sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d980f5664cc8c84a15f04a1fb30776a4eb70410c7d8bd05c8d74d27c5841dee7 | Python | 2,761 | 58 | from tensorflow.keras.layers import Activation, Add, AveragePooling2D, \
AveragePooling3D, BatchNormalization, \
Conv2D, Conv3D, Dense, Dropout, Flatten, \
GlobalAveragePooling2D, \
... |
2399e24f60cd513017808801a1ff91111d5f14b186e57271d9b8f1258db25ad2 | Python | 2,762 | 83 | import warnings
from langchain_core.globals import get_debug as core_get_debug
from langchain_core.globals import get_verbose as core_get_verbose
from langchain_core.globals import set_debug as core_set_debug
from langchain_core.globals import set_verbose as core_set_verbose
from langchain_classic.globals import get_... |
916d6a9bee6904623f72a00ba97c4a5b3f26d8a4c4e5dfc7f49f23103f7ba89a | Python | 2,763 | 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... |
b548c3c9b1bbcc9833888b2f6e27e7ab958ef9565daec9e348f7add9388e231d | Python | 2,764 | 86 | from __future__ import annotations
import asyncio
from collections.abc import AsyncIterator
from typing import Any, Literal, cast
from langchain_core.callbacks import AsyncCallbackHandler
from langchain_core.outputs import LLMResult
from typing_extensions import override
# TODO: If used by two LLM runs in parallel t... |
c9240513c98c52494c98c5c9f692dea6b21d515818801cec2b817f6a9cbed3ff | Python | 2,764 | 74 | import sys
sys.path.append(".")
import os
import argparse
import io
import tarfile
from crystallm import (
extract_space_group_symbol,
replace_symmetry_operators,
remove_atom_props_block,
)
def postprocess(cif: str, fname: str) -> str:
try:
# replace the symmetry operators with the correct op... |
dea6268d40f96d17c0c5f91ff4f1b2c099ecbbcb396001521c194658d60103e4 | Python | 2,768 | 102 | """
2023.08.30
author: femke.hurtak@epfl.ch
Script for drawing subsets of the network, zooming in on clusters.
"""
import os
import matplotlib.pyplot as plt
import neuron_params
import plot_params
from loaddata import load_graph_and_matrices
from draw_meta_network import load_communities
from common import (
sel... |
c78abd095fac243c58e9754ece82824de07e559dcf7c2b6c8307701baa3e00db | Python | 2,769 | 71 | #%%
import os
import gc
import warnings
from copy import deepcopy
from time import time
import re
import numpy as np
import pandas as pd
from scipy.io import loadmat, savemat
from load_intan_rhd_format import read_data, read_header
# from utils.read_mda import readmda
# from utils.write_mda import writemda16i
from ut... |
9e55308d668cf0b1c22e48168d2e9df40114655a270a60ebaef58beace5a138f | Python | 2,770 | 90 | import argparse
import numpy as np
def peak_det_simple(x, ff, flag=0, omit_close_peaks=0):
"""
peak_det_simple - Internal R-peak detector function
Syntax: peaks, peak_indexes = peak_det_simple(x, ff, flag, omit_close_peaks)
Inputs:
x: Vector of input data
ff: Approximate ECG be... |
f7b5fa511100ae0bd35c51e0d883a610aa8ed3cb9528b6616faad5cf4b44c4c0 | Python | 2,770 | 62 | import pandas as pd
import numpy as np
import re
def get_id(x,gtf_data_new):
# print(x)
chr=x.split('_')[0]
site=int(x.split('_')[-3].split('.')[0])
gtf=gtf_data_new.loc[(gtf_data_new['chr']==chr)&(gtf_data_new['strat']<=site)&(gtf_data_new['end']>=site),:]
if len(set(gtf['strand']))>1:
gtf... |
20eb7034ead2fcffbd94e8a996657c731117c6d956e24a36d77c3f16960a53f2 | Python | 2,771 | 78 | """
This module is an example of a barebones numpy reader plugin for napari.
It implements the ``napari_get_reader`` hook specification, (to create
a reader plugin) but your plugin may choose to implement any of the hook
specifications offered by napari.
see: https://napari.org/docs/dev/plugins/hook_specifications.htm... |
c4f2acc67eb5d09a5dfde46948570099b8a5ee658493250de503fa68a5740e1c | Python | 2,772 | 62 | import pandas as pd
import numpy as np
import re
def get_id(x,gtf_data_new):
# print(x)
chr=x.split('|')[0]
site=int(x.split('|')[1].split('.')[0])
gtf=gtf_data_new.loc[(gtf_data_new['chr']==chr)&(gtf_data_new['strat']<=site)&(gtf_data_new['end']>=site),:]
if len(set(gtf['strand']))>1:
gtf=... |
101608c969916888707f07bbd20700e284b546591dd1813dc48243baf1f42d2e | Python | 2,774 | 54 | """Extract regional labels without changing annotations or surface coordinates."""
from pathlib import Path
import nibabel as nib
import numpy as np
def read_regions(annotation):
codes, table, names = nib.freesurfer.read_annot(str(annotation), orig_ids=True)
if table.shape != (len(names), 5):
raise V... |
17ec038de91ecda076fb33854f8dddbffca5e9e1a10b91167c740971d847d3e6 | Python | 2,776 | 76 | import logging
from typing import Callable
from tensorboardX import SummaryWriter
import torch
import torch.nn as nn
from torch.optim import Optimizer
from torch.optim import lr_scheduler as _LRScheduler
from tqdm import tqdm
from HMSA.args import TrainArgs
from HMSA.data import MoleculeDataLoader
from HMSA.models imp... |
209634583d0a5c8391f73072e8b37070c6c67850981ba3e4e8d4a94e0070aa03 | Python | 2,776 | 78 | import numpy as np
import copy
from random import shuffle
from tqdm import trange
def train_Clust3D(epochs, lr_0, t1, t2, neurons, n_neurons, MDC_data, neighbors, std_mean_all, correlation, ord, random_state):
def masked_norm(a, b, ord=2):
mask = ~np.isnan(a) & ~np.isnan(b)
if not np.an... |
4c0a64d0152ab7e2fb6ed603c103176b3cff8cf5d752156376860c523cc81af9 | Python | 2,777 | 99 | # 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... |
bb1fa34166de4af096e3c05040e982d6fd3c199a7a03e9e9e1140ec857340fb9 | Python | 2,777 | 82 | """
This script is used to plot GO analysis produced by GOATOOLS.
It communicates with R, so rpy2 needs to be installed.
"""
import numpy as np
import pandas as pd
from glob import glob
import session_info, re
import rpy2.robjects as ro
from rpy2.robjects import pandas2ri
from rpy2.robjects.conversion import localconve... |
d280c98ce174c0de4dd8f52920ec911ab182e1df526c30bb3efe1614498d9b8e | Python | 2,777 | 92 | import torch
import torch.nn as nn
class DistanceNetwork(nn.Module):
def __init__(self, n_feat, p_drop=0.1):
super(DistanceNetwork, self).__init__()
#
self.proj_symm = nn.Linear(n_feat, 37*2)
self.proj_asymm = nn.Linear(n_feat, 37+19)
self.reset_parameter()
def... |
763e8cde43b3765d74a8266ea3e4fd6387f198ba674f1164aade7bd9ac0a9c5d | Python | 2,778 | 114 | # -*- coding: UTF-8 -*-
"""
@Author: Xingyan Liu
@File: _tmp_h5py.py
@Date: 2021-08-03
@Project: CAME
"""
import os
from pathlib import Path
from typing import Union, Optional, List, Mapping
import logging
import numpy as np
import h5py
def save_hidden_states(data_list: list, path: Union[Path, str]):
""" Save hid... |
622df9d36303dadd43b6560c318a3342762511ebbdfc9392be5efe46c1a37dc5 | Python | 2,779 | 82 | """
This script is used to plot GO analysis produced by GOATOOLS.
It communicates with R, so rpy2 needs to be installed.
"""
import numpy as np
import pandas as pd
from glob import glob
import session_info, re
import rpy2.robjects as ro
from rpy2.robjects import pandas2ri
from rpy2.robjects.conversion import localconve... |
585ef8b74ac7d3a8bb774a5b83b791380024f5615a994ac2d9d7a653148805e9 | Python | 2,782 | 71 | import unittest
from tests.test_utils import TestUtils
from tests.test_ImageModels import TestImageModels
from tests.test_Regularizers import TestRegularizers
from tests.test_LinearOperators import TestLinearOperators
from tests.test_Preconditioners import TestPreconditioners
from tests.test_InitializationMethods impo... |
6a9f8281e6ae27435f8fb33426b7c2029770653f0490a2df050384a28fdf68dd | Python | 2,782 | 68 | import logging
import os
import socket
import sys
import pandas as pd
from configs.configs import get_config_dict
from helpers.evaluation import leave_every_other_out_split, get_test_log_likelihood, get_test_location_estimated_covariance_structure
from helpers.rockland import get_rockland_subjects, load_rockland_data... |
224ed8aaff2ed75b88f71e4b40d8dae9d35bc461e7ee776cee97b5eac868e1a4 | Python | 2,783 | 91 | import numpy as np
import matplotlib.pyplot as plt
import argparse
import pickle
from sklearn import preprocessing
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier
"""
- Classification:
https://scikit-learn.org/stable/auto_exa... |
aa50911a8335b829bc163ccd6f10da92f2971382e58d389c0eaae5aaa2e51b94 | Python | 2,783 | 62 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 16:08:39 2021
@author: Neil J Thomson
"""
import os
import sys
from pensa import *
"""
for the pdb visualisation, the trajectory needs to be fit to the first frame of the simulation
so that the density and protein align with each other
"""
# #... |
092c739d4e963190c6e1c8309210f50ea9c2087978fe2b7862540df3b805dd4f | Python | 2,784 | 63 | import pandas as pd
import numpy as np
import re
def get_id(x,gtf_data_new):
# print(x)
chr=x.split('_')[0]
site=int(x.split('_')[1].split('.')[0])
gtf=gtf_data_new.loc[(gtf_data_new['chr']==chr)&(gtf_data_new['strat']<=site)&(gtf_data_new['end']>=site),:]
if len(set(gtf['strand']))>1:
gtf=... |
fff42ffb0d5b1826976dbbf869d1f895c212862d91708e637d069f64b28e3489 | Python | 2,785 | 67 | """Class registry for models, layers, optimizers, and schedulers.
"""
optimizer = {
"adam": "torch.optim.Adam",
"adamw": "torch.optim.AdamW",
"rmsprop": "torch.optim.RMSprop",
"sgd": "torch.optim.SGD",
"lamb": "src.utils.optim.lamb.JITLamb",
}
scheduler = {
"constant": "transformers.get_const... |
21cd1b1d46766055770c525994ec3883b49512f5190c1fee91d8d0cd370037de | Python | 2,787 | 86 | import logging
import torch
from torch import nn
from torch.nn import ModuleDict
from torch.nn.init import xavier_normal_
from .base import Shifter
logger = logging.getLogger(__name__)
class MLP(Shifter):
def __init__(self, input_features=2, hidden_channels=10, shift_layers=1, bias=True, **kwargs):
"""... |
def146b75a37a912291b3b41ef9a6809201dd191e85abe56200c0125d270e841 | Python | 2,787 | 76 | """Functions for formatting text.
"""
import logging
logger = logging.getLogger(__name__)
def tag_seq_overlap(overlap_labels, target_seq):
"""Add tags around where a sequence overlaps a target.
For example, if the target seq is 'ATCGAATTCCGG' and the overlap is the
indices {1,2,5,6,7}, the output is
... |
3f001fc859f40a589f0bd37c3492f65248e153f85405ddbabc96360cfa4f759e | Python | 2,789 | 101 | """
Tests for utility methods
"""
import os
import pytest
from openff.toolkit import unit
def test_subclasses():
"""Test that all subclasses (and descendents) are correctly identified by all_subclasses()"""
from openff.toolkit.utils.utils import all_subclasses
class Foo:
pass
class FooS... |
8da47a767bc5d5d02b36c05a0e40ce8380040755937539104c00f5ff0ba9aaaf | Python | 2,791 | 101 | import pathlib
study_name = "age-prediction-benchmark"
bids_root = pathlib.Path(
'/storage/store/data/camcan/BIDSsep/rest')
deriv_root = pathlib.Path('/storage/store3/derivatives/camcan-bids/derivatives')
subjects_dir = pathlib.Path('/storage/store/data/camcan-mne/freesurfer')
source_info_path_update = {'proce... |
22c401980257fe304ee3a7f62fdf50bbcaf6476cefee0366dbcd898359c1cad2 | Python | 2,794 | 81 | ####################################################################################
#
# 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... |
4903f474836371697f7cdeb63bae3bfae3a0ded7276e239242b7e5435801700f | Python | 2,795 | 81 | ####################################################################################
#
# 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... |
96b38251ec6608edc9cd21296d3ed4db972e054ccacbe3acc4d4d660351a1cb3 | Python | 2,795 | 84 | # Copyright 2022 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... |
d97398ad41fbc3183c738da1be776cc3e95b340eee71682792a62fa6d32689a3 | Python | 2,797 | 83 | import torch
import pandas as pd
from tqdm import tqdm
from torch.utils.data import Dataset
from torch_geometric.data import Data
from utils import build_crystal_graph, build_crystal
class MatDataset(Dataset):
def __init__(self, data_path, prop_name=None, graph_method='crystalnn'):
super().__init__()
... |
e66e6be3d89dccaac017fa86ae7d04a0b85cab88ce7d0977d83e055f6d2d263a | Python | 2,797 | 70 | import os
from pathlib import Path
import PIL.Image
from typing import List
import click
import numpy as np
import torch
from tqdm import tqdm
import legacy
import dnnlib
from training.training_loop import save_image_grid
from torch_utils import gen_utils
from gen_images import parse_range
@click.command()
@click.opt... |
1d19b7e97c39c173053616bd4cda762f032f946da81ee01c87d3ebdc1f826da7 | Python | 2,798 | 83 | from pathlib import Path
from rhe import run_genie, run_genie_mem, run_genie_multi_pheno
REPO_BASE_DIR = Path(__file__).parent.parent
EXAMPLE_DIR = REPO_BASE_DIR / 'example'
def test_genie():
gen = EXAMPLE_DIR / 'test'
phen = EXAMPLE_DIR / 'test.pheno'
covar = EXAMPLE_DIR / 'test.cov'
anno... |
4a2308df638abe7fb05a194e5247e4b8ff524aab85a80f3a3fe33d435a8b8740 | Python | 2,798 | 74 | from typing import Any
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_string
from typing_extensions import override
from langchain_classic.memory.chat_memory import BaseChatMemory
@deprecated(
si... |
dd718b1a08da945090d4ac1a5a2641a1c3b86ec09c4dc5adbee14ad125f7c4b0 | Python | 2,801 | 99 | # 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... |
e674cd5564a375c3636ed9236dbb5a3c880114480bb00657c6a162272658e300 | Python | 2,801 | 81 | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# 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 applic... |
121111d2143f306d3bceb8dc00b763a219326c8b14718cab26ce61ef53d41e40 | Python | 2,802 | 72 | from logging import Logger
from pathlib import Path
from typing import List
import re
import numpy as np # needed in eval
from ..data import (AnnotationTable, WorldlineTable)
from ._utilities import default_args
@default_args("_this, _now, *, _ACTIVE")
def change_provenance(dataset: Path, annotations: AnnotationTab... |
2ce102b8307a5cf66a6e28b8dd5789a6efdb0807f254371d432fec96504bee41 | Python | 2,802 | 104 | '''
MACS.py - Parser for MACS output
================================
:Tags: Python
The :mod:`Pipeline` module contains various utility functions
for parsing MACS output.
API
----
'''
import collections
import math
import csv
import cgatcore.csvutils as csvutils
MacsPeak = collections.namedtuple(
"MacsPeak"... |
e5e201c509b09e71f5df04a8db29b051cd1389c4323012cc40cbfa8509d1958b | Python | 2,802 | 86 | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
import torch.nn as nn
# Different encodings for the atom distance embeddings
class GaussianSmearing(torch.nn.Module):
def ... |
65eba09efa74129429e33c260c22faf359716b14413ebbe5cbd27a04af77f940 | Python | 2,803 | 56 | PREFIX = """Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text base... |
d09b592266b736bb2c6ba755c96aa4660da2788e5b79584b1de9ba22b9af7b32 | Python | 2,804 | 64 | import argparse
from pathlib import Path
from alpaca import ProvenanceGraph
def add_suffix_to_gexf_file(source, suffix):
return source.parent / (source.stem + f"_{suffix}.gexf")
def main(source, output):
source_path = Path(source).expanduser().absolute()
output_path = Path(output).expanduser().absolute... |
fa627573317b48da3d672a06444a7123be05dc7e945d9f47d465bfb12f9027e3 | Python | 2,806 | 89 | """
orderfile_generator.py
This script generates configuration files for the experiment.
"""
import random
import pandas as pd
import numpy as np
def main():
num_trial_def = 100 # trials per session
num_delay = 30 # perturbation trials
num_conditions = 5 # number of conditions (including fammiliariza... |
cf4b48afdf60c8079231f83ac7ca5055152a8cf23eff5b9aa2794559223a74a1 | Python | 2,807 | 92 | import logging
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.language_models import BaseLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts imp... |
1b77a0ebd193b8dceabc6d9422b77f146a532ce9de3e9173ce2e55c145e163f4 | Python | 2,810 | 88 | import numpy as np
import pandas as pd
import scanpy as sc
from anndata import AnnData
from prosstt.tree import Tree
from prosstt import simulation
from prosstt import count_model
def df_from_rank_gene_groups(adata, drop_duplicates=True):
'''Wraps results of sc.tl.rank_genes_groups in a df
'''
assert '... |
ff42bc4fbbeb6d1cd7c6447162b011780d08410c11ee365e311148822c0e89e4 | Python | 2,814 | 94 | """
This script is used to reformat the count output from featureCount: i.e., combine
count from different samples
"""
import pandas as pd
import argparse
def get_name_map(infile):
"""
Map bam files to their corresponding sample ids
Arguments
---------
infile: str
the map from bam file to... |
0fc7bc0aa5d8f7b3f367670cfd6a34f42a38cb4856ac28c02ba3299b43cf4e50 | Python | 2,819 | 101 | #!/usr/bin/python
import re,numpy as npy
import sys,os
#----------------------------------------------------------------------------------------------------------
# modify your stuff here
if len(sys.argv)>3 :
d=sys.argv[1]
if not os.path.exists(d):
sys.exit(" ERROR : the dx file provided does not exist . Breaking... |
1319f6c208b4420677c48f08ac2bae1fb25734daeb3658cfbb4024e6ba52c080 | Python | 2,819 | 68 | import argparse
import textwrap
#import argnet_lsaa as lsaa
#import argnet_lsnt as lsnt
#import argnet_ssaa as ssaa
#import argnet_ssnt as ssnt
import sys
parser = argparse.ArgumentParser(
prog='ARGNet',
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent("""\
ARGNet: a deep nueral net... |
e5307902d4068c9910eeb1d81b1133df8f489268fa8f53d49a9e180acf2e436e | Python | 2,821 | 72 | import trimesh
import os
import argparse # 导入 argparse 模块
import numpy as np # 导入 numpy 用于向量加法
# --- 参数解析 ---
parser = argparse.ArgumentParser(description="将输入的 OBJ 网格文件按固定的向量平移并保存。")
# 添加输入文件参数
parser.add_argument('--input_obj', required=True, type=str,
help="需要平移的输入 OBJ 文件的完整路径。")
# 添加输出文件参数
pa... |
1bb2c96f5987c768f1a4c9fb5e361c977b35b0b01f10b4183e4ef9b6f29b1a52 | Python | 2,822 | 64 | import os
import gdown
import torch
GoogleDriveLink = 'https://drive.google.com/drive/folders/10ebqIWLrZ672A9bFg9wLe48F-nsz7za3'
pretrained_model_parameters = {
'QH9Stable': {
'id': {
'name': 'QHNet-QH9-stable-id.pt',
'link': 'https://drive.google.com/file/d/115MOaWWr7JNP-SJ3IMaP3... |
bd16a0272285fc53e59ce9bc25a3e810e336e82f52b96fcc1b3eca00cc4e5733 | Python | 2,823 | 61 | import mne
import os.path as op
import numpy as np
from config_FaceName import MEG_data_path,group_name,Ids,bids_root
from mne.preprocessing import read_ica
import pandas as pd
from mne_bids import BIDSPath
for subject_id in Ids:
subject = group_name+"%02d" % subject_id
print("processing subject: %s" % su... |
70025260e5fcc02de58234f9cc3fcfa82b33d172e7f96c10d99c93b422611c1d | Python | 2,824 | 95 | """Compatibility helpers for Pydantic v1/v2 with langsmith `Run` objects.
!!! note
The generic helpers (`pydantic_to_dict`, `pydantic_copy`) detect Pydanti version
based on the langsmith `Run` model. They're intended for langsmith objects (`Run`,
`Example`) which migrate together.
For general Pydantic v1... |
7fa2cf0cf5741f8d34a384aa87a84ce1505fbf4071150d96f70ac3492ab25646 | Python | 2,824 | 94 | # Model based on convolutional layers, essentially reimplementing DeepSTARR in PyTorch
from typing import Tuple, Any, Optional
import os
import pytorch_lightning as pl
import torch
from torch import nn
from Bio import motifs
import numpy as np
class ConvolutionalClassificationModel(pl.LightningModule):
def __init_... |
482480bb70dc0b8221673a5c6df6c4fd2c461f717250f20c071ed63ee267eaef | Python | 2,828 | 60 | """links the Facial Anthropometrics workspace's own batch measurement
export to an existing cohort study, by mesh filename.
mirrors api/results_bundle.py's own list_cohort_patients, which already
joins a cohort's id-mapping file (file_name/file_path -> cohort_id -
private identity fields, kept out of the shared cohort... |
314d02dfe44f1760eb0c5f2ad2f931e08e08a0d114bfef01ef0db1e0146d6593 | Python | 2,830 | 87 | import os
import sys
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
import common.run_manager
import glob
import seq_des.util.canonicalize as canonicalize
import pickle
import seq_des.util.data as datasets
from torch.utils import data
import resourc... |
8752a6a233c7bd11889f38ecb57cf603f250af4f94941a55d76f29f162c91647 | Python | 2,832 | 157 | from neurogym.envs.collections import yang19
# DM1 to DM2
cur_1 = {
'name' : 'cur_1',
'config_files' : [
yang19.dm1(timing = {'fixation' : 300}),
yang19.dm2(timing = {'fixation' : 300}),
],
'indicators': [
0,
1,
]
}
# DM2 to DM1
cur_1R = {
'name' : 'cur_1R',
... |
a93bffa43f7ec00f9b90dd9e24ea8704ae08722c70f11481157690374d29a6a0 | Python | 2,833 | 75 | import tensorflow as tf
import tensorflow_addons as tfa
from dipgnn.utils.register import registers
from dipgnn.utils.common_util import LinearWarmupExponentialDecay
from abc import ABC, abstractmethod
@registers.task.register("base_trainer")
class BaseTrainer(ABC):
"""
Note: A significant portion o... |
4a3eb0274246d84cb55b01754d0a17558c9e766e2a251eb91969edcdc374cbb8 | Python | 2,834 | 66 | import pytest
from skbase.lookup import all_objects
from pgmpy.causal_discovery._base import BaseCausalDiscovery
from pgmpy.causal_discovery.bivariate_scores import BaseBivariateScore
from pgmpy.ci_tests import BaseCITest
from pgmpy.metrics._base import BaseSupervisedMetric, BaseUnsupervisedMetric
from pgmpy.registry ... |
646a518e2bcaef8e0c94eeeea4ceed57c18af359495a65ef403d358570d2283b | Python | 2,834 | 75 | def inputs(input_data, neighbors, n_neurons, epochs, lr, t1, t2, depth, max_n_neurons, dim_red, neuron_init):
"""
Validate and process input parameters for the Clust3D pipeline.
Returns:
tuple: Processed (MDC_data, neighbors, n_neurons, epochs, lr_0, t1, t2, depth, max_n_neurons)
"""
... |
b7bec03701dfa443b65e59fd8436371adc4fb591bcfd6f38ae3c6fe3ab7a5412 | Python | 2,834 | 104 | # 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... |
777189fd83ce7c94f7a5299c2fb834dbe5f1c8681505076d3f7778928c28ff3a | Python | 2,835 | 108 | #!/usr/bin/env python
import h5py
import sys
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.widgets import RadioButtons
import sys
W_JUST_HIT_ELEM = 1
def importOutput(filename: str):
"""
Import output h5 format and clean data
"""
# file will be closed when we exit from WITH sc... |
dc0393617435f9b7ef9646ef76b2f422837d77b7d9107d2b97081e4529939021 | Python | 2,835 | 83 | # Load in pre-trained model weights and evaluate its goodness (ECE, MCE, error) also saves logits.
from __future__ import print_function
import os.path
import densenet
import numpy as np
import sklearn.metrics as metrics
from keras.datasets import cifar10, cifar100
from keras.utils import np_utils
from keras.prepro... |
a7c93d622935390f2636415b61fee0c8fc21c2a3b0d944faebb99f57de08cf2d | Python | 2,838 | 88 | import warnings
from collections import OrderedDict
from typing import Dict
import torch
import torch.nn as nn
from e3nn.util.jit import compile_mode
import hienet._keys as KEY
from hienet._const import AtomGraphDataType
@compile_mode('script')
class AtomGraphSequential(nn.Sequential):
"""
same as nn.Sequen... |
dac334bbbd83a7e7d554d7f615fe821c4a8bdf7813e64e2162d41bc1562cf92b | Python | 2,839 | 78 | """
create torch's dataset for DrugComb
"""
import numpy as np
import pandas as pd
import pickle as pkl
import torch
import sklearn.feature_extraction as skfet
import sklearn.preprocessing as skpre
class DrugCombDataset:
"""
:param summary_data: drug combination data,
headers=['tripl... |
ec6ab1ea3e77f5b8ac83035f0c646c7219bcec996d476359472329a053ee2334 | Python | 2,839 | 68 | # Prepare images and labels for validation
# Copyright (c) 2021 Laurent Caplette
import numpy as np
import os, csv, h5py
from PIL import Image
np.random.seed()
homedir = '' ### path of home directory
nBlocks = 7
nTrialsInBlock = 50
nTrials = nBlocks * nTrialsInBlock
f = h5py.File('vis_visgen_ultimatefinal.h5', 'r')... |
aff22b9dca3da6aa66f97f6dad2f31ceee13ab85a4eb37f0a17d56108159fe8c | Python | 2,840 | 115 | """===========================
Pipeline template
===========================
.. Replace the documentation below with your own description of the
pipeline's purpose
Overview
========
This pipeline computes the word frequencies in the configuration
files :file:``pipeline.yml` and :file:`conf.py`.
Usage
=====
See ... |
b2dd500d5bd66885d225c8fe4983a8dea5f8e50758be24cf3e6c677989efc9d3 | Python | 2,840 | 86 | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@Description: :
@Date : 2022/06/15 16:42:02
@Author : Zilan Yu
@version : 1.0
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import SAGEConv, HeteroConv, Linear, TransformerConv, FiLMConv
class Heter... |
155271cdb6e22db1f27235cc2b8a25081f3f9fb63197a190c666e583df87aab3 | Python | 2,842 | 81 | # Load in pre-trained model weights and evaluate its goodness (ECE, MCE, error) also saves logits.
from __future__ import print_function
import os.path
import densenet
import numpy as np
import sklearn.metrics as metrics
from keras.datasets import cifar10, cifar100
from keras.utils import np_utils
from keras.prepro... |
264b5c3a606369c58213b2608135498f61068b89193019965919e4a06c733fcf | Python | 2,843 | 86 | import argparse
from dival import TaskTable
from dival.measure import PSNR, SSIM
from dliplib.utils import Params
from dliplib.utils.data.datasets import CachedDataset
from dliplib.utils.helper import select_hyper_best_parameters
from dliplib.utils.helper import load_standard_dataset
from dliplib.reconstructors impor... |
5c6614f8e3024f77e85cbed95ee63564a422db42dd3e90048c08c37b5ceab709 | Python | 2,845 | 74 | import sys
import os
import time
import warnings
from sklearn.exceptions import ConvergenceWarning
import gc
import psutil
import numpy as np
warnings.simplefilter(action='ignore', category=ConvergenceWarning)
warnings.simplefilter(action='ignore', category=RuntimeWarning)
warnings.simplefilter(action='error', cate... |
4cb0b49bb56c3001217303f1c5715344e2ad91037114c3b1f600f96293261771 | Python | 2,846 | 82 | import torch
from torch.utils.data import Dataset, DataLoader
from proto_net.utils.subs_to_list import subs_to_list
from processing.constants import ALL_TRIAL_LIST
import numpy as np
'''
This function was taken from the github repo of prototypical network
and was modified to fit the input data. If you are usin... |
a8a3cdc4d66bea3ec8c298c5f383c00570891c835a3f3150192d8a14e050316e | Python | 2,846 | 43 | #TO BE UPDATED
import numpy as np
from NeuronModel import integrate_and_fire, poissonPop
from NetworkModel import networkClass
from WeightModel import weightClass, lebastard, spikeBased
from InitFunctions import identity, dirac, dirac_sparse
from Simulator.SimulatorClass import simulatorClass
class simulatorLebastar... |
e25448ab3b13d6e2c86565591932c8efb30498d33940de5ee67a3b8fbb59552f | Python | 2,847 | 77 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import glob
import os
DESCRIPTION = 'Apply Eddy Warps to split data. Cornelius Eichner 2020'
FSL_LOCAL = '/data/pt_02101_dMRI/software/fsl6/bin/'
def buildArgsParser():
p = argparse.ArgumentParser(description=DESCRIPTION)
p.add_argument('--spl... |
fd76684dd0272151ceb43596b3c1311b86c116301a2e3efc7fb76b0af786acd6 | Python | 2,848 | 63 | """Indexed triangular mesh I/O and compatible STL loading."""
from pathlib import Path
import os
import numpy as np
import trimesh
def load_mesh(path, *, indexed=True):
mesh = trimesh.load(str(path), process=False)
if not isinstance(mesh, trimesh.Trimesh) or not len(mesh.faces):
raise ValueError('Ex... |
9f47c3e764860c263e4080f52eea5d3ca6be919ebebc02860c39dc2746190301 | Python | 2,849 | 80 | # https://github.com/CuriousAI/mean-teacher/blob/546348ff863c998c26be4339021425df973b4a36/pytorch/mean_teacher/losses.py#L15
import torch
from torch.nn import functional as F
from torch.autograd import Variable
from torch import nn
def softmax_mse_loss(input_logits, target_logits):
"""Takes softmax on both sides ... |
2c4c98268cbbb9dc32f6a894eb034f71d90d2c9c4a76b470d78bdd557ecf8203 | Python | 2,850 | 79 | import argparse
import chardet
from rdkit import Chem
import pickle
import pubchempy as pcp
import urllib.request, json
def extract_ghs_from_pictograms(sec, hazard):
for s in sec:
if "Name" in s.keys():
if s["Name"] == "Pictogram(s)":
for items in s["Value"]["StringWithMarkup"]:... |
461c6587a9abb5957fdd117b1a4632e389575a70ff3c6832654e773b30d86531 | Python | 2,852 | 95 | import threading
import TriggerDevice
import time
class MultiChannelTriggerMonitor():
monitoring = threading.Event()
def __init__(self, device, callback=None, *args):
super().__init__()
self.device = device
self.callback = callback
self.args = args
def __del__(self):
... |
26952d69a3347bf60f335aa2e173d04df71f9fa21faeb8757f7a07b3fac334cc | Python | 2,853 | 82 | """
Generate the barrel column positiosn from the annotation and flatmap.
This file performs the following steps:
1. Generate the barrel columns based on barrels, with use of ConvexHull around the barrel and flattened positions to measure the distances.
2. Translate the positions into the symmetric hemisphere
3. Annot... |
0d25ef3a45f63001b7d26203f9385b9c544e0b4a65c525323a2e917313941607 | Python | 2,854 | 117 | """===========================
Pipeline template
===========================
.. Replace the documentation below with your own description of the
pipeline's purpose
Overview
========
This pipeline computes the word frequencies in the configuration
files :file:``pipeline.yml` and :file:`conf.py`.
Usage
=====
See ... |
144c3d9d5d815a4366f4f8f923991b5d2b7f971335107c0638c9652f6098d66f | Python | 2,854 | 85 | import sys
import os
from numpy import random
from shutil import copyfile
import logging
logging.basicConfig(format='%(asctime)s %(levelname)s %(message)s', level=logging.DEBUG)
logger = logging.getLogger('train_test_split')
def __create_files_list(src_dir):
assert os.path.isdir(src_dir), f"{src_dir} is not exis... |
5bbdfd1cdee35e86f6ee98c7e190baa44f23521e775db6cf3616b81ad693ee2a | Python | 2,854 | 69 | import os
import numpy as np
import random
import csv
from sklearn.model_selection import train_test_split
from collections import Counter
import pandas as pd
def split_dataset(label_counts, val_split=.2):
print(label_counts)
train_indices, test_indices = [], []
for i in label_counts:
shuffled_indi... |
c49e39104a48dc98627c772c24485f1dd0b9162c1d621d2e976aedb5504b68d6 | Python | 2,854 | 89 | import numpy as np
import torch
from torch import nn
from torch.utils.data import TensorDataset
import torch.nn.functional as F
import sys; sys.path.append('/Users/danil/My')
from dt_utils import Timer
from data_utils import generate_recognition_data, data_to_tensor
from neural_net_utils import Sigmoid, Cro... |
5aa2531825807044bcd4c889757a5610fa82fa864db6eb2272f6345e925b2f9b | Python | 2,856 | 87 | import os
import sys
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
import common.run_manager
import glob
import seq_des.util.canonicalize as canonicalize
import pickle
import seq_des.util.data as datasets
from torch.utils import data
import resourc... |
73274d00473b44a476190a235c5fd177f1e7e03501a3298e47d2fead700076b9 | Python | 2,856 | 69 | from langchain_core.agents import AgentAction, AgentFinish
from langchain_classic.agents.output_parsers.xml import XMLAgentOutputParser
def test_tool_usage() -> None:
parser = XMLAgentOutputParser()
# Test when final closing </tool_input> is included
_input = """<tool>search</tool><tool_input>foo</tool_i... |
c4a27684443b5a4e77137a34022c86826358313d2fa1420e4bc411da893105f1 | Python | 2,856 | 80 | # third party modules
import numpy as np
# pnabind modules
from pnabind.utils import clipOutliers
from .laplacian_smoothing import laplacianSmoothing
def wfn(dist, cutoff, offset=0, weight_method='inverse_distance', minw=0.5, maxw=1.0):
if minw >= maxw:
raise ValueError("minw must be < maxw!")
# ... |
063d15582cf81f5be28da5077dbe8f66bdeb89784109a62a5c645fc7d52be6cb | Python | 2,858 | 83 | ####################################################################################
#
# 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... |
8c468d2eb021b1d2906530cb8ad53e2208eb004bc9858bf65163da34d0168c36 | Python | 2,858 | 86 | from logging import Logger
from pathlib import Path
from typing import List
from ..data import (get_channel_specific_slice_3D, Annotation, Worldline,
AnnotationTable, WorldlineTable, get_nearby_max)
from ._utilities import default_args
@default_args("0.01, 0.08, 0.005, 0.04, False, _ACTIVE, *")
d... |
ebb96f33c46a37a6552ee4f7a0a5fd6c028513e388d50603c06d56804b8de25d | Python | 2,858 | 77 | #!/usr/bin/python3
import argparse
import concurrent.futures
import matplotlib.pyplot as plt
import os
import os.path
import re
import subprocess
argp = argparse.ArgumentParser()
argp.add_argument('--basisu', type=str, required=True)
argp.add_argument('--graph', type=str, default="basisu.png")
argp.add_argument('--et... |
ac7f9984cec45d1e588bcd703c05b4cb4c34750d24bd4c3f1f197ccf56f4d2de | Python | 2,859 | 77 | from torchmetrics import Metric
import torch
class MinMax(Metric):
def __init__(self):
super().__init__()
self.add_state("min", default=torch.tensor(torch.inf), dist_reduce_fx="min")
self.add_state("max", default=torch.tensor(-torch.inf), dist_reduce_fx="max")
def update(self,... |
c644aa0db8eeed5152ff0e1a4bab14ec83cab09468bf3747f611cd380fbb0cd2 | Python | 2,859 | 83 | ####################################################################################
#
# 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... |
e138b9d43afbc73d7d26ac6d6fc239082c25dcd92ddfe75d37eeb7f81c1002b5 | Python | 2,859 | 78 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torch
from torch import nn
def batchmul2d(input, weights, emb):
temp = input * emb.unsqueeze(1)
out = torch.einsum("bixy,ioxy->boxy", temp, weights)
return out
class FreqLinear(nn.Module):
def __init__(self, in_channel, ... |
30d0bd4bfee241218a1c2aa98ffcf62ea24f658f7987fac8b7dbc2cde69950ff | Python | 2,862 | 52 |
pdb_protein = {
'amino_acid_names': [
'ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLU', 'GLN', 'GLY', 'HIS', 'ILE', 'LEU', 'LYS', 'MET', 'PHE', 'PRO', 'SER', 'THR', 'TRP', 'TYR', 'VAL'],
'NON_STANDARD_SUBSTITUTIONS': {
'2AS':'ASP', '3AH':'HIS', '5HP':'GLU', 'ACL':'ARG', 'AGM':'ARG', 'AIB':'ALA', 'ALM... |
f984b43acf3cd94f0d597edf9f955f0270f6114d7a4d70f103872324cc5370a1 | Python | 2,862 | 91 | from __future__ import annotations
import os
from typing import Any
from pgmpy.utils._warnings import _warn_external
class BaseReader:
"""
Base class for all model file readers.
Subclasses define the class attributes ``format_name`` and ``file_extensions``, load ``path`` or ``string`` in their
cons... |
2fa0c7d43cffb9cf34862f4ab35153ed46b858ae7e6e97dd7f01be4a2b312e18 | Python | 2,863 | 75 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 24 16:46:26 2020
@author: gritti
"""
import os, glob, sys
import PyQt5.QtWidgets
from morgana.GUIs import inspection
from morgana.DatasetTools.segmentation import io as ioSeg
from morgana.DatasetTools import io as ioDT
###################################################... |
95feb71ca34317c530de41d0427f06567220ba3e6d0ede47a46632023cf8bb25 | Python | 2,863 | 79 | # This file is a part of the `allegro` package. Please see LICENSE and README at the root for information on using it.
import torch
from e3nn.o3._irreps import Irreps
from e3nn.util.jit import compile_mode
from nequip.data import AtomicDataDict
from nequip.nn import GraphModuleMixin, ScalarMLPFunction, with_edge_type... |
11f083bc7fe55fa951a96b228e079b8a9134ae685125e7e0cd3678f32637fb21 | Python | 2,864 | 74 | # This file is a part of the `allegro` package. Please see LICENSE and README at the root for information on using it.
import torch
from nequip.data import AtomicDataDict
from nequip.scripts._compile_utils import (
LMP_OUTPUTS,
single_frame_batch_map_settings,
register_compile_targets,
)
PAIR_ALLEGRO_INPUT... |
acba465be3601fa33ebc01663f7442b9c06ecd0ab31a7f855c657f0231e629b1 | Python | 2,866 | 73 | ####################################################################################
#
# 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... |
16aa1c2467c7b8854d33fd423529c5e1b1e686501aa87a9041680a4ab09c5a0e | Python | 2,867 | 73 | ####################################################################################
#
# 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... |
2d3a8c50eacd6be9a409912ae8dc5336714fb7da7b8137995f7ab673e6b940ec | Python | 2,867 | 63 | import pandas as pd
## MGI_GenePheno.rpt
mgi_gene_pheno_df = pd.read_csv('MGI_GenePheno.rpt', sep='\t', header=None)
mgi_gene_pheno_df.columns = ['Allelic Composition', 'Allele Symbol(s)', 'Allele ID(s)', 'Genetic Background',
'Mammalian Phenotype ID', 'PubMed ID (pipe-delimited)', 'MGI M... |
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