sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a8f161260f326d0c07554067997e949b79e5e15ac1ed7423bf1b5c427d6fe32f | Python | 1,771 | 60 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
30972f25531eb8b336e1dd3bf49a7703f96f01230af0ceea2ec7c474de769dcd | Python | 1,772 | 55 | """
Optuna distributed optimization example that optimizes
an sklearn classifier for the Iris dataset on Kubernetes.
This example's code is mostly the same as the sklearn_simple.py example, except for two things:
1 - It gives a name to the study and sets load_if_exists to True
in order to avoid errors when the code i... |
9579b6b7329d635b31dc86c9e455313376d302b8c7823e674797c226f632eef2 | Python | 1,772 | 53 | import sys
from pybioshell.std import vector_std_shared_ptr_core_data_sequence_Sequence_t
from pybioshell.core.data.io import read_fasta_file
from pybioshell.core.alignment import AlignmentType
from pybioshell.core.protocols import PairwiseSequenceIdentityProtocol
if len(sys.argv) < 3 :
print("""
Reads a .fasta f... |
cf5d2984abb64b86ead02789ad56bd3d06cf75fa719d44e2fa27263693b4bc59 | Python | 1,772 | 47 | # Copyright 2020 The HuggingFace Team. 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 applicabl... |
d7e9bd35b69bfb0623e56cb8bef2e60b90cd39dab4efe57bcd96338eadb22b68 | Python | 1,773 | 37 | # Copyright (c) 2021, Ko Sugawara
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and t... |
282bd2ae15c55f1c81cd7692c3b15be2162403ab50f6ead45178253c4834c173 | Python | 1,774 | 52 | import os
import sys
read_dir_path = sys.argv[1]
ref_path = sys.argv[2]
model_name = sys.argv[3]
out_dir_path = sys.argv[4]
batch=0
modeltype = ''
if model_name == "default":
batch=512
modeltype = 'default'
elif model_name == "TINYX1":
batch=1600
modeltype = 'tinynoskipx1'
elif model_name == "... |
f6a1cd265b93fdbe7ad7a9721fbf15e44882fbbba4a22f2a9183e37b70f5d6c5 | Python | 1,774 | 45 | #----Figure_04_K_preparation-----------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code which has to be run in preparation for Figure 04 K
# for the connectivity analysis for Reinhard and Fukuda et al. 2024
#--------------... |
0a25f8c0f72accd311a368f719ce3d6e590ab02e286e800efaffe5b729e48a7b | Python | 1,775 | 57 | import numpy as np
from skimage import filters
from skimage.color import rgb2gray as skimage_rgb2gray
from skimage.morphology import erosion as skimage_erosion
from skimage.exposure import adjust_gamma as skimage_adjust_gamma
from src.preprocessing.helpers import cutdown, resize_padded
def preprocess_image(image, t... |
48b868b79c030df3c546bc6b83b13f4e9151d0d554e3c0b9f3c32a57ff132244 | Python | 1,775 | 56 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from .sr_backbone_utils import default_init_weights
class PixelShufflePack(nn.Module):
""" Pixel Shuffle upsample layer.
Args:
in_channels (int): Number of input channels.
out_channels (int)... |
6c46a7a6d4274e5c8d2c05fc0b13a35377819720a00e12b25cb0a7a813b22a48 | Python | 1,775 | 51 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 9 15:55:48 2023
@author: trianaa1
"""
import numpy as np
import pandas as pd
from scipy.io import loadmat
import matplotlib.pyplot as plt
###############################################################################
conn_path = '/m/cs/scratch/n... |
f30febdf12aef8174639c82820a9d8eac81f6bdf111cd2ca675721fff4cfaf3a | Python | 1,775 | 56 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from lab.reports import Attribute, arithmetic_mean, geometric_mean
import common_setup
from common_setup import IssueConfig, IssueExperiment
from rel... |
a72ad364bc9afd182a769016439ed92627f592d375197b588727079df5bce7a5 | Python | 1,776 | 39 | import tensorflow as tf
import os
#from tf_explain.callbacks.grad_cam import GradCAMCallback
class CallBacks:
def __init__(self, learning_rate=0.01, log_dir=None, optimizer=None, validation_data=None):
self.log_dir = log_dir
self.learning_rate = learning_rate
self.optimizer = optimizer
... |
6bf922fbcc53ed2660c0934fafb0b81e8a8e441fb25e4526e728b42abe456308 | Python | 1,777 | 51 | import numpy as np
import matplotlib.pyplot as plt
file=np.load('heatmap_SEModularity.npz')
SE=file['SE']
modularity=file['modularity']
file1=np.load('heatmap_SEModularity_0.9.npz')
modularity1=file1['modularity']
file2=np.load('heatmaps_spectrums.npz')
SD_KOP=file2['SD_KOP']
KOP=file2['KOP']
K_all_values=np.arang... |
9d707800d93b6c7340df0fa7b6510d060a8c52f5f22d046551197ee045a87749 | Python | 1,777 | 34 | # 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
#
# ht... |
e33ca8db6eb41f41a85a1c9a2b34126adee0aeb46186381d97f2417d6f52df80 | Python | 1,777 | 47 | import os
import os.path as op
import mne
import mne_bids
import nibabel as nib
from utils import load_raw
from params import BIDS_ROOT as bids_root
from params import SUBJECTS as subjects
from params import TEMPLATE as template
# get plotting information
subjects_dir = op.join(bids_root, "derivatives")
fs_subjects... |
a1aa15cd259a19be97d8d71fd1804eb9f478b6031bd72a02abc46651d782b2ca | Python | 1,778 | 47 | """register_ndim / registration() forward affine_method, affine_overrides,
reuse_affine into rg.runRegistration and record them in metadata."""
import inspect
import regtools.registration.pipeline as pipe
def test_register_ndim_signature_has_affine_params():
sig = inspect.signature(pipe.register_ndim)
... |
d33b7b7f5e93043c9c0826a1430b31ddd9bc063322934d0928ad82cf0b91fee9 | Python | 1,778 | 36 | # THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify: models/auto/modeling_auto.py
# 2. run: python utils/class_mapping_update.py
from collections import OrderedDict
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
[
("BigBirdConfig", "BigBirdForQuestionAnswering"),
("ConvBertConfig... |
0cdf76b4eb103e73efafeec6deaf34a40d0f367ec10cd1fef87ce5655ed72ad7 | Python | 1,779 | 53 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
17f658e991f4c02d302f1f431eed13b92cfac059af3a78df90676046e1d1db19 | Python | 1,779 | 53 | """HiDisc loss module.
Copyright (c) 2024 University of Michigan. All rights reserved.
Licensed under the MIT License. See LICENSE for license information.
"""
from typing import Tuple, Dict, Optional, Any
import torch
from torch import nn
from fastglioma.losses.supcon import SupConLoss
class HiDiscLoss(nn.Module)... |
41833e795686e8f92215a867a1e77ca02a5963d4edf6aca6b349ffa1da9eeb3c | Python | 1,779 | 52 | # Import package
import syncopy as spy
from syncopy.tests import synth_data
# basic dataset properties
nTrials = 100
samplerate = 5000 # in Hz
# add a harmonic with 200Hz
adata = 2 * synth_data.harmonic(nTrials, freq=200, samplerate=samplerate)
# add another harmonic with 1300Hz
adata += synth_data.harmonic(nTrials... |
14534fd33e4fc674e58dfb1e168660d2d123bf7b8dcef196ca14281fe9d4162b | Python | 1,780 | 65 | ############################################################################################################
# Set classes for handling pexceptions in the PySeg package
#
#
__author__ = 'martinez'
################################################################################################
# Exceptions
class PySe... |
8bf96a9826eb603e42059971bdee7111a7775785ed8491f35c95109ae4e264b3 | Python | 1,780 | 57 | """GPU (torch/CUDA) metric backend: parity with CPU + the CPU-force override.
The parity test skips on a CPU-only box (CI). The override test always runs -
it is the guarantee that a machine can opt out of the GPU path.
"""
import numpy as np
import pytest
from regtools.registration.core import metrics as M... |
0e8340299273f7d32c0338d573bd6ca82d3ce22cffec63a88d88dc689e20ea34 | Python | 1,781 | 53 | from sklearn.metrics import roc_auc_score
import numpy as np
from sklearn.model_selection import permutation_test_score
from tqdm import tqdm
def is_ndarray(img):
return isinstance(img, np.ndarray)
def permutation_test(v1, v2, fct, seed=1, permutations=2000, verbose=False):
if not is_ndarray(v1):
v1 =... |
4e8a382189824a313291c79c4a299e875dc25c045b4b8471c26fa20a767304a5 | Python | 1,781 | 65 | from datetime import datetime as dt
import numpy as np
import scipy.stats
from tabulate import tabulate
def timestamp_human():
return dt.now().strftime('%d-%m-%Y %H:%M:%S')
def timestamp_dir():
return dt.now().strftime("%Y-%m-%d_%H-%M-%S.%f")[:-3]
def print_dataframe(df, n=-1, n_digits=3):
print(tabul... |
52f7a7b73da935955f64eea3686e5d2402caef69c2c507a54abda7908db0855c | Python | 1,781 | 30 | # https://arxiv.org/abs/2105.02786
GENERAL_REGION_LIST = [['FP1', 'FPZ', 'FP2'],
['AF7', 'AF3', 'AFZ', 'AF4', 'AF8'],
['F7', 'F5', 'F3', 'F1', 'FZ', 'F2', 'F4', 'F6', 'F8'],
['FC5', 'FC3', 'FC1', 'FCZ', 'FC2', 'FC4', 'FC6'],
['C... |
da1ac084ed39306c59d084b4480a17ebcb44148f73b896bac1054087978c6ee6 | Python | 1,781 | 47 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F
def flow_warp(x,
flow,
interpolation='bilinear',
padding_mode='zeros',
align_corners=True):
"""Warp an image or a feature map with optical flow.
Args:
x... |
45bd77436f32a0b8748f5829c79494d239517ac35cb76d5e40246c9da3bdc4a0 | Python | 1,782 | 54 | import logging
from optparse import Values
from typing import List
from pip._internal.cli.base_command import Command
from pip._internal.cli.status_codes import ERROR, SUCCESS
from pip._internal.operations.check import (
check_package_set,
create_package_set_from_installed,
warn_legacy_versions_and_specifi... |
8789b0bce02d28a5f020f642966eaf47dc6acaf269182425d35932b714992226 | Python | 1,782 | 48 | import torch
from wsilearn.dl.pl.pl_modules import ModuleBase
class SurvModule(ModuleBase):
def __init__(self, loss=None, epoch_metrics=['cind'], **kwargs):
""" loss: name of the loss, dictionary(loss=Name,etc) or None """
if loss is None or len(loss)==0:
loss = {'name':'coxph'}
... |
9a825df5efe5225e39afa07c56aebf71fdc680acd1898fc0d772fcb24c9fc520 | Python | 1,782 | 50 | import torch
def compute_accuracy(config, outputs, targets, augmentation, topk=(1, )):
if augmentation:
if config.augmentation.use_mixup or config.augmentation.use_cutmix:
targets1, targets2, lam = targets
accs1 = accuracy(outputs, targets1, topk)
accs2 = accuracy(outpu... |
eedf82d2cc7d54924d0e8a35371063eef8daef70bc77b260d5d1d49083f72adf | Python | 1,783 | 51 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
7a31a5258678add8b4e9859277932bf2b6010fd60039311e5c8d1b9c7e2bc1f3 | Python | 1,784 | 58 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_DI... |
94ba192d5fe2c9e386773e8cd65b64216deba0e2b4027602aebab0b890bc6f23 | Python | 1,784 | 53 | import os
import anndata
import pytest
from cirrocumulus.envir import CIRRO_DB_URI, CIRRO_TEST
from cirrocumulus.launch import configure_app, create_app
from cirrocumulus.prepare_data import PrepareData
from cirrocumulus.serve import cached_app
@pytest.fixture(scope="session", params=[True, False])
def app_conf(req... |
ae7dc56d9b218cc4c3ae82b74beded984f2fa7b2c0167f15a564483f0bdbe8bf | Python | 1,784 | 52 | """A BIDS tabular file including its associated sidecar."""
import os
from hed.models.tabular_input import TabularInput
from hed.tools.bids.bids_file import BidsFile
from hed.tools.bids.bids_sidecar_file import BidsSidecarFile
class BidsTabularFile(BidsFile):
"""A BIDS tabular file including its associated side... |
fe1b21be12dfa57d8b827bb333f2cbf23b274f54bddd4212939cbfa0a46d96d3 | Python | 1,784 | 49 | from cloudvolume import CloudVolume, Storage
from shapely.geometry import Polygon, box
import numpy as np
from io import BytesIO
from PIL import Image, ImageDraw
mip = 6
mask = Storage('gs://neuroglancer/pinky100_v0/edge_mask')
order = Storage('gs://neuroglancer/pinky100_v0/z_order_corrected')
out = CloudVolume('gs://... |
4d917108cc2fdcaa4057c4c77eed6d8efc08275b997d95b7b60b87107d4a927d | Python | 1,785 | 52 | from torch.utils.data import Dataset
import linecache
import os
import numpy as np
base2code_dna = {'A': 0, 'C': 1, 'G': 2, 'T': 3, 'N': 4}
code2base_dna = {0: 'A', 1: 'C', 2: 'G', 3: 'T', 4: 'N'}
def clear_linecache():
# linecache should be treated carefully
linecache.clearcache()
def parse_... |
85e72b36c7c46da5614b7eab3aee863418e1fff5f9f05bc1d8a995917dbefee7 | Python | 1,785 | 47 | import torch
import numpy as np
from scipy.spatial import distance_matrix, minkowski_distance, distance
def calcADJ(coord, k=8, distanceType='euclidean', pruneTag='NA', all_conn = False):
r"""
Calculate spatial Matrix directly use X/Y coordinates
"""
spatialMatrix=coord
nodes=spatialMatrix.shape[0]
... |
e42365b134916202f0f7d92684d40659ed71615d854b5da61b74dd9d02aec81d | Python | 1,785 | 56 | import os
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.YAGO310")
class YAGO310(data.KnowledgeGraphDataset):
"""
Subset of YAGO3 knowledge base for knowledge graph reasoning.
Statistics:
- #Entity: 123... |
41160c1ec0da97150c09984700520ec099e5d7296e037b07dcab73e21c6ba0b3 | Python | 1,786 | 33 | """Python function to read CIFTI files without needing to install connectome workbench
Adapted and modified from https://nbviewer.org/github/neurohackademy/nh2020-curriculum/blob/master/we-nibabel-markiewicz/NiBabel.ipynb
"""
import nibabel as nib
import numpy as np
def read_cifti(file):
'''
file: filename... |
a56a68f28dc421aaacf5e566a39e75ca4a95b227790a01c5cac6854e3f0b795f | Python | 1,786 | 73 | import os
import time
import numpy as np
from plotter import plot_phi, plot_c_alpha, plot_phi_m
from ednm import model, solver
# number of units
N_units = 10
# distance between units [cm]
dxu = 5.0e-4
# boundary condition (0: closed, 1: periodic)
bc = 1
# simulation duration
Tstop = 1.5
# set synapse model (0, 1... |
a83629b450c320a6b60bf5e86649bf9fb1e30fcee37ff5bdc42c43ad428217ff | Python | 1,787 | 52 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from common_setup import IssueConfig, IssueExperiment, is_test_run
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["issue635-base", "issue635-v2"]
CONFIGS = [
IssueConfig(
... |
b40b104c1a6482f6419057877596b683dffe984d625d417cb901cadee7bb17a5 | Python | 1,787 | 52 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from common_setup import IssueConfig, IssueExperiment, is_test_run
BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"]
REVISIONS = ["issue635-base", "issue635-v3"]
CONFIGS = [
IssueConfig(
... |
cd2e4345c60f25066f345fd9aaee492ca2f6608521b8bedc3bfe75f3dbe7088d | Python | 1,787 | 84 | import autograd.numpy as np
import autograd.numpy.random as npr
from autograd import grad
from autograd import isinstance as ag_isinstance
from autograd import list as ag_list
from autograd.test_util import check_grads
npr.seed(1)
def test_getter():
def fun(input_list):
A = np.sum(input_list[0])
... |
d7e395347ea2d2bb5f38223ec51fd1ae9d105917c4181304a36b53499c68b6de | Python | 1,787 | 43 | import numpy as np
from trimesh.creation import icosphere
from src.utils.general import cart2spher
class Sphere:
# this is the sphere object, that is used for obtaining the right data for the GEM-CNN.
# it contains the points where the image intensities are sampled for each vertex in the mesh
def __init__... |
d8d62366d4e2dd70f32135d72d63a7162e968bda392a3b8d80fc4c92bd6fd7f4 | Python | 1,787 | 80 | import math
import matplotlib.pyplot as plt
import numpy as np
import meep as mp
def metal_cavity(w):
resolution = 50
sxy = 2
dpml = 1
sxy += 2 * dpml
cell = mp.Vector3(sxy, sxy)
pml_layers = [mp.PML(dpml)]
a = 1
t = 0.1
geometry = [
mp.Block(mp.Vector3(a + 2 * t, a + 2 ... |
e8f95dee82b8cf12ffd293a93ca2bf4b03c33b24133309cd71687f1c31ff578f | Python | 1,787 | 49 | try:
import binutil # required to import from dreamcoder modules
except ModuleNotFoundError:
import bin.binutil # alt import if called as module
from dreamcoder.dreaming import backgroundHelmholtzEnumeration
from dreamcoder.grammar import Grammar
from dreamcoder.program import Program
from dreamcoder.recogni... |
1472fc26fd0580738762a7b1deba8c789a7bbc1869e1e278dcfd48b0d9073580 | Python | 1,788 | 54 | import numpy as np
import h5py
from scipy.stats import pearsonr
with h5py.File(('seq_onehot.h5'), 'r') as hf:
X = hf['seq_onehot'][:]
with h5py.File(('expression.h5'), 'r') as hf:
Y = hf['expression'][:]
from sklearn.model_selection import train_test_split
# split into train and test datasets
X_train, X_tes... |
4afc6d3f95232edf7e1cb79ebd94ff4c8942c92f23f0388e1deff643975e1713 | Python | 1,788 | 50 | import os.path
from ckg.graphdb_builder import mapping as mp, builder_utils
######################################
# Databases at jensenLab.org) #
######################################
def parser(databases_directory, download=True):
result = {}
config = builder_utils.get_config(config_name="jensenlab... |
5b5d76daf3cdfced411c60d2b306d89d76e6cc28ce21338088950e7b8c82c985 | Python | 1,788 | 61 | # Taku Ito
# 2/21/2019
# Multiple linear regression (with L2 regularization option)
import numpy as np
def regression(data,regressors,alpha=0,constant=True,betasonly=False,residualsonly=False):
"""
Taku Ito
2/21/2019
Hand coded OLS regression using closed form equation: betas = (X'X + alpha*I)^(-1)... |
abfa77ad7475aeea3957654cf1d85e0defeab2bc4c2b2a40d902d9e2afff59d9 | Python | 1,788 | 50 | from pylearn2.utils import serial
import sys
_, model_path = sys.argv
model = serial.load(model_path)
from pylearn2.gui.patch_viewer import make_viewer
space = model.generator.get_output_space()
from pylearn2.space import VectorSpace
from pylearn2.config import yaml_parse
import numpy as np
match_train = True
if match... |
ac81a74326050b275ba6ed9fa5e075690e7d9b4274f692b51691d1445e44c5ca | Python | 1,788 | 55 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
c970cbf230d36da84950aa507eda8aba753894474582ba3b762d7138520059dd | Python | 1,788 | 48 | from . import loadDefaultParams as dp
from . import timeIntegration as ti
from ..model import Model
class WWModel(Model):
"""
Wong-Wang model. Original version and reduced version.
Main reference:
[original] Wong, K. F., & Wang, X. J. (2006). A recurrent network mechanism
of time integrat... |
e6284eeb96cda4fdadf0b6394681ca65ae7b921333dd1a514ef1be91437a5ddc | Python | 1,788 | 79 | import yaml
from src import conv_onet
method_dict = {
'conv_onet': conv_onet
}
def load_config(path, default_path=None):
"""
Loads config file.
Args:
path (str): path to config file.
default_path (str, optional): whether to use default path. Defaults to None.
Returns:
c... |
22ebfd768fa92e055f40b6ed6690c123fb9739bf62cac731508f569a4d40d760 | Python | 1,789 | 39 | import os
import pandas as pd
import numpy as np
import unittest
import sys
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/../../src")
from hornet.model import normalize
class TestNormalize(unittest.TestCase):
def setUp(self):
self.df = pd.DataFrame({
'etot': [1., 2., 3.],... |
5197ac0f113d9b7e0164050bda73369b7192fee3d9a7012681707c7a2b9db561 | Python | 1,789 | 59 | """
Optuna example that optimizes a classifier configuration for the Iris dataset using
scikit-learn and records hyperparameters and metrics using aim.
In this example we optimize random forest classifier for the Iris dataset. All
hyperparameters and metrics will be logged to aim via integration callback.
You can run... |
610d2e61a6567e1815959ec44955c93e5253da87693ed9012c22e7d29a4045b8 | Python | 1,789 | 80 | import os
import numpy as np
import torch
class BestMeter(object):
"""Computes and stores the best value"""
def __init__(self, best_type):
self.best_type = best_type
self.count = 0
self.reset()
def reset(self):
if self.best_type == 'min':
self.best = fl... |
1b893c1fcf2b53414a2375e7e3ebdcd2e824438bf7aae565334cdfe06235c5dc | Python | 1,791 | 50 | import yaml
import json
import argparse
import os
import open3d as o3d
parser = argparse.ArgumentParser(
description='Arguments for prep own data.'
)
parser.add_argument('--ouput_config', type=str, help='Path to config file.')
parser.add_argument('--scene_folder', type=str,
help='Path to the fol... |
26ccd52714846c85d156bff003254b9acb0e13c5013a8f8edf1df5c1ec04312b | Python | 1,791 | 63 | from typing import cast
from elasticai.creator.file_generation.in_memory_path import InMemoryFile, InMemoryPath
from elasticai.creator.nn.identity.layer import BufferedIdentity, BufferlessIdentity
def test_buffered_identity_generates_correct_vhdl_code() -> None:
expected = """library ieee;
use ieee.std_logic_116... |
b47029c6d8a7db0c4d7e2a30c43859c1b0ddec12acb1530fa026eba3c9cb5fa1 | Python | 1,791 | 61 | from setuptools import setup, find_packages
__version__ = '1.1.3'
URL = 'https://github.com/tczhangzhi/torcheeg'
install_requires = [
'tqdm>=4.64.0', 'numpy>=1.21.5', 'pandas>=1.3.5', 'xlrd>=2.0.1',
'scipy>=1.7.3, <= 1.10.1', 'scikit-learn>=1.0.2', 'lmdb>=1.3.0', 'einops>=0.4.1',
'mne>=1.0.3', 'xmltodict>... |
ded21297d3b4abfe7d284dd3bda1238c88521b02c105dde5c9cc5c374acc7704 | Python | 1,792 | 61 | import numpy as np
from netket.hilbert import AbstractHilbert
from netket.operator import ContinuousOperator, AbstractOperator
from .operator.tevo import UFromHOperator
COEFFS = [
(),
(1,),
((1 + 1j) / 2, (1 - 1j) / 2),
(
0.18673085336460016 + 0.4807738845503311j,
0.18673085336460016... |
3f1a6802a97e385b5ff3ed17c847c4c4aafe2b7f10e1244debe6c4f7fbb825ec | Python | 1,793 | 56 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Demonstrate usage of LFPy.NetworkPopulation with a ball-and-stick type
morphology with active HH channels inserted in the soma and passive-leak
channels distributed throughout the apical stick. The corresponding morphology
and template-specification is in the files BallA... |
4079f73fb7830b7ae565448e6d3138bf25d6855c36fdcf5f3d48f3f44f24914d | Python | 1,793 | 56 | import argparse
from pprint import pprint
import dataset as module_dataset
import datasets_manager as module_datasets_manager
import runner as module_runner
from parse_config import ConfigParser
from utils import set_random_seed
SEED = 1126
set_random_seed(SEED)
def main(config):
Datasets = {project_id: config.in... |
5248c42c8c47621c04226494ade26e5f2423bbab1a3b6dc512b168575da810cf | Python | 1,793 | 37 | from .common import MultiLayerPerceptron, GaussianSmearing, MutualInformation, PairNorm, InstanceNorm, Sequential, \
SinusoidalPositionEmbedding
from .block import ProteinResNetBlock, SelfAttentionBlock, ProteinBERTBlock
from .conv import MessagePassingBase, GraphConv, GraphAttentionConv, RelationalGraphConv, Grap... |
cd2898de6fcb3a9feac4e11d4630c15e9eefef2a23bb6c8b01ecb9821d834db0 | Python | 1,793 | 54 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites, configs
from downward.reports.compare import CompareConfigsReport
import common_setup
REVISIONS = ["issue462-base", "issue462-v1"]
CONFIGS = configs.default_configs_optimal()
# remove config that is disabled in this branch
del CONFIGS['asta... |
93b710a02c0862160cc45a85a126a5553d16bdc2a2a4a2c6e10d8703de876015 | Python | 1,794 | 65 | """Classes for generating and representing templates for HMP event detection.
Main class Pattern and including a half-sine wave template (`HalfSine`)
Classes
-------
Pattern - Main class
HalfSine
Generates a normalized half-sine wave template for use in
signal processing or event detection.
"""
f... |
20d68f29253e59035c18d694f398c865f44df07b8b72680002168b2cb664b2d9 | Python | 1,795 | 72 | import matplotlib.pyplot as plot
import math
import sys
import re
plot.figure()
A = len(sys.argv) - 1
assert A == 3
for a,fn in enumerate(sys.argv[1:]):
print(fn)
ss = []
hs = []
cs = []
es = []
with open(fn,"r") as handle:
for l in handle:
if not l.startswith("DATA"):
... |
299c9ab594c85b853794df7f7eb8764310d4349285afc62c734dce13cf352022 | Python | 1,795 | 39 | # 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
#
# ht... |
4b4e02043d7db13386dfdf3c920b72035079c2939e404789cd8c4174a8cc2124 | Python | 1,795 | 50 | """Run simulation
-----------------
Run a simulation of the mesocircuit model.
"""
###############################################################################
# Import the necessary modules.
import os
import sys
import mesocircuit.mesocircuit_framework as mesoframe
import mesocircuit.simulation.network as networ... |
a68c54a7a14f21d87f2481beb71b1c61f0d99a7178c5da902aa6973c3c8d450a | Python | 1,796 | 75 | import sys, os
sys.path.append(os.path.abspath("."))
import torch
import torch.nn as nn
import torch.optim as optim
from collections import OrderedDict
import prosper_nn.utils.neuron_correlation_hidden_layers as nchl
# %% Create dummy data
n_data = 10
X = torch.rand([n_data, 100])
Y = torch.rand([n_data, 1])
# %% ... |
e6f43ae519b340dc4c5bae4ebda9f53eece501eb030a21a20543389bc2ecffa5 | Python | 1,796 | 54 |
"""
Custom function
"""
import torch
def TwoStateFractionFoldedMod(
X = None,
trainable_parameters = {}):
"""
1-dimensional nonlinear transformation relating modified Gibbs free energy of folding to fraction of molecules folded.
:param X: list of tensors (required).
:param trainable_paramete... |
b6f8a52025e4e45651d9bdc7a4ffdb1de75905ed2d1b26c194125b064e578c38 | Python | 1,797 | 59 | import setuptools
import os
import sys
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
version_path = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(version_path)
from version import __version__
setuptools.setup(
name=__version__,
version="0.1.0",
... |
cee4f73ce29fb583f97e67352767ca9324583a6d7bc97799716a17d9e48c3c7c | Python | 1,797 | 51 | import librosa
import librosa.display
import io
import os
y, sr = librosa.load('7346_T1a_ADOS\\audio_clip.mp3')
y = librosa.effects.harmonic(y)
tonnetz = librosa.feature.tonnetz(y=y, sr=sr)
print(str(tonnetz))
import matplotlib.pyplot as plt
fig, ax = plt.subplots(nrows=2, sharex=True)
img1 = librosa.display.specsho... |
16c736acc7e83a929a85b28677462a96be9a2024a132afc7bfee9d84fd2ff51d | Python | 1,798 | 49 | import sys
import argparse
import os
import struct
import torch
from utils.torch_utils import select_device
def parse_args():
parser = argparse.ArgumentParser(description='Convert .pt file to .wts')
parser.add_argument('-w', '--weights', required=True, help='Input weights (.pt) file path (required)')
pars... |
56245f3f43c842a3d099e641e636a851b095212081d7d94a2596ef04946c54c5 | Python | 1,798 | 45 | #----Figure_05_A_preparation----------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code which has to be run in preparation for Figure 05 Panel A
# for the connectivity analysis for Reinhard and Fukuda et al. 2024
#---------... |
342980b907a2c43a5fe588d843131368ea46476e0f568f51b8ebd1e7023167f2 | Python | 1,799 | 65 | # Node group "Inverse Mass" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# Shared by several assets, which import it; not an asset itself.
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated... |
caf39c260ce8731cb14876b7a391375e806c638469ac8d5d8d9493dc415d6ccb | Python | 1,799 | 52 | """
A simple library to format JSON strings in the format that I wanted them.
Removes linebreaks that do not correspond to the start of a JSON object
"""
import re
import json
def clean_json_dump(object, fd, *args, **kwargs):
"""
Dump JSON representation of an object to a file
>>> clean_json_dump({'hello'... |
af7a4bbb4d69286db70d90bd4aecec00e2d133b69d20001a397947da3cd407d7 | Python | 1,800 | 51 | import urllib
import optuna
from optuna.integration.tensorboard import TensorBoardCallback
import tensorflow as tf
# TODO(crcrpar): Remove the below three lines once everything is ok.
# Register a global custom opener to avoid HTTP Error 403: Forbidden when downloading MNIST.
opener = urllib.request.build_opener()
o... |
29b6b96eec2c2ac938030ee09f097e0a8c4be05ae02353a59ab85feab6c7e203 | Python | 1,801 | 53 | import os
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
@R.register("datasets.Fold")
@utils.copy_args(data.ProteinDataset.load_lmdbs, ignore=("target_fields",))
class Fold(data.ProteinDataset):
"""
Fold labels for a set of proteins dete... |
6ffb59cb6f33f32d699916c18d0a12466064c0a0d9c4ee077c39bfdc66957dfe | Python | 1,801 | 46 | #----Figure_04_J_preparation----------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code which has to be run in preparation for Figure 04 Panel J
# for the connectivity analysis for Reinhard and Fukuda et al. 2024
#---------... |
11e3bf5de7d52fb3eee2233f0fccec321b43652b6265a4d9400beb0177f87944 | Python | 1,802 | 62 | #!/usr/bin/python3
import sys
import argparse
import os
import subprocess
def run_plink_regression(bfile, outfile, misspheno, pheno, covar,
chrom = None, remove = None, pheno_name = None):
cmd = ["plink", "--bfile", args.bfile, "--out", args.outfile,
"--missing-phenotype", args... |
4b8c345ced63ab81819d18ebb95c8602f00a27c9dd521a95a19ab8e92e19b7cd | Python | 1,802 | 50 | import torch
from torch import nn
def gaussian_smearing(distances, offset, widths, centered=False):
if not centered:
# compute width of Gaussian functions (using an overlap of 1 STDDEV)
coeff = -0.5 / torch.pow(widths, 2)
# Use advanced indexing to compute the individual components
... |
4bdd3e7675283f76e49d824bf88bb14099e0e454316e472e5dfe0cb7cdca2658 | Python | 1,802 | 50 | import torch.nn as nn
import torch
from models.tools import TransLastTwoC
def gen_mlp_module(node_dims, dropout=0.0, norm=True, activation='ReLU', last_acti=False, last_norm=True, norm_type='bn',
last_dropout: bool = None):
if last_dropout is None:
last_dropout = last_acti
... |
915067900111b247bb050584d56892af7dd52c5dc9bec9a658ebbebd53b850ec | Python | 1,802 | 66 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import itertools
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
from downward.reports.compare import ComparativeReport
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterP... |
fa7b8b77e530a599a9fb2b5b5ed807446e624dde5f2635acf089b67be05965b5 | Python | 1,802 | 50 | import io
import os
import unittest
from unittest.mock import patch
from hed.scripts.validate_bids import main
class Test(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data_root = os.path.realpath(
os.path.join(os.path.dirname(__file__), "../data/bids_tests/eeg_ds003645s_hed_d... |
1c8583881036f831c5855aeb741c1f3b61f117a518d500a350e166894d2dd746 | Python | 1,803 | 58 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_D... |
1cf83eb492448361ed2bdb0882c983a94ac372074085befd242db5cb3420793f | Python | 1,803 | 56 | import argparse
from pprint import pprint
import dataset as module_dataset
import datasets_manager as module_datasets_manager
import runner as module_runner
from parse_config import ConfigParser
from utils import set_random_seed
SEED = 1126
set_random_seed(SEED)
def main(config):
Datasets = {project_id: config.in... |
53a459d608dd0ebcfaf1fc69d75522f8c5c91a79e7eab0a26f67bfcf88161186 | Python | 1,804 | 62 | """Base jobs for DFTB+"""
from __future__ import annotations
from typing import TYPE_CHECKING
from ase.calculators.dftb import Dftb
from quacc.runners.ase import Runner
from quacc.schemas.ase import Summarize
from quacc.utils.dicts import recursive_dict_merge
if TYPE_CHECKING:
from typing import Any
from ... |
06f5a76b5168d1a02c0e73f92ae942c497885cdc468e73deaafd1e0e8c274bb9 | Python | 1,805 | 56 | # Plots smoothed population rates related to preferential parameters for Fig 6
from os.path import join
from get_paths import *
import pickle
import matplotlib.pyplot as plt
import numpy as np
from os.path import join
import h5py
from colors_paultol import colors
save_flag = 0
# Load simulation result... |
3447eb4fea478ff5f1c017926ac533235def7074cb4a7a18acad92dcb7ee848e | Python | 1,805 | 63 | import os
import re
import fnmatch
import torch
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
... |
f2e04aea1e01daad8b1c60baa1a228fd9a9495bed49cb822e093007efcdd9a28 | Python | 1,805 | 57 | # Cython compile instructions
import numpy
from setuptools import setup, Extension
from Cython.Build import build_ext
# To compile, use
# python setup.py build --inplace
#
extensions = [
Extension("pyshot",
sources=["pyshot.pyx", './src/shot_descriptor.cpp'],
include_dirs=[
... |
034df226042d9866bf37f88801d1cc21bb702e2c84f124fa810f35f127aefddd | Python | 1,806 | 54 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from lab.reports import Attribute, gm
from common_setup_no_benchmarks import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
def main(revisions=None):
suite = suites.suite_optimal_with_ipc11()
confi... |
07908679fa4f3b5f1b0431af78b94ba9ff7be3e5778f5793b671a8f70505ef2c | Python | 1,806 | 66 | """ Impute missing values using K-Nearest Neighbors"""
# import libraries
import numpy as np
import pickle
import sklearn
#from nmf_with_missing_values import nmf_with_missing_values
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import euclidean_distances
from sklearn.neighbors import KNeighborsRegress... |
68104d74cf85f9e6023a1a383baa849504c67a5997bf1b428a3766c22711b501 | Python | 1,806 | 56 | # -*- coding: utf-8 -*-
# @title Main
# @file main.py
# @author Romain Beaubois
# @date 06 Oct 2023
# @copyright
# SPDX-FileCopyrightText: © 2023 Romain Beaubois <refbeaubois@yahoo.com>
# SPDX-License-Identifier: GPL-3.0-or-later
#
# @brief
#
# @details
# > **06 Oct 2023** : file creat... |
c227733f022d66bb937f48802d03311d0f25982e6088cdfc70fea5145c473130 | Python | 1,806 | 70 | from function import *
'''
入力を受け取る
'''
parser = argparse.ArgumentParser(description='''
model training
''')
parser.add_argument("model_name", help="model name")
parser.add_argument("weight_path", help="weight_path")
args = parser.parse_args()
model_name = args.model_name
weight_path = args.weight_path
df = pd.read... |
95f440541997332dd0b60cac8779a2dc0aa372444ec93acb00077cbd7c648f12 | Python | 1,807 | 55 | import sys
import scanpy as sc
import anndata
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import cell2location
import scvi
from matplotlib import rcParams
rcParams['pdf.fonttype'] = 42 # enables correct plotting of text
import seaborn as sns
fpath = "/scratch/mshru... |
b0383938ac8f238e443331193b021fcdd47a5444c124fe8af84c606668cc643d | Python | 1,807 | 65 | from types import SimpleNamespace
import openmm
import pytest
from calvados.sim import Sim, _split_steps
@pytest.mark.parametrize("steps", [1, 7, 10, 11, 105])
def test_split_steps_preserves_requested_total(steps: int) -> None:
batches = _split_steps(steps)
assert sum(batches) == steps
assert len(batch... |
75acaa11d2bf7f9aaf10942a5408684736b72a486cc715a6d18b0740ecd24dfd | Python | 1,808 | 43 | # 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
#
# ht... |
7867acff53bb5fd53be76db809bbf574ebdc315e9cf17dea1c72f9acde20d59c | Python | 1,808 | 69 | # Node-group asset "Rotation cisTEM" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKI... |
cfaf1c416c30d32fb4e7754e807dd2cc3d0c09ed61ee932a98231eba8352c873 | Python | 1,808 | 71 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
from torch.optim import Adam
from collections import deque, namedtuple
import copy
from .initi_update import soft_update, hard_update, weights_init_
class Actor(nn.Module):
def ... |
72d40843f829135c43892a07d4319082f069b03470070658ad9b4ef5528d1a8c | Python | 1,809 | 59 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
91789a28243c71dc4084027808f805f359a452ba8371c0db16f7463cb6947a45 | Python | 1,809 | 55 | from PIL import Image
import numpy as np
import h5py
import os
import sys
import cv2
# Maybe consider implemeting more involved auto-balancing
# http://wiki.cmci.info/documents/120206pyip_cooking/python_imagej_cookbook#automatic_brightnesscontrast_button
def open(fn):
print "Opening " + fn
ext = os.path.splitext(fn... |
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