, plot_train_perf
#%%
if len(sys.argv)>=4:
R = int(... |
14eaf4487cfb6438dabcd44e4b408ec89277bdc4c575fdf77ddfee4fcb816e65 | Python | 2,358 | 69 | import torch
import torch_geometric
import hienet._keys as KEY
import hienet.util
# TODO: Now, I'm not sure why this class is required
class AtomGraphData(torch_geometric.data.Data):
"""
Args:
x (Tensor, optional): atomic numbers with shape :obj:`[num_nodes,
atomic_numbers]`. (default: :o... |
1a4dccb0413fe16a63fe0dcc0fdbaef17cd73b9b6d9b5cb48852a407c3bd3071 | Python | 2,358 | 96 | try:
import cPickle as pickle
except:
import pickle
import ast
import re
import inspect
import os
import logging
import numpy as np
def cross_entropy_npy(a, b):
return a * np.log(b + 1E-9) + (1 - a) * np.log(1 - b + 1E-9)
def safe_eval(expr):
if type(expr) is str:
return ast.literal_eval(expr... |
46c8e049feced88da1f9370afc2d53dbafe4e84a96586dffa18d692a00977226 | Python | 2,358 | 61 | import torch
from torch import nn
from torch.optim import Adam, lr_scheduler
from torch.utils.data import DataLoader
from torch.utils.data.sampler import WeightedRandomSampler
from processing.utils import make_weights_for_balanced_classes
from processing.train_utils import supervised_training
from typing import Tuple... |
6b102ef792f727a23cd8fdce45d7367612e0abb9e6e9e439bbe51bc4075bc7fa | Python | 2,358 | 59 | """Prompts for comparing the outputs of two models for a given question.
This prompt is used to compare two responses and evaluate which one best follows the instructions
and answers the question. The prompt is based on the paper from
Zheng, et. al. https://arxiv.org/abs/2306.05685
""" # noqa: E501
from langchain_co... |
bfa7ece0a00294f616a6c9497a4bd8da65ab0026bd19331ae3c91e6050da7ee4 | Python | 2,358 | 90 | # -*- coding: utf-8 -*-
import torch
import numpy as np
from PIL import Image
from torchvision import models, transforms
class ResNetDataset(torch.utils.data.Dataset):
def __init__(self, dataset):
super(ResNetDataset, self).__init__()
self.dataset = dataset
self.transform = transfor... |
d50141f5d7e44632a586646d0b8c9a1638de0c3e9403f3543d7580b872479232 | Python | 2,358 | 67 | import os
import sys
import adv
import argparse
import skimage.io
import numpy as np
def nrrd_details(f):
nrrd = adv.nrrd_read(f)
dtype = nrrd["type"]
path = nrrd["path"]
shape = nrrd["sizes"]
offset = nrrd.get("byte skip", 0)
dx, dy, dz = nrrd.get("spacings")
... |
7eefc4d9ed19921c84fa27e80049bc1f7dff3bf0118ded33223a54fca5372de7 | Python | 2,359 | 64 | import numpy as np
import torch
from medpy import metric
from scipy.ndimage import zoom
def calculate_metric_percase(pred, gt):
pred[pred > 0] = 1
gt[gt > 0] = 1
if pred.sum() > 0:
dice = metric.binary.dc(pred, gt)
hd95 = metric.binary.hd95(pred, gt)
return dice, hd95
else:
... |
be5a8fc7598a4b429746c9a872978477d9d45e932d4163f23473ad85a03b8e6d | Python | 2,361 | 65 | import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from scipy.stats import mannwhitneyu
from mantis_ml.config_class import Config
best_real_clf = {'CKD': 'XGBoost', 'Epilepsy': 'XGBoost', 'ALS': 'ExtraTreesClassifier'}
best_rand_clf = {'CKD':... |
c9541e5f47c15b5c04dbd2c1ef530b7890f9f54a12971c2a2e7510f6168f7f4f | Python | 2,363 | 56 | """
Calls CellExplorer connectivity function from Python. The input format fits Yu's curated data format.
2/27/2023 jiaaoZ
"""
import os
import re
import shutil
import subprocess
import numpy as np
from utils.mdaio import readmda, writemda64
def get_segment_index(segment_name: str) -> int:
return int(re.search(... |
7a83f0c8f73c50a72cd9663be6c6367e18b3971409fd9dddceff6961b23be739 | Python | 2,365 | 77 | #!/usr/bin/env python3
import logging
import os
import numpy as np
from enrichm.parser import ParseGenerate
from enrichm.writer import Writer
from enrichm.parser import Parser
class Predict:
predictions_output_file = 'predictions.tsv'
predictions_header = ["Sample", "Prediction", "Probability"]
def make_... |
25843ce147cb86cfa0cb30c75686ff18d58d629d86e2d426816e1874bc5cf57e | Python | 2,366 | 69 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# pylint: skip-file
import sklearn.linear_model
import scipy.io
import matplotlib.pyplot as plt
import numpy as np
from embanded.embanded_numpy import EMBanded
# Load data from the 'example02.mat' file
data = scipy.io.loadmat('example02.mat')
# Split the data into predi... |
aedb02bf919aac64700655308b8a6cf26f7a66d075f5d5233b64abd968317d9d | Python | 2,367 | 77 | # coding=utf-8
import numpy as np
import torch
import torch.nn.functional as F
from datautil.util import random_pairs_of_minibatches
from alg.algs.ERM import ERM
class Mixup(ERM):
def __init__(self, args):
super(Mixup, self).__init__(args)
self.args = args
def update(self, minibatches, opt, ... |
8a06d77103e8d9e8d726b6df537d79db7d7df920f63a948e4a0459014ebc2264 | Python | 2,368 | 80 | """A unit test meant to catch accidental introduction of non-optional dependencies."""
from collections.abc import Mapping
from pathlib import Path
from typing import Any
import pytest
import toml
from packaging.requirements import Requirement
HERE = Path(__file__).parent
PYPROJECT_TOML = HERE / "../../pyproject.to... |
afbbb46f03c7c10e3cfefea15126410472760469e075d4a979604a5d18fc661d | Python | 2,368 | 78 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 10 15:41:54 2021
@author: shni2598
"""
import numpy as np
#from brian2.only import *
#import matplotlib.pyplot as plt
from connection import coordination
#%%
def get_adaptation(base_amp = 16, max_decrease = [13,13], rang = [6, 6], sharpness =[1,1],... |
2787b7afffab30b80714b55756a956f33669c2327126a29f3cc6b3b4bd22efba | Python | 2,370 | 86 | """Training diffusion model on rna inverse design with given split."""
import ml_collections
import torch
def get_config():
config = ml_collections.ConfigDict()
# Misc config
config.exp_type = 'vpsde'
config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
con... |
f2eeeed5e411486c53902a29890ce0d9962436b5be86cd1b5c81799944a15074 | Python | 2,370 | 62 | """
openforcefields
"""
import sys
from setuptools import setup
import versioneer
short_description = __doc__.split("\n")
# from https://github.com/pytest-dev/pytest-runner#conditional-requirement
needs_pytest = {'pytest', 'test', 'ptr'}.intersection(sys.argv)
pytest_runner = ['pytest-runner'] if needs_pytest else []... |
713412cb8568762a835a760edad5549c3d5ee65252a16a43967b88e31f9fc822 | Python | 2,371 | 85 | '''test_style - test coding style confirmation of cgat code
===========================================================
:Author: Andreas Heger
:Release: $Id$
:Date: |today|
:Tags: Python
Purpose
-------
This script runs pep8 on all scripts in the cgat
code collection.
This script is best run within nosetests::
... |
f9d31cb6e978a5cded3ba3bd3587944946f10518c33decde2f666dcbedd46ea0 | Python | 2,372 | 89 | #!/usr/bin/env python3
"""
Calculate probability-weighted label volumes given a 4D probabilistic atlas
- assumes empirical probability is a good proxy for partial volume occupancy
- can handle a list of 4D prob atlas files
Authors
----
Mike Tyszka, Caltech Brain Imaging Center
License
----
This file is part of atlask... |
29c4d5a126471ed9889cbe097b97d9ad24a0d7c5542ce6b3002a90165bf48f8c | Python | 2,373 | 71 | # Copyright (c) DP Technology.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from hashlib import md5
def pad_1d_tokens(
values,
pad_idx,
left_pad=False,
pad_to_length=None,
pad_to_multiple=1,
):
"""Convert a list... |
09653c46b4602a5d2d0f2ff4d0fa6efb80958fc37cd268f58a29913c29cd10d3 | Python | 2,375 | 81 | # The script plots the neural and behavioural data contained in a .h5 file of the Diomedi et al., 2023 dataset
# in the form of a raster plot
# authors: Stefano Diomedi
# date: 10/2022
import numpy as np
import matplotlib.pyplot as plt
from supportFunctions import get_all_data_from_level_h5_rec
import tkinter as tk
fr... |
0b87654fade601f69b0cb95c4fab02f88777128f991faa8b78946e9b534542e7 | Python | 2,375 | 66 | # 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 --------------------------------------------------------------
# If wo... |
6676d02e39c8e98f95a223b241109abb53d22fcabdfa45b14a9b3921b98de3b5 | Python | 2,375 | 77 | import signal
import sys
import pytest
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_classic.agents.output_parsers.react_single_input import (
ReActSingleInputOutputParser,
)
def test_action() -> None:
"""Test standard p... |
248869dd392c83dd4d3ec39cd63da0bd1fb450bf44303671b9ac0d7489c0cf50 | Python | 2,380 | 66 | from mimic3models.metrics import 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('--test_listfile', type=str,
... |
47e9a3ded06da695afdc0d613d88afd5fb1e228b7aeab743718d0e604022cb0c | Python | 2,381 | 56 | from torch.utils.data import Dataset
import numpy as np
from torchvision import transforms
import torch
from src.napari_svetlana.PredictionDataset import max_to_1, min_max_norm
import cupy as cu
from cucim.skimage.morphology import dilation, ball
import matplotlib.pyplot as plt
class Prediction3DDataset(Dataset):
... |
7fd56f6444d4f33c6fd388e9f0d23191c1aa4fe325ec98a119d4582c4468c571 | Python | 2,382 | 65 | """**Document Transformers** are classes to transform Documents.
**Document Transformers** usually used to transform a lot of Documents in a single run.
"""
from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_transformers i... |
852bea6088a718352f350799916a4c9dba0e4692b2a9af9b68402f23644c7472 | Python | 2,383 | 70 | """
Post-processes deepsignal extract output: add high-confidence labels, removing sites w/o high confidence in methylation status.
"""
import sys
import pandas as pd
from utils.constants import METHYL_BED_COLS
from utils.constants import DEEPSIGNAL_EXTRACT_COLS
def label_extract(feat_filename, methyl_filename, wri... |
f2bb6da30c074309fc472f096ee4013d757af349ee190f473fcb3346a2fafc8e | Python | 2,386 | 39 | from pydantic import BaseModel
class Adaptor(BaseModel):
fasta_file: str = "" # url that can use to download a FASTA file, choose fasta_url or fasta_file as input
fasta_url: str = "" # fasta_file path, choose fasta_url or fasta_file as input
specificity_url: str = "" # specifi... |
29ac1b35db1ded0326173c50bd4aff7cce386b59733bcacb1a87c8c7331da8a1 | Python | 2,387 | 76 | import pandas as pd
import numpy as np
import sys
import os
def get_start_index(df, cnv_start):
if cnv_start <= df.iloc[0].start:
return 0
else:
start_index = df.index[df["start"] <= cnv_start].tolist()[-1]
if df.iloc[start_index].end <= cnv_start:
start_index += 1
... |
c1694f3310884e86ad3a541396ad33603e2d2bc3cc92009a6e5b4b71f152dfb2 | Python | 2,387 | 99 | #pip install pyarrow
#pip install fastparquet
import pandas as pd
import numpy as np
import pyarrow.parquet as pq
import copy as cp
import pickle as pk
import functions_paths as fpt
def normalize_vec(vec):
x = len(vec)
new_vec = np.zeros(x)
mi = min(vec)
ma = max(vec)
for i in range(0,x):
... |
bd78b145ac749c3ec6a1bbac1af160a145cb471bc83c14bfd4a4a2ff950faf01 | Python | 2,389 | 61 | import argparse
from pathlib import Path
from tempfile import TemporaryDirectory
from alpaca import ProvenanceGraph, AlpacaProvDocument
def add_suffix_to_gexf_file(source, suffix):
return source.parent / (source.stem + f"_{suffix}.gexf")
def merge_graphs(merged_file, *source_files):
doc = AlpacaProvDocumen... |
284fa2a81e48f71f4cf7645242959dbaff001a50473b21b215b4220c27094e0b | Python | 2,390 | 82 | from typing import Any
from unittest.mock import Mock, patch
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import ConfigurableField
from langchain_classic.runnables.hub import HubRunnable
@patch("langchain_classic.hub.pull")
def test_hub_runnable(mock_pull: Mock) -> None:
m... |
6f6d0c8ace70ac71ea8c42f99fd54af5d0183428ee29788063efa146ce7f8755 | Python | 2,390 | 82 | """
Write TIP3P-FB parameters into a SMIRNOFF force field. Based on
https://github.com/pandegroup/tip3p-tip4p-fb/blob/master/OpenMM/tip3p-fb/tip3p-fb.xml
"""
from openff.toolkit.typing.engines.smirnoff.forcefield import ForceField
from openff.toolkit.typing.engines.smirnoff.parameters import (
ConstraintHandler,
... |
dd207c66bc9231361b93fc51d6f6495fb893b02b2e7b324cfb431fc30fdbc359 | Python | 2,390 | 61 | import os
import pandas as pd
from src.PID import extract, k_order, full_order, batch
from src.PID import PID_utils
def get_performance_info(model_PID, activations, args):
model_PID['avg_reward'] = activations['avg_reward']
model_PID['accuracy'] = activations['accuracy']
if args.discrete_PID:
mod... |
0e986ea93ec33eeefb495a8d1b9da52d52e3f5bc69a8ba21d9e2ea08defd6e2f | Python | 2,391 | 62 | import os
import time
d='/jukebox/griffiths/bert-brains/'
jobs_per_sub=10
numbers=list(range(0,1001,jobs_per_sub))
with open("joblist.txt","w") as f:
for dataset in ['slumlordreach','black']:
#Slumlordreach
if dataset=='slumlordreach':
subs=['sub-145', 'sub-143', 'sub-016', 'sub-142', 'sub-141', 'sub... |
702a4fb7b18090216695fe5aca3ef73b194a3a9e439bc436c34e6134932ed1c1 | Python | 2,392 | 72 | import argparse
import numpy as np
from oset.ecg.peak_detection.peak_det_local_search import (
peak_det_local_search,
)
from scipy.signal import lfilter
def peak_det_matched_filter_robust(ref, fs, h, fmax, itr=1):
"""
peak_det_matched_filter_robust: R-peak detector based on a matched filter
Syntax: ... |
54475e9ff55cb26affddf8bae26caed773f01988fef01af6ddcffc9616e0bd24 | Python | 2,393 | 56 | import torch
from bindsnet.encoding import *
class TestEncodings:
"""
Tests all stable encoding functions and generators.
"""
def test_bernoulli(self):
for n in [1, 100]: # number of nodes in layer
for t in [1, 100]: # number of timesteps
for m in [0.1, 1.0]: #... |
85427416016947e38f75fc29b905aa03ca8aab1f99c1aae969ba80e82328a6fa | Python | 2,393 | 86 | from __future__ import print_function
import pandas
import numpy as np
from torchvision import transforms
from sklearn.metrics import balanced_accuracy_score
def obtain_transform(args : dict):
if 'eff' in args.model_name:
if 'b0' in args.model_name:
size = 224
elif 'b1' in args.mode... |
89c4f42c3759947a68d3340da0c66556ddf4a28efb7d4019703eb33d030028f1 | Python | 2,397 | 73 | #!/bin/env python3
import voxcell as vc
import numpy as np
import sys
fmap = vc.VoxelData.load_nrrd(sys.argv[1])
x = fmap.raw[:,:,:,0] # x coordinate in flatland
y = fmap.raw[:,:,:,1] # y coordinate in flatland
wx = np.array(np.where(x >= 0)).T
wy = np.array(np.where(y >= 0)).T
chk = np.equal(wx,wy).all()
if not c... |
bd97bf5cba0c552348d75b38bb530b29fd86cb94fc218d3759ef0b697bb29649 | Python | 2,397 | 67 | # main file for CNN classifier evaluation
# Updated: 2/27/2021
# Status: OK
# CUBLAS_WORKSPACE_CONFIG=:4096:8 python cnn_eval.py
import sys, os
import torch
import numpy as np
from networks import CNN_Wrapper
from utils import read_json
from tabulate import tabulate
def CNN_eval(model_name, config, Wrapper, num_exp... |
ce7b601d9c2b41c0c57ada5087df6a19e2463a8e77c0a3e60ba7bc3fab2a1e5e | Python | 2,397 | 65 | #!/usr/bin/env python
# ======================================================================
# MODULE DOCSTRING
# ======================================================================
"""
Test that the examples in the repo run without errors.
"""
# =================================================================... |
d9748b8822b98ad931c63be9bf938e53b11d48fbab28889d2fa4ea50e6b60cc6 | Python | 2,398 | 69 | from collections.abc import Hashable
from math import log
from pgmpy.structure_score.log_likelihood import LogLikelihood
class BIC(LogLikelihood):
r"""
BIC structure score for discrete Bayesian networks.
BIC, also known as the MDL score, balances discrete log-likelihood against model complexity. The loc... |
df1b67d800cba38bb3e66fdee2f8c3d0e7e145a7872d10103fb83b5458d1abad | Python | 2,399 | 81 | import numpy as np
import pytest
from pgmpy.inference.mplp import Mplp
from pgmpy.readwrite import UAIReader
@pytest.fixture
def mplp_instance():
reader_file = UAIReader("pgmpy/tests/test_readwrite/testdata/grid4x4_with_triplets.uai")
markov_model = reader_file.read()
for factor in markov_model.factors:... |
9070029034a36d8ae2e317ff8ca822df1b362d6bf338da5dec2f3d5647a50e2c | Python | 2,401 | 80 | conf = {}
conf_data = {}
conf_data['prop_name'] = 'heat_ref'
conf_data['graph_method'] = 'crystalnn'
conf_model = {}
conf_enc_backbone = {}
conf_enc_backbone['cutoff'] = 5.0
conf_enc_backbone['num_layers'] = 4
conf_enc_backbone['hidden_channels'] = 128
conf_enc_backbone['out_channels'] = 256
conf_enc_backbone['int_e... |
aaa73106e6fa5d8c88eaa7769eb2cb0b46209687b2eb7df661790e06f78d864c | Python | 2,403 | 69 | ####################################################################################
#
# 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... |
374dd537365211191d226dd702a0e32f4e65f4f614b1a38fe8e0e77243dfab78 | Python | 2,404 | 69 | ####################################################################################
#
# 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... |
b6261a4acff7e41a24f0bb5d403967a6011eeba29dd7803fde0fb710ce6fde4c | Python | 2,404 | 58 | #!/usr/bin/env python
"""
Cross-check energies of molecules from AlkEthOH set using SMIRNOFF xml file
versus energies from AMBER .prmtop and .crd files (parm@frosst params).
"""
import os
import glob
# datapath = './AlkEthOH_tripos/AlkEthOH_chain_filt1'
# datapath = './AlkEthOH_tripos/AlkEthOH_rings_filt1'
datapath ... |
1089783c0fabbc13b85e83e5757231604a11e34a8378ec5a57bc601e401cc958 | Python | 2,405 | 84 | import math
import torch
import torch.nn.functional as F
def log_standard_categorical(p):
"""
Calculates the cross entropy between a (one-hot) categorical vector
and a standard (uniform) categorical distribution.
:param p: one-hot categorical distribution
:return: H(p, u)
"""
# Uniform pr... |
82a639760b8face110a11b71d6c4199943e6d0ed28fac17920defd7bb42c2ae7 | Python | 2,405 | 66 | # preprocess the settings for simulation
class preprocess():
def __init__(self):
pass
def begin_of_program(self, settings):
# int parameter list
int_parameter_list = ["number_of_atoms",
"dimensions_start_line",
"dimensions... |
bb9d13d95c4b2f27c604229c56d59f4a266bb4ab9af2c3921f04921d12ad727a | Python | 2,405 | 78 | """**Embeddings** interface."""
from abc import ABC, abstractmethod
from langchain_core.runnables.config import run_in_executor
class Embeddings(ABC):
"""Interface for embedding models.
This is an interface meant for implementing text embedding models.
Text embedding models are used to map text to a v... |
1d3b0a2a0527b9a1f883ab95f35143f37c2b3a75f3761da7191add67fe32d730 | Python | 2,406 | 66 | """Verify _release.yml dropdown options match actual package directories.
Dropdown options are short names (e.g. `openai`, `core`). The workflow's
`EFFECTIVE_WORKING_DIR` expression re-adds the `libs/` prefix for top-level
packages and `libs/partners/` for everything else. This test reconstructs the
full path for each... |
ca3612df0a84ad054b79bbf50cb4246bcc14e46c05d2c9045759a0675e9c1066 | Python | 2,406 | 87 | from __future__ import annotations
from typing import TYPE_CHECKING, Any
from langchain_core.callbacks import Callbacks
from langchain_core.callbacks.manager import (
AsyncCallbackManager,
AsyncCallbackManagerForChainGroup,
AsyncCallbackManagerForChainRun,
AsyncCallbackManagerForLLMRun,
AsyncCallb... |
ba2df63d6cb1c81ec5448024db52f727ec5d095c4d9b7698ab2d4703a662d92b | Python | 2,408 | 80 | conf = {}
conf_data = {}
conf_data['prop_name'] = 'energy_per_atom'
conf_data['graph_method'] = 'crystalnn'
conf_model = {}
conf_enc_backbone = {}
conf_enc_backbone['cutoff'] = 5.0
conf_enc_backbone['num_layers'] = 4
conf_enc_backbone['hidden_channels'] = 128
conf_enc_backbone['out_channels'] = 256
conf_enc_backbone... |
4f562e32d64e7ffebdd5933e710049700b6f5aafdaa5ff927a0a2604baeff461 | Python | 2,410 | 52 | import numpy as np
import pandas as pd
from mantis_ml.config_class import Config
class HGNCtoEnsemblIDMapper:
def __init__(self, cfg):
self.cfg = cfg
def run(self):
hgnc_to_ens_gids_df = pd.read_csv('../../../../data/ensembl/hgnc_to_ens_gids.txt', sep='\t', header=None)
... |
777d0930d8a7b964bf8bb747a1a20191936a0987871b151ff3b9742cb2190f34 | Python | 2,410 | 76 | """
In this file the testing for the MLP for the 123 bus network is performed
"""
import numpy as np
import gym
from stable_baselines3 import PPO
import torch
from stable_baselines3 import A2C, PPO
from stable_baselines3.common.callbacks import CheckpointCallback, BaseCallback
from stable_baselines3.common... |
7b968f4ff98d0011ec2f14ae18b93b7651bc8df63133d4962ec8beab9e01632e | Python | 2,411 | 54 | from pandas.core.frame import DataFrame
from torch.utils.data import DataLoader, Dataset
from transformers.file_utils import TensorType
import torch
class VirHostNetData(Dataset):
def __init__(self, df: DataFrame, tokenizer, max_len, seq_col_a, seq_col_b, target_col, return_mock_data=False):
super().__init... |
acbbb08dab9cef57f3dd40dbd5d64d01670f7cc5329b0db80e174f14d36ea194 | Python | 2,411 | 64 | # Geometric substructure mmd evaluation; More advanced MMD metric could be used here.
import torch
def compute_mmd(source, target, batch_size=1000, kernel_mul=2.0, kernel_num=5, fix_sigma=None):
"""
From DIG.
Calculate the `maximum mean discrepancy distance <https://jmlr.csail.mit.edu/papers/v13/gretton1... |
b002c746a2b03c17b62f5223a08ca4d4ba7fce7af0735dc0b67b6311cae55343 | Python | 2,411 | 79 | import logging
import os
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats
import seaborn as sns
# from sklearn.linear_model import LinearRegression
# from sklearn.metrics import r2_score
# import statsmodels.api as sm
def plot_lengthscale_window_length_relation(
config_dict: dict,
kernel... |
12ed1cf5df5a79c5233e09a4bad5ea6468a34deec9bf152989f61781a880e398 | Python | 2,412 | 64 | # third party packages
import numpy as np
# pnabind packages
from .get_residue_id import getResidueID
from .get_atom_kdtree import getAtomKDTree
from .data import data
def getCV(structure, radius, residue_ids=None, ns=None, feature_name="cv", impute_hydrogens=False, bonds=None):
"""get CV values for every residue... |
2c0de53312cce771a9cb50ea26154bb6bbb212ef4c4ff76b1e104f57ca83db3b | Python | 2,412 | 75 | from typing import Iterable, Iterator, Optional, Union
import torch
from bindsnet.encoding.encodings import bernoulli, poisson, rank_order
def bernoulli_loader(
data: Union[torch.Tensor, Iterable[torch.Tensor]],
time: Optional[int] = None,
dt: float = 1.0,
**kwargs,
) -> Iterator[torch.Tensor]:
... |
243087238b38872e4574f2e9766b90d10b35c8664f03f60144fda28e19aa009c | Python | 2,415 | 44 | import numpy as np
def reconstruct_contour(mode, tp, rec_type='EFA', first_mode=0, last_mode=2):
N_modes = len(mode.locoL.values)
# timepoint -= cell->contour[cellnumber][cell->contourlength[cellnumber]].t*cell->locoefa[cellnumber][1].tau/(2.*M_PI)
if mode.r[1]<0.:
tp = -tp
x = [ 0. for i in ... |
c6593a4aac778a3231fe7a67c13e474e178c5fc3e3ea4122c7eca734bff71b21 | Python | 2,418 | 80 | conf = {}
conf_data = {}
conf_data['prop_name'] = 'formation_energy_per_atom'
conf_data['graph_method'] = 'crystalnn'
conf_model = {}
conf_enc_backbone = {}
conf_enc_backbone['cutoff'] = 5.0
conf_enc_backbone['num_layers'] = 4
conf_enc_backbone['hidden_channels'] = 128
conf_enc_backbone['out_channels'] = 256
conf_en... |
add2e46506c9b5792dab8c87ef16aa5a56bd22ee9a9c9f978c71cc39e9caef07 | Python | 2,421 | 86 | import glob
import logging
import os
import random
from pathlib import Path
from typing import Dict, List, Tuple
import hydra.utils
import numpy as np
import pytorch_lightning as pl
import torch
import wandb
from dotenv import load_dotenv
from omegaconf import DictConfig, OmegaConf, open_dict
from pytorch_lightning.ut... |
f8946fd869aa829bad892c92b0090f2aba2e71eca90b77934f84ac3b7487f5b7 | Python | 2,421 | 79 | import uncertainpy as un
import numpy as np
import matplotlib.pyplot as plt
from prettyplot import *
import os
### FIGURE7 ###
data = un.Data()
data.load(filename='../data/simulation_outputs/UQSA_pathological/phi_msn_5/phi_msn.h5')
sobol_1_sdp = data['start_depolarization_block']['sobol_first']
sobol_tot_sdp = data['... |
b5dace8f2479c607c7d0a1e9c9aa23032d993dc367b84b986d5dedb46205b19e | Python | 2,422 | 100 | import pathlib
import mne
study_name = "age-prediction-benchmark"
bids_root = pathlib.Path(
"/storage/store3/data/CHBMP_EEG_and_MRI/ds_bids_chbmp")
deriv_root = pathlib.Path("/storage/store3/derivatives/CHBMP_EEG_and_MRI/")
# "/storage/store2/derivatives/eeg-pred-modeling-summer-school/")
subjects_dir = pathlib... |
7970604aa4e211ac81bdcbf21437267839d25c2d63226b9d80c99748d2c7e06e | Python | 2,424 | 71 | from bindsnet.datasets.alov300 import ALOV300
from bindsnet.datasets.collate import time_aware_collate
from bindsnet.datasets.dataloader import DataLoader
from bindsnet.datasets.davis import Davis
from bindsnet.datasets.spoken_mnist import SpokenMNIST
from bindsnet.datasets.torchvision_wrapper import create_torchvision... |
50b2668bdd923c22159bb4a1ab95823628d9accf1e847ca2c525f321d0814751 | Python | 2,427 | 80 | import pytest
from langchain_classic.evaluation.parsing.json_schema import JsonSchemaEvaluator
@pytest.fixture
def json_schema_evaluator() -> JsonSchemaEvaluator:
return JsonSchemaEvaluator()
@pytest.mark.requires("jsonschema")
def test_json_schema_evaluator_requires_input(
json_schema_evaluator: JsonSchem... |
927a278b62ba142bedad15515b1b3dcad5622dca4f086a479e4e464cf0f59f9d | Python | 2,427 | 77 | import torch
import torch.nn as nn
class GCN(nn.Module):
def __init__(self, in_ft, out_ft, bias=True):
super(GCN, self).__init__()
self.fc = nn.Linear(in_ft, out_ft, bias=False)
self.act = nn.PReLU()
if bias:
self.bias = nn.Parameter(torch.FloatTensor(out_ft))
... |
f1ec3bf7b6ad2112e1a758ff067f514ce905851b5e7815da520710a48587fc41 | Python | 2,430 | 56 | # 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... |
6351ce46b291eb4fe52e1f09a474ae9de902783b1b89730152fc07eb34ec976d | Python | 2,431 | 77 | """Test in memory docstore."""
from typing import Any
from langchain_classic.output_parsers.combining import CombiningOutputParser
from langchain_classic.output_parsers.regex import RegexParser
from langchain_classic.output_parsers.structured import (
ResponseSchema,
StructuredOutputParser,
)
DEF_EXPECTED_RE... |
ccf29656a455d5a6d1052401b5fb0931b8ca7e0638a8c2cb5bb3a944eb154406 | Python | 2,432 | 71 | from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGeneration, Generation
from typing_extensions import override
from langchain_classic.agents.agent import MultiActionAgentOutputParser
from langchain_classic.agents.output_pa... |
b7023711463aa218a9c1d66a0946f1b7df2c6b8e0405b447cdbf82347ec69903 | Python | 2,433 | 77 | from omegaconf import OmegaConf
from pathlib import Path
import matplotlib.pyplot as plt
import mlflow.pytorch
import mlflow
import nibabel as nib
import numpy as np
import torch
from monai.config import print_config
from monai.utils import set_determinism
def main(args):
set_determinism(seed=args.seed)
prin... |
e18e88452017dbd5398a87f0e62d4959f28173f0c3dc96185c98fc1df5a175de | Python | 2,434 | 67 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# pylint: skip-file
import sklearn.linear_model
import scipy.io
import matplotlib.pyplot as plt
import numpy as np
import torch
from embanded.embanded_torch import EMBanded
# Load data from the 'example01.mat' file
data = scipy.io.loadmat('example01.mat')
device = torch... |
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