sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
60f80138fd762827433aefe5db35da018137f632c23f0c7a8927ac7cf3ac04f3 | Python | 3,424 | 103 | import torch
from bindsnet.network import Network
from bindsnet.network.nodes import (
AdaptiveLIFNodes,
IFNodes,
Input,
LIFNodes,
McCullochPitts,
Nodes,
SRM0Nodes,
)
class TestNodes:
"""
Tests all stable groups of neurons / nodes.
"""
def test_init(self):
network... |
62dffaefb4d0c456cbd46cfedc55c6edcb62c8958a6664a4dd447a94ea987a60 | Python | 3,424 | 103 | targetMean = 8
targetStd = 1.5
initStep = 0
import pandas as pd
resultsMean = []
resultsStd = []
for i in range(40):
base_name = 'R:/Temp/Threads16/Thread'+str(i)+'/Simulation/output/Data.csv'
df = pd.read_csv(base_name)
if df.iloc[0,0]>1000:
resultsMean.append(0)
else:
resultsMean.app... |
a04ebab5ceed331a2c3202f65359f358c4b8fea8d23214e6a4371973d346977a | Python | 3,425 | 112 | # 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... |
f32b4b816d701347dd3249c71b35640fa9c968b2829d754dc8944fe6f2931bb7 | Python | 3,425 | 87 | import torch
import torch_geometric
from torch_geometric.data import Data as PyGData
from torch_geometric.utils import dense_to_sparse, to_dense_batch, to_dense_adj
from utils.tensor_operator import tensor2array, GraphTensorPacking
from utils.tensor_operator import make_one_hot
# ATOM_ENCODER = {'C': 0, 'N': 1, 'O':... |
b9febb2128813800c3c045ff84a8d0e216298c7e34f32ecf659c0e35a07dced0 | Python | 3,426 | 93 | import os
os.nice(19)
import numpy as numx
import pydeep.rbm.model as MODEL
import pydeep.rbm.trainer as TRAINER
import pydeep.rbm.estimator as ESTIMATOR
import pydeep.misc.visualization as VISUALIZATION
import pydeep.misc.io as IO
import pydeep.misc.numpyextension as npExt
import mkl
import scipy.io as sio
mkl.set_nu... |
55b8faf658b0558ff44bd2fabdd6c0e8b09444ded40a11ae1cc33dcf35fea09b | Python | 3,431 | 104 | import matplotlib
matplotlib.use('agg')
import sys
from mantis_ml.modules.pre_processing.eda import EDA
from mantis_ml.config_class import Config
class EDAWrapper:
def __init__(self, cfg):
self.cfg = cfg
self.high_corr_thres = self.cfg.high_corr_thres
self.test_size = self.cfg.test_siz... |
cdd8cea39030300d84491b557c9f12beb39007361c9d2badc7760e817cb81f1d | Python | 3,431 | 100 | import warnings
from enum import Enum
from typing import Any
from typing import Callable
from typing import Dict
from typing import Optional
from typing import Union
import numpy as np
from neuralpredictors.measures.np_functions import corr
from torch.utils.data import DataLoader
from .predictions import get_model_pr... |
9666ea2bd7897544c7d87fedc4e4648cd45bad73b0137d239f1d740467b3fa70 | Python | 3,432 | 94 | # Copyright (c) 2008-2026 the MRtrix3 contributors.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Covered Software is provided under this License on an "as is"
#... |
e4b3d00a155ae1f15407634318a49910a9c05b25c9c415fac435951a236480c3 | Python | 3,432 | 162 | import pytest
import yaml
from unittest.mock import patch, mock_open
from animalai.envs.arena_config import (
Vector3,
RGB,
Item,
Arena,
ArenaConfig,
constructor_arena,
constructor_item,
)
item_yaml = """
!Item
name: Wall
positions:
- !Vector3 {x: 13.3, y: 0, z: 33}
- !Vector3 {x: ... |
1709586ff10e2c7af7b9470ebffcc53dca5ed8a49471b11a2b2fc5069341af91 | Python | 3,435 | 74 | # Script copied from https://github.com/flyyufelix/DenseNet-Keras/blob/master/custom_layers.py
from keras.engine import Layer, InputSpec
try:
from keras import initializations
except ImportError:
from keras import initializers as initializations
import keras.backend as K
class Scale(Layer):
'''Custom Laye... |
3881c342cc2b528efa03935bfb5c12663f03e2aa0ec667458e1fa82115af95ac | Python | 3,435 | 121 | #!/usr/bin/env python
import h5py
import sys
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.widgets import RadioButtons
import numpy as np
import sys
W_JUST_HIT_ELEM = 1
def intensity(complexEField : np.ndarray):
"""
Calculate the intensity from the electric field components.
"""
... |
e9c654ec9c7b2bc6248f3be708a62af0b32bba9f46a7b8e72e086d0d5b9c366e | Python | 3,435 | 117 | #!/usr/bin/env python3
"""
Smooth one or more labels within an integer atlas volume
Usage
----
smooth_labels.py -i <input label image> -o <output label image> [label numbers]
smooth_labels.py -h
Example
----
>>> smooth_labels.py -i atlas.nii.gz -o atlas_smooth_5.nii.gz 5 10 11
Authors
----
Mike Tyszka, Caltech Brain... |
1a69a13cb778a6c8b74e08b53318560c8d2300be177f59f864ca25bcee9dfc5f | Python | 3,436 | 105 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import numpy as np
import nibabel as nib
import os
DESCRIPTION = 'Swap the dimensions of acquired post-mortem dMRI data to resemble MNI space. Cornelius Eichner 2020'
np.set_printoptions(precision=2)
def buildArgsParser():
p = argparse.ArgumentP... |
c9d9ecddab9baeb3f7593250c04554acb40141e9ec59b76fad01b3b922955e71 | Python | 3,438 | 97 | import os
import sys
import time
import torch
import torch.nn
import argparse
from PIL import Image
from tensorboardX import SummaryWriter
from validate import validate
from data import create_dataloader
from earlystop import EarlyStopping
from networks.trainer import Trainer
from options.train_options import TrainOpt... |
ffccafaa09954fdd75a1d9b2de286a527c9714e32b6c272276b9982a74f9d920 | Python | 3,439 | 109 | """
Test that the examples in the repo run without errors.
"""
import pathlib
import re
import subprocess
import tempfile
import textwrap
import pytest
from openff.toolkit._tests.utils import _get_readme_path
from openff.toolkit.utils import RDKIT_AVAILABLE, get_data_file_path, temporary_cd
def run_script_file(fil... |
58e65e4310550889e752597257f435ceaaa15eb37f3d7394fa11ec315dff092e | Python | 3,440 | 83 | 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, load_standard_dataset
from dliplib.utils.reports import save_results_table
from dliplib.utils.weights im... |
8479563c0e56727c678ee2cb319e6350b70fc3f687f0acd7287e55d5554cb941 | Python | 3,440 | 85 | import sys
sys.path.append(".")
import argparse
import gzip
import pickle
import re
import io
import tarfile
from tqdm import tqdm
from mat2seq import CIFTokenizer
PATTERN_COMP = re.compile(r"(data_[^\n]*\n)", re.MULTILINE)
PATTERN_COMP_SG = re.compile(r"(data_[^\n]*\n)loop_[\s\S]*?(_symmetry_space_group_name_H-M[^\n]... |
37746c8ffa842e78d1baafb430a94db34eec6109f4a7357c5734d1a8b2a223b1 | Python | 3,441 | 108 | """Test formatting functionality."""
import pytest
from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
from langchain_core.documents import Document
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
ChatMessage,
ChatMessageChunk,
FunctionMessage,
Fun... |
48cd6b4bb72cc15c21a1d1331fb17e2cc22701d8f1694128707af7da15972cf8 | Python | 3,443 | 102 | """Collection of functions to use MCMC methods on MEG data with forward network model
-- uses cost (to find likelihood) and prior functions"""
import numpy as np
import math
from ..inverse import cost
from ..inverse import priors
from ..forward import runforward as rf
from ..inverse.cost import pearson_cost
# TO DO: ... |
9b46b478080db81f5397fb59661e9e59734c34176ab5e26e56ff9ff83b58ada8 | Python | 3,443 | 60 | """General-purpose test script for image-to-image translation.
Once you have trained your model with train.py, you can use this script to test the model.
It will load a saved model from '--checkpoints_dir' and save the results to '--results_dir'.
It first creates model and dataset given the option. It will hard-code ... |
be583593f7cf877675d8f887b70f7fb8741a6897f240626db7aab88b373b6d12 | Python | 3,445 | 96 | import numpy as np
def _rotation_matrix(axis, angle):
"""
This method produces a rotation matrix given an axis and an angle.
"""
axis = axis/np.linalg.norm(axis)
axis_squared = axis**2
cos_angle = np.cos(angle)
sin_angle = np.sin(angle)
rot_matrix_row_one = np.array([cos_angle+axis_squa... |
5d1f4db2f1f08f55500ad47ba124294088ebdb6117b36d5f9654604c3942abd4 | Python | 3,446 | 95 | import math
import torch
import torch.nn as nn
class GaussianSmearing(nn.Module):
def __init__(self, start=0.0, stop=5.0, num_gaussians=50):
super().__init__()
offset = torch.linspace(start, stop, num_gaussians)
delta = offset[1] - offset[0]
self.coeff = -0.5 / delta.item()**2
... |
80a24a00d62f384e80a00429e651704464efe6e055475975e50b1cfe49a9a439 | Python | 3,446 | 88 | """Iterate-N accumulation, the PURE aggregation of per-pass findings.
DeepMapper runs the ``arrange -> train -> attribute`` loop N times and accumulates
per-feature findings into robust, stability-scored attributions. These functions
turn a list of per-pass results into the final ranking + stability statistics.
No tor... |
996789425576b2479461dff9e48049d09a039c07484cd2595bed4b784d84e4d3 | Python | 3,446 | 107 | import torch
from torch_geometric.data import InMemoryDataset, Data
from torch_cluster import radius_graph
class PreprocessAcAc(InMemoryDataset):
def __init__(
self, pyg_dataset, cutoff, transform=None, pre_transform=None, pre_filter=None
):
self.dataset = pyg_dataset
self.cutoff = cut... |
bdf36ab6e3c249d687c9abb391c9f5f9ef48c858728d8d6235b8633c814fd081 | Python | 3,446 | 88 | import numpy as np
from voxcell import VoxelData
from os import cpu_count
import sys
from tqdm import tqdm
import scipy.ndimage as nd
import flatmap_util as fmutil
flatmap_nrrd = sys.argv[1]
pixel_res = int(sys.argv[2])
output_nrrd = sys.argv[3]
ncpu = cpu_count() // 2
if len(sys.argv) > 4:
ncpu = int(sys.argv... |
8296f3a1a30936088da2d166deaef9ab128a97406b22e440c381cd3bde3370ff | Python | 3,447 | 107 | import torch
from torch_geometric.data import InMemoryDataset, Data
from torch_cluster import radius_graph
class Preprocess3BPA(InMemoryDataset):
def __init__(
self, pyg_dataset, cutoff, transform=None, pre_transform=None, pre_filter=None
):
self.dataset = pyg_dataset
self.cutoff = cut... |
eb022b74a1575b9325d6815cab057a7d06c7a6c4a553d68857e3cb05ee724a53 | Python | 3,447 | 114 | #!/usr/bin/env python3
"""
Create a binary inclusive mask, including certain labels (brain areas), using a particular threshold (for the probabilistic atlas)
Usage
----
create_mask.py -i <probabilistic atlas image> -o <output mask image> -t <threshold> [label numbers]
create_mask.py -h
Authors
----
Wolfgang M. Pauli,... |
d4dc97c721eac2a768301707804971de279741595d7e30fa97474348e499680f | Python | 3,448 | 79 | import numpy as np
import os
import nevergrad as ng
import matplotlib.pyplot as plt
from scipy.signal import find_peaks
from astropy.convolution import convolve, Gaussian1DKernel, Box1DKernel
from Analysis.dataloader import dataloader_AP
experimentalist = 'Elodie'
neuron = 'MSN_In_Vitro'
path = '/users/gvignoud/Docu... |
5283c6a6b0b15aface47a937cb54b32dfd54c932d6529fec0397a670bb118177 | Python | 3,450 | 99 | from collections.abc import Hashable
import numpy as np
from pgmpy.structure_score._base import BaseStructureScore
class LogLikelihoodGauss(BaseStructureScore):
r"""
Log-likelihood structure score for Gaussian Bayesian networks.
This score evaluates a continuous Bayesian network structure by fitting a ... |
93a7d59b050424b1c53f745dc93406c4665ad5d9fdabcdf29af580c94f9a7fa6 | Python | 3,451 | 95 | '''
Author: Alexandra Fischbach
Github repo: https://github.com/AlexFischbach/local_csf_pipeline/tree/main
Date: 04-18-2025
Contact: fischbach.a@northeastern.edu
'''
def compute_timeseries(
func_mask_path,
threshold_roi_path,
mod_conf_path,
roi,
confound_vars="Default",
save_path=None,
verb... |
98ddcd9a00bc9c5fb54b9c899916da17dc7de85a648188c2b45a953b769caf1e | Python | 3,451 | 88 | import numpy as np
import torch
from src.logic import test_logic
from src.PID import PID_utils, k_order, runner
def compute_source_avg(pair_dict, idx, s_key):
# computes average 2-order synergy for a particular source
avg = 0
for i in range(len(idx)):
string = s_key + '_syn'
for j in range... |
9871d138525b5aaf3f00beb5f32ed85cd911e191a050b3b2f0981810185b606b | Python | 3,452 | 100 | # FCN model
# when tuning start with learning rate->mini_batch_size ->
# momentum-> #hidden_units -> # learning_rate_decay -> #layers
import tensorflow.keras as keras
import tensorflow as tf
import numpy as np
import time
from utils.utils import save_logs
from utils.utils import calculate_metrics
class Classifier_... |
343d72a8b21e7b9e7ceaf2d0a3bc9b9aea3fc36395f4fe18a5a17c494e2208bc | Python | 3,453 | 107 | import torch
class AlphaFoldLRScheduler(torch.optim.lr_scheduler._LRScheduler):
""" Implements the learning rate schedule defined in the AlphaFold 2
supplement. A linear warmup is followed by a plateau at the maximum
learning rate and then exponential decay.
Note that the initial... |
1998df8488161d53a54c22f31a50977f823870f6a700ba90c895d3c737916f3d | Python | 3,454 | 149 | import numpy as np
from sklearn.model_selection import KFold
# from keras.optimizers import Adam
# Classifier parameters selected with GridSearchCV (cv=5)
# Random Forest parameters
rf_params = {
'n_jobs': 1,#-1,
'n_estimators': 100,
'max_features' : 'auto',
'max_depth': 15,
'min_sampl... |
1ea90743ea540cdaa38029bf2d459c32b5526a595992734a125c83bd05bd972e | Python | 3,455 | 101 | ''' Toy example using BB-RBMs on Bars and Stripes.
:Version:
1.0
:Date:
29.08.2016
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2016 Jan Melchior
This program is free software: you can redistribute it and/or modify
... |
85d624dcbadd79ca1ae75db423ce02a8ce8627ec6377b0c81c8d9dcba86c0b2b | Python | 3,456 | 95 | """Input preparation shared by the notebook forms and headless E2E runs."""
from __future__ import annotations
import copy
import hashlib
import json
import re
from colabfold.rgi import require_supported_model
def chain_table(fold_input):
rows = []
for entity in fold_input["sequences"]:
kind, molec... |
d70eab168dca11090cb31770a40f1f1a61dc85eda3d80966458665201d50bd05 | Python | 3,456 | 103 | ''' Example using a small BB-RBMs on the MNIST handwritten digit database.
:Version:
1.0
:Date:
29.08.2016
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2016 Jan Melchior
This program is free software: you can redist... |
e1ed58a8cf9026dffb04b1dcc882e65a95b41926f53a4685e0259ce7576dccab | Python | 3,458 | 91 | import matplotlib.pyplot as plt
import numpy as np
from dliplib.utils.helper import set_use_latex
plt.style.use('seaborn-whitegrid')
set_use_latex()
sizes = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.10, 0.25, 0.50, 1.00]
ticks = range(len(sizes))
# performance on the different data sizes
learnedpd = {'PSNR [db]': ... |
eacb920fddee1ae19a29c7eb67055c884618f3c2c135267af153db6f3e0c4791 | Python | 3,460 | 83 | """
module to export plotted points for Nature's sourcedata requirements.
"""
import os
import sys
import numpy as np
import pandas as pd
def genotype_from_suptitle(figure_params):
string = figure_params["suptitle"]
if "MDN" in string:
return "MDN"
elif "DNp09" in string:
return "DNp09"
... |
fd0f1d7d31352c73655e06e46110d45c44231e5f6a2f7b06f84713d24c8f24b6 | Python | 3,463 | 95 | from ccg.astar import AStarSearch
import ccg
import torch
import os
import warnings
class Parser:
def __init__(self,
model_file : str,
words_per_batch : int = 25*100,
do_tokenization : bool = False,
max_steps : int = 1_000_000,
... |
956b1859e61bf5372c87c779b1c88b11038af4eb8339ac33dbf93719e7e78e60 | Python | 3,464 | 69 | """Compact, content-checked receipts for completed native-space results."""
import json
from pathlib import Path
from utils.files import sha256_file
from utils.preflight import digest
def result_receipt(args):
root = args.output_root.resolve() / args.subject
required = [root / 'logs' / name for name in ('ra... |
d30a05f7966d1e0e2a0b6ecd32b86d87851d3f1b21f8150bee82bbcc3287fc58 | Python | 3,465 | 116 | from __future__ import annotations
from typing import Any
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.output_parsers.json import parse_and_check_json_markdown
from pydantic import BaseModel
from typing_extensions import override
from langchain_classic.output_parsers.format_instruct... |
12c06fa2c26c7a9dab704fa672db6aafee7f94ed5558d65631a18bbb94aed0e7 | Python | 3,466 | 108 | import torch
from torch_geometric.data import InMemoryDataset, Data
from torch_cluster import radius_graph
class rMD17Preprocess(InMemoryDataset):
def __init__(
self, pyg_dataset, cutoff, transform=None, pre_transform=None, pre_filter=None
):
self.dataset = pyg_dataset
self.cutoff = cu... |
9efc987be58a7512a5ec060dd7f9f4d289e42fc3441fa34c365eddba8e237d7e | Python | 3,469 | 76 | import os
import socket
import sys
from configs.configs import get_config_dict
from helpers.hcp import get_human_connectome_project_subjects
from helpers.slurm import save_slurm_job_to_file, send_slurm_job_to_queue
if __name__ == "__main__":
hostname = socket.gethostname()
assert hostname == 'hivemind'
... |
bf31e6c5185e9b685617a145555a4ad7d7d5b60c5c214767b169c4964a4922a3 | Python | 3,469 | 117 | """Fake Embedding class for testing purposes."""
import math
from langchain_core.embeddings import Embeddings
from typing_extensions import override
fake_texts = ["foo", "bar", "baz"]
class FakeEmbeddings(Embeddings):
"""Fake embeddings functionality for testing."""
@override
def embed_documents(self,... |
648a05156b70e2e9dd24f4ade8d7f58939168c1a7ff98e2ba2f47747c9483918 | Python | 3,470 | 87 | # -*- coding: utf-8 -*-
import sys
import numpy as np
import os, gc
import csv, glob
import os.path as path
import torch, pickle
from scipy.spatial import distance
import networkx as nx
import Bio.PDB
from matplotlib import pylab
import pandas as pd
import scipy.sparse as sp
from collections import defaultdict
from s... |
aa7cd5aec8987961a1bfe1660daad0e5378a58afeab6240c5fc5e45a5f372311 | Python | 3,470 | 117 | from bindsnet.encoding import encodings
class Encoder:
# language=rst
"""
Base class for spike encodings transforms.
Calls ``self.enc`` from the subclass and passes whatever arguments were provided.
``self.enc`` must be callable with ``torch.Tensor``, ``*args``, ``**kwargs``
"""
def __in... |
c5c5e5fdde1c5142f8a732d9178022164ddd061bf1dbc22614ca39cd8133aa14 | Python | 3,470 | 102 | import logging
import os
import socket
import sys
import matplotlib.pyplot as plt
import pandas as pd
import ptitprince as pt
import seaborn as sns
from configs.configs import get_config_dict
from helpers.figures import set_size
def plot_kernel_lengthscales(
config_dict: dict,
model_name: str,
kernel_le... |
fea700fbcc4e76530d18f8040243cf2eb1a60091f4aa792804aa2ac2ee8a8237 | Python | 3,470 | 97 | """ Import Progenesis Output
Progenesis QI for proteomics allows user to export proteomic
quantitative data as .csv.
@author: Reis-de-Oliveira G <guioliveirareis@gmail.com>
"""
import numpy as np
import pandas as pd
class Input:
def __init__(self, Table, **kwargs):
""" Progenesis output for OmicSco... |
74497ca068cda1f7dbb997cb5d6657f93a8397ab7a77a1114ced671bdec7136b | Python | 3,471 | 98 | # 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... |
87a2c63c3db2586609d7c9d137d0a3cab7a3993e0fa3fc53b5cdbce12b4e015d | Python | 3,471 | 93 | import pylab
import spiketools
from bisect import bisect_right,bisect_left
import defaultSimulate as default
def compute_cluster_rates(spiketimes,N_E,Q,kernel_sigma=50,kernel_type = 'tri',tlim = None):
""" compute the avarage firing rate of the Q excitatory clusters in spiketimes. """
cluster_rates = []
cl... |
d10c5b1d0a1cb6f11c9300e06589266ebed959fdd1b2beb7d87d3d96f58700ff | Python | 3,472 | 101 | import sys
sys.path.append("/home/melchj57/workspacePy/PyDeep/")
import os
os.nice(19)
import numpy as numx
import pydeep.base.numpyextension as npExt
import DBM3LayerBinary.model as MODEL
import DBM3LayerBinary.trainer as TRAINER
import pydeep.rbm.model as RBM_MODEL
import pydeep.rbm.trainer as RBM_TRAINER
import D... |
4eccebde43a14665df7fb59e3759ebc2c3255cc63a10a96fda61c86358a3d709 | Python | 3,473 | 99 | # Collect layer activations to ImageNet images
# Copyright (c) 2021 Laurent Caplette
import torch
from torchvision import transforms
from torch import nn
import os, h5py, glob
from robustness.datasets import ImageNet
from robustness.model_utils import make_and_restore_model
import numpy as np
imagenet_mean = torch.te... |
e4d2c940255dd4f2e1ca729b8b02cae9f24562c7a37e87f93a4af27c770f4177 | Python | 3,476 | 85 | %matplotlib qt
import mne
from config_FaceName import Results_data_path,Ids,MEG_data_path,group_name,MRI_data_path,stats_conds_list,stats_comparisions,study_path
from custom_functions import do_cluster_permutation_1samp_test,prepare_X_and_one_data
import os.path as op
from config_FaceName import Results_data_path,stats... |
f43fc689b1a2066daad92dfdcbd3c06c66fb27910dc3f50ba3d4a4541ffd353c | Python | 3,476 | 116 | import equinox as eqx
import jax
import jax.numpy as jnp
import jax.nn as jnn
import jax.lax as lax
import diffrax
from CEL.utils.register import register
import jax.random as jr
from typing import Literal
from .meta_model import ModelClass
class ODEFunc(eqx.Module):
mlp: eqx.nn.MLP
input_channels: int
h... |
2d91b7149579fc1f40262adee702238e3af06a7fd17c7ccd822fd4121b2c567d | Python | 3,478 | 66 | # 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... |
610d7bde3978994525ba739bd54c30a950d046f2770cd73b2a2acd68223348b2 | Python | 3,479 | 95 | import configparser
import os
import numpy as np
import json
def read_predict_config(predict_config):
config = configparser.ConfigParser()
config.read(predict_config)
cfg_dict = {}
cfg_dict["batch_size"] = config.getint("Predict", "batch_size")
cfg_dict["train_dir"] = config.get("Predict", "train... |
9b6ec0131de826107f3793849a7873ff4a6689f68e90d6b4dfafd08c3c7434d0 | Python | 3,479 | 127 | """Callables annotated with names that are only imported for type checking.
Deliberately omits `from __future__ import annotations`, so on Python 3.14+ these
annotations are evaluated lazily and raise `NameError` when evaluated. Importing
this module requires Python 3.14+.
"""
# ruff: noqa: TC004
from typing import ... |
fd94575e10a58d1bf4d3f91a0883748f2cbf6d051c196be12ec9183088e481b0 | Python | 3,479 | 91 | # 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 __future__ import absolute_import, division, print_function
import os
import argparse
import json
import numpy as np
import pandas as pd
import joblib
from .d... |
b23d891ef310c0ed249071d3c457572a5365ae2dfc1476022f0806bc3d612e8c | Python | 3,480 | 114 | import os
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
import json
import re
import numpy as np
from tqdm import tqdm
from cloudfiles import CloudFiles
from cloudvolume.lib import Vec, Bbox, mkdir, save_images, yellow
from cloudvolume.paths import ExtractedPath
DEFAULT_PORT = 8080
RANGE_RE... |
c17926b59b778e591a8debcd3c435e3dd42165215caf79051028ea28670013e7 | Python | 3,482 | 101 | import numpy as np
class YOLO_Kmeans:
def __init__(self, cluster_number, filename):
self.cluster_number = cluster_number
self.filename = "2012_train.txt"
def iou(self, boxes, clusters): # 1 box -> k clusters
n = boxes.shape[0]
k = self.cluster_number
box_area = boxe... |
583754f399f99f630ac18a0f811b4c86c8e9ed58d9f0df4c90d5dea730ccd0cc | Python | 3,483 | 150 | import numpy as np
from sklearn.model_selection import KFold
# from keras.optimizers import Adam
# Classifier parameters selected with GridSearchCV (cv=5)
# Random Forest parameters
rf_params = {
'n_jobs': 1,#-1,
'n_estimators': 100,
'max_features' : 'auto',
'max_depth': 15,
'min_sampl... |
9936bfec66fd987eaa66880a3df6a72ca9483ae8b4f5667836779a53c98be9cb | Python | 3,484 | 106 | """Test few shot prompt template."""
import re
import pytest
from langchain_core.prompts.few_shot_with_templates import FewShotPromptWithTemplates
from langchain_core.prompts.prompt import PromptTemplate
EXAMPLE_PROMPT = PromptTemplate(
input_variables=["question", "answer"], template="{question}: {answer}"
)
... |
52d71727eacb3672c1ca7bfb3643b285207947f209a3191a8767e48e9c71c6ad | Python | 3,485 | 58 | # Python implementation of the GP based filter, USING THE DIAGONAL approximation for the covariances.
# This represents the benchmark (for the DIAGONAL models) - many flavours can be added and potentially improving the results. But this is the implementation that respects the model and offers a benchmark.
# It is the ... |
cb5e21da6172231d869ebf83848aafbde99b056b86ec76c2ed7352faf4c43b54 | Python | 3,485 | 52 | from optimization.ADMM import *
from EPI_MRI.EPIMRIDistortionCorrection import *
import argparse
import warnings
def main():
parser = argparse.ArgumentParser(description="PyHySCO: EPI-MRI Distortion Correction.")
parser.add_argument("file_1", type=str, help="Path to the input 1 data file (NIfTI format .nii.gz)... |
b9ecc669ef8288c4dcffdfd6b934d115eb0e8a55988fb555fb19507f8327dc9e | Python | 3,486 | 76 | # srun --nodes=1 --ntasks=1 --cpus-per-task=8 --gpus=1 --mem=100g --time=06:00:00 --pty bash -i
# Sconda conda activate ghmc1; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/GhmCN/reviewer_questions/distribution_of_genes_and_distances_activ72B; python
import pandas as pd
import numpy as np... |
1b818be959032ab56ee5ac8f79c77c71a2ea5f345ca9c5c0d3edc40fe93dd88a | Python | 3,487 | 109 | # Training procedure for CIFAR-100 using ResNet 110 (SD)
import numpy as np
import collections
import pickle
from resnet_sd import resnet_sd_model
from sklearn.model_selection import train_test_split
from keras.models import Model
from keras.callbacks import ModelCheckpoint
from keras.optimizers import SGD
from keras... |
83930fac3f2c24af23799442202bd6469f9db40ab58eacbe7c5fb04b97a0af94 | Python | 3,487 | 101 | """`OutputParser` classes parse the output of an LLM call into structured data.
!!! tip "Structured output"
Output parsers emerged as an early solution to the challenge of obtaining structured
output from LLMs.
Today, most LLMs support [structured output](https://docs.langchain.com/oss/python/langchain/m... |
343227d59f2a1853ad21f25df0caeb8f547ee1b52508b3eaa9f7dbaef63bc680 | Python | 3,489 | 73 | # 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... |
479a2ca24d8c93dfd8bbe3fd0e5626d60d1f4e9c344d119541e4ef13c50a377f | Python | 3,489 | 104 | import logging
import os
import socket
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from configs.configs import get_config_dict
from helpers.synthetic_covariance_structures import get_covariance_time_series
def _plot_synthetic_covariance_structures(
config_dict: dic... |
0ced285da8712a6e3e3a82a8418137a0e8bbcf92cddbbd9070448cd15eca432e | Python | 3,490 | 102 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to... |
512b6029888cb3d209c3e65c47b41175fb40f59fdee4623a72b257157ae95222 | Python | 3,492 | 109 | # coding=utf-8
""" Defines the MultiDataset Class"""
from torch.utils.data import Dataset
import logging
logger = logging.getLogger(__name__)
class MultiDataset(Dataset):
"""
Define a dataset for multi input networks.
Takes in a list of datasets, and whether or not their input_data
and target data s... |
38f94b06a97a8b0b43d2597f3a4202facfc03d98fa70bc236efd0fe5c9bcdbfc | Python | 3,493 | 98 | import json
import csv
import os, sys
import matlab.engine
import collections
import csv
import pandas as pd
import numpy as np
from scipy.interpolate import interp1d
from dataloader import B_IQ_Data
from utils import iqa_tensor, p_val, SSIM
from torch.utils.data import DataLoader
from tabulate import tabulate
class... |
94265a18619b1a80c8dadcbbe3edb8f79bcd658d93fd5a526e0c9b1d8c4c03a9 | Python | 3,493 | 121 | # -*- coding: utf-8 -*-
"""
Created on Wed Feb 3 23:07:34 2021
@author: Bruno Caneco
Code stolen from package in https://github.com/jobevers/diagonal-crop. Package was
converted into a single module to simplify integration with sscd's code structure
TODO
"""
from __future__ import division
import collections
im... |
b06d51007d9f1321d902733a8ecfbdc0a83f4017b2ea3ad31bee7bc8d9fc401a | Python | 3,494 | 111 | import signal
import sys
import pytest
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_classic.agents.mrkl.output_parser import (
MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,
MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
... |
06dca37f72fe7111dd8dd4f28df34b930be30dc2ad4b896480bd935dd04eda0b | Python | 3,495 | 129 | from EPI_MRI.LinearOperators import *
class QuadRegularizer:
"""
Defines the structure of an quadratic regularization term of the form S(x) = 1/2 ||x||_H^2 where H is positive semi-definite.
Attributes
----------
H :`LinearOperators.LinearOperator`
The linear operator used in the regu... |
f34afdcab51747e6e23d0595d81ae2b7fc98e43720e6733c5fc094da8ae665de | Python | 3,496 | 99 | import argparse
import pickle
import jcamp
import numpy as np
import pubchempy as pcp
import re
import requests
from bs4 import BeautifulSoup
from rdkit import Chem
nist_id_rexp = re.compile(r'[0-9]+\-[0-9]+\-[0-9]+')
def process_nist_id(response, results, site_list, site_addr, inchikey):
soup = BeautifulSoup(res... |
414601597e0176644028d0b38a0dfd35b9c055ecbff553e4ae14a4cfbcb38661 | Python | 3,497 | 85 | """Contains tests for the Conv2D-layer LRP implementations"""
import numpy as np
from tensorflow.keras import Model
from tensorflow.keras.layers import Conv3D, Flatten, Input
from explainability import LayerwiseRelevancePropagator
from explainability.layers import Conv3DLRP
def test_conv3d_lrp():
input = Input(... |
87c243440ea1caf17ccee2d5e236e9a9ecac304b2602f2963bbf94e96872aaa1 | Python | 3,500 | 90 | import logging
import os
import socket
import sys
import numpy as np
import pandas as pd
from configs.configs import get_config_dict
from helpers.hcp import get_human_connectome_project_subjects, load_human_connectome_project_data
from helpers.evaluation import leave_every_other_out_split, get_test_log_likelihood, ge... |
9dfe6976ce24e3faf9e6651c85cab208e154d4558a09d74c092a4aa50b3774af | Python | 3,500 | 103 | targetMean = 140
targetStd = 5
initStep = 0
import pandas as pd
resultsMean = []
resultsStd = []
for i in range(40):
base_name = 'R:/Temp/Threads10/Thread'+str(i)+'/Simulation/output/Data.csv'
df = pd.read_csv(base_name)
if df.iloc[0,0]>1000:
resultsMean.append(0)
else:
resultsMean.app... |
a5fe154858cd7cc478f8c1eb723ec9aff34559979a74ee9d70d62b522f568a56 | Python | 3,500 | 133 | """
Django settings for analyze_electrophysiology_data project.
Generated by 'django-admin startproject' using Django 3.1.3.
For more information on this file, see
https://docs.djangoproject.com/en/3.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/3.1/ref/sett... |
f40daa3ae5c963961a2931851164bbb7717cf191b60a07979afc174515150915 | Python | 3,504 | 69 | import rdkit.Chem as Chem
import numpy as np
from tqdm import tqdm
'''
This script prepares the data used in Wengong Jin's NIPS paper on predicting reaction outcomes for the modified
forward prediction script. Rather than just training to predict which bonds change, we make a direct prediction
on HOW those bonds chang... |
3e936133d7a6953c2beb423968f0336478f01c5c983f891b24b58fc983c3ec9b | Python | 3,506 | 88 | import numpy as np
import gym
from stable_baselines3 import PPO
import torch
from stable_baselines3 import A2C, PPO
from Policies.bus_13.CustomPolicies import ActorCriticGCAPSPolicy
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
from typing import Any, Callable, Dict, List, Optional, Tup... |
6dc2950405b595cb0966a3e57ba1e5e2b38f24c7125b85b844e9e2ae46bda80f | Python | 3,506 | 111 | # Training procedure for CIFAR-10 using ResNet 110 (SD)
import numpy as np
import collections
import pickle
from resnet_sd import resnet_sd_model
from sklearn.model_selection import train_test_split
from keras.models import Model
from keras.callbacks import ModelCheckpoint
from keras.optimizers import SGD
from keras.... |
88bf659b996504d4633be38712b7dcefcce7ba04cef1252bbf4d0cd9df52f171 | Python | 3,507 | 115 | import numpy as np
from os import listdir
from os.path import isfile, join
from PIL import Image
from sklearn.model_selection import train_test_split
# Paths to files, change the path if needed
LABELS_PATH = '../../data/data_imagenet/labels.txt'
DATA_PATH = '../../data/data_imagenet/val/'
IMG_MEAN = [103.939, 116.779,... |
bc584d4e1d28b5586204dfbdbfc51d5878e2aea2274aaf063d243e92d9bae96e | Python | 3,507 | 75 | """Dataset class template
This module provides a template for users to implement custom datasets.
You can specify '--dataset_mode template' to use this dataset.
The class name should be consistent with both the filename and its dataset_mode option.
The filename should be <dataset_mode>_dataset.py
The class name should... |
ac161359c413fa5de0912848f8ce029385194ce91439fcff22bd38694b0b1c1f | Python | 3,508 | 110 | from utils_stat import (
get_roc_info,
get_pr_info,
calc_neurologist_statistics,
read_raw_score,
)
import matplotlib.pyplot as plt
from utils_plot import plot_curve, plot_legend, plot_neorologist
from time import time
import numpy as np
def confusion_matrix(labels, scores):
matrix = [[0, 0], [0, 0... |
0c2d2fb107293d85bb0690d64e6336ac6ebdc2777b88600a178d015e43155408 | Python | 3,510 | 93 | import torch
from torch.utils.data import Dataset
import os
from itertools import islice
from math import inf
import logging
class ProcessedDataset(Dataset):
"""
Data structure for a pre-processed cormorant dataset. Extends PyTorch Dataset.
Parameters
----------
data : dict
Dictionary o... |
56bb0124e6a5354a266a3fa270b72129ce97ec587ec39820fe636d6e1de6a985 | Python | 3,510 | 121 | """
ZephOD feature detection.
Usage:
zephod -h | --help
zephod -v | --version
zephod --dataset=<dataset> --model=<model> [options]
Options:
-h --help show this message and exit.
-v --version show version information and exit.
--dataset=<dataset>... |
818e5d2e142c7fc54256b3f52563a0e11e00e90fb16e6d366abf885d9fb5c37d | Python | 3,513 | 81 | # HED batch processing script; modified from https://github.com/s9xie/hed/blob/master/examples/hed/HED-tutorial.ipynb
# Step 1: download the hed repo: https://github.com/s9xie/hed
# Step 2: download the models and protoxt, and put them under {caffe_root}/examples/hed/
# Step 3: put this script under {caffe_root}/exampl... |
dccc10f553390030daa26065054e4a22dd3f69357ec134b164fa3fda297e88e3 | Python | 3,514 | 84 | from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_message_histories import (
AstraDBChatMessageHistory,
CassandraChatMessageHistory,
ChatMessageHistory,
CosmosDBChatMessageHistory,
Dynamo... |
6fe0cd7b3909f7fb602e86c464d2f8c047bf293bf2957f8a98b44ebb26cc9dd8 | Python | 3,515 | 96 | # 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.
import os
from torchvision import datasets, transforms
from timm.data.constants import \
IMAGENET_DEFAULT_MEAN, IMA... |
d9a3fb97e95fc6cacecfce05bb404ae1830afd975b83a6e07f44c5b3890a2b63 | Python | 3,515 | 82 | import mne
from config_FaceName import Results_data_path,Ids,MEG_data_path,group_name,stats_conds_list,stats_comparisions
from custom_functions import do_cluster_permutation_1samp_test
from functools import partial
import os.path as op
import mne
from config_FaceName import Results_data_path,MRI_data_path,stats_compari... |
193bda05a6dbed06f05b7b196efe034c2cafbe4d8a87088c18a616a9d747e7b2 | Python | 3,516 | 95 | """
End to end test to check the basic usage
"""
import os
import shutil
import subprocess
from pathlib import Path
import pytest
REPO_ROOT = Path(__file__).parent.parent.parent.resolve()
DATA_DIR = REPO_ROOT / "tests" / "fixtures"
@pytest.mark.parametrize("model_id,extra_args,result_cols", (
(
"AF-A0A1... |
5d39776f9bdd38789dbc9a08508f339f8e4b95774d63dca24550dd7133d98307 | Python | 3,516 | 98 | """Configuration for unit tests."""
from collections.abc import Sequence
from importlib import util
import pytest
def pytest_addoption(parser: pytest.Parser) -> None:
"""Add custom command line options to pytest."""
parser.addoption(
"--only-extended",
action="store_true",
help="Only... |
dc108e0d9637f0fc5bd1d3b74298955f1ad21ac771e36b3792ad6f01f80eb487 | Python | 3,516 | 108 | """
Configuration file for pytest.
"""
import logging
import pytest
logger = logging.getLogger(__name__)
# Ensure QCPortal is imported before any OpenEye modules, see
# https://github.com/conda-forge/qcfractal-feedstock/issues/43
try:
import qcportal # noqa
except ImportError:
pass
def untar_full_alketho... |
992039ce6aaafbabe48851554b5706fc0f3f9eaf59e32d203f86ebf61aecd5a0 | Python | 3,519 | 102 | import gzip
import sys
from collections import defaultdict
from Bio.Seq import Seq
def main():
if len(sys.argv) < 7:
print("Usage: python createMAFFeatures.py <alignment file> <species names file> <output file> <chromosome> <reference species> <reference species chromosome length file>")
exit(1)
... |
e31ce6d0aa239b015730a8389a0d7990c7776ff93664af5e91667dde67458741 | Python | 3,519 | 100 | """
Monopolar triangulation of putative units
Code snippet from
https://github.com/SpikeInterface/spikeinterface/blob/master/spikeinterface/toolkit/postprocessing/unit_localization.py
Implementing method proposed in
https://www.biorxiv.org/content/10.1101/2021.11.05.467503v1
Modified 20220321 by jz103
"""
import nu... |
37d0addf27e76dc7b6b57299b160034c83b658ea94ef3e5b5af5ed1cf2d125e1 | Python | 3,522 | 109 | import tempfile
import typing as tp
from pathlib import Path
from typing import Tuple
import pydicom
import pytest
from hypothesis import strategies as st
from hypothesis.strategies import SearchStrategy
from MRdataset.dicom import DicomDataset
from MRdataset.tests.simulate import make_compliant_test_dataset, \
m... |
701d0fc40fc72b73bbc53ea259d1a0389b0f9cada2ca8b93f79af9b91eb5c8c5 | Python | 3,523 | 101 | """
openff-toolkit
A modern, extensible library for molecular mechanics force field science from the Open Force Field Consortium.
"""
import importlib
from importlib.metadata import version
from typing import TYPE_CHECKING
if TYPE_CHECKING:
# These types are imported lazily at runtime, but we need to tell type
... |
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