sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a51e264e1ef7dfd54c6d78a98007e84b242161fac116363d04dfe291cefb8d92 | Python | 6,812 | 178 |
import torch.nn as nn
from torchvision import models
# from torchvision.models.utils import load_state_dict_from_url
from torch.hub import load_state_dict_from_url
from torchvision.models.resnet import BasicBlock, Bottleneck
import copy
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
... |
ff64f0686451a47537fd9d717f313e9e1e45dc6b85fc656b82a2109f7b74f347 | Python | 6,812 | 167 | # 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... |
ff0af1c27a430cb9e98511836a3fac0726b44bfc567223548c5bee5ce4b5d6e4 | Python | 6,813 | 188 | #! /usr/bin/env python
# Time-stamp: <2021-03-23 22:02:10 christophe@pallier.org>
# License: Creative Commons Attribution-ShareAlike CC BY-SA
""" Implementation of Posner's endogeneous attention cueing task (see https://en.wikipedia.org/wiki/Posner_cueing_task)
Note: This experiment is meant to be run on FullHD (1920... |
3ca196772c45421aa9fc2a13a30923eea974245f0e67a5251a3d33b7fc9aca65 | Python | 6,814 | 201 | # 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... |
1d8ce1ddb0d46f61277e1b8601b1308576f78a49191818ef533faadd1a95d317 | Python | 6,815 | 189 | import pygame
import os
from dataclasses import dataclass
pygame.font.init()
pygame.mixer.init()
@dataclass
class G:
# Constants
ASSETS_PATH = os.path.join(os.path.dirname(__file__), "assets")
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
RED = (255, 0, 0)
YELLOW = (255, 255, 0)
FPS = 60
H... |
e7dff93904c929e123a0a4e11323f80a6a93c67a4d71fee2b17c739e16176a60 | Python | 6,818 | 217 | import glob
import os
import re
from enum import Enum
from pathlib import Path
import Calculation
import matplotlib.pyplot as plt
import pandas as pd
import Dicts
folder = '../../Experimente/'
# possible operations: LSF, ESF, CT
class Names(Enum):
FDG_11 = 'FDG_11'
FDG_12 = 'FDG_12'
FDG_61 = 'FDG_61'
... |
9077577f97e4a9a8cfac39dc3cebaece67059616053a279fc8fa9d67b7466a30 | Python | 6,819 | 167 | # Publication: "Protein Docking Model Evaluation by Graph Neural Networks", Xiao Wang, Sean T Flannery and Daisuke Kihara, (2020)
#GNN-Dove is a computational tool using graph neural network that can evaluate the quality of docking protein-complexes.
#Copyright (C) 2020 Xiao Wang, Sean T Flannery, Daisuke Kihara, and... |
af2fc402ed72592019e7636cfad0035f25727dca2ed0d4ff747a53510dd4c85d | Python | 6,819 | 156 | from typing import List, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.utils.data import DataLoader
from ..classifier import ClassifierTrainer
class EQLoss(nn.Module):
def __init__(self,
class_frequency: List[int],
... |
209760492227e8dc0416faf37205b60228d41a2e221155ebf0bbcaa0d2c0d33d | Python | 6,820 | 183 | # NQS class simulators
import numpy as np
import scipy as sp
import scipy.stats as stats
import pyclamp.dsp.discprob
from pyclamp.qnp.qmod import *
from pyclamp.dsp.optfunc import *
from pyclamp.dsp.fpfunc import *
def amp2wave(_A, si = 0.001, e = 0.05, T = [-2., 8.], ldc = [2., 1.], sn = None):
nd = nDim(_A)
A =... |
5956c6aaa6caeecd5c549d645ed576c7b5f4b58cef066acb8ab2c4517a040f47 | Python | 6,820 | 162 | from utils import *
class BoundingBox:
def __init__(self,
imageName,
classId,
x,
y,
w,
h,
typeCoordinates=CoordinatesType.Absolute,
imgSize=None,
bbType=BBType.G... |
9358b9914adf78e46510992c10afd4d1cb67f216ecedb5b1b9f7854a7bce4469 | Python | 6,821 | 167 | from pydrake.geometry import (ConnectDrakeVisualizer, DispatchLoadMessage)
from pydrake.lcm import DrakeLcm # Required else "ConnectDrakeVisualizer(): incompatible function arguments."
from pydrake.systems.framework import DiagramBuilder
from pydrake.systems.primitives import SignalLogger, Demultiplexer, LogOutput
d... |
367fb327afc13dfcea4bcdb623e97dc086f6fb1544d4a3a6fc69b938395b127e | Python | 6,822 | 224 | import cocotb
import numpy as np
import pytest
from cocotb.clock import Clock
from cocotb.triggers import ClockCycles, FallingEdge, ReadOnly, RisingEdge
from cocotb.types import LogicArray
from cocotb.utils import get_sim_time
from elasticai.creator.arithmetic import FxpParams, int_converter
from elasticai.creator.tes... |
3b1a8a1de570e91c7fe5515e48e2324d1ff8c1e00e67454b91a75ff3556dbfd9 | Python | 6,822 | 140 | from pddlstream.algorithms.common import add_facts, add_certified, is_instance_ready, UNKNOWN_EVALUATION
from pddlstream.algorithms.algorithm import remove_blocked
from pddlstream.language.constants import OptPlan
from pddlstream.language.function import FunctionResult
from pddlstream.language.stream import StreamResul... |
3402d6f336496e010bdbf3f7b3836e5ea201ea47f7af31e22e8c57173286f374 | Python | 6,826 | 193 | from curtsies import Input
import co2 as CO2
import co2_controller
import temp as Temp
import temp_controller
import adc as ADC
import multiprocessing as mp
import threading
import i2c as I2C
import logger
import numpy as np
import time
import RPi.GPIO as GPIO
def check_for_key_press(key_presses):
with In... |
34fc52af65db5246cea22e31166b09dd5e846565170866ebca36feaa2d6a82a0 | Python | 6,827 | 195 | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..initializer import create_initializer
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channels, out_channels, stride):
super().__init__()
self.conv1 = nn.Conv2d(
in_channels,
o... |
d1cac5be231fb46b88cb30716eebb485592d3878f2cf1a641cd066136c8f3be2 | Python | 6,827 | 173 | """
Prepare the input for PyOrg analysis package (Tutorial for unsigned membranes)
Input: - STAR files for the particles
- Surface models
- Surface models for simulations
- models resolution
- Segmentations:
+ Segmentation paths
... |
dbd5b2cdd5520d628af30d2ec6aa6ad63ade3b4b48ab2012325fde293f4f49a9 | Python | 6,827 | 204 | # 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... |
11e525c3eb0e27eb6d87c3acd46906101d4d68e399325332e8b4bc052e0b4b7c | Python | 6,828 | 222 | # 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... |
1078dc22dce1d8b61b6dccacad762db69c0053c49bf08be13c68a077ec7df144 | Python | 6,829 | 206 | # 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... |
4db205c8103cdb98c8300e4bbef37dc707c816432a10006b079aed1ac8f39d3d | Python | 6,829 | 154 | import math
import numpy
from scipy import stats
from OnlineDetectors.online_nab_detector import OnlineAnomalyDetector
class RelativeEntropyDetector(OnlineAnomalyDetector):
"""
This detector is an implementation of online anomaly detection using Relative Entropy statistic with multiple
hypotheses as des... |
bab801958dea91715fd6066a2a2639e9133841d1d701c32e2b8443b80246984e | Python | 6,829 | 200 | """
Optuna example that optimizes multi-layer perceptrons using PyTorch with checkpoint.
In this example, we optimize the validation accuracy of fastion product recognition using
PyTorch and FashionMNIST. We optimize the neural network architecture as well as the optimizer
configuration. As it is too time consuming to... |
79ad6ee79fb925a37f6b05a371a1d6b156eb20c6edbf37141d0c159ce43740ef | Python | 6,831 | 178 | """
Unit tests for NIfTI creation helpers in regtools.nii_creation.pipeline.
Covers:
- create_nifti_image
- _load_sections_from_multipage
- create_nii_images
"""
import os
import tempfile
import shutil
import numpy as np
import nibabel as nib
import pytest
DATA_DIR = os.path.join(os.path.dirn... |
dd983d4e39d8f3d82dcf1536038772c8d05ff64a390f75c1490f38f773bfbf19 | Python | 6,831 | 232 | # 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... |
d40dbd9f8ce2beee0854ae8556ae7f605d9206627f670745e97058aa95f65930 | Python | 6,833 | 196 | from utils import load_dataset, load_snp, null_dist_score1, FilterLCC, load_score
from sklearn.cluster import SpectralClustering
import pickle
from collections import defaultdict
import multiprocessing
from scipy.stats import norm
import numpy as np
def sig_score(gene):
gene_clust = set(cluster_results[gene])
... |
b4286695ef2a641be7f6fd02e1cecbc1b2ad25f21878b225ed502d81a93b223a | Python | 6,834 | 195 | from utils import load_dataset, load_snp, null_dist_score1, FilterLCC, load_score
from sklearn.cluster import SpectralClustering
import pickle
from collections import defaultdict
import multiprocessing
from scipy.stats import norm
import numpy as np
def sig_score(gene):
gene_clust = set(cluster_results[gene])
... |
835a47cccdb6564e645b878a147a1e0bf970790d42eb222592770652e0d8f71e | Python | 6,835 | 171 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
''' Do stats on timecourses extracted for regions of interest '''
proj_name = 'MINDLAB2020_MEG-AuditoryPatternRecognition'
wdir = '/projects/' + proj_name + '/scratch/working_memory/'
scripts_dir = '/projects/' + proj_name + '/scripts/working_memory/'
import sys
sys.path... |
9ec1d00d1bc0c193d4a490b67c22d8e8e8f8da303a064d5ba6fb0021185b32c9 | Python | 6,836 | 246 | # 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... |
5814a4a13316792e3b7c49362d109cf15c9fdb5901baeca1ee6cb1a891adee88 | Python | 6,837 | 191 | import warnings
warnings.filterwarnings('ignore')
import pandas as pd
from sklearn.utils import shuffle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from scipy.stats import pearsonr
from sklearn.linear_model impo... |
7cfd9e5b8eea59746d9bbbb115a732ce6211a178b1efc61a0484327fb8d3b07f | Python | 6,838 | 205 | # 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... |
99ba1db486070df1836062b33d652bb9aee3541df80f2bd0d89af496c77aa0d7 | Python | 6,839 | 188 | import os
import yaml
import glob
import warnings
def expand_config(config):
## accepts a human-generated config dictionary
## and adds in a bunch of entries for access later on
config['predictions_dir'] = os.path.join(config['output_dir'], 'predictions')
config['temp_dir'] = os.path.join(config['output_dir... |
52b5fa3e2e426ad8184b97121ecdeeaadd53eabc6ba6a8efe0bf8cd45a4a07ba | Python | 6,840 | 146 | # Main script to carry out the Snakemake rules of TrAnnoScope
from snakemake.utils import min_version
import yaml
import os
import sys
import argparse
import subprocess
import logging
# Set minimum Snakemake version
min_version('6.0')
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %... |
e6ae71384a01bd9b35a9bef8834af2fb039e565c494b37934b9a47ee382526dc | Python | 6,840 | 187 | import io
from collections import deque
from uuid import uuid1
import bl_ext.blender_org.molecularnodes as mn # type: ignore
import bpy
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from MDAnalysis.core.groups import AtomGroup
from PIL import Image
matplotlib.use("Agg")
# Annotations are auto-... |
c62013288cfbd68aa92517d347924eb88c6488aa8bbd00253346a8e3ad21c596 | Python | 6,842 | 226 | import os
from urllib.parse import urlparse
import fsspec
import numpy as np
import pandas as pd
import pandas._libs.json as ujson
from flask import make_response
from cirrocumulus.envir import CIRRO_DATASET_PROVIDERS
try:
dumps = ujson.dumps
except AttributeError:
dumps = ujson.ujson_dumps
def add_dataset... |
2b4c538005c8326c429d21a60bf2409a965ab5eba80ed60fcf94f1dcdc69f254 | Python | 6,843 | 181 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2024 Clinic for Diagnositic and Interventional Radiology, University Hospital Bonn, 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 ... |
a575a891f1482c74bcdadec965d7adcc8694f201e21d0fedf2a74c9d6ad5386b | Python | 6,844 | 174 | from unittest import TestCase
from codex.data.structured_search_filters import parse_search_query
from codex.utils.parsing import tokenize_and_fold_for_highlight
from codex.utils.formatting import (
highlight_annotations,
truncate,
nanometer_to_flywire_coordinates,
nanos_to_formatted_micros,
displa... |
dce1af50ec8b9740a7e69b9481f5bb25be7f539c7ab8e7f04f837607c9caa9a5 | Python | 6,844 | 189 | # Copyright (c) OpenBII
# Copyright (c) Lynxi Technologies Co., Ltd. All rights reserved.
# Copyright (c) OpenMMLab. All rights reserved.
import os, time
import torch as pt
import torch.nn as nn
from torch import ops
from torch.nn import Flatten
import numpy
from ..builder import BACKBONES
from ..layers.lif import C... |
23846b80bd8207e48a61a16ee1524bc6fb39eb06652a8297c373246ac161995b | Python | 6,845 | 241 | # 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... |
4c0b6837f89cd8d80bb98d9647ed7889b96ae7dcd676f44b77b58cf1a2f715d4 | Python | 6,845 | 192 | # part of this script was taken from https://github.com/jocicmarko/ultrasound-nerve-segmentation
import argparse
from glob import glob
import numpy as np
from PIL import Image
from keras import backend as K
from keras import losses
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from kera... |
e70839543e11042d6319aa186ea144015c23b0f1338fca27ce5eacf72a169c88 | Python | 6,845 | 207 | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..initializer import create_initializer
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channels, out_channels, stride):
super().__init__()
self.bn1 = nn.BatchNorm2d(in_channels)
... |
6afe288973d74525f32816bb3488754f96a0b54a19d758f1b3781bdac709dcac | Python | 6,847 | 158 | import os
from scipy.io import loadmat
import numpy as np
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from datasets.SequenceDatasets import dataset
from datasets.sequence_aug import *
from tqdm import tqdm
from itertools import islice
#Digital data was collected... |
c7925e53d5210d06c7c74e1de576ae9a68869cd1610bedc2b532ee8653eedf2d | Python | 6,847 | 207 | import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import euclidean_distances
from sklearn.linear_model import LogisticRegression
from sklearn.svm ... |
ef0728f9aa73f7095e99c52cebb6abfe1ce14f7c3d02118f4b739f2df36d5021 | Python | 6,847 | 183 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# 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 use, copy, modify, merge,... |
bd32b9ce176784a69efa9897664017b26c2b3e6d1490dac929c3618a051de58a | Python | 6,848 | 195 | #!/usr/bin/env python
'''
LFP simulation script for 4x4 mm2 network model
But the network is implemented with spatial connectivity, i.e., the neurons
are assigned positions and distance-dependent connectivity in terms of
cell-cell connectivity and transmission delays.
Synopsis of the main simulation procedure:
1. Loa... |
53b7cf4fd11dddd4379ad4a740eeec73b2ff2b51fc33aa3db5ab6a7110f31c16 | Python | 6,849 | 122 | #------------------------------------------------------------------------------------------------------------------------
#Author: Brandon S Coventry
# Date: 05/04/22 May the 4th be with you
# Purpose: This easily conducts a heirarchical bayesian linear regression. The goal of this program is to be more "plug
# and pl... |
adf2ac563fa5a5dcf10e413aabe25e7c84945d63d2369ffe341e13c82f3c536b | Python | 6,849 | 213 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# 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 use, copy, modify, merge,... |
eb840358baf98d837c4e48f05254021ffc01da3bc597c1d15559c398445ceed2 | Python | 6,849 | 145 | import os
import torch
from torch import nn
from torch_scatter import scatter_mean, scatter_add
from torchdrug import core, layers, utils, data
from torchdrug.layers import functional
from torchdrug.core import Registry as R
@R.register("models.Physicochemical")
class Physicochemical(nn.Module, core.Configurable):... |
201dded4dbda0ed74e1108801487262aaebdb8073c05efdd099d8c2eb0dc2095 | Python | 6,850 | 185 | ##
# \file convert_nifti_to_dicom.py
# \brief Script to convert a 3D NIfTI image to DICOM.
#
# \author Michael Ebner (michael.ebner.14@ucl.ac.uk)
# \date Mar 2018
#
# NOTES (quite some candidates were tried to get a working solution):
#
# - nifti2dicom (https://packages.ubuntu.com/xenial/science/nifti2d... |
ff26dfb4620b3b2219529f067f4c63b2ff3e036a5b01c0643c3779219ef090d6 | Python | 6,850 | 202 | from json import dump, load
from PyQt5.Qt import Qt
from PyQt5.QtGui import QColor
from PyQt5.QtWidgets import (QToolBar,
QToolButton,
)
from wonambi import Wonambi
from .test_scroll_data import (channel_make_group,
find_in_qt,
... |
d0f4e2709d195e56640aa30c7471d43c374f1fb44ff2b82040e96d853d23a489 | Python | 6,851 | 226 | #...............................................................................
#
# This file is part of the Doxyrest toolkit.
#
# Doxyrest is distributed under the MIT license.
# For details see accompanying license.txt file,
# the public copy of which is also available at:
# http://tibbo.com/downloads/archive/d... |
0e0d6e8de0ca31d85e8ebc2bfc204e47713241a924b25df5cd9c4bb50ea59c51 | Python | 6,852 | 175 | import io
import os
import json
import jinja2
from PIL import Image
from rdkit.Chem import AllChem, Draw
path = os.path.join(os.path.dirname(__file__), "template")
def reaction(reactants, products, save_file=None, figure_size=(3, 3), atom_map=False):
"""
Visualize a chemical reaction.
Parameters:
... |
1474f30bce9a32741f550327707fc0528861faa6e30f08a68861a7283c701341 | Python | 6,852 | 170 | """Tests for validate_hed_tabular script."""
import io
import json
import os
import tempfile
import unittest
from unittest.mock import patch
import pandas as pd
from hed.scripts.validate_hed_tabular import main
class TestValidateHedTabular(unittest.TestCase):
"""Test validate_hed_tabular script functionality."... |
4bccd586016152ae6de5b6b0fa36b843ff2b3a7aec319c7cf29da09a06f690af | Python | 6,855 | 179 | # Node-group asset "Build Elastic Network" (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_... |
5baf73ea92c98a2c6ed6e78eafd9f0ca8a5742cbc7c53bfd4f69973ec20bfa4b | Python | 6,855 | 175 | from analyse import *
import MDAnalysis
from MDAnalysis import transformations
import time
import os
import glob
import sys
import pandas as pd
import numpy as np
import mdtraj as md
import itertools
from mdtraj import element
from argparse import ArgumentParser
from scipy.optimize import least_squares
import time
par... |
c71dd32bcce32ed3b32565b58365a2434da8e1ca2fd80dfe13f984efd409d663 | Python | 6,855 | 206 | # 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... |
782794fceb5c4bd1bf7b28cef361d1e332ddac3f4dedc5085bb6f59b29d11153 | Python | 6,856 | 194 | """Device interface class."""
import json
from abc import ABC, abstractmethod
from ctypes import _SimpleCData, byref, c_char_p, c_int32, create_string_buffer
from logging import Logger
try:
import spcm_core as spcm
except Exception as exc: # noqa: BLE001, spcm_core raises a bare Exception if the driver is missing... |
3c65b9a3ca220b932580a9257086ae6e2d007a6c1ce3dbb4f3cdd2b92b11355f | Python | 6,858 | 219 | from __future__ import annotations
from ase import Atoms
from ase.build import bulk, molecule
from quacc import Remove, change_settings
from quacc.recipes.aims import _base, core
from quacc.utils.kpts import kspacing_to_kpts
def _record_calls(result):
calls = []
def recorder(*args, **kwargs):
calls... |
4b7536c5b076d1b230804e23e4584418dcd3dee23c5d33a013f2a9826ba018f8 | Python | 6,858 | 165 | from __future__ import print_function
import numpy as np
from examples.pybullet.utils.pybullet_tools.pr2_primitives import Conf, Trajectory, create_trajectory, Command
from examples.pybullet.utils.pybullet_tools.pr2_utils import visible_base_generator, get_detection_cone
from examples.pybullet.utils.pybullet_tools.ut... |
fc6582c05d5f5195664fa69c6d8173dd23d799efc7a1db4b5c8f86ad4299129e | Python | 6,859 | 200 | """Custodian handlers for VASP."""
from __future__ import annotations
import os
import shlex
from typing import TYPE_CHECKING
from custodian import Custodian
from custodian.vasp.handlers import (
FrozenJobErrorHandler,
IncorrectSmearingHandler,
KspacingMetalHandler,
LargeSigmaHandler,
MeshSymmetr... |
2b2ce78d62fa23e14121878a300b4a42685c1ffb5d2167bbc894408eb64dd977 | Python | 6,862 | 190 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Written by Tong He and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
"""
import os
import time
import random
import argparse
import numpy as np
from keras import backend as K
from keras.optimizers import SGD
from config import co... |
12b0645bb60aa43e23766ce60f394777295c370668b307d973d7fa73ab9af70f | Python | 6,863 | 206 | # -*- coding: utf-8 -*-
import numpy as np
import torch
import torchvision
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.utils.data as data
from torch import optim
from torchvision import datasets, transforms, models
from torchvision.models import resnet18, resnet50, ResNet50_Weights, efficie... |
fcce3c294d3b51451399e17bd18f68f9364082ce6e85bf08990de47f121388a5 | Python | 6,865 | 202 | """GUI wiring for the 5 advanced affine parameter fields -> --aff_* flags.
Two layers, mirroring test_reg_effort_cli_args.py (pure) and
test_parameters_tab_ants_overrides.py (Qt introspection + live widget).
"""
import inspect
import os
import sys
PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname... |
246683469e4b155bf97962c7862e3d9ddf32aa99badcdf276606b73b4fe900f3 | Python | 6,867 | 200 | import pytest
from elasticai.creator.file_generation.in_memory_path import InMemoryFile, InMemoryPath
@pytest.fixture
def saved_files(relu_layer, inputs):
relu_layer.forward(inputs)
relu_layer.eval()
design = relu_layer.create_design("relu_0")
destination = InMemoryPath("relu_0", parent=None)
de... |
da5ef014c33b9d48033b7269dc8afce93d4ae64aa4a2696c618c54633997ef9d | Python | 6,868 | 236 | import warnings
import numpy as _np
from autograd import builtins
from autograd.extend import notrace_primitive, primitive
if _np.lib.NumpyVersion(_np.__version__) >= "2.0.0":
from numpy._core.einsumfunc import _parse_einsum_input
else:
from numpy.core.einsumfunc import _parse_einsum_input
numpy_version = _... |
72149d5b98e5ff19a6c5126b7581f94a4000d8ec508ae8d5075c9aa593d5f0e7 | Python | 6,871 | 66 | """Render actual recorded state arrays; no simulated or decorative activity."""
from __future__ import annotations
import html
import json
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
def render(results: Path) -> None:
summary = json.loads((re... |
b0dbc87b9ec6e4584827ec75b3f9a2503c7f8edc0f58c2f577118108c1fb4771 | Python | 6,871 | 153 | # Importing necessary libraries for molecular processing, voxelization, rotation, and data preparation.
import torch
import rdkit
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import MolStandardize
from moleculekit.util import uniformRandomRotation # For random 3D rotations
from moleculekit.sma... |
b95fe78bfd24a855add429d372dfc6898afa45f0793322ed1b0e11ba96d28cb8 | Python | 6,871 | 220 | __author__ = "Zoe Wefers"
__credits__ = ["Ryan Huang"]
__version__ = "1.0.0"
__maintainer__ = "Zoe Wefers"
__email__ = "zoe.wefers@mail.mcgill.ca"
"""Purpose: Performs all the image cleaning, smoothing, processing etc to generate prints for every annoated cellular compartment
and skeletons for every dendrite."""
impo... |
ce21a905c20804a0a86e4a95a7133f9033752fdd23140bb6f912ca8c60a48da2 | Python | 6,874 | 122 | # 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... |
fad8926595724ebeb07d56909ee73c3e10106bac19e7f15ead90a88eed5e62e0 | Python | 6,874 | 199 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import numpy as np
import random
# write down train data path
data_path = '../../data/dream/train_sequences.txt'
# Globals.
base2int = {'A': 0, 'C': 1, 'G': 2, 'T': 3, 'N': 4}
one_hot_matrix = torch.t... |
9a30b16f3542e812daa3e715ce61d966692d1ef54e2ca35b9f71ba7b8a723056 | Python | 6,878 | 157 | from typing import Dict, List, Union
import numpy as np
from ..base_transform import EEGTransform
class PickElectrode(EEGTransform):
r'''
Select parts of electrode signals based on a given electrode index list.
.. code-block:: python
from torcheeg import transforms
from torcheeg.datase... |
dba27ad199843afb0f9b69da6e074debdc05992becfcb6ac4cbb754884be35b7 | Python | 6,878 | 124 | # 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... |
14772069d6327ffb4d0d9e4ae50627bfae2bbed4a489a6688014602f807e224f | Python | 6,879 | 226 | import time
import warnings
from importlib.util import find_spec
from pathlib import Path
from typing import Any, Callable, Dict, List
import hydra
from omegaconf import DictConfig
from pytorch_lightning import Callback
from pytorch_lightning.loggers import Logger
from pytorch_lightning.utilities import rank_zero_only... |
11a240a58ddbba4488aac9268cfdc3d5a95be43cb2d945b1d786baf9dc6a3278 | Python | 6,880 | 215 | # 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... |
67a86476a4004e5ce4aae51a2927107ee8221efc5c8223d9966a60e80cf11f94 | Python | 6,880 | 170 | # -*- coding:utf-8 -*-
import torch
from torch import nn
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
import os
from datetime import datetime
from sklearn.metrics import matthews_corrcoef,confusion_matrix,... |
87c6c0d2035c2ba4998f47bfd3f76ccf472595b96bb8cf07005ca83fb81a7e3b | Python | 6,881 | 218 | # Parameters
import argparse
# Create an ArgumentParser object
parser = argparse.ArgumentParser(description='Feedforward neural networks for pseudobulk data')
# Add parameters to the parser
parser.add_argument('--inPath', type=str, help='Folder with the input data')
parser.add_argument('--resultsPath', type=str, help... |
45d636589305540dbbf073dee00f09dde244c529ee3b2597c6e98644c583551e | Python | 6,882 | 176 | import time
import torch
from helpers import list_of_distances, make_one_hot
def _train_or_test(model, dataloader, optimizer=None, class_specific=True, use_l1_mask=True,
coefs=None, log=print):
'''
model: the multi-gpu model
dataloader:
optimizer: if None, will be test evaluation
... |
c011589b5f34a9e6bd24ab7ffd4ce14653513617333c31436aa183db5b1bbaca | Python | 6,882 | 217 | """
pygments.sphinxext
~~~~~~~~~~~~~~~~~~
Sphinx extension to generate automatic documentation of lexers,
formatters and filters.
:copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
:license: BSD, see LICENSE for details.
"""
import sys
from docutils import nodes
from docutils... |
38528922a5b848d366a918aaaba7111e702f98f2736adb81cd59a7b66d0484e5 | Python | 6,883 | 178 | import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.animation as animation
import time
import ray
import os
from neuron_model_parallel import NeuronSim
from elec_field import sparse_place_human
from pulse_train import PulseTrain_Sinusoid... |
04faaf250e4ba7e43a5529070658e27df8dd2c6d8b013b6e0494291bdfa29b56 | Python | 6,884 | 207 | import numpy as np
# from HypHC_master.utils.lca import hyp_lca
from manifolds.poincare import PoincareBall
import torch
def norm(x, axis=None):
return np.linalg.norm(x, axis=axis)
#-------------------------
#----- Poincaré Disk -----
#-------------------------
# NOTE: POSSIBLE ISSUE WITH DIFFERENT WAYS TO SPECIF... |
18b7a90a55a5aef6e6633ea540530d473c6dff9ca5a72aa19ea51aa34f763d9e | Python | 6,886 | 161 | """Hover-feedback mixin for the CCF boundary editor.
Carries the on-canvas hover behaviour: the scene mouse-move handler
(``_on_scene_mouse_moved``) that updates the cursor coordinate readout and,
when enabled, shows a region-info tooltip; the per-region cell-count chunk
used in that tooltip (``_hover_cell_chunk`... |
a5ec6a7faa3f47a5156388604e3688140edbddf6732b139a2ec17549de2238da | Python | 6,886 | 212 | # Node-group asset "Dihedral Chi Angle" (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_CHE... |
4465135712548fb7fb08fbe9da3e37e2f78009983f54c6852b42715917c031f4 | Python | 6,889 | 249 | """
Verifies that the magnitude and phase of the reflection coefficient of a
total internal reflected (TIR) mode of a flat interface of two lossless
materials given an incident planewave at oblique incidence computed using
the mode-decomposition feature matches the Fresnel equations.
ref: https://meep.readthedocs.io/e... |
2ade7887ad0971c42f2af8ca4fa39e12f64228c81e320e64c5c7ac37b114db8f | Python | 6,890 | 124 | # 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... |
8f68244a76354eba50c3aa64922c53fcdde4ab85c753f04e42c5c09cb51a2fce | Python | 6,890 | 236 | import numpy as np
from sella.linalg import ApproximateHessian
from sella.optimize.restricted_step import TrustRegion
from sella.optimize.stepper import (
PartitionedRationalFunctionOptimization,
QuasiNewton,
RationalFunctionOptimization,
)
class _StationaryQuadraticPES:
dim = 3
def __init__(sel... |
cffc89f243f1bb6fdee0d964e773085187a571c7b9c9c6889e2c273e34a3eedb | Python | 6,890 | 181 | """Implementation of CLI view command."""
import matplotlib.pyplot as plt
import matplotlib as mpl
from cycler import cycler
from treem.morph import Morph
from treem.morph import DGram
from treem.io import SWC
from treem.utils.plot import plot_neuron
from treem.utils.plot import plot_section
from treem.utils.plot i... |
d8ecdd688a6999b682adafbd8de2ffc325c120b023dc325843228f65802ca9c6 | Python | 6,894 | 157 | from jax import random, numpy as jnp, jit
from functools import partial
from ngclearn.utils.optim import get_opt_init_fn, get_opt_step_fn
from ngclearn import resolver, Component, Compartment
from ngclearn.components.synapses import DenseSynapse
from ngclearn.utils import tensorstats
class CSDPSynapse(DenseSyna... |
f9ae91c2c8bb7fe86ad1eacf0b126a24b219a448cb8c07e9bdb94de451509473 | Python | 6,894 | 178 | # Implements black-box variational inference, where the variational
# distribution is a mixture of Gaussians.
#
# This trick was written up by Alex Graves in this note:
# http://arxiv.org/abs/1607.05690
import matplotlib.pyplot as plt
import autograd.numpy as np
import autograd.numpy.random as npr
from autograd impor... |
864133440c176b7faaf679ded3168dee8cee39e9ba778ed40e41d915f297793c | Python | 6,895 | 178 | #
# Copyright Bradley Lowekamp
#
# 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.txt
#
# Unless required by applicable law or agreed ... |
c5bfae611331cad30bec913516365cc249302051bc745af6f2e7aed9fd50e9cb | Python | 6,895 | 188 | """
Created on Thu Mar 11 16:07:12 2021
@author: Haotian Teng
"""
import os
import toml
import torch
import itertools
import numpy as np
from typing import Union,Dict
from torch.utils.data.dataloader import DataLoader
from xron.xron_model import CRNN, REVCNN, CONFIG,DECODER_CONFIG,MM
class Trainer(object):
def _... |
4c270223c77822de971d28f930031802a89e8d0e63550da0329161de73b780f0 | Python | 6,896 | 172 | """Tests for the 2D match_fixed_resolution option.
When ON (the 2D default) the moving image's longest side is capped at the
template's, with its µm pitch scaled to match. Crucially, uploaded cell points
must project correctly through that resize (they ride the existing
moving_load_scale x point_scale composition... |
aaa3ad476114d0e0d0e1a63fcc93cc2e824e4e7d4ebf4cfede598683791b857c | Python | 6,896 | 183 | # 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... |
2d3e29855809e36b197df63af1bfdd2aaaa9027aaab112bc6ef443b61d8ef9e0 | Python | 6,897 | 184 | import argparse
from datetime import datetime
from pathlib import Path
from warnings import filterwarnings
import lightning.pytorch as pl
import pandas as pd
import torch
import yaml
from tqdm import tqdm
from dataset import TCGA_Program_Dataset
from datasets_manager import TCGA_Balanced_Datasets_Manager, TCGA_Datase... |
775a43e9c32119a4cd7df0960c99126d7d33700ef09b7cf6243f58ff779ed2f8 | Python | 6,897 | 161 | # 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... |
b077080e0e45367596d6b297365fd9428ccab95fc7798ca6b9a0f15c39ca6136 | Python | 6,898 | 318 | from __future__ import annotations
import pytest
prefect = pytest.importorskip("prefect")
import time
import psutil
from quacc import change_settings, flow, job, subflow
n_cpus = psutil.cpu_count() or 1
def test_patch():
@job
def task1(val: float) -> dict:
return {"input": val, "result": val * 1... |
d82e3c2e98a3e53f90429b9249823a7be50967e478ae407f43a70921d412441d | Python | 6,901 | 135 | import os
import sys
code_path = os.path.split(os.path.split(os.getcwd())[0])[0]
sys.path.append(code_path)
import numpy as np
import argparse
from setting import *
from utils import *
from loader import *
from feature_selection_sklearn import *
# Arguments
argParser = argparse.ArgumentParser()
argParser.add_argument... |
7c6558f73b2cf4c012c03493e026b71f1197981ea16c4ba71e838444de6b198c | Python | 6,903 | 152 | import torch
from torch.utils.data import Dataset, random_split, DataLoader
from typing import Any, List, Type
from pytorch_lightning import LightningDataModule
# local imports
from ..colors import Color, RandomRed, RandomBlue, Grey
from ..shapes import Pixel, Orientation, Rectangle
from ..textures import Texture
from ... |
77609d2e2b01a726f3e92a79ad10035c396f0d09e434de01edf508296f3ec2ea | Python | 6,904 | 161 | from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
def _temporal_same_padding(kernel_size: int) -> Tuple[int, int, int, int]:
r'''Compute temporal SAME padding for stride-one convolutions.'''
total_padding = kernel_size - 1
left_padding = total_padding // 2
re... |
62110e90f97fdb2b292a0c651cda83c584bf496852068b6480bdf79911883b57 | Python | 6,906 | 212 | # Defines different color classes used to generate artificial training stimuli.
import torch
from torch import Tensor
class Color:
"Base class"
def __init__(self):
self.color = self.colorval()
def colorval(self) -> Tensor:
raise NotImplementedError
def fill_tensor(self, height, wi... |
883eb9df6418aa7066ea1003ba52a3ad5f25f24149fbd7c4568a072471f784c8 | Python | 6,906 | 240 | import re
import sys
from contextlib import suppress
from typing import Iterable, NamedTuple, Optional
from .color import Color
from .style import Style
from .text import Text
re_ansi = re.compile(
r"""
(?:\x1b\](.*?)\x1b\\)|
(?:\x1b([(@-Z\\-_]|\[[0-?]*[ -/]*[@-~]))
""",
re.VERBOSE,
)
class _AnsiToken(Named... |
1df1fee8cccfa62d9ab54e538b8285bfe70253e20c21c636bec6e56211647882 | Python | 6,907 | 169 | # This file is for network initialization
# If you do not have a connection file
# you can fisrt construct the connection randomly via construct_connection
# then load the connection and generate a network via init_network
# Else
# you can load the connection and generate a network via init_network directly
... |
3f1580b950d9aadc21a7220c81d64bd204504523b51814afd35453f779709307 | Python | 6,907 | 190 | # 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... |
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