sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f8d9a9266d3a9801d042b581946d5370ce5bf29c314032d7b21d02ef278de53c | Python | 1,617 | 57 | """Implementations of key-value stores and storage helpers.
Module provides implementations of various key-value stores that conform
to a simple key-value interface.
The primary goal of these storages is to support implementation of caching.
"""
from typing import TYPE_CHECKING, Any
from langchain_core.stores impor... |
4daf18c02bc5d16a3882684e64b45fd13ce45f81e50c8f26d02f58cabf504663 | Python | 1,618 | 38 | import re
import os
from time import time
import utils.fpdf as fpdf
def get_segment_index(segment_name: str) -> int:
return int(re.search("seg([0-9]+)", segment_name)[1])
def get_cluslabel_from_figname(figname: str) -> int:
return int(re.search("waveform_clus([0-9]+).png", figname)[1])
os.makedirs("./tmp_pd... |
6c8a1414fbaec794a13b9d0ee8da47190c102f90706acc5d4ca7d89a61ae92dd | Python | 1,619 | 34 | from pdearena.data.twod.datapipes.coal2d import (
onestep_test_datapipe_coalSquare_grid,
onestep_valid_datapipe_coalSquare_grid,
onestep_train_datapipe_coalSquare_grid,
trajectory_test_datapipe_coalSquare_grid,
trajectory_valid_datapipe_coalSquare_grid,
trajectory_train_datapipe_coalSquare... |
cd560e1d4a4f5a5388326b07a45c71a09aa0580452626b4f668d4d3514579bf0 | Python | 1,619 | 63 | import numpy as np
from voxcell import VoxelData
from sys import argv, exit, stderr
from os import cpu_count
from tqdm import tqdm
from collections import defaultdict
import scipy.ndimage as nd
import flatmap_util as fmutil
argc = len(argv)
if argc < 3:
print('Usage: {} <flatmap> <flat xlen> [<ncpu>]'.format(ar... |
0b971d450b14725e52aec08f1c2f01f22f8008684a30b94b15444830b3735b2e | Python | 1,623 | 50 | import joblib
import matplotlib.pyplot as plt
from dt_utils import Timer
from plastic import PlasticNet
from plastic_gated import ThreeGateSynapse, GSP1
from recurrent_nets import VanillaRNN, LSTM, nnLSTM
from data_utils import generate_recognition_data, recog_chance
from neural_net_utils import plot_train_per... |
47f9df66f32f10bb24d8ccaa4a2d624a09d8a9056fe6a2915992587dd662575e | Python | 1,623 | 46 | import torch
import torch.nn as nn
from typing import List
from typing import Union
from typing import TypeVar
Self = TypeVar('Self', bound = 'VGG')
class VGG(nn.Module):
"""VGG-like network without a linear layer"""
def __init__(self: Self,
cfg: List[Union[int, str]],
in_c... |
40ae7c7a8d72e4e46b00d5707259edf7dcb8bc54479eb23e398847ac3f7671f7 | Python | 1,626 | 32 | import argparse
import numpy as np
def extract_peak_numbers(bed_file, bl_file):
chroms = np.loadtxt(bed_file, delimiter="\t", dtype=str, skiprows=1, usecols=0)
if bl_file is not None:
# blacklist reading specific lines
bl_chroms = np.atleast_1d(np.loadtxt(bl_file, dtype=str, usecols=0))
... |
6a0f40a8e80567512c0aa1c47da0fc37de102cee732f2a9c1aaf394da39a4098 | Python | 1,626 | 42 | from __future__ import annotations
import re
from langchain_core.output_parsers import BaseOutputParser
class RegexDictParser(BaseOutputParser[dict[str, str]]):
"""Parse the output of an LLM call into a Dictionary using a regex."""
regex_pattern: str = r"{}:\s?([^.'\n']*)\.?"
"""The regex pattern to us... |
e3210629cd9582fac4be6703838fb4c35f9fc8ad10b18f3428f2dac76cf81705 | Python | 1,627 | 43 | import numpy as np
import pandas as pd
from sklearn.neighbors import kneighbors_graph
from scipy.sparse.csgraph import dijkstra
def get_pairwise_distances(pX, return_predecessors=False, return_adjacency=False, verbose=False):
"""
Computes cells geodesic distances as shortest path in the minimal-fully-connecte... |
007d08e2c207027a4e0a12b029ae6d0430d85088d0a2f71f5fb34574487663e7 | Python | 1,628 | 51 | """Test LLMSummarization functionality."""
import pytest
from langchain_classic.chains.llm_summarization_checker.base import (
ARE_ALL_TRUE_PROMPT,
CHECK_ASSERTIONS_PROMPT,
CREATE_ASSERTIONS_PROMPT,
REVISED_SUMMARY_PROMPT,
LLMSummarizationCheckerChain,
)
from tests.unit_tests.llms.fake_llm import ... |
0a6fc00920b087447e5925be9432e549417b8866896b27ec9871c1a4abaaf0c1 | Python | 1,630 | 66 | from langchain_core.prompts import PromptTemplate
from langchain_classic.output_parsers.regex import RegexParser
output_parser = RegexParser(
regex=r"(.*?)\nScore: (\d*)",
output_keys=["answer", "score"],
)
prompt_template = """Use the following pieces of context to answer the question at the end. If you don... |
f7902b15ac367b5a781d8572899c5a1226a0f69a37b2979f7857abdab32e10b9 | Python | 1,632 | 28 | import argparse
def build_arg_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="PACE development")
parser.add_argument("--device", type=int, default=6)
parser.add_argument("--seed", help="random seed", type=int, default=3)
parser.add_argument("--split", help="train/tes... |
4c92e2f1a35e391a742ca13827a8d26624c785fe3e4e0c50102d042dbd4d4022 | Python | 1,633 | 58 | import torch as tr
# Mapping of nucleotide symbols
# R Guanine / Adenine (purine)
# Y Cytosine / Uracil (pyrimidine)
# K Guanine / Uracil
# M Adenine / Cytosine
# S Guanine / Cytosine
# W Adenine / Uracil
# B Guanine / Uracil / Cytosine
# D Guanine / Adenine / Uracil
# H Adenine / Cytosine / Uracil
# V Guanine / Cytos... |
443855683c3fc63cf2e9094fb91464a5c695f9f90a8f21c6d3dc1070f2ad74d8 | Python | 1,634 | 47 | # Copyright 2019 DeepMind Technologies Limited. 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... |
50481c1e4933cd786e2f4523b7c7160e4a9f78461ddee5c67bd9cf6f21fa8c33 | Python | 1,634 | 40 | import h5py
import numpy as np
def get_all_data_from_level_h5_rec(filename, group_name, markers, all_strings_mk, spikes, all_strings_sp):
"""
This function recursively reads data from an HDF5 file.
Parameters:
filename (str): A string that identifies the HDF5 file from which to extract data.
group... |
cf944bd01758c69eac07a303eeaeaf73b9c4a316ccae61d6ba7380436f66cc1a | Python | 1,637 | 41 | import torch
from torch.nn import Sequential, Conv2d, ReLU, MaxPool2d, AdaptiveAvgPool2d, Linear, Module, BatchNorm2d, Softmax
class CNN2D(Module):
def __init__(self, labels_number, channels_nb, depth, kersize):
super(CNN2D, self).__init__()
nconv = 16
npool = 2
layers_list = []
... |
11417533a8b2f7933eb2081876d36528e2c34851ef70955fcfe87db0925b0dc2 | Python | 1,638 | 37 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
import xarray as xr
import h5py
from tqdm import tqdm
import numpy as np
from argparse import ArgumentParser
def collect_data2h5(datapath, batch, mode, save_dir):
datals = os.path.join(datapath, "seed=*", "run*", "output.nc")
# ... |
3cb2d1f4336f05da57eb15528ea72fb9e6d576b8ae7c4428131bae71e674c3ad | Python | 1,638 | 39 | # import libraries
import ants
import numpy as np
import matplotlib.pyplot as plt
indir = '/xdisk/hutchinsone/cjoy1895/Data/Development/PM/TLobe/12_sample_dataset/ISMRM_data/'
outdir = '/xdisk/hutchinsone/cjoy1895/Data/Development/PM/TLobe/12_sample_dataset/data_analysis/1D_hist/'
maps ='PA_15-82' #map input
ext = '.n... |
932e9a18e40a5ef23679aae4729dce07d65ddf6b4c9f2edc88e95ab00f7f4731 | Python | 1,638 | 52 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class GCN(nn.Module):
def __init__(self, in_ft, out_ft, act=nn.PReLU(), drop_prob=0.0, isBias=False):
super().__init__()
self.linear = nn.Linear(in_ft, out_ft, bias=False)
# if isBias:
# self.... |
a13e5ecce372211e89410cecc298c84c162e9577cf521356f2aa12f88fa67a3e | Python | 1,638 | 68 | import os
import subprocess
from click.testing import CliRunner
from pkg_resources import resource_filename
from perses.app.cli import cli
test_yaml = """
protein_pdb: Tyk2_protein.pdb
ligand_file: Tyk2_ligands_shifted.sdf
old_ligand_index: 0
new_ligand_index: 3
forcefield_files:
- amber/ff14SB.xml
- amber/tip3p... |
b22949c4a29185b4d6d2bf2c364220af35ea1ba93abbad3a79a1f983156ddb6e | Python | 1,638 | 48 | 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.conversational.prompt import FORMAT_INSTRUCTIONS
class ConvoOutputParser(AgentOutputParser):
... |
4a7323a5b81701fe537ff9df104851cf5548bced714d299042cff1e4a03c6b60 | Python | 1,640 | 50 | import os
def getDSSP(model, dssp_map=None, feature_name='secondary_structure', formatstr="{}({})", clean=True):
try:
from Bio.PDB.DSSP import DSSP
except ModuleNotFoundError:
raise ModuleNotFoundError("The module 'Bio.PDB.DSSP' is required for this functionality!")
if dssp_map is None... |
19c52e81eb0cf9951999d8fe0075355f008739ec9868aba5ea346907d665e807 | Python | 1,642 | 53 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import math
from abc import abstractmethod
import torch
from torch import nn
def zero_module(module):
"""Zero out the parameters of a module and return it."""
for p in module.parameters():
p.detach().zero_()
return module
... |
2576c3943c7f67cde28c2e1a4b814dd94031896f400ce23c5a2f456a71889dd4 | Python | 1,643 | 32 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://w... |
15e2cdbc214a45c1526b8f47449d82915c71ddf7eeffd8b403ec25649091c3c1 | Python | 1,644 | 51 | import argparse
from dival import DataPairs
from dliplib.reconstructors import tv_reconstructor
from dliplib.reconstructors import tvadam_reconstructor
from dliplib.reconstructors import fbp_reconstructor
from dliplib.utils.helper import load_standard_dataset
from dliplib.utils.plot import plot_reconstructors_tests
... |
8cd7bf40c8d1ffe452207586a13dfd07daefd6852bca08dbbe050fbcb51c6d67 | Python | 1,644 | 47 | import argparse
from dival import DataPairs
from dliplib.reconstructors import get_reconstructor
from dliplib.utils.helper import load_standard_dataset
from dliplib.utils.plot import plot_reconstructors_tests
def get_parser():
"""Adds arguments to the command"""
parser = argparse.ArgumentParser()
parser.... |
1cebcca2dacfbb927b0ab582ec194abc487438098bf572357fd454afeef636bf | Python | 1,645 | 50 | import torch
import numpy as np
def MMD(x, y, kernel, device=None):
"""Emprical maximum mean discrepancy. The lower the result
the more evidence that distributions are the same.
Args:
x: first sample, distribution P
y: second sample, distribution Q
kernel: kernel type such as "... |
60ac7bb2d3e9026694cc0b0645ed184c48ddfed2688506889d53c516f67f265e | Python | 1,646 | 49 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os
from pdearena import utils
from pdearena.data.datamodule import PDEDataModule
from pdearena.lr_scheduler import LinearWarmupCosineAnnealingLR # noqa: F401
from pdearena.models.pdemodel import PDEModel
logger = utils.get_logger(__name_... |
c45bf7a339175d1ee6a7f1592990e25ece80297a89bb53f3fb5c7fd605a789fb | Python | 1,646 | 67 | import os
import yaml
from dlutils.layers.input_scaler import ScalingLayer
from dlutils.layers.padding import DynamicPaddingLayer, DynamicTrimmingLayer
CUSTOM_LAYERS = {
cls.__name__: cls
for cls in [
ScalingLayer, DynamicPaddingLayer,
DynamicTrimmingLayer
]
}
def get_number_of_output_ch... |
0dc20471c151489d4bbcee845ac1b49addc3e00ffc82080ce9ee6b37eca04a13 | Python | 1,647 | 53 | # Rdkit import should be first, do not move it
try:
from rdkit import Chem
except ModuleNotFoundError:
pass
import pickle
import torch.nn.functional as F
from qm9.analyze import analyze_stability_for_molecules
import numpy as np
import torch
def flatten_sample_dictionary(samples):
results = {'one_hot': [... |
1dd12592b09acbfb090008c984cb64e397fd85f4c516ce4af1ecfe651041635a | Python | 1,647 | 57 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 7 14:45:59 2021
@author: athanasiadis
"""
import numpy as np
def computer(x_data,global_vectors,gravity_centers):
"""
computer
______________________
This is a function that corrects the input dataset based on the
grav... |
c878ea840ed2c8b65023bb1ebc173c46a7bb3b6b669b73d61a0c0a4e24f82dbe | Python | 1,647 | 68 | import numpy as np
from edNEGmodel.edNEGmodel import *
from functions.initial_values import *
from functions.solve_edNEGmodel import *
import os
print('*************************************************')
print('* Computing referece simulation *')
print('*************************************************... |
ad52eaaf77ea27e17f4f0484d4f58237f8aff78b1aca964993a6b5bf69db17ba | Python | 1,648 | 70 | """
Functions to extract trajectory from a perses relative calculation
"""
import numpy as np
import mdtraj as md
from perses.analysis.utils import open_netcdf
def get_hybrid_topology(file):
""" Extracts hybrid_topology object from the .npy file generated by relative calculations
Parameters
----------... |
595d3c680d124fe2589cc78acdfa830acc49642e7a2ea2d6454d776ec7ddfbdf | Python | 1,649 | 49 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def get_mean_bucket_exp(X, pseudo, n_buckets=5, plot=False):
"""
Compute the expression profile based on bucket assignment
X - expression
bucket - bucket assignment
"""
# check pseudo is valid
if isinstance(pseudo, pd.S... |
b23b9b46f831e6326120c461c9101e469a25209aac24b3a2c2da7857b9111078 | Python | 1,650 | 38 | # pnabind packages
from .data import data
def getAchtleyFactors(structure, feature_name="achtley_factor", formatstr="{}{}"):
"""Citation: www.pnas.org/cgi/doi/10.1073/pnas.0408677102"""
atoms = structure.get_atoms()
achtley_factors = {
'A': [-0.591, -1.302, -0.733, 1.570, -0.146],
'C':... |
d21dbbd8cea7c55c97d222dc16b4fdf5f0748ccb6c3892b1f17789ef8e50061e | Python | 1,651 | 51 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# pylint: skip-file
import matplotlib
import matplotlib.pyplot as plt
from sklearn.linear_model import ARDRegression, BayesianRidge
from sklearn.linear_model import RidgeCV, LassoCV, LinearRegression
import numpy as np
from embanded.embanded_numpy import EMBanded
np.rand... |
08fa47feb97813af2c029415936a57a79a9802272798b276c5cb6ff6d1733bf9 | Python | 1,652 | 52 | from collections.abc import Sequence
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from typing_extensions import override
from langchain_classic.agents.agent import AgentOutputParser
class SelfAskOutputParser(AgentOutputParser):
"""Parses ... |
e2f1f73fb6ae2bfa1eacc82114c0319f5c003d4b2ff24e5dbc28edadfa114125 | Python | 1,652 | 41 | import numpy as np
from scipy.linalg import toeplitz
def notch_filter_cls(x, ff, gamma):
"""
A constrained least squares notch filter.
Apply a constrained least squares notch filter to an input signal.
This filter is designed to cancel mains frequency noise from a signal.
Parameters:
x (numpy... |
795390f4cfc0385b4a95ddc141c324f90cb6f50c54b2aacaadb22db417255e5b | Python | 1,653 | 89 | #!/usr/bin/env python
# coding: utf-8
# In[17]:
import pickle
import os
import yaml
from sklearn.impute import SimpleImputer
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScale... |
97f1162584ac0c09c77a5b84ce1c89450ba740fc4e5fb7e20d461b46284940cd | Python | 1,653 | 56 | import os
import requests
import argparse
import MDAnalysis as mda
from pensa import download_from_gpcrmd
def subsample(psf_file, xtc_file, out_file):
u = mda.Universe(psf_file,xtc_file)
print(len(u.trajectory))
a = u.select_atoms('all')
with mda.Writer(out_file, a.n_atoms) as W:
for ts in u.t... |
9ec615761fab1b402be0a464c6d005c39ceb20219d31e5aeedc880b751fca007 | Python | 1,653 | 44 | """This script adds marker genes for three brain regions to the pathway matrix """
import argparse
import os
import pandas as pd
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--marker_files',
nargs='*',
help='A series of fil... |
58b9ebefa2da947c1fc010c4045d7fb0ae153488a909451f498a8db13b12124a | Python | 1,654 | 68 | import wandb
import argparse
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.trainer import Trainer, seed_everything
from pytorch_lightning.loggers import WandbLogger, TensorBoardLogger
from genie.config import Config
from genie.data.data_module import SCOPeDataModule
from genie.diffusio... |
d4bb4ccdc5464ca2292213f859f05b5c84f9a61c5f226a64123d16a4d5ed1dc7 | Python | 1,654 | 50 | import torch
from torch_geometric.loader import DataLoader
from dataset.PygrMD17 import rMD17
class WeightedEnergyForcesLoss(torch.nn.Module):
def __init__(self, energy_weight=1.0, forces_weight=1.0) -> None:
super().__init__()
self.register_buffer(
"energy_weight",
torch.t... |
7630754e583f9b04d43790f52b40f6d87bebba1e08da294b50e26134ba2b97fb | Python | 1,655 | 48 | from setuptools import setup, find_packages
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setup(
name="AnnoSpat",
version="1.0.0",
author="Aanchal Mongia",
author_email="aanchal.mongia@pennmedicine.upenn.edu",
description="A tool for annotating and inferrin... |
4f768ecc0b56c7439433e44f699d055ec7b586f2bbe4cab030082dce49aaac14 | Python | 1,656 | 72 | from __future__ import annotations
import numpy as np
def Normalize(data: np.ndarray) -> np.ndarray:
return (data - data.min()) / (data.max() - data.min())
def NormalizeAxis(data: np.ndarray, axis: int) -> np.ndarray:
_min = np.min(data, axis=axis, keepdims=True)
_max = np.max(data, axis=axis, keepdims... |
4055e768acf03d5af7c139584b47c790be37bb9b4060990e9439d58a59ee9adc | Python | 1,657 | 48 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.llms import AzureMLOnlineEndpoint
from langchain_community.llms.azureml_endpoint import (
AzureMLEndpointClient,
ContentFormatterBase,
CustomOpenAIContent... |
7dedaef4ad86875bb9b8a2b8f48bda829cb3cbe244fe9015366ebfb7faba93f7 | Python | 1,658 | 63 | import numpy as np
from voxcell import VoxelData
import sys
from scipy.ndimage import distance_transform_edt
def is_background(array, val = np.nan):
return np.isnan(array) if np.isnan(val) else (array == val)
# CLI interface
rdepth_nrrd = sys.argv[1]
thresh = float(sys.argv[2])
side = sys.argv[3]
if side not ... |
9334a48590a0e112451bcf42e3356ee3845de9816e3300cc74cd293f6b9b62c6 | Python | 1,658 | 48 | import torch
import torch.nn as nn
from typing import List
from typing import Union
from typing import TypeVar
Self = TypeVar('Self', bound = 'VGG_CNNC')
class VGG_CNNC(nn.Module):
"""VGG-like network including a linear layer"""
def __init__(self: Self,
cfg: List[Union[int, str]],
... |
55315ca47ac88bbf001201cef839a9cbac7d1f756ce4bf4405a63ae86d03fb26 | Python | 1,659 | 50 | # 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... |
8445148190a8030e79bd2add81caf660e5521802045bce7565c9388d611ca313 | Python | 1,659 | 49 | import numpy as np
from dival import TaskTable
from dival.measure import PSNR, SSIM
from dliplib.reconstructors import learnedpd_dip_reconstructor
from dliplib.utils import Params
from dliplib.utils.data.datasets import CachedDataset
from dliplib.utils.helper import select_hyper_best_parameters, load_standard_dataset
... |
2991ec29f6af474463448825b1e0aeb4403384e109c491643e2d30507b99f870 | Python | 1,660 | 46 | """Setup script for uploading package to PyPI servers."""
from setuptools import setup, find_packages
tests_require = [
'pytest',
'numpydoc'
]
docs_require = [
'Sphinx', # TODO: maybe numpydoc?
]
with open('requirements.txt') as f:
install_requires = [l.strip() for l in f]
setup(
name='vulcanai... |
2c2bf478a7fdec07da6e4f4c50b3ba3c071405fe15b9db03bdc4c9f381cf5b51 | Python | 1,660 | 41 | import numpy as np
import pandas as pd
# Import xlsx file
df = pd.read_excel('data/PXD015912/Mapping_file_PXD015912.xlsx', sheet_name='PXD015912')
matrix = pd.read_csv('data/PXD015912/Peptide_intensity_matrix_b9369842-f3cf-4383-9e5c-6734dadcfbc9.csv', sep='\t')
matrix.index = matrix.loc[:, 'Peptide'].to_numpy()
# 18... |
4d73f776e2e8fbe867cbd42d85c0c5d36e9181873436d6133383efa3e7a74b9e | Python | 1,661 | 32 | import numpy as np
from src.PID import compute_store, format_data
def get_fullorder_FF(model_PID, activations, args):
measures = compute_store.compute_discrete_measures(activations['inputs'], activations['layers'][0], args)
model_PID = compute_store.store_measures(model_PID, args, 'x2h_', measures, True)
... |
751ae2cb8789da36e71b1e48128e749b64d4513a4b9234a3208917faae30c146 | Python | 1,662 | 59 | import logging
import os
import hydra
import pandas as pd
from omegaconf import DictConfig, OmegaConf
from tqdm import tqdm
from src import PROJECT_DIR
log = logging.getLogger(__name__)
@hydra.main(
config_path=os.path.join(PROJECT_DIR, 'configs'),
config_name='find_best_epoch',
version_base=None,
)
de... |
9d6c514d0a0fff298262c7b11ac038be3f4dfae2a367c16f5123fc8669f22bbd | Python | 1,663 | 44 | import torch
from torch import nn
import torch.nn.functional as F
import torch.nn as nn
class RecurrentNetwork(nn.Module):
"""
Recurrent network for neurogym experiments. Stores activations for easy extraction.
Uses leaky relu activation for continuous measures
"""
def __init__(self, input_size, o... |
ce2e4bd872ba753b5ace9dbe912e3adaea5e59fc436659397eae19acf123135a | Python | 1,663 | 51 | import glob
import tensorflow as tf
import numpy as np
def return_single_result(non_linear, nonlinear_cut_model, agg, data_dir, num_folds):
model_name = "model_non_linear" + \
str(non_linear) + '_'+str(nonlinear_cut_model)
cv_summary = []
for path in [glob.glob(data_dir+"/"+model_name+'_fold'+... |
2aaac306ec3fefc63a5eefcba582442bb9fa4153a5a3d3a5eeed5bcee89a873d | Python | 1,664 | 52 | from typing import Literal
from langchain_core.agents import AgentAction
def _escape(xml: str) -> str:
"""Replace XML tags with custom safe delimiters."""
replacements = {
"<tool>": "[[tool]]",
"</tool>": "[[/tool]]",
"<tool_input>": "[[tool_input]]",
"</tool_input>": "[[/tool... |
44ad208846f3e1dcdaf01db73e0258c182730e74829107bb60bcffbd4a785cf2 | Python | 1,664 | 59 | import argparse
import numpy as np
from dt_utils import Timer
from hebbian import LearnedSynHebb
from data_utils import generate_recognition_data
#%%
parser = argparse.ArgumentParser()
parser.add_argument('--R', type=int)
parser.add_argument('--T', type=int)
parser.add_argument('--batchsize', type=int)
pa... |
aa67514a26841dc8d5f812a1edc674bea57cf248be1cd96b4333f350157e4839 | Python | 1,664 | 56 | """Criteria or rubric based evaluators.
These evaluators are useful for evaluating the
output of a language model or chain against
specified criteria or rubric.
Classes
-------
CriteriaEvalChain : Evaluates the output of a language model or
chain against specified criteria.
Examples:
--------
Using a predefined crit... |
1b6c526bfebf8c0a068e2555baae4f4021175e2388aaf637392ecf6c954396ed | Python | 1,665 | 51 | """Tests for FlareChain.from_llm preserving supplied ChatOpenAI instance."""
from typing import cast
import pytest
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_core.runnables import RunnableSequence
from langchain_classic.chains.flare.base import Fl... |
23805acbe8463fefa6b30a7eb7e1bb1d5899ff1e17e6da9789b0ecd9cf07479e | Python | 1,666 | 52 | import numpy as np
from dival import TaskTable
from dival.measure import PSNR, SSIM
from dliplib.reconstructors import learnedpd_dip_reconstructor
from dliplib.utils import Params
from dliplib.utils.data.datasets import CachedDataset
from dliplib.utils.helper import select_hyper_best_parameters, load_standard_dataset
... |
404ef39e6dae643e7e0f155e4da6f56b851fa2d6633ed8b1566e60ef0c321f27 | Python | 1,667 | 26 | from batchgenerators.utilities.file_and_folder_operations import *
import numpy as np
if __name__ == '__main__':
input_file = '/home/xuesheng3/liuyuchen/Data/nnSegnext_raw_data_base/nnSegnext_preprocessed/Task004_BrainTissueIBSR/nnSegnextPlansv2.1_plans_3D.pkl'
output_file = '/home/xuesheng3/liuyuchen/Data/nnS... |
ea9e59964945d4bc56c62d34ef30f5657b0c2e1e64e7a43da823a44f0c35e96f | Python | 1,667 | 41 | import sys
import scipy.stats as stats
control1_1={}
control2_1={}
control1_2={}
control2_2={}
list1=[]
list2=[]
list4=[]
#################The script is for comparing the genes between GSCs samples and astrocyte samples.
rsemdir='/scratch/users/txie/RNA-seq/3rsem_out/'
f=open(sys.argv[1].replace("neoloop95... |
0802cc9f9a588d4f0be1cb4fba478e6250e3c31708175e3fffbb5be26aeac187 | Python | 1,669 | 60 | import torch
def get_rotation_frames(coords):
"""
Returns a local rotation frame defined by N, CA, C positions.
Args:
coords: coordinates, tensor of shape (batch_size x length x 3 x 3)
where the third dimension is in order of N, CA, C
Returns:
Local relative rotation frames in... |
8efdc74745c6ab64b2c64557c7a48652a15ac88a1e2fa2f76cb60398f1a1e116 | Python | 1,669 | 35 | import mne
from config_FaceName import Results_data_path,Ids,MEG_data_path,group_name,MRI_data_path,stats_conds_list,stats_comparisions
from custom_functions import do_cluster_permutation_1samp_test
from functools import partial
import os.path as op
bands = dict(theta=[4, 7],alpha=[8, 13],beta=[14, 30],gamma=[40, 90])... |
b36b703a38374c8753629a00b56a4b3e227c4d15a09255f119f2448a6b2552d8 | Python | 1,670 | 50 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders.parsers.pdf import (
AmazonTextractPDFParser,
DocumentIntelligenceParser,
PDFMinerParser,
PDFPlumberParser,
PyMuPDFParser,
... |
b9199b55108632ae822592404855e8d988aba232d56be9eef7664c1d645d2bfb | Python | 1,670 | 48 | from torch import nn
import SimpleITK as sitk
import torch
from nnsegnext.utilities.to_torch import maybe_to_torch, to_cuda
from nnsegnext.utilities.tensor_utilities import sum_tensor
def CannyEdgeDetectionImage(image,LH,UH,Var):
canny_op = sitk.CannyEdgeDetectionImageFilter()
canny_op.SetLowerThreshold(LH)
... |
fcee402966b6f03d464c3e193a2a16feefc1e5faa2deeb8c1f2b0410bbbedfa2 | Python | 1,671 | 40 | import sys
import warnings
from pathlib import Path
_PACKAGE_DIR = Path(__file__).resolve().parents[1]
# Leave tests outside the skipped prefixes so warnings identify the test caller.
_SKIP_FILE_PREFIXES = tuple(str(path) for path in _PACKAGE_DIR.iterdir() if path.name != "tests")
class ExperimentalWarning(UserWarni... |
3f42803316bb464c9a8914740cb408d2fcd4d869f93c9565d82033b68de32402 | Python | 1,672 | 51 | """One adapter, three transports.
`MCPAdapter` infers the transport from whatever you hand it, so the only thing
that changes between an in-process server, a local script over stdio, and a
remote URL is the target itself.
uv run examples/mcp/transports.py
"""
from __future__ import annotations
import asyncio
fr... |
1146407e749c105df87b07ed4273f9a15d64bb4ea4d793a7dc56b11672b5a657 | Python | 1,674 | 60 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
_base_ = [
'../_base_/models/upernet_convnext.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py... |
efb48c5157d200baab96378441e7e29971c7e47a9d81d5f194d763ea7b903310 | Python | 1,675 | 50 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_transformers import (
EmbeddingsClusteringFilter,
EmbeddingsRedundantFilter,
get_stateful_documents,
)
from langchain_community.document_tran... |
57aecd94415016725321945a6282593fc269bb6c129361d50f58995d6411cd5c | Python | 1,677 | 43 | #!/usr/bin/python
import sys
import json
import hashlib
import os
import shutil
import subprocess
def md5sum(filename, blocksize=65536):
hash = hashlib.sha1()
count = 1
with open(filename, "rb") as f:
# for block in iter(lambda: f.read(blocksize), b""):
# hash.update(block)
whi... |
687dfcdff94de5f435233d9772c3f90dc6e2b2f72c95169785219032bbde2221 | Python | 1,677 | 47 | # pnabind modules
from .data import data as D
def isAA(resname, regexes):
if regexes['PROTEIN']['STANDARD_RESIDUES'].search(resname):
return True
if resname in D.chem_components:
ccd = D.chem_components[resname]
if '_chem_comp.mon_nstd_parent_comp_id' in ccd:
parent = ... |
88fb75a54165d1ea22232ee85aa5608d4174d59592fdceab16876c25ef16a875 | Python | 1,677 | 59 | import warnings
from types import SimpleNamespace
from bernn.dl.train.train_ae_classifier_holdout import TrainAEClassifierHoldout # [`bernn.dl.train.train_ae_classifier_holdout.TrainAEClassifierHoldout`](bernn/dl/train/train_ae_classifier_holdout.py)
# 1) Minimal args — let TrainAE fill the rest with safe defaults
a... |
e6e1e8e940a874987a03b07e6501df5869789507e374b1f5d9214b359215646a | Python | 1,679 | 60 | from pytorch_lightning import LightningModule
import torch
import numpy as np
from torch.nn import MSELoss
class FCNNModel(LightningModule):
def __init__(
self,
input_dim: int,
output_dim: int = 1,
reduction: float = 3,
activation="ELU",
lr: float = 1e-3,
):
... |
f8dd08d6709fb80d5038d7f7adc5c92afad82f77138f06bd6b51fa055e7bc08e | Python | 1,679 | 61 | import os
import requests
# -- Functions to download from online repositories
def download_from_gpcrmd(filename, folder):
"""
Downloads a file from GPCRmd.
Parameters
----------
filename : str
Name of the file to download.
Must be a file that is in GPCRmd.
fo... |
f923d1368d5b80f63d45a643abbee27f701a59b955630f6de4cb47f12088ebe1 | Python | 1,680 | 48 | import torch
from torch_geometric.data import InMemoryDataset,Data
from os.path import join, isfile
from os import listdir
import numpy as np
import os.path as osp
from imports.read_abide_stats_parall_behavior import read_data
class ABIDEDataset(InMemoryDataset):
def __init__(self, root, name, transform=None, pre... |
6df840f29e6b4dd4d028239bd60e6e7a312ffd1840ef0f68860bce613957a9f9 | Python | 1,681 | 67 | import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 150)
disease_search_term = 'Kidney' # no spaces allowed
hgnc_genes_series = pd.read_csv('../../exac-broadinstitute/all_hgnc_genes.txt', header=None).loc[:, 0]
full_df = pd.read_csv('GTEx_Analysis_2016-01-15_v7_RNASe... |
800f0eef87ffc1a41b13742f1b1359281814aadc14cbf77836cfcf445bc8b0db | Python | 1,681 | 54 | import os
import argparse
import MDAnalysis as mda
from pensa.preprocessing import download_from_gpcrmd
def subsample(psf_file, xtc_file, out_file):
u = mda.Universe(psf_file, xtc_file)
print(len(u.trajectory))
a = u.select_atoms('all')
with mda.Writer(out_file, a.n_atoms) as W:
for ts in u.tr... |
498e78d9c5628736bca57cd1fbf1be2ccdd2e151ff73a1a18aee2ab65f539480 | Python | 1,683 | 61 | #!/usr/bin/env python3
"""Copy the openff-toolkit examples suite to a local directory"""
import argparse
from os import environ
from pathlib import Path
from shutil import copytree, ignore_patterns
class NoExamplesLibraryError(Exception):
"""Slightly more descriptive error for when the examples library is not fo... |
5446d54cc99c6b8e3bdd98fc04babf241e33fbe8c9ad25881772794ace9c037c | Python | 1,683 | 65 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import os.path as osp
from setuptools import find_packages, setup
# with open("README.md", encoding="utf-8") as fh:
# long_description = fh.read()
long_description = ""
exec(open(osp.join(osp.dirname(__file__), "pdearena", "version.py")).re... |
57642aca80a8474f3d112a42d8cd19bd878327114e4287182d304b83758496d6 | Python | 1,684 | 73 | import torch
from torch import nn
from genie.model.template import get_template_fn
class PairFeatureNet(nn.Module):
def __init__(self, c_s, c_p, relpos_k, template_type):
super(PairFeatureNet, self).__init__()
self.c_s = c_s
self.c_p = c_p
self.linear_s_p_i = nn.Linear(c_s, c_p)
self.linear_s_p_j = nn.... |
97b1c5247c46139ad4a76a34db42472032dd327f1b2bc7ababb82e2a7c23bc12 | Python | 1,684 | 59 | #%%
'''
This script is for training the proposed Sinc model. Before using it the marked lines corresponding the training and validation datagenerators should be altered.
'''
import os
import numpy as np
import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras.callbacks import EarlyStopping
fr... |
adb5c975c6a60239492678034cb66e536165760e2ca05b0326e355f92259dcb2 | Python | 1,684 | 62 | import os
import matplotlib.pyplot as plt
import torch
from bindsnet.analysis.pipeline_analysis import MatplotlibAnalyzer, TensorboardAnalyzer
class TestAnalyzer:
"""
Sanity checks all plotting functions for analyzers
"""
def test_init(self):
ma = MatplotlibAnalyzer()
assert plt.isi... |
e104898c89ab10a25913bde6285729d8a2220468ac1b063f489e6a1fe60826e2 | Python | 1,686 | 55 | # For this you need matlab and the new requirements.txt
import argparse
import matlab
import matlab.engine
import scipy.io
from oset.ecg.peak_detection.peak_det_matched_filter_robust import (
peak_det_matched_filter_robust,
)
import unit_test as testing
mat = scipy.io.loadmat("../../../datasets/sample-data/Sample... |
5eb4f366569997be6b2402cb03f7c9403bb1ed2d45d6e44779b8597043eea43b | Python | 1,687 | 50 | import os
# High-level locations
REPO_LOC = os.path.dirname(os.path.dirname(__file__))
DATA_LOC = os.path.join(REPO_LOC, "data")
OUT_LOC = os.path.join(REPO_LOC, "out")
# Data locations
WORKFLOW_SOURCES = os.path.join(DATA_LOC, "workflow_sources.txt")
TOOL_SOURCES = os.path.join(DATA_LOC, "tool_sources.txt")
RAW_GALA... |
86956f5dd98717baa0f7b7c95618ab0c479512b553013aac905bb12e44f62c82 | Python | 1,687 | 41 | # conding: utf-8
from pathlib import Path
SCRIPT_DIR = Path(__file__).parent.resolve()
GIT_DIR = SCRIPT_DIR.parent.resolve()
TOOL_DIR = GIT_DIR.joinpath("tool").resolve()
TEMPLATE_NO_TEST = "|[`{tool_file_name}`]({tool_file_path_from_script_dir})|no test|"
TEMPLATE_EXISTS_TEST = "|[`{tool_file_name}`]({tool_file_path... |
8870dcecf0876c7edff6f3f73fd341e302e4a9bfd38f6a73737e10c0e438f2aa | Python | 1,687 | 59 | #! /usr/local/bin/python
#'#################################################################################
#'#################################################################################
#' Train autoencoder from CNN network
#'#################################################################################
#'#... |
09117d6b8629831d4e0c4df53a584f1a70361753cd520b6813d58bd3a1bd96a3 | Python | 1,689 | 50 | from typing import Any
from typing import Callable
from typing import Dict
from typing import Optional
import torch.utils.model_zoo
from .cornet_r import CORnet_R
from .cornet_rt import CORnet_RT
from .cornet_s import CORnet_S
from .cornet_z import CORnet_Z
from ptrnets.utils.config import load_state_dict_from_model_... |
159fd7ad2b139d5ee97665d79cf572c0d9963c60df769b98c66cdfcc6cc65533 | Python | 1,690 | 62 | import os
import pandas as pd
import matplotlib.pyplot as plt
import re
from collections import OrderedDict
import numpy as np
def remove_duplicates_and_convert_npy(val_list):
tmp_dist = OrderedDict()
for ele in val_list:
tmp_dist[ele[0]] = ele[1:]
val_list = []
for k, v in tmp_dist.items():
... |
24d12d0d63ef1e6c3ca11c929e986da55f307cc54f772097bfaa6dca58bcfbf3 | Python | 1,692 | 39 | from omicscope.cli import main
def test_main():
assert main([]) == 0
class TestInput(object):
def test_Progenesis(self):
from omicscope.Input import Input
ctrlWT = Input('tests//data//proteins//progenesis.csv', Method='Progenesis')
ctrlNone = Input('tests//data//proteins//progenesis.... |
9f9af32238dfb0ef1227b1011e6315058a7201a79449cc690be2863ea6400ef6 | Python | 1,692 | 51 | # Batch acquisition of protein sequence, pocket sequence, and absolute position information of pockets
import os
import pandas as pd
aa_codes = {
'ALA':'A', 'CYS':'C', 'ASP':'D', 'GLU':'E',
'PHE':'F', 'GLY':'G', 'HIS':'H', 'LYS':'K',
'ILE':'I', 'LEU':'L', 'MET':'M', 'ASN':'N',
'PRO':'P', ... |
d43630e2f66053dde18e2b6da530246b1cc2b659d0325f4747f11639eb431ea7 | Python | 1,692 | 81 | from sys import path
from pathlib import Path
import mne
study_name = "age-prediction-benchmark"
# On drago
N_JOBS = 80
bids_root = Path("/storage/store2/data/TUAB-healthy-bids-bv")
#deriv_root = Path("/storage/store3/derivatives/TUAB-healthy-bids2")
deriv_root = Path("/storage/store3/derivatives/TUAB-healthy-bids3")... |
8317da5c8906d88d1e236339867bdeceb7b870d3ac6ef358e1743ac804b71439 | Python | 1,693 | 49 | import numpy as np
from dival import TaskTable
from dival.measure import PSNR, SSIM
from dliplib.reconstructors import learnedpd_dip_reconstructor
from dliplib.utils import Params
from dliplib.utils.helper import select_hyper_best_parameters, load_standard_dataset
from dliplib.utils.reports import save_results_table
... |
ba5951c2829f3b0eb724b27e66189b5d43d0c9762b19a76f1bfbc0da7add5bab | Python | 1,693 | 55 | import torch
import torch.nn as nn
import numpy as np
class LSTM(nn.Module):
def __init__(self, input_size, hidden_size):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.lstm = nn.LSTM(input_size=self.input_size, hidden_size=self.hidden_size, num_... |
e794d4d42d74801ddfb2efe273b32a266ff7cefdc37ccd5bb3057c10914934ad | Python | 1,694 | 56 | __all__ = ["LoggingCallbackHandler"]
import logging
from typing import Any
from uuid import UUID
from langchain_core.exceptions import TracerException
from langchain_core.tracers.stdout import FunctionCallbackHandler
from langchain_core.utils.input import get_bolded_text, get_colored_text
from typing_extensions impor... |
5b2886d7d3b1333ca9ae6c0554aec8087557348fa54d9397a03cfb36b858b43b | Python | 1,695 | 54 | import argparse
from dival import DataPairs
from dliplib.reconstructors import tvadam_reconstructor
from dliplib.reconstructors import diptv_reconstructor
from dliplib.reconstructors import fbp_reconstructor
from dliplib.utils.helper import load_standard_dataset
from dliplib.utils.plot import plot_reconstructors_test... |
c4fa4b2120af7054f91a57aad0a91919f85d148cd0b478cf364cff887ed45338 | Python | 1,695 | 42 | #!/usr/bin/env python
import os
import tempfile
from joblib import Parallel, delayed
class CombinedBAMs:
def __init__(self, bams, chromosomes):
self.bams = bams
self.chromosomes = chromosomes
def __enter__(self):
self.bam = tempfile.NamedTemporaryFile('rb', suffix='.bam')
... |
cacb0f53ccf601ca7e24abaea7318215610999b0e692a518428bd2190f79b66a | Python | 1,695 | 73 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 26 17:28:57 2017
@author: chiara
"""
import numpy as np
from scipy.io import loadmat
from scipy.stats import norm
import pandas as pd
filename = 'fit.mat'
mat = loadmat(filename)
param_pop = mat['param_pop'][0]
Covb = mat['stats']['c... |
eeac5384dfe8776a41c63a28a6a2cc72e36b8e7947af967044e8726b2c5cc3dc | Python | 1,695 | 83 | """Test the public API of the tools package."""
from langchain_classic.vectorstores import __all__ as public_api
_EXPECTED = [
"AlibabaCloudOpenSearch",
"AlibabaCloudOpenSearchSettings",
"AnalyticDB",
"Annoy",
"AstraDB",
"AtlasDB",
"AwaDB",
"AzureCosmosDBVectorSearch",
"AzureSearch... |
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