sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8867f0268cbc8b02868a3af6c91605c433f879537bf25d464185cfc36abbdd51 | Python | 3,237 | 113 | #!/usr/bin/python
#
## @file
#
# Control a Edmunds high speed filter wheel.
#
# Hazen 02/14
#
import os
import socket
import subprocess
import time
import win32com.client
class HSFW():
def __init__(self):
#self.excel = win32com.client.Dispatch("Excel.Application")
#self.excel.Visible = 1
... |
ec3f61d0aef32d88e5a8b877d8f19556a4472297da0677f6a354579f4ab08fa0 | Python | 3,238 | 109 | import h5py
import os
import matplotlib.pyplot as plt
import numpy as np
DIR = os.path.dirname(os.path.realpath(__file__))
HOME_DIR = DIR.rsplit('/', 1)[0]
HOME_DIR = HOME_DIR.rsplit('/', 1)[0]
print('> HOME: ', HOME_DIR)
DATA_DIR = HOME_DIR + '/data/'
print('> DATA: ', DATA_DIR)
# %%
f = h5py.File(DATA_DIR + '/0.... |
15e7a1d4804357c832efbf68745d3da93ba406e413bec363eaf31006b64cd3ab | Python | 3,239 | 96 | #!/usr/bin/env python3
# imports
import argparse
from ase import Atoms
from ase.io import read, write
from ase.optimize import *
from ase import units
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, ZeroRotation, Stationary
from ase.md.langevin import Langevin
from ase.io.trajectory import Trajec... |
4f5193db1d9ac5aef8293d630b84723f9a6a4c7584a3dae97df6675dc6bc4e09 | Python | 3,240 | 85 | import logging
from typing import Generator, Callable, List, Tuple, Any
from zigzag.classes.stages.Stage import Stage
from zigzag.classes.cost_model.cost_model import CostModelEvaluation
logger = logging.getLogger(__name__)
class GeneralParameterIteratorStage(Stage):
"""
General iterator over any parameter ... |
6afef497781eadc1644d043f050e7ab60f394c6d9140dbfe74d2aac143b62c14 | Python | 3,240 | 103 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
import shutil as sh
# external inputs
import numpy as np
import nibabel as nb
from nighres.laminar import profile_sampling
# local inputs
from ..io.affine import read_vox2ras_tkr
from ..cmap.generate_coordinate_mapping import generate_coordinate_mapp... |
dc16a34fde0e26c970f8c4aba902d107aed22d3da96e16f6bea8f40ad38a37ac | Python | 3,240 | 97 | import torch
import torch.nn as nn
class AsymmetricLossMultiLabel(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False):
super(AsymmetricLossMultiLabel, self).__init__()
self.gamma_neg = gamma_neg
self.gamma_pos = gamma_pos
... |
eac7ccd58b62ad4c410bb364b4a1c13437348c21fb89decac0a109d42555402d | Python | 3,240 | 87 | import os
import numpy as np
import nibabel as nib
import matplotlib
matplotlib.use("Agg") # 防止GUI后台冲突
import matplotlib.pyplot as plt
import imageio
# ======== User settings ========
flair_path = r"F:\BIDS\demo_BIDS\derivatives\xfm\sub-AFib0241\ses-baseline\sub-AFib0241_ses-baseline_acq-highres_space-T1w_desc-brain_... |
f76d6b82927bc2f6f434a3f636fbf3871da24bf6abc4f76ab726c108831c281c | Python | 3,240 | 88 | import pandas as pd
import torch
import os.path as osp
import numpy as np
import dgl
from ogb.io.read_graph_raw import read_csv_graph_raw, read_csv_heterograph_raw, read_binary_graph_raw, read_binary_heterograph_raw
from tqdm import tqdm
def read_graph_dgl(raw_dir, add_inverse_edge = False, additional_node_files = [],... |
5e632237c80a42e0cff0499a4d287723fd73dd7fbc04f0df2a770f5ee5faba86 | Python | 3,241 | 69 | import base64
import json
import os
import os.path as osp
import numpy as np
import PIL.Image
from labelme import utils
'''
制作自己的语义分割数据集需要注意以下几点：
1、我使用的labelme版本是3.16.7，建议使用该版本的labelme，有些版本的labelme会发生错误，
具体错误为：Too many dimensions: 3 > 2
安装方式为命令行pip install labelme==3.16.7
2、此处生成的标签图是8位彩色图，与视频中看起来的数据集格式不太一样。
... |
e9d4d56d65f4e2569e9913fe6453cc4baca6459b516bccd8176d9009797f9b8c | Python | 3,242 | 83 | from typing import Optional
from torch import Tensor
import torch_geometric.typing
from torch_geometric import is_compiling
from torch_geometric.typing import pyg_lib
from torch_geometric.utils import scatter, segment
from torch_geometric.utils.num_nodes import maybe_num_nodes
def softmax(
src: Tensor,
inde... |
2105b5e6b57a3e88376facb9c1ed87cbf91b667c1fbaa18104569cb81174f5a7 | Python | 3,243 | 70 | """Reconstruct a high-resolution, display-oriented AAL subcortical surface.
Unlike mesh-only smoothing, this starts from the labelled volume and uses a
Gaussian-smoothed occupancy field. The resulting isosurfaces do not retain
the voxel-grid ripples found in a direct label-to-mesh conversion.
"""
from __future__ imp... |
5cc411b63d5086c7f5d478cf234e305b2c4064997b469bcc8dd79c13e7fbce86 | Python | 3,243 | 91 | import numpy as np
from numba import njit
from EPGMotion import *
from Jacobian_MotionCorrection import *
@njit()
def EPGForwardMotionModel(theta, opt):
# Cache frequently used values
nTR = opt["nTR"].astype(np.int32)[0]
steadyStateTR = opt["SteadyStateTR"].astype(np.int32)[0]
nDummy = opt['nDummy'].as... |
80e4f8689364d8e2f124b6187355185262bb462446198d6c590d27a7a9ab4c0d | Python | 3,243 | 96 | import argparse
import pickle
import h5py
import nibabel as nb
import numpy as np
import pandas as pd
def get_nearest_point(idxs_res, idxs_to_impute):
nearest_point = {
tuple(idx): _get_nearest_point(idx, idxs_res) for idx in idxs_to_impute
}
return nearest_point
def _get_nearest_point(target, c... |
dd53d1b7fe84f79e00a190fad79af624992e0be39a204e42973e4deb9647cc3c | Python | 3,243 | 108 | from .. import fixtures
import unittest
import arbor as A
from arbor import units as U
"""
tests for (dynamically loaded) catalogues
"""
class recipe(A.recipe):
def __init__(self):
A.recipe.__init__(self)
self.tree = A.segment_tree()
self.tree.append(A.mnpos, (0, 0, 0, 10), (1, 0, 0, 10),... |
f5d5190d4a9b1f84ca2cffebe01fae0d60271f8719f46898ad26e791583b5d0f | Python | 3,243 | 94 | # This file is part of Mooney computational modeling project.
#
# Mooney computational modeling project is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) an... |
8ce2edc0e5ec4827d8e2e6456bf2669e58e085eeb8cbf08c589412f468681e43 | Python | 3,244 | 129 | # -*- coding: utf-8 -*-
# external inputs
import numpy as np
from numpy.linalg import norm
def _face_area(v, f):
"""Helper function to compute face areas."""
# indexed view into the vertex array
tris = v[f]
a = np.cross(tris[::, 1] - tris[::, 0], tris[::, 2] - tris[::, 0])
a = norm(a, axis=1)
... |
f68d00eb646336af762e999cf2ce77d2a2149154acca6117dcdffb3dc4f36975 | Python | 3,245 | 111 | """
Test hierarchical agglomerative clustering algorithms for comparison
"""
import numpy as np
import scanpy as sc
import pandas as pd
import squidpy as sq
import os
import matplotlib.pyplot as plt
import seaborn as sns
import random
import nest
from nest.synthetic.data import create_synthetic_data
from anndata imp... |
1f0db6c10a3651d46d2cbaebe5a47ab5537ce0b365a7859c509840b8607052d0 | Python | 3,246 | 91 | from collections.abc import Sequence
from dataclasses import dataclass
from typing import Literal
@dataclass
class ArrayOutputTypeConfig:
"""
:param normalization:
Which type of normalization to apply to the array data. If ``element``, will
normalize each element in the array independently. If... |
c6505d73d940c75b865fe57add150d192f06681fb718d465f79b6bb35c92436a | Python | 3,246 | 76 | from .CostModelStage import CostModelStage
from .DumpStage import DumpStage
from .PlotTemporalMappingsStage import PlotTemporalMappingsStage
from .SaveStage import CompleteSaveStage, SimpleSaveStage, PickleSaveStage
from .MPISaveStage import MPICompleteSaveStage, MPISimpleSaveStage, MPIPickleSaveStage
from .GeneralPara... |
016d284bc1d3da550ba31734cda2652d21ff370c510b5f643578f3bc0f8631a7 | Python | 3,247 | 99 | #!/usr/bin/env python3
import bisect;
def getSegs(pos,seglen,direction,exonstart,exonlen):
"""
This function returns the corresponding segstart and seg length (as in the list)
parameters:
pos: the position to be queried
seglen: the length of the segment
direction: 1 (forward), -1 (backward)
exonstart,ex... |
9ddd8ae49dbaabb29d9674e49e7ffe952ddade35a0a85faa84d87a251ee49c5a | Python | 3,247 | 94 | """Ordered endpoint registry and side-effect-free model selection helpers."""
from __future__ import annotations
from chemgraph.models.endpoints import EndpointSpec, ModelRequest
from chemgraph.models.endpoints import alcf as alcf_ep
from chemgraph.models.endpoints import anthropic_direct as anthropic_ep
from chemgra... |
966b0186df02d217db6d7a1429906972a8e040a0d7caa2edf2dd9daf97a2d457 | Python | 3,248 | 105 | import random
from dataclasses import dataclass
from pathlib import Path
from typing import TYPE_CHECKING
import numpy as np
from PIL import Image
from tests.setup_tests.setup_modelling_test_data.setup_targets_test_data import (
get_current_test_label_values,
get_test_label_file_fieldnames,
set_up_label_f... |
ab976a82638e67056c27a9777155f9f281921d677a2bd6a581f68c905d97d8ad | Python | 3,248 | 99 | # Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
import torch
def recursive_fn_factory(fn):
def recursive_fn(b):
if isinstance(b, dict):
return {k: recursive_fn(b[k]) for k in b}
if isinstance(b, list):
return [recursive_fn(t) for t in b]
if... |
24b81f0b4b68236aa2817972966441aabc1da9c16ec6063b45c076d92a9d21c1 | Python | 3,249 | 105 | import warnings
import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Union, Tuple, Optional
from functools import partial
from bism.modules.upsample_layer import UpSampleLayer2D
from bism.modules.concat import ConcatConv2D
"""
Re-implementation of Cellp... |
1ea36e4960daad36e72012f4f0679e135f5b33336a028893d603bc2627563920 | Python | 3,251 | 83 | """Compare MATLAB vs Python spike detection on the parity run.
Reports per-recording spike counts and the match rate within +/-1 ms, plus the
exact per-spike time offset once counts agree.
Note the known, deliberate 2-sample offset: MATLAB's spike times sit 2 samples
after the true negative peak (1-based frames + ali... |
61a46286c461801763cc8c001d159498c721292933ffbb7f82789a9741842f99 | Python | 3,251 | 106 | """
bootstrap_eval.py
-----------------
在完整训练集上反复自举 (B=1000) 评估 MLP，输出 95% 置信区间。
"""
import json
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import Subset, DataLoader
from dataset import StrabismusDataset
from models.mlp_model import Surgery... |
b1f62c9750adac14149c0472bb6353c297aa93dad05aeb76c55bc1207f50ba7a | Python | 3,251 | 87 | from pathlib import Path
from brainscore_core.supported_data_standards.brainio.stimuli import StimulusSet
from brainscore_core.supported_data_standards.brainio.packaging import package_stimulus_set
stimuli = []
image_paths = {}
stimuli_directory = '../datasets/false-colour/dnn/session-1'
'''
Dataset Meta Info (from h... |
f55ef0f44fb5fff9c9afd361353119f8faf5601f9a8212458ab63e2bc4a02154 | Python | 3,251 | 97 | import os
import time
import copy
import argparse
import torch
import torchvision
from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader, random_split
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from classification.ResnetClassification import ResnetClas... |
a06731b704ad0b3a32f57ff1d116ccae6f379f527a7c5d00b7374b32930731a8 | Python | 3,252 | 93 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
import numpy as np
import pytest
from mock import patch, Mock
from numpy.testing import assert_equal
from utils import create_fake_module
import connectome_manipulator.connectome_compari... |
bc9f9090c2eb5366c7bcdf8f8f0ae18f66ef2ca86b7ec3caddf8d5c166b497d5 | Python | 3,252 | 91 | from src.wrappers.OsipiBase import OsipiBase
from src.original.fitting.DT_IIITN.wls_ivim_fitting import wls_ivim_fit
import numpy as np
class DT_IIITN_WLS(OsipiBase):
"""
Segmented IVIM fitting with selectable regression method.
Two methods are available:
- WLS: Weighted Least Squares with Veraart S²... |
1e7ef298a0053b45961c7c88fb6a5990f04b730815d4c1af05e82ccba8ce9e1c | Python | 3,253 | 75 | import os, argparse, subprocess, sys
import nmrglue as ng
import numpy as np
import MRAi_P3 as P3
#####################################################################
def parse_input_file(filename):
params = {}
with open(filename) as f:
for line in f:
line = line.strip()
if not ... |
90f026d5b5a12c5a52f3cf1978fb67d4ea10ea138f7967ecb395396ca92084ca | Python | 3,253 | 99 | import nibabel as nib
import numpy as np
import numpy.testing as npt
from scipy.ndimage import distance_transform_edt
import AFQ.recognition.roi as abr
import AFQ.recognition.utils as abu
from AFQ.recognition.roi import check_sl_with_exclusion, check_sls_with_inclusion
shape = (15, 15, 15)
affine = np.eye(4)
streaml... |
e66561346e561a1bd427d862d0c035f69314e39e0680ebaad225f555784bfe9d | Python | 3,253 | 89 | _base_ = [
'RepLKNet_cascade_mask_rcnn_coco.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
large_kernel_sizes=[27,27,27,13],
channels=[256, 512, 1024, 2048],
small_kernel=None,
... |
f604e4d14ea2a2bd2a603c59da0d1fc972f2c85ac7ec88caea95dfd5a9b071ed | Python | 3,253 | 84 | # wfield - tools to analyse widefield data - hemodynamics correction
# Copyright (C) 2020 Joao Couto - jpcouto@gmail.com
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of ... |
f8165e4f69ba86f43a90ec6e723d4700c46a79223aa6dec4f4a2228925204274 | Python | 3,253 | 83 | #############################################################################
# Copyright (C) 2020-2026 MEmilio
#
# Authors: Carlotta Gerstein
#
# Contact: Martin J. Kuehn <Martin.Kuehn@DLR.de>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the ... |
0663d462d1ffc6c4f30c38bad3d9ae889034f6f613bead18d30954a34348d878 | Python | 3,254 | 72 | import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.check_submission import check_models
from brainscore_core.supported_data_standards.brainio.s3 import load_weight_file
from brainscore_vision.models.mobilenet_v2_0_5_192.model impo... |
1684f6f4d309b670bec065fc4c5134282907d72a6b8a60cda8e8f08386e959ba | Python | 3,254 | 73 | """
:class:`~allennlp.common.registrable.Registrable` is a "mixin" for endowing
any base class with a named registry for its subclasses and a decorator
for registering them.
"""
from collections import defaultdict
from typing import TypeVar, Type, Dict, List
import logging
from stog.utils.checks import ConfigurationEr... |
07ff374b2c53e8eb5897c4be7cc80c615f7a46ba92649b02592a984582bc0bfc | Python | 3,255 | 93 | '''
利用Conv2former结构作为链接构建一个PFA结构，这个结构要包含norm操作，我们最后替换的是模型中的norm结构
'''
import torch
import torch.nn as nn
from Conv2Former import Dual_ConvMod,Dual_Block
import torch.nn.functional as F
class LayerNorm(nn.Module):
r""" From ConvNeXt (https://arxiv.org/pdf/2201.03545.pdf)
"""
def __init__(self, normalized_... |
2e150080a4bf83f4c2800681535d0e445e6d6ed00bc1066afc6ccd4d108992b7 | Python | 3,255 | 83 | #!/usr/bin/env python3
"""Generate a synthetic Thalamus capture (``.tha``) file with no hardware.
This writes a recording that looks just like one produced by a STORAGE2 node:
a leading metadata record followed by a series of analog records. Two channels
are generated -- a 2 Hz sine wave and a 1 Hz square pulse -- so... |
428e0ad792924583d9eb502a0390035786c2d65c6497ad0c95ac9a4f901f5d40 | Python | 3,255 | 105 | from __future__ import annotations
from dataclasses import dataclass
from typing import Tuple
import kornia.geometry as KG
import kornia.geometry.transform as KGT
import kornia.utils as KU
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor
from brainways.transforms.base import B... |
8466e0c94165f4af67b764101231860fc9921ef75ccd4da0d5d5687663795633 | Python | 3,255 | 65 | from brainscore_core.supported_data_standards.brainio.fetch import get_assembly
from brainscore_core.supported_data_standards.brainio.packaging import package_data_assembly, package_stimulus_set
def adapt_stimulus_set(assembly, name_suffix):
stimulus_set_name = f"{assembly.stimulus_set.name}-{name_suffix}"
as... |
8bb877cfae3ec591c8b786e641b8b2b0af81b3f32f2105ab9015c44d4f93c691 | Python | 3,255 | 109 | '''
H3S
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for H3S:
P: potential energy, G: Gradient, D: Nonadiabatic coupling vector
This is listed at the end of description to show the availability.
For example: P/G means both potential energy and g... |
367780e5370b0e0ef40b3a31c4062cf2db3e90f1c8da39ab110e7e28b0eebc0d | Python | 3,256 | 98 | #!/usr/bin/env python3
# imports
import argparse
from ase import Atoms
from ase.io import read, write
from ase.optimize import *
from ase import units
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, ZeroRotation, Stationary
from ase.md.langevin import Langevin
from ase.io.trajectory import Trajec... |
414d6abcb2716234878a76b14e796cf24a22d63cfaa334f8a687e7d6ed63cca8 | Python | 3,256 | 101 | r"""
This module provides PyTorch modules for voxel-based processing.
"""
import torch
from torch import nn
def conv3d_block(in_channels: int, out_channels: int, **kwargs):
r"""
Return a block of the form Conv -> BatchNorm -> ReLU.
Examples
--------
>>> block = conv3d_block(4, 8, kernel_size=2, ... |
83effa58fc0a6856ae0732728f5012736ef05f30f69870ac6606654b6d1cd7b6 | Python | 3,256 | 103 | import base64
import json
from unittest.mock import MagicMock, patch
import pytest
from pyment.utils.download_file import download_file
def make_mock_response(chunks: list[bytes], content_length: int = 0):
response = MagicMock()
response.headers.get.return_value = content_length
response.iter_content.re... |
17dba1c2d1147204075dc5b4bcf9ad54fd8cc6b7cb09e8c3bc45fcf42d75c0bd | Python | 3,257 | 93 | import os
import numpy as np
parallel_starts = 8
available_gpus = ['6,1', '6,1']
# args dict
args_dict = {'device': 0, # gpu
'log_steps': 1,
'num_layers': 3,
'hidden_channels': 256,
'dropout': 0.0,
'batch_size': 64 * 1024,
'lr': 1.e-03,
'ep... |
828daf9081f2b0456f1e9124f6c64a2a215fadf51574765f95664802d2316661 | Python | 3,257 | 94 | import ot
import numpy as np
import scipy.sparse as sp
from sklearn.neighbors import NearestNeighbors
import torch
def construct_interaction(position, n_neighbors=3):
"""Constructing spot-to-spot interactive graph"""
# calculate distance matrix spot
distance_matrix = ot.dist(position, position, metric='e... |
8547c9340cc0186c4cad61a428af4e7f5b7794655cf28f7abde950f63df2e809 | Python | 3,257 | 107 | """
Loader for Calcium & TTX/NBQX Datasets
=====================================================
This module allows loading multi-frame TIFF recordings from:
1. Calcium dyes: calbr, cal520, fluo4, fluo8, ef630
2. TTX/NBQX datasets: ttx, nbqxap5
Examples:
---------------
# Calcium dataset
stack = load_recor... |
89a67afe88e20c820b878b0c2926756897fcf51928ced845f10466debe9fb7ba | Python | 3,257 | 99 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import numpy as np
import nibabel as nb
from nibabel.freesurfer.io import read_morph_data
from scipy.ndimage.filters import gaussian_filter
def map2grid(file_grid, file_input, sigma, path_output="", basename_output="",
... |
1323f7112098307f81b24ef594b9666636665dab751c7766cc464af8aed643f9 | Python | 3,258 | 85 | # Copyright (C) 2021-2022 Intel Corporation
#
# SPDX-License-Identifier: MIT
from django.conf import settings
from django.contrib.auth.models import Group
from django.db.models.signals import post_migrate, post_save
def register_groups(sender, **kwargs):
# Create all groups which corresponds system roles
for... |
26b4cddd6d6cadd90d5fba980c6074f26639eb5e62e677c42524c701d77912e0 | Python | 3,260 | 101 | from enum import Enum
from typing import List, Optional, Union
import torch
import torch.nn.functional as F
from torch import Tensor
class PoolingStrategy(Enum):
MEAN = 'mean'
LAST = 'last'
CLS = 'cls'
class SentenceTransformer(torch.nn.Module):
def __init__(
self,
model_name: str,
... |
4c9054612f8f70afa81365a2938a6a2d1df5762375cc9dc9d985f5bd748effa8 | Python | 3,260 | 98 | #!/usr/bin/env python3
# imports
import argparse
from ase import Atoms
from ase.io import read, write
from ase.optimize import *
from ase import units
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, ZeroRotation, Stationary
from ase.md.langevin import Langevin
from ase.io.trajectory import Trajec... |
fb0dae41e902687e886bfadac832804eaebf8775de2d0a55f8174d61f05311dd | Python | 3,260 | 76 | import cv2
import numpy as np
CLASS_NAMES_GEOMETRIC = {
1: 'rectangle',
2: 'triangle',
3: 'ellipse'
}
def random_triangle(image, mask, x, y, color, radius_range=(3, 28)):
a, b, c, d, e = np.random.randint(*radius_range, size=5)
triangle_cnt = np.array([[x, y - a], [x + b, y + c], [x - d, y + e]])... |
ea34a01183eeb4ef1c77ef43145d9eefb44fbfce7e56b2976e51ae851f02be22 | Python | 3,261 | 82 | # Generated by Django 3.2.15 on 2022-09-27 12:28
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("webhooks", "0001_initial"),
]
operations = [
migrations.AlterField(
model_name="webhookdelivery",
name="status_code... |
102497191b2c4b90be1b22321022fc830372eb786d60f70a67713a223d9eb3c0 | Python | 3,262 | 86 | import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool, GlobalAttention, Set2Set
import torch.nn.functional as F
from torch_geometric.nn.inits import uniform
from conv import GNN_node, GNN_node_Virtualnode
from torch_scatter import ... |
abea09db2f3f38afb87c39b2aef8ff2a6ecc171c971818f8ef444dc1eabd56f1 | Python | 3,262 | 89 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Compute the mean Apparent Fiber Density (AFD) and mean Radial fODF (radfODF)
maps along a bundle.
This is the "real" fixel-based fODF amplitude along every streamline
of the bundle provided, averaged at every voxel.
Please use a bundle file rather than a whole tracto... |
d28984355dc3e8b1563a2a5e0b7e10a467b8e3788c3ff0f9601b544d942fb111 | Python | 3,262 | 83 | import click
import dolfin as df
import numpy as np
import pantarei as pr
@click.command("refine_mesh")
@click.option("--input", "-i", type=str, required=True)
@click.option("--output", "-o", type=str, required=True)
@click.option("--threshold", "-t", type=float, default=2.0)
@click.option("--function", "-f", "functi... |
ebb19a4236e41903b7db193e367f9613823c0ff3120bb948e5ff735544c46a7a | Python | 3,262 | 109 | '''
List of default values and description of the tierpsy.processing.progressMultipleFilesFun
'''
import os
from tierpsy import IS_FROZEN
from .helper import repack_dflt_list
#get 90% of the number of cores as the default max_num_process
import multiprocessing as mp
_max_num_process = max(1, int(mp.cpu_count()*0.9))... |
ab79ee6a90ad8fed39a0e62caad86e0171c7f6cd87611514796f30faf10a72c5 | Python | 3,263 | 106 | import re
import os
import requests
# def parse_a2m(a2m_content,protein_name):
# a2m_lines = a2m_content.strip().split('\n')
# sequence_dict = {}
# for line in a2m_lines :
# if line.startswith('>') and '>'+protein_name+'/' not in line:
# uniprot_id = re.search(r'\|([A-Za-z0-9]+)\|',... |
f24f5fd2a3531f5f471a8356598cd1b9ae71d7c65e1dae687f4c95d02f1f033e | Python | 3,263 | 92 | import torch
from distances import CosineSimilarity
from reducers import DivisorReducer
from utils import common_functions as c_f
from utils import loss_and_miner_utils as lmu
from losses.base_metric_loss_function import BaseMetricLossFunction
from losses.mixins import WeightRegularizerMixin
# adapted from
# https:/... |
ff853c5a3bef969e06c410b52032c51d66546538e486ccf1725c8dcf0f2e6954 | Python | 3,263 | 93 | from typing import Literal, Final
import polars as pl
from argclz import argument, as_argument
from neuralib.plot import plot_figure
from neuralib.util.verbose import publish_annotation
from rscvp.statistic.core import StatPipeline
from rscvp.util.cli.cli_statistic import StatisticOptions
from rscvp.util.database imp... |
8416fe0b94f788419435c9126db682fe9afbed255836e4a85d6ecdfb35bd0a73 | Python | 3,265 | 84 | import tensorflow as tf
from tensorflow import keras
from utils import data_utils, argmanager
from utils.loss import multinomial_nll
import numpy as np
import os
import json
import scipy
import sklearn.metrics
import scipy.stats
from collections import OrderedDict
def softmax(x, temp=1):
norm_x = x - np.mean(x,ax... |
d27b4270a82489d813476ff3d73c61cc1aa2052e310f70cb2955548be818e19e | Python | 3,265 | 84 | """
Metrics to assess performance of direct-outcome (counterfactual prediction) models.
Function named as ``*_error`` return a scalar value to minimize:
the lower, the better.
Metrics' interface doesn't strictly follow skleran's metrics interface.
The outcome prediction is expected to be full potential outcome predict... |
4790345e54678c3d7589c2b8878c7c5e9f84ca1f59aa79f57f4441dd14fb6930 | Python | 3,266 | 105 | import h5py
import os
import matplotlib.pyplot as plt
import numpy as np
DIR = os.path.dirname(os.path.realpath(__file__))
HOME_DIR = DIR.rsplit('/', 1)[0]
HOME_DIR = HOME_DIR.rsplit('/', 1)[0]
print('> HOME: ', HOME_DIR)
DATA_DIR = HOME_DIR + '/data/'
print('> DATA: ', DATA_DIR)
# %%
f = h5py.File(DATA_DIR + '/0.... |
39c0564a803a41e29e9ac2d81c5a8fc94fe2abdabb8a6a4154ac1e71fd671112 | Python | 3,267 | 103 | #!/usr/bin/env python
import numpy as np
import pandas as pd
import click as ck
import sys
from collections import deque, Counter
import time
import logging
from utils import FUNC_DICT, Ontology, NAMESPACES
from matplotlib import pyplot as plt
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO... |
6dbfb8be71c696bf72cbefc4ca12ad5f5dccccf13fc7401ce2b0ff939868a357 | Python | 3,267 | 86 |
import yaml
import os
import sys
import h5py
import numpy as np
from numpy import array
pathpath = 'PATHS/filepaths_carlen.yml'
myrun = 'runCrespZI_brain'
cmethod = 'ward'
label_sorter = {'runC00dMP3_brain':{9:np.array([5,1,7,2,3,4,8,9,6])-1,\
8:np.array([4,1,6,2,3,7,8,5])-1,\
... |
9a33c7728f78e0cc925f56a5a83e3d736454beb9893666ec55b41112b05837c2 | Python | 3,267 | 90 | # This file was automatically generated by SWIG (http://www.swig.org).
# Version 4.0.2
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info as _swig_python_version_info
if _swig_python_version_info < (2, 7, 0):
raise Runtime... |
ad622b6dd75e094c5085b267e2ad89d76f64266f6c01b3d5378fa6f04073009e | Python | 3,267 | 87 | import shutil
from pathlib import Path
from typing import Callable, Dict, List
import importlib_resources
import numpy as np
from PIL import Image
from brainways.pipeline.atlas_registration import AtlasRegistration
from brainways.utils.atlas.brainways_atlas import BrainwaysAtlas
from brainways.utils.config import loa... |
30805e62bbb6984cebd5c20c40e85c136a4c7729a0468b532885eae646219c4a | Python | 3,268 | 103 | # 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, software
# distributed under th... |
8986818b83ff50ea0470b8630cae22058e88b611464f11d937dffb232d8147fa | Python | 3,268 | 76 |
import numpy as np
from helper_functions.cell_type_lib import CellType
import matplotlib.pyplot as plt
import json
import pathlib
from helper_functions import __file__
MODULE_BASE = pathlib.Path(__file__).parent.parent
if __name__ == '__main__':
SAVE = True
save_path = MODULE_BASE.joinpath('tests/terminal_ani... |
e1438ca45a7f3e33d86ab5cf11a812fc79228f6bf2b96025a088085f8e442daa | Python | 3,268 | 105 | import types
import typing
from collections.abc import Callable
from copy import copy
from functools import lru_cache, partial
from importlib import import_module
from typing import Any, Union, get_type_hints
try:
from toolz import curry
PARTIAL_TYPES: tuple[type, ...] = (partial, curry)
except ImportError: ... |
e751bf50118058dee4b4197f41fc4f1a582b932937d0ecfa2484950fa98130f7 | Python | 3,268 | 80 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
b82ab89bd64a5f3b8a54f8ccae2e0967810c75cc0c528749e44b9db412966446 | Python | 3,269 | 109 | import brainpy as bp
import brainpy.math as bm
import numpy as np
import matplotlib.pyplot as plt
from cann import CANN1D
import scipy
#set default ramndom seed for reproducibility
np.random.seed(111)
#set backend to cpu
bm.set_platform('cpu')
#build and run the network
cann = CANN1D(tau=3, tau_v=144., num=128, mbar=1... |
37206bee923aadc3891e3a3acc42ab2b2865e7c9e53601912a08352b50b13c85 | Python | 3,270 | 95 | from pathlib import Path
import numpy as np
import pandas as pd
import polars as pl
from neuralib.typing import PathLike
from rich.pretty import pprint
class PickleParquetViewer:
def __init__(self, file: PathLike,
view_array_shape: bool = False,
view_iterable_len: bool = False)... |
92fbe2620a9dce3f9f5aad2162997f0f29b9142296a31bd2d41a016e11d3ee9d | Python | 3,270 | 99 | #!/usr/bin/env python3
# imports
import argparse
from ase import Atoms
from ase.io import read, write
from ase.optimize import *
from ase import units
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, ZeroRotation, Stationary
from ase.md.langevin import Langevin
from ase.io.trajectory import Trajec... |
f89738e93765549f2a977497792d7be69d9bd45d1d3436b8ea65b37d05bf198e | Python | 3,273 | 105 | #Aidan Fike
#July 21, 2019
#File used to create random dihedral angles and random sequences
import random
import sys
import subprocess
import os
from shutil import copyfile
random.seed(a=None)
#Conversion between ints and amino acid names (used for random sequence creation)
def getNumLetterConv():
return {0: '... |
40460af7ac150d3f8ce91f11fcf1b4e52239073faf2c28e6968c46f427e861cd | Python | 3,274 | 96 | import copy
import pickle
import numpy as np
import pandas as pd
import fire
from joblib import Parallel, delayed
import sys
sys.path.insert(0,'..')
import contrasts
import nilearn
import nilearn.image
from nilearn.interfaces.bids import get_bids_files
def process_roi(roi, ref_cst, mask, all_betas, nruns, tmaps, re... |
831f86a23df373043e97738496be83f92e883496bfbc38ee16afc814dba620ab | Python | 3,275 | 84 | import unittest
import logging
import numpy as np
import pandas as pd
import diffxpy.api as de
class _TestSingleFullRank(unittest.TestCase):
def _test_single_full_rank(self):
"""
Test if de.wald() generates a uniform p-value distribution
if it is given data simulated based on the null mo... |
99ed29d5f21a9871c200b9c668542057dab7a67e24f2827f34793ee3626d595a | Python | 3,275 | 76 | import numpy as np
from scipy.stats import spearmanr
from brainscore_core.metrics import Score
from brainscore_core.supported_data_standards.brainio.assemblies import DataAssembly
from brainscore_vision.metrics import Ceiling
from brainscore_vision.metrics.rdm.metric import RDM
class RSACeiling(Ceiling):
"""Inte... |
b5211645035b99716dd3920db710e4e1a81142c4d2769df79d773095e603166f | Python | 3,275 | 86 | import torch
from utils import common_functions as c_f
from utils.module_with_records_and_reducer import ModuleWithRecordsAndDistance
class BaseMiner(ModuleWithRecordsAndDistance):
def mine(self, embeddings, labels, ref_emb, ref_labels, species, mnn, setting):
raise NotImplementedError
def output_as... |
e6b93d34da1b9144845a49307f12674f611021a244642fc998e43500fcf82ccb | Python | 3,275 | 94 | """Save the NSD-core ID and OOD test images of each subject.
Parameters
----------
data_ood_selection : str
If 'fmri', the ID/OD splits are defined based on fMRI responses.
If 'dnn', the ID/OD splits are defined based on DNN features.
nsd_dir : str
Directory of the NSD.
project_dir : str
Directory of the project f... |
df69ada9433d2f0f1700e15dc688de848f8cbb7a8541293ef1c724e5d8194185 | Python | 3,276 | 67 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'feed-viewer.ui'
#
# Created by: PyQt5 UI code generator 5.7
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjec... |
e16705180c702496b61c037a756a095c347058fc3fa74da7edc405bba1beea9b | Python | 3,276 | 97 | import os.path as op
import numpy as np
import numpy.testing as npt
import pytest
from bids.layout import BIDSLayout
import AFQ.definitions.image as afm
from AFQ.definitions.image import * # interprets images from eval # noqa F403
from AFQ.definitions.image import ImageFile, ThresholdedScalarImage
from AFQ.definitio... |
a6bc1174db6ee89008fc93d7e0de6af85bc543ef837855955ee411ba9d5893ac | Python | 3,277 | 132 | """Example script for running DyNeMo on simulated HMM-MVN data.
This script should take less than a couple minutes to run and
achieve a dice coefficient of ~0.99.
"""
print("Importing packages")
import os
import pickle
import numpy as np
from osl_dynamics.simulation import HMM_MVN
from osl_dynamics.data import Data... |
655efb2ae2f5e3cce789d77b109b7d5985eaddfcaa0440219ad11648d53c20b7 | Python | 3,281 | 93 | # Copyright (C) 2021-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import glob
import os.path as osp
import shutil
from datumaro.components.dataset import Dataset, DatasetItem
from datumaro.plugins.data_formats.open_images import OpenImagesPath
from datumaro.util.image im... |
b6505fb318f2407feb6f73b71db8fa0c30fee695ce9320bec6852356346f04c2 | Python | 3,281 | 108 | '''
O2H
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for O2H:
P: potential energy, G: Gradient, D: Nonadiabatic coupling vector
This is listed at the end of description to show the availability.
For example: P/G means both potential energy and g... |
367f42746d16e1151f82cc2c113c008998334203a3d211b0a0a76eee5508d7cf | Python | 3,282 | 110 | import tensorflow as tf
import numpy as np
import sys
from tensorflow.examples.tutorials.mnist import input_data
def xavier_init(size):
in_dim = size[0]
xavier_stddev = 1. / tf.sqrt(in_dim / 2.)
return tf.random_normal(shape=size, stddev=xavier_stddev)
def feedforward_net(D, H, C):
X = tf.placeholde... |
d3a2a5fb9aa5da178186d78100efdb1151c56d0479f78b65f5cbc63cdeaea58e | Python | 3,282 | 112 | # %%
from data_loading import load_dataset
import torch
from deepcardio import Trainer
from deepcardio.losses import LpLoss
import os
from deepcardio.neuralop_core.training import AdamW
from deepcardio.neuralop_core.utils import count_model_params
from deepcardio.utils import determine_batch_size
from torch_geometric... |
0f14848b84925f464505135bb83ee6927f2021607137a120f9fe3969aed05471 | Python | 3,283 | 100 | # /------------------------------------------------------------------------------+
# | 22-AUG-2022 |
# | Copyright (c) Bone Imaging Laboratory |
# | All rights reserved ... |
d3f4cd8f6ff7436deaed34a5c3fd26b5a759ebcefca6102cd30666bf34cbc9f1 | Python | 3,284 | 80 | import h5py
import numpy as np
import scipy.io
import pickle
def assign_data_from_file(var_path: str, filename: str, model_type: str):
"""
Loads a variable form a file given a particular way of finding variables
in the file, defined by model_type. Returns None if the model type is not
defined.
:pa... |
fb1b2e8069fa30c2b20264c303a54e7453a4f5457febefddf74934b601632f8a | Python | 3,284 | 107 | '''
CH4
'''
import numpy as np
import sys
def intro():
print("""
SHORT List of potential energy surfaces for CH4:
P: potential energy, G: Gradient, D: Nonadiabatic coupling vector
This is listed at the end of description to show the availability.
For example: P/G means both potential energy and g... |
4f204210902ae8752c6443c01e9e0c412cca9f5f9dcd0b894177d2b3f8f3eee5 | Python | 3,285 | 99 | #!/usr/bin/env python
from __future__ import print_function
import random
from logging import info
from keras.models import Model
from keras.layers import Input, Embedding, Flatten, Dense
from keras.layers import Reshape, Convolution2D, Dropout
from ltlib import filelog
from ltlib import conlldata
from ltlib import... |
9be242383f9eb640157ff21e25f51928d9a42d0dd91a20fbc7548374ced36110 | Python | 3,285 | 92 | from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerModel,
TransformerConfig,
TransformerEncoder,
TransformerEncoderLayer,
)
from dasa_fairseq.modules.multihead_attention_dasa import MultiheadAttentionDASA
class TransformerEncoder... |
15c8c94352471ee37caf3b8b89bac4e3ab4f8da01a1522a97b968825b9b9650f | Python | 3,286 | 80 | import numpy as np
import nibabel as nib
from nipype.interfaces.base import BaseInterface, BaseInterfaceInputSpec, TraitedSpec, Directory, TraitedSpec
from traits.api import Either, File, List, Bool
class MergeGiftiInputSpec(BaseInterfaceInputSpec):
mesh_gii_file = File(exists=True, mandatory=True, desc='Input GIF... |
539955119eb27290973d29c552858aae8da31dc2151f42c48463e6b50a559a92 | Python | 3,286 | 79 | from pathlib import Path
import numpy as np
import xarray as xr
from brainscore_core.supported_data_standards.brainio.assemblies import PropertyAssembly
from brainscore_core.supported_data_standards.brainio.packaging import package_data_assembly
import pandas as pd
DATASETS = ['short-2', 'short-4', 'short-6', 'short... |
023712a811a3376d6d67e92f7c5e818b9117f623b0651ceca51e905ea883ace1 | Python | 3,287 | 120 | import numpy as np
import pytest
import ccf_streamlines.coordinates as coordinates
def test_mismatch():
test_coords = np.array(
[
[0, 0, 0, 0],
[1, 1, 1, 1],
]
)
resolution = (10, 10, 10)
with pytest.raises(ValueError):
coordinates.coordinates_to_voxel... |
1b5d73ab3ac40e4b718e57890dcf68b7efd029cb6d971c050079e6f1134f17dd | Python | 3,287 | 111 | import brainpy as bp
import brainpy.math as bm
import numpy as np
import matplotlib.pyplot as plt
from cann import CANN1D
import scipy
#set default ramndom seed for reproducibility
np.random.seed(111)
#set backend to cpu
bm.set_platform('cpu')
#build and run the network
cann = CANN1D(tau=1., tau_v=48., num=128, mbar=2... |
c3ef84c26d85e3a0d5fde2873884ac52dd2be6e2dc3352637cfe8a794b8297e9 | Python | 3,287 | 122 | import numpy as np
import pytest
from lmfit import CompositeModel, Model
from impedancefitter.utils import get_equivalent_circuit_model
def test_single():
"""Test single resistor."""
model1 = "R"
model = get_equivalent_circuit_model(model1)
assert isinstance(model, Model)
def test_single2():
""... |
e5e8870e6280432ae5c77fdd1ac04fb23acfcab1c5273201b7b4d5ea8e487db5 | Python | 3,288 | 97 | from ..qt import *
from ..config import ObservableDict
from .. import thalamus_pb2_grpc
from ..observable_item_models import FlatObservableCollectionModel, TreeObservableCollectionModel, TreeObservableCollectionDelegate
import logging
LOGGER = logging.getLogger(__name__)
class Run2Widget(QTabWidget):
def __init__(... |
1ebde8783ddf61f3307271158a4fbedb2358c82cdff554bea15730ec3fc903c8 | Python | 3,290 | 102 | """
This module contains the functions to adapt the simulated reflectance spectra
to the TIVITA camera wavelengths.
"""
import os
import numpy as np
import pandas as pd
from dotenv import load_dotenv
from mcmlnet.susi.adapt_to_camera import adapt_to_camera
from mcmlnet.utils.logging import setup_logging
load_doten... |
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